Develop Reference

Can not use numba in the class

I recently want to use #njit(parallel=True) in package numba to speed up my nbodysimulation code, but when I separate the original function out of the class, my code can not work anymore. How to fix this problem?
The following block is the original code to calculate acceleration.
def _calculate_acceleration(self, mass, pos, rsoft):
"""
Calculate the acceleration.
"""
# TODO:
N = self.particles
rsoft = self.rsoft
posx = pos[:,0]
posy = pos[:,1]
posz = pos[:,2]
G = self.G
npts = self.nparticles
acc = np.zeros((npts, 3))
for i in prange(npts):
for j in prange(npts):
if (j>i):
x = (posx[i]-posx[j])
y = (posy[i]-posy[j])
z = (posz[i]-posz[j])
rsq = x**2 + y**2 + z**2
req = np.sqrt(x**2 + y**2)
f = -G*mass[i,0]*mass[j,0]/rsq
theta = np.arctan2(y, x)
phi = np.arctan2(z, req)
fx = f*np.cos(theta)*np.cos(phi)
fy = f*np.sin(theta)*np.cos(phi)
fz = f*np.sin(phi)
acc[i,0] += fx/mass[i]
acc[i,1] += fy/mass[i]
acc[i,2] += fz/mass[i]
acc[j,0] -= fx/mass[j]
acc[j,1] -= fy/mass[j]
acc[j,2] -= fz/mass[j]
return acc
def initialRandomParticles(N = 100, total_mass = 10):
"""
Initial particles
"""
particles = Particles(N)
masses = particles.masses
mass = total_mass/particles.nparticles
particles.masses = (masses*mass)
positions = np.random.randn(N,3)
velocities = np.random.randn(N,3)
accelerations = np.random.randn(N,3)
particles.positions = positions
particles.velocities = velocities
particles.accelerations = accelerations
return particles
particles = initialRandomParticles(N = 10**5, total_mass = 20)
sim = NbodySimulation(particles)
sim.setup(G=G,method="RK4",io_freq=200,io_title=problem_name,io_screen=True,visualized=False, rsoft=0.01)
sim.evolve(dt=0.01,tmax=10)
# Particles and NbodySimulation are defined class.
I spearate the original function out of the class, and define another function to call it in the class, but it's still can not work.
The following is new code which is out of the class.
#njit(nopython=True, parallel=True)
def _calculate_acceleration(n, npts, G, mass, pos, rsoft):
"""
Calculate the acceleration. This function is out of the class.
"""
# TODO:
posx = pos[:,0]
posy = pos[:,1]
posz = pos[:,2]
acc = np.zeros((n, 3))
sqrt = np.sqrt
for i in prange(npts):
for j in prange(npts):
if (j>i):
x = (posx[i]-posx[j])
y = (posy[i]-posy[j])
z = (posz[i]-posz[j])
rsq = x**2 + y**2 + z**2
req = sqrt(x**2 + y**2 + z**2)
f = -G*mass[i,0]*mass[j,0]/(req + rsoft)**2
fx = f*x**2/rsq
fy = f*y**2/rsq
fz = f*z**2/rsq
acc[i,0] = fx/mass[i] + acc[i,0]
acc[i,1] = fy/mass[i] + acc[i,1]
acc[i,2] = fz/mass[i] + acc[i,2]
acc[j,0] = fx/mass[j] - acc[j,0]
acc[j,1] = fy/mass[j] - acc[j,1]
acc[j,2] = fz/mass[j] - acc[j,2]
return acc
Here is the error:
TypingError Traceback (most recent call last)
:428, in NbodySimulation._update_particles_rk4(self, dt, particles)
426 position = particles.positions # y0[0]
427 velocity = particles.velocities # y0[1], k1[0]
--> 428 acceleration = self._calculate_acceleration_inclass() # k1[1]
430 position2 = position + 0.5*velocity * dt # y1[0]
431 velocity2 = velocity + 0.5*acceleration * dt # y1[1], k2[0]
:381, in NbodySimulation._calculate_acceleration_inclass(self)
377 def _calculate_acceleration_inclass(self):
378 """
379 Calculate the acceleration.
...
<source elided>
acc[i,0] = fx/mass[i] + acc[i,0]
^

How to excess scipy.solve_ivp solution.y_events?

