Related
I saw this tutorial on how to make a scatter plot with a histogram for the x and y axes and I thought it would be neat to also tack on a colorbar for an extra dimension of information. To do this, I utilized "the make_axes_locatable" function, like so:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
# generating fake data
tx = np.random.randn(1000)
ty = np.random.randn(1000)
tz = np.random.randn(1000)
fig = plt.figure(figsize=(5, 5))
gs = fig.add_gridspec(2, 2, width_ratios=(4, 1), height_ratios=(1, 4),
left=0.1, right=0.9, bottom=0.1, top=0.9,
wspace=0.05, hspace=0.05)
# Create the Axes.
ax = fig.add_subplot(gs[1, 0])
ax_histx = fig.add_subplot(gs[0, 0], sharex=ax)
ax_histy = fig.add_subplot(gs[1, 1], sharey=ax)
def scatter_hist_and_colorbar(x, y, c, ax, ax_histx, ax_histy,label):
# no labels
ax_histx.tick_params(axis="x", labelbottom=False)
ax_histy.tick_params(axis="y", labelleft=False)
# the scatter plot:
sc=ax.scatter(x,y,marker='o',label=label,c=c)
# now determine nice limits by hand:
binwidth = 0.25
xlim = (int(np.max(np.abs(x))/binwidth) + 1) * binwidth
ylim = (int(np.max(np.abs(y))/binwidth) + 1) * binwidth
xbins = np.arange(-xlim, xlim + binwidth, binwidth)
ybins = np.arange(-ylim, ylim + binwidth, binwidth)
ax_histx.hist(x, bins=xbins)
ax_histy.hist(y, bins=ybins, orientation='horizontal')
return sc
sc1= scatter_hist_and_colorbar(tx,ty,tz, ax, ax_histx, ax_histy,label='data')
ax.set_ylabel('x data')
ax.set_xlabel('y data')
ax.legend()
divider = make_axes_locatable(ax)
cax = divider.append_axes('left', size='5%', pad=1)
cbar=fig.colorbar(sc1, cax=cax, orientation='vertical')
cbar.ax.set_ylabel('z data',rotation=90,labelpad=5)
cbar.ax.yaxis.set_ticks_position("left")
plt.savefig('example.png')
plt.show()][2]][2]
This almost works except the "ax_histx" axis is now stretched and doesn't properly line up due to the addition of the colorbar. Is there a way to resize the "ax_histx" axis or is there a better way to add a colorbar to the "ax" subplot so that it wouldn't affect the "ax_histx" or "ax_histy" axes?
After getting a suggestion form #r-beginners , I tried tweaking this code to place a colorbar in the upper right, perpendicular to the histogram axes. This way, it doesn't distort the width/heights of the other shared axes:
# some random data
tx = np.random.randn(1000)
ty = np.random.randn(1000)
tz = np.random.randn(1000)
fig = plt.figure(figsize=(5, 5))
gs = fig.add_gridspec(2, 2, width_ratios=(4, 1), height_ratios=(1, 4),
left=0.1, right=0.9, bottom=0.1, top=0.9,
wspace=0.05, hspace=0.05)
# Create the Axes.
ax0 = fig.add_subplot(gs[0, 1])
ax = fig.add_subplot(gs[1, 0])
ax_histx = fig.add_subplot(gs[0, 0], sharex=ax)
ax_histy = fig.add_subplot(gs[1, 1], sharey=ax)
def scatter_hist_and_colorbar(x, y, c, ax, ax_histx, ax_histy,label):
# no labels
ax_histx.tick_params(axis="x", labelbottom=False)
ax_histy.tick_params(axis="y", labelleft=False)
# the scatter plot:
sc=ax.scatter(x,y,marker='o',label=label,c=c)
# now determine nice limits by hand:
binwidth = 0.25
xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))
lim = (int(xymax/binwidth) + 1) * binwidth
xlim = (int(np.max(np.abs(x))/binwidth) + 1) * binwidth
ylim = (int(np.max(np.abs(y))/binwidth) + 1) * binwidth
xbins = np.arange(-xlim, xlim + binwidth, binwidth)
ybins = np.arange(-ylim, ylim + binwidth, binwidth)
ax_histx.hist(x, bins=xbins)
ax_histy.hist(y, bins=ybins, orientation='horizontal')
return sc
sc1= scatter_hist_and_colorbar(tx,ty,tz, ax, ax_histx, ax_histy,label='data')
ax.set_ylabel('x data')
ax.set_xlabel('y data')
ax.legend()
divider = make_axes_locatable(ax)
divider = make_axes_locatable(ax0)
ca = divider.append_axes('left', size='50%')
ax0.axis('off')
cbar=fig.colorbar(sc1, cax=ca, orientation='vertical')
cbar.ax.set_ylabel('z data',rotation=270,labelpad=5)
cbar.ax.yaxis.set_ticks_position("right")
gs.tight_layout(fig,pad=1)
plt.savefig('example.png')
plt.show()
I have two working barplots about the sentiments of tweets (neutral, positive, negative). How can I merge them into one, side by side?
