/usr/lib/python2.7/dist-packages/nibabel/volumeutils.py is in python-nibabel 2.0.2-2.
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# vi: set ft=python sts=4 ts=4 sw=4 et:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the NiBabel package for the
# copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
''' Utility functions for analyze-like formats '''
from __future__ import division, print_function
import sys
import warnings
import gzip
import bz2
from os.path import exists, splitext
from operator import mul
from functools import reduce
import numpy as np
from .casting import (shared_range, type_info, OK_FLOATS)
from .openers import Opener
sys_is_le = sys.byteorder == 'little'
native_code = sys_is_le and '<' or '>'
swapped_code = sys_is_le and '>' or '<'
endian_codes = (# numpy code, aliases
('<', 'little', 'l', 'le', 'L', 'LE'),
('>', 'big', 'BIG', 'b', 'be', 'B', 'BE'),
(native_code, 'native', 'n', 'N', '=', '|', 'i', 'I'),
(swapped_code, 'swapped', 's', 'S', '!'))
# We'll put these into the Recoder class after we define it
#: default compression level when writing gz and bz2 files
default_compresslevel = 1
#: file-like classes known to hold compressed data
COMPRESSED_FILE_LIKES = (gzip.GzipFile, bz2.BZ2File)
#: file-like classes known to return string values that are safe to modify
SAFE_STRINGERS = (gzip.GzipFile, bz2.BZ2File)
class Recoder(object):
''' class to return canonical code(s) from code or aliases
The concept is a lot easier to read in the implementation and
tests than it is to explain, so...
>>> # If you have some codes, and several aliases, like this:
>>> code1 = 1; aliases1=['one', 'first']
>>> code2 = 2; aliases2=['two', 'second']
>>> # You might want to do this:
>>> codes = [[code1]+aliases1,[code2]+aliases2]
>>> recodes = Recoder(codes)
>>> recodes.code['one']
1
>>> recodes.code['second']
2
>>> recodes.code[2]
2
>>> # Or maybe you have a code, a label and some aliases
>>> codes=((1,'label1','one', 'first'),(2,'label2','two'))
>>> # you might want to get back the code or the label
>>> recodes = Recoder(codes, fields=('code','label'))
>>> recodes.code['first']
1
>>> recodes.code['label1']
1
>>> recodes.label[2]
'label2'
>>> # For convenience, you can get the first entered name by
>>> # indexing the object directly
>>> recodes[2]
2
'''
def __init__(self, codes, fields=('code',), map_maker=dict):
''' Create recoder object
``codes`` give a sequence of code, alias sequences
``fields`` are names by which the entries in these sequences can be
accessed.
By default ``fields`` gives the first column the name
"code". The first column is the vector of first entries
in each of the sequences found in ``codes``. Thence you can
get the equivalent first column value with ob.code[value],
where value can be a first column value, or a value in any of
the other columns in that sequence.
You can give other columns names too, and access them in the
same way - see the examples in the class docstring.
Parameters
----------
codes : seqence of sequences
Each sequence defines values (codes) that are equivalent
fields : {('code',) string sequence}, optional
names by which elements in sequences can be accessed
map_maker: callable, optional
constructor for dict-like objects used to store key value pairs.
Default is ``dict``. ``map_maker()`` generates an empty mapping.
The mapping need only implement ``__getitem__, __setitem__, keys,
values``.
'''
self.fields = tuple(fields)
self.field1 = {} # a placeholder for the check below
for name in fields:
if name in self.__dict__:
raise KeyError('Input name %s already in object dict'
% name)
self.__dict__[name] = map_maker()
self.field1 = self.__dict__[fields[0]]
self.add_codes(codes)
def add_codes(self, code_syn_seqs):
''' Add codes to object
Parameters
----------
code_syn_seqs : sequence
sequence of sequences, where each sequence ``S = code_syn_seqs[n]``
for n in 0..len(code_syn_seqs), is a sequence giving values in the
same order as ``self.fields``. Each S should be at least of the
same length as ``self.fields``. After this call, if ``self.fields
== ['field1', 'field2'], then ``self.field1[S[n]] == S[0]`` for all
n in 0..len(S) and ``self.field2[S[n]] == S[1]`` for all n in
0..len(S).
Examples
--------
>>> code_syn_seqs = ((1, 'one'), (2, 'two'))
>>> rc = Recoder(code_syn_seqs)
>>> rc.value_set() == set((1,2))
True
>>> rc.add_codes(((3, 'three'), (1, 'first')))
>>> rc.value_set() == set((1,2,3))
True
'''
for code_syns in code_syn_seqs:
# Add all the aliases
for alias in code_syns:
# For all defined fields, make every value in the sequence be an
# entry to return matching index value.
for field_ind, field_name in enumerate(self.fields):
self.__dict__[field_name][alias] = code_syns[field_ind]
def __getitem__(self, key):
''' Return value from field1 dictionary (first column of values)
Returns same value as ``obj.field1[key]`` and, with the
default initializing ``fields`` argument of fields=('code',),
this will return the same as ``obj.code[key]``
>>> codes = ((1, 'one'), (2, 'two'))
>>> Recoder(codes)['two']
2
'''
return self.field1[key]
def __contains__(self, key):
""" True if field1 in recoder contains `key`
"""
try:
self.field1[key]
except KeyError:
return False
return True
def keys(self):
''' Return all available code and alias values
Returns same value as ``obj.field1.keys()`` and, with the
default initializing ``fields`` argument of fields=('code',),
this will return the same as ``obj.code.keys()``
>>> codes = ((1, 'one'), (2, 'two'), (1, 'repeat value'))
>>> k = Recoder(codes).keys()
>>> set(k) == set([1, 2, 'one', 'repeat value', 'two'])
True
'''
return self.field1.keys()
def value_set(self, name=None):
''' Return set of possible returned values for column
By default, the column is the first column.
Returns same values as ``set(obj.field1.values())`` and,
with the default initializing``fields`` argument of
fields=('code',), this will return the same as
``set(obj.code.values())``
Parameters
----------
name : {None, string}
Where default of none gives result for first column
>>> codes = ((1, 'one'), (2, 'two'), (1, 'repeat value'))
>>> vs = Recoder(codes).value_set()
>>> vs == set([1, 2]) # Sets are not ordered, hence this test
True
>>> rc = Recoder(codes, fields=('code', 'label'))
>>> rc.value_set('label') == set(('one', 'two', 'repeat value'))
True
'''
if name is None:
d = self.field1
else:
d = self.__dict__[name]
return set(d.values())
# Endian code aliases
endian_codes = Recoder(endian_codes)
class DtypeMapper(object):
""" Specialized mapper for numpy dtypes
We pass this mapper into the Recoder class to deal with numpy dtype hashing.
