From 63b55c6d7dadb0e612b23a897f292d5ffc14cb52 Mon Sep 17 00:00:00 2001
From: David Schaefer <david.schaefer@ufz.de>
Date: Fri, 28 Oct 2022 12:07:32 +0200
Subject: [PATCH] Release 2.2
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commit f26d3ae32c6adcba340eb6fd866ab1471e78da2d
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Oct 28 11:57:51 2022 +0200
Release 2.2
commit 3d360de87a1992486f64f05297dbc3e24c3e1df1
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Oct 28 11:50:40 2022 +0200
fix failing doctest
commit 80c1508f1dc81b9e55f3f20b07519c26e00aa217
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Oct 28 11:42:42 2022 +0200
fixing doc references
commit 4fcfcee9e979e236b15aac519ec6ad483c0006f9
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Oct 28 11:34:26 2022 +0200
fixing doctest
commit c5f3cb49b96969a77ac5d7c2ba7939630d85d59f
Merge: 675330e3 68949366
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Oct 28 11:02:53 2022 +0200
Merge branch 'develop' of https://git.ufz.de/rdm-software/saqc into develop
commit 675330e31717f7584412bce16da154a126d20103
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Oct 28 10:55:17 2022 +0200
doc updates
commit 689493668385736b87cac33ae0ad16b2d47be2ea
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Oct 28 10:55:17 2022 +0200
doc typos
commit e90751455999628d70d8d745ef275a79f6846c5b
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Oct 28 10:17:45 2022 +0200
bump version to 2.2
commit 9f74ab6f1bea7ef324c0184a4ab3464230b38e22
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Oct 28 10:11:26 2022 +0200
change dfilter default value precedence order
commit 783ce026058f27366440c991e4af5008962e6431
Merge: 51948f4c 7e034781
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 15:34:54 2022 +0200
Merge branch 'deps' into 'develop'
Bump dependencies
See merge request rdm-software/saqc!565
commit 7e03478125d9c77b2dd163d2dd8cbe444484a3bf
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 15:34:54 2022 +0200
Bump dependencies
commit 51948f4c2d4e23444b63eca0390ebe3f84351bea
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Oct 27 15:03:51 2022 +0200
fix interpolation history writing
commit 604f460e8bba2a4e858b041b456b6a2187bb9f57
Merge: b04be6ca be8128c8
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:38:30 2022 +0200
Merge branch 'develop' of https://git.ufz.de/rdm-software/saqc into develop
commit b04be6cabab2f473dbd8918f2e1a58234ed8ed8d
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:36:53 2022 +0200
track interpolation routines in history
commit 59e5c183130f70df852169cf0968265193b74c2b
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:35:14 2022 +0200
docstring typos and comments
commit be8128c8f7289d8810ce734f036a74b93ea9c367
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:28:08 2022 +0200
Restrict pandas version to <1.5
commit 953ff5b3b9abe3cb7e2f39b7dd0cc1ec2579e1ad
Merge: 907c3114 cfa4f98b
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:27:16 2022 +0200
Merge branch 'butterworth' into 'develop'
Add `fitButterworth`
See merge request rdm-software/saqc!564
commit cfa4f98bc0455c2c5c8db604301a1e07514c77a7
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:27:16 2022 +0200
Add `fitButterworth`
commit 907c3114d866116a4918dd82d3d83c1515c0364e
Merge: 1194188c 03fa1202
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:21:49 2022 +0200
Merge branch 'dependabot/pip/sphinx-markdown-tables-0.0.17' into 'develop'
Bump sphinx-markdown-tables from 0.0.15 to 0.0.17
See merge request rdm-software/saqc!529
commit 1194188c0d33a7eb370e4ae5960e72c806c02802
Merge: ee321935 aeb52433
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:21:36 2022 +0200
Merge branch 'dependabot/pip/sphinx-tabs-3.4.1' into 'develop'
Bump sphinx-tabs from 3.3.1 to 3.4.1
See merge request rdm-software/saqc!531
commit ee3219357b923d8671388a856395c85c39c5d178
Merge: 7fee5fd0 c9335842
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:21:15 2022 +0200
Merge branch 'dependabot/pip/typing-extensions-4.3.0' into 'develop'
Bump typing-extensions from 4.2.0 to 4.3.0
See merge request rdm-software/saqc!528
commit 7fee5fd01f562e81376e2eb15a237439dbba5de8
Merge: 2a154f27 270ca8a7
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:21:03 2022 +0200
Merge branch 'dependabot/pip/pytz-2022.2.1' into 'develop'
Bump pytz from 2021.3 to 2022.2.1
See merge request rdm-software/saqc!540
commit 2a154f271a30140b5fbfe3bd3b6e4eacc1e7c93e
Merge: 9214fc39 1dd0b310
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:20:53 2022 +0200
Merge branch 'dependabot/pip/pyarrow-9.0.0' into 'develop'
Bump pyarrow from 8.0.0 to 9.0.0
See merge request rdm-software/saqc!536
commit 1dd0b3103dbf512290c4d2b4f70f9159a3c9d874
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Thu Oct 27 14:20:53 2022 +0200
Bump pyarrow from 8.0.0 to 9.0.0
commit 9214fc399e010fbadb341ccdf244106efdc6b410
Merge: b748981f ea7ea031
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:20:41 2022 +0200
Merge branch 'dependabot/pip/hypothesis-6.55.0' into 'develop'
Bump hypothesis from 6.46.11 to 6.55.0
See merge request rdm-software/saqc!549
commit ea7ea031e41235f7e9a76fc0ea5cb652be96e5a2
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Thu Oct 27 14:20:41 2022 +0200
Bump hypothesis from 6.46.11 to 6.55.0
commit b748981fc7d2ddda9f7f040c840e361a68921460
Merge: 9c8f5adb b8f04a4f
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:20:31 2022 +0200
Merge branch 'dependabot/pip/numba-0.56.2' into 'develop'
Bump numba from 0.55.2 to 0.56.2
See merge request rdm-software/saqc!551
commit b8f04a4fb6fa42326d725288c99970eaf80f1541
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Thu Oct 27 14:20:31 2022 +0200
Bump numba from 0.55.2 to 0.56.2
commit 9c8f5adb5bd38625d09fe60b66269f3706a9ee2f
Merge: d26d8ca5 7e8e7433
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 27 14:20:12 2022 +0200
Merge branch 'dependabot/pip/pytest-7.1.3' into 'develop'
Bump pytest from 7.1.2 to 7.1.3
See merge request rdm-software/saqc!554
commit 7e8e7433bd58e0212032bb9e2d20d26c18455c5a
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Thu Oct 27 14:20:12 2022 +0200
Bump pytest from 7.1.2 to 7.1.3
commit d26d8ca5370a6e26ba893885d7d6d9b32d0ff2c6
Merge: beb0cd41 12f35606
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Oct 27 10:01:15 2022 +0200
Merge branch 'docuMaintenance' into 'develop'
Docu maintenance
See merge request rdm-software/saqc!563
commit 12f35606abde4c7eeca110c018ea8eee7f6b9b7c
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Oct 27 10:01:15 2022 +0200
Docu maintenance
commit beb0cd412c388fb0e90964cb0ed8651812c04faa
Merge: 243910c6 b71d4a51
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed Oct 19 08:56:16 2022 +0200
Merge branch 'flagRangeDocstring' into 'develop'
reintroduced flagRange doc string
See merge request rdm-software/saqc!562
commit 243910c6b354a5a6469bce4520fa9b519cf0fbd9
Merge: 5d8f0b11 d69e2c00
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed Oct 19 08:56:00 2022 +0200
Merge branch 'addGrafixToFlagOffsetDoc' into 'develop'
Add grafix to flag offset doc
See merge request rdm-software/saqc!561
commit d69e2c0060eada6d32b624e73e1f4d28903e9ff1
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed Oct 19 08:56:00 2022 +0200
Add grafix to flag offset doc
commit b71d4a515cd5f3c2e366488348ce75a484def45c
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Oct 18 16:04:17 2022 +0200
reintroduced flagRange doc string
commit 5d8f0b1157f46e7e1bcb42ba1f8ab8f8337566c3
Merge: d0d15f2d 2b053879
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue Oct 18 15:20:16 2022 +0200
Merge branch 'flagJumpsfix' into 'develop'
Fix bugs in flagJums (solving #370)
See merge request rdm-software/saqc!560
commit 2b053879899a20804ab33fe1f5ef955e2aab0d5d
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Oct 18 15:20:16 2022 +0200
Fix bugs in flagJums (solving #370)
commit d0d15f2da92292141ed8d2703d7c74b7ac252aa9
Merge: 5a9b20ef 5da10cbf
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue Oct 11 17:48:35 2022 +0200
Merge branch 'fix-filterwarnings-for-windows-runner' into 'develop'
fix filterwarnings: in windows longlong_scalar instead of long_scalar is used...
See merge request rdm-software/saqc!559
commit 5da10cbfa0b67b9beb7b5375afadf7cd9dec9064
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue Oct 11 17:48:35 2022 +0200
fix filterwarnings: in windows longlong_scalar instead of long_scalar is used...
commit 5a9b20ef9a6b70c80d5f3993b9b811bb31e9d567
Merge: c65d9d99 58e9e889
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue Oct 11 16:47:51 2022 +0200
Merge branch 'ci-test-build' into 'develop'
setup github action
See merge request rdm-software/saqc!558
commit 58e9e889472197255b2a12e2cd78b4731aeae127
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue Oct 11 16:47:51 2022 +0200
setup github action
commit c65d9d99805b98328932e7e69c8f8a86ffa010f1
Merge: 3af7874a dba5c311
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Oct 5 09:42:43 2022 +0200
Merge branch 'fix_untriggered' into 'develop'
Fix untriggered
See merge request rdm-software/saqc!556
commit dba5c311dc06b3a81ad28fee2c9ff587409c0e03
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Oct 5 09:42:43 2022 +0200
Fix bug in function parameter checking
commit 270ca8a76d264459a910cf9faba7cb980ae9942b
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Sat Oct 1 02:08:03 2022 +0200
Bump pytz from 2021.3 to 2022.2.1
commit aeb52433716d4cc5d900497a77813b1b841fb056
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Sat Oct 1 02:07:50 2022 +0200
Bump sphinx-tabs from 3.3.1 to 3.4.1
commit 03fa120252a89b5124827fe9a7cb7d9cd437a15f
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Sat Oct 1 02:07:15 2022 +0200
Bump sphinx-markdown-tables from 0.0.15 to 0.0.17
commit c933584286fd2fe714666b39ed69078f9fd8630f
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Sat Oct 1 02:06:38 2022 +0200
Bump typing-extensions from 4.2.0 to 4.3.0
commit eacd558916d6506b0809166feea635ae400ba469
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Sep 29 14:44:36 2022 +0200
fix unnecessary field copies
commit 3af7874aaadd28c741da60d6e445bf0d854e3e6a
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Sep 29 11:47:43 2022 +0200
backwards compat
commit 06f6cd687d57981f022d02354806d7c38b852463
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Sep 29 11:37:22 2022 +0200
accidental left over
commit b72fc882d55bdc86ae89e1e7c5e2b80b37096605
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Sep 29 11:33:04 2022 +0200
sorting imports
commit 1b566c7b89e46064e4b9c72e1164a33d2fd5f0bb
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Sep 29 11:17:34 2022 +0200
type hints
commit 44d48f2eccfd608734ec344bf9a85ef0d3049ad8
Merge: eaaba441 cbcd24e1
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Sep 27 12:00:49 2022 +0200
Merge branch 'polyFitFix' into 'develop'
Poly fit fix
See merge request rdm-software/saqc!547
commit cbcd24e1fa3ccf5b1bbdbc77d534ff62eb590fb7
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Sep 27 12:00:49 2022 +0200
Poly fit fix
commit eaaba441febcb96eb3ea6df8a0e6fdf53e2d6569
Merge: 266d1d77 3385a478
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Sep 23 22:37:21 2022 +0200
Merge branch 'butterfix' into 'develop'
Butterfix
See merge request rdm-software/saqc!543
commit 3385a478ea7dd63fad15ecdf510e1a98ef802b90
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Sep 23 22:37:21 2022 +0200
Butterfix
commit 266d1d772a3a19dce296106127ea7e1dc1a05643
Merge: bf057900 2af75a96
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Sep 23 21:48:42 2022 +0200
Merge branch 'assignZnormNaNFix' into 'develop'
Assign znorm na n fix
See merge request rdm-software/saqc!546
commit 2af75a96e418123ca1cf22ac61bf188ead8ba606
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Sep 23 21:48:42 2022 +0200
Assign znorm na n fix
commit bf057900bd27531cef90e857a72fae3b0a6daf15
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Sep 23 14:14:12 2022 +0200
using nan sesnitive versions of numpy functions
commit 5a3cc45cb4c2a00ce09b69c8844950d7391e1d89
Merge: 5dd58121 9082425b
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Sep 23 09:36:20 2022 +0200
Merge branch 'develop' of https://git.ufz.de/rdm-software/saqc into develop
commit 9082425bed927fa88eda32d8f41569a71f3dd5c6
Merge: 713fcf07 fc46d1a4
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Sep 23 09:27:35 2022 +0200
Merge branch 'revert-713fcf07' into 'develop'
Revert "Merge branch 'statification' into 'develop'"
See merge request rdm-software/saqc!545
commit fc46d1a4cf1917945a96c82a0600fb82dad2de3a
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Sep 23 09:27:34 2022 +0200
Revert "Merge branch 'statification' into 'develop'"
commit 5dd58121204561d3234e2c8d283711e1eebc5fe1
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Sep 23 09:12:20 2022 +0200
type hints and docstring errors
commit 713fcf075864bcbe30798c9bb8d4441f0b0ca581
Merge: 3128d09a 065a0d89
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Sep 23 08:28:55 2022 +0200
Merge branch 'statification' into 'develop'
Make some SaQC methods static
See merge request rdm-software/saqc!506
commit 065a0d89b45a6f51dad9c4699e0d517fb9553e35
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Sep 23 08:28:55 2022 +0200
Make some SaQC methods static
commit 3128d09a5de9d2a0c87235b89341191231fbcc50
Merge: fa4c2e83 2b2981f4
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Sep 22 16:45:53 2022 +0200
Merge branch 'interpolRegressFix' into 'develop'
fixed recursion regress in interpolate
See merge request rdm-software/saqc!542
commit 2b2981f41b82174790f5f32fe85e8ae4fde051d9
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Sep 22 16:45:53 2022 +0200
fixed recursion regress in interpolate
commit fa4c2e83b0fdeca45ea4dec6990f6764f07235b0
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Sep 22 12:27:56 2022 +0200
try to optimize rolling.apply
commit 53ea48a14237e2899b85ffaa906caea986e70ade
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Sep 22 12:27:27 2022 +0200
docstring fix
commit c83937bf5bc0abf3be5e224f605c72396c4bc926
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Sep 22 12:26:13 2022 +0200
add parameter min_periods to flagConstants
commit 01559b5ac3f34e7549f81aea1e953308e7ab89ee
Author: David Schaefer <david.schaefer@ufz.de>
Date: Mon Sep 12 09:46:23 2022 +0200
new generic function clip
commit da617a9b1f30d7f5135bd4c838c6dcbb8207022d
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Sep 9 15:01:09 2022 +0200
BUGFIX: fix failing translation of scheme defaults
commit d290feb485b149555a6580125228eeaf3aaf007d
Merge: b2babd47 aae82834
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Sep 6 19:31:42 2022 +0200
Merge branch 'outlierDetectionLibraryConsolidation' into 'develop'
Outlier detection library consolidation
See merge request rdm-software/saqc!508
commit aae8283419d3e1181df3cc893a6a6b7f394a9ba4
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Sep 6 19:31:42 2022 +0200
Outlier detection library consolidation
commit b2babd47141f57997f50d614b50f409c63ff3bc0
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Jul 25 16:24:25 2022 +0200
typo fix in issue template
commit 2b8478ba5676c85a97024b249a5f6a5aa66d8125
Merge: 008ecb4f 7602d640
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Jul 25 14:51:41 2022 +0200
Merge branch 'IssueTemplate' into 'develop'
issue_template
See merge request rdm-software/saqc!502
commit 7602d640cf53d337595a22fb23e65559c6d046ca
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Jul 25 14:51:41 2022 +0200
issue_template
commit 008ecb4f251eedeafdb3016bb06850a009928e59
Merge: a9ccfcc9 82a203d1
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Jul 25 13:48:16 2022 +0200
Merge branch 'dfilter-translation' into 'develop'
added translation for dfilter
Closes #253
See merge request rdm-software/saqc!522
commit 82a203d175895d75e9cf9c0339e052c1d400202e
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Jul 25 13:48:16 2022 +0200
added translation for dfilter
commit a9ccfcc94a0ff25b55fe1488d00288e858d295c7
Merge: 9a4f146d 17a1949f
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Jul 21 09:50:23 2022 +0200
Merge branch 'test_outlier_detectionTypoFix' into 'develop'
fixed typo in test_outlier_detection filename
See merge request rdm-software/saqc!523
commit 17a1949fd8bfa0b62c6a115836fb50389c47c89b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Jul 20 14:34:50 2022 +0200
fixed typo in test_outlier_detection filename
commit 9a4f146d872b1d86f7beb975d98a727b64f2b832
Merge: 35604073 39519a85
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Jul 14 22:00:54 2022 +0200
Merge branch 'flagOffsetRework' into 'develop'
Flag offset rework
See merge request rdm-software/saqc!507
commit 39519a85b856188c9ef59ff9ecb481b1fec37c51
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Jul 14 22:00:54 2022 +0200
Flag offset rework
commit 356040730e8d6bb8d8d03a606931e6c29f12d050
Merge: 65d9dcc7 755c57fd
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jul 5 08:30:23 2022 +0200
Merge branch 'funcs-to-methods' into 'develop'
transform test functions to SaQC methods
See merge request rdm-software/saqc!510
commit 755c57fd82d3aab74d016ee6ebb35a91a63df99b
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jul 5 08:30:23 2022 +0200
transform test functions to SaQC methods
commit 65d9dcc744646cacee835025876667edf313d86a
Merge: 4dc18d8b d0c9de81
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Jul 4 08:47:52 2022 +0200
Merge branch 'flagIsolatedRework' into 'develop'
Flag isolated rework
Closes #358
See merge request rdm-software/saqc!503
commit d0c9de815e8ce2a116425908d15a1cb058fd30fe
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Jul 4 08:47:52 2022 +0200
Flag isolated rework
commit 4dc18d8b4c14a13779796a919b9176d970c73ec8
Merge: 37688f95 5da14b07
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Jul 4 08:30:54 2022 +0200
Merge branch 'coberturaFix' into 'develop'
updated cobertura inclusion
See merge request rdm-software/saqc!521
commit 5da14b072b3f4344e14614414f95cfd24cdc39f3
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Jul 4 08:30:54 2022 +0200
updated cobertura inclusion
commit 37688f958f7c4c80b9bf94157c93535569e664bc
Merge: d736c152 ed594cb8
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jul 1 09:04:44 2022 +0200
Merge branch 'dependabot/pip/sphinx-lt-6' into 'develop'
Update sphinx requirement from <5 to <6
See merge request rdm-software/saqc!516
commit ed594cb86f5e384402fe692dee8c8c0dbeac7eed
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Fri Jul 1 09:04:44 2022 +0200
Update sphinx requirement from <5 to <6
commit d736c1525661a55914e81750753614533f1500a2
Merge: ace8adf6 b6771a8c
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Jun 23 13:35:57 2022 +0200
Merge branch 'disparing-columns' into 'develop'
don't loose added flags columns
See merge request rdm-software/saqc!504
commit b6771a8cc29656184a7406e504b170db82ecba27
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Jun 23 13:35:56 2022 +0200
don't loose added flags columns
commit ace8adf65016aa2126124ee041c88bc8db7087a5
Merge: 91417853 fcc888e9
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Jun 22 20:08:27 2022 +0200
Merge branch 'test-refactorings' into 'develop'
make use of pytest features
See merge request rdm-software/saqc!505
commit fcc888e90b8127fd59dc1dc51aeffbfe179a8bf5
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Jun 22 20:01:50 2022 +0200
make use of pytest features
commit 91417853f394642287ef99d689300bd4f5cd9ddf
Merge: a9cd4648 854d2edb
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 14 13:22:57 2022 +0200
Merge branch 'release2.1' into 'develop'
Release2.1
See merge request rdm-software/saqc!499
commit 854d2edb9c80097bb610f0e2db55f33dfa24a49b
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 14 13:22:57 2022 +0200
Release2.1
commit a9cd464805dcafb2af916ed17d29bdc74f3ba4fd
Merge: 2ae300ad 47344cb6
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 14 13:02:58 2022 +0200
Merge branch 'fixPlotWarning' into 'develop'
fix for empty label warning
See merge request rdm-software/saqc!500
commit 47344cb6b0bf27ea321123a35fe08d4ba9e81e6b
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue Jun 14 12:54:15 2022 +0200
fix for empty label warning
commit 2ae300ad59139f13c0e5188f0e77dbb591011d44
Merge: 0ab9c4c6 9d172d96
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 14 12:43:04 2022 +0200
Merge branch 'dfilter-defaults' into 'develop'
respect function dfilter defaults
Closes #354
See merge request rdm-software/saqc!497
commit 9d172d9641a461e6f144be4a014bbb236df26fcb
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 14 12:43:04 2022 +0200
respect function dfilter defaults
commit 0ab9c4c6924cc785f74e45357baefa44910e209b
Merge: 63a8b1d0 a540773c
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 14 12:35:32 2022 +0200
Merge branch 'last-minute-fixes' into 'develop'
Last minute fixes
See merge request rdm-software/saqc!493
commit a540773cabd323ac4015e05dbd9aa487d6b37041
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 14 12:35:32 2022 +0200
Last minute fixes
commit 63a8b1d00052d46705ea7d80d6c1b65516381bea
Merge: b941a8ac 8e2844c7
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 14 12:34:54 2022 +0200
Merge branch 'adjustingShapeCycle' into 'develop'
added downward facing triangle
See merge request rdm-software/saqc!498
commit 8e2844c7bb0e6f20e5c5dbd598271f4dd1158151
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Jun 14 12:16:23 2022 +0200
added downward facing triangle
commit b941a8ac2c7016c222fdd34b6a9a0c41b4df1b24
Merge: 66562733 b05fc5ee
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 14 08:28:19 2022 +0200
Merge branch 'doctestFix' into 'develop'
fix for missing _api doctests (all tests in function docstrings)
See merge request rdm-software/saqc!495
commit b05fc5eeed60fa4da74cc060dd284a0fead821bc
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue Jun 14 02:35:16 2022 +0200
fix for missing _api doctests (all tests in function docstrings)
commit 665627335eafc9c459bc9a776707573a7b344ea8
Merge: 936e8626 664c96b8
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Jun 13 20:53:48 2022 +0200
Merge branch 'Make_isFlagged_testfunction_sensitive' into 'develop'
added label mechanics to isFlagged / implemented lebelMechanics test
Closes #336
See merge request rdm-software/saqc!491
commit 664c96b8e0b36a169e73db29a7f94752cb2ec964
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Jun 13 20:53:48 2022 +0200
added label mechanics to isFlagged / implemented lebelMechanics test
commit 936e86266fa45ce4bbe2cc56c32ec393d0afa9aa
Merge: 90af9e8a 1fe54e97
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Jun 13 16:51:43 2022 +0200
Merge branch 'FixInterpolations' into 'develop'
Fix interpolations
See merge request rdm-software/saqc!492
commit 1fe54e9793a186c229e4f8d6b55a119698848b94
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Jun 13 16:51:42 2022 +0200
Fix interpolations
commit 90af9e8a52ec1f76e93c8bc809afc0b0a69d6222
Merge: 93617e12 3bea8afe
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Jun 13 15:21:53 2022 +0200
Merge branch 'fixGL352' into 'develop'
fixed GL-#352 and added test for that
Closes #352
See merge request rdm-software/saqc!489
commit 3bea8afe2040a9777a7599c9fb03b6360b2a7aa5
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Jun 13 15:21:52 2022 +0200
fixed GL-#352 and added test for that
commit 93617e125cf8c0646003ccfebc496f7ef4c38b75
Merge: e7f6fec9 dcf00b66
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Jun 13 14:38:54 2022 +0200
Merge branch 'py37support' into 'develop'
Py37support
See merge request rdm-software/saqc!488
commit dcf00b665d4d46160d692791b5dc1220705ef939
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Jun 13 14:38:54 2022 +0200
Py37support
commit e7f6fec9dbe8edfe3fa32d19df02f1c111e0ee4d
Merge: 4e5453ff 289467fb
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Jun 13 10:37:13 2022 +0200
Merge branch 'docufixes' into 'develop'
Docufixes
See merge request rdm-software/saqc!486
commit 289467fb5b8e426cc1bcdf753915de5fe60be770
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Jun 13 10:37:12 2022 +0200
Docufixes
commit 4e5453ffb71c1228f2cab991768f2587d6c84dc8
Merge: 74d97644 fa97c356
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Sun Jun 12 17:35:10 2022 +0200
Merge branch 'docufixes' into 'develop'
Docufixes
See merge request rdm-software/saqc!484
commit fa97c356aea9a8464fc1439ade55d39ebc3f0ddb
Author: David Schäfer <david.schaefer@ufz.de>
Date: Sun Jun 12 17:35:10 2022 +0200
Docufixes
commit 74d9764453c17a5cee9c432d2b32d6b70bd5d2a8
Merge: 0104611d 7c7b459d
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Sun Jun 12 17:31:05 2022 +0200
Merge branch 'outlierDetectionRevision' into 'develop'
Outlier detection revision
See merge request rdm-software/saqc!485
commit 7c7b459d98647cbf432d2035780ff97f59fa52f4
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Sun Jun 12 17:31:04 2022 +0200
Outlier detection revision
commit 0104611d299bac54d0e23c5ac747b2608151ea3e
Merge: 679e887c 5f958b4e
Author: David Schäfer <david.schaefer@ufz.de>
Date: Sat Jun 11 19:31:53 2022 +0200
Merge branch 'varfixes' into 'develop'
Various fixes from CHS Pipeline
See merge request rdm-software/saqc!483
commit 5f958b4ee43ba19891115bfdc55e9a4859392fef
Author: David Schäfer <david.schaefer@ufz.de>
Date: Sat Jun 11 19:31:53 2022 +0200
Various fixes from CHS Pipeline
commit 679e887cbe8abe2f2c7af411ab84a500431108ed
Merge: f768ab41 78b34e6c
Author: David Schäfer <david.schaefer@ufz.de>
Date: Sat Jun 11 12:51:15 2022 +0200
Merge branch 'ci' into 'develop'
Ci
See merge request rdm-software/saqc!481
commit 78b34e6c6f9e96b89b6ca4e88462072c21ad1efc
Author: David Schäfer <david.schaefer@ufz.de>
Date: Sat Jun 11 12:51:15 2022 +0200
Ci
commit f768ab411282624975ad44341d45032db0946074
Merge: 67c9e5bf 84a4e433
Author: David Schäfer <david.schaefer@ufz.de>
Date: Sat Jun 11 12:18:11 2022 +0200
Merge branch 'setup.py' into 'develop'
update setup.py
Closes #349
See merge request rdm-software/saqc!479
commit 84a4e433b6b602014a775c830c6bf3c16ca2e7ca
Author: David Schäfer <david.schaefer@ufz.de>
Date: Sat Jun 11 12:18:10 2022 +0200
update setup.py
commit 67c9e5bf77662dd415c19c7f7f8c5b32608c83cf
Merge: e4a97e06 620917a8
Author: David Schäfer <david.schaefer@ufz.de>
Date: Sat Jun 11 12:11:51 2022 +0200
Merge branch 'logos' into 'develop'
Logos + Docu cleanups
See merge request rdm-software/saqc!480
commit 620917a87672fb18842969ea0359514a492c3b37
Author: David Schäfer <david.schaefer@ufz.de>
Date: Sat Jun 11 12:11:51 2022 +0200
Logos + Docu cleanups
commit e4a97e0605b0d34b26d4f83a6e28508be4a43042
Author: David Schaefer <david.schaefer@ufz.de>
Date: Sat Jun 11 00:34:47 2022 +0200
removed weird import
commit ae92bbdd3c2d8e8f334232d64ac641ad67496956
Merge: 73f36ed3 77cce8e5
Author: David Schaefer <david.schaefer@ufz.de>
Date: Sat Jun 11 00:16:44 2022 +0200
Merge branch 'develop' of https://git.ufz.de/rdm-software/saqc into develop
commit 73f36ed3fe3de8c83b8de58cc4f4e06f639f4ee4
Author: David Schaefer <david.schaefer@ufz.de>
Date: Sat Jun 11 00:16:21 2022 +0200
fixed failing doctests
commit 77cce8e5e611a32a413a90681626abb172e7608f
Author: Bert Palm <bert.palm@ufz.de>
Date: Fri Jun 10 15:56:42 2022 +0200
blackified
commit fd661ab2a9d7d11a9a2973102768d622a3545cc1
Author: Bert Palm <bert.palm@ufz.de>
Date: Fri Jun 10 14:16:37 2022 +0200
FIX: make job names unique for gitlab-ci
commit 1c0355a4b77d6fa6bff87c9c685ba012bb675bf6
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 14:03:39 2022 +0200
deleted scripts
commit edc81899156326f06e05b36779baaf468de9a08d
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 13:55:53 2022 +0200
removed file
commit 7062ef58a8f47f41e0f592431d3ce4627fdd5e24
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 13:35:16 2022 +0200
missing file
commit 9a4ee2c862efbf0b6c804a8fd3af8a8e2065ebfd
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 13:33:01 2022 +0200
skip ENVIRONMENT
commit bac1120b90e7e1ede0749304ca9c871aeb8acd30
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 13:30:00 2022 +0200
skip tempo
commit 17d62350fc7ecc02dffdd5f9ac1b0c0ccd72aea4
Merge: 854b49e4 028a0d33
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 13:12:19 2022 +0200
Merge branch 'develop' of https://git.ufz.de/rdm-software/saqc into develop
commit 854b49e4b09a8088cba049f5cf5c1a4afd568d20
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 13:08:14 2022 +0200
many more...
commit d1b388b7e07553d85401229ad3c720506a061fe6
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 09:19:55 2022 +0200
make cleaner
commit 63c8f749ee341d8540350f452f8a2bd807ca4db0
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 09:19:47 2022 +0200
cleanup
commit e318b09339aa6564f3d432c4e48dfc5074652e04
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 09:19:29 2022 +0200
fixed more warnings
commit 441fcc9e32f7725de762ffcc0133b9093e659192
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 09:07:24 2022 +0200
restructoring to fix warnings
commit 2496894bed73416c2ead6ce6d08681237a985fa9
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 09:00:26 2022 +0200
fromatting issues
commit 04d6c1886dbea880be8dfc02dd22b40de81a6b8a
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 09:00:04 2022 +0200
formatting
commit 8aeb6e911688215b374abe5582079aff91473495
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 08:59:54 2022 +0200
deleted obsolete files
commit 27dcbf98171c904ce0c8b2c90535272b4fb9702c
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 08:43:34 2022 +0200
removed obslete file
commit 02ec2d177726c3b6c692868e5fa9bbda89bb783a
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 08:38:36 2022 +0200
added new docs to tree
commit c34e9307ba6e7a340a57f57c0c04617aeceafa3a
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 08:38:25 2022 +0200
formatting issues
commit 3c416d50936e0b189830f07aa83ee644f2a80b8b
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 08:38:12 2022 +0200
added Cutomizations
commit 7475ab2bcea5c61f4d2a17a5f9313c404afd7260
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 08:06:41 2022 +0200
fixed docstring
commit 8c6ba8979595935c55844b69a7d91a8be213e509
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 08:02:44 2022 +0200
remove remaining data_raw
commit 63a13cf35159fbac3435ae9daec5d111841e90dd
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 10 08:02:30 2022 +0200
docstring fixes
commit 062a5f258f128a7721d084ec6caff1769f5ad9e0
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Jun 9 16:18:19 2022 +0200
renamed sphinxdoc -> docs
commit cdfecdf3e70487e56781de77db97637b22969f4e
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Jun 9 16:15:23 2022 +0200
rename modulesAPIs -> modules
commit 19615fdc651baed0b42c275b50ef85da08a33611
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Jun 9 16:13:49 2022 +0200
renamed how_to_doc -> howtodoc
commit 0f5a79318c92db0fe521a6256828cb01f1ac8a0f
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Jun 9 16:11:53 2022 +0200
renamed funcSummaries -> funcs
commit 5177f31c1c6169f82429e87dca7cc5b1043e8b89
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Jun 9 16:10:15 2022 +0200
renamed getting_started -> gettingstarted
commit c9e90c4e331422ae62f150653adc04988343ad0d
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Jun 9 16:03:54 2022 +0200
renamed cook_books -> cookbooks
commit 481e7b3c7c44ef5748242f25d42c52b97421642c
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Jun 9 15:59:12 2022 +0200
added requirements versions
commit 232ec0ce1b99a41c88522ef2c7d2d78bd3ef46be
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Jun 9 15:51:01 2022 +0200
renamed requirements file
commit 028a0d33e897e115cc3c8007814af5916a17604c
Merge: 4bf27009 4ebab7b3
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Jun 9 15:44:28 2022 +0200
Merge branch 'stray' into 'develop'
renamed parameter freq -> window
Closes #338
See merge request rdm-software/saqc!477
commit 4ebab7b369abf0c2ecd5f0f8c5228f1596f6b125
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Jun 9 15:41:02 2022 +0200
renamed parameter freq -> window
commit 4bf270094dab0819b0ed80da3082fd7711532d9b
Merge: f72ba6d0 fecc995e
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Jun 9 15:40:30 2022 +0200
Merge branch 'imports' into 'develop'
Import cleanups
See merge request rdm-software/saqc!476
commit fecc995ed45bd11b6ff74712ba88902c80f482ce
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Jun 9 15:40:30 2022 +0200
Import cleanups
commit f72ba6d014e7a5a6679f4eb7117ec17109d2ce84
Merge: ea7f430d d920a8dd
Author: Bert Palm <bert.palm@ufz.de>
Date: Thu Jun 9 14:22:05 2022 +0200
Merge branch 'fixGL333' into 'develop'
Fix gl333
Closes #333
See merge request rdm-software/saqc!475
commit d920a8dd5c25f23dc32fcb99c7ee12612c18afdd
Author: Bert Palm <bert.palm@ufz.de>
Date: Thu Jun 9 14:22:05 2022 +0200
Fix gl333
commit ea7f430dafc950441044fd2072c79a1f4bcdb9ab
Merge: 2c14d85b 3a75123a
Author: Bert Palm <bert.palm@ufz.de>
Date: Thu Jun 9 10:11:38 2022 +0200
Merge branch 'fixGL327' into 'develop'
FIX: GL-327 no data altering in plot even not with Exceptions
Closes #327
See merge request rdm-software/saqc!474
commit 2c14d85b4d44017985f1a5ac5e6ea716b12e00cd
Merge: 31dc477a 5d92a792
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed Jun 8 14:25:48 2022 +0200
Merge branch 'autoDocTypehints' into 'develop'
added autodoc-typehints, updated changelog
See merge request rdm-software/saqc!472
commit 31dc477ad9375e5adb12d746ccddbafc2e94ca90
Merge: 6d05ebaf de19465a
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Jun 8 14:04:02 2022 +0200
Merge branch 'signatures' into 'develop'
fix and check the signature wrapper
See merge request rdm-software/saqc!470
commit de19465a01dc3c075ede1a68f0afb659a6104e6f
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Jun 8 14:04:02 2022 +0200
fix and check the signature wrapper
commit 3a75123a4b85451b85456112c771fe646e163307
Author: Bert Palm <bert.palm@ufz.de>
Date: Wed Jun 8 13:40:05 2022 +0200
FIX: GL-327 no data altering in plot even not with Exceptions
commit 6d05ebaf1d897b77857a600c385ef606109d7587
Merge: 76b55583 255183fa
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed Jun 8 11:49:49 2022 +0200
Merge branch 'docStringParser' into 'develop'
Doc string parser
Closes #339
See merge request rdm-software/saqc!471
commit 255183fa82114dcc082324eac4c7e3eca28ab382
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed Jun 8 11:49:49 2022 +0200
Doc string parser
commit 76b55583782616114fc788f8bb531ae58d419ca8
Merge: 93e3ab35 7d5d80a4
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 7 19:43:43 2022 +0200
Merge branch 'accessors' into 'develop'
Simplified the data/flags accessors
See merge request rdm-software/saqc!466
commit 7d5d80a4468237e321f8109dad2072624388d2e2
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Jun 7 19:43:43 2022 +0200
Simplified the data/flags accessors
commit 5d92a79268defe8388c121e7c571a18492de2ce4
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Jun 7 18:50:12 2022 +0200
added autodoc-typehints, updated changelog
commit 93e3ab35f7d539851cab2930ef40cb41a24ac3f8
Merge: 4e892ce7 f3cc67fb
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 3 15:09:07 2022 +0200
Merge branch 'develop' of https://git.ufz.de/rdm-software/saqc into develop
commit 4e892ce7df83cdbf7667005d9212710a02f7571b
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 3 15:08:42 2022 +0200
added missing CHANGLOG entry
commit f3cc67fb67446486f94ff4d96f66a03a5926c008
Merge: 265519e2 512c5fcf
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jun 3 15:04:11 2022 +0200
Merge branch 'squeeze' into 'develop'
renamed History.max to History.squeeze
Closes #320
See merge request rdm-software/saqc!469
commit 512c5fcfec3d6da3d0d07dfac22ec051b114eaee
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Jun 3 15:01:23 2022 +0200
renamed History.max to History.squeeze
commit 265519e216492b6cdda23ee56f3649fdc230d06b
Merge: d4407046 46e8abf3
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jun 3 14:55:48 2022 +0200
Merge branch 'readme' into 'develop'
README changes and checks
See merge request rdm-software/saqc!467
commit 46e8abf35467450ec24e087fa55627e0a6bcd999
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jun 3 14:55:48 2022 +0200
README changes and checks
commit d4407046b74b6f194ce82be6ae94da49a97f842b
Merge: 7967c98a c2df261b
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jun 3 14:46:53 2022 +0200
Merge branch 'version_bumps' into 'develop'
Version bumps
See merge request rdm-software/saqc!468
commit c2df261b4d885f979065e8b859583bd4ea172be0
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jun 3 14:46:53 2022 +0200
Version bumps
commit 7967c98a62abda4cb44cf1dd01c3b2811258d9b7
Merge: 0d732baa d0add2de
Author: Bert Palm <bert.palm@ufz.de>
Date: Fri Jun 3 13:12:21 2022 +0200
Merge branch 'lowercase_inf_nan' into 'develop'
also allow lowercase inf/nan in config
See merge request rdm-software/saqc!464
commit 0d732baa888b3b757d6d120dc9cd48742747cb13
Merge: a86602ed 0cc74f63
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jun 3 13:08:29 2022 +0200
Merge branch 'immutability' into 'develop'
Make SaQC immutable again
See merge request rdm-software/saqc!465
commit 0cc74f636845bfb381e40b2a5b046ecd40b965f9
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jun 3 13:08:29 2022 +0200
Make SaQC immutable again
commit d0add2deb70f9696aa15a6b54f1827d49aa72bc2
Author: Bert Palm <bert.palm@ufz.de>
Date: Thu Jun 2 00:28:28 2022 +0200
also allow lowercase inf/nan in config
commit a86602ed74a3535c05e6341699ab87bcca9b2ae5
Merge: 15c37d13 95fc9a94
Author: Bert Palm <bert.palm@ufz.de>
Date: Wed Jun 1 15:38:01 2022 +0200
Merge branch 'more_tests' into 'develop'
More tests
Closes #145 and #216
See merge request rdm-software/saqc!462
commit 95fc9a941c9cd916613cbf4d1846257dc7d76d06
Author: Bert Palm <bert.palm@ufz.de>
Date: Wed Jun 1 15:38:01 2022 +0200
More tests
commit 15c37d1394aef385466bb8e7da16fb1c3ed61cef
Merge: 7abe825c d3cb4bb2
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue May 31 14:55:57 2022 +0200
Merge branch 'cleanup_kwargs' into 'develop'
ENH: rm `set_flags` and `return_residuals` from internal functions
See merge request rdm-software/saqc!463
commit d3cb4bb2c3583d93d7b532c03aa54135a66ba087
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue May 31 14:55:57 2022 +0200
ENH: rm `set_flags` and `return_residuals` from internal functions
commit 7abe825ce68a55aec964fb8a8728f89890815074
Author: David Schaefer <david.schaefer@ufz.de>
Date: Mon May 16 09:16:03 2022 +0200
fixed typehints
commit 03b7172bac7d80284314700fd9799b16ccae3901
Merge: a742bede ac0e80be
Author: David Schaefer <david.schaefer@ufz.de>
Date: Mon May 2 13:59:08 2022 +0200
Merge branch 'develop' of https://git.ufz.de/rdm-software/saqc into develop
commit a742bede43f54983c4cbc8b5cbaceb711f20dc90
Author: David Schaefer <david.schaefer@ufz.de>
Date: Mon May 2 13:58:11 2022 +0200
added new constant `INF`
commit ac0e80be69b6f6e7b2833aa27f0bddb07ae240e0
Merge: f147e428 4b243660
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon May 2 13:51:42 2022 +0200
Merge branch 'generic-fix' into 'develop'
[FIX] inconsistencies with generic flag handling and meta writing
See merge request rdm-software/saqc!461
commit 4b243660a8a1bed32401167d909c1e5cfdd0a39c
Author: David Schaefer <david.schaefer@ufz.de>
Date: Mon May 2 13:48:35 2022 +0200
[FIX] inconsistencies with generic flag handling and meta writing
commit f147e428e43561f92275417edcf4ffe9c0f9c083
Merge: eae8eac0 12e0c7fa
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Apr 26 09:15:23 2022 +0200
Merge branch 'flag_repeater' into 'develop'
Added a new function repeatFlags
Closes #226
See merge request rdm-software/saqc!441
commit 12e0c7fa52cd2a917c1483a97b70b82f990e9139
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Apr 26 09:15:23 2022 +0200
Added a new function repeatFlags
commit eae8eac09b5fa6a40c10781d39678eac238151ed
Merge: 05d0ff4a 719233eb
Author: Bert Palm <bert.palm@ufz.de>
Date: Thu Apr 7 20:47:51 2022 +0200
Merge branch 'newCI' into 'develop'
ENH: New CI/CD pipeline
Closes #337
See merge request rdm-software/saqc!453
commit 719233eb5f5121a911384566df7b49a43e01ee6e
Author: Bert Palm <bert.palm@ufz.de>
Date: Thu Apr 7 20:47:51 2022 +0200
ENH: New CI/CD pipeline
commit 05d0ff4af0af9c0850d7f02b5543b7f428658f8e
Merge: 0fd879c6 d0829da6
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Apr 5 20:08:58 2022 +0200
Merge branch 'docBugs' into 'develop'
doc errors mitigated
See merge request rdm-software/saqc!454
commit d0829da6d134a0595576aab63b464ac0e74ff493
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Apr 5 18:32:20 2022 +0200
doc errors mitigated
commit 0fd879c6c3a7c3fe3bce2c9461067e1c1d9aadc5
Merge: 6b260b62 cf856480
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Apr 4 12:57:05 2022 +0200
Merge branch 'periodicMaskFix' into 'develop'
Periodic mask fix
Closes #322 and #332
See merge request rdm-software/saqc!444
commit cf85648023adc6fec2a01c877ad12e24a8503a21
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Apr 4 12:57:04 2022 +0200
Periodic mask fix
commit 6b260b629092cde9360c56232910774cfae93338
Merge: 30a4576f aecb6fbd
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Apr 1 07:09:24 2022 +0200
Merge branch 'docuratorFix' into 'develop'
empty docstring fix
See merge request rdm-software/saqc!446
commit aecb6fbdb7b42f970876d1b2fd737ea89270a35d
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Apr 1 07:09:24 2022 +0200
empty docstring fix
commit 30a4576ff1951de52d3a893d1dd2f207059615b7
Merge: 5ce1e0d9 aa435ef2
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Mar 15 10:46:33 2022 +0100
Merge branch 'driftCorrectStability' into 'develop'
replaced exception by inpection (correctDrift)
See merge request rdm-software/saqc!445
commit aa435ef2f188704e3f219589eff5aba34f2810c3
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Mar 15 09:34:08 2022 +0100
replaced exception by inpection (correctDrift)
commit 5ce1e0d9a78077bdd399d4d87915d31c2cf9b974
Merge: 395e9a1b e3e2fc92
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Mar 14 15:25:41 2022 +0100
Merge branch 'interpolationFix' into 'develop'
Interpolation fix
See merge request rdm-software/saqc!443
commit e3e2fc920285615b726fc22f1a90fc6586aa9f19
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Mar 14 15:25:41 2022 +0100
Interpolation fix
commit 395e9a1b60aef701fb3d0803a7ebf0ebb635edf5
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Mar 14 08:34:35 2022 +0100
updated change log
commit 76bb830b0bcf836e85b3661af3f0d50cf953cf8e
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Mar 14 08:32:42 2022 +0100
renamed agg_func->func
commit c296971b679ca4cef99e80759eecda1649366c5b
Merge: b3c640df 768581ca
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Mar 8 11:13:47 2022 +0100
Merge branch 'develop' into filtertools
commit b3c640df3514d611d446d54d50f06f1a9a072532
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Mar 8 11:10:24 2022 +0100
black applied
commit 6997fbaa57b603e51b2724abe85fea7bc076a218
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Mar 7 16:35:35 2022 +0100
fixes and simplification
commit 768581ca6ce0817f69b03c492dfa0ed3746eed10
Merge: f1bcf777 39bf23d6
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Mar 4 10:38:49 2022 +0100
Merge branch 'concotSqueezeFix' into 'develop'
Concat Squeeze Fix (adressing SEEFO pipe error)
See merge request rdm-software/saqc!440
commit 39bf23d64e37fbcdadbad14a3d4f645c428b25e3
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Mar 4 10:38:49 2022 +0100
Concat Squeeze Fix (adressing SEEFO pipe error)
commit d81eebd413df1be760f93d6df8595cafc473ab6e
Author: Bert Palm <bert.palm@ufz.de>
Date: Wed Mar 2 12:39:21 2022 +0100
ah ok - didnt know about this one
commit 8db00cb89267390903abaf6e39731ec94801427c
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Mar 2 11:54:50 2022 +0100
changelog updated
commit 513c768ada677b2f5a2872c3479268f3cf241fcd
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Mar 2 11:52:47 2022 +0100
fixed untouched-unflagged indeifference bug
commit f1bcf7770414b09c552c026496984a178d3d977e
Merge: 157f8bc1 5121585a
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Feb 17 12:33:59 2022 +0100
Merge branch 'driftCorrectHotFix' into 'develop'
fixed driftcorrect bug when calculating single value corrections/updateed changelog
See merge request rdm-software/saqc!431
commit 5121585ab2101a86231602a60c814684297ca157
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 16 19:50:59 2022 +0100
fixed driftcorrect bug when calculating single value corrections/updateed changelog
commit 157f8bc1fc28a0f32c7c62a5aee4f16e9366fee6
Merge: a7c6e7ab 4dd56efb
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Feb 16 12:57:27 2022 +0100
Merge branch 'dmpfix' into 'develop'
DmpScheme: add missing DFILTER_DEFAULT constant
See merge request rdm-software/saqc!430
commit 4dd56efbaaf7289c441680c43a6e1eadcd747fb1
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Feb 16 12:57:26 2022 +0100
DmpScheme: add missing DFILTER_DEFAULT constant
commit a7c6e7ab61daca71c4356e36e6ee03eeebfb234d
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 16 07:49:52 2022 +0100
typos in doc fixed
commit 26f8fa89bbf8ffcd9360df7b7deae4685197a222
Merge: f185c6e6 75c8c8f9
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Feb 15 12:48:47 2022 +0100
Merge branch 'globalKwargDocs' into 'develop'
Global kwarg docs
See merge request rdm-software/saqc!429
commit f185c6e6aad8616fd48f4ea635c6ad9ddc35a119
Merge: 51a3f4d7 ac0fe57a
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Feb 15 11:20:58 2022 +0100
Merge branch 'dmp_refactoring' into 'develop'
dmpscheme refactorings
See merge request rdm-software/saqc!427
commit ac0fe57a15dee48ad9375996cd461a553ad51360
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Feb 15 11:20:58 2022 +0100
dmpscheme refactorings
commit 51a3f4d79c2a19e80489f1600756a16d76d8d917
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Feb 15 10:27:44 2022 +0100
FILTER_NONE and FILTER_ALL are now imported to saqc.__init__, changelog updated
commit 75c8c8f9262309015baf59c5295e095174c03dcc
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Feb 15 09:39:27 2022 +0100
added licence information
commit 569731b5edbabddf97bd87b3e7f5cd49833a9c60
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Feb 15 09:30:43 2022 +0100
added flaggingschme/flagging constants resources
commit db811aab4c12bbfd88d33bbcd4f2d5e6a23ddba8
Merge: f3784668 7bea02b8
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Feb 15 08:30:36 2022 +0100
Merge branch 'simplifyFlagsConcatenation' into 'develop'
simplify/unify flags concatenation workflows
See merge request rdm-software/saqc!410
commit 7bea02b86761fde98eaf7af78c0505baac377a80
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Feb 15 08:30:36 2022 +0100
simplify/unify flags concatenation workflows
commit 63a7d7c828e32f06ad7409a1978785b1d8c5794a
Merge: aac7628e f3784668
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 16:45:12 2022 +0100
...
commit aac7628ece5948e380bca558425cb283301e0d10
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 16:01:36 2022 +0100
constants bullet point added
commit f37846689564870a066cde8dc820cec5d0dadbc7
Merge: 9caad54b de84a082
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 14:02:42 2022 +0100
Merge branch 'residUE2residUAL' into 'develop'
corrected typo: residue->residual
Closes #324
See merge request rdm-software/saqc!422
commit de84a082a364c84ef47fe63f66341e7842fa7f40
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 14:02:42 2022 +0100
corrected typo: residue->residual
commit 9caad54bb7b2de172ddbe0fd1dc49700a5314257
Merge: ba84d441 c87e56f5
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 14:00:39 2022 +0100
Merge branch 'docDecorator' into 'develop'
Doc decorator
See merge request rdm-software/saqc!420
commit c87e56f5bb8d68853ee1dea52b5f7d8670d03304
Merge: 75395d72 e6b5c37e
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 13:36:22 2022 +0100
Merge branch 'develop' into docDecorator
commit f148d3f4a12d42737dd1001fadab3bb1a647169e
Merge: 56c4d0f6 e6b5c37e
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 13:24:10 2022 +0100
Merge branch 'develop' into globalKwargDocs
commit 56c4d0f6991bc3423af48ec15641e817b9bc1a37
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 13:19:55 2022 +0100
checked out licenced versions of autogenerated files from develop
commit e60bf247985b3c6e4c55f9c8c4bc048280f803e6
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 13:07:35 2022 +0100
updated changelog
commit 23122c9d39e77a97291330693767efc8b688210f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 13:06:10 2022 +0100
added global kwargs doc page
commit ba84d44141d8498870b2e62cadb6d65608b4dde9
Merge: e6b5c37e 76a326bb
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 10:45:59 2022 +0100
Merge branch 'crossStatisticsFixes' into 'develop'
Cross statistics fixes
Closes #302 and #217
See merge request rdm-software/saqc!424
commit 76a326bbc7057cd70a857fd83da30588c3933f5a
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Feb 14 10:45:59 2022 +0100
Cross statistics fixes
commit e6b5c37e23adfdd9d955237039dd46cb4a8a1110
Merge: 0a9d11db 9c90767b
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Feb 9 15:22:54 2022 +0100
Merge branch 'schueler/saqc-develop' into 'develop'
Update dependency versions to work with Py3.10
See merge request rdm-software/saqc!425
commit 9c90767b21a624be5a76b11df833272b2dcbbc62
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Feb 9 15:22:54 2022 +0100
Update dependency versions to work with Py3.10
commit 6bb304496bade1bca86300d266d8a669553d7fd8
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 14:05:07 2022 +0100
doc nicemaking
commit f55d2ca5ddff26db3cd005db08663e63688bfa71
Merge: 87f6179d 0a9d11db
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 13:41:10 2022 +0100
Merge branch 'develop' into globalKwargDocs
commit 87f6179dcc2709eccb91f4fdef6cbf16264bfda2
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 13:40:22 2022 +0100
wrote unflagging example
commit 0a9d11dbcdd698f5351846a1bc1994995f15f3b5
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 13:19:24 2022 +0100
level bug fixed
commit 8fafcd78005426d3bcd06f7336c1f5d8dfbafafe
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 13:09:23 2022 +0100
more doctext
commit 75395d725532d7486fde3d5dc6f9394e76c81f04
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 11:58:20 2022 +0100
modified changelog
commit d8a7af678b84aa927cbf1ed6121e6bf2a276061d
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 11:56:18 2022 +0100
generic doc library moved to saqc.lib.docurator / modified imports
commit a04f94cde4a95d0005e7b75a05bb2c7cfdb946ab
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 11:48:55 2022 +0100
docdocdoc
commit 28225863d14dc974ca97f7e28ca7e643ce1858e2
Merge: e5d357c3 ff3fb152
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 11:19:19 2022 +0100
Merge branch 'develop' into globalKwargDocs
commit e5d357c3678c1cf3b269b508ce67a4ce1ae5669a
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 11:18:11 2022 +0100
documented some more
commit ff3fb1521412e3448ff152e9f5351cb94afc98b6
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 10:02:12 2022 +0100
minor bfx in plot/changelog updated
commit 230eaeb7ae7ff8ed3999243f5691a806a4e90817
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 09:21:46 2022 +0100
global kwargs doc
commit 2f824cf668adf5a0f433494c118fb475684e946e
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 9 07:26:38 2022 +0100
..
commit 61395f933bf0589601585eb1111c9f7f0eb99709
Merge: 32b528e7 f54b931c
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Feb 8 12:28:38 2022 +0100
Merge branch 'contributingTypoLanguageFix' into 'develop'
fixed typos/language in CONTRIBUTING.md
See merge request rdm-software/saqc!421
commit f54b931cb8ab931054e8cd34dd5a8ee087c381d3
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Feb 8 12:28:38 2022 +0100
fixed typos/language in CONTRIBUTING.md
commit 2a9daad70dacd1d86fafa3ca6632426f16da5bfe
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue Feb 8 09:33:40 2022 +0100
okdok
commit a707c9668a5428daf8346a707a563be638235458
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Feb 8 09:32:50 2022 +0100
rm MC leftover
commit bd639178c808e3c2ce25f035df019b88018390cb
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Feb 8 09:27:24 2022 +0100
fixed typo in func name
commit 45c31146a1c599cdbf31d0624bc177672fcfda83
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Feb 8 09:20:27 2022 +0100
black applied
commit 40692f15325aef6ce817ff87cacf7aab92aa5e84
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Feb 8 09:20:07 2022 +0100
camelcased template funcs
commit ea07f9643d9fd28a4fa5250d2a3e0386a359f716
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue Feb 8 09:13:45 2022 +0100
Apply 1 suggestion(s) to 1 file(s)
commit 74280ae32448e5ebb044d52b3e6e920785cc9007
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Feb 3 17:26:51 2022 +0100
rm comments
commit ecc4accca4465523c2c49b8c1c209545bfab0a76
Author: Bert Palm <bert.palm@ufz.de>
Date: Thu Feb 3 17:09:38 2022 +0100
Apply 1 suggestion(s) to 1 file(s)
commit 967ed606895428d4a56b1617a9730475c3188dac
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Feb 3 17:07:41 2022 +0100
removed comments/renamed parameters
commit 22157f759eb6e249a21c211bb4a561f87a588a30
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Feb 3 12:23:05 2022 +0100
escaped invalid escape sequence trigger
commit 5d8c7008dd7e5fb6a19ad1361e3c596f78b516f8
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Feb 3 11:49:27 2022 +0100
black applied
commit 7d5e745f4f06b17f5a57a9898a0973a9a099e6b1
Merge: 93b5520b 32b528e7
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Feb 3 11:48:48 2022 +0100
MR solved
commit 93b5520b6a2fd2fbc4921e011d22b4143994f748
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Feb 3 11:46:58 2022 +0100
updated changelog
commit 4a44561066c575a2230d4faf2e1bf76534f6b1e8
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Feb 3 11:45:20 2022 +0100
added licence notes
commit 26e07005450ce1a0c41b7ee229892cde01cecab2
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Feb 3 11:43:31 2022 +0100
modified/simplified makefile
commit 3dd5ad35fd84f07515f779485e8374c29a7f6372
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Feb 3 11:41:37 2022 +0100
removed now superfluent hack scripts
commit 5bb83e88c0f6f509d9ff7824471ee9183c6a4258
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Feb 3 11:35:14 2022 +0100
added template.py to doc/integrated decorator based methods documentation
commit 32b528e72217c637540f36a4cfa2615d48cc7d9f
Merge: 9060a0d2 28273aaf
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed Feb 2 09:55:25 2022 +0100
Merge branch 'flagOffsetFix' into 'develop'
Flag offset fix
See merge request rdm-software/saqc!419
commit 28273aaf594c5b67f94e53a93b8ae714d9eb1bb1
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed Feb 2 09:55:25 2022 +0100
Flag offset fix
commit 50f81ce0ecf7c36673ecd800d8cb735ca12aff64
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Feb 2 09:53:41 2022 +0100
added doc decorator to core.modules
commit 9060a0d2c737587a1585d0662631d67b25b598a8
Merge: 6fca4e32 6fdd22e1
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Jan 24 16:12:12 2022 +0100
Merge branch 'morePlottingKWARGS' into 'develop'
More plotting kwargs
See merge request rdm-software/saqc!409
commit 6fdd22e16b6bfe1de6993add26439e072749464c
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Jan 24 16:12:12 2022 +0100
More plotting kwargs
commit 6fca4e325a79efb1edefd4d0aafa2fe68bc786b4
Merge: 7a2ad33a a13a947f
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jan 21 17:42:49 2022 +0100
Merge branch 'doi' into 'develop'
DOI
See merge request rdm-software/saqc!413
commit a13a947ffc750815b141fe9172293c2f81e031c0
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jan 21 17:42:49 2022 +0100
DOI
commit 7a2ad33ae87923dc64d1030fa3d2f60d6774a613
Merge: 6d64095c 36e74813
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jan 21 12:07:18 2022 +0100
Merge branch 'bestflei-develop-patch-10556' into 'develop'
Update CHANGELOG.md
See merge request rdm-software/saqc!412
commit 6d64095c8e8fe0c4b41380f22dea71015076ca35
Merge: 4fae3d6a c0ba73db
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Jan 21 10:30:16 2022 +0100
Merge branch 'bestflei-develop-patch-97070' into 'develop'
Add user support mailing list to README.md
See merge request rdm-software/saqc!411
commit 36e748134ecc08c56a87a37df1820e049ce92fae
Author: Norman Ziegner <norman.ziegner@ufz.de>
Date: Fri Jan 21 10:30:13 2022 +0100
Update CHANGELOG.md
commit c0ba73dba97189eeaa3cbb726987158d50cbaff2
Author: Norman Ziegner <norman.ziegner@ufz.de>
Date: Fri Jan 21 10:16:59 2022 +0100
Update README.md
commit 4fae3d6a8b97705cedfdabe244fca37e99d34d7d
Author: David Schaefer <david.schaefer@ufz.de>
Date: Mon Jan 10 12:54:21 2022 +0100
[FIX] route config function calls through 'saqc.core.modules' to
preserve funcion defaults.
commit 014511efe90e3c5b2df40735e6c03cdd3320262a
Merge: 67407651 e0a499fc
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Jan 10 10:21:50 2022 +0100
Merge branch 'dependabot/pip/hypothesis-6.34.1' into 'develop'
Bump hypothesis from 6.29.0 to 6.34.1
See merge request rdm-software/saqc!403
commit e0a499fcbefa4bd4add26ddca682c26a5277b3af
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Mon Jan 10 10:21:49 2022 +0100
Bump hypothesis from 6.29.0 to 6.34.1
commit 6740765183259f712f97c3c70aeed4cca5cb9a4e
Merge: 10bb1a48 8af3e769
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Jan 10 10:15:42 2022 +0100
Merge branch 'dependabot/pip/matplotlib-3.5.1' into 'develop'
Bump matplotlib from 3.5.0 to 3.5.1
See merge request rdm-software/saqc!404
commit 10bb1a48180fa6cac591ddab5c7db90842bc68e1
Merge: 4fd0801c 4df38c09
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Jan 10 10:15:31 2022 +0100
Merge branch 'dependabot/pip/pandas-1.3.5' into 'develop'
Bump pandas from 1.3.4 to 1.3.5
See merge request rdm-software/saqc!406
commit 4fd0801c8283c80283497b7f6ac9fcf810e89e58
Merge: 5b1b39c9 dae4126b
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Jan 10 10:15:20 2022 +0100
Merge branch 'dependabot/pip/scikit-learn-1.0.2' into 'develop'
Bump scikit-learn from 1.0.1 to 1.0.2
See merge request rdm-software/saqc!407
commit 5b1b39c98780557ce35da28952d2949a9264608d
Merge: cf034714 e90d447a
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Jan 10 10:15:11 2022 +0100
Merge branch 'dependabot/pip/typing-extensions-4.0.1' into 'develop'
Bump typing-extensions from 4.0.0 to 4.0.1
See merge request rdm-software/saqc!408
commit e90d447a30a16ad9c95ee4f557b8fac6afdf763a
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Sat Jan 1 01:06:04 2022 +0100
Bump typing-extensions from 4.0.0 to 4.0.1
Bumps [typing-extensions](https://github.com/python/typing) from 4.0.0 to 4.0.1.
- [Release notes](https://github.com/python/typing/releases)
- [Changelog](https://github.com/python/typing/blob/master/typing_extensions/CHANGELOG)
- [Commits](https://github.com/python/typing/compare/4.0.0...4.0.1)
commit dae4126bab80e12195e6889fff7d0e292580a6f1
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Sat Jan 1 01:05:58 2022 +0100
Bump scikit-learn from 1.0.1 to 1.0.2
Bumps [scikit-learn](https://github.com/scikit-learn/scikit-learn) from 1.0.1 to 1.0.2.
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](https://github.com/scikit-learn/scikit-learn/compare/1.0.1...1.0.2)
commit 4df38c09345fbe9b070c00b547456bde7898ad17
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Sat Jan 1 01:05:51 2022 +0100
Bump pandas from 1.3.4 to 1.3.5
Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.4 to 1.3.5.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.3.4...v1.3.5)
commit 8af3e76972cb0ce845d64c29cf7eed8b1823316f
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Sat Jan 1 01:05:34 2022 +0100
Bump matplotlib from 3.5.0 to 3.5.1
Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.5.0 to 3.5.1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.5.0...v3.5.1)
commit cf0347142dbd93a2cd0bbe56b29d6e12297e242c
Merge: a4bd45e5 df1e811e
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Dec 23 14:21:48 2021 +0100
Merge branch 'fix-misspelled-resources' into 'develop'
Fix typo in resources
See merge request rdm-software/saqc!399
commit a4bd45e58e46e676fd9c85a62af4de766bdf8ca0
Merge: 8cec45af f96b3b04
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Dec 22 14:51:01 2021 +0100
Merge branch 'update-LICENSE.md' into 'develop'
Update LICENSE.md
See merge request rdm-software/saqc!402
commit f96b3b04713015563dd800856fce4a3eb5ef8ddc
Author: Norman Ziegner <norman.ziegner@ufz.de>
Date: Wed Dec 22 11:24:04 2021 +0100
Update LICENSE.md
Signed-off-by: Norman Ziegner <norman.ziegner@ufz.de>
commit df1e811e719136d6bc449025136f87fdca396128
Author: Norman Ziegner <norman.ziegner@ufz.de>
Date: Wed Dec 22 10:43:24 2021 +0100
Fix typo in resources
Signed-off-by: Norman Ziegner <norman.ziegner@ufz.de>
commit 8cec45aff68b5e59c66f20776ec09b50b45c116a
Merge: 79ff5ae5 aa0be864
Author: Norman Ziegner <norman.ziegner@ufz.de>
Date: Wed Dec 22 10:57:49 2021 +0100
Merge branch 'add-copyright-information' into 'develop'
Add copyright and licensing information
See merge request rdm-software/saqc!397
commit aa0be8647e82233ab352925d2b64a2e174bd3aac
Author: Norman Ziegner <norman.ziegner@ufz.de>
Date: Wed Dec 22 10:57:49 2021 +0100
Add copyright and licensing information
commit 79ff5ae5d588b148561e3233efba920765eb23ae
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 22 04:38:41 2021 +0100
fixed even more paths
commit 651419c4866383ce875cb06ddbf176ba2bf471df
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 22 04:32:20 2021 +0100
fixed config file
commit 31d6e624d87323a9ccd90235ab6cecb6eba41574
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 22 04:24:28 2021 +0100
fixed paths
commit 5bbbd1b7fadcd38846859bd85a8dfab321194077
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 22 04:17:53 2021 +0100
removed test_packaged test (already tested by doctest)
commit 9becb87842ccc3a1b2936c0d5210f3457f067df0
Merge: 36f0a295 f296948e
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 22 04:12:50 2021 +0100
MCs solved
commit 36f0a29526353bb58284f5ff27283fc486d650c5
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 22 03:59:00 2021 +0100
fixed plot-not-occuring bug in TutorialCLI
commit 8f43c4ddf5b1813f0e50da72512e03085567abde
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 22 03:46:31 2021 +0100
aligned MV-tutorial notion with saqc tech note
commit f296948e39ff895765e4299aba4b8e8c258987d5
Merge: a634108e c744e2d3
Author: David Schaefer <david.schaefer@ufz.de>
Date: Mon Dec 20 17:55:40 2021 +0100
windows related fixes
commit a634108e67fc29287ecca67c7572da3c55bd83d2
Author: David Schaefer <david.schaefer@ufz.de>
Date: Mon Dec 20 17:02:47 2021 +0100
release 2.0.1 preparations
commit 29e1bb99a36b9210540fd197dbecce0e46d3c6c7
Merge: 80ee4605 fcad49cd
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Dec 20 12:46:40 2021 +0100
Merge branch 'plottingKeywords' into 'develop'
Added ax kwargs to SaQC.plot signature/removed stat-related parameters
See merge request rdm-software/saqc!398
commit fcad49cd03fa56660759e44f1c0a2a5d3c0ebe83
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Dec 20 12:46:39 2021 +0100
Added ax kwargs to SaQC.plot signature/removed stat-related parameters
commit 80ee4605b7e91737def05dc6fcfba36ba61e318e
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Dec 20 09:26:34 2021 +0100
README.md changes
commit 9202be36932768f1135daba548d449fbbb9b836d
Merge: 9a025a76 9b33c242
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Dec 20 09:25:15 2021 +0100
Merge branch 'geyr-develop-patch-35824' into 'develop'
Update README.md - Added sections Changelog, Contributing, Acknowledgements,...
