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How to increase FPS in ursina python

I want to create survival games with infinite block terrain(like Minecraft). So i using ursina python game engine, you can see it here
So i using perlin noise to create the terrain with build-in ursina block model. I test for first 25 block and it work pretty good with above 100 FPS, so i start increase to 250 block and more because I want a infinite terrain. But i ran to some problem, when i increase to 100 block or more, my FPS start to decrease below 30 FPS (With i create just one layer).
Here is my code:
#-------------------------------Noise.py(I got on the github)-------------------------
# Copyright (c) 2008, Casey Duncan (casey dot duncan at gmail dot com)
# see LICENSE.txt for details
"""Perlin noise -- pure python implementation"""
__version__ = '$Id: perlin.py 521 2008-12-15 03:03:52Z casey.duncan $'
from math import floor, fmod, sqrt
from random import randint
# 3D Gradient vectors
_GRAD3 = ((1,1,0),(-1,1,0),(1,-1,0),(-1,-1,0),
(1,0,1),(-1,0,1),(1,0,-1),(-1,0,-1),
(0,1,1),(0,-1,1),(0,1,-1),(0,-1,-1),
(1,1,0),(0,-1,1),(-1,1,0),(0,-1,-1),
)
# 4D Gradient vectors
_GRAD4 = ((0,1,1,1), (0,1,1,-1), (0,1,-1,1), (0,1,-1,-1),
(0,-1,1,1), (0,-1,1,-1), (0,-1,-1,1), (0,-1,-1,-1),
(1,0,1,1), (1,0,1,-1), (1,0,-1,1), (1,0,-1,-1),
(-1,0,1,1), (-1,0,1,-1), (-1,0,-1,1), (-1,0,-1,-1),
(1,1,0,1), (1,1,0,-1), (1,-1,0,1), (1,-1,0,-1),
(-1,1,0,1), (-1,1,0,-1), (-1,-1,0,1), (-1,-1,0,-1),
(1,1,1,0), (1,1,-1,0), (1,-1,1,0), (1,-1,-1,0),
(-1,1,1,0), (-1,1,-1,0), (-1,-1,1,0), (-1,-1,-1,0))
# A lookup table to traverse the simplex around a given point in 4D.
# Details can be found where this table is used, in the 4D noise method.
_SIMPLEX = (
(0,1,2,3),(0,1,3,2),(0,0,0,0),(0,2,3,1),(0,0,0,0),(0,0,0,0),(0,0,0,0),(1,2,3,0),
(0,2,1,3),(0,0,0,0),(0,3,1,2),(0,3,2,1),(0,0,0,0),(0,0,0,0),(0,0,0,0),(1,3,2,0),
(0,0,0,0),(0,0,0,0),(0,0,0,0),(0,0,0,0),(0,0,0,0),(0,0,0,0),(0,0,0,0),(0,0,0,0),
(1,2,0,3),(0,0,0,0),(1,3,0,2),(0,0,0,0),(0,0,0,0),(0,0,0,0),(2,3,0,1),(2,3,1,0),
(1,0,2,3),(1,0,3,2),(0,0,0,0),(0,0,0,0),(0,0,0,0),(2,0,3,1),(0,0,0,0),(2,1,3,0),
(0,0,0,0),(0,0,0,0),(0,0,0,0),(0,0,0,0),(0,0,0,0),(0,0,0,0),(0,0,0,0),(0,0,0,0),
(2,0,1,3),(0,0,0,0),(0,0,0,0),(0,0,0,0),(3,0,1,2),(3,0,2,1),(0,0,0,0),(3,1,2,0),
(2,1,0,3),(0,0,0,0),(0,0,0,0),(0,0,0,0),(3,1,0,2),(0,0,0,0),(3,2,0,1),(3,2,1,0))
# Simplex skew constants
_F2 = 0.5 * (sqrt(3.0) - 1.0)
_G2 = (3.0 - sqrt(3.0)) / 6.0
_F3 = 1.0 / 3.0
_G3 = 1.0 / 6.0
class BaseNoise:
"""Noise abstract base class"""
permutation = (151,160,137,91,90,15,
131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,23,
190,6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,32,57,177,33,
88,237,149,56,87,174,20,125,136,171,168,68,175,74,165,71,134,139,48,27,166,
77,146,158,231,83,111,229,122,60,211,133,230,220,105,92,41,55,46,245,40,244,
102,143,54,65,25,63,161,1,216,80,73,209,76,132,187,208,89,18,169,200,196,
135,130,116,188,159,86,164,100,109,198,173,186,3,64,52,217,226,250,124,123,
5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,182,189,28,42,
223,183,170,213,119,248,152,2,44,154,163,70,221,153,101,155,167,43,172,9,
129,22,39,253,9,98,108,110,79,113,224,232,178,185,112,104,218,246,97,228,
251,34,242,193,238,210,144,12,191,179,162,241, 81,51,145,235,249,14,239,107,
49,192,214,31,181,199,106,157,184,84,204,176,115,121,50,45,127,4,150,254,
138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,180)
period = len(permutation)
# Double permutation array so we don't need to wrap
permutation = permutation * 2
randint_function = randint
def __init__(self, period=None, permutation_table=None, randint_function=None):
"""Initialize the noise generator. With no arguments, the default
period and permutation table are used (256). The default permutation
table generates the exact same noise pattern each time.
An integer period can be specified, to generate a random permutation
table with period elements. The period determines the (integer)
interval that the noise repeats, which is useful for creating tiled
textures. period should be a power-of-two, though this is not
enforced. Note that the speed of the noise algorithm is indpendent of
the period size, though larger periods mean a larger table, which
consume more memory.
A permutation table consisting of an iterable sequence of whole
numbers can be specified directly. This should have a power-of-two
length. Typical permutation tables are a sequnce of unique integers in
the range [0,period) in random order, though other arrangements could
prove useful, they will not be "pure" simplex noise. The largest
element in the sequence must be no larger than period-1.
period and permutation_table may not be specified together.
A substitute for the method random.randint(a, b) can be chosen. The
