Font and colour for Python matplotlib legend- superscript - python

Can someone help me make the R2 writing look like the rest? Especially not italics. This is my code below:
While I'm here can anyone tell me how to either
get that R2=0.97 line in red writing to indicate that's what the red line is on the graph
or
insert a red line/red dash within the legend on that line?
I've seen other ways of doing it online but the way I've formatted my legend doesn't allow for it.
plt.rcParams["font.family"] = "Cambria"
fig, ax = plt.subplots()
ax.scatter(y_test, y_predicted ,s=10,color='darkslateblue',linewidths=1)
ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k-', lw=2,)
ax.set_xlabel('Actual (%)',fontsize='large')
ax.set_ylabel('Predicted (%)',fontsize='large')
y_test, y_predicted = y_test.reshape(-1,1), y_predicted.reshape(-1,1)
ax.plot(y_test, LinearRegression().fit(y_test, y_predicted).predict(y_test), color="red", lw=2)
ax.set_title('H2O REF')
handles = [mpl_patches.Rectangle((0, 0), 1, 1, fc="white", ec="white",
lw=0, alpha=0)] * 4
labels = []
labels.append("$R^2$ = {0:.2g}".format(Rsquared))
labels.append("RMSE = {0:.2g}".format(rmse))
labels.append("MAE = {0:.2g}".format(mae))
ax.legend(handles, labels, loc='best', fontsize='x-large',
fancybox=True, framealpha=0.7,
handlelength=0, handletextpad=0)
plt.show()
Thank you :)


For the first solution, a possible way to achieve it is by only typesetting ^2 in the math environment and than setting the first label text to red as described here, see code below.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpl_patches
x = np.linspace(0, 1)
y = x + np.random.normal(scale=0.1, size=50)
plt.rcParams["font.family"] = "Cambria"
Rsquared = 0.9
rmse = 0.8
mae = 1
fig, ax = plt.subplots()
ax.scatter(x, y)
ax.plot(x, x, c='r')
handles = [mpl_patches.Rectangle((0, 0), 1, 1, fc="white", ec="white",
lw=0, alpha=0)] * 4
labels = []
labels.append("R$^2$ = {0:.2g}".format(Rsquared))
labels.append("RMSE = {0:.2g}".format(rmse))
labels.append("MAE = {0:.2g}".format(mae))
leg = ax.legend(handles, labels, loc='best', fontsize='x-large',
fancybox=True, framealpha=0.7,
handlelength=0, handletextpad=0)
texts = leg.get_texts()
texts[0].set_color("red")
Alternatively, you can create legend entries including the red line with Line2D.
The corresponding code down below overrides handles[0].
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpl_patches
from matplotlib.lines import Line2D
x = np.linspace(0, 1)
y = x + np.random.normal(scale=0.1, size=50)
plt.rcParams["font.family"] = "Cambria"
Rsquared = 0.9
rmse = 0.8
mae = 1
fig, ax = plt.subplots()
ax.scatter(x, y)
ax.plot(x, x, c='r')
handles = [mpl_patches.Rectangle((0, 0), 1, 1, fc="white", ec="white",
lw=0, alpha=0)] * 4
lines = []
handles[0] = Line2D([0], [0], color='red')
labels = []
labels.append("R$^2$ = {0:.2g}".format(Rsquared))
labels.append("RMSE = {0:.2g}".format(rmse))
labels.append("MAE = {0:.2g}".format(mae))
leg = ax.legend(handles, labels, loc='best', fontsize='x-large',
fancybox=True, framealpha=0.7)

