XsocsH5.py 15.9 KB
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# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2015-2016 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.
#
# ###########################################################################*/

from __future__ import absolute_import

__authors__ = ["D. Naudet"]
__license__ = "MIT"
__date__ = "15/09/2016"

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import weakref
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from collections import OrderedDict, namedtuple
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from contextlib import contextmanager

import h5py as _h5py
import numpy as _np

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from .XsocsH5Base import XsocsH5Base
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from ._utils import str_to_h5_utf8, find_NX_class
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try:  # silx >= 0.14
    from silx.io.utils import h5py_read_dataset
except ImportError:  # Fall back for silx < 0.14
    from ..util.silx_io_utils import h5py_read_dataset

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class InvalidEntryError(Exception):
    pass
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ScanPositions = namedtuple('ScanPositions',
                           ['motor_0', 'pos_0', 'motor_1', 'pos_1', 'shape'])

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MOTORCOLS = {"pix": "adcY",
             "piy": "adcX",
             "piz": "adcZ"}
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def _process_entry(method):
    def _method(inst, entry=None, *args, **kwargs):
        if entry is None:
            entry = inst.get_entry_name()
        elif isinstance(entry, int):
            entry = inst.get_entry_name(entry)
        return method(inst, entry, *args, **kwargs)
    return _method


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class XsocsH5(XsocsH5Base):

    TOP_ENTRY = 'global'
    positioners_tpl = '/{0}/instrument/positioners'
    img_data_tpl = '/{0}/measurement/image/data'
    measurement_tpl = '/{0}/measurement'
    detector_tpl = '/{0}/instrument/detector'
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    scan_params_tpl = '/{0}/scan'
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    def __init__(self, h5_f, mode='r'):
        super(XsocsH5, self).__init__(h5_f, mode=mode)

        self.__entries = None

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    @_process_entry
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    def title(self, entry):
        with self._get_file() as h5_file:
            path = entry + '/title'
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            return h5py_read_dataset(h5_file[path], decode_ascii=True)
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    @_process_entry
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    def entry_filename(self, entry):
        with self._get_file() as h5_file:
            return h5_file[entry].file.filename

    def _update_entries(self):
        with self._get_file() as h5_file:
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            self.__entries = sorted(find_NX_class(h5_file, 'NXentry'))
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    def entries(self):
        if self.__entries is None:
            self._update_entries()
        return self.__entries[:]

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    @_process_entry
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    def scan_angle(self, entry):
        # TODO : get the correct angle name
        return self.positioner(entry, 'eta')

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    def get_entry_name(self, entry_idx=0):
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        """
        Get the entry found at position *entry_idx* (entries names sorted
        alphabeticaly).
        Raises InvalidEntryError if the entry is not found.
        """
        try:
            return self.entries()[entry_idx]
        except IndexError:
            raise InvalidEntryError('Entry not found (entry_idx={0}).'
                                    ''.format(entry_idx))

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    @_process_entry
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    def __detector_params(self, entry, param_names):
        with self._get_file() as h5_file:
            path = self.detector_tpl.format(entry) + '/{0}'
            if isinstance(param_names, (list, set, tuple)):
                return [h5_file.get(path.format(param), _np.array(None))[()]
                        for param in param_names]
            return h5_file.get(path.format(param_names), _np.array(None))[()]

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    @_process_entry
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    def beam_energy(self, entry):
        return self.__detector_params(entry, 'beam_energy')

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    @_process_entry
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    def direct_beam(self, entry):
        return self.__detector_params(entry, ['center_chan_dim0',
                                              'center_chan_dim1'])

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    @_process_entry
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    def chan_per_deg(self, entry):
        return self.__detector_params(entry, ['chan_per_deg_dim0',
                                              'chan_per_deg_dim1'])

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    @_process_entry
    def image_roi_offset(self, entry):
        """Image ROI offset that was saved in the hdf5 file

        :param str entry: Entry from which to retrieve the information
        :return: ROI offset (row_offset, column)
        :rtype: Union[List[int],None]
        """
        return self.__detector_params(entry, 'image_roi_offset')

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    @_process_entry
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    def n_images(self, entry):
        # TODO : make sure that data.ndims = 3
        path = self.img_data_tpl.format(entry)
        return self._get_array_data(path, shape=True)[0]

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    @_process_entry
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    def image_size(self, entry):
        # TODO : make sure that data.ndims = 3
        path = self.img_data_tpl.format(entry)
        return self._get_array_data(path, shape=True)[1:3]

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    @_process_entry
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    def image_dtype(self, entry):
        path = self.img_data_tpl.format(entry)
        return self._get_array_data(path, dtype=True)

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    @_process_entry
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    def dset_shape(self, path):
        return self._get_array_data(path, shape=True)

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    @_process_entry
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    def image_cumul(self, entry, dtype=None):
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        """Returns the summed intensity for each image.

