def test_slicing(temp_file):
dd = np.ones((10, 11, 12), dtype=np.uint16)
with emd.fileEMD(temp_file, readonly=False) as emd0:
dims = emd.defaultDims(dd)
emd0.put_emdgroup('test', dd, dims)
with emd.fileEMD(temp_file) as emd_obj:
assert _get_slice(emd_obj, 0).shape == (11, 12)
docs = list(ingest_NCEM_EMD([str(temp_file)]))
event_doc = docs[2][1]
data = event_doc['data']['raw']
assert data.shape == (10, 11, 12)
assert data[0].compute().shape == (11, 12)
def _metadata_from_dset(path, dset_num=0): # parameterized by path rather than emd_obj so that hashing lru hashing resolves easily
metaData = {}
metaData['veloxFlag'] = False
# EMD Berkeley
emd_obj = emd.fileEMD(path, readonly=True)
dataGroup = emd_obj.list_emds[dset_num]
dataset0 = dataGroup['data'] # get the dataset in the first group found
try:
name = dataGroup.name.split('/')[-1]
metaData[name] = {}
metaData[name].update(dataGroup.attrs)
except:
pass
# Get the dim vectors
dims = emd_obj.get_emddims(dataGroup)
if dataset0.ndim == 2:
dimZ = None
dimY = dims[0] # dataGroup['dim1']
dimX = dims[1] # dataGroup['dim2']
elif dataset0.ndim == 3:
dimZ = dims[0]
dimY = dims[1] # dataGroup['dim2']
dimX = dims[2] # dataGroup['dim3']
elif dataset0.ndim == 4:
dimZ = dims[1]
dimY = dims[2] # dataGroup['dim3']
dimX = dims[3] # dataGroup['dim4']
else:
dimZ = None
dimY = None
dimX = None
# Store the X and Y pixel size, offset and unit
try:
metaData['PhysicalSizeX'] = dimX[0][1] - dimX[0][0]
metaData['PhysicalSizeXOrigin'] = dimX[0][0]
metaData['PhysicalSizeXUnit'] = dimX[2].replace('_', '')
metaData['PhysicalSizeY'] = dimY[0][1] - dimY[0][0]
metaData['PhysicalSizeYOrigin'] = dimY[0][0]
metaData['PhysicalSizeYUnit'] = dimY[2].replace('_', '')
# metaData['PhysicalSizeZ'] = dimZ[0][1] - dimZ[0][0]
# metaData['PhysicalSizeZOrigin'] = dimZ[0][0]
# metaData['PhysicalSizeZUnit'] = dimZ[2]
except:
metaData['PhysicalSizeX'] = 1
metaData['PhysicalSizeXOrigin'] = 0
metaData['PhysicalSizeXUnit'] = ''
metaData['PhysicalSizeY'] = 1
metaData['PhysicalSizeYOrigin'] = 0
metaData['PhysicalSizeYUnit'] = ''
metaData['shape'] = dataset0.shape
_cleandict(metaData)
return metaData
def test_ingest_emd_berkeley(temp_file):
dd = np.ones((10, 11, 12), dtype=np.uint16)
with emd.fileEMD(temp_file, readonly=False) as emd0:
dims = emd.defaultDims(dd)
emd0.put_emdgroup('test', dd, dims)
# Test slicing
with emd.fileEMD(temp_file) as emd_obj:
dd0 = emd_obj.list_emds[0]['data']
assert dd0[0, :, :].shape == (11, 12)
# Test ingest
docs = list(ingest_NCEM_EMD([str(temp_file)]))
event_doc = docs[2][1]
data = event_doc['data']['raw']
assert data.shape == (10, 11, 12)
assert data[0].compute().shape == (11, 12)
def _metadata(path): # parameterized by path rather than emd_obj so that hashing lru hashing resolves easily
metaData = {}
metaData['veloxFlag'] = False
metaData['FileName'] = path
# EMD Berkeley
emd_obj = emd.fileEMD(path, readonly=True)
try:
metaData['user'] = {}
metaData['user'].update(emd_obj.file_hdl['/user'].attrs)
except:
pass
try:
metaData['microscope'] = {}
