lightkurve.seismology.
SeismologyQuantity
¶Bases: astropy.units.quantity.Quantity
Holds an asteroseismic value including its unit, error, and estimation method.
Compared to a traditional AstroPy Quantity
object, this
class has the following extra attributes:
name (e.g. ‘deltanu’ or ‘radius’);
error (i.e. the uncertainty);
method (e.g. specifying the asteroseismic scaling relation);
diagnostics;
diagnostics_plot_method.
Attributes Summary
Same as self.transpose(), except that self is returned if self.ndim < 2. 

Base object if memory is from some other object. 

Returns a copy of the current 

An object to simplify the interaction of the array with the ctypes module. 

Python buffer object pointing to the start of the array’s data. 

Datatype of the array’s elements. 

A list of equivalencies that will be applied by default during unit conversions. 

Information about the memory layout of the array. 

A 1D iterator over the Quantity array. 

The imaginary part of the array. 


Container for meta information like name, description, format. 
True if the 

Length of one array element in bytes. 

Total bytes consumed by the elements of the array. 

Number of array dimensions. 

The real part of the array. 

Tuple of array dimensions. 

Returns a copy of the current 

Number of elements in the array. 

Tuple of bytes to step in each dimension when traversing an array. 

A 

The numerical value of this instance. 
Methods Summary

Returns True if all elements evaluate to True. 

Returns True if any of the elements of 

Return indices of the maximum values along the given axis. 

Return indices of the minimum values along the given axis of 

Returns the indices that would partition this array. 

Returns the indices that would sort this array. 

Copy of the array, cast to a specified type. 

Swap the bytes of the array elements 

Use an index array to construct a new array from a set of choices. 

Return an array whose values are limited to 

Return selected slices of this array along given axis. 

Complexconjugate all elements. 
Return the complex conjugate, elementwise. 


Return a copy of the array. 

Return the cumulative product of the elements along the given axis. 

Return the cumulative sum of the elements along the given axis. 

Generates a new 

Return specified diagonals. 



Dot product of two arrays. 

Dump a pickle of the array to the specified file. 

Returns the pickle of the array as a string. 



Fill the array with a scalar value. 

Return a copy of the array collapsed into one dimension. 

Returns a field of the given array as a certain type. 

Insert values along the given axis before the given indices and return a new 

Copy an element of an array to a standard Python scalar and return it. 

Insert scalar into an array (scalar is cast to array’s dtype, if possible) 

Return the maximum along a given axis. 

Returns the average of the array elements along given axis. 

Return the minimum along a given axis. 



Return the array with the same data viewed with a different byte order. 

Return the indices of the elements that are nonzero. 

Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. 

Return the product of the array elements over the given axis 

Peak to peak (maximum  minimum) value along a given axis. 

Set 

Return a flattened array. 

Repeat elements of an array. 

Returns an array containing the same data with a new shape. 

Change shape and size of array inplace. 

Return 

Find indices where elements of v should be inserted in a to maintain order. 

Put a value into a specified place in a field defined by a datatype. 

Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively. 

Sort an array, inplace. 

Remove singledimensional entries from the shape of 

Returns the standard deviation of the array elements along given axis. 

Return the sum of the array elements over the given axis. 

Return a view of the array with 

Return an array formed from the elements of 

Return a new 

Generate a string representation of the quantity and its unit. 

The numerical value, possibly in a different unit. 

Construct Python bytes containing the raw data bytes in the array. 

Write array to a file as text or binary (default). 

Return the array as a (possibly nested) list. 

Construct Python bytes containing the raw data bytes in the array. 

Return the sum along diagonals of the array. 

Returns a view of the array with axes transposed. 

Returns the variance of the array elements, along given axis. 

