Exact SymPy expressions can be converted to floating-point approximations (decimal numbers) using either the .evalf() method or the N() function. N(expr, <args>) is equivalent to sympify(expr).evalf(<args>).
>>> from sympy import * >>> N(sqrt(2)*pi) 4.44288293815837 >>> (sqrt(2)*pi).evalf() 4.44288293815837
By default, numerical evaluation is performed to an accuracy of 15 decimal digits. You can optionally pass a desired accuracy (which should be a positive integer) as an argument to evalf or N:
>>> N(sqrt(2)*pi, 5) 4.4429 >>> N(sqrt(2)*pi, 50) 4.4428829381583662470158809900606936986146216893757
Complex numbers are supported:
>>> N(1/(pi + I), 20) 0.28902548222223624241 - 0.091999668350375232456*I
If the expression contains symbols or for some other reason cannot be evaluated numerically, calling .evalf() or N() returns the original expression, or in some cases a partially evaluated expression. For example, when the expression is a polynomial in expanded form, the coefficients are evaluated:
>>> x = Symbol('x') >>> (pi*x**2 + x/3).evalf() 0.333333333333333*x + 3.14159265358979*x**2
You can also use the standard Python functions float(), complex() to convert SymPy expressions to regular Python numbers:
>>> float(pi) 3.14159265359 >>> complex(pi+E*I) (3.14159265359+2.71828182846j)
If these functions are used, failure to evaluate the expression to an explicit number (for example if the expression contains symbols) will raise an exception.
There is essentially no upper precision limit. The following command, for example, computes the first 100,000 digits of π/e:
>>> N(pi/E, 100000) #doctest: +SKIP ...
This shows digits 999,951 through 1,000,000 of pi:
>>> str(N(pi, 10**6))[-50:] #doctest: +SKIP '95678796130331164628399634646042209010610577945815'
High-precision calculations can be slow. It is recommended (but entirely optional) to install gmpy (http://code.google.com/p/gmpy/), which will significantly speed up computations such as the one above.
Floating-point numbers in SymPy are instances of the class Real. A Real can be created with a custom precision as second argument:
>>> Real(0.1) 0.100000000000000 >>> Real(0.1, 10) 0.1000000000 >>> Real(0.1, 30) 0.100000000000000005551115123126
As the last example shows, Python floats are only accurate to about 15 digits as inputs. To create a Real from a high-precision decimal number, it is better to pass a string or evalf a Rational:
>>> Real('0.1', 30) 0.100000000000000000000000000000 >>> Rational(1,10).evalf(30) 0.100000000000000000000000000000
The precision of a number determines 1) the precision to use when performing arithmetic with the number, and 2) the number of digits to display when printing the number. When two numbers with different precision are used together in an arithmetic operation, the higher of the precisions is used for the result. Note that precision therefore cannot be used as a model of error propagation or significance arithmetic; rather, this scheme is designed to ensure stability of numerical algorithms.
