如何将颜色条放入 matplotlib 图例中

Question

I have code which produces this figure:

In this plot, the color indicates the horizontal offset of the lower end of each line. I would like a colorbar to appear in the legend (with 'start' and 'stop) which shows what the color means.

Here is my code:

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx


plt.clf()
plt.plot([0,100], [0,100], '--', linewidth=3, color='k', label = 'start')
plt.plot([100,100],[0,100], '-.', linewidth=3, color = 'k', label = 'stop')



jet = plt.get_cmap('jet') 
cNorm  = colors.Normalize(vmin=0, vmax=99)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)

for offset in range(1,100):
    colorVal = scalarMap.to_rgba(offset)
    plt.plot([offset, 100], [0,100], color=colorVal)

plt.legend()
plt.show()

So ideally I would have something looking like a standard colorbar, which ranges from 0 to 100, but appearing in the legend with a label 'offset' .

Answer 1

Here's some code to accomplish this based on ImportanceOfBeingErnest's comment approach (2).

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
from matplotlib.patches import Rectangle

fig, ax = plt.subplots(1)

plt.clf()
plt.plot([0,100], [0,100], '--', linewidth=3, color='k', label = 'start')
plt.plot([100,100],[0,100], '-.', linewidth=3, color = 'k', label = 'stop')

jet = plt.get_cmap('jet')
cNorm  = colors.Normalize(vmin=0, vmax=99)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
scalarMap.set_array([])

for offset in range(1,100):
    colorVal = scalarMap.to_rgba(offset)
    plt.plot([offset, 100], [0,100], color=colorVal)

plt.gca().add_patch(Rectangle((0.1, 45), 40, 55, edgecolor='gray',
                                            linewidth=3, fill=False))
plt.gca().text(25, 90, "-- start")
plt.gca().text(25, 80, "-. stop")
plt.gca().text(15, 50, "  offset")

cax = fig.add_axes([0.18, 0.48, 0.03, 0.35])

plt.colorbar(scalarMap, cax = cax, ticks=[range(0, 100, 10)],
                                            orientation='vertical')

plt.show()