Setting axes height - stretch axes height to legend height - python-2.7

I have a plot with a legend whose height is bigger than the axes height (like the result of the code below). Now, I would like to stretch the axes height in such a way that it ends at the legend's end.
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0., 10.5, 0.5)
fig, ax = plt.subplots()
for c in range(0, 20):
ax.plot(t, t*c/2, label='func {}'.format(c))
ax.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
What I would like to have is something like that (it does not matter, if the bottom of the grid or the bottom of the ticks ends with the legend)
I tried to reach my goal with appending following code but without any effect (legend_h is always =1.0):
legend_h = ax.get_legend().get_window_extent().height
ax_height = ax.get_window_extent().height
if ax_height < legend_h:
fig.set_figheight(legend_h/ax_height * fig.get_figheight())
Further on, it would be nice if I could only change the properties of the axes itself and not of the whole figure.
Edit:
My main purpose is to run the figure generation from a script but I also tried it in the Ipython notebook. One try was also to temporarily store the figure before getting the heights and setting the new figure height. But that also did not produce correct results.

I think you can achieve what you want by simply adding plt.draw() to what you already have, e.g.
fig, ax = plt.subplots()
for c in range(0, 20):
ax.plot(t, t*c/2, label='func {}'.format(c))
ax.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
plt.draw()
legend_h = ax.get_legend().get_window_extent().height
ax_height = ax.get_window_extent().height
if ax_height < legend_h:
fig.set_figheight(legend_h/ax_height * fig.get_figheight())
Update: Also, you can try (which should work from a script, and based on this answer):
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0., 10.5, 0.5)
fig, ax = plt.subplots()
for c in range(0, 20):
ax.plot(t, t*c/2, label='func {}'.format(c))
lgd = ax.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
fig.tight_layout()
fig.savefig('script.png', bbox_extra_artists=(lgd,), bbox_inches='tight')

In principle, #Matt Pitkin's answer shows the right approach. However, rather than set_figheight one would use set_size_inches. The calculation also needs to include the figure margins, which can be obtained from the fig.subplotpars.
Additionally to the height, we can also set the width of the figure, such that the legend is included.
import matplotlib.pyplot as plt
import numpy as np
t = np.linspace(0,10); c=20
fig, ax = plt.subplots()
for c in range(0, 20):
ax.plot(t, t*c/2, label='func {}'.format(c))
bbox = (1.01,1)
ax.legend(bbox_to_anchor=bbox, loc=2, borderaxespad=0.)
fig.canvas.draw()
legend_h = ax.get_legend().get_window_extent().height
ax_height = ax.get_window_extent().height
if ax_height < legend_h:
w,h = fig.get_size_inches()
h =legend_h/fig.dpi/(fig.subplotpars.top-fig.subplotpars.bottom)
fig.set_size_inches(w,h)
# set width as well
w,h = fig.get_size_inches()
r = ax.get_legend().get_window_extent().width/fig.dpi/w
fig.subplots_adjust(right=1-1.1*r)
plt.show()
The picture below is when running this as a script.
In Ipython or jupyter, the figure will automatically be cropped or expanded, because the png shown is automatically saved using the bbox_inches='tight' option. Therefore, the width adjustment is not necessary for a jupyter notebook.

Related

Python: plot different kinds of colors [duplicate]

