def __new__(cls, dtype):
try:
dtype = numeric.dtype(dtype)
except TypeError:
# In case a float instance was given
dtype = numeric.dtype(type(dtype))
obj = cls._finfo_cache.get(dtype,None)
if obj is not None:
return obj
dtypes = [dtype]
newdtype = numeric.obj2sctype(dtype)
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
if not issubclass(dtype, numeric.inexact):
raise ValueError, "data type %r not inexact" % (dtype)
obj = cls._finfo_cache.get(dtype,None)
if obj is not None:
return obj
if not issubclass(dtype, numeric.floating):
newdtype = _convert_to_float[dtype]
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
obj = cls._finfo_cache.get(dtype,None)
if obj is not None:
return obj
obj = object.__new__(cls)._init(dtype)
for dt in dtypes:
cls._finfo_cache[dt] = obj
return obj
def __new__(cls, dtype):
try:
dtype = numeric.dtype(dtype)
except TypeError:
# In case a float instance was given
dtype = numeric.dtype(type(dtype))
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
dtypes = [dtype]
newdtype = numeric.obj2sctype(dtype)
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
if not issubclass(dtype, numeric.inexact):
raise ValueError, "data type %r not inexact" % (dtype)
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
if not issubclass(dtype, numeric.floating):
newdtype = _convert_to_float[dtype]
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
obj = object.__new__(cls)._init(dtype)
for dt in dtypes:
cls._finfo_cache[dt] = obj
return obj
def __init__(self, int_type):
try:
self.dtype = numeric.dtype(int_type)
except TypeError:
self.dtype = numeric.dtype(type(int_type))
self.kind = self.dtype.kind
self.bits = self.dtype.itemsize * 8
self.key = "%s%d" % (self.kind, self.bits)
if not self.kind in 'iu':
raise ValueError("Invalid integer data type.")
def __init__(self, int_type):
try:
self.dtype = numeric.dtype(int_type)
except TypeError:
self.dtype = numeric.dtype(type(int_type))
self.kind = self.dtype.kind
self.bits = self.dtype.itemsize * 8
self.key = "%s%d" % (self.kind, self.bits)
if not self.kind in 'iu':
raise ValueError("Invalid integer data type.")
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 __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 __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 _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
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 _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
def view(self, obj):
try:
if issubclass(obj, ndarray):
return ndarray.view(self, obj)
except TypeError:
pass
dtype = sb.dtype(obj)
if dtype.fields is None:
return self.__array__().view(dtype)
return ndarray.view(self, obj)
def view(self, obj):
try:
if issubclass(obj, ndarray):
return ndarray.view(self, obj)
except TypeError:
pass
dtype = sb.dtype(obj)
if dtype.fields is None:
return self.__array__().view(dtype)
return ndarray.view(self, obj)
def view(self, dtype=None, type=None):
if dtype is None:
return ndarray.view(self, type)
elif type is None:
try:
if issubclass(dtype, ndarray):
return ndarray.view(self, dtype)
except TypeError:
pass
dtype = sb.dtype(dtype)
if dtype.fields is None:
return self.__array__().view(dtype)
return ndarray.view(self, dtype)
else:
return ndarray.view(self, dtype, type)
def __new__(subtype, data, dtype=None, copy=True):
if isinstance(data, matrix):
dtype2 = data.dtype
if (dtype is None):
dtype = dtype2
if (dtype2 == dtype) and (not copy):
return data
return data.astype(dtype)
if isinstance(data, N.ndarray):
if dtype is None:
intype = data.dtype
else:
intype = N.dtype(dtype)
new = data.view(subtype)
if intype != data.dtype:
return new.astype(intype)
if copy: return new.copy()
else: return new
if isinstance(data, str):
data = _convert_from_string(data)
# now convert data to an array
arr = N.array(data, dtype=dtype, copy=copy)
ndim = arr.ndim
shape = arr.shape
if (ndim > 2):
raise ValueError, "matrix must be 2-dimensional"
elif ndim == 0:
shape = (1, 1)
elif ndim == 1:
shape = (1, shape[0])
order = False
if (ndim == 2) and arr.flags.fortran:
order = True
if not (order or arr.flags.contiguous):
arr = arr.copy()
ret = N.ndarray.__new__(subtype,
shape,
arr.dtype,
buffer=arr,
order=order)
return ret
def __new__(subtype, data, dtype=None, copy=True):
if isinstance(data, matrix):
dtype2 = data.dtype
if (dtype is None):
dtype = dtype2
if (dtype2 == dtype) and (not copy):
return data
return data.astype(dtype)
if isinstance(data, N.ndarray):
if dtype is None:
intype = data.dtype
else:
intype = N.dtype(dtype)
new = data.view(subtype)
if intype != data.dtype:
return new.astype(intype)
if copy: return new.copy()
else: return new
if isinstance(data, str):
data = _convert_from_string(data)
# now convert data to an array
arr = N.array(data, dtype=dtype, copy=copy)
ndim = arr.ndim
shape = arr.shape
if (ndim > 2):
raise ValueError, "matrix must be 2-dimensional"
elif ndim == 0:
shape = (1,1)
elif ndim == 1:
shape = (1,shape[0])
order = False
if (ndim == 2) and arr.flags.fortran:
order = True
if not (order or arr.flags.contiguous):
arr = arr.copy()
ret = N.ndarray.__new__(subtype, shape, arr.dtype,
buffer=arr,
order=order)
return ret
def _init(self, dtype):
self.dtype = numeric.dtype(dtype)
if dtype is ntypes.double:
itype = ntypes.int64
fmt = '%24.16e'
