def download_junit(db, threads, client_class):
    """Download junit results for builds without them."""
    builds_to_grab = db.get_builds_missing_junit()
    pool = None
    if threads > 1:
        pool = multiprocessing.pool.ThreadPool(
            threads, mp_init_worker, ('', {}, client_class, False))
        test_iterator = pool.imap_unordered(
            get_junits, builds_to_grab)
    else:
        global WORKER_CLIENT  # pylint: disable=global-statement
        WORKER_CLIENT = client_class('', {})
        test_iterator = (
            get_junits(build_path) for build_path in builds_to_grab)
    for n, (build_id, build_path, junits) in enumerate(test_iterator, 1):
        print('%d/%d' % (n, len(builds_to_grab)),
              build_path, len(junits), len(''.join(junits.values())))
        junits = {k: remove_system_out(v) for k, v in junits.iteritems()}

        db.insert_build_junits(build_id, junits)
        if n % 100 == 0:
            db.commit()
    db.commit()
    if pool:
        pool.close()
        pool.join()

def _map_parallel(function, args, n_jobs):
    """multiprocessing.Pool(processors=n_jobs).map with some error checking"""
    # Following the error checking found in joblib
    multiprocessing = int(os.environ.get('JOBLIB_MULTIPROCESSING', 1)) or None
    if multiprocessing:
        try:
            import multiprocessing
            import multiprocessing.pool
        except ImportError:
            multiprocessing = None
        if sys.platform.startswith("win") and PY2:
            msg = "Multiprocessing is not supported on Windows with Python 2.X. Setting n_jobs=1"
            logger.warning(msg)
            n_jobs = 1
    # 2nd stage: validate that locking is available on the system and
    #            issue a warning if not
    if multiprocessing:
        try:
            _sem = multiprocessing.Semaphore()
            del _sem  # cleanup
        except (ImportError, OSError) as e:
            multiprocessing = None
            logger.warning('{}. _map_parallel will operate in serial mode'.format(e))
    if multiprocessing and int(n_jobs) not in (0, 1):
        if n_jobs == -1:
            n_jobs = None
        try:
            pool = multiprocessing.Pool(processes=n_jobs)
            map_result = pool.map(function, args)
        finally:
            pool.close()
            pool.join()
    else:
        map_result = list(map(function, args))
    return map_result

    def _listArtifacts(self, urls, gavs):
        """
        Loads maven artifacts from list of GAVs and tries to locate the artifacts in one of the
        specified repositories.

        :param urls: repository URLs where the given GAVs can be located
        :param gavs: List of GAVs
        :returns: Dictionary where index is MavenArtifact object and value is it's repo root URL.
        """
        def findArtifact(gav, urls, artifacts):
            artifact = MavenArtifact.createFromGAV(gav)
            for url in urls:
                if maven_repo_util.gavExists(url, artifact):
                    #Critical section?
                    artifacts[artifact] = ArtifactSpec(url, [ArtifactType(artifact.artifactType, True, set(['']))])
                    return

            logging.warning('Artifact %s not found in any url!', artifact)

        artifacts = {}
        pool = ThreadPool(maven_repo_util.MAX_THREADS)
        for gav in gavs:
            pool.apply_async(findArtifact, [gav, urls, artifacts])

        # Close the pool and wait for the workers to finnish
        pool.close()
        pool.join()

        return artifacts

def main():
    if len(sys.argv) < 3:
        print("Syntax:")
        print(
            "  {} [min_yeses] [out_csv_file]".format(
                sys.argv[0]
            )
        )
        sys.exit(1)

    min_yeses = eval(sys.argv[1])
    out_csv_file = sys.argv[2]

    pconfig = config.PaperworkConfig()
    pconfig.read()

    src_dir = pconfig.settings['workdir'].value
    print("Source work directory : {}".format(src_dir))
    src_dsearch = docsearch.DocSearch(src_dir)
    src_dsearch.reload_index()

    nb_threads = multiprocessing.cpu_count()
    pool = multiprocessing.pool.ThreadPool(processes=nb_threads)

    with open(out_csv_file, 'a', newline='') as csvfile:
        csvwriter = csv.writer(csvfile)
        for min_yes in min_yeses:
            pool.apply_async(
                _run_simulation,
                (src_dsearch, min_yes, csvwriter,)
            )
        pool.close()
        pool.join()
    print("All done !")

