Source code for silx.io.utils

# coding: utf-8
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""" I/O utility functions"""

__authors__ = ["P. Knobel", "V. Valls"]
__license__ = "MIT"
__date__ = "03/12/2020"

import enum
import os.path
import sys
import time
import logging
import collections
import urllib.parse

import numpy

from silx.utils.proxy import Proxy
import silx.io.url
from .._version import calc_hexversion

import h5py
import h5py.h5t
import h5py.h5a

try:
    import h5pyd
except ImportError as e:
    h5pyd = None

logger = logging.getLogger(__name__)

NEXUS_HDF5_EXT = [".h5", ".nx5", ".nxs", ".hdf", ".hdf5", ".cxi"]
"""List of possible extensions for HDF5 file formats."""


[docs]class H5Type(enum.Enum): """Identify a set of HDF5 concepts""" DATASET = 1 GROUP = 2 FILE = 3 SOFT_LINK = 4 EXTERNAL_LINK = 5 HARD_LINK = 6
_CLASSES_TYPE = None """Store mapping between classes and types""" string_types = (basestring,) if sys.version_info[0] == 2 else (str,) # noqa builtin_open = open
[docs]def supported_extensions(flat_formats=True): """Returns the list file extensions supported by `silx.open`. The result filter out formats when the expected module is not available. :param bool flat_formats: If true, also include flat formats like npy or edf (while the expected module is available) :returns: A dictionary indexed by file description and containing a set of extensions (an extension is a string like "\\*.ext"). :rtype: Dict[str, Set[str]] """ formats = collections.OrderedDict() formats["HDF5 files"] = set(["*.h5", "*.hdf", "*.hdf5"]) formats["NeXus files"] = set(["*.nx", "*.nxs", "*.h5", "*.hdf", "*.hdf5"]) formats["NeXus layout from spec files"] = set(["*.dat", "*.spec", "*.mca"]) if flat_formats: try: from silx.io import fabioh5 except ImportError: fabioh5 = None if fabioh5 is not None: formats["NeXus layout from fabio files"] = set(fabioh5.supported_extensions()) extensions = ["*.npz"] if flat_formats: extensions.append("*.npy") formats["Numpy binary files"] = set(extensions) formats["Coherent X-Ray Imaging files"] = set(["*.cxi"]) formats["FIO files"] = set(["*.fio"]) return formats
[docs]def save1D(fname, x, y, xlabel=None, ylabels=None, filetype=None, fmt="%.7g", csvdelim=";", newline="\n", header="", footer="", comments="#", autoheader=False): """Saves any number of curves to various formats: `Specfile`, `CSV`, `txt` or `npy`. All curves must have the same number of points and share the same ``x`` values. :param fname: Output file path, or file handle open in write mode. If ``fname`` is a path, file is opened in ``w`` mode. Existing file with a same name will be overwritten. :param x: 1D-Array (or list) of abscissa values. :param y: 2D-array (or list of lists) of ordinates values. First index is the curve index, second index is the sample index. The length of the second dimension (number of samples) must be equal to ``len(x)``. ``y`` can be a 1D-array in case there is only one curve to be saved. :param filetype: Filetype: ``"spec", "csv", "txt", "ndarray"``. If ``None``, filetype is detected from file name extension (``.dat, .csv, .txt, .npy``). :param xlabel: Abscissa label :param ylabels: List of `y` labels :param fmt: Format string for data. You can specify a short format string that defines a single format for both ``x`` and ``y`` values, or a list of two different format strings (e.g. ``["%d", "%.7g"]``). Default is ``"%.7g"``. This parameter does not apply to the `npy` format. :param csvdelim: String or character separating columns in `txt` and `CSV` formats. The user is responsible for ensuring that this delimiter is not used in data labels when writing a `CSV` file. :param newline: String or character separating lines/records in `txt` format (default is line break character ``\\n``). :param header: String that will be written at the beginning of the file in `txt` format. :param footer: String that will be written at the end of the file in `txt` format. :param comments: String that will be prepended to the ``header`` and ``footer`` strings, to