[i] general improvements

FHSTP/prep.py

@@ -185,22 +185,22 @@ def get_distance(lon_a: np.array, lat_a: np.array, lon_b: np.array, lat_b: np.ar
 
 
 def utm_to_lambert(lon: np.array, lat: np.array) -> tuple:
-    """Convert UTM coordinates to a Lambert Conic Conformal Projection
+    """Convert UTM coordinates to an Austrian Lambert Conic Conformal Projection (lcc)
+    EPSG code: 31287, see https://epsg.io/31287
     
     Parameters:
-        lon (np.array): Vector containing longitudes
-        lat (np.array): Vector containing latitudes
+        lon (np.array): Vector containing utm longitudes
+        lat (np.array): Vector containing utm latitudes
     
     Returns:
-        x, y (np.array): tuple of two vectors with coordinates
+        x, y (np.array): Vectors containing lcc coordinates
 	"""
     
     # convert utm coordinates to radians
     lon = np.radians(lon)
     lat = np.radians(lat)
-    
+
     # define standard parallels according to EPSG:31287 - MGI/Austria Lambert
-    # --> see https://epsg.io/31287
     lat1 = np.radians(49)
     lat2 = np.radians(46)
     
@@ -210,24 +210,28 @@ def utm_to_lambert(lon: np.array, lat: np.array) -> tuple:
     lon0 = np.radians(13.33333333333333)
     lat0 = np.radians(47.5)
     
+    # false easting/northing
     # INCA grid: 701x401 km
     # compensate for point of reference (middle of the grid)
-    # false easting: half of 701km = 350500m
-    # false northing: half of 401km = 200500m
+    # move point of reference (0, 0) to following x and y coordinates (x0, y0) to e.g. get strictly positive coordinates
+    # false easting: 3505000m
+    # false northing: 200500m
     x0 = 350500
     y0 = 200500
     
     # volumetric mean radius of the earth in m
     R = 6371000
 
-    # lambert conformal conic projection:
-    # --> see https://mathworld.wolfram.com/LambertConformalConicProjection.html
+    # lambert conformal conic projection as in https://pubs.usgs.gov/pp/1395/report.pdf
+    # reference:
+    # >>    J. P. Snyder, Map projections--a working manual. Washington: U.S. G.P.O.,
+    # >>    For sale by the Supt. of Docs., 1987., pp. 104-110
     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 * (1 / np.tan(np.pi/4 + lat/2))**n
     p0 = R * F * (1 / np.tan(np.pi/4 + lat0/2))**n
 
-    # calculate x and y
+    # calculate x and y in lcc
     x = p * np.sin(n * (lon - lon0)) + x0
     y = p0 - p * np.cos(n * (lon - lon0)) + y0
 
@@ -416,6 +420,10 @@ def prep() -> None:
     _log('Calculated distances between sites using a WGS84 ellipsoid')
     
     
+    # convert CAPACITYINTERFACE to int
+    df_config['CAPACITYINTERFACE'] = df_config['CAPACITYINTERFACE'].map(lambda x: str(x)[:-5]).astype('int')
+    _log('Converted CAPACITYINTERFACE to int')
+    
     # convert FREQUENCY to float
     df_config['FREQUENCY'] = df_config['FREQUENCY'].map(lambda x: str(x)[:-3]).astype('float')
     _log('Converted FREQUENCY to float')