# Scientific Programming notebooks and python code

Code and notes from reading Hans Peter Langtangen’s book and book repo Software and material.

Contains an ipython notebook with some formulas to accompany calculations, functions and scripts for sections, and a test file.

```
`document_format` contains notebook in distributable format like PDF
`scipro-primer` is the source material github repo by Hans
`function_formulas` contains python functions of formulas
`function_dev_module` contains a testing environment for python functions
`input_dev_module` contains a testing environment for python input functions
`intro_materials` is a composit of topics introducing coding examples
```

Examples of book contents as follows, also available as science_notebook.pdf

## Newton’s Second Law of Motion

```
5 * 0.6 - 0.5 * 9.81 * 0.6 ** 2
```

```
1.2342
```

## Height of an object

- v0 as initial velocity of objects
- g acceleration of gravity
- t as time

With y=0 as axis of object start when t=0 at initial time.

or

- time to move up and return to y=0, return seconds is and restricted to

```
# variables for newton's second law of motion
v0 = 5
g = 9.81
t = 0.6
y = v0*t - 0.5*g*t**2
print(y)
```

```
1.2342
```

```
# or using good pythonic naming conventions
initial_velocity = 5
acceleration_of_gravity = 9.81
TIME = 0.6
VerticalPositionOfBall = initial_velocity*TIME - \
0.5*acceleration_of_gravity*TIME**2
print(VerticalPositionOfBall)
```

```
1.2342
```

## Integral calculation

```
from numpy import *
def integrate(f, a, b, n=100):
"""
Integrate f from a to b
using the Trapezoildal rule with n intervals.
"""
x = linspace(a, b, n+1) # coords of intervals
h = x[1] - x[0]
I = h*(sum(f(x)) - 0.5*(f(a) + f(b)))
return I
# define integrand
def my_function(x):
return exp(-x**2)
minus_infinity = -20 # aprox for minus infinity
I = integrate(my_function, minus_infinity, 1, n=1000)
print("value of integral:", I)
```

```
value of integral: 1.6330240187288536
```

```
# Celsius-Fahrenheit Conversion
C = 21
F = (9/5)*C + 32
print(F)
```

```
69.80000000000001
```

## Time to reach height of

Quadratic equation to solve.

```
v0 = 5
g = 9.81
yc = 0.2
import math
t1 = (v0 - math.sqrt(v0**2 - 2 * g * yc)) / g
t2 = (v0 + math.sqrt(v0**2 - 2 * g * yc)) / g
print('At t=%g s and %g s, the height is %g m.' % (t1, t2, yc))
```

```
At t=0.0417064 s and 0.977662 s, the height is 0.2 m.
```

## The hyperbolic sine function and other math functions with right hand sides.

```
from math import sinh, exp, e, pi
x = 2*pi
r1 = sinh(x)
r2 = 0.5*(exp(x) - exp(-x))
r3 = 0.5*(e**x - e**(-x))
print(r1, r2, r3) # with rounding errors
```

```
267.74489404101644 267.74489404101644 267.7448940410163
```

```
# Math functions for complex numbers
from scipy import *
from cmath import sqrt
sqrt(-1) # complex number with cmath
from numpy.lib.scimath import sqrt
a = 1; b = 2; c = 100
r1 = (-b + sqrt(b**2 - 4*a*c))/(2*a)
r2 = (-b - sqrt(b**2 - 4*a*c))/(2*a)
print("""
t1={r1:g}
t2={r2:g}""".format(r1=r1, r2=r2))
```

```
t1=-1+9.94987j
t2=-1-9.94987j
```

```
# Symbolic computing
from sympy import (
symbols, # define symbols for symbolic math
diff, # differentiate expressions
integrate, # integrate expressions
Rational, # define rational numbers
lambdify, # turn symbolic expr. into python functions
)
# declare symbolic variables
t, v0, g = symbols('t v0 g')
# formula
y = v0*t - Rational(1,2)*g*t**2
dydt = diff(y ,t)
print("At time", dydt)
print("acceleration:", diff(y,t,t)) # 2nd derivative
y2 = integrate(dydt, t)
print("integration of dydt wrt t", y2)
# convert to python function
v = lambdify([t, v0, g], # arguments in v
dydt) # symbolic expression
print("As a function compute y = %g" % v(t=0, v0=5, g=9.81))
```

```
At time -g*t + v0
acceleration: -g
integration of dydt wrt t -g*t**2/2 + t*v0
As a function compute y = 5
```

```
# equation solving for expression e=0, t unknown
from sympy import solve
roots = solve(y, t) # e is y
print("""
If y = 0 for t then t solves y for [{},{}].
""".format(
y.subs(t, roots[0]),
y.subs(t, roots[1])
) )
```

```
If y = 0 for t then t solves y for [0,0].
```

```
# Taylor series to the order n in a variable t around the point t0
from sympy import exp, sin, cos
f = exp(t)
f.series(t, 0, 3)
f_sin = exp(sin(t))
f_sin.series(t, 0, 8)
```

