diff --git a/.gitattributes b/.gitattributes
index 612d4287d1f5adcf765cae9eff8fc7de1aeb630d..39c62b494d88330c4be6f8b8d21a3d9a8ef5eaac 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
*.feather filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text
-ressources/machine_learning/data/soil_moisture_mwe.feather filter=lfs diff=lfs merge=lfs -text
+resources/machine_learning/data/soil_moisture_mwe.feather filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
index c21f5007abe4013ceaf3094de94a14c237809322..dfacde84c06ace3bf99af6b853b13350a6a5cd06 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,13 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
**/__pycache__
*.pyc
+*.automodsumm
+*.automodapi
+docs/_api
+docs/_build
+coverage.xml
venv*/
**/.*
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index f5afe18a5b52bf0ee6b217090819734be0b445b7..1996835fa938594ba43ef996b612dbe4b13f4e17 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# ===========================================================
# preparation
# ===========================================================
@@ -5,33 +9,93 @@
variables:
GIT_SUBMODULE_STRATEGY: recursive
+stages:
+ - compliance
+ - test
+ - build
+ - deploy
+
default:
image: python:3.8
before_script:
- pip install --upgrade pip
- - pip install pytest
- pip install -r requirements.txt
+ - pip install -r tests/requirements.txt
+
+# ===========================================================
+# Compliance stage
+# ===========================================================
+
+# check if everything is properly formatted
+black:
+ stage: compliance
+ script:
+ - pip install black
+ - black --check .
+
+# check if imports are sorted
+isort:
+ stage: compliance
+ script:
+ - pip install isort
+ - isort --check .
+
+# Check compliance with the REUSE specification
+reuse:
+ stage: compliance
+ image:
+ name: fsfe/reuse:latest
+ entrypoint: [""]
+ before_script:
+ - echo "Override default 'before_script'..."
+ script:
+ - reuse lint
# ===========================================================
-# normal jobs (non scheduled)
+# Testing stage
# ===========================================================
+# make (visual) coverage in gitlab merge request diff's
+coverage:
+ stage: test
+ allow_failure: true
+ script:
+ - pip install pytest-cov coverage
+ - pytest --cov=saqc tests --ignore=tests/fuzzy -Werror
+ after_script:
+ - coverage xml
+ # regex to find the coverage percentage in the job output
+ coverage: '/^TOTAL.+?(\d+\%)$/'
+ artifacts:
+ when: always
+ reports:
+ cobertura: coverage.xml
+
+
# test saqc with python 3.7
python37:
stage: test
image: python:3.7
script:
- - pytest tests/core tests/funcs tests/integration dios/test -Werror
- - python -m saqc --config ressources/data/config.csv --data ressources/data/data.csv --outfile /tmp/test.csv
+ - pytest tests dios/test -Werror --junitxml=report.xml
+ - python -m saqc --config docs/resources/data/config.csv --data docs/resources/data/data.csv --outfile /tmp/test.csv
+ artifacts:
+ when: always
+ reports:
+ junit: report.xml
# test saqc with python 3.8
python38:
stage: test
script:
- - pytest tests/core tests/funcs tests/integration dios/test -Werror
- - python -m saqc --config ressources/data/config.csv --data ressources/data/data.csv --outfile /tmp/test.csv
+ - pytest tests dios/test -Werror --junitxml=report.xml
+ - python -m saqc --config docs/resources/data/config.csv --data docs/resources/data/data.csv --outfile /tmp/test.csv
+ artifacts:
+ when: always
+ reports:
+ junit: report.xml
# test saqc with python 3.9
@@ -39,36 +103,57 @@ python39:
stage: test
image: python:3.9
script:
- - pytest tests/core tests/funcs tests/integration dios/test -Werror
- - python -m saqc --config ressources/data/config.csv --data ressources/data/data.csv --outfile /tmp/test.csv
+ - pytest tests dios/test -Werror --junitxml=report.xml
+ - python -m saqc --config docs/resources/data/config.csv --data docs/resources/data/data.csv --outfile /tmp/test.csv
+ artifacts:
+ when: always
+ reports:
+ junit: report.xml
-# check if everthing is properly formatted
-black:
+# test saqc with python 3.10
+python310:
stage: test
+ image: python:3.10
script:
- - pip install black
- - black --check .
+ - pytest tests dios/test -Werror --junitxml=report.xml
+ - python -m saqc --config docs/resources/data/config.csv --data docs/resources/data/data.csv --outfile /tmp/test.csv
+ artifacts:
+ when: always
+ reports:
+ junit: report.xml
-# make (visual) coverage in gitlab merge request diff's
-coverage:
+doctest:
stage: test
- allow_failure: true
script:
- - pip install pytest-cov coverage
- - pytest --cov=saqc tests/core tests/funcs -Werror
- after_script:
- - coverage xml
+ - cd docs
+ - pip install -r requirements.txt
+ - make doc # needed for _api tests
+ - make test
- # regex to find the coverage percentage in the job output
- coverage: '/^TOTAL.+?(\d+\%)$/'
- artifacts:
- when: always
- reports:
- cobertura: coverage.xml
+# ===========================================================
+# Building stage
+# ===========================================================
+# check if we are able to build a wheel
+wheel:
+ stage: build
+ script:
+ - pip install wheel
+ - pip wheel .
+ - pip install .
+
+docs:
+ stage: build
+ script:
+ - cd docs
+ - pip install -r requirements.txt
+ - make doc
+# ===========================================================
+# Extra Pipeline (run with a successful run of all other jobs on develop)
+# ===========================================================
# make html docu with sphinx
pages:
@@ -78,33 +163,10 @@ pages:
except:
- schedules
script:
- - cd sphinxdoc/
- - pip install -r requirements_sphinx.txt
+ - cd docs/
+ - pip install -r requirements.txt
- make doc
- cp -r _build/html ../public
artifacts:
paths:
- public
-
-
-# ===========================================================
-# scheduled jobs
-# ===========================================================
-
-# fuzzy testing saqc
-fuzzy:
- stage: test
- only:
- - schedules
- script:
- - pytest tests/fuzzy -Werror
-
-
-# test lib saqc
-testLib:
- stage: test
- only:
- - schedules
- script:
- - pytest tests/lib -Werror
-
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 62b8c92c7286c9dc1a341ebeb66b420eea07e4f5..f22ec980c10ee3c75e49f5ceb7ba3c3ab0af0784 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,15 +1,65 @@
+<!--
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
+-->
+
# 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]
+## Unreleased
+[List of commits](https://git.ufz.de/rdm-software/saqc/-/compare/v2.1.0...develop)
+### Added
+### Changed
+### Removed
+### Fixed
+
+## [2.1.0](https://git.ufz.de/rdm-software/saqc/-/tags/v2.0.1) - 2022-06-14
+[List of commits](https://git.ufz.de/rdm-software/saqc/-/compare/v2.0.1...v2.1.0)
+
### Added
+- documentation of global keywords
+- generic documentation module `docurator.py`
+- documentation of flagging constants
+- `pyproject.toml`
+- new function `progagateFlags`
+- include function typehints in parameter documentation
+- `label` parameter to the generic function `isflagged`
+
### Changed
+- `flagOffsets` parameters `thresh` and `thresh_relative` are optional
+- corrected false notion of the term *residual* (replace all occurences of *residue* by *residual*)
+- `FILTER_NONE` and `FILTER_ALL` are top level constants (imported in `saqc.__init__`)
+- renamed `maskTime` to `selectTime`
+- `SaQC.data` returns `dios.DictOfSeries`
+- `SaQC.flags` returns `dios.DictOfSeries` or `pd.DataFrame`
+- `SaQC.data` and `SaQC.flags` are not mutated by function calls
+- renamed `History.max` to `History.squeeze`
+- renamed parameter `freq` of function flagByStray to `window`
+- `DmpScheme`: set `DFILTER_DEFAULT` to 1 in order to not mask the flag 'OK'
+
### Removed
+- data accessors `SaQC.result`, `SaQC.data_raw`, `SaQC.flags_raw`
+
### Fixed
+- `flagOffset` failure on falsy `thresh`
+- `flagCrossStatistics` failure on unaligned input variables
+- `plot` data loss when using *dfilter* kwarg
+- `correctDrift`: failure on single value intervals
+- `concatFlags`: information loss by appending squeezed histories
+- `interpolateInvalid`: replace flags by interpolated values
+- `resample`: pass resampling function to `history.appy()`
+- `tools.seasonalMask`: mask swapping with `include_bounds=True`
+- `flagGeneric`:
+ - fixed inconsistent history meta writing
+ - fixed handling of existing flags
+- `proGeneeric`: fixed inconsistent history meta writing
+- `docs`: removed documentation of data/flags parameters from automatic sphinx doc
-## [2.0.1] - 2021-12-20
+## [2.0.1](https://git.ufz.de/rdm-software/saqc/-/tags/v2.0.1) - 2021-12-20
+[List of commits](https://git.ufz.de/rdm-software/saqc/-/compare/v2.0.0...v2.0.1)
### Added
- CLI now accepts remote configuration and data files as URL
- new function `transferFlags`
@@ -35,5 +85,7 @@ This changelog starts with version 2.0.0. Basically all parts of the system, inc
- `field` was not masked for resampling functions
- allow custom registered functions to overwrite built-ins.
-## [2.0.0] - 2021-11-25
+## [2.0.0](https://git.ufz.de/rdm-software/saqc/-/tags/v2.0.0) - 2021-11-25
+[List of commits](https://git.ufz.de/rdm-software/saqc/-/compare/v1.5.0...v2.0.0)
+
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
index 4f6b69fbf66c3f6a6fa262917ee04360292dade5..bad9a580d774039dd193f65e27acf43808ee6014 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -3,7 +3,7 @@ title: SaQC - System for automated Quality Control
message: "Please cite this software using these metadata."
type: software
version: 2.0.0
-doi:
+doi: https://doi.org/10.5281/zenodo.5888547
date-released: "2021-11-25"
license: "GPL-3.0"
repository-code: "https://git.ufz.de/rdm-software/saqc"
@@ -24,7 +24,7 @@ authors:
affiliation: >-
Helmholtz Centre for Environmental Research -
UFZ
- orcid: 'https://orcid.org/0000-0000-0000-0000'
+ orcid: 'https://orcid.org/0000-0001-5106-9057'
- given-names: Peter
family-names: Lünenschloß
email: peter.luenenschloss@ufz.de
diff --git a/CITATION.cff.license b/CITATION.cff.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/CITATION.cff.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index f9dcdbbc8370ca06ed44a449e43657f17918c280..65c75b12fc8466c673720094987045760fc12cba 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,5 +1,11 @@
+<!--
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
+-->
+
# Development Environment
-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).
+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/gettingstarted/InstallationGuide.html#set-up-a-virtual-environment).
# Testing
SaQC comes with an extensive test suite based on [pytest](https://docs.pytest.org/en/latest/).
@@ -18,41 +24,38 @@ We implement the following naming conventions:
### Argument names in public functions signatures
-first, its not necessary to have *talking* arg-names, in contrast to variable names in
-code. This is, because one always must read the documentation. To use and parameterize a function,
-just by guessing the meaning of the argument names and not read the docs,
-will almost never work. thats why, we dont have the obligation to make names (very)
+First, in contrast to variable names in code, it is not necessary to have *talking* function argument names.
+A user is always expected to have had acknowledged the documentation. Using and parameterizing a function,
+just by guessing the meaning of the argument names, without having read the documentation,
+will almost never work. That is the reason, why we dont have the obligation to make names (very)
talkative.
-second, because of the nature of a function (to have a *simple* way to use complex code),
-its common to use simple and short names. This means, to omit any *irrelevant* information.
+Second, from the nature of a function to deliver a *simple* way of using complex code, it follows, that simple and short names are to prefer. This means, the encoding of *irrelevant* informations in names should be omitted.
-For example if we have a function that fit a polynomial on some data with three arguments.
+For example, take a function of three arguments, that fits a polynomial to some data.
Lets say we have:
- the data input,
- - a threshold that defines a cutoff point for a calculation on a polynomial and
+ - a threshold, that defines a cutoff point for a calculation on a polynomial and
- a third argument.
-one could name the args `data, poly_cutoff_threshold, ...`, but much better names would
-be `data, thresh, ...`, because a caller dont need the extra information,
-stuffed in the name.
+One could name the corresponding arguments: `data, poly_cutoff_threshold, ...`. However, much better names would
+be: `data, thresh, ...`, because a caller that is aware of a functions documentation doesnt need the extra information,
+encoded in the name.
If the third argument is also some kind of threshold,
one can use `data, cutoff, thresh`, because the *thresh-* information of the `cutoff`
-parameter is not crucial and the caller knows that this is a threshold from the docstring.
+parameter is not crucial, and the caller knows that this is a threshold from having studied the docstring, anyways.
-third, underscores give a nice feedback if one doing wrong or over complex.
-No underscore is fine, one underscore is ok, if the information is *really necessary* (see above),
-but if one use two or more underscores, one should think of a better naming,
-or omit some information.
-Sure, seldom but sometimes it is necessary to use 2 underscores, but we consider it as bad style.
-Using 3 or more underscores, is not allowed unless have write an reasoning and get it
-signed by at least as many core developers as underscores one want to use.
+Third, underscores give a nice implicit feedback, on whether one is doing wrong or getting over complex in the naming behavior.
+To have no underscore, is just fine. Having one underscore, is ok, if the encoded information appended through the underscore is *really necessary* (see above).
+If one uses two or more underscores, one should think of a better naming or omit some information.
+Sure, although it is seldom, it might sometimes be necessary to use two underscores, but still the usage of two underscores is considered bad style.
+Using three or more underscores is not allowed unless having issued a exhaustive and accepted (by at least one core developer per underscore) reasoning.
-In short the naming should *give a very, very rough idea* of the purpose of the argument,
+In short, the naming should *give a very, very rough idea* of the purpose of the argument,
but not *explain* the usage or the purpose.
-It is not a shame to name a parameter just `n` or `alpha` etc. if for example the algorithm
-(from the paper etc.) name it alike.
+It is not a shame to name a parameter just `n` or `alpha` etc., if, for example, the algorithm
+(from the paper etc.) names it alike.
### Test Functions
diff --git a/LICENSE.md b/LICENSE.md
index 804727ae457a5b291e0792b53bb9a211c2a836eb..2aadc3a22934008cf05c8b6fc3ba5d75c54292cf 100644
--- a/LICENSE.md
+++ b/LICENSE.md
@@ -1,16 +1,23 @@
+<!--
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
+-->
+
# SOFTWARE LICENCE
-This file is part of the "System for Automated Quality Control" developed by the Research Data Management Team
-of the Helmholtz-Centre for Environmental Research Leipzig.
+This file is part of the "System for Automated Quality Control" developed by
+the Research Data Management Team of the Helmholtz Centre for Environmental
+Research - UFZ.
# Copyright Notice
-Copyright(c) 2021, *Helmholtz-Zentrum für Umweltforschung GmbH -- UFZ*. All rights reserved.
+Copyright(c) 2021, *Helmholtz-Zentrum für Umweltforschung GmbH - UFZ*. All rights reserved.
**The code is a property of**
-*Helmholtz-Zentrum für Umweltforschung GmbH -- UFZ*\
+*Helmholtz-Zentrum für Umweltforschung GmbH - UFZ*\
Registered Office: Leipzig\
Registration Office: Amtsgericht Leipzig\
Trade Register Nr. B 4703\
@@ -37,8 +44,12 @@ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
Public License for more details.
You should have received a copy of the GNU General Public License along
-with this program; if not, it can be found at the end of this document
-or see at https://www.gnu.org/licenses/gpl-3.0.de.html.
+with this program; if not, it can be found at
+[LICENSES/GPL-3.0-or-later.txt](LICENSES/GPL-3.0-or-later.txt) or see at
+https://www.gnu.org/licenses/gpl-3.0.de.html.
+
+> **Hint:** We provided the copyright and license information in accordance to
+> the [REUSE Specification 3.0](https://reuse.software/spec/).
**Redistribution**
@@ -55,7 +66,7 @@ notice, this list of conditions, the following GNU General Public License
and the modification conditions in the documentation and/or other materials
provided with the distribution.
-- Neither the name of *Helmholtz-Zentrum für Umweltforschung GmbH -- UFZ*,
+- Neither the name of *Helmholtz-Zentrum für Umweltforschung GmbH - UFZ*,
nor the names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
@@ -63,684 +74,5 @@ derived from this software without specific prior written permission.
If software is modified to produce derivative works, such modified
software should be clearly marked, so as not to confuse it with the
-version available from *Helmholtz-Zentrum für Umweltforschung GmbH --
+version available from *Helmholtz-Zentrum für Umweltforschung GmbH -
UFZ*.
-
-
-# GNU GENERAL PUBLIC LICENSE
-
-Version 3, 29 June 2007
-
-Copyright (C) 2007 Free Software Foundation, Inc.
-<https://fsf.org/>
-
-Everyone is permitted to copy and distribute verbatim copies of this
-license document, but changing it is not allowed.
-
-# Preamble
-
-The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
-The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works. By contrast,
-the GNU General Public License is intended to guarantee your freedom
-to share and change all versions of a program--to make sure it remains
-free software for all its users. We, the Free Software Foundation, use
-the GNU General Public License for most of our software; it applies
-also to any other work released this way by its authors. You can apply
-it to your programs, too.
-
-When we speak of free software, we are referring to freedom, not
-price. Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
-To protect your rights, we need to prevent others from denying you
-these rights or asking you to surrender the rights. Therefore, you
-have certain responsibilities if you distribute copies of the
-software, or if you modify it: responsibilities to respect the freedom
-of others.
-
-For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must pass on to the recipients the same
-freedoms that you received. You must make sure that they, too, receive
-or can get the source code. And you must show them these terms so they
-know their rights.
-
-Developers that use the GNU GPL protect your rights with two steps:
-(1) assert copyright on the software, and (2) offer you this License
-giving you legal permission to copy, distribute and/or modify it.
-
-For the developers' and authors' protection, the GPL clearly explains
-that there is no warranty for this free software. For both users' and
-authors' sake, the GPL requires that modified versions be marked as
-changed, so that their problems will not be attributed erroneously to
-authors of previous versions.
-
-Some devices are designed to deny users access to install or run
-modified versions of the software inside them, although the
-manufacturer can do so. This is fundamentally incompatible with the
-aim of protecting users' freedom to change the software. The
-systematic pattern of such abuse occurs in the area of products for
-individuals to use, which is precisely where it is most unacceptable.
-Therefore, we have designed this version of the GPL to prohibit the
-practice for those products. If such problems arise substantially in
-other domains, we stand ready to extend this provision to those
-domains in future versions of the GPL, as needed to protect the
-freedom of users.
-
-Finally, every program is threatened constantly by software patents.
-States should not allow patents to restrict development and use of
-software on general-purpose computers, but in those that do, we wish
-to avoid the special danger that patents applied to a free program
-could make it effectively proprietary. To prevent this, the GPL
-assures that patents cannot be used to render the program non-free.
-
-The precise terms and conditions for copying, distribution and
-modification follow.
-
-# TERMS AND CONDITIONS
-
-## 0. Definitions.
-
-"This License" refers to version 3 of the GNU General Public License.
-
-"Copyright" also means copyright-like laws that apply to other kinds
-of works, such as semiconductor masks.
-
-"The Program" refers to any copyrightable work licensed under this
-License. Each licensee is addressed as "you". "Licensees" and
-"recipients" may be individuals or organizations.
-
-To "modify" a work means to copy from or adapt all or part of the work
-in a fashion requiring copyright permission, other than the making of
-an exact copy. The resulting work is called a "modified version" of
-the earlier work or a work "based on" the earlier work.
-
-A "covered work" means either the unmodified Program or a work based
-on the Program.
-
-To "propagate" a work means to do anything with it that, without
-permission, would make you directly or secondarily liable for
-infringement under applicable copyright law, except executing it on a
-computer or modifying a private copy. Propagation includes copying,
-distribution (with or without modification), making available to the
-public, and in some countries other activities as well.
-
-To "convey" a work means any kind of propagation that enables other
-parties to make or receive copies. Mere interaction with a user
-through a computer network, with no transfer of a copy, is not
-conveying.
-
-An interactive user interface displays "Appropriate Legal Notices" to
-the extent that it includes a convenient and prominently visible
-feature that (1) displays an appropriate copyright notice, and (2)
-tells the user that there is no warranty for the work (except to the
-extent that warranties are provided), that licensees may convey the
-work under this License, and how to view a copy of this License. If
-the interface presents a list of user commands or options, such as a
-menu, a prominent item in the list meets this criterion.
-
-## 1. Source Code.
-
-The "source code" for a work means the preferred form of the work for
-making modifications to it. "Object code" means any non-source form of
-a work.
-
-A "Standard Interface" means an interface that either is an official
-standard defined by a recognized standards body, or, in the case of
-interfaces specified for a particular programming language, one that
-is widely used among developers working in that language.
-
-The "System Libraries" of an executable work include anything, other
-than the work as a whole, that (a) is included in the normal form of
-packaging a Major Component, but which is not part of that Major
-Component, and (b) serves only to enable use of the work with that
-Major Component, or to implement a Standard Interface for which an
-implementation is available to the public in source code form. A
-"Major Component", in this context, means a major essential component
-(kernel, window system, and so on) of the specific operating system
-(if any) on which the executable work runs, or a compiler used to
-produce the work, or an object code interpreter used to run it.
-
-The "Corresponding Source" for a work in object code form means all
-the source code needed to generate, install, and (for an executable
-work) run the object code and to modify the work, including scripts to
-control those activities. However, it does not include the work's
-System Libraries, or general-purpose tools or generally available free
-programs which are used unmodified in performing those activities but
-which are not part of the work. For example, Corresponding Source
-includes interface definition files associated with source files for
-the work, and the source code for shared libraries and dynamically
-linked subprograms that the work is specifically designed to require,
-such as by intimate data communication or control flow between those
-subprograms and other parts of the work.
-
-The Corresponding Source need not include anything that users can
-regenerate automatically from other parts of the Corresponding Source.
-
-The Corresponding Source for a work in source code form is that same
-work.
-
-## 2. Basic Permissions.
-
-All rights granted under this License are granted for the term of
-copyright on the Program, and are irrevocable provided the stated
-conditions are met. This License explicitly affirms your unlimited
-permission to run the unmodified Program. The output from running a
-covered work is covered by this License only if the output, given its
-content, constitutes a covered work. This License acknowledges your
-rights of fair use or other equivalent, as provided by copyright law.
-
-You may make, run and propagate covered works that you do not convey,
-without conditions so long as your license otherwise remains in force.
-You may convey covered works to others for the sole purpose of having
-them make modifications exclusively for you, or provide you with
-facilities for running those works, provided that you comply with the
-terms of this License in conveying all material for which you do not
-control copyright. Those thus making or running the covered works for
-you must do so exclusively on your behalf, under your direction and
-control, on terms that prohibit them from making any copies of your
-copyrighted material outside their relationship with you.
-
-Conveying under any other circumstances is permitted solely under the
-conditions stated below. Sublicensing is not allowed; section 10 makes
-it unnecessary.
-
-## 3. Protecting Users' Legal Rights From Anti-Circumvention Law.
-
-No covered work shall be deemed part of an effective technological
-measure under any applicable law fulfilling obligations under article
-11 of the WIPO copyright treaty adopted on 20 December 1996, or
-similar laws prohibiting or restricting circumvention of such
-measures.
-
-When you convey a covered work, you waive any legal power to forbid
-circumvention of technological measures to the extent such
-circumvention is effected by exercising rights under this License with
-respect to the covered work, and you disclaim any intention to limit
-operation or modification of the work as a means of enforcing, against
-the work's users, your or third parties' legal rights to forbid
-circumvention of technological measures.
-
-## 4. Conveying Verbatim Copies.
-
-You may convey verbatim copies of the Program's source code as you
-receive it, in any medium, provided that you conspicuously and
-appropriately publish on each copy an appropriate copyright notice;
-keep intact all notices stating that this License and any
-non-permissive terms added in accord with section 7 apply to the code;
-keep intact all notices of the absence of any warranty; and give all
-recipients a copy of this License along with the Program.
-
-You may charge any price or no price for each copy that you convey,
-and you may offer support or warranty protection for a fee.
-
-## 5. Conveying Modified Source Versions.
-
-You may convey a work based on the Program, or the modifications to
-produce it from the Program, in the form of source code under the
-terms of section 4, provided that you also meet all of these
-conditions:
-
-- a) The work must carry prominent notices stating that you modified
- it, and giving a relevant date.
-- b) The work must carry prominent notices stating that it is
- released under this License and any conditions added under
- section 7. This requirement modifies the requirement in section 4
- to "keep intact all notices".
-- c) You must license the entire work, as a whole, under this
- License to anyone who comes into possession of a copy. This
- License will therefore apply, along with any applicable section 7
- additional terms, to the whole of the work, and all its parts,
- regardless of how they are packaged. This License gives no
- permission to license the work in any other way, but it does not
- invalidate such permission if you have separately received it.
-- d) If the work has interactive user interfaces, each must display
- Appropriate Legal Notices; however, if the Program has interactive
- interfaces that do not display Appropriate Legal Notices, your
- work need not make them do so.
-
-A compilation of a covered work with other separate and independent
-works, which are not by their nature extensions of the covered work,
-and which are not combined with it such as to form a larger program,
-in or on a volume of a storage or distribution medium, is called an
-"aggregate" if the compilation and its resulting copyright are not
-used to limit the access or legal rights of the compilation's users
-beyond what the individual works permit. Inclusion of a covered work
-in an aggregate does not cause this License to apply to the other
-parts of the aggregate.
-
-## 6. Conveying Non-Source Forms.
-
-You may convey a covered work in object code form under the terms of
-sections 4 and 5, provided that you also convey the machine-readable
-Corresponding Source under the terms of this License, in one of these
-ways:
-
-- a) Convey the object code in, or embodied in, a physical product
- (including a physical distribution medium), accompanied by the
- Corresponding Source fixed on a durable physical medium
- customarily used for software interchange.
-- b) Convey the object code in, or embodied in, a physical product
- (including a physical distribution medium), accompanied by a
- written offer, valid for at least three years and valid for as
- long as you offer spare parts or customer support for that product
- model, to give anyone who possesses the object code either (1) a
- copy of the Corresponding Source for all the software in the
- product that is covered by this License, on a durable physical
- medium customarily used for software interchange, for a price no
- more than your reasonable cost of physically performing this
- conveying of source, or (2) access to copy the Corresponding
- Source from a network server at no charge.
-- c) Convey individual copies of the object code with a copy of the
- written offer to provide the Corresponding Source. This
- alternative is allowed only occasionally and noncommercially, and
- only if you received the object code with such an offer, in accord
- with subsection 6b.
-- d) Convey the object code by offering access from a designated
- place (gratis or for a charge), and offer equivalent access to the
- Corresponding Source in the same way through the same place at no
- further charge. You need not require recipients to copy the
- Corresponding Source along with the object code. If the place to
- copy the object code is a network server, the Corresponding Source
- may be on a different server (operated by you or a third party)
- that supports equivalent copying facilities, provided you maintain
- clear directions next to the object code saying where to find the
- Corresponding Source. Regardless of what server hosts the
- Corresponding Source, you remain obligated to ensure that it is
- available for as long as needed to satisfy these requirements.
-- e) Convey the object code using peer-to-peer transmission,
- provided you inform other peers where the object code and
- Corresponding Source of the work are being offered to the general
- public at no charge under subsection 6d.
-
-A separable portion of the object code, whose source code is excluded
-from the Corresponding Source as a System Library, need not be
-included in conveying the object code work.
-
-A "User Product" is either (1) a "consumer product", which means any
-tangible personal property which is normally used for personal,
-family, or household purposes, or (2) anything designed or sold for
-incorporation into a dwelling. In determining whether a product is a
-consumer product, doubtful cases shall be resolved in favor of
-coverage. For a particular product received by a particular user,
-"normally used" refers to a typical or common use of that class of
-product, regardless of the status of the particular user or of the way
-in which the particular user actually uses, or expects or is expected
-to use, the product. A product is a consumer product regardless of
-whether the product has substantial commercial, industrial or
-non-consumer uses, unless such uses represent the only significant
-mode of use of the product.
-
-"Installation Information" for a User Product means any methods,
-procedures, authorization keys, or other information required to
-install and execute modified versions of a covered work in that User
-Product from a modified version of its Corresponding Source. The
-information must suffice to ensure that the continued functioning of
-the modified object code is in no case prevented or interfered with
-solely because modification has been made.
-
-If you convey an object code work under this section in, or with, or
-specifically for use in, a User Product, and the conveying occurs as
-part of a transaction in which the right of possession and use of the
-User Product is transferred to the recipient in perpetuity or for a
-fixed term (regardless of how the transaction is characterized), the
-Corresponding Source conveyed under this section must be accompanied
-by the Installation Information. But this requirement does not apply
-if neither you nor any third party retains the ability to install
-modified object code on the User Product (for example, the work has
-been installed in ROM).
-
-The requirement to provide Installation Information does not include a
-requirement to continue to provide support service, warranty, or
-updates for a work that has been modified or installed by the
-recipient, or for the User Product in which it has been modified or
-installed. Access to a network may be denied when the modification
-itself materially and adversely affects the operation of the network
-or violates the rules and protocols for communication across the
-network.
-
-Corresponding Source conveyed, and Installation Information provided,
-in accord with this section must be in a format that is publicly
-documented (and with an implementation available to the public in
-source code form), and must require no special password or key for
-unpacking, reading or copying.
-
-## 7. Additional Terms.
-
-"Additional permissions" are terms that supplement the terms of this
-License by making exceptions from one or more of its conditions.
-Additional permissions that are applicable to the entire Program shall
-be treated as though they were included in this License, to the extent
-that they are valid under applicable law. If additional permissions
-apply only to part of the Program, that part may be used separately
-under those permissions, but the entire Program remains governed by
-this License without regard to the additional permissions.
-
-When you convey a copy of a covered work, you may at your option
-remove any additional permissions from that copy, or from any part of
-it. (Additional permissions may be written to require their own
-removal in certain cases when you modify the work.) You may place
-additional permissions on material, added by you to a covered work,
-for which you have or can give appropriate copyright permission.
-
-Notwithstanding any other provision of this License, for material you
-add to a covered work, you may (if authorized by the copyright holders
-of that material) supplement the terms of this License with terms:
-
-- a) Disclaiming warranty or limiting liability differently from the
- terms of sections 15 and 16 of this License; or
-- b) Requiring preservation of specified reasonable legal notices or
- author attributions in that material or in the Appropriate Legal
- Notices displayed by works containing it; or
-- c) Prohibiting misrepresentation of the origin of that material,
- or requiring that modified versions of such material be marked in
- reasonable ways as different from the original version; or
-- d) Limiting the use for publicity purposes of names of licensors
- or authors of the material; or
-- e) Declining to grant rights under trademark law for use of some
- trade names, trademarks, or service marks; or
-- f) Requiring indemnification of licensors and authors of that
- material by anyone who conveys the material (or modified versions
- of it) with contractual assumptions of liability to the recipient,
- for any liability that these contractual assumptions directly
- impose on those licensors and authors.
-
-All other non-permissive additional terms are considered "further
-restrictions" within the meaning of section 10. If the Program as you
-received it, or any part of it, contains a notice stating that it is
-governed by this License along with a term that is a further
-restriction, you may remove that term. If a license document contains
-a further restriction but permits relicensing or conveying under this
-License, you may add to a covered work material governed by the terms
-of that license document, provided that the further restriction does
-not survive such relicensing or conveying.
-
-If you add terms to a covered work in accord with this section, you
-must place, in the relevant source files, a statement of the
-additional terms that apply to those files, or a notice indicating
-where to find the applicable terms.
-
-Additional terms, permissive or non-permissive, may be stated in the
-form of a separately written license, or stated as exceptions; the
-above requirements apply either way.
-
-## 8. Termination.
-
-You may not propagate or modify a covered work except as expressly
-provided under this License. Any attempt otherwise to propagate or
-modify it is void, and will automatically terminate your rights under
-this License (including any patent licenses granted under the third
-paragraph of section 11).
-
-However, if you cease all violation of this License, then your license
-from a particular copyright holder is reinstated (a) provisionally,
-unless and until the copyright holder explicitly and finally
-terminates your license, and (b) permanently, if the copyright holder
-fails to notify you of the violation by some reasonable means prior to
-60 days after the cessation.
-
-Moreover, your license from a particular copyright holder is
-reinstated permanently if the copyright holder notifies you of the
-violation by some reasonable means, this is the first time you have
-received notice of violation of this License (for any work) from that
-copyright holder, and you cure the violation prior to 30 days after
-your receipt of the notice.
-
-Termination of your rights under this section does not terminate the
-licenses of parties who have received copies or rights from you under
-this License. If your rights have been terminated and not permanently
-reinstated, you do not qualify to receive new licenses for the same
-material under section 10.
-
-## 9. Acceptance Not Required for Having Copies.
-
-You are not required to accept this License in order to receive or run
-a copy of the Program. Ancillary propagation of a covered work
-occurring solely as a consequence of using peer-to-peer transmission
-to receive a copy likewise does not require acceptance. However,
-nothing other than this License grants you permission to propagate or
-modify any covered work. These actions infringe copyright if you do
-not accept this License. Therefore, by modifying or propagating a
-covered work, you indicate your acceptance of this License to do so.
-
-## 10. Automatic Licensing of Downstream Recipients.
-
-Each time you convey a covered work, the recipient automatically
-receives a license from the original licensors, to run, modify and
-propagate that work, subject to this License. You are not responsible
-for enforcing compliance by third parties with this License.
-
-An "entity transaction" is a transaction transferring control of an
-organization, or substantially all assets of one, or subdividing an
-organization, or merging organizations. If propagation of a covered
-work results from an entity transaction, each party to that
-transaction who receives a copy of the work also receives whatever
-licenses to the work the party's predecessor in interest had or could
-give under the previous paragraph, plus a right to possession of the
-Corresponding Source of the work from the predecessor in interest, if
-the predecessor has it or can get it with reasonable efforts.
-
-You may not impose any further restrictions on the exercise of the
-rights granted or affirmed under this License. For example, you may
-not impose a license fee, royalty, or other charge for exercise of
-rights granted under this License, and you may not initiate litigation
-(including a cross-claim or counterclaim in a lawsuit) alleging that
-any patent claim is infringed by making, using, selling, offering for
-sale, or importing the Program or any portion of it.
-
-## 11. Patents.
-
-A "contributor" is a copyright holder who authorizes use under this
-License of the Program or a work on which the Program is based. The
-work thus licensed is called the contributor's "contributor version".
-
-A contributor's "essential patent claims" are all patent claims owned
-or controlled by the contributor, whether already acquired or
-hereafter acquired, that would be infringed by some manner, permitted
-by this License, of making, using, or selling its contributor version,
-but do not include claims that would be infringed only as a
-consequence of further modification of the contributor version. For
-purposes of this definition, "control" includes the right to grant
-patent sublicenses in a manner consistent with the requirements of
-this License.
-
-Each contributor grants you a non-exclusive, worldwide, royalty-free
-patent license under the contributor's essential patent claims, to
-make, use, sell, offer for sale, import and otherwise run, modify and
-propagate the contents of its contributor version.
-
-In the following three paragraphs, a "patent license" is any express
-agreement or commitment, however denominated, not to enforce a patent
-(such as an express permission to practice a patent or covenant not to
-sue for patent infringement). To "grant" such a patent license to a
-party means to make such an agreement or commitment not to enforce a
-patent against the party.
-
-If you convey a covered work, knowingly relying on a patent license,
-and the Corresponding Source of the work is not available for anyone
-to copy, free of charge and under the terms of this License, through a
-publicly available network server or other readily accessible means,
-then you must either (1) cause the Corresponding Source to be so
-available, or (2) arrange to deprive yourself of the benefit of the
-patent license for this particular work, or (3) arrange, in a manner
-consistent with the requirements of this License, to extend the patent
-license to downstream recipients. "Knowingly relying" means you have
-actual knowledge that, but for the patent license, your conveying the
-covered work in a country, or your recipient's use of the covered work
-in a country, would infringe one or more identifiable patents in that
-country that you have reason to believe are valid.
-
-If, pursuant to or in connection with a single transaction or
-arrangement, you convey, or propagate by procuring conveyance of, a
-covered work, and grant a patent license to some of the parties
-receiving the covered work authorizing them to use, propagate, modify
-or convey a specific copy of the covered work, then the patent license
-you grant is automatically extended to all recipients of the covered
-work and works based on it.
-
-A patent license is "discriminatory" if it does not include within the
-scope of its coverage, prohibits the exercise of, or is conditioned on
-the non-exercise of one or more of the rights that are specifically
-granted under this License. You may not convey a covered work if you
-are a party to an arrangement with a third party that is in the
-business of distributing software, under which you make payment to the
-third party based on the extent of your activity of conveying the
-work, and under which the third party grants, to any of the parties
-who would receive the covered work from you, a discriminatory patent
-license (a) in connection with copies of the covered work conveyed by
-you (or copies made from those copies), or (b) primarily for and in
-connection with specific products or compilations that contain the
-covered work, unless you entered into that arrangement, or that patent
-license was granted, prior to 28 March 2007.
-
-Nothing in this License shall be construed as excluding or limiting
-any implied license or other defenses to infringement that may
-otherwise be available to you under applicable patent law.
-
-## 12. No Surrender of Others' Freedom.
-
-If conditions are imposed on you (whether by court order, agreement or
-otherwise) that contradict the conditions of this License, they do not
-excuse you from the conditions of this License. If you cannot convey a
-covered work so as to satisfy simultaneously your obligations under
-this License and any other pertinent obligations, then as a
-consequence you may not convey it at all. For example, if you agree to
-terms that obligate you to collect a royalty for further conveying
-from those to whom you convey the Program, the only way you could
-satisfy both those terms and this License would be to refrain entirely
-from conveying the Program.
-
-## 13. Use with the GNU Affero General Public License.
-
-Notwithstanding any other provision of this License, you have
-permission to link or combine any covered work with a work licensed
-under version 3 of the GNU Affero General Public License into a single
-combined work, and to convey the resulting work. The terms of this
-License will continue to apply to the part which is the covered work,
-but the special requirements of the GNU Affero General Public License,
-section 13, concerning interaction through a network will apply to the
-combination as such.
-
-## 14. Revised Versions of this License.
-
-The Free Software Foundation may publish revised and/or new versions
-of the GNU General Public License from time to time. Such new versions
-will be similar in spirit to the present version, but may differ in
-detail to address new problems or concerns.
-
-Each version is given a distinguishing version number. If the Program
-specifies that a certain numbered version of the GNU General Public
-License "or any later version" applies to it, you have the option of
-following the terms and conditions either of that numbered version or
-of any later version published by the Free Software Foundation. If the
-Program does not specify a version number of the GNU General Public
-License, you may choose any version ever published by the Free
-Software Foundation.
-
-If the Program specifies that a proxy can decide which future versions
-of the GNU General Public License can be used, that proxy's public
-statement of acceptance of a version permanently authorizes you to
-choose that version for the Program.
-
-Later license versions may give you additional or different
-permissions. However, no additional obligations are imposed on any
-author or copyright holder as a result of your choosing to follow a
-later version.
-
-## 15. Disclaimer of Warranty.
-
-THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
-APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
-HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT
-WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND
-PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
-DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR
-CORRECTION.
-
-## 16. Limitation of Liability.
-
-IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR
-CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
-INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES
-ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT
-NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR
-LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM
-TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER
-PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
-
-## 17. Interpretation of Sections 15 and 16.
-
-If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-END OF TERMS AND CONDITIONS
-
-# How to Apply These Terms to Your New Programs
-
-If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these
-terms.
-
-To do so, attach the following notices to the program. It is safest to
-attach them to the start of each source file to most effectively state
-the exclusion of warranty; and each file should have at least the
-"copyright" line and a pointer to where the full notice is found.
-
- <one line to give the program's name and a brief idea of what it does.>
- Copyright (C) <year> <name of author>
-
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with this program. If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper
-mail.
-
-If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
- <program> Copyright (C) <year> <name of author>
- This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
- This is free software, and you are welcome to redistribute it
- under certain conditions; type `show c' for details.
-
-The hypothetical commands \`show w' and \`show c' should show the
-appropriate parts of the General Public License. Of course, your
-program's commands might be different; for a GUI interface, you would
-use an "about box".
-
-You should also get your employer (if you work as a programmer) or
-school, if any, to sign a "copyright disclaimer" for the program, if
-necessary. For more information on this, and how to apply and follow
-the GNU GPL, see <https://www.gnu.org/licenses/>.
-
-The GNU General Public License does not permit incorporating your
-program into proprietary programs. If your program is a subroutine
-library, you may consider it more useful to permit linking proprietary
-applications with the library. If this is what you want to do, use the
-GNU Lesser General Public License instead of this License. But first,
-please read <https://www.gnu.org/licenses/why-not-lgpl.html>.
-
-END OF TERMS AND CONDITIONS
diff --git a/LICENSES/GPL-3.0-or-later.txt b/LICENSES/GPL-3.0-or-later.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d41c0bd98feb776ef7d81efbc1b5447e0284a187
--- /dev/null
+++ b/LICENSES/GPL-3.0-or-later.txt
@@ -0,0 +1,232 @@
+GNU GENERAL PUBLIC LICENSE
+Version 3, 29 June 2007
+
+Copyright © 2007 Free Software Foundation, Inc. <http://fsf.org/>
+
+Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
+
+Preamble
+
+The GNU General Public License is a free, copyleft license for software and other kinds of works.
+
+The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.
+
+When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.
+
+To protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others.
+
+For example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights.
+
+Developers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.
+
+For the developers' and authors' protection, the GPL clearly explains that there is no warranty for this free software. For both users' and authors' sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions.
+
+Some devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users' freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users.
+
+Finally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free.
+
+The precise terms and conditions for copying, distribution and modification follow.
+
+TERMS AND CONDITIONS
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+Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:
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+All other non-permissive additional terms are considered “further restrictions†within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.
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+You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).
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+However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.
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+Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.
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+9. Acceptance Not Required for Having Copies.
+You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.
+
+10. Automatic Licensing of Downstream Recipients.
+Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License.
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+
+11. Patents.
+A “contributor†is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's “contributor versionâ€.
+
+A contributor's “essential patent claims†are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, “control†includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.
+
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+In the following three paragraphs, a “patent license†is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To “grant†such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party.
+
+If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. “Knowingly relying†means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.
+
+If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it.
+
+A patent license is “discriminatory†if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.
+
+Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.
+
+12. No Surrender of Others' Freedom.
+If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.
+
+13. Use with the GNU Affero General Public License.
+Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.
+
+14. Revised Versions of this License.
+The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.
+
+Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version†applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.
+
+If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program.
+
+Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.
+
+15. Disclaimer of Warranty.
+THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS†WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+16. Limitation of Liability.
+IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+17. Interpretation of Sections 15 and 16.
+If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.
+
+END OF TERMS AND CONDITIONS
+
+How to Apply These Terms to Your New Programs
+
+If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
+
+To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright†line and a pointer to where the full notice is found.
+
+ <one line to give the program's name and a brief idea of what it does.>
+ Copyright (C) <year> <name of author>
+
+ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:
+
+ <program> Copyright (C) <year> <name of author>
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an “about boxâ€.
+
+You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer†for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>.
+
+The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/README.md b/README.md
index 80533c112ba9969b40617dc882ef135209f3b2a8..05ca0fdd51adedb1f593a719e9fddc69c9b907ec 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,15 @@
+<!--
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
+-->
+
<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"/>
+ <img src="https://git.ufz.de/rdm-software/saqc/raw/develop/docs/resources/images/representative/UFZLogo.png" width="400"/>
</a>
<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"/>
+ <img src="https://git.ufz.de/rdm-software/saqc/raw/develop/docs/resources/images/representative/RDMLogo.png" align="right" width="220"/>
</a>
[](https://www.repostatus.org/#active)
@@ -34,7 +40,7 @@ can be installed using [pip](https://pip.pypa.io/en/stable/):
```sh
python -m pip install saqc
```
-For a more detailed installion guide, see the [installation guide](https://rdm-software.pages.ufz.de/saqc/getting_started/InstallationGuide.html).
+For a more detailed installion guide, see the [installation guide](https://rdm-software.pages.ufz.de/saqc/gettingstarted/InstallationGuide.html).
## Usage
@@ -45,11 +51,11 @@ 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](https://git.ufz.de/rdm-software/saqc/raw/develop/ressources/data/config.csv):
+could look like [this](https://git.ufz.de/rdm-software/saqc/raw/develop/docs/resources/data/config.csv):
```
varname ; test
-#----------; -----------------------------------------------------
+#----------; ---------------------------------------------------------------------
SM2 ; shift(freq="15Min")
'SM(1|2)+' ; flagMissing()
SM1 ; flagRange(min=10, max=60)
@@ -70,8 +76,8 @@ saqc \
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 \
+ --config https://git.ufz.de/rdm-software/saqc/raw/develop/docs/resources/data/config.csv \
+ --data https://git.ufz.de/rdm-software/saqc/raw/develop/docs/resources/data/data.csv \
--outfile saqc_test.csv
```
@@ -85,7 +91,7 @@ import pandas as pd
from saqc import SaQC
data = pd.read_csv(
- "https://git.ufz.de/rdm-software/saqc/raw/develop/ressources/data/data.csv",
+ "https://git.ufz.de/rdm-software/saqc/raw/develop/docs/resources/data/data.csv",
index_col=0, parse_dates=True,
)
@@ -100,15 +106,20 @@ saqc = (saqc
```
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)
+[respective section](https://rdm-software.pages.ufz.de/saqc/gettingstarted/TutorialAPI.html)
of the documentation.
## Changelog
All notable changes to this project will be documented in [CHANGELOG.md](CHANGELOG.md).
-## Contributing
+## Get involved
+
+### 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.
+### User support
+If you need help or have a question, you can use the SaQC user support mailing list: [saqc-support@ufz.de](mailto:saqc-support@ufz.de)
+
## Copyright and License
Copyright(c) 2021, [Helmholtz-Zentrum für Umweltforschung GmbH -- UFZ](https://www.ufz.de). All rights reserved.
@@ -121,8 +132,9 @@ For full details, see [LICENSE](LICENSE.md).
...
## Publications
-...
+coming soon...
## How to cite SaQC
-...
+If SaQC is advancing your research, please cite as:
+> Schäfer, David; Palm, Bert; Lünenschloß, Peter. (2021). System for automated Quality Control - SaQC. Zenodo. https://doi.org/10.5281/zenodo.5888547
diff --git a/dios/.gitignore b/dios/.gitignore
index 1b0574052da476dbbda5f136edecf7199a34628f..54b82b15527c60fd8a706a410f8d81ad22d379ed 100644
--- a/dios/.gitignore
+++ b/dios/.gitignore
@@ -1,2 +1,5 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
__pycache__/
diff --git a/dios/Readme.md b/dios/Readme.md
index 2a399caba9826c8636ecc8042bdf7b16802fc3bf..195b46a128fcf06aa2f71ff3d5d705004f650773 100644
--- a/dios/Readme.md
+++ b/dios/Readme.md
@@ -1,3 +1,9 @@
+<!--
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
+-->
+
DictOfSeries
============
diff --git a/dios/__init__.py b/dios/__init__.py
index 944c1c95ec6997eba2e4b6b2e2b6ffddc75bd886..7ed11a503edeb985d9a9beecedbbbac7d319f196 100644
--- a/dios/__init__.py
+++ b/dios/__init__.py
@@ -1,3 +1,7 @@
#!/usr/bin/env python
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from .dios import *
diff --git a/dios/dios/__init__.py b/dios/dios/__init__.py
index f5317eb22de1212b9fd6922a90ad67ec1b1387f6..6814f6d18cfde0d6b4d11f0398fbaabda041dc6a 100644
--- a/dios/dios/__init__.py
+++ b/dios/dios/__init__.py
@@ -1,5 +1,9 @@
-from .lib import *
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from .dios import *
+from .lib import *
__all__ = [
"DictOfSeries",
diff --git a/dios/dios/base.py b/dios/dios/base.py
index dfa64defc3a9d6da63a5f930c6d878703f0edcd0..674c939a1d79a24790f1d9a00ef184b421ad9c46 100644
--- a/dios/dios/base.py
+++ b/dios/dios/base.py
@@ -1,23 +1,24 @@
#!/usr/bin/env python
-from __future__ import annotations
-
-from copy import deepcopy, copy as shallowcopy
-from typing import Mapping, Hashable, Any, Sequence, overload
-from . import operators as ops
-from . import pandas_bridge as pdextra
-from . import lib
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
-from .lib import (
- _CAST_POLICIES,
- _throw_MixedItype_err_or_warn,
- _find_least_common_itype,
-)
+from __future__ import annotations
+import functools as ftools
+import operator as op
from abc import abstractmethod
+from copy import copy as shallowcopy
+from copy import deepcopy
+from typing import Any, Hashable, Mapping, Sequence, overload
+
import pandas as pd
-import operator as op
-import functools as ftools
+
+from . import lib
+from . import operators as ops
+from . import pandas_bridge as pdextra
+from .lib import _CAST_POLICIES, _find_least_common_itype, _throw_MixedItype_err_or_warn
__author__ = "Bert Palm"
__email__ = "bert.palm@ufz.de"
@@ -739,4 +740,4 @@ def _is_bool_dios_like(obj) -> bool:
# keep this here to prevent cyclic import
-from .indexer import _aLocIndexer, _iLocIndexer, _LocIndexer, _iAtIndexer, _AtIndexer
+from .indexer import _aLocIndexer, _AtIndexer, _iAtIndexer, _iLocIndexer, _LocIndexer
diff --git a/dios/dios/dios.py b/dios/dios/dios.py
index da9f4ca1993a30630cb032a3a12ff410c43e797d..659718d9df655806a964b563dc7f5efa10a6f00e 100644
--- a/dios/dios/dios.py
+++ b/dios/dios/dios.py
@@ -1,14 +1,18 @@
-from __future__ import annotations
-from typing import Any, Mapping, Hashable
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
-from .base import _DiosBase, _is_dios_like
-from .lib import Opts, OptsFields, dios_options
-from .lib import _find_least_common_itype
-from . import pandas_bridge as pdextra
+from __future__ import annotations
import functools as ftools
-import pandas as pd
+from typing import Any, Hashable, Mapping
+
import numpy as np
+import pandas as pd
+
+from . import pandas_bridge as pdextra
+from .base import _DiosBase, _is_dios_like
+from .lib import Opts, OptsFields, _find_least_common_itype, dios_options
class DictOfSeries(_DiosBase):
@@ -61,7 +65,6 @@ class DictOfSeries(_DiosBase):
"""Return pandas.Series with the indexes of all columns."""
return self.for_each("index")
- @property
def values(self):
"""Return a numpy.array of numpy.arrays with the values of all columns.
diff --git a/dios/dios/indexer.py b/dios/dios/indexer.py
index 10ccbd304a918be0925f53956eb1bb7a34da22df..f045a4f51d3d212f4a6cf4157c51903bc354caae 100644
--- a/dios/dios/indexer.py
+++ b/dios/dios/indexer.py
@@ -1,13 +1,13 @@
-from . import pandas_bridge as pdextra
-from .base import (
- _DiosBase,
- _is_dios_like,
- _is_bool_dios_like,
-)
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
import numpy as np
import pandas as pd
+from . import pandas_bridge as pdextra
+from .base import _DiosBase, _is_bool_dios_like, _is_dios_like
+
class _Indexer:
def __init__(self, obj: _DiosBase):
diff --git a/dios/dios/lib.py b/dios/dios/lib.py
index fc5e41305f920708b9e8e47f28af8911bb953f1a..c034e87b50f3033aee6893725dea1a0e27d55cd1 100644
--- a/dios/dios/lib.py
+++ b/dios/dios/lib.py
@@ -1,5 +1,18 @@
-import pandas as pd
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import warnings
+from contextlib import contextmanager
+
+import pandas as pd
+
+
+@contextmanager
+def no_index_warning():
+ with warnings.catch_warnings():
+ warnings.filterwarnings("ignore", category=FutureWarning)
+ yield
class ItypeWarning(RuntimeWarning):
@@ -29,15 +42,18 @@ class DtItype(__Itype):
class IntItype(__Itype):
name = "integer"
unique = True
- subtypes = (pd.RangeIndex, pd.Int64Index, pd.UInt64Index, int)
- min_pdindex = pd.Int64Index([])
+ with no_index_warning():
+ subtypes = (pd.RangeIndex, pd.Int64Index, pd.UInt64Index, int)
+ min_pdindex = pd.Int64Index([])
class FloatItype(__Itype):
name = "float"
- subtypes = (pd.Float64Index, float)
unique = True
- min_pdindex = pd.Float64Index([])
+
+ with no_index_warning():
+ subtypes = (pd.Float64Index, float)
+ min_pdindex = pd.Float64Index([])
# class MultiItype(__Itype):
@@ -51,7 +67,8 @@ class NumItype(__Itype):
_subitypes = (IntItype, FloatItype)
subtypes = _subitypes + IntItype.subtypes + FloatItype.subtypes
unique = False
- min_pdindex = pd.Float64Index([])
+ with no_index_warning():
+ min_pdindex = pd.Float64Index([])
class ObjItype(__Itype):
diff --git a/dios/dios/operators.py b/dios/dios/operators.py
index 365992efefa1951aeea7da7ce2a561dbe156e45d..e040310a41f2952b52c2e8d5420857381b1bdc68 100644
--- a/dios/dios/operators.py
+++ b/dios/dios/operators.py
@@ -1,7 +1,10 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# do not import dios-stuff here
import operator as op
-
_OP1_MAP = {
op.inv: "~",
op.neg: "-",
diff --git a/dios/dios/pandas_bridge.py b/dios/dios/pandas_bridge.py
index 3bbc08b0e1519c46c10d96827b2d38dd255cb0d5..1057785a7bd1e52eb9ecc0f375ef31e33871c9e4 100644
--- a/dios/dios/pandas_bridge.py
+++ b/dios/dios/pandas_bridge.py
@@ -1,26 +1,21 @@
#!/usr/bin/env python
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
__author__ = "Bert Palm"
__email__ = "bert.palm@ufz.de"
__copyright__ = "Copyright 2020, Helmholtz-Zentrum für Umweltforschung GmbH - UFZ"
-from pandas.core.common import (
- is_bool_indexer,
- is_null_slice,
-)
-from pandas.core.dtypes.common import (
- is_nested_list_like,
-)
-from pandas.api.types import (
- is_list_like,
+from pandas.api.types import ( # Unlike the example says, return lists False, not True; >>is_iterator([1, 2, 3]); >>False
+ is_dict_like,
is_hashable,
is_integer,
- is_dict_like,
- is_scalar,
- # Unlike the example says, return lists False, not True
- # >>is_iterator([1, 2, 3])
- # >>False
is_iterator,
+ is_list_like,
+ is_scalar,
)
+from pandas.core.common import is_bool_indexer, is_null_slice
+from pandas.core.dtypes.common import is_nested_list_like
diff --git a/dios/docs/.gitignore b/dios/docs/.gitignore
index 59b03a42eb191d1bf06f45dd6cfb3be394bbe06e..94b2ce4f6c97aab8fdc5b2a6fb7f5b3e4bb00da1 100644
--- a/dios/docs/.gitignore
+++ b/dios/docs/.gitignore
@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# ignore everything
_api
_build
diff --git a/dios/docs/Makefile b/dios/docs/Makefile
index 90e4cc6d6ce8338a0d7eacf2f93e96e8e3f2e6a2..5dc68a0e6a975d755f25cd006df1b5e66a041830 100644
--- a/dios/docs/Makefile
+++ b/dios/docs/Makefile
@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# Minimal makefile for Sphinx documentation
#
diff --git a/dios/docs/conf.py b/dios/docs/conf.py
index 52d7f39053fdcde753bf26ed2e1ba651ecb12128..245102bcab2ac28231cea6c109dad3092cfd4326 100644
--- a/dios/docs/conf.py
+++ b/dios/docs/conf.py
@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
diff --git a/dios/docs/dios_api.rst b/dios/docs/dios_api.rst
index 976d52c43c1ef9d02d9a55855808ff08cd2a60cd..4d3ad1ad820dbd18a470cd90891f59f9bae97174 100644
--- a/dios/docs/dios_api.rst
+++ b/dios/docs/dios_api.rst
@@ -1,3 +1,6 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
API
====
diff --git a/dios/docs/doc_cookbook.md b/dios/docs/doc_cookbook.md
index f32a52968935c927fca45591a1004663e19e37b8..94a3478495bf8ecaaf75be6f7132e908892a8cb8 100644
--- a/dios/docs/doc_cookbook.md
+++ b/dios/docs/doc_cookbook.md
@@ -1,3 +1,9 @@
+<!--
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
+-->
+
Cookbook
=========
diff --git a/dios/docs/doc_indexing.md b/dios/docs/doc_indexing.md
index ae67947ecbee8f5dda1577bdf67dd4f2ebef9b42..7d372f291ef27f46d22960736e277ed2de84e816 100644
--- a/dios/docs/doc_indexing.md
+++ b/dios/docs/doc_indexing.md
@@ -1,3 +1,9 @@
+<!--
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
+-->
+
Pandas-like indexing
====================
diff --git a/dios/docs/doc_itype.md b/dios/docs/doc_itype.md
index ea1d49f733b173a4799d9406f61a9932280d58fa..0420b2f4645bbbdd192ef998857a4b669286df8d 100644
--- a/dios/docs/doc_itype.md
+++ b/dios/docs/doc_itype.md
@@ -1,3 +1,9 @@
+<!--
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
+-->
+
Itype
=====
diff --git a/dios/docs/genindex.rst b/dios/docs/genindex.rst
index 1f62f6e9ecdf2abbb93ecc808c98f21d7afa99a6..d8bfa27e507b94ecf176d9606856f717b5765859 100644
--- a/dios/docs/genindex.rst
+++ b/dios/docs/genindex.rst
@@ -1,3 +1,6 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
# dummy file to be able to link to index
diff --git a/dios/docs/index.rst b/dios/docs/index.rst
index 8bfa3a91a0627774a1690216bcb6489d3466f7f9..9546d94886c7b14ea7046203560018bbffb7fd44 100644
--- a/dios/docs/index.rst
+++ b/dios/docs/index.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
.. dios documentation master file, created by
sphinx-quickstart on Sun Apr 19 02:36:37 2020.
