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https://github.com/n3pdf/pdfflow
PDFflow is parton distribution function interpolation library written in Python and based on the TensorFlow framework.
https://github.com/n3pdf/pdfflow
hep-ex hep-ph hep-th parton-distribution-functions pdf pdf-interpolation python tensorflow
Last synced: 2 months ago
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PDFflow is parton distribution function interpolation library written in Python and based on the TensorFlow framework.
- Host: GitHub
- URL: https://github.com/n3pdf/pdfflow
- Owner: N3PDF
- License: apache-2.0
- Created: 2020-02-06T16:19:43.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-01-23T08:52:43.000Z (12 months ago)
- Last Synced: 2024-10-16T09:16:23.878Z (3 months ago)
- Topics: hep-ex, hep-ph, hep-th, parton-distribution-functions, pdf, pdf-interpolation, python, tensorflow
- Language: Python
- Homepage: https://pdfflow.readthedocs.io
- Size: 350 KB
- Stars: 8
- Watchers: 4
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://zenodo.org/badge/latestdoi/238731330)
[](https://arxiv.org/abs/2009.06635)
[](https://pdfflow.readthedocs.io/en/latest/?badge=stable)
[](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