The hashing problem is that dtypes that compare equal may not have the same
hash. This is true for numpys up to the current at time of writing (1.6.0).
For numpy 1.2.1 at least, even dtypes that look exactly the same in terms of
fields don't always have the same hash. This makes dtypes difficult to use
as keys in a dictionary.
This class wraps a dictionary in order to implement a __getitem__ to deal
with dtype hashing. If the key doesn't appear to be in the mapping, and it
is a dtype, we compare (using ==) all known dtype keys to the input key, and
return any matching values for the matching key.
"""
def __init__(self):
self._dict = {}
self._dtype_keys = []
def keys(self):
return self._dict.keys()
def values(self):
return self._dict.values()
def __setitem__(self, key, value):
""" Set item into mapping, checking for dtype keys
Cache dtype keys for comparison test in __getitem__
"""
self._dict[key] = value
if hasattr(key, 'subdtype'):
self._dtype_keys.append(key)
def __getitem__(self, key):
""" Get item from mapping, checking for dtype keys
First do simple hash lookup, then check for a dtype key that has failed
the hash lookup. Look then for any known dtype keys that compare equal
to `key`.
"""
try:
return self._dict[key]
except KeyError:
pass
if hasattr(key, 'subdtype'):
for dt in self._dtype_keys:
if key == dt:
return self._dict[dt]
raise KeyError(key)
def pretty_mapping(mapping, getterfunc=None):
''' Make pretty string from mapping
Adjusts text column to print values on basis of longest key.
Probably only sensible if keys are mainly strings.
You can pass in a callable that does clever things to get the values
out of the mapping, given the names. By default, we just use
``__getitem__``
Parameters
----------
mapping : mapping
implementing iterator returning keys and .items()
getterfunc : None or callable
callable taking two arguments, ``obj`` and ``key`` where ``obj``
is the passed mapping. If None, just use ``lambda obj, key:
obj[key]``
Returns
-------
str : string
Examples
--------
>>> d = {'a key': 'a value'}
>>> print(pretty_mapping(d))
a key : a value
>>> class C(object): # to control ordering, show get_ method
... def __iter__(self):
... return iter(('short_field','longer_field'))
... def __getitem__(self, key):
... if key == 'short_field':
... return 0
... if key == 'longer_field':
... return 'str'
... def get_longer_field(self):
... return 'method string'
>>> def getter(obj, key):
... # Look for any 'get_<name>' methods
... try:
... return obj.__getattribute__('get_' + key)()
... except AttributeError:
... return obj[key]
>>> print(pretty_mapping(C(), getter))
short_field : 0
longer_field : method string
'''
if getterfunc is None:
getterfunc = lambda obj, key: obj[key]
lens = [len(str(name)) for name in mapping]
mxlen = np.max(lens)
fmt = '%%-%ds : %%s' % mxlen
out = []
for name in mapping:
value = getterfunc(mapping, name)
out.append(fmt % (name, value))
return '\n'.join(out)
def make_dt_codes(codes_seqs):
''' Create full dt codes Recoder instance from datatype codes
Include created numpy dtype (from numpy type) and opposite endian
numpy dtype
Parameters
----------
codes_seqs : sequence of sequences
contained sequences make be length 3 or 4, but must all be the same
length. Elements are data type code, data type name, and numpy
type (such as ``np.float32``). The fourth element is the nifti string
representation of the code (e.g. "NIFTI_TYPE_FLOAT32")
Returns
-------
rec : ``Recoder`` instance
Recoder that, by default, returns ``code`` when indexed with any
of the corresponding code, name, type, dtype, or swapped dtype.
You can also index with ``niistring`` values if codes_seqs had sequences
of length 4 instead of 3.
'''
fields=['code', 'label', 'type']
len0 = len(codes_seqs[0])
if not len0 in (3,4):
raise ValueError('Sequences must be length 3 or 4')
if len0 == 4:
fields.append('niistring')
dt_codes = []
for seq in codes_seqs:
if len(seq) != len0:
raise ValueError('Sequences must all have the same length')
np_type = seq[2]
this_dt = np.dtype(np_type)
# Add swapped dtype to synonyms
code_syns = list(seq) + [this_dt, this_dt.newbyteorder(swapped_code)]
dt_codes.append(code_syns)
return Recoder(dt_codes, fields + ['dtype', 'sw_dtype'], DtypeMapper)
@np.deprecate_with_doc('Please use arraywriter classes instead')
def can_cast(in_type, out_type, has_intercept=False, has_slope=False):
''' Return True if we can safely cast ``in_type`` to ``out_type``
Parameters
----------
in_type : numpy type
type of data we will case from
out_dtype : numpy type
type that we want to cast to
has_intercept : bool, optional
Whether we can subtract a constant from the data (before scaling)
before casting to ``out_dtype``. Default is False
has_slope : bool, optional
Whether we can use a scaling factor to adjust slope of
relationship of data to data in cast array. Default is False
Returns
-------
tf : bool
True if we can safely cast, False otherwise
Examples
--------
>>> can_cast(np.float64, np.float32)
True
>>> can_cast(np.complex128, np.float32)
False
>>> can_cast(np.int64, np.float32)
True
>>> can_cast(np.float32, np.int16)
False
>>> can_cast(np.float32, np.int16, False, True)
True
>>> can_cast(np.int16, np.uint8)
False
Whether we can actually cast int to uint when we don't have an intercept
depends on the data. That's why this function isn't very useful. But we
assume that an integer is using its full range, and check whether scaling
works in that situation.
