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model.py
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model.py
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# -*- coding: utf-8 -*-
"""
Copyright 2017 Bernard Giroux, Elie Dumas-Lefebvre, Jerome Simon
email: Bernard.Giroux@ete.inrs.ca
This file is part of BhTomoPy.
BhTomoPy is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import numpy as np
class Model:
def __init__(self, name=''):
self.name = name
self.grid = None
self.tt_covar = None
self.amp_covar = None
self.mogs = []
self.inv_res = [] # TODO: use DbList for this attribute
self.tlinv_res = None
self.modified = True
@property
def boreholes(self):
"""
Returns a list of all the boreholes contained in the mogs of a model, without duplicates.
"""
boreholes = []
for mog in self.mogs:
for borehole in mog.Tx, mog.Rx:
if borehole is not None:
if borehole not in boreholes: # guarantees there is no duplicate
boreholes.append(borehole)
return boreholes
@staticmethod
def getModelData(model, selected_mogs, type1, vlim=0, type2=''):
data = np.array([])
ind = np.array([])
tt = np.array([])
et = np.array([])
in_vect = np.array([])
mogs = []
for i in selected_mogs:
mogs.append(model.mogs[i])
if type1 == 'tt':
fac_dt = 1
mog = mogs[0]
ind = np.not_equal(mog.tt, -1).T
tt, t0 = mog.getCorrectedTravelTimes()
tt = tt.T
et = fac_dt * mog.f_et * mog.et.T
in_vect = mog.in_vect.T
no = np.arange(mog.data.ntrace).T
if len(mogs) > 1:
for n in range(1, len(model.mogs)):
mog = mogs[n]
ind = np.concatenate((ind, np.not_equal(mog.tt, -1).T), axis=0)
tt = np.concatenate((tt, mog.getCorrectedTravelTimes()[0].T), axis=0)
et = np.concatenate((et, fac_dt * mog.et * mog.f_et.T), axis=0)
in_vect = np.concatenate((in_vect, mog.in_vect.T), axis=0)
no = np.concatenate((no, np.arange(mog.ntrace + 1).T), axis=0)
elif type1 == "amp":
mog = mogs[0]
ind = np.not_equal(mog.tauApp, -1).T
tt = mog.tauApp.T
et = mog.tauApp_et.T * mog.f_et
in_vect = mog.in_vect.T
no = np.arange(mog.data.ntrace).T
if len(mogs) > 1:
for n in range(1, len(model.mogs)):
mog = mogs[n]
ind = np.concatenate((ind, np.not_equal(mog.tauApp, -1).T), axis=0)
tt = np.concatenate((tt, mog.tauApp.T), axis=0)
et = np.concatenate((et, mog.tauApp_et.T * mog.f_et), axis=0)
in_vect = np.concatenate((in_vect, mog.in_vect.T), axis=0)
no = np.concatenate((no, np.arange(mog.ntrace + 1).T), axis=0)
elif type1 == "fce":
mog = mogs[0]
ind = np.not_equal(mog.tauFce, -1).T
tt = mog.tauFce.T
et = mog.tauFce_et.T * mog.f_et
in_vect = mog.in_vect.T
no = np.arange(mog.data.ntrace).T
if len(mogs) > 1:
for n in range(1, len(model.mogs)):
mog = mogs[n]
ind = np.concatenate((ind, np.not_equal(mog.tauFce, -1).T), axis=0)
tt = np.concatenate((tt, mog.tauFce.T), axis=0)
et = np.concatenate((et, mog.tauFce_et.T * mog.f_et), axis=0)
in_vect = np.concatenate((in_vect, mog.in_vect.T), axis=0)
no = np.concatenate((no, np.arange(mog.ntrace + 1).T), axis=0)
elif type1 == "hyb":
mog = mogs[0]
ind = np.not_equal(mog.tauHyb, -1).T
tt = mog.tauHyb.T
et = mog.tauHyb_et.T * mog.f_et
in_vect = mog.in_vect.T
no = np.arange(mog.data.ntrace).T
if len(mogs) > 1:
for n in range(1, len(model.mogs)):
mog = mogs[n]
ind = np.concatenate((ind, np.not_equal(mog.tauHyb, -1).T), axis=0)
tt = np.concatenate((tt, mog.tauHyb.T), axis=0)
et = np.concatenate((et, mog.tauHyb_et.T * mog.f_et), axis=0)
in_vect = np.concatenate((in_vect, mog.in_vect.T), axis=0)
no = np.concatenate((no, np.arange(mog.ntrace + 1).T), axis=0)
elif type1 == 'depth':
if type2 == '':
return data, ind
_, ind = Model.getModelData(model, selected_mogs, type2) # @UndefinedVariable
mog = mogs[0]
tt = mog.Tx_z_orig.T
et = mog.Rx_z_orig.T
in_vect = mog.in_vect.T
if len(mogs) > 1:
for n in (1, len(mogs)):
tt = np.concatenate((tt, mogs[n].Tx_z_orig.T), axis=0)
et = np.concatenate((et, mogs[n].Rx_z_orig.T), axis=0)
in_vect = np.concatenate((in_vect, mogs[n].in_vect.T), axis=0)
else:
raise ValueError
if vlim != 0:
l = np.sqrt(np.sum((model.grid.Tx-model.grid.Rx)**2, axis=1)).T
vapp = l/tt
in2 = vapp<vlim
print(str(np.sum(~in2&ind)) + " rays with apparent velocity above " + str(vlim))
ind = ind & in2
ind = np.equal((ind.astype(int) + in_vect.astype(int)), 2)
data = np.array([tt[ind], et[ind], no[ind]]).T
return data, ind