mark them as comments. Default: ``#``. :param autoheader: In `CSV` or `txt`, ``True`` causes the first header line to be written as a standard CSV header line with column labels separated by the specified CSV delimiter. When saving to Specfile format, each curve is saved as a separate scan with two data columns (``x`` and ``y``). `CSV` and `txt` formats are similar, except that the `txt` format allows user defined header and footer text blocks, whereas the `CSV` format has only a single header line with columns labels separated by field delimiters and no footer. The `txt` format also allows defining a record separator different from a line break. The `npy` format is written with ``numpy.save`` and can be read back with ``numpy.load``. If ``xlabel`` and ``ylabels`` are undefined, data is saved as a regular 2D ``numpy.ndarray`` (contatenation of ``x`` and ``y``). If both ``xlabel`` and ``ylabels`` are defined, the data is saved as a ``numpy.recarray`` after being transposed and having labels assigned to columns. """ available_formats = ["spec", "csv", "txt", "ndarray"] if filetype is None: exttypes = {".dat": "spec", ".csv": "csv", ".txt": "txt", ".npy": "ndarray"} outfname = (fname if not hasattr(fname, "name") else fname.name) fileext = os.path.splitext(outfname)[1] if fileext in exttypes: filetype = exttypes[fileext] else: raise IOError("File type unspecified and could not be " + "inferred from file extension (not in " + "txt, dat, csv, npy)") else: filetype = filetype.lower() if filetype not in available_formats: raise IOError("File type %s is not supported" % (filetype)) # default column headers if xlabel is None: xlabel = "x" if ylabels is None: if numpy.array(y).ndim > 1: ylabels = ["y%d" % i for i in range(len(y))] else: ylabels = ["y"] elif isinstance(ylabels, (list, tuple)): # if ylabels is provided as a list, every element must # be a string ylabels = [ylabel if isinstance(ylabel, string_types) else "y%d" % i for ylabel in ylabels] if filetype.lower() == "spec": # Check if we have regular data: ref = len(x) regular = True for one_y in y: regular &= len(one_y) == ref if regular: if isinstance(fmt, (list, tuple)) and len(fmt) < (len(ylabels) + 1): fmt = fmt + [fmt[-1] * (1 + len(ylabels) - len(fmt))] specf = savespec(fname, x, y, xlabel, ylabels, fmt=fmt, scan_number=1, mode="w", write_file_header=True, close_file=False) else: y_array = numpy.asarray(y) # make sure y_array is a 2D array even for a single curve if y_array.ndim == 1: y_array.shape = 1, -1 elif y_array.ndim not in [1, 2]: raise IndexError("y must be a 1D or 2D array") # First curve specf = savespec(fname, x, y_array[0], xlabel, ylabels[0], fmt=fmt, scan_number=1, mode="w", write_file_header=True, close_file=False) # Other curves for i in range(1, y_array.shape[0]): specf = savespec(specf, x, y_array[i], xlabel, ylabels[i], fmt=fmt, scan_number=i + 1, mode="w", write_file_header=False, close_file=False) # close file if we created it if not hasattr(fname, "write"): specf.close() else: autoheader_line = xlabel + csvdelim + csvdelim.join(ylabels) if xlabel is not None and ylabels is not None and filetype == "csv": # csv format: optional single header line with labels, no footer if autoheader: header = autoheader_line + newline else: header = "" comments = "" footer = "" newline = "\n" elif filetype == "txt" and autoheader: # Comments string is added at the beginning of header string in # savetxt(). We add another one after the first header line and # before the rest of the header. if header: header = autoheader_line + newline + comments + header else: header = autoheader_line + newline # Concatenate x and y in a single 2D array X = numpy.vstack((x, y)) if filetype.lower() in ["csv", "txt"]: X = X.transpose() savetxt(fname, X, fmt=fmt, delimiter=csvdelim, newline=newline, header=header, footer=footer, comments=comments) elif filetype.lower() == "ndarray": if xlabel is not None and ylabels is not None: labels = [xlabel] + ylabels # .transpose is needed here because recarray labels # apply to columns X = numpy.core.records.fromrecords(X.transpose(), names=labels) numpy.save(fname, X)