I need to simulate a penalty where the ball shoots in every direction. For this I use solve_ivp and I terminate the integration when the ball crosses the backline. When this happens I want to use the values found when the integration stops to see if the ball at that point is within the dimensions of the goal (and count it as a goal). However, solution.y does not give the required precision that I want. The desired values are within solution.y_events, however, I can't seem to be able to get receive them. I also can't find information about this online. My code:
import numpy as np
from scipy import integrate
from scipy import constants
import matplotlib.pyplot as plt
#### Constants
# Number of simulations
number_of_penalty_shots = 10
# Angle of the shots
theta = np.random.uniform(0, 2.0*np.pi, number_of_penalty_shots)
phi = np.random.uniform(0, np.pi, number_of_penalty_shots)
# Velocity of the ball
v_magnitude = 80
### Starting Position Ball (defined as the penalty stip)
pos_x = 0.0
pos_y = 0.0
pos_z = 0.0
in_position = np.array([pos_x, pos_y, pos_z]) # Inital position in m
def homo_magnetic_field(t, vector):
vx = vector[3] # dx/dt = vx
vy = vector[4] # dy/dt = vy
vz = vector[5] # dz/dt = vz
# ax = -0.05*vector[3] # dvx/dt = ax
# ay = -0.05*vector[4] # dvy/dy = ay
# az = -0.05*vector[5] - constants.g #dvz/dt = az
ax = 0
ay = 0
az = 0
dvectordt = (vx,vy,vz,ax,ay,az)
return(dvectordt)
def goal(t, vector):
return vector[1] - 11
def own_goal(t,vector):
return vector[1] + 100
def ground(t,vector):
return vector[2]
goal.terminal=True
own_goal.terminal=True
def is_it_goal(vector):
if vector.status == 1:
if (vector.y[1][len(vector.y[1])-1] > 0) and (-3.36 < vector.y[0][len(vector.y[1])-1] < 3.36) and (vector.y[2][len(vector.y[1])-1] < 2.44):
print("GOAAAAAAAAAAAAL!")
elif (vector.y[1][len(vector.y[1])-1] < 0) and (-3.36 < vector.y[0][len(vector.y[1])-1] < 3.36) and (vector.y[2][len(vector.y[1])-1] < 2.44):
print("Own goal?! Why?")
else: print("Awwwwh")
else: print("Not even close, lol")
# Integrating
time_range = (0.0, 10**2)
for i in range(number_of_penalty_shots):
v_x = v_magnitude*np.sin(phi[i])*np.cos(theta[i])
v_y = v_magnitude*np.sin(phi[i])*np.sin(theta[i])
v_z = v_magnitude*np.cos(phi[i])
in_velocity = np.array([v_x, v_y, v_z])
initial_point = np.array([in_position, in_velocity])
start_point = initial_point.reshape(6,)
solution = integrate.solve_ivp(homo_magnetic_field , time_range, start_point,events=(goal, own_goal))
is_it_goal(solution)
Here I want to change vector.y[1][len(vector.y[1])-1] into something like vector.y_events...

What is wrong with my Implementation of 4th Order runge kutta in python for nonholonomic constraints?

I am trying to implement 4th order Runge Kutta for nonholonomic motion for car-like robots.
I don't know what I am doing wrong,essentially I am passing +-Pi/4 to calculate hard left and right turns to get different trajectories.
But no matter if I pass +pi/4 or -pi/4 to it, I get the same answer.
I cannot figure out what I am doing wrong.
The constraint equations that I am using are:
thetadot = (s/L)*tan(phi)
xdot = s*cos(theta)
ydot = s*sin(theta)
Where s is the speed and L is the length of the car like robot.
#! /usr/bin/env python
import sys, random, math, pygame
from pygame.locals import *
from math import sqrt,cos,sin,atan2,tan
import numpy as np
import matplotlib.pyplot as plt
XDIM = 640
YDIM = 480
WINSIZE = [XDIM, YDIM]
PHI = 45
s = 0.5
white = 255, 240, 200
black = 20, 20, 40
red = 255, 0, 0
green = 0, 255, 0
blue = 0, 0, 255
cyan = 0,255,255
pygame.init()
screen = pygame.display.set_mode(WINSIZE)
X = XDIM/2
Y = YDIM/2
THETA = 45
def main():
nodes = []
nodes.append(Node(XDIM/2.0,YDIM/2.0,0.0))
plt.plot(runge_kutta(nodes[0], (3.14/4))) #Hard Left turn
plt.plot(runge_kutta(nodes[0], 0)) #Straight ahead
plt.plot(runge_kutta(nodes[0], -(3.14/4))) #Hard Right turn
plt.show()
class Node:
x = 0
y = 0
theta = 0
distance=0
parent=None
def __init__(self,xcoord, ycoord, theta):
self.x = xcoord
self.y = ycoord
self.theta = theta
def rk4(f, x, y, n):
x0 = y0 = 0
vx = [0]*(n + 1)
vy = [0]*(n + 1)
h = 0.8
vx[0] = x = x0
vy[0] = y = y0
for i in range(1, n + 1):
k1 = h*f(x, y)
k2 = h*f(x + 0.5*h, y + 0.5*k1)
k3 = h*f(x + 0.5*h, y + 0.5*k2)
k4 = h*f(x + h, y + k3)
vx[i] = x = x0 + i*h
vy[i] = y = y + (k1 + k2 + k2 + k3 + k3 + k4)/6
print "1"
print vy
return vy
def fun1(x,y):
x = (0.5/2)*tan(y)
print "2"
print x
return x
def fun2(x,y):
x = 0.5*cos(y)
print "3"
print x
return x
def fun3(x,y):
x = 0.5*sin(y)
print "4"
print x
return x
def runge_kutta(p, phi):
x1 = p.x
y1 = p.y
theta1 = p.theta
fi = phi
for i in range(0,5):
x2 = rk4(fun2, x1, theta1, 5)
y2 = rk4(fun3, y1, theta1, 5)
theta2 = rk4(fun1, theta1 ,fi, 5)
theta1 = theta2
print "5"
print zip(x2,y2)
return zip(x2,y2)
# if python says run, then we should run
if __name__ == '__main__':
main()
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False

