两个 Matplotlib 颜色条具有不同（条件）标准化颜色图，用于一个散点 plot？

Question

I have a dataset of 5 columns: a,b,c,d,e. where the data type of b is str , and the others contain floats . The scatter axes are the columns a and e for x and y, respectively. The normalized colormap is based on the column e.

I want to add two colorbars where the filling colormap is the same as in the scatter plot (of course,). but the numerical ticks are based on the columns c and d, In one of my attempts, I tried to create two figures. but forcing the first colorbar to take its values from the second figure. But it didn't work.

My best attempts is the code below, which generates the attached image. (based on the data below) The code takes the data to plot the regular colorbar, and runs the same block again for the second colorbar, where the markers's surface (in the second time) is 0 (which is funny ;) ) EDIT I tied to add a third loop. thus: one real loop for column e, and two fake loops with marker surface is 0 for the colorbars from the c and d data. (see second code block), now the markers filling is not correct.

ie: 1/I need to normalize two colorbars from two different columns, but normalize the filling of the markers using one column. 2/ I wanna invert the second colorbar axis for clarity. (as if it has a reverse normalized colormap)

[EDIT]: To invert the second colorbar add _r to cmap in the second loop:

Thanks everyone !

Code 1

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, ax = plt.subplots()

a,b,c,d,e = np.loadtxt('test.txt', delimiter='\t', unpack=True, 
skiprows=1, dtype=str)

a = a.astype(np.float64)
c = c.astype(np.float64)
d = d.astype(np.float64)
e = e.astype(np.float64)
b = list(b)
#####################################################
# for c
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.Reds)
cmap.set_array([])
# for d
norm2 = mpl.colors.Normalize(vmin=d.min(), vmax=d.max())
cmap2 = mpl.cm.ScalarMappable(norm=norm2, cmap=mpl.cm.Reds)
cmap2.set_array([])
# real
for i in range(len(b)):
    if b[i] == 'alpha':
        sc = ax.scatter(a[i], e[i], marker='o', edgecolors='black', 
alpha=0.8, s=120, c=e[i], norm=norm,
                        cmap='Reds'
                        )
    if b[i] == 'beta':
        sc = ax.scatter(a[i], e[i], marker='d', edgecolors='black', 
alpha=0.8, s=120, c=e[i], norm=norm,
                        cmap='Reds'
                        )
# fake
for i in range(len(b)):
    if b[i] == 'alpha':
        sc1 = ax.scatter(a[i], e[i], marker=',', edgecolors='black', 
alpha=0.8, s=0, c=e[i], norm=norm2,
                         cmap='Reds'
                         )
    if b[i] == 'beta':
        sc1 = ax.scatter(a[i], e[i], marker=',', edgecolors='black', 
alpha=0.8, s=0, c=e[i], norm=norm2,
                         cmap='Reds'
                         )
years_ax = np.linspace(2000, 2021, 21, endpoint=True, dtype=int)
ax.set_xticks(years_ax)
plt.xticks(rotation=45)  # Rotates X-Axis Ticks by 45-degrees

plt.grid(True, which="both", ls="-", alpha=0.1)
plt.xlabel('Years', fontsize=14)
plt.ylabel('e', fontsize=14)
ax.tick_params(axis='both', which='major', labelsize=14)

divider = make_axes_locatable(ax)
cba = divider.new_vertical(size='5%', pad=0.5)
cbb = divider.new_vertical(size='5%', pad=0.5)

fig.add_axes(cba)
fig.add_axes(cbb)

# add colorbar #1
cba = plt.colorbar(sc, cax=cba, orientation='horizontal')
ax.xaxis.set_ticks_position('default')
cba.ax.xaxis.set_ticks_position('top')
# add colorbar #2
cbb = plt.colorbar(sc1, cax=cbb, orientation='horizontal')
cbb.ax.invert_yaxis()
ax.xaxis.set_ticks_position('default')
cbb.ax.xaxis.set_ticks_position('top')

plt.show()

