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134 lines
4.5 KiB
Python
134 lines
4.5 KiB
Python
from __future__ import division, print_function, absolute_import
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import numpy as np
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import torch as t
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from copy import copy
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import h5py
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import pathlib
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from CDTools.tools import data as cdtdata
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from CDTools.tools import plotting
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from torch.utils import data as torchdata
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from matplotlib import pyplot as plt
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from matplotlib.widgets import Slider
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from matplotlib import ticker
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#
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# This loads and stores all the kinds of metadata that are common to
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# All different kinds of diffraction experiments
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# Other datasets can subclass this and not worry about loading and
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# saving that metadata.
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#
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class CDataset(torchdata.Dataset):
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def __init__(self, entry_info=None, sample_info=None,
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wavelength=None,
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detector_geometry=None, mask=None,
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background=None):
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# Force pass-by-value-like behavior to stop strangeness
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self.entry_info = copy(entry_info)
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self.sample_info = copy(sample_info)
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self.wavelength = wavelength
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self.detector_geometry = copy(detector_geometry)
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if mask is not None:
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self.mask = t.tensor(mask)
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else:
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self.mask = None
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if background is not None:
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self.background = t.Tensor(background)
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else:
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self.background = None
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self.get_as(device='cpu')
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def to(self,*args,**kwargs):
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# The mask should always stay a uint8, but it should switch devices
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mask_kwargs = copy(kwargs)
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try:
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mask_kwargs.pop('dtype')
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except KeyError as r:
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pass
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if self.mask is not None:
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self.mask = self.mask.to(*args,**mask_kwargs)
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if self.background is not None:
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self.background = self.background.to(*args,**kwargs)
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def get_as(self, *args, **kwargs):
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self.get_as_args = (args, kwargs)
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def __getitem__(self, index):
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# Deals with loading to appropriate device/dtype, if
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# specified via a call to get_as
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inputs, outputs = self._load(index)
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if hasattr(self, 'get_as_args'):
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outputs = outputs.to(*self.get_as_args[0],**self.get_as_args[1])
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moved_inputs = []
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for inp in inputs:
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try:
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moved_inputs.append(inp.to(*self.get_as_args[0],**self.get_as_args[1]) )
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except:
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moved_inputs.append(inp)
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else:
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moved_inputs = inputs
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return moved_inputs, outputs
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def _load(self, index):
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# Internal function to load data
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raise NotImplementedError()
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@classmethod
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def from_cxi(cls, cxi_file):
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# If a bare string is passed
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if isinstance(cxi_file, str) or isinstance(cxi_file, pathlib.Path):
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with h5py.File(cxi_file,'r') as f:
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return cls.from_cxi(f)
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entry_info = cdtdata.get_entry_info(cxi_file)
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sample_info = cdtdata.get_sample_info(cxi_file)
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wavelength = cdtdata.get_wavelength(cxi_file)
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distance, basis, corner = cdtdata.get_detector_geometry(cxi_file)
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detector_geometry = {'distance' : distance,
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'basis' : basis,
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'corner' : corner}
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mask = cdtdata.get_mask(cxi_file)
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dark = cdtdata.get_dark(cxi_file)
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return cls(entry_info = entry_info,
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sample_info = sample_info,
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wavelength=wavelength,
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detector_geometry=detector_geometry,
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mask=mask, background=dark)
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def to_cxi(self, cxi_file):
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if self.entry_info is not None:
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cdtdata.add_entry_info(cxi_file, self.entry_info)
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if self.sample_info is not None:
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cdtdata.add_sample_info(cxi_file, self.sample_info)
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if self.wavelength is not None:
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cdtdata.add_source(cxi_file, self.wavelength)
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if self.detector_geometry is not None:
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if 'corner' in self.detector_geometry:
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corner = self.detector_geometry['corner']
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else:
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corner = None
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cdtdata.add_detector(cxi_file,
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self.detector_geometry['distance'],
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self.detector_geometry['basis'],
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corner = corner)
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if self.mask is not None:
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cdtdata.add_mask(cxi_file, self.mask)
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if self.background is not None:
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cdtdata.add_dark(cxi_file, self.background)
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from CDTools.datasets.ptycho_2d_dataset import Ptycho2DDataset
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