Here we need an intercept to scale the full range of an int to a uint
>>> can_cast(np.int16, np.uint8, False, True)
False
>>> can_cast(np.int16, np.uint8, True, True)
True
'''
in_dtype = np.dtype(in_type)
# Whether we can cast depends on the data, and we've only got the type.
# Let's assume integers use all of their range but floats etc not
if in_dtype.kind in 'iu':
info = np.iinfo(in_dtype)
data = np.array([info.min, info.max], dtype=in_dtype)
else: # Float or complex or something. Any old thing will do
data = np.ones((1,), in_type)
from .arraywriters import make_array_writer, WriterError
try:
_ = make_array_writer(data, out_type, has_slope, has_intercept)
except WriterError:
return False
return True
def _is_compressed_fobj(fobj):
""" Return True if fobj represents a compressed data file-like object
"""
return isinstance(fobj, COMPRESSED_FILE_LIKES)
def array_from_file(shape, in_dtype, infile, offset=0, order='F', mmap=True):
''' Get array from file with specified shape, dtype and file offset
Parameters
----------
shape : sequence
sequence specifying output array shape
in_dtype : numpy dtype
fully specified numpy dtype, including correct endianness
infile : file-like
open file-like object implementing at least read() and seek()
offset : int, optional
offset in bytes into `infile` to start reading array data. Default is 0
order : {'F', 'C'} string
order in which to write data. Default is 'F' (fortran order).
mmap : {True, False, 'c', 'r', 'r+'}
`mmap` controls the use of numpy memory mapping for reading data. If
False, do not try numpy ``memmap`` for data array. If one of {'c', 'r',
'r+'}, try numpy memmap with ``mode=mmap``. A `mmap` value of True
gives the same behavior as ``mmap='c'``. If `infile` cannot be
memory-mapped, ignore `mmap` value and read array from file.
Returns
-------
arr : array-like
array like object that can be sliced, containing data
Examples
--------
>>> from io import BytesIO
>>> bio = BytesIO()
>>> arr = np.arange(6).reshape(1,2,3)
>>> _ = bio.write(arr.tostring('F')) # outputs int in python3
>>> arr2 = array_from_file((1,2,3), arr.dtype, bio)
>>> np.all(arr == arr2)
True
>>> bio = BytesIO()
>>> _ = bio.write(b' ' * 10)
>>> _ = bio.write(arr.tostring('F'))
>>> arr2 = array_from_file((1,2,3), arr.dtype, bio, 10)
>>> np.all(arr == arr2)
True
'''
if not mmap in (True, False, 'c', 'r', 'r+'):
raise ValueError("mmap value should be one of True, False, 'c', "
"'r', 'r+'")
if mmap == True:
mmap = 'c'
in_dtype = np.dtype(in_dtype)
# Get file-like object from Opener instance
infile = getattr(infile, 'fobj', infile)
if mmap and not _is_compressed_fobj(infile):
try: # Try memmapping file on disk
return np.memmap(infile,
in_dtype,
mode=mmap,
shape=shape,
order=order,
offset=offset)
# The error raised by memmap, for different file types, has
# changed in different incarnations of the numpy routine
except (AttributeError, TypeError, ValueError):
pass
if len(shape) == 0:
return np.array([])
# Use reduce and mul to work around numpy integer overflow
n_bytes = reduce(mul, shape) * in_dtype.itemsize
if n_bytes == 0:
return np.array([])
# Read data from file
infile.seek(offset)
if hasattr(infile, 'readinto'):
data_bytes = bytearray(n_bytes)
n_read = infile.readinto(data_bytes)
needs_copy = False
else:
data_bytes = infile.read(n_bytes)
n_read = len(data_bytes)
needs_copy = not isinstance(infile, SAFE_STRINGERS)
if n_bytes != n_read:
raise IOError('Expected {0} bytes, got {1} bytes from {2}\n'
' - could the file be damaged?'.format(
n_bytes,
n_read,
getattr(infile, 'name', 'object')))
arr = np.ndarray(shape, in_dtype, buffer=data_bytes, order=order)
if needs_copy:
return arr.copy()
arr.flags.writeable = True
return arr
def array_to_file(data, fileobj, out_dtype=None, offset=0,
intercept=0.0, divslope=1.0,
mn=None, mx=None, order='F', nan2zero=True):
''' Helper function for writing arrays to file objects
Writes arrays as scaled by `intercept` and `divslope`, and clipped
at (prescaling) `mn` minimum, and `mx` maximum.
* Clip `data` array at min `mn`, max `max` where there are not None ->
``clipped`` (this is *pre scale clipping*)
* Scale ``clipped`` with ``clipped_scaled = (clipped - intercept) /
divslope``
* Clip ``clipped_scaled`` to fit into range of `out_dtype` (*post scale
clipping*) -> ``clipped_scaled_clipped``
* If converting to integer `out_dtype` and `nan2zero` is True, set NaN
values in ``clipped_scaled_clipped`` to 0
* Write ``clipped_scaled_clipped_n2z`` to fileobj `fileobj` starting at
offset `offset` in memory layout `order`
Parameters
----------
data : array-like
array or array-like to write.
fileobj : file-like
file-like object implementing ``write`` method.
out_dtype : None or dtype, optional
dtype to write array as. Data array will be coerced to this dtype
before writing. If None (default) then use input data type.
offset : None or int, optional
offset into fileobj at which to start writing data. Default is 0. None
means start at current file position
intercept : scalar, optional
scalar to subtract from data, before dividing by ``divslope``. Default
is 0.0
divslope : None or scalar, optional
scalefactor to *divide* data by before writing. Default is 1.0. If
None, there is no valid data, we write zeros.
mn : scalar, optional
minimum threshold in (unscaled) data, such that all data below this
value are set to this value. Default is None (no threshold). The typical
use is to set -np.inf in the data to have this value (which might be the
minimum non-finite value in the data).
mx : scalar, optional
maximum threshold in (unscaled) data, such that all data above this
value are set to this value. Default is None (no threshold). The typical
use is to set np.inf in the data to have this value (which might be the
maximum non-finite value in the data).
order : {'F', 'C'}, optional
memory order to write array. Default is 'F'
nan2zero : {True, False}, optional
Whether to set NaN values to 0 when writing integer output. Defaults to
True. If False, NaNs will be represented as numpy does when casting;
this depends on the underlying C library and is undefined. In practice
`nan2zero` == False might be a good choice when you completely sure
there will be no NaNs in the data. This value ignored for float ouptut
types. NaNs are treated as zero *before* applying `intercept` and
`divslope` - so an array ``[np.nan]`` with an `intercept` of 10 becomes
``[-10]`` after conversion to integer `out_dtype` with `nan2zero` set.
That is because you will likely apply `divslope` and `intercept` in
reverse order when reading the data back, returning the zero you
probably expected from the input NaN.
