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I have an issue with customizing the legend of my plot. I did lot's of customizing but couldnt get my head around this one. I want the symbols (not the labels) to be equally spaced in the legend. As you can see in the example, the space between the circles in the legend, gets smaller as the circles get bigger.
any ideas?
Also, how can I also add a color bar (in addition to the size), with smaller circles being light red (for example) and bigger circle being blue (for example)
here is my code so far:
import pandas as pd
import matplotlib.pyplot as plt
from vega_datasets import data as vega_data
gap = pd.read_json(vega_data.gapminder.url)
df = gap.loc[gap['year'] == 2000]
fig, ax = plt.subplots(1, 1,figsize=[14,12])
ax=ax.scatter(df['life_expect'], df['fertility'],
s = df['pop']/100000,alpha=0.7, edgecolor="black",cmap="viridis")
plt.xlabel("X")
plt.ylabel("Y");
kw = dict(prop="sizes", num=6, color="lightgrey", markeredgecolor='black',markeredgewidth=2)
plt.legend(*ax.legend_elements(**kw),bbox_to_anchor=(1, 0),frameon=False,
loc="lower left",markerscale=1,ncol=1,borderpad=2,labelspacing=4,handletextpad=2)
plt.grid()
plt.show()
It's a bit tricky, but you could measure the legend elements and reposition them to have a constant inbetween distance. Due to the pixel positioning, the plot can't be resized afterwards.
I tested the code inside PyCharm with the 'Qt5Agg' backend. And in a Jupyter notebook, both with %matplotlib inline and with %matplotlib notebook. I'm not sure whether it would work well in all environments.
Note that ax.scatter doesn't return an ax (countrary to e.g. sns.scatterplot) but a list of the created scatter dots.
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.transforms import IdentityTransform
from vega_datasets import data as vega_data
gap = pd.read_json(vega_data.gapminder.url)
df = gap.loc[gap['year'] == 2000]
fig, ax = plt.subplots(1, 1, figsize=[14, 12])
fig.subplots_adjust(right=0.8)
scat = ax.scatter(df['life_expect'], df['fertility'],
s=df['pop'] / 100000, alpha=0.7, edgecolor="black", cmap="viridis")
plt.xlabel("X")
plt.ylabel("Y")
x = 1.1
y = 0.1
is_first = True
kw = dict(prop="sizes", num=6, color="lightgrey", markeredgecolor='black', markeredgewidth=2)
handles, labels = scat.legend_elements(**kw)
inverted_transData = ax.transData.inverted()
for handle, label in zip(handles[::-1], labels[::-1]):
plt.setp(handle, clip_on=False)
for _ in range(1 if is_first else 2):
plt.setp(handle, transform=ax.transAxes)
if is_first:
xd, yd = x, y
else:
xd, yd = inverted_transData.transform((x, y))
handle.set_xdata([xd])
handle.set_ydata([yd])
ax.add_artist(handle)
bbox = handle.get_window_extent(fig.canvas.get_renderer())
y += y - bbox.y0 + 15 # 15 pixels inbetween
x = (bbox.x0 + bbox.x1) / 2
if is_first:
xd_text, _ = inverted_transData.transform((bbox.x1+10, y))
ax.text(xd_text, yd, label, transform=ax.transAxes, ha='left', va='center')
y = bbox.y1
is_first = False
plt.show()
I'm trying to define a function that can plot subplots within a 4-by-n grid. The code below works but I want to make the subplots share y axis, which I think I (definitely?) have to use the method mentioned in this post. However, because I'm doing a 4-by-n grid and I'm doing a loop, assigning a name to each subplot seems troublesome to me. Any ideas? Thank you in advance!
