def test_deferred_type(self):
node_type = deferred_type()
spec = OrderedDict()
spec['data'] = float32
spec['next'] = optional(node_type)
@njit
def get_data(node):
return node.data
@jitclass(spec)
class LinkedNode(object):
def __init__(self, data, next):
self.data = data
self.next = next
def get_next_data(self):
# use deferred type as argument
return get_data(self.next)
def append_to_tail(self, other):
cur = self
while cur.next is not None:
cur = cur.next
cur.next = other
node_type.define(LinkedNode.class_type.instance_type)
first = LinkedNode(123, None)
self.assertEqual(first.data, 123)
self.assertIsNone(first.next)
second = LinkedNode(321, first)
first_meminfo = _get_meminfo(first)
second_meminfo = _get_meminfo(second)
self.assertEqual(first_meminfo.refcount, 3)
self.assertEqual(second.next.data, first.data)
self.assertEqual(first_meminfo.refcount, 3)
self.assertEqual(second_meminfo.refcount, 2)
# Test using deferred type as argument
first_val = second.get_next_data()
self.assertEqual(first_val, first.data)
# Check setattr (issue #2606)
self.assertIsNone(first.next)
second.append_to_tail(LinkedNode(567, None))
self.assertIsNotNone(first.next)
self.assertEqual(first.next.data, 567)
self.assertIsNone(first.next.next)
second.append_to_tail(LinkedNode(678, None))
self.assertIsNotNone(first.next.next)
self.assertEqual(first.next.next.data, 678)
# Check ownership
self.assertEqual(first_meminfo.refcount, 3)
del second, second_meminfo
self.assertEqual(first_meminfo.refcount, 2)
def test_deferred_type(self):
node_type = deferred_type()
spec = OrderedDict()
spec['data'] = float32
spec['next'] = optional(node_type)
@njit
def get_data(node):
return node.data
@jitclass(spec)
class LinkedNode(object):
def __init__(self, data, next):
self.data = data
self.next = next
def get_next_data(self):
# use deferred type as argument
return get_data(self.next)
node_type.define(LinkedNode.class_type.instance_type)
first = LinkedNode(123, None)
self.assertEqual(first.data, 123)
self.assertIsNone(first.next)
second = LinkedNode(321, first)
first_meminfo = _get_meminfo(first)
second_meminfo = _get_meminfo(second)
self.assertEqual(first_meminfo.refcount, 3)
self.assertEqual(second.next.data, first.data)
self.assertEqual(first_meminfo.refcount, 3)
self.assertEqual(second_meminfo.refcount, 2)
# Test using deferred type as argument
first_val = second.get_next_data()
self.assertEqual(first_val, first.data)
# Check ownership
self.assertEqual(first_meminfo.refcount, 3)
del second, second_meminfo
self.assertEqual(first_meminfo.refcount, 2)
def test_deferred_type(self):
node_type = deferred_type()
spec = OrderedDict()
spec['data'] = float32
spec['next'] = optional(node_type)
@njit
def get_data(node):
return node.data
@jitclass(spec)
class LinkedNode(object):
def __init__(self, data, next):
self.data = data
self.next = next
def get_next_data(self):
# use deferred type as argument
return get_data(self.next)
node_type.define(LinkedNode.class_type.instance_type)
first = LinkedNode(123, None)
self.assertEqual(first.data, 123)
self.assertIsNone(first.next)
second = LinkedNode(321, first)
first_meminfo = _get_meminfo(first)
second_meminfo = _get_meminfo(second)
self.assertEqual(first_meminfo.refcount, 3)
self.assertEqual(second.next.data, first.data)
self.assertEqual(first_meminfo.refcount, 3)
self.assertEqual(second_meminfo.refcount, 2)
# Test using deferred type as argument
first_val = second.get_next_data()
self.assertEqual(first_val, first.data)
# Check ownership
self.assertEqual(first_meminfo.refcount, 3)
del second, second_meminfo
self.assertEqual(first_meminfo.refcount, 2)
for m in range(-L, L + 1):
yield L, m
else:
for m in range(L + 1):
if not m:
yield L, m
else:
for i in (m, -m):
yield L, i
#####################
# Basis set classes #
#####################
shell_type = deferred_type()
@jitclass([('L', int64), ('nprim', int64), ('ncont', int64),
('spherical', boolean), ('gaussian', boolean),
('alphas', float64[:]), ('_coef', float64[:]),
('rs', optional(int64[:])), ('ns', optional(int64[:]))])
class Shell(object):
"""The primary object used for all things basis set related.
Due to limitations in numba, contraction coefficients are stored
as a 1D-array and reshaped when calling contract methods.
Args:
coef (np.ndarray): 1D-array of contraction coefficients
alphas (np.ndarray): 1D-array of primitive exponents
nprim (int): number of primitives in the shell
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This is a more complicated jitclasses example.
Here, we implement a binarytree and iterative preorder and inorder traversal
function using a handwritten stack.
