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Save a subplot in matplotlib
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Closed 5 years ago.
Suppose I have the following code (modified version of matplotlib gridspec tutorial)
import matplotlib.pyplot as plt
def make_ticklabels_invisible(fig):
for i, ax in enumerate(fig.axes):
ax.text(0.5, 0.5, "ax%d" % (i+1), va="center", ha="center")
for tl in ax.get_xticklabels() + ax.get_yticklabels():
tl.set_visible(False)
plt.figure(0)
ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)
ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)
ax3 = plt.subplot2grid((3,3), (1, 2), rowspan=2)
ax4 = plt.subplot2grid((3,3), (2, 0))
plt.subplot2grid((3,3), (2, 1)) # OOPS! Forgot to store axes object
plt.suptitle("subplot2grid")
make_ticklabels_invisible(plt.gcf())
plt.show()
which results in
How can I 'extract' ax5 and plot it 'full screen' in a separate figure without having to recreate the plot?
I can't find anything in official documentation to back up what I'm saying, but my understanding is that it is impossible to "clone" an existing axes onto a new figure. In fact, no artist (line, text, legend) defined in one axes may be added to another axes. This discussion on Github may explain it to some degree.
For example, attempting to add a line from an axes defined on fig1 to an axes on a different figure fig2 raises an error:
import matplotlib.pyplot as plt
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
line, = ax1.plot([0,1])
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.add_line(line)
>>>RuntimeError: Can not put single artist in more than one figure`
And attempting to add a line that was drawn in ax1 to a second axes ax2 on the same figure raises an error:
fig1 = plt.figure()
ax1 = fig1.add_subplot(121)
line, = ax1.plot([0,1])
ax12 = fig1.add_subplot(122)
ax12.add_line(line)
>>>ValueError: Can not reset the axes. You are probably trying to re-use an artist in more than one Axes which is not supported
The best recommendation I can make is extract the data from the axes you want to copy, and manually plot that into a new axes object that is sized to your liking. Something like below demonstrates this. Note that this works for Line2D objects plotted via ax.plot. If the data was plotted using ax.scatter, then you need to change things just a little bit and I refer you here for instructions on how to extract data from a scatter.
import matplotlib.pyplot as plt
import numpy as np
def rd(n=5):
# Make random data
return np.sort(np.random.rand(n))
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
# Plot three lines on one axes
ax1.plot(rd(), rd(), rd(), rd(), rd(), rd())
xdata = []
ydata = []
# Iterate thru lines and extract x and y data
for line in ax1.get_lines():
xdata.append( line.get_xdata() )
ydata.append( line.get_ydata() )
# New figure and plot the extracted data
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
for X,Y in zip(xdata,ydata):
ax2.plot(X,Y)
Hope it helps.
Related
I want to code a function that generates an axes with a plot in it to be able to add it to a larger plot.
It should have the following structure:
def createAxes(data):
# create Axes object and plot data in it
return axes
# somewhere else
fig, axs = plt.subplots(ncols=2, nrows=1, figsize=(10, 5))
axs[0] = createAxes(data1)
axs[0].set_title('data 1')
axs[1] = createAxes(data2)
axs[1].set_title('data 2');
I have already tried to use plt.axes(), for example:
def createAxes(data):
plot = plt.axes()
plot.plot(1,1, color="black")
return plot
For this I get overlaying plots.
I have a pandas data frame, region, containing the prices of flats (Flat) and detached properties (Detached) in areas of the UK over time (the column Month). I'm trying to obtain plots of the change in price over time of both flats and detached properties, so that the plots have two different y axes - both of the average price but in different scales.
I've achieved this by using twinx(), however using the code below I get obviously get two figures. The first of these figures is exactly what I want, but I then get a second figure of blank plots. I have attached a screenshot of the kind of plot I want below.
