# /*##########################################################################
# Copyright (C) 2016-2023 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ############################################################################*/
"""This module provides a h5py-like API to access SpecFile data.
API description
+++++++++++++++
Specfile data structure exposed by this API:
::
/
1.1/
title = "…"
start_time = "…"
instrument/
specfile/
file_header = "…"
scan_header = "…"
positioners/
motor_name = value
…
mca_0/
data = …
calibration = …
channels = …
preset_time = …
elapsed_time = …
live_time = …
mca_1/
…
…
measurement/
colname0 = …
colname1 = …
…
mca_0/
data -> /1.1/instrument/mca_0/data
info -> /1.1/instrument/mca_0/
…
sample/
ub_matrix = …
unit_cell = …
unit_cell_abc = …
unit_cell_alphabetagamma = …
2.1/
…
``file_header`` and ``scan_header`` are the raw headers as they
appear in the original file, as a string of lines separated by newline (``\\n``) characters.
The title is the content of the ``#S`` scan header line without the leading
``#S`` and without the scan number (e.g ``"ascan ss1vo -4.55687 -0.556875 40 0.2"``).
The start time is converted to ISO8601 format (``"2016-02-23T22:49:05Z"``),
if the original date format is standard.
Numeric datasets are stored in *float32* format, except for scalar integers
which are stored as *int64*.
Motor positions (e.g. ``/1.1/instrument/positioners/motor_name``) can be
1D numpy arrays if they are measured as scan data, or else scalars as defined
on ``#P`` scan header lines. A simple test is done to check if the motor name
is also a data column header defined in the ``#L`` scan header line.
Scan data (e.g. ``/1.1/measurement/colname0``) is accessed by column,
the dataset name ``colname0`` being the column label as defined in the ``#L``
scan header line.
If a ``/`` character is present in a column label or in a motor name in the
original SPEC file, it will be substituted with a ``%`` character in the
corresponding dataset name.
MCA data is exposed as a 2D numpy array containing all spectra for a given
analyser. The number of analysers is calculated as the number of MCA spectra
per scan data line. Demultiplexing is then performed to assign the correct
spectra to a given analyser.
MCA calibration is an array of 3 scalars, from the ``#@CALIB`` header line.
It is identical for all MCA analysers, as there can be only one
``#@CALIB`` line per scan.
MCA channels is an array containing all channel numbers. This information is
computed from the ``#@CHANN`` scan header line (if present), or computed from
the shape of the first spectrum in a scan (``[0, … len(first_spectrum] - 1]``).
Accessing data
++++++++++++++
Data and groups are accessed in :mod:`h5py` fashion::
from silx.io.spech5 import SpecH5
# Open a SpecFile
sfh5 = SpecH5("test.dat")
# using SpecH5 as a regular group to access scans
scan1group = sfh5["1.1"]
instrument_group = scan1group["instrument"]
# alternative: full path access
measurement_group = sfh5["/1.1/measurement"]
# accessing a scan data column by name as a 1D numpy array
data_array = measurement_group["Pslit HGap"]
# accessing all mca-spectra for one MCA device
mca_0_spectra = measurement_group["mca_0/data"]
:class:`SpecH5` files and groups provide a :meth:`keys` method::
>>> sfh5.keys()
['96.1', '97.1', '98.1']
>>> sfh5['96.1'].keys()
['title', 'start_time', 'instrument', 'measurement']
They can also be treated as iterators:
.. code-block:: python
from silx.io import is_dataset
for scan_group in SpecH5("test.dat"):
dataset_names = [item.name in scan_group["measurement"] if
is_dataset(item)]
print("Found data columns in scan " + scan_group.name)
print(", ".join(dataset_names))
You can test for existence of data or groups::
>>> "/1.1/measurement/Pslit HGap" in sfh5
True
>>> "positioners" in sfh5["/2.1/instrument"]
True
>>> "spam" in sfh5["1.1"]
False
"""
import datetime
import logging
import re
import io
import h5py
import numpy
from silx import version as silx_version
from .specfile import SpecFile, SfErrColNotFound
from . import commonh5
__authors__ = ["P. Knobel", "D. Naudet"]
__license__ = "MIT"
__date__ = "17/07/2018"
logger1 = logging.getLogger(__name__)
text_dtype = h5py.special_dtype(vlen=str)
def to_h5py_utf8(str_list):
"""Convert a string or a list of strings to a numpy array of
unicode strings that can be written to HDF5 as utf-8.
