From 93c9ebbb132e67112aaef675f9e78a9938e90f08 Mon Sep 17 00:00:00 2001 From: Prakhar Sharma <1915438@swansea.ac.uk> Date: Tue, 18 Jul 2023 17:13:49 +0100 Subject: [PATCH 1/4] TODO updates --- todo.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/todo.md b/todo.md index 381422c..6f270e6 100644 --- a/todo.md +++ b/todo.md @@ -4,19 +4,23 @@ Last work : `DeepINN/constraint/gradients.py` ## Geometry - [ ] Geometry added but lot of bloats from TorchPhysics. Clean up geometry folder. - [ ] Clean up utils folder. +- [ ] Implement anchor points. + ## Gradients - [x] Implement basic gradients. - [ ] Implement gradient for multiple output neurons. -- [ ] Do we need retain_graph=True ? +- [x] Do we need retain_graph=True? ## Constraints - [X] Implement the prescribed BC part in constraint/ boundary_loss dirichletBC. - [X] Implement PDE loss constraint. - [ ] Implement gradients. - [ ] Implement lazy evaluation of gradients. +- [ ] Implement more constrainst. ## Architectures -- [ ] Implement fully connected NN. +- [x] Implement fully connected NN. +- [ ] Implement more neural networks ## Tutorials - [x] Basic geometry tutorials. From c1d161432db516f27a2ddf0744aeb4561b94a234 Mon Sep 17 00:00:00 2001 From: Prakhar Sharma <1915438@swansea.ac.uk> Date: Fri, 28 Jul 2023 16:00:16 +0100 Subject: [PATCH 2/4] Docs migrated to JupyterBook --- .gitignore | 1 + Tutorials/2. BC/1. dirichlet.ipynb | 32 +++-- Tutorials/4. Dataset/1. basic.ipynb | 7 + Tutorials/5. FCNN/1. basic.ipynb | 7 + Tutorials/5. FCNN/3. model.ipynb | 7 + docs/.vscode/settings.json | 4 - docs/Tutorials | 1 + docs/_config.yml | 32 +++++ docs/_toc.yml | 9 ++ docs/docs_tutorial/README.md | 1 + docs/intro.md | 11 ++ docs/logo.png | Bin 0 -> 9854 bytes docs/markdown-notebooks.md | 53 ++++++++ docs/markdown.md | 55 ++++++++ docs/notebooks.ipynb | 122 ++++++++++++++++++ docs/references.bib | 56 ++++++++ docs/requirements.txt | 13 +- .../Install/Installation.rst | 0 {docs => docs_legacy}/Makefile | 0 .../ReadtheDocs/Jupyter_notebook.ipynb | 0 {docs => docs_legacy}/ReadtheDocs/Markdown.md | 0 .../ReadtheDocs/Markdown_support.rst | 0 .../ReadtheDocs/ReadTheDocs.rst | 0 .../ReadtheDocs/Refresh toctree.md | 0 .../ReadtheDocs/Sphinx_Basics.rst | 0 .../ReadtheDocs/Sphinx_installation.rst | 0 {docs => docs_legacy}/conf.py | 0 {docs => docs_legacy}/index.rst | 0 {docs => docs_legacy}/make.bat | 0 docs_legacy/requirements.txt | 10 ++ requirements_docs.txt | 1 + 31 files changed, 398 insertions(+), 24 deletions(-) delete mode 100644 docs/.vscode/settings.json create mode 120000 docs/Tutorials create mode 100644 docs/_config.yml create mode 100644 docs/_toc.yml create mode 120000 docs/docs_tutorial/README.md create mode 100644 docs/intro.md create mode 100644 docs/logo.png create mode 100644 docs/markdown-notebooks.md create mode 100644 docs/markdown.md create mode 100644 docs/notebooks.ipynb create mode 100644 docs/references.bib rename {docs => docs_legacy}/Install/Installation.rst (100%) rename {docs => docs_legacy}/Makefile (100%) rename {docs => docs_legacy}/ReadtheDocs/Jupyter_notebook.ipynb (100%) rename {docs => docs_legacy}/ReadtheDocs/Markdown.md (100%) rename {docs => docs_legacy}/ReadtheDocs/Markdown_support.rst (100%) rename {docs => docs_legacy}/ReadtheDocs/ReadTheDocs.rst (100%) rename {docs => docs_legacy}/ReadtheDocs/Refresh toctree.md (100%) rename {docs => docs_legacy}/ReadtheDocs/Sphinx_Basics.rst (100%) rename {docs => docs_legacy}/ReadtheDocs/Sphinx_installation.rst (100%) rename {docs => docs_legacy}/conf.py (100%) rename {docs => docs_legacy}/index.rst (100%) rename {docs => docs_legacy}/make.bat (100%) create mode 100644 docs_legacy/requirements.txt create mode 100644 requirements_docs.txt diff --git a/.gitignore b/.gitignore index 9f4b154..1597e93 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ **/__pycache__/ # Sphinx documentation docs/_build/ +docs_legacy/_build/ # Jupyter notebook **.ipynb_checkpoints diff --git a/Tutorials/2. BC/1. dirichlet.ipynb b/Tutorials/2. BC/1. dirichlet.ipynb index 6a6199b..c6cab43 100644 --- a/Tutorials/2. BC/1. dirichlet.ipynb +++ b/Tutorials/2. BC/1. dirichlet.ipynb @@ -47,7 +47,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -89,7 +89,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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" ] @@ -291,10 +291,22 @@ "cell_type": "code", "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "__init__() missing 3 required positional arguments: 'layer_size', 'activation', and 'initialiser'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m base \u001b[39m=\u001b[39m dp\u001b[39m.\u001b[39;49mnn\u001b[39m.