"""
Record Arrays
=============
Record arrays expose the fields of structured arrays as properties.
Most commonly, ndarrays contain elements of a single type, e.g. floats,
integers, bools etc. However, it is possible for elements to be combinations
of these using structured types, such as::
>>> a = np.array([(1, 2.0), (1, 2.0)], dtype=[('x', int), ('y', float)])
>>> a
array([(1, 2.0), (1, 2.0)],
dtype=[('x', '<i4'), ('y', '<f8')])
Here, each element consists of two fields: x (and int), and y (a float).
This is known as a structured array. The different fields are analogous
to columns in a spread-sheet. The different fields can be accessed as
one would a dictionary::
>>> a['x']
array([1, 1])
>>> a['y']
array([ 2., 2.])
Record arrays allow us to access fields as properties::
>>> ar = np.rec.array(a)
>>> ar.x
array([1, 1])
>>> ar.y
array([ 2., 2.])
"""
from __future__ import division, absolute_import, print_function
import sys
import os
import warnings
from . import numeric as sb
from . import numerictypes as nt
from numpy.compat import isfileobj, bytes, long
from .arrayprint import get_printoptions
# All of the functions allow formats to be a dtype
__all__ = ['record', 'recarray', 'format_parser']
ndarray = sb.ndarray
_byteorderconv = {'b':'>',
'l':'<',
'n':'=',
'B':'>',
'L':'<',
'N':'=',
'S':'s',
's':'s',
'>':'>',
'<':'<',
'=':'=',
'|':'|',
'I':'|',
'i':'|'}
# formats regular expression
# allows multidimension spec with a tuple syntax in front
# of the letter code '(2,3)f4' and ' ( 2 , 3 ) f4 '
# are equally allowed
numfmt = nt.typeDict
def find_duplicate(list):
"""Find duplication in a list, return a list of duplicated elements"""
dup = []
for i in range(len(list)):
if (list[i] in list[i + 1:]):
if (list[i] not in dup):
dup.append(list[i])
return dup
class format_parser(object):
"""
Class to convert formats, names, titles description to a dtype.
After constructing the format_parser object, the dtype attribute is
the converted data-type:
``dtype = format_parser(formats, names, titles).dtype``
Attributes
----------
dtype : dtype
The converted data-type.
Parameters
----------
formats : str or list of str
The format description, either specified as a string with
comma-separated format descriptions in the form ``'f8, i4, a5'``, or
a list of format description strings in the form
``['f8', 'i4', 'a5']``.
names : str or list/tuple of str
The field names, either specified as a comma-separated string in the
form ``'col1, col2, col3'``, or as a list or tuple of strings in the
form ``['col1', 'col2', 'col3']``.
An empty list can be used, in that case default field names
('f0', 'f1', ...) are used.
titles : sequence
Sequence of title strings. An empty list can be used to leave titles
out.
aligned : bool, optional
If True, align the fields by padding as the C-compiler would.
Default is False.
byteorder : str, optional
If specified, all the fields will be changed to the
provided byte-order. Otherwise, the default byte-order is
used. For all available string specifiers, see `dtype.newbyteorder`.
See Also
--------
dtype, typename, sctype2char
Examples
--------
>>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
... ['T1', 'T2', 'T3']).dtype
dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'),
(('T3', 'col3'), '|S5')])
`names` and/or `titles` can be empty lists. If `titles` is an empty list,
titles will simply not appear. If `names` is empty, default field names
will be used.