You can adapt this file completely to your liking, but it should at least
diff --git a/dios/docs/make.bat b/dios/docs/make.bat
index 2119f51099bf37e4fdb6071dce9f451ea44c62dd..11b2c629f136ded491dca051ac52ddc62ec68000 100644
--- a/dios/docs/make.bat
+++ b/dios/docs/make.bat
@@ -1,3 +1,7 @@
+REM SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+REM
+REM SPDX-License-Identifier: GPL-3.0-or-later
+
@ECHO OFF
pushd %~dp0
diff --git a/dios/docs/requirements_sphinx.txt b/dios/docs/requirements_sphinx.txt
index d5bc791c87d490720b5910a238d776ac9370caa0..3c074c1f0388441cd4660f900a7cda4d828cfd7f 100644
--- a/dios/docs/requirements_sphinx.txt
+++ b/dios/docs/requirements_sphinx.txt
@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
alabaster==0.7.12
Babel==2.8.0
certifi==2020.6.20
diff --git a/dios/profiling/__init__.py b/dios/profiling/__init__.py
index 6252ac8ced4f8efaec94fe8702e7380bd67fcd16..609612eeb17be4a5bd61dd1ecf45cfab448dd546 100644
--- a/dios/profiling/__init__.py
+++ b/dios/profiling/__init__.py
@@ -1,2 +1,6 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from .generate_testsets import *
from .performance import find_index_range, gen_random_timestamps
diff --git a/dios/profiling/generate_testsets.py b/dios/profiling/generate_testsets.py
index 38dee39c12d01fad327d61b98aa8cf7fa469d98b..6c121c805066608b15d137ab4de37c0c2f00bdd2 100644
--- a/dios/profiling/generate_testsets.py
+++ b/dios/profiling/generate_testsets.py
@@ -1,11 +1,16 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import datetime as dt
+import os
+import pickle
import time
-import pandas as pd
import numpy as np
-import datetime as dt
+import pandas as pd
+
from ..dios import DictOfSeries
-import pickle
-import os
var_prefix = "var"
@@ -117,6 +122,6 @@ if __name__ == "__main__":
# t1 = time.time()
# print(t1-t0)
- rr = [10 ** r for r in range(1, 6)]
+ rr = [10**r for r in range(1, 6)]
c = range(10, 60, 10)
gen_all(rr, c)
diff --git a/dios/profiling/memory.py b/dios/profiling/memory.py
index 2226d5d031848f50ff971b345b629b0bb4d5e204..7341c3ab27bc14f10b2e162cb685e2a4b3e12c4a 100644
--- a/dios/profiling/memory.py
+++ b/dios/profiling/memory.py
@@ -1,4 +1,9 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import gc
+
from .generate_testsets import get_random_df_and_dios
diff --git a/dios/profiling/performance.py b/dios/profiling/performance.py
index caac9a101660d9d26e8c3b5a9e88045d091fd0f8..19e95c950ef918d81edc1bd25d47be3ba6ce24ee 100644
--- a/dios/profiling/performance.py
+++ b/dios/profiling/performance.py
@@ -1,6 +1,12 @@
-import pandas as pd
-import numpy as np
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import time
+
+import numpy as np
+import pandas as pd
+
from .generate_testsets import get_testset, var_prefix
profile_assignment = False
diff --git a/dios/profiling/testsets/.gitignore b/dios/profiling/testsets/.gitignore
index aa8d4bb337f54e8ae3685732eb6a29dcf4a23364..0bed86a4dfbb49bfbf843bc5b6485250dc8c93c5 100644
--- a/dios/profiling/testsets/.gitignore
+++ b/dios/profiling/testsets/.gitignore
@@ -1,3 +1,6 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
# ignore all
*
diff --git a/dios/requirements.txt b/dios/requirements.txt
index 4c87cfb3c08c272e57b405bf580e6e7f6632daa3..198c6b1f4e1ec9a956bea2961503dbd3610605ee 100644
--- a/dios/requirements.txt
+++ b/dios/requirements.txt
@@ -1,5 +1,9 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
numpy==1.21.2
-pandas==1.3.4
+pandas==1.3.5
python-dateutil==2.8.2
pytz==2021.3
six==1.16.0
diff --git a/dios/setup.py b/dios/setup.py
index f7ad2acae0012f9010965e8d4b29f660940899e6..2213f2088fa537fd577181cbc2802172844163d3 100644
--- a/dios/setup.py
+++ b/dios/setup.py
@@ -1,6 +1,10 @@
-from setuptools import setup, find_packages
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import subprocess
+from setuptools import find_packages, setup
with open("Readme.md", "r") as fh:
long_description = fh.read()
diff --git a/dios/test/__init__.py b/dios/test/__init__.py
index 04a9db79ad4e5abad209b03661ddc26349d4e086..38bfac50f92ecb8595a96b7c3c51fad6292b3efb 100644
--- a/dios/test/__init__.py
+++ b/dios/test/__init__.py
@@ -1 +1,5 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from .test_setup import *
diff --git a/dios/test/run_dios.py b/dios/test/run_dios.py
index 336ae3547862a786153b1f8a2901caae06c17dd6..6fc299dd61783fa37767e6ae0ccf16a0d698a52b 100644
--- a/dios/test/run_dios.py
+++ b/dios/test/run_dios.py
@@ -1,8 +1,13 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from builtins import range
-from dios import *
import numpy as np
+from dios import *
+
if __name__ == "__main__":
dios_options[OptsFields.mixed_itype_warn_policy] = Opts.itype_warn
print(dios_options)
@@ -32,7 +37,7 @@ if __name__ == "__main__":
)
def f(s):
- sec = 10 ** 9
+ sec = 10**9
s.index = pd.to_datetime(s.index * sec)
return s
diff --git a/dios/test/test__ops__.py b/dios/test/test__ops__.py
index 2ee9f579b86fa1d8257636d673a46d45790f336b..144ae93427ba9d2a9d83510396903ffff49ff5ea 100644
--- a/dios/test/test__ops__.py
+++ b/dios/test/test__ops__.py
@@ -1,9 +1,13 @@
#!/usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import pytest
from .test_setup import *
-
__author__ = "Bert Palm"
__email__ = "bert.palm@ufz.de"
__copyright__ = "Copyright 2018, Helmholtz-Centrum für Umweltforschung GmbH - UFC"
diff --git a/dios/test/test__setget__.py b/dios/test/test__setget__.py
index 281f0c5f95e5f12ba56a1d931961bfa4db7e1cb0..21e5d2175e5c3a178a92a58500495b5ab39eb15b 100644
--- a/dios/test/test__setget__.py
+++ b/dios/test/test__setget__.py
@@ -1,6 +1,11 @@
-from .test_setup import *
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from pandas.core.dtypes.common import is_scalar
+from .test_setup import *
+
@pytest.mark.parametrize(("idxer", "exp"), [("a", s1), ("c", s3)])
def test__getitem_single(dios_aligned, idxer, exp):
diff --git a/dios/test/test__setget__aloc.py b/dios/test/test__setget__aloc.py
index 2c47ec19e0502c89bfb7f5c910df44e46e0f26a8..0b9548d74ef5d8bc86c22c8f9b752628df216c51 100644
--- a/dios/test/test__setget__aloc.py
+++ b/dios/test/test__setget__aloc.py
@@ -1,6 +1,11 @@
-from .test_setup import *
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from pandas.core.dtypes.common import is_scalar
+from .test_setup import *
+
pytestmark = pytest.mark.skip
diff --git a/dios/test/test__setget__iloc.py b/dios/test/test__setget__iloc.py
index c0996e362f5122fcfaf268619060b37cc158f307..7fcad70fe51fe6efe656dc431c1eec645ca0ca80 100644
--- a/dios/test/test__setget__iloc.py
+++ b/dios/test/test__setget__iloc.py
@@ -1,6 +1,11 @@
-from .test_setup import *
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from pandas.core.dtypes.common import is_scalar
+from .test_setup import *
+
@pytest.mark.parametrize(
("idxer", "exp"),
diff --git a/dios/test/test__setget__loc.py b/dios/test/test__setget__loc.py
index a988b22546189d0f5a252f5f0b28af0d0e1bc7c1..3f5646f9b06cf3cc30cb921188d0b9bfee450ae7 100644
--- a/dios/test/test__setget__loc.py
+++ b/dios/test/test__setget__loc.py
@@ -1,6 +1,11 @@
-from .test_setup import *
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from pandas.core.dtypes.common import is_scalar
+from .test_setup import *
+
@pytest.mark.parametrize(("idxer", "exp"), [("a", s1), ("c", s3)])
def test__getitem_loc_singleCol(dios_aligned, idxer, exp):
diff --git a/dios/test/test__setitem__.py b/dios/test/test__setitem__.py
index 89c445f459a702d4782bb183b0d6a6b844073eab..1575768e321d6137da0edceb04c409949a188b5b 100644
--- a/dios/test/test__setitem__.py
+++ b/dios/test/test__setitem__.py
@@ -1,6 +1,11 @@
-from .test_setup import *
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import pytest
+from .test_setup import *
+
#
# s1 = pd.Series(range(10), index=range(10))
# s2 = pd.Series(range(5, 10), index=range(5, 10))
diff --git a/dios/test/test_dflike.py b/dios/test/test_dflike.py
index 1af90047d51bff19126c7b46e3491b0f9aa21cb6..550fd0e2e019372a691a4b1a3edb09e6dd9c982c 100644
--- a/dios/test/test_dflike.py
+++ b/dios/test/test_dflike.py
@@ -1,11 +1,17 @@
#!/usr/bin/env python
-import pytest
-from .test_setup import *
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from copy import deepcopy
+
+import numpy as np
import pandas as pd
+import pytest
from pandas.core.dtypes.common import is_dict_like, is_nested_list_like
-import numpy as np
-from copy import deepcopy
+
+from .test_setup import *
__author__ = "Bert Palm"
__email__ = "bert.palm@ufz.de"
diff --git a/dios/test/test_dflike__setget__.py b/dios/test/test_dflike__setget__.py
index f8d783a5e1538f67d0b6a970f670acf99f047591..9ce2aa2e3ed6129873ae97a74bc50f6297a31f70 100644
--- a/dios/test/test_dflike__setget__.py
+++ b/dios/test/test_dflike__setget__.py
@@ -1,6 +1,11 @@
-from .test_setup import *
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import pytest
+from .test_setup import *
+
def _test(res, exp):
diff --git a/dios/test/test_magic_methods.py b/dios/test/test_magic_methods.py
index adb06a52ba11748fdb34f3b0d3cf799af3cd26ed..33c80c714b28369f896e258cd94edd0ac3e4a79f 100644
--- a/dios/test/test_magic_methods.py
+++ b/dios/test/test_magic_methods.py
@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from .test_setup import *
diff --git a/dios/test/test_methods.py b/dios/test/test_methods.py
index a202b05f02565a3dcf776f2489b350185ba88e15..d1ebcd85b4050b6adb7e92318798b8161e55321d 100644
--- a/dios/test/test_methods.py
+++ b/dios/test/test_methods.py
@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from .test_setup import *
diff --git a/dios/test/test_setup.py b/dios/test/test_setup.py
index 0a59193bfe53689dbe5645bd74268d7cf9f381de..103afa1a4aede9e7a2b4c6962b799a4ee3384a66 100644
--- a/dios/test/test_setup.py
+++ b/dios/test/test_setup.py
@@ -1,15 +1,20 @@
-from dios import *
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import pytest
from numpy.random import randint
+from dios import *
+
try:
from dios.operators import (
- OP_MAP,
_OP1_MAP,
- _OP2_DIV_MAP,
_OP2_ARITH_MAP,
_OP2_BOOL_MAP,
_OP2_COMP_MAP,
+ _OP2_DIV_MAP,
+ OP_MAP,
)
except ModuleNotFoundError:
from dios.dios.operators import (
@@ -21,8 +26,8 @@ except ModuleNotFoundError:
_OP2_COMP_MAP,
)
-import pandas as pd
import numpy as np
+import pandas as pd
a = pd.Series(range(0, 70, 7), dtype=int)
b = pd.Series(range(5, 15, 1), dtype=int)
@@ -68,7 +73,7 @@ def df_unaligned__():
def dios_fuzzy__(nr_cols=None, mincol=0, maxcol=10, itype=None):
nr_of_cols = nr_cols if nr_cols else randint(mincol, maxcol + 1)
- ns = 10 ** 9
+ ns = 10**9
sec_per_year = 31536000
ITYPES = [IntItype, FloatItype, DtItype, ObjItype]
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..6b1029bbef2b64abf9a6c4e187e8d6cc549faf09
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,47 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS ?= -W
+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 docs/"$$k"; done
+
+# clean sphinx generated stuff
+clean:
+ rm -rf _build _static _api
+ rm -f *.automodsumm
+ rm -f *.automodapi
+ rm -f modules/*.automodsumm
+ rm -f modules/*.automodapi
+ rm -f */*.automodsumm
+
+# make documentation
+doc:
+ # generate environment table from dictionary
+ @$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+# make test, clean up
+test:
+ # generate parent fake module for the functions to be documented
+ @$(SPHINXBUILD) -M doctest "$(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/sphinxdoc/conf.py b/docs/conf.py
similarity index 76%
rename from sphinxdoc/conf.py
rename to docs/conf.py
index b58390666b1b7544bc1e4dc4406e3ec5e2206b6e..11ebee06bf571f1995e3c2607094824a4d4d386c 100644
--- a/sphinxdoc/conf.py
+++ b/docs/conf.py
@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
@@ -6,6 +10,8 @@
# -- Path setup --------------------------------------------------------------
+import logging
+
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
@@ -17,17 +23,33 @@ sys.path.insert(0, os.path.abspath(".."))
package_path = os.path.abspath("..")
os.environ["PYTHONPATH"] = ":".join((package_path, os.environ.get("PYTHONPATH", "")))
+# ---------- Version string --------------------------------------------------
+# read the version string without importing it
+vdict = {}
+with open("../saqc/version.py") as f:
+ exec(f.read(), vdict)
+version = vdict["__version__"]
+
+# -- Customize logging -------------------------------------------------------
+
+# couldn't get rid of a ignorable warning, so filter it
+# also see: https://issuemode.com/issues/sphinx-doc/sphinx/73994507
+
+
+def filterDuplicateObject(record):
+ return "duplicate object description of" not in record.getMessage()
+
+
+logging.getLogger("sphinx").addFilter(filterDuplicateObject)
# -- Project information -----------------------------------------------------
project = "SaQC"
-copyright = (
- "2020, Bert Palm, David Schäfer, Peter Lünenschloß, Lennart Schmidt, Juliane Geller"
-)
-author = "Bert Palm, David Schäfer, Peter Lünenschloß, Lennart Schmidt, Juliane Geller"
+copyright = "2020, Bert Palm, David Schäfer, Peter Lünenschloß, Florian Gransee"
+author = "Bert Palm, David Schäfer, Peter Lünenschloß, Florian Gransee"
# The full version, including alpha/beta/rc tags
-release = f"2.0"
+release = f"{version}"
# -- General configuration ---------------------------------------------------
@@ -65,8 +87,9 @@ extensions = [
"matplotlib.sphinxext.plot_directive",
# jupyter code execution
"jupyter_sphinx",
- # "sphinx_autodoc_typehints",
+ "sphinx_autodoc_typehints",
# "numpydoc"
+ "sphinx_tabs.tabs",
]
@@ -94,7 +117,6 @@ import numpy as np
from saqc.constants import *
"""
# -- Other options -----------------------------------------------------------
-
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
@@ -115,6 +137,9 @@ pygments_style = "sphinx"
# a list of builtin themes.
html_theme = "nature"
+html_logo = "resources/images/representative/SaQCLogo.png"
+html_favicon = "resources/images/representative/SaQCLogoSmall.png"
+
# use pandas theme
# html_theme = "pydata_sphinx_theme"
@@ -125,15 +150,15 @@ html_theme = "nature"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static", "css"]
+# html_static_path = ["_static"]
# -- RST options -------
rst_prolog = """
-.. |ufzLogo| image:: /ressources/images/Representative/UFZLogo.png
+.. |ufzLogo| image:: /resources/images/representative/UFZLogo.png
:width: 40 %
:target: https://www.ufz.de/
-.. |rdmLogo| image:: /ressources/images/Representative/RDMLogo.png
+.. |rdmLogo| image:: /resources/images/representative/RDMLogo.png
:width: 22 %
:target: https://www.ufz.de/index.php?de=45348
:class: align-right
diff --git a/sphinxdoc/cook_books/DataRegularisation.rst b/docs/cookbooks/DataRegularisation.rst
similarity index 74%
rename from sphinxdoc/cook_books/DataRegularisation.rst
rename to docs/cookbooks/DataRegularisation.rst
index f1debd6f40cd53d19f6a90579475c42897bd53de..4802066febed95b2a7917564eaf3ca036265be91 100644
--- a/sphinxdoc/cook_books/DataRegularisation.rst
+++ b/docs/cookbooks/DataRegularisation.rst
@@ -1,65 +1,69 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
Data Regularisation
===================
The tutorial aims to introduce the usage of ``SaQC`` methods, in order to obtain regularly sampled data derivatives
-from given time series data input. Regularly sampled time series data, is data, that exhibits a constant temporal
+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* :doc:`resampling <../funcSummaries/generic>`
+In the following steps, the tutorial guides through the usage of the *SaQC* :doc:`resampling <../funcs/generic>`
library.
#. Initially, we introduce and motivate regularisation techniques and we do import the tutorial data.
- * :ref:`Why Regularisation <cook_books/DataRegularisation:Why Regularisation>`
- * :ref:`Tutorial Data <cook_books/DataRegularisation:Tutorial Data>`
+ * :ref:`Why Regularisation <cookbooks/DataRegularisation:Why Regularisation>`
+ * :ref:`Tutorial Data <cookbooks/DataRegularisation:Tutorial Data>`
-#. We will get an overview over the main :ref:`Regularisation <cook_books/DataRegularisation:regularisations>` methods, starting with the shift.
+#. We will get an overview over the main :ref:`Regularisation <cookbooks/DataRegularisation:regularisations>` methods, starting with the shift.
- * :ref:`Shift <cook_books/DataRegularisation:shift>`
- * :ref:`Target Parameter <cook_books/DataRegularisation:target parameter>`
+ * :ref:`Shift <cookbooks/DataRegularisation:shift>`
+ * :ref:`Target Parameter <cookbooks/DataRegularisation:target parameter>`
- * :ref:`Freq Parameter <cook_books/DataRegularisation:freq parameter>`
- * :ref:`Method Parameter <cook_books/DataRegularisation:shifting method>`
- * :ref:`Valid Data <cook_books/DataRegularisation:Valid Data>`
+ * :ref:`Freq Parameter <cookbooks/DataRegularisation:freq parameter>`
+ * :ref:`Method Parameter <cookbooks/DataRegularisation:shifting method>`
+ * :ref:`Valid Data <cookbooks/DataRegularisation:Valid Data>`
#. We introduce the notion of *valid* data and see how sparse intervals and those with multiple values interact with
regularisation.
- * :ref:`Data Loss and Empty Intervals <cook_books/DataRegularisation:data loss and empty intervals>`
+ * :ref:`Data Loss and Empty Intervals <cookbooks/DataRegularisation:data loss and empty intervals>`
- * :ref:`Empty Intervals <cook_books/DataRegularisation:empty intervals>`
+ * :ref:`Empty Intervals <cookbooks/DataRegularisation:empty intervals>`
- * :ref:`Valid Data <cook_books/DataRegularisation:Valid Data>`
- * :ref:`Data Reduction <cook_books/DataRegularisation:data reduction>`
- * :ref:`Minimize Shifting <cook_books/DataRegularisation:minimize shifting distance>`
+ * :ref:`Valid Data <cookbooks/DataRegularisation:Valid Data>`
+ * :ref:`Data Reduction <cookbooks/DataRegularisation:data reduction>`
+ * :ref:`Minimize Shifting <cookbooks/DataRegularisation:minimize shifting distance>`
#. We use the Aggregation and the Interpolation method.
- * :ref:`Aggregation <cook_books/DataRegularisation:aggregation>`
+ * :ref:`Aggregation <cookbooks/DataRegularisation:aggregation>`
- * :ref:`Function Parameter <cook_books/DataRegularisation:aggregation functions>`
- * :ref:`Method Parameter <cook_books/DataRegularisation:shifting method>`
+ * :ref:`Function Parameter <cookbooks/DataRegularisation:aggregation functions>`
+ * :ref:`Method Parameter <cookbooks/DataRegularisation:shifting method>`
- * :ref:`Interpolation <cook_books/DataRegularisation:interpolation>`
+ * :ref:`Interpolation <cookbooks/DataRegularisation:interpolation>`
- * :ref:`Representing Data Sparsity <cook_books/DataRegularisation:interpolation and data sparsity>`
+ * :ref:`Representing Data Sparsity <cookbooks/DataRegularisation:interpolation and data sparsity>`
#. We see how regularisation interacts with Flags.
- * :ref:`Flags and Regularisation <cook_books/DataRegularisation:flags and regularisation>`
+ * :ref:`Flags and Regularisation <cookbooks/DataRegularisation:flags and regularisation>`
Why Regularisation
------------------
Often, measurement data does not come in regularly sampled time series. The reasons, why one usually would
like to have time series data, that exhibits a constant temporal gap size
-in between subsequent measurements, are manifold.
+in between subsequent measurements, are manifold.
-The 2 foremost important ones, may be, that statistics, such as *mean* and *standard deviation*
+The 2 foremost important ones, may be, that statistics, such as *mean* and *standard deviation*
usually presuppose the set of data points, they are computed of, to
-be equally weighted.
+be equally weighted.
The second reason, is, that, relating data of different sources to another, is impossible, if one
has not a mapping at hand, that relates the different date time indices to each other. One easy and intuitive
@@ -68,20 +72,20 @@ way of constructing such a mapping, is to just resample all data at the same (re
Tutorial Data
-------------
-The following `dataset <../ressources/data/SoilMoisture.csv>`_ of Soil Moisture measurements may serve as
+The following `dataset <../resources/data/SoilMoisture.csv>`_ of Soil Moisture measurements may serve as
example data set:
-.. image:: ../ressources/images/cbooks_SoilMoisture.png
- :target: ../ressources/images/cbooks_SoilMoisture.png
- :alt:
+.. image:: ../resources/images/cbooks_soil_moisture.png
+ :target: ../resources/images/cbooks_soil_moisture.png
+ :alt:
Lets import it and check out the first and last lines.
.. doctest:: example
>>> import pandas as pd
- >>> data_path = './ressources/data/SoilMoisture.csv'
+ >>> data_path = './resources/data/SoilMoisture.csv'
>>> data = pd.read_csv(data_path, index_col=0)
>>> data.index = pd.DatetimeIndex(data.index)
>>> data
@@ -101,30 +105,30 @@ Lets import it and check out the first and last lines.
[10607 rows x 1 columns]
-The data series seems to start with a sampling rate of roughly *10* minutes.
-Somewhere the sampling rate changes, and at the end it seems to exhibit an intended sampling
+The data series seems to start with a sampling rate of roughly *10* minutes.
+Somewhere the sampling rate changes, and at the end it seems to exhibit an intended sampling
rate of *15* minutes.
-Finding out about the proper sampling a series should be regularized to, is a subject on its own and wont be covered
+Finding out about the proper sampling a series should be regularized to, is a subject on its own and wont be covered
here. Usually, the intended sampling rate of sensor data is known from the specification of the sensor.
-If that is not the case, and if there seem to be more than one candidates for a rate regularisation, a rough rule of
-thumb, aiming at minimisation of data loss and data manipulation, may be,
+If that is not the case, and if there seem to be more than one candidates for a rate regularisation, a rough rule of
+thumb, aiming at minimisation of data loss and data manipulation, may be,
to go for the smallest rate seemingly present in the data.
Regularisations
---------------
-So lets transform the measurements timestamps to have a regular *10* minutes frequency. In order to do so,
+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 <../funcSummaries/resampling>` methods:
+Basically, there are three types of :doc:`regularisations <../funcs/resampling>` methods:
-#. We could keep the values as they are, and thus,
- just :ref:`shift <cook_books/DataRegularisation:Shift>` them in time to match the equidistant *10* minutes frequency grid, we want the data to exhibit.
-#. We could calculate new, synthetic data values for the regular timestamps, via an :ref:`interpolation <cook_books/DataRegularisation:Interpolation>` method.
-#. We could apply some :ref:`aggregation <cook_books/DataRegularisation:Aggregation>` to up- or down sample the data.
+#. We could keep the values as they are, and thus,
+ just :ref:`shift <cookbooks/DataRegularisation:Shift>` them in time to match the equidistant *10* minutes frequency grid, we want the data to exhibit.
+#. We could calculate new, synthetic data values for the regular timestamps, via an :ref:`interpolation <cookbooks/DataRegularisation:Interpolation>` method.
+#. We could apply some :ref:`aggregation <cookbooks/DataRegularisation:Aggregation>` to up- or down sample the data.
Shift
-----
@@ -149,7 +153,7 @@ 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 <../funcSummaries/resampling>` methods have such a frequency keyword,
+the :doc:`regularisations <../funcs/resampling>` methods have such a frequency keyword,
and it just determines the sampling rate, the resulting regular timeseries will have.
Shifting Method
@@ -158,17 +162,17 @@ 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:meth:`~saqc.SaQC.shift` documentation for more
-details on the keywords.)
+details on the keywords.)
-Lets see, how the data is now sampled. Therefore, we use the ``data_raw`` Atribute from the
+Lets see, how the data is now sampled. Therefore, we use the ``data`` Atribute from the
:py:class:`SaQC <saqc.core.core.SaQC>` object. This will prevent the methods output from
-being merged to a ``pandas.DataFrame`` object, and the changes from the resampling will be easier
+being merged to a ``pandas.DataFrame`` object, and the changes from the resampling will be easier
comprehensible from one look.
Shifted data
^^^^^^^^^^^^
- >>> qc.dataRaw # doctest: +SKIP
+ >>> qc.data # doctest: +SKIP
SoilMoisture | SoilMoisture_bshift |
================================ | ======================================= |
2021-01-01 00:09:07 23.429701 | 2021-01-01 00:00:00 23.429701 |
@@ -176,7 +180,7 @@ Shifted data
2021-01-01 00:28:42 23.343100 | 2021-01-01 00:20:00 23.343100 |
2021-01-01 00:38:30 23.476400 | 2021-01-01 00:30:00 23.476400 |
2021-01-01 00:48:18 23.343100 | 2021-01-01 00:40:00 23.343100 |
- ... | ... ... |
+ ... | ... ... |
2021-03-20 07:13:49 152.883102 | 2021-03-20 08:10:00 NaN |
2021-03-20 07:26:16 156.587906 | 2021-03-20 08:20:00 NaN |
2021-03-20 07:40:37 166.146194 | 2021-03-20 08:30:00 NaN |
@@ -193,7 +197,7 @@ 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 <../funcSummaries/resampling>` provided by ``saqc``.
+This is default behavior to all the :doc:`regularisations <../funcs/resampling>` provided by ``saqc``.
Data Loss and Empty Intervals
-----------------------------
@@ -204,8 +208,8 @@ transformation as well. That change stems from 2 sources mainly:
Empty Intervals
^^^^^^^^^^^^^^^
-If there is no :ref:`valid <cook_books/DataRegularisation:valid data>` data point available within an interval of the passed frequency,
-that could be shifted to match a multiple of the frequency, a ``NaN`` value gets inserted to represent the fact,
+If there is no :ref:`valid <cookbooks/DataRegularisation:valid data>` data point available within an interval of the passed frequency,
+that could be shifted to match a multiple of the frequency, a ``NaN`` value gets inserted to represent the fact,
that in the interval that is represented by that date time index, there was data missing.
Valid Data
@@ -214,29 +218,29 @@ Valid Data
Data points are referred to, as *valid*\ , in context of a regularisation, if:
-#.
+#.
the data points value is not ``NaN``
-#.
+#.
the *flag* of that datapoint has a value lower than the value passed to the methods
- ``to_mask`` keyword - since this keyword defaults to the highest flag level available,
+ ``to_mask`` keyword - since this keyword defaults to the highest flag level available,
defaultly, all data flagged :py:const:`~saqc.constants.BAD`, is considered invalid by that method.
-Note, that, from point *2* above, it follows, that flagging data values
-before regularisation, will effectively exclude them from the regularistaion process. See chapter
-:ref:`flagging and resampling <cook_books/DataRegularisation:flags and regularisation>` for an example of this effect and how it can help
-control :ref:`data reduction <cook_books/DataRegularisation:data reduction>`.
+Note, that, from point *2* above, it follows, that flagging data values
+before regularisation, will effectively exclude them from the regularistaion process. See chapter
+:ref:`flagging and resampling <cookbooks/DataRegularisation:flags and regularisation>` for an example of this effect and how it can help
+control :ref:`data reduction <cookbooks/DataRegularisation:data reduction>`.
data reduction
^^^^^^^^^^^^^^
-If there are multiple values present within an interval with size according to the passed frequency alias passed to
-``freq``\ , this values get reduced to one single value, that will get assigned to the timestamp associated with the
+If there are multiple values present within an interval with size according to the passed frequency alias passed to
+``freq``\ , this values get reduced to one single value, that will get assigned to the timestamp associated with the
interval.
-This reduction depends on the selected :doc:`regularisation <../funcSummaries/resampling>` method.
+This reduction depends on the selected :doc:`regularisation <../funcs/resampling>` method.
-For example, :ref:`above <cook_books/DataRegularisation:shift>`\ , we applied a backwards :py:meth:`~saqc.SaQC.shift` with a *10* minutes frequency.
+For example, :ref:`above <cookbooks/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.
@@ -245,7 +249,7 @@ 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.dataRaw["2021-01-01 07:00:00":"2021-01-01 08:00:00"] # doctest: +SKIP
+ >>> qc.data["2021-01-01 07:00:00":"2021-01-01 08:00:00"] # doctest: +SKIP
SoilMoisture_bshift | SoilMoisture |
================================ | ========================================= |
Date Time | Date Time |
@@ -259,51 +263,51 @@ for that interval.
Minimize Shifting Distance
^^^^^^^^^^^^^^^^^^^^^^^^^^
-Notice, how, for example, the data point for ``2021-01-01 07:49:41`` gets shifted all the way back, to
+Notice, how, for example, the data point for ``2021-01-01 07:49:41`` gets shifted all the way back, to
``2021-01-01 07:40:00`` - although, shifting it forward to ``07:40:00`` would be less a manipulation, since this timestamp
-appears to be closer to the original one.
+appears to be closer to the original one.
To shift to any frequncy aligned timestamp the value that is closest to that timestamp, we
can perform a *nearest shift* instead of a simple *back shift*\ , by using the shift method ``"nshift"``\ :
>>> qc = qc.shift('SoilMoisture', target='SoilMoisture_nshift', freq='10min', method='nshift')
- >>> qc.dataRaw['2021-01-01T07:00:00':'2021-01-01T08:00:00'] # doctest: +SKIP
- SoilMoisture_nshift | SoilMoisture |
- ================================ | ========================================= |
- Date Time | Date Time |
- 2021-01-01 07:00:00 23.3431 | 2021-01-01 07:00:41 23.3431 |
- 2021-01-01 07:10:00 23.3431 | 2021-01-01 07:10:29 23.3431 |
- 2021-01-01 07:20:00 23.2988 | 2021-01-01 07:20:17 23.2988 |
- 2021-01-01 07:30:00 23.3874 | 2021-01-01 07:30:05 23.3874 |
- 2021-01-01 07:40:00 23.3853 | 2021-01-01 07:39:53 23.3853 |
+ >>> qc.data['2021-01-01T07:00:00':'2021-01-01T08:00:00'] # doctest: +SKIP
+ SoilMoisture_nshift | SoilMoisture |
+ ================================ | ========================================= |
+ Date Time | Date Time |
+ 2021-01-01 07:00:00 23.3431 | 2021-01-01 07:00:41 23.3431 |
+ 2021-01-01 07:10:00 23.3431 | 2021-01-01 07:10:29 23.3431 |
+ 2021-01-01 07:20:00 23.2988 | 2021-01-01 07:20:17 23.2988 |
+ 2021-01-01 07:30:00 23.3874 | 2021-01-01 07:30:05 23.3874 |
+ 2021-01-01 07:40:00 23.3853 | 2021-01-01 07:39:53 23.3853 |
2021-01-01 07:50:00 23.3431 | 2021-01-01 07:49:41 23.3431 |
Now, any timestamp got assigned, the value that is nearest to it, *if* there is one valid data value available in the
-interval surrounding that timestamp with a range of half the frequency. In our example, this would mean, the regular
-timestamp would get assigned the nearest value of all the values, that preceed or succeed it by less than *5* minutes.
+interval surrounding that timestamp with a range of half the frequency. In our example, this would mean, the regular
+timestamp would get assigned the nearest value of all the values, that preceed or succeed it by less than *5* minutes.
Maybe check out, what happens with the chunk of the final 2 hours of our shifted *Soil Moisture* dataset, to get an idea.
- >>> qc.dataRaw['2021-03-20 07:00:00':] # doctest: +SKIP
- SoilMoisture_nshift | SoilMoisture |
- ================================ | ========================================= |
- Date Time | Date Time |
- 2021-03-20 07:00:00 145.027496 | 2021-03-20 07:13:49 152.883102 |
- 2021-03-20 07:10:00 152.883102 | 2021-03-20 07:26:16 156.587906 |
- 2021-03-20 07:20:00 NaN | 2021-03-20 07:40:37 166.146194 |
- 2021-03-20 07:30:00 156.587906 | 2021-03-20 07:54:59 164.690598 |
- 2021-03-20 07:40:00 166.146194 | 2021-03-20 08:40:41 155.318893 |
- 2021-03-20 07:50:00 164.690598 | 2021-03-20 08:40:41 155.318893 |
- 2021-03-20 08:00:00 NaN | |
- 2021-03-20 08:10:00 NaN | |
- 2021-03-20 08:20:00 NaN | |
- 2021-03-20 08:30:00 NaN | |
- 2021-03-20 08:40:00 155.318893 | |
+ >>> qc.data['2021-03-20 07:00:00':] # doctest: +SKIP
+ SoilMoisture_nshift | SoilMoisture |
+ ================================ | ========================================= |
+ Date Time | Date Time |
+ 2021-03-20 07:00:00 145.027496 | 2021-03-20 07:13:49 152.883102 |
+ 2021-03-20 07:10:00 152.883102 | 2021-03-20 07:26:16 156.587906 |
+ 2021-03-20 07:20:00 NaN | 2021-03-20 07:40:37 166.146194 |
+ 2021-03-20 07:30:00 156.587906 | 2021-03-20 07:54:59 164.690598 |
+ 2021-03-20 07:40:00 166.146194 | 2021-03-20 08:40:41 155.318893 |
+ 2021-03-20 07:50:00 164.690598 | 2021-03-20 08:40:41 155.318893 |
+ 2021-03-20 08:00:00 NaN | |
+ 2021-03-20 08:10:00 NaN | |
+ 2021-03-20 08:20:00 NaN | |
+ 2021-03-20 08:30:00 NaN | |
+ 2021-03-20 08:40:00 155.318893 | |
2021-03-20 08:50:00 NaN | |
-Since there is no valid data available, for example, in the interval from ``2021-03-20 07:55:00`` to ``2021-03-20 08:05:00`` - the new value
-for the regular timestamp ``2021-03-20 08:00:00``\ , that lies in the center of this interval, is ``NaN``.
+Since there is no valid data available, for example, in the interval from ``2021-03-20 07:55:00`` to ``2021-03-20 08:05:00`` - the new value
+for the regular timestamp ``2021-03-20 08:00:00``\ , that lies in the center of this interval, is ``NaN``.
Aggregation
-----------
@@ -314,51 +318,51 @@ Lets resample the *SoilMoisture* data to have a *20* minutes sample rate by aggr
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.dataRaw # doctest: +SKIP
- SoilMoisture | SoilMoisture_mean |
- ================================ | ===================================== |
- Date Time | Date Time |
- 2021-01-01 00:09:07 23.429701 | 2021-01-01 00:00:00 23.430800 |
- 2021-01-01 00:18:55 23.431900 | 2021-01-01 00:20:00 23.409750 |
- 2021-01-01 00:28:42 23.343100 | 2021-01-01 00:40:00 23.320950 |
- 2021-01-01 00:38:30 23.476400 | 2021-01-01 01:00:00 23.365250 |
- 2021-01-01 00:48:18 23.343100 | 2021-01-01 01:20:00 23.320950 |
- 2021-01-01 00:58:06 23.298800 | 2021-01-01 01:40:00 23.343100 |
- 2021-01-01 01:07:54 23.387400 | 2021-01-01 02:00:00 23.320950 |
- 2021-01-01 01:17:41 23.343100 | 2021-01-01 02:20:00 23.343100 |
- 2021-01-01 01:27:29 23.298800 | 2021-01-01 02:40:00 23.343100 |
- 2021-01-01 01:37:17 23.343100 | 2021-01-01 03:00:00 23.343100 |
- ... | ... ... |
- 2021-03-20 05:07:02 137.271500 | 2021-03-20 05:40:00 154.116806 |
- 2021-03-20 05:21:35 138.194107 | 2021-03-20 06:00:00 150.567505 |
- 2021-03-20 05:41:59 154.116806 | 2021-03-20 06:20:00 NaN |
- 2021-03-20 06:03:09 150.567505 | 2021-03-20 06:40:00 145.027496 |
- 2021-03-20 06:58:10 145.027496 | 2021-03-20 07:00:00 152.883102 |
- 2021-03-20 07:13:49 152.883102 | 2021-03-20 07:20:00 156.587906 |
- 2021-03-20 07:26:16 156.587906 | 2021-03-20 07:40:00 165.418396 |
- 2021-03-20 07:40:37 166.146194 | 2021-03-20 08:00:00 NaN |
- 2021-03-20 07:54:59 164.690598 | 2021-03-20 08:20:00 NaN |
+ >>> qc = qc.resample('SoilMoisture', target='SoilMoisture_mean', freq='20min', method='bagg', func=np.mean)
+ >>> qc.data # doctest: +SKIP
+ SoilMoisture | SoilMoisture_mean |
+ ================================ | ===================================== |
+ Date Time | Date Time |
+ 2021-01-01 00:09:07 23.429701 | 2021-01-01 00:00:00 23.430800 |
+ 2021-01-01 00:18:55 23.431900 | 2021-01-01 00:20:00 23.409750 |
+ 2021-01-01 00:28:42 23.343100 | 2021-01-01 00:40:00 23.320950 |
+ 2021-01-01 00:38:30 23.476400 | 2021-01-01 01:00:00 23.365250 |
+ 2021-01-01 00:48:18 23.343100 | 2021-01-01 01:20:00 23.320950 |
+ 2021-01-01 00:58:06 23.298800 | 2021-01-01 01:40:00 23.343100 |
+ 2021-01-01 01:07:54 23.387400 | 2021-01-01 02:00:00 23.320950 |
+ 2021-01-01 01:17:41 23.343100 | 2021-01-01 02:20:00 23.343100 |
+ 2021-01-01 01:27:29 23.298800 | 2021-01-01 02:40:00 23.343100 |
+ 2021-01-01 01:37:17 23.343100 | 2021-01-01 03:00:00 23.343100 |
+ ... | ... ... |
+ 2021-03-20 05:07:02 137.271500 | 2021-03-20 05:40:00 154.116806 |
+ 2021-03-20 05:21:35 138.194107 | 2021-03-20 06:00:00 150.567505 |
+ 2021-03-20 05:41:59 154.116806 | 2021-03-20 06:20:00 NaN |
+ 2021-03-20 06:03:09 150.567505 | 2021-03-20 06:40:00 145.027496 |
+ 2021-03-20 06:58:10 145.027496 | 2021-03-20 07:00:00 152.883102 |
+ 2021-03-20 07:13:49 152.883102 | 2021-03-20 07:20:00 156.587906 |
+ 2021-03-20 07:26:16 156.587906 | 2021-03-20 07:40:00 165.418396 |
+ 2021-03-20 07:40:37 166.146194 | 2021-03-20 08:00:00 NaN |
+ 2021-03-20 07:54:59 164.690598 | 2021-03-20 08:20:00 NaN |
2021-03-20 08:40:41 155.318893 | 2021-03-20 08:40:00 155.318893 |
[10607] [5643]
Aggregation functions
^^^^^^^^^^^^^^^^^^^^^
-You can pass arbitrary function objects to the ``agg_func`` parameter, to be applied to calculate every intervals result,
+You can pass arbitrary function objects to the ``func`` parameter, to be applied to calculate every intervals result,
as long as this function returns a scalar *float* value upon an array-like input. (So ``np.median`` would be propper
for calculating the median, ``sum``\ , for assigning the value sum, and so on.)
Aggregation method
^^^^^^^^^^^^^^^^^^
-As it is with the :ref:`shift <cook_books/DataRegularisation:Shift>` functionality, a ``method`` keyword controlls, weather the
-aggregation result for the interval in between 2 regular timestamps gets assigned to the left (=\ ``bagg``\ ) or to the
+As it is with the :ref:`shift <cookbooks/DataRegularisation:Shift>` functionality, a ``method`` keyword controlls, weather the
+aggregation result for the interval in between 2 regular timestamps gets assigned to the left (=\ ``bagg``\ ) or to the
right (\ ``fagg``\ ) boundary timestamp.
-* Also, analogous to to the shift functionality, intervals of size ``freq``\ , that do
- not contain any :ref:`valid <cook_books/DataRegularisation:valid data>` data, that could be aggregated, get ``ǹp.nan`` assigned.
+* Also, analogous to to the shift functionality, intervals of size ``freq``\ , that do
+ not contain any :ref:`valid <cookbooks/DataRegularisation:valid data>` data, that could be aggregated, get ``ǹp.nan`` assigned.
Interpolation
-------------
@@ -369,53 +373,53 @@ In the pool of py:mod:`regularisation <Functions.saqc.resampling>` methods, is a
: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
+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:meth:`~saqc.SaQC.interpolate>`
documentation. Lets check the results:
>>> qc = qc.interpolate('SoilMoisture', target='SoilMoisture_linear', freq='10min', method='time')
- >>> qc.dataRaw # doctest: +SKIP
- SoilMoisture | SoilMoisture_linear |
- ================================ | ========================================= |
- Date Time | Date Time |
- 2021-01-01 00:00:00 NaN | 2021-01-01 00:09:07 23.429701 |
- 2021-01-01 00:10:00 23.429899 | 2021-01-01 00:18:55 23.431900 |
- 2021-01-01 00:20:00 23.422067 | 2021-01-01 00:28:42 23.343100 |
- 2021-01-01 00:30:00 23.360782 | 2021-01-01 00:38:30 23.476400 |
- 2021-01-01 00:40:00 23.455997 | 2021-01-01 00:48:18 23.343100 |
- 2021-01-01 00:50:00 23.335415 | 2021-01-01 00:58:06 23.298800 |
- 2021-01-01 01:00:00 23.315977 | 2021-01-01 01:07:54 23.387400 |
- 2021-01-01 01:10:00 23.377891 | 2021-01-01 01:17:41 23.343100 |
- 2021-01-01 01:20:00 23.332627 | 2021-01-01 01:27:29 23.298800 |
- 2021-01-01 01:30:00 23.310176 | 2021-01-01 01:37:17 23.343100 |
- ... | ... ... |
- 2021-03-20 07:20:00 154.723105 | 2021-03-20 05:07:02 137.271500 |
- 2021-03-20 07:30:00 NaN | 2021-03-20 05:21:35 138.194107 |
- 2021-03-20 07:40:00 NaN | 2021-03-20 05:41:59 154.116806 |
- 2021-03-20 07:50:00 165.195497 | 2021-03-20 06:03:09 150.567505 |
- 2021-03-20 08:00:00 NaN | 2021-03-20 06:58:10 145.027496 |
- 2021-03-20 08:10:00 NaN | 2021-03-20 07:13:49 152.883102 |
- 2021-03-20 08:20:00 NaN | 2021-03-20 07:26:16 156.587906 |
- 2021-03-20 08:30:00 NaN | 2021-03-20 07:40:37 166.146194 |
- 2021-03-20 08:40:00 NaN | 2021-03-20 07:54:59 164.690598 |
+ >>> qc.data # doctest: +SKIP
+ SoilMoisture | SoilMoisture_linear |
+ ================================ | ========================================= |
+ Date Time | Date Time |
+ 2021-01-01 00:00:00 NaN | 2021-01-01 00:09:07 23.429701 |
+ 2021-01-01 00:10:00 23.429899 | 2021-01-01 00:18:55 23.431900 |
+ 2021-01-01 00:20:00 23.422067 | 2021-01-01 00:28:42 23.343100 |
+ 2021-01-01 00:30:00 23.360782 | 2021-01-01 00:38:30 23.476400 |
+ 2021-01-01 00:40:00 23.455997 | 2021-01-01 00:48:18 23.343100 |
+ 2021-01-01 00:50:00 23.335415 | 2021-01-01 00:58:06 23.298800 |
+ 2021-01-01 01:00:00 23.315977 | 2021-01-01 01:07:54 23.387400 |
+ 2021-01-01 01:10:00 23.377891 | 2021-01-01 01:17:41 23.343100 |
+ 2021-01-01 01:20:00 23.332627 | 2021-01-01 01:27:29 23.298800 |
+ 2021-01-01 01:30:00 23.310176 | 2021-01-01 01:37:17 23.343100 |
+ ... | ... ... |
+ 2021-03-20 07:20:00 154.723105 | 2021-03-20 05:07:02 137.271500 |
+ 2021-03-20 07:30:00 NaN | 2021-03-20 05:21:35 138.194107 |
+ 2021-03-20 07:40:00 NaN | 2021-03-20 05:41:59 154.116806 |
+ 2021-03-20 07:50:00 165.195497 | 2021-03-20 06:03:09 150.567505 |
+ 2021-03-20 08:00:00 NaN | 2021-03-20 06:58:10 145.027496 |
+ 2021-03-20 08:10:00 NaN | 2021-03-20 07:13:49 152.883102 |
+ 2021-03-20 08:20:00 NaN | 2021-03-20 07:26:16 156.587906 |
+ 2021-03-20 08:30:00 NaN | 2021-03-20 07:40:37 166.146194 |
+ 2021-03-20 08:40:00 NaN | 2021-03-20 07:54:59 164.690598 |
2021-03-20 08:50:00 NaN | 2021-03-20 08:40:41 155.318893 |
[11286] [10607]
Interpolation and Data Sparsity
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-The regularisation by interpolation is strict in the sense, that regular timestamps *only* get
-interpolated, if they have at least one :ref:`valid <cook_books/DataRegularisation:valid data>` data value preceeding them *and* one
+The regularisation by interpolation is strict in the sense, that regular timestamps *only* get
+interpolated, if they have at least one :ref:`valid <cookbooks/DataRegularisation:valid data>` data value preceeding them *and* one
succeeding them *within* the given frequency range (wich is controlled by the ``freq`` keyword.).
Thats, why, you have no interpolation value at ``2021-03-20 07:30:00`` - bacause it is preceeded
-by a :ref:`valid <cook_books/DataRegularisation:valid data>` value at ``2021-03-20 07:26:16``\ , but there is no :ref:`valid <cook_books/DataRegularisation:valid data>` value
+by a :ref:`valid <cookbooks/DataRegularisation:valid data>` value at ``2021-03-20 07:26:16``\ , but there is no :ref:`valid <cookbooks/DataRegularisation:valid data>` value
available in between the succeeding *10* minutes interval from ``2021-03-20 07:30:00`` to ``2021-03-20 07:30:00``.
On the other hand, there is an interpolated value assigned to ``2021-03-20 07:50:00``\ , it is preceeded by
-a :ref:`valid <cook_books/DataRegularisation:valid data>` value at ``2021-03-20 07:40:37`` and one succeeding at ``2021-03-20 07:54:59``.
+a :ref:`valid <cookbooks/DataRegularisation:valid data>` value at ``2021-03-20 07:40:37`` and one succeeding at ``2021-03-20 07:54:59``.
This behavior is intended to reflect the sparsity of the original data in the
regularized data set. The behavior can be circumvented by applying the more general
@@ -429,13 +433,13 @@ Note, that there is a wrapper available for linear interpolation: :py:meth:`~saq
Flags and Regularisation
------------------------
-Since data, that is flagged by a level higher or equal to the passed ``to_mask`` value
-(default=:py:const:~saqc.constants.BAD), is not regarded :ref:`valid <cook_books/DataRegularisation:valid data>` by the applied function,
+Since data, that is flagged by a level higher or equal to the passed ``to_mask`` value
+(default=:py:const:~saqc.constants.BAD), is not regarded :ref:`valid <cookbooks/DataRegularisation:valid data>` by the applied function,
it can be of advantage, to flag data before regularisation in order to effectively exclude it
from the resulting regularly sampled data set. Lets see an example for the *SoilMoisture* data set.
>>> qc = qc.linear('SoilMoisture', target='SoilMoisture_linear', freq='10min') # doctest: +SKIP
->>> qc.dataRaw['2021-01-01 15:00:00':'2021-01-01 16:00:00'] # doctest: +SKIP
+>>> qc.data['2021-01-01 15:00:00':'2021-01-01 16:00:00'] # doctest: +SKIP
SoilMoisture_linear | SoilMoisture |
================================ | ========================================= |
Date Time | Date Time |
@@ -453,7 +457,7 @@ should be percentage values in between *0* and *100*.
Since we dont exclude the value from interpolation, it gets included in the interpolation
process for the regular timstamp at ``2021-01-01 15:40:00`` - wich, as a result, also exhibits
a non - sence value of *-119.512446*. We could now flag the resulting regular dataset and
-exclude this calculated non sence value from further processing and analysis.
+exclude this calculated non sence value from further processing and analysis.
But, this would mean, that we would have a small data gap at this point.
@@ -466,15 +470,15 @@ do the interpolation.
>>> qc = qc.flagRange('SoilMoisture', min=0)
>>> qc = qc.interpolate('SoilMoisture', freq='10min', method='time')
- >>> qc.dataRaw['2021-01-01T07:00:00':'2021-01-01T08:00:00'] # doctest: +SKIP
+ >>> qc.data['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 |
- 2021-01-01 15:10:00 23.342964 | 2021-01-01 15:10:38 23.3431 |
- 2021-01-01 15:20:00 23.341092 | 2021-01-01 15:20:26 23.3410 |
- 2021-01-01 15:30:00 23.341000 | 2021-01-01 15:30:14 23.3410 |
- 2021-01-01 15:40:00 23.319971 | 2021-01-01 15:40:02 -120.0000 |
+ ================================ | ========================================= |
+ Date Time | Date Time |
+ 2021-01-01 15:00:00 23.341182 | 2021-01-01 15:00:51 23.3410 |
+ 2021-01-01 15:10:00 23.342964 | 2021-01-01 15:10:38 23.3431 |
+ 2021-01-01 15:20:00 23.341092 | 2021-01-01 15:20:26 23.3410 |
+ 2021-01-01 15:30:00 23.341000 | 2021-01-01 15:30:14 23.3410 |
+ 2021-01-01 15:40:00 23.319971 | 2021-01-01 15:40:02 -120.0000 |
2021-01-01 15:50:00 23.299553 | 2021-01-01 15:49:50 23.2988 |
back projection of flags
diff --git a/sphinxdoc/cook_books/MultivariateFlagging.rst b/docs/cookbooks/MultivariateFlagging.rst
similarity index 60%
rename from sphinxdoc/cook_books/MultivariateFlagging.rst
rename to docs/cookbooks/MultivariateFlagging.rst
index 6e6a9c48cceaac11eeee5b336a630cb33751944f..d89242d24a445afde0d777def978566940ce2d2f 100644
--- a/sphinxdoc/cook_books/MultivariateFlagging.rst
+++ b/docs/cookbooks/MultivariateFlagging.rst
@@ -1,20 +1,23 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
.. 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'
+ datapath = './resources/data/hydro_data.csv'
+ maintpath = './resources/data/hydro_maint.csv'
+ configpath = './resources/data/hydro_config.csv'
.. plot::
- :context:
+ :context: reset
:include-source: False
import matplotlib
import saqc
import pandas as pd
- datapath = '../ressources/data/hydro_data.csv'
- maintpath = '../ressources/data/hydro_maint.csv'
+ datapath = '../resources/data/hydro_data.csv'
+ maintpath = '../resources/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)
@@ -25,7 +28,7 @@
Multivariate Flagging
=====================
-The tutorial aims to introduce the usage of SaQC in the context of some more complex flagging and processing techniques.
+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.
@@ -42,8 +45,8 @@ 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>`_
+data <https://git.ufz.de/rdm-software/saqc/-/blob/develop/docs/resources/data/hydro_data.csv>`_ and the
+`maintenance data <https://git.ufz.de/rdm-software/saqc/-/blob/develop/docs/resources/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
@@ -73,7 +76,7 @@ was maintained. Lets have a look at those:
.. doctest:: exampleMV
- >>> qc.data_raw['maint'] # doctest:+SKIP
+ >>> qc.data['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
@@ -120,28 +123,29 @@ Lets check out the resulting flags for the *sac254* variable with the :py:meth:`
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
+constituting the sensor data. Since :py:attr:`saqc.SaQC.data` yields a
+`pandas.DataFrame <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_ like 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
+ sac254_raw | level_raw | water_temp_raw |
+ ============================== | ============================= | ================================== |
+ Timestamp | Timestamp | Timestamp |
+ 2016-01-01 00:02:00 18.4500 | 2016-01-01 00:02:00 103.290 | 2016-01-01 00:02:00 4.84 |
+ 2016-01-01 00:17:00 18.6437 | 2016-01-01 00:17:00 103.285 | 2016-01-01 00:17:00 4.82 |
+ 2016-01-01 00:32:00 18.9887 | 2016-01-01 00:32:00 103.253 | 2016-01-01 00:32:00 4.81 |
+ 2016-01-01 00:47:00 18.8388 | 2016-01-01 00:47:00 103.210 | 2016-01-01 00:47:00 4.80 |
+ 2016-01-01 01:02:00 18.7438 | 2016-01-01 01:02:00 103.167 | 2016-01-01 01:02:00 4.78 |
+ ... ... | ... ... | ... ... |
+ 2017-12-31 22:47:00 43.2275 | 2017-12-31 22:47:00 186.060 | 2017-12-31 22:47:00 5.49 |
+ 2017-12-31 23:02:00 43.6937 | 2017-12-31 23:02:00 186.115 | 2017-12-31 23:02:00 5.49 |
+ 2017-12-31 23:17:00 43.6012 | 2017-12-31 23:17:00 186.137 | 2017-12-31 23:17:00 5.50 |
+ 2017-12-31 23:32:00 43.2237 | 2017-12-31 23:32:00 186.128 | 2017-12-31 23:32:00 5.51 |
+ [70163] [70163] [70163]
<BLANKLINE>
- [70199 rows x 3 columns]
+ max: [70163 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:
@@ -149,24 +153,24 @@ But checking out values around *2017-10-29*, we notice, that the sampling rate s
.. 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
+ sac254_raw | level_raw | water_temp_raw |
+ ============================== | ============================= | ================================== |
+ Timestamp | Timestamp | Timestamp |
+ 2017-10-29 07:02:00 40.3050 | 2017-10-29 07:02:00 112.570 | 2017-10-29 07:02:00 10.91 |
+ 2017-10-29 07:17:00 39.6287 | 2017-10-29 07:17:00 112.497 | 2017-10-29 07:17:00 10.90 |
+ 2017-10-29 07:32:00 39.5800 | 2017-10-29 07:32:00 112.460 | 2017-10-29 07:32:00 10.88 |
+ 2017-10-29 07:32:01 39.9750 | 2017-10-29 07:32:01 111.837 | 2017-10-29 07:32:01 10.70 |
+ 2017-10-29 07:47:00 39.1350 | 2017-10-29 07:47:00 112.330 | 2017-10-29 07:47:00 10.84 |
+ 2017-10-29 07:47:01 40.6937 | 2017-10-29 07:47:01 111.615 | 2017-10-29 07:47:01 10.68 |
+ 2017-10-29 08:02:00 40.4938 | 2017-10-29 08:02:00 112.040 | 2017-10-29 08:02:00 10.77 |
+ 2017-10-29 08:02:01 39.3337 | 2017-10-29 08:02:01 111.552 | 2017-10-29 08:02:01 10.68 |
+ 2017-10-29 08:17:00 41.5238 | 2017-10-29 08:17:00 111.835 | 2017-10-29 08:17:00 10.72 |
+ 2017-10-29 08:17:01 38.6963 | 2017-10-29 08:17:01 111.750 | 2017-10-29 08:17:01 10.69 |
+ 2017-10-29 08:32:01 39.4337 | 2017-10-29 08:32:01 112.027 | 2017-10-29 08:32:01 10.66 |
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
+:doc:`resampling functions <../funcs/resampling>` onto the data, to obtain a regularly spaced timestamp.
+(See also the :ref:`harmonization tutorial <cookbooks/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,
@@ -189,19 +193,19 @@ 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
+ 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: 70177, dtype: float64
Since points, that were identified as malicous get excluded before the harmonization, the resulting regularly sampled
timeseries does not include them anymore:
@@ -244,23 +248,23 @@ 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
+ >>> plt.plot(qc.data['sac254_raw']['2016'], alpha=.5, color='black', label='original') # doctest:+SKIP
+ >>> plt.plot(qc.data['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.plot(qc.data['sac254_raw']['2016'], alpha=.5, color='black', label='original')
+ plt.plot(qc.data['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,
+Procedure for Detecting Technical Outliers in Water-Quality Data From In Situ Sensors. Water Resources Research,
55(11), 8547-8568.*
First, we define a transformation, we want the variables to be transformed with, to make them equally significant in
@@ -284,7 +288,8 @@ 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')
+ >>> qc = qc.transform(['sac254_corrected', 'level_raw', 'water_temp_raw'],
+ ... target=['sac254_norm', 'level_norm', 'water_temp_norm'], func=zscore_func, freq='30D')
.. plot::
@@ -293,7 +298,7 @@ Now we can pass the function to the :py:meth:`~saqc.SaQC.transform` method.
: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')
+ qc = qc.transform(['sac254_raw', 'level_raw', 'water_temp_raw'], target=['sac254_norm', 'level_norm', 'water_temp_norm'], 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
@@ -302,7 +307,8 @@ neighbors in feature space. We can do this, via the :py:meth:`~saqc.SaQC.assignK
.. doctest:: exampleMV
- >>> qc = qc.assignKNNScore(field=['sac_z', 'level_z', 'water_z'], target='kNNscores', freq='30D', n=5)
+ >>> qc = qc.assignKNNScore(field=['sac254_norm', 'level_norm', 'water_temp_norm'],
+ ... target='kNNscores', freq='30D', n=5)
Lets have a look at the resulting score variable.
@@ -316,7 +322,7 @@ Lets have a look at the resulting score variable.
:width: 80 %
:class: center
- qc = qc.assignKNNScore(field=['sac_z', 'level_z', 'water_z'], target='kNNscores', freq='30D', n=5)
+ qc = qc.assignKNNScore(field=['sac254_norm', 'level_norm', 'water_temp_norm'], 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
@@ -324,7 +330,7 @@ the projection of this feature space onto the 2 dimensional *sac* - *level* spac
.. doctest:: exampleMV
- >>> qc.plot('sac_z', phaseplot='level_z', xscope='2016-11') # doctest:+SKIP
+ >>> qc.plot('sac254_norm', phaseplot='level_norm', xscope='2016-11') # doctest:+SKIP
.. plot::
:context: close-figs
@@ -332,7 +338,7 @@ the projection of this feature space onto the 2 dimensional *sac* - *level* spac
:width: 80 %
:class: center
- qc.plot('sac_z', phaseplot='level_z', xscope='2016-11')
+ qc.plot('sac254_norm', phaseplot='level_norm', 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
@@ -342,16 +348,17 @@ correlated with relatively high *kNNscores*, we could try to calculate a thresho
: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.
+on the normalized *sac* and plot the flagged values in the *sac254_norm* - *level_norm* 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 = qc.transferFlags(field='kNNscores', target='sac254_norm', label='STRAY')
>>> qc.plot('sac254_corrected', xscope='2016-11') # doctest:+SKIP
- >>> qc.plot('sac_z', phaseplot='level_z', xscope='2016-11') # doctest:+SKIP
+ >>> qc.plot('sac254_norm', phaseplot='level_norm', xscope='2016-11') # doctest:+SKIP
.. plot::
:context: close-figs
@@ -359,7 +366,7 @@ on the normalized *sac* and plot the flagged values in the *sac_z* - *level_z* f
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 = qc.transferFlags(field='kNNscores', target='sac254_norm', label='STRAY')
.. plot::
:context: close-figs
@@ -375,7 +382,7 @@ on the normalized *sac* and plot the flagged values in the *sac_z* - *level_z* f
:width: 80 %
:class: center
- qc.plot('sac_z', phaseplot='level_z', xscope='2016-11')
+ qc.plot('sac254_norm', phaseplot='level_norm', xscope='2016-11')
Config
------
@@ -388,5 +395,5 @@ Config
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
+.. literalinclude:: ../resources/data/hydro_config.csv
diff --git a/sphinxdoc/cook_books/OutlierDetection.rst b/docs/cookbooks/OutlierDetection.rst
similarity index 54%
rename from sphinxdoc/cook_books/OutlierDetection.rst
rename to docs/cookbooks/OutlierDetection.rst
index 061a068c26d4b343920d793efb5378d0bf04a2dc..f3caa8fb878ba3f69a46bc21e2451afc44bae31e 100644
--- a/sphinxdoc/cook_books/OutlierDetection.rst
+++ b/docs/cookbooks/OutlierDetection.rst
@@ -1,3 +1,18 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+.. plot::
+ :context: reset
+ :include-source: False
+
+ import matplotlib
+ import saqc
+ import pandas as pd
+
+ data_path = '../resources/data/incidentsLKG.csv'
+ data = pd.read_csv(data_path, index_col=0)
+ data.index = pd.DatetimeIndex(data.index)
Outlier Detection and Flagging
==============================
@@ -8,42 +23,42 @@ The tutorial guides through the following steps:
#. We checkout and load the example data set. Subsequently, we initialise an :py:class:`SaQC <saqc.core.core.SaQC>` object.
- * :ref:`Preparation <cook_books/OutlierDetection:Preparation>`
+ * :ref:`Preparation <cookbooks/OutlierDetection:Preparation>`
- * :ref:`Data <cook_books/OutlierDetection:Data>`
- * :ref:`Initialisation <cook_books/OutlierDetection:Initialisation>`
+ * :ref:`Data <cookbooks/OutlierDetection:Data>`
+ * :ref:`Initialisation <cookbooks/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 residual
variables.
- * :ref:`Modelling <cook_books/OutlierDetection:Modelling>`
+ * :ref:`Modelling <cookbooks/OutlierDetection:Modelling>`
- * :ref:`Rolling Mean <cook_books/OutlierDetection:Rolling Mean>`
- * :ref:`Rolling Median <cook_books/OutlierDetection:Rolling Median>`
- * :ref:`Polynomial Fit <cook_books/OutlierDetection:Polynomial Fit>`
- * :ref:`Custom Models <cook_books/OutlierDetection:Custom Models>`
+ * :ref:`Rolling Mean <cookbooks/OutlierDetection:Rolling Mean>`
+ * :ref:`Rolling Median <cookbooks/OutlierDetection:Rolling Median>`
+ * :ref:`Polynomial Fit <cookbooks/OutlierDetection:Polynomial Fit>`
+ * :ref:`Custom Models <cookbooks/OutlierDetection:Custom Models>`
- * :ref:`Evaluation and Visualisation <cook_books/OutlierDetection:Visualisation>`
+ * :ref:`Evaluation and Visualisation <cookbooks/OutlierDetection:Visualisation>`
-#. We will see how we can obtain residues and scores from the calculated model curves.
+#. We will see how we can obtain residuals and scores from the calculated model curves.
- * :ref:`Residues and Scores <cook_books/OutlierDetection:Residues and Scores>`
+ * :ref:`Residuals and Scores <cookbooks/OutlierDetection:Residuals and Scores>`
- * :ref:`Residues <cook_books/OutlierDetection:Residues>`
- * :ref:`Scores <cook_books/OutlierDetection:Scores>`
- * :ref:`Optimization by Decomposition <cook_books/OutlierDetection:Optimization by Decomposition>`
+ * :ref:`Residuals <cookbooks/OutlierDetection:Residuals>`
+ * :ref:`Scores <cookbooks/OutlierDetection:Scores>`
+ * :ref:`Optimization by Decomposition <cookbooks/OutlierDetection:Optimization by Decomposition>`
#. Finally, we will see how to derive flags from the scores itself and impose additional conditions, functioning as
correctives.
- * :ref:`Setting and Correcting Flags <cook_books/OutlierDetection:Setting and Correcting Flags>`
+ * :ref:`Setting and Correcting Flags <cookbooks/OutlierDetection:Setting and Correcting Flags>`
- * :ref:`Flagging the Scores <cook_books/OutlierDetection:Flagging the Scores>`
+ * :ref:`Flagging the Scores <cookbooks/OutlierDetection:Flagging the Scores>`
* `Additional Conditions ("unflagging") <#Additional-Conditions>`_
- * :ref:`Including Multiple Conditions <cook_books/OutlierDetection:Including Multiple Conditions>`
+ * :ref:`Including Multiple Conditions <cookbooks/OutlierDetection:Including Multiple Conditions>`
Preparation
-----------
@@ -51,26 +66,29 @@ Preparation
Data
^^^^
-The example `data set <https://git.ufz.de/rdm-software/saqc/-/blob/cookBux/sphinx-doc/ressources/data/incidentsLKG.csv>`_
+The example `data set <https://git.ufz.de/rdm-software/saqc/-/blob/cookBux/sphinx-doc/resources/data/incidentsLKG.csv>`_
is selected to be small, comprehendable and its single anomalous outlier
-can be identified easily visually:
+can be identified easily visually:
+.. plot::
+ :context:
+ :include-source: False
+ :width: 80 %
+ :class: center
-.. image:: ../ressources/images/cbooks_incidents1.png
- :target: ../ressources/images/cbooks_incidents1.png
- :alt:
+ data.plot()
-It can be downloaded from the saqc git `repository <https://git.ufz.de/rdm-software/saqc/-/blob/cookBux/sphinx-doc/ressources/data/incidentsLKG.csv>`_.
+It can be downloaded from the saqc git `repository <https://git.ufz.de/rdm-software/saqc/-/blob/cookBux/sphinx-doc/resources/data/incidentsLKG.csv>`_.
-The data represents incidents of SARS-CoV-2 infections, on a daily basis, as reported by the
+The data represents incidents of SARS-CoV-2 infections, on a daily basis, as reported by the
`RKI <https://www.rki.de/DE/Home/homepage_node.html>`_ in 2020.
In June, an extreme spike can be observed. This spike relates to an incidence of so called "superspreading" in a local
`meat factory <https://www.heise.de/tp/features/Superspreader-bei-Toennies-identifiziert-4852400.html>`_.
For the sake of modelling the spread of Covid, it can be of advantage, to filter the data for such extreme events, since
-they may not be consistent with underlying distributional assumptions and thus interfere with the parameter learning
+they may not be consistent with underlying distributional assumptions and thus interfere with the parameter learning
process of the modelling. Also it can help to learn about the conditions severely facilitating infection rates.
To introduce into some basic ``saqc`` workflows, we will concentrate on classic variance based outlier detection approaches.
@@ -81,20 +99,17 @@ 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>`_.
-.. testsetup:: example
-
- data_path = './ressources/data/incidentsLKG.csv'
.. doctest:: exampleOD
- >>> data_path = './ressources/data/incidentsLKG.csv'
+ >>> data_path = './resources/data/incidentsLKG.csv'
>>> 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.
+of ``data`` is of the right type.
.. doctest:: exampleOD
@@ -103,6 +118,13 @@ of ``data`` is of the right type.
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.
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ import saqc
+ qc = saqc.SaQC(data)
+
.. doctest:: exampleOD
>>> import saqc
@@ -119,7 +141,7 @@ the method :py:meth:`~saqc.SaQC.plot`:
Modelling
---------
-First, we want to model our data in order to obtain a stationary, residuish variable with zero mean.
+First, we want to model our data in order to obtain a stationary, residuish variable with zero mean.
Rolling Mean
^^^^^^^^^^^^
@@ -132,6 +154,13 @@ model via the method :py:meth:`saqc.SaQC.roll`.
>>> import numpy as np
>>> qc = qc.roll(field='incidents', target='incidents_mean', func=np.mean, window='13D')
+.. plot::
+ :context:
+ :include-source: False
+
+ import numpy as np
+ qc = qc.roll(field='incidents', target='incidents_mean', func=np.mean, window='13D')
+
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.
@@ -141,20 +170,26 @@ Rolling Median
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.
+under the name ``np.median``. We just calculate another model curve for the ``"incidents"`` data with the ``np.median`` function from the ``numpy`` library.
.. doctest:: exampleOD
>>> 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.
-The :py:attr:`target` parameter can be passed to any call of a function from the
-saqc functions pool and will determine the result of the function to be written to the
-data, under the fieldname specified by it. If there already exists a field with the name passed to ``target``\ ,
+.. plot::
+ :context:
+ :include-source: False
+
+ 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.
+The :py:attr:`target` parameter can be passed to any call of a function from the
+saqc functions pool and will determine the result of the function to be written to the
+data, under the fieldname specified by it. If there already exists a field with the name passed to ``target``\ ,
the data stored to this field will be overridden.
-We will evaluate and visualize the different model curves :ref:`later <cook_books/OutlierDetection:Visualisation>`.
+We will evaluate and visualize the different model curves :ref:`later <cookbooks/OutlierDetection:Visualisation>`.
Beforehand, we will generate some more model data.
Polynomial Fit
@@ -167,16 +202,23 @@ Another common approach, is, to fit polynomials of certain degrees to the data.
>>> 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.
+.. plot::
+ :context:
+ :include-source: False
+
+ 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.
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:`~saqc.SaQC.process` function.
+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:`~saqc.SaQC.processGeneric` function.
-Lets apply a smoothing filter from the `scipy.signal <https://docs.scipy.org/doc/scipy/reference/signal.html>`_
+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:
.. testcode:: exampleOD
@@ -186,115 +228,181 @@ module. We wrap the filter generator up into a function first:
b, a = butter(N=filter_order, Wn=cutoff / nyq, btype=filter_type)
return pd.Series(filtfilt(b, a, x), index=x.index)
+.. plot::
+ :context:
+ :include-source: False
+
+ from scipy.signal import filtfilt, butter
+ def butterFilter(x, filter_order, nyq, cutoff, filter_type="lowpass"):
+ b, a = butter(N=filter_order, Wn=cutoff / nyq, btype=filter_type)
+ return pd.Series(filtfilt(b, a, x), index=x.index)
+
This function object, we can pass on to the :py:meth:`~saqc.SaQC.processGeneric` methods ``func`` argument.
.. doctest:: exampleOD
- >>> qc = qc.processGeneric(field='incidents', target='incidents_lowPass', func=lambda x: butterFilter(x, cutoff=0.1, nyq=0.5, filter_order=2))
+ >>> qc = qc.processGeneric(field='incidents', target='incidents_lowPass',
+ ... func=lambda x: butterFilter(x, cutoff=0.1, nyq=0.5, filter_order=2))
+
+.. plot::
+ :context:
+ :include-source: False
+
+ qc = qc.processGeneric(field='incidents', target='incidents_lowPass', func=lambda x: butterFilter(x, cutoff=0.1, nyq=0.5, filter_order=2))
+
Visualisation
-------------
We can obtain those updated informations by generating a `pandas dataframe <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_
-representation of it, with the :py:attr:`data <saqc.core.core.SaQC.data>` method:
+representation of it, with the :py:attr:`data <saqc.core.core.SaQC.data>` method:
.. doctest:: exampleOD
>>> data = qc.data
+.. plot::
+ :context:
+ :include-source: False
+
+ 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.
.. doctest:: exampleOD
- >>> data.plot()
+ >>> data.to_df().plot()
<AxesSubplot:>
-.. image:: ../ressources/images/cbooks_incidents2.png
- :target: ../ressources/images/cbooks_incidents2.png
- :alt:
+.. plot::
+ :context:
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ data.to_df().plot()
-Residues and Scores
--------------------
+Residuals and Scores
+--------------------
-Residues
-^^^^^^^^
+Residuals
+^^^^^^^^^
-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.
+We want to evaluate the residuals 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:`~saqc.SaQC.processGeneric` method.
+First, we retrieve the residuals 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.
+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,
+For obtaining the models residuals, we just subtract the model data from the original data and assign the result
+of this operation to a new variable, called ``incidents_residuals``. This Assignment, we, as usual,
control via the ``target`` parameter.
.. doctest:: exampleOD
- >>> qc = qc.processGeneric(['incidents', 'incidents_mean'], target='incidents_residues', func=lambda x, y: x - y)
+ >>> qc = qc.processGeneric(['incidents', 'incidents_mean'], target='incidents_residuals', func=lambda x, y: x - y)
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.processGeneric(['incidents', 'incidents_mean'], target='incidents_residuals', func=lambda x, y: x - y)
+
Scores
^^^^^^
-Next, we score the residues simply by computing their `Z-scores <https://en.wikipedia.org/wiki/Standard_score>`_.
-The Z-score of a point $\ ``x``\ $, relative to its surrounding $\ ``D``\ $, evaluates to $\ ``Z(x) = \frac{x - \mu(D)}{\sigma(D)}``\ $.
+Next, we score the residuals simply by computing their `Z-scores <https://en.wikipedia.org/wiki/Standard_score>`_.