Currently my function looks like this:
def make_plots(df, name_list):
n = len(name_list)
j = 1
plt.figure(1, dpi=500, figsize=(10,10/4*n/4))
for i in name_list:
plt.subplot(n/4,4,j)
plt.title('Table {}: {}'.format(j,i), size='xx-small')
plt.grid(False)
a = [i for i in list(df[i]) if i>0]
plt.hist(a,bins=7,rwidth=0.7)
plt.axvline(x=np.mean(a), color='red')
j+=1
plt.tight_layout(pad=0.2,h_pad=0.2, w_pad=0.2)
plt.show()
You could create the figure and subplots first and use the sharey = True argument. Then use zip to iterate over both name_list and the axes array at the same time:
def make_plots(df, name_list):
n = len(name_list)
j = 1
fig, axes = plt.subplots(n/4, 4, sharey=True, figsize=(10, 10/4*n/4))
for i, ax in zip(name_list, axes.flatten()):
ax.set_title('Table {}: {}'.format(j,i), size='xx-small')
ax.grid(False)
a = [i for i in list(df[i]) if i>0]
ax.hist(a,bins=7,rwidth=0.7)
ax.axvline(x=np.mean(a), color='red')
j+=1
plt.tight_layout(pad=0.2,h_pad=0.2, w_pad=0.2)
plt.show()
So I am currently plotting a scatter graph with many x and ys in matplotlib:
plt.scatter(x, y)
I want to draw a line on this scatter graph that crosses through the whole graph (i.e hits two 'borders') I know the gradient and the intercept - m and the c in the equation y = mx +c.
I have thought about acquiring the 4 points of the plot (calculating the min and max scatter x and ys) and from that calculating the min and max coords for the line and then plotting but that seems very convoluted. Is there any better way to do this bearing in mind the line may not even be 'within' the 'plot'?
Example of scatter graph:
as identified visually in the plot the four bordering coordinates are ruffly:
bottom left: -1,-2
top left: -1,2
bottom right: 6,-2
top right 6,2
I now have a line that I need to plot that must not exceed these boundaries but if it enters the plot must touch two of the boundary points.
So I could check what y equals when x = -1 and then check if that value is between -1 and 6 and if it is the line must cross the left border, so plot it, and so on and so fourth.
Ideally though I would create a line from -infinity to infinity and then crop it to fit the plot.
The idea here is to draw a line of some equation y=m*x+y0 into the plot. This can be achieved by transforming a horizontal line, originally given in axes coordinates, into data coordinates, applying the Affine2D transform according to the line equation and transforming back to screen coordinates.
The advantage here is that you do not need to know the axes limits at all. You may also freely zoom or pan your plot; the line will always stay within the axes boundaries. It hence effectively implements a line ranging from -infinity to + inifinty.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
def axaline(m,y0, ax=None, **kwargs):
if not ax:
ax = plt.gca()
tr = mtransforms.BboxTransformTo(
mtransforms.TransformedBbox(ax.viewLim, ax.transScale)) + \
ax.transScale.inverted()
aff = mtransforms.Affine2D.from_values(1,m,0,0,0,y0)
trinv = ax.transData
line = plt.Line2D([0,1],[0,0],transform=tr+aff+trinv, **kwargs)
ax.add_line(line)
x = np.random.rand(20)*6-0.7
y = (np.random.rand(20)-.5)*4
c = (x > 3).astype(int)
fig, ax = plt.subplots()
ax.scatter(x,y, c=c, cmap="bwr")
# draw y=m*x+y0 into the plot
m = 0.4; y0 = -1
axaline(m,y0, ax=ax, color="limegreen", linewidth=5)
plt.show()
While this solution looks a bit complicated on first sight, one does not need to fully understand it. Just copy the axaline function to your code and use it as it is.