\u001b[39;49mFullyConnected()\n\u001b[1;32m 2\u001b[0m base\u001b[39m.\u001b[39mforward()\n", + "\u001b[0;31mTypeError\u001b[0m: __init__() missing 3 required positional arguments: 'layer_size', 'activation', and 'initialiser'" + ] + } + ], "source": [ "base = dp.nn.FullyConnected()\n", - "base.forward()" + "#base.forward()" ] }, { @@ -328,7 +340,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.8.10" }, "orig_nbformat": 4 }, diff --git a/Tutorials/4. Dataset/1. basic.ipynb b/Tutorials/4. Dataset/1. basic.ipynb index b3cbda5..19bd12f 100644 --- a/Tutorials/4. Dataset/1. basic.ipynb +++ b/Tutorials/4. Dataset/1. basic.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to create the training dataset?" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/Tutorials/5. FCNN/1. basic.ipynb b/Tutorials/5. FCNN/1. basic.ipynb index 8728c67..d193a43 100644 --- a/Tutorials/5. FCNN/1. basic.ipynb +++ b/Tutorials/5. FCNN/1. basic.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basics of network design" + ] + }, { "cell_type": "code", "execution_count": 3, diff --git a/Tutorials/5. FCNN/3. model.ipynb b/Tutorials/5. FCNN/3. model.ipynb index 7011e63..7001569 100644 --- a/Tutorials/5. FCNN/3. model.ipynb +++ b/Tutorials/5. FCNN/3. model.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FCNN training" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/docs/.vscode/settings.json b/docs/.vscode/settings.json deleted file mode 100644 index a9ace50..0000000 --- a/docs/.vscode/settings.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "esbonio.sphinx.confDir": "${workspaceFolder}", - "restructuredtext.confPath": "${workspaceFolder}" -} \ No newline at end of file diff --git a/docs/Tutorials b/docs/Tutorials new file mode 120000 index 0000000..01a1731 --- /dev/null +++ b/docs/Tutorials @@ -0,0 +1 @@ +../Tutorials \ No newline at end of file diff --git a/docs/_config.yml b/docs/_config.yml new file mode 100644 index 0000000..5f534f8 --- /dev/null +++ b/docs/_config.yml @@ -0,0 +1,32 @@ +# Book settings +# Learn more at https://jupyterbook.org/customize/config.html + +title: My sample book +author: The Jupyter Book Community +logo: logo.png + +# Force re-execution of notebooks on each build. +# See https://jupyterbook.org/content/execute.html +execute: + execute_notebooks: force + +# Define the name of the latex output file for PDF builds +latex: + latex_documents: + targetname: book.tex + +# Add a bibtex file so that we can create citations +bibtex_bibfiles: + - references.bib + +# Information about where the book exists on the web +repository: + url: https://github.com/executablebooks/jupyter-book # Online location of your book + path_to_book: docs # Optional path to your book, relative to the repository root + branch: master # Which branch of the repository should be used when creating links (optional) + +# Add GitHub buttons to your book +# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository +html: + use_issues_button: true + use_repository_button: true diff --git a/docs/_toc.yml b/docs/_toc.yml new file mode 100644 index 0000000..f17a1bf --- /dev/null +++ b/docs/_toc.yml @@ -0,0 +1,9 @@ +# Table of contents +# Learn more at https://jupyterbook.org/customize/toc.html + +format: jb-book +root: intro +parts: + - caption: Installation + chapters: + - file: docs_tutorial/README.md \ No newline at end of file diff --git a/docs/docs_tutorial/README.md b/docs/docs_tutorial/README.md new file mode 120000 index 0000000..fe84005 --- /dev/null +++ b/docs/docs_tutorial/README.md @@ -0,0 +1 @@ +../../README.md \ No newline at end of file diff --git a/docs/intro.md b/docs/intro.md new file mode 100644 index 0000000..f8cdc73 --- /dev/null +++ b/docs/intro.md @@ -0,0 +1,11 @@ +# Welcome to your Jupyter Book + +This is a small sample book to give you a feel for how book content is +structured. +It shows off a few of the major file types, as well as some sample content. +It does not go in-depth into any particular topic - check out [the Jupyter Book documentation](https://jupyterbook.org) for more information. + +Check out the content pages bundled with this sample book to see more. + 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zB^upVY;ewq1QLHeD4y<6Oa4d6hgbOmM;(hw8Y(3@UM)XV_nC91+I{svghGcwL9DQC zyM&5PhT=%Y7C{j)JyC3slsF1h)J!2ju6cNc?HhXJLHl25entk$v!XYOzK=(rP~Rh! z*p(T?yl4h)l~Mkkoa1>4%y{DERdSd$UE=vGXLs>#A3q)Cuz$F)ey2S&b!HYf=Mm@i z$vBfjX|diF=}~}Se`0d%u`^s!Jiz*S>KK$b5mKnTyL{tReI0e>|9%tuAFB^Xu1EqI z2*~6xoM8t-esZMcNE3x1OqyN-Lp+FtX23bZdIhv1I_L3poeEE12w+x`r3BtK?UgS_ zgjv%6!;CN9dDzM}v3-DxU~SIgW9;-IvqR%OnBm4Ejqg0G}YnQC~*J|RZ=pB5-~vu$W~ z)Un>6^zlydKLzhIZ?b;=zuG68MC1PzKM`{<{lBe>Vh5EBTCLDuB`X-584ml0{G