# Integration (`scipy.integrate`)¶

The `scipy.integrate` sub-package provides several integration
techniques including an ordinary differential equation integrator. An
overview of the module is provided by the help command:

```
>>> help(integrate)
Methods for Integrating Functions given function object.
quad -- General purpose integration.
dblquad -- General purpose double integration.
tplquad -- General purpose triple integration.
fixed_quad -- Integrate func(x) using Gaussian quadrature of order n.
quadrature -- Integrate with given tolerance using Gaussian quadrature.
romberg -- Integrate func using Romberg integration.
Methods for Integrating Functions given fixed samples.
trapz -- Use trapezoidal rule to compute integral from samples.
cumtrapz -- Use trapezoidal rule to cumulatively compute integral.
simps -- Use Simpson's rule to compute integral from samples.
romb -- Use Romberg Integration to compute integral from
(2**k + 1) evenly-spaced samples.
See the special module's orthogonal polynomials (special) for Gaussian
quadrature roots and weights for other weighting factors and regions.
Interface to numerical integrators of ODE systems.
odeint -- General integration of ordinary differential equations.
ode -- Integrate ODE using VODE and ZVODE routines.
```

## General integration (`quad`)¶

The function `quad` is provided to integrate a function of one
variable between two points. The points can be \(\pm\infty\)
(\(\pm\) `inf`) to indicate infinite limits. For example,
suppose you wish to integrate a bessel function `jv(2.5, x)` along
the interval \([0, 4.5].\)

This could be computed using `quad`:

```
>>> import scipy.integrate as integrate
>>> import scipy.special as special
>>> result = integrate.quad(lambda x: special.jv(2.5,x), 0, 4.5)
>>> result
(1.1178179380783249, 7.8663172481899801e-09)
```

```
>>> from numpy import sqrt, sin, cos, pi
>>> I = sqrt(2/pi)*(18.0/27*sqrt(2)*cos(4.5) - 4.0/27*sqrt(2)*sin(4.5) +
... sqrt(2*pi) * special.fresnel(3/sqrt(pi))[0])
>>> I
1.117817938088701
```

```
>>> print(abs(result[0]-I))
1.03761443881e-11
```

The first argument to quad is a “callable” Python object (*i.e.* a
function, method, or class instance). Notice the use of a lambda-
function in this case as the argument. The next two arguments are the
limits of integration. The return value is a tuple, with the first
element holding the estimated value of the integral and the second
element holding an upper bound on the error. Notice, that in this
case, the true value of this integral is

where

is the Fresnel sine integral. Note that the numerically-computed integral is within \(1.04\times10^{-11}\) of the exact result — well below the reported error bound.

If the function to integrate takes additional parameters, the can be provided
in the *args* argument. Suppose that the following integral shall be calculated:

This integral can be evaluated by using the following code:

```
>>> from scipy.integrate import quad
>>> def integrand(x, a, b):
... return a * x + b
>>> a = 2
>>> b = 1
>>> I = quad(integrand, 0, 1, args=(a,b))
>>> I = (2.0, 2.220446049250313e-14)
```

Infinite inputs are also allowed in `quad` by using \(\pm\)
`inf` as one of the arguments. For example, suppose that a numerical
value for the exponential integral:

is desired (and the fact that this integral can be computed as
`special.expn(n,x)` is forgotten). The functionality of the function
`special.expn` can be replicated by defining a new function
`vec_expint` based on the routine `quad`:

```
>>> from scipy.integrate import quad
>>> def integrand(t, n, x):
... return np.exp(-x*t) / t**n
```

```
>>> def expint(n, x):
... return quad(integrand, 1, np.inf, args=(n, x))[0]
```

```
>>> vec_expint = np.vectorize(expint)
```

```
>>> vec_expint(3, np.arange(1.0, 4.0, 0.5))
array([ 0.1097, 0.0567, 0.0301, 0.0163, 0.0089, 0.0049])
>>> import scipy.special as special
>>> special.expn(3, np.arange(1.0,4.0,0.5))
array([ 0.1097, 0.0567, 0.0301, 0.0163, 0.0089, 0.0049])
```

The function which is integrated can even use the quad argument (though the
error bound may underestimate the error due to possible numerical error in the
integrand from the use of `quad` ). The integral in this case is

```
>>> result = quad(lambda x: expint(3, x), 0, np.inf)
>>> print(result)
(0.33333333324560266, 2.8548934485373678e-09)
```

```
>>> I3 = 1.0/3.0
>>> print(I3)
0.333333333333
```

```
>>> print(I3 - result[0])
8.77306560731e-11
```

This last example shows that multiple integration can be handled using
repeated calls to `quad`.

## General multiple integration (`dblquad`, `tplquad`, `nquad`)¶

The mechanics for double and triple integration have been wrapped up into the
functions `dblquad` and `tplquad`. These functions take the function
to integrate and four, or six arguments, respectively. The limits of all
inner integrals need to be defined as functions.

