Enlarging plot in mplot3D - python

I'm trying to compose an image with both 2D and 3D plot. so far I've done the following:
import idlsave
import matplotlib
from matplotlib import *
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
fig = plt.figure(figsize=(18,5))
ax = fig.add_subplot(1,3,1, projection='3d',azim=-133,elev=14)
l = ax.plot3D(X3D,Y3D,Z3D,lw=2,color='red')
ax.set_xlim3d(-10,10)
ax.set_ylim3d(-10,10)
ax.set_zlim3d(-10,10)
ax.text(-2,-7,-11,'b$_r$ [mT]','x')
ax.text(-5,-1,-11,'b$_p$ [mT]','y')
ax.set_zlabel(r'b$_t$ [mT]')
ax.plot([bEq[0],-bEq[0]],[bEq[1],-bEq[1]],[bEq[2],-bEq[2]],'b--',lw=2)
ax.plot([pLe[0],-pLe[0]],[pLe[1],-pLe[1]],[pLe[2],-pLe[2]],color='black',lw=2)
ax.text(3,12,9.2,'(a)', fontsize=14)
ax = fig.add_subplot(1,3,2)
l = ax.plot(br,bp,'k-',lw=2)
ax.set_xlabel(r'b$_{\lambda_1}$ [mT]')
ax.set_ylabel(r'b$_{\lambda_2}$ [mT]')
ax.set_xlim(-2,6.3)
ax.set_ylim(-5.5,5.5)
ax.plot([0,0],[-5.5,5.5],'k-.')
ax.plot([-2,6.3],[0,0],'k-.')
e=Ellipse((pf[2],pf[3]),2*pf[0],2*pf[1],- pf[4]*57.2958,fc='none',lw=2,ls='dashed',ec='red')
ax.add_artist(e)
ax.text(-1,4, '(b)', fontsize=14)
ax = fig.add_subplot(1,3,3)
ax.plot(-bxDip,-byDip,'b-',lw=2,label='$\mathcal{D}$')
ax.plot(-bxMon,-byMon,'r-',lw=2,label='$\mathcal{M}$')
ax.set_xlabel(r'b$_{\lambda_1}$')
ax.set_ylabel(r'b$_{\lambda_2}$')
ax.set_xlim(-4,12)
ax.set_ylim(-6,7)
ax.plot([-4,12],[0,0],'k-.')
ax.plot([0,0],[-6,7],'k-.')
ax.legend(loc='upper right')
ax.text(-3,5.5, '(c)', fontsize=14)
plt.savefig("../pdf_box/fig3.pdf",bbox_inches='tight')
Wit the present code I was able to produce the figure reported here http://img219.imageshack.us/i/fig3e.png/
There are two question which puzzle me.
1) As you can see the 3D plot is smaller than the other two and there is enough white spaces between the subplots to increase the size. How can I do this? i.e. How can I enlarge the size of one subplot, eventually decreasing the other two?
2) I would like to exclude the grey background in the 3D plot.
Any help is very welcomed.


Change ax.dist for the 3D plot. This will cause the rendered graphic to fill more of the subplot area. Here is a similar question. You may find some more info there.
You may also want to adjust the widths of the subplots with respect to each other (increase the width of the 3d plot and shrink the 2D plots. This can be accomplished with subplots_adjust

Related

Matplotlib: orthographic projection of 3D data (in 2D plot)

I'm trying to plot 3D data in 2D using orthographic projection. Here is partially what I'm looking for:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
fig = plt.figure(figsize=(10,10),facecolor='white')
axs = [fig.add_subplot(223)]
axs.append(fig.add_subplot(224))#,sharey=axs[0]))
axs.append(fig.add_subplot(221))#,sharex=axs[0]))
rng = np.random.default_rng(12345)
values = rng.random((100,3))-.5
values[:,1] = 1.6*values[:,1]
values[:,2] = .5*values[:,2]
for ax,axis in zip(axs,['y','x','z']):
axis1,axis2={'x':(1,2),'y':(0,2),'z':(0,1)}[axis]
ax.add_patch(plt.Circle([0,0], radius=.2, color='pink',zorder=-20))
ax.scatter(values[:,axis1],values[:,axis2])
axs[0].set_xlabel('x')
axs[2].set_ylabel('y')
axs[1].set_xlabel('y')
axs[0].set_ylabel('z')
fig.subplots_adjust(.08,.06,.99,.99,0,0)
plt.show()
There are some issues with this plot and the fixes I tried: I would need 'equal' aspect so that the circles are actually circle. I would also need the circles to be of the same size in each subplot. Finally, I would like the space to be optimized (i.e. with as little white space inside and between the subplots as possible).
I have tried sharing the axis between the subplots, then doing .axis('scaled') or .set_aspect('equal','box',share=True) for each axes, but the axis end up not being properly shared, and the circle in each subplot end up of different sizes. And while it crops the subplots to the data, it leaves a lot of space between the subplots. .axis('equal') or .set_aspect('equal','datalim',share=True) without axis shared leaves white space inside the subplots, and with shared axis, it leaves out some data.
Any way to make it work? And it would be perfect if it can work on matplotlib 3.4.3.