L>8MsHTOs~GC;Fmw literal 0 HcmV?d00001 diff --git a/docs/markdown-notebooks.md b/docs/markdown-notebooks.md new file mode 100644 index 0000000..a057a32 --- /dev/null +++ b/docs/markdown-notebooks.md @@ -0,0 +1,53 @@ +--- +jupytext: + formats: md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.11.5 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Notebooks with MyST Markdown + +Jupyter Book also lets you write text-based notebooks using MyST Markdown. +See [the Notebooks with MyST Markdown documentation](https://jupyterbook.org/file-types/myst-notebooks.html) for more detailed instructions. +This page shows off a notebook written in MyST Markdown. + +## An example cell + +With MyST Markdown, you can define code cells with a directive like so: + +```{code-cell} +print(2 + 2) +``` + +When your book is built, the contents of any `{code-cell}` blocks will be +executed with your default Jupyter kernel, and their outputs will be displayed +in-line with the rest of your content. + +```{seealso} +Jupyter Book uses [Jupytext](https://jupytext.readthedocs.io/en/latest/) to convert text-based files to notebooks, and can support [many other text-based notebook files](https://jupyterbook.org/file-types/jupytext.html). +``` + +## Create a notebook with MyST Markdown + +MyST Markdown notebooks are defined by two things: + +1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed). + See the YAML at the top of this page for example. +2. The presence of `{code-cell}` directives, which will be executed with your book. + +That's all that is needed to get started! + +## Quickly add YAML metadata for MyST Notebooks + +If you have a markdown file and you'd like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command: + +``` +jupyter-book myst init path/to/markdownfile.md +``` diff --git a/docs/markdown.md b/docs/markdown.md new file mode 100644 index 0000000..0ddaab3 --- /dev/null +++ b/docs/markdown.md @@ -0,0 +1,55 @@ +# Markdown Files + +Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or +in regular markdown files (`.md`), you'll write in the same flavor of markdown +called **MyST Markdown**. +This is a simple file to help you get started and show off some syntax. + +## What is MyST? + +MyST stands for "Markedly Structured Text". It +is a slight variation on a flavor of markdown called "CommonMark" markdown, +with small syntax extensions to allow you to write **roles** and **directives** +in the Sphinx ecosystem. + +For more about MyST, see [the MyST Markdown Overview](https://jupyterbook.org/content/myst.html). + +## Sample Roles and Directives + +Roles and directives are two of the most powerful tools in Jupyter Book. They +are kind of like functions, but written in a markup language. They both +serve a similar purpose, but **roles are written in one line**, whereas +**directives span many lines**. They both accept different kinds of inputs, +and what they do with those inputs depends on the specific role or directive +that is being called. + +Here is a "note" directive: + +```{note} +Here is a note +``` + +It will be rendered in a special box when you build your book. + +Here is an inline directive to refer to a document: {doc}`markdown-notebooks`. + + +## Citations + +You can also cite references that are stored in a `bibtex` file. For example, +the following syntax: `` {cite}`holdgraf_evidence_2014` `` will render like +this: {cite}`holdgraf_evidence_2014`. + +Moreover, you can insert a bibliography into your page with this syntax: +The `{bibliography}` directive must be used for all the `{cite}` roles to +render properly. +For example, if the references for your book are stored in `references.bib`, +then the bibliography is inserted with: + +```{bibliography} +``` + +## Learn more + +This is just a simple starter to get you started. +You can learn a lot more at [jupyterbook.org](https://jupyterbook.org). diff --git a/docs/notebooks.ipynb b/docs/notebooks.ipynb new file mode 100644 index 0000000..fdb7176 --- /dev/null +++ b/docs/notebooks.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Content with notebooks\n", + "\n", + "You can also create content with Jupyter Notebooks. This means that you can include\n", + "code blocks and their outputs in your book.\n", + "\n", + "## Markdown + notebooks\n", + "\n", + "As it is markdown, you can embed images, HTML, etc into your posts!