See merge request rdm-software/saqc!390
commit 9b33c2426923255aeed919266aca109863e448bb
Author: Ronny Gey <ronny.gey@ufz.de>
Date: Mon Dec 20 09:25:15 2021 +0100
Update README.md - Added sections Changelog, Contributing, Acknowledgements,...
commit 9a025a76691a4cd18c329813d7c7931303216a4e
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 17 15:59:15 2021 +0100
Add authors to CITATION.cff
commit 4126b47463526419965ea79e50a9ca1af5d82b71
Merge: 66e5bb41 fb061e31
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 17 15:56:20 2021 +0100
Merge branch 'develop' into 'develop'
added CITATION.cff
See merge request rdm-software/saqc!396
commit fb061e313311c482c5e44c4fd83550d199c7a13d
Author: Ronny Gey <ronny.gey@ufz.de>
Date: Fri Dec 17 15:56:19 2021 +0100
added CITATION.cff
commit 66e5bb4140305f70c18f1b169886b49eee5a1c21
Author: David Schäfer <david.schaefer@ufz.de>
Date: Mon Dec 13 12:31:38 2021 +0100
Update CHANGELOG.md
commit 1e702d1a785068b1dfef0c37327b71e8ba589e22
Author: David Schaefer <david.schaefer@ufz.de>
Date: Mon Dec 13 10:01:08 2021 +0100
Ã[FIX] custom registered functions overwrite built-in functions again
commit b4f6196f83c50a800f932500fe4a700e0bceb54d
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Sat Dec 11 12:49:06 2021 +0100
Update TutorialCLI.rst
commit 9ef7c9b4cb0921e09bbf69f98aa73db29484a64a
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Sat Dec 11 12:27:24 2021 +0100
checked out doc (CLI tutorial)
commit 5853405cee9acbbe9646cf32402e58d48c0e2c4b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Sat Dec 11 11:58:10 2021 +0100
making tutorial
commit 47f88a2a63bf2b0d97e0ccc33e7ee661c7877dd3
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Sat Dec 11 10:30:55 2021 +0100
made clean
commit e45a593e37bb852e3fd9a3c0b22f8ed4ba5d300a
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Sat Dec 11 10:30:38 2021 +0100
updating the CLI tutorial
commit 78a26398a56afc1baac59542c818e070197c0823
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 17:26:13 2021 +0100
made the out-of-date-note visible
commit 9426ba9dd1363dc665577df619257ac8c6d4441b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 17:16:05 2021 +0100
added final MV flagging doc to doc
commit c4ddb631d88a1904aacc2970fe791c47806b1daa
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 17:11:51 2021 +0100
MV flagging tutorial finished
commit 9b1cb8af5519f2da2317c959ee5194d30a73d074
Merge: f1f513f8 d2227e6c
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 15:39:43 2021 +0100
Merge branch 'develop' into cookBux
commit f1f513f853a45f76dacf251008f50dd80318758f
Merge: 84981365 557fc4e6
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 15:37:48 2021 +0100
Merge branch 'develop' into cookBux
commit d2227e6c2906f1262ba90917d79bb4ca08937063
Merge: da91c916 bb38dfb9
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 10 15:15:46 2021 +0100
Merge branch 'generic3' into 'develop'
generic improvements
Closes #290 and #289
See merge request rdm-software/saqc!395
commit bb38dfb90a468ea8702e847079479a6f53a9e48d
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 10 15:15:45 2021 +0100
generic improvements
commit da91c9162d971dd416d9bbf942a15d7c432c98b0
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 10 14:42:40 2021 +0100
Update CHANGELOG.md
commit 194c0d5d2eee84cf236950e9a8d521f782f4011a
Merge: 76ce086a ed562221
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 10 14:39:10 2021 +0100
Merge branch 'modifySomeRegisters' into 'develop'
modified resampling decorators
See merge request rdm-software/saqc!391
commit 76ce086af60a83b8625cf041fae723c4b6400036
Merge: b09e93f4 b32bb968
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 10 14:37:36 2021 +0100
Merge branch 'mo-targets' into 'develop'
Make more functions source-target ready
See merge request rdm-software/saqc!392
commit b32bb968e7a591f25dcf72cdb51db0707a69c1e2
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 10 14:37:35 2021 +0100
Make more functions source-target ready
commit b09e93f416bad37fb370f9187af76a1ab79192c7
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 10 14:10:08 2021 +0100
Update CONTRIBUTING.md
commit 7a9d4d1c96d73c02a279d4698318e175203982f7
Merge: 557fc4e6 ed786082
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 14:01:16 2021 +0100
Merge branch 'isflaggedWrapper' into 'develop'
Isflagged wrapper
See merge request rdm-software/saqc!394
commit ed786082f0590d064cb43ab5b30031cdf20ab925
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 14:01:15 2021 +0100
Isflagged wrapper
commit 84981365d76968ac188cc18933fe044a99dddd13
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 14:00:27 2021 +0100
updated the how-to-doc
commit 557fc4e66828474b21065ffecde29f3b9b1e892e
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 13:51:30 2021 +0100
checked out doc stuff
commit 09369d596e14e6d9a5912d5562892e7a2ff4db2c
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 13:49:36 2021 +0100
combined make doc doctest and cleanup
commit f02a667ba2d2293cee0e28ab22a99b154063c90a
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 11:18:47 2021 +0100
made clean
commit 19279e143d1697410bb7a97ae12545c73ed6c44d
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 11:18:20 2021 +0100
checked out updated documentation/removed sphinxdoc
commit 3dddc715e9f2ec86a85f8807bf13a581ce60c102
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 11:09:09 2021 +0100
included coredoc folder removal in make clean procedure
commit e66cd28dcaf7b3df51898d65f56acf94e3105159
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 10:53:37 2021 +0100
added configfile teting to MV flagging cookbook
commit 7b2b66a2ce0c68b3be2d6b3200b7a0f0b34d029b
Merge: 967c6db6 eeef1499
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 07:48:03 2021 +0100
MC solved
commit eeef1499e6b49adba29c3105d4d4600d5584394c
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 07:44:01 2021 +0100
black applied
commit ea0123bb0025b054f5d043170906e64d52d7b15d
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Fri Dec 10 07:36:39 2021 +0100
Added docstring Todo
commit 9ac28ee2e9f16911ba0c33f99cd905a179e37eca
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 18:04:06 2021 +0100
black applied
commit be683ef0e4a3911f2b147f2644867d37cb144456
Merge: 52300b58 7a15bb35
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 16:14:23 2021 +0100
MR solved
commit 52300b585c38c44f8c3bb35881d2c10eda347434
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 16:12:40 2021 +0100
made clean
commit 1d609fc61f2b966020448ac4135aacff8086d35a
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 16:12:21 2021 +0100
checked out updated documentation
commit 967c6db6a3e98260f2f4361620a3a41594eb1448
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 16:10:59 2021 +0100
black
commit 9d7f9b33616cef20783df86fb17298176936a197
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 16:08:46 2021 +0100
MV cookbook added
commit 7a15bb35746a5cb984a850ea45df42d37f411e82
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Dec 9 14:40:44 2021 +0100
fix obsolete now broken imports
commit db8244efca69130b6c00645a58fd8d12439d74ce
Merge: 07d5251f 6765f1fc
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Dec 9 14:33:37 2021 +0100
Merge branch 'develop' of https://git.ufz.de/rdm-software/saqc into develop
commit 07d5251fa578f389ac9a3896f8135dc1f592d708
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Dec 9 14:32:11 2021 +0100
CHANGELOG updates
commit 0c3accd77bb866a8e035ee81752ad963d60c2756
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Dec 9 14:19:25 2021 +0100
remove function flagDriftFromScaledNorm
commit 0d2ef036fcc32264093827ecba318588e68740be
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Dec 9 14:11:32 2021 +0100
rename function flagCrossStatitic -> flagCrossStatitics
commit 6d3464c5c9aca409596202e9371205f4fb7dff72
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Dec 8 17:56:13 2021 +0100
added some function writing documentation
commit 48f28bf09ab14ad55267cc340f01ad37330974e2
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Dec 8 16:02:38 2021 +0100
[FIX] drop existing targets instead of skipping the copy operation
commit 590bcde4c82eadc54d2ca05138d630f9f9f605ed
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Dec 8 14:52:40 2021 +0100
[FIX] handle non-existing targets
commit 664a965a3ff3d1fbbf23888dcb33cdac4ba5bed8
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Dec 8 14:51:43 2021 +0100
[FIX] don't fail if a squeeze argument is not given
commit 949113775b24d87e60d7047b26d73bfb8c9c7338
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 13:21:55 2021 +0100
checked out develop
commit 6765f1fc4b1134e3be0e13a92fbfa71a7ff0e1ad
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 13:17:31 2021 +0100
black
commit a58b0d39349618adb771f4c173929791e0ca5b6b
Merge: 91048c8c 41aefda4
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 12:52:25 2021 +0100
Merge branch 'develop' of https://git.ufz.de/rdm-software/saqc into develop
commit 91048c8cc840ba676c2f8ec5cc92f9bf317ee44c
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 12:52:07 2021 +0100
hotfix->kNNscores now handles 1-elements list in the target parameter
commit 3e20b867223eb750a03359d3d17ceed424e9d43c
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 9 07:12:29 2021 +0100
progress
commit 41aefda42ed491be80b2539b7cc96d361d7dba87
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed Dec 8 17:28:57 2021 +0100
Update CHANGELOG.md
commit 00bdb3f4aff2dd6763c77e6ba3761f2342b718f4
Merge: 3da74aeb 1aa24094
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 8 17:24:09 2021 +0100
merged develop
commit 1aa2409400cee39b8feb5049373a734332a3bb28
Merge: c1a79482 f6900fec
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 8 17:21:50 2021 +0100
Merge branch 'develop' of https://git.ufz.de/rdm-software/saqc into develop
commit c1a794828d09c0236d92f152f344e4b3b38d1bb9
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 8 17:21:24 2021 +0100
made clean
commit a95901d43cf672508e2926a0ff77d0ebef4771e8
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 8 17:20:59 2021 +0100
modified gitlab-ci.yml to generate documentation from develop only
commit 3da74aeb1285e0d1a53424ca425464142ea1843b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 8 17:13:11 2021 +0100
cooking content
commit ed562221b57d1349cb5d83aaa0e6105c6cfb7e5a
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 8 15:56:09 2021 +0100
modified resampling decorators
commit d39334206396a1636e14cd03730ad8b35df784e2
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 8 14:10:15 2021 +0100
multivar cookbook
commit 1906c7f02775a70b994e1fbe9917117eedf1d736
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Dec 7 13:00:52 2021 +0100
checked out develop-saqc
commit f6900fecdd6ca132dc5d78e561b9a4e6e135619a
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Dec 7 12:59:35 2021 +0100
fixed image links in readme.md to point at new sphinxdoc folder
commit 6979f47249a716ab305f635afe597f09d35e085d
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Dec 7 12:57:16 2021 +0100
renamed/updated sphinx doc content to make it available
commit 9289dda23cdd946c115d00c2b6d1c67c2b02bc65
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Dec 7 12:52:49 2021 +0100
commit
commit d04ba358502c90a5e6e04fa6bde0f56c7037b9bc
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 12:31:22 2021 +0100
Update CHANGELOG.md
commit e9c870d517d5bb2aa9556eee47535b6aeee794ac
Merge: ebc90b6d 1f07fa47
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 12:00:16 2021 +0100
Merge branch 'dependabot/pip/matplotlib-3.5.0' into 'develop'
Bump matplotlib from 3.4.3 to 3.5.0
See merge request rdm-software/saqc!376
commit 1f07fa47d4fc94e56fb206abf2ff991f96064df1
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Fri Dec 3 12:00:16 2021 +0100
Bump matplotlib from 3.4.3 to 3.5.0
commit ebc90b6de2f6b4b9e5f63b3cf9b9312f8b5c5542
Merge: 307e4d5c 1689c304
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:55:26 2021 +0100
Merge branch 'dependabot/pip/pyarrow-6.0.1' into 'develop'
Bump pyarrow from 4.0.1 to 6.0.1
See merge request rdm-software/saqc!379
commit 1689c3045a4d04198829f2f2c4923f0705ab82b1
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Fri Dec 3 11:55:25 2021 +0100
Bump pyarrow from 4.0.1 to 6.0.1
commit 307e4d5caf036ceb92c8a7a15134765bfb0aafdb
Merge: 37b096eb b8ad7914
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:53:59 2021 +0100
Merge branch 'genericfix' into 'develop'
[FIX] flagGeneric: don't fail on empty data
Closes #271
See merge request rdm-software/saqc!389
commit b8ad7914f29f2aa558a457d0e045e6371862ca1b
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:49:21 2021 +0100
[FIX] flagGeneric: don't fail on empty data
commit 37b096eb4e84f75b8f5d6da7fdf7453d9651f789
Merge: b28ff457 dc296083
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:43:15 2021 +0100
Merge branch 'dependabot/pip/scipy-1.7.3' into 'develop'
Bump scipy from 1.7.1 to 1.7.3
See merge request rdm-software/saqc!382
commit dc296083f70895e87bed80b8397765f65ed0b7f6
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Fri Dec 3 11:43:15 2021 +0100
Bump scipy from 1.7.1 to 1.7.3
commit b28ff45701701b997aee17db96cc2cf6104a4e8f
Merge: b1984f5d b0cca554
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:38:52 2021 +0100
Merge branch 'dependabot/pip/python-dateutil-2.8.2' into 'develop'
Bump python-dateutil from 2.8.1 to 2.8.2
See merge request rdm-software/saqc!384
commit b0cca554c509a08518d1187fc2d2248f3de85119
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Fri Dec 3 11:38:52 2021 +0100
Bump python-dateutil from 2.8.1 to 2.8.2
commit b1984f5d2750c743a2360bfe936c527d05be9edb
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:35:20 2021 +0100
Fix error message of copyField
commit 699d58d413171d5734cf2db77ccff52f2e9863f5
Merge: 6cd0a208 72ba4cf2
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:31:59 2021 +0100
Merge branch 'dependabot/pip/numba-0.54.1' into 'develop'
Bump numba from 0.54.0 to 0.54.1
See merge request rdm-software/saqc!377
commit 6cd0a208c8449be6644b6ed89102e9870df458d1
Merge: f0a6f7cd 3d77d622
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:31:43 2021 +0100
Merge branch 'dependabot/pip/scikit-learn-1.0.1' into 'develop'
Bump scikit-learn from 1.0 to 1.0.1
See merge request rdm-software/saqc!381
commit f0a6f7cd7c347d80166408e70b64c53f73f79590
Merge: 0e0a7460 60e82cc7
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:31:33 2021 +0100
Merge branch 'dependabot/pip/pandas-1.3.4' into 'develop'
Bump pandas from 1.3.3 to 1.3.4
See merge request rdm-software/saqc!380
commit 0e0a7460e6b7258ab4608f75bfb54d56e90d8453
Merge: 2e6f1228 fd78a634
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:30:50 2021 +0100
Merge branch 'dependabot/pip/hypothesis-6.29.0' into 'develop'
Bump hypothesis from 6.23.1 to 6.29.0
See merge request rdm-software/saqc!375
commit 2e6f12287b456a6205140d2d7c7e2c1f81da9fdf
Merge: b0cdb9be 41b93d69
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:30:42 2021 +0100
Merge branch 'dependabot/pip/click-8.0.3' into 'develop'
Bump click from 8.0.1 to 8.0.3
See merge request rdm-software/saqc!374
commit b0cdb9be281f199bfb7adca9503c266aee6716fe
Merge: 97287636 a979fa62
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:28:25 2021 +0100
Merge branch 'dependabot/pip/typing-extensions-4.0.0' into 'develop'
Bump typing-extensions from 3.10.0.2 to 4.0.0
See merge request rdm-software/saqc!383
commit 97287636603e824b7c1e2b351d81d07b10f8e145
Merge: bd4c74d6 57cb1e3b
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:27:06 2021 +0100
Merge branch 'dependabot/pip/pytz-2021.3' into 'develop'
Bump pytz from 2021.1 to 2021.3
See merge request rdm-software/saqc!385
commit bd4c74d69ef0244998a8334b44d807225ea07a37
Merge: a2acd418 a9ec7db0
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:26:42 2021 +0100
Merge branch 'readme' into 'develop'
README updates
Closes #276
See merge request rdm-software/saqc!388
commit a9ec7db01905002e9594a36ac4a30b67f4c1f4fa
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 11:26:42 2021 +0100
README updates
commit a2acd418a5a430ad6dab1373e7475ca1c8684279
Author: David Schäfer <david.schaefer@ufz.de>
Date: Fri Dec 3 09:40:07 2021 +0100
Absolute image paths in README.md
commit f5c075ab552db3ab42681a5d44da8f6c17238076
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 2 10:23:43 2021 +0100
fixed CI jamml
commit 708773252a73b7e5638968e887eb7fb119116b95
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 2 10:19:02 2021 +0100
added FuncsTOC/inserted saqc.SaQC cross references
commit c8ff7cca4d11f4ed354a2809838d2a0904a776de
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 2 09:48:55 2021 +0100
black
commit 53f483b064e248fa1c59e0c315e60fb77c155c33
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 2 09:47:53 2021 +0100
added funcSummaries folder parameter
commit 45efd8b41173023eb524475d31ad00adc13b9ab5
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 2 09:44:01 2021 +0100
clean up
commit ce3f4ee9caaa8074c66cfc1300ce3664eca6d4b0
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 2 09:43:01 2021 +0100
black
commit ba0c90e3aa1e0b936cfd3c69d62cba9a096975f1
Merge: a82ec2ed bdbd2638
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 2 09:35:03 2021 +0100
checkoed out dcstring fix from evelop
commit 91b6eb291e9598d245f6396f1af23307b482f8c2
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 2 09:27:47 2021 +0100
solved invalid esc syntax
commit a82ec2ed24de294f3d8599fcfc317c8b04100ab5
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 2 09:23:44 2021 +0100
dcfixes
commit fabdda9eaf091cfb709679b762d51ca4d8044840
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Dec 2 00:13:01 2021 +0100
solved some warnings
commit 01034fedf5d2c9321f72db01eee2f0e3ce2ef226
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 1 23:06:07 2021 +0100
saqc.SaQC properly documented
commit 1be015380a22a260b6fda3e1298df063d2ca387d
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 1 21:04:40 2021 +0100
omitting fake function modules/generating core doc in sphinx subfolder now
commit bdbd263867d08161f2559e5e81e3f1ab39a7b1c6
Merge: 2aed4a32 2f436fa5
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Dec 1 15:56:53 2021 +0100
Merge branch 'logos' into 'cookBux'
Update the logos
See merge request rdm-software/saqc!387
commit 2f436fa599ec80b4ba4084f730b13e8fb185d1a5
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Dec 1 15:56:52 2021 +0100
Update the logos
commit d324d6d760dbca3dedb2aab98f555cd7e4ff8f73
Merge: ac13a82e 71f3e9b4
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Dec 1 15:56:34 2021 +0100
Merge branch 'logos' into 'develop'
Update the logos
See merge request rdm-software/saqc!386
commit 71f3e9b4e0e076597ad91747ab18401045ba21e4
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Dec 1 15:56:34 2021 +0100
Update the logos
commit ef19f5e72ae017c350ff7f5071edd9e4400b7399
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 1 15:40:35 2021 +0100
simplifications
commit ac13a82ea87535675fb07fbaebd23027d5e0ccbb
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 1 14:29:17 2021 +0100
docstring fixes
commit 91faf6669a26fdec62d286c451d73757d30d3c20
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 1 14:27:32 2021 +0100
dc strings fiixed
commit 2aed4a3295dc9b1e77579331f0732c52aa9189b0
Merge: 2710bb9f 4b3563bf
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 1 14:12:34 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit 2710bb9f3d976a67d3f126977415cfa461356c15
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Dec 1 14:12:11 2021 +0100
clearing warnings / genericFunctions.rst moved to doc folder
commit d0ba4f22c25b0e4a512789154974f0a98ce937e3
Merge: 5b0cfdc9 ddfe2106
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Dec 1 10:44:04 2021 +0100
Merge branch 'fix-markdown-link-in-contribution.d' into 'develop'
Fix markdown link in CONTRIBUTING.md
See merge request rdm-software/saqc!373
commit 5b0cfdc9ea90a5bb167459858bd97019fdb048fb
Merge: a6d7f9a6 75347e25
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Dec 1 10:03:25 2021 +0100
Merge branch 'versionate' into 'develop'
fix version mess
Closes #273
See merge request rdm-software/saqc!371
commit 57cb1e3bfec6321bed2faf65a6385fe2179629fa
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Wed Dec 1 01:06:21 2021 +0100
Bump pytz from 2021.1 to 2021.3
Bumps [pytz](https://github.com/stub42/pytz) from 2021.1 to 2021.3.
- [Release notes](https://github.com/stub42/pytz/releases)
- [Commits](https://github.com/stub42/pytz/compare/release_2021.1...release_2021.3)
commit a979fa62eaf32f719d3a694557a0833de9ac763a
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Wed Dec 1 01:06:12 2021 +0100
Bump typing-extensions from 3.10.0.2 to 4.0.0
Bumps [typing-extensions](https://github.com/python/typing) from 3.10.0.2 to 4.0.0.
- [Release notes](https://github.com/python/typing/releases)
- [Changelog](https://github.com/python/typing/blob/master/typing_extensions/CHANGELOG)
- [Commits](https://github.com/python/typing/compare/3.10.0.2...4.0.0)
commit 3d77d62215dac6701438355d4ea9355d87843f51
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Wed Dec 1 01:05:59 2021 +0100
Bump scikit-learn from 1.0 to 1.0.1
Bumps [scikit-learn](https://github.com/scikit-learn/scikit-learn) from 1.0 to 1.0.1.
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](https://github.com/scikit-learn/scikit-learn/compare/1.0...1.0.1)
commit 60e82cc7ed43767dc274237b5a99672b4fb61b2d
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Wed Dec 1 01:05:52 2021 +0100
Bump pandas from 1.3.3 to 1.3.4
Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.3 to 1.3.4.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.3.3...v1.3.4)
commit 72ba4cf28daf4e5713de2be41ae26ca082b736e9
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Wed Dec 1 01:05:30 2021 +0100
Bump numba from 0.54.0 to 0.54.1
Bumps [numba](https://github.com/numba/numba) from 0.54.0 to 0.54.1.
- [Release notes](https://github.com/numba/numba/releases)
- [Commits](https://github.com/numba/numba/compare/0.54.0...0.54.1)
commit fd78a63465815af377c2edef4a142342127f268b
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Wed Dec 1 01:05:17 2021 +0100
Bump hypothesis from 6.23.1 to 6.29.0
Bumps [hypothesis](https://github.com/HypothesisWorks/hypothesis) from 6.23.1 to 6.29.0.
- [Release notes](https://github.com/HypothesisWorks/hypothesis/releases)
- [Commits](https://github.com/HypothesisWorks/hypothesis/compare/hypothesis-python-6.23.1...hypothesis-python-6.29.0)
commit 41b93d69c81959a7dcbe6c52fcb6a4572ace74d6
Author: WKDV Bot <adm-git+wkdv-bot@ufz.de>
Date: Wed Dec 1 01:05:09 2021 +0100
Bump click from 8.0.1 to 8.0.3
Bumps [click](https://github.com/pallets/click) from 8.0.1 to 8.0.3.
- [Release notes](https://github.com/pallets/click/releases)
- [Changelog](https://github.com/pallets/click/blob/main/CHANGES.rst)
- [Commits](https://github.com/pallets/click/compare/8.0.1...8.0.3)
commit ddfe2106cd6906b0a1b9716cd244ea96453bf822
Author: Norman Ziegner <norman.ziegner@ufz.de>
Date: Tue Nov 30 13:52:04 2021 +0100
Fix markdown link in CONTRIBUTING.md
commit 4b3563bfb44541ffa0963a3ec8fb3e9011fdcac4
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Nov 30 08:05:18 2021 +0100
Update .gitlab-ci.yml file
commit debb5e19c574a66c9207fead4dddfb6049f7abbe
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Nov 30 08:03:34 2021 +0100
Update .gitlab-ci.yml file
commit b4c9b5c1a4c3b8251609fb5a6befb1bb30e1dbd8
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Nov 30 07:54:51 2021 +0100
made clean/added all folder search for automodsumm removal
commit eaada95de73d4a788f4cd51a8a2d43c3f9602a68
Merge: a298b795 a6d7f9a6
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Nov 30 07:51:12 2021 +0100
MC solved
commit a298b7952879dcfed19ff9d49a705a92525eb34f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Nov 30 07:46:00 2021 +0100
added a usefull warning
commit c46bcad19427bc0ece6ed88a416424155b5fd0bf
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 14:04:32 2021 +0100
made clean
commit 4c25e566f64d4ba2c5ef8895422a82ee04304146
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 14:04:14 2021 +0100
fixed some hyperlinks
commit 1ad752fff7a75df77b6e10a61f5986b96341ceee
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 13:59:10 2021 +0100
made clean
commit ca7dcf5dca50359fececb988e0f3ceec80d19642
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 13:58:55 2021 +0100
fixed checkout mess
commit 5bdfbe26a19e2b7cf614cda36baa75d70436cf04
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 13:52:28 2021 +0100
checked out saqc
commit a6d7f9a63af94a7cac49414f661fcceb53ba32bb
Merge: 78930a9f 1aaa83da
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 13:49:59 2021 +0100
Merge branch 'snippet_fix' into 'develop'
fix doctest snippets
See merge request rdm-software/saqc!372
commit 1aaa83dadb19eb9fbe85f0f10e76b910f4c3b3ed
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Nov 29 13:49:59 2021 +0100
fix doctest snippets
commit b76b4080579bae6f2bf6cae30f31e6699d9ae69c
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 13:23:52 2021 +0100
black
commit d75de47e557e35dfbed68b47c243ab529a60dd06
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 13:23:28 2021 +0100
added doctest guide/cleaned up doc guide
commit 75347e25dc620f08ee38d5c95309d34ac20a479d
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Nov 29 11:47:22 2021 +0100
- introduced a version.py
- read version.py from setup.py without importing saqc
- set version to 2.0.1
commit c857743707fcca137295b04263c18b70db07c180
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 11:44:36 2021 +0100
black
commit 4210e7168d3950c7c58d2b51c779b53cbc74f202
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 11:44:04 2021 +0100
made clean
commit 72a396a27c6e8426c1751a75fde7507ec0c15d25
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 29 11:43:52 2021 +0100
fixed all the snippets/code examples
commit 1a7b772a4bda61a13b7b52d21d3ad6b42b5597d4
Merge: b96b1fa9 3040aae2
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Sun Nov 28 11:32:50 2021 +0100
MCs solved
commit 3040aae2dce22d1505723a3ad08caf19656b2808
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 26 18:54:47 2021 +0100
cleaned up doctest
commit 5bca71819b8a34156446f1e4cd5629342f729e60
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 26 13:52:53 2021 +0100
clearing
commit 7a2effac6bec3c746c6944d02ef0aba7e903087c
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 26 13:31:28 2021 +0100
made dataregularistaion snippets work
commit 8f18d5dc6dfcc420ac3b655739991a92ecdae1c1
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 26 13:31:14 2021 +0100
made dataregularistaion snippets work
commit db8dc382131814d2478829447d3b05d347ab0661
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 26 12:33:24 2021 +0100
made clean
commit 240440029bae6cf13ceaa06ca43979d978e59097
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 26 12:33:07 2021 +0100
made outlier cookbook doctest conform
commit 5fef77a2d2e605bbebe56ef3e46953247e233c54
Merge: aebb96c7 6b680932
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 26 10:28:21 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit aebb96c7b7f680338453387ca746ef591982a17a
Merge: de4a0e61 aa3544b1
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 26 09:39:21 2021 +0100
MCs solved
commit 6b680932290a51ea0854b58cf49c572329f3d9db
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Nov 26 00:19:22 2021 +0100
cross refernces and fixes
commit 45e929468810c2132d0dd3df4a1517b8251ffdfa
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Nov 26 00:11:38 2021 +0100
update cookbooks
commit 834f5628d3ae51af884ca1b404c88b4e43d7e30e
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 22:29:38 2021 +0100
code fixes
commit 452be5c4a7ae9d369c56cd939bf3059dd6184643
Merge: 087a3bef 0bd7c2a0
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 22:11:16 2021 +0100
Merge branch 'releaseprep' into cookBux
commit 0bd7c2a0ae56d8e22ebfb8228ed4cb667a4f6bce
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 22:10:41 2021 +0100
black
commit 087a3bef2503f1f84837c2744b5375d00ebfd1c1
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 22:09:39 2021 +0100
fixed stuff
commit 85fb44f39c0279e85e27ee9e9c1730b23709223e
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 22:09:31 2021 +0100
fixed references
commit f4973af6b76569532db9443089cf61bab85162a5
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 21:28:38 2021 +0100
fix references in reguaritzation cookbook
commit 5ca6d0a893bd08883b8a2aacc934dbddb1db45f6
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 20:57:43 2021 +0100
fix references in outlierdetection
commit 13f2796b20cadd5d3203f2458a0dc04068db9c6c
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 20:43:50 2021 +0100
moved scripts into seperate folder
commit 1ef08832eefe2ab7f8596874634aea2ead6afe50
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 20:39:04 2021 +0100
removed obsolete *_md folders
commit 65911f8b8d51fe32e9ee8a1c70198a7b8146020c
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 20:35:36 2021 +0100
minor cleanups
commit 5d344cee1e303f038241b9525b369ed051c5f8f9
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 20:34:56 2021 +0100
the skip directive triggers a sphinx-automodapi bug
commit a2bec75853ca76066efc6be1ab286e75bdbf45d1
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 20:34:30 2021 +0100
fix doctsring for removed constants
commit 13cbda4daed25886180d1db474934174a3ae0fdd
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 20:33:51 2021 +0100
remove git relicts
commit d0c2fada24655dc350e6eca694a8062712064d78
Merge: 6bb948c6 829500fc
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 19:54:17 2021 +0100
Merge branch 'develop' into cookBux
commit 6bb948c693669f9d2bc40d157800e74d24474ac4
Merge: 2deb254d 44660285
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 19:51:08 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit de4a0e6175e5907527d65716b0ee1c8841b968fb
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 19:34:22 2021 +0100
made clean
commit 891563a6d94bf3b63f5e2d81b2e472104a10f78f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 19:33:59 2021 +0100
working on cookbooks
commit 2deb254ddf76757f60d16c8a67d3aecd284141ee
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 19:26:07 2021 +0100
rewrite of the intrdocion text
commit 446602856d5a75103efa7777465c1e32c7f52493
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 18:24:38 2021 +0100
index page tidied up
commit 9287f5b3ed5d24f4929b8aa96e72ae732abbaf7d
Merge: e8c9e93e 250ec9d9
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 17:59:51 2021 +0100
MC solved
commit e8c9e93e4b5056c2f7c8c39e63308000b4f1276b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 17:58:34 2021 +0100
clean up
commit 250ec9d91c353b35e5344f9e46629ffd65ada515
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 14:59:40 2021 +0100
Update requirements_sphinx.txt
commit b96b1fa98b56d5f7fb82b5efce91ff3784552afb
Author: Bert Palm <bert.palm@ufz.de>
Date: Thu Nov 25 14:43:48 2021 +0100
Apply 1 suggestion(s) to 1 file(s)
commit d9cc0bbc997b119c33663c7ad22f81d8faec48c2
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 14:41:18 2021 +0100
added saqc obj to index
commit e26d88ba7ba13a4c3b9b8e77958308cb1da3c4fa
Author: Bert Palm <bert.palm@ufz.de>
Date: Thu Nov 25 14:40:14 2021 +0100
Apply 1 suggestion(s) to 1 file(s)
commit 4a4e9f4ecbd07fd04bfe9a772ce4df40aa15483b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 14:35:39 2021 +0100
black
commit 07a5e4aa816762d800268f020563d95547b79752
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 14:30:07 2021 +0100
Apply 1 suggestion(s) to 1 file(s)
commit 455339408c04b512fe9a324e2ccf3a63b7fe6321
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 14:22:28 2021 +0100
black added
commit f383f9f1856112153807e8c92d42595051f2e7e2
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 25 13:45:16 2021 +0100
working on the title text
commit 7fe0648a5d54963e46dd73d5a46e9550c728a4ef
Merge: dccf7ab3 afecd7fa
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 13:42:14 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit dccf7ab3a6096f79a91a52aaf900720a9996dfd6
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 13:41:13 2021 +0100
added Core fake
commit d207d4907cbf8c99f2cce1b46b2c31a9d24041b9
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 25 10:55:20 2021 +0100
data regularisation snippets fixed
commit d1e17419c19861ee2a3f16c101b855f9048af1d8
Merge: 2300da8f 218790d3
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 24 23:34:44 2021 +0100
Merge branch 'develop' into cookBux
commit 2300da8f1d6cddcb7ae9c36d819d346cdd8e78b5
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 24 23:29:02 2021 +0100
installation guide update
commit c39af8f5a4ce81df9bcecac2deebe71d7154aebe
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed Nov 24 22:30:16 2021 +0100
Apply 15 suggestion(s) to 4 file(s)
commit 4f1d598f3296dec0ed1c777dc46b37fd7deca9c8
Author: David Schäfer <schaefed>
Date: Wed Nov 24 22:00:42 2021 +0100
remove conda recipe as pip also works
commit 2a407d4212f8c29525b61d031466916d591f3669
Author: David Schäfer <schaefed>
Date: Wed Nov 24 21:59:08 2021 +0100
not doing paths right at the beginning, will hurt in the long run
commit e145d7ccff86663088104870ebe2d29438e8849d
Author: David Schäfer <schaefed>
Date: Wed Nov 24 21:45:28 2021 +0100
[FIX] windows only integer comparison bug
commit bb47658d5607361e8bbe720c0936b825f735b5a1
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 24 22:00:10 2021 +0100
clanup requirements
commit afecd7fafc3e14d700e65d7bdf3cc48a58b8977c
Merge: 96a42188 4c7a81ef
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 24 17:43:21 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit 96a42188a857d4e2e14be2e0e62eff99e2a1ecdc
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 24 17:42:18 2021 +0100
fix structural errors in docstrings
commit 177b40b17dd9dd5a42d22fe24c49986ad324c169
Merge: 4c7a81ef c341115d
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 24 15:13:42 2021 +0100
Merge branch 'develop' into cookBux
commit 4c7a81ef4237a35d51d988039af495e144dca65b
Merge: c29f40de 8271d1bb
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 24 15:12:57 2021 +0100
MC solved
commit c29f40de2e2cafdf27e9594a3f42b2ee07ce55ee
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 24 15:11:49 2021 +0100
fixed generic snippet bugs
commit b86e59b817f57ed9f640431fe08cd61da483ba5a
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 24 15:10:34 2021 +0100
debugging doc-ed code
commit 3231361e8ea8f01aca9ee456ce07acafc9c581b4
Author: Bert Palm <bert.palm@ufz.de>
Date: Tue Nov 23 01:01:33 2021 +0100
wip
commit 68f466b0257cc022fdf0042114fb2f8b9f06c95c
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Nov 22 23:47:52 2021 +0100
fixed curvefit.py
commit 410a3ea7d2caa08be22b670e92d67eb69fba18a7
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Nov 22 15:58:01 2021 +0100
docstrings in constants.py.
flagConstants now also take int's in window.
commit 96af27c49087af7ee81d0e0c4f685438b1385feb
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Nov 22 15:35:48 2021 +0100
docstrings in changepoints.py
commit 0a4a002fcb0b1ddbb6ce2868411f88fb266f84d9
Author: Bert Palm <bert.palm@ufz.de>
Date: Mon Nov 22 15:07:12 2021 +0100
docstrings in breaks.py
commit 1df6e0d5d1cf1517edc47f168819c129c3492178
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 24 09:57:43 2021 +0100
refernce upcoming version
commit 40d6bfe13cbcb59426eaf86fd0c8004a27c31c09
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 24 09:57:29 2021 +0100
dummy file
commit 14daf9ad1eae42a97cfc8d1c07021900a09bdd97
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 24 09:57:09 2021 +0100
reference upcome version
commit 8271d1bbaffb54da93eeb0431ec837c4ca30c7f4
Merge: ff7088e4 d22728be
Author: David Schaefer <david.schaefer@ufz.de>
Date: Tue Nov 23 09:51:47 2021 +0100
Merge branch 'develop' into cookBux
commit ff7088e4ba19386479c05ab7be20faeef5ecab3c
Merge: cab374e1 a976e4e1
Author: David Schaefer <david.schaefer@ufz.de>
Date: Tue Nov 23 09:45:40 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit a976e4e189073e2ed555effbc7b57c09f80132c2
Merge: 45062d54 9ff02246
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 19 11:32:25 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit 45062d5467e5b174e24f092dea8bc3562710bef4
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 19 11:30:03 2021 +0100
black
commit a0360fd5dfa7f5f3bd3e34ce7d06bc46f405734f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 19 11:29:23 2021 +0100
added and set-up jupyter support
commit cab374e1882cbda89aa5e97d85137e424694ad80
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Nov 19 08:08:00 2021 +0100
stuff I don't know where it is coming from...
commit 9ff0224618e7a26e795a3c4afcb1898e8b8df058
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Nov 19 00:42:12 2021 +0100
black
commit 5468f112d1035cbf1533783c35c32b6eaeba1acb
Merge: e0f00757 5d0c47bc
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Nov 19 00:39:38 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit e0f0075718b4f27bce645594a95eeb61e36b6aea
Author: David Schaefer <david.schaefer@ufz.de>
Date: Fri Nov 19 00:38:20 2021 +0100
logos everywhere
commit d6cb7bf636e88ca41e0dee5b77b8e40a1f34824b
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 18 23:46:59 2021 +0100
title images
commit 5d0c47bc58db1f1c18b44f897584559a88fcdbf7
Merge: a38a71dc fa9b8cc5
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 23:04:27 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit a38a71dc8a0293bdf872cf833f0845e7d8c64be1
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 23:04:01 2021 +0100
aligned snippets to doctest as far as possible
commit ab7e6144aa067a67c58229c015d6ae7524b3bb35
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 18 22:37:36 2021 +0100
added some empty placholder documents
commit fa9b8cc5237e31cb1978061171bf986dd45343b5
Merge: cbf2f8e2 39e740c2
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 18 22:24:39 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit cbf2f8e24afe119ec4d80c801f57f64b7ec66344
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 18 22:23:23 2021 +0100
work on
commit fc39ad434ed5016f31627f515fa48b513b892bda
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 21:33:11 2021 +0100
added doctest global environment
commit 74242e128cf8f04ceb574532ab71718e87fa5f7e
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 18 21:26:50 2021 +0100
added API tutorial
commit 4caecfbb7882abc4ba0d07a5b2d3baf4a592420e
Merge: 5b790950 4df1f908
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 18 20:22:01 2021 +0100
Merge branch 'develop' into cookBux
commit 5b790950b35a16ce0bb5c54428579c7d446980dc
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 18 18:02:17 2021 +0100
work on
commit 39e740c2601bc93dc89444ca5ad4fe46eca62261
Merge: 4699ea33 ac76bb79
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 17:50:06 2021 +0100
Merge branch 'cookBux' of https://git.ufz.de/rdm-software/saqc into cookBux
commit 4699ea33749c6ebcd6e311e72763ca99e2f65a0f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 17:49:40 2021 +0100
solved doctest import errors
commit ac76bb79f98e5d89b7538fe96e63b07a5ff55495
Author: David Schaefer <david.schaefer@ufz.de>
Date: Thu Nov 18 15:43:10 2021 +0100
seperated taining tour and installation guide
commit e4292ff9b68c6bd6918b86e3f4cc3b569c04da54
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 14:40:03 2021 +0100
black
commit af8119299ee36fb6df6d5ad68409b32cc380484d
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 14:22:27 2021 +0100
replaced links to temporal markdown processing products
commit 525447b2c3c93fa2c8a39281c0ec36c672a37cc4
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 14:18:10 2021 +0100
added automatic module API generation
commit e8aee95609797b6d9aca477301a917d5fdc87ab1
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 13:42:41 2021 +0100
getting started formatted
commit e83cf6e40818da585e415c2e6c71e11ab4f4157b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 13:32:41 2021 +0100
repaired getting started links
commit a59f358fa168e1a092518950c5760fe58e661d82
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 18 13:18:41 2021 +0100
cleaning-up/deprecated md - inclusion pipeline
commit e972f177333f5a14cc9f2635e5f4939779704317
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 17 14:50:34 2021 +0100
changes
commit c084eeac518bf77f57a4705501956b375aaea867
Merge: 0e74806f 7a633ab5
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 17 14:42:13 2021 +0100
MC solved
commit 0e74806ffdbda3843000cc0878e714bd9da0c2cd
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 17 13:41:04 2021 +0100
removed function cats -> use Functions-API
commit 820890543d0d8de7dc63787dba50917e9909210b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 17 13:40:14 2021 +0100
added alias examples
commit 2a9a4929de884893a5e136fb2add1f0e9ae5136f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 17 13:34:56 2021 +0100
doc stuff
commit ebe5bd86ed89de2e0f6393bf8807b2701bae7286
Merge: 4ab8444e 3b3c84ff
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 17 09:59:04 2021 +0100
MC solved
commit 4ab8444e2a2c7cdc54bbdc38ca508d8a818f1c86
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 17 08:55:13 2021 +0100
commit
commit fbeead0fc3e6f74a58bac835236e52fb6d8cf923
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Nov 16 12:56:38 2021 +0100
modified hydro_config.csv
commit ad585c73ea72c28730c18c8ca62240410a0dc578
Merge: 0b6a71b9 4348f87e
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Nov 16 12:36:42 2021 +0100
Merge branch 'driftModelKW' into cookBux
commit 4348f87e115ff1a453908d170fffc4a40df4ff32
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Nov 16 12:32:02 2021 +0100
black
commit 95ce990d6d2ef14398333460aef289ca9f07457a
Merge: 4f113c72 ac7a697e
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Nov 16 12:31:44 2021 +0100
Merge branch 'develop' into driftModelKW
commit 4f113c72f87ff8a15d4711618666c735efcc69ba
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Nov 16 12:30:47 2021 +0100
modified driftmodel keyword to Literal or Callable type
commit 0b6a71b9243e0d60f857132bf48bcec0990633a1
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 15 15:43:12 2021 +0100
hydro config added
commit be0e9af419c723272591124e2ca10fca901e7a59
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Sun Nov 14 12:13:07 2021 +0100
formatted title
commit 1364fb1a71c8334a1dec39e3de2972c578b444ee
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 12 21:59:58 2021 +0100
bulletmismatch solved
commit fa6ae695034031172deea3792db18af55e371d76
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 12 21:34:27 2021 +0100
black
commit a45ad76ae445226ef74d7c64d2546229aa6c3b7e
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 12 21:32:25 2021 +0100
bucks
commit 814cc3d5192af115c5a4d62165b477b65640a144
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 12 20:25:50 2021 +0100
black
commit 8e695b8c7438416be51275a3c2d494f09d29def8
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 12 17:25:57 2021 +0100
added pics
commit 0cdbbda0f27cf194db91da7e667772f9901bf37e
Merge: 8ab4705c 3480ccf2
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 12 15:38:26 2021 +0100
Merge branch 'develop' into cookBux
commit 8ab4705cd59c9b009b81a874d3ef5c81da24b0bc
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 12 15:36:53 2021 +0100
MR solved
commit 8fad2c951492fe15b8099e86ea43571b4074030c
Merge: b9df2ba1 cb4bb78c
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 12 15:14:17 2021 +0100
mr solved
commit b9df2ba124bebfc68245f1ac2f5eba33ea46b55b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 12 14:45:30 2021 +0100
nicemaking of doc page
commit bdc1a3abd89385ab37b49a8aa29a09a65c1afdcc
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 11 13:05:03 2021 +0100
docing
commit 1f303dc4915b343816772d6b51115774f1ab4080
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Nov 11 13:03:20 2021 +0100
titlepage md file added
commit 8720344a2a9026c324d91f4443af4343fed7d949
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 10 18:25:25 2021 +0100
...
commit 12594f8f3560f0a36963de589b53c01d7f4776ef
Merge: 4a193ca9 26b52fb6
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 10 17:58:47 2021 +0100
MC solved
commit 4a193ca9612fef1bb88d5163bd48965f568f5f4b
Merge: 0f7a12d1 2ffe84fd
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 10 17:45:54 2021 +0100
Merge branch 'preventProcessingfuncsFromDemasking' into cookBux
commit 0f7a12d1095da8806abaf9d6b2d85071b25d9267
Merge: 7eb93457 42c8eb68
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 10 17:43:24 2021 +0100
mr solved
commit 2ffe84fd0bb297950aa09285b46afeb3850ae73f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 10 17:37:40 2021 +0100
modified transformation function
commit b202e3d6040b0dbcda50c31d268bc7e3ebc8a6a8
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 10 17:21:03 2021 +0100
added target parameter to driftcorrection function/made drift correcion function not demask
commit 6e3b1d22f3e9b16769fb93a835dc2a1f87881ca7
Merge: 3540046b 42c8eb68
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 10 16:38:33 2021 +0100
Merge branch 'develop' into preventProcessingfuncsFromDemasking
commit 3540046bcb6ef16042e59f51810f11ffd11e6af0
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Nov 10 16:25:37 2021 +0100
msg
commit 60192ed4bf0f265c453960e2a2436dd5ab57b10d
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 10 12:14:31 2021 +0100
black
commit 7ab92adb96e8c0a1175ae6be783247e6dcef7aa0
Merge: 49cb9886 0fb9d12b
Author: David Schäfer <david.schaefer@ufz.de>
Date: Wed Nov 10 11:10:39 2021 +0000
Merge branch 'develop' into 'multifields'
# Conflicts:
# saqc/core/core.py
commit 49cb9886ce95c022a2f208dce9cfc050f0728d7a
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 10 09:15:56 2021 +0100
renamed function
commit 1aa6bf8024cf56e7a3ccfa263040be20e23861ec
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 10 08:31:14 2021 +0100
move the column consitency check after the function call to also
catch errors introduced by the last call in a call chain
commit 61dd6de392c1c2d13a4a533127402d6de9783ca0
Author: David Schaefer <david.schaefer@ufz.de>
Date: Wed Nov 10 08:30:08 2021 +0100
typing toSequence is hard...
commit 127cf0babea72f3867623235c21d566e28df0af3
Author: David Schaefer <david.schaefer@ufz.de>
Date: Tue Nov 9 17:25:17 2021 +0100
yeah black...
commit 15a190e2f7c252e129f9b3a6b1cdb7eac6399a24
Author: David Schaefer <david.schaefer@ufz.de>
Date: Tue Nov 9 17:20:37 2021 +0100
remove left over function
commit e741c90eeed09584fd33bd72a23b323a4da570d5
Author: David Schaefer <david.schaefer@ufz.de>
Date: Tue Nov 9 17:20:18 2021 +0100
introduce multivariate functions
commit 40a9022e5e97a8d78e91a7d0f33bb08f7690edd2
Author: David Schaefer <david.schaefer@ufz.de>
Date: Tue Nov 9 17:19:02 2021 +0100
new lib function
commit e5fff0192b32a7ce10ba7449c0b578d846e0c95d
Author: David Schaefer <david.schaefer@ufz.de>
Date: Tue Nov 9 17:18:28 2021 +0100
change return type of toSequence
commit fdefa1bb414e907520322b7f51e12b12b41bb4b0
Merge: 8d656395 719e50f1
Author: David Schaefer <david.schaefer@ufz.de>
Date: Tue Nov 9 13:15:25 2021 +0100
Merge branch 'notarget' of https://git.ufz.de/rdm-software/saqc into notarget
commit 8d65639508df7806eda6a3bf43502b586085b8a3
Author: David Schaefer <david.schaefer@ufz.de>
Date: Tue Nov 9 13:14:38 2021 +0100
bring back old parameter name in `renameField`
commit 719e50f1bab21ee0366511e56d8b8ada2a4c1143
Merge: 05cfeb51 d3583e07
Author: David Schäfer <david.schaefer@ufz.de>
Date: Tue Nov 9 11:27:05 2021 +0000
Merge branch 'develop' into 'notarget'
# Conflicts:
# saqc/funcs/tools.py
commit 05cfeb51e9aed72e2e0586ce2b83bd45b887c7ff
Author: David Schaefer <david.schaefer@ufz.de>
Date: Tue Nov 9 11:30:47 2021 +0100
remove the core `target` parameter
commit 7eb9345738b18ef8597cfdf71d07ee8fcf102a7d
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Nov 5 09:01:47 2021 +0100
new figs
commit 51f7d75ba4ccf6487d808d57df1fe2d839eb845b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Mon Nov 1 15:38:03 2021 +0100
added sac254 raw plot
commit a19d91fe14609b338dfa80fe9001dde35d28feef
Merge: 5da30798 d6fed5a8
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 29 17:14:01 2021 +0200
MR solved
commit 5da3079812c5a69329ece2d1070cd847b2bec4e4
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 29 17:12:10 2021 +0200
added hydro pics
commit a5675d4ff2e1da3ea68dc68175480c8b197cb512
Merge: d6cf3e69 8795ae33
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 29 09:09:22 2021 +0200
Merge branch 'plotInHistory' into cookBux
commit 8795ae339a0395c8249bc712a2395789144e69a8
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 29 09:08:33 2021 +0200
added flagMissingSkip
commit d6cf3e69ada0ee5e7c1d479fea9b14a0a4ec7abc
Merge: ce83a57c 1345b205
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 28 12:51:50 2021 +0200
Merge branch 'plotInHistory' into cookBux
commit 1345b205e949205c69efab0582001eb58b7f3dca
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 28 12:51:22 2021 +0200
catched it
commit ce83a57c4dc7384089b9beca964dc73d047d7fd8
Merge: 9b8160c1 f535d178
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 28 12:49:07 2021 +0200
Merge branch 'plotInHistory' into cookBux
commit f535d1787d767e51537d69310470ae1c6dafa36b
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 28 12:48:38 2021 +0200
catches flags_meta={} break
commit 9b8160c1affab20f55946a722a9e8b0a6fba21b6
Merge: f4749cf2 7ee25a85
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 28 11:10:15 2021 +0200
Merge branch 'plotInHistory' into cookBux
commit 7ee25a859653a2b64d9486ed098fbd4135fa82bb
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 28 11:09:44 2021 +0200
black
commit 76a68a86e1a2babdf8da13502e79d34837acc4b8
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 28 11:09:13 2021 +0200
bfx
commit f4749cf2c35ba1c1fbcc44c7062c1496274e96a2
Merge: 86389372 e119c357
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 27 19:06:12 2021 +0200
Merge branch 'plotInHistory' into cookBux
commit e119c357c31d311b5e0e2bd60a4ea9aace11d9a9
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 27 19:02:20 2021 +0200
added pahseplotting feature
commit 86389372aa8f3e3e45b960a20e17d19131d63d88
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 27 18:01:21 2021 +0200
config added
commit d6fed5a8ffbb6368ebb22aaf22e34d1c371b30e5
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 11:36:14 2021 +0200
Update hydro_config.csv
commit 444271550d292b1b33f8fdfd41b6dd3c42859440
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 11:32:18 2021 +0200
Update hydro_config.csv
commit 23a88fce8724d81a9f87a3020f277efe30e313c2
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 11:27:06 2021 +0200
Update hydro_config.csv
commit bf78b7a0288655e644476962ad988f8084855062
Merge: 66da1516 27e87a17
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 10:34:45 2021 +0200
Merge branch 'plotInHistory' into cookBux
commit 27e87a179e349d08e2984d9b7d0096cc19618cce
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 10:34:19 2021 +0200
max-gap error for empty series catched
commit 80836cd4fd161a9b5dec3c5802a9607325e7a2d7
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 10:15:28 2021 +0200
prevented ghost tests (tests that flagged nan-vals only) from appearing in valid-history legend
commit 66da15161c1c4d6afa109aba8172e31c1757d688
Merge: 3a869a80 02c51568
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 09:41:59 2021 +0200
Merge branch 'plotInHistory' into cookBux
commit 02c5156835c22dd29b538d8d5cb3347f72f8f90f
Merge: e93db50d a054a69a
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 09:38:11 2021 +0200
Merge branch 'plotInHistory' of https://git.ufz.de/rdm-software/saqc into plotInHistory
commit e93db50dda246e81c95b1b19b1b72a4b939c2ce4
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 09:37:42 2021 +0200
makeFig docstring
commit a054a69aaef43985214aa9f3bf98ae8aeafa9336
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 21 09:34:56 2021 +0200
Apply 1 suggestion(s) to 1 file(s)
commit c2b5746caa1e20ee6d9a551f4d3090db90911472
Author: David Schäfer <david.schaefer@ufz.de>
Date: Thu Oct 21 09:34:36 2021 +0200
Apply 1 suggestion(s) to 1 file(s)
commit 3bd219b6a9a91ad1b7ecac13cbf10bbaa1adf5a6
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 09:33:32 2021 +0200
fixed masking keyword for plotting
commit c6bda875ec3a299fedd358a4d0d8e57af069424f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 21 09:02:22 2021 +0200
added explicit to_mask kwarg to plot func
commit 3a869a807475321f7601aeb9ac4ba991c901e9c6
Merge: d81fd397 1aa97fe6
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 20 17:10:29 2021 +0200
Merge branch 'flagManualRework' into cookBux
commit 1aa97fe6b22cbfc902edeb128ade4cdfe64c0f4f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 20 17:06:36 2021 +0200
fixed closure confusion in flag manual
commit d81fd39764512368d0cbb7660d195a9d19f1ae71
Merge: 41281ffe 2c4db6eb
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 20 15:20:16 2021 +0200
MR solved
commit 41281ffedc211dda3ef0d12b92082d6be2651345
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 20 11:44:16 2021 +0200
moved hydro config
commit a6d323d3ac4cb419a9d23f1515944e74b10e6440
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 20 11:42:12 2021 +0200
added hydro config
commit 5273b4c52e2296ebe035adeca399e68204ba8e8d
Merge: 7514767b 4040e0f4
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 20 09:09:23 2021 +0200
Merge branch 'develop' into cookBux
commit 7514767b3ec3be0a78341a12eba49a9dace98797
Merge: 8faa7527 dc343ba0
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Oct 19 15:00:21 2021 +0200
Merge branch 'develop' into cookBux
commit 8faa75277ac0ba3482cfe156b98c34db1fdc6319
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Oct 19 13:18:44 2021 +0200
black
commit 715f53ee757e7b704f98da25f6f6e52e408a7a2e
Merge: b827d52e 9879b6f6
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Oct 19 13:17:53 2021 +0200
Merge branch 'develop' into cookBux
commit b827d52e56dad65f773133e4cce655b1a379e0f8
Merge: cbe190e9 ac065705
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Tue Oct 19 08:48:26 2021 +0200
Merge branch 'develop' into cookBux
commit 955a8b73a7229a386085ba0d835431988bf53abe
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 15 15:08:33 2021 +0200
added date-like check/removed test with list input
commit 2c4db6eb28cf65a1c898a4905c9cbfd148e932aa
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 15 14:24:51 2021 +0200
added seaborn to requirements
commit cbe190e9fb33ae38d36ef88d081f167d58be1128
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 15 14:21:04 2021 +0200
stuff
commit c31a354c403f5378250d1a6620892546777ad2b6
Merge: 37d283c9 e4fce003
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 15 13:57:47 2021 +0200
Merge branch 'plotInHistory' into cookBux
commit e4fce0038778d73b50a4dd38684f5d7443d1260a
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 15 13:57:13 2021 +0200
bläck
commit 417d540abab35411294071ff01916cce973ba0b0
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 15 13:56:46 2021 +0200
saqc theme defaults implemented
commit bb619492c3d949186725680313531d9f99b3f0a9
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Fri Oct 15 13:37:08 2021 +0200
removed kwargs keywords/made them module variables
commit 37d283c9a664ac6600d902dde36186444689aaa3
Merge: 58eeb836 19b7fcfe
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 14 15:37:43 2021 +0200
Merge branch 'plotInHistory' into cookBux
commit 58eeb836da7b7050fc4e6ac6fb0d695287ca970e
Merge: 72138bdb a69778a0
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 14 15:36:29 2021 +0200
Merge branch 'flagManualRework' into cookBux
commit a69778a06c11f9508c0be613e70cae30497d5cac
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 14 15:30:31 2021 +0200
blackattack
commit d5601a3b91c508497b4066b0e8172580546889a7
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Thu Oct 14 15:29:42 2021 +0200
included flag maintenance intervals workflow in flagManual function
commit 19b7fcfe647a14b8951f88fb3fbdd95899808472
Merge: 642e1dd6 fc7c1a8f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 13 18:17:19 2021 +0200
Merge branch 'develop' into plotInHistory
commit 642e1dd6d33d9c1e3f20e0d56e2b2647c80f8106
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 13 18:16:02 2021 +0200
fixed tests/black
commit 8c87a0a41e0865f448551a9adbe1165a0d380bde
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 13 17:53:53 2021 +0200
changed keyword thingies
commit 98f6a0d307faea5c844b402023b3f93cd1bdc133
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 13 15:46:29 2021 +0200
added istory/slicing keywords
commit 809bf37654cb2f51b50383807d0939afe78ba15f
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 13 14:57:28 2021 +0200
history evaluation in plotting now faster/makes more sence
commit 72138bdb38a0213bad3fd834ca61620da82023ee
Merge: bb58d669 60884feb
Author: luenensc <peter.luenenschloss@ufz.de>
Date: Wed Oct 13 11:26:41 2021 +0200
Merge branch 'plotInHistory' into cookBux
commit 60884febdd7e248ab4a478279ef52d6f3833c5ab
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed Oct 13 10:29:48 2021 +0200
black again
commit dfe5254fa781d1b024f4a6466ad36c5617c70d19
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed Oct 13 10:23:24 2021 +0200
bfx
commit 69259645653806e1b72aca3f6d34a8d8477cb86f
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed Oct 13 10:12:34 2021 +0200
made plotting functionality sensible to history
commit bb58d669033de93754a33fad46dea352b4511900
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Oct 12 15:18:12 2021 +0200
typo
commit 0e4f3715857d5a7a666f28ea0ad28b731accecaf
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Oct 12 15:01:50 2021 +0200
black
commit 6097680f12780a24c8e6177ed29efc315bfb767a
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Oct 12 14:58:48 2021 +0200
changes
commit bd191d6898df91b3c87b7dca125d8a538fa79a6f
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Oct 12 14:57:57 2021 +0200
guide updated
commit fe736c62f399eb8289a88c88e11c4752af4a5b80
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Oct 12 14:31:55 2021 +0200
mv flagging guide
commit b0b178d7109e0bae741840a49128585ea2c762af
Merge: d2367c27 cfab0bfb
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Fri Oct 8 15:44:31 2021 +0200
MR solved
commit d2367c2703590037b6bcddffdb89c3f594225256
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed Aug 11 07:40:44 2021 +0200
black applied
commit d73431c2f59486f0de91bb8dddb910f37ef02bca
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Aug 10 14:04:49 2021 +0200
merged develop
commit 0b6992d4f4c72287ce5418b79bc032e6941d21e0
Merge: b0261bef 4b26da07
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Aug 10 14:04:18 2021 +0200
Merge branch 'develop' into cookBux
commit b0261bef88118d9c3e4985d678e3d6ff98e04fb4
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Aug 10 14:04:00 2021 +0200
added tools/generic/resampling module
commit d47ec87ea6152f5daaac59a23e68be867b08cb8c
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Aug 10 13:52:15 2021 +0200
tools page added
commit cb9b26a8ff9d8878dd1eb66e5723d52ba5de1ed1
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Mon Jun 7 12:51:31 2021 +0200
content table added to regularisation tutorial
commit f8a7449be53655dac36e38144832bfd7109d8aba
Merge: 8a0ba2c8 118a3ba4
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Fri Jun 4 08:30:15 2021 +0200
merge conflicts solved
commit 8a0ba2c8f25f4ca0bf06a979183eb850cdc27a9a
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Jun 1 19:40:52 2021 +0200
some saqc constants doc
commit 637cabee530828d203572913be10bd02c085f2f4
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Jun 1 17:34:24 2021 +0200
landing page cleaned up
commit 44f2a35b72490ecc74d314e440c376fc86ea7dba
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Jun 1 17:05:04 2021 +0200
added how to link function cats guide to doc guide
commit 365dbb8cb53b6795a8a70a636ff9c2309d76ba38
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Jun 1 16:54:02 2021 +0200
docu how to added to pages pipe
commit 53ef3b493932e2732c3945b16d40110259fca7a9
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Jun 1 13:10:21 2021 +0200
some doc typo/struc rework / flagging constants documentation included
commit 24a9abd701eea29080c4538cea4958e7c26dc4e7
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Fri May 28 11:03:49 2021 +0200
tutorial flagging and regularisation
commit 86a0b5e7a4de92d0b3544718113a27ca1bc24786
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Fri May 28 10:20:34 2021 +0200
interpolation added to cookbux
commit 88bbdd3d71b720294dbab6132e6dc591a94d72b8
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Thu May 27 15:47:06 2021 +0200
more text
commit b3bd6101dec172300b3e8386bec84608d9bd4388
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Thu May 20 13:54:40 2021 +0200
page check commit
commit 3eb47fa06e13d3ebb7b2c4fb177b10dfa79f892d
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed May 19 21:08:09 2021 +0200
outlier det tutorial finished
commit b7489836853ae67c73d857a7beb220513e6068c9
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed May 19 09:17:27 2021 +0200
added saqc-obj doc api
commit e34c3c9c689b875f5318b6753b9efa4b46f250ed
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Mon May 17 20:15:29 2021 +0200
docdoc
commit 6604835868e6423126f147b7c6bf327a934d8289
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Mon May 17 20:08:59 2021 +0200
do
commit 592347b13fd9a334434db2323d7bed935cab9435
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Mon May 17 17:48:02 2021 +0200
docdocdoc
commit e4fb3f754338cba1c6addd3306aa6ebdadf720b7
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed May 12 16:48:18 2021 +0200
black again
commit 1313e3ed2bf5f02f2533754414d1d97a14492997
Merge: 6886f82d 875985a5
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed May 12 16:46:21 2021 +0200
Merge branch 'develop' into cookBux
commit 6886f82d0f06e3e34714c41fd617cf1c628386a7
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed May 12 16:42:26 2021 +0200
no book without a cook
commit fc2b4fc5a4b9e295dfc7460f13c932d9d406cb6e
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed May 12 15:14:28 2021 +0200
slimmer makefile
commit 254ccc0f45b235b200227b210cb863a485d12dd6
Merge: ae8bad45 5a614930
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed May 12 09:54:57 2021 +0200
Merge branch 'develop' into cookBux
commit ae8bad45f152c1f05f982f91961d83989dd19eae
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed May 12 08:22:51 2021 +0200
some slimfast for the doc pipe
commit c55ecec221d2de817079e3b1585a37300f5b79cc
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue May 11 15:52:48 2021 +0200
updated sphinx-pipe
commit 5c8447067108012b06744c90369369354635c02e
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon May 10 10:45:54 2021 +0000
Update OutlierDetection.md
commit 9632503e432a3e39ede88f27585f86fd923a3795
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon May 10 10:12:00 2021 +0000
Update OutlierDetection.md
commit 0b5a89b770a8fbbfee4e5e3c75eb0fd379557725
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Mon May 10 08:24:40 2021 +0000
Update OutlierDetection.md
commit b50608b87a4b6e3942977ec951916ebd54692c81
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Wed May 5 06:15:00 2021 +0000
Update OutlierDetection.md
commit 74d476ee137c7d11d68b0de2ed972237aa61b4be
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed Apr 28 09:24:30 2021 +0200
added score calc
commit 92fca973fa2c8171b421915453873ae8e7ec0459
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Apr 27 16:12:08 2021 +0200
docdocdoc
commit 26e7c83e24e73fb102360017c90d45af72c62f3d
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Apr 27 11:58:09 2021 +0200
progress
commit 1262861abfbb00d88a6a339e6bbe5cbb06a91b6c
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Apr 27 09:23:31 2021 +0200
pic added
commit 20fb4c5391ce0ae3928a421c6707823caf00e773
Author: Peter Lünenschloß <peter.luenenschloss@ufz.de>
Date: Tue Apr 27 07:11:19 2021 +0000
Update OutlierDetection.md
commit b279447570aa324051048673dbc997e183d2f3f4
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Apr 27 09:08:54 2021 +0200
pic added
commit 393d60bb57317901f46ef1e4a6f81d65df9b9b41
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Apr 27 08:41:06 2021 +0200
...
commit 79586be276b415b4486e410e7879440a8ee11561
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Apr 27 08:32:40 2021 +0200
data replaced
commit 87aca90a1e57760129b18f4d497d479416d7f24f
Merge: f0836f92 13baca75
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Apr 27 08:23:34 2021 +0200
Merge branch 'develop' into cookBux
commit f0836f9295ef28b132b7b6844e38e0246fc3b96b
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue Apr 27 08:04:30 2021 +0200
data added
commit c74883c90f8ad1bd053d9f06f43a6a4eedbcc137
Author: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Mon Apr 26 17:53:20 2021 +0200
outlierDetectionCookBook
---
.github/workflows/main.yml | 71 +
.gitlab-ci.yml | 4 +-
.gitlab/issue_templates/bug_report.md | 43 +
CHANGELOG.md | 32 +-
dios/dios/base.py | 21 +-
dios/requirements.txt | 2 +-
dios/test/test__ops__.py | 16 +-
docs/Makefile | 2 +-
docs/cookbooks/DataRegularisation.rst | 105 +-
docs/cookbooks/MultivariateFlagging.rst | 2 +-
docs/documentation/Customizations.rst | 13 +-
docs/documentation/GlobalKeywords.rst | 4 +-
docs/documentation/WritingFunctions.rst | 2 +-
docs/gettingstarted/InstallationGuide.rst | 6 +-
docs/gettingstarted/TutorialCLI.rst | 1 -
docs/index.rst | 11 +-
docs/misc/title.rst | 2 +-
docs/requirements.txt | 6 +-
docs/resources/images/flagJumpsPic.excalidraw | 7136 +++++++++++++++++
.../images/flagJumpsPic.excalidraw.license | 3 +
docs/resources/images/flagJumpsPic.png | Bin 0 -> 726144 bytes
.../resources/images/flagJumpsPic.png.license | 3 +
.../resources/images/flagOffsetPic.excalidraw | 1902 +++++
.../images/flagOffsetPic.excalidraw.license | 3 +
docs/resources/images/flagOffsetPic.png | Bin 0 -> 522250 bytes
.../images/flagOffsetPic.png.license | 3 +
docs/resources/temp/SM1processingResults.png | Bin 56186 -> 59435 bytes
docs/resources/temp/SM2processingResults.png | Bin 144765 -> 149755 bytes
requirements.txt | 10 +-
saqc/constants.py | 4 +
saqc/core/core.py | 145 +-
saqc/core/flags.py | 3 +
saqc/core/modules/__init__.py | 50 -
saqc/core/modules/breaks.py | 44 -
saqc/core/modules/changepoints.py | 54 -
saqc/core/modules/constants.py | 34 -
saqc/core/modules/curvefit.py | 31 -
saqc/core/modules/drift.py | 123 -
saqc/core/modules/flagtools.py | 74 -
saqc/core/modules/generic.py | 43 -
saqc/core/modules/interpolation.py | 60 -
saqc/core/modules/noise.py | 35 -
saqc/core/modules/outliers.py | 129 -
saqc/core/modules/pattern.py | 28 -
saqc/core/modules/resampling.py | 92 -
saqc/core/modules/residuals.py | 44 -
saqc/core/modules/rolling.py | 30 -
saqc/core/modules/scores.py | 36 -
saqc/core/modules/tools.py | 64 -
saqc/core/modules/transformation.py | 28 -
saqc/core/register.py | 675 +-
saqc/core/translation/basescheme.py | 9 +-
saqc/funcs/__init__.py | 58 +-
saqc/funcs/breaks.py | 396 +-
saqc/funcs/changepoints.py | 447 +-
saqc/funcs/constants.py | 304 +-
saqc/funcs/curvefit.py | 209 +-
saqc/funcs/drift.py | 1270 ++-
saqc/funcs/flagtools.py | 1049 ++-
saqc/funcs/generic.py | 438 +-
saqc/funcs/interpolation.py | 543 +-
saqc/funcs/noise.py | 142 +-
saqc/funcs/outliers.py | 2219 ++---
saqc/funcs/pattern.py | 216 +-
saqc/funcs/resampling.py | 927 +--
saqc/funcs/residuals.py | 271 +-
saqc/funcs/rolling.py | 117 +-
saqc/funcs/scores.py | 393 +-
saqc/funcs/tools.py | 663 +-
saqc/funcs/transformation.py | 123 +-
saqc/lib/tools.py | 52 +-
saqc/lib/ts_operators.py | 16 +-
saqc/lib/types.py | 16 +-
saqc/version.py | 2 +-
setup.py | 2 +-
tests/api/test_signatures.py | 48 -
tests/cli/test_integration.py | 8 +-
tests/common.py | 8 -
tests/core/test_FunctionsMixin.py | 33 -
tests/core/test_core.py | 220 +-
tests/core/test_reader.py | 6 +-
tests/funcs/test_constants_detection.py | 17 +-
tests/funcs/test_functions.py | 118 +-
tests/funcs/test_generic_api_functions.py | 291 +-
tests/funcs/test_generic_config_functions.py | 14 +-
tests/funcs/test_harm_funcs.py | 117 +-
tests/funcs/test_modelling.py | 122 -
...detection.py => test_outlier_detection.py} | 79 +-
tests/funcs/test_pattern_rec.py | 14 +-
tests/funcs/test_proc_functions.py | 122 +-
tests/funcs/test_tools.py | 1 -
tests/fuzzy/test_masking.py | 28 +-
tests/requirements.txt | 2 +-
93 files changed, 15192 insertions(+), 7167 deletions(-)
create mode 100644 .github/workflows/main.yml
create mode 100644 .gitlab/issue_templates/bug_report.md
create mode 100644 docs/resources/images/flagJumpsPic.excalidraw
create mode 100644 docs/resources/images/flagJumpsPic.excalidraw.license
create mode 100644 docs/resources/images/flagJumpsPic.png
create mode 100644 docs/resources/images/flagJumpsPic.png.license
create mode 100644 docs/resources/images/flagOffsetPic.excalidraw
create mode 100644 docs/resources/images/flagOffsetPic.excalidraw.license
create mode 100644 docs/resources/images/flagOffsetPic.png
create mode 100644 docs/resources/images/flagOffsetPic.png.license
delete mode 100644 saqc/core/modules/__init__.py
delete mode 100644 saqc/core/modules/breaks.py
delete mode 100644 saqc/core/modules/changepoints.py
delete mode 100644 saqc/core/modules/constants.py
delete mode 100644 saqc/core/modules/curvefit.py
delete mode 100644 saqc/core/modules/drift.py
delete mode 100644 saqc/core/modules/flagtools.py
delete mode 100644 saqc/core/modules/generic.py
delete mode 100644 saqc/core/modules/interpolation.py
delete mode 100644 saqc/core/modules/noise.py
delete mode 100644 saqc/core/modules/outliers.py
delete mode 100644 saqc/core/modules/pattern.py
delete mode 100644 saqc/core/modules/resampling.py
delete mode 100644 saqc/core/modules/residuals.py
delete mode 100644 saqc/core/modules/rolling.py
delete mode 100644 saqc/core/modules/scores.py
delete mode 100644 saqc/core/modules/tools.py
delete mode 100644 saqc/core/modules/transformation.py
delete mode 100644 tests/api/test_signatures.py
delete mode 100644 tests/core/test_FunctionsMixin.py
delete mode 100644 tests/funcs/test_modelling.py
rename tests/funcs/{test_outier_detection.py => test_outlier_detection.py} (68%)
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
new file mode 100644
index 000000000..01bc9ebc5
--- /dev/null
+++ b/.github/workflows/main.yml
@@ -0,0 +1,71 @@
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+# This is a basic workflow to help you get started with Actions
+
+name: CI
+
+# Controls when the workflow will run
+on:
+ push:
+ branches:
+ - master
+ - develop
+ tags:
+ - v**
+
+ pull_request:
+
+ # Allow to run this workflow manually from the Actions tab
+ workflow_dispatch:
+
+
+jobs:
+ build:
+ name: build (py${{ matrix.python-version }}, ${{ matrix.os }})
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: false
+ matrix:
+ os: ["windows-latest", "ubuntu-latest", "macos-latest"]
+ python-version: ["3.7", "3.8", "3.9", "3.10"]
+ defaults:
+ run:
+ # somehow this also works for windows O.o ??