>>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
... []).dtype
dtype([('col1', '<f8'), ('col2', '<i4'), ('col3', '|S5')])
>>> np.format_parser(['f8', 'i4', 'a5'], [], []).dtype
dtype([('f0', '<f8'), ('f1', '<i4'), ('f2', '|S5')])
"""
def __init__(self, formats, names, titles, aligned=False, byteorder=None):
self._parseFormats(formats, aligned)
self._setfieldnames(names, titles)
self._createdescr(byteorder)
self.dtype = self._descr
def _parseFormats(self, formats, aligned=0):
""" Parse the field formats """
if formats is None:
raise ValueError("Need formats argument")
if isinstance(formats, list):
if len(formats) < 2:
formats.append('')
formats = ','.join(formats)
dtype = sb.dtype(formats, aligned)
fields = dtype.fields
if fields is None:
dtype = sb.dtype([('f1', dtype)], aligned)
fields = dtype.fields
keys = dtype.names
self._f_formats = [fields[key][0] for key in keys]
self._offsets = [fields[key][1] for key in keys]
self._nfields = len(keys)
def _setfieldnames(self, names, titles):
"""convert input field names into a list and assign to the _names
attribute """
if (names):
if (type(names) in [list, tuple]):
pass
elif isinstance(names, str):
names = names.split(',')
else:
raise NameError("illegal input names %s" % repr(names))
self._names = [n.strip() for n in names[:self._nfields]]
else:
self._names = []
# if the names are not specified, they will be assigned as
# "f0, f1, f2,..."
# if not enough names are specified, they will be assigned as "f[n],
# f[n+1],..." etc. where n is the number of specified names..."
self._names += ['f%d' % i for i in range(len(self._names),
self._nfields)]
# check for redundant names
_dup = find_duplicate(self._names)
if _dup:
raise ValueError("Duplicate field names: %s" % _dup)
if (titles):
self._titles = [n.strip() for n in titles[:self._nfields]]
else:
self._titles = []
titles = []
if (self._nfields > len(titles)):
self._titles += [None] * (self._nfields - len(titles))
def _createdescr(self, byteorder):
descr = sb.dtype({'names':self._names,
'formats':self._f_formats,
'offsets':self._offsets,
'titles':self._titles})
if (byteorder is not None):
byteorder = _byteorderconv[byteorder[0]]
descr = descr.newbyteorder(byteorder)
self._descr = descr
class record(nt.void):
"""A data-type scalar that allows field access as attribute lookup.
"""
# manually set name and module so that this class's type shows up
# as numpy.record when printed
__name__ = 'record'
__module__ = 'numpy'
def __repr__(self):
if get_printoptions()['legacy'] == '1.13':
return self.__str__()
return super(record, self).__repr__()
def __str__(self):
if get_printoptions()['legacy'] == '1.13':
return str(self.item())
return super(record, self).__str__()
def __getattribute__(self, attr):
if attr in ['setfield', 'getfield', 'dtype']:
return nt.void.__getattribute__(self, attr)
try:
return nt.void.__getattribute__(self, attr)
except AttributeError:
pass
fielddict = nt.void.__getattribute__(self, 'dtype').fields
res = fielddict.get(attr, None)
if res:
obj = self.getfield(*res[:2])
# if it has fields return a record,
# otherwise return the object
try:
dt = obj.dtype
except AttributeError:
#happens if field is Object type
return obj
if dt.fields:
return obj.view((self.__class__, obj.dtype.fields))
return obj
else:
raise AttributeError("'record' object has no "
"attribute '%s'" % attr)
def __setattr__(self, attr, val):
if attr in ['setfield', 'getfield', 'dtype']:
raise AttributeError("Cannot set '%s' attribute" % attr)
fielddict = nt.void.__getattribute__(self, 'dtype').fields
res = fielddict.get(attr, None)
if res:
return self.setfield(val, *res[:2])
else:
if getattr(self, attr, None):
return nt.void.__setattr__(self, attr, val)
else:
raise AttributeError("'record' object has no "
"attribute '%s'" % attr)
def __getitem__(self, indx):
obj = nt.void.__getitem__(self, indx)
# copy behavior of record.__getattribute__,
if isinstance(obj, nt.void) and obj.dtype.fields:
return obj.view((self.__class__, obj.dtype.fields))
else:
# return a single element
return obj
def pprint(self):
"""Pretty-print all fields."""
# pretty-print all fields
names = self.dtype.names
maxlen = max(len(name) for name in names)
rows = []
fmt = '%% %ds: %%s' % maxlen
for name in names:
rows.append(fmt % (name, getattr(self, name)))
return "\n".join(rows)
# The recarray is almost identical to a standard array (which supports
# named fields already) The biggest difference is that it can use
# attribute-lookup to find the fields and it is constructed using
# a record.
# If byteorder is given it forces a particular byteorder on all
# the fields (and any subfields)
class recarray(ndarray):
"""Construct an ndarray that allows field access using attributes.
