import numpy as np
from ....core import Operator
[docs]class NormPerShot(Operator):
"""
Normalised a series of time traces to the maximum value of the set.
Parameters
----------
amplitude : bool, optional
Whether to keep the true amplitude of the modelled after
normalisation.
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.amplitude = kwargs.pop('amplitude', False)
self._num_traces = None
[docs] def forward(self, *traces, **kwargs):
self._num_traces = len(traces)
amplitude = kwargs.pop('amplitude', self.amplitude)
if amplitude:
scaling = self._norm(traces[0], **kwargs)
else:
scaling = 1.
normed = tuple([self._apply(each, scaling=scaling, **kwargs) for each in traces])
if len(normed) == 1:
normed = normed[0]
return normed
[docs] def adjoint(self, *d_traces, **kwargs):
d_traces = d_traces[:self._num_traces]
if len(d_traces) == 1:
d_traces = d_traces[0]
return d_traces
def _norm(self, traces, **kwargs):
norm_value = 0.
for index in range(traces.extended_shape[0]):
norm_value += np.sum(traces.extended_data[index] ** 2)
norm_value = np.sqrt(norm_value / traces.extended_shape[0]) + 1e-31
return norm_value
def _apply(self, traces, scaling, **kwargs):
norm_value = self._norm(traces, **kwargs)
out_traces = traces.alike(name='normed_%s' % traces.name)
out_traces.extended_data[:] = scaling * traces.extended_data / norm_value
return out_traces