chore: added geojson polygon function and custom colorscale per indicator

climateqa/engine/talk_to_data/drias/config.py

@@ -1,4 +1,7 @@
 
+from climateqa.engine.talk_to_data.ui_config import PRECIPITATION_COLORSCALE, TEMPERATURE_COLORSCALE
+
+
 DRIAS_TABLES = [
     "total_winter_precipitation",
     "total_summer_precipitation",
@@ -74,6 +77,24 @@ DRIAS_PLOT_PARAMETERS = [
     'year',
     'location'
 ]
+
+DRIAS_INDICATOR_TO_COLORSCALE = {
+    "total_winter_precipitation": PRECIPITATION_COLORSCALE,
+    "total_summer_precipitation": PRECIPITATION_COLORSCALE,
+    "total_annual_precipitation": PRECIPITATION_COLORSCALE,
+    "total_remarkable_daily_precipitation": PRECIPITATION_COLORSCALE,
+    "frequency_of_remarkable_daily_precipitation": PRECIPITATION_COLORSCALE,
+    "extreme_precipitation_intensity": PRECIPITATION_COLORSCALE,
+    "mean_winter_temperature":TEMPERATURE_COLORSCALE,
+    "mean_summer_temperature":TEMPERATURE_COLORSCALE,
+    "mean_annual_temperature":TEMPERATURE_COLORSCALE,
+    "number_tropical_nights": TEMPERATURE_COLORSCALE,
+    "maximum_summer_temperature":TEMPERATURE_COLORSCALE,
+    "number_of_days_with_tx_above_30": TEMPERATURE_COLORSCALE,
+    "number_of_days_with_tx_above_35": TEMPERATURE_COLORSCALE,
+    "number_of_days_with_dry_ground": TEMPERATURE_COLORSCALE
+}
+
 DRIAS_UI_TEXT = """
 Hi, I'm **Talk to Drias**, designed to answer your questions using [**DRIAS - TRACC 2023**](https://www.drias-climat.fr/accompagnement/sections/401) data.  
 I'll answer by displaying a list of SQL queries, graphs and data most relevant to your question.

climateqa/engine/talk_to_data/drias/plots.py

@@ -10,8 +10,29 @@ from climateqa.engine.talk_to_data.drias.queries import (
     indicator_for_given_year_query,
     indicator_per_year_at_location_query,
 )
-from climateqa.engine.talk_to_data.drias.config import DRIAS_INDICATOR_TO_UNIT
-
+from climateqa.engine.talk_to_data.drias.config import DRIAS_INDICATOR_TO_COLORSCALE, DRIAS_INDICATOR_TO_UNIT
+
+def generate_geojson_polygons(latitudes: list[float], longitudes: list[float], indicators: list[float]) -> geojson.FeatureCollection:
+    side_km = 8
+    delta_lat = side_km / 111
+    features = []
+    for idx, (lat, lon, val) in enumerate(zip(latitudes, longitudes, indicators)):
+        delta_lon  = side_km / (111 * cos(radians(lat)))
+        half_lat = delta_lat / 2
+        half_lon = delta_lon / 2
+        features.append(geojson.Feature(
+                geometry=geojson.Polygon([[
+                    [lon - half_lon, lat - half_lat],
+                    [lon + half_lon, lat - half_lat],
+                    [lon + half_lon, lat + half_lat],
+                    [lon - half_lon, lat + half_lat],
+                    [lon - half_lon, lat - half_lat]
+                ]]),
+                properties={"value": val},
+                id=str(idx)
+            ))
+
+    return geojson.FeatureCollection(features)
 
 def plot_indicator_evolution_at_location(params: dict) -> Callable[..., Figure]:
     """Generates a function to plot indicator evolution over time at a location.
@@ -359,50 +380,19 @@ def plot_map_of_france_of_indicator_for_given_year(
             longitudes = df_model["longitude"].astype(float).tolist()
             model_label = f"Model : {df['model'].unique()[0]}"
 
