# coding: utf-8
# /*##########################################################################
# Copyright (C) 2016-2017 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:
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# 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.
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# ############################################################################*/
"""This module provides functions to read fabio images as an HDF5 file.
>>> import silx.io.fabioh5
>>> f = silx.io.fabioh5.File("foobar.edf")
.. note:: This module has a dependency on the `h5py `_
and `fabio `_ libraries,
which are not a mandatory dependencies for `silx`. You might need
to install it if you don't already have it.
"""
import collections
import numpy
import numbers
import logging
from silx.third_party import six
_logger = logging.getLogger(__name__)
try:
import fabio
except ImportError as e:
_logger.error("Module %s requires fabio", __name__)
raise e
try:
import h5py
except ImportError as e:
_logger.error("Module %s requires h5py", __name__)
raise e
class Node(object):
"""Main class for all fabioh5 classes. Help to manage a tree."""
def __init__(self, name, parent=None):
self.__parent = parent
self.__basename = name
@property
def h5py_class(self):
"""Returns the h5py classes which is mimicked by this class. It can be
one of `h5py.File, h5py.Group` or `h5py.Dataset`
:rtype: Class
"""
raise NotImplementedError()
@property
def parent(self):
"""Returns the parent of the node.
:rtype: Node
"""
return self.__parent
@property
def file(self):
"""Returns the file node of this node.
:rtype: Node
"""
node = self
while node.__parent is not None:
node = node.__parent
if isinstance(node, File):
return node
else:
return None
def _set_parent(self, parent):
"""Set the parent of this node.
It do not update the parent object.
:param Node parent: New parent for this node
"""
self.__parent = parent
@property
def attrs(self):
"""Returns HDF5 attributes of this node.
:rtype: dict
"""
return {}
@property
def name(self):
"""Returns the HDF5 name of this node.
"""
if self.__parent is None:
return "/"
if self.__parent.name == "/":
return "/" + self.basename
return self.__parent.name + "/" + self.basename
@property
def basename(self):
"""Returns the HDF5 basename of this node.
"""
return self.__basename
class Dataset(Node):
"""Class which handle a numpy data as a mimic of a h5py.Dataset.
"""
def __init__(self, name, data, parent=None, attrs=None):
self.__data = data
Node.__init__(self, name, parent)
if attrs is None:
self.__attrs = {}
else:
self.__attrs = attrs
def _set_data(self, data):
"""Set the data exposed by the dataset.
It have to be called only one time before the data is used. It should
not be edited after use.
:param numpy.ndarray data: Data associated to the dataset
"""
self.__data = data
def _get_data(self):
"""Returns the exposed data
:rtype: numpy.ndarray
"""
return self.__data
@property
def attrs(self):
"""Returns HDF5 attributes of this node.
:rtype: dict
"""
return self.__attrs
@property
def h5py_class(self):
"""Returns the h5py classes which is mimicked by this class. It can be
one of `h5py.File, h5py.Group` or `h5py.Dataset`
:rtype: Class
"""
return h5py.Dataset
@property
def dtype(self):
"""Returns the numpy datatype exposed by this dataset.
:rtype: numpy.dtype
"""
return self._get_data().dtype
@property
def shape(self):
"""Returns the shape of the data exposed by this dataset.
:rtype: tuple
"""
if isinstance(self._get_data(), numpy.ndarray):
return self._get_data().shape
else:
return tuple()
@property
def size(self):
"""Returns the size of the data exposed by this dataset.
:rtype: int
"""
if isinstance(self._get_data(), numpy.ndarray):
return self._get_data().size
else:
# It is returned as float64 1.0 by h5py
return numpy.float64(1.0)
def __len__(self):
"""Returns the size of the data exposed by this dataset.
:rtype: int
"""
if isinstance(self._get_data(), numpy.ndarray):
return len(self._get_data())
else:
# It is returned as float64 1.0 by h5py
raise TypeError("Attempt to take len() of scalar dataset")
def __getitem__(self, item):
"""Returns the slice of the data exposed by this dataset.