New view of array with the same data. 
Attributes Documentation
T
¶Same as self.transpose(), except that self is returned if self.ndim < 2.
Examples
>>> x = np.array([[1.,2.],[3.,4.]])
>>> x
array([[ 1., 2.],
[ 3., 4.]])
>>> x.T
array([[ 1., 3.],
[ 2., 4.]])
>>> x = np.array([1.,2.,3.,4.])
>>> x
array([ 1., 2., 3., 4.])
>>> x.T
array([ 1., 2., 3., 4.])
base
¶Base object if memory is from some other object.
Examples
The base of an array that owns its memory is None:
>>> x = np.array([1,2,3,4])
>>> x.base is None
True
Slicing creates a view, whose memory is shared with x:
>>> y = x[2:]
>>> y.base is x
True
cgs
¶Returns a copy of the current Quantity
instance with CGS units. The
value of the resulting object will be scaled.
ctypes
¶An object to simplify the interaction of the array with the ctypes module.
This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library.
Possessing attributes data, shape, strides, etc.
See also
Notes
Below are the public attributes of this object which were documented in “Guide to NumPy” (we have omitted undocumented public attributes, as well as documented private attributes):
_ctypes.
data
¶A pointer to the memory area of the array as a Python integer.
This memory area may contain data that is not aligned, or not in correct
byteorder. The memory area may not even be writeable. The array
flags and datatype of this array should be respected when passing this
attribute to arbitrary Ccode to avoid trouble that can include Python
crashing. User Beware! The value of this attribute is exactly the same
as self._array_interface_['data'][0]
.
Note that unlike data_as
, a reference will not be kept to the array:
code like ctypes.c_void_p((a + b).ctypes.data)
will result in a
pointer to a deallocated array, and should be spelt
(a + b).ctypes.data_as(ctypes.c_void_p)
_ctypes.
shape
¶(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the Cinteger corresponding to dtype('p')
on this
platform. This basetype could be ctypes.c_int
, ctypes.c_long
, or
ctypes.c_longlong
depending on the platform.
The c_intp type is defined accordingly in numpy.ctypeslib
.
The ctypes array contains the shape of the underlying array.
_ctypes.
strides
¶(c_intp*self.ndim): A ctypes array of length self.ndim where the basetype is the same as for the shape attribute. This ctypes array contains the strides information from the underlying array. This strides information is important for showing how many bytes must be jumped to get to the next element in the array.
_ctypes.
data_as
(self, obj)¶Return the data pointer cast to a particular ctypes object.
For example, calling self._as_parameter_
is equivalent to
self.data_as(ctypes.c_void_p)
. Perhaps you want to use the data as a
pointer to a ctypes array of floatingpoint data:
self.data_as(ctypes.POINTER(ctypes.c_double))
.
The returned pointer will keep a reference to the array.
_ctypes.
shape_as
(self, obj)¶Return the shape tuple as an array of some other ctypes
type. For example: self.shape_as(ctypes.c_short)
.
_ctypes.
strides_as
(self, obj)¶Return the strides tuple as an array of some other
ctypes type. For example: self.strides_as(ctypes.c_longlong)
.
If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the as parameter attribute which will return an integer equal to the data attribute.
Examples
>>> import ctypes
>>> x
array([[0, 1],
[2, 3]])
>>> x.ctypes.data
30439712
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))
<ctypes.LP_c_long object at 0x01F01300>
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents
c_long(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents
c_longlong(4294967296L)
>>> x.ctypes.shape
<numpy.core._internal.c_long_Array_2 object at 0x01FFD580>
>>> x.ctypes.shape_as(ctypes.c_long)
<numpy.core._internal.c_long_Array_2 object at 0x01FCE620>
>>> x.ctypes.strides
<numpy.core._internal.c_long_Array_2 object at 0x01FCE620>
>>> x.ctypes.strides_as(ctypes.c_longlong)
<numpy.core._internal.c_longlong_Array_2 object at 0x01F01300>
data
¶Python buffer object pointing to the start of the array’s data.
dtype
¶Datatype of the array’s elements.
See also
Examples
>>> x
array([[0, 1],
[2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>
equivalencies
¶A list of equivalencies that will be applied by default during unit conversions.
flags
¶Information about the memory layout of the array.
Notes
The flags
object can be accessed dictionarylike (as in a.flags['WRITEABLE']
),
or by using lowercased attribute names (as in a.flags.writeable
). Short flag
names are only supported in dictionary access.
Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be
changed by the user, via direct assignment to the attribute or dictionary
entry, or by calling ndarray.setflags
.
The array flags cannot be set arbitrarily:
UPDATEIFCOPY can only be set False
.
WRITEBACKIFCOPY can only be set False
.
ALIGNED can only be set True
if the data is truly aligned.
WRITEABLE can only be set True
if the array owns its own memory
or the ultimate owner of the memory exposes a writeable buffer
interface or is a string.
Arrays can be both Cstyle and Fortranstyle contiguous simultaneously. This is clear for 1dimensional arrays, but can also be true for higher dimensional arrays.
Even for contiguous arrays a stride for a given dimension
arr.strides[dim]
may be arbitrary if arr.shape[dim] == 1
or the array has no elements.
It does not generally hold that self.strides[1] == self.itemsize
for Cstyle contiguous arrays or self.strides[0] == self.itemsize
for
Fortranstyle contiguous arrays is true.
The data is in a single, Cstyle contiguous segment.
The data is in a single, Fortranstyle contiguous segment.
The array owns the memory it uses or borrows it from another object.
