Reputation: 71
I have a file with 3 columns of data: Zenith (Z, from 0 to 90°) and Azimuth (A, from 0 to 360°). And radiance as the color variable.
I need to use python with matplotlib to plot this data into something resembling this:
This is my code so far (it returns an error):
import matplotlib.pyplot as plt
import numpy as np
# `data` has the following shape:
# [
# [Zenith value going from 0 to 90],
# [Azimuth values (0 to 365) increasing by 1 and looping back after 365],
# [radiance: floats that need to be mapped by the color value]
#]
data = [[6.000e+00 1.200e+01 1.700e+01 2.300e+01 2.800e+01 3.400e+01 3.900e+01
4.500e+01 5.000e+01 5.600e+01 6.200e+01 6.700e+01 7.300e+01 7.800e+01
8.400e+01 8.900e+01 3.934e+01 4.004e+01 4.054e+01 4.114e+01 4.154e+01
4.204e+01 4.254e+01 4.294e+01 4.334e+01 4.374e+01 4.414e+01 4.454e+01
4.494e+01 4.534e+01 4.564e+01 4.604e+01 4.644e+01 4.684e+01 4.714e+01
4.754e+01 4.794e+01 4.824e+01 4.864e+01 4.904e+01 4.944e+01 4.984e+01
5.014e+01 5.054e+01 5.094e+01 5.134e+01 5.174e+01 5.214e+01 5.264e+01
5.304e+01 5.344e+01 5.394e+01 5.444e+01 5.494e+01 5.544e+01 5.604e+01
5.674e+01 5.764e+01]
[1.960e+02 3.600e+01 2.360e+02 7.600e+01 2.760e+02 1.160e+02 3.160e+02
1.560e+02 3.560e+02 1.960e+02 3.600e+01 2.360e+02 7.600e+01 2.760e+02
1.160e+02 3.160e+02 6.500e+00 3.400e+00 3.588e+02 2.500e+00 3.594e+02
3.509e+02 5.000e-01 6.900e+00 1.090e+01 3.478e+02 1.250e+01 1.050e+01
7.300e+00 2.700e+00 3.571e+02 3.507e+02 1.060e+01 3.200e+00 3.556e+02
3.480e+02 7.300e+00 3.597e+02 3.527e+02 1.260e+01 6.600e+00 1.200e+00
3.570e+02 3.538e+02 3.520e+02 3.516e+02 3.528e+02 3.560e+02 1.200e+00
8.800e+00 3.567e+02 1.030e+01 6.800e+00 8.300e+00 3.583e+02 3.581e+02
3.568e+02 3.589e+02]
[3.580e-04 6.100e-04 3.220e-04 4.850e-04 4.360e-04 2.910e-04 1.120e-03
2.320e-04 4.300e-03 2.680e-04 1.700e-03 3.790e-04 7.460e-04 8.190e-04
1.030e-03 3.650e-03 3.050e-03 3.240e-03 3.340e-03 3.410e-03 3.490e-03
3.290e-03 3.630e-03 3.510e-03 3.320e-03 3.270e-03 3.280e-03 3.470e-03
3.720e-03 3.960e-03 3.980e-03 3.700e-03 3.630e-03 4.100e-03 4.080e-03
3.600e-03 3.990e-03 4.530e-03 4.040e-03 3.630e-03 4.130e-03 4.370e-03
4.340e-03 4.210e-03 4.100e-03 4.090e-03 4.190e-03 4.380e-03 4.460e-03
4.080e-03 4.420e-03 3.960e-03 4.230e-03 4.120e-03 4.440e-03 4.420e-03
4.370e-03 4.380e-03]]
rad = data[0]
azm = data[1]
# From what I understand, I need to create a meshgrid from the zenith and azimuth values
r, th = np.meshgrid(rad, azm)
z = data[2] # This doesn't work as `pcolormesh` expects this to be a 2d array
plt.subplot(projection="polar")
plt.pcolormesh(th, r, z, shading="auto")
plt.plot(azm, r, color="k", ls="none")
plt.show()
The example data above is my attempt to reduce the resolution of this massive file, so I only used 1/500 of the lines of data. This might be the wrong way to reduce the resolution, please correct me if it is!
Every tutorial I've seen generate the z
value from the r
array generated by meshgrid
. This is leaving me confused about how I would convert my z
column into a 2d array that would properly map to the zenith and azimuth values.
They'll use something like this:
z = (r ** 2.0) / 4.0
So, taking the exact shape of r
and applying a transformation to create the color.
