def __init__(self, multiprocessing_library = None):
self._pool = None
if multiprocessing_library is None:
multiprocessing_library = DataConstants().multiprocessing_library
self._multiprocessing_library = multiprocessing_library
self._thread_technique = 'na'
if multiprocessing_library == 'multiprocess':
try:
import multiprocess;
multiprocess.freeze_support()
except:
pass
elif multiprocessing_library == 'multiprocessing_on_dill':
try:
import multiprocessing_on_dill;
multiprocessing_on_dill.freeze_support()
except:
pass
elif multiprocessing_library == 'multiprocessing':
try:
import multiprocessing;
multiprocessing.freeze_support()
except:
pass
def analyze(self, threshold_multiplier, parallel=False):
exp_name = self.config['experiment']['name']
tr = Tracker(exp_name, self.outdir, threshold_multiplier, self.magnification, self.microscope, self.binning)
gen = self.readin_stacks()
multiprocess.freeze_support()
pool = multiprocess.Pool()
#This may need to be a function of memory
offset = 10
self.resultdir = join(os.path.dirname(self.outdir), 'results')
#Make directory in case it doesn't exist
fileutils.mkdir(self.resultdir)
self.exporter.prep_csv_file(self.resultdir, self.surv_fname)
def output(well, neurons, crop_val):
self.exporter.export(well, neurons, crop_val)
if not parallel:
for data in gen:
well, neurons = tr.track(data)
output(well, neurons, 20)
else:
stacks_left = True
while stacks_left:
try:
it = itertools.chain([next(gen)], itertools.islice(gen, 0, offset - 1))
for well, neurons in pool.imap_unordered(func=tr.track, iterable=it):
output(well, neurons, 20)
except StopIteration:
stacks_left = False
group_labels = self.config['experiment']['imaging']['group_labels']
group_control_label = self.config['experiment']['imaging']['group_control_label']
run_cox_analysis(self.config, self.outdir)
def __init__(self, multiprocessing_library=None):
self._pool = None
if multiprocessing_library is None:
multiprocessing_library = DataConstants.multiprocessing_library
self._multiprocessing_library = multiprocessing_library
if multiprocessing_library == 'multiprocess':
try:
import multiprocess
multiprocess.freeze_support()
except:
pass
elif multiprocessing_library == 'multiprocessing_on_dill':
try:
import multiprocessing_on_dill
multiprocessing_on_dill.freeze_support()
except:
pass
elif multiprocessing_library == 'multiprocessing':
try:
import multiprocessing
multiprocessing.freeze_support()
except:
pass
def start(self):
''' Create tasks and results queues, and start consumers. '''
mp.freeze_support()
self.tasks = mp.JoinableQueue()
self.results = mp.Queue()
self.consumers = [
Consumer(self.tasks, self.results)
for i in range(self.getNConsumers())
]
for c in self.consumers:
c.start()
def __init__(self, parallel_library=None):
self._pool = None
if parallel_library is None:
parallel_library = constants.parallel_library
self._parallel_library = parallel_library
if parallel_library == 'multiprocess':
try:
import multiprocess
multiprocess.freeze_support()
except:
pass
elif parallel_library == 'pathos':
try:
import pathos
pathos.helpers.freeze_support()
except:
pass
'''
Variation pathos, local method
Windows OS: Hangs/ multiprocess error with newer version of pathos
Mac OS:
Linux:
Debian (unclear because windows app but operated in same manner)
Cloud-based:
Repl.it: Works
Ideone.com: Fails-multiprocess error
'''
from multiprocess import freeze_support
from pathos.multiprocessing import ProcessPool
def f(vars):
return vars[0]**vars[1]
if __name__ == "__main__":
freeze_support()
pool = ProcessPool(4)
print(list(pool.imap(f, [(1, 5), (2, 8), (3, 9)])))
import multiprocess
multiprocess.freeze_support()
from osrsmath.apps.optimize.gui_single import Ui_MainWindow
from osrsmath.apps.optimize.logic.optimize import get_sets, get_best_sets
from osrsmath.combat.boosts import BoostingSchemes, Prayers, Potions
from osrsmath.combat.monsters import get_monster_data
from osrsmath.combat.monsters import Monster
from osrsmath.combat.fighter import Fighter
import osrsmath.apps.GUI.resources
import osrsmath.config as config
from PySide2 import QtCore, QtGui, QtWidgets
from pprint import pprint
from pathlib import Path
import textwrap
import time
import glob
import os
slots = [
'head', 'cape', 'neck', 'ammo', 'weapon', 'body', 'shield', 'legs',
'hands', 'feet', 'ring'
]
class GUI(Ui_MainWindow):
OVERVIEW_TEXT = textwrap.dedent("""\
This app allows you to determine the optimal equipment to wear against a set of opponents.
There are three main panels:
script for generating training and testing data for the
NMF portion of the SPS emulator
Notes:
* 2021/10/12: modified redshift range to 0.3 < z < 1.5 to construct training
data for LRG
* 2021/06/21: lower upper limit on metalliicty history; wider wavelength range
that extends to FUV
'''
import os, sys
import numpy as np
import multiprocess as mp
from provabgs import infer as Infer
from provabgs import models as Models
mp.freeze_support()
###########################################################################################
# input
###########################################################################################
name = 'nmf'
version = 'lrg.0.1'
try:
ibatch = int(sys.argv[1])
except ValueError:
ibatch = sys.argv[1]
assert ibatch == 'test'
ncpu = int(sys.argv[2])
# hardcoded to NERSC directory for LRG
#dat_dir='/global/cscratch1/sd/chahah/provabgs/emulator' # hardcoded to NERSC directory
# for LRG