Examples
--------
>>> from io import BytesIO
>>> sio = BytesIO()
>>> data = np.arange(10, dtype=np.float)
>>> array_to_file(data, sio, np.float)
>>> sio.getvalue() == data.tostring('F')
True
>>> _ = sio.truncate(0); _ = sio.seek(0) # outputs 0 in python 3
>>> array_to_file(data, sio, np.int16)
>>> sio.getvalue() == data.astype(np.int16).tostring()
True
>>> _ = sio.truncate(0); _ = sio.seek(0)
>>> array_to_file(data.byteswap(), sio, np.float)
>>> sio.getvalue() == data.byteswap().tostring('F')
True
>>> _ = sio.truncate(0); _ = sio.seek(0)
>>> array_to_file(data, sio, np.float, order='C')
>>> sio.getvalue() == data.tostring('C')
True
'''
# Shield special case
div_none = divslope is None
if not np.all(
np.isfinite((intercept, 1.0 if div_none else divslope))):
raise ValueError('divslope and intercept must be finite')
if divslope == 0:
raise ValueError('divslope cannot be zero')
data = np.asanyarray(data)
in_dtype = data.dtype
if out_dtype is None:
out_dtype = in_dtype
else:
out_dtype = np.dtype(out_dtype)
if offset is not None:
seek_tell(fileobj, offset)
if (div_none or
(mn, mx) == (0, 0) or
((mn is not None and mx is not None) and mx < mn)):
write_zeros(fileobj, data.size * out_dtype.itemsize)
return
if order not in 'FC':
raise ValueError('Order should be one of F or C')
# Simple cases
pre_clips = None if (mn is None and mx is None) else (mn, mx)
null_scaling = (intercept == 0 and divslope == 1)
if in_dtype.type == np.void:
if not null_scaling:
raise ValueError('Cannot scale non-numeric types')
if pre_clips is not None:
raise ValueError('Cannot clip non-numeric types')
return _write_data(data, fileobj, out_dtype, order)
if pre_clips is not None:
pre_clips = _dt_min_max(in_dtype, *pre_clips)
if null_scaling and np.can_cast(in_dtype, out_dtype):
return _write_data(data, fileobj, out_dtype, order,
pre_clips=pre_clips)
# Force upcasting for floats by making atleast_1d.
slope, inter = [np.atleast_1d(v) for v in (divslope, intercept)]
# Default working point type for applying slope / inter
if slope.dtype.kind in 'iu':
slope = slope.astype(float)
if inter.dtype.kind in 'iu':
inter = inter.astype(float)
in_kind = in_dtype.kind
out_kind = out_dtype.kind
if out_kind in 'fc':
return _write_data(data, fileobj, out_dtype, order,
slope=slope,
inter=inter,
pre_clips=pre_clips)
assert out_kind in 'iu'
if in_kind in 'iu':
if null_scaling:
# Must be large int to small int conversion; add clipping to
# pre scale thresholds
mn, mx = _dt_min_max(in_dtype, mn, mx)
mn_out, mx_out = _dt_min_max(out_dtype)
pre_clips = max(mn, mn_out), min(mx, mx_out)
return _write_data(data, fileobj, out_dtype, order,
pre_clips=pre_clips)
# In any case, we do not want to check for nans beause we've already
# disallowed scaling that generates nans
nan2zero = False
# We are either scaling into c/floats or starting with c/floats, then we're
# going to integers
# Because we're going to integers, complex inter and slope will only slow us
# down, cast to float
slope, inter = [v.astype(_matching_float(v.dtype)) for v in (slope, inter)]
# We'll do the thresholding on the scaled data, so turn off the thresholding
# on the unscaled data
pre_clips = None
# We may need to cast the original array to another type
cast_in_dtype = in_dtype
if in_kind == 'c':
# Cast to floats before anything else
cast_in_dtype = np.dtype(_matching_float(in_dtype))
elif in_kind == 'f' and in_dtype.itemsize == 2:
# Make sure we don't use float16 as a working type
cast_in_dtype = np.dtype(np.float32)
w_type = working_type(cast_in_dtype, slope, inter)
dt_mnmx = _dt_min_max(cast_in_dtype, mn, mx)
# We explore for a good precision to avoid infs and clipping
# Find smallest float type equal or larger than the current working
# type, that can contain range of extremes after scaling, without going
# to +-inf
extremes = np.array(dt_mnmx, dtype=cast_in_dtype)
w_type = best_write_scale_ftype(extremes, slope, inter, w_type)
# Push up precision by casting the slope, inter
slope, inter = [v.astype(w_type) for v in (slope, inter)]
# We need to know the result of applying slope and inter to the min and
# max of the array, in order to clip the output array, after applying
# the slope and inter. Otherwise we'd need to clip twice, once before
# applying (slope, inter), and again after, to ensure we have not hit
# over- or under-flow. For the same reason we need to know the result of
# applying slope, inter to 0, in order to fill in the nan output value
# after scaling etc. We could fill with 0 before scaling, but then we'd
# have to do an extra copy before filling nans with 0, to avoid
# overwriting the input array
# Run min, max, 0 through scaling / rint
specials = np.array(dt_mnmx + (0,), dtype=w_type)
if inter != 0.0:
specials = specials - inter
if slope != 1.0:
specials = specials / slope
assert specials.dtype.type == w_type
post_mn, post_mx, nan_fill = np.rint(specials)
if post_mn > post_mx: # slope could be negative
post_mn, post_mx = post_mx, post_mn
# Make sure that the thresholds exclude any value that will get badly cast
# to the integer type. This is not the same as using the maximumum of the
# output dtype as thresholds, because these may not be exactly represented
# in the float type.
#
# The thresholds assume that the data are in `wtype` dtype after applying
# the slope and intercept.
both_mn, both_mx = shared_range(w_type, out_dtype)
# Check that nan2zero output value is in range
if nan2zero and not both_mn <= nan_fill <= both_mx:
# Estimated error for (0 - inter) / slope is 2 * eps * abs(inter /
# slope). Assume errors are for working float type. Round for integer
# rounding
est_err = np.round(2 * np.finfo(w_type).eps * abs(inter / slope))
if ((nan_fill < both_mn and abs(nan_fill - both_mn) < est_err) or
(nan_fill > both_mx and abs(nan_fill - both_mx) < est_err)):
# nan_fill can be (just) outside clip range
nan_fill = np.clip(nan_fill, both_mn, both_mx)
else:
raise ValueError("nan_fill == {0}, outside safe int range "
"({1}-{2}); change scaling or "
"set nan2zero=False?".format(
nan_fill, int(both_mn), int(both_mx)))
# Make sure non-nan output clipped to shared range
post_mn = np.max([post_mn, both_mn])
post_mx = np.min([post_mx, both_mx])
in_cast = None if cast_in_dtype == in_dtype else cast_in_dtype
return _write_data(data, fileobj, out_dtype, order,
in_cast=in_cast,
pre_clips=pre_clips,
inter=inter,
slope=slope,
post_clips=(post_mn, post_mx),
nan_fill=nan_fill if nan2zero else None)
def _write_data(data,
fileobj,
out_dtype,
order,
in_cast=None,
pre_clips=None,
inter=0.,
slope=1.,
post_clips=None,
nan_fill=None):
""" Write array `data` to `fileobj` as `out_dtype` type, layout `order`
Does not modify `data` in-place.