## Taylor Series Polynomial to approximate functions;

```
# expanding and simplifying expressions
from sympy import simplify, expand
x, y = symbols('x y')
f = -sin(x) * sin(y) + cos(x) * cos(y)
print(f)
print(simplify(f))
print(expand(sin(x + y), trig=True)) # expand as trig funct
```

```
-sin(x)*sin(y) + cos(x)*cos(y)
cos(x + y)
sin(x)*cos(y) + sin(y)*cos(x)
```

## Trajectory of an object

```
# Trajectory of an object
g = 9.81 # m/s**2
v0 = 15 # km/h
theta = 60 # degree
x = 0.5 # m
y0 = 1 # m
print("""\
v0 = %.1f km/h
theta = %d degree
y0 = %.1f m
x = %.1f m\
""" % (v0, theta, y0, x))
from math import pi, tan, cos
v0 = v0/3.6 # km/h 1000/1 to m/s 1/60
theta = theta*pi/180 # degree to radians
y = x*tan(theta) - 1/(2*v0**2)*g*x**2/((cos(theta))**2)+y0
print("y = %.1f m" % y)
```

```
v0 = 15.0 km/h
theta = 60 degree
y0 = 1.0 m
x = 0.5 m
y = 1.6 m
```

## Conversion from meters to British units

```
# Convert meters to british length.
meters = 640
m = symbols('m')
in_m = m/(2.54)*100
ft_m = in_m / 12
yrd_m = ft_m / 3
bm_m = yrd_m / 1760
f_in_m = lambdify([m], in_m)
f_ft_m = lambdify([m], ft_m)
f_yrd_m = lambdify([m], yrd_m)
f_bm_m = lambdify([m], bm_m)
print("""
Given {meters:g} meters conversions for;
inches are {inches:.2f} in
feet are {feet:.2f} ft
yards are {yards:.2f} yd
miles are {miles:.3f} m
""".format(meters=meters,
inches=f_in_m(meters),
feet=f_ft_m(meters),
yards=f_yrd_m(meters),
miles=f_bm_m(meters)))
```

```
Given 640 meters conversions for;
inches are 25196.85 in
feet are 2099.74 ft
yards are 699.91 yd
miles are 0.398 m
```

## Gaussian function

```
from sympy import pi, exp, sqrt, symbols, lambdify
s, x, m = symbols("s x m")
y = 1/ (sqrt(2*pi)*s) * exp(-0.5*((x-m)/s)**2)
gaus_d = lambdify([m, s, x], y)
gaus_d(m = 0, s = 2, x = 1)
```

```
0.1760326633821498
```

## Drag force due to air resistance on an object as the expression;

Where

- drag coefficient (based on
roughness and shape)
- As 0.4

- is air density
- Air density of air is = 1.2 kg/

- V is velocity of the object
- A is the
cross-sectional area (normal to the velocity direction)
- for an object with a radius $a$
- = 11 cm

