chore: added information about grid point mapping to country inside plot information for map

climateqa/engine/talk_to_data/ipcc/plot_informations.py

@@ -40,11 +40,11 @@ def choropleth_map_informations(
     return f"""
 This plot displays a choropleth map showing the spatial distribution of **{indicator}** across all regions of **{location}** country ({country_name}) for the year **{year}** and the chosen scenario.
 
-Each region is colored according to the value of the indicator ({unit}), allowing you to visually compare how {indicator} varies geographically within the country for the selected year and scenario.
+Each grid point is colored according to the value of the indicator ({unit}), allowing you to visually compare how {indicator} varies geographically within the country for the selected year and scenario.
 
 **Data source:**  
 - The data come from the CMIP6 IPCC ATLAS data. The data were initially extracted from [this referenced website](https://digital.csic.es/handle/10261/332744) and then preprocessed to a tabular format and uploaded as parquet in this [Hugging Face dataset](https://huggingface.co/datasets/Ekimetrics/ipcc-atlas).
-- For each region of {location} country ({country_name}), the value of {indicator} in {year} and for the selected scenario is extracted and mapped to its geographic coordinates.
-- The regions correspond to 1-degree squares centered on the grid points of the IPCC dataset.
+- For each grid point of {location} country ({country_name}), the value of {indicator} in {year} and for the selected scenario is extracted and mapped to its geographic coordinates.
+- The grid points correspond to 1-degree squares centered on the grid points of the IPCC dataset. Each grid point has been mapped to a country using [**reverse_geocoder**](https://github.com/thampiman/reverse-geocoder). 
 - The coordinates used for each region are those of the closest available grid point in the IPCC database, which uses a regular grid with a spatial resolution of 1 degree.
 """