Parameters
----------
data : ndarray
fileobj : object
implementing ``obj.write``
out_dtype : numpy type
Type to which to cast output data just before writing
order : {'F', 'C'}
memory layout of array in fileobj after writing
in_cast : None or numpy type, optional
If not None, inital cast to do on `data` slices before further
processing
pre_clips : None or 2-sequence, optional
If not None, minimum and maximum of input values at which to clip.
inter : scalar or array, optional
Intercept to subtract before writing ``out = data - inter``
slope : scalar or array, optional
Slope by which to divide before writing ``out2 = out / slope``
post_clips : None or 2-sequence, optional
If not None, minimum and maximum of scaled values at which to clip.
nan_fill : None or scalar, optional
If not None, values that were NaN in `data` will receive `nan_fill`
in array as output to disk (after scaling).
"""
data = np.squeeze(data)
if data.ndim < 2: # Trick to allow loop over rows for 1D arrays
data = np.atleast_2d(data)
elif order == 'F':
data = data.T
nan_need_copy = ((pre_clips, in_cast, inter, slope, post_clips) ==
(None, None, 0, 1, None))
for dslice in data: # cycle over first dimension to save memory
if not pre_clips is None:
dslice = np.clip(dslice, *pre_clips)
if not in_cast is None:
dslice = dslice.astype(in_cast)
if inter != 0.0:
dslice = dslice - inter
if slope != 1.0:
dslice = dslice / slope
if not post_clips is None:
dslice = np.clip(np.rint(dslice), *post_clips)
if not nan_fill is None:
nans = np.isnan(dslice)
if np.any(nans):
if nan_need_copy:
dslice = dslice.copy()
dslice[nans] = nan_fill
if dslice.dtype != out_dtype:
dslice = dslice.astype(out_dtype)
fileobj.write(dslice.tostring())
def _dt_min_max(dtype_like, mn=None, mx=None):
dt = np.dtype(dtype_like)
if dt.kind in 'fc':
dt_mn, dt_mx = (-np.inf, np.inf)
elif dt.kind in 'iu':
info = np.iinfo(dt)
dt_mn, dt_mx = (info.min, info.max)
else:
raise ValueError("unknown dtype")
return dt_mn if mn is None else mn, dt_mx if mx is None else mx
_CSIZE2FLOAT = {
8: np.float32,
16: np.float64,
24: np.longdouble,
32: np.longdouble}
def _matching_float(np_type):
""" Return floating point type matching `np_type`
"""
dtype = np.dtype(np_type)
if dtype.kind not in 'cf':
raise ValueError('Expecting float or complex type as input')
if dtype.kind in 'f':
return dtype.type
return _CSIZE2FLOAT[dtype.itemsize]
def write_zeros(fileobj, count, block_size=8194):
""" Write `count` zero bytes to `fileobj`
Parameters
----------
fileobj : file-like object
with ``write`` method
count : int
number of bytes to write
block_size : int, optional
largest continuous block to write.
"""
nblocks = int(count // block_size)
rem = count % block_size
blk = b'\x00' * block_size
for bno in range(nblocks):
fileobj.write(blk)
fileobj.write(b'\x00' * rem)
def seek_tell(fileobj, offset, write0=False):
""" Seek in `fileobj` or check we're in the right place already
Parameters
----------
fileobj : file-like
object implementing ``seek`` and (if seek raises an IOError) ``tell``
offset : int
position in file to which to seek
write0 : {False, True}, optional
If True, and standard seek fails, try to write zeros to the file to
reach `offset`. This can be useful when writing bz2 files, that cannot
do write seeks.
"""
try:
fileobj.seek(offset)
except IOError as e:
# This can be a negative seek in write mode for gz file object or any
# seek in write mode for a bz2 file object
pos = fileobj.tell()
if pos == offset:
return
if not write0:
raise IOError(str(e))
if pos > offset:
raise IOError("Can't write to seek backwards")
fileobj.write(b'\x00' * (offset - pos))
assert fileobj.tell() == offset
def apply_read_scaling(arr, slope=None, inter=None):
""" Apply scaling in `slope` and `inter` to array `arr`
This is for loading the array from a file (as opposed to the reverse
scaling when saving an array to file)
Return data will be ``arr * slope + inter``. The trick is that we have to
find a good precision to use for applying the scaling. The heuristic is
that the data is always upcast to the higher of the types from `arr,
`slope`, `inter` if `slope` and / or `inter` are not default values. If the
dtype of `arr` is an integer, then we assume the data more or less fills
the integer range, and upcast to a type such that the min, max of
``arr.dtype`` * scale + inter, will be finite.
Parameters
----------
arr : array-like
slope : None or float, optional
slope value to apply to `arr` (``arr * slope + inter``). None
corresponds to a value of 1.0
inter : None or float, optional
intercept value to apply to `arr` (``arr * slope + inter``). None
corresponds to a value of 0.0
Returns
-------
ret : array
array with scaling applied. Maybe upcast in order to give room for the
scaling. If scaling is default (1, 0), then `ret` may be `arr` ``ret is
arr``.