Arrays may have a data-types containing fields, analogous
to columns in a spread sheet. An example is ``[(x, int), (y, float)]``,
where each entry in the array is a pair of ``(int, float)``. Normally,
these attributes are accessed using dictionary lookups such as ``arr['x']``
and ``arr['y']``. Record arrays allow the fields to be accessed as members
of the array, using ``arr.x`` and ``arr.y``.
Parameters
----------
shape : tuple
Shape of output array.
dtype : data-type, optional
The desired data-type. By default, the data-type is determined
from `formats`, `names`, `titles`, `aligned` and `byteorder`.
formats : list of data-types, optional
A list containing the data-types for the different columns, e.g.
``['i4', 'f8', 'i4']``. `formats` does *not* support the new
convention of using types directly, i.e. ``(int, float, int)``.
Note that `formats` must be a list, not a tuple.
Given that `formats` is somewhat limited, we recommend specifying
`dtype` instead.
names : tuple of str, optional
The name of each column, e.g. ``('x', 'y', 'z')``.
buf : buffer, optional
By default, a new array is created of the given shape and data-type.
If `buf` is specified and is an object exposing the buffer interface,
the array will use the memory from the existing buffer. In this case,
the `offset` and `strides` keywords are available.
Other Parameters
----------------
titles : tuple of str, optional
Aliases for column names. For example, if `names` were
``('x', 'y', 'z')`` and `titles` is
``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then
``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``.
byteorder : {'<', '>', '='}, optional
Byte-order for all fields.
aligned : bool, optional
Align the fields in memory as the C-compiler would.
strides : tuple of ints, optional
Buffer (`buf`) is interpreted according to these strides (strides
define how many bytes each array element, row, column, etc.
occupy in memory).
offset : int, optional
Start reading buffer (`buf`) from this offset onwards.
order : {'C', 'F'}, optional
Row-major (C-style) or column-major (Fortran-style) order.
Returns
-------
rec : recarray
Empty array of the given shape and type.
See Also
--------
rec.fromrecords : Construct a record array from data.
record : fundamental data-type for `recarray`.
format_parser : determine a data-type from formats, names, titles.
Notes
-----
This constructor can be compared to ``empty``: it creates a new record
array but does not fill it with data. To create a record array from data,
use one of the following methods:
1. Create a standard ndarray and convert it to a record array,
using ``arr.view(np.recarray)``
2. Use the `buf` keyword.
3. Use `np.rec.fromrecords`.
Examples
--------
Create an array with two fields, ``x`` and ``y``:
>>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)])
>>> x
array([(1.0, 2), (3.0, 4)],
dtype=[('x', '<f8'), ('y', '<i4')])
>>> x['x']
array([ 1., 3.])
View the array as a record array:
>>> x = x.view(np.recarray)
>>> x.x
array([ 1., 3.])
>>> x.y
array([2, 4])
Create a new, empty record array:
>>> np.recarray((2,),
... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP
rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),
(3471280, 1.2134086255804012e-316, 0)],
dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])
"""
# manually set name and module so that this class's type shows
# up as "numpy.recarray" when printed
__name__ = 'recarray'
__module__ = 'numpy'
def __new__(subtype, shape, dtype=None, buf=None, offset=0, strides=None,
formats=None, names=None, titles=None,
byteorder=None, aligned=False, order='C'):
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
if buf is None:
self = ndarray.__new__(subtype, shape, (record, descr), order=order)
else:
self = ndarray.__new__(subtype, shape, (record, descr),
buffer=buf, offset=offset,
strides=strides, order=order)
return self
def __array_finalize__(self, obj):
if self.dtype.type is not record and self.dtype.fields:
# if self.dtype is not np.record, invoke __setattr__ which will
# convert it to a record if it is a void dtype.
self.dtype = self.dtype
def __getattribute__(self, attr):
