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I am trying to understand the underlying concepts of matplotlib, especially Axes and Figure. Therefore I am trying to plot two scatters and then remove any superfluous space (the red one below) by accessing different APIs & objects in the hierarchy.
Yet I fail to understand where the remaining red space is coming from. This is the code:
# Random data
df = pd.DataFrame(np.random.randint(0,100,size=(100, 2)), columns=list('AB'))
# Create a single Axes and preconfigure the figure with red facecolor.
# Then plot a scatter
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10,5), facecolor='r')
ax1 = df.plot(kind='scatter', x='A', y='B', ax=axes[0])
ax2 = df.plot(kind='scatter', x='B', y='A', ax=axes[1])
# Remove except the scatter
for a in [ax1, ax2]:
a.set_xlabel(''), a.set_ylabel('') # Remove x and y labels
for loc in ['left', 'right', 'bottom', 'top']:
a.spines[loc].set_visible(False) # Remove spines
a.set_xticks([], []), a.set_yticks([], []) # Remove ticks
a.set_xmargin(0), a.set_ymargin(0) # No margin beyond outer values
# On figure-level we can make it more tight
fig.tight_layout()
It produces the following figure:
I saw that there is something like..
a.set_axis_off()
.. but this doesn't seem to be the right solution. Somewhere there seems to be some kind of padding that remains. It doesn't look like it's from some X/Y axis as it's the same for all four edges in both subplots.
Any help appreciated.
Solution
Two things are needed:
First we need to initialize the Figure with frameon=False:
fig, axes = plt.subplots(
// ...
frameon=False)
The space between the subplots can be removed using the subplot layout:
plt.subplots_adjust(wspace=.0, hspace=.0)
For the finest level of layout control, you can position your axes manually instead of relying on matplotlib to do it for you. There are a couple of ways of doing this.
One option is Axes.set_position
# Random data
df = pd.DataFrame(np.random.randint(0,100,size=(100, 2)), columns=list('AB'))
# Create a pair of Axes and preconfigure the figure with red facecolor.
# Then plot a scatter
fig, axes = plt.subplots(1, 2, figsize=(10, 5), facecolor='r')
df.plot(kind='scatter', x='A', y='B', ax=axes[0]).set_position([0, 0, 0.5, 1])
df.plot(kind='scatter', x='B', y='A', ax=axes[1]).set_position([0, 0.5, 0.5, 1])
You could also use the old-fashioned Figure.add_axes method:
# Random data
df = pd.DataFrame(np.random.randint(0,100,size=(100, 2)), columns=list('AB'))
# Create a pair of Axes and preconfigure the figure with red facecolor.
# Then plot a scatter
fig = plt.figure(figsize=(10, 5), facecolor='r')
df.plot(kind='scatter', x='A', y='B', ax=fig.add_axes([0, 0, 0.5, 1]))
df.plot(kind='scatter', x='B', y='A', ax=fig.add_axes([0, 0.5, 0.5, 1]))
I copied the code for adding legend via proxy artists from matplotlib's documentation but it doesn't work. I also tried the rest in matplotlib's legends guide but nothing works. I guess it's because the element is a shapely feature which ax.legend() somehow doesn't recognize.
Code
bounds = [116.9283371, 126.90534668, 4.58693981, 21.07014084]
stamen_terrain = cimgt.Stamen('terrain-background')
fault_line = ShapelyFeature(Reader('faultLines.shp').geometries(), ccrs.epsg(32651),
linewidth=1, edgecolor='black', facecolor='none') # geometry is multilinestring
fig = plt.figure(figsize=(15,10))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
ax.set_extent(bounds)
ax.add_image(stamen_terrain, 8)
a = ax.add_feature(fault_line, zorder=1, label='test')
ax.legend([a], loc='lower left', fancybox=True) #plt.legend() has the same result
plt.show()
Result
When copying the matplotlib example, you omitted the actual "proxy" artist line!
red_patch = mpatches.Patch(color='red', label='The red data')
plt.legend(handles=[red_patch])
That red_patch is the proxy artist. You have to create a dummy artist to pass to legend(). Your code as written is still passing the unrecognized Shapely feature.