Gravity Force on an object with mass is Where

- = 9.81 m/
- mass = 0.43kg

and results in a difference relationship between air resistance versus gravity at impact time

```
from sympy import (Rational, lambdify, symbols, pi)
g = 9.81 # gravity in m/s**(-2)
air_density = 1.2 # kg/m**(-3)
a = 11 # radius in cm
x_area = pi * a**2 # cross-sectional area
m = 0.43 # mass in kg
Fg = m * g # gravity force
high_velocity = 120 / 3.6 # impact velocity in km/h
low_velocity = 30 / 3.6 # impact velocity in km/h
Cd, Q, A, V = symbols("Cd Q A V")
y = Rational(1, 2) * Cd * Q * A * V**2
drag_force = lambdify([Cd, Q, A, V], y)
Fd_low_impact = drag_force(Cd=0.4,
Q=air_density,
A=x_area,
V=low_velocity)
Fd_high_impact = drag_force(Cd=0.4,
Q=air_density,
A=x_area,
V=high_velocity)
print("ratio of drag force=%.1f and gravity force=%.1f: %.1f" % \
(Fd_low_impact, Fg, float(Fd_low_impact/Fg)))
print("ratio of drag force=%.1f and gravity force=%.1f: %.1f" % \
(Fd_high_impact, Fg, float(Fd_high_impact/Fg)))
```

```
ratio of drag force=6335.5 and gravity force=4.2: 1501.9
ratio of drag force=101368.7 and gravity force=4.2: 24030.7
```

## Critical temperature of an object

An object heats at the center differently from it’s outside, an objects center may also have a different density than it’s outside.

```
def critical_temp(init_temp=4, final_temp=70, water_temp=100,
mass=47, density=1.038, heat_capacity=3.7,
thermal_conductivity=5.4*10**-3):
"""
Heating to a temperature with prevention to exceeding critical
points. Be defining critial temperature points based on
composition, e.g., 63 degrees celcius outter and 70 degrees
celcius inner we can express temperature and time as a
function.
Calculates the time for the center critical temp as a function
of temperature of applied heat where exceeding passes a critical point.
t = (M**(2/3)*c*rho**(1/3)/(K*pi**2*(4*pi/3)**(2/3)))*(ln(0.76*((To-Tw)/(Ty-Tw))))
Arguments:
init_temp: initial temperature in C of object e.g., 4, 20
final_temp: desired temperature in C of object e.g., 70
water_temp: temp in C for boiling water as a conductive fluid e.g., 100
mass: Mass in grams of an object, e.g., small: 47, large: 67
density: rho in g cm**-3 of the object e.g., 1.038
heat_capacity: c in J g**-1 K-1 e.g., 3.7
thermal_conductivity: in W cm**-1 K**-1 e.g., 5.4*10**-3
Returns: Time as a float in seconds to reach temperature Ty.
"""
from sympy import symbols
from sympy import lambdify
from sympy import sympify
from numpy import pi
from math import log as ln # using ln to represent natural log
# using non-pythonic math notation create variables
M, c, rho, K, To, Tw, Ty = symbols("M c rho K To Tw Ty")
# writing out the formula
t = sympify('(M**(2/3)*c*rho**(1/3)/(K*pi**2*(4*pi/3)**(2/3)))*(ln(0.76*((To-Tw)/(Ty-Tw))))')
# using symbolic formula representation to create a function
time_for_Ty = lambdify([M, c, rho, K, To, Tw, Ty], t)
# return the computed value
return time_for_Ty(M=mass, c=heat_capacity, rho=density, K=thermal_conductivity,
To=init_temp, Tw=water_temp, Ty=final_temp)
```

```
critical_temp()
```

```
313.09454902221626
```

```
critical_temp(init_temp=20)
```

```
248.86253747844728
```

```
critical_temp(mass=70)
```

```
408.3278117759983
```

```
critical_temp(init_temp=20, mass=70)
```

```
324.55849416396666
```

## Newtons second law of motion in direction x and y, aka accelerations:

is the sum of force, `m*a_x (mass * acceleration)`

, `ax = (d**2*x)/(d*t**2)`

With gravity from as 0 as ![$x(t)$(https://latex.codecogs.com/gif.latex?x%28t%29) is in the horizontal position at time t

