Develop Reference

Given two coordinates, a and b, how do I find a point that is x distance from a heading towards b?

I am working on a python script and I am sure there is an easier way to approach this problem then my solution so far.
Give two coordinates, say (0,0) and (40,40) and a set distance to travel, say 5, how would I find a new coordinate pair for the point that is 5 units from (0,0) heading along the line that connects (0,0) and (40,40)?
I am doing the following given the distance_to_travel and points p0, p1:
ang = math.atan2(p1.y - p0.y, p1.x - p0.x)
xx = node0.x + (distance_to_travel * math.cos(ang))
yy = node0.y + (distance_to_travel * math.sin(ang))

Following the method outlined in my comment:
# find the vector a-b
ab.x, ab.y = p1.x - p0.x, p1.y - p0.y
# find the length of this vector
len_ab = (ab.x * ab.x + ab.y * ab.y) ** 0.5
# scale to length 5
ab5.x, ab5.y = ab.x *5 / len_ab, ab.y *5 / len_ab
# add to a (== p0)
fin.x, fin.y = p0.x + ab5.x, p0.y + ab5.y

You should find the slope of the line between a and b |ab|.
Then you can use 2D spherical (polar) coordinate system to get r mount of distance with a given slope (The slope you found earlier).
Now you can convert from spherical to Cartesian to find x, y coordinates of new point. Then you add this values to values of point a:
import math
def slope(p1, p2):
return math.atan2(
(p2[1] - p1[1]), (p2[0] - p1[0])
)
def spherical_to_cartesian(r, theta):
x = r * math.cos(theta)
y = r * math.sin(theta)
return x, y
def add_points(p1, p2):
return p1[0] + p2[0], p1[1] + p2[1]
if __name__ == '__main__':
a = [0, 0]
b = [40, 40]
slp = slope(a, b)
r = 5
new_p = spherical_to_cartesian(r, slp)
res = add_points(a, new_p)
print(res)

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.

Intersections between Geodesics (shortest distance paths) on the surface of a sphere

