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 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)