    def count_intersect(self, threshold, frequency=True):

        self.counts = OrderedDict()
        self.rlen, self.qlen = {}, {}
        self.nalist = []

        if frequency:
            self.frequency = OrderedDict()

        # if self.mode_count == "bp":
        #    print2(self.parameter, "\n{0}\t{1}\t{2}\t{3}\t{4}".format("Reference","Length(bp)", "Query", "Length(bp)", "Length of Intersection(bp)"))
        # elif self.mode_count == "count":
        #    print2(self.parameter, "\n{0}\t{1}\t{2}\t{3}\t{4}".format("Reference","sequence_number", "Query", "sequence_number", "Number of Intersection"))

        for ty in self.groupedreference.keys():
            self.counts[ty] = OrderedDict()
            self.rlen[ty], self.qlen[ty] = OrderedDict(), OrderedDict()
            if frequency:
                self.frequency[ty] = OrderedDict()

            for r in self.groupedreference[ty]:
                if r.total_coverage() == 0 and len(r) > 0:
                    self.nalist.append(r.name)
                    continue
                else:
                    self.counts[ty][r.name] = OrderedDict()
                    if self.mode_count == "bp":
                        rlen = r.total_coverage()
                    elif self.mode_count == "count":
                        rlen = len(r)
                    self.rlen[ty][r.name] = rlen

                    mp_input = []
                    for q in self.groupedquery[ty]:
                        if r.name == q.name:
                            continue
                        else:
                            mp_input.append([q, self.nalist, self.mode_count, self.qlen, threshold,
                                             self.counts, frequency, self.frequency, ty, r])
                    # q, nalist, mode_count, qlen_dict, threshold, counts, frequency, self_frequency, ty, r
                    pool = multiprocessing.Pool(processes=multiprocessing.cpu_count() - 1)
                    mp_output = pool.map(mp_count_intersect, mp_input)
                    pool.close()
                    pool.join()

                    # qname, nalist, qlen_dict[ty][q.name], counts[ty][r.name][q.name], self_frequency[ty][q.name].append(c[2])
                    for output in mp_output:
                        if output[1]:
                            self.nalist.append(output[1])
                        else:
                            self.qlen[ty][output[0]] = output[2]
                            self.counts[ty][r.name][output[0]] = output[3]
                            # print(r.name)
                            # print(output[0])
                            # print(output[3])
                            try:
                                self.frequency[ty][output[0]][r.name] = output[3][2]
                            except:
                                self.frequency[ty][output[0]] = {}
                                self.frequency[ty][output[0]][r.name] = output[3][2]

    def run_abstraction_parallel(self):
        # initialization
        self.__get_methods()
        self.__read_config()
        self.__get_dataset()

        # get filename and properties
        filename_properties = []
        for filename, properties in self.files.iteritems():
            filename_properties.append((filename, properties))

        # run experiment in multiprocessing mode
        total_cpu = multiprocessing.cpu_count()
        pool = NoDaemonProcessPool(processes=total_cpu)
        results = pool.map(self, filename_properties)
        pool.close()
        pool.join()

        # open evaluation file
        self.__check_path(self.files['evaluation_directory'])
        f = open(self.files['evaluation_file'], 'wt')
        writer = csv.writer(f)

        # set header for evaluation file
        header = []
        if self.configuration['main']['abstraction'] == '1':
            header = self.configuration['abstraction_evaluation']['evaluation_file_header'].split('\n')
        writer.writerow(tuple(header))

        # write experiment result
        for result in results:
            writer.writerow(result)

        # close evaluation file
        f.close()

def process_iteration(Ns, ps, landscape, config):
	output_dir = config.output_dir + config.ext
	
	if config.background_image != None:
		background_path = config.input_dir + "/" + config.background_image
	else:
		background_path = None
	
