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Merge pull request #115 from mgrover1/reconfigure-page
ADD: Add revised toc notebooks
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‎Summer-Schools/README.md‎

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# Summer School Resources
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Here, we share resources from ARM Summer School events!
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## 2024 ARM Open Science Summer School
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- [Main Landing Page](https://arm-development.github.io/arm-summer-school-2024/)
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- [Github Repository](https://github.com/ARM-Development/arm-summer-school-2024)
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- [Project Cookbooks](https://arm-development.github.io/arm-summer-school-2024/projects/project-list.html)
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## 2025 CAPE-k Student Workshop
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- [Main Landing Page](https://arm-development.github.io/cape-k-student-workshop-2025/)
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- [Github Repository](https://github.com/ARM-Development/cape-k-student-workshop-2025)
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- [Project Cookbooks](https://arm-development.github.io/cape-k-student-workshop-2025/projects)
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## 2025 BNF Summer School
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- [Main Landing Page]()
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- [Github Repository](https://github.com/ARM-Development/arm-summer-school-2025)
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- [Project Cookbooks]()

‎Tutorials/ACT-Python-Tutorial/1-jupyter_intro.ipynb‎

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"metadata": {},
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"source": [
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"---"
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]
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"## Prerequisites\n",
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"| Concepts | Importance | Notes |\n",
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"| --- | --- | --- |\n",
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"| [Getting Started with Jupyter](getting-started-jupyter) | Helpful | |\n",
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"| [Getting Started with Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html) | Helpful | |\n",
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"| [Installing and Running Python: Python in Jupyter](https://foundations.projectpythia.org/foundations/jupyter.html) | Helpful | |\n",
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"\n",
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"- **Time to learn**: 30 minutes"
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"- [Markdown Guide](https://www.markdownguide.org/)\n",
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"- [Xdev Python Tutorial Seminar Series - Jupyter Notebooks](https://youtu.be/xSzXvwzFsDU)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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"version": "3.11.4"
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}
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},
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"nbformat": 4,

‎Tutorials/ACT-Python-Tutorial/2-Python-Basics.ipynb‎

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"- [Official Python tutorial (Python Docs)](https://docs.python.org/3/tutorial/index.html)\n",
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"- [ProjectPythia](https://foundations.projectpythia.org/landing-page.html)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a07e3574-4f6d-4ee3-97bb-e290895b9a9a",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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"version": "3.11.4"
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"nbformat": 4,

‎Tutorials/ACT-Python-Tutorial/2a-scientific_libraries_numpy.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"id": "a6432f38-cba9-4df0-8915-21f1545d509c",
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"metadata": {},
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"source": [
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"# Working with Numpy"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2bfdfc40-5ef5-44e6-b74d-f89a06a34973",
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"metadata": {},
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"source": [
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"# Working with Numpy\n",
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"\n",
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"From the [NumPy documentation](https://numpy.org/doc/stable/user/whatisnumpy.html):\n",
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"\n",
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"> NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation, and much more.\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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"version": "3.11.4"
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}
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},
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"nbformat": 4,

‎Tutorials/ACT-Python-Tutorial/2b-scientific_libraries_pandas.ipynb‎

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"cells": [
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{
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"id": "00d29a93-adb8-481b-9d93-bcc5c9f780d8",
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"metadata": {},
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"source": [
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"# Working with Pandas"
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]
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},
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{
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"cell_type": "markdown",
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"id": "75582b00-36b1-4fba-b23c-2348788cbf5e",
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"metadata": {},
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"source": [
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"# Working with Pandas\n",
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"\n",
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"From the [Pandas Documentation](https://pandas.pydata.org/docs/getting_started/overview.html)\n",
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"> pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. It is already well on its way toward this goal. \n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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"version": "3.11.4"
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"nbformat": 4,

‎Tutorials/ACT-Python-Tutorial/2c-scientific_libraries_xarray.ipynb‎

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"id": "ac7f7e4a-b762-4392-811f-0b9ddc51f841",
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"metadata": {},
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"source": [
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"# Working with Xarray"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b8ee587-5e1d-4ca8-9c6b-0da1600fa932",
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"metadata": {},
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"source": [
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"# Working with Xarray\n",
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"\n",
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"From the [Xarray Documentation](https://docs.xarray.dev/en/stable/getting-started-guide/why-xarray.html)\n",
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"> Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called “tensors”) are an essential part of computational science. They are encountered in a wide range of fields, including physics, astronomy, geoscience, bioinformatics, engineering, finance, and deep learning. In Python, NumPy provides the fundamental data structure and API for working with raw ND arrays. However, real-world datasets are usually more than just raw numbers; they have labels which encode information about how the array values map to locations in space, time, etc.\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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"nbformat": 4,