It's tedious, but the relevant code would be something like:
fault_line = ShapelyFeature(Reader('faultLines.shp').geometries(), ccrs.epsg(32651), linewidth=1, edgecolor='black', facecolor='none')
ax.add_feature(fault_line, zorder=1)
# Now make a dummy object that looks as similar as possible
import matplotlib.patches as mpatches
proxy_artist = mpatches.Rectangle((0, 0), 1, 0.1, linewidth=1, edgecolor='black', facecolor='none')
# And manually add the labels here
ax.legend([proxy_artist], ['test'], loc='lower left', fancybox=True)
Here I just used a Rectangle, but depending on the feature, you can use various supported matplotlib "artists".
I'm trying to add a legend to a matplotlib radar/polar graph. I am very new to matplotlib so please excuse the code. I also expect this is very simple but I've been at it an hour and got nowhere.
I have the following which produces a list of labels in the bottom left corner but whenever I try to add handles to give the color representing the label I lose the legend.
# Set color of axes
plt.rc('axes', linewidth=0.5, edgecolor="#888888")
# Create polar plot
ax = plt.subplot(111, polar=True)
# Set clockwise rotation. That is:
ax.set_theta_offset(pi / 2)
ax.set_theta_direction(-1)
# Set position of y-labels
ax.set_rlabel_position(0)
# Set color and linestyle of grid
ax.xaxis.grid(True, color="#888888", linestyle='solid', linewidth=0.5)
ax.yaxis.grid(True, color="#888888", linestyle='solid', linewidth=0.5)
# Plot data
ax.plot(x_as, values, linewidth=0, linestyle='solid', zorder=3)
# Fill area
ax.fill(x_as, values, 'r', alpha=0.3)
plt.legend(labels=[self.get_object().name], loc=(-.42,-.13))
if not self.get_object().subscription is None:
if self.get_object().subscription.benchmark:
bx = plt.subplot(111, polar=True)
bx.plot(x_as, baseline, linewidth=0, linestyle='solid', zorder=3)
bx.fill(x_as, baseline, 'b', alpha=0.3)
plt.legend(labels=[self.get_object().name, 'Benchmark'], loc=(-.42,-.13))
I believe I need
plt.lengend(handles=[some list], labels=[self.get_object().name, 'Benchmark'], loc=(-.42,-.13))
I do not understand what the list of handles should be and I've tried a number of things, including [ax, bx], [ax.plt(), bx.plt()], ['r', 'b']
From the documentation:
handles : sequence of Artist, optional
A list of Artists (lines, patches) to be added to the legend. Use this together with labels, if you need full control on what is shown
in the legend and the automatic mechanism described above is not
sufficient.
The length of handles and labels should be the same in this case. If they are not, they are truncated to the smaller length.
plt.plot returns a list a line2D objects which is what you need to pass to plt.legend(). Therefore a simplified example is as follows:
labels = ["Line 1", "Line 2"]
lines1, = plt.plot([1,2,3])
lines2, = plt.plot([3,2,1])
handles = [lines1, lines2]
plt.legend(handles, labels)
plt.show()
I am trying to make some figures for a scientific article, so I want my figures to have a specific size. I also see that Matplotlib by default adds a lot of padding on the border of the figures, which I don't need (since the figures will be on a white background anyway).
To set a specific figure size I simply use plt.figure(figsize = [w, h]), and I add the argument tight_layout = {'pad': 0} to remove the padding. This works perfectly, and even works if I add a title, y/x-labels etc. Example:
fig = plt.figure(
figsize = [3,2],
tight_layout = {'pad': 0}
)
ax = fig.add_subplot(111)
plt.title('title')
ax.set_ylabel('y label')
ax.set_xlabel('x label')
plt.savefig('figure01.pdf')
This creates a pdf file with exact size 3x2 (inches).
