I would like to know how I can dynamically update a stacked bar plot in matplotlib.
This question Dynamically updating a bar plot in matplotlib describes how it can be done for a normal bar chart, but not a stacked bar chart.
In a normal bar chart the update can be done via rect.set_height(h) assuming that rects = plt.bar(range(N), x, align='center')
But in a stacked bar chart we also need to set the bottom.
p2 = plt.bar(ind, womenMeans, width, color='y',
bottom=menMeans, yerr=menStd)
How can I dynamically set the bottom? Unfortunately it seems that the 'Rectangle' object has no attribute 'set_bottom'. Is there any alternative way to handle this?
For some reason, the set_bottom() function you want is set_y under patches in the return object from bar. The minimal example, based on the link you suggest would look like,
import numpy as np
import matplotlib.pyplot as plt
def setup_backend(backend='TkAgg'):
import sys
del sys.modules['matplotlib.backends']
del sys.modules['matplotlib.pyplot']
import matplotlib as mpl
mpl.use(backend) # do this before importing pyplot
import matplotlib.pyplot as plt
return plt
N = 5
width = 0.35 # the width of the bars: can also be len(x) sequence
def animate():
# http://www.scipy.org/Cookbook/Matplotlib/Animations
mu, sigma = 100, 15
h = mu + sigma * np.random.randn((N*2))
p1 = plt.bar(np.arange(N), h[:N], width, color='r')
p2 = plt.bar(np.arange(N), h[N:], width, color='b', bottom=h[:N])
assert len(p1) == len(p2)
maxh = 0.
for i in range(50):
for rect1, rect2 in zip(p1.patches, p2.patches):
h = mu + sigma * np.random.randn(2)
#Keep a record of maximum value of h
maxh = max(h[0]+h[1],maxh)
rect1.set_height(h[0])
rect2.set_y(rect1.get_height())
rect2.set_height(h[1])
#Set y limits to maximum value
ax.set_ylim((0,maxh))
fig.canvas.draw()
plt = setup_backend()
fig, ax = plt.subplots(1,1)
win = fig.canvas.manager.window
win.after(10, animate)
plt.show()
Note, I change the height generation using random numbers each iteration so the two arrays of patches can be zipped instead (would get a bit messy otherwise).
Related
I have plotted the grouped bar plot and I want to have spacing between orange and blue bar.
I am not sure how to.
It is the sample image - I want little space between blue and orange bar.
import numpy as np
import matplotlib.pyplot as plt
N=4
a = [63,13,12,45]
b = [22,6,9,9]
ind = np.arange(N)
width=0.35
fig, ax = plt.subplots()
b1 = ax.bar(ind, a, width)
b2 = ax.bar(ind+width, b, width)
ax.set_xticks(ind+width/2)
plt.show()
Just do this:
b2 = ax.bar(ind+ 1.2 * width, b, width)
Just edge processing in ax.bar() creates space.
b1 = ax.bar(ind, a, width, edgecolor="w", linewidth=3)
It's the full code of the modified sample.
import numpy as np
import matplotlib.pyplot as plt
N=4
a = [63,13,12,45]
b = [22,6,9,9]
ind = np.arange(N)
width=0.4
fig, ax = plt.subplots()
b1 = ax.bar(ind, a, width, edgecolor="w", linewidth=3)
b2 = ax.bar(ind+width, b, width, edgecolor="w",linewidth=3)
ax.set_xticks(ind+width/2)
plt.show()
I am unaware of an dedicated option for such a behavior. The reason is that it would indicate inaccurate measures. You would be no longer sure if the blue/orange bars belong to the same value on the x-axis.
