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
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
return workspace
def scipy_style(wavelet, workspace):
workspace['trace'] = sp.convolve1d(workspace['trace'], wavelet, axis=-1)
return workspace
def scipy_fft(wavelet, workspace):
workspace['trace'] = np.apply_along_axis(lambda m: fftconvolve(m, wavelet, mode='same'), axis=-1, arr=workspace['trace'])
return workspace
params = {}
wavelet = toolbox.ricker(60)
sutype = toolbox.typeSU(1000)
workspace1 = np.zeros(500, dtype=sutype)
workspace1['trace'][:,500] = 1
cProfile.run('numpy_style(wavelet, workspace1)')
workspace2 = np.zeros(500, dtype=sutype)
workspace2['trace'][:,500] = 1
cProfile.run('scipy_style(wavelet, workspace2)')
workspace3 = np.zeros(500, dtype=sutype)