‎Tutorials/ACT-Python-Tutorial/3-ACT-Basics-BNF.ipynb‎

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"id": "950099de-bfc3-4e1d-85a4-0184030e85a8",
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"# ACT Basics with BNF\n",
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"\n",
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‎Tutorials/ACT-Python-Tutorial/3-ACT-Basics.ipynb‎

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"id": "2131a83e-9bcf-4943-a3a9-9c362ca3d92e",
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"metadata": {},
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"source": [
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"# ACT Basics with TRACER"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3fb29e94-a430-4eb8-bd45-105b77f30277",
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‎Tutorials/ACT-Python-Tutorial/3a-ACT-TRACER-Dust.ipynb‎

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"id": "0a80c4e2-d09a-426c-9efb-c9a8622a3a65",
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"metadata": {},
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"source": [
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"# ACT TRACER Dust Exploration"
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]
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},
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‎Tutorials/ACT-Python-Tutorial/README.md‎

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The ACT tutorials were focused around exploring aerosol and lidar data from a dust event during TRacking Aerosol Convection interations ExpeRiment (TRACER) that occurred from July 16-19, 2022.
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# Tutorials
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### JupyterHub and Jupyter Notebooks
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## JupyterHub and Jupyter Notebooks
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[1-jupter_intro.ipynb](https://github.com/ARM-Development/ARM-Notebooks/blob/main/Tutorials/ACT-Python-Tutorial/1-jupyter_intro.ipynb) is a notebook to get started with ARM's JupyterHub resources and navigate what JupyterHub notebooks are and some useful shortcuts.
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### Python Tutorials
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## Python Tutorials
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[2-Python-Basics.ipynb](https://github.com/ARM-Development/ARM-Notebooks/blob/main/Tutorials/ACT-Python-Tutorial/2-Python-Basics.ipynb) is a what it seems, a notebook to go over beginner Python skills. For those wanting to explore some more complex libraries in Python, checkout the other notebooks
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- [2a-scientific_libraries_numpy.ipynb](https://github.com/ARM-Development/ARM-Notebooks/blob/main/Tutorials/ACT-Python-Tutorial/2a-scientific_libraries_numpy.ipynb)
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- [2b-scientific_libraries_pandas.ipynb](https://github.com/ARM-Development/ARM-Notebooks/blob/main/Tutorials/ACT-Python-Tutorial/2b-scientific_libraries_pandas.ipynb)
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- [2c-scientific_libraries_xarray.ipynb](https://github.com/ARM-Development/ARM-Notebooks/blob/main/Tutorials/ACT-Python-Tutorial/2c-scientific_libraries_xarray.ipynb)
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### Atmospheric data Community Toolkit (ACT) Tutorials
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## Atmospheric data Community Toolkit (ACT) Tutorials
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- [3-ACT-Basics-2023.ipynb](https://github.com/ARM-Development/ARM-Notebooks/blob/main/Tutorials/ACT-Python-Tutorial/3-ACT-Basics.ipynb) is a base tutorial using data from the TRACER Particle Soot Absorption Photometer (PSAP) to explore some of the base functionality of ACT including visualizations and working with quality control information.
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- [3a-ACT-TRACER-Dust.ipynb](https://github.com/ARM-Development/ARM-Notebooks/blob/main/Tutorials/ACT-Python-Tutorial/3a-ACT-TRACER-Dust.ipynb) is a notebook to bring a variety of datasets together for visualization and performing some of the more complex operations in ACT to produce a figure like the below.
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![Output from the advanced ACT tutorial](images/micropulse_lidar.png "Micropulse Lidar")
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Figure 1. From the top, micropulse lidar linear depolarization ratio, aerodynamic particle sizer concentration, scanning mobility particle sizer concentration, aerosol chemical speciation monitor chemical compositions, single particle soot photometer black carbon concentration, PSAP aerosol absorption coefficient in the blue channel, and the surface meteorology station wind direction.
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### Additional Tutorials
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# Additional Tutorials
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There are two additional tutorials for an [Introduction to GitHub and git](https://github.com/ARM-Development/ARM-Notebooks/blob/main/Tutorials/ACT-Python-Tutorial/optional_github_intro.md) and how to perform [Branching and Pull Requests](https://github.com/ARM-Development/ARM-Notebooks/blob/main/Tutorials/ACT-Python-Tutorial/optional_github_branching.md) which are all very important when contributing work back into these open-source packages.

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