+ shell: bash -l {0}
+
+ steps:
+
+ # checkout the repository under $GITHUB_WORKSPACE
+ - uses: actions/checkout@v3
+ - uses: conda-incubator/setup-miniconda@v2
+ with:
+ auto-update-conda: true
+ python-version: ${{ matrix.python-version }}
+ activate-environment: venv
+
+ - name: show conda info
+ run: conda info
+
+ - name: install requirements
+ run: |
+ pip install -r requirements.txt
+ pip install -r tests/requirements.txt
+
+ - name: show installed packages
+ run: conda list
+
+ - name: run SaQC test suite
+ run: |
+ pytest tests dios/test -Werror
+ python -m saqc --config docs/resources/data/config.csv --data docs/resources/data/data.csv --outfile /tmp/test.csv
+
+ - name: run doc tests
+ run: |
+ cd docs
+ pip install -r requirements.txt
+ make doc
+ make test
+
+
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 1996835fa..dfeafeac8 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -70,7 +70,9 @@ coverage:
artifacts:
when: always
reports:
- cobertura: coverage.xml
+ coverage_report:
+ coverage_format: cobertura
+ path: coverage.xml
# test saqc with python 3.7
diff --git a/.gitlab/issue_templates/bug_report.md b/.gitlab/issue_templates/bug_report.md
new file mode 100644
index 000000000..e0170fc13
--- /dev/null
+++ b/.gitlab/issue_templates/bug_report.md
@@ -0,0 +1,43 @@
+<!--
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
+-->
+
+## Summary
+
+(Summarize the bug encountered concisely)
+
+## Reproducible Example
+
+```python
+import numpy as np
+import pandas as pd
+import saqc
+...
+
+```
+
+## What is the current bug behavior?
+
+(What actually happens)
+
+## What is the expected correct behavior?
+
+(What you should see instead)
+
+## Stacktrace
+
+<details><summary>Click to expand</summary>
+
+```
+Paste any relevant stacktrace inside the ```
+```
+
+</details>
+
+## Possible fixes
+
+(If you can, link to the line of code that might be responsible for the problem)
+
+/label ~BUG ~needs-investigation
diff --git a/CHANGELOG.md b/CHANGELOG.md
index f22ec980c..9600623f8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,19 +6,38 @@ 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
-[List of commits](https://git.ufz.de/rdm-software/saqc/-/compare/v2.1.0...develop)
+[List of commits](https://git.ufz.de/rdm-software/saqc/-/compare/v2.2.0...develop)
### Added
### Changed
### Removed
### Fixed
+## [2.2.0](https://git.ufz.de/rdm-software/saqc/-/tags/v2.0.1) - 2022-10-28
+[List of commits](https://git.ufz.de/rdm-software/saqc/-/compare/v2.1.0...v2.2.0)
+### Added
+- translation of `dfilter`
+- new generic function `clip`
+- parameter `min_periods` to `SaQC.flagConstants`
+- function `fitButterworth`
+- tracking interpolation routines in `History`
+### Changed
+- test function interface changed to `func(saqc: SaQC, field: str | Sequence[str], *args, **kwargs)`
+- lib function `butterFilter` returns `NaN` for too-short series
+- `dfilter` default value precedence order
+### Removed
+- `closed` keyword in `flagJumps`
+### Fixed
+- fixed undesired behavior in `flagIsolated` for not harmonized data
+- fixed failing translation of `dfilter`-defaults
+- fixed unbound recursion error when interpolating with order-independent methods in `interpolateIndex`
+- fixed not working min_periods condition if `window=None` in `assignZScore`
+- fixed Exception occuring when fitting polynomials via `polyfit` to harmonized data, containing all-NaN gaps wider than the polynomial fitting window size.
+- fixed bug in function parameter checking
+- fixed bug one-off bug in `flagJumps`
+
## [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`
@@ -27,7 +46,6 @@ This changelog starts with version 2.0.0. Basically all parts of the system, inc
- 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*)
@@ -39,10 +57,8 @@ This changelog starts with version 2.0.0. Basically all parts of the system, inc
- 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
diff --git a/dios/dios/base.py b/dios/dios/base.py
index 674c939a1..29c0601bc 100644
--- a/dios/dios/base.py
+++ b/dios/dios/base.py
@@ -11,7 +11,7 @@ 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
+from typing import Any, Hashable, Mapping, Sequence, TypeVar, overload
import pandas as pd
@@ -25,10 +25,13 @@ __email__ = "bert.palm@ufz.de"
__copyright__ = "Copyright 2018, Helmholtz-Zentrum für Umweltforschung GmbH - UFZ"
+D = TypeVar("D", bound="_DiosBase")
+
+
class _DiosBase:
@property
@abstractmethod
- def _constructor(self) -> type[_DiosBase]:
+ def _constructor(self: D) -> type[D]:
raise NotImplementedError
def _finalize(self, other: _DiosBase):
@@ -187,19 +190,13 @@ class _DiosBase:
self._data.at[col] = val.copy(deep=True)
@overload
- def __getitem__(self, key: str | int) -> pd.Series:
- ...
-
- @overload
- def __getitem__(self, key: slice) -> pd.Series:
- ...
-
- @overload
- def __getitem__(self, key: "_DiosBase" | pd.DataFrame) -> "_DiosBase":
+ def __getitem__(self, key: str | int | slice) -> pd.Series:
...
@overload
- def __getitem__(self, key: Sequence[str | int]) -> "_DiosBase":
+ def __getitem__(
+ self: D, key: "_DiosBase" | pd.DataFrame | Sequence[str | int]
+ ) -> D:
...
def __getitem__(self, key):
diff --git a/dios/requirements.txt b/dios/requirements.txt
index 198c6b1f4..981ad1b64 100644
--- a/dios/requirements.txt
+++ b/dios/requirements.txt
@@ -5,5 +5,5 @@
numpy==1.21.2
pandas==1.3.5
python-dateutil==2.8.2
-pytz==2021.3
+pytz==2022.2.1
six==1.16.0
diff --git a/dios/test/test__ops__.py b/dios/test/test__ops__.py
index 144ae9342..dede30afb 100644
--- a/dios/test/test__ops__.py
+++ b/dios/test/test__ops__.py
@@ -27,8 +27,12 @@ def test__eq__(left, right):
assert res == exp
-@pytest.mark.filterwarnings("ignore: invalid value encountered in long_scalars")
-@pytest.mark.filterwarnings("ignore: divide by zero encountered in long_scalars")
+@pytest.mark.filterwarnings(
+ "ignore: invalid value encountered in .*_scalars", category=RuntimeWarning
+)
+@pytest.mark.filterwarnings(
+ "ignore: divide by zero encountered in .*_scalars", category=RuntimeWarning
+)
@pytest.mark.parametrize("left", diosFromMatr(DATA_ALIGNED))
@pytest.mark.parametrize("right", diosFromMatr(DATA_ALIGNED))
@pytest.mark.parametrize("op", OP2)
@@ -47,8 +51,12 @@ def test__op2__aligningops(left, right, op):
assert res == exp
-@pytest.mark.filterwarnings("ignore: invalid value encountered in long_scalars")
-@pytest.mark.filterwarnings("ignore: divide by zero encountered in long_scalars")
+@pytest.mark.filterwarnings(
+ "ignore: invalid value encountered in .*_scalars", category=RuntimeWarning
+)
+@pytest.mark.filterwarnings(
+ "ignore: divide by zero encountered in .*_scalars", category=RuntimeWarning
+)
@pytest.mark.parametrize("left", diosFromMatr(DATA_UNALIGNED))
@pytest.mark.parametrize("right", diosFromMatr(DATA_UNALIGNED))
@pytest.mark.parametrize("op", OPNOCOMP)
diff --git a/docs/Makefile b/docs/Makefile
index 6b1029bbe..411507f8e 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -35,7 +35,7 @@ doc:
# generate environment table from dictionary
@$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-# make test, clean up
+# run tests
test:
# generate parent fake module for the functions to be documented
@$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/cookbooks/DataRegularisation.rst b/docs/cookbooks/DataRegularisation.rst
index 4802066fe..ddd3c59d5 100644
--- a/docs/cookbooks/DataRegularisation.rst
+++ b/docs/cookbooks/DataRegularisation.rst
@@ -2,7 +2,7 @@
..
.. SPDX-License-Identifier: GPL-3.0-or-later
-Data Regularisation
+Data Regularization
===================
The tutorial aims to introduce the usage of ``SaQC`` methods, in order to obtain regularly sampled data derivatives
@@ -12,12 +12,12 @@ spacing in between subsequent data points.
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.
+#. Initially, we introduce and motivate regularization techniques and we do import the tutorial data.
- * :ref:`Why Regularisation <cookbooks/DataRegularisation:Why Regularisation>`
+ * :ref:`Why Regularization <cookbooks/DataRegularisation:Why Regularization?>`
* :ref:`Tutorial Data <cookbooks/DataRegularisation:Tutorial Data>`
-#. We will get an overview over the main :ref:`Regularisation <cookbooks/DataRegularisation:regularisations>` methods, starting with the shift.
+#. We will get an overview over the main :ref:`Regularization <cookbooks/DataRegularisation:regularization>` methods, starting with the shift.
* :ref:`Shift <cookbooks/DataRegularisation:shift>`
* :ref:`Target Parameter <cookbooks/DataRegularisation:target parameter>`
@@ -27,7 +27,7 @@ library.
* :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.
+ regularization.
* :ref:`Data Loss and Empty Intervals <cookbooks/DataRegularisation:data loss and empty intervals>`
@@ -50,12 +50,12 @@ library.
* :ref:`Representing Data Sparsity <cookbooks/DataRegularisation:interpolation and data sparsity>`
-#. We see how regularisation interacts with Flags.
+#. We see how regularization interacts with Flags.
- * :ref:`Flags and Regularisation <cookbooks/DataRegularisation:flags and regularisation>`
+ * :ref:`Flags and Regularization <cookbooks/DataRegularisation:flags and regularization>`
-Why Regularisation
-------------------
+Why Regularization?
+-------------------
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
@@ -112,17 +112,17 @@ rate of *15* minutes.
Finding out about the proper sampling a series should be regularized to, is a subject on its own and wont be covered
here. Usually, the intended sampling rate of sensor data is known from the specification of the sensor.
-If that is not the case, and if there seem to be more than one candidates for a rate regularisation, a rough rule of
-thumb, aiming at minimisation of data loss and data manipulation, may be,
+If that is not the case, and if there seem to be more than one candidates for a rate regularization, a rough rule of
+thumb, aiming at minimization of data loss and data manipulation, may be,
to go for the smallest rate seemingly present in the data.
-Regularisations
----------------
+Regularization
+--------------
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 <../funcs/resampling>` methods:
+Basically, there are three types of :doc:`regularization <../funcs/resampling>` methods:
#. We could keep the values as they are, and thus,
@@ -145,7 +145,7 @@ Lets apply a simple shift via the :py:meth:`~saqc.SaQC.shift` method.
Target parameter
^^^^^^^^^^^^^^^^
-We selected a new ``target`` field, to store the shifted data to a new field, so that our original data wouldnt be
+We selected a new ``target`` field, to store the shifted data to a new field, so that our original data wouldn't be
overridden.
Freq parameter
@@ -153,7 +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 <../funcs/resampling>` methods have such a frequency keyword,
+the :doc:`regularization <../funcs/resampling>` methods have such a frequency keyword,
and it just determines the sampling rate, the resulting regular timeseries will have.
Shifting Method
@@ -164,7 +164,7 @@ which applies a *backwards* shift, so data points get shifted *backwards*\ , unt
that is a multiple of *10* minutes. (See :py:meth:`~saqc.SaQC.shift` documentation for more
details on the keywords.)
-Lets see, how the data is now sampled. Therefore, we use the ``data`` Atribute from the
+Lets see, how the data is now sampled. Therefore, we use the ``data`` Attribute from the
:py:class:`SaQC <saqc.core.core.SaQC>` object. This will prevent the methods output from
being merged to a ``pandas.DataFrame`` object, and the changes from the resampling will be easier
comprehensible from one look.
@@ -192,17 +192,17 @@ Shifted data
<BLANKLINE>
-We see, the first and last *10* datapoints of both, the original data time series and the shifted one.
+We see, the first and last *10* data points of both, the original data time series and the shifted one.
-Obveously, the shifted data series now exhibits a regular sampling rate of *10* minutes, with the index
-ranging from the latest timestamp, that is a multiple of *10* minutes and preceeds the initial timestamp
-of the original data, up to the first *10* minutes multiple, that succeeds the last original datas timestamp.
-This is default behavior to all the :doc:`regularisations <../funcs/resampling>` provided by ``saqc``.
+Obviously, 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 precedes the initial timestamp
+of the original data, up to the first *10* minutes multiple, that succeeds the last original data timestamp.
+This is default behavior to all the :doc:`regularization <../funcs/resampling>` provided by ``saqc``.
Data Loss and Empty Intervals
-----------------------------
-The number of datapoints (displayed at the bottom of the table columns) has changed through the
+The number of data points (displayed at the bottom of the table columns) has changed through the
transformation as well. That change stems from 2 sources mainly:
Empty Intervals
@@ -215,20 +215,20 @@ that in the interval that is represented by that date time index, there was data
Valid Data
^^^^^^^^^^
-Data points are referred to, as *valid*\ , in context of a regularisation, if:
+Data points are referred to, as *valid*\ , in context of a regularization, if:
#.
the data points value is not ``NaN``
#.
- the *flag* of that datapoint has a value lower than the value passed to the methods
+ the *flag* of that data point has a value lower than the value passed to the methods
``to_mask`` keyword - since this keyword defaults to the highest flag level available,
- defaultly, all data flagged :py:const:`~saqc.constants.BAD`, is considered invalid by that method.
+ by default, 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 <cookbooks/DataRegularisation:flags and regularisation>` for an example of this effect and how it can help
+before regularization, will effectively exclude them from the regularization process. See chapter
+:ref:`flagging and regularization <cookbooks/DataRegularisation:flags and regularization>` for an example of this effect and how it can help
control :ref:`data reduction <cookbooks/DataRegularisation:data reduction>`.
data reduction
@@ -238,7 +238,7 @@ If there are multiple values present within an interval with size according to t
``freq``\ , this values get reduced to one single value, that will get assigned to the timestamp associated with the
interval.
-This reduction depends on the selected :doc:`regularisation <../funcs/resampling>` method.
+This reduction depends on the selected :doc:`regularization <../funcs/resampling>` method.
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
@@ -267,7 +267,7 @@ Notice, how, for example, the data point for ``2021-01-01 07:49:41`` gets shifte
``2021-01-01 07:40:00`` - although, shifting it forward to ``07:40:00`` would be less a manipulation, since this timestamp
appears to be closer to the original one.
-To shift to any frequncy aligned timestamp the value that is closest to that timestamp, we
+To shift to any frequency 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')
@@ -284,7 +284,7 @@ can perform a *nearest shift* instead of a simple *back shift*\ , by using the s
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.
+timestamp would get assigned the nearest value of all the values, that precede 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.
@@ -315,7 +315,7 @@ Aggregation
If we want to comprise several values by aggregation and assign the result to the new regular timestamp, instead of
selecting a single one, we can do this, with the :py:meth:`~saqc.SaQC.resample` method.
Lets resample the *SoilMoisture* data to have a *20* minutes sample rate by aggregating every *20* minutes intervals
-content with the arithmetic mean (which is implemented by numpies ``numpy.mean`` function for example).
+content with the arithmetic mean (which is provided by the ``numpy.mean`` function for example).
>>> import numpy as np
>>> qc = qc.resample('SoilMoisture', target='SoilMoisture_mean', freq='20min', method='bagg', func=np.mean)
@@ -333,7 +333,7 @@ content with the arithmetic mean (which is implemented by numpies ``numpy.mean``
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 |
@@ -350,32 +350,32 @@ Aggregation functions
^^^^^^^^^^^^^^^^^^^^^
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
+as long as this function returns a scalar *float* value upon an array-like input. (So ``np.median`` would be proper
for calculating the median, ``sum``\ , for assigning the value sum, and so on.)
Aggregation method
^^^^^^^^^^^^^^^^^^
-As it is with the :ref:`shift <cookbooks/DataRegularisation:Shift>` functionality, a ``method`` keyword controlls, weather the
+As it is with the :ref:`shift <cookbooks/DataRegularisation:Shift>` functionality, a ``method`` keyword controls, 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 <cookbooks/DataRegularisation:valid data>` data, that could be aggregated, get ``ǹp.nan`` assigned.
+* Also, analogous 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 ``np.nan`` assigned.
Interpolation
-------------
Another common way of obtaining regular timestamps, is, the interpolation of data at regular timestamps.
-In the pool of py:mod:`regularisation <Functions.saqc.resampling>` methods, is available the
+In the pool of py:mod:`regularization <Functions.saqc.resampling>` methods, is available the
:py:meth:`~saqc.SaQC.interpolate` method.
Lets apply a linear interpolation onto the dataset. To access
linear interpolation, we pass the ``method`` parameter the string ``"time"``. This
applies an interpolation, that is sensitive to the difference in temporal gaps
-(as opposed by ``"linear"``\ , wich expects all the gaps to be equal). Get an overview
+(as opposed by ``"linear"``\ , which 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:
@@ -394,7 +394,7 @@ documentation. Lets check the results:
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 |
@@ -410,15 +410,15 @@ documentation. Lets check the results:
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 <cookbooks/DataRegularisation:valid data>` data value preceeding them *and* one
-succeeding them *within* the given frequency range (wich is controlled by the ``freq`` keyword.).
+The regularization 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 preceding them *and* one
+succeeding them *within* the given frequency range (which is controlled by the ``freq`` keyword.).
-Thats, why, you have no interpolation value at ``2021-03-20 07:30:00`` - bacause it is preceeded
+That's why, you have no interpolation value at ``2021-03-20 07:30:00`` - because it is preceded
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
+On the other hand, there is an interpolated value assigned to ``2021-03-20 07:50:00``\ , it is preceded by
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
@@ -430,12 +430,12 @@ Linear Interpolation
Note, that there is a wrapper available for linear interpolation: :py:meth:`~saqc.SaQC.linear`.
-Flags and Regularisation
+Flags and Regularization
------------------------
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
+it can be of advantage, to flag data before regularization 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
@@ -454,12 +454,12 @@ At ``2021-01-01 15:40:02`` the original data exhibits a measurement value
of ``-120`` - which is obviously not a valid data point, regarding the fact, that *SoilMoisture* measurements
should be percentage values in between *0* and *100*.
-Since we dont exclude the value from interpolation, it gets included in the interpolation
-process for the regular timstamp at ``2021-01-01 15:40:00`` - wich, as a result, also exhibits
-a non - sence value of *-119.512446*. We could now flag the resulting regular dataset and
-exclude this calculated non sence value from further processing and analysis.
+Since we don't exclude the value from interpolation, it gets included in the interpolation
+process for the regular timestamp at ``2021-01-01 15:40:00`` - which, as a result, also exhibits
+a nonsense value of *-119.512446*. We could now flag the resulting regular dataset and
+exclude this calculated nonsense value from further processing and analysis.
-But, this would mean, that we would have a small data gap at this point.
+But, this would introduce a small data gap at this point.
We can circumvent having that gap, by flagging that value before interpolation. This
works, because there is actually another, now valid value, available in the interval
@@ -481,6 +481,7 @@ do the interpolation.
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/docs/cookbooks/MultivariateFlagging.rst b/docs/cookbooks/MultivariateFlagging.rst
index d89242d24..c7297ade1 100644
--- a/docs/cookbooks/MultivariateFlagging.rst
+++ b/docs/cookbooks/MultivariateFlagging.rst
@@ -170,7 +170,7 @@ But checking out values around *2017-10-29*, we notice, that the sampling rate s
Those instabilities do bias most statistical evaluations and it is common practice to apply some
: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
+(See also the :ref:`harmonization tutorial <cookbooks/DataRegularisation:data regularization>` for more informations
on that topic.)
We will apply :py:meth:`linear harmonisation <saqc.SaQC.linear>` to all the sensor data variables,
diff --git a/docs/documentation/Customizations.rst b/docs/documentation/Customizations.rst
index 473070fa3..3c76088f2 100644
--- a/docs/documentation/Customizations.rst
+++ b/docs/documentation/Customizations.rst
@@ -7,7 +7,7 @@ Customizations
SaQC comes with a continuously growing number of pre-implemented
quality checking and processing routines as well as flagging schemes.
-For any sufficiently large use case however it is very likely that the
+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
@@ -32,7 +32,7 @@ SaQC provides two ways to integrate custom routines into the system:
Interface
^^^^^^^^^
-In order to make a function usable within the evaluation framework of SaQC it needs to
+In order to make a function usable within the evaluation framework of SaQC, it needs to
implement the following function interface
.. code-block:: python
@@ -42,12 +42,11 @@ implement the following function interface
import saqc
def yourTestFunction(
- data: dios.DictOfSeries,
+ saqc: SaQC
field: str,
- flags: saqc.Flags,
*args,
**kwargs
- ) -> (dios.DictOfSeries, saqc.Flags)
+ ) -> SaQC
Argument Descriptions
~~~~~~~~~~~~~~~~~~~~~
@@ -81,8 +80,8 @@ test functions into SaQC. Here is a complete dummy example:
from saqc import register
@flagging()
- def yourTestFunction(data, field, flags, *args, **kwargs):
- return data, flags
+ def yourTestFunction(saqc: SaQC, field: str, *args, **kwargs):
+ return saqc
Example
^^^^^^^
diff --git a/docs/documentation/GlobalKeywords.rst b/docs/documentation/GlobalKeywords.rst
index 4bea09db6..781dfbc6f 100644
--- a/docs/documentation/GlobalKeywords.rst
+++ b/docs/documentation/GlobalKeywords.rst
@@ -225,7 +225,7 @@ We can make the value flagged by both the flagging functions by increasing the
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 = qc.flagRange('data', max=0, label='value > 0', dfilter=255)
qc.plot('data')
Unflagging Values
@@ -267,5 +267,5 @@ gets the already flagged values passed to test them.
:context: close-figs
:include-source: False
- qc = qc.flagConstants('data', window='2D', thresh=0, dfilter=300, flag=-np.inf)
+ qc = qc.flagConstants('data', window='2D', thresh=0, dfilter=255, flag=-np.inf)
qc.plot('data')
diff --git a/docs/documentation/WritingFunctions.rst b/docs/documentation/WritingFunctions.rst
index 6080a5e31..25ddb90bb 100644
--- a/docs/documentation/WritingFunctions.rst
+++ b/docs/documentation/WritingFunctions.rst
@@ -10,7 +10,7 @@ Writing non-standard functions
When implementing non-standard functions, i.e. all function not decorated with ``flagging``, some special care is
needed to comply to the standard ``SaQC`` behaviour. The following passages guide you through the jungle of
-``register`` arguments and there semantics.
+``register`` arguments and their semantics.
Masking
"""""""
diff --git a/docs/gettingstarted/InstallationGuide.rst b/docs/gettingstarted/InstallationGuide.rst
index ebe572bd8..700175b50 100644
--- a/docs/gettingstarted/InstallationGuide.rst
+++ b/docs/gettingstarted/InstallationGuide.rst
@@ -79,9 +79,9 @@ or
pip install git+https://git.ufz.de/rdm-software/saqc@master
-If you feel more adventurous feel free to use the latest development version from our
-`GitLab-repository <https://git.ufz.de/rdm-software/saqc>`_. While we try to keep the
-develop branch in a workable state, we sill won't make any guarantees here.
+If you feel more adventurous, feel free to use the latest development version from our
+`GitLab-repository <https://git.ufz.de/rdm-software/saqc>`_. We try to keep the
+develop branch in a workable state, but sill don't make any guarantees here.
.. code-block:: sh
diff --git a/docs/gettingstarted/TutorialCLI.rst b/docs/gettingstarted/TutorialCLI.rst
index f635ebd9a..e173f3c66 100644
--- a/docs/gettingstarted/TutorialCLI.rst
+++ b/docs/gettingstarted/TutorialCLI.rst
@@ -292,4 +292,3 @@ You can learn more about the syntax of these custom functions
:align: center
qc.plot('SM2')
-
diff --git a/docs/index.rst b/docs/index.rst
index f2b74478e..81a667b37 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -92,10 +92,11 @@ Getting Started
Writing Functions <documentation/WritingFunctions>
-Indices and tables
-==================
+..
+ Indices and tables
+ ==================
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
+ * :ref:`genindex`
+ * :ref:`modindex`
+ * :ref:`search`
diff --git a/docs/misc/title.rst b/docs/misc/title.rst
index f92b0cdd0..418249e55 100644
--- a/docs/misc/title.rst
+++ b/docs/misc/title.rst
@@ -69,7 +69,7 @@ Features
* or use SaQC as a commandline application and configure your pipelines via plain text
* - |sacRaw|
- * easily load data from multiple sources, concatenating them in a SaQC object
- * :ref:`preprocess your data, by aligning it to shared frequency grids <cookbooks/DataRegularisation:Data Regularisation>`
+ * :ref:`preprocess your data, by aligning it to shared frequency grids <cookbooks/DataRegularisation:Data Regularization>`
* - |sacFlagged|
- * apply basic plausibility checks, as well as
* more complex, univariat flagging Functions
diff --git a/docs/requirements.txt b/docs/requirements.txt
index b22a8edcc..2ff705310 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -3,11 +3,11 @@
# SPDX-License-Identifier: GPL-3.0-or-later
recommonmark==0.7.1
-sphinx<5
+sphinx<6
sphinx-automodapi==0.14.1
sphinxcontrib-fulltoc==1.2.0
-sphinx-markdown-tables==0.0.15
+sphinx-markdown-tables==0.0.17
m2r==0.2.1
jupyter-sphinx==0.3.2
sphinx_autodoc_typehints==1.18.2
-sphinx-tabs==3.3.1
+sphinx-tabs==3.4.1
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diff --git a/docs/resources/images/flagJumpsPic.excalidraw.license b/docs/resources/images/flagJumpsPic.excalidraw.license
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\ No newline at end of file
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diff --git a/docs/resources/images/flagJumpsPic.png.license b/docs/resources/images/flagJumpsPic.png.license
new file mode 100644
index 000000000..f8c6bf8cd
--- /dev/null
+++ b/docs/resources/images/flagJumpsPic.png.license
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\ No newline at end of file
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new file mode 100644
index 000000000..f8c6bf8cd
--- /dev/null
+++ b/docs/resources/images/flagOffsetPic.png.license
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diff --git a/requirements.txt b/requirements.txt
index 8a6bb169e..42bd4d01c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,13 +4,13 @@
Click==8.1.3
dtw==1.4.0
-hypothesis==6.46.11
-matplotlib==3.5.2
-numba==0.55.2
+hypothesis==6.55.0
+matplotlib==3.5.3
+numba==0.56.3
numpy==1.21.6
outlier-utils==0.0.3
-pyarrow==8.0.0
+pyarrow==9.0.0
pandas==1.3.5
scikit-learn==1.0.2
scipy==1.7.3
-typing_extensions==4.2.0
+typing_extensions==4.3.0
diff --git a/saqc/constants.py b/saqc/constants.py
index d425a9111..839795e0e 100644
--- a/saqc/constants.py
+++ b/saqc/constants.py
@@ -59,6 +59,9 @@ FILTER_NONE = np.inf
# ----------------------------------------------------------------------
# other
# ----------------------------------------------------------------------
+def clip(series, lower=None, upper=None):
+ return series.clip(lower=lower, upper=upper)
+
ENVIRONMENT = {
# Infinity constant
@@ -105,6 +108,7 @@ ENVIRONMENT = {
"madScore": ts_ops.standardizeByMedian,
# Standardize with Median and inter quantile range.
"iqsScore": ts_ops.standardizeByIQR,
+ "clip": clip,
"GOOD": GOOD,
"BAD": BAD,
"UNFLAGGED": UNFLAGGED,
diff --git a/saqc/core/core.py b/saqc/core/core.py
index b1ea076b9..abb47c7a5 100644
--- a/saqc/core/core.py
+++ b/saqc/core/core.py
@@ -10,19 +10,15 @@ from __future__ import annotations
import warnings
from copy import copy as shallowcopy
from copy import deepcopy
-from typing import Any, Callable, Hashable, List, MutableMapping, Sequence, Tuple
+from typing import Any, Hashable, MutableMapping
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.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.register import FUNC_MAP
from saqc.core.translation import (
DmpScheme,
FloatScheme,
@@ -30,8 +26,8 @@ from saqc.core.translation import (
SimpleScheme,
TranslationScheme,
)
-from saqc.lib.tools import concatDios, toSequence
-from saqc.lib.types import ExternalFlag, OptionalNone
+from saqc.funcs import FunctionsMixin
+from saqc.lib.tools import concatDios
# warnings
pd.set_option("mode.chained_assignment", "warn")
@@ -60,13 +56,13 @@ class SaQC(FunctionsMixin):
flags=None,
scheme: str | TranslationScheme = "float",
):
- self._data = self._initData(data)
- self._flags = self._initFlags(flags)
- self._scheme = self._initTranslationScheme(scheme)
- self._attrs = {}
+ self._data: DictOfSeries = self._initData(data)
+ self._flags: Flags = self._initFlags(flags)
+ self._scheme: TranslationScheme = self._initTranslationScheme(scheme)
+ self._attrs: dict = {}
self._validate(reason="init")
- def _construct(self, **attributes) -> SaQC:
+ def _construct(self, **attributes) -> "SaQC":
"""
Construct a new `SaQC`-Object from `self` and optionally inject
attributes with any chechking and overhead.
@@ -118,132 +114,17 @@ class SaQC(FunctionsMixin):
flags.attrs = self._attrs.copy()
return flags
- def _expandFields(
- self,
- regex: bool,
- field: str | Sequence[str],
- target: str | Sequence[str] = None,
- ) -> Tuple[List[str], List[str]]:
- """
- check and expand `field` and `target`
- """
-
- # expand regular expressions
- if regex:
- fmask = self._data.columns.str.match(field)
- fields = self._data.columns[fmask].tolist()
- else:
- fields = toSequence(field)
-
- targets = fields if target is None else toSequence(target)
-
- return fields, targets
-
- def _wrap(self, func: FunctionWrapper):
- """
- prepare user function input:
- - expand fields and targets
- - translate user given ``flag`` values or set the default ``BAD``
- - translate user given ``dfilter`` values or set the scheme default
- - dependeing on the workflow: initialize ``target`` variables
-
- Here we add the following parameters to all registered functions, regardless
- of their repsective definition:
- - ``regex``
- - ``target``
-
- """
-
- def inner(
- field: str | Sequence[str],
- *args,
- target: str | Sequence[str] = None,
- regex: bool = False,
- flag: ExternalFlag | OptionalNone = OptionalNone(),
- **kwargs,
- ) -> SaQC:
-
- 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
- # `None` so we introduce a special class here
- kwargs["flag"] = self._scheme(flag)
-
- fields, targets = self._expandFields(
- regex=regex, field=field, target=target
- )
- out = self.copy(deep=True)
-
- if not func.handles_target:
- if len(fields) != len(targets):
- raise ValueError(
- "expected the same number of 'field' and 'target' values"
- )
-
- # initialize all target variables
- for src, trg in zip(fields, targets):
- if src != trg:
- out = out._callFunction(
- FUNC_MAP["copyField"],
- field=src,
- target=trg,
- overwrite=True,
- )
-
- if func.multivariate:
- # pass all fields and targets
- out = out._callFunction(
- func,
- field=fields,
- target=targets,
- *args,
- **kwargs,
- )
- else:
- # call the function on target
- for src, trg in zip(fields, targets):
- fkwargs = {**kwargs, "field": src, "target": trg}
- if not func.handles_target:
- fkwargs["field"] = fkwargs.pop("target")
- out = out._callFunction(func, *args, **fkwargs)
-
- return out
-
- return inner
-
- def _callFunction(
- self,
- function: Callable,
- field: str | Sequence[str],
- *args: Any,
- **kwargs: Any,
- ) -> SaQC:
-
- res = function(data=self._data, flags=self._flags, field=field, *args, **kwargs)
-
- # keep consistence: if we modify data and flags inplace in a function,
- # but data is the original and flags is a copy (as currently implemented),
- # data and flags of the original saqc obj may change inconsistently.
- self._data, self._flags = res
- self._validate(reason=f"call to {repr(function.__name__)}")
-
- return self._construct(_data=self._data, _flags=self._flags)
-
def __getattr__(self, key):
"""
All failing attribute accesses are redirected to __getattr__.
We use this mechanism to make the registered functions appear
as `SaQC`-methods without actually implementing them.
"""
+ from functools import partial
+
if key not in FUNC_MAP:
raise AttributeError(f"SaQC has no attribute {repr(key)}")
- return self._wrap(FUNC_MAP[key])
+ return partial(FUNC_MAP[key], self)
def copy(self, deep=True):
copyfunc = deepcopy if deep else shallowcopy
@@ -286,7 +167,7 @@ class SaQC(FunctionsMixin):
raise TypeError(
"'data' must be of type pandas.Series, "
- "pandas.DataFrame or dios.DictOfSeries or"
+ "pandas.DataFrame or dios.DictOfSeries or "
"a list of those."
)
diff --git a/saqc/core/flags.py b/saqc/core/flags.py
index c5acb68c4..48482a703 100644
--- a/saqc/core/flags.py
+++ b/saqc/core/flags.py
@@ -25,6 +25,7 @@ DictLike = Union[
_Field = str
SelectT = Union[
_Field,
+ Tuple[np.ndarray, _Field],
Tuple[pd.Series, _Field],
Tuple[pd.Index, _Field],
Tuple[slice, _Field],
@@ -194,6 +195,8 @@ class Flags:
self, raw_data: Optional[Union[DictLike, Flags]] = None, copy: bool = False
):
+ self._data: dict[str, History]
+
if raw_data is None:
raw_data = {}
diff --git a/saqc/core/modules/__init__.py b/saqc/core/modules/__init__.py
deleted file mode 100644
index 1c9f1b6a3..000000000
--- a/saqc/core/modules/__init__.py
+++ /dev/null
@@ -1,50 +0,0 @@
-#! /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.changepoints import ChangePoints
-from saqc.core.modules.constants import Constants
-from saqc.core.modules.curvefit import Curvefit
-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.residuals import Residuals
-from saqc.core.modules.rolling import Rolling
-from saqc.core.modules.scores import Scores
-from saqc.core.modules.tools import Tools
-from saqc.core.modules.transformation import Transformation
-from saqc.core.register import FUNC_MAP
-
-
-class FunctionsMixin(
- Breaks,
- Noise,
- ChangePoints,
- Constants,
- Curvefit,
- Drift,
- FlagTools,
- Generic,
- Interpolation,
- Outliers,
- Pattern,
- Resampling,
- Residuals,
- Rolling,
- Scores,
- Tools,
- Transformation,
-):
- def _defer(self, fname, flocals):
- flocals.pop("self", None)
- fkwargs = flocals.pop("kwargs", {})
- return self._wrap(FUNC_MAP[fname])(**flocals, **fkwargs)
diff --git a/saqc/core/modules/breaks.py b/saqc/core/modules/breaks.py
deleted file mode 100644
index 76ffee5a9..000000000
--- a/saqc/core/modules/breaks.py
+++ /dev/null
@@ -1,44 +0,0 @@
-#! /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 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,
- ) -> saqc.SaQC:
- return self._defer("flagIsolated", locals())
-
- @doc(saqc.funcs.breaks.flagJumps.__doc__)
- def flagJumps(
- self,
- field: str,
- thresh: float,
- window: str,
- min_periods: int = 1,
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagJumps", locals())
diff --git a/saqc/core/modules/changepoints.py b/saqc/core/modules/changepoints.py
deleted file mode 100644
index dd1db1096..000000000
--- a/saqc/core/modules/changepoints.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#! /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, Tuple
-
-import numpy as np
-from typing_extensions import Literal
-
-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,
- stat_func: Callable[[np.ndarray, np.ndarray], float],
- thresh_func: Callable[[np.ndarray, np.ndarray], float],
- window: str | Tuple[str, str],
- min_periods: int | Tuple[int, int],
- closed: Literal["right", "left", "both", "neither"] = "both",
- reduce_window: str = None,
- reduce_func: Callable[[np.ndarray, np.ndarray], int] = lambda x, _: x.argmax(),
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagChangePoints", locals())
-
- @doc(saqc.funcs.changepoints.assignChangePointCluster.__doc__)
- def assignChangePointCluster(
- self,
- field: str,
- stat_func: Callable[[np.array, np.array], float],
- thresh_func: Callable[[np.array, np.array], float],
- window: str | Tuple[str, str],
- min_periods: int | Tuple[int, int],
- closed: Literal["right", "left", "both", "neither"] = "both",
- reduce_window: str = None,
- reduce_func: Callable[
- [np.ndarray, np.ndarray], float
- ] = lambda x, _: x.argmax(),
- model_by_resids: bool = False,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("assignChangePointCluster", locals())
diff --git a/saqc/core/modules/constants.py b/saqc/core/modules/constants.py
deleted file mode 100644
index 6391bb4e9..000000000
--- a/saqc/core/modules/constants.py
+++ /dev/null
@@ -1,34 +0,0 @@
-#! /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 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,
- thresh: float,
- maxna: int | None = None,
- maxna_group: int | None = None,
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagByVariance", locals())
-
- @doc(saqc.funcs.constants.flagConstants.__doc__)
- def flagConstants(
- 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
deleted file mode 100644
index f915ba5ce..000000000
--- a/saqc/core/modules/curvefit.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#! /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 typing_extensions import Literal
-
-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: int | str,
- order: int,
- min_periods: int = 0,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("fitPolynomial", locals())
diff --git a/saqc/core/modules/drift.py b/saqc/core/modules/drift.py
deleted file mode 100644
index 2e152d762..000000000
--- a/saqc/core/modules/drift.py
+++ /dev/null
@@ -1,123 +0,0 @@
-#! /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, Sequence, Union
-
-import numpy as np
-from scipy.spatial.distance import pdist
-from typing_extensions import Literal
-
-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],
- freq: str,
- spread: float,
- frac: float = 0.5,
- metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
- np.array([x, y]), metric="cityblock"
- )
- / len(x),
- method: LinkageString = "single",
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagDriftFromNorm", locals())
-
- @doc(saqc.funcs.drift.flagDriftFromReference.__doc__)
- def flagDriftFromReference(
- self,
- field: Sequence[str],
- reference: str,
- freq: str,
- thresh: float,
- metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
- np.array([x, y]), metric="cityblock"
- )
- / len(x),
- flag: float = BAD,
- **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,
- ) -> saqc.SaQC:
- return self._defer("correctDrift", locals())
-
- @doc(saqc.funcs.drift.correctRegimeAnomaly.__doc__)
- def correctRegimeAnomaly(
- self,
- field: str,
- cluster_field: str,
- model: CurveFitter,
- tolerance: Optional[str] = None,
- epoch: bool = False,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("correctRegimeAnomaly", locals())
-
- @doc(saqc.funcs.drift.correctOffset.__doc__)
- def correctOffset(
- self,
- field: str,
- max_jump: float,
- spread: float,
- window: str,
- min_periods: int,
- tolerance: Optional[str] = None,
- **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
deleted file mode 100644
index 66efc28d7..000000000
--- a/saqc/core/modules/flagtools.py
+++ /dev/null
@@ -1,74 +0,0 @@
-#! /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, Sequence, Union
-
-import numpy as np
-import pandas as pd
-from typing_extensions import Literal
-
-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,
- mdata: Union[pd.Series, pd.DataFrame, DictOfSeries, list, np.array],
- method: Literal[
- "left-open", "right-open", "closed", "plain", "ontime"
- ] = "left-open",
- mformat: Literal["start-end", "mflag"] = "start-end",
- mflag: Any = 1,
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagManual", locals())
-
- @doc(saqc.funcs.flagtools.transferFlags.__doc__)
- def transferFlags(
- self,
- field: str | Sequence[str],
- target: str | Sequence[str],
- **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
deleted file mode 100644
index 8759bebd7..000000000
--- a/saqc/core/modules/generic.py
+++ /dev/null
@@ -1,43 +0,0 @@
-#! /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
-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 = 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]],
- func: GenericFunction,
- target: Union[str, Sequence[str]] = None,
- flag: float = BAD,
- dfilter: float = FILTER_ALL,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagGeneric", locals())
diff --git a/saqc/core/modules/interpolation.py b/saqc/core/modules/interpolation.py
deleted file mode 100644
index 0a949a9bb..000000000
--- a/saqc/core/modules/interpolation.py
+++ /dev/null
@@ -1,60 +0,0 @@
-#! /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, Union
-
-import numpy as np
-import pandas as pd
-
-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,
- window: Union[str, int],
- func: Callable[[pd.Series], float] = np.median,
- center: bool = True,
- min_periods: int = 0,
- flag: float = UNFLAGGED,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("interpolateByRolling", locals())
-
- @doc(saqc.funcs.interpolation.interpolateInvalid.__doc__)
- def interpolateInvalid(
- self,
- field: str,
- method: _SUPPORTED_METHODS,
- order: int = 2,
- limit: int = 2,
- downgrade: bool = False,
- flag: float = UNFLAGGED,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("interpolateInvalid", locals())
-
- @doc(saqc.funcs.interpolation.interpolateIndex.__doc__)
- def interpolateIndex(
- self,
- field: str,
- freq: str,
- method: _SUPPORTED_METHODS,
- order: int = 2,
- limit: int = 2,
- downgrade: bool = False,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("interpolateIndex", locals())
diff --git a/saqc/core/modules/noise.py b/saqc/core/modules/noise.py
deleted file mode 100644
index 9b1209ad3..000000000
--- a/saqc/core/modules/noise.py
+++ /dev/null
@@ -1,35 +0,0 @@
-#! /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
-
-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.ndarray, pd.Series], float],
- window: str | pd.Timedelta,
- thresh: float,
- sub_window: str | pd.Timedelta = None,
- sub_thresh: float = None,
- min_periods: int = None,
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagByStatLowPass", locals())
diff --git a/saqc/core/modules/outliers.py b/saqc/core/modules/outliers.py
deleted file mode 100644
index 261c8146e..000000000
--- a/saqc/core/modules/outliers.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#! /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, Sequence, 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 Outliers:
- @doc(saqc.funcs.outliers.flagByStray.__doc__)
- def flagByStray(
- self,
- field: str,
- window: Optional[Union[int, str]] = None,
- min_periods: int = 11,
- iter_start: float = 0.5,
- alpha: float = 0.05,
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagByStray", locals())
-
- @doc(saqc.funcs.outliers.flagMVScores.__doc__)
- def flagMVScores(
- self,
- field: Sequence[str],
- trafo: Callable[[pd.Series], pd.Series] = lambda x: x,
- alpha: float = 0.05,
- n: int = 10,
- func: Callable[[pd.Series], float] = np.sum,
- iter_start: float = 0.5,
- partition: Optional[Union[int, str]] = None,
- partition_min: int = 11,
- stray_range: Optional[str] = None,
- drop_flagged: bool = False, # TODO: still a case ?
- thresh: float = 3.5,
- min_periods: int = 1,
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagMVScores", locals())
-
- @doc(saqc.funcs.outliers.flagRaise.__doc__)
- def flagRaise(
- self,
- field: str,
- thresh: float,
- raise_window: str,
- freq: str,
- average_window: Optional[str] = None,
- raise_factor: float = 2.0,
- slope: Optional[float] = None,
- weight: float = 0.8,
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagRaise", locals())
-
- @doc(saqc.funcs.outliers.flagMAD.__doc__)
- def flagMAD(
- self,
- field: str,
- window: str,
- z: float = 3.5,
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagMAD", locals())
-
- @doc(saqc.funcs.outliers.flagOffset.__doc__)
- def flagOffset(
- self,
- field: str,
- 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,
- window: Union[str, int],
- alpha: float = 0.05,
- min_periods: int = 8,
- pedantic: bool = False,
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagByGrubbs", locals())
-
- @doc(saqc.funcs.outliers.flagRange.__doc__)
- def flagRange(
- self,
- field: str,
- min: float = -np.inf,
- max: float = np.inf,
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagRange", locals())
-
- @doc(saqc.funcs.outliers.flagCrossStatistics.__doc__)
- def flagCrossStatistics(
- self,
- field: Sequence[str],
- thresh: float,
- method: Literal["modZscore", "Zscore"] = "modZscore",
- flag: float = BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagCrossStatistics", locals())
diff --git a/saqc/core/modules/pattern.py b/saqc/core/modules/pattern.py
deleted file mode 100644
index 2c37f51d8..000000000
--- a/saqc/core/modules/pattern.py
+++ /dev/null
@@ -1,28 +0,0 @@
-#! /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 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,
- reference,
- max_distance=0.0,
- normalize=True,
- plot=False,
- flag=BAD,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("flagPatternByDTW", locals())
diff --git a/saqc/core/modules/resampling.py b/saqc/core/modules/resampling.py
deleted file mode 100644
index 1dfa0b780..000000000
--- a/saqc/core/modules/resampling.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#! /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
-
-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.funcs.interpolation import _SUPPORTED_METHODS
-from saqc.lib.docurator import doc
-
-
-class Resampling:
- @doc(saqc.funcs.resampling.linear.__doc__)
- def linear(
- self,
- field: str,
- freq: str,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("linear", locals())
-
- @doc(saqc.funcs.resampling.interpolate.__doc__)
- def interpolate(
- self,
- field: str,
- freq: str,
- method: _SUPPORTED_METHODS,
- order: int = 1,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("interpolate", locals())
-
- @doc(saqc.funcs.resampling.shift.__doc__)
- def shift(
- self,
- field: str,
- freq: str,
- method: Literal["fshift", "bshift", "nshift"] = "nshift",
- freq_check: Optional[Literal["check", "auto"]] = None,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("shift", locals())
-
- @doc(saqc.funcs.resampling.resample.__doc__)
- def resample(
- self,
- field: str,
- freq: str,
- func: Callable[[pd.Series], pd.Series] = np.mean,
- method: Literal["fagg", "bagg", "nagg"] = "bagg",
- maxna: Optional[int] = None,
- maxna_group: Optional[int] = None,
- maxna_flags: Optional[int] = None, # TODO: still a case ??
- maxna_group_flags: Optional[int] = None,
- flag_func: Callable[[pd.Series], float] = max,
- freq_check: Optional[Literal["check", "auto"]] = None,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("resample", locals())
-
- @doc(saqc.funcs.resampling.concatFlags.__doc__)
- def concatFlags(
- self,
- field: str,
- target: str,
- method: Literal[
- "inverse_fagg",
- "inverse_bagg",
- "inverse_nagg",
- "inverse_fshift",
- "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
deleted file mode 100644
index e764099d1..000000000
--- a/saqc/core/modules/residuals.py
+++ /dev/null
@@ -1,44 +0,0 @@
-#! /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/rolling.py b/saqc/core/modules/rolling.py
deleted file mode 100644
index 108453d4e..000000000
--- a/saqc/core/modules/rolling.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#! /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, 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,
- window: Union[str, int],
- func: Callable[[pd.Series], np.ndarray] = np.mean,
- min_periods: int = 0,
- center: bool = True,
- **kwargs
- ):
- return self._defer("roll", locals())
diff --git a/saqc/core/modules/scores.py b/saqc/core/modules/scores.py
deleted file mode 100644
index 9fd3466ef..000000000
--- a/saqc/core/modules/scores.py
+++ /dev/null
@@ -1,36 +0,0 @@
-#! /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, 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],
- target: str,
- n: int = 10,
- func: Callable[[pd.Series], float] = np.sum,
- freq: Union[float, str] = np.inf,
- min_periods: int = 2,
- method: Literal["ball_tree", "kd_tree", "brute", "auto"] = "ball_tree",
- metric: str = "minkowski",
- p: int = 2,
- **kwargs,
- ) -> saqc.SaQC:
- return self._defer("assignKNNScore", locals())
diff --git a/saqc/core/modules/tools.py b/saqc/core/modules/tools.py
deleted file mode 100644
index 3eeb930fe..000000000
--- a/saqc/core/modules/tools.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#! /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
-
-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())
-
- @doc(saqc.funcs.tools.selectTime.__doc__)
- def selectTime(
- self,
- field: str,
- 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("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"] | 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
deleted file mode 100644
index 3952f89c4..000000000
--- a/saqc/core/modules/transformation.py
+++ /dev/null
@@ -1,28 +0,0 @@
-#! /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 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,
- ) -> saqc.SaQC:
- return self._defer("transform", locals())
diff --git a/saqc/core/register.py b/saqc/core/register.py
index 29ddcce97..148f37d8d 100644
--- a/saqc/core/register.py
+++ b/saqc/core/register.py
@@ -9,15 +9,21 @@ from __future__ import annotations
import functools
import inspect
import warnings
-from typing import Any, Callable, Dict, Sequence, Tuple
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Sequence, Tuple, TypeVar
import numpy as np
import pandas as pd
+from typing_extensions import ParamSpec
import dios
-from saqc.constants import FILTER_ALL, UNFLAGGED
+from saqc.constants import FILTER_ALL, FILTER_NONE, UNFLAGGED
from saqc.core.flags import Flags, History
+from saqc.core.translation.basescheme import TranslationScheme
from saqc.lib.tools import squeezeSequence, toSequence
+from saqc.lib.types import ExternalFlag, OptionalNone
+
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
# NOTE:
# the global SaQC function store,
@@ -26,329 +32,237 @@ FUNC_MAP: Dict[str, Callable] = {}
_is_list_like = pd.api.types.is_list_like
+T = TypeVar("T")
+P = ParamSpec("P")
-class FunctionWrapper:
- def __init__(
- self,
- func: Callable,
- mask: list,
- demask: list,
- squeeze: list,
- multivariate: bool = False,
- handles_target: bool = False,
- ):
- # todo:
- # - meta only is written with squeeze
-
- self.func = func
- self.func_name = func.__name__
- self.func_signature = inspect.signature(func)
-
- # ensure type and all elements exist in signature
- self._checkDecoratorKeywords(mask, demask, squeeze)
-
- self.decorator_mask = mask
- self.decorator_demask = demask
- self.decorator_squeeze = squeeze
- self.multivariate = multivariate
- self.handles_target = handles_target
-
- # set in __call__
- self.data = None
- self.flags = None
- self.fields = None
- self.args = None
- self.kwargs = None
- self.mask_thresh = None
- self.stored_data = None
-
- # make ourself look like the wrapped function, especially the docstring
- functools.update_wrapper(self, func)
-
- def _checkDecoratorKeywords(self, mask, demask, squeeze):
- params = self.func_signature.parameters.keys()
- for dec_arg, name in zip(
- [mask, demask, squeeze], ["mask", "demask", "squeeze"]
- ):
- typeerr = TypeError(
- f"type of decorator argument '{name}' must "
- f"be a list of strings, not {repr(type(dec_arg))}"
- )
- if not isinstance(dec_arg, list):
- raise typeerr
- for elem in dec_arg:
- if not isinstance(elem, str):
- raise typeerr
- if elem not in params:
- raise ValueError(
- f"passed value {repr(elem)} in {repr(name)} is not an "
- f"parameter in decorated function {repr(self.func_name)}"
- )
-
- @staticmethod
- def _argnamesToColumns(names: list, values: dict):
- clist = []
- for name in names:
- value = values.get(name) # eg. the value behind 'field'
-
- # NOTE: do not change order of the tests
- if value is None:
- pass
- elif isinstance(value, str):
- clist.append(value)
- # we ignore DataFrame, Series, DictOfSeries
- # and high order types alike
- elif hasattr(value, "columns"):
- pass
- elif _is_list_like(value) and all([isinstance(e, str) for e in value]):
- clist += value
- return pd.Index(clist)
-
- @staticmethod
- def _warn(missing, source):
- if len(missing) == 0:
- return
- action = source + "ed"
- obj = "flags" if source == "squeeze" else "data"
- warnings.warn(
- f"Column(s) {repr(missing)} cannot not be {action} "
- f"because they are not present in {obj}. ",
- RuntimeWarning,
- )
- def __call__(
- self, data: dios.DictOfSeries, field: str, flags: Flags, *args, **kwargs
- ) -> Tuple[dios.DictOfSeries, Flags]:
- """
- This wraps a call to a saqc function.
-
- Before the saqc function call it copies flags and maybe mask data (inplace).
- After the call it maybe squeezes modified histories and maybe reinsert the
- masked data locations.
-
- If the squeezing and/or the masking and/or the demasking will happen, depends
- on the decorator keywords `handles` and `datamask`. See ``_determineActions``,
- for that.
- """
- # keep this the original values
- self.data = data
- self.flags = flags
- self.fields = toSequence(field)
- self.args = args
- self.kwargs = self._checkKwargs(kwargs)
-
- self.mask_thresh = self._getMaskingThresh()
-
- # skip (data, field, flags)
- names = list(self.func_signature.parameters.keys())[3 : 3 + len(args)]
- all_args = {"field": field, **dict(zip(names, args)), **kwargs}
-
- # find columns that need masking
- columns = self._argnamesToColumns(self.decorator_mask, all_args)
- self._warn(columns.difference(self.data.columns).to_list(), source="mask")
- columns = columns.intersection(self.data.columns)
-
- masked, stored = self._maskData(
- data=self.data,
- flags=self.flags,
- columns=columns,
- thresh=self.mask_thresh,
+def _checkDecoratorKeywords(
+ func_signature, func_name, mask, demask, squeeze, handles_target
+):
+ params = func_signature.parameters.keys()
+ if "target" in params and not handles_target:
+ raise TypeError(
+ "functions defining a parameter named 'target' "
+ "need to decorated with 'handles_target=True'"
+ )
+ for dec_arg, name in zip([mask, demask, squeeze], ["mask", "demask", "squeeze"]):
+ typeerr = TypeError(
+ f"type of decorator argument '{name}' must "
+ f"be a list of strings, not {repr(type(dec_arg))}"
)
- self.data = masked
- self.stored_data = stored
-
- args, kwargs = self._prepareArgs()
- data, flags = self.func(*args, **kwargs)
-
- # find columns that need squeezing
- columns = self._argnamesToColumns(self.decorator_squeeze, all_args)
- self._warn(columns.difference(flags.columns).to_list(), source="squeeze")
- columns = columns.intersection(flags.columns)
-
- # if the function did not want to set any flags at all,
- # we assume a processing function that altered the flags
- # in an unpredictable manner or do nothing with the flags.
- # in either case we take the returned flags as the new truth.
- if columns.empty:
- result_flags = flags
- else:
- # even if this looks like a noop for columns=[],
- # it returns the old instead the new flags and
- # therefore ignores any possible processing changes
- result_flags = self._squeezeFlags(flags, columns)
-
- # find columns that need demasking
- columns = self._argnamesToColumns(self.decorator_demask, all_args)
- self._warn(columns.difference(data.columns).to_list(), source="demask")
- columns = columns.intersection(data.columns)
-
- result_data = self._unmaskData(data, self.stored_data, columns=columns)
-
- return result_data, result_flags
-
- @staticmethod
- def _checkKwargs(kwargs: dict) -> dict[str, Any]:
- if "dfilter" in kwargs and not isinstance(
- kwargs["dfilter"], (bool, float, int)
- ):
- raise TypeError(f"'dfilter' must be of type bool or float")
- return kwargs
-
- def _prepareArgs(self) -> Tuple[tuple, dict[str, Any]]:
- """
- Prepare the args and kwargs passed to the function
- Returns
- -------
- args: tuple
- arguments to be passed to the actual call
- kwargs: dict
- keyword-arguments to be passed to the actual call
- """
- kwargs = self.kwargs.copy()
- kwargs["dfilter"] = self.mask_thresh
-
- # always pass a list to multivariate functions and
- # unpack single element lists for univariate functions
- if self.multivariate:
- field = self.fields
- else:
- field = squeezeSequence(self.fields)
-
- args = self.data, field, self.flags.copy(), *self.args
- return args, kwargs
-
- def _getMaskingThresh(self) -> float:
- """
- Generate a float threshold by the value of the `dfilter` keyword
-
- Returns
- -------
- threshold: float
- All data gets masked, if the flags are equal or worse than the threshold.
-
- Notes
- -----
- If ``dfilter`` is **not** in the kwargs, the threshold defaults to `FILTER_ALL`.
- For any floatish value, it is taken as the threshold.
- """
- if "dfilter" not in self.kwargs:
- return FILTER_ALL
- return float(self.kwargs["dfilter"]) # handle int
-
- def _createMeta(self) -> dict:
- return {
- "func": self.func_name,
- "args": self.args,
- "kwargs": self.kwargs,
- }
-
- def _squeezeFlags(self, flags: Flags, columns: pd.Index) -> Flags:
- """
- Generate flags from the temporary result-flags and the original flags.
-
- Parameters
- ----------
- flags : Flags
- The flags-frame, which is the result from a saqc-function
-
- Returns
- -------
- Flags
- """
- out = self.flags.copy() # the old flags
- meta = self._createMeta()
- for col in columns:
-
- # todo: shouldn't we fail or warn here or even have a explicit test upstream
- # because the function should ensure consistence, especially because
- # a empty history maybe issnt what is expected, but this happens silently
- if col not in out: # ensure existence
- out.history[col] = History(index=flags.history[col].index)
-
- old_history = out.history[col]
- new_history = flags.history[col]
-
- # We only want to add new columns, that were appended during the last
- # function call. If no such columns exist, we end up with an empty
- # new_history.
- start = len(old_history.columns)
- new_history = self._sliceHistory(new_history, slice(start, None))
-
- squeezed = new_history.squeeze(raw=True)
- out.history[col] = out.history[col].append(squeezed, meta=meta)
-
- return out
-
- @staticmethod
- def _sliceHistory(history: History, sl: slice) -> History:
- history.hist = history.hist.iloc[:, sl]
- history.meta = history.meta[sl]
- return history
-
- @staticmethod
- def _maskData(
- data: dios.DictOfSeries, flags: Flags, columns: Sequence[str], thresh: float
- ) -> Tuple[dios.DictOfSeries, dios.DictOfSeries]:
- """
- Mask data with Nans, if the flags are worse than a threshold.
- - mask only passed `columns` (preselected by `datamask`-kw from decorator)
-
- Returns
- -------
- masked : dios.DictOfSeries
- masked data, same dim as original
- mask : dios.DictOfSeries
- dios holding iloc-data-pairs for every column in `data`
- """
- mask = dios.DictOfSeries(columns=columns)
-
- # we use numpy here because it is faster
- for c in columns:
- col_mask = _isflagged(flags[c].to_numpy(), thresh)
-
- if col_mask.any():
- col_data = data[c].to_numpy(dtype=np.float64)
-
- mask[c] = pd.Series(col_data[col_mask], index=np.where(col_mask)[0])
-
- col_data[col_mask] = np.nan
- data[c] = col_data
-
- return data, mask
-
- @staticmethod
- def _unmaskData(
- data: dios.DictOfSeries, mask: dios.DictOfSeries, columns: pd.Index = None
- ) -> dios.DictOfSeries:
- """
- Restore the masked data.
-
- Notes
- -----
- - Even if this returns data, it works inplace !
- - `mask` is not a boolean mask, instead it holds the original values.
- The index of mask is numeric and represent the integer location
- in the original data.
- """
- if columns is None:
- columns = data.columns # field was in old, is in mask and is in new
- columns = mask.columns.intersection(columns)
-
- for c in columns:
-
- # ignore
- if data[c].empty or mask[c].empty:
- continue
-
- # get the positions of values to unmask
- candidates = mask[c]
- # if the mask was removed during the function call, don't replace
- unmask = candidates[data[c].iloc[candidates.index].isna().to_numpy()]
- if unmask.empty:
- continue
- data[c].iloc[unmask.index] = unmask
-
- return data
+ if not isinstance(dec_arg, list):
+ raise typeerr
+ for elem in dec_arg:
+ if not isinstance(elem, str):
+ raise typeerr
+ if elem not in params:
+ raise ValueError(
+ f"passed value {repr(elem)} in {repr(name)} is not an "
+ f"parameter in decorated function {repr(func_name)}"
+ )
+
+
+def _argnamesToColumns(names: list, values: dict):
+ clist = []
+ for name in names:
+ value = values.get(name) # eg. the value behind 'field'
+
+ # NOTE: do not change order of the tests
+ if value is None:
+ pass
+ elif isinstance(value, str):
+ clist.append(value)
+ # we ignore DataFrame, Series, DictOfSeries
+ # and high order types alike
+ elif hasattr(value, "columns"):
+ pass
+ elif _is_list_like(value) and all([isinstance(e, str) for e in value]):
+ clist += value
+ return pd.Index(clist)
+
+
+def _warn(missing, source):
+ if len(missing) == 0:
+ return
+ action = source + "ed"
+ obj = "flags" if source == "squeeze" else "data"
+ warnings.warn(
+ f"Column(s) {repr(missing)} cannot not be {action} "
+ f"because they are not present in {obj}. ",
+ RuntimeWarning,
+ )
+
+
+def _getDfilter(
+ func_signature: inspect.Signature,
+ translation_scheme: TranslationScheme,
+ kwargs: Dict[str, Any],
+) -> float:
+ """
+ Find a default value for dfilter, either from the choosen translation scheme
+ or a possibly defined method default value. Translate, if necessary.
+ """
+ dfilter = kwargs.get("dfilter")
+ if dfilter is None or isinstance(dfilter, OptionalNone):
+ # let's see, if the function has an default value
+ default = func_signature.parameters.get("dfilter")
+ if default:
+ default = default.default
+ if default == inspect.Signature.empty:
+ # function did not define a positional dfilter argument
+ default = None
+ dfilter = translation_scheme.DFILTER_DEFAULT or default
+ else:
+ # try to translate dfilter
+ if dfilter not in {FILTER_ALL, FILTER_NONE, translation_scheme.DFILTER_DEFAULT}:
+ dfilter = translation_scheme(dfilter)
+ return float(dfilter)
+
+
+def _squeezeFlags(old_flags, new_flags: Flags, columns: pd.Index, meta) -> Flags:
+ """
+ Generate flags from the temporary result-flags and the original flags.
+
+ Parameters
+ ----------
+ flags : Flags
+ The flags-frame, which is the result from a saqc-function
+
+ Returns
+ -------
+ Flags
+ """
+ out = old_flags.copy() # the old flags
+
+ for col in columns.union(
+ new_flags.columns.difference(old_flags.columns)
+ ): # account for newly added columns
+
+ if col not in out: # ensure existence
+ out.history[col] = History(index=new_flags.history[col].index)
+
+ old_history = out.history[col]
+ new_history = new_flags.history[col]
+
+ # We only want to add new columns, that were appended during the last
+ # function call. If no such columns exist, we end up with an empty
+ # new_history.
+ start = len(old_history.columns)
+ new_history = _sliceHistory(new_history, slice(start, None))
+
+ squeezed = new_history.squeeze(raw=True)
+ out.history[col] = out.history[col].append(squeezed, meta=meta)
+
+ return out
+
+
+def _sliceHistory(history: History, sl: slice) -> History:
+ history.hist = history.hist.iloc[:, sl]
+ history.meta = history.meta[sl]
+ return history
+
+
+def _maskData(
+ data: dios.DictOfSeries, flags: Flags, columns: Sequence[str], thresh: float
+) -> Tuple[dios.DictOfSeries, dios.DictOfSeries]:
+ """
+ Mask data with Nans, if the flags are worse than a threshold.
+ - mask only passed `columns` (preselected by `datamask`-kw from decorator)
+
+ Returns
+ -------
+ masked : dios.DictOfSeries
+ masked data, same dim as original
+ mask : dios.DictOfSeries
+ dios holding iloc-data-pairs for every column in `data`
+ """
+ mask = dios.DictOfSeries(columns=columns)
+
+ # we use numpy here because it is faster
+ for c in columns:
+ col_mask = _isflagged(flags[c], thresh)
+
+ if col_mask.any():
+ col_data = data[c].to_numpy(dtype=np.float64)
+
+ mask[c] = pd.Series(col_data[col_mask], index=np.where(col_mask)[0])
+
+ col_data[col_mask] = np.nan
+ data[c] = col_data
+
+ return data, mask
+
+
+def _unmaskData(
+ data: dios.DictOfSeries, mask: dios.DictOfSeries, columns: pd.Index | None = None
+) -> dios.DictOfSeries:
+ """
+ Restore the masked data.
+
+ Notes
+ -----
+ - Even if this returns data, it works inplace !
+ - `mask` is not a boolean mask, instead it holds the original values.
+ The index of mask is numeric and represent the integer location
+ in the original data.
+ """
+ if columns is None:
+ columns = data.columns # field was in old, is in mask and is in new
+ columns = mask.columns.intersection(columns)
+
+ for c in columns:
+
+ # ignore
+ if data[c].empty or mask[c].empty:
+ continue
+
+ # get the positions of values to unmask
+ candidates = mask[c]
+ # if the mask was removed during the function call, don't replace
+ unmask = candidates[data[c].iloc[candidates.index].isna().to_numpy()]
+ if unmask.empty:
+ continue
+ data[c].iloc[unmask.index] = unmask
+
+ return data
+
+
+def _expandField(regex, columns, field) -> List[str]:
+ """
+ Expand regular expressions to concrete field names.
+ """
+ if regex:
+ fmask = columns.str.match(field)
+ return columns[fmask].tolist()
+ return toSequence(field)
+
+
+def _homogenizeFieldsTargets(
+ multivariate,
+ handles_target,
+ fields,
+ targets,
+):
+ """
+ Ensure, that fields and flags are of identical length.
+
+ Note
+ ----
+ We have four cases to consider:
+ 1. multivariate=False & handles_target=False
+ 2. multivariate=False & handles_target=True
+ 3. multivariate=True & handles_target=False
+ 4. multivariate=True & handles_target=True
+ """
+
+ if not (multivariate and handles_target):
+ if len(fields) != len(targets):
+ raise ValueError("expected the same number of 'field' and 'target' values")
+
+ if multivariate:
+ fields, targets = [fields], [targets]
+
+ return fields, targets
def register(
@@ -409,14 +323,112 @@ def register(
itself. Mandatory for multivariate functions.
"""
- def inner(func):
- wrapper = FunctionWrapper(
- func, mask, demask, squeeze, multivariate, handles_target
+ def outer(func: Callable[P, SaQC]) -> Callable[P, SaQC]:
+
+ func_signature = inspect.signature(func)
+ _checkDecoratorKeywords(
+ func_signature, func.__name__, mask, demask, squeeze, handles_target
)
- FUNC_MAP[wrapper.func_name] = wrapper
- return wrapper
- return inner
+ @functools.wraps(func)
+ def inner(
+ saqc,
+ field,
+ *args,
+ regex: bool = False,
+ flag: ExternalFlag | OptionalNone = OptionalNone(),
+ **kwargs,
+ ) -> "SaQC":
+
+ # args -> kwargs
+ paramnames = tuple(func_signature.parameters.keys())[
+ 2:
+ ] # skip (self, field)
+ kwargs = {**dict(zip(paramnames, args)), **kwargs}
+ kwargs["dfilter"] = _getDfilter(func_signature, saqc._scheme, kwargs)
+
+ # translate flag
+ if not isinstance(flag, OptionalNone):
+ # translation schemes might want to use a flag
+ # `None` so we introduce a special class here
+ kwargs["flag"] = saqc._scheme(flag)
+
+ fields = _expandField(regex, saqc._data.columns, field)
+ targets = toSequence(kwargs.pop("target", fields))
+
+ fields, targets = _homogenizeFieldsTargets(
+ multivariate, handles_target, fields, targets
+ )
+
+ out = saqc.copy(deep=True)
+
+ # initialize target fields
+ if not handles_target:
+ # initialize all target variables
+ for src, trg in zip(fields, targets):
+ if src != trg:
+ out = out.copyField(field=src, target=trg)
+
+ for src, trg in zip(fields, targets):
+ kwargs = {**kwargs, "field": src, "target": trg}
+ if not handles_target:
+ kwargs["field"] = kwargs.pop("target")
+
+ # find columns that need masking
+ # func_signature = func_signature.bind(field=field)
+ columns = _argnamesToColumns(mask, kwargs)
+ _warn(columns.difference(out._data.columns).to_list(), source="mask")
+ columns = columns.intersection(out._data.columns)
+
+ out._data, stored_data = _maskData(
+ data=out._data,
+ flags=out._flags,
+ columns=columns,
+ thresh=kwargs["dfilter"],
+ )
+
+ # always pass a list to multivariate functions and
+ # unpack single element lists for univariate functions
+ if not multivariate:
+ kwargs["field"] = squeezeSequence(kwargs["field"])
+
+ old_flags = out._flags.copy()
+
+ out = func(out, **kwargs)
+
+ # find columns that need squeezing
+ columns = _argnamesToColumns(squeeze, kwargs)
+ _warn(
+ columns.difference(out._flags.columns).to_list(), source="squeeze"
+ )
+ columns = columns.intersection(out._flags.columns)
+
+ # if the function did not want to set any flags at all,
+ # we assume a processing function that altered the flags
+ # in an unpredictable manner or do nothing with the flags.
+ # in either case we take the returned flags as the new truth.
+ if not columns.empty:
+ meta = {
+ "func": func.__name__,
+ "args": args,
+ "kwargs": kwargs,
+ }
+ out._flags = _squeezeFlags(old_flags, out._flags, columns, meta)
+
+ # find columns that need demasking
+ columns = _argnamesToColumns(demask, kwargs)
+ _warn(columns.difference(out._data.columns).to_list(), source="demask")
+ columns = columns.intersection(out._data.columns)
+
+ out._data = _unmaskData(out._data, stored_data, columns=columns)
+ out._validate(reason=f"call to {repr(func.__name__)}")
+
+ return out
+
+ FUNC_MAP[func.__name__] = inner
+ return inner
+
+ return outer
def flagging(**kwargs):
@@ -465,9 +477,10 @@ def processing(**kwargs):
return register(mask=[], demask=[], squeeze=[])
-def _isflagged(
- flagscol: np.ndarray | pd.Series, thresh: float
-) -> np.ndarray | pd.Series:
+A = TypeVar("A", np.ndarray, pd.Series)
+
+
+def _isflagged(flagscol: A, thresh: float) -> A:
"""
Return a mask of flags accordingly to `thresh`. Return type is same as flags.
"""
diff --git a/saqc/core/translation/basescheme.py b/saqc/core/translation/basescheme.py
index c9889cd20..1b42a8eff 100644
--- a/saqc/core/translation/basescheme.py
+++ b/saqc/core/translation/basescheme.py
@@ -8,7 +8,7 @@
from __future__ import annotations
-from typing import Any, Dict, MutableMapping, Union
+from typing import Any, Dict
import numpy as np
import pandas as pd
@@ -41,8 +41,11 @@ class TranslationScheme:
- 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`).
- - We need translations for the special flags `saqc.constants.UNFLAGGED` and
- `saqc.constants.BAD`. That implies, that every valid translation scheme
+ - We need translations for the special flags:
+ * `saqc.constants.UNFLAGGED`
+ * `saqc.constants.BAD`
+
+ . That implies, that every valid translation scheme
provides at least one user flag that maps to `BAD` and one that maps to
`UNFLAGGED`.
