When I do the steps exactly like its explained in the answer, I am able to replicate it. However, my case is a bit different. I have a dataframe with timeseries as it's index, and 'perc99_99' as a set of Z-scores.
I modified the SO answer to the best of my knowledge and this is what the code looks like:
fig, ax = plt.subplots()
# Index of dataframe = timestamps x = day_avg_zscore.index.values # Z score of 99th percentiles y = day_avg_zscore['perc99_99'].values
import numpy as np import matplotlib.pyplot as plt from matplotlib.collections import LineCollection from matplotlib.colors import ListedColormap, BoundaryNorm
def threshold_plot(ax, x, y, threshv, color, overcolor): """ Helper function to plot points above a threshold in a different color Parameters ---------- ax : Axes Axes to plot to x, y : array The x and y values threshv : float Plot using overcolor above this value color : color The color to use for the lower values overcolor: color The color to use for values over threshv """ # Create a colormap for red, green and blue and a norm to color # f' < -0.5 red, f' > 0.5 blue, and the rest green cmap = ListedColormap([color, overcolor]) norm = BoundaryNorm([np.min(y), threshv, np.max(y)], cmap.N)
# Create a set of line segments so that we can color them individually # This creates the points as a N x 1 x 2 array so that we can stack points # together easily to get the segments. The segments array for line collection # needs to be numlines x points per line x 2 (x and y) points = np.array([x, y]).T.reshape(-1, 1, 2) segments = np.concatenate([points[:-1], points[1:]], axis=1)
# Create the line collection object, setting the colormapping parameters. # Have to set the actual values used for colormapping separately. lc = LineCollection(segments, cmap=cmap, norm=norm) lc.set_array(y)