diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 0cdb40fae3fb1a10f93047c2a5a5de695778ecb8..f5afe18a5b52bf0ee6b217090819734be0b445b7 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -74,11 +74,11 @@ coverage:
pages:
stage: deploy
only:
- - cookBux
+ - develop
except:
- schedules
script:
- - cd sphinx-doc/
+ - cd sphinxdoc/
- pip install -r requirements_sphinx.txt
- make doc
- cp -r _build/html ../public
diff --git a/CHANGELOG.md b/CHANGELOG.md
index da88ffd8683a4b990ccc82e228ef41dc5e913d75..62b8c92c7286c9dc1a341ebeb66b420eea07e4f5 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,92 +1,39 @@
-# 1.1
-
-## Features
-- register is importable from the top level module
-- flagIsolated now respects time gaps in addition to value numbers
-- Make the comparator argument to isflagged available from the config
-
-
-## Bugfixes
-- Fixed missing constant lookup in the evaluator
-- Preserve untouched/checked variables and don't remove them from the data input
-
-
-## Refactorings
---
-
-## Breaking Changes
---
-
-# 1.2
-
-## Features
-- Python 3.8 support
-- exe: added the dmp flagger option
-- exe: use nodata argument as nodata-representation in output
-- flagging functions: implemented flagging function aiming to flag invalid value raises in a given time range
-- anaconda support
-
-## Bugfixes
-- pass the harmonization function names to the flagger
-- variables not listed in the varname column of the configuration file
- were not available in generic tests
-- Harmonization by interpolation, now will no longer insert a BAD-flagged but propperly interpolated value between two frequency alligned meassurements, that are seperated exactly by a margin of two times the frequency (instead, BAD flagged NaN gets inserted - as expected)
-- Fixed "not a frequency" - bug, occuring when trying to aggregate values to a 1-unit-frequency (1 Day, 1 Hour, ...)
-
-## Refactorings
-- configuration reader rework
-
-## Breaking Changes
---
-
-# 1.3
-
-## Features
-- spike detection test `spikes_flagRaise`
-- spike detection test `spikes_oddWater`
-- generic processing function `procGeneric`
-
-## Bugfixes
-- configuration: certain whitespace patterns broke the configuration parsing
-- configuration: multiple tests in one configuration row were not parsed correctly
-- reader: variables only available within the flagger were not transformed correctly
-
-## Refactorings
-- Improved logging
-
-## Breaking Changes
-- configuration: quoted variable names are handled as regular expressions
-- functions: renamed many test functions to a uniform naming scheme
-
-
-# 1.4
-
-## Features
-- added the data processing module `proc_functions`
-- `flagCrossValidation` implemented
-- CLI: added support for parquet files
-
-## Bugfixes
-- `spikes_flagRaise` - overestimation of value courses average fixed
-- `spikes_flagRaise` - raise check window now closed on both sides
-
-## Refactorings
-- renamed `spikes_oddWater` to `spikes_flagMultivarScores`
-- added STRAY auto treshing algorithm to `spikes_flagMultivarScores`
-- added "unflagging" - postprocess to `spikes_flagMultivarScores`
-- improved and extended masking
-
-## Breaking Changes
-- register is now a decorator instead of a wrapper
-
-# 1.5
-
-coming soon ...
-
-## Features
-
-## Bugfixes
-
-## Refactorings
-
-## Breaking Changes
+# Changelog
+
+This changelog starts with version 2.0.0. Basically all parts of the system, including the format of this changelog, have been reworked between the releases 1.4 and 2.0. Preceding the major breaking release 2.0, the maintenance of this file was rather sloppy, so we won't provide a detailed change history for early versions.
+
+
+## [Unreleased]
+### Added
+### Changed
+### Removed
+### Fixed
+
+## [2.0.1] - 2021-12-20
+### Added
+- CLI now accepts remote configuration and data files as URL
+- new function `transferFlags`
+- improved error messages from `flagGeneric` and `processGeneric`
+- new `ax_kwargs` keyword to `SaQC.plot` function
+### Changed
+- generate documentation from the `develop` branch
+- doctest is now ran upon push to the `develop` branch, failing doc snippets cause CI-pipeline to fail
+- renamed function `flagCrossStatistic` to `flagCrossStatistics`
+### Removed
+- removed function `flagDriftFromScaledNorm`
+- removed `stats` keywords and functionality from `SaQC.plot` function
+### Fixed
+- RDM/UFZ logos:
+ - use the English versions of the respective images
+ - use full URLs instead of the repository local URLs in `README.md`
+- fix code snippets in `README.md`
+- fix version confusion
+- `copyField`: fix misleading error message
+- `flagGeneric`: fix failure on empty data
+- existing `target` variables led to function calls on `target` instead of `field`
+- the functions `flagDriftFromNorm`, `flagDriftFromReference`, `flagCrossStatistics` and `flagMVScores` now properly support the field-target workflow
+- `field` was not masked for resampling functions
+- allow custom registered functions to overwrite built-ins.
+
+## [2.0.0] - 2021-11-25
+This release marks the beginning of a new release cycle. Basically the entire system got reworked between versions 1.4 and 2.0, a detailed changelog is not recoverable and/or useful.
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000000000000000000000000000000000000..4f6b69fbf66c3f6a6fa262917ee04360292dade5
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,34 @@
+cff-version: 1.2.0
+title: SaQC - System for automated Quality Control
+message: "Please cite this software using these metadata."
+type: software
+version: 2.0.0
+doi:
+date-released: "2021-11-25"
+license: "GPL-3.0"
+repository-code: "https://git.ufz.de/rdm-software/saqc"
+keywords:
+ - time series data
+ - environmental sensor data
+authors:
+ - given-names: David
+ family-names: Schäfer
+ email: david.schaefer@ufz.de
+ affiliation: >-
+ Helmholtz Centre for Environmental Research -
+ UFZ
+ orcid: 'https://orcid.org/0000-0003-4517-6459'
+ - given-names: Bert
+ family-names: Palm
+ email: bert.palm@ufz.de
+ affiliation: >-
+ Helmholtz Centre for Environmental Research -
+ UFZ
+ orcid: 'https://orcid.org/0000-0000-0000-0000'
+ - given-names: Peter
+ family-names: Lünenschloß
+ email: peter.luenenschloss@ufz.de
+ affiliation: >-
+ Helmholtz Centre for Environmental Research -
+ UFZ
+ orcid: 'https://orcid.org/0000-0000-0000-0000'
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 4cd12ace56e9e2fe51bd4530adef4c0eab10f4b4..f9dcdbbc8370ca06ed44a449e43657f17918c280 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,5 +1,5 @@
# Development Environment
-We recommend a virtual python environment for development. The setup process is described in detail in our [GettingStarted](docs/GettingStarted.md).
+We recommend a virtual python environment for development, a more detailed description of the setup process can be found in the [docs](https://rdm-software.pages.ufz.de/saqc/getting_started/InstallationGuide.html#set-up-a-virtual-environment).
# Testing
SaQC comes with an extensive test suite based on [pytest](https://docs.pytest.org/en/latest/).
@@ -59,7 +59,7 @@ It is not a shame to name a parameter just `n` or `alpha` etc. if for example th
- testnames: [testmodule_]flagTestName
## Formatting
-We use (black)[https://black.readthedocs.io/en/stable/] in its default settings.
+We use [black](https://black.readthedocs.io/en/stable/) in its default settings.
Within the `saqc` root directory run `black .`.
## Imports
@@ -71,12 +71,12 @@ Only absolute imports are accepted.
- `master` - branch:
+ Stable and usually protected.
- + Regular merges from `develop` according to the [release cycle](#release-cycle). These merges get a tag, increasing at least the minor version.
- + Irregular merges from `develop` in case if critical bugs. Such merges increase at least the patch level.
- + Merges into `master` usually lead to a PyPI release
+ + Regular merges from `develop`, these merges are tagged and increasing at least the minor version.
+ + Irregular merges from `develop` in case of critical bugs. Such merges increase at least the patch level.
+ + Merges into `master` usually lead to a PyPI release.
- `develop` - branch:
- + The main development branch, no hard stability requirements/guarantees
- + Merges into `develop` should mostly follow a Merge Request Workflow, minor changes can however be committed directly. Such minor changes include:
+ + The main development branch, no hard stability requirements/guarantees.
+ + Merges into `develop` should mostly follow a [Merge Request Workflow](#merge-request-workflow), minor changes can however be committed directly. Such minor changes include:
* Typos and white space changes
* Obvious bug in features implemented by the committing developer
diff --git a/README.md b/README.md
index dce9fa2b991795df90649c93e5f6a9abd58b8720..80533c112ba9969b40617dc882ef135209f3b2a8 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,12 @@
+<a href="https://www.ufz.de/index.php?en=33573">
+ <img src="https://git.ufz.de/rdm-software/saqc/raw/develop/sphinxdoc/ressources/images/Representative/UFZLogo.png" width="400"/>
+</a>
-<img src="sphinx-doc/ressources/images/Representative/UFZLogo.jpg" width="400"/>
+<a href="https://www.ufz.de/index.php?en=45348">
+ <img src="https://git.ufz.de/rdm-software/saqc/raw/develop/sphinxdoc/ressources/images/Representative/RDMLogo.png" align="right" width="220"/>
+</a>
-<img src="sphinx-doc/ressources/images/Representative/RDMlogo.jpg" align="right" width="180"/>
+[](https://www.repostatus.org/#active)
# System for automated Quality Control (SaQC)
@@ -40,24 +45,34 @@ and a python module with a simple API.
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:
+could look like [this](https://git.ufz.de/rdm-software/saqc/raw/develop/ressources/data/config.csv):
```
varname ; test
-#----------;------------------------------------
-SM2 ; shiftToFreq(freq="15Min")
-SM2 ; flagMissing()
-'SM(1|2)+' ; flagRange(min=10, max=60)
-SM2 ; flagMad(window="30d", z=3.5)
+#----------; -----------------------------------------------------
+SM2 ; shift(freq="15Min")
+'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)))
```
As soon as the basic inputs, dataset and configuration file, are
-prepared, `SaQC` is run with:
+prepared, run `SaQC`:
```sh
saqc \
- --config path_to_configuration.txt \
- --data path_to_data.csv \
- --outfile path_to_output.csv
+ --config PATH_TO_CONFIGURATION \
+ --data PATH_TO_DATA \
+ --outfile PATH_TO_OUTPUT
+```
+
+A full `SaQC` run against provided example data can be invoked with:
+```sh
+saqc \
+ --config https://git.ufz.de/rdm-software/saqc/raw/develop/ressources/data/config.csv \
+ --data https://git.ufz.de/rdm-software/saqc/raw/develop/ressources/data/data.csv \
+ --outfile saqc_test.csv
```
### SaQC as a python module
@@ -66,18 +81,48 @@ The following snippet implements the same configuration given above through
the Python-API:
```python
-import numpy as np
+import pandas as pd
from saqc import SaQC
-saqc = (SaQC(data)
- .shiftToFreq("SM2", freq="15Min")
- .flagMissing("SM2")
- .flagRange("SM(1|2)+", regex=True, min=10, max=60)
- .flagMad("SM2", window="30d", z=3.5))
-
-data, flags = saqc.getResult()
+data = pd.read_csv(
+ "https://git.ufz.de/rdm-software/saqc/raw/develop/ressources/data/data.csv",
+ index_col=0, parse_dates=True,
+)
+
+saqc = SaQC(data=data)
+saqc = (saqc
+ .shift("SM2", freq="15Min")
+ .flagMissing("SM(1|2)+", regex=True)
+ .flagRange("SM1", min=10, max=60)
+ .flagRange("SM2", min=10, max=40)
+ .flagMAD("SM2", window="30d", z=3.5)
+ .flagGeneric(field=["SM1", "SM2"], target="Dummy", func=lambda x, y: (isflagged(x) | isflagged(y))))
```
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.
+
+## Changelog
+All notable changes to this project will be documented in [CHANGELOG.md](CHANGELOG.md).
+
+## Contributing
+You found a bug or you want to suggest some cool features? Please refer to our [contributing guidelines](CONTRIBUTING.md) to see how you can contribute to SaQC.
+
+## Copyright and License
+Copyright(c) 2021, [Helmholtz-Zentrum für Umweltforschung GmbH -- UFZ](https://www.ufz.de). All rights reserved.
+
+- Documentation: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) <a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/80x15.png" /></a>
+- Source code: [GNU General Public License 3](https://www.gnu.org/licenses/gpl-3.0.html)
+
+For full details, see [LICENSE](LICENSE.md).
+
+## Acknowledgements
+...
+
+## Publications
+...
+
+## How to cite SaQC
+...
+
diff --git a/dios/requirements.txt b/dios/requirements.txt
index 139f044f250877d0aaf33406d2e4e87334691a46..4c87cfb3c08c272e57b405bf580e6e7f6632daa3 100644
--- a/dios/requirements.txt
+++ b/dios/requirements.txt
@@ -1,5 +1,5 @@
numpy==1.21.2
-pandas==1.3.3
-python-dateutil==2.8.1
-pytz==2021.1
+pandas==1.3.4
+python-dateutil==2.8.2
+pytz==2021.3
six==1.16.0
diff --git a/requirements.txt b/requirements.txt
index 1f9510979613e3a9e7a33d24e12ed37b54412251..5bf2d384d7b87874d58ed5a8b8866fff529a74ab 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,15 +1,15 @@
-Click==8.0.1
+Click==8.0.3
dtw==1.4.0
-hypothesis==6.23.1
-matplotlib==3.4.3
-numba==0.54.0
+hypothesis==6.29.0
+matplotlib==3.5.0
+numba==0.54.1
numpy==1.20.3
outlier-utils==0.0.3
-pyarrow==4.0.1
-pandas==1.3.3
+pyarrow==6.0.1
+pandas==1.3.4
pytest==6.2.5
pytest-lazy-fixture==0.6.3
-scikit-learn==1.0
-scipy==1.7.1
-typing_extensions==3.10.0.2
+scikit-learn==1.0.1
+scipy==1.7.3
+typing_extensions==4.0.0
seaborn==0.11.2
diff --git a/saqc/__init__.py b/saqc/__init__.py
index 7bd3bb9011a94d76edd502681c34855fee6c9d42..7b00e20cbdf8095266a203f1fd66f968837080d6 100644
--- a/saqc/__init__.py
+++ b/saqc/__init__.py
@@ -2,9 +2,8 @@
# -*- coding: utf-8 -*-
"""The system for automated quality controll package."""
-__version__ = "1.4"
+from saqc.version import __version__
-# import order: from small to big
from saqc.constants import (
UNFLAGGED,
GOOD,
@@ -12,6 +11,7 @@ from saqc.constants import (
BAD,
)
+# import order: from small to big, to a void cycles
from saqc.core import (
Flags,
SaQC,
diff --git a/saqc/__main__.py b/saqc/__main__.py
index 1326bd0be4a96628804a86b826963aad6e7297a1..8068d96c05deafc395986f2bd85dd635489bf128 100644
--- a/saqc/__main__.py
+++ b/saqc/__main__.py
@@ -65,14 +65,14 @@ def writeData(writer_dict, df, fname):
@click.option(
"-c",
"--config",
- type=click.Path(exists=True),
+ type=click.Path(),
required=True,
help="path to the configuration file",
)
@click.option(
"-d",
"--data",
- type=click.Path(exists=True),
+ type=click.Path(),
multiple=True,
required=True,
help="path to the data file",
diff --git a/saqc/core/__init__.py b/saqc/core/__init__.py
index cf86db46d2572cdca0c32b7ae2bc098bfa52ddef..5146d88130f3d449ff4fbdf59af0e744a4a50e9a 100644
--- a/saqc/core/__init__.py
+++ b/saqc/core/__init__.py
@@ -3,6 +3,7 @@
from saqc.core.register import register, flagging, processing
from saqc.core.flags import Flags, initFlagsLike
+from saqc.core.history import History
from saqc.core.core import SaQC
from saqc.core.translation import (
FloatScheme,
diff --git a/saqc/core/core.py b/saqc/core/core.py
index b7bb6050b0069252e7c63958e2d57bc744e1820f..2de8f7b987ed37a3f5878c1009a44886dac2e231 100644
--- a/saqc/core/core.py
+++ b/saqc/core/core.py
@@ -140,7 +140,6 @@ class SaQC(FunctionsMixin):
def _expandFields(
self,
regex: bool,
- multivariate: bool,
field: str | Sequence[str],
target: str | Sequence[str] = None,
) -> Tuple[List[str], List[str]]:
@@ -148,10 +147,6 @@ class SaQC(FunctionsMixin):
check and expand `field` and `target`
"""
- 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)
@@ -161,14 +156,6 @@ class SaQC(FunctionsMixin):
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
def _wrap(self, func: FunctionWrapper):
@@ -198,38 +185,48 @@ class SaQC(FunctionsMixin):
kwargs.setdefault("dfilter", self._scheme.DFILTER_DEFAULT)
if not isinstance(flag, OptionalNone):
- # translation schemes might want to use a flag,
- # None so we introduce a special class here
+ # translation schemes might want to use a flag
+ # `None` so we introduce a special class here
kwargs["flag"] = self._scheme(flag)
fields, targets = self._expandFields(
- regex=regex, multivariate=func.multivariate, field=field, target=target
+ regex=regex, field=field, target=target
)
-
out = self
- for field, target in zip(fields, targets):
-
- fkwargs = {
- **kwargs,
- "field": field,
- "target": target,
- }
+ if not func.handles_target:
+ if len(fields) != len(targets):
+ raise ValueError(
+ "expected the same number of 'field' and 'target' values"
+ )
- if not func.handles_target and field != target:
- if target not in self.data.columns:
+ # initialize all target variables
+ for src, trg in zip(fields, targets):
+ if src != trg:
out = out._callFunction(
FUNC_MAP["copyField"],
- *args,
- **fkwargs,
+ field=src,
+ target=trg,
+ overwrite=True,
)
- fkwargs["field"] = fkwargs.pop("target")
+ if func.multivariate:
+ # pass all fields and targets
out = out._callFunction(
func,
+ field=fields,
+ target=targets,
*args,
- **fkwargs,
+ **kwargs,
)
+ else:
+ # call the function on target
+ for src, trg in zip(fields, targets):
+ fkwargs = {**kwargs, "field": src, "target": trg}
+ if not func.handles_target:
+ fkwargs["field"] = fkwargs.pop("target")
+ out = out._callFunction(func, *args, **fkwargs)
+
return out
return inner
diff --git a/saqc/core/flags.py b/saqc/core/flags.py
index 6b119d70491d63ac59b442ca7aaf5e87b80eb1e6..fa990c1788e0339d6c4738260f664de8aeb5c4ad 100644
--- a/saqc/core/flags.py
+++ b/saqc/core/flags.py
@@ -70,51 +70,63 @@ class Flags:
We create an empty instance, by calling ``Flags`` without any arguments and then add a column to it.
- >>> from saqc.constants import UNFLAGGED, BAD, DOUBTFUL
- >>> flags = Flags()
- >>> flags
- Empty Flags
- Columns: []
+ .. doctest:: exampleFlags
+ >>> from saqc.constants import UNFLAGGED, BAD, DOUBTFUL
+ >>> flags = saqc.Flags()
+ >>> flags
+ Empty Flags
+ Columns: []
- >>> flags['v0'] = pd.Series([BAD,BAD,UNFLAGGED], dtype=float)
- >>> flags
- v0 |
- ======== |
- 0 255.0 |
- 1 255.0 |
- 2 -inf |
+ .. doctest:: exampleFlags
+
+ >>> flags['v0'] = pd.Series([BAD,BAD,UNFLAGGED], dtype=float)
+ >>> flags # doctest:+NORMALIZE_WHITESPACE
+ v0 |
+ ======== |
+ 0 255.0 |
+ 1 255.0 |
+ 2 -inf |
+ <BLANKLINE>
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 ...
- ValueError: Index does not match
+ .. doctest:: exampleFlags
+
+ >>> flags['v0'] = pd.Series([666.], dtype=float) # doctest:+ELLIPSIS
+ Traceback (most recent call last):
+ ...
+ ValueError: Index does not match
+
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,np.nan,DOUBT], dtype=float)
- >>> flags
- v0 |
- ======== |
- 0 25.0 |
- 1 255.0 |
- 2 25.0 |
+ .. doctest:: exampleFlags
+
+ >>> flags['v0'] = pd.Series([DOUBTFUL,np.nan,DOUBTFUL], dtype=float)
+ >>> flags # doctest:+NORMALIZE_WHITESPACE
+ v0 |
+ ======== |
+ 0 25.0 |
+ 1 255.0 |
+ 2 25.0 |
+ <BLANKLINE>
As we see above, the column now holds a combination from the values from the
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
- 1 255.0 nan
- 2 (-inf) 25.0
+ .. doctest:: exampleFlags
+
+ >>> flags['v0'] = pd.Series([DOUBTFUL, np.nan, DOUBTFUL], dtype=float)
+ >>> flags.history['v0'] # doctest:+NORMALIZE_WHITESPACE
+ 0 1 2
+ 0 255.0 25.0 25.0
+ 1 255.0 nan nan
+ 2 -inf 25.0 25.0
+
As we see now, the second call sets ``25.0`` and shadows (represented by the parentheses) ``(255.0)`` in the
first row and ``(-inf)`` in the last, but in the second row ``255.0`` still is valid, because it was
@@ -123,19 +135,25 @@ class Flags:
It is also possible to set values by a mask, which can be interpreted as condidional setting.
Imagine we want to `reset` all flags to ``0.`` if the existing flags are lower that ``255.``.
- >>> mask = flags['v0'] < BAD
- >>> mask
- 0 True
- 1 False
- 2 True
- dtype: bool
- >>> flags[mask, 'v0'] = 0
- >>> flags
- v0 |
- ======== |
- 0 0.0 |
- 1 255.0 |
- 2 0.0 |
+ .. doctest:: exampleFlags
+
+ >>> mask = flags['v0'] < BAD
+ >>> mask # doctest:+NORMALIZE_WHITESPACE
+ 0 True
+ 1 False
+ 2 True
+ Name: 2, dtype: bool
+
+ .. doctest:: exampleFlags
+
+ >>> flags[mask, 'v0'] = 0
+ >>> flags # doctest:+NORMALIZE_WHITESPACE
+ v0 |
+ ======== |
+ 0 0.0 |
+ 1 255.0 |
+ 2 0.0 |
+ <BLANKLINE>
The objects you can pass as a row selector (``flags[rows, column]``) are:
@@ -145,21 +163,27 @@ class Flags:
For example, to set `all` values to a scalar value, use a Null-slice:
- >>> flags[:, 'v0'] = 99.0
- >>> flags
- v0 |
- ======= |
- 0 99.0 |
- 1 99.0 |
- 2 99.0 |
-
- After all calls presented here, the history look like this:
-
- >>> flags.history['v0']
- 0 1 2 3
- 0 (255.0) (25.0) (0.0) 99.0
- 1 (255.0) (nan) (nan) 99.0
- 2 (-inf) (25.0) (0.0) 99.0
+ .. doctest:: exampleFlags
+
+ >>> flags[:, 'v0'] = 99.0
+ >>> flags # doctest:+NORMALIZE_WHITESPACE
+ v0 |
+ ======= |
+ 0 99.0 |
+ 1 99.0 |
+ 2 99.0 |
+ <BLANKLINE>
+
+
+ After all calls presented here, the history looks like this:
+
+ .. doctest:: exampleFlags
+
+ >>> flags.history['v0']
+ 0 1 2 3 4
+ 0 255.0 25.0 25.0 0.0 99.0
+ 1 255.0 nan nan nan 99.0
+ 2 -inf 25.0 25.0 0.0 99.0
"""
def __init__(
diff --git a/saqc/core/history.py b/saqc/core/history.py
index 9aa1859bdc996d6bc0cba75d037271e8c054e062..f035da7238ad929885feeee0a9e601609c6ff715 100644
--- a/saqc/core/history.py
+++ b/saqc/core/history.py
@@ -2,14 +2,12 @@
from __future__ import annotations
from copy import deepcopy, copy as shallowcopy
-import itertools
+from typing import Dict, Tuple, Union, List, Any
-from typing import Dict, Tuple, Type, Union, List, Any
-from typing_extensions import Literal
import pandas as pd
import numpy as np
-from saqc.constants import *
+from saqc.constants import UNFLAGGED
class History:
diff --git a/saqc/core/modules/drift.py b/saqc/core/modules/drift.py
index e2b6c4f2aed1ed01af14792db6907074d748e1f8..2cc22ff5c092426e08381a94e957350fac7d9e10 100644
--- a/saqc/core/modules/drift.py
+++ b/saqc/core/modules/drift.py
@@ -41,31 +41,11 @@ class Drift:
np.array([x, y]), metric="cityblock"
)
/ len(x),
- target=None,
flag: float = BAD,
**kwargs
) -> saqc.SaQC:
return self._defer("flagDriftFromReference", locals())
- def flagDriftFromScaledNorm(
- self,
- field: str,
- set_1: Sequence[str],
- set_2: Sequence[str],
- freq: str,
- spread: float,
- frac: float = 0.5,
- metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
- np.array([x, y]), metric="cityblock"
- )
- / len(x),
- method: LinkageString = "single",
- target: str = None,
- flag: float = BAD,
- **kwargs
- ) -> saqc.SaQC:
- return self._defer("flagDriftFromScaledNorm", locals())
-
def correctDrift(
self,
field: str,
diff --git a/saqc/core/modules/flagtools.py b/saqc/core/modules/flagtools.py
index 0df9dfc27d5b3e2c9761d40a2a6299f5f5fcbd47..bad840b58e2396dc9876ba966fad60bdc47f8d1e 100644
--- a/saqc/core/modules/flagtools.py
+++ b/saqc/core/modules/flagtools.py
@@ -39,3 +39,12 @@ class FlagTools:
**kwargs,
) -> saqc.SaQC:
return self._defer("flagManual", locals())
+
+ def transferFlags(
+ self,
+ field: str | Sequence[str],
+ target: str | Sequence[str],
+ flag: float = BAD,
+ **kwargs,
+ ) -> saqc.SaQC:
+ return self._defer("transferFlags", locals())
diff --git a/saqc/core/modules/outliers.py b/saqc/core/modules/outliers.py
index f66e3b10134d30340096dcfac7f8f668c7e7e49c..9d85d8e6bd08b0df35819ecb101f4ef632fb4173 100644
--- a/saqc/core/modules/outliers.py
+++ b/saqc/core/modules/outliers.py
@@ -39,7 +39,6 @@ class Outliers:
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:
@@ -104,13 +103,12 @@ class Outliers:
) -> saqc.SaQC:
return self._defer("flagRange", locals())
- def flagCrossStatistic(
+ def flagCrossStatistics(
self,
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/tools.py b/saqc/core/modules/tools.py
index cf6fd5d0a09df53c973c27580d486aa399e114ab..352922dc2db7381c507931c23722672f1380b6ea 100644
--- a/saqc/core/modules/tools.py
+++ b/saqc/core/modules/tools.py
@@ -12,7 +12,9 @@ from saqc.constants import FILTER_NONE
class Tools:
- def copyField(self, field: str, target: str, **kwargs) -> saqc.SaQC:
+ def copyField(
+ self, field: str, target: str, overwrite: bool = False, **kwargs
+ ) -> saqc.SaQC:
return self._defer("copyField", locals())
def dropField(self, field: str, **kwargs) -> saqc.SaQC:
@@ -38,11 +40,9 @@ class Tools:
field: str,
path: Optional[str] = None,
max_gap: Optional[str] = None,
- stats: bool = False,
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,
diff --git a/saqc/core/reader.py b/saqc/core/reader.py
index 1f1a6113a691e1bd8c16cbed1ced26ec764bbd48..c826a8cea762401538f5b10623b66f53375b29cb 100644
--- a/saqc/core/reader.py
+++ b/saqc/core/reader.py
@@ -4,6 +4,9 @@
import io
import ast
from pathlib import Path
+from urllib.request import urlopen
+from typing import TextIO
+
import pandas as pd
from saqc.core.core import SaQC
@@ -15,13 +18,29 @@ COMMENT = "#"
SEPARATOR = ";"
+def _openFile(fname) -> TextIO:
+ if isinstance(fname, (str, Path)):
+ try:
+ fobj = io.StringIO(urlopen(str(fname)).read().decode("utf-8"))
+ fobj.seek(0)
+ except ValueError:
+ fobj = io.open(fname, "r", encoding="utf-8")
+ else:
+ fobj = fname
+
+ return fobj
+
+
+def _closeFile(fobj):
+ try:
+ fobj.close()
+ except AttributeError:
+ pass
+
+
def readFile(fname) -> pd.DataFrame:
- fobj = (
- io.open(fname, "r", encoding="utf-8")
- if isinstance(fname, (str, Path))
- else fname
- )
+ fobj = _openFile(fname)
out = []
for i, line in enumerate(fobj):
@@ -39,10 +58,7 @@ def readFile(fname) -> pd.DataFrame:
)
out.append([i + 1] + parts)
- try:
- fobj.close()
- except AttributeError:
- pass
+ _closeFile(fobj)
df = pd.DataFrame(
out[1:],
@@ -54,6 +70,7 @@ def readFile(fname) -> pd.DataFrame:
return df
+# Todo: needs (maybe tiny) docstring!