I can't really say much about the algorithm, but the way you set up our rk4 function, the x, and y arguments will never have any effect:
def rk4(f, x, y, n):
x0 = y0 = 0 # x0 and y0 will both be 0 after this
vx = [0]*(n + 1)
vy = [0]*(n + 1)
h = 0.8
vx[0] = x = x0 # now x will be 0
vy[0] = y = y0 # and y will be 0 too
...
The rest of the function will use x=0 and y=0 in any case.
Also, I don't know if that's intentional, but the other functions fun1, fun2 and fun3 don't ever use the parameter passed as x, they only use y. They change x locally, but that won't reflect outside the function.

Creating a movie in Jython/Python

I am trying to make a movie, whilst creating frames through a loop. It is saving, but only the first frame (which it plays as a movie - short movie!) I've tried various things and cannot figure out what I am doing wrong. Thanks
def synthesiseFrame(folder):
folder =r"D:\FOLDER"
m=0.5
for x in range(1,121):
pic=makeEmptyPicture(960,540)
for x in range (0,960):
for y in range (0,540):
r=#some code
g=#some code
b=#some code
color =makeColor (r,g,b)
px= getPixel (pic, x, y)
setColor(px, color)
numStr=str(x)
m=m+0.0125
if x<10:
writePictureTo(pic, folder+"\pic00"+numStr+".png")
if x >=10 and x<100:
writePictureTo(pic, folder+"\pic0"+numStr+".png")
if x>=100:
writePictureTo(pic,folder+"\pic"+numStr+".png")
return movie
movie=synthesiseFrame(folder)
folder =r"D:\FOLDER"
file=r"D:\FOLDER\pic00.png"
movie=makeMovieFromInitialFile(file)
writeQuicktime(movie,"D:\FOLDER\movie.mov", 30)
playMovie(movie)

