python

SciPy is a Python library of mathematical routines. Many of the SciPy routines are Python "wrappers", that is, Python routines that provide a Python interface for numerical libraries and routines originally written in Fortran, C, or C++. Thus, SciPy lets you take advantage of the decades of work that has gone into creating and optimizing numerical routines for science and engineering. Because the Fortran, C, or C++ code that Python accesses is compiled, these routines typically run very fast. Therefore, there is no real downside---no speed penalty---for using Python in these cases.

single: SciPy

We have already encountered one of SciPy's routines,
`scipy.optimize.leastsq`

, for fitting nonlinear functions to
experimental data, which was introduced in the the chapter on `chap8`

.
Here we will provide a further introduction to a number of other SciPy
packages, in particular those on special functions, numerical
integration, including routines for numerically solving ordinary
differential equations (ODEs), discrete Fourier transforms, linear
algebra, and solving non-linear equations. Our introduction to these
capabilities does not include extensive background on the numerical
methods employed; that is a topic for another text. Here we simply
introduce the SciPy routines for performing some of the more frequently
required numerical tasks.

One final note: SciPy makes extensive use of NumPy arrays, so NumPy should always be imported with SciPy

single: SciPy; special functions

SciPy provides a plethora of special functions, including Bessel
functions (and routines for finding their zeros, derivatives, and
integrals), error functions, the gamma function, Legendre, Laguerre, and
Hermite polynomials (and other polynomial functions), Mathieu functions,
many statistical functions, and a number of other functions. Most are
contained in the `scipy.special`

library, and each has its own special
arguments and syntax, depending on the vagaries of the particular
function. We demonstrate a number of them in the code below that
produces a plot of the different functions called. For more information,
you should consult the SciPy web site on the `scipy.special`

library.

```
import numpy as np
import scipy.special
import matplotlib.pyplot as plt
# create a figure window
fig = plt.figure(1, figsize=(9,8))
# create arrays for a few Bessel functions and plot them
x = np.linspace(0, 20, 256)
j0 = scipy.special.jn(0, x)
j1 = scipy.special.jn(1, x)
y0 = scipy.special.yn(0, x)
y1 = scipy.special.yn(1, x)
ax1 = fig.add_subplot(321)
ax1.plot(x,j0, x,j1, x,y0, x,y1)
ax1.axhline(color="grey", ls="--", zorder=-1)
ax1.set_ylim(-1,1)
ax1.text(0.5, 0.95,'Bessel', ha='center', va='top',
transform = ax1.transAxes)
# gamma function
x = np.linspace(-3.5, 6., 3601)
g = scipy.special.gamma(x)
g = np.ma.masked_outside(g, -100, 400)
ax2 = fig.add_subplot(322)
ax2.plot(x,g)
ax2.set_xlim(-3.5, 6)
ax2.axhline(color="grey", ls="--", zorder=-1)
ax2.axvline(color="grey", ls="--", zorder=-1)
ax2.set_ylim(-20, 100)
ax2.text(0.5, 0.95,'Gamma', ha='center', va='top',
transform = ax2.transAxes)
# error function
x = np.linspace(0, 2.5, 256)
ef = scipy.special.erf(x)
ax3 = fig.add_subplot(323)
ax3.plot(x,ef)
ax3.set_ylim(0,1.1)
ax3.text(0.5, 0.95,'Error', ha='center', va='top',
transform = ax3.transAxes)
# Airy function
x = np.linspace(-15, 4, 256)
ai, aip, bi, bip = scipy.special.airy(x)
ax4 = fig.add_subplot(324)
ax4.plot(x,ai, x,bi)
ax4.axhline(color="grey", ls="--", zorder=-1)
ax4.axvline(color="grey", ls="--", zorder=-1)
ax4.set_xlim(-15,4)
ax4.set_ylim(-0.5,0.6)
ax4.text(0.5, 0.95,'Airy', ha='center', va='top',
transform = ax4.transAxes)
# Legendre polynomials
x = np.linspace(-1, 1, 256)
lp0 = np.polyval(scipy.special.legendre(0),x)
lp1 = np.polyval(scipy.special.legendre(1),x)
lp2 = np.polyval(scipy.special.legendre(2),x)
lp3 = np.polyval(scipy.special.legendre(3),x)
ax5 = fig.add_subplot(325)
ax5.plot(x,lp0, x,lp1, x,lp2, x,lp3)
ax5.axhline(color="grey", ls="--", zorder=-1)
ax5.axvline(color="grey", ls="--", zorder=-1)
ax5.set_ylim(-1,1.1)
ax5.text(0.5, 0.9,'Legendre', ha='center', va='top',
transform = ax5.transAxes)
# Laguerre polynomials
x = np.linspace(-5, 8, 256)
lg0 = np.polyval(scipy.special.laguerre(0),x)
lg1 = np.polyval(scipy.special.laguerre(1),x)
lg2 = np.polyval(scipy.special.laguerre(2),x)
lg3 = np.polyval(scipy.special.laguerre(3),x)
ax6 = fig.add_subplot(326)
ax6.plot(x,lg0, x,lg1, x,lg2, x,lg3)
ax6.axhline(color="grey", ls="--", zorder=-1)
ax6.axvline(color="grey", ls="--", zorder=-1)
ax6.set_xlim(-5,8)
ax6.set_ylim(-5,10)
ax6.text(0.5, 0.9,'Laguerre', ha='center', va='top',
transform = ax6.transAxes)
plt.show()
```

The arguments of the different functions depend, of course, on the
nature of the particular function. For example, the first argument of
the two types of Bessel functions called in lines 10-13 is the so-called
*order* of the Bessel function, and the second argument is the
independent variable. The Gamma and Error functions take one argument
each and produce one output. The Airy function takes only one input
argument, but returns four outputs, which correspond the two Airy
functions, normally designated $\mathrm{Ai}(x)$ and $\mathrm{Bi}(x)$,
and their derivatives $\mathrm{Ai}^\prime(x)$ and
$\mathrm{Bi}^\prime(x)$. The plot shows only $\mathrm{Ai}(x)$ and
$\mathrm{Bi}(x)$.