The data area can be written to. Setting this to False locks the data, making it readonly. A view (slice, etc.) inherits WRITEABLE from its base array at creation time, but a view of a writeable array may be subsequently locked while the base array remains writeable. (The opposite is not true, in that a view of a locked array may not be made writeable. However, currently, locking a base object does not lock any views that already reference it, so under that circumstance it is possible to alter the contents of a locked array via a previously created writeable view onto it.) Attempting to change a nonwriteable array raises a RuntimeError exception.
The data and all elements are aligned appropriately for the hardware.
This array is a copy of some other array. The CAPI function PyArray_ResolveWritebackIfCopy must be called before deallocating to the base array will be updated with the contents of this array.
(Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array. When this array is deallocated, the base array will be updated with the contents of this array.
F_CONTIGUOUS and not C_CONTIGUOUS.
F_CONTIGUOUS or C_CONTIGUOUS (onesegment test).
ALIGNED and WRITEABLE.
BEHAVED and C_CONTIGUOUS.
BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.
flat
¶A 1D iterator over the Quantity array.
This returns a QuantityIterator
instance, which behaves the same
as the flatiter
instance returned by flat
,
and is similar to, but not a subclass of, Python’s builtin iterator
object.
imag
¶The imaginary part of the array.
Examples
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0. , 0.70710678])
>>> x.imag.dtype
dtype('float64')
info
(option='attributes', out='')¶Container for meta information like name, description, format. This is required when the object is used as a mixin column within a table, but can be used as a general way to store meta information.
isscalar
¶True if the value
of this quantity is a scalar, or False if it
is an arraylike object.
Note
This is subtly different from numpy.isscalar
in that
numpy.isscalar
returns False for a zerodimensional array
(e.g. np.array(1)
), while this is True for quantities,
since quantities cannot represent true numpy scalars.
itemsize
¶Length of one array element in bytes.
Examples
>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16
nbytes
¶Total bytes consumed by the elements of the array.
Notes
Does not include memory consumed by nonelement attributes of the array object.
Examples
>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480
ndim
¶Number of array dimensions.
Examples
>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3
real
¶The real part of the array.
See also
numpy.real
equivalent function
Examples
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1. , 0.70710678])
>>> x.real.dtype
dtype('float64')
shape
¶Tuple of array dimensions.
The shape property is usually used to get the current shape of an array,
but may also be used to reshape the array inplace by assigning a tuple of
array dimensions to it. As with numpy.reshape
, one of the new shape
dimensions can be 1, in which case its value is inferred from the size of
the array and the remaining dimensions. Reshaping an array inplace will
fail if a copy is required.
See also
numpy.reshape
similar function
ndarray.reshape
similar method
Examples
>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (1,)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: incompatible shape for a noncontiguous array
si
¶Returns a copy of the current Quantity
instance with SI units. The
value of the resulting object will be scaled.
size
¶Number of elements in the array.
Equal to np.prod(a.shape)
, i.e., the product of the array’s
dimensions.
Notes
a.size
returns a standard arbitrary precision Python integer. This
may not be the case with other methods of obtaining the same value
(like the suggested np.prod(a.shape)
, which returns an instance
of np.int_
), and may be relevant if the value is used further in
calculations that may overflow a fixed size integer type.
Examples
>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30
strides
¶Tuple of bytes to step in each dimension when traversing an array.
The byte offset of element (i[0], i[1], ..., i[n])
in an array a
is:
offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in the “ndarray.rst” file in the NumPy reference guide.
See also
Notes
Imagine an array of 32bit integers (each 4 bytes):
x = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other
(known as a contiguous block of memory). The strides of an array tell
us how many bytes we have to skip in memory to move to the next position
along a certain axis. For example, we have to skip 4 bytes (1 value) to
move to the next column, but 20 bytes (5 values) to get to the same
position in the next row. As such, the strides for the array x
will be
(20, 4)
.
Examples
>>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813
value
¶The numerical value of this instance.
See also
to_value
Get the numerical value in a given unit.
Methods Documentation
all
(axis=None, out=None, keepdims=False)¶Returns True if all elements evaluate to True.
Refer to numpy.all
for full documentation.
See also
numpy.all
equivalent function
any
(axis=None, out=None, keepdims=False)¶Returns True if any of the elements of a
evaluate to True.
Refer to numpy.any
for full documentation.
See also
numpy.any
equivalent function
argmax
(axis=None, out=None)¶Return indices of the maximum values along the given axis.
Refer to numpy.argmax
for full documentation.
See also
numpy.argmax
equivalent function
argmin
(axis=None, out=None)¶Return indices of the minimum values along the given axis of a
.
Refer to numpy.argmin
for detailed documentation.
See also
numpy.argmin
equivalent function
argpartition
(kth, axis=1, kind='introselect', order=None)¶Returns the indices that would partition this array.
Refer to numpy.argpartition
for full documentation.
New in version 1.8.0.
