VBIndex 0.0.4
Library and command-line tool to calculate the Vogt-Bailey index of a dataset
Requires Python: >=3.6

# VBIndex
Vogt-Bailey index toolbox in Python

## Installation

It is possible to simply copy the folder vb_toobox to your project folder and
proceed from there. If this is the case, be sure you have the following
packages installed

```
multiprocess
nibabel
numpy
scipy
```

The preferred way to install is through pip. It is as easy as

```bash
pip install VBIndex
```

If your pip is properly configured, you can now use the program `vb_tool` from
your command line, and import any of the submodules in the `vb_toolbox` in your python 
interpreter.

## Usage of `vb_tool` CLI 

If VBIndex was installed via `pip`, the command line program `vb_tool` should
be available in your terminal. You can test if the program is correctly
installed by typing

```bash
vb_tool -h 
```

in your terminal. If you see the following output, the program has been
properly installed.

```
usage: app.py [-h] [-j N] [-n norm] [-fb] [-m file] [-c file] -s file -d file
              -o file

Calculate the Vogt-Bailey index of a dataset. For more information, check
https://github.com/VBIndex/py_vbindex.

optional arguments:
  -h, --help            show this help message and exit
  -j N, --jobs N        Maximum number of jobs to be used. If abscent, one job
                        per CPU will be spawned
  -n norm, --norm norm  Laplacian normalization to be used. Defaults to unnorm
  -fb, --full-brain     Calculate full brain spectral reordering.
  -m file, --mask file  File containing the labels to identify the cortex,
                        rather than the medial brain structures. This flag
                        must be set for normal analyses and full brain
                        analyses.
  -c file, --clusters file
                        File containing the surface clusters. Cluster with
                        index 0 are expected to denote the medial brain
                        structures and will be ignored.

required named arguments:
  -s file, --surface file
                        File containing the surface mesh
  -d file, --data file  File containing the data over the surface
  -o file, --output file
                        Base name for the output files
```

If you copied the program source code, the executable is found in `vb_toolbox/app.py`. 
You can test the program using 

```bash
python3 vb_toolbox/app.py
```

which should yield the results shown above.

There are three main uses for the `vb_tool`

1. Searchlight analyses
2. Whole brain gradient maps
3. Gradient maps in a specified set of regions of interest

### Searchlight analyses

The per vertex analyses can be carried with the following command

```bash
vb_tool --surface input_data/surface.surf.gii  --data input_data/data.func.gii --mask input_data/cortical_mask.shape.gii --output search_light 
```

The number of vertices in the surface mesh must match the number of entries in
the data and in the mask.

The cortical mask must contain a logical array, with `True` values in the
region on which the analyses will be carried out, and `False` in the regions to
be left out. This is most commonly used to mask out midbrain structures which
would otherwise influence the analysis of the cortical regions. 


### Whole brain analyses

To perform full brain analyses, the flag `-fb` or `--full-brain` must be set.
Otherwise, the flags are the same as in the searchlight analysis.

```bash
vb_tool --surface input_data/surface.surf.gii  --data input_data/data.func.gii --mask input_data/cortical_mask.shape.gii --full-brain --output full_brain_gradient
```

Be warned, however, that this analysis can take long, use a large amount of
RAM. In systems with 32k vertices, upwards of 30GB of RAM were used.

### Regions of Interest analyses

Sometimes, one is interested only in a small set of ROIs. In this case, the way
for calling the program changes slightly,

```bash
vb_tool --surface input_data/surface.surf.gii  --data input_data/data.func.gii  -c input_data/clusters.shape.gii --output clustered_analyses
```

The cluster file works similarly to the cortical mask in the previous
modalities. However, its structure is slightly different. Instead of an array
of logical values, the file must contain an array of integers, where each
integer corresponds to a different cluster. The 0th cluster is special, and
denotes an area which will *not* be analyzed. In these regards, it has a
similar use to the cortical mask. 

### Notes on parallelism

`vb_tool` uses a high level of parallelism. How many threads are spawned by
`vb_tool` itself can be controlled using the `-j/--jobs` flag. By default, it
will try to use all the CPUs in your computer at the same time to perform the
analyzes. Depending on the BLAS installation in your computer, this might not
be the best fastest approach, but rarely will be the slowest. If you are
unsure, leave the number of jobs at the default level.

Due to job structure of the `vb_tool`, the level of parallelism it can achieve
on its own depends on the specific analyses being carried out. 

1. Searchlight analyses: High level of parallelism. Will spawn as many jobs are
   there are CPUs
2. Whole brain analyses: Low lever of parallelism. Will only spawn one job
3. Region of Interest analyses: Medium level of parallelism. Will spawn as many
   jobs as there are ROIs, or number of CPUS, whichever is the lowest.

Specially in the whole brain analyses, having a well optimized BLAS
installation will grandly accelerate the process, and allow for a further
paralelism.  Both MKL and OpenBLAS have been shown to offer fast analyses. If
you are using the Anaconda distribution, you will have a good BLAS
pre-configured.

## Developer Information

### Build

We use setuptool and wheel to build the distribution code. The process is
described next. More information can be found
[here](https://packaging.python.org/tutorials/packaging-projects/).

1. Be sure that setuptools, twine, and wheel are up-to-dated

```bash
python3 -m pip install --user --upgrade setuptools wheel twine
```

2. Run the build command

```bash
python3 setup.py sdist bdist_wheel
```

3. Upload the package to pip

```bash
python3 -m twine upload dist/*
```