def test():
here = os.path.dirname(os.path.realpath(__file__))
config = os.path.join(here, 'config', 'in1.yaml')
preview = True
answer = plot.run(config, preview)
assert answer is True
import checksignal as cs
if cfg.quick == True:
cs.run(cfg.name + str(cfg.maxdepth), quick=True)
else:
cs.run(cfg.name + str(cfg.maxdepth))
if cfg.crossvalidation == True:
import crossvalidation as cv
if cfg.quick == True:
cv.run(cfg.name + str(cfg.maxdepth), quick=True)
else:
cv.run(cfg.name + str(cfg.maxdepth))
if cfg.plot == True:
import plot as p
if cfg.quick == True:
p.run(cfg.name + str(cfg.maxdepth), int(cfg.bins), quick=True)
else:
p.run(cfg.name + str(cfg.maxdepth), int(cfg.bins))
if cfg.write == True:
import write as w
if cfg.quick == True:
raise Exception("Requires full dataset")
else:
w.run(cfg.name + str(cfg.maxdepth), cfg.source)
end = time.time()
print time.asctime(time.localtime()), "Code Ended"
pl.show()
def main():
run("ecs_dp")
run("ecs_ml")
run("k8s")
run("swarm")
plot.run()
def plot():
import plot
plot.run()
import checksignal as cs
if cfg.quick == True:
cs.run(cfg.name + str(cfg.maxdepth), quick = True)
else:
cs.run(cfg.name + str(cfg.maxdepth))
if cfg.crossvalidation == True:
import crossvalidation as cv
if cfg.quick == True:
cv.run(cfg.name + str(cfg.maxdepth), quick = True)
else:
cv.run(cfg.name + str(cfg.maxdepth))
if cfg.plot == True:
import plot as p
if cfg.quick == True:
p.run(cfg.name + str(cfg.maxdepth), int(cfg.bins), quick = True)
else:
p.run(cfg.name + str(cfg.maxdepth), int(cfg.bins))
if cfg.write == True:
import write as w
if cfg.quick == True:
raise Exception("Requires full dataset")
else:
w.run(cfg.name + str(cfg.maxdepth),cfg.source)
end = time.time()
print time.asctime(time.localtime()), "Code Ended"
pl.show()
#Applies unsupervised learning to the datasets
if cfg.learn == True:
import learn as l
if cfg.quick == True:
l.run(str(cfg.name + "quick"), cfg.alpha, cfg.groups, quick = True)
else:
l.run(cfg.name, cfg.alpha, cfg.groups)
#Does something, probably.
if cfg.plot == True:
import plot as p
if cfg.quick == True:
p.run(cfg.name + "quick", quick = True)
else:
p.run(cfg.name)
#Classifies the data into a varying number of categories
if cfg.classify == True:
import classify as c
if cfg.quick == True:
c.run(cfg.name + "quick", cfg.fit, quick = True)
else:
c.run(cfg.name, cfg.fit)
#Creates Histograms for each category identied by learning algorithm
if cfg.onedhistogram == True:
def plot():
import plot
plot.run()
def run():
TFdict, Histonedict = get_fullpaths.run(TFdir, Histonedir)
calculate_results.run(TFdict, Histonedict, Bidirdir, Genedir, files)
plot.run(files)
exit(1)
# Spawn a CASA process to work with and put the config_file variable to
# the CASA variable list.
casa = drivecasa.Casapy(working_dir=os.path.curdir,
casa_logfile=False,
echo_to_stdout=True)
casa.run_script(["config_file = '{}'".format(args.config)])
# Top level simulation output directory.
# TODO-BM use the filename of the settings file as the path?
sim_dir = settings['path']
# Create a copy of the settings file.
if not os.path.isdir(sim_dir):
os.makedirs(sim_dir)
copyfile(args.config, join(sim_dir, args.config))
# Simulation.
simulate.run(settings)
# Average.
casa.run_script_from_file('average_ms.py')
# Image.
casa.run_script_from_file('image.py')
# Plot results.
plot.run(settings)
