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solving the wave 1-d equation with python and animate

I'm trying to solve the 1-d wave equation, and I coding the program for numerical computing solutions and animating, saving data in the file. I don't know how to fix the error and finally get the working code.
u_tt = a**2 * u_xx + f(x,t)
It is necessary for the program to solve equations when entering both an additional function and with non-zero initial and boundary conditions, with graphic visualization and saving data to a file.
So I attach my code (Python 3.9), and error message:
import numpy as np
import math
import matplotlib.pyplot as plt
import os
import time
import glob
def sol(I, V, f, a, L, C, T, U_0, U_L, dt, user_func = None):
"""
solver for wave equation
u_tt = a**2*u_xx + f(x,t) (0,L) where u=0 for
x=0,L, for t in (0,T].
:param I:
:param V:
:param f:
:param a:
:param L:
:param C:
:param T:
:param U_0:
:param U_L:
:param dt:
:param user_func:
:return:
"""
nt = int(round(T / dt))
t = np.linspace(0, nt * dt, nt + 1) # array for time points
dx = dt * a / float(C)
nx = int(round(L / dx))
x = np.linspace(0, L, nx + 1) # array for coord points
q = a ** 2
C2 = (dt / dx) ** 2
dt2 = dt * dt
# --- checking f(x,t) ---
if f is None or f == 0:
f = lambda x, t: 0
# --- check the initial conds dU(x,0)/dt ---
if V is None or V == 0:
V = lambda x: 0
# boundary conds
if U_0 is not None:
if isinstance(U_0, (float, int)) and U_0 == 0:
U_0 = lambda t: 0
if U_L is not None:
if isinstance(U_L, (float, int)) and U_L == 0:
U_L = lambda t: 0
# --- allocate memory ---
u = np.zeros(nx + 1)
u_n = np.zeros(nx + 1)
u_nm = np.zeros(nx + 1)
# --- valid indexing check ---
Ix = range(0, nx + 1)
It = range(0, nt + 1)
# --- set the boundary conds ---
for i in range(0, nx + 1):
u_n[i] = I(x[i])
if user_func is not None:
user_func(u_n, x, t, 0)
# --- finite difference step ---
for i in Ix[1:-1]:
u[i] = u_n[i] + dt * V(x[i]) + 0.5 * C2 * (0.5 * (q[i] + q[i + 1]) * (u_n[i + 1] - u_n[i]) -
0.5 * (q[i] + q[i - 1]) * (u_n[i] - u_n[i - 1])) + 0.5 * dt2 * f(x[i], t[0])
i = Ix[0]
if U_0 is None:
# set the boundary conds (x=0: i-1 -> i+1 u[i-1]=u[i+1]
# where du/dn = 0, on x=L: i+1 -> i-1 u[i+1]=u[i-1])
ip1 = i + 1
im1 = ip1 # i-1 -> i+1
u[i] = u_n[i] + dt * V(x[i]) + \
0.5 * C2 * (0.5 * (q[i] + q[ip1]) * (u_n[ip1] - u_n[i]) - 0.5 * (q[i] + q[im1])
* (u_n[i] - u_n[im1])) + 0.5 * dt2 * f(x[i], t[0])
else:
u[i] = U_0(dt)
i = Ix[-1]
if U_L is None:
im1 = i - 1
ip1 = im1 # i+1 -> i-1
u[i] = u_n[i] + dt * V(x[i]) + \
0.5 * C2 * (0.5 * (q[i] + q[ip1]) * (u_n[ip1] - u_n[i]) - 0.5 * (q[i] + q[im1]) * (u_n[i] - u_n[im1])) + \
0.5 * dt2 * f(x[i], t[0])
else:
u[i] = U_L(dt)
if user_func is not None:
user_func(u, x, t, 1)
# update data
u_nm, u_n, u = u_n, u, u_nm
# --- time looping ---
for n in It[1:-1]:
# update all inner points
for i in Ix[1:-1]:
u[i] = - u_nm[i] + 2 * u_n[i] + \
C2 * (0.5 * (q[i] + q[i + 1]) * (u_n[i + 1] - u_n[i]) -