First bar:
plt.figure(figsize=(6,5))
plt.title('Classification of All tweets into sentiment categories',fontsize=15)
plt.ylabel('Percentage [%]',fontsize=18)
ax = (df_navalny.sentiment.value_counts()/len(df_navalny)*100).plot(kind="bar", rot=0,color=['#04407F','#0656AC','#0A73E1'])
ax.set_yticks(np.arange(0, 110, 10))
plt.grid(color='#95a5a6', linestyle='-.', linewidth=1, axis='y', alpha=0.7)
ax2 = ax.twinx()
ax2.set_yticks(np.arange(0, 110, 10)*len(df_navalny)/100)
for p in ax.patches:
ax.annotate('{:.2f}%'.format(p.get_height()), (p.get_x()+0.15, p.get_height()+1))
Second bar:
plt.figure(figsize=(6,5))
plt.title('Classification of All tweets into sentiment categories',fontsize=15)
plt.ylabel('Percentage [%]',fontsize=18)
ax = (df_putin.sentiment.value_counts()/len(df_putin)*100).plot(kind="bar", rot=0,color=['#04407F','#0656AC','#0A73E1'])
ax.set_yticks(np.arange(0, 110, 10))
plt.grid(color='#95a5a6', linestyle='-.', linewidth=1, axis='y', alpha=0.7)
ax2 = ax.twinx()
ax2.set_yticks(np.arange(0, 110, 10)*len(df_putin)/100)
for p in ax.patches:
ax.annotate('{:.2f}%'.format(p.get_height()), (p.get_x()+0.15, p.get_height()+1))
It's a bit complicated but Matplotlib site offers a demo and when you copy and past you have the following
Here it is the code
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import host_subplot
from mpl_toolkits import axisartist
# fake data
a = [2341, 5670, 4822]
b = [4290, 5205, 3966]
pca, pcb = [[round(100*x/sum(l),2) for x in l] for l in (a, b)]
# prepare all the vertical axes
ax = host_subplot(111, axes_class=axisartist.Axes)
plt.subplots_adjust(right=0.67)
axa = ax.twinx() ; axb = ax.twinx()
axb.axis['right'] = axb.new_fixed_axis(loc="right", offset=(60, 0))
axa.axis['right'].toggle(all=True)
axb.axis['right'].toggle(all=True)
# plot the bars PLUS invisible lines to represent the samples numerosities
x, w = np.array((1,2,3)), 0.30
ax.bar(x-w, pca, width=w, align='edge', label='a', zorder=1)
ax.bar(x-0, pcb, width=w, align='edge', label='b', zorder=1)
axa.plot((1,1),(0,sum(a)),lw=0)
axb.plot((1,1),(0,sum(b)),lw=0)
# fix xtics, xlabels, 'regular' yticks
plt.xticks((1,2,3)) ; ax.set_xticklabels('NO == YES'.split())
ax.set_yticks(range(0, 101, 10))
# all the ylabels
ax.set_ylabel('Percentages')
axa.set_ylabel('Numerosity of a')
axb.set_ylabel('Numerosity of b')
axa.set_ylim(bottom=0.0)
axb.set_ylim(bottom=0.0)
plt.legend()
plt.grid(zorder=0)
plt.show()
I want to draw a horizontal line going through the 0.0 point over the plot produced by stratx's (https://github.com/parrt/stratx) plot_stratpd method.
How can I access the left Y-axis in this case, so that I can use y=0.0?
from stratx.partdep import *
X = df.drop('user_retained', axis=1)
y = df['user_retained']
plt.figure(figsize=(16,16), dpi= 80, facecolor='w', edgecolor='k')
plot_stratpd(X, y, 'percentage_of_points', 'user_retained', yrange=(-0.3, 0.6), n_trials=10)
plt.tight_layout()
plt.axhline(y=134, alpha=1, linewidth = 2, linestyle = '-')
plt.show()
Set up an Axes and pass it to plot_stratpd. You can then use this Axes to plot the horizontal line at regular data coordinates:
fig,ax = plt.subplots(figsize=(16,16), dpi= 80, facecolor='w', edgecolor='k')
plot_stratpd(X, y, 'percentage_of_points', 'user_retained', yrange=(-0.3, 0.6), n_trials=10, ax=ax)
ax.axhline(y=0, alpha=1, linewidth = 2, linestyle = '-')
Example:
from sklearn.datasets import load_diabetes
from stratx.partdep import *
import matplotlib.pyplot as plt
diabetes = load_diabetes()
df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
df['y'] = diabetes.target
X = df.drop('y', axis=1)
y = df['y']
fig,ax = plt.subplots()
plot_stratpd(X, y, 'bmi', 'y', n_trials=10, ax=ax)
ax.axhline(0)
plt.show()
I have written a code that plot some points and lines on the xy plane. It plots everything for a given value of n. So for different n I get my desired plots. But I want to animate these plots for different values of n, say, for n=1, 2, ..., 100. But I cannot do this animation.
