yt.units.yt_array module

YTArray class.

class yt.units.yt_array.YTArray[source]

Bases: numpy.ndarray

An ndarray subclass that attaches a symbolic unit object to the array data.

Parameters:
  • input_array (iterable) – A tuple, list, or array to attach units to
  • input_units (String unit specification, unit symbol object, or astropy units) – The units of the array. Powers must be specified using python syntax (cm**3, not cm^3).
  • registry (UnitRegistry) – The registry to create units from. If input_units is already associated with a unit registry and this is specified, this will be used instead of the registry associated with the unit object.
  • dtype (data-type) – The dtype of the array data. Defaults to the dtype of the input data, or, if none is found, uses np.float64
  • bypass_validation (boolean) – If True, all input validation is skipped. Using this option may produce corrupted, invalid units or array data, but can lead to significant speedups in the input validation logic adds significant overhead. If set, input_units must be a valid unit object. Defaults to False.

Examples

>>> from yt import YTArray
>>> a = YTArray([1, 2, 3], 'cm')
>>> b = YTArray([4, 5, 6], 'm')
>>> a + b
YTArray([ 401.,  502.,  603.]) cm
>>> b + a
YTArray([ 4.01,  5.02,  6.03]) m

NumPy ufuncs will pass through units where appropriate.

>>> import numpy as np
>>> a = YTArray(np.arange(8) - 4, 'g/cm**3')
>>> np.abs(a)
YTArray([4, 3, 2, 1, 0, 1, 2, 3]) g/cm**3

and strip them when it would be annoying to deal with them.

>>> np.log10(a)
array([       -inf,  0.        ,  0.30103   ,  0.47712125,  0.60205999,
        0.69897   ,  0.77815125,  0.84509804])

YTArray is tightly integrated with yt datasets:

>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> a = ds.arr(np.ones(5), 'code_length')
>>> a.in_cgs()
YTArray([  3.08600000e+24,   3.08600000e+24,   3.08600000e+24,
         3.08600000e+24,   3.08600000e+24]) cm

This is equivalent to:

>>> b = YTArray(np.ones(5), 'code_length', registry=ds.unit_registry)
>>> np.all(a == b)
True
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.])
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 data-type 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 byte-order 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 sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.
  • copy (bool, optional) – 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.
Returns:

arr_t – Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order.

Return type:

ndarray

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.

Raises:ComplexWarning – When casting from complex to float or int. To avoid this, one should use a.real.astype(t).

Examples

>>> x = np.array([1, 2, 2.5])
>>> x
array([ 1. ,  2. ,  2.5])
>>> x.astype(int)
array([1, 2, 2])
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
byteswap(inplace)

Swap the bytes of the array elements

Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place.

Parameters:inplace (bool, optional) – If True, swap bytes in-place, default is False.
Returns:out – The byteswapped array. If inplace is True, this is a view to self.
Return type:ndarray

Examples

>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> map(hex, A)
['0x1', '0x100', '0x2233']
>>> A.byteswap(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()

Complex-conjugate all elements.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate()
equivalent function
conjugate()

Return the complex conjugate, element-wise.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate()
equivalent function
convert_to_base(unit_system='cgs')[source]

Convert the array and units to the equivalent base units in the specified unit system.

Parameters:unit_system (string, optional) – The unit system to be used in the conversion. If not specified, the default base units of cgs are used.

Examples

>>> E = YTQuantity(2.5, "erg/s")
>>> E.convert_to_base(unit_system="galactic")
convert_to_cgs()[source]

Convert the array and units to the equivalent cgs units.

convert_to_mks()[source]

Convert the array and units to the equivalent mks units.

convert_to_units(units)[source]

Convert the array and units to the given units.

Parameters:units (Unit object or str) – The units you want to convert to.
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 C-order, ‘F’ means F-order, ‘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 :func:numpy.copy are very similar, but have different default values for their order= arguments.)

See also

numpy.copy(), numpy.copyto()

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
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 – Possessing attributes data, shape, strides, etc.
Return type:Python object

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 byte-order. The memory area may not even be writeable. The array flags and data-type of this array should be respected when passing this attribute to arbitrary C-code 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 C-integer corresponding to dtype(‘p’) on this platform. This base-type 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 c-types 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 floating-point data: self.data_as(ctypes.POINTER(ctypes.c_double)).
  • shape_as(obj): Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).
  • strides_as(obj): Return the strides tuple as an array of some other c-types 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 either c=a+b or ct=(a+b).ctypes. In the latter case, ct will hold a reference to the array until ct is deleted or re-assigned.

