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I'm working on a python module that creates a matplotlib figure with an on_resize listener. The listener forces the height of the lower axes to a specific number of pixels (rather than scaling relative to figure size). It works. However, if (in matplotlib interactive mode) after creating the plot the user calls fig.subplots_adjust() it messes up subplot sizes. Here's a radically simplified version of what the module does:
import matplotlib.pyplot as plt
plt.ion()
def make_plot():
fig = plt.figure()
gs = plt.GridSpec(10, 1, figure=fig)
ax_upper = fig.add_subplot(gs[:-1])
ax_lower = fig.add_subplot(gs[-1])
ax_upper.plot([0, 1])
ax_lower.plot([0, 1])
fig.canvas.mpl_connect('resize_event', on_resize)
return fig
def on_resize(event):
fig = event.canvas.figure
# get the current position
ax_lower_pos = list(fig.axes[1].get_position().bounds) # L,B,W,H
# compute desired height in figure-relative coords
desired_height_px = 40
xform = fig.transFigure.inverted()
desired_height_rel = xform.transform([0, desired_height_px])[1]
# set the new height
ax_lower_pos[-1] = desired_height_rel
fig.axes[1].set_position(ax_lower_pos)
# adjust ax_upper accordingly
ax_lower_top = fig.axes[1].get_position().extents[-1] # L,B,R,T
ax_upper_pos = list(fig.axes[0].get_position().bounds) # L,B,W,H
# new bottom
new_upper_bottom = ax_lower_top + desired_height_rel
ax_upper_pos[1] = new_upper_bottom
# new height
ax_upper_top = fig.axes[0].get_position().extents[-1] # L,B,R,T
new_upper_height = ax_upper_top - new_upper_bottom
ax_upper_pos[-1] = new_upper_height
# set the new position
fig.axes[0].set_position(ax_upper_pos)
fig.canvas.draw()
Here's the output if the user calls fig = make_plot():
Now if the user calls fig.subplots_adjust, the bottom axis is squished and the space between bottom and top axes is even more squished (the on_resize listener had set them both to 40px):
fig.subplots_adjust(top=0.7)
At this point, grabbing the corner of the window and dragging even a tiny bit is enough to trigger the on_resize listener and restore what I want (fixed pixel height for bottom axes and space between axes) while keeping the newly-added wide top margin intact:
How can I get that result without having to manually trigger a resize event? As far as I can tell, subplots_adjust does not fire off any events that I could listen for.
I think the problem lies in ax.update_params() updating the axes position with a figbox taken from the underlying subplotspec (which as far as I can tell doesn't get updated after initial figure creation?). (note: update_params is called from within subplots_adjust, see here).
The underlying problem seems to be to make an axes with a specific height in pixels. An easy solution to this is to use mpl_toolkits.axes_grid1's make_axes_locatable.
This allows to get rid of any callback and hence of the complete problem of the race condition in the events.
A note: The plot seems to be part of a bigger library. Since it is always nice not to patronize the users of such packages, one would usually allow them to specify the axes to plot to, such that they can put the plot into a bigger figure with other elements. The below solution makes this particularly easy.
Of course, also calling plt.subplots_adjust is still possible at any time.
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
desired_height_px = 40 #pixel
def make_plot(ax=None):
if not ax:
fig, ax = plt.subplots()
else:
fig = ax.figure
div = make_axes_locatable(ax)
cax = div.append_axes("bottom", desired_height_px/fig.dpi, pad=0.25)
sc1 = ax.scatter([2,1,3], [2,3,1], c=[1,2,3])
sc2 = cax.scatter([3,2,1],[2,3,1], c=[3,1,2])
return fig, ax, cax, (sc1, sc2)
fig, (ax1, ax2) = plt.subplots(1,2)
make_plot(ax=ax1)
#user plot on ax2
ax2.plot([1,3])
fig.subplots_adjust(top=0.7)
plt.show()
let say I have this code:
num_rows = 10
num_cols = 1
fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in xrange(num_rows):
ax = axs[i]
ax.plot(np.arange(10), np.arange(10)**i)
plt.show()
the result figure has too much info and now I want to pick 1 of the axes and draw it alone in a new figure
I tried doing something like this
def on_click(event):
axes = event.inaxes.get_axes()
fig2 = plt.figure(15)
fig2.axes.append(axes)
fig2.show()
fig.canvas.mpl_connect('button_press_event', on_click)
but it didn't quite work. what would be the correct way to do it? searching through the docs and throw SE gave hardly any useful result
edit:
I don't mind redrawing the chosen axes, but I'm not sure how can I tell which of the axes was chosen so if that information is available somehow then it is a valid solution for me
edit #2:
so I've managed to do something like this:
def on_click(event):
fig2 = plt.figure(15)
fig2.clf()
for line in event.inaxes.axes.get_lines():
xydata = line.get_xydata()
plt.plot(xydata[:, 0], xydata[:, 1])
fig2.show()
which seems to be "working" (all the other information is lost - labels, lines colors, lines style, lines width, xlim, ylim, etc...)
