Subtractively sweep a solid through another in python (or C++)

will be helpful for those in need Python (the language) doesn't say which form of garbage collection it uses. The main implementation (often known as CPython) acts as you describe. Other versions such as Jython or IronPython use a purely garbage collected system.
Yes, there is a benefit of earlier collection with reference counting, but the main reason CPython uses it is historical. Originally there was no garbage collection for cyclic objects so cycles led to memory leaks. The C APIs and data structures are based heavily around the principle of reference counting. When real garbage collection was added it wasn't an option to break the existing binary APIs and all the libraries that depended on them so the reference counting had to remain.

Any of those help I am learning some techniques for doing statistics with missing data from a book (Statistical Analysis with Missing Data by Little and Rubin). One particularly useful function for working with monotone non-response data is the Sweep Operator (details on page 148-151). I know that the R module gmm has the swp function which does this but I was wondering if anyone has implemented this function in Python, ideally for Numpy matrices to hold the input data. I search StackOverflow and several web searches without success. for any help.

def sweep(g, k):
    g = np.asarray(g)
    n = g.shape[0]
    if g.shape != (n, n):
        raise ValueError('Not a square array')
    if not np.allclose(g - g.T, 0):
        raise ValueError('Not a symmetrical array')
    if k >= n:
        raise ValueError('Not a valid row number')
    #  Fill with the general formula
    h = g - np.outer(g[:, k], g[k, :]) / g[k, k]
    # h = g - g[:, k:k+1] * g[k, :] / g[k, k]
    # Modify the k-th row and column
    h[:, k] = g[:, k] / g[k, k]
    h[k, :] = h[:, k]
    # Modify the pivot
    h[k, k] = -1 / g[k, k]
    return h

def sweep_non_sym(a, k):
    a = np.asarray(a)
    n = a.shape[0]
    if a.shape != (n, n):
        raise ValueError('Not a square array')
    if k >= n:
        raise ValueError('Not a valid row number')
    #  Fill with the general formula
    b = a - np.outer(a[:, k], a[k, :]) / a[k, k]
    # b = a - a[:, k:k+1] * a[k, :] / a[k, k]
    # Modify the k-th row and column
    b[k, :] = a[k, :] / a[k, k]
    b[:, k] = -a[:, k] / a[k, k]
    # Modify the pivot
    b[k, k] = 1 / a[k, k]
    return b

>>> a = [[2,4],[3,1]]
>>> sweep_non_sym(a, 0)
array([[ 0.5,  2. ],
       [-1.5, -5. ]])
>>> sweep_non_sym(sweep_non_sym(a, 0), 1)
array([[-0.1,  0.4],
       [ 0.3, -0.2]])
>>> np.dot(a, sweep_non_sym(sweep_non_sym(a, 0), 1))
array([[  1.00000000e+00,   0.00000000e+00],
       [  5.55111512e-17,   1.00000000e+00]])

This might help you Multiprocessing

#!/usr/bin/python2

import multiprocessing
import subprocess
import os

def pinger( job_q, results_q ):
    DEVNULL = open(os.devnull,'w')
    while True:
        ip = job_q.get()
        if ip is None: break

        try:
            subprocess.check_call(['ping','-c1',ip],
                                  stdout=DEVNULL)
            results_q.put(ip)
        except:
            pass

if __name__ == '__main__':
    pool_size = 255

    jobs = multiprocessing.Queue()
    results = multiprocessing.Queue()

    pool = [ multiprocessing.Process(target=pinger, args=(jobs,results))
             for i in range(pool_size) ]

    for p in pool:
        p.start()

    for i in range(1,255):
        jobs.put('192.168.1.{0}'.format(i))

    for p in pool:
        jobs.put(None)

    for p in pool:
        p.join()

    while not results.empty():
        ip = results.get()
        print(ip)

hop of those help? I want a sinus with linear increasing frequency in the range [f_start : f_stop] over time.

 sin(w*t)  => the angular velocity is   d(w*t)/dt =   w

sin(w*t*t) => the angular velocity is d(w*t*t)/dt = 2*w*t

I wish this helpful for you Remove the single quotation marks. They are necessary to delineate shell command line parameters, but not needed when there is no command line.

p3 = subprocess.Popen(["awk", "{print $4 \"is alive\"}"], ...)