My first sight at JES video functions and at your code tells me something like (fully working example):
import os
import random
def synthesizeFrameAndCreateMovie(folder):
# Create an empty movie to receive the frames
movie = makeMovie()
# Compute & save the frames
w = 40
h = 25
nb_frames = 60 # Will give 60 frames at 30 fps => movie duration : 2 sec.
for z in range(0, nb_frames):
pic=makeEmptyPicture(w, h)
for x in range (0, w):
for y in range (0, h):
#makeColor() takes red, green, and blue (in that order) between 0 and 255
r = random.randint(0, 255)
g = random.randint(0, 255)
b = random.randint(0, 255)
color = makeColor(r,g,b)
px = getPixel(pic, x, y)
setColor(px, color)
# Create one frame and inject in the movie object
filename = os.path.join(folder, 'pic%03d.png' % z)
writePictureTo(pic, filename)
addFrameToMovie(filename, movie)
# return the movie
return movie
movie = synthesizeFrameAndCreateMovie("D:\\FOLDER")
print movie
#writeQuicktime(movie,"D:\\FOLDER\\movie.mov", 30)
playMovie(movie)
Output (frames):
.............................................
EDIT :
More fun : animating a line (code taken form here)...
import os
import random
# Draw point, with check if the point is in the image area
def drawPoint(pic, col, x, y):
if (x >= 0) and (x < getWidth(pic)) and (y >= 0) and (y < getHeight(pic)):
px = getPixel(pic, x, y)
setColor(px, col)
# Draw line segment, given two points
# From Bresenham's line algorithm
# http://en.wikipedia.org/wiki/Bresenham%27s_line_algorithm
def drawLine(pic, col, x0, y0, x1, y1):
dx = abs(x1-x0)
dy = abs(y1-y0)
sx = sy = 0
#sx = 1 if x0 < x1 else -1
#sy = 1 if y0 < y1 else -1
if (x0 < x1):
sx = 1
else:
sx = -1
if (y0 < y1):
sy = 1
else:
sy = -1
err = dx - dy
while (True):
drawPoint(pic, col, x0, y0)
if (x0 == x1) and (y0 == y1):
break
e2 = 2 * err
if (e2 > -dy):
err = err - dy
x0 = x0 + sx
if (x0 == x1) and (y0 == y1):
drawPoint(pic, col, x0, y0)
break
if (e2 < dx):
err = err + dx
y0 = y0 + sy
# Draw infinite line from segment
def drawInfiniteLine(pic, col, x0, y0, x1, y1):
# y = m * x + b
m = (y0-y1) / (x0-x1)
if (abs(m) > 100.0):
m = 100.0
# y0 = m * x0 + b => b = y0 - m * x0
b = y0 - m * x0
x0 = 0
y0 = int(m*x0 + b)
# get a 2nd point far away from the 1st one
x1 = getWidth(pic)
y1 = int(m*x1 + b)
drawLine(pic, col, x0, y0, x1, y1)
# Draw infinite line from origin point and angle
# Angle 'theta' expressed in degres
def drawInfiniteLineA(pic, col, x, y, theta):
# y = m * x + b
dx = y * tan(theta * pi / 180.0) # (need radians)
dy = y
if (dx == 0):
dx += 0.000000001 # Avoid to divide by zero
m = dy / dx
# y = m * x + b => b = y - m * x
b = y - m * x
# get a 2nd point far away from the 1st one
x1 = 2 * getWidth(pic)
y1 = m*x1 + b
drawInfiniteLine(pic, col, x, y, x1, y1)
def synthesizeFrameAndCreateMovie(folder):
# Create an empty movie to receive the frames
movie = makeMovie()
# Compute & save the frames
w = 40
h = 25
nb_frames = 120 # Will give 120 frames at 30 fps => movie duration : 4 sec.
for z in range(0, nb_frames):
pic = makeEmptyPicture(w, h)
addRectFilled(pic, 0, 0, w-1, h-1)
#makeColor() takes red, green, and blue (in that order) between 0 and 255
r = random.randint(0, 255)
g = random.randint(0, 255)
b = random.randint(0, 255)
col = makeColor(r,g,b)
theta = z * 360 / nb_frames
if (theta != 180.0) and (theta != 0.0):
drawInfiniteLineA(pic, col, w//2, h//2, theta)
# Create one frame and inject in the movie object
filename = os.path.join(folder, 'pic%03d.png' % z)
writePictureTo(pic, filename)
addFrameToMovie(filename, movie)
# return the movie
return movie
movie = synthesizeFrameAndCreateMovie("/home/FOLDER")
print movie
#writeQuicktime(movie,"/home/golgauth/Desktop/FOLDER/movie.mov", 30)
#writeAVI(movie,"/home/golgauth/Desktop/FOLDER/movie.avi")
playMovie(movie)
Output (frames):
.............................................

I changed your code.
Used '%03d'%x instead of if*3.
change 'pic00.png' to 'pic001.png' because the loop in synthesiseFrame start from 1.
'\' -> os.path.join(..); Put import os if you didn't.
def synthesiseFrame(folder):
m = 0.5
for frameNumber in range(1,121):
pic=makeEmptyPicture(960,540)
for x in range (0,960):
for y in range (0,540):
r = #some code
g = #some code
b = #some code
color =makeColor (r,g,b)
px= getPixel (pic, x, y)
setColor(px, color)
m += 0.0125
writePictureTo(pic, os.path.join(folder, 'pic%03d.png' % frameNumber)) # 3 if -> no if
return movie
movie = synthesiseFrame(folder)
folder = r"D:\FOLDER"
file = r"D:\FOLDER\pic001.png" # 00 -> 001
movie=makeMovieFromInitialFile(file)
writeQuicktime(movie,"D:\FOLDER\movie.mov", 30)
playMovie(movie)
EDIT
x (in outer loop) -> frameNumber

Mesh Generation for Computational Science in Python

I have a need for a Python module/package that provides a mesh on which I can do computational science? I am not doing graphics, so I don't think the blender package is what I want.
Does anyone know of a good package?

The most useful packages out there are perhaps
mshr,
pygalmesh,
dmsh,
pygmsh, and
MeshPy,
meshzoo.
In addition, there is optimesh for improving the quality of any mesh.
(Disclaimer: I'm the author of pygmsh, pygalmesh, dmsh, meshzoo, and optimesh.)

If you're trying to solve FE or CFD style equations on a mesh you can use MeshPy in 2 and 3 dimensions. Meshpy is a nice wrapper around the existing tools tetgen and triangle.
If you're looking for more typical graphics style meshes, there was an interesting talk at PyCon 2011 "Algorithmic Generation of OpenGL Geometry", which described a pragmatic approach to procedural mesh generation. The code from the presentation is available online
If you're interested in reconstruction of surfaces from data, you can't go past the Standford 3D Scanning Repository, home of the Stanford Bunny
Edit:
A dependancy free alternative may be to use something like gmsh, which is platform independent, and uses similar tools to meshpy in its back-end.