\n", + "\n", + "![](https://myst-parser.readthedocs.io/en/latest/_static/logo-wide.svg)\n", + "\n", + "You can also $add_{math}$ and\n", + "\n", + "$$\n", + "math^{blocks}\n", + "$$\n", + "\n", + "or\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\mbox{mean} la_{tex} \\\\ \\\\\n", + "math blocks\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n", + "\n", + "## MyST markdown\n", + "\n", + "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n", + "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n", + "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n", + "\n", + "## Code blocks and outputs\n", + "\n", + "Jupyter Book will also embed your code blocks and output in your book.\n", + "For example, here's some sample Matplotlib code:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import rcParams, cycler\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.ion()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fixing random state for reproducibility\n", + "np.random.seed(19680801)\n", + "\n", + "N = 10\n", + "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n", + "data = np.array(data).T\n", + "cmap = plt.cm.coolwarm\n", + "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n", + "\n", + "\n", + "from matplotlib.lines import Line2D\n", + "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n", + " Line2D([0], [0], color=cmap(.5), lw=4),\n", + " Line2D([0], [0], color=cmap(1.), lw=4)]\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "lines = ax.plot(data)\n", + "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a lot more that you can do with outputs (such as including interactive outputs)\n", + "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/references.bib b/docs/references.bib new file mode 100644 index 0000000..783ec6a --- /dev/null +++ b/docs/references.bib @@ -0,0 +1,56 @@ +--- +--- + +@inproceedings{holdgraf_evidence_2014, + address = {Brisbane, Australia, Australia}, + title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}}, + booktitle = {International {Conference} on {Cognitive} {Neuroscience}}, + publisher = {Frontiers in Neuroscience}, + author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.}, + year = {2014} +} + +@article{holdgraf_rapid_2016, + title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility}, + volume = {7}, + issn = {2041-1723}, + url = {http://www.nature.com/doifinder/10.1038/ncomms13654}, + doi = {10.1038/ncomms13654}, + number = {May}, + journal = {Nature Communications}, + author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.}, + year = {2016}, + pages = {13654}, + file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf} +} + +@inproceedings{holdgraf_portable_2017, + title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science}, + volume = {Part F1287}, + isbn = {978-1-4503-5272-7}, + doi = {10.1145/3093338.3093370}, + abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.}, + booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}}, + author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.}, + year = {2017}, + keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy} +} + +@article{holdgraf_encoding_2017, + title = {Encoding and decoding models in cognitive electrophysiology}, + volume = {11}, + issn = {16625137}, + doi = {10.3389/fnsys.2017.00061}, + abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.}, + journal = {Frontiers in Systems Neuroscience}, + author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.}, + year = {2017}, + keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials} +} + +@book{ruby, + title = {The Ruby Programming Language}, + author = {Flanagan, David and Matsumoto, Yukihiro}, + year = {2008}, + publisher = {O'Reilly Media} +} diff --git a/docs/requirements.txt b/docs/requirements.txt index 5cca60a..7e821e4 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,10 +1,3 @@ -sphinx==5.0.2 -sphinx_rtd_theme==1.0.0 -myst-parser==0.18.1 -nbsphinx==0.8.9 -sphinxcontrib-applehelp==1.0.2 -sphinxcontrib-devhelp==1.0.2 -sphinxcontrib-htmlhelp==2.0.0 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==1.0.3 -sphinxcontrib-serializinghtml==1.1.5 +jupyter-book +matplotlib +numpy diff --git a/docs/Install/Installation.rst b/docs_legacy/Install/Installation.rst similarity index 100% rename from docs/Install/Installation.rst rename to docs_legacy/Install/Installation.rst diff --git a/docs/Makefile b/docs_legacy/Makefile similarity index 100% rename from docs/Makefile rename to docs_legacy/Makefile diff --git a/docs/ReadtheDocs/Jupyter_notebook.ipynb b/docs_legacy/ReadtheDocs/Jupyter_notebook.ipynb similarity index 100% rename from docs/ReadtheDocs/Jupyter_notebook.ipynb rename to docs_legacy/ReadtheDocs/Jupyter_notebook.ipynb diff --git a/docs/ReadtheDocs/Markdown.md b/docs_legacy/ReadtheDocs/Markdown.md similarity index 100% rename from docs/ReadtheDocs/Markdown.md rename to docs_legacy/ReadtheDocs/Markdown.md diff --git