I've searched far and wide but have yet to find a suitable answer to this problem. Given two lines on a sphere, each defined by their start and end points, determine whether or not and where they intersect. I've found this site (http://mathforum.org/library/drmath/view/62205.html) which runs through a good algorithm for the intersections of two great circles, although I'm stuck on determining whether the given point lies along the finite section of the great circles.
I've found several sites which claim they've implemented this, Including some questions here and on stackexchange, but they always seem to reduce back to the intersections of two great circles.
The python class I'm writing is as follows and seems to almost work:
class Geodesic(Boundary):
def _SecondaryInitialization(self):
self.theta_1 = self.point1.theta
self.theta_2 = self.point2.theta
self.phi_1 = self.point1.phi
self.phi_2 = self.point2.phi
sines = math.sin(self.phi_1) * math.sin(self.phi_2)
cosines = math.cos(self.phi_1) * math.cos(self.phi_2)
self.d = math.acos(sines - cosines * math.cos(self.theta_2 - self.theta_1))
self.x_1 = math.cos(self.theta_1) * math.cos(self.phi_1)
self.x_2 = math.cos(self.theta_2) * math.cos(self.phi_2)
self.y_1 = math.sin(self.theta_1) * math.cos(self.phi_1)
self.y_2 = math.sin(self.theta_2) * math.cos(self.phi_2)
self.z_1 = math.sin(self.phi_1)
self.z_2 = math.sin(self.phi_2)
self.theta_wraps = (self.theta_2 - self.theta_1 > PI)
self.phi_wraps = ((self.phi_1 < self.GetParametrizedCoords(0.01).phi and
self.phi_2 < self.GetParametrizedCoords(0.99).phi) or (
self.phi_1 > self.GetParametrizedCoords(0.01).phi) and
self.phi_2 > self.GetParametrizedCoords(0.99))
def Intersects(self, boundary):
A = self.y_1 * self.z_2 - self.z_1 * self.y_2
B = self.z_1 * self.x_2 - self.x_1 * self.z_2
C = self.x_1 * self.y_2 - self.y_1 * self.x_2
D = boundary.y_1 * boundary.z_2 - boundary.z_1 * boundary.y_2
E = boundary.z_1 * boundary.x_2 - boundary.x_1 * boundary.z_2
F = boundary.x_1 * boundary.y_2 - boundary.y_1 * boundary.x_2
try:
z = 1 / math.sqrt(((B * F - C * E) ** 2 / (A * E - B * D) ** 2)
+ ((A * F - C * D) ** 2 / (B * D - A * E) ** 2) + 1)
except ZeroDivisionError:
return self._DealWithZeroZ(A, B, C, D, E, F, boundary)
x = ((B * F - C * E) / (A * E - B * D)) * z
y = ((A * F - C * D) / (B * D - A * E)) * z
theta = math.atan2(y, x)
phi = math.atan2(z, math.sqrt(x ** 2 + y ** 2))
if self._Contains(theta, phi):
return point.SPoint(theta, phi)
theta = (theta + 2* PI) % (2 * PI) - PI
phi = -phi
if self._Contains(theta, phi):
return spoint.SPoint(theta, phi)
return None
def _Contains(self, theta, phi):
contains_theta = False
contains_phi = False
if self.theta_wraps:
contains_theta = theta > self.theta_2 or theta < self.theta_1
else:
contains_theta = theta > self.theta_1 and theta < self.theta_2
phi_wrap_param = self._PhiWrapParam()
if phi_wrap_param <= 1.0 and phi_wrap_param >= 0.0:
extreme_phi = self.GetParametrizedCoords(phi_wrap_param).phi
if extreme_phi < self.phi_1:
contains_phi = (phi < max(self.phi_1, self.phi_2) and
phi > extreme_phi)
else:
contains_phi = (phi > min(self.phi_1, self.phi_2) and
phi < extreme_phi)
else:
contains_phi = (phi > min(self.phi_1, self.phi_2) and
phi < max(self.phi_1, self.phi_2))
return contains_phi and contains_theta
def _PhiWrapParam(self):
a = math.sin(self.d)
b = math.cos(self.d)
c = math.sin(self.phi_2) / math.sin(self.phi_1)
param = math.atan2(c - b, a) / self.d
return param
def _DealWithZeroZ(self, A, B, C, D, E, F, boundary):
if (A - D) is 0:
y = 0
x = 1
elif (E - B) is 0:
y = 1
x = 0
else:
y = 1 / math.sqrt(((E - B) / (A - D)) ** 2 + 1)
x = ((E - B) / (A - D)) * y
theta = (math.atan2(y, x) + PI) % (2 * PI) - PI
return point.SPoint(theta, 0)
def GetParametrizedCoords(self, param_value):
A = math.sin((1 - param_value) * self.d) / math.sin(self.d)
B = math.sin(param_value * self.d) / math.sin(self.d)
x = A * math.cos(self.phi_1) * math.cos(self.theta_1) + (
B * math.cos(self.phi_2) * math.cos(self.theta_2))
y = A * math.cos(self.phi_1) * math.sin(self.theta_1) + (
B * math.cos(self.phi_2) * math.sin(self.theta_2))
z = A * math.sin(self.phi_1) + B * math.sin(self.phi_2)
new_phi = math.atan2(z, math.sqrt(x**2 + y**2))
new_theta = math.atan2(y, x)
return point.SPoint(new_theta, new_phi)
EDIT: I forgot to specify that if two curves are determined to intersect, I then need to have the point of intersection.

A simpler approach is to express the problem in terms of geometric primitive operations like the dot product, the cross product, and the triple product. The sign of the determinant of u, v, and w tells you which side of the plane spanned by v and w contains u. This enables us to detect when two points are on opposite sites of a plane. That's equivalent to testing whether a great circle segment crosses another great circle. Performing this test twice tells us whether two great circle segments cross each other.
The implementation requires no trigonometric functions, no division, no comparisons with pi, and no special behavior around the poles!
class Vector:
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def dot(v1, v2):
return v1.x * v2.x + v1.y * v2.y + v1.z * v2.z
def cross(v1, v2):
return Vector(v1.y * v2.z - v1.z * v2.y,
v1.z * v2.x - v1.x * v2.z,
v1.x * v2.y - v1.y * v2.x)
def det(v1, v2, v3):
return dot(v1, cross(v2, v3))
class Pair:
def __init__(self, v1, v2):
self.v1 = v1
self.v2 = v2
# Returns True if the great circle segment determined by s
# straddles the great circle determined by l
def straddles(s, l):
return det(s.v1, l.v1, l.v2) * det(s.v2, l.v1, l.v2) < 0
# Returns True if the great circle segments determined by a and b
# cross each other
def intersects(a, b):
return straddles(a, b) and straddles(b, a)
# Test. Note that we don't need to normalize the vectors.
print(intersects(Pair(Vector(1, 0, 1), Vector(-1, 0, 1)),
Pair(Vector(0, 1, 1), Vector(0, -1, 1))))
If you want to initialize unit vectors in terms of angles theta and phi, you can do that, but I recommend immediately converting to Cartesian (x, y, z) coordinates to perform all subsequent calculations.