In order to get the automatic updating working without the transforms doing this, one may add callbacks which would reset the transform every time something changes in the plot.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import transforms
class axaline():
def __init__(self, m,y0, ax=None, **kwargs):
if not ax: ax = plt.gca()
self.ax = ax
self.aff = transforms.Affine2D.from_values(1,m,0,0,0,y0)
self.line = plt.Line2D([0,1],[0,0], **kwargs)
self.update()
self.ax.add_line(self.line)
self.ax.callbacks.connect('xlim_changed', self.update)
self.ax.callbacks.connect('ylim_changed', self.update)
def update(self, evt=None):
tr = ax.transAxes - ax.transData
trinv = ax.transData
self.line.set_transform(tr+self.aff+trinv)
x = np.random.rand(20)*6-0.7
y = (np.random.rand(20)-.5)*4
c = (x > 3).astype(int)
fig, ax = plt.subplots()
ax.scatter(x,y, c=c, cmap="bwr")
# draw y=m*x+y0 into the plot
m = 0.4; y0 = -1
al = axaline(m,y0, ax=ax, color="limegreen", linewidth=5)
plt.show()
You may try:
import matplotlib.pyplot as plt
import numpy as np
m=3
c=-2
x1Data= np.random.normal(scale=2, loc=.4, size=25)
y1Data= np.random.normal(scale=3, loc=1.2, size=25)
x2Data= np.random.normal(scale=1, loc=3.4, size=25)
y2Data= np.random.normal(scale=.65, loc=-.2, size=25)
fig = plt.figure()
ax = fig.add_subplot( 1, 1, 1 )
ax.scatter(x1Data, y1Data)
ax.scatter(x2Data, y2Data)
ylim = ax.get_ylim()
xlim = ax.get_xlim()
ax.plot( xlim, [ m * x + c for x in xlim ], 'r:' )
ax.set_ylim( ylim )
ax.set_xlim( xlim )
plt.show()
which gives:
I'm trying to create a plot using pyplot that has a discontinuous x-axis. The usual way this is drawn is that the axis will have something like this:
(values)----//----(later values)
where the // indicates that you're skipping everything between (values) and (later values).
I haven't been able to find any examples of this, so I'm wondering if it's even possible. I know you can join data over a discontinuity for, eg, financial data, but I'd like to make the jump in the axis more explicit. At the moment I'm just using subplots but I'd really like to have everything end up on the same graph in the end.
Paul's answer is a perfectly fine method of doing this.
However, if you don't want to make a custom transform, you can just use two subplots to create the same effect.
Rather than put together an example from scratch, there's an excellent example of this written by Paul Ivanov in the matplotlib examples (It's only in the current git tip, as it was only committed a few months ago. It's not on the webpage yet.).
This is just a simple modification of this example to have a discontinuous x-axis instead of the y-axis. (Which is why I'm making this post a CW)
Basically, you just do something like this:
import matplotlib.pylab as plt
import numpy as np
# If you're not familiar with np.r_, don't worry too much about this. It's just
# a series with points from 0 to 1 spaced at 0.1, and 9 to 10 with the same spacing.
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)
fig,(ax,ax2) = plt.subplots(1, 2, sharey=True)
# plot the same data on both axes
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')
# zoom-in / limit the view to different portions of the data
ax.set_xlim(0,1) # most of the data
ax2.set_xlim(9,10) # outliers only
# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()
# Make the spacing between the two axes a bit smaller
plt.subplots_adjust(wspace=0.15)
plt.show()
To add the broken axis lines // effect, we can do this (again, modified from Paul Ivanov's example):
import matplotlib.pylab as plt
import numpy as np
# If you're not familiar with np.r_, don't worry too much about this. It's just
# a series with points from 0 to 1 spaced at 0.1, and 9 to 10 with the same spacing.
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)
fig,(ax,ax2) = plt.subplots(1, 2, sharey=True)
# plot the same data on both axes
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')
# zoom-in / limit the view to different portions of the data
ax.set_xlim(0,1) # most of the data
ax2.set_xlim(9,10) # outliers only
# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()
# Make the spacing between the two axes a bit smaller
plt.subplots_adjust(wspace=0.15)
# This looks pretty good, and was fairly painless, but you can get that
# cut-out diagonal lines look with just a bit more work. The important
# thing to know here is that in axes coordinates, which are always
# between 0-1, spine endpoints are at these locations (0,0), (0,1),
# (1,0), and (1,1). Thus, we just need to put the diagonals in the
# appropriate corners of each of our axes, and so long as we use the
# right transform and disable clipping.
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
ax.plot((1-d,1+d),(-d,+d), **kwargs) # top-left diagonal
ax.plot((1-d,1+d),(1-d,1+d), **kwargs) # bottom-left diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d,d),(-d,+d), **kwargs) # top-right diagonal
ax2.plot((-d,d),(1-d,1+d), **kwargs) # bottom-right diagonal
# What's cool about this is that now if we vary the distance between
# ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(),
# the diagonal lines will move accordingly, and stay right at the tips
# of the spines they are 'breaking'
plt.show()
I see many suggestions for this feature but no indication that it's been implemented. Here is a workable solution for the time-being. It applies a step-function transform to the x-axis. It's a lot of code, but it's fairly simple since most of it is boilerplate custom scale stuff. I have not added any graphics to indicate the location of the break, since that is a matter of style. Good luck finishing the job.
