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tsunami.py
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'''
A class that deals with seismic or high-rate GPS data (not finished)
Written by Z. Duputel, April 2013.
'''
# Externals
import os
import sys
import copy
import shutil
import numpy as np
import pyproj as pp
import matplotlib.pyplot as plt
# Personals
#xfrom WaveMod import sac
from .SourceInv import SourceInv
class tsunami(SourceInv):
'''
A class that handles tsunami data.
Args:
* name : Name of the dataset.
Kwargs:
* dtype : data type
* utmzone : UTM zone (optional, default=None)
* lon0 : Longitude of the center of the UTM zone
* lat0 : Latitude of the center of the UTM zone
* ellps : ellipsoid (optional, default='WGS84')
'''
def __init__(self,name,dtype='tsunami',utmzone=None,ellps='WGS84',lon0=None, lat0=None):
super(self.__class__,self).__init__(name,utmzone,ellps=ellps,lon0=lon0,lat0=lat0)
# Initialize the data set
self.dtype = dtype
# Data
self.d = []
self.Cd = None
self.sta = None
self.lat = None
self.lon = None
self.t0 = None
self.G = None
# All done
return
def readFromTxtFile(self,filename,factor=1.0,fileinfo=None):
'''
Read d, Cd from files filename.data filename.Cd
Args:
* filename : prefix of the filenames filename.d and filename.Cd
Kwargs:
* factor : scaling factor
* fileinfo : Information about the data (lon, lat and origintime)
Returns:
* None
'''
self.Cd = np.loadtxt(filename+'.Cd')*factor*factor
self.d = np.loadtxt(filename+'.data')*factor
self.sta = open(filename+'.id').readlines()
if fileinfo is not None:
f = open(fileinfo,'rt')
self.lon = []
self.lat = []
self.t0 = []
for l in f:
items = list(map(float,l.strip().split()[1:]))
self.lon.append(items[0])
self.lat.append(items[1])
self.t0.append(items[2])
f.close()
assert len(self.t0)==len(self.sta)
# All done
return
def getGF(self,filename,fault,factor=1.0):
'''
Read GF from file filename.gf
Args:
* filename : prefix of the file filename.gf
Kwargs:
* factor: scaling factor
Returns:
* 2d arrays: returns GF_SS and GF_DS
'''
GF = np.loadtxt(filename+'.gf')*factor
n = int(GF.shape[1]/2)
assert n == len(fault.slip), 'Incompatible tsunami GF size'
GF_SS = GF[:,:n]
GF_DS = GF[:,n:]
# All done
return GF_SS, GF_DS
def setGFsInFault(self, fault, G, vertical=False):
'''
From a dictionary of Green's functions, sets these correctly into the fault
object fault for future computation.
Args:
* fault : Instance of Fault
* G : Dictionary with 3 entries 'strikeslip', 'dipslip' and 'tensile'. These can be a matrix or None.
Kwargs:
* vertical : Set here for consistency with other data objects, but will always be set to False, whatever you do.
Returns:
* None
'''
# Get the values
try:
GssLOS = G['strikeslip']
except:
GssLOS = None
try:
GdsLOS = G['dipslip']
except:
GdsLOS = None
try:
GtsLOS = G['tensile']
except:
GtsLOS = None
try:
GcpLOS = G['coupling']
except:
GcpLOS = None
# set the GFs
fault.setGFs(self, strikeslip=[GssLOS], dipslip=[GdsLOS], tensile=[GtsLOS],
coupling=[GcpLOS], vertical=False)
# All done
return
def buildsynth(self, faults, direction='sd', poly=None):
'''
Takes the slip model in each of the faults and builds the synthetic displacement using the Green's functions.
Args:
* faults : list of faults to include.
Kwargs:
* direction : list of directions to use. Can be any combination of 's', 'd' and 't'.
* poly : if True, add an offseta in the data
Returns:
* None. Synthetics are stored in the synth attribute
'''
Nd = len(self.d)
# Clean synth
self.synth = np.zeros(self.d.shape)
for fault in faults:
# Get the good part of G
G = fault.G[self.name]
if ('s' in direction) and ('strikeslip' in G.keys()):
Gs = G['strikeslip']
Ss = fault.slip[:,0]
self.synth += np.dot(Gs,Ss)
if ('d' in direction) and ('dipslip' in G.keys()):
Gd = G['dipslip']
Sd = fault.slip[:,1]
self.synth += np.dot(Gd, Sd)
if poly is not None:
esti = self.getRampEstimator(fault.poly[self.name])
sol = fault.polysol[self.name]
self.shift = esti.dot(sol)
if poly == 'include':
self.synth += self.shift
# All done
return
def plot(self, nobs_per_trace, plot_synth=False,alpha=1.,figsize=(13,10),left=0.07,bottom=0.1,
right=0.99,top=0.9,wspace=0.31,hspace=0.47,scale=100.,ylim=None,yticks=None):
'''
Plot tsunami traces
:Note: We need a description of the options here...
'''
fig = plt.figure(figsize=figsize)
fig.subplots_adjust(bottom=bottom,top=top,left=left,right=right,wspace=wspace,hspace=hspace)
nsamp = nobs_per_trace
nstat = int(len(self.d)/nobs_per_trace)
for i in range(nstat):
data = self.d[i*nsamp:nsamp*i+nsamp]
if plot_synth == True:
if len(self.synth.shape)==2:
synth = self.synth[i*nsamp:nsamp*i+nsamp,:]
else:
synth = self.synth[i*nsamp:nsamp*i+nsamp]
plt.subplot(2,np.ceil(nstat/2.),i+1)
t = np.arange(len(data))
if self.t0 is not None:
t += int(self.t0[i])
plt.plot(t,data*scale,'k',label='data')
if plot_synth == True:
plt.plot(t,synth*scale,'r',alpha=alpha,label='predictions')
if i>=nstat/2:
plt.legend(loc='best')
#plt.grid()
plt.title(self.sta[i])
if not i%np.ceil(nstat/2.):
plt.ylabel('Water height, cm')
if i>=nstat/2:
if self.t0 is not None:
plt.xlabel('Time, min')
else:
plt.xlabel('Time since arrival, min')
if ylim is not None:
plt.ylim(ylim[0],ylim[1])
if yticks is not None:
plt.yticks(yticks)
plt.subplots_adjust(hspace=0.2, wspace=0.2)
# All done
return
def write2file(self, namefile, data='synth'):
'''
Write to a text file
Args:
* namefile : Name of the output file
Kwargs:
* data : can be data or synth
Returns:
* None
'''
if data == 'synth':
np.savetxt(namefile, self.synth.T)
elif data == 'data':
np.savetxt(namefile, self.d.T)
# All done
return
def getRampEstimator(self,order):
'''
Returns the Estimator of a constant offset in the data
Args:
* order : 1, estimate just a vertical shift in the data and ,2, estimate a ramp in the data. Order given as argument is in reality order*number_of_station
Returns:
* a 2d array
'''
nsta = len(self.sta)
nd = len(self.d)
obspersta = nd / nsta
order /= nsta
shift = np.zeros((nd,nsta*order))
ista = 0
for i in range(0,order*nsta,order):
ib = ista * obspersta
ie = (ista+1) * obspersta
ista += 1
shift[ib:ie,i] = 1.0
if order == 1:
continue
elif order == 2:
shift[ib:ie,i+1] = np.arange(0,obspersta)
return shift
#EOF