Intersection using plane trig can be calculated using the below code in UBasic.
5 'interx.ub adapted from code at
6 'https://rosettacode.org
7 '/wiki/Find_the_intersection_of_two_linesSinclair_ZX81_BASIC
8 'In U Basic by yuji kida https://en.wikipedia.org/wiki/UBASIC
10 XA=48.7815144526:'669595.708
20 YA=-117.2847245001:'2495736.332
30 XB=48.7815093807:'669533.412
40 YB=-117.2901673467:'2494425.458
50 XC=48.7824947147:'669595.708
60 YC=-117.28751374:'2495736.332
70 XD=48.77996737:'669331.214
80 YD=-117.2922957:'2494260.804
90 print "THE TWO LINES ARE:"
100 print "YAB=";YA-XA*((YB-YA)/(XB-XA));"+X*";((YB-YA)/(XB-XA))
110 print "YCD=";YC-XC*((YD-YC)/(XD-XC));"+X*";((YD-YC)/(XD-XC))
120 X=((YC-XC*((YD-YC)/(XD-XC)))-(YA-XA*((YB-YA)/(XB-XA))))/(((YB-YA)/(XB-XA))-((YD-YC)/(XD-XC)))
130 print "Lat = ";X
140 Y=YA-XA*((YB-YA)/(XB-XA))+X*((YB-YA)/(XB-XA))
150 print "Lon = ";Y
160 'print "YCD=";YC-XC*((YD-YC)/(XD-XC))+X*((YD-YC)/(XD-XC))

Find Arc/Circle equation given three points in space (3D)

Given 3 points in space (3D): A = (x1, y1, z1), B = (x2, y2, z2) C = (x3, y3, z3); then how to find the center and radius of the circle (arc) that passes through these three points, i.e. find circle equation? Using Python and Numpy here is my initial code
import numpy as np
A = np.array([x1, y1, z1])
B = np.array([x2, y2, z2])
C = np.array([x3, y3, z3])
#Find vectors connecting the three points and the length of each vector
AB = B - A
BC = C - B
AC = C - A
# Triangle Lengths
a = np.linalg.norm(AB)
b = np.linalg.norm(BC)
c = np.linalg.norm(AC)
From the Circumradius definition, the radius can be found using:
R = (a * b * c) / np.sqrt(2.0 * a**2 * b**2 +
2.0 * b**2 * c**2 +
2.0 * c**2 * a**2 -
a**4 - b**4 - c**4)
However, I am having problems finding the Cartesian coordinates of the center. One possible solution is to use the "Barycentric Coordinates" of the triangle points to find the trilinear coordinates of the circumcenter (Circumcenter).
First (using this source) we find the circumcenter barcyntric coordinates:
#barcyntric coordinates of center
b1 = a**2 * (b**2 + c**2 - a**2)
b2 = b**2 * (c**2 + a**2 - b**2)
b3 = c**2 * (a**2 + b**2 - c**2)
Then the Cartesian coordinates of the center (P) would be:
Px = (b1 * A[0]) + (b2 * B[0]) + (b3 * C[0])
Py = (b1 * A[1]) + (b2 * B[1]) + (b3 * C[1])
Pz = (b1 * A[2]) + (b2 * B[2]) + (b3 * C[2])
However, the barcyntric coordinates values above do not seem to be correct. When solved with an example of known values, the radius is correct, but the coordinates of the center are not.
Example: For these three points:
A = np.array([2.0, 1.5, 0.0])
B = np.array([6.0, 4.5, 0.0])
C = np.array([11.75, 6.25, 0.0])
The radius and center coordinates are:
R = 15.899002930062595
P = [13.4207317073, -9.56097560967, 0]
Any ideas on how to find the center coordinates?