+The *Z*-score of a point :math:`x`, relative to its surrounding :math:`D`,
+evaluates to :math:`Z(x) = \frac{x - \mu(D)}{\sigma(D)}`.
-So, if we would like to roll with a window of a fixed size of *27* periods through the data and calculate the *Z*\ -score
+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``\ :
.. doctest:: exampleOD
>>> z_score = lambda D: abs((D[14] - np.mean(D)) / np.std(D))
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ z_score = lambda D: abs((D[14] - np.mean(D)) / np.std(D))
+
And subsequently, use the :py:meth:`~saqc.SaQC.roll` method to make a rolling window application with the scoring
function:
.. doctest:: exampleOD
- >>> qc = qc.roll(field='incidents_residues', target='incidents_scores', func=z_score, window='27D')
+ >>> qc = qc.roll(field='incidents_residuals', target='incidents_scores', func=z_score, window='27D')
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.roll(field='incidents_residuals', target='incidents_scores', func=z_score, window='27D')
Optimization by Decomposition
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-There are 2 problems with the attempt presented :ref:`above <cook_books/OutlierDetection:Scores>`.
+There are 2 problems with the attempt presented :ref:`above <cookbooks/OutlierDetection:Scores>`.
-First, the rolling application of the customly
-defined function, might get really slow for large data sets, because our function ``z_scores`` does not get decomposed into optimized building blocks.
+First, the rolling application of the customly
+defined function, might get really slow for large data sets, because our function ``z_scores`` does not get decomposed into optimized building blocks.
-Second, and maybe more important, it relies heavily on every window having a fixed number of values and a fixed temporal extension.
-Otherwise, ``D[14]`` might not always be the value in the middle of the window, or it might not even exist,
+Second, and maybe more important, it relies heavily on every window having a fixed number of values and a fixed temporal extension.
+Otherwise, ``D[14]`` might not always be the value in the middle of the window, or it might not even exist,
and an error will be thrown.
-So the attempt works fine, only because our data set is small and strictly regularily sampled.
+So the attempt works fine, only because our data set is small and strictly regularily sampled.
Meaning that it has constant temporal distances between subsequent meassurements.
-In order to tweak our calculations and make them much more stable, it might be useful to decompose the scoring
+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:`~saqc.SaQC.roll` function, by calculating the series of the
-residues *mean* and *standard deviation* seperately:
+residuals *mean* and *standard deviation* seperately:
.. 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)
+ >>> qc = qc.roll(field='incidents_residuals', target='residuals_mean', window='27D', func=np.mean)
+ >>> qc = qc.roll(field='incidents_residuals', target='residuals_std', window='27D', func=np.std)
+ >>> qc = qc.processGeneric(field=['incidents_scores', "residuals_mean", "residuals_std"], target="residuals_norm",
+ ... func=lambda this, mean, std: (this - mean) / std)
+
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.roll(field='incidents_residuals', target='residuals_mean', window='27D', func=np.mean)
+ qc = qc.roll(field='incidents_residuals', target='residuals_std', window='27D', func=np.std)
+ qc = qc.processGeneric(field=['incidents_scores', "residuals_mean", "residuals_std"], target="residuals_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>`\ ,
+With huge datasets, this will be noticably faster, compared to the method presented :ref:`initially <cookbooks/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: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.
+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:`~saqc.SaQC.processGeneric` method, in order to obtain the scores:
.. doctest:: exampleOD
- >>> qc = qc.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_residuals','residuals_mean','residuals_std'],
+ ... target='incidents_scores', func=lambda x,y,z: abs((x-y) / z))
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.processGeneric(field=['incidents_residuals','residuals_mean','residuals_std'], target='incidents_scores', func=lambda x,y,z: abs((x-y) / z))
+
+
Let's have a look at the resulting scores:
@@ -302,9 +410,14 @@ Let's have a look at the resulting scores:
>>> qc.plot('incidents_scores') # doctest:+SKIP
-.. image:: ../ressources/images/cbook_incidents_scoresUnflagged.png
- :target: ../ressources/images/cbook_incidents_scoresUnflagged.png
- :alt:
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc.plot('incidents_scores')
Setting and correcting Flags
@@ -313,21 +426,32 @@ Setting and correcting Flags
Flagging the Scores
^^^^^^^^^^^^^^^^^^^
-We can now implement the common `rule of thumb <https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule>`_\ ,
-that any *Z*\ -score value above *3* may indicate an outlierish data point,
+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:`~saqc.SaQC.flagRange` method with a `max` value of *3*.
.. doctest:: exampleOD
>>> qc = qc.flagRange('incidents_scores', max=3)
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.flagRange('incidents_scores', max=3)
+
Now flags have been calculated for the scores:
>>> qc.plot('incidents_scores') # doctest:+SKIP
-.. image:: ../ressources/images/cbooks_incidents_scores.png
- :target: ../ressources/images/cbooks_incidents_scores.png
- :alt:
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc.plot('incidents_scores')
Projecting Flags
@@ -339,7 +463,14 @@ We now can project those flags onto our original incidents timeseries:
>>> 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
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.flagGeneric(field=['incidents_scores'], target='incidents', func=lambda x: isflagged(x))
+
+Note, that we could have skipped the :ref:`range flagging step <cookbooks/OutlierDetection:Flagging the scores>`\ , by including the cutting off in our
+
generic expression:
.. doctest:: exampleOD
@@ -352,53 +483,66 @@ Lets check out the results:
>>> qc.plot('incidents') # doctest: +SKIP
-.. image:: ../ressources/images/cbooks_incidentsOverflagged.png
- :target: ../ressources/images/cbooks_incidentsOverflagged.png
- :alt:
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc.plot('incidents')
Obveously, there are some flags set, that, relative to their 13 days surrounding, might relate to minor incidents spikes,
-but may not relate to superspreading events we are looking for.
+but may not relate to superspreading events we are looking for.
-Especially the left most flag seems not to relate to an extreme event at all.
+Especially the left most flag seems not to relate to an extreme event at all.
This overflagging stems from those values having a surrounding with very low data variance, and thus, evaluate to a relatively high Z-score.
-There are a lot of possibilities to tackle the issue. In the next section, we will see how we can improve the flagging results
+There are a lot of possibilities to tackle the issue. In the next section, we will see how we can improve the flagging results
by incorporating additional domain knowledge.
Additional Conditions
---------------------
-In order to improve our flagging result, we could additionally assume, that the events we are interested in,
+In order to improve our flagging result, we could additionally assume, that the events we are interested in,
are those with an incidents count that is deviating by a margin of more than
-*20* from the 2 week average.
+*20* from the 2 week average.
-This is equivalent to imposing the additional condition, that an outlier must relate to a sufficiently large residue.
+This is equivalent to imposing the additional condition, that an outlier must relate to a sufficiently large residual.
Unflagging
^^^^^^^^^^
-We can do that posterior to the preceeding flagging step, by *removing*
-some flags based on some condition.
+We can do that posterior to the preceeding flagging step, by *removing*
+some flags based on some condition.
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.
+sufficiently large residuals, we assign them the :py:const:`~saqc.constants.UNFLAGGED` flag.
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
+So, we check out, which residuals evaluate to a level below *20*\ , and assign the
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:`~Core.Core.SaQC>` object in the :ref:`beginning <cook_books/OutlierDetection:Initialisation>`.
+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:`~Core.Core.SaQC>` object in the :ref:`beginning <cookbooks/OutlierDetection:Initialisation>`.
.. doctest:: exampleOD
- >>> qc = qc.flagGeneric(field=['incidents','incidents_residues'], target="incidents", func=lambda x,y: isflagged(x) & (y < 50), flag=-np.inf)
+ >>> qc = qc.flagGeneric(field=['incidents','incidents_residuals'], target="incidents",
+ ... func=lambda x,y: isflagged(x) & (y < 50), flag=-np.inf)
+
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.flagGeneric(field=['incidents','incidents_residuals'], target="incidents", func=lambda x,y: isflagged(x) & (y < 50), flag=-np.inf)
+
Notice, that we passed the desired flag level to the :py:attr:`flag` keyword in order to perform an
"unflagging" instead of the usual flagging. The :py:attr:`flag` keyword can be passed to all the functions
-and defaults to the selected translation schemes :py:const:`BAD <saqc.constants.BAD>` flag level.
+and defaults to the selected translation schemes :py:const:`BAD <saqc.constants.BAD>` flag level.
Plotting proofs the tweaking did in deed improve the flagging result:
@@ -406,25 +550,34 @@ Plotting proofs the tweaking did in deed improve the flagging result:
>>> qc.plot("incidents") # doctest:+SKIP
-.. image:: ../ressources/images/cbooks_incidents_correctFlagged.png
- :target: ../ressources/images/cbooks_incidents_correctFlagged.png
- :alt:
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :class: center
+
+ qc.plot("incidents")
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:`~saqc.SaQC.flagRange` the condition for the residues having to be above *20*
+could circumvent the :ref:`unflagging <cookbooks/OutlierDetection:Unflagging>` step, by adding to the call to
+:py:meth:`~saqc.SaQC.flagRange` the condition for the residuals having to be above *20*
.. doctest:: exampleOD
- >>> qc = qc.flagGeneric(field=['incidents_scores', 'incidents_residues'], target='incidents', func=lambda x, y: (x > 3) & (y > 20))
+ >>> qc = qc.flagGeneric(field=['incidents_scores', 'incidents_residuals'], target='incidents',
+ ... func=lambda x, y: (x > 3) & (y > 20))
>>> qc.plot("incidents") # doctest: +SKIP
+.. plot::
+ :context: close-figs
+ :include-source: False
+ :width: 80 %
+ :class: center
-.. image:: ../ressources/images/cbooks_incidents_correctFlagged.png
- :target: ../ressources/images/cbooks_incidents_correctFlagged.png
- :alt:
-
+ qc = qc.flagGeneric(field=['incidents_scores', 'incidents_residuals'], target='incidents', func=lambda x, y: (x > 3) & (y > 20))
+ qc.plot("incidents")
diff --git a/docs/documentation/ConfigurationFiles.rst b/docs/documentation/ConfigurationFiles.rst
new file mode 100644
index 0000000000000000000000000000000000000000..4f56ecf77233084767b48ff0fa10548306ae1a4c
--- /dev/null
+++ b/docs/documentation/ConfigurationFiles.rst
@@ -0,0 +1,111 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+Configuration Files
+===================
+
+In addition to it's :doc:`Python API <../gettingstarted/TutorialAPI>`, SaQC also offers a text based
+configuration system. This option has proven as a valuable strategy in largely automated
+quality control workflows. The ability to change existing setups without changes
+to the source code of, e.g. Extract-Transform-Load (ETL) pipelines, simplies the operation and adaption
+of such workflows as well as the collaboration with less technical project partners.
+
+Format
+------
+
+Configuration files are expected to be semicolon-separated text files with exactly one header
+line. Each row of the configuration file lists one variable and a respective test function that
+is applied to the given variable.
+
+Header names
+^^^^^^^^^^^^
+
+The first line of every configuration file is dropped, so feel free to use header
+names to your liking.
+
+
+Test function notation
+^^^^^^^^^^^^^^^^^^^^^^
+
+The notation of test functions follows the function call notation of Python and
+many other programming languages and looks like this:
+
+.. code-block::
+
+ flagRange(min=0, max=100)
+
+Here the function :py:meth:`flagRange <saqc.SaQC.flagRange>` is called and the
+values ``0`` and ``100`` are passed to the parameters ``min`` and ``max`` respectively.
+As we value readablity of the configuration more than conciseness of the extension language, only
+keyword arguments are supported. That means that the notation ``flagRange(0, 100)``
+is not a valid replacement for the above example.
+
+Examples
+--------
+
+Every row lists one test per variable. If you want to call multiple tests on
+a specific variable (and you probably want to), list them in separate rows:
+
+.. code-block::
+
+ varname ; test
+ #-------;----------------------------------
+ x ; flagMissing()
+ x ; flagRange(min=0, max=100)
+ x ; flagConstants(window="3h")
+ y ; flagRange(min=-10, max=40)
+
+
+Available Test Functions
+^^^^^^^^^^^^^^^^^^^^^^^^
+All :ref:`test functions <modules/SaQCFunctions:Test Functions>` available in the
+:doc:`Python API <../gettingstarted/TutorialAPI>` are also available in the configuration
+system. The translation between API calls and the configuration syntax is straight forward
+and best described by an example. Let's consider the definition
+of :py:meth:`flagRange <saqc.SaQC.flagRange>`:
+
+.. code-block:: python
+
+ flagRange(field, min=-inf, max=inf, flag=255.)
+
+The signature consists of the prevalent parameter ``field``, the specific parameters ``min``
+and ``max`` as well as the :ref:`global parameter <documentation/GlobalKeywords:dfilter and flag keywords>`
+``flag``. The translation of the given API call to ``flagRange``
+
+.. code-block:: python
+
+ qc.flagRange("x", 0, 100, flag=BAD)
+
+
+into the configuration syntax look as follows:
+
+.. code-block::
+
+ varname ; test
+ #-------;------------------------------------
+ x ; flagRange(min=0, max=100, flag=BAD)
+
+We made the following changes: The value for ``field`` is given in the first column of the
+configuration file, the actual function including all parameter as name-value pairs are given
+in the second column.
+
+All other test functions can be used in the same manner.
+
+
+Regular Expressions in ``varname`` column
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Some of the tests (e.g. checks for missing values, range tests or interpolation
+functions) are very likely to be used on all or at least several variables of
+a given dataset. As it becomes quite cumbersome to list all these
+variables seperately, only to call the same functions with the same
+parameters, SaQC supports regular expressions on variables. To mark a given
+variable name as a regular expression, it needs to be quoted with ``'`` or ``"``.
+
+.. code-block::
+
+ varname ; test
+ #----------;------------------------------
+ '.*' ; shift(freq="15Min")
+ '(x | y)' ; flagMissing()
diff --git a/sphinxdoc/getting_started/Customizations.rst b/docs/documentation/Customizations.rst
similarity index 65%
rename from sphinxdoc/getting_started/Customizations.rst
rename to docs/documentation/Customizations.rst
index c5b0739f7b9e9182546af92bdcea6437d86f8888..473070fa383077b45e4e2b74c17ef11797c3c787 100644
--- a/sphinxdoc/getting_started/Customizations.rst
+++ b/docs/documentation/Customizations.rst
@@ -1,17 +1,19 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
Customizations
==============
SaQC comes with a continuously growing number of pre-implemented
-:doc:`quality check and processing routines <FunctionIndex>` and
-flagging schemes.
+quality checking and processing routines as well as flagging schemes.
For any sufficiently large use case however it is very likely that the
functions provided won't fulfill all your needs and requirements.
Acknowledging the impossibility to address all imaginable use cases, we
designed the system to allow for extensions and costumizations. The main extensions options, namely
-:ref:`quality check routines <getting_started/Customizations:custom quality check routines>`
-and the :ref:`flagging scheme <getting_started/Customizations:custom flagging schemes>`
+:ref:`quality check routines <documentation/Customizations:custom quality check routines>`
+and the :ref:`flagging scheme <documentation/Customizations:custom flagging schemes>`
are described within this documents.
Custom quality check routines
@@ -20,17 +22,18 @@ Custom quality check routines
In case you are missing quality check routines, you are of course very
welcome to file a feature request issue on the project's
`gitlab repository <https://git.ufz.de/rdm-software/saqc>`_. However, if
-you are more the "no-way-I-get-this-done-by-myself" type of person,
+you are more the "I-get-this-done-by-myself" type of person,
SaQC provides two ways to integrate custom routines into the system:
#. The :ref:`extension language <documentation/GenericFunctions:Generic Functions>`
-#. An :ref:`interface <getting_started/Customizations:interface>` to the evaluation machinery
+#. An :ref:`interface <documentation/Customizations:interface>` to the evaluation machinery
Interface
^^^^^^^^^
-In order to make a function usable within the evaluation framework of SaQC the following interface is needed:
+In order to make a function usable within the evaluation framework of SaQC it needs to
+implement the following function interface
.. code-block:: python
@@ -39,7 +42,7 @@ In order to make a function usable within the evaluation framework of SaQC the f
import saqc
def yourTestFunction(
- data: pandas.DataFrame,
+ data: dios.DictOfSeries,
field: str,
flags: saqc.Flags,
*args,
@@ -55,11 +58,11 @@ Argument Descriptions
* - Name
- Description
* - ``data``
- - The actual dataset.
+ - The actual dataset, an instance of ``dios.DictOfSeries``.
* - ``field``
- - The field/column within ``data``\ , that function is processing.
+ - The field/column within ``data``, that function is processing.
* - ``flags``
- - An instance of Flags, responsible for the translation of test results into quality attributes.
+ - An instance of saqc.Flags, responsible for the translation of test results into quality attributes.
* - ``args``
- Any other arguments needed to parameterize the function.
* - ``kwargs``
@@ -70,22 +73,22 @@ Integrate into SaQC
^^^^^^^^^^^^^^^^^^^
In order make your function available to the system it needs to be registered. We provide a decorator
-`\ ``register`` <saqc/functions/register.py>`_ with saqc, to integrate your
+`\ ``flagging`` <saqc/functions/register.py>`_ with saqc, to integrate your
test functions into SaQC. Here is a complete dummy example:
.. code-block:: python
from saqc import register
- @register()
+ @flagging()
def yourTestFunction(data, field, flags, *args, **kwargs):
return data, flags
Example
^^^^^^^
-The function `\ ``flagRange`` <saqc/funcs/functions.py>`_ provides a simple, yet complete implementation of
-a quality check routine. You might want to look into its implementation as a reference for your own.
+The function `\ ``flagRange`` <saqc/funcs/outliers.py>`_ provides a simple, yet complete implementation of
+a quality check routine. You might want to look into its implementation as an example.
Custom flagging schemes
-----------------------
diff --git a/docs/documentation/FlaggingTranslation.rst b/docs/documentation/FlaggingTranslation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..ac45572aba67a8ce3e2ec4d9af117d4e2fef5f33
--- /dev/null
+++ b/docs/documentation/FlaggingTranslation.rst
@@ -0,0 +1,10 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+.. _FlagsHistoryTranslations:
+
+Flags, History and Translations
+===============================
+
+TODO
diff --git a/docs/documentation/GenericFunctions.rst b/docs/documentation/GenericFunctions.rst
new file mode 100644
index 0000000000000000000000000000000000000000..bd017843aa675bc3698cc5e7543bcdd11393b348
--- /dev/null
+++ b/docs/documentation/GenericFunctions.rst
@@ -0,0 +1,799 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+Generic Functions
+=================
+
+Generic Flagging Functions
+--------------------------
+
+Generic flagging functions provide for custom cross-variable quality constraints, directly
+implemented using the :doc:`Python API <../gettingstarted/TutorialAPI>` or the
+:doc:`Configuration System <../documentation/ConfigurationFiles>`.
+
+Why?
+^^^^
+
+In most real world datasets many errors can be explained by the dataset itself. Think of a an
+active, fan-cooled measurement device: no matter how precise the instrument may work, problems
+are to be expected when the fan stops working or the power supply drops below a certain threshold.
+While these dependencies are easy to :ref:`formalize <documentation/GenericFunctions:a real world example>`
+on a per dataset basis, it is quite challenging to translate them into generic source code. That
+is why we instrumented SaQC to cope with such situations.
+
+
+Generic Flagging - Specification
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Generic flagging functions are used in the same manner as their
+non-generic counterparts. The basic signature looks like that:
+
+.. code-block::
+
+ flagGeneric(field, func=<expression>, flag=<flag_constant>)
+
+where ``<expression>`` is either a callable (Python API) or an expression
+composed of the `supported constructs`_
+and ``<flag_constant>`` is either one of the predefined
+:ref:`flagging constants <modules/saqcConstants:Flagging Constants>`
+(default: ``BAD``\ ) or a valid value of the chosen flagging scheme. Generic flagging functions
+are expected to return a collection of boolean values, i.e. one ``True`` or ``False`` for every
+value in ``field``. All other expressions will fail during runtime of SaQC.
+
+
+Examples
+^^^^^^^^
+
+The following sections show some contrived but realistic examples, highlighting the
+potential of :py:meth:`flagGeneric <saqq.SaQC.flagGeneric>`. Let's first generate a
+dummy dataset, to lead us through the following code snippets:
+
+.. testsetup:: python
+
+ from saqc import fromConfig
+ from tests.common import writeIO
+
+.. testcode:: python
+
+ from saqc import SaQC
+
+ x = np.array([12, 87, 45, 31, 18, 99])
+ y = np.array([2, 12, 33, 133, 8, 33])
+ z = np.array([34, 23, 89, 56, 5, 1])
+
+ dates = pd.date_range(start="2020-01-01", periods=len(x), freq="D")
+ data = pd.DataFrame({"x": x, "y": y, "z": z}, index=dates)
+
+ qc = SaQC(data)
+
+.. doctest:: python
+
+ >>> qc.data #doctest:+NORMALIZE_WHITESPACE
+ x | y | z |
+ ============== | =============== | ============== |
+ 2020-01-01 12 | 2020-01-01 2 | 2020-01-01 34 |
+ 2020-01-02 87 | 2020-01-02 12 | 2020-01-02 23 |
+ 2020-01-03 45 | 2020-01-03 33 | 2020-01-03 89 |
+ 2020-01-04 31 | 2020-01-04 133 | 2020-01-04 56 |
+ 2020-01-05 18 | 2020-01-05 8 | 2020-01-05 5 |
+ 2020-01-06 99 | 2020-01-06 33 | 2020-01-06 1 |
+
+
+Simple constraints
+~~~~~~~~~~~~~~~~~~
+
+**Task**: Flag all values of ``x`` where ``x`` is smaller than 30
+
+.. tabs::
+
+ .. tab:: API
+
+ .. testcode:: python
+
+ qc1 = qc.flagGeneric(field="x", func=lambda x: x < 30)
+
+ .. doctest:: python
+
+ >>> qc1.flags #doctest:+NORMALIZE_WHITESPACE
+ x | y | z |
+ ================= | =============== | =============== |
+ 2020-01-01 255.0 | 2020-01-01 -inf | 2020-01-01 -inf |
+ 2020-01-02 -inf | 2020-01-02 -inf | 2020-01-02 -inf |
+ 2020-01-03 -inf | 2020-01-03 -inf | 2020-01-03 -inf |
+ 2020-01-04 -inf | 2020-01-04 -inf | 2020-01-04 -inf |
+ 2020-01-05 255.0 | 2020-01-05 -inf | 2020-01-05 -inf |
+ 2020-01-06 -inf | 2020-01-06 -inf | 2020-01-06 -inf |
+
+ .. tab:: Configuration
+
+ .. code-block::
+
+ varname ; test
+ #-------;------------------------
+ x ; flagGeneric(func=x < 30)
+
+ .. doctest:: python
+ :hide:
+
+ >>> tmp = fromConfig(
+ ... writeIO(
+ ... """
+ ... varname ; test
+ ... #-------;------------------------
+ ... x ; flagGeneric(func=x < 30)
+ ... """
+ ... ),
+ ... data
+ ... )
+ >>> (tmp.flags == qc1.flags).all(axis=None) #doctest:+NORMALIZE_WHITESPACE
+ True
+
+
+As to be expected, the usual `comparison operators`_ are supported.
+
+
+Cross variable constraints
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+**Task**: Flag all values of ``x`` where ``y`` is larger than 30
+
+.. tabs::
+
+ .. tab:: API
+
+ .. testcode:: python
+
+ qc2 = qc.flagGeneric(field="y", target="x", func=lambda y: y > 30)
+
+ .. doctest:: python
+
+ >>> qc2.flags #doctest:+NORMALIZE_WHITESPACE
+ x | y | z |
+ ================= | =============== | =============== |
+ 2020-01-01 -inf | 2020-01-01 -inf | 2020-01-01 -inf |
+ 2020-01-02 -inf | 2020-01-02 -inf | 2020-01-02 -inf |
+ 2020-01-03 255.0 | 2020-01-03 -inf | 2020-01-03 -inf |
+ 2020-01-04 255.0 | 2020-01-04 -inf | 2020-01-04 -inf |
+ 2020-01-05 -inf | 2020-01-05 -inf | 2020-01-05 -inf |
+ 2020-01-06 255.0 | 2020-01-06 -inf | 2020-01-06 -inf |
+
+ We introduce another selection parameter, namely ``target``. While ``field`` is still used to select
+ a variable from the dataset, which is subsequently passed to the given function ``func``, ``target`` names the
+ variable to which SaQC writes the produced flags.
+
+ .. tab:: Configuration
+
+ .. code-block::
+
+ varname ; test
+ #-------;------------------------------------
+ x ; flagGeneric(field="y", func=y > 30)
+
+
+ Here the value in the first config column acts as the ``target``, while ``field`` needs to be given
+ as function argument. In case ``field`` is not explicitly given, the first column acts as both,
+ ``field`` and ``target``.
+
+ .. doctest:: python
+ :hide:
+
+ >>> tmp = fromConfig(
+ ... writeIO(
+ ... """
+ ... varname ; test
+ ... #-------;------------------------------------
+ ... x ; flagGeneric(field="y", func=y > 30)
+ ... """
+ ... ),
+ ... data
+ ... )
+ >>> (tmp.flags == qc2.flags).all(axis=None) #doctest:+NORMALIZE_WHITESPACE
+ True
+
+
+Multiple cross variable constraints
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+**Task**: Flag all values of ``x`` where ``y`` is larger than 30 and ``z`` is smaller than 50:
+
+In order to pass multiple variables to ``func``, we need to also specify multiple ``field`` elements.
+Note: to combine boolean expressions using one the available `logical operators`_, they single expressions
+need to be put in parentheses.
+
+.. tabs::
+
+ .. tab:: API
+
+ .. testcode:: python
+
+ qc3 = qc.flagGeneric(field=["y", "z"], target="x", func=lambda y, z: (y > 30) & (z < 50))
+
+ .. doctest:: python
+
+ >>> qc3.flags #doctest:+NORMALIZE_WHITESPACE
+ x | y | z |
+ ================= | =============== | =============== |
+ 2020-01-01 -inf | 2020-01-01 -inf | 2020-01-01 -inf |
+ 2020-01-02 -inf | 2020-01-02 -inf | 2020-01-02 -inf |
+ 2020-01-03 -inf | 2020-01-03 -inf | 2020-01-03 -inf |
+ 2020-01-04 -inf | 2020-01-04 -inf | 2020-01-04 -inf |
+ 2020-01-05 -inf | 2020-01-05 -inf | 2020-01-05 -inf |
+ 2020-01-06 255.0 | 2020-01-06 -inf | 2020-01-06 -inf |
+
+ The mapping
+ from ``field`` to the ``lambda`` function parameters is positional and not bound to names. That means
+ that the function parameters can be named arbitrarily.
+ .. tab:: Configuration
+
+ .. code-block::
+
+ varname ; test
+ #-------;--------------------------------------------------------
+ x ; flagGeneric(field=["y", "z"], func=(y > 30) & (z < 50))
+
+
+ Here the value in the first config column acts as the ``target``, while ``field`` needs to be given
+ as a function argument. In case ``field`` is not explicitly given, the first column acts as both,
+ ``field`` and ``target``.
+ The mapping from ``field`` to the names used in ``func`` is positional, i.e. the first value in ``field``
+ is mapped to the first variable found in ``func``.
+
+ .. doctest:: python
+ :hide:
+
+ >>> tmp = fromConfig(
+ ... writeIO(
+ ... """
+ ... varname ; test
+ ... #-------;--------------------------------------------------------
+ ... x ; flagGeneric(field=["y", "z"], func=(y > 30) & (z < 50))
+ ... """
+ ... ),
+ ... data
+ ... )
+ >>> (tmp.flags == qc3.flags).all(axis=None) #doctest:+NORMALIZE_WHITESPACE
+ True
+
+
+Arithmetics
+~~~~~~~~~~~
+**Task**: Flag all values of ``x``, that exceed the arithmetic mean of ``y`` and ``z``
+
+.. tabs::
+
+ .. tab:: API
+
+ .. testcode:: python
+
+ qc4 = qc.flagGeneric(field=["x", "y", "z"], target="x", func=lambda x, y, z: x > (y + z)/2)
+
+ .. doctest:: python
+
+ >>> qc4.flags #doctest:+NORMALIZE_WHITESPACE
+ x | y | z |
+ ================= | =============== | =============== |
+ 2020-01-01 -inf | 2020-01-01 -inf | 2020-01-01 -inf |
+ 2020-01-02 255.0 | 2020-01-02 -inf | 2020-01-02 -inf |
+ 2020-01-03 -inf | 2020-01-03 -inf | 2020-01-03 -inf |
+ 2020-01-04 -inf | 2020-01-04 -inf | 2020-01-04 -inf |
+ 2020-01-05 255.0 | 2020-01-05 -inf | 2020-01-05 -inf |
+ 2020-01-06 255.0 | 2020-01-06 -inf | 2020-01-06 -inf |
+
+ .. tab:: Configuration
+
+ .. code-block::
+
+ varname ; test
+ #-------;-------------------------------------------------------
+ x ; flagGeneric(field=["x", "y", "z"], func=x > (y + z)/2)
+
+
+ :py:meth:`flagGeneric <saqq.SaQC.flagGeneric>` supports the usual `arithmetic operators`_.
+
+ .. doctest:: python
+ :hide:
+
+ >>> tmp = fromConfig(
+ ... writeIO(
+ ... """
+ ... varname ; test
+ ... #-------;-------------------------------------------------------
+ ... x ; flagGeneric(field=["x", "y", "z"], func=x > (y + z)/2)
+ ... """
+ ... ),
+ ... data
+ ... )
+ >>> (tmp.flags == qc4.flags).all(axis=None) #doctest:+NORMALIZE_WHITESPACE
+ True
+
+
+
+Special functions
+~~~~~~~~~~~~~~~~~
+
+**Task**: Flag all values of ``x``, that exceed 2 standard deviations of ``z``.
+
+.. tabs::
+
+ .. tab:: API
+
+ .. testcode:: python
+
+ qc5 = qc.flagGeneric(field=["x", "z"], target="x", func=lambda x, z: x > np.std(z) * 2)
+
+ .. doctest:: python
+
+ >>> qc5.flags #doctest:+NORMALIZE_WHITESPACE
+ x | y | z |
+ ================= | =============== | =============== |
+ 2020-01-01 -inf | 2020-01-01 -inf | 2020-01-01 -inf |
+ 2020-01-02 255.0 | 2020-01-02 -inf | 2020-01-02 -inf |
+ 2020-01-03 -inf | 2020-01-03 -inf | 2020-01-03 -inf |
+ 2020-01-04 -inf | 2020-01-04 -inf | 2020-01-04 -inf |
+ 2020-01-05 -inf | 2020-01-05 -inf | 2020-01-05 -inf |
+ 2020-01-06 255.0 | 2020-01-06 -inf | 2020-01-06 -inf |
+
+ The selected variables are passed to ``func`` as arguments of type ``pd.Series``, so all functions
+ accepting such an argument can be used in generic expressions.
+
+ .. tab:: Configuration
+
+ .. code-block::
+
+ varname ; test
+ #-------;---------------------------------------------------
+ x ; flagGeneric(field=["x", "z"], func=x > std(z) * 2)
+
+ In configurations files, the number of available mathematical functions is more restricted. Instead
+ of basically all functions accepting array-like inputs, only certain built in
+ `<mathematical functions>`_ can be used.
+
+ .. doctest:: python
+ :hide:
+
+ >>> tmp = fromConfig(
+ ... writeIO(
+ ... """
+ ... varname ; test
+ ... #-------;---------------------------------------------------
+ ... x ; flagGeneric(field=["x", "z"], func=x > std(z) * 2)
+ ... """
+ ... ),
+ ... data
+ ... )
+ >>> (tmp.flags == qc5.flags).all(axis=None) #doctest:+NORMALIZE_WHITESPACE
+ True
+
+
+
+**Task**: Flag all values of ``x`` where ``y`` is flagged.
+
+.. tabs::
+
+ .. tab:: API
+
+ .. testcode:: python
+
+ qc6 = (qc
+ .flagRange(field="y", min=10, max=60)
+ .flagGeneric(field="y", target="x", func=lambda y: isflagged(y)))
+
+ .. doctest:: python
+
+ >>> qc6.flags #doctest:+NORMALIZE_WHITESPACE
+ x | y | z |
+ ================= | ================= | =============== |
+ 2020-01-01 255.0 | 2020-01-01 255.0 | 2020-01-01 -inf |
+ 2020-01-02 -inf | 2020-01-02 -inf | 2020-01-02 -inf |
+ 2020-01-03 -inf | 2020-01-03 -inf | 2020-01-03 -inf |
+ 2020-01-04 255.0 | 2020-01-04 255.0 | 2020-01-04 -inf |
+ 2020-01-05 255.0 | 2020-01-05 255.0 | 2020-01-05 -inf |
+ 2020-01-06 -inf | 2020-01-06 -inf | 2020-01-06 -inf |
+
+ .. tab:: Configuration
+
+ .. code-block::
+
+ varname ; test
+ #-------;------------------------------------------
+ y ; flagRange(min=10, max=60)
+ x ; flagGeneric(field="y", func=isflagged(y))
+
+ .. doctest:: python
+ :hide:
+
+ >>> tmp = fromConfig(
+ ... writeIO(
+ ... """
+ ... varname ; test
+ ... #-------;------------------------------------------
+ ... y ; flagRange(min=10, max=60)
+ ... x ; flagGeneric(field="y", func=isflagged(y))
+ ... """
+ ... ),
+ ... data
+ ... )
+ >>> (tmp.flags == qc6.flags).all(axis=None) #doctest:+NORMALIZE_WHITESPACE
+ True
+
+
+
+A real world example
+~~~~~~~~~~~~~~~~~~~~
+
+Let's consider the following dataset:
+
+.. testcode:: python
+
+ from saqc import SaQC
+
+ meas = np.array([3.56, 4.7, 0.1, 3.62])
+ fan = np.array([1, 0, 1, 1])
+ volt = np.array([12.1, 12.0, 11.5, 12.1])
+
+ dates = pd.to_datetime(["2018-06-01 12:00", "2018-06-01 12:10", "2018-06-01 12:20", "2018-06-01 12:30"])
+ data = pd.DataFrame({"meas": meas, "fan": fan, "volt": volt}, index=dates)
+
+ qc = SaQC(data)
+
+.. doctest:: python
+
+ >>> qc.data #doctest:+NORMALIZE_WHITESPACE
+ meas | fan | volt |
+ ========================= | ======================= | ========================= |
+ 2018-06-01 12:00:00 3.56 | 2018-06-01 12:00:00 1 | 2018-06-01 12:00:00 12.1 |
+ 2018-06-01 12:10:00 4.70 | 2018-06-01 12:10:00 0 | 2018-06-01 12:10:00 12.0 |
+ 2018-06-01 12:20:00 0.10 | 2018-06-01 12:20:00 1 | 2018-06-01 12:20:00 11.5 |
+ 2018-06-01 12:30:00 3.62 | 2018-06-01 12:30:00 1 | 2018-06-01 12:30:00 12.1 |
+
+
+**Task**: Flag ``meas`` where ``fan`` equals 0 and ``volt`` is lower than ``12.0``.
+
+**Configuration file**: There are various options. We can directly implement the condition as follows:
+
+.. tabs::
+
+ .. tab:: API
+
+ .. testcode:: python
+
+ qc7 = qc.flagGeneric(field=["fan", "volt"], target="meas", func=lambda x, y: (x == 0) | (y < 12))
+
+ .. doctest:: python
+
+ >>> qc7.flags #doctest:+NORMALIZE_WHITESPACE
+ meas | fan | volt |
+ ========================== | ======================== | ======================== |
+ 2018-06-01 12:00:00 -inf | 2018-06-01 12:00:00 -inf | 2018-06-01 12:00:00 -inf |
+ 2018-06-01 12:10:00 255.0 | 2018-06-01 12:10:00 -inf | 2018-06-01 12:10:00 -inf |
+ 2018-06-01 12:20:00 255.0 | 2018-06-01 12:20:00 -inf | 2018-06-01 12:20:00 -inf |
+ 2018-06-01 12:30:00 -inf | 2018-06-01 12:30:00 -inf | 2018-06-01 12:30:00 -inf |
+
+ .. tab:: Configuration
+
+ .. code-block::
+
+ varname ; test
+ #-------;---------------------------------------------------------------
+ meas ; flagGeneric(field=["fan", "volt"], func=(x == 0) | (y < 12.0))
+
+
+ .. doctest:: python
+ :hide:
+
+ >>> tmp = fromConfig(
+ ... writeIO(
+ ... """
+ ... varname ; test
+ ... #-------;---------------------------------------------------------------
+ ... meas ; flagGeneric(field=["fan", "volt"], func=(x == 0) | (y < 12.0))
+ ... """
+ ... ),
+ ... data
+ ... )
+ >>> (tmp.flags == qc7.flags).all(axis=None) #doctest:+NORMALIZE_WHITESPACE
+ True
+
+
+But we could also quality check our independent variables first and than leverage this information later on:
+
+.. tabs::
+
+ .. tab:: API
+
+ .. testcode:: python
+
+ qc8 = (qc
+ .flagMissing(".*", regex=True)
+ .flagGeneric(field="fan", func=lambda x: x == 0)
+ .flagGeneric(field="volt", func=lambda x: x < 12)
+ .flagGeneric(field=["fan", "volt"], target="meas", func=lambda x, y: isflagged(x) | isflagged(y)))
+
+ .. doctest:: python
+
+ >>> qc8.flags #doctest:+NORMALIZE_WHITESPACE
+ meas | fan | volt |
+ ========================== | ========================== | ========================== |
+ 2018-06-01 12:00:00 -inf | 2018-06-01 12:00:00 -inf | 2018-06-01 12:00:00 -inf |
+ 2018-06-01 12:10:00 255.0 | 2018-06-01 12:10:00 255.0 | 2018-06-01 12:10:00 -inf |
+ 2018-06-01 12:20:00 255.0 | 2018-06-01 12:20:00 -inf | 2018-06-01 12:20:00 255.0 |
+ 2018-06-01 12:30:00 -inf | 2018-06-01 12:30:00 -inf | 2018-06-01 12:30:00 -inf |
+
+ .. tab:: Configuration
+
+ .. code-block::
+
+ varname ; test
+ #-------;--------------------------------------------------------------------------
+ '.*' ; flagMissing()
+ fan ; flagGeneric(func=fan == 0)
+ volt ; flagGeneric(func=volt < 12.0)
+ meas ; flagGeneric(field=["fan", "volt"], func=isflagged(fan) | isflagged(volt))
+
+ .. doctest:: python
+ :hide:
+
+ >>> tmp = fromConfig(
+ ... writeIO(
+ ... """
+ ... varname ; test
+ ... #-------;--------------------------------------------------------------------------
+ ... '.*' ; flagMissing()
+ ... fan ; flagGeneric(func=fan == 0)
+ ... volt ; flagGeneric(func=volt < 12.0)
+ ... meas ; flagGeneric(field=["fan", "volt"], func=isflagged(fan) | isflagged(volt))
+ ... """
+ ... ),
+ ... data
+ ... )
+ >>> (tmp.flags == qc8.flags).all(axis=None) #doctest:+NORMALIZE_WHITESPACE
+ True
+
+
+Generic Processing
+------------------
+
+Generic processing functions provide a way to evaluate mathematical operations
+and functions on the variables of a given dataset.
+
+Why
+^^^
+
+In many real-world use cases, quality control is embedded into a larger data
+processing pipeline. It is not unusual to even have certain processing
+requirements as a part of the quality control itself. Generic processing
+functions make it easy to enrich a dataset through the evaluation of a
+given expression.
+
+Generic Processing - Specification
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The basic signature looks like that:
+
+.. code-block:: sh
+
+ processGeneric(field, func=<expression>)
+
+where ``<expression>`` is either a callable (Python API) or an expression composed of the
+`supported constructs`_ (Configuration File).
+
+Example
+^^^^^^^
+
+Let's use :py:meth:`flagGeneric <saqq.SaQC.processGeneric>` to calculate the mean value of several
+variables in a given dataset. We start with dummy data again:
+
+.. testcode:: python
+
+ from saqc import SaQC
+
+ x = np.array([12, 87, 45, 31, 18, 99])
+ y = np.array([2, 12, 33, 133, 8, 33])
+ z = np.array([34, 23, 89, 56, 5, 1])
+
+ dates = pd.date_range(start="2020-01-01", periods=len(x), freq="D")
+ data = pd.DataFrame({"x": x, "y": y, "z": z}, index=dates)
+
+ qc = SaQC(data)
+
+.. tabs::
+
+ .. tab:: API
+
+ .. testcode:: python
+
+ qc1 = qc.processGeneric(
+ field=["x", "y", "z"],
+ target="mean",
+ func=lambda x, y, z: (x + y + z) / 2
+ )
+
+ .. doctest:: python
+
+ >>> qc1.data #doctest:+NORMALIZE_WHITESPACE
+ x | y | z | mean |
+ ============== | =============== | ============== | ================= |
+ 2020-01-01 12 | 2020-01-01 2 | 2020-01-01 34 | 2020-01-01 24.0 |
+ 2020-01-02 87 | 2020-01-02 12 | 2020-01-02 23 | 2020-01-02 61.0 |
+ 2020-01-03 45 | 2020-01-03 33 | 2020-01-03 89 | 2020-01-03 83.5 |
+ 2020-01-04 31 | 2020-01-04 133 | 2020-01-04 56 | 2020-01-04 110.0 |
+ 2020-01-05 18 | 2020-01-05 8 | 2020-01-05 5 | 2020-01-05 15.5 |
+ 2020-01-06 99 | 2020-01-06 33 | 2020-01-06 1 | 2020-01-06 66.5 |
+
+ The call to :py:meth:`flagGeneric <saqq.SaQC.processGeneric>` added the new variable ``mean``
+ to the dataset.
+
+ .. tab:: Configuration
+
+ .. code-block::
+
+ varname ; test
+ #-------;------------------------------------------------------
+ mean ; processGeneric(field=["x", "y", "z"], func=(x+y+z)/2)
+
+ .. doctest:: python
+ :hide:
+
+ >>> tmp = fromConfig(
+ ... writeIO(
+ ... """
+ ... varname ; test
+ ... #-------;------------------------------------------------------
+ ... mean ; processGeneric(field=["x", "y", "z"], func=(x+y+z)/2)
+ ... """
+ ... ),
+ ... data
+ ... )
+ >>> (tmp.data == qc1.data).all(axis=None) #doctest:+NORMALIZE_WHITESPACE
+ True
+
+
+Supported constructs
+--------------------
+
+Operators
+^^^^^^^^^
+
+Comparison Operators
+~~~~~~~~~~~~~~~~~~~~
+
+The following comparison operators are available:
+
+.. list-table::
+ :header-rows: 1
+
+ * - Operator
+ - Description
+ * - ``==``
+ - ``True`` if the values of the operands are equal
+ * - ``!=``
+ - ``True`` if the values of the operands are not equal
+ * - ``>``
+ - ``True`` if the values of the left operand are greater than the values of the right operand
+ * - ``<``
+ - ``True`` if the values of the left operand are smaller than the values of the right operand
+ * - ``>=``
+ - ``True`` if the values of the left operand are greater or equal than the values of the right operand
+ * - ``<=``
+ - ``True`` if the values of the left operand are smaller or equal than the values of the right operand
+
+
+Logical operators
+~~~~~~~~~~~~~~~~~
+
+The bitwise operators act as logical operators in comparison chains
+
+.. list-table::
+ :header-rows: 1
+
+ * - Operator
+ - Description
+ * - ``&``
+ - binary and
+ * - ``|``
+ - binary or
+ * - ``^``
+ - binary xor
+ * - ``~``
+ - binary complement
+
+
+Arithmetic Operators
+~~~~~~~~~~~~~~~~~~~~
+
+The following arithmetic operators are supported:
+
+.. list-table::
+ :header-rows: 1
+
+ * - Operator
+ - Description
+ * - ``+``
+ - addition
+ * - ``-``
+ - subtraction
+ * - ``*``
+ - multiplication
+ * - ``/``
+ - division
+ * - ``**``
+ - exponentiation
+ * - ``%``
+ - modulus
+
+
+Functions
+^^^^^^^^^
+
+Mathematical Functions
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. list-table::
+ :header-rows: 1
+
+ * - Name
+ - Description
+ * - ``abs``
+ - absolute values of a variable
+ * - ``max``
+ - maximum value of a variable
+ * - ``min``
+ - minimum value of a variable
+ * - ``mean``
+ - mean value of a variable
+ * - ``sum``
+ - sum of a variable
+ * - ``std``
+ - standard deviation of a variable
+ * - ``abs``
+ - Pointwise absolute Value Function.
+ * - ``max``
+ - Maximum Value Function. Ignores NaN.
+ * - ``min``
+ - Minimum Value Function. Ignores NaN.
+ * - ``mean``
+ - Mean Value Function. Ignores NaN.
+ * - ``sum``
+ - Summation. Ignores NaN.
+ * - ``len``
+ - Standart Deviation. Ignores NaN.
+ * - ``exp``
+ - Pointwise Exponential.
+ * - ``log``
+ - Pointwise Logarithm.
+ * - ``nanLog``
+ - Logarithm, returning NaN for zero input, instead of -inf.
+ * - ``std``
+ - Standart Deviation. Ignores NaN.
+ * - ``var``
+ - Variance. Ignores NaN.
+ * - ``median``
+ - Median. Ignores NaN.
+ * - ``count``
+ - Count Number of values. Ignores NaNs.
+ * - ``id``
+ - Identity.
+ * - ``diff``
+ - Returns a Series` diff.
+ * - ``scale``
+ - Scales data to [0,1] Interval.
+ * - ``zScore``
+ - Standardize with Standart Deviation.
+ * - ``madScore``
+ - Standardize with Median and MAD.
+ * - ``iqsScore``
+ - Standardize with Median and inter quantile range.
+
+
+Miscellaneous Functions
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. list-table::
+ :header-rows: 1
+
+ * - Name
+ - Description
+ * - ``isflagged``
+ - Pointwise, checks if a value is flagged
+ * - ``len``
+ - Returns the length of passed series
diff --git a/docs/documentation/GlobalKeywords.rst b/docs/documentation/GlobalKeywords.rst
new file mode 100644
index 0000000000000000000000000000000000000000..4bea09db6298e180ee1027e7c729cf679a6c5dd1
--- /dev/null
+++ b/docs/documentation/GlobalKeywords.rst
@@ -0,0 +1,271 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+Global Keywords
+===============
+
+Introduction to the usage of the global keywords. (Keywords that can be passed to any :py:class:`saqc.SaQC` method.)
+
+0. `Set Up`_
+
+ * `Example data`_
+ * `Flagging Scheme Constraint`_
+
+1. `label keyword`_
+
+ * `label Example Usage`_
+
+2. `dfilter and flag keywords`_
+
+ * `Flags of Different Significance`_
+ * `Unflagging Values`_
+
+Set Up
+------
+
+Flagging Scheme Constraint
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The Tutorial currently only works when instantiating an :py:class:`~saqc.SaQC` object with the default
+:ref:`flagging scheme <FlagsHistoryTranslations>`, which is the :py:class:`~saqc.core.FloatScheme`.
+
+Example Data
+^^^^^^^^^^^^
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ import matplotlib.pyplot as plt
+ import pandas as pd
+ import numpy as np
+ import saqc
+ noise = np.random.normal(0, 1, 200)
+ data = pd.Series(noise, index=pd.date_range('2020','2021',periods=200), name='data')
+ data.iloc[20] = 16
+ data.iloc[100] = -17
+ data.iloc[160:180] = -3
+ qc = saqc.SaQC(data)
+
+Lets generate some example data and plot it:
+
+.. doctest:: exampleLabel
+
+ >>> import pandas as pd
+ >>> import numpy as np
+ >>> noise = np.random.normal(0, 1, 200) # some normally distributed noise
+ >>> data = pd.Series(noise, index=pd.date_range('2020','2021',periods=200), name='data') # index the noise with some dates
+ >>> data.iloc[20] = 16 # add some artificial anomalies:
+ >>> data.iloc[100] = -17
+ >>> data.iloc[160:180] = -3
+ >>> qc = saqc.SaQC(data)
+ >>> qc.plot('data') #doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc.plot('data')
+
+Label Keyword
+-------------
+
+The ``label`` keyword can be passed with any function call and serves as label to be plotted by a subsequent
+call to :py:meth:`saqc.SaQC.plot`.
+
+It is especially useful for enriching figures with custom context information, and for making results from
+different function calls distinguishable with respect to their purpose and parameterisation.
+Check out the following example:
+
+At first, we apply some flagging functions to mark anomalies without usage of the ``label`` keyword:
+
+.. doctest:: exampleLabel
+
+ >>> qc = qc.flagRange('data', max=15)
+ >>> qc = qc.flagRange('data', min=-16)
+ >>> qc = qc.flagConstants('data', window='2D', thresh=0)
+ >>> qc = qc.flagManual('data', mdata=pd.Series('2020-05', index=pd.DatetimeIndex(['2020-03'])))
+ >>> qc.plot('data') # doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.flagRange('data', max=15)
+ qc = qc.flagRange('data', min=-16)
+ qc = qc.flagConstants('data', window='2D', thresh=0)
+ qc = qc.flagManual('data', mdata=pd.Series('2020-05', index=pd.DatetimeIndex(['2020-03'])))
+ qc.plot('data')
+
+In the above plot, one might want to discern the two results from the call to :py:meth:`saqc.SaQC.flagRange` with
+respect to the parameters they where called with, also, one might want to give some hints about what is the context of
+the flags "manually" determined by the call to :py:meth:`saqc.SaQC.flagManual`. Lets repeat the procedure and
+enrich the call with this information by making use of the label keyword:
+
+Label Example Usage
+^^^^^^^^^^^^^^^^^^^
+
+.. doctest:: exampleLabel
+
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagRange('data', max=15, label='values < 15')
+ >>> qc = qc.flagRange('data', min=-16, label='values > -16')
+ >>> qc = qc.flagConstants('data', window='2D', thresh=0, label='values constant longer than 2 days')
+ >>> qc = qc.flagManual('data', mdata=pd.Series('2020-05', index=pd.DatetimeIndex(['2020-03'])), label='values collected while sensor maintenance')
+ >>> qc.plot('data') # doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = saqc.SaQC(data)
+ qc = qc.flagRange('data', max=15, label='values < 15')
+ qc = qc.flagRange('data', min=-16, label='values > -16')
+ qc = qc.flagConstants('data', window='2D', thresh=0, label='values constant longer than 2 days')
+ qc = qc.flagManual('data', mdata=pd.Series('2020-05', index=pd.DatetimeIndex(['2020-03'])), label='values collected while sensor maintenance')
+ qc.plot('data')
+
+
+dfilter and flag keywords
+-------------------------
+
+The ``flag`` keyword controls a tests level of flagging :math:`f(v)` for any value :math:`v`. So,
+in short, the keyword controls the output flag level of any flagging function.
+
+The ``dfilter`` keyword controls the threshold up to which a flagged value is masked, when passed
+on to any flagging function. So, in short, it controls the input threshold, up to which flagged values are visible to
+any function that operates on the values.
+
+In more detail: Any value :math:`v` with a flag :math:`f(v)` will be masked, if :math:`f(v) >=` ``dfilter``. A masked value
+will appear as ``NaN`` (`not a number`, or `missing`) to the flagging function and will be numerically treated as such.
+(This means, its excluded from most arithmetic calculations, but may be implicitly part of operations, such as `count(NaN)` or `isnan`).
+Lets at first visualize this interplay with the :py:meth:`saqc.SaqC.plot` method. (We are reusing data and code
+from the `Example Data`_ section). First, we set some flags to the data. As pointed out in
+`Flagging Scheme Constraint`_ , we are referring to defaultly instantiated :py:class:`saqc.SaQC` objects, that use the
+:py:class:`~saqc.core.FloatScheme` , (which uses a real valued scale of flags levels,
+ranging from ``-inf`` to ``255.0``).:
+
+.. doctest:: exampleLabel
+
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagRange('data', max=15, label='flaglevel=200', flag=200)
+ >>> qc = qc.flagRange('data', min=-16, label='flaglevel=100', flag=100)
+ >>> qc = qc.flagManual('data', mdata=pd.Series('2020-05', index=pd.DatetimeIndex(['2020-03'])), label='flaglevel=0', flag=0)
+ >>> qc.plot('data') # doctest:+SKIP
+
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = saqc.SaQC(data)
+ qc = qc.flagRange('data', max=15, label='flaglevel=200', flag=200)
+ qc = qc.flagRange('data', min=-16, label='flaglevel=100', flag=100)
+ qc = qc.flagManual('data', mdata=pd.Series('2020-05', index=pd.DatetimeIndex(['2020-03'])), label='flaglevel=0', flag=0)
+ qc.plot('data')
+
+With the ``dfilter`` Keyword, we can now control, which of the flags are passed on to the plot function.
+For example, if we set ``dfilter=50``, the flags set by the :py:meth:`saqc.SaQC.flagRange` method wont get passed on
+and thus, the resulting plot will be cleared from the flags:
+
+.. doctest:: exampleLabel
+
+ >>> qc.plot('data', dfilter=50) # doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc.plot('data', dfilter=50)
+
+Flags of Different Significance
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We can also use the interplay between the ``dfilter`` keyword and ``flag`` keyword, to order flags priorities.
+By default, the ``dfilter`` keyword is set to the highest flag value of the instantiated
+:ref:`flagging scheme <FlagsHistoryTranslations>`, referred to, as :py:attr:`~saqc.constants.BAD`.
+Since the flag set by a test also defaults to :py:attr:`~saqc.constants.BAD`, the second call
+to :py:meth:`saqc.SaQC.flagRange` in the example below, wont get passed the values already flagged by the first call to
+:py:meth:`saqc.SaQC.flagRange` - so it cant check the value level and assign no additional flag by its self.
+
+.. doctest:: exampleLabel
+
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagRange('data', max=15, label='value > 15')
+ >>> qc = qc.flagRange('data', max=0, label='value > 0')
+ >>> qc.plot('data') # doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = saqc.SaQC(data)
+ qc = qc.flagRange('data', max=15, label='value > 15')
+ qc = qc.flagRange('data', max=0, label='value > 0')
+ qc.plot('data')
+
+We can make the value flagged by both the flagging functions by increasing the
+``dfilter`` threshold of the flagging function called second, above the default flag level of
+:py:attr:`~saqc.constants.BAD`. This can be achieved, by passing the flagging constant
+:py:attr:`~saqc.constants.FILTER_NONE`,
+
+
+.. doctest:: exampleLabel
+
+ >>> from saqc.constants import FILTER_NONE
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagRange('data', max=15, label='value > 15')
+ >>> qc = qc.flagRange('data', max=0, label='value > 0', dfilter=FILTER_NONE)
+ >>> qc.plot('data') # doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = saqc.SaQC(data)
+ qc = qc.flagRange('data', max=15, label='value > 15')
+ qc = qc.flagRange('data', max=0, label='value > 0', dfilter=300)
+ qc.plot('data')
+
+Unflagging Values
+^^^^^^^^^^^^^^^^^
+
+With the ``flag`` keyword it is as also possible, to `revoke` or `unflag` a flag from a value.
+This way, it is possible to associate flags with conditions determined by other functions.
+For example, if we want to flag all values below a level of `0.5`, but not those that belong to a constant value
+course, we can achieve that, by combining the ``flag`` and the ``dfilter`` keyword.
+Lets first flag all the data below a level of `0.5`:
+
+.. doctest:: exampleLabel
+
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagRange('data', min=0.5)
+ >>> qc.plot('data') #doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = saqc.SaQC(data)
+ qc = qc.flagRange('data', min=0.5)
+ qc.plot('data')
+
+Now we can override the flags for the constant value course with the lowest (unflagged) flag level, which, for the
+:py:class:`~saqc.core.FloatScheme` is the value ``-np.inf``. Alternatively to the explicit value, we can use the
+:py:attr:`~saqc.constants.UNFLAGGED` constant.
+Also, for the override to work, we have to rise (or deactivate) the input filter, so that the :py:meth:`saqc.SaQC.flagConstants` method
+gets the already flagged values passed to test them.
+
+.. doctest:: exampleLabel
+
+ >>> from saqc.constants import UNFLAGGED, FILTER_NONE
+ >>> qc = qc.flagConstants('data', window='2D', thresh=0, dfilter=FILTER_NONE, flag=UNFLAGGED)
+ >>> qc.plot('data') #doctest:+SKIP
+
+.. plot::
+ :context: close-figs
+ :include-source: False
+
+ qc = qc.flagConstants('data', window='2D', thresh=0, dfilter=300, flag=-np.inf)
+ qc.plot('data')
diff --git a/docs/documentation/SourceTarget.rst b/docs/documentation/SourceTarget.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e5500fc07adcb0da4c28992a767318b03068aa72
--- /dev/null
+++ b/docs/documentation/SourceTarget.rst
@@ -0,0 +1,8 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+Assigning results to new variables
+==================================
+
+TODO
diff --git a/sphinxdoc/documentation/WritingFunctions.rst b/docs/documentation/WritingFunctions.rst
similarity index 95%
rename from sphinxdoc/documentation/WritingFunctions.rst
rename to docs/documentation/WritingFunctions.rst
index 5cf8e48fa23cb86576125d16e6626a0198f15e4d..6080a5e316d66b5ed68af94ddf5d183cdd7661a8 100644
--- a/sphinxdoc/documentation/WritingFunctions.rst
+++ b/docs/documentation/WritingFunctions.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
Writing Functions
=================
diff --git a/sphinxdoc/funcSummaries/breaks.rst b/docs/funcs/breaks.rst
similarity index 73%
rename from sphinxdoc/funcSummaries/breaks.rst
rename to docs/funcs/breaks.rst
index 78cc399bfef1b81fadadf36d104d3aac6cc3bddf..17408a2e91f1c3eb50c01c1cf94169d71c42609a 100644
--- a/sphinxdoc/funcSummaries/breaks.rst
+++ b/docs/funcs/breaks.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
breaks
======
diff --git a/sphinxdoc/funcSummaries/changepoints.rst b/docs/funcs/changepoints.rst
similarity index 50%
rename from sphinxdoc/funcSummaries/changepoints.rst
rename to docs/funcs/changepoints.rst
index 89625498cb9f271b38704a534887eb849372712f..59c80100ebc07dd8334a7ea580545027adcef332 100644
--- a/sphinxdoc/funcSummaries/changepoints.rst
+++ b/docs/funcs/changepoints.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
changepoints
============
diff --git a/docs/funcs/constants.rst b/docs/funcs/constants.rst
new file mode 100644
index 0000000000000000000000000000000000000000..0f6e00d05dea7c216da26f84d64e6755fe161078
--- /dev/null
+++ b/docs/funcs/constants.rst
@@ -0,0 +1,15 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+constants
+=========
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.flagConstants
+ ~SaQC.flagByVariance
diff --git a/docs/funcs/curvefit.rst b/docs/funcs/curvefit.rst
new file mode 100644
index 0000000000000000000000000000000000000000..616299a795a888b3f543f4e5b1fcfbab49111068
--- /dev/null
+++ b/docs/funcs/curvefit.rst
@@ -0,0 +1,14 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+curvefit
+========
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.fitPolynomial
diff --git a/sphinxdoc/funcSummaries/drift.rst b/docs/funcs/drift.rst
similarity index 65%
rename from sphinxdoc/funcSummaries/drift.rst
rename to docs/funcs/drift.rst
index 535bbc41d5721e4980e6fdced659a6a043399726..74ba1504b70ab84f4d8407e4c6073b7d2d18ec39 100644
--- a/sphinxdoc/funcSummaries/drift.rst
+++ b/docs/funcs/drift.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
drift
=====
diff --git a/sphinxdoc/funcSummaries/flagtools.rst b/docs/funcs/flagtools.rst
similarity index 59%
rename from sphinxdoc/funcSummaries/flagtools.rst
rename to docs/funcs/flagtools.rst
index 544c82ae44be43d253f3d4f943e1332508aa9a39..b508fc9051e720bb8478603c08503597694697c7 100644
--- a/sphinxdoc/funcSummaries/flagtools.rst
+++ b/docs/funcs/flagtools.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
flagtools
=========
diff --git a/docs/funcs/generic.rst b/docs/funcs/generic.rst
new file mode 100644
index 0000000000000000000000000000000000000000..be23839e7979f9125420ac8b04b82e201878f54f
--- /dev/null
+++ b/docs/funcs/generic.rst
@@ -0,0 +1,15 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+generic
+=======
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.processGeneric
+ ~SaQC.flagGeneric
diff --git a/sphinxdoc/funcSummaries/interpolation.rst b/docs/funcs/interpolation.rst
similarity index 54%
rename from sphinxdoc/funcSummaries/interpolation.rst
rename to docs/funcs/interpolation.rst
index 173ef11ccf1515d31bb8f8b5e1ea76e695b3663d..f3f96a0311e839bbb3bbd80b67202d0d281f12d7 100644
--- a/sphinxdoc/funcSummaries/interpolation.rst
+++ b/docs/funcs/interpolation.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
interpolation
=============
diff --git a/docs/funcs/noise.rst b/docs/funcs/noise.rst
new file mode 100644
index 0000000000000000000000000000000000000000..5fb919016cdc04702de5a11ce4abc400ad317999
--- /dev/null
+++ b/docs/funcs/noise.rst
@@ -0,0 +1,14 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+noise
+=====
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.flagByStatLowPass
diff --git a/sphinxdoc/funcSummaries/outliers.rst b/docs/funcs/outliers.rst
similarity index 64%
rename from sphinxdoc/funcSummaries/outliers.rst
rename to docs/funcs/outliers.rst
index 7c1bf5a78dc943f9d11a1700a850b48e8df7f5d5..696110e1526966aa6f660dad29188d40d26e05e5 100644
--- a/sphinxdoc/funcSummaries/outliers.rst
+++ b/docs/funcs/outliers.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
outliers
========
diff --git a/docs/funcs/pattern.rst b/docs/funcs/pattern.rst
new file mode 100644
index 0000000000000000000000000000000000000000..ed1b17e30388f60513b364e42b3ec4c78cc84d36
--- /dev/null
+++ b/docs/funcs/pattern.rst
@@ -0,0 +1,14 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+pattern
+=======
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.flagPatternByDTW
diff --git a/sphinxdoc/funcSummaries/resampling.rst b/docs/funcs/resampling.rst
similarity index 54%
rename from sphinxdoc/funcSummaries/resampling.rst
rename to docs/funcs/resampling.rst
index cffbee451560bcb56b136d62b1c9e02810b43ddc..88beeaf1c16833027dadde995f2b2674a34433df 100644
--- a/sphinxdoc/funcSummaries/resampling.rst
+++ b/docs/funcs/resampling.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
resampling
==========
diff --git a/docs/funcs/residuals.rst b/docs/funcs/residuals.rst
new file mode 100644
index 0000000000000000000000000000000000000000..5c36bd88bd0f4d3efd0d58a29c2fcbeadf81a4f7
--- /dev/null
+++ b/docs/funcs/residuals.rst
@@ -0,0 +1,15 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+residuals
+=========
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.calculatePolynomialResiduals
+ ~SaQC.calculateRollingResiduals
diff --git a/docs/funcs/rolling.rst b/docs/funcs/rolling.rst
new file mode 100644
index 0000000000000000000000000000000000000000..0d621221595bdaf9a13e768c45731c344d049f11
--- /dev/null
+++ b/docs/funcs/rolling.rst
@@ -0,0 +1,14 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+rolling
+=======
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.roll
diff --git a/docs/funcs/scores.rst b/docs/funcs/scores.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d433c4c4568032b3f5872a53734ae07b5b9d5902
--- /dev/null
+++ b/docs/funcs/scores.rst
@@ -0,0 +1,14 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+scores
+======
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.assignKNNScore
diff --git a/docs/funcs/tools.rst b/docs/funcs/tools.rst
new file mode 100644
index 0000000000000000000000000000000000000000..9887fe22a717979edb52b096d3358c30450be36a
--- /dev/null
+++ b/docs/funcs/tools.rst
@@ -0,0 +1,18 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+tools
+=====
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.copyField
+ ~SaQC.dropField
+ ~SaQC.renameField
+ ~SaQC.selectTime
+ ~SaQC.plot
diff --git a/docs/funcs/transformation.rst b/docs/funcs/transformation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..295519ae23d7cec31b7c874024e7586c7be6612d
--- /dev/null
+++ b/docs/funcs/transformation.rst
@@ -0,0 +1,14 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+transformation
+==============
+
+
+
+.. currentmodule:: saqc
+
+.. autosummary::
+
+ ~SaQC.transform
diff --git a/sphinxdoc/getting_started/InstallationGuide.rst b/docs/gettingstarted/InstallationGuide.rst
similarity index 95%
rename from sphinxdoc/getting_started/InstallationGuide.rst
rename to docs/gettingstarted/InstallationGuide.rst
index 4a91cd591efc0f73b1a7f790f0e93678db77085c..ebe572bd82b500f0cead98407ee11ce167f039be 100644
--- a/sphinxdoc/getting_started/InstallationGuide.rst
+++ b/docs/gettingstarted/InstallationGuide.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
Installation Guide
==================
diff --git a/sphinxdoc/getting_started/TutorialAPI.rst b/docs/gettingstarted/TutorialAPI.rst
similarity index 76%
rename from sphinxdoc/getting_started/TutorialAPI.rst
rename to docs/gettingstarted/TutorialAPI.rst
index bc8b530c197e2f6fb7b159ec0cb345059e102844..de56206a22151539a1e785cd14f3e67ab34c443e 100644
--- a/sphinxdoc/getting_started/TutorialAPI.rst
+++ b/docs/gettingstarted/TutorialAPI.rst
@@ -1,3 +1,7 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
.. testsetup:: python
np.random.seed(100)
@@ -12,12 +16,12 @@ The Python API of SaQC consists of three distinct components:
1. the core class, :py:class:`saqc.SaQC`
2. a number of flagging schemes
-3. a collection of :doc:`functions <../moduleAPIs/saqcFuncTOC>`.