def fromConfig(fname, *args, **kwargs):
saqc = SaQC(*args, **kwargs)
config = readFile(fname)
@@ -75,7 +92,7 @@ def fromConfig(fname, *args, **kwargs):
kwargs["field" if "field" not in kwargs else "target"] = fld
try:
- saqc = getattr(saqc, func_name)(regex=regex, **kwargs)
+ saqc = saqc.__getattr__(func_name)(regex=regex, **kwargs)
except Exception as e:
raise type(e)(f"failed to execute: {field} ; {expr}") from e
diff --git a/saqc/core/register.py b/saqc/core/register.py
index 8f7aec66886441b6eaa6dd524d9ed47d6b59eae1..72bfd0d079f4d5d1642fad44fce5d18ff15d016f 100644
--- a/saqc/core/register.py
+++ b/saqc/core/register.py
@@ -40,8 +40,6 @@ class FunctionWrapper:
# 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
@@ -85,7 +83,7 @@ class FunctionWrapper:
def _argnamesToColumns(names: list, values: dict):
clist = []
for name in names:
- value = values[name] # eg. the value behind 'field'
+ value = values.get(name) # eg. the value behind 'field'
# NOTE: do not change order of the tests
if value is None:
diff --git a/saqc/funcs/breaks.py b/saqc/funcs/breaks.py
index 92da3e89a6b4d0b4ff4a17260df9c1ee5e230c34..fc673eb37ade07507c9ce3fb40a10393f88396ef 100644
--- a/saqc/funcs/breaks.py
+++ b/saqc/funcs/breaks.py
@@ -2,10 +2,12 @@
# -*- coding: utf-8 -*-
-"""Detecting breakish changes in timeseries value courses.
+"""
+Detecting breaks in data.
-This module provides functions to detect and flag breakish changes in the data value course, like gaps
-(:py:func:`flagMissing`), jumps/drops (:py:func:`flagJumps`) or isolated values (:py:func:`flagIsolated`).
+This module provides functions to detect and flag breaks in data, for example temporal
+gaps (:py:func:`flagMissing`), jumps and drops (:py:func:`flagJumps`) or temporal
+isolated values (:py:func:`flagIsolated`).
"""
from typing import Tuple
@@ -20,7 +22,6 @@ from saqc.constants import *
from saqc.lib.tools import groupConsecutives
from saqc.funcs.changepoints import _assignChangePointCluster
from saqc.core.flags import Flags
-from saqc.core.history import History
from saqc.core.register import _isflagged, register, flagging
@@ -34,25 +35,34 @@ def flagMissing(
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- The function flags all values indicating missing data.
+ Flag NaNs in data.
+
+ By default only NaNs are flagged, that not already have a flag.
+ `to_mask` can be used to pass a flag that is used as threshold.
+ Each flag worse than the threshold is replaced by the function.
+ This is, because the data gets masked (with NaNs) before the
+ function evaluates the NaNs.
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ The data container.
+
field : str
- The fieldname of the column, holding the data-to-be-flagged.
+ Column(s) in flags and data.
+
flags : saqc.Flags
- Container to store quality flags to data.
+ The flags container.
+
flag : float, default BAD
- flag to set.
+ Flag to set.
Returns
-------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ Unmodified data container
flags : saqc.Flags
- The quality flags of data
+ The flags container
"""
datacol = data[field]
@@ -75,47 +85,53 @@ def flagIsolated(
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- The function flags arbitrary large groups of values, if they are surrounded by sufficiently
- large data gaps.
+ Find and flag temporal isolated groups of data.
- A gap is a timespan containing either no data or data invalid only (usually `nan`) .
+ The function flags arbitrarily large groups of values, if they are surrounded by
+ sufficiently large data gaps. A gap is a timespan containing either no data at all
+ or NaNs only.
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ The data container.
+
field : str
- The fieldname of the column, holding the data-to-be-flagged.
+ Column(s) in flags and data.
+
flags : saqc.Flags
- A flags object
+ The flags container.
+
gap_window : str
- The minimum size of the gap before and after a group of valid values, making this group considered an
- isolated group. See condition (2) and (3)
+ Minimum gap size required before and after a data group to consider it
+ isolated. See condition (2) and (3)
+
group_window : str
- The maximum temporal extension allowed for a group that is isolated by gaps of size 'gap_window',
- to be actually flagged as isolated group. See condition (1).
+ Maximum size of a data chunk to consider it a candidate for an isolated group.
+ Data chunks that are bigger than the ``group_window`` are ignored.
+ This does not include the possible gaps surrounding it.
+ See condition (1).
+
flag : float, default BAD
- flag to set.
+ Flag to set.
Returns
-------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ Unmodified data container
flags : saqc.Flags
- The flags object, holding flags and additional information related to `data`.
+ The flags container
Notes
-----
- A series of values :math:`x_k,x_{k+1},...,x_{k+n}`, with associated timestamps :math:`t_k,t_{k+1},...,t_{k+n}`,
- is considered to be isolated, if:
+ A series of values :math:`x_k,x_{k+1},...,x_{k+n}`, with associated
+ timestamps :math:`t_k,t_{k+1},...,t_{k+n}`, is considered to be isolated, if:
1. :math:`t_{k+1} - t_n <` `group_window`
- 2. None of the :math:`x_j` with :math:`0 < t_k - t_j <` `gap_window`, is valid (preceeding gap).
- 3. None of the :math:`x_j` with :math:`0 < t_j - t_(k+n) <` `gap_window`, is valid (succeding gap).
-
- See Also
- --------
- :py:func:`flagMissing`
+ 2. None of the :math:`x_j` with :math:`0 < t_k - t_j <` `gap_window`,
+ is valid (preceeding gap).
+ 3. None of the :math:`x_j` with :math:`0 < t_j - t_(k+n) <` `gap_window`,
+ is valid (succeding gap).
"""
gap_window = pd.tseries.frequencies.to_offset(gap_window)
group_window = pd.tseries.frequencies.to_offset(group_window)
@@ -155,26 +171,34 @@ def flagJumps(
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- Flag where the mean of the values significantly changes (the data "jumps").
+ Flag jumps and drops in data.
+
+ Flag data where the mean of its values significantly changes (the data "jumps").
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ The data container.
+
field : str
- The reference variable, the deviation from wich determines the flagging.
+ Column(s) in flags and data.
+
flags : saqc.Flags
- A flags object, holding flags and additional informations related to `data`.
+ The flags container.
+
thresh : float
- The threshold, the mean of the values have to change by, to trigger flagging.
+ Threshold value by which the mean of data has to change to trigger flagging.
+
window : str
- The temporal extension, of the rolling windows, the mean values that are to be
- compared, are obtained from.
+ Size of the moving window. This is the number of observations used
+ for calculating the statistic.
+
min_periods : int, default 1
- Minimum number of periods that have to be present in a window of size `window`,
- so that the mean value obtained from that window is regarded valid.
+ Minimum number of observations in window required to calculate a valid
+ mean value.
+
flag : float, default BAD
- flag to set.
+ Flag to set.
"""
return _assignChangePointCluster(
data,
diff --git a/saqc/funcs/changepoints.py b/saqc/funcs/changepoints.py
index 1cd87eff083fcb3cfaaaa4b22305a2dc944f28bb..46ba4f298836884ac046bf7ad9b9ca2f00449532 100644
--- a/saqc/funcs/changepoints.py
+++ b/saqc/funcs/changepoints.py
@@ -33,6 +33,8 @@ def flagChangePoints(
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
+ Flag data where it significantly changes.
+
Flag data points, where the parametrization of the process, the data is assumed to
generate by, significantly changes.
@@ -41,26 +43,29 @@ def flagChangePoints(
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ The data container.
field : str
- The reference variable, the deviation from wich determines the flagging.
+ A column in flags and data.
- flags : saqc.flags
- A flags object, holding flags and additional information related to `data`.
+ flags : saqc.Flags
+ The flags container.
- stat_func : Callable[numpy.array, numpy.array]
+ stat_func : Callable
A function that assigns a value to every twin window. The backward-facing
window content will be passed as the first array, the forward-facing window
content as the second.
- thresh_func : Callable[numpy.array, numpy.array]
+ thresh_func : Callable
A function that determines the value level, exceeding wich qualifies a
timestamps func value as denoting a change-point.
- window : str, tuple of string
- Size of the rolling windows the calculation is performed in. If it is a single
- frequency offset, it applies for the backward- and the forward-facing window.
+ window : str, tuple of str
+ Size of the moving windows. This is the number of observations used for
+ calculating the statistic.
+
+ If it is a single frequency offset, it applies for the backward- and the
+ forward-facing window.
If two offsets (as a tuple) is passed the first defines the size of the
backward facing window, the second the size of the forward facing window.
@@ -73,25 +78,35 @@ def flagChangePoints(
closed : {'right', 'left', 'both', 'neither'}, default 'both'
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.
+ reduce_window : str or None, 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.
+ 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.ndarray, numpy.ndarray], int], default lambda x, y: x.argmax()
+ 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.
+ 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`.
flag : float, default BAD
flag to set.
Returns
-------
+ data : dios.DictOfSeries
+ Unmodified data container
+ flags : saqc.Flags
+ The flags container
"""
return _assignChangePointCluster(
data,
@@ -128,12 +143,15 @@ def assignChangePointCluster(
**kwargs,
):
"""
+ Label data where it changes significantly.
+
+ The labels will be stored in data. Unless `target` is given the labels will
+ overwrite the data in `field`. The flags will always set to `UNFLAGGED`.
+
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.
Parameters
----------
@@ -187,10 +205,14 @@ def assignChangePointCluster(
that maximizes the stat_func.
model_by_resids : bool, default False
- If True, the stat_funcs results are written, otherwise the regime labels.
+ If True, the results of `stat_funcs` are written, otherwise the regime labels.
Returns
-------
+ data : dios.DictOfSeries
+ Modified data.
+ flags : saqc.Flags
+ The flags container
"""
reserved = ["assign_cluster", "set_flags", "flag"]
kwargs = filterKwargs(kwargs, reserved)
diff --git a/saqc/funcs/constants.py b/saqc/funcs/constants.py
index 18bf035497c79cbfc708f29753803707af0b5245..476d2d75cda99ba5eb8dfb319e52972313294bb6 100644
--- a/saqc/funcs/constants.py
+++ b/saqc/funcs/constants.py
@@ -1,7 +1,6 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
-from operator import mod
from typing import Tuple
import numpy as np
@@ -28,39 +27,49 @@ def flagConstants(
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- This functions flags plateaus/series of constant values of length `window` if
- their maximum total change is smaller than thresh.
+ Flag constant data values.
- Function flags plateaus/series of constant values. Any interval of values y(t),..y(t+n) is flagged, if:
+ Flags plateaus of constant data if their maximum total change in
+ a rolling window does not exceed a certain threshold.
- (1) n > `window`
- (2) `|(y(t + i) - (t + j)|` < `thresh`, for all i,j in [0, 1, ..., n]
-
- Flag values are (semi-)constant.
+ Any interval of values y(t),...,y(t+n) is flagged, if:
+ - (1): n > ``window``
+ - (2): abs(y(t + i) - (t + j)) < `thresh`, for all i,j in [0, 1, ..., n]
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ The data container.
+
field : str
- Name of the column, holding the data-to-be-flagged.
+ A column in flags and data.
+
flags : saqc.Flags
- Container to store quality flags to data.
+ The flags container.
+
thresh : float
- Upper bound for the maximum total change of an interval to be flagged constant.
- window : str
- Lower bound for the size of an interval to be flagged constant.
+ Maximum total change allowed per window.
+
+ window : str | int
+ Size of the moving window. This is the number of observations used
+ for calculating the statistic. Each window will be a fixed size.
+ If its an offset then this will be the time period of each window.
+ Each window will be a variable sized based on the observations included
+ in the time-period.
+
flag : float, default BAD
- flag to set.
+ Flag to set.
Returns
-------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ Unmodified data container
flags : saqc.Flags
- The flags object, holding flags and additional informations related to `data`.
- Flags values may have changed, relatively to the flags input.
+ The flags container
"""
+ if not isinstance(window, (str, int)):
+ raise TypeError("window must be offset string or int.")
+
d = data[field]
# min_periods=2 ensures that at least two non-nan values are present
@@ -84,7 +93,7 @@ def flagByVariance(
data: DictOfSeries,
field: str,
flags: Flags,
- window: str = "12h",
+ window: str,
thresh: float = 0.0005,
maxna: int = None,
maxna_group: int = None,
@@ -92,7 +101,12 @@ def flagByVariance(
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- Function flags plateaus/series of constant values. Any interval of values y(t),..y(t+n) is flagged, if:
+ Flag low-variance data.
+
+ Flags plateaus of constant data if the variance in a rolling window does not
+ exceed a certain threshold.
+
+ Any interval of values y(t),..y(t+n) is flagged, if:
(1) n > `window`
(2) variance(y(t),...,y(t+n) < `thresh`
@@ -100,30 +114,40 @@ def flagByVariance(
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ The data container.
+
field : str
- The fieldname of the column, holding the data-to-be-flagged.
+ A column in flags and data.
+
flags : saqc.Flags
- Container to store quality flags to data.
- window : str
- Only intervals of minimum size "window" have the chance to get flagged as constant intervals
- thresh : float
- The upper bound, the variance of an interval must not exceed, if the interval wants to be flagged a plateau.
+ The flags container.
+
+ window : str | int
+ Size of the moving window. This is the number of observations used
+ for calculating the statistic. Each window will be a fixed size.
+ If its an offset then this will be the time period of each window.
+ Each window will be sized, based on the number of observations included
+ in the time-period.
+
+ thresh : float, default 0.0005
+ Maximum total variance allowed per window.
+
maxna : int, default None
- Maximum number of NaNs tolerated in an interval. If more NaNs are present, the
- interval is not flagged as plateau.
+ Maximum number of NaNs allowed in window.
+ If more NaNs are present, the window is not flagged.
+
maxna_group : int, default None
Same as `maxna` but for consecutive NaNs.
+
flag : float, default BAD
- flag to set.
+ Flag to set.
Returns
-------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ Unmodified data container
flags : saqc.Flags
- The flags object, holding flags and additional informations related to `data`.
- Flags values may have changed, relatively to the flags input.
+ The flags container
"""
dataseries = data[field]
delta = getFreqDelta(dataseries.index)
diff --git a/saqc/funcs/curvefit.py b/saqc/funcs/curvefit.py
index a49cf98eb1e3b7df85c56fa7dc3fe6312d509999..d1be904674dbaa97cc1b8575875886d3252af6ac 100644
--- a/saqc/funcs/curvefit.py
+++ b/saqc/funcs/curvefit.py
@@ -1,7 +1,7 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
+from __future__ import annotations
-from math import floor
from typing import Tuple, Union
from typing_extensions import Literal
import numpy as np
@@ -25,7 +25,7 @@ def fitPolynomial(
data: DictOfSeries,
field: str,
flags: Flags,
- window: Union[int, str],
+ window: int | str,
order: int,
min_periods: int = 0,
**kwargs
@@ -52,37 +52,40 @@ def fitPolynomial(
Parameters
----------
- data : dios.DictOfSeries
- The data.
+ data : DictOfSeries
+ The data container.
field : str
- The column, holding the data-to-be-modelled.
+ A column in flags and data.
- flags : saqc.Flags
- Container to store quality flags to data.
+ flags : Flags
+ The flags container.
- window : {str, int}
- The size of the window you want to use for fitting. If an integer is passed,
+ window : str, int
+ 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.
+ data always a odd number of periods will be used for the fit (periods-1 if
+ periods is even).
order : int
- The degree of the polynomial used for fitting
+ Degree of the polynomial used for fitting
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.
+ Minimum number of observations in a window required to perform the fit,
+ otherwise NaNs will be assigned.
+ If ``None``, `min_periods` defaults to 1 for integer windows and to the
+ size of the window for offset based windows.
+ Passing 0, disables the feature and will result in over-fitting for too
+ sparse windows.
Returns
-------
data : dios.DictOfSeries
+ Modified data
flags : saqc.Flags
+ Flags
"""
reserved = ["residues", "set_flags"]
filterKwargs(kwargs, reserved)
diff --git a/saqc/funcs/drift.py b/saqc/funcs/drift.py
index 98a1c9e380cbddf41d93d6d8b051f2d788fccca2..ff874b53593df69e9c0d4afddfc0efb7030d484b 100644
--- a/saqc/funcs/drift.py
+++ b/saqc/funcs/drift.py
@@ -1,22 +1,23 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
+
+
from __future__ import annotations
import functools
-from typing import Optional, Tuple, Sequence, Callable, Optional
+from typing import Tuple, Sequence, Callable
from typing_extensions import Literal
import numpy as np
import pandas as pd
-from scipy import stats
from scipy.optimize import curve_fit
from scipy.spatial.distance import pdist
from dios import DictOfSeries
from saqc.constants import BAD
-from saqc.core.register import register, flagging
-from saqc.core import Flags
+
+from saqc.core.register import register, flagging, Flags
from saqc.funcs.changepoints import _assignChangePointCluster
from saqc.funcs.tools import dropField, copyField
@@ -33,7 +34,13 @@ LinkageString = Literal[
MODELDICT = {"linear": linearDriftModel, "exponential": expDriftModel}
-@flagging()
+@register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=["field"], # reference is written !
+ multivariate=True,
+ handles_target=False,
+)
def flagDriftFromNorm(
data: DictOfSeries,
field: Sequence[str],
@@ -50,81 +57,81 @@ def flagDriftFromNorm(
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- The function flags value courses that significantly deviate from a group of normal value courses.
+ Flags data that deviates from an avarage data course.
- "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 "field".
+ "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 "field".
See the Notes section for a more detailed presentation of the algorithm
Parameters
----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- 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`.
+ data : DictOfSeries
+ The data container.
+
+ field : str
+ A column in flags and data.
+
+ flags : Flags
+ The flags container.
+
freq : str
- An offset string, determining the size of the seperate datachunks that the algorihm is to be piecewise
- applied on.
+ Frequency, that split the data in chunks.
+
spread : float
- A parameter limiting the maximum "spread" of the timeseries, allowed in the "normal" group. See Notes section
- for more details.
+ Maximum spread allowed in the group of *normal* data. See Notes section for more details.
+
frac : float, default 0.5
- Has to be in [0,1]. Determines the minimum percentage of variables, the "normal" group has to comprise to be the
- normal group actually. The higher that value, the more stable the algorithm will be with respect to false
- positives. Also, nobody knows what happens, if this value is below 0.5.
- metric : Callable[[numpy.array, numpy.array], float]
- 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.
+ Fraction defining the normal group. Use a value from the interval [0,1].
+ The higher the value, the more stable the algorithm will be. For values below
+ 0.5 the results are undefined.
+
+ metric : Callable, default ``lambda x,y:pdist(np.array([x,y]),metric="cityblock")/len(x)``
+ Distance function that takes two arrays as input and returns a scalar float.
+ This value is interpreted as the distance of the two input arrays.
+ Defaults to the `averaged manhattan metric` (see Notes).
+
method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single"
- The linkage method used for hierarchical (agglomerative) clustering of the timeseries.
- See the Notes section for more details.
- The keyword gets passed on to scipy.hierarchy.linkage. See its documentation to learn more about the different
- keywords (References [1]).
- See wikipedia for an introduction to hierarchical clustering (References [2]).
+ Linkage method used for hierarchical (agglomerative) clustering of the data.
+ `method` is directly passed to ``scipy.hierarchy.linkage``. See its documentation [1] for
+ more details. For a general introduction on hierarchical clustering see [2].
+
flag : float, default BAD
flag to set.
Returns
-------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
flags : saqc.Flags
- The quality flags of data
- Flags values may have changed relatively to the input flags.
Notes
-----
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 `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 field.
+ 1. Calculate distances :math:`d(x_i,x_j)` for all :math:`x_i` in parameter `field`.
+ (with :math:`d` denoting the distance function, specified by `metric`.
+ 2. Calculate a dendogram with a hierarchical linkage algorithm, specified by `method`.
+ 3. Flatten the dendogram at the level, the agglomeration costs exceed `spread`
+ 4. check if a cluster containing more than `frac` variables.
1. if yes: flag all the variables that are not in that cluster (inside the segment)
2. if no: flag nothing
- The main parameter giving control over the algorithms behavior is the `spread` parameter, that determines
- the maximum spread of a normal group by limiting the costs, a cluster agglomeration must not exceed in every
- linkage step.
- For singleton clusters, that costs just equal half the distance, the timeseries in the clusters, have to
- each other. So, no timeseries can be clustered together, that are more then
- 2*`spread` distanted from each other.
- When timeseries get clustered together, this new clusters distance to all the other timeseries/clusters is
- calculated according to the linkage method specified by `method`. By default, it is the minimum distance,
- the members of the clusters have to each other.
- Having that in mind, it is advisable to choose a distance function, that can be well interpreted in the units
- dimension of the measurement and where the interpretation is invariant over the length of the timeseries.
- 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).
+ The main parameter giving control over the algorithms behavior is the `spread` parameter,
+ that determines the maximum spread of a normal group by limiting the costs, a cluster
+ agglomeration must not exceed in every linkage step.
+ For singleton clusters, that costs just equal half the distance, the data in the
+ clusters, have to each other. So, no data can be clustered together, that are more then
+ 2*`spread` distances away from each other. When data get clustered together, this new
+ clusters distance to all the other data/clusters is calculated according to the linkage
+ method specified by `method`. By default, it is the minimum distance, the members of the
+ clusters have to each other. Having that in mind, it is advisable to choose a distance
+ function, that can be well interpreted in the units dimension of the measurement and where
+ the interpretation is invariant over the length of the data. That is, why,
+ the "averaged manhattan metric" is set as the metric default, since it corresponds to the
+ averaged value distance, two data sets have (as opposed by euclidean, for example).