I recommend using NumPy (especially if you've used MATLAB before). Many computational scientists / mechanical engineers working in python might agree, but I'm biased as it found it's way into much of the last year of my research. It's part of SciPy: http://numpy.scipy.org/
I was fond of numpy.linspace(a,b,N) which makes an N length vector of equally spaced values from a to b. You can use numpy.ndarray to make a N x M matrix, or if you want 2D arrays use numpy.meshgrid.

Here is code adapted from Kardontchik's port,
import numpy as np
from numpy import pi as pi
from scipy.spatial import Delaunay
import matplotlib.pylab as plt
from scipy.optimize import fmin
import matplotlib.pylab as plt
def ktrimesh(p,bars,pflag=0):
# create the (x,y) data for the plot
xx1 = p[bars[:,0],0]; yy1 = p[bars[:,0],1]
xx2 = p[bars[:,1],0]; yy2 = p[bars[:,1],1]
xmin = np.min(p[:,0])
xmax = np.max(p[:,0])
ymin = np.min(p[:,1])
ymax = np.max(p[:,1])
xmin = xmin - 0.05*(xmax - xmin)
xmax = xmax + 0.05*(xmax - xmin)
ymin = ymin - 0.05*(ymax - ymin)
ymax = ymax + 0.05*(ymax - ymin)
plt.figure()
for i in range(len(xx1)):
xp = np.array([xx1[i],xx2[i]])
yp = np.array([yy1[i],yy2[i]])
plt.plot(xmin,ymin,'.',xmax,ymax,'.',markersize=0.1)
plt.plot(xp,yp,'k')
plt.axis('equal')
if pflag == 0:
stitle = 'Triangular Mesh'
if pflag == 1:
stitle = 'Visual Boundary Integrity Check'
#plt.title('Triangular Mesh')
plt.title(stitle)
plt.xlabel('x')
plt.ylabel('y')
plt.show()
return 1
def ccw_tri(p,t):
"""
orients all the triangles counterclockwise
"""
# vector A from vertex 0 to vertex 1
# vector B from vertex 0 to vertex 2
A01x = p[t[:,1],0] - p[t[:,0],0]
A01y = p[t[:,1],1] - p[t[:,0],1]
B02x = p[t[:,2],0] - p[t[:,0],0]
B02y = p[t[:,2],1] - p[t[:,0],1]
# if vertex 2 lies to the left of vector A the component z of
# their vectorial product A^B is positive
Cz = A01x*B02y - A01y*B02x
a = t[np.where(Cz<0)]
b = t[np.where(Cz>=0)]
a[:,[1,2]] = a[:,[2,1]]
t = np.concatenate((a, b))
return t
def triqual_flag(p,t):
# a(1,0), b(2,0), c(2,1)
a = np.sqrt((p[t[:,1],0] - p[t[:,0],0])**2 + (p[t[:,1],1] - p[t[:,0],1])**2)
b = np.sqrt((p[t[:,2],0] - p[t[:,0],0])**2 + (p[t[:,2],1] - p[t[:,0],1])**2)
c = np.sqrt((p[t[:,2],0] - p[t[:,1],0])**2 + (p[t[:,2],1] - p[t[:,1],1])**2)
A = 0.25*np.sqrt((a+b+c)*(b+c-a)*(a+c-b)*(a+b-c))
R = 0.25*(a*b*c)/A
r = 0.5*np.sqrt( (a+b-c)*(b+c-a)*(a+c-b)/(a+b+c) )
q = 2.0*(r/R)
min_edge = np.minimum(np.minimum(a,b),c)
min_angle_deg = (180.0/np.pi)*np.arcsin(0.5*min_edge/R)
min_q = np.min(q)
min_ang = np.min(min_angle_deg)
return min_q, min_ang
def triqual(p,t,fh,qlim=0.2):
# a(1,0), b(2,0), c(2,1)
a = np.sqrt((p[t[:,1],0] - p[t[:,0],0])**2 + (p[t[:,1],1] - p[t[:,0],1])**2)
b = np.sqrt((p[t[:,2],0] - p[t[:,0],0])**2 + (p[t[:,2],1] - p[t[:,0],1])**2)
c = np.sqrt((p[t[:,2],0] - p[t[:,1],0])**2 + (p[t[:,2],1] - p[t[:,1],1])**2)
A = 0.25*np.sqrt((a+b+c)*(b+c-a)*(a+c-b)*(a+b-c))
R = 0.25*(a*b*c)/A
r = 0.5*np.sqrt( (a+b-c)*(b+c-a)*(a+c-b)/(a+b+c) )
q = 2.0*(r/R)
pmid = (p[t[:,0]] + p[t[:,1]] + p[t[:,2]])/3.0
hmid = fh(pmid)
Ah = A/hmid
Anorm = Ah/np.mean(Ah)
min_edge = np.minimum(np.minimum(a,b),c)
min_angle_deg = (180.0/np.pi)*np.arcsin(0.5*min_edge/R)
