Since you’ve tagged this question with sympy, I will give such an example. From the documentation: http://docs.sympy.org/latest/modules/plotting.html. from sympy import var, plot_implicit var(‘x y’) plot_implicit(x*y**3 – y*x**3)
First of all, at the moment SymPy does not guarantee support for numpy arrays which is what you want in this case. Check this bug report http://code.google.com/p/sympy/issues/detail?id=537 Second, If you want to evaluate something numerically for many values SymPy is not the best choice (it is a symbolic library after all). Use numpy and scipy. … Read more
From the SymPy documentation == represents exact structural equality testing. “Exact” here means that two expressions will compare equal with == only if they are exactly equal structurally. Here, (x+1)^2 and x^2+2x+1 are not the same symbolically. One is the power of an addition of two terms, and the other is the addition of three … Read more
all_coeffs() can be sometime better than using coeffs() for a Poly. The difference lies in output of these both. coeffs() returns a list containing all coefficients which has a value and ignores those whose coefficient is 0 whereas all_coeffs() returns all coefficients including those whose coefficient is zero. >>> a = Poly(x**3 + a*x**2 – … Read more
Nice find! The documentation doesn’t make things clear. Diving into the source code, leads to these lines in plot.py: for marker in parent.markers: # make a copy of the marker dictionary # so that it doesn’t get altered m = marker.copy() args = m.pop(‘args’) ax.plot(*args, **m) So, sympy just calls matplotlib’s plot with: the args … Read more
By using masked arrays you can avoid plotting selected regions of a curve. To remove the singularity at x=2: import matplotlib.numerix.ma as M # for older versions, prior to .98 #import numpy.ma as M # for newer versions of matplotlib from pylab import * figure() xx = np.arange(-0.5,5.5,0.01) vals = 1/(xx-2) vals = M.array(vals) mvals … Read more
for numerical solution, you can use fsolve: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fsolve.html#scipy.optimize.fsolve from scipy.optimize import fsolve import math def equations(p): x, y = p return (x+y**2-4, math.exp(x) + x*y – 3) x, y = fsolve(equations, (1, 1)) print equations((x, y))
I don’t believe there’s very good support for this, but you could try something like import matplotlib.pyplot from numpy import arange from numpy import meshgrid delta = 0.025 xrange = arange(-5.0, 20.0, delta) yrange = arange(-5.0, 20.0, delta) X, Y = meshgrid(xrange,yrange) # F is one side of the equation, G is the other F … Read more