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Problem with implicit_plot

I have three huge degree 31 bivariate polynomials (20,000 characters long each) I want to plot, but I keep getting a lot of noise in my plot. I can't upload it, but the point is that in some regions I just get colorful noise. I've tried defining the polynomials over RealField(n) and increasing the number of plot_points, but neither of these approaches work. Any ideas on how to work around this? Thanks.

Problem with implicit_plot

I have three huge degree 31 bivariate polynomials (20,000 characters long each) I want to plot, but I keep getting a lot of noise in my plot. I can't upload it, but the point is that in some regions I just get colorful noise. I've tried defining the polynomials over RealField(n) and increasing the number of plot_points, but neither of these approaches work. Any ideas on how to work around this? Thanks.

Edit: Tried using sympy's plot_implicit and it's so (SO!) slow. Then used numpy's contour_plot and it actually has the same issue.

Here's the code that produces the polynomials and plot. Be patient as it is quite computationally demanding at first.

plot = Graphics()
m = 32-1
D = [(i,j) for i in range(0,m+1) for j in range(0,m+1) if i+j<m+1]

#Polygon
P = Polyhedron( vertices= [(0, 0), (32, 44), (23, 0), (10, 14), (2, 3)] )
points = P.integral_points()

plot_pts = point(points, rgbcolor=(0, 0, 0), size = 20).plot()
plot_np = P.plot(fill = False, point=False, line='black')

M = matrix(ZZ, len(points), len(D), 0)

for row_num, row in enumerate(points):
    for col_num, column in enumerate(D):
        i, j = row
        a, b= column
        #Matrix for interpolation:
        M[row_num, col_num] = (i^a)*(j^b) 

R = PolynomialRing(QQ, 2, 'xy') 
S = PolynomialRing(RealField(500), 2, 'uv') 
x, y = R.gens()       
u, v = S.gens() 
K = M.right_kernel() 
Kdim = K.dimension() 
print(Kdim) 
if Kdim > 0: 
     for l in range(Kdim): 
        K_basis = K.basis()[l] 
        #Writing the interpolating polynomial 
        f=0 
        for order, bidegree in enumerate(D): 
            a, b = bidegree 
            f += list(K_basis)[order] * u^a * v^b 
        #print(f.factor()) 
        cols = ['red', 'blue', 'green'] 
        interpolation = implicit_plot(f, (u,-1,34), (v,-1,45), plot_points=100, color=cols[l]) 
        plot += interpolation 
plot += plot_pts + plot_np 
plot.show(figsize=10)

Problem with implicit_plot

I have three huge degree 31 bivariate polynomials (20,000 characters long each) I want to plot, but I keep getting a lot of noise in my plot. I can't upload it, but the point is that in some regions I just get colorful noise. I've tried defining the polynomials over RealField(n) and increasing the number of plot_points, but neither of these approaches work. Any ideas on how to work around this? Thanks.

Edit: Tried using sympy's plot_implicit and it's so (SO!) slow. Then used numpy's contour_plot and it actually has the same issue.

Here's the code that produces the polynomials and plot. Be patient as it is quite computationally demanding at first.could be a bit slow (depending on your machine).

plot = Graphics()
m = 32-1
D = [(i,j) for i in range(0,m+1) for j in range(0,m+1) if i+j<m+1]

#Polygon
P = Polyhedron( vertices= [(0, 0), (32, 44), (23, 0), (10, 14), (2, 3)] )
points = P.integral_points()

plot_pts = point(points, rgbcolor=(0, 0, 0), size = 20).plot()
plot_np = P.plot(fill = False, point=False, line='black')

M = matrix(ZZ, len(points), len(D), 0)

for row_num, row in enumerate(points):
    for col_num, column in enumerate(D):
        i, j = row
        a, b= column
        #Matrix for interpolation:
        M[row_num, col_num] = (i^a)*(j^b) 

