Using the GitHub API we gather information about a repository, and use pandas to display the information.
First, create a folder for your module (following the file structure of this template, module_template would be that folder). Then, create another folder called code within that folder. Inside the code folder, place all of your Python code for your module. For each library you import, add it to a file called requirements.txt within the code folder. Lastly, copy the Dockerfile from this template into the folder of the module itself.
cd into the module folder you created, then (in your Docker terminal) run docker build -t your_module .
This will create a Docker image with the name your_module. To run a container of that image, run docker run your_module [cliArgs], where [cliArgs] are optional command line arguments passed directly to the entry file. Note that all modules must begin execution at a script named in the Dockerfile as app.py - feel free to change the name in the Dockerfile of your own module to fit this requirement, or simply name your entry script app.py as well.
In your Docker terminal, cd into the module_template folder, run docker build -t template . . When that finishes, run docker run template word , where word is a word of your choice to print, showing how to pass command line arguments through Docker to Python. The script also prints the results of a basic HTTP GET request, to utilize the requests library that serves as a placeholder in the example requirements.txt . The result should look like this:
Hello, World!
Your first command line argument is:
word
{'userId': 1, 'id': 1, 'title': 'delectus aut autem', 'completed': False}
To share files between your computer and your Docker containers, you need to create a volume. This should be pretty simple on *nix machines - I haven't had a chance to test on Mac/Linux, but I have been able to make it work on Windows, which is a more complicated process.
First, assuming you are not on an enterprise version of Windows and therefore you are using Docker Toolbox with Oracle VirtualBox, you will need to create a "Shared Folder".
I suggest following this tutorial and changing all instances of "Divio" in the commands to "metrics-vol" or something else you recognize: http://support.divio.com/en/articles/646695-how-to-use-a-directory-outside-c-users-with-docker-toolbox-docker-for-windows
The second part of this process, as mentioned in the tutorial, is permanently mounting the new shared folder in your default Docker machine. The tutorial provides good instructions on how to do this, and I will note that one way to edit the profile document is to use the default Linux echo command. First do a sudo su to give yourself proper permissions, then run echo -e '\nsudo mkdir /metrics-vol\nsudo mount -t vboxsf -o uid=1000,gid=50 metrics-vol /metrics-vol' >> profile.
After following the tutorial above as described, you should be able to read and write files in the shared folder you set up on your host machine from your Docker containers at the path /metrics-vol, so long as you mount a volume when you run the container. This command should look like docker run -v /metrics-vol:/metrics-vol image-name command_line_argument
I've included some test code in module_template/app.py that is currently commented out - if you correctly add a shared folder on your host machine, mount the folder on your default Docker machine, and mount it as a volume when you run the container (after building the image with the lines uncommented), it should write your command line argument
to a file called voltest.txt that exists in your shared folder on your host machine.
docker volume create metrics
docker build . -t name_of_image
docker run -v metrics:/metrics <name_of_image> github.com/owner/repo_name
get docker id:
docker container ls -a
copy data:
docker cp id:/metrics /path/to/file/on/host
docker run -v metrics:/metrics -p 5000:5000 <name_of_image>
To run the scripts, run the following commands while in the top SSLMetrics directory:
chmod +x ./all_metrics.sh
chmod +x ./Commits/metrics.sh
chmod +x ./Issues/metrics.sh
chmod +x ./Lines_Of_Code_Num_Of_Chars/metrics.sh
chmod +x ./Issue_Spoilage/metrics.sh
chmod +x ./Defect_Density/metrics.sh
./all_metrics.sh github.com/<owner>/<repo_name> <api_token>
Note: the chmod commands need to be run only initially. After that, just run the all_metrics.sh script with the GitHub repo as the command line argument.