metaData['microscope'].update(emd_obj.file_hdl['/microscope'].attrs)
except:
pass
try:
metaData['sample'] = {}
metaData['sample'].update(emd_obj.file_hdl['/sample'].attrs)
except:
pass
try:
metaData['comments'] = {}
metaData['comments'].update(emd_obj.file_hdl['/comments'].attrs)
except:
pass
try:
metaData['stage'] = {}
# Check for legacy keys in stage group. Skip the rest
good_keys = ('position', 'type', 'Type')
for k in good_keys:
if k in emd_obj.file_hdl['/stage'].attrs:
metaData['stage'][k] = emd_obj.file_hdl['/stage'].attrs[k]
except:
pass
_cleandict(metaData)
return metaData
def EMD_multi_path():
"""Write a small Berkeley EMD file with 2 data sets to a tempfile
"""
dd, _, _ = np.mgrid[0:30, 0:40, 0:50]
dd = dd.astype('<u2')
dd2, _, _ = np.mgrid[0:60, 0:80, 0:100]
dd2 = dd2.astype('<u2')
tmp = tempfile.NamedTemporaryFile(mode='wb')
tmp.close() # need to close the file to use it later
fPath = str(Path(tmp.name))
with emd.fileEMD(fPath, readonly=False) as f0:
dims = emd.defaultDims(dd)
f0.put_emdgroup('test1', dd, dims)
dims2 = emd.defaultDims(dd2)
f0.put_emdgroup('test2', dd2, dims2)
return fPath
def test_multi_device(temp_file):
dd = np.ones((10, 11, 12), dtype=np.uint16)
with emd.fileEMD(temp_file, readonly=False) as emd0:
dims = emd.defaultDims(dd)
emd0.put_emdgroup('test', dd, dims)
# Change shape and write again to simulate a second data set
dd2 = dd.reshape(5, 22, 12)
dims2 = emd.defaultDims(dd2)
emd0.put_emdgroup('test2', dd2, dims2)
del dd, dd2, dims, dims2
# Ingest and get the first data set.
docs = list(ingest_NCEM_EMD([str(temp_file)]))
event_doc = docs[2][1]
data = event_doc['data']['raw']
assert data.shape == (10, 11, 12)
assert data[0].compute().shape == (11, 12)
event_doc = docs[4][1]
data = event_doc['data']['raw']
assert data.shape == (5, 22, 12)
assert data[0].compute().shape == (22, 12)
catalog = BlueskyInMemoryCatalog()
start = docs[0][1]
stop = docs[-1][1]
others = docs[1:-2]
def doc_gen():
yield from docs
catalog.upsert(start, stop, doc_gen, [], {})
run_catalog = catalog[-1]
stream_names = list(run_catalog)
print(stream_names)
run_catalog[stream_names[0]].to_dask()['raw'].compute()
run_catalog[stream_names[1]].to_dask()['raw'].compute()
def emd_sniffer(path, first_bytes):
if not Path(path).suffix.lower() == '.emd':
return
test_velox = False
try:
# Test for Berkeley EMD
with emd.fileEMD(path, readonly=True) as emd1:
if len(emd1.list_emds) > 0:
return 'application/x-EMD'
else:
test_velox = True
except OSError:
# Not a HDF5 file
return
if test_velox:
# Test for Velox
with emdVelox.fileEMDVelox(path) as emd2:
ver = emd2._file_hdl['Version'][0].decode('ASCII')
if ver.find('Velox') > -1:
return 'application/x-EMD-VELOX'
else:
return
def ExtractSignalsFromEMD(InputEMD=None,
SignalNames=['HAADF', 'Mg_K', 'Fe_Ka'],
Binning=4):
''' Read in a set of Bruker bcf files containing EDS acquisitions.
Parameters:
InputEMD (str): Name of EMD file containing the tilt stacks.
SignalNames (list of str): A list of signals that should be extracted from the Bruker files.
