fix temp and humidity

APIs/meteo.py

@@ -5,9 +5,9 @@ from retry_requests import retry
 
 
 # Setup the Open-Meteo API client with cache and retry on error
-cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)
-retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
-openmeteo = openmeteo_requests.Client(session = retry_session)
+cache_session = requests_cache.CachedSession('.cache', expire_after=3600)
+retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
+openmeteo = openmeteo_requests.Client(session=retry_session)
 
 
 def get_info_meteo(latitude, longitude):
@@ -15,22 +15,22 @@ def get_info_meteo(latitude, longitude):
     ARGS: 
         latitude (Float) : latitude coordinate
         longitude (Float): longitude coordinate
-    
+
     """
     url = "https://api.open-meteo.com/v1/forecast"
     params = {
-        "latitude": latitude, 
-        "longitude": longitude, #"cloud_cover_low", "cloud_cover_mid","cloud_cover_high"
+        "latitude": latitude,
+        "longitude": longitude,  # "cloud_cover_low", "cloud_cover_mid","cloud_cover_high"
         "hourly": {"relative_humidity_2m", "soil_temperature_0cm", "soil_moisture_0_to_1cm", "cloud_cover"},
-        "daily": {"temperature_2m_max","precipitation_sum", "wind_speed_10m_max", "sunshine_duration", "rain_sum"},
-        "past_days" : 5,
-        "forecast_days" : 7
-        
-    } 
+        "daily": {"temperature_2m_max", "precipitation_sum", "wind_speed_10m_max", "sunshine_duration", "rain_sum"},
+        "past_days": 5,
+        "forecast_days": 7
+
+    }
     responses = openmeteo.weather_api(url, params=params)
     response = responses[0]
 
-    #Hourly dataframe
+    # Hourly dataframe
 
     hourly = response.Hourly()
     hourly_relative_humidity_2m = hourly.Variables(0).ValuesAsNumpy()
@@ -39,10 +39,10 @@ def get_info_meteo(latitude, longitude):
     hourly_cloud_cover = hourly.Variables(3).ValuesAsNumpy()
 
     hourly_data = {"date": pd.date_range(
-        start = pd.to_datetime(hourly.Time(), unit = "s", utc = True),
-        end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True),
-        freq = pd.Timedelta(seconds = hourly.Interval()),
-        inclusive = "left"
+        start=pd.to_datetime(hourly.Time(), unit="s", utc=True),
+        end=pd.to_datetime(hourly.TimeEnd(), unit="s", utc=True),
+        freq=pd.Timedelta(seconds=hourly.Interval()),
+        inclusive="left"
     )}
 
     hourly_data["relative_humidity_2m_%"] = hourly_relative_humidity_2m
@@ -50,26 +50,34 @@ def get_info_meteo(latitude, longitude):
     hourly_data["soil_moisture_0_to_1cm_m³"] = hourly_soil_moisture_0_to_1cm
     hourly_data["cloud_cover_%"] = hourly_cloud_cover
 
-    hourly_dataframe = pd.DataFrame(data = hourly_data)
-
+    hourly_dataframe = pd.DataFrame(data=hourly_data)
 
-    hourly_dataframe = pd.DataFrame(data = hourly_data)
+    hourly_dataframe = pd.DataFrame(data=hourly_data)
 
-    #Average hourly data per day
-    hourly_dataframe['day_date'] = hourly_dataframe['date'].dt.strftime('%Y-%m-%d')
+    # Average hourly data per day
+    hourly_dataframe['day_date'] = hourly_dataframe['date'].dt.strftime(
+        '%Y-%m-%d')
 
-    tmp1 = hourly_dataframe.groupby('day_date')[['relative_humidity_2m_%', 'soil_temperature_0cm_C', 'soil_moisture_0_to_1cm_m³']].mean()
+    tmp1 = hourly_dataframe.groupby('day_date')[
+        ['relative_humidity_2m_%', 'soil_temperature_0cm_C', 'soil_moisture_0_to_1cm_m³']].mean()
 