method must take two integer parameters a and b and return an integer N
such that a <= N <= b.
"""
if randint_function is not None: # do this before calling randomize()
if not hasattr(randint_function, '__call__'):
raise TypeError(
'randint_function has to be a function')
self.randint_function = randint_function
if period is None:
period = self.period # enforce actually calling randomize()
if period is not None and permutation_table is not None:
raise ValueError(
'Can specify either period or permutation_table, not both')
if period is not None:
self.randomize(period)
elif permutation_table is not None:
self.permutation = tuple(permutation_table) * 2
self.period = len(permutation_table)
def randomize(self, period=None):
"""Randomize the permutation table used by the noise functions. This
makes them generate a different noise pattern for the same inputs.
"""
if period is not None:
self.period = period
perm = list(range(self.period))
perm_right = self.period - 1
for i in list(perm):
j = self.randint_function(0, perm_right)
perm[i], perm[j] = perm[j], perm[i]
self.permutation = tuple(perm) * 2
class SimplexNoise(BaseNoise):
"""Perlin simplex noise generator
Adapted from Stefan Gustavson's Java implementation described here:
http://staffwww.itn.liu.se/~stegu/simplexnoise/simplexnoise.pdf
To summarize:
"In 2001, Ken Perlin presented 'simplex noise', a replacement for his classic
noise algorithm. Classic 'Perlin noise' won him an academy award and has
become an ubiquitous procedural primitive for computer graphics over the
years, but in hindsight it has quite a few limitations. Ken Perlin himself
designed simplex noise specifically to overcome those limitations, and he
spent a lot of good thinking on it. Therefore, it is a better idea than his
original algorithm. A few of the more prominent advantages are:
* Simplex noise has a lower computational complexity and requires fewer
multiplications.
* Simplex noise scales to higher dimensions (4D, 5D and up) with much less
computational cost, the complexity is O(N) for N dimensions instead of
the O(2^N) of classic Noise.
* Simplex noise has no noticeable directional artifacts. Simplex noise has
a well-defined and continuous gradient everywhere that can be computed
quite cheaply.
* Simplex noise is easy to implement in hardware."
"""
def noise2(self, x, y):
"""2D Perlin simplex noise.
Return a floating point value from -1 to 1 for the given x, y coordinate.
The same value is always returned for a given x, y pair unless the
permutation table changes (see randomize above).
"""
# Skew input space to determine which simplex (triangle) we are in
s = (x + y) * _F2
i = floor(x + s)
j = floor(y + s)
t = (i + j) * _G2
x0 = x - (i - t) # "Unskewed" distances from cell origin
y0 = y - (j - t)
if x0 > y0:
i1 = 1; j1 = 0 # Lower triangle, XY order: (0,0)->(1,0)->(1,1)
else:
i1 = 0; j1 = 1 # Upper triangle, YX order: (0,0)->(0,1)->(1,1)
x1 = x0 - i1 + _G2 # Offsets for middle corner in (x,y) unskewed coords
y1 = y0 - j1 + _G2
x2 = x0 + _G2 * 2.0 - 1.0 # Offsets for last corner in (x,y) unskewed coords
y2 = y0 + _G2 * 2.0 - 1.0
# Determine hashed gradient indices of the three simplex corners
perm = self.permutation
ii = int(i) % self.period
jj = int(j) % self.period
gi0 = perm[ii + perm[jj]] % 12
gi1 = perm[ii + i1 + perm[jj + j1]] % 12
gi2 = perm[ii + 1 + perm[jj + 1]] % 12
# Calculate the contribution from the three corners
tt = 0.5 - x0**2 - y0**2
if tt > 0:
g = _GRAD3[gi0]
noise = tt**4 * (g[0] * x0 + g[1] * y0)
else:
noise = 0.0
tt = 0.5 - x1**2 - y1**2
if tt > 0:
g = _GRAD3[gi1]
noise += tt**4 * (g[0] * x1 + g[1] * y1)
tt = 0.5 - x2**2 - y2**2
if tt > 0:
g = _GRAD3[gi2]
noise += tt**4 * (g[0] * x2 + g[1] * y2)
return noise * 70.0 # scale noise to [-1, 1]
def noise3(self, x, y, z):
"""3D Perlin simplex noise.
Return a floating point value from -1 to 1 for the given x, y, z coordinate.
The same value is always returned for a given x, y, z pair unless the
permutation table changes (see randomize above).
"""
# Skew the input space to determine which simplex cell we're in
s = (x + y + z) * _F3
i = floor(x + s)
j = floor(y + s)
k = floor(z + s)
t = (i + j + k) * _G3
x0 = x - (i - t) # "Unskewed" distances from cell origin
y0 = y - (j - t)
z0 = z - (k - t)
# For the 3D case, the simplex shape is a slightly irregular tetrahedron.
# Determine which simplex we are in.
if x0 >= y0:
if y0 >= z0:
i1 = 1; j1 = 0; k1 = 0
i2 = 1; j2 = 1; k2 = 0
elif x0 >= z0:
i1 = 1; j1 = 0; k1 = 0
i2 = 1; j2 = 0; k2 = 1
else:
i1 = 0; j1 = 0; k1 = 1
i2 = 1; j2 = 0; k2 = 1
else: # x0 < y0
if y0 < z0:
i1 = 0; j1 = 0; k1 = 1
i2 = 0; j2 = 1; k2 = 1
elif x0 < z0:
i1 = 0; j1 = 1; k1 = 0
i2 = 0; j2 = 1; k2 = 1
else:
i1 = 0; j1 = 1; k1 = 0
i2 = 1; j2 = 1; k2 = 0
# Offsets for remaining corners
x1 = x0 - i1 + _G3
y1 = y0 - j1 + _G3
z1 = z0 - k1 + _G3
x2 = x0 - i2 + 2.0 * _G3
y2 = y0 - j2 + 2.0 * _G3