References
----------
@@ -134,6 +141,7 @@ def flagDriftFromNorm(
[2] https://en.wikipedia.org/wiki/Hierarchical_clustering
"""
fields = toSequence(field)
+
data_to_flag = data[fields].to_df()
data_to_flag.dropna(inplace=True)
@@ -151,13 +159,12 @@ def flagDriftFromNorm(
return data, flags
-# default multivariate flagging
@register(
mask=["field", "reference"],
demask=["field", "reference"],
squeeze=["field", "reference"], # reference is written !
multivariate=True,
- handles_target=True,
+ handles_target=False,
)
def flagDriftFromReference(
data: DictOfSeries,
@@ -170,64 +177,66 @@ def flagDriftFromReference(
np.array([x, y]), metric="cityblock"
)
/ len(x),
- target=None,
flag: float = BAD,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- The function flags value courses that deviate from a reference course by a margin exceeding a certain threshold.
+ Flags data that deviates from a reference course.
- The deviation is measured by the distance function passed to parameter metric.
+ The deviation is measured by a passed distance function.
Parameters
----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- 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`.
- reference : str
- The reference variable, the deviation from wich determines the flagging.
+ data : DictOfSeries
+ The data container.
+
+ field : str
+ A column in flags and data.
+
+ flags : Flags
+ The flags container.
+
freq : str
- An offset string, determining the size of the seperate datachunks that the algorihm is to be piecewise
- applied on.
+ Frequency, that split the data in chunks.
+
+ reference : str
+ Reference variable, the deviation is calculated from.
+
thresh : float
- The threshod by wich normal variables can deviate from the reference variable at max.
- metric : Callable[(numpyp.array, numpy-array), float]
- 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.
+ Maximum deviation from reference.
+
+ metric : Callable
+ Distance function. Takes two arrays as input and returns a scalar float.
+ This value is interpreted as the mutual distance of the two input arrays.
+ Defaults to the `averaged manhattan metric` (see Notes).
+
target : None
- ignored.
+ Ignored.
+
flag : float, default BAD
- flag to set.
+ Flag to set.
Returns
-------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
flags : saqc.Flags
- The quality flags of data
- Flags values may have changed relatively to the input flags.
Notes
-----
- it is advisable to choose a distance function, that can be well interpreted in the units
- dimension of the measurement and where the interpretation is invariant over the length of the timeseries.
- 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).
+ It is advisable to choose a distance function, that can be well interpreted in
+ the units dimension of the measurement and where the interpretation is invariant over the
+ length of the data. That is, why, the "averaged manhatten metric" is set as the metric
+ default, since it corresponds to the averaged value distance, two data sets have (as opposed
+ by euclidean, for example).
"""
- if target:
- raise NotImplementedError("target is not implemented for this function")
fields = toSequence(field)
- data_to_flag = data[fields].to_df().dropna()
- fields = list(fields)
if reference not in fields:
fields.append(reference)
+ data_to_flag = data[fields].to_df().dropna()
+
segments = data_to_flag.groupby(pd.Grouper(freq=freq))
for segment in segments:
@@ -245,156 +254,6 @@ def flagDriftFromReference(
return data, flags
-# 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: str,
- spread: float,
- frac: float = 0.5,
- metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
- np.array([x, y]), metric="cityblock"
- )
- / len(x),
- method: LinkageString = "single",
- target: str = None,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- The function linearly rescales one set of variables to another set of variables
- with a different scale and then flags value courses that significantly deviate
- from a group of normal value courses.
-
- The two sets of variables can be linearly scaled one to another and hence the
- scaling transformation is performed via linear regression: A linear regression is
- performed on each pair of variables giving a slope and an intercept. The
- transformation is then calculated a the median of all the calculated slopes and
- intercepts.
-
- Once the transformation is performed, the function flags those values,
- that deviate from a group of normal values. "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".
-
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
-
- field : str
- A dummy parameter.
-
- flags : saqc.Flags
- A flags object, holding flags and additional informations related to `data`.
-
- set_1 : str
- The first set of fieldnames in data to be included into the flagging process.
-
- set_2 : str
- The second set of fieldnames in data 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.
-
- spread : float
- A parameter limiting the maximum "spread" of the timeseries, allowed in the
- "normal" group. See Notes section for more details.
-
- frac : float, default 0.5
- Has to be in [0,1]. Determines the minimum percentage of variables,
- the "normal" group has to comprise to be the normal group actually. The
- higher that value, the more stable the algorithm will be with respect to
- false positives. Also, nobody knows what happens, if this value is below 0.5.
-
- metric : Callable[(numpyp.array, numpy-array), float]
- 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.
-
- method : str, default "single"
- The linkage method used for hierarchical (agglomerative) clustering of the
- timeseries. See the Notes section for more details. The keyword gets passed
- on to scipy.hierarchy.linkage. See its documentation to learn more about the
- different keywords (References [1]). See wikipedia for an introduction to
- hierarchical clustering (References [2]).
-
- target : None
- ignored
-
- flag : float, default BAD
- flag to set.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- Flags values may have changed relatively to the input flags.
-
- References
- ----------
- Documentation of the underlying hierarchical clustering algorithm:
- [1] https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html
- 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().dropna()
-
- convert_slope = []
- convert_intercept = []
-
- for field1 in set_1:
- for field2 in set_2:
- slope, intercept, *_ = stats.linregress(
- data_to_flag[field1], data_to_flag[field2]
- )
- convert_slope.append(slope)
- convert_intercept.append(intercept)
-
- factor_slope = np.median(convert_slope)
- factor_intercept = np.median(convert_intercept)
-
- dat = DictOfSeries()
- for field1 in set_1:
- dat[field1] = factor_intercept + factor_slope * data_to_flag[field1]
- for field2 in set_2:
- dat[field2] = data_to_flag[field2]
-
- dat_to_flag = dat[fields].to_df()
-
- segments = dat_to_flag.groupby(pd.Grouper(freq=freq))
- for segment in segments:
-
- if segment[1].shape[0] <= 1:
- continue
-
- drifters = detectDeviants(segment[1], metric, spread, frac, method, "variables")
-
- for var in drifters:
- flags[segment[1].index, fields[var]] = flag
-
- return data, flags
-
-
@register(mask=["field"], demask=[], squeeze=[])
def correctDrift(
data: DictOfSeries,
@@ -412,17 +271,21 @@ def correctDrift(
Parameters
----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ data : DictOfSeries
+ The data container.
+
field : str
- The fieldname of the data column, you want to correct.
+ Column in data and flags.
+
flags : saqc.Flags
- Container to store quality flags to data.
+ Flags container.
+
maintenance_field : str
- The fieldname of the datacolumn holding the support-points information.
- 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.
+ Column holding the support-points information.
+ The data is expected to have the following form:
+ The index of the series represents the beginning of a maintenance
+ event, wheras the values represent its endings.
+
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.
@@ -431,9 +294,10 @@ def correctDrift(
After the data parameter, there can occure an arbitrary number of model calibration arguments in
the signature.
See the Notes section for an extensive description.
+
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)
+ Number of values to calculate the mean of, for obtaining the value level directly
+ after and directly before a maintenance event. Needed for shift calibration.
Returns
-------
@@ -445,25 +309,27 @@ 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).
+ 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).
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:::
- 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 next support point.
@@ -473,12 +339,13 @@ def correctDrift(
Linear drift modell (no free parameters).
- >>> M = lambda t, origin, target: origin + t * target
+
+ >>> Model = 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))
+ >>> Model = 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``.
@@ -538,7 +405,7 @@ def correctRegimeAnomaly(
flags: Flags,
cluster_field: str,
model: CurveFitter,
- tolerance: Optional[str] = None,
+ tolerance: str = None,
epoch: bool = False,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
@@ -659,7 +526,7 @@ def correctOffset(
spread: float,
window: str,
min_periods: int,
- tolerance: Optional[str] = None,
+ tolerance: str = None,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
@@ -837,7 +704,7 @@ def assignRegimeAnomaly(
cluster_field: str,
spread: float,
method: LinkageString = "single",
- metric: Callable[[np.array, np.array], float] = lambda x, y: np.abs(
+ metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: np.abs(
np.nanmean(x) - np.nanmean(y)
),
frac: float = 0.5,
diff --git a/saqc/funcs/flagtools.py b/saqc/funcs/flagtools.py
index 12bea62a516241f287aa7f6bcd7ae793b854474a..cafe7e1d74c36c6d6eb4248e2e5245bfed927ec0 100644
--- a/saqc/funcs/flagtools.py
+++ b/saqc/funcs/flagtools.py
@@ -1,6 +1,7 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
-from typing import Any, Tuple, Union
+from __future__ import annotations
+from typing import Any, Tuple, Union, Sequence
from typing_extensions import Literal
import pandas as pd
from dateutil.parser import ParserError
@@ -12,6 +13,7 @@ from saqc.core import register, Flags
import warnings
from saqc.core.register import flagging
+from saqc.funcs.generic import flagGeneric
@register(mask=[], demask=[], squeeze=["field"])
@@ -206,54 +208,64 @@ def flagManual(
--------
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
- 2000-03-01 0
- 2001-05-01 1
- dtype: int64
+ .. doctest:: ExampleFlagManual
- On *dayly* data, with the 'ontime' method, only the provided timestamnps are used.
+ >>> mdata = pd.Series([1,0,1], index=pd.to_datetime(['2000-02', '2000-03', '2001-05']))
+ >>> mdata
+ 2000-02-01 1
+ 2000-03-01 0
+ 2001-05-01 1
+ dtype: int64
+
+ On *dayly* data, with the 'ontime' method, only the provided timestamps 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
- 2000-02-01 True
- 2000-02-02 False
- 2000-02-03 False
- .. ..
- 2000-02-29 False
- 2000-03-01 True
- 2000-03-02 False
- Freq: D, dtype: bool
+ .. doctest:: ExampleFlagManual
+
+ >>> data = a=pd.Series(0, index=pd.date_range('2000-01-31', '2000-03-02', freq='1D'), name='dailyData')
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagManual('dailyData', mdata, mflag=1, mformat='mdata', method='ontime')
+ >>> qc.flags['dailyData'] > UNFLAGGED #doctest:+SKIP
+ 2000-01-31 False
+ 2000-02-01 True
+ 2000-02-02 False
+ 2000-02-03 False
+ .. ..
+ 2000-02-29 False
+ 2000-03-01 True
+ 2000-03-02 False
+ 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
- 2000-02-01 True
- 2000-02-02 True
- .. ..
- 2000-02-29 True
- 2000-03-01 False
- 2000-03-02 False
- Freq: D, dtype: bool
+ .. doctest:: ExampleFlagManual
+
+ >>> qc = qc.flagManual('dailyData', mdata, mflag=1, mformat='mdata', method='right-open')
+ >>> qc.flags['dailyData'] > UNFLAGGED #doctest:+SKIP
+ 2000-01-31 False
+ 2000-02-01 True
+ 2000-02-02 True
+ .. ..
+ 2000-02-29 True
+ 2000-03-01 False
+ 2000-03-02 False
+ 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
- 2000-02-01 False
- 2000-02-02 True
- .. ..
- 2000-02-29 True
- 2000-03-01 True
- 2000-03-02 False
- Freq: D, dtype: bool
+ .. doctest:: ExampleFlagManual
+
+ >>> qc = qc.flagManual('dailyData', mdata, mflag=1, mformat='mdata', method='left-open')
+ >>> qc.flags['dailyData'] > UNFLAGGED #doctest:+SKIP
+ 2000-01-31 False
+ 2000-02-01 False
+ 2000-02-02 True
+ .. ..
+ 2000-02-29 True
+ 2000-03-01 True
+ 2000-03-02 False
+ Freq: D, dtype: bool
"""
dat = data[field]
# internal not-mflag-value -> cant go for np.nan
@@ -346,3 +358,88 @@ def flagDummy(
The quality flags of data
"""
return data, flags
+
+
+@register(
+ mask=[],
+ demask=[],
+ squeeze=["target"],
+ handles_target=True,
+)
+def transferFlags(
+ data: DictOfSeries,
+ field: str | Sequence[str],
+ flags: Flags,
+ target: str | Sequence[str],
+ **kwargs,
+):
+ """
+ Transfer Flags of one variable to another.
+
+ Parameters
+ ----------
+
+ data : {pd.DataFrame, dios.DictOfSeries}
+ data
+
+ field : str or List of str
+ Variable or list of variables, the flags of which are to be transferred.
+
+ flags : {pd.DataFrame, dios.DictOfSeries, saqc.flagger}
+ Flags or flagger object
+
+ target : str or List of str
+ Variable or list of variables, the flags of `field` are to be transferred to.
+
+ See Also
+ --------
+ * :py:meth:`saqc.SaQC.flagGeneric`
+
+ Examples
+ --------
+ First, generate some data with some flags:
+
+ .. doctest:: exampleTransfer
+
+ >>> import pandas as pd #doctest:+SKIP
+ >>> import saqc #doctest:+SKIP
+ >>> data = pd.DataFrame({'a':[1,2], 'b':[1,2], 'c':[1,2]})
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagRange('a', max=1.5)
+ >>> qc.flags
+ columns a b c
+ 0 -inf -inf -inf
+ 1 255.0 -inf -inf
+
+ Now we can project the flag from `a` to `b` via
+
+ .. doctest:: exampleTransfer
+
+ >>> qc = qc.transferFlags('a', target='b')
+ >>> qc.flags
+ columns a b c
+ 0 -inf -inf -inf
+ 1 255.0 255.0 -inf
+
+ You can skip the explicit target parameter designation:
+
+ .. doctest:: exampleTransfer
+
+ >>> qc = qc.transferFlags('a', 'b')
+
+ To project the flags of `a` to both the variables `b` and `c` in one call, align the field and target variables in
+ 2 lists:
+
+ .. doctest:: exampleTransfer
+
+ >>> qc = qc.transferFlags(['a','a'], ['b', 'c'])
+ >>> qc.flags
+ columns a b c
+ 0 -inf -inf -inf
+ 1 255.0 255.0 255.0
+ """
+
+ data, flags = flagGeneric(
+ data, field, flags, target=target, func=lambda x: isflagged(x), **kwargs
+ )
+ return data, flags
diff --git a/saqc/funcs/generic.py b/saqc/funcs/generic.py
index 5f7977b684c0ae382b95f3d83370a213a2541c53..aa1da90e4986df65074498c7a47e7276eb450882 100644
--- a/saqc/funcs/generic.py
+++ b/saqc/funcs/generic.py
@@ -45,6 +45,12 @@ def _execGeneric(
data = data.to_frame()
out = func(*[data[c] for c in data.columns])
+ if pd.api.types.is_scalar(out):
+ raise ValueError(
+ "generic function should return a sequence object, "
+ f"got '{type(out)}' instead"
+ )
+
return DictOfSeries(out)
@@ -121,7 +127,12 @@ def processGeneric(
Compute the sum of the variables 'rainfall' and 'snowfall' and save the result to
a (new) variable 'precipitation'
- >>> saqc.genericProcess(field=["rainfall", "snowfall"], target="precipitation'", func=lambda x, y: x + y)
+ .. testsetup::
+
+ qc = saqc.SaQC(pd.DataFrame({'rainfall':[0], 'snowfall':[0], 'precipitation':[0]}, index=pd.DatetimeIndex([0])))
+
+
+ >>> qc = qc.processGeneric(field=["rainfall", "snowfall"], target="precipitation'", func=lambda x, y: x + y)
"""
fields = toSequence(field)
@@ -134,6 +145,7 @@ def processGeneric(
"func": "procGeneric",
"args": (),
"kwargs": {
+ **kwargs,
"field": field,
"func": func.__name__,
"target": target,
@@ -228,17 +240,27 @@ def flagGeneric(
Examples
--------
+ .. testsetup:: exampleFlagGeneric
+
+ qc = saqc.SaQC(pd.DataFrame({'temperature':[0], 'uncertainty':[0], 'rainfall':[0], 'fan':[0]}, index=pd.DatetimeIndex([0])))
+
1. Flag the variable 'rainfall', if the sum of the variables 'temperature' and 'uncertainty' is below zero:
- >>> saqc.flagGeneric(field=["temperature", "uncertainty"], target="rainfall", func= lambda x, y: temperature + uncertainty < 0
+ .. testcode:: exampleFlagGeneric
+
+ qc.flagGeneric(field=["temperature", "uncertainty"], target="rainfall", func= lambda x, y: x + y < 0)
2. Flag the variable 'temperature', where the variable 'fan' is flagged:
- >>> saqc.flagGeneric(field="fan", target="temperature", func=lambda x: isflagged(x))
+ .. testcode:: exampleFlagGeneric
+
+ qc.flagGeneric(field="fan", target="temperature", func=lambda x: isflagged(x))
3. The generic functions also support all pandas and numpy functions:
- >>> saqc.flagGeneric(field="fan", target="temperature", func=lambda x: np.sqrt(x) < 7)
+ .. testcode:: exampleFlagGeneric
+
+ qc = qc.flagGeneric(field="fan", target="temperature", func=lambda x: np.sqrt(x) < 7)
"""
fields = toSequence(field)
@@ -252,13 +274,14 @@ def flagGeneric(
f"the generic function returned {len(result.columns)} field(s), but only {len(targets)} target(s) were given"
)
- if not (result.dtypes == bool).all():
+ if not result.empty and not (result.dtypes == bool).all():
raise TypeError(f"generic expression does not return a boolean array")
meta = {
"func": "flagGeneric",
"args": (),
"kwargs": {
+ **kwargs,
"field": field,
"func": func.__name__,
"target": target,
diff --git a/saqc/funcs/interpolation.py b/saqc/funcs/interpolation.py
index 6076ba88ce053d4c7b236e1d6f48049008905b98..ba131d8fc84b606ae358ada6e021ce4ad9772f26 100644
--- a/saqc/funcs/interpolation.py
+++ b/saqc/funcs/interpolation.py
@@ -206,7 +206,7 @@ def _resampleOverlapping(data: pd.Series, freq: str, fill_value):
return data.fillna(fill_value).astype(dtype)
-@processing()
+@register(mask=["field"], demask=[], squeeze=[])
def interpolateIndex(
data: DictOfSeries,
field: str,
diff --git a/saqc/funcs/outliers.py b/saqc/funcs/outliers.py
index adeb2091302651e449d4cd0f6b5086131e0add69..c41079481420a3f5d5752b6af54d42bf6fa897d3 100644
--- a/saqc/funcs/outliers.py
+++ b/saqc/funcs/outliers.py
@@ -1,6 +1,7 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
-import warnings
+
+import uuid
from typing import Optional, Union, Tuple, Sequence, Callable
from typing_extensions import Literal
@@ -13,7 +14,7 @@ from dios import DictOfSeries
from outliers import smirnov_grubbs
from scipy.optimize import curve_fit
-from saqc.constants import *
+from saqc.constants import BAD, UNFLAGGED
from saqc.core import register, Flags
from saqc.core.register import flagging
from saqc.lib.tools import customRoller, findIndex, getFreqDelta, toSequence
@@ -134,7 +135,7 @@ def _evalStrayLabels(
data: DictOfSeries,
field: str,
flags: Flags,
- fields: Sequence[str],
+ target: Sequence[str],
reduction_range: Optional[str] = None,
reduction_drop_flagged: bool = False, # TODO: still a case ?
reduction_thresh: float = 3.5,
@@ -158,7 +159,7 @@ def _evalStrayLabels(
flags : saqc.Flags
Container to store quality flags to data.
- fields : list of str
+ target : list of str
A list of strings, holding the column names of the variables, the stray labels
shall be projected onto.
@@ -173,7 +174,7 @@ def _evalStrayLabels(
reduction_range : {None, str}
An offset string, denoting the range of the temporal surrounding to include
into the MAD testing. If ``None`` is passed, no testing will be performed and
- all fields will have the stray flag projected.
+ all targets will have the stray flag projected.
reduction_drop_flagged : bool, default False
Wheather or not to drop flagged values other than the value under test, from the
@@ -196,17 +197,17 @@ def _evalStrayLabels(
----------
[1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
"""
- val_frame = data[fields].to_df()
+ val_frame = data[target].to_df()
stray_detects = flags[field] > UNFLAGGED
stray_detects = stray_detects[stray_detects]
- to_flag_frame = pd.DataFrame(False, columns=fields, index=stray_detects.index)
+ to_flag_frame = pd.DataFrame(False, columns=target, index=stray_detects.index)
if reduction_range is None:
for field in to_flag_frame.columns:
flags[to_flag_frame.index, field] = flag
return data, flags
- for var in fields:
+ for var in target:
for index in enumerate(to_flag_frame.index):
index_slice = slice(
@@ -403,7 +404,7 @@ def _expFit(
demask=["field"],
squeeze=["field"],
multivariate=True,
- handles_target=True,
+ handles_target=False,
)
def flagMVScores(
data: DictOfSeries,
@@ -420,7 +421,6 @@ def flagMVScores(
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]:
@@ -506,9 +506,6 @@ def flagMVScores(
necessarily present in a reduction interval for reduction actually to be
performed.
- target : None
- ignored.
-
flag : float, default BAD
flag to set.
@@ -558,19 +555,24 @@ 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)
+
+ fields_ = []
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)
+ field_ = str(uuid.uuid4())
+ data, flags = copyField(data, field=f, flags=flags, target=field_)
+ data, flags = transform(
+ data, field=field_, flags=flags, func=trafo, freq=partition
+ )
+ fields_.append(field_)
+ knn_field = str(uuid.uuid4())
data, flags = assignKNNScore(
data=data,
- field=[f"trafo_{f}" for f in fields],
+ field=fields_,
flags=flags,
- target="kNN",
+ target=knn_field,
n=n,
func=func,
freq=partition,
@@ -578,11 +580,13 @@ def flagMVScores(
min_periods=partition_min,
**kwargs,
)
+ for field_ in fields_:
+ data, flags = dropField(data, field_, flags)
data, flags = flagByStray(
- data,
- "kNN",
- flags,
+ data=data,
+ field=knn_field,
+ flags=flags,
freq=partition,
min_periods=partition_min,
iter_start=iter_start,
@@ -593,8 +597,8 @@ def flagMVScores(
data, flags = _evalStrayLabels(
data=data,
- field="kNN",
- fields=fields,
+ field=knn_field,
+ target=fields,
flags=flags,
reduction_range=stray_range,
reduction_drop_flagged=drop_flagged,
@@ -603,9 +607,7 @@ def flagMVScores(
flag=flag,
**kwargs,
)
- data, flags = dropField(data, "kNN", flags)
- for f in fields:
- data, flags = dropField(data, f"trafo_{f}", flags)
+ data, flags = dropField(data, knn_field, flags)
return data, flags
@@ -1202,16 +1204,15 @@ def flagRange(
demask=["field"],
squeeze=["field"],
multivariate=True,
- handles_target=True,
+ handles_target=False,
)
-def flagCrossStatistic(
+def flagCrossStatistics(
data: DictOfSeries,
field: Sequence[str],
flags: Flags,
thresh: float,
method: Literal["modZscore", "Zscore"] = "modZscore",
flag: float = BAD,
- target: str = None,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
@@ -1248,10 +1249,6 @@ def flagCrossStatistic(
flag : float, default BAD
flag to set.
- target : None
- ignored
-
-
Returns
-------
data : dios.DictOfSeries
@@ -1264,10 +1261,16 @@ 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)
+
+ for src in fields[1:]:
+ if (data[src].index != data[fields[0]].index).any():
+ raise ValueError(
+ f"indices of '{fields[0]}' and '{src}' are not compatibble, "
+ "please resample all variables to a common (time-)grid"
+ )
+
df = data[fields].loc[data[fields].index_of("shared")].to_df()
if isinstance(method, str):
@@ -1303,7 +1306,7 @@ def flagCrossStatistic(
if mask.empty:
return data, flags
- for var in fields:
- flags[mask[var], var] = flag
+ for f in fields:
+ flags[mask[f], f] = flag
return data, flags
diff --git a/saqc/funcs/resampling.py b/saqc/funcs/resampling.py
index 35a1ce6626aa069d48ada12faeedb6bbf201a9d7..79566f5ed20734d8e117ff5932e686ca6b524a05 100644
--- a/saqc/funcs/resampling.py
+++ b/saqc/funcs/resampling.py
@@ -27,7 +27,7 @@ METHOD2ARGS = {
}
-@processing()
+@register(mask=["field"], demask=[], squeeze=[])
def linear(
data: DictOfSeries,
field: str,
@@ -75,7 +75,7 @@ def linear(
return interpolateIndex(data, field, flags, freq, "time", **kwargs)
-@processing()
+@register(mask=["field"], demask=[], squeeze=[])
def interpolate(
data: DictOfSeries,
field: str,
@@ -141,7 +141,6 @@ def interpolate(
)
-# for @processing this would need to handle dfilter
@register(mask=["field"], demask=[], squeeze=[])
def shift(
data: DictOfSeries,
@@ -219,7 +218,6 @@ def shift(
return data, flags
-# for @processing this would need to handle dfilter
@register(mask=["field"], demask=[], squeeze=[])
def resample(
data: DictOfSeries,
diff --git a/saqc/funcs/scores.py b/saqc/funcs/scores.py
index 5c41170b68f3decd4aabd9e81e05737ab8e080eb..e931b72c7ad8d632c83b5cd569714a39583cdb1b 100644
--- a/saqc/funcs/scores.py
+++ b/saqc/funcs/scores.py
@@ -101,8 +101,13 @@ def assignKNNScore(
----------
[1] https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html
"""
- if target in data.columns:
- raise ValueError(f"'target' must not exist.")
+ if isinstance(target, list):
+ if (len(target) > 1) or (target[0] in data.columns):
+ raise ValueError(
+ f"'target' must not exist and be of length 1. {target} was passed instead."
