I have a simple task that should have a simple solution, but I have been trying for days now. I try to be specific.
I try to plot a surface using matplotlib's mplot3d and plot_surface.
When I plot the surface of a dataset 'z' and try to scale the colormap to a certain maximum value I change the 'vmax' property to this value. That works great.
When I try to plot a surface of one dataset (z) and use the facecolors of a second dataset (fc), this also works fine.
When I want to scale the colormap of the facecolors, the vmax property is overruled by the facecolors values. Vmax therefore has no effect (attempt1). The lines also disappeared, but that's another issue.
Also trying to change the values of the facecolor dataset (fc) did not have the desired effect (attempt2).
I try to get a figure with a scaled colormap (as in the figure 'scaled' below) but scaled to the facecolors, and not the z-values.
The code below is what I have now, and the results look like this:
Does anyone know what I am missing here? Any thoughts are appreciated!
import pylab as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
plt.ion()
# creating dataset
profile = np.arange(20)**2
z = profile.repeat(20).reshape(20,20)
fc= np.rot90(z.copy())
x = np.arange(z.shape[0])
y = np.arange(z.shape[1])
X, Y = np.meshgrid(x,y)
# plotting
vmax = 100
fig = plt.figure()
ax = fig.add_subplot(1,4,1, projection='3d', azim=210)
ax.plot_surface(X,Y,z, cmap=plt.cm.jet, cstride=1, rstride=1)
ax.set_title('normal')
ax = fig.add_subplot(1,4,2, projection='3d', azim=210)
ax.plot_surface(X,Y,z, cmap=plt.cm.jet, cstride=1, rstride=1, vmax=vmax)
ax.set_title('scaled')
ax = fig.add_subplot(1,4,3, projection='3d', azim=210)
ax.plot_surface(X,Y,z, facecolors=plt.cm.jet(fc), cstride=1, rstride=1, vmax=vmax)
ax.set_title('rotated (attempt1)')
ax = fig.add_subplot(1,4,4, projection='3d', azim=210)
fc[fc> vmax] = vmax
ax.plot_surface(X,Y,z, facecolors=plt.cm.jet(fc), cstride=1, rstride=1)
ax.set_title('rotated (attempt2)')
One - dirty - solution would be to rescale the clipped facecolors such that the maximum is equal to the maximum of your height map (in addition to basically what you suggested as attempt 2):
ax.plot_surface(X,Y,z, facecolors=plt.cm.jet(np.clip(fc,0,vmax)*np.max(z)/vmax), cstride=1, rstride=1, vmax=vmax)
Does this give the result you are looking for?
Related
Consider the following picture. How do I create distance between the x-axis numbering and the label?
The plot is created following the steps.
The structure of the code is more or less like this:
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d;
ax = plt.axes(projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1,
cmap='viridis')
ax.set_title('surface');
You can specify a value of your choice to the labelpad argument as following. The same can be done for y and z axis labels as well.
ax.set_xlabel('xxxxxxxxx', labelpad=10)
I have a matplotlib scatter plot with many markers:
plt.scatter(x_position,y_position,c=z_position,s=90, cmap=cm.bwr,linewidth=1,edgecolor='k')
Sometimes the markers overlap. I want the zorder of each to be based on the z_position of the individual marker.
Is this possible in a scatterplot or would I have to have an separate line for each data point with its own zorder value?
Thank you.
import numpy as np
import matplotlib.pyplot as plt
x = np.array([0,1,0,1])
y = np.array([0,0,1,1])
z = np.array([8,4,6,2])
If you now call
plt.scatter(x, y, c=z, s=1000, marker="X",
cmap=plt.cm.bwr, linewidth=1, edgecolor='k')
markers overlap:
The last marker in the arrays is drawn last, hence the one with z=2 is in front.
You can sort the arrays by z to change the order of appearance.
order = np.argsort(z)
plt.scatter(x[order], y[order], c=z[order], s=1000, marker="X",
cmap=plt.cm.bwr, linewidth=1, edgecolor='k')
I'm working with python 3.4. I'm trying to plot a simple surface from a 2D array (11x13), but the colour map is not graduating properly. There's only a small range, but the colour bar looks reasonable while the plot just has stripes. Doesn't seem to matter what colourmap I use I get an equivalent result.
From another post, I tried using the rstride & cstride parameters, but that just turned the entire surface pink (with this map).
Any suggestions? I adapted the code from one of the mplot3d tutorial examples - basically just replaced X, Y, Z and the axis limits and tried different colour maps.
