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
The symbols function can be used to easily generate lists of symbols In [1]: symbols(‘a0:3’) Out[1]: (a₀, a₁, a₂) In [2]: numEquations = 15 In [3]: symbols(‘a0:%d’%numEquations) Out[3]: (a₀, a₁, a₂, a₃, a₄, a₅, a₆, a₇, a₈, a₉, a₁₀, a₁₁, a₁₂, a₁₃, a₁₄)
If you are looking to print the ± symbol, just use: print(u”\u00B1″)
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
To substitute several values: >>> from sympy import Symbol >>> x, y = Symbol(‘x y’) >>> f = x + y >>> f.subs({x:10, y: 20}) >>> f 30
I found the sympy library way in this link http://docs.sympy.org/latest/tutorial/manipulation.html Use .args atribute in the function or result object. If I have a function: >>>func = Eq(u(x),−x+sin(x)) >>>func u(x) = -x + sin(x) >>>func.args[0] u(x) >>>func.args[1] -x+sin(x) The same applies for a result that is a finite set type.
This looks like the most straightforward: np.array(g).astype(np.float64) If you skip the astype method, numpy will create a matrix of type ‘object’, which won’t work with common array operations.
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
sympy.Function is for undefined functions. Like if f = Function(‘f’) then f(x) remains unevaluated in expressions. If you want an actual function (like if you do f(1) it evaluates x**2 + 1 at x=1, you can use a Python function def f(x): return x**2 + 1 Then f(Symbol(‘x’)) will give a symbolic x**2 + 1 … Read more