def loop(dt,itters,particles,file_name,domain,cross_sections): file = open("number_densities.csv",'w+') dsmc = DSMC(cross_sections) for i in range(itters): print('Started pusher') pusher.push(particles,dt) execute_boundary(particles,domain) print('Started building octree') tree = create_leafs(particles,domain) print('Started dsmc') first_leaf = tree.leafs[0] _execute_next_level(first_leaf, dt, dsmc) number_densities = diagnose(1000, domain.zmin, domain.zmax,particles,(domain.xmax - domain.xmin)*(domain.ymax - domain.ymin)) print('Stared writing number deinsities') for density in number_densities: file.write('{},'.format(density)) file.write('\n') #print_praView_file(file_name,i,particles) print('Itterartion {} complete'.format(i + 1)) print(40*"-") file.close()
def main(rawargs): args = get_parser().parse_args(rawargs) if args.s3_path is not None: scene_root = os.path.basename(args.s3_path) args.local_path = scene_root pusher.pull_scene(scene_root, args.local_path, verbose=args.verbose) else: if args.local_path is None: print 'ERROR: Please specify one of --s3-path or --local-path' sys.exit(1) scene_root = os.path.basename(args.local_path) upload = reprocess(scene_root, args.local_path, verbose=args.verbose) if upload and args.s3_path: scene_dict = {} pusher.push(scene_root, args.local_path, scene_dict, verbose=args.verbose, overwrite=True) if args.s3_path: shutil.rmtree(args.local_path)
def process(source, scene_root, verbose=False, clean=False, list_file=None, overwrite=False): if pusher.check_existance(scene_root): print 'Scene %s already exists on destination bucket.' % scene_root if not overwrite: return collect_missing_entry(scene_root, verbose, clean, list_file) if verbose: print 'Processing scene: %s' % scene_root scene_dict = {} local_tarfile = puller.pull(source, scene_root, scene_dict, verbose=verbose) local_dir = splitter.split(scene_root, local_tarfile, verbose=verbose) scene_info.add_mtl_info(scene_dict, scene_root, local_dir) thumbnailer.thumbnail(scene_root, local_dir, verbose=verbose) scene_index_maker.make_index(scene_root, local_dir, verbose=verbose) pusher.push(scene_root, local_dir, scene_dict, verbose=verbose, overwrite=overwrite) if clean: os.unlink(local_tarfile) shutil.rmtree(local_dir) if list_file: scene_info.append_scene_line(list_file, scene_dict) return scene_dict
def process(source, scene_root, verbose=False, clean=False, list_file=None, overwrite=False): if pusher.check_existance(scene_root): print 'Scene %s already exists on destination bucket.' % scene_root if not overwrite: return collect_missing_entry(scene_root, verbose, clean, list_file) if verbose: print 'Processing scene: %s' % scene_root scene_dict = {} local_tarfile = puller.pull(source, scene_root, scene_dict, verbose=verbose) try: local_dir = splitter.split(scene_root, local_tarfile, verbose=verbose) except: if source == 's3queue': # Remove problematic scenes from the queue directory puller_s3queue.clean_queued_tarfile(scene_root) return scene_info.add_mtl_info(scene_dict, scene_root, local_dir) thumbnailer.thumbnail(scene_root, local_dir, verbose=verbose) scene_index_maker.make_index(scene_root, local_dir, verbose=verbose) pusher.push(scene_root, local_dir, scene_dict, verbose=verbose, overwrite=overwrite) if clean: os.unlink(local_tarfile) shutil.rmtree(local_dir) if list_file: scene_info.append_scene_line(list_file, scene_dict) return scene_dict
arr2.append(row2) arr3.append(row3) arr4.append(row4) c = c + 1 else: vmList = pullVMList() print(vmList) print("abcdefshgduycfhbscnklkmlwes") #df=pd.DataFrame(arr,columns=['date','month','year','day','usage']) df = pd.DataFrame( arr, columns=['date', 'month', 'year', 'day', 'usage1', 'usage2']) print(df) #os.system('python pusher.py') if '1' in vmList: df.to_csv('AppData_1.csv', index=False) push('1') #df=pd.DataFrame(arr2,columns=['date','month','year','day','usage']) df = pd.DataFrame( arr2, columns=['date', 'month', 'year', 'day', 'usage1', 'usage2']) print(df) #os.system('python pusher.py') if '2' in vmList: df.to_csv('AppData_2.csv', index=False) push('2') #df=pd.DataFrame(arr3,columns=['date','month','year','day','usage']) df = pd.DataFrame( arr3, columns=['date', 'month', 'year', 'day', 'usage1', 'usage2']) print(df) #os.system('python pusher.py')