# Replace with numpy.savetxt when dropping support of numpy < 1.7.0
[docs]def savetxt(fname, X, fmt="%.7g", delimiter=";", newline="\n", header="", footer="", comments="#"): """``numpy.savetxt`` backport of header and footer arguments from numpy=1.7.0. See ``numpy.savetxt`` help: http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.savetxt.html """ if not hasattr(fname, "name"): ffile = builtin_open(fname, 'wb') else: ffile = fname if header: if sys.version_info[0] >= 3: header = header.encode("utf-8") ffile.write(header) numpy.savetxt(ffile, X, fmt, delimiter, newline) if footer: footer = (comments + footer.replace(newline, newline + comments) + newline) if sys.version_info[0] >= 3: footer = footer.encode("utf-8") ffile.write(footer) if not hasattr(fname, "name"): ffile.close()
[docs]def savespec(specfile, x, y, xlabel="X", ylabel="Y", fmt="%.7g", scan_number=1, mode="w", write_file_header=True, close_file=False): """Saves one curve to a SpecFile. The curve is saved as a scan with two data columns. To save multiple curves to a single SpecFile, call this function for each curve by providing the same file handle each time. :param specfile: Output SpecFile name, or file handle open in write or append mode. If a file name is provided, a new file is open in write mode (existing file with the same name will be lost) :param x: 1D-Array (or list) of abscissa values :param y: 1D-array (or list), or list of them of ordinates values. All dataset must have the same length as x :param xlabel: Abscissa label (default ``"X"``) :param ylabel: Ordinate label, may be a list of labels when multiple curves are to be saved together. :param fmt: Format string for data. You can specify a short format string that defines a single format for both ``x`` and ``y`` values, or a list of two different format strings (e.g. ``["%d", "%.7g"]``). Default is ``"%.7g"``. :param scan_number: Scan number (default 1). :param mode: Mode for opening file: ``w`` (default), ``a``, ``r+``, ``w+``, ``a+``. This parameter is only relevant if ``specfile`` is a path. :param write_file_header: If ``True``, write a file header before writing the scan (``#F`` and ``#D`` line). :param close_file: If ``True``, close the file after saving curve. :return: ``None`` if ``close_file`` is ``True``, else return the file handle. """ # Make sure we use binary mode for write # (issue with windows: write() replaces \n with os.linesep in text mode) if "b" not in mode: first_letter = mode[0] assert first_letter in "rwa" mode = mode.replace(first_letter, first_letter + "b") x_array = numpy.asarray(x) y_array = numpy.asarray(y) if y_array.ndim > 2: raise IndexError("Y columns must have be packed as 1D") if y_array.shape[-1] != x_array.shape[0]: raise IndexError("X and Y columns must have the same length") if y_array.ndim == 2: assert isinstance(ylabel, (list, tuple)) assert y_array.shape[0] == len(ylabel) labels = (xlabel, *ylabel) else: labels = (xlabel, ylabel) data = numpy.vstack((x_array, y_array)) ncol = data.shape[0] assert len(labels) == ncol print(xlabel, ylabel, fmt, ncol, x_array, y_array) if isinstance(fmt, string_types) and fmt.count("%") == 1: full_fmt_string = " ".join([fmt] * ncol) elif isinstance(fmt, (list, tuple)) and len(fmt) == ncol: full_fmt_string = " ".join(fmt) else: raise ValueError("`fmt` must be a single format string or a list of " + "format strings with as many format as ncolumns") if not hasattr(specfile, "write"): f = builtin_open(specfile, mode) else: f = specfile current_date = "#D %s" % (time.ctime(time.time())) if write_file_header: lines = [ "#F %s" % f.name, current_date, ""] else: lines = [""] lines += [ "#S %d %s" % (scan_number, labels[1]), current_date, "#N %d" % ncol, "#L " + " ".join(labels)] for i in data.T: lines.append(full_fmt_string % tuple(i)) lines.append("") output = "\n".join(lines) f.write(output.encode()) if close_file: f.close() return None return f