Related

Gradient fill from zero till a curve

I have been using Is it possible to get color gradients under curve in matplotlib? as a reference (you can see the similarities, however i cant for the life of me figure out how to push the shading all the way down to 0 on the Y AXIS, for some reason which i cant find out, it has an upward sloping straight line cutting off the shading, i cant find anything in my data to suggest why its doing this.
for context the y axis can show positive and negative and i want to fill the scale the whole way so using gradient colour to fill from 0 to the line (positive) then fill from 0 to the negative line (see my blue example from a previous chart -same data-)
Here is my code
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.patches import Polygon
# Variables
AUM = df['#AHD_AUM'].head(104)
MM = df['#AHD_Managed_Money_Net'].head(104)
PRICE = df['#AHD_Price'].head(104)
DATES = df['DATES'].head(104)
# Date Friendly Variables for Plot
List_AUM = df['#AHD_AUM'].head(104).to_list()
List_MM = df['#AHD_Managed_Money_Net'].head(104).to_list()
List_DATES = df['DATES'].head(104).to_list()
X = 0 * df['#AHD_AUM'].head(104)
# Make a date list changing dates with numbers to avoid the issue with the plot
interpreting dates
for i in range(len(df['DATES'].head(104))):
count = i
df['count'][i] = 120 - i
# X and Y data variables changed to arrays as when i had these set as dates
matplotlib hates it
x = df['count'].head(104).to_numpy()
y = df['#AHD_Managed_Money_Net'].head(104).to_numpy()
#DD = AUM.to_numpy()
#MMM = MM.to_numpy()
def main():
for _ in range(len(DD)):
gradient_fill(x,y)
plt.show()
def gradient_fill(x,y, fill_color=None, ax=None, **kwargs):
"""
"""
if ax is None:
ax = plt.gca()
line, = ax.plot(x, y, **kwargs)
if fill_color is None:
fill_color = line.get_color()
zorder = line.get_zorder()
alpha = line.get_alpha()
alpha = 1.0 if alpha is None else alpha
z = np.empty((100, 1, 4), dtype=float)
rgb = mcolors.colorConverter.to_rgb(fill_color)
z[:,:,:3] = rgb
z[:,:,-1] = np.linspace(0, alpha, 100)[:,None]
xmin, xmax, ymin, ymax = x.min(), x.max(), y.min(), y.max()
im = ax.imshow(z, aspect='auto', extent=[xmin, xmax, ymin, ymax],
origin='lower', zorder=zorder)
xy = np.column_stack([x, y])
# xy = np.vstack([[xmin, ymin], xy, [xmax, ymin], [xmin, ymin]]) ### i dont
need this so i have just commented it out
clip_path = Polygon(xy, facecolor='none', edgecolor='none', closed=True)
ax.add_patch(clip_path)
im.set_clip_path(clip_path)
ax.autoscale(True)
return line, im
main()
this is my current output

An easier way to clip the gradient by the curve, is to use a polygon obtained from fill_between.
Here is some example code to get you started.
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(123)
x = np.linspace(0, 10, 200)
y = np.random.normal(0.01, 1, 200).cumsum()
fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(x, y)
ylim = ax.get_ylim()
grad1 = ax.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap='Blues', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), 0, y.max()], origin='lower')
poly_pos = ax.fill_between(x, y.min(), y, alpha=0.1)
grad1.set_clip_path(poly_pos.get_paths()[0], transform=ax.transData)
poly_pos.remove()
grad2 = ax.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap='Reds', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), y.min(), 0], origin='upper')
poly_neg = ax.fill_between(x, y, y.max(), alpha=0.1)
grad2.set_clip_path(poly_neg.get_paths()[0], transform=ax.transData)
poly_neg.remove()
ax.set_ylim(ylim)
ax.axhline(0, color='black') # show a line at x=0
plt.show()
PS: vmin in imshow can be used to remove the color range where it's very light:
grad1 = ax.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap='Blues', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), 0, y.max()], origin='lower')
grad2 = ax.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap='Reds', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), y.min(), 0], origin='upper')