	#Create a point to hold the iteration
	p = Point()
	p.add_iteration()
	
	#draw_population(Ns[0], landscape, ps.totalK, 0, output_dir, 2.0, background_path)
	
	if config.display:
		pool = mp.Pool(config.num_processors)

	for t in xrange(min(ps.max_time_steps, len(Ns))):
		if config.display:
			pool.apply_async(draw_population, [Ns[t], landscape, ps.totalK, t, output_dir, 2.0, background_path])
		
		p.add_time_step([t] + population_statistics(ps, landscape, Ns[t]))
	
	pool.close()

	#Write the iteration results to file as a trajectory containing a single point
	write_trajectories([Trajectory(points=[p])], None, ps.sentinels, output_dir + "/results.txt")

	if config.save_time_steps:
		np.savez(output_dir + "/populations.npz", *Ns)

	pool.join()

def _CompileDeps(aapt_path, dep_subdirs, temp_dir):
  partials_dir = os.path.join(temp_dir, 'partials')
  build_utils.MakeDirectory(partials_dir)
  partial_compile_command = [
      aapt_path + '2',
      'compile',
      # TODO(wnwen): Turn this on once aapt2 forces 9-patch to be crunched.
      # '--no-crunch',
  ]
  pool = multiprocessing.pool.ThreadPool(10)
  def compile_partial(directory):
    dirname = os.path.basename(directory)
    partial_path = os.path.join(partials_dir, dirname + '.zip')
    compile_command = (partial_compile_command +
                       ['--dir', directory, '-o', partial_path])
    build_utils.CheckOutput(compile_command)

    # Sorting the files in the partial ensures deterministic output from the
    # aapt2 link step which uses order of files in the partial.
    sorted_partial_path = os.path.join(partials_dir, dirname + '.sorted.zip')
    _SortZip(partial_path, sorted_partial_path)

    return sorted_partial_path

  partials = pool.map(compile_partial, dep_subdirs)
  pool.close()
  pool.join()
  return partials

def test_no_thread_pool():
    pool = xmon_stepper.ThreadlessPool()
    result = pool.map(lambda x: x + 1, range(10))
    assert result == [x + 1 for x in range(10)]
    # No ops.
    pool.terminate()
    pool.join()

 def test():
     print("Creating 5 (non-daemon) workers and jobs in main process.")
     pool = MyPool(5)
     result = pool.map(work, [randint(1, 5) for x in range(5)])
     pool.close()
     pool.join()
     print(result)

def run_trajectory(t, ps, landscape, ptv, num_iterations, num_processors):
    # Get the points in the trajectory
    points = t.points()

    # Determine the index of each unique point (sometimes points are equal due to rounding)
    uinds = [i for i, p in enumerate(points) if i == 0 or not p.equals(points[i - 1])]

    # Create a process pool, using as many processors as are available, or
    # are required to allow each point to run concurrently
    pool = mp.Pool(processes=min(num_processors, len(points)))

    results = []
    for i in uinds:
        # Modify the parameter set to match the current point
        psm = ps.copy()
        psm.modify_for_point(points[i], ptv)
        psm.convert_to_age_classes()

        # Launch a process to run the simulation(s) for the point. This modifies the point in place
        args = [points[i], psm, landscape, num_iterations, num_processors]
        results.append(pool.apply_async(run_iterations_for_point, args))

    pool.close()
    pool.join()

    # Merge the unique and non-unique points back together
    for i, r in zip(uinds, results):
        points[i] = r.get(None)

    # Return a new trajectory containing the results for each point
    return io.Trajectory(points=points)

def _map_parallel(function, args, n_jobs):
    """multiprocessing.Pool(processors=n_jobs).map with some error checking"""
    # Following the error checking found in joblib
    multiprocessing = int(os.environ.get('JOBLIB_MULTIPROCESSING', 1)) or None
    if multiprocessing:
        try:
            import multiprocessing
            import multiprocessing.pool
        except ImportError:
            multiprocessing = None
    # 2nd stage: validate that locking is available on the system and
    #            issue a warning if not
    if multiprocessing:
        try:
            _sem = multiprocessing.Semaphore()
            del _sem  # cleanup
        except (ImportError, OSError) as e:
            multiprocessing = None
            warnings.warn('%s. _map_parallel will operate in serial mode' % (e,))
    if multiprocessing and int(n_jobs) not in (0, 1):
        if n_jobs == -1:
            n_jobs = None
        pool = multiprocessing.Pool(processes=n_jobs)
        map_result = pool.map(function, args)
        pool.close()
        pool.join()
    else:
        map_result = list(map(function, args))
    return map_result

