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zooming into mandelbrot set does not work as planned

I changed some things but i still have a similar problem. I am working on Mandelbrot zoom. I try to zoom in deeper at branches. I count the consecutive points in the set and return the branch if it reaches the defined length. Then I zoom into that area and repeat. But the change gets smaller and the returned branch is nearly the same as the last one.
variables
X0,Y0,X1 = verticies of current area
branch = current branch
n = number of iterations
p = how many times i want to zoom
b = minimum length of branch i am looking for
c = current complex number
z = current value
k = pixel size
the function that returns the branch
def fractal(n, X0, Y0, X1): # searching for new branch
branch = []
k = (X1 - X0) / x_axis # pixel size
b = 5 # the number of black points int the set i am looking for
for y in range(y_axis):
try:
for x in range(x_axis):
c = X0 + k * x + Y0 - k * y * 1.j # new c
z = c
for m in range(n):
if abs(z) <= 2:
z = z * z + c # new z
else:
break
if abs(z) <= 2:
branch.append((x,y)) # the coordinates for those points
else:
if b < len(branch) < 50:
raise BreakOutOfALoop # break if a branch was found
branch = []
except BreakOutOfALoop:
break
return branch, k
the function that calculates the verticies of the new area
def new_area(branch, X0, Y0, X1, k): # calculating new area
print(branch)
X0 = X0 + k * branch[0][0]
Y0 = Y0 - k * branch[0][1]
X1 = X1 + k * branch[-1][0] # new area verticies
return X0, Y0, X1
the loop that calls the functions
for i in range(p):
areaImage = Image.new('RGB', (x_axis,y_axis), "white")
area_pixels = areaImage.load() # image load
branch, k = fractal(n,X0, Y0, X1)
X0, Y0, X1 = new_area(branch, X0, Y0, X1, k)
file_name = "/" + str(i) + "_" + str(n) + "_" + str(x_axis) + "_" + str(y_axis) + ".png" # image save
areaImage = areaImage.save(f"{areaImage_path}{file_name}")
What am I overlooking?
Here are some pictures for different p.
p = 3
p = 10

Python: got an output image with unexpected grid lines

I am writing a function that scales the input image into times of
its input size. The function Resize(Mat I, float s) first fills in the and Mat’s
that contained the query point coordinates. Then I calculate the query value by
using bilinear interpolation.
The output image seems to be alright except it has an unexpected # shape grid on it. Can you provide any hint for the resolution?
Output image:
Code:
import numpy as np
import cv2 as cv
import math
import matplotlib.pyplot as plt
#Mat I, float s
def Resize(I, s):
orig_x = I.shape[0];
orig_y = I.shape[1];
tar_x = int (orig_x * s) #int tar_x and tar_y
tar_y = int (orig_y * s);
#print(tar_x)
# Query points
X = np.empty((tar_y, tar_x), np.float32)
Y = np.empty((tar_y, tar_x), np.float32)
# calc interval between output points
interval = (orig_x-1) / (tar_x-1)
# Setting the query points
for i in range(0, tar_y):
for j in range(0, tar_x):
#set X[i, j] and Y[i,j]
X[i][j] = j * interval
Y[i][j] = i * interval
# Output image
output = np.empty((tar_y, tar_x), np.uint8)
# Performing the interpolation
for i in range(0, tar_y):
for j in range(0, tar_x):
#set output[i,j] using X[i, j] and Y[i,j]
x = X[i][j]
y = Y[i][j]
x1 = math.floor(x)
x2 = math.ceil(x)
y1 = math.floor(y)
y2 = math.ceil(y)
vq1= (x-x1)*I[y1,x2] + (x2-x)*I[y1,x1]
vq2= (x-x1)*I[y2,x2] + (x2-x)*I[y2,x1]
output[i,j] = (y-y1)*vq2 + (y2-y)*vq1
return output
s= 640 / 256
I = cv.imread("aerial_256.png", cv.IMREAD_GRAYSCALE)
output = Resize(I,s)
output = cv.cvtColor(output, cv.COLOR_BGR2RGB)
plt.imshow(output)
plt.savefig("aerial_640.png",bbox_inches='tight',transparent=True, pad_inches=0)
plt.show()