"""
diff --git a/saqc/funcs/__init__.py b/saqc/funcs/__init__.py
index 5aa3335ad..f2f925324 100644
--- a/saqc/funcs/__init__.py
+++ b/saqc/funcs/__init__.py
@@ -6,22 +6,42 @@
# -*- coding: utf-8 -*-
-# imports needed to make the functions register themself
-from saqc.core.register import register
-from saqc.funcs.breaks import *
-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.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.breaks import BreaksMixin
+from saqc.funcs.changepoints import ChangepointsMixin
+from saqc.funcs.constants import ConstantsMixin
+from saqc.funcs.curvefit import CurvefitMixin
+from saqc.funcs.drift import DriftMixin
+from saqc.funcs.flagtools import FlagtoolsMixin
+from saqc.funcs.generic import GenericMixin
+from saqc.funcs.interpolation import InterpolationMixin
+from saqc.funcs.noise import NoiseMixin
+from saqc.funcs.outliers import OutliersMixin
+from saqc.funcs.pattern import PatternMixin
+from saqc.funcs.resampling import ResamplingMixin
+from saqc.funcs.residuals import ResidualsMixin
+from saqc.funcs.rolling import RollingMixin
+from saqc.funcs.scores import ScoresMixin
+from saqc.funcs.tools import ToolsMixin
+from saqc.funcs.transformation import TransformationMixin
+
+
+class FunctionsMixin(
+ BreaksMixin,
+ ChangepointsMixin,
+ ConstantsMixin,
+ CurvefitMixin,
+ DriftMixin,
+ FlagtoolsMixin,
+ GenericMixin,
+ InterpolationMixin,
+ NoiseMixin,
+ OutliersMixin,
+ PatternMixin,
+ ResamplingMixin,
+ ResidualsMixin,
+ RollingMixin,
+ ScoresMixin,
+ ToolsMixin,
+ TransformationMixin,
+):
+ pass
diff --git a/saqc/funcs/breaks.py b/saqc/funcs/breaks.py
index 754d77231..0b700d6ae 100644
--- a/saqc/funcs/breaks.py
+++ b/saqc/funcs/breaks.py
@@ -17,205 +17,213 @@ isolated values (:py:func:`flagIsolated`).
from __future__ import annotations
-from typing import Tuple
+from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
-from dios import DictOfSeries
from saqc.constants import BAD, FILTER_ALL
-from saqc.core.flags import Flags
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"])
-def flagMissing(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- flag: float = BAD,
- dfilter: float = FILTER_ALL,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flag NaNs in data.
-
- By default only NaNs are flagged, that not already have a flag.
- `to_mask` can be used to pass a flag that is used as threshold.
- Each flag worse than the threshold is replaced by the function.
- This is, because the data gets masked (with NaNs) before the
- function evaluates the NaNs.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data container.
-
- field : str
- Column(s) in flags and data.
-
- flags : saqc.Flags
- The flags container.
-
- flag : float, default BAD
- Flag to set.
-
- Returns
- -------
- data : dios.DictOfSeries
- Unmodified data container
- flags : saqc.Flags
- The flags container
- """
-
- datacol = data[field]
- mask = datacol.isna()
-
- mask = ~_isflagged(flags[field], dfilter) & mask
-
- flags[mask, field] = flag
- return data, flags
-
-
-@flagging()
-def flagIsolated(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- gap_window: str,
- group_window: str,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Find and flag temporal isolated groups of data.
-
- The function flags arbitrarily large groups of values, if they are surrounded by
- sufficiently large data gaps. A gap is a timespan containing either no data at all
- or NaNs only.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data container.
-
- field : str
- Column(s) in flags and data.
-
- flags : saqc.Flags
- The flags container.
-
- gap_window : str
- Minimum gap size required before and after a data group to consider it
- isolated. See condition (2) and (3)
-
- group_window : str
- Maximum size of a data chunk to consider it a candidate for an isolated group.
- Data chunks that are bigger than the ``group_window`` are ignored.
- This does not include the possible gaps surrounding it.
- See condition (1).
-
- flag : float, default BAD
- Flag to set.
-
- Returns
- -------
- data : dios.DictOfSeries
- Unmodified data container
- flags : saqc.Flags
- The flags container
-
- Notes
- -----
- A series of values :math:`x_k,x_{k+1},...,x_{k+n}`, with associated
- timestamps :math:`t_k,t_{k+1},...,t_{k+n}`, is considered to be isolated, if:
-
- 1. :math:`t_{k+1} - t_n <` `group_window`
- 2. None of the :math:`x_j` with :math:`0 < t_k - t_j <` `gap_window`,
- is valid (preceeding gap).
- 3. None of the :math:`x_j` with :math:`0 < t_j - t_(k+n) <` `gap_window`,
- is valid (succeding gap).
- """
- gap_window = pd.tseries.frequencies.to_offset(gap_window)
- group_window = pd.tseries.frequencies.to_offset(group_window)
-
- mask = data[field].isna()
-
- bools = pd.Series(data=0, index=mask.index, dtype=bool)
- for srs in groupConsecutives(mask):
- if np.all(~srs):
- # we found a chunk of non-nan values
- start = srs.index[0]
- stop = srs.index[-1]
- if stop - start <= group_window:
- # the chunk is large enough
- left = mask[start - gap_window : start].iloc[:-1]
- if left.all():
- # the section before our chunk is nan-only
- right = mask[stop : stop + gap_window].iloc[1:]
- if right.all():
- # the section after our chunk is nan-only
- # -> we found a chunk of isolated non-values
- bools[start:stop] = True
-
- flags[bools, field] = flag
- return data, flags
-
-
-@flagging()
-def flagJumps(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- thresh: float,
- window: str,
- min_periods: int = 1,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flag jumps and drops in data.
-
- Flag data where the mean of its values significantly changes (the data "jumps").
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data container.
-
- field : str
- Column(s) in flags and data.
-
- flags : saqc.Flags
- The flags container.
-
- thresh : float
- Threshold value by which the mean of data has to change to trigger flagging.
-
- window : str
- Size of the moving window. This is the number of observations used
- for calculating the statistic.
-
- min_periods : int, default 1
- Minimum number of observations in window required to calculate a valid
- mean value.
-
- flag : float, default BAD
- Flag to set.
- """
- return _assignChangePointCluster(
- data,
- field,
- flags,
- stat_func=lambda x, y: np.abs(np.mean(x) - np.mean(y)),
- thresh_func=lambda x, y: thresh,
- window=window,
- min_periods=min_periods,
- set_flags=True,
- model_by_resids=False,
- assign_cluster=False,
- flag=flag,
+
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
+
+class BreaksMixin:
+ @register(mask=[], demask=[], squeeze=["field"])
+ def flagMissing(
+ self: "SaQC",
+ field: str,
+ flag: float = BAD,
+ dfilter: float = FILTER_ALL,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flag NaNs in data.
+
+ By default only NaNs are flagged, that not already have a flag.
+ `dfilter` can be used to pass a flag that is used as threshold.
+ Each flag worse than the threshold is replaced by the function.
+ This is, because the data gets masked (with NaNs) before the
+ function evaluates the NaNs.
+
+ Parameters
+ ----------
+ field : str
+ Column(s) in flags and data.
+
+ flag : float, default BAD
+ Flag to set.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+
+ datacol = self._data[field]
+ mask = datacol.isna()
+
+ mask = ~_isflagged(self._flags[field], dfilter) & mask
+
+ self._flags[mask, field] = flag
+ return self
+
+ @flagging()
+ def flagIsolated(
+ self: "SaQC",
+ field: str,
+ gap_window: str,
+ group_window: str,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Find and flag temporal isolated groups of data.
+
+ The function flags arbitrarily large groups of values, if they are surrounded by
+ sufficiently large data gaps. A gap is a timespan containing either no data at all
+ or NaNs only.
+
+ Parameters
+ ----------
+ field : str
+ Column(s) in flags and data.
+
+ gap_window : str
+ Minimum gap size required before and after a data group to consider it
+ isolated. See condition (2) and (3)
+
+ group_window : str
+ Maximum size of a data chunk to consider it a candidate for an isolated group.
+ Data chunks that are bigger than the ``group_window`` are ignored.
+ This does not include the possible gaps surrounding it.
+ See condition (1).
+
+ flag : float, default BAD
+ Flag to set.
+
+ Returns
+ -------
+ saqc.SaQC
+
+ Notes
+ -----
+ A series of values :math:`x_k,x_{k+1},...,x_{k+n}`, with associated
+ timestamps :math:`t_k,t_{k+1},...,t_{k+n}`, is considered to be isolated, if:
+
+ 1. :math:`t_{k+1} - t_n <` `group_window`
+ 2. None of the :math:`x_j` with :math:`0 < t_k - t_j <` `gap_window`,
+ is valid (preceeding gap).
+ 3. None of the :math:`x_j` with :math:`0 < t_j - t_(k+n) <` `gap_window`,
+ is valid (succeding gap).
+ """
+
+ dat = self._data[field].dropna()
+ if dat.empty:
+ return self
+
+ gap_ends = dat.rolling(gap_window).count() == 1
+ gap_ends[0] = False
+ gap_ends = gap_ends[gap_ends]
+ gap_starts = dat[::-1].rolling(gap_window).count()[::-1] == 1
+ gap_starts[-1] = False
+ gap_starts = gap_starts[gap_starts]
+ if gap_starts.empty:
+ return self
+
+ gap_starts = gap_starts[1:]
+ gap_ends = gap_ends[:-1]
+ isolated_groups = gap_starts.index - gap_ends.index < group_window
+ gap_starts = gap_starts[isolated_groups]
+ gap_ends = gap_ends[isolated_groups]
+ to_flag = pd.Series(False, index=dat.index)
+ for s, e in zip(gap_starts.index, gap_ends.index):
+ # what gets flagged are the groups between the gaps, those range from
+ # the end of one gap (gap_end) to the beginning of the next (gap_start)
+ to_flag[e:s] = True
+
+ to_flag = to_flag.reindex(self._data[field].index, fill_value=False)
+ self._flags[to_flag.to_numpy(), field] = flag
+ return self
+
+ @flagging()
+ def flagJumps(
+ self: "SaQC",
+ field: str,
+ thresh: float,
+ window: str,
+ min_periods: int = 1,
+ flag: float = BAD,
**kwargs,
- )
+ ) -> "SaQC":
+ """
+ Flag jumps and drops in data.
+
+ Flag data where the mean of its values significantly changes (, where the data "jumps" from one value level to
+ another).
+ The changes in value level are detected by comparing the mean for two adjacently rolling windows.
+ Whenever the difference between the mean in the two windows exceeds `thresh`, the value between the windows
+ is flagged a jump.
+
+
+ Parameters
+ ----------
+ field : str
+ Column(s) in flags and data.
+
+ thresh : float
+ Threshold value by which the mean of data has to jump, to trigger flagging.
+
+ window : str
+ Size of the two moving windows. This determines the number of observations used
+ for calculating the mean in every window.
+ The window size should be big enough to yield enough samples for a reliable mean calculation,
+ but it should also not be arbitrarily big, since it also limits the density of jumps that can be detected.
+ More precisely: Jumps that are not distanced to each other by more than three fourth (3/4) of the
+ selected window size, will not be detected reliably.
+
+ min_periods : int, default 1
+ The minimum number of observations in window required to calculate a valid
+ mean value.
+
+ flag : float, default BAD
+ Flag to set.
+
+ Examples
+ --------
+
+ Below picture gives an abstract interpretation of the parameter interplay in case of a positive value jump,
+ initialising a new mean level.
+
+ .. figure:: /resources/images/flagJumpsPic.png
+
+ The two adjacent windows of size `window` roll through the whole data series. Whenever the mean values in
+ the two windows differ by more than `thresh`, flagging is triggered.
+
+ Notes
+ -----
+
+ Jumps that are not distanced to each other by more than three fourth (3/4) of the
+ selected window size, will not be detected reliably.
+
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ self._data, self._flags = _assignChangePointCluster(
+ self._data,
+ field,
+ self._flags,
+ stat_func=lambda x, y: np.abs(np.mean(x) - np.mean(y)),
+ thresh_func=lambda x, y: thresh,
+ window=window,
+ min_periods=min_periods,
+ set_flags=True,
+ model_by_resids=False,
+ assign_cluster=False,
+ flag=flag,
+ **kwargs,
+ )
+ return self
diff --git a/saqc/funcs/changepoints.py b/saqc/funcs/changepoints.py
index 08af9cc0b..c4d6db095 100644
--- a/saqc/funcs/changepoints.py
+++ b/saqc/funcs/changepoints.py
@@ -7,7 +7,7 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Callable, Tuple
+from typing import TYPE_CHECKING, Callable, Tuple
import numba
import numpy as np
@@ -20,235 +20,211 @@ from saqc.core.flags import Flags
from saqc.core.register import flagging, register
from saqc.lib.tools import customRoller, filterKwargs
-
-@flagging()
-def flagChangePoints(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- stat_func: Callable[[np.ndarray, np.ndarray], float],
- thresh_func: Callable[[np.ndarray, np.ndarray], float],
- window: str | Tuple[str, str],
- min_periods: int | Tuple[int, int],
- closed: Literal["right", "left", "both", "neither"] = "both",
- reduce_window: str = None,
- reduce_func: Callable[[np.ndarray, np.ndarray], int] = lambda x, _: x.argmax(),
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flag data where it significantly changes.
-
- Flag data points, where the parametrization of the process, the data is assumed to
- generate by, significantly changes.
-
- The change points detection is based on a sliding window search.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data container.
-
- field : str
- A column in flags and data.
-
- flags : saqc.Flags
- The flags container.
-
- stat_func : Callable
- A function that assigns a value to every twin window. The backward-facing
- window content will be passed as the first array, the forward-facing window
- content as the second.
-
- thresh_func : Callable
- A function that determines the value level, exceeding wich qualifies a
- timestamps func value as denoting a change-point.
-
- window : str, tuple of str
- Size of the moving windows. This is the number of observations used for
- calculating the statistic.
-
- If it is a single frequency offset, it applies for the backward- and the
- forward-facing window.
-
- If two offsets (as a tuple) is passed the first defines the size of the
- backward facing window, the second the size of the forward facing window.
-
- min_periods : int or tuple of int
- Minimum number of observations in a window required to perform the changepoint
- test. If it is a tuple of two int, the first refer to the backward-,
- the second to the forward-facing window.
-
- closed : {'right', 'left', 'both', 'neither'}, default 'both'
- Determines the closure of the sliding windows.
-
- reduce_window : str or None, default None
- The sliding window search method is not an exact CP search method and usually
- there wont be detected a single changepoint, but a "region" of change around
- a changepoint.
-
- If `reduce_window` is given, for every window of size `reduce_window`, there
- will be selected the value with index `reduce_func(x, y)` and the others will
- be dropped.
-
- If `reduce_window` is None, the reduction window size equals the twin window
- size, the changepoints have been detected with.
-
- reduce_func : Callable, default ``lambda x, y: x.argmax()``
- A function that must return an index value upon input of two arrays x and y.
- First input parameter will hold the result from the stat_func evaluation for
- every reduction window. Second input parameter holds the result from the
- `thresh_func` evaluation.
- The default reduction function just selects the value that maximizes the
- `stat_func`.
-
- flag : float, default BAD
- flag to set.
-
- Returns
- -------
- data : dios.DictOfSeries
- Unmodified data container
- flags : saqc.Flags
- The flags container
- """
- return _assignChangePointCluster(
- data,
- field,
- flags,
- stat_func=stat_func,
- thresh_func=thresh_func,
- window=window,
- min_periods=min_periods,
- closed=closed,
- reduce_window=reduce_window,
- reduce_func=reduce_func,
- set_flags=True,
- model_by_resids=False,
- assign_cluster=False,
- flag=flag,
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
+
+class ChangepointsMixin:
+ @flagging()
+ def flagChangePoints(
+ self: "SaQC",
+ field: str,
+ stat_func: Callable[[np.ndarray, np.ndarray], float],
+ thresh_func: Callable[[np.ndarray, np.ndarray], float],
+ window: str | Tuple[str, str],
+ min_periods: int | Tuple[int, int],
+ reduce_window: str | None = None,
+ reduce_func: Callable[[np.ndarray, np.ndarray], int] = lambda x, _: x.argmax(),
+ flag: float = BAD,
**kwargs,
- )
-
-
-@register(mask=["field"], demask=[], squeeze=[])
-def assignChangePointCluster(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- stat_func: Callable[[np.array, np.array], float],
- thresh_func: Callable[[np.array, np.array], float],
- window: str | Tuple[str, str],
- min_periods: int | Tuple[int, int],
- closed: Literal["right", "left", "both", "neither"] = "both",
- reduce_window: str = None,
- reduce_func: Callable[[np.ndarray, np.ndarray], float] = lambda x, _: x.argmax(),
- model_by_resids: bool = False,
- **kwargs,
-):
- """
- Label data where it changes significantly.
-
- The labels will be stored in data. Unless `target` is given the labels will
- overwrite the data in `field`. The flags will always set to `UNFLAGGED`.
-
- Assigns label to the data, aiming to reflect continuous regimes of the processes
- the data is assumed to be generated by. The regime change points detection is
- based on a sliding window search.
-
-
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
-
- field : str
- The reference variable, the deviation from wich determines the flagging.
-
- flags : saqc.flags
- A flags object, holding flags and additional informations related to `data`.
-
- stat_func : Callable[[numpy.array, numpy.array], float]
- A function that assigns a value to every twin window. Left window content will
- be passed to first variable,
- right window content will be passed to the second.
-
- thresh_func : Callable[numpy.array, numpy.array], float]
- A function that determines the value level, exceeding wich qualifies a
- timestamps func func value as denoting a changepoint.
-
- window : str, tuple of string
- Size of the rolling windows the calculation is performed in. If it is a single
- frequency offset, it applies for the backward- and the forward-facing window.
-
- If two offsets (as a tuple) is passed the first defines the size of the
- backward facing window, the second the size of the forward facing window.
-
- min_periods : int or tuple of int
- Minimum number of observations in a window required to perform the changepoint
- test. If it is a tuple of two int, the first refer to the backward-,
- the second to the forward-facing window.
-
- closed : {'right', 'left', 'both', 'neither'}, default 'both'
- Determines the closure of the sliding windows.
-
- reduce_window : {None, str}, default None
- The sliding window search method is not an exact CP search method and usually
- there wont be detected a single changepoint, but a "region" of change around
- a changepoint. If `reduce_window` is given, for every window of size
- `reduce_window`, there will be selected the value with index `reduce_func(x,
- y)` and the others will be dropped. If `reduce_window` is None, the reduction
- window size equals the twin window size, the changepoints have been detected
- with.
-
- reduce_func : callable, default lambda x,y: x.argmax()
- A function that must return an index value upon input of two arrays x and y.
- First input parameter will hold the result from the stat_func evaluation for
- every reduction window. Second input parameter holds the result from the
- thresh_func evaluation. The default reduction function just selects the value
- that maximizes the stat_func.
-
- model_by_resids : bool, default False
- If True, the results of `stat_funcs` are written, otherwise the regime labels.
-
- Returns
- -------
- data : dios.DictOfSeries
- Modified data.
- flags : saqc.Flags
- The flags container
- """
- reserved = ["assign_cluster", "set_flags", "flag"]
- kwargs = filterKwargs(kwargs, reserved)
- return _assignChangePointCluster(
- data=data,
- field=field,
- flags=flags,
- stat_func=stat_func,
- thresh_func=thresh_func,
- window=window,
- min_periods=min_periods,
- closed=closed,
- reduce_window=reduce_window,
- reduce_func=reduce_func,
- model_by_resids=model_by_resids,
+ ) -> "SaQC":
+ """
+ Flag data where it significantly changes.
+
+ Flag data points, where the parametrization of the process, the data is assumed to
+ generate by, significantly changes.
+
+ The change points detection is based on a sliding window search.
+
+ Parameters
+ ----------
+ field : str
+ A column in flags and data.
+
+ stat_func : Callable
+ A function that assigns a value to every twin window. The backward-facing
+ window content will be passed as the first array, the forward-facing window
+ content as the second.
+
+ thresh_func : Callable
+ A function that determines the value level, exceeding wich qualifies a
+ timestamps func value as denoting a change-point.
+
+ window : str, tuple of str
+ Size of the moving windows. This is the number of observations used for
+ calculating the statistic.
+
+ If it is a single frequency offset, it applies for the backward- and the
+ forward-facing window.
+
+ If two offsets (as a tuple) is passed the first defines the size of the
+ backward facing window, the second the size of the forward facing window.
+
+ min_periods : int or tuple of int
+ Minimum number of observations in a window required to perform the changepoint
+ test. If it is a tuple of two int, the first refer to the backward-,
+ the second to the forward-facing window.
+
+ reduce_window : str or None, default None
+ The sliding window search method is not an exact CP search method and usually
+ there wont be detected a single changepoint, but a "region" of change around
+ a changepoint.
+
+ If `reduce_window` is given, for every window of size `reduce_window`, there
+ will be selected the value with index `reduce_func(x, y)` and the others will
+ be dropped.
+
+ If `reduce_window` is None, the reduction window size equals the twin window
+ size, the changepoints have been detected with.
+
+ reduce_func : Callable, default ``lambda x, y: x.argmax()``
+ A function that must return an index value upon input of two arrays x and y.
+ First input parameter will hold the result from the stat_func evaluation for
+ every reduction window. Second input parameter holds the result from the
+ `thresh_func` evaluation.
+ The default reduction function just selects the value that maximizes the
+ `stat_func`.
+
+ flag : float, default BAD
+ flag to set.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ self._data, self._flags = _assignChangePointCluster(
+ self._data,
+ field,
+ self._flags,
+ stat_func=stat_func,
+ thresh_func=thresh_func,
+ window=window,
+ min_periods=min_periods,
+ reduce_window=reduce_window,
+ reduce_func=reduce_func,
+ set_flags=True,
+ model_by_resids=False,
+ assign_cluster=False,
+ flag=flag,
+ **kwargs,
+ )
+ return self
+
+ @register(mask=["field"], demask=[], squeeze=[])
+ def assignChangePointCluster(
+ self: "SaQC",
+ field: str,
+ stat_func: Callable[[np.ndarray, np.ndarray], float],
+ thresh_func: Callable[[np.ndarray, np.ndarray], float],
+ window: str | Tuple[str, str],
+ min_periods: int | Tuple[int, int],
+ reduce_window: str | None = None,
+ reduce_func: Callable[
+ [np.ndarray, np.ndarray], float
+ ] = lambda x, _: x.argmax(),
+ model_by_resids: bool = False,
**kwargs,
- # control args
- assign_cluster=True,
- set_flags=False,
- )
+ ) -> "SaQC":
+ """
+ Label data where it changes significantly.
+
+ The labels will be stored in data. Unless `target` is given the labels will
+ overwrite the data in `field`. The flags will always set to `UNFLAGGED`.
+
+ Assigns label to the data, aiming to reflect continuous regimes of the processes
+ the data is assumed to be generated by. The regime change points detection is
+ based on a sliding window search.
+
+
+ Parameters
+ ----------
+ field : str
+ The reference variable, the deviation from wich determines the flagging.
+
+ stat_func : Callable[[numpy.array, numpy.array], float]
+ A function that assigns a value to every twin window. Left window content will
+ be passed to first variable,
+ right window content will be passed to the second.
+
+ thresh_func : Callable[numpy.array, numpy.array], float]
+ A function that determines the value level, exceeding wich qualifies a
+ timestamps func func value as denoting a changepoint.
+
+ window : str, tuple of string
+ Size of the rolling windows the calculation is performed in. If it is a single
+ frequency offset, it applies for the backward- and the forward-facing window.
+
+ If two offsets (as a tuple) is passed the first defines the size of the
+ backward facing window, the second the size of the forward facing window.
+
+ min_periods : int or tuple of int
+ Minimum number of observations in a window required to perform the changepoint
+ test. If it is a tuple of two int, the first refer to the backward-,
+ the second to the forward-facing window.
+
+ reduce_window : {None, str}, default None
+ The sliding window search method is not an exact CP search method and usually
+ there wont be detected a single changepoint, but a "region" of change around
+ a changepoint. If `reduce_window` is given, for every window of size
+ `reduce_window`, there will be selected the value with index `reduce_func(x,
+ y)` and the others will be dropped. If `reduce_window` is None, the reduction
+ window size equals the twin window size, the changepoints have been detected
+ with.
+
+ reduce_func : callable, default lambda x,y: x.argmax()
+ A function that must return an index value upon input of two arrays x and y.
+ First input parameter will hold the result from the stat_func evaluation for
+ every reduction window. Second input parameter holds the result from the
+ thresh_func evaluation. The default reduction function just selects the value
+ that maximizes the stat_func.
+
+ model_by_resids : bool, default False
+ If True, the results of `stat_funcs` are written, otherwise the regime labels.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ reserved = ["assign_cluster", "set_flags", "flag"]
+ kwargs = filterKwargs(kwargs, reserved)
+ self._data, self._flags = _assignChangePointCluster(
+ data=self._data,
+ field=field,
+ flags=self._flags,
+ stat_func=stat_func,
+ thresh_func=thresh_func,
+ window=window,
+ min_periods=min_periods,
+ reduce_window=reduce_window,
+ reduce_func=reduce_func,
+ model_by_resids=model_by_resids,
+ **kwargs,
+ # control args
+ assign_cluster=True,
+ set_flags=False,
+ )
+ return self
def _assignChangePointCluster(
data: DictOfSeries,
field: str,
flags: Flags,
- stat_func: Callable[[np.array, np.array], float],
- thresh_func: Callable[[np.array, np.array], float],
+ stat_func: Callable[[np.ndarray, np.ndarray], float],
+ thresh_func: Callable[[np.ndarray, np.ndarray], float],
window: str | Tuple[str, str],
min_periods: int | Tuple[int, int],
- closed: Literal["right", "left", "both", "neither"] = "both",
- reduce_window: str = None,
+ reduce_window: str | None = None,
reduce_func: Callable[[np.ndarray, np.ndarray], float] = lambda x, _: x.argmax(),
model_by_resids: bool = False,
set_flags: bool = False,
@@ -274,22 +250,16 @@ def _assignChangePointCluster(
)
reduce_window = f"{s}s"
- roller = customRoller(data_ser, window=bwd_window, min_periods=bwd_min_periods)
- bwd_start, bwd_end = roller.window_indexer.get_window_bounds(
- len(data_ser), min_periods=bwd_min_periods, closed=closed
- )
+ roller = customRoller(data_ser, window=bwd_window, min_periods=0)
+ bwd_start, bwd_end = roller.window_indexer.get_window_bounds(len(data_ser))
- roller = customRoller(
- data_ser, window=fwd_window, forward=True, min_periods=fwd_min_periods
- )
- fwd_start, fwd_end = roller.window_indexer.get_window_bounds(
- len(data_ser), min_periods=fwd_min_periods, closed=closed
- )
+ roller = customRoller(data_ser, window=fwd_window, forward=True, min_periods=0)
+ fwd_start, fwd_end = roller.window_indexer.get_window_bounds(len(data_ser))
- min_mask = ~(
- (fwd_end - fwd_start <= fwd_min_periods)
- | (bwd_end - bwd_start <= bwd_min_periods)
+ min_mask = (fwd_end - fwd_start >= fwd_min_periods) & (
+ bwd_end - bwd_start >= bwd_min_periods
)
+
fwd_end = fwd_end[min_mask]
split = bwd_end[min_mask]
bwd_start = bwd_start[min_mask]
@@ -338,6 +308,15 @@ def _assignChangePointCluster(
)
det_index = det_index[detected]
+ # the changepoint is the point "after" the change - so detected index has to be shifted once with regard to the
+ # data index:
+ shifted = (
+ pd.Series(True, index=det_index)
+ .reindex(data_ser.index, fill_value=False)
+ .shift(fill_value=False)
+ )
+ det_index = shifted.index[shifted]
+
if assign_cluster:
cluster = pd.Series(False, index=data[field].index)
cluster[det_index] = True
@@ -385,7 +364,7 @@ def _reduceCPCluster(stat_arr, thresh_arr, start, end, obj_func, num_val):
s, e = start[win_i], end[win_i]
x = stat_arr[s:e]
y = thresh_arr[s:e]
- pos = s + obj_func(x, y) + 1
+ pos = s + obj_func(x, y)
out_arr[s:e] = False
out_arr[pos] = True
diff --git a/saqc/funcs/constants.py b/saqc/funcs/constants.py
index d7a98d20c..fc1a77f99 100644
--- a/saqc/funcs/constants.py
+++ b/saqc/funcs/constants.py
@@ -9,173 +9,155 @@
from __future__ import annotations
import operator
-from typing import Tuple
+from typing import TYPE_CHECKING
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 customRoller, getFreqDelta, statPass
from saqc.lib.ts_operators import varQC
-
-@flagging()
-def flagConstants(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- thresh: float,
- window: int | str,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flag constant data values.
-
- Flags plateaus of constant data if their maximum total change in
- a rolling window does not exceed a certain threshold.
-
- Any interval of values y(t),...,y(t+n) is flagged, if:
- - (1): n > ``window``
- - (2): abs(y(t + i) - (t + j)) < `thresh`, for all i,j in [0, 1, ..., n]
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data container.
-
- field : str
- A column in flags and data.
-
- flags : saqc.Flags
- The flags container.
-
- thresh : float
- Maximum total change allowed per window.
-
- window : str | int
- Size of the moving window. This is the number of observations used
- for calculating the statistic. Each window will be a fixed size.
- If its an offset then this will be the time period of each window.
- Each window will be a variable sized based on the observations included
- in the time-period.
-
- flag : float, default BAD
- Flag to set.
-
- Returns
- -------
- data : dios.DictOfSeries
- Unmodified data container
- flags : saqc.Flags
- The flags container
- """
- if not isinstance(window, (str, int)):
- raise TypeError("window must be offset string or int.")
-
- d = data[field]
-
- # min_periods=2 ensures that at least two non-nan values are present
- # in each window and also min() == max() == d[i] is not possible.
- kws = dict(window=window, min_periods=2, expand=False)
-
- # 1. find starting points of consecutive constant values as a boolean mask
- # 2. fill the whole window with True's
- rolling = customRoller(d, **kws)
- starting_points_mask = rolling.max() - rolling.min() <= thresh
- rolling = customRoller(starting_points_mask, **kws, forward=True)
- # mimic any()
- mask = (rolling.sum() > 0) & d.notna()
-
- flags[mask, field] = flag
- return data, flags
-
-
-@flagging()
-def flagByVariance(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- window: str,
- thresh: float,
- maxna: int | None = None,
- maxna_group: int | None = None,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flag low-variance data.
-
- Flags plateaus of constant data if the variance in a rolling window does not
- exceed a certain threshold.
-
- Any interval of values y(t),..y(t+n) is flagged, if:
-
- (1) n > `window`
- (2) variance(y(t),...,y(t+n) < `thresh`
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data container.
-
- field : str
- A column in flags and data.
-
- flags : saqc.Flags
- The flags container.
-
- window : str | int
- Size of the moving window. This is the number of observations used
- for calculating the statistic. Each window will be a fixed size.
- If its an offset then this will be the time period of each window.
- Each window will be sized, based on the number of observations included
- in the time-period.
-
- thresh : float, default 0.0005
- Maximum total variance allowed per window.
-
- maxna : int, default None
- Maximum number of NaNs allowed in window.
- If more NaNs are present, the window is not flagged.
-
- maxna_group : int, default None
- Same as `maxna` but for consecutive NaNs.
-
- flag : float, default BAD
- Flag to set.
-
- Returns
- -------
- data : dios.DictOfSeries
- Unmodified data container
- flags : saqc.Flags
- The flags container
- """
- dataseries = data[field]
- delta = getFreqDelta(dataseries.index)
- if not delta:
- raise IndexError("Timeseries irregularly sampled!")
-
- if maxna is None:
- maxna = np.inf
-
- if maxna_group is None:
- maxna_group = np.inf
-
- min_periods = int(np.ceil(pd.Timedelta(window) / pd.Timedelta(delta)))
- window = pd.Timedelta(window)
- to_set = statPass(
- dataseries,
- lambda x: varQC(x, maxna, maxna_group),
- window,
- thresh,
- operator.lt,
- min_periods=min_periods,
- )
-
- flags[to_set, field] = flag
- return data, flags
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
+
+class ConstantsMixin:
+ @flagging()
+ def flagConstants(
+ self: "SaQC",
+ field: str,
+ thresh: float,
+ window: int | str,
+ min_periods: int = 2,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flag constant data values.
+
+ Flags plateaus of constant data if their maximum total change in
+ a rolling window does not exceed a certain threshold.
+
+ Any interval of values y(t),...,y(t+n) is flagged, if:
+ - (1): n > ``window``
+ - (2): abs(y(t + i) - (t + j)) < `thresh`, for all i,j in [0, 1, ..., n]
+
+ Parameters
+ ----------
+ field : str
+ A column in flags and data.
+
+ thresh : float
+ Maximum total change allowed per window.
+
+ window : str | int
+ Size of the moving window. This is the number of observations used
+ for calculating the statistic. Each window will be a fixed size.
+ If its an offset then this will be the time period of each window.
+ Each window will be a variable sized based on the observations included
+ in the time-period.
+
+ flag : float, default BAD
+ Flag to set.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ if not isinstance(window, (str, int)):
+ raise TypeError("window must be offset string or int.")
+
+ d = self._data[field]
+
+ # min_periods=2 ensures that at least two non-nan values are present
+ # in each window and also min() == max() == d[i] is not possible.
+ kws = dict(window=window, min_periods=min_periods, expand=False)
+
+ # 1. find starting points of consecutive constant values as a boolean mask
+ # 2. fill the whole window with True's
+ rolling = customRoller(d, **kws)
+ starting_points_mask = rolling.max() - rolling.min() <= thresh
+ rolling = customRoller(starting_points_mask, **kws, forward=True)
+ # mimic any()
+ mask = (rolling.sum() > 0) & d.notna()
+
+ self._flags[mask, field] = flag
+ return self
+
+ @flagging()
+ def flagByVariance(
+ self: "SaQC",
+ field: str,
+ window: str,
+ thresh: float,
+ maxna: int | None = None,
+ maxna_group: int | None = None,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flag low-variance data.
+
+ Flags plateaus of constant data if the variance in a rolling window does not
+ exceed a certain threshold.
+
+ Any interval of values y(t),..y(t+n) is flagged, if:
+
+ (1) n > `window`
+ (2) variance(y(t),...,y(t+n) < `thresh`
+
+ Parameters
+ ----------
+ field : str
+ A column in flags and data.
+
+ window : str | int
+ Size of the moving window. This is the number of observations used
+ for calculating the statistic. Each window will be a fixed size.
+ If its an offset then this will be the time period of each window.
+ Each window will be sized, based on the number of observations included
+ in the time-period.
+
+ thresh : float, default 0.0005
+ Maximum total variance allowed per window.
+
+ maxna : int, default None
+ Maximum number of NaNs allowed in window.
+ If more NaNs are present, the window is not flagged.
+
+ maxna_group : int, default None
+ Same as `maxna` but for consecutive NaNs.
+
+ flag : float, default BAD
+ Flag to set.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ dataseries = self._data[field]
+ delta = getFreqDelta(dataseries.index)
+ if not delta:
+ raise IndexError("Timeseries irregularly sampled!")
+
+ if maxna is None:
+ maxna = np.inf
+
+ if maxna_group is None:
+ maxna_group = np.inf
+
+ min_periods = int(np.ceil(pd.Timedelta(window) / pd.Timedelta(delta)))
+ window = pd.Timedelta(window)
+ to_set = statPass(
+ dataseries,
+ lambda x: varQC(x, maxna, maxna_group),
+ window,
+ thresh,
+ operator.lt,
+ min_periods=min_periods,
+ )
+
+ self._flags[to_set, field] = flag
+ return self
diff --git a/saqc/funcs/curvefit.py b/saqc/funcs/curvefit.py
index aecdfa674..bed9088ae 100644
--- a/saqc/funcs/curvefit.py
+++ b/saqc/funcs/curvefit.py
@@ -7,16 +7,18 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Tuple, Union
+from typing import TYPE_CHECKING, Tuple, Union
import numpy as np
import pandas as pd
+from typing_extensions import Literal
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 (
+ butterFilter,
polyRoller,
polyRollerIrregular,
polyRollerNoMissing,
@@ -24,83 +26,138 @@ from saqc.lib.ts_operators import (
polyRollerNumba,
)
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
-@register(mask=["field"], demask=[], squeeze=[])
-def fitPolynomial(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- window: int | str,
- order: int,
- min_periods: int = 0,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Fits a polynomial model to the data.
-
- The fit is calculated by fitting a polynomial of degree `order` to a data slice
- of size `window`, that has x at its center.
-
- Note that the result is stored in `field` and overwrite it unless a
- `target` is given.
-
- In case your data is sampled at an equidistant frequency grid:
-
- (1) If you know your data to have no significant number of missing values,
- or if you do not want to calculate 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.
-
- Each residual gets assigned the worst flag present in the interval of
- the original data.
-
- Parameters
- ----------
- data : DictOfSeries
- The data container.
-
- field : str
- A column in flags and data.
-
- flags : Flags
- The flags container.
-
- window : str, int
- Size of the window you want to use for fitting. If an integer is passed,
- the size refers to the number of periods for every fitting window. If an
- offset string is passed, the size refers to the total temporal extension. The
- window will be centered around the vaule-to-be-fitted. For regularly sampled
- data always a odd number of periods will be used for the fit (periods-1 if
- periods is even).
-
- order : int
- Degree of the polynomial used for fitting
-
- min_periods : int or None, default 0
- Minimum number of observations in a window required to perform the fit,
- otherwise NaNs will be assigned.
- If ``None``, `min_periods` defaults to 1 for integer windows and to the
- size of the window for offset based windows.
- Passing 0, disables the feature and will result in over-fitting for too
- sparse windows.
-
- Returns
- -------
- data : dios.DictOfSeries
- Modified data
- flags : saqc.Flags
- Flags
- """
- return _fitPolynomial(
- data=data,
- field=field,
- flags=flags,
- window=window,
- order=order,
- min_periods=min_periods,
+_FILL_METHODS = Literal[
+ "linear",
+ "nearest",
+ "zero",
+ "slinear",
+ "quadratic",
+ "cubic",
+ "spline",
+ "barycentric",
+ "polynomial",
+]
+
+
+class CurvefitMixin:
+ @register(mask=["field"], demask=[], squeeze=[])
+ def fitPolynomial(
+ self: "SaQC",
+ field: str,
+ window: int | str,
+ order: int,
+ min_periods: int = 0,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Fits a polynomial model to the data.
+
+ The fit is calculated by fitting a polynomial of degree `order` to a data slice
+ of size `window`, that has x at its center.
+
+ Note that the result is stored in `field` and overwrite it unless a
+ `target` is given.
+
+ In case your data is sampled at an equidistant frequency grid:
+
+ (1) If you know your data to have no significant number of missing values,
+ or if you do not want to calculate 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.
+
+ Each residual gets assigned the worst flag present in the interval of
+ the original data.
+
+ Parameters
+ ----------
+ field : str
+ A column in flags and data.
+
+ window : str, int
+ Size of the window you want to use for fitting. If an integer is passed,
+ the size refers to the number of periods for every fitting window. If an
+ offset string is passed, the size refers to the total temporal extension. The
+ window will be centered around the vaule-to-be-fitted. For regularly sampled
+ data always a odd number of periods will be used for the fit (periods-1 if
+ periods is even).
+
+ order : int
+ Degree of the polynomial used for fitting
+
+ min_periods : int or None, default 0
+ Minimum number of observations in a window required to perform the fit,
+ otherwise NaNs will be assigned.
+ If ``None``, `min_periods` defaults to 1 for integer windows and to the
+ size of the window for offset based windows.
+ Passing 0, disables the feature and will result in over-fitting for too
+ sparse windows.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ self._data, self._flags = _fitPolynomial(
+ data=self._data,
+ field=field,
+ flags=self._flags,
+ window=window,
+ order=order,
+ min_periods=min_periods,
+ **kwargs,
+ )
+ return self
+
+ @register(mask=["field"], demask=[], squeeze=[])
+ def fitLowpassFilter(
+ self: "SaQC",
+ field: str,
+ cutoff: float | str,
+ nyq: float = 0.5,
+ filter_order: int = 2,
+ fill_method: _FILL_METHODS = "linear",
**kwargs,
- )
+ ):
+ """
+ Fits the data using the butterworth filter.
+
+ Note
+ ----
+ The data is expected to be regularly sampled.
+
+ Parameters
+ ----------
+ field: str
+ A column in flags and data.
+
+ 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.
+
+ fill_method: Literal[‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’]
+ Fill method to be applied on the data before filtering (butterfilter cant
+ handle ''np.nan''). See documentation of pandas.Series.interpolate method for
+ details on the methods associated with the different keywords.
+
+ filter_type: Literal["lowpass", "highpass", "bandpass", "bandstop"]
+ The type of filter. Default is ‘lowpass’.
+
+ """
+
+ self._data[field] = butterFilter(
+ self._data[field],
+ cutoff=cutoff,
+ nyq=nyq,
+ filter_order=filter_order,
+ fill_method=fill_method,
+ filter_type="lowpass",
+ )
+ return self
def _fitPolynomial(
diff --git a/saqc/funcs/drift.py b/saqc/funcs/drift.py
index 6b07c3ef0..9e8cf172a 100644
--- a/saqc/funcs/drift.py
+++ b/saqc/funcs/drift.py
@@ -11,7 +11,7 @@ from __future__ import annotations
import functools
import inspect
-from typing import Callable, Optional, Sequence, Tuple
+from typing import TYPE_CHECKING, Callable, Optional, Sequence, Tuple
import numpy as np
import pandas as pd
@@ -23,11 +23,14 @@ from dios import DictOfSeries
from saqc.constants import BAD
from saqc.core.register import Flags, flagging, register
from saqc.funcs.changepoints import _assignChangePointCluster
-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
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
+
LinkageString = Literal[
"single", "complete", "average", "weighted", "centroid", "median", "ward"
]
@@ -35,556 +38,674 @@ LinkageString = Literal[
MODELDICT = {"linear": linearDriftModel, "exponential": expDriftModel}
-@register(
- mask=["field"],
- demask=["field"],
- squeeze=["field"], # reference is written !
- multivariate=True,
- handles_target=False,
-)
-def flagDriftFromNorm(
- data: DictOfSeries,
- field: Sequence[str],
- flags: Flags,
- freq: str,
- spread: float,
- frac: float = 0.5,
- metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
- np.array([x, y]), metric="cityblock"
+class DriftMixin:
+ @register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=["field"], # reference is written !
+ multivariate=True,
+ handles_target=False,
)
- / len(x),
- method: LinkageString = "single",
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flags data that deviates from an avarage data course.
-
- "Normality" is determined in terms of a maximum spreading distance,
- that members of a normal group must not exceed. In addition, only a group is considered
- "normal" if it contains more then `frac` percent of the variables in "field".
-
- See the Notes section for a more detailed presentation of the algorithm
-
- Parameters
- ----------
- data : DictOfSeries
- The data container.
-
- field : str
- A column in flags and data.
-
- flags : Flags
- The flags container.
-
- freq : str
- Frequency, that split the data in chunks.
-
- spread : float
- Maximum spread allowed in the group of *normal* data. See Notes section for more details.
-
- frac : float, default 0.5
- Fraction defining the normal group. Use a value from the interval [0,1].
- The higher the value, the more stable the algorithm will be. For values below
- 0.5 the results are undefined.
-
- metric : Callable, default ``lambda x,y:pdist(np.array([x,y]),metric="cityblock")/len(x)``
- Distance function that takes two arrays as input and returns a scalar float.
- This value is interpreted as the distance of the two input arrays.
- Defaults to the `averaged manhattan metric` (see Notes).
-
- method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single"
- Linkage method used for hierarchical (agglomerative) clustering of the data.
- `method` is directly passed to ``scipy.hierarchy.linkage``. See its documentation [1] for
- more details. For a general introduction on hierarchical clustering see [2].
-
- flag : float, default BAD
- flag to set.
-
- Returns
- -------
- data : dios.DictOfSeries
- flags : saqc.Flags
-
- Notes
- -----
- following steps are performed for every data "segment" of length `freq` in order to find the
- "abnormal" data:
-
- 1. Calculate distances :math:`d(x_i,x_j)` for all :math:`x_i` in parameter `field`.
- (with :math:`d` denoting the distance function, specified by `metric`.
- 2. Calculate a dendogram with a hierarchical linkage algorithm, specified by `method`.
- 3. Flatten the dendogram at the level, the agglomeration costs exceed `spread`
- 4. check if a cluster containing more than `frac` variables.
-
- 1. if yes: flag all the variables that are not in that cluster (inside the segment)
- 2. if no: flag nothing
-
- The main parameter giving control over the algorithms behavior is the `spread` parameter,
- that determines the maximum spread of a normal group by limiting the costs, a cluster
- agglomeration must not exceed in every linkage step.
- For singleton clusters, that costs just equal half the distance, the data in the
- clusters, have to each other. So, no data can be clustered together, that are more then
- 2*`spread` distances away from each other. When data get clustered together, this new
- clusters distance to all the other data/clusters is calculated according to the linkage
- method specified by `method`. By default, it is the minimum distance, the members of the
- clusters have to each other. Having that in mind, it is advisable to choose a distance
- function, that can be well interpreted in the units dimension of the measurement and where
- the interpretation is invariant over the length of the data. That is, why,
- the "averaged manhattan metric" is set as the metric default, since it corresponds to the
- averaged value distance, two data sets have (as opposed by euclidean, for example).
-
- References
- ----------
- Documentation of the underlying hierarchical clustering algorithm:
- [1] https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html
- Introduction to Hierarchical clustering:
- [2] https://en.wikipedia.org/wiki/Hierarchical_clustering
- """
- fields = toSequence(field)
-
- data_to_flag = data[fields].to_df()
- data_to_flag.dropna(inplace=True)
-
- segments = data_to_flag.groupby(pd.Grouper(freq=freq))
- for segment in segments:
-
- if segment[1].shape[0] <= 1:
- continue
-
- drifters = detectDeviants(segment[1], metric, spread, frac, method, "variables")
-
- for var in drifters:
- flags[segment[1].index, fields[var]] = flag
+ def flagDriftFromNorm(
+ self: "SaQC",
+ field: Sequence[str],
+ freq: str,
+ spread: float,
+ frac: float = 0.5,
+ metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
+ np.array([x, y]), metric="cityblock"
+ )
+ / len(x),
+ method: LinkageString = "single",
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flags data that deviates from an avarage data course.
+
+ "Normality" is determined in terms of a maximum spreading distance,
+ that members of a normal group must not exceed. In addition, only a group is considered
+ "normal" if it contains more then `frac` percent of the variables in "field".
+
+ See the Notes section for a more detailed presentation of the algorithm
+
+ Parameters
+ ----------
+ field : str
+ A column in flags and data.
+
+ freq : str
+ Frequency, that split the data in chunks.
+
+ spread : float
+ Maximum spread allowed in the group of *normal* data. See Notes section for more details.
+
+ frac : float, default 0.5
+ Fraction defining the normal group. Use a value from the interval [0,1].
+ The higher the value, the more stable the algorithm will be. For values below
+ 0.5 the results are undefined.
+
+ metric : Callable, default ``lambda x,y:pdist(np.array([x,y]),metric="cityblock")/len(x)``
+ Distance function that takes two arrays as input and returns a scalar float.
+ This value is interpreted as the distance of the two input arrays.
+ Defaults to the `averaged manhattan metric` (see Notes).
+
+ method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single"
+ Linkage method used for hierarchical (agglomerative) clustering of the data.
+ `method` is directly passed to ``scipy.hierarchy.linkage``. See its documentation [1] for
+ more details. For a general introduction on hierarchical clustering see [2].
+
+ flag : float, default BAD
+ flag to set.
+
+ Returns
+ -------
+ saqc.SaQC
+
+ Notes
+ -----
+ following steps are performed for every data "segment" of length `freq` in order to find the
+ "abnormal" data:
+
+ 1. Calculate distances :math:`d(x_i,x_j)` for all :math:`x_i` in parameter `field`.
+ (with :math:`d` denoting the distance function, specified by `metric`.
+ 2. Calculate a dendogram with a hierarchical linkage algorithm, specified by `method`.
+ 3. Flatten the dendogram at the level, the agglomeration costs exceed `spread`
+ 4. check if a cluster containing more than `frac` variables.
+
+ 1. if yes: flag all the variables that are not in that cluster (inside the segment)
+ 2. if no: flag nothing
+
+ The main parameter giving control over the algorithms behavior is the `spread` parameter,
+ that determines the maximum spread of a normal group by limiting the costs, a cluster
+ agglomeration must not exceed in every linkage step.
+ For singleton clusters, that costs just equal half the distance, the data in the
+ clusters, have to each other. So, no data can be clustered together, that are more then
+ 2*`spread` distances away from each other. When data get clustered together, this new
+ clusters distance to all the other data/clusters is calculated according to the linkage
+ method specified by `method`. By default, it is the minimum distance, the members of the
+ clusters have to each other. Having that in mind, it is advisable to choose a distance
+ function, that can be well interpreted in the units dimension of the measurement and where
+ the interpretation is invariant over the length of the data. That is, why,
+ the "averaged manhattan metric" is set as the metric default, since it corresponds to the
+ averaged value distance, two data sets have (as opposed by euclidean, for example).
+
+ References
+ ----------
+ Documentation of the underlying hierarchical clustering algorithm:
+ [1] https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html
+ Introduction to Hierarchical clustering:
+ [2] https://en.wikipedia.org/wiki/Hierarchical_clustering
+ """
+ fields = toSequence(field)
+
+ data_to_flag = self._data[fields].to_df()
+ data_to_flag.dropna(inplace=True)
+
+ segments = data_to_flag.groupby(pd.Grouper(freq=freq))
+ for segment in segments:
+
+ if segment[1].shape[0] <= 1:
+ continue
+
+ drifters = detectDeviants(
+ segment[1], metric, spread, frac, method, "variables"
+ )
- return data, flags
+ for var in drifters:
+ self._flags[segment[1].index, fields[var]] = flag
+ return self
-@register(
- mask=["field", "reference"],
- demask=["field", "reference"],
- squeeze=["field", "reference"], # reference is written !
- multivariate=True,
- handles_target=False,
-)
-def flagDriftFromReference(
- data: DictOfSeries,
- field: Sequence[str],
- flags: Flags,
- reference: str,
- freq: str,
- thresh: float,
- metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
- np.array([x, y]), metric="cityblock"
+ @register(
+ mask=["field", "reference"],
+ demask=["field", "reference"],
+ squeeze=["field", "reference"], # reference is written !
+ multivariate=True,
+ handles_target=False,
)
- / len(x),
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flags data that deviates from a reference course.
+ def flagDriftFromReference(
+ self: "SaQC",
+ field: Sequence[str],
+ reference: str,
+ freq: str,
+ thresh: float,
+ metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
+ np.array([x, y]), metric="cityblock"
+ )
+ / len(x),
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flags data that deviates from a reference course.
- The deviation is measured by a passed distance function.
+ The deviation is measured by a passed distance function.
- Parameters
- ----------
- data : DictOfSeries
- The data container.
+ Parameters
+ ----------
+ field : str
+ A column in flags and data.
- field : str
- A column in flags and data.
+ freq : str
+ Frequency, that split the data in chunks.
- flags : Flags
- The flags container.
+ reference : str
+ Reference variable, the deviation is calculated from.
- freq : str
- Frequency, that split the data in chunks.
+ thresh : float
+ Maximum deviation from reference.
- reference : str
- Reference variable, the deviation is calculated from.
+ metric : Callable
+ Distance function. Takes two arrays as input and returns a scalar float.
+ This value is interpreted as the mutual distance of the two input arrays.
+ Defaults to the `averaged manhattan metric` (see Notes).
- thresh : float
- Maximum deviation from reference.
+ target : None
+ Ignored.
- metric : Callable
- Distance function. Takes two arrays as input and returns a scalar float.
- This value is interpreted as the mutual distance of the two input arrays.
- Defaults to the `averaged manhattan metric` (see Notes).
+ flag : float, default BAD
+ Flag to set.
- target : None
- Ignored.
+ Returns
+ -------
+ saqc.SaQC
- flag : float, default BAD
- Flag to set.
+ Notes
+ -----
+ It is advisable to choose a distance function, that can be well interpreted in
+ the units dimension of the measurement and where the interpretation is invariant over the
+ length of the data. That is, why, the "averaged manhatten metric" is set as the metric
+ default, since it corresponds to the averaged value distance, two data sets have (as opposed
+ by euclidean, for example).
+ """
- Returns
- -------
- data : dios.DictOfSeries
- flags : saqc.Flags
+ fields = toSequence(field)
- Notes
- -----
- It is advisable to choose a distance function, that can be well interpreted in
- the units dimension of the measurement and where the interpretation is invariant over the
- length of the data. That is, why, the "averaged manhatten metric" is set as the metric
- default, since it corresponds to the averaged value distance, two data sets have (as opposed
- by euclidean, for example).
- """
+ if reference not in fields:
+ fields.append(reference)
- fields = toSequence(field)
+ data_to_flag = self._data[fields].to_df().dropna()
- if reference not in fields:
- fields.append(reference)
+ segments = data_to_flag.groupby(pd.Grouper(freq=freq))
+ for segment in segments:
- data_to_flag = data[fields].to_df().dropna()
+ if segment[1].shape[0] <= 1:
+ continue
- segments = data_to_flag.groupby(pd.Grouper(freq=freq))
- for segment in segments:
+ for i in range(len(fields)):
+ dist = metric(
+ segment[1].iloc[:, i].values, segment[1].loc[:, reference].values
+ )
- if segment[1].shape[0] <= 1:
- continue
+ if dist > thresh:
+ self._flags[segment[1].index, fields[i]] = flag
- for i in range(len(fields)):
- dist = metric(
- segment[1].iloc[:, i].values, segment[1].loc[:, reference].values
- )
+ return self
- if dist > thresh:
- flags[segment[1].index, fields[i]] = flag
+ @register(mask=["field"], demask=[], squeeze=[])
+ def correctDrift(
+ self: "SaQC",
+ field: str,
+ maintenance_field: str,
+ model: Callable[..., float] | Literal["linear", "exponential"],
+ cal_range: int = 5,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ The function corrects drifting behavior.
- return data, flags
+ See the Notes section for an overview over the correction algorithm.
+ Parameters
+ ----------
+ field : str
+ Column in data and flags.
-@register(mask=["field"], demask=[], squeeze=[])
-def correctDrift(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- maintenance_field: str,
- model: Callable[..., float] | Literal["linear", "exponential"],
- cal_range: int = 5,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- The function corrects drifting behavior.
+ maintenance_field : str
+ Column holding the support-points information.
+ The data is expected to have the following form:
+ The index of the series represents the beginning of a maintenance
+ event, wheras the values represent its endings.
- See the Notes section for an overview over the correction algorithm.
+ model : Callable or {'exponential', 'linear'}
+ A modelfunction describing the drift behavior, that is to be corrected.
+ Either use built-in exponential or linear drift model by passing a string, or pass a custom callable.
+ The model function must always contain the keyword parameters 'origin' and 'target'.
+ The starting parameter must always be the parameter, by wich the data is passed to the model.
+ After the data parameter, there can occure an arbitrary number of model calibration arguments in
+ the signature.
+ See the Notes section for an extensive description.
- Parameters
- ----------
- data : DictOfSeries
- The data container.
+ cal_range : int, default 5
+ Number of values to calculate the mean of, for obtaining the value level directly
+ after and directly before a maintenance event. Needed for shift calibration.
- field : str
- Column in data and flags.
+ Returns
+ -------
+ saqc.SaQC
- flags : saqc.Flags
- Flags container.
+ Notes
+ -----
+ It is assumed, that between support points, there is a drift effect shifting the
+ meassurements in a way, that can be described, by a model function M(t, p, origin, target).
+ (With 0<=t<=1, p being a parameter set, and origin, target being floats).
- maintenance_field : str
- Column holding the support-points information.
- The data is expected to have the following form:
- The index of the series represents the beginning of a maintenance
- event, wheras the values represent its endings.
+ Note, that its possible for the model to have no free parameters p at all. (linear drift mainly)
- model : Callable or {'exponential', 'linear'}
- A modelfunction describing the drift behavior, that is to be corrected.
- Either use built-in exponential or linear drift model by passing a string, or pass a custom callable.
- The model function must always contain the keyword parameters 'origin' and 'target'.
- The starting parameter must always be the parameter, by wich the data is passed to the model.
- After the data parameter, there can occure an arbitrary number of model calibration arguments in
- the signature.
- See the Notes section for an extensive description.
+ The drift model, directly after the last support point (t=0),
+ should evaluate to the origin - calibration level (origin), and directly before the next
+ support point (t=1), it should evaluate to the target calibration level (target).
- cal_range : int, default 5
- Number of values to calculate the mean of, for obtaining the value level directly
- after and directly before a maintenance event. Needed for shift calibration.
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
+ M(0, p, origin, target) = origin
+ M(1, p, origin, target) = target
- Notes
- -----
- It is assumed, that between support points, there is a drift effect shifting the
- meassurements in a way, that can be described, by a model function M(t, p, origin, target).
- (With 0<=t<=1, p being a parameter set, and origin, target being floats).
- Note, that its possible for the model to have no free parameters p at all. (linear drift mainly)
+ The model is than fitted to any data chunk in between support points, by optimizing
+ the parameters p, and thus, obtaining optimal parameterset P.
- The drift model, directly after the last support point (t=0),
- should evaluate to the origin - calibration level (origin), and directly before the next
- support point (t=1), it should evaluate to the target calibration level (target).
+ The new values at t are computed via:::
+ new_vals(t) = old_vals(t) + M(t, P, origin, target) - M_drift(t, P, origin, new_target)
- M(0, p, origin, target) = origin
- M(1, p, origin, target) = target
+ Wheras ``new_target`` represents the value level immediately after the next support point.
+ Examples
+ --------
+ Some examples of meaningful driftmodels.
- The model is than fitted to any data chunk in between support points, by optimizing
- the parameters p, and thus, obtaining optimal parameterset P.
+ Linear drift modell (no free parameters).
- The new values at t are computed via:::
- new_vals(t) = old_vals(t) + M(t, P, origin, target) - M_drift(t, P, origin, new_target)
+ >>> Model = lambda t, origin, target: origin + t*target
- Wheras ``new_target`` represents the value level immediately after the next support point.
+ exponential drift model (exponential raise!)
- Examples
- --------
- Some examples of meaningful driftmodels.
+ >>> expFunc = lambda t, a, b, c: a + b * (np.exp(c * x) - 1)
+ >>> Model = lambda t, p, origin, target: expFunc(t, (target - origin) / (np.exp(abs(c)) - 1), abs(c))
- Linear drift modell (no free parameters).
+ Exponential and linear driftmodels are part of the ``ts_operators`` library, under the names
+ ``expDriftModel`` and ``linearDriftModel``.
+ """
+ # extract model func:
+ if isinstance(model, str):
+ if model not in MODELDICT:
+ raise ValueError(
+ f"invalid model '{model}', choose one of '{MODELDICT.keys()}'"
+ )
+ model = MODELDICT[model]
- >>> Model = lambda t, origin, target: origin + t*target
+ # 1: extract fit intervals:
+ if self._data[maintenance_field].empty:
+ return self
- exponential drift model (exponential raise!)
+ to_correct = self._data[field].copy()
+ maint_data = self._data[maintenance_field].copy()
- >>> expFunc = lambda t, a, b, c: a + b * (np.exp(c * x) - 1)
- >>> Model = lambda t, p, origin, target: expFunc(t, (target - origin) / (np.exp(abs(c)) - 1), abs(c))
+ to_correct_clean = to_correct.dropna()
+ d = {"drift_group": np.nan, to_correct.name: to_correct_clean.values}
+ drift_frame = pd.DataFrame(d, index=to_correct_clean.index)
- Exponential and linear driftmodels are part of the ``ts_operators`` library, under the names
- ``expDriftModel`` and ``linearDriftModel``.
+ # group the drift frame
+ for k in range(0, maint_data.shape[0] - 1):
+ # assign group numbers for the timespans in between one maintenance ending and the beginning of the next
+ # maintenance time itself remains np.nan assigned
+ drift_frame.loc[
+ maint_data.values[k] : pd.Timestamp(maint_data.index[k + 1]),
+ "drift_group",
+ ] = k
- """
- # extract model func:
- if isinstance(model, str):
- if model not in MODELDICT:
- raise ValueError(
- f"invalid model '{model}', choose one of '{MODELDICT.keys()}'"
- )
- model = MODELDICT[model]
-
- # 1: extract fit intervals:
- if data[maintenance_field].empty:
- return data, flags
-
- to_correct = data[field].copy()
- maint_data = data[maintenance_field].copy()
-
- to_correct_clean = to_correct.dropna()
- d = {"drift_group": np.nan, to_correct.name: to_correct_clean.values}
- drift_frame = pd.DataFrame(d, index=to_correct_clean.index)
-
- # group the drift frame
- for k in range(0, maint_data.shape[0] - 1):
- # assign group numbers for the timespans in between one maintenance ending and the beginning of the next
- # maintenance time itself remains np.nan assigned
- drift_frame.loc[
- maint_data.values[k] : pd.Timestamp(maint_data.index[k + 1]), "drift_group"
- ] = k
-
- # define target values for correction
- drift_grouper = drift_frame.groupby("drift_group")
- shift_targets = drift_grouper.aggregate(lambda x: x[:cal_range].mean()).shift(-1)
-
- for k, group in drift_grouper:
- data_series = group[to_correct.name]
- data_fit, data_shiftTarget = _driftFit(
- data_series, shift_targets.loc[k, :][0], cal_range, model
+ # define target values for correction
+ drift_grouper = drift_frame.groupby("drift_group")
+ shift_targets = drift_grouper.aggregate(lambda x: x[:cal_range].mean()).shift(
+ -1
)
- data_fit = pd.Series(data_fit, index=group.index)
- data_shiftTarget = pd.Series(data_shiftTarget, index=group.index)
- data_shiftVektor = data_shiftTarget - data_fit
- shiftedData = data_series + data_shiftVektor
- to_correct[shiftedData.index] = shiftedData
-
- data[field] = to_correct
-
- return data, flags
-
-@register(mask=["field", "cluster_field"], demask=["cluster_field"], squeeze=[])
-def correctRegimeAnomaly(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- cluster_field: str,
- model: CurveFitter,
- tolerance: Optional[str] = None,
- epoch: bool = False,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Function fits the passed model to the different regimes in data[field] and tries to correct
- those values, that have assigned a negative label by data[cluster_field].
-
- Currently, the only correction mode supported is the "parameter propagation."
-
- This means, any regime :math:`z`, labeled negatively and being modeled by the parameters p, gets corrected via:
-
- :math:`z_{correct} = z + (m(p^*) - m(p))`,
-
- where :math:`p^*` denotes the parameter set belonging to the fit of the nearest not-negatively labeled cluster.
-
- 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
- Container to store flags of the data.
- cluster_field : str
- A string denoting the field in data, holding the cluster label for the data you want to correct.
- model : Callable
- The model function to be fitted to the regimes.
- It must be a function of the form :math:`f(x, *p)`, where :math:`x` is the ``numpy.array`` holding the
- independent variables and :math:`p` are the model parameters that are to be obtained by fitting.
- Depending on the `x_date` parameter, independent variable x will either be the timestamps
- of every regime transformed to seconds from epoch, or it will be just seconds, counting the regimes length.
- tolerance : {None, str}, default None:
- If an offset string is passed, a data chunk of length `offset` right at the
- start and right at the end is ignored when fitting the model. This is to account for the
- unreliability of data near the changepoints of regimes.
- epoch : bool, default False
- If True, use "seconds from epoch" as x input to the model func, instead of "seconds from regime start".
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- """
- cluster_ser = data[cluster_field]
- unique_successive = pd.unique(cluster_ser.values)
- data_ser = data[field]
- regimes = data_ser.groupby(cluster_ser)
- para_dict = {}
- x_dict = {}
- x_mask = {}
- if tolerance is not None:
- # get seconds
- tolerance = pd.Timedelta(tolerance).total_seconds()
- for label, regime in regimes:
- if epoch is False:
- # get seconds data:
- xdata = (regime.index - regime.index[0]).to_numpy(dtype=float) * 10 ** (-9)
- else:
- # get seconds from epoch data
- xdata = regime.index.to_numpy(dtype=float) * 10 ** (-9)
- ydata = regime.values
- valid_mask = ~np.isnan(ydata)
+ for k, group in drift_grouper:
+ data_series = group[to_correct.name]
+ data_fit, data_shiftTarget = _driftFit(
+ data_series, shift_targets.loc[k, :][0], cal_range, model
+ )
+ data_fit = pd.Series(data_fit, index=group.index)
+ data_shiftTarget = pd.Series(data_shiftTarget, index=group.index)
+ data_shiftVektor = data_shiftTarget - data_fit
+ shiftedData = data_series + data_shiftVektor
+ to_correct[shiftedData.index] = shiftedData
+
+ self._data[field] = to_correct
+
+ return self
+
+ @register(mask=["field", "cluster_field"], demask=["cluster_field"], squeeze=[])
+ def correctRegimeAnomaly(
+ self: "SaQC",
+ field: str,
+ cluster_field: str,
+ model: CurveFitter,
+ tolerance: Optional[str] = None,
+ epoch: bool = False,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Function fits the passed model to the different regimes in data[field] and tries to correct
+ those values, that have assigned a negative label by data[cluster_field].
+
+ Currently, the only correction mode supported is the "parameter propagation."
+
+ This means, any regime :math:`z`, labeled negatively and being modeled by the parameters p, gets corrected via:
+
+ :math:`z_{correct} = z + (m(p^*) - m(p))`,
+
+ where :math:`p^*` denotes the parameter set belonging to the fit of the nearest not-negatively labeled cluster.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the data column, you want to correct.
+
+ cluster_field : str
+ A string denoting the field in data, holding the cluster label for the data you want to correct.
+
+ model : Callable
+ The model function to be fitted to the regimes.
+ It must be a function of the form :math:`f(x, *p)`, where :math:`x` is the ``numpy.array`` holding the
+ independent variables and :math:`p` are the model parameters that are to be obtained by fitting.
+ Depending on the `x_date` parameter, independent variable x will either be the timestamps
+ of every regime transformed to seconds from epoch, or it will be just seconds, counting the regimes length.
+
+ tolerance : {None, str}, default None:
+ If an offset string is passed, a data chunk of length `offset` right at the
+ start and right at the end is ignored when fitting the model. This is to account for the
+ unreliability of data near the changepoints of regimes.
+
+ epoch : bool, default False
+ If True, use "seconds from epoch" as x input to the model func, instead of "seconds from regime start".
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ cluster_ser = self._data[cluster_field]
+ unique_successive = pd.unique(cluster_ser.values)
+ data_ser = self._data[field]
+ regimes = data_ser.groupby(cluster_ser)
+ para_dict = {}
+ x_dict = {}
+ x_mask = {}
if tolerance is not None:
- valid_mask &= xdata > xdata[0] + tolerance
- valid_mask &= xdata < xdata[-1] - tolerance
- try:
- p, *_ = curve_fit(model, xdata[valid_mask], ydata[valid_mask])
- except (RuntimeError, ValueError):
- p = np.array([np.nan])
- para_dict[label] = p
- x_dict[label] = xdata
- x_mask[label] = valid_mask
-
- first_normal = unique_successive > 0
- first_valid = np.array(
- [
- ~pd.isna(para_dict[unique_successive[i]]).any()
- for i in range(0, unique_successive.shape[0])
- ]
- )
- first_valid = np.where(first_normal & first_valid)[0][0]
- last_valid = 1
-
- for k in range(0, unique_successive.shape[0]):
- if unique_successive[k] < 0 & (
- not pd.isna(para_dict[unique_successive[k]]).any()
- ):
- ydata = data_ser[regimes.groups[unique_successive[k]]].values
- xdata = x_dict[unique_successive[k]]
- ypara = para_dict[unique_successive[k]]
- if k > 0:
- target_para = para_dict[unique_successive[k - last_valid]]
+ # get seconds
+ tolerance = pd.Timedelta(tolerance).total_seconds()
+ for label, regime in regimes:
+ if epoch is False:
+ # get seconds data:
+ xdata = (regime.index - regime.index[0]).to_numpy(dtype=float) * 10 ** (
+ -9
+ )
else:
- # first regime has no "last valid" to its left, so we use first valid to the right:
- target_para = para_dict[unique_successive[k + first_valid]]
- y_shifted = ydata + (model(xdata, *target_para) - model(xdata, *ypara))
- data_ser[regimes.groups[unique_successive[k]]] = y_shifted
- if k > 0:
+ # get seconds from epoch data
+ xdata = regime.index.to_numpy(dtype=float) * 10 ** (-9)
+ ydata = regime.values
+ valid_mask = ~np.isnan(ydata)
+ if tolerance is not None:
+ valid_mask &= xdata > xdata[0] + tolerance
+ valid_mask &= xdata < xdata[-1] - tolerance
+ try:
+ p, *_ = curve_fit(model, xdata[valid_mask], ydata[valid_mask])
+ except (RuntimeError, ValueError):
+ p = np.array([np.nan])
+ para_dict[label] = p
+ x_dict[label] = xdata
+ x_mask[label] = valid_mask
+
+ first_normal = unique_successive > 0
+ first_valid = np.array(
+ [
+ ~pd.isna(para_dict[unique_successive[i]]).any()
+ for i in range(0, unique_successive.shape[0])
+ ]
+ )
+ first_valid = np.where(first_normal & first_valid)[0][0]
+ last_valid = 1
+
+ for k in range(0, unique_successive.shape[0]):
+ if unique_successive[k] < 0 & (
+ not pd.isna(para_dict[unique_successive[k]]).any()
+ ):
+ ydata = data_ser[regimes.groups[unique_successive[k]]].values
+ xdata = x_dict[unique_successive[k]]
+ ypara = para_dict[unique_successive[k]]
+ if k > 0:
+ target_para = para_dict[unique_successive[k - last_valid]]
+ else:
+ # first regime has no "last valid" to its left, so we use first valid to the right:
+ target_para = para_dict[unique_successive[k + first_valid]]
+ y_shifted = ydata + (model(xdata, *target_para) - model(xdata, *ypara))
+ data_ser[regimes.groups[unique_successive[k]]] = y_shifted
+ if k > 0:
+ last_valid += 1
+ elif pd.isna(para_dict[unique_successive[k]]).any() & (k > 0):
last_valid += 1
- elif pd.isna(para_dict[unique_successive[k]]).any() & (k > 0):
- last_valid += 1
- else:
- last_valid = 1
-
- data[field] = data_ser
- return data, flags
-
-
-@register(mask=["field"], demask=[], squeeze=[])
-def correctOffset(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- max_jump: float,
- spread: float,
- window: str,
- min_periods: int,
- tolerance: Optional[str] = None,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- 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
- Container to store flags of the data.