When removing the second fig line fig, ax2 = ..., I get the error NameError: name 'ax2' is not defined. Also bringing the line ax2 = ax.twinx() outside of the loop gives the error AttributeError: 'numpy.ndarray' object has no attribute 'twinx'. I can't seem to figure out how to get this plot to work without having the duplicate blank figure, any help is much appreciated.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
region_list = sorted(region['Area'].unique().tolist())
fig, ax = plt.subplots(nrows=len(region_list), figsize=(13.8,len(region_list)*7))
fig, ax2 = plt.subplots(nrows=len(region_list), figsize=(13.8,len(region_list)*7))
for i in region_list:
ind = region_list.index(i)
filt = region['Area'] == i
ax2[ind] = ax[ind].twinx()
ax[ind].plot(region.loc[filt]['Month'],region.loc[filt]['Flat'], color='red', marker='o')
ax[ind].set_ylabel('Average price of flats', color='red', fontsize=14)
ax2[ind].plot(region.loc[filt]['Month'],region.loc[filt]['Detached'],color='blue',marker='o')
ax2[ind].set_ylabel('Average price of detached properties',color='blue',fontsize=14)
ax[ind].set_title(i, size=14)
ax[ind].xaxis.set_tick_params(labelsize=10)
ax[ind].yaxis.set_tick_params(labelsize=10)
plt.tight_layout()
When creating a secondary axis for a subplot, the result is a new object, and can't be referenced using array indices like the subplot axes (unless you specifically add the new twin axes to an array).
You've probably seen the following:
# with one axis
fig, ax = plt.subplots()
ax2 = ax.twinx()
ax2.plot(...)
But with multiple subplots, the same logic applies:
# with one axis
fig, axes = plt.subplots(1, 2)
ax2 = axes[0].twinx()
ax2.plot(...) # secondary axis on subplot 0
ax2 = axes[1].twinx()
ax2.plot(...) # secondary axis on subplot 1
In your case:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
region_list = sorted(region['Area'].unique().tolist())
fig, ax = plt.subplots(nrows=len(region_list), figsize=(13.8,len(region_list)*7))
# don't add a second plot - this would be blank
# fig, ax2 = plt.subplots(nrows=len(region_list), figsize=(13.8,len(region_list)*7))
for i in region_list:
ind = region_list.index(i)
filt = region['Area'] == i
# don't index into ax2
# ax2[ind] = ax[ind].twinx()
# instead, create a local variable ax2 which is the secondary axis
# on the subplot ax[ind]
ax2 = ax[ind].twinx()
ax[ind].plot(region.loc[filt]['Month'],region.loc[filt]['Flat'], color='red', marker='o')
ax[ind].set_ylabel('Average price of flats', color='red', fontsize=14)
ax2.plot(region.loc[filt]['Month'],region.loc[filt]['Detached'],color='blue',marker='o')
ax2.set_ylabel('Average price of detached properties',color='blue',fontsize=14)
ax[ind].set_title(i, size=14)
ax[ind].xaxis.set_tick_params(labelsize=10)
ax[ind].yaxis.set_tick_params(labelsize=10)
plt.tight_layout()
I'm trying to remove the white space from the plot that I created:
As it is possible to see, there a big white spot on the right and also on the bottom, how to fix it? Here is my script:
fig = plt.figure(figsize=(7,7))
ax1 = plt.subplot2grid((4,3), (0,0),)
ax2 = plt.subplot2grid((4,3), (1,0),)
ax3 = plt.subplot2grid((4,3), (0,1),)
ax4 = plt.subplot2grid((4,3), (1,1),)
data = self.dframe[i]
tes = print_data(data, self.issues, self.color, self.type_user)
tes.print_top(data=data, top=10, ax=ax1, typegraph="hbar", problem=self.issues[i], tone=self.color[i])
tes.print_top(data=data, top=10, ax=ax2, typegraph="prod_bar", problem=self.issues[i], tone=self.color[i])
tes.print_top(data=data, top=10, ax=ax3, typegraph="reg_hbar", problem=self.issues[i], tone=self.color[i])
tes.print_top(data=data, top=10, ax=ax4, typegraph=self.type_user, problem=self.issues[i], tone=self.color[i])
problem = self.issues[i]
plt.tight_layout()
name = problem + str('.PNG')
plt.close(fig)
fig.savefig(name)
You are creating too many subplots!
