I am Plotting and printing a large dataset to eps:
plot(Voltage,Torque,'b.')
print -depsc figure.eps
Through these million data points I will fit a graph. However since the sizes of the Voltage and Torque vectors are enormous my eps file is 64.5 MB.
Most of the plotted points however lie on top of other points or very close. How can I reduce the size of the .eps while still having limited effects on the way the data is shown in the graph? Can I make matlab detect and remove data points close enough to other already plotted points?
although it is a scatter plot, I am not using scatterplot since all points should have the same size and color. Is it possible to use scatterplot to remove visual obsolete datapoints?
Beyond stackoverflow, the File Exchange is always a good place to start the search for a solution.
In this case I found the following submissions:
Plot (Big):
This simple tool intercepts data going into a plot and reduces it to the smallest possible set that looks identical given the number of pixels available on the screen.
DSPLOT:
This version of "plot" will allow you to visualize data that has very large number of elements. Plotting large data set makes your graphics sluggish, but most times you don't need all of the information displayed in the plot.
If you end up using the plot in a LaTeX-file, you should consider using
matlab2tikz:
This is matlab2tikz, a MATLAB(R) script for converting MATLAB figures into native TikZ/Pgfplots figures.
For use in LaTeX you don't have to go the detour of PostScript and it will make for beautiful plots.
It also provides a function called: CLEANFIGURE('minimumPointsDistance', DOUBLE,...), that will help you reduce the data points. (Possibly you could also combine this with the above solutions.)
If your vector Voltage is already sorted and more or less regularly spaced, you can simply plot a fraction of the data:
plot(Voltage(1:step:end),Torque(1:step:end),'b.')
with step set to find the right tradeoff between accuracy and size of your eps file.
If needed, first sort your vectors with:
[Voltage,I] = sort(Voltage);
Torque = Torque(I);
Related
I have a 2D image consisting in the simulation of a band structure 1. I would like to track the maxima on the image to have lines (indicating the bands). The first thing that I did is to calculate the maxima in each line (showed in 2). Then I have a set of points. However, I dont know how to connect each of the points. Does Matlab have a function capable of doing that? Or is there any reasonable way for doing it? So far, I am connecting the points by hand, but that is not scalable.
Many thanks
contourplot showing the raw data
plot showing the maxima of the colorplot in (1)
I want to produce a figure like the following one (found in a paper)
I think it is done using histfit
However, histfit doesen't really work with my data. The bars exceed the curve. My data is not really normally distributed but I want all the bins to be inside the curve except some outliers. Is there any way to fit a gaussian and plot it like in the above figure?
Edit
This is what histfit(data)has given
I want to fit a gaussian to it and keep some values as ouliers. I need to only use a normal distribution as it is going to be used in a Kalman filter based on the assumption that the data is normally distributed. The fact that is not really normally distributed will certainly affect the performance of the filter but I have to feed it first with the parameters of a normal distribution , i.e mean and std.
I'm not sure you understand how a fit works, if your data is kinda gaussian the function will plot the fitted curve based on the values, some bars will be above some below, it all depends on how the least squares are minimized over the entire curve. you can't force the fit to look different, this is the result of the fitting process. If your data is not normally distributed then the goodness of the fit is poor. without having more info or data, this is the best I can answer :)
I'm using the griddata() command in MATLAB to go from a spherical grid of sizes on the order (128x256x1500) to a Cartesian cubic grid, centered on the sphere and containing N^3 regularly-spaced points (where N is between 128 and 512). I need to do this for dozens or hundreds of checkpoints in my simulation, and several variables per checkpoint. I'm going to need to interpolate from the same spherical grid to the same cubic grid several hundred or several thousand times over, using new data on the spherical grid each time!
Since the most computationally expensive part of this routine is the triangulation and interpolation, I would like to cache some information the first time the routine is run and use that information for subsequent runs.
I think I could probably cache a table of vertex indices and associated interpolation weights for every point on the cubic grid, but I'm not sure how/where to do this....
As far as I can tell, this is not possible using the current implementation of griddata(). Is there any way I could do something like this -- perhaps re-writing the griddata() routine?
