Numerical differentiation

The functions I have defined use the numdifftools package,see The curl, divergence, and laplacian operators, are not explicitly a part of the package (they probably should be), but the package does return the Jacobian matrix of all first-order partial derivatives of a vector-valued function (, and the Hessian matrix of second-order partial derivatives of a scalar-valued function (, from which it is easy to compute the desired operators.

In [1]:
import scipy as sp
import numdifftools as nd  # See

import matplotlib as mpl       # As of July 2017 Bucknell computers use v. 2.x 
import matplotlib.pyplot as plt

# Following is an Ipython magic command that puts figures in the  notebook.
# For figures in separate windows, comment out following line and uncomment
# the next line
# Must come before defaults are changed.
%matplotlib notebook

# As of Aug. 2017 reverting to 1.x defaults.
# In 2.x text.ustex requires dvipng, texlive-latex-extra, and texlive-fonts-recommended, 
# which don't seem to be universal
# See'classic')
# M.L. modifications of matplotlib defaults using syntax of v.2.0 
# More info at
# Changes can also be put in matplotlibrc file, or effected using mpl.rcParams[]
plt.rc('figure', figsize = (6, 4.5))            # Reduces overall size of figures
plt.rc('axes', labelsize=16, titlesize=14)
plt.rc('figure', autolayout = True)             # Adjusts supblot parameters for new size
In [2]:
# Divergence of vector-valued function f evaluated at x
def div(f,x):
    jac = nd.Jacobian(f)(x)
    return jac[0,0] + jac[1,1] + jac[2,2]
In [3]:
# Gradient of scalar-valued function f evaluated at x
def grad(f,x):
    return nd.Gradient(f)(x)
In [4]:
# Curl of vector field f evaluated at x
def curl(f,x):
    jac = nd.Jacobian(f)(x)
    return sp.array([jac[2,1]-jac[1,2],jac[0,2]-jac[2,0],jac[1,0]-jac[0,1]])
In [5]:
# Laplacian of scalar field f evaluated at x
def laplacian(f,x):
    hes = nd.Hessdiag(f)(x)
    return sum(hes)

Example from documentation


In [6]:
import matplotlib.pyplot as plt
%matplotlib notebook

x = sp.linspace(-2, 2, 100)
for i in range(10):
    df = nd.Derivative(sp.tanh, n=i)
    y = df(x)
    h = plt.plot(x, y/sp.absolute(y).max())

Scalar function single variable: $\quad f(x) = 4x^3\quad\left.\frac{df}{dx}\right|_{x_0}\quad\left.\frac{d^2f}{dx^2}\right|_{x_0}$

In [7]:
def f(x):
    return 4*x**3
In [8]:
x0 = 3
d1 = nd.Derivative(f,n=1)  # OR nd.Derivative(f)
d2 = nd.Derivative(f,n=2)
(array(108.00000000000003), array(72.00000000000017))

Scalar field (3D): $\quad g({\bf r}) = x^2 + y^3 + 1\left.\quad\nabla g\right|_{{\bf r}_0}\quad \left.\nabla^2g\right|_{{\bf r}_0}$

In [9]:
def g(x):
    return x[0]**2+x[1]**3+1
In [10]:
r0 = sp.array([1,2,3])
array([  2.,  12.,   0.])
In [11]:

Vector field (3D): $\quad {\bf v}({\bf r})\quad\nabla\cdot {\bf v}\quad \nabla\times{\bf v}$

In [12]:
def v1(x):
    return sp.array([x[1]**3,x[0]**2,x[0]])

def v2(x):
    return sp.array([x[0]**3*x[2],x[1],x[2]*x[1]])
In [13]:
r0 = sp.array([1,2,3])
In [14]:
array([ 0., -1., -10.])
In [15]:
div(v1,r0), div(v2,r0)
(0.0, 12.0)

Version Information

version_information is from J.R. Johansson (jrjohansson at
See Introduction to scientific computing with Python:
for more information and instructions for package installation.

If version_information has been installed system wide (as it has been on Bucknell linux computers with shared file systems), continue with next cell as written. If not, comment out top line in next cell and uncomment the second line.

In [16]:
%load_ext version_information

Loading extensions from ~/.ipython/extensions is deprecated. We recommend managing extensions like any other Python packages, in site-packages.
In [17]:
version_information scipy, matplotlib, numdifftools
Python3.6.1 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
OSLinux 3.10.0 327.36.3.el7.x86_64 x86_64 with redhat 7.2 Maipo
Tue Aug 01 12:25:30 2017 EDT
In [ ]: