Component A : Setup config and Pre-processing pipeline
\nStep 1: Code pipeline
\nStep 2: Setting up global config
\nStep 3: Structure Sub National region
\nStep 4: Convert Power plants from csv file to Geolocation shape file
\nTo get power plants in a provided sub national regional polygon as a data frame use below code.
\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"from emissionspatial import emissionSpatialLayer\nfrom emissionglobals import appGlobal\nfrom emissionconfig import appConfig\n\nobjConfig = appConfig(\"/kaggle/input/ds4gconfiguration/config_kaggle.json\")\noutputPath = \"/kaggle/working/output/\"\n\naGlobal = appGlobal(objConfig, outputPath)\n\nlayerList = objConfig.getLayerList()\n\ndef getGeneratorXYLocaiton():\n \"\"\"\n This method get power plant x,y location as dataframe\n \"\"\"\n for layer in layerList:\n print('processing layer ' + layer)\n spl = emissionSpatialLayer(aGlobal, layer) \n dictSubRegions = spl.getSpatialLayerNPArr()\n\n for subRegion in dictSubRegions:\n # get power plant x,y location as data frame\n dfGeoLocation = spl.getGeneratorXYLocation(subRegion)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Step 5: Separate Sentinel 5P, GFS, GLDAS satelite image by Timeseries
\nCompenent B - Emissions / Weather Data Processing
\nStep 2: Separate Sentinel 5P, GFS, GLDAS by Sub National region
\nStep 3: Merge GFS, GLDAS satelite imagery
\nStep 4: Calculate Emission Factor
\nStep 5: Mean Weather caluclation
\n