is the sum of force, `m*a_y`

, `ay = (d**2*y)/(d*t**2)`

With gravity from as since is in the veritcal postion at time t

Let coodinate be horizontal and verical positions to time t then we can integrate Newton’s two components, using the second law twice with initial velocity and position with respect to t

```
"""
Derive the trajectory of an object from basic physics.
Newtons second law of motion in direction x and y, aka accelerations:
F_x = ma_x is the sum of force, m*a_x (mass * acceleration)
F_y = ma_y is the sum of force, m*a_y
let coordinates (x(t), y(t)) be position horizontal and vertical to time t
relations between acceleration, velocity, and position are derivatives of t
$a_x = \frac {d^{2}x}{dt^{2}}$, ax = (d**2*x)/(d*t**2)
$a_y = \frac {d^{2}y}{dt^{2}}$ ay = (d**2*y)/(d*t**2)
With gravity and F_x = 0 and F_y = -mg
integrate Newton's the two components, (x(t), y(t)) second law twice with
initial velocity and position wrt t
$\frac{d}{dt}x(0)=v_0 cos\theta$
$\frac{d}{dt}y(0)=v_0 sin\theta$
$x(0) = 0$
$y(0) = y_0$
Derivative(t)x(0) = v0*cos(theta) ; x(0) = 0
Derivative(t)y(0) = v0*sin(theta) ; y(0) = y0
from sympy import *
diff(Symbol(v0)*cos(Symbol(theta)))
diff(Symbol(v0)*sin(Symbol(theta)))
theta: some angle, e.g, pi/2 or 90
Return: relationship between x and y
# the expression for x(t) and y(t)
# if theta = pi/2 then motion is vertical e.g., the y position formula
# if t = 0, or is eliminated then x and y are the object coordinates
"""
```

#### there isn’t any code to this, it just looks at newtons second law of motion

## Sine function as a polynomial

```
x, N, k, sign = 1.2, 25, 1, 1.0
s = x
import math
while k < N:
sign = - sign
k = k + 2
term = sign*x**x/math.factorial(k)
s = s + term
print("sin(%g) = %g (approximation with %d terms)" % (x, s, N))
```

```
sin(1.2) = 1.0027 (approximation with 25 terms)
```

## Print table using an approximate Fahrenheit-Celcius conversiion.

For the approximate formula farenheit to celcius conversions are calculated. Adds a third to conversation_table with an approximate value .

```
F=0; step=10; end=100 # declare
print('------------------')
while F <= end:
C = F/(9.0/5) - 32
C_approx = (F-30)/2
print("{:>3} {:>5.1f} {:>3.0f}".format(F, C, C_approx))
F = F + step
print('------------------')
```

```
------------------
0 -32.0 -15
10 -26.4 -10
20 -20.9 -5
30 -15.3 0
40 -9.8 5
50 -4.2 10
60 1.3 15
70 6.9 20
80 12.4 25
90 18.0 30
100 23.6 35
------------------
```

## Create sequences of odds from 1 to any number.

```
n = 9 # specify any number
c = 1
while 1 <= n:
if c%2 == 1:
print(c)
c += 1
n -= 1
```

```
1
3
5
7
9
```

## Compute energy levels in an atom

Compute the n-th energy level for an electron in an atom, e.g., Hydrogen:

where:

kg is the electron mass

C is the elementary charge

is electrical permittivity of vacuum

Js

Calculates energy level for

```
def formula():
# Symbolic computing
from sympy import (
symbols, # define symbols for symbolic math
lambdify, # turn symbolic expr. into python functions
)
# declare symbolic variables
m_e, e, epsilon_0, h, n = symbols('m_e e epsilon_0 h n')
# formula
En = -(m_e*e**4)/(8*epsilon_0*h**2)*(1/n**2)
# convert to python function
return lambdify([m_e, e, epsilon_0, h, n], # arguments in En
En) # symbolic expression
def compute_atomic_energy(m_e=9.094E-34,
e=1.6022E-19,
epsilon_0=9.9542E-12,
h=6.6261E-34):
En = 0 # energy level of an atom
for n in range(1, 20): # Compute for 1,...,20
En += formula()(m_e, e, epsilon_0, h, n)
return En
```

```
compute_atomic_energy()
```

```
-2.7315307541142e-32
```