This ensures that the type will be consistent between python 2 and
python 3, if attributes or datasets are saved to an HDF5 file.
"""
return numpy.array(str_list, dtype=text_dtype)
def _get_number_of_mca_analysers(scan):
"""
:param SpecFile sf: :class:`SpecFile` instance
"""
number_of_mca_spectra = len(scan.mca)
# Scan.data is transposed
number_of_data_lines = scan.data.shape[1]
if not number_of_data_lines == 0:
# Number of MCA spectra must be a multiple of number of data lines
assert number_of_mca_spectra % number_of_data_lines == 0
return number_of_mca_spectra // number_of_data_lines
elif number_of_mca_spectra:
# Case of a scan without data lines, only MCA.
# Our only option is to assume that the number of analysers
# is the number of #@CHANN lines
return len(scan.mca.channels)
else:
return 0
def _motor_in_scan(sf, scan_key, motor_name):
"""
:param sf: :class:`SpecFile` instance
:param scan_key: Scan identification key (e.g. ``1.1``)
:param motor_name: Name of motor as defined in file header lines
:return: ``True`` if motor exists in scan, else ``False``
:raise: ``KeyError`` if scan_key not found in SpecFile
"""
if scan_key not in sf:
raise KeyError(
"Scan key %s " % scan_key + "does not exist in SpecFile %s" % sf.filename
)
ret = motor_name in sf[scan_key].motor_names
if not ret and "%" in motor_name:
motor_name = motor_name.replace("%", "/")
ret = motor_name in sf[scan_key].motor_names
return ret
def _column_label_in_scan(sf, scan_key, column_label):
"""
:param sf: :class:`SpecFile` instance
:param scan_key: Scan identification key (e.g. ``1.1``)
:param column_label: Column label as defined in scan header
:return: ``True`` if data column label exists in scan, else ``False``
:raise: ``KeyError`` if scan_key not found in SpecFile
"""
if scan_key not in sf:
raise KeyError(
"Scan key %s " % scan_key + "does not exist in SpecFile %s" % sf.filename
)
ret = column_label in sf[scan_key].labels
if not ret and "%" in column_label:
column_label = column_label.replace("%", "/")
ret = column_label in sf[scan_key].labels
return ret
def _parse_UB_matrix(header_line):
"""Parse G3 header line and return UB matrix
:param str header_line: G3 header line
:return: UB matrix
:raises ValueError: For malformed UB matrix header line
"""
values = list(map(float, header_line.split())) # Can raise ValueError
if len(values) < 9:
raise ValueError("Not enough values in UB matrix")
return numpy.array(values).reshape((1, 3, 3))
def _ub_matrix_in_scan(scan):
"""Return True if scan header has a G3 line and all values are not 0.
:param scan: specfile.Scan instance
:return: True or False
"""
header_line = scan.scan_header_dict.get("G3", None)
if header_line is None:
return False
try:
ub_matrix = _parse_UB_matrix(header_line)
except ValueError:
logger1.warning("Malformed G3 header line")
return False
return numpy.any(ub_matrix)
def _parse_unit_cell(header_line):
"""Parse G1 header line and return unit cell
:param str header_line: G1 header line
:return: unit cell
:raises ValueError: For malformed unit cell header line
"""
values = list(map(float, header_line.split()[0:6])) # can raise ValueError
if len(values) < 6:
raise ValueError("Not enough values in unit cell")
return numpy.array(values).reshape((1, 6))
def _unit_cell_in_scan(scan):
"""Return True if scan header has a G1 line and all values are not 0.
