Energy¶

class
gammapy.utils.energy.
Energy
[source]¶ Bases:
astropy.units.quantity.Quantity
Energy quantity scalar or array.
This is a
Quantity
subclass that adds convenience methods to handle common tasks for energy bin center arrays, like FITS I/O or generating equallogspaced grids of energies.See Energy handling in Gammapy for further information.
Parameters: energy :
array
, scalar,Quantity
Energy
unit :
UnitBase
, str, optionalThe unit of the value specified for the energy. This may be any string that
Unit
understands, but it is better to give an actual unit object.dtype :
dtype
, optionalSee
Quantity
.copy : bool, optional
See
Quantity
.Attributes Summary
T
Same as self.transpose(), except that self is returned if self.ndim < 2. base
Base object if memory is from some other object. cgs
Returns a copy of the current Quantity
instance with CGS units.ctypes
An object to simplify the interaction of the array with the ctypes module. data
Python buffer object pointing to the start of the array’s data. dtype
Datatype of the array’s elements. equivalencies
A list of equivalencies that will be applied by default during unit conversions. flags
Information about the memory layout of the array. flat
A 1D iterator over the Quantity array. imag
The imaginary part of the array. info
Container for meta information like name, description, format. isscalar
True if the value
of this quantity is a scalar, or False if it is an arraylike object.itemsize
Length of one array element in bytes. nbins
The number of bins. nbytes
Total bytes consumed by the elements of the array. ndim
Number of array dimensions. range
The covered energy range (tuple). real
The real part of the array. shape
Tuple of array dimensions. si
Returns a copy of the current Quantity
instance with SI units.size
Number of elements in the array. strides
Tuple of bytes to step in each dimension when traversing an array. unit
A UnitBase
object representing the unit of this quantity.value
The numerical value of this instance. Methods Summary
all
([axis, out, keepdims])Returns True if all elements evaluate to True. any
([axis, out, keepdims])Returns True if any of the elements of a
evaluate to True.argmax
([axis, out])Return indices of the maximum values along the given axis. argmin
([axis, out])Return indices of the minimum values along the given axis of a
.argpartition
(kth[, axis, kind, order])Returns the indices that would partition this array. argsort
([axis, kind, order])Returns the indices that would sort this array. astype
(dtype[, order, casting, subok, copy])Copy of the array, cast to a specified type. byteswap
([inplace])Swap the bytes of the array elements choose
(choices[, out, mode])Use an index array to construct a new array from a set of choices. clip
([min, max, out])Return an array whose values are limited to [min, max]
.compress
(condition[, axis, out])Return selected slices of this array along given axis. conj
()Complexconjugate all elements. conjugate
()Return the complex conjugate, elementwise. copy
([order])Return a copy of the array. cumprod
([axis, dtype, out])Return the cumulative product of the elements along the given axis. cumsum
([axis, dtype, out])Return the cumulative sum of the elements along the given axis. decompose
([bases])Generates a new Quantity
with the units decomposed.diagonal
([offset, axis1, axis2])Return specified diagonals. diff
([n, axis])dot
(b[, out])Dot product of two arrays. dump
(file)Dump a pickle of the array to the specified file. dumps
()Returns the pickle of the array as a string. ediff1d
([to_end, to_begin])equal_log_spacing
(emin, emax, nbins[, unit, …])Create Energy with equal logspacing ( Energy
).fill
(value)Fill the array with a scalar value. flatten
([order])Return a copy of the array collapsed into one dimension. from_fits
(hdu[, unit])Read ENERGIES fits extension ( Energy
).getfield
(dtype[, offset])Returns a field of the given array as a certain type. insert
(obj, values[, axis])Insert values along the given axis before the given indices and return a new Quantity
object.item
(*args)Copy an element of an array to a standard Python scalar and return it. itemset
(*args)Insert scalar into an array (scalar is cast to array’s dtype, if possible) max
([axis, out, keepdims])Return the maximum along a given axis. mean
([axis, dtype, out, keepdims])Returns the average of the array elements along given axis. min
([axis, out, keepdims])Return the minimum along a given axis. nansum
([axis, out, keepdims])newbyteorder
([new_order])Return the array with the same data viewed with a different byte order. nonzero
()Return the indices of the elements that are nonzero. partition
(kth[, axis, kind, order])Rearranges the elements in the array in such a way that value of the element in kth position is in the position it would be in a sorted array. prod
