I am trying to plot an image from numpy.array. Y axe-labels are linear but I need the values multiplied by a number. In this example Y labels should go from 0 to 4000, so multiplied by 2.
Any ideas?
My code so far:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
fig = plt.figure('Nav Panel')
fig.set_size_inches(12, 12)
imgplot = plt.imshow(nupyarray,interpolation='bicubic')
imgplot.set_cmap('Greys_r')
plt.colorbar()
plt.show()
Thanks
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What I got:
Expected result:
In the chamber measurement, Theta varies from -180:180 and phi varies from 0:180. I have to plot theta from 0:180 and phi from 0:360. How can I plot that in python without altering the dataset file but just in code?
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
from mpl_toolkits import mplot3d
import pandas as pd
from matplotlib import rcParams
df= pd.read_csv('Data.csv')
df.head()
Z=df.pivot(index="Phi", columns="Theta", values="E_total").T.values
X_unique = np.sort(df.Theta.unique())
Y_unique = np.sort(df.Phi.unique())
X, Y = np.meshgrid(X_unique, Y_unique)
fig = plt.figure()
ax = fig.add_subplot(111)
cpf = ax.contourf(X,Y,Z,20, cmap=cm.jet)
plt.colorbar(cpf)
ax.set_xlabel('Phi')
ax.set_ylabel('Theta')
enter image description here
I have this following python code, which displays the following 3D plot.
My code is:
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
# Generate data example
X,Y = np.meshgrid(np.arange(-99,-90), np.arange(-200,250,50))
Z = np.zeros_like(X)
Z[:,0] = 100.
Z[4][7] = 10
# Normalize to [0,1]
Z = (Z-Z.min())/(Z.max()-Z.min())
colors = cm.viridis(Z)
rcount, ccount, _ = colors.shape
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z, rcount=rcount, ccount=ccount,
facecolors=colors, shade=False)
surf.set_facecolor((0,0,0,0))
plt.show()
I want to color the irregularities on the XY plane in a different color. I want to be able to highlight the bumps on the XY plane.
How do I do that?
The problem is that the grid is not very dense. The bump consist of a single pixel. So there are 4 cells in the grid, 3 of which have their lower left corner at 0, and would hence not receive a different color according to their value. Only the one pixel which actually is the bump gets colorized.
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
X,Y = np.meshgrid(np.arange(-99,-90), np.arange(-200,250,50))
Z = np.zeros_like(X)
Z[:,0] = 100.
Z[4][7] = 10
norm = plt.Normalize(Z.min(),Z.min()+10 )
colors = cm.viridis(norm(Z))
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z, facecolors=colors, shade=False)
surf.set_facecolor((0,0,0,0))
plt.show()
Now you may expand the colorized part of the plot, e.g. using scipy.ndimage.grey_dilation, such that all pixels that are adjacent also become yellow.
from scipy import ndimage
C = ndimage.grey_dilation(Z, size=(2,2), structure=np.ones((2, 2)))
norm = plt.Normalize(Z.min(),Z.min()+10 )
colors = cm.viridis(norm(C))
Hi I am trying to use stripplot in seaborn with log scale for the x-axis. It seems that the path I have taken does not work as intended. I would appreciate if someone could help me with that.
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
x = np.logspace(-8, -2, 10)
y = np.linspace(0, 100, 10)
sns.stripplot(x,y)
plt.gca().set_xscale('log')
all the xvalues are collapsed on the right edge of the plot (see plot). I works fine if I set the y-axis to be log.
PS: I would also need to restrict the number of x tick labels.
Thanks.
A scatter plot on a log scale using pyplot.scatter:
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
x = np.logspace(-8, -2, 10)
y = np.linspace(0, 100, 10)
c = np.random.rand(10)
s = 20+np.random.rand(10)*40
plt.scatter(x,y, c=c, s=s, cmap="jet")
plt.gca().set_xscale('log')
plt.xlim(5e-9, 5e-2)
plt.show()
The same scatter plot on a linear scale:
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
x = np.logspace(-8, -2, 10)
y = np.linspace(0, 100, 10)
c = np.random.rand(10)
s = 20+np.random.rand(10)*40
plt.scatter(x,y, c=c, s=s, cmap="jet")
plt.xlim(-0.003, 0.012)
plt.show()
I have the following script
import matplotlib.pyplot as plt
import numpy as np
data = np.random.rand(24,7)
heatmap = plt.pcolor(data)
plt.show()
Which results into this image
How can I remove the white bar at the very top?
You have to manually set the x and y limits sometimes when you're using pcolor.
import matplotlib.pyplot as plt
import numpy as np
data = np.random.rand(24,7)
heatmap = plt.pcolor(data)
plt.ylim(0, 24)
plt.show()
I am assuming here that your matrix is not a jagged matrix:
import matplotlib.pyplot as plt
import numpy as np
data = np.random.rand(24,7)
nrow, ncol = data.shape
heatmap = plt.pcolor(data)
# put the major ticks
heatmap.axes.set_xticks(np.arange(ncol), minor=False)
heatmap.axes.set_yticks(np.arange(nrow), minor=False)
heatmap.axes.set_xlim(0,ncol) # Assuming a non jagged matrix
heatmap.axes.set_ylim(0,nrow)
plt.show()
Just simple change. np.random.rand(24,7) replace to np.random.rand(25,7)
import matplotlib.pyplot as plt
import numpy as np
data = np.random.rand(25,7)
heatmap = plt.pcolor(data)
plt.show()
Output:
Or add axis Like plt.axis([0,7,0,24])
import matplotlib.pyplot as plt
import numpy as np
data = np.random.rand(24,7)
heatmap = plt.pcolor(data)
plt.axis([0,7,0,24])
plt.show()
Output:
Let's say I have a greyscale image (size: 550x150 px). I load the image with matplolib
import matplotlib.pyplot as plt
import matplotlib.image as mp_img
image = mp_img.imread("my-cat.png")
plt.imshow(image)
plt.show()
Now, plt.imshow displays the image on the screen. But what I want is a surface plot of the greyscale values, something like this:
.Colour is not really a necessity, but it would be helpful for the height lines. I know, that I need a function of the form f(x,y) -> z to create the surface plot. So, I want to use the greyscale value at (x_pixel,y_pixel) in my image to get the value of f. This leads to my problem:
I'd like to do some interpolation (e.g. smoothing) of my image values during plotting. This depends also on the size of my meshgrid, so how do I control this? And,
how do I make a surface plot of the greyscale values from my image?
So this is pretty straightforward. Load the data, build the plot:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# generate some sample data
import scipy.misc
lena = scipy.misc.lena()
# downscaling has a "smoothing" effect
lena = scipy.misc.imresize(lena, 0.15, interp='cubic')
# create the x and y coordinate arrays (here we just use pixel indices)
xx, yy = np.mgrid[0:lena.shape[0], 0:lena.shape[1]]
# create the figure
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(xx, yy, lena ,rstride=1, cstride=1, cmap=plt.cm.gray,
linewidth=0)
# show it
plt.show()
Result:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import cv2
# generate some sample data
import scipy.misc
lena = cv2.imread("./data/lena.png", 0)
# downscaling has a "smoothing" effect
lena = cv2.resize(lena, (100,100))
# create the x and y coordinate arrays (here we just use pixel indices)
xx, yy = np.mgrid[0:lena.shape[0], 0:lena.shape[1]]
# create the figure
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(xx, yy, lena ,rstride=1, cstride=1, cmap=plt.cm.jet,
linewidth=0)
# show it
plt.show()
If you want to get color plot, change the code to: "cmap=plt.cm.jet".
So you can get something like this:
color plot