from matplotlib import pyplot as plt
from matplotlib import scale as mscale
from matplotlib import transforms as mtransforms
import numpy as np
def CustomScaleFactory(l, u):
class CustomScale(mscale.ScaleBase):
name = 'custom'
def __init__(self, axis, **kwargs):
mscale.ScaleBase.__init__(self)
self.thresh = None #thresh
def get_transform(self):
return self.CustomTransform(self.thresh)
def set_default_locators_and_formatters(self, axis):
pass
class CustomTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
lower = l
upper = u
def __init__(self, thresh):
mtransforms.Transform.__init__(self)
self.thresh = thresh
def transform(self, a):
aa = a.copy()
aa[a>self.lower] = a[a>self.lower]-(self.upper-self.lower)
aa[(a>self.lower)&(a<self.upper)] = self.lower
return aa
def inverted(self):
return CustomScale.InvertedCustomTransform(self.thresh)
class InvertedCustomTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
lower = l
upper = u
def __init__(self, thresh):
mtransforms.Transform.__init__(self)
self.thresh = thresh
def transform(self, a):
aa = a.copy()
aa[a>self.lower] = a[a>self.lower]+(self.upper-self.lower)
return aa
def inverted(self):
return CustomScale.CustomTransform(self.thresh)
return CustomScale
mscale.register_scale(CustomScaleFactory(1.12, 8.88))
x = np.concatenate((np.linspace(0,1,10), np.linspace(9,10,10)))
xticks = np.concatenate((np.linspace(0,1,6), np.linspace(9,10,6)))
y = np.sin(x)
plt.plot(x, y, '.')
ax = plt.gca()
ax.set_xscale('custom')
ax.set_xticks(xticks)
plt.show()
Check the brokenaxes package:
import matplotlib.pyplot as plt
from brokenaxes import brokenaxes
import numpy as np
fig = plt.figure(figsize=(5,2))
bax = brokenaxes(
xlims=((0, .1), (.4, .7)),
ylims=((-1, .7), (.79, 1)),
hspace=.05
)
x = np.linspace(0, 1, 100)
bax.plot(x, np.sin(10 * x), label='sin')
bax.plot(x, np.cos(10 * x), label='cos')
bax.legend(loc=3)
bax.set_xlabel('time')
bax.set_ylabel('value')
A very simple hack is to
scatter plot rectangles over the axes' spines and
draw the "//" as text at that position.
Worked like a charm for me:
# FAKE BROKEN AXES
# plot a white rectangle on the x-axis-spine to "break" it
xpos = 10 # x position of the "break"
ypos = plt.gca().get_ylim()[0] # y position of the "break"
plt.scatter(xpos, ypos, color='white', marker='s', s=80, clip_on=False, zorder=100)
# draw "//" on the same place as text
plt.text(xpos, ymin-0.125, r'//', fontsize=label_size, zorder=101, horizontalalignment='center', verticalalignment='center')
Example Plot:
For those interested, I've expanded upon #Paul's answer and added it to the matplotlib wrapper proplot. It can do axis "jumps", "speedups", and "slowdowns".
There is no way currently to add "crosses" that indicate the discrete jump like in Joe's answer, but I plan to add this in the future. I also plan to add a default "tick locator" that sets sensible default tick locations depending on the CutoffScale arguments.
Adressing Frederick Nord's question how to enable parallel orientation of the diagonal "breaking" lines when using a gridspec with ratios unequal 1:1, the following changes based on the proposals of Paul Ivanov and Joe Kingtons may be helpful. Width ratio can be varied using variables n and m.
import matplotlib.pylab as plt
import numpy as np
import matplotlib.gridspec as gridspec
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)
n = 5; m = 1;
gs = gridspec.GridSpec(1,2, width_ratios = [n,m])
plt.figure(figsize=(10,8))
ax = plt.subplot(gs[0,0])
ax2 = plt.subplot(gs[0,1], sharey = ax)
plt.setp(ax2.get_yticklabels(), visible=False)
plt.subplots_adjust(wspace = 0.1)
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')
ax.set_xlim(0,1)
ax2.set_xlim(10,8)
# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
on = (n+m)/n; om = (n+m)/m;
ax.plot((1-d*on,1+d*on),(-d,d), **kwargs) # bottom-left diagonal
ax.plot((1-d*on,1+d*on),(1-d,1+d), **kwargs) # top-left diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d*om,d*om),(-d,d), **kwargs) # bottom-right diagonal
ax2.plot((-d*om,d*om),(1-d,1+d), **kwargs) # top-right diagonal
plt.show()
This is a hacky but pretty solution for x-axis breaks.