:param scan: specfile.Scan instance
:return: True or False
"""
header_line = scan.scan_header_dict.get("G1", None)
if header_line is None:
return False
try:
unit_cell = _parse_unit_cell(header_line)
except ValueError:
logger1.warning("Malformed G1 header line")
return False
return numpy.any(unit_cell)
def _parse_ctime(ctime_lines, analyser_index=0):
"""
:param ctime_lines: e.g ``@CTIME %f %f %f``, first word ``@CTIME`` optional
When multiple CTIME lines are present in a scan header, this argument
is a concatenation of them separated by a ``\\n`` character.
:param analyser_index: MCA device/analyser index, when multiple devices
are in a scan.
:return: (preset_time, live_time, elapsed_time)
"""
ctime_lines = ctime_lines.lstrip("@CTIME ")
ctimes_lines_list = ctime_lines.split("\n")
if len(ctimes_lines_list) == 1:
# single @CTIME line for all devices
ctime_line = ctimes_lines_list[0]
else:
ctime_line = ctimes_lines_list[analyser_index]
if not len(ctime_line.split()) == 3:
raise ValueError(
"Incorrect format for @CTIME header line " + '(expected "@CTIME %f %f %f").'
)
return list(map(float, ctime_line.split()))
def spec_date_to_iso8601(date, zone=None):
"""Convert SpecFile date to Iso8601.
:param date: Date (see supported formats below)
:type date: str
:param zone: Time zone as it appears in a ISO8601 date
Supported formats:
* ``DDD MMM dd hh:mm:ss YYYY``
* ``DDD YYYY/MM/dd hh:mm:ss YYYY``
where `DDD` is the abbreviated weekday, `MMM` is the month abbreviated
name, `MM` is the month number (zero padded), `dd` is the weekday number
(zero padded) `YYYY` is the year, `hh` the hour (zero padded), `mm` the
minute (zero padded) and `ss` the second (zero padded).
All names are expected to be in english.
Examples::
>>> spec_date_to_iso8601("Thu Feb 11 09:54:35 2016")
'2016-02-11T09:54:35'
>>> spec_date_to_iso8601("Sat 2015/03/14 03:53:50")
'2015-03-14T03:53:50'
"""
months = [
"Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec",
]
days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
days_rx = "(?P<day>" + "|".join(days) + ")"
months_rx = "(?P<month>" + "|".join(months) + ")"
year_rx = r"(?P<year>\d{4})"
day_nb_rx = r"(?P<day_nb>[0-3 ]\d)"
month_nb_rx = r"(?P<month_nb>[0-1]\d)"
hh_rx = r"(?P<hh>[0-2]\d)"
mm_rx = r"(?P<mm>[0-5]\d)"
ss_rx = r"(?P<ss>[0-5]\d)"
tz_rx = r"(?P<tz>[+-]\d\d:\d\d){0,1}"
# date formats must have either month_nb (1..12) or month (Jan, Feb, ...)
re_tpls = [
"{days} {months} {day_nb} {hh}:{mm}:{ss}{tz} {year}",
"{days} {year}/{month_nb}/{day_nb} {hh}:{mm}:{ss}{tz}",
]
grp_d = None
for rx in re_tpls:
full_rx = rx.format(
days=days_rx,
months=months_rx,
year=year_rx,
day_nb=day_nb_rx,
month_nb=month_nb_rx,
hh=hh_rx,
mm=mm_rx,
ss=ss_rx,
tz=tz_rx,
)
m = re.match(full_rx, date)
if m:
grp_d = m.groupdict()
break
if not grp_d:
raise ValueError("Date format not recognized : {0}".format(date))
year = grp_d["year"]
month = grp_d.get("month_nb")
if not month:
month = "{0:02d}".format(months.index(grp_d.get("month")) + 1)
day = grp_d["day_nb"]
tz = grp_d["tz"]
if not tz:
tz = zone
time = "{0}:{1}:{2}".format(grp_d["hh"], grp_d["mm"], grp_d["ss"])
full_date = "{0}-{1}-{2}T{3}{4}".format(year, month, day, time, tz if tz else "")
return full_date
def _demultiplex_mca(scan, analyser_index):
"""Return MCA data for a single analyser.