import pandas as pd # For data handling
import seaborn as sns # For plotting
import numpy as np
import matplotlib.pyplot as plt # For plotting
import matplotlib
#some preferred user settings
plt.rcParams['figure.figsize'] = (18.0, 12.0)
pd.set_option('display.max_columns', None)
%matplotlib inline
import warnings
warnings.filterwarnings(action='ignore')
from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import datetime as dt
import matplotlib.dates as mdates
import pandas
Metal = CAD
# Variables
AUM = Metal.iloc[:,[7]].head(104)
MM = Metal.iloc[:,[0]].head(104)
PRICE = Metal.iloc[:,[8]].head(104)
#Last_Report = Metal.iloc[:,[9]].head(1).dt.strftime('%d %b %Y').to_list()
DATES = Metal.iloc[:,[10]].head(104)
# Dataframe for Net Position High
Net_High = Metal[Metal.iloc[:,[0]] == Metal.iloc[:,[0]].max()]
# Variables for Chart Annotation for Net Position High
Pos_High_Date = Net_High.iloc[:, [0]]
Pos_High_AUM = Net_High.iloc[:, [7]][0]/[1000000000]
Pos_High_Price = Net_High.iloc[:, [8]].to_numpy()[0].round().astype('int')
Pos_High = Net_High.iloc[:, [0]][0].astype('int')
Str_Date = mdates.num2date(Pos_High_Date)
Str_Date = pd.to_datetime(Str_Date[0]).strftime("%d %b %y")[0]
# Dataframe for Net Position Low
Net_Low = df[df['#CAD_Managed_Money_Net'] == df['#CAD_Managed_Money_Net'].head(104).min()]
# Variables for Chart Annotation for Net Position High
Pos_Low_Date = Net_Low.iloc[:, [55]].to_numpy()
Pos_Low_AUM = Net_Low.iloc[:, [26]].to_numpy()[0].round()/[1000000000]
Pos_Low_Price = Net_Low.iloc[:, [27]].to_numpy()[0].round().astype('int')
Pos_Low = Net_Low['#CAD_Managed_Money_Net'][0].astype('int')
Str_Date_Low = mdates.num2date(Pos_Low_Date)
Str_Date_Low = pd.to_datetime(Str_Date_Low[0]).strftime("%d %b %y")[0]
# C Brand Colour Scheme
C = ['deepskyblue', '#003399', 'slategray', '#027608','#cc0000']
def make_patch_spines_invisible(ax):
ax.set_frame_on(True)
ax.patch.set_visible(False)
for sp in ax.spines.values():
sp.set_visible(False)
fig, host = plt.subplots(figsize=(25,15))
fig.subplots_adjust(right=0.8)
#twinx() creates another axes sharing the x axis we do this twice
par1 = host.twinx()
par2 = host.twinx()
# Offset the right spine of par2 the ticks
par2.spines["right"].set_position(("axes",1.08))
#because par2 was created by twinx the frame is off so we need to use the method created above
make_patch_spines_invisible(par2)
# second, show the right spine
par2.spines["right"].set_visible(True)
######### Colouring in Plots
x = DATES
y = MM
ylim = host.get_ylim()
Long = host.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap= 'Blues', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), 0, y.max()], origin='lower')
poly_pos = host.fill_between(x, y.min(), y, alpha=0.1)
Long.set_clip_path(poly_pos.get_paths()[0], transform=host.transData)
poly_pos.remove()
Short = host.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap='OrRd', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), y.min(), 0], origin='upper')
poly_neg = host.fill_between(x, y, y.max(), alpha=0.1)
Short.set_clip_path(poly_neg.get_paths()[0], transform=host.transData)
poly_neg.remove()
##########
#plot data
p1, = host.plot(DATES, MM, label="Managed Money Net Position", linewidth=0.0,color = Citi[1], alpha = 0.8)
p2, = par1.plot(DATES, AUM, label="AUM",linewidth=1, marker = '$A$',mew = 1,mfc = 'w', color = Citi[0], alpha = 0.8)
