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POHow to make a 4d plot with matplotlib using arbitrary data
 

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  1. POHow to make a 4d plot with matplotlib using arbitrary data
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    <p>This question is related to this <a href="https://stackoverflow.com/questions/7855229/how-to-make-a-4d-plot-using-python-with-matplotlib">one</a>.</p> <p>What I would like to know is how to apply the suggested solution to a bunch of data (4 columns), e.g.:</p> <pre><code>0.1 0 0.1 2.0 0.1 0 1.1 -0.498121712998 0.1 0 2.1 -0.49973005075 0.1 0 3.1 -0.499916082038 0.1 0 4.1 -0.499963726586 0.1 1 0.1 -0.0181405895692 0.1 1 1.1 -0.490774988618 0.1 1 2.1 -0.498653742846 0.1 1 3.1 -0.499580747953 0.1 1 4.1 -0.499818696063 0.1 2 0.1 -0.0107079119572 0.1 2 1.1 -0.483641823093 0.1 2 2.1 -0.497582061233 0.1 2 3.1 -0.499245863438 0.1 2 4.1 -0.499673749657 0.1 3 0.1 -0.0075248589089 0.1 3 1.1 -0.476713038166 0.1 3 2.1 -0.49651497615 0.1 3 3.1 -0.498911427589 0.1 3 4.1 -0.499528887295 0.1 4 0.1 -0.00579180003048 0.1 4 1.1 -0.469979974092 0.1 4 2.1 -0.495452458086 0.1 4 3.1 -0.498577439505 0.1 4 4.1 -0.499384108904 1.1 0 0.1 302.0 1.1 0 1.1 -0.272727272727 1.1 0 2.1 -0.467336140806 1.1 0 3.1 -0.489845926622 1.1 0 4.1 -0.495610916847 1.1 1 0.1 -0.000154915998165 1.1 1 1.1 -0.148803329865 1.1 1 2.1 -0.375881358454 1.1 1 3.1 -0.453749548548 1.1 1 4.1 -0.478942841849 1.1 2 0.1 -9.03765566114e-05 1.1 2 1.1 -0.0972702806613 1.1 2 2.1 -0.314291859842 1.1 2 3.1 -0.422606253083 1.1 2 4.1 -0.463359353084 1.1 3 0.1 -6.31234088628e-05 1.1 3 1.1 -0.0720095219203 1.1 3 2.1 -0.270015786897 1.1 3 3.1 -0.395462300716 1.1 3 4.1 -0.44875793248 1.1 4 0.1 -4.84199181874e-05 1.1 4 1.1 -0.0571187054704 1.1 4 2.1 -0.236660992042 1.1 4 3.1 -0.371593983211 1.1 4 4.1 -0.4350485869 2.1 0 0.1 1102.0 2.1 0 1.1 0.328324567994 2.1 0 2.1 -0.380952380952 2.1 0 3.1 -0.462992178846 2.1 0 4.1 -0.48400342421 2.1 1 0.1 -4.25137933034e-05 2.1 1 1.1 -0.0513190921508 2.1 1 2.1 -0.224866151101 2.1 1 3.1 -0.363752470126 2.1 1 4.1 -0.430700436658 2.1 2 0.1 -2.48003822279e-05 2.1 2 1.1 -0.0310025255124 2.1 2 2.1 -0.158022037087 2.1 2 3.1 -0.29944612818 2.1 2 4.1 -0.387965424205 2.1 3 0.1 -1.73211484062e-05 2.1 3 1.1 -0.0220466245862 2.1 3 2.1 -0.12162780064 2.1 3 3.1 -0.254424041889 2.1 3 4.1 -0.35294082311 2.1 4 0.1 -1.32862131387e-05 2.1 4 1.1 -0.0170828002197 2.1 4 2.1 -0.0988138417802 2.1 4 3.1 -0.221154587294 2.1 4 4.1 -0.323713596671 3.1 0 0.1 2402.0 3.1 0 1.1 1.30503380917 3.1 0 2.1 -0.240578771191 3.1 0 3.1 -0.41935483871 3.1 0 4.1 -0.465141248676 3.1 1 0.1 -1.95102493785e-05 3.1 1 1.1 -0.0248114638773 3.1 1 2.1 -0.135153019304 3.1 1 3.1 -0.274125336409 3.1 1 4.1 -0.36965644171 3.1 2 0.1 -1.13811197906e-05 3.1 2 1.1 -0.0147116366819 3.1 2 2.1 -0.0872950700627 3.1 2 3.1 -0.202935925412 3.1 2 4.1 -0.306612285308 3.1 3 0.1 -7.94877050259e-06 3.1 3 1.1 -0.0103624783432 3.1 3 2.1 -0.0642253568271 3.1 3 3.1 -0.160970897235 3.1 3 4.1 -0.261906474418 3.1 4 0.1 -6.09709039262e-06 3.1 4 1.1 -0.00798626913355 3.1 4 2.1 -0.0507564081263 3.1 4 3.1 -0.133349565782 3.1 4 4.1 -0.228563754423 4.1 0 0.1 4202.0 4.1 0 1.1 2.65740045079 4.1 0 2.1 -0.0462153115214 4.1 0 3.1 -0.358933906213 4.1 0 4.1 -0.439024390244 4.1 1 0.1 -1.11538537794e-05 4.1 1 1.1 -0.0144619860317 4.1 1 2.1 -0.0868190343718 4.1 1 3.1 -0.203767982755 4.1 1 4.1 -0.308519215265 4.1 2 0.1 -6.50646078271e-06 4.1 2 1.1 -0.0085156584289 4.1 2 2.1 -0.0538784714494 4.1 2 3.1 -0.140215240068 4.1 2 4.1 -0.23746380125 4.1 3 0.1 -4.54421180079e-06 4.1 3 1.1 -0.00597669061814 4.1 3 2.1 -0.038839789599 4.1 3 3.1 -0.106675396816 4.1 3 4.1 -0.192922262523 4.1 4 0.1 -3.48562423225e-06 4.1 4 1.1 -0.00459693165308 4.1 4 2.1 -0.0303305231375 4.1 4 3.1 -0.0860368842133 4.1 4 4.1 -0.162420599686 </code></pre> <p>The solution to the initial problem is:</p> <pre><code># Python-matplotlib Commands from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.gca(projection='3d') X = np.arange(-5, 5, .25) Y = np.arange(-5, 5, .25) X, Y = np.meshgrid(X, Y) R = np.sqrt(X**2 + Y**2) Z = np.sin(R) Gx, Gy = np.gradient(Z) # gradients with respect to x and y G = (Gx**2+Gy**2)**.5 # gradient magnitude N = G/G.max() # normalize 0..1 surf = ax.plot_surface( X, Y, Z, rstride=1, cstride=1, facecolors=cm.jet(N), linewidth=0, antialiased=False, shade=False) plt.show() </code></pre> <p>As far as I can see, and this applies to all matplotlib-demos, the variables X, Y and Z are nicely prepared. In practical cases this is not always the case. </p> <p>Ideas how to reuse the given solution with arbitrary data? </p>
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    1. COis it reading X and Y from the columns that you're having trouble with?
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      1. POHow to make a 4d plot with matplotlib using arbitrary data
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    2. CONo, but the fact that (X;Y;Z) are not on a grid like the example.
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      1. POHow to make a 4d plot with matplotlib using arbitrary data
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