0.5 * (q[i] + q[i - 1]) * (u_n[i] - u_n[i - 1])) + dt2 * f(x[i], t[n])
# --- set boundary conds ---
i = Ix[0]
if U_0 is None:
# set the boundary conds
# x=0: i-1 -> i+1 u[i-1]=u[i+1] where du/dn=0
# x=L: i+1 -> i-1 u[i+1]=u[i-1] where du/dn=0
ip1 = i + 1
im1 = ip1
u[i] = - u_nm[i] + 2 * u_n[i] + \
C2 * (0.5 * (q[i] + q[ip1]) * (u_n[ip1] - u_n[i]) - 0.5 * (q[i] + q[im1]) * (u_n[i] - u_n[im1])) + \
dt2 * f(x[i], t[n])
else:
u[i] = U_0(t[n + 1])
i = Ix[-1]
if U_L is None:
im1 = i - 1
ip1 = im1
u[i] = - u_nm[i] + 2 * u_n[i] + \
C2 * (0.5 * (q[i] + q[ip1]) * (u_n[ip1] - u_n[i]) - 0.5 * (q[i] + q[im1]) * (u_n[i] - u_n[im1])) + \
dt2 * f(x[i], t[n])
else:
u[i] = U_L(t[n + 1])
if user_func is not None:
if user_func(u, x, t, n + 1):
break
u_nm, u_n, u = u_n, u, u_nm
return u, x, t
# --- here function for return functions ---
# return func(x)
def func(x):
"""
:param x:
:return:
"""
return # expression
# start simulate and animate or visualisation and savin the data from file
def simulate(
I, V, f, a, L, C, T, U_0, U_L, dt, # params
umin, umax, # amplitude
animate = True, # animate or not?
solver_func = sol, # call the solver
mode = 'plotter', # mode: plotting the graphic or saving to file
):
# code for visualization and simulate
...........
# start simulate
solver_func(I, V, f, a, L, C, T, U_0, U_L, dt, user_func)
return 0
def task( ):
'''
test tasking for solver and my problem
:return:
'''
I
L = 1
a = 1
C = 0.85
T = 1
dt = 0.05
U_0, U_L, V, f
umax = 2
umin = -umax
simulate(I, V, f, a, L, C, T, U_0, U_L, dt, umax, umin, animate = True, solver_func = sol, mode = 'plotter',)
if __name__ == '__main__':
task()
And I get the same error:
File "C:\\LR2-rep\wave_eq_1d.py", line 102, in sol
u[i] = u_n[i] + dt * V(x[i]) + 0.5 * C2 * (0.5 * (q[i] + q[i + 1]) * (u_n[i + 1] - u_n[i]) -
TypeError: 'int' object is not subscriptable
I understand the meaning of the error, but I do not understand how it can be fixed, and for almost two weeks I have not been able to write a program ... I ask for help with solving this problem! Thank you very much in advance!

offset a parallel line to a given line python

I want to draw parallel line to given X,Y coordinate below code helps to draw ,
import numpy as np
import matplotlib.pyplot as plt
x = [187, 879, 722, 322]
y = [341, 344, 112, 112]
newX = []
newY = []
def findIntesection(p1x, p1y, p2x, p2y, p3x,p3y, p4x, p4y):
dx12 = p2x - p1x
dy12 = p2y - p1y
dx34 = p4x - p3x
dy34 = p4y - p3y
denominator = (dy12*dx34-dx12*dy34)
t1 = ((p1x - p3x) * dy34 + (p3y - p1y) * dx34)/ denominator
t2 = ((p3x - p1x) * dy12 + (p1y - p3y) * dx12)/ -denominator;
intersectX = p1x + dx12 * t1
intersectY = p1y + dy12 * t1
if (t1 < 0): t1 = 0
elif (t1 > 1): t1 = 1
if (t2 < 0): t2 = 0
elif (t2 > 1): t2 = 1
return intersectX,intersectY
def normalizeVec(x,y):
distance = np.sqrt(x*x+y*y)
return x/distance, y/distance
def getEnlarged(oldX, oldY, offset):
num_points = len(oldX)
for j in range(num_points):
