python-bindings | Python language bindings for preCICE
kandi X-RAY | python-bindings Summary
kandi X-RAY | python-bindings Summary
This package provides python language bindings for the C++ library preCICE. Note that the first three digits of the version number of the bindings indicate the preCICE version that the bindings support. The last digit represents the version of the bindings. Example: v2.0.0.1 and v2.0.0.2 of the bindings represent versions 1 and 2 of the bindings that are compatible with preCICE v2.0.0.
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Top functions reviewed by kandi - BETA
- Returns a dict of the command class
- Return a ConfigParser instance from root
- Get project root directory
- Extract the version information from the VCS
- Create a versioneer config file
- Install versioneer
- Initialize the options
- Return a list of C ++ extensions
- Get the list of installed versions
- Scans the setup py py file and checks if it is missing
- Finalize the options
python-bindings Key Features
python-bindings Examples and Code Snippets
Lines of Code : 9License : Weak Copyleft (LGPL-3.0)
Copy /tmp/pip-install-d_fjyo1h/pyprecice/precice.cpp:643:10: fatal error: precice/SolverInterface.hpp: No such file or directory
643 | #include "precice/SolverInterface.hpp"
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
$ python3 -m pip install pyprecice
Collecting pyprecice
...
/tmp/pip-build-7rj4_h93/pyprecice/precice.cpp:25:20: fatal error: Python.h: No such file or directory
compilation terminated.
error: command 'x86_64-linux-gnu-gcc' failed with exit sta $ python3 -c "import precice"
Traceback (most recent call last):
File "", line 1, in
ImportError: libprecice.so.2: cannot open shared object file: No such file or directory
Community Discussions
Trending Discussions on python-bindings
QUESTION
This has been asked twice already one answer seems very popular:
How to use compile_commands.json with clang python bindings?
This other one not as much: How to use compile_commands.json with llvm clang (version 7.0.1) python bindings?
However neither solution seems to work. If you try the most popular solution, i.e. if you do this:
...ANSWER
Answered 2022-Jan-31 at 05:43The correct way seems to be doing this:
QUESTION
I want to create a special case of a room-impulse-response. I am following this implemetation for a room-impulse-response generator. I am also following this tutorial for integrating c++\c with python.
According to the tutorial:
- You want to speed up a particular section of your Python code by converting a critical section to C. Not only does C have a faster execution speed, but it also allows you to break free from the limitations of the GIL, provided you’re careful.
However, when looking at the MATLAB example, all I see the cpp code segment doing, are regular loops and mathematical computations. In what way will c\cpp be faster than python\MATLAB in this example or any other? Will any general c\cpp code run faster? If so, why? If not, what are the indicators I need to look for, when opting for a c\cpp segment implementation? which operations are faster in c\cpp?
...ANSWER
Answered 2022-Jan-16 at 15:31C++ code compiles into machine code. This makes it faster compared to interpreter languages (however not every code written in C++ is faster than Python code if you don't know what you are doing). in C++ you can access the data pointers directly and use SIMD instructions on them to make them multiple times faster. You can also multi-thread your loops and codes to make them run even faster (either explicit multi-threading or tools like OpenMP). You can't do these things (at least properly) in a high level language).
When to use C++ to speedup PythonNot every part of the code is worth optimizing. You should only optimize the parts that are computationally expensive and are a bottleneck of your program. These parts can be written in C or C++ and exposed to python by using bindings (by using pybind11 for example). Big machine learning libraries like PyTorch and TensorFlow do this.
Dedicated HardwareSometimes having a well optimized C++ CPU code is not enough. Then you can assess your problem and if it is suitable, you can use dedicated hardware. These hardware can go from low-level (like FPGA) to high-level hardware like dedicated graphics cards we usually have on our system (like CUDA programming for NVIDIA GPUs).
Regular Code Difference in Low and High Level LanguagesUsing a language that compiles has great advantages even if you don't use multi-threading or SIMD operations. For example, looping over a C array or std::vector in C++ can be more than 100x faster compared to looping over Python arrays or using for in MATLAB (recently JIT compiling is being used to speed up high-level languages but still, the difference exists). This has many reasons, some of which are basic data types that are recognized at compile time and having contiguous arrays. This is why people recommend using Numpy vectorized operations over simple Python loops (same is recommended for MATLAB).