The solution is based on https://matplotlib.org/stable/gallery/subplots_axes_and_figures/broken_axis.html, which gets rid of the problem with positioning the break above the spine, solved by How can I plot points so they appear over top of the spines with matplotlib?
from matplotlib.patches import Rectangle
import matplotlib.pyplot as plt
def axis_break(axis, xpos=[0.1, 0.125], slant=1.5):
d = slant # proportion of vertical to horizontal extent of the slanted line
anchor = (xpos[0], -1)
w = xpos[1] - xpos[0]
h = 1
kwargs = dict(marker=[(-1, -d), (1, d)], markersize=12, zorder=3,
linestyle="none", color='k', mec='k', mew=1, clip_on=False)
axis.add_patch(Rectangle(
anchor, w, h, fill=True, color="white",
transform=axis.transAxes, clip_on=False, zorder=3)
)
axis.plot(xpos, [0, 0], transform=axis.transAxes, **kwargs)
fig, ax = plt.subplots(1,1)
plt.plot(np.arange(10))
axis_break(ax, xpos=[0.1, 0.12], slant=1.5)
axis_break(ax, xpos=[0.3, 0.31], slant=-10)
if you want to replace an axis label, this would do the trick:
from matplotlib import ticker
def replace_pos_with_label(fig, pos, label, axis):
fig.canvas.draw() # this is needed to set up the x-ticks
labs = axis.get_xticklabels()
labels = []
locs = []
for text in labs:
x = text._x
lab = text._text
if x == pos:
lab = label
labels.append(lab)
locs.append(x)
axis.xaxis.set_major_locator(ticker.FixedLocator(locs))
axis.set_xticklabels(labels)
fig, ax = plt.subplots(1,1)
plt.plot(np.arange(10))
replace_pos_with_label(fig, 0, "-10", axis=ax)
replace_pos_with_label(fig, 6, "$10^{4}$", axis=ax)
axis_break(ax, xpos=[0.1, 0.12], slant=2)
I'm trying to create a plot using pyplot that has a discontinuous x-axis. The usual way this is drawn is that the axis will have something like this:
(values)----//----(later values)
where the // indicates that you're skipping everything between (values) and (later values).
I haven't been able to find any examples of this, so I'm wondering if it's even possible. I know you can join data over a discontinuity for, eg, financial data, but I'd like to make the jump in the axis more explicit. At the moment I'm just using subplots but I'd really like to have everything end up on the same graph in the end.
Paul's answer is a perfectly fine method of doing this.
However, if you don't want to make a custom transform, you can just use two subplots to create the same effect.
Rather than put together an example from scratch, there's an excellent example of this written by Paul Ivanov in the matplotlib examples (It's only in the current git tip, as it was only committed a few months ago. It's not on the webpage yet.).
This is just a simple modification of this example to have a discontinuous x-axis instead of the y-axis. (Which is why I'm making this post a CW)
Basically, you just do something like this:
import matplotlib.pylab as plt
import numpy as np
# If you're not familiar with np.r_, don't worry too much about this. It's just
# a series with points from 0 to 1 spaced at 0.1, and 9 to 10 with the same spacing.
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)
fig,(ax,ax2) = plt.subplots(1, 2, sharey=True)
# plot the same data on both axes
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')
# zoom-in / limit the view to different portions of the data
ax.set_xlim(0,1) # most of the data
ax2.set_xlim(9,10) # outliers only
# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()
# Make the spacing between the two axes a bit smaller
plt.subplots_adjust(wspace=0.15)
plt.show()
To add the broken axis lines // effect, we can do this (again, modified from Paul Ivanov's example):