I am using matplotlib to create the plots. I have to identify each plot with a different color which should be automatically generated by Python.
Can you please give me a method to put different colors for different plots in the same figure?
Matplotlib does this by default.
E.g.:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
plt.plot(x, x)
plt.plot(x, 2 * x)
plt.plot(x, 3 * x)
plt.plot(x, 4 * x)
plt.show()
And, as you may already know, you can easily add a legend:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
plt.plot(x, x)
plt.plot(x, 2 * x)
plt.plot(x, 3 * x)
plt.plot(x, 4 * x)
plt.legend(['y = x', 'y = 2x', 'y = 3x', 'y = 4x'], loc='upper left')
plt.show()
If you want to control the colors that will be cycled through:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
plt.gca().set_color_cycle(['red', 'green', 'blue', 'yellow'])
plt.plot(x, x)
plt.plot(x, 2 * x)
plt.plot(x, 3 * x)
plt.plot(x, 4 * x)
plt.legend(['y = x', 'y = 2x', 'y = 3x', 'y = 4x'], loc='upper left')
plt.show()
If you're unfamiliar with matplotlib, the tutorial is a good place to start.
Edit:
First off, if you have a lot (>5) of things you want to plot on one figure, either:
Put them on different plots (consider using a few subplots on one figure), or
Use something other than color (i.e. marker styles or line thickness) to distinguish between them.
Otherwise, you're going to wind up with a very messy plot! Be nice to who ever is going to read whatever you're doing and don't try to cram 15 different things onto one figure!!
Beyond that, many people are colorblind to varying degrees, and distinguishing between numerous subtly different colors is difficult for more people than you may realize.
That having been said, if you really want to put 20 lines on one axis with 20 relatively distinct colors, here's one way to do it:
import matplotlib.pyplot as plt
import numpy as np
num_plots = 20
# Have a look at the colormaps here and decide which one you'd like:
# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
colormap = plt.cm.gist_ncar
plt.gca().set_prop_cycle(plt.cycler('color', plt.cm.jet(np.linspace(0, 1, num_plots))))
# Plot several different functions...
x = np.arange(10)
labels = []
for i in range(1, num_plots + 1):
plt.plot(x, i * x + 5 * i)
labels.append(r'$y = %ix + %i$' % (i, 5*i))
# I'm basically just demonstrating several different legend options here...
plt.legend(labels, ncol=4, loc='upper center',
bbox_to_anchor=[0.5, 1.1],
columnspacing=1.0, labelspacing=0.0,
handletextpad=0.0, handlelength=1.5,
fancybox=True, shadow=True)
plt.show()
Setting them later
If you don't know the number of the plots you are going to plot you can change the colours once you have plotted them retrieving the number directly from the plot using .lines, I use this solution:
Some random data
import matplotlib.pyplot as plt
import numpy as np
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
for i in range(1,15):
ax1.plot(np.array([1,5])*i,label=i)
The piece of code that you need:
colormap = plt.cm.gist_ncar #nipy_spectral, Set1,Paired
colors = [colormap(i) for i in np.linspace(0, 1,len(ax1.lines))]
for i,j in enumerate(ax1.lines):
j.set_color(colors[i])
ax1.legend(loc=2)
The result is the following:
TL;DR No, it can't be done automatically. Yes, it is possible.
import matplotlib.pyplot as plt
my_colors = plt.rcParams['axes.prop_cycle']() # <<< note that we CALL the prop_cycle
fig, axes = plt.subplots(2,3)
for ax in axes.flatten(): ax.plot((0,1), (0,1), **next(my_colors))
Each plot (axes) in a figure (figure) has its own cycle of colors — if you don't force a different color for each plot, all the plots share the same order of colors but, if we stretch a bit what "automatically" means, it can be done.
The OP wrote
[...] I have to identify each plot with a different color which should be automatically generated by [Matplotlib].
But... Matplotlib automatically generates different colors for each different curve
In [10]: import numpy as np
...: import matplotlib.pyplot as plt
In [11]: plt.plot((0,1), (0,1), (1,2), (1,0));
Out[11]:
So why the OP request? If we continue to read, we have
Can you please give me a method to put different colors for different plots in the same figure?
and it make sense, because each plot (each axes in Matplotlib's parlance) has its own color_cycle (or rather, in 2018, its prop_cycle) and each plot (axes) reuses the same colors in the same order.
In [12]: fig, axes = plt.subplots(2,3)
In [13]: for ax in axes.flatten():
...: ax.plot((0,1), (0,1))
If this is the meaning of the original question, one possibility is to explicitly name a different color for each plot.
If the plots (as it often happens) are generated in a loop we must have an additional loop variable to override the color automatically chosen by Matplotlib.
In [14]: fig, axes = plt.subplots(2,3)
In [15]: for ax, short_color_name in zip(axes.flatten(), 'brgkyc'):
...: ax.plot((0,1), (0,1), short_color_name)
Another possibility is to instantiate a cycler object
from cycler import cycler
my_cycler = cycler('color', ['k', 'r']) * cycler('linewidth', [1., 1.5, 2.])
actual_cycler = my_cycler()
fig, axes = plt.subplots(2,3)
for ax in axes.flat:
ax.plot((0,1), (0,1), **next(actual_cycler))
Note that type(my_cycler) is cycler.Cycler but type(actual_cycler) is itertools.cycle.
I would like to offer a minor improvement on the last loop answer given in the previous post (that post is correct and should still be accepted). The implicit assumption made when labeling the last example is that plt.label(LIST) puts label number X in LIST with the line corresponding to the Xth time plot was called. I have run into problems with this approach before. The recommended way to build legends and customize their labels per matplotlibs documentation ( http://matplotlib.org/users/legend_guide.html#adjusting-the-order-of-legend-item) is to have a warm feeling that the labels go along with the exact plots you think they do:
...
# Plot several different functions...
labels = []
plotHandles = []
for i in range(1, num_plots + 1):
x, = plt.plot(some x vector, some y vector) #need the ',' per ** below
plotHandles.append(x)
labels.append(some label)
plt.legend(plotHandles, labels, 'upper left',ncol=1)
**: Matplotlib Legends not working
Matplot colors your plot with different colors , but incase you wanna put specific colors
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
plt.plot(x, x)
plt.plot(x, 2 * x,color='blue')
plt.plot(x, 3 * x,color='red')
plt.plot(x, 4 * x,color='green')
plt.show()
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from skspatial.objects import Line, Vector
for count in range(0,len(LineList),1):
Line_Color = np.random.rand(3,)
Line(StartPoint,EndPoint)).plot_3d(ax,c="Line"+str(count),label="Line"+str(count))
plt.legend(loc='lower left')
plt.show(block=True)
The above code might help you to add 3D lines with different colours in a randomized fashion. Your colored lines can also be referenced with a help of a legend as mentioned in the label="... " parameter.
Honestly, my favourite way to do this is pretty simple: Now this won't work for an arbitrarily large number of plots, but it will do you up to 1163. This is by using the map of all matplotlib's named colours and then selecting them at random.
from random import choice
import matplotlib.pyplot as plt
from matplotlib.colors import mcolors
# Get full named colour map from matplotlib
colours = mcolors._colors_full_map # This is a dictionary of all named colours
# Turn the dictionary into a list
color_lst = list(colours.values())
# Plot using these random colours
for n, plot in enumerate(plots):
plt.scatter(plot[x], plot[y], color=choice(color_lst), label=n)

