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https://github.com/schluppeck/dockerize-analysis

build a container from which EH's analysis code (and others) can be run, without relying on windows/mac compatibility
https://github.com/schluppeck/dockerize-analysis

docker fsl nipype

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build a container from which EH's analysis code (and others) can be run, without relying on windows/mac compatibility

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README 

        
# dockerize-analysis



Setting up for reproducible code for running data analysis workflows at UNUK



:cinema: I have also made a **small youtube playlist** with [videos introducing the ideas](https://www.youtube.com/playlist?list=PLCZfmSQp7dzKdvEf5TP4erdlDZPkqFl20) available. They might give some useful context to what's here.



The idea for this test is to have a near complete install of `nipype` (sans matlab) as a docker image. this includes all the python dependencies, `fsl` (version 5.0 for now) and various other tools.



By installig docker desktop (https://www.docker.com/products/docker-desktop) on your local machine (Windows/Mac), you can run the container which is actually built on Linux. This will remove a lot of the Windows-to-Mac headaches that we have been having - at the cost of this small extra step, which is hopefully not too burdensome.



## TL;DR



If you think you have everything installed and just need a reminder of what the command line options for running the container are:



```bash 

# go to home directory and make "testdata if it doesn not exist

cd ~ && mkdir -p testdata



# allow x windows to localhost

xhost + 127.0.0.1



# now run container with shell

docker run --rm -it \

  --name fsl_dev \

  --mount type=bind,source="$(pwd)"/testdata,target=/home/data \

  docker.pkg.github.com/schluppeck/dockerize-analysis/nipype_test:1.0 \

  bash

```



If this looks confusing, then have a look at the following description, which takes you through to this point step by step.



## Running from terminal



For testing and/or if all you need is `vim`, `emacs`, etc and the command line, you can also use `bash` as your endpoint like this



```bash

# to run this with shell

docker run -it --rm \

  docker.pkg.github.com/schluppeck/dockerize-analysis/nipype_fsl:1.0 bash

```



This will pop you into a running container with everything installed. Check with `hostname`, `uname -a`, etc and snoop around.



## Running also with fslview



- make sure you have `Xquartz` (macos) or `Xming` (windows) running to display X11 windows / GUIs



- make sure that X windows are allowed to be launched by using `xhost + 127.0.0.1` as per this [blog post](https://medium.com/@mreichelt/how-to-show-x11-windows-within-docker-on-mac-50759f4b65cb) - [gist](https://gist.github.com/mreichelt/de62cc78e3d12993dc60140b2a43cee7)



- start the container, making sure you have a folder called `testdata` present in your current working directory (`pwd`) - this is the folder shared with your container (at `/home/data`)



```bash

docker run  --rm -it \

  --mount type=bind,source="$(pwd)"/testdata,target=/home/data \

  -e DISPLAY=host.docker.internal:0 \

  docker.pkg.github.com/schluppeck/dockerize-analysis/nipype_fsl:1.0 \

  bash

```



- try out a programme that requires `X11`, like `fsl &` or `fslview &`



- `python3` should also work. Share some python code in your mounted drive and run to test!



## Running the notebook server



```bash

# to run this with a notebook server

docker run -it --rm -p8888:8888 docker.pkg.github.com/schluppeck/dockerize-analysis/nipype_test:1.0

```



Under `macos` this looks as follows (under Windows with Docker Desktop installed you should be something very similar). Copy and paste the link you see in the terminal into your browser and start using the `jupyter` notebook.



![starting container](docker-run.png)



Interact with the `jupyter` notebook in the way you have come to apreciate and :heart:



![connecting to notebook server](notebook-running.png)



## Note on download / pulling the image



The first time you run this, docker needs to download the image (~4gb), so have some patience... but after this you should be able to start up the container very quickly.



Alternatively, you can docker pull the image first as per instructions under the packages tab and the `docker run` your downloaded version.



## Details on how to access data in there?



Example use case:



1. you are in a folder, say `~/demo` on your machine, which contains a folder called `testdata` that you want to access inside your container (read/write).



2. from within `~/demo`,  Make sure you have a local folder and run the container:



```bash

# go to folder that contains testdata

cd ~/demo  

hostname # your computer

ls # what's here ??



# now run container with shell

docker run -it \

  --name devtest \

  --mount type=bind,source="$(pwd)"/testdata,target=/home/data \

  --rm \

  docker.pkg.github.com/schluppeck/dockerize-analysis/nipype_test:1.0 \

  bash

```



3. **Check** - if everything is running ok, you are now running Linux (rather than `macos` or Windows)



```bash

hostname

#neuro@b29b9e2e54cc:/home/data$ hostname

#b29b9e2e54cc



# and 

uname -a

# Linux b29b9e2e54cc 4.19.76-linuxkit #1 SMP Tue May 26 11:42:35 UTC 2020 x86_64 GNU/Linux



# on container, data is at /home/data where we mounted!

ls /home/data

```



4. run something on some test data. On local machine copy a nifti file into `~/demo/testdata` (I tried a short fMRI run) and run mcflirt on it



```bash

# in container:

cd /home/data

# run motion comp

mcflirt -in dafni_01_FSL_4_1.nii -plots

```



5. when you exit container, files will persist (look in `~/demo/testdata`)



## Notes



To see what was done to create the docker image for here, have a look at the [Readme file](building/Readme.md) and generated `Dockerfile` in the "building" directory