import numpy as np
from libertem.udf import UDF
[docs]
class SignalMonitorUDF(UDF):
'''
Return the most recently processed signal space element (frame).
This is useful for live processing as a beam monitor. Individual frames in
an offline dataset are more easily accessible with
:class:`~libertem.udf.raw.PickUDF`.
The result is most likely the last frame of each partition for the
individual merge steps. The end result depends on the merge order.
'''
def get_backends(self):
''
return [
backend for backend in self.BACKEND_ALL
if backend not in {self.BACKEND_CUPY_SCIPY_COO, self.BACKEND_SCIPY_COO}
]
# def get_preferred_input_dtype(self):
# ''
# return self.USE_NATIVE_DTYPE
def get_result_buffers(self):
''
return {
"intensity": self.buffer(kind='sig', dtype=self.meta.input_dtype, where='device')
}
def process_tile(self, tile):
''
# Assign the portion from the last frame within the tile
# to the result buffer
self.results.intensity[:] = self.forbuf(tile[-1], self.results.intensity)
def merge(self, dest, src):
''
dest.intensity[:] = src.intensity
# Largely copied from SumUDF, but separate implementation is
# desirable, for example since `merge_all()` doesn't make sense
# here.
[docs]
class PartitionMonitorUDF(UDF):
"""
Sum up frames in a partition and update result with
the latest partition result.
This is useful for live processing as a beam monitor if
individual frames contain not enough signal. Partial sums of
an offline dataset are more easily accessible with
:class:`~libertem.udf.sum.SumUDF` with a ROI.
The result is the sum of the partition that was merged last.
The end result depends on the merge order.
Parameters
----------
dtype : numpy.dtype, optional
Preferred dtype for computation, default 'float32'. The actual dtype will be determined
from this value and the dataset's dtype using :meth:`numpy.result_type`.
See also :ref:`udf dtype`.
"""
def __init__(self, dtype='float32'):
super().__init__(dtype=dtype)
def get_preferred_input_dtype(self):
''
return self.params.dtype
def get_backends(self):
''
return self.BACKEND_ALL
def get_result_buffers(self):
''
return {
'intensity': self.buffer(
kind='sig', dtype=self.meta.input_dtype, where='device'
)
}
def process_tile(self, tile):
''
self.results.intensity[:] += self.forbuf(
np.sum(tile, axis=0),
self.results.intensity
)
def merge(self, dest, src):
''
dest.intensity[:] = src.intensity