QUESTION
I have been trying to use some code which is uses openmesh and igl libraries.
Unfortunately, the only way to install igl I found was via conda into its Python 3.9 environment (conda install -c conda-forge igl). Openmesh on the other hand I could only install via outdated pip 20.x into Python 2.7 (pip install openmesh).
How could I use code, which necessitates both libraries? Is there an easy way to import the methods from one of the two libraries into either Python 2.7 or 3.9 environment?
...ANSWER
Answered 2022-Jan-11 at 23:15First, openmesh on Conda Forge refers to the OpenMesh software, which is not equivalent to the Python bindings (openmesh on PyPI). The latter goes by openmesh-python on Conda Forge and has builds for Python 2.7, 3.6, and 3.7.
Second, I wouldn't recommend trying to install into an existing environment. Rather, specify exactly the packages that you require and let Conda resolve the dependency versions.
Using the command:
QUESTION
I am using the python client for GPT 3 search model on my own Jsonlines files. When I run the code on Google Colab Notebook for test purposes, it works fine and returns the search responses. But when I run the code on my local machine (Mac M1) as a web application (running on localhost) using flask for web service functionalities, it gives the following error:
...ANSWER
Answered 2021-Dec-20 at 13:05The problem was on this line:
file = openai.File.create(file=open(jsonFileName), purpose="search")
It returns the call with a file ID and status uploaded which makes it seem like the upload and file processing is complete. I then passed that fileID to the search API, but in reality it had not completed processing and so the search API threw the error openai.error.InvalidRequestError: File is still processing. Check back later.
The returned file object looks like this (misleading):
It worked in google colab because the openai.File.create call and the search call were in 2 different cells, which gave it the time to finish processing as I executed the cells one by one. If I write all of the same code in one cell, it gave me the same error there.
So, I had to introduce a wait time for 4-7 seconds depending on the size of your data, time.sleep(5) after openai.File.create call before calling the openai.Engine("davinci").search call and that solved the issue. :)
QUESTION
I have a project, cloudgen, that I would like to add bindings for Python so I can access some of the underlying functions. I have stubbed out the initial work on a branch. Because the main executable is built with cmake, I decided to use scikit-build to manage the build and use pybind11 to deal with the binding (following this example repo).
When I run pip install . in a virtual environment, everything appears to work as expected. I find the executable is installed to /bin, the library goes into /lib, and the module goes into /lib/pythonX.Y/site-packages/cloudgen. In fact, if I run pip uninstall cloudgen, all of the correct files are uninstalled. However, my problems arise when I start to test the Python bindings. I find two separate but related problems.
- If I installed into an Anaconda environment, the module is able to resolve the path to the shared library and pass the tests, but the executable does not resolve the path to the library.
- On the other hand, if I installed into a virtual environment using
python -m venv, both the module and the executable are unable to resolve the path to the shared library.
Searching around, I came across this question which notes I could manipulate LD_LIBRARY_PATH (or equivalently DYLD_LIBRARY_PATH on macOS or PATH on Windows), but that is normally frowned upon. That question references an open issue that refers to including additional build products (which as I said appears to not be my problem) but doesn't address the library path resolution. I also came across this question asking about distributing the build products using scikit-build and this question using setuptools directly. Neither of the questions or answers address the library path resolution.
My question is: What is the correct way to distribute a package that contains an executable, shared library, and Python binding module and have the path resolution Just Work™?
A minimal working example is a bit much, but I created a gist to demonstrate the behavior.
...ANSWER
Answered 2021-Nov-30 at 00:52After a bit more digging (and carefully reading the CMake documentation on RPATH), the correct answer appears to be explicitly setting RPATH on installation. The relevant change to the linked gist is to add the following to the CMakeLists.txt after creating the targets (adapted from the linked Wiki):
QUESTION
I would appreciate it if I can see an example for using offset_surface on python as follow:
- read object in stl format
- offset object with certain distance
- write new object (offsetted object) in stl as well.
long story short, I am looking for something like this but with python instead of matlab
Thank you so much
I tried it here:
...ANSWER
Answered 2021-Jun-25 at 11:41I found out the answer for the question and wanted to shar it wit you:
instead of
QUESTION
I'm trying to help the owner of tkdnd generate a wheel and distribute it to Pypi so users could simply install the tkdnd extension with a simple pip install TkinterDnD2.