If we look at this line:
ax1 = plt.subplot2grid((4,3), (0,0),)
We can see the first argument given to subplot2grid are the dimensions of the subplot grid to be made, in this case 4 rows, and 3 columns. You are then plotting in the subplots in the top left of your figure (the second argument given) which leaves a lot of space that's not used.
So to solve this, reduce the number of subplots by using:
ax1 = plt.subplot2grid((2,2), (0,0),)
Full example:
import numpy as np
import matplotlib.pyplot as plt
data = np.random.randn(25)
fig = plt.figure(figsize=(7,7))
ax1 = plt.subplot2grid((2,2), (0,0),)
ax2 = plt.subplot2grid((2,2), (1,0),)
ax3 = plt.subplot2grid((2,2), (0,1),)
ax4 = plt.subplot2grid((2,2), (1,1),)
ax1.plot(data)
ax2.plot(data)
ax3.plot(data)
ax4.plot(data)
plt.show()
Giving:
you can use
plt.subplots_adjust(left=0.09, bottom=0.07, right=0.98, top=0.97, wspace=0.2 , hspace=0.17 ) to adjust the window.
But the issue is that a lot of the space in your plot is empty
maybe you should change
plt.subplot2grid((4,3)... to plt.subplot2grid((2,2)
I am trying to create a figure with 6 sub-plots in python but I am having a problem. Here is a simplified version of my code:
import matplotlib.pyplot as plt
import numpy
g_width = 200
g_height = 200
data = numpy.zeros(g_width*g_height).reshape(g_height,g_width)
ax1 = plt.subplot(231)
im1 = ax1.imshow(data)
ax2 = plt.subplot(232)
im2 = ax2.imshow(data)
ax3 = plt.subplot(233)
im3 = ax3.imshow(data)
ax0 = plt.subplot(234)
im0 = ax0.imshow(data)
ax4 = plt.subplot(235)
im4 = ax4.imshow(data)
ax5 = plt.subplot(236)
ax5.plot([1,2], [1,2])
plt.show()
The above figure has 5 "imshow-based" sub-plots and one simple-data-based sub-plot. Can someone explain to me why the box of the last sub-plot does not have the same size with the other sub-plots? If I replace the last sub-plot with an "imshow-based" sub-plot the problem disappears. Why is this happening? How can I fix it?
The aspect ratio is set to "equal" for the 5imshow()calls (check by callingax1.get_aspect()) while forax5it is set toautowhich gives you the non-square shape you observe. I'm guessingimshow()` defaults to equal while plot does not.
To fix this set all the axis aspect ratios manually e.g when creating the plot ax5 = plt.subplot(236, aspect="equal")
On a side node if your creating many axis like this you may find this useful:
fig, ax = plt.subplots(ncols=3, nrows=2, subplot_kw={'aspect':'equal'})
Then ax is a tuple (in this case ax = ((ax1, ax2, ax3), (ax4, ax5, ax6))) so to plot in the i, j plot just call
ax[i,j].plot(..)
Define data
x = np.linspace(0,2*np.pi,100)
y = 2*np.sin(x)
Plot
fig = plt.figure()
ax = plt.axes()
fig.add_subplot(ax)
ax.plot(x,y)
Add second axis
newax = plt.axes(axisbg='none')
Gives me ValueError: Unknown element o, even though it does the same thing as what I am about to describe. I can also see that this works (no error) to do the same thing:
newax = plt.axes()
fig.add_subplot(newax)
newax.set_axis_bgcolor('none')
However, it turns the background color of the original figure "gray" (or whatever the figure background is)? I don't understand, as I thought this would make newax transparent except for the axes and box around the figure. Even if I switch the order, same thing:
plt.close('all')
fig = plt.figure()
newax = plt.axes()
fig.add_subplot(newax)
newax.set_axis_bgcolor('none')
ax = plt.axes()
fig.add_subplot(ax)
ax.plot(x,y)
This is surprising because I thought the background of one would be overlaid on the other, but in either case it is the newax background that appears to be visible (or at least this is the color I see).