:rtype: numpy.ndarray
"""
if not isinstance(self._get_data(), numpy.ndarray):
if item == Ellipsis:
return numpy.array(self._get_data())
elif item == tuple():
return self._get_data()
else:
raise ValueError("Scalar can only be reached with an ellipsis or an empty tuple")
return self._get_data().__getitem__(item)
def __str__(self):
basename = self.name.split("/")[-1]
return '' % \
(basename, self.shape, self.dtype.str)
def __getslice__(self, i, j):
"""Returns the slice of the data exposed by this dataset.
Deprecated but still in use for python 2.7
:rtype: numpy.ndarray
"""
return self.__getitem__(slice(i, j, None))
@property
def value(self):
"""Returns the data exposed by this dataset.
Deprecated by h5py. It is prefered to use indexing `[()]`.
:rtype: numpy.ndarray
"""
return self._get_data()
@property
def compression(self):
"""Returns compression as provided by `h5py.Dataset`.
There is no compression."""
return None
@property
def compression_opts(self):
"""Returns compression options as provided by `h5py.Dataset`.
There is no compression."""
return None
@property
def chunks(self):
"""Returns chunks as provided by `h5py.Dataset`.
There is no chunks."""
return None
class LazyLoadableDataset(Dataset):
"""Abstract dataset which provide a lazy loading of the data.
The class have to be inherited and the :meth:`_create_data` have to be
implemented to return the numpy data exposed by the dataset. This factory
is only called ones, when the data is needed.
"""
def __init__(self, name, parent=None, attrs=None):
super(LazyLoadableDataset, self).__init__(name, None, parent, attrs=attrs)
self.__is_initialized = False
def _create_data(self):
"""
Factory to create the data exposed by the dataset when it is needed.
It have to be implemented to work.
:rtype: numpy.ndarray
"""
raise NotImplementedError()
def _get_data(self):
"""Returns the data exposed by the dataset.
Overwrite Dataset method :meth:`_get_data` to implement the lazy
loading feature.
:rtype: numpy.ndarray
"""
if not self.__is_initialized:
data = self._create_data()
self._set_data(data)
self.__is_initialized = True
return super(LazyLoadableDataset, self)._get_data()
class Group(Node):
"""Class which mimic a `h5py.Group`."""
def __init__(self, name, parent=None, attrs=None):
Node.__init__(self, name, parent)
self.__items = collections.OrderedDict()
if attrs is None:
attrs = {}
self.__attrs = attrs
def _get_items(self):
"""Returns the child items as a name-node dictionary.
:rtype: dict
"""
return self.__items
def add_node(self, node):
"""Add a child to this group.
:param Node node: Child to add to this group
"""
self._get_items()[node.basename] = node
node._set_parent(self)
@property
def h5py_class(self):
"""Returns the h5py classes which is mimicked by this class.
It returns `h5py.Group`
:rtype: Class
"""
return h5py.Group
@property
def attrs(self):
"""Returns HDF5 attributes of this node.
:rtype: dict
"""
return self.__attrs
def items(self):
"""Returns items iterator containing name-node mapping.
:rtype: iterator
"""
return self._get_items().items()
def get(self, name, default=None, getclass=False, getlink=False):
""" Retrieve an item or other information.
If getlink only is true, the returned value is always HardLink
cause this implementation do not use links. Like the original
implementation.
:param str name: name of the item
:param object default: default value returned if the name is not found
:param bool getclass: if true, the returned object is the class of the object found
:param bool getlink: if true, links object are returned instead of the target
:return: An object, else None
:rtype: object
"""
if name not in self._get_items():
return default
if getlink:
node = h5py.HardLink()
else:
node = self._get_items()[name]
if getclass:
obj = node.h5py_class
else:
obj = node
return obj
def __len__(self):
"""Returns the number of child contained in this group.
:rtype: int
"""
return len(self._get_items())
def __iter__(self):
"""Iterate over member names"""
for x in self._get_items().__iter__():
yield x
def __getitem__(self, name):
"""Return a child from is name.
:param name str: name of a member or a path throug members using '/'
separator. A '/' as a prefix access to the root item of the tree.
:rtype: Node
"""
if name is None or name == "":
raise ValueError("No name")
if "/" not in name:
return self._get_items()[name]
if name.startswith("/"):
root = self
while root.parent is not None:
root = root.parent
if name == "/":
return root
return root[name[1:]]
path = name.split("/")
result = self
for item_name in path:
if not isinstance(result, Group):
raise KeyError("Unable to open object (Component not found)")
result = result._get_items()[item_name]
return result
def __contains__(self, name):
"""Returns true is a name is an existing child of this group.