def ScopedPool(*args, **kwargs):
  """Context Manager which returns a multiprocessing.pool instance which
  correctly deals with thrown exceptions.

  *args - Arguments to multiprocessing.pool

  Kwargs:
    kind ('threads', 'procs') - The type of underlying coprocess to use.
    **etc - Arguments to multiprocessing.pool
  """
  if kwargs.pop('kind', None) == 'threads':
    pool = multiprocessing.pool.ThreadPool(*args, **kwargs)
  else:
    orig, orig_args = kwargs.get('initializer'), kwargs.get('initargs', ())
    kwargs['initializer'] = _ScopedPool_initer
    kwargs['initargs'] = orig, orig_args
    pool = multiprocessing.pool.Pool(*args, **kwargs)

  try:
    yield pool
    pool.close()
  except:
    pool.terminate()
    raise
  finally:
    pool.join()

def pass_data_to_search(symbol,path,start_time_seconds,end_time_seconds,date,time_interval,tt,code_path):

    jobs=[]
    dic_files={}
    lis=[]
    slot_results=[]
    
    file_name = path+'b'+date+'.l.bz2'
    # file_name = path + date+'/'+dic_files[lis[index]]+'.bz2'
        
    size=os.path.getsize(file_name)
    total_rows=size/69
    total_processes1=40
    slots=total_rows/total_processes1

    #Multiprocessing each file as chunk
    # mapper(0,slots,total_processes1,symbol,start_time_seconds,end_time_seconds,date,time_interval,file_name,tt,code_path)
    # mapper(1,slots,total_processes1,symbol,start_time_seconds,end_time_seconds,date,time_interval,file_name,tt,code_path)
    
    pool = multiprocessing.Pool(total_processes1)
    

    for i in range(total_processes1):

        pool.apply_async(mapper, args = (i,slots,total_processes1,symbol,start_time_seconds,end_time_seconds,date,time_interval,file_name,tt,code_path))
        
    pool.close()
    pool.join()    

def slippy_test(test_options, width=TILE_WIDTH, height=TILE_HEIGHT, tile_factor=TILE_FACTOR):
	#assume each screen is a 10x5 grid of tiles
	#this approximately the OTM map size at full screen
	#at my desk
	z = test_options['z']
	x = test_options['x']
	y = test_options['y']
	url_prefix = test_options['url_prefix']


	tiles_to_request = []
	for x_iter in range(x - width/2, x + width/2 - 1):
		for y_iter in range(y - height/2, y + height/2 - 1):
			tiles_to_request.append(url_prefix + '%d/%d/%d.png' % (z, x_iter, y_iter))

	pool = multiprocessing.Pool(processes=tile_factor)
	start_time = time.time()
	results = pool.map(slippy_test_helper, tiles_to_request)
	end_time = time.time()
	pool.close()
	pool.join()
	sys.stderr.write('.')

	if(False in results):
		return '%d,ERROR,%f' % (-1, float('nan'))
	return '%d,OK,' % z + str(end_time - start_time)

def parallel_compile(self, sources, output_dir=None, macros=None,
                     include_dirs=None, debug=0, extra_preargs=None,
                     extra_postargs=None, depends=None):
    """New compile function that we monkey patch into the existing compiler instance.
    """
    import multiprocessing.pool

    # Copied from the regular compile function
    macros, objects, extra_postargs, pp_opts, build = \
            self._setup_compile(output_dir, macros, include_dirs, sources,
                                depends, extra_postargs)
    cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)

    def _single_compile(obj):
        try:
            src, ext = build[obj]
        except KeyError:
            return
        self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)

