tight_layout automatically adjusts subplot params so that the subplot(s) fits in to the figure area. This is an experimental feature and may not work for some cases. It only checks the extents of ticklabels, axis labels, and titles.
In matplotlib location of axes (including subplots) are specified in normalized figure coordinate. It can happen that your axis labels or titles (or sometimes even ticklabels) go outside the figure area thus clipped.
plt.rcParams['savefig.facecolor'] = "0.8"
def example_plot(ax, fontsize=12):
ax.plot([1, 2])
ax.locator_params(nbins=3)
ax.set_xlabel('x-label', fontsize=fontsize)
ax.set_ylabel('y-label', fontsize=fontsize)
ax.set_title('Title', fontsize=fontsize)
plt.close('all')
fig, ax = plt.subplots()
example_plot(ax, fontsize=24)
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To prevent this, the location of axes need to be adjusted. For subplots, this can be done by adjusting the subplot params (Move the edge of an axes to make room for tick labels). Matplotlib v1.1 introduces a new command tight_layout() that does this automatically for you.
plt.tight_layout()
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When you have multiple subplots, often you see labels of different axes overlaps each other.
plt.close('all')
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)
example_plot(ax1)
example_plot(ax2)
example_plot(ax3)
example_plot(ax4)
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tight_layout will also adjust spacing betweens subplots to minimize the overlaps.
plt.tight_layout()
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tight_layout() can take keyword arguments of pad, w_pad and h_pad. These controls the extra pad around the figure border and between subplots. The pads are specified in fraction of fontsize.
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
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tight_layout() will work even if the sizes of subplot are different as far as their grid specification is compatible. In the example below, ax1 and ax2 are subplots of 2x2 grid, while ax3 is of 1x2 grid.
plt.close('all')
fig = plt.figure()
ax1 = plt.subplot(221)
ax2 = plt.subplot(223)
ax3 = plt.subplot(122)
example_plot(ax1)
example_plot(ax2)
example_plot(ax3)
plt.tight_layout()
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It works with subplots created with subplot2grid(). In general, subplots created from the gridspec (Customizing Location of Subplot Using GridSpec) will work.
plt.close('all')
fig = plt.figure()
ax1 = plt.subplot2grid((3, 3), (0, 0))
ax2 = plt.subplot2grid((3, 3), (0, 1), colspan=2)
ax3 = plt.subplot2grid((3, 3), (1, 0), colspan=2, rowspan=2)
ax4 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)
example_plot(ax1)
example_plot(ax2)
example_plot(ax3)
example_plot(ax4)
plt.tight_layout()
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Although not thoroughly tested, it seems to work for subplots with aspect != “auto” (e.g., axes with images).
arr = np.arange(100).reshape((10,10))
plt.close('all')
fig = plt.figure(figsize=(5,4))
ax = plt.subplot(111)
im = ax.imshow(arr, interpolation="none")
plt.tight_layout()
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- tight_layout only considers ticklabels, axis labels, and titles. Thus, other atists may be clipped and also may overlap.
- It assumes that the extra space needed for ticklabels, axis labels, and titles is independent of original location of axes. This is often True, but there are rare cases it is not.
- pad=0 clips some of the texts by a few pixels. This may be a bug or a limitation of the current algorithm and it is not clear why it happens. Meanwhile, use of pad at least larger than 0.3 is recommended.
GridSpec has its own tight_layout method (the pyplot api tight_layout() also works).
plt.close('all')
fig = plt.figure()
import matplotlib.gridspec as gridspec
gs1 = gridspec.GridSpec(2, 1)
ax1 = fig.add_subplot(gs1[0])
ax2 = fig.add_subplot(gs1[1])
example_plot(ax1)
example_plot(ax2)
gs1.tight_layout(fig)
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You may provide an optional rect parameter, which specify the bbox that the subplots will be fit in. The coordinate must be in normalized figure coordinate and the default is (0, 0, 1, 1).
gs1.tight_layout(fig, rect=[0, 0, 0.5, 1])
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For example, this can be used for a figure with multiple grid_spec’s.
gs2 = gridspec.GridSpec(3, 1)
for ss in gs2:
ax = fig.add_subplot(ss)
example_plot(ax)
ax.set_title("")
ax.set_xlabel("")
ax.set_xlabel("x-label", fontsize=12)
gs2.tight_layout(fig, rect=[0.5, 0, 1, 1], h_pad=0.5)
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We may try to match the top and bottom of two grids
top = min(gs1.top, gs2.top)
bottom = max(gs1.bottom, gs2.bottom)
gs1.update(top=top, bottom=bottom)
gs2.update(top=top, bottom=bottom)
While this should be mostly good enough, but adjusting top and bottom may requires adjustment in hspace also. To update hspace & vspace, we call tight_layout again with updated rect argument. Note the rect argument specifies area including the ticklabels etc. Thus we will increase the bottom (which is 0 in normal case) by the difference between the bottom from above and bottom of each gridspec. Same thing for top.
top = min(gs1.top, gs2.top)
bottom = max(gs1.bottom, gs2.bottom)
gs1.tight_layout(fig, rect=[None, 0 + (bottom-gs1.bottom),
0.5, 1 - (gs1.top-top)])
gs2.tight_layout(fig, rect=[0.5, 0 + (bottom-gs2.bottom),
None, 1 - (gs2.top-top)],
h_pad=0.5)
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While limited, axes_grid1 toolkit is also supported.
plt.close('all')
fig = plt.figure()
from mpl_toolkits.axes_grid1 import Grid
grid = Grid(fig, rect=111, nrows_ncols=(2,2),
axes_pad=0.25, label_mode='L',
)
for ax in grid:
example_plot(ax)
ax.title.set_visible(False)
plt.tight_layout()
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If you create colorbar with colorbar() command, the created colorbar is an instance of Axes not Subplot, thus tight_layout does not work. With Matplotlib v1.1, you may create a colobar as a subplot using the gridspec.
plt.close('all')
fig = plt.figure(figsize=(4, 4))
im = plt.imshow(arr, interpolation="none")
plt.colorbar(im, use_gridspec=True)
plt.tight_layout()
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Another option is to use AxesGrid1 toolkit to explicitly create an axes for colorbar.
plt.close('all')
fig = plt.figure(figsize=(4, 4))
im = plt.imshow(arr, interpolation="none")
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes("right", "5%", pad="3%")
plt.colorbar(im, cax=cax)
plt.tight_layout()
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