pyorps.graph.api package

Submodules

pyorps.graph.api.graph_api module

class pyorps.graph.api.graph_api.ABC

Bases: object

Helper class that provides a standard way to create an ABC using inheritance.

_abc_impl = <_abc._abc_data object>

class pyorps.graph.api.graph_api.GraphAPI(raster_data, steps)

Bases: ABC

Base class for all graph APIs defining the minimal required interface.

_abc_impl = <_abc._abc_data object>

abstractmethod shortest_path(source_indices, target_indices, algorithm='dijkstra', **kwargs)

Find the shortest path(s) between source and target indices.

Parameters:

• source_indices (Union[int, list[int], ndarray[int]]) – Source node indices

• target_indices (Union[int, list[int], ndarray[int]]) – Target node indices

• algorithm (str) – Algorithm name (e.g., “dijkstra”, “astar”)

• **kwargs –

  pairwisebool

  If True, compute pairwise shortest paths between source_indices and target_indices. Only allowed if len(source_indices) == len(target_indices)

  heuristiccallable, optional

  A function that takes two node indices (u, target) and returns an estimate of the distance between them. Only used when algorithm=”astar”.

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int], list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]]

Returns:

list of path indices for each source-target pair

pyorps.graph.api.graph_api.abstractmethod(funcobj)

A decorator indicating abstract methods.

Requires that the metaclass is ABCMeta or derived from it. A class that has a metaclass derived from ABCMeta cannot be instantiated unless all of its abstract methods are overridden. The abstract methods can be called using any of the normal ‘super’ call mechanisms. abstractmethod() may be used to declare abstract methods for properties and descriptors.

Usage:

class C(metaclass=ABCMeta):

@abstractmethod def my_abstract_method(self, arg1, arg2, argN):

…

class pyorps.graph.api.graph_api.ndarray

Bases: object

ndarray(shape, dtype=float, buffer=None, offset=0,

strides=None, order=None)

An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)

Arrays should be constructed using array, zeros or empty (refer to the See Also section below). The parameters given here refer to a low-level method (ndarray(…)) for instantiating an array.

For more information, refer to the numpy module and examine the methods and attributes of an array.

Parameters:

• below) ((for the __new__ method; see Notes)

• shape (tuple of ints) – Shape of created array.

• dtype (data-type, optional) – Any object that can be interpreted as a numpy data type.

• buffer (object exposing buffer interface, optional) – Used to fill the array with data.

• offset (int, optional) – Offset of array data in buffer.

• strides (tuple of ints, optional) – Strides of data in memory.

• order ({'C', 'F'}, optional) – Row-major (C-style) or column-major (Fortran-style) order.

T

Transpose of the array.

Type:

ndarray

data

The array’s elements, in memory.

Type:

buffer

dtype

Describes the format of the elements in the array.

Type:

dtype object

flags

Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, etc.

Type:

dict

flat

Flattened version of the array as an iterator. The iterator allows assignments, e.g., x.flat = 3 (See ndarray.flat for assignment examples; TODO).

Type:

numpy.flatiter object

imag

Imaginary part of the array.

Type:

ndarray

real

Real part of the array.

Type:

ndarray

size

Number of elements in the array.

Type:

int

itemsize

The memory use of each array element in bytes.

Type:

int

nbytes

The total number of bytes required to store the array data, i.e., itemsize * size.

Type:

int

ndim

The array’s number of dimensions.

Type:

int

shape

Shape of the array.

Type:

tuple of ints

strides

The step-size required to move from one element to the next in memory. For example, a contiguous (3, 4) array of type int16 in C-order has strides (8, 2). This implies that to move from element to element in memory requires jumps of 2 bytes. To move from row-to-row, one needs to jump 8 bytes at a time (2 * 4).

Type:

tuple of ints

ctypes

Class containing properties of the array needed for interaction with ctypes.

Type:

ctypes object

base

If the array is a view into another array, that array is its base (unless that array is also a view). The base array is where the array data is actually stored.

Type:

ndarray

See also

array

Construct an array.

zeros

Create an array, each element of which is zero.

empty

Create an array, but leave its allocated memory unchanged (i.e., it contains “garbage”).

dtype

Create a data-type.

numpy.typing.NDArray

An ndarray alias generic w.r.t. its dtype.type <numpy.dtype.type>.

Notes

There are two modes of creating an array using __new__:

1. If buffer is None, then only shape, dtype, and order are used.

2. If buffer is an object exposing the buffer interface, then all keywords are interpreted.

No __init__ method is needed because the array is fully initialized after the __new__ method.

Examples

These examples illustrate the low-level ndarray constructor. Refer to the See Also section above for easier ways of constructing an ndarray.

First mode, buffer is None:

>>> import numpy as np
>>> np.ndarray(shape=(2,2), dtype=float, order='F')
array([[0.0e+000, 0.0e+000], # random
       [     nan, 2.5e-323]])

Second mode:

>>> np.ndarray((2,), buffer=np.array([1,2,3]),
...            offset=np.int_().itemsize,
...            dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])

T

View of the transposed array.

Same as self.transpose().

Examples

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.T
array([[1, 3],
       [2, 4]])

>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> a.T
array([1, 2, 3, 4])

See also

transpose

all(axis=None, out=None, keepdims=False, *, where=True)

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, *, where=True)

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, *, keepdims=False)

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, *, keepdims=False)

Return indices of the minimum values along the given axis.

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.

See also

numpy.argpartition

equivalent function

argsort(axis=-1, kind=None, 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

Raises:

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

Examples

>>> import numpy as np
>>> 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:

>>> import numpy as np
>>> 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=False)

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. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.

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

>>> import numpy as np
>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> list(map(hex, A))
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([  256,     1, 13090], dtype=int16)
>>> list(map(hex, A))
['0x100', '0x1', '0x3322']

Arrays of byte-strings are not swapped

>>> A = np.array([b'ceg', b'fac'])
>>> A.byteswap()
array([b'ceg', b'fac'], dtype='|S3')

A.view(A.dtype.newbyteorder()).byteswap() produces an array with the same values but different representation in memory

>>> A = np.array([1, 2, 3],dtype=np.int64)
>>> A.view(np.uint8)
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
       0, 0], dtype=uint8)
>>> A.view(A.dtype.newbyteorder()).byteswap(inplace=True)
array([1, 2, 3], dtype='>i8')
>>> A.view(np.uint8)
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
       0, 3], dtype=uint8)

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, **kwargs)

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

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 numpy.copy() are very similar but have different default values for their order= arguments, and this function always passes sub-classes through.)

See also

numpy.copy

Similar function with different default behavior

numpy.copyto

Notes

This function is the preferred method for creating an array copy. The function numpy.copy() is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default.

Examples

>>> import numpy as np
>>> 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

For arrays containing Python objects (e.g. dtype=object), the copy is a shallow one. The new array will contain the same object which may lead to surprises if that object can be modified (is mutable):

>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
>>> b = a.copy()
>>> b[2][0] = 10
>>> a
array([1, 'm', list([10, 3, 4])], dtype=object)

To ensure all elements within an object array are copied, use copy.deepcopy:

>>> import copy
>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
>>> c = copy.deepcopy(a)
>>> c[2][0] = 10
>>> c
array([1, 'm', list([10, 3, 4])], dtype=object)
>>> a
array([1, 'm', list([2, 3, 4])], dtype=object)

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):

_ctypes.data

A pointer to the memory area of the array as a Python integer. This memory area may contain data that is not aligned, or not in correct 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].

Note that unlike data_as, a reference won’t be kept to the array: code like ctypes.c_void_p((a + b).ctypes.data) will result in a pointer to a deallocated array, and should be spelt (a + b).ctypes.data_as(ctypes.c_void_p)

_ctypes.shape

A ctypes array of length self.ndim where the basetype is the C-integer corresponding to dtype('p') on this platform (see ~numpy.ctypeslib.c_intp). This base-type could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The ctypes array contains the shape of the underlying array.

Type:

(c_intp*self.ndim)

_ctypes.strides

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.

Type:

(c_intp*self.ndim)

_ctypes.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)).

The returned pointer will keep a reference to the array.

_ctypes.shape_as(obj)

Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).

_ctypes.strides_as(obj)

Return the strides tuple as an array of some other c-types type. For example: self.strides_as(ctypes.c_longlong).

If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the as_parameter attribute which will return an integer equal to the data attribute.

Examples

>>> import numpy as np
>>> import ctypes
>>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)
>>> x
array([[0, 1],
       [2, 3]], dtype=int32)
>>> x.ctypes.data
31962608 # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))
<__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents
c_uint(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents
c_ulong(4294967296)
>>> x.ctypes.shape
<numpy._core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary
>>> x.ctypes.strides
<numpy._core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary

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

data

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

device

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()

dtype

Data-type of the array’s elements.

Warning

Setting arr.dtype is discouraged and may be deprecated in the future. Setting will replace the dtype without modifying the memory (see also ndarray.view and ndarray.astype).

Parameters:

None

Returns:

d

Return type:

numpy dtype object

See also

ndarray.astype

Cast the values contained in the array to a new data-type.

ndarray.view

Create a view of the same data but a different data-type.

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 or Path) – A string naming the dump file.

dumps()

Returns the pickle of the array as a string. pickle.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

>>> import numpy as np
>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([1.,  1.])

Fill expects a scalar value and always behaves the same as assigning to a single array element. The following is a rare example where this distinction is important:

>>> a = np.array([None, None], dtype=object)
>>> a[0] = np.array(3)
>>> a
array([array(3), None], dtype=object)
>>> a.fill(np.array(3))
>>> a
array([array(3), array(3)], dtype=object)

Where other forms of assignments will unpack the array being assigned:

>>> a[...] = np.array(3)
>>> a
array([3, 3], dtype=object)

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.

WRITEBACKIFCOPY(X)

This array is a copy of some other array. The C-API function PyArray_ResolveWritebackIfCopy must be called before deallocating to 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 WRITEBACKIFCOPY, 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:

• WRITEBACKIFCOPY can only be set False.