An example of using double integration to compute several values of \(I_{n}\) is shown below:

```
>>> from scipy.integrate import quad, dblquad
>>> def I(n):
... return dblquad(lambda t, x: np.exp(-x*t)/t**n, 0, np.inf, lambda x: 1, lambda x: np.inf)
```

```
>>> print(I(4))
(0.25000000000435768, 1.0518245707751597e-09)
>>> print(I(3))
(0.33333333325010883, 2.8604069919261191e-09)
>>> print(I(2))
(0.49999999999857514, 1.8855523253868967e-09)
```

As example for non-constant limits consider the integral

This integral can be evaluated using the expression below (Note the use of the non-constant lambda functions for the upper limit of the inner integral):

```
>>> from scipy.integrate import dblquad
>>> area = dblquad(lambda x, y: x*y, 0, 0.5, lambda x: 0, lambda x: 1-2*x)
>>> area
(0.010416666666666668, 1.1564823173178715e-16)
```

For n-fold integration, scipy provides the function `nquad`. The
integration bounds are an iterable object: either a list of constant bounds,
or a list of functions for the non-constant integration bounds. The order of
integration (and therefore the bounds) is from the innermost integral to the
outermost one.

The integral from above

can be calculated as

```
>>> from scipy import integrate
>>> N = 5
>>> def f(t, x):
... return np.exp(-x*t) / t**N
>>> integrate.nquad(f, [[1, np.inf],[0, np.inf]])
(0.20000000000002294, 1.2239614263187945e-08)
```

Note that the order of arguments for *f* must match the order of the
integration bounds; i.e. the inner integral with respect to \(t\) is on
the interval \([1, \infty]\) and the outer integral with respect to
\(x\) is on the interval \([0, \infty]\).

Non-constant integration bounds can be treated in a similar manner; the example from above

can be evaluated by means of

```
>>> from scipy import integrate
>>> def f(x, y):
... return x*y
>>> def bounds_y():
... return [0, 0.5]
>>> def bounds_x(y):
... return [0, 1-2*y]
>>> integrate.nquad(f, [bounds_x, bounds_y])
(0.010416666666666668, 4.101620128472366e-16)
```

which is the same result as before.

## Gaussian quadrature¶

A few functions are also provided in order to perform simple Gaussian
quadrature over a fixed interval. The first is `fixed_quad` which
performs fixed-order Gaussian quadrature. The second function is
`quadrature` which performs Gaussian quadrature of multiple
orders until the difference in the integral estimate is beneath some
tolerance supplied by the user. These functions both use the module
`special.orthogonal` which can calculate the roots and quadrature
weights of a large variety of orthogonal polynomials (the polynomials
themselves are available as special functions returning instances of
the polynomial class — e.g. `special.legendre`).

## Romberg Integration¶

Romberg’s method [WPR] is another method for numerically evaluating an
integral. See the help function for `romberg` for further details.

## Integrating using Samples¶

If the samples are equally-spaced and the number of samples available
is \(2^{k}+1\) for some integer \(k\), then Romberg `romb`
integration can be used to obtain high-precision estimates of the
integral using the available samples. Romberg integration uses the
trapezoid rule at step-sizes related by a power of two and then
performs Richardson extrapolation on these estimates to approximate
the integral with a higher-degree of accuracy.

In case of arbitrary spaced samples, the two functions trapz (defined in numpy
[NPT]) and `simps` are available. They are using Newton-Coates formulas
of order 1 and 2 respectively to perform integration. The trapezoidal rule
approximates the function as a straight line between adjacent points, while
Simpson’s rule approximates the function between three adjacent points as a
parabola.

For an odd number of samples that are equally spaced Simpson’s rule is exact if the function is a polynomial of order 3 or less. If the samples are not equally spaced, then the result is exact only if the function is a polynomial of order 2 or less.

```
>>> import numpy as np
>>> def f1(x):
... return x**2
>>> def f2(x):
... return x**3
>>> x = np.array([1,3,4])
>>> y1 = f1(x)
>>> from scipy.integrate import simps
>>> I1 = simps(y1, x)
>>> print(I1)
21.0
```

This corresponds exactly to

whereas integrating the second function

```
>>> y2 = f2(x)
>>> I2 = integrate.simps(y2, x)
>>> print(I2)
61.5
```

does not correspond to

because the order of the polynomial in f2 is larger than two.

## Faster integration using Ctypes¶

A user desiring reduced integration times may pass a C function pointer through
`ctypes` to `quad`, `dblquad`, `tplquad` or `nquad` and it will be integrated
and return a result in Python. The performance increase here arises from two
factors. The primary improvement is faster function evaluation, which is
provided by compilation. This can also be achieved using a library like Cython
or F2Py that compiles Python. Additionally we have a speedup provided by the
removal of function calls between C and Python in `quad` - this cannot be
achieved through Cython or F2Py. This method will provide a speed increase of
~2x for trivial functions such as sine but can produce a much more noticeable
increase (10x+) for more complex functions. This feature then, is geared
towards a user with numerically intensive integrations willing to write a
little C to reduce computation time significantly.