The polynomial functions shown have a special syntax that uses NumPy's
`polyval`

function for generating polynomials. If `p`

is a list or array
of `N`

numbers and `x`

is an array, then

```
polyval(p, x) = p[0]*x**(N-1) + p[1]*x**(N-2) + ... + p[N-2]*x +
p[N-1]
```

For example, if `p = [2.0, 5.0, 1.0]`

, `polyval(p, x)`

generates the
following quadratic polynomial: $2x^2 + 5x +1$.

SciPy's `special.legendre(n)`

and `special.laguerre(n)`

functions output
the coefficients `p`

needed in `polyval`

to produce the
$n^\mathrm{th}$-order Legendre and Laguerre polynomials, respectively.
The `scipy.special`

library has functions that specify many other
polynomial functions in this same way.

single: SciPy; numerical integration single: numerical integration; single integrals

When a function cannot be integrated analytically, or is very difficult
to integrate analytically, one generally turns to numerical integration
methods. SciPy has a number of routines for performing numerical
integration. Most of them are found in the same `scipy.integrate`

library. We list them here for reference.

+---------------+-------------------------------------------------------+
| **Function** | **Description** |
+===============+=======================================================+
| `quad`

| single integration |
+---------------+-------------------------------------------------------+
| `dblquad`

| double integration |
+---------------+-------------------------------------------------------+
| `tplquad`

| triple integration |
+---------------+-------------------------------------------------------+
| `nquad`

| $n$-fold multiple integration |
+---------------+-------------------------------------------------------+
| `fixed_quad`

| Gaussian quadrature, order n |
+---------------+-------------------------------------------------------+
| `quadrature`

| Gaussian quadrature to tolerance |
+---------------+-------------------------------------------------------+
| `romberg`

| Romberg integration |
+---------------+-------------------------------------------------------+
+---------------+-------------------------------------------------------+
| `trapz`

| trapezoidal rule |
+---------------+-------------------------------------------------------+
| `cumtrapz`

| trapezoidal rule to cumulatively compute integral |
+---------------+-------------------------------------------------------+
| `simps`

| Simpson's rule |
+---------------+-------------------------------------------------------+
| `romb`

| Romberg integration |
+---------------+-------------------------------------------------------+
+---------------+-------------------------------------------------------+
| `polyint`

| Analytical polynomial integration (NumPy) |
+---------------+-------------------------------------------------------+
| `poly1d`

| Helper function for `polyint`

(NumPy) |
+---------------+-------------------------------------------------------+

The function `quad`

is the workhorse of SciPy's integration functions.
Numerical integration is sometimes called *quadrature*, hence the name.
It is normally the default choice for performing single integrals of a
function $f(x)$ over a given fixed range from $a$ to $b$

The general form of `quad`

is `scipy.integrate.quad(f, a, b)`

, where `f`

is the name of the function to be integrated and `a`

and `b`

are the
lower and upper limits, respectively. The routine uses *adaptive
quadrature* methods to numerically evaluate integrals, meaning it
successively refines the subintervals (makes them smaller) until a
desired level of numerical precision is achieved. For the `quad`

routine, this is about $10^{-8}$, although it usually does even better.

As an example, let's integrate a Gaussian function over the range from 0 to 1

$$\int_0^1 e^{-x^2} dx$$We first need to define the function $f(x)=e^{-x^2}$, which we do using
a lambda expression, and then we call the function `quad`

to perform the
integration.

```
ipython
In [1]: import scipy.integrate
In [2]: f = lambda x : exp(-x**2)
In [3]: scipy.integrate.quad(f, 0, 1)
Out[3]: (0.7468241328124271, 8.291413475940725e-15)
```

The function call `scipy.integrate.quad(f, 0, 1)`

returns two numbers.
The first is `0.7468...`

, which is the value of the integral, and the
second is `8.29...e-15`

, which is an estimate of the absolute error in
the value of the integral, which we see is quite small compared to
`0.7468`

.

Because `quad`

requires a function *name* as its first argument, we
can't simply use the expression `exp(-x**2)`

. On the other hand, we
could use the usual `def`

statement to create a normal function, and
then use the name of that function in `quad`

. However, it's simpler here
to use a lambda expression. In fact, we can just put the lambda
expression directly into the first argument, as illustrated here

```
ipython
In [4]: scipy.integrate.quad(lambda x : exp(-x**2), 0, 1)
Out[4]: (0.7468241328124271, 8.291413475940725e-15)
```

That works too! Thus we see a `lambda`

expression used as an *anonymous
function*, a function with no name, as promised in the section `lambda`

.

Note

The `quad`

function accepts positive and negative infinity as limits.

```
ipython
In [5]: scipy.integrate.quad(lambda x : exp(-x**2), 0, inf)
Out[5]: (0.8862269254527579, 7.101318390472462e-09)
In [6]: scipy.integrate.quad(lambda x : exp(-x**2), -inf, 1)
Out[6]: (1.6330510582651852, 3.669607414547701e-11)
```

The `quad`

function handles infinite limits just fine. The absolute
errors are somewhat larger but still well within acceptable bounds for
practical work.