There are two issues with your code.
The first is in the naming convention. For all the formulas you are using to hold, the side of length a has to be the one opposite the point A, and similarly for b and B and c and C. You can solve that by computing them as:
a = np.linalg.norm(C - B)
b = np.linalg.norm(C - A)
c = np.linalg.norm(B - A)
The second has to do with the note in your source for the barycentric coordinates of the circumcenter: not necessarily homogeneous. That is, they need not be normalized in any way, and the formula you are using to compute the Cartesian coordinates from the barycentric ones is only valid when they add up to one.
Fortunately, you only need to divide the resulting Cartesian coordinates by b1 + b2 + b3 to get the result you are after. Streamlining a little bit your code for efficiency, I get the results you expect:
>>> A = np.array([2.0, 1.5, 0.0])
>>> B = np.array([6.0, 4.5, 0.0])
>>> C = np.array([11.75, 6.25, 0.0])
>>> a = np.linalg.norm(C - B)
>>> b = np.linalg.norm(C - A)
>>> c = np.linalg.norm(B - A)
>>> s = (a + b + c) / 2
>>> R = a*b*c / 4 / np.sqrt(s * (s - a) * (s - b) * (s - c))
>>> b1 = a*a * (b*b + c*c - a*a)
>>> b2 = b*b * (a*a + c*c - b*b)
>>> b3 = c*c * (a*a + b*b - c*c)
>>> P = np.column_stack((A, B, C)).dot(np.hstack((b1, b2, b3)))
>>> P /= b1 + b2 + b3
>>> R
15.899002930062531
>>> P
array([ 13.42073171, -9.56097561, 0. ])

As an extension to the original problem: Now suppose we have an arc of known length (e.g. 1 unit) extending from point A as defined above (away from point B). How to find the 3D coordinates of the end point (N)? The new point N lies on the same circle passing through points A, B & C.
This can be solved by first finding the angle between the two vectors PA & PN:
# L = Arc Length
theta = L / R
Now all we need to do is rotate the vector PA (Radius) by this angle theta in the correct direction. In order to do that, we need the 3D rotation matrix. For that we use the Euler–Rodrigues formula:
def rotation_matrix_3d(axis, theta):
axis = axis / np.linalg.norm(axis)
a = np.cos(theta / 2.0)
b, c, d = axis * np.sin(theta / 2.0)
rot = np.array([[a*a+b*b-c*c-d*d, 2*(b*c-a*d), 2*(b*d+a*c)],
[ 2*(b*c+a*d), a*a+c*c-b*b-d*d, 2*(c*d-a*b)],
[ 2*(b*d-a*c), 2*(c*d+a*b), a*a+d*d-b*b-c*c]])
return rot
Next, we need to find the rotation axis to rotate PA around. This can be achieved by finding the axis normal to the plane of the circle passing through A, B & C. The coordinates of point N can then be found from the center of the circle.
PA = P - A
PB = P - B
xx = np.cross(PB, PA)
r3d = rotation_matrix_3d(xx, theta)
PN = np.dot(r3d, PA)
N = P - PN
as an example, we want to find coordinates of point N that are 3 degrees away from point A, the coordinates would be
N = (1.43676498, 0.8871264, 0.)
Which is correct! (manually verified using CAD program)

How can you determine a point is between two other points on a line segment?

Let's say you have a two dimensional plane with 2 points (called a and b) on it represented by an x integer and a y integer for each point.
How can you determine if another point c is on the line segment defined by a and b?
I use python most, but examples in any language would be helpful.

Check if the cross product of (b-a) and (c-a) is 0, as tells Darius Bacon, tells you if the points a, b and c are aligned.
But, as you want to know if c is between a and b, you also have to check that the dot product of (b-a) and (c-a) is positive and is less than the square of the distance between a and b.
In non-optimized pseudocode:
def isBetween(a, b, c):
crossproduct = (c.y - a.y) * (b.x - a.x) - (c.x - a.x) * (b.y - a.y)
# compare versus epsilon for floating point values, or != 0 if using integers
if abs(crossproduct) > epsilon:
return False
dotproduct = (c.x - a.x) * (b.x - a.x) + (c.y - a.y)*(b.y - a.y)
if dotproduct < 0:
return False
squaredlengthba = (b.x - a.x)*(b.x - a.x) + (b.y - a.y)*(b.y - a.y)
if dotproduct > squaredlengthba:
return False
return True