Valid values include:
HAADF: For the HAADF signal
Element_Line: e.g. Fe_Ka for a weighted sum of Fe_Ka1 and Ka2, or Fe_Ka1 for just that line, or Mg_K for a weighted sum of all Mg-K lines. There is no brehmsstrahlung removal.
eV1-eV2: Start and stop energies in eV. The signal will be the sum of all energies over the range [eV1, eV2).
Binning (int): How much binning to do on EDS signals to reduce noise. 1 means no binning. 2 means each output voxel is 2x2x2 input voxels. HAADF signals are not rebinned as they are usually not noisy.
Returns:
SignalDict (OrderedDict of np.ndarray): Dictionary with names matching SignalNames and dimensions of (tilt, x, y).
Tilts (list of floats): A list of tilt angles.
'''
# for s in SignalNames:
# if '_' in s:
# El, Line = s.split('_')
# print(GetFluorescenceLineEnergy(El, Series=Line[0], Line=Line))
# Make an ordered dictionary with one entry for each signal.
SignalDict = OrderedDict()
for n in SignalNames:
SignalDict[n] = []
# Open the emd file that has our data.
EMD = fileEMD(InputEMD, readonly=True)
# We will compute the FWHM of peaks using the Mn-Ka reported in the EMD.
EnergyResolutionMnKa = EMD.microscope.attrs['MnKaResolution[eV]']
Mn_Ka_Energy = GetFluorescenceLineEnergy('Mn', Series='K', Line='Ka')
K = EnergyResolutionMnKa / np.sqrt(Mn_Ka_Energy)
print('Energy resolution of Mn-Ka is: ' + str(EnergyResolutionMnKa) +
' eV.')
print(
'Assumed FWHM of peaks will be (%g*sqrt(E))/2., hence at Mn-Ka: %g eV.'
% (K, K * np.sqrt(Mn_Ka_Energy) / 2))
# Get links to the data we'll need from the EMD.
HAADF, HAADF_dims = EMD.get_emdgroup(EMD.data['HAADF_TiltStack'])
EDS, EDS_dims = EMD.get_emdgroup(EMD.data['EDS_TiltStack'])
Tilts = HAADF_dims[0][0]
print('HAADF dimensions are (%d, %d).' %
(len(HAADF_dims[1][0]), len(HAADF_dims[1][0])))
# Calculate the rebinning size for the EDS data.
rebinsize_m = int(len(EDS_dims[1][0]) / Binning)
rebinsize_n = int(len(EDS_dims[2][0]) / Binning)
print(
'Binning is %d so rebinned EDS cubes will have spatial dimension (%d, %d).'
% (Binning, rebinsize_m, rebinsize_n))
for sig in SignalDict.keys():
print(sig, end='')
if sig == 'HAADF':
SignalDict['HAADF'] = HAADF[:].astype(
"float32") # The HAADF doesn't get rebinned.
print('')
if '_' in sig:
# This is a fluorescence line.
El, Line = sig.split('_')
CenterEnergy = GetFluorescenceLineEnergy(El,
Series=Line[0],
Line=Line)
if CenterEnergy is None:
print(
'Unrecognized fluorescence line: %s-%s, ignoring this signal.'
% (El, Line))
continue
LowEnergy = CenterEnergy - K * np.sqrt(CenterEnergy) / 2
HighEnergy = CenterEnergy + K * np.sqrt(CenterEnergy) / 2
LowEnergyIndex = np.argmin((EDS_dims[3][0] - LowEnergy)**2)
HighEnergyIndex = np.argmin((EDS_dims[3][0] - HighEnergy)**2)
print(', %g-%g eV window, energy bins: %d-%d.' %
(LowEnergy, HighEnergy, LowEnergyIndex, HighEnergyIndex))
Cube = np.sum(EDS[:, :, :, LowEnergyIndex:HighEnergyIndex + 1],
axis=-1)
SignalDict[sig] = BinEDSSpatialDimensions(Cube, Binning)
# fluor[0].rebin((rebinsize_m,rebinsize_n)).data.copy().astype("float32"))
if '-' in sig:
print('Energy range signals not implemented yet.')
print('Signals Extracted.')
