I have a large dataset from which I derive a squared matrix I would like to visualize as a heatmap. I'm using Matplotlib and Seaborn. Unfortunately, it seems to work only for a relatively small amount of data.
size = 10000
similarity_matrix = np.random.rand(size, size)
fig, ax = plt.subplots()
sns.heatmap(similarity_matrix, vmin=0, vmax=1)
plt.savefig("matrix.png")
This stops working from approximately size=6000, resulting in a white heatmap.
imshow or matshow seems to work fine:
np.random.seed(42)
size = 10000
similarity_matrix = np.random.rand(size, size)
plt.imshow(similarity_matrix, cmap='hot')
plt.colorbar()
Output:
The original code didn't generate a plot for me
Changing fig, ax = plt.subplots() to plt.figure(figsize=(14, 14)), worked to create the plot.
At figsize=(10, 10), the figure didn't render in Jupyter, but the correct image did save to a file.
A figure smaller than figsize=(14, 14), wouldn't render in Jupyter.
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# create matrix
size = 10000
similarity_matrix = np.random.rand(size, size)
# plot matrix
# create figure and set size
plt.figure(figsize=(14, 14))
# add heatmap
sns.heatmap(similarity_matrix, vmin=0, vmax=1)
# save the figure
plt.savefig('test.png', dpi=600)
# show the figure; this was slow
plt.show()
Related
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_xlim([0, 1])
plt.show()
results in the following plot:
There is a small margin between the axis and there limits.
How can I get rid of this offset of the axis.
For example ax.set_xlim([0, 1]) has not effect!
I'm a beginner in Python and specially in Matplotlib. I have a 22797x3 array, built from a multiplication between two other arrays, one 22797x400 long and the other 400x3 long. In the resulted array (22797x3),each line represents a point with (x,y,z) coordinates, hence the 3 columns. How could I plot that resulted array in a 3D surface, where I can see all the 22797 points spread in 3D space? This data is for future Kmeans clustering, so I need to visualise it.
So far I've tried:
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#building the 22797x3 array:
#loading the first array from .txt file, 22797x400 long.
array = np.loadtxt('C:\Users\Scripts/final_array.txt', usecols=range(400))
array = np.float32(array)
#loading the second array from .txt file, 400x3 long.
small_vh2 = np.loadtxt('C:\Users\Scripts/small_vh2.txt', usecols=range(3))
small_vh2 = np.float32(small_vh2)
#multiplying and getting result array 22797x3 long:
Y = np.array(np.matmul(array,small_vh2))
#I've checked Y dimensions, it's 22797x3 long, working fine.
#now I must plot it in 3D:
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(Y[:, 0], Y[:, 1], Y[:, 2])
plt.show()
I keep getting the result shown in the image below:
https://i.stack.imgur.com/jRyHM.jpg
What I need is to get is the 22797 points, and I keep getting only 4 points plotted. Does anybody know what is wrong with the code?
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from matplotlib import pyplot as plt
# made 2 random arrays of the same size as yours
array = np.random.rand(22797, 400)
small_vh2 = np.random.rand(400,3)
Y = np.matmul(array,small_vh2)
#now I must plot it in 3D:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(Y[:, 0], Y[:, 1], Y[:, 2], alpha = 0.1)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()
I am plotting a function on the surface of a sphere. To test my code, I simply plot the spherical coordinate phi divided by pi. I get
Unexpectedly, half of the sphere is of the same color, and the colors on the other half aren't correct (at phi=pi, i should get 1, not 2). If I divide the data array by 2, the problem disappears. Can someone explain to me what is happening?
Here is the code I use:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
# prepare the sphere surface
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_zlabel('Z axis')
phi = np.linspace(0,2*np.pi, 50)
theta = np.linspace(0, np.pi, 25)
x=np.outer(np.cos(phi), np.sin(theta))
y=np.outer(np.sin(phi), np.sin(theta))
z=np.outer(np.ones(np.size(phi)), np.cos(theta))
# prepare function to plot
PHI=np.outer(phi,np.ones(np.size(theta)))
THETA=np.outer(np.ones(np.size(phi)),theta)
data = PHI/np.pi
# plot
surface=ax.plot_surface(x, y, z, cstride=1, rstride=1,
facecolors=cm.jet(data),cmap=plt.get_cmap('jet'))
# add colorbar
m = cm.ScalarMappable(cmap=surface.cmap,norm=surface.norm)
m.set_array(data)
plt.colorbar(m)
plt.show()
There is a little bit of chaos in the code.
When specifying facecolors, there is no reason to supply a colormap, because the facecolors do not need to be retrieved from a colormap.