You are getting a black pixel where x is an integer and where y is an integer.
Take a look at the following code:
x1 = math.floor(x)
x2 = math.ceil(x)
vq1= (x-x1)*I[y1,x2] + (x2-x)*I[y1,x1]
vq2= (x-x1)*I[y2,x2] + (x2-x)*I[y2,x1]
Assume: x = 85.0
x1 = floor(x) = 85
x2 = ceil(x) = 85
(x-x1) = (85-85) = 0
(x2-x) = (85-85) = 0
vq1 = (x-x1)*I[y1,x2] + (x2-x)*I[y1,x1] = 0*I[y1,x2] + 0*I[y1,x1] = 0
vq2 = (x-x1)*I[y2,x2] + (x2-x)*I[y2,x1] = 0*I[y2,x2] + 0*I[y2,x1] = 0
output[i,j] = (y-y1)*vq2 + (y2-y)*vq1 = (y-y1)*0 + (y2-y)*0 = 0
Result:
In the entire column where x = 85.0 the value of output[i,j] is zero (we are getting a black column).
Same result applied to y = 85.0 - we are getting a black row.
When does x value is an integer?
Take a look at the following code:
# calc interval between output points
interval = (orig_x-1) / (tar_x-1)
# Setting the query points
for i in range(0, tar_y):
for j in range(0, tar_x):
#set X[i, j] and Y[i,j]
X[i][j] = j * interval
interval = (orig_x-1) / (tar_x-1) = 255/639 = (3*5*17/(3*3*71) = 85/213
j * interval = j * 85/213
Each time j is a multiple of 213, j * interval is an integer (we are getting a black column).
It happens when j=0, j=213, j=426, j=639, so there are two black columns (beside margins).
There are also two visible black rows (beside margins).
Suggested solution:
Replace x2 = math.ceil(x) with x2 = min(x1 + 1, orig_x-1).
Replace y2 = math.ceil(y) with y2 = min(y1 + 1, orig_y-1).
Corrected loop:
for i in range(0, tar_y):
for j in range(0, tar_x):
#set output[i,j] using X[i, j] and Y[i,j]
x = X[i][j]
y = Y[i][j]
x1 = math.floor(x)
x2 = min(x1 + 1, orig_x-1)
y1 = math.floor(y)
y2 = min(y1 + 1, orig_y-1)
vq1= (x-x1)*I[y1,x2] + (x2-x)*I[y1,x1]
vq2= (x-x1)*I[y2,x2] + (x2-x)*I[y2,x1]
output[i,j] = (y-y1)*vq2 + (y2-y)*vq1
Result:

Monte Carlo simulation of a system of Lennard-Jones + FENE potential

I want to generate two linear chains of 20 monomers each at some distance to each other. The following code generates a single chain. Could someone help me with how to generate the second chain?
The two chains are fixed to a surface i.e the first monomer of the chain is fixed and the rest of the monomers move freely in x-y-z directions but the z component of the monomers should be positive.
Something like this:
import numpy as np
import numba as nb
#import pandas as pd
#nb.jit()
def gen_chain(N):
x = np.zeros(N)
y = np.zeros(N)
z = np.linspace(0, (N)*0.9, num=N)
return np.column_stack((x, y, z)), np.column_stack((x1, y1, z1))
#coordinates = np.loadtxt('2GN_50_T_10.txt', skiprows=199950)
#return coordinates
#nb.jit()
def lj(rij2):
sig_by_r6 = np.power(sigma**2 / rij2, 3)
sig_by_r12 = np.power(sigma**2 / rij2, 6)
lje = 4 * epsilon * (sig_by_r12 - sig_by_r6)
return lje
#nb.jit()
def fene(rij2):
return (-0.5 * K * np.power(R, 2) * np.log(1 - ((np.sqrt(rij2) - r0) / R)**2))
#nb.jit()
def total_energy(coord):
# Non-bonded energy.
e_nb = 0.0
for i in range(N):
for j in range(i - 1):
ri = coord[i]
rj = coord[j]
rij = ri - rj
rij2 = np.dot(rij, rij)
if (rij2 < rcutoff_sq):
e_nb += lj(rij2)
# Bonded FENE potential energy.
e_bond = 0.0
for i in range(1, N):
ri = coord[i]
rj = coord[i - 1] # Can be [i+1] ??
rij = ri - rj
rij2 = np.dot(rij, rij)
e_bond += fene(rij2)
return e_nb + e_bond
#nb.jit()
def move(coord):
trial = np.ndarray.copy(coord)
for i in range(1, N):
while True:
delta = (2 * np.random.rand(3) - 1) * max_delta
trial[i] += delta
#while True:
if trial[i,2] > 0.0:
break
trial[i] -= delta
return trial
#nb.jit()
def accept(delta_e):
beta = 1.0 / T
if delta_e < 0.0:
return True
random_number = np.random.rand(1)
p_acc = np.exp(-beta * delta_e)
if random_number < p_acc:
return True
return False
if __name__ == "__main__":
# FENE potential parameters.
K = 40.0
R = 0.3
r0 = 0.7
# L-J potential parameters
sigma = 0.5716
epsilon = 1.0
# MC parameters
N = 20 # Numbers of monomers
rcutoff = 2.5 * sigma
rcutoff_sq = rcutoff * rcutoff
max_delta = 0.01
n_steps = 100000
T = 10
# MAIN PART OF THE CODE
coord = gen_chain(N)
energy_current = total_energy(coord)
traj = open('2GN_20_T_10.xyz', 'w')
traj_txt = open('2GN_20_T_10.txt', 'w')
for step in range(n_steps):
if step % 1000 == 0:
traj.write(str(N) + '\n\n')
for i in range(N):
traj.write("C %10.5f %10.5f %10.5f\n" % (coord[i][0], coord[i][1], coord[i][2]))
traj_txt.write("%10.5f %10.5f %10.5f\n" % (coord[i][0], coord[i][1], coord[i][2]))
print(step, energy_current)
coord_trial = move(coord)
energy_trial = total_energy(coord_trial)
delta_e = energy_trial - energy_current
if accept(delta_e):
coord = coord_trial
energy_current = energy_trial
traj.close()
I except the chain of particles to collapse into a globule.