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>
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
d

Get a view of the array data.

data

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

diagonal(offset=0, axis1=0, axis2=1)

Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.

Refer to numpy.diagonal() for full documentation.

See also

numpy.diagonal()
equivalent function
dot(b, out=None)[source]
dtype

Data-type of the array’s elements.

Parameters:None
Returns:d
Return type:numpy dtype object

See also

numpy.dtype

Examples

>>> x
array([[0, 1],
       [2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>
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
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.])
flags

Information about the memory layout of the array.

C_CONTIGUOUS(C)

The data is in a single, C-style contiguous segment.

F_CONTIGUOUS(F)

The data is in a single, Fortran-style 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 read-only. 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 non-writeable array raises a RuntimeError exception.

ALIGNED(A)

The data and all elements are aligned appropriately for the hardware.

UPDATEIFCOPY(U)

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 (one-segment test).

BEHAVED(B)

ALIGNED and WRITEABLE.

CARRAY(CA)

BEHAVED and C_CONTIGUOUS.

FARRAY(FA)

BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.

Notes

The flags object can be accessed dictionary-like (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 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.
  • 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 C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional 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 C-style contiguous arrays or self.strides[0] == self.itemsize for Fortran-style contiguous arrays is true.

flat

A 1-D iterator over the array.

This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object.

See also

flatten
Return a copy of the array collapsed into one dimension.

flatiter

Examples

>>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
       [4, 5, 6]])
>>> x.flat[3]
4
>>> x.T
array([[1, 4],
       [2, 5],
       [3, 6]])
>>> x.T.flat[3]
5
>>> type(x.flat)
<type 'numpy.flatiter'>

An assignment example:

>>> x.flat = 3; x
array([[3, 3, 3],
       [3, 3, 3]])
>>> x.flat[[1,4]] = 1; x
array([[3, 1, 3],
       [3, 1, 3]])
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 row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.
Returns:y – A copy of the input array, flattened to one dimension.
Return type:ndarray

See also

ravel()
Return a flattened array.
flat()
A 1-D flat iterator over the array.

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_astropy(arr, unit_registry=None)[source]

Convert an AstroPy “Quantity” to a YTArray or YTQuantity.

Parameters:
  • arr (AstroPy Quantity) – The Quantity to convert from.
  • unit_registry (yt UnitRegistry, optional) – A yt unit registry to use in the conversion. If one is not supplied, the default one will be used.
classmethod from_hdf5(filename, dataset_name=None, group_name=None)[source]

Attempts read in and convert a dataset in an hdf5 file into a YTArray.

Parameters:
  • filename (string) –
  • filename to of the hdf5 file. (The) –
  • dataset_name (string) – The name of the dataset to read from. If the dataset has a units attribute, attempt to infer units as well.
  • group_name (string) – An optional group to read the arrays from. If not specified, the arrays are datasets at the top level by default.
classmethod from_pint(arr, unit_registry=None)[source]

Convert a Pint “Quantity” to a YTArray or YTQuantity.

Parameters:
  • arr (Pint Quantity) – The Quantity to convert from.
  • unit_registry (yt UnitRegistry, optional) – A yt unit registry to use in the conversion. If one is not supplied, the default one will be used.

Examples

>>> from pint import UnitRegistry
>>> import numpy as np
>>> ureg = UnitRegistry()
>>> a = np.random.random(10)
>>> b = ureg.Quantity(a, "erg/cm**3")
>>> c = yt.YTArray.from_pint(b)
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 data-type. 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 16-byte elements. If taking a view with a 32-bit 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.]])
has_equivalent(equiv)[source]

Check to see if this YTArray or YTQuantity has an equivalent unit in equiv.

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')
in_base(unit_system='cgs')[source]

Creates a copy of this array with the data in the specified unit system, and returns it in that system’s base units.

Parameters:unit_system (string, optional) – The unit system to be used in the conversion. If not specified, the default base units of cgs are used.

Examples

>>> E = YTQuantity(2.5, "erg/s")
>>> E_new = E.in_base(unit_system="galactic")
in_cgs()[source]

Creates a copy of this array with the data in the equivalent cgs units, and returns it.

