style: auto format non-python files on save

and currently format more non-python files
DeepRank · Jan 22, 2024 · 6fb26b3 · 6fb26b3
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diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -1,16 +1,16 @@
 ---
 name: Bug report
 about: Create a report to help us improve
-title: 'Bug: <please fill here>'
-labels: 'bug'
-assignees: ''
-
+title: "Bug: <please fill here>"
+labels: "bug"
+assignees: ""
 ---
 
 **Describe the bug**
 A clear and concise description of what the bug is.
 
 **Environment:**
+
 - OS system:
 - Version:
 - Branch commit ID:
@@ -19,11 +19,11 @@ A clear and concise description of what the bug is.
 **To Reproduce**
 Steps/commands/screenshots to reproduce the behaviour:
 
- 1.
+1.
 
- 2.
+2.
 
- 3.
+3.
 
 **Expected Results**
 A clear and concise description of what you expected to happen.

diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -1,10 +1,9 @@
 ---
 name: Feature request
 about: Suggest an idea for this project
-title: 'Add/edit'
-labels: 'feature'
-assignees: ''
-
+title: "Add/edit"
+labels: "feature"
+assignees: ""
 ---
 
 **Is your feature request related to a problem? Please describe.**
@@ -21,6 +20,7 @@ Add any other context or screenshots about the feature request here.
 
 **Checks for the developer**
 After having implemented the request, please remember to:
+
 - [ ] Add all the necessary tests. Make sure that the parameter functionality is well tested, from all points of views.
 - [ ] Add the proper documentation to the source code (docstrings).
 - [ ] Add the proper documentation to the readme. Examples about how using the new feature should be clear and easy to follow.
diff --git a/.vscode/settings.json b/.vscode/settings.json
@@ -1,19 +1,23 @@
 {
-    // Python
-    "[python]": {
-        "editor.formatOnSave": true,
-        "editor.codeActionsOnSave": {
-            "source.fixAll": "explicit"
-        },
-        "editor.defaultFormatter": "charliermarsh.ruff"
+  // Python
+  "[python]": {
+    "editor.formatOnSave": true,
+    "editor.codeActionsOnSave": {
+      "source.fixAll": "explicit"
     },
-    "autoDocstring.docstringFormat": "google",
+    "editor.defaultFormatter": "charliermarsh.ruff"
+  },
+  "autoDocstring.docstringFormat": "google",
 
-    // Notebooks
-    "notebook.lineNumbers": "on",
-    "notebook.formatOnSave.enabled": true,
-    "notebook.codeActionsOnSave": {
-        "notebook.source.fixAll": "explicit",
-    },
-    "notebook.diff.ignoreMetadata": true,
+  // Notebooks
+  "notebook.lineNumbers": "on",
+  "notebook.formatOnSave.enabled": true,
+  "notebook.codeActionsOnSave": {
+    "notebook.source.fixAll": "explicit"
+  },
+  "notebook.diff.ignoreMetadata": true,
+
+  // Format all files on save
+  "editor.formatOnSave": true,
+  "editor.defaultFormatter": "esbenp.prettier-vscode"
 }
diff --git a/CHANGELOG.md b/CHANGELOG.md
diff --git a/README.dev.md b/README.dev.md
@@ -50,16 +50,21 @@ If you are using VS code, please install and activate the [Ruff extension](https
 
 Otherwise, please ensure check both linting (`ruff fix .`) and formatting (`ruff format .`) before requesting a review.
 
+We use [prettier](https://prettier.io/) for formatting most other files. If you are editing or adding non-python files and using VS code, the [Prettier extension](https://marketplace.visualstudio.com/items?itemName=esbenp.prettier-vscode) can be installed to auto-format these files as well.
+
 ## Versioning
 
 Bumping the version across all files is done before creating a new package release, running `bump2version [part]` from command line after having installed [bump2version](https://pypi.org/project/bump2version/) on your local environment. Instead of `[part]`, type the part of the version to increase, e.g. minor. The settings in `.bumpversion.cfg` will take care of updating all the files containing version strings.
 
