Heat maps are a more condensed, information-rich, and efficient version of the peri-stimulus time histograms (PSTHs) we saw in the previous lessons. Heat maps allow us to plot three dimensions rather than only two; we have the x and y axes, and then we can plot a third variable (dimension) using colour (or intensity of colour). We can use Matplotlib’s ax.imshow() method (or plt.imshow() function) to generate a heat map.
Learning Objectives:
- Visualize spike train data along two dimensions using heat maps
- Create a 2D NumPy array of histograms to generate this plot
- Format a heat map, including adding a colourbar
- Understand the process of interpolation
- Understand advantages and disadvantages of different ways of interpolating
- Understand how colour choice can influence the interpretation of heat maps
- Make well-reasoned choices about colour map selection
From the course, NESC 3505 Neural Data Science, by Aaron J Newman, Dalhousie University, Halifax, NS, Canada.
All course videos are here: https://bit.ly/neural_data_science_videos
Textbook is available here: http://neuraldatascience.io/intro.html
