I'd like to plot a series with x and y error bars, then plot a second series with x and y error bars on a second y axis all on the same subplot. Can this be done with matplotlib?
import matplotlib.pyplot as plt
plt.figure()
ax1 = plt.errorbar(voltage, dP, xerr=voltageU, yerr=dPU)
ax2 = plt.errorbar(voltage, current, xerr=voltageU, yerr=currentU)
plt.show()
Basically, I'd like to put ax2 on a second axis and have the scale on the right side.
Thanks!
twinx() is your friend for adding a secondary y-axis, e.g.:
import matplotlib.pyplot as pl
import numpy as np
pl.figure()
ax1 = pl.gca()
ax1.errorbar(np.arange(10), np.arange(10), xerr=np.random.random(10), yerr=np.random.random(10), color='g')
ax2 = ax1.twinx()
ax2.errorbar(np.arange(10), np.arange(10)+5, xerr=np.random.random(10), yerr=np.random.random(10), color='r')
There is not a lot of documentation except for:
matplotlib.pyplot.twinx(ax=None)
Make a second axes that shares the x-axis. The new axes will overlay ax (or the current axes if ax is None). The ticks for ax2 will be placed on the right, and the ax2 instance is returned.
I was struggling to share the x-axis, but thank you #Bart you saved me!
The simple solution is use twiny instead of twinx
ax1.errorbar(layers, scores_means[str(epoch)][h,:],np.array(scores_stds[str(epoch)][h,:]))
# Make the y-axis label, ticks and tick labels match the line color.
ax1.set_xlabel('depth', color='b')
ax1.tick_params('x', colors='b')
ax2 = ax1.twiny()
ax2.errorbar(hidden_dim, scores_means[str(epoch)][:,l], np.array(scores_stds[str(epoch)][:,l]))
ax2.set_xlabel('width', color='r')
ax2.tick_params('x', colors='r')
fig.tight_layout()
plt.show()
Related
I have two subplots of horizontal bars done in matplotlib. For the first subplot, the number of y-axis ticks is appropriate, but I'm unable to figure out why specifying number of ticks for the second subplot is coming out to be wrong. This is the code:
import matplotlib.pyplot as plt
import numpy as np
# Plot separate subplots for genders
fig, (axes1, axes2) = plt.subplots(nrows=1, ncols=2,
sharex=False,
sharey=False,
figsize=(15,10))
labels = list(out.index)
x = ["20%", "40%", "60%", "80%", "100%"]
y = np.arange(len(out))
width = 0.5
axes1.barh(y, female_distr, width, color="olive",
align="center", alpha=0.8)
axes1.ticks_params(nbins=6)
axes1.set_yticks(y)
axes1.set_yticklabels(labels)
axes1.set_xticklabels(x)
axes1.yaxis.grid(False)
axes1.set_xlabel("Occurence (%)")
axes1.set_ylabel("Language")
axes1.set_title("Language Distribution (Women)")
axes2.barh(y, male_distr, width, color="chocolate",
align="center", alpha=0.8)
axes2.locator_params(nbins=6)
axes2.set_yticks(y)
axes2.set_yticklabels(labels)
axes2.set_xticklabels(x)
axes2.yaxis.grid(False)
axes2.set_xlabel("Occurence (%)")
axes2.set_ylabel("Language")
axes2.set_title("Language Distribution (Men)")
The rest of the objects like out are simple data frames that I don't think need to be described here. The above code returns the following plot:
I would like the second subplot to have equal number of ticks but experimenting with nbins always results in either more or fewer ticks than the first subplot.
First, if you want your two plots to have the same x-axis, why not use sharex=True?
x_ticks = [0,20,40,60,80,100]
fig, (ax1,ax2) = plt.subplots(1,2, sharex=True)
ax1.set_xticks(x_ticks)
ax1.set_xticklabels(['{:.0f}%'.format(x) for x in x_ticks])
ax1.set_xlim(0,100)
ax1.grid(True, axis='x')
ax2.grid(True, axis='x')
I am using seaborn and twinx to plot two lines in one figure. However, as replicated below, the blue line is below the horizontal line because it is overlayed by the second plot:
import seaborn as sns
import matplotlib.pyplot as plt
l1 = sns.lineplot(x=[0,1,2],y=[1,2,3],color="#0188A8")
ax1 = plt.gca()
ax2 = ax1.twinx()
l2 = sns.lineplot(x=[0,1,2], y=[100,200,300],color="#D42227")
plt.xlabel('Number of Selves',fontsize=13)
ax1.set_xticks([0,1,2])
ax1.set_yticks([0,1,2])
ax2.set_yticks([100,200,300])
After doing some googling, I found this which was close, but didn't help. Trying out their solution, the axis ticks will get distorted, as both lines are plotted on the second plot:
ax1 = plt.gca()
ax2 = ax1.twinx()
l1 = sns.lineplot(x=[0,1,2],y=[1,2,3],color="#0188A8")
l2 = sns.lineplot(x=[0,1,2], y=[100,200,300],color="#D42227")
plt.xlabel('Number of Selves',fontsize=13)
ax1.set_xticks([0,1,2])
ax1.set_yticks([0,1,2])
ax2.set_yticks([100,200,300])
My question is, how can the blue line be on top of the horizontal grid lines while maintaining the ticks to be at the same position as they are in the first picture?
You cannot easily obtain the desired effect because all the artists of ax2 are drawn above the artists of ax1, regardless of their respective z-order.