but I feel like there must be a nicer way to do it
thanks
Copying the axes
The inital answer here does not work, we keep it for future reference and also to see why a more sophisticated approach is needed.
#There are some pitfalls on the way with the initial approach.
#Adding an `axes` to a figure can be done via `fig.add_axes(axes)`. However, at this point,
#the axes' figure needs to be the figure the axes should be added to.
#This may sound a bit like running in circles but we can actually set the axes'
#figure as `axes.figure = fig2` and hence break out of this.
#One might then also position the axes in the new figure to take the usual dimensions.
#For this a dummy axes can be added first, the axes can change its position to the position
#of the dummy axes and then the dummy axes is removed again. In total, this would look as follows.
import matplotlib.pyplot as plt
import numpy as np
num_rows = 10
num_cols = 1
fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in xrange(num_rows):
ax = axs[i]
ax.plot(np.arange(10), np.arange(10)**i)
def on_click(event):
axes = event.inaxes
if not axes: return
fig2 = plt.figure()
axes.figure=fig2
fig2.axes.append(axes)
fig2.add_axes(axes)
dummy = fig2.add_subplot(111)
axes.set_position(dummy.get_position())
dummy.remove()
fig2.show()
fig.canvas.mpl_connect('button_press_event', on_click)
plt.show()
#So far so good, however, be aware that now after a click the axes is somehow
#residing in both figures, which can cause all sorts of problems, e.g. if you
# want to resize or save the initial figure.
Instead, the following will work:
Pickling the figure
The problem is that axes cannot be copied (even deepcopy will fail). Hence to obtain a true copy of an axes, you may need to use pickle. The following will work. It pickles the complete figure and removes all but the one axes to show.
import matplotlib.pyplot as plt
import numpy as np
import pickle
import io
num_rows = 10
num_cols = 1
fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in range(num_rows):
ax = axs[i]
ax.plot(np.arange(10), np.arange(10)**i)
def on_click(event):
if not event.inaxes: return
inx = list(fig.axes).index(event.inaxes)
buf = io.BytesIO()
pickle.dump(fig, buf)
buf.seek(0)
fig2 = pickle.load(buf)
for i, ax in enumerate(fig2.axes):
if i != inx:
fig2.delaxes(ax)
else:
axes=ax
axes.change_geometry(1,1,1)
fig2.show()
fig.canvas.mpl_connect('button_press_event', on_click)
plt.show()
Recreate plots
The alternative to the above is of course to recreate the plot in a new figure each time the axes is clicked. To this end one may use a function that creates a plot on a specified axes and with a specified index as input. Using this function during figure creation as well as later for replicating the plot in another figure ensures to have the same plot in all cases.
import matplotlib.pyplot as plt
import numpy as np
num_rows = 10
num_cols = 1
colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
labels = ["Label {}".format(i+1) for i in range(num_rows)]
def myplot(i, ax):
ax.plot(np.arange(10), np.arange(10)**i, color=colors[i])
ax.set_ylabel(labels[i])
fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in xrange(num_rows):
myplot(i, axs[i])
def on_click(event):
axes = event.inaxes
if not axes: return
inx = list(fig.axes).index(axes)
fig2 = plt.figure()
ax = fig2.add_subplot(111)
myplot(inx, ax)
fig2.show()
fig.canvas.mpl_connect('button_press_event', on_click)
plt.show()
If you have, for example, a plot with three lines generated by the function plot_something, you can do something like this:
fig, axs = plot_something()
ax = axs[2]
l = list(ax.get_lines())[0]
l2 = list(ax.get_lines())[1]
l3 = list(ax.get_lines())[2]
plot(l.get_data()[0], l.get_data()[1])
plot(l2.get_data()[0], l2.get_data()[1])
plot(l3.get_data()[0], l3.get_data()[1])
ylim(0,1)
I want to increase the grey area around the plot, but keeping the plot the same size. I've already tried changing the figure size, which ends up stretching the plot.