You can use a common xlim, ylim for your subplots and set your equal ratio with ax.set_aspect(aspect='equal', adjustable='datalim'):
See full code below:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
fig = plt.figure(figsize=(10,10),facecolor='white')
axs = [fig.add_subplot(223)]
axs.append(fig.add_subplot(224))#,sharey=axs[0]))
axs.append(fig.add_subplot(221))#,sharex=axs[0]))
rng = np.random.default_rng(12345)
values = rng.random((100,3))-.5
values[:,1] = 1.6*values[:,1]
values[:,2] = .5*values[:,2]
for ax,axis in zip(axs,['y','x','z']):
axis1,axis2={'x':(1,2),'y':(0,2),'z':(0,1)}[axis]
ax.add_patch(plt.Circle([0,0], radius=.2, color='pink',zorder=-20))
ax.scatter(values[:,axis1],values[:,axis2])
ax.set_xlim([np.amin(values),np.amax(values)])
ax.set_ylim([np.amin(values),np.amax(values)])
ax.set_aspect('equal', adjustable='datalim')
axs[0].set_xlabel('x')
axs[2].set_ylabel('y')
axs[1].set_xlabel('y')
axs[0].set_ylabel('z')
fig.subplots_adjust(.08,.06,.99,.99,0,0)
plt.show()
The output gives:

I made it work using gridspec (I changed scatter for plot to visually make sure no data gets left out). It requires some tweaking of the figsize to really minimize the white space within the axes. Thank you to #jylls for the intermediate solution.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
%matplotlib inline
rng = np.random.default_rng(12345)
values = rng.random((100,3))-.5
values[:,1] = 1.6*values[:,1]
values[:,2] = .5*values[:,2]
fig = plt.figure(figsize=(10,8),facecolor='white')
ranges = np.ptp(values,axis=0)
gs = GridSpec(2, 2, None,.08,.06,.99,.99,0,0, width_ratios=[ranges[0], ranges[1]], height_ratios=[ranges[1], ranges[2]])
axs = [fig.add_subplot(gs[2])]
axs.append(fig.add_subplot(gs[3]))#,sharey=axs[0]))
axs.append(fig.add_subplot(gs[0]))#,sharex=axs[0]))
for ax,axis in zip(axs,['y','x','z']):
axis1,axis2={'x':(1,2),'y':(0,2),'z':(0,1)}[axis]
ax.add_patch(plt.Circle([0,0], radius=.2, color='pink',zorder=-20))
ax.plot(values[:,axis1],values[:,axis2])
ax.set_aspect('equal', adjustable='datalim')
axs[0].set_xlabel('x')
axs[2].set_ylabel('y')
axs[1].set_xlabel('y')
axs[0].set_ylabel('z')
plt.show()

How can I rotate axis tickmark labels if I set axis properties before making my plot?

I'm experimenting with seaborn and have a question about specifying axes properties. In my code below, I've taken two approaches to creating a heatmap of a matrix and placing the results on two sets of axes in a figure.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
A=np.random.randn(4,4)
labels=['a','b','c','d']
fig, ax = plt.subplots(2)
sns.heatmap(ax =ax[0], data = A)
ax[0].set_xticks(range(len(labels)))
ax[0].set_xticklabels(labels,fontsize=10,rotation=45)
ax[0].set_yticks(range(len(labels)))
ax[0].set_yticklabels(labels,fontsize=10,rotation=45)
ax[1].set_xticks(range(len(labels)))
ax[1].set_xticklabels(labels,fontsize=10,rotation=45)
ax[1].set_yticks(range(len(labels)))
ax[1].set_yticklabels(labels,fontsize=10,rotation=45)
sns.heatmap(ax =ax[1], data = A,xticklabels=labels, yticklabels=labels)
plt.show()
The resulting figure looks like this:
Normally, I would always take the first approach of creating the heatmap and then specifying axis properties. However, when creating an animation (to be embedded on a tkinter canvas), which is what I'm ultimately interested in doing, I found such an ordering in my update function leads to "flickering" of axis labels. The second approach will eliminate this effect, and it also centers the tickmarks within squares along the axes.
However, the second approach does not rotate the y-axis tickmark labels as desired. Is there a simple fix to this?

