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import os
import multiprocessing
import time
import numpy as np
import pyfits
import glob
import subprocess
import matplotlib.pyplot as plt
"""
Script that runs tests of purify on real data.
"""
nice_value = 0
home = os.path.expanduser("~")
stokes_v_calculation = False
outpath = home + "/purify_real_data/"
if stokes_v_calculation:
outpath = home + "/purify_real_data_v/"
data = home + "/Dropbox/Visibility_Files/"
atca = "atca/"
vla = "vla/"
names = [atca + "0332-391", atca + "0114-476", vla + "CygA-X", vla + "3C129BC"]
imsize = ["1024", "1024", "1024", "1024"]
cellsizes = ["2", "2", "0.5","0.4"]
residual_convergences = ["3700", "-1", "20000", "-1"]
def make_path(name, outpath, epsilon_factor):
return outpath + name + "/" + str(epsilon_factor) + "/"
def make_directory(path):
if not os.path.exists(path):
os.makedirs(path)
def run_test((name, inpath, cellsize, size, epsilon_factor, residual_convergence)):
path = make_path(name, outpath, epsilon_factor)
noise = " "
make_directory(path)
if stokes_v_calculation == True:
noise = " --noise " + inpath + ".ms "
cmd = ["screen -S "+ name.split("/")[-1] + "_" + str(epsilon_factor) +
" -d -m ~/dev/purify/build/purify" +
" --name " + path + name.split("/")[-1] +
" --measurement_set " + inpath + ".ms --n_mean " + str(epsilon_factor) +
noise +
" --cellsize " + cellsize +
" --size " + str(size) +
" --diagnostic --power_iterations 200" +
" --residual_convergence " + residual_convergence +
" --relative_variation 0.005" +
" --adapt_iter 100" +
" --niters 500" +
" --relative_gamma_adapt 1e-3"
]
proc = subprocess.Popen(cmd, shell=True)
print cmd
print "Waiting for " + path
while not os.path.exists(path + name.split("/")[-1] + "_solution_whiten_final.fits"):
time.sleep(10)
return
params = []
for i in range(len(names)):
i = 2
n = names[i]
print "Looking for " + data + n
if os.path.exists(data + n + ".ms"):
print n + " found!"
image_size = imsize[i]
pix_size = cellsizes[i]
params.append((n, data + n, pix_size, image_size, 7, residual_convergences[i]))
else:
print n + " not found!"
break
n_processors = multiprocessing.cpu_count() + 1
p = multiprocessing.Pool(min(n_processors, 1))
p.map(run_test, params)
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