# Turn those signal readouts into 3D numpy arrays (tilt, x, y).
for k, v in SignalDict.items():
npStack = np.array(v)
SignalDict[k] = npStack
return SignalDict, Tilts
def ExtractRawSignalsFromBrukerSequence(InputDirectory=None, OutputEMD=None):
''' Read in a set of Bruker bcf files containing EDS acquisitions and write an EMD file with the data.
Parameters:
InputDirectory (str): Name of directory containing the bcf files.
OutputEMD (str): Name of the output file (may include a path).
Returns:
None.
'''
# Find all the bcf files first.
Tilts = GetTiltsFromBrukerSequence(Directory=InputDirectory)
Tilts = np.array(Tilts)
# We need some filename if none was given to us.
if OutputEMD is None:
OutputEMD = 'test.emd'
print('Extracting Signals:')
for i, t in enumerate(Tilts):
# Load the bruker file for this tilt.
fname = os.path.join(InputDirectory, str(int(t))+'.bcf')
x = hs.load(fname)
# Only the first time, we need to calculate the sizes of all the arrays we are going to make.
if 'HAADFsize_m' not in locals():
# HAADF's have x,y dimensions.
HAADFsize_m = GetSpatialDimension(x[0].axes_manager['width'])
HAADFsize_n = GetSpatialDimension(x[0].axes_manager['height'])
HAADFDim = (len(Tilts), len(HAADFsize_m), len(HAADFsize_n))
print('HAADF has dimensions ' + str(HAADFDim))
# EDS cubes have x,y, energy dimensions.
EDSsize_m = GetSpatialDimension(x[1].axes_manager['width'])
EDSsize_n = GetSpatialDimension(x[1].axes_manager['height'])
EDSsize_e = GetEnergyDimension(x[1].axes_manager['Energy'])
EDSDim = (len(Tilts), len(EDSsize_m), len(EDSsize_n), len(EDSsize_e))
print('EDS has dimensions ' + str(EDSDim))
HAADF = np.zeros(HAADFDim)
EDS = np.zeros(EDSDim, dtype='float32')
BeamEnergy = x[0].metadata['Acquisition_instrument']['TEM']['beam_energy']*1000 # eV
EnergyResolutionMnKa = x[1].metadata['Acquisition_instrument']['TEM']['Detector']['EDS']['energy_resolution_MnKa'] # eV
DetectorTiltAngle = x[1].metadata['Acquisition_instrument']['TEM']['Detector']['EDS']['elevation_angle'] # degrees
RealTime = x[1].metadata['Acquisition_instrument']['TEM']['Detector']['EDS']['real_time'] # seconds
print(str(fname))
HAADF[i,:,:] = x[0].data.astype("float32")
EDS[i,:,:,:] = x[1].data.astype("float32")
# open nonexisting file for writing
if os.path.isfile(OutputEMD):
os.remove(OutputEMD)
EMD = fileEMD(OutputEMD)
data = HAADF
dims = ( (Tilts, 'angle', '[deg]'),
(HAADFsize_m, 'x', '[m]'),
(HAADFsize_n, 'y', '[m]'))
print('Writing HAADF tilt stack.')
EMD.put_emdgroup('HAADF_TiltStack', data, dims)
data = EDS
dims = ( (Tilts, 'angle', '[deg]'),
(EDSsize_m, 'x', '[m]'),
(EDSsize_n, 'y', '[m]'),
(EDSsize_e, 'E', '[eV]'))
print('Writing EDS tilt stack.')
EMD.put_emdgroup('EDS_TiltStack', data, dims)
EMD.microscope.attrs['BeamVoltage[eV]'] = BeamEnergy
EMD.microscope.attrs['MnKaResolution[eV]'] = EnergyResolutionMnKa
EMD.microscope.attrs['DetectorTiltAngle[deg]'] = DetectorTiltAngle
EMD.microscope.attrs['RealTime[s]'] = RealTime * len(Tilts)
EMD.put_comment('File created.')
del EMD
print('Created file ' + OutputEMD)
def writeEMD(self, filename):
""" Write SER data to an EMD file.