The axes inside the figure is positionned relative to the figure. Per default you have e.g. a fraction of 0.125 of figure width as space at the left. This means that resizing the figure, scales the axes as well.
You may calculate how much the spacings need to change such that if the figure is rescaled, the axes size remains constant. The new spacings then need to be set using fig.subplots_adjust.
import matplotlib.pyplot as plt
def set_figsize(figw,figh, fig=None):
if not fig: fig=plt.gcf()
w, h = fig.get_size_inches()
l = fig.subplotpars.left
r = fig.subplotpars.right
t = fig.subplotpars.top
b = fig.subplotpars.bottom
hor = 1.-w/float(figw)*(r-l)
ver = 1.-h/float(figh)*(t-b)
fig.subplots_adjust(left=hor/2., right=1.-hor/2., top=1.-ver/2., bottom=ver/2.)
fig, ax=plt.subplots()
ax.plot([1,3,2])
set_figsize(9,7)
plt.show()
You may then also use this function to update the subplot params when the figure window is resized.
import matplotlib.pyplot as plt
class Resizer():
def __init__(self,fig=None):
if not fig: fig=plt.gcf()
self.fig=fig
self.w, self.h = self.fig.get_size_inches()
self.l = self.fig.subplotpars.left
self.r = self.fig.subplotpars.right
self.t = self.fig.subplotpars.top
self.b = self.fig.subplotpars.bottom
def set_figsize(self, figw,figh):
hor = 1.-self.w/float(figw)*(self.r-self.l)
ver = 1.-self.h/float(figh)*(self.t-self.b)
self.fig.subplots_adjust(left=hor/2., right=1.-hor/2., top=1.-ver/2., bottom=ver/2.)
def resize(self, event):
figw = event.width/self.fig.dpi
figh = event.height/self.fig.dpi
self.set_figsize( figw,figh)
fig, ax=plt.subplots()
ax.plot([1,3,2])
r = Resizer()
cid = fig.canvas.mpl_connect("resize_event", r.resize)
plt.show()
In the window of a matplotlib figure, there's a button called 'Configure subplots' (see below picture, screenshot on Windows 10 with matplotlib version 1.5.2). Try to change the parameters 'left' and 'right'. You can also change these parameters with plt.subplots_adjust(left=..., bottom=..., right=..., top=..., wspace=..., hspace=...).
I'm trying to get a colorbar for the following minimal example of my code.
g1 = gridspec.GridSpec(1, 1)
f, ((ax0)) = plt.subplots(1, 1)
ax0 = subplot(g1[0])
cmap = matplotlib.cm.get_cmap('viridis')
for i in linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
im = ax0.plot(x,y,color=rgba)
f.colorbar(im)
I also tried f.colorbar(cmap)
Probably pretty obvious, but I get errors such as
'ListedColormap' object has no attribute 'autoscale_None'
In reality, the value defining i is more complex, but I think this should do the trick. My data is plotted with plot and not with imshow (for which I know how to make the colormap).
The answers so far seem overly complicated. fig.colorbar() expects a ScalarMappable as its first argument. Often ScalarMappables are produced by imshow or contourplots and are readily avaible.
In this case you would need to define your custom ScalarMappable to provide to the colorbar.
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
cmap = plt.cm.get_cmap('viridis')
for i in np.linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
im = ax.plot(x,y,color=rgba)
sm = plt.cm.ScalarMappable(cmap=cmap)
sm.set_array([])
fig.colorbar(sm)
plt.show()
You should pass an Image or ContourSet when you call colorbar on a Figure.