See also
numpy.argpartition
equivalent function
argsort
(axis=1, kind='quicksort', order=None)¶Returns the indices that would sort this array.
Refer to numpy.argsort
for full documentation.
See also
numpy.argsort
equivalent function
astype
(dtype, order='K', casting='unsafe', subok=True, copy=True)¶Copy of the array, cast to a specified type.
Typecode or datatype to which the array is cast.
Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.
Controls what kind of data casting may occur. Defaults to ‘unsafe’ for backwards compatibility.
‘no’ means the data types should not be cast at all.
‘equiv’ means only byteorder changes are allowed.
‘safe’ means only casts which can preserve values are allowed.
‘same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.
‘unsafe’ means any data conversions may be done.
If True, then subclasses will be passedthrough (default), otherwise the returned array will be forced to be a baseclass array.
By default, astype always returns a newly allocated array. If this
is set to false, and the dtype
, order
, and subok
requirements are satisfied, the input array is returned instead
of a copy.
When casting from complex to float or int. To avoid this,
one should use a.real.astype(t)
.
Notes
Starting in NumPy 1.9, astype method now returns an error if the string dtype to cast to is not long enough in ‘safe’ casting mode to hold the max value of integer/float array that is being casted. Previously the casting was allowed even if the result was truncated.
Examples
>>> x = np.array([1, 2, 2.5])
>>> x
array([ 1. , 2. , 2.5])
>>> x.astype(int)
array([1, 2, 2])
byteswap
(inplace=False)¶Swap the bytes of the array elements
Toggle between lowendian and bigendian data representation by returning a byteswapped array, optionally swapped inplace.
If True
, swap bytes inplace, default is False
.
The byteswapped array. If inplace
is True
, this is
a view to self.
Examples
>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> map(hex, A)
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([ 256, 1, 13090], dtype=int16)
>>> map(hex, A)
['0x100', '0x1', '0x3322']
Arrays of strings are not swapped
>>> A = np.array(['ceg', 'fac'])
>>> A.byteswap()
array(['ceg', 'fac'],
dtype='S3')
choose
(choices, out=None, mode='raise')¶Use an index array to construct a new array from a set of choices.
Refer to numpy.choose
for full documentation.
See also
numpy.choose
equivalent function
clip
(min=None, max=None, out=None)¶Return an array whose values are limited to [min, max]
.
One of max or min must be given.
Refer to numpy.clip
for full documentation.
See also
numpy.clip
equivalent function
compress
(condition, axis=None, out=None)¶Return selected slices of this array along given axis.
Refer to numpy.compress
for full documentation.
See also
numpy.compress
equivalent function
conj
()¶Complexconjugate all elements.
Refer to numpy.conjugate
for full documentation.
See also
numpy.conjugate
equivalent function
conjugate
()¶Return the complex conjugate, elementwise.
Refer to numpy.conjugate
for full documentation.
See also
numpy.conjugate
equivalent function
copy
(order='C')¶Return a copy of the array.
Controls the memory layout of the copy. ‘C’ means Corder,
‘F’ means Forder, ‘A’ means ‘F’ if a
is Fortran contiguous,
‘C’ otherwise. ‘K’ means match the layout of a
as closely
as possible. (Note that this function and numpy.copy()
are very
similar, but have different default values for their order=
arguments.)
See also
Examples
>>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x
array([[0, 0, 0],
[0, 0, 0]])
>>> y
array([[1, 2, 3],
[4, 5, 6]])
>>> y.flags['C_CONTIGUOUS']
True
cumprod
(axis=None, dtype=None, out=None)¶Return the cumulative product of the elements along the given axis.
Refer to numpy.cumprod
for full documentation.
See also
numpy.cumprod
equivalent function
cumsum
(axis=None, dtype=None, out=None)¶Return the cumulative sum of the elements along the given axis.
Refer to numpy.cumsum
for full documentation.
See also
numpy.cumsum
equivalent function
decompose
(self, bases=[])¶Generates a new Quantity
with the units
decomposed. Decomposed units have only irreducible units in
them (see astropy.units.UnitBase.decompose
).
The bases to decompose into. When not provided,
decomposes down to any irreducible units. When provided,
the decomposed result will only contain the given units.
This will raises a UnitsError
if it’s not possible
to do so.
Quantity
A new object equal to this quantity with units decomposed.
diagonal
(offset=0, axis1=0, axis2=1)¶Return specified diagonals. In NumPy 1.9 the returned array is a readonly view instead of a copy as in previous NumPy versions. In a future version the readonly restriction will be removed.
Refer to numpy.diagonal()
for full documentation.
See also
numpy.diagonal
equivalent function
diff
(self, n=1, axis=1)¶dot
(b, out=None)¶Dot product of two arrays.
Refer to numpy.dot
for full documentation.
See also
numpy.dot
equivalent function
Examples
>>> a = np.eye(2)
>>> b = np.ones((2, 2)) * 2
>>> a.dot(b)
array([[ 2., 2.],
[ 2., 2.]])
This array method can be conveniently chained:
>>> a.dot(b).dot(b)
array([[ 8., 8.],
[ 8., 8.]])
dump
(file)¶Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load.
A string naming the dump file.
dumps
()¶Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array.
ediff1d
(self, to_end=None, to_begin=None)¶fill
(value)¶Fill the array with a scalar value.
All elements of a
will be assigned this value.
Examples
>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([ 1., 1.])
flatten
(order='C')¶Return a copy of the array collapsed into one dimension.
‘C’ means to flatten in rowmajor (Cstyle) order.
‘F’ means to flatten in columnmajor (Fortran
style) order. ‘A’ means to flatten in columnmajor
order if a
is Fortran contiguous in memory,
