I am trying to get a scatterplot like the one seen below in matlab: But I absolutely cannot get any matlab scatterplot techniques to collaborate with me in the slighest.
I have used my data to create boxplots with no issue, so suppose there is something wrong with my plotting approach.
My code currently looks like this:
Data = readtable("Dataset.xlsx"); gscatter(Data.Var1,Data.Var2)
Anyone got any guides or code advice that can help me create the intended plot?
Thanks ahead.
Related
I'm new to matlab and am trying to work with three dimensional data. It has latitude, longitude and time as the three dimensions, and I want to create a PDF for each of these matrices and then put all of them on the same plot rather than have three separate PDF plots. I don't know how to create reproducible data for matlab for this question so if there are more questions I can provide more guidance (also would be happy to hear guidance about how to create reproducible 3 dimensional data).
Essentially I need help creating a probability distribution function for three dimensional data, and then I want to plot multiple PDF lines on the same figure.
I've tried using the histogram function and normplot() but neither has worked.
I am using Matlab 2015a.
I have got electricity consumption data to cluster it. Initially i am trying to cluster it against hours and dates. I have created three different variables, one for time, one for dates and third for data. I am unable to understand how should i combine these in a matrix form so that the loads are distributed according to time? Then i have tried to look how can i plot a line graph for k-means but i can only find scatter command graphs but no line plots.
Further how can i plot it as a 3-d plot?
Further at a later stage i want to include temperature variable aswel. But when the 4th variable is involved, what will the plot be? will it still be 3-d?
Any suggestions, links?
In Matlab you can create N-dimensional matrices, so you can arrange your 3D data in a N*M*3 matrix (you might want to look for the cat() function to help you out).
There are several functions that allow you to plot in 3D, one of these is scatter3() which is perfect for K-Means clustering. I don't really understand which lines you do want to plot: K-Means is about clusters and centroids (i.e. points).
If a 4th variable is involved, you can as well create a 4D matrix. Although I reckon plotting a 4D graph isn't going to be easy. A first approach might be using several colours for your scatter points with different colours for different temperatures (or temperatures range). In this case the 5th input argument for scatter3() will be helpful.
Help for scatter3() here.
Help for cat() here.
As a part of my project I need to generate high dimensional non linear dataset / spherical dataset...I am using Matlab to generate it...I have tried using randn,surf and plot3 functions ..I referred
http://in.mathworks.com/help/matlab/math/numbers-placed-randomly-within-volume-of-sphere.html
I got the output but I dont have any clear idea....Can anyone help with this or suggest how to generate the dataset mentioned .. like any function?.Also I would like to view the dataset like by using plot or something.....Thanx in advance...
When doing multiple plots on the same figure in MATLAB, is there any way of annotating them such that the legend entries have arrows pointing to the plots that they're named for?
Here's an example of what I have in mind. I'd like to do this using code.
Note that the MATLAB website mentions a way to do this using the annotation function. The problem with that function is that it takes x and y values (normalized for the plot) and puts the text there. Given that I am not certain where the datapoints will lie, this is unhelpful for what I want to do.
I don't mind if the text shows up at a random place, actually. What's important is to have an arrow or some way of pointing to the plot that it is referencing.
Take a look at the following Math works file exchange post:
http://www.mathworks.co.uk/matlabcentral/fileexchange/10656-data-space-to-figure-units-conversion
The code given here allows you to convert between data-space units and normalised figure units. The example given in the post seems to be doing almost exactly what you are asking.
I have a set of 3D points which numbers up around 1 million points. I am looking to visualise these with matlab.
I have tried the following functions:
plot3
scatter3
But they are both very sluggish. Is there a more efficient way to visualise this level of points in matlab? Maybe a way to mesh the points?
If not can anyone suggest a plug-in or even a different program for visualising 3D points?
You're going to run into efficiency issues no matter what plugin/program you use if you want all million+ points to show up in a plot. My suggestion would be to downsample. Use the plot3 or scatter3 function on every other point, or every nth point, until you get a figure that is not sluggish. As long as the variance in your data isn't astronomical, downsampling a little bit shouldn't affect the overall distribution of points (given that you have a million+ points). And any software that is able to display that much data without being sluggish is most likely downsampling/binning or using some interpolation technique to do so (so you might as well have control over it).
fscatter3 from the file exchange, does what you like.
Is there a specific reason to actually have it display that many points?
I Googled around a bit and found some people who have had similar issues (someone suggested Avizo as an alternate program but I've never used it):
http://www.mathworks.com/matlabcentral/newsreader/view_thread/308948
mathworks.com/matlabcentral/newsreader/view_thread/134022 (not clickable because I don't have enough rep to post more than two links)
An alternate solution would be to generate a histogram if you're more interested in the density of the data:
http://blogs.mathworks.com/videos/2010/01/22/advanced-making-a-2d-or-3d-histogram-to-visualize-data-density/
I you know beforehand roughly the coordinates of the feature you are looking for, try passing the cloud through a simple pass-through-filter, which essentially crops your point cloud. I.e. if you know that the feature is at a x-coordinate > 5, remove all points with x-coordinate < 5.
This filter could for the first coordinated be realized as
data = data(data(1,:) > 5,:);
Provided that your 3d data is stored in an n by 3 matrix.
This, together with downsampling, could help you out. If you still find the performance lagging, consider using something like the PCD viewer in PointCloudLibrary, check half way down the page at
http://pointclouds.org/documentation/overview/visualization.php
It is a stand alone app you could launch from matlab. I find it's performance far better than the sluggish matlab plotting tools.
For anyone who is interested I ended up finding a Point cloud visualiser called Cloud Compare. It is extremely fast and allows selection and segmentation as well as filtering on point clouds.