def imshow_sw(dat,seis_coord = None,well_files = None,horfiles = None,rockprop = None) :
minval = 0
maxval = np.max(dat)
fig, ax = plt.subplots()
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.05)
if (seis_coord is None):
pos =ax.imshow(dat,interpolation='bicubic',cmap = 'nipy_spectral', aspect='auto',vmin = 0,vmax =maxval)
fig.colorbar(pos, cax=cax, orientation='vertical')
else:
#dist = calc_dist(seis_coord)[0]
tmin = seis_coord['tseis'][0]
tmax = seis_coord['tseis'][-1]
xmin = 0
xmax = dat.shape[1]*seis_coord['bins'][0]
pos = ax.imshow(dat,interpolation='bicubic',cmap = 'nipy_spectral', aspect='auto',vmin =0,vmax =maxval,\
extent = [xmin,xmax,tmax,tmin])
nwells = well_files.size
numm = 40
wellcoord = seis_coord['wellcoord']
for i in range(nwells):
wlog = mfun.load_obj(well_files[i])
rho = wlog.Rhob
if (rho is None):
raise Exception ('Density log does not exist')
wtmin = np.int(wlog.time[0])
wtmax = np.int(wlog.time[-1])
if(wtmin < tmin):
wtmin = tmin
elif( wtmax > tmax):
wtmax = tmax
zone = np.array([wtmin,wtmax], dtype = int)
rho = mfun.segment_logs(zone,wlog.time,rho)[0]
wxmin = xmin + wellcoord['indx'][i]*seis_coord['bins'][0]
wxmax = wxmin + numm
ai_mat = np.tile(rho,[numm,1]).T
ax.imshow(ai_mat,interpolation='bicubic',cmap = 'nipy_spectral', aspect='auto',vmin = 0,vmax =maxval,\
extent = [wxmin,wxmax,wtmax,wtmin])
if(horfiles is not None):
nhor = horfiles.size
indx = seis_coord['coord_indx']
hor_x = np.arange(xmin,xmax,seis_coord['bins'][0]) # temp
for xd in range(nhor):
hor = mfun.load_obj(horfiles[xd])
hor_y = hor.Z[indx]
ax.plot(hor_x,hor_y,'k')
fig.colorbar(pos, cax=cax, orientation='vertical')
ax.autoscale()
ax.set_xlabel('Distance(m)')
ax.set_ylabel('Time(ms)')
if (rockprop is None):
rockprop = 'Porosity'
ax.set_title(rockprop)
w19_19A.apply_TD()
w19_19A_time = well()
w19_19A_time = mfun.load_obj('well_files//volve//time//w19A')
# debug
zone = np.zeros(2)
if (w19_19A.time [0] >= w19_19A_time.time[0] ):
zone[0] = w19_19A.time[0]
else:
zone[0] = w19_19A_time.time[0]
if (w19_19A.time [-1]<=w19_19A_time.time[-1] ):
zone[1] = w19_19A_time.time[-1]
else:
zone[1] = w19_19A.time[-1]
zone[0] = 2350
zone[1] = 2515
w19_19A.calc_Vp()
ax2.plot(w19_19A.time,w19_19A.Vp)
vp_time_zn,new_time_zn = mfun.segment_logs(zone,w19_19A.time,w19_19A.Vp)
org_vp_time_zn,org_time_zn = mfun.segment_logs(zone,w19_19A_time.time,w19_19A_time.Vp)
fig, (ax1, ax2) = plt.subplots(2,1)
ax1.plot(new_time_zn,vp_time_zn)
ax2.plot(org_time_zn,org_vp_time_zn)
w19_19A.save_obj('w19_19A_jan2020',w19_19A)
def predict_ver2(params, wlog, LOGS, tseis):
dt = tseis[1] - tseis[0]
nwells, nlogs = LOGS.size
logs = mfun.cell(nwells, nlogs)
for i in range(nwells):
t1 = np.min(tseis)
t2 = np.min(LOGS[i, 0]) - dt
upper = np.arange(t1, t2 + dt, dt)
z = upper
if (upper.size == 0):
upperzone_data = z # there is data above the horizon
else:
if (wlog == 'AI'):
scal = 1
upperzone_data = scal * fcompactntrend_AI(z, params)
elif (wlog == 'Vp'):
scal = 1
upperzone_data = scal * fcompactntrend_vp(z, params)
elif (wlog == 'Vs'):
scal = 1
upperzone_data = scal * fcompactntrend_vs(z, params)
elif (wlog == 'Rhob'):
upperzone_data = fcompactntrend_rho(z, params)
elif (wlog == 'GR'):
upperzone_data = fcompactntrend_gr(z, params)
else:
raise Exception('not yet coded for this lognames')
t2 = np.max(tseis)
t1 = np.max(LOGS[i, 0]) + dt
lower = np.arange(t1, t2 + dt, dt)
z = lower
if (lower.size == 0):
lowerzone_data = lower # there is data beneath tseis
else:
if (wlog == 'AI'):
scal = 1
lowerzone_data = scal * fcompactntrend_AI(z, params)
elif (wlog == 'Vp'):
scal = 1
lowerzone_data = scal * fcompactntrend_vp(z, params)
elif (wlog == 'Vs'):
scal = 1
lowerzone_data = scal * fcompactntrend_vs(z, params)
elif (wlog == 'Rhob'):
scal = 1
lowerzone_data = fcompactntrend_rho(z, params)
elif (wlog == 'GR'):
lowerzone_data = fcompactntrend_gr(z, params)
else:
raise Exception('not yet coded for this lognames')
if (upper.size == 0 and lower.size != 0):
zone = np.array([np.min(tseis), LOGS[i, 0][-1]], dtype=int)
data, t = mfun.segment_logs(zone, LOGS[i, 0], LOGS[i, 3])
dat_out = np.concatenate((data, lowerzone_data), axis=None)
elif (upper.size != 0 and lower.size == 0):
zone = np.array([LOGS[i, 0][0], tseis[-1]], dtype=int)
data, t = mfun.segment_logs(zone, LOGS[i, 0], LOGS[i, 3])
dat_out = np.concatenate((upperzone_data, data), axis=None)
elif (upper.size == 0 and lower.size == 0):
zone = np.array([np.min(tseis), np.max(tseis)], dtype=int)
data, t = mfun.segment_logs(zone, LOGS[i, 0], LOGS[i, 3])
dat_out = data
else: #upper and lower zone zero
dat_out = np.concatenate(
(upperzone_data, LOGS[i, 3], lowerzone_data), axis=None)
logs[i, 0] = tseis
logs[i, 1] = LOGS[i, 1]
logs[i, 2] = LOGS[i, 2]
logs[i, 3] = dat_out
return logs