"""
from __future__ import print_function, absolute_import
import random
from collections import OrderedDict
from numba import njit
from numba import jitclass
from numba import int32, deferred_type, optional
from numba.runtime import rtsys
node_type = deferred_type()
spec = OrderedDict()
spec['data'] = int32
spec['left'] = optional(node_type)
spec['right'] = optional(node_type)
@jitclass(spec)
class TreeNode(object):
def __init__(self, data):
self.data = data
self.left = None
self.right = None
@numba.njit(fastmath=True)
def func(x):
if x > lbs[0] and x < ubs[-1]:
ind = _get_ind(x, ubs)
a = lbs[ind]
b = ubs[ind]
_x = 2*(x-a)/(b-a) - 1.0
return _numba_chbevl(_x, cs[ind])
else:
return np.nan
return func
################################################################################
# This is slow for now :(
leaf_type = numba.deferred_type()
leaf_spec = OrderedDict()
leaf_spec['a'] = numba.float64
leaf_spec['b'] = numba.float64
leaf_spec['ancestor'] = numba.optional(leaf_type)
leaf_spec['m'] = numba.float64
leaf_spec['ind'] = numba.int64
leaf_spec['parent'] = numba.boolean
leaf_spec['left'] = numba.optional(leaf_type)
leaf_spec['right'] = numba.optional(leaf_type)
@numba.jitclass(leaf_spec)
class Leaf(object):
def __init__(self, a, b, ancestor, ind):
self.a = a
self.b = b
return self.get_misfit_incompatible(rffactor)
if self.to[i] >= 0:
temp += ((self.rfo[i] - self.rfp[i])**2 / (self.stdrfo[i]**2))
k += 1
if self.to[i] > 10:
break
self.misfit = np.sqrt(temp / k)
tS = temp / rffactor
if tS > 50.:
tS = np.sqrt(tS * 50.)
self.L = np.exp(-0.5 * tS)
return True
# define type of disp object
disp_type = numba.deferred_type()
disp_type.define(disp.class_type.instance_type)
# define type of rf object
rf_type = numba.deferred_type()
rf_type.define(rf.class_type.instance_type)
####################################################
# Predefine the parameters for the data1d object
####################################################
spec_data = [ # Rayleigh/Love dispersion data
('dispR', disp_type),
('dispL', disp_type),
# radial/transverse receiver function data
('rfr', rf_type),
('rft', rf_type),
from scipy import interpolate
from utils import *
#c = 2.998e8 # m/s
#c = 1.0 # m/s
spec_Dipole = [
('r', float64[:]),
('k', float64[:]),
('E0', float64),
('phase', float64),
('wavelength', float64),
]
Dipole_type = deferred_type()
@jitclass(spec_Dipole)
class Dipole(object):
def __init__(self, r, k, E0, phase, wavelength):
self.r = r
self.k = unit_vector(k) * 2 * np.pi / wavelength
self.E0 = E0
self.phase = phase
self.wavelength = wavelength
Dipole_type.define(Dipole.class_type.instance_type)
spec_DipoleSet = [
:Copyright:
Author: Lili Feng
Graduate Research Assistant
CIEI, Department of Physics, University of Colorado Boulder
email: [email protected]
"""
import fast_surf
import numba
import numpy as np
import vmodel
import copy
# define type of vmodel.model1d
model_type = numba.deferred_type()
model_type.define(vmodel.model1d.class_type.instance_type)
#--------------------------------------------------------------------------------------------------
#- fundamental array manipulations
#--------------------------------------------------------------------------------------------------
@numba.jit(numba.float32[:](numba.float32, numba.float32, numba.float32))
def _get_array(xmin, xmax, dx):
xlst = []
Nx = int((xmax - xmin) / dx + 1)
for i in xrange(Nx):
xlst.append(dx * i + xmin)
return np.array(xlst, dtype=np.float32)
@property
def nx(self):
return self.__nx
@property
def nu(self):
return self.__nu
@property
def ny(self):
return self.__ny
NB_TYPE_DYNAMIC = nb.deferred_type()
NB_TYPE_DYNAMIC.define(Dynamic.class_type.instance_type)
class Simulate:
def __init__(self,steps,nx,u,y,nbases,L,R=0.1,PFweightNum=30):
# self.__dynamic = dynamic
self.__u = u
self.__y = y
self.__nu = self.__u.shape[0]
self.__ny = self.__y.shape[0]
self.__nx = nx
self.__steps = steps
self.__nbases = nbases #assumed to be equal to all x and u
self.__A = np.zeros((self.__nx,self.nbases**(self.__nx+self.__nu)),dtype=np.float64,order='C')
yield L, m
else:
for m in range(L + 1):
if not m:
yield L, m
else:
for i in (m, -m):
yield L, i
#####################
# Basis set classes #
#####################
shell_type = deferred_type()
@jitclass([('L', int64), ('nprim', int64), ('ncont', int64),
('spherical', boolean), ('gaussian', boolean),
('alphas', float64[:]), ('_coef', float64[:]),
('rs', optional(int64[:])), ('ns', optional(int64[:]))])
class Shell(object):
"""The primary object used for all things basis set related.
Due to limitations in numba, contraction coefficients are stored
as a 1D-array and reshaped when calling contract methods.