and energy released moving from level to is

```
# Symbolic computing
from sympy import (
symbols, # define symbols for symbolic math
lambdify, # turn symbolic expr. into python functions
)
# declare symbolic variables
m_e, e, epsilon_0, h, ni, nf = symbols('m_e e epsilon_0 h ni nf')
# formula
delta_E = -(m_e*e**4)/(8*epsilon_0*h**2)*((1/ni**2)-(1/nf**2))
# convert to python function
y = lambdify([m_e, e, epsilon_0, h, ni, nf], # arguments in En
delta_E) # symbolic expression
def compute_change_in_energy(m_e=9.094E-34,
e=1.6022E-19,
epsilon_0=9.9542E-12,
h=6.6261E-34):
print("Energy released going from level to level.")
En = y(m_e, e, epsilon_0, h, 2, 1) # energy at level 1
for n in range(2, 20): # Compute for 1,...,20
En += y(m_e, e, epsilon_0, h, n-1, n)
print("{:23.2E} {:7} to level {:2}".format(
y(m_e, e, epsilon_0, h, n-1, n),
n-1,
n))
print("Total energy: {:.2E}".format(compute_atomic_energy()))
```

```
compute_change_in_energy()
```

```
Energy released going from level to level.
-1.29E-32 1 to level 2
-2.38E-33 2 to level 3
-8.33E-34 3 to level 4
-3.86E-34 4 to level 5
-2.09E-34 5 to level 6
-1.26E-34 6 to level 7
-8.20E-35 7 to level 8
-5.62E-35 8 to level 9
-4.02E-35 9 to level 10
-2.97E-35 10 to level 11
-2.26E-35 11 to level 12
-1.76E-35 12 to level 13
-1.40E-35 13 to level 14
-1.13E-35 14 to level 15
-9.22E-36 15 to level 16
-7.65E-36 16 to level 17
-6.41E-36 17 to level 18
-5.42E-36 18 to level 19
Total energy: -2.73E-32
```

## Numerical root finding, nonlinear approximation:

solve ;

Given the example equation;

Move all terms on the left hand side to make the ** root** of the equation.

Example 1: Bisection method.

On an interval, , where the root lies that contains a root of the interval is halved at if the sign of changes in the left half, , continue on that side of the halved interval, otherwise continue on the right half interval, . The root is guaranteed to be inside an interval of length .

</br>

Example 2: Newton’s method.

Generates a sequence where if the sequence converges to 0, approaches the root of . That is where solves the equation .

When is not linear i.e., is not in the form with constant and we have a nonlinear difference equation. If we have an approximate solution and if were linear, , we could solve and if is approximatly close to then , the slope would be approximately , , , then the approximate line function would be

Which is the first two terms in Taylor series approximation, and solving for

.

Newton’s method relies on convergence to an approximation root with number of a sequence where divergence may occur, thus we increase until a small with increasing until of some small and some maximum for accounting to divergence.

```
# Example 1.
def f(x):
"""The function f(x) = x - 1 sin x"""
from math import sin
return x - 1 - sin(x)
a = 0
b = 10
fa = f(a)
if fa*f(b) > 0:
m = 0
i = 0
while b-a > 1E-6:
i += 1
m = (a + b)/2.0
fm = f(m)
if fa*fm <= 0:
b = m
else:
a = m
fa = fm
m, i
```

```
(1.9345635175704956, 24)
```

```
# Example 2.
from math import sin, cos
def g(x):
return x - 1 - sin(x)
def dg(x):
return 1 - cos(x)
x = 2
n = 0
N = 100
epsilon = 1.0E-7
f_value = g(x)
while abs(f_value) > epsilon and n <= N:
dfdx_value = float(dg(x))
x = x - f_value/dfdx_value
n += 1
f_value = g(x)
x, n, f_value
```

```
(1.9345632107521757, 3, 2.0539125955565396e-13)
```

## Newton’s second law of motion

A car driver driving at velocity breaks, how to determine the stopping diststance. Newtons second law of motion, an energy equation;

```
def stopping_length_function(initial_velocity=120, friction_coefficient=0.3):
"""Newton's second law of motion for measuring stoppnig distance
Newton's second law of motion is d = (1/2)*(v0**2/(mu*g)) so the stopping
distance of an object in motion, like a car, can be measured. The
friction coefficient measures how slick a road is with a default of 0.3.
Arguments:
initial_velocity, v0: 120 km/h or 50 km/h
friction_coefficient, mu: = 0.3
>>> stopping_length(50, 0.3)
188.77185034167707
>>> stopping_length_function(50, 0.05)
196.63734410591357
Returns a real number as a floating point number.
"""
g = 9.81
v0 = initial_velocity/3.6
mu = friction_coefficient
return (1/2)*(v0**2/(mu*g))
```

```
stopping_length_function()
```

```
188.77185034167707
```