Returns:
Return type:Quantity object with data converted to cgs units.
in_mks()[source]

Creates a copy of this array with the data in the equivalent mks units, and returns it.

Returns:
Return type:Quantity object with data converted to mks units.
in_units(units)[source]

Creates a copy of this array with the data in the supplied units, and returns it.

Parameters:units (Unit object or string) – The units you want to get a new quantity in.
Returns:
Return type:YTArray
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 nd-index into the array.
Returns:z – A copy of the specified element of the array as a suitable Python scalar
Return type:Standard Python scalar object

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.

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 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 look-up 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]])
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
list_equivalencies()[source]

Lists the possible equivalencies associated with this YTArray or YTQuantity.

max(axis=None, out=None)

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)[source]
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
nbytes

Total bytes consumed by the elements of the array.

Notes

Does not include memory consumed by non-element 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
ndarray_view()[source]

Returns a view into the array, but as an ndarray rather than ytarray.

Returns:
Return type:View of this array’s data.
ndim

Number of array dimensions.

Examples

>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3
ndview

Get a view of the array data.

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 sub-arrays 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 case-insensitive 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 big-endian.

Returns:new_arr – New array object with the dtype reflecting given change to the byte order.
Return type:array
nonzero()

Return the indices of the elements that are non-zero.

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)[source]
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 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.
real

The real part of the array.

Examples

>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1.        ,  0.70710678])
>>> x.real.dtype
dtype('float64')

See also

numpy.real
equivalent function
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
resize(new_shape, refcheck=True)

Change shape and size of array in-place.

Parameters:
  • new_shape (tuple of ints, or n ints) – Shape of resized array.
  • refcheck (bool, optional) – If False, reference count will not be checked. Default is True.
Returns:

Return type:

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 data-type.

Place val into a‘s field defined by dtype and beginning offset bytes into the field.

Parameters:
  • val (object) – Value to be placed in field.
  • dtype (dtype object) – Data-type of the field in which to place val.
  • offset (int, optional) – The number of bytes into the field at which to place val.
Returns:

Return type:

None

See also

getfield()

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,   1.48219694e-323,   1.48219694e-323],
       [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],
       [  1.48219694e-323,   1.48219694e-323,   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, and UPDATEIFCOPY, respectively.

These Boolean-valued 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 UPDATEIFCOPY flag 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 6 Boolean flags in use, only three of which can be changed by the user: 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) this array is a copy of some other array (referenced by .base). When this array is deallocated, the base array will be updated with the contents of this array.

All flags can be accessed using their first (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
  UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : False
  ALIGNED : False
  UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: cannot set UPDATEIFCOPY flag to True
shape

Tuple of array dimensions.

Notes

May be used to “reshape” the array, as long as this would not require a change in the total number of elements

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
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
sort(axis=-1, kind='quicksort', order=None)

Sort an array, in-place.

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 single-dimensional 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)[source]
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.

Notes

Imagine an array of 32-bit 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
sum(axis=None, dtype=None, out=None)[source]
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(units)[source]

An alias for YTArray.in_units().

See the docstrings of that function for details.

to_astropy(**kwargs)[source]

Creates a new AstroPy quantity with the same unit information.

to_equivalent(unit, equiv, **kwargs)[source]

Convert a YTArray or YTQuantity to an equivalent, e.g., something that is related by only a constant factor but not in the same units.

Parameters:
  • unit (string) – The unit that you wish to convert to.
  • equiv (string) – The equivalence you wish to use. To see which equivalencies are supported for this unitful quantity, try the list_equivalencies() method.

Examples

>>> a = yt.YTArray(1.0e7,"K")
>>> a.to_equivalent("keV", "thermal")
to_ndarray()[source]

Creates a copy of this array with the unit information stripped

to_pint(unit_registry=None)[source]

Convert a YTArray or YTQuantity to a Pint Quantity.

Parameters:
  • arr (YTArray or YTQuantity) – The unitful quantity to convert from.
  • unit_registry (Pint UnitRegistry, optional) – The Pint UnitRegistry to use in the conversion. If one is not supplied, the default one will be used. NOTE: This is not the same as a yt UnitRegistry object.

