Lab exercises are distributed as Jupyter notebooks.
9/30 Introduction to Jupyter; tree files and formats; tree visualization
10/7 Sequence alignment; tree searches using parsimony
10/14 Nucleotide models and model selection; tree searches using maximum likelihood
10/21 Divergence time estimation
10/28 Estimating rates of diversification
11/4 Multilocus tree inference ("species tree" estimation)
11/11 Ancestral state estimation; independent contrasts; state-dependent diversification
11/18 Review session
These instructions are for installation on your own laptop. Lab computers will have all software pre-installed.
Install the Miniconda Python 3 distribution
Go to https://conda.io/miniconda.html and download and run the latest installer for your operating system.
Open a Terminal and run the installer (assuming it is now in the Downloads folder):
bash Downloads/Miniconda3-latest-MacOSX-x86_64.sh
Press space to page through to the end of the agreement and agree: type yes.
Answer yes (type it or just hit Enter) when it asks you about prepending the install location to your PATH. This is for convenience in running the various Miniconda programs from the command line.
Close the Terminal.
You now have a bare-bones Python 3 installation that is isolated in your home directory, separate from any Python used by your operating system.
2. Create a new conda environment
A conda environment is like a "sandbox" into which you can install just the packages you need for a particular task. This way, you can make different environments for different projects. So, let's make one to use throughout the quarter -- let's call it rtol -- and populate it with some essential packages.
Open a new Terminal and type:
conda create -n rtol jupyter biopython pandas r-irkernel
To use the environment it must be activated:
source activate rtol
Your Terminal prompt should now begin with (rtol).
2. Run Jupyter
In the same Terminal, or a new one, run:
jupyter-notebook
This will open a browser window showing files and folders in the current directory. Jupyter notebook files are conventionally named with an .ipynb extension, reflecting their derivation from IPython notebooks.