-        side_km = 8
-        delta_lat = side_km / 111
-        features = []
-        for idx, (lat, lon, val) in enumerate(zip(latitudes, longitudes, indicators)):
-            delta_lon  = side_km / (111 * cos(radians(lat)))
-            half_lat = delta_lat / 2
-            half_lon = delta_lon / 2
-            features.append(geojson.Feature(
-                    geometry=geojson.Polygon([[
-                        [lon - half_lon, lat - half_lat],
-                        [lon + half_lon, lat - half_lat],
-                        [lon + half_lon, lat + half_lat],
-                        [lon - half_lon, lat + half_lat],
-                        [lon - half_lon, lat - half_lat]
-                    ]]),
-                    properties={"value": val},
-                    id=str(idx)
-                ))
-
-        geojson_data = geojson.FeatureCollection(features)
-
-        custom_colorscale = [
-            [0.0, "rgb(5, 48, 97)"],
-            [0.10, "rgb(33, 102, 172)"],
-            [0.20, "rgb(67, 147, 195)"],
-            [0.30, "rgb(146, 197, 222)"],
-            [0.40, "rgb(209, 229, 240)"],
-            [0.50, "rgb(247, 247, 247)"],
-            [0.60, "rgb(253, 219, 199)"],
-            [0.75, "rgb(244, 165, 130)"],
-            [0.85, "rgb(214, 96, 77)"],
-            [0.90, "rgb(178, 24, 43)"],
-            [1.0, "rgb(103, 0, 31)"]
-        ]
+
+
+        geojson_data = generate_geojson_polygons(latitudes, longitudes, indicators)
 
         fig = go.Figure(go.Choroplethmapbox(
             geojson=geojson_data,
             locations=[str(i) for i in range(len(indicators))],
             featureidkey="id",
             z=indicators,
-            colorscale=custom_colorscale,
+            colorscale=DRIAS_INDICATOR_TO_COLORSCALE[indicator],
             zmin=min(indicators),
             zmax=max(indicators),
-            marker_opacity=0.7,
+            marker_opacity=0.6,
             marker_line_width=0,
             colorbar_title=f"{indicator_label} ({unit})",
             text=[f"{indicator_label}: {value:.2f} {unit}" for value in indicators],  # Add hover text showing the indicator value

climateqa/engine/talk_to_data/ipcc/config.py

@@ -1,3 +1,6 @@
+from climateqa.engine.talk_to_data.ui_config import PRECIPITATION_COLORSCALE, TEMPERATURE_COLORSCALE
+
+
 IPCC_DATASET_URL = "hf://datasets/ekimetrics/ipcc-atlas"
 IPCC_TABLES = [
     "mean_temperature",
@@ -55,6 +58,11 @@ MACRO_COUNTRIES = ['JP',
  'RU'
 ]
 
+IPCC_INDICATOR_TO_COLORSCALE = {
+    "mean_temperature": TEMPERATURE_COLORSCALE,
+    "total_precipitation": PRECIPITATION_COLORSCALE
+}
+
 IPCC_UI_TEXT = """
 Hi, I'm **Talk to IPCC**, designed to answer your questions using [**IPCC - ATLAS**](https://interactive-atlas.ipcc.ch/regional-information#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) data.  
 I'll answer by displaying a list of SQL queries, graphs and data most relevant to your question.

climateqa/engine/talk_to_data/ipcc/plots.py

@@ -4,10 +4,29 @@ import plotly.graph_objects as go
 import pandas as pd
 import geojson
 
-from climateqa.engine.talk_to_data.ipcc.config import IPCC_INDICATOR_TO_UNIT, IPCC_SCENARIO
+from climateqa.engine.talk_to_data.ipcc.config import IPCC_INDICATOR_TO_COLORSCALE, IPCC_INDICATOR_TO_UNIT, IPCC_SCENARIO
 from climateqa.engine.talk_to_data.ipcc.queries import indicator_for_given_year_query, indicator_per_year_at_location_query
 from climateqa.engine.talk_to_data.objects.plot import Plot
 
+def generate_geojson_polygons(latitudes: list[float], longitudes: list[float], indicators: list[float]) -> geojson.FeatureCollection:
+    features = [
+        geojson.Feature(
+            geometry=geojson.Polygon([[
+                [lon - 0.5, lat - 0.5],
+                [lon + 0.5, lat - 0.5],
+                [lon + 0.5, lat + 0.5],
+                [lon - 0.5, lat + 0.5],
+                [lon - 0.5, lat - 0.5]
+            ]]),
+            properties={"value": val},
+            id=str(idx)
+        )
+        for idx, (lat, lon, val) in enumerate(zip(latitudes, longitudes, indicators))
+    ]
+
+    geojson_data = geojson.FeatureCollection(features)
+    return geojson_data
+
 def plot_indicator_evolution_at_location_historical_and_projections(
     params: dict,
 ) -> Callable[[pd.DataFrame], Figure]:
@@ -100,57 +119,31 @@ def plot_choropleth_map_of_country_indicator_for_specific_year(
         showing the indicator's spatial distribution as a choropleth map for the specified year.
     """
     indicator = params["indicator_column"]
-    year = params.get('year', 2050)
+    year = params.get('year')
+    if year is None: 
+        year = 2050
     country_name = params['country_name']
     location = params['location']
     indicator_label = " ".join(word.capitalize() for word in indicator.split("_"))
     unit = IPCC_INDICATOR_TO_UNIT.get(indicator, "")
 