+ )
+ target = target[0]
+
fields = toSequence(field)
val_frame = data[fields].copy()
score_index = val_frame.index_of("shared")
diff --git a/saqc/funcs/tools.py b/saqc/funcs/tools.py
index 9bd2ee6d668dc842c97d8f66f2adc764b462e1e9..c67584fa6d4ab0c75d57fb6e7b3eeb28f851a6c5 100644
--- a/saqc/funcs/tools.py
+++ b/saqc/funcs/tools.py
@@ -22,7 +22,12 @@ _MPL_DEFAULT_BACKEND = mpl.get_backend()
@register(mask=[], demask=[], squeeze=[], handles_target=True)
def copyField(
- data: DictOfSeries, field: str, flags: Flags, target: str, **kwargs
+ data: DictOfSeries,
+ field: str,
+ flags: Flags,
+ target: str,
+ overwrite: bool = False,
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
Copy data and flags to a new name (preserve flags history).
@@ -47,8 +52,13 @@ def copyField(
The quality flags of data
Flags shape may have changed relatively to the flags input.
"""
+ if field == target:
+ return data, flags
+
if target in flags.columns.union(data.columns):
- raise ValueError(f"{field}: field already exist")
+ if not overwrite:
+ raise ValueError(f"{target}: already exist")
+ data, flags = dropField(data=data, flags=flags, field=target)
data[target] = data[field].copy()
flags.history[target] = flags.history[field].copy()
@@ -249,12 +259,11 @@ def plot(
flags: Flags,
path: Optional[str] = None,
max_gap: Optional[str] = None,
- stats: bool = False,
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,
+ ax_kwargs: Optional[dict] = None,
dfilter: float = FILTER_ALL,
**kwargs,
):
@@ -290,9 +299,6 @@ def plot(
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.
-
history : {"valid", "complete", None}, default "valid"
Discriminate the plotted flags with respect to the tests they originate from.
@@ -315,35 +321,10 @@ def plot(
``{'pickle': True}``, but note that all other store_kwargs are ignored then.
Reopen with: ``pickle.load(open(savepath,'w')).show()``
- stats_dict: dict, default None
- (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)}``
-
- The lambda args ``x``,``y``,``z`` will be fed by:
-
- * ``x``: the data (``data[field]``).
- * ``y``: the flags (``flags[field]``).
- * ``z``: The passed flags level (``kwargs[flag]``)
+ ax_kwargs : dict, default {}
+ Axis keywords. Change the axis labeling defaults. Most important keywords:
+ 'x_label', 'y_label', 'title', 'fontsize'.
- See examples section for examples
-
- Examples
- --------
- Summary statistic function examples:
-
- >>> func = lambda x, y, z: len(x)
-
- Total number of nan-values:
-
- >>> func = lambda x, y, z: x.isna().sum()
-
- Percentage of values, flagged greater than passed flag (always round float results
- to avoid table cell overflow):
-
- >>> func = lambda x, y, z: round((x.isna().sum()) / len(x), 2)
"""
interactive = path is None
@@ -356,6 +337,9 @@ def plot(
if store_kwargs is None:
store_kwargs = {}
+ if ax_kwargs is None:
+ ax_kwargs = {}
+
if interactive:
mpl.use(_MPL_DEFAULT_BACKEND)
@@ -368,11 +352,10 @@ def plot(
flags=flags,
level=level,
max_gap=max_gap,
- stats=stats,
history=history,
xscope=xscope,
phaseplot=phaseplot,
- stats_dict=stats_dict,
+ ax_kwargs=ax_kwargs,
)
if interactive:
diff --git a/saqc/lib/plotting.py b/saqc/lib/plotting.py
index 08305933f2d25838cc8f4cb627fbaa7d46740078..588954a7b21b7570ed4b9b83dff5f5861d394fa8 100644
--- a/saqc/lib/plotting.py
+++ b/saqc/lib/plotting.py
@@ -24,7 +24,6 @@ STATSDICT = {
}
PLOT_KWARGS = {"alpha": 0.8, "linewidth": 1}
-AX_KWARGS = {}
FIG_KWARGS = {"figsize": (16, 9)}
SCATTER_KWARGS = {
"marker": ["s", "D", "^", "o"],
@@ -42,11 +41,10 @@ def makeFig(
flags: Flags,
level: float,
max_gap: Optional[str] = None,
- stats: bool = False,
history: Optional[Literal["valid", "complete"]] = "valid",
xscope: Optional[slice] = None,
phaseplot: Optional[str] = None,
- stats_dict: Optional[dict] = None,
+ ax_kwargs: Optional[dict] = None,
):
"""
Returns a figure object, containing data graph with flag marks for field.
@@ -71,8 +69,6 @@ def makeFig(
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.
history : {"valid", "complete", None}, default "valid"
Discriminate the plotted flags with respect to the tests they originate from.
@@ -88,45 +84,14 @@ def makeFig(
phaseplot :
- stats_dict: dict, default None
- (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)}
-
- The lambda args ``x``,``y``,``z`` will be fed by:
-
- * ``x``: the data (``data[field]``).
- * ``y``: the flags (``flags[field]``).
- * ``z``: The passed flags level (``kwargs[flag]``)
-
- See examples section for examples
-
Returns
-------
fig : matplotlib.pyplot.figure
figure object.
- Examples
- --------
- Summary statistic function examples:
-
- >>> func = lambda x, y, z: len(x)
-
- Total number of nan-values:
-
- >>> func = lambda x, y, z: x.isna().sum()
-
- Percentage of values, flagged greater than passed flag (always round float results to avoid table cell overflow):
-
- >>> func = lambda x, y, z: round((x.isna().sum()) / len(x), 2)
"""
- if stats_dict is None:
- stats_dict = {}
-
# data retrieval
d = data[field]
# data slicing:
@@ -135,9 +100,10 @@ def makeFig(
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)
+
+ # set fontsize:
+ default = plt.rcParams["font.size"]
+ plt.rcParams["font.size"] = ax_kwargs.pop("fontsize", None) or default
na_mask = d.isna()
d = d[~na_mask]
@@ -151,10 +117,9 @@ def makeFig(
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}
+ 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 and not d.empty:
@@ -163,13 +128,7 @@ def makeFig(
# figure composition
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])
- ax = fig.add_subplot(tab_gs)
- _plotStatsTable(ax, stats_dict)
- ax = fig.add_subplot(plot_gs)
- else:
- ax = fig.add_subplot(grid[0])
+ ax = fig.add_subplot(grid[0])
_plotVarWithFlags(
ax,
@@ -184,29 +143,9 @@ def makeFig(
ax_kwargs,
SCATTER_KWARGS,
)
- return fig
-
-def _evalStatsDict(in_dict, datser, flagser, level):
- out_dict = {}
- for key in in_dict:
- out_dict[key] = str(in_dict[key](datser, flagser, level))
- return out_dict
-
-
-def _plotStatsTable(ax, stats_dict):
- ax.axis("tight")
- ax.axis("off")
- tab_obj = ax.table(
- cellText=[[a, b] for a, b in stats_dict.items()],
- in_layout=True,
- loc="center",
- # make the table a bit smaller than the plot
- bbox=[0.0, 0.1, 0.95, 0.8],
- )
- tab_obj.auto_set_column_width(False)
- tab_obj.auto_set_font_size(False)
- tab_obj.set_fontsize(10)
+ plt.rcParams["font.size"] = default
+ return fig
def _plotVarWithFlags(
diff --git a/saqc/lib/tools.py b/saqc/lib/tools.py
index 94205e5a5863a873f1682d0103b721fae8bb4995..95c9e294dab3e492bb15216098bac22d80699298 100644
--- a/saqc/lib/tools.py
+++ b/saqc/lib/tools.py
@@ -25,17 +25,6 @@ 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'")
@@ -44,7 +33,7 @@ def assertScalar(name, value, optional=False):
def toSequence(value: T | Sequence[T]) -> List[T]:
- if isinstance(value, (str, int, float)):
+ if isinstance(value, T.__constraints__):
return [value]
return list(value)
diff --git a/saqc/version.py b/saqc/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..423813e43f505ef32b3ce488943b4ec6bd249e3e
--- /dev/null
+++ b/saqc/version.py
@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+
+__version__ = "2.0.1"
diff --git a/setup.py b/setup.py
index 2ea09a26956e2bbd508bf54ac5e82fb0633dc3eb..7e6ddfc9f0f809093f3d73ba04d862741bd2185e 100644
--- a/setup.py
+++ b/setup.py
@@ -1,11 +1,21 @@
from setuptools import setup, find_packages
+from distutils.util import convert_path
+
+# read the version string from saqc without importing it. See the
+# link for a more detailed description of the problem and the solution
+# https://stackoverflow.com/questions/2058802/how-can-i-get-the-version-defined-in-setup-py-setuptools-in-my-package
+vdict = {}
+version_fpath = convert_path("saqc/version.py")
+with open(version_fpath) as f:
+ exec(f.read(), vdict)
+version = vdict["__version__"]
with open("README.md", "r") as fh:
long_description = fh.read()
setup(
name="saqc",
- version="2.0.0",
+ version=version,
author="Bert Palm, David Schaefer, Peter Luenenschloss, Lennard Schmidt",
author_email="david.schaefer@ufz.de",
description="Data quality checking and processing tool/framework",
@@ -15,17 +25,17 @@ setup(
packages=find_packages(exclude=("tests",)),
python_requires=">=3.7, <3.10",
install_requires=[
+ "Click==8.0.*",
+ "dtw==1.4.*",
+ "matplotlib>=3.4,<3.6",
+ "numba==0.54.*",
"numpy==1.20.*",
+ "outlier-utils==0.0.3",
+ "pyarrow==6.0.*",
"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.*",
+ "scipy==1.7.*",
+ "typing_extensions==4.*",
"seaborn==0.11.*",
],
license_files=("LICENSE.md",),
diff --git a/sphinx-doc/.gitignore b/sphinx-doc/.gitignore
deleted file mode 100644
index 16f05fe5bcb986845d46c131c0e10447581670b2..0000000000000000000000000000000000000000
--- a/sphinx-doc/.gitignore
+++ /dev/null
@@ -1,6 +0,0 @@
-
-_api/
-_build/
-_static/
-*.automodsumm
-_static/*
\ No newline at end of file
diff --git a/sphinx-doc/Makefile b/sphinx-doc/Makefile
deleted file mode 100644
index f3db1da13588941376be9065785a5055e0a97e63..0000000000000000000000000000000000000000
--- a/sphinx-doc/Makefile
+++ /dev/null
@@ -1,53 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS ?=
-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/"$$k"; done
-
-# clean sphinx generated stuff
-clean:
- rm -rf _build _static
- rm -rf ../$(FUNCTIONS)
- rm -rf ../$(CORE)
- mkdir _static
- # for k in $(MDLIST); do rm -r "$$k"_m2r; done
- rm -f *.automodsumm
- rm -f *.automodapi
- rm -f moduleAPIs/*.automodsumm
- rm -f moduleAPIs/*.automodapi
-
-# trigger (saqc) customized documentation pipeline
-doc:
- # generate 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
- # 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)
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
- @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
diff --git a/sphinx-doc/cook_books/MultivariateFlagging.rst b/sphinx-doc/cook_books/MultivariateFlagging.rst
deleted file mode 100644
index c7ba19919e41d7ec4c2e2d6ad718d3ef977ff391..0000000000000000000000000000000000000000
--- a/sphinx-doc/cook_books/MultivariateFlagging.rst
+++ /dev/null
@@ -1,67 +0,0 @@
-
-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/documentation/GenericFunctions.rst b/sphinx-doc/documentation/GenericFunctions.rst
deleted file mode 100644
index b630a24832881431393c8f8de938ce5a8da9077e..0000000000000000000000000000000000000000
--- a/sphinx-doc/documentation/GenericFunctions.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Generic Functions
-=================
-
-TODO
diff --git a/sphinx-doc/environment/freqAliases.rst b/sphinx-doc/environment/freqAliases.rst
deleted file mode 100644
index f33af8a092ca93ab3d729ff9bb010f2ee2c3bcee..0000000000000000000000000000000000000000
--- a/sphinx-doc/environment/freqAliases.rst
+++ /dev/null
@@ -1,18 +0,0 @@
-.. 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/getting_started/TutorialCLI.rst b/sphinx-doc/getting_started/TutorialCLI.rst
deleted file mode 100644
index dd15a5df37170bcc6f6a35e04fdf6ed624fd7b6c..0000000000000000000000000000000000000000
--- a/sphinx-doc/getting_started/TutorialCLI.rst
+++ /dev/null
@@ -1,285 +0,0 @@
-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/how_to_doc/HowToDoc.rst b/sphinx-doc/how_to_doc/HowToDoc.rst
deleted file mode 100644
index 273ef245705123cc957ee4d080d0a6034fe066a6..0000000000000000000000000000000000000000
--- a/sphinx-doc/how_to_doc/HowToDoc.rst
+++ /dev/null
@@ -1,237 +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>`_\ ] :
-
- .. 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/misc/ExponentialModel.rst b/sphinx-doc/misc/ExponentialModel.rst
deleted file mode 100644
index abd06681b914f680045e559cffdb2852c110e445..0000000000000000000000000000000000000000
--- a/sphinx-doc/misc/ExponentialModel.rst
+++ /dev/null
@@ -1,31 +0,0 @@
-
-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/moduleAPIs/Functionsbreaks.rst b/sphinx-doc/moduleAPIs/Functionsbreaks.rst
deleted file mode 100644
index 102ff3d23ea480793e5def5820cdd7d8ac6550af..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsbreaks.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-breaks
-======
-
-.. automodapi:: Functions.breaks
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionschangepoints.rst b/sphinx-doc/moduleAPIs/Functionschangepoints.rst
deleted file mode 100644
index d3b545582d5403d065de1ef64f1ed96f65de29b8..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionschangepoints.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-changepoints
-============
-
-.. automodapi:: Functions.changepoints
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsconstants.rst b/sphinx-doc/moduleAPIs/Functionsconstants.rst
deleted file mode 100644
index 3aeb8feda3132a3eba0799d62a610ae1f9be9edd..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsconstants.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-constants
-=========
-
-.. automodapi:: Functions.constants
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionscurvefit.rst b/sphinx-doc/moduleAPIs/Functionscurvefit.rst
deleted file mode 100644
index 4e64814b5da5bfb1dc3fa3376473d36675601ab0..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionscurvefit.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-curvefit
-========
-
-.. automodapi:: Functions.curvefit
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsdrift.rst b/sphinx-doc/moduleAPIs/Functionsdrift.rst
deleted file mode 100644
index 6fff728821955cf745be0fb42274ac696508c46b..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsdrift.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-drift
-=====
-
-.. automodapi:: Functions.drift
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsflagtools.rst b/sphinx-doc/moduleAPIs/Functionsflagtools.rst
deleted file mode 100644
index d07f78c06d045ee97241a41bcd44a76d2fa81746..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsflagtools.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-flagtools
-=========
-
-.. automodapi:: Functions.flagtools
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsgeneric.rst b/sphinx-doc/moduleAPIs/Functionsgeneric.rst
deleted file mode 100644
index fa0f435dc27fdccca1fc907630bd59054fe4ebfd..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsgeneric.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-generic
-=======
-
-.. automodapi:: Functions.generic
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsinterpolation.rst b/sphinx-doc/moduleAPIs/Functionsinterpolation.rst
deleted file mode 100644
index b30ebf19c0dde054fdc82c8b3d1307e1e5ef3a57..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsinterpolation.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-interpolation
-=============
-
-.. automodapi:: Functions.interpolation
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsnoise.rst b/sphinx-doc/moduleAPIs/Functionsnoise.rst
deleted file mode 100644
index 6a8a74657cd3554a40498b6048de354a8c77c1ea..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsnoise.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-noise
-=====
-
-.. automodapi:: Functions.noise
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsoutliers.rst b/sphinx-doc/moduleAPIs/Functionsoutliers.rst
deleted file mode 100644
index 13dc3d806da548c1e5bbe62460746e69e1fb2057..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsoutliers.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-outliers
-========
-
-.. automodapi:: Functions.outliers
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionspattern.rst b/sphinx-doc/moduleAPIs/Functionspattern.rst
deleted file mode 100644
index bd010b4d1d8f23305a6f380da07fcecae40bddcb..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionspattern.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-pattern
-=======
-
-.. automodapi:: Functions.pattern
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsresampling.rst b/sphinx-doc/moduleAPIs/Functionsresampling.rst
deleted file mode 100644
index 5e7e22367ad71274b16519fde5cb3f794e4990c1..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsresampling.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-resampling
-==========
-
-.. automodapi:: Functions.resampling
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsresidues.rst b/sphinx-doc/moduleAPIs/Functionsresidues.rst
deleted file mode 100644
index c97bfed02a2614d47ce1f25e9c0ce45e55a25e7e..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsresidues.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-residues
-========
-
-.. automodapi:: Functions.residues
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsrolling.rst b/sphinx-doc/moduleAPIs/Functionsrolling.rst
deleted file mode 100644
index 8cd668265f234bfc1e7fb8705e4e4d16038a9bc6..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsrolling.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-rolling
-=======
-
-.. automodapi:: Functions.rolling
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionssaqc.rst b/sphinx-doc/moduleAPIs/Functionssaqc.rst
deleted file mode 100644
index 54c41273bafb912a4b31b9a6f00c662dab00ae01..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionssaqc.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-saqc
-====
-
-.. automodapi:: Functions.saqc
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsscores.rst b/sphinx-doc/moduleAPIs/Functionsscores.rst
deleted file mode 100644
index c3867522989e9abfff6872c9fd8b5da042d5f7af..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionsscores.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-scores
-======
-
-.. automodapi:: Functions.scores
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionstools.rst b/sphinx-doc/moduleAPIs/Functionstools.rst
deleted file mode 100644
index 42989321f245e2f5328683e3e709e4f87bb2a5bb..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionstools.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-tools
-=====
-
-.. automodapi:: Functions.tools
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionstransformation.rst b/sphinx-doc/moduleAPIs/Functionstransformation.rst
deleted file mode 100644
index 8b20b6ebc1676c2b4523f92add02135f8306e8e2..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/Functionstransformation.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-transformation
-==============
-
-.. automodapi:: Functions.transformation
- :no-heading:
diff --git a/sphinx-doc/moduleAPIs/saqcClass.rst b/sphinx-doc/moduleAPIs/saqcClass.rst
deleted file mode 100644
index 97f70bd99dcf779343cc68156707cfb3f91598a7..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/saqcClass.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-
-SaQC
-====
-
-* :py:class:`saqc.SaQC <Core.Core.SaQC>`
\ No newline at end of file
diff --git a/sphinx-doc/moduleAPIs/saqcInit.rst b/sphinx-doc/moduleAPIs/saqcInit.rst
deleted file mode 100644
index c0fc167dcc27967152d8793e1e30ebe8f0353fa8..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/saqcInit.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-
-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
deleted file mode 100644
index 3b15895e0979ecfce96f57ed803bf4146d7a3ddb..0000000000000000000000000000000000000000
--- a/sphinx-doc/moduleAPIs/saqcVisitor.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-saqc
-====
-
-.. automodapi:: saqc.core.visitor
- :include-all-objects:
\ No newline at end of file
diff --git a/sphinx-doc/ressources/data/hydro_config.csv b/sphinx-doc/ressources/data/hydro_config.csv
deleted file mode 100644
index 3589f46f9ccb2d2d58defb85ed6da900ccd165f1..0000000000000000000000000000000000000000
--- a/sphinx-doc/ressources/data/hydro_config.csv
+++ /dev/null
@@ -1,26 +0,0 @@
-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/scripts/make_md_to_rst.py b/sphinx-doc/scripts/make_md_to_rst.py
deleted file mode 100644
index a02eecdcb7db4cf2663c0ca698186b74077466a9..0000000000000000000000000000000000000000
--- a/sphinx-doc/scripts/make_md_to_rst.py
+++ /dev/null
@@ -1,181 +0,0 @@
-"""
-The script generates a folder of rest files from a folder of markdown files.
-Markdown Hyperlinks between the files in the folder get converted to rest links so that they function properly in a
-sphinx generated html build obtained from the resulting rest folder.
-"""
-
-import os
-import click
-import shutil
-from m2r import parse_from_file
-import re
-
-new_line_re = "(\r\n|[\r\n])"
-
-
-def rebaseAbsRoot(path, target, root):
- """
- If path and target intersect at root, return relative path from path to target
- Functionality is limited.
- path and target must be path strings pointing at FILES!
- root is only allowed to appear once in every path
- you cant root to os.sep (no folder seperators allowed in the root string)
- """
-
- p = path.find(root)
- t = target.find(root)
- if (p == -1) or (t == -1) or (".." in path):
- return target
-
- path = path[path.find(root) :].split(os.sep)
- target = target[target.find(root) :].split(os.sep)
- # remove common path chunks:
- while path[0] == target[0]:
- del path[0]
- del target[0]
-
- up_steps = (len(path) - 1) * f"..{os.sep}"
- down_steps = os.sep.join(target)
- new_path = os.path.join(up_steps, down_steps)
- return new_path
-
-
-def fixTables(f_rst):
- body_re = f"((.+){new_line_re})*{new_line_re}((.+){new_line_re})*"
- tables = list(re.finditer(f"\.\. list-table::{new_line_re}" + body_re, f_rst))
- for t in tables:
- tab = t[0]
-
- def pic_repl(match):
- leading = match.groupdict()["list_level"]
- pic_dir = match.groupdict()["pic_directive"]
- pic_pad = re.match("^[ ]*", pic_dir).span()[1]
- pic_dir = re.sub(f'{" " * pic_pad}', " " * len(leading), pic_dir)
- pic_dir = leading + pic_dir[len(leading) :]
- end_space = re.search(f"{new_line_re}[ ]*$", match[0])
- if end_space:
- pic_dir = re.sub(f"{new_line_re}[ ]*$", end_space[0], pic_dir)
- return pic_dir
-
- messy_re = (
- f"(?P<list_level>.*){new_line_re}(?P<pic_directive>[ ]*.. image::[^*-]*)"
- )
- # using while loop cause messed pic patterns overlap
- tab, repnum = re.subn(messy_re, pic_repl, tab, 1)
- while repnum:
- tab, repnum = re.subn(messy_re, pic_repl, tab, 1)
-
- bullets = tab.split(" *")[1:]
- items = [bullet.split(" -") for bullet in bullets]
- last_items = items[-1]
- item_num = len(items[0])
- last_item_num = len(last_items)
- if item_num > last_item_num:
- has_content = (
- len([content for content in last_items if re.search("[^\s-]", content)])
- > 0
- )
- if has_content:
- # append empty cells
- tab += " - \n" * (item_num - last_item_num)
- else:
- # delete last row (using replace to avoid false meta char interpretation
- tab = tab.replace(bullets[-1][0], "")
-
- bullet_num = len(
- list(re.finditer(f" \*(?P<items>([ ]+-.*{new_line_re})*)", tab))
- )
- if bullet_num == 1:
- # fix empty body table error:
- tab = re.sub(":header-rows: [0-9]", ":header-rows: 0", tab)
-
- if tab != t[0]:
- f_rst = f_rst.replace(t[0], tab)
-
- return f_rst
-
-
-def fixLinks(f_rst, f, targetpath):
- md_links = list(
- re.finditer(
- "(?P<numbered>\. )?`(?P<link_name>[^<`]*) <(?P<md_link>\S*.md)?(#)?(?P<section>[^>]*)?>`_?",
- f_rst,
- )
- )
- for link in md_links:
- # change directory:
- link_path = link.groupdict()["md_link"]
- if not link_path:
- link_path = f
- # change directory to point at temporal rest dir (if link isnt relative):
- if os.path.dirname(link_path) != "":
- link_path = os.path.join(
- os.path.dirname(link_path) + "_m2r", os.path.basename(link_path)
- )
- # rebase the link to relative link if its not
- link_path = rebaseAbsRoot(os.path.join(targetpath, f), link_path, "sphinx-doc")
- # remove extension name (rst syntax)
- link_path = re.sub("\.md$", "", link_path)
- if link.groupdict()["section"]:
- # while document links have to be relative - section links have to be absolute from sphinx doc dir -
- # markdown space representation by dash has to be removed...
- abs_path = os.path.basename(os.path.abspath(""))
- abs_path = targetpath[targetpath.find(abs_path) + len(abs_path) + 1 :]
- link_path = os.path.join(abs_path, os.path.basename(link_path))
- role = ":ref:"
- section = ":" + link.groupdict()["section"].replace("-", " ")
- # one more regex spell for the sake of numbered section linking:
- if link.groupdict()["numbered"]:
- section = re.sub("(:[0-9]+)", "\g<1>.", section)
- else:
- role = ":doc:"
- section = ""
-
- f_rst = re.sub(
- f'`(?P<link_name>{link.groupdict()["link_name"]}) '
- f'<({link.groupdict()["md_link"]})?(#[^>]*)?>`(_)?',
- r"{}`\g<link_name> <{}{}>`".format(role, link_path, section),
- f_rst,
- )
- return f_rst
-
-
-@click.command()
-@click.option(
- "-p",
- "--mdpath",
- type=str,
- required=True,
- default="sphinx-doc/getting_started_md",
- help="Relative path to the folder containing the .md files to be converted (relative to sphinx root).",
-)
-@click.option(
- "-sr",
- "--sphinxroot",
- type=str,
- required=True,
- default="..",
- help="Relative path to the sphinx root.",
-)
-def main(mdpath, sphinxroot):
- root_path = os.path.abspath(sphinxroot)
- mdpath = os.path.join(root_path, mdpath)
- targetpath = mdpath + "_m2r"
-
- # clear target directory:
- if os.path.isdir(targetpath):
- shutil.rmtree(targetpath)
- os.mkdir(targetpath)
-
- mdfiles = [f for f in os.listdir(mdpath) if os.path.splitext(f)[1] == ".md"]
- for f in mdfiles:
- f_rst = parse_from_file(os.path.join(mdpath, f))
- # regex magic- replace invalid links:
- f_rst = fixLinks(f_rst, f, targetpath)
- f_rst = fixTables(f_rst)
- with open(os.path.join(targetpath, f.replace(".md", ".rst")), "w+") as file_:
- file_.write(f_rst)
-
-
-if __name__ == "__main__":
- main()
diff --git a/sphinxdoc/Makefile b/sphinxdoc/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..b3a754c158ab6913ce6d2761884b3b568086bbb0
--- /dev/null
+++ b/sphinxdoc/Makefile
@@ -0,0 +1,75 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS ?=
+SPHINXBUILD ?= sphinx-build
+SOURCEDIR = .
