Develop Reference

How to make a progressive line graph with each timestep?

import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import numpy as np
import random
MAXROWS = 20
MAXCOLS = 20
def flipCoords(row, col, limits):
xpos = col
ypos = row
return (xpos, ypos)
def plot_runner_scatter(runners, limits):
xlist = []
ylist = []
slist = []
clist = []
mlist = []
for k in range(len(runners)):
ylist.append(runners[k].getRow())
xlist.append(runners[k].getCol())
slist.append(80)
clist.append(k)
plt.scatter(xlist,ylist,s=slist,c=clist,marker=6,edgecolors='white')
def main():
runnerNames = ["1","2","3","4","5"]
limits = [MAXROWS, MAXCOLS]
light = True
numRunners = 5
runnerList = []
for j in range(numRunners):
runnerList.append(Runner(limits, runnerNames[j]))
runnerList[j].row = 0
runnerList[j].col = (MAXCOLS/numRunners)/2 + j*(MAXCOLS/numRunners)
for t in range(5):
ax = plt.axes()
print("### Timestep ", t, "###")
for i in range(8):
xlist = [2,7,12,17,4,10,16,10]
ylist = [2,2,2,2,9,9,9,15]
rect1width = 2
rect1height = 2
rect = Rectangle((xlist[i],ylist[i]), rect1width, rect1height)
rect.set_color('k')
rect.zorder = 0
ax.add_patch(rect)
if light:
plt.title("RUN! at timestep "+ str(t) +" first one wins")
ax.set_facecolor("orange")
else:
plt.title("Beware! lights are off at timestep "+ str(t) +" watch your step")
ax.set_facecolor("gray")
for i in range(numRunners):
if light and not runnerList[i].felloff:
runnerList[i].run()
for j in range(8):
if runnerList[i].row == (ylist[j]+1) and ((xlist[j]) < runnerList[i].col < (xlist[j] + rect1width)):
runnerList[i].row -= 1
runnerList[i].col += 1
print(runnerList[i].name, "survives at row", runnerList[i].row, "and col", runnerList[i].col)
elif not light and not runnerList[i].felloff:
runnerList[i].blinded()
for j in range(8):
if runnerList[i].row == (ylist[j]+1) and ((xlist[j]) < runnerList[i].col < (xlist[j] + rect1width)):
runnerList[i].felloff = True
runnerList[i].getFallen()
if runnerList[i].row < 0 or runnerList[i].col < 0 or runnerList[i].col > MAXCOLS:
runnerList[i].felloff = True
runnerList[i].getFallen()
if not runnerList[i].felloff:
print(runnerList[i].name, "survives at row", runnerList[i].row, "and col", runnerList[i].col)
else:
print(runnerList[i].name, "fell off the platform at row", runnerList[i].row, "and col", runnerList[i].col)
if random.randint(1,4) == 1:
light = not light
for i in range(numRunners):
plot_runner_scatter(runnerList, limits)
plt.xlabel("Columns")
plt.ylabel("Rows")
for i in range(numRunners):
plt.annotate(runnerList[i].name, flipCoords(runnerList[i].getRow(), runnerList[i].getCol(), limits), color = "white")
plt.xlim(-1,MAXCOLS)
plt.ylim(-1,MAXROWS)
plt.pause(1)
plt.clf()
for i in range(numRunners):
if runnerList[i].row == 20:
print("Runner", runnerList[i].name, "WINS THE RACE!")
break
if __name__ == "__main__":
main()
This is a simulation where there are runners in a platform and they have to race to the end. I want to have a line graph with 4 lines where on the y axis is the row of each runner and x axis is the time. So as the simulation goes on the row of each runner is updated with time. How can I achieve this?