     def plot_data(df: pd.DataFrame) -> Figure:
-        custom_colorscale = [
-            [0.0, "rgb(5, 48, 97)"],
-            [0.10, "rgb(33, 102, 172)"],
-            [0.20, "rgb(67, 147, 195)"],
-            [0.30, "rgb(146, 197, 222)"],
-            [0.40, "rgb(209, 229, 240)"],
-            [0.50, "rgb(247, 247, 247)"],
-            [0.60, "rgb(253, 219, 199)"],
-            [0.75, "rgb(244, 165, 130)"],
-            [0.85, "rgb(214, 96, 77)"],
-            [0.90, "rgb(178, 24, 43)"],
-            [1.0, "rgb(103, 0, 31)"]
-        ]
 
         indicators = df[indicator].astype(float).tolist()
         latitudes = df["latitude"].astype(float).tolist()
         longitudes = df["longitude"].astype(float).tolist()
 
-        features = [
-            geojson.Feature(
-                geometry=geojson.Polygon([[
-                    [lon - 0.5, lat - 0.5],
-                    [lon + 0.5, lat - 0.5],
-                    [lon + 0.5, lat + 0.5],
-                    [lon - 0.5, lat + 0.5],
-                    [lon - 0.5, lat - 0.5]
-                ]]),
-                properties={"value": val},
-                id=str(idx)
-            )
-            for idx, (lat, lon, val) in enumerate(zip(latitudes, longitudes, indicators))
-        ]
-
-        geojson_data = geojson.FeatureCollection(features)
+        geojson_data = generate_geojson_polygons(latitudes, longitudes, indicators)
 
         fig = go.Figure(go.Choroplethmapbox(
             geojson=geojson_data,
             locations=[str(i) for i in range(len(indicators))],
             featureidkey="id",
             z=indicators,
-            colorscale=custom_colorscale,
+            colorscale=IPCC_INDICATOR_TO_COLORSCALE[indicator],
             zmin=min(indicators),
             zmax=max(indicators),
-            marker_opacity=0.7,
+            marker_opacity=0.6,
             marker_line_width=0,
             colorbar_title=f"{indicator_label} ({unit})",
             text=[f"{indicator_label}: {value:.2f} {unit}" for value in indicators],  # Add hover text showing the indicator value

climateqa/engine/talk_to_data/ui_config.py

@@ -0,0 +1,27 @@
+TEMPERATURE_COLORSCALE = [
+    [0.0, "rgb(5, 48, 97)"],
+    [0.10, "rgb(33, 102, 172)"],
+    [0.20, "rgb(67, 147, 195)"],
+    [0.30, "rgb(146, 197, 222)"],
+    [0.40, "rgb(209, 229, 240)"],
+    [0.50, "rgb(247, 247, 247)"],
+    [0.60, "rgb(253, 219, 199)"],
+    [0.75, "rgb(244, 165, 130)"],
+    [0.85, "rgb(214, 96, 77)"],
+    [0.90, "rgb(178, 24, 43)"],
+    [1.0, "rgb(103, 0, 31)"]
+]
+
+PRECIPITATION_COLORSCALE = [
+    [0.0, "rgb(84, 48, 5)"],
+    [0.10, "rgb(140, 81, 10)"],
+    [0.20, "rgb(191, 129, 45)"],
+    [0.30, "rgb(223, 194, 125)"],
+    [0.40, "rgb(246, 232, 195)"],
+    [0.50, "rgb(245, 245, 245)"],
+    [0.60, "rgb(199, 234, 229)"],
+    [0.75, "rgb(128, 205, 193)"],
+    [0.85, "rgb(53, 151, 143)"],
+    [0.90, "rgb(1, 102, 94)"],
+    [1.0, "rgb(0, 60, 48)"]
+]