Download Introduction to Python Programming Nolan Matthews

Introduction to
Python Programming
Nolan Matthews
What is Python and why use it?
From Wiki:
○ High-level, widely used programming
language.
○
Emphasis on code readability, and
intended to enable clear programs on
small and large scale.
○
Free and open-sourced.
Pros/Cons to using Python
Pros:
-Easy to read/write
-Extremely popular
(and growing)
-Already ‘infrastructure’
for scientific analysis.
Cons:
-Slower than lower-level
languages (C,C++,..)
-Certain design restrictions
(I personally don’t know
much about this…)
Some basics..
-To start the python interpreter (where you can run code) in
the terminal type:
$python
-You are now within the python environment.
>>>print(“Hello World!”)
Hello World!
-To leave the python environment type:
>>>exit()
Some more basics..
The interpreter allows you to test commands on the fly.
However, if you want to run a script that is within your local
directory type:
$python test_script.py
where test_script.py is code written in a file of your favorite
text editor. (Must have prefix .py)
(Example helloworld.py)
Even more basics...
Here’s some basic
examples:
>>>local_var=17
>>>print(local_var)
17
>>>local_var*2
34
>>>local_var/3
5
Note that int/int == int
>>>local_var/3.0
5.666666667
>>>type(local_var)
‘int’
>>>type(3.0)
‘float’
Loops
Logical Operators
>>>a=3
>>>for i in range(5):
>>>a==3
>>>
True
print(i)
>>>a!=3
>>>N=10
False
>>>while j <= N:
>>>
print(j)
>>>
j+=1
Control Statements
if a==3:
print(‘A equals 3’)
elif a==2:
print(‘A equals 2’)
else:
print(‘A does not =equal 2 or 3’)
Defining functions
In a file named counter.py
def counter(N):
#define function names/inputs
i=0
#initialize index
while i <=N:
#begin while loop
print(i)
#prints out iteration number
i+=1
#adds 1 to i (eqvil. i = i+1)
counter(10)
Notice syntax used in loops! Controlled by indentation…
$python counter.py
What if we want to force N to be an integer?
Defining Functions (cont…)
In many cases you want a function to output a parameter
based on input parameters. Here is a simple example:
def addone(x):
return x+1
y=addone(4)
print(y)
#Returns the value of x+1
#Assigns addone(4) to local var. y
Defining Functions (cont…)
In many cases you want a function to output a parameter
based on input parameters. Here is a simple example:
def addone(x):
return x+1
y=addone(4)
print(y)
TASK: Write a function that takes
in two inputs ‘x’ and ‘c’ and
outputs x^(3/2) + c
Working with Strings/Lists
>>>basket=[“apple”,”banana”,”orange”,”pear”]
>>>for fruits in basket:
#Iterates through ‘basket’
>>>
#Prints out current element in iteration
print(fruits)
Working with Strings/Lists
>>>basket=[“apple”,”banana”,”orange”,”pear”]
>>>for fruits in basket:
#Iterates through ‘basket’
>>>
#Prints out current element in iteration
print(fruits)
-Note that this is equivalent to the more complex method generally used,
>>>for i in range(len(basket)): #Iterates through index number
>>>
print(basket[i])
#Calls specific element of ‘basket’ at index ‘i’
Working with Strings/Lists
>>>basket=[“apple”,”banana”,”orange”,”pear”]
>>>for fruits in basket:
#Iterates through ‘basket’
>>>
#Prints out current element in iteration
print(fruits)
-Note that this is equivalent to the more complex method generally used,
>>>for i in range(len(basket)): #Iterates through index number
>>>
print(basket[i])
#Calls specific element of ‘basket’ at index ‘i’
-There are many list methods which can be utilized in an OOP way.
>>>basket.append(“lemon”) #Adds an element to the end
>>>basket.remove(“orange”) #Removes elements == orange
>>>print(basket)
Importing Packages (“libraries”)
-Similar to C++, python has a lot of pre-built
modules for widely varying tasks. The most
commonly used are the NumPy, SciPy, and
MatPlotLib packages.