The `quad`

function can integrate standard predefined NumPy functions of
a single variable, like `exp`

, `sin`

, and `cos`

.

```
ipython
In [7]: scipy.integrate.quad(exp, 0, 1)
Out[7]: (1.7182818284590453, 1.9076760487502457e-14)
In [8]: scipy.integrate.quad(sin, -0.5, 0.5)
Out[8]: (0.0, 2.707864644566304e-15)
In [9]: scipy.integrate.quad(cos, -0.5, 0.5)
Out[9]: (0.9588510772084061, 1.0645385431034061e-14)
```

Let's integrate the first order Bessel function of the first kind,
usually denoted $J_1(x)$, over the interval from 0 to 5. Here is how we
do it, using `scipy.special.jn(v,x)`

where `v`

is the (real) order of
the Bessel function:

```
ipython
In [10]: import scipy.special
In [11]: scipy.integrate.quad(lambda x: scipy.special.jn(1,x),0,5)
Out[11]: (1.177596771314338, 1.8083362065765924e-14)
```

Because the SciPy function `scipy.special.jn(v, x)`

is a function of two
variables, `v`

and `x`

, we cannot use the function name
`scipy.special.jn`

in `quad`

. So we use a `lambda`

expression, which is
a function of only one variable, `x`

, because we have set the `v`

argument equal to 1.

single: numerical integration; integrals of polynomials

Working in concert with the NumPy `poly1d`

, the NumPy function `polyint`

takes the $n^\mathrm{th}$ antiderivative of a polynomial and can be used
to evaluate definite integrals. The function `poly1d`

essentially does
the same thing as `polyval`

that we encountered in the section
`specFunc`

, but with a different syntax. Suppose we want to make the
polynomial function $p(x) = 2x^2 + 5x +1$. Then we write

```
ipython
In [12]: p = np.poly1d([2, 5, 1])
In [13]: p
Out[13]: poly1d([2, 5, 1])
```

The polynomial $p(x) = 2x^2 + 5x +1$ is evaluated using the syntax
`p(x)`

. Below, we evaluate the polynomial at three different values of
`x`

.

```
ipython
In [14]: p(1), p(2), p(3.5)
Out[14]: (8, 19, 43.0)
```

Thus `polyval`

allows us to define the function $p(x) = 2x^2 + 5x +1$.
Now the antiderivative of $p(x) = 2x^2 + 5x +1$ is
$P(x) = \frac{2}{3}x^3 + \frac{5}{2}x^2 +x+C$ where $C$ is the
integration constant. The NumPy function `polyint`

, which takes the
$n^\mathrm{th}$ antiderivative of a polynomial, works as follows

```
ipython
In [15]: P = polyint(p)
In [16]: P
Out[16]: poly1d([ 0.66666667, 2.5 , 1. , 0. ])
```

When `polyint`

has a single input, `p`

is this case, `polyint`

returns
the coefficients of the antiderivative with the integration constant set
to zero, as `Out[16]`

illustrates. It is then an easy matter to
determine any definite integral of the polynomial $p(x) = 2x^2 + 5x +1$
since

For example, if $a=1$ and $b=5$,

```
ipython
In [17]: q=P(5)-P(1)
In [18]: q
Out[18]: 146.66666666666666
```

or

$$\int_1^5 \left(2x^2 + 5x +1\right)\, dx = 146\tfrac{2}{3} \;.$$single: numerical integration; double integrals

The `scipy.integrate`

function `dblquad`

can be used to numerically
evaluate double integrals of the form

The general form of `dblquad`

is

```
ipython
scipy.integrate.dblquad(func, a, b, gfun, hfun)
```

where `func`

if the name of the function to be integrated, `a`

and `b`

are the lower and upper limits of the `x`

variable, respectively, and
`gfun`

and `hfun`

are the *names* of the functions that define the lower
and upper limits of the `y`

variable.

As an example, let's perform the double integral

$$\int_0^{1/2} dy \int_0^{\sqrt{1-4y^2}} 16xy\, dx$$We define the functions f, g, and h, using lambda expressions. Note that even if g, and h are constants, as they may be in many cases, they must be defined as functions, as we have done here for the lower limit.

```
ipython
In [19]: f = lambda x, y : 16*x*y
In [20]: g = lambda x : 0
In [21]: h = lambda y : sqrt(1-4*y**2)
In [22]: scipy.integrate.dblquad(f, 0, 0.5, g, h)
Out[22]: (0.5, 5.551115123125783e-15)
```

Once again, there are two outputs: the first is the value of the integral and the second is its absolute uncertainty.

Of course, the lower limit can also be a function of $y$, as we demonstrate here by performing the integral

$$\int_0^{1/2} dy \int_{1-2y}^{\sqrt{1-4y^2}} 16xy\, dx$$The code for this is given by

```
ipython
In [23]: g = lambda y : 1-2*y
In [24]: scipy.integrate.dblquad(f, 0, 0.5, g, h)
Out[24]: (0.33333333333333326, 3.700743415417188e-15)
```

In addition to the routines described above, `scipy.integrate`

has a
number of other integration routines, including `nquad`

, which performs
$n$-fold multiple integration, as well as other routines that implement
other integration algorithms. You will find, however, that `quad`

and
`dblquad`

meet most of your needs for numerical integration.

single: ODEs; numerical solutions single: SciPy; ODEs

The `scipy.integrate`

library has two powerful powerful routines, `ode`

and `odeint`

, for numerically solving systems of coupled first order
ordinary differential equations (ODEs). While `ode`

is more versatile,
`odeint`

(ODE integrator) has a simpler Python interface works very well
for most problems. It can handle both stiff and non-stiff problems. Here
we provide an introduction to `odeint`

.

A typical problem is to solve a second or higher order ODE for a given
set of initial conditions. Here we illustrate using `odeint`

to solve
the equation for a driven damped pendulum. The equation of motion for
the angle $\theta$ that the pendulum makes with the vertical is given by

where $t$ is time, $Q$ is the quality factor, $d$ is the forcing amplitude, and $\Omega$ is the driving frequency of the forcing. Reduced variables have been used such that the natural (angular) frequency of oscillation is 1. The ODE is nonlinear owing to the $\sin\theta$ term. Of course, it's precisely because there are no general methods for solving nonlinear ODEs that one employs numerical techniques, so it seems appropriate that we illustrate the method with a nonlinear ODE.