There is some problem with the logic of the MC you are implementing.
To perform a MC you need to ATTEMPT a move, evaluate the energy of the new state and then accept/reject according to a random number.
In your code there is not the slightest sign of the attempt to move a particle.
You need to move one (or more of them), evaluate the energy, and then update your coordinates.
By the way, I suppose this is not your entire code. There are many parameters that are not defined like the "k" and the "R0" in your fene potential

The FENE potential models bond interactions. What your code is saying is that all particles within the cutoff are bonded by FENE springs, and that the bonds are not fixed but rather defined by the cutoff. With a r_cutoff = 3.0, larger than equilibrium distance of the LJ well, you are essentially considering that each particle is bonded to potentially many others. You are treating the FENE potential as a non-bonded one.
For the bond interactions you should ignore the cutoff and only evaluate the energy for the actual pairs that are bonded according to your topology, which means that first you need to define a topology. I suggest generating a linear molecule of N atoms in a box big enough to contain the whole stretched molecule, and consider the i-th atom as bonded to the (i-1)-th atom, with i = 2, ..., N. In this way the topology is well defined and persistent. Then consider both interactions separately, non-bonded and bond, and add them at the end.
Something like this, in pseudo-code:
e_nb = 0
for particle i = 1 to N:
for particle j = 1 to i-1:
if (dist(i, j) < rcutoff):
e_nb += lj(i, j)
e_bond = 0
for particle i = 2 to N:
e_bond += fene(i, i-1)
e_tot = e_nb + e_bond