Therefore, you need to come up with a small workaround by shifting the data (or rather the two data arrays) around the index on the x-axis. For this, I introduced the variable dist in the code below. Note that it should be larger than half of the width of a bar.
import numpy as np
import matplotlib.pyplot as plt
N=4
a = [63,13,12,45]
b = [22,6,9,9]
ind = np.arange(N)
width = 0.1
dist = 0.08 # should be larger than width/2
fig, ax = plt.subplots()
b1 = ax.bar(ind-dist, a, width)
b2 = ax.bar(ind+dist, b, width)
plt.show()
generic solution
For a somewhat more generic solution, we need first to calculate the width of the grouped bars and than shift the group around the index:
import numpy as np
import matplotlib.pyplot as plt
N=4
a = [63,13,12,45]
b = [22,6,9,9]
ind = np.arange(N) # index / x-axis value
width = 0.1 # width of each bar
DistBetweenBars = 0.01 # distance between bars
Num = 5 # number of bars in a group
# calculate the width of the grouped bars (including the distance between the individual bars)
WithGroupedBars = Num*width + (Num-1)*DistBetweenBars
fig, ax = plt.subplots()
for i in range(Num):
data = np.random.rand(N)
ax.bar(ind-WithGroupedBars/2 + (width+DistBetweenBars)*i,data, width)
plt.show()
I am trying to animate a histogram using matplotlib and I want to show the different bars using a colormap, e.g:
I have this working when I clear the complete figure every frame and then redraw everything. But this is very slow, so I am trying out the example by matplotlib itself.
This works and is very fast, but unfortunately I have no idea on how to specify a colormap because it is using the patches.PathPatch object to draw the histogram now. I can only get it to work with the same single color for every individual bar.
How can I specify a gradient or colormap to achieve the desired result shown above?
Here is an example of a working animation with a single color which I am currently using.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.path as path
import matplotlib.animation as animation
# Fixing random state for reproducibility
np.random.seed(19680801)
# histogram our data with numpy
data = np.random.randn(1000)
n, bins = np.histogram(data, 100)
# get the corners of the rectangles for the histogram
left = np.array(bins[:-1])
right = np.array(bins[1:])
bottom = np.zeros(len(left))
top = bottom + n
nrects = len(left)
nverts = nrects * (1 + 3 + 1)
verts = np.zeros((nverts, 2))
codes = np.ones(nverts, int) * path.Path.LINETO
codes[0::5] = path.Path.MOVETO
codes[4::5] = path.Path.CLOSEPOLY
verts[0::5, 0] = left
verts[0::5, 1] = bottom
verts[1::5, 0] = left
verts[1::5, 1] = top
verts[2::5, 0] = right
verts[2::5, 1] = top
verts[3::5, 0] = right
verts[3::5, 1] = bottom
patch = None
def animate(i):
# simulate new data coming in
data = np.random.randn(1000)
n, bins = np.histogram(data, 100)
top = bottom + n
verts[1::5, 1] = top
verts[2::5, 1] = top
return [patch, ]
fig, ax = plt.subplots()
barpath = path.Path(verts, codes)
patch = patches.PathPatch(
barpath, facecolor='green', edgecolor='yellow', alpha=0.5)
ax.add_patch(patch)
ax.set_xlim(left[0], right[-1])
ax.set_ylim(bottom.min(), top.max())
ani = animation.FuncAnimation(fig, animate, 100, repeat=False, blit=True)
plt.show()
I recommend u using BarContainer, you can change bar color individually. In your example, the path is single object, matplotlib seems not to support gradient color for a single patch (not sure though).
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
# histogram our data with numpy
data = np.random.randn(1000)
colors = plt.cm.coolwarm(np.linspace(0, 1, 100))
def animate(i):
data = np.random.randn(1000)
bc = ax.hist(data, 100)[2]
for i, e in enumerate(bc):
e.set_color(colors[i])
return bc
fig, ax = plt.subplots(1, 1, figsize=(7.2, 7.2))
ani = animation.FuncAnimation(fig, animate, 100, repeat=False, blit=True)
I'm trying to animate a stem plot in matplotlib and I can't find the necessary documentation to help me. I have a series of data files which each look like this:
1 0.345346
2 0.124325
3 0.534585
and I want plot each file as a separate frame.