+3. a collection of :doc:`functions <../modules/SaQCFunctions>`.
One and two are implemented as distinct classes, the core object is called ``SaQC`` and we currently
provide three flagging schemes, namely:
-1. ``FloatScheme``: Provides the quality flags ``-np.inf`` and ``[0..255]``.
+1. ``FloatScheme``: The default flagging scheme provides the quality flags ``-np.inf`` and ``[0..255]``.
``-np.inf`` denotes the absence of quality flags and ``255`` denotes, that
the associated data value is considered to be bad. the absence of a flags,
``1`` the presence of flag (i.e. one of the tests provided a positive result)
@@ -26,8 +30,7 @@ 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
-:py:class:`~saqc.SaQC` instances.
+The third component, the actual test functions, appear as methods of :py:class:`~saqc.SaQC` instances.
Getting started - Put something in
@@ -45,13 +48,13 @@ with something more elaborate, is in fact a one line change. So let's start with
from saqc import SaQC
# we need some dummy data
- values = np.random.randint(low=0, high=100, size=100)
+ values = np.array([12, 24, 36, 33, 89, 87, 45, 31, 18, 99])
dates = pd.date_range(start="2020-01-01", periods=len(values), freq="D")
data = pd.DataFrame({"a": values}, index=dates)
# let's insert some constant values ...
- data.iloc[30:40] = values.mean()
+ data.iloc[3:6] = values.mean()
# ... and an outlier
- data.iloc[70] = 175
+ data.iloc[8] = 175
# initialize saqc
qc = SaQC(data=data, scheme="simple")
@@ -67,7 +70,7 @@ available functions appear as methods of the :py:class:`~saqc.SaQC` class, so w
qc = qc.flagRange("a", min=20, max=80)
-:py:meth:`~saqc.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
@@ -97,36 +100,33 @@ number of different attributes, of which you likely might want to use the follow
.. 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]
+ >>> qc.data #doctest:+NORMALIZE_WHITESPACE
+ a |
+ ================= |
+ 2020-01-01 12.0 |
+ 2020-01-02 24.0 |
+ 2020-01-03 36.0 |
+ 2020-01-04 47.4 |
+ 2020-01-05 47.4 |
+ 2020-01-06 47.4 |
+ 2020-01-07 45.0 |
+ 2020-01-08 31.0 |
+ 2020-01-09 175.0 |
+ 2020-01-10 99.0 |
+
+ >>> qc.flags #doctest:+NORMALIZE_WHITESPACE
+ a |
+ ===================== |
+ 2020-01-01 BAD |
+ 2020-01-02 UNFLAGGED |
+ 2020-01-03 UNFLAGGED |
+ 2020-01-04 UNFLAGGED |
+ 2020-01-05 UNFLAGGED |
+ 2020-01-06 UNFLAGGED |
+ 2020-01-07 UNFLAGGED |
+ 2020-01-08 UNFLAGGED |
+ 2020-01-09 BAD |
+ 2020-01-10 BAD |
Putting it together - The complete workflow
@@ -157,10 +157,10 @@ The snippet below provides you with a compete example from the things we have se
.flagByGrubbs("a", window="10D")
.flagRange("a", min=20, max=80))
- # retrieve the data as a pandas.DataFrame
+ # retrieve the data
qc.data
- # retrieve the flags as a pandas.DataFrame
+ # retrieve the flags
qc.flags
@@ -169,11 +169,11 @@ Can I get something visual, please?
-----------------------------------
We provide an elaborated plotting method to generate and show or write matplotlib figures. Building on
-the example :ref:`above <getting_started/TutorialAPI:putting it together - the complete workflow>` above
+the example :ref:`above <gettingstarted/TutorialAPI:putting it together - the complete workflow>` above
simply call:
.. testcode:: python
qc.plot("a")
-.. image:: /ressources/images/tutorial_api_1.png
+.. image:: /resources/images/tutorial_api_1.png
diff --git a/sphinxdoc/getting_started/TutorialCLI.rst b/docs/gettingstarted/TutorialCLI.rst
similarity index 77%
rename from sphinxdoc/getting_started/TutorialCLI.rst
rename to docs/gettingstarted/TutorialCLI.rst
index f0cdc5816946c02aeac798cb7f152a4c6327b44d..f635ebd9a9973289feda03be7210f2db288263cb 100644
--- a/sphinxdoc/getting_started/TutorialCLI.rst
+++ b/docs/gettingstarted/TutorialCLI.rst
@@ -1,8 +1,12 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
.. testsetup:: exampleCLI
- datapath = './ressources/data/data.csv'
- configpath = lambda x: f'./ressources/data/myconfig{x}.csv'
- temppath = lambda x: f'./ressources/temp/{x}'
+ datapath = './resources/data/data.csv'
+ configpath = lambda x: f'./resources/data/myconfig{x}.csv'
+ temppath = lambda x: f'./resources/temp/{x}'
data = pd.read_csv(datapath, index_col=0)
data.index = pd.DatetimeIndex(data.index)
@@ -13,9 +17,9 @@
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}'
+ datapath = '../resources/data/data.csv'
+ configpath = lambda x: f'../resources/data/myconfig{x}.csv'
+ temppath = lambda x: f'../resources/temp/{x}'
data = pd.read_csv(datapath, index_col=0)
data.index = pd.DatetimeIndex(data.index)
@@ -45,13 +49,15 @@ 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
+If you take a look into the folder ``saqc/resources/data`` you will find a toy
dataset ``data.csv`` which contains the following data:
-.. literalinclude:: ../ressources/data/data.csv
+
+.. literalinclude:: ../resources/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
@@ -59,19 +65,20 @@ 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>`_.
+`this one <https://git.ufz.de/rdm-software/saqc/-/blob/develop/docs/resources/data/myconfig.csv>`_.
It contains the following lines:
-.. literalinclude:: ../ressources/data/myconfig.csv
+.. literalinclude:: ../resources/data/myconfig.csv
These lines illustrate how different quality control tests can be specified for
-different variables.
+different variables, for a more detailed explanation of the configuration format, please refer
+to respective :doc:`documentation page <../documentation/ConfigurationFiles>`
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
+:ref:`here <modules/SaQCFunctions:Test 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.
@@ -97,7 +104,7 @@ 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
+ python3 -m saqc -c docs/resources/data/myconfig.csv -d docs/resources/data/data.csv
On Windows
""""""""""
@@ -119,7 +126,7 @@ 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
+ py -3 -m saqc -c docs/resources/data/myconfig.csv -d docs/resources/data/data.csv
If you installed saqc via PYPi, you can omit ``sh python -m``.
@@ -152,7 +159,7 @@ use of the ``-o`` option:
.. code-block:: sh
- saqc -c ressources/data/config.csv -d ressources/data/data.csv -o ressources/data/out.csv
+ saqc -c docs/resources/data/config.csv -d docs/resources/data/data.csv -o 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:
@@ -180,7 +187,7 @@ 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
+.. literalinclude:: ../resources/data/myconfig2.csv
Rerunning SaQC as above produces the following plot:
@@ -212,9 +219,9 @@ 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.
+get stored to files. (We route the storage to the repos resources folder...)
-.. literalinclude:: ../ressources/data/myconfig4.csv
+.. literalinclude:: ../resources/data/myconfig4.csv
.. plot::
@@ -223,23 +230,20 @@ get stored to files.
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:
-
+which gives you separate plots for each call to plot:
.. 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:
+ * - .. image:: ../resources/temp/SM1processingResults.png
+ :target: ../resources/temp/SM1processingResults.png
+ :alt:
+
+ - .. image:: ../resources/temp/SM2processingResults.png
+ :target: ../resources/temp/SM2processingResults.png
+ :alt:
Data harmonization and custom functions
@@ -249,7 +253,7 @@ 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
+.. literalinclude:: ../resources/data/myconfig3.csv
.. testcode:: exampleCLI
:hide:
@@ -268,10 +272,10 @@ series. Also, you can write your own tests using a python-based
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>`.
+found in the :doc:`Resampling cookbook <../cookbooks/DataRegularisation>`.
-.. literalinclude:: ../ressources/temp/TutorialCLIHarmData.csv
+.. literalinclude:: ../resources/temp/TutorialCLIHarmData.csv
:lines: 1-10
@@ -288,3 +292,4 @@ You can learn more about the syntax of these custom functions
:align: center
qc.plot('SM2')
+
diff --git a/sphinxdoc/how_to_doc/HowToDoc.rst b/docs/howtodoc/HowToDoc.rst
similarity index 97%
rename from sphinxdoc/how_to_doc/HowToDoc.rst
rename to docs/howtodoc/HowToDoc.rst
index 6dd9f8086ba63cfd34e80a6acae80631a21323e1..2b0c36afaa012a3692722c2d749f2695675f7fdb 100644
--- a/sphinxdoc/how_to_doc/HowToDoc.rst
+++ b/docs/howtodoc/HowToDoc.rst
@@ -1,3 +1,6 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
Documentation Guide
===================
@@ -290,10 +293,10 @@ 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:
+There for go to the docs directory and run:
.. code-block::
- make testonly
+ make test
-To only run the doctests.
\ No newline at end of file
+To only run the doctests.
diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f2b74478ef9d0afeca51cd660676458ef7f9aa8a
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1,101 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+.. include:: misc/title.rst
+
+
+.. toctree::
+ :hidden:
+ :maxdepth: 1
+
+ Repository <https://git.ufz.de/rdm-software/saqc>
+
+Getting Started
+===============
+
+.. toctree::
+ :caption: Getting started
+ :maxdepth: 1
+ :hidden:
+
+ gettingstarted/InstallationGuide
+ gettingstarted/TutorialAPI
+ gettingstarted/TutorialCLI
+
+
+.. toctree::
+ :caption: Cookbooks
+ :maxdepth: 1
+ :hidden:
+
+ cookbooks/DataRegularisation
+ cookbooks/OutlierDetection
+ cookbooks/MultivariateFlagging
+
+.. toctree::
+ :caption: Documentation
+ :maxdepth: 1
+ :hidden:
+
+ documentation/ConfigurationFiles
+ documentation/GenericFunctions
+ documentation/GlobalKeywords
+ documentation/Customizations
+ documentation/SourceTarget
+ documentation/FlaggingTranslation
+
+..
+ .. toctree::
+ :caption: Functions
+ :hidden:
+ :maxdepth: 1
+ :glob:
+ :titlesonly:
+
+ resampling <funcs/resampling>
+ generic <funcs/generic>
+ outliers <funcs/outliers>
+ breaks <funcs/breaks>
+ constants <funcs/constants>
+ changepoints <funcs/changepoints>
+ drift <funcs/drift>
+ curvefit <funcs/curvefit>
+ interpolation <funcs/interpolation>
+ residuals <funcs/residuals>
+ tools <funcs/tools>
+ flagtools <funcs/flagtools>
+ rolling <funcs/rolling>
+ scores <funcs/scores>
+ transformation <funcs/transformation>
+ noise <funcs/noise>
+ pattern <funcs/pattern>
+
+
+.. toctree::
+ :caption: SaQC
+ :hidden:
+ :maxdepth: 2
+
+ Package <modules/SaQC>
+ Core <modules/SaQCCore>
+ Test Functions <modules/SaQCFunctions>
+ Translations <modules/SaQCTranslation>
+ Constants <modules/SaQCConstants>
+
+.. toctree::
+ :caption: Developer Resources
+ :hidden:
+ :maxdepth: 1
+
+ Documentation Guide <howtodoc/HowToDoc>
+ Writing Functions <documentation/WritingFunctions>
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+
diff --git a/sphinxdoc/make.bat b/docs/make.bat
similarity index 81%
rename from sphinxdoc/make.bat
rename to docs/make.bat
index 6247f7e231716482115f34084ac61030743e0715..6e460bde347d06a89bab5d003ccaf471407f3dcf 100644
--- a/sphinxdoc/make.bat
+++ b/docs/make.bat
@@ -1,3 +1,7 @@
+REM SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+REM
+REM SPDX-License-Identifier: GPL-3.0-or-later
+
@ECHO OFF
pushd %~dp0
diff --git a/sphinxdoc/misc/title.rst b/docs/misc/title.rst
similarity index 66%
rename from sphinxdoc/misc/title.rst
rename to docs/misc/title.rst
index f7e5641ddd143abebf06602c64bfdd98c27f2f3a..f92b0cdd09e6bd6b78548800d764d1873b7e538d 100644
--- a/sphinxdoc/misc/title.rst
+++ b/docs/misc/title.rst
@@ -1,35 +1,36 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
-.. |sacRaw| image:: /ressources/images/Representative/RawData.png
+.. |sacRaw| image:: /resources/images/representative/RawData.png
:height: 150 px
:width: 288 px
-.. |sacFlagged| image:: /ressources/images/Representative/DifferentFlags.png
+.. |sacFlagged| image:: /resources/images/representative/DifferentFlags.png
:height: 150 px
:width: 288 px
-.. |sacMV| image:: /ressources/images/Representative/MultivarSAC.png
+.. |sacMV| image:: /resources/images/representative/MultivarSAC.png
:height: 150 px
:width: 288 px
-.. |sacProc| image:: /ressources/images/Representative/ProcessingDrift.png
+.. |sacProc| image:: /resources/images/representative/ProcessingDrift.png
:height: 150 px
:width: 288 px
-.. |pyLogo| image:: /ressources/images/Representative/pythonLogo.png
+.. |pyLogo| image:: /resources/images/representative/PythonLogo.png
:height: 108 px
:width: 105 px
-.. |csvConfig| image:: /ressources/images/Representative/csvConfig.png
+.. |csvConfig| image:: /resources/images/representative/CsvConfig.png
:height: 100 px
:width: 176 px
-
-.. |legendEXMPL| image:: /ressources/images/Representative/legendEXMPL.png
+.. |legendEXMPL| image:: /resources/images/representative/LegendEXMPL.png
:height: 100 px
:width: 200
-
===========================================
SaQC - System for automated Quality Control
===========================================
@@ -48,7 +49,7 @@ SaQC is developed and maintained by the
`Research Data Management <https://www.ufz.de/index.php?en=45348>`_ Team at the
`Helmholtz-Centre for Environmental Research - UFZ <https://www.ufz.de/>`_.
It manifests the requirements and experiences made from the implementation and
-operation of fully automated quality control pipelines for environmental sensor data.
+operation of fully automated quality control pipelines for environmental sensor data.
The diversity of communities involved in this process and the special needs within the
realm of scientific data acquisition and its provisioning, have shaped SaQC into
its current state. We define this state as: inherently consistent, yet externally
@@ -63,12 +64,12 @@ Features
.. list-table::
* - |pyLogo| |csvConfig|
- - * :ref:`get and install SaQC <getting_started/InstallationGuide:installation guide>`
- * :ref:`use the SaQC python API, enabling integration into larger programs <getting_started/TutorialAPI:python api>`
+ - * :ref:`get and install SaQC <gettingstarted/InstallationGuide:installation guide>`
+ * :ref:`use the SaQC python API, enabling integration into larger programs <gettingstarted/TutorialAPI:python api>`
* or use SaQC as a commandline application and configure your pipelines via plain text
* - |sacRaw|
- * easily load data from multiple sources, concatenating them in a SaQC object
- * :ref:`preprocess your data, by aligning it to shared frequency grids <cook_books/DataRegularisation:Data Regularisation>`
+ * :ref:`preprocess your data, by aligning it to shared frequency grids <cookbooks/DataRegularisation:Data Regularisation>`
* - |sacFlagged|
- * apply basic plausibility checks, as well as
* more complex, univariat flagging Functions
@@ -76,7 +77,7 @@ Features
- * automatically keep track of flagging history and flags significance for every datapoint
* define and use custom schemes to translate your flags to and from SaQC
* - |sacProc|
- - * modify your data by :ref:`interpolations <cook_books/DataRegularisation:Interpolation>`, corrections and :ref:`transformations <cook_books/DataRegularisation:Aggregation>`
- * calculate data products, such as :ref:`residues or outlier scores <cook_books/OutlierDetection:Residues and Scores>`
+ - * modify your data by :ref:`interpolations <cookbooks/DataRegularisation:Interpolation>`, corrections and :ref:`transformations <cookbooks/DataRegularisation:Aggregation>`
+ * calculate data products, such as :ref:`residuals or outlier scores <cookbooks/OutlierDetection:Residuals and Scores>`
* - |sacMV|
- - * apply :ref:`multivariate flagging functions <cook_books/MultivariateFlagging:Multivariate Flagging>`
+ - * apply :ref:`multivariate flagging functions <cookbooks/MultivariateFlagging:Multivariate Flagging>`
diff --git a/docs/modules/SaQC.rst b/docs/modules/SaQC.rst
new file mode 100644
index 0000000000000000000000000000000000000000..a1c9ecddc81fa96f66efea98b4254c7b5e1579b2
--- /dev/null
+++ b/docs/modules/SaQC.rst
@@ -0,0 +1,11 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+SaQC
+====
+
+.. automodapi:: saqc
+
+
+
diff --git a/docs/modules/SaQCConstants.rst b/docs/modules/SaQCConstants.rst
new file mode 100644
index 0000000000000000000000000000000000000000..eaed103df6a9dd2854d429be603671ee2d6c3ddb
--- /dev/null
+++ b/docs/modules/SaQCConstants.rst
@@ -0,0 +1,9 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+SaQC
+====
+
+.. automodapi:: saqc.constants
+ :skip: ENVIRONMENT
diff --git a/docs/modules/SaQCCore.rst b/docs/modules/SaQCCore.rst
new file mode 100644
index 0000000000000000000000000000000000000000..197da183d6809feed687dcf07a360f7fc76c5aaf
--- /dev/null
+++ b/docs/modules/SaQCCore.rst
@@ -0,0 +1,9 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+SaQC
+====
+
+.. automodapi:: saqc.core
+ :include-all-objects:
diff --git a/docs/modules/SaQCFunctions.rst b/docs/modules/SaQCFunctions.rst
new file mode 100644
index 0000000000000000000000000000000000000000..68d0021b952da7d072c06d0cca14f63df09510c3
--- /dev/null
+++ b/docs/modules/SaQCFunctions.rst
@@ -0,0 +1,15 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+SaQC
+====
+
+Test Functions
+--------------
+
+.. toctree::
+ :glob:
+
+ ../funcs/*
+
diff --git a/docs/modules/SaQCTranslation.rst b/docs/modules/SaQCTranslation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d853456038f4d950cc1c7fb633fe6be3f3a729f9
--- /dev/null
+++ b/docs/modules/SaQCTranslation.rst
@@ -0,0 +1,9 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+SaQC
+====
+
+.. automodapi:: saqc.core.translation
+ :include-all-objects:
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b22a8edcca0d70768082e5c1fd2e8055feb9f625
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,13 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+recommonmark==0.7.1
+sphinx<5
+sphinx-automodapi==0.14.1
+sphinxcontrib-fulltoc==1.2.0
+sphinx-markdown-tables==0.0.15
+m2r==0.2.1
+jupyter-sphinx==0.3.2
+sphinx_autodoc_typehints==1.18.2
+sphinx-tabs==3.3.1
diff --git a/sphinxdoc/ressources/data/SoilMoisture.csv b/docs/resources/data/SoilMoisture.csv
similarity index 100%
rename from sphinxdoc/ressources/data/SoilMoisture.csv
rename to docs/resources/data/SoilMoisture.csv
diff --git a/docs/resources/data/SoilMoisture.csv.license b/docs/resources/data/SoilMoisture.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/SoilMoisture.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/TutorialCLIHarmData.csv b/docs/resources/data/TutorialCLIHarmData.csv
similarity index 100%
rename from sphinxdoc/ressources/data/TutorialCLIHarmData.csv
rename to docs/resources/data/TutorialCLIHarmData.csv
diff --git a/docs/resources/data/TutorialCLIHarmData.csv.license b/docs/resources/data/TutorialCLIHarmData.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/TutorialCLIHarmData.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/docs/resources/data/config.csv b/docs/resources/data/config.csv
new file mode 100644
index 0000000000000000000000000000000000000000..261482b57c42ea28e9e0c34df5223ff413012843
--- /dev/null
+++ b/docs/resources/data/config.csv
@@ -0,0 +1,6 @@
+varname ; test
+#----------;-----------------------------
+SM2 ; shift(freq="15Min")
+SM2 ; flagMissing()
+'SM(1|2)+' ; flagRange(min=10, max=60)
+SM2 ; flagMAD(window="30d", z=3.5)
diff --git a/docs/resources/data/config.csv.license b/docs/resources/data/config.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/config.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/config_ci.csv b/docs/resources/data/config_ci.csv
similarity index 100%
rename from sphinxdoc/ressources/data/config_ci.csv
rename to docs/resources/data/config_ci.csv
diff --git a/docs/resources/data/config_ci.csv.license b/docs/resources/data/config_ci.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/config_ci.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/ressources/data/data.csv b/docs/resources/data/data.csv
similarity index 100%
rename from ressources/data/data.csv
rename to docs/resources/data/data.csv
diff --git a/docs/resources/data/data.csv.license b/docs/resources/data/data.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/data.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/hydro_config.csv b/docs/resources/data/hydro_config.csv
similarity index 100%
rename from sphinxdoc/ressources/data/hydro_config.csv
rename to docs/resources/data/hydro_config.csv
diff --git a/docs/resources/data/hydro_config.csv.license b/docs/resources/data/hydro_config.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/hydro_config.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/hydro_data.csv b/docs/resources/data/hydro_data.csv
similarity index 100%
rename from sphinxdoc/ressources/data/hydro_data.csv
rename to docs/resources/data/hydro_data.csv
diff --git a/docs/resources/data/hydro_data.csv.license b/docs/resources/data/hydro_data.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/hydro_data.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/hydro_maint.csv b/docs/resources/data/hydro_maint.csv
similarity index 100%
rename from sphinxdoc/ressources/data/hydro_maint.csv
rename to docs/resources/data/hydro_maint.csv
diff --git a/docs/resources/data/hydro_maint.csv.license b/docs/resources/data/hydro_maint.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/hydro_maint.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/incidentsLKG.csv b/docs/resources/data/incidentsLKG.csv
similarity index 100%
rename from sphinxdoc/ressources/data/incidentsLKG.csv
rename to docs/resources/data/incidentsLKG.csv
diff --git a/docs/resources/data/incidentsLKG.csv.license b/docs/resources/data/incidentsLKG.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/incidentsLKG.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/myconfig.csv b/docs/resources/data/myconfig.csv
similarity index 100%
rename from sphinxdoc/ressources/data/myconfig.csv
rename to docs/resources/data/myconfig.csv
diff --git a/docs/resources/data/myconfig.csv.license b/docs/resources/data/myconfig.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/myconfig.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/myconfig2.csv b/docs/resources/data/myconfig2.csv
similarity index 100%
rename from sphinxdoc/ressources/data/myconfig2.csv
rename to docs/resources/data/myconfig2.csv
diff --git a/docs/resources/data/myconfig2.csv.license b/docs/resources/data/myconfig2.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/myconfig2.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/data/myconfig3.csv b/docs/resources/data/myconfig3.csv
similarity index 100%
rename from sphinxdoc/ressources/data/myconfig3.csv
rename to docs/resources/data/myconfig3.csv
diff --git a/docs/resources/data/myconfig3.csv.license b/docs/resources/data/myconfig3.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/myconfig3.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/docs/resources/data/myconfig4.csv b/docs/resources/data/myconfig4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5977ac246ae962366e9c8fbfa87b0bcd13ad840c
--- /dev/null
+++ b/docs/resources/data/myconfig4.csv
@@ -0,0 +1,9 @@
+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(path='../resources/temp/SM1processingResults')
+SM2;plot(path='../resources/temp/SM2processingResults')
+
diff --git a/docs/resources/data/myconfig4.csv.license b/docs/resources/data/myconfig4.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/myconfig4.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/images/cbooks_SoilMoisture.png b/docs/resources/images/cbooks_soil_moisture.png
similarity index 100%
rename from sphinxdoc/ressources/images/cbooks_SoilMoisture.png
rename to docs/resources/images/cbooks_soil_moisture.png
diff --git a/docs/resources/images/cbooks_soil_moisture.png.license b/docs/resources/images/cbooks_soil_moisture.png.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/images/cbooks_soil_moisture.png.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/ressources/images/example_plot_1.png b/docs/resources/images/example_plot_1.png
similarity index 100%
rename from ressources/images/example_plot_1.png
rename to docs/resources/images/example_plot_1.png
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new file mode 100644
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+
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\ No newline at end of file
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similarity index 100%
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@@ -0,0 +1,3 @@
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+
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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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similarity index 100%
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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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similarity index 100%
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similarity index 100%
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+
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diff --git a/sphinxdoc/ressources/images/Representative/RawData.png b/docs/resources/images/representative/RawData.png
similarity index 100%
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+++ b/docs/resources/images/representative/RawData.png.license
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+
+SPDX-License-Identifier: GPL-3.0-or-later
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diff --git a/docs/resources/images/representative/SaQCLogo.png b/docs/resources/images/representative/SaQCLogo.png
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+++ b/docs/resources/images/representative/SaQCLogo.png.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
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+++ b/docs/resources/images/representative/SaQCLogoSmall.png.license
@@ -0,0 +1,3 @@
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+
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\ No newline at end of file
diff --git a/sphinxdoc/ressources/images/Representative/UFZLogo.png b/docs/resources/images/representative/UFZLogo.png
similarity index 100%
rename from sphinxdoc/ressources/images/Representative/UFZLogo.png
rename to docs/resources/images/representative/UFZLogo.png
diff --git a/docs/resources/images/representative/UFZLogo.png.license b/docs/resources/images/representative/UFZLogo.png.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/images/representative/UFZLogo.png.license
@@ -0,0 +1,3 @@
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+
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\ No newline at end of file
diff --git a/sphinxdoc/ressources/images/tutorial_api_1.png b/docs/resources/images/tutorial_api_1.png
similarity index 100%
rename from sphinxdoc/ressources/images/tutorial_api_1.png
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diff --git a/docs/resources/images/tutorial_api_1.png.license b/docs/resources/images/tutorial_api_1.png.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/images/tutorial_api_1.png.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/docs/resources/temp/SM1processingResults.png b/docs/resources/temp/SM1processingResults.png
new file mode 100644
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diff --git a/docs/resources/temp/SM1processingResults.png.license b/docs/resources/temp/SM1processingResults.png.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/temp/SM1processingResults.png.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/docs/resources/temp/SM2processingResults.png b/docs/resources/temp/SM2processingResults.png
new file mode 100644
index 0000000000000000000000000000000000000000..644eaadfa21b5c9031ad779e71c010f9e2003fb3
Binary files /dev/null and b/docs/resources/temp/SM2processingResults.png differ
diff --git a/docs/resources/temp/SM2processingResults.png.license b/docs/resources/temp/SM2processingResults.png.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/temp/SM2processingResults.png.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/sphinxdoc/ressources/temp/TutorialCLIHarmData.csv b/docs/resources/temp/TutorialCLIHarmData.csv
similarity index 100%
rename from sphinxdoc/ressources/temp/TutorialCLIHarmData.csv
rename to docs/resources/temp/TutorialCLIHarmData.csv
diff --git a/docs/resources/temp/TutorialCLIHarmData.csv.license b/docs/resources/temp/TutorialCLIHarmData.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/temp/TutorialCLIHarmData.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..a80fb39505209ec95a6a1f765229d474e825d72d
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,13 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+[tool.isort]
+profile = "black"
+
+[tool.pytest.ini_options]
+# trigger an Error for unknown mark's
+addopts = "--strict-markers"
+testpaths = "tests"
+markers = "slow: marks tests as slow (deselect with '-m \"not slow\"')"
+
diff --git a/requirements.txt b/requirements.txt
index 5bf2d384d7b87874d58ed5a8b8866fff529a74ab..8a6bb169e96fa31961a0fa9302a933ce7472b3b1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,15 +1,16 @@
-Click==8.0.3
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+Click==8.1.3
dtw==1.4.0
-hypothesis==6.29.0
-matplotlib==3.5.0
-numba==0.54.1
-numpy==1.20.3
+hypothesis==6.46.11
+matplotlib==3.5.2
+numba==0.55.2
+numpy==1.21.6
outlier-utils==0.0.3
-pyarrow==6.0.1
-pandas==1.3.4
-pytest==6.2.5
-pytest-lazy-fixture==0.6.3
-scikit-learn==1.0.1
+pyarrow==8.0.0
+pandas==1.3.5
+scikit-learn==1.0.2
scipy==1.7.3
-typing_extensions==4.0.0
-seaborn==0.11.2
+typing_extensions==4.2.0
diff --git a/ressources/data/config.csv b/ressources/data/config.csv
deleted file mode 100644
index 1f11becdb3eada375a1b1ef8fa1ffd9917abbcc3..0000000000000000000000000000000000000000
--- a/ressources/data/config.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-varname ; test
-#----------; -----------------------------------------------------
-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)))
diff --git a/ressources/images/cbooks_outliers_incidents.png b/ressources/images/cbooks_outliers_incidents.png
deleted file mode 100644
index ea4e0482bf93b543d2ec5d94ac30ef6c61140f7c..0000000000000000000000000000000000000000
Binary files a/ressources/images/cbooks_outliers_incidents.png and /dev/null differ
diff --git a/ressources/images/readme_image.png b/ressources/images/readme_image.png
deleted file mode 100644
index 3bb830f6c8bce1aa16b9c4b4021637c3cd0cfe2c..0000000000000000000000000000000000000000
Binary files a/ressources/images/readme_image.png and /dev/null differ
diff --git a/saqc/__init__.py b/saqc/__init__.py
index 7b00e20cbdf8095266a203f1fd66f968837080d6..c6db9151c878416e559f1b78824df83596dea397 100644
--- a/saqc/__init__.py
+++ b/saqc/__init__.py
@@ -1,19 +1,15 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-"""The system for automated quality controll package."""
-from saqc.version import __version__
+"""The System for automated Quality Control package."""
-from saqc.constants import (
- UNFLAGGED,
- GOOD,
- DOUBTFUL,
- BAD,
-)
+from saqc.constants import BAD, DOUBTFUL, FILTER_ALL, FILTER_NONE, GOOD, UNFLAGGED
# import order: from small to big, to a void cycles
-from saqc.core import (
- Flags,
- SaQC,
- fromConfig,
-)
+from saqc.core import Flags, SaQC, fromConfig
+from saqc.version import __version__
diff --git a/saqc/__main__.py b/saqc/__main__.py
index 8068d96c05deafc395986f2bd85dd635489bf128..58d83731a2b675b4386db604b650b027beec9a1b 100644
--- a/saqc/__main__.py
+++ b/saqc/__main__.py
@@ -1,4 +1,9 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
import logging
@@ -6,14 +11,13 @@ from functools import partial
from pathlib import Path
import click
-
import numpy as np
import pandas as pd
import pyarrow as pa
-from saqc.core.reader import fromConfig
+from dios.dios.dios import DictOfSeries
from saqc.core.core import TRANSLATION_SCHEMES
-
+from saqc.core.reader import fromConfig
logger = logging.getLogger("SaQC")
@@ -46,7 +50,7 @@ def readData(reader_dict, fname):
reader = reader_dict.get(extension)
if not reader:
raise ValueError(
- f"Unsupported file format '{extension}', use one of {tuple(reader.keys())}"
+ f"Unsupported file format '{extension}', use one of {tuple(reader_dict.keys())}"
)
return reader(fname)
@@ -56,7 +60,7 @@ def writeData(writer_dict, df, fname):
writer = writer_dict.get(extension)
if not writer:
raise ValueError(
- f"Unsupported file format '{extension}', use one of {tuple(writer.keys())}"
+ f"Unsupported file format '{extension}', use one of {tuple(writer_dict.keys())}"
)
writer(df, fname)
@@ -99,11 +103,7 @@ def main(config, data, scheme, outfile, nodata, log_level):
_setupLogging(log_level)
reader, writer = setupIO(nodata)
- _data = []
- for dfile in data:
- df = readData(reader, dfile)
- _data.append(df)
- data = _data
+ data = [readData(reader, f) for f in data]
saqc = fromConfig(
config,
@@ -111,7 +111,10 @@ def main(config, data, scheme, outfile, nodata, log_level):
scheme=TRANSLATION_SCHEMES[scheme or "simple"](),
)
- data_result, flags_result = saqc.result.data, saqc.result.flags
+ data_result = saqc.data.to_df()
+ flags_result = saqc.flags
+ if isinstance(flags_result, DictOfSeries):
+ flags_result = flags_result.to_df()
if outfile:
diff --git a/saqc/constants.py b/saqc/constants.py
index 781de815580c477a125a5bacb6b4f10af93fac68..d425a9111abac83d5cf0ab3757624088d529dc40 100644
--- a/saqc/constants.py
+++ b/saqc/constants.py
@@ -1,10 +1,27 @@
#!/usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
"""
The module comprises flag value constants in use throughout saqc.
-The constants order as follows (from "worse" to "best"):
-:py:const:`~saqc.constants.BAD` > :py:const:`~saqc.constants.DOUBTFUL` > :py:const:`~saqc.constants.GOOD` >
-:py:const:`~saqc.constants.UNFLAGGED`
+Flagging Constants
+------------------
+* :py:const:`~saqc.constants.UNFLAGGED`: indicates that no flag has been assigned yet
+* :py:const:`~saqc.constants.GOOD`: the lowest possible flag value larget than :py:const:`~saqc.constants.UNFLAGGED`
+* :py:const:`~saqc.constants.DOUBFUL`: a modest flag value, usually indicating some sort of suspiciousness
+* :py:const:`~saqc.constants.BAD`: the highest flag value available
+
+The flagging constants are ordered (from "worse" to "best") as:
+
+:py:const:`~saqc.constants.BAD` > :py:const:`~saqc.constants.DOUBTFUL` > :py:const:`~saqc.constants.GOOD` > :py:const:`~saqc.constants.UNFLAGGED`
+
+Filtering Constants
+-------------------
+* :py:const:`~saqc.constants.FILTER_ALL`: mask/filter all flagged data
+* :py:const:`~saqc.constants.FILTER_NONE`: mask/filter no data
"""
__all__ = [
@@ -20,51 +37,36 @@ __all__ = [
import numpy as np
import scipy.stats as st
+
import saqc.lib.ts_operators as ts_ops
# ----------------------------------------------------------------------
# global flag constants
# ----------------------------------------------------------------------
-#: A :py:mod:`flag level constant <saqc.constants>`
-#: , evaluating to the level, that indicates, no flag has been assigned to yet.
UNFLAGGED = -np.inf
-
-#: A :py:mod:`flag level constant <saqc.constants>`
-#: , evaluating to the lowest level level of flagging, that is
-#: not :py:const:`UNFLAGGED <saqc.constants.UNFLAGGED>`.
GOOD = 0
-
-#: A :py:mod:`flag level constant <saqc.constants>`
-#: , evaluating to a somewhat modest flag level.
DOUBTFUL = 25.0
-
-#: A :py:mod:`flag level constant <saqc.constants>`
-#: , evaluating to the highest (internal) flag level available.
BAD = 255.0
-
# ----------------------------------------------------------------------
# global dfilter constants
# ----------------------------------------------------------------------
-#: A :py:mod:`dfilter constant <saqc.constants>`
-#: , mask/filter all flagged data.
FILTER_ALL = -np.inf
-
-#: A :py:mod:`dfilter constant <saqc.constants>`
-#: , mask/filter no data at all.
FILTER_NONE = np.inf
-
# ----------------------------------------------------------------------
# other
# ----------------------------------------------------------------------
-#: A :py:mod:`flag level constant <saqc.constants>`
ENVIRONMENT = {
+ # Infinity constant
+ "inf": np.inf,
+ "INF": np.inf,
# Not A number Constant.
"NAN": np.nan,
+ "nan": np.nan,
# Pointwise absolute Value Function.
"abs": np.abs,
# Maximum Value Function. Ignores NaN.
@@ -107,4 +109,6 @@ ENVIRONMENT = {
"BAD": BAD,
"UNFLAGGED": UNFLAGGED,
"DOUBTFUL": DOUBTFUL,
+ "FILTER_ALL": FILTER_ALL,
+ "FILTER_NONE": FILTER_NONE,
}
diff --git a/saqc/core/__init__.py b/saqc/core/__init__.py
index 5146d88130f3d449ff4fbdf59af0e744a4a50e9a..7d30f6416abb5611e17cbfb7fae6025e48243296 100644
--- a/saqc/core/__init__.py
+++ b/saqc/core/__init__.py
@@ -1,14 +1,14 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-from saqc.core.register import register, flagging, processing
+from saqc.core.core import SaQC
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,
- DmpScheme,
- PositionalScheme,
- SimpleScheme,
-)
from saqc.core.reader import fromConfig
+from saqc.core.register import flagging, processing, register
+from saqc.core.translation import DmpScheme, FloatScheme, PositionalScheme, SimpleScheme
diff --git a/saqc/core/core.py b/saqc/core/core.py
index 2de8f7b987ed37a3f5878c1009a44886dac2e231..b1ea076b9c4a3a58fc92b4ac152db56a4747e7bc 100644
--- a/saqc/core/core.py
+++ b/saqc/core/core.py
@@ -1,42 +1,38 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
import warnings
-from typing import (
- Any,
- Callable,
- List,
- Sequence,
- Hashable,
- Tuple,
-)
-from copy import deepcopy, copy as shallowcopy
+from copy import copy as shallowcopy
+from copy import deepcopy
+from typing import Any, Callable, Hashable, List, MutableMapping, Sequence, Tuple
-import pandas as pd
import numpy as np
+import pandas as pd
+# the import is needed to trigger the registration
+# of the built-in (test-)functions
+import saqc.funcs # noqa
from dios import DictOfSeries, to_dios
-from saqc.constants import BAD
-
-from saqc.core.modules import FunctionsMixin
-from saqc.core.flags import initFlagsLike, Flags
+from saqc.core.flags import Flags, initFlagsLike
from saqc.core.history import History
+from saqc.core.modules import FunctionsMixin
from saqc.core.register import FUNC_MAP, FunctionWrapper
from saqc.core.translation import (
- TranslationScheme,
+ DmpScheme,
FloatScheme,
- SimpleScheme,
PositionalScheme,
- DmpScheme,
+ SimpleScheme,
+ TranslationScheme,
)
-from saqc.lib.tools import toSequence, concatDios
+from saqc.lib.tools import concatDios, toSequence
from saqc.lib.types import ExternalFlag, OptionalNone
-# the import is needed to trigger the registration
-# of the built-in (test-)functions
-import saqc.funcs # noqa
-
# warnings
pd.set_option("mode.chained_assignment", "warn")
np.seterr(invalid="ignore")
@@ -56,7 +52,6 @@ class SaQC(FunctionsMixin):
"_flags",
"_scheme",
"_attrs",
- "_called",
}
def __init__(
@@ -64,12 +59,10 @@ class SaQC(FunctionsMixin):
data=None,
flags=None,
scheme: str | TranslationScheme = "float",
- copy: bool = True,
):
- self._data = self._initData(data, copy)
- self._flags = self._initFlags(flags, copy)
+ self._data = self._initData(data)
+ self._flags = self._initFlags(flags)
self._scheme = self._initTranslationScheme(scheme)
- self._called = []
self._attrs = {}
self._validate(reason="init")
@@ -114,28 +107,16 @@ class SaQC(FunctionsMixin):
self._attrs = dict(value)
@property
- def data_raw(self) -> DictOfSeries:
- return self._data
-
- @property
- def flags_raw(self) -> Flags:
- return self._flags
-
- @property
- def data(self) -> pd.DataFrame:
- data: pd.DataFrame = self._data.to_df()
- data.attrs = self._attrs.copy()
- return data
-
- @property
- def flags(self) -> pd.DataFrame:
- data: pd.DataFrame = self._scheme.backward(self._flags, attrs=self._attrs)
+ def data(self) -> MutableMapping:
+ data = self._data
data.attrs = self._attrs.copy()
return data
@property
- def result(self) -> SaQCResult:
- return SaQCResult(self._data, self._flags, self._attrs, self._scheme)
+ def flags(self) -> MutableMapping:
+ flags = self._scheme.backward(self._flags, attrs=self._attrs, raw=True)
+ flags.attrs = self._attrs.copy()
+ return flags
def _expandFields(
self,
@@ -182,7 +163,12 @@ class SaQC(FunctionsMixin):
**kwargs,
) -> SaQC:
- kwargs.setdefault("dfilter", self._scheme.DFILTER_DEFAULT)
+ if "dfilter" not in kwargs:
+ # let's see, if the function has an default value
+ default = func.func_signature.parameters.get("dfilter")
+ if default:
+ default = default.default
+ kwargs["dfilter"] = default or self._scheme.DFILTER_DEFAULT
if not isinstance(flag, OptionalNone):
# translation schemes might want to use a flag
@@ -192,7 +178,7 @@ class SaQC(FunctionsMixin):
fields, targets = self._expandFields(
regex=regex, field=field, target=target
)
- out = self
+ out = self.copy(deep=True)
if not func.handles_target:
if len(fields) != len(targets):
@@ -245,12 +231,9 @@ class SaQC(FunctionsMixin):
# but data is the original and flags is a copy (as currently implemented),
# data and flags of the original saqc obj may change inconsistently.
self._data, self._flags = res
- self._called += [(field, (function, args, kwargs))]
self._validate(reason=f"call to {repr(function.__name__)}")
- return self._construct(
- _data=self._data, _flags=self._flags, _called=self._called
- )
+ return self._construct(_data=self._data, _flags=self._flags)
def __getattr__(self, key):
"""
@@ -287,7 +270,7 @@ class SaQC(FunctionsMixin):
f"or an initialized Translator object, got '{scheme}'"
)
- def _initData(self, data, copy: bool) -> DictOfSeries:
+ def _initData(self, data) -> DictOfSeries:
if data is None:
return DictOfSeries()
@@ -295,11 +278,11 @@ class SaQC(FunctionsMixin):
if isinstance(data, list):
results = []
for d in data:
- results.append(self._castToDios(d, copy=copy))
+ results.append(self._castToDios(d))
return concatDios(results, warn=True, stacklevel=3)
if isinstance(data, (DictOfSeries, pd.DataFrame, pd.Series)):
- return self._castToDios(data, copy)
+ return self._castToDios(data)
raise TypeError(
"'data' must be of type pandas.Series, "
@@ -307,7 +290,7 @@ class SaQC(FunctionsMixin):
"a list of those."
)
- def _castToDios(self, data, copy: bool):
+ def _castToDios(self, data):
if isinstance(data, pd.Series):
if not isinstance(data.name, str):
raise ValueError(f"Cannot init from unnamed pd.Series")
@@ -320,18 +303,16 @@ class SaQC(FunctionsMixin):
for c in data.columns:
if not isinstance(c, str):
raise TypeError("columns labels must be of type string")
- if copy:
- data = data.copy()
return data
- def _initFlags(self, flags, copy: bool) -> Flags:
+ def _initFlags(self, flags) -> Flags:
if flags is None:
return initFlagsLike(self._data)
if isinstance(flags, list):
result = Flags()
for f in flags:
- f = self._castToFlags(f, copy=copy)
+ f = self._castToFlags(f)
for c in f.columns:
if c in result.columns:
warnings.warn(
@@ -343,7 +324,7 @@ class SaQC(FunctionsMixin):
flags = result
elif isinstance(flags, (pd.DataFrame, DictOfSeries, Flags)):
- flags = self._castToFlags(flags, copy=copy)
+ flags = self._castToFlags(flags)
else:
raise TypeError(
@@ -366,91 +347,11 @@ class SaQC(FunctionsMixin):
)
return flags
- def _castToFlags(self, flags, copy):
+ def _castToFlags(self, flags):
if isinstance(flags, pd.DataFrame):
for idx in [flags.index, flags.columns]:
if isinstance(idx, pd.MultiIndex):
raise TypeError("'flags' should not have MultiIndex")
if not isinstance(flags, Flags):
flags = Flags(flags)
- if copy:
- flags = flags.copy()
return flags
-
-
-class SaQCResult:
- def __init__(
- self,
- data: DictOfSeries,
- flags: Flags,
- attrs: dict,
- scheme: TranslationScheme,
- ):
- assert isinstance(data, DictOfSeries)
- assert isinstance(flags, Flags)
- assert isinstance(attrs, dict)
- assert isinstance(scheme, TranslationScheme)
- self._data = data.copy()
- self._flags = flags.copy()
- self._attrs = attrs.copy()
- self._scheme = scheme
- self._validate()
-
- try:
- self._scheme.backward(self._flags, attrs=self._attrs)
- except Exception as e:
- raise RuntimeError("Translation of flags failed") from e
-
- def _validate(self):
- if not self._data.columns.equals(self._flags.columns):
- raise AssertionError(
- "Consistency broken. data and flags have not the same columns"
- )
-
- @property
- def data(self) -> pd.DataFrame:
- data: pd.DataFrame = self._data.copy().to_df()
- data.attrs = self._attrs.copy()
- return data
-
- @property
- def flags(self) -> pd.DataFrame:
- data: pd.DataFrame = self._scheme.backward(self._flags, attrs=self._attrs)
- data.attrs = self._attrs.copy()
- return data
-
- @property
- def data_raw(self) -> DictOfSeries:
- return self._data
-
- @property
- def flags_raw(self) -> Flags:
- return self._flags
-
- @property
- def columns(self) -> DictOfSeries():
- self._validate()
- return self._data.columns
-
- def __getitem__(self, key):
- self._validate()
- if key not in self.columns:
- raise KeyError(key)
- data_series = self._data[key].copy()
- # slice flags to one column
- flags = Flags({key: self._flags._data[key]}, copy=True)
-
- df = self._scheme.backward(flags, attrs=self._attrs)
- if isinstance(df.columns, pd.MultiIndex):
- df = df.droplevel(level=0, axis=1)
-
- if len(df.columns) == 1:
- df.columns = ["flags"]
-
- df.insert(0, column="data", value=data_series)
- df.columns.name = None
- df.index.name = None
- return df
-
- def __repr__(self):
- return f"SaQCResult\nColumns: {self.columns.to_list()}"
diff --git a/saqc/core/flags.py b/saqc/core/flags.py
index fa990c1788e0339d6c4738260f664de8aeb5c4ad..c5acb68c43df3317e316043aa733e7b1fac5acb5 100644
--- a/saqc/core/flags.py
+++ b/saqc/core/flags.py
@@ -1,15 +1,19 @@
#!/usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from __future__ import annotations
-import pandas as pd
+from typing import DefaultDict, Dict, Iterable, Mapping, Optional, Tuple, Type, Union
+
import numpy as np
-import dios
-from typing import Mapping, Union, Dict, DefaultDict, Optional, Type, Tuple, Iterable
+import pandas as pd
-from saqc.constants import *
+import dios
from saqc.core.history import History
-
_VAL = Union[pd.Series, History]
DictLike = Union[
pd.DataFrame,
@@ -45,7 +49,7 @@ class _HistAccess:
class Flags:
"""
- Saqc's flags container.
+ SaQC's flags container.
This container class holds the quality flags associated with the data. It hold key-value pairs, where
the key is the name of the column and the value is a ``pandas.Series`` of flags. The index of the series
@@ -243,7 +247,7 @@ class Flags:
errm += f"of column {colname} "
# this ensures that the mask does not shadow UNFLAGGED with a NaN.
- if history.max().hasnans:
+ if history.squeeze().hasnans:
raise ValueError(errm + "is not valid (result of max() contains NaNs)")
return history
@@ -315,7 +319,7 @@ class Flags:
# item access
def __getitem__(self, key: str) -> pd.Series:
- return self._data[key].max()
+ return self._data[key].squeeze()
def __setitem__(self, key: SelectT, value: ValueT):
# force-KW is only internally available
diff --git a/saqc/core/history.py b/saqc/core/history.py
index f035da7238ad929885feeee0a9e601609c6ff715..37285b5dc455342d3321bc52c1a97df7440ab56f 100644
--- a/saqc/core/history.py
+++ b/saqc/core/history.py
@@ -1,11 +1,17 @@
#!/usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from __future__ import annotations
-from copy import deepcopy, copy as shallowcopy
-from typing import Dict, Tuple, Union, List, Any
+from copy import copy as shallowcopy
+from copy import deepcopy
+from typing import Any, Callable, Dict, List, Tuple, Union
-import pandas as pd
import numpy as np
+import pandas as pd
from saqc.constants import UNFLAGGED
@@ -19,9 +25,8 @@ class History:
names are increasing integers starting with 0. After initialisation
the FH is empty and has no columns at all.
- To get the worst flags (highest value) that are currently stored in
- the FH, we provide a ``max()`` method. It returns a pd.Series indicating
- the worst flag per row.
+ To get the latest flags, that are currently stored in the FH, we provide
+ a ``squeeze()`` method.
For more details and a detailed discussion, why this is needed, how this
works and possible other implementations, see #GL143 [1].
@@ -38,7 +43,7 @@ class History:
createHistoryFromData: function to create History from existing data
"""
- def __init__(self, index: pd.Index):
+ def __init__(self, index: pd.Index | None):
self.hist = pd.DataFrame(index=index)
self.meta = []
@@ -119,7 +124,9 @@ class History:
return self
- def append(self, value: Union[pd.Series, History], meta: dict = None) -> History:
+ def append(
+ self, value: Union[pd.Series, History], meta: dict | None = None
+ ) -> History:
"""
Create a new FH column and insert given pd.Series to it.
@@ -203,13 +210,13 @@ class History:
self.meta += value_meta
return self
- def max(self, raw=False) -> pd.Series:
+ def squeeze(self, raw=False) -> pd.Series:
"""
- Get the maximum value per row of the FH.
+ Get the last flag value per row of the FH.
Returns
-------
- pd.Series: maximum values
+ pd.Series
"""
result = self.hist.astype(float)
if result.empty:
@@ -260,7 +267,7 @@ class History:
def apply(
self,
index: pd.Index,
- func: callable,
+ func: Callable,
func_kws: dict,
func_handle_df: bool = False,
copy: bool = True,
@@ -275,8 +282,7 @@ class History:
- the functions mustn't alter the passed objects
- the functions are not allowed to add or remove columns
- the function must return same type as first argument
- - the returned object must have same index as the passed ``index`` to ``apply``
- as first argument
+ - the returned object must have same index as the passed ``index`` to ``apply`` as first argument
Parameters
----------
diff --git a/saqc/core/modules/__init__.py b/saqc/core/modules/__init__.py
index c3e7f09a588c2f7be9c3f75c00eaba864fcfc4b8..1c9f1b6a35911fa0258044f3062beaa93600bc90 100644
--- a/saqc/core/modules/__init__.py
+++ b/saqc/core/modules/__init__.py
@@ -1,7 +1,11 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from saqc.core.modules.breaks import Breaks
-from saqc.core.modules.noise import Noise
from saqc.core.modules.changepoints import ChangePoints
from saqc.core.modules.constants import Constants
from saqc.core.modules.curvefit import Curvefit
@@ -9,10 +13,11 @@ from saqc.core.modules.drift import Drift
from saqc.core.modules.flagtools import FlagTools
from saqc.core.modules.generic import Generic
from saqc.core.modules.interpolation import Interpolation
+from saqc.core.modules.noise import Noise
from saqc.core.modules.outliers import Outliers
from saqc.core.modules.pattern import Pattern
from saqc.core.modules.resampling import Resampling
-from saqc.core.modules.residues import Residues
+from saqc.core.modules.residuals import Residuals
from saqc.core.modules.rolling import Rolling
from saqc.core.modules.scores import Scores
from saqc.core.modules.tools import Tools
@@ -33,7 +38,7 @@ class FunctionsMixin(
Outliers,
Pattern,
Resampling,
- Residues,
+ Residuals,
Rolling,
Scores,
Tools,
diff --git a/saqc/core/modules/breaks.py b/saqc/core/modules/breaks.py
index e76e783cd92bb440878958e12c0321b651545c0f..76ffee5a937a84060efa8f51bdf66924431bb11d 100644
--- a/saqc/core/modules/breaks.py
+++ b/saqc/core/modules/breaks.py
@@ -1,27 +1,37 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-from saqc.constants import BAD, FILTER_ALL
import saqc
+import saqc.funcs
+from saqc.constants import BAD, FILTER_ALL
+from saqc.lib.docurator import doc
class Breaks:
+ @doc(saqc.funcs.breaks.flagMissing.__doc__)
def flagMissing(
self, field: str, flag: float = BAD, dfilter: float = FILTER_ALL, **kwargs
) -> saqc.SaQC:
return self._defer("flagMissing", locals())
+ @doc(saqc.funcs.breaks.flagIsolated.__doc__)
def flagIsolated(
self,
field: str,
gap_window: str,
group_window: str,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("flagIsolated", locals())
+ @doc(saqc.funcs.breaks.flagJumps.__doc__)
def flagJumps(
self,
field: str,
@@ -29,6 +39,6 @@ class Breaks:
window: str,
min_periods: int = 1,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("flagJumps", locals())
diff --git a/saqc/core/modules/changepoints.py b/saqc/core/modules/changepoints.py
index 0fffc914caddff3f173fd2a7723520670c07d59c..dd1db109676b2d5d126d9c5a370fe2224c358916 100644
--- a/saqc/core/modules/changepoints.py
+++ b/saqc/core/modules/changepoints.py
@@ -1,4 +1,9 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
@@ -7,11 +12,14 @@ from typing import Callable, Tuple
import numpy as np
from typing_extensions import Literal
-from saqc.constants import BAD
import saqc
+import saqc.funcs
+from saqc.constants import BAD
+from saqc.lib.docurator import doc
class ChangePoints:
+ @doc(saqc.funcs.changepoints.flagChangePoints.__doc__)
def flagChangePoints(
self,
field: str,
@@ -27,6 +35,7 @@ class ChangePoints:
) -> saqc.SaQC:
return self._defer("flagChangePoints", locals())
+ @doc(saqc.funcs.changepoints.assignChangePointCluster.__doc__)
def assignChangePointCluster(
self,
field: str,
diff --git a/saqc/core/modules/constants.py b/saqc/core/modules/constants.py
index 035147e67dac5a7ca956bbe7b1c1d618ab7e912e..6391bb4e903bfd4278f3ece68d63f03c773deb2d 100644
--- a/saqc/core/modules/constants.py
+++ b/saqc/core/modules/constants.py
@@ -1,25 +1,34 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-from saqc.constants import BAD
import saqc
+import saqc.funcs
+from saqc.constants import BAD
+from saqc.lib.docurator import doc
class Constants:
+ @doc(saqc.funcs.constants.flagByVariance.__doc__)
def flagByVariance(
self,
field: str,
- window: str = "12h",
- thresh: float = 0.0005,
- maxna: int = None,
- maxna_group: int = None,
+ window: str,
+ thresh: float,
+ maxna: int | None = None,
+ maxna_group: int | None = None,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("flagByVariance", locals())
+ @doc(saqc.funcs.constants.flagConstants.__doc__)
def flagConstants(
- self, field: str, thresh: float, window: str, flag: float = BAD, **kwargs
+ self, field: str, thresh: float, window: int | str, flag: float = BAD, **kwargs
) -> saqc.SaQC:
return self._defer("flagConstants", locals())
diff --git a/saqc/core/modules/curvefit.py b/saqc/core/modules/curvefit.py
index ba595d0659e6e745396ee1cdd463ac6e776e8c16..f915ba5ce56d5a8f9c0095969529a9a7039f4995 100644
--- a/saqc/core/modules/curvefit.py
+++ b/saqc/core/modules/curvefit.py
@@ -1,22 +1,31 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
+
from typing import Union
-from dios import DictOfSeries
from typing_extensions import Literal
-from saqc.constants import BAD
import saqc
+import saqc.funcs
+from dios import DictOfSeries
+from saqc.constants import BAD
+from saqc.lib.docurator import doc
class Curvefit:
+ @doc(saqc.funcs.curvefit.fitPolynomial.__doc__)
def fitPolynomial(
self,
field: str,
- window: Union[int, str],
+ window: int | str,
order: int,
min_periods: int = 0,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("fitPolynomial", locals())
diff --git a/saqc/core/modules/drift.py b/saqc/core/modules/drift.py
index 2cc22ff5c092426e08381a94e957350fac7d9e10..2e152d7629ea9589fc4aeae3fc895d2246e4e617 100644
--- a/saqc/core/modules/drift.py
+++ b/saqc/core/modules/drift.py
@@ -1,20 +1,28 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Sequence, Callable, Optional, Union
-from typing_extensions import Literal
+from typing import Callable, Optional, Sequence, Union
import numpy as np
from scipy.spatial.distance import pdist
+from typing_extensions import Literal
-from saqc.constants import BAD
import saqc
+import saqc.funcs
+from saqc.constants import BAD
from saqc.funcs import LinkageString
+from saqc.lib.docurator import doc
from saqc.lib.types import CurveFitter
class Drift:
+ @doc(saqc.funcs.drift.flagDriftFromNorm.__doc__)
def flagDriftFromNorm(
self,
field: Sequence[str],
@@ -27,10 +35,11 @@ class Drift:
/ len(x),
method: LinkageString = "single",
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("flagDriftFromNorm", locals())
+ @doc(saqc.funcs.drift.flagDriftFromReference.__doc__)
def flagDriftFromReference(
self,
field: Sequence[str],
@@ -42,20 +51,22 @@ class Drift:
)
/ len(x),
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("flagDriftFromReference", locals())
+ @doc(saqc.funcs.drift.correctDrift.__doc__)
def correctDrift(
self,
field: str,
maintenance_field: str,
model: Callable[..., float] | Literal["linear", "exponential"],
cal_range: int = 5,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("correctDrift", locals())
+ @doc(saqc.funcs.drift.correctRegimeAnomaly.__doc__)
def correctRegimeAnomaly(
self,
field: str,
@@ -63,10 +74,11 @@ class Drift:
model: CurveFitter,
tolerance: Optional[str] = None,
epoch: bool = False,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("correctRegimeAnomaly", locals())
+ @doc(saqc.funcs.drift.correctOffset.__doc__)
def correctOffset(
self,
field: str,
@@ -75,6 +87,37 @@ class Drift:
window: str,
min_periods: int,
tolerance: Optional[str] = None,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("correctOffset", locals())
+
+ @doc(saqc.funcs.drift.flagRegimeAnomaly.__doc__)
+ def flagRegimeAnomaly(
+ self,
+ field: str,
+ cluster_field: str,
+ spread: float,
+ method: LinkageString = "single",
+ metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: np.abs(
+ np.nanmean(x) - np.nanmean(y)
+ ),
+ frac: float = 0.5,
+ flag: float = BAD,
+ **kwargs,
+ ) -> saqc.SaQC:
+ return self._defer("flagRegimeAnomaly", locals())
+
+ @doc(saqc.funcs.drift.assignRegimeAnomaly.__doc__)
+ def assignRegimeAnomaly(
+ self,
+ field: str,
+ cluster_field: str,
+ spread: float,
+ method: LinkageString = "single",
+ metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: np.abs(
+ np.nanmean(x) - np.nanmean(y)
+ ),
+ frac: float = 0.5,
+ **kwargs,
+ ) -> saqc.SaQC:
+ return self._defer("assignRegimeAnomaly", locals())
diff --git a/saqc/core/modules/flagtools.py b/saqc/core/modules/flagtools.py
index bad840b58e2396dc9876ba966fad60bdc47f8d1e..66efc28d70f0a56bdfa12c157380fb3093628bc0 100644
--- a/saqc/core/modules/flagtools.py
+++ b/saqc/core/modules/flagtools.py
@@ -1,31 +1,43 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Any, Union
+from typing import Any, Sequence, Union
-import pandas as pd
import numpy as np
-from dios import DictOfSeries
+import pandas as pd
from typing_extensions import Literal
-from saqc.constants import BAD
import saqc
+import saqc.funcs
+from dios import DictOfSeries
+from saqc.constants import BAD, FILTER_ALL
+from saqc.lib.docurator import doc
class FlagTools:
+ @doc(saqc.funcs.flagtools.clearFlags.__doc__)
def clearFlags(self, field: str, **kwargs) -> saqc.SaQC:
return self._defer("clearFlags", locals())
+ @doc(saqc.funcs.flagtools.forceFlags.__doc__)
def forceFlags(self, field: str, flag: float = BAD, **kwargs) -> saqc.SaQC:
return self._defer("forceFlags", locals())
+ @doc(saqc.funcs.flagtools.forceFlags.__doc__)
def flagDummy(self, field: str, **kwargs) -> saqc.SaQC:
return self._defer("flagDummy", locals())
+ @doc(saqc.funcs.flagtools.flagUnflagged.__doc__)
def flagUnflagged(self, field: str, flag: float = BAD, **kwargs) -> saqc.SaQC:
return self._defer("flagUnflagged", locals())
+ @doc(saqc.funcs.flagtools.flagManual.__doc__)
def flagManual(
self,
field: str,
@@ -40,11 +52,23 @@ class FlagTools:
) -> saqc.SaQC:
return self._defer("flagManual", locals())
+ @doc(saqc.funcs.flagtools.transferFlags.__doc__)
def transferFlags(
self,
field: str | Sequence[str],
target: str | Sequence[str],
- flag: float = BAD,
**kwargs,
) -> saqc.SaQC:
return self._defer("transferFlags", locals())
+
+ @doc(saqc.funcs.flagtools.propagateFlags.__doc__)
+ def propagateFlags(
+ self,
+ field: str | Sequence[str],
+ window: Union[str, int],
+ method: Literal["ffill", "bfill"] = "ffill",
+ flag: float = BAD,
+ dfilter: float = FILTER_ALL,
+ **kwargs,
+ ) -> saqc.SaQC:
+ return self._defer("propagateFlags", locals())
diff --git a/saqc/core/modules/generic.py b/saqc/core/modules/generic.py
index c0ed0a21ff0eec046d88c8bd56d46dec1c77e84d..8759bebd74d5583c66c8ba62a899b975306febea 100644
--- a/saqc/core/modules/generic.py
+++ b/saqc/core/modules/generic.py
@@ -1,26 +1,36 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
from typing import Sequence, Union
+import numpy as np
+
import saqc
-from saqc.constants import UNFLAGGED, BAD, FILTER_ALL
+import saqc.funcs
+from saqc.constants import BAD, FILTER_ALL
+from saqc.lib.docurator import doc
from saqc.lib.types import GenericFunction
class Generic:
+ @doc(saqc.funcs.generic.processGeneric.__doc__)
def processGeneric(
self,
field: str | Sequence[str],
func: GenericFunction,
- target: str | Sequence[str] = None,
- flag: float = UNFLAGGED,
+ target: str | Sequence[str] | None = None,
dfilter: float = FILTER_ALL,
**kwargs,
) -> saqc.SaQC:
return self._defer("processGeneric", locals())
+ @doc(saqc.funcs.generic.flagGeneric.__doc__)
def flagGeneric(
self,
field: Union[str, Sequence[str]],
diff --git a/saqc/core/modules/interpolation.py b/saqc/core/modules/interpolation.py
index 3eed7ac7ed31df0b816239348d2e50b9464e0717..0a949a9bbe9efbf56a9621afc14b5996986810d6 100644
--- a/saqc/core/modules/interpolation.py
+++ b/saqc/core/modules/interpolation.py
@@ -1,18 +1,26 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Union, Callable
+from typing import Callable, Union
import numpy as np
import pandas as pd
-from saqc.constants import UNFLAGGED
import saqc
+import saqc.funcs
+from saqc.constants import UNFLAGGED
from saqc.funcs.interpolation import _SUPPORTED_METHODS
+from saqc.lib.docurator import doc
class Interpolation:
+ @doc(saqc.funcs.interpolation.interpolateByRolling.__doc__)
def interpolateByRolling(
self,
field: str,
@@ -25,6 +33,7 @@ class Interpolation:
) -> saqc.SaQC:
return self._defer("interpolateByRolling", locals())
+ @doc(saqc.funcs.interpolation.interpolateInvalid.__doc__)
def interpolateInvalid(
self,
field: str,
@@ -37,6 +46,7 @@ class Interpolation:
) -> saqc.SaQC:
return self._defer("interpolateInvalid", locals())
+ @doc(saqc.funcs.interpolation.interpolateIndex.__doc__)
def interpolateIndex(
self,
field: str,
diff --git a/saqc/core/modules/noise.py b/saqc/core/modules/noise.py
index dfc734162930c8a4075c379b994de869be4966cf..9b1209ad37b6860903a53542498343ad447cb919 100644
--- a/saqc/core/modules/noise.py
+++ b/saqc/core/modules/noise.py
@@ -1,26 +1,35 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
+from typing import Callable
+
import numpy as np
import pandas as pd
-from typing import Callable
-from saqc.constants import BAD
import saqc
+import saqc.funcs
+from saqc.constants import BAD
+from saqc.lib.docurator import doc
class Noise:
+ @doc(saqc.funcs.noise.flagByStatLowPass.__doc__)
def flagByStatLowPass(
self,
field: str,
- func: Callable[[np.array, pd.Series], float],
- window: str,
+ func: Callable[[np.ndarray, pd.Series], float],
+ window: str | pd.Timedelta,
thresh: float,
- sub_window: str = None,
+ sub_window: str | pd.Timedelta = None,
sub_thresh: float = None,
min_periods: int = None,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("flagByStatLowPass", locals())
diff --git a/saqc/core/modules/outliers.py b/saqc/core/modules/outliers.py
index 9d85d8e6bd08b0df35819ecb101f4ef632fb4173..261c8146e734ff94c6f191a8839d0904f4eda12b 100644
--- a/saqc/core/modules/outliers.py
+++ b/saqc/core/modules/outliers.py
@@ -1,22 +1,30 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Optional, Union, Callable, Sequence
+from typing import Callable, Optional, Sequence, Union
import numpy as np
import pandas as pd
from typing_extensions import Literal
-from saqc.constants import BAD
import saqc
+import saqc.funcs
+from saqc.constants import BAD
+from saqc.lib.docurator import doc
class Outliers:
+ @doc(saqc.funcs.outliers.flagByStray.__doc__)
def flagByStray(
self,
field: str,
- freq: Optional[Union[int, str]] = None,
+ window: Optional[Union[int, str]] = None,
min_periods: int = 11,
iter_start: float = 0.5,
alpha: float = 0.05,
@@ -25,6 +33,7 @@ class Outliers:
) -> saqc.SaQC:
return self._defer("flagByStray", locals())
+ @doc(saqc.funcs.outliers.flagMVScores.__doc__)
def flagMVScores(
self,
field: Sequence[str],
@@ -44,6 +53,7 @@ class Outliers:
) -> saqc.SaQC:
return self._defer("flagMVScores", locals())
+ @doc(saqc.funcs.outliers.flagRaise.__doc__)
def flagRaise(
self,
field: str,
@@ -59,6 +69,7 @@ class Outliers:
) -> saqc.SaQC:
return self._defer("flagRaise", locals())
+ @doc(saqc.funcs.outliers.flagMAD.__doc__)
def flagMAD(
self,
field: str,
@@ -69,18 +80,20 @@ class Outliers:
) -> saqc.SaQC:
return self._defer("flagMAD", locals())
+ @doc(saqc.funcs.outliers.flagOffset.__doc__)
def flagOffset(
self,
field: str,
- thresh: float,
tolerance: float,
window: Union[int, str],
+ thresh: Optional[float] = None,
thresh_relative: Optional[float] = None,
flag: float = BAD,
**kwargs,
) -> saqc.SaQC:
return self._defer("flagOffset", locals())
+ @doc(saqc.funcs.outliers.flagByGrubbs.__doc__)
def flagByGrubbs(
self,
field: str,
@@ -93,6 +106,7 @@ class Outliers:
) -> saqc.SaQC:
return self._defer("flagByGrubbs", locals())
+ @doc(saqc.funcs.outliers.flagRange.__doc__)
def flagRange(
self,
field: str,
@@ -103,6 +117,7 @@ class Outliers:
) -> saqc.SaQC:
return self._defer("flagRange", locals())
+ @doc(saqc.funcs.outliers.flagCrossStatistics.__doc__)
def flagCrossStatistics(
self,
field: Sequence[str],
@@ -111,4 +126,4 @@ class Outliers:
flag: float = BAD,
**kwargs,
) -> saqc.SaQC:
- return self._defer("flagCrossStatistic", locals())
+ return self._defer("flagCrossStatistics", locals())
diff --git a/saqc/core/modules/pattern.py b/saqc/core/modules/pattern.py
index f38de24416b852376ea85c356d81e1e34f2c30b9..2c37f51d88b31b483711e43f0931b32d103104ab 100644
--- a/saqc/core/modules/pattern.py
+++ b/saqc/core/modules/pattern.py
@@ -1,12 +1,20 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-from saqc.constants import BAD
import saqc
+import saqc.funcs
+from saqc.constants import BAD
+from saqc.lib.docurator import doc
class Pattern:
+ @doc(saqc.funcs.pattern.flagPatternByDTW.__doc__)
def flagPatternByDTW(
self,
field,
@@ -15,6 +23,6 @@ class Pattern:
normalize=True,
plot=False,
flag=BAD,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("flagPatternByDTW", locals())
diff --git a/saqc/core/modules/resampling.py b/saqc/core/modules/resampling.py
index 1331647e5079cc4b366d2785f417622ac1f32ecf..1dfa0b780d1d4121bb68f88d0aa47faec739751d 100644
--- a/saqc/core/modules/resampling.py
+++ b/saqc/core/modules/resampling.py
@@ -1,19 +1,27 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Optional, Callable
+from typing import Callable, Optional
import numpy as np
import pandas as pd
from typing_extensions import Literal
-from saqc.constants import BAD
import saqc
+import saqc.funcs
+from saqc.constants import BAD
from saqc.funcs.interpolation import _SUPPORTED_METHODS
+from saqc.lib.docurator import doc
class Resampling:
+ @doc(saqc.funcs.resampling.linear.__doc__)
def linear(
self,
field: str,
@@ -22,6 +30,7 @@ class Resampling:
) -> saqc.SaQC:
return self._defer("linear", locals())
+ @doc(saqc.funcs.resampling.interpolate.__doc__)
def interpolate(
self,
field: str,
@@ -32,6 +41,7 @@ class Resampling:
) -> saqc.SaQC:
return self._defer("interpolate", locals())
+ @doc(saqc.funcs.resampling.shift.__doc__)
def shift(
self,
field: str,
@@ -42,6 +52,7 @@ class Resampling:
) -> saqc.SaQC:
return self._defer("shift", locals())
+ @doc(saqc.funcs.resampling.resample.__doc__)
def resample(
self,
field: str,
@@ -58,6 +69,7 @@ class Resampling:
) -> saqc.SaQC:
return self._defer("resample", locals())
+ @doc(saqc.funcs.resampling.concatFlags.__doc__)
def concatFlags(
self,
field: str,
@@ -70,9 +82,11 @@ class Resampling:
"inverse_bshift",
"inverse_nshift",
"inverse_interpolation",
- ],
+ "match",
+ ] = "match",
freq: Optional[str] = None,
drop: Optional[bool] = False,
+ squeeze: Optional[bool] = False,
**kwargs,
) -> saqc.SaQC:
return self._defer("concatFlags", locals())
diff --git a/saqc/core/modules/residuals.py b/saqc/core/modules/residuals.py
new file mode 100644
index 0000000000000000000000000000000000000000..e764099d1c4f0f13c4ac12d836f0f5cc6ed2242c
--- /dev/null
+++ b/saqc/core/modules/residuals.py
@@ -0,0 +1,44 @@
+#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+from typing import Callable, Optional, Union
+
+import numpy as np
+import pandas as pd
+from typing_extensions import Literal
+
+import saqc
+import saqc.funcs
+from saqc.constants import BAD
+from saqc.lib.docurator import doc
+
+
+class Residuals:
+ @doc(saqc.funcs.residuals.calculatePolynomialResiduals.__doc__)
+ def calculatePolynomialResiduals(
+ self,
+ field: str,
+ window: Union[str, int],
+ order: int,
+ min_periods: Optional[int] = 0,
+ **kwargs,
+ ) -> saqc.SaQC:
+ return self._defer("calculatePolynomialResiduals", locals())
+
+ @doc(saqc.funcs.residuals.calculateRollingResiduals.__doc__)
+ def calculateRollingResiduals(
+ self,
+ field: str,
+ window: Union[str, int],
+ func: Callable[[pd.Series], np.ndarray] = np.mean,
+ min_periods: Optional[int] = 0,
+ center: bool = True,
+ **kwargs,
+ ) -> saqc.SaQC:
+ return self._defer("calculateRollingResiduals", locals())
diff --git a/saqc/core/modules/residues.py b/saqc/core/modules/residues.py
deleted file mode 100644
index 30503a46d2e79dd19e6e2d97b0f2cc79b5fdbc08..0000000000000000000000000000000000000000
--- a/saqc/core/modules/residues.py
+++ /dev/null
@@ -1,35 +0,0 @@
-#! /usr/bin/env python
-# -*- coding: utf-8 -*-
-from __future__ import annotations
-
-from typing import Optional, Union, Callable
-
-import numpy as np
-import pandas as pd
-from typing_extensions import Literal
-
-from saqc.constants import BAD
-import saqc
-
-
-class Residues:
- def calculatePolynomialResidues(
- self,
- field: str,
- window: Union[str, int],
- order: int,
- min_periods: Optional[int] = 0,
- **kwargs
- ) -> saqc.SaQC:
- return self._defer("calculatePolynomialResidues", locals())
-
- def calculateRollingResidues(
- self,
- field: str,
- window: Union[str, int],
- func: Callable[[pd.Series], np.ndarray] = np.mean,
- min_periods: Optional[int] = 0,
- center: bool = True,
- **kwargs
- ) -> saqc.SaQC:
- return self._defer("calculateRollingResidues", locals())
diff --git a/saqc/core/modules/rolling.py b/saqc/core/modules/rolling.py
index 67e0e10b103dbf6aee3b9e83c0036956af2b497d..108453d4e04f760f07eb35a1486afb1a7cd0cd9b 100644
--- a/saqc/core/modules/rolling.py
+++ b/saqc/core/modules/rolling.py
@@ -1,15 +1,23 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-from typing import Union, Callable
+from typing import Callable, Union
import numpy as np
import pandas as pd
+import saqc.funcs
from saqc.constants import BAD
+from saqc.lib.docurator import doc
class Rolling:
+ @doc(saqc.funcs.rolling.roll.__doc__)
def roll(
self,
field: str,
diff --git a/saqc/core/modules/scores.py b/saqc/core/modules/scores.py
index feaa89b8a70d9c79a62ef202ebfd1b5d3cce41ac..9fd3466efb716b0c088051f99876b470a41018a8 100644
--- a/saqc/core/modules/scores.py
+++ b/saqc/core/modules/scores.py
@@ -1,17 +1,25 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Sequence, Callable, Union
+from typing import Callable, Sequence, Union
import numpy as np
import pandas as pd
from typing_extensions import Literal
import saqc
+import saqc.funcs
+from saqc.lib.docurator import doc
class Scores:
+ @doc(saqc.funcs.scores.assignKNNScore.__doc__)
def assignKNNScore(
self,
field: Sequence[str],
diff --git a/saqc/core/modules/tools.py b/saqc/core/modules/tools.py
index 352922dc2db7381c507931c23722672f1380b6ea..3eeb930feac622b810c3e187ba6508f25bc8e2ca 100644
--- a/saqc/core/modules/tools.py
+++ b/saqc/core/modules/tools.py
@@ -1,50 +1,64 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
from typing import Optional
-from typing_extensions import Literal
-import saqc
import numpy as np
+from typing_extensions import Literal
+import saqc
+import saqc.funcs
from saqc.constants import FILTER_NONE
+from saqc.lib.docurator import doc
class Tools:
+ @doc(saqc.funcs.tools.copyField.__doc__)
def copyField(
self, field: str, target: str, overwrite: bool = False, **kwargs
) -> saqc.SaQC:
return self._defer("copyField", locals())
+ @doc(saqc.funcs.tools.dropField.__doc__)
def dropField(self, field: str, **kwargs) -> saqc.SaQC:
return self._defer("dropField", locals())
+ @doc(saqc.funcs.tools.renameField.__doc__)
def renameField(self, field: str, new_name: str, **kwargs) -> saqc.SaQC:
return self._defer("renameField", locals())
- def maskTime(
+ @doc(saqc.funcs.tools.selectTime.__doc__)
+ def selectTime(
self,
field: str,
- mode: Literal["periodic", "mask_field"],
- mask_field: Optional[str] = None,
+ mode: Literal["periodic", "selection_field"],
+ selection_field: Optional[str] = None,
start: Optional[str] = None,
end: Optional[str] = None,
closed: bool = True,
**kwargs,
) -> saqc.SaQC:
- return self._defer("maskTime", locals())
+ return self._defer("selectTime", locals())
+ @doc(saqc.funcs.tools.plot.__doc__)
def plot(
self,
field: str,
path: Optional[str] = None,
max_gap: Optional[str] = None,
- history: Optional[Literal["valid", "complete", "clear"]] = "valid",
+ history: Optional[Literal["valid", "complete"] | list] = "valid",
xscope: Optional[slice] = None,
phaseplot: Optional[str] = None,
store_kwargs: Optional[dict] = None,
+ ax_kwargs: Optional[dict] = None,
dfilter: Optional[float] = FILTER_NONE,
**kwargs,
) -> saqc.SaQC:
+
return self._defer("plot", locals())
diff --git a/saqc/core/modules/transformation.py b/saqc/core/modules/transformation.py
index 622040d42797c52440aa3ccec96a52dda1126bc9..3952f89c423339d8aef3c184e1f375f4a07c6fde 100644
--- a/saqc/core/modules/transformation.py
+++ b/saqc/core/modules/transformation.py
@@ -1,4 +1,9 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
@@ -7,14 +12,17 @@ from typing import Callable, Optional, Union
import pandas as pd
import saqc
+import saqc.funcs
+from saqc.lib.docurator import doc
class Transformation:
+ @doc(saqc.funcs.transformation.transform.__doc__)
def transform(
self,
field: str,
func: Callable[[pd.Series], pd.Series],
freq: Optional[Union[float, str]] = None,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("transform", locals())
diff --git a/saqc/core/reader.py b/saqc/core/reader.py
index c826a8cea762401538f5b10623b66f53375b29cb..781190cc85e761c056e6371a67fea01ef67c37d0 100644
--- a/saqc/core/reader.py
+++ b/saqc/core/reader.py
@@ -1,11 +1,17 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-import io
import ast
+import io
from pathlib import Path
-from urllib.request import urlopen
from typing import TextIO
+from urllib.error import URLError
+from urllib.request import urlopen
import pandas as pd
@@ -13,7 +19,6 @@ from saqc.core.core import SaQC
from saqc.core.visitor import ConfigFunctionParser
from saqc.lib.tools import isQuoted
-
COMMENT = "#"
SEPARATOR = ";"
@@ -23,7 +28,7 @@ def _openFile(fname) -> TextIO:
try:
fobj = io.StringIO(urlopen(str(fname)).read().decode("utf-8"))
fobj.seek(0)
- except ValueError:
+ except (ValueError, URLError):
fobj = io.open(fname, "r", encoding="utf-8")
else:
fobj = fname
@@ -70,9 +75,9 @@ def readFile(fname) -> pd.DataFrame:
return df
-# Todo: needs (maybe tiny) docstring!
