t0 = dt.datetime.now() #----------------------------------------------------------------------------- # INITIALISE CLASS STATES #----------------------------------------------------------------------------- # 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()
#cd Desktop/Link\ to\ SIMULATIONS/Network_Tracks/smarter_model/
grad_ideal, grad_check1, grad_check2, H_avg1, H_avg2 = 0, 0, 0, 0, 0
SHAPE = (1,1)
perc_high = 0.01
low_counter = 0
random_counter = 0
#new_coord = 0
infinite_counter = 0
find_it = []
check_coord = None
use_old_path = False
searches_per_point = 3
factor = 0.05
cluster = False
COORDS = Coordinates()
INPOLY = InPoly(shape_path)
while infinite_counter <= 1:
t0 = datetime.datetime.now()
#----------------------------------------------------------------------
# Generate N new point coordinates
#----------------------------------------------------------------------
if cluster:
new_coords = N_cluster_points
else:
new_coords = ps.points_in_shape(shape_path, N)
coords = np.append(coords, new_coords, axis=0)
#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 ")
DENSITY = Density(paths=paths1)
H, xedges, yedges = DENSITY.hist2d(paths=paths1)
grad = DENSITY.hgrad(H=H)
H_avg1 = np.average(H)
grad_check1 = np.std(grad)
H_masked = DENSITY.transform_h(H=H)
grad = DENSITY.transform_grad(grad=grad)
DENSITY.plot_field(SHAPE=UNIQUE_SHAPE)
quit()
COORDS = Coordinates()
for i in [0]:
t0 = datetime.datetime.now()
#----------------------------------------------------------------------
# Generate N new point coordinates
#----------------------------------------------------------------------
#new_coords = N_cluster_points
# if cluster:
# new_coords = N_cluster_points
# else:
# new_coords = ps.points_in_shape(shape_path, N)
t0 = dt.datetime.now() # ----------------------------------------------------------------------------- # INITIALISE CLASS STATES # ----------------------------------------------------------------------------- # 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])
DENSITY = Density(paths=paths1)
H, xedges, yedges = DENSITY.hist2d(paths=paths1)
grad = DENSITY.hgrad(H=H)
H_avg1 = np.average(H)
grad_check1 = np.std(grad)
H_masked = DENSITY.transform_h(H=H)
grad = DENSITY.transform_grad(grad=grad)
DENSITY.plot_field(SHAPE=UNIQUE_SHAPE)
quit()
COORDS = Coordinates()
for i in [0]:
t0 = datetime.datetime.now()
#----------------------------------------------------------------------
# Generate N new point coordinates
#----------------------------------------------------------------------
#new_coords = N_cluster_points
# if cluster:
# new_coords = N_cluster_points
# else:
# new_coords = ps.points_in_shape(shape_path, N)
#coords = np.append(coords, new_coords, axis=0)