a/docs/ReadtheDocs/Markdown_support.rst b/docs_legacy/ReadtheDocs/Markdown_support.rst similarity index 100% rename from docs/ReadtheDocs/Markdown_support.rst rename to docs_legacy/ReadtheDocs/Markdown_support.rst diff --git a/docs/ReadtheDocs/ReadTheDocs.rst b/docs_legacy/ReadtheDocs/ReadTheDocs.rst similarity index 100% rename from docs/ReadtheDocs/ReadTheDocs.rst rename to docs_legacy/ReadtheDocs/ReadTheDocs.rst diff --git a/docs/ReadtheDocs/Refresh toctree.md b/docs_legacy/ReadtheDocs/Refresh toctree.md similarity index 100% rename from docs/ReadtheDocs/Refresh toctree.md rename to docs_legacy/ReadtheDocs/Refresh toctree.md diff --git a/docs/ReadtheDocs/Sphinx_Basics.rst b/docs_legacy/ReadtheDocs/Sphinx_Basics.rst similarity index 100% rename from docs/ReadtheDocs/Sphinx_Basics.rst rename to docs_legacy/ReadtheDocs/Sphinx_Basics.rst diff --git a/docs/ReadtheDocs/Sphinx_installation.rst b/docs_legacy/ReadtheDocs/Sphinx_installation.rst similarity index 100% rename from docs/ReadtheDocs/Sphinx_installation.rst rename to docs_legacy/ReadtheDocs/Sphinx_installation.rst diff --git a/docs/conf.py b/docs_legacy/conf.py similarity index 100% rename from docs/conf.py rename to docs_legacy/conf.py diff --git a/docs/index.rst b/docs_legacy/index.rst similarity index 100% rename from docs/index.rst rename to docs_legacy/index.rst diff --git a/docs/make.bat b/docs_legacy/make.bat similarity index 100% rename from docs/make.bat rename to docs_legacy/make.bat diff --git a/docs_legacy/requirements.txt b/docs_legacy/requirements.txt new file mode 100644 index 0000000..5cca60a --- /dev/null +++ b/docs_legacy/requirements.txt @@ -0,0 +1,10 @@ +sphinx==5.0.2 +sphinx_rtd_theme==1.0.0 +myst-parser==0.18.1 +nbsphinx==0.8.9 +sphinxcontrib-applehelp==1.0.2 +sphinxcontrib-devhelp==1.0.2 +sphinxcontrib-htmlhelp==2.0.0 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==1.0.3 +sphinxcontrib-serializinghtml==1.1.5 diff --git a/requirements_docs.txt b/requirements_docs.txt new file mode 100644 index 0000000..47a56ab --- /dev/null +++ b/requirements_docs.txt @@ -0,0 +1 @@ +jupyter-book \ No newline at end of file From 6d5583d20c0fefd7edd668cb765fd003e36aca5e Mon Sep 17 00:00:00 2001 From: Prakhar Sharma <1915438@swansea.ac.uk> Date: Fri, 28 Jul 2023 16:35:52 +0100 Subject: [PATCH 3/4] basic docs created --- docs/_config.yml | 7 +- docs/_toc.yml | 8 +- docs/docs_tutorial/README.md | 1 - docs/docs_tutorial/contribution.md | 31 ++++++ docs/docs_tutorial/docs_contribution.md | 64 +++++++++++++ docs/docs_tutorial/installation.md | 53 ++++++++++ docs/intro.md | 14 +-- docs/markdown-notebooks.md | 53 ---------- docs/markdown.md | 55 ----------- docs/notebooks.ipynb | 122 ------------------------ 10 files changed, 163 insertions(+), 245 deletions(-) delete mode 120000 docs/docs_tutorial/README.md create mode 100644 docs/docs_tutorial/contribution.md create mode 100644 docs/docs_tutorial/docs_contribution.md create mode 100644 docs/docs_tutorial/installation.md delete mode 100644 docs/markdown-notebooks.md delete mode 100644 docs/markdown.md delete mode 100644 docs/notebooks.ipynb diff --git a/docs/_config.yml b/docs/_config.yml index 5f534f8..c993e45 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -1,8 +1,9 @@ # Book settings # Learn more at https://jupyterbook.org/customize/config.html -title: My sample book -author: The Jupyter Book Community +title: DeepINN +author: Prakhar Sharma +copyright: "2023" logo: logo.png # Force re-execution of notebooks on each build. @@ -21,7 +22,7 @@ bibtex_bibfiles: # Information about where the book exists on the web repository: - url: https://github.com/executablebooks/jupyter-book # Online location of your book + url: https://github.com/praksharma/DeepINN # Online location of your book path_to_book: docs # Optional path to your book, relative to the repository root branch: master # Which branch of the repository should be used when creating links (optional) diff --git a/docs/_toc.yml b/docs/_toc.yml index f17a1bf..78eaeec 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -6,4 +6,10 @@ root: intro parts: - caption: Installation chapters: - - file: docs_tutorial/README.md \ No newline at end of file + - file: docs_tutorial/installation.md + - caption: Contribution + chapters: + - file: docs_tutorial/contribution.md + - caption: Documentation compilation + chapters: + - file: docs_tutorial/docs_contribution.md \ No newline at end of file diff --git a/docs/docs_tutorial/README.md b/docs/docs_tutorial/README.md deleted file mode 120000 index fe84005..0000000 --- a/docs/docs_tutorial/README.md +++ /dev/null @@ -1 +0,0 @@ -../../README.md \ No newline at end of file diff --git