CAMS data textures

[sebsteinig]

This is the implementation for #73.

Changes include:

• process to create data textures added to ETL pipeline
• data textures for each pollutant PM2.5, PM10, O3, NO2, SO2 (grayscale) and winds (RGB) in WEBP or PNG format
• textures are chunked in time for sequential loading
• textures are saved in shared Docker volume /app/data_textures
• metadata and file URI are added to MongoDB in a new collection "data_textures"
• conversion metadata (units, dynamical min and max values to convert back from [0,255] range) added to collection
• shared volume added to Docker compose file
• unit tests for new forecast_adapter methods

[github-actions[bot]]

☂️ Python Coverage

current status: ✅

Overall Coverage

Lines	Covered	Coverage	Threshold	Status
1189	1144	96%	80%	🟢

New Files

File	Coverage	Status
air-quality-backend/etl/src/forecast/forecast_texture_storer.py	94%	🟢
air-quality-backend/etl/tests/forecast/forecast_texture_storer_test.py	100%	🟢
TOTAL	97%	🟢

Modified Files

File	Coverage	Status
air-quality-backend/etl/scripts/run_forecast_etl.py	97%	🟢
air-quality-backend/etl/src/forecast/forecast_adapter.py	96%	🟢
air-quality-backend/etl/src/forecast/forecast_dao.py	100%	🟢
air-quality-backend/etl/src/forecast/forecast_data.py	95%	🟢
air-quality-backend/etl/tests/forecast/forecast_adapter_test.py	100%	🟢
air-quality-backend/etl/tests/forecast/forecast_dao_test.py	100%	🟢
air-quality-backend/etl/tests/scripts/run_forecast_etl_test.py	100%	🟢
TOTAL	98%	🟢

updated for commit: a83ddf5 by action🐍