plt.figure()
plt.subplot(3,1,1)
plt.hist(q)
plt.title('Histogram;Triangle Statistics:q-factor,Minimum Angle and Area')
plt.subplot(3,1,2)
plt.hist(min_angle_deg)
plt.ylabel('Number of Triangles')
plt.subplot(3,1,3)
plt.hist(Anorm)
plt.xlabel('Note: for equilateral triangles q = 1 and angle = 60 deg')
plt.show()
indq = np.where(q < qlim) # indq is a tuple: len(indq) = 1
if list(indq[0]) != []:
print ('List of triangles with q < %5.3f and the (x,y) location of their nodes' % qlim)
print ('')
print ('q t[i] t[nodes] [x,y][0] [x,y][1] [x,y][2]')
for i in indq[0]:
print ('%.2f %4d [%4d,%4d,%4d] [%+.2f,%+.2f] [%+.2f,%+.2f] [%+.2f,%+.2f]' % \
(q[i],i,t[i,0],t[i,1],t[i,2],p[t[i,0],0],p[t[i,0],1],p[t[i,1],0],p[t[i,1],1],p[t[i,2],0],p[t[i,2],1]))
print ('')
# end of detailed data on worst offenders
return q,min_angle_deg,Anorm
class Circle:
def __init__(self,xc,yc,r):
self.xc, self.yc, self.r = xc, yc, r
def __call__(self,p):
xc, yc, r = self.xc, self.yc, self.r
d = np.sqrt((p[:,0] - xc)**2 + (p[:,1] - yc)**2) - r
return d
class Rectangle:
def __init__(self,x1,x2,y1,y2):
self.x1, self.x2, self.y1, self.y2 = x1,x2,y1,y2
def __call__(self,p):
x1,x2,y1,y2 = self.x1, self.x2, self.y1, self.y2
d1 = p[:,1] - y1 # if p inside d1 > 0
d2 = y2 - p[:,1] # if p inside d2 > 0
d3 = p[:,0] - x1 # if p inside d3 > 0
d4 = x2 - p[:,0] # if p inside d4 > 0
d = -np.minimum(np.minimum(np.minimum(d1,d2),d3),d4)
return d
class Polygon:
def __init__(self,verts):
self.verts = verts
def __call__(self,p):
verts = self.verts
# close the polygon
cverts = np.zeros((len(verts)+1,2))
cverts[0:-1] = verts
cverts[-1] = verts[0]
# initialize
inside = np.zeros(len(p))
dist = np.zeros(len(p))
Cz = np.zeros(len(verts)) # z-components of the vectorial products
dist_to_edge = np.zeros(len(verts))
in_ref = np.ones(len(verts))
# if np.sign(Cz) == in_ref then point is inside
for j in range(len(p)):
Cz = (cverts[1:,0] - cverts[0:-1,0])*(p[j,1] - cverts[0:-1,1]) - \
(cverts[1:,1] - cverts[0:-1,1])*(p[j,0] - cverts[0:-1,0])
dist_to_edge = Cz/np.sqrt( \
(cverts[1:,0] - cverts[0:-1,0])**2 + \
(cverts[1:,1] - cverts[0:-1,1])**2)
inside[j] = int(np.array_equal(np.sign(Cz),in_ref))
dist[j] = (1 - 2*inside[j])*np.min(np.abs(dist_to_edge))
return dist
class Union:
def __init__(self,fd1,fd2):
self.fd1, self.fd2 = fd1, fd2
def __call__(self,p):
fd1,fd2 = self.fd1, self.fd2
d = np.minimum(fd1(p),fd2(p))
return d
class Diff:
def __init__(self,fd1,fd2):
self.fd1, self.fd2 = fd1, fd2
def __call__(self,p):
fd1,fd2 = self.fd1, self.fd2
d = np.maximum(fd1(p),-fd2(p))
return d
class Intersect:
def __init__(self,fd1,fd2):
self.fd1, self.fd2 = fd1, fd2
def __call__(self,p):
fd1,fd2 = self.fd1, self.fd2
d = np.maximum(fd1(p),fd2(p))
return d
class Protate:
def __init__(self,phi):
self.phi = phi
def __call__(self,p):
phi = self.phi
c = np.cos(phi)
s = np.sin(phi)
temp = np.copy(p[:,0])
rp = np.copy(p)
rp[:,0] = c*p[:,0] - s*p[:,1]
rp[:,1] = s*temp + c*p[:,1]
return rp
class Pshift:
def __init__(self,x0,y0):
self.x0, self.y0 = x0,y0
def __call__(self,p):
x0, y0 = self.x0, self.y0
p[:,0] = p[:,0] + x0
p[:,1] = p[:,1] + y0
return p
def Ellipse_dist_to_minimize(t,p,xc,yc,a,b):
