def display(input, agc=1): def update(val): vmax = smax.val vmin = smin.val im.set_clim(vmax=vmax, vmin=vmin) fig.canvas.draw_idle() fig = pylab.figure() '''displays a gather using imshow''' dataset = toolbox.read(input) vmax = np.amax(dataset['trace']) vmin = np.amin(dataset['trace']) if agc: dataset = toolbox.agc(dataset) im = pylab.imshow(dataset['trace'].T, aspect='auto', cmap='spectral', vmax =vmax, vmin=vmin) pylab.colorbar() axcolor = 'lightgoldenrodyellow' axmax = pylab.axes([0.08, 0.06, 0.65, 0.01], axisbg=axcolor) #rect = [left, bottom, width, height] in normalized (0, 1) units smax = Slider(axmax, 'vmax', vmin, vmax, valinit=vmax) smax.on_changed(update) axmin = pylab.axes([0.08, 0.03, 0.65, 0.01], axisbg=axcolor) #rect = [left, bottom, width, height] in normalized (0, 1) units smin = Slider(axmin, 'vmin', vmin, vmax, valinit=vmin) smin.on_changed(update) smin.on_changed(update) pylab.show()
def display(input, agc=1):
def update(val):
vmax = smax.val
vmin = smin.val
im.set_clim(vmax=vmax, vmin=vmin)
fig.canvas.draw_idle()
fig = pylab.figure()
'''displays a gather using imshow'''
dataset = toolbox.read(input)
vmax = np.amax(dataset['trace'])
vmin = np.amin(dataset['trace'])
if agc:
dataset = toolbox.agc(dataset)
im = pylab.imshow(dataset['trace'].T,
aspect='auto',
cmap='spectral',
vmax=vmax,
vmin=vmin)
pylab.colorbar()
axcolor = 'lightgoldenrodyellow'
axmax = pylab.axes(
[0.08, 0.06, 0.65, 0.01], axisbg=axcolor
) #rect = [left, bottom, width, height] in normalized (0, 1) units
smax = Slider(axmax, 'vmax', vmin, vmax, valinit=vmax)
smax.on_changed(update)
axmin = pylab.axes(
[0.08, 0.03, 0.65, 0.01], axisbg=axcolor
) #rect = [left, bottom, width, height] in normalized (0, 1) units
smin = Slider(axmin, 'vmin', vmin, vmax, valinit=vmin)
smin.on_changed(update)
smin.on_changed(update)
pylab.show()
def semb(input, vels): dataset = toolbox.read(input) nvels = vels.size ns = dataset['ns'][0] result = np.zeros((nvels,ns),'f') for v in range(nvels): gather = nmo(input, None, [vels[v]], [0], 20) gather = toolbox.agc(gather) result[v,:] += np.abs(_stack_gather(gather)['trace']) pylab.imshow(result.T, aspect='auto', extent=(800.,4400.,1.,0.), cmap='spectral', vmin=0, vmax=0.6) pylab.colorbar() pylab.show()
def semb(input, vels):
dataset = toolbox.read(input)
nvels = vels.size
ns = dataset['ns'][0]
result = np.zeros((nvels, ns), 'f')
for v in range(nvels):
gather = nmo(input, None, [vels[v]], [0], 20)
gather = toolbox.agc(gather)
result[v, :] += np.abs(_stack_gather(gather)['trace'])
pylab.imshow(result.T,
aspect='auto',
extent=(800., 4400., 1., 0.),
cmap='spectral',
vmin=0,
vmax=0.6)
pylab.colorbar()
pylab.show()
x = ((panel["trace"] * scalar) + trace_centers).ravel()
xlines = np.split(x, 284)
ylines = np.split(y, 284)
lines = [zip(xlines[a], ylines[a]) for a in range(len(xlines))]
fig, ax = pylab.subplots()
ax.fill_betweenx(y, offsets, x, where=(x > offsets), color="k")
col1 = collections.LineCollection(lines)
col1.set_color("k")
ax.add_collection(col1, autolim=True)
pylab.xlim([0, 284])
pylab.ylim([0, 1500])
ax.set_ylim(ax.get_ylim()[::-1])
pylab.tight_layout()
pylab.show()
file = "/home/sfletcher/Downloads/2d_land_data/2D_Land_data_2ms/Line_001.su"
data, params = toolbox.initialise(file)
dmap = np.memmap(file, dtype=toolbox.typeSU(1501), mode="r")
eps = np.unique(dmap["ep"])
for ep in eps[:1]:
params["window"] = 500
panel = dmap[dmap["ep"] == ep].copy()
panel = toolbox.agc(panel, None, **params)
func(panel)
# t = Timer("""func(panel)""", setup="from __main__ import func; from __main__ import panel")
# print t.timeit(100)
# 777.01802206
import toolbox
import numpy as np
import pylab
#~ for mute in range(110,200, 10):
#initialise
data, params = toolbox.initialise('prepro.su')
#load vels
params['vels'] = np.fromfile('vels_full.bin').reshape(-1, params['ns'])
params['smute'] = 150 #mute
#normal moveout correction
toolbox.nmo(data, None, **params)
#AGC
toolbox.agc(data, None, **params)
#stack
stack = toolbox.stack(data, None, **params)
params['gamma'] = -1
toolbox.tar(stack, None, **params)
data['tstat'] /= 2
toolbox.apply_statics(stack, None, **params)
#~ stack.tofile("smute_%d.su" %mute)
stack.tofile("1st_vels_stack_elev.su")
# i think I like 150
ax.fill(x,y, 'k', aa=True)
ax.set_xlim([0,nt])
ax.set_ylim([ns,0])
pylab.tight_layout()
pylab.show()
if __name__ == "__main__":
file = "/home/stewart/su/2d_land_data/2D_Land_data_2ms/su/Line_001.su"
#file = "/home/sfletcher/Downloads/2d_land_data/2D_Land_data_2ms/Line_001.su"
data, params = toolbox.initialise(file)
eps = np.unique(data['ep'])
for ep in eps[:1]:
frame = data[data['ep'] == ep]
params['window'] = 500
toolbox.agc(frame, None, **params)
wiggle(frame, scale=2)
#~ trace_centers = np.linspace(1,284, panel.size).reshape(-1,1)
#~ scalar = 284/(panel.size*0.1)
#~ panel['trace'][:,-1] = np.nan
#~ x = panel['trace'].ravel()
#~ y = np.arange(x.size)
#~ dx = np.signbit(x)
#~ before = np.where(np.diff(np.signbit(x)))[0]
#~ after = before + 1
data = su.readSU('record.su')
cdp_gathers =
cdp300 = cdp_gathers[cdp_gathers['cdp'] == 300]
#~ toolbox.display(cdp300)
#~ velrange = np.arange(800.0, 4500.0, 100.0)
#~ result = semb(toolbox.agc(cdp300), velrange)
velx = [0.91,0.23,0.38]
vely = [1531,1630, 1919]
vector = build_vel_trace(velx, vely)
stack = stack_volume(cdp_gathers, vector)
stack = toolbox.agc(stack)
#~ toolbox.display(stack)
pylab.imshow(stack['trace'].T, aspect='auto', extent=(1,stack.size,1.,0.), cmap='spectral' , vmin=-8, vmax=8)
pylab.colorbar()
pylab.show()
#~ offsets = np.unique(data['offset'])
#~ aoffsets = sorted(np.abs(offsets))
#~ print aoffsets
#~ supergathers = toolbox.build_supergather(10, 3, aoffsets[::2], data)
#~ inds = supergathers['ns1']
#~ print inds
#-----------------------------------------------------------------------
# useful functions
#-----------------------------------------------------------------------
None
#-----------------------------------------------------------------------
# main functions
#-----------------------------------------------------------------------
if __name__ == "__main__":
#intialise workspace and parameter dictionary
print 'initialising'
workspace, params = toolbox.initialise('../../../../foybrook.su')
cdp400 = workspace[workspace['cdp'] == 480]
toolbox.agc(cdp400, None, **params)
params['velocities'] = np.arange(2000, 7000, 50)
toolbox.semb(cdp400, **params)
#set our TAR
#~ print "applying tar"
#~ params['gamma'] = 3
#~ tar(workspace, None, **params)
#apply LMO
#~ print "applying lmo"
#~ params['lmo'] =1000.0
#~ lmo(workspace, None, **params)
#~ workspace['trace'][:,:30] *= 0
#~ workspace['trace'][:,1850:] *= 0
displays multiple stacks ''' import toolbox import pylab import numpy as np filelist = ["1st_vels_stack.su", #"1st_vels_stack_elev.su", "fk_stack.su", #"model.su", "model_filtered.su", #"trim_stack.su", "trim_stack2.su", ] dataset, params = toolbox.initialise(filelist[0]) dataset['fldr'] = 0 for index, file in enumerate(filelist[1:]): data, junk = toolbox.initialise(file) data['fldr'] = index + 1 dataset = np.column_stack([dataset, data]) params['window'] = 1000 toolbox.agc(dataset, None, **params) params['primary'] = 'fldr' params['secondary'] = 'cdp' params['clip'] = 0.02 toolbox.display(dataset, **params) pylab.show()
import toolbox
import numpy as np
import pylab
#extract shot record
data, params = toolbox.initialise("prepro.su")
mask = data['fldr'] == 221
shot = data[mask].copy()
#agc
toolbox.agc(shot, None, **params)
params['primary'] = 'fldr'
params['secondary'] = 'tracf'