i = j - 1
if i < 0:
i += num_points
k = (j + 1) % num_points
vec1X = oldX[j] - oldX[i]
vec1Y = oldY[j] - oldY[i]
v1normX, v1normY = normalizeVec(vec1X,vec1Y)
v1normX *= offset
v1normY *= offset
n1X = -v1normY
n1Y = v1normX
pij1X = oldX[i] + n1X
pij1Y = oldY[i] + n1Y
pij2X = oldX[j] + n1X
pij2Y = oldY[j] + n1Y
vec2X = oldX[k] - oldX[j]
vec2Y = oldY[k] - oldY[j]
v2normX, v2normY = normalizeVec(vec2X,vec2Y)
v2normX *= offset
v2normY *= offset
n2X = -v2normY
n2Y = v2normX
pjk1X = oldX[j] + n2X
pjk1Y = oldY[j] + n2Y
pjk2X = oldX[k] + n2X
pjk2Y = oldY[k] + n2Y
intersectX,intersetY = findIntesection(pij1X,pij1Y,pij2X,pij2Y,pjk1X,pjk1Y,pjk2X,pjk2Y)
#print(intersectX,intersetY)
newX.append(intersectX)
newY.append(intersetY)
getEnlarged(x, y, 20)
plt.plot(x, y)
plt.plot(newX, newY)
plt.show()
This gives result as below
Here it is giving good result by drawing parallel line to each line of our trapezoidal shaped , but i want it to be a closed shape in place of open shape
i want to join the 1st and last coordinate so that it should form a closed shape. Any help will be appreciated .

Using approach from here
outer_ccw parameters combines vertex order and desired offset direction. For CCW order and outer polygon it is 1, for inner polygon it should be -1.
def makeOffsetPoly(oldX, oldY, offset, outer_ccw = 1):
num_points = len(oldX)
for curr in range(num_points):
prev = (curr + num_points - 1) % num_points
next = (curr + 1) % num_points
vnX = oldX[next] - oldX[curr]
vnY = oldY[next] - oldY[curr]
vnnX, vnnY = normalizeVec(vnX,vnY)
nnnX = vnnY
nnnY = -vnnX
vpX = oldX[curr] - oldX[prev]
vpY = oldY[curr] - oldY[prev]
vpnX, vpnY = normalizeVec(vpX,vpY)
npnX = vpnY * outer_ccw
npnY = -vpnX * outer_ccw
bisX = (nnnX + npnX) * outer_ccw
bisY = (nnnY + npnY) * outer_ccw
bisnX, bisnY = normalizeVec(bisX, bisY)
bislen = offset / np.sqrt((1 + nnnX*npnX + nnnY*npnY)/2)
newX.append(oldX[curr] + bislen * bisnX)
newY.append(oldY[curr] + bislen * bisnY)
x = [0, 100, 60, 40]
y = [0, 0, 50, 50]
makeOffsetPoly(x, y, 20)
print(newX, newY)
>>>[-29.424478775259594, 129.4244787752596, 66.79706177729007, 33.202938222709925]
[-14.14213562373095, -14.14213562373095, 64.14213562373095, 64.14213562373095]

Just append the first coordinates to the end of your lists.
x.append(x[0])
y.append(y[0])
newX.append(newX[0])
newY.append(newY[0])
Place this right before you plot. Here's my output

How can I know the dimension of my variable?

I get this error :
ValueError: operands could not be broadcast together with shapes (365,) (2,)
But I'm surprised by this (2,).
How do I know which variable does this dimension (2,) please?
Because none of my variables should have it.
Thank you for your help !
Here, you can see the first script, where I define my function. It include a loop and also another function so I don't know if I can.
I have a lot of variable with (365, ) for the dimension because, it's function of the time, so for 365 days.