## A hat function translated into if, else if, else clause

Widely used in advanced computer simulations, a can be represented as an branching statement.

```
def N(x):
if x < 0:
return 0.0
elif 0 <= x < 1:
return x
elif 1 <= x < 2:
return 2-x
elif x >= 2:
return 0.0
```

## Numerical integration by the Simpson’s rule:

Given an integral, it’s approximation is given by the Simpson’s rule.

```
def Simpson(f, a, b, n=500):
"""Simpsons's rule approximation."""
h = (b - a)/float(n)
sum1 = 0
for i in range(1, n//2 + 1):
sum1 += f(a + (2*i-1)*h)
sum2 = 0
for i in range(1, n//2):
sum2 += f(a + 2*i*h)
integral = (b-a)/(3*n)*(f(a) + f(b) + 4*sum1 + 2*sum2)
return integral
```

```
def test_Simpson():
"""A test function is used in pytest or nose framework format."""
a = 1.5
b = 2.0
n = 8
g = lambda x: 3.0*x**2 - 7*x + 2.5 # to test the integrand
G = lambda x: x**3.0 - 3.5*x**2 + 2.5*x # the integral of g
exact = G(b) - G(a)
approx = Simpson(g, a, b, n)
success = abs(exact - approx) < 1E-14
msg = "Simpson: {:g}, exact: {:g}".format(approx, exact)
assert success, msg
```

#### Using the integral:

```
def h(x):
return 3*x**2 - 7*x + 2.5
```

```
test_Simpson()
```

```
a, b, = 3.5, 2.0
Simpson(h, a, b)
```

```
-9.749999999999996
```

### Compute a polynomial via a product

Given roots of a polynomial of degree can be computed by

```
x, poly, roots = 1, 1, [2, 4, 7]
for r in roots:
poly *= x - r
poly
```

```
-18
```

### Integrate a function by the Trapezoidal rule

An approximation to the integral of a function over an interval can be found by approximating by the straight line of and and then finding the area under the straight line, which is the area of a trapezoid.

```
%matplotlib inline
import numpy as np
from scipy.integrate import trapz
from scipy import sqrt
import matplotlib.pyplot as plt
def f(x):
return x**2
fig, axs = plt.subplots(nrows=1, ncols=1)
x=np.arange(0,9,0.01)
y=f(x)
axs.plot(y,x, 'k-')
#Trapizium
xstep = np.arange(0,10,3)
area=trapz(y,x)
print(area)
axs.fill_between(f(xstep), 0, xstep)
lwbase = plt.rcParams['lines.linewidth']
thin = lwbase / 2
lwx, lwy = thin, thin
for i, x in enumerate(f(xstep)):
axs.axvline(x, color='white', lw=lwy)
axs.text(x, xstep[i], (x, xstep[i]), color='darkgrey')
plt.show()
```

```
242.19104950000002
```

```
def trap_approx(f,a=0,b=5):
area_trap_rule = (b-a)/2 * (f(a) + f(b))
print(area_trap_rule)
return area_trap_rule
```

```
from scipy import cos, tan, pi, sin
x = trap_approx(lambda x:cos(x)-tan(x))
y = trap_approx(lambda x:x**2)
z = trap_approx(lambda x:2**tan(x))
cosx = trap_approx(lambda x:cos(x),a=0,b=pi)
sinx1 = trap_approx(lambda x:sin(x), a=0, b=pi)
sinx2 = trap_approx(lambda x:sin(x), a=0, b=pi/2)
```

```
11.66044297927453
62.5
2.7400510395289186
0.0
1.9236706937217898e-16
0.7853981633974483
```

```
def trapzint2(f, a=0, b=5):
half = (a+b)/2
area = lambda x, y: (b-a)/2 * (f(x) + f(y))
area1 = area(a, half)
area2 = area(half, b)
return (area1 + area2), half, a, b
```

```
f = lambda x: x**2
area, half, a, b = trapzint2(lambda x:x**2)
fig, axs = plt.subplots(nrows=1, ncols=2)
a1, a2 = trap_approx(f, a, half), trap_approx(f, half, b)
x1 = np.arange(a, half, 0.01)
y1 = f(x1)
axs[0].plot(y1, x1, 'k-')
axs[0].fill_between(y1, x1)
axs[0].set_title(a1)
axs[0].set_ylim(0.0, 5.2)
x2 = np.arange(half, b, 0.01)
y2 = f(x2)
axs[1].plot(y2, x2, 'k-')
axs[1].fill_between(y2, x2)
axs[1].set_title(a2)
from scipy.integrate import quad
area_actual = quad(lambda x:x**2, 0, 5)
fig.suptitle("Approx: " + str(a1+a2) + " Actual: " \
+ str(round(area_actual[0], 2)))
```

```
7.8125
39.0625
Text(0.5, 0.98, 'Approx: 46.875 Actual: 41.67')
```

```
def trapezint(f, a, b, n):
h = (b - a)/float(n)
xi = lambda i: a + i * h
f_list = [f(xi(j)) + f(xi(j+1)) for j in range(1, n)]
return sum((0.5*h) * z for z in f_list), f_list, a, b, n
```

```
f = lambda x: x**2
trapezint(f, 0, 5, 10)
```

```
(41.8125,
[1.25, 3.25, 6.25, 10.25, 15.25, 21.25, 28.25, 36.25, 45.25],
0,
5,
10)
```

```
area, _, a, b, n = trapezint(f, 0, 5, 100)
area
```

```
41.668687500000004
```