+BUILDDIR = _build
+
+# 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 sphinxdoc/"$$k"; done
+
+# clean sphinx generated stuff
+clean:
+ rm -rf _build _static
+ mkdir _static
+ rm -f *.automodsumm
+ rm -f *.automodapi
+ rm -f moduleAPIs/*.automodsumm
+ rm -f moduleAPIs/*.automodapi
+ rm -f */*.automodsumm
+ rm -f -r coredoc
+
+# make doctest, make documentation, make clean
+doc:
+ # generate parent fake module for the functions to be documented
+ python scripts/make_doc_module.py -p "saqc/funcs" -sr ".." -su "funcSummaries"
+ # generate environment table from dictionary
+ python scripts/make_env_tab.py
+ @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+ @$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+ python scripts/modify_html_API.py
+ rm -f *.automodsumm
+ rm -f *.automodapi
+ rm -f moduleAPIs/*.automodsumm
+ rm -f moduleAPIs/*.automodapi
+ rm -f */*.automodsumm
+ rm -f -r coredoc
+
+# make documentation
+doconly:
+ # generate parent fake module for the functions to be documented
+ python scripts/make_doc_module.py -p "saqc/funcs" -sr ".." -su "funcSummaries"
+ # generate environment table from dictionary
+ python scripts/make_env_tab.py
+ @$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+ python scripts/modify_html_API.py
+
+
+# make test, clean up
+testonly:
+ # generate parent fake module for the functions to be documented
+ python scripts/make_doc_module.py -p "saqc/funcs" -sr ".." -su "funcSummaries"
+ # generate environment table from dictionary
+ python scripts/make_env_tab.py
+ @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+ rm -f *.automodsumm
+ rm -f *.automodapi
+ rm -f moduleAPIs/*.automodsumm
+ rm -f moduleAPIs/*.automodapi
+ rm -f */*.automodsumm
+ rm -f -r coredoc
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+ @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
diff --git a/sphinx-doc/conf.py b/sphinxdoc/conf.py
similarity index 92%
rename from sphinx-doc/conf.py
rename to sphinxdoc/conf.py
index bed7ae63676e534c3b34c3d404bbdb1f1ed96059..b58390666b1b7544bc1e4dc4406e3ec5e2206b6e 100644
--- a/sphinx-doc/conf.py
+++ b/sphinxdoc/conf.py
@@ -71,9 +71,12 @@ extensions = [
# -- Params of the extensions ------------------------------------------------
+autosummary_ignore_module_all = True
+autosummary_imported_members = False
add_module_names = False
numpydoc_show_class_members = False
-
+plot_html_show_formats = False
+plot_html_show_source_link = False
automodsumm_inherited_members = True
# write out the files generated by automodapi, mainly for debugging
automodsumm_writereprocessed = True
@@ -88,6 +91,7 @@ doctest_global_setup = """
import saqc
import pandas as pd
import numpy as np
+from saqc.constants import *
"""
# -- Other options -----------------------------------------------------------
@@ -125,12 +129,12 @@ html_static_path = ["_static", "css"]
# -- RST options -------
rst_prolog = """
-.. |ufzLogo| image:: /ressources/images/Representative/UFZLogo.jpg
+.. |ufzLogo| image:: /ressources/images/Representative/UFZLogo.png
:width: 40 %
:target: https://www.ufz.de/
-.. |rdmLogo| image:: /ressources/images/Representative/RDMlogo.jpg
- :width: 18 %
+.. |rdmLogo| image:: /ressources/images/Representative/RDMLogo.png
+ :width: 22 %
:target: https://www.ufz.de/index.php?de=45348
:class: align-right
diff --git a/sphinx-doc/cook_books/DataRegularisation.rst b/sphinxdoc/cook_books/DataRegularisation.rst
similarity index 87%
rename from sphinx-doc/cook_books/DataRegularisation.rst
rename to sphinxdoc/cook_books/DataRegularisation.rst
index 3bf9fd5c7107cb258c986e0938686f31cf2df807..f1debd6f40cd53d19f6a90579475c42897bd53de 100644
--- a/sphinx-doc/cook_books/DataRegularisation.rst
+++ b/sphinxdoc/cook_books/DataRegularisation.rst
@@ -5,7 +5,7 @@ The tutorial aims to introduce the usage of ``SaQC`` methods, in order to obtain
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>`
+In the following steps, the tutorial guides through the usage of the *SaQC* :doc:`resampling <../funcSummaries/generic>`
library.
#. Initially, we introduce and motivate regularisation techniques and we do import the tutorial data.
@@ -78,32 +78,27 @@ example data set:
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)
+.. doctest:: example
+
+ >>> 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]
The data series seems to start with a sampling rate of roughly *10* minutes.
@@ -123,7 +118,7 @@ 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:
+Basically, there are three types of :doc:`regularisations <../funcSummaries/resampling>` methods:
#. We could keep the values as they are, and thus,
@@ -134,16 +129,14 @@ Basically, there are three types of :doc:`regularisations <../moduleAPIs/Functio
Shift
-----
-Lets apply a simple shift via the :py:func:`shift <Functions.saqc.shift>` method.
+Lets apply a simple shift via the :py:meth:`~saqc.SaQC.shift` method.
->>> import saqc
->>> qc = saqc.SaQC(data)
->>> qc = qc.shift('SoilMoisture', target='SoilMoisture_bshift', freq='10min', method='bshift')
+.. doctest::
-.. testsetup::
+ >>> import saqc
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.shift('SoilMoisture', target='SoilMoisture_bshift', freq='10min', method='bshift')
- qc = saqc.SaQC(data)
- qc = qc.shift('SoilMoisture', target='SoilMoisture_bshift', freq='10min', method='bshift')
Target parameter
^^^^^^^^^^^^^^^^
@@ -156,7 +149,7 @@ 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,
+the :doc:`regularisations <../funcSummaries/resampling>` methods have such a frequency keyword,
and it just determines the sampling rate, the resulting regular timeseries will have.
Shifting Method
@@ -164,7 +157,7 @@ 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
+that is a multiple of *10* minutes. (See :py:meth:`~saqc.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
@@ -175,7 +168,7 @@ comprehensible from one look.
Shifted data
^^^^^^^^^^^^
- >>> qc.data_raw
+ >>> qc.dataRaw # doctest: +SKIP
SoilMoisture | SoilMoisture_bshift |
================================ | ======================================= |
2021-01-01 00:09:07 23.429701 | 2021-01-01 00:00:00 23.429701 |
@@ -200,7 +193,7 @@ We see, the first and last *10* datapoints of both, the original data time serie
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``.
+This is default behavior to all the :doc:`regularisations <../funcSummaries/resampling>` provided by ``saqc``.
Data Loss and Empty Intervals
-----------------------------
@@ -241,9 +234,9 @@ If there are multiple values present within an interval with size according to t
``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.
+This reduction depends on the selected :doc:`regularisation <../funcSummaries/resampling>` method.
-For example, :ref:`above <cook_books/DataRegularisation:shift>`\ , we applied a backwards :py:func:`shift <Functions.saqc.shift>` with a *10* minutes frequency.
+For example, :ref:`above <cook_books/DataRegularisation:shift>`\ , we applied a backwards :py:meth:`~saqc.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.
@@ -252,7 +245,7 @@ within the *10* minutes interval ranging from ``2021-01-01 07:30:00`` to ``2021-
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"]
+ >>> qc.dataRaw["2021-01-01 07:00:00":"2021-01-01 08:00:00"] # doctest: +SKIP
SoilMoisture_bshift | SoilMoisture |
================================ | ========================================= |
Date Time | Date Time |
@@ -274,7 +267,7 @@ To shift to any frequncy aligned timestamp the value that is closest to that tim
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']
+ >>> qc.dataRaw['2021-01-01T07:00:00':'2021-01-01T08:00:00'] # doctest: +SKIP
SoilMoisture_nshift | SoilMoisture |
================================ | ========================================= |
Date Time | Date Time |
@@ -291,7 +284,7 @@ timestamp would get assigned the nearest value of all the values, that preceed o
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':]
+ >>> qc.dataRaw['2021-03-20 07:00:00':] # doctest: +SKIP
SoilMoisture_nshift | SoilMoisture |
================================ | ========================================= |
Date Time | Date Time |
@@ -316,13 +309,13 @@ 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.
+selecting a single one, we can do this, with the :py:meth:`~saqc.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
+ >>> qc.dataRaw # doctest: +SKIP
SoilMoisture | SoilMoisture_mean |
================================ | ===================================== |
Date Time | Date Time |
@@ -373,17 +366,17 @@ 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.
+:py:meth:`~saqc.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>`
+of the possible interpolation methods in the :py:meth:`~saqc.SaQC.interpolate>`
documentation. Lets check the results:
>>> qc = qc.interpolate('SoilMoisture', target='SoilMoisture_linear', freq='10min', method='time')
- >>> qc.data_raw
+ >>> qc.dataRaw # doctest: +SKIP
SoilMoisture | SoilMoisture_linear |
================================ | ========================================= |
Date Time | Date Time |
@@ -426,12 +419,12 @@ a :ref:`valid <cook_books/DataRegularisation:valid data>` value at ``2021-03-20
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>`.
+:py:meth:`~saqc.SaQC.interpolateIndex`.
Linear Interpolation
~~~~~~~~~~~~~~~~~~~~
-Note, that there is a wrapper available for linear interpolation: :py:func:`saqc.linear <Functions.saqc.linear>`.
+Note, that there is a wrapper available for linear interpolation: :py:meth:`~saqc.SaQC.linear`.
Flags and Regularisation
------------------------
@@ -441,17 +434,17 @@ Since data, that is flagged by a level higher or equal to the passed ``to_mask``
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 |
+>>> qc = qc.linear('SoilMoisture', target='SoilMoisture_linear', freq='10min') # doctest: +SKIP
+>>> qc.dataRaw['2021-01-01 15:00:00':'2021-01-01 16:00:00'] # doctest: +SKIP
+ 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
@@ -468,13 +461,13 @@ We can circumvent having that gap, by flagging that value before interpolation.
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,
+with the :py:meth:`~saqc.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 |
+ >>> qc.dataRaw['2021-01-01T07:00:00':'2021-01-01T08:00:00'] # doctest: +SKIP
+ SoilMoisture | SoilMoisture_original |
================================ | ========================================= |
Date Time | Date Time |
2021-01-01 15:00:00 23.341182 | 2021-01-01 15:00:51 23.3410 |
diff --git a/sphinxdoc/cook_books/MultivariateFlagging.rst b/sphinxdoc/cook_books/MultivariateFlagging.rst
new file mode 100644
index 0000000000000000000000000000000000000000..6e6a9c48cceaac11eeee5b336a630cb33751944f
--- /dev/null
+++ b/sphinxdoc/cook_books/MultivariateFlagging.rst
@@ -0,0 +1,392 @@
+
+.. testsetup:: exampleMV
+
+ import matplotlib.pyplot as plt
+ datapath = './ressources/data/hydro_data.csv'
+ maintpath = './ressources/data/hydro_maint.csv'
+ configpath = './ressources/data/hydro_config.csv'
+
+.. plot::
+ :context:
+ :include-source: False
+
+ import matplotlib
+ import saqc
+ import pandas as pd
+ datapath = '../ressources/data/hydro_data.csv'
+ maintpath = '../ressources/data/hydro_maint.csv'
+ data = pd.read_csv(datapath, index_col=0)
+ maint = pd.read_csv(maintpath, index_col=0)
+ maint.index = pd.DatetimeIndex(maint.index)
+ data.index = pd.DatetimeIndex(data.index)
+ qc = saqc.SaQC([data, maint])
+
+
+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.
+
+
+#. `Data Preparation`_
+
+#. `Drift Correction`_
+
+#. `Multivariate Flagging Procedure`_
+
+#. `Config`_
+
+
+Data Preparation
+----------------
+
+First import the data (from the repository), and generate an saqc instance from it. You will need to download the `sensor
+data <https://git.ufz.de/rdm-software/saqc/-/blob/develop/sphinxdoc/ressources/data/hydro_config.csv>`_ and the
+`maintenance data <https://git.ufz.de/rdm-software/saqc/-/blob/develop/sphinxdoc/ressources/data/hydro_maint.csv>`_
+from the `repository <https://git.ufz.de/rdm-software/saqc.git>`_ and make variables `datapath` and `maintpath` be
+paths pointing at those downloaded files. Note, that the :py:class:`~saqc.SaQC` digests the loaded data in a list.
+This is done, to circumvent having to concatenate both datasets in a pandas Dataframe instance, which would introduce
+`NaN` values to both the datasets, wherever their timestamps missmatch. `SaQC` can handle those unaligned data
+internally without introducing artificial fill values to them.
+
+.. testcode:: exampleMV
+
+ import saqc
+ import pandas as pd
+ data = pd.read_csv(datapath, index_col=0)
+ maint = pd.read_csv(maintpath, index_col=0)
+ maint.index = pd.DatetimeIndex(maint.index)
+ data.index = pd.DatetimeIndex(data.index)
+ qc = saqc.SaQC([data, maint]) # dataframes "data" and "maint" are integrated internally
+
+We can check out the fields, the newly generated :py:class:`~saqc.SaQC` object contains as follows:
+
+.. doctest:: exampleMV
+
+ >>> qc.data.columns
+ Index(['sac254_raw', 'level_raw', 'water_temp_raw', 'maint'], dtype='object', name='columns')
+
+The variables represent meassurements of *water level*, the *specific absorption coefficient* at 254 nm Wavelength,
+the *water temperature* and there is also a variable, *maint*, that refers to time periods, where the *sac254* sensor
+was maintained. Lets have a look at those:
+
+.. doctest:: exampleMV
+
+ >>> qc.data_raw['maint'] # doctest:+SKIP
+ Timestamp
+ 2016-01-10 11:15:00 2016-01-10 12:15:00
+ 2016-01-12 14:40:00 2016-01-12 15:30:00
+ 2016-02-10 13:40:00 2016-02-10 14:40:00
+ 2016-02-24 16:40:00 2016-02-24 17:30:00
+ .... ....
+ 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
+ Name: maint, dtype: object
+
+Measurements collected while maintenance are not trustworthy, so any measurement taken, in any of the listed
+intervals should be flagged right away. This can be achieved, with the :py:meth:`~saqc.SaQC.flagManual` method. Also,
+we will flag out-of-range values in the data with the :py:meth:`~saqc.SaQC.flagRange` method:
+
+.. doctest:: exampleMV
+
+ >>> qc = qc.flagManual('sac254_raw', mdata='maint', method='closed', label='Maintenance')
+ >>> qc = qc.flagRange('level_raw', min=0)
+ >>> qc = qc.flagRange('water_temp_raw', min=-1, max=40)
+ >>> qc = qc.flagRange('sac254_raw', min=0, max=60)
+
+.. plot::
+ :context:
+ :include-source: False
+
+ qc = qc.flagManual('sac254_raw', mdata='maint', method='closed', label='Maintenance')
+ qc = qc.flagRange('level_raw', min=0)
+ qc = qc.flagRange('water_temp_raw', min=-1, max=40)
+ qc = qc.flagRange('sac254_raw', min=0, max=60)
+
+Lets check out the resulting flags for the *sac254* variable with the :py:meth:`~saqc.SaQC.plot` method:
+
+>>> qc.plot('sac254_raw') #doctest:+SKIP
+
+.. plot::
+ :context:
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc.plot('sac254_raw')
+
+
+Now we should figure out, what sampling rate the data is intended to have, by accessing the *_raw* variables
+constituting the sensor data. Since :py:attr:`saqc.SaQC.data` yields a common
+`pandas.DataFrame <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_ object, we can index it with
+the desired variables as column names and have a look at the console output to get a first impression.
+
+.. doctest:: exampleMV
+
+ >>> qc.data[['sac254_raw', 'level_raw', 'water_temp_raw']] # doctest:+NORMALIZE_WHITESPACE
+ columns sac254_raw level_raw water_temp_raw
+ Timestamp
+ 2016-01-01 00:02:00 18.4500 103.290 4.84
+ 2016-01-01 00:17:00 18.6437 103.285 4.82
+ 2016-01-01 00:32:00 18.9887 103.253 4.81
+ 2016-01-01 00:47:00 18.8388 103.210 4.80
+ 2016-01-01 01:02:00 18.7438 103.167 4.78
+ ... ... ...
+ 2017-12-31 22:47:00 43.2275 186.060 5.49
+ 2017-12-31 23:02:00 43.6937 186.115 5.49
+ 2017-12-31 23:17:00 43.6012 186.137 5.50
+ 2017-12-31 23:32:00 43.2237 186.128 5.51
+ 2017-12-31 23:47:00 43.7438 186.130 5.53
+ <BLANKLINE>
+ [70199 rows x 3 columns]
+
+The data seems to have a fairly regular sampling rate of *15* minutes at first glance.
+But checking out values around *2017-10-29*, we notice, that the sampling rate seems not to be totally stable:
+
+.. doctest:: exampleMV
+
+ >>> qc.data[['sac254_raw', 'level_raw', 'water_temp_raw']]['2017-10-29 07:00:00':'2017-10-29 09:00:00'] # doctest:+NORMALIZE_WHITESPACE
+ columns sac254_raw level_raw water_temp_raw
+ Timestamp
+ 2017-10-29 07:02:00 40.3050 112.570 10.91
+ 2017-10-29 07:17:00 39.6287 112.497 10.90
+ 2017-10-29 07:32:00 39.5800 112.460 10.88
+ 2017-10-29 07:32:01 39.9750 111.837 10.70
+ 2017-10-29 07:47:00 39.1350 112.330 10.84
+ 2017-10-29 07:47:01 40.6937 111.615 10.68
+ 2017-10-29 08:02:00 40.4938 112.040 10.77
+ 2017-10-29 08:02:01 39.3337 111.552 10.68
+ 2017-10-29 08:17:00 41.5238 111.835 10.72
+ 2017-10-29 08:17:01 38.6963 111.750 10.69
+ 2017-10-29 08:32:01 39.4337 112.027 10.66
+ 2017-10-29 08:47:01 40.4987 112.450 10.64
+
+Those instabilities do bias most statistical evaluations and it is common practice to apply some
+:doc:`resampling functions <../funcSummaries/resampling>` onto the data, to obtain a regularly spaced timestamp.
+(See also the :ref:`harmonization tutorial <cook_books/DataRegularisation:data regularisation>` for more informations
+on that topic.)
+
+We will apply :py:meth:`linear harmonisation <saqc.SaQC.linear>` to all the sensor data variables,
+to interpolate pillar points of multiples of *15* minutes linearly.
+
+.. doctest:: exampleMV
+
+ >>> qc = qc.linear(['sac254_raw', 'level_raw', 'water_temp_raw'], freq='15min')
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.linear(['sac254_raw', 'level_raw', 'water_temp_raw'], freq='15min')
+
+
+The resulting timeseries now has has regular timestamp.
+
+
+.. doctest:: exampleMV
+
+ >>> qc.data['sac254_raw'] #doctest:+NORMALIZE_WHITESPACE
+ Timestamp
+ 2016-01-01 00:00:00 NaN
+ 2016-01-01 00:15:00 18.617873
+ 2016-01-01 00:30:00 18.942700
+ 2016-01-01 00:45:00 18.858787
+ 2016-01-01 01:00:00 18.756467
+ ...
+ 2017-12-31 23:00:00 43.631540
+ 2017-12-31 23:15:00 43.613533
+ 2017-12-31 23:30:00 43.274033
+ 2017-12-31 23:45:00 43.674453
+ 2018-01-01 00:00:00 NaN
+ Name: sac254_raw, Length: 70194, dtype: float64
+
+Since points, that were identified as malicous get excluded before the harmonization, the resulting regularly sampled
+timeseries does not include them anymore:
+
+.. doctest:: exampleMV
+
+ >>> qc.plot('sac254_raw') # doctest:+SKIP
+
+.. plot::
+ :context:
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc.plot('sac254_raw')
+
+
+Drift Correction
+----------------
+
+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 method
+:py:meth:`~saqc.SaQC.correctDrift`, that will correct the data in between any two such maintenance intervals.
+
+.. doctest:: exampleMV
+
+ >>> qc = qc.correctDrift('sac254_raw', target='sac254_corrected',maintenance_field='maint', model='exponential')
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc = qc.correctDrift('sac254_raw', target='sac254_corrected',maintenance_field='maint', model='exponential')
+
+Check out the results for the year *2016*
+
+.. doctest:: exampleMV
+
+ >>> plt.plot(qc.data_raw['sac254_raw']['2016'], alpha=.5, color='black', label='original') # doctest:+SKIP
+ >>> plt.plot(qc.data_raw['sac254_corrected']['2016'], color='black', label='corrected') # doctest:+SKIP
+
+.. plot::
+ :context:
+ :include-source: False
+
+ plt.figure(figsize=(16,9))
+ plt.plot(qc.data_raw['sac254_raw']['2016'], alpha=.5, color='black', label='original')
+ plt.plot(qc.data_raw['sac254_corrected']['2016'], color='black', label='corrected')
+ plt.legend()
+
+Multivariate Flagging Procedure
+-------------------------------
+
+We are 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.*
+
+First, we define a transformation, we want the variables to be transformed with, to make them equally significant in
+their common feature space. We go for the common pick of just *zScoring* the variables.
+Therefor, we just import *scipys* `zscore` function and wrap it, so that it will be able to digest *nan* values,
+without returning *nan*.
+
+.. doctest:: exampleMV
+
+ >>> from scipy.stats import zscore
+ >>> zscore_func = lambda x: zscore(x, nan_policy='omit')
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ from scipy.stats import zscore
+ zscore_func = lambda x: zscore(x, nan_policy='omit')
+
+Now we can pass the function to the :py:meth:`~saqc.SaQC.transform` method.
+
+.. doctest:: exampleMV
+
+ >>> qc = qc.transform(['sac254_corrected', 'level_raw', 'water_temp_raw'], target=['sac_z', 'level_z', 'water_z'], func=zscore_func, freq='30D')
+
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc = qc.transform(['sac254_raw', 'level_raw', 'water_temp_raw'], target=['sac_z', 'level_z', 'water_z'], func=zscore_func, freq='30D')
+
+The idea of the multivariate flagging approach we are going for, is,
+to assign any datapoint a score, derived from the distance this datapoint has to its *k* nearest
+neighbors in feature space. We can do this, via the :py:meth:`~saqc.SaQC.assignKNNScore` method.
+
+
+.. doctest:: exampleMV
+
+ >>> qc = qc.assignKNNScore(field=['sac_z', 'level_z', 'water_z'], target='kNNscores', freq='30D', n=5)
+
+Lets have a look at the resulting score variable.
+
+.. doctest:: exampleMV
+
+ >>> qc.plot('kNNscores') # doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc = qc.assignKNNScore(field=['sac_z', 'level_z', 'water_z'], target='kNNscores', freq='30D', n=5)
+ qc.plot('kNNscores')
+
+Those scores roughly correlate with the isolation of the scored points in the feature space. For example, have a look at
+the projection of this feature space onto the 2 dimensional *sac* - *level* space, in november 2016:
+
+.. doctest:: exampleMV
+
+ >>> qc.plot('sac_z', phaseplot='level_z', xscope='2016-11') # doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc.plot('sac_z', phaseplot='level_z', xscope='2016-11')
+
+
+We can clearly see some outliers, that seem to be isolated from the cloud of the normalish points. Since those outliers are
+correlated with relatively high *kNNscores*, we could try to calculate a threshold that determines, how extreme an
+*kNN* score has to be to qualify an outlier. Therefor, we will use the saqc-implementation of the
+`STRAY <https://arxiv.org/pdf/1908.04000.pdf>`_ algorithm, which is available as the method:
+:py:meth:`~saqc.SaQC.flagByStray`. This method will mark some samples of the `kNNscore` variable as anomaly.
+Subsequently we project this marks (or *flags*) on to the *sac* variable with a call to
+:py:meth:`~saqc.SaQC.transferFlags`. For the sake of demonstration, we also project the flags
+on the normalized *sac* and plot the flagged values in the *sac_z* - *level_z* feature space.