z2 = z0 - k2 + 2.0 * _G3
x3 = x0 - 1.0 + 3.0 * _G3
y3 = y0 - 1.0 + 3.0 * _G3
z3 = z0 - 1.0 + 3.0 * _G3
# Calculate the hashed gradient indices of the four simplex corners
perm = self.permutation
ii = int(i) % self.period
jj = int(j) % self.period
kk = int(k) % self.period
gi0 = perm[ii + perm[jj + perm[kk]]] % 12
gi1 = perm[ii + i1 + perm[jj + j1 + perm[kk + k1]]] % 12
gi2 = perm[ii + i2 + perm[jj + j2 + perm[kk + k2]]] % 12
gi3 = perm[ii + 1 + perm[jj + 1 + perm[kk + 1]]] % 12
# Calculate the contribution from the four corners
noise = 0.0
tt = 0.6 - x0**2 - y0**2 - z0**2
if tt > 0:
g = _GRAD3[gi0]
noise = tt**4 * (g[0] * x0 + g[1] * y0 + g[2] * z0)
else:
noise = 0.0
tt = 0.6 - x1**2 - y1**2 - z1**2
if tt > 0:
g = _GRAD3[gi1]
noise += tt**4 * (g[0] * x1 + g[1] * y1 + g[2] * z1)
tt = 0.6 - x2**2 - y2**2 - z2**2
if tt > 0:
g = _GRAD3[gi2]
noise += tt**4 * (g[0] * x2 + g[1] * y2 + g[2] * z2)
tt = 0.6 - x3**2 - y3**2 - z3**2
if tt > 0:
g = _GRAD3[gi3]
noise += tt**4 * (g[0] * x3 + g[1] * y3 + g[2] * z3)
return noise * 32.0
def lerp(t, a, b):
return a + t * (b - a)
def grad3(hash, x, y, z):
g = _GRAD3[hash % 16]
return x*g[0] + y*g[1] + z*g[2]
class TileableNoise(BaseNoise):
"""Tileable implemention of Perlin "improved" noise. This
is based on the reference implementation published here:
http://mrl.nyu.edu/~perlin/noise/
"""
def noise3(self, x, y, z, repeat, base=0.0):
"""Tileable 3D noise.
repeat specifies the integer interval in each dimension
when the noise pattern repeats.
base allows a different texture to be generated for
the same repeat interval.
"""
i = int(fmod(floor(x), repeat))
j = int(fmod(floor(y), repeat))
k = int(fmod(floor(z), repeat))
ii = (i + 1) % repeat
jj = (j + 1) % repeat
kk = (k + 1) % repeat
if base:
i += base; j += base; k += base
ii += base; jj += base; kk += base
x -= floor(x); y -= floor(y); z -= floor(z)
fx = x**3 * (x * (x * 6 - 15) + 10)
fy = y**3 * (y * (y * 6 - 15) + 10)
fz = z**3 * (z * (z * 6 - 15) + 10)
perm = self.permutation
A = perm[i]
AA = perm[A + j]
AB = perm[A + jj]
B = perm[ii]
BA = perm[B + j]
BB = perm[B + jj]
return lerp(fz, lerp(fy, lerp(fx, grad3(perm[AA + k], x, y, z),
grad3(perm[BA + k], x - 1, y, z)),
lerp(fx, grad3(perm[AB + k], x, y - 1, z),
grad3(perm[BB + k], x - 1, y - 1, z))),
lerp(fy, lerp(fx, grad3(perm[AA + kk], x, y, z - 1),
grad3(perm[BA + kk], x - 1, y, z - 1)),
lerp(fx, grad3(perm[AB + kk], x, y - 1, z - 1),
grad3(perm[BB + kk], x - 1, y - 1, z - 1))))
#--------------------------Math.py(For InverseLefp)--------------------------------
def Clamp(t: float, minimum: float, maximum: float):
"""Float result between a min and max values."""
value = t
if t < minimum:
value = minimum
elif t > maximum:
value = maximum
return value
def InverseLefp(a: float, b: float, value: float):
if a != b:
return Clamp((value - a) / (b - a), 0, 1)
return 0
#-----------------------------Game.py(Main code)----------------------
from ursina import *
from ursina.prefabs import *
from ursina.prefabs.first_person_controller import *
from Math import InverseLefp
import Noise
app = Ursina()
#The maximum height of the terrain
maxHeight = 10
#Control the width and height of the map
mapWidth = 10
mapHeight = 10
#A class that create a block
class Voxel(Button):
def __init__(self, position=(0,0,0)):
super().__init__(
parent = scene,
position = position,
model = 'cube',
origin_y = .5,
texture = 'white_cube',
color = color.color(0, 0, random.uniform(.9, 1.0)),
highlight_color = color.lime,
)
#Detect user key input
def input(self, key):
if self.hovered:
if key == 'right mouse down':
#Place block if user right click
voxel = Voxel(position=self.position + mouse.normal)
if key == 'left mouse down':
#Break block if user left click
destroy(self)
if key == 'escape':
#Exit the game if user press the esc key
app.userExit()
#Return perlin noise value between 0 and 1 with x, y position with scale = noiseScale
def GeneratedNoiseMap(y: int, x: int, noiseScale: float):
#Check if the noise scale was invalid or not
if noiseScale <= 0:
noiseScale = 0.001
sampleX = x / noiseScale
sampleY = y / noiseScale
#The Noise.SimplexNoise().noise2 will return the value between -1 and 1
perlinValue = Noise.SimplexNoise().noise2(sampleX, sampleY)
#The InverseLefp will make the value scale to between 0 and 1
perlinValue = InverseLefp(-1, 1, perlinValue)
return perlinValue
for z in range(mapHeight):
for x in range(mapWidth):
#Calculating the height of the block and round it to integer
height = round(GeneratedNoiseMap(z, x, 20) * maxHeight)
#Place the block and make it always below the player
block = Voxel(position=(x, height - maxHeight - 1, z))
#Set the collider of the block
block.collider = 'mesh'
#Character movement
player = FirstPersonController()
#Run the game
app.run()
All file in same folder.
It was working fine but the FPS is very low, so can anyone help?

