Familar with Matplotlib and Basemap, but I haven't noticed that some one has tried to plot intersected figure to visualize multi-dimension data.
Here is an practical scene: Satellite data now can capture the information of atmosphere in 3-d dimension which contain the spatial distribution in several vertical level.
The example figure here contain several subplots:
(1) Vertical profile of dust extinction coefficient from calipso satellite data. (2) The average profile for each slice of (1)
(3) The background figure represent the ground-level wind field of East Asia.
(4) Some purple streamline(look carefully) which pass through each slice represent the air mass trajectory.
The figure here represent essential information in one frame.
The Matplotlib and Basemap can help me to generate each subplot. Is there any useful tools or python package can organize all these elements into one.
Related
I am making maps of meteorological data (x,y-coordinates in m) using matplotlib.pyplot.contourf(). I want to plot a coastline, but all the examples I find on internet use lat-lon data (with cartopy or basemap).
Is there a way (without transforming the data to a lat-lon grid) to plot a coastline on my cartesian grid? I know size of the grid, and its center's lat-lon coordinates.
I haven't tried anything but look for similar examples, which I could not find.
The solution is to use cartopy's gnomonic projection: https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html#gnomonic , e.g.
proj =ccrs.Gnomonic(central_latitude=0, central_longitude= 0)
The origin of the data need to be specified (in lat-lon), and it expects the data coordinates to be distance in meters from that origin. Then, the normal cartopy features (like coastlines) work as usual.
Consider a Manhattan plot for a genome-wide association study. The density of dots at the bottom of the plot is very high -- individual points are no longer visible. I'd like to skip plotting the points that completely overlap with other points (even though their x,y is not identical) to reduce the plotting time and the size of the exported PDF. Any recipes for achieving this? Collision detection? Subsampling?
I'd like to use matplotlib, though this requirement is optional. Ideally, the output should be visually identical to the "full" plot.
Some background info on the plot type:
https://en.wikipedia.org/wiki/Manhattan_plot
I'm working with data consisting in 2M points of aceleration in time, what im doing is apllying FFT to de dataset and ploting it to see behaviours.
I did it with Matplotlib but i have the problem that i can not see or select any point to see to wich (x,y) value correspond, too that i trully need (in matlab plot you can do it with a plot tool). I tried Bokeh but the problem is that is too slow plotting and ive been having some issues showing the plots (sometimes it plot, sometimes dont).
So my question is is there is any way to select (x,y) value (by clicking) in Matplotlib or if there is any plot tool to do this in a fast way?
this is the ploting data
In Atmospheric Research, the cross-section plots are often used to represent the average meteorology vertical profiles.
Using Python, I can plot the cross-section figure based on netcdf file(Simulation result).
I use this post for reference
But how to represent the terrain in specific cutting line. Example like this:
I've used
plt.fill_between(axis, terrain, 0, facecolor='black')
I have data points in x,y,z format. They form a point cloud of a closed manifold. How can I interpolate them using R-Project or Python? (Like polynomial splines)
It depends on what the points originally represented. Just having an array of points is generally not enough to derive the original manifold from. You need to know which points go together.
The most common low-level boundary representation ("brep") is a bunch of triangles. This is e.g. what OpenGL and Directx get as input. I've written a Python software that can convert triangular meshes in STL format to e.g. a PDF image. Maybe you can adapt that to for your purpose. Interpolating a triangle is usually not necessary, but rather trivail to do. Create three new points each halfway between two original point. These three points form an inner triangle, and the rest of the surface forms three triangles. So with this you have transformed one triangle into four triangles.
If the points are control points for spline surface patches (like NURBS, or Bézier surfaces), you have to know which points together form a patch. Since these are parametric surfaces, once you know the control points, all the points on the surface can be determined. Below is the function for a Bézier surface. The parameters u and v are the the parametric coordinates of the surface. They run from 0 to 1 along two adjecent edges of the patch. The control points are k_ij.
The B functions are weight functions for each control point;
Suppose you want to approximate a Bézier surface by a grid of 10x10 points. To do that you have to evaluate the function p for u and v running from 0 to 1 in 10 steps (generating the steps is easily done with numpy.linspace).
For each (u,v) pair, p returns a 3D point.
If you want to visualise these points, you could use mplot3d from matplotlib.
By "compact manifold" do you mean a lower dimensional function like a trajectory or a surface that is embedded in 3d? You have several alternatives for the surface-problem in R depending on how "parametric" or "non-parametric" you want to be. Regression splines of various sorts could be applied within the framework of estimating mean f(x,y) and if these values were "tightly" spaced you may get a relatively accurate and simple summary estimate. There are several non-parametric methods such as found in packages 'locfit', 'akima' and 'mgcv'. (I'm not really sure how I would go about statistically estimating a 1-d manifold in 3-space.)
Edit: But if I did want to see a 3D distribution and get an idea of whether is was a parametric curve or trajectory, I would reach for package:rgl and just plot it in a rotatable 3D frame.
If you are instead trying to form the convex hull (for which the word interpolate is probably the wrong choice), then I know there are 2-d solutions and suspect that searching would find 3-d solutions as well. Constructing the right search strategy will depend on specifics whose absence the 2 comments so far reflects. I'm speculating that attempting to model lower and higher order statistics like the 1st and 99th percentile as a function of (x,y) could be attempted if you wanted to use a regression effort to create boundaries. There is a quantile regression package, 'rq' by Roger Koenker that is well supported.