Why does python use both reference counting and markandsweep for gc?
Date : March 29 2020, 07:55 AM
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.

Python implementation of statistical Sweep operator
Tag : python , By : George Handlin
Date : March 29 2020, 07:55 AM
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 nonresponse data is the Sweep Operator (details on page 148151). 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 kth 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 kth 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],
[ 5.55111512e17, 1.00000000e+00]])

Fast ping sweep in python
Date : March 29 2020, 07:55 AM
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)

Plot Frequency Sweep In Python
Date : March 29 2020, 07:55 AM
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

using awk in python in ping sweep
Tag : python , By : PsyberMonkey
Date : March 29 2020, 07:55 AM
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\"}"], ...)