• ALIGNED can only be set True if the data is truly aligned.

• WRITEABLE can only be set True if the array owns its own memory or the ultimate owner of the memory exposes a writeable buffer interface or is a string.

Arrays can be both 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

>>> import numpy as np
>>> 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)
<class '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

>>> import numpy as np
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])

getfield(dtype, offset=0)

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

A field is a view of the array data with a given 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

>>> import numpy as np
>>> 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.]])

imag

The imaginary part of the array.

Examples

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

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

>>> import numpy as np
>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1

For an array with object dtype, elements are returned as-is.

>>> a = np.array([np.int64(1)], dtype=object)
>>> a.item() #return np.int64
np.int64(1)

itemset

itemsize

Length of one array element in bytes.

Examples

>>> import numpy as np
>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16

mT

View of the matrix transposed array.

The matrix transpose is the transpose of the last two dimensions, even if the array is of higher dimension.

Added in version 2.0.

Raises:

ValueError – If the array is of dimension less than 2.

Examples

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.mT
array([[1, 3],
       [2, 4]])

>>> a = np.arange(8).reshape((2, 2, 2))
>>> a
array([[[0, 1],
        [2, 3]],

       [[4, 5],
        [6, 7]]])
>>> a.mT
array([[[0, 2],
        [1, 3]],

       [[4, 6],
        [5, 7]]])

max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the maximum along a given axis.

Refer to numpy.amax for full documentation.

See also

numpy.amax

equivalent function

mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)

Returns the average of the array elements along given axis.

Refer to numpy.mean for full documentation.

See also

numpy.mean

equivalent function

min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

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.

See also

sys.getsizeof

Memory consumed by the object itself without parents in case view. This does include memory consumed by non-element attributes.

Examples

>>> import numpy as np
>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480

ndim

Number of array dimensions.

Examples

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

newbyteorder

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)

Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array. In the output array, all elements smaller than the k-th element are located to the left of this element and all equal or greater are located to its right. The ordering of the elements in the two partitions on the either side of the k-th element in the output array is undefined.

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 of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.

  Deprecated since version 1.22.0: Passing booleans as index is deprecated.

• 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 to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

See also

numpy.partition

Return a partitioned copy of an array.

argpartition

Indirect partition.

sort

Full sort.

Notes

See np.partition for notes on the different algorithms.

Examples

>>> import numpy as np
>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4]) # may vary

>>> a.partition((1, 3))
>>> a
array([1, 2, 3, 4])

prod()

a.prod(axis=None, dtype=None, out=None, keepdims=False,

initial=1, where=True)

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

See also

numpy.prod

equivalent function

ptp

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

>>> import numpy as np
>>> 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', copy=None)

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

Refer to numpy.reshape for full documentation.

See also

numpy.reshape

equivalent function

Notes

Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11)).

resize(new_shape, refcheck=True)

Change shape and size of array 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.

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:

>>> import numpy as np

>>> 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 references or is 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.

Return type:

None

See also

getfield

Examples

>>> import numpy as np
>>> 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]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
       [1.5e-323, 1.0e+000, 1.5e-323],
       [1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])

setflags(write=None, align=None, uic=None)

Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, 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 WRITEBACKIFCOPY 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 7 Boolean flags in use, only three of which can be changed by the user: WRITEBACKIFCOPY, 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);

WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the C-API function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array.

All flags can be accessed using the single (upper case) letter as well as the full name.

Examples

>>> import numpy as np
>>> y = np.array([[3, 1, 7],
...               [2, 0, 0],
...               [8, 5, 9]])
>>> y
array([[3, 1, 7],
       [2, 0, 0],
       [8, 5, 9]])
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : False
  ALIGNED : False
  WRITEBACKIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True

shape

Tuple of array dimensions.

The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.

Warning

Setting arr.shape is discouraged and may be deprecated in the future. Using ndarray.reshape is the preferred approach.

Examples

>>> import numpy as np
>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: Incompatible shape for in-place modification. Use
`.reshape()` to make a copy with the desired shape.

See also

numpy.shape

Equivalent getter function.

numpy.reshape

Function similar to setting shape.

ndarray.reshape

Method similar to setting shape.

size

Number of elements in the array.

Equal to np.prod(a.shape), i.e., the product of the array’s dimensions.

Notes

a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested np.prod(a.shape), which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type.

Examples

>>> import numpy as np
>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30

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

Sort an array in-place. Refer to numpy.sort for full documentation.

Parameters:

• axis (int, optional) – Axis along which to sort. Default is -1, which means sort along the last axis.

• kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, optional) – Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with datatype. The ‘mergesort’ option is retained for backwards compatibility.

• 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.

numpy.argsort

Indirect sort.

numpy.lexsort

Indirect stable sort on multiple keys.

numpy.searchsorted

Find elements in sorted array.

numpy.partition

Partial sort.

Notes

See numpy.sort for notes on the different sorting algorithms.

Examples

>>> import numpy as np
>>> 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([(b'c', 1), (b'a', 2)],
      dtype=[('x', 'S1'), ('y', '<i8')])

squeeze(axis=None)

Remove axes of length one from a.

Refer to numpy.squeeze for full documentation.

See also

numpy.squeeze

equivalent function

std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

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

Refer to numpy.std for full documentation.

See also

numpy.std

equivalent function

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 arrays.ndarray.

Warning

Setting arr.strides is discouraged and may be deprecated in the future. numpy.lib.stride_tricks.as_strided should be preferred to create a new view of the same data in a safer way.

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).

See also

numpy.lib.stride_tricks.as_strided

Examples

>>> import numpy as np
>>> 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, keepdims=False, initial=0, where=True)

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

Refer to numpy.sum for full documentation.

See also

numpy.sum

equivalent function

swapaxes(axis1, axis2)

Return a view of the array with axis1 and axis2 interchanged.

Refer to numpy.swapaxes for full documentation.

See also

numpy.swapaxes

equivalent function

take(indices, axis=None, out=None, mode='raise')

Return an array formed from the elements of a at the given indices.

Refer to numpy.take for full documentation.

See also

numpy.take

equivalent function

to_device()

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 is produced in C-order by default. This behavior is controlled by the order parameter.

Parameters:

order ({'C', 'F', 'A'}, optional) – Controls the memory layout of the bytes object. ‘C’ means C-order, ‘F’ means F-order, ‘A’ (short for Any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. Default is ‘C’.

Returns:

s – Python bytes exhibiting a copy of a’s raw data.

Return type:

bytes

See also

frombuffer

Inverse of this operation, construct a 1-dimensional array from Python bytes.

Examples

>>> import numpy as np
>>> x = np.array([[0, 1], [2, 3]], dtype='<u2')
>>> x.tobytes()
b'\x00\x00\x01\x00\x02\x00\x03\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x02\x00\x01\x00\x03\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 or Path) – 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.

When fid is a file object, array contents are directly written to the file, bypassing the file object’s write method. As a result, tofile cannot be used with files objects supporting compression (e.g., GzipFile) or file-like objects that do not support fileno() (e.g., BytesIO).

tolist()

Return the array as an a.ndim-levels deep nested list of Python scalars.

Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible builtin Python type, via the ~numpy.ndarray.item function.

If a.ndim is 0, then since the depth of the nested list is 0, it will not be a list at all, but a simple Python scalar.

Parameters:

none

Returns:

y – The possibly nested list of array elements.

Return type:

object, or list of object, or list of list of object, or …

Notes

The array may be recreated via a = np.array(a.tolist()), although this may sometimes lose precision.

Examples

For a 1D array, a.tolist() is almost the same as list(a), except that tolist changes numpy scalars to Python scalars:

>>> import numpy as np
>>> a = np.uint32([1, 2])
>>> a_list = list(a)
>>> a_list
[np.uint32(1), np.uint32(2)]
>>> type(a_list[0])
<class 'numpy.uint32'>
>>> a_tolist = a.tolist()
>>> a_tolist
[1, 2]
>>> type(a_tolist[0])
<class 'int'>

Additionally, for a 2D array, tolist applies recursively:

>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]

The base case for this recursion is a 0D array:

>>> a = np.array(1)
>>> list(a)
Traceback (most recent call last):
  ...
TypeError: iteration over a 0-d array
>>> a.tolist()
1

tostring(order='C')

A compatibility alias for ~ndarray.tobytes, with exactly the same behavior.

Despite its name, it returns bytes not strs.

Deprecated since version 1.19.0.

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.

Refer to numpy.transpose for full documentation.

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 that the array’s i-th axis becomes the transposed array’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:

p – View of the array with its axes suitably permuted.

Return type:

ndarray

See also

transpose

Equivalent function.

ndarray.T

Array property returning the array transposed.

ndarray.reshape

Give a new shape to an array without changing its data.

Examples

>>> import numpy as np
>>> 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]])

>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> a.transpose()
array([1, 2, 3, 4])

var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

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][, type])

New view of array with the same data.

Note

Passing None for dtype is different from omitting the parameter, since the former invokes dtype(None) which is an alias for dtype('float64').

Parameters:

• dtype (data-type or ndarray sub-class, optional) – Data-type descriptor of the returned view, e.g., float32 or int16. Omitting it 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, omission of the parameter 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 last axis of a must be contiguous. This axis will be resized in the result.

Changed in version 1.23.0: Only the last axis needs to be contiguous. Previously, the entire array had to be C-contiguous.