I have some fixed variable like the soil parameter, so the dimension for these is (1,)
But I don't know which variable get (2,) dimension ?
import pandas as pd
import numpy as np
def SA(MO = 0,
ETPr = 0,
SWSa = 0,
pb = 1.70 ):
DB = pd.read_excel("~/Documents/Spider/Data/data_base.xlsx", sheet_name = "DB")
DB1 = pd.read_excel("~/Documents/Spider/Bilan_Courgette.xlsx", sheet_name = "sol")
DB2 = pd.read_excel("~/Documents/Spider/Bilan_Courgette.xlsx", sheet_name = "culture")
#Calculs inter. pour déterminer ET0/day
#Array qui reprend "date" en une série 1 -> 365
JourDeLAnnee = pd.Series(range(1,366))
#Mauves
dist_TS = 1+(0.033*np.cos(0.0172 * JourDeLAnnee))
decli_So = 0.409*np.sin((0.0172 * JourDeLAnnee)-1.39)
lat = 0.87266463
ang_Hor_So =np.arccos(-np.tan(lat)*np.tan(decli_So))
gamma = 0.067
#Jaunes
delta = 2504*np.exp((17.27*DB.tsa_by_day)/(DB.tsa_by_day +237.3))/(DB.tsa_by_day +237.3)**2
rg = DB.ens_by_day / 1000000 * 86400
ra = 37.6 * dist_TS * ((ang_Hor_So * np.sin(lat) * np.sin(decli_So)) + \
(np.cos(lat) * np.cos(decli_So) * np.sin(ang_Hor_So)))
rso = (0.75 + (2*0.00001*120)) * ra
tw =(DB.tsa_by_day * np.arctan(0.151977 * ((DB.hra_by_day + 8.313659)**0.5))) + \
np.arctan(DB.tsa_by_day + DB.hra_by_day) - np.arctan(DB.hra_by_day - 1.676331) + \
(0.00391838 * ((DB.hra_by_day)**1.5) * np.arctan(0.023101 * DB.hra_by_day)) - 4.686035
ed = (0.611 * np.exp((17.27 * tw) / (tw + 237.3))) - (0.0008 *(DB.tsa_by_day-tw) * 101.325)
ea =((0.611 * np.exp((17.27*DB.tsa_max) / (DB.tsa_max + 237.3))) + \
(0.611 * np.exp((17.27 * DB.tsa_min) / (DB.tsa_min +237.3)))) / 2.0
rn = (0.77 * rg) - (((1.35 * (rg / rso)) - 0.35) \
* (0.34 - (0.14 * (ed**0.5))) * (4.9E-9) * ((((273+DB.tsa_max)**4)+((273+DB.tsa_min)**4))/2))
#Calcul de G
from typing import List
def get_g_constant(tsa_by_day: List[float], day: int):
assert day >= 1
return 0.38 * (tsa_by_day[day] - tsa_by_day[day-1])
def get_g_for_year(tsa_by_day: List[int]) -> List[float]:
g_list = []
for i in range(1, len(tsa_by_day)):
g_value = get_g_constant(tsa_by_day, i)
g_list.append(g_value)
return g_list
G = get_g_for_year(DB.tsa_by_day)
G = [DB.tsa_by_day[0]] + G
#Le fameux ET0
ET0 = ((0.408 * delta * (rn - G)) + (gamma * (900 /(DB.tsa_by_day + 273)) * DB.vtt_by_day * (ea - ed))) / \
(delta + (0.067*(1+(0.34 * DB.vtt_by_day))))
# Calcul des paramètres du sol
Profil = 500
pb = 100 / ((MO / 224000) + ((100-MO) / (1.64)))
Os = 0.6355+0.0013* DB1.A -0.1631* pb
Or = 0
lnα = (-4.3003) - (0.0097*DB1.A) + (0.0138* DB1.S ) - (0.0992*MO)
lnn = -1.0846-0.0236 * DB1.A -0.0085 * DB1.S +0.0001 * (DB1.S)**2
nn = np.exp(lnn) + 1
m = 1 - (1/nn)
lnK0 = 1.9582 + 0.0308*DB1.S - 0.6142* pb - 0.1566*MO
λ = -1.8642 - 0.1317*DB1.A + 0.0067*DB1.S
α = np.exp(lnα)
K0 = np.exp(lnK0)
θPf2 =(((1 + ((α*(10**2.5))**nn))**(-m))*( Os - Or)) + Or
θPf4 =(((1 + ((α*(10**4.2))**nn))**(-m))*( Os - Or)) + Or
SWS = θPf2 - θPf4
diff = SWS*SWSa
aj = diff / 2
θPf2New = θPf2 + aj
θPf4New = θPf4 - aj
#Calcul du volume de stock p à atteindre
p = 0.04 *(5 - ET0) + DB2.ptab[0]
θp =(1 - p) * ( θPf2New - θPf4New )+ θPf4New
Vp = θp * Profil
#Le fameux ETP
import datetime
DateS = datetime.datetime.strptime('30/03/2019','%d/%m/%Y').timetuple().tm_yday
DateR = datetime.datetime.strptime('15/09/2019','%d/%m/%Y').timetuple().tm_yday
ETP=ET0.copy()
for n in range(364):