The first step is always to transform any $n^\mathrm{th}$-order ODE into a system of $n$ first order ODEs of the form:

$$\begin{aligned} \frac{dy_1}{dt} &= f_1(t, y_1, ..., y_n) \\ \frac{dy_2}{dt} &= f_2(t, y_1, ..., y_n) \\ \vdots\quad &= \quad\vdots \\ \frac{dy_n}{dt} &= f_n(t, y_1, ..., y_n) \;. \end{aligned}$$We also need $n$ initial conditions, one for each variable $y_i$. Here we have a second order ODE so we will have two coupled ODEs and two initial conditions.

We start by transforming our second order ODE into two coupled first order ODEs. The transformation is easily accomplished by defining a new variable $\omega \equiv d\theta/dt$. With this definition, we can rewrite our second order ODE as two coupled first order ODEs:

$$\begin{aligned} \frac{d\theta}{dt} &= \omega \\ \frac{d\omega}{dt} &= -\frac{1}{Q}\,\omega + \sin\theta + d \cos\Omega t \;. \end{aligned}$$In this case the functions on the right hand side of the equations are

$$\begin{aligned} f_1(t, \theta, \omega) &= \omega \\ f_2(t, \theta, \omega) &= -\frac{1}{Q}\,\omega + \sin\theta + d \cos\Omega t \;. \end{aligned}$$Note that there are no explicit derivatives on the right hand side of the functions $f_i$; they are all functions of $t$ and the various $y_i$, in this case $\theta$ and $\omega$.

The initial conditions specify the values of $\theta$ and $\omega$ at $t=0$.

SciPy's ODE solver `scipy.integrate.odeint`

has three required arguments
and many optional keyword arguments, of which we only need one, `args`

,
for this example. So in this case, `odeint`

has the form

```
ipython
odeint(func, y0, t, args=())
```

The first argument `func`

is the name of a Python function that returns
a list of values of the $n$ functions $f_i(t, y_1, ..., y_n)$ at a given
time $t$. The second argument `y0`

is an array (or list) of the values
of the initial conditions of $y_1, ..., y_n)$. The third argument is the
array of times at which you want `odeint`

to return the values of
$y_1, ..., y_n)$. The keyword argument `args`

is a tuple that is used to
pass parameters (besides `y0`

and `t`

) that are needed to evaluate
`func`

. Our example should make all of this clear.

After having written the $n^\mathrm{th}$-order ODE as a system of $n$
first-order ODEs, the next task is to write the function `func`

. The
function `func`

should have three arguments: (1) the list (or array) of
current `y`

values, the current time `t`

, and a list of any other
parameters `params`

needed to evaluate `func`

. The function `func`

returns the values of the derivatives $dy_i/dt = f_i(t, y_1, ..., y_n)$
in a list (or array). Lines 5-11 illustrate how to write `func`

for our
example of a driven damped pendulum. Here we name the function simply
`f`

, which is the name that appears in the call to `odeint`

in line 33
below.

The only other tasks remaining are to define the parameters needed in
the function, bundling them into a list (see line 22 below), and to
define the initial conditions, and bundling them into another list (see
line 25 below). After defining the time array in lines 28-30, the only
remaining task is to call `odeint`

with the appropriate arguments and a
variable, `psoln`

in this case to store output. The output `psoln`

is an
$n$ element array where each element is itself an array corresponding
the the values of $y_i$ for each time in the time `t`

array that was an
argument of `odeint`

. For this example, the first element `psoln[:,0]`

is the $y_0$ or `theta`

array, and the second element `psoln[:,1]`

is
the $y_1$ or `omega`

array. The remainder of the code simply plots out
the results in different formats. The resulting plots are shown in the
figure `fig:odePend`

after the code.

```
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
def f(y, t, params):
theta, omega = y # unpack current values of y
Q, d, Omega = params # unpack parameters
derivs = [omega, # list of dy/dt=f functions
-omega/Q + np.sin(theta) + d*np.cos(Omega*t)]
return derivs
# Parameters
Q = 2.0 # quality factor (inverse damping)
d = 1.5 # forcing amplitude
Omega = 0.65 # drive frequency
# Initial values
theta0 = 0.0 # initial angular displacement
omega0 = 0.0 # initial angular velocity
# Bundle parameters for ODE solver
params = [Q, d, Omega]
# Bundle initial conditions for ODE solver
y0 = [theta0, omega0]
# Make time array for solution
tStop = 200.
tInc = 0.05
t = np.arange(0., tStop, tInc)
# Call the ODE solver
psoln = odeint(f, y0, t, args=(params,))
# Plot results
fig = plt.figure(1, figsize=(8,8))
# Plot theta as a function of time
ax1 = fig.add_subplot(311)
ax1.plot(t, psoln[:,0])
ax1.set_xlabel('time')
ax1.set_ylabel('theta')
# Plot omega as a function of time
ax2 = fig.add_subplot(312)
ax2.plot(t, psoln[:,1])
ax2.set_xlabel('time')
ax2.set_ylabel('omega')
# Plot omega vs theta
ax3 = fig.add_subplot(313)
twopi = 2.0*np.pi
ax3.plot(psoln[:,0]%twopi, psoln[:,1], '.', ms=1)
ax3.set_xlabel('theta')
ax3.set_ylabel('omega')
ax3.set_xlim(0., twopi)
plt.tight_layout()
plt.show()
```

The plots above reveal that for the particular set of input parameters
chosen, `Q = 2.0`

, `d = 1.5`

, and `Omega = 0.65`

, the pendulum
trajectories are chaotic. Weaker forcing (smaller $d$) leads to what is
perhaps the more familiar behavior of sinusoidal oscillations with a
fixed frequency which, at long times, is equal to the driving frequency.

single: discrete Fourier transforms single: SciPy; discrete Fourier transforms see: fast Fourier transforms; discrete Fourier transforms see: FFTs; discrete Fourier transforms

The SciPy library has a number of routines for performing discrete Fourier transforms. Before delving into them, we provide a brief review of Fourier transforms and discrete Fourier transforms.

