I have a 3 column irregular data in the format [X Y Z]. I am having difficulty in creating a contourf
plot for the same since it requires one to create a meshgrid and also that data be uniform among the grid.
I need some direction or hint to get started.
I am providing two ways in which you can create a contour/density plot for the data which is in 3-column format and irregular, as you have mentioned.
You can use Mathematica: see the documentation of ListDensityPlot. You can directly provide the data as, ListDensityPlot[{{x1,y1,f1},…,{xk,yk,fk}}], and this will plot the sought density plot.
There is also a simple way to do this in python: You can see the documentation of tricontourf, a module of matplotlib. Its functionality is similar to that of contourf, except that you give 1D arrays rather than the data in mesh grid format.
Related
I'm plotting a Matrix with contourf, the Matrix is 883x883, the problem is that when plotting it the axis in the plots go from 0 to 883, but I would like to give it another values, more exactly, I'd like it to go from -20 to 20. How can I set that? I am very new in python, so I'd appreciate your help.
When you use contourf, you can provide the location of your data points using the optional X and Y arguments. This will only work as expected if your data is structured, meaning if you can generate a grid made of rectangles for which the nodes would represent the location of your data points. If this is not the case, then I would suggest using a triangulation and provide it to tricontourf.
I have some data of soil's moisture content (Theta) in the form of 3D-domain points (CSV file of the columns x, y, z, Theta). I want to take cross sections from the 3D domain in some specific positions (section ABCD in the figure). I want to calculate the value of Theta in a 5*5 grid in the cross-section, but the points around each node of the grid are not coplanar with the unknown point. I did this before for 2D domains in python, but the 3D domains seem more complicated for me. I found that plotly can make something like that in its virtual environment but I want this to output a numpy array or pandas DataFrame to draw it as a contour in the jupyter notebook.
I know that finding the grid involves finding the value of each point like P0 in the figure by interpolation or gridding from its neighbors, then to draw the cross section using matplotlib, but I don' know how to do it.
Related question, Is slicing 2D grids from 3D grids available in matplotlib or similar libraries?
Thanks for all help.
The underlying problem is 3D interpolation. There are numerous packages which can do this type of thing, or you can write your own (using, e.g. KDE, which is basically just a type of smoothing/binning). There is a lot of material on the topic, like
This answer https://stackoverflow.com/a/15753011/230468
The scipy docs
This extensive set of option on scicomp.stack
And this blog post (with some good examples)
Have you tried playing with pyugrid? It's a library specifically for manipulating unstructured grids, so it sounds like it might be of some use to you. Check out these example notebooks.
I'm working with some instrument data that has records the temperature at a specific latitude, longitude, and pressure (height) coordinate. I need to create a 3d grid from this instrument data that I can then use to take a vertical cross sections of the interpolated gridded data. I've looked at pretty much every interpolation function/library I can find and I'm still having trouble just wrapping my head around how to do this.
I'd prefer not to use Mayavi, since it seems to bug out on my school's server and I'd rather not try to deal with fixing it right now.
The data is currently in 4 separate 1d arrays and I used those to mock up some scatter plots of what I'm trying to get.
Here is the structure of my instrument data points:
And here is what I'm trying to create:
Ultimately, I'd like to create some kind of 3d contour from these points that I can take slices of. Each of the plotted points has a corresponding temperature attached to it, which is really what I think is throwing me off in terms of dimensions and whatnot.
There are a few options to go from the unstructured data which you have to a structured dataset.
The simplest option might be to use the scipy interpolate.griddata method which can interpolate unstructured points using, linear or cubic interpolation.
Another option is to define your grid and then average all of the unstructured points which fall into each grid cell, giving you some gridded representation of the data. You could use a tool such as CIS to do this easily (full disclosure, I wrote this package to do exactly this kind of thing).
Or, there are more complicated methods of interpolating the data by trying to determine the most likely value of the grid points based on the unstructured data, for example using kriging with the pyKriging package, though I've never used this.
In a standard 3D python plot, each data point is, by default, represented as a sphere in 3D. For the data I'm plotting, the z-axis is very sensitive, while the x and y axes are very general, so is there a way to make each point on the scatter plot spread out over the x and y direction as it normally would with, for example, s=500, but not spread at all along the z-axis? Ideally this would look like a set of stacked discs, rather than overlapping spheres.
Any ideas? I'm relatively new to python and I don't know if there's a way to make custom data points like this with a scatter plot.
I actually was able to do this using the matplotlib.patches library, creating a patch for every data point, and then making it whatever shape I wanted with the help of mpl_toolkits.mplot3d.art3d.
You might look for something called "jittering". Take a look at
Matplotlib: avoiding overlapping datapoints in a "scatter/dot/beeswarm" plot
It works by adding random noise to your data.
Another way might be to reduce the variance of the data on your z-axis (e.g. applying a log-function) or adjusting the scale. You could do that with ax.set_zscale("log"). It is documented here http://matplotlib.org/mpl_toolkits/mplot3d/api.html#mpl_toolkits.mplot3d.axes3d.Axes3D.set_zscale
I am trying to create a 2D Contour Map in Python that looks like this:
In this case, it is a map of chemical concentration for a number of points on the map. But for the sake of simplicity, we could just say it's elevation.
I am given the map, in this case 562 by 404px. I am given a number of X & Y coordinates with the given value at that point. I am not given enough points to smoothly connect the line, and sometimes very few data points to draw from. It's my understanding that Spline plots should be used to smoothly connect the points.
I see that there are a number of libraries out there for Python which assist in creation of the contour maps similar to this.
Matplotlib's Pyplot Contour looks promising.
Numpy also looks to have some potential
But to me, I don't see a clear winner. I'm not really sure where to start, being new to this programming graphical data such as this.
So my question really is, what's the best library to use? Simpler would be preferred. Any insight you could provide that would help get me started the proper way would be fantastic.
Thank you.
In the numpy example that you show, the author is actually using Matplotlib. While there are several plotting libraries, Matplotlib is the most popular for simple 2D plots like this. I'd probably use that unless there is a compelling reason not to.
A general strategy would be to try to find something that looks like what you want in the Matplotlib example gallery and then modify the source code. Another good source of high quality Matplotlib examples that I like is:
http://astroml.github.com/book_figures/
Numpy is actually a N-dimensional array object, not a plotting package.
You don't need every pixel with data. Simply mask your data array. Matplotlib will automatically plot the area that it can and leave other area blank.
I was having this same question. I found that matplotlib has interpolation which can be used to smoothly connect discrete X-Y points.
See the following docs for what helped me through:
Matplotlib's matplotlib.tri.LinearTriInterpolator docs.
Matplotlib's Contour Plot of Irregularly Spaced Data example
How I used the above resources loading x, y, z points in from a CSV to make a topomap end-to-end