if n >= (DateS - 1) and n <= (DateR - 1) :
ETP[n] = ET0[n] * DB2.Kc[0]
else:
ETP[n] = ET0[n] * DB2.SolNu[0]
ETP[0] = 0
ETPNew = ET0.copy()
ETPNew = ETP - ETP * ETPr
#Le Bilan Hydrique
Stock = ET0.copy()
θ = ET0.copy()
Drainage = ET0.copy()
Irrigation = ET0.copy()
Se = ET0.copy()
SeC = ET0.copy()
θ[0] = θPf2New
Stock[0] = θ[0]*Profil
for i in range(364) :
Se[i] = (θ[i] - Or)/( Os - Or)
if Se[i] > 1 :
SeC[i] = 1
else:
SeC[i] = Se[i]
Drainage[i] = K0 *(((SeC[i])**λ )*(1-(1- SeC[i]**(nn/(nn-1)))**m)**2)*10
if Vp[i] - Stock[i] > 0 : #Ici stock non défini
Irrigation[i] = Vp[i] - Stock[i]
else:
Irrigation[i] = 0
Stock[i+1] = Stock[i] + DB.plu_by_day[i] - ETPNew[i] - Drainage[i] + Irrigation[i]
θ[i+1] = Stock[i+1] / Profil
return (Irrigation.sum())
After, i use a second script to do a sensitivity analysis. And It's here, when I run this script, I get the error 'ValueError: operands could not be broadcast together with shapes (365,) (2,)'
import numpy as np
from SALib.analyze import sobol
from SALib.sample import saltelli
from test import*
import matplotlib.pyplot as plt
# Set up dictionary with system parameters
problem = {
'num_vars': 4,
'names': ['MO', 'ETPr', 'SWSa', 'K0'],
'bounds': [[0, 10],
[0, 0.04135],
[0, 0.2615],
[1.40, 1.70],
]}
# Array with n's to use
nsamples = np.arange(50, 400, 50)
# Arrays to store the index estimates
S1_estimates = np.zeros([problem['num_vars'],len(nsamples)])
ST_estimates = np.zeros([problem['num_vars'],len(nsamples)])
# Loop through all n values, create sample, evaluate model and estimate S1 & ST
for i in range(len(nsamples)):
print('n= '+ str(nsamples[i]))
# Generate samples
sampleset = saltelli.sample(problem, nsamples[i],calc_second_order=False)
# Run model for all samples
output = [SA(*sampleset[j,:]) for j in range(len(sampleset))]
# Perform analysis
results = sobol.analyze(problem, np.asarray(output), calc_second_order=False,print_to_console=False)
# Store estimates
ST_estimates[:,i]=results['ST']
S1_estimates[:,i]=results['S1']
np.save('ST_estimates.npy', ST_estimates)
np.save('S1_estimates.npy', S1_estimates)
S1_estimates = np.load('S1_estimates.npy')
ST_estimates = np.load('ST_estimates.npy')
# Generate figure showing evolution of indices
fig = plt.figure(figsize=(18,9))
ax1 = fig.add_subplot(1,2,1)
handles = []
for j in range(problem['num_vars']):
handles += ax1.plot(nsamples, S1_estimates[j,:], linewidth=5)
ax1.set_title('Evolution of S1 index estimates', fontsize=20)
ax1.set_ylabel('S1', fontsize=18)
ax1.set_xlabel('Number of samples (n)', fontsize=18)
ax1.tick_params(axis='both', which='major', labelsize=14)
ax2 = fig.add_subplot(1,2,2)
for j in range(problem['num_vars']):
ax2.plot(nsamples, ST_estimates[j,:], linewidth=5)
ax2.set_title('Evolution of ST index estimates', fontsize=20)
ax2.set_ylabel('ST', fontsize=18)
ax2.tick_params(axis='both', which='major', labelsize=14)
ax2.set_xlabel('Number of samples (n)', fontsize=18)
fig.legend(handles, problem['names'], loc = 'right', fontsize=11)
plt.savefig('indexevolution.png')
# Calculate parameter rankings
S1_ranks = np.zeros_like(S1_estimates)
ST_ranks = np.zeros_like(ST_estimates)
for i in range(len(nsamples)):
orderS1 = np.argsort(S1_estimates[:,i])
orderST = np.argsort(ST_estimates[:,i])
S1_ranks[:,i] = orderS1.argsort()
ST_ranks[:,i] = orderST.argsort()
Thank you for your help !