precname = 'double'
elif dtype is ntypes.single:
itype = ntypes.int32
fmt = '%15.7e'
precname = 'single'
elif dtype is ntypes.longdouble:
itype = ntypes.longlong
fmt = '%s'
precname = 'long double'
elif dtype is ntypes.half:
itype = ntypes.int16
fmt = '%12.5e'
precname = 'half'
else:
raise ValueError(repr(dtype))
machar = MachAr(lambda v: array([v], dtype),
lambda v: _frz(v.astype(itype))[0],
lambda v: array(_frz(v)[0], dtype),
lambda v: fmt % array(_frz(v)[0], dtype),
'numpy %s precision floating point number' % precname)
for word in [
'precision', 'iexp', 'maxexp', 'minexp', 'negep', 'machep'
]:
setattr(self, word, getattr(machar, word))
for word in ['tiny', 'resolution', 'epsneg']:
setattr(self, word, getattr(machar, word).flat[0])
self.max = machar.huge.flat[0]
self.min = -self.max
self.eps = machar.eps.flat[0]
self.nexp = machar.iexp
self.nmant = machar.it
self.machar = machar
self._str_tiny = machar._str_xmin.strip()
self._str_max = machar._str_xmax.strip()
self._str_epsneg = machar._str_epsneg.strip()
self._str_eps = machar._str_eps.strip()
self._str_resolution = machar._str_resolution.strip()
return self
def _init(self, dtype):
self.dtype = numeric.dtype(dtype)
if dtype is ntypes.double:
itype = ntypes.int64
fmt = "%24.16e"
precname = "double"
elif dtype is ntypes.single:
itype = ntypes.int32
fmt = "%15.7e"
precname = "single"
elif dtype is ntypes.longdouble:
itype = ntypes.longlong
fmt = "%s"
precname = "long double"
elif dtype is ntypes.half:
itype = ntypes.int16
fmt = "%12.5e"
precname = "half"
else:
raise ValueError(repr(dtype))
machar = MachAr(
lambda v: array([v], dtype),
lambda v: _frz(v.astype(itype))[0],
lambda v: array(_frz(v)[0], dtype),
lambda v: fmt % array(_frz(v)[0], dtype),
"numpy %s precision floating point number" % precname,
)
for word in ["precision", "iexp", "maxexp", "minexp", "negep", "machep"]:
setattr(self, word, getattr(machar, word))
for word in ["tiny", "resolution", "epsneg"]:
setattr(self, word, getattr(machar, word).flat[0])
self.max = machar.huge.flat[0]
self.min = -self.max
self.eps = machar.eps.flat[0]
self.nexp = machar.iexp
self.nmant = machar.it
self.machar = machar
self._str_tiny = machar._str_xmin.strip()
self._str_max = machar._str_xmax.strip()
self._str_epsneg = machar._str_epsneg.strip()
self._str_eps = machar._str_eps.strip()
self._str_resolution = machar._str_resolution.strip()
return self
def _init(self, dtype):
self.dtype = numeric.dtype(dtype)
if dtype is ntypes.double:
itype = ntypes.int64
fmt = '%24.16e'
precname = 'double'
elif dtype is ntypes.single:
itype = ntypes.int32
fmt = '%15.7e'
precname = 'single'
elif dtype is ntypes.longdouble:
itype = ntypes.longlong
fmt = '%s'
precname = 'long double'
elif dtype is ntypes.half:
itype = ntypes.int16
fmt = '%12.5e'
precname = 'half'
else:
raise ValueError, repr(dtype)
machar = MachAr(lambda v:array([v], dtype),
lambda v:_frz(v.astype(itype))[0],
lambda v:array(_frz(v)[0], dtype),
lambda v: fmt % array(_frz(v)[0], dtype),
'numpy %s precision floating point number' % precname)
for word in ['precision', 'iexp',
'maxexp','minexp','negep',
'machep']:
setattr(self,word,getattr(machar, word))
for word in ['tiny','resolution','epsneg']:
setattr(self,word,getattr(machar, word).flat[0])
self.max = machar.huge.flat[0]
self.min = -self.max
self.eps = machar.eps.flat[0]
self.nexp = machar.iexp
self.nmant = machar.it
self.machar = machar
self._str_tiny = machar._str_xmin.strip()
self._str_max = machar._str_xmax.strip()
self._str_epsneg = machar._str_epsneg.strip()
self._str_eps = machar._str_eps.strip()
self._str_resolution = machar._str_resolution.strip()
return self
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 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()
res = new.view(recarray)
if issubclass(res.dtype.type, nt.void):
res.dtype = sb.dtype((record, res.dtype))
return res
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)
res = obj.view(recarray)
if issubclass(res.dtype.type, nt.void):
res.dtype = sb.dtype((record, res.dtype))
return res
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.
>>> 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 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
chararray(['dbe', 'de'],
dtype='|S3')
>>> import cPickle
>>> print cPickle.loads(cPickle.dumps(r))
[(456, 'dbe', 1.2) (2, 'de', 1.3)]
"""
nfields = len(recList[0])
if formats is None and dtype is None: # slower
obj = sb.array(recList, dtype=object)
arrlist = [sb.array(obj[..., i].tolist()) for i in xrange(nfields)]
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
try:
retval = sb.array(recList, dtype=descr)
except TypeError: # list of lists instead of list of tuples
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 xrange(_array.size):
_array[k] = tuple(recList[k])
return _array
else:
if shape is not None and retval.shape != shape:
retval.shape = shape
res = retval.view(recarray)
return 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 += _typestr[obj.dtype.type]
if issubclass(obj.dtype.type, nt.flexible):
formats += `obj.itemsize`
formats += ','
formats = formats[:-1]
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 = len(descr[k].shape)
testshape = obj.shape[:len(obj.shape) - 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