According to this and this other tutorial, I should create a function which updates the data contained in each plot object (artist? I'm not sure about the terminology)
From the second link, this is the update function
def update(frame):
global P, C, S
# Every ring is made more transparent
C[:,3] = np.maximum(0, C[:,3] - 1.0/n)
# Each ring is made larger
S += (size_max - size_min) / n
# Reset ring specific ring (relative to frame number)
i = frame % 50
P[i] = np.random.uniform(0,1,2)
S[i] = size_min
C[i,3] = 1
# Update scatter object
scat.set_edgecolors(C)
scat.set_sizes(S)
scat.set_offsets(P)
# Return the modified object
return scat,
How can I adapt this kind of update function for a stem plot? The documentation for stem is horribly brief (in fact this is a recurring issue as I'm learning matplotlib), but the example code shows that the output of stem is a tuple markerline, stemlines, baseline rather than an artist object like for plt.plot or plt.imshow.
So when I write my update function for the animation, how can I update the data inside the stem plot?
Here you go!
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np
fig, ax = plt.subplots()
x = np.linspace(0.1, 2*np.pi, 10)
markerline, stemlines, baseline = ax.stem(x, np.cos(x), '-.')
def update(i):
ax.cla()
markerline, stemlines, baseline = ax.stem(x, np.cos(x+i/10), '-.')
ax.set_ylim((-1, 1))
anim = FuncAnimation(fig, update, frames=range(10, 110, 10), interval=500)
anim.save('so.gif', dpi=80, writer='imagemagick')
I think there can be better ways of achieving this- not requiring to clear the plot each time. However, this works!
When using the keyword use_line_collection=True (default behavior since Matplotlib 3.3) one can update the three elements
markerline
stemlines
baseline
individualy. Here is the code for the sine wave example:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
fig, ax = plt.subplots()
x = np.linspace(0.1, 2*np.pi, 10)
y = np.cos(x)
bottom = 0
h_stem = ax.stem(x, y, bottom=bottom, use_line_collection=True, linefmt='-.')
def update(i):
y = np.cos(x+i/10)
# markerline
h_stem[0].set_ydata(y)
h_stem[0].set_xdata(x) # not necessary for constant x
# stemlines
h_stem[1].set_paths([np.array([[xx, bottom],
[xx, yy]]) for (xx, yy) in zip(x, y)])
# baseline
h_stem[2].set_xdata([np.min(x), np.max(x)])
h_stem[2].set_ydata([bottom, bottom]) # not necessary for constant bottom
anim = FuncAnimation(fig, update, frames=range(10, 110, 10), interval=1)
anim.save('so.gif', dpi=80, writer='imagemagick')
Depending on what values (x, y, bottom) should be updated you can omit some parts of this update or reuse the current values. I wrote a more general function, where you can pass an arbitrary combination of these values:
def update_stem(h_stem, x=None, y=None, bottom=None):
if x is None:
x = h_stem[0].get_xdata()
else:
h_stem[0].set_xdata(x)
h_stem[2].set_xdata([np.min(x), np.max(x)])
if y is None:
y = h_stem[0].get_ydata()
else:
h_stem[0].set_ydata(y)
if bottom is None:
bottom = h_stem[2].get_ydata()[0]
else:
h_stem[2].set_ydata([bottom, bottom])
h_stem[1].set_paths([np.array([[xx, bottom],
[xx, yy]]) for (xx, yy) in zip(x, y)])
I want to automatically scale the vertical height of subplots for shared x-axis figures based on their data span! I want to compare the relative intensity of the displayed data. If i use the sharey=True kwarg for the subbplots the data is displayed in a way that the relative intensity is recognizable:
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
SIZE = (12, 8) #desired overall figure size
# Simple data to display in various forms
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
y2 = 2*(np.sin(x ** 2))
y3 = 3*(np.sin(x ** 2))
fig, ax = plt.subplots(3,ncols=1, sharex=True, sharey=True)
fig.set_size_inches(SIZE[1], SIZE[0])
fig.subplots_adjust(hspace=0.001)
ax[0].plot(x, y)
ax[1].plot(x, y2)
ax[2].plot(x, y3)
plt.show()
All subplots have the same height now and the data span in the y-Axis is recognizable as the data is displayed with the correct relative proportion.