What is going on here?
You're not actually adding a new axes.
Matplotlib is detecting that there's already a plot in that position and returning it instead of a new axes object.
(Check it for yourself. ax and newax will be the same object.)
There's probably not a reason why you'd want to, but here's how you'd do it.
(Also, don't call newax = plt.axes() and then call fig.add_subplot(newax) You're doing the same thing twice.)
Edit: With newer (>=1.2, I think?) versions of matplotlib, you can accomplish the same thing as the example below by using the label kwarg to fig.add_subplot. E.g. newax = fig.add_subplot(111, label='some unique string')
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
# If you just call `plt.axes()` or equivalently `fig.add_subplot()` matplotlib
# will just return `ax` again. It _won't_ create a new axis unless we
# call fig.add_axes() or reset fig._seen
newax = fig.add_axes(ax.get_position(), frameon=False)
ax.plot(range(10), 'r-')
newax.plot(range(50), 'g-')
newax.axis('equal')
plt.show()
Of course, this looks awful, but it's what you're asking for...
I'm guessing from your earlier questions that you just want to add a second x-axis? If so, this is a completely different thing.
If you want the y-axes linked, then do something like this (somewhat verbose...):
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
newax = ax.twiny()
# Make some room at the bottom
fig.subplots_adjust(bottom=0.20)
# I'm guessing you want them both on the bottom...
newax.set_frame_on(True)
newax.patch.set_visible(False)
newax.xaxis.set_ticks_position('bottom')
newax.xaxis.set_label_position('bottom')
newax.spines['bottom'].set_position(('outward', 40))
ax.plot(range(10), 'r-')
newax.plot(range(21), 'g-')
ax.set_xlabel('Red Thing')
newax.set_xlabel('Green Thing')
plt.show()
If you want to have a hidden, unlinked y-axis, and an entirely new x-axis, then you'd do something like this:
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
newax = fig.add_axes(ax.get_position())
newax.patch.set_visible(False)
newax.yaxis.set_visible(False)
for spinename, spine in newax.spines.iteritems():
if spinename != 'bottom':
spine.set_visible(False)
newax.spines['bottom'].set_position(('outward', 25))
ax.plot(range(10), 'r-')
x = np.linspace(0, 6*np.pi)
newax.plot(x, 0.001 * np.cos(x), 'g-')
plt.show()
Note that the y-axis values for anything plotted on newax are never shown.
If you wanted, you could even take this one step further, and have independent x and y axes (I'm not quite sure what the point of it would be, but it looks neat...):
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2, right=0.85)
newax = fig.add_axes(ax.get_position())
newax.patch.set_visible(False)
newax.yaxis.set_label_position('right')
newax.yaxis.set_ticks_position('right')
newax.spines['bottom'].set_position(('outward', 35))
ax.plot(range(10), 'r-')
ax.set_xlabel('Red X-axis', color='red')
ax.set_ylabel('Red Y-axis', color='red')
x = np.linspace(0, 6*np.pi)
newax.plot(x, 0.001 * np.cos(x), 'g-')
newax.set_xlabel('Green X-axis', color='green')
newax.set_ylabel('Green Y-axis', color='green')
plt.show()
You can also just add an extra spine at the bottom of the plot. Sometimes this is easier, especially if you don't want ticks or numerical things along it. Not to plug one of my own answers too much, but there's an example of that here: How do I plot multiple X or Y axes in matplotlib?
As one last thing, be sure to look at the parasite axes examples if you want to have the different x and y axes linked through a specific transformation.