Upload app.py

app.py

@@ -70,35 +70,8 @@ gpx_path = pathlib.Path(gpx_file)
 with open('weather_icons_custom.json', 'r') as file:
 	icons = json.load(file)
 
-def mid_point(df):
-
-    def add_ele(x):
-        return elevation_data.get_elevation(x['latitude'], x['longitude'], 0)
-
-    df['srtm'] = df.apply(lambda x: add_ele(x), axis=1)
-
-    # Distance estimation function
-
-    def eukarney(lat1, lon1, alt1, lat2, lon2, alt2):
-        p1 = (lat1, lon1)
-        p2 = (lat2, lon2)
-        karney = distance.distance(p1, p2).m
-        return np.sqrt(karney**2 + (alt2 - alt1)**2)
-
-    # Create shifted columns in order to facilitate distance calculation
-
-    df['lat_shift'] = df['latitude'].shift(periods=-1).fillna(df['latitude'])
-    df['lon_shift'] = df['longitude'].shift(periods=-1).fillna(df['longitude'])
-    df['alt_shift'] = df['srtm'].shift(periods=-1).fillna(df['srtm'])
-
-    # Apply the distance function to the dataframe
-
-    df['distances'] = df.apply(lambda x: eukarney(x['latitude'], x['longitude'], x['srtm'], x['lat_shift'], x['lon_shift'], x['alt_shift']), axis=1).fillna(0)
-    df['distance'] = df['distances'].cumsum().round(decimals = 0).astype(int)
-
-    gpx_dict = df.loc[df.distance==df.distance.median()].iloc[-1].to_dict()
-
-    return gpx_dict
+def add_ele(x):
+    return elevation_data.get_elevation(x['latitude'], x['longitude'], 0)
 
 def map_icons(df):
 
@@ -254,7 +227,7 @@ def coor_gpx(gpx):
         global sunrise
         global sunset
 
-        with open(gpx.name) as f:
+        with open(gpx) as f:
             gpx_parsed = gpxpy.parse(f)
         # Convert to a dataframe one point at a time.
         points = []
@@ -268,7 +241,29 @@ def coor_gpx(gpx):
                 })
         df_gpx = pd.DataFrame.from_records(points)
         #gpx_dict = df_gpx.iloc[-1].to_dict()
-        gpx_dict = mid_point(df_gpx)
+
+        df_gpx['srtm'] = df_gpx.apply(lambda x: add_ele(x), axis=1)
+
+        # Distance estimation function
+
+        def eukarney(lat1, lon1, alt1, lat2, lon2, alt2):
+            p1 = (lat1, lon1)
+            p2 = (lat2, lon2)
+            karney = distance.distance(p1, p2).m
+            return np.sqrt(karney**2 + (alt2 - alt1)**2)
+
+        # Create shifted columns in order to facilitate distance calculation
+
+        df_gpx['lat_shift'] = df_gpx['latitude'].shift(periods=-1).fillna(df_gpx['latitude'])
+        df_gpx['lon_shift'] = df_gpx['longitude'].shift(periods=-1).fillna(df_gpx['longitude'])
+        df_gpx['alt_shift'] = df_gpx['srtm'].shift(periods=-1).fillna(df_gpx['srtm'])
+
+        # Apply the distance function to the dataframe
+
+        df_gpx['distances'] = df_gpx.apply(lambda x: eukarney(x['latitude'], x['longitude'], x['srtm'], x['lat_shift'], x['lon_shift'], x['alt_shift']), axis=1).fillna(0)
+        df_gpx['distance'] = df_gpx['distances'].cumsum().round(decimals = 0).astype(int)
+
+        gpx_dict = df_gpx.iloc[(df_gpx.distance - df_gpx.distance.median()).abs().argsort()[:1]].to_dict('records')[0]
 
         params['latitude'] = gpx_dict['latitude']
         params['longitude'] = gpx_dict['longitude']