문제

I have been playing around with writing my own physics engine in Python as an exercise in physics and programming. I started out by following the tutorial located here. That went well, but then I found the article "Advanced character physics" by thomas jakobsen, which covered using Verlet integration for simulations, which I found fascinating.

I have been attempting to write my own basic physics simulator using verlet integration, but it turns out to be slightly more difficult than I first expected. I was out browsing for example programs to read, and stumbled accross this one written in Python and I also found this tutorial which uses Processing.

What impresses me about the Processing version is how fast it runs. The cloth alone has 2400 different points being simulated, and that's not including the bodies.

The python example only uses 256 particles for the cloth, and it runs at about 30 frames per second. I tried increasing the number of particles to 2401 (it has to be square for that program to work), it ran at about 3 fps.


Both of these work by storing instances of a particle object in a list, and then iterating through the list, calling each particles "update position" method. As an example, this is the part of the code from the Processing sketch that calculates each particle's new postion:

for (int i = 0; i < pointmasses.size(); i++) {
    PointMass pointmass = (PointMass) pointmasses.get(i);
    pointmass.updateInteractions();
    pointmass.updatePhysics(fixedDeltaTimeSeconds);
}

EDIT: Here is the code from the python version I linked earlier:

"""
verletCloth01.py
Eric Pavey - 2010-07-03 - www.akeric.com

Riding on the shoulders of giants.
I wanted to learn now to do 'verlet cloth' in Python\Pygame.  I first ran across
this post \ source:
http://forums.overclockers.com.au/showthread.php?t=870396
http://dl.dropbox.com/u/3240460/cloth5.py

Which pointed to some good reference, that was a dead link.  After some searching,
I found it here:
http://www.gpgstudy.com/gpgiki/GDC%202001%3A%20Advanced%20Character%20Physics
Which is a 2001 SIGGRAPH paper by Thomas Jakobsen called:
"GDC 2001: Advanced Characer Physics".

This code is a Python\Pygame interpretation of that 2001 Siggraph paper.  I did
borrow some code from 'domlebo's source code, it was a great starting point.  But
I'd like to think I put my own flavor on it.
"""

#--------------
# Imports & Initis
import sys
from math import sqrt

# Vec2D comes from here: http://pygame.org/wiki/2DVectorClass
from vec2d import Vec2d
import pygame
from pygame.locals import *
pygame.init()

#--------------
# Constants
TITLE = "verletCloth01"
WIDTH = 600
HEIGHT = 600
FRAMERATE = 60
# How many iterations to run on our constraints per frame?
# This will 'tighten' the cloth, but slow the sim.
ITERATE = 2
GRAVITY = Vec2d(0.0,0.05)
TSTEP = 2.8

# How many pixels to position between each particle?
PSTEP = int(WIDTH*.03)
# Offset in pixels from the top left of screen to position grid:
OFFSET = int(.25*WIDTH)

#-------------
# Define helper functions, classes

class Particle(object):
    """
    Stores position, previous position, and where it is in the grid.
    """
    def __init__(self, screen, currentPos, gridIndex):
        # Current Position : m_x
        self.currentPos = Vec2d(currentPos)
        # Index [x][y] of Where it lives in the grid
        self.gridIndex = gridIndex
        # Previous Position : m_oldx
        self.oldPos = Vec2d(currentPos)
        # Force accumulators : m_a
        self.forces = GRAVITY
        # Should the particle be locked at its current position?
        self.locked = False
        self.followMouse = False

        self.colorUnlocked = Color('white')
        self.colorLocked = Color('green')
        self.screen = screen

    def __str__(self):
        return "Particle <%s, %s>"%(self.gridIndex[0], self.gridIndex[1])

    def draw(self):
        # Draw a circle at the given Particle.
        screenPos = (self.currentPos[0], self.currentPos[1])
        if self.locked:
            pygame.draw.circle(self.screen, self.colorLocked, (int(screenPos[0]),
                                                         int(screenPos[1])), 4, 0)
        else:
            pygame.draw.circle(self.screen, self.colorUnlocked, (int(screenPos[0]),
                                                         int(screenPos[1])), 1, 0)

