"""
NXcanSAS 1/2D data reader for writing HDF5 formatted NXcanSAS files.
"""
import h5py
import numpy as np
import re
import os
from sas.sascalc.dataloader.readers.cansas_reader_HDF5 import Reader
from sas.sascalc.dataloader.data_info import Data1D, Data2D
[docs]class NXcanSASWriter(Reader):
"""
A class for writing in NXcanSAS data files. Any number of data sets may be
written to the file. Currently 1D and 2D SAS data sets are supported
NXcanSAS spec: http://download.nexusformat.org/sphinx/classes/contributed_definitions/NXcanSAS.html
:Dependencies:
The NXcanSAS writer requires h5py => v2.5.0 or later.
"""
[docs] def write(self, dataset, filename):
"""
Write an array of Data1d or Data2D objects to an NXcanSAS file, as
one SASEntry with multiple SASData elements. The metadata of the first
elememt in the array will be written as the SASentry metadata
(detector, instrument, sample, etc).
:param dataset: A list of Data1D or Data2D objects to write
:param filename: Where to write the NXcanSAS file
"""
def _h5_string(string):
"""
Convert a string to a numpy string in a numpy array. This way it is
written to the HDF5 file as a fixed length ASCII string and is
compatible with the Reader read() method.
"""
if isinstance(string, np.ndarray):
return string
elif not isinstance(string, str):
string = str(string)
return np.array([np.string_(string)])
def _write_h5_string(entry, value, key):
entry[key] = _h5_string(value)
def _h5_float(x):
if not (isinstance(x, list)):
x = [x]
return np.array(x, dtype=np.float32)
def _write_h5_float(entry, value, key):
entry.create_dataset(key, data=_h5_float(value))
def _write_h5_vector(entry, vector, names=['x_position', 'y_position'],
units=None, write_fn=_write_h5_string):
"""
Write a vector to an h5 entry
:param entry: The H5Py entry to write to
:param vector: The Vector to write
:param names: What to call the x,y and z components of the vector
when writing to the H5Py entry
:param units: The units of the vector (optional)
:param write_fn: A function to convert the value to the required
format and write it to the H5Py entry, of the form
f(entry, value, name) (optional)
"""
if len(names) < 2:
raise ValueError("Length of names must be >= 2.")
if vector.x is not None:
write_fn(entry, vector.x, names[0])
if units is not None:
entry[names[0]].attrs['units'] = units
if vector.y is not None:
write_fn(entry, vector.y, names[1])
if units is not None:
entry[names[1]].attrs['units'] = units
if len(names) == 3 and vector.z is not None:
write_fn(entry, vector.z, names[2])
if units is not None:
entry[names[2]].attrs['units'] = units
valid_data = all([isinstance(d, (Data1D, Data2D)) for d in dataset])
if not valid_data:
raise ValueError("All entries of dataset must be Data1D or Data2D"
"objects")
# Get run name and number from first Data object
data_info = dataset[0]
run_number = ''
run_name = ''
if len(data_info.run) > 0:
run_number = data_info.run[0]
if len(data_info.run_name) > 0:
run_name = data_info.run_name[run_number]
f = h5py.File(filename, 'w')
sasentry = f.create_group('sasentry01')
sasentry['definition'] = _h5_string('NXcanSAS')
sasentry['run'] = _h5_string(run_number)
sasentry['run'].attrs['name'] = run_name
sasentry['title'] = _h5_string(data_info.title)
sasentry.attrs['canSAS_class'] = 'SASentry'
sasentry.attrs['version'] = '1.0'
for i, data_obj in enumerate(dataset):
data_entry = sasentry.create_group("sasdata{0:0=2d}".format(i+1))
data_entry.attrs['canSAS_class'] = 'SASdata'
if isinstance(data_obj, Data1D):
self._write_1d_data(data_obj, data_entry)
elif isinstance(data_obj, Data2D):
self._write_2d_data(data_obj, data_entry)
data_info = dataset[0]
# Sample metadata
sample_entry = sasentry.create_group('sassample')
sample_entry.attrs['canSAS_class'] = 'SASsample'
sample_entry['ID'] = _h5_string(data_info.sample.name)
sample_attrs = ['thickness', 'temperature', 'transmission']
for key in sample_attrs:
if getattr(data_info.sample, key) is not None:
sample_entry.create_dataset(key,
data=_h5_float(getattr(data_info.sample, key)))
_write_h5_vector(sample_entry, data_info.sample.position)
# NXcanSAS doesn't save information about pitch, only roll
# and yaw. The _write_h5_vector method writes vector.y, but we
# need to write vector.z for yaw
data_info.sample.orientation.y = data_info.sample.orientation.z
_write_h5_vector(sample_entry, data_info.sample.orientation,
names=['polar_angle', 'azimuthal_angle'])
if data_info.sample.details is not None\