 ## Branching workflow
 
 We use a [Git Flow](https://nvie.com/posts/a-successful-git-branching-model/)-inspired branching workflow for development. DeepRank2's repository is based on two main branches with infinite lifetime:
+
 - `main` — this branch contains production (stable) code. All development code is merged into `main` in sometime.
 - `dev` — this branch contains pre-production code. When the features are finished then they are merged into `dev`.
+
 During the development cycle, three main supporting branches are used:
+
 - Feature branches - Branches that branch off from `dev` and must merge into `dev`: used to develop new features for the upcoming releases.
 - Hotfix branches - Branches that branch off from `main` and must merge into `main` and `dev`: necessary to act immediately upon an undesired status of `main`.
 - Release branches - Branches that branch off from `dev` and must merge into `main` and `dev`: support preparation of a new production release. They allow many minor bug to be fixed and preparation of meta-data for a release.
@@ -77,12 +82,13 @@ During the development cycle, three main supporting branches are used:
 1. Branch from `dev` and prepare the branch for the release (e.g., removing the unnecessary dev files such as the current one, fix minor bugs if necessary).
 2. [Bump the version](https://github.com/DeepRank/deeprank2/blob/dev/README.dev.md#versioning).
 3. Verify that the information in `CITATION.cff` is correct (update the release date), and that `.zenodo.json` contains equivalent data.
-5. Merge the release branch into `main` (and `dev`), and [run the tests](https://github.com/DeepRank/deeprank2/blob/dev/README.dev.md#running-the-tests).
-6. Go to https://github.com/DeepRank/deeprank2/releases and draft a new release; create a new tag for the release, generate release notes automatically and adjust them, and finally publish the release as latest. This will trigger [a GitHub action](https://github.com/DeepRank/deeprank2/actions/workflows/release.yml) that will take care of publishing the package on PyPi.
-7. Update the doi in `CITATION.cff` with the one corresponding to the new release.
+4. Merge the release branch into `main` (and `dev`), and [run the tests](https://github.com/DeepRank/deeprank2/blob/dev/README.dev.md#running-the-tests).
+5. Go to https://github.com/DeepRank/deeprank2/releases and draft a new release; create a new tag for the release, generate release notes automatically and adjust them, and finally publish the release as latest. This will trigger [a GitHub action](https://github.com/DeepRank/deeprank2/actions/workflows/release.yml) that will take care of publishing the package on PyPi.
+6. Update the doi in `CITATION.cff` with the one corresponding to the new release.
 
 ## UML
 
 Code-base class diagrams updated on 02/11/2023, generated with https://www.gituml.com (save the images and open them in the browser for zooming).
+
 - Data processing classes and functions: <img src="./tests/utils/uml_data_processing.svg" width="50">
 - ML pipeline classes and functions: <img src="./tests/utils/uml_training.svg" width="50">
diff --git a/README.md b/README.md
@@ -1,13 +1,13 @@
 # Deeprank2
 