What i would like to achieve is that the scales of each plot end where the data ends. Essentially eliminating the not used white space. The size of the subplot would than represent the relative height ratios of the data. They should still have the same scaling on the Y axis in order for the viewer to estimate the relative data height ( which cold be a countrate for example).
I found the following links to similar problems but none really helped me to solve my issue:
Link1 Link2
Here an example that determines the ratio for you and creates the subplots accordingly:
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
SIZE = (12, 8) #desired overall figure size
# Simple data to display in various forms
x = np.linspace(0, 2 * np.pi, 400)
# the maximum multiplier for the function
N = 3
# the y-ranges:
ys = [i * np.sin(x**2) for i in range(1,N+1)]
# the maximum extent of the plot in y-direction (cast as int)
hs = [int(np.ceil(np.max(np.abs(y)))) for y in ys]
# determining the size of the GridSpec:
gs_size = np.sum(hs)
gs = gridspec.GridSpec(gs_size,1)
# the figure
fig = plt.figure(figsize = SIZE)
# creating the subplots
base = 0
ax = []
for y,h in zip(ys,hs):
ax.append(fig.add_subplot(gs[base:h+base,:]))
base += h
ax[-1].plot(x,y)
##fig, ax = plt.subplots(3,ncols=1, sharex=True, sharey=True)
##fig.set_size_inches(SIZE[1], SIZE[0])
fig.subplots_adjust(hspace=0.001)
##ax[0].plot(x, ys[0])
##ax[1].plot(x, ys[1])
##ax[2].plot(x, ys[2])
plt.show()
The code determines the maximum y-extend for each set of data, casts it into an integer and then divides the figure into subplots using the sum of these extends as scale for the GridSpec.
The resulting figure looks like this:
Tested on Python 3.5
EDIT:
If the maximum and minimum extents of your data are not comparable, it may be better to change the way hs is calculated into
hs = [int(np.ceil(np.max(y))) - int(np.floor(np.min(y))) for y in ys]
I have a bar graph which retrieves its y values from a dict. Instead of showing several graphs with all the different values and me having to close every single one, I need it to update values on the same graph. Is there a solution for this?
Here is an example of how you can animate a bar plot.
You call plt.bar only once, save the return value rects, and then call rect.set_height to modify the bar plot.
Calling fig.canvas.draw() updates the figure.
import matplotlib
matplotlib.use('TKAgg')
import matplotlib.pyplot as plt
import numpy as np
def animated_barplot():
# http://www.scipy.org/Cookbook/Matplotlib/Animations
mu, sigma = 100, 15
N = 4
x = mu + sigma*np.random.randn(N)
rects = plt.bar(range(N), x, align = 'center')
for i in range(50):
x = mu + sigma*np.random.randn(N)
for rect, h in zip(rects, x):
rect.set_height(h)
fig.canvas.draw()
fig = plt.figure()
win = fig.canvas.manager.window
win.after(100, animated_barplot)
plt.show()
I've simplified the above excellent solution to its essentials, with more details at my blogpost:
import numpy as np
import matplotlib.pyplot as plt
numBins = 100
numEvents = 100000
file = 'datafile_100bins_100000events.histogram'
histogramSeries = np.loadtext(file)
fig, ax = plt.subplots()
rects = ax.bar(range(numBins), np.ones(numBins)*40) # 40 is upper bound of y-axis
for i in range(numEvents):
for rect,h in zip(rects,histogramSeries[i,:]):
rect.set_height(h)
fig.canvas.draw()
plt.pause(0.001)