def energies(seq_list):
energy_list = []
energy_list = multiprocessing(rna_folder, [sequence for sequence in seq_list], 12)
# for sequence in seq_list:
# #fc = RNA.fold_compound(str(sequence))
# (structure, MFE) = RNA.fold(str(sequence)) # calculate and define variables for mfe and structure
# energy_list.append(MFE) # adds the native fragment to list
return energy_list;
def __init__(
self,
constraint,
caption = "concurrency",
single = single(),
multicore = multicore(),
extras = [ batch() ],
conf_threading = threading(),
conf_multiprocessing = multiprocessing(),
prefer_mp = False,
):
self.caption = caption
self.single = single
self.multicore = multicore
if prefer_mp:
technologies = [ conf_multiprocessing, conf_threading ]
else:
technologies = [ conf_threading, conf_multiprocessing ]
self.option_for = {}
try:
self.option_for[ single.caption ] = single(
constraint = constraint,
technologies = technologies,
)
except ConfigurationError:
raise ConfigurationError("'%s' option not valid with this setup" % single.caption)
try:
self.option_for[ multicore.caption ] = multicore(
constraint = constraint,
technologies = technologies,
)
except ConfigurationError:
pass
self.extras = []
for platform in extras:
try:
self.option_for[ platform.caption ] = platform( constraint = constraint )
except ConfigurationError:
continue
self.extras.append( platform )
def __init__(
self,
constraint,
caption = "concurrency",
single = single(),
multicore = multicore(),
extras = [ batch() ],
conf_threading = threading(),
conf_multiprocessing = multiprocessing(),
prefer_mp = False,
):
self.caption = caption
self.single = single
self.multicore = multicore
if prefer_mp:
technologies = [ conf_multiprocessing, conf_threading ]
else:
technologies = [ conf_threading, conf_multiprocessing ]
self.option_for = {}
try:
self.option_for[ single.caption ] = single(
constraint = constraint,
technologies = technologies,
)
except ConfigurationError:
raise ConfigurationError, "'%s' option not valid with this setup" % single.caption
try:
self.option_for[ multicore.caption ] = multicore(
constraint = constraint,
technologies = technologies,
)
except ConfigurationError:
pass
self.extras = []
for platform in extras:
try:
self.option_for[ platform.caption ] = platform( constraint = constraint )
except ConfigurationError:
continue
self.extras.append( platform )
def scramble(text, randomizations, type):
frag = str(text)
frag_seqs = []
if type == "di":
for _ in range(randomizations):
result = dinuclShuffle(frag)
frag_seqs.append(result)
elif type == "mono":
frag_seqs = multiprocessing(randomizer, [frag for i in range(randomizations)], 12)
# for _ in range(int(randomizations)):
# result = ''.join(random.sample(frag,len(frag)))
# frag_seqs.append(result)
else:
print("Shuffle type not properly designated; please input \"di\" or \"mono\"")
return frag_seqs;
y_axis = np.power((y - float(grid_np_list_y[m])), 2)
z_axis = np.power((z - float(grid_np_list_z[m])), 2)
distance_to_atoms = (np.sqrt(x_axis + y_axis + z_axis))
n = 0
count = 0
while n < len(x_axis):
if distance_to_atoms[n] >= (surface_probe_diameter / 2):
count += 1
else:
break
n += 1
if count == len(x_axis):
return check_line
multiprocessing()
vs.pdb.divided_points("protein_points.txt", "protein_grid.pdb")
vs.pdb.divided_points("void_points.txt", "void_grid.pdb")
vs.pdb.divided_points("probe_points.txt", "probe_grid.pdb")
print("Done step 6/6")
protein_infile = open("protein_points.txt", "r").read().split()
void_infile = open("void_points.txt", "r").read().split()
void_volume = np.power(probe_size, 3) * len(void_infile)
protein_volume = np.power(probe_size, 3) * len(protein_infile) #cubic
print("\n\n\n" + str(protein_volume) + " A^3 protein volume")
print(str(void_volume) + " A^3 void volume")
total_volume = protein_volume + void_volume
__osplat = fn.extplatform()
executeable = fn.copasiexec()
fn.copasiexist()
print ("Found OS: %s\n" % __osplat)
print ("I'm going to use the following executeable: %s\n" % executeable)
print ("Please select the run mode:\n")
print ("Press 'c' for Multi config file mode:")
print ("Edits the filename of the data set in multiple cpsfiles (Models) \n\n\n")
print ("Press 'd' for Multi dataset file mode")
print ("Adds multiple Datasets to one cps file\n\n")
runmode = str ( input() )
runfileprocessing = []
if runmode in ("c", "C"):
returncode, runfileprocessing = multicpsfiles() # List backref
elif runmode in ("d", "D"):
returncode, runfileprocessing = multidataset() # Tupel backref
if returncode:
multiprocessing(runfileprocessing)
else:
sys.exit("Error: Method failed!")
if '.minion' in i:
try:
test_names.append(subfolder + '/' + i.replace('.minion', ''))
subprocess.check_call(['rm', test_names[-1] + '.xls'
]) # , shell=True)
except:
continue
if __name__ == '__main__':
for i in range(10):
test_names = []
benchmark = []
try:
subdirs = [x[0] for x in os.walk('./testcases/')]
#subdirs = subdirs[0:]
for i in subdirs:
test_name_add(i)
except:
subdirs = []
if len(sys.argv) > 2:
if sys.argv[2] == 'c':
clear_results(test_names)
else:
benchmark = ['x'] * len(test_names)
benchmark = multiprocessing(parproc, range(len(test_names)),
min(len(test_names), 4))
bm = get_benchmark()
print('done')
def hardest_read2(self):
read = multiprocessing(avg_vowels, url_list)
read.sort(reverse=True)
return read[0]
# # jobs = []
# #
# # start_time = time.time()
# # for i in range(0, 100):
# # p = multiprocessing.Process(target=worker, args=(i,))
# # jobs.append(p)
# # p.start()
# #
# # print(time.time() - start_time)
# #
# # print('-' * 50)
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
def multithreading(func, args, workers):
with ThreadPoolExecutor(workers) as ex:
res = ex.map(func, args)
return list(res)
def multiprocessing(func, args, workers):
with ProcessPoolExecutor(workers) as ex:
res = ex.map(func, args)
return list(res)
if __name__ == '__main__':
# multithreading(worker, range(1000), 6)
multiprocessing(worker, range(1000), 6)