"""
if slope is None:
slope = 1.0
if inter is None:
inter = 0.0
if (slope, inter) == (1, 0):
return arr
shape = arr.shape
# Force float / float upcasting by promoting to arrays
arr, slope, inter = [np.atleast_1d(v) for v in (arr, slope, inter)]
if arr.dtype.kind in 'iu':
# int to float; get enough precision to avoid infs
# Find floating point type for which scaling does not overflow,
# starting at given type
default = (slope.dtype.type if slope.dtype.kind == 'f'
else np.float64)
ftype = int_scinter_ftype(arr.dtype, slope, inter, default)
slope = slope.astype(ftype)
inter = inter.astype(ftype)
if slope != 1.0:
arr = arr * slope
if inter != 0.0:
arr = arr + inter
return arr.reshape(shape)
def working_type(in_type, slope=1.0, inter=0.0):
""" Return array type from applying `slope`, `inter` to array of `in_type`
Numpy type that results from an array of type `in_type` being combined with
`slope` and `inter`. It returns something like the dtype type of
``((np.zeros((2,), dtype=in_type) - inter) / slope)``, but ignoring the
actual values of `slope` and `inter`.
Note that you would not necessarily get the same type by applying slope and
inter the other way round. Also, you'll see that the order in which slope
and inter are applied is the opposite of the order in which they are passed.
Parameters
----------
in_type : numpy type specifier
Numpy type of input array. Any valid input for ``np.dtype()``
slope : scalar, optional
slope to apply to array. If 1.0 (default), ignore this value and its
type.
inter : scalar, optional
intercept to apply to array. If 0.0 (default), ignore this value and
its type.
Returns
-------
wtype: numpy type
Numpy type resulting from applying `inter` and `slope` to array of type
`in_type`.
"""
val = np.array([1], dtype=in_type)
slope = np.array(slope)
inter = np.array(inter)
# Don't use real values to avoid overflows. Promote to 1D to avoid scalar
# casting rules. Don't use ones_like, zeros_like because of a bug in numpy
# <= 1.5.1 in converting complex192 / complex256 scalars.
if inter != 0:
val = val + np.array([0], dtype=inter.dtype)
if slope != 1:
val = val / np.array([1], dtype=slope.dtype)
return val.dtype.type
@np.deprecate_with_doc('Please use arraywriter classes instead')
def calculate_scale(data, out_dtype, allow_intercept):
''' Calculate scaling and optional intercept for data
Parameters
----------
data : array
out_dtype : dtype
output data type in some form understood by ``np.dtype``
allow_intercept : bool
If True allow non-zero intercept
Returns
-------
scaling : None or float
scalefactor to divide into data. None if no valid data
intercept : None or float
intercept to subtract from data. None if no valid data
mn : None or float
minimum of finite value in data or None if this will not
be used to threshold data
mx : None or float
minimum of finite value in data, or None if this will not
be used to threshold data
'''
# Code here is a compatibility shell around arraywriters refactor
in_dtype = data.dtype
out_dtype = np.dtype(out_dtype)
if np.can_cast(in_dtype, out_dtype):
return 1.0, 0.0, None, None
from .arraywriters import make_array_writer, WriterError, get_slope_inter
try:
writer = make_array_writer(data, out_dtype, True, allow_intercept)
except WriterError as e:
raise ValueError(str(e))
if out_dtype.kind in 'fc':
return (1.0, 0.0, None, None)
mn, mx = writer.finite_range()
if (mn, mx) == (np.inf, -np.inf): # No valid data
return (None, None, None, None)
if not in_dtype.kind in 'fc':
mn, mx = (None, None)
return get_slope_inter(writer) + (mn, mx)
@np.deprecate_with_doc('Please use arraywriter classes instead')
def scale_min_max(mn, mx, out_type, allow_intercept):
''' Return scaling and intercept min, max of data, given output type
Returns ``scalefactor`` and ``intercept`` to best fit data with
given ``mn`` and ``mx`` min and max values into range of data type
with ``type_min`` and ``type_max`` min and max values for type.
The calculated scaling is therefore::
scaled_data = (data-intercept) / scalefactor
Parameters
----------
mn : scalar
data minimum value
mx : scalar
data maximum value
out_type : numpy type
numpy type of output
allow_intercept : bool
If true, allow calculation of non-zero intercept. Otherwise,
returned intercept is always 0.0
Returns
-------
scalefactor : numpy scalar, dtype=np.maximum_sctype(np.float)
scalefactor by which to divide data after subtracting intercept
intercept : numpy scalar, dtype=np.maximum_sctype(np.float)
value to subtract from data before dividing by scalefactor
Examples
--------
>>> scale_min_max(0, 255, np.uint8, False)
(1.0, 0.0)
>>> scale_min_max(-128, 127, np.int8, False)
(1.0, 0.0)
>>> scale_min_max(0, 127, np.int8, False)
(1.0, 0.0)
>>> scaling, intercept = scale_min_max(0, 127, np.int8, True)
>>> np.allclose((0 - intercept) / scaling, -128)
True
>>> np.allclose((127 - intercept) / scaling, 127)
True
>>> scaling, intercept = scale_min_max(-10, -1, np.int8, True)
>>> np.allclose((-10 - intercept) / scaling, -128)
True
>>> np.allclose((-1 - intercept) / scaling, 127)
True
>>> scaling, intercept = scale_min_max(1, 10, np.int8, True)
>>> np.allclose((1 - intercept) / scaling, -128)
True
>>> np.allclose((10 - intercept) / scaling, 127)
True
Notes
-----
We don't use this function anywhere in nibabel now, it's here for API
compatibility only.
The large integers lead to python long types as max / min for type.
To contain the rounding error, we need to use the maximum numpy
float types when casting to float.
'''
if mn > mx:
raise ValueError('min value > max value')
info = type_info(out_type)
mn, mx, type_min, type_max = np.array(
[mn, mx, info['min'], info['max']], np.maximum_sctype(np.float))
# with intercept
if allow_intercept:
data_range = mx-mn
if data_range == 0:
return 1.0, mn
type_range = type_max - type_min
scaling = data_range / type_range
intercept = mn - type_min * scaling
return scaling, intercept
# without intercept
if mx == 0 and mn == 0:
return 1.0, 0.0
if type_min == 0: # uint
if mn < 0 and mx > 0:
raise ValueError('Cannot scale negative and positive '
'numbers to uint without intercept')
if mx < 0:
scaling = mn / type_max
else:
scaling = mx / type_max
else: # int
if abs(mx) >= abs(mn):
scaling = mx / type_max
else:
scaling = mn / type_min
return scaling, 0.0
def int_scinter_ftype(ifmt, slope=1.0, inter=0.0, default=np.float32):
""" float type containing int type `ifmt` * `slope` + `inter`
Return float type that can represent the max and the min of the `ifmt` type
after multiplication with `slope` and addition of `inter` with something
like ``np.array([imin, imax], dtype=ifmt) * slope + inter``.
