This is a website that is a library of worked python/ipython solutions to specific, narrow-scope, problems. The intent is that one can construct meaningful programs by gathering the needed examples here, binding them, and running their own unique problems.

The directories are organized into single topics. Within each directory there will be an HTML file, which is the notebook run once through a rendering program to make a static content file -- i.e. what the notebook should look like without errors (syntax errors are suppressed). The notebook itself is the filename.ipynb file. There is a .tar file which can be downloaded, extracted, and run on your own instance of JupyterLab on your own machine.

The collection of notebooks, is categorized by topic. The link will take you to the directory where the notebook is housed, something like the image below.

If you simply want to look at the notebook, rendered as a web page, select the .html file. Otherwise select and download the .ipynb file. If there are other files, they are usually needed too. In complete examples with multiple files required there will be a .zip file which will contain all the depnedencies, download that file, extract and you can run the notebook.

The notebook directory is also replicated on remote shared instances that you can access and run the notebooks yourself

Hello World Classic first program

Variables and Operators Variable types; operators (*,/,+,- .....)

Variables and Operators Data types; integer; real; string ; lists

Conditionals IF ... THEN, program flow control; for loops; while loops.

Sorting Sorting lists

Numerical Integration Definite integrals of functions (NOT symbolic manipulation)

Tabular Data Integration Integration of Tabular Data

Approximation of Derivatives Finite difference approximation of derivatives; directional derivatives

Newton's Method Root finding by classical and quasi-newton methods; single variable.

User-built functions Prototype functions for program clarity and memory leak reduction

Numerical Linear Algebra Matrices and vectors as lists of lists (python primative). Does not require numpy

Matrix Inversion

Systems of Linear Equations Jacobi iteration; Gaussian reduction; with pivoting, use of numpy tools

Linear Systems Applications Statically determinate truss by method of joints and numerical linear algebra, uses Gaussian reduction; with pivoting (as python primatives)

Linear Systems Applications Water quality in series reactors with feedback

Linear Systems Applications Linear heat flow in media with varying thermal properties

Non-linear systems Simultaneous systems of non-linear equations; Newton-Raphson with analytical derivatives; Quasi-Newton using finite differences to approximate detrivatives, Linearization by method of darts

Non-linear System Application Branched pipes/3-Reservoirs connected to a common node

Application of Non-linear Systems Flow and pressure in pipeline networks.

Get data from a URL Script to retrieve and load a database without making a copy of the remote file

Parametric and Non-Parametric Tests Two (2) notebooks with various examples, using data from field studys, small sample sizes

Evaporation analysis is global dimming having an impact on climatic behavior?

Solids in Rivers Estimating river bed load transport mass using weighted n-Nearest neighbor database lookup compared to regression equations

Concrete Strength Prediction Concrete Strength Estimation using Random Forests Model of Observations in a Database

Time Series (Stock Prices) Time series analysis using ARIMA and seasonal decomposition models

Artifical Neural Network for Image Classification ANN to classify images of hand drawn numerals

Linear Regression

Classical Linear Regression Normal equations, Example from an old Texas Instruments TI-55 User Manual

Linear Regression my Matrix Arithmetic Regression as solution to system of linear equations; python primitive version -- uses scripts from Numerical Methods section

Scatter (x-y) plots Single plot, two plots, plot with a drawn line.

Gridding XYZ data; Contour plotting

DAT.src Example Data Files

Notebook Collection FTP-type directory listing for the Notebook -- For development, and maintenance

Excel Spreadsheets Source files for spreadsheets

Site Tour A tour of this website

SharePYTHON Tour A tour of the SharePYTHON site where you can run the notebooks yourself!

Install Anaconda macOS Video showing install onto a bare-iron Catalina implementation. (Recorded 19 June 2020)

Install Anaconda win10 Video showing install onto a Windows 10 Build XXXX implementation

SharePYTHON Environment The vnc#007 shared python environment; remember to disconnect before closing the browser!

PLACEHOLDER describe

Computational and Inferential Thinking Ani Adhikari and John DeNero, Computational and Inferential Thinking, The Foundations of Data Science, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND)

PLACEHOLDER authors