Args:
coef (np.ndarray): 1D-array of contraction coefficients
alphas (np.ndarray): 1D-array of primitive exponents
nprim (int): number of primitives in the shell
ncont (int): number of contracted functions in the shell
]
@jitclass(matSpec)
class Material:
def __init__(self, refractive_index, albedo, color, spec):
self.albedo = np.array(albedo)
self.difuse_color = np.array(color)
self.specular_exponent = float(spec)
self.refractive_index = float(refractive_index)
if os.environ.get('NUMBA_DISABLE_JIT') == '1':
material_type = None
else:
material_type = deferred_type()
material_type.define(Material.class_type.instance_type)
sphereSpec = [
('center', numba.float64[:]),
('radius', numba.float64),
('radius2', numba.float64),
('material', material_type),
]
@jitclass(sphereSpec)
class Sphere:
def __init__(self, center, radius, material):
self.center = np.array(center)
self.radius = float(radius)
lst.push_front(3)
lst.show()#321
@nb.jit
def sum_list(lst):
result=0
node=lst.head
while node is not None:
result+=node.value
node=node.next
return result
lst1=LinkedList()
[lst1.push_front(i) for i in range(1000)]
# print(sum_list(lst1))#无法推断类型
#可使用装饰器nb.jitclass 来编译Node和LinkedList类，装饰器接受一个参数，其中包含被装饰类的属性的类型。
#首先必须先声明属性，在定义类，使用nb.deferred_type()函数，其次属性next可以使NOne，也可以是一个Node实例，这被称为可选类型，nb.optional
node_type=nb.deferred_type()
node_spec=[('next',nb.optional(node_type)),('value',nb.int64)]
@nb.jitclass(node_spec)
class Node1:
def __init__(self,value):
self.next=None
self.value=value
node_type.define(Node.class_type.instance_type)
ll_spec=[('head',nb.optional(Node.class_type.instance_type))]
@nb.jitclass(ll_spec)
class LinkedList1:#实现链表
def __init__(self):
self.head=None
def push_front(self,value):
if self.head==None:
self.head=Node(value)
import numba
from numba import jit
from numba import int32, float32, boolean, float64
from numba import jitclass, deferred_type
@jitclass([('p_pos', float64[:]), ('p_vel', float64[:])])
class EntityState_nb(object):
def __init__(self):
# physical position
self.p_pos
# physical velocity
self.p_vel
EntityState_type = deferred_type()
EntityState_type.define(EntityState_nb.class_type.instance_type)
@jitclass([('size', float32), ('movable', boolean), ('collide', boolean),
('state', EntityState_type)])
class Entity_nb(object):
def __init__(self):
# state
self.state = EntityState_nb()
# entity collides with others
self.collide = True
self.size = 0.050
self.movable = False
import numpy as np
import numba as nb
import mcmc.util as util
import mcmc.fourier as fourier
# import mcmc.layer as layer
import mcmc.randomGenerator as randomGenerator
import mcmc.measurement as meas
Rand_gen_type = nb.deferred_type()
Rand_gen_type.define(randomGenerator.RandomGenerator.class_type.instance_type)
meas_type = nb.deferred_type()
meas_type.define(meas.Measurement.class_type.instance_type)
fourier_type = nb.deferred_type()
fourier_type.define(fourier.FourierAnalysis.class_type.instance_type)
spec = [
('n_layers',nb.int64),
('record_skip',nb.int64),
('record_count',nb.int64),
('max_record_history',nb.int64),
('beta',nb.float64),
('betaZ',nb.float64),
('random_gen',Rand_gen_type),
('measurement',meas_type),
('fourier',fourier_type),
('H',nb.complex128[:,::1]),
('I',nb.float64[:,::1]),
('H_t_H',nb.float64[:,::1]),
('H_dagger',nb.complex128[:,::1]),
('yBar',nb.float64[::1]),
import numpy as np
import numba as nb
import mcmc.util as util
import mcmc.fourier as fourier
import mcmc.L as L
import mcmc.randomGenerator as randomGenerator
L_matrix_type = nb.deferred_type()
Rand_gen_type = nb.deferred_type()
L_matrix_type.define(L.Lmatrix.class_type.instance_type)
Rand_gen_type.define(randomGenerator.RandomGenerator.class_type.instance_type)
spec = [
('LMat', L_matrix_type),
('rand_gen', Rand_gen_type),
('uStdev', nb.complex128[:]),
('beta', nb.float64),
('betaZ', nb.float64),
('current_sample', nb.complex128[:]),
('norm_current_sample', nb.float64),
('current_log_det_L', nb.float64),
]
@nb.jitclass(spec)
class pCN():
def __init__(self, LMat, rg, uStdev, init_sample, beta=1):
self.LMat = LMat
self.rand_gen = rg
self.uStdev = uStdev
self.beta = beta
self.betaZ = 1 - np.sqrt(beta**2)
"""
This example demonstrates jitclasses and deferred types for writing a
singly-linked-list.
"""
from __future__ import print_function, absolute_import
from collections import OrderedDict
import numpy as np
from numba import njit
from numba import jitclass
from numba import int32, deferred_type, optional
from numba.runtime import rtsys
node_type = deferred_type()
spec = OrderedDict()
spec['data'] = int32
spec['next'] = optional(node_type)
@jitclass(spec)
class LinkedNode(object):
def __init__(self, data, next):
self.data = data
self.next = next
def prepend(self, data):
return LinkedNode(data, self)
@njit
def make_linked_node(data):
# -*- coding: utf-8 -*-
"""
This is a more complicated jitclasses example.
Here, we implement a binarytree and iterative preorder and inorder traversal
function using a handwritten stack.
"""
from __future__ import print_function, absolute_import
import random
from collections import OrderedDict
from numba import njit
from numba import jitclass
from numba import int32, deferred_type, optional
from numba.runtime import rtsys
node_type = deferred_type()
spec = OrderedDict()
spec['data'] = int32
spec['left'] = optional(node_type)
spec['right'] = optional(node_type)
@jitclass(spec)
class TreeNode(object):
def __init__(self, data):
self.data = data
self.left = None
self.right = None
Class for tracking the result of selection to allow for chaining.