# See if ndarray has this attr, and return it if so. (note that this
# means a field with the same name as an ndarray attr cannot be
# accessed by attribute).
try:
return object.__getattribute__(self, attr)
except AttributeError: # attr must be a fieldname
pass
# look for a field with this name
fielddict = ndarray.__getattribute__(self, 'dtype').fields
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError("recarray has no attribute %s" % attr)
obj = self.getfield(*res)
# At this point obj will always be a recarray, since (see
# PyArray_GetField) the type of obj is inherited. Next, if obj.dtype is
# non-structured, convert it to an ndarray. Then if obj is structured
# with void type convert it to the same dtype.type (eg to preserve
# numpy.record type if present), since nested structured fields do not
# inherit type. Don't do this for non-void structures though.
if obj.dtype.fields:
if issubclass(obj.dtype.type, nt.void):
return obj.view(dtype=(self.dtype.type, obj.dtype))
return obj
else:
return obj.view(ndarray)
# Save the dictionary.
# If the attr is a field name and not in the saved dictionary
# Undo any "setting" of the attribute and do a setfield
# Thus, you can't create attributes on-the-fly that are field names.
def __setattr__(self, attr, val):
# Automatically convert (void) structured types to records
# (but not non-void structures, subarrays, or non-structured voids)
if attr == 'dtype' and issubclass(val.type, nt.void) and val.fields:
val = sb.dtype((record, val))
newattr = attr not in self.__dict__
try:
ret = object.__setattr__(self, attr, val)
except Exception:
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
if attr not in fielddict:
exctype, value = sys.exc_info()[:2]
raise exctype(value)
else:
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
if attr not in fielddict:
return ret
if newattr:
# We just added this one or this setattr worked on an
# internal attribute.
try:
object.__delattr__(self, attr)
except Exception:
return ret
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError("record array has no attribute %s" % attr)
return self.setfield(val, *res)
def __getitem__(self, indx):
obj = super(recarray, self).__getitem__(indx)
# copy behavior of getattr, except that here
# we might also be returning a single element
if isinstance(obj, ndarray):
if obj.dtype.fields:
obj = obj.view(type(self))
if issubclass(obj.dtype.type, nt.void):
return obj.view(dtype=(self.dtype.type, obj.dtype))
return obj
else:
return obj.view(type=ndarray)
else:
# return a single element
return obj
def __repr__(self):
repr_dtype = self.dtype
if (self.dtype.type is record
or (not issubclass(self.dtype.type, nt.void))):
# If this is a full record array (has numpy.record dtype),
# or if it has a scalar (non-void) dtype with no records,
# represent it using the rec.array function. Since rec.array
# converts dtype to a numpy.record for us, convert back
# to non-record before printing
if repr_dtype.type is record:
repr_dtype = sb.dtype((nt.void, repr_dtype))
prefix = "rec.array("
fmt = 'rec.array(%s,%sdtype=%s)'
else:
# otherwise represent it using np.array plus a view
# This should only happen if the user is playing
# strange games with dtypes.
prefix = "array("
fmt = 'array(%s,%sdtype=%s).view(numpy.recarray)'
# get data/shape string. logic taken from numeric.array_repr
if self.size > 0 or self.shape == (0,):
lst = sb.array2string(
self, separator=', ', prefix=prefix, suffix=',')
else:
# show zero-length shape unless it is (0,)
lst = "[], shape=%s" % (repr(self.shape),)
lf = '\n'+' '*len(prefix)
if get_printoptions()['legacy'] == '1.13':
lf = ' ' + lf # trailing space
return fmt % (lst, lf, repr_dtype)
def field(self, attr, val=None):
if isinstance(attr, int):
names = ndarray.__getattribute__(self, 'dtype').names
attr = names[attr]
fielddict = ndarray.__getattribute__(self, 'dtype').fields
res = fielddict[attr][:2]
if val is None:
obj = self.getfield(*res)
if obj.dtype.fields:
return obj
return obj.view(ndarray)
else:
return self.setfield(val, *res)
def fromarrays(arrayList, dtype=None, shape=None, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
""" create a record array from a (flat) list of arrays
>>> x1=np.array([1,2,3,4])
>>> x2=np.array(['a','dd','xyz','12'])
>>> x3=np.array([1.1,2,3,4])
>>> r = np.core.records.fromarrays([x1,x2,x3],names='a,b,c')
>>> print(r[1])
(2, 'dd', 2.0)
>>> x1[1]=34
>>> r.a
array([1, 2, 3, 4])
"""
arrayList = [sb.asarray(x) for x in arrayList]
if shape is None or shape == 0:
shape = arrayList[0].shape
if isinstance(shape, int):
shape = (shape,)
if formats is None and dtype is None:
# go through each object in the list to see if it is an ndarray
# and determine the formats.
formats = []
for obj in arrayList:
if not isinstance(obj, ndarray):
raise ValueError("item in the array list must be an ndarray.")
formats.append(obj.dtype.str)
formats = ','.join(formats)
if dtype is not None:
descr = sb.dtype(dtype)
_names = descr.names
else:
parsed = format_parser(formats, names, titles, aligned, byteorder)
_names = parsed._names
descr = parsed._descr
# Determine shape from data-type.
if len(descr) != len(arrayList):
raise ValueError("mismatch between the number of fields "
"and the number of arrays")
d0 = descr[0].shape
nn = len(d0)
if nn > 0:
shape = shape[:-nn]
for k, obj in enumerate(arrayList):
nn = descr[k].ndim
testshape = obj.shape[:obj.ndim - nn]
if testshape != shape:
raise ValueError("array-shape mismatch in array %d" % k)
_array = recarray(shape, descr)
# populate the record array (makes a copy)
for i in range(len(arrayList)):
_array[_names[i]] = arrayList[i]
return _array
def fromrecords(recList, dtype=None, shape=None, formats=None, names=None,
titles=None, aligned=False, byteorder=None):
""" create a recarray from a list of records in text form
The data in the same field can be heterogeneous, they will be promoted
to the highest data type. This method is intended for creating
smaller record arrays. If used to create large array without formats
defined
r=fromrecords([(2,3.,'abc')]*100000)
it can be slow.
If formats is None, then this will auto-detect formats. Use list of
tuples rather than list of lists for faster processing.
>>> r=np.core.records.fromrecords([(456,'dbe',1.2),(2,'de',1.3)],
... names='col1,col2,col3')
>>> print(r[0])
(456, 'dbe', 1.2)
>>> r.col1
array([456, 2])
>>> r.col2
array(['dbe', 'de'],
dtype='|S3')
>>> import pickle
>>> print(pickle.loads(pickle.dumps(r)))
[(456, 'dbe', 1.2) (2, 'de', 1.3)]
"""
if formats is None and dtype is None: # slower
obj = sb.array(recList, dtype=object)
arrlist = [sb.array(obj[..., i].tolist()) for i in range(obj.shape[-1])]
return fromarrays(arrlist, formats=formats, shape=shape, names=names,
titles=titles, aligned=aligned, byteorder=byteorder)
if dtype is not None:
descr = sb.dtype((record, dtype))
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
# deprecated back-compat block for numpy 1.14, to be removed in a later
# release. This converts list-of-list input to list-of-tuples in some
# cases, as done in numpy <= 1.13. In the future we will require tuples.
if (isinstance(recList, list) and len(recList) > 0
and isinstance(recList[0], list) and len(recList[0]) > 0
and not isinstance(recList[0][0], (list, tuple))):
try:
memoryview(recList[0][0])
except:
if (shape is None or shape == 0):
shape = len(recList)
if isinstance(shape, (int, long)):
shape = (shape,)
if len(shape) > 1:
raise ValueError("Can only deal with 1-d array.")
_array = recarray(shape, descr)
for k in range(_array.size):
_array[k] = tuple(recList[k])
