Is there anyway to plot my data consisting of lat/lon and some feature values in google map from matlab. I have certain data points having different properties based upon that I want to show like markers with different color/size on google map. Is that possible
Google Maps allows you to import data in the form of a KML file. There are various tutorials available online that show how to perform this import step (here's one that I just quickly found). Also, here is some basic info on KML from google.
So then the only challenge becomes exporting your data from MATLAB into KML form. If you have MATLAB's Mapping Toolbox, then this is extremely easy. Just use the kmlwrite command.
If you don't have the Mapping Toolbox, already, it's probably a good idea to have if you are performing any sort of complex mapping operations (things get pretty complicated when you try to flatten a round globe into a map). If this is just a one-off project and that toolbox is overkill, then you may be able to manually create a KML file by writing XML from MATLAB (either using xmlwrite or going the very manual route of writing with fprintf).
Additionally, I would not be too surprised if Google Maps allows you to import certain data in the form of CSV files (though perhaps this has limitations compared to KML). If so, you can simply make use of csvwrite from MATLAB to export your data (no extra toolboxes required).
==EDIT==
If you'd like to find out how to convert from CSV to KML, this previous SO post might help.
There is the KLM-Toolbox that doesn't require the matlab Mapping Toolbox:
http://www.mathworks.com/matlabcentral/fileexchange/34694
It should do the job.
Related
I am simulating the case "Cavity driven lid" and I try to get all the stream lines with the stream tracer of paraview, but I only get the ones that intersect the reference line, and because of that there are vortices that are not visible. How can I see all the stream-lines in the domain?
Thanks a lot in adavance.
To add a little bit to Mathieu's answer, if you really want streamlines everywhere, then you can create a Stream Tracer With Custom Source (as Mathieu suggested) and set your data to both the Input and the Seed Source. That will create a streamline originating from every point in your dataset, which is pretty much what you asked for.
However, while you can do this, you will probably not be happy with the results. First of all, unless your data is trivially small, this will take a long time to compute and create a large amount of data. Even worse, the result will be so dense that you won't be able to see anything. You will get all those interesting streamlines through vortices, but they will be completely hidden by all the boring streamlines around them.
Thus, you are better off with trying to derive a data set that contains seed points that are likely to trace a stream through the vortices that you are interested in. One thing you might want to try is to compute the vorticity of your vector field (Gradient Of Unstructured Data Set when turning on advanced option Compute Vorticity), find the magnitude of that (Calculator), and then use the Threshold filter to pull out the cells with large vorticity. Then use that as your Seed Source.
Another (probably better) option if your data is 2D or you can extract an interesting surface along the flow of your data is to use the Surface LIC plugin. Details can be found at https://www.paraview.org/Wiki/ParaView/Line_Integral_Convolution.
You have to choose a representative source for your streamline.
You could use a "Sphere Source", so in the StreamTracer properties.
If that fails, you can use a StreamTracerWithCustomSource and use your own source that you will have to create yourself first.
I know Matlab has the function TrainAutoencoder(input, settings) to create and train an autoencoder. The result is capable of running the two functions of "Encode" and "Decode".
But this is only applicable to the case of normal autoencoders. What if you want to have a denoising autoencoder? I searched and found some sample codes, where they used the "Network" function to convert the autoencoder to a normal network and then Train(network, noisyInput, smoothOutput)like a denoising autoencoder.
But there are multiple missing parts:
How to use this new network object to "encode" new data points? it doesn't support the encode().
How to get the "latent" variables to the features, out of this "network'?
I appreciate if anyone could help me resolve this issue.
Thanks,
-Moein
At present (2019a), MATALAB does not permit users to add layers manually in autoencoder. If you want to build up your own, you will have start from the scratch by using layers provided by MATLAB;
In order to to use TrainNetwork(...) to train your model, you will have you find out a way to insert your data into an object called imDatastore. The difficulty for autoencoder's data is that there is NO label, which is required by imDatastore, hence you will have to find out a smart way to avoid it--essentially you are to deal with a so-called OCC (One Class Classification) problem.
https://www.mathworks.com/help/matlab/ref/matlab.io.datastore.imagedatastore.html
Use activations(...) to dump outputs from intermediate (hidden) layers
https://www.mathworks.com/help/deeplearning/ref/activations.html?searchHighlight=activations&s_tid=doc_srchtitle
I swang between using MATLAB and Python (Keras) for deep learning for a couple of weeks, eventually I chose the latter, albeit I am a long-term and loyal user to MATLAB and a rookie to Python. My two cents are that there are too many restrictions in the former regarding deep learning.
