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)