Public code for the OOSA course.

There is a separate directory for each week's work. As we progress, suggested answers will be uploaded.

Foundations

The foundations folder contains some warm up exercises to transition on to the course. There are two folders within:

• basic_features
• iterating

The basic_features folder contains a set of scripts introducing the basic Python features (data types, loops, plotting etc.). Other than 06_objects.py, which will be covered in week 2, these should already be familar to you and are included here for revision.

The iterating directory contains some exercises to practice using loops and work up to moving around a raster dataset.

Week 1

Week 1 covers:

Aspects

• Github version control and code repository
• Computer basics
• Revision of loops and file I/O

Algorithm

• Introduction to algorithm design: Finding minima and sorting

fileIO

Contains two scripts to demonstrate reading from a text file and writing to a text file. The data folder contains the data for the file reading example.

data

Contains a text file with some sample data to practice reading with the fileIO code.

sort

Contains an example solution for a simple sort. Note that there are multiple sort solutions, such as bubble sort, and then many more complex and efficient algorithms.

Week 2

Week 2 covers

Aspects

• Using the command line to make programmable programs
• Objects and classes

Algorithm

• Binary search: Loop and recursion

main

Contains an example of the main block in order to ease importing code in to other programs

docu_strings

Contains examples of document strings. Week 2 covers

Aspects

• Using the command line to make programmable programs
• Objects and classes

Algorithm

• Binary search: Loop and recursion

command_line

Contains two example python files, which can be used to alter the behaviour of a program at run time. This allows you to create a single python program and then reuse it with different input files, options etc.

commandExample.py: Minimum workable example of a command line
commandLineIllus.py: Illustrates the common command 

objects

Includes a script with a simple example of an object; a grouping of data and functions.

data

Contains some text data files for use in this week's exercises.

binary_search

Contains the answer for week 2's algorithm.

binarySearches.py: contains suggested answers for binary search by loop and recursion
finishedQuartiles.py: uses the above to find quartiles in a sorted dataset
searchObject.py: begins an object for sorting data
makeData.py: makes data for testing algorithms
randomWages.py: generates random wage data for testing algorithms

Week 3

Week 3 covers

Aspects

• Geospatial packages: pyproj and gdal
• A note on function input/output
• Function fitting
• A mention of pandas
• Geospatial data formats; HDF5

Algorithm

• Douglas-Peucker line generalization

reproject

Shows an example of using the dgal package to reproject raster or vector data.

function_fit

Shows an example of fitting a function to data.

pointer_reference

Demonstrates the difference between variables that point to an array and copying a whole array.

pandas

Shows an example of reading data into a pandas object.

hdf

Contains two files:

lvisClass.py: Shows an example of reading HDF data in to RAM, in this case using LVIS data. 
lvisCompleteExample.py: Shows an examp[le of reprojecting and plotting data.

line_distance

A function to find the orthogonal distance between a line and a point, to be used in the Douglas-Peucker line generalization algorithm.

dp-line-general

An example solution for Douglas-Peucker line-generalisation. This one uses recursion and imports a script from a different folder usingf PYTHONPATH.

data

Contains data to be used in week 3's exercises.

Week 4

Week 4 covers:

Aspects

• Geospatial data formats; geotiff
• Raster-vector calculations

Algorithm

• Batch processing: Raster-vector intersection
• Raster operations: Focal functions

geotiff

Contains a function to pass a raster array to and then uses GDAL to write to a geotiff that can be read into GIS or back into python.

lvis

This contains four files:

lvisClass.py:   A class to hold data from LVIS HDF5 files
processLVIS.py: Processes LVIS data to estimate ground elevation
lvisExample.py: An example of reading an LVIS file into RAM
lvisCompleteExample.py: Reprojects and plots LVIS data

Week 5

Week 5 covers:

Aspects

• A few more geospatial packages
• More batch processing
• Raster analysis

Algorithm

• Practice all course material

geopandas

Contains a minimum workable example of reading data into geopandas.

rasters

Contains a script to read a geotiff raster in to RAM.

data

Contains data to use in the above.

batch_data

Contains lots of data files for use in the batch processing exercise.

line_interp

Contains a function to interpolate a line and return a list of raster pixels intersected.