"""
import numba
import numpy as np
from .flags import *
from .indexset import IndexSet
from .sel import _sel
from .omit import _omit
from collections import OrderedDict
sel_result_spec = OrderedDict()
indexset_type = numba.deferred_type()
sel_result_spec['start'] = numba.int64
sel_result_spec['stop'] = numba.int64
sel_result_spec['indexset'] = indexset_type
sel_result_spec['flags'] = numba.boolean[:]
sel_result_spec['_num_flags'] = numba.int64
sel_result_spec['has_flags'] = numba.bool_
@numba.jitclass(sel_result_spec)
class SelResult_:
def __init__(self, indexset):
self.indexset = indexset
self.start = 0
self.stop = indexset.n
class OrientationSpaceExplorer(object):
def __init__(
self,
minimum_radius=0.2,
maximum_radius=2.96, # 2.5
minimum_clearance=1.02,
neighbors=32,
maximum_curvature=0.2,
timeout=1.0,
overlap_rate=0.5):
self.minimum_radius = minimum_radius
self.maximum_radius = maximum_radius
self.minimum_clearance = minimum_clearance
self.neighbors = neighbors
self.maximum_curvature = maximum_curvature
self.timeout = timeout
self.overlap_rate = overlap_rate
# planning related
self.start = None
self.goal = None
self.grid_ori = None
self.grid_map = None
self.grid_res = None
self.grid_pad = None
self.obstacle = 255
def exploring(self, plotter=None):
close_set, open_set = numba.typed.List(), [(self.start.f, self.start)]
close_set.append((0., 0., 0., 0.)), close_set.pop()
while open_set:
circle = self.pop_top(open_set)
if self.goal.f < circle.f:
return True
if not self.exist(circle, close_set):
expansion = self.expand(circle)
self.merge(expansion, open_set)
if self.overlap(circle, self.goal) and circle.f < self.goal.g:
self.goal.g = circle.f
self.goal.f = self.goal.g + self.goal.h
self.goal.set_parent(circle)
close_set.append((circle.x, circle.y, circle.a, circle.r))
if plotter:
plotter([circle])
return False
@property
def circle_path(self):
if self.goal:
path, parent = [self.goal], self.goal.parent
while parent:
path.append(parent)
parent = parent.parent
path.reverse()
return path
return []
def path(self):
circles = self.circle_path
if circles:
return [(p.x, p.y, p.a, p.r) for p in circles]
return []
def initialize(self,
start,
goal,
grid_map,
grid_res,
grid_ori,
obstacle=255):
# type: (CircleNode, CircleNode, np.ndarray, float, CircleNode, int) -> OrientationSpaceExplorer
"""
:param start: start circle-node
:param goal: goal circle-node
:param grid_map: occupancy map(0-1), 2d-square, with a certain resolution: gird_res
:param grid_res: resolution of occupancy may (m/pixel)
:param grid_ori:
:param obstacle: value of the pixels of obstacles region on occupancy map.
"""
self.start, self.goal = start, goal
self.grid_map, self.grid_res, self.grid_ori, self.obstacle = grid_map, grid_res, grid_ori, obstacle
# padding grid map for clearance calculation
s = int(
np.ceil((self.maximum_radius + self.minimum_clearance) /
self.grid_res))
self.grid_pad = np.pad(self.grid_map, ((s, s), (s, s)),
'constant',
constant_values=((self.obstacle, self.obstacle),
(self.obstacle,
self.obstacle)))
# complete the start and goal
self.start.r, self.start.g = self.clearance(
self.start) - self.minimum_clearance, 0
self.start.h = reeds_shepp.path_length(
(start.x, start.y, start.a),
(self.goal.x, self.goal.y, self.goal.a),
1. / self.maximum_curvature)
self.goal.r, self.goal.h, self.goal.g = self.clearance(
self.goal) - self.minimum_clearance, 0, np.inf
self.start.f, self.goal.f = self.start.g + self.start.h, self.goal.g + self.goal.h
return self
@staticmethod
def merge(expansion, open_set):
"""
:param expansion: expansion is a set in which items are unordered.
:param open_set: we define the open set as a set in which items are sorted from Large to Small by cost.
"""
open_set.extend(zip(map(lambda x: x.f, expansion), expansion))
open_set.sort(reverse=True)
@staticmethod
def pop_top(open_set):
"""
:param open_set: we define the open set as a set in which items are sorted from Large to Small by cost.
"""
return open_set.pop()[-1]
def exist(self, circle, close_set):
state = (circle.x, circle.y, circle.a)
return self.jit_exist(state, close_set, self.maximum_curvature)
@staticmethod
@njit
def jit_exist(state, close_set, maximum_curvature):
def distance(one, another, curvature):
euler = np.sqrt((one[0] - another[0])**2 +
(one[1] - another[1])**2)
angle = np.abs(one[2] - another[2])
angle = (angle + np.pi) % (2 * np.pi) - np.pi
# angle = np.pi - angle if angle > np.pi / 2 else angle
heuristic = angle / curvature
return euler if euler > heuristic else heuristic
for item in close_set:
if distance(state, item, maximum_curvature) < item[-1] - 0.1:
return True
return False
def overlap(self, circle, goal):
"""
check if two circles overlap with each other
in a certain margin (overlap_rate[e.g., 50%] of the radius of the smaller circle),
which guarantees enough space for a transition motion.