Below you can find a modified version of your code. To make things simpler, in this version there is no box and no boundary conditions, just a chain in free space. The chain is initialized as a linear sequence of particles each distant 80% of R0 from the next, since R0 is the maximum length of the FENE bond. The code considers that particle i is bonded with i+1 and the bond is not broken. This code is just a proof of concept.
#!/usr/bin/python
import numpy as np
def gen_chain(N, R):
x = np.linspace(0, (N-1)*R*0.8, num=N)
y = np.zeros(N)
z = np.zeros(N)
return np.column_stack((x, y, z))
def lj(rij2):
sig_by_r6 = np.power(sigma/rij2, 3)
sig_by_r12 = np.power(sig_by_r6, 2)
lje = 4.0 * epsilon * (sig_by_r12 - sig_by_r6)
return lje
def fene(rij2):
return (-0.5 * K * R0**2 * np.log(1-(rij2/R0**2)))
def total_energy(coord):
# Non-bonded
e_nb = 0
for i in range(N):
for j in range(i-1):
ri = coord[i]
rj = coord[j]
rij = ri - rj
rij2 = np.dot(rij, rij)
if (rij2 < rcutoff):
e_nb += lj(rij2)
# Bonded
e_bond = 0
for i in range(1, N):
ri = coord[i]
rj = coord[i-1]
rij = ri - rj
rij2 = np.dot(rij, rij)
e_bond += fene(rij2)
return e_nb + e_bond
def move(coord):
trial = np.ndarray.copy(coord)
for i in range(N):
delta = (2.0 * np.random.rand(3) - 1) * max_delta
trial[i] += delta
return trial
def accept(delta_e):
beta = 1.0/T
if delta_e <= 0.0:
return True
random_number = np.random.rand(1)
p_acc = np.exp(-beta*delta_e)
if random_number < p_acc:
return True
return False
if __name__ == "__main__":
# FENE parameters
K = 40
R0 = 1.5
# LJ parameters
sigma = 1.0
epsilon = 1.0
# MC parameters
N = 50 # number of particles
rcutoff = 3.5
max_delta = 0.01
n_steps = 10000000
T = 1.5
coord = gen_chain(N, R0)
energy_current = total_energy(coord)
traj = open('traj.xyz', 'w')
for step in range(n_steps):
if step % 1000 == 0:
traj.write(str(N) + '\n\n')
for i in range(N):
traj.write("C %10.5f %10.5f %10.5f\n" % (coord[i][0], coord[i][1], coord[i][2]))
print(step, energy_current)
coord_trial = move(coord)
energy_trial = total_energy(coord_trial)
delta_e = energy_trial - energy_current
if accept(delta_e):
coord = coord_trial
energy_current = energy_trial
traj.close()
The code prints the current configuration at each step, you can just load it up on VMD and see how it behaves. The bonds will not show correctly at first on VMD, you must use a bead representation for the particles and define the bonds manually or with a script within VMD. In any case, you don't need to see the bonds to notice that the chain does not collapse.
Please bear in mind that if you want to simulate a chain at a certain density, you need to be careful to generate the correct topology. I recommend the EMC package to efficiently generate polymers at the desired thermodynamic conditions. It is by no means a trivial problem, especially for larger chains.
By the way, your code had an error in the FENE energy evaluation. rij2 is already squared, you squared it again.
Below you can see how the total energy as a function of the number of steps behaves for T = 1.0, N = 20, rcutoff = 3.5, and also the last current configuration after 10 thousand steps.
And below for N = 50, T = 1.5, max_delta = 0.01, K = 40, R = 1.5, rcutoff = 3.5, and 10 million steps. This is the last current configuration.
The full "trajectory", which isn't really a trajectory since this is MC, you can find here (it's under 6 MB).

Normalized correlation

I have trouble with the use of the normalized correlation. I am using python to plot the different plots, such as correlation, normalized correlation and auto-correlation.
When I use my own defined function with a sinus it works well, but when I try the Wikipedia example with a triangle and a box wave the normalized correlation does not work anymore.
This is the code I use (only the functions):
def crosscor(sig, known):
ans = np.array([])
temp = 0
for i in range(len(sig)-len(known)):
for j in range(len(known)):
temp += sig[i+j]*known[j]
ans = np.append(ans, temp)
temp = 0
return ans
def normcrosscor(sig, known):
ans = np.array([])
temp = 0
x2 = 0
y2 = 0
for i in range(len(sig)-len(known)):
for j in range(len(known)):
x2 += (sig[i+j]-np.mean(sig[i:i+len(known)]))**2
y2 += (known[j]-np.mean(known))**2
sigx = (1/len(known)*x2)**(1/2)
sigy = (1/len(known)*y2)**(1/2)
for j in range(len(known)):
if sigx != 0 and sigy != 0:
temp += (sig[i+j]-np.mean(sig[i:i+len(known)]))*(known[j]-np.mean(known))/(sigx*sigy)
else:
temp += 0
temp = 1/len(known)*temp
ans = np.append(ans, temp)
x2 = 0
y2 = 0
temp = 0
return ans
def autocor(sig):
x = sig
y = sig
ans = np.array([])
y = np.lib.pad(y, (len(sig), len(sig)), 'constant', constant_values=(0, 0))
for t in range(len(y)-len(sig)+1):
s = 0
for i in range(len(x)):
s += x[i]*y[i+t]
ans = np.append(ans, s)
return ans
When I try this with a sinus my result is as follow:
And when I use a triangle wave and a blok wave to compare the signals I get:
I expect that the normalized correlation would also give me a result that has a maximum near 100.
Can somebody help me?

How does Python handle complicated calculations?

