update method: change replace by update and correct tzinfo
2 files changed, 8 insertions(+), 4 deletions(-)

M meteo_scraper/ecmwf_scraper.py
M meteo_scraper/gfs_scraper.py
M meteo_scraper/ecmwf_scraper.py +4 -2
@@ 107,17 107,19 @@ def exploit_grib_file_hres(tsa, gribfile
             val_zone = values[:, mask_zone]
             val_mean_zone = np.mean(val_zone, axis=1)
             ts = pd.Series(data=val_mean_zone, index = index)
+            ts = ts.tz_localize('utc')
 
             seriesname = f'meteo.area.{zone.lower()}.{param}.{unit.lower()}.hres.ecmwf.fcst.3-6h'
-            tsa.replace(seriesname, ts, author='scraper-ecmwf') #replace
+            tsa.update(seriesname, ts, author='scraper-ecmwf') #replace
         print(f'retrieving {param} for cities...')
         for row in CITIES.iterrows():
             index_city = nearest_gridpoint(row[1].lng, row[1].lat, 'ecmwf')
             val_city = values[:, index_city]
             ts = pd.Series(data=val_city, index = index)
+            ts = ts.tz_localize('utc')
 
             seriesname = f"meteo.city.{(row[1].country).replace(' ', '').lower()}.{(row[1].city_ascii).replace(' ', '').lower()}.{param}.{unit.lower()}.hres.ecmwf.fcst.3-6h"
-            tsa.replace(seriesname, ts, author='scraper-ecmwf')
+            tsa.update(seriesname, ts, author='scraper-ecmwf')
 
 
 def exploit_grib_file_ens(tsa, gribfile, param, unit):

          
M meteo_scraper/gfs_scraper.py +4 -2
@@ 60,14 60,16 @@ def get_last_run_data(tsa):
                 val_zone = data[:, mask_zone]
                 val_mean_zone = np.mean(val_zone, axis=1)
                 ts = pd.Series(data=val_mean_zone, index = time)
+                ts = ts.tz_localize('utc')
                 seriesname = f'meteo.area.{zone.lower()}.{param}.{unit.lower()}.gfs.1p00.fcst.3h'
-                tsa.replace(seriesname, ts, author='scraper-gfs')
+                tsa.update(seriesname, ts, author='scraper-gfs')
 
             print(f'retrieving {param} for cities...')
             for row in CITIES.iterrows():
                 index_city = nearest_gridpoint(row[1].lng, row[1].lat, '1p00')
                 val_city = data[:, index_city]
                 ts = pd.Series(data=val_city, index = time)
+                ts = ts.tz_localize('utc')
                 seriesname = f"meteo.city.{(row[1].country).replace(' ', '').lower()}.{(row[1].city_ascii).replace(' ', '').lower()}.{param}.{unit.lower()}.gfs.1p00.fcst.3h"
-                tsa.replace(seriesname, ts, author='scraper-gfs')
+                tsa.update(seriesname, ts, author='scraper-gfs')