rowmajor order otherwise. ‘K’ means to flatten
a
in the order the elements occur in memory.
The default is ‘C’.
A copy of the input array, flattened to one dimension.
Examples
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])
getfield
(dtype, offset=0)¶Returns a field of the given array as a certain type.
A field is a view of the array data with a given datatype. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16byte elements. If taking a view with a 32bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.
The data type of the view. The dtype size of the view can not be larger than that of the array itself.
Number of bytes to skip before beginning the element view.
Examples
>>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[ 1.+1.j, 0.+0.j],
[ 0.+0.j, 2.+4.j]])
>>> x.getfield(np.float64)
array([[ 1., 0.],
[ 0., 2.]])
By choosing an offset of 8 bytes we can select the complex part of the array for our view:
>>> x.getfield(np.float64, offset=8)
array([[ 1., 0.],
[ 0., 4.]])
insert
(self, obj, values, axis=None)¶Insert values along the given axis before the given indices and return
a new Quantity
object.
This is a thin wrapper around the numpy.insert
function.
Object that defines the index or indices before which values
is
inserted.
Values to insert. If the type of values
is different
from that of quantity, values
is converted to the matching type.
values
should be shaped so that it can be broadcast appropriately
The unit of values
must be consistent with this quantity.
Axis along which to insert values
. If axis
is None then
the quantity array is flattened before insertion.
Quantity
A copy of quantity with values
inserted. Note that the
insertion does not occur inplace: a new quantity array is returned.
Examples
>>> import astropy.units as u
>>> q = [1, 2] * u.m
>>> q.insert(0, 50 * u.cm)
<Quantity [ 0.5, 1., 2.] m>
>>> q = [[1, 2], [3, 4]] * u.m
>>> q.insert(1, [10, 20] * u.m, axis=0)
<Quantity [[ 1., 2.],
[ 10., 20.],
[ 3., 4.]] m>
>>> q.insert(1, 10 * u.m, axis=1)
<Quantity [[ 1., 10., 2.],
[ 3., 10., 4.]] m>
item
(*args)¶Copy an element of an array to a standard Python scalar and return it.
none: in this case, the method only works for arrays
with one element (a.size == 1
), which element is
copied into a standard Python scalar object and returned.
int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return.
tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an ndindex into the array.
A copy of the specified element of the array as a suitable Python scalar
Notes
When the data type of a
is longdouble or clongdouble, item() returns
a scalar array object because there is no available Python scalar that
would not lose information. Void arrays return a buffer object for item(),
unless fields are defined, in which case a tuple is returned.
item
is very similar to a[args], except, instead of an array scalar,
a standard Python scalar is returned. This can be useful for speeding up
access to elements of the array and doing arithmetic on elements of the
array using Python’s optimized math.
Examples
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[3, 1, 7],
[2, 8, 3],
[8, 5, 3]])
>>> x.item(3)
2
>>> x.item(7)
5
>>> x.item((0, 1))
1
>>> x.item((2, 2))
3
itemset
(*args)¶Insert scalar into an array (scalar is cast to array’s dtype, if possible)
There must be at least 1 argument, and define the last argument
as item. Then, a.itemset(*args)
is equivalent to but faster
than a[args] = item
. The item should be a scalar value and args
must select a single item in the array a
.
If one argument: a scalar, only used in case a
is of size 1.
If two arguments: the last argument is the value to be set
and must be a scalar, the first argument specifies a single array
element location. It is either an int or a tuple.
Notes
Compared to indexing syntax, itemset
provides some speed increase
for placing a scalar into a particular location in an ndarray
,
if you must do this. However, generally this is discouraged:
among other problems, it complicates the appearance of the code.
Also, when using itemset
(and item
) inside a loop, be sure
to assign the methods to a local variable to avoid the attribute
lookup at each loop iteration.
Examples
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[3, 1, 7],
[2, 8, 3],
[8, 5, 3]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[3, 1, 7],
[2, 0, 3],
[8, 5, 9]])
max
(axis=None, out=None, keepdims=False)¶Return the maximum along a given axis.
Refer to numpy.amax
for full documentation.
See also
numpy.amax
equivalent function
mean
(axis=None, dtype=None, out=None, keepdims=False)¶Returns the average of the array elements along given axis.
Refer to numpy.mean
for full documentation.
See also
numpy.mean
equivalent function
min
(axis=None, out=None, keepdims=False)¶Return the minimum along a given axis.
Refer to numpy.amin
for full documentation.
See also
numpy.amin
equivalent function
nansum
(self, axis=None, out=None, keepdims=False)¶newbyteorder
(new_order='S')¶Return the array with the same data viewed with a different byte order.
Equivalent to:
arr.view(arr.dtype.newbytorder(new_order))
Changes are also made in all fields and subarrays of the array data type.
Byte order to force; a value from the byte order specifications
below. new_order
codes can be any of:
‘S’  swap dtype from current to opposite endian
{‘<’, ‘L’}  little endian
{‘>’, ‘B’}  big endian
{‘=’, ‘N’}  native order
{‘’, ‘I’}  ignore (no change to byte order)
The default value (‘S’) results in swapping the current
byte order. The code does a caseinsensitive check on the first
letter of new_order
for the alternatives above. For example,
any of ‘B’ or ‘b’ or ‘biggish’ are valid to specify bigendian.
New array object with the dtype reflecting given change to the byte order.
nonzero
()¶Return the indices of the elements that are nonzero.