- max_jump : float
- when searching for changepoints in mean - this is the threshold a mean difference in the
- sliding window search must exceed to trigger changepoint detection.
- spread : float
- threshold denoting the maximum, regimes are allowed to abolutely differ in their means
- to form the "normal group" of values.
- window : str
- Size of the adjacent windows that are used to search for the mean changepoints.
- min_periods : int
- Minimum number of periods a search window has to contain, for the result of the changepoint
- detection to be considered valid.
- tolerance : {None, str}, default None:
- If an offset string is passed, a data chunk of length `offset` right from the
- start and right before the end of any regime is ignored when calculating a regimes mean for data correcture.
- This is to account for the unrelyability of data near the changepoints of regimes.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- """
- data, flags = copyField(data, field, flags, field + "_CPcluster")
- data, flags = _assignChangePointCluster(
- data,
- field + "_CPcluster",
- flags,
- lambda x, y: np.abs(np.mean(x) - np.mean(y)),
- lambda x, y: max_jump,
- window=window,
- min_periods=min_periods,
- )
- data, flags = _assignRegimeAnomaly(data, field, flags, field + "_CPcluster", spread)
- data, flags = correctRegimeAnomaly(
- data,
- field,
- flags,
- field + "_CPcluster",
- lambda x, p1: np.array([p1] * x.shape[0]),
- tolerance=tolerance,
- )
- data, flags = dropField(data, field + "_CPcluster", flags)
-
- return data, flags
+ else:
+ last_valid = 1
+
+ self._data[field] = data_ser
+ return self
+
+ @register(mask=["field"], demask=[], squeeze=[])
+ def correctOffset(
+ self: "SaQC",
+ field: str,
+ max_jump: float,
+ spread: float,
+ window: str,
+ min_periods: int,
+ tolerance: Optional[str] = None,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Parameters
+ ----------
+ field : str
+ The fieldname of the data column, you want to correct.
+
+ max_jump : float
+ when searching for changepoints in mean - this is the threshold a mean difference in the
+ sliding window search must exceed to trigger changepoint detection.
+
+ spread : float
+ threshold denoting the maximum, regimes are allowed to abolutely differ in their means
+ to form the "normal group" of values.
+
+ window : str
+ Size of the adjacent windows that are used to search for the mean changepoints.
+
+ min_periods : int
+ Minimum number of periods a search window has to contain, for the result of the changepoint
+ detection to be considered valid.
+
+ tolerance : {None, str}, default None:
+ If an offset string is passed, a data chunk of length `offset` right from the
+ start and right before the end of any regime is ignored when calculating a regimes mean for data correcture.
+ This is to account for the unrelyability of data near the changepoints of regimes.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ self = self.copyField(field, field + "_CPcluster")
+ self._data, self._flags = _assignChangePointCluster(
+ self._data,
+ field + "_CPcluster",
+ self._flags,
+ lambda x, y: np.abs(np.mean(x) - np.mean(y)),
+ lambda x, y: max_jump,
+ window=window,
+ min_periods=min_periods,
+ )
+ self._data, self._flags = _assignRegimeAnomaly(
+ self._data, field, self._flags, field + "_CPcluster", spread
+ )
+ self = self.correctRegimeAnomaly(
+ field,
+ field + "_CPcluster",
+ lambda x, p1: np.array([p1] * x.shape[0]),
+ tolerance=tolerance,
+ )
+ self = self.dropField(field + "_CPcluster")
+ return self
+
+ @flagging()
+ def flagRegimeAnomaly(
+ self: "SaQC",
+ 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":
+ """
+ Flags anomalous regimes regarding to modelling regimes of field.
+
+ "Normality" is determined in terms of a maximum spreading distance,
+ regimes must not exceed in respect to a certain metric and linkage method.
+
+ In addition, only a range of regimes is considered "normal", if it models
+ more then `frac` percentage of the valid samples in "field".
+
+ Note, that you must detect the regime changepoints prior to calling this function.
+
+ Note, that it is possible to perform hypothesis tests for regime equality
+ by passing the metric a function for p-value calculation and selecting linkage
+ method "complete".
+
+ Parameters
+ ----------
+ field : str
+ Name of the column to process
+
+ cluster_field : str
+ Column in data, holding the cluster labels for the samples in field.
+ (has to be indexed equal to field)
+
+ spread : float
+ A threshold denoting the value level, up to wich clusters a agglomerated.
+
+ method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single"
+ The linkage method for hierarchical (agglomerative) clustering of the variables.
+
+ metric : Callable, default lambda x,y: np.abs(np.nanmean(x) - np.nanmean(y))
+ A metric function for calculating the dissimilarity between 2 regimes.
+ Defaults to the difference in mean.
+
+ frac : float
+ Has to be in [0,1]. Determines the minimum percentage of samples,
+ the "normal" group has to comprise to be the normal group actually.
+
+ flag : float, default BAD
+ flag to set.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ reserverd = ["set_cluster", "set_flags"]
+ kwargs = filterKwargs(kwargs, reserverd)
+ self._data, self._flags = _assignRegimeAnomaly(
+ data=self._data,
+ field=field,
+ flags=self._flags,
+ cluster_field=cluster_field,
+ spread=spread,
+ method=method,
+ metric=metric,
+ frac=frac,
+ flag=flag,
+ **kwargs,
+ set_cluster=False,
+ set_flags=True,
+ )
+ return self
+
+ @register(mask=["field", "cluster_field"], demask=["cluster_field"], squeeze=[])
+ def assignRegimeAnomaly(
+ self: "SaQC",
+ 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":
+ """
+ A function to detect values belonging to an anomalous regime regarding modelling
+ regimes of field.
+
+ The function changes the value of the regime cluster labels to be negative.
+ "Normality" is determined in terms of a maximum spreading distance, regimes must
+ not exceed in respect to a certain metric and linkage method. In addition,
+ only a range of regimes is considered "normal", if it models more then `frac`
+ percentage of the valid samples in "field". Note, that you must detect the regime
+ changepoints prior to calling this function. (They are expected to be stored
+ parameter `cluster_field`.)
+
+ Note, that it is possible to perform hypothesis tests for regime equality by
+ passing the metric a function for p-value calculation and selecting linkage
+ method "complete".
+
+ Parameters
+ ----------
+ field : str
+ Name of the column to process
+
+ cluster_field : str
+ Column in data, holding the cluster labels for the samples in field.
+ (has to be indexed equal to field)
+
+ spread : float
+ A threshold denoting the value level, up to wich clusters a agglomerated.
+
+ method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single"
+ The linkage method for hierarchical (agglomerative) clustering of the variables.
+
+ metric : Callable, default lambda x,y: np.abs(np.nanmean(x) - np.nanmean(y))
+ A metric function for calculating the dissimilarity between 2 regimes.
+ Defaults to the difference in mean.
+
+ frac : float
+ Has to be in [0,1]. Determines the minimum percentage of samples,
+ the "normal" group has to comprise to be the normal group actually.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ reserverd = ["set_cluster", "set_flags", "flag"]
+ kwargs = filterKwargs(kwargs, reserverd)
+ self._data, self._flags = _assignRegimeAnomaly(
+ data=self._data,
+ field=field,
+ flags=self._flags,
+ cluster_field=cluster_field,
+ spread=spread,
+ method=method,
+ metric=metric,
+ frac=frac,
+ **kwargs,
+ # control args
+ set_cluster=True,
+ set_flags=False,
+ )
+ return self
def _driftFit(x, shift_target, cal_mean, driftModel):
@@ -617,165 +738,6 @@ def _driftFit(x, shift_target, cal_mean, driftModel):
return data_fit, data_shift
-@flagging()
-def flagRegimeAnomaly(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- 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,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flags anomalous regimes regarding to modelling regimes of field.
-
- "Normality" is determined in terms of a maximum spreading distance,
- regimes must not exceed in respect to a certain metric and linkage method.
-
- In addition, only a range of regimes is considered "normal", if it models
- more then `frac` percentage of the valid samples in "field".
-
- Note, that you must detect the regime changepoints prior to calling this function.
-
- Note, that it is possible to perform hypothesis tests for regime equality
- by passing the metric a function for p-value calculation and selecting linkage
- method "complete".
-
- Parameters
- ----------
- data : dios.DictOfSeries
- Data to process
- field : str
- Name of the column to process
- flags : saqc.Flags
- Container to store flags of the data.
- cluster_field : str
- Column in data, holding the cluster labels for the samples in field.
- (has to be indexed equal to field)
- spread : float
- A threshold denoting the value level, up to wich clusters a agglomerated.
- method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single"
- The linkage method for hierarchical (agglomerative) clustering of the variables.
- metric : Callable, default lambda x,y: np.abs(np.nanmean(x) - np.nanmean(y))
- A metric function for calculating the dissimilarity between 2 regimes.
- Defaults to the difference in mean.
- frac : float
- Has to be in [0,1]. Determines the minimum percentage of samples,
- the "normal" group has to comprise to be the normal group actually.
- flag : float, default BAD
- flag to set.
-
- Returns
- -------
-
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The flags object, holding flags and additional informations related to `data`.
- Flags values may have changed, relatively to the flags input.
- """
- reserverd = ["set_cluster", "set_flags"]
- kwargs = filterKwargs(kwargs, reserverd)
- return _assignRegimeAnomaly(
- data=data,
- field=field,
- flags=flags,
- cluster_field=cluster_field,
- spread=spread,
- method=method,
- metric=metric,
- frac=frac,
- flag=flag,
- **kwargs,
- set_cluster=False,
- set_flags=True,
- )
-
-
-@register(mask=["field", "cluster_field"], demask=["cluster_field"], squeeze=[])
-def assignRegimeAnomaly(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- 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,
-) -> Tuple[DictOfSeries, Flags]:
- """
- A function to detect values belonging to an anomalous regime regarding modelling
- regimes of field.
-
- The function changes the value of the regime cluster labels to be negative.
- "Normality" is determined in terms of a maximum spreading distance, regimes must
- not exceed in respect to a certain metric and linkage method. In addition,
- only a range of regimes is considered "normal", if it models more then `frac`
- percentage of the valid samples in "field". Note, that you must detect the regime
- changepoints prior to calling this function. (They are expected to be stored
- parameter `cluster_field`.)
-
- Note, that it is possible to perform hypothesis tests for regime equality by
- passing the metric a function for p-value calculation and selecting linkage
- method "complete".
-
- Parameters
- ----------
- data : dios.DictOfSeries
- Data to process
- field : str
- Name of the column to process
- flags : saqc.Flags
- Container to store flags of the data.
- cluster_field : str
- Column in data, holding the cluster labels for the samples in field.
- (has to be indexed equal to field)
- spread : float
- A threshold denoting the value level, up to wich clusters a agglomerated.
- method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single"
- The linkage method for hierarchical (agglomerative) clustering of the variables.
- metric : Callable, default lambda x,y: np.abs(np.nanmean(x) - np.nanmean(y))
- A metric function for calculating the dissimilarity between 2 regimes.
- Defaults to the difference in mean.
- frac : float
- Has to be in [0,1]. Determines the minimum percentage of samples,
- the "normal" group has to comprise to be the normal group actually.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The flags object, holding flags and additional informations related to `data`.
- Flags values may have changed, relatively to the flags input.
- """
- reserverd = ["set_cluster", "set_flags", "flag"]
- kwargs = filterKwargs(kwargs, reserverd)
- return _assignRegimeAnomaly(
- data=data,
- field=field,
- flags=flags,
- cluster_field=cluster_field,
- spread=spread,
- method=method,
- metric=metric,
- frac=frac,
- **kwargs,
- # control args
- set_cluster=True,
- set_flags=False,
- )
-
-
def _assignRegimeAnomaly(
data: DictOfSeries,
field: str,
@@ -783,7 +745,7 @@ def _assignRegimeAnomaly(
cluster_field: str,
spread: float,
method: LinkageString = "single",
- metric: Callable[[np.array, np.array], float] = lambda x, y: np.abs(
+ metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: np.abs(
np.nanmean(x) - np.nanmean(y)
),
frac: float = 0.5,
diff --git a/saqc/funcs/flagtools.py b/saqc/funcs/flagtools.py
index effa0e1db..d253dc8ff 100644
--- a/saqc/funcs/flagtools.py
+++ b/saqc/funcs/flagtools.py
@@ -8,7 +8,7 @@
from __future__ import annotations
import warnings
-from typing import Any, Sequence, Tuple, Union
+from typing import TYPE_CHECKING, Any, Union
import numpy as np
import pandas as pd
@@ -16,553 +16,510 @@ from typing_extensions import Literal
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"])
-def forceFlags(
- data: DictOfSeries, field: str, flags: Flags, flag: float = BAD, **kwargs
-) -> Tuple[DictOfSeries, Flags]:
- """
- Set whole column to a flag value.
-
- Parameters
- ----------
- data : DictOfSeries
- data container
- field : str
- columns name that holds the data
- flags : saqc.Flags
- flags object
- flag : float, default BAD
- flag to set
- kwargs : dict
- unused
-
- Returns
- -------
- data : DictOfSeries
- flags : saqc.Flags
-
- See Also
- --------
- clearFlags : set whole column to UNFLAGGED
- flagUnflagged : set flag value at all unflagged positions
- """
- flags[:, field] = flag
- return data, flags
-
-
-@register(mask=[], demask=[], squeeze=["field"])
-def clearFlags(
- data: DictOfSeries, field: str, flags: Flags, **kwargs
-) -> Tuple[DictOfSeries, Flags]:
- """
- Set whole column to UNFLAGGED.
-
- Parameters
- ----------
- data : DictOfSeries
- data container
- field : str
- columns name that holds the data
- flags : saqc.Flags
- flags object
- kwargs : dict
- unused
-
- Returns
- -------
- data : DictOfSeries
- flags : saqc.Flags
-
- Notes
- -----
- This function ignores the ``dfilter`` keyword, because the data is not relevant
- for processing.
- A warning is triggered if the ``flag`` keyword is given, because the flags are
- always set to `UNFLAGGED`.
-
-
- See Also
- --------
- forceFlags : set whole column to a flag value
- flagUnflagged : set flag value at all unflagged positions
- """
- # NOTE: do we really need this?
- if "flag" in kwargs:
- kwargs = {**kwargs} # copy
- flag = kwargs.pop("flag")
- warnings.warn(f"`flag={flag}` is ignored here.")
-
- return forceFlags(data, field, flags, flag=UNFLAGGED, **kwargs)
-
-
-@register(mask=[], demask=[], squeeze=["field"])
-def flagUnflagged(
- data: DictOfSeries, field: str, flags: Flags, flag: float = BAD, **kwargs
-) -> Tuple[DictOfSeries, Flags]:
- """
- Function sets a flag at all unflagged positions.
-
- 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`.
- flag : float, default BAD
- flag value to set
- kwargs : Dict
- unused
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
-
- Notes
- -----
- This function ignores the ``dfilter`` keyword, because the data is not relevant
- for processing.
-
- See Also
- --------
- clearFlags : set whole column to UNFLAGGED
- forceFlags : set whole column to a flag value
- """
- unflagged = flags[field].isna() | (flags[field] == UNFLAGGED)
- flags[unflagged, field] = flag
- return data, flags
-
-
-@register(mask=["field"], demask=["field"], squeeze=["field"])
-def flagManual(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- mdata: Union[pd.Series, pd.DataFrame, DictOfSeries, list, np.array],
- method: Literal[
- "left-open", "right-open", "closed", "plain", "ontime"
- ] = "left-open",
- mformat: Literal["start-end", "mflag"] = "start-end",
- mflag: Any = 1,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flag data by given, "manually generated" data.
-
- The data is flagged at locations where `mdata` is equal to a provided flag (`mflag`).
- The format of mdata can be an indexed object, like pd.Series, pd.Dataframe or dios.DictOfSeries,
- but also can be a plain list- or array-like.
- How indexed mdata is aligned to data is specified via the `method` parameter.
-
- 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`.
- mdata : pd.Series, pd.DataFrame, DictOfSeries, str, list or np.ndarray
- The Data determining, wich intervals are to be flagged, or a string, denoting under which field the data is
- accessable.
- method : {'plain', 'ontime', 'left-open', 'right-open', 'closed'}, default 'plain'
- Defines how mdata is projected on data. Except for the 'plain' method, the methods assume mdata to have an
- index.
-
- * 'plain': mdata must have the same length as data and is projected one-to-one on data.
- * 'ontime': works only with indexed mdata. mdata entries are matched with data entries that have the same index.
- * 'right-open': mdata defines intervals, values are to be projected on.
- The intervals are defined,
-
- (1) Either, by any two consecutive timestamps t_1 and 1_2 where t_1 is valued with mflag, or by a series,
- (2) Or, a Series, where the index contains in the t1 timestamps nd the values the respective t2 stamps.
-
- The value at t_1 gets projected onto all data timestamps t with t_1 <= t < t_2.
-
- * 'left-open': like 'right-open', but the projected interval now covers all t with t_1 < t <= t_2.
- * 'closed': like 'right-open', but the projected interval now covers all t with t_1 <= t <= t_2.
-
- mformat : {"start-end", "mflag"}, default "start-end"
-
- * "start-end": mdata is a Series, where every entry indicates an interval to-flag. The index defines the left
- bound, the value defines the right bound.
- * "mflag": mdata is an array like, with entries containing 'mflag',where flags shall be set. See documentation
- for examples.
-
- mflag : scalar
- The flag that indicates data points in `mdata`, of wich the projection in data should be flagged.
- flag : float, default BAD
- flag to set.
-
- Returns
- -------
- data : original data
- flags : modified flags
-
- Examples
- --------
- An example for mdata
-
- .. doctest:: ExampleFlagManual
-
- >>> 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
- 2000-05-01 1
- dtype: int64
-
- On *dayly* data, with the 'ontime' method, only the provided timestamps are used.
- Bear in mind that only exact timestamps apply, any offset will result in ignoring
- the timestamp.
-
- .. doctest:: ExampleFlagManual
-
- >>> 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('daily_data', mdata, mflag=1, mformat='mdata', method='ontime')
- >>> qc.flags['daily_data'] > UNFLAGGED
- 2000-01-31 False
- 2000-02-01 True
- 2000-02-02 False
- 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('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
- 2000-03-01 False
- 2000-05-01 True
- Name: daily_data, dtype: bool
-
- With the 'left-open' method, backward filling is used:
-
- .. doctest:: ExampleFlagManual
-
- >>> 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 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
- not_mflag = -1 if mflag == 0 else 0
- if isinstance(mdata, str):
- mdata = data[mdata]
-
- if isinstance(mdata, (pd.DataFrame, DictOfSeries)):
- mdata = mdata[field]
-
- hasindex = isinstance(mdata, (pd.Series, pd.DataFrame, DictOfSeries))
- if not hasindex:
- if method != "plain":
- raise ValueError("mdata has no index")
- else:
- mdata = pd.Series(mdata, index=dat.index)
-
- # check, if intervals where passed in format (index:start-time, data:end-time)
- if mformat == "start-end":
- if method in ["plain", "ontime"]:
- raise ValueError(
- "'Start-End' formatting not compatible to 'plain' or 'ontime' methods"
- )
+
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
+
+class FlagtoolsMixin:
+ @flagging()
+ def flagDummy(self: "SaQC", field: str, **kwargs) -> "SaQC":
+ """
+ Function does nothing but returning data and flags.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ return self
+
+ @register(mask=[], demask=[], squeeze=["field"])
+ def forceFlags(self: "SaQC", field: str, flag: float = BAD, **kwargs) -> "SaQC":
+ """
+ Set whole column to a flag value.
+
+ Parameters
+ ----------
+ field : str
+ columns name that holds the data
+
+ flag : float, default BAD
+ flag to set
+
+ kwargs : dict
+ unused
+
+ Returns
+ -------
+ saqc.SaQC
+
+ See Also
+ --------
+ clearFlags : set whole column to UNFLAGGED
+ flagUnflagged : set flag value at all unflagged positions
+ """
+ self._flags[:, field] = flag
+ return self
+
+ @register(mask=[], demask=[], squeeze=["field"])
+ def clearFlags(self: "SaQC", field: str, **kwargs) -> "SaQC":
+ """
+ Set whole column to UNFLAGGED.
+
+ Parameters
+ ----------
+ field : str
+ columns name that holds the data
+
+ kwargs : dict
+ unused
+
+ Returns
+ -------
+ saqc.SaQC
+
+ Notes
+ -----
+ This function ignores the ``dfilter`` keyword, because the data is not relevant
+ for processing.
+ A warning is triggered if the ``flag`` keyword is given, because the flags are
+ always set to `UNFLAGGED`.
+
+ See Also
+ --------
+ forceFlags : set whole column to a flag value
+ flagUnflagged : set flag value at all unflagged positions
+ """
+ # NOTE: do we really need this?
+ if "flag" in kwargs:
+ kwargs = {**kwargs} # copy
+ flag = kwargs.pop("flag")
+ warnings.warn(f"`flag={flag}` is ignored here.")
+
+ return self.forceFlags(field, flag=UNFLAGGED, **kwargs)
+
+ @register(mask=[], demask=[], squeeze=["field"])
+ def flagUnflagged(self: "SaQC", field: str, flag: float = BAD, **kwargs) -> "SaQC":
+ """
+ Function sets a flag at all unflagged positions.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
+
+ flag : float, default BAD
+ flag value to set
+
+ kwargs : Dict
+ unused
+
+ Returns
+ -------
+ saqc.SaQC
+
+ Notes
+ -----
+ This function ignores the ``dfilter`` keyword, because the data is not relevant
+ for processing.
+
+ See Also
+ --------
+ clearFlags : set whole column to UNFLAGGED
+ forceFlags : set whole column to a flag value
+ """
+ unflagged = self._flags[field].isna() | (self._flags[field] == UNFLAGGED)
+ self._flags[unflagged, field] = flag
+ return self
+
+ @register(mask=["field"], demask=["field"], squeeze=["field"])
+ def flagManual(
+ self: "SaQC",
+ field: str,
+ mdata: Union[pd.Series, pd.DataFrame, DictOfSeries, list, np.ndarray],
+ method: Literal[
+ "left-open", "right-open", "closed", "plain", "ontime"
+ ] = "left-open",
+ mformat: Literal["start-end", "mflag"] = "start-end",
+ mflag: Any = 1,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flag data by given, "manually generated" data.
+
+ The data is flagged at locations where `mdata` is equal to a provided flag (`mflag`).
+ The format of mdata can be an indexed object, like pd.Series, pd.Dataframe or dios.DictOfSeries,
+ but also can be a plain list- or array-like.
+ How indexed mdata is aligned to data is specified via the `method` parameter.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
+
+ mdata : pd.Series, pd.DataFrame, DictOfSeries, str, list or np.ndarray
+ The Data determining, wich intervals are to be flagged, or a string, denoting under which field the data is
+ accessable.
+
+ method : {'plain', 'ontime', 'left-open', 'right-open', 'closed'}, default 'plain'
+ Defines how mdata is projected on data. Except for the 'plain' method, the methods assume mdata to have an
+ index.
+
+ * 'plain': mdata must have the same length as data and is projected one-to-one on data.
+ * 'ontime': works only with indexed mdata. mdata entries are matched with data entries that have the same index.
+ * 'right-open': mdata defines intervals, values are to be projected on.
+ The intervals are defined,
+
+ (1) Either, by any two consecutive timestamps t_1 and 1_2 where t_1 is valued with mflag, or by a series,
+ (2) Or, a Series, where the index contains in the t1 timestamps nd the values the respective t2 stamps.
+
+ The value at t_1 gets projected onto all data timestamps t with t_1 <= t < t_2.
+
+ * 'left-open': like 'right-open', but the projected interval now covers all t with t_1 < t <= t_2.
+ * 'closed': like 'right-open', but the projected interval now covers all t with t_1 <= t <= t_2.
+
+ mformat : {"start-end", "mflag"}, default "start-end"
+
+ * "start-end": mdata is a Series, where every entry indicates an interval to-flag. The index defines the left
+ bound, the value defines the right bound.
+ * "mflag": mdata is an array like, with entries containing 'mflag',where flags shall be set. See documentation
+ for examples.
+
+ mflag : scalar
+ The flag that indicates data points in `mdata`, of wich the projection in data should be flagged.
+
+ flag : float, default BAD
+ flag to set.
+
+ Returns
+ -------
+ saqc.SaQC
+
+ Examples
+ --------
+ An example for mdata
+
+ .. doctest:: ExampleFlagManual
+
+ >>> 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
+ 2000-05-01 1
+ dtype: int64
+
+ On *dayly* data, with the 'ontime' method, only the provided timestamps are used.
+ Bear in mind that only exact timestamps apply, any offset will result in ignoring
+ the timestamp.
+
+ .. doctest:: ExampleFlagManual
+
+ >>> 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('daily_data', mdata, mflag=1, mformat='mdata', method='ontime')
+ >>> qc.flags['daily_data'] > UNFLAGGED
+ 2000-01-31 False
+ 2000-02-01 True
+ 2000-02-02 False
+ 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('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
+ 2000-03-01 False
+ 2000-05-01 True
+ Name: daily_data, dtype: bool
+
+ With the 'left-open' method, backward filling is used:
+
+ .. doctest:: ExampleFlagManual
+
+ >>> 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 True
+ 2000-02-02 True
+ 2000-03-01 True
+ 2000-05-01 True
+ Name: daily_data, dtype: bool
+ """
+ dat = self._data[field]
+ # internal not-mflag-value -> cant go for np.nan
+ not_mflag = -1 if mflag == 0 else 0
+ if isinstance(mdata, str):
+ mdata = self._data[mdata]
+
+ if isinstance(mdata, (pd.DataFrame, DictOfSeries)):
+ mdata = mdata[field]
+
+ hasindex = isinstance(mdata, (pd.Series, pd.DataFrame, DictOfSeries))
+ if not hasindex:
+ if method != "plain":
+ raise ValueError("mdata has no index")
+ else:
+ mdata = pd.Series(mdata, index=dat.index)
+
+ # check, if intervals where passed in format (index:start-time, data:end-time)
+ if mformat == "start-end":
+ if method in ["plain", "ontime"]:
+ raise ValueError(
+ "'Start-End' formatting not compatible to 'plain' or 'ontime' methods"
+ )
+ else:
+ mdata = pd.Series(
+ not_mflag,
+ index=mdata.index.join(pd.DatetimeIndex(mdata.values), how="outer"),
+ )
+ mdata[::2] = mflag
+
+ # get rid of values that are neither mflag nor not_mflag (for bw-compatibillity mainly)
+ mdata[mdata != mflag] = not_mflag
+
+ # evaluate methods
+ if method == "plain":
+ pass
+ # reindex will do the job later
+ elif method == "ontime":
+ pass
+
+ elif method in ["left-open", "right-open", "closed"]:
+ mdata = mdata.drop(mdata.index[mdata.diff() == 0])
+ app_entry = pd.Series(mdata[-1], dat.index.shift(freq="1min")[-1:])
+ mdata = mdata.reindex(dat.index.union(mdata.index))
+
+ if method == "right-open":
+ mdata = mdata.ffill()
+
+ if method == "left-open":
+ mdata = pd.concat(
+ [mdata.replace({mflag: not_mflag, not_mflag: mflag}), app_entry]
+ ).bfill()
+
+ if method == "closed":
+ mdata[mdata.ffill() == mflag] = mflag
+ mdata.replace({not_mflag: mflag}, inplace=True)
else:
- mdata = pd.Series(
- not_mflag,
- index=mdata.index.join(pd.DatetimeIndex(mdata.values), how="outer"),
- )
- mdata[::2] = mflag
-
- # get rid of values that are neither mflag nor not_mflag (for bw-compatibillity mainly)
- mdata[mdata != mflag] = not_mflag
-
- # evaluate methods
- if method == "plain":
- pass
- # reindex will do the job later
- elif method == "ontime":
- pass
-
- elif method in ["left-open", "right-open", "closed"]:
- mdata = mdata.drop(mdata.index[mdata.diff() == 0])
- app_entry = pd.Series(mdata[-1], dat.index.shift(freq="1min")[-1:])
- mdata = mdata.reindex(dat.index.union(mdata.index))
-
- if method == "right-open":
- mdata = mdata.ffill()
-
- if method == "left-open":
- mdata = pd.concat(
- [mdata.replace({mflag: not_mflag, not_mflag: mflag}), app_entry]
- ).bfill()
-
- if method == "closed":
- mdata[mdata.ffill() == mflag] = mflag
- mdata.replace({not_mflag: mflag}, inplace=True)
- else:
- raise ValueError(method)
-
- mask = mdata == mflag
- mask = mask.reindex(dat.index).fillna(False)
-
- flags[mask, field] = flag
- return data, flags
-
-
-@flagging()
-def flagDummy(
- data: DictOfSeries, field: str, flags: Flags, **kwargs
-) -> Tuple[DictOfSeries, Flags]:
- """
- Function does nothing but returning data and flags.
-
- 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`.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- """
- return data, flags
-
-
-@register(
- mask=[],
- demask=[],
- squeeze=["target"],
- handles_target=True,
-)
-def transferFlags(
- data: DictOfSeries,
- field: str | Sequence[str],
- flags: Flags,
- target: str | Sequence[str],
- **kwargs,
-):
- """
- Transfer Flags of one variable to another.
-
- Parameters
- ----------
-
- data : {pd.DataFrame, dios.DictOfSeries}
- data
-
- field : str or List of str
- Variable or list of variables, the flags of which are to be transferred.
-
- flags : {pd.DataFrame, dios.DictOfSeries, saqc.flagger}
- Flags or flagger object
-
- target : str or List of str
- Variable or list of variables, the flags of `field` are to be transferred to.
-
- See Also
- --------
- * :py:meth:`saqc.SaQC.flagGeneric`
- * :py:meth:`saqc.SaQC.concatFlags`
-
- Examples
- --------
- First, generate some data with some flags:
-
- .. doctest:: exampleTransfer
-
- >>> 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.to_df()
- columns a b c
- 0 -inf -inf -inf
- 1 255.0 -inf -inf
-
- Now we can project the flag from `a` to `b` via
-
- .. doctest:: exampleTransfer
-
- >>> qc = qc.transferFlags('a', target='b')
- >>> qc.flags.to_df()
- columns a b c
- 0 -inf -inf -inf
- 1 255.0 255.0 -inf
-
- You can skip the explicit target parameter designation:
-
- .. doctest:: exampleTransfer
-
- >>> qc = qc.transferFlags('a', 'b')
-
- To project the flags of `a` to both the variables `b` and `c` in one call, align the field and target variables in
- 2 lists:
-
- .. doctest:: exampleTransfer
-
- >>> qc = qc.transferFlags(['a','a'], ['b', 'c'])
- >>> qc.flags.to_df()
- columns a b c
- 0 -inf -inf -inf
- 1 255.0 255.0 255.0
- """
-
- 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)
- )
+ raise ValueError(method)
+
+ mask = mdata == mflag
+ mask = mask.reindex(dat.index).fillna(False)
- if method == "bfill":
- repeated = repeated[::-1]
+ self._flags[mask, field] = flag
+ return self
- flags[repeated, field] = flag
+ @register(
+ mask=[],
+ demask=[],
+ squeeze=["target"],
+ handles_target=True, # function defines a target parameter, so it needs to handle it
+ )
+ def transferFlags(
+ self: "SaQC",
+ field: str,
+ target: str,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Transfer Flags of one variable to another.
+
+ Parameters
+ ----------
+
+ field : str or List of str
+ Variable or list of variables, the flags of which are to be transferred.
- return data, flags
+ target : str or List of str
+ Variable or list of variables, the flags of `field` are to be transferred to.
+
+ Returns
+ -------
+ saqc.SaQC
+
+ See Also
+ --------
+ * :py:meth:`saqc.SaQC.flagGeneric`
+ * :py:meth:`saqc.SaQC.concatFlags`
+
+ Examples
+ --------
+ First, generate some data with some flags:
+
+ .. doctest:: exampleTransfer
+
+ >>> 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.to_df()
+ columns a b c
+ 0 -inf -inf -inf
+ 1 255.0 -inf -inf
+
+ Now we can project the flag from `a` to `b` via
+
+ .. doctest:: exampleTransfer
+
+ >>> qc = qc.transferFlags('a', target='b')
+ >>> qc.flags.to_df()
+ columns a b c
+ 0 -inf -inf -inf
+ 1 255.0 255.0 -inf
+
+ You can skip the explicit target parameter designation:
+
+ .. doctest:: exampleTransfer
+
+ >>> qc = qc.transferFlags('a', 'b')
+
+ To project the flags of `a` to both the variables `b` and `c` in one call, align the field and target variables in
+ 2 lists:
+
+ .. doctest:: exampleTransfer
+
+ >>> qc = qc.transferFlags(['a','a'], ['b', 'c'])
+ >>> qc.flags.to_df()
+ columns a b c
+ 0 -inf -inf -inf
+ 1 255.0 255.0 255.0
+ """
+
+ return self.concatFlags(field, target=target, method="match", squeeze=False)
+
+ @flagging()
+ def propagateFlags(
+ self: "SaQC",
+ field: str,
+ window: Union[str, int],
+ method: Literal["ffill", "bfill"] = "ffill",
+ flag: float = BAD,
+ dfilter: float = FILTER_ALL,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flag values before or after flags set by the last test.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
+
+ 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
+ -------
+ saqc.SaQC
+
+ 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 = self._flags.history[field].hist.iloc[:, -1].astype(float)
+
+ if method == "bfill":
+ hc = hc[::-1]
+
+ # get dfilter from meta or get of rid of this and
+ # consider everything != np.nan as flag
+ flagged = _isflagged(hc, dfilter)
+
+ repeated = (
+ flagged.rolling(window, min_periods=1, closed="left")
+ .max()
+ .fillna(0)
+ .astype(bool)
+ )
+
+ if method == "bfill":
+ repeated = repeated[::-1]
+
+ self._flags[repeated, field] = flag
+
+ return self
diff --git a/saqc/funcs/generic.py b/saqc/funcs/generic.py
index 53b0d77c6..a4125acab 100644
--- a/saqc/funcs/generic.py
+++ b/saqc/funcs/generic.py
@@ -7,7 +7,7 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Sequence, Tuple, Union
+from typing import TYPE_CHECKING, Sequence, Tuple, Union
import numpy as np
import pandas as pd
@@ -16,10 +16,13 @@ from dios import DictOfSeries
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.core.register import _isflagged, _maskData, register
from saqc.lib.tools import toSequence
from saqc.lib.types import GenericFunction, PandasLike
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
def _flagSelect(field, flags, label=None):
if label is None:
@@ -48,7 +51,7 @@ def _prepare(
fchunk = Flags({f: flags[f] for f in columns})
for f in fchunk.columns:
fchunk.history[f] = flags.history[f]
- dchunk, _ = FunctionWrapper._maskData(
+ dchunk, _ = _maskData(
data=data.loc[:, columns].copy(), flags=fchunk, columns=columns, thresh=dfilter
)
return dchunk, fchunk.copy()
@@ -83,257 +86,248 @@ def _execGeneric(
return DictOfSeries(out)
-@register(
- mask=[],
- demask=[],
- squeeze=[],
- multivariate=True,
- handles_target=True,
-)
-def processGeneric(
- data: DictOfSeries,
- field: str | Sequence[str],
- flags: Flags,
- func: GenericFunction,
- target: str | Sequence[str] | None = None,
- dfilter: float = FILTER_ALL,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Generate/process data with user defined functions.
-
- Formally, what the function does, is the following:
-
- 1. Let F be a Callable, depending on fields f_1, f_2,...f_K, (F = F(f_1, f_2,...f_K))
- Than, for every timestamp t_i that occurs in at least one of the timeseries data[f_j] (outer join),
- The value v_i is computed via:
- v_i = data([f_1][t_i], data[f_2][t_i], ..., data[f_K][t_i]), if all data[f_j][t_i] do exist
- v_i = ``np.nan``, if at least one of the data[f_j][t_i] is missing.
- 2. The result is stored to ``data[target]``, if ``target`` is given or to ``data[field]`` otherwise
-
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- field : str or list of str
- The variable(s) passed to func.
- flags : saqc.Flags
- Container to store flags of the data.
- func : callable
- Function to call on the variables given in ``field``. The return value will be written
- to ``target`` or ``field`` if the former is not given. This implies, that the function
- needs to accept the same number of arguments (of type pandas.Series) as variables given
- in ``field`` and should return an iterable of array-like objects with the same number
- of elements as given in ``target`` (or ``field`` if ``target`` is not specified).
- target: str or list of str
- The variable(s) to write the result of ``func`` to. If not given, the variable(s)
- specified in ``field`` will be overwritten. If a ``target`` is not given, it will be
- created.
- flag: float, default ``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.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- The shape of the data may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- The flags shape may have changed relatively to the input flags.
-
- Note
- -----
- All the numpy functions are available within the generic expressions.
-
- Examples
- --------
- Compute the sum of the variables 'rainfall' and 'snowfall' and save the result to
- a (new) variable 'precipitation'
-
- >>> 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)
- targets = fields if target is None else toSequence(target)
-
- dchunk, fchunk = _prepare(data, flags, fields, dfilter)
- result = _execGeneric(fchunk, dchunk, func, dfilter=dfilter)
-
- meta = {
- "func": "procGeneric",
- "args": (field, target),
- "kwargs": {
- "func": func.__name__,
- "dfilter": dfilter,
- **kwargs,
- },
- }
+class GenericMixin:
+ @register(
+ mask=[],
+ demask=[],
+ squeeze=[],
+ multivariate=True,
+ handles_target=True,
+ )
+ def processGeneric(
+ self: "SaQC",
+ field: str | Sequence[str],
+ func: GenericFunction,
+ target: str | Sequence[str] | None = None,
+ dfilter: float = FILTER_ALL,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Generate/process data with user defined functions.
+
+ Formally, what the function does, is the following:
+
+ 1. Let F be a Callable, depending on fields f_1, f_2,...f_K, (F = F(f_1, f_2,...f_K))
+ Than, for every timestamp t_i that occurs in at least one of the timeseries data[f_j] (outer join),
+ The value v_i is computed via:
+ v_i = data([f_1][t_i], data[f_2][t_i], ..., data[f_K][t_i]), if all data[f_j][t_i] do exist
+ v_i = ``np.nan``, if at least one of the data[f_j][t_i] is missing.
+ 2. The result is stored to ``data[target]``, if ``target`` is given or to ``data[field]`` otherwise
+
+ Parameters
+ ----------
+ field : str or list of str
+ The variable(s) passed to func.
+
+ func : callable
+ Function to call on the variables given in ``field``. The return value will be written
+ to ``target`` or ``field`` if the former is not given. This implies, that the function
+ needs to accept the same number of arguments (of type pandas.Series) as variables given
+ in ``field`` and should return an iterable of array-like objects with the same number
+ of elements as given in ``target`` (or ``field`` if ``target`` is not specified).
+
+ target: str or list of str
+ The variable(s) to write the result of ``func`` to. If not given, the variable(s)
+ specified in ``field`` will be overwritten. If a ``target`` is not given, it will be
+ created.
+
+ flag: float, default ``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.
+
+ Returns
+ -------
+ saqc.SaQC
+
+ Note
+ -----
+ All the numpy functions are available within the generic expressions.
+
+ Examples
+ --------
+ Compute the sum of the variables 'rainfall' and 'snowfall' and save the result to
+ a (new) variable 'precipitation'
+
+ >>> 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)
+ targets = fields if target is None else toSequence(target)
+
+ dchunk, fchunk = _prepare(self._data, self._flags, fields, dfilter)
+ result = _execGeneric(fchunk, dchunk, func, dfilter=dfilter)
+
+ meta = {
+ "func": "procGeneric",
+ "args": (field, target),
+ "kwargs": {
+ "func": func.__name__,
+ "dfilter": dfilter,
+ **kwargs,
+ },
+ }
+
+ # update data & flags
+ for i, col in enumerate(targets):
+
+ datacol = result.iloc[:, i]
+ self._data[col] = datacol
+
+ if col not in self._flags:
+ self._flags.history[col] = History(datacol.index)
+
+ if not self._flags[col].index.equals(datacol.index):
+ raise ValueError(
+ f"cannot assign function result to the existing variable {repr(col)} "
+ "because of incompatible indices, please choose another 'target'"
+ )
+
+ self._flags.history[col].append(
+ pd.Series(np.nan, index=datacol.index), meta
+ )
- # update data & flags
- for i, col in enumerate(targets):
+ return self
- datacol = result.iloc[:, i]
- data[col] = datacol
+ @register(
+ mask=[],
+ demask=[],
+ squeeze=[],
+ multivariate=True,
+ handles_target=True,
+ )
+ def flagGeneric(
+ self: "SaQC",
+ field: str | Sequence[str],
+ func: GenericFunction,
+ target: str | Sequence[str] | None = None,
+ flag: float = BAD,
+ dfilter: float = FILTER_ALL,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flag data with user defined functions.
- if col not in flags:
- flags.history[col] = History(datacol.index)
+ Formally, what the function does, is the following:
+ Let X be a Callable, depending on fields f_1, f_2,...f_K, (X = X(f_1, f_2,...f_K))
+ Than for every timestamp t_i in data[field]:
+ data[field][t_i] is flagged if X(data[f_1][t_i], data[f_2][t_i], ..., data[f_K][t_i]) is True.
- if not flags[col].index.equals(datacol.index):
- raise ValueError(
- f"cannot assign function result to the existing variable {repr(col)} "
- "because of incompatible indices, please choose another 'target'"
- )
+ Parameters
+ ----------
+ field : str or list of str
+ The variable(s) passed to func.
- flags.history[col].append(pd.Series(np.nan, index=datacol.index), meta)
+ func : callable
+ Function to call on the variables given in ``field``. The function needs to accept the same
+ number of arguments (of type pandas.Series) as variables given in ``field`` and return an
+ iterable of array-like objects of with dtype bool and with the same number of elements as
+ given in ``target`` (or ``field`` if ``target`` is not specified). The function output
+ determines the values to flag.
- return data, flags
+ target: str or list of str
+ The variable(s) to write the result of ``func`` to. If not given, the variable(s)
+ specified in ``field`` will be overwritten. If a ``target`` is not given, it will be
+ created.
+ flag: float, default ``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.
-@register(
- mask=[],
- demask=[],
- squeeze=[],
- multivariate=True,
- handles_target=True,
-)
-def flagGeneric(
- data: DictOfSeries,
- field: Union[str, Sequence[str]],
- flags: Flags,
- func: GenericFunction,
- target: Union[str, Sequence[str]] = None,
- flag: float = BAD,
- dfilter: float = FILTER_ALL,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flag data with user defined functions.
+ dfilter: float, default ``FILTER_ALL``
+ Threshold flag. Flag values greater than ``dfilter`` indicate that the associated
+ data value is inappropiate for further usage.
- Formally, what the function does, is the following:
- Let X be a Callable, depending on fields f_1, f_2,...f_K, (X = X(f_1, f_2,...f_K))
- Than for every timestamp t_i in data[field]:
- data[field][t_i] is flagged if X(data[f_1][t_i], data[f_2][t_i], ..., data[f_K][t_i]) is True.
+ Returns
+ -------
+ saqc.SaQC
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- field : str or list of str
- The variable(s) passed to func.
- flags : saqc.Flags
- Container to store flags of the data.
- func : callable
- Function to call on the variables given in ``field``. The function needs to accept the same
- number of arguments (of type pandas.Series) as variables given in ``field`` and return an
- iterable of array-like objects of with dtype bool and with the same number of elements as
- given in ``target`` (or ``field`` if ``target`` is not specified). The function output
- determines the values to flag.
- target: str or list of str
- The variable(s) to write the result of ``func`` to. If not given, the variable(s)
- specified in ``field`` will be overwritten. If a ``target`` is not given, it will be
- created.
- flag: float, default ``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``
- Threshold flag. Flag values greater than ``dfilter`` indicate that the associated
- data value is inappropiate for further usage.
+ Note
+ -----
+ All the numpy functions are available within the generic expressions.
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- Flags values may have changed relatively to the flags input.
+ Examples
+ --------
- Note
- -----
- All the numpy functions are available within the generic expressions.
+ .. testsetup:: exampleFlagGeneric
- Examples
- --------
+ qc = saqc.SaQC(pd.DataFrame({'temperature':[0], 'uncertainty':[0], 'rainfall':[0], 'fan':[0]}, index=pd.DatetimeIndex([0])))
- .. testsetup:: exampleFlagGeneric
+ 1. Flag the variable 'rainfall', if the sum of the variables 'temperature' and 'uncertainty' is below zero:
- qc = saqc.SaQC(pd.DataFrame({'temperature':[0], 'uncertainty':[0], 'rainfall':[0], 'fan':[0]}, index=pd.DatetimeIndex([0])))
+ .. testcode:: exampleFlagGeneric
- 1. Flag the variable 'rainfall', if the sum of the variables 'temperature' and 'uncertainty' is below zero:
+ qc.flagGeneric(field=["temperature", "uncertainty"], target="rainfall", func= lambda x, y: x + y < 0)
- .. testcode:: exampleFlagGeneric
+ 2. Flag the variable 'temperature', where the variable 'fan' is flagged:
- qc.flagGeneric(field=["temperature", "uncertainty"], target="rainfall", func= lambda x, y: x + y < 0)
+ .. testcode:: exampleFlagGeneric
- 2. Flag the variable 'temperature', where the variable 'fan' is flagged:
+ qc.flagGeneric(field="fan", target="temperature", func=lambda x: isflagged(x))
- .. testcode:: exampleFlagGeneric
+ 3. The generic functions also support all pandas and numpy functions:
- qc.flagGeneric(field="fan", target="temperature", func=lambda x: isflagged(x))
+ .. testcode:: exampleFlagGeneric
- 3. The generic functions also support all pandas and numpy functions:
+ qc = qc.flagGeneric(field="fan", target="temperature", func=lambda x: np.sqrt(x) < 7)
+ """
- .. testcode:: exampleFlagGeneric
+ fields = toSequence(field)
+ targets = fields if target is None else toSequence(target)
- qc = qc.flagGeneric(field="fan", target="temperature", func=lambda x: np.sqrt(x) < 7)
- """
+ dchunk, fchunk = _prepare(self._data, self._flags, fields, dfilter)
+ result = _execGeneric(fchunk, dchunk, func, dfilter=dfilter)
- fields = toSequence(field)
- targets = fields if target is None else toSequence(target)
+ if len(targets) != len(result.columns):
+ raise ValueError(
+ f"the generic function returned {len(result.columns)} field(s), but only {len(targets)} target(s) were given"
+ )
- dchunk, fchunk = _prepare(data, flags, fields, dfilter)
- result = _execGeneric(fchunk, dchunk, func, dfilter=dfilter)
+ if not result.empty and not (result.dtypes == bool).all():
+ raise TypeError(f"generic expression does not return a boolean array")
- if len(targets) != len(result.columns):
- raise ValueError(
- f"the generic function returned {len(result.columns)} field(s), but only {len(targets)} target(s) were given"
- )
-
- if not result.empty and not (result.dtypes == bool).all():
- raise TypeError(f"generic expression does not return a boolean array")
-
- meta = {
- "func": "flagGeneric",
- "args": (field, target),
- "kwargs": {
- "func": func.__name__,
- "flag": flag,
- "dfilter": dfilter,
- **kwargs,
- },
- }
+ meta = {
+ "func": "flagGeneric",
+ "args": (field, target),
+ "kwargs": {
+ "func": func.__name__,
+ "flag": flag,
+ "dfilter": dfilter,
+ **kwargs,
+ },
+ }
- # update flags & data
- for i, col in enumerate(targets):
+ # update flags & data
+ for i, col in enumerate(targets):
- maskcol = result.iloc[:, i]
+ maskcol = result.iloc[:, i]
- # make sure the column exists
- if col not in flags:
- flags.history[col] = History(maskcol.index)
+ # make sure the column exists
+ if col not in self._flags:
+ self._flags.history[col] = History(maskcol.index)
- # dummy column to ensure consistency between flags and data
- if col not in data:
- data[col] = pd.Series(np.nan, index=maskcol.index)
+ # dummy column to ensure consistency between flags and data
+ if col not in self._data:
+ self._data[col] = pd.Series(np.nan, index=maskcol.index)
- flagcol = maskcol.replace({False: np.nan, True: flag}).astype(float)
+ flagcol = maskcol.replace({False: np.nan, True: flag}).astype(float)
- # we need equal indices to work on
- if not flags[col].index.equals(maskcol.index):
- raise ValueError(
- f"cannot assign function result to the existing variable {repr(col)} "
- "because of incompatible indices, please choose another 'target'"
- )
+ # we need equal indices to work on
+ if not self._flags[col].index.equals(maskcol.index):
+ raise ValueError(
+ f"cannot assign function result to the existing variable {repr(col)} "
+ "because of incompatible indices, please choose another 'target'"
+ )
- flags.history[col].append(flagcol, meta)
+ self._flags.history[col].append(flagcol, meta)
- return data, flags
+ return self
diff --git a/saqc/funcs/interpolation.py b/saqc/funcs/interpolation.py
index 3a005a719..e25401429 100644
--- a/saqc/funcs/interpolation.py
+++ b/saqc/funcs/interpolation.py
@@ -7,18 +7,20 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Callable, Tuple, Union
+from typing import TYPE_CHECKING, Callable, Union
import numpy as np
import pandas as pd
from typing_extensions import Literal
-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
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
+
_SUPPORTED_METHODS = Literal[
"linear",
"time",
@@ -38,171 +40,6 @@ _SUPPORTED_METHODS = Literal[
]
-@register(
- mask=["field"],
- demask=["field"],
- squeeze=[], # func handles history by itself
-)
-def interpolateByRolling(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- window: Union[str, int],
- func: Callable[[pd.Series], float] = np.median,
- center: bool = True,
- min_periods: int = 0,
- flag: float = UNFLAGGED,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Interpolates nan-values in the data by assigning them the aggregation result of the window surrounding them.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data container.
-
- field : str
- Name of the column, holding the data-to-be-interpolated.
-
- flags : saqc.Flags
- A flags object, holding flags and additional Information related to `data`.
-
- window : int, str
- The size of the window, the aggregation is computed from. An integer define the number of periods to be used,
- an string is interpreted as an offset. ( see `pandas.rolling` for more information).
- Integer windows may result in screwed aggregations if called on none-harmonized or irregular data.
-
- func : Callable
- The function used for aggregation.
-
- center : bool, default True
- Center the window around the value. Can only be used with integer windows, otherwise it is silently ignored.
-
- min_periods : int
- Minimum number of valid (not np.nan) values that have to be available in a window for its aggregation to be
- computed.
-
- flag : float or None, default UNFLAGGED
- Flag that is to be inserted for the interpolated values.
- If `None` the old flags are kept, even if the data is valid now.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- """
- datcol = data[field]
- roller = datcol.rolling(window=window, center=center, min_periods=min_periods)
- try:
- func_name = func.__name__
- if func_name[:3] == "nan":
- func_name = func_name[3:]
- rolled = getattr(roller, func_name)()
- except AttributeError:
- rolled = roller.apply(func)
-
- na_mask = datcol.isna()
- interpolated = na_mask & rolled.notna()
- datcol[na_mask] = rolled[na_mask]
- data[field] = datcol
-
- new_col = pd.Series(np.nan, index=flags[field].index)
- new_col.loc[interpolated] = np.nan if flag is None else flag
-
- # todo kwargs must have all passed args except data,field,flags
- flags.history[field].append(
- new_col, {"func": "interpolateByRolling", "args": (), "kwargs": kwargs}
- )
-
- return data, flags
-
-
-@register(
- mask=["field"],
- demask=["field"],
- squeeze=[], # func handles history by itself
-)
-def interpolateInvalid(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- method: _SUPPORTED_METHODS,
- order: int = 2,
- limit: int = 2,
- downgrade: bool = False,
- flag: float = UNFLAGGED,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Function to interpolate nan values in the data.
-
- There are available all the interpolation methods from the pandas.interpolate method and they are applicable by
- the very same key words, that you would pass to the ``pd.Series.interpolate``'s method parameter.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data container.
-
- field : str
- Name of the column, holding the data-to-be-interpolated.
-
- flags : saqc.Flags
- A flags object, holding flags and additional Information related to `data`.
-
- method : {"linear", "time", "nearest", "zero", "slinear", "quadratic", "cubic", "spline", "barycentric",
- "polynomial", "krogh", "piecewise_polynomial", "spline", "pchip", "akima"}
- The interpolation method to use.
-
- order : int, default 2
- If there your selected interpolation method can be performed at different 'orders' - here you pass the desired
- order.
-
- limit : int, default 2
- Maximum number of consecutive 'nan' values allowed for a gap to be interpolated. This really restricts the
- interpolation to chunks, containing not more than `limit` successive nan entries.
-
- flag : float or None, default UNFLAGGED
- Flag that is set for interpolated values. If ``None``, no flags are set at all.
-
- downgrade : bool, default False
- If `True` and the interpolation can not be performed at current order, retry with a lower order.
- This can happen, because the chosen ``method`` does not support the passed ``order``, or
- simply because not enough values are present in a interval.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- """
- inter_data = interpolateNANs(
- data[field],
- method,
- order=order,
- inter_limit=limit,
- downgrade_interpolation=downgrade,
- )
-
- 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
-
- # todo kwargs must have all passed args except data,field,flags
- flags.history[field].append(
- new_col, {"func": "interpolateInvalid", "args": (), "kwargs": kwargs}
- )
-
- return data, flags
-
-
def _resampleOverlapping(data: pd.Series, freq: str, fill_value):
"""TODO: docstring needed"""
dtype = data.dtype
@@ -214,111 +51,269 @@ def _resampleOverlapping(data: pd.Series, freq: str, fill_value):
return data.fillna(fill_value).astype(dtype)
-@register(mask=["field"], demask=[], squeeze=[])
-def interpolateIndex(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- freq: str,
- method: _SUPPORTED_METHODS,
- order: int = 2,
- limit: int = 2,
- downgrade: bool = False,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Function to interpolate the data at regular (equidistant) timestamps (or Grid points).
-
- Note, that the interpolation will only be calculated, for grid timestamps that have a preceding AND a succeeding
- valid data value within "freq" range.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data container.
-
- field : str
- Name of the column, holding the data-to-be-interpolated.
-
- flags : saqc.Flags
- A flags object, holding flags and additional Information related to `data`.
-
- freq : str
- An Offset String, interpreted as the frequency of
- the grid you want to interpolate your data at.
-
- method : {"linear", "time", "nearest", "zero", "slinear", "quadratic", "cubic", "spline", "barycentric",
- "polynomial", "krogh", "piecewise_polynomial", "spline", "pchip", "akima"}: string
- The interpolation method you want to apply.
-
- order : int, default 2
- If there your selected interpolation method can be performed at different 'orders' - here you pass the desired
- order.
-
- limit : int, default 2
- Maximum number of consecutive 'nan' values allowed for a gap to be interpolated. This really restricts the
- interpolation to chunks, containing not more than `limit` successive nan entries.
-
- downgrade : bool, default False
- If `True` and the interpolation can not be performed at current order, retry with a lower order.
- This can happen, because the chosen ``method`` does not support the passed ``order``, or
- simply because not enough values are present in a interval.
-
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values and shape may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- Flags values and shape may have changed relatively to the flags input.
- """
- if data[field].empty:
- return data, flags
-
- datcol = data[field].copy()
-
- start, end = datcol.index[0].floor(freq), datcol.index[-1].ceil(freq)
- grid_index = pd.date_range(start=start, end=end, freq=freq, name=datcol.index.name)
-
- # todo:
- # in future we could use `register(mask=[field], [], [])`
- # and dont handle masking manually here
- flagged = _isflagged(flags[field], kwargs["dfilter"])
-
- # drop all points that hold no relevant grid information
- datcol = datcol[~flagged].dropna()
-
- # account for annoying case of subsequent frequency aligned values,
- # that differ exactly by the margin of 2*freq
- gaps = datcol.index[1:] - datcol.index[:-1] == 2 * pd.Timedelta(freq)
- gaps = datcol.index[1:][gaps]
- gaps = gaps.intersection(grid_index).shift(-1, freq)
-
- # prepare grid interpolation:
- datcol = datcol.reindex(datcol.index.union(grid_index))
-
- # do the grid interpolation
- inter_data = interpolateNANs(
- data=datcol,
- method=method,
- order=order,
- inter_limit=limit,
- downgrade_interpolation=downgrade,
+class InterpolationMixin:
+ @register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=[], # func handles history by itself
)
-
- # override falsely interpolated values:
- inter_data[gaps] = np.nan
-
- # store interpolated grid
- data[field] = inter_data[grid_index]
-
- history = flags.history[field].apply(
- index=data[field].index,
- func=_resampleOverlapping,
- func_kws=dict(freq=freq, fill_value=UNFLAGGED),
+ def interpolateByRolling(
+ self: "SaQC",
+ field: str,
+ window: Union[str, int],
+ func: Callable[[pd.Series], float] = np.median,
+ center: bool = True,
+ min_periods: int = 0,
+ flag: float = UNFLAGGED,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Interpolates nan-values in the data by assigning them the aggregation result of the window surrounding them.
+
+ Parameters
+ ----------
+ field : str
+ Name of the column, holding the data-to-be-interpolated.
+
+ window : int, str
+ The size of the window, the aggregation is computed from. An integer define the number of periods to be used,
+ an string is interpreted as an offset. ( see `pandas.rolling` for more information).
+ Integer windows may result in screwed aggregations if called on none-harmonized or irregular data.
+
+ func : Callable
+ The function used for aggregation.
+
+ center : bool, default True
+ Center the window around the value. Can only be used with integer windows, otherwise it is silently ignored.
+
+ min_periods : int
+ Minimum number of valid (not np.nan) values that have to be available in a window for its aggregation to be
+ computed.
+
+ flag : float or None, default UNFLAGGED
+ Flag that is to be inserted for the interpolated values.
+ If `None` the old flags are kept, even if the data is valid now.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ datcol = self._data[field]
+ roller = datcol.rolling(window=window, center=center, min_periods=min_periods)
+ try:
+ func_name = func.__name__
+ if func_name[:3] == "nan":
+ func_name = func_name[3:]
+ rolled = getattr(roller, func_name)()
+ except AttributeError:
+ rolled = roller.apply(func)
+
+ na_mask = datcol.isna()
+ interpolated = na_mask & rolled.notna()
+ datcol[na_mask] = rolled[na_mask]
+ self._data[field] = datcol
+
+ flagcol = pd.Series(np.nan, index=self._flags[field].index)
+ flagcol.loc[interpolated] = np.nan if flag is None else flag
+
+ # todo kwargs must have all passed args except data,field,flags
+ meta = {
+ "func": "interpolateByRolling",
+ "args": (field,),
+ "kwargs": {
+ "window": window,
+ "func": func,
+ "center": center,
+ "min_periods": min_periods,
+ "flag": flag,
+ **kwargs,
+ },
+ }
+ self._flags.history[field].append(flagcol, meta)
+
+ return self
+
+ @register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=[], # func handles history by itself
)
-
- flags.history[field] = history
- return data, flags
+ def interpolateInvalid(
+ self: "SaQC",
+ field: str,
+ method: _SUPPORTED_METHODS,
+ order: int = 2,
+ limit: int = 2,
+ downgrade: bool = False,
+ flag: float = UNFLAGGED,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Function to interpolate nan values in the data.
+
+ There are available all the interpolation methods from the pandas.interpolate method and they are applicable by
+ the very same key words, that you would pass to the ``pd.Series.interpolate``'s method parameter.
+
+ Parameters
+ ----------
+ field : str
+ Name of the column, holding the data-to-be-interpolated.
+
+ method : {"linear", "time", "nearest", "zero", "slinear", "quadratic", "cubic", "spline", "barycentric",
+ "polynomial", "krogh", "piecewise_polynomial", "spline", "pchip", "akima"}
+ The interpolation method to use.
+
+ order : int, default 2
+ If there your selected interpolation method can be performed at different 'orders' - here you pass the desired
+ order.
+
+ limit : int, default 2
+ Maximum number of consecutive 'nan' values allowed for a gap to be interpolated. This really restricts the
+ interpolation to chunks, containing not more than `limit` successive nan entries.
+
+ flag : float or None, default UNFLAGGED
+ Flag that is set for interpolated values. If ``None``, no flags are set at all.
+
+ downgrade : bool, default False
+ If `True` and the interpolation can not be performed at current order, retry with a lower order.
+ This can happen, because the chosen ``method`` does not support the passed ``order``, or
+ simply because not enough values are present in a interval.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ inter_data = interpolateNANs(
+ self._data[field],
+ method,
+ order=order,
+ inter_limit=limit,
+ downgrade_interpolation=downgrade,
+ )
+
+ interpolated = self._data[field].isna() & inter_data.notna()
+ self._data[field] = inter_data
+ new_col = pd.Series(np.nan, index=self._flags[field].index)
+ new_col.loc[interpolated] = np.nan if flag is None else flag
+
+ # todo kwargs must have all passed args except data,field,flags
+ self._flags.history[field].append(
+ new_col, {"func": "interpolateInvalid", "args": (), "kwargs": kwargs}
+ )
+
+ return self
+
+ @register(mask=["field"], demask=[], squeeze=[])
+ def interpolateIndex(
+ self: "SaQC",
+ field: str,
+ freq: str,
+ method: _SUPPORTED_METHODS,
+ order: int = 2,
+ limit: int = 2,
+ downgrade: bool = False,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Function to interpolate the data at regular (equidistant) timestamps (or Grid points).
+
+ Note, that the interpolation will only be calculated, for grid timestamps that have a preceding AND a succeeding
+ valid data value within "freq" range.
+
+ Parameters
+ ----------
+ field : str
+ Name of the column, holding the data-to-be-interpolated.
+
+ freq : str
+ An Offset String, interpreted as the frequency of
+ the grid you want to interpolate your data at.
+
+ method : {"linear", "time", "nearest", "zero", "slinear", "quadratic", "cubic", "spline", "barycentric",
+ "polynomial", "krogh", "piecewise_polynomial", "spline", "pchip", "akima"}: string
+ The interpolation method you want to apply.
+
+ order : int, default 2
+ If there your selected interpolation method can be performed at different 'orders' - here you pass the desired
+ order.
+
+ limit : int, default 2
+ Maximum number of consecutive 'nan' values allowed for a gap to be interpolated. This really restricts the
+ interpolation to chunks, containing not more than `limit` successive nan entries.
+
+ downgrade : bool, default False
+ If `True` and the interpolation can not be performed at current order, retry with a lower order.
+ This can happen, because the chosen ``method`` does not support the passed ``order``, or
+ simply because not enough values are present in a interval.
+
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ if self._data[field].empty:
+ return self
+
+ datcol = self._data[field].copy()
+
+ start, end = datcol.index[0].floor(freq), datcol.index[-1].ceil(freq)
+ grid_index = pd.date_range(
+ start=start, end=end, freq=freq, name=datcol.index.name
+ )
+
+ # TODO:
+ # in future we could use `register(mask=[field], [], [])`
+ # and dont handle masking manually here
+ flagged = _isflagged(self._flags[field], kwargs["dfilter"])
+
+ # drop all points that hold no relevant grid information
+ datcol = datcol[~flagged].dropna()
+
+ # account for annoying case of subsequent frequency aligned values,
+ # that differ exactly by the margin of 2*freq
+ gaps = datcol.index[1:] - datcol.index[:-1] == 2 * pd.Timedelta(freq)
+ gaps = datcol.index[1:][gaps]
+ gaps = gaps.intersection(grid_index).shift(-1, freq)
+
+ # prepare grid interpolation:
+ datcol = datcol.reindex(datcol.index.union(grid_index))
+
+ # do the grid interpolation
+ inter_data = interpolateNANs(
+ data=datcol,
+ method=method,
+ order=order,
+ inter_limit=limit,
+ downgrade_interpolation=downgrade,
+ )
+
+ # override falsely interpolated values:
+ inter_data[gaps] = np.nan
+
+ # store interpolated grid
+ self._data[field] = inter_data[grid_index]
+
+ history = self._flags.history[field].apply(
+ index=self._data[field].index,
+ func=_resampleOverlapping,
+ func_kws=dict(freq=freq, fill_value=np.nan),
+ )
+
+ meta = {
+ "func": "interpolateIndex",
+ "args": (field,),
+ "kwargs": {
+ "freq": freq,
+ "method": method,
+ "order": order,
+ "limit": limit,
+ "downgrade": downgrade,
+ **kwargs,
+ },
+ }
+ flagcol = pd.Series(UNFLAGGED, index=history.index)
+ history.append(flagcol, meta)
+
+ self._flags.history[field] = history
+
+ return self
diff --git a/saqc/funcs/noise.py b/saqc/funcs/noise.py
index 6fe5fb8c2..8945f2233 100644
--- a/saqc/funcs/noise.py
+++ b/saqc/funcs/noise.py
@@ -8,77 +8,89 @@
from __future__ import annotations
import operator
-from typing import Callable
+from typing import TYPE_CHECKING, Callable
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
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
-@flagging()
-def flagByStatLowPass(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- func: Callable[[np.ndarray, pd.Series], float],
- window: str | pd.Timedelta,
- thresh: float,
- sub_window: str | pd.Timedelta = None,
- sub_thresh: float = None,
- min_periods: int = None,
- flag: float = BAD,
- **kwargs,
-):
- """
- Flag *chunks* of length, `window`:
-
- 1. If they excexceed `thresh` with regard to `stat`:
- 2. If all (maybe overlapping) *sub-chunks* of *chunk*, with length `sub_window`,
- `excexceed `sub_thresh` with regard to `stat`:
-
- 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
- Container to store quality flags to data.
- func: Callable[[np.array, pd.Series], float]
- Function to aggregate chunk contnent with.
- window: str
- Temporal extension of the chunks to test
- thresh: float
- Threshold, that triggers flagging, if exceeded by stat value.
- sub_window: str, default None,
- Window size of the sub chunks, that are additionally tested for exceeding
- `sub_thresh` with respect to `stat`.
- sub_thresh: float, default None
- min_periods: int, default None
- flag : float, default BAD
- flag to set
-
- Returns
- -------
- """
-
- datcol = data[field]
- if not min_periods:
- min_periods = 0
- if not sub_thresh:
- sub_thresh = thresh
- window = pd.Timedelta(window)
-
- if sub_window:
- sub_window = pd.Timedelta(sub_window)
-
- to_set = statPass(
- datcol, func, window, thresh, operator.gt, sub_window, sub_thresh, min_periods
- )
- flags[to_set, field] = flag
- return data, flags
+
+class NoiseMixin:
+ @flagging()
+ def flagByStatLowPass(
+ self: "SaQC",
+ field: str,
+ func: Callable[[np.ndarray, pd.Series], float],
+ window: str | pd.Timedelta,
+ thresh: float,
+ sub_window: str | pd.Timedelta | None = None,
+ sub_thresh: float | None = None,
+ min_periods: int | None = None,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flag *chunks* of length, `window`:
+
+ 1. If they excexceed `thresh` with regard to `stat`:
+ 2. If all (maybe overlapping) *sub-chunks* of *chunk*, with length `sub_window`,
+ `excexceed `sub_thresh` with regard to `stat`:
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
+
+ func: Callable[[np.array, pd.Series], float]
+ Function to aggregate chunk contnent with.
+
+ window: str
+ Temporal extension of the chunks to test
+
+ thresh: float
+ Threshold, that triggers flagging, if exceeded by stat value.
+
+ sub_window: str, default None,
+ Window size of the sub chunks, that are additionally tested for exceeding
+ `sub_thresh` with respect to `stat`.
+
+ sub_thresh: float, default None
+
+ min_periods: int, default None
+
+ flag : float, default BAD
+ flag to set
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+
+ datcol = self._data[field]
+ if not min_periods:
+ min_periods = 0
+ if not sub_thresh:
+ sub_thresh = thresh
+ window = pd.Timedelta(window)
+
+ if sub_window is not None:
+ sub_window = pd.Timedelta(sub_window)
+
+ to_set = statPass(
+ datcol,
+ func,
+ window,
+ thresh,
+ operator.gt,
+ sub_window,
+ sub_thresh,
+ min_periods,
+ )
+ self._flags[to_set, field] = flag
+ return self
diff --git a/saqc/funcs/outliers.py b/saqc/funcs/outliers.py
index f7d137dcd..7e0575e21 100644
--- a/saqc/funcs/outliers.py
+++ b/saqc/funcs/outliers.py
@@ -9,1283 +9,1288 @@
from __future__ import annotations
import uuid
-from typing import Callable, Optional, Sequence, Tuple, Union
+from typing import TYPE_CHECKING, Callable, Optional, Sequence, Tuple, Union
import numba
import numpy as np
import numpy.polynomial.polynomial as poly
import pandas as pd
from outliers import smirnov_grubbs
+from scipy.stats import median_abs_deviation
from typing_extensions import Literal
from dios import DictOfSeries
from saqc.constants import BAD, UNFLAGGED
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
+from saqc.funcs.scores import _univarScoring
from saqc.lib.tools import customRoller, getFreqDelta, toSequence
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
-@flagging()
-def flagByStray(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- window: Optional[Union[int, str]] = None,
- min_periods: int = 11,
- iter_start: float = 0.5,
- alpha: float = 0.05,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Flag outliers in 1-dimensional (score) data with the STRAY Algorithm.
-
- Find more information on the algorithm in References [1].
-
- 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
- Container to store quality flags to data.
-
- freq : str, int, or None, default None
- Determines the segmentation of the data into partitions, the kNN algorithm is
- applied onto individually.
-
- * ``np.inf``: Apply Scoring on whole data set at once
- * ``x`` > 0 : Apply scoring on successive data chunks of periods length ``x``
- * Offset String : Apply scoring on successive partitions of temporal extension
- matching the passed offset string
-
- min_periods : int, default 11
- Minimum number of periods per partition that have to be present for a valid
- outlier dettection to be made in this partition. (Only of effect, if `freq`
- is an integer.) Partition min value must always be greater then the
- nn_neighbors value.