-def fromConfig(fname, *args, **kwargs):
- saqc = SaQC(*args, **kwargs)
+# Todo: needs a (maybe tiny) docstring!
+def fromConfig(fname, *args, **func_kwargs):
+ saqc = SaQC(*args, **func_kwargs)
config = readFile(fname)
for _, field, expr in config.itertuples():
@@ -86,13 +91,16 @@ def fromConfig(fname, *args, **kwargs):
try:
tree = ast.parse(expr, mode="eval")
- func_name, kwargs = ConfigFunctionParser().parse(tree.body)
+ func_name, func_kwargs = ConfigFunctionParser().parse(tree.body)
except Exception as e:
raise type(e)(f"failed to parse: {field} ; {expr}") from e
- kwargs["field" if "field" not in kwargs else "target"] = fld
+ func_kwargs["field" if "field" not in func_kwargs else "target"] = fld
try:
- saqc = saqc.__getattr__(func_name)(regex=regex, **kwargs)
+ # We explictly route all function calls through SaQC.__getattr__
+ # in order to do a FUNC_MAP lookup. Otherwise we wouldn't be able
+ # to overwrite exsiting test functions with custom register calls.
+ saqc = saqc.__getattr__(func_name)(regex=regex, **func_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 72bfd0d079f4d5d1642fad44fce5d18ff15d016f..29ddcce9765156a3b34459a51449e6178f9f2184 100644
--- a/saqc/core/register.py
+++ b/saqc/core/register.py
@@ -1,15 +1,21 @@
#!/usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from __future__ import annotations
+import functools
import inspect
import warnings
-from typing import Dict, Tuple, Callable, Sequence, Any
-import functools
+from typing import Any, Callable, Dict, Sequence, Tuple
+
import numpy as np
import pandas as pd
-import dios
-from saqc.constants import UNFLAGGED, FILTER_ALL
+import dios
+from saqc.constants import FILTER_ALL, UNFLAGGED
from saqc.core.flags import Flags, History
from saqc.lib.tools import squeezeSequence, toSequence
@@ -268,7 +274,7 @@ class FunctionWrapper:
start = len(old_history.columns)
new_history = self._sliceHistory(new_history, slice(start, None))
- squeezed = new_history.max(raw=True)
+ squeezed = new_history.squeeze(raw=True)
out.history[col] = out.history[col].append(squeezed, meta=meta)
return out
@@ -459,7 +465,9 @@ def processing(**kwargs):
return register(mask=[], demask=[], squeeze=[])
-def _isflagged(flagscol: np.ndarray | pd.Series, thresh: float) -> np.array | pd.Series:
+def _isflagged(
+ flagscol: np.ndarray | pd.Series, thresh: float
+) -> np.ndarray | pd.Series:
"""
Return a mask of flags accordingly to `thresh`. Return type is same as flags.
"""
diff --git a/saqc/core/translation/__init__.py b/saqc/core/translation/__init__.py
index b5acd9cf759f5ab036c86d275404c9ee44efed36..45aa077963b0f90265238a23bb945aa58a2b79e8 100644
--- a/saqc/core/translation/__init__.py
+++ b/saqc/core/translation/__init__.py
@@ -1,9 +1,14 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from saqc.core.translation.basescheme import (
- TranslationScheme,
FloatScheme,
SimpleScheme,
+ TranslationScheme,
)
-from saqc.core.translation.positionalscheme import PositionalScheme
from saqc.core.translation.dmpscheme import DmpScheme
+from saqc.core.translation.positionalscheme import PositionalScheme
diff --git a/saqc/core/translation/basescheme.py b/saqc/core/translation/basescheme.py
index ecfeff35f07c511a353e0450394416a0abed11d6..c9889cd2043c351744ae7c6c0ff47995135cbc3f 100644
--- a/saqc/core/translation/basescheme.py
+++ b/saqc/core/translation/basescheme.py
@@ -1,25 +1,23 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Dict, Union, Any
+from typing import Any, Dict, MutableMapping, Union
import numpy as np
import pandas as pd
from dios import DictOfSeries
-from saqc.constants import (
- FILTER_ALL,
- UNFLAGGED,
- BAD,
- GOOD,
-)
-
+from saqc.constants import BAD, FILTER_ALL, GOOD, UNFLAGGED
from saqc.core.flags import Flags
from saqc.lib.types import ExternalFlag
-
ForwardMap = Dict[ExternalFlag, float]
BackwardMap = Dict[float, ExternalFlag]
@@ -39,6 +37,7 @@ class TranslationScheme:
the inverse of the 'forward' translation.
The translation mechanism imposes a few restrictions:
+
- The scheme must be well definied, i.e. we need a backward translation for
every forward translation (each value in `self._forward` needs a key in
`self._backward`).
@@ -79,8 +78,8 @@ class TranslationScheme:
@staticmethod
def _translate(
- flags: Union[Flags, pd.DataFrame, pd.Series],
- trans_map: Union[ForwardMap, BackwardMap],
+ flags: Flags | pd.DataFrame | pd.Series,
+ trans_map: ForwardMap | BackwardMap,
) -> DictOfSeries:
"""
Translate a given flag data structure to another according to the
@@ -149,7 +148,7 @@ class TranslationScheme:
raw: bool = False,
attrs: dict | None = None,
**kwargs,
- ) -> Union[pd.DataFrame, DictOfSeries]:
+ ) -> pd.DataFrame | DictOfSeries:
"""
Translate from 'internal flags' to 'external flags'
diff --git a/saqc/core/translation/dmpscheme.py b/saqc/core/translation/dmpscheme.py
index aefad4018dc4c2c13ebff6ddbfb4482980b2abf6..f5354871f0fc7c6fef8de603b57708b6875f5d6d 100644
--- a/saqc/core/translation/dmpscheme.py
+++ b/saqc/core/translation/dmpscheme.py
@@ -1,29 +1,23 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
import json
-from typing import Any
from functools import reduce
import numpy as np
import pandas as pd
+from saqc.constants import BAD, DOUBTFUL, GOOD, UNFLAGGED
+from saqc.core.flags import Flags
from saqc.core.history import History
-from saqc.core.flags import (
- Flags,
- UNFLAGGED,
- GOOD,
- DOUBTFUL,
- BAD,
-)
-from saqc.core.translation.basescheme import (
- BackwardMap,
- TranslationScheme,
- ForwardMap,
-)
-
+from saqc.core.translation.basescheme import BackwardMap, ForwardMap, TranslationScheme
_QUALITY_CAUSES = [
"",
@@ -55,6 +49,8 @@ class DmpScheme(TranslationScheme):
ARGUMENTS = {"comment": "", "cause": "OTHER"}
+ DFILTER_DEFAULT = GOOD + 1
+
_FORWARD: ForwardMap = {
"NIL": UNFLAGGED,
"OK": GOOD,
@@ -73,6 +69,28 @@ class DmpScheme(TranslationScheme):
def __init__(self):
super().__init__(forward=self._FORWARD, backward=self._BACKWARD)
+ def toHistory(self, field_flags: pd.DataFrame):
+ """
+ Translate a single field of external ``Flags`` to a ``History``
+ """
+ field_history = History(field_flags.index)
+
+ for (flag, cause, comment), values in field_flags.groupby(_QUALITY_LABELS):
+ try:
+ comment = json.loads(comment)
+ except json.decoder.JSONDecodeError:
+ comment = {"test": "unknown", "comment": ""}
+
+ histcol = pd.Series(np.nan, index=field_flags.index)
+ histcol.loc[values.index] = self(flag)
+
+ meta = {
+ "func": comment["test"],
+ "kwargs": {"comment": comment["comment"], "cause": cause},
+ }
+ field_history.append(histcol, meta=meta)
+ return field_history
+
def forward(self, df: pd.DataFrame) -> Flags:
"""
Translate from 'external flags' to 'internal flags'
@@ -91,27 +109,8 @@ class DmpScheme(TranslationScheme):
data = {}
- for field in df.columns.get_level_values(0):
-
- field_flags = df[field]
- field_history = History(field_flags.index)
-
- for (flag, cause, comment), values in field_flags.groupby(_QUALITY_LABELS):
- try:
- comment = json.loads(comment)
- except json.decoder.JSONDecodeError:
- comment = {"test": "unknown", "comment": ""}
-
- histcol = pd.Series(np.nan, index=field_flags.index)
- histcol.loc[values.index] = self(flag)
-
- meta = {
- "func": comment["test"],
- "kwargs": {"comment": comment["comment"], "cause": cause},
- }
- field_history.append(histcol, meta=meta)
-
- data[str(field)] = field_history
+ for field in df.columns.get_level_values(0).drop_duplicates():
+ data[str(field)] = self.toHistory(df[field])
return Flags(data)
diff --git a/saqc/core/translation/positionalscheme.py b/saqc/core/translation/positionalscheme.py
index d2e7623dad980c5011c6c3d7c548276c19fe4064..1fb581d56a45f620ce79c5bfdafcf5ba42e41363 100644
--- a/saqc/core/translation/positionalscheme.py
+++ b/saqc/core/translation/positionalscheme.py
@@ -1,23 +1,18 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
import numpy as np
import pandas as pd
-from saqc.core.flags import (
- Flags,
- History,
- UNFLAGGED,
- GOOD,
- DOUBTFUL,
- BAD,
-)
-from saqc.core.translation.basescheme import (
- TranslationScheme,
- ForwardMap,
- BackwardMap,
-)
+from saqc.constants import BAD, DOUBTFUL, GOOD, UNFLAGGED
+from saqc.core.flags import Flags, History
+from saqc.core.translation.basescheme import BackwardMap, ForwardMap, TranslationScheme
class PositionalScheme(TranslationScheme):
@@ -32,6 +27,7 @@ class PositionalScheme(TranslationScheme):
-6: UNFLAGGED,
-5: UNFLAGGED,
-2: UNFLAGGED,
+ -1: UNFLAGGED,
0: UNFLAGGED,
1: DOUBTFUL,
2: BAD,
@@ -94,10 +90,10 @@ class PositionalScheme(TranslationScheme):
"""
out = {}
for field in flags.columns:
- thist = flags.history[field].hist.replace(self._BACKWARD).astype(int)
+ thist = flags.history[field].hist.replace(self._BACKWARD).astype(float)
# concatenate the single flag values
ncols = thist.shape[-1]
- init = 9 * 10 ** ncols
+ init = 9 * 10**ncols
bases = 10 ** np.arange(ncols - 1, -1, -1)
tflags = init + (thist * bases).sum(axis=1)
diff --git a/saqc/core/visitor.py b/saqc/core/visitor.py
index 1ec26b48817f9e5e7e3bff9a87858c5dafb560aa..b5a7b33b902d36e749f2723b2b5c57dc6c4aa481 100644
--- a/saqc/core/visitor.py
+++ b/saqc/core/visitor.py
@@ -1,4 +1,9 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
import ast
diff --git a/saqc/funcs/__init__.py b/saqc/funcs/__init__.py
index d4fabccfc98765f48221838d54f21f9db73f5301..5aa3335ada54797b09eab01d0e7a18b1f22447f5 100644
--- a/saqc/funcs/__init__.py
+++ b/saqc/funcs/__init__.py
@@ -1,4 +1,9 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
# imports needed to make the functions register themself
@@ -8,15 +13,15 @@ from saqc.funcs.changepoints import *
from saqc.funcs.constants import *
from saqc.funcs.curvefit import *
from saqc.funcs.drift import *
+from saqc.funcs.flagtools import *
from saqc.funcs.generic import *
from saqc.funcs.interpolation import *
+from saqc.funcs.noise import *
from saqc.funcs.outliers import *
from saqc.funcs.pattern import *
from saqc.funcs.resampling import *
-from saqc.funcs.residues import *
+from saqc.funcs.residuals import *
from saqc.funcs.rolling import *
from saqc.funcs.scores import *
from saqc.funcs.tools import *
from saqc.funcs.transformation import *
-from saqc.funcs.flagtools import *
-from saqc.funcs.noise import *
diff --git a/saqc/funcs/breaks.py b/saqc/funcs/breaks.py
index fc673eb37ade07507c9ce3fb40a10393f88396ef..754d77231e3332d6401da242c23673dea0360f00 100644
--- a/saqc/funcs/breaks.py
+++ b/saqc/funcs/breaks.py
@@ -1,4 +1,9 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
@@ -10,19 +15,19 @@ gaps (:py:func:`flagMissing`), jumps and drops (:py:func:`flagJumps`) or tempora
isolated values (:py:func:`flagIsolated`).
"""
+from __future__ import annotations
+
from typing import Tuple
import numpy as np
import pandas as pd
-import pandas.tseries.frequencies
from dios import DictOfSeries
-
-from saqc.constants import *
-from saqc.lib.tools import groupConsecutives
-from saqc.funcs.changepoints import _assignChangePointCluster
+from saqc.constants import BAD, FILTER_ALL
from saqc.core.flags import Flags
-from saqc.core.register import _isflagged, register, flagging
+from saqc.core.register import _isflagged, flagging, register
+from saqc.funcs.changepoints import _assignChangePointCluster
+from saqc.lib.tools import groupConsecutives
@register(mask=[], demask=[], squeeze=["field"])
diff --git a/saqc/funcs/changepoints.py b/saqc/funcs/changepoints.py
index 46ba4f298836884ac046bf7ad9b9ca2f00449532..08af9cc0b399d16b95e8f2858649a7e1bf543613 100644
--- a/saqc/funcs/changepoints.py
+++ b/saqc/funcs/changepoints.py
@@ -1,20 +1,24 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-import pandas as pd
-import numpy as np
-import numba
-
from typing import Callable, Tuple
+
+import numba
+import numpy as np
+import pandas as pd
from typing_extensions import Literal
from dios import DictOfSeries
-
-from saqc.constants import *
-from saqc.core.register import flagging
+from saqc.constants import BAD, UNFLAGGED
+from saqc.core.flags import Flags
+from saqc.core.register import flagging, register
from saqc.lib.tools import customRoller, filterKwargs
-from saqc.core import register, Flags
@flagging()
@@ -314,9 +318,9 @@ def _assignChangePointCluster(
result_arr = stat_arr > thresh_arr
if model_by_resids:
- residues = pd.Series(np.nan, index=data[field].index)
- residues[masked_index] = stat_arr
- data[field] = residues
+ residuals = pd.Series(np.nan, index=data[field].index)
+ residuals[masked_index] = stat_arr
+ data[field] = residuals
flags[:, field] = UNFLAGGED
return data, flags
diff --git a/saqc/funcs/constants.py b/saqc/funcs/constants.py
index 476d2d75cda99ba5eb8dfb319e52972313294bb6..d7a98d20c4f0f88d422dac6a49efa926c99bd3b1 100644
--- a/saqc/funcs/constants.py
+++ b/saqc/funcs/constants.py
@@ -1,19 +1,25 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+import operator
from typing import Tuple
import numpy as np
import pandas as pd
-import operator
from dios import DictOfSeries
-
-from saqc.constants import *
-from saqc.core import register, Flags
+from saqc.constants import BAD
+from saqc.core.flags import Flags
from saqc.core.register import flagging
-from saqc.lib.ts_operators import varQC
from saqc.lib.tools import customRoller, getFreqDelta, statPass
+from saqc.lib.ts_operators import varQC
@flagging()
@@ -22,7 +28,7 @@ def flagConstants(
field: str,
flags: Flags,
thresh: float,
- window: str,
+ window: int | str,
flag: float = BAD,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
@@ -94,9 +100,9 @@ def flagByVariance(
field: str,
flags: Flags,
window: str,
- thresh: float = 0.0005,
- maxna: int = None,
- maxna_group: int = None,
+ thresh: float,
+ maxna: int | None = None,
+ maxna_group: int | None = None,
flag: float = BAD,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
diff --git a/saqc/funcs/curvefit.py b/saqc/funcs/curvefit.py
index d1be904674dbaa97cc1b8575875886d3252af6ac..aecdfa674477f256fc9ae9b00c21ce6639bf13cf 100644
--- a/saqc/funcs/curvefit.py
+++ b/saqc/funcs/curvefit.py
@@ -1,22 +1,27 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
from typing import Tuple, Union
-from typing_extensions import Literal
+
import numpy as np
import pandas as pd
-from dios import DictOfSeries
-from saqc.constants import *
-from saqc.core import register, Flags
-from saqc.lib.tools import getFreqDelta, filterKwargs
+from dios import DictOfSeries
+from saqc.core.flags import Flags
+from saqc.core.register import register
+from saqc.lib.tools import getFreqDelta
from saqc.lib.ts_operators import (
- polyRollerIrregular,
- polyRollerNumba,
polyRoller,
- polyRollerNoMissingNumba,
+ polyRollerIrregular,
polyRollerNoMissing,
+ polyRollerNoMissingNumba,
+ polyRollerNumba,
)
@@ -28,7 +33,7 @@ def fitPolynomial(
window: int | str,
order: int,
min_periods: int = 0,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
Fits a polynomial model to the data.
@@ -42,7 +47,7 @@ def fitPolynomial(
In case your data is sampled at an equidistant frequency grid:
(1) If you know your data to have no significant number of missing values,
- or if you do not want to calculate residues for windows containing missing values
+ or if you do not want to calculate residuals for windows containing missing values
any way, performance can be increased by setting min_periods=window.
Note, that the initial and final window/2 values do not get fitted.
@@ -87,8 +92,6 @@ def fitPolynomial(
flags : saqc.Flags
Flags
"""
- reserved = ["residues", "set_flags"]
- filterKwargs(kwargs, reserved)
return _fitPolynomial(
data=data,
field=field,
@@ -97,9 +100,6 @@ def fitPolynomial(
order=order,
min_periods=min_periods,
**kwargs,
- # ctrl args
- return_residues=False,
- set_flags=True,
)
@@ -109,10 +109,8 @@ def _fitPolynomial(
flags: Flags,
window: Union[int, str],
order: int,
- set_flags: bool = True,
min_periods: int = 0,
- return_residues: bool = False,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
# TODO: some (rather large) parts are functional similar to saqc.funcs.rolling.roll
@@ -135,7 +133,7 @@ def _fitPolynomial(
).floor()
centers = centers.drop(centers[centers.isna()].index)
centers = centers.astype(int)
- residues = to_fit.rolling(
+ fitted = to_fit.rolling(
pd.Timedelta(window), closed="both", min_periods=min_periods
).apply(polyRollerIrregular, args=(centers, order))
@@ -148,11 +146,11 @@ def _fitPolynomial(
.apply(center_func, raw=False)
.astype(int)
)
- temp = residues.copy()
+ temp = fitted.copy()
for k in centers_iloc.iteritems():
- residues.iloc[k[1]] = temp[k[0]]
- residues[residues.index[0] : residues.index[centers_iloc[0]]] = np.nan
- residues[residues.index[centers_iloc[-1]] : residues.index[-1]] = np.nan
+ fitted.iloc[k[1]] = temp[k[0]]
+ fitted[fitted.index[0] : fitted.index[centers_iloc[0]]] = np.nan
+ fitted[fitted.index[centers_iloc[-1]] : fitted.index[-1]] = np.nan
else:
if isinstance(window, str):
window = pd.Timedelta(window) // regular
@@ -180,7 +178,7 @@ def _fitPolynomial(
na_mask = to_fit.isna()
to_fit[na_mask] = miss_marker
if numba:
- residues = to_fit.rolling(window).apply(
+ fitted = to_fit.rolling(window).apply(
polyRollerNumba,
args=(miss_marker, val_range, center_index, order),
raw=True,
@@ -189,18 +187,18 @@ def _fitPolynomial(
)
# due to a tiny bug - rolling with center=True doesnt work
# when using numba engine.
- residues = residues.shift(-int(center_index))
+ fitted = fitted.shift(-int(center_index))
else:
- residues = to_fit.rolling(window, center=True).apply(
+ fitted = to_fit.rolling(window, center=True).apply(
polyRoller,
args=(miss_marker, val_range, center_index, order),
raw=True,
)
- residues[na_mask] = np.nan
+ fitted[na_mask] = np.nan
else:
# we only fit fully populated intervals:
if numba:
- residues = to_fit.rolling(window).apply(
+ fitted = to_fit.rolling(window).apply(
polyRollerNoMissingNumba,
args=(val_range, center_index, order),
engine="numba",
@@ -209,21 +207,16 @@ def _fitPolynomial(
)
# due to a tiny bug - rolling with center=True doesnt work
# when using numba engine.
- residues = residues.shift(-int(center_index))
+ fitted = fitted.shift(-int(center_index))
else:
- residues = to_fit.rolling(window, center=True).apply(
+ fitted = to_fit.rolling(window, center=True).apply(
polyRollerNoMissing,
args=(val_range, center_index, order),
raw=True,
)
- if return_residues:
- residues = to_fit - residues
-
- data[field] = residues
- if set_flags:
- # TODO: we does not get any flags here, because of masking=field
- worst = flags[field].rolling(window, center=True, min_periods=min_periods).max()
- flags[field] = worst
+ data[field] = fitted
+ worst = flags[field].rolling(window, center=True, min_periods=min_periods).max()
+ flags[field] = worst
return data, flags
diff --git a/saqc/funcs/drift.py b/saqc/funcs/drift.py
index ff874b53593df69e9c0d4afddfc0efb7030d484b..6b07c3ef0f95b2a0226ae472b7d5b14001fe49de 100644
--- a/saqc/funcs/drift.py
+++ b/saqc/funcs/drift.py
@@ -1,31 +1,32 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
import functools
-from typing import Tuple, Sequence, Callable
-from typing_extensions import Literal
+import inspect
+from typing import Callable, Optional, Sequence, Tuple
import numpy as np
import pandas as pd
-
from scipy.optimize import curve_fit
from scipy.spatial.distance import pdist
+from typing_extensions import Literal
from dios import DictOfSeries
from saqc.constants import BAD
-
-from saqc.core.register import register, flagging, Flags
+from saqc.core.register import Flags, flagging, register
from saqc.funcs.changepoints import _assignChangePointCluster
-from saqc.funcs.tools import dropField, copyField
-
-from saqc.lib.tools import detectDeviants, toSequence, filterKwargs
-from saqc.lib.ts_operators import linearDriftModel, expDriftModel
+from saqc.funcs.tools import copyField, dropField
+from saqc.lib.tools import detectDeviants, filterKwargs, toSequence
+from saqc.lib.ts_operators import expDriftModel, linearDriftModel
from saqc.lib.types import CurveFitter
-from saqc.lib.ts_operators import linearDriftModel, expDriftModel
-
LinkageString = Literal[
"single", "complete", "average", "weighted", "centroid", "median", "ward"
@@ -405,7 +406,7 @@ def correctRegimeAnomaly(
flags: Flags,
cluster_field: str,
model: CurveFitter,
- tolerance: str = None,
+ tolerance: Optional[str] = None,
epoch: bool = False,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
@@ -526,7 +527,7 @@ def correctOffset(
spread: float,
window: str,
min_periods: int,
- tolerance: str = None,
+ tolerance: Optional[str] = None,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
@@ -589,26 +590,27 @@ def correctOffset(
def _driftFit(x, shift_target, cal_mean, driftModel):
x_index = x.index - x.index[0]
x_data = x_index.total_seconds().values
- x_data = x_data / x_data[-1]
+ x_data = x_data / x_data[-1] if len(x_data) > 1 else x_data
y_data = x.values
origin_mean = np.mean(y_data[:cal_mean])
target_mean = np.mean(y_data[-cal_mean:])
dataFitFunc = functools.partial(driftModel, origin=origin_mean, target=target_mean)
# if drift model has free parameters:
- try:
- # try fitting free parameters
- fit_paras, *_ = curve_fit(dataFitFunc, x_data, y_data)
- data_fit = dataFitFunc(x_data, *fit_paras)
- data_shift = driftModel(
- x_data, *fit_paras, origin=origin_mean, target=shift_target
- )
- except RuntimeError:
- # if fit fails -> make no correction
- data_fit = np.array([0] * len(x_data))
- data_shift = np.array([0] * len(x_data))
+ if len(inspect.getfullargspec(dataFitFunc).args) > 1:
+ try:
+ # try fitting free parameters
+ fit_paras, *_ = curve_fit(dataFitFunc, x_data, y_data)
+ data_fit = dataFitFunc(x_data, *fit_paras)
+ data_shift = driftModel(
+ x_data, *fit_paras, origin=origin_mean, target=shift_target
+ )
+ except RuntimeError:
+ # if fit fails -> make no correction
+ data_fit = np.array([0] * len(x_data))
+ data_shift = np.array([0] * len(x_data))
# when there are no free parameters in the model:
- except ValueError:
+ else:
data_fit = dataFitFunc(x_data)
data_shift = driftModel(x_data, origin=origin_mean, target=shift_target)
diff --git a/saqc/funcs/flagtools.py b/saqc/funcs/flagtools.py
index cafe7e1d74c36c6d6eb4248e2e5245bfed927ec0..effa0e1dbae6bc3934c7ad1428f62ee6b740aa25 100644
--- a/saqc/funcs/flagtools.py
+++ b/saqc/funcs/flagtools.py
@@ -1,19 +1,24 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
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
-import numpy as np
-from dios import DictOfSeries
-from saqc.constants import BAD, UNFLAGGED
-from saqc.core import register, Flags
import warnings
+from typing import Any, Sequence, Tuple, Union
+
+import numpy as np
+import pandas as pd
+from typing_extensions import Literal
-from saqc.core.register import flagging
-from saqc.funcs.generic import flagGeneric
+from dios import DictOfSeries
+from saqc.constants import BAD, FILTER_ALL, UNFLAGGED
+from saqc.core.flags import Flags
+from saqc.core.register import _isflagged, flagging, register
+from saqc.funcs.resampling import concatFlags
@register(mask=[], demask=[], squeeze=["field"])
@@ -210,11 +215,11 @@ def flagManual(
.. doctest:: ExampleFlagManual
- >>> mdata = pd.Series([1,0,1], index=pd.to_datetime(['2000-02', '2000-03', '2001-05']))
+ >>> mdata = pd.Series([1, 0, 1], index=pd.to_datetime(['2000-02-01', '2000-03-01', '2000-05-01']))
>>> mdata
2000-02-01 1
2000-03-01 0
- 2001-05-01 1
+ 2000-05-01 1
dtype: int64
On *dayly* data, with the 'ontime' method, only the provided timestamps are used.
@@ -223,49 +228,42 @@ def flagManual(
.. doctest:: ExampleFlagManual
- >>> data = a=pd.Series(0, index=pd.date_range('2000-01-31', '2000-03-02', freq='1D'), name='dailyData')
+ >>> data = pd.Series(0, index=pd.to_datetime(['2000-01-31', '2000-02-01', '2000-02-02', '2000-03-01', '2000-05-01']), name='daily_data')
>>> qc = saqc.SaQC(data)
- >>> qc = qc.flagManual('dailyData', mdata, mflag=1, mformat='mdata', method='ontime')
- >>> qc.flags['dailyData'] > UNFLAGGED #doctest:+SKIP
+ >>> qc = qc.flagManual('daily_data', mdata, mflag=1, mformat='mdata', method='ontime')
+ >>> qc.flags['daily_data'] > UNFLAGGED
2000-01-31 False
- 2000-02-01 True
+ 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
+ 2000-03-01 False
+ 2000-05-01 True
+ Name: daily_data, dtype: bool
With the 'right-open' method, the mdata is forward fill:
.. doctest:: ExampleFlagManual
- >>> qc = qc.flagManual('dailyData', mdata, mflag=1, mformat='mdata', method='right-open')
- >>> qc.flags['dailyData'] > UNFLAGGED #doctest:+SKIP
+ >>> qc = qc.flagManual('daily_data', mdata, mflag=1, mformat='mdata', method='right-open')
+ >>> qc.flags['daily_data'] > UNFLAGGED
2000-01-31 False
- 2000-02-01 True
- 2000-02-02 True
- .. ..
- 2000-02-29 True
+ 2000-02-01 True
+ 2000-02-02 True
2000-03-01 False
- 2000-03-02 False
- Freq: D, dtype: bool
+ 2000-05-01 True
+ Name: daily_data, dtype: bool
With the 'left-open' method, backward filling is used:
.. doctest:: ExampleFlagManual
- >>> qc = qc.flagManual('dailyData', mdata, mflag=1, mformat='mdata', method='left-open')
- >>> qc.flags['dailyData'] > UNFLAGGED #doctest:+SKIP
+ >>> qc = qc.flagManual('daily_data', mdata, mflag=1, mformat='mdata', method='left-open')
+ >>> qc.flags['daily_data'] > 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
+ 2000-02-01 True
+ 2000-02-02 True
+ 2000-03-01 True
+ 2000-05-01 True
+ Name: daily_data, dtype: bool
"""
dat = data[field]
# internal not-mflag-value -> cant go for np.nan
@@ -315,11 +313,9 @@ def flagManual(
mdata = mdata.ffill()
if method == "left-open":
- mdata = (
- mdata.replace({mflag: not_mflag, not_mflag: mflag})
- .append(app_entry)
- .bfill()
- )
+ mdata = pd.concat(
+ [mdata.replace({mflag: not_mflag, not_mflag: mflag}), app_entry]
+ ).bfill()
if method == "closed":
mdata[mdata.ffill() == mflag] = mflag
@@ -394,6 +390,7 @@ def transferFlags(
See Also
--------
* :py:meth:`saqc.SaQC.flagGeneric`
+ * :py:meth:`saqc.SaQC.concatFlags`
Examples
--------
@@ -401,12 +398,10 @@ def transferFlags(
.. doctest:: exampleTransfer
- >>> import pandas as pd #doctest:+SKIP
- >>> import saqc #doctest:+SKIP
- >>> data = pd.DataFrame({'a':[1,2], 'b':[1,2], 'c':[1,2]})
+ >>> 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
+ >>> qc.flags.to_df()
columns a b c
0 -inf -inf -inf
1 255.0 -inf -inf
@@ -416,7 +411,7 @@ def transferFlags(
.. doctest:: exampleTransfer
>>> qc = qc.transferFlags('a', target='b')
- >>> qc.flags
+ >>> qc.flags.to_df()
columns a b c
0 -inf -inf -inf
1 255.0 255.0 -inf
@@ -433,13 +428,141 @@ def transferFlags(
.. doctest:: exampleTransfer
>>> qc = qc.transferFlags(['a','a'], ['b', 'c'])
- >>> qc.flags
+ >>> qc.flags.to_df()
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
+ data, flags = concatFlags(
+ data, field, flags, target=target, method="match", squeeze=False
+ )
+ return data, flags
+
+
+@flagging()
+def propagateFlags(
+ data: DictOfSeries,
+ field: str,
+ flags: Flags,
+ window: Union[str, int],
+ method: Literal["ffill", "bfill"] = "ffill",
+ flag: float = BAD,
+ dfilter: float = FILTER_ALL,
+ **kwargs,
+) -> Tuple[DictOfSeries, Flags]:
+ """
+ Flag values before or after flags set by the last test.
+
+ Parameters
+ ----------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
+ flags : saqc.Flags
+ A flags object, holding flags and additional informations related to `data`.
+ window : int, str
+ Size of the repetition window. An integer defines the exact number of repetitions,
+ strings are interpreted as time offsets to fill with .
+ method : {"ffill", "bfill"}
+ Direction of repetetion. With "ffill" the subsequent values receive the flag to
+ repeat, with "bfill" the previous values.
+ flag : float, default BAD
+ Flag to set.
+ dfilter : float, default FILTER_ALL
+ Threshold flag.
+
+ Returns
+ -------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ flags : saqc.Flags
+ The quality flags of data
+
+ Examples
+ --------
+ First, generate some data and some flags:
+
+ .. doctest:: propagateFlags
+
+ >>> data = pd.DataFrame({"a": [-3, -2, -1, 0, 1, 2, 3]})
+ >>> flags = pd.DataFrame({"a": [-np.inf, -np.inf, -np.inf, 255.0, -np.inf, -np.inf, -np.inf]})
+ >>> qc = saqc.SaQC(data=data, flags=flags)
+ >>> qc.flags["a"]
+ 0 -inf
+ 1 -inf
+ 2 -inf
+ 3 255.0
+ 4 -inf
+ 5 -inf
+ 6 -inf
+ Name: a, dtype: float64
+
+ Now, to repeat the flag '255.0' two times in direction of ascending indices, execute:
+
+ .. doctest:: propagateFlags
+
+ >>> qc.propagateFlags('a', window=2, method="ffill").flags["a"]
+ 0 -inf
+ 1 -inf
+ 2 -inf
+ 3 255.0
+ 4 255.0
+ 5 255.0
+ 6 -inf
+ Name: a, dtype: float64
+
+ Choosing "bfill" will result in
+
+ .. doctest:: propagateFlags
+
+ >>> qc.propagateFlags('a', window=2, method="bfill").flags["a"]
+ 0 -inf
+ 1 255.0
+ 2 255.0
+ 3 255.0
+ 4 -inf
+ 5 -inf
+ 6 -inf
+ Name: a, dtype: float64
+
+ If an explicit flag is passed, it will be used to fill the repetition window
+
+ .. doctest:: propagateFlags
+
+ >>> qc.propagateFlags('a', window=2, method="bfill", flag=111).flags["a"]
+ 0 -inf
+ 1 111.0
+ 2 111.0
+ 3 255.0
+ 4 -inf
+ 5 -inf
+ 6 -inf
+ Name: a, dtype: float64
+ """
+
+ if method not in {"bfill", "ffill"}:
+ raise ValueError(f"supported methods are 'bfill', 'ffill', got '{method}'")
+
+ # get the last history column
+ hc = flags.history[field].hist.iloc[:, -1].astype(float)
+
+ if method == "bfill":
+ hc = hc[::-1]
+
+ flagged = _isflagged(hc, dfilter)
+
+ repeated = (
+ flagged.rolling(window, min_periods=1, closed="left")
+ .max()
+ .fillna(0)
+ .astype(bool)
)
+
+ if method == "bfill":
+ repeated = repeated[::-1]
+
+ flags[repeated, field] = flag
+
return data, flags
diff --git a/saqc/funcs/generic.py b/saqc/funcs/generic.py
index aa1da90e4986df65074498c7a47e7276eb450882..53b0d77c6e34dbe8905ff1ccf2f2c2771274048f 100644
--- a/saqc/funcs/generic.py
+++ b/saqc/funcs/generic.py
@@ -1,4 +1,9 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
@@ -8,19 +13,41 @@ import numpy as np
import pandas as pd
from dios import DictOfSeries
-
-from saqc.constants import BAD, UNFLAGGED, ENVIRONMENT, FILTER_ALL
+from saqc.constants import BAD, ENVIRONMENT, FILTER_ALL
+from saqc.core.flags import Flags
from saqc.core.history import History
+from saqc.core.register import FunctionWrapper, _isflagged, register
from saqc.lib.tools import toSequence
from saqc.lib.types import GenericFunction, PandasLike
-from saqc.core.flags import Flags
-from saqc.core.register import register, _isflagged, FunctionWrapper
+
+
+def _flagSelect(field, flags, label=None):
+ if label is None:
+ return flags[field]
+
+ h_meta = flags.history[field].meta
+ trg_col = None
+
+ for idx, item in enumerate(h_meta):
+ kwargs = item.get("kwargs")
+ if kwargs is None or "label" not in kwargs:
+ continue
+ if kwargs["label"] == label:
+ trg_col = idx
+
+ if trg_col is None:
+ raise KeyError(f"no such label {label} for field {field}")
+
+ out = flags.history[field].hist[trg_col].astype(float)
+ return out.fillna(-np.inf)
def _prepare(
data: DictOfSeries, flags: Flags, columns: Sequence[str], dfilter: float
) -> Tuple[DictOfSeries, Flags]:
fchunk = Flags({f: flags[f] for f in columns})
+ for f in fchunk.columns:
+ fchunk.history[f] = flags.history[f]
dchunk, _ = FunctionWrapper._maskData(
data=data.loc[:, columns].copy(), flags=fchunk, columns=columns, thresh=dfilter
)
@@ -35,7 +62,9 @@ def _execGeneric(
) -> DictOfSeries:
globs = {
- "isflagged": lambda data: _isflagged(flags[data.name], thresh=dfilter),
+ "isflagged": lambda data, label=None: _isflagged(
+ _flagSelect(data.name, flags, label), thresh=dfilter
+ ),
**ENVIRONMENT,
}
@@ -66,8 +95,7 @@ def processGeneric(
field: str | Sequence[str],
flags: Flags,
func: GenericFunction,
- target: str | Sequence[str] = None,
- flag: float = UNFLAGGED,
+ target: str | Sequence[str] | None = None,
dfilter: float = FILTER_ALL,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
@@ -101,10 +129,9 @@ def processGeneric(
The variable(s) to write the result of ``func`` to. If not given, the variable(s)
specified in ``field`` will be overwritten. If a ``target`` is not given, it will be
created.
- flag: float, default ``UNFLAGGED``
- The quality flag to set. The default ``UNFLAGGED`` states the general idea, that
- ``processGeneric`` generates 'new' data without direct relation to the potentially
- already present flags.
+ flag: float, default ``np.nan``
+ The quality flag to set. The default ``np.nan`` states the general idea, that
+ ``processGeneric`` generates 'new' data without any flags.
dfilter: float, default ``FILTER_ALL``
Threshold flag. Flag values greater than ``dfilter`` indicate that the associated
data value is inappropiate for further usage.
@@ -127,12 +154,12 @@ def processGeneric(
Compute the sum of the variables 'rainfall' and 'snowfall' and save the result to
a (new) variable 'precipitation'
- .. 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)
+ >>> from saqc import SaQC
+ >>> qc = SaQC(pd.DataFrame({'rainfall':[1], 'snowfall':[2]}, index=pd.DatetimeIndex([0])))
+ >>> qc = qc.processGeneric(field=["rainfall", "snowfall"], target="precipitation", func=lambda x, y: x + y)
+ >>> qc.data.to_df()
+ columns rainfall snowfall precipitation
+ 1970-01-01 1 2 3
"""
fields = toSequence(field)
@@ -143,14 +170,11 @@ def processGeneric(
meta = {
"func": "procGeneric",
- "args": (),
+ "args": (field, target),
"kwargs": {
- **kwargs,
- "field": field,
"func": func.__name__,
- "target": target,
- "flag": flag,
"dfilter": dfilter,
+ **kwargs,
},
}
@@ -169,7 +193,7 @@ def processGeneric(
"because of incompatible indices, please choose another 'target'"
)
- flags.history[col].append(pd.Series(flag, index=datacol.index), meta)
+ flags.history[col].append(pd.Series(np.nan, index=datacol.index), meta)
return data, flags
@@ -217,8 +241,8 @@ def flagGeneric(
The variable(s) to write the result of ``func`` to. If not given, the variable(s)
specified in ``field`` will be overwritten. If a ``target`` is not given, it will be
created.
- flag: float, default ``UNFLAGGED``
- The quality flag to set. The default ``UNFLAGGED`` states the general idea, that
+ flag: float, default ``BAD``
+ The quality flag to set. The default ``BAD`` states the general idea, that
``processGeneric`` generates 'new' data without direct relation to the potentially
already present flags.
dfilter: float, default ``FILTER_ALL``
@@ -279,14 +303,12 @@ def flagGeneric(
meta = {
"func": "flagGeneric",
- "args": (),
+ "args": (field, target),
"kwargs": {
- **kwargs,
- "field": field,
"func": func.__name__,
- "target": target,
"flag": flag,
"dfilter": dfilter,
+ **kwargs,
},
}
@@ -303,7 +325,6 @@ def flagGeneric(
if col not in data:
data[col] = pd.Series(np.nan, index=maskcol.index)
- maskcol = maskcol & ~_isflagged(flags[col], dfilter)
flagcol = maskcol.replace({False: np.nan, True: flag}).astype(float)
# we need equal indices to work on
diff --git a/saqc/funcs/interpolation.py b/saqc/funcs/interpolation.py
index ba131d8fc84b606ae358ada6e021ce4ad9772f26..3a005a719f9c36172465bbe04619f5130e5f6d03 100644
--- a/saqc/funcs/interpolation.py
+++ b/saqc/funcs/interpolation.py
@@ -1,14 +1,22 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-from typing import Tuple, Union, Callable
-from typing_extensions import Literal
+from __future__ import annotations
+
+from typing import Callable, Tuple, Union
+
import numpy as np
import pandas as pd
-from dios import DictOfSeries
+from typing_extensions import Literal
-from saqc.constants import *
-from saqc.core import register, Flags
-from saqc.core.register import _isflagged, processing
+from dios import DictOfSeries
+from saqc.constants import UNFLAGGED
+from saqc.core.flags import Flags
+from saqc.core.register import _isflagged, register
from saqc.lib.ts_operators import interpolateNANs
_SUPPORTED_METHODS = Literal[
@@ -181,9 +189,9 @@ def interpolateInvalid(
inter_limit=limit,
downgrade_interpolation=downgrade,
)
- data[field] = inter_data
interpolated = data[field].isna() & inter_data.notna()
+ data[field] = inter_data
new_col = pd.Series(np.nan, index=flags[field].index)
new_col.loc[interpolated] = np.nan if flag is None else flag
diff --git a/saqc/funcs/noise.py b/saqc/funcs/noise.py
index d9e808561e45becf21c301c90874d25f71ba1141..6fe5fb8c2a8c7a2b5e627e54dee9ac0687030ce9 100644
--- a/saqc/funcs/noise.py
+++ b/saqc/funcs/noise.py
@@ -1,12 +1,21 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-import pandas as pd
-import numpy as np
+from __future__ import annotations
+
import operator
-from dios import DictOfSeries
from typing import Callable
-from saqc.constants import *
-from saqc.core import register, Flags
+
+import numpy as np
+import pandas as pd
+
+from dios import DictOfSeries
+from saqc.constants import BAD
+from saqc.core.flags import Flags
from saqc.core.register import flagging
from saqc.lib.tools import statPass
@@ -16,10 +25,10 @@ def flagByStatLowPass(
data: DictOfSeries,
field: str,
flags: Flags,
- func: Callable[[np.array, pd.Series], float],
- window: str,
+ func: Callable[[np.ndarray, pd.Series], float],
+ window: str | pd.Timedelta,
thresh: float,
- sub_window: str = None,
+ sub_window: str | pd.Timedelta = None,
sub_thresh: float = None,
min_periods: int = None,
flag: float = BAD,
diff --git a/saqc/funcs/outliers.py b/saqc/funcs/outliers.py
index c41079481420a3f5d5752b6af54d42bf6fa897d3..f7d137dcdd9ca75060c6bbde6e78b2818376ae79 100644
--- a/saqc/funcs/outliers.py
+++ b/saqc/funcs/outliers.py
@@ -1,27 +1,31 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
+from __future__ import annotations
+
import uuid
-from typing import Optional, Union, Tuple, Sequence, Callable
-from typing_extensions import Literal
+from typing import Callable, Optional, Sequence, Tuple, Union
import numba
import numpy as np
import numpy.polynomial.polynomial as poly
import pandas as pd
-
-from dios import DictOfSeries
from outliers import smirnov_grubbs
-from scipy.optimize import curve_fit
+from typing_extensions import Literal
+from dios import DictOfSeries
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
+from saqc.core.flags import Flags
+from saqc.core.register import flagging, register
from saqc.funcs.scores import assignKNNScore
from saqc.funcs.tools import copyField, dropField
from saqc.funcs.transformation import transform
-import saqc.lib.ts_operators as ts_ops
+from saqc.lib.tools import customRoller, getFreqDelta, toSequence
@flagging()
@@ -29,7 +33,7 @@ def flagByStray(
data: DictOfSeries,
field: str,
flags: Flags,
- freq: Optional[Union[int, str]] = None,
+ window: Optional[Union[int, str]] = None,
min_periods: int = 11,
iter_start: float = 0.5,
alpha: float = 0.05,
@@ -87,17 +91,17 @@ def flagByStray(
if scores.empty:
return data, flags
- if not freq:
- freq = scores.shape[0]
+ if not window:
+ window = scores.shape[0]
- if isinstance(freq, str):
- partitions = scores.groupby(pd.Grouper(freq=freq))
+ if isinstance(window, str):
+ partitions = scores.groupby(pd.Grouper(freq=window))
else:
grouper_series = pd.Series(
data=np.arange(0, scores.shape[0]), index=scores.index
)
- grouper_series = grouper_series.transform(lambda x: int(np.floor(x / freq)))
+ grouper_series = grouper_series.transform(lambda x: int(np.floor(x / window)))
partitions = scores.groupby(grouper_series)
# calculate flags for every partition
@@ -238,12 +242,12 @@ def _evalStrayLabels(
x = test_slice.index.values.astype(float)
x_0 = x[0]
- x = (x - x_0) / 10 ** 12
+ x = (x - x_0) / 10**12
polyfitted = poly.polyfit(y=test_slice.values, x=x, deg=polydeg)
testval = poly.polyval(
- (float(index[1].to_numpy()) - x_0) / 10 ** 12, polyfitted
+ (float(index[1].to_numpy()) - x_0) / 10**12, polyfitted
)
testval = val_frame[var][index[1]] - testval
@@ -265,140 +269,6 @@ def _evalStrayLabels(
return data, flags
-def _expFit(
- val_frame,
- scoring_method="kNNMaxGap",
- n_neighbors=10,
- iter_start=0.5,
- alpha=0.05,
- bin_frac=10,
-):
- """
- Find outliers in multi dimensional observations.
-
- The general idea is to assigning scores to every observation based on the observations neighborhood in the space
- of observations. Then, the gaps between the (greatest) scores are tested for beeing drawn from the same
- distribution, as the majority of the scores.
-
- Note, that no normalizations/transformations are applied to the different components (data columns)
- - those are expected to be applied previously, if necessary.
-
- Parameters
- ----------
- val_frame : (N,M) ndarray
- Input NxM array of observations, where N is the number of observations and M the number of components per
- observation.
- scoring_method : {'kNNSum', 'kNNMaxGap'}, default 'kNNMaxGap'
- Scoring method applied.
- `'kNNSum'`: Assign to every point the sum of the distances to its 'n' nearest neighbors.
- `'kNNMaxGap'`: Assign to every point the distance to the neighbor with the "maximum gap" to its predecessor
- in the hierarchy of the `n` nearest neighbors. (see reference section for further descriptions)
- n_neighbors : int, default 10
- Number of neighbors included in the scoring process for every datapoint.
- iter_start : float, default 0.5
- Float in [0,1] that determines which percentage of data is considered "normal". 0.5 results in the expfit
- algorithm to search only the upper 50 % of the scores for the cut off point. (See reference section for more
- information)
- alpha : float, default 0.05
- Niveau of significance by which it is tested, if a score might be drawn from another distribution, than the
- majority of the data.
- bin_frac : {int, str}, default 10
- Controls the binning for the histogram in the fitting step. If an integer is passed, the residues will
- equidistantly be covered by `bin_frac` bins, ranging from the minimum to the maximum of the residues.
- If a string is passed, it will be passed on to the ``numpy.histogram_bin_edges`` method.
- """
-
- kNNfunc = getattr(ts_ops, scoring_method)
- resids = kNNfunc(val_frame.values, n_neighbors=n_neighbors, algorithm="ball_tree")
- data_len = resids.shape[0]
-
- # sorting
- sorted_i = resids.argsort()
- resids = resids[sorted_i]
- iter_index = int(np.floor(resids.size * iter_start))
- # initialize condition variables:
- crit_val = np.inf
- test_val = 0
- neg_log_alpha = -np.log(alpha)
-
- # define exponential dist density function:
- def fit_function(x, lambd):
- return lambd * np.exp(-lambd * x)
-
- # initialise sampling bins
- if isinstance(bin_frac, int):
- binz = np.linspace(
- resids[0], resids[-1], 10 * int(np.ceil(data_len / bin_frac))
- )
- elif bin_frac in [
- "auto",
- "fd",
- "doane",
- "scott",
- "stone",
- "rice",
- "sturges",
- "sqrt",
- ]:
- binz = np.histogram_bin_edges(resids, bins=bin_frac)
- else:
- raise ValueError(f"Can't interpret {bin_frac} as an binning technique.")
-
- binzenters = np.array([0.5 * (binz[i] + binz[i + 1]) for i in range(len(binz) - 1)])
- # inititialize full histogram:
- full_hist, binz = np.histogram(resids, bins=binz)
- # check if start index is sufficiently high (pointing at resids value beyond histogram maximum at least):
- hist_argmax = full_hist.argmax()
-
- if hist_argmax >= findIndex(binz, resids[iter_index - 1], 0):
- raise ValueError(
- "Either the data histogram is too strangely shaped for oddWater OD detection - "
- "or a too low value for 'offset' was passed "
- "(offset better be much greater 0.5)"
- )
- # GO!
- iter_max_bin_index = findIndex(binz, resids[iter_index - 1], 0)
- upper_tail_index = int(np.floor(0.5 * hist_argmax + 0.5 * iter_max_bin_index))
- resids_tail_index = findIndex(resids, binz[upper_tail_index], 0)
- upper_tail_hist, bins = np.histogram(
- resids[resids_tail_index:iter_index],
- bins=binz[upper_tail_index : iter_max_bin_index + 1],
- )
-
- while (test_val < crit_val) & (iter_index < resids.size - 1):
- iter_index += 1
- new_iter_max_bin_index = findIndex(binz, resids[iter_index - 1], 0)
- # following if/else block "manually" expands the data histogram and circumvents calculation of the complete
- # histogram in any new iteration.
- if new_iter_max_bin_index == iter_max_bin_index:
- upper_tail_hist[-1] += 1
- else:
- upper_tail_hist = np.append(
- upper_tail_hist, np.zeros([new_iter_max_bin_index - iter_max_bin_index])
- )
- upper_tail_hist[-1] += 1
- iter_max_bin_index = new_iter_max_bin_index
- upper_tail_index_new = int(
- np.floor(0.5 * hist_argmax + 0.5 * iter_max_bin_index)
- )
- upper_tail_hist = upper_tail_hist[upper_tail_index_new - upper_tail_index :]
- upper_tail_index = upper_tail_index_new
-
- # fitting
-
- lambdA, _ = curve_fit(
- fit_function,
- xdata=binzenters[upper_tail_index:iter_max_bin_index],
- ydata=upper_tail_hist,
- p0=[-np.log(alpha / resids[iter_index])],
- )
-
- crit_val = neg_log_alpha / lambdA
- test_val = resids[iter_index]
-
- return val_frame.index[sorted_i[iter_index:]]
-
-
@register(
mask=["field"],
demask=["field"],
@@ -873,27 +743,29 @@ def flagOffset(
data: DictOfSeries,
field: str,
flags: Flags,
- thresh: float,
tolerance: float,
window: Union[int, str],
+ thresh: Optional[float] = None,
thresh_relative: Optional[float] = None,
flag: float = BAD,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- A basic outlier test that work on regular and irregular sampled data
+ A basic outlier test that works on regularly and irregularly sampled data.
The test classifies values/value courses as outliers by detecting not only a rise
- in value, but also, checking for a return to the initial value level.
+ in value, but also, by checking for a return to the initial value level.
Values :math:`x_n, x_{n+1}, .... , x_{n+k}` of a timeseries :math:`x` with
associated timestamps :math:`t_n, t_{n+1}, .... , t_{n+k}` are considered spikes, if
1. :math:`|x_{n-1} - x_{n + s}| >` `thresh`, for all :math:`s \\in [0,1,2,...,k]`
- 2. :math:`|x_{n-1} - x_{n+k+1}| <` `tolerance`
+ 2. :math:`(x_{n + s} - x_{n - 1}) / x_{n - 1} >` `thresh_relative`
- 3. :math:`|t_{n-1} - t_{n+k+1}| <` `window`
+ 3. :math:`|x_{n-1} - x_{n+k+1}| <` `tolerance`
+
+ 4. :math:`|t_{n-1} - t_{n+k+1}| <` `window`
Note, that this definition of a "spike" not only includes one-value outliers, but
also plateau-ish value courses.
@@ -906,15 +778,19 @@ def flagOffset(
The field in data.
flags : saqc.Flags
Container to store flags of the data.
- thresh : float
- Minimum difference between to values, to consider the latter one as a spike. See condition (1)
tolerance : float
- Maximum difference between pre-spike and post-spike values. See condition (2)
+ Maximum difference allowed, between the value, directly preceding and the value, directly succeeding an offset,
+ to trigger flagging of the values forming the offset.
+ See condition (3).
window : {str, int}, default '15min'
- Maximum length of "spiky" value courses. See condition (3). Integer defined window length are only allowed for
- regularly sampled timeseries.
+ Maximum length allowed for offset value courses, to trigger flagging of the values forming the offset.
+ See condition (4). Integer defined window length are only allowed for regularly sampled timeseries.
+ thresh : float: {float, None}, default None
+ Minimum difference between a value and its successors, to consider the successors an anomalous offset group.
+ See condition (1). If None is passed, condition (1) is not tested.
thresh_relative : {float, None}, default None
- Relative threshold.