Parameters
----------
filename: str or pathlib.Path
Name of the EMD file.
"""
# Todo: Update this to be much simpler. Can write this in a couple of lines now using the fileEMD class
from ncempy.io import emd
# create the EMD file and set version attributes
try:
f = emd.fileEMD(filename)
except:
raise IOError('Cannot write to file "{}"!'.format(filename))
# create EMD group
grp = f.file_hdl['data'].create_group(
os.path.basename(self._file_hdl.name))
grp.attrs['emd_group_type'] = 1
# use first dataset to layout memory
data, first_meta = self.getDataset(0)
first_tag = self._getTag(0)
if self.head['DataTypeID'] == 0x4122:
# 2D datasets
self.head[
'ExperimentType'] = 'image' # text indicator of the experiment type
if first_tag['TagTypeID'] == 0x4142:
# 2D mapping
dset = grp.create_dataset(
'data',
(self.head['Dimensions'][1]['DimensionSize'],
self.head['Dimensions'][0]['DimensionSize'],
first_meta['ArrayShape'][1], first_meta['ArrayShape'][0]),
dtype=self._dictDataType[first_meta['DataType']])
# collect time
time = np.zeros((self.head['Dimensions'][0]['DimensionSize'],
self.head['Dimensions'][1]['DimensionSize']),
dtype='i4')
# create mapping dims for checking
map_xdim = self._createDim(
self.head['Dimensions'][0]['DimensionSize'],
self.head['Dimensions'][0]['CalibrationOffset'],
self.head['Dimensions'][0]['CalibrationDelta'],
self.head['Dimensions'][0]['CalibrationElement'])
map_ydim = self._createDim(
self.head['Dimensions'][1]['DimensionSize'],
self.head['Dimensions'][1]['CalibrationOffset'],
self.head['Dimensions'][1]['CalibrationDelta'],
self.head['Dimensions'][1]['CalibrationElement'])
# weird direction depend half pixel shifting
map_xdim += 0.5 * self.head['Dimensions'][0]['CalibrationDelta']
map_ydim -= 0.5 * self.head['Dimensions'][1]['CalibrationDelta']
for y in range(self.head['Dimensions'][0]['DimensionSize']):
for x in range(
self.head['Dimensions'][1]['DimensionSize']):
index = int(
x +
y * self.head['Dimensions'][0]['DimensionSize'])
print('converting dataset {} of {}, items ({}, {})'.
format(index + 1,
self.head['ValidNumberElements'], x, y))
# retrieve dataset and put into buffer
data, meta = self.getDataset(index)
dset[y, x, :, :] = data[:, :]
# get tag data per image
tag = self._getTag(index)
time[y, x] = tag['Time']
assert (np.abs(tag['PositionX'] - map_xdim[x]) <
np.abs(tag['PositionX'] * 1e-8))
assert (np.abs(tag['PositionY'] - map_ydim[y]) <
np.abs(tag['PositionY'] * 1e-8))
del data, meta, tag
# create dimension datasets
dims = []
dims_time = []
# Position Y
assert self.head['Dimensions'][1]['Description'] == 'Position'
dims.append(
(map_ydim, self.head['Dimensions'][1]['Description'],
'[{}]'.format(self.head['Dimensions'][1]['Units'])))
dims_time.append(
(map_ydim, self.head['Dimensions'][1]['Description'],
'[{}]'.format(self.head['Dimensions'][1]['Units'])))
# Position X
assert self.head['Dimensions'][0]['Description'] == 'Position'
dims.append(
(map_xdim, self.head['Dimensions'][0]['Description'],
'[{}]'.format(self.head['Dimensions'][0]['Units'])))
dims_time.append(
(map_xdim, self.head['Dimensions'][0]['Description'],
'[{}]'.format(self.head['Dimensions'][0]['Units'])))
dim = self._createDim(
first_meta['ArrayShape'][1],
first_meta['Calibration'][1]['CalibrationOffset'],
first_meta['Calibration'][1]['CalibrationDelta'],
first_meta['Calibration'][1]['CalibrationElement'])
dims.append((dim, 'y', '[m]'))