([axis, dtype, out, keepdims])Return the product of the array elements over the given axis ptp
([axis, out])Peak to peak (maximum  minimum) value along a given axis. put
(indices, values[, mode])Set a.flat[n] = values[n]
for alln
in indices.ravel
([order])Return a flattened array. repeat
(repeats[, axis])Repeat elements of an array. reshape
(shape[, order])Returns an array containing the same data with a new shape. resize
(new_shape[, refcheck])Change shape and size of array inplace. round
([decimals, out])Return a
with each element rounded to the given number of decimals.searchsorted
(v[, side, sorter])Find indices where elements of v should be inserted in a to maintain order. setfield
(val, dtype[, offset])Put a value into a specified place in a field defined by a datatype. setflags
([write, align, uic])Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively. sort
([axis, kind, order])Sort an array, inplace. squeeze
([axis])Remove singledimensional entries from the shape of a
.std
([axis, dtype, out, ddof, keepdims])Returns the standard deviation of the array elements along given axis. sum
([axis, dtype, out, keepdims])Return the sum of the array elements over the given axis. swapaxes
(axis1, axis2)Return a view of the array with axis1
andaxis2
interchanged.take
(indices[, axis, out, mode])Return an array formed from the elements of a
at the given indices.to
(unit[, equivalencies])Return a new Quantity
object with the specified unit.to_fits
()Write ENERGIES fits extension to_value
([unit, equivalencies])The numerical value, possibly in a different unit. tobytes
([order])Construct Python bytes containing the raw data bytes in the array. tofile
(fid[, sep, format])Write array to a file as text or binary (default). tolist
()Return the array as a (possibly nested) list. tostring
([order])Construct Python bytes containing the raw data bytes in the array. trace
([offset, axis1, axis2, dtype, out])Return the sum along diagonals of the array. transpose
(*axes)Returns a view of the array with axes transposed. var
([axis, dtype, out, ddof, keepdims])Returns the variance of the array elements, along given axis. view
([dtype, type])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.
Parameters: None
Returns: c : Python object
Possessing attributes data, shape, strides, etc.
See also
numpy.ctypeslib
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):
 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].
 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 c_int, c_long, or 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.
 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.
 data_as(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)).
 shape_as(obj): Return the shape tuple as an array of some other ctypes type. For example: self.shape_as(ctypes.c_short).
 strides_as(obj): Return the strides tuple as an array of some other ctypes type. For example: self.strides_as(ctypes.c_longlong).
Be careful using the ctypes attribute  especially on temporary arrays or arrays constructed on the fly. For example, calling
(a+b).ctypes.data_as(ctypes.c_void_p)
returns a pointer to memory that is invalid because the array created as (a+b) is deallocated before the next Python statement. You can avoid this problem using eitherc=a+b
orct=(a+b).ctypes
. In the latter case, ct will hold a reference to the array until ct is deleted or reassigned.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.
Parameters: None Returns: d : numpy dtype object 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 ina.flags['WRITEABLE']
), or by using lowercased attribute names (as ina.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 ifarr.shape[dim] == 1
or the array has no elements. It does not generally hold thatself.strides[1] == self.itemsize
for Cstyle contiguous arrays orself.strides[0] == self.itemsize
for Fortranstyle contiguous arrays is true.Attributes
C_CONTIGUOUS (C) The data is in a single, Cstyle contiguous segment. F_CONTIGUOUS (F) The data is in a single, Fortranstyle contiguous segment. OWNDATA (O) The array owns the memory it uses or borrows it from another object. WRITEABLE (W) 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. ALIGNED (A) The data and all elements are aligned appropriately for the hardware. WRITEBACKIFCOPY (X) 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. UPDATEIFCOPY (U) (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. FNC F_CONTIGUOUS and not C_CONTIGUOUS. FORC F_CONTIGUOUS or C_CONTIGUOUS (onesegment test). BEHAVED (B) ALIGNED and WRITEABLE. CARRAY (CA) BEHAVED and C_CONTIGUOUS. FARRAY (FA) BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.  UPDATEIFCOPY can only be set