p3, = par2.plot(DATES, PRICE, label="3M Price",linewidth=1, marker = '$p$', color = Citi[2], alpha = 0.8)
#Automatically scale and format
host_labels = ['{:,.0f}'.format(x) + 'K Lots' for x in host.get_yticks()/1000]
host.set_yticklabels(host_labels)
par1_labels = ['{:,.1f}'.format(x) + ' $Billion' for x in par1.get_yticks()/1000000000]
par1.set_yticklabels(par1_labels)
par2_labels = ['{:,.0f}'.format(x) + ' $' for x in par2.get_yticks()]
par2.set_yticklabels(par2_labels)
# x Axis formatting (date)
formatter = matplotlib.dates.DateFormatter('%b- %Y')
host.xaxis.set_major_formatter(formatter)
# Rotates and right-aligns the x labels so they don't crowd each other.
for label in host.get_xticklabels(which='major'):
label.set(rotation=30, horizontalalignment='right')
# Axis Labels
host.set_xlabel("Date")
host.set_ylabel("Managed Money Net Position")
par1.set_ylabel("AUM")
par2.set_ylabel("3M Price")
# Tick Parameters
tkw = dict(size=10, width=2.5)
# Set tick colours
host.tick_params(axis = 'y', colors = Citi[1], **tkw)
par1.tick_params(axis = 'y', colors = Citi[0], **tkw)
par2.tick_params(axis = 'y', colors = Citi[2], **tkw)
#host.tick_params(which='major',axis = 'x',direction='out', colors = Citi[2], **tkw)
#plt.xticks(x, rotation='vertical')
#host.xaxis.set_major_locator(AutoMajorLocator())
host.xaxis.set_major_locator(MultipleLocator(24))
host.tick_params('x',which='major', length=7)
#Label colours taken from plot
host.yaxis.label.set_color(p1.get_color())
par1.yaxis.label.set_color(p2.get_color())
par2.yaxis.label.set_color(p3.get_color())
# Map Title
host.set_title('Aluminium Managed Money Net Positioning as of %s'% Last_Report[0],fontsize='large')
#Colour Spines cant figure out how to do it for the host
par1.spines["right"].set_edgecolor(p2.get_color())
par2.spines["right"].set_edgecolor(p3.get_color())
###### Annotation Tests ##########
## Net Position High Box
host.annotate(f' Net Position High | {Pos_High} \n Date | {Str_Date} \n AUM | ${Pos_High_AUM[0].round(1)} Billion\n 3M Price | ${Pos_High_Price[0]}$',
xy=(Pos_High_Date, Pos_High), xycoords='data',
xytext=(0.02, .85), textcoords='axes fraction',
horizontalalignment='left',
verticalalignment='bottom',
color='white',
bbox=dict(boxstyle="round", fc= Citi[1],edgecolor='white'),
arrowprops=dict(
facecolor='black',
arrowstyle= '->'))
## Net Position Low Box
host.annotate(f' Net Position Low | {Pos_Low} \n Date | {Str_Date_Low} \n AUM | ${Pos_Low_AUM[0].round(1)} Billion\n 3M Price | ${Pos_Low_Price[0]}$',
xy=(Pos_Low_Date, Pos_Low), xycoords='data',
xytext=(0.02, .80), textcoords='axes fraction',
horizontalalignment='left',
verticalalignment='top',
color='white',
bbox=dict(boxstyle="round", fc= Citi[4],edgecolor='white'),
arrowprops=dict(
facecolor='black',
arrowstyle= '->'))
################
# Legend - a little complicated as we have to take from multiple axis
lines = [p1, p2, p3]
########## Plot text and line on chart if you want to
# host.axvline(x = DATES[52] , linestyle='dotted', color='black') ###Dotted Line when Needed
# host.text(2020.3, 10, 'Managed Money \n Aluminium')
# host.text(2020.5, 92, r'Ali',color='black')
# host.text(2020.8,15, r'some event', rotation=90)
host.legend(lines,[l.get_label() for l in lines],loc=2, fontsize=12,frameon=False)
plt.savefig('multiple_axes.png', dpi=300, bbox_inches='tight')