Examples

>>> a = YTQuantity(4.0, "cm**2/s")
>>> b = a.to_pint()
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 C-order 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 – Python bytes exhibiting a copy of a‘s raw data.
Return type:bytes

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 – The possibly nested list of array elements.
Return type:list

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 C-order 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 – Python bytes exhibiting a copy of a‘s raw data.
Return type:bytes

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 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.) For a 2-D array, this is the usual matrix transpose. For an n-D 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[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... 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 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 n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form)
Returns:out – View of a, with axes suitably permuted.
Return type:ndarray

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]])
ua

Get a YTArray filled with ones with the same unit and shape as this array

unit_array

Get a YTArray filled with ones with the same unit and shape as this array

unit_quantity

Get a YTQuantity with the same unit as this array and a value of 1.0

uq

Get a YTQuantity with the same unit as this array and a value of 1.0

v

Get a copy of the array data as a numpy ndarray

value

Get a copy of the array data as a numpy ndarray

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 (data-type or ndarray sub-class, optional) – Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type 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) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. 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 C-ordered versus fortran-ordered, 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, fortran-ordering, 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')])
write_hdf5(filename, dataset_name=None, info=None, group_name=None)[source]

Writes a YTArray to hdf5 file.

Parameters:
  • filename (string) – The filename to create and write a dataset to
  • dataset_name (string) – The name of the dataset to create in the file.
  • info (dictionary) – A dictionary of supplementary info to write to append as attributes to the dataset.
  • group_name (string) – An optional group to write the arrays to. If not specified, the arrays are datasets at the top level by default.

Examples

>>> a = YTArray([1,2,3], 'cm')
>>> myinfo = {'field':'dinosaurs', 'type':'field_data'}
>>> a.write_hdf5('test_array_data.h5', dataset_name='dinosaurs',
...              info=myinfo)
class yt.units.yt_array.YTQuantity[source]

Bases: yt.units.yt_array.YTArray

A scalar associated with a unit.

Parameters:
  • input_scalar (an integer or floating point scalar) – The scalar to attach units to
  • input_units (String unit specification, unit symbol object, or astropy units) – The units of the quantity. Powers must be specified using python syntax (cm**3, not cm^3).
  • registry (A UnitRegistry object) – The registry to create units from. If input_units is already associated with a unit registry and this is specified, this will be used instead of the registry associated with the unit object.
  • dtype (data-type) – The dtype of the array data.

Examples

>>> from yt import YTQuantity
>>> a = YTQuantity(1, 'cm')
>>> b = YTQuantity(2, 'm')
>>> a + b
201.0 cm
>>> b + a
2.01 m

NumPy ufuncs will pass through units where appropriate.

>>> import numpy as np
>>> a = YTQuantity(12, 'g/cm**3')
>>> np.abs(a)
12 g/cm**3

and strip them when it would be annoying to deal with them.

>>> print(np.log10(a))
1.07918124605

YTQuantity is tightly integrated with yt datasets:

>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> a = ds.quan(5, 'code_length')
>>> a.in_cgs()
1.543e+25 cm

This is equivalent to:

>>> b = YTQuantity(5, 'code_length', registry=ds.unit_registry)
>>> np.all(a == b)
True
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.])
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 data-type 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 byte-order 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 sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.
  • copy (bool, optional) – 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.
Returns:

arr_t – Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order.

Return type:

ndarray

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.

Raises:ComplexWarning – When casting from complex to float or int. To avoid this, one should use a.real.astype(t).

Examples

>>> x = np.array([1, 2, 2.5])
>>> x
array([ 1. ,  2. ,  2.5])
>>> x.astype(int)
array([1, 2, 2])
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
byteswap(inplace)

Swap the bytes of the array elements

Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place.

Parameters:inplace (bool, optional) – If True, swap bytes in-place, default is False.
Returns:out – The byteswapped array. If inplace is True, this is a view to self.
Return type:ndarray

Examples

>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> map(hex, A)
['0x1', '0x100', '0x2233']
>>> A.byteswap(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()

Complex-conjugate all elements.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate()
equivalent function
conjugate()

Return the complex conjugate, element-wise.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate()
equivalent function
convert_to_base(unit_system='cgs')

Convert the array and units to the equivalent base units in the specified unit system.

Parameters:unit_system (string, optional) – The unit system to be used in the conversion. If not specified, the default base units of cgs are used.

Examples

>>> E = YTQuantity(2.5, "erg/s")
>>> E.convert_to_base(unit_system="galactic")
convert_to_cgs()

Convert the array and units to the equivalent cgs units.

convert_to_mks()

Convert the array and units to the equivalent mks units.

convert_to_units(units)

Convert the array and units to the given units.