Upvotes: 0
Views: 1645
Reputation: 80339
The simplest way to plot the given data is with a polar scatter plot. Using blue for low values and red for high values, it could look like:
import matplotlib.pyplot as plt
import numpy as np
data = [[6.000e+00, 1.200e+01, 1.700e+01, 2.300e+01, 2.800e+01, 3.400e+01, 3.900e+01, 4.500e+01, 5.000e+01, 5.600e+01, 6.200e+01, 6.700e+01, 7.300e+01, 7.800e+01, 8.400e+01, 8.900e+01, 3.934e+01, 4.004e+01, 4.054e+01, 4.114e+01, 4.154e+01, 4.204e+01, 4.254e+01, 4.294e+01, 4.334e+01, 4.374e+01, 4.414e+01, 4.454e+01, 4.494e+01, 4.534e+01, 4.564e+01, 4.604e+01, 4.644e+01, 4.684e+01, 4.714e+01, 4.754e+01, 4.794e+01, 4.824e+01, 4.864e+01, 4.904e+01, 4.944e+01, 4.984e+01, 5.014e+01, 5.054e+01, 5.094e+01, 5.134e+01, 5.174e+01, 5.214e+01, 5.264e+01, 5.304e+01, 5.344e+01, 5.394e+01, 5.444e+01, 5.494e+01, 5.544e+01, 5.604e+01, 5.674e+01, 5.764e+01],
[1.960e+02, 3.600e+01, 2.360e+02, 7.600e+01, 2.760e+02, 1.160e+02, 3.160e+02, 1.560e+02, 3.560e+02, 1.960e+02, 3.600e+01, 2.360e+02, 7.600e+01, 2.760e+02, 1.160e+02, 3.160e+02, 6.500e+00, 3.400e+00, 3.588e+02, 2.500e+00, 3.594e+02, 3.509e+02, 5.000e-01, 6.900e+00, 1.090e+01, 3.478e+02, 1.250e+01, 1.050e+01, 7.300e+00, 2.700e+00, 3.571e+02, 3.507e+02, 1.060e+01, 3.200e+00, 3.556e+02, 3.480e+02, 7.300e+00, 3.597e+02, 3.527e+02, 1.260e+01, 6.600e+00, 1.200e+00, 3.570e+02, 3.538e+02, 3.520e+02, 3.516e+02, 3.528e+02, 3.560e+02, 1.200e+00, 8.800e+00, 3.567e+02, 1.030e+01, 6.800e+00, 8.300e+00, 3.583e+02, 3.581e+02, 3.568e+02, 3.589e+02],
[3.580e-04, 6.100e-04, 3.220e-04, 4.850e-04, 4.360e-04, 2.910e-04, 1.120e-03, 2.320e-04, 4.300e-03, 2.680e-04, 1.700e-03, 3.790e-04, 7.460e-04, 8.190e-04, 1.030e-03, 3.650e-03, 3.050e-03, 3.240e-03, 3.340e-03, 3.410e-03, 3.490e-03, 3.290e-03, 3.630e-03, 3.510e-03, 3.320e-03, 3.270e-03, 3.280e-03, 3.470e-03, 3.720e-03, 3.960e-03, 3.980e-03, 3.700e-03, 3.630e-03, 4.100e-03, 4.080e-03, 3.600e-03, 3.990e-03, 4.530e-03, 4.040e-03, 3.630e-03, 4.130e-03, 4.370e-03, 4.340e-03, 4.210e-03, 4.100e-03, 4.090e-03, 4.190e-03, 4.380e-03, 4.460e-03, 4.080e-03, 4.420e-03, 3.960e-03, 4.230e-03, 4.120e-03, 4.440e-03, 4.420e-03, 4.370e-03, 4.380e-03]]
rad = np.radians(data[1])
azm = data[0]
z = data[2]
plt.subplot(projection="polar")
plt.scatter(rad, azm, c=z, cmap='coolwarm')
plt.colorbar()
plt.show()
Creating such a scatter plot with your real data gives an idea how it looks like. You might want to choose a different colormap, depending on what you want to convey. You also can choose a smaller dot size (for example plt.scatter(rad, azm, c=z, cmap='plasma', s=1, ec='none')
) if there would be too many points.
A simple way to create a filled image from non-gridded data uses tricontourf
with 256 colors (it looks quite dull with the given data, so I didn't add an example plot):
plt.subplot(projection="polar")
plt.tricontourf(rad, azm, z, levels=256, cmap='coolwarm')
Upvotes: 1
Reputation: 71
The solution was in the data file all along. I needed to better understand what np.meshrid
actually did. Turns out the data already is a 2d array, it just needed to be reshaped. I also found a flaw in the file, fixing it reduced its lines from 56k to 15k. This was small enough that I did not need to reduce the resolution.
Here's how I reshaped my data, and what the solution looked like:
import matplotlib.pyplot as plt
import numpy as np
with open("data.txt") as f:
lines = np.array(
[
[float(n) for n in line.split("\t")]
for i, line in enumerate(f.read().splitlines())
]
)
data = [np.reshape(a, (89, 180)) for a in lines.T]
rad = np.radians(data[1])
azm = data[0]
z = data[2]
plt.subplot(projection="polar")
plt.pcolormesh(rad, azm, z, cmap="coolwarm", shading="auto")
plt.colorbar()
plt.show()
Upvotes: 1