[docs]def h5ls(h5group, lvl=0): """Return a simple string representation of a HDF5 tree structure. :param h5group: Any :class:`h5py.Group` or :class:`h5py.File` instance, or a HDF5 file name :param lvl: Number of tabulations added to the group. ``lvl`` is incremented as we recursively process sub-groups. :return: String representation of an HDF5 tree structure Group names and dataset representation are printed preceded by a number of tabulations corresponding to their depth in the tree structure. Datasets are represented as :class:`h5py.Dataset` objects. Example:: >>> print(h5ls("Downloads/sample.h5")) +fields +fieldB <HDF5 dataset "z": shape (256, 256), type "<f4"> +fieldE <HDF5 dataset "x": shape (256, 256), type "<f4"> <HDF5 dataset "y": shape (256, 256), type "<f4"> .. note:: This function requires `h5py <http://www.h5py.org/>`_ to be installed. """ h5repr = '' if is_group(h5group): h5f = h5group elif isinstance(h5group, string_types): h5f = open(h5group) # silx.io.open else: raise TypeError("h5group must be a hdf5-like group object or a file name.") for key in h5f.keys(): # group if hasattr(h5f[key], 'keys'): h5repr += '\t' * lvl + '+' + key h5repr += '\n' h5repr += h5ls(h5f[key], lvl + 1) # dataset else: h5repr += '\t' * lvl h5repr += str(h5f[key]) h5repr += '\n' if isinstance(h5group, string_types): h5f.close() return h5repr
def _open_local_file(filename): """ Load a file as an `h5py.File`-like object. Format supported: - h5 files, if `h5py` module is installed - SPEC files exposed as a NeXus layout - raster files exposed as a NeXus layout (if `fabio` is installed) - fio files exposed as a NeXus layout - Numpy files ('npy' and 'npz' files) The file is opened in read-only mode. :param str filename: A filename :raises: IOError if the file can't be loaded as an h5py.File like object :rtype: h5py.File """ if not os.path.isfile(filename): raise IOError("Filename '%s' must be a file path" % filename) debugging_info = [] try: _, extension = os.path.splitext(filename) if extension in [".npz", ".npy"]: try: from . import rawh5 return rawh5.NumpyFile(filename) except (IOError, ValueError) as e: debugging_info.append((sys.exc_info(), "File '%s' can't be read as a numpy file." % filename)) if h5py.is_hdf5(filename): try: return h5py.File(filename, "r") except OSError: return h5py.File(filename, "r", libver='latest', swmr=True) try: from . import fabioh5 return fabioh5.File(filename) except ImportError: debugging_info.append((sys.exc_info(), "fabioh5 can't be loaded.")) except Exception: debugging_info.append((sys.exc_info(), "File '%s' can't be read as fabio file." % filename)) try: from . import spech5 return spech5.SpecH5(filename) except ImportError: debugging_info.append((sys.exc_info(), "spech5 can't be loaded.")) except IOError: debugging_info.append((sys.exc_info(), "File '%s' can't be read as spec file." % filename)) try: from . import fioh5 return fioh5.FioH5(filename) except IOError: debugging_info.append((sys.exc_info(), "File '%s' can't be read as fio file." % filename)) finally: for exc_info, message in debugging_info: logger.debug(message, exc_info=exc_info) raise IOError("File '%s' can't be read as HDF5" % filename) class _MainNode(Proxy): """A main node is a sub node of the HDF5 tree which is responsible of the closure of the file. It is a proxy to the sub node, plus support context manager and `close` method usually provided by `h5py.File`. :param h5_node: Target to the proxy. :param h5_file: Main file. This object became the owner of this file. """ def __init__(self, h5_node, h5_file): super(_MainNode, self).__init__(h5_node) self.__file = h5_file self.__class = get_h5_class(h5_node) @property def h5_class(self): """Returns the HDF5 class which is mimicked by this class. :rtype: H5Type """ return self.__class @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: h5py class """ return h5type_to_h5py_class(self.__class) def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.close() def close(self): """Close the file""" self.__file.close() self.__file = None
