titlepage md file added

sphinx-doc/cook_books_md_m2r/MultivariateFlagging.rst

@@ -24,7 +24,7 @@ Data preparation
 
 * Flagging missing values via :py:func:`flagMissing <Functions.saqc.flagMissing>`.
 * Flagging out of range values via :py:func:`flagRange <Functions.saqc.flagRange>`.
-* Flagging values, where the Specific Conductance (\ *K25*\ ) drops down to near zero. (via :py:func:`flagGeneric <Functions.saqc.flagGeneric>`)
+* Flagging values, where the Specific Conductance (\ *K25*\ ) drops down to near zero. (via :py:func:`flagGeneric <Functions.saqc.flag>`)
 * Resampling the data via linear Interpolation (:py:func:`linear <Functions.saqc.linear>`).
 
 Drift Correction
@@ -63,11 +63,11 @@ Basically following the *oddWater* procedure, as suggested in *Talagala, P.D. et
 * Variables *SAK254*\ , *Turbidity*\ , *Pegel*\ , *NO3N*\ , *WaterTemp* and *pH* get transformed to comparable scales
 * We are obtaining nearest neighbor scores and assigign those to a new variable, via :py:func:`assignKNNScores <Functions.saqc.assignKNNScores>`.
 * We are applying the *STRAY* Algorithm to find the cut_off points for the scores, above which values qualify as outliers. (:py:func:`flagByStray <Functions.saqc.flagByStray>`)
-* We project the calculated flags onto the input variables via :py:func:`assignKNNScores <Functions.saqc.assignKNNScores>`.
+* We project the calculated flags onto the input variables via :py:func:`assignKNNScore <Functions.saqc.assignKNNScore>`.
 
-Posprocessing
--------------
+Postprocessing
+--------------
 
 
 * (Flags reduction onto subspaces)
-* Backprojection of calculated flags from resampled Data onto original data via :py:func ``mapToOriginal <Functions.saqc.assignKNNScores>``
+* Back projection of calculated flags from resampled Data onto original data via :py:func: ``mapToOriginal <Functions.saqc.mapToOriginal>``

sphinx-doc/cook_books_md_m2r/OutlierDetection.rst

@@ -243,6 +243,8 @@ To see all the results obtained so far, plotted in one figure window, we make us
    :alt: 
 
 
+<<<<<<< HEAD
+
 Residues and Scores
 -------------------
 
@@ -263,6 +265,16 @@ control via the :py:attr:`target` parameter.
 .. code-block:: python
 
    i_saqc = i_saqc.procesGeneric(['incidents', 'incidents_model'], target='incidents_residues', func=lambda x, y: x - y)
+   =======
+   We want to evaluate the residues of the model, in order to score the outlierish-nes of every point. 
+   First, we retrieve the residues via the :py:func:`saqc.genericProcess <docs.func_modules.genericProcess>` method.
+   The method generates a new variable, resulting from the processing of other variables. It automatically
+   generates the field name it gets passed - so we do not have to generate new variable beforehand. The function we apply 
+   is just the computation of the variables difference for any timestep.
+
+   ```python
+   i_saqc = i_saqc.genericProcess('incidents_residues', func=lambda incidents, incidents_model:incidents - incidents_model)
+   >>>>>>> develop
 
 Scores
 ^^^^^^
@@ -311,7 +323,7 @@ residues *mean* and *standard deviation* seperately:
    i_saqc = i_saqc.rolling.roll(field='incidents_residues', target='residues_std',
                                 window='27D',
                                 func=np.std)
-   i_saqc = i_saqc.generic.process(field='incidents_scores',
+   i_saqc = i_saqc.genericProcess(field='incidents_scores',
                                    func=lambda This, residues_mean, residues_std: (
                                                                                               This - residues_mean) / residues_std)
 

sphinx-doc/index.rst

@@ -2,6 +2,8 @@
 SaQC documentation
 ==================
 
+.. include:: misc_md_m2r/TitlePage.rst
+
 .. toctree::
    :hidden:
    :maxdepth: 1

sphinx-doc/misc_md/TitlePage.md

+# SaQC