I have a little issue in my code and I hope someone will have an idea to help me. So, I have created a pie chart and, with RadioButtons, I would like to 'control' the 'explode parameter'. Expected result : we have a pie chart, we have Radiobutton and when we click on a radio button, in the pie chart the 'serie' that matches with the Radiobutton clicked is pulled out (thanks to 'explode') and we can do that as long as we want.
My problem : I have created the Radio Buttons, and when they are clicked, it opens a new window with the expected result but I would like to have the pie chart and the Radiobuttons in the same window.
My code just below :
import matplotlib.pyplot as plt
from matplotlib.widgets import RadioButtons
rax = plt.axes([0.05, 0.7, 0.15, 0.15])
radio = RadioButtons(rax, ('0-4 ans', '5-10 ans', '11-13 ans', '14-17 ans'))
def explode_function(label):
labels = '0-4 ans', '5-10 ans', '11-13 ans', '14-17 ans'
sizes = [17.4, 25.7, 18.6, 38.3]
explode = (0, 0, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
fig1, ax = plt.subplots()
if label == '0-4 ans':
explode = (0.15, 0, 0, 0)
elif label == '5-10 ans':
explode = (0, 0.15, 0, 0)
elif label == '11-13 ans':
explode = (0, 0, 0.15, 0)
elif label == '14-17 ans':
explode = (0, 0, 0, 0.15)
ax.pie (sizes, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, colors=['lightblue', 'lightgreen', 'salmon', 'lightgray'])
plt.show()
radio.on_clicked(explode_function)
plt.show()```
Thanks a lot, and good evening
By creating the fig and ax inside the explode_function, a new plot will be generated each time a radio button is clicked. Creating the fig and ax beforehand, allows to constantly work with the same plot. As an initialization, explode_function(labels[0]) already shows one variant of the pie chart. fig.canvas.redraw() forces the updated chart to be drawn.
import matplotlib.pyplot as plt
from matplotlib.widgets import RadioButtons
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
rax = plt.axes([0.05, 0.7, 0.15, 0.15])
labels = '0-4 ans', '5-10 ans', '11-13 ans', '14-17 ans'
radio = RadioButtons(rax, labels)
def explode_function(label):
ax.cla()
sizes = [17.4, 25.7, 18.6, 38.3]
explode = [0.15 if label == label_i else 0 for label_i in labels]
ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, colors=['lightblue', 'lightgreen', 'salmon', 'lightgray'])
fig.canvas.draw()
explode_function(labels[0])
radio.on_clicked(explode_function)
plt.show()
Related
I want the picker event to simply display the legend label when I click on any of the points on my scatter plot. This is what I have and looks like:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# x y data and legend labels
x = np.random.uniform(0, 100, 50)
y = np.random.uniform(0, 100, 50)
ID = np.random.randint(0,25,50)
# define the event
def onpick(event):
ind = event.ind
print('x:', x[ind], 'y:', y[ind])
# create the plot
fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c = ID, picker=True)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.legend(*scatter.legend_elements(num=list(np.unique(ID))),
loc="center left",
title='ID',
bbox_to_anchor=(1, 0.5),
ncol=2
)
ax.ticklabel_format(useOffset=False)
ax.tick_params(axis = 'x',labelrotation = 45)
plt.tight_layout()
# call the event
fig.canvas.mpl_connect('pick_event', onpick)
The scatter plot:
The current output on click:
I want it to print something like:
x: [76.25650514] y: [59.85198124] ID: 11 # the corresponding legend label
I have been searching through the web and couldn't find much I can duplicate from.
Generally, the way you would get the label of the point you clicked on would be print(event.artist.get_label()) but with your custom legends labels, the only thing that prints is _child0. However, due to your custom labels, you can use your variable ID just like how you are using your x and y variables e.g. print('id:', ID[ind]).
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# x y data and legend labels
x = np.random.uniform(0, 100, 50)
y = np.random.uniform(0, 100, 50)
ID = np.random.randint(0,25,50)
# define the event
def onpick(event):
ind = event.ind
print(event.artist.get_label()) # How you normally get the legend label
print('id:', ID[ind]) # How you can get your custom legend label
print('x:', x[ind], 'y:', y[ind])
# create the plot
fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c = ID, picker=True)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.legend(*scatter.legend_elements(num=list(np.unique(ID))),
loc="center left",
title='ID',
bbox_to_anchor=(1, 0.5),
ncol=2
)
ax.ticklabel_format(useOffset=False)
ax.tick_params(axis = 'x',labelrotation = 45)
plt.tight_layout()
# call the event
fig.canvas.mpl_connect('pick_event', onpick)
plt.show()
Clicking on the yellow most point gives:
_child0
id: [24]
x: [84.73899472] y: [3.07532246]
Clicking on a very purple point gives:
_child0
id: [2]
x: [99.88397652] y: [98.89144833]
I have a subplot and its tick labels overlap with the data. I would like to set the x-tick labels to have a background colour (e.g. white). Currently I have only been able to find how to change the label's colour, but not the background. I know how to get the effect using a text object as shown below. (NB - I don't want the whole subplot's margin to be coloured, but just the tick label).