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d')
# Make data.
X = np.arange(0,550,50)
Y = np.arange(-12,1,1)
X, Y = np.meshgrid(X, Y)
# Array calculated with a process in excel...I'll learn to python it later.
Z = np.array([[0,0,0,0,0,0,0,0,9.318546297,9.32278014,9.324432509],
[0,0,0,0,0,0,9.270465014,9.281098393,9.287418343,9.291620087,9.293257704],
[0,0,0,9.183895053,9.20724459,9.226419172,9.239450746,9.25002849,9.257383697,9.261920422,9.263632585],
[9,9.086332608,9.126397936,9.153091476,9.176369804,9.195477721,9.210108238,9.22128034,9.229169896,9.233975347,9.235758722],
[9,9.0556775,9.095671635,9.125345711,9.149164982,9.168378335,9.183464661,9.194945002,9.203079816,9.208024884,9.209844315],
[9,9.040637647,9.075058225,9.10310915,9.126085,9.144796641,9.15970021,9.171125191,9.179264934,9.184223248,9.186039585],
[9,9.031750626,9.060617885,9.085619791,9.106813693,9.12444679,9.13872781,9.149807723,9.157769849,9.162644172,9.164426795],
[9,9.025687536,9.049859786,9.071632721,9.09067842,9.106911657,9.120316269,9.130877982,9.138559174,9.14329971,9.145034568],
[9,9.021085904,9.041333766,9.060092969,9.076965929,9.091711302,9.104158214,9.114155647,9.121547513,9.126169075,9.127867857],
[9,9.017274923,9.034147314,9.050188629,9.065030255,9.07836292,9.08991453,9.099425441,9.106628029,9.111235402,9.112955947],
[9,9.01382656,9.027663537,9.041265696,9.054295453,9.066400007,9.077233581,9.086451516,9.09368943,9.098527457,9.100440892],
[9,9.010336943,9.021310255,9.032733661,9.044224745,9.055367181,9.065750219,9.074968252,9.082599449,9.088144367,9.090799207],
[9,9.00619218,9.014431776,9.023994936,9.034293996,9.04481216,9.055077078,9.064646767,9.073106766,9.080108346,9.085588416]])
# replacing the zeroes with NaN
for i in range(np.shape(Z)[0]):
for j in range(np.shape(Z)[1]):
if Z[i,j] == 0:
Z[i,j] = float('nan')
# Plot the surface.
surf = ax.plot_surface(X, Y, Z, cmap=cm.cool,
linewidth=0, antialiased=False)
# Customize the z axis.
ax.set_zlim(9, 9.5)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
For anyone else that finds this thread with a similar question:
After reading a question about handling of NaN cells, I tried modifying the ax.plot_surface command with nanmin and nanmax arguments and that sorted out the colour stretching:
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm, linewidth=0, antialiased=False, rstride=1, cstride=1,
vmin=np.nanmin(Z), vmax=np.nanmax(Z))
I try to make colorful scatter plot using third variable to define color. It is simple to use the following code:
plt.scatter(mH, mA, s=1, c=mHc)
plt.colorbar()
plt.show()
But I do not have many choices to modify the frame of the plot. I am trying the following code to make colorful scatter plot, at the same time I try to optimize the frame of the plot:
import numpy as np
import math
from matplotlib import rcParams
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator
fig, ax = plt.subplots()
cax = ax.scatter(mH,mA,s=0.5,c=mHc) ### mH, mA, mHC are the dataset
fig.colorbar(cax)
minor_locator1 = AutoMinorLocator(6)
minor_locator2 = AutoMinorLocator(6)
ax.xaxis.set_minor_locator(minor_locator1)
ax.yaxis.set_minor_locator(minor_locator2)
ax.tick_params('both', length=10, width=2, which='major')
ax.tick_params('both', length=5, width=2, which='minor')
ax.set_xlabel(r'$m_H$')
ax.set_ylabel(r'$m_A$')
ax.set_xticks([300,600,900,1200,1500])
ax.set_yticks([300,600,900,1200,1500])
plt.savefig('mH_mA.png',bbox_inches='tight')
plt.show()
But the plot I got is black-white. It looks like the problem lies in the marker size argument, but I do not have much idea how to correct it. I want to have smaller marker size. Anyone can offer me some idea to approach this issue. Thanks.
