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 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, 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.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 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 slice(input, output, key, values): dataset = toolbox.read(input) filter = ~np.isfinite(dataset['tracl']) for value in values: bool = dataset[key] == value filter = (bool) | (filter) toolbox.write(dataset[filter], output)
def slice(input, output, key, values):
dataset = toolbox.read(input)
filter = ~np.isfinite(dataset['tracl'])
for value in values:
bool = dataset[key] == value
filter = (bool) | (filter)
toolbox.write(dataset[filter], output)
def stack(input, output): dataset = toolbox.read(input) cdps = np.unique(dataset['cdp']) sutype = np.result_type(dataset) holder = np.zeros(cdps.size, dtype=sutype) for index, cdp in enumerate(cdps): gather = dataset[dataset['cdp'] == cdp] trace = _stack_gather(gather) holder[index] = trace toolbox.write(holder, output)
def stack(input, output):
dataset = toolbox.read(input)
cdps = np.unique(dataset['cdp'])
sutype = np.result_type(dataset)
holder = np.zeros(cdps.size, dtype=sutype)
for index, cdp in enumerate(cdps):
gather = dataset[dataset['cdp'] == cdp]
trace = _stack_gather(gather)
holder[index] = trace
toolbox.write(holder, output)
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 nmo(input, output, vels, times, smute): dataset = toolbox.read(input) nt = dataset.size holder = dataset.copy() holder['trace'].fill(0) vector = build_vel_trace(times, vels) for i in range(nt): holder[i]['trace'] += _nmo_trace(dataset[i], vector, smute)['trace'] if output: toolbox.write(holder, output) else: return holder
def nmo(input, output, vels, times, smute):
dataset = toolbox.read(input)
nt = dataset.size
holder = dataset.copy()
holder['trace'].fill(0)
vector = build_vel_trace(times, vels)
for i in range(nt):
holder[i]['trace'] += _nmo_trace(dataset[i], vector, smute)['trace']
if output:
toolbox.write(holder, output)
else:
return holder
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 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
def agc(input, output, window=100): dataset = toolbox.read(input) dataset = toolbox.agc(dataset, window=window) toolbox.write(dataset, output)
def scan_headers(input):
dataset = toolbox.read(input)
print dataset.shape
for key, t in toolbox.su_header_dtype.descr:
print key, np.amin(dataset[key]), np.amax(dataset[key])
def agc(input, output, window=100):
dataset = toolbox.read(input)
dataset = toolbox.agc(dataset, window=window)
toolbox.write(dataset, output)
def window_rms(arr=None, window_size=100):
a2 = np.power(arr, 2)
window = np.ones(window_size) / float(window_size)
return np.sqrt(np.convolve(a2, window, 'valid'))
@_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
def scan_headers(input): dataset = toolbox.read(input) print dataset.shape for key, t in toolbox.su_header_dtype.descr: print key, np.amin(dataset[key]), np.amax(dataset[key])