Populating an Array in a loop not working

I am dumbfounded right now, I have some code that works generating an array of data and operating on it.
I am trying to sample random sections from my code, in order to check the calculations I am doing.
I have done this before and it has worked fine. I
target_sample =[1,2,10,25,83,62]
df, s_array_track ,z_array_track = MonteCarloValuationAntithetic(df,target_sample)
#df,z,s_array,lookback_scenario = MonteCarloValuation(df)
target_sample =[1,2,10,25,83,62]
lookback = []
for i in range(n_samples):
s = df["current_index"][i]
s_max = df["max_index"][i]
t = df["time to maturity_Months"][i]
sigma = df["volatility"][i]
cap = df["cap_rate"][i]
r = df["interest_rate"][i]
z = np.zeros((int(index_crediting_term*12)+1,n_scenarios))
s_array_track=np.zeros((len(target_sample),int(index_crediting_term*12)+1,n_scenarios))
z_array_track = np.zeros((len(target_sample),int(index_crediting_term*12)+1,n_scenarios))
df_track = df
s_start = df['initial_index'][i]
s_array = np.zeros((int(index_crediting_term*12)+1,n_scenarios))
for k in range(int(n_scenarios/2)):
for j in range(int(t)+1):
drift =( r - .5 *(sigma**2)) * (1/12)
z[j][k] = np.random.normal(0, 1)
diffusion = sigma* z[j][k] * (np.sqrt(1/12))
if j == 0:
s_array[j][k] = s
if (0 < j) and (j < t):
s_array[j][k] = s_array[j-1][k]*np.exp(drift + diffusion)
if j==t:
s_array[j][k] = s_max
else:
continue
for k in range(int(n_scenarios/2),int(n_scenarios)):
for j in range(int(t)+1):
drift =( r - .5 *(sigma**2)) * (1/12)
z[j][k] = -z[j][int(k-n_scenarios/2)]
diffusion = sigma* z[j][k] * (np.sqrt(1/12))
if j == 0:
s_array[j][k] = s
if (0 < j) and (j < t):
s_array[j][k] = s_array[j-1][k]*np.exp(drift + diffusion)
if j == t:
s_array[j][k] = s_max
else:
continue
if i in target_sample:
print(str(i) + " is in Target")
h = target_sample.index(i)
print(str(h))
s_array_track[h] = s_array
z_array_track[h] = z
lookback_temp = max(0,np.mean(np.clip(np.max(((s_array[:][:] / s_start)-1) ,axis =0 ),None,cap))))
lookback.append(lookback_temp)
df["Lookback"] = lookback
I am not getting the results I am expecting. When I do
s_array_track[h] = s_array
Outside of the code it works as expected. What is going on in my loop? I have spent hours on this and I am really confused as to why its not working.

plt.matshow().set_data() is not updating the AxesImage

Here's a program that I am trying to run for Potts Model. It gives the plot once and then keeps repeating the line <Figure size 432x288 with 0 Axes>. What's the solution to get various plots after certain time steps showing the evolution of the system?
import math
import numpy as np
import matplotlib.pyplot as plt
def largest_primes_under(N):
n = N - 1
while n >= 2:
if all(n % d for d in range(2, int(n ** 0.5 + 1))):
return n
n -= 1
def Neighbors(Lattice,i,j,n=1):
''' Returns an flat array of all neighboring sites in the n-th coordination sphere including the center'''
N, M = Lattice.shape
rows = [(i-1) % N, i, (i+1) % N]
cols = [(j-1) % N, j, (j+1) % M]
return Lattice[rows][:, cols].flatten()
def calc_dE(Lattice, x, y, z):
N, M = Lattice.shape
old_energy = 0
new_energy = 0
for i in [0,1,-1]:
for j in [0,1,-1]:
if i == 0 and j == 0:
continue
if Lattice[x%N,y%M] == Lattice[(x+i)%N,(y+j)%M]:
old_energy += 1
elif z == Lattice[(x+i)%N,(y+j)%M]:
new_energy += 1
return old_energy-new_energy
N, M = 100,100
orientations = 3
MCS = int(10)
a = largest_primes_under(N*M)
L = np.random.randint(1,orientations+1,size=(N,M))
mat = plt.matshow(L,cmap = plt.get_cmap('plasma', orientations+1), vmin = -0.5, vmax = orientations+0.5, interpolation='kaiser')
plt.axis('off')
for t in range(1,MCS+1):
rand = np.random.random_integers(N*M)
for i in range(0,N**2):
index = (a*i + rand) % (N**2)
x = index % N
y = index // N
n = Neighbors(L,x,y)
if len(n)-1 == 0:
continue
else:
z = np.random.choice(n)
dE = calc_dE(L,x,y,z)
if (dE < 0):
L[x%N,y%N] = z
elif np.random.sample() < math.exp(-dE*2.5):
L[x%N,y%N] = z
mat.set_data(L)
plt.draw()
plt.pause(0.1)