I'm not able to test this code at the moment but this should serve as a starting point:
level_parent = Entity(model=Mesh(vertices=[], uvs=[]))
for z in range(mapHeight):
for x in range(mapWidth):
height = round(GeneratedNoiseMap(z, x, 20) * maxHeight)
block = Voxel(position=(x, height - maxHeight - 1, z))
level_parent.model.vertices.extend(block.model.vertices)
level_parent.collider = 'mesh' # call this only once after all vertices are set up
For texturing, you might have to add the block.uvs from each block to level_parent.model.uvs as well. Alternatively, call level_parent.model.project_uvs() after setting up the vertices.

On my version of ursina engine (5.0.0) only this code:
`
level_parent = Entity(model=Mesh(vertices=[], uvs=[]))
for z in range(mapHeight):
for x in range(mapWidth):
height = round(GeneratedNoiseMap(z, x, 20) * maxHeight)
block = Voxel(position=(x, height - maxHeight - 1, z))
#level_parent.model.vertices.extend(block.model.vertices)
level_parent.combine().vertices.extend(block.combine().vertices)
level_parent.collider = 'mesh'
`
is working.

How to generate squares (randomly located, equally sized, randomly rotated) that don't intersect each other?

I've been working on generating a layer of randomly rotated and placed squares on a 1x1 grid. I have been able to generate a single square that is randomly placed and rotated on the grid, but I'm not sure how to improve the code to generate more random squares that do not intersect with each other. Current code seen below:
Example of my One Randomized Square
from math import cos, pi, sin
from random import randint
from matplotlib.mlab import frange
from matplotlib.pyplot import plot, axis, show
def flake_position_layer1(): #Determines the initial position of one corner of the square
x0 = randint(0, 100) / 100
y0 = randint(0, 100) / 100
theta = randint(0, 90) * pi / 180 #Angle of rotation for the square
return x0, y0, theta
def flake_shape(): #generates the other 3 corners of the square
x0, y0, z, theta = flake_position_layer1()
x1 = x0 + (0.1 * cos(theta))
x2 = x1 + (0.1 * cos((90 * pi/180) + theta))
x3 = x2 + (0.1 * cos((180 * pi/180) + theta))
y1 = y0 + (0.1 * sin(theta))
y2 = y1 + (0.1 * sin((90 * pi/180) + theta))
y3 = y2 + (0.1 * sin((180 * pi/180) + theta))
return x0, x1, x2, x3, y0, y1, y2, y3
def display(): #connects the 4 corners on a plot
x0, x1, x2, x3, y0, y1, y2, y3 = flake_shape()
return plot([x0, x1, x2, x3, x0], [y0, y1, y2, y3, y0])
display()
axis([0,1,0,1]) #1x1 grid
show()
I do not have a CS background (I'm an environmental engineering major) and I am extremely inexperienced with coding. Please give me any recommendations that you may have for me to try and tackle this problem with!

