I need a python equivalent of the MATLAB command findchangepts . I am unable to find one yet. Essentially I want to find K abrupt changes in my time series.
I am able to use the matlab command but unable to find an equivalent function in python. Any help/pointers to existing libraries will be very helpful. I am not proficient in the optimization modules of scipy and thus would prefer an existing python package.
Thanks.
If you cannot use Matlab under the hood there is e.g. ruptures which implements change point detection (docs).
Just in case the answer might help:
I have both MATLAB and Python on my computer. I was able to use MATLAB bindings for python to use findchangepts in my python code.
Source for bindings : MATLAB API for Python
Related
In my program in need to reconstruct matlab code in python but not in very low level. The problem is that i got following lines of matlab
p = sobolset(problem_size, 'Skip', 1e4, 'Leap', 1e3);
p = scramble(p, 'MatousekAffineOwen');
rand0 = net(p, pop_size);
And I'm not able to reconstruct it on my own. Didn't find python functions in python libraries that would do exacly the same. So my question is if i could easly blend in this matlab code to python code or do someone have other idea how I can deal with it?
Your best bet here is probably to use either MATLAB Compiler SDK to generate a Python package from your MATLAB code or to use MATLAB Engine for Python.
If you are going to share this code with others that don't have a MATLAB installation or if you're running on a machine that doesn't have a MATLAB installation, you CANNOT use MATLAB Engine. Compiler SDK does require you to have that specific license though. If you are licensed through a university, you probably already have access to that license.
I am using an open-source Matlab toolbox for brain-computer interface (BCI). I want to send the brain imaging data over to Tensorflow for classification and get the results back to Matlab. Is there any way to pass data structures from Matlab to Tensorflow and get the results back into Matlab?
In case someone lands here with a similar question, I'd like to suggest a Matlab package I am currently writing. It's called tensorflow.m and it's available on GitHub. There's no stable release yet, but simple functionality like importing a frozen graph and running an inference is already possible (see the examples) - this is all you'd need to classify the images in Matlab (only).
The advantage is that you don't need any expensive toolbox nor a Python/Tensorflow installation on your machine. The Python interface of Matlab also seems to be rather adventurous, while tensorflow.m is pure Matlab/C.
I'd be glad if the package can be of use for someone looking for similar solutions; even more so, in case you extend/implement something and open a PR.
So far the best way I found is to run your python module in matlab through matlab's now built-in mechanism for connecting to python:
I wrote my python script in a .py file and in there I imported tensorflow and used it in different functions. You can then return the results to matlab by calling
results = py.myModule.myFunction(arg1,arg2,...,argN)
More detailed instructions for calling user-defined python modules in matlab could be found in the following link:
http://www.mathworks.com/help/matlab/matlab_external/call-user-defined-custom-module.html
Does anybody know which is the equivalent (or any equivalent) matlab damp function for Python (in scipy, numpy or some other)?
Thanks
Whilst I haven't personally used it, a quick google suggests that the python control systems library might be what you're after.
It appears to implement the functionality of the MATLAB control systems toolbox with a compatibility mode which matches the same interface as in MATLAB.
Specifically the function you have asked for is here.
I'd like to use OpenCV functions, like Hough Transform and Corner Detection, in Sikuli.
I tried the OpenCV installation instructions for CPython on Sikuli and it's a no go.
I understand that Sikuli is Jython and this might be the hard way to do things. What are the easier alternatives?
I'd still like to use Sikuli & Python because the code I write just works; but maybe I'm hitting the limits of Sikuli.
You could use Automa as an alternative to Sikuli. It is a pure Python library (so you can use any other Python library with it) and its image search algorithms are fully compatible with Sikuli. In fact, it uses OpenCV's Python bindings itself, and thus ships with them.
Disclaimer: I'm one of Automa's developers.
Generally spoken: Python modules containing C-based code (which is the case for Python OpenCV) cannot be used in the Sikuli Script (Jython).
The integration of some OpenCV features in Sikuli is realized using the Java-To-C interface (JNI) and the usage of OpenCV features itself is coded in C++.
If you want to use OpenCV features not supported by Sikuli, then you either have to use the above mentioned JNI approach or use the Java implementation of OpenCV (javacv) (Java stuff can be used seamlessly from Jython scripts).
Another option of course is to implement the additional things in plain Python scripts using the Python/OpenCV and call these scripts via the C-Python interpreter in a subprocess.
A researcher has created a small simulation in MATLAB and we want to make it accessible to others. My plan is to take the simulation, clean up a few things and turn it into a set of functions. Then I plan to compile it into a C library and use SWIG to create a Python wrapper. At that point, I should be able to call the simulation from a small Django application. At least I hope so.
Do I have the right plan? Are there are any serious pitfalls that I'm not aware of at the moment?
One thing to remember is that the MATLAB compiler does not actually compile the MATLAB code into native machine instructions. It simply wraps it into a stand-alone executable or a library with its own runtime engine that runs it. You would be able to run your code without MATLAB installed, and you would be able to interface it with other languages, but it will still be interpreted MATLAB code, so there would be no speedup.
Matlab Coder, on the other hand, is the thing that can generate C code from Matlab. There are some limitations, though. Not all Matlab functions are supported for code generation, and there are things you cannot do, like change the type of a variable on the fly.
I remember that I was able to wrap a MATLAB simulation into a DLL file and then call it from a Delphi application. It worked really well.
I'd also try ctypes first.
Use the MATLAB compiler to compile the code into C.
Compile the C code into a DLL.
Use ctypes to load and call code from this DLL
The hardest step is probably 1, but if you already know MATLAB and have used the MATLAB compiler, you should not have serious problems with it.
Perhaps try ctypes instead of SWIG. If it has been included as a part of Python 2.5, then it must be good :-)