Tutorial 5: Working with Data Files

Tutorial 5: Working with Data Files¶

This tutorial demonstrates how to work with data files in your course using the {nma-data} directive.

Declaring Data Files¶

The {nma-data} directive tells the course kit which files your tutorial needs:

This declaration:

• In the browser: embeds files ≤2MB into the page (they load instantly from memory)

• In notebooks: generates a hidden download cell that fetches them from GitHub

Files must live in data/ or image/ inside your day’s folder.

Using Images¶

Images work the same way as data files - declare them in {nma-data} and reference them in your content:

In the browser, the image loads instantly from the embedded data. In generated notebooks, the download cell fetches it from GitHub.

Loading Data¶

Load the data the same way in both environments - normal NumPy works:

Visualizing Spike Trains¶

Let’s create a raster plot showing spike times for the first neuron across all trials:

Computing Firing Rates¶

Calculate mean firing rate by condition:

Interactive Exploration¶

Use a widget to explore different neurons and conditions:

Summary¶

When adding data and images to your tutorials:

1. Create data/ and/or image/ folders in your day’s directory

2. Keep files small (<2MB total) so they embed in browser pages

3. Declare all files with {nma-data} at the top of the tutorial

4. Reference normally - images with {figure}, data with np.load() or similar

5. Validate with nma-kit check before committing

The kit automatically:

• Embeds small files for instant browser loading

• Generates download cells for Colab/Kaggle notebooks

• Validates that your code references only declared files

Note: This tutorial includes generation scripts (generate_data.py, generate_schematic.py) for reproducibility, but you don’t need to commit generation scripts in your own courses - just commit the files themselves.