Examples

>>> import numpy as np
>>> x = np.array([(-1, 2)], dtype=[('a', np.int8), ('b', np.int8)])

Viewing array data using a different type and dtype:

>>> nonneg = np.dtype([("a", np.uint8), ("b", np.uint8)])
>>> y = x.view(dtype=nonneg, type=np.recarray)
>>> x["a"]
array([-1], dtype=int8)
>>> y.a
array([255], dtype=uint8)

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
>>> x
array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])

Using a view to convert an array to a recarray:

>>> z = x.view(np.recarray)
>>> z.a
array([1, 3], dtype=int8)

Views share data:

>>> x[0] = (9, 10)
>>> z[0]
np.record((9, 10), dtype=[('a', 'i1'), ('b', 'i1')])

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[:, ::2]
>>> y
array([[1, 3],
       [4, 6]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
    ...
ValueError: To change to a dtype of a different size, the last axis must be contiguous
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 3)],
       [(4, 6)]], dtype=[('width', '<i2'), ('length', '<i2')])

However, views that change dtype are totally fine for arrays with a contiguous last axis, even if the rest of the axes are not C-contiguous:

>>> x = np.arange(2 * 3 * 4, dtype=np.int8).reshape(2, 3, 4)
>>> x.transpose(1, 0, 2).view(np.int16)
array([[[ 256,  770],
        [3340, 3854]],

       [[1284, 1798],
        [4368, 4882]],

       [[2312, 2826],
        [5396, 5910]]], dtype=int16)

pyorps.graph.api.graph_library_api module

This file contains the abstract base class for the interface to the graph libraries. All specific graph library interfaces should inherit from this class. The workflow of the specific interfaces are determined by the respective graph library. The workflow of the graph libraries can vary!

• For rustworkx and igraph the nodes need to be created before the edges can be added

• For networkit and networkx the edges can be added on the fly when adding the nodes

• For rustworkx and igraph the edges can only be added as a list of tuples. This means

that the edge information as retrieved by numpy arrays, need to be converted into a list, which leads to a much higher (more than double) memory usage! - For networkit and networkx edges can be added as a sparse matrix or as numpy arrays

Please see the specific interfaces to the specific graph libraries for more details!

class pyorps.graph.api.graph_library_api.Any(*args, **kwargs)

Bases: object

Special type indicating an unconstrained type.

• Any is compatible with every type.

• Any assumed to have all methods.

• All values assumed to be instances of Any.

Note that all the above statements are true from the point of view of static type checkers. At runtime, Any should not be used with instance checks.

class pyorps.graph.api.graph_library_api.GraphAPI(raster_data, steps)

Bases: ABC

Base class for all graph APIs defining the minimal required interface.

_abc_impl = <_abc._abc_data object>

abstractmethod shortest_path(source_indices, target_indices, algorithm='dijkstra', **kwargs)

Find the shortest path(s) between source and target indices.

Parameters:

• source_indices (Union[int, list[int], ndarray[int]]) – Source node indices

• target_indices (Union[int, list[int], ndarray[int]]) – Target node indices

• algorithm (str) – Algorithm name (e.g., “dijkstra”, “astar”)

• **kwargs –

  pairwisebool

  If True, compute pairwise shortest paths between source_indices and target_indices. Only allowed if len(source_indices) == len(target_indices)

  heuristiccallable, optional

  A function that takes two node indices (u, target) and returns an estimate of the distance between them. Only used when algorithm=”astar”.

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int], list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]]

Returns:

list of path indices for each source-target pair

class pyorps.graph.api.graph_library_api.GraphLibraryAPI(raster_data, steps, from_nodes=None, to_nodes=None, cost=None, ignore_max=True, **kwargs)

Bases: GraphAPI

Base class for all graph library-based APIs.

This class extends GraphAPI with common functionality needed by standard graph libraries that require edge data to be explicitly provided and a graph to be constructed.

_abc_impl = <_abc._abc_data object>

abstractmethod _all_pairs_shortest_path(sources, targets, algorithm, **kwargs)

Computes shortest paths between all pairs of sources and targets.

Parameters:

• sources (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of source node identifiers

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation.

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths for all source-target combinations

_compute_all_pairs_shortest_paths(sources, targets, algorithm, **kwargs)

Computes paths individually for each source-target pair using the specified algorithm. Returns empty paths for unreachable targets.

Parameters:

• sources (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of source node identifiers

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths for all source-target combinations

abstractmethod _compute_single_path(source, target, algorithm, **kwargs)

Computes shortest path between a single source and target.

Parameters:

• source (Union[int, int32, int64, uint32, uint64]) – Source node identifier

• target (Union[int, int32, int64, uint32, uint64]) – Target node identifier

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]

Returns:

List of node identifiers representing the shortest path

abstractmethod _compute_single_source_multiple_targets(source, targets, algorithm, **kwargs)

Computes shortest paths from a single source to multiple targets.

Parameters:

• source (Union[int, int32, int64, uint32, uint64]) – Source node identifier

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths from the source to each target

static _ensure_path_endpoints(path, source, target)

Ensures the path starts with the source node and ends with the target node.

Parameters:

• path (list[int]) – List of node IDs representing a path

• source (int) – ID of the source node that should be at the start of the path

• target (int) – ID of the target node that should be at the end of the path

Return type:

list[int]

Returns:

list of node IDs with source and target at endpoints if needed

_pairwise_shortest_path(sources, targets, algorithm, **kwargs)

Default implementation for pairwise shortest path computation. Subclasses can override this for library-specific optimizations.

Parameters:

• sources (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of source node identifiers

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths, each connecting corresponding source-target pairs

_vectorized_bresenham(x0, y0, x1, y1)

Optimized implementation of Bresenham’s line algorithm that avoids generating duplicate coordinates.

Parameters:

• x0 (int) – x-coordinate of the first point

• y0 (int) – y-coordinate of the first point

• x1 (int) – x-coordinate of the second point

• y1 (int) – y-coordinate of the second point

Return type:

ndarray

Returns:

Array of (x,y) coordinates of cells that the line passes through

abstractmethod create_graph(from_nodes, to_nodes, cost=None, **kwargs)

Creates a graph object with the graph library specified in the selected interface.

Parameters:

• from_nodes (ndarray[int]) – The starting node indices from the edge data

• to_nodes (ndarray[int]) – The ending node indices from the edge data

• cost (Optional[ndarray[int]]) – The weight of the edge data

• kwargs – Additional parameters for the underlying graph library

Return type:

Any

Returns:

The graph object

get_a_star_heuristic(target, source=None, **kwargs)

Calculate the A* heuristic based on the Euclidean distance from the target node.

Parameters:

• target (Union[int, int32, int64, uint32, uint64]) – The index of the target node in the raster data

• source (Union[int, int32, int64, uint32, uint64, None]) – Optional source node for calculating area-specific minimum values

• kwargs – Additional parameters, including optional heu_weight for scaling

Returns:

• An array of node indices in the graph

• An array of heuristic values corresponding to each node

Return type:

tuple containing

get_advanced_a_star_heuristic(target, source=None, **kwargs)

Calculate the A* heuristic based on the Euclidean distance from the target node.

Parameters:

• target (Union[int, int32, int64, uint32, uint64]) – The index of the target node in the raster data

• source (Union[int, int32, int64, uint32, uint64, None]) – Optional source node for calculating area-specific minimum values

• kwargs – Additional parameters, including optional heu_weight for scaling

Returns:

• An array of node indices in the graph

• An array of heuristic values corresponding to each node

Return type:

tuple containing

abstractmethod get_nodes()

This method returns the nodes in the graph as a list or numpy array of node indices.

Return type:

Union[List[int], ndarray]

Returns:

List or array of node indices of the nodes in the graph

abstractmethod get_number_of_edges()

Returns the number of edges in the graph.

Return type:

int

Returns:

The number of edges

abstractmethod get_number_of_nodes()

Returns the number of nodes in the graph.

Return type:

int

Returns:

The number of nodes

abstractmethod remove_isolates()

If the graph object was initialized with the maximum number of nodes, this function helps to reduce the occupied memory by removing nodes without any edge (degree == 0).

Return type:

None

Returns:

None

shortest_path(source_indices, target_indices, algorithm='dijkstra', **kwargs)

This method applies the specified shortest path algorithm on the created graph object and finds the shortest path between source(s) and target(s) as a list of node indices.

Parameters:

• source_indices (Union[int, int32, int64, uint32, uint64, list[Union[int, int32, int64, uint32, uint64]], ndarray[int], None]) – Index or indices of source node(s) (int or list[int])

• target_indices (Union[int, int32, int64, uint32, uint64, list[Union[int, int32, int64, uint32, uint64]], ndarray[int], None]) – Index or indices of target node(s) (int or list[int])

• algorithm (str) – Algorithm to use for shortest path computation. Options depend on the specific library implementation.

• kwargs – Additional parameters for specific algorithms library including

• and (specific keywords) – “pairwise”: If True, compute pairwise shortest paths between source_indices and target_indices. Only allowed if len(source_indices) == len(target_indices)

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int], list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]]

Returns:

List of node indices representing the shortest path(s)

exception pyorps.graph.api.graph_library_api.NoPathFoundError(source, target)

Bases: Exception

Custom exception if no path can be found in the graph for source and target

exception pyorps.graph.api.graph_library_api.PairwiseError

Bases: Exception

Custom exception if pairwise computation fails

pyorps.graph.api.graph_library_api.abstractmethod(funcobj)

A decorator indicating abstract methods.

Requires that the metaclass is ABCMeta or derived from it. A class that has a metaclass derived from ABCMeta cannot be instantiated unless all of its abstract methods are overridden. The abstract methods can be called using any of the normal ‘super’ call mechanisms. abstractmethod() may be used to declare abstract methods for properties and descriptors.

Usage:

class C(metaclass=ABCMeta):

@abstractmethod def my_abstract_method(self, arg1, arg2, argN):

…

pyorps.graph.api.graph_library_api.construct_edges(raster, steps, ignore_max=True)

Construct graph edges from rasterized geodata using specified neighborhood steps.

This is the main function for converting rasterized cost data into a weighted graph representation suitable for least-cost path analysis. It processes each step direction in the neighborhood to create edges between valid grid cells.