The Fourier transform of a function $g(t)$ is given by

$$G(f) = \int_{-\infty}^\infty g(t)\, e^{-i\, 2\pi f t}\, dt \;,$$where $f$ is the Fourier transform variable; if $t$ is time, then $f$ is frequency. The inverse transform is given by

$$g(t) = \int_{-\infty}^\infty G(f)\, e^{i\, 2\pi ft}\, df$$Here we define the Fourier transform in terms of the frequency $f$ rather than the angular frequency $\omega = 2\pi f$.

The conventional Fourier transform is defined for continuous functions,
or at least for functions that are dense and thus have an infinite
number of data points. When doing numerical analysis, however, you work
with *discrete* data sets, that is, data sets defined for a finite
number of points. The discrete Fourier transform (DFT) is defined for a
function $g_n$ consisting of a set of $N$ discrete data points. Those
$N$ data points must be defined at *equally-spaced* times
$t_n=n\Delta t$ where $\Delta t$ is the time between successive data
points and $n$ runs from 0 to $N-1$. The discrete Fourier transform
(DFT) of $g_n$ is defined as

where $l$ runs from 0 to $N-1$. The inverse discrete Fourier transform (iDFT) is defined as

$$g_n = \frac{1}{N} \sum_{l=0}^{N-1} G_l\, e^{i\,(2\pi/N)\,ln} \;.$$The DFT is usually implemented on computers using the well-known Fast Fourier Transform (FFT) algorithm, generally credited to Cooley and Tukey who developed it at AT&T Bell Laboratories during the 1960s. But their algorithm is essentially one of many independent rediscoveries of the basic algorithm dating back to Gauss who described it as early as 1805.

The SciPy library `scipy.fftpack`

has routines that implement a
souped-up version of the FFT algorithm along with many ancillary
routines that support working with DFTs. The basic FFT routine in
`scipy.fftpack`

is appropriately named `fft`

. The program below
illustrates its use, along with the plots that follow.

```
import numpy as np
from scipy import fftpack
import matplotlib.pyplot as plt
width = 2.0
freq = 0.5
t = np.linspace(-10, 10, 101) # linearly space time array
g = np.exp(-np.abs(t)/width) * np.sin(2.0*np.pi*freq*t)
dt = t[1]-t[0] # increment between times in time array
G = fftpack.fft(g) # FFT of g
f = fftpack.fftfreq(g.size, d=dt) # frequenies f[i] of g[i]
f = fftpack.fftshift(f) # shift frequencies from min to max
G = fftpack.fftshift(G) # shift G order to coorespond to f
fig = plt.figure(1, figsize=(8,6), frameon=False)
ax1 = fig.add_subplot(211)
ax1.plot(t, g)
ax1.set_xlabel('t')
ax1.set_ylabel('g(t)')
ax2 = fig.add_subplot(212)
ax2.plot(f, np.real(G), color='dodgerblue', label='real part')
ax2.plot(f, np.imag(G), color='coral', label='imaginary part')
ax2.legend()
ax2.set_xlabel('f')
ax2.set_ylabel('G(f)')
plt.show()
```

The DFT has real and imaginary parts, both of which are plotted in the figure.

The `fft`

function returns the $N$ Fourier components of $G_n$ starting
with the zero-frequency component $G_0$ and progressing to the maximum
positive frequency component $G_{(N/2)-1}$ (or $G_{(N-1)/2}$ if $N$ is
odd). From there, `fft`

returns the maximum *negative* component
$G_{N/2}$ (or $G_{(N-1)/2}$ if $N$ is odd) and continues upward in
frequency until it reaches the minimum negative frequency component
$G_{N-1}$. This is the standard way that DFTs are ordered by most
numerical DFT packages. The `scipy.fftpack`

function `fftfreq`

creates
the array of frequencies in this non-intuitive order such that `f[n]`

in
the above routine is the correct frequency for the Fourier component
`G[n]`

. The arguments of `fftfreq`

are the size of the the orignal array
`g`

and the keyword argument `d`

that is the spacing between the
(equally spaced) elements of the time array (`d=1`

if left unspecified).
The package `scipy.fftpack`

provides the convenience function `fftshift`

that reorders the frequency array so that the zero-frequency occurs at
the middle of the array, that is, so the frequencies proceed
monotonically from smallest (most negative) to largest (most positive).
Applying `fftshift`

to both `f`

and `G`

puts the frequencies `f`

in
ascending order and shifts `G`

so that the frequency of `G[n]`

is given
by the shifted `f[n]`

.

The `scipy.fftpack`

module also contains routines for performing
2-dimensional and $n$-dimensional DFTs, named `fft2`

and `fftn`

,
respectively, using the FFT algorithm.

As for most FFT routines, the `scipy.fftpack`

FFT routines are most
efficient if $N$ is a power of 2. Nevertheless, the FFT routines are
able to handle data sets where $N$ is not a power of 2.