params['wiggle'] = True
toolbox.display(shot, **params)
#fk plot
params['dx'] = 33.5 #m
#~ toolbox.fk_view(shot, **params)
#~ #fk filter design
params['fkVelocity'] = 2000
params['fkSmooth'] = 20
params['fkFilter'] = toolbox.fk_design(shot, **params)
shot = toolbox.fk_filter(shot, None, **params)
toolbox.display(shot, **params)
##############end of testing
#~ data, nparams = toolbox.initialise("prepro.su")
def agc(input, output, window=100):
dataset = toolbox.read(input)
dataset = toolbox.agc(dataset, window=window)
toolbox.write(dataset, output)
# useful functions
#-----------------------------------------------------------------------
None
#-----------------------------------------------------------------------
# main functions
#-----------------------------------------------------------------------
if __name__ == "__main__":
#intialise workspace and parameter dictionary
print 'initialising'
workspace, params = toolbox.initialise('../../../../foybrook.su')
cdp400 = workspace[workspace['cdp'] ==480]
toolbox.agc(cdp400, None, **params)
params['velocities'] = np.arange(2000,7000,50)
toolbox.semb(cdp400, **params)
#set our TAR
#~ print "applying tar"
#~ params['gamma'] = 3
#~ tar(workspace, None, **params)
#apply LMO
#~ print "applying lmo"
#~ params['lmo'] =1000.0
#~ lmo(workspace, None, **params)
#~ workspace['trace'][:,:30] *= 0
ax.fill(x, y, 'k', aa=True)
ax.set_xlim([0, nt])
ax.set_ylim([ns, 0])
pylab.tight_layout()
pylab.show()
if __name__ == "__main__":
file = "/home/stewart/su/2d_land_data/2D_Land_data_2ms/su/Line_001.su"
#file = "/home/sfletcher/Downloads/2d_land_data/2D_Land_data_2ms/Line_001.su"
data, params = toolbox.initialise(file)
eps = np.unique(data['ep'])
for ep in eps[:1]:
frame = data[data['ep'] == ep]
params['window'] = 500
toolbox.agc(frame, None, **params)
wiggle(frame, scale=2)
#~ trace_centers = np.linspace(1,284, panel.size).reshape(-1,1)
#~ scalar = 284/(panel.size*0.1)
#~ panel['trace'][:,-1] = np.nan
#~ x = panel['trace'].ravel()
#~ y = np.arange(x.size)
#~ dx = np.signbit(x)
#~ before = np.where(np.diff(np.signbit(x)))[0]
#~ after = before + 1
#~ #y = mx + c.
#~ x1= x[before].astype(np.float)
def agc(input, output, window=100): dataset = toolbox.read(input) dataset = toolbox.agc(dataset, window=window) toolbox.write(dataset, output)
for index, cdp in enumerate(cdps):
gather = dataset[dataset['cdp'] == cdp]
trace = _stack_gather(gather)
result[index] = trace
return result
if __name__ == '__main__':
#initialise your test cdp first
#first do the true amplitude recovery
#set nmo parameters
params['smute'] = 0
params['vels'] = toolbox.build_vels([0.5], [1], ns=params['ns'])
#then apply NMO
#we will apply a pre-stack agc
toolbox.agc(workspace, None, None)
#stack it
section = stack(workspace, None, **params)
#display it
#params['clip'] = 1e-5
toolbox.display(section, None, **params)
pylab.show()
#apply tar
params['gamma'] = 5
toolbox.tar(workspace, None, **params)
#copy vels from previous exercise
vels = {}
vels[225] = (0.06, 1537.38) , (0.28, 2876.21) , (0.87, 4608.10)
vels[270] = (0.05, 1525.09) , (0.18, 2483.16) , (0.36, 3171.00) , (0.66, 4079.93) , (0.98, 4816.90)
vels[315] = (0.04, 1365.42) , (0.14, 2728.82) , (0.22, 3134.15) , (0.57, 4116.78) , (0.74, 4571.25) , (0.97, 5013.43)
vels[360] = (0.04, 1697.05) , (0.10, 2520.01) , (0.21, 2937.62) , (0.43, 3244.70) , (0.64, 3981.67) , (0.98, 4239.61)
vels[405] = (0.06, 1439.11) , (0.27, 2753.38) , (0.49, 3957.10) , (0.97, 5381.92)
vels[450] = (0.06, 1340.85) , (0.41, 2741.10) , (0.52, 3625.47) , (0.02, 1144.32) , (0.29, 3060.45) , (0.54, 3711.45) , (0.97, 4313.31)
vels[495] = (0.04, 1611.07) , (0.11, 3072.74) , (0.23, 3318.39) , (0.35, 3772.86) , (0.48, 3981.67) , (0.94, 5099.41)