"""
return self.jit_overlap((circle.x, circle.y, circle.r),
(goal.x, goal.y, goal.r), self.overlap_rate)
@staticmethod
@njit
def jit_overlap(circle, goal, rate):
euler = np.sqrt((circle[0] - goal[0])**2 + (circle[1] - goal[1])**2)
r1, r2 = min([circle[2], goal[2]]), max([circle[2], goal[2]])
return euler < r1 * rate + r2
def expand(self, circle):
def buildup(state):
child = self.CircleNode(state[0], state[1], state[2], state[3])
# build the child
child.set_parent(circle)
# child.h = self.distance(child, self.goal)
child.h = reeds_shepp.path_length(
(child.x, child.y, child.a),
(self.goal.x, self.goal.y, self.goal.a),
1. / self.maximum_curvature)
child.g = circle.g + reeds_shepp.path_length(
(circle.x, circle.y, circle.a),
(child.x, child.y, child.a), 1. / self.maximum_curvature)
child.f = child.g + child.h
# add the child to expansion set
return child
neighbors = self.jit_neighbors((circle.x, circle.y, circle.a),
circle.r, self.neighbors)
children = self.jit_children(
neighbors, (self.grid_ori.x, self.grid_ori.y, self.grid_ori.a),
self.grid_pad, self.grid_map, self.grid_res, self.maximum_radius,
self.minimum_radius, self.minimum_clearance, self.obstacle)
return map(buildup, children)
@staticmethod
@njit
def jit_children(neighbors, origin, grid_pad, grid_map, grid_res,
maximum_radius, minimum_radius, minimum_clearance,
obstacle):
def clearance(state):
s_x, s_y, s_a = origin[0], origin[1], origin[2]
c_x, c_y, c_a = state[0], state[1], state[2]
x = (c_x - s_x) * np.cos(s_a) + (c_y - s_y) * np.sin(s_a)
y = -(c_x - s_x) * np.sin(s_a) + (c_y - s_y) * np.cos(s_a)
u = int(np.floor(y / grid_res + grid_map.shape[0] / 2))
v = int(np.floor(x / grid_res + grid_map.shape[0] / 2))
size = int(np.ceil(
(maximum_radius + minimum_clearance) / grid_res))
subspace = grid_pad[u:u + 2 * size + 1, v:v + 2 * size + 1]
rows, cols = np.where(subspace >= obstacle)
if len(rows):
row, col = np.fabs(rows - size) - 1, np.fabs(cols - size) - 1
rs = np.sqrt(row**2 + col**2) * grid_res
return rs.min()
else:
return size * grid_res
children = numba.typed.List()
children.append((0., 0., 0., 0.)), children.pop()
for neighbor in neighbors:
r = min([clearance(neighbor) - minimum_clearance, maximum_radius])
if r > minimum_radius:
children.append((neighbor[0], neighbor[1], neighbor[2], r))
return children
@staticmethod
@njit
def jit_neighbors(state, radius, number):
def lcs2gcs(point):
x, y, a = point
xo, yo, ao = state
x1 = x * np.cos(ao) - y * np.sin(ao) + xo
y1 = x * np.sin(ao) + y * np.cos(ao) + yo
a1 = a + ao
return x1, y1, a1
neighbors = numba.typed.List()
neighbors.append((0., 0., 0.)), neighbors.pop()
for n in np.radians(np.linspace(-90, 90, number / 2)):
neighbor = (radius * np.cos(n), radius * np.sin(n), n)
opposite = (radius * np.cos(n + np.pi), radius * np.sin(n + np.pi),
n)
neighbor = lcs2gcs(neighbor)
opposite = lcs2gcs(opposite)
neighbors.extend([neighbor, opposite])
return neighbors
def clearance(self, circle):
origin, coord = (self.grid_ori.x, self.grid_ori.y,
self.grid_ori.a), (circle.x, circle.y, circle.a)
return self.jit_clearance(coord, origin, self.grid_pad, self.grid_map,
self.grid_res, self.maximum_radius,
self.minimum_clearance, self.obstacle)
@staticmethod
@njit
def jit_clearance(coord, origin, grid_pad, grid_map, grid_res,
maximum_radius, minimum_clearance, obstacle):
s_x, s_y, s_a = origin[0], origin[1], origin[2]
c_x, c_y, c_a = coord[0], coord[1], coord[2]
x = (c_x - s_x) * np.cos(s_a) + (c_y - s_y) * np.sin(s_a)
y = -(c_x - s_x) * np.sin(s_a) + (c_y - s_y) * np.cos(s_a)
u = int(np.floor(y / grid_res + grid_map.shape[0] / 2))
v = int(np.floor(x / grid_res + grid_map.shape[0] / 2))
size = int(np.ceil((maximum_radius + minimum_clearance) / grid_res))
subspace = grid_pad[u:u + 2 * size + 1, v:v + 2 * size + 1]
rows, cols = np.where(subspace >= obstacle)
if len(rows):
row, col = np.fabs(rows - size) - 1, np.fabs(cols - size) - 1
rs = np.sqrt(row**2 + col**2) * grid_res
return rs.min()
else:
return size * grid_res
circle_node = numba.deferred_type()
spec = [('x', numba.float64), ('y', numba.float64), ('a', numba.float64),
('r', numba.optional(numba.float64)), ('h', numba.float64),
('g', numba.float64), ('f', numba.float64),
("parent", numba.optional(circle_node)),
('children', numba.optional(numba.types.List(circle_node)))]
# @numba.jitclass(spec)
class CircleNode(object):
def __init__(self, x=None, y=None, a=None, r=None):
self.x = x
self.y = y
self.a = a
self.r = r
self.h = np.inf # cost from here to goal, heuristic distance or actual one
self.g = np.inf # cost from start to here, actual distance
self.f = self.h + self.g
self.parent = None
self.children = None
def set_parent(self, circle):
self.parent = circle
def lcs2gcs(self, circle):
# type: (OrientationSpaceExplorer.CircleNode) -> OrientationSpaceExplorer.CircleNode
"""
transform self's coordinate from local coordinate system (LCS) to global coordinate system (GCS)
:param circle: the circle-node contains the coordinate (in GCS) of the origin of LCS.
