say I want to print the equation g(x) in the form g(x) = x^2 *........
how do I do it? This is my first time using python
import numpy as np
import matplotlib.pyplot as plt
import math
from sympy.mpmath import *
f = lambda x: ((x^2)*math.exp(-x))
dfdx = lambda x: diff(f,x)
d2fdx2 = lambda x: diff(dfdx,x)
g = lambda x: ((math.exp(x)/math.factorial(2))*d2fdx2)
print(g)
(edit) the output im getting is
function lambda at 0x0671C198
First of drop all the the unnecessary imports and stick to what you really need. Symbolic math is what sympy was made for so check out the documentation for that.
In sympy you have to define symbols first
import sympy
x = symbols('x')
Now you would use the symbol x to construct an expression using builtin operators and functions in the sympy module. Be aware that ** is exponentiation and ^ is logical xor.
f = x ** 2 * sympy.exp(-x)
dfdx = sympy.diff(f, x)
d2fdx2 = sympy.diff(f, x, x)
g = sympy.exp(x) / sympy.factorial(2) * d2fdx2
When you write g in the interactive interpreter it will write the expression the way you want it. Can't show that here but atleast I can do this:
>>> print(g)
x**2/2 - 2*x + 1
You cannot do what you want with the math, sympy.mpmath and numpy modules as they exist for numerical evalutions - they want numbers and give you number.
If you later want to evaluate your expression for a given value of x you could do
val_at_point = g.evalf(subs={x: 1.5})
where subs is a dictionary.
Or you could turn g into a python lambda function:
fun_g = sympy.lambdify(x, g)
val_at_point = fun_g(1.5)
If you're doing this for a math class you probably want to be working in the interpreter anyway in which case you can start by writing
>>> from sympy import *
so that you can skip all the sympy. stuff in the above code samples. I left them there just to show where symbols come from.
Related
Given an expression, we can convert it into a function using sympy.lambdify. Similarly, given a function, we can convert it into an expression by evaluating it at symbol x. We would naturally expect that these two operations are inverses of each other. And, this expected behaviour is displayed when I use polynomial expressions. For example,
import sympy as sym
x = sym.symbols('x')
expr = 5*x**2 + 2*x + 3
f = sym.lambdify([x],expr)
f_expr = f(x)
print(expr == f_expr)
gives True as its output.
On the other hand, the following code does not run
import sympy as sym
x = sym.symbols('x')
expr = sym.sin(x)
f = sym.lambdify([x],expr)
f_expr = f(x)
print(expr == f_expr)
and throws the error "TypeError: loop of ufunc does not support argument 0 of type Symbol which has no callable sin method". Could you please explain why this is happening? My guess would be that sym.sin(x) does not return an "expression" analogous to 5x**2 + 2x + 3. But, I would like to understand it a bit better. Thanks in advance.
For a non-numeric object the lambdify code tries to do x.sin()
with making sure the sin function is from library sympy not numpy to avoid confusions.
you can try :
import sympy as sym
from sympy import sin
x = sym.symbols('x')
expr = sin(x)
# f = sym.lambdify(x,expr)
f = lambda x:sin(x)
f_expr = f(x)
print(expr == f_expr)
The python script below evaluates the first term in the expression,
4.939e-3xAxB+8.7989 at (A,B) = (1.0,1.0):
import sympy
from sympy import *
A = sympy.Symbol(A)
B = sympy.Symbol(B)
F = 4.939e-3*A*B+8.7989
G = str(F).split("+")[0]
H = lambdify([A,B], G, "numpy")
print H(1,1)
The ouput is: 0.004939
Now the code below aims to achieve the same objective:
A = sympy.Symbol(A)
B = sympy.Symbol(B)
F = 4.939e-3*A*B+8.7989
G = str(F).split("+")[0]
H = lambdify(A, G, "numpy")
I = lambdify(B, H(1), "numpy")
print I(1)
But this returns the error:
NameError: global name 'B' is not defined
Can someone kindly explain this?
You are conflating strings and symbols at multiple instances. The only reason that any of this works at all is that SymPy applies sympify to many inputs (sympify("A")==Symbol("A")) and Python’s duck typing. Specifically, G is a string, when it should be a symbolic expression, and all first arguments passed to lambdify are strings or lists of strings, when they should be symbols or lists thereof.
A clean version of your first code would be:
from sympy.abc import A,B
import sympy
G = 4.939e-3*A*B # now this is a symbolic expression, not a string
H = lambdify( [A,B], G, "numpy" )
print(H(1,1))
And the same for your second code:
from sympy.abc import A,B
import sympy
G = 4.939e-3*A*B # now this is a symbolic expression, not a string
H = lambdify( A, G, "numpy" )
I = lambdify( B, H(1), "numpy" )
print(I(1))
To get all variables from a sympy expression, one can call .free_symbols on the expression. I would like to retrieve all functions used in an expression. For example, from y in
from sympy import *
f = Function('f')
g = Function('g')
x = Symbol('x')
y = f(x) + 2*g(x)
I'd like to get f and g.