Can anyone here help me to do this? Thank you.. I paste my code here:
My Code
import matplotlib as mpl
mpl.rc('text', usetex = True)
mpl.rc('font', family = 'serif')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Circle
fig = plt.subplots()
ax = plt.axes(xlim=(-1.2, 1.2), ylim=(-1.2, 1.2))
plt.gca().set_aspect('equal', adjustable='box')
plt.style.use(['ggplot','dark_background'])
plt.rcParams['figure.figsize'] = (12, 8)
n = 10 #I want to animate this n.
p = 2
for k in range(0,n,1):
theta1 = np.pi + 2*k*np.pi / n
theta2 = np.pi + 2*p*k*np.pi / n
x, y = np.cos(theta1), np.sin(theta1)
x1, y1 = np.cos(theta2), np.sin(theta2)
plt.scatter(x, y, s=50, c='violet', zorder=3)
plt.plot([x,x1], [y,y1], color = 'w')
circle = plt.Circle((0, 0), 1, color='c', fill=False, lw = 1)
ax.add_artist(circle)
#Customize the axes and gridlines:
ax.grid(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
#TickMarks Customization:
ax.set_xticks([])
ax.set_yticks([])
#plt.savefig('nthRoots.png', format='png', dpi=1000,bbox_inches='tight')
plt.show()
Output
Is it possible to animate n over different values?
EDIT: Here I donot have only scatter plots ...so I cannot understand how to do this job using those links..!
My Attempt
#Animation.
import matplotlib as mpl
mpl.rc('text', usetex = True) #for LaTex notation in the Plot
mpl.rc('font', family = 'serif')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Circle
from matplotlib import animation, rc
rc('animation', html='html5')
fig = plt.subplots()
ax = plt.axes(xlim=(-1.2, 1.2), ylim=(-1.2, 1.2))
plt.gca().set_aspect('equal', adjustable='box')
plt.style.use(['ggplot','dark_background'])
plt.rcParams['figure.figsize'] = (12, 8)
p = 2
#Plotting Function:
def f(n):
for k in range(0,n,1):
theta1 = np.pi + 2*k*np.pi / n
theta2 = np.pi + 2*p*k*np.pi / n
x, y = np.cos(theta1), np.sin(theta1)
x1, y1 = np.cos(theta2), np.sin(theta2)
plt.scatter(x, y, s=50, c='violet', zorder=3)
plt.plot([x,x1], [y,y1], color = 'w')
circle = plt.Circle((0, 0), 1, color='c', fill=False, lw = 1)
ax.add_artist(circle)
#Customize the axes and gridlines:
ax.grid(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
#TickMarks Customization:
ax.set_xticks([])
ax.set_yticks([])
plt.show()
#Now I try to define a function for animating n in f(n)
def animate(n):
f(n)
anim = animation.FuncAnimation(fig, animate,
frames=100, interval=100, blit=True)
#anim.save('Wave.mp4', writer = 'ffmpeg', fps = 2, dpi=500,extra_args=['-vcodec', 'libx264'])
That's all I had... But this idea didn't work...I think I have to properly define animate(n).
Any suggestion...! thanks.
Several problems in your code (most are unrelated to animations)
rcParams need to be defined before creating the figure
plt.subplots returns a tuple of figure and axes.
The animation must return a sequence of artist objects when blitting is used. You might turn it off though
plt.show() should be called once at the end of the script.