[docs]def open(filename): # pylint:disable=redefined-builtin """ Open a file as an `h5py`-like object. Format supported: - h5 files, if `h5py` module is installed - SPEC files exposed as a NeXus layout - raster files exposed as a NeXus layout (if `fabio` is installed) - fio files exposed as a NeXus layout - Numpy files ('npy' and 'npz' files) The filename can be trailled an HDF5 path using the separator `::`. In this case the object returned is a proxy to the target node, implementing the `close` function and supporting `with` context. The file is opened in read-only mode. :param str filename: A filename which can containt an HDF5 path by using `::` separator. :raises: IOError if the file can't be loaded or path can't be found :rtype: h5py-like node """ url = silx.io.url.DataUrl(filename) if url.scheme() in [None, "file", "silx"]: # That's a local file if not url.is_valid(): raise IOError("URL '%s' is not valid" % filename) h5_file = _open_local_file(url.file_path()) elif url.scheme() in ["fabio"]: raise IOError("URL '%s' containing fabio scheme is not supported" % filename) else: # That's maybe an URL supported by h5pyd uri = urllib.parse.urlparse(filename) if h5pyd is None: raise IOError("URL '%s' unsupported. Try to install h5pyd." % filename) path = uri.path endpoint = "%s://%s" % (uri.scheme, uri.netloc) if path.startswith("/"): path = path[1:] return h5pyd.File(path, 'r', endpoint=endpoint) if url.data_slice(): raise IOError("URL '%s' containing slicing is not supported" % filename) if url.data_path() in [None, "/", ""]: # The full file is requested return h5_file else: # Only a children is requested if url.data_path() not in h5_file: msg = "File '%s' does not contain path '%s'." % (filename, url.data_path()) raise IOError(msg) node = h5_file[url.data_path()] proxy = _MainNode(node, h5_file) return proxy
def _get_classes_type(): """Returns a mapping between Python classes and HDF5 concepts. This function allow an lazy initialization to avoid recurssive import of modules. """ global _CLASSES_TYPE from . import commonh5 if _CLASSES_TYPE is not None: return _CLASSES_TYPE _CLASSES_TYPE = collections.OrderedDict() _CLASSES_TYPE[commonh5.Dataset] = H5Type.DATASET _CLASSES_TYPE[commonh5.File] = H5Type.FILE _CLASSES_TYPE[commonh5.Group] = H5Type.GROUP _CLASSES_TYPE[commonh5.SoftLink] = H5Type.SOFT_LINK _CLASSES_TYPE[h5py.Dataset] = H5Type.DATASET _CLASSES_TYPE[h5py.File] = H5Type.FILE _CLASSES_TYPE[h5py.Group] = H5Type.GROUP _CLASSES_TYPE[h5py.SoftLink] = H5Type.SOFT_LINK _CLASSES_TYPE[h5py.HardLink] = H5Type.HARD_LINK _CLASSES_TYPE[h5py.ExternalLink] = H5Type.EXTERNAL_LINK if h5pyd is not None: _CLASSES_TYPE[h5pyd.Dataset] = H5Type.DATASET _CLASSES_TYPE[h5pyd.File] = H5Type.FILE _CLASSES_TYPE[h5pyd.Group] = H5Type.GROUP _CLASSES_TYPE[h5pyd.SoftLink] = H5Type.SOFT_LINK _CLASSES_TYPE[h5pyd.HardLink] = H5Type.HARD_LINK _CLASSES_TYPE[h5pyd.ExternalLink] = H5Type.EXTERNAL_LINK return _CLASSES_TYPE
[docs]def get_h5_class(obj=None, class_=None): """ Returns the HDF5 type relative to the object or to the class. :param obj: Instance of an object :param class_: A class :rtype: H5Type """ if class_ is None: class_ = obj.__class__ classes = _get_classes_type() t = classes.get(class_, None) if t is not None: return t if obj is not None: if hasattr(obj, "h5_class"): return obj.h5_class for referencedClass_, type_ in classes.items(): if issubclass(class_, referencedClass_): classes[class_] = type_ return type_ classes[class_] = None return None
[docs]def h5type_to_h5py_class(type_): """ Returns an h5py class from an H5Type. None if nothing found. :param H5Type type_: :rtype: H5py class """ if type_ == H5Type.FILE: return h5py.File if type_ == H5Type.GROUP: return h5py.Group if type_ == H5Type.DATASET: return h5py.Dataset if type_ == H5Type.SOFT_LINK: return h5py.SoftLink if type_ == H5Type.HARD_LINK: return h5py.HardLink if type_ == H5Type.EXTERNAL_LINK: return h5py.ExternalLink return None
