Merge pull request #413 from mir-group/notebooks

Fix broken link to sparse GP tutorial

.github/workflows/flare.yml

@@ -112,6 +112,18 @@ jobs:
         cd tests
         pytest test_lammps.py
 
+    - name: Run tutorial
+      run: |
+        pip install -U jupyter nbconvert
+        cp tutorials/sparse_gp_tutorial.ipynb tutorial.ipynb
+        jupyter nbconvert --to script tutorial.ipynb
+        sed -i '/^get_ipython()/s/^/# /' tutorial.py
+        sed -i '/^plt/s/^/# /' tutorial.py
+        wget http://quantum-machine.org/gdml/data/npz/md17_aspirin.npz
+        wget https://www.ctcms.nist.gov/potentials/Download/1999--Mishin-Y-Farkas-D-Mehl-M-J-Papaconstantopoulos-D-A--Al/2/Al99.eam.alloy
+        python tutorial.py
+        rm Al* aluminum.txt aspirin.txt md17_aspirin.npz tutorial.ipynb tutorial.py
+
     - name: Install Sphinx and Breathe
       run: |
         sudo apt-get update
@@ -141,23 +153,3 @@ jobs:
         # Change the directory if changes in Doxyfile
         publish_dir: ./docs/build/html
       if: github.event_name == 'pull_request' && matrix.lapack == 'on' && matrix.omp == 'on'
-
-#     - name: Run tutorial
-#       run: |
-#         cd tests
-#         # Download colab notebook
-#         export fileid="18_pTcWM19AUiksaRyCgg9BCpVyw744xv"
-#         wget -O tutorial.ipynb 'https://docs.google.com/uc?export=download&id='${fileid}
-#         # Convert notebook into python script
-#         pip install -U jupyter nbconvert
-#         jupyter nbconvert --to script tutorial.ipynb
-#         # Remove bash commands in the notebook
-#         sed '/!/d' tutorial.txt > tutorial.py
-#         cat test_tutorial.py tutorial.py > tuttest.py
-#         # Download datasets needed for the tutorial
-#         wget http://quantum-machine.org/gdml/data/npz/aspirin_dft.npz
-#         wget https://www.ctcms.nist.gov/potentials/Download/1999--Mishin-Y-Farkas-D-Mehl-M-J-Papaconstantopoulos-D-A--Al/2/Al99.eam.alloy
-#         # Run script
-#         pytest -s tuttest.py
-#         # Remove output files
-#         rm Al* aspirin_dft.npz tutorial.ipynb tuttest.py tutorial.py tutorial.txt

.gitignore

@@ -73,3 +73,7 @@ _C_flare*
 **/xml
 **/dist
 **egg-info
+tutorials/Al*
+tutorials/*txt*
+tutorials/*npz*
+tutorials/*checkpoint*

README.md

@@ -1,7 +1,5 @@
 [![Build Status](https://github.com/mir-group/flare/actions/workflows/flare.yml/badge.svg)](https://github.com/mir-group/flare/actions) [![pypi](https://img.shields.io/pypi/v/mir-flare)](https://pypi.org/project/mir-flare/) [![activity](https://img.shields.io/github/commit-activity/m/mir-group/flare)](https://github.com/mir-group/flare/commits/master) [![codecov](https://codecov.io/gh/mir-group/flare/branch/master/graph/badge.svg)](https://codecov.io/gh/mir-group/flare)
 
-***NOTE: This is the latest release [1.3.3](https://github.com/mir-group/flare/releases/tag/1.3.3) which includes significant changes compared to the previous version [0.2.4](https://github.com/mir-group/flare/releases/tag/0.2.4). Please check the updated tutorials and documentations from the links below.***
-
 # FLARE: Fast Learning of Atomistic Rare Events
 
 <p align="center">
@@ -16,24 +14,16 @@ FLARE is an open-source Python package for creating fast and accurate interatomi
   <img src="https://github.com/mir-group/flare/blob/development/docs/images/Flare_features.jpg?raw=true">
 </p>
 
-Note:
-
-We implement Sparse GP, all the kernels and descriptors in C++ with Python interface.
-
-We implement Full GP, Mapped GP, RBCM, Squared Exponential kernel and 2+3-body descriptors in Python.
-
-Please do NOT mix them.
-
 ## Documentations and Tutorials
 
 Documentation of the code can be accessed here: https://mir-group.github.io/flare
 
 [Applications using FLARE and gallery](https://mir-group.github.io/flare/related.html)
 
-### Google Colab Tutorials
+### Tutorials
 
-[FLARE (ACE descriptors + sparse GP)](https://colab.research.google.com/drive/1QcHf5FVU_juZOvQ49FliJVzhon8MJ6PO)
-The tutorial shows how to run flare with ACE and SGP on energy and force data, demoing "offline" training on the MD17 dataset and "online" on-the-fly training of a simple aluminum force field.
+[FLARE (ACE descriptors + sparse GP)](https://github.com/mir-group/flare/blob/notebooks/tutorials/sparse_gp_tutorial.ipynb)
+This tutorial shows how to run flare with a sparse Gaussian process model trained on energy and force data, demoing "offline" training on the MD17 dataset and "online" on-the-fly training of a simple aluminum force field.
 