import matplotlib.pylab as plt
import numpy as np
# If you're not familiar with np.r_, don't worry too much about this. It's just
# a series with points from 0 to 1 spaced at 0.1, and 9 to 10 with the same spacing.
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)
fig,(ax,ax2) = plt.subplots(1, 2, sharey=True)
# plot the same data on both axes
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')
# zoom-in / limit the view to different portions of the data
ax.set_xlim(0,1) # most of the data
ax2.set_xlim(9,10) # outliers only
# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()
# Make the spacing between the two axes a bit smaller
plt.subplots_adjust(wspace=0.15)
# This looks pretty good, and was fairly painless, but you can get that
# cut-out diagonal lines look with just a bit more work. The important
# thing to know here is that in axes coordinates, which are always
# between 0-1, spine endpoints are at these locations (0,0), (0,1),
# (1,0), and (1,1). Thus, we just need to put the diagonals in the
# appropriate corners of each of our axes, and so long as we use the
# right transform and disable clipping.
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
ax.plot((1-d,1+d),(-d,+d), **kwargs) # top-left diagonal
ax.plot((1-d,1+d),(1-d,1+d), **kwargs) # bottom-left diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d,d),(-d,+d), **kwargs) # top-right diagonal
ax2.plot((-d,d),(1-d,1+d), **kwargs) # bottom-right diagonal
# What's cool about this is that now if we vary the distance between
# ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(),
# the diagonal lines will move accordingly, and stay right at the tips
# of the spines they are 'breaking'
plt.show()
I see many suggestions for this feature but no indication that it's been implemented. Here is a workable solution for the time-being. It applies a step-function transform to the x-axis. It's a lot of code, but it's fairly simple since most of it is boilerplate custom scale stuff. I have not added any graphics to indicate the location of the break, since that is a matter of style. Good luck finishing the job.
from matplotlib import pyplot as plt
from matplotlib import scale as mscale
from matplotlib import transforms as mtransforms
import numpy as np
def CustomScaleFactory(l, u):
class CustomScale(mscale.ScaleBase):
name = 'custom'
def __init__(self, axis, **kwargs):
mscale.ScaleBase.__init__(self)
self.thresh = None #thresh
def get_transform(self):
return self.CustomTransform(self.thresh)
def set_default_locators_and_formatters(self, axis):
pass
class CustomTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
lower = l
upper = u
def __init__(self, thresh):
mtransforms.Transform.__init__(self)
self.thresh = thresh
def transform(self, a):
aa = a.copy()
aa[a>self.lower] = a[a>self.lower]-(self.upper-self.lower)
aa[(a>self.lower)&(a<self.upper)] = self.lower
return aa
def inverted(self):
return CustomScale.InvertedCustomTransform(self.thresh)
class InvertedCustomTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
lower = l
upper = u
def __init__(self, thresh):
mtransforms.Transform.__init__(self)
self.thresh = thresh
def transform(self, a):
aa = a.copy()
aa[a>self.lower] = a[a>self.lower]+(self.upper-self.lower)
return aa
def inverted(self):
return CustomScale.CustomTransform(self.thresh)
return CustomScale
mscale.register_scale(CustomScaleFactory(1.12, 8.88))
x = np.concatenate((np.linspace(0,1,10), np.linspace(9,10,10)))
xticks = np.concatenate((np.linspace(0,1,6), np.linspace(9,10,6)))
y = np.sin(x)
plt.plot(x, y, '.')
ax = plt.gca()
ax.set_xscale('custom')
ax.set_xticks(xticks)
plt.show()
Check the brokenaxes package:
import matplotlib.pyplot as plt
from brokenaxes import brokenaxes
import numpy as np
fig = plt.figure(figsize=(5,2))
bax = brokenaxes(
xlims=((0, .1), (.4, .7)),
ylims=((-1, .7), (.79, 1)),
hspace=.05
)
x = np.linspace(0, 1, 100)
bax.plot(x, np.sin(10 * x), label='sin')
bax.plot(x, np.cos(10 * x), label='cos')
bax.legend(loc=3)
bax.set_xlabel('time')
bax.set_ylabel('value')
A very simple hack is to
scatter plot rectangles over the axes' spines and
draw the "//" as text at that position.