:rtype: bool
"""
return name in self._get_items()
def keys(self):
return self._get_items().keys()
class LazyLoadableGroup(Group):
"""Abstract group which provide a lazy loading of the child.
The class have to be inherited and the :meth:`_create_child` have to be
implemented to add (:meth:`_add_node`) all child. This factory
is only called ones, when child are needed.
"""
def __init__(self, name, parent=None, attrs=None):
Group.__init__(self, name, parent, attrs)
self.__is_initialized = False
def _get_items(self):
"""Returns internal structure which contains child.
It overwrite method :meth:`_get_items` to implement the lazy
loading feature.
:rtype: dict
"""
if not self.__is_initialized:
self.__is_initialized = True
self._create_child()
return Group._get_items(self)
def _create_child(self):
"""
Factory to create the child contained by the group when it is needed.
It have to be implemented to work.
"""
raise NotImplementedError()
class FrameData(LazyLoadableDataset):
"""Expose a cube of image from a Fabio file using `FabioReader` as
cache."""
def __init__(self, name, fabio_reader, parent=None):
attrs = {"interpretation": "image"}
LazyLoadableDataset.__init__(self, name, parent, attrs=attrs)
self.__fabio_reader = fabio_reader
def _create_data(self):
return self.__fabio_reader.get_data()
class RawHeaderData(LazyLoadableDataset):
"""Lazy loadable raw header"""
def __init__(self, name, fabio_file, parent=None):
LazyLoadableDataset.__init__(self, name, parent)
self.__fabio_file = fabio_file
def _create_data(self):
"""Initialize hold data by merging all headers of each frames.
"""
headers = []
for frame in range(self.__fabio_file.nframes):
if self.__fabio_file.nframes == 1:
header = self.__fabio_file.header
else:
header = self.__fabio_file.getframe(frame).header
data = []
for key, value in header.items():
data.append("%s: %s" % (str(key), str(value)))
headers.append(u"\n".join(data))
# create the header list
return numpy.array(headers)
class MetadataGroup(LazyLoadableGroup):
"""Abstract class for groups containing a reference to a fabio image.
"""
def __init__(self, name, metadata_reader, kind, parent=None, attrs=None):
LazyLoadableGroup.__init__(self, name, parent, attrs)
self.__metadata_reader = metadata_reader
self.__kind = kind
def _create_child(self):
keys = self.__metadata_reader.get_keys(self.__kind)
for name in keys:
data = self.__metadata_reader.get_value(self.__kind, name)
dataset = Dataset(name, data)
self.add_node(dataset)
@property
def _metadata_reader(self):
return self.__metadata_reader
class DetectorGroup(LazyLoadableGroup):
"""Define the detector group (sub group of instrument) using Fabio data.
"""
def __init__(self, name, fabio_reader, parent=None, attrs=None):
if attrs is None:
attrs = {"NX_class": "NXdetector"}
LazyLoadableGroup.__init__(self, name, parent, attrs)
self.__fabio_reader = fabio_reader
def _create_child(self):
data = FrameData("data", self.__fabio_reader)
self.add_node(data)
# TODO we should add here Nexus informations we can extract from the
# metadata
others = MetadataGroup("others", self.__fabio_reader, kind=FabioReader.DEFAULT)
self.add_node(others)
class ImageGroup(LazyLoadableGroup):
"""Define the image group (sub group of measurement) using Fabio data.
"""
def __init__(self, name, fabio_reader, parent=None, attrs=None):
LazyLoadableGroup.__init__(self, name, parent, attrs)
self.__fabio_reader = fabio_reader
def _create_child(self):
data = FrameData("data", self.__fabio_reader)
self.add_node(data)
# TODO detector should be a real soft-link
detector = DetectorGroup("info", self.__fabio_reader)
self.add_node(detector)
class SampleGroup(LazyLoadableGroup):
"""Define the image group (sub group of measurement) using Fabio data.