[docs]def get_h5py_class(obj): """Returns the h5py class from an object. If it is an h5py object or an h5py-like object, an h5py class is returned. If the object is not an h5py-like object, None is returned. :param obj: An object :return: An h5py object """ if hasattr(obj, "h5py_class"): return obj.h5py_class type_ = get_h5_class(obj) return h5type_to_h5py_class(type_)
[docs]def is_file(obj): """ True is the object is an h5py.File-like object. :param obj: An object """ t = get_h5_class(obj) return t == H5Type.FILE
[docs]def is_group(obj): """ True if the object is a h5py.Group-like object. A file is a group. :param obj: An object """ t = get_h5_class(obj) return t in [H5Type.GROUP, H5Type.FILE]
[docs]def is_dataset(obj): """ True if the object is a h5py.Dataset-like object. :param obj: An object """ t = get_h5_class(obj) return t == H5Type.DATASET
def _visitall(item, path=''): """Helper function for func:`visitall`. :param item: Item to visit :param str path: Relative path of the item """ if not is_group(item): return for name, child_item in item.items(): if isinstance(child_item, (h5py.Group, h5py.Dataset)): link = item.get(name, getlink=True) else: link = child_item child_path = '/'.join((path, name)) ret = link if link is not None and is_link(link) else child_item yield child_path, ret yield from _visitall(child_item, child_path)
[docs]def visitall(item): """Visit entity recursively including links. It does not follow links. This is a generator yielding (relative path, object) for visited items. :param item: The item to visit. """ yield from _visitall(item, '')
[docs]def get_data(url): """Returns a numpy data from an URL. Examples: >>> # 1st frame from an EDF using silx.io.open >>> data = silx.io.get_data("silx:/users/foo/image.edf::/scan_0/instrument/detector_0/data[0]") >>> # 1st frame from an EDF using fabio >>> data = silx.io.get_data("fabio:/users/foo/image.edf::[0]") Yet 2 schemes are supported by the function. - If `silx` scheme is used, the file is opened using :meth:`silx.io.open` and the data is reach using usually NeXus paths. - If `fabio` scheme is used, the file is opened using :meth:`fabio.open` from the FabIO library. No data path have to be specified, but each frames can be accessed using the data slicing. This shortcut of :meth:`silx.io.open` allow to have a faster access to the data. .. seealso:: :class:`silx.io.url.DataUrl` :param Union[str,silx.io.url.DataUrl]: A data URL :rtype: Union[numpy.ndarray, numpy.generic] :raises ImportError: If the mandatory library to read the file is not available. :raises ValueError: If the URL is not valid or do not match the data :raises IOError: If the file is not found or in case of internal error of :meth:`fabio.open` or :meth:`silx.io.open`. In this last case more informations are displayed in debug mode. """ if not isinstance(url, silx.io.url.DataUrl): url = silx.io.url.DataUrl(url) if not url.is_valid(): raise ValueError("URL '%s' is not valid" % url.path()) if not os.path.exists(url.file_path()): raise IOError("File '%s' not found" % url.file_path()) if url.scheme() == "silx": data_path = url.data_path() data_slice = url.data_slice() with open(url.file_path()) as h5: if data_path not in h5: raise ValueError("Data path from URL '%s' not found" % url.path()) data = h5[data_path] if not silx.io.is_dataset(data): raise ValueError("Data path from URL '%s' is not a dataset" % url.path()) if data_slice is not None: data = h5py_read_dataset(data, index=data_slice) else: # works for scalar and array data = h5py_read_dataset(data) elif url.scheme() == "fabio": import fabio data_slice = url.data_slice() if data_slice is None: data_slice = (0,) if data_slice is None or len(data_slice) != 1: raise ValueError("Fabio slice expect a single frame, but %s found" % data_slice) index = data_slice[0] if not isinstance(index, int): raise ValueError("Fabio slice expect a single integer, but %s found" % data_slice) try: fabio_file = fabio.open(url.file_path()) except Exception: logger.debug("Error while opening %s with fabio", url.file_path(), exc_info=True) raise IOError("Error while opening %s with fabio (use debug for more information)" % url.path()) if fabio_file.nframes == 1: if index != 0: raise ValueError("Only a single frame available. Slice %s out of range" % index) data = fabio_file.data else: data = fabio_file.getframe(index).data # There is no explicit close fabio_file = None else: raise ValueError("Scheme '%s' not supported" % url.scheme()) return data
