jax.numpy.dtype — JAX documentation (original) (raw)

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• dtype
  • dtype.__init__()

jax.numpy.dtype#

class jax.numpy.dtype(dtype, align=False, _copy=False_[, _metadata_])#

Create a data type object.

A numpy array is homogeneous, and contains elements described by a dtype object. A dtype object can be constructed from different combinations of fundamental numeric types.

Parameters:

• dtype – Object to be converted to a data type object.
• align (bool, optional) – Add padding to the fields to match what a C compiler would output for a similar C-struct. Can be True only if obj is a dictionary or a comma-separated string. If a struct dtype is being created, this also sets a sticky alignment flag isalignedstruct.
• copy (bool, optional) – Make a new copy of the data-type object. If False, the result may just be a reference to a built-in data-type object.
• metadata (dict, optional) – An optional dictionary with dtype metadata.

Examples

Using array-scalar type:

import numpy as np np.dtype(np.int16) dtype('int16')

Structured type, one field name ‘f1’, containing int16:

np.dtype([('f1', np.int16)]) dtype([('f1', '<i2')])

Structured type, one field named ‘f1’, in itself containing a structured type with one field:

np.dtype([('f1', [('f1', np.int16)])]) dtype([('f1', [('f1', '<i2')])])

Structured type, two fields: the first field contains an unsigned int, the second an int32:

np.dtype([('f1', np.uint64), ('f2', np.int32)]) dtype([('f1', '<u8'), ('f2', '<i4')])

Using array-protocol type strings:

np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', 'S10')])

Using comma-separated field formats. The shape is (2,3):

np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])

Using tuples. int is a fixed type, 3 the field’s shape. voidis a flexible type, here of size 10:

np.dtype([('hello',(np.int64,3)),('world',np.void,10)]) dtype([('hello', '<i8', (3,)), ('world', 'V10')])

Subdivide int16 into 2 int8’s, called x and y. 0 and 1 are the offsets in bytes:

np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)})) dtype((numpy.int16, [('x', 'i1'), ('y', 'i1')]))

Using dictionaries. Two fields named ‘gender’ and ‘age’:

np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]}) dtype([('gender', 'S1'), ('age', 'u1')])

Offsets in bytes, here 0 and 25:

np.dtype({'surname':('S25',0),'age':(np.uint8,25)}) dtype([('surname', 'S25'), ('age', 'u1')])

__init__()#

Methods

Attributes