Code 2:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, ax = plt.subplots()

a,b,c,d,e = np.loadtxt('test.txt', delimiter='\t', unpack=True, 
skiprows=1, dtype=str)

a = a.astype(np.float64)
c = c.astype(np.float64)
d = d.astype(np.float64)
e = e.astype(np.float64)
b = list(b)
#####################################################
# for e
norme = mpl.colors.Normalize(vmin=e.min(), vmax=e.max())
cmape = mpl.cm.ScalarMappable(norm=norme, cmap=mpl.cm.Reds)
cmape.set_array([])
# for c
normc = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmapc = mpl.cm.ScalarMappable(norm=normc, cmap=mpl.cm.Reds)
cmapc.set_array([])
# for d
normd = mpl.colors.Normalize(vmin=e.min(), vmax=e.max())
cmapd = mpl.cm.ScalarMappable(norm=normd, cmap=mpl.cm.Reds_r)
cmapd.set_array([])
# real
for i in range(len(b)):
    if b[i] == 'alpha':
        sc = ax.scatter(a[i], e[i], marker='o', edgecolors='black', 
alpha=0.8, s=120, c=e[i], norm=norme,
                        cmap='Reds'
                        )
    if b[i] == 'beta':
        sc = ax.scatter(a[i], e[i], marker='d', edgecolors='black', 
alpha=0.8, s=120, c=e[i], norm=norme,
                        cmap='Reds'
                        )
# fake for c
for i in range(len(b)):
    if b[i] == 'alpha':
        sc = ax.scatter(a[i], e[i], marker='o', edgecolors='black', 
alpha=0.8, s=0, c=e[i], norm=normc,
                        cmap='Reds'
                        )
    if b[i] == 'beta':
        sc = ax.scatter(a[i], e[i], marker='d', edgecolors='black', 
alpha=0.8, s=0, c=e[i], norm=normc,
                        cmap='Reds'
                        )
# fake for d
for i in range(len(b)):
    if b[i] == 'alpha':
        sc1 = ax.scatter(a[i], e[i], marker=',', edgecolors='black', 
alpha=0.8, s=0, c=e[i], norm=normd,
                         cmap='Reds_r'
                         )
    if b[i] == 'beta':
        sc1 = ax.scatter(a[i], e[i], marker=',', edgecolors='black', 
alpha=0.8, s=0, c=e[i], norm=normd,
                         cmap='Reds_r'
                         )
years_ax = np.linspace(2000, 2021, 21, endpoint=True, dtype=int)
ax.set_xticks(years_ax)
plt.xticks(rotation=45)  # Rotates X-Axis Ticks by 45-degrees

plt.grid(True, which="both", ls="-", alpha=0.1)
plt.xlabel('Years', fontsize=14)
plt.ylabel('e', fontsize=14)
ax.tick_params(axis='both', which='major', labelsize=14)

divider = make_axes_locatable(ax)
cba = divider.new_vertical(size='5%', pad=0.5)
cbb = divider.new_vertical(size='5%', pad=0.5)

fig.add_axes(cba)
fig.add_axes(cbb)

# add colorbar #1
cba = plt.colorbar(sc, cax=cba, orientation='horizontal')
ax.xaxis.set_ticks_position('default')
cba.ax.xaxis.set_ticks_position('top')
# add colorbar #2
cbb = plt.colorbar(sc1, cax=cbb, orientation='horizontal')
cbb.ax.invert_yaxis()
ax.xaxis.set_ticks_position('default')
cbb.ax.xaxis.set_ticks_position('top')
plt.show()

Result: result image

Result from third attempt (with three loops, and corrected inverted bars)

second attempt

data sample -small-:

a   b   c   d   e
2013    alpha   21.8    47  101.3302752
2013    alpha   5   23  105.8
2009    alpha   38  58  88.52631579
2009    alpha   47  77  126.1489362
2009    alpha   188 55  16.09042553
2009    alpha   267 77  22.20599251
2019    alpha   1.67    10  59.88023952
2020    alpha   31.5    57  103.1428571
2013    beta    19  50  131.5789474
2013    beta    26.8    62.5    145.755597
2013    beta    54.5    95  165.5963303
2013    beta    120.1   123 125.970025
2013    beta    147.6   137.5   128.0911247
2007    alpha   14.5    14.5    14.5
2007    alpha   28.7    28  27.31707317
2007    alpha   42.5    40  37.64705882

