#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]