+ Minimum relative change between and its successors, to consider the successors an anomalous offset group.
+ See condition (2). If None is passed, condition (2) is not tested.
flag : float, default BAD
flag to set.
@@ -926,6 +802,99 @@ def flagOffset(
The quality flags of data
Flags values may have changed, relatively to the flags input.
+ Examples
+ --------
+
+ .. plot::
+ :context:
+ :include-source: False
+
+ import matplotlib
+ import saqc
+ import pandas as pd
+ data = pd.DataFrame({'data':np.array([5,5,8,16,17,7,4,4,4,1,1,4])}, index=pd.date_range('2000',freq='1H', periods=12))
+
+
+
+ Lets generate a simple, regularly sampled timeseries with an hourly sampling rate and generate an
+ :py:class:`saqc.SaQC` instance from it.
+
+ .. doctest:: flagOffsetExample
+
+ >>> data = pd.DataFrame({'data':np.array([5,5,8,16,17,7,4,4,4,1,1,4])}, index=pd.date_range('2000',freq='1H', periods=12))
+ >>> data
+ data
+ 2000-01-01 00:00:00 5
+ 2000-01-01 01:00:00 5
+ 2000-01-01 02:00:00 8
+ 2000-01-01 03:00:00 16
+ 2000-01-01 04:00:00 17
+ 2000-01-01 05:00:00 7
+ 2000-01-01 06:00:00 4
+ 2000-01-01 07:00:00 4
+ 2000-01-01 08:00:00 4
+ 2000-01-01 09:00:00 1
+ 2000-01-01 10:00:00 1
+ 2000-01-01 11:00:00 4
+ >>> qc = saqc.SaQC(data)
+
+ Now we are applying :py:meth:`~saqc.SaQC.flagOffset` and try to flag offset courses, that dont extend longer than
+ *6 hours* in time (``window``) and that have an initial value jump higher than *2* (``thresh``), and that do return
+ to the initial value level within a tolerance of *1.5* (``tolerance``).
+
+ .. doctest:: flagOffsetExample
+
+ >>> qc = qc.flagOffset("data", thresh=2, tolerance=1.5, window='6H')
+ >>> qc.plot('data') # doctest:+SKIP
+
+ .. plot::
+ :context: close-figs
+ :include-source: False
+
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagOffset("data", thresh=2, tolerance=1.5, window='6H')
+ >>> qc.plot('data')
+
+ Note, that both, negative and positive jumps are considered starting points of negative or positive offsets.
+ If you want to impose the additional condition, that the initial value jump must exceed *+90%* of the value level,
+ you can additionally set the ``thresh_relative`` parameter:
+
+ .. doctest:: flagOffsetExample
+
+ >>> qc = qc.flagOffset("data", thresh=2, thresh_relative=.9, tolerance=1.5, window='6H')
+ >>> qc.plot('data') # doctest:+SKIP
+
+ .. plot::
+ :context: close-figs
+ :include-source: False
+
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagOffset("data", thresh=2, thresh_relative=.9, tolerance=1.5, window='6H')
+ >>> qc.plot('data')
+
+ Now, only positive jumps, that exceed a value gain of *+90%* are considered starting points of offsets.
+
+ In the same way, you can aim for only negative offsets, by setting a negative relative threshold. The below
+ example only flags offsets, that fall off by at least *50 %* in value, with an absolute value drop of at least *2*.
+
+ .. doctest:: flagOffsetExample
+
+ >>> qc = qc.flagOffset("data", thresh=2, thresh_relative=-.5, tolerance=1.5, window='6H')
+ >>> qc.plot('data') # doctest:+SKIP
+
+ .. plot::
+ :context: close-figs
+ :include-source: False
+
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagOffset("data", thresh=2, thresh_relative=-.5, tolerance=1.5, window='6H')
+ >>> qc.plot('data')
+
+
+
+
+
+
References
----------
The implementation is a time-window based version of an outlier test from the UFZ Python library,
@@ -934,6 +903,12 @@ def flagOffset(
https://git.ufz.de/chs/python/blob/master/ufz/level1/spike.py
"""
+ if (thresh is None) and (thresh_relative is None):
+ raise ValueError(
+ "At least one of parameters 'thresh' and 'thresh_relative' has to be given. Got 'thresh'=None, "
+ "'thresh_relative'=None instead."
+ )
+
dataseries = data[field].dropna()
if dataseries.empty:
return data, flags
@@ -949,19 +924,19 @@ def flagOffset(
window = delta * window
if not delta:
raise TypeError(
- "Only offset string defined window sizes allowed for irrgegularily sampled timeseries"
+ "Only offset string defined window sizes allowed for timeseries not sampled regularly."
)
# get all the entries preceding a significant jump
- if thresh:
+ if thresh is not None:
post_jumps = dataseries.diff().abs() > thresh
- if thresh_relative:
+ if thresh_relative is not None:
s = np.sign(thresh_relative)
rel_jumps = s * (dataseries.shift(1) - dataseries).div(dataseries.abs()) > abs(
thresh_relative
)
- if thresh:
+ if thresh is not None:
post_jumps = rel_jumps & post_jumps
else:
post_jumps = rel_jumps
@@ -977,11 +952,13 @@ def flagOffset(
).dropna()
to_roll = dataseries[to_roll]
- if thresh_relative:
+ if thresh_relative is not None:
- def spikeTester(chunk, thresh=abs(thresh_relative), tol=tolerance):
+ def spikeTester(
+ chunk, thresh_r=abs(thresh_relative), thresh_a=thresh or 0, tol=tolerance
+ ):
jump = chunk[-2] - chunk[-1]
- thresh = thresh * abs(jump)
+ thresh = max(thresh_r * abs(chunk[-1]), thresh_a)
chunk_stair = (np.sign(jump) * (chunk - chunk[-1]) < thresh)[::-1].cumsum()
initial = np.searchsorted(chunk_stair, 2)
if initial == len(chunk):
@@ -1257,6 +1234,14 @@ def flagCrossStatistics(
The quality flags of data
Flags values may have changed relatively to the input flags.
+
+ Notes
+ -----
+
+ The input variables dont necessarily have to be aligned. If the variables are unaligned, scoring
+ and flagging will be only performed on the subset of inices shared among all input variables.
+
+
References
----------
[1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
@@ -1264,13 +1249,6 @@ def flagCrossStatistics(
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):
@@ -1307,6 +1285,7 @@ def flagCrossStatistics(
return data, flags
for f in fields:
- flags[mask[f], f] = flag
+ m = mask[f].reindex(index=flags[f].index, fill_value=False)
+ flags[m, f] = flag
return data, flags
diff --git a/saqc/funcs/pattern.py b/saqc/funcs/pattern.py
index f1d3c86926f8eab5e698b268fd231885736567a9..96a5b176c3aed3666470696ac785d00b6acdaa9c 100644
--- a/saqc/funcs/pattern.py
+++ b/saqc/funcs/pattern.py
@@ -1,116 +1,19 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-import pandas as pd
import dtw
+import pandas as pd
from saqc.constants import BAD
from saqc.core.register import flagging
from saqc.lib.tools import customRoller
-# todo should we mask `reference` even if the func fail if reference has NaNs
-@flagging()
-def flagPatternByWavelet(
- data,
- field,
- flags,
- reference,
- widths=(1, 2, 4, 8),
- waveform="mexh",
- flag=BAD,
- **kwargs,
-):
- """
- Pattern recognition via wavelets.
-
- The steps are:
- 1. work on chunks returned by a moving window
- 2. each chunk is compared to the given pattern, using the wavelet algorithm as
- presented in [1]
- 3. if the compared chunk is equal to the given pattern it gets flagged
-
- Parameters
- ----------
-
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
-
- field : str
- The fieldname of the data column, you want to correct.
-
- flags : saqc.Flags
- The flags belonging to ``data``.
-
- reference: str
- The fieldname in ``data`' which holds the pattern.
-
- widths: tuple of int
- Widths for wavelet decomposition. [1] recommends a dyadic scale.
- Default: (1,2,4,8)
-
- waveform: str
- Wavelet to be used for decomposition. Default: 'mexh'. See [2] for a list.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
-
- flags : saqc.Flags
- The flags belongiong to `data`.
-
- References
- ----------
-
- The underlying pattern recognition algorithm using wavelets is documented here:
- [1] Maharaj, E.A. (2002): Pattern Recognition of Time Series using Wavelets. In: Härdle W., Rönz B. (eds) Compstat. Physica, Heidelberg, 978-3-7908-1517-7.
-
- The documentation of the python package used for the wavelt decomposition can be found here:
- [2] https://pywavelets.readthedocs.io/en/latest/ref/cwt.html#continuous-wavelet-families
- """
-
- dat = data[field]
- ref = data[reference].to_numpy()
- cwtmat_ref, _ = pywt.cwt(ref, widths, waveform)
- wavepower_ref = np.power(cwtmat_ref, 2)
- len_width = len(widths)
- sz = len(ref)
-
- assert len_width
- assert sz
-
- def func(x, y):
- return x.sum() / y.sum()
-
- def pvalue(chunk):
- cwtmat_chunk, _ = pywt.cwt(chunk, widths, waveform)
- wavepower_chunk = np.power(cwtmat_chunk, 2)
-
- # Permutation test on Powersum of matrix
- for i in range(len_width):
- x = wavepower_ref[i]
- y = wavepower_chunk[i]
- pval = permutation_test(
- x, y, method="approximate", num_rounds=200, func=func, seed=0
- )
- pval = min(pval, 1 - pval)
- return pval # noqa # existence ensured by assert
-
- rolling = customRoller(dat, window=sz, min_periods=sz, forward=True)
- pvals = rolling.apply(pvalue, raw=True)
- markers = pvals > 0.01 # nans -> False
-
- # the markers are set on the left edge of the window. thus we must propagate
- # `sz`-many True's to the right of every marker.
- rolling = customRoller(markers, window=sz, min_periods=sz)
- mask = rolling.sum().fillna(0).astype(bool)
-
- flags[mask, field] = flag
- return data, flags
-
-
def calculateDistanceByDTW(
data: pd.Series, reference: pd.Series, forward=True, normalize=True
):
@@ -191,13 +94,16 @@ def flagPatternByDTW(
flag=BAD,
**kwargs,
):
- """Pattern Recognition via Dynamic Time Warping.
+ """
+ Pattern Recognition via Dynamic Time Warping.
The steps are:
- 1. work on a moving window
- 2. for each data chunk extracted from each window, a distance to the given pattern
- is calculated, by the dynamic time warping algorithm [1]
- 3. if the distance is below the threshold, all the data in the window gets flagged
+ 1. work on a moving window
+
+ 2. for each data chunk extracted from each window, a distance to the given pattern
+ is calculated, by the dynamic time warping algorithm [1]
+
+ 3. if the distance is below the threshold, all the data in the window gets flagged
Parameters
----------
diff --git a/saqc/funcs/resampling.py b/saqc/funcs/resampling.py
index 79566f5ed20734d8e117ff5932e686ca6b524a05..9c2b4565f901ec384ea3bb38434c8e725ae8f0e9 100644
--- a/saqc/funcs/resampling.py
+++ b/saqc/funcs/resampling.py
@@ -1,20 +1,26 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-from typing import Callable, Tuple, Optional, Union
-from typing_extensions import Literal
+from __future__ import annotations
+
+from typing import Callable, Optional, Tuple, Union
+
import numpy as np
import pandas as pd
-from dios import DictOfSeries
-
-from saqc.constants import *
-from saqc.core import register, Flags
-from saqc.core.register import _isflagged, processing
-from saqc.lib.tools import evalFreqStr, getFreqDelta, filterKwargs
-from saqc.lib.ts_operators import shift2Freq, aggregate2Freq
-from saqc.funcs.interpolation import interpolateIndex, _SUPPORTED_METHODS
-import saqc.funcs.tools as tools
+from typing_extensions import Literal
+import saqc.funcs.tools as tools
+from dios import DictOfSeries, DtItype
+from saqc.core.flags import Flags
+from saqc.core.register import _isflagged, register
+from saqc.funcs.interpolation import _SUPPORTED_METHODS, interpolateIndex
+from saqc.lib.tools import evalFreqStr, filterKwargs, getFreqDelta
+from saqc.lib.ts_operators import aggregate2Freq, shift2Freq
METHOD2ARGS = {
"inverse_fshift": ("backward", pd.Timedelta),
@@ -112,7 +118,7 @@ def interpolate(
The fieldname of the column, holding the data-to-be-regularized.
flags : saqc.Flags
- Container to store flags of the data. freq
+ Container to store flags of the data.
freq : str
An offset string. The frequency of the grid you want to interpolate your data at.
@@ -239,18 +245,18 @@ def resample(
The data will be sampled at regular (equidistant) timestamps aka. Grid points.
Sampling intervals therefore get aggregated with a function, specified by
- 'agg_func' parameter and the result gets projected onto the new timestamps with a
+ 'func' parameter and the result gets projected onto the new timestamps with a
method, specified by "method". The following method (keywords) are available:
* ``'nagg'``: all values in the range (+/- `freq`/2) of a grid point get
- aggregated with agg_func and assigned to it.
- * ``'bagg'``: all values in a sampling interval get aggregated with agg_func and
+ aggregated with func and assigned to it.
+ * ``'bagg'``: all values in a sampling interval get aggregated with func and
the result gets assigned to the last grid point.
- * ``'fagg'``: all values in a sampling interval get aggregated with agg_func and
+ * ``'fagg'``: all values in a sampling interval get aggregated with func and
the result gets assigned to the next grid point.
- Note, that. if possible, functions passed to agg_func will get projected
+ Note, that. if possible, functions passed to func will get projected
internally onto pandas.resample methods, wich results in some reasonable
performance boost - however, for this to work, you should pass functions that
have the __name__ attribute initialised and the according methods name assigned
@@ -322,6 +328,11 @@ def resample(
"""
datcol = data[field]
+
+ # workaround for #GL-333
+ if datcol.empty and data.itype in [None, DtItype]:
+ datcol = pd.Series(index=pd.DatetimeIndex([]), dtype=datcol.dtype)
+
freq = evalFreqStr(freq, freq_check, datcol.index)
datcol = aggregate2Freq(
@@ -431,39 +442,45 @@ def concatFlags(
"inverse_bshift",
"inverse_nshift",
"inverse_interpolation",
- ],
+ "match",
+ ] = "match",
freq: Optional[str] = None,
drop: Optional[bool] = False,
+ squeeze: Optional[bool] = False,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
The Function appends flags history of ``fields`` to flags history of ``target``.
- Before Appending, columns in ``field`` history are projected onto the target index via ``method``
+ Before appending, columns in ``field`` history are projected onto the target index via ``method``
+
+ method: (field_flag associated with "field", source_flags associated with "source")
+
+ * 'inverse_nagg' - all target_flags within the range +/- freq/2 of a field_flag, get assigned this field flags value.
+ (if field_flag > target_flag)
- method: (field_flag in associated with "field", source_flags associated with "source")
+ * 'inverse_bagg' - all target_flags succeeding a field_flag within the range of "freq", get assigned this field flags
+ value. (if field_flag > target_flag)
- 'inverse_nagg' - all target_flags within the range +/- freq/2 of a field_flag, get assigned this field flags value.
- (if field_flag > target_flag)
- 'inverse_bagg' - all target_flags succeeding a field_flag within the range of "freq", get assigned this field flags
- value. (if field_flag > target_flag)
- 'inverse_fagg' - all target_flags preceeding a field_flag within the range of "freq", get assigned this field flags
- value. (if field_flag > target_flag)
+ * 'inverse_fagg' - all target_flags preceeding a field_flag within the range of "freq", get assigned this field flags
+ value. (if field_flag > target_flag)
- 'inverse_interpolation' - all target_flags within the range +/- freq of a field_flag, get assigned this source flags value.
- (if field_flag > target_flag)
+ * 'inverse_interpolation' - all target_flags within the range +/- freq of a field_flag, get assigned this source flags value.
+ (if field_flag > target_flag)
- 'inverse_nshift' - That target_flag within the range +/- freq/2, that is nearest to a field_flag, gets the source
- flags value. (if field_flag > target_flag)
- 'inverse_bshift' - That target_flag succeeding a field flag within the range freq, that is nearest to a
- field_flag, gets assigned this field flags value. (if field_flag > target_flag)
- 'inverse_nshift' - That target_flag preceeding a field flag within the range freq, that is nearest to a
- field_flag, gets assigned this field flags value. (if field_flag > target_flag)
+ * 'inverse_nshift' - That target_flag within the range +/- freq/2, that is nearest to a field_flag, gets the source
+ flags value. (if field_flag > target_flag)
- 'match' - any target_flag with a timestamp matching a field_flags timestamp gets this field_flags value
- (if field_flag > target_flag)
+ * 'inverse_bshift' - That target_flag succeeding a field flag within the range freq, that is nearest to a
+ field_flag, gets assigned this field flags value. (if field_flag > target_flag)
+
+ * 'inverse_nshift' - That target_flag preceeding a field flag within the range freq, that is nearest to a
+ field_flag, gets assigned this field flags value. (if field_flag > target_flag)
+
+ * 'match' - any target_flag with a timestamp matching a field_flags timestamp gets this field_flags value
+ (if field_flag > target_flag)
Note, to undo or backtrack a resampling/shifting/interpolation that has been performed with a certain method,
- you can just pass the associated "inverse" method. Also you should pass the same drop flags keyword.
+ you can just pass the associated "inverse" method. Also you should pass the same ``drop`` keyword.
Parameters
----------
@@ -479,17 +496,20 @@ def concatFlags(
target : str
Field name of flags history to append to.
- method : {'inverse_fagg', 'inverse_bagg', 'inverse_nagg', 'inverse_fshift', 'inverse_bshift', 'inverse_nshift',
- 'match'}
+ method : {'inverse_fagg', 'inverse_bagg', 'inverse_nagg', 'inverse_fshift', 'inverse_bshift', 'inverse_nshift', 'match'}, default 'match'
The method used for projection of ``field`` flags onto ``target`` flags. See description above for more details.
- freq : {None, str},default None
+ freq : str or None, default None
The ``freq`` determines the projection range for the projection method. See above description for more details.
Defaultly (None), the sampling frequency of ``field`` is used.
- drop : default False
+ drop : bool, default False
If set to `True`, the `field` column will be removed after processing
+ squeeze : bool, default False
+ If set to `True`, the appended flags frame will be squeezed - resulting in function specific flags informations
+ getting lost.
+
Returns
-------
data : dios.DictOfSeries
@@ -546,7 +566,23 @@ def concatFlags(
raise ValueError(f"unknown method {method}")
history = flags.history[field].apply(dummy.index, func, func_kws)
- flags.history[target].append(history)
+ if squeeze:
+ history = history.squeeze(raw=True)
+
+ meta = {
+ "func": f"concatFlags({field})",
+ "args": (field, target),
+ "kwargs": {
+ "method": method,
+ "freq": freq,
+ "drop": drop,
+ "squeeze": squeeze,
+ **kwargs,
+ },
+ }
+ flags.history[target].append(history, meta)
+ else:
+ flags.history[target].append(history)
if drop:
data, flags = tools.dropField(data=data, flags=flags, field=field)
diff --git a/saqc/funcs/residues.py b/saqc/funcs/residuals.py
similarity index 82%
rename from saqc/funcs/residues.py
rename to saqc/funcs/residuals.py
index 3adea11ed60bd7b1137846602f5df60b488b3bde..dc8f05e364ebea15cbd818e2911d0767940fd2ea 100644
--- a/saqc/funcs/residues.py
+++ b/saqc/funcs/residuals.py
@@ -1,44 +1,48 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
+from __future__ import annotations
-from typing import Tuple, Union, Optional, Callable
+from typing import Callable, Optional, Tuple, Union
-import pandas as pd
-from typing_extensions import Literal
import numpy as np
-from dios import DictOfSeries
+import pandas as pd
-from saqc.constants import *
-from saqc.core import register, Flags
-from saqc.funcs.rolling import _roll
+from dios import DictOfSeries
+from saqc.core.flags import Flags
+from saqc.core.register import register
from saqc.funcs.curvefit import _fitPolynomial
-from saqc.lib.tools import filterKwargs
+from saqc.funcs.rolling import _roll
@register(mask=["field"], demask=[], squeeze=[])
-def calculatePolynomialResidues(
+def calculatePolynomialResiduals(
data: DictOfSeries,
field: str,
flags: Flags,
window: Union[str, int],
order: int,
min_periods: Optional[int] = 0,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- Fits a polynomial model to the data and calculate the residues.
+ Fits a polynomial model to the data and calculate the residuals.
- The residue is calculated by fitting a polynomial of degree `order` to a data
+ The residual is calculated by fitting a polynomial of degree `order` to a data
slice of size `window`, that has x at its center.
- Note, that calculating the residues tends to be quite costy, because a function
+ Note, that calculating the residuals tends to be quite costy, because a function
fitting is performed for every sample. To improve performance, consider the
following possibilities:
In case your data is sampled at an equidistant frequency grid:
(1) If you know your data to have no significant number of missing values,
- or if you do not want to calculate residues for windows containing missing values
+ or if you do not want to calculate residuals for windows containing missing values
any way, performance can be increased by setting min_periods=window.
Note, that the initial and final window/2 values do not get fitted.
@@ -80,9 +84,8 @@ def calculatePolynomialResidues(
data : dios.DictOfSeries
flags : saqc.Flags
"""
- reserved = ["residues", "set_flags"]
- filterKwargs(kwargs, reserved)
- return _fitPolynomial(
+ orig = data[field]
+ data, flags = _fitPolynomial(
data=data,
field=field,
flags=flags,
@@ -90,14 +93,13 @@ def calculatePolynomialResidues(
order=order,
min_periods=min_periods,
**kwargs,
- # ctrl args
- return_residues=True,
- set_flags=True,
)
+ data[field] = orig - data[field]
+ return data, flags
@register(mask=["field"], demask=[], squeeze=[])
-def calculateRollingResidues(
+def calculateRollingResiduals(
data: DictOfSeries,
field: str,
flags: Flags,
@@ -105,7 +107,7 @@ def calculateRollingResidues(
func: Callable[[pd.Series], np.ndarray] = np.mean,
min_periods: Optional[int] = 0,
center: bool = True,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
Calculate the diff of a rolling-window function and the data.
@@ -141,9 +143,8 @@ def calculateRollingResidues(
flags : saqc.Flags
The quality flags of data
"""
- reserved = ["return_residues", "set_flags"]
- kwargs = filterKwargs(kwargs, reserved)
- return _roll(
+ orig = data[field].copy()
+ data, flags = _roll(
data=data,
field=field,
flags=flags,
@@ -152,7 +153,8 @@ def calculateRollingResidues(
min_periods=min_periods,
center=center,
**kwargs,
- # ctrl args
- set_flags=True,
- return_residues=True,
)
+
+ # calculate residual
+ data[field] = orig - data[field]
+ return data, flags
diff --git a/saqc/funcs/rolling.py b/saqc/funcs/rolling.py
index ff73d7887838f9d532cfcbaf4e6eac742dc84c21..fa5cabecdbd0036010080cee7b15222ce34d25a6 100644
--- a/saqc/funcs/rolling.py
+++ b/saqc/funcs/rolling.py
@@ -1,14 +1,20 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-from typing import Union, Callable, Tuple
+from typing import Callable, Tuple, Union
+
import numpy as np
import pandas as pd
-from dios import DictOfSeries
-from saqc.constants import *
-from saqc.core import register, Flags
-from saqc.lib.tools import getFreqDelta, filterKwargs
+from dios import DictOfSeries
+from saqc.core.flags import Flags
+from saqc.core.register import register
+from saqc.lib.tools import getFreqDelta
@register(mask=["field"], demask=[], squeeze=[])
@@ -56,8 +62,6 @@ def roll(
flags : saqc.Flags
The quality flags of data
"""
- reserved = ["return_residues", "set_flags"]
- kwargs = filterKwargs(kwargs, reserved)
return _roll(
data=data,
field=field,
@@ -67,9 +71,6 @@ def roll(
min_periods=min_periods,
center=center,
**kwargs,
- # ctrl args
- set_flags=True,
- return_residues=False,
)
@@ -79,10 +80,8 @@ def _roll(
flags: Flags,
window: Union[str, int],
func: Callable[[pd.Series], np.ndarray] = np.mean,
- set_flags: bool = True,
min_periods: int = 0,
center: bool = True,
- return_residues=False,
**kwargs
):
to_fit = data[field].copy()
@@ -148,12 +147,8 @@ def _roll(
func
)
- if return_residues:
- means = to_fit - means
-
data[field] = means
- if set_flags:
- worst = flags[field].rolling(window, center=True, min_periods=min_periods).max()
- flags[field] = worst
+ worst = flags[field].rolling(window, center=True, min_periods=min_periods).max()
+ flags[field] = worst
return data, flags
diff --git a/saqc/funcs/scores.py b/saqc/funcs/scores.py
index e931b72c7ad8d632c83b5cd569714a39583cdb1b..3af6854b7317a20dc7cd8d5d753d0c371b624fd5 100644
--- a/saqc/funcs/scores.py
+++ b/saqc/funcs/scores.py
@@ -1,15 +1,24 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-from typing import Union, Tuple, Callable, Sequence, Optional
-from typing_extensions import Literal
+from __future__ import annotations
+
+from typing import Callable, Sequence, Tuple, Union
+
import numpy as np
import pandas as pd
-from dios import DictOfSeries
+from typing_extensions import Literal
-from saqc.constants import *
-from saqc.core import register, Flags
-from saqc.lib.tools import toSequence
import saqc.lib.ts_operators as ts_ops
+from dios import DictOfSeries
+from saqc.constants import UNFLAGGED
+from saqc.core.flags import Flags
+from saqc.core.register import register
+from saqc.lib.tools import toSequence
@register(
diff --git a/saqc/funcs/tools.py b/saqc/funcs/tools.py
index c67584fa6d4ab0c75d57fb6e7b3eeb28f851a6c5..f418f26afb1ee5194054af5b9d8e387ccc21da20 100644
--- a/saqc/funcs/tools.py
+++ b/saqc/funcs/tools.py
@@ -1,21 +1,26 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
+from __future__ import annotations
+import pickle
from typing import Optional, Tuple
-from typing_extensions import Literal
-import numpy as np
-from dios import DictOfSeries
-
import matplotlib as mpl
import matplotlib.pyplot as plt
-import pickle
+import numpy as np
+from typing_extensions import Literal
-from saqc.constants import *
-from saqc.core.register import processing
-from saqc.core import register, Flags
-from saqc.lib.tools import periodicMask, filterKwargs
+from dios import DictOfSeries
+from saqc.constants import FILTER_NONE, UNFLAGGED
+from saqc.core.flags import Flags
+from saqc.core.register import processing, register
from saqc.lib.plotting import makeFig
+from saqc.lib.tools import periodicMask
_MPL_DEFAULT_BACKEND = mpl.get_backend()
@@ -129,12 +134,12 @@ def renameField(
@register(mask=[], demask=[], squeeze=["field"])
-def maskTime(
+def selectTime(
data: DictOfSeries,
field: str,
flags: Flags,
- mode: Literal["periodic", "mask_field"],
- mask_field: Optional[str] = None,
+ mode: Literal["periodic", "selection_field"],
+ selection_field: Optional[str] = None,
start: Optional[str] = None,
end: Optional[str] = None,
closed: bool = True,
@@ -151,7 +156,7 @@ def maskTime(
Here comes a recipe on how to apply a flagging function only on a masked chunk of the variable field:
1. dublicate "field" in the input data (`copyField`)
- 2. mask the dublicated data (this, `maskTime`)
+ 2. mask the dublicated data (this, `selectTime`)
3. apply the tests you only want to be applied onto the masked data chunks (a saqc function)
4. project the flags, calculated on the dublicated and masked data onto the original field data
(`concateFlags` or `flagGeneric`)
@@ -167,11 +172,11 @@ def maskTime(
The fieldname of the column, holding the data-to-be-masked.
flags : saqc.Flags
Container to store flags of the data.
- mode : {"periodic", "mask_var"}
+ mode : {"periodic", "mask_field"}
The masking mode.
- "periodic": parameters "period_start", "end" are evaluated to generate a periodical mask
- "mask_var": data[mask_var] is expected to be a boolean valued timeseries and is used as mask.
- mask_field : {None, str}, default None
+ selection_field : {None, str}, default None
Only effective if mode == "mask_var"
Fieldname of the column, holding the data that is to be used as mask. (must be boolean series)
Neither the series` length nor its labels have to match data[field]`s index and length. An inner join of the
@@ -209,41 +214,38 @@ def maskTime(
The highest date unit gives the period.
For example:
- >>> period_start = "01T15:00:00"
+ >>> start = "01T15:00:00"
>>> end = "13T17:30:00"
Will result in all values sampled between 15:00 at the first and 17:30 at the 13th of every month get masked
- >>> period_start = "01:00"
+ >>> start = "01:00"
>>> end = "04:00"
All the values between the first and 4th minute of every hour get masked.
- >>> period_start = "01-01T00:00:00"
+ >>> start = "01-01T00:00:00"
>>> end = "01-03T00:00:00"
Mask january and february of evcomprosed in theery year. masking is inclusive always, so in this case the mask will
include 00:00:00 at the first of march. To exclude this one, pass:
- >>> period_start = "01-01T00:00:00"
+ >>> start = "01-01T00:00:00"
>>> end = "02-28T23:59:59"
To mask intervals that lap over a seasons frame, like nights, or winter, exchange sequence of season start and
season end. For example, to mask night hours between 22:00:00 in the evening and 06:00:00 in the morning, pass:
- >>> period_start = "22:00:00"
+ >>> start = "22:00:00"
>>> end = "06:00:00"
-
- When inclusive_selection="season", all above examples work the same way, only that you now
- determine wich values NOT TO mask (=wich values are to constitute the "seasons").
"""
datcol_idx = data[field].index
if mode == "periodic":
- mask = periodicMask(datcol_idx, start, end, closed)
- elif mode == "mask_field":
- idx = data[mask_field].index.intersection(datcol_idx)
- mask = data.loc[idx, mask_field]
+ mask = periodicMask(datcol_idx, start, end, ~closed)
+ elif mode == "selection_field":
+ idx = data[selection_field].index.intersection(datcol_idx)
+ mask = data.loc[idx, selection_field]
else:
raise ValueError("Keyword passed as masking mode is unknown ({})!".format(mode))
@@ -259,12 +261,12 @@ def plot(
flags: Flags,
path: Optional[str] = None,
max_gap: Optional[str] = None,
- history: Optional[Literal["valid", "complete", "clear"]] = "valid",
+ history: Optional[Literal["valid", "complete"] | list] = "valid",
xscope: Optional[slice] = None,
phaseplot: Optional[str] = None,
store_kwargs: Optional[dict] = None,
ax_kwargs: Optional[dict] = None,
- dfilter: float = FILTER_ALL,
+ dfilter: Optional[float] = FILTER_NONE,
**kwargs,
):
"""
@@ -299,14 +301,14 @@ 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.
- history : {"valid", "complete", None}, default "valid"
+ history : {"valid", "complete", None, list of strings}, default "valid"
Discriminate the plotted flags with respect to the tests they originate from.
* "valid" - Only plot those flags, that do not get altered or "unflagged" by subsequent tests. Only list tests
in the legend, that actually contributed flags to the overall resault.
* "complete" - plot all the flags set and list all the tests ran on a variable. Suitable for debugging/tracking.
- * "clear" - clear plot from all the flagged values
* None - just plot the resulting flags for one variable, without any historical meta information.
+ * list of strings - plot only flags set by those tests listed.
xscope : slice or Offset, default None
Parameter, that determines a chunk of the data to be plotted
@@ -323,15 +325,17 @@ def plot(
ax_kwargs : dict, default {}
Axis keywords. Change the axis labeling defaults. Most important keywords:
- 'x_label', 'y_label', 'title', 'fontsize'.
+ 'x_label', 'y_label', 'title', 'fontsize', 'cycleskip'.
"""
+ # keep the very original, not the copy
+ orig = data, flags
+ data, flags = data.copy(), flags.copy()
interactive = path is None
- level = kwargs.get("flag", BAD)
+ level = kwargs.get("flag", UNFLAGGED)
if dfilter < np.inf:
- data = data.copy()
data.loc[flags[field] >= dfilter, field] = np.nan
if store_kwargs is None:
@@ -368,4 +372,4 @@ def plot(
else:
fig.savefig(path, **store_kwargs)
- return data, flags
+ return orig
diff --git a/saqc/funcs/transformation.py b/saqc/funcs/transformation.py
index 560dc4bb143b9d5473f7c3a7798fe77475111c61..0b3b363c6cd829f5430fade0255e1e516770ba5a 100644
--- a/saqc/funcs/transformation.py
+++ b/saqc/funcs/transformation.py
@@ -1,12 +1,20 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+from typing import Callable, Optional, Tuple, Union
-from typing import Optional, Callable, Tuple, Union
import numpy as np
import pandas as pd
-from dios import DictOfSeries
-from saqc.core import register, Flags
+from dios import DictOfSeries
+from saqc.core.flags import Flags
+from saqc.core.register import register
@register(mask=["field"], demask=[], squeeze=[])
@@ -16,7 +24,7 @@ def transform(
flags: Flags,
func: Callable[[pd.Series], pd.Series],
freq: Optional[Union[float, str]] = None,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
Function to transform data columns with a transformation that maps series onto series of the same length.
diff --git a/saqc/lib/__init__.py b/saqc/lib/__init__.py
index e1f7e6e8bf04d3d59cff68b83d91fbf791f0faa2..1d74ada495ad00a20887cdee50a426347f37efae 100644
--- a/saqc/lib/__init__.py
+++ b/saqc/lib/__init__.py
@@ -1,2 +1,7 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
diff --git a/saqc/lib/docurator.py b/saqc/lib/docurator.py
new file mode 100644
index 0000000000000000000000000000000000000000..59b27d7393d7943c7818e018914cf8f1911aeca4
--- /dev/null
+++ b/saqc/lib/docurator.py
@@ -0,0 +1,164 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import re
+
+FUNC_NAPOLEAN_STYLE_ORDER = [
+ "Head",
+ "Parameters",
+ "Returns",
+ "Notes",
+ "See also",
+ "Examples",
+ "References",
+]
+
+
+def doc(doc_string: str, template="saqc_methods", source="function_string"):
+ def docFunc(meth):
+ if template == "saqc_methods":
+ meth.__doc__ = saqcMethodsTemplate(doc_string, source)
+ return meth
+
+ return docFunc
+
+
+def getDocstringIndent(lines: list) -> str:
+ """returns a whitespace string matching the indent size of the passed docstring_list"""
+ for line in lines:
+ if len(line) == 0 or re.match(" *$", line):
+ continue
+ return re.match(" *", line)[0]
+ return ""
+
+
+def getSections(doc_string: list, indent_str: str) -> dict:
+ """Returns a dictionary of sections, with section names as keys"""
+ section_lines = [0]
+ section_headings = ["Head"]
+ for k in range(len(doc_string) - 1):
+ # check if next line is an underscore line (section signator):
+ if re.match(indent_str + "-+$", doc_string[k + 1]):
+ # check if underscore length matches heading length
+ if len(doc_string[k + 1]) == len(doc_string[k]):
+ section_lines.append(k)
+ # skip leading whitespaces
+ skip = re.match("^ *", doc_string[k]).span()[-1]
+ section_headings.append(doc_string[k][skip:])
+ section_lines.append(len(doc_string))
+ section_content = [
+ doc_string[section_lines[k] : section_lines[k + 1]]
+ for k in range(len(section_lines) - 1)
+ ]
+ section_content = [clearTrailingWhitespace(p) for p in section_content]
+ sections = dict(zip(section_headings, section_content))
+ return sections
+
+
+def getParameters(section: list, indent_str: str) -> dict:
+ """Returns a dictionary of Parameter documentations, with parameter names as keys"""
+ parameter_lines = []
+ parameter_names = []
+ for k in range(len(section)):
+ # try catch a parameter definition start (implicitly assuming parameter names have no
+ # whitespaces):
+ param = re.match(indent_str + r"(\S+) *:", section[k])
+ if param:
+ parameter_lines.append(k)
+ parameter_names.append(param.group(1))
+
+ parameter_lines.append(len(section))
+ parameter_content = [
+ section[parameter_lines[k] : parameter_lines[k + 1]]
+ for k in range(len(parameter_lines) - 1)
+ ]
+ parameter_content = [clearTrailingWhitespace(p) for p in parameter_content]
+ parameter_dict = dict(zip(parameter_names, parameter_content))
+ return parameter_dict
+
+
+def mkParameter(
+ parameter_name: str, parameter_type: str, parameter_doc: str, indent_str: str
+) -> dict:
+ parameter_doc = parameter_doc.splitlines()
+ parameter_doc = [indent_str + " " * 4 + p for p in parameter_doc]
+ content = [indent_str + f"{parameter_name} : {parameter_type}"]
+ content += parameter_doc
+ return {parameter_name: content}
+
+
+def makeSection(section_name: str, indent_str: str, doc_content: str = None) -> dict:
+ content = [indent_str + section_name]
+ content += [indent_str + "_" * len(section_name)]
+ content += [" "]
+ if doc_content:
+ content += doc_content.splitlines()
+
+ return {section_name: content}
+
+
+def composeDocstring(
+ section_dict: dict, order: list = FUNC_NAPOLEAN_STYLE_ORDER
+) -> str:
+ """Compose final docstring from a sections dictionary"""
+ doc_string = []
+ section_dict = section_dict.copy()
+ for sec in order:
+ dc = section_dict.pop(sec, [])
+ doc_string += dc
+ # blank line at section end
+ if len(dc) > 0:
+ doc_string += [""]
+
+ return "\n".join(doc_string)
+
+
+def clearTrailingWhitespace(doc: list) -> list:
+ """Clears trailing whitespace lines"""
+ for k in range(len(doc), 0, -1):
+ if not re.match(r"^\s*$", doc[k - 1]):
+ break
+ return doc[:k]
+
+
+def rmParameter(para_name: str, section: list, indent_str: str):
+ params = getParameters(section, indent_str=indent_str)
+ if para_name in params:
+ rm_idx = [
+ sidx
+ for sidx in range(len(section))
+ if params[para_name] == section[sidx : sidx + len(params[para_name])]
+ ][0]
+ section = section[:rm_idx] + section[rm_idx + len(params[para_name]) :]
+ return section
+
+
+def saqcMethodsTemplate(doc_string: str, source="function_string"):
+ if source == "function_string":
+ doc_string = doc_string.splitlines()
+ indent_string = getDocstringIndent(doc_string)
+ sections = getSections(doc_string, indent_str=indent_string)
+ # modify returns section
+ sections.pop("Returns", None)
+ returns_section = makeSection(section_name="Returns", indent_str=indent_string)
+ out_para = mkParameter(
+ parameter_name="out",
+ parameter_type="saqc.SaQC",
+ parameter_doc="An :py:meth:`saqc.SaQC` object, holding the (possibly) modified data",
+ indent_str=indent_string,
+ )
+ returns_section["Returns"] += out_para["out"]
+ sections.update(returns_section)
+ # remove flags and data parameter from docstring
+ if "Parameters" in sections:
+ para_sec = sections["Parameters"]
+ sections.pop("Parameters", None)
+ para_sec = rmParameter("data", para_sec, indent_string)
+ para_sec = rmParameter("flags", para_sec, indent_string)
+ sections["Parameters"] = para_sec
+
+ doc_string = composeDocstring(
+ section_dict=sections, order=FUNC_NAPOLEAN_STYLE_ORDER
+ )
+ return doc_string
diff --git a/saqc/lib/plotting.py b/saqc/lib/plotting.py
index 588954a7b21b7570ed4b9b83dff5f5861d394fa8..109284b092e44d2c48b55f14ad7232dc8a5d11ea 100644
--- a/saqc/lib/plotting.py
+++ b/saqc/lib/plotting.py
@@ -1,19 +1,23 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-from typing import Optional
-from typing_extensions import Literal
-from saqc.lib.tools import toSequence
-import pandas as pd
-import numpy as np
-import matplotlib as mpl
import itertools
+from typing import Optional, Union
+
+import matplotlib as mpl
import matplotlib.pyplot as plt
-import seaborn
-from saqc.constants import *
-from saqc.core import Flags
-from saqc.lib.types import DiosLikeT
+import numpy as np
+import pandas as pd
+from typing_extensions import Literal
+from saqc.core.flags import Flags
+from saqc.lib.tools import toSequence
+from saqc.lib.types import DiosLikeT
STATSDICT = {
"values total": lambda x, y, z: len(x),
@@ -25,9 +29,23 @@ STATSDICT = {
PLOT_KWARGS = {"alpha": 0.8, "linewidth": 1}
FIG_KWARGS = {"figsize": (16, 9)}
+
+_seaborn_color_palette = [
+ (0.00784313725490196, 0.24313725490196078, 1.0),
+ (1.0, 0.48627450980392156, 0.0),
+ (0.10196078431372549, 0.788235294117647, 0.2196078431372549),
+ (0.9098039215686274, 0.0, 0.043137254901960784),
+ (0.5450980392156862, 0.16862745098039217, 0.8862745098039215),
+ (0.6235294117647059, 0.2823529411764706, 0.0),
+ (0.9450980392156862, 0.2980392156862745, 0.7568627450980392),
+ (0.6392156862745098, 0.6392156862745098, 0.6392156862745098),
+ (1.0, 0.7686274509803922, 0.0),
+ (0.0, 0.8431372549019608, 1.0),
+]
+
SCATTER_KWARGS = {
- "marker": ["s", "D", "^", "o"],
- "color": seaborn.color_palette("bright"),
+ "marker": ["s", "D", "^", "o", "v"],
+ "color": _seaborn_color_palette,
"alpha": 0.7,
"zorder": 10,
"edgecolors": "black",
@@ -41,7 +59,7 @@ def makeFig(
flags: Flags,
level: float,
max_gap: Optional[str] = None,
- history: Optional[Literal["valid", "complete"]] = "valid",
+ history: Union[Optional[Literal["valid", "complete"]], list] = "valid",
xscope: Optional[slice] = None,
phaseplot: Optional[str] = None,
ax_kwargs: Optional[dict] = None,
@@ -70,13 +88,16 @@ def makeFig(
below `max_gap` get connected via the plotting line.
- history : {"valid", "complete", None}, default "valid"
+ history : {"valid", "complete", None, list of strings}, default "valid"
Discriminate the plotted flags with respect to the tests they originate from.
+
* "valid" - Only plot those flags, that do not get altered or "unflagged" by subsequent tests. Only list tests
in the legend, that actually contributed flags to the overall resault.
* "complete" - plot all the flags set and list all the tests ran on a variable. Suitable for debugging/tracking.
* "clear" - clear plot from all the flagged values
* None - just plot the resulting flags for one variable, without any historical meta information.
+ * list of strings - for any string ``s`` in the list, plot the flags set by test labeled, ``s`` - if ``s`` is
+ not present in the history labels, plot any flags, set by a test labeled ``s``
xscope : slice or Offset, default None
Parameter, that determines a chunk of the data to be plotted /
@@ -92,6 +113,8 @@ def makeFig(
"""
+ if ax_kwargs is None:
+ ax_kwargs = {}
# data retrieval
d = data[field]
# data slicing:
@@ -105,6 +128,9 @@ def makeFig(
default = plt.rcParams["font.size"]
plt.rcParams["font.size"] = ax_kwargs.pop("fontsize", None) or default
+ # set shapecycle start:
+ cyclestart = ax_kwargs.pop("cycleskip", 0)
+
na_mask = d.isna()
d = d[~na_mask]
if phaseplot:
@@ -142,6 +168,7 @@ def makeFig(
plot_kwargs,
ax_kwargs,
SCATTER_KWARGS,
+ cyclestart,
)
plt.rcParams["font.size"] = default
@@ -160,10 +187,11 @@ def _plotVarWithFlags(
plot_kwargs,
ax_kwargs,
scatter_kwargs,
+ cyclestart,
):
scatter_kwargs = scatter_kwargs.copy()
ax.set_title(datser.name)
- ax.plot(datser, color="black", **plot_kwargs)
+ ax.plot(datser, color="black", label=" ", **plot_kwargs)
ax.set(**ax_kwargs)
shape_cycle = scatter_kwargs.get("marker", "o")
shape_cycle = itertools.cycle(toSequence(shape_cycle))
@@ -171,8 +199,25 @@ def _plotVarWithFlags(
"color", plt.rcParams["axes.prop_cycle"].by_key()["color"]
)
color_cycle = itertools.cycle(toSequence(color_cycle))
+ for k in range(0, cyclestart):
+ next(color_cycle)
+ next(shape_cycle)
+
if history:
for i in flags_hist.columns:
+ if isinstance(history, list):
+ meta_field = "label" if "label" in flags_meta[i].keys() else "func"
+ to_plot = (
+ flags_meta[i][meta_field]
+ if flags_meta[i][meta_field] in history
+ else None
+ )
+ if not to_plot:
+ continue
+ else:
+ hist_key = "valid"
+ else:
+ hist_key = history
# catch empty but existing history case (flags_meta={})
if len(flags_meta[i]) == 0:
continue
@@ -182,18 +227,18 @@ def _plotVarWithFlags(
)
scatter_kwargs.update({"label": label})
flags_i = flags_hist[i].astype(float)
- if history == "complete":
+ if hist_key == "complete":
scatter_kwargs.update(
{"color": next(color_cycle), "marker": next(shape_cycle)}
)
_plotFlags(ax, datser, flags_i, na_mask, level, scatter_kwargs)
- if history == "valid":
+ if hist_key == "valid":
# only plot those flags, that do not get altered later on:
mask = flags_i.eq(flags_vals)
flags_i[~mask] = np.nan
# Skip plot, if the test did not have no effect on the all over flagging result. This avoids
# legend overflow
- if ~(flags_i >= level).any():
+ if ~(flags_i > level).any():
continue
# Also skip plot, if all flagged values are np.nans (to catch flag missing and masked results mainly)
@@ -214,6 +259,7 @@ def _plotVarWithFlags(
level,
scatter_kwargs,
)
+
ax.legend()
else:
scatter_kwargs.update({"color": next(color_cycle), "marker": next(shape_cycle)})
@@ -221,7 +267,7 @@ def _plotVarWithFlags(
def _plotFlags(ax, datser, flags, na_mask, level, scatter_kwargs):
- is_flagged = flags.astype(float) >= level
+ is_flagged = flags.astype(float) > level
is_flagged = is_flagged[~na_mask]
is_flagged = datser[is_flagged[is_flagged].index]
ax.scatter(is_flagged.index, is_flagged.values, **scatter_kwargs)
diff --git a/saqc/lib/rolling.py b/saqc/lib/rolling.py
index 4e12f07fca3636f5c54e688de2aa24bc07de9a56..aa2faee30cf283df0dec4593c1ba18e065139d4b 100644
--- a/saqc/lib/rolling.py
+++ b/saqc/lib/rolling.py
@@ -1,11 +1,15 @@
#!/usr/bin/env python
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from __future__ import annotations
import warnings
+
import numpy as np
import pandas as pd
-
from pandas.api.types import is_bool
if pd.__version__ < "1.4":
diff --git a/saqc/lib/tools.py b/saqc/lib/tools.py
index 95c9e294dab3e492bb15216098bac22d80699298..1c2d8b9d19b974f2e996ddf2a132c232f110d9f3 100644
--- a/saqc/lib/tools.py
+++ b/saqc/lib/tools.py
@@ -1,38 +1,48 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
from __future__ import annotations
-import re
-import datetime
+import collections
import itertools
+import re
import warnings
-from typing import List, Sequence, TypeVar, Union, Any, Iterator, Callable, Collection
+from typing import Callable, Collection, Iterator, List, Sequence, TypeVar, Union
-import numpy as np
import numba as nb
+import numpy as np
import pandas as pd
from scipy import fft
+from scipy.cluster.hierarchy import fcluster, linkage
import dios
-import collections
-from scipy.cluster.hierarchy import linkage, fcluster
# keep this for external imports
# TODO: fix the external imports
from saqc.lib.rolling import customRoller
-
T = TypeVar("T", str, float, int)
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'")
- elif (not np.isscalar(value)) and optional is False:
- raise ValueError(f"'{name}' needs to be a scalar")
+ if optional and value is None:
+ return
+ if np.isscalar(value):
+ return
+
+ msg = f"'{name}' needs to be a scalar"
+ if optional:
+ msg += " or 'None'"
+ raise ValueError(msg)
def toSequence(value: T | Sequence[T]) -> List[T]:
+ if value is None: # special case
+ return [None]
if isinstance(value, T.__constraints__):
return [value]
return list(value)
@@ -44,101 +54,6 @@ def squeezeSequence(value: Sequence[T]) -> Union[T, Sequence[T]]:
return value
-def toOffset(freq_string: str, raw: bool = False) -> datetime.timedelta:
- offset = pd.tseries.frequencies.to_offset(freq_string)
- if raw:
- return offset
- return offset.delta.to_pytimedelta()
-
-
-@nb.jit(nopython=True, cache=True)
-def findIndex(iterable, value, start):
- i = start
- while i < len(iterable):
- v = iterable[i]
- if v >= value:
- return i
- i = i + 1
- return -1
-
-
-@nb.jit(nopython=True, cache=True)
-def valueRange(iterable):
- minval = iterable[0]
- maxval = minval
- for v in iterable:
- if v < minval:
- minval = v
- elif v > maxval:
- maxval = v
- return maxval - minval
-
-
-def slidingWindowIndices(dates, window_size, iter_delta=None):
- """
- this function is a building block of a custom implementation of
- the pandas 'rolling' method. A number of shortcomings in the
- 'rolling' implementation might made this a worthwhil endavour,
- namly:
- + There is no way to provide a step size, i.e. to not start the
- next rolling window at the very next row in the DataFrame/Series
- + The inconsistent bahaviour with numerical vs frequency based
- window sizes. When window is an integer, all windows are equally
- large (window=5 -> windows contain 5 elements), but variable in
- size, when the window is a frequency string (window="2D" ->
- window grows from size 1 during the first iteration until it
- covers the given frequency). Especially the bahaviour with
- frequency strings is quite unfortunate when calling methods
- relying on the size of the window (sum, mean, median)
- """
-
- if not isinstance(dates, pd.DatetimeIndex):
- raise TypeError("Must pass pd.DatetimeIndex")
-
- # lets work on numpy data structures for performance reasons
- dates = np.array(dates, dtype=np.int64)
-
- if np.any(np.diff(dates) <= 0):
- raise ValueError("strictly monotonic index needed")
-
- window_size = pd.to_timedelta(window_size).to_timedelta64().astype(np.int64)
- if iter_delta:
- iter_delta = pd.to_timedelta(iter_delta).to_timedelta64().astype(np.int64)
-
- start_date = dates[0]
- last_date = dates[-1]
- start_idx = 0
- end_idx = start_idx
-
- while True:
- end_date = start_date + window_size
- if (end_date > last_date) or (start_idx == -1) or (end_idx == -1):
- break
-
- end_idx = findIndex(dates, end_date, end_idx)
- yield start_idx, end_idx
-
- if iter_delta:
- start_idx = findIndex(dates, start_date + iter_delta, start_idx)
- else:
- start_idx += 1
-
- start_date = dates[start_idx]
-
-
-def inferFrequency(data: pd.Series) -> pd.DateOffset:
- return pd.tseries.frequencies.to_offset(pd.infer_freq(data.index))
-
-
-def offset2seconds(offset):
- """Function returns total seconds upon "offset like input
-
- :param offset: offset string or pandas offset object.
- """
-
- return pd.Timedelta.total_seconds(pd.Timedelta(offset))
-
-
def periodicMask(dtindex, season_start, season_end, include_bounds):
"""
This function generates date-periodic/seasonal masks from an index passed.
@@ -165,7 +80,7 @@ def periodicMask(dtindex, season_start, season_end, include_bounds):
Returns
-------
dfilter : pandas.Series[bool]
- A series, indexed with the input index and having value `True` for all the values that are to be masked.
+ A series, indexed with the input index and having value `False` for all the values that are to be masked.
Examples
--------
@@ -212,46 +127,48 @@ def periodicMask(dtindex, season_start, season_end, include_bounds):
stamp_kwargs = dict(zip(keys, stamp_list))
def _replace(index):
+ if len(index) == 0:
+ return None
if "day" in stamp_kwargs:
stamp_kwargs["day"] = min(stamp_kwargs["day"], index[0].daysinmonth)
-
out = index[0].replace(**stamp_kwargs)
return out.strftime("%Y-%m-%dT%H:%M:%S")
return _replace
- mask = pd.Series(include_bounds, index=dtindex)
+ mask = pd.Series(True, index=dtindex)
start_replacer = _replaceBuilder(season_start)
end_replacer = _replaceBuilder(season_end)
- if pd.Timestamp(start_replacer(dtindex)) <= pd.Timestamp(end_replacer(dtindex)):
+ invert = False
- def _selector(x, base_bool=include_bounds):
- x[start_replacer(x.index) : end_replacer(x.index)] = not base_bool
+ if pd.Timestamp(start_replacer(dtindex)) > pd.Timestamp(end_replacer(dtindex)):
+ start_replacer, end_replacer = end_replacer, start_replacer
+ include_bounds = not include_bounds
+ invert = True
+
+ if include_bounds:
+
+ def _selector(x):
+ x[start_replacer(x.index) : end_replacer(x.index)] = False
return x
else:
- def _selector(x, base_bool=include_bounds):
- x[: end_replacer(x.index)] = not base_bool
- x[start_replacer(x.index) :] = not base_bool
+ def _selector(x):
+ s = start_replacer(x.index)
+ e = end_replacer(x.index)
+ x[s:e] = False
+ x[s:s] = True
+ x[e:e] = True
return x
freq = "1" + "mmmhhhdddMMMYYY"[len(season_start)]
- return mask.groupby(pd.Grouper(freq=freq)).transform(_selector)
-
-
-def assertDictOfSeries(df: Any, argname: str = "arg") -> None:
- if not isinstance(df, dios.DictOfSeries):
- raise TypeError(
- f"{argname} must be of type dios.DictOfSeries, {type(df)} was given"
- )
-
-
-def assertSeries(srs: Any, argname: str = "arg") -> None:
- if not isinstance(srs, pd.Series):
- raise TypeError(f"{argname} must be of type pd.Series, {type(srs)} was given")
+ out = mask.groupby(pd.Grouper(freq=freq)).transform(_selector)
+ if invert:
+ out = ~out
+ return out
@nb.jit(nopython=True, cache=True)
@@ -425,11 +342,11 @@ def estimateFrequency(
min_energy = delta_f[0] * min_energy
# calc/assign low/high freq cut offs (makes life easier):
min_rate_i = int(
- len_f / (pd.Timedelta(min_rate).total_seconds() * (10 ** delta_precision))
+ len_f / (pd.Timedelta(min_rate).total_seconds() * (10**delta_precision))
)
delta_f[:min_rate_i] = 0
max_rate_i = int(
- len_f / (pd.Timedelta(max_rate).total_seconds() * (10 ** delta_precision))
+ len_f / (pd.Timedelta(max_rate).total_seconds() * (10**delta_precision))
)
hf_cutoff = min(max_rate_i, len_f // 2)
delta_f[hf_cutoff:] = 0
@@ -639,8 +556,12 @@ def statPass(
exceeds = exceeding_sub & exceeds
to_set = pd.Series(False, index=exceeds.index)
- for exceed, group in exceeds.groupby(by=exceeds.values):
- if exceed:
+ exceeds_df = pd.DataFrame(
+ {"g": exceeds.diff().cumsum().values, "ex_val": exceeds.values},
+ index=exceeds.index,
+ )
+ for _, group in exceeds_df.groupby(by="g"):
+ if group["ex_val"].iloc[0]:
# dt-slices include both bounds, so we subtract 1ns
start = group.index[0] - (winsz - pd.Timedelta("1ns"))
end = group.index[-1]
diff --git a/saqc/lib/ts_operators.py b/saqc/lib/ts_operators.py
index 151b2e77e50916856f7038e9826389400f0896b7..27db26955a8515a052b60080eb92914a3acf1e09 100644
--- a/saqc/lib/ts_operators.py
+++ b/saqc/lib/ts_operators.py
@@ -1,25 +1,44 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
"""
The module gathers all kinds of timeseries tranformations.
"""
import re
+import sys
import warnings
from typing import Union
-import sys
-import pandas as pd
-import numpy as np
import numba as nb
-from sklearn.neighbors import NearestNeighbors
-from scipy.stats import iqr, median_abs_deviation
-from scipy.signal import filtfilt, butter
+import numpy as np
import numpy.polynomial.polynomial as poly
+import pandas as pd
+from scipy.signal import butter, filtfilt
+from scipy.stats import iqr, median_abs_deviation
+from sklearn.neighbors import NearestNeighbors
+
+from saqc.lib.tools import getFreqDelta
def identity(ts):
- # identity function
+ """
+ Returns the input.
+
+ Parameters
+ ----------
+ ts : pd.Series
+ A series with datetime index.
+
+ Returns
+ -------
+ ts: pd.Series
+ the original
+ """
return ts
@@ -30,36 +49,56 @@ def count(ts):
return ts.count()
-def first(ts):
- # first is a dummy to trigger according built in count method of resamplers when
- # passed to aggregate2freq. For consistency reasons, it works accordingly when
- # applied directly:
- return ts.first()
-
-
-def last(ts):
- # last is a dummy to trigger according built in count method of resamplers when
- # passed to aggregate2freq. For consistency reasons, it works accordingly when
- # applied directly:
- return ts.last()
+def zeroLog(ts):
+ """
+ Calculate log of values of series for (0, inf] and NaN otherwise.
+ Parameters
+ ----------
+ ts : pd.Series
+ A series with datetime index.
-def zeroLog(ts):
- # zero log returns np.nan instead of -np.inf, when passed 0. Usefull, because
- # in internal processing, you only have to check for nan values if you need to
- # remove "invalidish" values from the data.
+ Returns
+ -------
+ pd.Series
+ """
log_ts = np.log(ts)
log_ts[log_ts == -np.inf] = sys.float_info.min
return log_ts
def derivative(ts, unit="1min"):
- # calculates derivative of timeseries, expressed in slope per "unit"
- return ts / (deltaT(ts, unit=unit))
+ """
+ Calculates derivative of timeseries, expressed in slope per `unit`.
+
+ Parameters
+ ----------
+ ts : pd.Series
+ A series with datetime index.
+
+ unit : str
+ Datetime offset unit.
+
+ Returns
+ -------
+ pd.Series
+ """
+ return ts / deltaT(ts, unit=unit)
def deltaT(ts, unit="1min"):
- # calculates series of time gaps in ts
+ """
+ Calculate the time difference of the index-values in seconds.
+
+ Parameters
+ ----------
+ ts : pd.Series
+ A series with datetime index.
+
+ Returns
+ -------
+ pd.Series
+ """
return (
ts.index.to_series().diff().dt.total_seconds()
/ pd.Timedelta(unit).total_seconds()
@@ -67,11 +106,34 @@ def deltaT(ts, unit="1min"):
def difference(ts):
- # NOTE: index of input series gets lost!
- return np.diff(ts, prepend=np.nan)
+ """
+ Calculate the difference of subsequent values in the series.
+
+ Parameters
+ ----------
+ ts : pd.Series
+ A series with datetime index.
+
+ Returns
+ -------
+ pd.Series
+ """
+ return ts.diff(1)
def rateOfChange(ts):
+ """
+ Calculate the rate of change of the series values.
+
+ Parameters
+ ----------
+ ts : pd.Series
+ A series with datetime index.
+
+ Returns
+ -------
+ pd.Series
+ """
return difference(ts) / ts
@@ -83,7 +145,22 @@ def relativeDifference(ts):
def scale(ts, target_range=1, projection_point=None):
- # scales input series to have values ranging from - target_rang to + target_range
+ """
+ Scales input series values to a given range.
+
+
+ Parameters
+ ----------
+ ts : pd.Series
+ A series with datetime index.
+ target_range : int
+ The projection will range from ``[-target_range, target_range]``
+
+ Returns
+ -------
+ scaled: pd.Series
+ The scaled Series
+ """
if not projection_point:
projection_point = np.max(np.abs(ts))
return (ts / projection_point) * target_range
@@ -133,9 +210,22 @@ def maxGap(in_arr):
@nb.njit
-def _maxConsecutiveNan(arr, max_consec):
- # checks if arr (boolean array) has not more then "max_consec" consecutive True
- # values
+def _exceedConsecutiveNanLimit(arr, max_consec):
+ """
+ Check if array has more consecutive NaNs than allowed.
+
+ Parameters
+ ----------
+ arr : bool array
+ boolean array
+ max_consec : int
+ maximum allowed consecutive `True`s
+
+ Returns
+ -------
+ exceeded: bool
+ True if more than allowed consecutive NaNs appear, False otherwise.
+ """
current = 0
idx = 0
while idx < arr.size:
@@ -143,10 +233,10 @@ def _maxConsecutiveNan(arr, max_consec):
current += 1
idx += 1
if current > max_consec:
- return False
+ return True
current = 0
idx += 1
- return True
+ return False
def validationTrafo(data, max_nan_total, max_nan_consec):
@@ -154,22 +244,17 @@ def validationTrafo(data, max_nan_total, max_nan_consec):
# True-array of input array size for invalid input arrays False array for valid
# ones
data = data.copy()
- if (max_nan_total is np.inf) & (max_nan_consec is np.inf):
+ if max_nan_total is np.inf and max_nan_consec is np.inf:
return data
- # nan_mask = np.isnan(data)
-
- if data.sum() <= max_nan_total:
- if max_nan_consec is np.inf:
- data[:] = False
- return data
- elif _maxConsecutiveNan(np.asarray(data), max_nan_consec):
- data[:] = False
- else:
- data[:] = True
+ if data.sum() > max_nan_total:
+ value = True
+ elif max_nan_consec is np.inf:
+ value = False
else:
- data[:] = True
+ value = _exceedConsecutiveNanLimit(np.asarray(data), max_nan_consec)
+ data[:] = value
return data
@@ -231,7 +316,12 @@ def interpolateNANs(
)
pre_index = data.index
- data = data[gap_mask]
+
+ if data[gap_mask].empty:
+ return data
+
+ else:
+ data = data[gap_mask]
if method in ["linear", "time"]:
@@ -251,7 +341,7 @@ def interpolateNANs(
except (NotImplementedError, ValueError):
warnings.warn(
f"Interpolation with method {method} is not supported at order "
- f"{wrap_order}. and will be performed at order {wrap_order-1}"
+ f"{wrap_order}. and will be performed at order {wrap_order - 1}"
)
return _interpolWrapper(x, int(wrap_order - 1), wrap_method)
elif x.size < 3:
@@ -378,8 +468,8 @@ def butterFilter(
x: pd.Series
input timeseries
- cutoff: float
- The cutoff-frequency, expressed in multiples of the sampling rate.
+ cutoff: {float, str}
+ The cutoff-frequency, either an offset freq string, or expressed in multiples of the sampling rate.
nyq: float
The niquist-frequency. expressed in multiples if the sampling rate.
@@ -393,6 +483,9 @@ def butterFilter(
Returns
-------
"""
+ if isinstance(cutoff, str):
+ cutoff = getFreqDelta(x.index) / pd.Timedelta(cutoff)
+
na_mask = x.isna()
x = x.interpolate(fill_method).interpolate("ffill").interpolate("bfill")
b, a = butter(N=filter_order, Wn=cutoff / nyq, btype=filter_type)
@@ -410,7 +503,7 @@ def _coeffMat(x, deg):
mat_[:, 1] = x
if deg > 1:
for n in range(2, deg + 1):
- mat_[:, n] = x ** n
+ mat_[:, n] = x**n
return mat_
diff --git a/saqc/lib/types.py b/saqc/lib/types.py
index 55fe7392a6cf49b2483ce69771b2eda7b1ce7ca6..6c0a61f352f4f40983d4813bfec7ee3ac6a14728 100644
--- a/saqc/lib/types.py
+++ b/saqc/lib/types.py
@@ -1,4 +1,9 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
__all__ = [
"T",
@@ -10,10 +15,12 @@ __all__ = [
"OptionalNone",
]
-from typing import Any, TypeVar, Union, Dict
-from typing_extensions import Protocol, Literal
+from typing import Any, Dict, TypeVar, Union
+
import numpy as np
import pandas as pd
+from typing_extensions import Literal, Protocol
+
from dios import DictOfSeries
T = TypeVar("T")
@@ -27,7 +34,7 @@ ExternalFlag = Union[str, float, int]
# needed for deeper type hinting magic
class CurveFitter(Protocol):
def __call__(self, data: np.ndarray, *params: float) -> np.ndarray:
- ...
+ ... # pragma: no cover
class GenericFunction(Protocol):
@@ -36,7 +43,7 @@ class GenericFunction(Protocol):
__globals__: Dict[str, Any]
def __call__(self, *args: pd.Series) -> PandasLike:
- ...