a/docs/docs_tutorial/contribution.md b/docs/docs_tutorial/contribution.md new file mode 100644 index 0000000..c6c16cb --- /dev/null +++ b/docs/docs_tutorial/contribution.md @@ -0,0 +1,31 @@ +# Contribution +Create a `venv` in the root of the repo. Here the assumption is that the `python` is symlink to `python3`. +```sh +python -m venv .venv +``` +Activate the environment. +```sh +source .venv/bin/activate +``` +Confirm that the Python path is updated. +```sh +which python +``` +The `STDOUT` should point to the .venv directory. Now, upgrade the pip. +```sh +python -m pip install --upgrade pip +``` +Install the required packages. +```sh +pip install -r requirements.txt +``` +If you want to build the docs using the same environment, then install the relevant dependencies. +```sh +pip install -r docs/requirements.txt +``` + +# Testing +The testing is very simple. Just run the test.py file in the current Python virtual environment. +```sh +python test.py +``` diff --git a/docs/docs_tutorial/docs_contribution.md b/docs/docs_tutorial/docs_contribution.md new file mode 100644 index 0000000..9f70ad3 --- /dev/null +++ b/docs/docs_tutorial/docs_contribution.md @@ -0,0 +1,64 @@ + +## Documentation compilation +The doc is created using [Jupyter-book](https://jupyterbook.org/en/stable/intro.html). + +### Setting up Jupyter-books +These steps will allows you to create a basic setup. For more details visit [here](https://jupyterbook.org/en/stable/start/your-first-book.html). +1. Create a new Python virtual environment or use an existing one. + +```sh +python3 -m venv jupyter_env +``` + +2. Install Jupyter-book. +```sh +pip3 install -U jupyter-book +``` + +3. Create a template for quick start. +```sh +jupyter-book create docs/ +``` +This will create a directory in the ```$PWD``` named ```docs/```. The table of contents are stored in ```_toc.yml``` and the configuration is stored in ```_config.yml```. + +4. Building a project + +``` +jupyter-book build docs/ +``` + +For a full rebuild: + +``` +jupyter-book build --all docs/ +``` + +If the toc doesn't update. This will update the entire project. + +5. Publish the docs in the new branch. + +``` +ghp-import -n -p -f docs/_build/html +``` + +6. Deploy website +Go to "Settings->Pages" of the repo. Set the "Source" to "Deploy from a branch". In the "Branch", select the "gh-pages" branch and location as the "/root". + +7. GitHub pages force build +GitHub pages is known for its laziness. To force deploy the website go to "Setting->Pages". Here, search for the following line: + +> Your site was last deployed to the github-pages environment by the pages build and deployment workflow. + +Click on "pages build and deployment" and click on the button "Re-run all jobs" on the top right corner. + +8. Include notebooks outside the docs/ directory +You can create a soft link in the book directory to the directory with the notebooks you want to include: +```sh +ln -s ../Tutorials ./Tutorials +``` + +You can also link to a document as follows: + +```sh +ln -s ../../README.md ./README.md +``` \ No newline at end of file diff --git a/docs/docs_tutorial/installation.md b/docs/docs_tutorial/installation.md new file mode 100644 index 0000000..acc96a6 --- /dev/null +++ b/docs/docs_tutorial/installation.md @@ -0,0 +1,53 @@ +# Installation +## Using pip +DeepINN can be installed via pip using the following command: +```sh +pip install DeepINN +``` + +## Docker image +Pull the image with suitable tagname. The image is available [here](https://hub.docker.com/r/prakhars962/deepinn). + +```sh +docker pull prakhars962/deepinn:tagname +``` +### CPU Only +The image opens a jupyter server by default. +```sh +docker run -p 8888:8888 prakhars962/deepinn:pre-release +``` + +You can override the jupyter server entrypoint using the following command. +```sh +docker run -it --entrypoint /bin/bash prakhars962/deepinn:pre-release +``` +### GPU passthrough +First install `nvidia-docker` using this [guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#step-2-install-nvidia-container-toolkit). + +Now run the container with `nvidia-docker`. +```sh +nvidia-docker run -it --entrypoint /bin/bash prakhars962/deepinn:pre-release +``` +This command will bind the `pwd` to `/workspace/tutorials` and open a jupyter-lab with GPU support. +```sh +nvidia-docker run -v $(pwd):/workspace/tutorials -p 8888:8888 prakhars962/deepinn:pre-release +``` +Alternatively, one can run interactive session. +```sh +nvidia-docker run -v $(pwd):/workspace/tutorials -it --entrypoint /bin/bash prakhars962/deepinn:pre-release +``` + +### Tagless copy +Each time you pull the updated image, docker will create a tagless copy