[i] 1/tan faster than cos/sin

FHSTP/prep.py

@@ -189,10 +189,10 @@ def utm_to_lambert(lon: np.array, lat: np.array) -> tuple:
 
     # lambert conformal conic projection:
     # --> see https://mathworld.wolfram.com/LambertConformalConicProjection.html
-    n = np.log(np.cos(lat1) * (1 / np.cos(lat2))) / np.log(np.tan(np.pi/4 + lat2/2) * (np.cos(np.pi/4 + lat1/2) / np.sin(np.pi/4 + lat1/2)))
+    n = np.log(np.cos(lat1) * (1 / np.cos(lat2))) / np.log(np.tan(np.pi/4 + lat2/2) * (1 / np.tan(np.pi/4 + lat1/2)))
     F = (np.cos(lat1) * np.tan(np.pi/4 + lat1/2)**n) / n
-    p = R * F * (np.cos(np.pi/4 + lat/2) / np.sin(np.pi/4 + lat/2))**n
-    p0 = R * F * (np.cos(np.pi/4 + lat0/2) / np.sin(np.pi/4 + lat0/2))**n
+    p = R * F * (1 / np.tan(np.pi/4 + lat/2))**n
+    p0 = R * F * (1 / np.tan(np.pi/4 + lat0/2))**n
 
     # calculate lambert conic conformal x and y
     x = p * np.sin(n * (lon - lon0)) + x0
@@ -386,6 +386,11 @@ def prep() -> None:
     _log('Converted BEGINTIME to UTC')
     
     
+    # only use transmissions with begintime matching INCA date
+    date_inca = np.datetime64(f'{str(dir_inca.stem)[0:4]}-{str(dir_inca.stem)[-4:-2]}-{str(dir_inca.stem)[-2:]}')
+    df_link = df_link[df_link['BEGINTIME'].dt.date == date_inca]
+    _log('Filtered transmissions to match BEGINTIME with INCA date')
+    
     # copy REMOTERXLEVEL to PMIN and PMAX (for aggregation in 15min window conversion)
     df_link['PMIN'] = df_link['REMOTERXLEVEL']
     df_link['PMAX'] = df_link['REMOTERXLEVEL']
@@ -457,7 +462,7 @@ def prep() -> None:
     
     # load inca data
     inca_data = load_inca_data(dir_inca)
-    _log(f'Loaded INCA data from {str(dir_inca).split("/")[-1]}')
+    _log(f'Loaded INCA data from {str(dir_inca).split("/")[-1]} with shape {inca_data.shape}')
     
     # set INCA RR data based on datetime and coordinates
     df_link['RRStart'] = get_inca_data(inca_data, df_link['DateTime'], df_link['XStart'], df_link['YStart'])