README.md

# What is this tool?

Let's all be frank, root sucks and the root file format is horrible.
It's among humanities worst pieces of software. With this small tool I hope to fix
the damage that was done, at least a little, by converting root files into
native Python formats.


It's using [Numpy](https://numpy.org) and a library called [Uproot](https://github.com/scikit-hep/uproot5)
to read and process these damn root files. So far it is specialist for one task
and I will have to work on it to make it actually viable for more use cases. That task
is to extract PXD data from Belle 2 data files.


## How to use this?

This is a single class, that needs to be instantiated, it doesn't take any arguments.
Just import it like this:

```python
from rootable import Rootable
```

Then you can create an instance:

```python
loadFromRoot = Rootable()
```

and load the root file and all the data:

```python
loadFromRoot.loadData('/root-files/slow_pions_2.root')
loadFromRoot.getClusters()
loadFromRoot.getCoordisnates()
loadFromRoot.getLayers()
loadFromRoot.getMatrices()
loadFromRoot.getMCData()
```

One can now specify how many events should be loaded from the root file.
Keep in mind, that this is different from 'entries'. Each event consists of an
irregular number of entries. The user can also set if events should be
selected randomly

```python
loadFromRoot.loadData('/root-files/slow_pions_2.root', events = 50)
loadFromRoot.loadData('/root-files/slow_pions_2.root', events = 50, selection = 'random')
```

The 'get' commands don't have any return value, but instead work in-place.
Then all data is stored inside the object as dict:

```python
loadFromRoot.data
```

Here follows a list of keywords contained in the dict:

- cluster data:
    - 'eventNumber'
    - 'clsCharge'
    - 'seedCharge'
    - 'clsSize'
    - 'uSize'
    - 'vSize'
    - 'uPosition'
    - 'vPosition'
    - 'sensorID'
- coordinates:
    - 'xPosition'
    - 'yPosition'
    - 'zPosition'
- layers:
    - 'layer'
    - 'ladder'
- matrices:
    - 'cluster'
- Monte Carlo data:
    - 'momentumX'
    - 'momentumY'
    - 'momentumZ'
    - 'pdg'
    - 'clsNumber'

Since the class is subscriptable one can access every element directly using the keywords
like this:

```python
loadFromRoot['eventNumber']
```

or

```python
loadFromRoot[0]
```

will return either the array containing the event numbers of the first entry of every
array contained in the classes dict.

It is possible to filter through the data:

```python
loadFromRoot.where('clsSize == 1')
loadFromRoot.where('clsSize > 1')
```

or even:

```python
loadFromRoot.where('eventNumbers in [0,1,2]')
```

And finally you can convert the dict into a structured Numpy array by simply writing:

```python
loadFromRoot.getStructuredArray()
```

This last command returns a Numpy array. From there the user can save it using
Numpys build-in functions, convert it to Pandas or use it in any way that is
compatible with Numpy.

The class itself is iterable, it's a bit different from typical python dicts,
I iterate over rows and return it as a dict, not sure if that's actually useful.

## Installation

You will need to the [wheel](https://pypi.org/project/wheel/) and [setuptools](https://pypi.org/project/setuptools/) packages of python in order to install
Download the repo, navigate in the terminal to the folder and run the following script:

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

and then:

```bash
pip3 install .
```