    # Set by fix_compiler
    global glob_use_njobs
    if glob_use_njobs == 1:
        # This is equivalent to regular compile function
        for obj in objects:
            _single_compile(obj)
    else:
        # Use ThreadPool, rather than Pool, since the objects are picklable.
        pool = multiprocessing.pool.ThreadPool(glob_use_njobs)
        pool.map(_single_compile, objects)
        pool.close()
        pool.join()

    # Return *all* object filenames, not just the ones we just built.
    return objects

def from_carrays(path, format_categories='bcolz', format_codes='bcolz', format_values='bcolz', parallel=True):
    assert os.path.exists(path), 'No path {}'.format(path)
    df_columns = glob.glob(os.path.join(path, '*'))
    df = dict()
    if parallel:
        pool = multiprocessing.pool.ThreadPool()
        results = []
        for i, k in enumerate(df_columns):
            p = pool.apply_async(_from_carray, args=(k,), kwds={'format_categories': format_categories, 'format_codes': format_codes, 'format_values': format_values})
            results.append(p)
        pool.close()
        pool.join()
        for x in results:
            meta, s = x.get()
            df[meta['name']] = s
    else:
        for i, k in enumerate(df_columns):
            meta, s = _from_carray(k, format_categories=format_categories, format_codes=format_codes, format_values=format_values)
            df[meta['name']] = s

    # # # this is slow when we have non categoricals as series for some reason
    with log.timedlogger('constructing dataframe from %s column dict' % len(df)):
        df = pandas.DataFrame(df)  # TODO: fast DataFrame constructor

    return df

def threshold(X, e, a, b, k, num_workers, metric):
    """ Get all threshold clusters (algorithm 7, lines 1-6)
    :param X: Data matrix
    :param e: lower bound on fractional size of each cluster
    :param a: lower bound on fractional size of a set inside own cluster for which stability holds
    :param b: lower bound on fractional size of a set outside own cluster for which stability holds
    :param k: Number of clusters
    :param num_workers: Number of workers
    :param metric: metric is in the set {avg, min, max}
    :return: Threshold clusters
    """
    print("Populating list with all threshold clusters with metric:", metric)
    start = time.time()
    n = len(X)
    minsize = int(e * n)
    with Pool(num_workers) as pool:
        func = partial(get_thresholds, X, minsize, num_workers, metric)
        items = pool.map(func, range(n))
        pool.close()
        pool.join()
    threshold_lists = [item[0] for item in items]
    L = [item for sublist in threshold_lists for item in sublist]
    D = dict([(item[1], item[2]) for item in items])
    end = time.time()
    print("Length of L = ", len(L))
    print("time = {0:.2f}s".format(end - start))
    return refine(L, X, D, e, a, b, k, num_workers, metric)

def refine(L, X, D, e, a, b, k, num_workers, metric):
    """ Throw out bad points (algorithm 7, lines 7-17)
    :param L: List of subsets
    :param X: Data matrix
    :param D: dictionary
    :param e: lower bound on fractional size of each cluster
    :param a: lower bound on fractional size of a set inside own cluster for which stability holds
    :param b: lower bound on fractional size of a set outside own cluster for which stability holds
    :param k: Number of clusters
    :param num_workers: Number of workers
    :param metric: metric is in {avg, max, min}
    :return: Refined clusters
    """
    print("Getting rid of bad points")
    print("Length of L at start = ", len(L))
    start = time.time()
    n = len(X)
    T = int((e - 2 * a - b * k) * n)
    t = int((e - a) * n)
    with Pool() as pool:
        func = partial(refine_individual, D, T, t)
        L = pool.map(func, L)
        pool.close()
        pool.join()
    end = time.time()
    print("Length of L on end = ", len(L))
    print("time = {0:.2f}s".format(end - start))
    return grow(L, X, a, num_workers, metric)

def create_process_pool(index):
    print index
    li = range(3)
    pool = multiprocessing.Pool(processes = len(li))
    for sub_index in li:
        pool.apply_async(print_process_index, (index, sub_index))
    pool.close()
    pool.join()