#connect SQL database SQL_db = os.path.join(DATABASE_DIR, 'timeline.db') stations, subdir_len = psstationSQL.get_stationsSQL(SQL_db, xml_inventories=xml_inventories, dataless_inventories=dataless_inventories, startday=FIRSTDAY, endday=LASTDAY, verbose=False) DECLUSTER = False if DECLUSTER: stat_coords = np.asarray([station.coord for station in stations]) COORDS = Coordinates(input_list=stat_coords) declustered_coords = COORDS.decluster(degree_dist=0.1) stations = [station for station in stations if station.coord in declustered_coords] # Loop on time interval #number of time steps N = int(((LASTDAY - FIRSTDAY).days + 1)*60*24 / XCORR_INTERVAL) dates = [FIRSTDAY + dt.timedelta(minutes=i) for i in \ [j*XCORR_INTERVAL for j in range(N)]] #begin = raw_input("\nPress enter to begin the program ") # initialise preprocess class: METHOD - Bensen et al. (2007)
#----------------------------------------------------------------------------- # Generate InShape class SHAPE = InShape(shape_path) # Create shapely polygon from imported shapefile UNIQUE_SHAPE = SHAPE.shape_poly() print type(UNIQUE_SHAPE) # Generate InPoly class INPOLY = InPoly(shape_path) GEODESIC = Geodesic() COORDS = Coordinates() INPOLY = InPoly(shape_path) POLY_NODES = INPOLY.poly_nodes() # decluster the points to desired specifications. coords = COORDS.decluster(inputs=coords, degree_dist=0.5) lonmin, lonmax = np.floor(min(coords[:, 0])), np.ceil(max(coords[:, 0])) latmin, latmax = np.floor(min(coords[:, 1])), np.ceil(max(coords[:, 1])) print lonmin, lonmax, latmin, latmax plt.figure() plt.scatter(coords[:, 0], coords[:, 1]) plt.show() kappa = [np.vstack([[coord1[0],coord1[1],coord2[0],coord2[1]]\ for coord2 in coords]) for coord1 in coords]
# Generate InShape class SHAPE = InShape(shape_path) # Create shapely polygon from imported shapefile UNIQUE_SHAPE = SHAPE.shape_poly() print type(UNIQUE_SHAPE) # Generate InPoly class INPOLY = InPoly(shape_path) GEODESIC = Geodesic() COORDS = Coordinates() INPOLY = InPoly(shape_path) POLY_NODES = INPOLY.poly_nodes() # decluster the points to desired specifications. coords = COORDS.decluster(inputs=coords, degree_dist=0.5) lonmin, lonmax = np.floor(min(coords[:, 0])), np.ceil(max(coords[:, 0])) latmin, latmax = np.floor(min(coords[:, 1])), np.ceil(max(coords[:, 1])) print lonmin, lonmax, latmin, latmax plt.figure() plt.scatter(coords[:, 0], coords[:, 1]) plt.show() kappa = [np.vstack([[coord1[0], coord1[1], coord2[0], coord2[1]] for coord2 in coords]) for coord1 in coords]