dim = self._createDim(
first_meta['ArrayShape'][0],
first_meta['Calibration'][0]['CalibrationOffset'],
first_meta['Calibration'][0]['CalibrationDelta'],
first_meta['Calibration'][0]['CalibrationElement'])
dims.append((dim, 'x', '[m]'))
# write dimensions
for ii in range(len(dims)):
f.write_dim('dim{:d}'.format(ii + 1), dims[ii], grp)
# write out time as additional dataset
_ = f.put_emdgroup('timestamp', time, dims_time, parent=grp)
else:
# 1 entry series to single image
if self.head['ValidNumberElements'] == 1:
# get image
data, meta = self.getDataset(0)
tag = self._getTag(0)
# create dimensions
dims = []
dim = self._createDim(
first_meta['ArrayShape'][1],
first_meta['Calibration'][1]['CalibrationOffset'],
first_meta['Calibration'][1]['CalibrationDelta'],
first_meta['Calibration'][1]['CalibrationElement'])
dims.append((dim, 'y', '[m]'))
dim = self._createDim(
first_meta['ArrayShape'][0],
first_meta['Calibration'][0]['CalibrationOffset'],
first_meta['Calibration'][0]['CalibrationDelta'],
first_meta['Calibration'][0]['CalibrationElement'])
dims.append((dim, 'x', '[m]'))
dset = grp.create_dataset(
'data', (first_meta['ArrayShape'][1],
first_meta['ArrayShape'][0]),
dtype=self._dictDataType[first_meta['DataType']])
dset[:, :] = data[:, :]
for i in range(len(dims)):
f.write_dim('dim{:d}'.format(i + 1), dims[i], grp)
dset.attrs['timestamp'] = tag['Time']
else:
# simple series
dset = grp.create_dataset(
'data', (self.head['ValidNumberElements'],
first_meta['ArrayShape'][1],
first_meta['ArrayShape'][0]),
dtype=self._dictDataType[first_meta['DataType']])
# collect time
time = np.zeros(self.head['ValidNumberElements'],
dtype='i4')
for i in range(self.head['ValidNumberElements']):
print('converting dataset {} of {}'.format(
i + 1, self.head['ValidNumberElements']))
# retrieve dataset and put into buffer
data, meta = self.getDataset(i)
dset[i, :, :] = data[:, :]
# get tag data per image
tag = self._getTag(i)
time[i] = tag['Time']
# create dimension data sets
dims = []
# first SER dimension is number
assert self.head['Dimensions'][0][
'Description'] == 'Number'
dim = self._createDim(
self.head['Dimensions'][0]['DimensionSize'],
self.head['Dimensions'][0]['CalibrationOffset'],
self.head['Dimensions'][0]['CalibrationDelta'],
self.head['Dimensions'][0]['CalibrationElement'])
dims.append(
(dim[0:self.head['ValidNumberElements']],
self.head['Dimensions'][0]['Description'],
'[{}]'.format(self.head['Dimensions'][0]['Units'])))
dim = self._createDim(
first_meta['ArrayShape'][1],
first_meta['Calibration'][1]['CalibrationOffset'],
first_meta['Calibration'][1]['CalibrationDelta'],
first_meta['Calibration'][1]['CalibrationElement'])
dims.append((dim, 'y', '[m]'))
dim = self._createDim(
first_meta['ArrayShape'][0],
first_meta['Calibration'][0]['CalibrationOffset'],
first_meta['Calibration'][0]['CalibrationDelta'],
first_meta['Calibration'][0]['CalibrationElement'])
dims.append((dim, 'x', '[m]'))
# write dimensions
for i in range(len(dims)):
f.write_dim('dim{:d}'.format(i + 1), dims[i], grp)
# write out time as additional dim vector
f.write_dim('dim1_time', (time, 'timestamp', '[s]'), grp)
elif self.head['DataTypeID'] == 0x4120:
# 1D datasets; spectra
self.head[
'ExperimentType'] = 'spectrum' # text indicator of the experiment type
if first_tag['TagTypeID'] == 0x4142:
# 2D mapping
dset = grp.create_dataset(
'data', (self.head['Dimensions'][1]['DimensionSize'],