Parameters:units (Unit object or str) – The units you want to convert to.
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 C-order, ‘F’ means F-order, ‘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 :func:numpy.copy are very similar, but have different default values for their order= arguments.)

See also

numpy.copy(), numpy.copyto()

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
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 – Possessing attributes data, shape, strides, etc.
Return type:Python object

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 byte-order. The memory area may not even be writeable. The array flags and data-type of this array should be respected when passing this attribute to arbitrary C-code 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 C-integer corresponding to dtype(‘p’) on this platform. This base-type 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 c-types 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 floating-point data: self.data_as(ctypes.POINTER(ctypes.c_double)).
  • shape_as(obj): Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).
  • strides_as(obj): Return the strides tuple as an array of some other c-types 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 either c=a+b or ct=(a+b).ctypes. In the latter case, ct will hold a reference to the array until ct is deleted or re-assigned.

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>
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
d

Get a view of the array data.

data

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

diagonal(offset=0, axis1=0, axis2=1)

Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.

Refer to numpy.diagonal() for full documentation.

See also

numpy.diagonal()
equivalent function
dot(b, out=None)
dtype

Data-type of the array’s elements.

Parameters:None
Returns:d
Return type:numpy dtype object

See also

numpy.dtype

Examples

>>> x
array([[0, 1],
       [2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>
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
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.])
flags

Information about the memory layout of the array.

C_CONTIGUOUS(C)

The data is in a single, C-style contiguous segment.

F_CONTIGUOUS(F)

The data is in a single, Fortran-style 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 read-only. 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 non-writeable array raises a RuntimeError exception.

ALIGNED(A)

The data and all elements are aligned appropriately for the hardware.

UPDATEIFCOPY(U)

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 (one-segment test).

BEHAVED(B)

ALIGNED and WRITEABLE.

CARRAY(CA)

BEHAVED and C_CONTIGUOUS.

FARRAY(FA)

BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.

Notes

The flags object can be accessed dictionary-like (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 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.
  • 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 C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional 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 C-style contiguous arrays or self.strides[0] == self.itemsize for Fortran-style contiguous arrays is true.

flat

A 1-D iterator over the array.

This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object.

See also

flatten
Return a copy of the array collapsed into one dimension.

flatiter

Examples

>>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
       [4, 5, 6]])
>>> x.flat[3]
4
>>> x.T
array([[1, 4],
       [2, 5],
       [3, 6]])
>>> x.T.flat[3]
5
>>> type(x.flat)
<type 'numpy.flatiter'>

An assignment example:

>>> x.flat = 3; x
array([[3, 3, 3],
       [3, 3, 3]])
>>> x.flat[[1,4]] = 1; x
array([[3, 1, 3],
       [3, 1, 3]])
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 row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.
Returns:y – A copy of the input array, flattened to one dimension.
Return type:ndarray

See also

ravel()
Return a flattened array.
flat()
A 1-D flat iterator over the array.

Examples

>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])
from_astropy(arr, unit_registry=None)

Convert an AstroPy “Quantity” to a YTArray or YTQuantity.

Parameters:
  • arr (AstroPy Quantity) – The Quantity to convert from.
  • unit_registry (yt UnitRegistry, optional) – A yt unit registry to use in the conversion. If one is not supplied, the default one will be used.
from_hdf5(filename, dataset_name=None, group_name=None)

Attempts read in and convert a dataset in an hdf5 file into a YTArray.

Parameters:
  • filename (string) –
  • filename to of the hdf5 file. (The) –
  • dataset_name (string) – The name of the dataset to read from. If the dataset has a units attribute, attempt to infer units as well.
  • group_name (string) – An optional group to read the arrays from. If not specified, the arrays are datasets at the top level by default.
from_pint(arr, unit_registry=None)

Convert a Pint “Quantity” to a YTArray or YTQuantity.

Parameters:
  • arr (Pint Quantity) – The Quantity to convert from.
  • unit_registry (yt UnitRegistry, optional) – A yt unit registry to use in the conversion. If one is not supplied, the default one will be used.