`scipy.fftpack`

also supplies an inverse DFT function `ifft`

. It is
written to act on the *unshifted* FFT so take care! Note also that
`ifft`

returns a *complex* array. Because of machine roundoff error, the
imaginary part of the function returned by `ifft`

will, in general, be
very near zero but not exactly zero even when the original function is a
purely real function.

single: linear algebra single: SciPy; linear algebra

Python's mathematical libraries, NumPy and SciPy, have extensive tools
for numerically solving problems in linear algebra. Here we focus on two
problems that arise commonly in scientific and engineering settings: (1)
solving a system of linear equations and (2) eigenvalue problems. In
addition, we also show how to perform a number of other basic
computations, such as finding the determinant of a matrix, matrix
inversion, and $LU$ decomposition. The SciPy package for linear algebra
is called `scipy.linalg`

.

SciPy has a number of routines for performing basic operations with
matrices. The determinant of a matrix is computed using the
`scipy.linalg.det`

function:

```
ipython
In [1]: import scipy.linalg
In [2]: a = array([[-2, 3], [4, 5]])
In [3]: a
Out[4]: array([[-2, 3],
[ 4, 5]])
In [5]: scipy.linalg.det(a)
Out[5]: -22.0
```

The inverse of a matrix is computed using the `scipy.linalg.inv`

function, while the product of two matrices is calculated using the
NumPy `dot`

function:

```
ipython
In [6]: b = scipy.linalg.inv(a)
In [6]: b
Out[6]: array([[-0.22727273, 0.13636364],
[ 0.18181818, 0.09090909]])
In [7]: dot(a,b)
Out[7]: array([[ 1., 0.],
[ 0., 1.]])
```

single: linear algebra; solving systems of equations single: SciPy; solving systems of equations

Solving systems of equations is nearly as simple as constructing a coefficient matrix and a column vector. Suppose you have the following system of linear equations to solve:

$$\begin{aligned} 2x_1 + 4x_2 + 6x_3 &= 4\\ x_1 - 3x_2 - 9x_3 &= -11\\ 8x_1 + 5x_2 - 7x_3 &= 1\\ \end{aligned}$$The first task is to recast this set of equations as a matrix equation of the form $\mathsf{A}\, \mathbf{x} = \mathbf{b}$. In this case, we have:

$$\begin{aligned} \mathsf{A} = \left(\begin{array}{ccc}2 & 4 & 6 \\ 1 & -3 & -9 \\ 8 & 5 & -7 \end{array}\right) \;,\quad \mathbf{x} = \left(\begin{array}{c}x_1 \\x_2 \\x_3\end{array}\right) \;,\quad \mathbf{b} = \left(\begin{array}{c}4 \\-11 \\1\end{array}\right) \;. \end{aligned}$$Next we construct the array $\mathsf{A}$ and vector $\mathbf{b}$ as NumPy arrays:

```
ipython
In [8]: A = array([[2, 4, 6], [1, -3, -9], [8, 5, -7]])
In [9]: b = array([4, -11, 2])
```

Finally we use the SciPy function `scipy.linalg.solve`

to find $x_1$,
$x_2$, and $x_3$.

```
ipython
In [10]: scipy.linalg.solve(A,b)
Out[10]: array([ -8.91304348, 10.2173913 , -3.17391304])
```

which gives the results: $x_1=-8.91304348$, $x_2= 10.2173913$, and $x_3= -3.17391304$. Of course, you can get the same answer by noting that $\mathbf{x}=\mathsf{A}^{-1}\mathbf{b}$. Following this approach, we can use the scipy.linalg.inv introduced in the previous section:

```
ipython
Ainv = scipy.linalg.inv(A)
In [10]: dot(Ainv, b)
Out[10]: array([ -8.91304348, 10.2173913 , -3.17391304])
```

which is the same answer we obtained using `scipy.linalg.solve`

. Using
`scipy.linalg.solve`

is numerically more stable and a faster than using
$\mathbf{x}=\mathsf{A}^{-1}\mathbf{b}$, so it is the preferred method
for solving systems of equations.

You might wonder what happens if the system of equations are not all linearly independent. For example if the matrix $\mathsf{A}$ is given by

$$\begin{aligned} \mathsf{A} = \left(\begin{array}{ccc}2 & 4 & 6 \\ 1 & -3 & -9 \\ 1 & 2 & 3 \end{array}\right) \end{aligned}$$where the third row is a multiple of the first row. Let's try it out and see what happens. First we change the bottom row of the matrix $\mathsf{A}$ and then try to solve the system as we did before.

```
ipython
In [11]: A[2] = array([1, 2, 3])
In [12]: A
Out[12]: array([[ 2, 4, 6],
[ 1, -3, -9],
[ 1, 2, 3]])
In [13]: scipy.linalg.solve(A,b)
LinAlgError: Singular matrix
In [14]: Ainv = scipy.linalg.inv(A)
LinAlgError: Singular matrix
```

Whether we use `scipy.linalg.solve`

or `scipy.linalg.inv`

, SciPy raises
an error because the matrix is singular.

single: linear algebra; eigenvalue problems single: eigenvalue problems single: anonymous functions; lambda expressions single: lambda expressions

One of the most common problems in science and engineering is the eigenvalue problem, which in matrix form is written as

$$\mathsf{A}\mathbf{x} = \lambda \mathbf{x}$$where $\mathsf{A}$ is a square matrix, $\mathbf{x}$ is a column vector, and $\lambda$ is a scalar (number). Given the matrix $\mathsf{A}$, the problem is to find the set of eigenvectors $\mathbf{x}$ and their corresponding eigenvalues $\lambda$ that solve this equation.