flat
¶ A 1D iterator over the Quantity array.
This returns a
QuantityIterator
instance, which behaves the same as theflatiter
instance returned byflat
, 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
¶ 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 thatnumpy.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

nbins
¶ The number of bins.

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

range
¶ The covered energy range (tuple).

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.
Equivalent to
np.prod(a.shape)
, i.e., the product of the array’s dimensions.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 arraya
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.
Parameters: dtype : str or dtype
Typecode or datatype to which the array is cast.
order : {‘C’, ‘F’, ‘A’, ‘K’}, optional
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’.
casting : {‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional
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.
subok : bool, optional
If True, then subclasses will be passedthrough (default), otherwise the returned array will be forced to be a baseclass array.
copy : bool, optional
By default, astype always returns a newly allocated array. If this is set to false, and the
dtype
,order
, andsubok
requirements are satisfied, the input array is returned instead of a copy.Returns: arr_t : ndarray
Raises: ComplexWarning
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.
Parameters: inplace : bool, optional
If
True
, swap bytes inplace, default isFalse
.Returns: out : ndarray
The byteswapped array. If
inplace
isTrue
, 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.
Parameters: order : {‘C’, ‘F’, ‘A’, ‘K’}, optional
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 ofa
as closely as possible. (Note that this function andnumpy.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
(bases=[])¶ Generates a new
Quantity
with the units decomposed. Decomposed units have only irreducible units in them (seeastropy.units.UnitBase.decompose
).Parameters: bases : sequence of UnitBase, optional
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.Returns: newq :
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
(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.
Parameters: file : str
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.
Parameters: None

ediff1d
(to_end=None, to_begin=None)¶

classmethod
equal_log_spacing
(emin, emax, nbins, unit=None, per_decade=False)[source]¶ Create Energy with equal logspacing (
Energy
).if no unit is given, it will be taken from emax
Parameters: emin :
Quantity
, floatLowest energy bin
emax :
Quantity
, floatHighest energy bin
nbins : int
Number of bins
unit :
UnitBase
, strEnergy unit
per_decade : bool
Whether nbins is per decade.

fill
(value)¶ Fill the array with a scalar value.
Parameters: value : scalar
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.
Parameters: order : {‘C’, ‘F’, ‘A’, ‘K’}, optional
‘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 flattena
in the order the elements occur in memory. The default is ‘C’.Returns: y : ndarray
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])

classmethod
from_fits
(hdu, unit=None)[source]¶ Read ENERGIES fits extension (
Energy
).Parameters: hdu: `~astropy.io.fits.BinTableHDU`
ENERGIES
extensions.unit :
UnitBase
, str, NoneEnergy unit

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.
Parameters: dtype : str or dtype
The data type of the view. The dtype size of the view can not be larger than that of the array itself.
offset : int
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
(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.Parameters: obj : int, slice or sequence of ints
Object that defines the index or indices before which
values
is inserted.values : arraylike
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 ofvalues
must be consistent with this quantity.axis : int, optional
Axis along which to insert
values
. Ifaxis
is None then the quantity array is flattened before insertion.Returns: out :
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.
Parameters: *args : Arguments (variable number and type)
 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.
Returns: z : Standard Python scalar object
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
 none: in this case, the method only works for arrays
with one element (

