Source code for silx.io.utils
# coding: utf-8
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""" I/O utility functions"""
import numpy
import os.path
import sys
import time
import logging
from silx.utils.decorators import deprecated
try:
import h5py
except ImportError as e:
h5py_missing = True
h5py_import_error = e
else:
h5py_missing = False
__authors__ = ["P. Knobel"]
__license__ = "MIT"
__date__ = "13/12/2016"
logger = logging.getLogger(__name__)
string_types = (basestring,) if sys.version_info[0] == 2 else (str,) # noqa
builtin_open = open
[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 len(numpy.array(y).shape) > 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 = [ylabels[i] if ylabels[i] is not None else "y%d" % i
for i in range(len(ylabels))]
if filetype.lower() == "spec":
y_array = numpy.asarray(y)
# make sure y_array is a 2D array even for a single curve
if len(y_array.shape) == 1:
y_array.shape = (1, y_array.shape[0])
elif len(y_array.shape) > 2 or len(y_array.shape) < 1:
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) of ordinates values
:param xlabel: Abscissa label (default ``"X"``)
:param ylabel: Ordinate label
: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.shape[0] != x_array.shape[0]:
raise IndexError("X and Y columns must have the same length")
if isinstance(fmt, string_types) and fmt.count("%") == 1:
full_fmt_string = fmt + " " + fmt + "\n"
elif isinstance(fmt, (list, tuple)) and len(fmt) == 2:
full_fmt_string = " ".join(fmt) + "\n"
else:
raise ValueError("fmt must be a single format string or a list of " +
"two format strings")
if not hasattr(specfile, "write"):
f = builtin_open(specfile, mode)
else:
f = specfile
output = ""
current_date = "#D %s\n" % (time.ctime(time.time()))
if write_file_header:
output += "#F %s\n" % f.name
output += current_date
output += "\n"
output += "#S %d %s\n" % (scan_number, ylabel)
output += current_date
output += "#N 2\n"
output += "#L %s %s\n" % (xlabel, ylabel)
for i in range(y_array.shape[0]):
output += full_fmt_string % (x_array[i], y_array[i])
output += "\n"
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.
"""
if h5py_missing:
logger.error("h5ls requires h5py")
raise h5py_import_error
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
[docs]def open(filename): # pylint:disable=redefined-builtin
"""
Load a file as an `h5py.File`-like object.
Format supported:
- h5 files, if `h5py` module is installed
- Spec files if `SpecFile` module is installed
- a set of raster image formats (tiff, edf...) if `fabio` is installed
: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)
if not h5py_missing:
if h5py.is_hdf5(filename):
return h5py.File(filename)
try:
from . import fabioh5
return fabioh5.File(filename)
except ImportError:
logger.debug("fabioh5 can't be loaded.", exc_info=True)
except Exception:
logger.debug("File '%s' can't be read as fabio file.", filename, exc_info=True)
try:
from . import spech5
return spech5.SpecH5(filename)
except ImportError:
logger.debug("spech5 can't be loaded.", exc_info=True)
except IOError:
logger.debug("File '%s' can't be read as spec file.", filename, exc_info=True)
raise IOError("File '%s' can't be read as HDF5" % filename)
@deprecated
[docs]def load(filename):
"""
Load a file as an `h5py.File`-like object.
Format supported:
- h5 files, if `h5py` module is installed
- Spec files if `SpecFile` module is installed
.. deprecated:: 0.4
Use :meth:`open`, or :meth:`silx.io.open`. Will be removed in
Silx 0.5.
:param str filename: A filename
:raises: IOError if the file can't be loaded as an h5py.File like object
:rtype: h5py.File
"""
return open(filename)
[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
elif isinstance(obj, (h5py.File, h5py.Group, h5py.Dataset)):
return obj.__class__
else:
return None
[docs]def is_file(obj):
"""
True is the object is an h5py.File-like object.
:param obj: An object
"""
class_ = get_h5py_class(obj)
if class_ is None:
return False
return issubclass(class_, h5py.File)
[docs]def is_group(obj):
"""
True is the object is an h5py.Group-like object.
:param obj: An object
"""
class_ = get_h5py_class(obj)
if class_ is None:
return False
return issubclass(class_, h5py.Group)
[docs]def is_dataset(obj):
"""
True is the object is an h5py.Dataset-like object.
:param obj: An object
"""
class_ = get_h5py_class(obj)
if class_ is None:
return False
return issubclass(class_, h5py.Dataset)
if h5py_missing:
def raise_h5py_missing(obj):
logger.error("get_h5py_class/is_file/is_group/is_dataset requires h5py")
raise h5py_import_error
get_h5py_class = raise_h5py_missing
is_file = raise_h5py_missing
is_group = raise_h5py_missing
is_dataset = raise_h5py_missing