MWE
import matplotlib as mpl
rc_fonts = {
"text.usetex": True,
'text.latex.preview': True,
"font.size": 50,
'mathtext.default': 'regular',
'axes.titlesize': 55,
"axes.labelsize": 55,
"legend.fontsize": 50,
"xtick.labelsize": 50,
"ytick.labelsize": 50,
'figure.titlesize': 55,
'figure.figsize': (10, 6.5), # 15, 9.3
'text.latex.preamble': [
r"""\usepackage{lmodern,amsmath,amssymb,bm,physics,mathtools,nicefrac,letltxmacro,fixcmex}
"""],
"font.family": "serif",
"font.serif": "computer modern roman",
}
mpl.rcParams.update(rc_fonts)
import matplotlib.pylab as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes, InsetPosition, mark_inset
from numpy import linspace, sin
x = linspace(0, 1, 100)
plt.clf()
ax1 = plt.gca()
ax2 = plt.axes([0, 0, 1, 1], label=str(2))
ip = InsetPosition(ax1, [0.08, 0.63, 0.45, 0.3])
ax2.set_axes_locator(ip)
ax1.plot(x, x)
ax1.plot(x, x + 0.3)
ax1.set_xlim(0, 1)
ax1.set_ylim(0, 1)
ax2.xaxis.set_tick_params(labelcolor='r')
ax1.text(0.3, 0.3, '$1$', transform=ax1.transAxes, horizontalalignment='center', verticalalignment='center', color='black', backgroundcolor='white')
To set a label's background color you may use the same property as for a text, essentially because a label is a text.
plt.setp(ax2.get_xticklabels(), backgroundcolor="limegreen")
For more sophisticated backgrounds, you could also use the bbox property.
bbox = dict(boxstyle="round", ec="limegreen", fc="limegreen", alpha=0.5)
plt.setp(ax2.get_xticklabels(), bbox=bbox)
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
ax.plot(np.linspace(0, 1, 5), np.random.rand(5))
# set xticklabels
xtl = []
for x in ax.get_xticks():
xtl += ['lbl: {:.1f}'.format(x)]
ax.set_xticklabels(xtl)
# modify labels
for tl in ax.get_xticklabels():
txt = tl.get_text()
if txt == 'lbl: 1.0':
txt += ' (!)'
tl.set_backgroundcolor('C3')
tl.set_text(txt)
Following the Slider Demo of Matplotlib https://matplotlib.org/gallery/widgets/slider_demo.html, I would like to update the Slider ranges, so that every time I change the slider values, those are re-centred in the Slider.
I have tried to define the Sliders as
sfreq = Slider(axfreq, 'Freq', freq-10, freq+10, valinit=freq)
samp = Slider(axamp, 'Amp', amp-5, amp+5, valinit=amp)
but since the update() function does not return anything, that does not work. I also tried making these variables global inside the function, which also did not work. I finally tried defining the Sliders inside the update function,
def update(val):
amp = samp.val
freq = sfreq.val
l.set_ydata(amp*np.sin(2*np.pi*freq*t))
fig.canvas.draw_idle()
Slider(axfreq, 'Freq', freq-10, freq+10, valinit=freq)
Slider(axamp, 'Amp', amp-5, amp+5, valinit=amp)
but that overlays more and more Sliders as I change the values. Any suggestions?
So I just decided to make the range of the slider cover several orders of magnitude of the parameter, and display the values in a logarithmic scale. In case anyone wonders, and following the matplotlib demo:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25)
t = np.arange(0.0, 1.0, 0.001)
a0 = 5
f0 = 10
delta_f = 5.0
s = a0*np.sin(2*np.pi*f0*t)
l, = plt.plot(t, s, lw=2, color='red')
plt.axis([0, 1, -10, 10])
axcolor = 'lightgoldenrodyellow'
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
sfreq = Slider(axfreq, 'Freq', np.log(1), np.log10(1000), valinit=np.log10(f0), valfmt='%4.2E')
samp = Slider(axamp, 'Amp', a0-5, a0+5, valinit=a0)
def update(val):
amp = samp.val
freq = sfreq.val
sfreq.valtext.set_text('{:4.2E}'.format(10**freq))
l.set_ydata(amp*np.sin(2*np.pi*10**freq*t))
fig.canvas.draw_idle()
sfreq.on_changed(update)
samp.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):
sfreq.reset()
samp.reset()
button.on_clicked(reset)
rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)
radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
def colorfunc(label):
l.set_color(label)
fig.canvas.draw_idle()
radio.on_clicked(colorfunc)
plt.show()
I am using the check_buttons widget like in the example (http://matplotlib.org/examples/widgets/check_buttons.html)
On a plot with many lines, it is difficult to know from the text which check box goes with which plotted line, at least until the box is clicked and the user looks to see if he can tell which line has disappeared.