 [FLARE (LAMMPS active learning)](https://bit.ly/flarelmpotf)
 This tutorial demonstrates new functionality for running active learning all within LAMMPS, with LAMMPS running the dynamics to allow arbitrarily complex molecular dynamics workflows while maintaining a simple interface. This also demonstrates how to use the C++ API directly from Python through `pybind11`. Finally, there's a simple demonstration of phonon calculations with FLARE using `phonopy`.

docs/source/tutorials/colabs.rst

@@ -1,20 +1,23 @@
 FLARE: Active Learning Bayesian Force Fields
 ============================================
 
-We have a few Google Colab tutorials that you can check out and play with.
+We have a few tutorial notebooks that you can check out and play with.
 
-`FLARE (ACE descriptors + sparse GP <https://colab.research.google.com/drive/1rZ-p3kN5CJbPJgD8HuQHSc7ecmwZYse6>`_.
-The tutorial shows how to run flare with ACE and SGP on energy and force data, demoing "offline" training on the MD17 dataset and "online" on-the-fly training of a simple aluminum force field. All the trainings use yaml files for configuration.
+`FLARE (ACE descriptors + sparse GP) <https://github.com/mir-group/flare/blob/notebooks/tutorials/sparse_gp_tutorial.ipynb>`_.
+This tutorial shows how to run flare with a sparse Gaussian process model trained on energy and force data, demoing "offline" training on the MD17 dataset and "online" on-the-fly training of a simple aluminum force field.
 
 `FLARE (ACE descriptors + sparse GP) with LAMMPS <https://colab.research.google.com/drive/1qgGlfu1BlXQgSrnolS4c4AYeZ-2TaX5Y>`_.
 The tutorial shows how to compile LAMMPS with FLARE pair style and uncertainty compute code, and use LAMMPS for Bayesian active learning and uncertainty-aware molecular dynamics.
 
-`FLARE (ACE descriptors + sparse GP) Python API <https://colab.research.google.com/drive/18_pTcWM19AUiksaRyCgg9BCpVyw744xv>`_.
-The tutorial shows how to do the offline and online trainings with python scripts.
-A video walkthrough of the tutorial, including detailed discussion of expected outputs, is available `here <https://youtu.be/-FH_VqRQrso>`_.
+`FLARE (LAMMPS active learning) <https://bit.ly/flarelmpotf>`_.
+This tutorial demonstrates new functionality for running active learning all within LAMMPS, with LAMMPS running the dynamics to allow arbitrarily complex molecular dynamics workflows while maintaining a simple interface. This also demonstrates how to use the C++ API directly from Python through `pybind11`. Finally, there's a simple demonstration of phonon calculations with FLARE using `phonopy`.
 
-`FLARE (2+3-body + GP) <https://colab.research.google.com/drive/1Q2NCCQWYQdTW9-e35v1W-mBlWTiQ4zfT>`_.
-The tutorial shows how to use flare 2+3 body descriptors and squared exponential kernel to train a Gaussian Process force field on-the-fly.
+.. `FLARE (ACE descriptors + sparse GP) Python API <https://colab.research.google.com/drive/18_pTcWM19AUiksaRyCgg9BCpVyw744xv>`_.
+.. The tutorial shows how to do the offline and online trainings with python scripts.
+.. A video walkthrough of the tutorial, including detailed discussion of expected outputs, is available `here <https://youtu.be/-FH_VqRQrso>`_.
+
+.. `FLARE (2+3-body + GP) <https://colab.research.google.com/drive/1Q2NCCQWYQdTW9-e35v1W-mBlWTiQ4zfT>`_.
+.. The tutorial shows how to use flare 2+3 body descriptors and squared exponential kernel to train a Gaussian Process force field on-the-fly.
 
 `Compute thermal conductivity from FLARE and Boltzmann transport equations <https://phoebe.readthedocs.io/en/develop/tutorials/mlPhononTransport.html>`_.
 The tutorial shows how to use FLARE (LAMMPS) potential to compute lattice thermal conductivity from Boltzmann transport equation method,