import toolbox
import pylab
import numpy as np
model, mparams = toolbox.initialise("model_filtered.su")
gathers, params = toolbox.initialise("fk_nmo_gathers.su")
params['model'] = model
params['gate'] = (.4, 1.0) #seconds
params['maxshift'] = 4 #samples
toolbox.trim(gathers, None, **params)
toolbox.apply_statics(gathers, None, **params)
stack = toolbox.stack(gathers, None, **params)
params['gamma'] = -1
toolbox.tar(stack, None, **params)
stack.tofile("trim_stack2.su")
import toolbox
import numpy as np
import pylab
#--------------------------------------------------
# useful functions
#-------------------------------------------------
None
if __name__ == "__main__":
#initialise dataset
print "initialising dataset"
workspace, params = toolbox.initialise('al_dynamite.su')
#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)
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")
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
#processing a real dataset
#step 1 - import dataset and check the gathers
import toolbox
import numpy as np
import pylab
if __name__ == "__main__":
#import dataset
print "initialising dataset"
workspace, params = toolbox.initialise('al_dynamite.su')
#set gather order to shot gather
params['primary'] = 'sx'
params['secondary'] = 'gx'
#display
toolbox.display(workspace, None, **params)
pylab.show()
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
data, params = toolbox.initialise('prepro.su')
cdps = np.unique(data['cdp'])
#recreate original velocity field
#~ vels = {}
#~ vels[753]= (2456.0, 0.153), (2772.1, 0.413), (3003.2, 0.612), (3076.1, 0.704), (3270.7, 1.056), (3367.9, 1.668), (3538.2, 2.204), (3671.9, 3.566), (3915.1, 5.908),
#~ vels[3056]=(2456.0, 0.153), (2772.1, 0.413), (3003.2, 0.612), (3076.1, 0.704), (3270.7, 1.056), (3367.9, 1.668), (3538.2, 2.204), (3671.9, 3.566), (3915.1, 5.908),
#~ params['cdp'] = cdps
#~ params['vels'] = toolbox.build_vels(vels, **params)
#~ np.array(params['vels']).tofile('vels_initial.bin')
params['vels'] = np.fromfile('vels_initial.bin').reshape(-1, params['ns'])
#~ pylab.imshow(params['vels'].T, aspect='auto')
#~ pylab.colorbar()
#~ pylab.show()
#agc and stack
toolbox.agc(data, None, **params)
params['smute'] = 200
toolbox.nmo(data, None, **params)
stack = toolbox.stack(data, None, **params)
params['gamma'] = -1
toolbox.tar(stack, None, **params)
stack.tofile('field_stack.su')
#display
import toolbox
import numpy as np
import pylab
#initialise dataset
#~ data, params = toolbox.initialise("geometries.su")
#trim data
#~ params['ns'] = 1500
#~ data = toolbox.slice(data, None, **params)
#~ data.tofile("geom_short.su")
#initialise dataset
data, params = toolbox.initialise("geom_short.su")
#agc
#~ toolbox.agc(data, None, **params)
params['gamma'] = 1.5
toolbox.tar(data, None, **params)
kills = [270, 300, 374, 614] #fldr
mask = toolbox.build_mask(data['fldr'], kills)
data = data[mask]
data.tofile("prepro.su")
#display
#~ params['primary'] = 'fldr'
#~ params['secondary'] = 'tracf'
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
import toolbox
import numpy as np
import pylab
stack, params = toolbox.initialise("fk_stack.su")
stack['fldr'] = 1
params['dx'] = 33.5/2.0 #m
params['fkVelocity'] = 6000
params['fkSmooth'] = 20
params['fkFilter'] = toolbox.fk_design(stack, **params)
stack = toolbox.fk_filter(stack, None, **params)
#bandpass
params['lowcut'] = 10.0
params['highcut'] = 100.0
toolbox.bandpass(stack, None, **params)
stack.tofile("model_filtered.su")
#display
#~ params['primary'] = None
#~ params['secondary'] = 'cdp'
#~ toolbox.display(stack, **params)
#~ pylab.show()
#spectral analysis, bandpass filters
#test a few filters to find the best
import toolbox
import numpy as np
import pylab
#--------------------------------------------------
# useful functions
#-------------------------------------------------
None
if __name__ == "__main__":
#initialise dataset
print "initialising dataset"
workspace, params = toolbox.initialise('stack100.su')
params['primary'] = None
#basic spectral analysis
#~ toolbox.fx(workspace, None, **params)
params['highcut'] = 100
params['lowcut'] = 30
toolbox.bandpass(workspace, None, **params)
toolbox.display(workspace, None, **params)
pylab.show()
#-----------------------------------------------------------------------
# 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
import toolbox
import numpy as np
import pylab
data, params = toolbox.initialise('field_stack.su')
toolbox.fx(data, None, **params)
pylab.show()
#shift from amplitudes to plotting coordinates
x_shift, y = y[order].__divmod__(ns)
ax.plot(x[order] * scalar + x_shift + 1, y, 'k')
x[x < 0] = np.nan
x = x[order] * scalar + x_shift + 1
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)
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
#initialise dataset
#~ data, params = toolbox.initialise("geometries.su")
#trim data
#~ params['ns'] = 1500
#~ data = toolbox.slice(data, None, **params)
#~ data.tofile("geom_short.su")
#initialise dataset
data, params = toolbox.initialise("geom_short.su")
#agc
#~ toolbox.agc(data, None, **params)
params['gamma'] = 1.5
toolbox.tar(data, None, **params)
kills = [270, 300, 374, 614] #fldr
mask = toolbox.build_mask(data['fldr'], kills)
data = data[mask]
data.tofile("prepro.su")
#display
#~ params['primary'] = 'fldr'
#~ params['secondary'] = 'tracf'
#this time we want to do a few more locations
import toolbox
import numpy as np
import pylab
#--------------------------------------------------
# useful functions
#-------------------------------------------------
None
if __name__ == "__main__":
#initialise dataset
print "initialising dataset"
workspace, params = toolbox.initialise('foybrook.su')
#apply TAR
print "applying true amplitude recovery"
params['gamma'] = 3
toolbox.tar(workspace, None, **params)
#lets see how many cdps there are
print np.unique(workspace['cdp'])[25::45].tolist()
params['smoother'] = 5
#copy your list of cdps here... it will make it easier later
cdps = [219, 264, 309, 354, 399, 444, 489, 534, 579]
#~ params['velocities'] = np.arange(2000,6000,50)
#spectral analysis, bandpass filters
#test a few filters to find the best
import toolbox
import numpy as np
import pylab
#--------------------------------------------------
# useful functions
#-------------------------------------------------
None
if __name__ == "__main__":
#initialise dataset
print "initialising dataset"
workspace, params = toolbox.initialise('stack100.su')
params['primary'] = None
#basic spectral analysis
#~ toolbox.fx(workspace, None, **params)
params['highcut'] = 100
params['lowcut'] = 30
toolbox.bandpass(workspace, None, **params)
toolbox.display(workspace, None, **params)
pylab.show()