Does anyone know if the background color of the check box could be made the same as the line it affects...something like a legend?
You can set the widget box colour as follows,
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import CheckButtons
t = np.arange(0.0, 2.0, 0.01)
s0 = np.sin(2*np.pi*t)
s1 = np.sin(4*np.pi*t)
s2 = np.sin(6*np.pi*t)
fig, ax = plt.subplots()
l0, = ax.plot(t, s0, visible=False, lw=2)
l1, = ax.plot(t, s1, lw=2)
l2, = ax.plot(t, s2, lw=2)
plt.subplots_adjust(left=0.2)
rax = plt.axes([0.05, 0.4, 0.1, 0.15])
check = CheckButtons(rax, ('2 Hz', '4 Hz', '6 Hz'), (False, True, True))
#Define colours for rectangles and set them
c = ['b', 'g', 'r']
[rec.set_facecolor(c[i]) for i, rec in enumerate(check.rectangles)]
def func(label):
if label == '2 Hz': l0.set_visible(not l0.get_visible())
elif label == '4 Hz': l1.set_visible(not l1.get_visible())
elif label == '6 Hz': l2.set_visible(not l2.get_visible())
plt.draw()
check.on_clicked(func)
plt.show()
The checkbutton panel has each tick box as a matplotlib.patches.Rectangle object which can be customised as needed.
I have such a plot, and would like to add a the colorbar code (which color corresponds to what number) on the right hand below. I saw some example which where used for imshow not pie chart.
#!/usr/bin/env python
"""
http://matplotlib.sf.net/matplotlib.pylab.html#-pie for the docstring.
"""
from pylab import *
fracs = [33,33,33]
starting_angle = 90
axis('equal')
for item in range(9):
color_vals = [-1, 0, 1]
my_norm = matplotlib.colors.Normalize(-1, 1) # maps your data to the range [0, 1]
my_cmap = matplotlib.cm.get_cmap('RdBu') # can pick your color map
patches, texts, autotexts = pie(fracs, labels = None, autopct='%1.1f%%', startangle=90, colors=my_cmap(my_norm(color_vals)))
subplot(3,3,item+1)
fracs = [33,33,33]
starting_angle = 90
axis('equal')
patches, texts, autotexts = pie(fracs, labels = None, autopct='%1.1f%%', startangle=90, colors=my_cmap(my_norm(color_vals)))
for item in autotexts:
item.set_text("")
subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9, wspace=0.0, hspace=0.5)
savefig('/home/superiois/Downloads/projectx3/GRAIL/pie1.png')
show()
Also, it would be great if you tell me how to customize the size and location of colorbar code; Thanks.
Usually a legend is more appropriate for discrete values and a colorbar for continuous values. That said, its off course possible since mpl allows you to create a colorbar from scratch.
import matplotlib.pyplot as plt
import matplotlib as mpl
fracs = [33,33,33]
starting_angle = 90
fig, axs = plt.subplots(3,3, figsize=(6,6))
fig.subplots_adjust(hspace=0.1,wspace=0.0)
axs = axs.ravel()
for n in range(9):
color_vals = [-1, 0, 1]
my_norm = mpl.colors.Normalize(-1, 1) # maps your data to the range [0, 1]
my_cmap = mpl.cm.get_cmap('RdBu', len(color_vals)) # can pick your color map
patches, texts, autotexts = axs[n].pie(fracs, labels = None, autopct='%1.1f%%', startangle=90, colors=my_cmap(my_norm(color_vals)))
axs[n].set_aspect('equal')
for item in autotexts:
item.set_text("")
ax_cb = fig.add_axes([.9,.25,.03,.5])
cb = mpl.colorbar.ColorbarBase(ax_cb, cmap=my_cmap, norm=my_norm, ticks=color_vals)
cb.set_label('Some label [-]')
cb.set_ticklabels(['One', 'Two', 'Three'])
I have added custom ticklabels just to show how that would work, to get the default values simply remove the last line.