def make_dask(darray, output, attribute_class, attribute_type, kernel):
if output == '2d':
if attribute_class == 'Amplitude':
x = SignalProcess()
darray, chunks_init = SignalProcess.create_array(x, darray, kernel,
preview=None)
darray = darray.T
if attribute_class == 'CompleTrace':
x = ComplexAttributes()
darray, chunks_init = ComplexAttributes.create_array(x, darray, kernel,
preview=None)
darray = darray.T
if attribute_class == 'DipAzm':
x = DipAzm()
darray, chunks_init = DipAzm.create_array(x, darray, kernel=None,
preview=None)
darray = darray.T
if attribute_class == 'EdgeDetection':
x = EdgeDetection()
darray, chunks_init = EdgeDetection.create_array(x, darray, kernel,
preview=None)
darray = darray.T
if output == '3d':
if attribute_class == 'Amplitude':
x = SignalProcess()
darray, chunks_init = SignalProcess.create_array(x, darray, kernel,
preview=None)
if attribute_class == 'CompleTrace':
x = ComplexAttributes()
darray, chunks_init = ComplexAttributes.create_array(x, darray, kernel,
preview=None)
if attribute_class == 'DipAzm':
x = DipAzm()
darray, chunks_init = DipAzm.create_array(x, darray, kernel=None,
preview=None)
if attribute_class == 'EdgeDetection':
x = EdgeDetection()
darray, chunks_init = EdgeDetection.create_array(x, darray, kernel,
preview=None)
return(x, darray)
def compute(x, darray, attribute_class, attribute_type, kernel,
sample_rate, dip_factor, axis):
if attribute_class == 'Amplitude':
if attribute_type == 'fder':
result = SignalProcess.first_derivative(x, darray, axis=-1,
preview=None)
return(result)
if attribute_type == 'sder':
result = SignalProcess.second_derivative(x, darray, axis=-1,
preview=None)
return(result)
if attribute_type == 'rms':
result = SignalProcess.rms(x, darray, kernel=(1,1,9),
preview=None)
return(result)
if attribute_type == 'gradmag':
result = SignalProcess.gradient_magnitude(x, darray, sigmas=(1,1,1),
preview=None)
return(result)
if attribute_type == 'reflin':
result = SignalProcess.reflection_intensity(x, darray, kernel=(1,1,9),
preview=None)
return(result)
if attribute_class == 'CompleTrace':
if attribute_type == 'enve':
result = ComplexAttributes.envelope(x, darray, preview=None)
return(result)
if attribute_type == 'inphase':
result = ComplexAttributes.instantaneous_phase(x, darray, preview=None)
return(result)
if attribute_type == 'cosphase':
result = ComplexAttributes.cosine_instantaneous_phase(x, darray,
preview=None)
return(result)
if attribute_type == 'ampcontrast':
result = ComplexAttributes.relative_amplitude_change(x, darray,
preview=None)
return(result)
if attribute_type == 'ampacc':
result = ComplexAttributes.amplitude_acceleration(x, darray,
preview=None)
return(result)
if attribute_type == 'infreq':
result = ComplexAttributes.instantaneous_frequency(x, darray,
sample_rate=4,
preview=None)
return(result)
if attribute_type == 'inband':
result = ComplexAttributes.instantaneous_bandwidth(x, darray,
preview=None)
return(result)
if attribute_type == 'domfreq':
result = ComplexAttributes.dominant_frequency(x, darray, sample_rate=4,
preview=None)
return(result)
if attribute_type == 'freqcontrast':
result = ComplexAttributes.dominant_frequency(x, darray, sample_rate=4,
preview=None)
return(result)
if attribute_type == 'sweet':
result = ComplexAttributes.sweetness(x, darray, sample_rate=4,
preview=None)
return(result)
if attribute_type == 'quality':
result = ComplexAttributes.quality_factor(x, darray, sample_rate=4,
preview=None)
return(result)
if attribute_type == 'resphase':
result = ComplexAttributes.response_phase(x, darray, preview=None)
return(result)
if attribute_type == 'resfreq':
result = ComplexAttributes.response_frequency(x, darray, sample_rate=4,
preview=None)
return(result)
if attribute_type == 'resamp':
result = ComplexAttributes.response_amplitude(x, darray, preview=None)
return(result)
if attribute_type == 'apolar':
result = ComplexAttributes.apparent_polarity(x, darray, preview=None)
return(result)
if attribute_class == 'DipAzm':
if attribute_type == 'dipgrad':
result = DipAzm.gradient_dips(x, darray, dip_factor=10, kernel=(3,3,3),
preview=None)
return(result) # result is il_dip, xl_dip
if attribute_type == 'gst':
result = DipAzm.gradient_structure_tensor(x, darray, kernel,
preview=None)
return(result) # result is gi2, gj2, gk2, gigj, gigk, gjgk
if attribute_type == 'gstdip2d':
result = DipAzm.gst_2D_dips(x, darray, dip_factor=10, kernel=(3,3,3),
preview=None)
return(result) # result is il_dip, xl_dip
if attribute_type == 'gstdip3d':
result = DipAzm.gst_3D_dip(x, darray, dip_factor=10, kernel=(3,3,3),
preview=None)
return(result)
if attribute_type == 'gstazm3d':
result = DipAzm.gst_3D_azm(x, darray, dip_factor=10, kernel=(3,3,3),
preview=None)
return(result)
if attribute_class == 'EdgeDetection':
if attribute_type == 'semblance':
result = EdgeDetection.semblance(x, darray, kernel=(3,3,9),
preview=None)
return(result)
if attribute_type == 'gstdisc':
result = EdgeDetection.gradient_structure_tensor(x, darray,
kernel=(3,3,9),
preview=None)
return(result)
if attribute_type == 'eigen':
result = EdgeDetection.eig_complex(x, darray, kernel=(3,3,9),
preview=None)
return(result)
if attribute_type == 'chaos':
result = EdgeDetection.chaos(x, darray, kernel=(3,3,9),
preview=None)
return(result)
if attribute_type == 'curv':
# compute first inline and xline dips from gst
x = DipAzm()
darray_il, darray_xl = DipAzm.gradient_dips(x, darray, dip_factor=10,
kernel=(3,3,3),
preview=None)
# compute curvature
result = EdgeDetection.volume_curvature(x, darray_il, darray_xl,
dip_factor=10,
kernel=(3,3,3),
preview=None)
return(result) # result is H, K, Kmax, Kmin, KMPos, KMNeg