I am trying to resample/recreate already recorded data for plotting purposes. I thought this is best place to ask the question (besides dsp.se).
The data is sampled at high frequency, contains to much data points and not suitable for plotting in time domain (not enough memory). i want to sample it with minimal loss. The sampling interval of the resulting data doesn't need to be same (well it is again for plotting purposes, not analysis) although input data in equally sampled.
When we use the regular resample command from matlab/octave, it can distort stiff pieces of the curve.
What is the best approach here?
For reference I put two pictures found in tex.se)
First image is regular resample
Second image is a better resampled data that can well behave around peaks.
You should try this set of files from the File Exchange. It computes optimal lookup table based on either the maximum set of points or a given error. You can choose from natural, linear, or spline for the interpolation methods. Spline will have the smallest table size but is slower than linear. I don't use natural unless I have a really good reason.
Sincerely,
Jason
I am trying to plot a 2 GB matrix using MATLAB hist on a computer with 4 GB RAM. The operation is taking hours. Are there ways to increase the performance of the computation, by pre-sorting the data, pre-determining bin sizes, breaking the data into smaller groups, deleting the raw data as the data is added to bins, etc?
Also, after the data is plotted, I need to adjust the binning to ensure the curve is smooth. This requires starting over and re-binning the raw data. I assume the strategy involving the least computation would be to first bin the data using very small bins and then manipulate the bin size of the output, rather than re-binning the raw data. What is the best way to adjust bin sizes post-binning (assuming the bin sizes can only grow and not shrink)?
I don't like answers to StackOverflow Questions of the form "well even though you asked how to do X, you don't really want to do X, you really want to do Y, so here's a solution to Y"
But that's what i am going to do here. I think such an answer is justified in this rare instance becuase the answer below is in accord with sound practices in statistical analysis and because it avoids the current problem in front of you which is crunching 4 GB of datda.
If you want to represent the distribution of a population using a non-parametric density estimator, and you wwish to avoid poor computational performance, a kernel density estimator (KDE) will do the job far better than a histogram.
To begin with, there's a clear preference for KDEs versus histograms among the majority of academic and practicing statisticians. Among the numerous texts on this topic, ne that i think is particularly good is An introduction to kernel density estimation )
Reasons why KDE is preferred to histogram
the shape of a histogram is strongly influenced by the choice of
total number of bins; yet there is no authoritative technique for
calculating or even estimating a suitable value. (Any doubts about this, just plot a histogram from some data, then watch the entire shape of the histogram change as you adjust the number of bins.)
the shape of the histogram is strongly influenced by the choice of
location of the bin edges.
a histogram gives a density estimate that is not smooth.
KDE eliminates completely histogram properties 2 and 3. Although KDE doesn't produce a density estimate with discrete bins, an analogous parameter, "bandwidth" must still be supplied.
To calculate and plot a KDE, you need to pass in two parameter values along with your data:
kernel function: the most common options (all available in the MATLAB kde function) are: uniform, triangular, biweight, triweight, Epanechnikov, and normal. Among these, gaussian (normal) is probably most often used.
bandwith: the choice of value for bandwith will almost certainly have a huge effect on the quality of your KDE. Therefore, sophisticated computation platforms like MATLAB, R, etc. include utility functions (e.g., rusk function or MISE) to estimate bandwith given oother parameters.
KDE in MATLAB
kde.m is the function in MATLAB that implementes KDE:
[h, fhat, xgrid] = kde(x, 401);
Notice that bandwith and kernel are not supplied when calling kde.m. For bandwitdh: kde.m wraps a function for bandwidth selection; and for the kernel function, gaussian is used.
But will using KDE in place of a histogram solve or substantially eliminate the very slow performance given your 2 GB dataset?
It certainly should.
In your Question, you stated that the lagging performance occurred during plotting. A KDE does not require mapping of thousands (missions?) of data points a symbol, color, and specific location on a canvas--instead it plots a single smooth line. And because the entire data set doesn't need to be rendered one point at a time on the canvas, they don't need to be stored (in memory!) while the plot is created and rendered.