R = PolynomialRing(QQ, 2, 'xy') 
S = PolynomialRing(RealField(500), 2, 'uv') 
x, y = R.gens()       
u, v = S.gens() 
K = M.right_kernel() 
Kdim = K.dimension() 
print(Kdim) 
if Kdim > 0: 
     for l in range(Kdim): 
        K_basis = K.basis()[l] 
        #Writing the interpolating polynomial 
        f=0 
        for order, bidegree in enumerate(D): 
            a, b = bidegree 
            f += list(K_basis)[order] * u^a * v^b 
        #print(f.factor()) 
        cols = ['red', 'blue', 'green'] 
        interpolation = implicit_plot(f, (u,-1,34), (v,-1,45), plot_points=100, color=cols[l]) 
        plot += interpolation 
plot += plot_pts + plot_np 
plot.show(figsize=10)

Problem with implicit_plot

I have three huge degree 31 bivariate polynomials (20,000 characters long each) I want to plot, but I keep getting a lot of noise in my plot. I can't upload it, but the point is that in some regions I just get colorful noise. I've tried defining the polynomials over RealField(n) and increasing the number of plot_points, but neither of these approaches work. Any ideas on how to work around this? Thanks.

Edit: Tried using sympy's plot_implicit and it's so (SO!) slow. Then used numpy's contour_plot and it actually has the same issue.

Here's the code that produces the polynomials and plot. Be patient as it could be a bit slow (depending on your machine).

plot = Graphics()
m = 32-1
D = [(i,j) for i in range(0,m+1) for j in range(0,m+1) if i+j<m+1]

#Polygon
P = Polyhedron( vertices= [(0, 0), (32, 44), (23, 0), (10, 14), (2, 3)] )
points = P.integral_points()

plot_pts = point(points, rgbcolor=(0, 0, 0), size = 20).plot()
plot_np = P.plot(fill = False, point=False, line='black')

M = matrix(ZZ, len(points), len(D), 0)

for row_num, row in enumerate(points):
    for col_num, column in enumerate(D):
        i, j = row
        a, b= column
        #Matrix for interpolation:
        M[row_num, col_num] = (i^a)*(j^b) 

R = PolynomialRing(QQ, 2, 'xy') 
S = PolynomialRing(RealField(500), 2, 'uv') 
x, y = R.gens()       
u, v = S.gens() 
K = M.right_kernel() 
Kdim = K.dimension() 
print(Kdim) 
if Kdim > 0: 
     for l in range(Kdim): 
        K_basis = K.basis()[l] 
        #Writing the interpolating polynomial 
        f=0 
        for order, bidegree in enumerate(D): 
            a, b = bidegree 
            f += list(K_basis)[order] * u^a * v^b 
        #print(f.factor()) F = f.factor()
        f = F[6][0]

        cols = ['red', 'blue', 'green'] 
        interpolation = implicit_plot(f, (u,-1,34), (v,-1,45), plot_points=100, color=cols[l]) 
        plot += interpolation 
plot += plot_pts + plot_np 
plot.show(figsize=10)

Problem with implicit_plot

I have three huge degree 31 bivariate polynomials (20,000 characters long each) I want to plot, but I keep getting a lot of noise in my plot. I can't upload it, but the point is that in some regions I just get colorful noise. I've tried defining the polynomials over RealField(n) and increasing the number of plot_points, but neither of these approaches work. Any ideas on how to work around this? Thanks.

Edit: Tried using sympy's plot_implicit and it's so (SO!) slow. Then used numpy's contour_plot and it actually it's fast, but has the same issue. problem as sage.

Here's the code that produces the polynomials and plot. Be patient as it could be a bit slow (depending on your machine).

plot = Graphics()
m = 32-1
D = [(i,j) for i in range(0,m+1) for j in range(0,m+1) if i+j<m+1]

#Polygon
P = Polyhedron( vertices= [(0, 0), (32, 44), (23, 0), (10, 14), (2, 3)] )
points = P.integral_points()

plot_pts = point(points, rgbcolor=(0, 0, 0), size = 20).plot()
plot_np = P.plot(fill = False, point=False, line='black')

M = matrix(ZZ, len(points), len(D), 0)

for row_num, row in enumerate(points):
    for col_num, column in enumerate(D):
        i, j = row
        a, b= column
        #Matrix for interpolation:
        M[row_num, col_num] = (i^a)*(j^b) 