Each MCA spectrum is a 1D array. For each analyser, there is one
spectrum recorded per scan data line. When there are more than a single
MCA analyser in a scan, the data will be multiplexed. For instance if
there are 3 analysers, the consecutive spectra for the first analyser must
be accessed as ``mca[0], mca[3], mca[6]…``.
:param scan: :class:`Scan` instance containing the MCA data
:param analyser_index: 0-based index referencing the analyser
:type analyser_index: int
:return: 2D numpy array containing all spectra for one analyser
"""
number_of_analysers = _get_number_of_mca_analysers(scan)
number_of_spectra = len(scan.mca)
number_of_spectra_per_analyser = number_of_spectra // number_of_analysers
len_spectrum = len(scan.mca[analyser_index])
mca_array = numpy.empty((number_of_spectra_per_analyser, len_spectrum))
for i in range(number_of_spectra_per_analyser):
mca_array[i, :] = scan.mca[analyser_index + i * number_of_analysers]
return mca_array
# Node classes
[docs]
class SpecH5Dataset(object):
"""This convenience class is to be inherited by all datasets, for
compatibility purpose with code that tests for
``isinstance(obj, SpecH5Dataset)``.
This legacy behavior is deprecated. The correct way to test
if an object is a dataset is to use :meth:`silx.io.utils.is_dataset`.
Datasets must also inherit :class:`SpecH5NodeDataset` or
:class:`SpecH5LazyNodeDataset` which actually implement all the
API."""
pass
[docs]
class SpecH5NodeDataset(commonh5.Dataset, SpecH5Dataset):
"""This class inherits :class:`commonh5.Dataset`, to which it adds
little extra functionality. The main additional functionality is the
proxy behavior that allows to mimic the numpy array stored in this
class.
"""
def __init__(self, name, data, parent=None, attrs=None):
# get proper value types, to inherit from numpy
# attributes (dtype, shape, size)
if isinstance(data, str):
# use unicode (utf-8 when saved to HDF5 output)
value = to_h5py_utf8(data)
elif isinstance(data, float):
# use 32 bits for float scalars
value = numpy.float32(data)
elif isinstance(data, int):
value = numpy.int_(data)
else:
# Enforce numpy array
array = numpy.array(data)
data_kind = array.dtype.kind
if data_kind in ["S", "U"]:
value = numpy.asarray(array, dtype=text_dtype)
elif data_kind in ["f"]:
value = numpy.asarray(array, dtype=numpy.float32)
else:
value = array
commonh5.Dataset.__init__(self, name, value, parent, attrs)
[docs]
def __getattr__(self, item):
"""Proxy to underlying numpy array methods."""
if hasattr(self[()], item):
return getattr(self[()], item)
raise AttributeError("SpecH5Dataset has no attribute %s" % item)
class SpecH5LazyNodeDataset(commonh5.LazyLoadableDataset, SpecH5Dataset):
"""This class inherits :class:`commonh5.LazyLoadableDataset`,
to which it adds a proxy behavior that allows to mimic the numpy
array stored in this class.
The class has to be inherited and the :meth:`_create_data` method has to be
implemented to return the numpy data exposed by the dataset. This factory
method is only called once, when the data is needed.
"""
def __getattr__(self, item):
"""Proxy to underlying numpy array methods."""
if hasattr(self[()], item):
return getattr(self[()], item)
raise AttributeError("SpecH5Dataset has no attribute %s" % item)
def _create_data(self):
"""
Factory to create the data exposed by the dataset when it is needed.
It has to be implemented for the class to work.
:rtype: numpy.ndarray
"""
raise NotImplementedError()
[docs]
class SpecH5Group(object):
"""This convenience class is to be inherited by all groups, for
compatibility purposes with code that tests for
``isinstance(obj, SpecH5Group)``.
This legacy behavior is deprecated. The correct way to test
if an object is a group is to use :meth:`silx.io.utils.is_group`.
Groups must also inherit :class:`silx.io.commonh5.Group`, which
actually implements all the methods and attributes."""
pass
[docs]
class SpecH5(commonh5.File, SpecH5Group):
"""This class opens a SPEC file and exposes it as a *h5py.File*.