class Constraint(object):
    """
    Stores 'constraint' data between two Particle objects.  Stores this data
    before the sim runs, to speed sim and draw operations.
    """
    def __init__(self, screen, particles):
        self.particles = sorted(particles)
        # Calculate restlength as the initial distance between the two particles:
        self.restLength = sqrt(abs(pow(self.particles[1].currentPos.x -
                                       self.particles[0].currentPos.x, 2) +
                                   pow(self.particles[1].currentPos.y -
                                       self.particles[0].currentPos.y, 2)))
        self.screen = screen
        self.color = Color('red')

    def __str__(self):
        return "Constraint <%s, %s>"%(self.particles[0], self.particles[1])

    def draw(self):
        # Draw line between the two particles.
        p1 = self.particles[0]
        p2 = self.particles[1]
        p1pos = (p1.currentPos[0],
                 p1.currentPos[1])
        p2pos = (p2.currentPos[0],
                 p2.currentPos[1])
        pygame.draw.aaline(self.screen, self.color,
                           (p1pos[0], p1pos[1]), (p2pos[0], p2pos[1]), 1)

class Grid(object):
    """
    Stores a grid of Particle objects.  Emulates a 2d container object.  Particle
    objects can be indexed by position:
        grid = Grid()
        particle = g[2][4]
    """
    def __init__(self, screen, rows, columns, step, offset):

        self.screen = screen
        self.rows = rows
        self.columns = columns
        self.step = step
        self.offset = offset

        # Make our internal grid:
        # _grid is a list of sublists.
        #    Each sublist is a 'column'.
        #        Each column holds a particle object per row:
        # _grid =
        # [[p00, [p10, [etc,
        #   p01,  p11,
        #   etc], etc],     ]]
        self._grid = []
        for x in range(columns):
            self._grid.append([])
            for y in range(rows):
                currentPos = (x*self.step+self.offset, y*self.step+self.offset)
                self._grid[x].append(Particle(self.screen, currentPos, (x,y)))

    def getNeighbors(self, gridIndex):
        """
        return a list of all neighbor particles to the particle at the given gridIndex:

        gridIndex = [x,x] : The particle index we're polling
        """
        possNeighbors = []
        possNeighbors.append([gridIndex[0]-1, gridIndex[1]])
        possNeighbors.append([gridIndex[0], gridIndex[1]-1])
        possNeighbors.append([gridIndex[0]+1, gridIndex[1]])
        possNeighbors.append([gridIndex[0], gridIndex[1]+1])

        neigh = []
        for coord in possNeighbors:
            if (coord[0] < 0) | (coord[0] > self.rows-1):
                pass
            elif (coord[1] < 0) | (coord[1] > self.columns-1):
                pass
            else:
                neigh.append(coord)

        finalNeighbors = []
        for point in neigh:
            finalNeighbors.append((point[0], point[1]))

        return finalNeighbors

    #--------------------------
    # Implement Container Type:

    def __len__(self):
        return len(self.rows * self.columns)

    def __getitem__(self, key):
        return self._grid[key]

    def __setitem__(self, key, value):
        self._grid[key] = value

    #def __delitem__(self, key):
        #del(self._grid[key])

    def __iter__(self):
        for x in self._grid:
            for y in x:
                yield y

    def __contains__(self, item):
        for x in self._grid:
            for y in x:
                if y is item:
                    return True
        return False


class ParticleSystem(Grid):
    """
    Implements the verlet particles physics on the encapsulated Grid object.
    """

    def __init__(self, screen, rows=49, columns=49, step=PSTEP, offset=OFFSET):
        super(ParticleSystem, self).__init__(screen, rows, columns, step, offset)

        # Generate our list of Constraint objects.  One is generated between
        # every particle connection.
        self.constraints = []
        for p in self:
            neighborIndices = self.getNeighbors(p.gridIndex)
            for ni in neighborIndices:
                # Get the neighbor Particle from the index:
                n = self[ni[0]][ni[1]]
                # Let's not add duplicate Constraints, which would be easy to do!
                new = True
                for con in self.constraints:
                    if n in con.particles and p in con.particles:
                        new = False
                if new:
                    self.constraints.append( Constraint(self.screen, (p,n)) )