Parameters:

• raster (np.ndarray) – 2D cost raster representing terrain/construction costs

• steps (np.ndarray) – Array of neighborhood step directions (Rk neighborhood)

• ignore_max (bool) – If True, treats maximum cost values as forbidden areas

Returns:

Complete edge list for graph

Return type:

Tuple[np.ndarray, np.ndarray, np.ndarray]

References

[1]

pyorps.graph.api.graph_library_api.time() → floating point number

Return the current time in seconds since the Epoch. Fractions of a second may be present if the system clock provides them.

pyorps.graph.api.igraph_api module

pyorps.graph.api.networkit_api module

class pyorps.graph.api.networkit_api.AStar(G, heu, source, target, storePred=True)

Bases: STSP

A* path-finding algorithm.

Parameters:

• G (networkit.Graph) – The input graph.

• heu (list(float)) – List of lower bounds of the distance of each node to the target.

• source (int) – The source node.

• target (int) – The target node.

• storePred (bool, optional) – If True, the algorithm will also store the predecessors and reconstruct a shortest path from source and target. Default: True

exception pyorps.graph.api.networkit_api.AlgorthmNotImplementedError(algorithm, graph_library)

Bases: Exception

Custom exception if a specific algorithm is not implemented in the API or the graph library

class pyorps.graph.api.networkit_api.BidirectionalDijkstra(G, source, target, storePred=True)

Bases: STSP

Bidirectional implementation of the Dijkstra algorithm from two given source and target nodes. Explores the graph from both the source and target nodes until the two explorations meet.

Parameters:

Gnetworkit.Graph

The input graph.

sourceint

The source node.

targetint

The target node.

storePredbool, optional

If True, the algorithm will also store the predecessors and reconstruct a shortest path from source and target.

class pyorps.graph.api.networkit_api.Dijkstra(G, source, storePaths=True, storeNodesSortedByDistance=False, target=None)

Bases: SSSP

Dijkstra’s SSSP algorithm. Returns list of weighted distances from node source, i.e. the length of the shortest path from source to any other node.

Parameters:

• G (networkit.Graph) – The graph.

• source (int) – The source node of the Dijkstra search.

• storePaths (bool, optional) – Controls whether to store paths and number of paths. Default: True

• storeNodesSortedByDistance (bool, optional) – Controls whether to store nodes sorted by distance. Default: False

• target (int or None, optional) – Terminate search when the target has been reached. In default-mode, this target is set to None.

class pyorps.graph.api.networkit_api.Graph(n=0, weighted=False, directed=False, edgesIndexed=False)

Bases: object

An undirected graph (with optional weights) and parallel iterator methods.

Create a graph of n nodes. The graph has assignable edge weights if weighted is set to True. If weighted is set to False each edge has edge weight 1.0 and any other weight assignment will be ignored.

Parameters:

• n (int, optional) – Number of nodes. Default: 0

• weighted (bool, optional) – If set to True, the graph can have edge weights other than 1.0. Default: False

• directed (bool, optional) – If set to True, the graph will be directed. Default: False

• edgesIndexed (bool, optional) – If set to True, the graph’s edges will be indexed. Default: False

addEdge(u, v, w=1.0, addMissing=False, checkMultiEdge=False)

Insert an undirected edge between the nodes u and v. If the graph is weighted you can optionally set a weight for this edge. The default weight is 1.0. If one or both end-points do not exists and addMissing is set, they are silently added.

Note

By default it is not checked whether this edge already exists, thus it is possible to create multi-edges. Multi-edges are not supported and will NOT be handled consistently by the graph data structure. To enable set checkMultiEdge to True. Note that this increases the runtime of the function by O(max(deg(u), deg(v))).

Parameters:

• u (int) – Endpoint of edge.

• v (int) – Endpoint of edge.

• w (float, optional) – Edge weight. Default: 1.0

• addMissing (bool, optional) – Add missing endpoints if necessary (i.e., increase numberOfNodes). Default: False

• checkMultiEdge (bool, optional) – Check if edge is already present in the graph. If detected, do not insert the edge. Default: False

Returns:

Indicates whether the edge has been added. Is False in case checkMultiEdge is set to True and the new edge would have been a multi-edge.

Return type:

bool

addEdges(inputData)

Inserts edges from several sources based on the type of inputData.

If the graph is undirected, each pair (i,j) in inputData is inserted twice twice: once as (i,j) and once as (j,i).

Parameter inputData can be one of the following:

• scipy.sparse.coo_matrix

• (data, (i,j)) where data, i and j are of type np.ndarray

• (i,j) where i and j are of type np.ndarray

Note

If only pairs of row and column indices (i,j) are given, each edge is given weight 1.0 (even in case of a weighted graph).

Parameters:

• inputData (several) – Input data encoded as one of the supported formats.

• addMissing (bool, optional) – Add missing endpoints if necessary (i.e., increase numberOfNodes). Default: False

• checkMultiEdge (bool, optional) – Check if edge is already present in the graph. If detected, do not insert the edge. Default: False

addNode()

Add a new node to the graph and return it.

Returns:

The new node.

Return type:

int

addNodes(numberOfNewNodes)

Add numberOfNewNodes many new nodes to the graph and return the id of the last node added.

Parameters:

numberOfNewNodes (int) – Number of nodes to be added.

Returns:

The id of the last node added.

Return type:

int

attachEdgeAttribute(name, ofType)

Attaches an edge attribute to the graph and returns it.

A = G.attachEdgeAttribute("attributeIdentifier", ofType)

All values are initially undefined for existing edges values can be set/get by

A[edgeId] = value # set
value = A[edgeId] # get

Getting undefined values raises a ValueError removing an edge makes all its attributes undefined

Notes

Using edge attributes is in experimental state. The API may change in future updates.

Parameters:

• name (str) – Name for this attribute

• ofType (type) – Type of the attribute (either int, float, or str)

Returns:

The resulting edge attribute container.

Return type:

networkit.graph.EdgeAttribute

attachNodeAttribute(name, ofType)

Attaches a node attribute to the graph and returns it.

A = G.attachNodeAttribute("attributeIdentifier", ofType)

All values are initially undefined for existing nodes values can be set/get by

A[node] = value # set
value = A[node] # get

Getting undefined values raises a ValueError removing a node makes all its attributes undefined

Notes

Using node attributes is in experimental state. The API may change in future updates.

Parameters:

• name (str) – Name for this attribute

• ofType (type) – Type of the attribute (either int, float, or str)

Returns:

The resulting node attribute container.

Return type:

networkit.graph.NodeAttribute

checkConsistency()

Check for invalid graph states, such as multi-edges.

Returns:

True if graph contains invalid graph states.

Return type:

bool

compactEdges()

Compact the edge storage, this should be called after executing many edge deletions.

degree(u)

Get the number of neighbors of u.

Note

The existence of the node is not checked. Calling this function with a non-existing node results in a segmentation fault. Node existence can be checked by calling hasNode(u).

Parameters:

u (int) – The input Node.

Returns:

The number of neighbors.

Return type:

int

degreeIn(u)

Get the number of in-neighbors of u.

Note

The existence of the node is not checked. Calling this function with a non-existing node results in a segmentation fault. Node existence can be checked by calling hasNode(u).

Parameters:

u (int) – The input Node.

Returns:

The number of in-neighbors.

Return type:

int

degreeOut(u)

Get the number of out-neighbors of u.

Note

The existence of the node is not checked. Calling this function with a non-existing node results in a segmentation fault. Node existence can be checked by calling hasNode(u).

Parameters:

u (int) – The Input Node.i

Returns:

The number of out-neighbors.

Return type:

int

detachEdgeAttribute(name)

Detaches an edge attribute from the graph.

Notes

Using edge attributes is in experimental state. The API may change in future updates.

Parameters:

name (str) – The distinguished name for the attribute to detach.

detachNodeAttribute(name)

Detaches a node attribute from the graph.

Notes

Using node attributes is in experimental state. The API may change in future updates.

Parameters:

name (str) – The distinguished name for the attribute to detach.

edgeId(u, v)

Parameters:

• u (node) – Node Id from u.

• v (node) – Node Id from v.

Returns:

Id of the edge.

Return type:

int

forEdges(callback)

Experimental edge iterator interface

Parameters:

callback (object) – Any callable object that takes the parameter tuple(int, int, float, int). Parameter list refering to (node id, node id, edge weight, edge id).

forEdgesOf(u, callback)

Experimental incident (outgoing) edge iterator interface

Parameters:

• u (int) – The node of which incident edges shall be passed to the callback

• callback (object) – Any callable object that takes the parameter tuple(int, int, float, int). Parameter list refering to (node id, node id, edge weight, edge id).

forInEdgesOf(u, callback)

Experimental incident edge iterator interface

Parameters:

• u (int) – The node of which incident edges shall be passed to the callback

• callback (object) – Any callable object that takes the parameter tuple(int, int, float, int). Parameter list refering to (node id, node id, edge weight, edge id).

forNodePairs(callback)

Experimental node pair iterator interface

Parameters:

callback (object) – Any callable object that takes the parameters tuple(int, int). Parameter list refering to (node id, node id).

forNodes(callback)

Experimental node iterator interface

Parameters:

callback (object) – Any callable object that takes the parameter node.

forNodesInRandomOrder(callback)

Experimental node iterator interface

Parameters:

callbackobject

Any callable object that takes the parameter node.

getEdgeAttribute(name, ofType)

Gets an edge attribute that is already attached to the graph and returns it.

A = G.getEdgeAttribute("attributeIdentifier", ofType)

Notes

Using edge attributes is in experimental state. The API may change in future updates.

Parameters:

• name (str) – Name for this attribute

• ofType (type) – Type of the attribute (either int, float, or str)

Returns:

The resulting edge attribute container.

Return type:

networkit.graph.EdgeAttribute

getNodeAttribute(name, ofType)

Gets a node attribute that is already attached to the graph and returns it.

A = G.getNodeAttribute("attributeIdentifier", ofType)

Notes

Using node attributes is in experimental state. The API may change in future updates.

Parameters:

• name (str) – Name for this attribute

• ofType (type) – Type of the attribute (either int, float, or str)

Returns:

The resulting node attribute container.