NumPy→ Numerical Package.
SciPy→ Scientific Package
MatPlotLib → Graphing/plotting package.
Using Packages/Modules
To import a module:
>>>import numpy
#imports numpy module
>>>numpy.version.version #checks version
>>>import numpy as np #imports under different call
>>>np.sqrt(4)
#mathematical functions..
>>>np.sqrt(6)
#notice ‘int’ to ‘float’ (depends
on version)
NumPy Array Handling
>>>array=np.arange(10)
>>>help(np.arange)
>>>print(array)
>>>print(array[5:8])
>>>print(array[5:])
>>>print(array/2)
>>>array[3] = 15
>>>print array
TASK: Write a function
that takes in an array of
numbers and returns an
array which contains only
the even numbers.
HINTS: % and np.delete
Plotting using MatPlotLib (MPL)
>>>from matplotlib import pyplot as plt
>>>import numpy as np
>>>x=np.arange(0,100)
#Set up X/Y pts.
>>>y=2*x
>>>plt.plot(x,y)
#Put on figure
>>>plt.show()
#Display Figure
Extremely Simple!
>>>help(plt.plot)
Interacting with MPL figures
-We will look at an example from the matplotlib
website (slightly modified) to some of the
capabilities of MPL figures using the
integral_demo.py script
$python integral_demo.py
Interacting w/ MPL figures (cont..)
TASK: Modify the integral_demo.py script to
implement the following steps:
-Include labels for the X/Y axes and also a title.
-Remove the x and y text at the corners of the graph.
-Below the LaTeX text include a line (using ‘LaTeX’ font)
that describes what f(x) is. Be sure that it is properly
displayed on the graph.
-Force the line color of f(x) to be red.
Working with SciPy
-SciPy contains routines that often show up in
scientific work (e.g. numerical integrals,
solution of diff. eq’s, optimization..)
-Contains broad range of functions. Here we
will focus on the optimization package.
Curve Fitting in Python
-Lets first create some simulated data. Let’s do
this in a file called cfitting.py
>>>mu,sigma,numPts=0,2.0,1000
>>>x=np.arange(numPts)
>>>help(np.random.normal)
>>>y=x+np.random.normal(mu,sigma,numPts)
>>>plt.plot(x,y,’.’)
>>>plt.show()
Curve Fitting in Python (cont…)
Now let’s define the function to be fit to the
data.
>>>def line(x,m,b):
>>>
return m*x+b
Curve Fitting in Python (cont…)
Using the scipy.optimize.curve_fit module we
perform the fitting.
>>>from scipy.optimize import curve_fit
>>>help(curve_fit)
>>>fit,success=curve_fit(f=line,xdata=x,ydata=y)
>>>print(fit)
Let’s check the results...
>>>x_vals=np.arange(0,np.size(x),0.01)
#Sets up x array.
>>>theory=line(x_vals,fit[0],fit[1])
#Creates theory line from fit
>>>plt.plot(x_vals,theory)
#Plot fit results as line
>>>plt.plot(x,y,’.’)
#Plot data as points
>>>plt.show()
TASK
-Define a function that produces a 1D gaussian curve using the following inputs: mean position,
amplitude, and standard deviation. Verify this by plotting it on a graph. To start set mu=0.0,sigma=1.0,
amp=1.0.
-Create simulated data by adding normally distributed (np.random.normal) random values to a ‘pure’
gaussian. Again, verify by plotting on a graph.
-Using the curve_fit function, fit the gaussian to the simulated data. Do the fit coefficients match the
expected values?
(Helpful commands: np.shape,np.zeros,np.arange)
-->Could we improve the fitting?
-->How could we check the fit results? Or, in other words, how do we estimate how accurate are
the fit parameters to the true value?