Deploying to gh-pages from @ 8ca93ae 🚀

.buildinfo

@@ -1,4 +1,4 @@
 # 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: becfbf9143b6ea489c5afed7c3ad7082
+config: 2fea2bf74694d431d1bb43b8c647c1f4
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/basics.md.txt

@@ -0,0 +1,8 @@
+# Python Basics
+
+```{toctree} Table of Contents
+
+python-basics.ipynb
+pandas.ipynb
+visualization.ipynb
+```

_sources/case-studies.md.txt

@@ -0,0 +1,7 @@
+# Case Studies
+
+```{toctree} Table of Contents
+
+linear_fitting.ipynb
+xps_processing.ipynb
+```

_sources/index.md.txt

@@ -1,11 +1,9 @@
 # Python Scripting for Experimental Chemists
 
-```{toctree} Table of Contents
-
-notebooks/python-basics.ipynb
-notebooks/pandas.ipynb
-notebooks/visualization.ipynb
-notebooks/linear_fitting.ipynb
-notebooks/xps_processing.ipynb
+```{toctree} Table of 
+:maxdepth: 2
 
+setup.md
+basics.md
+case-studies.md
 ```

_sources/setup.md.txt

@@ -0,0 +1,75 @@
+# Set Up
+
+## Running Python in the Cloud
+
+If you would just like to try out these notebooks to see if Python is right for you, we recommend running the notebooks in the cloud using GitHub Codespaces.
+
+To use GitHub Codespaces, you will need a GitHub account. If you do not have one, you can create one for free at [github.com](https://github.com).
+
+Next, click the following button to open the notebooks in GitHub Codespaces:
+
+[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/MolSSI-Education/python-experimental-chem?editor=jupyter)
+
+This will take a few minutes to start up. 
+By default, the Codespace will open in an interface called VSCode. 
+However, we recommend using the Jupyter Lab interface.
+To use this interface, click [this link](https://github.com/codespaces) to see your Codespaces.
+GitHub will have given your CodeSpace a random name.
+Click the buttons with three dots to the right of your CodeSpace and select "Open with Jupyter Lab".
+
+![jupyter-lab](./images/open-in-jupyterlab.png)
+
+## Installing Python Locally
+
+If you would like to install Python and run these notebooks on your own computer, we reccomend the following:
+
+
+1. [Download and install miniconda](https://docs.conda.io/projects/miniconda/en/latest/miniconda-install.html) - Miniconda is a package manager that will allow you to install Python and other software needed for this workshop. If you already have conda installed, you can skip this step.
+
+2. Install solver - We recommend changing a setting having to do with how packages are installed. After you have the conda package manager installed, open your terminal (if on Mac or Linux) or the Anaconda Prompt (if on Windows).
+
+Note, if you have previously installed conda, you will first need to do the following in your terminal:
+
+```bash
+conda update -n base conda
+```
+Then, execute the following command
+
+```bash
+conda install -n base conda-libmamba-solver -c conda-forge
+```
+
+Then run this command
+
+```bash
+conda config --set solver libmamba
+```
+
+3. Clone or download the files for the workshop from the GitHub reposistory.
+
+```bash
+git clone https://github.com/MolSSI-Education/python-experimental-chem.git
+```
+
+4. Install the environment for the workshop
+Once you've cloned the workshop materials, you can create an environment with all the necessary packages by navigating to the workshop directory and running the following command:
+
+```bash
+conda env create -f environment.yml
+```
+
+5. Test your installation
+
+You can test your installation by activating the environment and opening the jupyter lab interface.
+
+Type the following to activate your environment
+
+```bash
+conda activate sermacs-workshop
+```
+
+Type the following to open a Jupyter Lab interface:
+
+```bash
+jupyter lab
+```

_sources/notebooks/xps_processing.ipynb.txt → _sources/xps_processing.ipynb.txt

@@ -379,17 +379,6 @@
     "</div>"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2aa3816a-5d48-41a4-88a1-adba7d9c55b2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add your label for oxygen here.\n",
-    "\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "edd39b5d-1e19-42b6-a230-9e6429f22514",
@@ -633,7 +622,7 @@
    "id": "241e5c0b-65ae-490b-a2df-01a09c0367b3",
    "metadata": {},
    "source": [
-    "## Numerical Integration of Peaks\n",
+    "### Numerical Integration of Peaks\n",
     "\n",
     "Although it is possible to fit a  function to each peak and perform  integration,\n",
     "we can also analyze our spectrum by doing numerical integration.\n",
@@ -736,7 +725,7 @@
    "id": "d9007712-db67-4251-b675-dfba9a5dd677",
    "metadata": {},
    "source": [
-    "## Fitting Peaks with Gaussian Functions\n",
+    "### Fitting Peaks with Gaussian Functions\n",
     "\n",
     "The following is supplemental material. \n",
     "If you would like to be more rigorous with your peak fitting, you might choose to fit the peaks with a Gaussian instead of performing numerical integration.\n",
@@ -896,7 +885,7 @@
    "id": "17046b5b-797c-4380-b8e3-2528b1863e67",
    "metadata": {},
    "source": [
-    "## Peak Integration\n",
+    "### Peak Integration\n",
     "\n",
     "Now that we have our peak, we can integrate it using SciPy.\n",
     "We will use [the `quad` function](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.quad.html) we imported at the top of the file.\n",