mat.set_data(L) is not updating the data
In the for-loop, replace mat.set_data(L) with:
mat = plt.matshow(L, cmap = plt.get_cmap('plasma', orientations+1), vmin = -0.5, vmax = orientations+0.5, interpolation='kaiser')
The plots successfully showed up when I tested the code with the change.
Also np.random.random_integers(N*M) is deprecated in numpy v1.20.1. In the code below, np.random.randint(N*M) is used, but this change isn't related to the question in the OP.
for t in range(1, MCS+1):
rand = np.random.randint(N*M)
for i in range(0, N**2):
index = (a*i + rand) % (N**2)
x = index % N
y = index // N
n = Neighbors(L, x, y)
if len(n)-1 == 0:
continue
else:
z = np.random.choice(n)
dE = calc_dE(L, x, y, z)
if (dE < 0):
L[x%N, y%N] = z
elif np.random.sample() < math.exp(-dE*2.5):
L[x%N, y%N] = z
mat = plt.matshow(L, cmap = plt.get_cmap('plasma', orientations+1), vmin = -0.5, vmax = orientations+0.5, interpolation='kaiser')
# mat.set_data(L)
plt.draw()
plt.pause(0.1)
Alternative
In this case it might be more interesting to animate the progression
Implemented with Animate quadratic grid changes (matshow)
In the following code, save_count=MCS takes the place of the original outer loop for t in range(1, MCS+1), where t was just a throwaway variable.
import matplotlib.animation as animation
def generate_data():
rand = np.random.randint(N*M)
for i in range(0, N**2):
index = (a*i + rand) % (N**2)
x = index % N
y = index // N
n = Neighbors(L, x, y)
if len(n)-1 == 0:
continue
else:
z = np.random.choice(n)
dE = calc_dE(L, x, y, z)
if (dE < 0):
L[x%N, y%N] = z
elif np.random.sample() < math.exp(-dE*2.5):
L[x%N, y%N] = z
return L
def update(data):
mat.set_data(data)
return mat
def data_gen():
while True:
yield generate_data()
N, M = 100, 100
orientations = 3
MCS = 10
a = largest_primes_under(N*M)
L = np.random.randint(1, orientations+1, size=(N, M))
fig, ax = plt.subplots()
mat = ax.matshow(generate_data(), cmap=plt.get_cmap('plasma', orientations+1), vmin=-0.5, vmax=orientations+0.5, interpolation='kaiser')
plt.colorbar(mat)
ani = animation.FuncAnimation(fig, update, data_gen, interval=500, save_count=MCS)
plt.show()
ani.save('animation.gif')

Using matplotlib.pyplot to make the animation of the 1D wave equation

I have been using matplotlib from python to show the animation of 1D wave equation.But I got a problem of making the animation.I want the image of the wave to change with time.It means that I may need a loop to form many different pictures of the wave equation.But it seems that the time cannot be put into the wave functions ,so the images do not change at all.Please help me with the mistake that I made.
Here are the codes that I wrote:(Part of the codes comes from the book "Python Scripting for Computational Science")
from numpy import zeros,linspace,sin,pi
import matplotlib.pyplot as mpl
def I(x):
return sin(2*x*pi/L)
def f(x,t):
return sin(x*t)
def solver0(I,f,c,L,n,dt,tstop):
# f is a function of x and t, I is a function of x
x = linspace(0,L,n+1)
dx = L/float(n)
if dt <= 0:
dt = dx/float(c)
C2 = (c*dt/dx)**2
dt2 = dt*dt
up = zeros(n+1)
u = up.copy()
um = up.copy()
t = 0.0
for i in range(0,n):
u[i] = I(x[i])
for i in range(1,n-1):
um[i] = u[i]+0.5*C2*(u[i-1] - 2*u[i] + u[i+1]) + dt2*f(x[i],t)
um[0] = 0
um[n] = 0
while t <= tstop:
t_old = t
t += dt
#update all inner points:
for i in range(1,n-1):
up[i] = -um[i] + 2*u[i] + C2*(u[i-1] - 2*u[i] + u[i+1]) + dt2*f(x[i],t_old)
#insert boundary conditions:
up[0] = 0
up[n] = 0
#update data structures for next step
um = u.copy()
u = up.copy()
return u
c = 3.0 #given by myself
L = 10
n = 100
dt = 0
tstart = 0
tstop = 6
x = linspace(0,L,n+1)
t_values = linspace(tstart,tstop,31)
mpl.ion()
y = solver0(I, f, c, L, n, dt, tstop)
lines = mpl.plot(x,y)
mpl.axis([x[0], x[-1], -1.0, 1.0])
mpl.xlabel('x')
mpl.ylabel('y')
counter = 0
for t in t_values:
y = solver0(I,f,c,L,n,dt,tstop)
lines[0].set_ydata(y)
mpl.draw()
mpl.legend(['t=%4.1f' % t])
mpl.savefig('sea_%04d.png' %counter)
counter += 1