I'm not sure this is what you're looking for. It looks like you create your figure after you change the yticklabels. so the figure is overwriting your yticklabels.
Below would fix your issue.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
A=np.random.randn(4,4)
labels=['a','b','c','d']
fig, ax = plt.subplots(2)
sns.heatmap(ax =ax[0], data = A)
ax[0].set_xticks(range(len(labels)))
ax[0].set_xticklabels(labels,fontsize=10,rotation=45)
ax[0].set_yticks(range(len(labels)))
ax[0].set_yticklabels(labels,fontsize=10,rotation=45)
ax[1].set_xticks(range(len(labels)))
ax[1].set_xticklabels(labels,fontsize=10,rotation=45)
ax[1].set_yticks(range(len(labels)))
sns.heatmap(ax =ax[1], data = A,xticklabels=labels, yticklabels=labels)
ax[1].set_yticklabels(labels,fontsize=10,rotation=45)
plt.show()

Total figure width with external legend in matplotlib

I'm using
plt.legend(bbox_to_anchor = (1,1))
to put the legend outside my figure. The journal to which I'm submitting requires specific sizes for the figures. When I use this method, it increases the total width of my figure beyond the required size. I want to have the figure sized exactly to specification. Is there a way to calculate the total width of the figure including the external legend, so that I can reduce my figsize parameter accordingly?

The following works fine; I've just coloured the figure so you can see its size.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
fig, ax = plt.subplots(figsize=(3, 3), constrained_layout=True)
fig.set_facecolor('0.5')
ax.plot(np.arange(10), label='Boo')
ax.legend(bbox_to_anchor=(1, 1))
fig.savefig('boo.png')

Save figure with clip box from another figure

Normally if you plot two different figures using the default settings in pyplot, they will be exactly the same size, and if saved can be neatly aligned in PowerPoint or the like. I'd like to generate one figure, however, which has a legend outside of the figure. The script I'm using is shown below.
import numpy as np
import matplotlib.pyplot as plt
x=np.linspace(0,1,201)
y1=x**2
y2=np.sin(x)
fig1=plt.figure(1)
plt.plot(x,y1,label='y1')
handles1,labels1=plt.gca().get_legend_handles_labels()
lgd1=plt.gca().legend(handles1,labels1,bbox_to_anchor=(1.27,1),borderaxespad=0.)
fig2=plt.figure(2)
plt.plot(x,y2)
fig1.savefig('fig1',bbox_extra_artists=(lgd1,),bbox_inches='tight')
fig2.savefig('fig2')
plt.show()
The problem is that in PowerPoint, I can no longer align the two figures left and have their axes aligned. Due to the use of the 'extra artists' and 'bbox_inches=tight' arguments for the first figure, the width of its margins becomes different from the second figure.
Is there any way to 'transfer' the clip box from the first figure to the second figure, such that they can be aligned by 'align left' in PowerPoint?

I think an easier way to achieve what you want is to just construct one figure with two subplots, and let matplotlib align everything for you.
Do you think doing something like this is a good idea?
import matplotlib.pyplot as plt
import numpy as np
x=np.linspace(0,1,201)
y1=x**2
y2=np.sin(x)
fig = plt.figure()
a = fig.add_subplot(211)
a.plot(x,y1, label='y1')
lgd1 = a.legend(bbox_to_anchor = (1.27,1), borderaxespad=0.)
a = fig.add_subplot(212)
a.plot(x,y2)
fig.savefig('fig',bbox_extra_artists=(lgd1,),bbox_inches='tight')

Python matplotlib graph problem

import matplotlib
import matplotlib.pyplot as plt
import pylab as PL
matplotlib.rcParams['axes.unicode_minus'] = False
fig = plt.figure()
ax = fig.add_subplot(111)
PL.loglog(a, b,'o')
ax.set_title('Graph Example')
plt.show()
1) This displays the graph with points on the plot. Is there a way to join these points with a smooth curve.
2) I want to draw more than one plot in the same graph(i.e. for a different set of values of lists a and b) . How do I do that? I want to represent points of each graph with a different symbol(cross,square,circle) or color.

See #Ber's comment
Simply call PL.loglog multiple times.

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