import functools
import warnings
import h5py
import numpy as np
import qpimage
from .dataset import SeriesData
class NoSinogramDataFoundError(BaseException):
pass
class SeriesHDF5GenericWarning(UserWarning):
pass
class SeriesHDF5SinogramGeneric(SeriesData):
"""Base class for HDF5-based raw sinogram QPI data
"""
def __len__(self):
return len(self._get_data_indices())
@functools.lru_cache()
def _get_data_indices(self):
"""Get all experiments from the hdf5 file"""
with h5py.File(name=self.path, mode="r") as h5:
if "sinogram" not in h5:
raise NoSinogramDataFoundError(
f"Group 'sinogram' not found in '{self.path}'!")
indices = sorted(h5["sinogram"].keys(), key=lambda x: int(x))
if not indices:
# if this error is raised, the signal_type is probably not
# set to "hologram".
raise NoSinogramDataFoundError(
f"No sinogram data found in '{self.path}'!")
return indices
def _get_metadata(self, dataset):
"""Return the metadata of the specified `h5py.Dataset`"""
meta = qpimage.meta.MetaDict()
for key in qpimage.META_KEYS:
if key in dataset.attrs:
meta[key] = dataset.attrs[key]
return meta
@staticmethod
def verify(path):
"""Verify that `path` has generic sinogram data"""
valid = False
try:
h5 = h5py.File(path, mode="r")
except (OSError, IsADirectoryError):
pass
else:
if ("file_format" in h5.attrs
and "qpformat" in h5.attrs["file_format"].lower()
and "sinogram" in h5):
valid = True
h5.close()
return valid
@property
@functools.lru_cache()
def shape(self):
with h5py.File(name=self.path, mode="r") as h5:
group = h5["sinogram"]["0"]
for key in ["field", "phase", "amplitude", "intensity"]:
if key in group:
return len(self), group[key].shape[0], group[key].shape[1]
else:
# Fallback to expensive shape computation
warnings.warn(f"Using fallback `shape` for '{self.path}'!",
SeriesHDF5GenericWarning)
return super(SeriesHDF5SinogramGeneric, self).shape
[docs]class SeriesHDF5SinogramMeep(SeriesHDF5SinogramGeneric):
"""sinograms extracted from Meep/FDTD simulations
I introduced this format in 2022 as part of my efforts to make
the finite-difference time domain simulations from the ODTbrain
manuscript :cite:`Mueller2015` publicly available.
The HDF5 file contains a "background" and a "sinogram" group.
The subgroups of "sinogram" are enumerated starting with "0".
Each of them contain the complex "field" at a plane behind the
scattering phantom as an HDF5 Dataset. The location of the plane
(and all other relevant metadata) is stored in the attributes
of this Dataset. In the same group, there are also the C++
"simulation_code" and the log "simulation_output" which can
be used to reproduce the simulation.
"""
storage_type = "field"
def __init__(self, path, meta_data=None, *args, **kwargs):
"""Initialize with default wavelength of 500nm"""
if meta_data is None:
meta_data = {}
if "wavelength" not in meta_data:
meta_data["wavelength"] = 500e-9
super(SeriesHDF5SinogramMeep, self).__init__(
path=path, meta_data=meta_data, *args, **kwargs)
# set background data
with h5py.File(path, "r") as h5:
if "background" in h5:
bgds = h5["background"]["field"]
meta_data = self._get_metadata(bgds)
qpi_bg = qpimage.QPImage(data=bgds[:],
which_data="field",
meta_data=meta_data,
h5dtype=self.as_type)
self.set_bg(qpi_bg)
def _get_metadata(self, dataset):
"""Return simulation-specific metadata
This uses the metadata previously extracted
from the simulation and set in `dataset.attrs`
to populate the QPI metadata.
"""
meta = super(SeriesHDF5SinogramMeep, self)._get_metadata(dataset)
meta.update(self.meta_data)
if "ACQUISITION_PHI" in dataset.attrs:
meta["angle"] = dataset.attrs["ACQUISITION_PHI"]
if "MEDIUM_RI" in dataset.attrs:
meta["medium index"] = dataset.attrs["MEDIUM_RI"]
if "SAMPLING" in dataset.attrs:
meta["pixel size"] = meta["wavelength"] / dataset.attrs["SAMPLING"]
if "extraction focus distance [px]" in dataset.attrs:
focus_px = dataset.attrs["extraction focus distance [px]"]
meta["focus"] = focus_px * meta["pixel size"]
meta["sim center"] = np.array(dataset.shape) / 2
meta["sim model"] = "fdtd"
return meta
def get_qpimage_raw(self, idx=0):
"""Return QPImage without background correction"""
name = self._get_data_indices()[idx]
with h5py.File(name=self.path, mode="r") as h5:
dataset = h5["sinogram"][name]["field"]
meta_data = self._get_metadata(dataset)
meta_data["time"] = float(idx)
qpi = qpimage.QPImage(data=dataset[:],
which_data="field",
meta_data=meta_data,
h5dtype=self.as_type)
# set identifier
qpi["identifier"] = self.get_identifier(idx)
return qpi
@staticmethod
def verify(path):
"""Verify the file format
The "file_format" attribute of the HDF5 file must contain
the strings "qpformat" and "meep".
"""
valid = SeriesHDF5SinogramGeneric.verify(path)
if valid:
with h5py.File(path, "r") as h5:
valid = "meep" in h5.attrs["file_format"].lower()
return valid