The issue I have is that when I for example add a text box outside the axis (typically a legend box), Matplotlib does not make room for the text box like it does when adding titles/axis labels. Typically the text box is cut off, or does not show in the saved figure at all. Example:
plt.close('all')
fig = plt.figure(
figsize = [3,2],
tight_layout = {'pad': 0}
)
ax = fig.add_subplot(111)
plt.title('title')
ax.set_ylabel('y label')
ax.set_xlabel('x label')
t = ax.text(0.7, 1.1, 'my text here', bbox = dict(boxstyle = 'round'))
plt.savefig('figure02.pdf')
A solution I found elsewhere on SO was to add the argument bbox_inches = 'tight' to the savefig command. The text box is now included like I wanted, but the pdf is now the wrong size. It seems like Matplotlib just makes the figure bigger, instead of reducing the size of the axes like it does when adding titles and x/y-labels.
Example:
plt.close('all')
fig = plt.figure(
figsize = [3,2],
tight_layout = {'pad': 0}
)
ax = fig.add_subplot(111)
plt.title('title')
ax.set_ylabel('y label')
ax.set_xlabel('x label')
t = ax.text(0.7, 1.1, 'my text here', bbox = dict(boxstyle = 'round'))
plt.savefig('figure03.pdf', bbox_inches = 'tight')
(This figure is 3.307x2.248)
Is there any solution to this that covers most cases with a legend just outside the axes?
So the requirements are:
Having a fixed, predefined figure size
Adding a text label or legend outside the axes
Axes and text cannot overlap
The axes, together with the title and axis labels, sits tightly agains the figure border.
So tight_layout with pad = 0, solves 1. and 4. but contradicts 2.
One could think on setting pad to a larger value. This would solve 2. However, since it's is symmetric in all directions, it would contradict 4.
Using bbox_inches = 'tight' changes the figure size. Contradicts 1.
So I think there is no generic solution to this problem.
Something I can come up with is the following: It sets the text in figure coordinates and then resizes the axes either in horizontal or in vertical direction such that there is no overlap between the axes and the text.
import matplotlib.pyplot as plt
import matplotlib.transforms
fig = plt.figure(figsize = [3,2])
ax = fig.add_subplot(111)
plt.title('title')
ax.set_ylabel('y label')
ax.set_xlabel('x label')
def text_legend(ax, x0, y0, text, direction = "v", padpoints = 3, margin=1.,**kwargs):
ha = kwargs.pop("ha", "right")
va = kwargs.pop("va", "top")
t = ax.figure.text(x0, y0, text, ha=ha, va=va, **kwargs)
otrans = ax.figure.transFigure
plt.tight_layout(pad=0)
ax.figure.canvas.draw()
plt.tight_layout(pad=0)
offs = t._bbox_patch.get_boxstyle().pad * t.get_size() + margin # adding 1pt
trans = otrans + \
matplotlib.transforms.ScaledTranslation(-offs/72.,-offs/72.,fig.dpi_scale_trans)
t.set_transform(trans)
ax.figure.canvas.draw()
ppar = [0,-padpoints/72.] if direction == "v" else [-padpoints/72.,0]
trans2 = matplotlib.transforms.ScaledTranslation(ppar[0],ppar[1],fig.dpi_scale_trans) + \
ax.figure.transFigure.inverted()
tbox = trans2.transform(t._bbox_patch.get_window_extent())
bbox = ax.get_position()
if direction=="v":
ax.set_position([bbox.x0, bbox.y0,bbox.width, tbox[0][1]-bbox.y0])
else:
ax.set_position([bbox.x0, bbox.y0,tbox[0][0]-bbox.x0, bbox.height])
# case 1: place text label at top right corner of figure (1,1). Adjust axes height.
#text_legend(ax, 1,1, 'my text here', bbox = dict(boxstyle = 'round'), )
# case 2: place text left of axes, (1, y), direction=="v"
text_legend(ax, 1., 0.8, 'my text here', margin=2., direction="h", bbox = dict(boxstyle = 'round') )
plt.savefig(__file__+'.pdf')
plt.show()
case 1 (left) and case 2 (right):
Doin the same with a legend is slightly easier, because we can directly use the bbox_to_anchor argument and don't need to control the fancy box around the legend.