vels[539] = (0.04, 2028.69) , (0.11, 3072.74) , (0.32, 3883.41) , (0.51, 4485.27) , (0.96, 5222.24)
vels[584] = (0.06, 1623.36) , (0.20, 2495.44) , (0.32, 3121.87) , (0.95, 4411.57)
#build vels
vels = toolbox.build_vels(vels, **params)
params['primary'] = None
params['smute'] = 30
params['vels'] = vels
v100 = toolbox.co_nmo(workspace, None, **params)
toolbox.agc(v100, None, **params)
section100 = toolbox.stack(v100, None, **params)
toolbox.cp(section100, 'stack100.su', None)
toolbox.display(section100, None, **params)
pylab.show()
sutype = np.result_type(dataset)
result = np.zeros(cdps.size, dtype=sutype)
for index, cdp in enumerate(cdps):
gather = dataset[dataset['cdp'] == cdp]
trace = _stack_gather(gather)
result[index] = trace
return result
if __name__ == '__main__':
#initialise your test cdp first
#first do the true amplitude recovery
#set nmo parameters
params['smute'] = 0
params['vels'] = toolbox.build_vels([0.5], [1], ns=params['ns'])
#then apply NMO
#we will apply a pre-stack agc
toolbox.agc(workspace, None, None)
#stack it
section = stack(workspace, None, **params)
#display it
#params['clip'] = 1e-5
toolbox.display(section, None, **params)
pylab.show()
#~ lmo(workspace, None, **params)
#~ workspace['trace'][:,:30] *= 0
#~ workspace['trace'][:,1850:] *= 0
#~ params['lmo'] = -1000.0
#~ lmo(workspace, None, **params)
#apply our NMO
print "applying nmo"
params['smute'] = 30
v = [3000]
t = [0.5]
params['vels'] = toolbox.build_vels(t, v, ns=params['ns'])
nmo(workspace, None, **params)
#~ #apply AGC
toolbox.agc(workspace, None, **params)
#~ #stack
print "stacking"
stack(workspace, 'stack1.su', **params)
#view
params['primary'] = None
toolbox.display('stack1.su', None, **params)
pylab.show()
vels[539] = (0.04, 2028.69) , (0.11, 3072.74) , (0.32, 3883.41) , (0.51, 4485.27) , (0.96, 5222.24)
vels[584] = (0.06, 1623.36) , (0.20, 2495.44) , (0.32, 3121.87) , (0.95, 4411.57)
#build vels
vels = toolbox.build_vels(vels, **params)
params['primary'] = None
params['highcut'] = 100
params['lowcut'] = 30
params['smute'] = 30
params['vels'] = vels
v100 = toolbox.co_nmo(workspace, None, **params)
toolbox.agc(v100, None, **params)
section100 = toolbox.stack(v100, None, **params)
toolbox.bandpass(section100, None, **params)
toolbox.display(section100, None, **params)
params['vels'] = vels * .9
v90 = toolbox.co_nmo(workspace, None, **params)
toolbox.agc(v90, None, **params)
section90 = toolbox.stack(v90, None, **params)
toolbox.bandpass(section90, None, **params)
toolbox.display(section90, None, **params)
params['vels'] = vels *1.1
v110 = toolbox.co_nmo(workspace, None, **params)
toolbox.agc(v110, None, **params)
@io
def add_noise(dataset, **kwargs):
noise = np.random.normal(0.0, 1e-8, size=(dataset.shape))
dataset += noise
return dataset
@io
def convolve_wavelet(dataset, **kwargs):
wavelet = toolbox.ricker(60)
dataset = toolbox.conv(dataset, wavelet)
return dataset
if __name__ == '__main__':
#initialise
workspace, params = initialise()
#build record
build_combined(workspace, None, **params)
#add wavelet
workspace = convolve_wavelet(workspace, None, **params)
#add noise
workspace = add_noise(workspace, None, **params)
#display
toolbox.agc(workspace, None, **params)
toolbox.display(workspace, None, **params)
import toolbox
import numpy as np
import pylab
#initialise
data, params = toolbox.initialise('prepro.su')
#load vels
params['vels'] = np.fromfile('vels_full.bin').reshape(-1, params['ns'])
params['smute'] = 200
#normal moveout correction
nmo = toolbox.nmo(data, None, **params)
#AGC
toolbox.agc(nmo, None, **params)
#stack
stack = toolbox.stack(nmo, None, **params)
params['gamma'] = -1
toolbox.tar(stack, None, **params)
stack.tofile("field_stack2.su")
params['primary'] = None
params['secondary'] = 'cdp'
toolbox.display(stack, **params)
pylab.show()