Here's how I'd do it:
def distance(a,b):
return sqrt((a.x - b.x)**2 + (a.y - b.y)**2)
def is_between(a,c,b):
return distance(a,c) + distance(c,b) == distance(a,b)

Check if the cross product of b-a and c-a is0: that means all the points are collinear. If they are, check if c's coordinates are between a's and b's. Use either the x or the y coordinates, as long as a and b are separate on that axis (or they're the same on both).
def is_on(a, b, c):
"Return true iff point c intersects the line segment from a to b."
# (or the degenerate case that all 3 points are coincident)
return (collinear(a, b, c)
and (within(a.x, c.x, b.x) if a.x != b.x else
within(a.y, c.y, b.y)))
def collinear(a, b, c):
"Return true iff a, b, and c all lie on the same line."
return (b.x - a.x) * (c.y - a.y) == (c.x - a.x) * (b.y - a.y)
def within(p, q, r):
"Return true iff q is between p and r (inclusive)."
return p <= q <= r or r <= q <= p
This answer used to be a mess of three updates. The worthwhile info from them: Brian Hayes's chapter in Beautiful Code covers the design space for a collinearity-test function -- useful background. Vincent's answer helped to improve this one. And it was Hayes who suggested testing only one of the x or the y coordinates; originally the code had and in place of if a.x != b.x else.
(This is coded for exact arithmetic with integers or rationals; if you pass in floating-point numbers instead, there will be problems with round-off errors. I'm not even sure what's a good way to define betweenness of 2-d points in float coordinates.)

Here's another approach:
Lets assume the two points be A (x1,y1) and B (x2,y2)
The equation of the line passing through those points is (x-x1)/(y-y1)=(x2-x1)/(y2-y1) .. (just making equating the slopes)
Point C (x3,y3) will lie between A & B if:
x3,y3 satisfies the above equation.
x3 lies between x1 & x2 and y3 lies between y1 & y2 (trivial check)

The length of the segment is not important, thus using a square root is not required and should be avoided since we could lose some precision.
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
class Segment:
def __init__(self, a, b):
self.a = a
self.b = b
def is_between(self, c):
# Check if slope of a to c is the same as a to b ;
# that is, when moving from a.x to c.x, c.y must be proportionally
# increased than it takes to get from a.x to b.x .
# Then, c.x must be between a.x and b.x, and c.y must be between a.y and b.y.
# => c is after a and before b, or the opposite
# that is, the absolute value of cmp(a, b) + cmp(b, c) is either 0 ( 1 + -1 )
# or 1 ( c == a or c == b)
a, b = self.a, self.b
return ((b.x - a.x) * (c.y - a.y) == (c.x - a.x) * (b.y - a.y) and
abs(cmp(a.x, c.x) + cmp(b.x, c.x)) <= 1 and
abs(cmp(a.y, c.y) + cmp(b.y, c.y)) <= 1)
Some random example of usage :
a = Point(0,0)
b = Point(50,100)
c = Point(25,50)
d = Point(0,8)
print Segment(a,b).is_between(c)
print Segment(a,b).is_between(d)

You can use the wedge and dot product:
def dot(v,w): return v.x*w.x + v.y*w.y
def wedge(v,w): return v.x*w.y - v.y*w.x
def is_between(a,b,c):
v = a - b
w = b - c
return wedge(v,w) == 0 and dot(v,w) > 0

Using a more geometric approach, calculate the following distances:
ab = sqrt((a.x-b.x)**2 + (a.y-b.y)**2)
ac = sqrt((a.x-c.x)**2 + (a.y-c.y)**2)
bc = sqrt((b.x-c.x)**2 + (b.y-c.y)**2)
and test whether ac+bc equals ab:
is_on_segment = abs(ac + bc - ab) < EPSILON
That's because there are three possibilities:
The 3 points form a triangle => ac+bc > ab
They are collinear and c is outside the ab segment => ac+bc > ab
They are collinear and c is inside the ab segment => ac+bc = ab

