I've been learning how to use Cartopy and Matplotlib to plot map. But I have a question regarding the argument transform. According to the Cartopy document, transform specifies "what coordinate system your data are defined in". Suppose I am going to plot temperatures of an area, and the area has been split into several grid cells. Each grid cells has a corresponding coordinate defined in lat and lon (Geodetic Systems). Based on the Cartopy document, I need to use crs.PlateCarree() instead of crs.Geodetic(). I'm a bit confused about it. Because,I think the PlateCarree is a way of projection. In other words, coordinates defined in PlateCarree projections are projected data. But latitude and longitude should be unprojected data. Can anyone help me with it? Thanks!
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So I've produced a 3-d graph on python using trisruf:
ax.plot_trisurf(x,y,z)
and I end up with the following:
3d plot
So now I want to plot contours on the base of this same plot, When I tried using ax.contour(x,y,z) I get an error saying my z should be in 2-dimensions, however my data comes from three 1-d arrays.
How can I go about plotting contours on the base of my graph?
Ok so I managed to find the answer after a bit of searching,
ax.tricontourf(angle_x,angle_y,nlist,zdir='-z', offset = -0.859, cmap='coolwarm') worked, its important to make the offset just slightly lower than the lowest z point (or whatever direction you want the projection) so you can actually see the contour plot!
Here's the result:
updated plot with contour
I am trying to use Julia to create a gif animation showing the change of density of data points with time (the data points are at the beginning concentrated at the center, and than spread to the sides, a little bit like 2D Gaussian of variance increasing with time). I have checked a catalogue of available kinds of plots in Julia:
http://docs.juliaplots.org/latest/examples/gr/
And I have tried contour plot, heatmap and 2D histogram. However, it seems that the grids of a heatmap or a contour plot have to be manually specified which is highly inconvenient. A 2D histogram serves the purpose better, but it's more related to the number of data points and when I want the plot to be more continuous by setting more bins, it cannot describe the density of data points well. Are there any good substitutes of the 2D density plot in matplotlib in Julia as the following?
https://python-graph-gallery.com/85-density-plot-with-matplotlib/
You use a package like KernelDensity to calculate the point density, then plot that. Here's an example
using StatsPlots, KernelDensity
a, b = randn(10000), randn(10000)
dens = kde((a,b))
plot(dens)
The philosophy, in the Plots package and other places in Julia, is that you generate the object you are interested in first, and then dispatch takes care of plotting it correctly.
Alternatively, you can always use PyPlot to plot anything using matplotlib directly.
I'm constructing a graph plot in Julia and need to color each edge of the graph differently, based on some weighting factor. I can't find a way to get a specific RGB (or HSV, it doesn't matter) value from a colormap. Let's say I'd like to get the RGB value on 'jet' that would correspond to a data value of n on imshow plot.
In python, I would just use jet(n), where n is the value along the colormap in which I am interested. PyPlot in Julia doesn't seem to have wrapped this functionality. I've also already tried indexing into the cmap object returned from get_cmap(). Any advice?
I'm stumped, so even an approximate solution would help. Thanks!
Maybe you can look at the Colors.jl package (https://github.com/JuliaGraphics/Colors.jl):
using Colors
palette = colormap("Oranges", 100)
Then you can access each color with palette[n]. Or are you using PyCall? A code describing what you're trying to do would help.
I'm trying to determine what the data points are on a matplotlib axes. Is there an attribute I'm missing on the Axes object to get the x/y data values?
For example, say my code is passed a line plot, and I want to print out the x/y values that are plotted.
Your plot call will give you a lines.Line2D, which has the get_xdata(orig=True) and the get_ydata(orig=True) methods.
You can check axes.get_children() for Line2D instances.
Note that what you're doing sounds horrible from a software design point of view. You should rather implement something like a wrapper for plot that prints your raw data.
#JRichardSnape adds that iff your plot is only lines, you can use get_lines() rather than filtering the output of get_children().
I have some data defined on a sphere (a sphere not the earth): is it possible with Python 2.6 and matplotlib to draw them on map (of the type of Mercator map) "automatically" or do I have to project the data?
Edit: All of my data are lat-long.
It really depends on what you have and what you want: x-y and/or lat-lon? It looks like your question is similar to a problem I had and more-or-less answered:
matplotlib and apect ratio of geographical-data plots
Consider using set_aspect(), using the reciprocal of the mean latitude of your data.
See matplotlib and apect ratio of geographical-data plots for a working example.