-| Badges | |
-|:----:|----|
-| **fairness** |  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu) [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/6403/badge)](https://bestpractices.coreinfrastructure.org/projects/6403) |
-| **package** |  [![PyPI version](https://badge.fury.io/py/deeprank2.svg)](https://badge.fury.io/py/deeprank2) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/f3f98b2d1883493ead50e3acaa23f2cc)](https://app.codacy.com/gh/DeepRank/deeprank2?utm_source=github.com&utm_medium=referral&utm_content=DeepRank/deeprank2&utm_campaign=Badge_Grade) |
-| **docs** | [![Documentation Status](https://readthedocs.org/projects/deeprank2/badge/?version=latest)](https://deeprank2.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/450496579.svg)](https://zenodo.org/badge/latestdoi/450496579) |
-| **tests** | [![Build Status](https://github.com/DeepRank/deeprank2/actions/workflows/build.yml/badge.svg)](https://github.com/DeepRank/deeprank2/actions) ![Linting status](https://github.com/DeepRank/deeprank2/actions/workflows/linting.yml/badge.svg?branch=main) [![Coverage Status](https://coveralls.io/repos/github/DeepRank/deeprank2/badge.svg?branch=main)](https://coveralls.io/github/DeepRank/deeprank2?branch=main) ![Python](https://img.shields.io/badge/python-3.10-blue.svg)  ![Python](https://img.shields.io/badge/python-3.11-blue.svg) |
-| **running on** | ![Ubuntu](https://img.shields.io/badge/Ubuntu-E95420?style=for-the-badge&logo=ubuntu&logoColor=white) |
-| **license** |  [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/license/apache-2-0/)  |
+|     Badges     |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| :------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+|  **fairness**  | [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu) [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/6403/badge)](https://bestpractices.coreinfrastructure.org/projects/6403)                                                                                                                                                                                                                 |
+|  **package**   | [![PyPI version](https://badge.fury.io/py/deeprank2.svg)](https://badge.fury.io/py/deeprank2) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/f3f98b2d1883493ead50e3acaa23f2cc)](https://app.codacy.com/gh/DeepRank/deeprank2?utm_source=github.com&utm_medium=referral&utm_content=DeepRank/deeprank2&utm_campaign=Badge_Grade)                                                                                                                                                                                                      |
+|    **docs**    | [![Documentation Status](https://readthedocs.org/projects/deeprank2/badge/?version=latest)](https://deeprank2.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/450496579.svg)](https://zenodo.org/badge/latestdoi/450496579)                                                                                                                                                                                                                                                                                              |
+|   **tests**    | [![Build Status](https://github.com/DeepRank/deeprank2/actions/workflows/build.yml/badge.svg)](https://github.com/DeepRank/deeprank2/actions) ![Linting status](https://github.com/DeepRank/deeprank2/actions/workflows/linting.yml/badge.svg?branch=main) [![Coverage Status](https://coveralls.io/repos/github/DeepRank/deeprank2/badge.svg?branch=main)](https://coveralls.io/github/DeepRank/deeprank2?branch=main) ![Python](https://img.shields.io/badge/python-3.10-blue.svg) ![Python](https://img.shields.io/badge/python-3.11-blue.svg) |
+| **running on** | ![Ubuntu](https://img.shields.io/badge/Ubuntu-E95420?style=for-the-badge&logo=ubuntu&logoColor=white)                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+|  **license**   | [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/license/apache-2-0/)                                                                                                                                                                                                                                                                                                                                                                                                                                |
 
 ## Overview
 
@@ -18,6 +18,7 @@ DeepRank2 is an open-source deep learning (DL) framework for data mining of prot
 DeepRank2 allows for transformation of (pdb formatted) molecular data into 3D representations (either grids or graphs) containing structural and physico-chemical information, which can be used for training neural networks. DeepRank2 also offers a pre-implemented training pipeline, using either [CNNs](https://en.wikipedia.org/wiki/Convolutional_neural_network) (for grids) or [GNNs](https://en.wikipedia.org/wiki/Graph_neural_network) (for graphs), as well as output exporters for evaluating performances.
 
 Main features:
+
 - Predefined atom-level and residue-level feature types
   - e.g. atom/residue type, charge, size, potential energy
   - All features' documentation is available [here](https://deeprank2.readthedocs.io/en/latest/features.html)
@@ -58,19 +59,19 @@ The package officially supports ubuntu-latest OS only, whose functioning is wide
 
 Before installing deeprank2 you need to install some dependencies. We advise to use a [conda environment](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) with Python >= 3.10 installed. The following dependency installation instructions are updated as of 14/09/2023, but in case of issues during installation always refer to the official documentation which is linked below:
 