N and evalf can be used to change the precision of existing floating-point numbers:
>>> N(3.5) 3.50000000000000 >>> N(3.5, 5) 3.5000 >>> N(3.5, 30) 3.50000000000000000000000000000
When the input to N or evalf is a complicated expression, numerical error propagation becomes a concern. As an example, consider the 100’th Fibonacci number and the excellent (but not exact) approximation φ^100 / √5 where φ is the golden ratio. With ordinary floating-point arithmetic, subtracting these numbers from each other erroneously results in a complete cancellation:
>>> float(GoldenRatio**1000/sqrt(5)) 4.34665576869e+208 >>> float(fibonacci(1000)) 4.34665576869e+208 >>> float(fibonacci(1000)) - float(GoldenRatio**1000/sqrt(5)) 0.0
N and evalf keep track of errors and automatically increase the precision used internally in order to obtain a correct result:
>>> N(fibonacci(100) - GoldenRatio**100/sqrt(5)) -5.64613129282185e-22
Unfortunately, numerical evaluation cannot tell an expression that is exactly zero apart from one that is merely very small. The working precision is therefore capped, by default to around 100 digits. If we try with the 1000’th Fibonacci number, the following happens:
>>> N(fibonacci(1000) - (GoldenRatio)**1000/sqrt(5)) -.0e+84
The lack of digits in the returned number indicates that N failed to achieve full accuracy. The result indicates that the magnitude of the expression is something less than 10^84, but that is not a particularly good answer. To force a higher working precision, the maxprec keyword argument can be used:
>>> N(fibonacci(1000) - (GoldenRatio)**1000/sqrt(5), maxprec=500) -4.60123853010113e-210
Normally, maxprec can be set very high (thousands of digits), but be aware that this may cause significant slowdown in extreme cases. Alternatively, the strict=True option can be set to force an exception instead of silently returning a value with less than the requested accuracy:
>>> N(fibonacci(1000) - (GoldenRatio)**1000/sqrt(5), strict=True) Traceback (most recent call last): ... PrecisionExhausted: Failed to distinguish the expression: <BLANKLINE> 43466557686937456435688527675040625802564660517371780402481729089536555417949051890403879840079255169295922593080322634775209689623239873322471161642996440906533187938298969649928516003704476137795166849228875 - 5**(1/2)*GoldenRatio**1000/5 <BLANKLINE> from zero. Try simplifying the input, using chop=True, or providing a higher maxprec for evalf
If we add a term so that the Fibonacci approximation becomes exact (the full form of Binet’s formula), we get an expression that is exactly zero, but N does not know this:
>>> f = fibonacci(100) - (GoldenRatio**100 - (GoldenRatio-1)**100)/sqrt(5) >>> N(f) .0e-113 >>> N(f, maxprec=1000) .0e-1345
In situations where such cancellations are known to occur, the chop options is useful. This basically replaces very small numbers with exact zeros:
>>> N(f, chop=True) 0
The chop option is very useful for cleaning up small real or imaginary parts:
>>> N((E+pi*I)*(E-pi*I)) 17.25866050002 - 1.30104260698261e-17*I >>> N((E+pi*I)*(E-pi*I), chop=True) 17.2586605000200
Sums (in particular infinite series) and integrals can be used like regular closed-form expressions, and support arbitrary-precision evaluation:
>>> var('n x') (n, x) >>> Sum(1/n**n, (n, 1, oo)).evalf() 1.29128599706266 >>> Integral(x**(-x), (x, 0, 1)).evalf() 1.29128599706266 >>> Sum(1/n**n, (n, 1, oo)).evalf(50) 1.2912859970626635404072825905956005414986193682745 >>> Integral(x**(-x), (x, 0, 1)).evalf(50) 1.2912859970626635404072825905956005414986193682745 >>> (Integral(exp(-x**2), (x, -oo, oo)) ** 2).evalf(30) 3.14159265358979323846264338328
By default, the tanh-sinh quadrature algorithm is used to evaluate integrals. This algorithm is very efficient and robust for smooth integrands (and even integrals with endpoint singularities), but may struggle with integrals that are highly oscillatory or have mid-interval discontinuities. In many cases, evalf/N will correctly estimate the error. With the following integral, the result is accurate but only good to four digits:
>>> f = abs(sin(x)) >>> Integral(abs(sin(x)), (x, 0, 4)).evalf() 2.346
It is better to split this integral into two pieces:
>>> (Integral(f, (x, 0, pi)) + Integral(f, (x, pi, 4))).evalf() 2.34635637913639
A similar example is the following oscillatory integral:
>>> Integral(sin(x)/x**2, (x, 1, oo)).evalf(maxprec=20) 5.e-1
It can be dealt with much more efficiently by telling evalf or N to use an oscillatory quadrature algorithm:
>>> Integral(sin(x)/x**2, (x, 1, oo)).evalf(quad='osc') 0.504067061906928 >>> Integral(sin(x)/x**2, (x, 1, oo)).evalf(20, quad='osc') 0.50406706190692837199
Oscillatory quadrature requires an integrand containing a factor cos(ax+b) or sin(ax+b). Note that many other oscillatory integrals can be transformed to this form with a change of variables:
>>> from sympy import pprint >>> import sys >>> sys.displayhook = pprint >>> intgrl = Integral(sin(1/x), (x, 0, 1)).transform(x, 1/x) >>> intgrl oo / | | sin(x) | ------ dx | 2 | x | / 1 >>> N(intgrl, quad='osc') 0.504067061906928
Infinite series use direct summation if the series converges quickly enough. Otherwise, extrapolation methods (generally the Euler-Maclaurin formula but also Richardson extrapolation) are used to speed up convergence. This allows high-precision evaluation of slowly convergent series:
>>> var('k') k >>> Sum(1/k**2, (k, 1, oo)).evalf() 1.64493406684823 >>> zeta(2).evalf() 1.64493406684823 >>> Sum(1/k-log(1+1/k), (k, 1, oo)).evalf() 0.577215664901533 >>> Sum(1/k-log(1+1/k), (k, 1, oo)).evalf(50) 0.57721566490153286060651209008240243104215933593992 >>> EulerGamma.evalf(50) 0.57721566490153286060651209008240243104215933593992
The Euler-Maclaurin formula is also used for finite series, allowing them to be approximated quickly without evaluating all terms:
>>> Sum(1/k, (k, 10000000, 20000000)).evalf() 0.693147255559946
Note that evalf makes some assumptions that are not always optimal. For fine-tuned control over numerical summation, it might be worthwhile to manually use the method Sum.euler_maclaurin.
Special optimizations are used for rational hypergeometric series (where the term is a product of polynomials, powers, factorials, binomial coefficients and the like). N/evalf sum series of this type very rapidly to high precision. For example, this Ramanujan formula for pi can be summed to 10,000 digits in a fraction of a second with a simple command:
>>> f = factorial >>> n = Symbol('n', integer=True) >>> R = 9801/sqrt(8)/Sum(f(4*n)*(1103+26390*n)/f(n)**4/396**(4*n), (n, 0, oo)) #doctest: +SKIP >>> N(R, 10000) #doctest: +SKIP 3.141592653589793238462643383279502884197169399375105820974944592307816406286208 99862803482534211706798214808651328230664709384460955058223172535940812848111745 02841027019385211055596446229489549303819644288109756659334461284756482337867831 ...
The function nsimplify attempts to find a formula that is numerically equal to the given input. This feature can be used to guess an exact formula for an approximate floating-point input, or to guess a simpler formula for a complicated symbolic input. The algorithm used by nsimplify is capable of identifying simple fractions, simple algebraic expressions, linear combinations of given constants, and certain elementary functional transformations of any of the preceding.
Optionally, nsimplify can be passed a list of constants to include (e.g. pi) and a minimum numerical tolerance. Here are some elementary examples:
>>> nsimplify(0.1) 1/10 >>> nsimplify(6.28, [pi], tolerance=0.01) 2*pi >>> nsimplify(pi, tolerance=0.01) 22/7 >>> nsimplify(pi, tolerance=0.001) 355 --- 113 >>> nsimplify(0.33333, tolerance=1e-4) 1/3 >>> nsimplify(2.0**(1/3.), tolerance=0.001) 635 --- 504 >>> nsimplify(2.0**(1/3.), tolerance=0.001, full=True) 3 ___ \/ 2
Here are several more advanced examples:
>>> nsimplify(Real('0.130198866629986772369127970337',30), [pi, E]) 1 ---------- 5*pi 2*E + ---- 7 >>> nsimplify(cos(atan('1/3'))) ____ 3*\/ 10 -------- 10 >>> nsimplify(4/(1+sqrt(5)), [GoldenRatio]) -2 + 2*GoldenRatio >>> nsimplify(2 + exp(2*atan('1/4')*I)) 49 8*I -- + --- 17 17 >>> nsimplify((1/(exp(3*pi*I/5)+1))) _____________ / ___ / \/ 5 1/2 - I* / 1/4 + ----- \/ 10 >>> nsimplify(I**I, [pi]) -pi --- 2 e >>> n = Symbol('n') >>> nsimplify(Sum(1/n**2, (n, 1, oo)), [pi]) 2 pi --- 6 >>> nsimplify(gamma('1/4')*gamma('3/4'), [pi]) ___ pi*\/ 2