How to make horizon plots in Python

Horizon plots were invented about a decade ago to facilitate visual comparison between two time series. They are not intuitive to read right away, but they are great for comparing and presenting many sets of timeseries together. They can take advantage of a minimal design by avoiding titles and ticks on every axis and packing them close together to convey a bigger picture. The example below shows percent changes in the price of various food items in 25 years.

The way they are produced and read is by dividing the values along the y axis in bands based on ranges. The color of each band is given by a divergent color map. By collapsing the bands to the zero axis and layering the higher bands on top, one can create a time-varying heatmap of sorts.

Source: http://idl.cs.washington.edu/papers/horizon

I wasn’t able to find a script that could produce this in Python, besides some code in this github repository, that is about a decade old and cannot really run in Python 3. I cleaned it up and updated the scripts with some additional features. I also added example data comparing USGS streamflow data with model simulation data for the same locations for 38 years. The code can be found here and can be used with any two datasets that one would like to compare with as many points of comparison as needed (I used eight below, but the script can accept larger csv files with more or less comparison points, which will be detected automatically). The script handles the transformation of the data to uniform bands and produces the following figure, with every subplot comparing model output with observations at eight gauges, i.e. model prediction error. When the model is over predicting the area is colored blue, when the area is underpredicting, the area is colored red. Darker shades indicate further divergence from the zero axis. The script automatically uses three bands for both positive or negative divergence, but more can be added, as long as the user defines additional colors to be used.

Using this type of visualization for these data allows for time-varying comparisons of multiple locations in the same basin. The benefit of it is most exploited with many subplots that make up a bigger picture.

Future extensions in this repository will include code to accept more file types than csv, more flexibility in how the data is presented and options to select different colormaps when executing.

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