Note that ``slope`` and ``inter`` get promoted to 1D arrays for this purpose
to avoid the numpy scalar casting rules, which prevent scalars upcasting the
array.
Parameters
----------
ifmt : object
numpy integer type (e.g. np.int32)
slope : float, optional
slope, default 1.0
inter : float, optional
intercept, default 0.0
default_out : object, optional
numpy floating point type, default is ``np.float32``
Returns
-------
ftype : object
numpy floating point type
Examples
--------
>>> int_scinter_ftype(np.int8, 1.0, 0.0) == np.float32
True
>>> int_scinter_ftype(np.int8, 1e38, 0.0) == np.float64
True
Notes
-----
It is difficult to make floats overflow with just addition because the
deltas are so large at the extremes of floating point. For example::
>>> arr = np.array([np.finfo(np.float32).max], dtype=np.float32)
>>> res = arr + np.iinfo(np.int16).max
>>> arr == res
array([ True], dtype=bool)
"""
ii = np.iinfo(ifmt)
tst_arr = np.array([ii.min, ii.max], dtype=ifmt)
try:
return _ftype4scaled_finite(tst_arr, slope, inter, 'read', default)
except ValueError:
raise ValueError('Overflow using highest floating point type')
def best_write_scale_ftype(arr, slope = 1.0, inter = 0.0, default=np.float32):
""" Smallest float type to contain range of ``arr`` after scaling
Scaling that will be applied to ``arr`` is ``(arr - inter) / slope``.
Note that ``slope`` and ``inter`` get promoted to 1D arrays for this purpose
to avoid the numpy scalar casting rules, which prevent scalars upcasting the
array.
Parameters
----------
arr : array-like
array that will be scaled
slope : array-like, optional
scalar such that output array will be ``(arr - inter) / slope``.
inter : array-like, optional
scalar such that output array will be ``(arr - inter) / slope``
default : numpy type, optional
minimum float type to return
Returns
-------
ftype : numpy type
Best floating point type for scaling. If no floating point type
prevents overflow, return the top floating point type. If the input
array ``arr`` already contains inf values, return the greater of the
input type and the default type.
Examples
--------
>>> arr = np.array([0, 1, 2], dtype=np.int16)
>>> best_write_scale_ftype(arr, 1, 0) is np.float32
True
Specify higher default return value
>>> best_write_scale_ftype(arr, 1, 0, default=np.float64) is np.float64
True
Even large values that don't overflow don't change output
>>> arr = np.array([0, np.finfo(np.float32).max], dtype=np.float32)
>>> best_write_scale_ftype(arr, 1, 0) is np.float32
True
Scaling > 1 reduces output values, so no upcast needed
>>> best_write_scale_ftype(arr, np.float32(2), 0) is np.float32
True
Scaling < 1 increases values, so upcast may be needed (and is here)
>>> best_write_scale_ftype(arr, np.float32(0.5), 0) is np.float64
True
"""
default = better_float_of(arr.dtype.type, default)
if not np.all(np.isfinite(arr)):
return default
try:
return _ftype4scaled_finite(arr, slope, inter, 'write', default)
except ValueError:
return OK_FLOATS[-1]
def better_float_of(first, second, default=np.float32):
""" Return more capable float type of `first` and `second`
Return `default` if neither of `first` or `second` is a float
Parameters
----------
first : numpy type specifier
Any valid input to `np.dtype()``
second : numpy type specifier
Any valid input to `np.dtype()``
default : numpy type specifier, optional
Any valid input to `np.dtype()``
Returns
-------
better_type : numpy type
More capable of `first` or `second` if both are floats; if only one is
a float return that, otherwise return `default`.
Examples
--------
>>> better_float_of(np.float32, np.float64) is np.float64
True
>>> better_float_of(np.float32, 'i4') is np.float32
True
>>> better_float_of('i2', 'u4') is np.float32
True
>>> better_float_of('i2', 'u4', np.float64) is np.float64
True
"""
first = np.dtype(first)
second = np.dtype(second)
default = np.dtype(default).type
kinds = (first.kind, second.kind)
if not 'f' in kinds:
return default
if kinds == ('f', 'f'):
if first.itemsize >= second.itemsize:
return first.type
return second.type
if first.kind == 'f':
return first.type
return second.type
def _ftype4scaled_finite(tst_arr, slope, inter, direction='read',
default=np.float32):
""" Smallest float type for scaling of `tst_arr` that does not overflow
"""
assert direction in ('read', 'write')
if not default in OK_FLOATS and default is np.longdouble:
# Omitted longdouble
return default
def_ind = OK_FLOATS.index(default)
# promote to arrays to avoid numpy scalar casting rules
tst_arr = np.atleast_1d(tst_arr)
slope = np.atleast_1d(slope)
inter = np.atleast_1d(inter)
warnings.filterwarnings('ignore', '.*overflow.*', RuntimeWarning)
try:
for ftype in OK_FLOATS[def_ind:]:
tst_trans = tst_arr.copy()
slope = slope.astype(ftype)
inter = inter.astype(ftype)
if direction == 'read': # as in reading of image from disk
if slope != 1.0:
tst_trans = tst_trans * slope
if inter != 0.0:
tst_trans = tst_trans + inter
elif direction == 'write':
if inter != 0.0:
tst_trans = tst_trans - inter
if slope != 1.0:
tst_trans = tst_trans / slope
if np.all(np.isfinite(tst_trans)):
return ftype
finally:
warnings.filters.pop(0)
raise ValueError('Overflow using highest floating point type')
def finite_range(arr, check_nan=False):
''' Return range (min, max) or range and flag (min, max, has_nan) from `arr`
Parameters
----------
arr : array-like
check_nan : {False, True}, optional
Whether to return third output, a bool signaling whether there are NaN
values in `arr`
Returns
-------
mn : scalar
minimum of values in (flattened) array
mx : scalar
maximum of values in (flattened) array
has_nan : bool
Returned if `check_nan` is True. `has_nan` is True if there are one or
more NaN values in `arr`
Examples
--------
>>> a = np.array([[-1, 0, 1],[np.inf, np.nan, -np.inf]])
>>> finite_range(a)
(-1.0, 1.0)
>>> a = np.array([[-1, 0, 1],[np.inf, np.nan, -np.inf]])
>>> finite_range(a, check_nan=True)
(-1.0, 1.0, True)
>>> a = np.array([[np.nan],[np.nan]])
>>> finite_range(a) == (np.inf, -np.inf)
True
>>> a = np.array([[-3, 0, 1],[2,-1,4]], dtype=np.int)
>>> finite_range(a)
(-3, 4)
>>> a = np.array([[1, 0, 1],[2,3,4]], dtype=np.uint)
>>> finite_range(a)
(0, 4)
>>> a = a + 1j
>>> finite_range(a)
(1j, (4+1j))
>>> a = np.zeros((2,), dtype=[('f1', 'i2')])
>>> finite_range(a)
Traceback (most recent call last):
...