of the old one. +```sh +╰─ docker images +REPOSITORY TAG IMAGE ID CREATED SIZE +prakhars962/deepinn pre-release 886808706155 4 minutes ago 6.99GB +prakhars962/deepinn 0bb744f6159e 38 minutes ago 6.99GB +prakhars962/deepinn 4ffbb67f8447 About an hour ago 6.8GB +prakhars962/deepinn fe16ca34f9d9 About an hour ago 6.8GB +``` +The only solution is to delete them one by one using the IMAGE_ID. +```sh +docker image rm -f IMAGE_ID +``` \ No newline at end of file diff --git a/docs/intro.md b/docs/intro.md index f8cdc73..8d2f0d9 100644 --- a/docs/intro.md +++ b/docs/intro.md @@ -1,11 +1,5 @@ -# Welcome to your Jupyter Book +# DeepINN +[![DeepINN CI](https://github.com/praksharma/DeepINN/actions/workflows/main.yml/badge.svg)](https://github.com/praksharma/DeepINN/actions/workflows/main.yml) [![docker_container](https://github.com/praksharma/DeepINN/actions/workflows/docker.yml/badge.svg)](https://github.com/praksharma/DeepINN/actions/workflows/docker.yml) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/a5c43d9b9e6a45759061ac654bdc1e3f)](https://www.codacy.com/gh/praksharma/DeepINN/dashboard?utm_source=github.com&utm_medium=referral&utm_content=praksharma/DeepINN&utm_campaign=Badge_Grade)![Travis (.org) branch](https://app.travis-ci.com/praksharma/DeepINN.svg?branch=main) +[![Documentation Status](https://readthedocs.org/projects/deepinn/badge/?version=latest)](https://deepinn.readthedocs.io/en/latest/index.html?badge=latest) [![License](https://img.shields.io/badge/License-AGPL_v3-red.svg)](https://github.com/praksharma/DeepINN/blob/main/LICENSE) -This is a small sample book to give you a feel for how book content is -structured. -It shows off a few of the major file types, as well as some sample content. -It does not go in-depth into any particular topic - check out [the Jupyter Book documentation](https://jupyterbook.org) for more information. - -Check out the content pages bundled with this sample book to see more. - -```{tableofcontents} -``` +[DeepINN](https://github.com/praksharma/DeepINN) is a deep-learning framework for solving forward and inverse problem involving PDEs using physics-informed neural networks (PINNs). diff --git a/docs/markdown-notebooks.md b/docs/markdown-notebooks.md deleted file mode 100644 index a057a32..0000000 --- a/docs/markdown-notebooks.md +++ /dev/null @@ -1,53 +0,0 @@ ---- -jupytext: - formats: md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.11.5 -kernelspec: - display_name: Python 3 - language: python - name: python3 ---- - -# Notebooks with MyST Markdown - -Jupyter Book also lets you write text-based notebooks using MyST Markdown. -See [the Notebooks with MyST Markdown documentation](https://jupyterbook.org/file-types/myst-notebooks.html) for more detailed instructions. -This page shows off a notebook written in MyST Markdown. - -## An example cell - -With MyST Markdown, you can define code cells with a directive like so: - -```{code-cell} -print(2 + 2) -``` - -When your book is built, the contents of any `{code-cell}` blocks will be -executed with your default Jupyter kernel, and their outputs will be displayed -in-line with the rest of your content. - -```{seealso} -Jupyter Book uses [Jupytext](https://jupytext.readthedocs.io/en/latest/) to convert text-based files to notebooks, and can support [many other text-based notebook files](https://jupyterbook.org/file-types/jupytext.html). -``` - -## Create a notebook with MyST Markdown - -MyST Markdown notebooks are defined by two things: - -1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed). - See the YAML at the top of this page for example. -2. The presence of `{code-cell}` directives, which will be executed with your book. - -That's all that is needed to get started! - -## Quickly add YAML metadata for MyST Notebooks - -If you have a markdown file and you'd like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command: - -``` -jupyter-book myst init path/to/markdownfile.md -``` diff --git a/docs/markdown.md b/docs/markdown.md deleted file mode 100644 index 0ddaab3..0000000 --- a/docs/markdown.md +++ /dev/null @@ -1,55 +0,0 @@ -# Markdown Files - -Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or -in regular markdown files (`.md`), you'll write in the same flavor of markdown -called **MyST Markdown**. -This is a simple file to help you get started and show off some syntax. - -## What is MyST? - -MyST stands for "Markedly Structured Text". It -is a slight variation on a flavor of markdown called "CommonMark" markdown, -with small syntax extensions to allow you to write **roles** and **directives** -in the Sphinx ecosystem. - -For more about MyST, see [the MyST Markdown