and data_info.sample.details != []:
details = None
if len(data_info.sample.details) > 1:
details = [np.string_(d) for d in data_info.sample.details]
details = np.array(details)
elif data_info.sample.details != []:
details = _h5_string(data_info.sample.details[0])
if details is not None:
sample_entry.create_dataset('details', data=details)
# Instrument metadata
instrument_entry = sasentry.create_group('sasinstrument')
instrument_entry.attrs['canSAS_class'] = 'SASinstrument'
instrument_entry['name'] = _h5_string(data_info.instrument)
# Source metadata
source_entry = instrument_entry.create_group('sassource')
source_entry.attrs['canSAS_class'] = 'SASsource'
if data_info.source.radiation is None:
source_entry['radiation'] = _h5_string('neutron')
else:
source_entry['radiation'] = _h5_string(data_info.source.radiation)
if data_info.source.beam_shape is not None:
source_entry['beam_shape'] = _h5_string(data_info.source.beam_shape)
wavelength_keys = { 'wavelength': 'incident_wavelength',
'wavelength_min':'wavelength_min',
'wavelength_max': 'wavelength_max',
'wavelength_spread': 'incident_wavelength_spread' }
for sasname, nxname in wavelength_keys.items():
value = getattr(data_info.source, sasname)
units = getattr(data_info.source, sasname + '_unit')
if value is not None:
source_entry[nxname] = _h5_float(value)
source_entry[nxname].attrs['units'] = units
_write_h5_vector(source_entry, data_info.source.beam_size,
names=['beam_size_x', 'beam_size_y'],
units=data_info.source.beam_size_unit, write_fn=_write_h5_float)
# Collimation metadata
if len(data_info.collimation) > 0:
for i, coll_info in enumerate(data_info.collimation):
collimation_entry = instrument_entry.create_group(
'sascollimation{0:0=2d}'.format(i + 1))
collimation_entry.attrs['canSAS_class'] = 'SAScollimation'
if coll_info.length is not None:
_write_h5_float(collimation_entry, coll_info.length, 'SDD')
collimation_entry['SDD'].attrs['units'] =\
coll_info.length_unit
if coll_info.name is not None:
collimation_entry['name'] = _h5_string(coll_info.name)
else:
# Create a blank one - at least 1 collimation required by format
instrument_entry.create_group('sascollimation01')
# Detector metadata
if len(data_info.detector) > 0:
i = 1
for i, det_info in enumerate(data_info.detector):
detector_entry = instrument_entry.create_group(
'sasdetector{0:0=2d}'.format(i + 1))
detector_entry.attrs['canSAS_class'] = 'SASdetector'
if det_info.distance is not None:
_write_h5_float(detector_entry, det_info.distance, 'SDD')
detector_entry['SDD'].attrs['units'] =\
det_info.distance_unit
if det_info.name is not None:
detector_entry['name'] = _h5_string(det_info.name)
else:
detector_entry['name'] = _h5_string('')
if det_info.slit_length is not None:
_write_h5_float(detector_entry, det_info.slit_length,
'slit_length')
detector_entry['slit_length'].attrs['units'] =\
det_info.slit_length_unit
_write_h5_vector(detector_entry, det_info.offset)
# NXcanSAS doesn't save information about pitch, only roll
# and yaw. The _write_h5_vector method writes vector.y, but we
# need to write vector.z for yaw
det_info.orientation.y = det_info.orientation.z
_write_h5_vector(detector_entry, det_info.orientation,
names=['polar_angle', 'azimuthal_angle'])
_write_h5_vector(detector_entry, det_info.beam_center,
names=['beam_center_x', 'beam_center_y'],
write_fn=_write_h5_float, units=det_info.beam_center_unit)
_write_h5_vector(detector_entry, det_info.pixel_size,
names=['x_pixel_size', 'y_pixel_size'],
write_fn=_write_h5_float, units=det_info.pixel_size_unit)
else:
# Create a blank one - at least 1 detector required by format
detector_entry = instrument_entry.create_group('sasdetector01')
detector_entry.attrs['canSAS_class'] = 'SASdetector'
detector_entry.attrs['name'] = ''
# Process meta data
for i, process in enumerate(data_info.process):
process_entry = sasentry.create_group('sasprocess{0:0=2d}'.format(
i + 1))
process_entry.attrs['canSAS_class'] = 'SASprocess'
if process.name:
name = _h5_string(process.name)
process_entry.create_dataset('name', data=name)
if process.date:
date = _h5_string(process.date)
process_entry.create_dataset('date', data=date)
if process.description:
desc = _h5_string(process.description)
process_entry.create_dataset('description', data=desc)
for j, term in enumerate(process.term):
# Don't save empty terms
if term:
h5_term = _h5_string(term)
process_entry.create_dataset('term{0:0=2d}'.format(
j + 1), data=h5_term)
for j, note in enumerate(process.notes):