### Midpoint integration rule

```
def midpointint(f, a, b, n):
h = (b - a)/float(n)
area_list = [f(a + i * h + 0.5 * h) for i in range(0, n)]
area = h * sum(area_list)
return area
```

```
midpointint(f, 0, 5, 10)
```

```
41.5625
```

### Adaptive Trapezoidal rule

By using the following equation, a maximum can be computed by evaluating at large points in $[a, b]$ of the absolute value of . where and is the difference between the exact integral and that produced by the trapezoidal rule, and is a small number.

The double derivative can be computed by a finite difference formula:

With the estimated max and finding h from setting the right hand side equal to the desired tolerance:

Solving with respect to h gives With we have n that corresponds to the desired accuracy .

```
def adaptive_trapezint(f, a, b, eps=1E-5):
n_limit = 1000000 # Used to avoid infinite loop.
n = 2
integral_n = trapezint(f, a, b, n)
integral_2n = trapeint(f, a, b, 2*n)
diff = abs(integral_2n - integral_n)
print("trapezoidal diff: {}".format(diff))
while (diff > eps) and (n < n_limit):
integral_n = trapeint(f, a, b, n)
integral_2n = trapeint(f, a, b, n)
diff = abs(integral_2n - integral_n)
print("trapezoidal diff: {}".format(diff))
n *= 2
if diff <= eps:
print("The integral computes to: {}".format(integral_2n))
return n
else:
return -n # If not found returns negative n.
```

### Area of a triangle

```
def triangle_area(vertices):
x1, y1 = vertices[0][0], vertices[0][1]
x2, y2 = vertices[1][0], vertices[1][1]
x3, y3 = vertices[2][0], vertices[2][1]
A = 0.5*abs(x2*y3-x3*y2-x1*y3+x3*y1+x1*y2-x2*y1)
return A
```

### Computing the length of a path

The total length of a path from to is the sum of the individual line segments to

```
def pathlength(x,y):
x_list = []
for i, xi in enumerate(x):
x_list.append(xi-x[i-1]**2)
y_list = []
for i, yi in enumerate(y):
y_list.append(yi-y[i-1]**2)
from math import sqrt
return sum([sqrt(abs(i)) for i in list(map(sum, zip(X, Y)))])
```

```
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
```

```
pathlength(x, y)
```

```
29.168806202379237
```

```
def test_pathlength():
success = round(pathlength([1, 2], [2, 4])) == 29
msg = "pathlength for [1, 2], [2, 4] != approx 29"
assert success, msg
```

```
test_pathlength() # all pass
```