self.head['Dimensions'][0]['DimensionSize'],
first_meta['ArrayShape'][0]),
dtype=self._dictDataType[first_meta['DataType']])
time = np.zeros((self.head['Dimensions'][0]['DimensionSize'],
self.head['Dimensions'][1]['DimensionSize']),
dtype='i4')
# create mapping dims for checking
map_xdim = self._createDim(
self.head['Dimensions'][0]['DimensionSize'],
self.head['Dimensions'][0]['CalibrationOffset'],
self.head['Dimensions'][0]['CalibrationDelta'],
self.head['Dimensions'][0]['CalibrationElement'])
map_ydim = self._createDim(
self.head['Dimensions'][1]['DimensionSize'],
self.head['Dimensions'][1]['CalibrationOffset'],
self.head['Dimensions'][1]['CalibrationDelta'],
self.head['Dimensions'][1]['CalibrationElement'])
# weird direction depend half pixel shifting
map_xdim += 0.5 * self.head['Dimensions'][0]['CalibrationDelta']
map_ydim -= 0.5 * self.head['Dimensions'][1]['CalibrationDelta']
for y in range(self.head['Dimensions'][0]['DimensionSize']):
for x in range(
self.head['Dimensions'][1]['DimensionSize']):
index = int(
x +
y * self.head['Dimensions'][0]['DimensionSize'])
print('converting dataset {} of {}, items ({}, {})'.
format(index + 1,
self.head['ValidNumberElements'], x, y))
# retrieve dataset and put into buffer
data, meta = self.getDataset(index)
dset[y, x, :] = np.copy(data[:])
# get tag data per image
tag = self._getTag(index)
time[y, x] = tag['Time']
assert (np.abs(tag['PositionX'] - map_xdim[x]) <
np.abs(tag['PositionX'] * 1e-8))
assert (np.abs(tag['PositionY'] - map_ydim[y]) <
np.abs(tag['PositionY'] * 1e-8))
del data, meta, tag
# create dimension datasets
dims = []
dims_time = []
# Position Y
assert self.head['Dimensions'][1]['Description'] == 'Position'
dims.append(
(map_ydim, self.head['Dimensions'][1]['Description'],
'[{}]'.format(self.head['Dimensions'][1]['Units'])))
dims_time.append(
(map_ydim, self.head['Dimensions'][1]['Description'],
'[{}]'.format(self.head['Dimensions'][1]['Units'])))
# Position X
assert self.head['Dimensions'][0]['Description'] == 'Position'
dims.append(
(map_xdim, self.head['Dimensions'][0]['Description'],
'[{}]'.format(self.head['Dimensions'][0]['Units'])))
dims_time.append(
(map_xdim, self.head['Dimensions'][0]['Description'],
'[{}]'.format(self.head['Dimensions'][0]['Units'])))
dim = self._createDim(
first_meta['ArrayShape'][0],
first_meta['Calibration'][0]['CalibrationOffset'],
first_meta['Calibration'][0]['CalibrationDelta'],
first_meta['Calibration'][0]['CalibrationElement'])
dims.append((dim, 'E', '[m_eV]'))
# write dimensions
for i in range(len(dims)):
f.write_dim('dim{:d}'.format(i + 1), dims[i], grp)
# write out time as additional dataset
_ = f.put_emdgroup('timestamp', time, dims_time, parent=grp)
else:
# simple series
dset = grp.create_dataset(
'data', (self.head['ValidNumberElements'],
first_meta['ArrayShape'][0]),
dtype=self._dictDataType[first_meta['DataType']])
# collect time
time = np.zeros(self.head['ValidNumberElements'], dtype='i4')
for i in range(self.head['ValidNumberElements']):
print('converting dataset {} of {}'.format(
i + 1, self.head['ValidNumberElements']))
# retrieve dataset and put into buffer
data, meta = self.getDataset(i)
dset[i, :] = data[:]
# get tag data per image
tag = self._getTag(i)
time[i] = tag['Time']
# create dimension datasets
dims = []
# first SER dimension is number
assert self.head['Dimensions'][0]['Description'] == 'Number'
dim = self._createDim(