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 thana[args] = item
. The item should be a scalar value andargs
must select a single item in the arraya
.Parameters: *args : Arguments
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 anndarray
, if you must do this. However, generally this is discouraged: among other problems, it complicates the appearance of the code. Also, when usingitemset
(anditem
) 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
(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.
Parameters: new_order : string, optional
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.Returns: new_arr : array
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 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.
Parameters: kth : int or sequence of ints
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 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 : int, optional
Axis along which to sort. Default is 1, which means sort along the last axis.
kind : {‘introselect’}, optional
Selection algorithm. Default is ‘introselect’.
order : str or list of str, optional
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.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)¶ 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 alln
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 onndarray
allows the elements of the shape parameter to be passed in as separate arguments. For example,a.reshape(10, 11)
is equivalent toa.reshape((10, 11))
.

resize
(new_shape, refcheck=True)¶ Change shape and size of array inplace.
Parameters: new_shape : tuple of ints, or
n
intsShape of resized array.
refcheck : bool, optional
If False, reference count will not be checked. Default is True.
Returns: None
Raises: ValueError
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.SystemError
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
intoa
’s field defined bydtype
and beginningoffset
bytes into the field.Parameters: val : object
Value to be placed in field.
dtype : dtype object
Datatype of the field in which to place
val
.offset : int, optional
The number of bytes into the field at which to place
val
.Returns: None
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.)Parameters: write : bool, optional
Describes whether or not
a
can be written to.align : bool, optional
Describes whether or not
a
is aligned properly for its type.uic : bool, optional
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.
Parameters: axis : int, optional
Axis along which to sort. Default is 1, which means sort along the last axis.
kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, optional
Sorting algorithm. Default is ‘quicksort’.
order : str or list of str, optional
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
andaxis2
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
(unit, equivalencies=[])¶ Return a new
Quantity
object with the specified unit.Parameters: unit :
UnitBase
instance, strequivalencies : list of equivalence pairs, optional
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 forQuantity
, but may be set for subclasses) IfNone
, 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_fits
()[source]¶ Write ENERGIES fits extension
Returns: hdu:
BinTableHDU
ENERGIES fits extension

to_value
(unit=None, equivalencies=[])¶ The numerical value, possibly in a different unit.
Parameters: unit :
UnitBase
instance or str, optionalThe unit in which the value should be given. If not given or
None
, use the current unit.equivalencies : list of equivalence pairs, optional
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 forQuantity
, but may be set for subclasses). IfNone
, no equivalencies will be applied at all, not even any set globally or within a context.Returns: value :
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.
Parameters: order : {‘C’, ‘F’, None}, optional
Order of the data for multidimensional arrays: C, Fortran, or the same as for the original array.
Returns: s : bytes
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().Parameters: fid : file or str
An open file object, or a string containing a filename.
sep : str
Separator between array items for text output. If “” (empty), a binary file is written, equivalent to
file.write(a.tobytes())
.format : str
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.

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.
Parameters: none
Returns: y : list
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.
Parameters: order : {‘C’, ‘F’, None}, optional
Order of the data for multidimensional arrays: C, Fortran, or the same as for the original array.
Returns: s : bytes
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])
, thena.transpose().shape = (i[n1], i[n2], ... i[1], i[0])
.Parameters: axes : None, tuple of ints, or
n
ints None or no argument: reverses the order of the axes.
 tuple of ints:
i
in thej
th place in the tuple meansa
’si
th axis becomesa.transpose()
’sj
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)
Returns: out : ndarray
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.
Parameters: dtype : datatype or ndarray subclass, optional
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 thetype
parameter).type : Python type, optional
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)
ora.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)
ora.view(type=ndarray_subclass)
just returns an instance ofndarray_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)
, ifsome_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 ofa
(shown byprint(a)
). It also depends on exactly howa
is stored in memory. Therefore ifa
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')])