Good luck.:-)
If you 'simulation' means prediction/inference, simply use activations(...) to dump outputs from any intermediate (hidden) layers as I mentioned earlier so that you can check them.
Another way is that you construct an identical network but with the encoding part only, copy your trained parameters into it, and feed your simulated signals.
I'm relatively new to Matlab and currently using it to calculate pressure cards for rapid dynamic applications on RADIOSS.
The function is done and can calculate Time-Pressure points.
For the moment I generated only .ascii files to import as curves into the software but I'd like to directly write a text file readable by RADIOSS. (after conversion)
The formatting I need is very specific and I'd like to know if such a thing is possible to do on Matlab. I've been searching on my own for some time now and didn't find really specific formatting options so I come seeking for your advice.
For example I have n time Arrays Te{1 to n} an n Pressure Arrays Pr{1 to n} the format needed is presented in the image linked. How can it be done if it is possible ?
The sprintf function is quite powerful and should provide all the facilities you need. Having looked at the image you linked, I don't see anything particularly special.
I've asked this before, but I feel I wasn't clear enough so I'll try again.
I am running a network simulation, and I have several hundreds output files. Each file holds the simulation's test result for different parameters.
There are 5 different parameters and 16 different tests for each simulation. I need a method to store all this information (and again, there's a lot of it) in Matlab with the purpose of plotting graphs using a script. suppose the script input is parameter_1 and test_2, so I get a graph where parameter_1 is the X axis and test_2 is the Y axis.
My problem is that I'm not quite familier to Matlab, and I need to be directed so it doesn't take me forever (I'm short on time).
How do I store this information in Matlab? I was thinking of two options:
Each output file is imported separately to a different variable (matrix)
All output files are merged to one output file and imprted together. In the resulted matrix each line is a different output file, and each column is a different test. Problem is, I don't know how to store the simulation parameters
Edit: maybe I can use a dataset?
So, I would appreciate any suggestion of how to store the information, and what functions might help me fetch the only the data I need.
If you're still looking to give matlab a try with this problem, you can iterate through all the files and import them one by one. You can create a list of the contents of a folder with the function
ls(name)
and you can import data like this:
A = importdata(filename)
if your data is in txt files, you should consider this Prev Q
A good strategy to avoid cluttering your workspace is to import them all into a single matrix. SO if you have a matrix called VAR, then VAR{1,1}.{1,1} could be where you put your test results and VAR{1,1}.{2,1} could be where you put your simulation parameters of the first file. I think that is simpler than making a data structure. Just make sure you uniformly place the information in the same indexes of the arrays. You could also organize your VAR row v col by parameter vs test.
This is more along the lines of your first suggestion
Each output file is imported separately to a different variable
(matrix)
Your second suggestion seems unnecessary since you can just iterate through your files.
You can use the command save to store your data.
It is very convenient, and can store as much data as your hard disk can bear.
The documentation is there:
http://www.mathworks.fr/help/techdoc/ref/save.html
Describe the format of text files. Because if it has a systematic format then you can use dlmread or similar commands in matlab and read the text file in a matrix. From there, you can plot easily. If you try to do it in excel, it will be much slower than reading from a text file. If speed is an issue for you, I suggest that you don't go for Excel.
Hi I have some data thats represented like this:
0,tcp,http,SF,239,486,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,19,19,1.00,0.00,0.05,0.00,0.00,0.00,0.00,0.00,normal.
Its from the kdd cup 1999 which was based on the darpa set.
the text file I have has rows and rows of data like this, in matlab there is the generic clustering tool you can use by typing findcluster but it only accepts .dat files.
Im also not very sure if it will accept the format like this. Im also not sure why there is so many trailing zeros in the dump files.
Can anyone help how I can utilise the text document and run it thru a fcm clustering method in matlab? Code help is really needed.
FINDCLUSTER is simply a GUI interface for two clustering algorithms: FCM and SUBCLUST
You first need to read the data from file, look into the TEXTSCAN function for that.
Then you need to deal with non-numeric attributes; either remove them or convert them somehow. As far as I can tell, the two algorithms mentioned only support numeric data.
Visit the original website of the KDD cup dataset to find out the description of each attribute.