Run brain locally
355120
def run(dataset):
    """"""
    config = _get_config()

    if dataset:
        _print("getting dataset from brains...")
        cprint("done", 'green')

        # check dataset cache for dataset
        # if not exists
        # r = requests.get('https://api.github.com/events', stream=True)
        # with open(filename, 'wb') as fd:
        #     for chunk in r.iter_content(chunk_size):
        #         fd.write(chunk)

    cprint('Running "%s"' % config["run"], 'green', attrs=("underline",))
    call(config["run"].split())
304668
def run(self, **options):
        """"""
        if not self.is_setup:
            self.setup()

        if self.twisted:
            self.run_with_twisted(**options)
        elif self.gunicorn:
            self.run_with_gunicorn(**options)
        else:
            self.run_with_werkzeug(**options)
1281220
def run():
    """"""
    
    # Grab server args
    args = client_helper.grab_server_args()

    # Start up workbench connection
    workbench = zerorpc.Client(timeout=300, heartbeat=60)
    workbench.connect('tcp://'+args['server']+':'+args['port'])

    # Upload the files into workbench
    my_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),
                           '../data/pcap/gold_xxx.pcap')
    with open(my_file,'rb') as f:

        # We're going to upload the file in chunks to workbench 
        filename = os.path.basename(my_file)
        raw_bytes = f.read()
        md5_list = []
        for chunk in chunks(raw_bytes, 1024*1024):
            md5_list.append(workbench.store_sample(chunk, filename, 'exe'))

        # Now we just ask Workbench to combine these
        combined_md5 = workbench.combine_samples(md5_list, filename, 'exe')
        real_md5 = workbench.store_sample(raw_bytes, filename, 'exe')
        assert(combined_md5 == real_md5)
1237490
def tempput(local_path=None, remote_path=None, use_sudo=False,
            mirror_local_mode=False, mode=None):
    """"""
    import warnings
    warnings.simplefilter('ignore', RuntimeWarning)
    if remote_path is None:
        remote_path = os.tempnam()
    put(local_path, remote_path, use_sudo, mirror_local_mode, mode)
    yield remote_path
    run("rm '{}'".format(remote_path))
361921
public static void main(String[] args) {

    try {
      MnistDAO.init("/usr/local/Datasets"); // setup data
    System.out.println("here");
      MnistImageDisplayApp mnistImageDisplayApp = new MnistImageDisplayApp();
      mnistImageDisplayApp.go(args);
    } catch (Exception e) {
      // eat it.
    } finally {
      MnistDAO.release(); // release data resources
    }

  }
565390
def run(self):
        
        loop = GLib.MainLoop()
        context = loop.get_context()
        while True:
            time.sleep(0.1)
            if context.pending():
                context.iteration()
                self._manager[ATTR_POSITION] = self._position()
            try:
                method, args = self._task_queue.get(False)
                getattr(self, method)(**args)
            except queue.Empty:
                pass
            if self.state != STATE_IDLE:
                continue
            try:
                uri = self._media_queue.get(False)
                self.media(uri)
            except queue.Empty:
                pass
760067
def run(self, dw, m, area):
        """"""
        return _fitz.DisplayList_run(self, dw, m, area)
973393
def _run_cortex(fastq, indexes, params, out_base, dirs, config):
    