+
+
+.. doctest:: exampleMV
+
+ >>> qc = qc.flagByStray(field='kNNscores', freq='30D', alpha=.3)
+ >>> qc = qc.transferFlags(field='kNNscores', target='sac254_corrected', label='STRAY')
+ >>> qc = qc.transferFlags(field='kNNscores', target='sac_z', label='STRAY')
+ >>> qc.plot('sac254_corrected', xscope='2016-11') # doctest:+SKIP
+ >>> qc.plot('sac_z', phaseplot='level_z', xscope='2016-11') # doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.flagByStray(field='kNNscores', freq='30D', alpha=.3)
+ qc = qc.transferFlags(field='kNNscores', target='sac254_corrected', label='STRAY')
+ qc = qc.transferFlags(field='kNNscores', target='sac_z', label='STRAY')
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :align: center
+
+ qc.plot('sac254_corrected', xscope='2016-11')
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc.plot('sac_z', phaseplot='level_z', xscope='2016-11')
+
+Config
+------
+
+.. testcode:: exampleMV
+ :hide:
+
+ saqc.fromConfig(configpath, [data, maint])
+
+To configure `saqc` to execute the above data processing and flagging steps, the config file would have to look
+as follows:
+
+.. literalinclude:: ../ressources/data/hydro_config.csv
+
diff --git a/sphinx-doc/cook_books/OutlierDetection.rst b/sphinxdoc/cook_books/OutlierDetection.rst
similarity index 70%
rename from sphinx-doc/cook_books/OutlierDetection.rst
rename to sphinxdoc/cook_books/OutlierDetection.rst
index a464b67e5fe408096fd7ad89fa3aa18160878d8c..061a068c26d4b343920d793efb5378d0bf04a2dc 100644
--- a/sphinx-doc/cook_books/OutlierDetection.rst
+++ b/sphinxdoc/cook_books/OutlierDetection.rst
@@ -13,8 +13,7 @@ The tutorial guides through the following steps:
* :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
+#. We will see how to apply different smoothing methods and models to the data in order to obtain usefull residue
variables.
@@ -27,8 +26,7 @@ The tutorial guides through the following steps:
* :ref:`Evaluation and Visualisation <cook_books/OutlierDetection:Visualisation>`
-#.
- We will see how we can obtain residues and scores from the calculated model curves.
+#. 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>`
@@ -37,8 +35,7 @@ The tutorial guides through the following steps:
* :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
+#. Finally, we will see how to derive flags from the scores itself and impose additional conditions, functioning as
correctives.
@@ -82,35 +79,42 @@ 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>`_.
+library and use its csv file parser `pd.read_csv <https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html>`_.
-.. code-block:: python
+.. testsetup:: example
+
+ data_path = './ressources/data/incidentsLKG.csv'
+
+.. doctest:: exampleOD
+
+ >>> data_path = './ressources/data/incidentsLKG.csv'
+ >>> import pandas as pd
+ >>> data = pd.read_csv(data_path, index_col=0)
- 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
+.. doctest:: exampleOD
- data.index = pd.DatetimeIndex(data.index)
+ >>> 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
+.. doctest:: exampleOD
- import saqc
- saqc = saqc.SaQC(data)
+ >>> import saqc
+ >>> qc = 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>`:
+the method :py:meth:`~saqc.SaQC.plot`:
+
+.. doctest:: exampleOD
-.. code-block:: python
+ >>> qc.plot('incidents') # doctest: +SKIP
- >>> saqc.plot('incidents')
Modelling
---------
@@ -121,27 +125,27 @@ Rolling Mean
^^^^^^^^^^^^
Easiest thing to do, would be, to apply some rolling mean
-model via the method :py:meth:`roll <Functions.saqc.roll>`.
+model via the method :py:meth:`saqc.SaQC.roll`.
-.. code-block:: python
+.. doctest:: exampleOD
>>> import numpy as np
- >>> saqc = saqc.roll(field='incidents', target='incidents_mean', func=np.mean, window='13D')
+ >>> qc = qc.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.
+The ``field`` parameter is passed the variable name, we want to calculate the rolling mean of.
+The ``target`` parameter holds the name, we want to store the results of the calculation to.
+The ``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".
+You can pass arbitrary function objects to the ``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
+.. doctest:: exampleOD
- >>> saqc = saqc.roll(field='incidents', target='incidents_median', func=np.median, window='13D')
+ >>> qc = qc.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.
@@ -157,12 +161,11 @@ 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>`:
+:py:class:`SaQC <Core.Core.SaQC>` provides the polynomial fit function :py:meth:`~saqc.SaQC.fitPolynomial`:
-.. code-block:: python
-
- >>> saqc = saqc.fitPolynomial(field='incidents', target='incidents_polynomial', order=2, window='13D')
+.. doctest:: exampleOD
+ >>> qc = qc.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.
@@ -171,26 +174,24 @@ 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.
+you can make use of the more general :py:meth:`~saqc.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
+.. testcode:: exampleOD
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)
+ 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.
+This function object, we can pass on to the :py:meth:`~saqc.SaQC.processGeneric` methods ``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))
+.. doctest:: exampleOD
+ >>> qc = qc.processGeneric(field='incidents', target='incidents_lowPass', func=lambda x: butterFilter(x, cutoff=0.1, nyq=0.5, filter_order=2))
Visualisation
-------------
@@ -198,16 +199,16 @@ 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
+.. doctest:: exampleOD
- >>> data = saqc.data
+ >>> data = qc.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
+.. doctest:: exampleOD
>>> data.plot()
-
+ <AxesSubplot:>
.. image:: ../ressources/images/cbooks_incidents2.png
:target: ../ressources/images/cbooks_incidents2.png
@@ -223,18 +224,17 @@ 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.
+First, we retrieve the residues via the :py:meth:`~saqc.SaQC.processGeneric` 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.
+control via the ``target`` parameter.
-.. code-block:: python
-
- saqc = saqc.processGeneric(['incidents', 'incidents_mean'], target='incidents_residues', func=lambda x, y: x - y)
+.. doctest:: exampleOD
+ >>> qc = qc.processGeneric(['incidents', 'incidents_mean'], target='incidents_residues', func=lambda x, y: x - y)
Scores
^^^^^^
@@ -245,16 +245,16 @@ The Z-score of a point $\ ``x``\ $, relative to its surrounding $\ ``D``\ $, eva
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
+.. doctest:: exampleOD
- z_score = lambda D: abs((D[14] - np.mean(D)) / np.std(D))
+ >>> 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
+And subsequently, use the :py:meth:`~saqc.SaQC.roll` method to make a rolling window application with the scoring
function:
-.. code-block:: python
+.. doctest:: exampleOD
- saqc = saqc.roll(field='incidents_residues', target='incidents_scores', func=z_score, window='27D')
+ >>> qc = qc.roll(field='incidents_residues', target='incidents_scores', func=z_score, window='27D')
Optimization by Decomposition
@@ -273,35 +273,34 @@ So the attempt works fine, only because our data set is small and strictly regul
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
+into seperate calls to the :py:meth:`~saqc.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)
+.. doctest:: exampleOD
+
+ >>> qc = qc.roll(field='incidents_residues', target='residues_mean', window='27D', func=np.mean)
+ >>> qc = qc.roll(field='incidents_residues', target='residues_std', window='27D', func=np.std)
+ >>> qc = qc.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,
+Also, as a result of the :py:meth:`~saqc.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:
+:py:meth:`~saqc.SaQC.processGeneric` method, in order to obtain the scores:
-.. code-block:: python
+.. doctest:: exampleOD
- saqc = saqc.processGeneric(field=['incidents_residues','incidents_mean','incidents_median'], target='incidents_scores', func=lambda x,y,z: abs((x-y) / z))
+ >>> qc = qc.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')
+.. doctest:: exampleOD
+ >>> qc.plot('incidents_scores') # doctest:+SKIP
.. image:: ../ressources/images/cbook_incidents_scoresUnflagged.png
:target: ../ressources/images/cbook_incidents_scoresUnflagged.png
@@ -316,18 +315,15 @@ 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*.
+by applying the :py:meth:`~saqc.SaQC.flagRange` method with a `max` value of *3*.
-.. code-block:: python
+.. doctest:: exampleOD
- saqc = saqc.flagRange('incidents_scores', max=3)
+ >>> qc = qc.flagRange('incidents_scores', max=3)
Now flags have been calculated for the scores:
-.. code-block:: python
-
- >>> saqc.plot('incidents_scores')
-
+>>> qc.plot('incidents_scores') # doctest:+SKIP
.. image:: ../ressources/images/cbooks_incidents_scores.png
:target: ../ressources/images/cbooks_incidents_scores.png
@@ -339,23 +335,22 @@ Projecting Flags
We now can project those flags onto our original incidents timeseries:
-.. code-block:: python
+.. doctest:: exampleOD
- >>> saqc = saqc.flagGeneric(field=['incidents_scores'], target='incidents', func=lambda x: isflagged(x))
+ >>> qc = qc.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
+.. doctest:: exampleOD
- >>> saqc = saqc.flagGeneric(field=['incidents_scores'], target='incidents', func=lambda x: x > 3)
+ >>> qc = qc.flagGeneric(field=['incidents_scores'], target='incidents', func=lambda x: x > 3)
Lets check out the results:
-.. code-block:: python
-
- >>> saqc.plot('incidents')
+.. doctest:: exampleOD
+ >>> qc.plot('incidents') # doctest: +SKIP
.. image:: ../ressources/images/cbooks_incidentsOverflagged.png
:target: ../ressources/images/cbooks_incidentsOverflagged.png
@@ -386,20 +381,20 @@ 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.
+In order want to *unflag* those values, that do not relate to
+sufficiently large residues, we assign them the :py:const:`~saqc.constants.UNFLAGGED` flag.
-Therefore, we make use of the :py:meth:`flagGeneric <Functions.saqc.flag>` method.
+Therefore, we make use of the :py:meth:`~saqc.SaQC.flagGeneric` 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
+flag value for :py:const:`~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>`.
+generation of the :py:class:`~Core.Core.SaQC>` object in the :ref:`beginning <cook_books/OutlierDetection:Initialisation>`.
-.. code-block:: python
+.. doctest:: exampleOD
- >>> saqc = saqc.flagGeneric(field=['incidents','incidents_residues'], target="incidents", func=lambda x,y: isflagged(x) & (y < 50), flag=-np.inf)
+ >>> qc = qc.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
@@ -407,10 +402,9 @@ and defaults to the selected translation schemes :py:const:`BAD <saqc.constants.
Plotting proofs the tweaking did in deed improve the flagging result:
-.. code-block:: python
-
- >>> saqc.plot("incidents")
+.. doctest:: exampleOD
+ >>> qc.plot("incidents") # doctest:+SKIP
.. image:: ../ressources/images/cbooks_incidents_correctFlagged.png
:target: ../ressources/images/cbooks_incidents_correctFlagged.png
@@ -422,12 +416,12 @@ 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*
+:py:meth:`~saqc.SaQC.flagRange` the condition for the residues having to be above *20*
-.. code-block:: python
+.. doctest:: exampleOD
- >>> saqc = saqc.flagGeneric(field=['incidents_scores', 'incidents_residues'], target='incidents', func=lambda x, y: (x > 3) & (y > 20))
- >>> saqc.plot("incidents")
+ >>> qc = qc.flagGeneric(field=['incidents_scores', 'incidents_residues'], target='incidents', func=lambda x, y: (x > 3) & (y > 20))
+ >>> qc.plot("incidents") # doctest: +SKIP
.. image:: ../ressources/images/cbooks_incidents_correctFlagged.png
diff --git a/sphinx-doc/documentation/FlaggingTranslation.rst b/sphinxdoc/documentation/FlaggingTranslation.rst
similarity index 100%
rename from sphinx-doc/documentation/FlaggingTranslation.rst
rename to sphinxdoc/documentation/FlaggingTranslation.rst
diff --git a/sphinx-doc/getting_started/GenericFunctions.rst b/sphinxdoc/documentation/GenericFunctions.rst
similarity index 88%
rename from sphinx-doc/getting_started/GenericFunctions.rst
rename to sphinxdoc/documentation/GenericFunctions.rst
index 6907ecc97a18045b679ff5bc960d5add1aff7e98..93d313645c75d44db262d9edf23da2712b693835 100644
--- a/sphinx-doc/getting_started/GenericFunctions.rst
+++ b/sphinxdoc/documentation/GenericFunctions.rst
@@ -16,7 +16,7 @@ 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
+:ref:`formalize <documentation/GenericFunctions:a real world example>` on a per dataset basis, it is quite
challenging to translate them into generic source code.
Specification
@@ -30,9 +30,9 @@ signature looks like that:
flagGeneric(func=<expression>, flag=<flagging_constant>)
-where ``<expression>`` is composed of the :ref:`supported constructs <getting_started_md_m2r/GenericFunctions:supported constructs>`
+where ``<expression>`` is composed of the `supported constructs`_
and ``<flag_constant>`` is one of the predefined
-:ref:`flagging constants <getting_started_md_m2r/ParameterDescriptions:flagging constants>` (default: ``BAD``\ ).
+:ref:`flagging constants <getting_started/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``.
@@ -177,7 +177,7 @@ The basic signature looks like that:
procGeneric(func=<expression>)
-where ``<expression>`` is composed of the :ref:`supported constructs <getting_started_md_m2r/GenericFunctions:supported constructs>`.
+where ``<expression>`` is composed of the `supported constructs`_.
Variable References
-------------------
@@ -185,7 +185,7 @@ 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:
+:ref:`example <documentation/GenericFunctions:calculations>` could be rewritten as:
.. code-block::
@@ -274,8 +274,8 @@ The bitwise operators also act as logical operators in comparison chains
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>`\ )
+All functions expect a :ref:`variable reference <documentation/GenericFunctions:variable references>`
+as the only non-keyword argument (see :ref:`here <documentation/GenericFunctions:special functions>`\ )
Mathematical Functions
~~~~~~~~~~~~~~~~~~~~~~
@@ -319,4 +319,4 @@ Constants
^^^^^^^^^
Generic functions support the same constants as normal functions, a detailed
-list is available :ref:`here <getting_started_md_m2r/ParameterDescriptions:constants>`.
+list is available :ref:`here <getting_started/ParameterDescriptions:constants>`.
diff --git a/sphinx-doc/documentation/SourceTarget.rst b/sphinxdoc/documentation/SourceTarget.rst
similarity index 100%
rename from sphinx-doc/documentation/SourceTarget.rst
rename to sphinxdoc/documentation/SourceTarget.rst
diff --git a/sphinxdoc/documentation/WritingFunctions.rst b/sphinxdoc/documentation/WritingFunctions.rst
new file mode 100644
index 0000000000000000000000000000000000000000..5cf8e48fa23cb86576125d16e6626a0198f15e4d
--- /dev/null
+++ b/sphinxdoc/documentation/WritingFunctions.rst
@@ -0,0 +1,72 @@
+Writing Functions
+=================
+
+Writing non-standard functions
+------------------------------
+
+When implementing non-standard functions, i.e. all function not decorated with ``flagging``, some special care is
+needed to comply to the standard ``SaQC`` behaviour. The following passages guide you through the jungle of
+``register`` arguments and there semantics.
+
+Masking
+"""""""
+TODO
+
+Demasking
+"""""""""
+TODO
+
+History squeezing
+"""""""""""""""""
+TODO
+
+Target handling
+"""""""""""""""
+Functions decorated with ``register(handles_target=True)`` are fully in charge to implement the source-target
+workflow. That means they need to handle the following cases:
+
+- Existing ``target``: one ore more of the given targets might already exist in ``data`` and ``flags``.
+ There is (currently) no hard rule how we expect functions to handle this case, the default functions
+ however drop pre-existing ``target`` with:
+
+ .. code-block:: python
+
+ from saqc.funcs import dropField
+
+ data, flags = dropField(data=data, fags=flags, field=field, target=target)
+
+- Non-existing ``target``: we stick to the idea, that non existing targets will be handled
+ gracefully. Usually that implies, that after a function returns all names given in ``target`` exist
+ in ``data`` as well as in ``flags``. The easiest solution here is to create an explicit copy:
+
+ .. code-block:: python
+
+ from saqc.funcs import copyField
+
+ data, flags = copyField(data=data, flags=flags, field=field, target=target)
+
+ However, such a direct mapping/lineage between ``field`` and ``target`` is not always meaningful. Usually
+ the function itself either provides new ``flags`` or new ``data``, but we need to also provide values for
+ for the respective other structure. For ``data`` we default to a series with all ``np.nan`` values, e.g.
+
+ .. code-block:: python
+
+ data[target] = pd.Series(np.nan, index=data[field].index)
+
+ and empty ``flags``, e.g.
+
+ .. code-block:: python
+
+ from saqc.core import History
+ flags.history[target] = saqc.core.history.History(data[target].index)
+
+- Incompatible lengths of ``field`` and ``target``: Certain functions might have specific mapping
+ needs, some multivariate functions, for example, might map exactly three ``fields`` to one ``target``,
+ or need exactly on ``target`` for every field. If this is the case, such invriants should be checked
+ accordingly.
+
+- Incompatible indices for ``field`` and ``target``: Depending on the function, differing indices for
+ ``field`` and ``target`` might or might not impose problems for the implementation. If not coded carefully,
+ chances are however high, that this might cause breakage. It is therefore recommended to check this
+ accordingly.
+
diff --git a/sphinx-doc/environment/configEnv.rst b/sphinxdoc/environment/configEnv.rst
similarity index 100%
rename from sphinx-doc/environment/configEnv.rst
rename to sphinxdoc/environment/configEnv.rst
diff --git a/sphinxdoc/funcSummaries/breaks.rst b/sphinxdoc/funcSummaries/breaks.rst
new file mode 100644
index 0000000000000000000000000000000000000000..78cc399bfef1b81fadadf36d104d3aac6cc3bddf
--- /dev/null
+++ b/sphinxdoc/funcSummaries/breaks.rst
@@ -0,0 +1,16 @@
+breaks
+======
+
+Detecting breaks in data.
+
+This module provides functions to detect and flag breaks in data, for example temporal
+gaps (:py:func:`flagMissing`), jumps and drops (:py:func:`flagJumps`) or temporal
+isolated values (:py:func:`flagIsolated`).
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.flagMissing
+ ~SaQC.flagIsolated
+ ~SaQC.flagJumps
diff --git a/sphinxdoc/funcSummaries/changepoints.rst b/sphinxdoc/funcSummaries/changepoints.rst
new file mode 100644
index 0000000000000000000000000000000000000000..89625498cb9f271b38704a534887eb849372712f
--- /dev/null
+++ b/sphinxdoc/funcSummaries/changepoints.rst
@@ -0,0 +1,11 @@
+changepoints
+============
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.flagChangePoints
+ ~SaQC.assignChangePointCluster
diff --git a/sphinxdoc/funcSummaries/constants.rst b/sphinxdoc/funcSummaries/constants.rst
new file mode 100644
index 0000000000000000000000000000000000000000..a2b9ac669b91d90da7de0d085ef3c7f507060db6
--- /dev/null
+++ b/sphinxdoc/funcSummaries/constants.rst
@@ -0,0 +1,11 @@
+constants
+=========
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.flagConstants
+ ~SaQC.flagByVariance
diff --git a/sphinxdoc/funcSummaries/curvefit.rst b/sphinxdoc/funcSummaries/curvefit.rst
new file mode 100644
index 0000000000000000000000000000000000000000..8e7a8d384540782c4f3844b7c4f1037ddfb48740
--- /dev/null
+++ b/sphinxdoc/funcSummaries/curvefit.rst
@@ -0,0 +1,10 @@
+curvefit
+========
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.fitPolynomial
diff --git a/sphinxdoc/funcSummaries/drift.rst b/sphinxdoc/funcSummaries/drift.rst
new file mode 100644
index 0000000000000000000000000000000000000000..535bbc41d5721e4980e6fdced659a6a043399726
--- /dev/null
+++ b/sphinxdoc/funcSummaries/drift.rst
@@ -0,0 +1,16 @@
+drift
+=====
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.flagDriftFromNorm
+ ~SaQC.flagDriftFromReference
+ ~SaQC.correctDrift
+ ~SaQC.correctRegimeAnomaly
+ ~SaQC.correctOffset
+ ~SaQC.flagRegimeAnomaly
+ ~SaQC.assignRegimeAnomaly
diff --git a/sphinxdoc/funcSummaries/flagtools.rst b/sphinxdoc/funcSummaries/flagtools.rst
new file mode 100644
index 0000000000000000000000000000000000000000..544c82ae44be43d253f3d4f943e1332508aa9a39
--- /dev/null
+++ b/sphinxdoc/funcSummaries/flagtools.rst
@@ -0,0 +1,15 @@
+flagtools
+=========
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.forceFlags
+ ~SaQC.clearFlags
+ ~SaQC.flagUnflagged
+ ~SaQC.flagManual
+ ~SaQC.flagDummy
+ ~SaQC.transferFlags
diff --git a/sphinxdoc/funcSummaries/generic.rst b/sphinxdoc/funcSummaries/generic.rst
new file mode 100644
index 0000000000000000000000000000000000000000..88fb0454bb9372ca9a49bc098795a1ddf7185804
--- /dev/null
+++ b/sphinxdoc/funcSummaries/generic.rst
@@ -0,0 +1,11 @@
+generic
+=======
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.processGeneric
+ ~SaQC.flagGeneric
diff --git a/sphinxdoc/funcSummaries/interpolation.rst b/sphinxdoc/funcSummaries/interpolation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..173ef11ccf1515d31bb8f8b5e1ea76e695b3663d
--- /dev/null
+++ b/sphinxdoc/funcSummaries/interpolation.rst
@@ -0,0 +1,12 @@
+interpolation
+=============
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.interpolateByRolling
+ ~SaQC.interpolateInvalid
+ ~SaQC.interpolateIndex
diff --git a/sphinxdoc/funcSummaries/noise.rst b/sphinxdoc/funcSummaries/noise.rst
new file mode 100644
index 0000000000000000000000000000000000000000..5db1a3fdf5ffb726b4207d439896e16db66180db
--- /dev/null
+++ b/sphinxdoc/funcSummaries/noise.rst
@@ -0,0 +1,10 @@
+noise
+=====
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.flagByStatLowPass
diff --git a/sphinxdoc/funcSummaries/outliers.rst b/sphinxdoc/funcSummaries/outliers.rst
new file mode 100644
index 0000000000000000000000000000000000000000..7c1bf5a78dc943f9d11a1700a850b48e8df7f5d5
--- /dev/null
+++ b/sphinxdoc/funcSummaries/outliers.rst
@@ -0,0 +1,17 @@
+outliers
+========
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.flagByStray
+ ~SaQC.flagMVScores
+ ~SaQC.flagRaise
+ ~SaQC.flagMAD
+ ~SaQC.flagOffset
+ ~SaQC.flagByGrubbs
+ ~SaQC.flagRange
+ ~SaQC.flagCrossStatistics
diff --git a/sphinxdoc/funcSummaries/pattern.rst b/sphinxdoc/funcSummaries/pattern.rst
new file mode 100644
index 0000000000000000000000000000000000000000..652b5425a1f3dbf18ec61d51270feb675b3d3773
--- /dev/null
+++ b/sphinxdoc/funcSummaries/pattern.rst
@@ -0,0 +1,12 @@
+pattern
+=======
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.flagPatternByWavelet
+ ~SaQC.calculateDistanceByDTW
+ ~SaQC.flagPatternByDTW
diff --git a/sphinxdoc/funcSummaries/resampling.rst b/sphinxdoc/funcSummaries/resampling.rst
new file mode 100644
index 0000000000000000000000000000000000000000..cffbee451560bcb56b136d62b1c9e02810b43ddc
--- /dev/null
+++ b/sphinxdoc/funcSummaries/resampling.rst
@@ -0,0 +1,14 @@
+resampling
+==========
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.linear
+ ~SaQC.interpolate
+ ~SaQC.shift
+ ~SaQC.resample
+ ~SaQC.concatFlags
diff --git a/sphinxdoc/funcSummaries/residues.rst b/sphinxdoc/funcSummaries/residues.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f4a876ca78c0f5624de7dd25ef08fc2c92b68a5d
--- /dev/null
+++ b/sphinxdoc/funcSummaries/residues.rst
@@ -0,0 +1,11 @@
+residues
+========
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.calculatePolynomialResidues
+ ~SaQC.calculateRollingResidues
diff --git a/sphinxdoc/funcSummaries/rolling.rst b/sphinxdoc/funcSummaries/rolling.rst
new file mode 100644
index 0000000000000000000000000000000000000000..37facd6199c4d20c59a5260e7564f89292f9e849
--- /dev/null
+++ b/sphinxdoc/funcSummaries/rolling.rst
@@ -0,0 +1,10 @@
+rolling
+=======
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.roll
diff --git a/sphinxdoc/funcSummaries/scores.rst b/sphinxdoc/funcSummaries/scores.rst
new file mode 100644
index 0000000000000000000000000000000000000000..50a1c1dec02280b2c4c40d104f40e70393384aca
--- /dev/null
+++ b/sphinxdoc/funcSummaries/scores.rst
@@ -0,0 +1,10 @@
+scores
+======
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.assignKNNScore
diff --git a/sphinxdoc/funcSummaries/tools.rst b/sphinxdoc/funcSummaries/tools.rst
new file mode 100644
index 0000000000000000000000000000000000000000..18d408ddbf89a440b6e97350e5e9fd0bd3e76ba4
--- /dev/null
+++ b/sphinxdoc/funcSummaries/tools.rst
@@ -0,0 +1,14 @@
+tools
+=====
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.copyField
+ ~SaQC.dropField
+ ~SaQC.renameField
+ ~SaQC.maskTime
+ ~SaQC.plot
diff --git a/sphinxdoc/funcSummaries/transformation.rst b/sphinxdoc/funcSummaries/transformation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..8f2789bc644cf4eee299a9d4a9e23f8eb550a107
--- /dev/null
+++ b/sphinxdoc/funcSummaries/transformation.rst
@@ -0,0 +1,10 @@
+transformation
+==============
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.transform
diff --git a/sphinx-doc/getting_started/ConfigurationFiles.rst b/sphinxdoc/getting_started/ConfigurationFiles.rst
similarity index 97%
rename from sphinx-doc/getting_started/ConfigurationFiles.rst
rename to sphinxdoc/getting_started/ConfigurationFiles.rst
index e530d39dcb8f591246655656e074a8d08d1a1b09..41d3f0aaee0827e34991956a64c2f9527acc8e50 100644
--- a/sphinx-doc/getting_started/ConfigurationFiles.rst
+++ b/sphinxdoc/getting_started/ConfigurationFiles.rst
@@ -29,7 +29,7 @@ configuration headers have a look `here <saqc/core/config.py>`_.