matplotlib line that follows point

How to make line that follows points. Shadow of move.
Something like that: https://www.youtube.com/watch?v=pEjZd-AvPco
Pastebin with code: https://pastebin.com/AkHaEM4i
Everything is in the link, so I can't add some more details. Gonna paste lorem ipsum...
It looks like your post is mostly code; please add some more details.
class DoublePendulum:
def __init__(self,
init_state = [120,0,-20,0],
L1 = .5,
L2 = .5,
M1 = 1.0,
M2 = 2.0,
G = 9.8,
origin=(0,0)):
self.init_state = np.asarray(init_state,dtype='float')
self.params = (L1,L2,M1,M2,G)
self.origin = origin
self.time_elapsed = 0
self.state = self.init_state * np.pi/180
def position(self):
(L1, L2, M1, M2, G) = self.params
x = np.cumsum([self.origin[0],
L1 * sin(self.state[0]),
L2 * sin(self.state[2])])
y = np.cumsum([self.origin[1],
-L1 * cos(self.state[0]),
-L2 * cos(self.state[2])])
return (-x,-y)
def dstate_dt(self,state,t):
(M1,M2,L1,L2,G)=self.params
dydx = np.zeros_like(state)
dydx[0] = state[1]
dydx[2] = state[3]
cos_delta = cos(state[2] - state[0])
sin_delta = sin(state[2] - state[0])
den1 = (M1 + M2) * L1 - M2 * L1 * cos_delta * cos_delta
dydx[1] = (M2 * L1 * state[1] * state[1] * sin_delta * cos_delta
+ M2 * G * sin(state[2]) * cos_delta
+ M2 * L2 * state[3] * state[3] * sin_delta
- (M1+M2) * G * sin(state[0])) / den1
den2 = (L2 / L1) * den1
dydx[3] = (-M2 * L2 * state[3] * state[3] * sin_delta * cos_delta
+ (M1 + M2) * G * sin(state[0]) * cos_delta
- (M1 + M2) * L1 * state[1] * state[1] * sin_delta
- (M1 + M2) * G * sin(state[2])) / den2
return dydx
def step(self,dt):
self.state = integrate.odeint(self.dstate_dt, self.state, [0,dt])[1]
self.time_elapsed += dt
pendulum = DoublePendulum([120.,0.0,180.,0.0],.5,.5,10,10,10)
dt = 1./30 #fps
fig = plt.figure(1)
lim1,lim2 = 2,-2
ax = fig.add_subplot(111,aspect='equal', autoscale_on=False,
xlim=(lim1,lim2),ylim=(lim1,lim2),alpha=0.5)
ax.grid()
line, = ax.plot([],[],'o-',lw=2)
time_text = ax.text(0.02,0.95,'', transform=ax.transAxes)
def init():
line.set_data([],[])
time_text.set_text('')
return line, time_text
def animate(i):
global pendulum, dt
pendulum.step(dt)
line.set_data(*pendulum.position())
time_text.set_text('time = %.1f' % pendulum.time_elapsed)
return line, time_text
from time import time
t0 = time()
animate(0)
t1 = time()
interval = 100 * dt - (t1-t0)
ani = animation.FuncAnimation(fig,animate,frames=150,
interval=interval, blit=True, init_func=init)
fig.set_size_inches(6.5, 6.5)
plt.show()

I think the referred youtube-video uses code very similiar to the code I published here:
https://github.com/jonas37/double_pendulum/

ray caster, cast_ray function incorrectly accounts for obscured light

I am getting an error that says I am not accounting for obscured light and that my specular is getting added when the light is obscured. This is what the specular part that is being added onto is with x representing r, g, orb of my Color class: light.color.x * s.finish.specular * specIntense
