I am doing a very simple task of plotting a 2d numpy histogram and displaying with with
mayavi.mlab.imshow(my2dhistogram, interpolate=False)
For a 5x5 array the output is the following,
I would like the bins along the border to be the same size as the ones in the center. I understand the logic of what mayavi is doing but for this application I absolutely need the bins to be equal size. This is for a scientific visualization where each bin represents a measurement on a detector surface.
Any suggestions?
I don't know how to do this the right way (it seems like it would be very difficult to get right from what I know about imshow), but I have a conceptual suggestion.
Represent your NxN matrix of items on the surface with an (N+2)x(N+2) matrix and set the border entries to be -1. Then make a customized colormap such that your desired colormap is contained between 0 and 1, with all other entries as (0,0,0,0). I'm not exactly sure how to do that -- iirc mayavi modules don't allow you to setup discontinuous color tables, but you could still hack it in this way. Let me know if the part about the color table is confusing, and I can provide some code to make it work.
Also, is there a reason you need to use mayavi's imshow as opposed to say matplotlib for this essentially 2D problem?
Related
Hi I am writing a code which uses ax.plot_surface() to plot data on a unit sphere for theta = linspace(0,pi,100) and phi = linspace(0,2*pi,100).
For some reason my image is distorted in the sense that the sphere is ahead of the axis. Does anyone have any idea of why this would be the case?
3D plotting isn't necessarily a good place to start learning how to use plotting libraries; the fundamentals are more often explained in simpler 2d plots. But to get started,
read the 3d tutorial
use the 3d examples for reference
experiment! Produce the same figure with different parameter settings.
The specific parameters you asked about:
linewidth is not relevant for the plot_surface, but does make a big difference in the closely related plot_wireframe. See this example and experiment with the linewidth value. 1 is default.
alpha refers to transparency, of a graphical element. Any value <1 will mean it is possible to see other lines etc, even directly behind. This example uses alpha=0.3 in 3d
antialiased controls whether the rendering is done with anti-aliasing or not. It is more expensive to use, but the result is to reduce visual distortions. See https://stackoverflow.com/a/8750463 which also links this explanation of the method.
I am stuck with python and matplotlib imshow(). Aim is it to show a twodimensonal color map which represents three dimensions.
My x-axis is represented by an array'TG'(93 entries). My y-axis is a set of arrays dependend of my 'TG' To be precise we have 93 different arrays with the length of 340. My z-axis is also a set of arrays depended of my 'TG' equally sized then y (93x340).
Basically what I have is a set of two-dimensonal measurements which I want to plot in color dependend on a third array. Is there a clever way to do that. I was trying to find out on my own first, but all I found is that most common is the problem with just a z-plane(two-dimensonal plot). So I have two matrices of the order of (93x340) and one array(93). Do you know a helpful advise.
Without more detail on your specific problem, it's hard to guess what is the best way to represent your data. I am going to give an example, hopefully it is relevant.
Suppose we are collecting height and weight of a group of people. Maybe the index of the person is your first dimension, and the height and weight depends on who it is. Then one way to represent this data is use height and weight as the x and y axes, and plot each person as a dot in that two dimensional space.
In this example, the person index doesn't really have much meaning, thus no color is needed.
I am currently working on a project where I have to bin up to 10-dimensional data. This works totally fine with numpy.histogramdd, however with one have a serious obstacle:
My parameter space is pretty large, but only a fraction is actually inhabited by data (say, maybe a few % or so...). In these regions, the data is quite rich, so I would like to use relatively small bin widths. The problem here, however, is that the RAM usage totally explodes. I see usage of 20GB+ for only 5 dimensions which is already absolutely not practical. I tried defining the grid myself, but the problem persists...
My idea would be to manually specify the bin edges, where I just use very large bin widths for empty regions in the data space. Only in regions where I actually have data, I would need to go to a finer scale.
I was wondering if anyone here knows of such an implementation already which works in arbitrary numbers of dimensions.
thanks 😊
I think you should first remap your data, then create the histogram, and then interpret the histogram knowing the values have been transformed. One possibility would be to tweak the histogram tick labels so that they display mapped values.
One possible way of doing it, for example, would be:
Sort one dimension of data as an unidimensional array;
Integrate this array, so you have a cumulative distribution;
Find the steepest part of this distribution, and choose a horizontal interval corresponding to a "good" bin size for the peak of your histogram - that is, a size that gives you good resolution;
Find the size of this same interval along the vertical axis. That will give you a bin size to apply along the vertical axis;
Create the bins using the vertical span of that bin - that is, "draw" horizontal, equidistant lines to create your bins, instead of the most common way of drawing vertical ones;
That way, you'll have lots of bins where data is more dense, and lesser bins where data is more sparse.
Two things to consider:
The mapping function is the cumulative distribution of the sorted values along that dimension. This can be quite arbitrary. If the distribution resembles some well known algebraic function, you could define it mathematically and use it to perform a two-way transform between actual value data and "adaptive" histogram data;
This applies to only one dimension. Care must be taken as how this would work if the histograms from multiple dimensions are to be combined.
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
I'm trying to plot a series of rectangles and lines based on a tab delimited text file in matplotlib. The coordinates are quite large in the data and shown be drawn to scale -- except scaled down by some factor X -- in matplotlib.
What's the easiest way to do this in matplotlib? I know that there are transformations, but I am not sure how to define my own transformation (i.e. where the origin is and what the scale factor is) in matplotlib and have it easily convert between "data space" and "plot space". Can someone please show a quick example or point me to the right place?
If you simply use matplotlib's plot function, the plot will fit into one online window, so you don't really need to 'rescale' explicitly. Linearly rescaling is pretty easy, if you include some code sample to show your formatting of the data, somebody can help you in translating the origin and scaling the coordinates.