import matplotlib.pyplot as plt
import matplotlib.transforms
fig = plt.figure(figsize = [3.5,2])
ax = fig.add_subplot(111)
ax.set_title('title')
ax.set_ylabel('y label')
ax.set_xlabel('x label')
ax.plot([1,2,3], marker="o", label="quantity 1")
ax.plot([2,1.7,1.2], marker="s", label="quantity 2")
def legend(ax, x0=1,y0=1, direction = "v", padpoints = 3,**kwargs):
otrans = ax.figure.transFigure
t = ax.legend(bbox_to_anchor=(x0,y0), loc=1, bbox_transform=otrans,**kwargs)
plt.tight_layout(pad=0)
ax.figure.canvas.draw()
plt.tight_layout(pad=0)
ppar = [0,-padpoints/72.] if direction == "v" else [-padpoints/72.,0]
trans2=matplotlib.transforms.ScaledTranslation(ppar[0],ppar[1],fig.dpi_scale_trans)+\
ax.figure.transFigure.inverted()
tbox = t.get_window_extent().transformed(trans2 )
bbox = ax.get_position()
if direction=="v":
ax.set_position([bbox.x0, bbox.y0,bbox.width, tbox.y0-bbox.y0])
else:
ax.set_position([bbox.x0, bbox.y0,tbox.x0-bbox.x0, bbox.height])
# case 1: place text label at top right corner of figure (1,1). Adjust axes height.
#legend(ax, borderaxespad=0)
# case 2: place text left of axes, (1, y), direction=="h"
legend(ax,y0=0.8, direction="h", borderaxespad=0.2)
plt.savefig(__file__+'.pdf')
plt.show()
Why 72? The 72 is the number of points per inch (ppi). This is a fixed typographic unit e.g. fontsizes are always given in points (like 12pt). Because matplotlib defines the padding of the text box in units relative to fontsize, which is points, we need to use 72 to transform back to inches (and then to display coordinates). The default dots per inch (dpi) is not touched here, but is accounted for in fig.dpi_scale_trans. If you want to change dpi you need to make sure the figure dpi is set when creating the figure as well as when saving it (use dpi=.. in the call to plt.figure() as well as plt.savefig()).
As of matplotlib==3.1.3, you can use constrained_layout=True to achieve the desired result. This is currently experimental, but see the docs for a very helpful guide (and the section specifically on legends). Note that the legend will steal space from the plot, but this is unavoidable. I've found that as long as the legend does not take up too much space relative to the size of the plot, then the figure gets saved without cropping anything.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(3, 2), constrained_layout=True)
ax.set_title('title')
ax.set_ylabel('y label')
ax.set_xlabel('x label')
ax.plot([0,1], [0,1], label='my text here')
ax.legend(loc='center left', bbox_to_anchor=(1.1, 0.5))
fig.savefig('figure03.pdf')
The code below produces the plot and legend correctly, however the legend does not show the specified label text, but "Container object of 10 artists" instead.
ax = plt.subplot(212)
plt.tight_layout()
l1= plt.bar(X+0.25, Y1, 0.45, align='center', color='r', label='A', edgecolor='black', hatch="/")
l2= plt.bar(X, Y2, 0.45, align='center', color='b', label='N',hatch='o', fill=False)
ax.autoscale(tight=True)
plt.xticks(X, X_values)
ymax1 = max(Y1) + 1
ymax2 = max(Y2) + 1
ymax = max(ymax1,ymax2)+1
plt.ylim(0, ymax)
plt.grid(True)
plt.legend([l1,l2], loc='upper right', prop={'size':20})
The output is shown below:
How can I correctly display the labels for each bar (as specified in the plt.bar() function) on the legend?
The problem stems from mixing two approaches to using plt.legend(). You have two options:
Manually specify the labels for the legend
Use ax.get_legend_handles_labels() to fill them in with the label parameters you passed to plt.bar()
To manually specify the labels, pass them as the second argument to your call to plt.legend() as follows:
plt.legend([l1,l2], ["A", "N"], loc='upper right', prop={'size':20})
If instead you want to automatically populate the legend you can use the following to find legend-able objects in the plot and their labels:
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc='upper right', prop={'size':20})