Examples

>>> from pint import UnitRegistry
>>> import numpy as np
>>> ureg = UnitRegistry()
>>> a = np.random.random(10)
>>> b = ureg.Quantity(a, "erg/cm**3")
>>> c = yt.YTArray.from_pint(b)
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 data-type. 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 16-byte elements. If taking a view with a 32-bit 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.]])
has_equivalent(equiv)

Check to see if this YTArray or YTQuantity has an equivalent unit in equiv.

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')
in_base(unit_system='cgs')

Creates a copy of this array with the data in the specified unit system, and returns it in that system’s base units.

Parameters:unit_system (string, optional) – The unit system to be used in the conversion. If not specified, the default base units of cgs are used.

Examples

>>> E = YTQuantity(2.5, "erg/s")
>>> E_new = E.in_base(unit_system="galactic")
in_cgs()

Creates a copy of this array with the data in the equivalent cgs units, and returns it.

Returns:
Return type:Quantity object with data converted to cgs units.
in_mks()

Creates a copy of this array with the data in the equivalent mks units, and returns it.

Returns:
Return type:Quantity object with data converted to mks units.
in_units(units)

Creates a copy of this array with the data in the supplied units, and returns it.

Parameters:units (Unit object or string) – The units you want to get a new quantity in.
Returns:
Return type:YTArray
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 nd-index into the array.
Returns:z – A copy of the specified element of the array as a suitable Python scalar
Return type:Standard Python scalar object

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.

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 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 look-up 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]])
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
list_equivalencies()

Lists the possible equivalencies associated with this YTArray or YTQuantity.

max(axis=None, out=None)

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)
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
nbytes

Total bytes consumed by the elements of the array.

Notes

Does not include memory consumed by non-element 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
ndarray_view()

Returns a view into the array, but as an ndarray rather than ytarray.

Returns:
Return type:View of this array’s data.
ndim

Number of array dimensions.

Examples

>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3
ndview

Get a view of the array data.

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 sub-arrays 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 case-insensitive 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 big-endian.

Returns:new_arr – New array object with the dtype reflecting given change to the byte order.
Return type:array
nonzero()

Return the indices of the elements that are non-zero.

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)
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 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.
real

The real part of the array.

Examples

>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1.        ,  0.70710678])
>>> x.real.dtype
dtype('float64')

See also

numpy.real
equivalent function
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
resize(new_shape, refcheck=True)

Change shape and size of array in-place.

Parameters:
  • new_shape (tuple of ints, or n ints) – Shape of resized array.
  • refcheck (bool, optional) – If False, reference count will not be checked. Default is True.
Returns:

Return type:

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 data-type.

Place val into a‘s field defined by dtype and beginning offset bytes into the field.

Parameters:
  • val (object) – Value to be placed in field.
  • dtype (dtype object) – Data-type of the field in which to place val.
  • offset (int, optional) – The number of bytes into the field at which to place val.
Returns:

Return type:

None

See also

getfield()

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,   1.48219694e-323,   1.48219694e-323],
       [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],
       [  1.48219694e-323,   1.48219694e-323,   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, and UPDATEIFCOPY, respectively.

These Boolean-valued 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 UPDATEIFCOPY flag 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 6 Boolean flags in use, only three of which can be changed by the user: 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) this array is a copy of some other array (referenced by .base). When this array is deallocated, the base array will be updated with the contents of this array.

All flags can be accessed using their first (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
  UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : False
  ALIGNED : False
  UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: cannot set UPDATEIFCOPY flag to True
shape

Tuple of array dimensions.

Notes

May be used to “reshape” the array, as long as this would not require a change in the total number of elements

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
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
sort(axis=-1, kind='quicksort', order=None)

Sort an array, in-place.

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 single-dimensional 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)
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.

Notes

Imagine an array of 32-bit 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
sum(axis=None, dtype=None, out=None)
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(units)

An alias for YTArray.in_units().

See the docstrings of that function for details.

to_astropy(**kwargs)

Creates a new AstroPy quantity with the same unit information.

to_equivalent(unit, equiv, **kwargs)

Convert a YTArray or YTQuantity to an equivalent, e.g., something that is related by only a constant factor but not in the same units.

Parameters:
  • unit (string) – The unit that you wish to convert to.
  • equiv (string) – The equivalence you wish to use. To see which equivalencies are supported for this unitful quantity, try the list_equivalencies() method.

Examples

>>> a = yt.YTArray(1.0e7,"K")
>>> a.to_equivalent("keV", "thermal")
to_ndarray()

Creates a copy of this array with the unit information stripped

to_pint(unit_registry=None)

Convert a YTArray or YTQuantity to a Pint Quantity.