It inherits :class:`silx.io.commonh5.Group` (via :class:`commonh5.File`),
which implements most of its API.
"""
def __init__(self, filename):
"""
:param filename: Path to SpecFile in filesystem
:type filename: str
"""
if isinstance(filename, io.IOBase):
# see https://github.com/silx-kit/silx/issues/858
filename = filename.name
self._sf = SpecFile(filename)
attrs = {
"NX_class": to_h5py_utf8("NXroot"),
"file_time": to_h5py_utf8(datetime.datetime.now().isoformat()),
"file_name": to_h5py_utf8(filename),
"creator": to_h5py_utf8("silx spech5 %s" % silx_version),
}
commonh5.File.__init__(self, filename, attrs=attrs)
for scan_key in self._sf.keys():
scan = self._sf[scan_key]
scan_group = ScanGroup(scan_key, parent=self, scan=scan)
self.add_node(scan_group)
[docs]
def close(self):
self._sf.close()
self._sf = None
class ScanGroup(commonh5.Group, SpecH5Group):
def __init__(self, scan_key, parent, scan):
"""
:param parent: parent Group
:param str scan_key: Scan key (e.g. "1.1")
:param scan: specfile.Scan object
"""
commonh5.Group.__init__(
self, scan_key, parent=parent, attrs={"NX_class": to_h5py_utf8("NXentry")}
)
# take title in #S after stripping away scan number and spaces
s_hdr_line = scan.scan_header_dict["S"]
title = s_hdr_line.lstrip("0123456789").lstrip()
self.add_node(
SpecH5NodeDataset(name="title", data=to_h5py_utf8(title), parent=self)
)
if "D" in scan.scan_header_dict:
try:
start_time_str = spec_date_to_iso8601(scan.scan_header_dict["D"])
except (IndexError, ValueError):
logger1.warning(
"Could not parse date format in scan %s header."
+ " Using original date not converted to ISO-8601",
scan_key,
)
start_time_str = scan.scan_header_dict["D"]
elif "D" in scan.file_header_dict:
logger1.warning(
"No #D line in scan %s header. " + "Using file header for start_time.",
scan_key,
)
try:
start_time_str = spec_date_to_iso8601(scan.file_header_dict["D"])
except (IndexError, ValueError):
logger1.warning(
"Could not parse date format in scan %s header. "
+ "Using original date not converted to ISO-8601",
scan_key,
)
start_time_str = scan.file_header_dict["D"]
else:
logger1.warning(
"No #D line in %s header. Setting date to empty string.", scan_key
)
start_time_str = ""
self.add_node(
SpecH5NodeDataset(
name="start_time", data=to_h5py_utf8(start_time_str), parent=self
)
)
self.add_node(InstrumentGroup(parent=self, scan=scan))
self.add_node(MeasurementGroup(parent=self, scan=scan))
if _unit_cell_in_scan(scan) or _ub_matrix_in_scan(scan):
self.add_node(SampleGroup(parent=self, scan=scan))
class InstrumentGroup(commonh5.Group, SpecH5Group):
def __init__(self, parent, scan):
"""
:param parent: parent Group
:param scan: specfile.Scan object
"""
commonh5.Group.__init__(
self,
name="instrument",
parent=parent,
attrs={"NX_class": to_h5py_utf8("NXinstrument")},
)
self.add_node(InstrumentSpecfileGroup(parent=self, scan=scan))
self.add_node(PositionersGroup(parent=self, scan=scan))
num_analysers = _get_number_of_mca_analysers(scan)
for anal_idx in range(num_analysers):
self.add_node(
InstrumentMcaGroup(parent=self, analyser_index=anal_idx, scan=scan)
)
class InstrumentSpecfileGroup(commonh5.Group, SpecH5Group):
def __init__(self, parent, scan):
commonh5.Group.__init__(
self,
name="specfile",
parent=parent,
attrs={"NX_class": to_h5py_utf8("NXcollection")},
)
self.add_node(
SpecH5NodeDataset(
name="file_header",
data=to_h5py_utf8(scan.file_header),
parent=self,
attrs={},
)
)
self.add_node(
SpecH5NodeDataset(
name="scan_header",
data=to_h5py_utf8(scan.scan_header),
parent=self,
attrs={},
)
)
class PositionersGroup(commonh5.Group, SpecH5Group):
def __init__(self, parent, scan):
commonh5.Group.__init__(
self,
name="positioners",
parent=parent,
attrs={"NX_class": to_h5py_utf8("NXcollection")},
)
dataset_info = [] # Store list of positioner's (name, value)
is_error = False # True if error encountered
for motor_name in scan.motor_names:
safe_motor_name = motor_name.replace("/", "%")
if motor_name in scan.labels and scan.data.shape[0] > 0:
# return a data column if one has the same label as the motor
motor_value = scan.data_column_by_name(motor_name)
else:
# Take value from #P scan header.
# (may return float("inf") if #P line is missing from scan hdr)
try:
motor_value = scan.motor_position_by_name(motor_name)
except SfErrColNotFound:
is_error = True
motor_value = float("inf")
dataset_info.append((safe_motor_name, motor_value))
if is_error: # Filter-out scalar values
logger1.warning("Mismatching number of elements in #P and #O: Ignoring")
dataset_info = [
(name, value)
for name, value in dataset_info
if not isinstance(value, float)
]
for name, value in dataset_info:
self.add_node(SpecH5NodeDataset(name=name, data=value, parent=self))
class InstrumentMcaGroup(commonh5.Group, SpecH5Group):
def __init__(self, parent, analyser_index, scan):
name = "mca_%d" % analyser_index
commonh5.Group.__init__(
self,
name=name,
parent=parent,
attrs={"NX_class": to_h5py_utf8("NXdetector")},
)
mcaDataDataset = McaDataDataset(
parent=self, analyser_index=analyser_index, scan=scan
)
self.add_node(mcaDataDataset)
spectrum_length = mcaDataDataset.shape[-1]
mcaDataDataset = None
if len(scan.mca.channels) == 1:
# single @CALIB line applying to multiple devices
calibration_dataset = scan.mca.calibration[0]
channels_dataset = scan.mca.channels[0]
else:
calibration_dataset = scan.mca.calibration[analyser_index]
channels_dataset = scan.mca.channels[analyser_index]
channels_length = len(channels_dataset)
if (channels_length > 1) and (spectrum_length > 0):
logger1.info("Spectrum and channels length mismatch")
# this should always be the case
if channels_length > spectrum_length:
channels_dataset = channels_dataset[:spectrum_length]
elif channels_length < spectrum_length:
# only trust first channel and increment
channel0 = channels_dataset[0]
increment = channels_dataset[1] - channels_dataset[0]
channels_dataset = numpy.linspace(
channel0,
channel0 + increment * spectrum_length,
spectrum_length,
endpoint=False,
)
self.add_node(
SpecH5NodeDataset(name="calibration", data=calibration_dataset, parent=self)
)
self.add_node(
SpecH5NodeDataset(name="channels", data=channels_dataset, parent=self)
)
if "CTIME" in scan.mca_header_dict:
ctime_line = scan.mca_header_dict["CTIME"]
preset_time, live_time, elapsed_time = _parse_ctime(
ctime_line, analyser_index
)
self.add_node(
SpecH5NodeDataset(name="preset_time", data=preset_time, parent=self)
)
self.add_node(
SpecH5NodeDataset(name="live_time", data=live_time, parent=self)
)
self.add_node(
SpecH5NodeDataset(name="elapsed_time", data=elapsed_time, parent=self)
)
class McaDataDataset(SpecH5LazyNodeDataset):
"""Lazy loadable dataset for MCA data"""
def __init__(self, parent, analyser_index, scan):
commonh5.LazyLoadableDataset.__init__(
self,
name="data",
parent=parent,
attrs={
"interpretation": to_h5py_utf8("spectrum"),
},
)
self._scan = scan
self._analyser_index = analyser_index
self._shape = None
self._num_analysers = _get_number_of_mca_analysers(self._scan)
def _create_data(self):
return _demultiplex_mca(self._scan, self._analyser_index)
@property
def shape(self):
if self._shape is None:
num_spectra_in_file = len(self._scan.mca)
num_spectra_per_analyser = num_spectra_in_file // self._num_analysers
len_spectrum = len(self._scan.mca[self._analyser_index])