-
- iter_start : float, default 0.5
- Float in [0,1] that determines which percentage of data is considered
- "normal". 0.5 results in the stray 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
- Level of significance by which it is tested, if a score might be drawn from
- another distribution, than the majority of the data.
-
- flag : float, default BAD
- flag to set.
-
- References
- ----------
- [1] Talagala, P. D., Hyndman, R. J., & Smith-Miles, K. (2019). Anomaly detection in
- high dimensional data. arXiv preprint arXiv:1908.04000.
- """
- scores = data[field].dropna()
-
- if scores.empty:
- return data, flags
-
- if not window:
- window = scores.shape[0]
-
- 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 / window)))
- partitions = scores.groupby(grouper_series)
-
- # calculate flags for every partition
- for _, partition in partitions:
-
- if partition.empty | (partition.shape[0] < min_periods):
- continue
-
- sample_size = partition.shape[0]
-
- sorted_i = partition.values.argsort()
- resids = partition.values[sorted_i]
- gaps = np.append(0, np.diff(resids))
-
- tail_size = int(max(min(50, np.floor(sample_size / 4)), 2))
- tail_indices = np.arange(2, tail_size + 1)
-
- i_start = int(max(np.floor(sample_size * iter_start), 1) + 1)
- ghat = np.array([np.nan] * sample_size)
-
- for i in range(i_start - 1, sample_size):
- ghat[i] = sum((tail_indices / (tail_size - 1)) * gaps[i - tail_indices + 1])
-
- log_alpha = np.log(1 / alpha)
- for iter_index in range(i_start - 1, sample_size):
- if gaps[iter_index] > log_alpha * ghat[iter_index]:
- index = partition.index[sorted_i[iter_index:]]
- flags[index, field] = flag
- break
-
- return data, flags
-
-
-def _evalStrayLabels(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- target: Sequence[str],
- reduction_range: Optional[str] = None,
- reduction_drop_flagged: bool = False, # TODO: still a case ?
- reduction_thresh: float = 3.5,
- reduction_min_periods: int = 1,
- at_least_one: bool = True,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- The function "reduces" an observations flag to components of it, by applying MAD
- (See references) test onto every components temporal surrounding.
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+class OutliersMixin:
+ @flagging()
+ def flagRange(
+ self: "SaQC",
+ field: str,
+ min: float = -np.inf,
+ max: float = np.inf,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Function flags values exceeding the closed interval [`min`, `max`].
+
+ Parameters
+ ----------
+ field : str
+ The field name of the column, holding the data-to-be-flagged.
+ min : float
+ Lower bound for valid data.
+ max : float
+ Upper bound for valid data.
+ flag : float, default BAD
+ flag to set.
+
+ Returns
+ -------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ flags : saqc.Flags
+ The quality flags of data
+ """
+
+ # using .values is much faster
+ datacol = self._data[field].to_numpy()
+ mask = (datacol < min) | (datacol > max)
+ self._flags[mask, field] = flag
+ return self
+
+ @flagging()
+ def flagByStray(
+ self: "SaQC",
+ field: str,
+ window: int | str | None = None,
+ min_periods: int = 11,
+ iter_start: float = 0.5,
+ alpha: float = 0.05,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flag outliers in 1-dimensional (score) data with the STRAY Algorithm.
- field : str
- The fieldname of the column, holding the labels to be evaluated.
+ Find more information on the algorithm in References [1].
- flags : saqc.Flags
- Container to store quality flags to data.
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
- target : list of str
- A list of strings, holding the column names of the variables, the stray labels
- shall be projected onto.
+ freq : str, int, or None, default None
+ Determines the segmentation of the data into partitions, the kNN algorithm is
+ applied onto individually.
- val_frame : (N,M) pd.DataFrame
- Input NxM DataFrame of observations, where N is the number of observations and
- M the number of components per observation.
+ * ``np.inf``: Apply Scoring on whole data set at once
+ * ``x`` > 0 : Apply scoring on successive data chunks of periods length ``x``
+ * Offset String : Apply scoring on successive partitions of temporal extension
+ matching the passed offset string
- to_flag_frame : pandas.DataFrame
- Input dataframe of observations to be tested, where N is the number of
- observations and M the number of components per observation.
+ min_periods : int, default 11
+ Minimum number of periods per partition that have to be present for a valid
+ outlier dettection to be made in this partition. (Only of effect, if `freq`
+ is an integer.) Partition min value must always be greater then the
+ nn_neighbors value.
- reduction_range : {None, str}
- An offset string, denoting the range of the temporal surrounding to include
- into the MAD testing. If ``None`` is passed, no testing will be performed and
- all targets will have the stray flag projected.
+ iter_start : float, default 0.5
+ Float in [0,1] that determines which percentage of data is considered
+ "normal". 0.5 results in the stray algorithm to search only the upper 50 % of
+ the scores for the cut off point. (See reference section for more information)
- reduction_drop_flagged : bool, default False
- Wheather or not to drop flagged values other than the value under test, from the
- temporal surrounding before checking the value with MAD.
+ alpha : float, default 0.05
+ Level of significance by which it is tested, if a score might be drawn from
+ another distribution, than the majority of the data.
- reduction_thresh : float, default 3.5
- The `critical` value, controlling wheather the MAD score is considered
- referring to an outlier or not. Higher values result in less rigid flagging.
- The default value is widely used in the literature. See references section
- for more details ([1]).
+ flag : float, default BAD
+ flag to set.
- at_least_one : bool, default True
- If none of the variables, the outlier label shall be reduced to, is an outlier
- with regard to the test, all (True) or none (False) of the variables are flagged
+ Returns
+ -------
+ saqc.SaQC
- flag : float, default BAD
- flag to set.
+ References
+ ----------
+ [1] Talagala, P. D., Hyndman, R. J., & Smith-Miles, K. (2019). Anomaly detection in
+ high dimensional data. arXiv preprint arXiv:1908.04000.
+ """
+ scores = self._data[field].dropna()
- References
- ----------
- [1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
- """
- val_frame = data[target].to_df()
- stray_detects = flags[field] > UNFLAGGED
- stray_detects = stray_detects[stray_detects]
- to_flag_frame = pd.DataFrame(False, columns=target, index=stray_detects.index)
+ if scores.empty:
+ return self
- if reduction_range is None:
- for field in to_flag_frame.columns:
- flags[to_flag_frame.index, field] = flag
- return data, flags
+ if not window:
+ window = scores.shape[0]
- for var in target:
- for index in enumerate(to_flag_frame.index):
+ if isinstance(window, str):
+ partitions = scores.groupby(pd.Grouper(freq=window))
- index_slice = slice(
- index[1] - pd.Timedelta(reduction_range),
- index[1] + pd.Timedelta(reduction_range),
+ else:
+ grouper_series = pd.Series(
+ data=np.arange(0, scores.shape[0]), index=scores.index
)
- test_slice = val_frame[var][index_slice].dropna()
-
- # check, wheather value under test is sufficiently centered:
- first = test_slice.first_valid_index()
- last = test_slice.last_valid_index()
- min_range = pd.Timedelta(reduction_range) / 4
-
- if (
- pd.Timedelta(index[1] - first) < min_range
- or pd.Timedelta(last - index[1]) < min_range
- ):
- polydeg = 0
- else:
- polydeg = 2
+ grouper_series = grouper_series.transform(
+ lambda x: int(np.floor(x / window))
+ )
+ partitions = scores.groupby(grouper_series)
- if reduction_drop_flagged:
- test_slice = test_slice.drop(to_flag_frame.index, errors="ignore")
+ # calculate flags for every partition
+ for _, partition in partitions:
- if test_slice.shape[0] < reduction_min_periods:
- to_flag_frame.loc[index[1], var] = True
+ if partition.empty | (partition.shape[0] < min_periods):
continue
- x = test_slice.index.values.astype(float)
- x_0 = x[0]
- x = (x - x_0) / 10**12
+ sample_size = partition.shape[0]
- polyfitted = poly.polyfit(y=test_slice.values, x=x, deg=polydeg)
+ sorted_i = partition.values.argsort()
+ resids = partition.values[sorted_i]
+ gaps = np.append(0, np.diff(resids))
- testval = poly.polyval(
- (float(index[1].to_numpy()) - x_0) / 10**12, polyfitted
- )
- testval = val_frame[var][index[1]] - testval
+ tail_size = int(max(min(50, np.floor(sample_size / 4)), 2))
+ tail_indices = np.arange(2, tail_size + 1)
- resids = test_slice.values - poly.polyval(x, polyfitted)
- med_resids = np.median(resids)
- MAD = np.median(np.abs(resids - med_resids))
- crit_val = 0.6745 * (abs(med_resids - testval)) / MAD
+ i_start = int(max(np.floor(sample_size * iter_start), 1) + 1)
+ ghat = np.array([np.nan] * sample_size)
- if crit_val > reduction_thresh:
- to_flag_frame.loc[index[1], var] = True
-
- if at_least_one:
- to_flag_frame[~to_flag_frame.any(axis=1)] = True
-
- for field in to_flag_frame.columns:
- col = to_flag_frame[field]
- flags[col[col].index, field] = flag
+ for i in range(i_start - 1, sample_size):
+ ghat[i] = sum(
+ (tail_indices / (tail_size - 1)) * gaps[i - tail_indices + 1]
+ )
- return data, flags
+ log_alpha = np.log(1 / alpha)
+ for iter_index in range(i_start - 1, sample_size):
+ if gaps[iter_index] > log_alpha * ghat[iter_index]:
+ index = partition.index[sorted_i[iter_index:]]
+ self._flags[index, field] = flag
+ break
+
+ return self
+
+ @register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=["field"],
+ multivariate=True,
+ handles_target=False,
+ )
+ def flagMVScores(
+ self: "SaQC",
+ field: Sequence[str],
+ trafo: Callable[[pd.Series], pd.Series] = lambda x: x,
+ alpha: float = 0.05,
+ n: int = 10,
+ func: Callable[[pd.Series], float] = np.sum,
+ iter_start: float = 0.5,
+ partition: Optional[Union[int, str]] = None,
+ partition_min: int = 11,
+ stray_range: Optional[str] = None,
+ drop_flagged: bool = False, # TODO: still a case ?
+ thresh: float = 3.5,
+ min_periods: int = 1,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ The algorithm implements a 3-step outlier detection procedure for simultaneously
+ flagging of higher dimensional data (dimensions > 3).
+
+ In references [1], the procedure is introduced and exemplified with an
+ application on hydrological data. See the notes section for an overview over the
+ algorithms basic steps.
+
+ Parameters
+ ----------
+ field : list of str
+ List of fieldnames, corresponding to the variables that are to be included
+ into the flagging process.
+
+ trafo : callable, default lambda x:x
+ Transformation to be applied onto every column before scoring. Will likely
+ get deprecated soon. Its better to transform the data in a processing step,
+ preceeeding the call to ``flagMVScores``.
+
+ alpha : float, default 0.05
+ Level of significance by which it is tested, if an observations score might
+ be drawn from another distribution than the majority of the observation.
+
+ n : int, default 10
+ Number of neighbors included in the scoring process for every datapoint.
+
+ func : Callable[numpy.array, float], default np.sum
+ The function that maps the set of every points k-nearest neighbor distances
+ onto a certain scoring.
+
+ iter_start : float, default 0.5
+ Float in [0,1] that determines which percentage of data is considered
+ "normal". 0.5 results in the threshing algorithm to search only the upper 50
+ % of the scores for the cut off point. (See reference section for more
+ information)
+
+ partition : {None, str, int}, default None
+ Only effective when `threshing` = 'stray'. Determines the size of the data
+ partitions, the data is decomposed into. Each partition is checked seperately
+ for outliers. If a String is passed, it has to be an offset string and it
+ results in partitioning the data into parts of according temporal length. If
+ an integer is passed, the data is simply split up into continous chunks of
+ `freq` periods. if ``None`` is passed (default), all the data will be tested
+ in one run.
+
+ partition_min : int, default 11
+ Only effective when `threshing` = 'stray'. Minimum number of periods per
+ partition that have to be present for a valid outlier detection to be made in
+ this partition. (Only of effect, if `stray_partition` is an integer.)
+
+ partition_trafo : bool, default True
+ Whether or not to apply the passed transformation on every partition the
+ algorithm is applied on, separately.
+
+ stray_range : {None, str}, default None
+ If not None, it is tried to reduce the stray result onto single outlier
+ components of the input fields. An offset string, denoting the range of the
+ temporal surrounding to include into the MAD testing while trying to reduce
+ flags.
+
+ drop_flagged : bool, default False
+ Only effective when `range` is not ``None``. Whether or not to drop flagged
+ values other than the value under test from the temporal surrounding before
+ checking the value with MAD.
+
+ thresh : float, default 3.5
+ Only effective when `range` is not ``None``. The `critical` value,
+ controlling wheather the MAD score is considered referring to an outlier or
+ not. Higher values result in less rigid flagging. The default value is widely
+ considered apropriate in the literature.
+
+ min_periods : int, 1
+ Only effective when `range` is not ``None``. Minimum number of meassurements
+ necessarily present in a reduction interval for reduction actually to be
+ performed.
+
+ flag : float, default BAD
+ flag to set.
+
+ Returns
+ -------
+ saqc.SaQC
+
+ Notes
+ -----
+ The basic steps are:
+
+ 1. transforming
+
+ The different data columns are transformed via timeseries transformations to
+ (a) make them comparable and
+ (b) make outliers more stand out.
+
+ This step is usually subject to a phase of research/try and error. See [1] for more
+ details.
+
+ Note, that the data transformation as an built-in step of the algorithm,
+ will likely get deprecated soon. Its better to transform the data in a processing
+ step, preceeding the multivariate flagging process. Also, by doing so, one gets
+ mutch more control and variety in the transformation applied, since the `trafo`
+ parameter only allows for application of the same transformation to all of the
+ variables involved.
+
+ 2. scoring
+
+ Every observation gets assigned a score depending on its k nearest neighbors. See
+ the `scoring_method` parameter description for details on the different scoring
+ methods. Furthermore [1], [2] may give some insight in the pro and cons of the
+ different methods.
+
+ 3. threshing
+
+ The gaps between the (greatest) scores are tested for beeing drawn from the same
+ distribution as the majority of the scores. If a gap is encountered, that,
+ with sufficient significance, can be said to not be drawn from the same
+ distribution as the one all the smaller gaps are drawn from, than the observation
+ belonging to this gap, and all the observations belonging to gaps larger then
+ this gap, get flagged outliers. See description of the `threshing` parameter for
+ more details. Although [2] gives a fully detailed overview over the `stray`
+ algorithm.
+ """
+
+ fields = toSequence(field)
+
+ fields_ = []
+ for f in fields:
+ field_ = str(uuid.uuid4())
+ self = self.copyField(field=f, target=field_)
+ self = self.transform(field=field_, func=trafo, freq=partition)
+ fields_.append(field_)
+
+ knn_field = str(uuid.uuid4())
+ self = self.assignKNNScore(
+ field=fields_,
+ target=knn_field,
+ n=n,
+ func=func,
+ freq=partition,
+ method="ball_tree",
+ min_periods=partition_min,
+ **kwargs,
+ )
+ for field_ in fields_:
+ self = self.dropField(field_)
+
+ self = self.flagByStray(
+ field=knn_field,
+ freq=partition,
+ min_periods=partition_min,
+ iter_start=iter_start,
+ alpha=alpha,
+ flag=flag,
+ **kwargs,
+ )
+ self._data, self._flags = _evalStrayLabels(
+ data=self._data,
+ field=knn_field,
+ target=fields,
+ flags=self._flags,
+ reduction_range=stray_range,
+ reduction_drop_flagged=drop_flagged,
+ reduction_thresh=thresh,
+ reduction_min_periods=min_periods,
+ flag=flag,
+ **kwargs,
+ )
+ return self.dropField(knn_field)
+
+ @flagging()
+ def flagRaise(
+ self: "SaQC",
+ field: str,
+ thresh: float,
+ raise_window: str,
+ freq: str,
+ average_window: Optional[str] = None,
+ raise_factor: float = 2.0,
+ slope: Optional[float] = None,
+ weight: float = 0.8,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ The function flags raises and drops in value courses, that exceed a certain threshold
+ within a certain timespan.
-@register(
- mask=["field"],
- demask=["field"],
- squeeze=["field"],
- multivariate=True,
- handles_target=False,
-)
-def flagMVScores(
- data: DictOfSeries,
- field: Sequence[str],
- flags: Flags,
- trafo: Callable[[pd.Series], pd.Series] = lambda x: x,
- alpha: float = 0.05,
- n: int = 10,
- func: Callable[[pd.Series], float] = np.sum,
- iter_start: float = 0.5,
- partition: Optional[Union[int, str]] = None,
- partition_min: int = 11,
- stray_range: Optional[str] = None,
- drop_flagged: bool = False, # TODO: still a case ?
- thresh: float = 3.5,
- min_periods: int = 1,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- The algorithm implements a 3-step outlier detection procedure for simultaneously
- flagging of higher dimensional data (dimensions > 3).
+ The parameter variety of the function is owned to the intriguing
+ case of values, that "return" from outlierish or anomalious value levels and
+ thus exceed the threshold, while actually being usual values.
- In references [1], the procedure is introduced and exemplified with an
- application on hydrological data. See the notes section for an overview over the
- algorithms basic steps.
+ NOTE: the dataset is NOT supposed to be harmonized to a time series with an
+ equidistant frequency grid.
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
- field : list of str
- List of fieldnames, corresponding to the variables that are to be included
- into the flagging process.
+ thresh : float
+ The threshold, for the total rise (thresh > 0), or total drop (thresh < 0),
+ value courses must not exceed within a timespan of length `raise_window`.
- flags : saqc.Flags
- Container to store quality flags to data.
+ raise_window : str
+ An offset string, determining the timespan, the rise/drop thresholding refers
+ to. Window is inclusively defined.
- trafo : callable, default lambda x:x
- Transformation to be applied onto every column before scoring. Will likely
- get deprecated soon. Its better to transform the data in a processing step,
- preceeeding the call to ``flagMVScores``.
-
- alpha : float, default 0.05
- Level of significance by which it is tested, if an observations score might
- be drawn from another distribution than the majority of the observation.
-
- n : int, default 10
- Number of neighbors included in the scoring process for every datapoint.
-
- func : Callable[numpy.array, float], default np.sum
- The function that maps the set of every points k-nearest neighbor distances
- onto a certain scoring.
-
- iter_start : float, default 0.5
- Float in [0,1] that determines which percentage of data is considered
- "normal". 0.5 results in the threshing algorithm to search only the upper 50
- % of the scores for the cut off point. (See reference section for more
- information)
-
- partition : {None, str, int}, default None
- Only effective when `threshing` = 'stray'. Determines the size of the data
- partitions, the data is decomposed into. Each partition is checked seperately
- for outliers. If a String is passed, it has to be an offset string and it
- results in partitioning the data into parts of according temporal length. If
- an integer is passed, the data is simply split up into continous chunks of
- `freq` periods. if ``None`` is passed (default), all the data will be tested
- in one run.
-
- partition_min : int, default 11
- Only effective when `threshing` = 'stray'. Minimum number of periods per
- partition that have to be present for a valid outlier detection to be made in
- this partition. (Only of effect, if `stray_partition` is an integer.)
-
- partition_trafo : bool, default True
- Whether or not to apply the passed transformation on every partition the
- algorithm is applied on, separately.
-
- stray_range : {None, str}, default None
- If not None, it is tried to reduce the stray result onto single outlier
- components of the input fields. An offset string, denoting the range of the
- temporal surrounding to include into the MAD testing while trying to reduce
- flags.
-
- drop_flagged : bool, default False
- Only effective when `range` is not ``None``. Whether or not to drop flagged
- values other than the value under test from the temporal surrounding before
- checking the value with MAD.
-
- thresh : float, default 3.5
- Only effective when `range` is not ``None``. The `critical` value,
- controlling wheather the MAD score is considered referring to an outlier or
- not. Higher values result in less rigid flagging. The default value is widely
- considered apropriate in the literature.
-
- min_periods : int, 1
- Only effective when `range` is not ``None``. Minimum number of meassurements
- necessarily present in a reduction interval for reduction actually to be
- performed.
+ freq : str
+ An offset string, determining The frequency, the timeseries to-be-flagged is
+ supposed to be sampled at. The window is inclusively defined.
- flag : float, default BAD
- flag to set.
+ average_window : {None, str}, default None
+ See condition (2) of the description linked in the references. Window is
+ inclusively defined. The window defaults to 1.5 times the size of `raise_window`
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- Flags values may have changed, relatively to the flags input.
-
- Notes
- -----
- The basic steps are:
-
- 1. transforming
-
- The different data columns are transformed via timeseries transformations to
- (a) make them comparable and
- (b) make outliers more stand out.
-
- This step is usually subject to a phase of research/try and error. See [1] for more
- details.
-
- Note, that the data transformation as an built-in step of the algorithm,
- will likely get deprecated soon. Its better to transform the data in a processing
- step, preceeding the multivariate flagging process. Also, by doing so, one gets
- mutch more control and variety in the transformation applied, since the `trafo`
- parameter only allows for application of the same transformation to all of the
- variables involved.
-
- 2. scoring
-
- Every observation gets assigned a score depending on its k nearest neighbors. See
- the `scoring_method` parameter description for details on the different scoring
- methods. Furthermore [1], [2] may give some insight in the pro and cons of the
- different methods.
-
- 3. threshing
-
- The gaps between the (greatest) scores are tested for beeing drawn from the same
- distribution as the majority of the scores. If a gap is encountered, that,
- with sufficient significance, can be said to not be drawn from the same
- distribution as the one all the smaller gaps are drawn from, than the observation
- belonging to this gap, and all the observations belonging to gaps larger then
- this gap, get flagged outliers. See description of the `threshing` parameter for
- more details. Although [2] gives a fully detailed overview over the `stray`
- algorithm.
- """
+ raise_factor : float, default 2
+ See second condition listed in the notes below.
- fields = toSequence(field)
+ slope : {None, float}, default None
+ See third condition listed in the notes below.
- fields_ = []
- for f in fields:
- field_ = str(uuid.uuid4())
- data, flags = copyField(data, field=f, flags=flags, target=field_)
- data, flags = transform(
- data, field=field_, flags=flags, func=trafo, freq=partition
- )
- fields_.append(field_)
-
- knn_field = str(uuid.uuid4())
- data, flags = assignKNNScore(
- data=data,
- field=fields_,
- flags=flags,
- target=knn_field,
- n=n,
- func=func,
- freq=partition,
- method="ball_tree",
- min_periods=partition_min,
- **kwargs,
- )
- for field_ in fields_:
- data, flags = dropField(data, field_, flags)
-
- data, flags = flagByStray(
- data=data,
- field=knn_field,
- flags=flags,
- freq=partition,
- min_periods=partition_min,
- iter_start=iter_start,
- alpha=alpha,
- flag=flag,
- **kwargs,
- )
+ weight : float, default 0.8
+ See third condition listed in the notes below.
- data, flags = _evalStrayLabels(
- data=data,
- field=knn_field,
- target=fields,
- flags=flags,
- reduction_range=stray_range,
- reduction_drop_flagged=drop_flagged,
- reduction_thresh=thresh,
- reduction_min_periods=min_periods,
- flag=flag,
- **kwargs,
- )
- data, flags = dropField(data, knn_field, flags)
+ flag : float, default BAD
+ flag to set.
- return data, flags
+ Returns
+ -------
+ saqc.SaQC
+ Notes
+ -----
+ The value :math:`x_{k}` of a time series :math:`x` with associated
+ timestamps :math:`t_i`, is flagged a raise, if:
-@flagging()
-def flagRaise(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- thresh: float,
- raise_window: str,
- freq: str,
- average_window: Optional[str] = None,
- raise_factor: float = 2.0,
- slope: Optional[float] = None,
- weight: float = 0.8,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- The function flags raises and drops in value courses, that exceed a certain threshold
- within a certain timespan.
+ * There is any value :math:`x_{s}`, preceeding :math:`x_{k}` within `raise_window`
+ range, so that:
- The parameter variety of the function is owned to the intriguing
- case of values, that "return" from outlierish or anomalious value levels and
- thus exceed the threshold, while actually being usual values.
+ * :math:`M = |x_k - x_s | >` `thresh` :math:`> 0`
- NOTE, the dataset is NOT supposed to be harmonized to a time series with an
- equidistant frequency grid.
+ * The weighted average :math:`\\mu^{*}` of the values, preceding :math:`x_{k}`
+ within `average_window`
+ range indicates, that :math:`x_{k}` does not return from an "outlierish" value
+ course, meaning that:
- 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
- Container to store flags of the data.
- thresh : float
- The threshold, for the total rise (thresh > 0), or total drop (thresh < 0),
- value courses must not exceed within a timespan of length `raise_window`.
- raise_window : str
- An offset string, determining the timespan, the rise/drop thresholding refers
- to. Window is inclusively defined.
- freq : str
- An offset string, determining The frequency, the timeseries to-be-flagged is
- supposed to be sampled at. The window is inclusively defined.
- average_window : {None, str}, default None
- See condition (2) of the description linked in the references. Window is
- inclusively defined. The window defaults to 1.5 times the size of `raise_window`
- raise_factor : float, default 2
- See second condition listed in the notes below.
- slope : {None, float}, default None
- See third condition listed in the notes below.
- weight : float, default 0.8
- See third condition listed in the notes below.
- flag : float, default BAD
- flag to set.
+ * :math:`x_k > \\mu^* + ( M` / `mean_raise_factor` :math:`)`
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- Flags values may have changed, relatively to the flags input.
+ * Additionally, if ``min_slope`` is not `None`, :math:`x_{k}` is checked for being
+ sufficiently divergent from its very predecessor :math:`x_{k-1}`, meaning that, it
+ is additionally checked if:
- Notes
- -----
- The value :math:`x_{k}` of a time series :math:`x` with associated
- timestamps :math:`t_i`, is flagged a raise, if:
+ * :math:`x_k - x_{k-1} >` `min_slope`
+ * :math:`t_k - t_{k-1} >` `weight` :math:`\\times` `freq`
- * There is any value :math:`x_{s}`, preceeding :math:`x_{k}` within `raise_window`
- range, so that:
+ """
- * :math:`M = |x_k - x_s | >` `thresh` :math:`> 0`
+ # prepare input args
+ dataseries = self._data[field].dropna()
+ raise_window_td = pd.Timedelta(raise_window)
+ freq_dt = pd.Timedelta(freq)
+ if slope is not None:
+ slope = np.abs(slope)
- * The weighted average :math:`\\mu^{*}` of the values, preceding :math:`x_{k}`
- within `average_window`
- range indicates, that :math:`x_{k}` does not return from an "outlierish" value
- course, meaning that:
+ if average_window is None:
+ average_window = 1.5 * raise_window_td
- * :math:`x_k > \\mu^* + ( M` / `mean_raise_factor` :math:`)`
+ if thresh < 0:
+ dataseries *= -1
+ thresh *= -1
- * Additionally, if ``min_slope`` is not `None`, :math:`x_{k}` is checked for being
- sufficiently divergent from its very predecessor :math:`x_{k-1}`, meaning that, it
- is additionally checked if:
+ def raise_check(x, thresh):
+ test_set = x[-1] - x[0:-1]
+ max_val = np.max(test_set)
+ if max_val >= thresh:
+ return max_val
+ else:
+ return np.nan
- * :math:`x_k - x_{k-1} >` `min_slope`
- * :math:`t_k - t_{k-1} >` `weight` :math:`\\times` `freq`
+ def custom_rolling_mean(x):
+ return np.sum(x[:-1])
- """
+ # get invalid-raise/drop mask:
+ raise_series = dataseries.rolling(raise_window_td, min_periods=2, closed="both")
- # prepare input args
- dataseries = data[field].dropna()
- raise_window = pd.Timedelta(raise_window)
- freq = pd.Timedelta(freq)
- if slope is not None:
- slope = np.abs(slope)
-
- if average_window is None:
- average_window = 1.5 * pd.Timedelta(raise_window)
-
- if thresh < 0:
- dataseries *= -1
- thresh *= -1
-
- def raise_check(x, thresh):
- test_set = x[-1] - x[0:-1]
- max_val = np.max(test_set)
- if max_val >= thresh:
- return max_val
+ numba_boost = True
+ if numba_boost:
+ raise_check_boosted = numba.jit(raise_check, nopython=True)
+ raise_series = raise_series.apply(
+ raise_check_boosted, args=(thresh,), raw=True, engine="numba"
+ )
else:
- return np.nan
-
- def custom_rolling_mean(x):
- return np.sum(x[:-1])
-
- # get invalid-raise/drop mask:
- raise_series = dataseries.rolling(raise_window, min_periods=2, closed="both")
-
- numba_boost = True
- if numba_boost:
- raise_check = numba.jit(raise_check, nopython=True)
- raise_series = raise_series.apply(
- raise_check, args=(thresh,), raw=True, engine="numba"
+ raise_series = raise_series.apply(raise_check, args=(thresh,), raw=True)
+
+ if raise_series.isna().all():
+ return self
+
+ # "unflag" values of insufficient deviation to their predecessors
+ if slope is not None:
+ w_mask = (
+ pd.Series(dataseries.index).diff().dt.total_seconds()
+ / freq_dt.total_seconds()
+ ) > weight
+ slope_mask = np.abs(dataseries.diff()) < slope
+ to_unflag = raise_series.notna() & w_mask.values & slope_mask
+ raise_series[to_unflag] = np.nan
+
+ # calculate and apply the weighted mean weights (pseudo-harmonization):
+ weights = (
+ pd.Series(dataseries.index).diff(periods=2).shift(-1).dt.total_seconds()
+ / freq_dt.total_seconds()
+ / 2
)
- else:
- raise_series = raise_series.apply(raise_check, args=(thresh,), raw=True)
- if raise_series.isna().all():
- return data, flags
+ weights.iloc[0] = 0.5 + (
+ pd.Timestamp(dataseries.index[1]) - pd.Timestamp(dataseries.index[0])
+ ).total_seconds() / (freq_dt.total_seconds() * 2)
- # "unflag" values of insufficient deviation to their predecessors
- if slope is not None:
- w_mask = (
- pd.Series(dataseries.index).diff().dt.total_seconds() / freq.total_seconds()
- ) > weight
- slope_mask = np.abs(dataseries.diff()) < slope
- to_unflag = raise_series.notna() & w_mask.values & slope_mask
- raise_series[to_unflag] = np.nan
-
- # calculate and apply the weighted mean weights (pseudo-harmonization):
- weights = (
- pd.Series(dataseries.index).diff(periods=2).shift(-1).dt.total_seconds()
- / freq.total_seconds()
- / 2
- )
-
- weights.iloc[0] = 0.5 + (
- dataseries.index[1] - dataseries.index[0]
- ).total_seconds() / (freq.total_seconds() * 2)
+ weights.iloc[-1] = 0.5 + (
+ pd.Timestamp(dataseries.index[-1]) - pd.Timestamp(dataseries.index[-2])
+ ).total_seconds() / (freq_dt.total_seconds() * 2)
- weights.iloc[-1] = 0.5 + (
- dataseries.index[-1] - dataseries.index[-2]
- ).total_seconds() / (freq.total_seconds() * 2)
+ weights[weights > 1.5] = 1.5
+ weights.index = dataseries.index
+ weighted_data = dataseries.mul(weights)
- weights[weights > 1.5] = 1.5
- weights.index = dataseries.index
- weighted_data = dataseries.mul(weights)
-
- # rolling weighted mean calculation
- weighted_rolling_mean = weighted_data.rolling(
- average_window, min_periods=2, closed="both"
- )
- weights_rolling_sum = weights.rolling(average_window, min_periods=2, closed="both")
- if numba_boost:
- custom_rolling_mean = numba.jit(custom_rolling_mean, nopython=True)
- weighted_rolling_mean = weighted_rolling_mean.apply(
- custom_rolling_mean, raw=True, engine="numba"
+ # rolling weighted mean calculation
+ weighted_rolling_mean = weighted_data.rolling(
+ average_window, min_periods=2, closed="both"
)
- weights_rolling_sum = weights_rolling_sum.apply(
- custom_rolling_mean, raw=True, engine="numba"
+ weights_rolling_sum = weights.rolling(
+ average_window, min_periods=2, closed="both"
)
- else:
- weighted_rolling_mean = weighted_rolling_mean.apply(
- custom_rolling_mean, raw=True
- )
- weights_rolling_sum = weights_rolling_sum.apply(
- custom_rolling_mean, raw=True, engine="numba"
- )
-
- weighted_rolling_mean = weighted_rolling_mean / weights_rolling_sum
- # check means against critical raise value:
- to_flag = dataseries >= weighted_rolling_mean + (raise_series / raise_factor)
- to_flag &= raise_series.notna()
- flags[to_flag[to_flag].index, field] = flag
-
- return data, flags
-
-
-@flagging()
-def flagMAD(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- window: str,
- z: float = 3.5,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- The function represents an implementation of the modyfied Z-score outlier detection method.
-
- See references [1] for more details on the algorithm.
-
- Note, that the test needs the input data to be sampled regularly (fixed sampling rate).
-
- 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. (Here a dummy, for structural reasons)
- flags : saqc.Flags
- Container to store flags of the data.
- window : str
- Offset string. Denoting the windows size that the "Z-scored" values have to lie in.
- z: float, default 3.5
- The value the Z-score is tested against. Defaulting to 3.5 (Recommendation of [1])
- flag : float, default BAD
- flag to set.
+ if numba_boost:
+ custom_rolling_mean_boosted = numba.jit(custom_rolling_mean, nopython=True)
+ weighted_rolling_mean = weighted_rolling_mean.apply(
+ custom_rolling_mean_boosted, raw=True, engine="numba"
+ )
+ weights_rolling_sum = weights_rolling_sum.apply(
+ custom_rolling_mean_boosted, raw=True, engine="numba"
+ )
+ else:
+ weighted_rolling_mean = weighted_rolling_mean.apply(
+ custom_rolling_mean, raw=True
+ )
+ weights_rolling_sum = weights_rolling_sum.apply(
+ custom_rolling_mean, raw=True, engine="numba"
+ )
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- Flags values may have changed, relatively to the flags input.
+ weighted_rolling_mean = weighted_rolling_mean / weights_rolling_sum
+ # check means against critical raise value:
+ to_flag = dataseries >= weighted_rolling_mean + (raise_series / raise_factor)
+ to_flag &= raise_series.notna()
+ self._flags[to_flag[to_flag].index, field] = flag
+
+ return self
+
+ @flagging()
+ def flagMAD(
+ self: "SaQC",
+ field: str,
+ window: Optional[str, int] = None,
+ z: float = 3.5,
+ min_residuals: Optional[int] = None,
+ min_periods: Optional[int] = None,
+ center: bool = False,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ The function represents an implementation of the modyfied Z-score outlier detection method.
+
+ See references [1] for more details on the algorithm.
+
+ Note, that the test needs the input data to be sampled regularly (fixed sampling rate).
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged. (Here a dummy, for structural reasons)
+ window : {str, int}, default None
+ Size of the window. Either determined via an Offset String, denoting the windows temporal extension or
+ by an integer, denoting the windows number of periods.
+ `NaN` measurements also count as periods.
+ If `None` is passed, All data points share the same scoring window, which than equals the whole
+ data.
+ z: float, default 3.5
+ The value the Z-score is tested against. Defaulting to 3.5 (Recommendation of [1])
+ min_periods
+ Minimum number of valid meassurements in a scoring window, to consider the resulting score valid.
+ center
+ Weather or not to center the target value in the scoring window. If `False`, the
+ target value is the last value in the window.
+ flag : float, default BAD
+ flag to set.
+
+ Returns
+ -------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ flags : saqc.Flags
+ The quality flags of data
+ Flags values may have changed, relatively to the flags input.
+
+ References
+ ----------
+ [1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
+ """
+
+ self = self.flagZScore(
+ field,
+ window=window,
+ thresh=z,
+ min_residuals=min_residuals,
+ model_func=np.median,
+ norm_func=lambda x: median_abs_deviation(
+ x, scale="normal", nan_policy="omit"
+ ),
+ center=center,
+ min_periods=min_periods,
+ flag=flag,
+ )
- References
- ----------
- [1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
- """
- d = data[field]
- if d.empty:
- return data, flags
+ return self
+
+ @flagging()
+ def flagOffset(
+ self: "SaQC",
+ field: str,
+ tolerance: float,
+ window: Union[int, str],
+ thresh: Optional[float] = None,
+ thresh_relative: Optional[float] = None,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ 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, 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. if `thresh_relative` > 0, :math:`x_{n + s} > x_{n - 1}*(1+` `thresh_relative` :math:`)`
+
+ 3. if `thresh_relative` < 0, :math:`x_{n + s} < x_{n - 1}*(1+` `thresh_relative` :math:`)`
+
+ 4. :math:`|x_{n-1} - x_{n+k+1}| <` `tolerance`
+
+ 5. :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.
+
+ Parameters
+ ----------
+ field : str
+ The field in data.
+ tolerance : float
+ 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 (4).
+ window : {str, int}, default '15min'
+ Maximum length allowed for offset value courses, to trigger flagging of the values forming the offset.
+ See condition (5). 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
+ Minimum relative change between a value 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.
- median = d.rolling(window=window, closed="both").median()
- diff = (d - median).abs()
- mad = diff.rolling(window=window, closed="both").median()
- mask = (mad > 0) & (0.6745 * diff > z * mad)
- # NOTE:
- # In pandas <= 0.25.3, the window size is not fixed if the
- # window-argument to rolling is a frequency. That implies,
- # that during the first iterations the window has a size of
- # 1, 2, 3, ... until it eventually covers the desired time
- # span. For stuff like the calculation of median, that is rather
- # unfortunate, as the size of the calculation base might differ
- # heavily. So don't flag something until, the window reaches
- # its target size
- if not isinstance(window, int):
- index = mask.index
- mask.loc[index < index[0] + pd.to_timedelta(window)] = False
-
- flags[mask, field] = flag
- return data, flags
+ Returns
+ -------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ flags : saqc.Flags
+ The quality flags of data
+ Flags values may have changed, relatively to the flags input.
+ Examples
+ --------
+ Below picture gives an abstract interpretation of the parameter interplay in case of a positive value jump,
+ initialising an offset course.
-@flagging()
-def flagOffset(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- 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 works on regularly and irregularly sampled data.
+ .. figure:: /resources/images/flagOffsetPic.png
- The test classifies values/value courses as outliers by detecting not only a rise
- in value, but also, by checking for a return to the initial value level.
+ The four values marked red, are flagged, because (1) the initial value jump *exceeds* the value given by `thresh`,
+ (2) the temporal extension of the group does *not exceed* the range given by `window` and (3) the returning
+ value after the group, lies *within* the value range determined by `tolerance`
- 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]`
+ .. plot::
+ :context:
+ :include-source: False
- 2. :math:`(x_{n + s} - x_{n - 1}) / x_{n - 1} >` `thresh_relative`
+ 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))
- 3. :math:`|x_{n-1} - x_{n+k+1}| <` `tolerance`
- 4. :math:`|t_{n-1} - t_{n+k+1}| <` `window`
+ Lets generate a simple, regularly sampled timeseries with an hourly sampling rate and generate an
+ :py:class:`saqc.SaQC` instance from it.
- Note, that this definition of a "spike" not only includes one-value outliers, but
- also plateau-ish value courses.
+ .. doctest:: flagOffsetExample
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- field : str
- The field in data.
- flags : saqc.Flags
- Container to store flags of the data.
- tolerance : float
- 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 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
- 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.
+ >>> 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)
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- Flags values may have changed, relatively to the flags input.
-
- Examples
- --------
+ 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``).
- .. plot::
- :context:
- :include-source: False
+ .. doctest:: flagOffsetExample
- 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))
+ >>> 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')
- Lets generate a simple, regularly sampled timeseries with an hourly sampling rate and generate an
- :py:class:`saqc.SaQC` instance from it.
+ 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
+ .. 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)
+ >>> qc = qc.flagOffset("data", thresh=2, thresh_relative=-.5, tolerance=1.5, window='6H')
+ >>> qc.plot('data') # doctest:+SKIP
- 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``).
+ .. plot::
+ :context: close-figs
+ :include-source: False
- .. doctest:: flagOffsetExample
+ >>> qc = saqc.SaQC(data)
+ >>> qc = qc.flagOffset("data", thresh=2, thresh_relative=-.5, tolerance=1.5, window='6H')
+ >>> qc.plot('data')
- >>> qc = qc.flagOffset("data", thresh=2, tolerance=1.5, window='6H')
- >>> qc.plot('data') # doctest:+SKIP
- .. plot::
- :context: close-figs
- :include-source: False
+ References
+ ----------
+ The implementation is a time-window based version of an outlier test from the UFZ Python library,
+ that can be found here:
- >>> qc = saqc.SaQC(data)
- >>> qc = qc.flagOffset("data", thresh=2, tolerance=1.5, window='6H')
- >>> qc.plot('data')
+ https://git.ufz.de/chs/python/blob/master/ufz/level1/spike.py
- 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:
+ """
+ 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."
+ )
+ if thresh is None:
+ thresh = 0
+
+ dat = self._data[field].dropna()
+ if thresh_relative is not None:
+ rel_jumps = np.sign(thresh_relative) * dat > np.sign(
+ thresh_relative
+ ) * dat.shift(+1) * (1 + thresh_relative)
+
+ data_diff = dat.diff()
+ initial_jumps = data_diff.abs() > thresh
+ if thresh_relative:
+ initial_jumps &= rel_jumps
+ return_in_time = (
+ dat[::-1]
+ .rolling(window, min_periods=2)
+ .apply(lambda x: np.abs(x[-1] - x[:-1]).min() < tolerance, raw=True)[::-1]
+ .astype(bool)
+ )
+ return_in_time = return_in_time & initial_jumps.reindex(
+ dat.index, fill_value=False
+ ).shift(-1, fill_value=False)
+ offset_start_candidates = dat[return_in_time]
+ win_delta = pd.Timedelta(window)
+ corners = pd.Series(False, index=dat.index)
+ to_flag = pd.Series(False, index=dat.index)
+ ns = pd.Timedelta("1ns")
+ for c in zip(offset_start_candidates.index, offset_start_candidates.values):
+ ret = (dat[c[0]] - dat[c[0] + ns : c[0] + win_delta]).abs()[1:] < tolerance
+ if not ret.empty:
+ r = ret.idxmax()
+ chunk = dat[c[0] : r]
+ sgn = np.sign(chunk[1] - c[1])
+ t_val = ((chunk[1:-1] - c[1]) * sgn > thresh).all()
+ r_val = True
+ if thresh_relative:
+ r_val = (
+ np.sign(thresh_relative) * chunk[1:-1]
+ > np.sign(thresh_relative) * c[1] * (1 + thresh_relative)
+ ).all()
+ if t_val and r_val and (not corners[c[0]]):
+ flag_i = dat[c[0] + ns : chunk.index[-1] - ns].index
+ to_flag[flag_i] = True
+ corners.loc[flag_i[-1]] = True
+ to_flag = to_flag.reindex(self._data[field].index, fill_value=False)
+
+ self._flags[to_flag, field] = flag
+ return self
+
+ @flagging()
+ def flagByGrubbs(
+ self: "SaQC",
+ field: str,
+ window: Union[str, int],
+ alpha: float = 0.05,
+ min_periods: int = 8,
+ pedantic: bool = False,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ The function flags values that are regarded outliers due to the grubbs test.
+
+ See reference [1] for more information on the grubbs tests definition.
+
+ The (two-sided) test gets applied onto data chunks of size "window". The tests
+ application will be iterated on each data-chunk under test, till no more
+ outliers are detected in that chunk.
+
+ Note, that the test performs poorely for small data chunks (resulting in heavy
+ overflagging). Therefor you should select "window" so that every window contains
+ at least > 8 values and also adjust the min_periods values accordingly.
+
+ Note, that the data to be tested by the grubbs test are expected to be distributed
+ "normalish".
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
+
+ window : {int, str}
+ The size of the window you want to use for outlier testing. If an integer is
+ passed, the size refers to the number of periods of every testing window. If a
+ string is passed, it has to be an offset string, and will denote the total
+ temporal extension of every window.
+
+ alpha : float, default 0.05
+ The level of significance, the grubbs test is to be performed at. (between 0 and 1)
+
+ min_periods : int, default 8
+ The minimum number of values that have to be present in an interval under test,
+ for a grubbs test result to be accepted. Only makes sence in case `window` is
+ an offset string.
+
+ pedantic: boolean, default False
+ If True, every value gets checked twice for being an outlier. Ones in the
+ initial rolling window and one more time in a rolling window that is lagged
+ by half the windows delimeter (window/2). Recommended for avoiding false
+ positives at the window edges. Only available when rolling with integer
+ defined window size.
+
+ flag : float, default BAD
+ flag to set.
+
+ Returns
+ -------
+ saqc.SaQC
+
+ References
+ ----------
+ introduction to the grubbs test:
+
+ [1] https://en.wikipedia.org/wiki/Grubbs%27s_test_for_outliers
+ """
+ datcol = self._data[field].copy()
+ rate = getFreqDelta(datcol.index)
+
+ # if timeseries that is analyzed, is regular,
+ # window size can be transformed to a number of periods:
+ if rate and isinstance(window, str):
+ window = pd.Timedelta(window) // rate
+
+ to_group = pd.DataFrame(data={"ts": datcol.index, "data": datcol})
+ to_flag = pd.Series(False, index=datcol.index)
+
+ # period number defined test intervals
+ if isinstance(window, int):
+ grouper_series = pd.Series(
+ data=np.arange(0, datcol.shape[0]), index=datcol.index
+ )
+ grouper_series_lagged = grouper_series + (window / 2)
+ grouper_series = grouper_series.transform(lambda x: x // window)
+ grouper_series_lagged = grouper_series_lagged.transform(
+ lambda x: x // window
+ )
+ partitions = to_group.groupby(grouper_series)
+ partitions_lagged = to_group.groupby(grouper_series_lagged)
- .. doctest:: flagOffsetExample
+ # offset defined test intervals:
+ else:
+ partitions = to_group.groupby(pd.Grouper(freq=window))
+ partitions_lagged = []
- >>> qc = qc.flagOffset("data", thresh=2, thresh_relative=.9, tolerance=1.5, window='6H')
- >>> qc.plot('data') # doctest:+SKIP
+ for _, partition in partitions:
+ if partition.shape[0] > min_periods:
+ detected = smirnov_grubbs.two_sided_test_indices(
+ partition["data"].values, alpha=alpha
+ )
+ detected = partition["ts"].iloc[detected]
+ to_flag[detected.index] = True
+
+ if isinstance(window, int) and pedantic:
+ to_flag_lagged = pd.Series(False, index=datcol.index)
+
+ for _, partition in partitions_lagged:
+ if partition.shape[0] > min_periods:
+ detected = smirnov_grubbs.two_sided_test_indices(
+ partition["data"].values, alpha=alpha
+ )
+ detected = partition["ts"].iloc[detected]
+ to_flag_lagged[detected.index] = True
+
+ to_flag &= to_flag_lagged
+
+ self._flags[to_flag, field] = flag
+ return self
+
+ @register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=["field"],
+ multivariate=True,
+ handles_target=False,
+ )
+ def flagCrossStatistics(
+ self: "SaQC",
+ field: Sequence[str],
+ thresh: float,
+ method: Literal["modZscore", "Zscore"] = "modZscore",
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Function checks for outliers relatively to the "horizontal" input data axis.
- .. plot::
- :context: close-figs
- :include-source: False
+ For `fields` :math:`=[f_1,f_2,...,f_N]` and timestamps :math:`[t_1,t_2,...,t_K]`, the following steps are taken
+ for outlier detection:
- >>> qc = saqc.SaQC(data)
- >>> qc = qc.flagOffset("data", thresh=2, thresh_relative=.9, tolerance=1.5, window='6H')
- >>> qc.plot('data')
+ 1. All timestamps :math:`t_i`, where there is one :math:`f_k`, with :math:`data[f_K]` having no entry at
+ :math:`t_i`, are excluded from the following process (inner join of the :math:`f_i` fields.)
+ 2. for every :math:`0 <= i <= K`, the value
+ :math:`m_j = median(\\{data[f_1][t_i], data[f_2][t_i], ..., data[f_N][t_i]\\})` is calculated
+ 3. for every :math:`0 <= i <= K`, the set
+ :math:`\\{data[f_1][t_i] - m_j, data[f_2][t_i] - m_j, ..., data[f_N][t_i] - m_j\\}` is tested for outliers with the
+ specified method (`cross_stat` parameter).
- Now, only positive jumps, that exceed a value gain of *+90%* are considered starting points of offsets.
+ Parameters
+ ----------
+ field : list of str
+ List of fieldnames in data, determining wich variables are to be included into the flagging process.
- 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*.
+ thresh : float
+ Threshold which the outlier score of an value must exceed, for being flagged an outlier.
- .. doctest:: flagOffsetExample
+ method : {'modZscore', 'Zscore'}, default 'modZscore'
+ Method used for calculating the outlier scores.
- >>> qc = qc.flagOffset("data", thresh=2, thresh_relative=-.5, tolerance=1.5, window='6H')
- >>> qc.plot('data') # doctest:+SKIP
+ * ``'modZscore'``: Median based "sigma"-ish approach. See Referenecs [1].
+ * ``'Zscore'``: Score values by how many times the standard deviation they differ from the median.
+ See References [1]
- .. plot::
- :context: close-figs
- :include-source: False
+ flag : float, default BAD
+ flag to set.
- >>> qc = saqc.SaQC(data)
- >>> qc = qc.flagOffset("data", thresh=2, thresh_relative=-.5, tolerance=1.5, window='6H')
- >>> qc.plot('data')
+ Returns
+ -------
+ saqc.SaQC
+ 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
+ """
- References
- ----------
- The implementation is a time-window based version of an outlier test from the UFZ Python library,
- that can be found here:
+ fields = toSequence(field)
- https://git.ufz.de/chs/python/blob/master/ufz/level1/spike.py
+ df = self._data[fields].loc[self._data[fields].index_of("shared")].to_df()
- """
- 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."
- )
+ if isinstance(method, str):
- dataseries = data[field].dropna()
- if dataseries.empty:
- return data, flags
+ if method == "modZscore":
+ MAD_series = df.subtract(df.median(axis=1), axis=0).abs().median(axis=1)
+ diff_scores = (
+ (0.6745 * (df.subtract(df.median(axis=1), axis=0)))
+ .divide(MAD_series, axis=0)
+ .abs()
+ )
- # using reverted series - because ... long story.
- ind = dataseries.index
- rev_ind = ind[0] + ((ind[-1] - ind)[::-1])
- map_i = pd.Series(ind, index=rev_ind)
- dataseries = pd.Series(dataseries.values, index=rev_ind)
-
- if isinstance(window, int):
- delta = getFreqDelta(dataseries.index)
- window = delta * window
- if not delta:
- raise TypeError(
- "Only offset string defined window sizes allowed for timeseries not sampled regularly."
- )
+ elif method == "Zscore":
+ diff_scores = (
+ df.subtract(df.mean(axis=1), axis=0)
+ .divide(df.std(axis=1), axis=0)
+ .abs()
+ )
- # get all the entries preceding a significant jump
- if thresh is not None:
- post_jumps = dataseries.diff().abs() > thresh
+ else:
+ raise ValueError(method)
- 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 is not None:
- post_jumps = rel_jumps & post_jumps
else:
- post_jumps = rel_jumps
-
- post_jumps = post_jumps[post_jumps]
- if post_jumps.empty:
- return data, flags
-
- # get all the entries preceding a significant jump
- # and its successors within "length" range
- to_roll = post_jumps.reindex(
- dataseries.index, method="bfill", tolerance=window, fill_value=False
- ).dropna()
- to_roll = dataseries[to_roll]
-
- if thresh_relative is not None:
-
- def spikeTester(
- chunk, thresh_r=abs(thresh_relative), thresh_a=thresh or 0, tol=tolerance
- ):
- jump = chunk[-2] - chunk[-1]
- 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):
- return 0
- if np.abs(chunk[-initial - 1] - chunk[-1]) < tol:
- return initial - 1
- return 0
-
- else:
-
- # define spike testing function to roll with (no rel_check):
- def spikeTester(chunk, thresh=thresh, tol=tolerance):
- # signum change!!!
- chunk_stair = (
- np.sign(chunk[-2] - chunk[-1]) * (chunk - chunk[-1]) < thresh
- )[::-1].cumsum()
- initial = np.searchsorted(chunk_stair, 2)
- if initial == len(chunk):
- return 0
- if np.abs(chunk[-initial - 1] - chunk[-1]) < tol:
- return initial - 1
- return 0
-
- roller = customRoller(to_roll, window=window, min_periods=2, closed="both")
- engine = None if len(to_roll) < 200000 else "numba"
- result = roller.apply(spikeTester, raw=True, engine=engine)
-
- ignore = pd.Series(True, index=to_roll.index)
- ignore[post_jumps.index] = False
- result[ignore] = np.nan
-
- result.index = map_i[result.index]
-
- # correct the result: only those values define plateaus, that do not have
- # values at their left starting point, that belong to other plateaus themself:
- def calcResult(result):
- var_num = result.shape[0]
- flag_scopes = np.zeros(var_num, dtype=bool)
- for k in range(var_num):
- if result[k] > 0:
- k_r = int(result[k])
- # validity check: plateuas start isnt another plateaus end:
- if not flag_scopes[k - k_r - 1]:
- flag_scopes[(k - k_r) : k] = True
- return pd.Series(flag_scopes, index=result.index)
-
- cresult = calcResult(result)
- cresult = cresult[cresult].index
- flags[cresult, field] = flag
- return data, flags
-
-
-@flagging()
-def flagByGrubbs(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- window: Union[str, int],
- alpha: float = 0.05,
- min_periods: int = 8,
- pedantic: bool = False,
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- The function flags values that are regarded outliers due to the grubbs test.
-
- See reference [1] for more information on the grubbs tests definition.
-
- The (two-sided) test gets applied onto data chunks of size "window". The tests
- application will be iterated on each data-chunk under test, till no more
- outliers are detected in that chunk.
- Note, that the test performs poorely for small data chunks (resulting in heavy
- overflagging). Therefor you should select "window" so that every window contains
- at least > 8 values and also adjust the min_periods values accordingly.
-
- Note, that the data to be tested by the grubbs test are expected to be distributed
- "normalish".
-
- 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
- Container to store flags of the data.
- window : {int, str}
- The size of the window you want to use for outlier testing. If an integer is
- passed, the size refers to the number of periods of every testing window. If a
- string is passed, it has to be an offset string, and will denote the total
- temporal extension of every window.
- alpha : float, default 0.05
- The level of significance, the grubbs test is to be performed at. (between 0 and 1)
- min_periods : int, default 8
- The minimum number of values that have to be present in an interval under test,
- for a grubbs test result to be accepted. Only makes sence in case `window` is
- an offset string.
- pedantic: boolean, default False
- If True, every value gets checked twice for being an outlier. Ones in the
- initial rolling window and one more time in a rolling window that is lagged
- by half the windows delimeter (window/2). Recommended for avoiding false
- positives at the window edges. Only available when rolling with integer
- defined window size.
- flag : float, default BAD
- flag to set.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- Flags values may have changed relatively to the flags input.
-
- References
- ----------
- introduction to the grubbs test:
-
- [1] https://en.wikipedia.org/wiki/Grubbs%27s_test_for_outliers
- """
- datcol = data[field].copy()
- rate = getFreqDelta(datcol.index)
-
- # if timeseries that is analyzed, is regular,
- # window size can be transformed to a number of periods:
- if rate and isinstance(window, str):
- window = pd.Timedelta(window) // rate
-
- to_group = pd.DataFrame(data={"ts": datcol.index, "data": datcol})
- to_flag = pd.Series(False, index=datcol.index)
-
- # period number defined test intervals
- if isinstance(window, int):
- grouper_series = pd.Series(
- data=np.arange(0, datcol.shape[0]), index=datcol.index
+ try:
+ stat = getattr(df, method.__name__)(axis=1)
+ except AttributeError:
+ stat = df.aggregate(method, axis=1)
+
+ diff_scores = df.subtract(stat, axis=0).abs()
+
+ mask = diff_scores > thresh
+ if mask.empty:
+ return self
+
+ for f in fields:
+ m = mask[f].reindex(index=self._flags[f].index, fill_value=False)
+ self._flags[m, f] = flag
+
+ return self
+
+ @flagging()
+ def flagZScore(
+ self: "SaQC",
+ field: str,
+ window: str | int | None = None,
+ thresh: float = 3,
+ min_residuals: int | None = None,
+ min_periods: int | None = None,
+ model_func: Callable = np.nanmean,
+ norm_func: Callable = np.nanstd,
+ center: bool = True,
+ flag: float = BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Flag data where its (rolling) Zscore exceeds a threshold.
+
+ The function implements flagging derived from a basic Zscore calculation.
+ To handle non stationary data, the Zscoring can be applied with a rolling window.
+ Therefor, the function allows for a minimum residual to be specified in order to mitigate overflagging in
+ local regimes of low variance.
+
+ See the Notes section for a detailed overview of the calculation
+
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged. (Here a dummy, for structural reasons)
+ window : {str, int}, default None
+ Size of the window. Either determined via an Offset String, denoting the windows temporal extension or
+ by an integer, denoting the windows number of periods.
+ `NaN` measurements also count as periods.
+ If `None` is passed, All data points share the same scoring window, which than equals the whole
+ data.
+ thresh
+ Cutoff level for the Zscores, above which associated points are getting flagged.
+ min_residuals
+ Minimum residual level points must have to be considered outliers.
+ min_periods
+ Minimum number of valid meassurements in a scoring window, to consider the resulting score valid.
+ model_func
+ Function to calculate the center moment in every window.
+ norm_func
+ Function to calculate the scaling for every window
+ center
+ Weather or not to center the target value in the scoring window. If `False`, the
+ target value is the last value in the window.
+
+ Returns
+ -------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ flags : saqc.Flags
+ The quality flags of data
+ Flags values may have changed, relatively to the flags input.
+
+ Notes
+ -----
+ Steps of calculation:
+
+ 1. Consider a window :math:`W` of successive points :math:`W = x_{1},...x_{w}`
+ containing the value :math:`y_{K}` which is to be checked.
+ (The index of :math:`K` depends on the selection of the parameter `center`.)
+
+ 2. The "moment" :math:`M` for the window gets calculated via :math:`M=` `model_func(:math:`W`)
+
+ 3. The "scaling" :math:`N` for the window gets calculated via :math:`N=` `norm_func(:math:`W`)
+
+ 4. The "score" :math:`S` for the point :math:`x_{k}`gets calculated via :math:`S=(x_{k} - M) / N`
+
+ 5. Finally, :math:`x_{k}` gets flagged, if :math:`|S| >` `thresh` and :math:`|M - x_{k}| >= `min_residuals`
+ """
+ datser = self._data[field]
+ if min_residuals is None:
+ min_residuals = 0
+
+ score, model, _ = _univarScoring(
+ datser,
+ window=window,
+ norm_func=norm_func,
+ model_func=model_func,
+ center=center,
+ min_periods=min_periods,
)
- grouper_series_lagged = grouper_series + (window / 2)
- grouper_series = grouper_series.transform(lambda x: x // window)
- grouper_series_lagged = grouper_series_lagged.transform(lambda x: x // window)
- partitions = to_group.groupby(grouper_series)
- partitions_lagged = to_group.groupby(grouper_series_lagged)
-
- # offset defined test intervals:
- else:
- partitions = to_group.groupby(pd.Grouper(freq=window))
- partitions_lagged = []
-
- for _, partition in partitions:
- if partition.shape[0] > min_periods:
- detected = smirnov_grubbs.two_sided_test_indices(
- partition["data"].values, alpha=alpha
- )
- detected = partition["ts"].iloc[detected]
- to_flag[detected.index] = True
-
- if isinstance(window, int) and pedantic:
- to_flag_lagged = pd.Series(False, index=datcol.index)
-
- for _, partition in partitions_lagged:
- if partition.shape[0] > min_periods:
- detected = smirnov_grubbs.two_sided_test_indices(
- partition["data"].values, alpha=alpha
- )
- detected = partition["ts"].iloc[detected]
- to_flag_lagged[detected.index] = True
+ to_flag = (score.abs() > thresh) & ((model - datser).abs() >= min_residuals)
+ self._flags[to_flag, field] = flag
+ return self
- to_flag &= to_flag_lagged
-
- flags[to_flag, field] = flag
- return data, flags
-
-@flagging()
-def flagRange(
+def _evalStrayLabels(
data: DictOfSeries,
field: str,
flags: Flags,
- min: float = -np.inf,
- max: float = np.inf,
+ target: Sequence[str],
+ reduction_range: Optional[str] = None,
+ reduction_drop_flagged: bool = False, # TODO: still a case ?
+ reduction_thresh: float = 3.5,
+ reduction_min_periods: int = 1,
+ at_least_one: bool = True,
flag: float = BAD,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- Function flags values not covered by the closed interval [`min`, `max`].
+ The function "reduces" an observations flag to components of it, by applying MAD
+ (See references) test onto every components temporal surrounding.
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
- Container to store flags of the data.
- min : float
- Lower bound for valid data.
- max : float
- Upper bound for valid data.
- flag : float, default BAD
- flag to set.
+ The fieldname of the column, holding the labels to be evaluated.
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
flags : saqc.Flags
- The quality flags of data
- """
+ Container to store quality flags to data.
- # using .values is much faster
- datacol = data[field].values
- mask = (datacol < min) | (datacol > max)
- flags[mask, field] = flag
- return data, flags
+ target : list of str
+ A list of strings, holding the column names of the variables, the stray labels
+ shall be projected onto.
+ val_frame : (N,M) pd.DataFrame
+ Input NxM DataFrame of observations, where N is the number of observations and
+ M the number of components per observation.
-@register(
- mask=["field"],
- demask=["field"],
- squeeze=["field"],
- multivariate=True,
- handles_target=False,
-)
-def flagCrossStatistics(
- data: DictOfSeries,
- field: Sequence[str],
- flags: Flags,
- thresh: float,
- method: Literal["modZscore", "Zscore"] = "modZscore",
- flag: float = BAD,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Function checks for outliers relatively to the "horizontal" input data axis.
+ to_flag_frame : pandas.DataFrame
+ Input dataframe of observations to be tested, where N is the number of
+ observations and M the number of components per observation.
- For `fields` :math:`=[f_1,f_2,...,f_N]` and timestamps :math:`[t_1,t_2,...,t_K]`, the following steps are taken
- for outlier detection:
+ reduction_range : {None, str}
+ An offset string, denoting the range of the temporal surrounding to include
+ into the MAD testing. If ``None`` is passed, no testing will be performed and
+ all targets will have the stray flag projected.
- 1. All timestamps :math:`t_i`, where there is one :math:`f_k`, with :math:`data[f_K]` having no entry at
- :math:`t_i`, are excluded from the following process (inner join of the :math:`f_i` fields.)
- 2. for every :math:`0 <= i <= K`, the value
- :math:`m_j = median(\\{data[f_1][t_i], data[f_2][t_i], ..., data[f_N][t_i]\\})` is calculated
- 3. for every :math:`0 <= i <= K`, the set
- :math:`\\{data[f_1][t_i] - m_j, data[f_2][t_i] - m_j, ..., data[f_N][t_i] - m_j\\}` is tested for outliers with the
- specified method (`cross_stat` parameter).
+ reduction_drop_flagged : bool, default False
+ Wheather or not to drop flagged values other than the value under test, from the
+ temporal surrounding before checking the value with MAD.
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- field : list of str
- List of fieldnames in data, determining wich variables are to be included into the flagging process.
- flags : saqc.Flags
- A flags object, holding flags and additional informations related to `data`.
- thresh : float
- Threshold which the outlier score of an value must exceed, for being flagged an outlier.
- method : {'modZscore', 'Zscore'}, default 'modZscore'
- Method used for calculating the outlier scores.
+ reduction_thresh : float, default 3.5
+ The `critical` value, controlling wheather the MAD score is considered
+ referring to an outlier or not. Higher values result in less rigid flagging.
+ The default value is widely used in the literature. See references section
+ for more details ([1]).
- * ``'modZscore'``: Median based "sigma"-ish approach. See Referenecs [1].
- * ``'Zscore'``: Score values by how many times the standard deviation they differ from the median.
- See References [1]
+ at_least_one : bool, default True
+ If none of the variables, the outlier label shall be reduced to, is an outlier
+ with regard to the test, all (True) or none (False) of the variables are flagged
flag : float, default BAD
flag to set.
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- Flags values may have changed relatively to the input flags.
-
-
- Notes
- -----
-
- 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
"""
+ val_frame = data[target].to_df()
+ stray_detects = flags[field] > UNFLAGGED
+ stray_detects = stray_detects[stray_detects]
+ to_flag_frame = pd.DataFrame(False, columns=target, index=stray_detects.index)
- fields = toSequence(field)
-
- df = data[fields].loc[data[fields].index_of("shared")].to_df()
+ if reduction_range is None:
+ for field in to_flag_frame.columns:
+ flags[to_flag_frame.index, field] = flag
+ return data, flags
- if isinstance(method, str):
+ for var in target:
+ for index in enumerate(to_flag_frame.index):
- if method == "modZscore":
- MAD_series = df.subtract(df.median(axis=1), axis=0).abs().median(axis=1)
- diff_scores = (
- (0.6745 * (df.subtract(df.median(axis=1), axis=0)))
- .divide(MAD_series, axis=0)
- .abs()
+ index_slice = slice(
+ index[1] - pd.Timedelta(reduction_range),
+ index[1] + pd.Timedelta(reduction_range),
)
+ test_slice = val_frame[var][index_slice].dropna()
- elif method == "Zscore":
- diff_scores = (
- df.subtract(df.mean(axis=1), axis=0)
- .divide(df.std(axis=1), axis=0)
- .abs()
- )
+ # check, wheather value under test is sufficiently centered:
+ first = test_slice.first_valid_index()
+ last = test_slice.last_valid_index()
+ min_range = pd.Timedelta(reduction_range) / 4
- else:
- raise ValueError(method)
+ if (
+ pd.Timedelta(index[1] - first) < min_range
+ or pd.Timedelta(last - index[1]) < min_range
+ ):
+ polydeg = 0
+ else:
+ polydeg = 2
- else:
+ if reduction_drop_flagged:
+ test_slice = test_slice.drop(to_flag_frame.index, errors="ignore")
- try:
- stat = getattr(df, method.__name__)(axis=1)
- except AttributeError:
- stat = df.aggregate(method, axis=1)
+ if test_slice.shape[0] < reduction_min_periods:
+ to_flag_frame.loc[index[1], var] = True
+ continue
- diff_scores = df.subtract(stat, axis=0).abs()
+ x = test_slice.index.values.astype(float)
+ x_0 = x[0]
+ x = (x - x_0) / 10**12
- mask = diff_scores > thresh
- if mask.empty:
- return data, flags
+ polyfitted = poly.polyfit(y=test_slice.values, x=x, deg=polydeg)
+
+ testval = poly.polyval(
+ (float(index[1].to_numpy()) - x_0) / 10**12, polyfitted
+ )
+ testval = val_frame[var][index[1]] - testval
- for f in fields:
- m = mask[f].reindex(index=flags[f].index, fill_value=False)
- flags[m, f] = flag
+ resids = test_slice.values - poly.polyval(x, polyfitted)
+ med_resids = np.median(resids)
+ MAD = np.median(np.abs(resids - med_resids))
+ crit_val = 0.6745 * (abs(med_resids - testval)) / MAD
+
+ if crit_val > reduction_thresh:
+ to_flag_frame.loc[index[1], var] = True
+
+ if at_least_one:
+ to_flag_frame[~to_flag_frame.any(axis=1)] = True
+
+ for field in to_flag_frame.columns:
+ col = to_flag_frame[field]
+ flags[col[col].index, field] = flag
return data, flags
diff --git a/saqc/funcs/pattern.py b/saqc/funcs/pattern.py
index 96a5b176c..43e4f5f97 100644
--- a/saqc/funcs/pattern.py
+++ b/saqc/funcs/pattern.py
@@ -5,6 +5,9 @@
# SPDX-License-Identifier: GPL-3.0-or-later
# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
import dtw
import pandas as pd
@@ -13,6 +16,9 @@ from saqc.constants import BAD
from saqc.core.register import flagging
from saqc.lib.tools import customRoller
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
def calculateDistanceByDTW(
data: pd.Series, reference: pd.Series, forward=True, normalize=True
@@ -81,112 +87,104 @@ def calculateDistanceByDTW(
return distances.reindex(index=data.index) # reinsert NaNs
-# todo should we mask `reference` even if the func fail if reference has NaNs
-@flagging()
-def flagPatternByDTW(
- data,
- field,
- flags,
- reference,
- max_distance=0.0,
- normalize=True,
- plot=False,
- flag=BAD,
- **kwargs,
-):
- """
- 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
-
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
-
- field : str
- The name of the data column
-
- flags : saqc.Flags
- The flags belonging to `data`.
-
- reference : str
- The name in `data` which holds the pattern. The pattern must not have NaNs,
- have a datetime index and must not be empty.
-
- max_distance : float, default 0.0
- Maximum dtw-distance between chunk and pattern, if the distance is lower than
- ``max_distance`` the data gets flagged. With default, ``0.0``, only exact
- matches are flagged.
-
- normalize : bool, default True
- If `False`, return unmodified distances.
- If `True`, normalize distances by the number of observations of the reference.
- This helps to make it easier to find a good cutoff threshold for further
- processing. The distances then refer to the mean distance per datapoint,
- expressed in the datas units.
-
- plot: bool, default False
- Show a calibration plot, which can be quite helpful to find the right threshold
- for `max_distance`. It works best with `normalize=True`. Do not use in automatic
- setups / pipelines. The plot show three lines:
-
- - data: the data the function was called on
- - distances: the calculated distances by the algorithm
- - indicator: have to distinct levels: `0` and the value of `max_distance`.
- If `max_distance` is `0.0` it defaults to `1`. Everywhere where the
- indicator is not `0` the data will be flagged.
-
- 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 belonging to `data`.