        :param str entry:
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        :param dtype: dtype passed to the numpy.sum function.
            Default is numpy.double.
        :type dtype: numpy.dtype
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        """
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        if dtype is None:
            dtype = _np.double

        with self.image_dset_ctx(entry) as ctx:
            shape = ctx.shape
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            intensity = _np.zeros(shape=(shape[0],), dtype=dtype)
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            img_buffer = _np.array(ctx[0], dtype=dtype)
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            for idx in range(0, shape[0]):
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                ctx.read_direct(img_buffer, idx)
                intensity[idx] = _np.sum(img_buffer)
        return intensity
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    @_process_entry
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    def scan_positions(self, entry):
        path = self.measurement_tpl.format(entry)
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        params = self.scan_params(entry)
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        motors = [m.decode() if hasattr(m, 'decode') else m
                  for m in (params['motor_0'], params['motor_1'])]

        m0 = '/{0}'.format(MOTORCOLS[motors[0]])
        m1 = '/{0}'.format(MOTORCOLS[motors[1]])
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        n_0 = params['motor_0_steps']
        n_1 = params['motor_1_steps']
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        x_pos = self._get_array_data(path + m0)
        y_pos = self._get_array_data(path + m1)
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        return ScanPositions(motor_0=motors[0],
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                             pos_0=x_pos,
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                             motor_1=motors[1],
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                             pos_1=y_pos,
                             shape=(n_0, n_1))
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    @_process_entry
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    def acquisition_params(self, entry):
        beam_energy = self.beam_energy(entry)
        direct_beam = self.direct_beam(entry)
        chan_per_deg = self.chan_per_deg(entry)

        result = OrderedDict()
        result['beam_energy'] = beam_energy
        result['direct_beam'] = direct_beam
        result['chan_per_deg'] = chan_per_deg

        return result

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    @_process_entry
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    def is_regular_grid(self, entry):
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        """For now grids are always regular

        :param str entry:
        :rtype: bool
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        """
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        # TODO
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        return True

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    @_process_entry
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    def scan_params(self, entry):
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        # TODO : make this more generic to make it compatible
        #  with irregular grids
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        param_names = ['motor_0', 'motor_0_start',
                       'motor_0_end', 'motor_0_steps',
                       'motor_1', 'motor_1_start',
                       'motor_1_end', 'motor_1_steps',
                       'delay']
        with self._get_file() as h5_file:
            path = self.scan_params_tpl.format(entry) + '/{0}'
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            result = OrderedDict()
            for param in param_names:
                value = h5_file.get(path.format(param), _np.array(None))[()]
                if hasattr(value, 'decode'):
                    value = value.decode()
                result[param] = value
            return result
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    @_process_entry
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    def positioner(self, entry, positioner):
        path = self.positioners_tpl.format(entry) + '/' + positioner
        return self._get_scalar_data(path)

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    @_process_entry
    def positioners(self, entry):
        """Returns names of positioners.

        :return: List of dataset names in positioners
        :rtype: List[str]
        """
        positioners = []

        path = self.positioners_tpl.format(entry) + '/'
        with self._get_file() as h5_file:
            for name, node in h5_file[path].items():
                if (isinstance(node, _h5py.Dataset) and
                        node.dtype.kind in 'iuf'):
                    positioners.append(name)

        return positioners

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    @_process_entry
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    def measurement(self, entry, measurement):
        path = self.measurement_tpl.format(entry) + '/' + measurement
        return self._get_array_data(path)

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    @_process_entry
    def normalizers(self, entry):
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        """Returns names of dataset in measurement that can be used to normalize data
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        It returns names of 1D datasets with same number of elements as images
        that are available in the measurement group.
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        :return: List of dataset names in measurement that might be normalizers
        :rtype: List[str]
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        """
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        normalizers = []
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        nb_images = self.n_images(entry)

        path = self.measurement_tpl.format(entry) + '/'
        with self._get_file() as h5_file:
            for name, node in h5_file[path].items():
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                if (isinstance(node, _h5py.Dataset) and
                        node.dtype.kind in 'iuf' and
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                        len(node.shape) == 1 and
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                        node.shape[0] == nb_images):
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                    # Only get (u)int and float datasets
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                    # with same number of values as number of images
                    normalizers.append(name)
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        return normalizers

