def initialise(file, memmap=False, scan=False):
#intialise empty parameter dictionary
#kwargs stands for keyword arguments
kwargs = {}
#load file
if memmap == True:
ns = np.fromfile(file, dtype=toolbox.su_header_dtype, count=1)['ns']
sutype = toolbox.typeSU(ns)
dataset = np.memmap(file, dtype=sutype)
else:
dataset = toolbox.read(file)
#allocate stuff
#~
ns = kwargs['ns'] = dataset['ns'][0]
dt = kwargs['dt'] = dataset['dt'][0] / 1e6
#also add the time vector - it's useful later
kwargs['times'] = np.arange(0, dt * ns, dt)
dataset['trace'] /= np.amax(dataset['trace'])
dataset['tracr'] = np.arange(dataset.size)
kwargs['primary'] = 'cdp'
kwargs['secondary'] = 'offset'
kwargs['cdp'] = np.sort(np.unique(dataset['cdp']))
kwargs['step'] = 1
if scan:
toolbox.scan(dataset)
return dataset, kwargs
def initialise(file):
#intialise empty parameter dictionary
#kwargs stands for keyword arguments
kwargs = {}
#load file
dataset = toolbox.read(file)
#allocate stuff
#~
ns = kwargs['ns'] = dataset['ns'][0]
dt = kwargs['dt'] = dataset['dt'][0] / 1e6
#also add the time vector - it's useful later
kwargs['times'] = np.arange(0, dt * ns, dt)
dataset['trace'] /= np.amax(dataset['trace'])
dataset['tracr'] = np.arange(dataset.size)
kwargs['primary'] = 'cdp'
kwargs['secondary'] = 'offset'
kwargs['cdp'] = np.sort(np.unique(dataset['cdp']))
kwargs['step'] = 1
toolbox.scan(dataset)
return dataset, kwargs
def initialise(file, memmap=False, scan=False):
#intialise empty parameter dictionary
#kwargs stands for keyword arguments
kwargs = {}
#load file
if memmap == True:
ns = np.fromfile(file, dtype=toolbox.su_header_dtype, count=1)['ns']
sutype = toolbox.typeSU(ns)
dataset = np.memmap(file, dtype=sutype)
else:
dataset = toolbox.read(file)
#allocate stuff
#~
ns = kwargs['ns'] = dataset['ns'][0]
dt = kwargs['dt'] = dataset['dt'][0]/1e6
#also add the time vector - it's useful later
kwargs['times'] = np.arange(0, dt*ns, dt)
dataset['trace'] /= np.amax(dataset['trace'])
dataset['tracr'] = np.arange(dataset.size)
kwargs['primary'] = 'cdp'
kwargs['secondary'] = 'offset'
kwargs['cdp'] = np.sort(np.unique(dataset['cdp']))
kwargs['step'] = 1
if scan:
toolbox.scan(dataset)
return dataset, kwargs
def initialise(file):
#intialise empty parameter dictionary
#kwargs stands for keyword arguments
kwargs = {}
#load file
dataset = toolbox.read(file)
#allocate stuff
#~
ns = kwargs['ns'] = dataset['ns'][0]
dt = kwargs['dt'] = dataset['dt'][0]/1e6
#also add the time vector - it's useful later
kwargs['times'] = np.arange(0, dt*ns, dt)
dataset['trace'] /= np.amax(dataset['trace'])
dataset['tracr'] = np.arange(dataset.size)
kwargs['primary'] = 'cdp'
kwargs['secondary'] = 'offset'
kwargs['cdp'] = np.sort(np.unique(dataset['cdp']))
kwargs['step'] = 1
toolbox.scan(dataset)
return dataset, kwargs
def initialise(file):
#intialise empty parameter dictionary
#kwargs stands for keyword arguments
kwargs = {}
#load file
dataset = toolbox.read(file)
#allocate stuff
ns = kwargs['ns'] = dataset['ns'][0]
dt = kwargs['dt'] = dataset['dt'][0]/1e6
#also add the time vector - it's useful later
kwargs['times'] = np.linspace(dt, ns*dt, ns*dt*1000)
dataset['trace'] /= np.amax(dataset['trace'])
kwargs['primary'] = 'sx'
kwargs['secondary'] = 'gx'
kwargs['step'] = 1
toolbox.scan(dataset)
return dataset, kwargs
@_2d
def scale(dataset):
dataset['trace'] /= np.amax(np.abs(dataset['trace']))
if __name__ == "__main__":
file = "/home/sfletcher/Downloads/2d_land_data/2D_Land_data_2ms/Line_001.su"
data = toolbox.read(file)
data = data[data['tracf'] > 0]
toolbox.scan(data)
ffids = np.unique(data['fldr'])
parms = {}
parms['dt'] = data['dt'][0]
for shot in ffids:
panel = data[data['fldr'] == shot]
#print shot
parms["primary"] = 'fldr'
parms["step"] = 1
parms["window"] = 1000
dataset, params = initialise('cleaned.su')
# we are going to pull out 1 cdp for testing with.
#firstly, use the scroll tool to view the cdps, and
#then pick one near the middle of the volume
print dataset['cdp']
params['primary'] = 'cdp'
params['secondary'] = 'offset'
params['step'] = 20
#~ toolbox.display(dataset, None, **params)
#we then want to extract that single cdp
#for testing with later. we can do that
#the following way
cdp500 = dataset[dataset['cdp'] == 500]
toolbox.scan(cdp500)
#view it
#~ toolbox.display(cdp500, None, **params)
#we have the right cdp = but the traces are in the wrong
#order. lets sort by offset
cdp500 = np.sort(cdp500, order=['cdp', 'offset'])
#output it for later
toolbox.cp(cdp500, 'cdp500.su', None)
params['clip'] = 6e-4
toolbox.display(cdp500, None, **params)
pylab.show()
#intialise dataset and parameter dictionary
dataset, params = initialise('cleaned.su')
# we are going to pull out 1 cdp for testing with.
#firstly, use the scroll tool to view the cdps, and
#then pick one near the middle of the volume
print dataset['cdp']
params['primary'] = 'cdp'
params['secondary'] = 'offset'
params['step'] = 20
#~ toolbox.display(dataset, None, **params)
#we then want to extract that single cdp
#for testing with later. we can do that
#the following way
cdp500 = dataset[dataset['cdp'] == 500]
toolbox.scan(cdp500)
#view it
#~ toolbox.display(cdp500, None, **params)
#we have the right cdp = but the traces are in the wrong
#order. lets sort by offset
cdp500 = np.sort(cdp500, order=['cdp', 'offset'])
#output it for later
toolbox.cp(cdp500, 'cdp500.su', None)
params['clip'] = 6e-4
toolbox.display(cdp500, None, **params)
pylab.show()