+ ... # pragma: no cover
class OptionalNone:
diff --git a/saqc/version.py b/saqc/version.py
index 423813e43f505ef32b3ce488943b4ec6bd249e3e..777c3104ada32dc027e6fa0a3a3d44c381a44138 100644
--- a/saqc/version.py
+++ b/saqc/version.py
@@ -1,3 +1,7 @@
#!/usr/bin/env python
-__version__ = "2.0.1"
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+__version__ = "2.1.0"
diff --git a/setup.py b/setup.py
index 7e6ddfc9f0f809093f3d73ba04d862741bd2185e..950f9aba023e36d7b79f7d811e0276c8b1c5643d 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,11 @@
-from setuptools import setup, find_packages
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from distutils.util import convert_path
+from setuptools import find_packages, setup
+
# 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
@@ -16,29 +21,28 @@ with open("README.md", "r") as fh:
setup(
name="saqc",
version=version,
- author="Bert Palm, David Schaefer, Peter Luenenschloss, Lennard Schmidt",
+ author="Bert Palm, David Schaefer, Florian Gransee, Peter Luenenschloss",
author_email="david.schaefer@ufz.de",
- description="Data quality checking and processing tool/framework",
+ description="A timeseries data quality control and processing tool/framework",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://git.ufz.de/rdm-software/saqc",
- packages=find_packages(exclude=("tests",)),
- python_requires=">=3.7, <3.10",
+ packages=find_packages(exclude=("tests", "docs")),
+ python_requires=">=3.7",
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.*",
- "scikit-learn==1.0.*",
- "scipy==1.7.*",
- "typing_extensions==4.*",
- "seaborn==0.11.*",
+ "Click",
+ "dtw",
+ "matplotlib>=3.4",
+ "numba",
+ "numpy",
+ "outlier-utils",
+ "pyarrow",
+ "pandas>=1.2",
+ "scikit-learn",
+ "scipy",
+ "typing_extensions",
],
- license_files=("LICENSE.md",),
+ license_files=("LICENSE.md", "LICENSES/GPL-3.0-or-later.txt"),
entry_points={
"console_scripts": ["saqc=saqc.__main__:main"],
},
diff --git a/sphinxdoc/Makefile b/sphinxdoc/Makefile
deleted file mode 100644
index b3a754c158ab6913ce6d2761884b3b568086bbb0..0000000000000000000000000000000000000000
--- a/sphinxdoc/Makefile
+++ /dev/null
@@ -1,75 +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
-
-# 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/sphinxdoc/documentation/FlaggingTranslation.rst b/sphinxdoc/documentation/FlaggingTranslation.rst
deleted file mode 100644
index 84548d8358d01f7db300a40edfa433b741650872..0000000000000000000000000000000000000000
--- a/sphinxdoc/documentation/FlaggingTranslation.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Flags, History and Translations
-===============================
-
-TODO
diff --git a/sphinxdoc/documentation/GenericFunctions.rst b/sphinxdoc/documentation/GenericFunctions.rst
deleted file mode 100644
index 93d313645c75d44db262d9edf23da2712b693835..0000000000000000000000000000000000000000
--- a/sphinxdoc/documentation/GenericFunctions.rst
+++ /dev/null
@@ -1,322 +0,0 @@
-
-Generic Functions
-=================
-
-Generic Flagging Functions
---------------------------
-
-Generic flagging functions provide for cross-variable quality
-constraints and to implement simple quality checks directly within the configuration.
-
-Why?
-^^^^
-
-In most real world datasets many errors
-can be explained by the dataset itself. Think of a an active, fan-cooled
-measurement device: no matter how precise the instrument may work, problems
-are to be expected when the fan stops working or the power supply
-drops below a certain threshold. While these dependencies are easy to
-: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
-^^^^^^^^^^^^^
-
-Generic flagging functions are used in the same manner as their
-:doc:`non-generic counterparts <FunctionIndex>`. The basic
-signature looks like that:
-
-.. code-block:: sh
-
- flagGeneric(func=<expression>, flag=<flagging_constant>)
-
-where ``<expression>`` is composed of the `supported constructs`_
-and ``<flag_constant>`` is one of the predefined
-: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``.
-
-Examples
-^^^^^^^^
-
-Simple comparisons
-~~~~~~~~~~~~~~~~~~
-
-Task
-""""
-
-Flag all values of ``x`` where ``y`` falls below 0.
-
-Configuration file
-""""""""""""""""""
-
-.. code-block::
-
- varname ; test
- #-------;------------------------
- x ; flagGeneric(func=y < 0)
-
-Calculations
-~~~~~~~~~~~~
-
-Task
-""""
-
-Flag all values of ``x`` that exceed 3 standard deviations of ``y``.
-
-Configuration file
-""""""""""""""""""
-
-.. code-block::
-
- varname ; test
- #-------;---------------------------------
- x ; flagGeneric(func=x > std(y) * 3)
-
-Special functions
-~~~~~~~~~~~~~~~~~
-
-Task
-""""
-
-Flag all values of ``x`` where: ``y`` is flagged and ``z`` has missing values.
-
-Configuration file
-""""""""""""""""""
-
-.. code-block::
-
- varname ; test
- #-------;----------------------------------------------
- x ; flagGeneric(func=isflagged(y) & ismissing(z))
-
-A real world example
-~~~~~~~~~~~~~~~~~~~~
-
-Let's consider the following dataset:
-
-.. list-table::
- :header-rows: 1
-
- * - date
- - meas
- - fan
- - volt
- * - 2018-06-01 12:00
- - 3.56
- - 1
- - 12.1
- * - 2018-06-01 12:10
- - 4.7
- - 0
- - 12.0
- * - 2018-06-01 12:20
- - 0.1
- - 1
- - 11.5
- * - 2018-06-01 12:30
- - 3.62
- - 1
- - 12.1
- * - ...
- -
- -
- -
-
-
-Task
-""""
-
-Flag ``meas`` where ``fan`` equals 0 and ``volt``
-is lower than ``12.0``.
-
-Configuration file
-""""""""""""""""""
-
-There are various options. We can directly implement the condition as follows:
-
-.. code-block::
-
- varname ; test
- #-------;-----------------------------------------------
- meas ; flagGeneric(func=(fan == 0) \| (volt < 12.0))
-
-But we could also quality check our independent variables first
-and than leverage this information later on:
-
-.. code-block::
-
- varname ; test
- #-------;----------------------------------------------------
- '.*' ; flagMissing()
- fan ; flagGeneric(func=fan == 0)
- volt ; flagGeneric(func=volt < 12.0)
- meas ; flagGeneric(func=isflagged(fan) \| isflagged(volt))
-
-Generic Processing
-------------------
-
-Generic processing functions provide a way to evaluate mathmetical operations
-and functions on the variables of a given dataset.
-
-Why
-^^^
-
-In many real-world use cases, quality control is embedded into a larger data
-processing pipeline and it is not unusual to even have certain processing
-requirements as a part of the quality control itself. Generic processing
-functions make it easy to enrich a dataset through the evaluation of a
-given expression.
-
-Specification
-^^^^^^^^^^^^^
-
-The basic signature looks like that:
-
-.. code-block:: sh
-
- procGeneric(func=<expression>)
-
-where ``<expression>`` is composed of the `supported constructs`_.
-
-Variable References
--------------------
-
-All variables of the processed dataset are available within generic functions,
-so arbitrary cross references are possible. The variable of interest
-is furthermore available with the special reference ``this``\ , so the second
-:ref:`example <documentation/GenericFunctions:calculations>` could be rewritten as:
-
-.. code-block::
-
- varname ; test
- #-------;------------------------------------
- x ; flagGeneric(func=this > std(y) * 3)
-
-When referencing other variables, their flags will be respected during evaluation
-of the generic expression. So, in the example above only values of ``x`` and ``y``\ , that
-are not already flagged with ``BAD`` will be used the avaluation of ``x > std(y)*3``.
-
-Supported constructs
---------------------
-
-Operators
-^^^^^^^^^
-
-Comparison
-~~~~~~~~~~
-
-The following comparison operators are available:
-
-.. list-table::
- :header-rows: 1
-
- * - Operator
- - Description
- * - ``==``
- - ``True`` if the values of the operands are equal
- * - ``!=``
- - ``True`` if the values of the operands are not equal
- * - ``>``
- - ``True`` if the values of the left operand are greater than the values of the right operand
- * - ``<``
- - ``True`` if the values of the left operand are smaller than the values of the right operand
- * - ``>=``
- - ``True`` if the values of the left operand are greater or equal than the values of the right operand
- * - ``<=``
- - ``True`` if the values of the left operand are smaller or equal than the values of the right operand
-
-
-Arithmetics
-~~~~~~~~~~~
-
-The following arithmetic operators are supported:
-
-.. list-table::
- :header-rows: 1
-
- * - Operator
- - Description
- * - ``+``
- - addition
- * - ``-``
- - subtraction
- * - ``*``
- - multiplication
- * - ``/``
- - division
- * - ``**``
- - exponentiation
- * - ``%``
- - modulus
-
-
-Bitwise
-~~~~~~~
-
-The bitwise operators also act as logical operators in comparison chains
-
-.. list-table::
- :header-rows: 1
-
- * - Operator
- - Description
- * - ``&``
- - binary and
- * - ``|``
- - binary or
- * - ``^``
- - binary xor
- * - ``~``
- - binary complement
-
-
-Functions
-^^^^^^^^^
-
-All functions expect a :ref:`variable reference <documentation/GenericFunctions:variable references>`
-as the only non-keyword argument (see :ref:`here <documentation/GenericFunctions:special functions>`\ )
-
-Mathematical Functions
-~~~~~~~~~~~~~~~~~~~~~~
-
-.. list-table::
- :header-rows: 1
-
- * - Name
- - Description
- * - ``abs``
- - absolute values of a variable
- * - ``max``
- - maximum value of a variable
- * - ``min``
- - minimum value of a variable
- * - ``mean``
- - mean value of a variable
- * - ``sum``
- - sum of a variable
- * - ``std``
- - standard deviation of a variable
- * - ``len``
- - the number of values for variable
-
-
-Special Functions
-~~~~~~~~~~~~~~~~~
-
-.. list-table::
- :header-rows: 1
-
- * - Name
- - Description
- * - ``ismissing``
- - check for missing values
- * - ``isflagged``
- - check for flags
-
-
-Constants
-^^^^^^^^^
-
-Generic functions support the same constants as normal functions, a detailed
-list is available :ref:`here <getting_started/ParameterDescriptions:constants>`.
diff --git a/sphinxdoc/documentation/SourceTarget.rst b/sphinxdoc/documentation/SourceTarget.rst
deleted file mode 100644
index 06a4e466459f824d9d554d4cd34af54f61882c01..0000000000000000000000000000000000000000
--- a/sphinxdoc/documentation/SourceTarget.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Assigning results to new variables
-==================================
-
-TODO
diff --git a/sphinxdoc/environment/configEnv.rst b/sphinxdoc/environment/configEnv.rst
deleted file mode 100644
index b765aee302cb14e46ab48c13ef05a32ec97acb7f..0000000000000000000000000000000000000000
--- a/sphinxdoc/environment/configEnv.rst
+++ /dev/null
@@ -1,56 +0,0 @@
-Config file Environment
-=======================
-
-.. list-table::
- :header-rows: 1
-
- * - Env-Value
- - Env-Doc
- * - "NAN"
- - Pointwise absolute Value Function.
- * - "abs"
- - Maximum Value Function. Ignores NaN.
- * - "max"
- - Minimum Value Function. Ignores NaN.
- * - "min"
- - Mean Value Function. Ignores NaN.
- * - "mean"
- - Summation. Ignores NaN.
- * - "sum"
- - Standart Deviation. Ignores NaN.
- * - "len"
- - Pointwise Exponential.
- * - "exp"
- - Pointwise Logarithm.
- * - "log"
- - Logarithm, returning NaN for zero input, instead of -inf.
- * - "nanLog"
- - Standart Deviation. Ignores NaN.
- * - "std"
- - Variance. Ignores NaN.
- * - "var"
- - Median. Ignores NaN.
- * - "median"
- - Count Number of values. Ignores NaNs.
- * - "count"
- - Identity.
- * - "id"
- - Returns a Series` diff.
- * - "diff"
- - Scales data to [0,1] Interval.
- * - "scale"
- - Standardize with Standart Deviation.
- * - "zScore"
- - Standardize with Median and MAD.
- * - "madScore"
- - Standardize with Median and inter quantile range.
- * - "iqsScore"
- -
- * - "GOOD"
- -
- * - "BAD"
- -
- * - "UNFLAGGED"
- -
- * - "DOUBTFUL"
- -
diff --git a/sphinxdoc/funcSummaries/constants.rst b/sphinxdoc/funcSummaries/constants.rst
deleted file mode 100644
index a2b9ac669b91d90da7de0d085ef3c7f507060db6..0000000000000000000000000000000000000000
--- a/sphinxdoc/funcSummaries/constants.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-constants
-=========
-
-
-
-.. currentmodule:: saqc
-
-.. autosummary::
-
- ~SaQC.flagConstants
- ~SaQC.flagByVariance
diff --git a/sphinxdoc/funcSummaries/curvefit.rst b/sphinxdoc/funcSummaries/curvefit.rst
deleted file mode 100644
index 8e7a8d384540782c4f3844b7c4f1037ddfb48740..0000000000000000000000000000000000000000
--- a/sphinxdoc/funcSummaries/curvefit.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-curvefit
-========
-
-
-
-.. currentmodule:: saqc
-
-.. autosummary::
-
- ~SaQC.fitPolynomial
diff --git a/sphinxdoc/funcSummaries/generic.rst b/sphinxdoc/funcSummaries/generic.rst
deleted file mode 100644
index 88fb0454bb9372ca9a49bc098795a1ddf7185804..0000000000000000000000000000000000000000
--- a/sphinxdoc/funcSummaries/generic.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-generic
-=======
-
-
-
-.. currentmodule:: saqc
-
-.. autosummary::
-
- ~SaQC.processGeneric
- ~SaQC.flagGeneric
diff --git a/sphinxdoc/funcSummaries/noise.rst b/sphinxdoc/funcSummaries/noise.rst
deleted file mode 100644
index 5db1a3fdf5ffb726b4207d439896e16db66180db..0000000000000000000000000000000000000000
--- a/sphinxdoc/funcSummaries/noise.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-noise
-=====
-
-
-
-.. currentmodule:: saqc
-
-.. autosummary::
-
- ~SaQC.flagByStatLowPass
diff --git a/sphinxdoc/funcSummaries/pattern.rst b/sphinxdoc/funcSummaries/pattern.rst
deleted file mode 100644
index 652b5425a1f3dbf18ec61d51270feb675b3d3773..0000000000000000000000000000000000000000
--- a/sphinxdoc/funcSummaries/pattern.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-pattern
-=======
-
-
-
-.. currentmodule:: saqc
-
-.. autosummary::
-
- ~SaQC.flagPatternByWavelet
- ~SaQC.calculateDistanceByDTW
- ~SaQC.flagPatternByDTW
diff --git a/sphinxdoc/funcSummaries/residues.rst b/sphinxdoc/funcSummaries/residues.rst
deleted file mode 100644
index f4a876ca78c0f5624de7dd25ef08fc2c92b68a5d..0000000000000000000000000000000000000000
--- a/sphinxdoc/funcSummaries/residues.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-residues
-========
-
-
-
-.. currentmodule:: saqc
-
-.. autosummary::
-
- ~SaQC.calculatePolynomialResidues
- ~SaQC.calculateRollingResidues
diff --git a/sphinxdoc/funcSummaries/rolling.rst b/sphinxdoc/funcSummaries/rolling.rst
deleted file mode 100644
index 37facd6199c4d20c59a5260e7564f89292f9e849..0000000000000000000000000000000000000000
--- a/sphinxdoc/funcSummaries/rolling.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-rolling
-=======
-
-
-
-.. currentmodule:: saqc
-
-.. autosummary::
-
- ~SaQC.roll
diff --git a/sphinxdoc/funcSummaries/scores.rst b/sphinxdoc/funcSummaries/scores.rst
deleted file mode 100644
index 50a1c1dec02280b2c4c40d104f40e70393384aca..0000000000000000000000000000000000000000
--- a/sphinxdoc/funcSummaries/scores.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-scores
-======
-
-
-
-.. currentmodule:: saqc
-
-.. autosummary::
-
- ~SaQC.assignKNNScore
diff --git a/sphinxdoc/funcSummaries/tools.rst b/sphinxdoc/funcSummaries/tools.rst
deleted file mode 100644
index 18d408ddbf89a440b6e97350e5e9fd0bd3e76ba4..0000000000000000000000000000000000000000
--- a/sphinxdoc/funcSummaries/tools.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-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
deleted file mode 100644
index 8f2789bc644cf4eee299a9d4a9e23f8eb550a107..0000000000000000000000000000000000000000
--- a/sphinxdoc/funcSummaries/transformation.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-transformation
-==============
-
-
-
-.. currentmodule:: saqc
-
-.. autosummary::
-
- ~SaQC.transform
diff --git a/sphinxdoc/getting_started/ConfigurationFiles.rst b/sphinxdoc/getting_started/ConfigurationFiles.rst
deleted file mode 100644
index 41d3f0aaee0827e34991956a64c2f9527acc8e50..0000000000000000000000000000000000000000
--- a/sphinxdoc/getting_started/ConfigurationFiles.rst
+++ /dev/null
@@ -1,133 +0,0 @@
-
-Configuration Files
-===================
-
-The behaviour of SaQC can be completely controlled by a text based configuration file.
-
-Format
-------
-
-SaQC expects configuration files to be semicolon-separated text files with a
-fixed header. Each row of the configuration file lists
-one variable and one or several test functions that are applied on the given variable.
-
-Header names
-^^^^^^^^^^^^
-
-The header names are basically fixed, but if you really insist in custom
-configuration headers have a look `here <saqc/core/config.py>`_.
-
-.. list-table::
- :header-rows: 1
-
- * - Name
- - Data Type
- - Description
- - Required
- * - varname
- - string
- - name of a variable
- - yes
- * - test
- - :ref:`function notation <getting_started/ConfigurationFiles:test function notation>`
- - test function
- - yes
- * - plot
- - boolean (\ ``True``\ /\ ``False``\ )
- - plot the test's result
- - no
-
-
-Test function notation
-^^^^^^^^^^^^^^^^^^^^^^
-
-The notation of test functions follows the function call notation of Python and
-many other programming languages and looks like this:
-
-.. code-block::
-
- flagRange(min=0, max=100)
-
-Here the function ``flagRange`` is called and the values ``0`` and ``100`` are passed
-to the parameters ``min`` and ``max`` respectively. As we value readablity
-of the configuration more than conciseness of the extrension language, only
-keyword arguments are supported. That means that the notation ``flagRange(0, 100)``
-is not a valid replacement for the above example.
-
-Examples
---------
-
-Single Test
-^^^^^^^^^^^
-
-Every row lists one test per variable. If you want to call multiple tests on
-a specific variable (and you probably want to), list them in separate rows:
-
-.. code-block::
-
- varname | test
- #-------|----------------------------------
- x | flagMissing()
- x | flagRange(min=0, max=100)
- x | constants_flagBasic(window="3h")
- y | flagRange(min=-10, max=40)
-
-Multiple Tests
-^^^^^^^^^^^^^^
-
-A row lists multiple tests for a specific variable in separate columns. All test
-columns need to share the common prefix ``test``\ :
-
-.. code-block::
-
- varname ; test_1 ; test_2 ; test_3
- #-------;----------------------------;---------------------------;---------------------------------
- x ; flagMissing() ; flagRange(min=0, max=100) ; constants_flagBasic(window="3h")
- y ; flagRange(min=-10, max=40) ; ;
-
-The evaluation of such a configuration is in columns-major order, so the given
-example is identical to the following:
-
-.. code-block::
-
- varname ; test_1
- #-------;---------------------------------
- x ; flagMissing()
- y ; flagRange(min=-10, max=40)
- x ; flagRange(min=0, max=100)
- x ; constants_flagBasic(window="3h")
-
-Plotting
-^^^^^^^^
-
-As the process of finding a good quality check setup is somewhat experimental, SaQC
-provides a possibility to plot the results of the test functions. To use this feature, add the optional column ``plot`` and set it
-to ``True`` for all results you want to plot. These plots are
-meant to provide a quick and easy visual evaluation of the test.
-
-.. code-block::
-
- varname ; test ; plot
- #-------;----------------------------------;-----
- x ; flagMissing() ;
- x ; flagRange(min=0, max=100) ; False
- x ; constants_flagBasic(window="3h") ; True
- y ; flagRange(min=-10, max=40)` ;
-
-Regular Expressions in ``varname`` column
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Some of the tests (e.g. checks for missing values, range tests or interpolation
-functions) are very likely to be used on all or at least several variables of
-the processed dataset. As it becomes quite cumbersome to list all these
-variables seperately, only to call the same functions with the same
-parameters, SaQC supports regular expressions
-within the ``varname`` column. Please not that a ``varname`` needs to be quoted
-(with ``'`` or ``"``\ ) in order to be interpreted as a regular expression.
-
-.. code-block::
-
- varname ; test
- #----------;------------------------------
- '.*' ; harm_shift2Grid(freq="15Min")
- '(x \| y)' ; flagMissing()
diff --git a/sphinxdoc/getting_started/FlaggingSchemes.rst b/sphinxdoc/getting_started/FlaggingSchemes.rst
deleted file mode 100644
index 4162147258a7c962c735227dc0bb7c50217ca5d1..0000000000000000000000000000000000000000
--- a/sphinxdoc/getting_started/FlaggingSchemes.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-
-DMP flagging scheme
-===================
-
-Possible flags
---------------
-
-The DMP scheme produces the following flag constants:
-
-
-* "ok"
-* "doubtfull"
-* "bad"
diff --git a/sphinxdoc/getting_started/ParameterDescriptions.rst b/sphinxdoc/getting_started/ParameterDescriptions.rst
deleted file mode 100644
index 1f7386125dcaebc91576908adb11535429e2cc2e..0000000000000000000000000000000000000000
--- a/sphinxdoc/getting_started/ParameterDescriptions.rst
+++ /dev/null
@@ -1,64 +0,0 @@
-
-Offset Strings
---------------
-
-All the `pandas offset aliases <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_ ars supported by SaQC. The following table lists some of the more relevant options:
-
-.. list-table::
- :header-rows: 1
-
- * - Alias
- - Description
- * - ``"S"``\ , ``"s"``
- - second
- * - ``"T"``\ , ``"Min"``\ , ``"min"``
- - minute
- * - ``"H"``\ , ``"h"``
- - hour
- * - ``"D"``\ , ``"d"``
- - calendar day
- * - ``"W"``\ , ``"w"``
- - week
- * - ``"M"``\ , ``"m"``
- - month
- * - ``"Y"``\ , ``"y"``
- - year
-
-
-Multiples are build by preceeding the alias with the desired multiply (e.g ``"5Min"``\ , ``"4W"``\ )
-
-Constants
----------
-
-Flagging Constants
-^^^^^^^^^^^^^^^^^^
-
-The following flag constants are available and can be used to mark the quality of a data point:
-
-.. list-table::
- :header-rows: 1
-
- * - Alias
- - Description
- * - ``GOOD``
- - A value did pass all the test and is therefore considered to be valid
- * - ``BAD``
- - At least on test failed on the values and is therefore considered to be invalid
- * - ``UNFLAGGED``
- - The value has not got a flag yet. This might mean, that all tests passed or that no tests ran
-
-
-How these aliases will be translated into 'real' flags (output of SaQC) dependes on the :doc:`flagging scheme <FlaggingSchemes>`
-and might range from numerical values to string constants.
-
-Numerical Constants
-^^^^^^^^^^^^^^^^^^^
-
-.. list-table::
- :header-rows: 1
-
- * - Alias
- - Description
- * - ``NAN``
- - Not a number
-
diff --git a/sphinxdoc/index.rst b/sphinxdoc/index.rst
deleted file mode 100644
index 8466583581b3c6cffabf6fdc40ebf43f224c6f81..0000000000000000000000000000000000000000
--- a/sphinxdoc/index.rst
+++ /dev/null
@@ -1,93 +0,0 @@
-
-.. include:: misc/title.rst
-
-
-.. toctree::
- :hidden:
- :maxdepth: 1
-
- Repository <https://git.ufz.de/rdm-software/saqc>
-
-Getting Started
-===============
-
-.. toctree::
- :caption: Getting started
- :maxdepth: 1
- :hidden:
-
- getting_started/InstallationGuide
- getting_started/TutorialAPI
- getting_started/TutorialCLI
-
-
-.. toctree::
- :caption: Cookbooks
- :maxdepth: 1
- :hidden:
-
- cook_books/DataRegularisation
- cook_books/OutlierDetection
- cook_books/MultivariateFlagging
-
-.. toctree::
- :caption: Documentation
- :maxdepth: 1
- :hidden:
-
- documentation/FlaggingTranslation
- documentation/GenericFunctions
- documentation/SourceTarget
-
-
-.. toctree::
- :caption: Functions
- :hidden:
- :maxdepth: 1
- :glob:
- :titlesonly:
-
- 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: 2
-
- Package <moduleAPIs/saqc>
- Config Environment <environment/configEnv>
-
-
-.. toctree::
- :caption: Developer Ressources
- :hidden:
- :maxdepth: 1
-
- Documentation Guide <how_to_doc/HowToDoc>
- Writing Functions <documentation/WritingFunctions>
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-
diff --git a/sphinxdoc/moduleAPIs/saqc.rst b/sphinxdoc/moduleAPIs/saqc.rst
deleted file mode 100644
index 9cdee31aabb0fddda2d6686f0cc6c3217beea29c..0000000000000000000000000000000000000000
--- a/sphinxdoc/moduleAPIs/saqc.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-
-SaQC
-====
-
-.. automodapi:: saqc
-
-
-
diff --git a/sphinxdoc/moduleAPIs/saqcConstants.rst b/sphinxdoc/moduleAPIs/saqcConstants.rst
deleted file mode 100644
index db20cc67b9289435bb59f631a7236a8f0067f4ea..0000000000000000000000000000000000000000
--- a/sphinxdoc/moduleAPIs/saqcConstants.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-
-saqc
-====
-
-.. automodapi:: saqc.constants
- :include-all-objects:
- :skip: ENVIRONMENT
diff --git a/sphinxdoc/moduleAPIs/saqcFuncClass.rst b/sphinxdoc/moduleAPIs/saqcFuncClass.rst
deleted file mode 100644
index c537ab13ca0819ec19a8828b0d3f74151f198fd8..0000000000000000000000000000000000000000
--- a/sphinxdoc/moduleAPIs/saqcFuncClass.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-
-SaQC
-====
-
-.. automodapi:: sphinxdoc.coredoc
-
-
-
diff --git a/sphinxdoc/moduleAPIs/saqcFuncTOC.rst b/sphinxdoc/moduleAPIs/saqcFuncTOC.rst
deleted file mode 100644
index 6a3ff1aa1673f522cf65af0fbef5f518545290fb..0000000000000000000000000000000000000000
--- a/sphinxdoc/moduleAPIs/saqcFuncTOC.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-SaQC Flagging Functions
------------------------
-
-.. toctree::
- :glob:
-
- ../funcSummaries/*
-
-
-
diff --git a/sphinxdoc/requirements_sphinx.txt b/sphinxdoc/requirements_sphinx.txt
deleted file mode 100644
index 3ebf7c5bdf83131dfeadb917893b5678820d50e2..0000000000000000000000000000000000000000
--- a/sphinxdoc/requirements_sphinx.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-recommonmark
-sphinx
-sphinx-automodapi
-sphinxcontrib-fulltoc
-sphinx-markdown-tables
-m2r
-jupyter-sphinx
diff --git a/sphinxdoc/ressources/data/config.csv b/sphinxdoc/ressources/data/config.csv
deleted file mode 100644
index 6f31389afc600155fb18cd9710738d3fc809d065..0000000000000000000000000000000000000000
--- a/sphinxdoc/ressources/data/config.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-varname ; test ; plot
-#----------;-------------------------------------;------
-SM2 ; shift(freq="15Min") ; False
-SM2 ; flagMissing() ; False
-'SM(1|2)+' ; flagRange(min=10, max=60) ; False
-SM2 ; flagMAD(window="30d", z=3.5) ; True
diff --git a/sphinxdoc/ressources/data/data.csv b/sphinxdoc/ressources/data/data.csv
deleted file mode 100644
index bb853b10bcffe98dbfc8d11348bcfe3fdf47eb9a..0000000000000000000000000000000000000000
--- a/sphinxdoc/ressources/data/data.csv
+++ /dev/null
@@ -1,14694 +0,0 @@
-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
-2016-04-01 01:05:26,3572,32.736999999999995,29.3131
-2016-04-01 01:20:21,3571,32.6186,29.3157
-2016-04-01 01:35:16,3571,32.736999999999995,29.3157
-2016-04-01 01:50:11,3571,32.736999999999995,29.4727
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-2016-08-31 10:57:27,3599,10.9808,14.4838
-2016-08-31 11:07:29,3599,10.9808,14.4526
-2016-08-31 11:17:32,3599,10.9808,14.4539
-2016-08-31 11:27:35,3599,10.9808,14.4552
-2016-08-31 11:37:37,3599,10.9818,14.4838
-2016-08-31 11:47:40,3599,10.953,14.4877
-2016-08-31 11:57:43,3599,10.9808,14.4552
-2016-08-31 12:07:46,3599,10.9818,14.4864
-2016-08-31 12:17:49,3599,10.9818,14.4526
-2016-08-31 12:27:52,3599,10.9808,14.4864
-2016-08-31 12:37:54,3599,10.952,14.4851
-2016-08-31 12:47:57,3599,10.9242,14.4825
-2016-08-31 12:58:00,3599,10.8954,14.4501
-2016-08-31 13:08:03,3599,10.8954,14.4539
-2016-08-31 13:18:05,3599,10.8954,14.4851
-2016-08-31 13:28:08,3599,10.8944,14.4526
-2016-08-31 13:38:11,3599,10.8983,14.4526
-2016-08-31 13:48:14,3599,10.8686,14.48
-2016-08-31 13:58:17,3599,10.8983,14.3891
-2016-08-31 14:08:19,3599,10.8408,14.3567
-2016-08-31 14:18:22,3599,10.8686,14.3865
-2016-08-31 14:28:25,3599,10.8686,14.3891
-2016-08-31 14:38:28,3599,10.8686,14.4189
-2016-08-31 14:48:31,3599,10.8973,14.3891
-2016-08-31 14:58:34,3599,10.8973,14.4176
-2016-08-31 15:08:39,3599,10.8973,14.3865
-2016-08-31 15:18:42,3599,10.8695,14.3891
-2016-08-31 15:28:45,3599,10.8695,14.3865
-2016-08-31 15:38:48,3599,10.9002,14.358
-2016-08-31 15:48:51,3599,10.9012,14.3878
-2016-08-31 15:58:54,3599,10.8715,14.3567
-2016-08-31 16:08:57,3599,10.8724,14.3554
-2016-08-31 16:19:00,3599,10.8724,14.4176
-2016-08-31 16:29:03,3599,10.8753,14.3865
-2016-08-31 16:39:06,3599,10.8753,14.3554
-2016-08-31 16:49:09,3599,10.8734,14.3878
-2016-08-31 16:59:12,3599,10.9041,14.3865
-2016-08-31 17:09:15,3599,10.8753,14.3554
-2016-08-31 17:19:18,3599,10.8753,14.3865
-2016-08-31 17:29:21,3599,10.9031,14.3878
-2016-08-31 17:39:24,3599,10.8753,14.3891
-2016-08-31 17:49:27,3599,10.9041,14.3865
-2016-08-31 17:59:30,3599,10.9041,14.3865
-2016-08-31 18:09:33,3599,10.9031,14.3865
-2016-08-31 18:19:36,3599,10.8744,14.3865
-2016-08-31 18:29:39,3599,10.9061,14.3567
-2016-08-31 18:39:42,3599,10.8773,14.3592
-2016-08-31 18:49:45,3599,10.907,14.3554
-2016-08-31 18:59:48,3599,10.8485,14.4189
-2016-08-31 19:09:51,3599,10.8773,14.3256
-2016-08-31 19:19:53,3599,10.9051,14.3554
-2016-08-31 19:29:56,3599,10.8773,14.3891
-2016-08-31 19:39:59,3599,10.9061,14.4202
-2016-08-31 19:50:02,3599,10.8763,14.4202
-2016-08-31 20:00:05,3599,10.9051,14.3878
-2016-08-31 20:10:08,3599,10.8773,14.3567
-2016-08-31 20:20:11,3599,10.9061,14.4202
-2016-08-31 20:30:13,3599,10.8773,14.3878
-2016-08-31 20:40:16,3599,10.8773,14.3878
-2016-08-31 20:50:19,3599,10.907,14.3878
-2016-08-31 21:00:22,3599,10.8773,14.3865
-2016-08-31 21:10:25,3599,10.8773,14.3891
-2016-08-31 21:20:27,3599,10.8763,14.3865
-2016-08-31 21:30:30,3599,10.8495,14.3567
-2016-08-31 21:40:33,3599,10.9061,14.3865
-2016-08-31 21:50:36,3599,10.8782,14.3865
-2016-08-31 22:00:38,3599,10.9061,14.3852
-2016-08-31 22:10:41,3599,10.9051,14.3903
-2016-08-31 22:20:44,3599,10.9061,14.3541
-2016-08-31 22:30:46,3599,10.907,14.3865
-2016-08-31 22:40:49,3599,10.9051,14.3865
-2016-08-31 22:50:52,3599,10.9061,14.3891
-2016-08-31 23:00:54,3599,10.9061,14.3852
-2016-08-31 23:10:57,3599,10.9051,14.3891
-2016-08-31 23:20:59,3599,10.8773,14.3541
-2016-08-31 23:31:02,3599,10.9061,14.3865
-2016-08-31 23:41:04,3599,10.9061,14.3878
-2016-08-31 23:51:07,3599,10.8773,14.3865
diff --git a/sphinxdoc/ressources/data/myconfig4.csv b/sphinxdoc/ressources/data/myconfig4.csv
deleted file mode 100644
index dd7a9a6b17152307389153325d78628771b52f6a..0000000000000000000000000000000000000000
--- a/sphinxdoc/ressources/data/myconfig4.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-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/sphinxdoc/ressources/images/Representative/RDMlogo.jpg b/sphinxdoc/ressources/images/Representative/RDMlogo.jpg
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diff --git a/sphinxdoc/ressources/images/hydro_usecase/water_temp_raw.png b/sphinxdoc/ressources/images/hydro_usecase/water_temp_raw.png
deleted file mode 100644
index 6db9141146cca24dd938a5b28dbf04ead2283f19..0000000000000000000000000000000000000000
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diff --git a/sphinxdoc/ressources/images/readme_image.png b/sphinxdoc/ressources/images/readme_image.png
deleted file mode 100644
index 3bb830f6c8bce1aa16b9c4b4021637c3cd0cfe2c..0000000000000000000000000000000000000000
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diff --git a/sphinxdoc/scripts/make_doc_module.py b/sphinxdoc/scripts/make_doc_module.py
deleted file mode 100644
index 6223e24f8378f1593b6dd0429dc6dc70bd745c85..0000000000000000000000000000000000000000
--- a/sphinxdoc/scripts/make_doc_module.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import ast
-import os
-import click
-import pkgutil
-import shutil
-import re
-from collections import OrderedDict
-import pickle
-
-new_line_re = "(\r\n|[\r\n])"
-
-
-def rm_section(dcstring, section, _return_section=False):
- """
- Detects a section in a docstring and (default) removes it, or (_return_section=True) returns it
- """
- section_re = f"{new_line_re}(?P<s_name>[^\n\r]{{2,}}){new_line_re}(?P<s_dash>-{{2,}}){new_line_re}"
- triggers = re.finditer(section_re, dcstring)
- matches = [
- (trigger.groupdict()["s_name"], trigger.span())
- for trigger in triggers
- if len(trigger.groupdict()["s_name"]) == len(trigger.groupdict()["s_dash"])
- ] + [(None, (len(dcstring), None))]
- sections = [m[0] for m in matches]
- starts = ends = 0
- if section in sections:
- i = sections.index(section)
- starts = matches[i][1][0]
- ends = matches[i + 1][1][0]
-
- if _return_section:
- return dcstring[starts:ends]
- else:
- return dcstring[:starts] + dcstring[ends:]
-
-
-def rm_parameter(dcstring, parameter):
- """
- remove a parameters documentation from a function docstring
- """
- paramatches = _get_paramatches(dcstring)
- start = end = 0
- for p in paramatches:
- if parameter == p.groupdict()["paraname"]:
- start = re.search(p[0], dcstring).span()[0]
- try:
- end = dcstring.find(next(paramatches)[0])
- except StopIteration:
- end = len(re.sub(new_line_re + "$", "", dcstring))
-
- return dcstring[0:start] + dcstring[end:]
-
-
-def get_parameter(dcstr):
- """
- returns the list of parameters and their defaults, documented in a docstrings Parameters section
- """
- paramatches = _get_paramatches(dcstr)
- return [
- (p.groupdict()["paraname"], p.groupdict()["paradefaults"]) for p in paramatches
- ]
-
-
-def _get_paramatches(dcstr):
- parastr = rm_section(dcstr, "Parameters", _return_section=True)
- match_re = f"{new_line_re}(?P<paraname>[\S]+) : [^\n\r]*(default (?P<paradefaults>[^\n\r]*))?"
- return re.finditer(match_re, parastr)
-
-
-def parse_func_dcstrings(m_paths):
- func_dict = {}
- for m in m_paths:
- with open(m) as f:
- lines = f.readlines()
- module_ast = ast.parse("".join(lines))
- funcs = [node for node in module_ast.body if isinstance(node, ast.FunctionDef)]
- for func in funcs:
- dcstr = ast.get_docstring(func)
- if func.name[0] == "_" or (dcstr is None):
- continue
- dcstr = rm_section(dcstr, "Returns")
- dcstr = rm_parameter(dcstr, "data")
- dcstr = rm_parameter(dcstr, "flags")
- parameters = get_parameter(dcstr)
- parameters = [f"{p[0]}={p[1]}" if p[1] else p[0] for p in parameters]
- signature = f"def {func.name}({', '.join(parameters)}):"
- # get @register module registration if present
- reg_module = None
- r = [d for d in func.decorator_list if d.func.id == "register"]
- if r:
- rm = [kw.value.s for kw in r[0].keywords if kw.arg == "module"]
- if rm:
- reg_module = rm[0]
-
- func_dict[f"{os.path.splitext(os.path.basename(m))[0]}.{func.name}"] = (
- signature,
- dcstr,
- reg_module,
- )
-
- return func_dict
-
-
-def parse_module_dcstrings(m_paths):
- mod_dict = {}
- for m in m_paths:
- with open(m) as f:
- lines = f.readlines()
-
- mod_docstr = ast.get_docstring(ast.parse("".join(lines)))
- mod_dict[f"{os.path.splitext(os.path.basename(m))[0]}"] = mod_docstr or ""
- return mod_dict
-
-
-def make_doc_module(targetpath, func_dict, doc_mod_structure):
- for doc_mod in [
- d for d in doc_mod_structure.keys() if not re.search("_dcstring$", d)
- ]:
- with open(os.path.join(targetpath, f"{doc_mod}.py"), "w+") as f:
- mod_string = [
- '"""\n' + doc_mod_structure.get(doc_mod + "_dcstring", "") + '\n"""'
- ]
- mod_funcs = doc_mod_structure[doc_mod]
- for func in mod_funcs:
- mod_string.append(func_dict[func][0])
- mod_string.append(' """')
- # indent the docstring:
- indented_doc_string = "\n".join(
- [f" {l}" for l in func_dict[func][1].splitlines()]
- )
- mod_string.append(indented_doc_string)
- mod_string.append(' """')
- mod_string.append(" pass")
- mod_string.append("")
- mod_string.append("")
- f.write("\n".join(mod_string))
-
- return 0
-
-
-def make_doc_core(sphinxroot, func_dict, doc_mod_structure):
- targetfolder = os.path.join(sphinxroot, "sphinxdoc/coredoc")
- coresource = os.path.join(sphinxroot, os.path.normpath("saqc/core/core.py"))
-
- if os.path.isdir(targetfolder):
- shutil.rmtree(targetfolder)
- os.makedirs(targetfolder, exist_ok=True)
-
- # parse real core.py
- with open(coresource) as f:
- corelines = f.readlines()
-
- # find SaQC class def
- coreast = ast.parse("".join(corelines))
- startline = None
- endline = None
- for node in coreast.body:
- if isinstance(node, ast.ClassDef):
- if node.name == "SaQC":
- startline = node.lineno
- elif startline and (not endline):
- endline = node.lineno
-
- start = corelines[: endline - 1]
- end = corelines[endline - 1 :]
- tab = " "
- for doc_mod in [
- d for d in doc_mod_structure.keys() if not re.search("_dcstring$", d)
- ]:
- with open(os.path.join(targetfolder, f"core.py"), "w+") as f:
- mod_string = []
- mod_funcs = doc_mod_structure[doc_mod]
- for func in mod_funcs:
- def_string = func_dict[func][0]
- i_pos = re.match("def [^ ]*\(", def_string).span()[-1]
- def_string = def_string[:i_pos] + "self, " + def_string[i_pos:]
- def_string = tab + def_string
- mod_string.append(def_string)
- mod_string.append(2 * tab + '"""')
- # indent the docstring:
- indented_doc_string = "\n".join(
- [2 * tab + f"{l}" for l in func_dict[func][1].splitlines()]
- )
- mod_string.append(indented_doc_string)
- mod_string.append(2 * tab + '"""')
- mod_string.append(2 * tab + "pass")
- mod_string.append("")
- mod_string.append("")
-
- newcore = (
- "".join(start) + "\n" + "\n".join(mod_string) + "\n" + "".join(end)
- )
- f.write(newcore)
-
- 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
-
-
-def makeModuleAPIs(modules, folder_path="moduleAPIs", pck_path="Functions"):
- f_path = os.path.abspath(folder_path)
- for m in modules:
- lines = []
- lines += [m]
- lines += ["=" * len(m)]
- lines += [""]
- lines += [f".. automodapi:: {pck_path}.{m}"]
- lines += [" " * 3 + ":no-heading:"]
- with open(os.path.join(f_path, f"{pck_path}{m}.rst"), "w") as f:
- for l in lines:
- f.write(l + "\n")
-
- pass
-
-
-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",
- "--pckpath",
- type=str,
- required=True,
- default="saqc/funcs",
- help="Relative path to the package to be documented (relative to sphinx root).",
-)
-@click.option(
- "-sr",
- "--sphinxroot",
- type=str,
- required=True,
- default="../..",
- help="Relative path to the sphinx root.",
-)
-@click.option(
- "-su",
- "--summaries",
- type=str,
- required=True,
- default="funcSummaries",
- help="Target path for summaries.",
-)
-def main(pckpath, sphinxroot, summaries):
- root_path = os.path.abspath(sphinxroot)
- pkg_path = os.path.join(root_path, pckpath)
- 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)
-
- # if os.path.isdir(coretrg):
- # shutil.rmtree(coretrg)
- # os.makedirs(coretrg, exist_ok=True)
-
- # parse all the functions
- module_paths = [os.path.join(pkg_path, f"{m}.py") for m in modules]
- mod_dict = parse_module_dcstrings(module_paths)
- mod_dict = dict(
- zip([k + "_dcstring" for k in mod_dict.keys()], list(mod_dict.values()))
- )
- func_dict = parse_func_dcstrings(module_paths)
-
- # 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__":
- main()
diff --git a/sphinxdoc/scripts/make_doc_rst.py b/sphinxdoc/scripts/make_doc_rst.py
deleted file mode 100644
index e3084a69e3cd08cefdc14e14ee524d635736faa8..0000000000000000000000000000000000000000
--- a/sphinxdoc/scripts/make_doc_rst.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import os
-import click
-import pkgutil
-import ast
-import shutil
-
-
-def parse_imports(path):
- modules = []
- file = open(path)
- lines = file.readlines()
- for node in ast.iter_child_nodes(ast.parse("".join(lines))):
- if isinstance(node, ast.ImportFrom) | isinstance(node, ast.Import):
- modules += [x.name for x in node.names] + [
- x.asname for x in node.names if x.asname is not None
- ]
- file.close()
- return modules
-
-
-@click.command()
-@click.option(
- "-p",
- "--pckpath",
- type=str,
- required=True,
- 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="sphinxdoc/internal_doc_rst",
- help="Output folder path (relative to sphinx root). Will be overridden if already existent.",
-)
-@click.option(
- "-sr",
- "--sphinxroot",
- type=str,
- required=True,
- default="..",
- help="Relative path to the sphinx root.",
-)
-def main(pckpath, targetpath, sphinxroot):
- root_path = os.path.abspath(sphinxroot)
- targetpath = os.path.join(root_path, targetpath)
- pkg_path = os.path.join(root_path, pckpath)
- modules = []
- for _, modname, _ in pkgutil.walk_packages(path=[pkg_path], onerror=lambda x: None):
- modules.append(modname)
-
- emptyline = [""]
-
- # clear target directory:
- if os.path.isdir(targetpath):
- shutil.rmtree(targetpath)
- os.mkdir(targetpath)
-
- for module in modules:
- imports = parse_imports(os.path.join(pkg_path, f"{module}.py"))
- skiplist = [f"\t:skip: {k}" for k in imports]
- section = [module] + ["=" * len(module)]
- automodapi_directive = [
- ".. automodapi:: " + pckpath.replace("/", ".") + "." + module
- ]
- no_heading = [f"\t:no-heading:"]
- to_write = (
- emptyline
- + section
- + emptyline
- + automodapi_directive
- + skiplist
- + no_heading
- )
- to_write = "".join([f"{k}\r\n" for k in to_write])
- with open(os.path.join(targetpath, f"{module}.rst"), "w+") as f:
- f.write(to_write)
-
-
-if __name__ == "__main__":
- main()
diff --git a/sphinxdoc/scripts/make_env_tab.py b/sphinxdoc/scripts/make_env_tab.py
deleted file mode 100644
index 6f0914792bc8b8bc66d14642eb3a2af223a09193..0000000000000000000000000000000000000000
--- a/sphinxdoc/scripts/make_env_tab.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import ast
-import os
-import click
-import pkgutil
-import shutil
-import re
-from collections import OrderedDict
-import pickle
-
-
-@click.command()
-@click.option(
- "-p",
- "--envpath",
- type=str,
- required=True,
- default="saqc/constants.py",
- help="Relative path to the module containing the env dict (relative to sphinx root).",
-)
-@click.option(
- "-t",
- "--targetpath",
- type=str,
- required=True,
- default="sphinxdoc/environment",
- help="Output path to contain configEnv.rst (relative to sphinx root).",
-)
-@click.option(
- "-sr",
- "--sphinxroot",
- type=str,
- required=True,
- default="..",
- help="Relative path to the sphinx root.",
-)
-def main(envpath, targetpath, sphinxroot):
- root_path = os.path.abspath(sphinxroot)
- source_path = os.path.join(root_path, envpath)
- target_path = os.path.join(root_path, targetpath)
-
- with open(source_path) as f:
- lines = f.readlines()
-
- # get ENV definition linenumber
- lino_st = None
- lino_end = None
- nodes = ast.parse("".join(lines))
- for node in nodes.body:
- if lino_st:
- lino_end = node.lineno
- break
- if isinstance(node, ast.Assign):
- if isinstance(node.targets[0], ast.Name):
- if node.targets[0].id == "ENVIRONMENT":
- lino_st = node.lineno
-
- env_dict = lines[slice(lino_st, lino_end)]
- entry = 0
- val_dict = {}
- doc_dict = {}
- for line in env_dict:
- val = re.match(' *(?P<Key>".*"):', line)
- doc = re.match(" *#(?P<Comment>.*)", line)
- if val:
- if len(val.groups()) > 0:
- entry += 1
- val_dict.update({entry: val["Key"]})
- if doc:
- if len(doc.groups()) > 0:
- doc_dict.update({entry: doc_dict.get(entry, "") + doc["Comment"]})
-
- # make heading
- heading = "Config file Environment"
- lines = []
- lines += [heading, len(heading) * "=", ""]
- # make table directive
- tab = " " * 3
- new_row = tab + "* - "
- new_col = tab + " " + " - "
- # directive:
- lines += [".. list-table::", tab + ":header-rows: 1", ""]
- # table header:
- lines += [new_row + "Env-Value"]
- lines += [new_col + "Env-Doc"]
- # table body:
- for k in val_dict.keys():
- lines += [new_row + val_dict[k]]
- lines += [new_col + doc_dict.get(k, "")]
-
- with open(os.path.join(target_path, "configEnv.rst"), "w") as f:
- for s in lines:
- f.write(s + "\n")
-
-
-if __name__ == "__main__":
- main()
diff --git a/sphinxdoc/scripts/modify_html_API.py b/sphinxdoc/scripts/modify_html_API.py
deleted file mode 100644
index 4e8208a18a69363f49d75b751349d9aedb77535c..0000000000000000000000000000000000000000
--- a/sphinxdoc/scripts/modify_html_API.py
+++ /dev/null
@@ -1,43 +0,0 @@
-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/__init__.py b/tests/__init__.py
index 4265cc3e6c16c09774190fa55d609cd9fe0808e4..6b7d6f94a7f566f32065da988e7fcc3eec8692fc 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -1 +1,5 @@
#!/usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
diff --git a/tests/api/__init__.py b/tests/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d74ada495ad00a20887cdee50a426347f37efae
--- /dev/null
+++ b/tests/api/__init__.py
@@ -0,0 +1,7 @@
+#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+# -*- coding: utf-8 -*-
diff --git a/tests/core/test_creation.py b/tests/api/test_creation.py
similarity index 70%
rename from tests/core/test_creation.py
rename to tests/api/test_creation.py
index 9d2badf6df87fc51a340688514e714203db87f86..85fbebeb871c5d1d379fae84859b0162a463a8ac 100644
--- a/tests/core/test_creation.py
+++ b/tests/api/test_creation.py
@@ -1,12 +1,17 @@
#!/usr/bin/env python
-import pandas as pd
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import numpy as np
+import pandas as pd
+
import dios
def test_init():
- from saqc import SaQC, Flags
+ from saqc import Flags, SaQC
arr = np.array(
[
diff --git a/tests/api/test_signatures.py b/tests/api/test_signatures.py
new file mode 100644
index 0000000000000000000000000000000000000000..f229b52efbb377b2030936741bf97b5b812a99c3
--- /dev/null
+++ b/tests/api/test_signatures.py
@@ -0,0 +1,48 @@
+#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+# -*- coding: utf-8 -*-
+
+import inspect
+from typing import Callable, Set
+
+import pandas as pd
+import pytest
+
+from saqc import SaQC
+from saqc.core.register import FUNC_MAP, FunctionWrapper
+
+
+def _filterSignature(func: Callable, skip: Set):
+ sig = inspect.signature(func)
+ return {k: v for k, v in sig.parameters.items() if k not in skip}
+
+
+@pytest.mark.parametrize("name,func", FUNC_MAP.items())
+def test_signatureConformance(name: str, func: FunctionWrapper):
+
+ method = getattr(SaQC, name, None)
+ # check a wrapper function is defined at all
+ assert method is not None
+
+ fsig = _filterSignature(func.func, {"data", "field", "flags"})
+ msig = _filterSignature(method, {"self", "field"})
+ assert fsig.keys() == msig.keys()
+
+ for key, fp in fsig.items():
+ mp = msig[key]
+ try:
+ assert fp == mp
+ except AssertionError:
+ assert mp.annotation == fp.annotation
+ if pd.isna(fp.default) and pd.isna(mp.default): # both NA: OK
+ pass
+ elif isinstance(fp.default, Callable) and isinstance(
+ mp.default, Callable
+ ): # hard to check: ignore
+ pass
+ else:
+ assert mp.default == fp.default
diff --git a/tests/cli/__init__.py b/tests/cli/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d74ada495ad00a20887cdee50a426347f37efae
--- /dev/null
+++ b/tests/cli/__init__.py
@@ -0,0 +1,7 @@
+#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+# -*- coding: utf-8 -*-
diff --git a/tests/cli/test_integration.py b/tests/cli/test_integration.py
new file mode 100644
index 0000000000000000000000000000000000000000..81447f3aef7450610e82d2d9efa36a9ac3572a19
--- /dev/null
+++ b/tests/cli/test_integration.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+from pathlib import Path
+
+import pytest
+from click.testing import CliRunner
+
+FLOAT = [
+ ",Battery,Battery,SM1,SM1,SM2,SM2\n",
+ ",data,flags,data,flags,data,flags\n",
+ "Date,,,,,,\n",
+ "2016-04-01 00:00:00,nan,nan,nan,nan,29.3157,-inf\n",
+ "2016-04-01 00:05:48,3573.0,-inf,32.685,-inf,nan,nan\n",
+ "2016-04-01 00:15:00,nan,nan,nan,nan,29.3157,-inf\n",
+ "2016-04-01 00:20:42,3572.0,-inf,32.7428,-inf,nan,nan\n",
+ "2016-04-01 00:30:00,nan,nan,nan,nan,29.3679,-inf\n",
+ "2016-04-01 00:35:37,3572.0,-inf,32.6186,-inf,nan,nan\n",
+ "2016-04-01 00:45:00,nan,nan,nan,nan,29.3679,-inf\n",
+]
+
+SIMPLE = [
+ ",Battery,Battery,SM1,SM1,SM2,SM2\n",
+ ",data,flags,data,flags,data,flags\n",
+ "Date,,,,,,\n",
+ "2016-04-01 00:00:00,nan,nan,nan,nan,29.3157,UNFLAGGED\n",
+ "2016-04-01 00:05:48,3573.0,UNFLAGGED,32.685,UNFLAGGED,nan,nan\n",
+ "2016-04-01 00:15:00,nan,nan,nan,nan,29.3157,UNFLAGGED\n",
+ "2016-04-01 00:20:42,3572.0,UNFLAGGED,32.7428,UNFLAGGED,nan,nan\n",
+ "2016-04-01 00:30:00,nan,nan,nan,nan,29.3679,UNFLAGGED\n",
+ "2016-04-01 00:35:37,3572.0,UNFLAGGED,32.6186,UNFLAGGED,nan,nan\n",
+ "2016-04-01 00:45:00,nan,nan,nan,nan,29.3679,UNFLAGGED\n",
+]
+
+POSITIONAL = [
+ ",Battery,Battery,SM1,SM1,SM2,SM2\n",
+ ",data,flags,data,flags,data,flags\n",
+ "Date,,,,,,\n",
+ "2016-04-01 00:00:00,nan,-9999,nan,-9999,29.3157,9000\n",
+ "2016-04-01 00:05:48,3573.0,9,32.685,90,nan,-9999\n",
+ "2016-04-01 00:15:00,nan,-9999,nan,-9999,29.3157,9000\n",
+ "2016-04-01 00:20:42,3572.0,9,32.7428,90,nan,-9999\n",
+ "2016-04-01 00:30:00,nan,-9999,nan,-9999,29.3679,9000\n",
+ "2016-04-01 00:35:37,3572.0,9,32.6186,90,nan,-9999\n",
+ "2016-04-01 00:45:00,nan,-9999,nan,-9999,29.3679,9000\n",
+]
+
+DMP = [
+ ",Battery,Battery,Battery,Battery,SM1,SM1,SM1,SM1,SM2,SM2,SM2,SM2\n",
+ ",data,quality_flag,quality_cause,quality_comment,data,quality_flag,quality_cause,quality_comment,data,quality_flag,quality_cause,quality_comment\n",
+ "Date,,,,,,,,,,,,\n",
+ "2016-04-01 00:00:00,nan,nan,nan,nan,nan,nan,nan,nan,29.3157,NIL,,\n",
+ "2016-04-01 00:05:48,3573.0,NIL,,,32.685,NIL,,,nan,nan,nan,nan\n",
+ "2016-04-01 00:15:00,nan,nan,nan,nan,nan,nan,nan,nan,29.3157,NIL,,\n",
+ "2016-04-01 00:20:42,3572.0,NIL,,,32.7428,NIL,,,nan,nan,nan,nan\n",
+ "2016-04-01 00:30:00,nan,nan,nan,nan,nan,nan,nan,nan,29.3679,NIL,,\n",
+ "2016-04-01 00:35:37,3572.0,NIL,,,32.6186,NIL,,,nan,nan,nan,nan\n",
+ "2016-04-01 00:45:00,nan,nan,nan,nan,nan,nan,nan,nan,29.3679,NIL,,\n",
+]
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize(
+ "scheme, expected",
+ [("float", FLOAT), ("simple", SIMPLE), ("positional", POSITIONAL), ("dmp", DMP)],
+)
+def test__main__py(tmp_path, scheme, expected):
+ import saqc.__main__
+
+ # if not run from project root
+ projpath = Path(saqc.__file__).parents[1]
+ outfile = Path(tmp_path, "test.csv") # the filesystem's temp dir
+ args = [
+ "--config",
+ Path(projpath, "docs/resources/data/config.csv"),
+ "--data",
+ Path(projpath, "docs/resources/data/data.csv"),
+ "--outfile",
+ outfile,
+ "--scheme",
+ scheme,
+ ]
+ result = CliRunner().invoke(saqc.__main__.main, args)
+ assert result.exit_code == 0, result.output
+ with open(outfile, "r") as f:
+ result = f.readlines()[:10]
+ assert result == expected
diff --git a/tests/common.py b/tests/common.py
index 6ec9788b038076fcca3681a5697a72c7aebffd9f..54d987e8100a971e244fde1144939e137a064bb7 100644
--- a/tests/common.py
+++ b/tests/common.py
@@ -1,12 +1,18 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
import io
+
import numpy as np
import pandas as pd
-import dios
-from saqc.constants import *
+import dios
+from saqc.constants import BAD
from saqc.core import Flags
from saqc.core.history import History, createHistoryFromData
diff --git a/tests/core/__init__.py b/tests/core/__init__.py
index e1f7e6e8bf04d3d59cff68b83d91fbf791f0faa2..1d74ada495ad00a20887cdee50a426347f37efae 100644
--- a/tests/core/__init__.py
+++ b/tests/core/__init__.py
@@ -1,2 +1,7 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
diff --git a/tests/core/test_FunctionsMixin.py b/tests/core/test_FunctionsMixin.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f57509665c3f02a7c902f2131ad6d3921fbdaee
--- /dev/null
+++ b/tests/core/test_FunctionsMixin.py
@@ -0,0 +1,33 @@
+#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+# -*- coding: utf-8 -*-
+import inspect
+
+import pytest
+
+from saqc.core.modules import FunctionsMixin
+
+methods = [
+ attr
+ for attr in dir(FunctionsMixin)
+ if callable(getattr(FunctionsMixin, attr)) and not attr.startswith("_")
+]
+
+
+@pytest.mark.parametrize("name", methods)
+def test_redirect_call(name):
+ fmixin = FunctionsMixin()
+ method = getattr(fmixin, name)
+ params = inspect.signature(method).parameters
+ assert "field" in params
+ assert "kwargs" in params
+ dummy_params = dict.fromkeys(params.keys())
+ dummy_params.pop("kwargs")
+
+ err_msg = "'FunctionsMixin' object has no attribute '_wrap'"
+ with pytest.raises(AttributeError, match=err_msg):
+ method(**dummy_params)
diff --git a/tests/core/test_core.py b/tests/core/test_core.py
index a1e709507910850aa051194e728828ae31cc8bbd..62ead32ff074849cb933ae44f0ae096bb5e11cdf 100644
--- a/tests/core/test_core.py
+++ b/tests/core/test_core.py
@@ -1,20 +1,25 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-import pytest
+import copy
+
import numpy as np
import pandas as pd
+import pytest
import saqc
-from saqc.core import initFlagsLike, SaQC, register
+from saqc.core import SaQC, initFlagsLike, register
from saqc.core.flags import Flags
-from saqc.core.register import processing
-
-from tests.common import initData, flagAll
+from saqc.core.register import flagging, processing
+from tests.common import flagAll, initData
OPTIONAL = [False, True]
-
register(mask=["field"], demask=["field"], squeeze=["field"])(flagAll)
@@ -35,22 +40,10 @@ def test_errorHandling(data):
raise TypeError
var1 = data.columns[0]
+ qc = SaQC(data)
with pytest.raises(TypeError):
- SaQC(data).raisingFunc(var1)
-
-
-def test_duplicatedVariable():
- data = initData(1)
- var1 = data.columns[0]
-
- pflags = SaQC(data).flagDummy(var1).result.flags
-
- if isinstance(pflags.columns, pd.MultiIndex):
- cols = pflags.columns.get_level_values(0).drop_duplicates()
- assert np.all(cols == [var1])
- else:
- assert (pflags.columns == [var1]).all()
+ qc.raisingFunc(var1)
@pytest.mark.parametrize("optional", OPTIONAL)
@@ -62,7 +55,7 @@ def test_dtypes(data, flags):
flags_raw = flags.toDios()
var1, var2 = data.columns[:2]
- pflags = SaQC(data, flags=flags_raw).flagAll(var1).flagAll(var2).result.flags_raw
+ pflags = SaQC(data, flags=flags_raw).flagAll(var1).flagAll(var2).flags
for c in pflags.columns:
assert pflags[c].dtype == flags[c].dtype
@@ -78,7 +71,6 @@ def test_copy(data):
for copy in [deep, shallow]:
assert copy is not qc
- assert copy._called is not qc._called
assert copy._scheme is not qc._scheme
assert copy._attrs is not qc._attrs
@@ -186,3 +178,70 @@ def test_sourceTargetMulti():
return data, flags
SaQC(data, flags).flagMulti(field=fields, target=targets)
+
+
+def test_unknown_attribute():
+ qc = SaQC()
+ with pytest.raises(AttributeError):
+ qc._construct(_spam="eggs")
+
+
+def test_validation(data):
+ """Test if validation detects different columns in data and flags."""
+ df = pd.DataFrame(
+ data=np.arange(8).reshape(4, 2),
+ index=pd.date_range("2020", None, 4, "1d"),
+ columns=list("ab"),
+ )
+ qc = SaQC(df)
+
+ @flagging()
+ def flagFoo(data, field, flags, **kwargs):
+ data["spam"] = data[field]
+ return data, flags
+
+ with pytest.raises(RuntimeError):
+ qc.flagFoo("a")
+
+
+@pytest.mark.skip(reason="bug in register, see #GL 342")
+def test_validation_flags(data):
+ """Test if validation detects different columns in data and flags."""