-
- Notes
- -----
- The window size of the moving window is set to equal the temporal extension of the
- reference datas datetime index.
-
- References
- ----------
- Find a nice description of underlying the Dynamic Time Warping Algorithm here:
-
- [1] https://cran.r-project.org/web/packages/dtw/dtw.pdf
- """
- ref = data[reference]
- dat = data[field]
-
- distances = calculateDistanceByDTW(dat, ref, forward=True, normalize=normalize)
- winsz = ref.index.max() - ref.index.min()
-
- # prevent nan propagation
- distances = distances.fillna(max_distance + 1)
-
- # find minima filter by threshold
- fw = customRoller(distances, window=winsz, forward=True, closed="both", expand=True)
- bw = customRoller(distances, window=winsz, closed="both", expand=True)
- minima = (fw.min() == bw.min()) & (distances <= max_distance)
-
- # Propagate True's to size of pattern.
- rolling = customRoller(minima, window=winsz, closed="both", expand=True)
- mask = rolling.sum() > 0
-
- if plot:
- df = pd.DataFrame()
- df["data"] = dat
- df["distances"] = distances
- df["indicator"] = mask.astype(float) * (max_distance or 1)
- df.plot()
-
- flags[mask, field] = flag
- return data, flags
+class PatternMixin:
+
+ # todo should we mask `reference` even if the func fail if reference has NaNs
+ @flagging()
+ def flagPatternByDTW(
+ self: "SaQC",
+ field,
+ reference,
+ max_distance=0.0,
+ normalize=True,
+ plot=False,
+ flag=BAD,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ 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
+
+ Parameters
+ ----------
+ field : str
+ The name of the data column
+
+ reference : str
+ The name in `data` which holds the pattern. The pattern must not have NaNs,
+ have a datetime index and must not be empty.
+
+ max_distance : float, default 0.0
+ Maximum dtw-distance between chunk and pattern, if the distance is lower than
+ ``max_distance`` the data gets flagged. With default, ``0.0``, only exact
+ matches are flagged.
+
+ normalize : bool, default True
+ If `False`, return unmodified distances.
+ If `True`, normalize distances by the number of observations of the reference.
+ This helps to make it easier to find a good cutoff threshold for further
+ processing. The distances then refer to the mean distance per datapoint,
+ expressed in the datas units.
+
+ plot: bool, default False
+ Show a calibration plot, which can be quite helpful to find the right threshold
+ for `max_distance`. It works best with `normalize=True`. Do not use in automatic
+ setups / pipelines. The plot show three lines:
+
+ - data: the data the function was called on
+ - distances: the calculated distances by the algorithm
+ - indicator: have to distinct levels: `0` and the value of `max_distance`.
+ If `max_distance` is `0.0` it defaults to `1`. Everywhere where the
+ indicator is not `0` the data will be flagged.
+
+ Returns
+ -------
+ saqc.SaQC
+
+ Notes
+ -----
+ The window size of the moving window is set to equal the temporal extension of the
+ reference datas datetime index.
+
+ References
+ ----------
+ Find a nice description of underlying the Dynamic Time Warping Algorithm here:
+
+ [1] https://cran.r-project.org/web/packages/dtw/dtw.pdf
+ """
+ ref = self._data[reference]
+ dat = self._data[field]
+
+ distances = calculateDistanceByDTW(dat, ref, forward=True, normalize=normalize)
+ winsz = ref.index.max() - ref.index.min()
+
+ # prevent nan propagation
+ distances = distances.fillna(max_distance + 1)
+
+ # find minima filter by threshold
+ fw = customRoller(
+ distances, window=winsz, forward=True, closed="both", expand=True
+ )
+ bw = customRoller(distances, window=winsz, closed="both", expand=True)
+ minima = (fw.min() == bw.min()) & (distances <= max_distance)
+
+ # Propagate True's to size of pattern.
+ rolling = customRoller(minima, window=winsz, closed="both", expand=True)
+ mask = rolling.sum() > 0
+
+ if plot:
+ df = pd.DataFrame()
+ df["data"] = dat
+ df["distances"] = distances
+ df["indicator"] = mask.astype(float) * (max_distance or 1)
+ df.plot()
+
+ self._flags[mask, field] = flag
+ return self
diff --git a/saqc/funcs/resampling.py b/saqc/funcs/resampling.py
index 9c2b4565f..4f227d897 100644
--- a/saqc/funcs/resampling.py
+++ b/saqc/funcs/resampling.py
@@ -8,20 +8,22 @@
from __future__ import annotations
-from typing import Callable, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Callable, Optional, Union
import numpy as np
import pandas as pd
from typing_extensions import Literal
-import saqc.funcs.tools as tools
-from dios import DictOfSeries, DtItype
-from saqc.core.flags import Flags
+from dios import DtItype
from saqc.core.register import _isflagged, register
-from saqc.funcs.interpolation import _SUPPORTED_METHODS, interpolateIndex
+from saqc.funcs.interpolation import _SUPPORTED_METHODS
from saqc.lib.tools import evalFreqStr, filterKwargs, getFreqDelta
from saqc.lib.ts_operators import aggregate2Freq, shift2Freq
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
+
METHOD2ARGS = {
"inverse_fshift": ("backward", pd.Timedelta),
"inverse_bshift": ("forward", pd.Timedelta),
@@ -33,336 +35,442 @@ METHOD2ARGS = {
}
-@register(mask=["field"], demask=[], squeeze=[])
-def linear(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- freq: str,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- A method to "regularize" data by interpolating linearly the data at regular timestamp.
-
- A series of data is considered "regular", if it is sampled regularly (= having uniform sampling rate).
-
- Interpolated values will get assigned the worst flag within freq-range.
-
- Note, that the data only gets interpolated at those (regular) timestamps, that have a valid (existing and
- not-na) datapoint preceeding them and one succeeding them within freq range.
- Regular timestamp that do not suffice this condition get nan assigned AND The associated flag will be of value
- ``UNFLAGGED``.
-
- 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-regularized.
-
- flags : saqc.Flags
- Container to store flags of the data. freq
-
- freq : str
- An offset string. The frequency of the grid you want to interpolate your data at.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values and shape may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- Flags values and shape may have changed relatively to the flags input.
- """
- reserved = ["method", "order", "limit", "downgrade"]
- kwargs = filterKwargs(kwargs, reserved)
- return interpolateIndex(data, field, flags, freq, "time", **kwargs)
-
-
-@register(mask=["field"], demask=[], squeeze=[])
-def interpolate(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- freq: str,
- method: _SUPPORTED_METHODS,
- order: int = 1,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- A method to "regularize" data by interpolating the data at regular timestamp.
-
- A series of data is considered "regular", if it is sampled regularly (= having uniform sampling rate).
-
- Interpolated values will get assigned the worst flag within freq-range.
-
- There are available all the interpolations from the pandas.Series.interpolate method and they are called by
- the very same keywords.
-
- Note, that, to perform a timestamp aware, linear interpolation, you have to pass ``'time'`` as `method`,
- and NOT ``'linear'``.
-
- Note, that the data only gets interpolated at those (regular) timestamps, that have a valid (existing and
- not-na) datapoint preceeding them and one succeeding them within freq range.
- Regular timestamp that do not suffice this condition get nan assigned AND The associated flag will be of value
- ``UNFLAGGED``.
-
- 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-regularized.
-
- flags : saqc.Flags
- Container to store flags of the data.
-
- freq : str
- An offset string. The frequency of the grid you want to interpolate your data at.
-
- method : {"linear", "time", "nearest", "zero", "slinear", "quadratic", "cubic", "spline", "barycentric",
- "polynomial", "krogh", "piecewise_polynomial", "spline", "pchip", "akima"}
- The interpolation method you want to apply.
-
- order : int, default 1
- If your selected interpolation method can be performed at different *orders* - here you pass the desired
- order.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values and shape may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- Flags values and shape may have changed relatively to the flags input.
- """
- reserved = ["limit", "downgrade"]
- kwargs = filterKwargs(kwargs, reserved)
- return interpolateIndex(
- data, field, flags, freq, method=method, order=order, **kwargs
- )
+class ResamplingMixin:
+ @register(mask=["field"], demask=[], squeeze=[])
+ def linear(
+ self: "SaQC",
+ field: str,
+ freq: str,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ A method to "regularize" data by interpolating linearly the data at regular timestamp.
+
+ A series of data is considered "regular", if it is sampled regularly (= having uniform sampling rate).
+
+ Interpolated values will get assigned the worst flag within freq-range.
+
+ Note, that the data only gets interpolated at those (regular) timestamps, that have a valid (existing and
+ not-na) datapoint preceeding them and one succeeding them within freq range.
+ Regular timestamp that do not suffice this condition get nan assigned AND The associated flag will be of value
+ ``UNFLAGGED``.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-regularized.
+
+ freq : str
+ An offset string. The frequency of the grid you want to interpolate your data at.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ reserved = ["method", "order", "limit", "downgrade"]
+ kwargs = filterKwargs(kwargs, reserved)
+ return self.interpolateIndex(field, freq, "time", **kwargs)
+
+ @register(mask=["field"], demask=[], squeeze=[])
+ def interpolate(
+ self: "SaQC",
+ field: str,
+ freq: str,
+ method: _SUPPORTED_METHODS,
+ order: int = 1,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ A method to "regularize" data by interpolating the data at regular timestamp.
+
+ A series of data is considered "regular", if it is sampled regularly (= having uniform sampling rate).
+
+ Interpolated values will get assigned the worst flag within freq-range.
+
+ There are available all the interpolations from the pandas.Series.interpolate method and they are called by
+ the very same keywords.
+
+ Note, that, to perform a timestamp aware, linear interpolation, you have to pass ``'time'`` as `method`,
+ and NOT ``'linear'``.
+
+ Note, that the data only gets interpolated at those (regular) timestamps, that have a valid (existing and
+ not-na) datapoint preceeding them and one succeeding them within freq range.
+ Regular timestamp that do not suffice this condition get nan assigned AND The associated flag will be of value
+ ``UNFLAGGED``.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-regularized.
+
+ freq : str
+ An offset string. The frequency of the grid you want to interpolate your data at.
+
+ method : {"linear", "time", "nearest", "zero", "slinear", "quadratic", "cubic", "spline", "barycentric",
+ "polynomial", "krogh", "piecewise_polynomial", "spline", "pchip", "akima"}
+ The interpolation method you want to apply.
+
+ order : int, default 1
+ If your selected interpolation method can be performed at different *orders* - here you pass the desired
+ order.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ reserved = ["limit", "downgrade"]
+ kwargs = filterKwargs(kwargs, reserved)
+ return self.interpolateIndex(field, freq, method=method, order=order, **kwargs)
+
+ @register(mask=["field"], demask=[], squeeze=[])
+ def shift(
+ self: "SaQC",
+ field: str,
+ freq: str,
+ method: Literal["fshift", "bshift", "nshift"] = "nshift",
+ freq_check: Optional[Literal["check", "auto"]] = None,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Function to shift data and flags to a regular (equidistant) timestamp grid, according to ``method``.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-shifted.
+
+ freq : str
+ An frequency Offset String that will be interpreted as the sampling rate you want the data to be shifted to.
+
+ method : {'fshift', 'bshift', 'nshift'}, default 'nshift'
+ Specifies how misaligned data-points get propagated to a grid timestamp.
+ Following choices are available:
+
+ * 'nshift' : every grid point gets assigned the nearest value in its range. (range = +/- 0.5 * `freq`)
+ * 'bshift' : every grid point gets assigned its first succeeding value, if one is available in
+ the succeeding sampling interval.
+ * 'fshift' : every grid point gets assigned its ultimately preceding value, if one is available in
+ the preceeding sampling interval.
+
+ freq_check : {None, 'check', 'auto'}, default None
+
+ * ``None`` : do not validate frequency-string passed to `freq`
+ * 'check' : estimate frequency and log a warning if estimate miss matches frequency string passed to `freq`,
+ or if no uniform sampling rate could be estimated
+ * 'auto' : estimate frequency and use estimate. (Ignores `freq` parameter.)
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ datcol = self._data[field]
+ if datcol.empty:
+ return self
+
+ freq = evalFreqStr(freq, freq_check, datcol.index)
+
+ # do the shift
+ datcol = shift2Freq(datcol, method, freq, fill_value=np.nan)
+
+ # do the shift on the history
+ kws = dict(method=method, freq=freq)
+
+ history = self._flags.history[field].apply(
+ index=datcol.index,
+ func_handle_df=True,
+ func=shift2Freq,
+ func_kws={**kws, "fill_value": np.nan},
+ )
+ self._flags.history[field] = history
+ self._data[field] = datcol
+ return self
+
+ @register(mask=["field"], demask=[], squeeze=[])
+ def resample(
+ self: "SaQC",
+ field: str,
+ freq: str,
+ func: Callable[[pd.Series], pd.Series] = np.mean,
+ method: Literal["fagg", "bagg", "nagg"] = "bagg",
+ maxna: Optional[int] = None,
+ maxna_group: Optional[int] = None,
+ maxna_flags: Optional[int] = None, # TODO: still a case ??
+ maxna_group_flags: Optional[int] = None,
+ flag_func: Callable[[pd.Series], float] = max,
+ freq_check: Optional[Literal["check", "auto"]] = None,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Function to resample the data.
+
+ The data will be sampled at regular (equidistant) timestamps aka. Grid points.
+ Sampling intervals therefore get aggregated with a function, specified by
+ '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 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 func and
+ the result gets assigned to the next grid point.
+
+
+ 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
+ to it. Furthermore, you shouldnt pass numpys nan-functions (``nansum``,
+ ``nanmean``,...) because those for example, have ``__name__ == 'nansum'`` and
+ they will thus not trigger ``resample.func()``, but the slower ``resample.apply(
+ nanfunc)``. Also, internally, no nans get passed to the functions anyway,
+ so that there is no point in passing the nan functions.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-resampled.
+
+ freq : str
+ An Offset String, that will be interpreted as the frequency you want to
+ resample your data with.
+
+ func : Callable
+ The function you want to use for aggregation.
+
+ method: {'fagg', 'bagg', 'nagg'}, default 'bagg'
+ Specifies which intervals to be aggregated for a certain timestamp. (preceding,
+ succeeding or "surrounding" interval). See description above for more details.
+
+ maxna : {None, int}, default None
+ Maximum number NaNs in a resampling interval. If maxna is exceeded, the interval
+ is set entirely to NaN.
+
+ maxna_group : {None, int}, default None
+ Same as `maxna` but for consecutive NaNs.
+
+ maxna_flags : {None, int}, default None
+ Same as `max_invalid`, only applying for the flags. The flag regarded
+ as "invalid" value, is the one passed to empty_intervals_flag (
+ default=``BAD``). Also this is the flag assigned to invalid/empty intervals.
+
+ maxna_group_flags : {None, int}, default None
+ Same as `maxna_flags`, only applying onto flags. The flag regarded as
+ "invalid" value, is the one passed to empty_intervals_flag. Also this is the
+ flag assigned to invalid/empty intervals.
+
+ flag_func : Callable, default: max
+ The function you want to aggregate the flags with. It should be capable of
+ operating on the flags dtype (usually ordered categorical).
+
+ freq_check : {None, 'check', 'auto'}, default None
+
+ * ``None``: do not validate frequency-string passed to `freq`
+ * ``'check'``: estimate frequency and log a warning if estimate miss matchs
+ frequency string passed to 'freq', or if no uniform sampling rate could be
+ estimated
+ * ``'auto'``: estimate frequency and use estimate. (Ignores `freq` parameter.)
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+
+ datcol = self._data[field]
+
+ # workaround for #GL-333
+ if datcol.empty and self._data.itype in [None, DtItype]:
+ datcol = pd.Series(index=pd.DatetimeIndex([]), dtype=datcol.dtype)
+
+ freq = evalFreqStr(freq, freq_check, datcol.index)
+
+ datcol = aggregate2Freq(
+ datcol,
+ method,
+ freq,
+ func,
+ fill_value=np.nan,
+ max_invalid_total=maxna,
+ max_invalid_consec=maxna_group,
+ )
-@register(mask=["field"], demask=[], squeeze=[])
-def shift(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- freq: str,
- method: Literal["fshift", "bshift", "nshift"] = "nshift",
- freq_check: Optional[Literal["check", "auto"]] = None,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Function to shift data and flags to a regular (equidistant) timestamp grid, according to ``method``.
-
- 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-shifted.
-
- flags : saqc.Flags
- Container to store flags of the data.
-
- freq : str
- An frequency Offset String that will be interpreted as the sampling rate you want the data to be shifted to.
-
- method : {'fshift', 'bshift', 'nshift'}, default 'nshift'
- Specifies how misaligned data-points get propagated to a grid timestamp.
- Following choices are available:
-
- * 'nshift' : every grid point gets assigned the nearest value in its range. (range = +/- 0.5 * `freq`)
- * 'bshift' : every grid point gets assigned its first succeeding value, if one is available in
- the succeeding sampling interval.
- * 'fshift' : every grid point gets assigned its ultimately preceding value, if one is available in
- the preceeding sampling interval.
-
- freq_check : {None, 'check', 'auto'}, default None
-
- * ``None`` : do not validate frequency-string passed to `freq`
- * 'check' : estimate frequency and log a warning if estimate miss matches frequency string passed to `freq`,
- or if no uniform sampling rate could be estimated
- * 'auto' : estimate frequency and use estimate. (Ignores `freq` parameter.)
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values and shape may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- Flags values and shape may have changed relatively to the flags input.
- """
- datcol = data[field]
- if datcol.empty:
- return data, flags
-
- freq = evalFreqStr(freq, freq_check, datcol.index)
-
- # do the shift
- datcol = shift2Freq(datcol, method, freq, fill_value=np.nan)
-
- # do the shift on the history
- kws = dict(method=method, freq=freq)
-
- history = flags.history[field].apply(
- index=datcol.index,
- func_handle_df=True,
- func=shift2Freq,
- func_kws={**kws, "fill_value": np.nan},
- )
+ kws = dict(
+ method=method,
+ freq=freq,
+ agg_func=flag_func,
+ fill_value=np.nan,
+ max_invalid_total=maxna_flags,
+ max_invalid_consec=maxna_group_flags,
+ )
- flags.history[field] = history
- data[field] = datcol
- return data, flags
+ history = self._flags.history[field].apply(
+ index=datcol.index,
+ func=aggregate2Freq,
+ func_kws=kws,
+ )
+ self._data[field] = datcol
+ self._flags.history[field] = history
+ return self
-@register(mask=["field"], demask=[], squeeze=[])
-def resample(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- freq: str,
- func: Callable[[pd.Series], pd.Series] = np.mean,
- method: Literal["fagg", "bagg", "nagg"] = "bagg",
- maxna: Optional[int] = None,
- maxna_group: Optional[int] = None,
- maxna_flags: Optional[int] = None, # TODO: still a case ??
- maxna_group_flags: Optional[int] = None,
- flag_func: Callable[[pd.Series], float] = max,
- freq_check: Optional[Literal["check", "auto"]] = None,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Function to resample the data.
-
- The data will be sampled at regular (equidistant) timestamps aka. Grid points.
- Sampling intervals therefore get aggregated with a function, specified by
- '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 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 func and
- the result gets assigned to the next grid point.
-
-
- 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
- to it. Furthermore, you shouldnt pass numpys nan-functions (``nansum``,
- ``nanmean``,...) because those for example, have ``__name__ == 'nansum'`` and
- they will thus not trigger ``resample.func()``, but the slower ``resample.apply(
- nanfunc)``. Also, internally, no nans get passed to the functions anyway,
- so that there is no point in passing the nan functions.
-
- 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-resampled.
-
- flags : saqc.Flags
- Container to store flags of the data.
-
- freq : str
- An Offset String, that will be interpreted as the frequency you want to
- resample your data with.
-
- func : Callable
- The function you want to use for aggregation.
-
- method: {'fagg', 'bagg', 'nagg'}, default 'bagg'
- Specifies which intervals to be aggregated for a certain timestamp. (preceding,
- succeeding or "surrounding" interval). See description above for more details.
-
- maxna : {None, int}, default None
- Maximum number NaNs in a resampling interval. If maxna is exceeded, the interval
- is set entirely to NaN.
-
- maxna_group : {None, int}, default None
- Same as `maxna` but for consecutive NaNs.
-
- maxna_flags : {None, int}, default None
- Same as `max_invalid`, only applying for the flags. The flag regarded
- as "invalid" value, is the one passed to empty_intervals_flag (
- default=``BAD``). Also this is the flag assigned to invalid/empty intervals.
-
- maxna_group_flags : {None, int}, default None
- Same as `maxna_flags`, only applying onto flags. The flag regarded as
- "invalid" value, is the one passed to empty_intervals_flag. Also this is the
- flag assigned to invalid/empty intervals.
-
- flag_func : Callable, default: max
- The function you want to aggregate the flags with. It should be capable of
- operating on the flags dtype (usually ordered categorical).
-
- freq_check : {None, 'check', 'auto'}, default None
-
- * ``None``: do not validate frequency-string passed to `freq`
- * ``'check'``: estimate frequency and log a warning if estimate miss matchs
- frequency string passed to 'freq', or if no uniform sampling rate could be
- estimated
- * ``'auto'``: estimate frequency and use estimate. (Ignores `freq` parameter.)
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values and shape may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- Flags values and shape may have changed relatively to the flags input.
- """
-
- 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(
- datcol,
- method,
- freq,
- func,
- fill_value=np.nan,
- max_invalid_total=maxna,
- max_invalid_consec=maxna_group,
+ @register(
+ mask=[],
+ demask=[],
+ squeeze=[],
+ handles_target=True, # target is mandatory in func, so its allowed
)
+ def concatFlags(
+ self: "SaQC",
+ field: str,
+ target: str,
+ method: Literal[
+ "inverse_fagg",
+ "inverse_bagg",
+ "inverse_nagg",
+ "inverse_fshift",
+ "inverse_bshift",
+ "inverse_nshift",
+ "inverse_interpolation",
+ "match",
+ ] = "match",
+ freq: Optional[str] = None,
+ drop: Optional[bool] = False,
+ squeeze: Optional[bool] = False,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ The Function appends flags history of ``fields`` to flags history of ``target``.
+ Before appending, columns in ``field`` history are projected onto the target index via ``method``
+
+ method: (field_flag 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)
+
+ * 'inverse_bagg' - all target_flags succeeding a field_flag within the range of "freq", get assigned this field flags
+ value. (if field_flag > target_flag)
+
+ * 'inverse_fagg' - all target_flags preceeding a field_flag within the range of "freq", get assigned this field flags
+ value. (if field_flag > target_flag)
+
+ * 'inverse_interpolation' - all 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)
+
+ * '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`` keyword.
+
+ Parameters
+ ----------
+ field : str
+ Fieldname of flags history to append.
+
+ target : str
+ Field name of flags history to append to.
+
+ 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 : 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 : 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
+ -------
+ saqc.SaQC
+ """
+ flagscol = self._flags[field]
+ target_datcol = self._data[target]
+ target_flagscol = self._flags[target]
+
+ if target_datcol.empty or flagscol.empty:
+ return self
+
+ dummy = pd.Series(np.nan, target_flagscol.index, dtype=float)
+
+ if freq is None:
+ freq = getFreqDelta(flagscol.index)
+ if freq is None and not method == "match":
+ raise ValueError(
+ 'To project irregularly sampled data, either use method="match", or '
+ "pass custom projection range to freq parameter."
+ )
+
+ if method[-13:] == "interpolation":
+ ignore = _getChunkBounds(target_datcol, flagscol, freq)
+ func = _inverseInterpolation
+ func_kws = dict(freq=freq, chunk_bounds=ignore, target=dummy)
+
+ elif method[-3:] == "agg":
+ projection_method = METHOD2ARGS[method][0]
+ tolerance = METHOD2ARGS[method][1](freq)
+ func = _inverseAggregation
+ func_kws = dict(freq=tolerance, method=projection_method, target=dummy)
+
+ elif method[-5:] == "shift":
+ drop_mask = target_datcol.isna() | _isflagged(
+ target_flagscol, kwargs["dfilter"]
+ )
+ projection_method = METHOD2ARGS[method][0]
+ tolerance = METHOD2ARGS[method][1](freq)
+ func = _inverseShift
+ kws = dict(
+ freq=tolerance,
+ method=projection_method,
+ drop_mask=drop_mask,
+ target=dummy,
+ )
+ func_kws = {**kws, "fill_value": np.nan}
- kws = dict(
- method=method,
- freq=freq,
- agg_func=flag_func,
- fill_value=np.nan,
- max_invalid_total=maxna_flags,
- max_invalid_consec=maxna_group_flags,
- )
+ elif method == "match":
+ func = lambda x: x
+ func_kws = {}
- history = flags.history[field].apply(
- index=datcol.index,
- func=aggregate2Freq,
- func_kws=kws,
- )
+ else:
+ raise ValueError(f"unknown method {method}")
+
+ history = self._flags.history[field].apply(dummy.index, func, func_kws)
+ 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,
+ },
+ }
+ self._flags.history[target].append(history, meta)
+ else:
+ self._flags.history[target].append(history)
- data[field] = datcol
- flags.history[field] = history
- return data, flags
+ if drop:
+ return self.dropField(field=field)
+
+ return self
def _getChunkBounds(target: pd.Series, flagscol: pd.Series, freq: str):
@@ -421,170 +529,3 @@ def _inverseShift(
source.loc[target_drops.index] = target_drops.values
return source.fillna(fill_value).astype(dtype, copy=False)
-
-
-@register(
- mask=[],
- demask=[],
- squeeze=[],
- handles_target=True, # target is mandatory in func, so its allowed
-)
-def concatFlags(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- target: str,
- method: Literal[
- "inverse_fagg",
- "inverse_bagg",
- "inverse_nagg",
- "inverse_fshift",
- "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``
-
- 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)
-
- * 'inverse_bagg' - all target_flags succeeding a field_flag within the range of "freq", get assigned this field flags
- value. (if field_flag > target_flag)
-
- * 'inverse_fagg' - all target_flags preceeding a field_flag within the range of "freq", get assigned this field flags
- value. (if field_flag > target_flag)
-
- * 'inverse_interpolation' - all 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)
-
- * '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`` keyword.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
-
- field : str
- Fieldname of flags history to append.
-
- flags : saqc.Flags
- Container to store flags of the data.
-
- target : str
- Field name of flags history to append to.
-
- 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 : 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 : 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
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- Flags values and shape may have changed relatively to the flags input.
- """
- flagscol = flags[field]
- target_datcol = data[target]
- target_flagscol = flags[target]
-
- if target_datcol.empty or flagscol.empty:
- return data, flags
-
- dummy = pd.Series(np.nan, target_flagscol.index, dtype=float)
-
- if freq is None:
- freq = getFreqDelta(flagscol.index)
- if freq is None and not method == "match":
- raise ValueError(
- 'To project irregularly sampled data, either use method="match", or '
- "pass custom projection range to freq parameter."
- )
-
- if method[-13:] == "interpolation":
- ignore = _getChunkBounds(target_datcol, flagscol, freq)
- func = _inverseInterpolation
- func_kws = dict(freq=freq, chunk_bounds=ignore, target=dummy)
-
- elif method[-3:] == "agg":
- projection_method = METHOD2ARGS[method][0]
- tolerance = METHOD2ARGS[method][1](freq)
- func = _inverseAggregation
- func_kws = dict(freq=tolerance, method=projection_method, target=dummy)
-
- elif method[-5:] == "shift":
- drop_mask = target_datcol.isna() | _isflagged(
- target_flagscol, kwargs["dfilter"]
- )
- projection_method = METHOD2ARGS[method][0]
- tolerance = METHOD2ARGS[method][1](freq)
- func = _inverseShift
- kws = dict(
- freq=tolerance, method=projection_method, drop_mask=drop_mask, target=dummy
- )
- func_kws = {**kws, "fill_value": np.nan}
-
- elif method == "match":
- func = lambda x: x
- func_kws = {}
-
- else:
- raise ValueError(f"unknown method {method}")
-
- history = flags.history[field].apply(dummy.index, func, func_kws)
- 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)
-
- return data, flags
diff --git a/saqc/funcs/residuals.py b/saqc/funcs/residuals.py
index dc8f05e36..dec6681e2 100644
--- a/saqc/funcs/residuals.py
+++ b/saqc/funcs/residuals.py
@@ -7,154 +7,145 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Callable, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Callable, Optional, Union
import numpy as np
import pandas as pd
-from dios import DictOfSeries
-from saqc.core.flags import Flags
from saqc.core.register import register
from saqc.funcs.curvefit import _fitPolynomial
from saqc.funcs.rolling import _roll
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
-@register(mask=["field"], demask=[], squeeze=[])
-def calculatePolynomialResiduals(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- window: Union[str, int],
- order: int,
- min_periods: Optional[int] = 0,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Fits a polynomial model to the data and calculate the residuals.
-
- 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 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 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.
-
- Each residual gets assigned the worst flag present in the interval of
- the original data.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data.
-
- field : str
- The column, holding the data-to-be-modelled.
-
- flags : saqc.Flags
- Container to store quality flags to data.
-
- window : {str, int}
- The size of the window you want to use for fitting. If an integer is passed,
- the size refers to the number of periods for every fitting window. If an
- offset string is passed, the size refers to the total temporal extension. The
- window will be centered around the vaule-to-be-fitted. For regularly sampled
- timeseries the period number will be casted down to an odd number if even.
-
- order : int
- The degree of the polynomial used for fitting
-
- min_periods : int or None, default 0
- The minimum number of periods, that has to be available in every values
- fitting surrounding for the polynomial fit to be performed. If there are not
- enough values, np.nan gets assigned. Default (0) results in fitting
- regardless of the number of values present (results in overfitting for too
- sparse intervals). To automatically set the minimum number of periods to the
- number of values in an offset defined window size, pass np.nan.
-
- Returns
- -------
- data : dios.DictOfSeries
- flags : saqc.Flags
- """
- orig = data[field]
- data, flags = _fitPolynomial(
- data=data,
- field=field,
- flags=flags,
- window=window,
- order=order,
- min_periods=min_periods,
+
+class ResidualsMixin:
+ @register(mask=["field"], demask=[], squeeze=[])
+ def calculatePolynomialResiduals(
+ self: "SaQC",
+ field: str,
+ window: str | int,
+ order: int,
+ min_periods: int = 0,
**kwargs,
- )
- data[field] = orig - data[field]
- return data, flags
-
-
-@register(mask=["field"], demask=[], squeeze=[])
-def calculateRollingResiduals(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- window: Union[str, int],
- func: Callable[[pd.Series], np.ndarray] = np.mean,
- min_periods: Optional[int] = 0,
- center: bool = True,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Calculate the diff of a rolling-window function and the data.
-
- Note, that the data gets assigned the worst flag present in the original data.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data.
- field : str
- The column to calculate on.
- flags : saqc.Flags
- Container to store quality flags to data.
- window : {int, str}
- The size of the window you want to roll with. If an integer is passed, the size
- refers to the number of periods for every fitting window. If an offset string
- is passed, the size refers to the total temporal extension. For regularly
- sampled timeseries, the period number will be casted down to an odd number if
- ``center=True``.
- func : Callable, default np.mean
- Function to roll with.
- min_periods : int, default 0
- The minimum number of periods to get a valid value
- center : bool, default True
- If True, center the rolling window.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- """
- orig = data[field].copy()
- data, flags = _roll(
- data=data,
- field=field,
- flags=flags,
- window=window,
- func=func,
- min_periods=min_periods,
- center=center,
+ ) -> "SaQC":
+ """
+ Fits a polynomial model to the data and calculate the residuals.
+
+ 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 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 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.
+
+ Each residual gets assigned the worst flag present in the interval of
+ the original data.
+
+ Parameters
+ ----------
+ field : str
+ The column, holding the data-to-be-modelled.
+
+ window : {str, int}
+ The size of the window you want to use for fitting. If an integer is passed,
+ the size refers to the number of periods for every fitting window. If an
+ offset string is passed, the size refers to the total temporal extension. The
+ window will be centered around the vaule-to-be-fitted. For regularly sampled
+ timeseries the period number will be casted down to an odd number if even.
+
+ order : int
+ The degree of the polynomial used for fitting
+
+ min_periods : int or None, default 0
+ The minimum number of periods, that has to be available in every values
+ fitting surrounding for the polynomial fit to be performed. If there are not
+ enough values, np.nan gets assigned. Default (0) results in fitting
+ regardless of the number of values present (results in overfitting for too
+ sparse intervals). To automatically set the minimum number of periods to the
+ number of values in an offset defined window size, pass np.nan.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ orig = self._data[field]
+ data, _ = _fitPolynomial(
+ data=self._data,
+ field=field,
+ flags=self._flags,
+ window=window,
+ order=order,
+ min_periods=min_periods,
+ **kwargs,
+ )
+ self._data[field] = orig - data[field]
+ return self
+
+ @register(mask=["field"], demask=[], squeeze=[])
+ def calculateRollingResiduals(
+ self: "SaQC",
+ field: str,
+ window: str | int,
+ func: Callable[[pd.Series], np.ndarray] = np.mean,
+ min_periods: int = 0,
+ center: bool = True,
**kwargs,
- )
-
- # calculate residual
- data[field] = orig - data[field]
- return data, flags
+ ) -> "SaQC":
+ """
+ Calculate the diff of a rolling-window function and the data.
+
+ Note, that the data gets assigned the worst flag present in the original data.
+
+ Parameters
+ ----------
+ field : str
+ The column to calculate on.
+
+ flags : saqc.Flags
+ Container to store quality flags to data.
+
+ window : {int, str}
+ The size of the window you want to roll with. If an integer is passed, the size
+ refers to the number of periods for every fitting window. If an offset string
+ is passed, the size refers to the total temporal extension. For regularly
+ sampled timeseries, the period number will be casted down to an odd number if
+ ``center=True``.
+
+ func : Callable, default np.mean
+ Function to roll with.
+
+ min_periods : int, default 0
+ The minimum number of periods to get a valid value
+
+ center : bool, default True
+ If True, center the rolling window.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ orig = self._data[field].copy()
+ data, _ = _roll(
+ data=self._data,
+ field=field,
+ flags=self._flags,
+ window=window,
+ func=func,
+ min_periods=min_periods,
+ center=center,
+ **kwargs,
+ )
+
+ # calculate residual
+ self._data[field] = orig - data[field]
+ return self
diff --git a/saqc/funcs/rolling.py b/saqc/funcs/rolling.py
index fa5cabecd..80699200d 100644
--- a/saqc/funcs/rolling.py
+++ b/saqc/funcs/rolling.py
@@ -6,7 +6,7 @@
# -*- coding: utf-8 -*-
-from typing import Callable, Tuple, Union
+from typing import TYPE_CHECKING, Callable, Union
import numpy as np
import pandas as pd
@@ -16,62 +16,65 @@ from saqc.core.flags import Flags
from saqc.core.register import register
from saqc.lib.tools import getFreqDelta
-
-@register(mask=["field"], demask=[], squeeze=[])
-def roll(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- window: Union[str, int],
- func: Callable[[pd.Series], np.ndarray] = np.mean,
- min_periods: int = 0,
- center: bool = True,
- **kwargs
-) -> Tuple[DictOfSeries, Flags]:
- """
- Calculate a rolling-window function on the data.
-
- Note, that the data gets assigned the worst flag present in the original data.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- The data.
- field : str
- The column to calculate on.
- flags : saqc.Flags
- Container to store quality flags to data.
- window : {int, str}
- The size of the window you want to roll with. If an integer is passed, the size
- refers to the number of periods for every fitting window. If an offset string
- is passed, the size refers to the total temporal extension. For regularly
- sampled timeseries, the period number will be casted down to an odd number if
- ``center=True``.
- func : Callable, default np.mean
- Function to roll with.
- min_periods : int, default 0
- The minimum number of periods to get a valid value
- center : bool, default True
- If True, center the rolling window.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- """
- return _roll(
- data=data,
- field=field,
- flags=flags,
- window=window,
- func=func,
- min_periods=min_periods,
- center=center,
- **kwargs,
- )
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
+
+class RollingMixin:
+ @register(mask=["field"], demask=[], squeeze=[])
+ def roll(
+ self: "SaQC",
+ field: str,
+ window: Union[str, int],
+ func: Callable[[pd.Series], np.ndarray] = np.mean,
+ min_periods: int = 0,
+ center: bool = True,
+ **kwargs
+ ) -> "SaQC":
+ """
+ Calculate a rolling-window function on the data.
+
+ Note, that the data gets assigned the worst flag present in the original data.
+
+ Parameters
+ ----------
+ field : str
+ The column to calculate on.
+
+ flags : saqc.Flags
+ Container to store quality flags to data.
+
+ window : {int, str}
+ The size of the window you want to roll with. If an integer is passed, the size
+ refers to the number of periods for every fitting window. If an offset string
+ is passed, the size refers to the total temporal extension. For regularly
+ sampled timeseries, the period number will be casted down to an odd number if
+ ``center=True``.
+
+ func : Callable, default np.mean
+ Function to roll with.
+
+ min_periods : int, default 0
+ The minimum number of periods to get a valid value
+
+ center : bool, default True
+ If True, center the rolling window.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ self._data, self._flags = _roll(
+ data=self._data,
+ field=field,
+ flags=self._flags,
+ window=window,
+ func=func,
+ min_periods=min_periods,
+ center=center,
+ **kwargs,
+ )
+ return self
def _roll(
diff --git a/saqc/funcs/scores.py b/saqc/funcs/scores.py
index 3af6854b7..8e0be0b36 100644
--- a/saqc/funcs/scores.py
+++ b/saqc/funcs/scores.py
@@ -7,158 +7,293 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Callable, Sequence, Tuple, Union
+from typing import TYPE_CHECKING, Callable, Optional, Sequence, Tuple
import numpy as np
import pandas as pd
from typing_extensions import Literal
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(
- mask=["field"],
- demask=[],
- squeeze=["target"],
- multivariate=True,
- handles_target=True,
-)
-def assignKNNScore(
- data: DictOfSeries,
- field: Sequence[str],
- flags: Flags,
- target: str,
- n: int = 10,
- func: Callable[[pd.Series], float] = np.sum,
- freq: Union[float, str] = np.inf,
- min_periods: int = 2,
- method: Literal["ball_tree", "kd_tree", "brute", "auto"] = "ball_tree",
- metric: str = "minkowski",
- p: int = 2,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
+from saqc.lib.tools import getApply, toSequence
+
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
+
+def _univarScoring(
+ data: pd.Series,
+ window: Optional[str, int] = None,
+ norm_func: Callable = np.nanstd,
+ model_func: Callable = np.nanmean,
+ center: bool = True,
+ min_periods: Optional[int] = None,
+) -> Tuple[pd.Series, pd.Series, pd.Series]:
"""
- TODO: docstring need a rework
- Score datapoints by an aggregation of the dictances to their k nearest neighbors.
-
- The function is a wrapper around the NearestNeighbors method from pythons sklearn library (See reference [1]).
-
- The steps taken to calculate the scores are as follows:
-
- 1. All the timeseries, given through ``field``, are combined to one feature space by an *inner* join on their
- date time indexes. thus, only samples, that share timestamps across all ``field`` will be included in the
- feature space.
- 2. Any datapoint/sample, where one ore more of the features is invalid (=np.nan) will get excluded.
- 3. For every data point, the distance to its `n` nearest neighbors is calculated by applying the
- metric `metric` at grade `p` onto the feature space. The defaults lead to the euclidian to be applied.
- If `radius` is not None, it sets the upper bound of distance for a neighbor to be considered one of the
- `n` nearest neighbors. Furthermore, the `freq` argument determines wich samples can be
- included into a datapoints nearest neighbors list, by segmenting the data into chunks of specified temporal
- extension and feeding that chunks to the kNN algorithm seperatly.
- 4. For every datapoint, the calculated nearest neighbors distances get aggregated to a score, by the function
- passed to `func`. The default, ``sum`` obviously just sums up the distances.
- 5. The resulting timeseries of scores gets assigned to the field target.
+ Calculate (rolling) normalisation scores.
Parameters
----------
- data : dios.DictOfSeries
+ data
A dictionary of pandas.Series, holding all the data.
- field : list of str
- input variable names.
- flags : saqc.flags
- A flags object, holding flags and additional informations related to `data`.
- target : str, default "kNNscores"
- A new Column name, where the result is stored.
- n : int, default 10
- The number of nearest neighbors to which the distance is comprised in every datapoints scoring calculation.
- func : Callable[numpy.array, float], default np.sum
- A function that assigns a score to every one dimensional array, containing the distances
- to every datapoints `n` nearest neighbors.
- freq : {np.inf, float, str}, default np.inf
- Determines the segmentation of the data into partitions, the kNN algorithm is
- applied onto individually.
-
- * ``np.inf``: Apply Scoring on whole data set at once
- * ``x`` > 0 : Apply scoring on successive data chunks of periods length ``x``
- * Offset String : Apply scoring on successive partitions of temporal extension matching the passed offset
- string
-
- min_periods : int, default 2
- The minimum number of periods that have to be present in a partition for the kNN scoring
- to be applied. If the number of periods present is below `min_periods`, the score for the
- datapoints in that partition will be np.nan.
- method : {'ball_tree', 'kd_tree', 'brute', 'auto'}, default 'ball_tree'
- The search algorithm to find each datapoints k nearest neighbors.
- The keyword just gets passed on to the underlying sklearn method.
- See reference [1] for more information on the algorithm.
- metric : str, default 'minkowski'
- The metric the distances to any datapoints neighbors is computed with. The default of `metric`
- together with the default of `p` result in the euclidian to be applied.
- The keyword just gets passed on to the underlying sklearn method.
- See reference [1] for more information on the algorithm.
- p : int, default 2
- The grade of the metrice specified by parameter `metric`.
- The keyword just gets passed on to the underlying sklearn method.
- See reference [1] for more information on the algorithm.
-
- References
- ----------
- [1] https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html
+ window : {str, int}, default None
+ Size of the window. Either determined via an Offset String, denoting the windows temporal extension or
+ by an integer, denoting the windows number of periods.
+ `NaN` measurements also count as periods.
+ If `None` is passed, All data points share the same scoring window, which than equals the whole
+ data.
+ model_func
+ Function to calculate the center moment in every window.
+ norm_func
+ Function to calculate the scaling for every window
+ center
+ Weather or not to center the target value in the scoring window. If `False`, the
+ target value is the last value in the window.
+ min_periods
+ Minimum number of valid meassurements in a scoring window, to consider the resulting score valid.
"""
- if isinstance(target, list):
- if (len(target) > 1) or (target[0] in data.columns):
- raise ValueError(
- f"'target' must not exist and be of length 1. {target} was passed instead."
+ if data.empty:
+ return data, data, data
+ if min_periods is None:
+ min_periods = 0
+
+ if window is None:
+ if data.notna().sum() >= min_periods:
+ # in case of stationary analysis, broadcast statistics to series for compatibility reasons
+ norm = pd.Series(norm_func(data.values), index=data.index)
+ model = pd.Series(model_func(data.values), index=data.index)
+ else:
+ norm = pd.Series(np.nan, index=data.index)
+ model = pd.Series(np.nan, index=data.index)
+ else:
+ # wrap passed func with rolling built in if possible and rolling.apply else
+ roller = data.rolling(window=window, min_periods=min_periods, center=center)
+ norm = getApply(roller, norm_func)
+ model = getApply(roller, model_func)
+
+ score = (data - model) / norm
+ return score, model, norm
+
+
+class ScoresMixin:
+ @register(
+ mask=["field"],
+ demask=[],
+ squeeze=["target"],
+ multivariate=True,
+ handles_target=True,
+ )
+ def assignKNNScore(
+ self: "SaQC",
+ field: Sequence[str],
+ target: str,
+ n: int = 10,
+ func: Callable[[pd.Series], float] = np.sum,
+ freq: float | str | None = np.inf,
+ min_periods: int = 2,
+ method: Literal["ball_tree", "kd_tree", "brute", "auto"] = "ball_tree",
+ metric: str = "minkowski",
+ p: int = 2,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ TODO: docstring need a rework
+ Score datapoints by an aggregation of the dictances to their k nearest neighbors.
+
+ The function is a wrapper around the NearestNeighbors method from pythons sklearn library (See reference [1]).
+
+ The steps taken to calculate the scores are as follows:
+
+ 1. All the timeseries, given through ``field``, are combined to one feature space by an *inner* join on their
+ date time indexes. thus, only samples, that share timestamps across all ``field`` will be included in the
+ feature space.
+ 2. Any datapoint/sample, where one ore more of the features is invalid (=np.nan) will get excluded.
+ 3. For every data point, the distance to its `n` nearest neighbors is calculated by applying the
+ metric `metric` at grade `p` onto the feature space. The defaults lead to the euclidian to be applied.
+ If `radius` is not None, it sets the upper bound of distance for a neighbor to be considered one of the
+ `n` nearest neighbors. Furthermore, the `freq` argument determines wich samples can be
+ included into a datapoints nearest neighbors list, by segmenting the data into chunks of specified temporal
+ extension and feeding that chunks to the kNN algorithm seperatly.
+ 4. For every datapoint, the calculated nearest neighbors distances get aggregated to a score, by the function
+ passed to `func`. The default, ``sum`` obviously just sums up the distances.
+ 5. The resulting timeseries of scores gets assigned to the field target.
+
+ Parameters
+ ----------
+ field : list of str
+ input variable names.
+
+ target : str, default "kNNscores"
+ A new Column name, where the result is stored.
+
+ n : int, default 10
+ The number of nearest neighbors to which the distance is comprised in every datapoints scoring calculation.
+
+ func : Callable[numpy.array, float], default np.sum
+ A function that assigns a score to every one dimensional array, containing the distances
+ to every datapoints `n` nearest neighbors.
+
+ freq : {float, str, None}, default np.inf
+ Determines the segmentation of the data into partitions, the kNN algorithm is
+ applied onto individually.
+
+ * ``np.inf``: Apply Scoring on whole data set at once
+ * ``x`` > 0 : Apply scoring on successive data chunks of periods length ``x``
+ * Offset String : Apply scoring on successive partitions of temporal extension matching the passed offset
+ string
+
+ min_periods : int, default 2
+ The minimum number of periods that have to be present in a partition for the kNN scoring
+ to be applied. If the number of periods present is below `min_periods`, the score for the
+ datapoints in that partition will be np.nan.
+
+ method : {'ball_tree', 'kd_tree', 'brute', 'auto'}, default 'ball_tree'
+ The search algorithm to find each datapoints k nearest neighbors.
+ The keyword just gets passed on to the underlying sklearn method.
+ See reference [1] for more information on the algorithm.
+
+ metric : str, default 'minkowski'
+ The metric the distances to any datapoints neighbors is computed with. The default of `metric`
+ together with the default of `p` result in the euclidian to be applied.
+ The keyword just gets passed on to the underlying sklearn method.
+ See reference [1] for more information on the algorithm.
+
+ p : int, default 2
+ The grade of the metrice specified by parameter `metric`.
+ The keyword just gets passed on to the underlying sklearn method.
+ See reference [1] for more information on the algorithm.
+
+ Returns
+ -------
+ saqc.SaQC
+
+ References
+ ----------
+ [1] https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html
+ """
+ if isinstance(target, list):
+ if (len(target) > 1) or (target[0] in self._data.columns):
+ raise ValueError(
+ f"'target' must not exist and be of length 1. {target} was passed instead."
+ )
+ target = target[0]
+
+ fields = toSequence(field)
+ val_frame = self._data[fields].copy()
+ score_index = val_frame.index_of("shared")
+ score_ser = pd.Series(np.nan, index=score_index, name=target)
+
+ val_frame = val_frame.loc[val_frame.index_of("shared")].to_df()
+ val_frame.dropna(inplace=True)
+
+ if val_frame.empty:
+ return self
+
+ # partitioning
+ if not freq:
+ freq = val_frame.shape[0]
+
+ if isinstance(freq, str):
+ grouper = pd.Grouper(freq=freq)
+ else:
+ grouper = pd.Series(
+ data=np.arange(0, val_frame.shape[0]), index=val_frame.index
+ )
+ grouper = grouper.transform(lambda x: int(np.floor(x / freq)))
+
+ partitions = val_frame.groupby(grouper)
+
+ for _, partition in partitions:
+ if partition.empty or (partition.shape[0] < min_periods):
+ continue
+
+ sample_size = partition.shape[0]
+ nn_neighbors = min(n, max(sample_size, 2) - 1)
+ dist, *_ = ts_ops.kNN(
+ partition.values, nn_neighbors, algorithm=method, metric=metric, p=p
)
- target = target[0]
+ try:
+ resids = getattr(dist, func.__name__)(axis=1)
+ except AttributeError:
+ resids = np.apply_along_axis(func, 1, dist)
- fields = toSequence(field)
- val_frame = data[fields].copy()
- score_index = val_frame.index_of("shared")
- score_ser = pd.Series(np.nan, index=score_index, name=target)
+ score_ser[partition.index] = resids
- val_frame = val_frame.loc[val_frame.index_of("shared")].to_df()
- val_frame.dropna(inplace=True)
+ self._flags[target] = pd.Series(UNFLAGGED, index=score_ser.index, dtype=float)
+ self._data[target] = score_ser
- if val_frame.empty:
- return data, flags
+ return self
- # partitioning
- if not freq:
- freq = val_frame.shape[0]
+ @register(mask=["field"], demask=[], squeeze=[])
+ def assignZScore(
+ self: "SaQC",
+ field: str,
+ window: Optional[str] = None,
+ norm_func: Callable = np.nanstd,
+ model_func: Callable = np.nanmean,
+ center: bool = True,
+ min_periods: Optional[int] = None,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Calculate (rolling) Zscores.
- if isinstance(freq, str):
- grouper = pd.Grouper(freq=freq)
- else:
- grouper = pd.Series(
- data=np.arange(0, val_frame.shape[0]), index=val_frame.index
- )
- grouper = grouper.transform(lambda x: int(np.floor(x / freq)))
+ See the Notes section for a detailed overview of the calculation
- partitions = val_frame.groupby(grouper)
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged. (Here a dummy, for structural reasons)
+ window : {str, int}, default None
+ Size of the window. Either determined via an Offset String, denoting the windows temporal extension or
+ by an integer, denoting the windows number of periods.
+ `NaN` measurements also count as periods.
+ If `None` is passed, All data points share the same scoring window, which than equals the whole
+ data.
+ model_func
+ Function to calculate the center moment in every window.
+ norm_func
+ Function to calculate the scaling for every window
+ center
+ Weather or not to center the target value in the scoring window. If `False`, the
+ target value is the last value in the window.
+ min_periods
+ Minimum number of valid meassurements in a scoring window, to consider the resulting score valid.
- for _, partition in partitions:
- if partition.empty or (partition.shape[0] < min_periods):
- continue
+ Returns
+ -------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ flags : saqc.Flags
+ The quality flags of data
+ Flags values may have changed, relatively to the flags input.
- sample_size = partition.shape[0]
- nn_neighbors = min(n, max(sample_size, 2) - 1)
- dist, *_ = ts_ops.kNN(
- partition.values, nn_neighbors, algorithm=method, metric=metric, p=p
- )
- try:
- resids = getattr(dist, func.__name__)(axis=1)
- except AttributeError:
- resids = np.apply_along_axis(func, 1, dist)
+ Notes
+ -----
+ Steps of calculation:
+
+ 1. Consider a window :math:`W` of successive points :math:`W = x_{1},...x_{w}`
+ containing the value :math:`y_{K}` wich is to be checked.
+ (The index of :math:`K` depends on the selection of the parameter `center`.)
+
+ 2. The "moment" :math:`M` for the window gets calculated via :math:`M=` `model_func(:math:`W`)
- score_ser[partition.index] = resids
+ 3. The "scaling" :math:`N` for the window gets calculated via :math:`N=` `norm_func(:math:`W`)
- flags[target] = pd.Series(UNFLAGGED, index=score_ser.index, dtype=float)
- data[target] = score_ser
+ 4. The "score" :math:`S` for the point :math:`x_{k}`gets calculated via :math:`S=(x_{k} - M) / N`
+ """
- return data, flags
+ if min_periods is None:
+ min_periods = 0
+
+ score, _, _ = _univarScoring(
+ self._data[field],
+ window=window,
+ norm_func=norm_func,
+ model_func=model_func,
+ center=center,
+ min_periods=min_periods,
+ )
+ self._data[field] = score
+ return self
diff --git a/saqc/funcs/tools.py b/saqc/funcs/tools.py
index f418f26af..0967a823a 100644
--- a/saqc/funcs/tools.py
+++ b/saqc/funcs/tools.py
@@ -8,368 +8,333 @@
from __future__ import annotations
import pickle
-from typing import Optional, Tuple
+from typing import TYPE_CHECKING, Optional
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from typing_extensions import Literal
-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
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
+
+
_MPL_DEFAULT_BACKEND = mpl.get_backend()
-@register(mask=[], demask=[], squeeze=[], handles_target=True)
-def copyField(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- target: str,
- overwrite: bool = False,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Copy data and flags to a new name (preserve flags history).
-
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- field : str
- The fieldname of the data column, you want to fork (copy).
- flags : saqc.Flags
- Container to store quality flags to data.
- target: str
- Target name.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- data shape may have changed relatively to the flags input.
- flags : saqc.Flags
- The quality flags of data
- Flags shape may have changed relatively to the flags input.
- """
- if field == target:
- return data, flags
-
- if target in flags.columns.union(data.columns):
- if not overwrite:
- raise ValueError(f"{target}: already exist")
- data, flags = dropField(data=data, flags=flags, field=target)
-
- data[target] = data[field].copy()
- flags.history[target] = flags.history[field].copy()
-
- return data, flags
-
-
-@processing()
-def dropField(
- data: DictOfSeries, field: str, flags: Flags, **kwargs
-) -> Tuple[DictOfSeries, Flags]:
- """
- Drops field from the data and flags.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- field : str
- The fieldname of the data column, you want to drop.
- flags : saqc.Flags
- Container to store quality flags to data.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- data shape may have changed relatively to the flags input.
- flags : saqc.Flags
- The quality flags of data
- Flags shape may have changed relatively to the flags input.
- """
- del data[field]
- del flags[field]
- return data, flags
-
-
-@processing()
-def renameField(
- data: DictOfSeries, field: str, flags: Flags, new_name: str, **kwargs
-) -> Tuple[DictOfSeries, Flags]:
- """
- Rename field in data and flags.
-
- Parameters
- ----------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- field : str
- The fieldname of the data column, you want to rename.
- flags : saqc.Flags
- Container to store flags of the data.
- new_name : str
- String, field is to be replaced with.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- flags : saqc.Flags
- The quality flags of data
- """
- data[new_name] = data[field]
- flags.history[new_name] = flags.history[field]
- del data[field]
- del flags[field]
- return data, flags
-
-
-@register(mask=[], demask=[], squeeze=["field"])
-def selectTime(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- mode: Literal["periodic", "selection_field"],
- selection_field: Optional[str] = None,
- start: Optional[str] = None,
- end: Optional[str] = None,
- closed: bool = True,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Realizes masking within saqc.
-
- Due to some inner saqc mechanics, it is not straight forwardly possible to exclude
- values or datachunks from flagging routines. This function replaces flags with UNFLAGGED
- value, wherever values are to get masked. Furthermore, the masked values get replaced by
- np.nan, so that they dont effect calculations.
-
- 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, `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`)
- 5. drop the dublicated data (`dropField`)
-
- To see an implemented example, checkout flagSeasonalRange in the saqc.functions module
-
- 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-masked.
- flags : saqc.Flags
- Container to store flags of the data.
- 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.
- 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
- indices will be calculated and values get masked where the values of the inner join are ``True``.
- start : {None, str}, default None
- Only effective if mode == "seasonal"
- String denoting starting point of every period. Formally, it has to be a truncated instance of "mm-ddTHH:MM:SS".
- Has to be of same length as `end` parameter.
- See examples section below for some examples.
- end : {None, str}, default None
- Only effective if mode == "periodic"
- String denoting starting point of every period. Formally, it has to be a truncated instance of "mm-ddTHH:MM:SS".
- Has to be of same length as `end` parameter.
- See examples section below for some examples.
- closed : boolean
- Wheather or not to include the mask defining bounds to the mask.
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- Flags values may have changed relatively to the flags input.
-
-
- Examples
- --------
- The `period_start` and `end` parameters provide a conveniant way to generate seasonal / date-periodic masks.
- They have to be strings of the forms: "mm-ddTHH:MM:SS", "ddTHH:MM:SS" , "HH:MM:SS", "MM:SS" or "SS"
- (mm=month, dd=day, HH=hour, MM=minute, SS=second)
- Single digit specifications have to be given with leading zeros.
- `period_start` and `seas on_end` strings have to be of same length (refer to the same periodicity)
- The highest date unit gives the period.
- For example:
-
- >>> 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
-
- >>> start = "01:00"
- >>> end = "04:00"
-
- All the values between the first and 4th minute of every hour get masked.
-
- >>> 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:
-
- >>> 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:
-
- >>> start = "22:00:00"
- >>> end = "06:00:00"
- """
- datcol_idx = data[field].index
-
- if mode == "periodic":
- 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))
-
- data.aloc[mask, field] = np.nan
- flags[mask, field] = UNFLAGGED
- return data, flags
-
-
-@register(mask=[], demask=[], squeeze=[])
-def plot(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- path: Optional[str] = None,
- max_gap: Optional[str] = None,
- 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,
-):
- """
- Plot data and flags or store plot to file.
-
- There are two modes, 'interactive' and 'store', which are determind through the
- ``save_path`` keyword. In interactive mode (default) the plot is shown at runtime
- and the program execution stops until the plot window is closed manually. In
- store mode the generated plot is stored to disk and no manually interaction is
- needed.
-
- Parameters
- ----------
- data : {pd.DataFrame, dios.DictOfSeries}
- data
-
- field : str
- Name of the variable-to-plot
-
- flags : {pd.DataFrame, dios.DictOfSeries, saqc.flagger}
- Flags or flagger object
-
- path : str, default None
- If ``None`` is passed, interactive mode is entered; plots are shown immediatly
- and a user need to close them manually before execution continues.
- If a filepath is passed instead, store-mode is entered and
- the plot is stored unter the passed location.
-
- max_gap : str, default None
- If None, all the points in the data will be connected, resulting in long linear
- lines, where continous chunks of data is missing. Nans in the data get dropped
- before plotting. If an offset string is passed, only points that have a distance
- below `max_gap` get connected via the plotting line.
-
- 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.
- * 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
- processed. `xscope` can be anything, that is a valid argument to the ``pandas.Series.__getitem__`` method.
-
- phaseplot : str or None, default None
- If a string is passed, plot ``field`` in the phase space it forms together with the Variable ``phaseplot``.
-
- store_kwargs : dict, default {}
- Keywords to be passed on to the ``matplotlib.pyplot.savefig`` method, handling
- the figure storing. To store an pickle object of the figure, use the option
- ``{'pickle': True}``, but note that all other store_kwargs are ignored then.
- Reopen with: ``pickle.load(open(savepath,'w')).show()``
-
- ax_kwargs : dict, default {}
- Axis keywords. Change the axis labeling defaults. Most important keywords:
- '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", UNFLAGGED)
-
- if dfilter < np.inf:
- data.loc[flags[field] >= dfilter, field] = np.nan
-
- if store_kwargs is None:
- store_kwargs = {}
-
- if ax_kwargs is None:
- ax_kwargs = {}
-
- if interactive:
- mpl.use(_MPL_DEFAULT_BACKEND)
-
- else:
- mpl.use("Agg")
-
- fig = makeFig(
- data=data,
- field=field,
- flags=flags,
- level=level,
- max_gap=max_gap,
- history=history,
- xscope=xscope,
- phaseplot=phaseplot,
- ax_kwargs=ax_kwargs,
- )
-
- if interactive:
- plt.show()
-
- else:
- if store_kwargs.pop("pickle", False):
- with open(path, "wb") as f:
- pickle.dump(fig, f)
+class ToolsMixin:
+ @register(mask=[], demask=[], squeeze=[], handles_target=True)
+ def copyField(
+ self: "SaQC",
+ field: str,
+ target: str,
+ overwrite: bool = False,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Copy data and flags to a new name (preserve flags history).
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the data column, you want to fork (copy).
+
+ target: str
+ Target name.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ if field == target:
+ return self
+
+ if target in self._flags.columns.union(self._data.columns):
+ if not overwrite:
+ raise ValueError(f"{target}: already exist")
+ self = self.dropField(field=target)
+
+ self._data[target] = self._data[field].copy()
+ self._flags.history[target] = self._flags.history[field].copy()
+
+ return self
+
+ @processing()
+ def dropField(self: "SaQC", field: str, **kwargs) -> "SaQC":
+ """
+ Drops field from the data and flags.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the data column, you want to drop.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ del self._data[field]
+ del self._flags[field]
+ return self
+
+ @processing()
+ def renameField(self: "SaQC", field: str, new_name: str, **kwargs) -> "SaQC":
+ """
+ Rename field in data and flags.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the data column, you want to rename.
+
+ new_name : str
+ String, field is to be replaced with.
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ self._data[new_name] = self._data[field]
+ self._flags.history[new_name] = self._flags.history[field]
+ del self._data[field]
+ del self._flags[field]
+ return self
+
+ @register(mask=[], demask=[], squeeze=["field"])
+ def selectTime(
+ self: "SaQC",
+ field: str,
+ mode: Literal["periodic", "selection_field"],
+ selection_field: Optional[str] = None,
+ start: Optional[str] = None,
+ end: Optional[str] = None,
+ closed: bool = True,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Realizes masking within saqc.
+
+ Due to some inner saqc mechanics, it is not straight forwardly possible to exclude
+ values or datachunks from flagging routines. This function replaces flags with UNFLAGGED
+ value, wherever values are to get masked. Furthermore, the masked values get replaced by
+ np.nan, so that they dont effect calculations.
+
+ 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, `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`)
+ 5. drop the dublicated data (`dropField`)
+
+ To see an implemented example, checkout flagSeasonalRange in the saqc.functions module
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-masked.
+
+ flags : saqc.Flags
+ Container to store flags of the data.
+
+ 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.
+
+ 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
+ indices will be calculated and values get masked where the values of the inner join are ``True``.
+
+ start : {None, str}, default None
+ Only effective if mode == "seasonal"
+ String denoting starting point of every period. Formally, it has to be a truncated instance of "mm-ddTHH:MM:SS".
+ Has to be of same length as `end` parameter.
+ See examples section below for some examples.
+
+ end : {None, str}, default None
+ Only effective if mode == "periodic"
+ String denoting starting point of every period. Formally, it has to be a truncated instance of "mm-ddTHH:MM:SS".
+ Has to be of same length as `end` parameter.
+ See examples section below for some examples.
+
+ closed : boolean
+ Wheather or not to include the mask defining bounds to the mask.
+
+ Returns
+ -------
+ saqc.SaQC
+
+ Examples
+ --------
+ The `period_start` and `end` parameters provide a conveniant way to generate seasonal / date-periodic masks.
+ They have to be strings of the forms: "mm-ddTHH:MM:SS", "ddTHH:MM:SS" , "HH:MM:SS", "MM:SS" or "SS"
+ (mm=month, dd=day, HH=hour, MM=minute, SS=second)
+ Single digit specifications have to be given with leading zeros.
+ `period_start` and `seas on_end` strings have to be of same length (refer to the same periodicity)
+ The highest date unit gives the period.
+ For example:
+
+ >>> 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
+
+ >>> start = "01:00"
+ >>> end = "04:00"
+
+ All the values between the first and 4th minute of every hour get masked.
+
+ >>> 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:
+
+ >>> 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:
+
+ >>> start = "22:00:00"
+ >>> end = "06:00:00"
+ """
+ datcol_idx = self._data[field].index
+
+ if mode == "periodic":
+ mask = periodicMask(datcol_idx, start, end, ~closed)
+ elif mode == "selection_field":
+ idx = self._data[selection_field].index.intersection(datcol_idx)
+ mask = self._data.loc[idx, selection_field]
+ else:
+ raise ValueError(
+ "Keyword passed as masking mode is unknown ({})!".format(mode)
+ )
+
+ self._data.aloc[mask, field] = np.nan
+ self._flags[mask, field] = UNFLAGGED
+ return self
+
+ @register(mask=[], demask=[], squeeze=[])
+ def plot(
+ self: "SaQC",
+ field: str,
+ path: Optional[str] = None,
+ max_gap: Optional[str] = None,
+ 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_NONE,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Plot data and flags or store plot to file.
+
+ There are two modes, 'interactive' and 'store', which are determind through the
+ ``save_path`` keyword. In interactive mode (default) the plot is shown at runtime
+ and the program execution stops until the plot window is closed manually. In
+ store mode the generated plot is stored to disk and no manually interaction is
+ needed.
+
+ Parameters
+ ----------
+ field : str
+ Name of the variable-to-plot
+
+ path : str, default None
+ If ``None`` is passed, interactive mode is entered; plots are shown immediatly
+ and a user need to close them manually before execution continues.
+ If a filepath is passed instead, store-mode is entered and
+ the plot is stored unter the passed location.
+
+ max_gap : str, default None
+ If None, all the points in the data will be connected, resulting in long linear
+ lines, where continous chunks of data is missing. Nans in the data get dropped
+ before plotting. If an offset string is passed, only points that have a distance
+ below `max_gap` get connected via the plotting line.
+
+ 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.
+ * 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
+ processed. `xscope` can be anything, that is a valid argument to the ``pandas.Series.__getitem__`` method.
+
+ phaseplot : str or None, default None
+ If a string is passed, plot ``field`` in the phase space it forms together with the Variable ``phaseplot``.
+
+ store_kwargs : dict, default {}
+ Keywords to be passed on to the ``matplotlib.pyplot.savefig`` method, handling
+ the figure storing. To store an pickle object of the figure, use the option
+ ``{'pickle': True}``, but note that all other store_kwargs are ignored then.
+ Reopen with: ``pickle.load(open(savepath,'w')).show()``
+
+ ax_kwargs : dict, default {}
+ Axis keywords. Change the axis labeling defaults. Most important keywords:
+ 'x_label', 'y_label', 'title', 'fontsize', 'cycleskip'.
+
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ data, flags = self._data.copy(), self._flags.copy()
+
+ interactive = path is None
+ level = kwargs.get("flag", UNFLAGGED)
+
+ if dfilter < np.inf:
+ data.loc[flags[field] >= dfilter, field] = np.nan
+
+ if store_kwargs is None:
+ store_kwargs = {}
+
+ if ax_kwargs is None:
+ ax_kwargs = {}
+
+ if interactive:
+ mpl.use(_MPL_DEFAULT_BACKEND)
+
+ else:
+ mpl.use("Agg")
+
+ fig = makeFig(
+ data=data,
+ field=field,
+ flags=flags,
+ level=level,
+ max_gap=max_gap,
+ history=history,
+ xscope=xscope,
+ phaseplot=phaseplot,
+ ax_kwargs=ax_kwargs,
+ )
+
+ if interactive:
+ plt.show()
+
else:
- fig.savefig(path, **store_kwargs)
+ if store_kwargs.pop("pickle", False):
+ with open(path, "wb") as f:
+ pickle.dump(fig, f)
+ else:
+ fig.savefig(path, **store_kwargs)
- return orig
+ return self
diff --git a/saqc/funcs/transformation.py b/saqc/funcs/transformation.py
index 0b3b363c6..a55568ddb 100644
--- a/saqc/funcs/transformation.py
+++ b/saqc/funcs/transformation.py
@@ -7,73 +7,70 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Callable, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Callable, Optional, Union
import numpy as np
import pandas as pd
-from dios import DictOfSeries
-from saqc.core.flags import Flags
from saqc.core.register import register
+if TYPE_CHECKING:
+ from saqc.core.core import SaQC
-@register(mask=["field"], demask=[], squeeze=[])
-def transform(
- data: DictOfSeries,
- field: str,
- flags: Flags,
- func: Callable[[pd.Series], pd.Series],
- freq: Optional[Union[float, str]] = None,
- **kwargs,
-) -> Tuple[DictOfSeries, Flags]:
- """
- Function to transform data columns with a transformation that maps series onto series of the same length.
-
- Note, that flags get preserved.
-
- 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-transformed.
- flags : saqc.Flags
- Container to store quality flags to data.
- func : Callable[{pd.Series, np.array}, np.array]
- Function to transform data[field] with.
- freq : {None, float, str}, default None
- Determines the segmentation of the data into partitions, the transformation is applied on individually
-
- * ``np.inf``: Apply transformation on whole data set at once
- * ``x`` > 0 : Apply transformation on successive data chunks of periods length ``x``
- * Offset String : Apply transformation on successive partitions of temporal extension matching the passed offset
- string
-
- Returns
- -------
- data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
- flags : saqc.Flags
- The quality flags of data
- """
- val_ser = data[field].copy()
- # partitioning
- if not freq:
- freq = val_ser.shape[0]
-
- if isinstance(freq, str):
- grouper = pd.Grouper(freq=freq)
- else:
- grouper = pd.Series(data=np.arange(0, val_ser.shape[0]), index=val_ser.index)
- grouper = grouper.transform(lambda x: int(np.floor(x / freq)))
-
- partitions = val_ser.groupby(grouper)
-
- for _, partition in partitions:
- if partition.empty:
- continue
- val_ser[partition.index] = func(partition)
-
- data[field] = val_ser
- return data, flags
+
+class TransformationMixin:
+ @register(mask=["field"], demask=[], squeeze=[])
+ def transform(
+ self: "SaQC",
+ field: str,
+ func: Callable[[pd.Series], pd.Series],
+ freq: Optional[Union[float, str]] = None,
+ **kwargs,
+ ) -> "SaQC":
+ """
+ Function to transform data columns with a transformation that maps series onto series of the same length.
+
+ Note, that flags get preserved.
+
+ Parameters
+ ----------
+ field : str
+ The fieldname of the column, holding the data-to-be-transformed.
+
+ func : Callable[{pd.Series, np.array}, np.array]
+ Function to transform data[field] with.