I'm after this 'challange' for the last two days but could not solve it by myself so far, but I'm sure someone with a deep understanding python packaging and installing process could help as solve it in short time.
currently in order to get this extension work you need to do the following steps(as mentioned here):
- download the compiled tkdnd files for your os. now go to your base interpreter directory/tcl and copy this folder under. for example:
ANSWER
Answered 2021-Jun-05 at 17:02Just answering for the case someone else got stuck on it for weeks like I did.
You can see dist repo that pmgagne created here: https://github.com/pmgagne/tkinterdnd2
The package is not yet published on pypi but you can build a wheel and install it following the instructions on this issue I opened: https://github.com/pmgagne/tkinterdnd2/issues/5
Hopefully, the author will build and publish it soon, so you could simply do python -m pip install tkinterdnd2 and enjoy tkinter inter-windows drag and drop support!
If you don't want to build it your self here's a wheel download: tkinterdnd2-0.3.0-py2.py3-none-any.zip
extract zip and then you can do python -m pip install tkinterdnd2-0.3.0-py2.py3-none-any.whl and then you will be able to import tkinterdnd2 in your python project.
the author did not respond so I forked it published it myself. you can now install simply using
QUESTION
I have to read images coming from a camera, add some shapes with OpenCV and return the image to a webpage with Flask. This application is running in Windows 10. The detailed steps are:
- Reading the images from a camera is done. I have a Python wrapper for the C++ library which seems to work well.
- Adding shapes with OpenCV is done. I use the OpenCV wrapper for Python.
- Returning the image with Flask is the problem. For that, I need to convert the image from raw to JPG or PNG format with
cv2.imencode(the OpenCV Python wrapper). Nevertheless, this slows down the frame rate of the video to be shown on the website.
I've seen at some places, that OpenCV can be used to perform some operations over the GPU (Nvidia GeForce RTX). I've tried with cv2.UMat just before calling cv2.imencode but there is no difference.
And here is my question:
- should I try with more Python wrappers (as described here)?
- should I start learning C++? Will I need to migrate all the existing application from Python to C++ or just a wrapper? Can this be done in a few lines?
ANSWER
Answered 2020-Aug-25 at 08:17I finally compiled OpenCV-python with the GPU flags, with the needed CUDA flags. It took me weeks to install all the needed dependencies (image libraries, video libraries, Fortran, LINPAK, BLAS...).
The result is that the call to cv2.imencode is way faster and I can see the GPU consumption increased in the resources manager of Windows.
QUESTION
I am using Selenium 2.35.0 to test a Chromium based browser. I am using Chrome driver version 2.43.600233 and set my application name to Chrome and version to 2.43.600233 as recommended in
https://forum.qt.io/topic/96202/unrecognized-chrome-version-when-using-selenium-python-bindings-and-chromedriver
"I had the same problem, and a way to hack it is to set your QT applicationName and applicationVersion to "Chrome" and "69.0.3497.128". "
Everything then worked fine. I only ran to an issue when I tried maximizing,minimizing, changing window size of my window using
...ANSWER
Answered 2020-Jul-07 at 15:22I can tell you that on all recent Chrome versions (going back years), with up-to-date Chromedriver, in C#, setting driver.Manage().Window.Size works fine for me. But, I'm unfamiliar with setting app names and versions like you're doing. Does it work if you don't call maximize() first?
QUESTION
Edit: accidentally copy pasted a line twice.
I'm making a C++ Python extension using a combination of boost and the normal C API, but I can't compile it. I've read the following documentations:
documentation from python wiki
Python bindings for C++ medium
here's the code from the cpp file:
...ANSWER
Answered 2020-Jun-28 at 21:27Like (almost?) everything (that isn't a macro) in Boost, class_ is in a namespace, in this case boost::python. Either:
- Add
using namespace boost::python;at the top of your file (just after the includes). - Refer to
boost::python::class_instead.
The same applies to the other symbols to.
This is shown in the quickstart part of the documentation. For most of the rest of it they only show short code snippets so I think it's meant to be implicit.
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