I want to call python from the java code and pass the parameters to function in python. I have encapsulated a function in python. But the thing is I have also imported third party library sklearn in the python program. When I call a test program and pass the value to python, I am able to run the code. But when I tried using third party library such as sklearn and import in the python code, I got following error.
ImportError: No module named sklearn
I initialized object for the class PythonInterpreter and then using the following command, I included the path for the sklearn
this.interpreter.exec("import sys");
this.interpreter.exec("sys.path.append(\"/usr/local/lib/python2.7/dist-packages/sklearn\")");
Can anyone tell how to fix this problem? How to include third party library in the jython code and run the program?
As sklearn depends on native CPython extensions, it currently won't work with Jython. However, keep an eye on JyNI (www.jyni.org), which will vastly improve this issue, but is still in an early state. Until JyNI is sufficiently mature, you can use one of the following projects:
JEP (https://github.com/mrj0/jep) or
JPY (https://github.com/bcdev/jpy).
They work by embedding the CPython interpreter and don't integrate with Jython. Still, if you depend on things like scikit or numpy, these are currently the only workable approaches to use your Python-code from Java.
Related
I want to write golang bindings for an existing (third party) Python module.
The purpose is that I want to use the API that the Python module provides in Golang.
I already found golang bindings for Python's C API (go-python3 for py3 and go-python for py2), but I still haven't figured out how to translate a relatively complex Python module into Golang (i.e. how to deal with type safety in go for unsafe inputs and returns in python, etc).
What would be a good approach? Are there any pre-existing tools in that space? Are there any good examples for Golang bindings for Python code? (I couldn't find many tbh).
I want to use the API that the Python module provides in Golang.
Calling Python from Go is detailed recently in "Python and Go : Part I - gRPC" by Miki Tebeka.
You can see an example in ardanlabs/python-go/grpc
But, as shown in their next two articles, you can also:
compiled Go code to a shared library and used it from the Python interactive shell.
use a Python module that hides the low level details of working with a shared library and then package this code as a Python package.
Full example: ardanlabs/python-go/pyext.
I am testing VS code and I like it very much. But I have an issue with a Python module.
The module is xspec (https://heasarc.gsfc.nasa.gov/docs/xanadu/xspec/python/html/index.html). The issue is that VS code does not recognize the module, underlining in red all the functions.
This is probably because xspec needs to be initialized before using it. To inizialize it, an entire software has to be initialized (https://heasarc.gsfc.nasa.gov/docs/software/heasoft/). Basically, every time I need the xspec module I have to inizialized the heasoft sofwtware before running python, otherwise the module is not recognized.
There is a way to solve it? Or there is a way to add exceptions to the VS code highlining errors?
I'm developing a C++ library, which has python embedded. What I would like to do is to statically link Python library, so that there won't be configuration issues, when I switch to production server. So far, I'm able to link libpython3.5m.a statically (I had to build Python from sources though, because it seems, that packaged libraries aren't compiled with -fPIC flag). However, I came to a problem, that it seems, there's no Numpy: When I run application, which uses my library, it prompts me with an error:
ImportError: numpy.core.multiarray failed to import
And this error is caused by import_array1() macro, that (AFAIK) is used to import the numpy routines to C++. I tried linking libnpymath.a as well as libnpysort.a, which I found in numpy build dir, but to no avail. Do you happen to know, if such static linking is possible and how to do it? I guess it should be possible, since numpy is written in C...
What I would like to do is to statically link Python library, so that there won't be configuration issues, when I switch to production server.
This would only be the Python core, it would exclude all of the Python libraries. You still need to ship all of the Python code.
...since numpy is written in C...
This is incorrect. NumPy is written about half in C and half in Python. It looks like the C part is the part that's not loading here, since numpy.core.multiarray is written in C, and you wouldn't normally import that yourself, it would normally be imported by the Python part of NumPy.
Linking in the C code is not enough anyway, you need to load initialize the associated Python modules exported by the C code. Without static linking, Python would just find the multiarray.so file in the right place and load it. When you build Python statically, you would would normally edit the Modules/Setup.local file with the modules you want statically compiled into Python. However, this is not designed to work with arbitrary third-party modules like NumPy. See: Compile the Python interpreter statically?
Honestly, if you are just trying to make sure that the same version of Python runs on both development and production systems, there are vastly easier ways to do this, like virtualenv. CPython is simply not designed to be statically linked.
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
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 :-)