"""
xo, yo, ao = circle.x, circle.y, circle.a
x = self.x * np.cos(ao) - self.y * np.sin(ao) + xo
y = self.x * np.sin(ao) + self.y * np.cos(ao) + yo
a = self.a + ao
self.x, self.y, self.a = x, y, a
return self
def gcs2lcs(self, circle):
# type: (OrientationSpaceExplorer.CircleNode) -> OrientationSpaceExplorer.CircleNode
"""
transform self's coordinate from global coordinate system (LCS) to local coordinate system (GCS)
:param circle: the circle-node contains the coordinate (in GCS) of the origin of LCS.
"""
xo, yo, ao = circle.x, circle.y, circle.a
x = (self.x - xo) * np.cos(ao) + (self.y - yo) * np.sin(ao)
y = -(self.x - xo) * np.sin(ao) + (self.y - yo) * np.cos(ao)
a = self.a - ao
self.x, self.y, self.a = x, y, a
return self
import numba
from collections import OrderedDict
linked_node_spec = OrderedDict()
node_type = numba.deferred_type()
linked_node_spec["parent"] = node_type
linked_node_spec["child"] = node_type
linked_node_spec["depth"] = numba.int32
linked_node_spec["start"] = numba.int32
linked_node_spec["end"] = numba.int32
linked_node_spec["index"] = numba.int32
linked_node_spec["node_mean"] = numba.float32
linked_node_spec["node_impurity"] = numba.float32
linked_node_spec["right_child_mean"] = numba.float32
linked_node_spec["right_child_impurity"] = numba.float32
linked_node_spec["sorted_dim"] = numba.int32
@numba.jitclass(linked_node_spec)
class LinkedNode:
def __init__(self, depth):
self.parent = self
self.child = self
self.depth = depth
return np.array([x, y, z], dtype=np.float64)
@numba.njit
def vec(x, y, z):
return np.array([x, y, z], dtype=np.float64)
@numba.njit
def unit(v):
return v / np.linalg.norm(v)
# fast primitives
ray_type = numba.deferred_type()
tree_box_type = numba.deferred_type()
@numba.experimental.jitclass([
('origin', numba.types.Array(numba.float64, 1, layout='C')),
('direction', numba.types.Array(numba.float64, 1, layout='C')),
('inv_direction', numba.types.Array(numba.float64, 1, layout='C')),
('sign', numba.uint8[:]),
('color', numba.types.Array(numba.float64, 1, layout='C')),
('local_color', numba.types.Array(numba.float64, 1, layout='C')),
('i', numba.int32),
('j', numba.int32),
('bounces', numba.int32),
('p', numba.float64),
('local_p', numba.float64),
def test_deferred_type(self):
node_type = deferred_type()
spec = OrderedDict()
spec['data'] = float32
spec['next'] = optional(node_type)
@njit
def get_data(node):
return node.data
@jitclass(spec)
class LinkedNode(object):
def __init__(self, data, next):
self.data = data
self.next = next
def get_next_data(self):
# use deferred type as argument
return get_data(self.next)
def append_to_tail(self, other):
cur = self
while cur.next is not None:
cur = cur.next
cur.next = other
node_type.define(LinkedNode.class_type.instance_type)
first = LinkedNode(123, None)
self.assertEqual(first.data, 123)
self.assertIsNone(first.next)
second = LinkedNode(321, first)
first_meminfo = _get_meminfo(first)
second_meminfo = _get_meminfo(second)
self.assertEqual(first_meminfo.refcount, 3)
self.assertEqual(second.next.data, first.data)
self.assertEqual(first_meminfo.refcount, 3)
self.assertEqual(second_meminfo.refcount, 2)
# Test using deferred type as argument
first_val = second.get_next_data()
self.assertEqual(first_val, first.data)
# Check setattr (issue #2606)
self.assertIsNone(first.next)
second.append_to_tail(LinkedNode(567, None))
self.assertIsNotNone(first.next)
self.assertEqual(first.next.data, 567)
self.assertIsNone(first.next.next)
second.append_to_tail(LinkedNode(678, None))
self.assertIsNotNone(first.next.next)
self.assertEqual(first.next.next.data, 678)
# Check ownership
self.assertEqual(first_meminfo.refcount, 3)
del second, second_meminfo
self.assertEqual(first_meminfo.refcount, 2)
Numba is used for speeding up of the code.