self.head['Dimensions'][0]['DimensionSize'],
self.head['Dimensions'][0]['CalibrationOffset'],
self.head['Dimensions'][0]['CalibrationDelta'],
self.head['Dimensions'][0]['CalibrationElement'])
dims.append(
(dim[0:self.head['ValidNumberElements']],
self.head['Dimensions'][0]['Description'],
'[{}]'.format(self.head['Dimensions'][0]['Units'])))
dim = self._createDim(
first_meta['ArrayShape'][0],
first_meta['Calibration'][0]['CalibrationOffset'],
first_meta['Calibration'][0]['CalibrationDelta'],
first_meta['Calibration'][0]['CalibrationElement'])
dims.append((dim, 'E', '[m_eV]'))
# write dimensions
for i in range(len(dims)):
f.write_dim('dim{:d}'.format(i + 1), dims[i], grp)
# write out time as additional dim vector
f.write_dim('dim1_time', (time, 'timestamp', '[s]'), grp)
else:
raise RuntimeError('Unknown DataTypeID')
# put meta information from _emi to Microscope group, if available
if self._emi:
for key in self._emi:
if not self._emi[key] is None:
f.microscope.attrs[key] = self._emi[key]
# write comment into Comment group
f.put_comment(
'Converted SER file "{}" to EMD using the openNCEM tools.'.format(
self._file_hdl.name))
def ingest_NCEM_EMD(paths):
assert len(paths) == 1
path = paths[0]
emd_handle = emd.fileEMD(path, readonly=True)
# Compose run start
run_bundle = event_model.compose_run() # type: event_model.ComposeRunBundle
start_doc = run_bundle.start_doc
start_doc["sample_name"] = Path(paths[0]).resolve().stem
metadata = _metadata(path)
metadata.update(start_doc)
start_doc = metadata
yield 'start', start_doc
for device_index, device_name in enumerate(_dset_names(emd_handle)):
num_t = _num_t(emd_handle, dset_num=device_index)
first_frame = _get_slice(emd_handle, 0, dset_num=device_index)
shape = first_frame.shape
dtype = first_frame.dtype
delayed_get_slice = dask.delayed(_get_slice)
dask_data = da.stack([da.from_delayed(delayed_get_slice(emd_handle, t, dset_num=device_index), shape=shape, dtype=dtype)
for t in range(num_t)])
# Compose descriptor
source = 'NCEM'
frame_data_keys = {'raw': {'source': source,
'dtype': 'number',
'shape': (num_t, *shape)}}
frame_stream_name = f'primary_{device_name}'
stream_metadata = _metadata_from_dset(path, dset_num=device_index)
configuration = {key: {"data": {key: value},
"timestamps": {key: time.time()},
"data_keys": {key: {"source": path,
"dtype": _guess_type(value),
"shape": [],
"units": "",
#"related_value": 0, ... # i.e. soft limits, precision
}}}
for key, value in stream_metadata.items() if _guess_type(value)}
frame_stream_bundle = run_bundle.compose_descriptor(data_keys=frame_data_keys,
name=frame_stream_name,
configuration=configuration
)
yield 'descriptor', frame_stream_bundle.descriptor_doc
# NOTE: Resource document may be meaningful in the future. For transient access it is not useful
# # Compose resource
# resource = run_bundle.compose_resource(root=Path(path).root, resource_path=path, spec='NCEM_DM', resource_kwargs={})
# yield 'resource', resource.resource_doc
# Compose datum_page
# z_indices, t_indices = zip(*itertools.product(z_indices, t_indices))
# datum_page_doc = resource.compose_datum_page(datum_kwargs={'index_z': list(z_indices), 'index_t': list(t_indices)})
# datum_ids = datum_page_doc['datum_id']
# yield 'datum_page', datum_page_doc
yield 'event', frame_stream_bundle.compose_event(data={'raw': dask_data},
timestamps={'raw': time.time()})
yield 'stop', run_bundle.compose_stop()