Modifying saved plot with matplotlib

I am having a problem right now. I have run an extremely heavy simulation and, thus, generated a plot with matplotlib containing the results and saved it (as .jpg). However, there are some elemnts of the plot I would like to change, such as labels size and one vertical line. Is there a straighforward way to do this using matplotlib? I know I could have stored the data and now just replot changing the parameters (and, actually, I have done this), but I was wondering whether there is an easier way. Maybe something like:
fig, ax = plt.figure(path_to_figure)
ax.set_ylabel("Y_label")
...

You can refer to below example, which gives you more idea on how you can do this while plotting everything.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
plt.rc('text', usetex=True)
def f(t):
return t ** 2
t1 = np.arange(0.0, 2.0, 0.1)
noise = np.random.randn(len(t1)) * 0.04
# x coordinates for the lines
xcoords = [0.1, 0.3, 0.5]
# colors for the lines
colors = ['r','k','b']
fig = plt.figure(figsize=(4, 3), dpi=200)
ax = fig.add_subplot(1, 1, 1)
plt.scatter(t1, f(t1 + noise), color = 'hotpink', label='Values obtained by experiment', edgecolors='k')
plt.plot(t1, f(t1), ls='solid', label='Theoretical expectation', color='b')
plt.title(r'This is latex title example $\mathbf{E = m \times c^2}$', fontsize='small')
for xc,c in zip(xcoords,colors):
plt.axvline(x=xc, label='line at x = {}'.format(xc), c=c)
plt.grid()
plt.legend(loc=0)
If you want to make all the fonts bold, you can also use below code to make everything bold:
font = {'weight' : 'bold',
'size' : 14 }
plt.rc('font', **font)
def f(t):
return t ** 2
t1 = np.arange(0.0, 2.0, 0.1)
noise = np.random.randn(len(t1)) * 0.04
# x coordinates for the lines
xcoords = [0.1, 0.3, 0.5]
# colors for the lines
colors = ['r','k','b']
fig = plt.figure(figsize=(4, 3), dpi=200)
ax = fig.add_subplot(1, 1, 1)
plt.scatter(t1, f(t1 + noise), color = 'hotpink', label='Values obtained by experiment', edgecolors='k')
plt.plot(t1, f(t1), ls='solid', label='Theoretical expectation', color='b')
plt.title(r'This is latex title example $\mathbf{E = m \times c^2}$', fontsize='small')
plt.xlabel("This is X-label.", fontsize=12)
plt.ylabel("This is Y-label.", fontsize=16)
for xc,c in zip(xcoords,colors):
plt.axvline(x=xc, label='line at x = {}'.format(xc), c=c)
plt.grid()
plt.legend(loc=(1.15,0.2))

Append data with different colour in matplotlib in real time

I'm updating dynamically a plot in a loop:
dat=[0, max(X[:, 0])]
fig = plt.figure()
ax = fig.add_subplot(111)
Ln, = ax.plot(dat)
Ln2, = ax.plot(dat)
plt.ion()
plt.show()
for i in range(1, 40):
ax.set_xlim(int(len(X[:i])*0.8), len(X[:i])) #show last 20% data of X
Ln.set_ydata(X[:i])
Ln.set_xdata(range(len(X[:i])))
Ln2.set_ydata(Y[:i])
Ln2.set_xdata(range(len(Y[:i])))
plt.pause(0.1)
But now I want to update it in a different way: append some values and show them in other colour:
X.append(other_data)
# change colour just to other_data in X
The result should look something like this:
How could I do that?

Have a look at the link I posted. Linesegments can be used to plot colors at a particular location differently. If you want to do it in real-time you can still use line-segments. I leave that up to you.
# adjust from https://stackoverflow.com/questions/38051922/how-to-get-differents-colors-in-a-single-line-in-a-matplotlib-figure
import numpy as np, matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib.colors import ListedColormap, BoundaryNorm
# my func
x = np.linspace(-2 * np.pi, 2 * np.pi, 100)
y = 3000 * np.sin(x)
# select how to color
cmap = ListedColormap(['r','b'])
norm = BoundaryNorm([2000,], cmap.N)
# get segments
xy = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.hstack([xy[:-1], xy[1:]])
# control which values have which colors
n = y.shape[0]
c = np.array([plt.cm.RdBu(0) if i < n//2 else plt.cm.RdBu(255) for i in range(n)])
# c = plt.cm.Reds(np.arange(0, n))
# make line collection
lc = LineCollection(segments,
colors = c
# norm = norm,
)
# plot
fig, ax = plt.subplots()
ax.add_collection(lc)
ax.autoscale()
ax.axvline(x[n//2], linestyle = 'dashed')
ax.annotate("Half-point", (x[n//2], y[n//2]), xytext = (4, 1000),
arrowprops = dict(headwidth = 30))
fig.show()