x = xc + a*np.cos(t) # coord x of the point on the ellipse
y = yc + b*np.sin(t) # coord y of the point on the ellipse
dist = (p[0] - x)**2 + (p[1] - y)**2
return dist
class Ellipse:
def __init__(self,xc,yc,a,b):
self.xc, self.yc, self.a, self.b = xc, yc, a, b
self.t, self.verts = self.pick_points_on_shape()
def pick_points_on_shape(self):
xc, yc, a, b = self.xc, self.yc, self.a, self.b
c = np.array([xc,yc])
t = np.linspace(0,(7.0/4.0)*pi,8)
verts = np.zeros((8,2))
verts[:,0] = c[0] + a*np.cos(t)
verts[:,1] = c[1] + b*np.sin(t)
return t, verts
def inside_ellipse(self,p):
xc, yc, a, b = self.xc, self.yc, self.a, self.b
c = np.array([xc,yc])
r, phase = self.rect_to_polar(p-c)
r_ellipse = self.rellipse(phase)
in_ref = np.ones(len(p))
inside = 0.5 + 0.5*np.sign(r_ellipse-r)
return inside
def rect_to_polar(self,p):
r = np.sqrt(p[:,0]**2 + p[:,1]**2)
phase = np.arctan2(p[:,1],p[:,0])
# note: np.arctan2(y,x) order; phase in +/- pi (+/- 180deg)
return r, phase
def rellipse(self,phi):
a, b = self.a, self.b
r = a*b/np.sqrt((b*np.cos(phi))**2 + (a*np.sin(phi))**2)
return r
def find_closest_vertex(self,point):
t, verts = self.t, self.verts
dist = np.zeros(len(t))
for i in range(len(t)):
dist[i] = (point[0] - verts[i,0])**2 + (point[1] - verts[i,1])**2
ind = np.argmin(dist)
t0 = t[ind]
return t0
def __call__(self,p):
xc, yc, a, b = self.xc, self.yc, self.a, self.b
t, verts = self.t, self.verts
dist = np.zeros(len(p))
inside = self.inside_ellipse(p)
for j in range(len(p)):
t0 = self.find_closest_vertex(p[j]) # initial guess to minimizer
opt = fmin(Ellipse_dist_to_minimize,t0, \
args=(p[j],xc,yc,a,b),full_output=1,disp=0)
# add full_output=1 so we can retrieve the min dist(squared)
# (2nd argument of opt array, 1st argument is the optimum t)
min_dist = np.sqrt(opt[1])
dist[j] = min_dist*(1 - 2*inside[j])
return dist
def distmesh(fd,fh,h0,xmin,ymin,xmax,ymax,pfix,ttol=0.1,dptol=0.001,Iflag=1,qmin=1.0):
geps = 0.001*h0; deltat = 0.2; Fscale = 1.2
deps = h0 * np.sqrt(np.spacing(1))
random_seed = 17
h0x = h0; h0y = h0*np.sqrt(3)/2 # to obtain equilateral triangles
Nx = int(np.floor((xmax - xmin)/h0x))
Ny = int(np.floor((ymax - ymin)/h0y))
x = np.linspace(xmin,xmax,Nx)
y = np.linspace(ymin,ymax,Ny)
# create the grid in the (x,y) plane
xx,yy = np.meshgrid(x,y)
xx[1::2] = xx[1::2] + h0x/2.0 # shifts even rows by h0x/2
p = np.zeros((np.size(xx),2))
p[:,0] = np.reshape(xx,np.size(xx))
p[:,1] = np.reshape(yy,np.size(yy))
p = np.delete(p,np.where(fd(p) > geps),axis=0)
np.random.seed(random_seed)
r0 = 1.0/fh(p)**2
p = np.concatenate((pfix,p[np.random.rand(len(p))<r0/max(r0),:]))
pold = np.inf
Num_of_Delaunay_triangulations = 0
Num_of_Node_movements = 0 # dp = F*dt
while (1):
Num_of_Node_movements += 1
if Iflag == 1 or Iflag == 3: # Newton flag
print ('Num_of_Node_movements = %3d' % (Num_of_Node_movements))
if np.max(np.sqrt(np.sum((p - pold)**2,axis = 1))) > ttol:
Num_of_Delaunay_triangulations += 1
if Iflag == 1 or Iflag == 3: # Delaunay flag
print ('Num_of_Delaunay_triangulations = %3d' % \
(Num_of_Delaunay_triangulations))
pold = p
tri = Delaunay(p) # instantiate a class
t = tri.vertices
