cython

问题 How to grant read-only (non-copy) access to an object in one process to another process? For example, in the diagram below, how to grant Process 2 and Process 3 access to the class Tasks which is in Process 1 ? Process 1 is the only process which will write to the class Tasks and it is not important to maintain any state about which tasks have been processed etc. Process 2 Process 1 Process 3 +-------------------+ +-------------------+ +-------------------+ | | | | | | | Perform job A for | <

问题 I wrote a tree object in cython that has many nodes, each containing a single unicode character. I wanted to test whether the character gets interned if I use Py_UNICODE or str as the variable type. I'm trying to test this by creating multiple instances of the node class and getting the memory address of the character for each, but somehow I end up with the same memory address, even if the different instances contain different characters. Here is my code: from libc.stdint cimport uintptr_t

问题 I am having trouble compiling a c extension using cython in python 3. It compiles and works fine with python 2.7, but in python 3.4.3 I get the following error when building with 3.4 (anaconda distribution): python setup.py build_ext --inplace running build_ext building 'module' extension gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Iincludes -I/home/user/anaconda/include/python3.4m -c module.cpp -o build/temp.linux-x86_64-3.4/module.o cc1plus: warning: command line

问题 I was using the pandas grouby mean function like the following on a very large dataset: import pandas as pd df=pd.read_csv("large_dataset.csv") df.groupby(['variable']).mean() It looks like the function is not using multi-processing, and therefore, I implemented a paralleled version: import pandas as pd from multiprocessing import Pool, cpu_count def meanFunc(tmp_name, df_input): df_res=df_input.mean().to_frame().transpose() return df_res def applyParallel(dfGrouped, func): num_process=int

问题 so I'm trying to create a Cython module which uses a .c file that I've written. That .c file needs a special linking option (to compile it, I need gcc -o mycode mycode.c -lfftw3f ). I could potentially just rewrite my .c file in Cython, but I want to know how to do this. I am using fftw3, and when compiling, you need to use the -lfftw3f option IN ADDITION to #include <fftw3.h> in the .c file if you want to use the float version, which I do. My setup.py looks like the following: from distutils

问题 I am trying to speed up my Numpy code and decided that I wanted to implement one particular function where my code spent most of the time in C. I'm actually a rookie in C, but I managed to write the function which normalizes every row in a matrix to sum to 1. I can compile it and I tested it with some data (in C) and it does what I want. At that point I was very proud of myself. Now I'm trying to call my glorious function from Python where it should accept a 2d-Numpy array. The various things

问题 I am trying to speed up my Numpy code and decided that I wanted to implement one particular function where my code spent most of the time in C. I'm actually a rookie in C, but I managed to write the function which normalizes every row in a matrix to sum to 1. I can compile it and I tested it with some data (in C) and it does what I want. At that point I was very proud of myself. Now I'm trying to call my glorious function from Python where it should accept a 2d-Numpy array. The various things

问题 I'm trying to write Cython code to dump a dense feature matrix, target vector pair to libsvm format faster than sklearn's built in code. I get a compilation error complaining about a type issue with passing the target vector (a numpy array of ints) to the relevant c function. Here's the code: import numpy as np cimport numpy as np cimport cython cdef extern from "cdump.h": int filedump( double features[], int numexemplars, int numfeats, int target[], char* outfname) @cython.boundscheck(False)

问题 I am working on a project which combines high-performance algorithms written in C++ with a Python interface. C++ classes and functions are wrapped and exposed to Python via the Cython compiler. Suppose I call a long running native function from the Python interpreter (my preferred one is IPython). Is it somehow possible to interrupt or abort the execution of that code without killing the interpreter? 回答1: Here is a possible implementation using multiprocessing as suggested by Ricardo C.,

问题 I have a large collection of large images (ex. 15000x15000 pixels) that I would like to blur. I need to blur the images using a distance function, so the further away I move from some areas in the image the more heavier the blurring should be. I have a distance map describing how far a given pixel is from the areas. Due to the large amount of images I have to consider performance. I have looked at NumPY/SciPY, they have some great functions but they seem to use a fixed kernel size and I need