Return type:

networkit.graph.NodeAttribute

hasEdge(u, v)

hasEdge(u, v)

Checks if undirected edge {u,`v`} exists in the graph.

Parameters:

• u (int) – Endpoint of edge.

• v (int) – Endpoint of edge.

Returns:

True if the edge exists, False otherwise.

Return type:

bool

hasEdgeIds()

Returns true if edges have been indexed

Returns:

If edges have been indexed

Return type:

bool

hasNode(u)

Checks if the Graph has the node u, i.e. if u hasn’t been deleted and is in the range of valid ids.

Parameters:

u (int) – Id of node queried.

Returns:

Indicates whether node u is part of the graph.

Return type:

bool

increaseWeight(u, v, w)

Increase the weight of an edge. If the edge does not exist, it will be inserted.

Parameters:

• u (int) – Endpoint of edge.

• v (int) – Endpoint of edge.

• w (float) – Edge weight.

indexEdges(force=False)

Assign integer ids to edges.

Parameters:

force (bool, optional) – Force re-indexing of edges. Default: False

isDirected()

Returns whether a graph is directed.

Returns:

True if graph is directed.

Return type:

bool

isIsolated(u)

If the node u is isolated.

Parameters:

u (int) – The input node.

Returns:

Indicates whether the node is isolated.

Return type:

bool

isWeighted()

Returns whether a graph is weighted.

Returns:

True if this graph supports edge weights other than 1.0.

Return type:

bool

iterEdges()

Iterates over the edges of the graph.

For each node u in the graph in ascending node id order, the iterator yields the out-edges of u in directed graphs and the edges (u,v) in which u < v for undirected graphs.

It does not follow the order of edge ids (if present).

iterEdgesWeights()

iterEdgeWeights()

Iterates over the edges of the graph and their weights.

iterInNeighbors(u)

Iterates over a range of the in-neighbors of a node.

Parameters:

u (int) – The input node.

iterInNeighborsWeights(u)

Iterates over a range of the in-neighbors of a node including the edge weights. The iterator is not safe to use with unweighted graphs. To avoid unsafe behavior a runtime error will be thrown.

Parameters:

u (int) – The input node.

iterNeighbors(u)

Iterates over a range of the neighbors of a node.

Parameters:

u (int) – The input node.

iterNeighborsWeights(u)

Iterates over a range of the neighbors of a node including the edge weights. The iterator is not safe to use with unweighted graphs. To avoid unsafe behavior a runtime error will be thrown.

Parameters:

u (int) – The input node.

iterNodes()

Iterates over the nodes of the graph.

numberOfEdges()

Get the number of edges in the graph.

Returns:

The number of edges.

Return type:

int

numberOfNodes()

Get the number of nodes in the graph.

Returns:

The number of nodes.

Return type:

int

numberOfSelfLoops()

Get number of self-loops, i.e. edges {v, v}.

Returns:

Number of self-loops.

Return type:

int

removeAllEdges()

Removes all the edges in the graph.

removeEdge(u, v)

Removes the undirected edge {u,`v`}.

Parameters:

• u (int) – Endpoint of edge.

• v (int) – Endpoint of edge.

removeMultiEdges()

Removes all multi-edges from the graph.

removeNode(u)

Remove a node u and all incident edges from the graph.

Incoming as well as outgoing edges will be removed.

Parameters:

u (int) – Id of node to be removed.

removeSelfLoops()

Removes all self-loops from the graph.

restoreNode(u)

Restores a previously deleted node u with its previous id in the graph.

Parameters:

u (int) – The input node.

setWeight(u, v, w)

Set the weight of an edge. If the edge does not exist, it will be inserted.

Parameters:

• u (int) – Endpoint of edge.

• v (int) – Endpoint of edge.

• w (float) – Edge weight.

sortEdges()

Sorts the adjacency arrays by node id. While the running time is linear this temporarily duplicates the memory.

swapEdge(s1, t1, s2, t2)

Changes the edge (s1, t1) into (s1, t2) and the edge (s2, t2) into (s2, t1).

If there are edge weights or edge ids, they are preserved.

Note

No check is performed if the swap is actually possible, i.e. does not generate duplicate edges.

Parameters:

• s1 (int) – Source node of the first edge.

• t1 (int) – Target node of the first edge.

• s2 (int) – Source node of the second edge.

• t2 (int) – Target node of the second edge.

totalEdgeWeight()

Get the sum of all edge weights.

Returns:

The sum of all edge weights.

Return type:

float

upperEdgeIdBound()

Get an upper bound for the edge ids in the graph.

Returns:

An upper bound for the edge ids in the graph.

Return type:

int

upperNodeIdBound()

Get an upper bound for the node ids in the graph.

Returns:

An upper bound for the node ids in the graph.

Return type:

int

weight(u, v)

Get edge weight of edge {u , v}. Returns 0 if edge does not exist.

Parameters:

• u (int) – Endpoint of edge.

• v (int) – Endpoint of edge.

Returns:

Edge weight of edge {u , v} or 0 if edge does not exist.

Return type:

float

weightedDegree(u, countSelfLoopsTwice=False)

Returns the weighted out-degree of u.

For directed graphs this is the sum of weights of all outgoing edges of u.

Parameters:

• u (int) – The input Node.

• countSelfLoopsTwice (bool, optional) – If set to True, self-loops will be counted twice. Default: False

Returns:

The weighted out-degree of u.

Return type:

float

weightedDegreeIn(u, countSelfLoopsTwice=False)

Returns the weighted in-degree of u.

For directed graphs this is the sum of weights of all ingoing edges of u.

Parameters:

• u (int) – The input node.

• countSelfLoopsTwice (bool, optional) – If set to True, self-loops will be counted twice. Default: False

Returns:

The weighted in-degree of u.

Return type:

float

class pyorps.graph.api.networkit_api.GraphLibraryAPI(raster_data, steps, from_nodes=None, to_nodes=None, cost=None, ignore_max=True, **kwargs)

Bases: GraphAPI

Base class for all graph library-based APIs.

This class extends GraphAPI with common functionality needed by standard graph libraries that require edge data to be explicitly provided and a graph to be constructed.

_abc_impl = <_abc._abc_data object>

abstractmethod _all_pairs_shortest_path(sources, targets, algorithm, **kwargs)

Computes shortest paths between all pairs of sources and targets.

Parameters:

• sources (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of source node identifiers

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation.

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths for all source-target combinations

_compute_all_pairs_shortest_paths(sources, targets, algorithm, **kwargs)

Computes paths individually for each source-target pair using the specified algorithm. Returns empty paths for unreachable targets.

Parameters:

• sources (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of source node identifiers

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths for all source-target combinations

abstractmethod _compute_single_path(source, target, algorithm, **kwargs)

Computes shortest path between a single source and target.

Parameters:

• source (Union[int, int32, int64, uint32, uint64]) – Source node identifier

• target (Union[int, int32, int64, uint32, uint64]) – Target node identifier

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]

Returns:

List of node identifiers representing the shortest path

abstractmethod _compute_single_source_multiple_targets(source, targets, algorithm, **kwargs)

Computes shortest paths from a single source to multiple targets.

Parameters:

• source (Union[int, int32, int64, uint32, uint64]) – Source node identifier

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths from the source to each target

static _ensure_path_endpoints(path, source, target)

Ensures the path starts with the source node and ends with the target node.

Parameters:

• path (list[int]) – List of node IDs representing a path

• source (int) – ID of the source node that should be at the start of the path

• target (int) – ID of the target node that should be at the end of the path

Return type:

list[int]

Returns:

list of node IDs with source and target at endpoints if needed

_pairwise_shortest_path(sources, targets, algorithm, **kwargs)

Default implementation for pairwise shortest path computation. Subclasses can override this for library-specific optimizations.

Parameters:

• sources (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of source node identifiers

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths, each connecting corresponding source-target pairs

_vectorized_bresenham(x0, y0, x1, y1)

Optimized implementation of Bresenham’s line algorithm that avoids generating duplicate coordinates.

Parameters:

• x0 (int) – x-coordinate of the first point

• y0 (int) – y-coordinate of the first point

• x1 (int) – x-coordinate of the second point

• y1 (int) – y-coordinate of the second point

Return type:

ndarray

Returns:

Array of (x,y) coordinates of cells that the line passes through

abstractmethod create_graph(from_nodes, to_nodes, cost=None, **kwargs)

Creates a graph object with the graph library specified in the selected interface.

Parameters:

• from_nodes (ndarray[int]) – The starting node indices from the edge data

• to_nodes (ndarray[int]) – The ending node indices from the edge data

• cost (Optional[ndarray[int]]) – The weight of the edge data

• kwargs – Additional parameters for the underlying graph library

Return type:

Any

Returns:

The graph object

get_a_star_heuristic(target, source=None, **kwargs)

Calculate the A* heuristic based on the Euclidean distance from the target node.

Parameters:

• target (Union[int, int32, int64, uint32, uint64]) – The index of the target node in the raster data

• source (Union[int, int32, int64, uint32, uint64, None]) – Optional source node for calculating area-specific minimum values

• kwargs – Additional parameters, including optional heu_weight for scaling

Returns:

• An array of node indices in the graph

• An array of heuristic values corresponding to each node

Return type:

tuple containing

get_advanced_a_star_heuristic(target, source=None, **kwargs)

Calculate the A* heuristic based on the Euclidean distance from the target node.

Parameters:

• target (Union[int, int32, int64, uint32, uint64]) – The index of the target node in the raster data

• source (Union[int, int32, int64, uint32, uint64, None]) – Optional source node for calculating area-specific minimum values

• kwargs – Additional parameters, including optional heu_weight for scaling

Returns:

• An array of node indices in the graph

• An array of heuristic values corresponding to each node

Return type:

tuple containing

abstractmethod get_nodes()

This method returns the nodes in the graph as a list or numpy array of node indices.