Correcting for those you get
import matplotlib as mpl
mpl.rc('font', family = 'serif')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Circle
from matplotlib import animation, rc
plt.rcParams['figure.figsize'] = (12, 8)
plt.style.use(['ggplot','dark_background'])
fig, ax = plt.subplots()
p = 2
#Plotting Function:
def f(n):
ax.clear()
ax.set(xlim=(-1.2, 1.2), ylim=(-1.2, 1.2))
ax.set_aspect('equal', adjustable='box')
for k in range(0,n,1):
theta1 = np.pi + 2*k*np.pi / n
theta2 = np.pi + 2*p*k*np.pi / n
x, y = np.cos(theta1), np.sin(theta1)
x1, y1 = np.cos(theta2), np.sin(theta2)
plt.scatter(x, y, s=50, c='violet', zorder=3)
plt.plot([x,x1], [y,y1], color = 'w')
circle = Circle((0, 0), 1, color='c', fill=False, lw = 1)
ax.add_artist(circle)
#Customize the axes and gridlines:
ax.grid(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
#TickMarks Customization:
ax.set_xticks([])
ax.set_yticks([])
anim = animation.FuncAnimation(fig, f, frames=100, interval=100, blit=False)
plt.show()
I would like to set the colorbar of my plot to a custom height, not necessarily to match the size of the plot. In fact I would like the height of the colorbar PLUS the title on top of it to match the height of the figure.
With
ax3 = divider.append_axes('right', size='10%', pad=0.3)
cb = plt.colorbar(Q, cax=ax3, ticks=[0.0, 3.0, 6.0, 9.0, 12.0, 15.0], format='%.1f')
I managed to have a colorbar with the same height as the plot, which has been asked for many other times, now I would like to shrink it.
Following suggestion provided in other questions I decided to explicitly give the colorbar its own axes with add_axes, after getting the position of the last plot axes with get_position. Here is what I'm trying to do. There are no data and no colorbar in this example, just to show that I'm not getting the result I expected:
from __future__ import unicode_literals
import numpy as np
from scipy.interpolate import griddata
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from matplotlib.pylab import cm
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size
matplotlib.rcParams.update({'font.size': 8})
fig = plt.figure()
fig.set_size_inches(6.3,6.3)
ax1 = plt.subplot(111)
divider = make_axes_locatable(ax1)
ax2 = divider.append_axes('right', size='100%', pad=0.3)
axes = [ax1, ax2]
ltypes = ['dashed', 'solid']
xi = np.linspace(-18.125, 18.125, 11)
yi = np.linspace(0, 28, 9)
xv, yv = np.meshgrid(xi, yi)
xcOdd = 0.2
zcOdd = 0.725
xcEven = 0.6
zcEven = 0.725
maskRadius = 0.15
for i in range(2):
ax = axes[i]
ax.set_xlabel('distance [m]')
if i == 0:
ax.set_ylabel('depth [m]')
if i == 1:
ax.set_yticklabels([])
ax.invert_yaxis()
ax.tick_params(direction='in')
ax.set_aspect('equal')
odd = Circle((xcOdd, zcOdd), .15, linewidth=1.2, color='k', fill=False)
even = Circle((xcEven, zcEven), .15, linewidth=1.2, linestyle=ltypes[i], color='k', fill=False)
vmax = 15.
vmin = 0.
norm = matplotlib.colors.Normalize(vmin,vmax, clip=False)
color_map = matplotlib.colors.ListedColormap(plt.cm.Greys(np.linspace(0.25, 1, 5)), "name")
ax.add_patch(odd)
pad = 0.03
width = 0.03
pos = ax2.get_position()
ax3 = fig.add_axes([pos.xmax + pad, pos.ymin, width, 0.7*(pos.ymax-pos.ymin) ])
plt.savefig('prova-vect-paper-test-2.eps', format='eps')
Why is get_position returning the wrong boundingbox?
You need to draw the canvas before obtaining the actual position from .get_position(). This is because due to the equal aspect ratio, the axes changes size and position at draw time.
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
matplotlib.rcParams.update({'font.size': 8})
fig = plt.figure()
fig.set_size_inches(6.3,6.3)
ax1 = plt.subplot(111)
divider = make_axes_locatable(ax1)
ax2 = divider.append_axes('right', size='100%', pad=0.3)
axes = [ax1, ax2]
xi = np.linspace(-18.125, 18.125, 11)
yi = np.linspace(0, 28, 9)
xv, yv = np.meshgrid(xi, yi)
for i in range(2):
ax = axes[i]
ax.set_xlabel('distance [m]')
if i == 0:
ax.set_ylabel('depth [m]')
if i == 1:
ax.set_yticklabels([])
ax.invert_yaxis()
ax.tick_params(direction='in')
ax.set_aspect('equal')
vmax = 15.
vmin = 0.
norm = colors.Normalize(vmin,vmax, clip=False)
color_map = colors.ListedColormap(plt.cm.Greys(np.linspace(0.25, 1, 5)), "name")
im = ax.imshow(yv, cmap=color_map, norm=norm)
pad = 0.03
width = 0.03
fig.canvas.draw()
pos = ax2.get_position()
ax3 = fig.add_axes([pos.xmax + pad, pos.ymin, width, 0.7*(pos.ymax-pos.ymin) ])
fig.colorbar(im, cax=ax3)
plt.show()