        # Lock our top left and right particles by default:
        self[0][0].locked = True
        self[1][0].locked = True
        self[-2][0].locked = True
        self[-1][0].locked = True

    def verlet(self):
        # Verlet integration step:
        for p in self:
            if not p.locked:
                # make a copy of our current position
                temp = Vec2d(p.currentPos)
                p.currentPos += p.currentPos - p.oldPos + p.forces * TSTEP**2
                p.oldPos = temp
            elif p.followMouse:
                temp = Vec2d(p.currentPos)
                p.currentPos = Vec2d(pygame.mouse.get_pos())
                p.oldPos = temp

    def satisfyConstraints(self):
        # Keep particles together:
        for c in self.constraints:
            delta =  c.particles[0].currentPos - c.particles[1].currentPos
            deltaLength = sqrt(delta.dot(delta))
            try:
                # You can get a ZeroDivisionError here once, so let's catch it.
                # I think it's when particles sit on top of one another due to
                # being locked.
                diff = (deltaLength-c.restLength)/deltaLength
                if not c.particles[0].locked:
                    c.particles[0].currentPos -= delta*0.5*diff
                if not c.particles[1].locked:
                    c.particles[1].currentPos += delta*0.5*diff
            except ZeroDivisionError:
                pass

    def accumulateForces(self):
        # This doesn't do much right now, other than constantly reset the
        # particles 'forces' to be 'gravity'.  But this is where you'd implement
        # other things, like drag, wind, etc.
        for p in self:
            p.forces = GRAVITY

    def timeStep(self):
        # This executes the whole shebang:
        self.accumulateForces()
        self.verlet()
        for i in range(ITERATE):
            self.satisfyConstraints()

    def draw(self):
        """
        Draw constraint connections, and particle positions:
        """
        for c in self.constraints:
            c.draw()
        #for p in self:
        #    p.draw()

    def lockParticle(self):
        """
        If the mouse LMB is pressed for the first time on a particle, the particle
        will assume the mouse motion.  When it is pressed again, it will lock
        the particle in space.
        """
        mousePos = Vec2d(pygame.mouse.get_pos())
        for p in self:
            dist2mouse = sqrt(abs(pow(p.currentPos.x -
                                      mousePos.x, 2) +
                                  pow(p.currentPos.y -
                                      mousePos.y, 2)))
            if dist2mouse < 10:
                if not p.followMouse:
                    p.locked = True
                    p.followMouse = True
                    p.oldPos = Vec2d(p.currentPos)
                else:
                    p.followMouse = False

    def unlockParticle(self):
        """
        If the RMB is pressed on a particle, if the particle is currently
        locked or being moved by the mouse, it will be 'unlocked'/stop following
        the mouse.
        """
        mousePos = Vec2d(pygame.mouse.get_pos())
        for p in self:
            dist2mouse = sqrt(abs(pow(p.currentPos.x -
                                      mousePos.x, 2) +
                                  pow(p.currentPos.y -
                                      mousePos.y, 2)))
            if dist2mouse < 5:
                p.locked = False

#------------
# Main Program
def main():
    # Screen Setup
    screen = pygame.display.set_mode((WIDTH, HEIGHT))
    clock = pygame.time.Clock()

    # Create our grid of particles:
    particleSystem = ParticleSystem(screen)
    backgroundCol = Color('black')

    # main loop
    looping = True
    while looping:
        clock.tick(FRAMERATE)
        pygame.display.set_caption("%s -- www.AKEric.com -- LMB: move\lock - RMB: unlock - fps: %.2f"%(TITLE, clock.get_fps()) )
        screen.fill(backgroundCol)

        # Detect for events
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                looping = False
            elif event.type == MOUSEBUTTONDOWN:
                if event.button == 1:
                    # See if we can make a particle follow the mouse and lock
                    # its position when done.
                    particleSystem.lockParticle()
                if event.button == 3:
                    # Try to unlock the current particles position:
                    particleSystem.unlockParticle()

        # Do stuff!
        particleSystem.timeStep()
        particleSystem.draw()

        # update our display:
        pygame.display.update()

#------------
# Execution from shell\icon:
if __name__ == "__main__":
    print "Running Python version:", sys.version
    print "Running PyGame version:", pygame.ver
    print "Running %s.py"%TITLE
    sys.exit(main())

Because both programs work roughly the same way, but the Python version is SO much slower, it makes me wonder:

  • Is this performance difference part of the nature of Python?
  • What should I do differently from the above if I want to get better performance from my own Python programs? E.g store the properties of all particles inside an array instead of using individual objects, etc.

