- Updated the readme on grompy commandline

README.md

@@ -28,7 +28,7 @@ This includes the paths to the different CSV files, the path to the shapefile wi
 the URI for the database where the data have to be written. The `grompy.yaml` file is the entry point for all
 other grompy operations as well as the `DataAccessProvider`.
 
-The `grompy.yaml` file can be generated with the command `grompy init <data path>` and for doing so, grompy assumes 
+The `grompy.yaml` file can be generated with the command `grompy init <year> <data path>` and for doing so, grompy assumes 
 a certain folder structure which looks like this:
 ```
 <data path> /BRP/gewaspercelen_<year>.shp
@@ -36,11 +36,13 @@ a certain folder structure which looks like this:
             /Radar/ - CSV with radar data 
 ```
 
-In practice it is most convenient to keep the `grompy.yaml` file together with the data. So change directory
+In practice it is most convenient to keep the `grompy.yaml` file together with the data and use paths relative
+to the location of the `grompy.yaml` file. In this way you can copy to the grompy.yaml file and the corresponding 
+database to another location without having to edit the `grompy.yaml` file. So change directory
 to the data folder and execute:
 ```commandline
 cd <data path>
-grompy init .
+grompy init 2019 .
 ```
 
 The init command creates the `grompy.yaml` and sets the path to the inputs/outputs based on the input
@@ -49,30 +51,30 @@ for `<data path>`. In this case, the current directory `.`. The `grompy.yaml` no
 grompy:
   version: 1.0
 parcel_info:
-  dsn: sqlite:////home/wit015/Data/groenmonitor/parcel_info.db3
-  counts_file: /home/wit015/Data/groenmonitor/Optisch/perceelscount.csv
-  shape_file: /home/wit015/Data/groenmonitor/BRP/gewaspercelen_2019.shp
+  dsn: sqlite:///./parcel_info.db3
+  counts_file: ./Optisch/perceelscount.csv
+  shape_file: ./BRP/gewaspercelen_2019.shp
   table_name: parcel_info
 datasets:
   sentinel2_reflectance_values:
-    dsn: sqlite:////home/wit015/Data/groenmonitor/sentinel2_reflectance_values.db3
+    dsn: sqlite:///./sentinel2_reflectance_values.db3
     bands:
-      NDVI: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_2019_ADC.csv
-      B02: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_B02_2019_ADC.csv
-      B03: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_B03_2019_ADC.csv
-      B04: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_B04_2019_ADC.csv
-      B05: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_B05_2019_ADC.csv
-      B06: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_B06_2019_ADC.csv
-      B07: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_B07_2019_ADC.csv
-      B08: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_B08_2019_ADC.csv
-      B11: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_B11_2019_ADC.csv
-      B12: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_B12_2019_ADC.csv
-      B8A: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_mean_B8A_2019_ADC.csv
+      NDVI: ./Optisch/zonal_stats_mean_2019_ADC.csv
+      B02: ./Optisch/zonal_stats_mean_B02_2019_ADC.csv
+      B03: ./Optisch/zonal_stats_mean_B03_2019_ADC.csv
+      B04: ./Optisch/zonal_stats_mean_B04_2019_ADC.csv
+      B05: ./Optisch/zonal_stats_mean_B05_2019_ADC.csv
+      B06: ./Optisch/zonal_stats_mean_B06_2019_ADC.csv
+      B07: ./Optisch/zonal_stats_mean_B07_2019_ADC.csv
+      B08: ./Optisch/zonal_stats_mean_B08_2019_ADC.csv
+      B11: ./Optisch/zonal_stats_mean_B11_2019_ADC.csv
+      B12: ./Optisch/zonal_stats_mean_B12_2019_ADC.csv
+      B8A: ./Optisch/zonal_stats_mean_B8A_2019_ADC.csv
   sentinel2_reflectance_std:
-    dsn: sqlite:////home/wit015/Data/groenmonitor/sentinel2_reflectance_std.db3
+    dsn: sqlite:///./sentinel2_reflectance_std.db3
     bands:
-      NDVI: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_std_2019_ADC.csv
-      B02: /home/wit015/Data/groenmonitor/Optisch/zonal_stats_std_B02_2019_ADC.csv
+      NDVI: ./Optisch/zonal_stats_std_2019_ADC.csv
+      B02: ./Optisch/zonal_stats_std_B02_2019_ADC.csv
 ...
 ```
 The `grompy.yaml` file specifies several sections:
@@ -103,13 +105,13 @@ The `grompy.yaml` is a relatively complex input structure and manually checking
 Therefore, grompy can check if the YAML file is OK by executing:
 
 ```commandline
-grompy check
+grompy check <grompy_yaml>
 ```
-It assumes that the `grompy.yaml` resides in the current directory. Grompy will now read the YAML and carry out 
-several checks, including:
+Grompy will now read the YAML and carry out several checks, including:
  - If files exists.
  - If connections to database can be opened.
  - If the CSV files of the different datasets all have the same number of lines.
+ - If the shapefile with parcel info has the required attributes.
 
 Grompy will display a lot of output on the screen. If everything is fine, the last line will show:
 ```commandline
@@ -125,8 +127,7 @@ run `grompy check` first.
 ### loading
 
 The final step is to load the parcel information and satellite observations into the database tables. This can be
-done with the `grompy load` command. Also here grompy assumes that the `grompy.yaml` file resides in the current
-directory. Grompy will now show the following output:
+done with the `grompy load <grompy_yaml>` command. Grompy will now show the following output:
 ```commandline
 Start loading parcel information. This will take some time...
 Starting loading of: sentinel1_backscatter