Source code for libertem.udf.record

from typing import Tuple
import numpy as np

from libertem.common import Shape
from libertem.udf.base import UDF
from libertem.common.math import prod
from libertem.common.buffers import reshaped_view

[docs]class RecordUDF(UDF): ''' Record input data as NumPy .npy file Parameters ---------- filename : str or path-like Filename where to save. The file will be overwritten if it exists. _is_master : bool Internal flag, keep at default value. ''' def __init__(self, filename, _is_master=True): self._is_master = _is_master super().__init__(filename=filename, _is_master=False) def get_preferred_input_dtype(self): '' return self.USE_NATIVE_DTYPE @property def _ds_shape(self) -> Shape: # only valid during run_udf hence _ property return self.meta.dataset_shape @property def _memmap_flat_shape(self) -> Tuple[int, ...]: # only valid during run_udf hence _ property return (prod(self._ds_shape.nav), *self._ds_shape.sig) def preprocess(self): '' if self.meta.roi is not None: raise RuntimeError('Recording with ROI is not supported.') # create the file once in the preprocess method on the master node if self._is_master: np.lib.format.open_memmap( self.params.filename, mode='w+', dtype=self.meta.input_dtype, shape=tuple(self._ds_shape), ) def get_result_buffers(self): '' return {} def get_task_data(self): '' m = np.lib.format.open_memmap( self.params.filename, mode='r+', dtype=self.meta.input_dtype, shape=tuple(self._ds_shape) ) return { 'memmap': reshaped_view(m, self._memmap_flat_shape) } def process_tile(self, tile): '' self.meta.slice.get(self.task_data.memmap)[:] = tile