Refer to numpy.nonzero
for full documentation.
See also
numpy.nonzero
equivalent function
partition
(kth, axis=1, kind='introselect', order=None)¶Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.
New in version 1.8.0.
Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.
Axis along which to sort. Default is 1, which means sort along the last axis.
Selection algorithm. Default is ‘introselect’.
When a
is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need to be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
See also
numpy.partition
Return a parititioned copy of an array.
argpartition
Indirect partition.
sort
Full sort.
Notes
See np.partition
for notes on the different algorithms.
Examples
>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
>>> a.partition((1, 3))
array([1, 2, 3, 4])
prod
(axis=None, dtype=None, out=None, keepdims=False)¶Return the product of the array elements over the given axis
Refer to numpy.prod
for full documentation.
See also
numpy.prod
equivalent function
ptp
(axis=None, out=None, keepdims=False)¶Peak to peak (maximum  minimum) value along a given axis.
Refer to numpy.ptp
for full documentation.
See also
numpy.ptp
equivalent function
put
(indices, values, mode='raise')¶Set a.flat[n] = values[n]
for all n
in indices.
Refer to numpy.put
for full documentation.
See also
numpy.put
equivalent function
ravel
([order])¶Return a flattened array.
Refer to numpy.ravel
for full documentation.
See also
numpy.ravel
equivalent function
ndarray.flat
a flat iterator on the array.
repeat
(repeats, axis=None)¶Repeat elements of an array.
Refer to numpy.repeat
for full documentation.
See also
numpy.repeat
equivalent function
reshape
(shape, order='C')¶Returns an array containing the same data with a new shape.
Refer to numpy.reshape
for full documentation.
See also
numpy.reshape
equivalent function
Notes
Unlike the free function numpy.reshape
, this method on ndarray
allows
the elements of the shape parameter to be passed in as separate arguments.
For example, a.reshape(10, 11)
is equivalent to
a.reshape((10, 11))
.
resize
(new_shape, refcheck=True)¶Change shape and size of array inplace.
n
intsShape of resized array.
If False, reference count will not be checked. Default is True.
If a
does not own its own data or references or views to it exist,
and the data memory must be changed.
PyPy only: will always raise if the data memory must be changed, since
there is no reliable way to determine if references or views to it
exist.
If the order
keyword argument is specified. This behaviour is a
bug in NumPy.
See also
resize
Return a new array with the specified shape.
Notes
This reallocates space for the data area if necessary.
Only contiguous arrays (data elements consecutive in memory) can be resized.
The purpose of the reference count check is to make sure you
do not use this array as a buffer for another Python object and then
reallocate the memory. However, reference counts can increase in
other ways so if you are sure that you have not shared the memory
for this array with another Python object, then you may safely set
refcheck
to False.
Examples
Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped:
>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
[1]])
>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
[2]])
Enlarging an array: as above, but missing entries are filled with zeros:
>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
[3, 0, 0]])
Referencing an array prevents resizing…
>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that has been referenced ...
Unless refcheck
is False:
>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])
round
(decimals=0, out=None)¶Return a
with each element rounded to the given number of decimals.
Refer to numpy.around
for full documentation.
See also
numpy.around
equivalent function
searchsorted
(v, side='left', sorter=None)¶Find indices where elements of v should be inserted in a to maintain order.
For full documentation, see numpy.searchsorted
See also
numpy.searchsorted
equivalent function
setfield
(val, dtype, offset=0)¶Put a value into a specified place in a field defined by a datatype.
Place val
into a
’s field defined by dtype
and beginning offset
bytes into the field.
Value to be placed in field.
Datatype of the field in which to place val
.
The number of bytes into the field at which to place val
.
See also
Examples
>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
[3, 3, 3],
[3, 3, 3]])
>>> x
array([[ 1.00000000e+000, 1.48219694e323, 1.48219694e323],
[ 1.48219694e323, 1.00000000e+000, 1.48219694e323],
[ 1.48219694e323, 1.48219694e323, 1.00000000e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
setflags
(write=None, align=None, uic=None)¶Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively.
These Booleanvalued flags affect how numpy interprets the memory
area used by a
(see Notes below). The ALIGNED flag can only
be set to True if the data is actually aligned according to the type.
The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set
to True. The flag WRITEABLE can only be set to True if the array owns its
own memory, or the ultimate owner of the memory exposes a writeable buffer
interface, or is a string. (The exception for string is made so that
unpickling can be done without copying memory.)
Describes whether or not a
can be written to.
Describes whether or not a
is aligned properly for its type.
Describes whether or not a
is a copy of another “base” array.
Notes
Array flags provide information about how the memory area used for the array is to be interpreted. There are 7 Boolean flags in use, only four of which can be changed by the user: WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.
WRITEABLE (W) the data area can be written to;
ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler);
UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;
WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the CAPI function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array.
All flags can be accessed using the single (upper case) letter as well as the full name.
Examples
>>> y
array([[3, 1, 7],
[2, 0, 0],
[8, 5, 9]])
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : False
ALIGNED : False
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True
sort
(axis=1, kind='quicksort', order=None)¶Sort an array, inplace.
Axis along which to sort. Default is 1, which means sort along the last axis.
Sorting algorithm. Default is ‘quicksort’.
When a
is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
See also
numpy.sort
Return a sorted copy of an array.
argsort
Indirect sort.
lexsort
Indirect stable sort on multiple keys.
searchsorted
Find elements in sorted array.
partition
Partial sort.
Notes
See sort
for notes on the different sorting algorithms.
Examples
>>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
[1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
[1, 4]])
Use the order
keyword to specify a field to use when sorting a
structured array:
>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([('c', 1), ('a', 2)],
dtype=[('x', 'S1'), ('y', '<i4')])
squeeze
(axis=None)¶Remove singledimensional entries from the shape of a
.
Refer to numpy.squeeze
for full documentation.
See also
numpy.squeeze
equivalent function
std
(axis=None, dtype=None, out=None, ddof=0, keepdims=False)¶Returns the standard deviation of the array elements along given axis.
Refer to numpy.std
for full documentation.
See also
numpy.std
equivalent function
sum
(axis=None, dtype=None, out=None, keepdims=False)¶Return the sum of the array elements over the given axis.
Refer to numpy.sum
for full documentation.
See also
numpy.sum
equivalent function
swapaxes
(axis1, axis2)¶Return a view of the array with axis1
and axis2
interchanged.
Refer to numpy.swapaxes
for full documentation.
See also
numpy.swapaxes
equivalent function
take
(indices, axis=None, out=None, mode='raise')¶Return an array formed from the elements of a
at the given indices.
Refer to numpy.take
for full documentation.
See also
numpy.take
equivalent function
to
(self, unit, equivalencies=[])¶Return a new Quantity
object with the specified unit.
UnitBase
instance, strAn object that represents the unit to convert to. Must be
an UnitBase
object or a string parseable
by the units
package.
A list of equivalence pairs to try if the units are not
directly convertible. See Equivalencies.
If not provided or []
, class default equivalencies will be used
(none for Quantity
, but may be set for subclasses)
If None
, no equivalencies will be applied at all, not even any
set globally or within a context.
See also
to_value
get the numerical value in a given unit.
to_string
(self, unit=None, precision=None, format=None, subfmt=None)¶Generate a string representation of the quantity and its unit.
The behavior of this function can be altered via the
numpy.set_printoptions
function and its various keywords. The
exception to this is the threshold
keyword, which is controlled via
the [units.quantity]
configuration item latex_array_threshold
.
This is treated separately because the numpy default of 1000 is too big
for most browsers to handle.
UnitBase
, optionalSpecifies the unit. If not provided, the unit used to initialize the quantity will be used.
The level of decimal precision. If None
, or not provided,
it will be determined from NumPy print options.
The format of the result. If not provided, an unadorned string is returned. Supported values are:
‘latex’: Return a LaTeXformatted string
Subformat of the result. For the moment, only used for format=”latex”. Supported values are:
‘inline’: Use $ ... $
as delimiters.
‘display’: Use $\displaystyle ... $
as delimiters.
A string with the contents of this Quantity
to_value
(self, unit=None, equivalencies=[])¶The numerical value, possibly in a different unit.
UnitBase
instance or str, optionalThe unit in which the value should be given. If not given or None
,
use the current unit.
A list of equivalence pairs to try if the units are not directly
convertible (see Equivalencies). If not provided or
[]
, class default equivalencies will be used (none for
Quantity
, but may be set for subclasses).
If None
, no equivalencies will be applied at all, not even any
set globally or within a context.
ndarray
or scalarThe value in the units specified. For arrays, this will be a view of the data if no unit conversion was necessary.
See also
to
Get a new instance in a different unit.
tobytes
(order='C')¶Construct Python bytes containing the raw data bytes in the array.
Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object can be produced in either ‘C’ or ‘Fortran’, or ‘Any’ order (the default is ‘C’order). ‘Any’ order means Corder unless the F_CONTIGUOUS flag in the array is set, in which case it means ‘Fortran’ order.
New in version 1.9.0.
Order of the data for multidimensional arrays: C, Fortran, or the same as for the original array.
Python bytes exhibiting a copy of a
’s raw data.
Examples
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tobytes()
b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'
tofile
(fid, sep="", format="%s")¶Write array to a file as text or binary (default).
Data is always written in ‘C’ order, independent of the order of a
.
The data produced by this method can be recovered using the function
fromfile().
An open file object, or a string containing a filename.
Separator between array items for text output.
If “” (empty), a binary file is written, equivalent to
file.write(a.tobytes())
.
Format string for text file output. Each entry in the array is formatted to text by first converting it to the closest Python type, and then using “format” % item.