Colormaps range from 0 to 1. Your data ranges from 0 to 2. Hence half of the facecolors are just the same. So you first need to normalize the data to the (0,1)-range, e.g. using a Normalize instance, then you can apply the colormap.
norm = plt.Normalize(vmin=data.min(), vmax=data.max())
surface=ax.plot_surface(x, y, z, cstride=1, rstride=1,
facecolors=cm.jet(norm(data)))
For the colorbar you should then use the same colormap and the same normalization as for the plot itself.
m = cm.ScalarMappable(cmap=cm.jet,norm=norm)
m.set_array(data)
Complete code:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
# prepare the sphere surface
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_zlabel('Z axis')
phi = np.linspace(0,2*np.pi, 50)
theta = np.linspace(0, np.pi, 25)
x=np.outer(np.cos(phi), np.sin(theta))
y=np.outer(np.sin(phi), np.sin(theta))
z=np.outer(np.ones(np.size(phi)), np.cos(theta))
# prepare function to plot
PHI=np.outer(phi,np.ones(np.size(theta)))
THETA=np.outer(np.ones(np.size(phi)),theta)
data = PHI/np.pi
# plot
norm = plt.Normalize(vmin=data.min(), vmax=data.max())
surface=ax.plot_surface(x, y, z, cstride=1, rstride=1,
facecolors=cm.jet(norm(data)))
# add colorbar
m = cm.ScalarMappable(cmap=cm.jet,norm=norm)
m.set_array(data)
plt.colorbar(m)
plt.show()
ipython notebook 3.0.0
matplotlib 1.4.3
OS X 10.11.4
I am creating an interactive 3D scatter plot of a 3D data cube.
I've included here a toy example that generates the same problems I am encountering trying to plot my data cube.
If I generate a matplot window outside of the notebook, when I manually close it (clicking the red x) it stalls with 'the wheel' until I force quit.
#Generate matplot window outside of the notebook
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
#from matplot3d tutorial
def randrange(n, vmin, vmax):
return (vmax - vmin)*np.random.rand(n) + vmin
fig = plt.figure() ax = fig.add_subplot(111, projection='3d') n = 100 for c, m, zl, zh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:
xs = randrange(n, 23, 32)
ys = randrange(n, 0, 100)
zs = randrange(n, zl, zh)
ax.scatter(xs, ys, zs, c=c, marker=m)
ax.set_xlabel('X Label') ax.set_ylabel('Y Label') ax.set_zlabel('Z Label')
plt.show()
I've tried using mpld3 within the notebook but a non-interactive image displays along with the error
"TypeError: array([ 2., 20.]) is not JSON serializable"
#Use mpld3 within notebook
import matplotlib.pyplot as plt
import numpy as np
import mpld3
from mpl_toolkits.mplot3d import Axes3D
mpld3.enable_notebook()
def randrange(n, vmin, vmax):
return (vmax - vmin)*np.random.rand(n) + vmin
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
n = 100
for c, m, zl, zh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:
xs = randrange(n, 23, 32)
ys = randrange(n, 0, 100)
zs = randrange(n, zl, zh)
ax.scatter(xs, ys, zs, c=c, marker=m)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
Some quick research into JSON serialization has been non-fruitful.
What is the best way to create an interactive 3D matplotlib scatter plot that won't stall?
In IPython, even if you aren't using the inline backend, it's best to use %matplotlib. This tells IPython and matplotlib to work together with eventloops and should help with the hang. To use the default GUI backend, use:
%matplotlib
Or to specify the qt backend:
%matplotlib qt
This avoids the need for plt.show() and the blocking of the kernel when plots are being drawn.
For best results, run this in he first cell of your notebook, on its own before any plotting commands.
I am not able to get nicer spaces between the xticks with the following code:
import random
import matplotlib.pyplot as plt
coverages = [random.randint(1,10)*2] * 100
contig_names = ['AAB0008r'] * len(coverages)
fig = plt.figure()
fig.clf()
ax = fig.add_subplot(111)
ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=0.5)
ind = range(len(coverages))
rects = ax.bar(ind, coverages, width=0.2, align='center', color='thistle')
ax.set_xticks(ind)
ax.set_xticklabels(contig_names)
#function to auto-rotate the x axis labels
fig.autofmt_xdate()
plt.show()
How to get more space between the xticks so they do not look like overlapped anymore?
Thank you in advance.
You can try changing the figure size, the size of the xticklabels, their angle of rotation, etc.
# Set the figure size
fig = plt.figure(1, [20, 8])
# Set the x-axis limit
ax.set_xlim(-1,100)
# Change of fontsize and angle of xticklabels
plt.setp(ax.get_xticklabels(), fontsize=10, rotation='vertical')