Update documentation

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: e7ccd46b6f10102da33c75fda9ac2b27
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/ingestion.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data ingestion and formatting\n",
+    "\n",
+    "This notebook explains how to convert the Climate TRACE dataset to a format that is more appropriate for data science. \n",
+    "\n",
+    "```{note}\n",
+    "This section is relevant for data engineers, or data scientists who want to understand how the data \n",
+    "has been prepared. Skip if you just want to access the final, prepared data.\n",
+    "```\n",
+    "\n",
+    "The original data from Climate TRACE is offered as a series of CSV files bundled in ZIP archives. That format is universally understood, but it is not the most effective for effective analysis.\n",
+    "\n",
+    "Instead, we are going to use the Parquet format. This format has a number of advantages:\n",
+    "- it is _column-based_ : data systems can process big chunks of data at once, rather than line by line. Also, depending on the information requested, systems will read only the relevant columns and skip the rest very effectively\n",
+    "- it is _structured_ : basic information about numbers, categories, ... are preserved. This provides a large speed boost\n",
+    "- it is _universal_ : most modern data systems will be able to read it\n",
+    "\n",
+    "```{admonition} TODO\n",
+    "complete this notebook\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  },
+  "widgets": {
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+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

_sources/intro.md

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+# Saving the planet with data: deriving insights from the Climate Trace project.
+
+Our human activities are responsible for most of the greenhouse gases (GHG) 
+released in the atmosphere. These gas stay in the atmosphere for long periods  of time and they drive profound changes in the physical systems and the ecosystems around us. Where do they come from? What can we do about it?
+
+You cannot control what you cannot measure. Identifying the source of these emissions is a key step for eventually reducing them. The [Climate TRACE](https://climatetrace.org/) project is a consortium of research labs, non-profit organizations and companies that aims at providing a comprehensive, global understanding of all the emissions around the planet. It makes high level datasets available for anyone to browse and share.
+
+This handbook is a series of tutorials aimed at data scientists and technical people who may be wondering how to explore the dataset offered by Climate TRACE. I hope that this series of notebooks inspires scientists and engineers to understand better the source of emissions around them, and to give them the curiosity to engage and improve this information.
+
+Even if we are going to analyze hundreds of millions of sources of GHG gases on a planetary scale, any reasonably modern laptop should be enough to run these. Along the way, I will use and point out the modern data science techniques that makes this analysis so fast and streamlined. 
+The technical sections that also explain the how the code is running will be clearly marked with a `Technical` label. You can skip them if you are just interested in the results.
+
+After reading this handbook, you should be able to do the following:
+- access and manipulate the Climate Trace dataset with Python and Jupyter
+- derive basic insights about emissions from this dataset: which countries emit the most, the least, which sorts of gas, etc.
+- where our knowledge is the most uncertain
+- where independent measurements of the same sources gives inconsistent values
+
+```{warning}
+
+This handbook is still a draft. Numbers are still being checked with experts.
+
+```
+
+```{tableofcontents}
+```

_sources/markdown-notebooks.md

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+---
+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
+```

_sources/markdown.md

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+# 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 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).

_sources/notebooks.ipynb

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+{
+ "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 (ipykernel)",
+   "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": "3.10.12"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}