Answer 1

[Solved] Using two figures, where the second figure colorbar is just plotted in the second as follows:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, ax = plt.subplots()
fig2, ax2 = plt.subplots()

a,b,c,d,e = np.loadtxt('test.txt', delimiter='\t', unpack=True, skiprows=1, dtype=str)

a = a.astype(np.float64)
c = c.astype(np.float64)
d = d.astype(np.float64)
e = e.astype(np.float64)
b = list(b)
#####################################################
# for e
norme = mpl.colors.Normalize(vmin=e.min(), vmax=e.max())
cmape = mpl.cm.ScalarMappable(norm=norme, cmap=mpl.cm.Reds)
cmape.set_array([])
# for c
normc = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmapc = mpl.cm.ScalarMappable(norm=normc, cmap=mpl.cm.Reds)
cmapc.set_array([])
# for d
normd = mpl.colors.Normalize(vmin=d.min(), vmax=d.max())
cmapd = mpl.cm.ScalarMappable(norm=normd, cmap=mpl.cm.Reds_r)
cmapd.set_array([])
# real
for i in range(len(b)):
    if b[i] == 'alpha':
        sc = ax.scatter(a[i], e[i], marker='o', edgecolors='black', alpha=0.8, s=120, c=c[i], norm=normc,
                        cmap='Reds'
                        )
    if b[i] == 'beta':
        sc = ax.scatter(a[i], e[i], marker='d', edgecolors='black', alpha=0.8, s=120, c=c[i], norm=normc,
                        cmap='Reds'
                        )
# fake for c
for i in range(len(b)):
    if b[i] == 'alpha':
        sc = ax.scatter(a[i], e[i], marker='o', edgecolors='black', alpha=0.8, s=0, c=e[i], norm=normc,
                        cmap='Reds'
                        )
    if b[i] == 'beta':
        sc = ax.scatter(a[i], e[i], marker='d', edgecolors='black', alpha=0.8, s=0, c=e[i], norm=normc,
                        cmap='Reds'
                        )
# # fake for d
for i in range(len(b)):
    if b[i] == 'alpha':
        sc1 = ax2.scatter(a[i], e[i], marker=',', edgecolors='black', alpha=0.8, s=100,c=d[i], norm=normd,
                         cmap='Reds_r'
                         )
    if b[i] == 'beta':
        sc1 = ax2.scatter(a[i], e[i], marker=',', edgecolors='black', alpha=0.8, s=100,c=d[i], norm=normd,
                         cmap='Reds_r'
                         )
years_ax = np.linspace(2000, 2021, 21, endpoint=True, dtype=int)
ax.set_xticks(years_ax)
plt.xticks(rotation=45)  # Rotates X-Axis Ticks by 45-degrees

plt.grid(True, which="both", ls="-", alpha=0.1)
plt.xlabel('Years', fontsize=14)
plt.ylabel('e', fontsize=14)
ax.tick_params(axis='both', which='major', labelsize=14)

divider = make_axes_locatable(ax)

cba = divider.new_vertical(size='5%', pad=0.5)
cbb = divider.new_vertical(size='5%', pad=0.5)

fig.add_axes(cba)
fig.add_axes(cbb)

# add colorbar #1
cba = plt.colorbar(sc, cax=cba, orientation='horizontal')
ax.xaxis.set_ticks_position('default')
cba.ax.xaxis.set_ticks_position('top')
# add colorbar #2
cbb = plt.colorbar(sc1, cax=cbb, orientation='horizontal')
cbb.ax.invert_yaxis()
ax.xaxis.set_ticks_position('default')
cbb.ax.xaxis.set_ticks_position('top')
plt.show()