# list of lists instead of list of tuples ?
# 2018-02-07, 1.14.1
warnings.warn(
"fromrecords expected a list of tuples, may have received a "
"list of lists instead. In the future that will raise an error",
FutureWarning, stacklevel=2)
return _array
else:
pass
retval = sb.array(recList, dtype=descr)
if shape is not None and retval.shape != shape:
retval.shape = shape
return retval.view(recarray)
def fromstring(datastring, dtype=None, shape=None, offset=0, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
""" create a (read-only) record array from binary data contained in
a string"""
if dtype is None and formats is None:
raise ValueError("Must have dtype= or formats=")
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
itemsize = descr.itemsize
if (shape is None or shape == 0 or shape == -1):
shape = (len(datastring) - offset) // itemsize
_array = recarray(shape, descr, buf=datastring, offset=offset)
return _array
def get_remaining_size(fd):
try:
fn = fd.fileno()
except AttributeError:
return os.path.getsize(fd.name) - fd.tell()
st = os.fstat(fn)
size = st.st_size - fd.tell()
return size
def fromfile(fd, dtype=None, shape=None, offset=0, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
"""Create an array from binary file data
If file is a string then that file is opened, else it is assumed
to be a file object. The file object must support random access
(i.e. it must have tell and seek methods).
>>> from tempfile import TemporaryFile
>>> a = np.empty(10,dtype='f8,i4,a5')
>>> a[5] = (0.5,10,'abcde')
>>>
>>> fd=TemporaryFile()
>>> a = a.newbyteorder('<')
>>> a.tofile(fd)
>>>
>>> fd.seek(0)
>>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
... byteorder='<')
>>> print(r[5])
(0.5, 10, 'abcde')
>>> r.shape
(10,)
"""
if (shape is None or shape == 0):
shape = (-1,)
elif isinstance(shape, (int, long)):
shape = (shape,)
name = 0
if isinstance(fd, str):
name = 1
fd = open(fd, 'rb')
if (offset > 0):
fd.seek(offset, 1)
size = get_remaining_size(fd)
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
itemsize = descr.itemsize
shapeprod = sb.array(shape).prod()
shapesize = shapeprod * itemsize
if shapesize < 0:
shape = list(shape)
shape[shape.index(-1)] = size / -shapesize
shape = tuple(shape)
shapeprod = sb.array(shape).prod()
nbytes = shapeprod * itemsize
if nbytes > size:
raise ValueError(
"Not enough bytes left in file for specified shape and type")
# create the array
_array = recarray(shape, descr)
nbytesread = fd.readinto(_array.data)
if nbytesread != nbytes:
raise IOError("Didn't read as many bytes as expected")
if name:
fd.close()
return _array
def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None,
names=None, titles=None, aligned=False, byteorder=None, copy=True):
"""Construct a record array from a wide-variety of objects.
"""
if ((isinstance(obj, (type(None), str)) or isfileobj(obj)) and
(formats is None) and (dtype is None)):
raise ValueError("Must define formats (or dtype) if object is "
"None, string, or an open file")
kwds = {}
if dtype is not None:
dtype = sb.dtype(dtype)
elif formats is not None:
dtype = format_parser(formats, names, titles,
aligned, byteorder)._descr
else:
kwds = {'formats': formats,
'names': names,
'titles': titles,
'aligned': aligned,
'byteorder': byteorder
}
if obj is None:
if shape is None:
raise ValueError("Must define a shape if obj is None")
return recarray(shape, dtype, buf=obj, offset=offset, strides=strides)
elif isinstance(obj, bytes):
return fromstring(obj, dtype, shape=shape, offset=offset, **kwds)
elif isinstance(obj, (list, tuple)):
if isinstance(obj[0], (tuple, list)):
return fromrecords(obj, dtype=dtype, shape=shape, **kwds)
else:
return fromarrays(obj, dtype=dtype, shape=shape, **kwds)
elif isinstance(obj, recarray):
if dtype is not None and (obj.dtype != dtype):
new = obj.view(dtype)
else:
new = obj
if copy:
new = new.copy()
return new
elif isfileobj(obj):
return fromfile(obj, dtype=dtype, shape=shape, offset=offset)
elif isinstance(obj, ndarray):
if dtype is not None and (obj.dtype != dtype):
new = obj.view(dtype)
else:
new = obj
if copy:
new = new.copy()
return new.view(recarray)
else:
interface = getattr(obj, "__array_interface__", None)
if interface is None or not isinstance(interface, dict):
raise ValueError("Unknown input type")
obj = sb.array(obj)
if dtype is not None and (obj.dtype != dtype):
obj = obj.view(dtype)
return obj.view(recarray)