Return type:

Union[List[int], ndarray]

Returns:

List or array of node indices of the nodes in the graph

abstractmethod get_number_of_edges()

Returns the number of edges in the graph.

Return type:

int

Returns:

The number of edges

abstractmethod get_number_of_nodes()

Returns the number of nodes in the graph.

Return type:

int

Returns:

The number of nodes

abstractmethod remove_isolates()

If the graph object was initialized with the maximum number of nodes, this function helps to reduce the occupied memory by removing nodes without any edge (degree == 0).

Return type:

None

Returns:

None

shortest_path(source_indices, target_indices, algorithm='dijkstra', **kwargs)

This method applies the specified shortest path algorithm on the created graph object and finds the shortest path between source(s) and target(s) as a list of node indices.

Parameters:

• source_indices (Union[int, int32, int64, uint32, uint64, list[Union[int, int32, int64, uint32, uint64]], ndarray[int], None]) – Index or indices of source node(s) (int or list[int])

• target_indices (Union[int, int32, int64, uint32, uint64, list[Union[int, int32, int64, uint32, uint64]], ndarray[int], None]) – Index or indices of target node(s) (int or list[int])

• algorithm (str) – Algorithm to use for shortest path computation. Options depend on the specific library implementation.

• kwargs – Additional parameters for specific algorithms library including

• and (specific keywords) – “pairwise”: If True, compute pairwise shortest paths between source_indices and target_indices. Only allowed if len(source_indices) == len(target_indices)

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int], list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]]

Returns:

List of node indices representing the shortest path(s)

class pyorps.graph.api.networkit_api.NetworkitAPI(raster_data, steps, from_nodes=None, to_nodes=None, cost=None, ignore_max=True, **kwargs)

Bases: GraphLibraryAPI

_abc_impl = <_abc._abc_data object>

_all_pairs_shortest_path(sources, targets, algorithm, **kwargs)

Computes shortest paths between all pairs of sources and targets.

Parameters:

• sources (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of source node identifiers

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths for all source-target combinations

_compute_multi_target_dijkstra(source, targets)

_compute_single_path(source, target, algorithm, **kwargs)

Computes shortest path between a single source and target.

Parameters:

• source (Union[int, int32, int64, uint32, uint64]) – Source node identifier

• target (Union[int, int32, int64, uint32, uint64]) – Target node identifier

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]

Returns:

List of node identifiers representing the shortest path

_compute_single_source_multiple_targets(source, targets, algorithm, **kwargs)

Computes shortest paths from a single source to multiple targets.

Parameters:

• source (Union[int, int32, int64, uint32, uint64]) – Source node identifier

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths from the source to each target

create_graph(from_nodes, to_nodes, cost=None, **kwargs)

Creates a graph object with the networkit library.

Parameters:

• from_nodes (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – The starting node indices from the edge data

• to_nodes (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – The ending node indices from the edge data

• cost (Optional[ndarray[int]]) – The weight of the edge data

• kwargs – Additional parameters for the underlying graph library

Return type:

Graph

Returns:

The graph object

get_nodes()

This method returns the nodes in the graph as a list or numpy array of node indices.

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]

Returns:

List or array of node indices of the nodes in the graph

get_number_of_edges()

Returns the number of edges in the graph.

Returns:

The number of edges

get_number_of_nodes()

Returns the number of nodes in the graph.

Returns:

The number of nodes

remove_isolates()

If the graph object was initialized with the maximum number of nodes, this function helps to reduce the occupied memory by removing nodes without any edge (degree == 0).

Returns:

None

exception pyorps.graph.api.networkit_api.NoPathFoundError(source, target)

Bases: Exception

Custom exception if no path can be found in the graph for source and target

class pyorps.graph.api.networkit_api.ndarray

Bases: object

ndarray(shape, dtype=float, buffer=None, offset=0,

strides=None, order=None)

An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)

Arrays should be constructed using array, zeros or empty (refer to the See Also section below). The parameters given here refer to a low-level method (ndarray(…)) for instantiating an array.

For more information, refer to the numpy module and examine the methods and attributes of an array.

Parameters:

• below) ((for the __new__ method; see Notes)

• shape (tuple of ints) – Shape of created array.

• dtype (data-type, optional) – Any object that can be interpreted as a numpy data type.

• buffer (object exposing buffer interface, optional) – Used to fill the array with data.

• offset (int, optional) – Offset of array data in buffer.

• strides (tuple of ints, optional) – Strides of data in memory.

• order ({'C', 'F'}, optional) – Row-major (C-style) or column-major (Fortran-style) order.

T

Transpose of the array.

Type:

ndarray

data

The array’s elements, in memory.

Type:

buffer

dtype

Describes the format of the elements in the array.

Type:

dtype object

flags

Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, etc.

Type:

dict

flat

Flattened version of the array as an iterator. The iterator allows assignments, e.g., x.flat = 3 (See ndarray.flat for assignment examples; TODO).

Type:

numpy.flatiter object

imag

Imaginary part of the array.

Type:

ndarray

real

Real part of the array.

Type:

ndarray

size

Number of elements in the array.

Type:

int

itemsize

The memory use of each array element in bytes.

Type:

int

nbytes

The total number of bytes required to store the array data, i.e., itemsize * size.

Type:

int

ndim

The array’s number of dimensions.

Type:

int

shape

Shape of the array.

Type:

tuple of ints

strides

The step-size required to move from one element to the next in memory. For example, a contiguous (3, 4) array of type int16 in C-order has strides (8, 2). This implies that to move from element to element in memory requires jumps of 2 bytes. To move from row-to-row, one needs to jump 8 bytes at a time (2 * 4).

Type:

tuple of ints

ctypes

Class containing properties of the array needed for interaction with ctypes.

Type:

ctypes object

base

If the array is a view into another array, that array is its base (unless that array is also a view). The base array is where the array data is actually stored.

Type:

ndarray

See also

array

Construct an array.

zeros

Create an array, each element of which is zero.

empty

Create an array, but leave its allocated memory unchanged (i.e., it contains “garbage”).

dtype

Create a data-type.

numpy.typing.NDArray

An ndarray alias generic w.r.t. its dtype.type <numpy.dtype.type>.

Notes

There are two modes of creating an array using __new__:

1. If buffer is None, then only shape, dtype, and order are used.

2. If buffer is an object exposing the buffer interface, then all keywords are interpreted.

No __init__ method is needed because the array is fully initialized after the __new__ method.

Examples

These examples illustrate the low-level ndarray constructor. Refer to the See Also section above for easier ways of constructing an ndarray.

First mode, buffer is None:

>>> import numpy as np
>>> np.ndarray(shape=(2,2), dtype=float, order='F')
array([[0.0e+000, 0.0e+000], # random
       [     nan, 2.5e-323]])

Second mode:

>>> np.ndarray((2,), buffer=np.array([1,2,3]),
...            offset=np.int_().itemsize,
...            dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])

T

View of the transposed array.

Same as self.transpose().

Examples

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.T
array([[1, 3],
       [2, 4]])

>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> a.T
array([1, 2, 3, 4])

See also

transpose

all(axis=None, out=None, keepdims=False, *, where=True)

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, *, where=True)

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, *, keepdims=False)

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, *, keepdims=False)

Return indices of the minimum values along the given axis.

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.

See also

numpy.argpartition

equivalent function

argsort(axis=-1, kind=None, 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

Raises:

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

Examples

>>> import numpy as np
>>> 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:

>>> import numpy as np
>>> 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=False)

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. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.

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

>>> import numpy as np
>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> list(map(hex, A))
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([  256,     1, 13090], dtype=int16)
>>> list(map(hex, A))
['0x100', '0x1', '0x3322']

Arrays of byte-strings are not swapped

>>> A = np.array([b'ceg', b'fac'])
>>> A.byteswap()
array([b'ceg', b'fac'], dtype='|S3')

A.view(A.dtype.newbyteorder()).byteswap() produces an array with the same values but different representation in memory

>>> A = np.array([1, 2, 3],dtype=np.int64)
>>> A.view(np.uint8)
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
       0, 0], dtype=uint8)
>>> A.view(A.dtype.newbyteorder()).byteswap(inplace=True)
array([1, 2, 3], dtype='>i8')
>>> A.view(np.uint8)
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
       0, 3], dtype=uint8)

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, **kwargs)

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

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 numpy.copy() are very similar but have different default values for their order= arguments, and this function always passes sub-classes through.)

See also

numpy.copy

Similar function with different default behavior

numpy.copyto

Notes

This function is the preferred method for creating an array copy. The function numpy.copy() is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default.

Examples

>>> import numpy as np
>>> 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

For arrays containing Python objects (e.g. dtype=object), the copy is a shallow one. The new array will contain the same object which may lead to surprises if that object can be modified (is mutable):

>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
>>> b = a.copy()
>>> b[2][0] = 10
>>> a
array([1, 'm', list([10, 3, 4])], dtype=object)

To ensure all elements within an object array are copied, use copy.deepcopy:

>>> import copy
>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
>>> c = copy.deepcopy(a)
>>> c[2][0] = 10
>>> c
array([1, 'm', list([10, 3, 4])], dtype=object)
>>> a
array([1, 'm', list([2, 3, 4])], dtype=object)

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):

_ctypes.data

A pointer to the memory area of the array as a Python integer. This memory area may contain data that is not aligned, or not in correct 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].

Note that unlike data_as, a reference won’t be kept to the array: code like ctypes.c_void_p((a + b).ctypes.data) will result in a pointer to a deallocated array, and should be spelt (a + b).ctypes.data_as(ctypes.c_void_p)

_ctypes.shape

A ctypes array of length self.ndim where the basetype is the C-integer corresponding to dtype('p') on this platform (see ~numpy.ctypeslib.c_intp). This base-type could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The ctypes array contains the shape of the underlying array.

Type:

(c_intp*self.ndim)

_ctypes.strides

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.

Type:

(c_intp*self.ndim)

_ctypes.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)).