"""
def __init__(self, name, fabio_reader, parent=None):
attrs = {"NXclass": "NXsample"}
LazyLoadableGroup.__init__(self, name, parent, attrs)
self.__fabio_reader = fabio_reader
def _create_child(self):
if self.__fabio_reader.has_ub_matrix():
scalar = {"interpretation": "scalar"}
data = self.__fabio_reader.get_unit_cell_abc()
data = Dataset("unit_cell_abc", data, attrs=scalar)
self.add_node(data)
unit_cell_data = numpy.zeros((1, 6), numpy.float32)
unit_cell_data[0, :3] = data
data = self.__fabio_reader.get_unit_cell_alphabetagamma()
data = Dataset("unit_cell_alphabetagamma", data, attrs=scalar)
self.add_node(data)
unit_cell_data[0, 3:] = data
data = Dataset("unit_cell", unit_cell_data, attrs=scalar)
self.add_node(data)
data = self.__fabio_reader.get_ub_matrix()
data = Dataset("ub_matrix", data, attrs=scalar)
self.add_node(data)
class MeasurementGroup(LazyLoadableGroup):
"""Define the measurement group for fabio file.
"""
def __init__(self, name, fabio_reader, parent=None, attrs=None):
LazyLoadableGroup.__init__(self, name, parent, attrs)
self.__fabio_reader = fabio_reader
def _create_child(self):
keys = self.__fabio_reader.get_keys(FabioReader.COUNTER)
# create image measurement but take care that no other metadata use
# this name
for i in range(1000):
name = "image_%i" % i
if name not in keys:
data = ImageGroup(name, self.__fabio_reader)
self.add_node(data)
break
else:
raise Exception("image_i for 0..1000 already used")
# add all counters
for name in keys:
data = self.__fabio_reader.get_value(FabioReader.COUNTER, name)
dataset = Dataset(name, data)
self.add_node(dataset)
class FabioReader(object):
"""Class which read and cache data and metadata from a fabio image."""
DEFAULT = 0
COUNTER = 1
POSITIONER = 2
def __init__(self, fabio_file):
self.__fabio_file = fabio_file
self.__counters = {}
self.__positioners = {}
self.__measurements = {}
self.__data = None
self.__frame_count = self.__fabio_file.nframes
self._read(self.__fabio_file)
def fabio_file(self):
return self.__fabio_file
def _create_data(self):
"""Initialize hold data by merging all frames into a single cube.
Choose the cube size which fit the best the data. If some images are
smaller than expected, the empty space is set to 0.
The computation is cached into the class, and only done ones.
"""
images = []
for frame in range(self.__fabio_file.nframes):
if self.__fabio_file.nframes == 1:
image = self.__fabio_file.data
else:
image = self.__fabio_file.getframe(frame).data
images.append(image)
# get the max size
max_shape = [0, 0]
for image in images:
if image.shape[0] > max_shape[0]:
max_shape[0] = image.shape[0]
if image.shape[1] > max_shape[1]:
max_shape[1] = image.shape[1]
max_shape = tuple(max_shape)
# fix smallest images
for index, image in enumerate(images):
if image.shape == max_shape:
continue
right_image = numpy.zeros(max_shape)
right_image[0:image.shape[0], 0:image.shape[1]] = image
images[index] = right_image
# create a cube
return numpy.array(images)
def __get_dict(self, kind):
"""Returns a dictionary from according to an expected kind"""
if kind == self.DEFAULT:
return self.__measurements
elif kind == self.COUNTER:
return self.__counters
elif kind == self.POSITIONER:
return self.__positioners
else:
raise Exception("Unexpected kind %s", kind)
def get_data(self):
"""Returns a cube from all available data from frames
:rtype: numpy.ndarray
"""
if self.__data is None:
self.__data = self._create_data()
return self.__data
def get_keys(self, kind):
"""Get all available keys according to a kind of metadata.
:rtype: list
"""
return self.__get_dict(kind).keys()
def get_value(self, kind, name):
"""Get a metadata value according to the kind and the name.