# Don't save empty notes
if note:
h5_note = _h5_string(note)
process_entry.create_dataset('note{0:0=2d}'.format(
j + 1), data=h5_note)
# Transmission Spectrum
for i, trans in enumerate(data_info.trans_spectrum):
trans_entry = sasentry.create_group(
'sastransmission_spectrum{0:0=2d}'.format(i + 1))
trans_entry.attrs['canSAS_class'] = 'SAStransmission_spectrum'
trans_entry.attrs['signal'] = 'T'
trans_entry.attrs['T_axes'] = 'T'
trans_entry.attrs['name'] = trans.name
if trans.timestamp is not '':
trans_entry.attrs['timestamp'] = trans.timestamp
transmission = trans_entry.create_dataset('T',
data=trans.transmission)
transmission.attrs['unertainties'] = 'Tdev'
trans_entry.create_dataset('Tdev',
data=trans.transmission_deviation)
trans_entry.create_dataset('lambda', data=trans.wavelength)
note_entry = sasentry.create_group('sasnote'.format(i))
note_entry.attrs['canSAS_class'] = 'SASnote'
notes = None
if len(data_info.notes) > 1:
notes = [np.string_(n) for n in data_info.notes]
notes = np.array(notes)
elif data_info.notes != []:
notes = _h5_string(data_info.notes[0])
if notes is not None:
note_entry.create_dataset('SASnote', data=notes)
f.close()
def _write_1d_data(self, data_obj, data_entry):
"""
Writes the contents of a Data1D object to a SASdata h5py Group
:param data_obj: A Data1D object to write to the file
:param data_entry: A h5py Group object representing the SASdata
"""
data_entry.attrs['signal'] = 'I'
data_entry.attrs['I_axes'] = 'Q'
data_entry.attrs['Q_indices'] = [0]
q_entry = data_entry.create_dataset('Q', data=data_obj.x)
q_entry.attrs['units'] = data_obj.x_unit
i_entry = data_entry.create_dataset('I', data=data_obj.y)
i_entry.attrs['units'] = data_obj.y_unit
if data_obj.dy is not None:
i_entry.attrs['uncertainties'] = 'Idev'
i_dev_entry = data_entry.create_dataset('Idev', data=data_obj.dy)
i_dev_entry.attrs['units'] = data_obj.y_unit
if data_obj.dx is not None:
q_entry.attrs['resolutions'] = 'dQ'
dq_entry = data_entry.create_dataset('dQ', data=data_obj.dx)
dq_entry.attrs['units'] = data_obj.x_unit
elif data_obj.dxl is not None:
q_entry.attrs['resolutions'] = ['dQl','dQw']
dql_entry = data_entry.create_dataset('dQl', data=data_obj.dxl)
dql_entry.attrs['units'] = data_obj.x_unit
dqw_entry = data_entry.create_dataset('dQw', data=data_obj.dxw)
dqw_entry.attrs['units'] = data_obj.x_unit
def _write_2d_data(self, data, data_entry):
"""
Writes the contents of a Data2D object to a SASdata h5py Group
:param data: A Data2D object to write to the file
:param data_entry: A h5py Group object representing the SASdata
"""
data_entry.attrs['signal'] = 'I'
data_entry.attrs['I_axes'] = 'Qx,Qy'
data_entry.attrs['Q_indices'] = [0,1]
(n_rows, n_cols) = (len(data.y_bins), len(data.x_bins))
if (n_rows == 0 and n_cols == 0) or (n_cols*n_rows != data.data.size):
# Calculate rows and columns, assuming detector is square
# Same logic as used in PlotPanel.py _get_bins
n_cols = int(np.floor(np.sqrt(len(data.qy_data))))
n_rows = int(np.floor(len(data.qy_data) / n_cols))
if n_rows * n_cols != len(data.qy_data):
raise ValueError("Unable to calculate dimensions of 2D data")
intensity = np.reshape(data.data, (n_rows, n_cols))
qx = np.reshape(data.qx_data, (n_rows, n_cols))
qy = np.reshape(data.qy_data, (n_rows, n_cols))
i_entry = data_entry.create_dataset('I', data=intensity)
i_entry.attrs['units'] = data.I_unit
qx_entry = data_entry.create_dataset('Qx', data=qx)
qx_entry.attrs['units'] = data.Q_unit
qy_entry = data_entry.create_dataset('Qy', data=qy)
qy_entry.attrs['units'] = data.Q_unit
if (data.err_data is not None
and not all(v is None for v in data.err_data)):
d_i = np.reshape(data.err_data, (n_rows, n_cols))
i_entry.attrs['uncertainties'] = 'Idev'
i_dev_entry = data_entry.create_dataset('Idev', data=d_i)
i_dev_entry.attrs['units'] = data.I_unit
if (data.dqx_data is not None
and not all(v is None for v in data.dqx_data)):
qx_entry.attrs['resolutions'] = 'dQx'
dqx_entry = data_entry.create_dataset('dQx', data=data.dqx_data)
dqx_entry.attrs['units'] = data.Q_unit
if (data.dqy_data is not None
and not all(v is None for v in data.dqy_data)):
qy_entry.attrs['resolutions'] = 'dQy'
dqy_entry = data_entry.create_dataset('dQy', data=data.dqy_data)
dqy_entry.attrs['units'] = data.Q_unit
if data.mask is not None and not all(v is None for v in data.mask):
data_entry.attrs['mask'] = "mask"
mask = np.invert(np.asarray(data.mask, dtype=bool))
data_entry.create_dataset('mask', data=mask)