pdfflow 1.2.2
PDF interpolation with Tensorflow
Requires Python: >=3.6

[![DOI](https://zenodo.org/badge/238731330.svg)](https://zenodo.org/badge/latestdoi/238731330)
[![arxiv](https://img.shields.io/badge/arXiv-hep--ph%2F2009.06635-%23B31B1B.svg)](https://arxiv.org/abs/2009.06635)

[![Documentation Status](https://readthedocs.org/projects/pdfflow/badge/?version=stable)](https://pdfflow.readthedocs.io/en/latest/?badge=stable)
![pytest](https://github.com/N3PDF/pdfflow/workflows/pytest/badge.svg)
[![AUR](https://img.shields.io/aur/version/python-pdfflow)](https://aur.archlinux.org/packages/python-pdfflow)

# PDFFlow

PDFFlow is parton distribution function interpolation library written in Python and based on the [TensorFlow](https://www.tensorflow.org/) framework. It is developed with a focus on speed and efficiency, enabling researchers to perform very expensive calculation as quick and easy as possible.

The key features of PDFFlow is the possibility to query PDF sets on GPU accelerators.

## Documentation

The documentation for PDFFlow can be consulted in the readthedocs page: [pdfflow.readthedocs.io](https://pdfflow.readthedocs.io/en/latest).

## Installation

The package can be installed with pip:

```bash
python3 -m pip install pdfflow[MODE]
```

If you prefer a manual installation just `cd` in the cloned folder and use:

```bash
pip install .[MODE]
```

or if you are planning to extend or develop code just install the package in
editable mode:

```bash
pip install -e .[MODE]
```

`PDFFlow` assumes that the user has already installed the most optimized version
of TensorFlow for his platform. As such, by default, `pip` will not check it as
a requirement.

However, the user can also install it specifying a `MODE` option in the
`pip` command. The list below summarizes the valid choices for the `MODE` flag:

- `tf`: installs the `tensorflow` package
- `tf-cpu`: installs the `tensorflow-cpu` package
- `tf-gpu`: installs the `tensorflow-gpu` package
- `tf-amd`: installs the `tensorflow-rocm` package

**⚠ Note: Use the latest version of TensorFlow!**

TensorFlow is updated frequently and a later version of TensorFlow will often
offer better performance in both GPUs and CPUs.
Although it can be made to work with earlier versions, `PDFFlow` is only
supported for TensorFlow>2.1.

## PDF set management

PDFFlow does not do management of PDF sets, which is left to LHAPDF and so a lhapdf installation is needed.
A full lhapdf installation can be obtained by utilizing the `lhapdf_management` library.

```bash
  python3 -m pip install lhapdf_management
  lhapdf_management install NNPDF31_nnlo_as_0118
```

## Minimal Working Example

Below a minimalistic example where `PDFFlow` is used to generate a 10 values of the PDF
for 2 members for three different flavours.

```python
from pdfflow import mkPDFs
import tensorflow as tf

pdf = mkPDFs("NNPDF31_nnlo_as_0118", [0,2])
x = tf.random.uniform([10], dtype=tf.float64)
q2 = tf.random.uniform([10], dtype=tf.float64)*20 + 10
pid = tf.cast([-1,21,1], dtype=tf.int32)

result = pdf.xfxQ2(pid, x, q2)
```

Note the usage of the `dtype` keyword inm the TensorFlow calls.
This is used to ensure that `float64` is being used all across the program.
For convenience, we ship two functions, `int_me` and `float_me` which are simply
wrappers to `tf.cast` with the right types.

These wrappers can be used over TensorFlow types but also numpy values:

```python
from pdfflow import mkPDFs, int_me, float_me
import tensorflow as tf
import numpy as np

pdf = mkPDFs("NNPDF31_nnlo_as_0118", [0,2])
x = float_me(np.random.rand(10))
q2 = float_me(tf.random.uniform([10])*20 + 10)
pid = int_me([-1,21,1])

result = pdf.xfxQ2(pid, x, q2)
```

## Citation policy

If you use the package pelase cite the following paper and zenodo references:

- https://doi.org/10.5281/zenodo.3964190
- https://arxiv.org/abs/2009.06635