:rtype: numpy.ndarray
"""
value = self.__get_dict(kind)[name]
if not isinstance(value, numpy.ndarray):
value = self._convert_metadata_vector(value)
self.__get_dict(kind)[name] = value
return value
def _set_counter_value(self, frame_id, name, value):
"""Set a counter metadata according to the frame id"""
if name not in self.__counters:
self.__counters[name] = [None] * self.__frame_count
self.__counters[name][frame_id] = value
def _set_positioner_value(self, frame_id, name, value):
"""Set a positioner metadata according to the frame id"""
if name not in self.__positioners:
self.__positioners[name] = [None] * self.__frame_count
self.__positioners[name][frame_id] = value
def _set_measurement_value(self, frame_id, name, value):
"""Set a measurement metadata according to the frame id"""
if name not in self.__measurements:
self.__measurements[name] = [None] * self.__frame_count
self.__measurements[name][frame_id] = value
def _read(self, fabio_file):
"""Read all metadata from the fabio file and store it into this
object."""
for frame in range(fabio_file.nframes):
if fabio_file.nframes == 1:
header = fabio_file.header
else:
header = fabio_file.getframe(frame).header
self._read_frame(frame, header)
def _read_frame(self, frame_id, header):
"""Read all metadata from a frame and store it into this
object."""
for key, value in header.items():
self._read_key(frame_id, key, value)
def _read_key(self, frame_id, name, value):
"""Read a key from the metadata and cache it into this object."""
self._set_measurement_value(frame_id, name, value)
def _convert_metadata_vector(self, values):
"""Convert a list of numpy data into a numpy array with the better
fitting type."""
converted = []
types = set([])
has_none = False
for v in values:
if v is None:
converted.append(None)
has_none = True
else:
c = self._convert_value(v)
converted.append(c)
types.add(c.dtype)
if has_none and len(types) == 0:
# That's a list of none values
return numpy.array([0] * len(values), numpy.int8)
result_type = numpy.result_type(*types)
if issubclass(result_type.type, numpy.string_):
# use the raw data to create the array
result = values
elif issubclass(result_type.type, numpy.unicode_):
# use the raw data to create the array
result = values
else:
result = converted
if has_none:
# Fix missing data according to the array type
if result_type.kind in ["S", "U"]:
none_value = ""
elif result_type.kind == "f":
none_value = numpy.float("NaN")
elif result_type.kind == "i":
none_value = numpy.int(0)
elif result_type.kind == "u":
none_value = numpy.int(0)
elif result_type.kind == "b":
none_value = numpy.bool(False)
else:
none_value = None
for index, r in enumerate(result):
if r is not None:
continue
result[index] = none_value
return numpy.array(result, dtype=result_type)
def _convert_value(self, value):
"""Convert a string into a numpy object (scalar or array).
The value is most of the time a string, but it can be python object
in case if TIFF decoder for example.
"""
if isinstance(value, list):
# convert to a numpy array
return numpy.array(value)
if isinstance(value, dict):
# convert to a numpy associative array
key_dtype = numpy.min_scalar_type(list(value.keys()))
value_dtype = numpy.min_scalar_type(list(value.values()))
associative_type = [('key', key_dtype), ('value', value_dtype)]
assert key_dtype.kind != "O" and value_dtype.kind != "O"
return numpy.array(list(value.items()), dtype=associative_type)
if isinstance(value, numbers.Number):
dtype = numpy.min_scalar_type(value)
assert dtype.kind != "O"
return dtype.type(value)
if isinstance(value, six.binary_type):
try:
value = value.decode('utf-8')
except UnicodeDecodeError:
return numpy.void(value)
if " " in value:
result = self._convert_list(value)
else:
result = self._convert_scalar_value(value)
return result
def _convert_scalar_value(self, value):
"""Convert a string into a numpy int or float.
If it is not possible it returns a numpy string.
"""
try:
value = int(value)
dtype = numpy.min_scalar_type(value)
assert dtype.kind != "O"
return dtype.type(value)
except ValueError:
try:
# numpy.min_scalar_type is not able to do very well the job
# when there is a lot of digit after the dot
# https://github.com/numpy/numpy/issues/8207
# Let's count the digit of the string
digits = len(value) - 1 # minus the dot
if digits <= 7:
# A float32 is accurate with about 7 digits
return numpy.float32(value)
elif digits <= 16:
# A float64 is accurate with about 16 digits
return numpy.float64(value)
else:
if hasattr(numpy, "float128"):
return numpy.float128(value)
else:
return numpy.float64(value)
except ValueError:
return numpy.string_(value)
def _convert_list(self, value):
"""Convert a string into a typed numpy array.
