pnlpipe | based framework

Python >= 3.6, FreeSurfer >= 5.3.0 and FSL >= 5.0.11 (ignore the one(s) you have already):.
Now that you have installed the prerequisite software, you are ready to install the pipelines (std, epi, hcp):.
Premade pipelines are in the pnlpipe_pipelines directory. For example, the standard PNL pipeline is defined in pnlpipe_pipelines/std.py, and the EPI correction pipeline is defined in pnlpipe_pipelines/epi.py. You can also get a list of available pipelines by running ./pnlpipe -h. As an example, we will run the PNL standard pipeline, the one named std. Before running a pipeline, we need to configure it. This involves two steps: one, we need to specify its parameters, and two, we need to build the software it requires. To specify the parameters, we put them in a [yaml](http://www.yaml.org/start.html) configuration file, in this case called pnlpipe_params/std.params. To make a default version of this file, run. This makes a parameter file with the pipeline’s default parameters. For the std pipeline, the most important ones are the input keys, inputDwiKey, inputT1Key, etc. These are the keys the pipeline uses to find its input data, by looking up their paths in pnlpipe_config.INPUT_KEYS. For example, inputDwiKey: [dwi] means that the pipeline will find its DWI input by looking up 'dwi' in INPUT_KEYS. Likewise, inputT1Key: [t1] means that the pipeline will find its T1w input by looking up 't1' in INPUT_KEYS. The reason it is done this way is that if you happen to reorganize your data, you just have to update your pnlpipe_config.INPUT_KEYS, and your parameters remain the same. Another important field is caseid; the default is ./caselist.txt, which means the pipeline will look in that file to find the case ids you want to use with this pipeline. Make it by putting each case id on its own line. You will notice that the parameter values are wrapped in square brackets. This is because you can specify more than one value for each parameter. For example, if you wanted to run the std pipeline using a DWI masking bet threshold of 0.1 as well as a 0.15, you would write: bet_threshold: [0.1, 0.15]. For more details on specifying multiple parameter combinations, see further down in this README. Now you’re ready to build the software needed by the pipeline. The required software is determined by the parameters that end in '_version' and '_hash' (a Github commit hash). Before building the software packages, you need to specify the directory to install them to, and you do this by setting a global environment variable called $PNLPIPE_SOFT (e.g. export PNLPIPE_SOFT=path/to/software/dir). Now build the software by running-. (if any of the software packages already exist, they will not rebuild). You should now see the results in $PNLPIPE_SOFT, such as BRAINSTools-bin-2d5eccb/ and UKFTractography-421a7ad/.