I'm making plots where the (0,0) position is not necessarily in the bottom left corner of the plot frame, meaning that the x and y axes and their ticks cross within the plot frame. It's fine to have the lines, ticks and their values within the frame, but having the axis titles so near to their lines interferes with visualization of certain data points.
I'd simply like to force the x and y axis titles to be at the bottom and far left of the plot frame, respectively; it doesn't work to simply 'pad' the xlabel or ylabel titles because the padding I'd need would vary between plots.
How to get the titles always in these consistent locations of the plot frame, even if the corresponding axis lines, ticks/values may vary in the plot frame space?
Thank you.
I'd like to add a note here. It seemed to me that it could be possible to define the xlabel and ylabel 'labelpad' values directly from the data range:
For example, one could find the minimum x-value of the minimum y-value (this would set the position of the x-axis label), and the minimum x-value of the minimum x-value (this would set the position of the y-axis label):
xpad = ax.get_ylim()[0]
ypad = ax.get_xlim()[0]
Then use these values in xlabel and ylabel parameters as labelpads:
pylab.xlabel("X Title", fontsize=12, labelpad=abs(xpad))
pylab.ylabel("Y Title", fontsize=12, labelpad=abs(ypad))
Unfortunately, it doesn't look like labelpad can accept variables. Any suggestions that might allow a work-around?
Related
What knobs must I tweak to prevent these problems:
overlapping axes labels
overlapping plots with cropped axes labels
I'm using matplotlib 3.5.1 with the PGF backend. Some solutions for older versions no longer work.
fig, axes = plt.subplots\
(2, 3, constrained_layout=True, subplot_kw=dict(projection="3d"))
#it = np.nditer(axes, flags=["refs_ok","multi_index"])
#for ax in it:
# # Plot the surfaces, add row and column title annotations.
# pass
width = 150 * 0.8 * mm
height = width * 0.65
fig.set_size_inches(width, height)
fig.savefig("something.pgf", dpi=300)
plt.close(fig)
Getting rid of the constrained layout and using plt.subplots_adjust(wspace=<value>, hspace=<value>) worked for me
from matplotlib import pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=3, subplot_kw=dict(projection="3d"))
plt.subplots_adjust(wspace=0.5,hspace=0.5)
labels_x, labels_y, labels_z = [['x-axis']*3]*2, [['y-axis']*3]*2, [['z-axis']*3]*2
for i in range(len(axes)):
for j in range(len(axes[i])):
axes[i,j].set_xlabel(labels_x[i][j])
axes[i,j].set_ylabel(labels_y[i][j])
axes[i,j].set_zlabel(labels_z[i][j])
plt.show()
While both tight and constrained layouts can be used with 3d projection (mplot3d) it seems that constrained layout does not understand how to pad 3d tick labels, leading to overlapping or trimmed labels. Both layout managers adjust subplot padding and axes size given a fixed figure size. Neither can fit the figure size to the contents. To do so with tight layout, extrapolate the desired figure size from the current figure tight bbox over multiple iterations. When using the constrained layout manager, sum the axes tight bboxes and padding to determine any extraneous space. Tight layout adjusts subplotpars (axes size and figure padding) and supports "h_pad" and "w_pad" parameters. Constrained layout adjusts axes size and supports "wspace", "hspace", "w_pad", and "h_pad" parameters. The tight layout squeezes subplots into a tight group which is then centered in the available space. The constrained layout distributes subplots evenly across all available space. Regardless of the layout manager, if the ticks overlap or are clustered too tightly, switch the tick locator to "MaxNLocator" for some smaller "n".
The root problem is that the 3d projection tick labels are empty until a full canvas draw. The "_draw_disabled" draw performed by the constrained layout manager isn't sufficient to trigger the tick labels. If you trace the axes tight bbox you'll notice they don't include the labels until after a call to "fig.canvas.draw", before then the tick labels are just "Text(0, 0, '')". Be sure to include this call after sizing the figure and then the layout will be constrained as expected. Given a fixed figure width, set the height based on aesthetics or sum the axes tight bboxes with padding to determine the minimum possible height.
I want to show values as pixels. But my x-axis and y-axis show different range.I don't know what these values are telling us. But I read somewhere that we have to read the values as intensity values. They will not be visualized on the axis. Then what these axis are telling us? Secondly if I use xticks and yticks to label x and y axis does it has any effect on the image? Does it flip anything in the image?
`# a is an array of shape(500,2)
plt.xticks(np.arange(0,2))
plt.yticks(np.arange(0,500))
plt.imshow(a,aspect='auto',cmap=plt.cm.gray)`
I have a horizontal bar chart where I decided to hide the Y axis and display the axis labels inside the chart:
Now I'd like to know how I can tell Dimple to use the free space (see red rectangle) available from hiding the Y axis.
You set the bounds of the plot area with setMargins or setBounds. Dimple doesn't automatically size to accommodate axes. So just set the left margin to about 10px.
Is there a way to render the tick labels just right inside the axes, i.e, something like the direction property there is on the ticks themself?
Right now I'm setting the x property to a positive value on the ticklabels to draw them inside of the axis, i.e.,
ax2.set_yticklabels(['0', '2500', '5000', '7500'], minor=False, x=0.05)
But this doesn't really work on resizable plots, as the 0.05 figure is absolute (and too big on big plots).
Any ideas?
I'm assuming that ax2 is constructed as ax2 = ax.twinx(), which is to say that it is on the right side of the axes.
You could do something like the following:
ax2.set_yticklabels(['0', '2500', '5000', '7500'], minor=False, horizontalalignment='right')
for tick in ax2.yaxis.get_major_ticks():
tick.set_pad(-8)
If you want the left side axis on the inside too, then you'd simply switch the horizontal alignment to 'left' and change the pad from -8 to -25.
The two numbers might not be exact and could depend on other matplotlib settings you might have (e.g. length of major ticks) so you may want to increase or decrease those values slightly.
I'd like to have one axis label centered over two axes in matplotlib.
For example, I set up the axes as follows:
figure = pyplot.figure(figsize=10,10))
diagram1 = figure.add_axes([0.01,0.62,0.90,0.30])
diagram2 = figure.add_axes([0.01,0.32,0.90,0.30])
This will generate two diagrams on top of each other. How can i now define one axis label for the y axis centered on both diagrams.
I think this can be done with subplots, but I prefer to set every diagram individually, as shown above.
You can manually change the y-coordinate of the label.
diagram1.set_ylabel('y label').set_y(0)
# Alternatively you can use
# diagram2.set_ylabel('y label').set_y(1)
The coordinate is in the axes coordinate space, meaning 0 is the bottom and 1 is the top of the Axes.
Seeing how your Axes are placed at x=0.01, you can make the label appear at the right side of the plots as well
diagram1.yaxis.set_label_position("right")