Practice it now so that when you actually need to make a plot you will be prepared. Plot the fake gravitational potential of a Lagrange point. Plot the gravitational potential of the Earth-moon system. ![]() Make some type of three dimensional plot. It's better have sloppy code that you made yourself than some pre-built code that you have no idea how it works. However, I like to remind everyone that I am just a human. Did I do some things the hard way? Absolutely.By going through each data point, I can both use vectors and check to make sure the potential values are behaving. I don't like to do this because I couldn't (at least not easily in my mind) use vectors. Python can handle these array calculations. Do you actually have to go through each element in the meshgrid? No.Also, VPython did all the hard work in making the vectors. Why? Because it's always easier to write things as a vector when it's actually a vector. I like to import the vector from VPython.# Set up a regular grid of interpolation points If you have millions of points, this implementation will be inefficient, but as a starting point: import numpy as np a "thin-plate-spline" is a particular type of radial basis function) is often a good choice. There's no one way to do this, and the "best" method depends entirely on the a-priori information you should be incorporating into the interpolation.īefore I go into a rant on "black-box" interpolation methods, though, a radial basis function (e.g. Now if x had N unique values, y had M unique values, then zvals will be a (N,M) 2d-array which can be fed to plt.contour.Īppendix: Example data import numpy as np.Since the values of pandas dataframe columns are just numpy arrays, you can call the reshape() method to create the needed 2d-array.This is kind of the inverse of "meshgrid" operation. ![]() Get all the unique values for x- and y-data with unique().This makes the values given by unique() in the next step, sorted. Then, use the df.sort_values() method to sort the x- and y-data.Then, create pandas.DataFrame df as intermediate medium.It it is not, you can interpolate it to new grid. First, the data has to be gridded, since that is how a plt.contour plot works.Zvals = df.values.reshape(len(xvals), len(yvals)).T (Example data given in the Appendix) import pandas as pdĭf = pd.DataFrame(dict(x=xdata, y=ydata, z=zdata)) If one has gridded data stored in three 1D-arrays, and for some reason does not want to use tricontourf, there is how you could make a contourf plot out of it. Just use the plt.tricontourf function like this (see creation of the example data in the appendix) from matplotlib import pyplot as plt After interpolating your data, you can use the technique shown in Option 2. You can represent this on a two dimensional plot where the z-value is indicated by a contour line or. Each spot on a map will have an x value, a y value, and a z value (the elevation). A type of contour plot you may be familar with depicts land elevation. There are many 3d interpolation questions which can help you with that. A contour plot can be used when you have data which has three dimensions ( x, y and z ). If your data is not gridded, and you do not want to use tricontourf, you can interpolate the data into a grid and plot it with contourf. ![]()
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