import numpy as num
uncertainty=False
output_path='/Users/francesco/Desktop/Single_Station_Location/MOLQUAKE_V_0.3/ridge_2staz'
data_path='/Users/francesco/Desktop/Single_Station_Location/MOLQUAKE_V_0.3/ridge_2staz'
datafiles=['ridgecrest_refcat_15.dat','ridgecrest_picks_2sta_15.dat']
catalogue_file='refcat_15.dat'
out_catalogue='outcat_ridgecrest_two_stations.txt'
Vp=6000 #velocities in m/s !!!!!
Vps=1.73
Vs=Vp/Vps
nref=20
datafile='ridgecrest_Vp_'+str(Vp)
dataobj=molquake.molquake_dat(data_path,datafiles,one_sta=False)
locobj=molquake.molquake_loc(Vp,Vs)
references=dataobj.references
nref=num.size(references[:,0])
locobj.location_2stations(dataobj.data, references)
catevs=dataobj.read_catalogue(data_path,catalogue_file)
locobj.catalogue_creation(dataobj.evids, dataobj.origin, nref, out_catalogue, output_path)
if uncertainty:
print('uncertainty estimation')
multiloc=locobj.uncertainty_estimation_2sta(dataobj.data, references, 25, [5500,6500], 1.73)
locboot=num.array([0.,0.,0])
for i in range(25):
locboot=num.vstack((locboot,multiloc[i,:,:]))
num.save('locboot_real.npy',locboot)
num.save('locreal.npy',dataobj.references)
num.save('catevs.npy',catevs)
path = '/Users/francesco/Desktop/Single_Station_Location/Synthetic_data'
data_path = '/Users/francesco/Desktop/Single_Station_Location/'
filecat = 'catalogue'
filepicks = 'picks'
offset = 10000
extension = 1000
Vp = 6500
Vs = Vp / num.sqrt(3)
kv = (Vp * Vs) / (Vp - Vs)
data = num.load(path + '/synth1.data.mol.npy')
references = num.load(path + '/synth1.reference.mol.npy')
references = references[:10, :]
data = num.sqrt(data[:, 0]**2 + data[:, 1]**2 + data[:, 2]**2) / kv
objloc = molquake.molquake_loc(offset, extension)
Vpmin = 6000
Vpmax = 7000
locations = objloc.location(data, references, Vp, Vs)
num.save('locations_single.npy', locations)
for i in range(1):
Vp = Vpmin + (2 * num.random.random_sample() - 1) * (Vpmax - Vpmin)
Vs = Vp / num.sqrt(3)
locs = objloc.location(data, references, Vp, Vs)
locations = num.vstack((locations, locs))
num.save('locations_bootstrap.npy', locations)
#multiloc=objloc.uncertainty_estimation(data, references, 1, [5.4, 5.6], num.sqrt(3))
#print(num.shape(multiloc))
#data=data_in_sphere(n_points, radius=1000, origin_shift=10000.)
#references=data_in_sphere(n_points, radius=1000, origin_shift=10000.)