The returned pointer will keep a reference to the array.

_ctypes.shape_as(obj)

Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).

_ctypes.strides_as(obj)

Return the strides tuple as an array of some other c-types type. For example: self.strides_as(ctypes.c_longlong).

If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the as_parameter attribute which will return an integer equal to the data attribute.

Examples

>>> import numpy as np
>>> import ctypes
>>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)
>>> x
array([[0, 1],
       [2, 3]], dtype=int32)
>>> x.ctypes.data
31962608 # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))
<__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents
c_uint(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents
c_ulong(4294967296)
>>> x.ctypes.shape
<numpy._core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary
>>> x.ctypes.strides
<numpy._core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary

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

data

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

device

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()

dtype

Data-type of the array’s elements.

Warning

Setting arr.dtype is discouraged and may be deprecated in the future. Setting will replace the dtype without modifying the memory (see also ndarray.view and ndarray.astype).

Parameters:

None

Returns:

d

Return type:

numpy dtype object

See also

ndarray.astype

Cast the values contained in the array to a new data-type.

ndarray.view

Create a view of the same data but a different data-type.

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 or Path) – A string naming the dump file.

dumps()

Returns the pickle of the array as a string. pickle.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

>>> import numpy as np
>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([1.,  1.])

Fill expects a scalar value and always behaves the same as assigning to a single array element. The following is a rare example where this distinction is important:

>>> a = np.array([None, None], dtype=object)
>>> a[0] = np.array(3)
>>> a
array([array(3), None], dtype=object)
>>> a.fill(np.array(3))
>>> a
array([array(3), array(3)], dtype=object)

Where other forms of assignments will unpack the array being assigned:

>>> a[...] = np.array(3)
>>> a
array([3, 3], dtype=object)

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.

WRITEBACKIFCOPY(X)

This array is a copy of some other array. The C-API function PyArray_ResolveWritebackIfCopy must be called before deallocating to 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 WRITEBACKIFCOPY, 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:

• WRITEBACKIFCOPY can only be set False.

• ALIGNED can only be set True if the data is truly aligned.

• WRITEABLE can only be set True if the array owns its own memory or the ultimate owner of the memory exposes a writeable buffer interface or is a string.

Arrays can be both 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

>>> import numpy as np
>>> 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)
<class '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

>>> import numpy as np
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])

getfield(dtype, offset=0)

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

A field is a view of the array data with a given 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

>>> import numpy as np
>>> 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.]])

imag

The imaginary part of the array.

Examples

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

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

>>> import numpy as np
>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1

For an array with object dtype, elements are returned as-is.

>>> a = np.array([np.int64(1)], dtype=object)
>>> a.item() #return np.int64
np.int64(1)

itemset

itemsize

Length of one array element in bytes.

Examples

>>> import numpy as np
>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16

mT

View of the matrix transposed array.

The matrix transpose is the transpose of the last two dimensions, even if the array is of higher dimension.

Added in version 2.0.

Raises:

ValueError – If the array is of dimension less than 2.

Examples

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.mT
array([[1, 3],
       [2, 4]])

>>> a = np.arange(8).reshape((2, 2, 2))
>>> a
array([[[0, 1],
        [2, 3]],

       [[4, 5],
        [6, 7]]])
>>> a.mT
array([[[0, 2],
        [1, 3]],

       [[4, 6],
        [5, 7]]])

max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the maximum along a given axis.

Refer to numpy.amax for full documentation.

See also

numpy.amax

equivalent function

mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)

Returns the average of the array elements along given axis.

Refer to numpy.mean for full documentation.

See also

numpy.mean

equivalent function

min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

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.

See also

sys.getsizeof

Memory consumed by the object itself without parents in case view. This does include memory consumed by non-element attributes.

Examples

>>> import numpy as np
>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480

ndim

Number of array dimensions.

Examples

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

newbyteorder

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)

Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array. In the output array, all elements smaller than the k-th element are located to the left of this element and all equal or greater are located to its right. The ordering of the elements in the two partitions on the either side of the k-th element in the output array is undefined.

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 of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.

  Deprecated since version 1.22.0: Passing booleans as index is deprecated.

• 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 to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

See also

numpy.partition

Return a partitioned copy of an array.

argpartition

Indirect partition.

sort

Full sort.

Notes

See np.partition for notes on the different algorithms.

Examples

>>> import numpy as np
>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4]) # may vary

>>> a.partition((1, 3))
>>> a
array([1, 2, 3, 4])

prod()

a.prod(axis=None, dtype=None, out=None, keepdims=False,

initial=1, where=True)

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

See also

numpy.prod

equivalent function

ptp

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

>>> import numpy as np
>>> 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', copy=None)

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

Refer to numpy.reshape for full documentation.

See also

numpy.reshape

equivalent function

Notes

Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11)).

resize(new_shape, refcheck=True)

Change shape and size of array 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.

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:

>>> import numpy as np

>>> 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 references or is 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.

Return type:

None

See also

getfield

Examples

>>> import numpy as np
>>> 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]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
       [1.5e-323, 1.0e+000, 1.5e-323],
       [1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])

setflags(write=None, align=None, uic=None)

Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, 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 WRITEBACKIFCOPY 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 7 Boolean flags in use, only three of which can be changed by the user: WRITEBACKIFCOPY, 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);

WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the C-API function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array.

All flags can be accessed using the single (upper case) letter as well as the full name.

Examples

>>> import numpy as np
>>> y = np.array([[3, 1, 7],
...               [2, 0, 0],
...               [8, 5, 9]])
>>> y
array([[3, 1, 7],
       [2, 0, 0],
       [8, 5, 9]])
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : False
  ALIGNED : False
  WRITEBACKIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True

shape

Tuple of array dimensions.

The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.

Warning

Setting arr.shape is discouraged and may be deprecated in the future. Using ndarray.reshape is the preferred approach.

Examples

>>> import numpy as np
>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: Incompatible shape for in-place modification. Use
`.reshape()` to make a copy with the desired shape.

See also

numpy.shape

Equivalent getter function.

numpy.reshape

Function similar to setting shape.

ndarray.reshape

Method similar to setting shape.

size

Number of elements in the array.

Equal to np.prod(a.shape), i.e., the product of the array’s dimensions.

Notes

a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested np.prod(a.shape), which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type.

Examples

>>> import numpy as np
>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30

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

Sort an array in-place. Refer to numpy.sort for full documentation.

Parameters:

• axis (int, optional) – Axis along which to sort. Default is -1, which means sort along the last axis.

• kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, optional) – Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with datatype. The ‘mergesort’ option is retained for backwards compatibility.

• 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.

numpy.argsort

Indirect sort.

numpy.lexsort

Indirect stable sort on multiple keys.

numpy.searchsorted

Find elements in sorted array.

numpy.partition

Partial sort.

Notes

See numpy.sort for notes on the different sorting algorithms.

Examples

>>> import numpy as np
>>> 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([(b'c', 1), (b'a', 2)],
      dtype=[('x', 'S1'), ('y', '<i8')])

squeeze(axis=None)

Remove axes of length one from a.

Refer to numpy.squeeze for full documentation.

See also

numpy.squeeze

equivalent function

std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

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

Refer to numpy.std for full documentation.

See also

numpy.std

equivalent function

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 arrays.ndarray.

Warning

Setting arr.strides is discouraged and may be deprecated in the future. numpy.lib.stride_tricks.as_strided should be preferred to create a new view of the same data in a safer way.

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).

See also

numpy.lib.stride_tricks.as_strided

Examples

>>> import numpy as np
>>> 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, keepdims=False, initial=0, where=True)

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

Refer to numpy.sum for full documentation.

See also

numpy.sum

equivalent function

swapaxes(axis1, axis2)

Return a view of the array with axis1 and axis2 interchanged.

Refer to numpy.swapaxes for full documentation.

See also

numpy.swapaxes

equivalent function

take(indices, axis=None, out=None, mode='raise')

Return an array formed from the elements of a at the given indices.

Refer to numpy.take for full documentation.

See also

numpy.take

equivalent function

to_device()

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 is produced in C-order by default. This behavior is controlled by the order parameter.

Parameters:

order ({'C', 'F', 'A'}, optional) – Controls the memory layout of the bytes object. ‘C’ means C-order, ‘F’ means F-order, ‘A’ (short for Any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. Default is ‘C’.

Returns:

s – Python bytes exhibiting a copy of a’s raw data.

Return type:

bytes

See also

frombuffer

Inverse of this operation, construct a 1-dimensional array from Python bytes.

Examples

>>> import numpy as np
>>> x = np.array([[0, 1], [2, 3]], dtype='<u2')
>>> x.tobytes()
b'\x00\x00\x01\x00\x02\x00\x03\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x02\x00\x01\x00\x03\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 or Path) – 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.

When fid is a file object, array contents are directly written to the file, bypassing the file object’s write method. As a result, tofile cannot be used with files objects supporting compression (e.g., GzipFile) or file-like objects that do not support fileno() (e.g., BytesIO).

tolist()

Return the array as an a.ndim-levels deep nested list of Python scalars.

Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible builtin Python type, via the ~numpy.ndarray.item function.

If a.ndim is 0, then since the depth of the nested list is 0, it will not be a list at all, but a simple Python scalar.

Parameters:

none

Returns:

y – The possibly nested list of array elements.

Return type:

object, or list of object, or list of list of object, or …

Notes

The array may be recreated via a = np.array(a.tolist()), although this may sometimes lose precision.

Examples

For a 1D array, a.tolist() is almost the same as list(a), except that tolist changes numpy scalars to Python scalars:

>>> import numpy as np
>>> a = np.uint32([1, 2])
>>> a_list = list(a)
>>> a_list
[np.uint32(1), np.uint32(2)]
>>> type(a_list[0])
<class 'numpy.uint32'>
>>> a_tolist = a.tolist()
>>> a_tolist
[1, 2]
>>> type(a_tolist[0])
<class 'int'>

Additionally, for a 2D array, tolist applies recursively:

>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]

The base case for this recursion is a 0D array:

>>> a = np.array(1)
>>> list(a)
Traceback (most recent call last):
  ...
TypeError: iteration over a 0-d array
>>> a.tolist()
1

tostring(order='C')

A compatibility alias for ~ndarray.tobytes, with exactly the same behavior.