Parameters:
  • arr (YTArray or YTQuantity) – The unitful quantity to convert from.
  • unit_registry (Pint UnitRegistry, optional) – The Pint UnitRegistry to use in the conversion. If one is not supplied, the default one will be used. NOTE: This is not the same as a yt UnitRegistry object.

Examples

>>> a = YTQuantity(4.0, "cm**2/s")
>>> b = a.to_pint()
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 C-order 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 – Python bytes exhibiting a copy of a‘s raw data.
Return type:bytes

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 – The possibly nested list of array elements.
Return type:list

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 C-order 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 – Python bytes exhibiting a copy of a‘s raw data.
Return type:bytes

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 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.) For a 2-D array, this is the usual matrix transpose. For an n-D 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[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... 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 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 n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form)
Returns:out – View of a, with axes suitably permuted.
Return type:ndarray

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]])
ua

Get a YTArray filled with ones with the same unit and shape as this array

unit_array

Get a YTArray filled with ones with the same unit and shape as this array

unit_quantity

Get a YTQuantity with the same unit as this array and a value of 1.0

uq

Get a YTQuantity with the same unit as this array and a value of 1.0

v

Get a copy of the array data as a numpy ndarray

value

Get a copy of the array data as a numpy ndarray

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 (data-type or ndarray sub-class, optional) – Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type 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) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. 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 C-ordered versus fortran-ordered, 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, fortran-ordering, 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')])
write_hdf5(filename, dataset_name=None, info=None, group_name=None)

Writes a YTArray to hdf5 file.

Parameters:
  • filename (string) – The filename to create and write a dataset to
  • dataset_name (string) – The name of the dataset to create in the file.
  • info (dictionary) – A dictionary of supplementary info to write to append as attributes to the dataset.
  • group_name (string) – An optional group to write the arrays to. If not specified, the arrays are datasets at the top level by default.

Examples

>>> a = YTArray([1,2,3], 'cm')
>>> myinfo = {'field':'dinosaurs', 'type':'field_data'}
>>> a.write_hdf5('test_array_data.h5', dataset_name='dinosaurs',
...              info=myinfo)
yt.units.yt_array.arctan2_unit(unit1, unit2)[source]
yt.units.yt_array.array_like_field(data, x, field)[source]
yt.units.yt_array.bitop_units(unit1, unit2)[source]
yt.units.yt_array.coerce_iterable_units(input_object)[source]
yt.units.yt_array.comparison_unit(unit1, unit2=None)[source]
yt.units.yt_array.divide_units(unit1, unit2)[source]
yt.units.yt_array.get_binary_op_return_class(cls1, cls2)[source]
yt.units.yt_array.get_inp_u_binary(ufunc, inputs)[source]
yt.units.yt_array.get_inp_u_unary(ufunc, inputs, out_arr=None)[source]
yt.units.yt_array.handle_comparison_units(inps, units, ufunc, ret_class, raise_error=False)[source]
yt.units.yt_array.handle_multiply_divide_units(unit, units, out, out_arr)[source]
yt.units.yt_array.handle_preserve_units(inps, units, ufunc, ret_class)[source]
yt.units.yt_array.invert_units(unit)[source]
yt.units.yt_array.iterable(obj)[source]
yt.units.yt_array.loadtxt(fname, dtype='float', delimiter='\t', usecols=None, comments='#')[source]

Load YTArrays with unit information from a text file. Each row in the text file must have the same number of values.

Parameters:
  • fname (str) – Filename to read.
  • dtype (data-type, optional) – Data-type of the resulting array; default: float.
  • delimiter (str, optional) – The string used to separate values. By default, this is any whitespace.
  • usecols (sequence, optional) – Which columns to read, with 0 being the first. For example, usecols = (1,4,5) will extract the 2nd, 5th and 6th columns. The default, None, results in all columns being read.
  • comments (str, optional) – The character used to indicate the start of a comment; default: ‘#’.