Here's a different way to go about it, with code given in C++. Given two points, l1 and l2 it's trivial to express the line segment between them as
l1 + A(l2 - l1)
where 0 <= A <= 1. This is known as the vector representation of a line if you're interested any more beyond just using it for this problem. We can split out the x and y components of this, giving:
x = l1.x + A(l2.x - l1.x)
y = l1.y + A(l2.y - l1.y)
Take a point (x, y) and substitute its x and y components into these two expressions to solve for A. The point is on the line if the solutions for A in both expressions are equal and 0 <= A <= 1. Because solving for A requires division, there's special cases that need handling to stop division by zero when the line segment is horizontal or vertical. The final solution is as follows:
// Vec2 is a simple x/y struct - it could very well be named Point for this use
bool isBetween(double a, double b, double c) {
// return if c is between a and b
double larger = (a >= b) ? a : b;
double smaller = (a != larger) ? a : b;
return c <= larger && c >= smaller;
}
bool pointOnLine(Vec2<double> p, Vec2<double> l1, Vec2<double> l2) {
if(l2.x - l1.x == 0) return isBetween(l1.y, l2.y, p.y); // vertical line
if(l2.y - l1.y == 0) return isBetween(l1.x, l2.x, p.x); // horizontal line
double Ax = (p.x - l1.x) / (l2.x - l1.x);
double Ay = (p.y - l1.y) / (l2.y - l1.y);
// We want Ax == Ay, so check if the difference is very small (floating
// point comparison is fun!)
return fabs(Ax - Ay) < 0.000001 && Ax >= 0.0 && Ax <= 1.0;
}

The scalar product between (c-a) and (b-a) must be equal to the product of their lengths (this means that the vectors (c-a) and (b-a) are aligned and with the same direction). Moreover, the length of (c-a) must be less than or equal to that of (b-a). Pseudocode:
# epsilon = small constant
def isBetween(a, b, c):
lengthca2 = (c.x - a.x)*(c.x - a.x) + (c.y - a.y)*(c.y - a.y)
lengthba2 = (b.x - a.x)*(b.x - a.x) + (b.y - a.y)*(b.y - a.y)
if lengthca2 > lengthba2: return False
dotproduct = (c.x - a.x)*(b.x - a.x) + (c.y - a.y)*(b.y - a.y)
if dotproduct < 0.0: return False
if abs(dotproduct*dotproduct - lengthca2*lengthba2) > epsilon: return False
return True

I needed this for javascript for use in an html5 canvas for detecting if the users cursor was over or near a certain line. So I modified the answer given by Darius Bacon into coffeescript:
is_on = (a,b,c) ->
# "Return true if point c intersects the line segment from a to b."
# (or the degenerate case that all 3 points are coincident)
return (collinear(a,b,c) and withincheck(a,b,c))
withincheck = (a,b,c) ->
if a[0] != b[0]
within(a[0],c[0],b[0])
else
within(a[1],c[1],b[1])
collinear = (a,b,c) ->
# "Return true if a, b, and c all lie on the same line."
((b[0]-a[0])*(c[1]-a[1]) < (c[0]-a[0])*(b[1]-a[1]) + 1000) and ((b[0]-a[0])*(c[1]-a[1]) > (c[0]-a[0])*(b[1]-a[1]) - 1000)
within = (p,q,r) ->
# "Return true if q is between p and r (inclusive)."
p <= q <= r or r <= q <= p

Here's how I did it at school. I forgot why it is not a good idea.
EDIT:
#Darius Bacon: cites a "Beautiful Code" book which contains an explanation why the belowed code is not a good idea.
#!/usr/bin/env python
from __future__ import division
epsilon = 1e-6
class Point:
def __init__(self, x, y):
self.x, self.y = x, y
class LineSegment:
"""
>>> ls = LineSegment(Point(0,0), Point(2,4))
>>> Point(1, 2) in ls
True
>>> Point(.5, 1) in ls
True
>>> Point(.5, 1.1) in ls
False
>>> Point(-1, -2) in ls
False
>>> Point(.1, 0.20000001) in ls
True
>>> Point(.1, 0.2001) in ls
False
>>> ls = LineSegment(Point(1, 1), Point(3, 5))
>>> Point(2, 3) in ls
True
>>> Point(1.5, 2) in ls
True
>>> Point(0, -1) in ls
False
>>> ls = LineSegment(Point(1, 2), Point(1, 10))
>>> Point(1, 6) in ls
True
>>> Point(1, 1) in ls
False
>>> Point(2, 6) in ls
False
>>> ls = LineSegment(Point(-1, 10), Point(5, 10))
>>> Point(3, 10) in ls
True
>>> Point(6, 10) in ls
False
>>> Point(5, 10) in ls
True
>>> Point(3, 11) in ls
False
"""
def __init__(self, a, b):
if a.x > b.x:
a, b = b, a
(self.x0, self.y0, self.x1, self.y1) = (a.x, a.y, b.x, b.y)
self.slope = (self.y1 - self.y0) / (self.x1 - self.x0) if self.x1 != self.x0 else None
def __contains__(self, c):
return (self.x0 <= c.x <= self.x1 and
min(self.y0, self.y1) <= c.y <= max(self.y0, self.y1) and
(not self.slope or -epsilon < (c.y - self.y(c.x)) < epsilon))
def y(self, x):
return self.slope * (x - self.x0) + self.y0
if __name__ == '__main__':
import doctest
doctest.testmod()