Maybe that's what you need?
y = solver0(I,f,c,L,n,dt,t)

Python Scatter plot

Got this question from 'how to think like a computer scientist' course:
Interpret the data file labdata.txt such that each line contains a an x,y coordinate pair. Write a function called plotRegression that reads the data from this file and uses a turtle to plot those points and a best fit line according to the following formulas:
y=y¯+m(x−x¯)
m=∑xiyi−nx¯y¯∑x2i−nx¯2
http://interactivepython.org/runestone/static/thinkcspy/Files/Exercises.html?lastPosition=1308
my code doesnt seem to be working and i cant figure out why. it looks like python is interpreting the data as str as opposed to float.
def plotregression(t):
labfile = open('labdata.txt','r')
sumx = 0
sumy = 0
count = 0
sumprod = 0
sumsqrx =0
sumsqrnx = 0
for i in labfile:
points = i.split()
print (points)
t.up()
t.setpos(points[0],points[1])
t.stamp()
sumx = sumx + int(points[0])
sumy = sumy + int(points[1])
prod = points[0]*int(points[1])
sumprod = sumprod + prod
count += 1
sqrx = int(points[0])**2
sumsqrx = sumsqrx + sqrx
sqrnx = int(points[0])**(-2)
sumsqrnx = sumsqrnx + sqrnx
avgx = sumx/count
avgy = sumy/count
m = (sumprod - count(avgx*avgy))/sumsqrx- (count(avgx**2))
print(m)
for bestline in labfile:
line = bestline.split()
y= avgy + m(int(line[0])-avgx)
t.down()
t.setpos(0,0)
t.setpos(line[0],y)
plotregression(kj)
Appreciate your help.
Thnx

I actually worked out the problem myself and it finally seems to be doing what i'm telling it to. But i would love to know if i can cut out any unnecessary lines of code. I'm thinking its a bit too long and i'm missing out something which would make this more simpler to do.
import turtle
wn= turtle.Screen()
kj = turtle.Turtle()
kj.shape('circle')
kj.turtlesize(0.2)
kj.color('blue')
kj.speed(1)
def plotregression(t):
sumx = 0
sumy = 0
count = 0
sumprod = 0
sumsqrx =0
labfile = open('labdata.txt','r')
for i in labfile:
points = i.split()
print (points)
t.up()
t.setpos(int(points[0]),int(points[1]))
t.stamp()
sumx = sumx + int(points[0])
sumy = sumy + int(points[1])
prod = int(points[0])*int(points[1])
sumprod = sumprod + prod
count += 1
sqrx = int(points[0])**2
sumsqrx = sumsqrx + sqrx
avgx = sumx/count
avgy = sumy/count
m = (sumprod - count*(avgx*avgy))/(sumsqrx- (count*(avgx**2)))
print('M is: ',m )
labfile.close()
labfile = open('labdata.txt','r')
besttfit = open('bestfit.txt','w')
for bestline in labfile:
line = bestline.split()
y = avgy + m*(int(line[0])-avgx)
print('y is:' ,y)
besttfit.write((line[0])+'\t'+str(y)+'\n')
labfile.close()
besttfit.close()
bestfitline = open('bestfit.txt','r')
for regline in bestfitline:
reg = regline.split()
t.goto(float(reg[0]),float(reg[1]))
t.down()
t.write('Best fit line')
bestfitline.close()
wn.setworldcoordinates(-10,-10,120,120)
figure = plotregression(kj)
wn.exitonclick()
please let me know if i can cut down anywhere