Math background
1. Algebra
1st degree function (or [Wikipedia]: Linear function) is a function ([Wikipedia]: function) whose:
Expression can be written as: f(x) = a * x + b (a and b constants, x variable)
Graphical representation is a straight line in the xOy plane ([Wikipedia]: Cartesian coordinate system)
2. Plane Geometry
A plane consists of an (infinite) number of points: let's refer to a point by its coordinates which can be referred to as:
abscissa or horizontal coordinate or (simply) x
ordinate or vertical coordinate or (simply) y
The points in the plane spread across 2 dimensions
In the plane, every point can be uniquely identified by its x and y
Some of the points in the plane might have some common characteristics: e.g. a bunch of points that are on a straight line... a point that is on a straight line satisfies the line straight line equation (which is an expression generally defined as ${function (from previous paragraph) result} = ${value})
So, in short: for a point P0(x0, y0), if y0 == f(x0), the point is located on that straight line (and more: depending on y0 being greater / lower than f(x0), P0 is located above / below the straight line in the xOy plane). Again, I want to state that for non linear functions, y = f(x) still applies (as it's the general equation formula), but other operators (e.g. <, >) don't
! Important Note !: everything discussed here applies to a variety of functions (don't want to say all), but I'm limiting to linear functions, for clarity's sake
A straight line is determined by 2 distinct points (e.g. P0(x0, y0), P1(x1, y1)) - the equation for that straight line would be y = a * x + b (in our example: y = ((y0 - y1) / (x0 - x1)) * x + (y0 - x0 * ((y0 - y1) / (x0 - x1)))); !! Of course it worth mentioning the "vertical" (parallel to Oy) line which is !! not a function !!
Example: having 2 distinct parallel (non Bolyai :) ) lines: f0(x) = a * x + b0 and f1(x) = a * x + b1 (a is the same for both lines - this is the condition for them to be parallel) and an external point P0(x0, y0) (that obviously doesn't belong to any of the lines). How to determine if P0 is between the 2 lines? Well, the point must be above (the lower) one and below the other (the higher one). Translated into math (considering f0 being the lower one):
y0 > f0(x0) (y0 - f0(x0) > 0)
y0 < f1(x0) (y0 - f1(x0) < 0)
From the above observations (and there may be more wisdom), this is the condition that the point coordinates should satisfy: (y0 - f0(x0)) * (y0 - f1(x0)) < 0
Going further: a square consists of 2 sets of parallel lines (its sides); if a point is between each lines pair, then the point is in the square.
code00.py:
#!/usr/bin/env python3
import sys
from random import random, seed
from math import pi, sin, cos, sqrt
import matplotlib.pyplot as plt
pi_2 = pi / 2
MINX = MINY = 0
MAXX = MAXY = 1
DEFAULT_SIDE = 0.1
DEFAULT_SAFETY_MARGIN = DEFAULT_SIDE * sqrt(2)
MAX_SQUARES = 30
__global_generation_counter = 0
def get_func_deg1(p0, p1):
(x0, y0), (x1, y1) = p0, p1
if x0 == x1:
return None
a = (y0 - y1)/(x0 - x1)
b = y0 - x0 * a
return lambda x: a * x + b
def is_point_in_square(p, sq):
x, y = p
p0, p1, p2, p3 = sq
side_func0 = get_func_deg1(p0, p1)
side_func1 = get_func_deg1(p1, p2)
side_func2 = get_func_deg1(p2, p3)
side_func3 = get_func_deg1(p3, p0)
if not side_func0 or not side_func1 or not side_func2 or not side_func3:
xmin = min(p0[0], p2[0])
xmax = max(p0[0], p2[0])
ymin = min(p0[1], p2[1])
ymax = max(p0[1], p2[1])
return xmin <= x <= xmax and ymin <= y <= ymax
return ((y - side_func0(x)) * (y - side_func2(x))) <= 0 and \
((y - side_func1(x)) * (y - side_func3(x))) <= 0
def squares_overlap(square0, square1):
for p0 in square0:
if is_point_in_square(p0, square1):
return True
for p1 in square1:
if is_point_in_square(p1, square0):
return True
xc0 = (square0[0][0] + square0[2][0]) / 2
yc0 = (square0[0][1] + square0[2][1]) / 2
if is_point_in_square((xc0, yc0), square1):
return True
# The "reverse center check" not needed, since squares are congruent
"""
xc1 = (square1[0][0] + square1[2][0]) / 2
yc1 = (square1[0][1] + square1[2][1]) / 2
if is_point_in_square((xc1, yc1), square0):
return True
"""
return False
def __generation_monitor():
global __global_generation_counter
__global_generation_counter += 1
def generate_random_point(minx=MINX, miny=MINY, maxx=MAXX, maxy=MAXY, safety_margin=DEFAULT_SAFETY_MARGIN):
if maxx - minx < 2 * safety_margin or maxy - miny < 2 * safety_margin:
print("MUEEE")
safety_margin = 0
x = safety_margin + random() * (maxx - minx - 2 * safety_margin)
y = safety_margin + random() * (maxy - miny - 2 * safety_margin)
__generation_monitor()
return x, y
def generate_random_angle(max_val=pi_2):
return random() * max_val
def generate_random_square(side=DEFAULT_SIDE, squares_to_avoid=()):
while 1:
restart = False
x0, y0 = generate_random_point()
angle = generate_random_angle()
x1 = x0 + side * cos(angle)
y1 = y0 + side * sin(angle)
angle += pi_2
x2 = x1 + side * cos(angle)
y2 = y1 + side * sin(angle)
angle += pi_2
x3 = x2 + side * cos(angle)
y3 = y2 + side * sin(angle)
ret = (x0, y0), (x1, y1), (x2, y2), (x3, y3)
for square in squares_to_avoid:
if squares_overlap(ret, square):
restart = True
if restart:
continue
return ret
def square_to_plot(square):
xs, ys = zip(square[0], square[1], square[2], square[3])
return xs + (xs[0],), ys + (ys[0],)
def main():
seed()
squares = list()
allow_overlapping = False # CHANGE to True to allow square to overlap
for _ in range(MAX_SQUARES):
#print("Generating:", _)
if allow_overlapping:
square = generate_random_square()
else:
square = generate_random_square(squares_to_avoid=squares)
squares.append(square)
plot_squares = tuple()
for sq in squares:
plot_squares += square_to_plot(sq)
print("STATS:\n Squares: {}\n Allow overlapping: {}\n Generated values: {}".format(MAX_SQUARES, allow_overlapping, __global_generation_counter))
plt.plot(*plot_squares)
plt.axis([MINX, MAXX, MINY, MAXY])
plt.show()
if __name__ == "__main__":
print("Python {:s} on {:s}\n".format(sys.version, sys.platform))
main()
Notes:
I didn't work with matplotlib before (actually, I pip installed it for this task)
General comments:
A point is represented by a tuple representing its coordinates: (x, y)
A square is a tuple consisting of 4 points (p0, p1, p2, p3)
get_func_deg1:
Returns the function that describes the line that contains the 2 points given as arguments
If the 2 points are on a line that is parallel to Oy (there's no "normal" function to describe it), simply return None
is_point_in_square:
Determines if a point is inside a square
Uses the logic explained above, except
For the special case when the square edges are parallel to Ox and Oy when it uses some simple arithmetic operations
squares_overlap:
Determines whether 2 squares overlap (I'm sure there are faster "algorithms")
Checks if any of the 1st square corners are inside the 2nd one
The other way around: checks if any of the 2nd square corners are inside the 1st one
Since the above 2 checks are not enough (imagine a regular [Wikipedia]: Octagon and unifying its vertices every 2nd one: there will be 2 squares neither having its corners in the other one, but sharing their "central" areas), also check that one square's center is inside the other one
generate_random_point:
Generates a point in the given bounding box
safety_margin specifies the (minimum) distance that the generated point should be away from any of the bounding box sides, in order that any square that will have this point as a corner, would fit entirely in the bounding box
generate_random_angle:
Generates a random angle between 0 and (π / 2)
generate_random_square:
Generates a random point, a random angle, and constructs a square starting from there, using the specified side
squares_to_avoid is a list of squares. After the square is generated, it is checked against every square from that list. If the 2 squares overlap, the square is regenerated
square_to_plot:
Converts a square (from a tuple of points) to matplotlib format (2 tuples consisting of xs and ys with the 1st element duplicated as the last)
main:
The main function
__generation_monitor (0):
Internal function used for profiling
In order to change the number of squares, modify MAX_SQUARES
Output:
[cfati#CFATI-5510-0:e:\Work\Dev\StackOverflow\q046081491]> "e:\Work\Dev\VEnvs\py_064_03.05.04_test0\Scripts\python.exe" code00.py
STATS:
Squares: 30
Allow overlapping: False
Generated values: 1135
Few words about the squares generation
As seen in the output, for 30 displayed squares, 1135 were generated (at this run). That is because they were overlapping
If changing from main allow_overlapping = True, Generated values in the output will match the number of squares (MAX_SQUARES)
If increasing MAX_SQUARES to values let's say higher than 50, the number of generated values will increase exponentially (so will the time needed to generate them), and the chance that the program will enter an infinite loop (because it won't be able to generate a square that doesn't overlap to another one) will grow as well