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    @contextmanager
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    @_process_entry
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    def image_dset_ctx(self,
                       entry,
                       create=False,
                       **kwargs):
        """
        Context manager for the image dataset.
        WARNING: only to be used as a context manager!
        """
        dset_path = self.img_data_tpl.format(entry)
        with self._get_file() as h5_file:
            if create:
                try:
                    image_dset = h5_file.require_dataset(dset_path,
                                                         **kwargs)
                except TypeError:
                    image_dset = h5_file.create_dataset(dset_path,
                                                        **kwargs)
            else:
                image_dset = h5_file[dset_path]
            yield weakref.proxy(image_dset)
            del image_dset


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class XsocsH5Writer(XsocsH5):
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    def __init__(self, h5_f, mode='a'):
        super(XsocsH5Writer, self).__init__(h5_f, mode=mode)
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    def __set_detector_params(self, entry, params):
        with self._get_file() as h5_file:
            path = self.detector_tpl.format(entry) + '/{0}'
            for param_name, param_value in params.items():
                self._set_scalar_data(path.format(param_name), param_value)

    def set_beam_energy(self, beam_energy, entry):
        return self.__set_detector_params(entry, {'beam_energy': beam_energy})

    def set_direct_beam(self, direct_beam, entry):
        value = {'center_chan_dim0': direct_beam[0],
                 'center_chan_dim1': direct_beam[1]}
        return self.__set_detector_params(entry, value)

    def set_chan_per_deg(self, chan_per_deg, entry):
        value = {'chan_per_deg_dim0': chan_per_deg[0],
                 'chan_per_deg_dim1': chan_per_deg[1]}
        return self.__set_detector_params(entry, value)

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    def set_image_roi_offset(self, offset, entry):
        """Store image ROI offset information in the hdf5 file

        :param List[int] offset:
            Offset of the ROI in pixels (row_offset, column_offset)
        :param str entry: Entry for which to store information
        """
        return self.__set_detector_params(entry, {'image_roi_offset': offset})

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    def set_scan_params(self,
                        entry,
                        motor_0,
                        motor_0_start,
                        motor_0_end,
                        motor_0_steps,
                        motor_1,
                        motor_1_start,
                        motor_1_end,
                        motor_1_steps,
                        delay,
                        **kwargs):
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        params = OrderedDict([('motor_0', str_to_h5_utf8(motor_0)),
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                              ('motor_0_start', float(motor_0_start)),
                              ('motor_0_end', float(motor_0_end)),
                              ('motor_0_steps', int(motor_0_steps)),
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                              ('motor_1', str_to_h5_utf8(motor_1)),
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                              ('motor_1_start', float(motor_1_start)),
                              ('motor_1_end', float(motor_1_end)),
                              ('motor_1_steps', int(motor_1_steps)),
                              ('delay', float(delay))])
        with self._get_file():
            path = self.scan_params_tpl.format(entry) + '/{0}'
            for param_name, param_value in params.items():
                self._set_scalar_data(path.format(param_name), param_value)
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    def create_entry(self, entry):
        with self._get_file() as h5_file:
            entry_grp = h5_file.require_group(entry)
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            entry_grp.attrs['NX_class'] = str_to_h5_utf8('NXentry')
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            # creating mandatory groups and setting their Nexus attributes
            grp = entry_grp.require_group('measurement/image')
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            grp.attrs['interpretation'] = str_to_h5_utf8('image')
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            # setting the nexus classes
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            # entry_grp.attrs['NX_class'] = str_to_h5_utf8('NXentry')
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            grp = entry_grp.require_group('instrument')
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            grp.attrs['NX_class'] = str_to_h5_utf8('NXinstrument')
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            grp = entry_grp.require_group('instrument/detector')
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            grp.attrs['NX_class'] = str_to_h5_utf8('NXdetector')
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            grp = entry_grp.require_group('instrument/positioners')
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            grp.attrs['NX_class'] = str_to_h5_utf8('NXcollection')
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            grp = entry_grp.require_group('measurement')
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            grp.attrs['NX_class'] = str_to_h5_utf8('NXcollection')
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            grp = entry_grp.require_group('measurement/image')
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            grp.attrs['NX_class'] = str_to_h5_utf8('NXcollection')
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            # creating some links
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            grp = entry_grp.require_group('measurement/image')
            det_grp = entry_grp.require_group('instrument/detector')
            grp['info'] = det_grp
            det_grp['data'] = _h5py.SoftLink(self.img_data_tpl.format(entry))

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            self._update_entries()
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class XsocsH5MasterWriter(XsocsH5Writer):
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    def add_entry_file(self, entry, entry_file, master_entry=None):
        """Add an external link to an entry in a sub-file

        :param str entry: Name of the entry in the sub-file
        :param str entry_file: Name of the file the entry belongs to
        :param str master_entry: Optional alternative entry name in master file
        """
        if master_entry is None:
            master_entry = entry

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        with self._get_file() as h5_file:
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            h5_file[master_entry] = _h5py.ExternalLink(entry_file, entry)