University:
Boston University
Course:
MET CS 201 | Introduction to Programming
Academic year:

2020
Views:

102

Pages:

20
Author:

menajam08eh

Try Out Dictionaries  Create a dictionary in the Python console or Spyder editor.  Add some values to it just by using a new key as an index. Can you overwrite a value?  Try x.keys() and x.values()  Try: del x[valid_key]  deletes a key/value pair from the dictionary. Tutorial Outline – Part 2       Lists Tuples and dictionaries Modules numpy and matplotlib modules Script setup Development notes Modules  Python modules, aka libraries or packages, add functionality to the core Python language.  The Python Standard Library provides a very wide assortment of functions and data structures.  Check out their Brief Tour for a quick intro.  Distributions like Anaconda provides dozens or hundreds more  You can write your own libraries or install your own. PyPI  The Python Package Index is a central repository for Python software.  Mostly but not always written in Python.  A tool, pip, can be used to install packages from it into your Python setup.  Anaconda provides a similar tool called conda  Number of projects (as of January 2019): 164,947  You should always do your due diligence when using software from a place like PyPI. Make sure it does what you think it’s doing! Python Modules on the SCC  Python modules should not be confused with the SCC module command.  For the SCC there are instructions on how to install Python software for your account or project.  Many SCC modules provide Python packages as well.  Example: tensorflow, pycuda, others. Importing modules  The import command is used to load a module.  The name of the module is prepended to function names and data structures in the module.  The preserves the module namespace  This allows different modules to have the same function names – when loaded the module name keeps them separate. Fun with import  The import command can strip away the module name: from math import *  Or it can import select functions: from math import cos from math import cos,sqrt  Or rename on the import: from math import sin as pySin Easter Eggs from __future__ import braces import antigravity Fun with import  The import command can also load your own Python files.  The Python file to the right can be used in another Python script: # Don't use the .py ending import myfuncs x = [1,2,3,4] y = myfuncs.get_odds(x) myfuncs.py def get_odds(lst): ''' Gets the odd numbers in a list. lst: incoming list of integers return: list of odd integers ''' odds = [] for elem in lst: # Odd if there's a remainder when # dividing by 2. if elem % 2 != 0: odds.append(elem) return odds Import details  Python reads and executes a file when the file  is opened directly: python somefile.py  is imported: import somefile  Lines that create variables, call functions, etc. are all executed.  Here these lines will run when it’s imported into another script! myfuncs.py def get_odds(lst): ''' Gets the odd numbers in a list. lst: incoming list of integers return: list of odd integers ''' odds = [] for elem in lst: # Odd if there's a remainder when # dividing by 2. if elem % 2 != 0: odds.append(elem) return odds x = [1,2,3,4] y = get_odds(x) print(y) The __name__ attribute  Python stores object information in hidden fields called attributes  Every file has one called __name__ whose value depends on how the file is used. # in another Python # script import myfuncs __name__  myfuncs (i.e. the file name) # called directly python myfuncs.py __name__  __main__ myfuncs.py The __name__ attribute  __name__ can be used to make a Python scripts usable as a standalone program and as imported code.  Now: myfuncs.py def get_odds(lst): ''' Gets the odd numbers in a list. lst: incoming list of integers return: list of odd integers ''' odds = [] for elem in lst: # Odd if there's a remainder when # dividing by 2. if elem % 2 != 0: odds.append(elem) return odds  python myfuncs.py  __name__ has the value of ‘__main__’ and the code in the if statement is executed.  import myfuncs  __name__ is ‘myfuncs’ and the if statement does not run. if __name__=='__main__': x = [1,2,3,4] y = get_odds(x) print(y) Tutorial Outline – Part 2       Lists Tuples and dictionaries Modules numpy and matplotlib modules Script setup Development notes A brief into to numpy and matplotlib  numpy is a Python library that provides efficient multidimensional matrix and basic linear algrebra  The syntax is very similar to Matlab or Fortran  matplotlib is a popular plotting library  Remarkably similar to Matlab plotting commands!  A third library, scipy, provides a wide variety of numerical algorithms:  Integrations, curve fitting, machine learning, optimization, root finding, etc.  Built on top of numpy  Investing the time in learning these three libraries is worth the effort!! numpy  numpy provides data structures written in compiled C code  Many of its operations are executed in compiled C or Fortran code, not Python.  Check out numpy_basics.py numpy datatypes  Unlike Python lists, which are generic containers, numpy arrays are typed.  If you don’t specify a type, numpy will assign one automatically.  A wide variety of numerical types are available.  Proper assignment of data types can sometimes have a significant effect on memory usage and performance. Numpy operators  Numpy arrays will do element-wise arithmetic: + / - * **  Matrix (or vector/matrix, etc.) multiplication needs the .dot() function.  Numpy has its own sin(), cos(), log(), etc. functions that will operate elementby-element on its arrays. Plotting with matplotlib  Matplotlib is probably the most popular Python plotting library  Plotly is another good one  If you are familiar with Matlab plotting then matplotlib is very easy to learn!  Plots can be made from lists, tuples, numpy arrays, etc.  Some sample images from matplotlib.org  A vast array of plot types in 2D and 3D are available in this library. A numpy and matplotlib example  numpy_matplotlib_fft.py is a short example on using numpy and matplotlib together.  Open numpy_matplotlib_fft.py  Let’s walk through this…