- name of a variable
- yes
* - test
- - :ref:`function notation <getting_started_md_m2r/ConfigurationFiles:test function notation>`
+ - :ref:`function notation <getting_started/ConfigurationFiles:test function notation>`
- test function
- yes
* - plot
diff --git a/sphinx-doc/getting_started/Customizations.rst b/sphinxdoc/getting_started/Customizations.rst
similarity index 87%
rename from sphinx-doc/getting_started/Customizations.rst
rename to sphinxdoc/getting_started/Customizations.rst
index f5c624e3160cb5614ecd7a3ff5c2320aa9853224..c5b0739f7b9e9182546af92bdcea6437d86f8888 100644
--- a/sphinx-doc/getting_started/Customizations.rst
+++ b/sphinxdoc/getting_started/Customizations.rst
@@ -10,8 +10,8 @@ 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>`
+:ref:`quality check routines <getting_started/Customizations:custom quality check routines>`
+and the :ref:`flagging scheme <getting_started/Customizations:custom flagging schemes>`
are described within this documents.
Custom quality check routines
@@ -24,8 +24,8 @@ 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
+#. The :ref:`extension language <documentation/GenericFunctions:Generic Functions>`
+#. An :ref:`interface <getting_started/Customizations:interface>` to the evaluation machinery
Interface
^^^^^^^^^
diff --git a/sphinx-doc/getting_started/FlaggingSchemes.rst b/sphinxdoc/getting_started/FlaggingSchemes.rst
similarity index 100%
rename from sphinx-doc/getting_started/FlaggingSchemes.rst
rename to sphinxdoc/getting_started/FlaggingSchemes.rst
diff --git a/sphinx-doc/getting_started/InstallationGuide.rst b/sphinxdoc/getting_started/InstallationGuide.rst
similarity index 100%
rename from sphinx-doc/getting_started/InstallationGuide.rst
rename to sphinxdoc/getting_started/InstallationGuide.rst
diff --git a/sphinx-doc/getting_started/ParameterDescriptions.rst b/sphinxdoc/getting_started/ParameterDescriptions.rst
similarity index 100%
rename from sphinx-doc/getting_started/ParameterDescriptions.rst
rename to sphinxdoc/getting_started/ParameterDescriptions.rst
diff --git a/sphinx-doc/getting_started/TutorialAPI.rst b/sphinxdoc/getting_started/TutorialAPI.rst
similarity index 76%
rename from sphinx-doc/getting_started/TutorialAPI.rst
rename to sphinxdoc/getting_started/TutorialAPI.rst
index 52f456871475755984648371700b7dc1c9475e9e..bc8b530c197e2f6fb7b159ec0cb345059e102844 100644
--- a/sphinx-doc/getting_started/TutorialAPI.rst
+++ b/sphinxdoc/getting_started/TutorialAPI.rst
@@ -1,3 +1,7 @@
+.. testsetup:: python
+
+ np.random.seed(100)
+
Python API
==========
@@ -6,9 +10,9 @@ General structure
The Python API of SaQC consists of three distinct components:
-1. the core class ``SaQC``
+1. the core class, :py:class:`saqc.SaQC`
2. a number of flagging schemes
-3. a collection of functions
+3. a collection of :doc:`functions <../moduleAPIs/saqcFuncTOC>`.
One and two are implemented as distinct classes, the core object is called ``SaQC`` and we currently
provide three flagging schemes, namely:
@@ -22,7 +26,8 @@ provide three flagging schemes, namely:
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``.
+The third component, the actual test functions, appear as methods of
+:py:class:`~saqc.SaQC` instances.
Getting started - Put something in
@@ -33,7 +38,7 @@ of an appropriate flagging scheme. For reasons of simplicity, we'll use the ``Si
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
+.. testcode:: python
import numpy as np
import pandas as pd
@@ -56,13 +61,13 @@ 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:
+available functions appear as methods of the :py:class:`~saqc.SaQC` class, so we can add a tests to our suite with:
-.. code-block:: python
+.. testcode:: python
- qc.flagRange("a", min=20, max=80)
+ qc = qc.flagRange("a", min=20, max=80)
-:py:func:`flagRange <Functions.saqc.flagRange>` is the easiest of all functions and simply marks all values
+:py:meth:`~saqc.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
@@ -70,10 +75,10 @@ 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.
+the additional methods :py:meth:`~saqc.SaQC.flagConstants` and
+:py:meth:`~saqc.SaQC.flagByGrubbs`) is therefore simply a matter of method chaining.
-.. code-block:: python
+.. testcode:: python
# execute some tests
qc = (qc
@@ -90,20 +95,45 @@ we have seen above, calling quality checks does however not immediately return t
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
+.. doctest:: python
+
+ >>> qc.data # retrieve the data as a pandas.DataFrame
+ columns a
+ 2020-01-01 8.0
+ 2020-01-02 24.0
+ 2020-01-03 67.0
+ 2020-01-04 87.0
+ 2020-01-05 79.0
+ ... ...
+ 2020-04-05 38.0
+ 2020-04-06 86.0
+ 2020-04-07 94.0
+ 2020-04-08 98.0
+ 2020-04-09 42.0
+ <BLANKLINE>
+ [100 rows x 1 columns]
+ >>> qc.flags # retrieve the flags as a pandas.DataFrame
+ columns a
+ 2020-01-01 BAD
+ 2020-01-02 UNFLAGGED
+ 2020-01-03 UNFLAGGED
+ 2020-01-04 BAD
+ 2020-01-05 UNFLAGGED
+ ... ...
+ 2020-04-05 UNFLAGGED
+ 2020-04-06 BAD
+ 2020-04-07 BAD
+ 2020-04-08 BAD
+ 2020-04-09 UNFLAGGED
+ <BLANKLINE>
+ [100 rows x 1 columns]
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
+.. testcode:: python
import numpy as np
import pandas as pd
@@ -142,7 +172,7 @@ We provide an elaborated plotting method to generate and show or write matplotli
the example :ref:`above <getting_started/TutorialAPI:putting it together - the complete workflow>` above
simply call:
-.. code-block:: python
+.. testcode:: python
qc.plot("a")
diff --git a/sphinxdoc/getting_started/TutorialCLI.rst b/sphinxdoc/getting_started/TutorialCLI.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f0cdc5816946c02aeac798cb7f152a4c6327b44d
--- /dev/null
+++ b/sphinxdoc/getting_started/TutorialCLI.rst
@@ -0,0 +1,290 @@
+.. testsetup:: exampleCLI
+
+ datapath = './ressources/data/data.csv'
+ configpath = lambda x: f'./ressources/data/myconfig{x}.csv'
+ temppath = lambda x: f'./ressources/temp/{x}'
+ data = pd.read_csv(datapath, index_col=0)
+ data.index = pd.DatetimeIndex(data.index)
+
+.. plot::
+ :include-source: False
+ :context: close-figs
+
+ import pandas as pd
+ import saqc
+ import matplotlib
+ datapath = '../ressources/data/data.csv'
+ configpath = lambda x: f'../ressources/data/myconfig{x}.csv'
+ temppath = lambda x: f'../ressources/temp/{x}'
+ data = pd.read_csv(datapath, index_col=0)
+ data.index = pd.DatetimeIndex(data.index)
+
+Command Line Application
+========================
+
+
+Contents
+--------
+
+
+* `1. Get toy data and configuration`_
+* `2. Run SaQC`_
+* `3. Configure SaQC`_
+
+ * `Change test parameters`_
+
+* `4. Explore the functionality`_
+
+ * `Process multiple variables`_
+ * `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 data:
+
+.. literalinclude:: ../ressources/data/data.csv
+ :lines: 1-6
+
+
+These are the first entries of 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 have a look at a basic sonfiguration file, as
+`this one <https://git.ufz.de/rdm-software/saqc/-/blob/develop/sphinxdoc/ressources/data/myconfig.csv>`_.
+It contains the following lines:
+
+.. literalinclude:: ../ressources/data/myconfig.csv
+
+These lines illustrate how different quality control tests can be specified for
+different variables.
+
+In this case, we trigger a :py:meth:`range <saqc.SaQC.flagRange>` test, that flags all values exceeding
+the range of the bounds of the interval *[10,60]*. Subsequently, a test to detect spikes, is applied,
+using the MAD-method. (:py:meth:`~saqc.SaQC.flagMAD`).
+You can find an overview of all available quality control tests
+:ref:`here <moduleAPI/saqcFuncTOC:SaQC Flagging Functions>`. 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.
+
+.. testcode:: exampleCLI
+ :hide:
+
+ qc = saqc.fromConfig(configpath(''), data)
+
+2. Run SaQC
+-----------
+
+On Unix/Mac-systems
+"""""""""""""""""""
+
+Remember to have your virtual environment activated:
+
+.. code-block:: sh
+
+ source env_saqc/bin/activate
+
+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:
+
+.. code-block:: sh
+
+ python3 -m saqc -c ressources/data/myconfig.csv -d ressources/data/data.csv
+
+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:
+
+
+.. 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:
+
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :align: center
+
+ qc = saqc.fromConfig(configpath(''), data)
+
+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 :py:meth:`~saqc.SaQC.flagRange` -test that flags
+ all values outside the range [10,60] was executed and after that,
+ the :py:meth:`~saqc.SaQC.flagMAD` -test to identify spikes in the data
+* Finally we triggered the generation of a plot, by adding the :py:meth:`~saqc.SaQC.plot` function in the last line.
+
+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:
+
+.. literalinclude:: ../ressources/data/myconfig2.csv
+
+Rerunning SaQC as above produces the following plot:
+
+.. testcode:: exampleCLI
+ :hide:
+
+ qc = saqc.fromConfig(configpath('2'), data)
+
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :align: center
+
+ qc = saqc.fromConfig(configpath('2'), data)
+
+
+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. To not interrupt processing, the plots
+get stored to files.
+
+.. literalinclude:: ../ressources/data/myconfig4.csv
+
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = saqc.fromConfig(configpath('4'), data)
+
+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
+ * - .. image:: ../ressources/images/SM1processingResults.png
+ :target: ../ressources/images/SM1processingResults.png
+ :alt:
+
+ - .. image:: ../ressources/images/SM2processingResults.png
+ :target: ../ressources/images/SM2processingResults.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
+:ref:`extension language <documentation/GenericFunctions:Generic Functions>`. This would look like this:
+
+.. literalinclude:: ../ressources/data/myconfig3.csv
+
+.. testcode:: exampleCLI
+ :hide:
+
+ qc = saqc.fromConfig(configpath('3'), data)
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :nofigs:
+
+ import os
+ qc = saqc.fromConfig(configpath('3'), data)
+ qc.data.to_csv(temppath('TutorialCLIHarmData.csv'))
+
+
+The above executes an internal framework that aligns the timestamps of SM2
+to a 15min-grid (:py:meth:`saqc.SaQC.shift`). Further information on harmonization can be
+found in the :doc:`Resampling cookbook <../cook_books/DataRegularisation>`.
+
+
+.. literalinclude:: ../ressources/temp/TutorialCLIHarmData.csv
+ :lines: 1-10
+
+
+Also, all values where SM2 is below 30 are flagged via the custom function (see
+plot below) and the plot is labeled with the string passed to the `label` keyword.
+You can learn more about the syntax of these custom functions
+:ref:`here <documentation/GenericFunctions:Generic Functions>`.
+
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :align: center
+
+ qc.plot('SM2')
diff --git a/sphinxdoc/how_to_doc/HowToDoc.rst b/sphinxdoc/how_to_doc/HowToDoc.rst
new file mode 100644
index 0000000000000000000000000000000000000000..6dd9f8086ba63cfd34e80a6acae80631a21323e1
--- /dev/null
+++ b/sphinxdoc/how_to_doc/HowToDoc.rst
@@ -0,0 +1,299 @@
+
+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``)
+* 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.
+* 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:`coolMethod <saqc.SaQC.flagRange>`
+
+
+* The saqc object and/or its content is available via:
+
+ .. code-block::
+
+ :py:class:`saqc.SaQC`
+ :py:meth:`saqc.SaQC.flagRange`
+
+
+* 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.
+
+Integrating doctested code snippets
+-----------------------------------
+
+code-block
+^^^^^^^^^^
+
+If you want to ONLY RENDER code blocks, use the common `code-block` directive:
+
+.. code-block:: rest
+
+ .. code-block:: python
+
+ a = 1
+ b = 2
+ a + b
+
+This results in:
+
+.. code-block:: python
+
+ a = 1
+ b = 2
+ a + b
+
+testcode
+^^^^^^^^
+
+If you want code to be RENDERED and TESTED, use the `testcode` directive. You can specify a group, where
+assignments and imports will be stored to, and available for later `testcode` directives
+of the same group and same document. Code is executed, and the doctest will fail, if execution causes an exception to be thrown.
+
+.. code-block:: rest
+
+ .. testcode:: group1
+
+ a = 1
+ b = 2
+ a + b
+
+This will be rendered as:
+
+.. testcode:: group1
+
+ a = 1
+ b = 2
+ a + b
+
+Assignments (and imports) will be available in any other `testcode` directive, that has the same group assigned.
+So the following wont fail, since ``a`` is known in ``group1``:
+
+.. code-block:: rest
+
+ .. testcode:: group1
+
+ a - 4
+
+testsetup
+^^^^^^^^^
+
+If you want to setup the example environment in a hidden manor, you can use the `testsetup` directive:
+
+.. code-block:: rest
+
+ .. testsetup:: group1
+
+ import scipy
+
+Will import scipy into the group environment, omitting rendering/display of the code.
+
+testoutput
+^^^^^^^^^^
+
+If you want to additionally check the final *std_out* output of a `testcode` block, you can use
+the `testoutput` directive:
+
+.. code-block:: rest
+
+ .. testcode:: group1
+
+ a - 4
+
+ .. testoutput::
+
+ -3
+
+This will be rendered as follows:
+
+.. testcode:: group1
+
+ a - 4
+
+.. testoutput::
+
+ -3
+
+You can omit displaying of the testoutput, by adding the `hidden` option.
+
+doctest
+^^^^^^^
+
+If you want to have code tested and rendered in the doctest style rendering,
+(including *>>>*), you can use doctest syntax:
+
+.. code-block:: rest
+
+ >>> 1+1
+ 2
+
+This will be rendered as:
+
+>>> 1+1
+2
+
+It can be a little tricky, to match complexer std_out strings, like dios or DataFrames. There are some
+doctest flags that can mitigate frustration:
+
+#. NORMALIZE_WHITESPACE will map all whitespace/tab combos onto a single whitespace. Use like:
+
+ .. code-block:: rest
+
+ >>> 'ab c' #doctest:+NORMALIZE_WHITESPACE
+ 'ab c'
+
+#. ELLIPSIS will allow usage of the '...'-Wildcard in the expected output. (Usefull, if output contains unpredictable substrings, like memory adresses or filepaths
+
+ .. code-block:: rest
+
+ >>> 'abcdefg' #doctest:+ELLIPSIS
+ 'a...b'
+
+#. SKIP skips the check (and execution of the line) all together. (usually used, if display is demanded, but testing would somehow be unstable, due to random/unpredictable components)
+
+ .. code-block:: rest
+
+ >>> time #doctest:+SKIP
+ CPU times: user 5 µs, sys: 3 µs, total: 8 µs
+ Wall time: 13.8 µs
+
+ .. caution::
+ Skipped lines are NOT tested! The execution of the line is skipped all together with the check against the
+ expected output.
+
+To assign a group to doctest snippets, use the more verbose `doctest` directive:
+
+.. code-block:: rest
+
+ .. doctest:: group1
+
+ >>> time #doctest:+SKIP
+ CPU times: user 5 µs, sys: 3 µs, total: 8 µs
+ Wall time: 13.8 µs
+
+Will be rendered, as:
+
+.. doctest:: group1
+
+ >>> time #doctest:+SKIP
+ CPU times: user 5 µs, sys: 3 µs, total: 8 µs
+ Wall time: 13.8 µs
+
+
+Run doctest locally
+-------------------
+
+Since doctest checks guard the push to the develop branch, you might wish to chek if your local modification passes
+all doctests beforehand.
+
+There for go to the sphinxdoc directory and run:
+
+.. code-block::
+
+ make testonly
+
+To only run the doctests.
\ No newline at end of file
diff --git a/sphinx-doc/index.rst b/sphinxdoc/index.rst
similarity index 55%
rename from sphinx-doc/index.rst
rename to sphinxdoc/index.rst
index 3ee256b2d00b5afa2591beaba1db9d8e104d5b96..8466583581b3c6cffabf6fdc40ebf43f224c6f81 100644
--- a/sphinx-doc/index.rst
+++ b/sphinxdoc/index.rst
@@ -7,7 +7,6 @@
:maxdepth: 1
Repository <https://git.ufz.de/rdm-software/saqc>
- Package API <moduleAPIs/saqcInit>
Getting Started
===============
@@ -48,32 +47,31 @@ Getting Started
:glob:
:titlesonly:
- 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>
+ resampling <funcSummaries/resampling>
+ generic <funcSummaries/generic>
+ outliers <funcSummaries/outliers>
+ breaks <funcSummaries/breaks>
+ constants <funcSummaries/constants>
+ changepoints <funcSummaries/changepoints>
+ drift <funcSummaries/drift>
+ curvefit <funcSummaries/curvefit>
+ interpolation <funcSummaries/interpolation>
+ residues <funcSummaries/residues>
+ tools <funcSummaries/tools>
+ flagtools <funcSummaries/flagtools>
+ rolling <funcSummaries/rolling>
+ scores <funcSummaries/scores>
+ transformation <funcSummaries/transformation>
+ noise <funcSummaries/noise>
+ pattern <funcSummaries/pattern>
.. toctree::
:caption: SaQC
:hidden:
- :maxdepth: 1
+ :maxdepth: 2
- SaQC Object <moduleAPIs/saqcClass>
- Constants <moduleAPI/saqcConstants>
+ Package <moduleAPIs/saqc>
Config Environment <environment/configEnv>
@@ -83,6 +81,7 @@ Getting Started
:maxdepth: 1
Documentation Guide <how_to_doc/HowToDoc>
+ Writing Functions <documentation/WritingFunctions>
Indices and tables
diff --git a/sphinx-doc/make.bat b/sphinxdoc/make.bat
similarity index 100%
rename from sphinx-doc/make.bat
rename to sphinxdoc/make.bat
diff --git a/sphinx-doc/misc/title.rst b/sphinxdoc/misc/title.rst
similarity index 93%
rename from sphinx-doc/misc/title.rst
rename to sphinxdoc/misc/title.rst
index bb92d288f350908b1b0727c4c0ff03947f752e01..f7e5641ddd143abebf06602c64bfdd98c27f2f3a 100644
--- a/sphinx-doc/misc/title.rst
+++ b/sphinxdoc/misc/title.rst
@@ -68,7 +68,7 @@ Features
* 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
+ * :ref:`preprocess your data, by aligning it to shared frequency grids <cook_books/DataRegularisation:Data Regularisation>`
* - |sacFlagged|
- * apply basic plausibility checks, as well as
* more complex, univariat flagging Functions
@@ -79,4 +79,4 @@ Features
- * 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
+ - * apply :ref:`multivariate flagging functions <cook_books/MultivariateFlagging:Multivariate Flagging>`
diff --git a/sphinxdoc/moduleAPIs/saqc.rst b/sphinxdoc/moduleAPIs/saqc.rst
new file mode 100644
index 0000000000000000000000000000000000000000..9cdee31aabb0fddda2d6686f0cc6c3217beea29c
--- /dev/null
+++ b/sphinxdoc/moduleAPIs/saqc.rst
@@ -0,0 +1,8 @@
+
+SaQC
+====
+
+.. automodapi:: saqc
+
+
+
diff --git a/sphinx-doc/moduleAPIs/saqcConstants.rst b/sphinxdoc/moduleAPIs/saqcConstants.rst
similarity index 70%
rename from sphinx-doc/moduleAPIs/saqcConstants.rst
rename to sphinxdoc/moduleAPIs/saqcConstants.rst
index 3c6636edbcd85673ae95ccc19e41728fa231034f..db20cc67b9289435bb59f631a7236a8f0067f4ea 100644
--- a/sphinx-doc/moduleAPIs/saqcConstants.rst
+++ b/sphinxdoc/moduleAPIs/saqcConstants.rst
@@ -4,3 +4,4 @@ saqc
.. automodapi:: saqc.constants
:include-all-objects:
+ :skip: ENVIRONMENT
diff --git a/sphinxdoc/moduleAPIs/saqcFuncClass.rst b/sphinxdoc/moduleAPIs/saqcFuncClass.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c537ab13ca0819ec19a8828b0d3f74151f198fd8
--- /dev/null
+++ b/sphinxdoc/moduleAPIs/saqcFuncClass.rst
@@ -0,0 +1,8 @@
+
+SaQC
+====
+
+.. automodapi:: sphinxdoc.coredoc
+
+
+
diff --git a/sphinxdoc/moduleAPIs/saqcFuncTOC.rst b/sphinxdoc/moduleAPIs/saqcFuncTOC.rst
new file mode 100644
index 0000000000000000000000000000000000000000..6a3ff1aa1673f522cf65af0fbef5f518545290fb
--- /dev/null
+++ b/sphinxdoc/moduleAPIs/saqcFuncTOC.rst
@@ -0,0 +1,10 @@
+SaQC Flagging Functions
+-----------------------
+
+.. toctree::
+ :glob:
+
+ ../funcSummaries/*
+
+
+
diff --git a/sphinx-doc/requirements_sphinx.txt b/sphinxdoc/requirements_sphinx.txt
similarity index 100%
rename from sphinx-doc/requirements_sphinx.txt
rename to sphinxdoc/requirements_sphinx.txt
diff --git a/sphinx-doc/ressources/data/SoilMoisture.csv b/sphinxdoc/ressources/data/SoilMoisture.csv
similarity index 100%
rename from sphinx-doc/ressources/data/SoilMoisture.csv
rename to sphinxdoc/ressources/data/SoilMoisture.csv
diff --git a/sphinxdoc/ressources/data/TutorialCLIHarmData.csv b/sphinxdoc/ressources/data/TutorialCLIHarmData.csv
new file mode 100644
index 0000000000000000000000000000000000000000..09e1c045a5c31631e45cd29564638c989b98463e
--- /dev/null
+++ b/sphinxdoc/ressources/data/TutorialCLIHarmData.csv
@@ -0,0 +1,29365 @@
+Date,Battery,SM1,SM2
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diff --git a/sphinx-doc/ressources/data/config.csv b/sphinxdoc/ressources/data/config.csv
similarity index 100%
rename from sphinx-doc/ressources/data/config.csv
rename to sphinxdoc/ressources/data/config.csv
diff --git a/sphinx-doc/ressources/data/config_ci.csv b/sphinxdoc/ressources/data/config_ci.csv
similarity index 100%
rename from sphinx-doc/ressources/data/config_ci.csv
rename to sphinxdoc/ressources/data/config_ci.csv
diff --git a/sphinx-doc/ressources/data/data.csv b/sphinxdoc/ressources/data/data.csv
similarity index 100%
rename from sphinx-doc/ressources/data/data.csv
rename to sphinxdoc/ressources/data/data.csv
diff --git a/sphinxdoc/ressources/data/hydro_config.csv b/sphinxdoc/ressources/data/hydro_config.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c97df2be1da86607ce0a1e0ddff58ab945d63180
--- /dev/null
+++ b/sphinxdoc/ressources/data/hydro_config.csv
@@ -0,0 +1,21 @@
+varname ; test
+#-----------------------;-------------------------------------------
+# Data Preparation
+sac254_raw ; flagManual(mdata='maint', method='closed')
+level_raw ; flagRange(min=0)
+water_temp_raw ; flagRange(min=-1)
+sac254_raw ; flagRange(min=0, max=60)
+level_raw ; linear(freq='15min')
+water_temp_raw ; linear(freq='15min')
+sac254_raw ; linear(freq='15min')
+# Drift Correcture
+sac254_raw ; correctDrift(target='sac254_corr', maintenance_field='maint', model='exponential')
+# Multivariate Flagging Procedure
+level_z ; transform(field=['level_raw'], func=zScore(x), freq='20D')
+water_z ; transform(field=['water_temp_raw'], func=zScore(x), freq='20D')
+sac_z ; transform(field=['sac254_raw'], func=zScore(x), freq='20D')
+kNN_scores ; assignKNNScore(field=['level_z', 'water_z', 'sac_z'], freq='20D')
+kNN_scores ; flagByStray(freq='20D')
+level_raw ; transferFlags(field=['kNN_scores'], label='STRAY')
+sac254_corr ; transferFlags(field=['kNN_scores'], label='STRAY')
+water_temp_raw ; transferFlags(field=['kNN_scores'], label='STRAY')
\ No newline at end of file
