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"))
Related
Here's my code so far:
QE_ellip_fixed = [-1.04e-3,-1.04e-2,-0.1,-0.76,-2.34,-2.54]
QL_ellip_fixed = [1.77e-4,9.89e-4,-6e-2,-2.9,-4.45,-2.74]
QP_ellip_fixed = [1.26e-3,1.45e-2,0.14,0.98,2.6,2.5]
QE_ellip_varied = [-1.73e-4,-1.73e-3,-1.71e-2,-0.15,-0.86,-3.16]
QL_ellip_varied = [7.57e-5,7.53e-4,5.4e-3,-0.13,-4.15,-7.3]
QP_ellip_varied = [1.41e-3,1.77e-3,2.34e-2,0.22,1.33,3.14]
RHScalls_ellip = [764021,76388,7625,750,63,3]
RHScalls_circ = [629171,62864,6234,577,41,5]
QE_circ_fixed= [-1.26e-4,-1.26e-3,-1.24e-2,-0.11,-0.57,-2.98]
QL_circ_fixed = [-1.32e-4,5.89e-4,1.5e-3,-0.51,0.4,-9.57]
QP_circ_fixed = [1.45e-2,9.25e-3,4.62e-2,0.58,3.5,8.54]
QE_circ_varied = [-1.26e-4,-1.25e-3,-1.24e-2,-0.11,-0.56,-2.13]
QL_circ_varied = [-1.33e-4,5.88e-4,1.69e-3,-0.45,-0.64,-6.58]
QP_circ_varied = [1.45e-2,9.32e-3,5.2e-2,0.55,3.11,13.05]
alp = [1e-5,1e-4,1e-3,1e-2,1e-1,1]
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(alp,np.abs(QE_ellip_varied),label='$|Q_E|$')
ax1.plot(alp,np.abs(QL_ellip_varied),label='$|Q_L|$')
ax1.plot(alp,np.abs(QP_ellip_varied),label='$|Q_P|$')
ax2 = ax1.twiny()
ax2.set_xticks([1e-5,1e-4,1e-3,1e-2,1e-1,1])
ax2.set_xticklabels(RHScalls_ellip)
ax1.set_xscale('log')
plt.yscale('log')
ax1.grid()
ax1.set_xlabel('alpha')
ax1.set_ylabel('Score (unitless)')
ax1.legend()
plt.show()
And here's the outputted image:
I want to have the values on the top axis have ticklabels in line with the grid lines already imposed, as they actually correspond to those gridlines, but I can't seem to make the top axis not behave in this annoying logarithmic way. I've only specified for axis 1 to have a logarithmic scale, but it seems to have applied to axis 2 as well...
it's because the axes share the y-axis, but not the x-axis. The xlimits are different for each..the following worked for me:
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
ax1.set_xscale('log')
ax2.set_xscale('log') #make sure both log
plt.yscale('log')
ax1.plot(alp,np.abs(QE_ellip_varied),label='$|Q_E|$')
ax1.plot(alp,np.abs(QL_ellip_varied),label='$|Q_L|$')
ax1.plot(alp,np.abs(QP_ellip_varied),label='$|Q_P|$')
ax2.set_xlim(ax1.get_xlim()) #make sure same limits
ax2.set_xticks([1e-5,1e-4,1e-3,1e-2,1e-1,1])
ax2.set_xticklabels(RHScalls_ellip)
ax1.grid()
ax1.set_xlabel('alpha')
ax1.set_ylabel('Score (unitless)')
ax1.legend()
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.
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()
I am trying to add custom xticks to a relatively complicated bar graph plot and I am stuck.
I am plotting from two data frames, merged_90 and merged_15:
merged_15
Volume y_err_x Area_2D y_err_y
TripDate
2015-09-22 1663.016032 199.507503 1581.591701 163.473202
merged_90
Volume y_err_x Area_2D y_err_y
TripDate
1990-06-10 1096.530711 197.377497 1531.651913 205.197493
I want to create a bar graph with two axes (i.e. Area_2D and Volume) where the Area_2D and Volume bars are grouped based on their respective data frame. An example script would look like:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy
fig = plt.figure()
ax1 = fig.add_subplot(111)
merged_90.Volume.plot(ax=ax1, color='orange', kind='bar',position=2.5, yerr=merged_90['y_err_x'] ,use_index=False , width=0.1)
merged_15.Volume.plot(ax=ax1, color='red', kind='bar',position=0.9, yerr=merged_15['y_err_x'] ,use_index=False, width=0.1)
ax2 = ax1.twinx()
merged_90.Area_2D.plot(ax=ax2,color='green', kind='bar',position=3.5, yerr=merged_90['y_err_y'],use_index=False, width=0.1)
merged_15.Area_2D.plot(ax=ax2,color='blue', kind='bar',position=0, yerr=merged_15['y_err_y'],use_index=False, width=0.1)
ax1.set_xlim(-0.5,0.2)
x = scipy.arange(1)
ax2.set_xticks(x)
ax2.set_xticklabels(['2015'])
plt.tight_layout()
plt.show()
The resulting plot is:
One would think I could change:
x = scipy.arange(1)
ax2.set_xticks(x)
ax2.set_xticklabels(['2015'])
to
x = scipy.arange(2)
ax2.set_xticks(x)
ax2.set_xticklabels(['1990','2015'])
but that results in:
I would like to see the ticks ordered in chronological order (i.e. 1990,2015)
Thanks!
Have you considered dropping the second axis and plotting them as follows:
ind = np.array([0,0.3])
width = 0.1
fig, ax = plt.subplots()
Rects1 = ax.bar(ind, [merged_90.Volume.values, merged_15.Volume.values], color=['orange', 'red'] ,width=width)
Rects2 = ax.bar(ind + width, [merged_90.Area_2D.values, merged_15.Area_2D.values], color=['green', 'blue'] ,width=width)
ax.set_xticks([.1,.4])
ax.set_xticklabels(('1990','2015'))
This produces:
I omitted the error and colors but you can easily add them. That would produce a readable graph given your test data. As you mentioned in comments you would still rather have two axes, presumably for different data with proper scales. To do this you could do:
fig = plt.figure()
ax1 = fig.add_subplot(111)
merged_90.Volume.plot(ax=ax, color='orange', kind='bar',position=2.5, use_index=False , width=0.1)
merged_15.Volume.plot(ax=ax, color='red', kind='bar',position=1.0, use_index=False, width=0.1)
ax2 = ax1.twinx()
merged_90.Area_2D.plot(ax=ax,color='green', kind='bar',position=3.5,use_index=False, width=0.1)
merged_15.Area_2D.plot(ax=ax,color='blue', kind='bar',position=0,use_index=False, width=0.1)
ax1.set_xlim([-.45, .2])
ax2.set_xlim(-.45, .2])
ax1.set_xticks([-.35, 0])
ax1.set_xticklabels([1990, 2015])
This produces:
Your problem was with resetting just one axis limit and not the other, they are created as twins but do not necessarily follow the changes made to one another.
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()