It might be a very simple problem but seems I am not able to see it.
I have a list of point ordered clockwise and want to calculate the centroid of these point (a convex polygon) using the following function according to this:
and
def calculateCentroid(raLinks,raNodes, links, nodes):
orderedPointsOfLinks = orderClockwise(raLinks,raNodes, links, nodes)
arg1 = 0
arg2 = 0
Xc = 0
Yc = 0
i = 0
for point in orderedPointsOfLinks:
arg1 += point.Y*(orderedPointsOfLinks[i+1 if i+1<len(orderedPointsOfLinks) else 0].X)
arg2 += (orderedPointsOfLinks[i+1 if i+1<len(orderedPointsOfLinks) else 0].Y)*point.X
Xc += (point.X+(orderedPointsOfLinks[i+1 if i+1<len(orderedPointsOfLinks) else 0].X))*(((orderedPointsOfLinks[i+1 if i+1<len(orderedPointsOfLinks) else 0].Y)*point.X)-(point.Y*(orderedPointsOfLinks[i+1 if i+1<len(orderedPointsOfLinks) else 0].X)))
Yc += (point.Y+(orderedPointsOfLinks[i+1 if i+1<len(orderedPointsOfLinks) else 0].Y))*(((orderedPointsOfLinks[i+1 if i+1<len(orderedPointsOfLinks) else 0].Y)*point.X)-(point.Y*(orderedPointsOfLinks[i+1 if i+1<len(orderedPointsOfLinks) else 0].X)))
i+=1
area = (arg1-arg2)*0.5
print area
X = -Xc/(6*area)
Y = -Yc/(6*area)
print X , " ", Y
calculating the area and the centorid using Arcpy shows that the calculated area by the above function is correct but the centroid is wrong.
what is the problem with Xc and Yc that I cant fix it?
If I change the for loop in the following way it works:
for point in orderedPointsOfLinks:
y0 = point.Y
x0 = point.X
x1 = orderedPointsOfLinks[i+1 if i+1<len(orderedPointsOfLinks) else 0].X
y1 = orderedPointsOfLinks[i+1 if i+1<len(orderedPointsOfLinks) else 0].Y
a = x0*y1 - x1*y0
area += a
Xc += (x0+x1)*a
Yc += (y0+y1)*a
i+=1
area *= 0.5
print area
X = Xc/(6*area)
Y = Yc/(6*area)
print X , " ", Y
here is a list of nodes to examine the code:
[(371623.876, 6159668.714),(371625.994, 6159661.094), (371624.319, 6159654.634), (371619.654, 6159649.86), (371614.194, 6159647.819), (371608.401, 6159648.449), (371601.544, 6159652.652), (371598.77, 6159658.058), (371599.318, 6159665.421), (371603.025, 6159671.805), (371611.372, 6159674.882 ), (371619.417, 6159673.065)]

source
Try:
import numpy
tp = [(371623.876, 6159668.714),(371625.994, 6159661.094), (371624.319, 6159654.634), (371619.654, 6159649.86),\
(371614.194, 6159647.819), (371608.401, 6159648.449), (371601.544, 6159652.652), (371598.77, 6159658.058), \
(371599.318, 6159665.421), (371603.025, 6159671.805), (371611.372, 6159674.882 ), (371619.417, 6159673.065),(371623.876, 6159668.714)]
# cx = sigma (x[i]+x[i+1])*((x[i]*y[i+1]) - (x[i+1]*y[i] ))
# cy = sigma (y[i]+y[i+1])*((x[i]*y[i+1]) - (x[i+1]*y[i] ))
cx = 0
cy = 0
p = numpy.array(tp)
x = p[:, 0]
y = p[:, 1]
a = x[:-1] * y[1:]
b = y[:-1] * x[1:]
cx = x[:-1] + x[1:]
cy = y[:-1] + y[1:]
tp = tp[:-1] #dont need repeat
def area():
tox=0
toy=0
for i in range(len(tp)):
if i+1 == len(tp):
tox += tp[-1][0]*tp[0][1]
else:
tox += tp[i][0]*tp[i+1][1]
for i in range(len(tp)):
if i+1 == len(tp):
toy += tp[-1][1]*tp[0][0]
else:
toy += tp[i][1]*tp[i+1][0]
return abs(tox-toy)*0.5
ar = area()
Cx = abs(numpy.sum(cx * (a - b)) / (6. * ar))
Cy = abs(numpy.sum(cy * (a - b)) / (6. * ar))
print Cx,Cy
Warning !
tp[0] == tp[-1]
So: first and last coordinates are same value...