I was solving the same problem form the interactive python. Here is how I did it.
import turtle
def plotRegression(data):
win = turtle.Screen()
win.bgcolor('pink')
t = turtle.Turtle()
t.shape('circle')
t.turtlesize(0.2)
x_list, y_list = [i[0] for i in plot_data], [i[1] for i in plot_data]
x_list, y_list = [float(i) for i in x_list], [float(i) for i in y_list]
x_sum, y_sum = sum(x_list), sum(y_list)
x_bar, y_bar = x_sum / len(x_list), y_sum / len(y_list)
x_list_square = [i ** 2 for i in x_list]
x_list_square_sum = sum(x_list_square)
xy_list = [x_list[i] * y_list[i] for i in range(len(x_list))]
xy_list_sum = sum(xy_list)
m = (xy_list_sum - len(x_list) * x_bar * y_bar) / (x_list_square_sum - len(x_list) * x_bar ** 2)
# best y
y_best = [ (y_bar + m * (x_list[i] - x_bar)) for i in range( len(x_list) ) ]
# plot points
max_x = max(x_list)
max_y = max(y_list)
win.setworldcoordinates(0, 0, max_x, max_y)
for i in range(len(x_list)):
t.penup()
t.setposition(x_list[i], y_list[i])
t.stamp()
#plot best y
t.penup()
t.setposition(0,0)
t.color('blue')
for i in range(len(x_list)):
t.setposition(x_list[i],y_best[i])
t.pendown()
win.exitonclick()
with open('files/labdata.txt', 'r') as f:
plot_data = [aline.split() for aline in f]
plotRegression(plot_data)

I am about 5 years too late but here is my two cents.
The problem might be in the line:
t.setpos(points[0],points[1])
This is telling the turtle to go to the string value of the points[0] and points[1].
For example, if points[0] stores the value of "50" and points[1] holds the value "60" then "50" + "60" would be return the string "5060"
This line might have problems as well:
prod = points[0]*int(points[1])
This is adding the string value in points[0] to the integer value in points[1]
In this case, using the previous values points[0] would be "50" and int(points[1]) would be 60. That is 60 and not "60". So you cant add the string "50" with the integer 60.
Here is how I worked out the problem:
import turtle
import math
import statistics as stats
def get_line(means, slope, xlist):
"""Return a list of best y values."""
line = [(means[1] + slope * (xlist[x] + means[0]))
for x in range(len(xlist))]
return line
def get_mtop(xlist, ylist, n, means):
"""Return top half of m expression."""
xbyy_list = [xlist[x] * ylist[x] for x in range(len(xlist))]
xbyy_sum = sum(xbyy_list)
nby_means = n * (means[0] * means[1])
top = xbyy_sum - nby_means
return top
def get_mbot(xlist, n, means):
"""Return bottom half of m expression."""
sqr_comprehension = [x**2 for x in xlist]
sqr_sum = sum(sqr_comprehension)
nbymean_sqr = n * means[0]**2
bot = sqr_sum - nbymean_sqr
return bot
def get_mean(xlist, ylist):
"""Return a tuple that contains the means of xlist and ylist
in form of (xmean,ymean)."""
xmean = stats.mean(xlist)
ymean = stats.mean(ylist)
return xmean, ymean
def plotRegression(input_file, input_turtle):
"""Draw the plot regression.""""
infile = open(input_file, 'r')
input_turtle.shape("circle")
input_turtle.penup()
# Get a list of xcoor and a list of ycoor
xcoor = []
ycoor = []
for line in infile:
coor = line.split()
xcoor.append(int(coor[0]))
ycoor.append(int(coor[1]))
# Plot and count the points
num_points = 0
for count in range(len(xcoor)):
input_turtle.goto(xcoor[count], ycoor[count])
input_turtle.stamp()
num_points += 1
# Get the mean values of the xcoor and ycoor lists
means_tup = get_mean(xcoor, ycoor)
print(means_tup)
# Get the value for M
mtop = get_mtop(xcoor, ycoor, num_points, means_tup)
mbot = get_mbot(xcoor, num_points, means_tup)
m = mtop / mbot
print(m)
# Draw the line
yline = get_line(means_tup, m, xcoor)
input_turtle.color("green")
input_turtle.goto(xcoor[0], yline[0])
input_turtle.pendown()
for x in range(len(xcoor)):
print(xcoor[x], yline[x])
input_turtle.goto(xcoor[x], yline[x])
input_turtle.hideturtle()
def main():
"""Create the canvas and the turtle. Call the function(s)"""
# Set up the screen
sc = turtle.Screen()
sc.setworldcoordinates(0, 0, 100, 100)
sc.bgcolor("black")
# Create the turtle
Donatello = turtle.Turtle()
Donatello.color("purple")
# Run plot Regression
labdata = """C:\\Users\\user\\pathtofile\\labdata.txt"""
plotRegression(labdata, Donatello)
sc.exitonclick()
if __name__ == "__main__":
main()
I don't know if this is the correct slope but it seems to be in the right direction. Hopefully this helps some one who has the same problem.