EDIT: Answered!!

@Mr E's linked PyCon talk in the comments, and @A. Rosa answer with the linked resources all helped ENORMOUSLY in better understanding how to write good, fast python code. I am now bookmarking this page for future reference :D

도움이 되었습니까?

해결책

There is a Guido van Rossum's article linked in the section Performance Tips of the Python Wiki. In its conclusion, you can read the following sentence:

If you feel the need for speed, go for built-in functions - you can't beat a loop written in C.

The essay continues with a list of guidelines for loop optimization. I recommend both resources, since they give concrete and practical advices about optimizing Python code.

There is also a well-known group of benchmarks in benchmarksgame.alioth.debian.org, where you can find comparasions among different programs and languages in distinct machines. As can be seen, there are lots of variables in play that makes impossible state something as broad as Java is faster than Python. This is commonly summed up in the sentence "Languages don't have speeds; implementations do".

In your code can be applied more pythonic and faster alternatives using built-in functions. For example, there are several nested loops (some of them don't require processing the whole list) which can be rewritten using imap or list comprehensions. PyPy is also another interesting option to improve the performance. I'm not an expert about Python optimization, but there are lots of tips which are extremely useful (Notice that don't write Java in Python is one of them!).

Resources and another related questions on SO:

다른 팁

If you write Python like you write Java, of course it's going to be slower, idiomatic java does not translate well to idiomatic python.

Is this performance difference part of the nature of Python? What should I do differently from the above if I want to get better performance from my own Python programs? E.g store the properties of all particles inside an array instead of using individual objects, etc.

Hard to say without seeing your code.

Here are an incomplete list of differences between python and java that may sometimes affect performance:

  1. Processing uses immediate mode canvas, if you want a comparable performance in Python, you also need to use immediate mode canvas. Canvases in most GUI framework (including Tkinter canvas) is retained mode, which is easier to use, but inherently slower than immediate mode. You'll need to use immediate mode canvas like those provided by pygame, SDL, or Pyglet.

  2. Python is dynamic language, that means instance member access, module member access, and global variable access is resolved at run time. Instance member access, module member access, and global variable access in python is really dictionary access. In java, they are resolved at compile time and by its nature much faster. Cache frequently accessed globals, module variables, and attributes to a local variable.

  3. In python 2.x, range() produces a concrete list, in python, iteration done using iterator, for item in list, is usually faster than iteration done using iteration variable, for n in range(len(list)). You should almost always iterate directly using iterator instead of iterating using range(len(...)).

  4. Python's numbers is immutable, this means any arithmetic calculation allocates a new object. This is one reason why plain python is not very suitable for low level calculations; most people that want to be able to write low level calculations without having to resort to writing C extension typically uses cython, psyco, or numpy. This usually only becomes a problem when you have millions of calculations though.

This are just partial, very incomplete list, there are many other reasons why translating java to python would produce suboptimal code. Without seeing your code it's impossible to tell what you need to do differently. Optimized python code generally looks very different than optimized java code.

I would also suggest to read about other physics engines. There are a few open source engines which use a variety of methods for calculating the "physics".

  • Newton Game Dynamics
  • Chipmunk
  • Bullet
  • Box2D
  • ODE (Open Dynamics Engine)

There are also ports of most of the engines:

  • Pymunk
  • PyBullet
  • PyBox2D
  • PyODE

If you read through the documentation of those engines you will often find statements saying that they are optimized for speed (30fps - 60fps). But if you think they can do this while calculating "real" physics you are wrong. Most engines calculate physics to a point where a normal user cannot optically distinguish between "real" physical behavior and "simulated" physical behavior. However if you investigate the error it is neglectable if you want to write games. But if you want to do physics, all of those engines are of no use to you. Thats why I would say if you are doing a real physical simulation you are slower than those engines by design and you will never outrun another physics engine.

Particle-based physics simulation translates easily into linear algebra operations ie. matrix operations. Numpy offers such operations, which are implemented in Fortran/C/C++ under the hood. Well-written python/Numpy code (taking full advantage of language & library) allows to write decently fast code.

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