[docs]def rawfile_to_h5_external_dataset(bin_file, output_url, shape, dtype, overwrite=False): """ Create a HDF5 dataset at `output_url` pointing to the given vol_file. Either `shape` or `info_file` must be provided. :param str bin_file: Path to the .vol file :param DataUrl output_url: HDF5 URL where to save the external dataset :param tuple shape: Shape of the volume :param numpy.dtype dtype: Data type of the volume elements (default: float32) :param bool overwrite: True to allow overwriting (default: False). """ assert isinstance(output_url, silx.io.url.DataUrl) assert isinstance(shape, (tuple, list)) v_majeur, v_mineur, v_micro = [int(i) for i in h5py.version.version.split('.')[:3]] if calc_hexversion(v_majeur, v_mineur, v_micro)< calc_hexversion(2,9,0): raise Exception('h5py >= 2.9 should be installed to access the ' 'external feature.') with h5py.File(output_url.file_path(), mode="a") as _h5_file: if output_url.data_path() in _h5_file: if overwrite is False: raise ValueError('data_path already exists') else: logger.warning('will overwrite path %s' % output_url.data_path()) del _h5_file[output_url.data_path()] external = [(bin_file, 0, h5py.h5f.UNLIMITED)] _h5_file.create_dataset(output_url.data_path(), shape, dtype=dtype, external=external)
[docs]def vol_to_h5_external_dataset(vol_file, output_url, info_file=None, vol_dtype=numpy.float32, overwrite=False): """ Create a HDF5 dataset at `output_url` pointing to the given vol_file. If the vol_file.info containing the shape is not on the same folder as the vol-file then you should specify her location. :param str vol_file: Path to the .vol file :param DataUrl output_url: HDF5 URL where to save the external dataset :param Union[str,None] info_file: .vol.info file name written by pyhst and containing the shape information :param numpy.dtype vol_dtype: Data type of the volume elements (default: float32) :param bool overwrite: True to allow overwriting (default: False). :raises ValueError: If fails to read shape from the .vol.info file """ _info_file = info_file if _info_file is None: _info_file = vol_file + '.info' if not os.path.exists(_info_file): logger.error('info_file not given and %s does not exists, please' 'specify .vol.info file' % _info_file) return def info_file_to_dict(): ddict = {} with builtin_open(info_file, "r") as _file: lines = _file.readlines() for line in lines: if not '=' in line: continue l = line.rstrip().replace(' ', '') l = l.split('#')[0] key, value = l.split('=') ddict[key.lower()] = value return ddict ddict = info_file_to_dict() if 'num_x' not in ddict or 'num_y' not in ddict or 'num_z' not in ddict: raise ValueError( 'Unable to retrieve volume shape from %s' % info_file) dimX = int(ddict['num_x']) dimY = int(ddict['num_y']) dimZ = int(ddict['num_z']) shape = (dimZ, dimY, dimX) return rawfile_to_h5_external_dataset(bin_file=vol_file, output_url=output_url, shape=shape, dtype=vol_dtype, overwrite=overwrite)
[docs]def h5py_decode_value(value, encoding="utf-8", errors="surrogateescape"): """Keep bytes when value cannot be decoded :param value: bytes or array of bytes :param encoding str: :param errors str: """ try: if numpy.isscalar(value): return value.decode(encoding, errors=errors) str_item = [b.decode(encoding, errors=errors) for b in value.flat] return numpy.array(str_item, dtype=object).reshape(value.shape) except UnicodeDecodeError: return value
[docs]def h5py_encode_value(value, encoding="utf-8", errors="surrogateescape"): """Keep string when value cannot be encoding :param value: string or array of strings :param encoding str: :param errors str: """ try: if numpy.isscalar(value): return value.encode(encoding, errors=errors) bytes_item = [s.encode(encoding, errors=errors) for s in value.flat] return numpy.array(bytes_item, dtype=object).reshape(value.shape) except UnicodeEncodeError: return value