Overview](https://jupyterbook.org/content/myst.html). - -## Sample Roles and Directives - -Roles and directives are two of the most powerful tools in Jupyter Book. They -are kind of like functions, but written in a markup language. They both -serve a similar purpose, but **roles are written in one line**, whereas -**directives span many lines**. They both accept different kinds of inputs, -and what they do with those inputs depends on the specific role or directive -that is being called. - -Here is a "note" directive: - -```{note} -Here is a note -``` - -It will be rendered in a special box when you build your book. - -Here is an inline directive to refer to a document: {doc}`markdown-notebooks`. - - -## Citations - -You can also cite references that are stored in a `bibtex` file. For example, -the following syntax: `` {cite}`holdgraf_evidence_2014` `` will render like -this: {cite}`holdgraf_evidence_2014`. - -Moreover, you can insert a bibliography into your page with this syntax: -The `{bibliography}` directive must be used for all the `{cite}` roles to -render properly. -For example, if the references for your book are stored in `references.bib`, -then the bibliography is inserted with: - -```{bibliography} -``` - -## Learn more - -This is just a simple starter to get you started. -You can learn a lot more at [jupyterbook.org](https://jupyterbook.org). diff --git a/docs/notebooks.ipynb b/docs/notebooks.ipynb deleted file mode 100644 index fdb7176..0000000 --- a/docs/notebooks.ipynb +++ /dev/null @@ -1,122 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Content with notebooks\n", - "\n", - "You can also create content with Jupyter Notebooks. This means that you can include\n", - "code blocks and their outputs in your book.\n", - "\n", - "## Markdown + notebooks\n", - "\n", - "As it is markdown, you can embed images, HTML, etc into your posts!\n", - "\n", - "![](https://myst-parser.readthedocs.io/en/latest/_static/logo-wide.svg)\n", - "\n", - "You can also $add_{math}$ and\n", - "\n", - "$$\n", - "math^{blocks}\n", - "$$\n", - "\n", - "or\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\mbox{mean} la_{tex} \\\\ \\\\\n", - "math blocks\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n", - "\n", - "## MyST markdown\n", - "\n", - "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n", - "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n", - "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n", - "\n", - "## Code blocks and outputs\n", - "\n", - "Jupyter Book will also embed your code blocks and output in your book.\n", - "For example, here's some sample Matplotlib code:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import rcParams, cycler\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "plt.ion()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fixing random state for reproducibility\n", - "np.random.seed(19680801)\n", - "\n", - "N = 10\n", - "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n", - "data = np.array(data).T\n", - "cmap = plt.cm.coolwarm\n", - "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n", - "\n", - "\n", - "from matplotlib.lines import Line2D\n", - "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n", - " Line2D([0], [0], color=cmap(.5), lw=4),\n", - " Line2D([0], [0], color=cmap(1.), lw=4)]\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 5))\n", - "lines = ax.plot(data)\n", - "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is a lot more that you can do with outputs (such as including interactive outputs)\n", - "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.0" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 73706e2e4c668c7221fdec204c74774de246e990 Mon Sep 17 00:00:00 2001 From: Prakhar Sharma <1915438@swansea.ac.uk> Date: Fri, 28 Jul 2023 16:39:59 +0100 Subject: [PATCH 4/4] docs toc modified --- docs/_toc.yml | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/docs/_toc.yml b/docs/_toc.yml index 78eaeec..6d7b616 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -4,12 +4,14 @@ format: jb-book root: intro parts: - - caption: Installation + - caption: Installation and contribution chapters: - file: docs_tutorial/installation.md - - caption: Contribution - chapters: - file: docs_tutorial/contribution.md - - caption: Documentation compilation - chapters: - - file: docs_tutorial/docs_contribution.md \ No newline at end of file + - file: docs_tutorial/docs_contribution.md + # - caption: Contribution + # chapters: + # - file: docs_tutorial/contribution.md + # - caption: Documentation compilation + # chapters: + # - file: docs_tutorial/docs_contribution.md \ No newline at end of file