Despite its name, it returns bytes not strs.

Deprecated since version 1.19.0.

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.

Refer to numpy.transpose for full documentation.

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 that the array’s i-th axis becomes the transposed array’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:

p – View of the array with its axes suitably permuted.

Return type:

ndarray

See also

transpose

Equivalent function.

ndarray.T

Array property returning the array transposed.

ndarray.reshape

Give a new shape to an array without changing its data.

Examples

>>> import numpy as np
>>> 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]])

>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> a.transpose()
array([1, 2, 3, 4])

var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

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][, type])

New view of array with the same data.

Note

Passing None for dtype is different from omitting the parameter, since the former invokes dtype(None) which is an alias for dtype('float64').

Parameters:

• dtype (data-type or ndarray sub-class, optional) – Data-type descriptor of the returned view, e.g., float32 or int16. Omitting it 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, omission of the parameter 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 last axis of a must be contiguous. This axis will be resized in the result.

Changed in version 1.23.0: Only the last axis needs to be contiguous. Previously, the entire array had to be C-contiguous.

Examples

>>> import numpy as np
>>> x = np.array([(-1, 2)], dtype=[('a', np.int8), ('b', np.int8)])

Viewing array data using a different type and dtype:

>>> nonneg = np.dtype([("a", np.uint8), ("b", np.uint8)])
>>> y = x.view(dtype=nonneg, type=np.recarray)
>>> x["a"]
array([-1], dtype=int8)
>>> y.a
array([255], dtype=uint8)

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
>>> x
array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])

Using a view to convert an array to a recarray:

>>> z = x.view(np.recarray)
>>> z.a
array([1, 3], dtype=int8)

Views share data:

>>> x[0] = (9, 10)
>>> z[0]
np.record((9, 10), dtype=[('a', 'i1'), ('b', 'i1')])

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[:, ::2]
>>> y
array([[1, 3],
       [4, 6]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
    ...
ValueError: To change to a dtype of a different size, the last axis must be contiguous
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 3)],
       [(4, 6)]], dtype=[('width', '<i2'), ('length', '<i2')])

However, views that change dtype are totally fine for arrays with a contiguous last axis, even if the rest of the axes are not C-contiguous:

>>> x = np.arange(2 * 3 * 4, dtype=np.int8).reshape(2, 3, 4)
>>> x.transpose(1, 0, 2).view(np.int16)
array([[[ 256,  770],
        [3340, 3854]],

       [[1284, 1798],
        [4368, 4882]],

       [[2312, 2826],
        [5396, 5910]]], dtype=int16)

pyorps.graph.api.networkx_api module

pyorps.graph.api.rustworkx_api module

Module contents

Graph API abstractions for different graph libraries and routing algorithms.

This module provides base classes for graph APIs that are implemented by various backend libraries.

class pyorps.graph.api.GraphAPI(raster_data, steps)

Bases: ABC

Base class for all graph APIs defining the minimal required interface.

_abc_impl = <_abc._abc_data object>

abstractmethod shortest_path(source_indices, target_indices, algorithm='dijkstra', **kwargs)

Find the shortest path(s) between source and target indices.

Parameters:

• source_indices (Union[int, list[int], ndarray[int]]) – Source node indices

• target_indices (Union[int, list[int], ndarray[int]]) – Target node indices

• algorithm (str) – Algorithm name (e.g., “dijkstra”, “astar”)

• **kwargs –

  pairwisebool

  If True, compute pairwise shortest paths between source_indices and target_indices. Only allowed if len(source_indices) == len(target_indices)

  heuristiccallable, optional

  A function that takes two node indices (u, target) and returns an estimate of the distance between them. Only used when algorithm=”astar”.

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int], list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]]

Returns:

list of path indices for each source-target pair

class pyorps.graph.api.GraphLibraryAPI(raster_data, steps, from_nodes=None, to_nodes=None, cost=None, ignore_max=True, **kwargs)

Bases: GraphAPI

Base class for all graph library-based APIs.

This class extends GraphAPI with common functionality needed by standard graph libraries that require edge data to be explicitly provided and a graph to be constructed.

_abc_impl = <_abc._abc_data object>

abstractmethod _all_pairs_shortest_path(sources, targets, algorithm, **kwargs)

Computes shortest paths between all pairs of sources and targets.

Parameters:

• sources (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of source node identifiers

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation.

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths for all source-target combinations

_compute_all_pairs_shortest_paths(sources, targets, algorithm, **kwargs)

Computes paths individually for each source-target pair using the specified algorithm. Returns empty paths for unreachable targets.

Parameters:

• sources (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of source node identifiers

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths for all source-target combinations

abstractmethod _compute_single_path(source, target, algorithm, **kwargs)

Computes shortest path between a single source and target.

Parameters:

• source (Union[int, int32, int64, uint32, uint64]) – Source node identifier

• target (Union[int, int32, int64, uint32, uint64]) – Target node identifier

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]

Returns:

List of node identifiers representing the shortest path

abstractmethod _compute_single_source_multiple_targets(source, targets, algorithm, **kwargs)

Computes shortest paths from a single source to multiple targets.

Parameters:

• source (Union[int, int32, int64, uint32, uint64]) – Source node identifier

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths from the source to each target

static _ensure_path_endpoints(path, source, target)

Ensures the path starts with the source node and ends with the target node.

Parameters:

• path (list[int]) – List of node IDs representing a path

• source (int) – ID of the source node that should be at the start of the path

• target (int) – ID of the target node that should be at the end of the path

Return type:

list[int]

Returns:

list of node IDs with source and target at endpoints if needed

_pairwise_shortest_path(sources, targets, algorithm, **kwargs)

Default implementation for pairwise shortest path computation. Subclasses can override this for library-specific optimizations.

Parameters:

• sources (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of source node identifiers

• targets (Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]) – List of target node identifiers

• algorithm (str) – Algorithm to use for computation

• kwargs – Additional algorithm-specific parameters

Return type:

list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]

Returns:

List of paths, each connecting corresponding source-target pairs

_vectorized_bresenham(x0, y0, x1, y1)

Optimized implementation of Bresenham’s line algorithm that avoids generating duplicate coordinates.

Parameters:

• x0 (int) – x-coordinate of the first point

• y0 (int) – y-coordinate of the first point

• x1 (int) – x-coordinate of the second point

• y1 (int) – y-coordinate of the second point

Return type:

ndarray

Returns:

Array of (x,y) coordinates of cells that the line passes through

abstractmethod create_graph(from_nodes, to_nodes, cost=None, **kwargs)

Creates a graph object with the graph library specified in the selected interface.

Parameters:

• from_nodes (ndarray[int]) – The starting node indices from the edge data

• to_nodes (ndarray[int]) – The ending node indices from the edge data

• cost (Optional[ndarray[int]]) – The weight of the edge data

• kwargs – Additional parameters for the underlying graph library

Return type:

Any

Returns:

The graph object

get_a_star_heuristic(target, source=None, **kwargs)

Calculate the A* heuristic based on the Euclidean distance from the target node.

Parameters:

• target (Union[int, int32, int64, uint32, uint64]) – The index of the target node in the raster data

• source (Union[int, int32, int64, uint32, uint64, None]) – Optional source node for calculating area-specific minimum values

• kwargs – Additional parameters, including optional heu_weight for scaling

Returns:

• An array of node indices in the graph

• An array of heuristic values corresponding to each node

Return type:

tuple containing

get_advanced_a_star_heuristic(target, source=None, **kwargs)

Calculate the A* heuristic based on the Euclidean distance from the target node.

Parameters:

• target (Union[int, int32, int64, uint32, uint64]) – The index of the target node in the raster data

• source (Union[int, int32, int64, uint32, uint64, None]) – Optional source node for calculating area-specific minimum values

• kwargs – Additional parameters, including optional heu_weight for scaling

Returns:

• An array of node indices in the graph

• An array of heuristic values corresponding to each node

Return type:

tuple containing

abstractmethod get_nodes()

This method returns the nodes in the graph as a list or numpy array of node indices.

Return type:

Union[List[int], ndarray]

Returns:

List or array of node indices of the nodes in the graph

abstractmethod get_number_of_edges()

Returns the number of edges in the graph.

Return type:

int

Returns:

The number of edges

abstractmethod get_number_of_nodes()

Returns the number of nodes in the graph.

Return type:

int

Returns:

The number of nodes

abstractmethod remove_isolates()

If the graph object was initialized with the maximum number of nodes, this function helps to reduce the occupied memory by removing nodes without any edge (degree == 0).

Return type:

None

Returns:

None

shortest_path(source_indices, target_indices, algorithm='dijkstra', **kwargs)

This method applies the specified shortest path algorithm on the created graph object and finds the shortest path between source(s) and target(s) as a list of node indices.

Parameters:

• source_indices (Union[int, int32, int64, uint32, uint64, list[Union[int, int32, int64, uint32, uint64]], ndarray[int], None]) – Index or indices of source node(s) (int or list[int])

• target_indices (Union[int, int32, int64, uint32, uint64, list[Union[int, int32, int64, uint32, uint64]], ndarray[int], None]) – Index or indices of target node(s) (int or list[int])

• algorithm (str) – Algorithm to use for shortest path computation. Options depend on the specific library implementation.

• kwargs – Additional parameters for specific algorithms library including

• and (specific keywords) – “pairwise”: If True, compute pairwise shortest paths between source_indices and target_indices. Only allowed if len(source_indices) == len(target_indices)

Return type:

Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int], list[Union[list[Union[int, int32, int64, uint32, uint64]], ndarray[int]]]]

Returns:

List of node indices representing the shortest path(s)