We can solve eigenvalue equations like this using `scipy.linalg.eig`

.
the outputs of this function is an array whose entries are the
eigenvalues and a matrix whose rows are the eigenvectors. Let's return
to the matrix we were using previously and find its eigenvalues and
eigenvectors.

```
ipython
A = array([[2, 4, 6],[1, -3, -9],[8, 5, -7]])
In [15]: A
Out[15]: array([[ 2, 4, 6],
[ 1, -3, -9],
[ 8, 5, -7]])
In [16]: lam, evec = scipy.linalg.eig(A)
In [17]: lam
Out[17]: array([ 2.40995356+0.j, -8.03416016+0.j,
-2.37579340+0.j])
In [18]: evec
Out[18]: array([[-0.77167559, -0.52633654, 0.57513303],
[ 0.50360249, 0.76565448, -0.80920669],
[-0.38846018, 0.36978786, 0.12002724]])
```

The first eigenvalue and its corresponding eigenvector are given by

```
ipython
In [19]: lam[0]
Out[19]: (2.4099535647625494+0j)
In [20]: evec[:,0]
Out[20]: array([-0.77167559, 0.50360249, -0.38846018])
```

We can check that they satisfy the $\mathsf{A}\mathbf{x} = \lambda \mathbf{x}:$

```
ipython
In [21]: dot(A,evec[:,0])
Out[21]: array([-1.85970234, 1.21365861, -0.93617101])
In [22]: lam[0]*evec[:,0]
Out[22]: array([-1.85970234+0.j, 1.21365861+0.j,
-0.93617101+0.j])
```

Thus we see by direct substitution that the left and right sides of $\mathsf{A}\mathbf{x} = \lambda \mathbf{x}:$ are equal. In general, the eigenvalues can be complex, so their values are reported as complex numbers.

single: linear algebra; generalized eigenvalue problem

The `scipy.linalg.eig`

function can also solve the *generalized*
eigenvalue problem

where $\mathsf{B}$ is a square matrix with the same size as $\mathsf{A}$. Suppose, for example, that we have

```
ipython
In [22]: A = array([[2, 4, 6], [1, -3, -9], [8, 5, -7]])
Out[22]: B = array([[5, 9, 1], [-3, 1, 6], [4, 2, 8]])
```

Then we can solve the generalized eigenvalue problem by entering
$\mathsf{B}$ as the optional second argument to `scipy.linalg.eig`

```
ipython
In [23]: lam, evec = scipy.linalg.eig(A,B)
```

The solutions are returned in the same fashion as before, as an array
`lam`

whose entries are the eigenvalues and a matrix `evac`

whose rows
are the eigenvectors.

```
ipython
In [24]: lam
Out[24]: array([-1.36087907+0.j, 0.83252442+0.j,
-0.10099858+0.j])
In [25]: evec
Out[25]: array([[-0.0419907 , -1. , 0.93037493],
[-0.43028153, 0.17751302, -1. ],
[ 1. , -0.29852465, 0.4226201 ]])
```

single: linear algebra; Hermitian and banded matrices

SciPy has a specialized routine for solving eigenvalue problems for
Hermitian (or real symmetric) matrices. The routine for hermitian
matrices is `scipy.linalg.eigh`

. It is more efficient (faster and uses
less memory) than `scipy.linalg.eig`

. The basic syntax of the two
routines is the same, although some of the *optional* arguments are
different. Both routines can solve generalized as well as standard
eigenvalue problems.

SciPy also has a specialized routine `scipy.linalg.eig_banded`

for
solving eigenvalue problems for real symmetric or complex hermitian
banded matrices.

single: non-linear equations single: non-linear equations; solving single: SciPy; non-linear equations see: solving non-linear equations; non-linear equations see: roots of equations; non-linear equations

SciPy has many different routines for numerically solving non-linear equations or systems of non-linear equations. Here we will introduce only a few of these routines, the ones that are relatively simple and appropriate for the most common types of nonlinear equations.

Solving a single nonlinear equation is enormously simpler than solving a system of nonlinear equations, so that is where we start. A word of caution: solving non-linear equations can be a tricky business so it is important that you have a good sense of the behavior of the function you are trying to solve. The best way to do this is to plot the function over the domain of interest before trying to find the solutions. This will greatly assist you in finding the solutions you seek and avoiding spurious solutions.

We begin with a concrete example. Suppose we want to find the solutions to the equation

$$\tan x=\sqrt{(8/x)^2-1}$$Plots of $\tan x$ and $\sqrt{(8/x)^2-1}$ *vs* $x$ are shown in the top
plot in the figure `fig-subplotDemo`

, albeit with $x$ replaced by
$\theta$. The solutions to this equation are those $x$ values where the
two curves $\tan x$ and $\sqrt{(8/x)^2-1}$ cross each other. The first
step towards obtaining a numerical solution is to rewrite the equation
to be solved in the form $f(x)=0$. Doing so, the above equation becomes

Obviously the two equations above have the same solutions for $x$.
Parenthetically we mention that the problem of finding the solutions to
equations of the form $f(x)=0$ is often referred to as *finding the
roots* of $f(x)$.

Next, we plot $f(x)$ over the domain of interest, in this case from $x=0$ to 8. We are only interested in positive solutions and for $x>8$, the equation has no real solutions as the argument of the square root becomes negative. The solutions, the points where $f(x)=0$ are indicated by green circles; there are three of them. Another notable feature of the function is that it diverges to $\pm\infty$ at $x = \{0, \pi/2, 3\pi/2, 5\pi/2\}$.

single: non-linear equations; Brent method

One of the workhorses for finding solutions to a single variable
nonlinear equation is the method of Brent, discussed in many texts on
numerical methods. SciPy's implementation of the Brent algorithm is the
function `scipy.optimize.brentq(f, a, b)`