The only "good" solution that I can suggest, is, as you had found out, draw both lines on ax2, but you have to use the data transform of ax1 for the first line so that it matches the numbers on the left axis.
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
l1 = sns.lineplot(x=[0,1,2],y=[1,2,3],color="#0188A8", ax=ax2, transform=ax1.transData)
l2 = sns.lineplot(x=[0,1,2], y=[100,200,300],color="#D42227", ax=ax2)
ax1.set_xlabel('Number of Selves',fontsize=13)
ax1.set_xticks([0,1,2])
ax1.set_yticks([0,1,2])
ax2.set_yticks([100,200,300])
ax1.set_ylim(-0.5,3.5)
Note that, because there are actually no data on ax1, you have to manually specify the y-axis limits, it won't autoscale for you.
I'm using Matplotlib to plot some simple figure. An exmaple is illustrated like follows:
x = np.linspace(0, 5, 1000)
fig =plt.figure(figsize=(7,7))
ax =plt.subplot(211)
ax.plot(x, np.sin(x), '-b', label='Sine')
ax.axis('equal')
leg1 = ax.legend(loc= (0.8,0.85));
leg2 = ax.legend(loc= (0.8,-0.15));
leg2.set_zorder(14)
ax.add_artist(leg1)
ax.add_artist(leg2)
ax =plt.subplot(212)
I want to keep the upper and lower subplot both have the same legend as the subplot 1.
However, when I turn down the leg2 in order to within the second subplot, it is covered by the canvas. Here is my question, Is there any method to duplicate the legend of one subplot to another subplot?
The problem is that the second legend is part of the first subplot, and the first subplot is completely behind the second.
Ususally, you would add the legend to the second subplot instead of the first.
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 5, 1000)
fig =plt.figure(figsize=(7,7))
ax =plt.subplot(211)
ax.plot(x, np.sin(x), '-b', label='Sine')
leg1 = ax.legend(loc= (0.8,0.85))
ax2 =plt.subplot(212)
leg2 = ax2.legend(*ax.get_legend_handles_labels(), loc= (0.8,0.85))
plt.show()
In pyplot, you can change the order of different graphs using the zorder option or by changing the order of the plot() commands. However, when you add an alternative axis via ax2 = twinx(), the new axis will always overlay the old axis (as described in the documentation).
Is it possible to change the order of the axis to move the alternative (twinned) y-axis to background?
In the example below, I would like to display the blue line on top of the histogram:
import numpy as np
import matplotlib.pyplot as plt
import random
# Data
x = np.arange(-3.0, 3.01, 0.1)
y = np.power(x,2)
y2 = 1/np.sqrt(2*np.pi) * np.exp(-y/2)
data = [random.gauss(0.0, 1.0) for i in range(1000)]
# Plot figure
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax2.hist(data, bins=40, normed=True, color='g',zorder=0)
ax2.plot(x, y2, color='r', linewidth=2, zorder=2)
ax1.plot(x, y, color='b', linewidth=2, zorder=5)
ax1.set_ylabel("Parabola")
ax2.set_ylabel("Normal distribution")
ax1.yaxis.label.set_color('b')
ax2.yaxis.label.set_color('r')
plt.show()
Edit: For some reason, I am unable to upload the image generated by this code. I will try again later.
You can set the zorder of an axes, ax.set_zorder(). One would then need to remove the background of that axes, such that the axes below is still visible.
ax2 = ax1.twinx()
ax1.set_zorder(10)
ax1.patch.set_visible(False)
How do I show a plot with twin axes such that the aspect of the top and right axes are 'equal'. For example, the following code will produce a square plot
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.set_aspect('equal')
ax.plot([0,1],[0,1])
But this changes as soon as you use the twinx function.
ax2 = ax.twinx()
ax2.set_ylim([0,2])
ax3 = ax.twiny()
ax3.set_xlim([0,2])
Using set_aspect('equal') on ax2 and ax3 seems to force it the the aspect of ax, but set_aspect(0.5) doesn't seem to change anything either.
Put simply, I would like the plot to be square, the bottom and left axes to run from 0 to 1 and the top and right axes to run from 0 to 2.
Can you set the aspect between two twined axes? I've tried stacking the axes:
ax3 = ax2.twiny()
ax3.set_aspect('equal')
I've also tried using the adjustable keyword in set_aspect:
ax.set_aspect('equal', adjustable:'box-forced')
The closest I can get is:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.set_aspect('equal', adjustable='box-forced')
ax.plot([0,1],[0,1])
ax2=ax.twinx()
ax3 = ax2.twiny()
ax3.set_aspect(1, adjustable='box-forced')
ax2.set_ylim([0,2])
ax3.set_xlim([0,2])
ax.set_xlim([0,1])
ax.set_ylim([0,1])
Which produces:
I would like to remove the extra space to the right and left of the plot
It seems overly complicated to use two different twin axes to get two independent set of axes. If the aim is to create one square plot with one axis on each side of the plot, you may use two axes, both at the same position but with different scales. Both can then be set to have equal aspect ratios.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.set_aspect('equal')
ax.plot([0,1],[0,1])
ax2 = fig.add_axes(ax.get_position())
ax2.set_facecolor("None")
ax2.set_aspect('equal')
ax2.plot([2,0],[0,2], color="red")
ax2.tick_params(bottom=0, top=1, left=0, right=1,
labelbottom=0, labeltop=1, labelleft=0, labelright=1)
plt.show()