Homec4science
Diffusion Public Numerical Analysis Jupyter Notebook (Modifications-for-2025) Browse

PubNumAnalysisIpynb/NonLinearEquationsLib.py
901afe7fc84aModifications-for-2025

Last Change
Raw File

NonLinearEquationsLib.py
View Options

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 30
@author: Simone Deparis
"""
import numpy as np
def plotPhi (a,b,phi,label='$\phi$', N = 100) :
'''
simple plot of fonction with bisectrice of first quadrant
useful for preparing grpahics of fixed point iterations
[a,b] : interval, used for both x and y axis
phi : funciton to plot
label : label for the fonction, usually '$\phi$'
N : number of points for plotting
'''
import matplotlib.pyplot as plt
z = np.linspace(a,b,N)
plt.plot(z,phi(z),'k-')
plt.plot([a,b],[a,b],':', linewidth=0.5)
plt.xlabel('x'); plt.ylabel(label);
# Plot the x,y-axis
plt.plot([a,b], [0,0], 'k-',linewidth=0.1)
plt.plot([0,0], [a,b], 'k-',linewidth=0.1)
plt.legend([label, 'y=x'])
plt.title('Graph de la fonction ' + label)
def plotPhiIterations (x) :
# plot the graphical interpretation of the Fixed Point method
import matplotlib.pyplot as plt
plt.plot([x[0],x[0]], [0,x[1]], 'g:')
for k in range(x.size-2) :
plt.plot([x[k],x[k+1]], [x[k+1],x[k+1]], 'g:')
plt.plot([x[k+1],x[k+1]], [x[k+1],x[k+2]], 'g:')
k=x.size-2
plt.plot([x[k],x[k+1]], [x[k+1],x[k+1]], 'g:')
# Putting a sign at the initial point
deltaAxis = plt.gca().axes.get_ylim()[1] - plt.gca().axes.get_ylim()[0]
if (x[1] < 0) :
plt.annotate("$x_0$", (x[0], 0.02*deltaAxis) )
else :
plt.annotate("$x_0$", (x[0], -0.05*deltaAxis) )
def plotNewtonIterations (a,b,f,x,N=200) :
# plot the graphical interpretation of the Newton method
import matplotlib.pyplot as plt
z = np.linspace(a,b,N)
plt.plot(z,f(z), 'b-', x,f(x), 'rx')
# Putting a sign at the initial point
deltaAxis = plt.gca().axes.get_ylim()[1] - plt.gca().axes.get_ylim()[0]
# plot the graphical interpretation of the Newton method
plt.plot([x[0],x[0]], [0,f(x[0])], 'g:')
for k in range(x.size-1) :
plt.plot([x[k],x[k+1]], [f(x[k]),0], 'g-')
plt.plot([x[k+1],x[k+1]], [0,f(x[k+1])], 'g:')
# Putting a sign at the initial point
if (f(x[k]) < 0) :
plt.annotate("$x_"+str(k)+"$", (x[k], 0.02*deltaAxis) )
else :
plt.annotate("$x_"+str(k)+"$", (x[k], -0.05*deltaAxis) )
plt.ylabel('$f$'); plt.xlabel('$x$');
# Plot the x,y-axis
plt.plot([a,b], [0,0], 'k-',linewidth=0.1)
plt.plot([0,0], [np.min(f(z)),np.max(f(z))], 'k-',linewidth=0.1)
plt.legend(['$f$','($x_k$,$f(x_k)$)'])
def plotBisectionIterations (a,b,f,x,N=200) :
# plot the graphical interpretation of the Bisection method
import matplotlib.pyplot as plt
def putSign(y,f,text) :
if (f(y) < 0) :
plt.annotate(text, (y, 0.02*deltaAxis) )
else :
plt.annotate(text, (y, -0.05*deltaAxis) )
z = np.linspace(a,b,N)
plt.plot(z,f(z), 'b-')
plt.plot([a,a], [0,f(a)], 'g:')
plt.plot([b,b], [0,f(b)], 'g:')
# For putting a sign at the initial point
deltaAxis = plt.gca().axes.get_ylim()[1] - plt.gca().axes.get_ylim()[0]
putSign(a,f,"a")
putSign(b,f,"b")
for k in range(x.size) :
plt.plot([x[k],x[k]], [0, f(x[k])], 'g:')
putSign(x[k],f,"$x_"+str(k)+"$")
# Plot the x,y-axis
plt.plot([a,b], [0,0], 'k-',linewidth=0.1)
plt.plot([0,0], [np.min(f(z)),np.max(f(z))], 'k-',linewidth=0.1)
plt.ylabel('$f$'); plt.xlabel('$x$');
return
c4science · Help