+
+ freq : {None, float, str}, default None
+ Determines the segmentation of the data into partitions, the transformation is applied on individually
+
+ * ``np.inf``: Apply transformation on whole data set at once
+ * ``x`` > 0 : Apply transformation on successive data chunks of periods length ``x``
+ * Offset String : Apply transformation on successive partitions of temporal extension matching the passed offset
+ string
+
+ Returns
+ -------
+ saqc.SaQC
+ """
+ val_ser = self._data[field].copy()
+ # partitioning
+ if not freq:
+ freq = val_ser.shape[0]
+
+ if isinstance(freq, str):
+ grouper = pd.Grouper(freq=freq)
+ else:
+ grouper = pd.Series(
+ data=np.arange(0, val_ser.shape[0]), index=val_ser.index
+ )
+ grouper = grouper.transform(lambda x: int(np.floor(x / freq)))
+
+ partitions = val_ser.groupby(grouper)
+
+ for _, partition in partitions:
+ if partition.empty:
+ continue
+ val_ser[partition.index] = func(partition)
+
+ self._data[field] = val_ser
+ return self
diff --git a/saqc/lib/tools.py b/saqc/lib/tools.py
index 1c2d8b9d1..5f3fdd7a0 100644
--- a/saqc/lib/tools.py
+++ b/saqc/lib/tools.py
@@ -11,9 +11,8 @@ import collections
import itertools
import re
import warnings
-from typing import Callable, Collection, Iterator, List, Sequence, TypeVar, Union
+from typing import Callable, Collection, List, Sequence, TypeVar, Union
-import numba as nb
import numpy as np
import pandas as pd
from scipy import fft
@@ -24,6 +23,7 @@ import dios
# keep this for external imports
# TODO: fix the external imports
from saqc.lib.rolling import customRoller
+from saqc.lib.types import CompT
T = TypeVar("T", str, float, int)
@@ -171,35 +171,6 @@ def periodicMask(dtindex, season_start, season_end, include_bounds):
return out
-@nb.jit(nopython=True, cache=True)
-def otherIndex(values: np.ndarray, start: int = 0) -> int:
- """
- returns the index of the first non value not equal to values[0]
- -> values[start:i] are all identical
- """
- val = values[start]
- for i in range(start, len(values)):
- if values[i] != val:
- return i
- return -1
-
-
-def groupConsecutives(series: pd.Series) -> Iterator[pd.Series]:
- """
- group consecutive values into distinct pd.Series
- """
- index = series.index
- values = series.values
-
- start = 0
- while True:
- stop = otherIndex(values, start)
- if stop == -1:
- break
- yield pd.Series(data=values[start:stop], index=index[start:stop])
- start = stop
-
-
def concatDios(data: List[dios.DictOfSeries], warn: bool = True, stacklevel: int = 2):
# fast path for most common case
if len(data) == 1 and data[0].columns.is_unique:
@@ -509,7 +480,7 @@ def getFreqDelta(index):
return delta
-def getApply(in_obj, apply_obj, attr_access="__name__", attr_or="apply"):
+def getApply(in_obj, apply_obj, attr_access="__name__", attr_or="apply") -> pd.Series:
"""
For the repeating task of applying build in (accelerated) methods/funcs (`apply_obj`),
of rolling/resampling - like objects (`in_obj`) ,
@@ -519,20 +490,25 @@ def getApply(in_obj, apply_obj, attr_access="__name__", attr_or="apply"):
try:
out = getattr(in_obj, getattr(apply_obj, attr_access))()
except AttributeError:
- out = getattr(in_obj, attr_or)(apply_obj)
+ try:
+ # let's try to run it somewhat optimized
+ out = getattr(in_obj, attr_or)(apply_obj, raw=True)
+ except:
+ # did't work out, fallback
+ out = getattr(in_obj, attr_or)(apply_obj)
return out
def statPass(
datcol: pd.Series,
- stat: Callable[[np.array, pd.Series], float],
+ stat: Callable[[np.ndarray, pd.Series], float],
winsz: pd.Timedelta,
thresh: float,
- comparator: Callable[[float, float], bool],
- sub_winsz: pd.Timedelta = None,
- sub_thresh: float = None,
- min_periods: int = None,
+ comparator: Callable[[CompT, CompT], bool],
+ sub_winsz: pd.Timedelta | None = None,
+ sub_thresh: float | None = None,
+ min_periods: int | None = None,
) -> pd.Series:
"""
Check `datcol`, if it contains chunks of length `window`, exceeding `thresh` with
diff --git a/saqc/lib/ts_operators.py b/saqc/lib/ts_operators.py
index 27db26955..094875fcc 100644
--- a/saqc/lib/ts_operators.py
+++ b/saqc/lib/ts_operators.py
@@ -335,7 +335,9 @@ def interpolateNANs(
data = pd.merge(gap_mask, data, how="inner", left_index=True, right_index=True)
def _interpolWrapper(x, wrap_order=order, wrap_method=method):
- if x.count() > wrap_order:
+ if wrap_order < 0:
+ return x
+ elif x.count() > wrap_order:
try:
return x.interpolate(method=wrap_method, order=int(wrap_order))
except (NotImplementedError, ValueError):
@@ -446,8 +448,8 @@ def shift2Freq(
}
direction, tolerance = methods[method](freq)
target_ind = pd.date_range(
- start=data.index[0].floor(freq),
- end=data.index[-1].ceil(freq),
+ start=pd.Timestamp(data.index[0]).floor(freq),
+ end=pd.Timestamp(data.index[-1]).ceil(freq),
freq=freq,
name=data.index.name,
)
@@ -457,7 +459,7 @@ def shift2Freq(
def butterFilter(
- x, cutoff, nyq=0.5, filter_order=2, fill_method="linear", filter_type="low"
+ x, cutoff, nyq=0.5, filter_order=2, fill_method="linear", filter_type="lowpass"
):
"""
Applies butterworth filter.
@@ -479,6 +481,8 @@ def butterFilter(
handle ''np.nan''). See documentation of pandas.Series.interpolate method for
details on the methods associated with the different keywords.
+ filter_type: Literal["lowpass", "highpass", "bandpass", "bandstop"]
+ The type of filter. Default is ‘lowpass’.
Returns
-------
@@ -489,6 +493,8 @@ def butterFilter(
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)
+ if x.shape[0] < 3 * max(len(a), len(b)):
+ return pd.Series(np.nan, x.index, name=x.name)
y = pd.Series(filtfilt(b, a, x), x.index, name=x.name)
y[na_mask] = np.nan
return y
@@ -554,6 +560,8 @@ def polyRoller(in_slice, miss_marker, val_range, center_index, poly_deg):
miss_mask = in_slice == miss_marker
x_data = val_range[~miss_mask]
y_data = in_slice[~miss_mask]
+ if len(x_data) == 0:
+ return np.nan
fitted = poly.polyfit(x=x_data, y=y_data, deg=poly_deg)
return poly.polyval(center_index, fitted)
diff --git a/saqc/lib/types.py b/saqc/lib/types.py
index 6c0a61f35..0e84ff162 100644
--- a/saqc/lib/types.py
+++ b/saqc/lib/types.py
@@ -5,6 +5,10 @@
# SPDX-License-Identifier: GPL-3.0-or-later
# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+from abc import abstractmethod
+
__all__ = [
"T",
"ArrayLike",
@@ -15,11 +19,12 @@ __all__ = [
"OptionalNone",
]
+
from typing import Any, Dict, TypeVar, Union
import numpy as np
import pandas as pd
-from typing_extensions import Literal, Protocol
+from typing_extensions import Protocol
from dios import DictOfSeries
@@ -46,5 +51,14 @@ class GenericFunction(Protocol):
... # pragma: no cover
+class Comparable(Protocol):
+ @abstractmethod
+ def __gt__(self: CompT, other: CompT) -> bool:
+ pass
+
+
+CompT = TypeVar("CompT", bound=Comparable)
+
+
class OptionalNone:
pass
diff --git a/saqc/version.py b/saqc/version.py
index 777c3104a..0afc143e5 100644
--- a/saqc/version.py
+++ b/saqc/version.py
@@ -4,4 +4,4 @@
#
# SPDX-License-Identifier: GPL-3.0-or-later
-__version__ = "2.1.0"
+__version__ = "2.2.0"
diff --git a/setup.py b/setup.py
index 950f9aba0..5aedd38f5 100644
--- a/setup.py
+++ b/setup.py
@@ -37,7 +37,7 @@ setup(
"numpy",
"outlier-utils",
"pyarrow",
- "pandas>=1.2",
+ "pandas>=1.2,<1.5",
"scikit-learn",
"scipy",
"typing_extensions",
diff --git a/tests/api/test_signatures.py b/tests/api/test_signatures.py
deleted file mode 100644
index f229b52ef..000000000
--- a/tests/api/test_signatures.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#! /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/test_integration.py b/tests/cli/test_integration.py
index 81447f3ae..5d4aef522 100644
--- a/tests/cli/test_integration.py
+++ b/tests/cli/test_integration.py
@@ -16,7 +16,7 @@ FLOAT = [
"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 00:30:00,nan,nan,nan,nan,29.3679,255.0\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",
]
@@ -29,7 +29,7 @@ SIMPLE = [
"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 00:30:00,nan,nan,nan,nan,29.3679,BAD\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",
]
@@ -42,7 +42,7 @@ POSITIONAL = [
"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 00:30:00,nan,-9999,nan,-9999,29.3679,9002\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",
]
@@ -55,7 +55,7 @@ DMP = [
"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 00:30:00,nan,nan,nan,nan,nan,nan,nan,nan,29.3679,BAD,OTHER,"{""test"": ""flagMAD"", ""comment"": """"}"\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",
]
diff --git a/tests/common.py b/tests/common.py
index 54d987e81..3a973e4c9 100644
--- a/tests/common.py
+++ b/tests/common.py
@@ -12,18 +12,10 @@ import numpy as np
import pandas as pd
import dios
-from saqc.constants import BAD
from saqc.core import Flags
from saqc.core.history import History, createHistoryFromData
-def flagAll(data, field, flags, **kwargs):
- # NOTE: remember to rename flag -> flag_values
- flags.copy()
- flags[:, field] = BAD
- return data, flags
-
-
def initData(
cols=2, start_date="2017-01-01", end_date="2017-12-31", freq=None, rows=None
):
diff --git a/tests/core/test_FunctionsMixin.py b/tests/core/test_FunctionsMixin.py
deleted file mode 100644
index 9f5750966..000000000
--- a/tests/core/test_FunctionsMixin.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#! /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 62ead32ff..82b301864 100644
--- a/tests/core/test_core.py
+++ b/tests/core/test_core.py
@@ -13,15 +13,15 @@ import pandas as pd
import pytest
import saqc
-from saqc.core import SaQC, initFlagsLike, register
+from saqc.constants import BAD, FILTER_ALL, FILTER_NONE, UNFLAGGED
+from saqc.core import SaQC, initFlagsLike
from saqc.core.flags import Flags
-from saqc.core.register import flagging, processing
-from tests.common import flagAll, initData
+from saqc.core.register import flagging, processing, register
+from saqc.lib.types import OptionalNone
+from tests.common import initData
OPTIONAL = [False, True]
-register(mask=["field"], demask=["field"], squeeze=["field"])(flagAll)
-
@pytest.fixture
def data():
@@ -36,7 +36,7 @@ def flags(data, optional):
def test_errorHandling(data):
@processing()
- def raisingFunc(data, field, flags, **kwargs):
+ def raisingFunc(saqc, field, **kwargs):
raise TypeError
var1 = data.columns[0]
@@ -51,6 +51,12 @@ def test_dtypes(data, flags):
"""
Test if the categorical dtype is preserved through the core functionality
"""
+
+ @register(mask=["field"], demask=["field"], squeeze=["field"])
+ def flagAll(saqc, field, **kwargs):
+ saqc._flags[:, field] = BAD
+ return saqc
+
flags = initFlagsLike(data)
flags_raw = flags.toDios()
var1, var2 = data.columns[:2]
@@ -61,6 +67,11 @@ def test_dtypes(data, flags):
assert pflags[c].dtype == flags[c].dtype
+def test_new_call(data):
+ qc = saqc.SaQC(data)
+ qc = qc.flagRange("var1", max=5)
+
+
def test_copy(data):
qc = saqc.SaQC(data)
@@ -100,9 +111,9 @@ def test_sourceTargetCopy():
target = "new"
@register(mask=["field"], demask=["field"], squeeze=["field"], handles_target=False)
- def flagTarget(data, field, flags, **kwargs):
+ def flagTarget(saqc, field, **kwargs):
assert "target" not in kwargs
- return data, flags
+ return saqc
qc = SaQC(data, flags=Flags({var1: pd.Series(127.0, index=data[var1].index)}))
qc = qc.flagTarget(field=var1, target=target)
@@ -120,18 +131,18 @@ def test_sourceTargetNoCopy():
target = "new"
@register(mask=["field"], demask=["field"], squeeze=["field"], handles_target=True)
- def flagField(data, field, flags, **kwargs):
+ def flagField(saqc, field, **kwargs):
assert "target" in kwargs
- assert "target" not in data
- assert "target" not in flags
- return data, flags
+ assert "target" not in saqc._data
+ assert "target" not in saqc._flags
+ return saqc
SaQC(data).flagField(field=var1, target=target)
def test_sourceTargetMultivariate():
"""
- test bypassing of the imlpicit copy machiners
+ test bypassing of the imlpicit copy machinery
"""
data = initData(3)
@@ -142,12 +153,12 @@ def test_sourceTargetMultivariate():
handles_target=True,
multivariate=True,
)
- def flagMulti(data, field, flags, **kwargs):
+ def flagMulti(saqc, field, **kwargs):
assert "target" in kwargs
- assert "target" not in data
- assert "target" not in flags
+ assert "target" not in saqc._data
+ assert "target" not in saqc._flags
assert field == kwargs["target"]
- return data, flags
+ return saqc
SaQC(data).flagMulti(field=data.columns, target=data.columns)
@@ -162,20 +173,17 @@ def test_sourceTargetMulti():
mask=["field"],
demask=["field"],
squeeze=["field"],
- handles_target=False,
+ handles_target=True,
multivariate=True,
)
- def flagMulti(data, field, flags, target, **kwargs):
+ def flagMulti(saqc, field, target, **kwargs):
assert len(field) == len(target)
for src, trg in zip(field, target):
- assert src in data
- assert trg in data
- assert src in flags
- assert trg in flags
-
- assert (data[src] == data[trg]).all(axis=None)
- assert (flags[src] == flags[trg]).all(axis=None)
- return data, flags
+ assert src in saqc._data
+ assert src in saqc._flags
+ assert trg not in saqc._data
+ assert trg not in saqc._flags
+ return saqc
SaQC(data, flags).flagMulti(field=fields, target=targets)
@@ -196,28 +204,9 @@ def test_validation(data):
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
+ def flagFoo(saqc, field, **kwargs):
+ saqc._data["spam"] = saqc._data[field]
+ return saqc
with pytest.raises(RuntimeError):
qc.flagFoo("a")
@@ -245,3 +234,136 @@ def test_immutability(data):
saqc_after = saqc_before.flagDummy(field)
for name in SaQC._attributes:
assert getattr(saqc_before, name) is not getattr(saqc_after, name)
+
+
+@pytest.mark.parametrize(
+ "field,target",
+ [
+ (["a"], ["x", "y"]),
+ (["a", "b"], ["x"]),
+ ],
+)
+def test_fieldsTargetsExpansionFail(field, target):
+ # check that the field/target handling works as expected for the
+ # different function types
+
+ @register(mask=[], demask=[], squeeze=[], multivariate=False, handles_target=False)
+ def foo(saqc, field, **kwargs):
+ return saqc
+
+ @register(mask=[], demask=[], squeeze=[], multivariate=False, handles_target=True)
+ def bar(saqc, field, target, **kwargs):
+ return saqc
+
+ @register(mask=[], demask=[], squeeze=[], multivariate=True, handles_target=False)
+ def baz(saqc, field, **kwargs):
+ return saqc
+
+ @register(mask=[], demask=[], squeeze=[], multivariate=False, handles_target=True)
+ def fooBar(saqc, field, **kwargs):
+ return saqc
+
+ data = pd.DataFrame({"a": [1, 2], "b": [2, 3], "c": [3, 4], "d": [4, 5]})
+ qc = SaQC(data)
+ with pytest.raises(ValueError):
+ qc.foo(field, target=target)
+ with pytest.raises(ValueError):
+ qc.bar(field, target=target)
+ with pytest.raises(ValueError):
+ qc.baz(field, target=target)
+ with pytest.raises(ValueError):
+ qc.fooBar(field, target=target)
+
+
+@pytest.mark.parametrize(
+ "field,target",
+ [
+ (["a"], ["x"]),
+ (["a", "a"], ["x", "y"]),
+ ],
+)
+def test_fieldsTargetsExpansion(field, target):
+ # check that the field/target handling works as expected for the
+ # different function types
+
+ @register(mask=[], demask=[], squeeze=[], multivariate=False, handles_target=False)
+ def foo(saqc, field, **kwargs):
+ return saqc
+
+ @register(mask=[], demask=[], squeeze=[], multivariate=False, handles_target=True)
+ def bar(saqc, field, target, **kwargs):
+ return saqc
+
+ @register(mask=[], demask=[], squeeze=[], multivariate=True, handles_target=False)
+ def baz(saqc, field, **kwargs):
+ return saqc
+
+ @register(mask=[], demask=[], squeeze=[], multivariate=False, handles_target=True)
+ def fooBar(saqc, field, **kwargs):
+ return saqc
+
+ data = pd.DataFrame({"a": [1, 2], "b": [2, 3], "c": [3, 4], "d": [4, 5]})
+ qc = SaQC(data)
+ qc.foo(field, target=target)
+ qc.bar(field, target=target)
+ qc.baz(field, target=target)
+ qc.fooBar(field, target=target)
+
+
+@pytest.mark.parametrize(
+ "field,target",
+ [
+ (["a"], ["x"]),
+ (["a", "a"], ["x", "y"]),
+ (["a"], ["x", "y"]),
+ (["a", "a"], ["x"]),
+ ],
+)
+def test_fieldsTargetsExpansionMultivariate(field, target):
+ @register(mask=[], demask=[], squeeze=[], multivariate=True, handles_target=True)
+ def foo(saqc, field, target, **kwargs):
+ return saqc
+
+ @register(mask=[], demask=[], squeeze=[], multivariate=True, handles_target=True)
+ def bar(saqc, field, **kwargs):
+ return saqc
+
+ data = pd.DataFrame(
+ {"a": [1, 2, 3], "b": [2, 3, 4], "c": [3, 4, 5], "d": [4, 5, 6]}
+ )
+ qc = SaQC(data)
+ qc.foo(field, target)
+ qc.bar(field, target)
+
+
+def test_columnConsitency(data):
+ @flagging()
+ def flagFoo(saqc, field, **kwargs):
+ saqc._flags["spam"] = saqc._flags[field]
+ return saqc
+
+ field = data.columns[0]
+ qc = SaQC(data)
+ with pytest.raises(RuntimeError):
+ qc.flagFoo(field)
+
+
+@pytest.mark.parametrize(
+ "user_flag,internal_flag",
+ (
+ [FILTER_ALL, FILTER_ALL],
+ [FILTER_NONE, FILTER_NONE],
+ [OptionalNone(), FILTER_ALL],
+ ["BAD", BAD],
+ ["UNFLAGGED", UNFLAGGED],
+ ),
+)
+def test_dfilterTranslation(data, user_flag, internal_flag):
+ @flagging()
+ def flagFoo(saqc, field, dfilter, **kwargs):
+ assert dfilter == internal_flag
+ return saqc
+
+ field = data.columns[0]
+ qc = SaQC(data, scheme="simple")
+ qc.flagFoo(field, dfilter=user_flag)
diff --git a/tests/core/test_reader.py b/tests/core/test_reader.py
index 7df2cf625..70a00d016 100644
--- a/tests/core/test_reader.py
+++ b/tests/core/test_reader.py
@@ -141,9 +141,9 @@ def test_supportedArguments(data):
# TODO: necessary?
@flagging()
- def func(data, field, flags, kwarg, **kwargs):
- flags[:, field] = np.nan
- return data, flags
+ def func(saqc, field, kwarg, **kwargs):
+ saqc._flags[:, field] = np.nan
+ return saqc
var1 = data.columns[0]
diff --git a/tests/funcs/test_constants_detection.py b/tests/funcs/test_constants_detection.py
index c3f9d3667..381d5260f 100644
--- a/tests/funcs/test_constants_detection.py
+++ b/tests/funcs/test_constants_detection.py
@@ -10,8 +10,7 @@ import numpy as np
import pytest
from saqc.constants import BAD, UNFLAGGED
-from saqc.core import initFlagsLike
-from saqc.funcs.constants import flagByVariance, flagConstants
+from saqc.core import SaQC, initFlagsLike
from tests.common import initData
@@ -27,10 +26,9 @@ def data():
def test_constants_flagBasic(data):
field, *_ = data.columns
flags = initFlagsLike(data)
- data, flags_result = flagConstants(
- data, field, flags, window="15Min", thresh=0.1, flag=BAD
- )
- flagscol = flags_result[field]
+ qc = SaQC(data, flags)
+ qc = qc.flagConstants(field, window="15Min", thresh=0.1, flag=BAD)
+ flagscol = qc._flags[field]
assert np.all(flagscol[5:25] == BAD)
assert np.all(flagscol[:5] == UNFLAGGED)
assert np.all(flagscol[25 + 1 :] == UNFLAGGED)
@@ -39,11 +37,10 @@ def test_constants_flagBasic(data):
def test_constants_flagVarianceBased(data):
field, *_ = data.columns
flags = initFlagsLike(data)
- data, flags_result1 = flagByVariance(
- data, field, flags, window="1h", thresh=0.0005, flag=BAD
- )
+ qc = SaQC(data, flags)
+ qc = qc.flagByVariance(field, window="1h", thresh=0.0005, flag=BAD)
- flagscol = flags_result1[field]
+ flagscol = qc._flags[field]
assert np.all(flagscol[5:25] == BAD)
assert np.all(flagscol[:5] == UNFLAGGED)
assert np.all(flagscol[25 + 1 :] == UNFLAGGED)
diff --git a/tests/funcs/test_functions.py b/tests/funcs/test_functions.py
index 416bd1c9f..c898cb8af 100644
--- a/tests/funcs/test_functions.py
+++ b/tests/funcs/test_functions.py
@@ -14,13 +14,7 @@ import dios
import saqc
from saqc.constants import BAD, DOUBTFUL, UNFLAGGED
from saqc.core import initFlagsLike
-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.resampling import concatFlags
-from saqc.funcs.tools import copyField, dropField, selectTime
+from saqc.core.core import SaQC
from tests.common import initData
from tests.fixtures import char_dict, course_1
@@ -42,19 +36,20 @@ def test_statPass():
data[200:210] = noise[:10]
data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
- data, flags = flagByStatLowPass(
- data, "data", flags, np.std, "20D", 0.999, "5D", 0.999, 0, flag=BAD
+ qc = SaQC(data, flags).flagByStatLowPass(
+ "data", np.std, "20D", 0.999, "5D", 0.999, 0, flag=BAD
)
- assert (flags["data"].iloc[:100] == UNFLAGGED).all()
- assert (flags["data"].iloc[100:120] == BAD).all()
- assert (flags["data"].iloc[121:] == UNFLAGGED).all()
+ assert (qc.flags["data"].iloc[:100] == UNFLAGGED).all()
+ assert (qc.flags["data"].iloc[100:120] == BAD).all()
+ assert (qc.flags["data"].iloc[121:] == UNFLAGGED).all()
def test_flagRange(data, field):
min, max = 10, 90
flags = initFlagsLike(data)
- data, flags = flagRange(data, field, flags, min=min, max=max, flag=BAD)
- flagged = flags[field] > UNFLAGGED
+ qc = SaQC(data, flags)
+ qc = qc.flagRange(field, min=min, max=max, flag=BAD)
+ flagged = qc.flags[field] > UNFLAGGED
expected = (data[field] < min) | (data[field] > max)
assert all(flagged == expected)
@@ -89,31 +84,26 @@ def test_flagSesonalRange(data, field):
),
]
+ flags = initFlagsLike(data)
+ qc = SaQC(data, flags)
for test, expected in tests:
- flags = initFlagsLike(data)
newfield = f"{field}_masked"
start = f"{test['startmonth']:02}-{test['startday']:02}T00:00:00"
end = f"{test['endmonth']:02}-{test['endday']:02}T00:00:00"
- data, flags = copyField(data, field, flags, field + "_masked")
- data, flags = selectTime(
- data,
+ qc = qc.copyField(field, field + "_masked")
+ qc = qc.selectTime(
newfield,
- flags,
mode="periodic",
start=start,
end=end,
closed=True,
flag=BAD,
)
- data, flags = flagRange(
- data, newfield, flags, min=test["min"], max=test["max"], flag=BAD
- )
- data, flags = concatFlags(
- data, newfield, flags, method="match", target=field, flag=BAD
- )
- data, flags = dropField(data, newfield, flags)
- flagged = flags[field] > UNFLAGGED
+ qc = qc.flagRange(newfield, min=test["min"], max=test["max"], flag=BAD)
+ qc = qc.concatFlags(newfield, method="match", target=field, flag=BAD)
+ qc = qc.dropField(newfield)
+ flagged = qc._flags[field] > UNFLAGGED
assert flagged.sum() == expected
@@ -122,8 +112,9 @@ def test_clearFlags(data, field):
flags[:, field] = BAD
assert all(flags[field] == BAD)
- _, flags = clearFlags(data, field, flags)
- assert all(flags[field] == UNFLAGGED)
+ qc = SaQC(data, flags)
+ qc = qc.clearFlags(field)
+ assert all(qc._flags[field] == UNFLAGGED)
def test_forceFlags(data, field):
@@ -131,13 +122,13 @@ def test_forceFlags(data, field):
flags[:, field] = BAD
assert all(flags[field] == BAD)
- _, flags = forceFlags(data, field, flags, flag=DOUBTFUL)
- assert all(flags[field] == DOUBTFUL)
+ qc = SaQC(data, flags).forceFlags(field, flag=DOUBTFUL)
+ assert all(qc._flags[field] == DOUBTFUL)
def test_flagIsolated(data, field):
flags = initFlagsLike(data)
-
+ d_len = data.shape[0][0]
data.iloc[1:3, 0] = np.nan
data.iloc[4:5, 0] = np.nan
flags[data[field].index[5:6], field] = BAD
@@ -155,22 +146,22 @@ def test_flagIsolated(data, field):
# 2016-01-08 7.0 -inf
# .. .. ..
- _, flags_result = flagIsolated(
- data, field, flags, group_window="1D", gap_window="2.1D", flag=BAD
+ qc = SaQC(data, flags).flagIsolated(
+ field, group_window="1D", gap_window="2.1D", flag=BAD
)
+ assert (qc._flags[field].iloc[[3, 5]] == BAD).all()
+ neg_list = [k for k in range(d_len) if k not in [3, 5]]
+ assert (qc._flags[field].iloc[neg_list] == UNFLAGGED).all()
- assert flags_result[field].iloc[[3, 5]].all()
-
- data, flags_result = flagIsolated(
- data,
+ qc = qc.flagIsolated(
field,
- flags_result,
group_window="2D",
gap_window="2.1D",
- continuation_range="1.1D",
flag=BAD,
)
- assert flags_result[field].iloc[[3, 5, 13, 14]].all()
+ assert (qc._flags[field].iloc[[3, 5, 13, 14]] == BAD).all()
+ neg_list = [k for k in range(d_len) if k not in [3, 5, 13, 14]]
+ assert (qc._flags[field].iloc[neg_list] == UNFLAGGED).all()
def test_flagManual(data, field):
@@ -190,22 +181,20 @@ def test_flagManual(data, field):
]
for kw in kwargs_list:
- _, fl = flagManual(data.copy(), field, flags.copy(), **kw)
- isflagged = fl[field] > UNFLAGGED
+ qc = SaQC(data, flags).flagManual(field, **kw)
+ isflagged = qc._flags[field] > UNFLAGGED
assert isflagged[isflagged].index.equals(index_exp)
# flag not exist in mdata
- _, fl = flagManual(
- data.copy(),
+ qc = SaQC(data, flags).flagManual(
field,
- flags.copy(),
mdata=mdata,
mflag="i do not exist",
method="ontime",
mformat="mflag",
flag=BAD,
)
- isflagged = fl[field] > UNFLAGGED
+ isflagged = qc._flags[field] > UNFLAGGED
assert isflagged[isflagged].index.equals(pd.DatetimeIndex([]))
# check closure methods
@@ -233,17 +222,15 @@ def test_flagManual(data, field):
]
bound_drops = {"right-open": [1], "left-open": [0], "closed": []}
for method in ["right-open", "left-open", "closed"]:
- _, fl = flagManual(
- data.copy(),
+ qc = qc.flagManual(
field,
- flags.copy(),
mdata=mdata,
mflag=1,
method=method,
mformat="mflag",
flag=BAD,
)
- isflagged = fl[field] > UNFLAGGED
+ isflagged = qc._flags[field] > UNFLAGGED
for flag_i in flag_intervals:
f_i = isflagged[slice(flag_i[0], flag_i[-1])].index
check_i = f_i.drop(
@@ -264,15 +251,13 @@ def test_flagDriftFromNorm(dat):
fields = ["field1", "field2", "field3"]
flags = initFlagsLike(data)
- _, flags_norm = flagDriftFromNorm(
- data=data.copy(),
+ qc = SaQC(data, flags).flagDriftFromNorm(
field=fields,
- flags=flags.copy(),
freq="200min",
spread=5,
flag=BAD,
)
- assert all(flags_norm["field3"] > UNFLAGGED)
+ assert all(qc._flags["field3"] > UNFLAGGED)
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_1")])
@@ -285,16 +270,14 @@ def test_flagDriftFromReference(dat):
flags = initFlagsLike(data)
- _, flags_ref = flagDriftFromReference(
- data=data.copy(),
+ qc = SaQC(data, flags).flagDriftFromReference(
field=fields,
- flags=flags.copy(),
reference="field1",
freq="3D",
thresh=20,
flag=BAD,
)
- assert all(flags_ref["field3"] > UNFLAGGED)
+ assert all(qc._flags["field3"] > UNFLAGGED)
def test_transferFlags():
@@ -302,9 +285,18 @@ def test_transferFlags():
qc = saqc.SaQC(data)
qc = qc.flagRange("a", max=1.5)
qc = qc.transferFlags(["a", "a"], ["b", "c"])
- assert np.all(
- qc.flags["b"].values == np.array([saqc.constants.UNFLAGGED, saqc.constants.BAD])
+ assert np.all(qc.flags["b"].values == np.array([UNFLAGGED, BAD]))
+ assert np.all(qc.flags["c"].values == np.array([UNFLAGGED, BAD]))
+
+
+def test_flagJumps():
+ data = pd.DataFrame(
+ {"a": [1, 1, 1, 1, 1, 6, 6, 6, 6, 6]},
+ index=pd.date_range(start="2020-01-01", periods=10, freq="D"),
)
- assert np.all(
- qc.flags["c"].values == np.array([saqc.constants.UNFLAGGED, saqc.constants.BAD])
+ qc = SaQC(data=data)
+ qc = qc.flagJumps(field="a", thresh=1, window="2D")
+ assert qc.flags["a"][5] == BAD
+ assert np.all(qc.flags["a"].values[:5] == UNFLAGGED) & np.all(
+ qc.flags["a"].values[6:] == UNFLAGGED
)
diff --git a/tests/funcs/test_generic_api_functions.py b/tests/funcs/test_generic_api_functions.py
index 64ca9173e..6f3c77691 100644
--- a/tests/funcs/test_generic_api_functions.py
+++ b/tests/funcs/test_generic_api_functions.py
@@ -35,156 +35,157 @@ def test_emptyData():
assert saqc.flags.empty
-def test_writeTargetFlagGeneric(data):
- params = [
+@pytest.mark.parametrize(
+ "targets, func",
+ [
(["tmp"], lambda x, y: pd.Series(True, index=x.index.union(y.index))),
(
["tmp1", "tmp2"],
lambda x, y: [pd.Series(True, index=x.index.union(y.index))] * 2,
),
- ]
- for targets, func in params:
- expected_meta = {
- "func": "flagGeneric",
- "args": (data.columns.tolist(), targets),
- "kwargs": {
- "func": func.__name__,
- "flag": BAD,
- "dfilter": FILTER_ALL,
- },
- }
-
- saqc = SaQC(data=data)
- saqc = saqc.flagGeneric(field=data.columns, target=targets, func=func, flag=BAD)
- for target in targets:
- assert saqc._flags.history[target].hist.iloc[0].tolist() == [BAD]
- assert saqc._flags.history[target].hist.iloc[0].tolist() == [BAD]
- assert saqc._flags.history[target].meta[0] == expected_meta
-
-
-def test_overwriteFieldFlagGeneric(data):
- params = [
+ ],
+)
+def test_writeTargetFlagGeneric(data, targets, func):
+ expected_meta = {
+ "func": "flagGeneric",
+ "args": (data.columns.tolist(), targets),
+ "kwargs": {
+ "func": func.__name__,
+ "flag": BAD,
+ "dfilter": FILTER_ALL,
+ },
+ }
+
+ saqc = SaQC(data=data)
+ saqc = saqc.flagGeneric(field=data.columns, target=targets, func=func, flag=BAD)
+ for target in targets:
+ assert saqc._flags.history[target].hist.iloc[0].tolist() == [BAD]
+ assert saqc._flags.history[target].meta[0] == expected_meta
+
+
+@pytest.mark.parametrize(
+ "fields, func",
+ [
(["var1"], lambda x: pd.Series(True, index=x.index)),
(
["var1", "var2"],
lambda x, y: [pd.Series(True, index=x.index.union(y.index))] * 2,
),
- ]
+ ],
+)
+def test_overwriteFieldFlagGeneric(data, fields, func):
flag = 12
- for fields, func in params:
- expected_meta = {
- "func": "flagGeneric",
- "args": (fields, fields),
- "kwargs": {
- "func": func.__name__,
- "flag": flag,
- "dfilter": FILTER_ALL,
- },
- }
-
- saqc = SaQC(
- data=data.copy(),
- flags=Flags(
- {
- k: pd.Series(data[k] % 2, index=data[k].index).replace(
- {0: UNFLAGGED, 1: 127}
- )
- for k in data.columns
- }
- ),
- )
-
- res = saqc.flagGeneric(field=fields, func=func, flag=flag)
- for field in fields:
- histcol1 = res._flags.history[field].hist[1]
- 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
-
-
-def test_writeTargetProcGeneric(data):
- fields = ["var1", "var2"]
- params = [
+ expected_meta = {
+ "func": "flagGeneric",
+ "args": (fields, fields),
+ "kwargs": {
+ "func": func.__name__,
+ "flag": flag,
+ "dfilter": FILTER_ALL,
+ },
+ }
+
+ saqc = SaQC(
+ data=data.copy(),
+ flags=Flags(
+ {
+ k: pd.Series(data[k] % 2, index=data[k].index).replace(
+ {0: UNFLAGGED, 1: 127}
+ )
+ for k in data.columns
+ }
+ ),
+ )
+
+ res = saqc.flagGeneric(field=fields, func=func, flag=flag)
+ for field in fields:
+ histcol1 = res._flags.history[field].hist[1]
+ 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
+
+
+@pytest.mark.parametrize(
+ "targets, func",
+ [
(["tmp"], lambda x, y: x + y),
(["tmp1", "tmp2"], lambda x, y: (x + y, y * 2)),
- ]
+ ],
+)
+def test_writeTargetProcGeneric(data, targets, func):
+ fields = ["var1", "var2"]
dfilter = 128
- for targets, func in params:
-
- expected_data = DictOfSeries(
- func(*[data[f] for f in fields]), columns=toSequence(targets)
- ).squeeze()
-
- expected_meta = {
- "func": "procGeneric",
- "args": (fields, targets),
- "kwargs": {
- "func": func.__name__,
- "dfilter": dfilter,
- "label": "generic",
- },
- }
- saqc = SaQC(
- data=data,
- flags=Flags(
- {k: pd.Series(127.0, index=data[k].index) for k in data.columns}
- ),
- )
- res = saqc.processGeneric(
- field=fields,
- target=targets,
- func=func,
- 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].isna().all()
- assert res._flags.history[target].meta[0] == expected_meta
-
-
-def test_overwriteFieldProcGeneric(data):
- params = [
+
+ expected_data = DictOfSeries(
+ func(*[data[f] for f in fields]), columns=toSequence(targets)
+ ).squeeze()
+
+ expected_meta = {
+ "func": "procGeneric",
+ "args": (fields, targets),
+ "kwargs": {
+ "func": func.__name__,
+ "dfilter": dfilter,
+ "label": "generic",
+ },
+ }
+ saqc = SaQC(
+ data=data,
+ flags=Flags({k: pd.Series(127.0, index=data[k].index) for k in data.columns}),
+ )
+ res = saqc.processGeneric(
+ field=fields,
+ target=targets,
+ func=func,
+ 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].isna().all()
+ assert res._flags.history[target].meta[0] == expected_meta
+
+
+@pytest.mark.parametrize(
+ "fields, func",
+ [
(["var1"], lambda x: x * 2),
(["var1", "var2"], lambda x, y: (x + y, y * 2)),
- ]
+ ],
+)
+def test_overwriteFieldProcGeneric(data, fields, func):
dfilter = 128
- for fields, func in params:
- expected_data = DictOfSeries(
- func(*[data[f] for f in fields]), columns=fields
- ).squeeze()
-
- expected_meta = {
- "func": "procGeneric",
- "args": (fields, fields),
- "kwargs": {
- "func": func.__name__,
- "dfilter": dfilter,
- "label": "generic",
- },
- }
-
- saqc = SaQC(
- data=data,
- flags=Flags(
- {k: pd.Series(127.0, index=data[k].index) for k in data.columns}
- ),
- )
-
- res = saqc.processGeneric(
- field=fields, 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].isna().all()
- assert res._flags.history[field].meta[0] == {}
- assert res._flags.history[field].meta[1] == expected_meta
+ expected_data = DictOfSeries(
+ func(*[data[f] for f in fields]), columns=fields
+ ).squeeze()
+
+ expected_meta = {
+ "func": "procGeneric",
+ "args": (fields, fields),
+ "kwargs": {
+ "func": func.__name__,
+ "dfilter": dfilter,
+ "label": "generic",
+ },
+ }
+
+ saqc = SaQC(
+ data=data,
+ flags=Flags({k: pd.Series(127.0, index=data[k].index) for k in data.columns}),
+ )
+
+ res = saqc.processGeneric(field=fields, 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].isna().all()
+ assert res._flags.history[field].meta[0] == {}
+ assert res._flags.history[field].meta[1] == expected_meta
def test_label():
@@ -202,3 +203,31 @@ def test_label():
func=lambda x, y: isflagged(x, "out of range") | isflagged(y),
)
assert list((qc.flags["data2"] > 0).values) == [False, False, True, False, False]
+
+
+@pytest.mark.parametrize(
+ "kwargs, got, expected",
+ [
+ (
+ {
+ "lower": 0,
+ },
+ [-9, -2, 1, 2, 9],
+ [0, 0, 1, 2, 9],
+ ),
+ ({"upper": 3}, [-9, -2, 1, 2, 9], [-9, -2, 1, 2, 3]),
+ ({"lower": -6, "upper": 3}, [-9, -2, 1, 2, 9], [-6, -2, 1, 2, 3]),
+ ],
+)
+def test_processGenericClip(kwargs, got, expected):
+ field = "data"
+ got = pd.DataFrame(
+ got, columns=[field], index=pd.date_range("2020-06-30", periods=len(got))
+ )
+ expected = pd.DataFrame(
+ expected,
+ columns=[field],
+ index=pd.date_range("2020-06-30", periods=len(expected)),
+ )
+ qc = SaQC(got).processGeneric(field, func=lambda x: clip(x, **kwargs))
+ assert (qc._data[field] == expected[field]).all()
diff --git a/tests/funcs/test_generic_config_functions.py b/tests/funcs/test_generic_config_functions.py
index a9e4d09a6..1106cb217 100644
--- a/tests/funcs/test_generic_config_functions.py
+++ b/tests/funcs/test_generic_config_functions.py
@@ -222,10 +222,10 @@ def test_callableArgumentsUnary(data):
window = 5
@register(mask=["field"], demask=["field"], squeeze=["field"])
- def testFuncUnary(data, field, flags, func, **kwargs):
- value = data[field].rolling(window=window).apply(func)
- data[field] = value
- return data, initFlagsLike(data)
+ def testFuncUnary(saqc, field, func, **kwargs):
+ value = saqc._data[field].rolling(window=window).apply(func)
+ saqc._data[field] = value
+ return saqc
var = data.columns[0]
@@ -252,9 +252,9 @@ def test_callableArgumentsBinary(data):
var1, var2 = data.columns[:2]
@register(mask=["field"], demask=["field"], squeeze=["field"])
- def testFuncBinary(data, field, flags, func, **kwargs):
- data[field] = func(data[var1], data[var2])
- return data, initFlagsLike(data)
+ def testFuncBinary(saqc, field, func, **kwargs):
+ saqc._data[field] = func(data[var1], data[var2])
+ return saqc
config = f"""
varname ; test
diff --git a/tests/funcs/test_harm_funcs.py b/tests/funcs/test_harm_funcs.py
index 53ff995a4..1dbed141d 100644
--- a/tests/funcs/test_harm_funcs.py
+++ b/tests/funcs/test_harm_funcs.py
@@ -12,9 +12,7 @@ import pytest
import dios
from saqc.constants import BAD, UNFLAGGED
-from saqc.core import Flags, initFlagsLike
-from saqc.funcs.resampling import concatFlags, interpolate, linear, resample, shift
-from saqc.funcs.tools import copyField, dropField
+from saqc.core import SaQC, initFlagsLike
from tests.common import checkDataFlagsInvariants
@@ -55,14 +53,13 @@ def test_wrapper(data, func, kws):
for c in flags.columns:
flags[:, c] = BAD
- import saqc
+ qc = SaQC(data, flags)
- func = getattr(saqc.funcs, func)
- data, flags = func(data, field, flags, freq, **kws)
+ qc = getattr(qc, func)(field, freq, **kws)
# check minimal requirements
- checkDataFlagsInvariants(data, flags, field)
- assert data[field].index.inferred_freq == freq
+ checkDataFlagsInvariants(qc._data, qc._flags, field)
+ assert qc.data[field].index.inferred_freq == freq
_SUPPORTED_METHODS = [
@@ -105,31 +102,27 @@ def test_gridInterpolation(data, method, fill_history):
for c in flags.columns:
flags[::2, c] = UNFLAGGED
+ qc = SaQC(data, flags)
+
# we are just testing if the interpolation gets passed to the series without
# causing an error:
- res = interpolate(
- data.copy(),
+ res = qc.interpolate(
field,
- flags.copy(),
freq,
method=method,
downcast_interpolation=True,
)
if method == "polynomial":
- res = interpolate(
- data.copy(),
+ res = qc.interpolate(
field,
- flags.copy(),
freq,
order=2,
method=method,
downcast_interpolation=True,
)
- res = interpolate(
- data.copy(),
+ res = qc.interpolate(
field,
- flags.copy(),
freq,
order=10,
method=method,
@@ -137,9 +130,8 @@ def test_gridInterpolation(data, method, fill_history):
)
# check minimal requirements
- rdata, rflags = res
- checkDataFlagsInvariants(rdata, rflags, field, identical=False)
- assert rdata[field].index.inferred_freq == freq
+ checkDataFlagsInvariants(res._data, res._flags, field, identical=False)
+ assert res.data[field].index.inferred_freq == freq
@pytest.mark.parametrize(
@@ -181,21 +173,27 @@ def test_harmSingleVarIntermediateFlagging(data, reshaper):
pre_data = data.copy()
pre_flags = flags.copy()
- data, flags = copyField(data, field, flags, field + "_interpolated")
- data, flags = linear(data, field + "_interpolated", flags, freq=freq)
- checkDataFlagsInvariants(data, flags, field + "_interpolated", identical=True)
- assert data[field + "_interpolated"].index.inferred_freq == freq
+ qc = SaQC(data, flags)
+
+ qc = qc.copyField(field, field + "_interpolated")
+ qc = qc.linear(field + "_interpolated", freq=freq)
+ checkDataFlagsInvariants(
+ qc._data, qc._flags, field + "_interpolated", identical=True
+ )
+ assert qc._data[field + "_interpolated"].index.inferred_freq == freq
# flag something bad
- flags[data[field + "_interpolated"].index[3:4], field + "_interpolated"] = BAD
- data, flags = concatFlags(
- data, field + "_interpolated", flags, method="inverse_" + reshaper, target=field
+ qc._flags[
+ qc._data[field + "_interpolated"].index[3:4], field + "_interpolated"
+ ] = BAD
+ qc = qc.concatFlags(
+ field + "_interpolated", method="inverse_" + reshaper, target=field
)
- data, flags = dropField(data, field + "_interpolated", flags)
+ qc = qc.dropField(field + "_interpolated")
- assert len(data[field]) == len(flags[field])
- assert data[field].equals(pre_data[field])
- assert flags[field].index.equals(pre_flags[field].index)
+ assert len(qc.data[field]) == len(qc.flags[field])
+ assert qc.data[field].equals(pre_data[field])
+ assert qc.flags[field].index.equals(pre_flags[field].index)
if "agg" in reshaper:
if reshaper == "nagg":
@@ -207,9 +205,9 @@ def test_harmSingleVarIntermediateFlagging(data, reshaper):
else:
raise NotImplementedError("untested test case")
- assert all(flags[field].iloc[start:end] > UNFLAGGED)
- assert all(flags[field].iloc[:start] == UNFLAGGED)
- assert all(flags[field].iloc[end:] == UNFLAGGED)
+ assert all(qc._flags[field].iloc[start:end] > UNFLAGGED)
+ assert all(qc._flags[field].iloc[:start] == UNFLAGGED)
+ assert all(qc._flags[field].iloc[end:] == UNFLAGGED)
elif "shift" in reshaper:
if reshaper == "nshift":
@@ -221,7 +219,7 @@ def test_harmSingleVarIntermediateFlagging(data, reshaper):
else:
raise NotImplementedError("untested test case")
- flagged = flags[field] > UNFLAGGED
+ flagged = qc._flags[field] > UNFLAGGED
assert all(flagged == exp)
elif reshaper == "interpolation":
@@ -281,21 +279,20 @@ def test_harmSingleVarInterpolationAgg(data, params, expected):
pre_flaggger = flags.copy()
method, freq = params
- data_harm, flags_harm = copyField(data, "data", flags, "data_harm")
- data_harm, flags_harm = resample(
- data_harm, h_field, flags_harm, freq, func=np.sum, method=method
- )
- checkDataFlagsInvariants(data_harm, flags_harm, h_field, identical=True)
- assert data_harm[h_field].index.freq == pd.Timedelta(freq)
- assert data_harm[h_field].equals(expected)
+ qc = SaQC(data, flags)
- data_deharm, flags_deharm = concatFlags(
- data_harm, h_field, flags_harm, target=field, method="inverse_" + method
- )
- data_deharm, flags_deharm = dropField(data_deharm, h_field, flags_deharm)
- checkDataFlagsInvariants(data_deharm, flags_deharm, field, identical=True)
- assert data_deharm[field].equals(pre_data[field])
- assert flags_deharm[field].equals(pre_flaggger[field])
+ qc = qc.copyField("data", "data_harm")
+ qc = qc.resample(h_field, freq, func=np.sum, method=method)
+
+ checkDataFlagsInvariants(qc._data, qc._flags, h_field, identical=True)
+ assert qc._data[h_field].index.freq == pd.Timedelta(freq)
+ assert qc._data[h_field].equals(expected)
+
+ qc = qc.concatFlags(h_field, target=field, method="inverse_" + method)
+ qc = qc.dropField(h_field)
+ checkDataFlagsInvariants(qc._data, qc._flags, field, identical=True)
+ assert qc.data[field].equals(pre_data[field])
+ assert qc.flags[field].equals(pre_flaggger[field])
@pytest.mark.parametrize(
@@ -365,16 +362,16 @@ def test_harmSingleVarInterpolationShift(data, params, expected):
pre_flags = flags.copy()
method, freq = params
- data_harm, flags_harm = copyField(data, "data", flags, "data_harm")
- data_harm, flags_harm = shift(data_harm, h_field, flags_harm, freq, method=method)
- assert data_harm[h_field].equals(expected)
- checkDataFlagsInvariants(data_harm, flags_harm, field, identical=True)
+ qc = SaQC(data, flags)
- data_deharm, flags_deharm = concatFlags(
- data_harm, h_field, flags_harm, target=field, method="inverse_" + method
- )
- checkDataFlagsInvariants(data_deharm, flags_deharm, field, identical=True)
+ qc = qc.copyField("data", "data_harm")
+ qc = qc.shift(h_field, freq, method=method)
+ assert qc.data[h_field].equals(expected)
+ checkDataFlagsInvariants(qc._data, qc._flags, field, identical=True)
+
+ qc = qc.concatFlags(h_field, target=field, method="inverse_" + method)
+ checkDataFlagsInvariants(qc._data, qc._flags, field, identical=True)
- data_deharm, flags_deharm = dropField(data_deharm, h_field, flags_deharm)
- assert data_deharm[field].equals(pre_data[field])
- assert flags_deharm[field].equals(pre_flags[field])
+ qc = qc.dropField(h_field)
+ assert qc.data[field].equals(pre_data[field])
+ assert qc.flags[field].equals(pre_flags[field])
diff --git a/tests/funcs/test_modelling.py b/tests/funcs/test_modelling.py
deleted file mode 100644
index a768c8658..000000000
--- a/tests/funcs/test_modelling.py
+++ /dev/null
@@ -1,122 +0,0 @@
-#! /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.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")
-@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_2")])
-def test_modelling_polyFit_forRegular(dat):
- data, _ = dat(
- freq="10min", periods=30, initial_level=0, final_level=100, out_val=-100
- )
- # add some nice sine distortion
- data = data + 10 * np.sin(np.arange(0, len(data.indexes[0])))
- data = dios.DictOfSeries(data)
- flags = initFlagsLike(data)
- 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, _ = calculatePolynomialResiduals(
- data, "data", flags, 11, 2, numba=True, min_periods=11
- )
- assert (result4["data"] - result2["data"]).abs().max() < 10**-10
- data.iloc[13:16] = np.nan
- result5, _ = calculatePolynomialResiduals(
- data, "data", flags, 11, 2, numba=True, min_periods=9
- )
- assert result5["data"].iloc[10:19].isna().all()
-
-
-@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_2")])
-def test_modelling_rollingMean_forRegular(dat):
- data, _ = dat(
- freq="10min", periods=30, initial_level=0, final_level=100, out_val=-100
- )
- data = dios.DictOfSeries(data)
- flags = initFlagsLike(data)
- calculateRollingResiduals(
- data,
- "data",
- flags,
- 5,
- func=np.mean,
- min_periods=0,
- center=True,
- )
- calculateRollingResiduals(
- data,
- "data",
- flags,
- 5,
- func=np.mean,
- min_periods=0,
- center=False,
- )
-
-
-@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_1")])
-def test_modelling_mask(dat):
- data, _ = dat()
- data = dios.DictOfSeries(data)
- flags = initFlagsLike(data)
- field = "data"
-
- # set flags everywhere to test unflagging
- flags[:, field] = BAD
-
- common = dict(data=data, field=field, flags=flags, mode="periodic")
- 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)
- assert all(flags_seasonal[field][m] == UNFLAGGED)
- assert all(data_seasonal[field][m].isna())
-
- 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 = selectTime(
- **common, start="03T00:00:00", end="10T00:00:00"
- )
- flagscol = flags_seasonal[field]
- m = (3 <= flagscol.index.hour) & (flagscol.index.hour <= 10)
- assert all(flags_seasonal[field][m] == UNFLAGGED)
- assert all(data_seasonal[field][m].isna())
-
- mask_ser = pd.Series(False, index=data["data"].index)
- mask_ser[::5] = True
- data["mask_ser"] = mask_ser
- flags = initFlagsLike(data)
- 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)
- assert all(data_masked[field][m].isna())
diff --git a/tests/funcs/test_outier_detection.py b/tests/funcs/test_outlier_detection.py
similarity index 68%
rename from tests/funcs/test_outier_detection.py
rename to tests/funcs/test_outlier_detection.py
index 21da1d6ef..921a82e53 100644
--- a/tests/funcs/test_outier_detection.py
+++ b/tests/funcs/test_outlier_detection.py
@@ -13,16 +13,9 @@ import pandas as pd
import pytest
import dios
+import saqc
from saqc.constants import BAD, UNFLAGGED
-from saqc.core import initFlagsLike
-from saqc.funcs.outliers import (
- flagByGrubbs,
- flagCrossStatistics,
- flagMAD,
- flagMVScores,
- flagOffset,
- flagRaise,
-)
+from saqc.core import SaQC, initFlagsLike
from tests.fixtures import char_dict, course_1, course_2, course_3, course_4
@@ -40,8 +33,8 @@ def test_flagMad(spiky_data):
data = spiky_data[0]
field, *_ = data.columns
flags = initFlagsLike(data)
- data, flags_result = flagMAD(data, field, flags, "1H", flag=BAD)
- flag_result = flags_result[field]
+ qc = SaQC(data, flags).flagMAD(field, "1H", flag=BAD)
+ flag_result = qc.flags[field]
test_sum = (flag_result[spiky_data[1]] == BAD).sum()
assert test_sum == len(spiky_data[1])
@@ -50,10 +43,10 @@ def test_flagSpikesBasic(spiky_data):
data = spiky_data[0]
field, *_ = data.columns
flags = initFlagsLike(data)
- data, flags_result = flagOffset(
- data, field, flags, thresh=60, tolerance=10, window="20min", flag=BAD
+ qc = SaQC(data, flags).flagOffset(
+ field, thresh=60, tolerance=10, window="20min", flag=BAD
)
- flag_result = flags_result[field]
+ flag_result = qc.flags[field]
test_sum = (flag_result[spiky_data[1]] == BAD).sum()
assert test_sum == len(spiky_data[1])
@@ -73,19 +66,17 @@ def test_flagSpikesLimitRaise(dat):
data, characteristics = dat()
field, *_ = data.columns
flags = initFlagsLike(data)
- _, flags_result = flagRaise(
- data,
+ qc = SaQC(data, flags).flagRaise(
field,
- flags,
thresh=2,
freq="10min",
raise_window="20min",
numba_boost=False,
flag=BAD,
)
- assert np.all(flags_result[field][characteristics["raise"]] > UNFLAGGED)
- assert not np.any(flags_result[field][characteristics["return"]] > UNFLAGGED)
- assert not np.any(flags_result[field][characteristics["drop"]] > UNFLAGGED)
+ assert np.all(qc.flags[field][characteristics["raise"]] > UNFLAGGED)
+ assert not np.any(qc.flags[field][characteristics["return"]] > UNFLAGGED)
+ assert not np.any(qc.flags[field][characteristics["drop"]] > UNFLAGGED)
# see test/functs/fixtures.py for the 'course_N'
@@ -110,16 +101,14 @@ def test_flagMVScores(dat):
s2 = pd.Series(data=s2.values, index=s1.index)
data = dios.DictOfSeries([s1, s2], columns=["field1", "field2"])
flags = initFlagsLike(data)
- _, flags_result = flagMVScores(
- data=data,
+ qc = SaQC(data, flags).flagMVScores(
field=fields,
- flags=flags,
trafo=np.log,
iter_start=0.95,
n=10,
flag=BAD,
)
- _check(fields, flags_result, characteristics)
+ _check(fields, qc.flags, characteristics)
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_3")])
@@ -134,10 +123,8 @@ def test_grubbs(dat):
out_val=-10,
)
flags = initFlagsLike(data)
- data, result_flags = flagByGrubbs(
- data, "data", flags, window=20, min_periods=15, flag=BAD
- )
- assert np.all(result_flags["data"][char_dict["drop"]] > UNFLAGGED)
+ qc = SaQC(data, flags).flagByGrubbs("data", window=20, min_periods=15, flag=BAD)
+ assert np.all(qc.flags["data"][char_dict["drop"]] > UNFLAGGED)
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_2")])
@@ -151,9 +138,39 @@ def test_flagCrossStatistics(dat):
data = dios.DictOfSeries([s1, s2], columns=["field1", "field2"])
flags = initFlagsLike(data)
- _, flags_result = flagCrossStatistics(
- data, fields, flags, thresh=3, method=np.mean, flag=BAD
+ qc = SaQC(data, flags).flagCrossStatistics(
+ fields, thresh=3, method=np.mean, flag=BAD
)
for field in fields:
- isflagged = flags_result[field] > UNFLAGGED
+ isflagged = qc.flags[field] > UNFLAGGED
assert isflagged[characteristics["raise"]].all()
+
+
+def test_flagZScores():
+ np.random.seed(seed=1)
+ data = pd.Series(
+ [np.random.normal() for k in range(100)],
+ index=pd.date_range("2000", freq="1D", periods=100),
+ name="data",
+ )
+ data.iloc[[5, 80]] = 5
+ data.iloc[[40]] = -6
+ qc = saqc.SaQC(data)
+ qc = qc.flagZScore("data", window=None)
+
+ assert (qc.flags.to_df().iloc[[5, 40, 80], 0] > 0).all()
+
+ qc = saqc.SaQC(data)
+ qc = qc.flagZScore("data", window=None, min_residuals=10)
+
+ assert (qc.flags.to_df()["data"] < 0).all()
+
+ qc = saqc.SaQC(data)
+ qc = qc.flagZScore("data", window="20D")
+
+ assert (qc.flags.to_df().iloc[[40, 80], 0] > 0).all()
+
+ qc = saqc.SaQC(data)
+ qc = qc.flagZScore("data", window=20)
+
+ assert (qc.flags.to_df().iloc[[40, 80], 0] > 0).all()
diff --git a/tests/funcs/test_pattern_rec.py b/tests/funcs/test_pattern_rec.py
index 037bba240..7b9e08716 100644
--- a/tests/funcs/test_pattern_rec.py
+++ b/tests/funcs/test_pattern_rec.py
@@ -8,12 +8,10 @@
import pandas as pd
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
+from saqc.core import SaQC, initFlagsLike
from tests.common import initData
@@ -36,16 +34,14 @@ def test_flagPattern_dtw(plot, normalize):
data = dios.DictOfSeries(dict(data=data, pattern_data=pattern))
flags = initFlagsLike(data, name="data")
- data, flags = flagPatternByDTW(
- data,
+ qc = SaQC(data, flags).flagPatternByDTW(
"data",
- flags,
reference="pattern_data",
plot=plot,
normalize=normalize,
flag=BAD,
)
- assert all(flags["data"].iloc[10:18] == BAD)
- assert all(flags["data"].iloc[:9] == UNFLAGGED)
- assert all(flags["data"].iloc[18:] == UNFLAGGED)
+ assert all(qc.flags["data"].iloc[10:18] == BAD)
+ assert all(qc.flags["data"].iloc[:9] == UNFLAGGED)
+ assert all(qc.flags["data"].iloc[18:] == UNFLAGGED)
diff --git a/tests/funcs/test_proc_functions.py b/tests/funcs/test_proc_functions.py
index 056a78b42..b1dd89607 100644
--- a/tests/funcs/test_proc_functions.py
+++ b/tests/funcs/test_proc_functions.py
@@ -16,15 +16,7 @@ import pytest
import dios
import saqc
from saqc.constants import UNFLAGGED
-from saqc.core import initFlagsLike
-from saqc.funcs.drift import correctOffset
-from saqc.funcs.interpolation import (
- interpolateByRolling,
- interpolateIndex,
- interpolateInvalid,
-)
-from saqc.funcs.resampling import resample
-from saqc.funcs.transformation import transform
+from saqc.core import SaQC, initFlagsLike
from saqc.lib.ts_operators import linearInterpolation, polynomialInterpolation
from tests.fixtures import char_dict, course_3, course_5
@@ -34,28 +26,24 @@ def test_rollingInterpolateMissing(course_5):
field = data.columns[0]
data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
- dataInt, *_ = interpolateByRolling(
- data.copy(),
+ qc = SaQC(data, flags).interpolateByRolling(
field,
- flags.copy(),
3,
func=np.median,
center=True,
min_periods=0,
interpol_flag=UNFLAGGED,
)
- assert dataInt[field][characteristics["missing"]].notna().all()
- dataInt, *_ = interpolateByRolling(
- data.copy(),
+ assert qc.data[field][characteristics["missing"]].notna().all()
+ qc = SaQC(data, flags).interpolateByRolling(
field,
- flags.copy(),
3,
func=np.nanmean,
center=False,
min_periods=3,
interpol_flag=UNFLAGGED,
)
- assert dataInt[field][characteristics["missing"]].isna().all()
+ assert qc.data[field][characteristics["missing"]].isna().all()
def test_interpolateMissing(course_5):
@@ -63,23 +51,23 @@ def test_interpolateMissing(course_5):
field = data.columns[0]
data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
- dataLin, *_ = interpolateInvalid(data, field, flags, method="linear")
- dataPoly, *_ = interpolateInvalid(data, field, flags, method="polynomial")
- assert dataLin[field][characteristics["missing"]].notna().all()
- assert dataPoly[field][characteristics["missing"]].notna().all()
+ qc = SaQC(data, flags)
+
+ qc_lin = qc.interpolateInvalid(field, method="linear")
+ qc_poly = qc.interpolateInvalid(field, method="polynomial")
+ assert qc_lin.data[field][characteristics["missing"]].notna().all()
+ assert qc_poly.data[field][characteristics["missing"]].notna().all()
+
data, characteristics = course_5(periods=10, nan_slice=[5, 6, 7])
- dataLin1, *_ = interpolateInvalid(
- data.copy(), field, flags, method="linear", limit=2
- )
- dataLin2, *_ = interpolateInvalid(
- data.copy(), field, flags, method="linear", limit=3
- )
- dataLin3, *_ = interpolateInvalid(
- data.copy(), field, flags, method="linear", limit=4
- )
- assert dataLin1[field][characteristics["missing"]].isna().all()
- assert dataLin2[field][characteristics["missing"]].isna().all()
- assert dataLin3[field][characteristics["missing"]].notna().all()
+
+ qc = SaQC(data, flags)
+ qc_lin_1 = qc.interpolateInvalid(field, method="linear", limit=2)
+ qc_lin_2 = qc.interpolateInvalid(field, method="linear", limit=3)
+ qc_lin_3 = qc.interpolateInvalid(field, method="linear", limit=4)
+
+ assert qc_lin_1.data[field][characteristics["missing"]].isna().all()
+ assert qc_lin_2.data[field][characteristics["missing"]].isna().all()
+ assert qc_lin_3.data[field][characteristics["missing"]].notna().all()
def test_transform(course_5):
@@ -87,50 +75,45 @@ def test_transform(course_5):
field = data.columns[0]
data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
- data1, *_ = transform(data, field, flags, func=linearInterpolation)
- assert data1[field][characteristics["missing"]].isna().all()
- data1, *_ = transform(
- data, field, flags, func=lambda x: linearInterpolation(x, inter_limit=3)
- )
- assert data1[field][characteristics["missing"]].notna().all()
- data1, *_ = transform(
- data,
+ qc = SaQC(data, flags)
+
+ result = qc.transform(field, func=linearInterpolation)
+ assert result.data[field][characteristics["missing"]].isna().all()
+
+ result = qc.transform(field, func=lambda x: linearInterpolation(x, inter_limit=3))
+ assert result.data[field][characteristics["missing"]].notna().all()
+
+ result = qc.transform(
field,
- flags,
func=lambda x: polynomialInterpolation(x, inter_limit=3, inter_order=3),
)
- assert data1[field][characteristics["missing"]].notna().all()
+ assert result.data[field][characteristics["missing"]].notna().all()
def test_resample(course_5):
- data, characteristics = course_5(
- freq="1min", periods=30, nan_slice=[1, 11, 12, 22, 24, 26]
- )
+ data, _ = course_5(freq="1min", periods=30, nan_slice=[1, 11, 12, 22, 24, 26])
field = data.columns[0]
data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
- data1, *_ = resample(
- data,
+ qc = SaQC(data, flags).resample(
field,
- flags,
"10min",
np.mean,
maxna=2,
maxna_group=1,
)
- assert ~np.isnan(data1[field].iloc[0])
- assert np.isnan(data1[field].iloc[1])
- assert np.isnan(data1[field].iloc[2])
+ assert ~np.isnan(qc.data[field].iloc[0])
+ assert np.isnan(qc.data[field].iloc[1])
+ assert np.isnan(qc.data[field].iloc[2])
def test_interpolateGrid(course_5, course_3):
data, _ = course_5()
- data_grid, characteristics = course_3()
+ data_grid, _ = course_3()
data["grid"] = data_grid.to_df()
- # data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
- dataInt, *_ = interpolateIndex(
- data, "data", flags, "1h", "time", grid_field="grid", limit=10
+ SaQC(data, flags).interpolateIndex(
+ "data", "1h", "time", grid_field="grid", limit=10
)
@@ -139,13 +122,34 @@ def test_offsetCorrecture():
data = pd.Series(0, index=pd.date_range("2000", freq="1d", periods=100), name="dat")
data.iloc[30:40] = -100
data.iloc[70:80] = 100
- data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
- data, _ = correctOffset(data, "dat", flags, 40, 20, "3d", 1)
- assert (data == 0).all()[0]
+ qc = SaQC(data, flags).correctOffset("dat", 40, 20, "3d", 1)
+ assert (qc.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")
+
+
+@pytest.mark.parametrize(
+ "data",
+ [
+ pd.Series(
+ [
+ np.random.normal(loc=1 + k * 0.1, scale=3 * (1 - (k * 0.001)))
+ for k in range(100)
+ ],
+ index=pd.date_range("2000", freq="1D", periods=100),
+ name="data",
+ )
+ ],
+)
+def test_assignZScore(data):
+ qc = saqc.SaQC(data)
+ qc = qc.assignZScore("data", window="20D")
+ mean_res = qc.data["data"].mean()
+ std_res = qc.data["data"].std()
+ assert -0.1 < mean_res < 0.1
+ assert 0.9 < std_res < 1.1
diff --git a/tests/funcs/test_tools.py b/tests/funcs/test_tools.py
index 0d6c634f9..8ec0b5262 100644
--- a/tests/funcs/test_tools.py
+++ b/tests/funcs/test_tools.py
@@ -8,7 +8,6 @@ import pytest
import dios
import saqc
-from saqc.lib.plotting import makeFig
@pytest.mark.slow
diff --git a/tests/fuzzy/test_masking.py b/tests/fuzzy/test_masking.py
index 3f49df708..0d0a49e92 100644
--- a/tests/fuzzy/test_masking.py
+++ b/tests/fuzzy/test_masking.py
@@ -11,7 +11,7 @@ import pytest
from hypothesis import given, settings
from saqc.constants import BAD, UNFLAGGED
-from saqc.core.register import FunctionWrapper
+from saqc.core.register import _maskData, _unmaskData
from tests.fuzzy.lib import MAX_EXAMPLES, dataFieldFlags
@@ -23,7 +23,7 @@ def test_maskingMasksData(data_field_flags):
test if flagged values are replaced by np.nan
"""
data_in, field, flags = data_field_flags
- data_masked, mask = FunctionWrapper._maskData(
+ data_masked, mask = _maskData(
data_in, flags, columns=[field], thresh=UNFLAGGED
) # thresh UNFLAGGED | np.inf
assert data_masked[field].iloc[mask[field].index].isna().all()
@@ -42,11 +42,9 @@ def test_dataMutationPreventsUnmasking(data_field_flags):
data_in, field, flags = data_field_flags
- data_masked, mask = FunctionWrapper._maskData(
- data_in, flags, columns=[field], thresh=UNFLAGGED
- )
+ data_masked, mask = _maskData(data_in, flags, columns=[field], thresh=UNFLAGGED)
data_masked[field] = filler
- data_out = FunctionWrapper._unmaskData(data_masked, mask)
+ data_out = _unmaskData(data_masked, mask)
assert (data_out[field] == filler).all(axis=None)
@@ -60,11 +58,9 @@ def test_flagsMutationPreventsUnmasking(data_field_flags):
"""
data_in, field, flags = data_field_flags
- data_masked, mask = FunctionWrapper._maskData(
- data_in, flags, columns=[field], thresh=UNFLAGGED
- )
+ data_masked, mask = _maskData(data_in, flags, columns=[field], thresh=UNFLAGGED)
flags[:, field] = UNFLAGGED
- data_out = FunctionWrapper._unmaskData(data_masked, mask)
+ data_out = _unmaskData(data_masked, mask)
assert (data_out.loc[flags[field] == BAD, field].isna()).all(axis=None)
@@ -82,9 +78,7 @@ def test_reshapingPreventsUnmasking(data_field_flags):
data_in, field, flags = data_field_flags
- data_masked, mask = FunctionWrapper._maskData(
- data_in, flags, columns=[field], thresh=UNFLAGGED
- )
+ data_masked, mask = _maskData(data_in, flags, columns=[field], thresh=UNFLAGGED)
# mutate indexes of `data` and `flags`
index = data_masked[field].index.to_series()
index.iloc[-len(data_masked[field]) // 2 :] += pd.Timedelta("7.5Min")
@@ -94,7 +88,7 @@ def test_reshapingPreventsUnmasking(data_field_flags):
flags.drop(field)
flags[field] = pd.Series(data=fflags.values, index=index)
- data_out = FunctionWrapper._unmaskData(data_masked, mask)
+ data_out = _unmaskData(data_masked, mask)
assert (data_out[field] == filler).all(axis=None)
@@ -107,10 +101,8 @@ def test_unmaskingInvertsMasking(data_field_flags):
"""
data_in, field, flags = data_field_flags
- data_masked, mask = FunctionWrapper._maskData(
- data_in, flags, columns=[field], thresh=UNFLAGGED
- )
- data_out = FunctionWrapper._unmaskData(data_masked, mask)
+ data_masked, mask = _maskData(data_in, flags, columns=[field], thresh=UNFLAGGED)
+ data_out = _unmaskData(data_masked, mask)
assert pd.DataFrame.equals(
data_out.to_df().astype(float), data_in.to_df().astype(float)
)
diff --git a/tests/requirements.txt b/tests/requirements.txt
index a18d98d1b..539d74b9d 100644
--- a/tests/requirements.txt
+++ b/tests/requirements.txt
@@ -2,7 +2,7 @@
#
# SPDX-License-Identifier: GPL-3.0-or-later
-pytest==7.1.2
+pytest==7.1.3
pytest-lazy-fixture==0.6.3
Markdown==3.3.7
beautifulsoup4==4.11.1
--
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