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If you have not yet completed the in-class work or the weekly reading, then you may want to finish that first. Recent lecture notes on Canvas may also be useful...
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0.5) Checkout the repository:
Use this link to accept the assignment and create your repository on GitHub: : https://classroom.github.com/a/wIjIwZVv
After you accept the assignment and the repository and it exists in your GitHub then clone the repository into your working area on Rivanna.
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Write a second program (or notebook) iris_loadtxt.py (or .ipynb) to read-in each one of these files into NumPy arrays using the function np.loadtxt (you will have 3 NumPy arrays with 4 columns of values in each). Use these columns along with NumPy functions to print summary statistics to the screen (mean and standard deviation). Make sure it is clear what you are printing to the screen.
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Done? Make sure you answered any questions, then clean up your code and include some useful comments, then push: Setosa.out, Versicolour.out, Virginica.out, iris_parse.py, and iris_loadtxt.py (or iris_parse.ipynb and iris_loadtxt.ipynb if you use a Notebook), plotting_pi_mc.py , pi_mc_10.png, pi_mc_100.png, pi_mc_1000.png, pi_mc_10000.png, pi_mc_all4.png, gaussian.py, and PlotGaussian.png to GitHub..
Start your work early, so you can get assistance if needed.