-    avg_hourly_dataframe = tmp1.rename(columns={'relative_humidity_2m_%': 'avg_hourly_relative_humidity_2m_%', 'soil_temperature_0cm_C': 'avg_hourly_soil_temperature_0cm_C', 'soil_moisture_0_to_1cm_m³': 'avg_hourly_soil_moisture_0_to_1cm_m³'})
+    avg_hourly_dataframe = tmp1.rename(columns={
+        'relative_humidity_2m_%': 'avg_hourly_relative_humidity_2m_%',
+        'soil_temperature_0cm_C': 'avg_hourly_soil_temperature_0cm_C',
+        'soil_moisture_0_to_1cm_m³': 'avg_hourly_soil_moisture_0_to_1cm_m³'
+    }
+    )
 
-    #med cloud couverture
+    # med cloud couverture
     tmp2 = hourly_dataframe.groupby('day_date')[['cloud_cover_%']].median()
 
-    med_cloud_cover = tmp2.rename(columns={'cloud_cover_%': 'med_cloud_cover_%'})
+    med_cloud_cover = tmp2.rename(
+        columns={'cloud_cover_%': 'med_cloud_cover_%'})
 
-    #Daily dataframe 
+    # Daily dataframe
     daily = response.Daily()
     daily_temperature_2m_max = daily.Variables(0).ValuesAsNumpy()
+    print(daily_temperature_2m_max)
     daily_precipitation_sum = daily.Variables(1).ValuesAsNumpy()
     daily_wind_speed_10m_max = daily.Variables(2).ValuesAsNumpy()
     daily_sunshine_duration = daily.Variables(3).ValuesAsNumpy()
@@ -78,7 +86,7 @@ def get_info_meteo(latitude, longitude):
     daily_data = {"date": pd.date_range(
         start=pd.to_datetime(daily.Time(), unit="s", utc=True),
         end=pd.to_datetime(daily.TimeEnd(), unit="s", utc=True),
-        freq='D',  
+        freq='D',
         inclusive="left"
     )}
 
@@ -90,13 +98,20 @@ def get_info_meteo(latitude, longitude):
 
     daily_dataframe = pd.DataFrame(data=daily_data)
 
-    daily_dataframe['day_date'] = daily_dataframe['date'].dt.strftime('%Y-%m-%d')
+    daily_dataframe['day_date'] = daily_dataframe['date'].dt.strftime(
+        '%Y-%m-%d')
 
-    total_df = pd.merge(daily_dataframe, avg_hourly_dataframe, on="day_date", how="left")
+    total_df = pd.merge(daily_dataframe, avg_hourly_dataframe,
+                        on="day_date", how="left")
     total_df = pd.merge(total_df, med_cloud_cover, on="day_date", how="left")
 
     total_df['date'] = total_df['day_date']
     total_df = total_df.drop('day_date', axis=1)
-    
+
+    total_df.to_json("weather_data.json", orient='columns')
     return total_df
-    #total_df.to_json("weather_data.json", orient='columns')
+
+
+if __name__ == "__main__":
+    df = get_info_meteo(48.832, 2.286)
+    print(df)

utils.py

@@ -32,8 +32,8 @@ query_engine = index.as_query_engine(similarity_top_k=15)
 def on_init(lat, lon, address):
     map_html, fig = show_map(lat, lon, address)
     df = get_info_meteo(lat, lon)
-    avg_temp = df.loc[:, 'daily_temperature_2m_max_C'].mean()
-    avg_humidity = df.loc[:, 'avg_hourly_soil_moisture_0_to_1cm_m³'].mean()
+    avg_temp = df.loc[:, 'avg_hourly_soil_temperature_0cm_C'].mean()
+    avg_humidity = df.loc[:, 'avg_hourly_relative_humidity_2m_%'].mean()
     # avg_snowfall = df.loc[:, 'daily_precipitation_sum_mm'].mean()
     avg_cloud_cover = df.loc[:, 'med_cloud_cover_%'].mean()
     avg_precipitations = df.loc[:, 'daily_rain_sum_mm'].mean()