:Copyright:
Author: Lili Feng
Graduate Research Assistant
CIEI, Department of Physics, University of Colorado Boulder
email: [email protected]
"""
import numpy as np
import math
import numba
import random
import vmodel, modparam, data, eigenkernel
model1d_type = numba.deferred_type()
model1d_type.define(vmodel.model1d.class_type.instance_type)
data_type = numba.deferred_type()
data_type.define(data.data1d.class_type.instance_type)
eigk_type = numba.deferred_type()
eigk_type.define(eigenkernel.eigkernel.class_type.instance_type)
####################################################
# Predefine the parameters for the ttisolver object
####################################################
spec_ttisolver = [('model', model1d_type), ('indata', data_type),
('eigkR', eigk_type), ('eigkL', eigk_type),
('hArr', numba.float32[:])]
import numpy as np
import numba as nb
import mcmc.util as util
import mcmc.fourier as fourier
import mcmc.L as L
import mcmc.pCN as pCN
L_matrix_type = nb.deferred_type()
L_matrix_type.define(L.Lmatrix.class_type.instance_type)
pCN_type = nb.deferred_type()
pCN_type.define(pCN.pCN.class_type.instance_type)
fourier_type = nb.deferred_type()
fourier_type.define(fourier.FourierAnalysis.class_type.instance_type)
spec = [
('is_stationary', nb.boolean),
('sqrt_beta', nb.float64),
('order_number', nb.int64),
('n_samples', nb.int64),
('pcn', pCN_type),
('i_record', nb.int64),
('stdev', nb.complex128[::1]),
('stdev_sym', nb.complex128[::1]),
('samples_history', nb.complex128[:, ::1]),
('current_sample', nb.complex128[::1]),
('current_sample_sym', nb.complex128[::1]),
('current_sample_scaled_norm', nb.float64),
('current_log_L_det', nb.float64),
('new_sample', nb.complex128[::1]),
('new_sample_sym', nb.complex128[::1]),
('new_sample_scaled_norm', nb.float64),
('new_log_L_det', nb.float64),
import numpy as np
from numba import intc, float64, deferred_type
from numba import types
from numba.experimental import jitclass
from numba.extending import register_jitable
from numba.core.errors import TypingError
from numba import types, typeof
from kalmantv.numba.kalmantv import KalmanTV, _mvn_sim, _quad_form
from rodeo.utils import rand_mat
kalman_type = deferred_type()
kalman_type.define(KalmanTV.class_type.instance_type)
@register_jitable
def _fempty(shape):
"""
Create an empty ndarray with the given shape in fortran order.
Numba cannot create fortran arrays directly, so this is done by transposing a C order array, as explained [here](https://numba.pydata.org/numba-doc/dev/user/faq.html#how-can-i-create-a-fortran-ordered-array).
"""
return np.empty(shape[::-1]).T
@register_jitable
def _interrogate_chkrebtii(x_meas, var_meas,
fun, t, theta,
wgt_meas, mu_state_pred, var_state_pred, z_state,
tx_state, twgt_meas, tchol_state):
"""
Interrogate method of Chkrebtii et al (2016).
import numpy as np
from . import BiQuad
import numba
from tqdm import tqdm
BiQuad_type = numba.deferred_type()
BiQuad_type.define(BiQuad.BiQuad.class_type.instance_type)
spec = [
('filters', BiQuad_type[:]),
]
# simple LF-Rd wavetable oscillator based on Fant 1985, Fant 1995
class SerialFilterBank:
def tick(self, x, verbose=False):
if np.isscalar(x):
y = x
for filter in self.filters:
y = filter.tick(y)
return y
else:
y = x
for i in tqdm(range(len(y)), disable=(not verbose)):
for filter in self.filters:
y[i] = filter.tick(y[i])
return y
import numpy as np
from Weno import Weno
from numba import jitclass
from numba import int32, float64, deferred_type
#------------------------------------------------------
Weno_type = deferred_type()
Weno_type.define(Weno.class_type.instance_type)
spec = [('c21', float64), ('c22', float64), ('c31', float64), ('c32', float64),
('size', int32), ('u1', float64[:]), ('u2', float64[:]),
("W", Weno_type)]
#-------------------------------------------------------
@jitclass(spec)
class RK3TVD:
def __init__(self, size, L):
self.c21 = 3. / 4.
self.c22 = 1. / 4.
self.c31 = 1. / 3.
self.c32 = 2. / 3.
self.size = size
self.u1 = np.empty(self.size)
self.u2 = np.empty(self.size)
self.W = Weno(size, L)
def op(self, Meth, InOut, dt):
self.W.weno(Meth, InOut, self.u1)
self.u1 = InOut + dt * self.u1
This is an extension to the simpler singly-linked-list example in
``linkedlist.py``.
Here, we make a better interface in the Stack class that encapsuate the
underlying linked-list.
