#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()
#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)
section110 = toolbox.stack(v110, None, **params)
toolbox.bandpass(section110, None, **params)
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()