How to specify 2D histogram image size and scale in pyplot

I wish to produce a series of 2D histograms using pyplot.
I want to be able to specify the size and scale (or aspect ratio) of the generated image. In addition to this, I would like to remove the ticks and axes labels and borders.
This does not seem to be possible in the arguments to the plt.hist2d() method.
Rather than share my (rather complex) code, I post the pyplot demo script. If what I want is possible with this code, then it will be possible with mine.
import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(1000)
y = np.random.randn(1000) + 5
# normal distribution center at x=0 and y=5
plt.hist2d(x, y, bins=40)
plt.show()
Thanks for your help in advance.
Specifying the aspect alone will not help, you need the figure size in width or height in addition.
To get rid of the margins you can use subplots_adjust. And in order to turn the axes off you need axis("off").
import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(1000)
y = np.random.randn(1000) + 5
width=4 # inch
aspect=0.8 # height/width ratio
height = width*aspect
plt.figure(figsize=(width, height ))
plt.hist2d(x, y, bins=40)
plt.subplots_adjust(bottom=0, top=1, left=0, right=1)
plt.gca().axis("off")
plt.show()
The figsize should do what you want:
plt.figure(figsize=(20,10))
plt.hist2d(x, y, bins=40)
plt.show()
The following snippet shows how to quickly and easily
set the figure size (and implicitly the aspect ratio)
disable the axis bounding box and tick annotations
set the axis to fill the whole figure (removes borders)
save the resulting figure to an image file.
.
import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(1000)
y = np.random.randn(1000) + 5
plt.figure(figsize=[7, 2]) # set figure dimensions to weird but illustrative aspect ratio
plt.hist2d(x, y, bins=40)
plt.box(False) # disable axis box
plt.xticks([]) # no x axis ticks
plt.yticks([]) # no y axis ticks
plt.subplots_adjust(left=0, right=1, top=1, bottom=0) # remove borders
plt.savefig('output.png')
plt.show()