    print(out_base)
    fastaq_index = "{0}.fastaq_index".format(out_base)
    se_fastq_index = "{0}.se_fastq".format(out_base)
    pe_fastq_index = "{0}.pe_fastq".format(out_base)
    reffasta_index = "{0}.list_ref_fasta".format(out_base)
    with open(se_fastq_index, "w") as out_handle:
        out_handle.write(fastq + "\n")
    with open(pe_fastq_index, "w") as out_handle:
        out_handle.write("")
    with open(fastaq_index, "w") as out_handle:
        out_handle.write("{0}\t{1}\t{2}\t{2}\n".format(params["sample"], se_fastq_index,
                                                       pe_fastq_index))
    with open(reffasta_index, "w") as out_handle:
        for x in indexes["fasta"]:
            out_handle.write(x + "\n")
    os.environ["PERL5LIB"] = "{0}:{1}:{2}".format(
        os.path.join(dirs["cortex"], "scripts/calling"),
        os.path.join(dirs["cortex"], "scripts/analyse_variants/bioinf-perl/lib"),
        os.environ.get("PERL5LIB", ""))
    kmers = sorted(params["kmers"])
    kmer_info = ["--first_kmer", str(kmers[0])]
    if len(kmers) > 1:
        kmer_info += ["--last_kmer", str(kmers[-1]),
                      "--kmer_step", str(kmers[1] - kmers[0])]
    subprocess.check_call(["perl", os.path.join(dirs["cortex"], "scripts", "calling", "run_calls.pl"),
                           "--fastaq_index", fastaq_index,
                           "--auto_cleaning", "yes", "--bc", "yes", "--pd", "yes",
                           "--outdir", os.path.dirname(out_base), "--outvcf", os.path.basename(out_base),
                           "--ploidy", str(config["algorithm"].get("ploidy", 2)),
                           "--stampy_hash", indexes["stampy"],
                           "--stampy_bin", os.path.join(dirs["stampy"], "stampy.py"),
                           "--refbindir", os.path.dirname(indexes["cortex"][0]),
                           "--list_ref_fasta",  reffasta_index,
                           "--genome_size", str(params["genome_size"]),
                           "--max_read_len", "30000",
                           #"--max_var_len", "4000",
                           "--format", "FASTQ", "--qthresh", "5", "--do_union", "yes",
                           "--mem_height", "17", "--mem_width", "100",
                           "--ref", "CoordinatesAndInCalling", "--workflow", "independent",
                           "--vcftools_dir", dirs["vcftools"],
                           "--logfile", "{0}.logfile,f".format(out_base)]
                          + kmer_info)
    final = glob.glob(os.path.join(os.path.dirname(out_base), "vcfs",
                                   "{0}*FINALcombined_BC*decomp.vcf".format(os.path.basename(out_base))))
    # No calls, need to setup an empty file
    if len(final) != 1:
        print("Did not find output VCF file for {0}".format(out_base))
        return None
    else:
        return final[0]
1039928
function load (robot) {
  robot.respond(/^(sync users with brain|sync brain users)/i, async (res) => {
    const addedUsers = await syncBrainUsers(robot)
    if (typeof addedUsers === 'undefined') {
      res.reply(`Sorry I can't do that.`)
    } else {
      const names = '@' + addedUsers.join(', @').replace(/,(?!.*,)/gmi, ' and')
      res.reply(`${names} were added to my brain.`)
    }
  })
}
511859
def run(bam_file, data, out_dir):
    """"""
    m = {"base": None, "secondary": []}
    m.update(_mirbase_stats(data, out_dir))
    m["secondary"].append(_seqcluster_stats(data, out_dir))
220419
def local_project(v, V, u=None):
    """"""
    dv = TrialFunction(V)
    v_ = TestFunction(V)
    a_proj = inner(dv, v_)*dx
    b_proj = inner(v, v_)*dx
    solver = LocalSolver(a_proj, b_proj)
    solver.factorize()
    if u is None:
        u = Function(V)
        solver.solve_local_rhs(u)
        return u
    else:
        solver.solve_local_rhs(u)
        return
89262
def runctx(self, cmd, globals, locals):
        """"""
        with self():
            exec(cmd, globals, locals)
        return self
161699
def local_global_attention(x,
                           self_attention_bias,
                           hparams,
                           q_padding="LEFT",
                           kv_padding="LEFT"):
  """"""
  with tf.variable_scope("self_local_global_att"):
    [x_global, x_local] = tf.split(x, 2, axis=-1)
    split_hidden_size = int(hparams.hidden_size / 2)
    split_heads = int(hparams.num_heads / 2)
    if self_attention_bias is not None:
      self_attention_bias = get_self_attention_bias(x)
    y_global = common_attention.multihead_attention(
        x_global,
        None,
        self_attention_bias,
        hparams.attention_key_channels or split_hidden_size,
        hparams.attention_value_channels or split_hidden_size,
        split_hidden_size,
        split_heads,
        hparams.attention_dropout,
        q_filter_width=hparams.q_filter_width,
        kv_filter_width=hparams.kv_filter_width,
        q_padding=q_padding,
        kv_padding=kv_padding,
        name="global_self_att")
    y_local = common_attention.multihead_attention(
        x_local,
        None,
        None,
        hparams.attention_key_channels or split_hidden_size,
        hparams.attention_value_channels or split_hidden_size,
        split_hidden_size,
        split_heads,
        hparams.attention_dropout,
        attention_type="local_masked",
        block_length=hparams.block_length,
        block_width=hparams.block_width,
        q_filter_width=hparams.q_filter_width,
        kv_filter_width=hparams.kv_filter_width,
        q_padding=q_padding,
        kv_padding=kv_padding,
        name="local_self_att")
    y = tf.concat([y_global, y_local], axis=-1)
    return y
1347419
function run(keywordsToRun, keywordsDefined, passedArguments) {
        var vars = helpers.createLocalEnv(passedArguments);
        keywordsToRun = helpers.createKeywords(keywordsToRun);

        return {
            then: function(callback) {
                runKeywords(keywordsToRun, keywordsDefined, vars, callback);
            }
        };
    }
944505
def run(self):
        

        eta = self.settings['noise_strength']
        gamma = 2 * np.pi * self.settings['noise_bandwidth']
        dt = 1. / self.settings['update frequency']
        control = self.settings['control']

        self._state = self._output
        while self._stop is False:

            A = -gamma * dt

            noise = np.sqrt(2*gamma*eta)*np.random.randn()
            self._state *= (1. + A)
            self._state += noise + control
            self._output =  self._state

            self.msleep(int(1e3 / self.settings['update frequency']))
1045281
def run(self):
        '''
        
        '''
        if self.opts['chunked']:
            ret = self.run_chunked()
        else:
            ret = self.run_oldstyle()

        salt.output.display_output(
                ret,
                self.opts.get('output', 'nested'),
                self.opts)
783513
def _run_chunk(self, chunk, kernel_options, device_options, tuning_options):
        """"""

        #detect language and create high-level device interface
        self.dev = DeviceInterface(kernel_options.kernel_string, iterations=tuning_options.iterations, **device_options)

        #move data to the GPU
        gpu_args = self.dev.ready_argument_list(kernel_options.arguments)

        results = []

        for element in chunk:
            params = dict(OrderedDict(zip(tuning_options.tune_params.keys(), element)))

            try:
                time = self.dev.compile_and_benchmark(gpu_args, params, kernel_options, tuning_options)

                params['time'] = time
                results.append(params)
            except Exception:
                params['time'] = None
                results.append(params)

        return results