R = PolynomialRing(QQ, 2, 'xy') 
S = PolynomialRing(RealField(500), 2, 'uv') 
x, y = R.gens()       
u, v = S.gens() 
K = M.right_kernel() 
Kdim = K.dimension() 
print(Kdim) 
if Kdim > 0: 
     for l in range(Kdim): 
        K_basis = K.basis()[l] 
        #Writing the interpolating polynomial 
        f=0 
        for order, bidegree in enumerate(D): 
            a, b = bidegree 
            f += list(K_basis)[order] * u^a * v^b 
        F = f.factor()
        f = F[6][0]

        cols = ['red', 'blue', 'green'] 
        interpolation = implicit_plot(f, (u,-1,34), (v,-1,45), plot_points=100, color=cols[l]) 
        plot += interpolation 
plot += plot_pts + plot_np 
plot.show(figsize=10)

Problem with implicit_plot

I have three huge degree 31 bivariate polynomials (20,000 characters long each) I want to plot, but I keep getting a lot of noise in my plot. I can't upload it, but the point is that in some regions I just get colorful noise. I've tried defining the polynomials over RealField(n) and increasing the number of plot_points, but neither of these approaches work. Any ideas on how to work around this? Thanks.

Edit: Tried using sympy's plot_implicit and it's so (SO!) slow. Then used numpy's contour_plot and it's fast, but has the same problem as sage.

Here's the code that produces the polynomials and plot. Be patient as it could be a bit slow (depending on your machine).

plot = Graphics()
m = 32-1
D = [(i,j) for i in range(0,m+1) for j in range(0,m+1) if i+j<m+1]

#Polygon
P = Polyhedron( vertices= [(0, 0), (32, 44), (23, 0), (10, 14), (2, 3)] )
points = P.integral_points()

plot_pts = point(points, rgbcolor=(0, 0, 0), size = 20).plot()
plot_np = P.plot(fill = False, point=False, line='black')

M = matrix(ZZ, len(points), len(D), 0)

for row_num, row in enumerate(points):
    for col_num, column in enumerate(D):
        i, j = row
        a, b= column
        #Matrix for interpolation:
        M[row_num, col_num] = (i^a)*(j^b) 

R = PolynomialRing(QQ, 2, 'xy') 
S = PolynomialRing(RealField(500), 2, 'uv') 
x, y = R.gens()       
u, v = S.gens() 
K = M.right_kernel() 
Kdim = K.dimension() 
print(Kdim) 
if Kdim > 0: 
     for l in range(Kdim): 
        K_basis = K.basis()[l] 
        #Writing the interpolating polynomial 
        f=0 
        for order, bidegree in enumerate(D): 
            a, b = bidegree 
            f += list(K_basis)[order] * u^a * v^b 
        F = f.factor()
        f = F[6][0]

        cols = ['red', 'blue', 'green'] 
        interpolation = implicit_plot(f, (u,-1,34), (v,-1,45), plot_points=100, color=cols[l]) 
        plot += interpolation 
plot += plot_pts + plot_np 
plot.show(figsize=10)

Edit 2: Using the mpmath library in Python and with the aid of S├ębastien's code below I wrote a routine that allows us to control the root finding method and precision of our computations. I tried several methods, secant (default), newton, hailley, mnewton, etc. without success. Changing the precision and tolerance of the root finding function from mpmath didn't help either. I think this polynomial just behaves too wildly in the region of the plot.

Here's the code and relevant documentation for the "mpmath.findroot" function:

http://mpmath.org/doc/current/calculus/optimization.html :

from sympy import *
import matplotlib.pyplot as plt
import mpmath as mp

mp.dps = 100
stepx = 0.1
stepy = 0.5
xrange = np.arange(7.5,12.5, stepx)
yrange = np.arange(0, 5, stepy)

def plot_roots_of_f(f):
    L = []
    for u in xrange:
        for v in yrange:
            Root = mp.findroot(f.eval({x:u}),v, method = 'mnewton', tol = E-60, verbose=False,verify=False)
            L.append([u,Root])
    return plt.plot(*zip(*L),linestyle='None', marker='.')

plot_roots_of_f(f)
plt.show()