Any hints?
atoms does the trick:
for f in y.atoms(Function):
print(f.func)
For all functions, use atoms(Function).
In [40]: (f(x) + sin(x)).atoms(Function)
Out[40]: set([f(x), sin(x)])
For only undefined functions, use atoms(AppliedUndef).
In [41]: from sympy.core.function import AppliedUndef
In [42]: (f(x) + sin(x)).atoms(AppliedUndef)
Out[42]: set([f(x)])
I have a complicated equation which is function of several variables and I want to manipulate like this example:
y = (x + a) / z
x = y*z - a
Is it possible to do this kind of manipulation matlab or python?
If there is possibility then please point out method or function to do this operation.
I tried following code in Sympy Shell:
x,y,z,a = symbols ('x y z a')
solve ( y = (x-a)/z, x)
I am getting following error:
Traceback (most recent call last):
File "<string>", line 1
SyntaxError: non-keyword arg after keyword arg
In Matlab you'd need the symbolic math toolbox (which I don't have so I can't test) and then you should be able to do use the solve function:
syms y x a z
solve(y == (x+a)/z, x)
I have NO experince with sympy but pretty sure based on the docs this is how you do it:
from sympy import solve, Poly, Eq, Function, exp
from sympy.abc import x, y, z, a
solve(y - (x+a)/z, x)
SymPy is a Python library, so your SymPy code needs to be valid Python. In Python, = is the assignment operator, which is why solve ( y = (x-a)/z, x) gives a SyntaxError. See http://docs.sympy.org/latest/gotchas.html#equals-signs.
To create an equality in SymPy use Eq, like solve(Eq(y, (x - a)/z, x), or use the fact that expressions in SymPy are assumed to be equal to zero, like solve(y - (x - a)/z, x).
I'm looking for an example of operations with logarithms in Python. I've tried with sympy and numpy and I still can't do what I want. For example, for an input like this:
log(x+1)+log(4-x)=log(100) # it's just an example
the output should give me the x value. I need to do this with any other functions like log(x+1)=4 or log(x)-log(x+1)=log(x).
Is there some method or somewhere (documentation or similar) where can I find how to do this?
I may be misunderstanding what you need to do because you said you tried sympy already. However, it looks like you just want to solve for x in an algebraic equation.
Solving for x in the equation
log(x+1)+log(4-x)=log(100)
using sympy would be
>>> from sympy import Symbol, solve, log
>>> x = Symbol('x')
>>> solve(log(x+1) + log(4-x) - log(100), x)
[3/2 - 5*sqrt(15)*I/2, 3/2 + 5*sqrt(15)*I/2]
If you want, you can check that these two solutions are correct with numpy.
>>> import numpy as np
>>> a = 3/2 - 5*np.sqrt(15)*1j/2
>>> b = 3/2 + 5*np.sqrt(15)*1j/2
>>> np.log(a + 1) + np.log(4-a)
(4.6051701859880918+0j)
>>> np.log(b + 1) + np.log(4-b)
(4.6051701859880918+0j)
>>> np.log(100)
4.6051701859880918
Is that not what you are looking for?
Since log is a non-linear function, you will need to use a non-linear solver like scipy.optimize.fsolve. It take in a function and a guess value and returns the answer in the form of an array. For simplicity reason, I defined the function as a lambda function since we don't need it outside of this line, but creating a function using standard def methods would work as well. The [0] on the back end get the value out of the array to return just the float.
import scipy.optimize
import math
scipy.optimize.fsolve(lambda x: math.log(x+1) - 4, 5)[0] # 5 is guess value
>>> 53.598
# Check
math.exp(4) - 1
>>> 53.598
Good advice already given. I just note that you can also check the answer in SymPy.
>>> L, R = log(x+1)+log(4-x), log(100)
>>> eq = Eq(L, R)
>>> eq
log(-x + 4) + log(x + 1) == log(100)
>>> sol = solve(eq)
>>> [eq.subs(x, i) for i in sol]
[True, True]
So in the Eq form the solutions were verified automatically. This is not always
true but you can use numerical evaluation to check the value:
>>> f = eq.lhs - eq.rhs; f
log(-x + 4) + log(x + 1) - log(100)
>>> f.subs(x, sol[0])
-log(100) + log(5/2 - 5*sqrt(15)*I/2) + log(5/2 + 5*sqrt(15)*I/2)
>>> _.n()
0.e-124 + 0.e-125*I
>>> f.subs(x, sol[0]).n(chop=True) # the small numbers can be chopped
0