+ df = pd.DataFrame(
+ data=np.arange(8).reshape(4, 2),
+ index=pd.date_range("2020", None, 4, "1d"),
+ columns=list("ab"),
+ )
+ qc = SaQC(df)
+
+ @flagging()
+ def flagFoo(data, field, flags, **kwargs):
+ flags["spam"] = flags[field]
+ return data, flags
+
+ with pytest.raises(RuntimeError):
+ qc.flagFoo("a")
+
+
+def test__copy__():
+ orig = SaQC()
+ orig.attrs["spam"] = [] # a higher object
+ shallow = copy.copy(orig)
+ assert shallow is not orig
+ assert shallow.attrs["spam"] is orig.attrs["spam"]
+
+
+def test__deepcopy__():
+ orig = SaQC()
+ orig.attrs["spam"] = [] # a higher object
+ shallow = copy.deepcopy(orig)
+ assert shallow is not orig
+ assert shallow.attrs["spam"] is not orig.attrs["spam"]
+
+
+def test_immutability(data):
+ field = data.columns[0]
+ saqc_before = SaQC(data)
+ saqc_after = saqc_before.flagDummy(field)
+ for name in SaQC._attributes:
+ assert getattr(saqc_before, name) is not getattr(saqc_after, name)
diff --git a/tests/core/test_flags.py b/tests/core/test_flags.py
index 1e66ce62f42ba99f0869d5c77859e7dc567d50c2..6be9479869c464c7de609ca1618479fdf1668a30 100644
--- a/tests/core/test_flags.py
+++ b/tests/core/test_flags.py
@@ -1,19 +1,22 @@
#!/usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
from typing import Dict, Union
-import dios
-import pytest
+
import numpy as np
import pandas as pd
+import pytest
-from saqc.constants import *
+import dios
+from saqc.constants import UNFLAGGED
from saqc.core.flags import Flags
+from tests.core.test_history import History
+from tests.core.test_history import is_equal as hist_equal
-from tests.core.test_history import (
- History,
- is_equal as hist_equal,
-)
-
-_data = [
+_arrays = [
np.array([[]]),
np.zeros((1, 1)),
np.zeros((3, 4)),
@@ -35,23 +38,16 @@ _data = [
),
]
-data = []
-for d in _data:
+testdata = []
+for d in _arrays:
columns = list("abcdefgh")[: d.shape[1]]
df = pd.DataFrame(d, dtype=float, columns=columns)
dis = dios.DictOfSeries(df)
di = {}
di.update(df.items())
- data.append(df)
- data.append(di)
- data.append(dis)
-
-
-@pytest.mark.parametrize("data", data)
-def test_init(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]):
- flags = Flags(data)
- assert isinstance(flags, Flags)
- assert len(data.keys()) == len(flags)
+ testdata.append(df)
+ testdata.append(di)
+ testdata.append(dis)
def is_equal(f1, f2):
@@ -60,7 +56,53 @@ def is_equal(f1, f2):
assert hist_equal(f1.history[c], f2.history[c])
-@pytest.mark.parametrize("data", data)
+@pytest.mark.parametrize("data", testdata)
+@pytest.mark.parametrize("copy", [True, False])
+def test_init(data, copy):
+ flags = Flags(data, copy=copy)
+ assert isinstance(flags, Flags)
+ assert len(data.keys()) == len(flags)
+
+
+@pytest.mark.parametrize("data", testdata)
+@pytest.mark.parametrize("copy", [True, False])
+def test_init_from_other(data, copy):
+ first = Flags(data)
+ second = Flags(first, copy=copy)
+ assert isinstance(second, Flags)
+ assert first is not second
+ if copy:
+ assert first._data is not second._data
+ else:
+ assert first._data is second._data
+
+
+@pytest.mark.parametrize(
+ "data,msg",
+ [
+ ({1: pd.Series([1.0])}, "column names must be string"),
+ ({"a": pd.Series([1, 2], dtype=int)}, "dtype must be float"),
+ ],
+)
+def test_init_raise_ValueError(data, msg):
+ with pytest.raises(ValueError) as e:
+ Flags(data)
+ assert msg in str(e.value)
+
+
+@pytest.mark.parametrize(
+ "data,msg",
+ [
+ ({"a": [1, 2]}, "cannot init from 'dict' of 'list'"),
+ ],
+)
+def test_init_raise_TypeError(data, msg):
+ with pytest.raises(TypeError) as e:
+ Flags(data)
+ assert msg in str(e.value)
+
+
+@pytest.mark.parametrize("data", testdata)
def test_copy(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]):
flags = Flags(data)
shallow = flags.copy(deep=False)
@@ -88,7 +130,7 @@ def test_copy(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]
assert deep._data[c].index is not flags._data[c].index
-@pytest.mark.parametrize("data", data)
+@pytest.mark.parametrize("data", testdata)
def test_flags_history(
data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]
):
@@ -110,7 +152,7 @@ def test_flags_history(
assert len(hist) == hlen + 1
-@pytest.mark.parametrize("data", data)
+@pytest.mark.parametrize("data", testdata)
def test_get_flags(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]):
flags = Flags(data)
@@ -119,7 +161,7 @@ def test_get_flags(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Ser
var = flags[c]
assert isinstance(var, pd.Series)
assert not var.empty
- assert var.equals(flags._data[c].max())
+ assert var.equals(flags._data[c].squeeze())
# always a copy
assert var is not flags[c]
@@ -129,7 +171,7 @@ def test_get_flags(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Ser
assert all(flags[c] != var)
-@pytest.mark.parametrize("data", data)
+@pytest.mark.parametrize("data", testdata)
def test_set_flags(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]):
flags = Flags(data)
@@ -140,25 +182,25 @@ def test_set_flags(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Ser
flags[c] = new
assert len(flags.history[c]) == hlen + 1
- assert all(flags.history[c].max() == 9999.0)
- assert all(flags.history[c].max() == flags[c])
+ assert all(flags.history[c].squeeze() == 9999.0)
+ assert all(flags.history[c].squeeze() == flags[c])
# check if deep-copied correctly
new[:] = 8888.0
- assert all(flags.history[c].max() == 9999.0)
+ assert all(flags.history[c].squeeze() == 9999.0)
# flags always overwrite former
flags[c] = new
assert len(flags.history[c]) == hlen + 2
- assert all(flags.history[c].max() == 8888.0)
- assert all(flags.history[c].max() == flags[c])
+ assert all(flags.history[c].squeeze() == 8888.0)
+ assert all(flags.history[c].squeeze() == flags[c])
# check if deep-copied correctly
new[:] = 7777.0
- assert all(flags.history[c].max() == 8888.0)
+ assert all(flags.history[c].squeeze() == 8888.0)
-@pytest.mark.parametrize("data", data)
+@pytest.mark.parametrize("data", testdata)
def test_set_flags_with_mask(
data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]
):
@@ -205,7 +247,7 @@ def test_set_flags_with_mask(
flags[mask, c] = wrong_len
-@pytest.mark.parametrize("data", data)
+@pytest.mark.parametrize("data", testdata)
def test_set_flags_with_index(
data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]
):
@@ -249,7 +291,7 @@ def _validate_flags_equals_frame(flags, df):
assert df[c].equals(flags[c]) # respects nan's
-@pytest.mark.parametrize("data", data)
+@pytest.mark.parametrize("data", testdata)
def test_to_dios(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]):
flags = Flags(data)
df = flags.toDios()
@@ -258,10 +300,41 @@ def test_to_dios(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Serie
_validate_flags_equals_frame(flags, df)
-@pytest.mark.parametrize("data", data)
+@pytest.mark.parametrize("data", testdata)
def test_to_frame(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]):
flags = Flags(data)
df = flags.toFrame()
assert isinstance(df, pd.DataFrame)
_validate_flags_equals_frame(flags, df)
+
+
+@pytest.mark.parametrize(
+ "columns",
+ [["x", "y"], pd.Index(["x", "y"])],
+)
+def test_columns_setter(columns):
+ flags = Flags(
+ {"a": pd.Series([1, 2], dtype=float), "b": pd.Series([1, 2], dtype=float)}
+ )
+ flags.columns = columns
+ for c in columns:
+ assert c in flags.columns
+
+
+@pytest.mark.parametrize(
+ "columns,err",
+ [
+ ("foooo", TypeError), # cannot cast to Index
+ (pd.Index(["x", "x"]), TypeError), # duplicates
+ (pd.Index([1, 2]), TypeError), # not string
+ (pd.Index(["x", "y", "z"]), ValueError), # wrong length
+ (pd.Index(["x"]), ValueError), # wrong length
+ ],
+)
+def test_columns_setter_raises(columns, err):
+ flags = Flags(
+ {"a": pd.Series([1, 2], dtype=float), "b": pd.Series([1, 2], dtype=float)}
+ )
+ with pytest.raises(err):
+ flags.columns = columns
diff --git a/tests/core/test_history.py b/tests/core/test_history.py
index 6cfcffc336dd968329e50f7a728351ad0781d7dd..3481b6ce1ac121e83e037dae1622117fe73a6888 100644
--- a/tests/core/test_history.py
+++ b/tests/core/test_history.py
@@ -1,8 +1,12 @@
#!/usr/bin/env python
-import pytest
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import numpy as np
import pandas as pd
+import pytest
from saqc.core.history import History, createHistoryFromData
from tests.common import dummyHistory
@@ -80,7 +84,7 @@ def check_invariants(hist):
# advanced
assert hist.columns.equals(pd.Index(range(len(hist))))
- assert isinstance(hist.max(), pd.Series)
+ assert isinstance(hist.squeeze(), pd.Series)
def is_equal(hist1: History, hist2: History):
@@ -231,4 +235,4 @@ def test_append_force(__hist, s, max_val):
hist = __hist
hist.append(s)
check_invariants(hist)
- assert all(hist.max() == max_val)
+ assert all(hist.squeeze() == max_val)
diff --git a/tests/core/test_reader.py b/tests/core/test_reader.py
index d04d463a513a1f4ce2788071bfa234e2b87b87ce..7df2cf625e71e9424ef6c8570735cc77d57c3dd2 100644
--- a/tests/core/test_reader.py
+++ b/tests/core/test_reader.py
@@ -1,15 +1,18 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-import pytest
import numpy as np
-import pandas as pd
-import dios
-from pathlib import Path
+import pytest
+import dios
+from saqc.core.flags import Flags
from saqc.core.reader import fromConfig, readFile
-from saqc.core.register import FUNC_MAP, flagging
-
+from saqc.core.register import flagging
from tests.common import initData, writeIO
@@ -18,25 +21,19 @@ def data() -> dios.DictOfSeries:
return initData(3)
-def test_packagedConfig():
-
- path = Path(__file__).parents[2] / "ressources/data"
-
- config_path = path / "config.csv"
- data_path = path / "data.csv"
-
- data = pd.read_csv(
- data_path,
- index_col=0,
- parse_dates=True,
- )
- saqc = fromConfig(config_path, data)
- saqc.result._validate()
+def getTestedVariables(flags: Flags, test: str):
+ out = []
+ for col in flags.columns:
+ for m in flags.history[col].meta:
+ if m["func"] == test:
+ out.append(col)
+ return out
def test_variableRegex(data):
header = f"varname;test"
+ function = "flagDummy"
tests = [
("'.*'", data.columns),
("'var(1|2)'", [c for c in data.columns if c[-1] in ("1", "2")]),
@@ -45,19 +42,19 @@ def test_variableRegex(data):
]
for regex, expected in tests:
- fobj = writeIO(header + "\n" + f"{regex} ; flagDummy()")
+ fobj = writeIO(header + "\n" + f"{regex} ; {function}()")
saqc = fromConfig(fobj, data=data)
- result = [field for field, _ in saqc._called]
+ result = getTestedVariables(saqc._flags, function)
assert np.all(result == expected)
tests = [
- ("var[12]", ["var[12]"]), # not quoted -> not a regex
+ ("var[12]", []), # not quoted -> not a regex
]
for regex, expected in tests:
- fobj = writeIO(header + "\n" + f"{regex} ; flagDummy()")
+ fobj = writeIO(header + "\n" + f"{regex} ; {function}()")
with pytest.warns(RuntimeWarning):
saqc = fromConfig(fobj, data=data)
- result = [field for field, _ in saqc._called]
+ result = getTestedVariables(saqc._flags, function)
assert np.all(result == expected)
@@ -68,12 +65,12 @@ def test_inlineComments(data):
"""
config = f"""
varname ; test
- pre2 ; flagDummy() # test
+ var1 ; flagDummy() # test
"""
saqc = fromConfig(writeIO(config), data)
- _, func = saqc._called[0]
- assert func[0] == FUNC_MAP["flagDummy"]
+ func = saqc._flags.history["var1"].meta[0]["func"]
+ assert func == "flagDummy"
def test_configReaderLineNumbers():
diff --git a/tests/core/test_translator.py b/tests/core/test_translator.py
index 581aeceb4f2c7dfd271280d095aa3089e8642e0b..61d789e405b33bcaa1c70eaf02227ba71f59cdd3 100644
--- a/tests/core/test_translator.py
+++ b/tests/core/test_translator.py
@@ -1,23 +1,22 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
import json
-from typing import Dict, Union, Sequence
+from typing import Dict, Sequence, Union
import numpy as np
import pandas as pd
-
import pytest
-from saqc.constants import UNFLAGGED, BAD, DOUBTFUL, FILTER_NONE
-from saqc.core.translation import (
- PositionalScheme,
- TranslationScheme,
- DmpScheme,
-)
-from saqc.core.flags import Flags
+from saqc.constants import BAD, DOUBTFUL, FILTER_NONE, UNFLAGGED
from saqc.core.core import SaQC
-
+from saqc.core.flags import Flags
+from saqc.core.translation import DmpScheme, PositionalScheme, TranslationScheme
from tests.common import initData
@@ -152,7 +151,7 @@ def test_positionalTranslatorIntegration():
scheme = PositionalScheme()
saqc = SaQC(data=data, scheme=scheme)
saqc = saqc.flagMissing(col).flagRange(col, min=3, max=10, flag=DOUBTFUL)
- flags = saqc.result.flags
+ flags = saqc.flags
for field in flags.columns:
assert flags[field].astype(str).str.match("^9[012]*$").all()
@@ -172,7 +171,7 @@ def test_dmpTranslatorIntegration():
scheme = DmpScheme()
saqc = SaQC(data=data, scheme=scheme)
saqc = saqc.flagMissing(col).flagRange(col, min=3, max=10)
- flags = saqc.result.flags
+ flags = saqc.flags
qflags = flags.xs("quality_flag", axis="columns", level=1)
qfunc = flags.xs("quality_comment", axis="columns", level=1).applymap(
@@ -204,11 +203,11 @@ def test_dmpValidCombinations():
scheme = DmpScheme()
saqc = SaQC(data=data, scheme=scheme)
- with pytest.raises(RuntimeError):
- saqc.flagRange(col, min=3, max=10, cause="SOMETHING_STUPID").result
+ with pytest.raises(ValueError):
+ saqc.flagRange(col, min=3, max=10, cause="SOMETHING_STUPID").flags
- with pytest.raises(RuntimeError):
- saqc.flagRange(col, min=3, max=10, cause="").result
+ with pytest.raises(ValueError):
+ saqc.flagRange(col, min=3, max=10, cause="").flags
def _buildupSaQCObjects():
@@ -236,8 +235,8 @@ def _buildupSaQCObjects():
def test_translationPreservesFlags():
saqc1, saqc2 = _buildupSaQCObjects()
- flags1 = saqc1.result.flags_raw
- flags2 = saqc2.result.flags_raw
+ flags1 = saqc1._flags
+ flags2 = saqc2._flags
for k in flags2.columns:
got = flags2.history[k].hist
@@ -251,8 +250,8 @@ def test_translationPreservesFlags():
def test_multicallsPreserveHistory():
saqc1, saqc2 = _buildupSaQCObjects()
- flags1 = saqc1.result.flags_raw
- flags2 = saqc2.result.flags_raw
+ flags1 = saqc1._flags
+ flags2 = saqc2._flags
# check, that the `History` is duplicated
for col in flags2.columns:
@@ -275,8 +274,8 @@ def test_positionalMulitcallsPreserveState():
saqc1, saqc2 = _buildupSaQCObjects()
scheme = PositionalScheme()
- flags1 = saqc1.result.flags_raw
- flags2 = saqc2.result.flags_raw
+ flags1 = saqc1._flags
+ flags2 = saqc2._flags
tflags1 = scheme.backward(flags1).astype(str)
tflags2 = scheme.backward(flags2).astype(str)
diff --git a/tests/docs/test_markdown.py b/tests/docs/test_markdown.py
new file mode 100644
index 0000000000000000000000000000000000000000..182ec151edc4f2fe5a93619aedd0e18f0fc24003
--- /dev/null
+++ b/tests/docs/test_markdown.py
@@ -0,0 +1,55 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import re
+import subprocess
+
+import markdown
+import pytest
+import requests
+from bs4 import BeautifulSoup
+
+
+@pytest.mark.parametrize("fname", ["README.md"])
+def test_codeSnippets(fname):
+ with open(fname, "r") as f:
+ content = f.read()
+
+ snippets = re.findall("`{3}python(.*)```", content, flags=re.M | re.DOTALL)
+
+ for snippet in snippets:
+ try:
+ subprocess.check_output(["python", "-c", snippet])
+ except subprocess.CalledProcessError:
+ print(snippet)
+ raise AssertionError("failed to execute code snippet")
+
+
+FILEURL = "https://git.ufz.de/rdm-software/saqc/-/blob/develop/"
+
+
+@pytest.mark.parametrize("fname", ["README.md"])
+def test_links(fname):
+ with open(fname, "r") as f:
+ content = f.read()
+
+ soup = BeautifulSoup(markdown.markdown(content), "html.parser")
+
+ links = []
+ # links
+ for link in soup.find_all("a"):
+ l = link.get("href").strip()
+ if not l.startswith("mailto"):
+ links.append(l)
+
+ # images
+ for link in soup.find_all("img"):
+ links.append(link.get("src").strip())
+
+ for link in links:
+ if not link.startswith("http"):
+ link = f"{FILEURL}/link"
+ res = requests.get(link)
+ if res.status_code != requests.codes.ok:
+ raise AssertionError(f"failed to retrieve: {link}")
diff --git a/tests/fixtures.py b/tests/fixtures.py
index a14d60f75525f967278ef17549168ed04e1c6790..f1c356590bd4f3a87c954ce123ae40eb546f24f2 100644
--- a/tests/fixtures.py
+++ b/tests/fixtures.py
@@ -1,10 +1,13 @@
-import pytest
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
import numpy as np
import pandas as pd
+import pytest
from dios import DictOfSeries
-
# TODO: this is odd
# Why not simple fixtures with talking-names,
# that also take parameter, if needed
@@ -159,8 +162,9 @@ def course_3(char_dict):
insertion_index = pd.DatetimeIndex(dates)
data.iloc[int(np.floor(periods / 2))] = out_val
- data = data.append(pd.Series(data=out_val, index=insertion_index))
- data.sort_index(inplace=True)
+ data = pd.concat(
+ [data, pd.Series(data=out_val, index=insertion_index)]
+ ).sort_index()
anomaly_index = insertion_index.insert(
0, data.index[int(np.floor(periods / 2))]
)
diff --git a/tests/funcs/__init__.py b/tests/funcs/__init__.py
index e1f7e6e8bf04d3d59cff68b83d91fbf791f0faa2..1d74ada495ad00a20887cdee50a426347f37efae 100644
--- a/tests/funcs/__init__.py
+++ b/tests/funcs/__init__.py
@@ -1,2 +1,7 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
diff --git a/tests/funcs/test_constants_detection.py b/tests/funcs/test_constants_detection.py
index 302672611e88854a465630512ab7b5aa77e0d76b..c3f9d36678e42b35143e5eec518625717c78b611 100644
--- a/tests/funcs/test_constants_detection.py
+++ b/tests/funcs/test_constants_detection.py
@@ -1,13 +1,17 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-import pytest
import numpy as np
+import pytest
-from saqc.constants import *
-from saqc.funcs.constants import flagConstants, flagByVariance
-from saqc.core import initFlagsLike, Flags
-
+from saqc.constants import BAD, UNFLAGGED
+from saqc.core import initFlagsLike
+from saqc.funcs.constants import flagByVariance, flagConstants
from tests.common import initData
@@ -33,10 +37,11 @@ def test_constants_flagBasic(data):
def test_constants_flagVarianceBased(data):
- expected = np.arange(5, 25)
field, *_ = data.columns
flags = initFlagsLike(data)
- data, flags_result1 = flagByVariance(data, field, flags, window="1h", flag=BAD)
+ data, flags_result1 = flagByVariance(
+ data, field, flags, window="1h", thresh=0.0005, flag=BAD
+ )
flagscol = flags_result1[field]
assert np.all(flagscol[5:25] == BAD)
diff --git a/tests/funcs/test_flagtools.py b/tests/funcs/test_flagtools.py
new file mode 100644
index 0000000000000000000000000000000000000000..457edab8ab666175cafc9bb5df6cf01f5bb70af1
--- /dev/null
+++ b/tests/funcs/test_flagtools.py
@@ -0,0 +1,100 @@
+#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import numpy as np
+import pandas as pd
+
+# -*- coding: utf-8 -*-
+import pytest
+
+from saqc import BAD as B
+from saqc import UNFLAGGED as U
+from saqc import SaQC
+
+N = np.nan
+
+
+@pytest.mark.parametrize(
+ "got, expected, kwargs",
+ [
+ ([N, N, B, N, N], [N, N, N, B, N], {"window": 1, "method": "ffill"}),
+ ([N, N, B, N, N], [N, B, N, N, N], {"window": 1, "method": "bfill"}),
+ ([B, N, N, N, B], [N, B, N, N, N], {"window": 1, "method": "ffill"}),
+ ([B, N, N, N, B], [N, N, N, B, N], {"window": 1, "method": "bfill"}),
+ ([N, N, B, N, N], [N, N, N, B, N], {"window": "1D", "method": "ffill"}),
+ ([N, N, B, N, N], [N, B, N, N, N], {"window": "1D", "method": "bfill"}),
+ ([B, N, N, N, B], [N, B, N, N, N], {"window": "1D", "method": "ffill"}),
+ ([B, N, N, N, B], [N, N, N, B, N], {"window": "1D", "method": "bfill"}),
+ ([N, N, B, N, N], [N, N, N, B, B], {"window": 2, "method": "ffill"}),
+ ([N, N, B, N, N], [B, B, N, N, N], {"window": 2, "method": "bfill"}),
+ ([B, N, N, N, B], [N, B, B, N, N], {"window": 2, "method": "ffill"}),
+ ([B, N, N, N, B], [N, N, B, B, N], {"window": 2, "method": "bfill"}),
+ ([N, N, B, N, N], [N, N, N, B, B], {"window": "2D", "method": "ffill"}),
+ ([N, N, B, N, N], [B, B, N, N, N], {"window": "2D", "method": "bfill"}),
+ ([B, N, N, N, B], [N, B, B, N, N], {"window": "2D", "method": "ffill"}),
+ ([B, N, N, N, B], [N, N, B, B, N], {"window": "2D", "method": "bfill"}),
+ # window larger then data
+ ([U, U, B, U, U], [N, N, N, B, B], {"window": 10, "method": "ffill"}),
+ ([U, U, B, U, U], [B, B, N, N, N], {"window": 10, "method": "bfill"}),
+ ([B, U, U, U, U], [N, B, B, B, B], {"window": "10D", "method": "ffill"}),
+ ([B, U, U, U, U], [N, N, N, N, N], {"window": "10D", "method": "bfill"}),
+ # playing with dfilter
+ (
+ [1, 1, B, 1, 1],
+ [N, B, B, B, B],
+ {"window": 2, "method": "ffill", "dfilter": 0},
+ ),
+ (
+ [1, 1, B, 1, 1],
+ [B, B, B, B, N],
+ {"window": 2, "method": "bfill", "dfilter": 0},
+ ),
+ (
+ [B, 1, 1, 1, 1],
+ [N, B, B, B, B],
+ {"window": "2D", "method": "ffill", "dfilter": 0},
+ ),
+ (
+ [B, 1, 1, 1, 1],
+ [B, B, B, B, N],
+ {"window": "2D", "method": "bfill", "dfilter": 0},
+ ),
+ ],
+)
+def test_propagateFlagsRegularIndex(got, expected, kwargs):
+ index = pd.date_range("2000-01-01", periods=len(got))
+ flags = pd.DataFrame({"x": got}, index=index)
+ expected = pd.Series(expected, index=index)
+ data = pd.DataFrame({"x": np.nan}, index=index)
+ saqc = SaQC(data=data, flags=flags).propagateFlags(field="x", **kwargs)
+ result = saqc._flags.history["x"].hist[1].astype(float)
+ assert result.equals(expected)
+
+
+@pytest.mark.parametrize(
+ "got, expected, kwargs",
+ [
+ ([N, N, B, N, N], [N, N, N, N, N], {"window": "1D", "method": "ffill"}),
+ ([N, N, B, N, N], [N, N, N, N, N], {"window": "1D", "method": "bfill"}),
+ ([B, N, N, N, B], [N, B, N, N, N], {"window": "1D", "method": "ffill"}),
+ ([B, N, N, N, B], [N, N, N, N, N], {"window": "1D", "method": "bfill"}),
+ ([N, N, B, N, N], [N, N, N, B, N], {"window": "3D", "method": "ffill"}),
+ ([N, N, B, N, N], [B, B, N, N, N], {"window": "3D", "method": "bfill"}),
+ ([B, N, N, N, B], [N, B, N, N, N], {"window": "2D", "method": "ffill"}),
+ ([B, N, N, N, B], [N, N, N, N, N], {"window": "2D", "method": "bfill"}),
+ ([B, U, U, U, U], [N, B, B, B, N], {"window": "10D", "method": "ffill"}),
+ ],
+)
+def test_propagateFlagsIrregularIndex(got, expected, kwargs):
+ index = pd.to_datetime(
+ ["2000-01-01", "2000-01-02", "2000-01-04", "2000-01-07", "2000-01-18"]
+ )
+ flags = pd.DataFrame({"x": got}, index=index)
+ expected = pd.Series(expected, index=index)
+ data = pd.DataFrame({"x": np.nan}, index=index)
+ saqc = SaQC(data=data, flags=flags).propagateFlags(field="x", **kwargs)
+ result = saqc._flags.history["x"].hist[1].astype(float)
+ assert result.equals(expected)
diff --git a/tests/funcs/test_functions.py b/tests/funcs/test_functions.py
index 535fa632fc3697d9cf907027ff515e1a6a942b60..416bd1c9f8b081bb0db590f1beea6fbfa85ab9a6 100644
--- a/tests/funcs/test_functions.py
+++ b/tests/funcs/test_functions.py
@@ -1,26 +1,28 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-import dios
-import pandas as pd
import numpy as np
-import saqc
+import pandas as pd
+import pytest
-from saqc.funcs.noise import flagByStatLowPass
-from saqc.constants import *
+import dios
+import saqc
+from saqc.constants import BAD, DOUBTFUL, UNFLAGGED
from saqc.core import initFlagsLike
-from saqc.funcs.drift import (
- flagDriftFromNorm,
- flagDriftFromReference,
-)
+from saqc.funcs.breaks import flagIsolated
+from saqc.funcs.drift import flagDriftFromNorm, flagDriftFromReference
+from saqc.funcs.flagtools import clearFlags, flagManual, forceFlags
+from saqc.funcs.noise import flagByStatLowPass
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
-from saqc.funcs.breaks import flagIsolated
-
-from tests.fixtures import *
+from saqc.funcs.tools import copyField, dropField, selectTime
from tests.common import initData
+from tests.fixtures import char_dict, course_1
@pytest.fixture
@@ -94,7 +96,7 @@ def test_flagSesonalRange(data, field):
end = f"{test['endmonth']:02}-{test['endday']:02}T00:00:00"
data, flags = copyField(data, field, flags, field + "_masked")
- data, flags = maskTime(
+ data, flags = selectTime(
data,
newfield,
flags,
diff --git a/tests/funcs/test_generic_api_functions.py b/tests/funcs/test_generic_api_functions.py
index 91f41a67fdcf310aed6e85f876629d457bbff78a..64ca9173effe6a1017ff4ea09fb4504e358c01a2 100644
--- a/tests/funcs/test_generic_api_functions.py
+++ b/tests/funcs/test_generic_api_functions.py
@@ -1,16 +1,19 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-import pytest
import pandas as pd
-from dios.dios.dios import DictOfSeries
+import pytest
-from saqc.constants import BAD, UNFLAGGED, FILTER_ALL
-from saqc.core.flags import Flags
+from dios.dios.dios import DictOfSeries
from saqc import SaQC
-from saqc.core.register import _isflagged
+from saqc.constants import BAD, FILTER_ALL, UNFLAGGED
+from saqc.core.flags import Flags
from saqc.lib.tools import toSequence
-
from tests.common import initData
@@ -43,11 +46,9 @@ def test_writeTargetFlagGeneric(data):
for targets, func in params:
expected_meta = {
"func": "flagGeneric",
- "args": (),
+ "args": (data.columns.tolist(), targets),
"kwargs": {
- "field": data.columns.tolist(),
"func": func.__name__,
- "target": targets,
"flag": BAD,
"dfilter": FILTER_ALL,
},
@@ -75,10 +76,8 @@ def test_overwriteFieldFlagGeneric(data):
for fields, func in params:
expected_meta = {
"func": "flagGeneric",
- "args": (),
+ "args": (fields, fields),
"kwargs": {
- "field": fields,
- "target": fields,
"func": func.__name__,
"flag": flag,
"dfilter": FILTER_ALL,
@@ -99,11 +98,9 @@ def test_overwriteFieldFlagGeneric(data):
res = saqc.flagGeneric(field=fields, func=func, flag=flag)
for field in fields:
- assert (data[field] == res.data[field]).all(axis=None)
- histcol0 = res._flags.history[field].hist[0]
histcol1 = res._flags.history[field].hist[1]
- assert (histcol1[histcol0 == 127.0].isna()).all()
- assert (histcol1[histcol0 != 127.0] == flag).all()
+ assert (histcol1 == flag).all()
+ assert (data[field] == res.data[field]).all(axis=None)
assert res._flags.history[field].meta[0] == {}
assert res._flags.history[field].meta[1] == expected_meta
@@ -123,12 +120,9 @@ def test_writeTargetProcGeneric(data):
expected_meta = {
"func": "procGeneric",
- "args": (),
+ "args": (fields, targets),
"kwargs": {
- "field": data.columns.tolist(),
- "target": targets,
"func": func.__name__,
- "flag": BAD,
"dfilter": dfilter,
"label": "generic",
},
@@ -143,14 +137,13 @@ def test_writeTargetProcGeneric(data):
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
for target in targets:
- assert res._flags.history[target].hist.iloc[0].tolist() == [BAD]
+ assert res._flags.history[target].hist.iloc[0].isna().all()
assert res._flags.history[target].meta[0] == expected_meta
@@ -160,7 +153,6 @@ def test_overwriteFieldProcGeneric(data):
(["var1", "var2"], lambda x, y: (x + y, y * 2)),
]
dfilter = 128
- flag = 12
for fields, func in params:
expected_data = DictOfSeries(
func(*[data[f] for f in fields]), columns=fields
@@ -168,12 +160,9 @@ def test_overwriteFieldProcGeneric(data):
expected_meta = {
"func": "procGeneric",
- "args": (),
+ "args": (fields, fields),
"kwargs": {
- "field": fields,
- "target": fields,
"func": func.__name__,
- "flag": flag,
"dfilter": dfilter,
"label": "generic",
},
@@ -187,12 +176,29 @@ def test_overwriteFieldProcGeneric(data):
)
res = saqc.processGeneric(
- field=fields, func=func, flag=flag, dfilter=dfilter, label="generic"
+ field=fields, func=func, dfilter=dfilter, label="generic"
)
assert (expected_data == res.data[fields].squeeze()).all(axis=None)
# check that the histories got appended
for field in fields:
assert (res._flags.history[field].hist[0] == 127.0).all()
- assert (res._flags.history[field].hist[1] == 12.0).all()
+ assert res._flags.history[field].hist[1].isna().all()
assert res._flags.history[field].meta[0] == {}
assert res._flags.history[field].meta[1] == expected_meta
+
+
+def test_label():
+ dat = pd.DataFrame(
+ {"data1": [1, 1, 5, 2, 1], "data2": [1, 1, 2, 3, 4], "data3": [1, 1, 2, 3, 4]},
+ index=pd.date_range("2000", "2005", periods=5),
+ )
+
+ qc = SaQC(dat)
+ qc = qc.flagRange("data1", max=4, label="out of range")
+ qc = qc.flagRange("data1", max=0, label="out of range2")
+ qc = qc.flagGeneric(
+ ["data1", "data3"],
+ target="data2",
+ func=lambda x, y: isflagged(x, "out of range") | isflagged(y),
+ )
+ assert list((qc.flags["data2"] > 0).values) == [False, False, True, False, False]
diff --git a/tests/funcs/test_generic_config_functions.py b/tests/funcs/test_generic_config_functions.py
index 556aeba816c9ae38345a3ac5bab965791a2ca55a..a9e4d09a61bf5a12c0e7b63afc074b26aba5428a 100644
--- a/tests/funcs/test_generic_config_functions.py
+++ b/tests/funcs/test_generic_config_functions.py
@@ -1,21 +1,26 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
import ast
-from saqc.core.flags import Flags
-from saqc.core.reader import fromConfig
-import pytest
+
import numpy as np
import pandas as pd
-import dios
+import pytest
-from saqc.constants import *
+import dios
+from saqc import SaQC
+from saqc.constants import BAD, UNFLAGGED
from saqc.core import initFlagsLike
-from saqc.core.visitor import ConfigFunctionParser
+from saqc.core.flags import Flags
+from saqc.core.reader import fromConfig
from saqc.core.register import register
+from saqc.core.visitor import ConfigFunctionParser
from saqc.funcs.generic import _execGeneric
-from saqc import SaQC
-
from tests.common import initData, writeIO
@@ -102,7 +107,7 @@ def test_arithmeticOperators(data):
("var1 * 100 > 200", data * 100 > 200),
("var1 / 100 > .1", data / 100 > 0.1),
("var1 % 2 == 1", data % 2 == 1),
- ("var1 ** 2 == 0", data ** 2 == 0),
+ ("var1 ** 2 == 0", data**2 == 0),
]
for test, expected in tests:
@@ -164,7 +169,7 @@ def test_processExistingTarget(data):
config = f"""
varname ; test
var2 ; flagMissing()
- var2 ; processGeneric(func=y - 1, flag=DOUBTFUL)
+ var2 ; processGeneric(func=y - 1)
"""
fobj = writeIO(config)
@@ -172,7 +177,7 @@ def test_processExistingTarget(data):
assert (saqc._data["var2"] == data["var2"] - 1).all()
assert len(saqc._flags.history["var2"]) == 2
assert saqc._flags.history["var2"].hist[0].isna().all()
- assert (saqc._flags.history["var2"].hist[1] == DOUBTFUL).all()
+ assert saqc._flags.history["var2"].hist[1].isna().all()
def test_flagTargetExisting(data):
@@ -211,6 +216,7 @@ def test_flagTargetExistingFail(data_diff):
fromConfig(fobj, data_diff)
+@pytest.mark.slow
def test_callableArgumentsUnary(data):
window = 5
@@ -235,8 +241,8 @@ def test_callableArgumentsUnary(data):
for (name, func) in tests:
fobj = writeIO(config.format(name))
- result_config = fromConfig(fobj, data).result.data
- result_api = SaQC(data).testFuncUnary(var, func=func).result.data
+ result_config = fromConfig(fobj, data).data
+ result_api = SaQC(data).testFuncUnary(var, func=func).data
expected = data[var].rolling(window=window).apply(func)
assert (result_config[var].dropna() == expected.dropna()).all(axis=None)
assert (result_api[var].dropna() == expected.dropna()).all(axis=None)
@@ -262,8 +268,8 @@ def test_callableArgumentsBinary(data):
for (name, func) in tests:
fobj = writeIO(config.format(name))
- result_config = fromConfig(fobj, data).result.data
- result_api = SaQC(data).testFuncBinary(var1, func=func).result.data
+ result_config = fromConfig(fobj, data).data
+ result_api = SaQC(data).testFuncBinary(var1, func=func).data
expected = func(data[var1], data[var2])
assert (result_config[var1].dropna() == expected.dropna()).all(axis=None)
assert (result_api[var1].dropna() == expected.dropna()).all(axis=None)
diff --git a/tests/funcs/test_harm_funcs.py b/tests/funcs/test_harm_funcs.py
index 2819a884bc7c1532fd168e80f091743329b93874..53ff995a4fefb45d383153c2698ff5e19f9d5272 100644
--- a/tests/funcs/test_harm_funcs.py
+++ b/tests/funcs/test_harm_funcs.py
@@ -1,14 +1,19 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-import pytest
import numpy as np
import pandas as pd
-import dios
+import pytest
-from saqc.core import initFlagsLike, Flags
+import dios
from saqc.constants import BAD, UNFLAGGED
-from saqc.funcs.resampling import linear, interpolate, shift, concatFlags, resample
+from saqc.core import Flags, initFlagsLike
+from saqc.funcs.resampling import concatFlags, interpolate, linear, resample, shift
from saqc.funcs.tools import copyField, dropField
from tests.common import checkDataFlagsInvariants
@@ -45,6 +50,11 @@ def test_wrapper(data, func, kws):
freq = "15T"
flags = initFlagsLike(data)
+ # GL-#352
+ # make a History, otherwise nothing important is tested
+ for c in flags.columns:
+ flags[:, c] = BAD
+
import saqc
func = getattr(saqc.funcs, func)
@@ -55,15 +65,46 @@ def test_wrapper(data, func, kws):
assert data[field].index.inferred_freq == freq
-@pytest.mark.parametrize("method", ["time", "polynomial"])
-def test_gridInterpolation(data, method):
+_SUPPORTED_METHODS = [
+ "linear",
+ "time",
+ "nearest",
+ "zero",
+ "slinear",
+ "quadratic",
+ "cubic",
+ "spline",
+ "barycentric",
+ "polynomial",
+ "krogh",
+ "piecewise_polynomial",
+ "spline",
+ "pchip",
+ "akima",
+]
+
+
+@pytest.mark.parametrize("method", _SUPPORTED_METHODS)
+@pytest.mark.parametrize("fill_history", ["some", "all", "none"])
+def test_gridInterpolation(data, method, fill_history):
freq = "15T"
field = "data"
data = data[field]
- data = (data * np.sin(data)).append(data.shift(1, "2h")).shift(1, "3s")
+ data = pd.concat([data * np.sin(data), data.shift(1, "2h")]).shift(1, "3s")
data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
+ if fill_history == "none":
+ pass
+
+ if fill_history == "all":
+ for c in flags.columns:
+ flags[:, c] = BAD
+
+ if fill_history == "some":
+ for c in flags.columns:
+ flags[::2, c] = UNFLAGGED
+
# we are just testing if the interpolation gets passed to the series without
# causing an error:
res = interpolate(
diff --git a/tests/funcs/test_modelling.py b/tests/funcs/test_modelling.py
index 33d4fee458db6a8c26e6c0cbbd447fa8e5c41a41..a768c86586bf1e9329f731a9e74ea9778f502a29 100644
--- a/tests/funcs/test_modelling.py
+++ b/tests/funcs/test_modelling.py
@@ -1,18 +1,23 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
+import numpy as np
+import pandas as pd
# see test/functs/fixtures.py for global fixtures "course_..."
import pytest
import dios
-
from saqc import BAD, UNFLAGGED
from saqc.core import initFlagsLike
-from saqc.funcs.tools import maskTime
-from saqc.funcs.residues import calculatePolynomialResidues, calculateRollingResidues
-
-from tests.fixtures import *
+from saqc.funcs.residuals import calculatePolynomialResiduals, calculateRollingResiduals
+from saqc.funcs.tools import selectTime
+from tests.fixtures import char_dict, course_1, course_2
@pytest.mark.filterwarnings("ignore: The fit may be poorly conditioned")
@@ -25,19 +30,19 @@ def test_modelling_polyFit_forRegular(dat):
data = data + 10 * np.sin(np.arange(0, len(data.indexes[0])))
data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
- result1, _ = calculatePolynomialResidues(data, "data", flags, 11, 2, numba=False)
- result2, _ = calculatePolynomialResidues(data, "data", flags, 11, 2, numba=True)
- assert (result1["data"] - result2["data"]).abs().max() < 10 ** -10
- result3, _ = calculatePolynomialResidues(
+ result1, _ = calculatePolynomialResiduals(data, "data", flags, 11, 2, numba=False)
+ result2, _ = calculatePolynomialResiduals(data, "data", flags, 11, 2, numba=True)
+ assert (result1["data"] - result2["data"]).abs().max() < 10**-10
+ result3, _ = calculatePolynomialResiduals(
data, "data", flags, "110min", 2, numba=False
)
assert result3["data"].equals(result1["data"])
- result4, _ = calculatePolynomialResidues(
+ result4, _ = calculatePolynomialResiduals(
data, "data", flags, 11, 2, numba=True, min_periods=11
)
- assert (result4["data"] - result2["data"]).abs().max() < 10 ** -10
+ assert (result4["data"] - result2["data"]).abs().max() < 10**-10
data.iloc[13:16] = np.nan
- result5, _ = calculatePolynomialResidues(
+ result5, _ = calculatePolynomialResiduals(
data, "data", flags, 11, 2, numba=True, min_periods=9
)
assert result5["data"].iloc[10:19].isna().all()
@@ -50,7 +55,7 @@ def test_modelling_rollingMean_forRegular(dat):
)
data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
- calculateRollingResidues(
+ calculateRollingResiduals(
data,
"data",
flags,
@@ -59,7 +64,7 @@ def test_modelling_rollingMean_forRegular(dat):
min_periods=0,
center=True,
)
- calculateRollingResidues(
+ calculateRollingResiduals(
data,
"data",
flags,
@@ -81,21 +86,23 @@ def test_modelling_mask(dat):
flags[:, field] = BAD
common = dict(data=data, field=field, flags=flags, mode="periodic")
- data_seasonal, flags_seasonal = maskTime(
+ data_seasonal, flags_seasonal = selectTime(
**common, start="20:00", end="40:00", closed=False
)
flagscol = flags_seasonal[field]
- m = (20 <= flagscol.index.minute) & (flagscol.index.minute <= 40)
+ m = (20 > flagscol.index.minute) | (flagscol.index.minute > 40)
assert all(flags_seasonal[field][m] == UNFLAGGED)
assert all(data_seasonal[field][m].isna())
- data_seasonal, flags_seasonal = maskTime(**common, start="15:00:00", end="02:00:00")
+ data_seasonal, flags_seasonal = selectTime(
+ **common, start="15:00:00", end="02:00:00"
+ )
flagscol = flags_seasonal[field]
m = (15 <= flagscol.index.hour) & (flagscol.index.hour <= 2)
assert all(flags_seasonal[field][m] == UNFLAGGED)
assert all(data_seasonal[field][m].isna())
- data_seasonal, flags_seasonal = maskTime(
+ data_seasonal, flags_seasonal = selectTime(
**common, start="03T00:00:00", end="10T00:00:00"
)
flagscol = flags_seasonal[field]
@@ -107,8 +114,8 @@ def test_modelling_mask(dat):
mask_ser[::5] = True
data["mask_ser"] = mask_ser
flags = initFlagsLike(data)
- data_masked, flags_masked = maskTime(
- data, "data", flags, mode="mask_field", mask_field="mask_ser"
+ data_masked, flags_masked = selectTime(
+ data, "data", flags, mode="selection_field", selection_field="mask_ser"
)
m = mask_ser
assert all(flags_masked[field][m] == UNFLAGGED)
diff --git a/tests/funcs/test_outier_detection.py b/tests/funcs/test_outier_detection.py
index 6cc2a91c4f2e77e803a8c2f78802b5a345b7e4db..21da1d6efb242b49187a2f0e32f85b8b26083bab 100644
--- a/tests/funcs/test_outier_detection.py
+++ b/tests/funcs/test_outier_detection.py
@@ -1,20 +1,29 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
+import numpy as np
+import pandas as pd
+
# see test/functs/fixtures.py for global fixtures "course_..."
-import dios
-from tests.fixtures import *
+import pytest
+import dios
+from saqc.constants import BAD, UNFLAGGED
+from saqc.core import initFlagsLike
from saqc.funcs.outliers import (
+ flagByGrubbs,
+ flagCrossStatistics,
flagMAD,
+ flagMVScores,
flagOffset,
flagRaise,
- flagMVScores,
- flagByGrubbs,
- flagCrossStatistics,
)
-from saqc.constants import *
-from saqc.core import initFlagsLike
+from tests.fixtures import char_dict, course_1, course_2, course_3, course_4
@pytest.fixture(scope="module")
@@ -49,10 +58,11 @@ def test_flagSpikesBasic(spiky_data):
assert test_sum == len(spiky_data[1])
-# see test/functs/fixtures.py for the 'course_N'
+@pytest.mark.slow
@pytest.mark.parametrize(
"dat",
[
+ # see test/functs/fixtures.py for the 'course_N'
pytest.lazy_fixture("course_1"),
pytest.lazy_fixture("course_2"),
pytest.lazy_fixture("course_3"),
diff --git a/tests/funcs/test_pattern_rec.py b/tests/funcs/test_pattern_rec.py
index 18bb3384087fc63d502d15d2bdfcd8dfe2da37c1..037bba240edb8622ad173a49848c60b0b31963f6 100644
--- a/tests/funcs/test_pattern_rec.py
+++ b/tests/funcs/test_pattern_rec.py
@@ -1,10 +1,16 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
-import pytest
import pandas as pd
-import dios
+import pytest
+from pandas.testing import assert_series_equal
+import dios
from saqc.constants import BAD, UNFLAGGED
from saqc.core import initFlagsLike
from saqc.funcs.pattern import flagPatternByDTW
@@ -21,7 +27,9 @@ def field(data):
return data.columns[0]
-def test_flagPattern_dtw():
+@pytest.mark.parametrize("plot", [True, False])
+@pytest.mark.parametrize("normalize", [True, False])
+def test_flagPattern_dtw(plot, normalize):
data = pd.Series(0, index=pd.date_range(start="2000", end="2001", freq="1d"))
data.iloc[10:18] = [0, 5, 6, 7, 6, 8, 5, 0]
pattern = data.iloc[10:18]
@@ -29,7 +37,13 @@ def test_flagPattern_dtw():
data = dios.DictOfSeries(dict(data=data, pattern_data=pattern))
flags = initFlagsLike(data, name="data")
data, flags = flagPatternByDTW(
- data, "data", flags, reference="pattern_data", flag=BAD
+ data,
+ "data",
+ flags,
+ reference="pattern_data",
+ plot=plot,
+ normalize=normalize,
+ flag=BAD,
)
assert all(flags["data"].iloc[10:18] == BAD)
diff --git a/tests/funcs/test_proc_functions.py b/tests/funcs/test_proc_functions.py
index 0d63371877ddef401d5643db9fd5ecb6998d55c6..056a78b42314a4badc8cf4f0de9f9a970ebabff4 100644
--- a/tests/funcs/test_proc_functions.py
+++ b/tests/funcs/test_proc_functions.py
@@ -1,24 +1,32 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
+import numpy as np
+import pandas as pd
+
# see test/functs/fixtures.py for global fixtures "course_..."
+import pytest
import dios
-
-from saqc.constants import *
+import saqc
+from saqc.constants import UNFLAGGED
from saqc.core import initFlagsLike
-from saqc.funcs.transformation import transform
from saqc.funcs.drift import correctOffset
from saqc.funcs.interpolation import (
interpolateByRolling,
- interpolateInvalid,
interpolateIndex,
+ interpolateInvalid,
)
from saqc.funcs.resampling import resample
+from saqc.funcs.transformation import transform
from saqc.lib.ts_operators import linearInterpolation, polynomialInterpolation
-
-from tests.fixtures import *
+from tests.fixtures import char_dict, course_3, course_5
def test_rollingInterpolateMissing(course_5):
@@ -126,6 +134,7 @@ def test_interpolateGrid(course_5, course_3):
)
+@pytest.mark.slow
def test_offsetCorrecture():
data = pd.Series(0, index=pd.date_range("2000", freq="1d", periods=100), name="dat")
data.iloc[30:40] = -100
@@ -134,3 +143,9 @@ def test_offsetCorrecture():
flags = initFlagsLike(data)
data, _ = correctOffset(data, "dat", flags, 40, 20, "3d", 1)
assert (data == 0).all()[0]
+
+
+# GL-333
+def test_resampleSingleEmptySeries():
+ qc = saqc.SaQC(pd.DataFrame(1, columns=["a"], index=pd.DatetimeIndex([])))
+ qc.resample("a", freq="1d")
diff --git a/tests/funcs/test_tools.py b/tests/funcs/test_tools.py
index 3686b2e7d657abca2cfc4b11db7f0809b4256f85..0d6c634f927df5e2e0de216b17c31f48aa50df85 100644
--- a/tests/funcs/test_tools.py
+++ b/tests/funcs/test_tools.py
@@ -1,12 +1,17 @@
-import pytest
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
-from saqc.lib.plotting import makeFig
-import pandas as pd
import numpy as np
-import saqc
+import pandas as pd
+import pytest
+
import dios
+import saqc
+from saqc.lib.plotting import makeFig
+@pytest.mark.slow
def test_makeFig():
# just testing for no errors to occure...
data = dios.DictOfSeries(
diff --git a/tests/fuzzy/__init__.py b/tests/fuzzy/__init__.py
index 4265cc3e6c16c09774190fa55d609cd9fe0808e4..6b7d6f94a7f566f32065da988e7fcc3eec8692fc 100644
--- a/tests/fuzzy/__init__.py
+++ b/tests/fuzzy/__init__.py
@@ -1 +1,5 @@
#!/usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
diff --git a/tests/fuzzy/lib.py b/tests/fuzzy/lib.py
index 69ab3c6df58091c3f6d4236d1614e0009eff5e0f..b8f4435e32341f43efde4cc81fc910a9e4d341b4 100644
--- a/tests/fuzzy/lib.py
+++ b/tests/fuzzy/lib.py
@@ -1,30 +1,32 @@
#!/usr/bin/env python
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
import numbers
-import dios
-import numpy as np
-import pandas as pd
-from typing import get_type_hints
from contextlib import contextmanager
+from typing import get_type_hints
+import numpy as np
+import pandas as pd
+from hypothesis.extra.numpy import arrays, from_dtype
from hypothesis.strategies import (
- lists,
- sampled_from,
composite,
- from_regex,
- sampled_from,
datetimes,
+ from_regex,
+ from_type,
integers,
+ lists,
register_type_strategy,
- from_type,
+ sampled_from,
)
-from hypothesis.extra.numpy import arrays, from_dtype
from hypothesis.strategies._internal.types import _global_type_lookup
-from saqc.constants import *
-from saqc.core.register import FUNC_MAP
+import dios
+from saqc.constants import BAD
from saqc.core import initFlagsLike
+from saqc.core.register import FUNC_MAP
MAX_EXAMPLES = 50
# MAX_EXAMPLES = 100000
diff --git a/tests/fuzzy/test_masking.py b/tests/fuzzy/test_masking.py
index 5faaa466a99a98670c0729603f8d83d07f88cc45..3f49df708ff34f97b8b9dd815e4e2e8e4b1b2fde 100644
--- a/tests/fuzzy/test_masking.py
+++ b/tests/fuzzy/test_masking.py
@@ -1,16 +1,21 @@
#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
# -*- coding: utf-8 -*-
import pandas as pd
-
+import pytest
from hypothesis import given, settings
-from saqc.constants import UNFLAGGED, BAD
+from saqc.constants import BAD, UNFLAGGED
from saqc.core.register import FunctionWrapper
-
-from tests.fuzzy.lib import dataFieldFlags, MAX_EXAMPLES
+from tests.fuzzy.lib import MAX_EXAMPLES, dataFieldFlags
+@pytest.mark.slow
@settings(max_examples=MAX_EXAMPLES, deadline=None)
@given(data_field_flags=dataFieldFlags())
def test_maskingMasksData(data_field_flags):
@@ -25,6 +30,7 @@ def test_maskingMasksData(data_field_flags):
assert (flags[field].iloc[mask[field].index] > UNFLAGGED).all()
+@pytest.mark.slow
@settings(max_examples=MAX_EXAMPLES, deadline=None)
@given(data_field_flags=dataFieldFlags())
def test_dataMutationPreventsUnmasking(data_field_flags):
@@ -44,6 +50,7 @@ def test_dataMutationPreventsUnmasking(data_field_flags):
assert (data_out[field] == filler).all(axis=None)
+@pytest.mark.slow
@settings(max_examples=MAX_EXAMPLES, deadline=None)
@given(data_field_flags=dataFieldFlags())
def test_flagsMutationPreventsUnmasking(data_field_flags):
@@ -61,6 +68,7 @@ def test_flagsMutationPreventsUnmasking(data_field_flags):
assert (data_out.loc[flags[field] == BAD, field].isna()).all(axis=None)
+@pytest.mark.slow
@settings(max_examples=MAX_EXAMPLES, deadline=None)
@given(data_field_flags=dataFieldFlags())
def test_reshapingPreventsUnmasking(data_field_flags):
@@ -90,6 +98,7 @@ def test_reshapingPreventsUnmasking(data_field_flags):
assert (data_out[field] == filler).all(axis=None)
+@pytest.mark.slow
@settings(max_examples=MAX_EXAMPLES, deadline=None)
@given(data_field_flags=dataFieldFlags())
def test_unmaskingInvertsMasking(data_field_flags):
diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/tests/integration/test_integration.py b/tests/integration/test_integration.py
deleted file mode 100644
index 7e47dd1544134847f67844420b1da00a632d4ad6..0000000000000000000000000000000000000000
--- a/tests/integration/test_integration.py
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/usr/bin/env python
-from click.testing import CliRunner
-from pathlib import Path
-
-
-def test__main__py(tmp_path):
- import saqc.__main__
-
- # if not run from project root
- projpath = Path(saqc.__file__).parents[1]
- args = [
- "--config",
- Path(projpath, "ressources/data/config.csv"),
- "--data",
- Path(projpath, "ressources/data/data.csv"),
- "--outfile",
- Path(tmp_path, "test.csv"), # the filesystem temp dir
- ]
- runner = CliRunner()
- for scheme in ["float", "positional", "dmp", "simple"]:
- result = runner.invoke(saqc.__main__.main, args + ["--scheme", scheme])
- assert result.exit_code == 0, result.output
diff --git a/tests/lib/__init__.py b/tests/lib/__init__.py
index 4265cc3e6c16c09774190fa55d609cd9fe0808e4..6b7d6f94a7f566f32065da988e7fcc3eec8692fc 100644
--- a/tests/lib/__init__.py
+++ b/tests/lib/__init__.py
@@ -1 +1,5 @@
#!/usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
diff --git a/tests/lib/test_periodicMask.py b/tests/lib/test_periodicMask.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac2997848e6373609fff7a2fe3fcb0dd1cbcd0e6
--- /dev/null
+++ b/tests/lib/test_periodicMask.py
@@ -0,0 +1,124 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from saqc.lib.tools import periodicMask
+
+unmasked, masked = True, False
+
+
+def assert_equal(result, exp):
+ assert exp.index.equals(result.index)
+ assert result.dtype == exp.dtype
+
+ if result.equals(exp):
+ return
+
+ df = pd.DataFrame(
+ data=np.array([result, exp]).T, index=exp.index, columns=["result", "exp"]
+ )
+ print("\n", df)
+ assert result.equals(exp)
+
+
+@pytest.mark.parametrize(
+ "index",
+ [
+ pd.DatetimeIndex(
+ [
+ "1999-10",
+ "1999-11",
+ "1999-12",
+ "2000-01",
+ "2000-02",
+ "2000-03",
+ ]
+ )
+ ],
+)
+@pytest.mark.parametrize(
+ "start, end, closed, exp",
+ [
+ (
+ "01-01T00:00:00",
+ "03-01T00:00:00",
+ True,
+ [unmasked, unmasked, unmasked, masked, masked, masked],
+ ),
+ (
+ "01-01T00:00:00",
+ "03-01T00:00:00",
+ False,
+ [unmasked, unmasked, unmasked, unmasked, masked, unmasked],
+ ),
+ (
+ "03-01T00:00:00",
+ "01-01T00:00:00",
+ True,
+ [masked, masked, masked, masked, unmasked, masked],
+ ),
+ (
+ "03-01T00:00:00",
+ "01-01T00:00:00",
+ False,
+ [masked, masked, masked, unmasked, unmasked, unmasked],
+ ),
+ ],
+)
+def test_bounds(index, start, end, closed, exp):
+ exp = pd.Series(exp, index=index, dtype=bool)
+ result = periodicMask(index, start, end, closed)
+ assert_equal(result, exp)
+
+
+@pytest.mark.parametrize(
+ "index",
+ [
+ pd.DatetimeIndex(
+ [
+ "1990-01",
+ "1990-02",
+ "1990-03",
+ "1990-04",
+ "2000-01",
+ "2000-02",
+ "2000-03",
+ "2000-04",
+ ]
+ )
+ ],
+)
+@pytest.mark.parametrize(
+ "start, end, closed, exp",
+ [
+ (
+ "01-01T00:00:00",
+ "03-01T00:00:00",
+ True,
+ [masked, masked, masked, unmasked, masked, masked, masked, unmasked],
+ ),
+ (
+ "01-01T00:00:00",
+ "03-01T00:00:00",
+ False,
+ [
+ unmasked,
+ masked,
+ unmasked,
+ unmasked,
+ unmasked,
+ masked,
+ unmasked,
+ unmasked,
+ ],
+ ),
+ ],
+)
+def test_season(index, start, end, closed, exp):
+ exp = pd.Series(exp, index=index, dtype=bool)
+ result = periodicMask(index, start, end, closed)
+ assert_equal(result, exp)
diff --git a/tests/lib/test_rolling.py b/tests/lib/test_rolling.py
index c78fb61d03149215691852cb49504a5349227e3c..d0a5b54caca81b62217e7bd1cca9b28447b30c94 100644
--- a/tests/lib/test_rolling.py
+++ b/tests/lib/test_rolling.py
@@ -1,19 +1,28 @@
-import pytest
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
-import pandas as pd
import numpy as np
+import pandas as pd
+import pytest
+
from saqc.lib.rolling import customRoller
n = np.nan
def test_rolling_existence_of_attrs():
- r = pd.DataFrame().rolling(0).validate()
+ r = pd.DataFrame().rolling(0)
+ if int(pd.__version__.replace(".", "")) < 140:
+ r = r.validate()
+ else:
+ # after pandas 1.4 Rolling.validate() is deprecated,
+ # the wrapped method, however, is not. For now, we use this
+ r = r._validate()
c = customRoller(pd.DataFrame(), 0, min_periods=0)
expected = [attr for attr in dir(r) if not attr.startswith("_")]
result = [attr for attr in dir(c) if not attr.startswith("_")]
diff = [attr for attr in expected if attr not in result]
- print(diff)
assert len(diff) == 0
@@ -125,6 +134,7 @@ def test_rolling_expand_forward(data, kws, expected):
assert np.allclose(result, expected, rtol=0, atol=0, equal_nan=True)
+@pytest.mark.slow
@pytest.mark.parametrize("window", ["0H", "1H", "2H", "3H", "4H"])
@pytest.mark.parametrize("closed", ["both", "neither", "left", "right"])
@pytest.mark.parametrize("center", [False, True], ids=lambda x: f" center={x} ")
diff --git a/tests/lib/test_tools.py b/tests/lib/test_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..b282f24758ce4735f42dce82c69c43dddaf5af1c
--- /dev/null
+++ b/tests/lib/test_tools.py
@@ -0,0 +1,101 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+import numpy as np
+import pandas as pd
+import pytest
+
+import saqc.lib.tools as tools
+from dios import DictOfSeries as DoS
+
+
+@pytest.mark.parametrize("optional", [False, True])
+@pytest.mark.parametrize("value", [1, 0, "foo", np.nan, np.inf])
+def test_assertScalar(value, optional):
+ tools.assertScalar("value", value, optional)
+
+
+@pytest.mark.parametrize("optional", [False, True])
+@pytest.mark.parametrize("value", [[1], [0, 1], {}, {1, 2}, pd.Series([1, 2])])
+def test_assertScalar_raises(value, optional):
+ with pytest.raises(ValueError):
+ tools.assertScalar("value", value, optional)
+
+
+def test_assertScalar_optional():
+ tools.assertScalar("value", None, optional=True)
+ with pytest.raises(ValueError):
+ tools.assertScalar("value", None, optional=False)
+
+
+class _ListLike(list):
+ pass
+
+
+@pytest.mark.parametrize(
+ "value,expected",
+ [
+ (None, [None]),
+ ([None], [None]),
+ (1, [1]),
+ (np.nan, [np.nan]),
+ ([1], [1]),
+ ("foo", ["foo"]),
+ (["foo"], ["foo"]),
+ ([1, 2], [1, 2]),
+ (_ListLike("ab"), ["a", "b"]),
+ ],
+)
+def test_toSequence(value, expected):
+ result = tools.toSequence(value)
+ assert isinstance(result, list)
+ assert result == expected
+
+
+@pytest.mark.parametrize(
+ "value,expected",
+ [
+ # squeeze
+ ([1], 1),
+ ([[1, 2]], [1, 2]),
+ ([[]], []),
+ (_ListLike("a"), "a"),
+ # no squeeze
+ ([], []),
+ ([1, 2], [1, 2]),
+ (_ListLike("ab"), ["a", "b"]),
+ ],
+)
+def test_squeezeSequence(value, expected):
+ result = tools.squeezeSequence(value)
+ assert result == expected
+
+
+@pytest.mark.parametrize(
+ "data, expected",
+ [
+ # 2c + 1c -> 3c
+ ([DoS(dict(a=[1], b=[2])), DoS(dict(c=[3]))], DoS(dict(a=[1], b=[2], c=[3]))),
+ # 1c + 1c + 1c -> 3c
+ (
+ [DoS(dict(a=[1])), DoS(dict(b=[1])), DoS(dict(c=[1]))],
+ DoS(dict(a=[1], b=[1], c=[1])),
+ ),
+ # 2c + 1c (overwrite) = 2c
+ ([DoS(dict(a=[1], b=[2])), DoS(dict(b=[22]))], DoS(dict(a=[1], b=[22]))),
+ # 1c + 1c + 1c (all overwrite) -> 1c
+ (
+ [DoS(dict(a=[1])), DoS(dict(a=[11])), DoS(dict(a=[111]))],
+ DoS(dict(a=[111])),
+ ),
+ ],
+)
+def test_concatDios(data, expected):
+ result = tools.concatDios(data, warn=False)
+ assert result == expected
+
+
+@pytest.mark.parametrize("data", [[DoS(dict(a=[1], b=[2])), DoS(dict(b=[22]))]])
+def test_concatDios_warning(data):
+ with pytest.warns(UserWarning):
+ tools.concatDios(data, warn=True, stacklevel=0)
diff --git a/tests/lib/test_ts_operators.py b/tests/lib/test_ts_operators.py
index 510fce2e4f5d84b9c86684098b196345f841b753..f5f8b3ef2f7891e1047dd471feaa16b75ecdf01e 100644
--- a/tests/lib/test_ts_operators.py
+++ b/tests/lib/test_ts_operators.py
@@ -1,10 +1,195 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+import numpy as np
+import pandas as pd
import pytest
+from numpy.testing import assert_array_equal, assert_equal
+from pandas.testing import assert_series_equal
-from saqc.lib.ts_operators import butterFilter
-import pandas as pd
+import saqc.lib.ts_operators as tsops
def test_butterFilter():
assert (
- butterFilter(pd.Series([1, -1] * 100), cutoff=0.1) - pd.Series([1, -1] * 100)
+ tsops.butterFilter(pd.Series([1, -1] * 100), cutoff=0.1)
+ - pd.Series([1, -1] * 100)
).mean() < 0.5
+
+
+T = True
+F = False
+
+
+@pytest.mark.parametrize(
+ "arr,maxc,expected",
+ [
+ (np.array([]), 1, False),
+ (np.array([F]), 1, False),
+ (np.array([F, F, F]), 1, False),
+ #
+ (np.array([T]), 0, True),
+ (np.array([T]), 1, False),
+ #
+ (np.array([F, T, F]), 0, True),
+ (np.array([F, T, F]), 1, False),
+ #
+ (np.array([F, T, T, T, T, F]), 0, True),
+ (np.array([F, T, T, T, T, F]), 1, True),
+ (np.array([F, T, T, T, T, F]), 2, True),
+ (np.array([F, T, T, T, T, F]), 3, True),
+ (np.array([F, T, T, T, T, F]), 4, False),
+ (np.array([F, T, T, T, T, F]), 5, False),
+ #
+ (np.array([F, T, T, F, T, T, F]), 2, False),
+ ],
+)
+def test__exceedConsecutiveNanLimit(arr, maxc, expected):
+ result = tsops._exceedConsecutiveNanLimit(arr, maxc)
+ assert result is expected
+
+
+def dtSeries(data, freq="1d"):
+ index = pd.date_range(start="2020", periods=len(data), freq=freq)
+ return pd.Series(data=data, index=index, dtype=float)
+
+
+@pytest.mark.parametrize(
+ "data",
+ [dtSeries([0, 1, 2]), dtSeries([0, np.nan, 2])],
+)
+def test_identity(data):
+ from saqc.lib.ts_operators import identity
+
+ result = identity(data)
+ assert result is data
+
+
+@pytest.mark.parametrize(
+ "data,expected",
+ [
+ (dtSeries([0, 1, 2]), 3),
+ (dtSeries([0, np.nan, 2]), 2),
+ ],
+)
+def test_count(data, expected):
+ # count is labeled as a dummy function, this means
+ # we need to ensure it exists with a resampler object.
+ resampler = data.resample("2d")
+ assert hasattr(resampler, "count")
+
+ from saqc.lib.ts_operators import count
+
+ result = count(data)
+ assert result == expected
+
+
+@pytest.mark.parametrize(
+ "data,expected",
+ [
+ (
+ dtSeries([1, 2, np.inf, np.nan]),
+ dtSeries([np.log(1), np.log(2), np.inf, np.nan]),
+ ),
+ pytest.param(
+ dtSeries(
+ [
+ 0,
+ -2,
+ -1,
+ -np.inf,
+ ]
+ ),
+ dtSeries([np.nan, np.nan, np.nan, np.nan]),
+ marks=pytest.mark.xfail(reason="zeroLog(0) did not return NaN for 0"),
+ ),
+ ],
+)
+def test_zeroLog(data, expected):
+ from saqc.lib.ts_operators import zeroLog
+
+ result = zeroLog(data)
+ assert_series_equal(result, expected, check_freq=False, check_names=False)
+
+
+@pytest.mark.parametrize(
+ "data,expected",
+ [
+ (dtSeries([1, 2, 3]), dtSeries([np.nan, 1440, 1440])),
+ (
+ pd.Series(
+ [1, 2, 3],
+ index=pd.DatetimeIndex(["2020-01-01", "2020-01-03", "2020-01-13"]),
+ ),
+ dtSeries([np.nan, 2880, 14400]),
+ ),
+ ],
+)
+def test_deltaT(data, expected):
+ from saqc.lib.ts_operators import deltaT
+
+ result = deltaT(data)
+ assert_series_equal(
+ result,
+ expected,
+ check_dtype=False,
+ check_names=False,
+ check_index=False,
+ check_freq=False,
+ )
+
+
+@pytest.mark.parametrize(
+ "data,expected",
+ [
+ pytest.param(
+ pd.Series(
+ # We use as values the delta of total seconds from the last value.
+ # This way the 'derivative' should be 1 for each result value.
+ [1, 2880, 14400],
+ index=pd.DatetimeIndex(["2020-01-01", "2020-01-03", "2020-01-13"]),
+ ),
+ pd.Series(
+ [np.nan, 1, 1],
+ index=pd.DatetimeIndex(["2020-01-01", "2020-01-03", "2020-01-13"]),
+ ),
+ ),
+ ],
+)
+def test_derivative(data, expected):
+ from saqc.lib.ts_operators import derivative
+
+ result = derivative(data)
+ assert_series_equal(result, expected, check_dtype=False, check_names=False)
+
+
+@pytest.mark.parametrize(
+ "data,expected",
+ [
+ (dtSeries([1, 1, 1]), dtSeries([np.nan, 0, 0])),
+ (dtSeries([1, 10, 100]), dtSeries([np.nan, 9, 90])),
+ (dtSeries([-np.inf, np.inf, 0]), dtSeries([np.nan, np.inf, -np.inf])),
+ (dtSeries([0, np.nan, 0]), dtSeries([np.nan, np.nan, np.nan])),
+ ],
+)
+def test_difference(data, expected):
+ from saqc.lib.ts_operators import difference
+
+ result = difference(data)
+ assert_series_equal(result, expected, check_names=False)
+
+
+@pytest.mark.parametrize(
+ "data,expected",
+ [
+ (dtSeries([1, 1, 1]), dtSeries([np.nan, 0, 0])),
+ (dtSeries([1, 10, 100]), dtSeries([np.nan, 0.9, 0.9])),
+ (dtSeries([-np.inf, np.inf, 0]), dtSeries([np.nan, np.nan, -np.inf])),
+ (dtSeries([0, np.nan, 0]), dtSeries([np.nan, np.nan, np.nan])),
+ ],
+)
+def test_rateOfChange(data, expected):
+ from saqc.lib.ts_operators import rateOfChange
+
+ result = rateOfChange(data)
+ assert_series_equal(result, expected, check_names=False)
diff --git a/tests/requirements.txt b/tests/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a18d98d1bc61ff30cb421bc37afddd2208659061
--- /dev/null
+++ b/tests/requirements.txt
@@ -0,0 +1,9 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+pytest==7.1.2
+pytest-lazy-fixture==0.6.3
+Markdown==3.3.7
+beautifulsoup4==4.11.1
+requests==2.27.1
diff --git a/tests/test__main__.py b/tests/test__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cea574998a0cf40675ef543b18926d9709132ba
--- /dev/null
+++ b/tests/test__main__.py
@@ -0,0 +1,21 @@
+#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import numpy as np
+import pandas as pd
+
+# -*- coding: utf-8 -*-
+import pytest
+
+
+def test_unknownFileExtention():
+ from saqc.__main__ import readData, setupIO, writeData
+
+ reader, writer = setupIO(np.nan)
+ with pytest.raises(ValueError):
+ readData(reader, "foo.unknown")
+ with pytest.raises(ValueError):
+ writeData(reader, pd.DataFrame(), "foo.unknown")