Examples

>>> temp, velx = yt.loadtxt("sphere.dat", usecols=(1,2), delimiter="\t")
yt.units.yt_array.multiply_units(unit1, unit2)[source]
yt.units.yt_array.passthrough_unit(unit, unit2=None)[source]
yt.units.yt_array.power_unit(unit, power)[source]
yt.units.yt_array.preserve_units(unit1, unit2=None)[source]
yt.units.yt_array.reciprocal_unit(unit)[source]
yt.units.yt_array.return_arr(func)[source]
yt.units.yt_array.return_without_unit(unit, unit2=None)[source]
yt.units.yt_array.sanitize_units_add(this_object, other_object, op_string)[source]
yt.units.yt_array.sanitize_units_mul(this_object, other_object)[source]
yt.units.yt_array.savetxt(fname, arrays, fmt='%.18e', delimiter='\t', header='', footer='', comments='#')[source]

Write YTArrays with unit information to a text file.

Parameters:
  • fname (str) – The file to write the YTArrays to.
  • arrays (list of YTArrays or single YTArray) – The array(s) to write to the file.
  • fmt (str or sequence of strs, optional) – A single format (%10.5f), or a sequence of formats.
  • delimiter (str, optional) – String or character separating columns.
  • header (str, optional) – String that will be written at the beginning of the file, before the unit header.
  • footer (str, optional) – String that will be written at the end of the file.
  • comments (str, optional) – String that will be prepended to the header and footer strings, to mark them as comments. Default: ‘# ‘, as expected by e.g. yt.loadtxt.

Examples

>>> sp = ds.sphere("c", (100,"kpc"))
>>> a = sp["density"]
>>> b = sp["temperature"]
>>> c = sp["velocity_x"]
>>> yt.savetxt("sphere.dat", [a,b,c], header='My sphere stuff', delimiter="\t")
yt.units.yt_array.sqrt_unit(unit)[source]
yt.units.yt_array.square_unit(unit)[source]
yt.units.yt_array.uconcatenate(arrs, axis=0)[source]

Concatenate a sequence of arrays.

This wrapper around numpy.concatenate preserves units. All input arrays must have the same units. See the documentation of numpy.concatenate for full details.

Examples

>>> A = yt.YTArray([1, 2, 3], 'cm')
>>> B = yt.YTArray([2, 3, 4], 'cm')
>>> uconcatenate((A, B))
YTArray([ 1., 2., 3., 2., 3., 4.]) cm
yt.units.yt_array.ucross(arr1, arr2, registry=None, axisa=-1, axisb=-1, axisc=-1, axis=None)[source]

Applies the cross product to two YT arrays.

This wrapper around numpy.cross preserves units. See the documentation of numpy.cross for full details.

yt.units.yt_array.udot(op1, op2)[source]

Matrix or vector dot product that preservs units

This is a wrapper around np.dot that preserves units.

yt.units.yt_array.uhstack(arrs)[source]

Stack arrays in sequence horizontally (column wise) while preserving units

This is a wrapper around np.hstack that preserves units.

yt.units.yt_array.uintersect1d(arr1, arr2, assume_unique=False)[source]

Find the sorted unique elements of the two input arrays.

A wrapper around numpy.intersect1d that preserves units. All input arrays must have the same units. See the documentation of numpy.intersect1d for full details.

Examples

>>> A = yt.YTArray([1, 2, 3], 'cm')
>>> B = yt.YTArray([2, 3, 4], 'cm')
>>> uintersect1d(A, B)
YTArray([ 2., 3.]) cm
yt.units.yt_array.unorm(data, ord=None, axis=None, keepdims=False)[source]

Matrix or vector norm that preserves units

This is a wrapper around np.linalg.norm that preserves units. See the documentation for that function for descriptions of the keyword arguments.

The keepdims argument is ignored if the version of numpy installed is older than numpy 1.10.0.

yt.units.yt_array.uunion1d(arr1, arr2)[source]

Find the union of two arrays.

A wrapper around numpy.intersect1d that preserves units. All input arrays must have the same units. See the documentation of numpy.intersect1d for full details.

Examples

>>> A = yt.YTArray([1, 2, 3], 'cm')
>>> B = yt.YTArray([2, 3, 4], 'cm')
>>> uunion1d(A, B)
YTArray([ 1., 2., 3., 4.]) cm
yt.units.yt_array.uvstack(arrs)[source]

Stack arrays in sequence vertically (row wise) while preserving units

This is a wrapper around np.vstack that preserves units.

yt.units.yt_array.validate_comparison_units(this, other, op_string)[source]
yt.units.yt_array.validate_numpy_wrapper_units(v, arrs)[source]