pmid = (p[t[:,0]] + p[t[:,1]] + p[t[:,2]])/3.0
t = t[np.where(fd(pmid) < -geps)]
bars = np.concatenate((t[:,[0,1]],t[:,[0,2]], t[:,[1,2]]))
bars = np.unique(np.sort(bars),axis=0)
if Iflag == 4:
min_q, min_angle_deg = triqual_flag(p,t)
print ('Del iter: %3d, min q = %5.2f, min angle = %3.0f deg' \
% (Num_of_Delaunay_triangulations, min_q, min_angle_deg))
if min_q > qmin:
break
if Iflag == 2 or Iflag == 3:
ktrimesh(p,bars)
# move mesh points based on bar lengths L and forces F
barvec = p[bars[:,0],:] - p[bars[:,1],:]
L = np.sqrt(np.sum(barvec**2,axis=1))
hbars = 0.5*(fh(p[bars[:,0],:]) + fh(p[bars[:,1],:]))
L0 = hbars*Fscale*np.sqrt(np.sum(L**2)/np.sum(hbars**2))
F = np.maximum(L0-L,0)
Fvec = np.column_stack((F,F))*(barvec/np.column_stack((L,L)))
Ftot = np.zeros((len(p),2))
n = len(bars)
for j in range(n):
Ftot[bars[j,0],:] += Fvec[j,:] # the : for the (x,y) components
Ftot[bars[j,1],:] -= Fvec[j,:]
# force = 0 at fixed points, so they do not move:
Ftot[0: len(pfix),:] = 0
# update the node positions
p = p + deltat*Ftot
# bring outside points back to the boundary
d = fd(p); ix = d > 0 # find points outside (d > 0)
dpx = np.column_stack((p[ix,0] + deps,p[ix,1]))
dgradx = (fd(dpx) - d[ix])/deps
dpy = np.column_stack((p[ix,0], p[ix,1] + deps))
dgrady = (fd(dpy) - d[ix])/deps
p[ix,:] = p[ix,:] - np.column_stack((dgradx*d[ix], dgrady*d[ix]))
# termination criterium: all interior nodes move less than dptol:
if max(np.sqrt(np.sum(deltat*Ftot[d<-geps,:]**2,axis=1))/h0) < dptol:
break
final_tri = Delaunay(p) # another instantiation of the class
t = final_tri.vertices
pmid = (p[t[:,0]] + p[t[:,1]] + p[t[:,2]])/3.0
# keep the triangles whose geometrical center is inside the shape
t = t[np.where(fd(pmid) < -geps)]
bars = np.concatenate((t[:,[0,1]],t[:,[0,2]], t[:,[1,2]]))
# delete repeated bars
#bars = unique_rows(np.sort(bars))
bars = np.unique(np.sort(bars),axis=0)
# orient all the triangles counterclockwise (ccw)
t = ccw_tri(p,t)
# graphical output of the current mesh
ktrimesh(p,bars)
triqual(p,t,fh)
return p,t,bars
def boundary_bars(t):
# create the bars (edges) of every triangle
bars = np.concatenate((t[:,[0,1]],t[:,[0,2]], t[:,[1,2]]))
# sort all the bars
data = np.sort(bars)
# find the bars that are not repeated
Delaunay_bars = dict()
for row in data:
row = tuple(row)
if row in Delaunay_bars:
Delaunay_bars[row] += 1
else:
Delaunay_bars[row] = 1
# return the keys of Delaunay_bars whose value is 1 (non-repeated bars)
bbars = []
for key in Delaunay_bars:
if Delaunay_bars[key] == 1:
bbars.append(key)
bbars = np.asarray(bbars)
return bbars
def plot_shapes(xc,yc,r):
# circle for plotting
t_cir = np.linspace(0,2*pi)
x_cir = xc + r*np.cos(t_cir)
y_cir = yc + r*np.sin(t_cir)
plt.figure()
plt.plot(x_cir,y_cir)
plt.grid()
plt.title('Shapes')
plt.xlabel('x')
plt.ylabel('y')
plt.axis('equal')
#plt.show()
return
plt.close('all')
xc = 0; yc = 0; r = 1.0
x1,y1 = -1.0,-2.0
x2,y2 = 2.0,3.0
plot_shapes(xc,yc,r)
xmin = -1.5; ymin = -1.5
xmax = 1.5; ymax = 1.5
h0 = 0.4
pfix = np.zeros((0,2)) # null 2D array, no fixed points provided
fd = Circle(xc,yc,r)
fh = lambda p: np.ones(len(p))
p,t,bars = distmesh(fd,fh,h0,xmin,ymin,xmax,ymax,pfix,Iflag=4)