kwcoco.demo.boids

Module Contents

Classes

Boids

Efficient numpy based backend for generating boid positions.

Functions

clamp_mag(vec, mag, axis=None)	vec = np.random.rand(10, 2)
triu_condense_multi_index(multi_index, dims, symetric=False)	Like np.ravel_multi_index but returns positions in an upper triangular
_spatial_index_scratch()
closest_point_on_line_segment(pts, e1, e2)	Finds the closet point from p on line segment (e1, e2)
_pygame_render_boids()	Fast and responsive BOID rendering. This is an easter egg.
_yeah_boid()

class kwcoco.demo.boids.Boids(num, dims=2, rng=None, **kwargs)

Bases: ubelt.NiceRepr

Efficient numpy based backend for generating boid positions.

BOID = bird-oid object

References

https://www.youtube.com/watch?v=mhjuuHl6qHM https://medium.com/better-programming/boids-simulating-birds-flock-behavior-in-python-9fff99375118 https://en.wikipedia.org/wiki/Boids

Example

>>> from kwcoco.demo.boids import *  # NOQA
>>> num_frames = 10
>>> num_objects = 3
>>> rng = None
>>> self = Boids(num=num_objects, rng=rng).initialize()
>>> paths = self.paths(num_frames)
>>> #
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> from mpl_toolkits.mplot3d import Axes3D  # NOQA
>>> ax = plt.gca(projection='3d')
>>> ax.cla()
>>> #
>>> for path in paths:
>>>     time = np.arange(len(path))
>>>     ax.plot(time, path.T[0] * 1, path.T[1] * 1, ',-')
>>> ax.set_xlim(0, num_frames)
>>> ax.set_ylim(-.01, 1.01)
>>> ax.set_zlim(-.01, 1.01)
>>> ax.set_xlabel('time')
>>> ax.set_ylabel('u-pos')
>>> ax.set_zlabel('v-pos')
>>> kwplot.show_if_requested()

import xdev _ = xdev.profile_now(self.compute_forces)() _ = xdev.profile_now(self.update_neighbors)()

Example

>>> # Test determenism
>>> from kwcoco.demo.boids import *  # NOQA
>>> num_frames = 2
>>> num_objects = 1
>>> rng = 4532
>>> self = Boids(num=num_objects, rng=rng).initialize()
>>> #print(ub.hash_data(self.pos))
>>> #print(ub.hash_data(self.vel))
>>> #print(ub.hash_data(self.acc))
>>> tocheck = []
>>> for i in range(100):
>>>     self = Boids(num=num_objects, rng=rng).initialize()
>>>     self.step()
>>>     self.step()
>>>     self.step()
>>>     tocheck.append(self.pos.copy())
>>> assert ub.allsame(list(map(ub.hash_data, tocheck)))

__nice__(self)

initialize(self)

update_neighbors(self)

compute_forces(self)

boundary_conditions(self)

step(self)

Update positions, velocities, and accelerations

paths(self, num_steps)

kwcoco.demo.boids.clamp_mag(vec, mag, axis=None)

vec = np.random.rand(10, 2) mag = 1.0 axis = 1 new_vec = clamp_mag(vec, mag, axis) np.linalg.norm(new_vec, axis=axis)

kwcoco.demo.boids.triu_condense_multi_index(multi_index, dims, symetric=False)

Like np.ravel_multi_index but returns positions in an upper triangular condensed square matrix

Examples

multi_index (Tuple[ArrayLike]):

indexes for each dimension into the square matrix

dims (Tuple[int]):

shape of each dimension in the square matrix (should all be the same)

symetric (bool):

if True, converts lower triangular indices to their upper triangular location. This may cause a copy to occur.

References

https://stackoverflow.com/a/36867493/887074 https://numpy.org/doc/stable/reference/generated/numpy.ravel_multi_index.html#numpy.ravel_multi_index

Examples

>>> dims = (3, 3)
>>> symetric = True
>>> multi_index = (np.array([0, 0, 1]), np.array([1, 2, 2]))
>>> condensed_idxs = triu_condense_multi_index(multi_index, dims, symetric=symetric)
>>> assert condensed_idxs.tolist() == [0, 1, 2]

>>> n = 7
>>> symetric = True
>>> multi_index = np.triu_indices(n=n, k=1)
>>> condensed_idxs = triu_condense_multi_index(multi_index, [n] * 2, symetric=symetric)
>>> assert condensed_idxs.tolist() == list(range(n * (n - 1) // 2))
>>> from scipy.spatial.distance import pdist, squareform
>>> square_mat = np.zeros((n, n))
>>> conden_mat = squareform(square_mat)
>>> conden_mat[condensed_idxs] = np.arange(len(condensed_idxs)) + 1
>>> square_mat = squareform(conden_mat)
>>> print('square_mat =\n{}'.format(ub.repr2(square_mat, nl=1)))

>>> n = 7
>>> symetric = True
>>> multi_index = np.tril_indices(n=n, k=-1)
>>> condensed_idxs = triu_condense_multi_index(multi_index, [n] * 2, symetric=symetric)
>>> assert sorted(condensed_idxs.tolist()) == list(range(n * (n - 1) // 2))
>>> from scipy.spatial.distance import pdist, squareform
>>> square_mat = np.zeros((n, n))
>>> conden_mat = squareform(square_mat, checks=False)
>>> conden_mat[condensed_idxs] = np.arange(len(condensed_idxs)) + 1
>>> square_mat = squareform(conden_mat)
>>> print('square_mat =\n{}'.format(ub.repr2(square_mat, nl=1)))

kwcoco.demo.boids._spatial_index_scratch()

kwcoco.demo.boids.closest_point_on_line_segment(pts, e1, e2)

Finds the closet point from p on line segment (e1, e2)

Parameters

• pts (ndarray) – xy points [Nx2]

• e1 (ndarray) – the first xy endpoint of the segment

• e2 (ndarray) – the second xy endpoint of the segment

Returns

pt_on_seg - the closest xy point on (e1, e2) from ptp

Return type

ndarray

References

http://en.wikipedia.org/wiki/Distance_from_a_point_to_a_line http://stackoverflow.com/questions/849211/shortest-distance-between-a-point-and-a-line-segment

Example

>>> # ENABLE_DOCTEST
>>> from kwcoco.demo.boids import *  # NOQA
>>> verts = np.array([[ 21.83012702,  13.16987298],
>>>                   [ 16.83012702,  21.83012702],
>>>                   [  8.16987298,  16.83012702],
>>>                   [ 13.16987298,   8.16987298],
>>>                   [ 21.83012702,  13.16987298]])
>>> rng = np.random.RandomState(0)
>>> pts = rng.rand(64, 2) * 20 + 5
>>> e1, e2 = verts[0:2]
>>> closest_point_on_line_segment(pts, e1, e2)

kwcoco.demo.boids._pygame_render_boids()

Fast and responsive BOID rendering. This is an easter egg.

Requirements:

pip install pygame

CommandLine

python -m kwcoco.demo.boids
pip install pygame kwcoco -U && python -m kwcoco.demo.boids

kwcoco.demo.boids._yeah_boid()