Okay, here is what I've come up with with a little help from the shapely package. Installation help is at the bottom here. The ultimate result:
Code walkthrough
distance is a helper function using the Point class from shapely to find the distance between two coordinates. Just a helper function for later.
Square instantiations a new polygon. It has 4 corners, each an (x,y) pair, one coordinate for its center, and a scalar value equal to half the distance of its diagonal.
test_overlap is pretty self explanatory by title. But logically what it does is this: find the distance from center-to-center between the two shapes. Then find the sum of the half-diagonal of each shape. If the center-to-center distance is greater than the sum, the squares cannot overlap.
Squares starts out with an empty container (empty list) and attempts to add squares to it. But for each possible new addition, it firsts tests that there is no overlap with existing squares.
Code
import math
import random
from shapely.geometry import Polygon, Point
def distance(a, b):
return Point(a).distance(Point(b))
class Square(object):
def __init__(self):
self.x0, self.y0 = random.random(), random.random()
theta = random.randint(0, 90) * math.pi / 180 # Angle of rotation
self.x1 = self.x0 + (0.1 * math.cos(theta))
self.x2 = self.x1 + (0.1 * math.cos((90 * math.pi/180) + theta))
self.x3 = self.x2 + (0.1 * math.cos((180 * math.pi/180) + theta))
self.y1 = self.y0 + (0.1 * math.sin(theta))
self.y2 = self.y1 + (0.1 * math.sin((90 * math.pi/180) + theta))
self.y3 = self.y2 + (0.1 * math.sin((180 * math.pi/180) + theta))
self.corners = ((self.x0, self.y0), (self.x1, self.y1),
(self.x2, self.y2), (self.x3, self.y3))
#property
def center(self):
"""(x, y) of the center of the polygon."""
return Polygon(self.corners).centroid.coords[0]
#property
def half_diag(self):
"""The distance of 1/2 the shape's diagonal (center-to-corner)."""
p0, p1, p2, p3 = self.corners
return 0.5 * distance(p0, p1) * math.sqrt(2)
def test_overlap(square1, square2):
"""Do two shapes overlap?
Note this is a 'conservative' test. May return True if they do not
(false positive), but will never return False if they do (false negative).
"""
# Distance between two centers
ctc = distance(square1.center, square2.center)
# Sum of half-diagonals
halfdiags = square1.half_diag + square2.half_diag
res = ctc < halfdiags
return res
class Squares(object):
def __init__(self):
self.squares = []
def add_square(self):
new_square = Square()
if not self.squares:
# Initial empty list/container - just add without any tests
self.squares.append(new_square)
else:
while True:
# Test that new_square overlaps with existing
res = [test_overlap(square, new_square) for square in self.squares]
if any(res):
# We have at least 1 case of overlap (1 True)
new_square = Square()
else:
# Safe to add
self.squares.append(new_square)
break
def plot_squares(self):
for square in self.squares:
(x0, y0), (x1, y1), (x2, y2), (x3, y3) = square.corners
plt.plot([x0, x1, x2, x3, x0], [y0, y1, y2, y3, y0])
Example
import itertools
%matplotlib inline
for _ in itertools.repeat(None, 10):
# Add 10 squares; you could also just build this into the class
sqs.add_square()
sqs.plot_squares()
Installing shapely
Install the Anaconda distribution if you don't already. Then just use conda-forge to install shapely. From cmd run:
conda config --add channels conda-forge
conda install shapely
Glaring deficiency
At a certain point, your container of squares gets filled up and there is minimal room left to add new shapes. Even if there is available space, the function is basically trial-and-error, so will take long at high shape counts. That happens at around 20-25 squares (in a 1.0x1.0 box) at the moment.

You need a function to determine whether two cube intersect.
from math import cos, pi, sin
from random import random
from matplotlib.mlab import frange
from matplotlib.pyplot import plot, axis, show,axes
LEN = 0.1
def rotate(point, theta):
x = point[0]
y = point[1]
x_ = x * cos(theta) + y * sin(theta)
y_ = - x * sin(theta) + y * cos(theta)
return x_, y_
class CUBE(object):
def __init__(self, x, y, theta):
self.corner = [(LEN / 2, LEN / 2),
(-LEN / 2, LEN / 2),
(-LEN / 2, -LEN / 2),
(LEN / 2, -LEN / 2)
]
self.theta = theta
self.x = x
self.y = y
for i in range(4):
self.corner[i] = rotate(self.corner[i], theta)
self.corner[i] = (self.corner[i][0] + x,
self.corner[i][1] + y)
def is_include(cube, point):
point = [point[0] - cube.x, point[1] - cube.y]
point = rotate(point, -cube.theta)
if (point[0] < -LEN / 2
or point[0] > LEN / 2
or point[1] < -LEN / 2
or point[1] > LEN / 2
):
return False
else:
return True
def is_intersect(cube1, cube2):
if (any([is_include(cube1, point) for point in cube2.corner])
or any([is_include(cube2, point) for point in cube1.corner])
or is_include(cube1, (cube2.x, cube2.y))):
return True
else:
return False
def plot_cube(cube,n):
plot(
[cube.corner[i][0] for i in [0, 1, 2, 3, 0]],
[cube.corner[i][1] for i in [0, 1, 2, 3, 0]])
ax = axes()
ax.text(cube.x,cube.y,str(n))
def display(cubelist): # connects the 4 corners on a plot
for i,cube in enumerate(cubelist):
plot_cube(cube,i)
axis([0, 1, 0, 1]) # 1x1 grid
show()
cubelist = []
for i in range(100):
x0 = random()
y0 = random()
theta = random() * pi
cube = CUBE(x0, y0, theta)
if any(is_intersect(cube,cb) for cb in cubelist):
continue
else:
cubelist.append(cube)
display(cubelist)

Is there a better way to find points along a curve than Bresenham's line algorithm