If it is not possible it returns a numpy string.
"""
try:
numpy_values = []
values = value.split(" ")
types = set([])
for string_value in values:
v = self._convert_scalar_value(string_value)
numpy_values.append(v)
types.add(v.dtype.type)
result_type = numpy.result_type(*types)
if issubclass(result_type.type, numpy.string_):
# use the raw data to create the result
return numpy.string_(value)
elif issubclass(result_type.type, numpy.unicode_):
# use the raw data to create the result
return numpy.unicode_(value)
else:
return numpy.array(numpy_values, dtype=result_type)
except ValueError:
return numpy.string_(value)
def has_sample_information(self):
"""Returns true if there is information about the sample in the
file
:rtype: bool
"""
return self.has_ub_matrix()
def has_ub_matrix(self):
"""Returns true if a UB matrix is available.
:rtype: bool
"""
return False
class EdfFabioReader(FabioReader):
"""Class which read and cache data and metadata from a fabio image.
It is mostly the same as FabioReader, but counter_mne and
motor_mne are parsed using a special way.
"""
def __init__(self, fabio_file):
FabioReader.__init__(self, fabio_file)
self.__unit_cell_abc = None
self.__unit_cell_alphabetagamma = None
self.__ub_matrix = None
def _read_frame(self, frame_id, header):
"""Overwrite the method to check and parse special keys: counter and
motors keys."""
self.__catch_keys = set([])
if "motor_pos" in header and "motor_mne" in header:
self.__catch_keys.add("motor_pos")
self.__catch_keys.add("motor_mne")
self._read_mnemonic_key(frame_id, "motor", header)
if "counter_pos" in header and "counter_mne" in header:
self.__catch_keys.add("counter_pos")
self.__catch_keys.add("counter_mne")
self._read_mnemonic_key(frame_id, "counter", header)
FabioReader._read_frame(self, frame_id, header)
def _read_key(self, frame_id, name, value):
"""Overwrite the method to filter counter or motor keys."""
if name in self.__catch_keys:
return
FabioReader._read_key(self, frame_id, name, value)
def _get_mnemonic_key(self, base_key, header):
mnemonic_values_key = base_key + "_mne"
mnemonic_values = header.get(mnemonic_values_key, "")
mnemonic_values = mnemonic_values.split()
pos_values_key = base_key + "_pos"
pos_values = header.get(pos_values_key, "")
pos_values = pos_values.split()
result = collections.OrderedDict()
nbitems = max(len(mnemonic_values), len(pos_values))
for i in range(nbitems):
if i < len(mnemonic_values):
mnemonic = mnemonic_values[i]
else:
# skip the element
continue
if i < len(pos_values):
pos = pos_values[i]
else:
pos = None
result[mnemonic] = pos
return result
def _read_mnemonic_key(self, frame_id, base_key, header):
"""Parse a mnemonic key"""
is_counter = base_key == "counter"
is_positioner = base_key == "motor"
data = self._get_mnemonic_key(base_key, header)
for mnemonic, pos in data.items():
if is_counter:
self._set_counter_value(frame_id, mnemonic, pos)
elif is_positioner:
self._set_positioner_value(frame_id, mnemonic, pos)
else:
raise Exception("State unexpected (base_key: %s)" % base_key)
def has_ub_matrix(self):
"""Returns true if a UB matrix is available.
:rtype: bool
"""
header = self.fabio_file().header
expected_keys = set(["UB_mne", "UB_pos", "sample_mne", "sample_pos"])
return expected_keys.issubset(header)
def parse_ub_matrix(self):
header = self.fabio_file().header
ub_data = self._get_mnemonic_key("UB", header)
s_data = self._get_mnemonic_key("sample", header)
if len(ub_data) > 9:
_logger.warning("UB_mne and UB_pos contains more than expected keys.")
if len(s_data) > 6:
_logger.warning("sample_mne and sample_pos contains more than expected keys.")
data = numpy.array([s_data["U0"], s_data["U1"], s_data["U2"]], dtype=float)
unit_cell_abc = data
data = numpy.array([s_data["U3"], s_data["U4"], s_data["U5"]], dtype=float)
unit_cell_alphabetagamma = data
ub_matrix = numpy.array([[
[ub_data["UB0"], ub_data["UB1"], ub_data["UB2"]],
[ub_data["UB3"], ub_data["UB4"], ub_data["UB5"]],
[ub_data["UB6"], ub_data["UB7"], ub_data["UB8"]]]], dtype=float)
self.__unit_cell_abc = unit_cell_abc
self.__unit_cell_alphabetagamma = unit_cell_alphabetagamma
self.__ub_matrix = ub_matrix
def get_unit_cell_abc(self):
"""Get a numpy array data as defined for the dataset unit_cell_abc
from the NXsample dataset.