def in_shadow (sphere_list, sphere, ray_to_light, light):
new_list = list()
for s in sphere_list:
if sphere != s:
new_list.append(s)
for s in new_list:
if sphere_intersection_point(ray_to_light, s):
x1 = ray_to_light.pt.x - light.pt.x
y1 = ray_to_light.pt.y - light.pt.y
z1 = ray_to_light.pt.z - light.pt.z
dist1 = math.sqrt(x1 + y1 + z1)
x2 = ray_to_light.pt.x - s.center.x
y2 = ray_to_light.pt.y - s.center.y
z2 = ray_to_light.pt.z - s.center.z
dist2 = math.sqrt(x2 + y2 + z2)
# distance to light, distance to sphere
# check if distance to sphere < distance to light
# if so return 0
if dist2 < dist1:
return 0
return 1
def cast_ray(ray, sphere_list, color, light, point):
# count = 0
dist = -1
cp = Color(1.0, 1.0, 1.0)
for s in sphere_list:
if sphere_intersection_point(ray, s):
# count += 1
p = sphere_intersection_point(ray, s)
vec = vector_from_to(s.center, p)
N = normalize_vector(vec)
norm_scaled = scale_vector(N, 0.01)
pe = translate_point(p, norm_scaled)
l = vector_from_to(pe, light.pt)
l_dir = normalize_vector(l)
dot = dot_vector(N, l_dir)
r = Ray(pe, l_dir)
dotNScaled = dot * 2
reflecVec = difference_vector(l_dir, scale_vector(N, dotNScaled))
V = vector_from_to(point, pe)
Vdir = normalize_vector(V)
spec = dot_vector(reflecVec, Vdir)
m = in_shadow(sphere_list, s, r, light)
if (dot <= 0):
m = 0
x = (ray.pt.x - p.x) ** 2
y = (ray.pt.y - p.y) ** 2
z = (ray.pt.z - p.z) ** 2
curdist = math.sqrt(x + y + z)
# print curdist
if (dist < 0) or (dist > curdist):
dist = curdist
if (spec <= 0 ):
r = ( s.color.r * s.finish.ambient * color.r ) \
+ ( light.color.r * s.finish.diffuse * dot * s.color.r * m )
g = ( s.color.g * s.finish.ambient * color.g ) \
+ (light.color.g * s.finish.diffuse * dot * s.color.g * m )
b = ( s.color.b * s.finish.ambient * color.b ) \
+ (light.color.b * s.finish.diffuse * dot * s.color.b * m )
cp = Color(r, g, b)
if ( spec >= 0 ):
specIntense = spec ** (1/s.finish.roughness)
print type(s.finish.diffuse)
r = (s.color.r * s.finish.ambient * color.r) \
+ (light.color.r * s.finish.diffuse * dot * s.color.r * m) \
+ (light.color.r * s.finish.specular * specIntense)
g = (s.color.g * s.finish.ambient * color.g) \
+ (light.color.g * s.finish.diffuse * dot * s.color.g * m) \
+ (light.color.g * s.finish.specular * specIntense)
b = (s.color.b * s.finish.ambient * color.b) \
+ (light.color.b * s.finish.diffuse * dot * s.color.b * m) \
+ (light.color.b * s.finish.specular * specIntense)
cp = Color(r, g, b)
# if count > 1:
# print 'intersects two!'
return cp
I think somewhere I am not accounting for the case where the sphere has another one in front of it therefore the specular part is being added to it when it shouldn't, creating this weird white light behind the first sphere that isn't supposed to be there. I'm sure there is a bug in this code somewhere but I cannot find it.