matplotlib zorder of elements in polar plot superimposed on cartesian plot

I'm having a difficulty controlling the zorder of the elements of a polar plot superimposed on a cartesian plot.
Consider this example:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(1, 1, marker='*', s=2000, c='r', zorder=2)
ax2 = fig.add_axes(ax.get_position(), frameon=False, polar=True)
ax2.scatter(1., 0.1, marker='*', s=1000, c='b', zorder=1)
plt.xlim(0, 2)
plt.ylim(0, 2)
plt.show()
The result is:
It looks like matplotlib ignored the zorder of scatter plots. I would expect the red star to be on top of the blue one.
Could you please explain what I'm doing wrong here?
I found one question, which is kind of similar to mine, but concerns ticklines and grids instead. Maybe it's the same bug?
P.S. I'm running Linux x86_64 with Python 2.7.6 and matplotlib 1.3.1.
The problem is that you are setting the z-order of the marks on different axes ax and ax2 but since ax2 has a greater z-order all the plots in it will be on top of ax. One solution could be to set a higher z-order to ax but then you need to make the background transparent or set frameon=False (and that's maybe not desirable for your case), this is a demonstration of what I'm saying:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(1, 1, marker='*', s=2000, c='r', zorder=2)
ax2 = fig.add_axes(ax.get_position(), frameon=False, polar=True)
ax2.scatter(1., 0.1, marker='*', s=1000, c='b', zorder=1)
ax.set_zorder(3)
ax.patch.set_facecolor('none')
#ax.patch.set_visible(False)
plt.xlim(0, 2)
plt.ylim(0, 2)
plt.show()
Plot:

2D Histogram from a 3x3 array in PYTHON

This might sound trivial, but I am unable find a solution in PYTHON. No problem in ROOT or MATLAB.
So, I have a 3x3 array, and I would like each element in the array to represent the height (frequency) of a bin. I should have a histogram with 9 bins. Here's an example of what I have been trying.
import numpy as np
import matplotlib.pyplot as plt
H = np.array([[21,33,6],[25,20,2],[80,40,0]])
hist, bin = np.histogramdd(H, bins=3)
center = 0.5*(bin[:-1] + bin[1:])
plt.bar(center, hist)
plt.show()
I've tried histogram2D, I just can't find any to get this to work with PYTHON. Thanks in advance for any help on this.
If im not mistaken shouldnt this just be:
H=H.reshape(-1)
plt.bar(np.arange(H.shape[0]),H)
You can also do a 3D histogram:
extent = [0,2,0,2]
plt.imshow(H, extent=extent, interpolation='nearest')
plt.colorbar()
plt.show()
3D Bar histogram:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for z,height in enumerate(H):
cs = [c] * len(xs)
cs[0] = 'c'
ax.bar(np.arange(3), height, zs=z, zdir='y', color=cs, alpha=0.8)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
The above should work, I dont have my laptop with me at the moment. More example can be found here. A great example for 3D bars can be found here.

Matplotlib Slider Widget and changing colorbar threshold

I am currently trying to work on a program that will allow the user to display their dataset in the form of a colormap and through the use of sliders, it will also allow the user to adjust the threshold of the colormap and thus update the colormap accordingly. The best to describe this would be through the use of a picture:
This image shows how the colorbar should look before (the image on the left) and after (the image on the right) the adjustment. As the threshold values of the colrobar are changed, the colormap would be updated accordingly.
Now I am mainly using matplotlib and I found that matplotlib does support some widgets, such as a slider. However the area I need help in is devising a piece of code which will update the colorbar and colormap (like the way shown in the picture above) when the slider is adjusted. I was wondering if anyone has done this before and might have a piece of code they would be willing to share and might have pointers as to how this can be achieved.
This should get you 80% of the way there:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button
ax = plt.subplot(111)
plt.subplots_adjust(left=0.25, bottom=0.25)
img_data = np.random.rand(50,50)
c_min = 0
c_max = 1
img = ax.imshow(img_data, interpolation='nearest')
cb = plt.colorbar(img)
axcolor = 'lightgoldenrodyellow'
ax_cmin = plt.axes([0.25, 0.1, 0.65, 0.03])
ax_cmax = plt.axes([0.25, 0.15, 0.65, 0.03])
s_cmin = Slider(ax_cmin, 'min', 0, 1, valinit=c_min)
s_cmax = Slider(ax_cmax, 'max', 0, 1, valinit=c_max)
def update(val, s=None):
_cmin = s_cmin.val
_cmax = s_cmax.val
img.set_clim([_cmin, _cmax])
plt.draw()
s_cmin.on_changed(update)
s_cmax.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
s_cmin.reset()
s_cmax.reset()
button.on_clicked(reset)
plt.show()
This is a minimally edited version of the official demo.