, which has three required
arguments. The first `f`

is the name of the user-defined function to be
solved. The next two, `a`

and `b`

are the $x$ values that bracket the
solution you are looking for. You should choose `a`

and `b`

so that
there is only one solutions in the interval between `a`

and `b`

. Brent's
method also requires that `f(a)`

and `f(b)`

have opposite signs; an
error message is returned if they do not. Thus to find the three
solutions to $\tan x - \sqrt{(8/x)^2-1} = 0$, we need to run
`scipy.optimize.brentq(f, a, b)`

three times using three different
values of `a`

and `b`

that bracket each of the three solutions. The
program below illustrates the how to use `scipy.optimize.brentq`

```
import numpy as np
import scipy.optimize
import matplotlib.pyplot as plt
def tdl(x):
y = 8./x
return np.tan(x) - np.sqrt(y*y-1.0)
# Find true roots
rx1 = scipy.optimize.brentq(tdl, 0.5, 0.49*np.pi)
rx2 = scipy.optimize.brentq(tdl, 0.51*np.pi, 1.49*np.pi)
rx3 = scipy.optimize.brentq(tdl, 1.51*np.pi, 2.49*np.pi)
rx = np.array([rx1, rx2, rx3])
ry = np.zeros(3)
# print using a list comprehension
print('\nTrue roots:')
print('\n'.join('f({0:0.5f}) = {1:0.2e}'.format(x, tdl(x)) for x in rx))
# Find false roots
rx1f = scipy.optimize.brentq(tdl, 0.49*np.pi, 0.51*np.pi)
rx2f = scipy.optimize.brentq(tdl, 1.49*np.pi, 1.51*np.pi)
rx3f = scipy.optimize.brentq(tdl, 2.49*np.pi, 2.51*np.pi)
rxf = np.array([rx1f, rx2f, rx3f])
# print using a list comprehension
print('\nFalse roots:')
print('\n'.join('f({0:0.5f}) = {1:0.2e}'.format(x, tdl(x)) for x in rxf))
# Plot function and various roots
x = np.linspace(0.7, 8, 128)
y = tdl(x)
# Create masked array for plotting
ymask = np.ma.masked_where(np.abs(y)>20., y)
plt.figure(figsize=(6, 4))
plt.plot(x, ymask)
plt.axhline(color='black')
plt.axvline(x=np.pi/2., color="gray", linestyle='--', zorder=-1)
plt.axvline(x=3.*np.pi/2., color="gray", linestyle='--', zorder=-1)
plt.axvline(x=5.*np.pi/2., color="gray", linestyle='--', zorder=-1)
plt.xlabel(r'$x$')
plt.ylabel(r'$\tan x - \sqrt{(8/x)^2-1}$')
plt.ylim(-8, 8)
plt.plot(rx, ry, 'og', ms=5, label='true roots')
plt.plot(rxf, ry, 'xr', ms=5, label='false roots')
plt.legend(numpoints=1, fontsize='small', loc = 'upper right',
bbox_to_anchor = (0.92, 0.97))
plt.tight_layout()
plt.show()
```

Running this code generates the following output:

```
ipython
In [1]: run rootbrentq.py
True roots:
f(1.39547) = -6.39e-14
f(4.16483) = -7.95e-14
f(6.83067) = -1.11e-15
False roots:
f(1.57080) = -1.61e+12
f(4.71239) = -1.56e+12
f(7.85398) = 1.16e+12
```

The Brent method finds the three true roots of the equation quickly and
accurately when you provide values for the brackets `a`

and `b`

that are
valid. However, like many numerical methods for finding roots, the Brent
method can produce spurious roots as it does in the above example when
`a`

and `b`

bracket singularities like those at
$x = \{\pi/2, 3\pi/2, 5\pi/2\}$. Here we evaluated the function at the
purported roots found by `brentq`

to verify that the values of $x$ found
were indeed roots. For the true roots, the values of the function were
very near zero, to within an acceptable roundoff error of less than
$10^{-13}$. For the false roots, exceedingly large numbers on the order
of $10^{12}$ were obtained, indicating a possible problem with these
roots. These results, together with the plots, allow you to
unambiguously identify the true solutions to this nonlinear function.

The `brentq`

function has a number of optional keyword arguments that
you may find useful. One keyword argument causes `brentq`

to return not
only the solution but the value of the function evaluated at the
solution. Other arguments allow you to specify a tolerance to which the
solution is found as well as a few other parameters possibly of
interest. Most of the time, you can leave the keyword arguments at their
default values. See the `brentq`

entry online on the SciPy web site for
more information.

single: non-linear equations; Newton-Raphson method

SciPy provides a number of other methods for solving nonlinear equations
of a single variable. It has an implementation of the Newton-Raphson
method called `scipy.optimize.newton`

. It's the racecar of such methods;
its super fast but less stable that the Brent method. To fully realize
its speed, you need to specify not only the function to be solved, but
also its first derivative, which is often more trouble than its worth.
You can also specify its second derivative, which may further speed up
finding the solution. If you do not specify the first or second
derivatives, the method uses the secant method, which is usually slower
than the Brent method.

single: non-linear equations; Ridder method single: non-linear equations; Bisection method

Other methods, including the Ridder (`scipy.optimize.ridder`

) and
bisection (`scipy.optimize.bisect`

), are also available, although the
Brent method is generally superior. SciPy let's you use your favorite.

single: non-linear equations; systems of nonlinear equations

Solving systems of nonlinear equations is not for the faint of heart. It is a difficult problem that lacks any general purpose solutions. Nevertheless, SciPy provides quite an assortment of numerical solvers for nonlinear systems of equations. However, because of the complexity and subtleties of this class of problems, we do not discuss their use here.

- Use NumPy's
`polyval`

function together with SciPy to plot the following functions:- The first four Chebyshev polynomials of first kind. Plot these over the interval from -1 to +1.
- The first four Hermite polynomials
*multiplied*by $e^{-x^2/2}$. Plot these on the interval from -5 to +5. These are the first four wave functions of the quantum mechanical simple harmonic oscillator.