Update GettingStarted.md

docs/GettingStarted.md

@@ -79,8 +79,8 @@ Now create your our own configuration file `saqc/ressources/data/myconfig.csv`
 and paste the following lines into it:
 	
 	varname;test;plot
-	SM2;range(min=10, max=60);False
-	SM2;spikes_simpleMad(window="30d", z=3.5);True
+	SM2;flagRange(min=10, max=60);False
+	SM2;spikes_flagMad(window="30d", z=3.5);True
 
 These lines illustrate how different quality control tests can be specified for
 different variables by following the pattern:
@@ -128,6 +128,26 @@ So, what do we see here?
   only. Thus, the plot aggregates all preceeding tests (here: `range`) to black
   points and highlights the flags of the selected test as red points.
 
+
+#### Save outputs to file
+If you want the final results to be saved to a csv-file, you can do so by the
+use of the `-o` option:
+
+```sh
+saqc -c ressources/data/config.csv -d ressources/data/data.csv -o ressources/data/out.csv 
+```
+
+Which saves a dataframe that contains both the original data and the quality
+flags that were assigned by SaQC for each of the variables:
+
+	Date,SM1,SM1_flags,SM2,SM2_flags
+	2016-04-01 00:05:48,32.685,OK,29.3157,OK
+	2016-04-01 00:20:42,32.7428,OK,29.3157,OK
+	2016-04-01 00:35:37,32.6186,OK,29.3679,OK
+	2016-04-01 00:50:32,32.736999999999995,OK,29.3679,OK
+	...
+
+
 ### Configure SaQC
 
 #### Change test parameters
@@ -138,8 +158,8 @@ example, you could modify your `myconfig.csv` and change the parameters of the
 range-test:
 	
 	varname;test;plot
-	SM2;range(min=-20, max=60);False
-	SM2;spikes_simpleMad(window="30d", z=3.5);True
+	SM2;flagRange(min=-20, max=60);False
+	SM2;spikes_flagMad(window="30d", z=3.5);True
 Rerunning SaQC as above produces the following plot:
 
 ![Changing the config](images/example_plot_2.png "Changing the config")
@@ -156,10 +176,10 @@ are then executed sequentially and can be plotted seperately. E.g. you could do
 something like this:
 
 	varname;test;plot
-	SM1;range(min=10, max=60);False
-	SM2;range(min=10, max=60);False
-	SM1;spikes_simpleMad(window="15d", z=3.5);True
-	SM2;spikes_simpleMad(window="30d", z=3.5);True
+	SM1;flagRange(min=10, max=60);False
+	SM2;flagRange(min=10, max=60);False
+	SM1;spikes_flagMad(window="15d", z=3.5);True
+	SM2;spikes_flagMad(window="30d", z=3.5);True
 
 which gives you separate plots for each line where the plotting option is set to
 `True` as well as one summary "data plot" that depicts the joint flags from all
@@ -178,7 +198,7 @@ series. Also, you can write your own tests using a python-based
 [extension language](docs/GenericFunctions.md). This would look like this:
 
 	varname;test;plot
-	SM2;harmonize_shift2Grid(freq="15Min");False
+	SM2;harm_shift2Grid(freq="15Min");False
 	SM2;flagGeneric(func=(SM2 < 30));True
 
 The above executes an internal framework that harmonizes the timestamps of SM2
@@ -196,23 +216,4 @@ Also, all values where SM2 is below 30 are flagged via the custom function (see
 plot below). You can learn more about the syntax of these custom functions
 [here](docs/GenericFunctions.md).
 
-![Example custom function](images/example_plot_4.png "Example custom function")
-
-#### Save outputs to file
-If you want the final results to be saved to a csv-file, you can do so by the
-use of the `-o` option:
-
-```sh
-saqc -c ressources/data/config.csv -d ressources/data/data.csv -o ressources/data/out.csv 
-```
-
-Which saves a dataframe that contains both the original data and the quality
-flags that were assigned by SaQC for each of the variables:
-
-	Date,SM1,SM1_flags,SM2,SM2_flags
-	2016-04-01 00:05:48,32.685,OK,29.3157,OK
-	2016-04-01 00:20:42,32.7428,OK,29.3157,OK
-	2016-04-01 00:35:37,32.6186,OK,29.3679,OK
-	2016-04-01 00:50:32,32.736999999999995,OK,29.3679,OK
-	...
-
+![Example custom function](images/example_plot_4.png "Example custom function")
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