size=0.5 is extremely small - probably all you are seeing is the marker outlines. I would suggest you increase the size a bit, and perhaps pass edgecolors="none" to turn off the marker edge stroke:
import numpy as np
from matplotlib import pyplot as plt
n = 10000
x, y = np.random.randn(2, n)
z = -(x**2 + y**2)**0.5
fig, ax = plt.subplots(1, 1)
ax.scatter(x, y, s=5, c=z, cmap="jet", edgecolors="none")
You might also want to experiment with making the points semi-transparent using the alpha= parameter:
ax.scatter(x, y, s=20, c=z, alpha=0.1, cmap="jet", edgecolors="none")
It can be difficult to get scatter plots to look nice when you have such a massive number of overlapping points. I would be tempted to plot your data as a 2D histogram or contour plot instead, or perhaps even a combination of a scatter plot and a contour plot:
density, xe, ye = np.histogram2d(x, y, bins=20, normed=True)
ax.hold(True)
ax.scatter(x, y, s=5, c=z, cmap="jet", edgecolors="none")
ax.contour(0.5*(xe[:-1] + xe[1:]), 0.5*(ye[:-1] + ye[1:]), density,
colors='k')
My question is almost similar to this on:
smoothing surface plot from matrix
only that my toolset is matplotlib and numpy (so far).
I have sucessfully generated a X, Y and Z-grid to plot
with
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='summer', rstride=1, cstride=1, alpa=None)
However, as the values are quite jumpy, it looks terribly.
I'd like to smoothen things up, make at least the vertices connected, or look like that.
My data is generated like that:
I have a function
svOfMatrix(x, y)
which produces a matrix in dependence on x, calculates its y-th power, selects a subset of columns and rows, and calculates the maximum singular value.
So, Z[x,y] is svOfMatrix(x, y)
As this calculation is quite expensive, I don't want to make the steps for x too small, and Y is bound to be integer
Further, even for very small steps, there might be quite some changes, I don't want see. So I'd like to interpolate it somehow.
I found
http://docs.scipy.org/doc/scipy-0.14.0/reference/tutorial/interpolate.html
but I don't get it to work.
From the link you suggested, the example here is probably closest to what you want. You can use the example with your values,
import numpy as np
from scipy import interpolate
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d, Axes3D
X, Y = np.mgrid[-1:1:20j, -1:1:20j]
Z = (X+Y) * np.exp(-6.0*(X*X+Y*Y)) + np.random.rand(X.shape[0])
xnew, ynew = np.mgrid[-1:1:80j, -1:1:80j]
tck = interpolate.bisplrep(X, Y, Z, s=0)
znew = interpolate.bisplev(xnew[:,0], ynew[0,:], tck)
fig = plt.figure(figsize=(12,12))
ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, Z, cmap='summer', rstride=1, cstride=1, alpha=None)
plt.show()
fig = plt.figure(figsize=(12,12))
ax = fig.gca(projection='3d')
ax.plot_surface(xnew, ynew, znew, cmap='summer', rstride=1, cstride=1, alpha=None, antialiased=True)
plt.show()
Also, antialiased=True may make it look better but I think is on by default. The first plot looks like this,
and the smoothed plot like this,
The problem with your the low frequency noise in your data is that it will be difficult to define a grid fine enough to resolve. You can adjust the level of smoothing with the s argument to interpolate.bisplrep or perhaps coarse grain/filter your data to leave only major trends (e.g. using scipy.ndimage.interpolation.zoom if you have regular gridded data). Alternatively, consider a different type of plot such as pcolormesh as the data is essentially 2D.
Simply put the data_frame into this function. You'll get a proper smoothen surface plot. Incase you face any error, just choose only those features from data_frame which are numerical.
'data_frame = data_frame.select_dtypes(include='number')'
from scipy import interpolate
from mpl_toolkits.mplot3d import axes3d, Axes3D
def surface(data_frame, title=None, title_x=0.5, title_y=0.9):
X, Y = np.mgrid[-10:10:complex(0,data_frame.shape[0]),
-10:10:complex(0,data_frame.shape[1])]
Z = data_frame.values
xnew, ynew = np.mgrid[-1:1:80j, -1:1:80j]
tck = interpolate.bisplrep(X, Y, Z, s=0)
znew = interpolate.bisplev(xnew[:,0], ynew[0,:], tck)
fig = go.Figure(data=[go.Surface(z=znew)])
fig.update_layout(template='plotly_dark',
width=800,
height=800,
title = title,
title_x = title_x,
title_y = title_y
)
return fig