plot multicolor line with x as datetime and change color by y-CodePudding

For 2 days I am trying to plot a multicolor line. My data looks like this:

                     gradient result
date                                
2022-04-15 09:43:20  0.206947      E
2022-04-15 10:25:00  0.102620      E
2022-04-15 11:06:40  0.019450      C
2022-04-15 11:48:20  0.025945      D
2022-04-15 12:30:00  0.022455      D
                      ...    ...
2022-05-02 14:13:20  0.003770      A
2022-05-02 14:55:00  0.084120      E
2022-05-02 15:36:40  0.134970      E
2022-05-02 16:18:20  0.261385      E
2022-05-02 17:00:00  0.955833    NaN

I have used to following script to plot the graph:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
from matplotlib.collections import LineCollection

dates = data_646_mean.index
y = data_646_mean.gradient

s = pd.Series(y, index=dates)

fig, ax = plt.subplots()

#convert dates to numbers first
inxval = mdates.date2num(s.index.to_pydatetime())
points = np.array([inxval, s.values]).T.reshape(-1,1,2)
segments = np.concatenate([points[:-1],points[1:]], axis=1)

lc = LineCollection(segments, cmap="plasma", linewidth=3)
# set color to date values
#lc.set_array(inxval)
# note that you could also set the colors according to y values
lc.set_array(s.values())
# add collection to axes
ax.add_collection(lc)


ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_minor_locator(mdates.DayLocator())
monthFmt = mdates.DateFormatter("%b")
ax.xaxis.set_major_formatter(monthFmt)
ax.autoscale_view()
plt.show()

This has worked but the different colors are not set within a certain range for y:

And I would really like to make the colors depend on the category in the result column. And if that is not possible I would like to give certain intervals:

blue = 0.1 - 0.2
red = 0.2 - 0.5
green = 0.5 - 0.9
black = 0.9 1.4

I hope somewan could help me out.

CodePudding user response：

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
from matplotlib.collections import LineCollection
import matplotlib.cm as cm

data_646_mean = pd.read_csv("path/to/file")
data_646_mean["date"] = pd.to_datetime(data_646_mean["date"])
dates = data_646_mean["date"].values
y = data_646_mean["gradient"].values
# convert results to categories so that we can apply it
# to the color value of the line
cat_series = pd.Categorical(data_646_mean["result"])

fig, ax = plt.subplots()

# convert dates to numbers first
dates_num = mdates.date2num(dates)
points = np.array([dates_num, y]).T.reshape(-1,1,2)
segments = np.concatenate([points[:-1],points[1:]], axis=1)

# use a subset of Tab10 (categorical color map)
cmap = cm.get_cmap('tab10', len(cat_series.categories))
lc = LineCollection(segments, cmap=cmap, linewidth=3)
lc.set_array(cat_series.codes)
ax.add_collection(lc)

# add a colorbar, modify the position of the ticks
cb = fig.colorbar(lc, ticks=np.linspace(-0.5, 3.5, 5))
# set the categories to be the value of the ticks
cb.ax.set_yticklabels(cat_series.categories)

ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_minor_locator(mdates.DayLocator())
monthFmt = mdates.DateFormatter("%b")
ax.xaxis.set_major_formatter(monthFmt)
ax.autoscale_view()
plt.show()