TypeError: Can only handle numeric types
'''
arr = np.asarray(arr)
if arr.size == 0:
return (np.inf, -np.inf) + (False,) * check_nan
# Resort array to slowest->fastest memory change indices
stride_order = np.argsort(arr.strides)[::-1]
sarr = arr.transpose(stride_order)
kind = sarr.dtype.kind
if kind in 'iu':
if check_nan:
return np.min(sarr), np.max(sarr), False
return np.min(sarr), np.max(sarr)
if kind not in 'cf':
raise TypeError('Can only handle numeric types')
# Deal with 1D arrays in loop below
sarr = np.atleast_2d(sarr)
# Loop to avoid big temporary arrays
t_info = np.finfo(sarr.dtype)
t_mn, t_mx = t_info.min, t_info.max
has_nan = False
n_slices = sarr.shape[0]
maxes = np.zeros(n_slices, dtype=sarr.dtype) - np.inf
mins = np.zeros(n_slices, dtype=sarr.dtype) + np.inf
for s in range(n_slices):
this_slice = sarr[s] # view
if not has_nan:
maxes[s] = np.max(this_slice)
# May have a non-nan non-inf max before we trip on min. If so,
# record so we don't recalculate
max_good = False
if np.isnan(maxes[s]):
has_nan = True
elif maxes[s] != np.inf:
max_good = True
mins[s] = np.min(this_slice)
if mins[s] != -np.inf:
# Only case where we escape the default np.isfinite
# algorithm
continue
tmp = this_slice[np.isfinite(this_slice)]
if tmp.size == 0: # No finite values
# Reset max, min in case set in tests above
maxes[s] = -np.inf
mins[s] = np.inf
continue
if not max_good:
maxes[s] = np.max(tmp)
mins[s] = np.min(tmp)
if check_nan:
return np.nanmin(mins), np.nanmax(maxes), has_nan
return np.nanmin(mins), np.nanmax(maxes)
def shape_zoom_affine(shape, zooms, x_flip=True):
''' Get affine implied by given shape and zooms
We get the translations from the center of the image (implied by
`shape`).
Parameters
----------
shape : (N,) array-like
shape of image data. ``N`` is the number of dimensions
zooms : (N,) array-like
zooms (voxel sizes) of the image
x_flip : {True, False}
whether to flip the X row of the affine. Corresponds to
radiological storage on disk.
Returns
-------
aff : (4,4) array
affine giving correspondance of voxel coordinates to mm
coordinates, taking the center of the image as origin
Examples
--------
>>> shape = (3, 5, 7)
>>> zooms = (3, 2, 1)
>>> shape_zoom_affine((3, 5, 7), (3, 2, 1))
array([[-3., 0., 0., 3.],
[ 0., 2., 0., -4.],
[ 0., 0., 1., -3.],
[ 0., 0., 0., 1.]])
>>> shape_zoom_affine((3, 5, 7), (3, 2, 1), False)
array([[ 3., 0., 0., -3.],
[ 0., 2., 0., -4.],
[ 0., 0., 1., -3.],
[ 0., 0., 0., 1.]])
'''
shape = np.asarray(shape)
zooms = np.array(zooms) # copy because of flip below
ndims = len(shape)
if ndims != len(zooms):
raise ValueError('Should be same length of zooms and shape')
if ndims >= 3:
shape = shape[:3]
zooms = zooms[:3]
else:
full_shape = np.ones((3,))
full_zooms = np.ones((3,))
full_shape[:ndims] = shape[:]
full_zooms[:ndims] = zooms[:]
shape = full_shape
zooms = full_zooms
if x_flip:
zooms[0] *= -1
# Get translations from center of image
origin = (shape-1) / 2.0
aff = np.eye(4)
aff[:3, :3] = np.diag(zooms)
aff[:3, -1] = -origin * zooms
return aff
def rec2dict(rec):
''' Convert recarray to dictionary
Also converts scalar values to scalars
Parameters
----------
rec : ndarray
structured ndarray
Returns
-------
dct : dict
dict with key, value pairs as for `rec`
Examples
--------
>>> r = np.zeros((), dtype = [('x', 'i4'), ('s', 'S10')])
>>> d = rec2dict(r)
>>> d == {'x': 0, 's': b''}
True
'''
dct = {}
for key in rec.dtype.fields:
val = rec[key]
try:
val = np.asscalar(val)
except ValueError:
pass
dct[key] = val
return dct
class BinOpener(Opener):
# Adds .mgz as gzipped file name type
__doc__ = Opener.__doc__
compress_ext_map = Opener.compress_ext_map.copy()
compress_ext_map['.mgz'] = Opener.gz_def
def fname_ext_ul_case(fname):
""" `fname` with ext changed to upper / lower case if file exists
Check for existence of `fname`. If it does exist, return unmodified. If
it doesn't, check for existence of `fname` with case changed from lower to
upper, or upper to lower. Return this modified `fname` if it exists.
Otherwise return `fname` unmodified
Parameters
----------
fname : str
filename.
Returns
-------
mod_fname : str
filename, maybe with extension of opposite case
"""
if exists(fname):
return fname
froot, ext = splitext(fname)
if ext == ext.lower():
mod_fname = froot + ext.upper()
if exists(mod_fname):
return mod_fname
elif ext == ext.upper():
mod_fname = froot + ext.lower()
if exists(mod_fname):
return mod_fname
return fname
def allopen(fileish, *args, **kwargs):
""" Compatibility wrapper for old ``allopen`` function
Wraps creation of ``BinOpener`` instance, while picking up module global
``default_compresslevel``.
Please see docstring for ``BinOpener`` and ``Opener`` for details.
"""
warnings.warn("Please use BinOpener class instead of this function",
DeprecationWarning,
stacklevel=2)
class MyOpener(BinOpener):
default_compresslevel = default_compresslevel
return MyOpener(fileish, *args, **kwargs)
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