@@ -24,8 +24,11 @@ The order in which the scripts in the present repository need to be run to produ
8. If the OpenQuake input files for the SERA model distributed onto a grid are desired (i.e. not GDE, just SERA), run `SERA_create_OQ_input_files.py` with the desired distribution method.
9. If a CSV summarising the number of buildings, dwellings, people and costs by cell according to the SERA model is desired (i.e. not GDE, just SERA), run `SERA_create_visual_output_of_grid_model_full_files.py` with the desired distribution method.
10. Run `OBM_buildings_per_cell.py` with the desired distribution method.
11. Run `GDE_gather_SERA_and_OBM.py` with the desired distribution method. The output is a series of CSV files that serve as input for damage/risk calculations to be run in OpenQuake (https://github.com/gem/oq-engine), a CSV file that summarises results per cell and contains the geometry of the cells so that it can all be visualised with a GIS, and a CSV file that summarises results per adminstrative unit and contains the geometry of the administrative boundaries so that it can all be visualised with a GIS.
11. Run `GDE_gather_SERA_and_OBM.py` with the desired distribution method. The output is:
a. a series of CSV files that serve as input for damage/risk calculations to be run in OpenQuake (https://github.com/gem/oq-engine);
b. a CSV file that summarises results per cell and contains the geometry of the cells so that it can all be visualised with a GIS;
c. a CSV file that summarises results per adminstrative unit and contains the geometry of the administrative boundaries so that it can all be visualised with a GIS;
d. a series of HDF5 files (one per cell) that contain the final GDE model.
## Testing Scripts
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@@ -41,6 +44,7 @@ The order in which the scripts in the present repository need to be run to produ
- The script `GDE_check_OQ_input_files.py` can be run after step 11 above. It prints to screen some summary values of the files and checks that the asset ID values are all unique.
- The script `GDE_check_tiles_vs_visual_CSVs.py` can be run after step 11 above. It reads the visual CSV output by cell and the corresponding GDE tiles HDF5 files and compares the number of buildings, cost and number of people in each cell according to each of the two. An output CSV file collects the discrepancies found, if any.