How to highlight the regions in a plot to indicate the sliding windows in Python?

Question

I have a time series with extreme events and I tried to get the width of these extreme events using sliding window approach. I used the code:

def moving_window(s, length, step =1):
       streams = it.tee(s, length)
       return zip(*[it.islice(stream, i, None, step*length) for stream, i in zip(streams, it.count(step=step))])
x_=list(moving_window(s, 15))
x_=np.asarray(x_) #windows
print(x_)

and I have an ouput for the time series:

[[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14]
 [ 15  16  17  18  19  20  21  22  23  24  25  26  27  28  29]
 [ 30  31  32  33  34  35  36  37  38  39  40  41  42  43  44]
 [ 45  46  47  48  49  50  51  52  53  54  55  56  57  58  59]
 [ 60  61  62  63  64  65  66  67  68  69  70  71  72  73  74]
 [ 75  76  77  78  79  80  81  82  83  84  85  86  87  88  89]
 [ 90  91  92  93  94  95  96  97  98  99 100 101 102 103 104]
 [105 106 107 108 109 110 111 112 113 114 115 116 117 118 119]
 [120 121 122 123 124 125 126 127 128 129 130 131 132 133 134]
 [135 136 137 138 139 140 141 142 143 144 145 146 147 148 149]
 [150 151 152 153 154 155 156 157 158 159 160 161 162 163 164]
 [165 166 167 168 169 170 171 172 173 174 175 176 177 178 179]
 [180 181 182 183 184 185 186 187 188 189 190 191 192 193 194]]

I want to highlight the sliding windows with a colormap. What I want is something like the following image:

I want to know how to use a colormap to do this (There are 20 time series in the image but consider only one.). Can anyone help?

Could you add some example data, some code to plot one series and create a [minimal-reproducible-example](https://stackoverflow.com/help/minimal-reproducible-example)? — JohanC, Jan 09 '20 at 14:28
This time series was generated for the coupled fitzhugh nagumo oscillators using JITCODE in Python. I am asking in general for any time series — Monali, Jan 09 '20 at 14:37
I think this might provide a solution for your issue: https://stackoverflow.com/questions/5296335/gradient-facecolor-matplotlib-bar-plot — Wololo, Jan 09 '20 at 14:58
@magnus Your link shows how to draw a standalone colorbar. It is not clear how this could be used as background inside regions in a plot. — JohanC, Jan 09 '20 at 17:39

JohanC · Accepted Answer · 2020-01-09T15:49:54.293

Here is an example using a sine function to demonstrate the concept. axvspan draws vertical spans. The color can be set from a colormap. color=0 would be at the left of the map, color=1 totally at the right. Here 'Reds' is used. Some experimenting with alpha and indices suggests an alpha=0.6 and indices 0.75 and lowering gives some similar colors as in the given example.

import matplotlib.pyplot as plt
import numpy as np

x_min = 0
x_max = 120
x = np.linspace(x_min, x_max, 10000)
y = np.sin(x/3)

fig, ax = plt.subplots(figsize=(12,2))
ax.plot(x, y, color='royalblue')

cmap = plt.cm.Reds # e.g. plt.cm.plasma_r or plt.cm.YlOrRd also seem interesting
current_x = 105
x_step = 16
for i in range(8):
    ax.axvspan(current_x - (i + 1) * x_step, current_x - i * x_step,
               alpha=0.6, color=cmap(0.75 - i / 20))
ax.set_xlim(x_min, x_max)
plt.tight_layout()
plt.show()

Alternatively, instead of varying the color, one could vary the alpha. In the example with only reds, the following leads to something similar:

for i in range(8):
    ax.axvspan(current_x - (i + 1) * x_step, current_x - i * x_step,
               alpha=0.5 - i / 20, color='red')

Of course, one could vary both alpha and the color together for more fine-tuning. Some experimenting is needed, to find colors that are sufficiently different and that don't scream too much.

Here an example with cmap = plt.cm.inferno_r and ax.axvspan(..., alpha=0.4, color=cmap(0.8 - i / 10)):

How to highlight the regions in a plot to indicate the sliding windows in Python?

1 Answers1