:rtype: numpy.ndarray
"""
if self.__unit_cell_abc is None:
self.parse_ub_matrix()
return self.__unit_cell_abc
def get_unit_cell_alphabetagamma(self):
"""Get a numpy array data as defined for the dataset
unit_cell_alphabetagamma from the NXsample dataset.
:rtype: numpy.ndarray
"""
if self.__unit_cell_alphabetagamma is None:
self.parse_ub_matrix()
return self.__unit_cell_alphabetagamma
def get_ub_matrix(self):
"""Get a numpy array data as defined for the dataset ub_matrix
from the NXsample dataset.
:rtype: numpy.ndarray
"""
if self.__ub_matrix is None:
self.parse_ub_matrix()
return self.__ub_matrix
class File(Group):
"""Class which handle a fabio image as a mimick of a h5py.File.
"""
def __init__(self, file_name=None, fabio_image=None):
self.__must_be_closed = False
if file_name is not None and fabio_image is not None:
raise TypeError("Parameters file_name and fabio_image are mutually exclusive.")
if file_name is not None:
self.__fabio_image = fabio.open(file_name)
self.__must_be_closed = True
elif fabio_image is not None:
self.__fabio_image = fabio_image
Group.__init__(self, name="", parent=None, attrs={"NX_class": "NXroot"})
self.__fabio_reader = self.create_fabio_reader(self.__fabio_image)
scan = self.create_scan_group(self.__fabio_image, self.__fabio_reader)
self.add_node(scan)
def create_scan_group(self, fabio_image, fabio_reader):
"""Factory to create the scan group.
:param FabioImage fabio_image: A Fabio image
:param FabioReader fabio_reader: A reader for the Fabio image
:rtype: Group
"""
scan = Group("scan_0", attrs={"NX_class": "NXentry"})
instrument = Group("instrument", attrs={"NX_class": "NXinstrument"})
measurement = MeasurementGroup("measurement", fabio_reader, attrs={"NX_class": "NXcollection"})
file_ = Group("file", attrs={"NX_class": "NXcollection"})
positioners = MetadataGroup("positioners", fabio_reader, FabioReader.POSITIONER, attrs={"NX_class": "NXpositioner"})
raw_header = RawHeaderData("scan_header", fabio_image, self)
detector = DetectorGroup("detector_0", fabio_reader)
scan.add_node(instrument)
instrument.add_node(positioners)
instrument.add_node(file_)
instrument.add_node(detector)
file_.add_node(raw_header)
scan.add_node(measurement)
if fabio_reader.has_sample_information():
sample = SampleGroup("sample", fabio_reader)
scan.add_node(sample)
return scan
def create_fabio_reader(self, fabio_file):
"""Factory to create fabio reader.
:rtype: FabioReader"""
if isinstance(fabio_file, fabio.edfimage.EdfImage):
metadata = EdfFabioReader(fabio_file)
else:
metadata = FabioReader(fabio_file)
return metadata
@property
def h5py_class(self):
return h5py.File
@property
def filename(self):
return self.__fabio_image.filename
def __enter__(self):
return self
def __exit__(self, type, value, tb): # pylint: disable=W0622
"""Called at the end of a `with` statement.
It will close the internal FabioImage only if the FabioImage was
created by the class itself. The reference to the FabioImage is anyway
broken.
"""
if self.__must_be_closed:
self.close()
else:
self.__fabio_image = None
def close(self):
"""Close the object, and free up associated resources.
The associated FabioImage is closed anyway the object was created from
a filename or from a FabioImage.
After calling this method, attempts to use the object may fail.
"""
# It looks like there is no close on FabioImage
# self.__fabio_image.close()
self.__fabio_image = None