How to plot matplotlib errorbars

I tried to plot error bar with Matplotlib like graphic attached, I can't made it, any suggestion?
import numpy as np
import matplotlib.pyplot as plt
Media = data["Media"]
Periodo = data["Periodo"]
P10th = data["P10th"]
P90th = data["P90th"]
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
fig, ax = plt.subplots()
ax.errorbar(Media, P90th, P10th, color='red', ls='--', marker='o', capsize=5, capthick=1, ecolor='black')
plt.xticks(ind, ('1910-1940', '1950-1990', '1990-2000', '2001-2010') )
ax.set_ylim(ylims)
, please can you help me.
This is my output

Here's the plot for your data:
p_10 = [.19,.62, .77, 1]
p_90 = [7.19, 6.67, 7.36, 8.25]
M = [1.16, 2.06, 2.17, 2.52]
fig = plt.figure()
x = [1, 2, 3, 4]
y = M
yerr = [p_10, # 'down' error
p_90] # 'up' error
plt.errorbar(x, y, yerr=yerr, capsize=3, fmt="r--o", ecolor = "black")

How to combine two matplotlib (python) colormaps from scatter plot

I am trying to combine two colourmap legends in one. Colour values are defined from third (z) data.
I am trying plot one legend colormap with two color scheme.
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_excel('C:\\Users\user1\\PycharmProjects\\untitled\\Python_test.xlsx')
x = df['Vp_dry']
y = df['Vs_dry']
q = df['Vp_wet']
w = df['Vs_wet']
fig, ax = plt.subplots()
popt, pcov = curve_fit(lambda fx, a, b: a * fx ** -b, x, y)
x_linspace = np.linspace(min(x - 100), max(x + 100), 100)
power_y = popt[0]*x_linspace ** -popt[1]
ax1 = plt.scatter(x, y, c=df['Porosity'], cmap=plt.cm.Greys, vmin=2, vmax=df['Porosity'].max(), edgecolors="#B6BBBD")
plt.plot(x_linspace, power_y, color='grey', label='Dry')
popt, pcov = curve_fit(lambda fx, a, b: a * fx ** -b, q, w)
q_linspace = np.linspace(min(q - 100), max(q + 100), 100)
power_w = popt[0]*q_linspace ** -popt[1]
ax2 = plt.scatter(q, w, c=df['Porosity'], cmap=plt.cm.Blues, vmin=2, vmax=df['Porosity'].max(), edgecolors="#3D83C1")
plt.plot(q_linspace, power_w, label='Wet')
cbar = fig.colorbar(ax2)
cbar = fig.colorbar(ax1)
cbar.set_label("Porosity (%)")
plt.xlabel('Vp (m/s)')
plt.ylabel('Vs (m/s)')
plt.grid()
plt.legend()
plt.show()
Desired result:

You seem to need a colorbar with two color maps combined, one of them reversed, and have the ticks changed to percentage values.
An approach is to manually create a second subplot, use two images and make it look like a colorbar:
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
# first create some dummy data to plot
N = 100
x = np.random.uniform(0, 10, N)
y = np.random.normal(15, 2, N)
q = np.random.uniform(0, 10, N)
w = np.random.normal(10, 2, N)
df_porosity = np.random.uniform(0, 5, N)
fig, (ax, ax2) = plt.subplots(ncols=2, figsize=(6, 4), gridspec_kw={"width_ratios": [1, 0.08]})
plot1 = ax.scatter(x, y, c=df_porosity, cmap=plt.cm.Greys, vmin=2, vmax=df_porosity.max(), edgecolors="#B6BBBD")
plot2 = ax.scatter(q, w, c=df_porosity, cmap=plt.cm.Blues, vmin=2, vmax=df_porosity.max(), edgecolors="#3D83C1")
img_cbar = np.linspace(0, 1, 256).reshape(256, 1)
ax2.imshow(img_cbar, cmap=plt.cm.Blues, extent=[0, 1, 1, 0]) # aspect='auto')
ax2.imshow(img_cbar, cmap=plt.cm.Greys, extent=[0, 1, -1, 0])
ax2.set_ylim(-1, 1)
ax2.set_aspect(10)
ax2.set_ylabel("Porosity (%)")
ax2.yaxis.set_label_position("right")
ax2.set_xticks([])
ax2.yaxis.tick_right()
# optionally show the ticks as percentage, where 1.0 corresponds to 100 %
ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))
plt.tight_layout()
plt.show()

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