To install this repository you can download TBarrier, unzip it, rename the resulting directory to TBarrier and move it to your development directory.
Next, you will need Python 3 and a bunch of Python libraries. The simplest way to install these is to download and install Anaconda, which is a great cross-platform Python distribution for scientific computing. If you prefer a lighter weight Anaconda distribution, you can install Miniconda, which contains the bare minimum to run the conda packaging tool. You should install the latest version of Anaconda (or Miniconda) available.
Once Anaconda (or Miniconda) is installed, run the following command to update the conda packaging tool to the latest version:
$ conda update -n base -c defaults conda
Note: if you don't like Anaconda for some reason, then you can install Python 3 and use pip to install the required libraries manually (this is not recommended, unless you really know what you are doing). I recommend using Python 3.7, since some libs don't support Python 3.8 or 3.9 yet.
Next, make sure you're in the TBarrier directory and run the following command. It will create a new conda environment containing every library you will need to run all the notebooks (by default, the environment will be named TBarrier, but you can choose another name using the -n option):
$ conda env create -f environment.yml
Next, activate the new environment (from the terminal):
$ conda activate TBarrier
It's almost done! You just need to register the TBarrier conda environment to Jupyter. The notebooks in this project will default to the environment named python3, so it's best to register this environment using the name python3 (if you prefer to use another name, you will have to select it in the "Kernel > Change kernel..." menu in Jupyter every time you open a notebook):
$ python3 -m ipykernel install --user --name=python3
And that's it! You can now start Jupyter like this:
$ jupyter notebook
This should open up your browser, and you should see Jupyter's tree view, with the contents of the current directory. If your browser does not open automatically, visit localhost:8888.
Congrats! You are now ready to learn how to extract transport barriers from velocity data!
When you're done with Jupyter, you can close it by typing Ctrl-C in the Terminal window where you started it. Every time you want to work on this project, you will need to open a Terminal, and run:
$ cd Name-of-Directory $ # or whatever development directory you chose earlier
$ cd TBarrier
$ conda activate TBarrier
$ jupyter notebook
I regularly update the notebooks to fix issues and add support for new libraries. So make sure you update this project regularly.
For this, open a terminal, and run:
$ cd Name-of-Directory # or whatever development directory you chose earlier
$ cd TBarrier # go to this project's directory
$ git pull
Next, let's update the libraries. First, let's update conda itself:
$ conda update -c defaults -n base conda
Then we'll delete this project's TBarrier environment:
$ conda activate base
$ conda env remove -n TBarrier
And recreate the environment:
$ conda env create -f environment.yml
Lastly, we reactivate the environment and start Jupyter:
$ conda activate TBarrier
$ jupyter notebook