diff --git a/sphinx-doc/ressources/data/hydro_data.csv b/sphinxdoc/ressources/data/hydro_data.csv
similarity index 100%
rename from sphinx-doc/ressources/data/hydro_data.csv
rename to sphinxdoc/ressources/data/hydro_data.csv
diff --git a/sphinx-doc/ressources/data/hydro_maint.csv b/sphinxdoc/ressources/data/hydro_maint.csv
similarity index 100%
rename from sphinx-doc/ressources/data/hydro_maint.csv
rename to sphinxdoc/ressources/data/hydro_maint.csv
diff --git a/sphinx-doc/ressources/data/incidentsLKG.csv b/sphinxdoc/ressources/data/incidentsLKG.csv
similarity index 100%
rename from sphinx-doc/ressources/data/incidentsLKG.csv
rename to sphinxdoc/ressources/data/incidentsLKG.csv
diff --git a/sphinxdoc/ressources/data/myconfig.csv b/sphinxdoc/ressources/data/myconfig.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ea8d6c09063b95789adb7d2244ad7ff939222f00
--- /dev/null
+++ b/sphinxdoc/ressources/data/myconfig.csv
@@ -0,0 +1,5 @@
+varname;test
+#------;--------------------------
+SM2 ;flagRange(min=10, max=60)
+SM2 ;flagMAD(window="30d", z=3.5)
+SM2 ;plot()
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/myconfig2.csv b/sphinxdoc/ressources/data/myconfig2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..26f29d7d928a97629c5abe38080f88e303bc38c7
--- /dev/null
+++ b/sphinxdoc/ressources/data/myconfig2.csv
@@ -0,0 +1,5 @@
+varname;test
+#------;--------------------------
+SM2 ;flagRange(min=-20, max=60)
+SM2 ;flagMAD(window="30d", z=3.5)
+SM2 ;plot()
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/myconfig3.csv b/sphinxdoc/ressources/data/myconfig3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1f030fe445a337b92dc1cb507ed4af3a138d0fd4
--- /dev/null
+++ b/sphinxdoc/ressources/data/myconfig3.csv
@@ -0,0 +1,5 @@
+varname;test
+#------;--------------------------
+SM2 ;shift(freq="15Min")
+SM2 ;flagGeneric(func=(SM2 < 30), label='Value too low')
+SM2 ;plot()
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/myconfig4.csv b/sphinxdoc/ressources/data/myconfig4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..dd7a9a6b17152307389153325d78628771b52f6a
--- /dev/null
+++ b/sphinxdoc/ressources/data/myconfig4.csv
@@ -0,0 +1,8 @@
+varname;test
+#------;--------------------------
+SM1;flagRange(min=10, max=60)
+SM2;flagRange(min=10, max=60)
+SM1;flagMAD(window="15d", z=3.5)
+SM2;flagMAD(window="30d", z=3.5)
+SM1;plot(store_kwargs={'format':'png','fname':'../ressources/temp/SM1processingResults'})
+SM2;plot(store_kwargs={'format':'png','fname':'../ressources/temp/SM2processingResults'})
\ No newline at end of file
diff --git a/sphinx-doc/ressources/images/Representative/DifferentFlags.png b/sphinxdoc/ressources/images/Representative/DifferentFlags.png
similarity index 100%
rename from sphinx-doc/ressources/images/Representative/DifferentFlags.png
rename to sphinxdoc/ressources/images/Representative/DifferentFlags.png
diff --git a/sphinx-doc/ressources/images/Representative/MultivarSAC.png b/sphinxdoc/ressources/images/Representative/MultivarSAC.png
similarity index 100%
rename from sphinx-doc/ressources/images/Representative/MultivarSAC.png
rename to sphinxdoc/ressources/images/Representative/MultivarSAC.png
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rename to sphinxdoc/ressources/images/Representative/ProcessingDrift.png
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rename to sphinxdoc/ressources/images/Representative/pythonLogo.png
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rename to sphinxdoc/ressources/images/cbook_incidents_scoresUnflagged.png
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rename to sphinxdoc/ressources/images/cbooks_incidents1.png
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similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/sphinx-doc/scripts/make_doc_module.py b/sphinxdoc/scripts/make_doc_module.py
similarity index 79%
rename from sphinx-doc/scripts/make_doc_module.py
rename to sphinxdoc/scripts/make_doc_module.py
index e68178771a1895ab4463e221eba82752dde8cd22..6223e24f8378f1593b6dd0429dc6dc70bd745c85 100644
--- a/sphinx-doc/scripts/make_doc_module.py
+++ b/sphinxdoc/scripts/make_doc_module.py
@@ -139,9 +139,13 @@ def make_doc_module(targetpath, func_dict, doc_mod_structure):
def make_doc_core(sphinxroot, func_dict, doc_mod_structure):
- targetfolder = os.path.join(sphinxroot, "Core")
+ targetfolder = os.path.join(sphinxroot, "sphinxdoc/coredoc")
coresource = os.path.join(sphinxroot, os.path.normpath("saqc/core/core.py"))
+ if os.path.isdir(targetfolder):
+ shutil.rmtree(targetfolder)
+ os.makedirs(targetfolder, exist_ok=True)
+
# parse real core.py
with open(coresource) as f:
corelines = f.readlines()
@@ -163,7 +167,7 @@ def make_doc_core(sphinxroot, func_dict, doc_mod_structure):
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:
+ 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:
@@ -188,6 +192,14 @@ def make_doc_core(sphinxroot, func_dict, doc_mod_structure):
)
f.write(newcore)
+ with open(os.path.join(targetfolder, f"__init__.py"), "w+") as f:
+ init_content = [
+ "# ! /usr/bin/env python",
+ "# -*- coding: utf-8 -*-",
+ "from sphinxdoc.coredoc.core import SaQC",
+ ]
+ f.write("\n".join(init_content))
+
return 0
@@ -207,6 +219,31 @@ def makeModuleAPIs(modules, folder_path="moduleAPIs", pck_path="Functions"):
pass
+def makeModuleSummaries(modules, folder_path="funcSummaries"):
+ f_path = os.path.abspath(folder_path)
+ if os.path.isdir(f_path):
+ shutil.rmtree(f_path)
+ os.makedirs(f_path, exist_ok=True)
+
+ for m in [m for m in modules.keys() if m.split("_")[-1] != "dcstring"]:
+ lines = []
+ lines += [m]
+ lines += ["=" * len(m)]
+ lines += [""]
+ lines += [modules[m + "_dcstring"]]
+ lines += [""]
+ lines += [f".. currentmodule:: saqc", ""]
+ lines += [".. autosummary::", ""]
+ for func in modules[m]:
+ lines += [3 * " " + f"~SaQC.{func.split('.')[-1]}"]
+
+ with open(os.path.join(f_path, f"{m}.rst"), "w") as f:
+ for l in lines:
+ f.write(l + "\n")
+
+ pass
+
+
@click.command()
@click.option(
"-p",
@@ -216,50 +253,35 @@ def makeModuleAPIs(modules, folder_path="moduleAPIs", pck_path="Functions"):
default="saqc/funcs",
help="Relative path to the package to be documented (relative to sphinx root).",
)
-@click.option(
- "-t",
- "--targetpath",
- type=str,
- required=True,
- default="docs/intro_modules",
- help="Output folder path (relative to sphinx root). Will be overridden if already existent.",
-)
@click.option(
"-sr",
"--sphinxroot",
type=str,
required=True,
- default="..",
+ default="../..",
help="Relative path to the sphinx root.",
)
@click.option(
- "-mo",
- "--mode",
+ "-su",
+ "--summaries",
type=str,
required=True,
- default="Functions",
- help="either 'Functions' or 'module_doc' or 'registered_doc' (All but 'Functions' is deprecated and will be remove "
- "soon).",
+ default="funcSummaries",
+ help="Target path for summaries.",
)
-def main(pckpath, targetpath, sphinxroot, mode):
+def main(pckpath, sphinxroot, summaries):
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")
+ coretrg = os.path.join(sphinxroot, "sphinxdoc/coredoc")
modules = []
# collect modules
for _, modname, _ in pkgutil.walk_packages(path=[pkg_path], onerror=lambda x: None):
modules.append(modname)
- # clear target dir
- if os.path.isdir(targetpath):
- shutil.rmtree(targetpath)
- os.makedirs(targetpath, exist_ok=True)
-
- if os.path.isdir(coretrg):
- shutil.rmtree(coretrg)
- os.makedirs(coretrg, 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]
@@ -268,22 +290,16 @@ def main(pckpath, targetpath, sphinxroot, mode):
zip([k + "_dcstring" for k in mod_dict.keys()], list(mod_dict.values()))
)
func_dict = parse_func_dcstrings(module_paths)
- if mode == "Functions":
- # module docs
- doc_struct = {m: [] for m in modules}
- for dm in func_dict.keys():
- module = re.search("([^ .]*)\.[^ ]*$", dm).group(1)
- doc_struct[module].append(dm)
- makeModuleAPIs(list(doc_struct.keys()))
- doc_struct.update(mod_dict)
- make_doc_module(targetpath, func_dict, doc_struct)
-
- # complete docs
- doc_mod_structure = {"saqc": [f for f in func_dict.keys()], "saqc_dcstring": ""}
- makeModuleAPIs(["saqc"])
- make_doc_module(targetpath, func_dict, doc_mod_structure)
-
- make_doc_core(root_path, func_dict, doc_mod_structure)
+
+ # module docs
+ doc_struct = {m: [] for m in modules}
+ for dm in func_dict.keys():
+ module = re.search("([^ .]*)\.[^ ]*$", dm).group(1)
+ doc_struct[module].append(dm)
+ doc_struct.update(mod_dict)
+ makeModuleSummaries(doc_struct, summaries)
+ doc_mod_structure = {"saqc": [f for f in func_dict.keys()], "saqc_dcstring": ""}
+ make_doc_core(root_path, func_dict, doc_mod_structure)
if __name__ == "__main__":
diff --git a/sphinx-doc/scripts/make_doc_rst.py b/sphinxdoc/scripts/make_doc_rst.py
similarity index 98%
rename from sphinx-doc/scripts/make_doc_rst.py
rename to sphinxdoc/scripts/make_doc_rst.py
index b5f3cfb25fb5808db1aabf359de89b29f65c726c..e3084a69e3cd08cefdc14e14ee524d635736faa8 100644
--- a/sphinx-doc/scripts/make_doc_rst.py
+++ b/sphinxdoc/scripts/make_doc_rst.py
@@ -32,7 +32,7 @@ def parse_imports(path):
"--targetpath",
type=str,
required=True,
- default="sphinx-doc/internal_doc_rst",
+ default="sphinxdoc/internal_doc_rst",
help="Output folder path (relative to sphinx root). Will be overridden if already existent.",
)
@click.option(
diff --git a/sphinx-doc/scripts/make_env_tab.py b/sphinxdoc/scripts/make_env_tab.py
similarity index 98%
rename from sphinx-doc/scripts/make_env_tab.py
rename to sphinxdoc/scripts/make_env_tab.py
index af92bcb06e0bbce22c70af284b8b02f7d4b5a69d..6f0914792bc8b8bc66d14642eb3a2af223a09193 100644
--- a/sphinx-doc/scripts/make_env_tab.py
+++ b/sphinxdoc/scripts/make_env_tab.py
@@ -22,7 +22,7 @@ import pickle
"--targetpath",
type=str,
required=True,
- default="sphinx-doc/environment",
+ default="sphinxdoc/environment",
help="Output path to contain configEnv.rst (relative to sphinx root).",
)
@click.option(
diff --git a/sphinxdoc/scripts/modify_html_API.py b/sphinxdoc/scripts/modify_html_API.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e8208a18a69363f49d75b751349d9aedb77535c
--- /dev/null
+++ b/sphinxdoc/scripts/modify_html_API.py
@@ -0,0 +1,43 @@
+import os
+import click
+import time
+
+
+@click.command()
+@click.option(
+ "-src",
+ "--source",
+ type=str,
+ required=True,
+ default="sphinxdoc.coredoc.SaQC",
+)
+@click.option(
+ "-trg",
+ "--target",
+ type=str,
+ required=True,
+ default="saqc.SaQC",
+)
+@click.option(
+ "-br",
+ "--builddir",
+ type=str,
+ required=True,
+ default="_build",
+ help="Relative path to the build dir.",
+)
+def main(source, target, builddir):
+ builddir = os.path.abspath(builddir)
+ apidir = os.path.join(builddir, os.path.normpath("html/_api"))
+ os.remove(os.path.join(apidir, target + ".html"))
+ with open(os.path.join(apidir, source + ".html"), "r") as f:
+ APIstring = f.read()
+ # APIstring = APIstring.replace('sphinxdoc.coredoc.core', 'saqc')
+
+ APIstring = APIstring.replace(source, target)
+ with open(os.path.join(apidir, target + ".html"), "w+") as f:
+ f.write(APIstring)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tests/core/test_core.py b/tests/core/test_core.py
index 5662ba2e5d5c973871f5574df46c01f8516d1fb3..a1e709507910850aa051194e728828ae31cc8bbd 100644
--- a/tests/core/test_core.py
+++ b/tests/core/test_core.py
@@ -158,3 +158,31 @@ def test_sourceTargetMultivariate():
return data, flags
SaQC(data).flagMulti(field=data.columns, target=data.columns)
+
+
+def test_sourceTargetMulti():
+ data = initData(3)
+ flags = initFlagsLike(data)
+ fields = data.columns
+ targets = [f"target{i + 1}" for i in range(len(fields))]
+
+ @register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=["field"],
+ handles_target=False,
+ multivariate=True,
+ )
+ def flagMulti(data, field, flags, target, **kwargs):
+ assert len(field) == len(target)
+ for src, trg in zip(field, target):
+ assert src in data
+ assert trg in data
+ assert src in flags
+ assert trg in flags
+
+ assert (data[src] == data[trg]).all(axis=None)
+ assert (flags[src] == flags[trg]).all(axis=None)
+ return data, flags
+
+ SaQC(data, flags).flagMulti(field=fields, target=targets)
diff --git a/tests/core/test_reader.py b/tests/core/test_reader.py
index a1ddc8c5784b96a7dcfe50b4d51f4fe849ee1ef4..d04d463a513a1f4ce2788071bfa234e2b87b87ce 100644
--- a/tests/core/test_reader.py
+++ b/tests/core/test_reader.py
@@ -8,7 +8,7 @@ import dios
from pathlib import Path
from saqc.core.reader import fromConfig, readFile
-from saqc.core.register import FUNC_MAP, register, flagging
+from saqc.core.register import FUNC_MAP, flagging
from tests.common import initData, writeIO
diff --git a/tests/funcs/test_functions.py b/tests/funcs/test_functions.py
index ab8fa8f4985586b4b3f55ed2c6152464282eeaae..535fa632fc3697d9cf907027ff515e1a6a942b60 100644
--- a/tests/funcs/test_functions.py
+++ b/tests/funcs/test_functions.py
@@ -4,6 +4,7 @@
import dios
import pandas as pd
import numpy as np
+import saqc
from saqc.funcs.noise import flagByStatLowPass
from saqc.constants import *
@@ -11,9 +12,8 @@ from saqc.core import initFlagsLike
from saqc.funcs.drift import (
flagDriftFromNorm,
flagDriftFromReference,
- flagDriftFromScaledNorm,
)
-from saqc.funcs.outliers import flagCrossStatistic, flagRange
+from saqc.funcs.outliers import flagRange
from saqc.funcs.flagtools import flagManual, forceFlags, clearFlags
from saqc.funcs.tools import dropField, copyField, maskTime
from saqc.funcs.resampling import concatFlags
@@ -171,24 +171,6 @@ def test_flagIsolated(data, field):
assert flags_result[field].iloc[[3, 5, 13, 14]].all()
-@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_2")])
-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)
- fields = ["data1", "data2"]
- s1, s2 = data1.squeeze(), data2.squeeze()
- s1 = pd.Series(data=s1.values, index=s1.index)
- s2 = pd.Series(data=s2.values, index=s1.index)
- data = dios.DictOfSeries([s1, s2], columns=["data1", "data2"])
- flags = initFlagsLike(data)
- _, flags_result = flagCrossStatistic(
- data, fields, flags, thresh=3, method=np.mean, flag=BAD
- )
- for field in fields:
- isflagged = flags_result[field] > UNFLAGGED
- assert isflagged[characteristics["raise"]].all()
-
-
def test_flagManual(data, field):
flags = initFlagsLike(data)
dat = data[field]
@@ -272,44 +254,55 @@ def test_flagManual(data, field):
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_1")])
-def test_flagDriftFromNormal(dat):
- data = dat(periods=200, peak_level=5, name="d1")[0]
- data["d2"] = dat(periods=200, peak_level=10, name="d2")[0]["d2"]
- data["d3"] = dat(periods=200, peak_level=100, name="d3")[0]["d3"]
- data["d4"] = 3 + 4 * data["d1"]
- data["d5"] = 3 + 4 * data["d1"]
+def test_flagDriftFromNorm(dat):
+ data = dat(periods=200, peak_level=5, name="field1")[0]
+ data["field2"] = dat(periods=200, peak_level=10, name="field2")[0]["field2"]
+ data["field3"] = dat(periods=200, peak_level=100, name="field3")[0]["field3"]
+
+ fields = ["field1", "field2", "field3"]
flags = initFlagsLike(data)
_, flags_norm = flagDriftFromNorm(
data=data.copy(),
- field=["d1", "d2", "d3"],
+ field=fields,
flags=flags.copy(),
freq="200min",
spread=5,
flag=BAD,
)
+ assert all(flags_norm["field3"] > UNFLAGGED)
+
+
+@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_1")])
+def test_flagDriftFromReference(dat):
+ data = dat(periods=200, peak_level=5, name="field1")[0]
+ data["field2"] = dat(periods=200, peak_level=10, name="field2")[0]["field2"]
+ data["field3"] = dat(periods=200, peak_level=100, name="field3")[0]["field3"]
+
+ fields = ["field1", "field2", "field3"]
+
+ flags = initFlagsLike(data)
_, flags_ref = flagDriftFromReference(
data=data.copy(),
- field=["d1", "d2", "d3"],
+ field=fields,
flags=flags.copy(),
- reference="d1",
+ reference="field1",
freq="3D",
thresh=20,
flag=BAD,
)
+ assert all(flags_ref["field3"] > UNFLAGGED)
- _, flags_scale = flagDriftFromScaledNorm(
- data.copy(),
- "dummy",
- flags.copy(),
- ["d1", "d3"],
- ["d4", "d5"],
- freq="3D",
- thresh=20,
- spread=5,
- flag=BAD,
+
+def test_transferFlags():
+ data = pd.DataFrame({"a": [1, 2], "b": [1, 2], "c": [1, 2]})
+ qc = saqc.SaQC(data)
+ qc = qc.flagRange("a", max=1.5)
+ qc = qc.transferFlags(["a", "a"], ["b", "c"])
+ assert np.all(
+ qc.flags["b"].values == np.array([saqc.constants.UNFLAGGED, saqc.constants.BAD])
+ )
+ assert np.all(
+ qc.flags["c"].values == np.array([saqc.constants.UNFLAGGED, saqc.constants.BAD])
)
- assert all(flags_norm["d3"] > UNFLAGGED)
- assert all(flags_ref["d3"] > UNFLAGGED)
- assert all(flags_scale["d3"] > UNFLAGGED)
diff --git a/tests/funcs/test_generic_api_functions.py b/tests/funcs/test_generic_api_functions.py
index c8eecb617f1ff4cf6e94647b20310fcb4b5ac3d5..91f41a67fdcf310aed6e85f876629d457bbff78a 100644
--- a/tests/funcs/test_generic_api_functions.py
+++ b/tests/funcs/test_generic_api_functions.py
@@ -8,6 +8,7 @@ from dios.dios.dios import DictOfSeries
from saqc.constants import BAD, UNFLAGGED, FILTER_ALL
from saqc.core.flags import Flags
from saqc import SaQC
+from saqc.core.register import _isflagged
from saqc.lib.tools import toSequence
from tests.common import initData
@@ -18,6 +19,19 @@ def data():
return initData()
+def test_emptyData():
+ # test that things do not break with empty data sets
+ saqc = SaQC(data=pd.DataFrame({"x": [], "y": []}))
+
+ saqc.flagGeneric("x", func=lambda x: x < 0)
+ assert saqc.data.empty
+ assert saqc.flags.empty
+
+ saqc = saqc.processGeneric(field="x", target="y", func=lambda x: x + 2)
+ assert saqc.data.empty
+ assert saqc.flags.empty
+
+
def test_writeTargetFlagGeneric(data):
params = [
(["tmp"], lambda x, y: pd.Series(True, index=x.index.union(y.index))),
@@ -116,6 +130,7 @@ def test_writeTargetProcGeneric(data):
"func": func.__name__,
"flag": BAD,
"dfilter": dfilter,
+ "label": "generic",
},
}
saqc = SaQC(
@@ -125,7 +140,12 @@ def test_writeTargetProcGeneric(data):
),
)
res = saqc.processGeneric(
- field=fields, target=targets, func=func, flag=BAD, dfilter=dfilter
+ field=fields,
+ target=targets,
+ func=func,
+ flag=BAD,
+ dfilter=dfilter,
+ label="generic",
)
assert (expected_data == res.data[targets].squeeze()).all(axis=None)
# check that new histories where created
@@ -155,6 +175,7 @@ def test_overwriteFieldProcGeneric(data):
"func": func.__name__,
"flag": flag,
"dfilter": dfilter,
+ "label": "generic",
},
}
@@ -165,7 +186,9 @@ def test_overwriteFieldProcGeneric(data):
),
)
- res = saqc.processGeneric(field=fields, func=func, flag=flag, dfilter=dfilter)
+ res = saqc.processGeneric(
+ field=fields, func=func, flag=flag, dfilter=dfilter, label="generic"
+ )
assert (expected_data == res.data[fields].squeeze()).all(axis=None)
# check that the histories got appended
for field in fields:
diff --git a/tests/funcs/test_spikes_detection.py b/tests/funcs/test_outier_detection.py
similarity index 75%
rename from tests/funcs/test_spikes_detection.py
rename to tests/funcs/test_outier_detection.py
index 9c951fc4d849d2a06288b2099f4277bb4c6e8157..6cc2a91c4f2e77e803a8c2f78802b5a345b7e4db 100644
--- a/tests/funcs/test_spikes_detection.py
+++ b/tests/funcs/test_outier_detection.py
@@ -11,6 +11,7 @@ from saqc.funcs.outliers import (
flagRaise,
flagMVScores,
flagByGrubbs,
+ flagCrossStatistics,
)
from saqc.constants import *
from saqc.core import initFlagsLike
@@ -79,18 +80,25 @@ def test_flagSpikesLimitRaise(dat):
# see test/functs/fixtures.py for the 'course_N'
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_3")])
-def test_flagMultivarScores(dat):
+def test_flagMVScores(dat):
+ def _check(fields, flags, characteristics):
+ for field in fields:
+ isflagged = flags[field] > UNFLAGGED
+ assert isflagged[characteristics["raise"]].all()
+ assert not isflagged[characteristics["return"]].any()
+ assert not isflagged[characteristics["drop"]].any()
+
data1, characteristics = dat(
periods=1000, initial_level=5, final_level=15, out_val=50
)
data2, characteristics = dat(
periods=1000, initial_level=20, final_level=1, out_val=30
)
- fields = ["data1", "data2"]
+ fields = ["field1", "field2"]
s1, s2 = data1.squeeze(), data2.squeeze()
s1 = pd.Series(data=s1.values, index=s1.index)
s2 = pd.Series(data=s2.values, index=s1.index)
- data = dios.DictOfSeries([s1, s2], columns=["data1", "data2"])
+ data = dios.DictOfSeries([s1, s2], columns=["field1", "field2"])
flags = initFlagsLike(data)
_, flags_result = flagMVScores(
data=data,
@@ -101,11 +109,7 @@ def test_flagMultivarScores(dat):
n=10,
flag=BAD,
)
- for field in fields:
- isflagged = flags_result[field] > UNFLAGGED
- assert isflagged[characteristics["raise"]].all()
- assert not isflagged[characteristics["return"]].any()
- assert not isflagged[characteristics["drop"]].any()
+ _check(fields, flags_result, characteristics)
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_3")])
@@ -124,3 +128,22 @@ def test_grubbs(dat):
data, "data", flags, window=20, min_periods=15, flag=BAD
)
assert np.all(result_flags["data"][char_dict["drop"]] > UNFLAGGED)
+
+
+@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_2")])
+def test_flagCrossStatistics(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)
+ fields = ["field1", "field2"]
+ s1, s2 = data1.squeeze(), data2.squeeze()
+ s1 = pd.Series(data=s1.values, index=s1.index)
+ s2 = pd.Series(data=s2.values, index=s1.index)
+ data = dios.DictOfSeries([s1, s2], columns=["field1", "field2"])
+ flags = initFlagsLike(data)
+
+ _, flags_result = flagCrossStatistics(
+ data, fields, flags, thresh=3, method=np.mean, flag=BAD
+ )
+ for field in fields:
+ isflagged = flags_result[field] > UNFLAGGED
+ assert isflagged[characteristics["raise"]].all()
diff --git a/tests/funcs/test_tools.py b/tests/funcs/test_tools.py
index eb3daefa524309aff04c45082bd1be98e4624289..3686b2e7d657abca2cfc4b11db7f0809b4256f85 100644
--- a/tests/funcs/test_tools.py
+++ b/tests/funcs/test_tools.py
@@ -30,5 +30,5 @@ def test_makeFig():
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, ax_kwargs={"ylim": "2000-10"}, stats=True
+ field="data", path=dummy_path, ax_kwargs={"ylabel": "data is data"}, stats=True
)