Notes
This is a convenience function for quick storage of array data. Information on endianness and precision is lost, so this method is not a good choice for files intended to archive data or transport data between machines with different endianness. Some of these problems can be overcome by outputting the data as text files, at the expense of speed and file size.
When fid is a file object, array contents are directly written to the
file, bypassing the file object’s write
method. As a result, tofile
cannot be used with files objects supporting compression (e.g., GzipFile)
or filelike objects that do not support fileno()
(e.g., BytesIO).
tolist
()¶Return the array as a (possibly nested) list.
Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible Python type.
The possibly nested list of array elements.
Notes
The array may be recreated, a = np.array(a.tolist())
.
Examples
>>> a = np.array([1, 2])
>>> a.tolist()
[1, 2]
>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]
tostring
(order='C')¶Construct Python bytes containing the raw data bytes in the array.
Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object can be produced in either ‘C’ or ‘Fortran’, or ‘Any’ order (the default is ‘C’order). ‘Any’ order means Corder unless the F_CONTIGUOUS flag in the array is set, in which case it means ‘Fortran’ order.
This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.
Order of the data for multidimensional arrays: C, Fortran, or the same as for the original array.
Python bytes exhibiting a copy of a
’s raw data.
Examples
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tobytes()
b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'
trace
(offset=0, axis1=0, axis2=1, dtype=None, out=None)¶Return the sum along diagonals of the array.
Refer to numpy.trace
for full documentation.
See also
numpy.trace
equivalent function
transpose
(*axes)¶Returns a view of the array with axes transposed.
For a 1D array, this has no effect. (To change between column and
row vectors, first cast the 1D array into a matrix object.)
For a 2D array, this is the usual matrix transpose.
For an nD array, if axes are given, their order indicates how the
axes are permuted (see Examples). If axes are not provided and
a.shape = (i[0], i[1], ... i[n2], i[n1])
, then
a.transpose().shape = (i[n1], i[n2], ... i[1], i[0])
.
n
intsNone or no argument: reverses the order of the axes.
tuple of ints: i
in the j
th place in the tuple means a
’s
i
th axis becomes a.transpose()
’s j
th axis.
n
ints: same as an ntuple of the same ints (this form is
intended simply as a “convenience” alternative to the tuple form)
View of a
, with axes suitably permuted.
See also
ndarray.T
Array property returning the array transposed.
Examples
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
[3, 4]])
>>> a.transpose()
array([[1, 3],
[2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
[2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
[2, 4]])
var
(axis=None, dtype=None, out=None, ddof=0, keepdims=False)¶Returns the variance of the array elements, along given axis.
Refer to numpy.var
for full documentation.
See also
numpy.var
equivalent function
view
(dtype=None, type=None)¶New view of array with the same data.
Datatype descriptor of the returned view, e.g., float32 or int16. The
default, None, results in the view having the same datatype as a
.
This argument can also be specified as an ndarray subclass, which
then specifies the type of the returned object (this is equivalent to
setting the type
parameter).
Type of the returned view, e.g., ndarray or matrix. Again, the default None results in type preservation.
Notes
a.view()
is used two different ways:
a.view(some_dtype)
or a.view(dtype=some_dtype)
constructs a view
of the array’s memory with a different datatype. This can cause a
reinterpretation of the bytes of memory.
a.view(ndarray_subclass)
or a.view(type=ndarray_subclass)
just
returns an instance of ndarray_subclass
that looks at the same array
(same shape, dtype, etc.) This does not cause a reinterpretation of the
memory.
For a.view(some_dtype)
, if some_dtype
has a different number of
bytes per entry than the previous dtype (for example, converting a
regular array to a structured array), then the behavior of the view
cannot be predicted just from the superficial appearance of a
(shown
by print(a)
). It also depends on exactly how a
is stored in
memory. Therefore if a
is Cordered versus fortranordered, versus
defined as a slice or transpose, etc., the view may give different
results.
Examples
>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
Viewing array data using a different type and dtype:
>>> y = x.view(dtype=np.int16, type=np.matrix)
>>> y
matrix([[513]], dtype=int16)
>>> print(type(y))
<class 'numpy.matrixlib.defmatrix.matrix'>
Creating a view on a structured array so it can be used in calculations
>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(1,2)
>>> xv
array([[1, 2],
[3, 4]], dtype=int8)
>>> xv.mean(0)
array([ 2., 3.])
Making changes to the view changes the underlying array
>>> xv[0,1] = 20
>>> print(x)
[(1, 20) (3, 4)]
Using a view to convert an array to a recarray:
>>> z = x.view(np.recarray)
>>> z.a
array([1], dtype=int8)
Views share data:
>>> x[0] = (9, 10)
>>> z[0]
(9, 10)
Views that change the dtype size (bytes per entry) should normally be avoided on arrays defined by slices, transposes, fortranordering, etc.:
>>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
>>> y = x[:, 0:2]
>>> y
array([[1, 2],
[4, 5]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: new type not compatible with array.
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 2)],
[(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])
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