Ok, lots of mentions of linear algebra (cross product of vectors) and this works in a real (ie continuous or floating point) space but the question specifically stated that the two points were expressed as integers and thus a cross product is not the correct solution although it can give an approximate solution.
The correct solution is to use Bresenham's Line Algorithm between the two points and to see if the third point is one of the points on the line. If the points are sufficiently distant that calculating the algorithm is non-performant (and it'd have to be really large for that to be the case) I'm sure you could dig around and find optimisations.

Any point on the line segment (a, b) (where a and b are vectors) can be expressed as a linear combination of the two vectors a and b:
In other words, if c lies on the line segment (a, b):
c = ma + (1 - m)b, where 0 <= m <= 1
Solving for m, we get:
m = (c.x - b.x)/(a.x - b.x) = (c.y - b.y)/(a.y - b.y)
So, our test becomes (in Python):
def is_on(a, b, c):
"""Is c on the line segment ab?"""
def _is_zero( val ):
return -epsilon < val < epsilon
x1 = a.x - b.x
x2 = c.x - b.x
y1 = a.y - b.y
y2 = c.y - b.y
if _is_zero(x1) and _is_zero(y1):
# a and b are the same point:
# so check that c is the same as a and b
return _is_zero(x2) and _is_zero(y2)
if _is_zero(x1):
# a and b are on same vertical line
m2 = y2 * 1.0 / y1
return _is_zero(x2) and 0 <= m2 <= 1
elif _is_zero(y1):
# a and b are on same horizontal line
m1 = x2 * 1.0 / x1
return _is_zero(y2) and 0 <= m1 <= 1
else:
m1 = x2 * 1.0 / x1
if m1 < 0 or m1 > 1:
return False
m2 = y2 * 1.0 / y1
return _is_zero(m2 - m1)

c#
From http://www.faqs.org/faqs/graphics/algorithms-faq/
-> Subject 1.02: How do I find the distance from a point to a line?
Boolean Contains(PointF from, PointF to, PointF pt, double epsilon)
{
double segmentLengthSqr = (to.X - from.X) * (to.X - from.X) + (to.Y - from.Y) * (to.Y - from.Y);
double r = ((pt.X - from.X) * (to.X - from.X) + (pt.Y - from.Y) * (to.Y - from.Y)) / segmentLengthSqr;
if(r<0 || r>1) return false;
double sl = ((from.Y - pt.Y) * (to.X - from.X) - (from.X - pt.X) * (to.Y - from.Y)) / System.Math.Sqrt(segmentLengthSqr);
return -epsilon <= sl && sl <= epsilon;
}

An answer in C# using a Vector2D class
public static bool IsOnSegment(this Segment2D #this, Point2D c, double tolerance)
{
var distanceSquared = tolerance*tolerance;
// Start of segment to test point vector
var v = new Vector2D( #this.P0, c ).To3D();
// Segment vector
var s = new Vector2D( #this.P0, #this.P1 ).To3D();
// Dot product of s
var ss = s*s;
// k is the scalar we multiply s by to get the projection of c onto s
// where we assume s is an infinte line
var k = v*s/ss;
// Convert our tolerance to the units of the scalar quanity k
var kd = tolerance / Math.Sqrt( ss );
// Check that the projection is within the bounds
if (k <= -kd || k >= (1+kd))
{
return false;
}
// Find the projection point
var p = k*s;
// Find the vector between test point and it's projection
var vp = (v - p);
// Check the distance is within tolerance.
return vp * vp < distanceSquared;
}
Note that
s * s
is the dot product of the segment vector via operator overloading in C#
The key is taking advantage of the projection of the point onto the infinite line and observing that the scalar quantity of the projection tells us trivially if the projection is on the segment or not. We can adjust the bounds of the scalar quantity to use a fuzzy tolerance.
If the projection is within bounds we just test if the distance from the point to the projection is within bounds.
The benefit over the cross product approach is that the tolerance has a meaningful value.