I have a travel time map, I want to get the integer points along the shortest path from source to receiver.
My present solution is that I make a runge-kutta integration from the receiver location and get a series of float points. Then I sample every 5 or some number of points and assume it a straight line between in order to use the Bresenham's line algorithm. With this approach, I will get the integer points.
However, it's not enough fast. Because I need to calculate a lot of receivers' shortest path, the sum of time will be very large.
I used line_profiler to analysis the time-consuming, which shows the major part of time is for function ruge-kutta and its calling function get_velocity
codes are below
def optimal_path_2d(gradx_interp,
grady_interp,
starting_point,
dx,
N=100):
"""
Find the optimal path from starting_point to the zero contour
of travel_time. dx is the grid spacing
Solve the equation x_t = - grad t / | grad t |
"""
def get_velocity(position):
""" return normalized velocity at pos """
x, y = position
vel = np.array([gradx_interp(y, x)[0][0], grady_interp(y, x)[0][0]])
return vel / np.linalg.norm(vel)
def runge_kutta(pos, ds):
""" Fourth order Runge Kutta point update """
k1 = ds * get_velocity(pos)
k2 = ds * get_velocity(pos - k1 / 2.0)
k3 = ds * get_velocity(pos - k2 / 2.0)
k4 = ds * get_velocity(pos - k3)
return pos - (k1 + 2 * k2 + 2 * k3 + k4) / 6.0
x = runge_kutta(starting_point, dx)
xl, yl = [], []
for i in range(N):
xl.append(x[0])
yl.append(x[1])
x = runge_kutta(x, dx)
distance = ((x[0] - xl[-1])**2 +
(x[1] - yl[-1])**2)**0.5
if distance < dx*0.9:
break
return yl, xl
def get_curve(x_curve, y_curve, num_interval):
"""Curve Algorithm based on Bresenham's Line Algorithm
Produces a list of tuples
"""
num = len(x_curve)
if num < num_interval:
print("num_interval is too large.")
ret_set = set()
x0 = x_curve[0]
y0 = y_curve[0]
for i in range(num_interval, num, num_interval):
x1 = x_curve[i]
y1 = y_curve[i]
points_on_line = get_line((x0, y0), (x1, y1))
ret_set.update(points_on_line)
x0 = x1
y0 = y1
if num % num_interval != 0:
n = int(num/num_interval)*num_interval
x0 = x_curve[n]
y0 = y_curve[n]
x1 = x_curve[-1]
y1 = y_curve[-1]
points_on_line = get_line((x0, y0), (x1, y1))
ret_set.update(points_on_line)
return list(ret_set)
def get_line(start, end):
"""modifed version of Bresenham's Line Algorithm
Produces a list of tuples from start and end
>>> points1 = get_line((0, 0), (3, 4))
>>> points2 = get_line((3, 4), (0, 0))
>>> assert(set(points1) == set(points2))
>>> print points1
[(0, 0), (1, 1), (1, 2), (2, 3), (3, 4)]
>>> print points2
[(3, 4), (2, 3), (1, 2), (1, 1), (0, 0)]
"""
# Setup initial conditions
x1, y1 = (int(x) for x in start)
x2, y2 = (int(x) for x in end)
dx = x2 - x1
dy = y2 - y1
# Determine how steep the line is
is_steep = abs(dy) > abs(dx)
# Rotate line
if is_steep:
x1, y1 = y1, x1
x2, y2 = y2, x2
# Swap start and end points if necessary and store swap state
swapped = False
if x1 > x2:
x1, x2 = x2, x1
y1, y2 = y2, y1
swapped = True
# Recalculate differentials
dx = x2 - x1
dy = y2 - y1
# Calculate error
error = int(dx / 2.0)
ystep = 1 if y1 < y2 else -1
# Iterate over bounding box generating points between start and end
y = y1
points = []
for x in range(x1, x2 + 1):
coord = (y, x) if is_steep else (x, y)
points.append(coord)
error -= abs(dy)
if error < 0:
y += ystep
error += dx
# Reverse the list if the coordinates were swapped
if swapped:
points.reverse()
return points
nx = 100
ny = 100
num_interval = 5
loc_src = (10, 10)
loc_rec = (70, 90)
coordx = np.arange(nx)
coordy = np.arange(ny)
X, Y = np.meshgrid(coordx, coords)
travel_time = (X-loc_src[0])**2/5 + (Y-loc_src[1])**2/10 # for simplicity
grad_t_y, grad_t_x = np.gradient(travel_time, dx)
if isinstance(travel_time, np.ma.MaskedArray):
grad_t_y[grad_t_y.mask] = 0.0
grad_t_y = grad_t_y.data
grad_t_x[grad_t_x.mask] = 0.0
grad_t_x = grad_t_x.data
gradx_interp = RectBivariateSpline(coordy, coordx, grad_t_x)
grady_interp = RectBivariateSpline(coordy, coordx, grad_t_y)
yl, xl = optimal_path(gradx_interp, grady_interp, loc_rec, dx)
grid_indx = get_curve(xl, yl, num_interval)
I hear that Cython will be faster, then I learn a little recently and try it. the result is only 2 faster than codes above because I'm really new to Cython. The code below is incomplete, and I just wrote it for testing.
import numpy as np
from numpy.core.umath_tests import inner1d
def func(X_interp, Y_interp):
def get_velocity(double x, double y ):
""" return normalized velocity at pos """
cdef double vel[2], norm
a = X_interp(y, x)
vel[0] = a[0][0]
b = Y_interp(y, x)
vel[1] = b[0][0]
# norm = (vel[0]**2 + vel[1]**2)**0.5
# vel[0] = vel[0]/norm
# vel[1] = vel[1]/norm
return vel
def runge_kutta(double x, double y, double ds):
""" Fourth order Runge Kutta point update """
cdef double k1[2], k2[2], k3[2], k4[2], r[2], pos[2]
pos[0] = x; pos[1] = y
k1 = get_velocity(pos[0], pos[1])
k2 = get_velocity(pos[0] - k1[0]/2.0*ds,pos[1] - k1[1]/2.0*ds)
k3 = get_velocity(pos[0] - k2[0]/2.0*ds,pos[1] - k2[1]/2.0*ds)
k4 = get_velocity(pos[0] - k3[0]/2.0*ds,pos[1] - k3[1]/2.0*ds)
cdef size_t i
for i in range(2):
r[i] = pos[i] - ds * (k1[i] + 2*k2[i] + 2*k3[i] + k4[i])/6.0
return r
for i in range(50):
runge_kutta(0, 0, 1.)
# print(runge_kutta(0, 0, 1.))

How to find the closest point on a line segment to an arbitrary point?

