bucks

sphinx-doc/misc/title.rst

@@ -13,6 +13,32 @@
     :width: 184 px
     :target: https://www.ufz.de/index.php?de=45348
 
+
+.. |sacRaw| image:: /ressources/images/Representative/RawData.png
+    :height: 150 px
+    :width: 288 px
+
+.. |sacFlagged| image:: /ressources/images/Representative/DifferentFlags.png
+    :height: 150 px
+    :width: 288 px
+
+.. |sacMV| image:: /ressources/images/Representative/MultivarSAC.png
+    :height: 150 px
+    :width: 288 px
+
+.. |sacProc| image:: /ressources/images/Representative/ProcessingDrift.png
+    :height: 165 px
+    :width: 316 px
+
+.. |pyLogo| image:: /ressources/images/Representative/pythonLogo.png
+    :height: 108 px
+    :width: 105 px
+
+.. |csvConfig| image:: /ressources/images/Representative/csvConfig.png
+    :height: 100 px
+    :width: 176 px
+
+
 .. image:: /ressources/images/Representative/gitlabLogo.png
     :height: 100 px
     :width: 110 px
@@ -33,8 +59,35 @@ even for experts. SaQC is both, a Python framework and a command line applicatio
 addresses the exploratory nature of quality control by offering a
 continuously growing number of quality check routines through a flexible
 and simple configuration system.
+
+
 Below its user interface, SaQC is highly customizable and extensible.
 A modular structure and well-defined interfaces make it easy to extend
 the system with custom quality checks. Furthermore, even core components like
 the flagging scheme are exchangeable.
 
+
+--------
+Features
+--------
+
+.. list-table::
+
+    * - |pyLogo| |csvConfig|
+        * SaQC is readily available from PyPI
+        * use SaQC as a commandline application and configure your labeling pipelines via plain .csv files
+        * or use the SaQC python API, enabling integration in your python processing script
+    * - |sacRaw|
+      - * easily load data from multiple sources, concatenating them in a SaQC object
+        * preprocess your data, by aligning it to equidistant frequency grids
+    * - |sacFlagged|
+      - * ally basic plausibillity checks, as well as
+        * more complex, univariat flagging Functions
+        * automatically keep track of labeling history and label significance
+    * - |sacProc|
+      - * modifyyour data, by interpolations, corrections and transformations
+        * calculate data products, such as residues or outlier scores
+        * automatically keep track of labeling history and label significance
+    * - |sacMV|
+      - * apply multivariate flagging function
+

sphinx-doc/ressources/data/hydro_config.csv

-varname    ; test
-#----------;---------------------------------------
-level      ; outliers.flagRange(min=0)
-water_temp ; outliers.flagRange(min=-.5)
-sak254 	   ; outliers.flagRange(min=0, max=70)
-turbidity  ; outliers.flagRange(min=0, max=140)
-# interpolation resampling
-level      ; resample.linear(freq='15min')
-water_temp ; resample.linear(freq='15min')
-sak254 	   ; resample.linear(freq='15min')
-turbidity  ; resample.linear(freq='15min')
-# Drift Correction
-sak254 		; drift.correctDrift(target='sak254_corr', maintenance_field='maint', model=expDriftModel)
-turbidity 	; drift.correctDrift(target='turbidity_corr', maintenance_field='maint', model=expDriftModel)
-# flag maintenance intervals
-sak254 		; flagtools.flagManual(mdata='maint')
-sak254_corr ; flagtools.flagManual(mdata='maint')
-turbidity 	; flagtools.flagManual(mdata='maint')
-turbidity_corr ; flagtools.flagManual(mdata='maint')
-# linearly fill singleton gaps
-level      ; interpolation.interpolateInvalid('linear', limit=2)
-water_temp ; interpolation.interpolateInvalid('linear', limit=2)
-sak254 	   ; interpolation.interpolateInvalid('linear', limit=2)
-turbidity  ; interpolation.interpolateInvalid('linear', limit=2)
-# transform variables
-level 	   ; transformation.transform(target='norm_level', func='zNorm', freq='20D')
-sac_corr   ; transformation.transform(target='norm_SAK', func='zNorm', freq='20D')
-turbidity_corr 	; transformation.transform(target='norm_turbidity_corr', func='zNorm', freq='20D')
-water_temp 		; transformation.transform('norm_water_temp', func='zNorm', freq='20D')
-# nearest neighbor scoring
+varname    	; test
+#---------------;---------------------------------------
+level      	; flagRange(min=0)
+water_temp 	; flagRange(min=-.5)
+sak254 	   	; flagRange(min=0, max=70)
+turbidity  	; flagRange(min=0, max=140)
+level      	; linear(freq='15min')
+water_temp 	; linear(freq='15min')
+sak254 	   	; linear(freq='15min')
+turbidity  	; linear(freq='15min')
+sak254 	   	; correctDrift(target='sak254_corr', maintenance_field='maint', model=expDriftModel)
+turbidity  	; correctDrift(target='turbidity_corr', maintenance_field='maint', model=expDriftModel)
+sak254 	   	; flagManual(mdata='maint')
+sak254_corr	; flagManual(mdata='maint')
+turbidity  	; flagManual(mdata='maint')
+turbidity_corr  ; flagManual(mdata='maint')
+level      	; interpolateInvalid('linear', limit=2)
+water_temp 	; interpolateInvalid('linear', limit=2)
+sak254 	   	; interpolateInvalid('linear', limit=2)
+turbidity  	; interpolateInvalid('linear', limit=2)
+level 	   	; transform(target='norm_level', func='zNorm', freq='20D')
+sac_corr   	; transform(target='norm_SAK', func='zNorm', freq='20D')
+turbidity_corr 	; transform(target='norm_turbidity_corr', func='zNorm', freq='20D')
+water_temp 	; transform('norm_water_temp', func='zNorm', freq='20D')
 norm_level, norm_sac_corr, norm_turbidity_corr, norm_water_temp ; scores.assignKNNScore(targe='kNN_scores', freq='20D')
 kNN_scores 	; outliers.flagByStray(freq='20D')
-# project stray results
 level, sac_corr, turbidity_corr, water_temp;generic.projectFlags(source='kNNscores', 'match')
 # undo interpolation resampling
 level 			; generic.projectFlags(source='level_original', 'inverse_interpolation')