C# version of Jules' answer:
public static double CalcDistanceBetween2Points(double x1, double y1, double x2, double y2)
{
return Math.Sqrt(Math.Pow (x1-x2, 2) + Math.Pow (y1-y2, 2));
}
public static bool PointLinesOnLine (double x, double y, double x1, double y1, double x2, double y2, double allowedDistanceDifference)
{
double dist1 = CalcDistanceBetween2Points(x, y, x1, y1);
double dist2 = CalcDistanceBetween2Points(x, y, x2, y2);
double dist3 = CalcDistanceBetween2Points(x1, y1, x2, y2);
return Math.Abs(dist3 - (dist1 + dist2)) <= allowedDistanceDifference;
}

Here is some Java code that worked for me:
boolean liesOnSegment(Coordinate a, Coordinate b, Coordinate c) {
double dotProduct = (c.x - a.x) * (c.x - b.x) + (c.y - a.y) * (c.y - b.y);
return (dotProduct < 0);
}

You could also use the very convenient scikit-spatial library.
For instance, you could create a Line object defined by the two points a and b:
>>> point_a = [0, 0]
>>> point_b = [1, 0]
>>> line = Line.from_points(point_a, point_b)
then you can use the side_point method of the Line class to check whether point c lies on line or not.
>>> line.side_point([0.5, 0])
0
If the output is 0, then point c lies on line.

how about just ensuring that the slope is the same and the point is between the others?
given points (x1, y1) and (x2, y2) ( with x2 > x1)
and candidate point (a,b)
if (b-y1) / (a-x1) = (y2-y2) / (x2-x1) And x1 < a < x2
Then (a,b) must be on line between (x1,y1) and (x2, y2)

Here is my solution with C# in Unity.
private bool _isPointOnLine( Vector2 ptLineStart, Vector2 ptLineEnd, Vector2 ptPoint )
{
bool bRes = false;
if((Mathf.Approximately(ptPoint.x, ptLineStart.x) || Mathf.Approximately(ptPoint.x, ptLineEnd.x)))
{
if(ptPoint.y > ptLineStart.y && ptPoint.y < ptLineEnd.y)
{
bRes = true;
}
}
else if((Mathf.Approximately(ptPoint.y, ptLineStart.y) || Mathf.Approximately(ptPoint.y, ptLineEnd.y)))
{
if(ptPoint.x > ptLineStart.x && ptPoint.x < ptLineEnd.x)
{
bRes = true;
}
}
return bRes;
}

You can do it by solving the line equation for that line segment with the point coordinates you will know whether that point is on the line and then checking the bounds of the segment to know whether it is inside or outside of it. You can apply some threshold because well it is somewhere in space mostl likely defined by a floating point value and you must not hit the exact one.
Example in php
function getLineDefinition($p1=array(0,0), $p2=array(0,0)){
$k = ($p1[1]-$p2[1])/($p1[0]-$p2[0]);
$q = $p1[1]-$k*$p1[0];
return array($k, $q);
}
function isPointOnLineSegment($line=array(array(0,0),array(0,0)), $pt=array(0,0)){
// GET THE LINE DEFINITION y = k.x + q AS array(k, q)
$def = getLineDefinition($line[0], $line[1]);
// use the line definition to find y for the x of your point
$y = $def[0]*$pt[0]+$def[1];
$yMin = min($line[0][1], $line[1][1]);
$yMax = max($line[0][1], $line[1][1]);
// exclude y values that are outside this segments bounds
if($y>$yMax || $y<$yMin) return false;
// calculate the difference of your points y value from the reference value calculated from lines definition
// in ideal cases this would equal 0 but we are dealing with floating point values so we need some threshold value not to lose results
// this is up to you to fine tune
$diff = abs($pt[1]-$y);
$thr = 0.000001;
return $diff<=$thr;
}