[docs]class H5pyDatasetReadWrapper: """Wrapper to handle H5T_STRING decoding on-the-fly when reading a dataset. Uniform behaviour for h5py 2.x and h5py 3.x h5py abuses H5T_STRING with ASCII character set to store `bytes`: dset[()] = b"..." Therefore an H5T_STRING with ASCII encoding is not decoded by default. """ H5PY_AUTODECODE_NONASCII = int(h5py.version.version.split(".")[0]) < 3 def __init__(self, dset, decode_ascii=False): """ :param h5py.Dataset dset: :param bool decode_ascii: """ try: string_info = h5py.h5t.check_string_dtype(dset.dtype) except AttributeError: # h5py < 2.10 try: idx = dset.id.get_type().get_cset() except AttributeError: # Not an H5T_STRING encoding = None else: encoding = ["ascii", "utf-8"][idx] else: # h5py >= 2.10 try: encoding = string_info.encoding except AttributeError: # Not an H5T_STRING encoding = None if encoding == "ascii" and not decode_ascii: encoding = None if encoding != "ascii" and self.H5PY_AUTODECODE_NONASCII: # Decoding is already done by the h5py library encoding = None if encoding == "ascii": # ASCII can be decoded as UTF-8 encoding = "utf-8" self._encoding = encoding self._dset = dset def __getitem__(self, args): value = self._dset[args] if self._encoding: return h5py_decode_value(value, encoding=self._encoding) else: return value
[docs]class H5pyAttributesReadWrapper: """Wrapper to handle H5T_STRING decoding on-the-fly when reading an attribute. Uniform behaviour for h5py 2.x and h5py 3.x h5py abuses H5T_STRING with ASCII character set to store `bytes`: dset[()] = b"..." Therefore an H5T_STRING with ASCII encoding is not decoded by default. """ H5PY_AUTODECODE = int(h5py.version.version.split(".")[0]) >= 3 def __init__(self, attrs, decode_ascii=False): """ :param h5py.Dataset dset: :param bool decode_ascii: """ self._attrs = attrs self._decode_ascii = decode_ascii def __getitem__(self, args): value = self._attrs[args] # Get the string encoding (if a string) try: dtype = self._attrs.get_id(args).dtype except AttributeError: # h5py < 2.10 attr_id = h5py.h5a.open(self._attrs._id, self._attrs._e(args)) try: idx = attr_id.get_type().get_cset() except AttributeError: # Not an H5T_STRING return value else: encoding = ["ascii", "utf-8"][idx] else: # h5py >= 2.10 try: encoding = h5py.h5t.check_string_dtype(dtype).encoding except AttributeError: # Not an H5T_STRING return value if self.H5PY_AUTODECODE: if encoding == "ascii" and not self._decode_ascii: # Undo decoding by the h5py library return h5py_encode_value(value, encoding="utf-8") else: if encoding == "ascii" and self._decode_ascii: # Decode ASCII as UTF-8 for consistency return h5py_decode_value(value, encoding="utf-8") # Decoding is already done by the h5py library return value def items(self): for k in self._attrs.keys(): yield k, self[k]
[docs]def h5py_read_dataset(dset, index=tuple(), decode_ascii=False): """Read data from dataset object. UTF-8 strings will be decoded while ASCII strings will only be decoded when `decode_ascii=True`. :param h5py.Dataset dset: :param index: slicing (all by default) :param bool decode_ascii: """ return H5pyDatasetReadWrapper(dset, decode_ascii=decode_ascii)[index]
[docs]def h5py_read_attribute(attrs, name, decode_ascii=False): """Read data from attributes. UTF-8 strings will be decoded while ASCII strings will only be decoded when `decode_ascii=True`. :param h5py.AttributeManager attrs: :param str name: attribute name :param bool decode_ascii: """ return H5pyAttributesReadWrapper(attrs, decode_ascii=decode_ascii)[name]
[docs]def h5py_read_attributes(attrs, decode_ascii=False): """Read data from attributes. UTF-8 strings will be decoded while ASCII strings will only be decoded when `decode_ascii=True`. :param h5py.AttributeManager attrs: :param bool decode_ascii: """ return dict(H5pyAttributesReadWrapper(attrs, decode_ascii=decode_ascii).items())