"""
from __future__ import print_function, absolute_import
from numba.utils import OrderedDict
from numba import njit
from numba import jitclass
from numba import deferred_type, intp, optional
from numba.runtime import rtsys
linkednode_spec = OrderedDict()
linkednode_type = deferred_type()
linkednode_spec['data'] = data_type = deferred_type()
linkednode_spec['next'] = optional(linkednode_type)
@jitclass(linkednode_spec)
class LinkedNode(object):
def __init__(self, data):
self.data = data
self.next = None
linkednode_type.define(LinkedNode.class_type.instance_type)
stack_spec = OrderedDict()
stack_spec['head'] = optional(linkednode_type)
def fourierTransformHalf(self, z):
return u2.rfft2(z, self.basis_number)
def constructU(self, uHalf2D):
"""
Construct Toeplitz Matrix
"""
return u2.constructU(uHalf2D, self.Index)
def constructMatexplicit(self, uHalf2D, fun):
temp = fun(self.inverseFourierLimited(uHalf2D).T)
temp2 = self.fourierTransformHalf(temp)
return self.constructU(temp2)
fourier_type = nb.deferred_type()
fourier_type.define(FourierAnalysis_2D.class_type.instance_type)
specL = [
('fourier', fourier_type),
('sqrt_beta', nb.float64),
('current_L', nb.complex128[:, ::1]),
('latest_computed_L', nb.complex128[:, ::1]),
]
@nb.jitclass(specL)
class Lmatrix_2D:
def __init__(self, f, sqrt_beta):
self.fourier = f
self.sqrt_beta = sqrt_beta
import numpy as np
import numba as nb
import mcmc.util as util
import mcmc.fourier as fourier
import mcmc.L as L
import mcmc.randomGenerator as randomGenerator
import mcmc.pCN as pCN
import mcmc.measurement as meas
import mcmc.simulationResults as simRes
# import scipy as scp
import time
fourier_type = nb.deferred_type()
fourier_type.define(fourier.FourierAnalysis.class_type.instance_type)
L_matrix_type = nb.deferred_type()
L_matrix_type.define(L.Lmatrix.class_type.instance_type)
Rand_gen_type = nb.deferred_type()
Rand_gen_type.define(randomGenerator.RandomGenerator.class_type.instance_type)
pCN_type = nb.deferred_type()
pCN_type.define(pCN.pCN.class_type.instance_type)
meas_type = nb.deferred_type()
meas_type.define(meas.Measurement.class_type.instance_type)
sim_result_type = nb.deferred_type()
sim_result_type.define(simRes.SimulationResult.class_type.instance_type)
spec = [
('fourier', fourier_type),
('random_gen', Rand_gen_type),
('pcn', pCN_type),
('sim_result', sim_result_type),
# ('LMat',L_matrix_type),
('measurement', meas_type),
return np.array([x, y, z], dtype=np.float64)
@numba.njit
def vec(x, y, z):
return np.array([x, y, z], dtype=np.float64)
@numba.njit
def unit(v):
return v / np.linalg.norm(v)
# fast primitives
ray_type = numba.deferred_type()
node_type = numba.deferred_type()
box_type = numba.deferred_type()
@numba.experimental.jitclass([
('origin', numba.float64[3::1]),
('direction', numba.float64[3::1]),
('inv_direction', numba.float64[3::1]),
('sign', numba.uint8[3::1]),
('color', numba.float64[3::1]),
('local_color', numba.float64[3::1]),
('i', numba.int32),
('j', numba.int32),
('bounces', numba.int32),
('p', numba.float64),
This example demonstrates jitclasses and deferred type.
This is an extension to the simpler singly-linked-list example in
``linkedlist.py``.
Here, we make a better interface in the Stack class that encapsuate the
underlying linked-list.
"""
from __future__ import print_function, absolute_import
from collections import OrderedDict
from numba import njit
from numba import deferred_type, intp, optional
from numba.core.runtime import rtsys
from numba.experimental import jitclass
linkednode_spec = OrderedDict()
linkednode_type = deferred_type()
linkednode_spec['data'] = data_type = deferred_type()
linkednode_spec['next'] = optional(linkednode_type)
@jitclass(linkednode_spec)
class LinkedNode(object):
def __init__(self, data):
self.data = data
self.next = None
linkednode_type.define(LinkedNode.class_type.instance_type)
stack_spec = OrderedDict()
stack_spec['head'] = optional(linkednode_type)
point_spec = [('x', nb.float32), ('y', nb.float32)]
@nb.jitclass(point_spec)
class Point(object):
def __init__(self, x, y):
self.x = x
self.y = y
# def __str__(self):
# return "(%s, %s)" % (self.x, self.y)
point_type = nb.deferred_type()
point_type.define(Point.class_type.instance_type)
################ Just for tests ######################
@nb.njit(nb.float32(nb.float32[2], nb.float32[2]))
def get_distance_to_4(p1, p2):
dx = p1[0] - p2[0]
dy = p1[1] - p2[1]
return m.hypot(dx, dy)
@nb.njit
def get_distance_to(from_object, to_object):
dx = to_object.x - from_object.x
class MoveType(IntEnum):
Standard, Double, Prime = range(3)
class Pieces(IntEnum):
UR, UF, UL, UB, DF, DB = range(6)
def convert_move_to_string(move, move_type):
return ("U" if move == MoveSet.U else
"M") + ("" if move_type == MoveType.Standard else
"2" if move_type == MoveType.Double else "'")
lse_type = deferred_type()
'''@jitclass([
# Constructor variables
('lse_state', lse_type),#LSE_State.class_type.instance_type)
('edge_orientation_state', typeof([True])),
('edge_permutation_state', typeof(numpy.zeros(1, dtype = int))),
('center_AUF_state', typeof(numpy.zeros(1, dtype = int))),
('misoriented_centers_orientation_state', typeof([True])),
('edge_orientation_state_replacement', typeof([True])),
('edge_permutation_state_in_place', typeof(numpy.zeros(1, dtype = int))),
# toString variables
('data', typeof("")),
# Equality variables
('other', lse_type),
# swap_pieces_permutation method
('piece1', typeof(Pieces.UR)),
import numpy as np
from Weno import Weno
from numba import jitclass
from numba import int32, float64,deferred_type
#------------------------------------------------------
Weno_type = deferred_type()
Weno_type.define(Weno.class_type.instance_type)
spec = [
('c21', float64), ('c22', float64),
('c31', float64), ('c32', float64),
('size',int32),
('u1', float64[:]),
('u2', float64[:]),
("W", Weno_type)
]
#-------------------------------------------------------
@jitclass(spec)
class RK3TVD:
def __init__(self,size,L):
self.c21=3./4.
self.c22=1./4.
self.c31=1./3.
self.c32=2./3.
self.size=size
self.u1=np.empty(self.size)
self.u2=np.empty(self.size)
self.W=Weno(size,L)
def op(self,Meth,InOut,dt):