Worked like a charm for me:
# FAKE BROKEN AXES
# plot a white rectangle on the x-axis-spine to "break" it
xpos = 10 # x position of the "break"
ypos = plt.gca().get_ylim()[0] # y position of the "break"
plt.scatter(xpos, ypos, color='white', marker='s', s=80, clip_on=False, zorder=100)
# draw "//" on the same place as text
plt.text(xpos, ymin-0.125, r'//', fontsize=label_size, zorder=101, horizontalalignment='center', verticalalignment='center')
Example Plot:
For those interested, I've expanded upon #Paul's answer and added it to the matplotlib wrapper proplot. It can do axis "jumps", "speedups", and "slowdowns".
There is no way currently to add "crosses" that indicate the discrete jump like in Joe's answer, but I plan to add this in the future. I also plan to add a default "tick locator" that sets sensible default tick locations depending on the CutoffScale arguments.
Adressing Frederick Nord's question how to enable parallel orientation of the diagonal "breaking" lines when using a gridspec with ratios unequal 1:1, the following changes based on the proposals of Paul Ivanov and Joe Kingtons may be helpful. Width ratio can be varied using variables n and m.
import matplotlib.pylab as plt
import numpy as np
import matplotlib.gridspec as gridspec
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)
n = 5; m = 1;
gs = gridspec.GridSpec(1,2, width_ratios = [n,m])
plt.figure(figsize=(10,8))
ax = plt.subplot(gs[0,0])
ax2 = plt.subplot(gs[0,1], sharey = ax)
plt.setp(ax2.get_yticklabels(), visible=False)
plt.subplots_adjust(wspace = 0.1)
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')
ax.set_xlim(0,1)
ax2.set_xlim(10,8)
# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
on = (n+m)/n; om = (n+m)/m;
ax.plot((1-d*on,1+d*on),(-d,d), **kwargs) # bottom-left diagonal
ax.plot((1-d*on,1+d*on),(1-d,1+d), **kwargs) # top-left diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d*om,d*om),(-d,d), **kwargs) # bottom-right diagonal
ax2.plot((-d*om,d*om),(1-d,1+d), **kwargs) # top-right diagonal
plt.show()
This is a hacky but pretty solution for x-axis breaks.
The solution is based on https://matplotlib.org/stable/gallery/subplots_axes_and_figures/broken_axis.html, which gets rid of the problem with positioning the break above the spine, solved by How can I plot points so they appear over top of the spines with matplotlib?
from matplotlib.patches import Rectangle
import matplotlib.pyplot as plt
def axis_break(axis, xpos=[0.1, 0.125], slant=1.5):
d = slant # proportion of vertical to horizontal extent of the slanted line
anchor = (xpos[0], -1)
w = xpos[1] - xpos[0]
h = 1
kwargs = dict(marker=[(-1, -d), (1, d)], markersize=12, zorder=3,
linestyle="none", color='k', mec='k', mew=1, clip_on=False)
axis.add_patch(Rectangle(
anchor, w, h, fill=True, color="white",
transform=axis.transAxes, clip_on=False, zorder=3)
)
axis.plot(xpos, [0, 0], transform=axis.transAxes, **kwargs)
fig, ax = plt.subplots(1,1)
plt.plot(np.arange(10))
axis_break(ax, xpos=[0.1, 0.12], slant=1.5)
axis_break(ax, xpos=[0.3, 0.31], slant=-10)
if you want to replace an axis label, this would do the trick:
from matplotlib import ticker
def replace_pos_with_label(fig, pos, label, axis):
fig.canvas.draw() # this is needed to set up the x-ticks
labs = axis.get_xticklabels()
labels = []
locs = []
for text in labs:
x = text._x
lab = text._text
if x == pos:
lab = label
labels.append(lab)
locs.append(x)
axis.xaxis.set_major_locator(ticker.FixedLocator(locs))
axis.set_xticklabels(labels)
fig, ax = plt.subplots(1,1)
plt.plot(np.arange(10))
replace_pos_with_label(fig, 0, "-10", axis=ax)
replace_pos_with_label(fig, 6, "$10^{4}$", axis=ax)
axis_break(ax, xpos=[0.1, 0.12], slant=2)