self._shape = num_spectra_per_analyser, len_spectrum
return self._shape
@property
def size(self):
return numpy.prod(self.shape, dtype=numpy.intp)
@property
def dtype(self):
# we initialize the data with numpy.empty() without specifying a dtype
# in _demultiplex_mca()
return numpy.empty((1,)).dtype
def __len__(self):
return self.shape[0]
def __getitem__(self, item):
# optimization for fetching a single spectrum if data not already loaded
if not self._is_initialized:
if isinstance(item, int):
if item < 0:
# negative indexing
item += len(self)
return self._scan.mca[self._analyser_index + item * self._num_analysers]
# accessing a slice or element of a single spectrum [i, j:k]
try:
spectrum_idx, channel_idx_or_slice = item
assert isinstance(spectrum_idx, int)
except (ValueError, TypeError, AssertionError):
pass
else:
if spectrum_idx < 0:
item += len(self)
idx = self._analyser_index + spectrum_idx * self._num_analysers
return self._scan.mca[idx][channel_idx_or_slice]
return super(McaDataDataset, self).__getitem__(item)
class MeasurementGroup(commonh5.Group, SpecH5Group):
def __init__(self, parent, scan):
"""
:param parent: parent Group
:param scan: specfile.Scan object
"""
commonh5.Group.__init__(
self,
name="measurement",
parent=parent,
attrs={
"NX_class": to_h5py_utf8("NXcollection"),
},
)
for label in scan.labels:
safe_label = label.replace("/", "%")
self.add_node(
SpecH5NodeDataset(
name=safe_label, data=scan.data_column_by_name(label), parent=self
)
)
num_analysers = _get_number_of_mca_analysers(scan)
for anal_idx in range(num_analysers):
self.add_node(MeasurementMcaGroup(parent=self, analyser_index=anal_idx))
class MeasurementMcaGroup(commonh5.Group, SpecH5Group):
def __init__(self, parent, analyser_index):
basename = "mca_%d" % analyser_index
commonh5.Group.__init__(self, name=basename, parent=parent, attrs={})
target_name = self.name.replace("measurement", "instrument")
self.add_node(
commonh5.SoftLink(name="data", path=target_name + "/data", parent=self)
)
self.add_node(commonh5.SoftLink(name="info", path=target_name, parent=self))
class SampleGroup(commonh5.Group, SpecH5Group):
def __init__(self, parent, scan):
"""
:param parent: parent Group
:param scan: specfile.Scan object
"""
commonh5.Group.__init__(
self,
name="sample",
parent=parent,
attrs={
"NX_class": to_h5py_utf8("NXsample"),
},
)
if _unit_cell_in_scan(scan):
self.add_node(
SpecH5NodeDataset(
name="unit_cell",
data=_parse_unit_cell(scan.scan_header_dict["G1"]),
parent=self,
attrs={"interpretation": to_h5py_utf8("scalar")},
)
)
self.add_node(
SpecH5NodeDataset(
name="unit_cell_abc",
data=_parse_unit_cell(scan.scan_header_dict["G1"])[0, 0:3],
parent=self,
attrs={"interpretation": to_h5py_utf8("scalar")},
)
)
self.add_node(
SpecH5NodeDataset(
name="unit_cell_alphabetagamma",
data=_parse_unit_cell(scan.scan_header_dict["G1"])[0, 3:6],
parent=self,
attrs={"interpretation": to_h5py_utf8("scalar")},
)
)
if _ub_matrix_in_scan(scan):
self.add_node(
SpecH5NodeDataset(
name="ub_matrix",
data=_parse_UB_matrix(scan.scan_header_dict["G3"]),
parent=self,
attrs={"interpretation": to_h5py_utf8("scalar")},
)
)