This function is supposed to take in a point parameter which will be used to find the closest point to it that lies on the line segment object. In the example assertion code the function getClosestPoint(Point()) takes Point(10, 0) as parameters and should return Point(5,5) as the closest point to Point(10, 0) that is on the line of l1 = Line(Point(5,5), Point(20,35)) With the endpoints being A Point(5,5), B Point(20,35) I'm not sure how to go about solving this problem. My current solution will return (4,3) and that is not on the line segment but is on the line.
from point import Point
import math
class Line:
def __init__(self,aPoint=Point(), bPoint=Point()):
self.firstPoint = aPoint
self.secondPoint = bPoint
def getClosestPoint(self,point=Point()):
m1 = self.getSlope()
m2 = -1 / float(m1)
b1 = self.p1.y - m1 * self.p1.x
b2 = point.y - m2 * point.x
x = float(b2 - b1) / float(m1 - m2)
y = m1 * x + b1
return Point(x, y)
if __name__ == "__main__":
p1 = Point(5,5)
p2 = Point(20,35)
l1 = Line(p1,p2)
assert l1.getClosestPoint(Point(10,0)) == Point(5,5)
assert l2.getClosestPoint(Point(25/2,25/2)
class Point:
def __init__(self,x=0,y=0):
self.x = x
self.y = y

The general answer is to project the point onto the line.
One way to see it is to transform the point into the reference frame defined by your segment (p1 is the new origin (0, 0), p2 the new (1, 0)).
Then, you get rid of the new y coordinate (that's where the actual projection occurs) and transform the new point (x, 0) back into the original frame.
Concretely, you'll have to find the transformations.
The second one, from the new space into the original space is easy to write (just draw it on paper, you'll see):
x = (x2 - x1) * nx + (y2 - y1) * ny + x1
y = (y1 - y2) * nx + (x2 - x1) * ny + y1
But you can invert these equations to find (nx, ny) that correspond to a point (x, y).
When you do that, and assuming neither of us has made any mistakes nor typo, you should get something like:
dx = x2 - x1
dy = y2 - y1
d2 = dx*dx + dy*dy
nx = ((x3-x1)*dx + (y3-y1)*dy) / d2
return (dx*nx + x1, dy*nx + y1)
edit: If you actually have to find the closest point on the segment instead of the line, it is easy to find because if the projection falls within the segment, you have 0 <= nx <= 1 (it's a necessary and sufficient condition).
Before the return statement, you can just force nx to stay in this interval:
nx = min(1, max(0, nx))
reedit:
The statement above is equivalent to:
if nx<0:
nx = 0
if nx>1:
nx = 1
This way, the projection of the point on the line (which can be outside the segment) gets pushed back inside the segment (defined by 0 <= nx <= 1) at the closest point.

Circle and line collision detection in python tkinter

I writing a python program in which a circle bounces off of user drawn lines. There are multiple circles that bounce off the wall. For each one, the shortest distance from the center of the circle and the ball should be calculated. I would prefer if this code was very efficient because my current algorithm lags the computer a lot. If point a is the starting point ,and point b is the end point, and point c is the center, and r is the radius, how would I calculate the shortest distance between the ball? This algorithm should also work if the X coordinate of the ball is out of range of x coordinates in segment AB.
Please post python code
Any help would be appreciated!
Here's what I have so far:
lineList is a list with 4 values that contains beginning and end coordinates of the user drawn lines
center is the center of the ball
global lineList, numobjects
if not(0 in lineList):
beginCoord = [lineList[0],lineList[1]]
endCoord = [lineList[2]-500,lineList[3]-500]
center = [xCoordinate[i],yCoordinate[i]+15]
distance1 = math.sqrt((lineList[1] - center[1])**2 + (lineList[0] - center[0])**2)
slope1 = math.tan((lineList[1] - lineList[3]) / (lineList[0] - lineList[2]))
try:
slope2 = math.tan((center[1] - beginCoord[1])/(center[0]-beginCoord[0]))
angle1 = slope2 + slope1
circleDistance = distance1 * math.sin(angle1)
except:
#If the circle is directly above beginCoord
circleDistance = center[1] - lineList[1]
global numbounces
if circleDistance < 2 and circleDistance > -2:
print(circleDistance)
b = False
b2=False
if xCoordinate[i] < 0:
xCoordinate[i] += 1
speed1[i] *= -1
b=True
elif xCoordinate[i] > 0:
xCoordinate[i] -= 1
speed1[i] *= -1
b=True
if yCoordinate[i] < 0:
yCoordinate[i] += 1
speed2[i] *= -1
b2=True
elif yCoordinate[i] > 0:
yCoordinate[i] -= 1
speed2[i] *= -1
b2=True
if b and b2:
#Only delete the line if the ball reversed directions
numbounces += 1
#Add a ball after 5 bounces
if numbounces % 5 == 0 and numbounces != 0:
numobjects = 1
getData(numobjects)
canvas.delete("line")
lineList = [0,0,0,0]

I don't know what is the mean of shortest distance between the ball, but if you want to calculation the point where the circle will contact the line you can use sympy to figure the formula:
from sympy import *
from sympy.geometry import *
x1, y1, x2, y2, xc, yc = symbols("x1,y1,x2,y2,xc,yc")
p1 = Point(x1, y1)
p2 = Point(x2, y2)
pc = Point(xc, yc)
line = Line(p1, p2)
pline = line.perpendicular_line(pc)
p = line.intersection(pline)[0]
cse(p, symbols=numbered_symbols("t"))
the output is :
([(t0, x1 - x2), (t1, y1 - y2), (t2, x1*y2 - x2*y1), (t3, t0**2 + t1**2)],
[Point((t0**2*xc + t0*t1*yc - t1*t2)/t3, (t0*t1*xc + t0*t2 + t1**2*yc)/t3)])
this means that you can calculate the perpendicular point as:
t0 = x1 - x2
t1 = y1 - y2
t2 = x1*y2 - x2*y1
t3 = t0**2 + t1**2
xp = (t0**2*xc + t0*t1*yc - t1*t2)/t3
yp = (t0*t1*xc + t0*t2 + t1**2*yc)/t3