-*  [MSMS](https://anaconda.org/bioconda/msms): `conda install -c bioconda msms`.
-    * [Here](https://ssbio.readthedocs.io/en/latest/instructions/msms.html) for MacOS with M1 chip users.
-*  [PyTorch](https://pytorch.org/get-started/locally/)
-    * We support torch's CPU library as well as CUDA.
-    * Currently, the package is tested using [PyTorch 2.0.1](https://pytorch.org/get-started/previous-versions/#v201).
-*  [PyG](https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html) and its optional dependencies: `torch_scatter`, `torch_sparse`, `torch_cluster`, `torch_spline_conv`.
-*  [DSSP 4](https://swift.cmbi.umcn.nl/gv/dssp/)
-    * Check if `dssp` is installed: `dssp --version`. If this gives an error or shows a version lower than 4:
-      * on ubuntu 22.04 or newer: `sudo apt-get install dssp`. If the package cannot be located, first run `sudo apt-get update`.
-      * on older versions of ubuntu or on mac or lacking sudo priviliges: install from [here](https://github.com/pdb-redo/dssp), following the instructions listed. Alternatively, follow [this](https://github.com/PDB-REDO/libcifpp/issues/49) thread.
-*  [GCC](https://gcc.gnu.org/install/)
-    * Check if gcc is installed: `gcc --version`. If this gives an error, run `sudo apt-get install gcc`.
-*  For MacOS with M1 chip users only install [the conda version of PyTables](https://www.pytables.org/usersguide/installation.html).
+- [MSMS](https://anaconda.org/bioconda/msms): `conda install -c bioconda msms`.
+  - [Here](https://ssbio.readthedocs.io/en/latest/instructions/msms.html) for MacOS with M1 chip users.
+- [PyTorch](https://pytorch.org/get-started/locally/)
+  - We support torch's CPU library as well as CUDA.
+  - Currently, the package is tested using [PyTorch 2.0.1](https://pytorch.org/get-started/previous-versions/#v201).
+- [PyG](https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html) and its optional dependencies: `torch_scatter`, `torch_sparse`, `torch_cluster`, `torch_spline_conv`.
+- [DSSP 4](https://swift.cmbi.umcn.nl/gv/dssp/)
+  - Check if `dssp` is installed: `dssp --version`. If this gives an error or shows a version lower than 4:
+    - on ubuntu 22.04 or newer: `sudo apt-get install dssp`. If the package cannot be located, first run `sudo apt-get update`.
+    - on older versions of ubuntu or on mac or lacking sudo priviliges: install from [here](https://github.com/pdb-redo/dssp), following the instructions listed. Alternatively, follow [this](https://github.com/PDB-REDO/libcifpp/issues/49) thread.
+- [GCC](https://gcc.gnu.org/install/)
+  - Check if gcc is installed: `gcc --version`. If this gives an error, run `sudo apt-get install gcc`.
+- For MacOS with M1 chip users only install [the conda version of PyTables](https://www.pytables.org/usersguide/installation.html).
 
 ## Deeprank2 Package
 
@@ -110,6 +111,7 @@ For more details, see the [extended documentation](https://deeprank2.rtfd.io/).
 For each protein-protein complex (or protein structure containing a missense variant), a `Query` can be created and added to the `QueryCollection` object, to be processed later on. Two subtypes of `Query` exist: `ProteinProteinInterfaceQuery` and `SingleResidueVariantQuery`.
 
 A `Query` takes as inputs:
+
 - a `.pdb` file, representing the protein-protein structure,
 - the resolution (`"residue"` or `"atom"`), i.e. whether each node should represent an amino acid residue or an atom,
 - the ids of the chains composing the structure, and
@@ -350,10 +352,10 @@ For more details about how to run a pre-trained model on new data, see the [docs
 We measured the efficiency of data generation in DeepRank2 using the tutorials' [PDB files](https://zenodo.org/record/8187806) (~100 data points per data set), averaging the results run on Apple M1 Pro, using a single CPU.
 Parameter settings were: atomic resolution, `distance_cutoff` of 5.5 Å, radius (for SRV only) of 10 Å. The [features modules](https://deeprank2.readthedocs.io/en/latest/features.html) used were `components`, `contact`, `exposure`, `irc`, `secondary_structure`, `surfacearea`, for a total of 33 features for PPIs and 26 for SRVs (the latter do not use `irc` features).
 
-|      |         Data processing speed <br />[seconds/structure]        |                Memory <br />[megabyte/structure]               |
-|------|:--------------------------------------------------------:|:--------------------------------------------------------:|
+|      |            Data processing speed <br />[seconds/structure]             |                   Memory <br />[megabyte/structure]                    |
+| ---- | :--------------------------------------------------------------------: | :--------------------------------------------------------------------: |
 | PPIs | graph only: **2.99** (std 0.23) <br />graph+grid: **11.35** (std 1.30) | graph only: **0.54** (std 0.07) <br />graph+grid: **16.09** (std 0.44) |
-| SRVs | graph only: **2.20** (std 0.08)  <br />graph+grid: **2.85** (std 0.10) | graph only: **0.05** (std 0.01) <br />graph+grid: **17.52** (std 0.59) |
+| SRVs | graph only: **2.20** (std 0.08) <br />graph+grid: **2.85** (std 0.10)  | graph only: **0.05** (std 0.01) <br />graph+grid: **17.52** (std 0.59) |
 
 ## Package development