You can make an image of the data points by calling plt.imshow with the data. You can start with this:
data = []
for i in np.linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
ax0.plot(x,y,color=rgba)
data.append([x, y])
image = plt.imshow(data)
figure.colorbar(image)
plt.show()
Reference:
https://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure.colorbar
Oluwafemi Sule's solution almost works, but it plots the matrix into the same figure as the lines. Here a solution that opens a second figure, does the imshow call on that second figure, uses the result to draw the colorbar in the first figure, and then closes the second figure before calling plt.show():
import matplotlib
from matplotlib import pyplot as plt
from matplotlib import gridspec
import numpy as np
cmap = matplotlib.cm.get_cmap('viridis')
g1 = gridspec.GridSpec(1, 1)
f0, ((ax0)) = plt.subplots(1, 1)
f1, ((ax1)) = plt.subplots(1, 1)
for i in np.linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
ax0.plot(x,y,color=rgba)
data = np.linspace(0,1,100).reshape((10,10))
image = ax1.imshow(data)
f0.colorbar(image)
plt.close(f1)
plt.show()
The result looks like this:
I'd like to draw a (vertical) colorbar, which has two different scales (corresponding to two different units for the same quantity) on each side. Think Fahrenheit on one side and Celsius on the other side. Obviously, I'd need to specify the ticks for each side individually.
Any idea how I can do this?
That should get you started:
import matplotlib.pyplot as plt
import numpy as np
# generate random data
x = np.random.randint(0,200,(10,10))
plt.pcolormesh(x)
# create the colorbar
# the aspect of the colorbar is set to 'equal', we have to set it to 'auto',
# otherwise twinx() will do weird stuff.
cbar = plt.colorbar()
pos = cbar.ax.get_position()
cbar.ax.set_aspect('auto')
# create a second axes instance and set the limits you need
ax2 = cbar.ax.twinx()
ax2.set_ylim([-2,1])
# resize the colorbar (otherwise it overlays the plot)
pos.x0 +=0.05
cbar.ax.set_position(pos)
ax2.set_position(pos)
plt.show()
If you create a subplot for the colorbar, you can create a twin axes for that subplot and manipulate it like a normal axes.
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-1,2.7)
X,Y = np.meshgrid(x,x)
Z = np.exp(-X**2-Y**2)*.9+0.1
fig, (ax, cax) = plt.subplots(ncols=2, gridspec_kw={"width_ratios":[15,1]})
im =ax.imshow(Z, vmin=0.1, vmax=1)
cbar = plt.colorbar(im, cax=cax)
cax2 = cax.twinx()
ticks=np.arange(0.1,1.1,0.1)
iticks=1./np.array([10,3,2,1.5,1])
cbar.set_ticks(ticks)
cbar.set_label("z")
cbar.ax.yaxis.set_label_position("left")
cax2.set_ylim(0.1,1)
cax2.set_yticks(iticks)
cax2.set_yticklabels(1./iticks)
cax2.set_ylabel("1/z")
plt.show()
Note that in newer version of matplotlib, the above answers no long work (as #Ryan Skene pointed out). I'm using v3.3.2. The secondary_yaxis function works for the colorbars in the same way as for regular plot axes and gives one colorbar with two scales: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.secondary_yaxis.html#matplotlib.axes.Axes.secondary_yaxis
import matplotlib.pyplot as plt
import numpy as np
# generate random data
x = np.random.randint(0,200,(10,10)) #let's assume these are temperatures in Fahrenheit
im = plt.imshow(x)
# create the colorbar
cbar = plt.colorbar(im,pad=0.1) #you may need to adjust this padding for the secondary colorbar label[enter image description here][1]
cbar.set_label('Temperature ($^\circ$F)')
# define functions that relate the two colorbar scales
# e.g., Celcius to Fahrenheit and vice versa
def F_to_C(x):
return (x-32)*5/9
def C_to_F(x):
return (x*9/5)+32
# create a second axes
cbar2 = cbar.ax.secondary_yaxis('left',functions=(F_to_C,C_to_F))
cbar2.set_ylabel('Temperatrue ($\circ$C)')
plt.show()
I am using an inset axis for my colorbar and, for some reason, I found the above to answers no longer worked as of v3.4.2. The twinx took up the entire original subplot.
So I just replicated the inset axis (instead of using twinx) and increased the zorder on the original inset.
axkws = dict(zorder=2)
cax = inset_axes(
ax, width="100%", height="100%", bbox_to_anchor=bbox,
bbox_transform=ax.transAxes, axes_kwargs=axkws
)
cbar = self.fig.colorbar(mpl.cm.ScalarMappable(cmap=cmap), cax=cax)
cbar.ax.yaxis.set_ticks_position('left')
caxx = inset_axes(
ax, width="100%", height="100%",
bbox_to_anchor=bbox, bbox_transform=ax.transAxes
)
caxx.yaxis.set_ticks_position('right')