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()
Related
How can I set the labels on the extra axes?
The ticks and labels should be the same on all 4 axes. I'm doing something wrong... Thanks!
import matplotlib.pyplot as plt
plt.rcParams['text.usetex'] = True
plt.figure(figsize=(5,5))
f, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax3 = ax1.twiny()
plt.show()
# create reusable ticks and labels
ticks = [0,1/2,3.14159/4,3.14159/2,1]
labels = [r"$0$", r"$\displaystyle\frac{1}{2}$", r"$\displaystyle\frac{\pi}{4}$", r"$\displaystyle\frac{\pi}{2}$", r"$1$"]
# Version 1: twinx() + xaxis.set_ticks()
plt.figure(figsize=(5,5))
f, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax3 = ax1.twiny()
ax1.xaxis.set_ticks(ticks, labels=labels)
ax1.yaxis.set_ticks(ticks, labels=labels)
ax2.xaxis.set_ticks(ticks, labels=labels)
ax3.yaxis.set_ticks(ticks, labels=labels)
plt.show()
# Version 2: twinx() + set_xticklabels)()
plt.figure(figsize=(5,5))
f, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax3 = ax1.twiny()
ax1.set_xticks(ticks)
ax1.set_xticklabels(labels)
ax1.set_yticks(ticks)
ax1.set_yticklabels(labels)
ax2.set_xticks(ticks)
ax2.set_xticklabels(labels)
ax3.set_yticks(ticks)
ax3.set_yticklabels(labels)
plt.show()
Confused: How come ax1 has both xaxis and yaxis, while ax2, ax3 do not appear to?
A unintuitive solution based on matplotlib.axes.Axes.twinx:
Create a new Axes with an invisible x-axis and an independent y-axis
positioned opposite to the original one (i.e. at right).
This means unintuitively (at least for me) you have to switch x/y at the .twin call.
unintuitively not concerning the general matplotlib twinx functionality, but concerning such a manual ticks and label assignment
To highlight that a bit more I used ax2_x and ax3_y in the code.
Disclaimer: Not sure if that will break your plot intention when data is added.
Probably at least you have to take special care with the data assignment to those twin axes - keeping that "axis switch" in mind.
Also keep that axis switch" in mind when assigning different ticks and labels to the x/y axis.
But for now I think that's the plot you were looking for:
Code:
import matplotlib.pyplot as plt
plt.rcParams['text.usetex'] = True
# create reusable ticks and labels
ticks = [0,1/2,3.14159/4,3.14159/2,1]
labels = [r"$0$", r"$\displaystyle\frac{1}{2}$", r"$\displaystyle\frac{\pi}{4}$", r"$\displaystyle\frac{\pi}{2}$", r"$1$"]
plt.figure(figsize=(5,5))
f, ax1 = plt.subplots()
ax1.xaxis.set_ticks(ticks, labels=labels)
ax1.yaxis.set_ticks(ticks, labels=labels)
ax2_x = ax1.twiny() # switch
ax3_y = ax1.twinx() # switch
ax2_x.xaxis.set_ticks(ticks, labels=labels)
ax3_y.yaxis.set_ticks(ticks, labels=labels)
plt.show()
Or switch the x/yaxis.set_ticks - with the same effect:
On second thought, I assume that's the preferred way to do it, especially when data comes into play.
ax2_x = ax1.twinx()
ax3_y = ax1.twiny()
ax2_x.yaxis.set_ticks(ticks, labels=labels) # switch
ax3_y.xaxis.set_ticks(ticks, labels=labels) # switch
In case you don't intend to use the twin axis functionality (that means having different data with different scaling assigned to those axis) but 'only' want the ticks and labels on all 4 axis for better plot readability:
Solution based on answer of ImportanceOfBeingErnest with the same plot result:
import matplotlib.pyplot as plt
plt.rcParams['text.usetex'] = True
# create reusable ticks and labels
ticks = [0,1/2,3.14159/4,3.14159/2,1]
labels = [r"$0$", r"$\displaystyle\frac{1}{2}$", r"$\displaystyle\frac{\pi}{4}$", r"$\displaystyle\frac{\pi}{2}$", r"$1$"]
plt.figure(figsize=(5,5))
f, ax1 = plt.subplots()
ax1.xaxis.set_ticks(ticks, labels=labels)
ax1.yaxis.set_ticks(ticks, labels=labels)
ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True, labeltop=True)
ax1.tick_params(axis="y", left=True, right=True, labelleft=True, labelright=True)
plt.show()
ax2 = ax1.twinx() shares the x-axis with ax1.
ax3 = ax1.twiny() shares the y-axis with ax1.
As a result, the two lines where you set ax2.xaxis and ax3.yaxis's ticks and ticklabels are redundant with the changes you already applied on ax1.
import matplotlib.pyplot as plt
plt.rcParams['text.usetex'] = False # My computer doesn't have LaTeX, don't mind me.
# Create reusable ticks and labels.
ticks = [0, 1/2, 3.14159/4, 3.14159/2, 1]
labels = [r"$0$", r"$\frac{1}{2}$", r"$\frac{\pi}{4}$", r"$\frac{\pi}{2}$", r"$1$"]
# Set the ticks and ticklabels for each axis.
fig = plt.figure(figsize=(5,5))
ax1 = fig.add_subplot()
ax2 = ax1.twinx()
ax3 = ax1.twiny()
for axis in (ax1.xaxis,
ax1.yaxis,
ax2.yaxis,
ax3.xaxis):
axis.set_ticks(ticks)
axis.set_ticklabels(labels)
fig.show()
Notice that if I comment out the work on ax2 and ax3, we get exactly what you have in your question:
for axis in (ax1.xaxis, ax1.yaxis,
# ax2.yaxis,
# ax3.xaxis,
):
axis.set_ticks(ticks)
axis.set_ticklabels(labels)
Now let's ruin ax1 via modifications on ax2, just to show that the bound between twins works well:
ax2.xaxis.set_ticks(range(10))
ax2.xaxis.set_ticklabels(tuple("abcdefghij"))
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 have a matplotlib bar chart, which bars are colored according to some rules through a colormap. I need a colorbar on the right of the main axes, so I added a new axes with
fig, (ax, ax_cbar) = plt.subplots(1,2)
and managed to draw my color bar in the ax_bar axes, while I have my data displayed in the ax axes. Now I need to reduce the width of the ax_bar, because it looks like this:
How can I do?
Using subplots will always divide your figure equally. You can manually divide up your figure in a number of ways. My preferred method is using subplot2grid.
In this example, we are setting the figure to have 1 row and 10 columns. We then set ax to be the start at row,column = (0,0) and have a width of 9 columns. Then set ax_cbar to start at (0,9) and has by default a width of 1 column.
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8,6))
num_columns = 10
ax = plt.subplot2grid((1,num_columns), (0,0), colspan=num_columns-1)
ax_cbar = plt.subplot2grid((1,num_columns), (0,num_columns-1))
The ususal way to add a colorbar is by simply putting it next to the axes:
fig.colorbar(sm)
where fig is the figure and sm is the scalar mappable to which the colormap refers. In the case of the bars, you need to create this ScalarMappable yourself. Apart from that there is no need for complex creation of multiple axes.
import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
fig , ax = plt.subplots()
x = [0,1,2,3]
y = np.array([34,40,38,50])*1e3
norm = matplotlib.colors.Normalize(30e3, 60e3)
ax.bar(x,y, color=plt.cm.plasma_r(norm(y)) )
ax.axhline(4.2e4, color="gray")
ax.text(0.02, 4.2e4, "42000", va='center', ha="left", bbox=dict(facecolor="w",alpha=1),
transform=ax.get_yaxis_transform())
sm = plt.cm.ScalarMappable(cmap=plt.cm.plasma_r, norm=norm)
sm.set_array([])
fig.colorbar(sm)
plt.show()
If you do want to create a special axes for the colorbar yourself, the easiest method would be to set the width already inside the call to subplots:
fig , (ax, cax) = plt.subplots(ncols=2, gridspec_kw={"width_ratios" : [10,1]})
and later put the colorbar to the cax axes,
fig.colorbar(sm, cax=cax)
Note that the following questions have been asked for this homework assignment already:
Point picker event_handler drawing line and displaying coordinates in matplotlib
Matplotlib's widget to select y-axis value and change barplot
Display y axis value horizontal line drawn In bar chart
How to change colors automatically once a parameter is changed
Interactively Re-color Bars in Matplotlib Bar Chart using Confidence Intervals
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()