`ctypes` integration can be done in a few simple steps:

1.) Write an integrand function in C with the function signature
`double f(int n, double args[n])`, where `args` is an array containing the
arguments of the function f.

```
//testlib.c
double f(int n, double args[n]){
return args[0] - args[1] * args[2]; //corresponds to x0 - x1 * x2
}
```

2.) Now compile this file to a shared/dynamic library (a quick search will help with this as it is OS-dependent). The user must link any math libraries, etc. used. On linux this looks like:

```
$ gcc -shared -o testlib.so -fPIC testlib.c
```

The output library will be referred to as `testlib.so`, but it may have a
different file extension. A library has now been created that can be loaded
into Python with `ctypes`.

3.) Load shared library into Python using `ctypes` and set `restypes` and
`argtypes` - this allows Scipy to interpret the function correctly:

```
>>> import ctypes
>>> from scipy import integrate
>>> lib = ctypes.CDLL('/**/testlib.so') # Use absolute path to testlib
>>> func = lib.f # Assign specific function to name func (for simplicity)
>>> func.restype = ctypes.c_double
>>> func.argtypes = (ctypes.c_int, ctypes.c_double)
```

Note that the `argtypes` will always be `(ctypes.c_int, ctypes.c_double)`
regardless of the number of parameters, and `restype` will always be
`ctypes.c_double`.

4.) Now integrate the library function as normally, here using `nquad`:

```
>>> integrate.nquad(func, [[0, 10], [-10, 0], [-1, 1]])
(1000.0, 1.1102230246251565e-11)
```

And the Python tuple is returned as expected in a reduced amount of time. All optional parameters can be used with this method including specifying singularities, infinite bounds, etc.

## Ordinary differential equations (`odeint`)¶

Integrating a set of ordinary differential equations (ODEs) given
initial conditions is another useful example. The function
`odeint` is available in SciPy for integrating a first-order
vector differential equation:

given initial conditions \(\mathbf{y}\left(0\right)=y_{0}\), where \(\mathbf{y}\) is a length \(N\) vector and \(\mathbf{f}\) is a mapping from \(\mathcal{R}^{N}\) to \(\mathcal{R}^{N}.\) A higher-order ordinary differential equation can always be reduced to a differential equation of this type by introducing intermediate derivatives into the \(\mathbf{y}\) vector.

For example suppose it is desired to find the solution to the following second-order differential equation:

with initial conditions \(w\left(0\right)=\frac{1}{\sqrt[3]{3^{2}}\Gamma\left(\frac{2}{3}\right)}\) and \(\left.\frac{dw}{dz}\right|_{z=0}=-\frac{1}{\sqrt[3]{3}\Gamma\left(\frac{1}{3}\right)}.\) It is known that the solution to this differential equation with these boundary conditions is the Airy function

which gives a means to check the integrator using `special.airy`.

First, convert this ODE into standard form by setting \(\mathbf{y}=\left[\frac{dw}{dz},w\right]\) and \(t=z\). Thus, the differential equation becomes

In other words,

As an interesting reminder, if \(\mathbf{A}\left(t\right)\) commutes with \(\int_{0}^{t}\mathbf{A}\left(\tau\right)\, d\tau\) under matrix multiplication, then this linear differential equation has an exact solution using the matrix exponential:

However, in this case, \(\mathbf{A}\left(t\right)\) and its integral do not commute.

There are many optional inputs and outputs available when using odeint
which can help tune the solver. These additional inputs and outputs
are not needed much of the time, however, and the three required input
arguments and the output solution suffice. The required inputs are the
function defining the derivative, *fprime*, the initial conditions
vector, *y0*, and the time points to obtain a solution, *t*, (with
the initial value point as the first element of this sequence). The
output to `odeint` is a matrix where each row contains the
solution vector at each requested time point (thus, the initial
conditions are given in the first output row).

The following example illustrates the use of odeint including the
usage of the *Dfun* option which allows the user to specify a gradient
(with respect to \(\mathbf{y}\) ) of the function,
\(\mathbf{f}\left(\mathbf{y},t\right)\).

```
>>> from scipy.integrate import odeint
>>> from scipy.special import gamma, airy
>>> y1_0 = 1.0 / 3**(2.0/3.0) / gamma(2.0/3.0)
>>> y0_0 = -1.0 / 3**(1.0/3.0) / gamma(1.0/3.0)
>>> y0 = [y0_0, y1_0]
>>> def func(y, t):
... return [t*y[1],y[0]]
```

```
>>> def gradient(y, t):
... return [[0,t], [1,0]]
```

```
>>> x = np.arange(0, 4.0, 0.01)
>>> t = x
>>> ychk = airy(x)[0]
>>> y = odeint(func, y0, t)
>>> y2 = odeint(func, y0, t, Dfun=gradient)
```

```
>>> ychk[:36:6]
array([0.355028, 0.339511, 0.324068, 0.308763, 0.293658, 0.278806])
```

```
>>> y[:36:6,1]
array([0.355028, 0.339511, 0.324067, 0.308763, 0.293658, 0.278806])
```

```
>>> y2[:36:6,1]
array([0.355028, 0.339511, 0.324067, 0.308763, 0.293658, 0.278806])
```