Parse a WMIC array.
496917
def parse_array(raw_array):
    """"""
    array_strip_brackets = raw_array.replace('{', '').replace('}', '')
    array_strip_spaces = array_strip_brackets.replace('"', '').replace(' ', '')
    return array_strip_spaces.split(',')
557479
def _parse(self):
        """"""
        while self._cur_token['type'] in (TT.ws, TT.lbreak):
            self._skip_whitespace()
            self._skip_newlines()

        self._data = self._parse_value()

        return self._data
161419
def __parse_inchi():
    ''''''
    filename = get_file('chebiId_inchi.tsv')

    with io.open(filename, 'r', encoding='cp1252') as textfile:
        next(textfile)

        for line in textfile:
            tokens = line.strip().split('\t')
            __INCHIS[int(tokens[0])] = tokens[1]
799277
def _parse_array(self, tensor_proto):
        """"""
        try:
            from onnx.numpy_helper import to_array
        except ImportError as e:
            raise ImportError("Unable to import onnx which is required {}".format(e))
        np_array = to_array(tensor_proto).reshape(tuple(tensor_proto.dims))
        return mx.nd.array(np_array)
90318
def Parse(self, cmd, args, stdout, stderr, return_val, time_taken,
            knowledge_base):
    """"""
    _ = stderr, time_taken, args, knowledge_base  # Unused
    self.CheckReturn(cmd, return_val)

    plist = biplist.readPlist(io.BytesIO(stdout))

    if len(plist) > 1:
      raise parser.ParseError("SPHardwareDataType plist has too many items.")

    hardware_list = plist[0]["_items"][0]
    serial_number = hardware_list.get("serial_number", None)
    system_product_name = hardware_list.get("machine_model", None)
    bios_version = hardware_list.get("boot_rom_version", None)

    yield rdf_client.HardwareInfo(
        serial_number=serial_number,
        bios_version=bios_version,
        system_product_name=system_product_name)
761323
def array(self, size_chunk, start, bytesize):
        ''' 
        '''

        with open(self.img, 'rb') as f1:
            f1.seek(self.start_byte + start * self.bytesize)
            data = f1.read(size_chunk * self.bytesize)
            Z = np.fromstring(data, dtype=self.dtype, count=size_chunk)
            if self.grid == 'LOLA':
                return Z * float(self.SCALING_FACTOR)
            else:
                return Z
288025
def _Parse(self):
    """"""
    vshadow_store = self._file_entry.GetVShadowStore()

    self._AddAttribute(volume_system.VolumeAttribute(
        'identifier', vshadow_store.identifier))
    self._AddAttribute(volume_system.VolumeAttribute(
        'copy_identifier', vshadow_store.copy_identifier))
    self._AddAttribute(volume_system.VolumeAttribute(
        'copy_set_identifier', vshadow_store.copy_set_identifier))
    self._AddAttribute(volume_system.VolumeAttribute(
        'creation_time', vshadow_store.get_creation_time_as_integer()))

    volume_extent = volume_system.VolumeExtent(0, vshadow_store.volume_size)
    self._extents.append(volume_extent)
1286579
def parse(self, data):
        """"""

        ASNlist = []
        for line in data.splitlines()[1:]:
            line = plain_str(line)
            if "|" not in line:
                continue
            asn, ip, desc = [elt.strip() for elt in line.split('|')]
            if asn == "NA":
                continue
            asn = "AS%s" % asn
            ASNlist.append((ip, asn, desc))
        return ASNlist
358037
def parse_header(input_array):
    """"""
    codec = struct.unpack(mmtf.utils.constants.NUM_DICT[4], input_array[0:4])[0]
    length = struct.unpack(mmtf.utils.constants.NUM_DICT[4], input_array[4:8])[0]
    param = struct.unpack(mmtf.utils.constants.NUM_DICT[4], input_array[8:12])[0]
    return codec,length,param,input_array[12:]
1233742
def _Parse(self):
    """"""
    root_file_entry = self._file_system.GetRootFileEntry()

    for sub_file_entry in root_file_entry.sub_file_entries:
      volume = APFSVolume(sub_file_entry)
      self._AddVolume(volume)
1132020
def read_array(self, infile, var_name):
        """"""
        file_handle = self.read_cdf(infile)
        try:
            # return the data array
            return file_handle.variables[var_name][:]
        except KeyError:
            print("Cannot find variable: {0}".format(var_name))
            raise KeyError
1050751
function parseArray(array) {
  const templates = array.map(parse);
  const templateParameters = templates.reduce(
    (parameters, template) => parameters.concat(template.parameters),
    []
  );
  const templateFn = context => templates.map(template => template(context));

  return Template(templateFn, templateParameters);
}
789425
def _parse(root):
    """"""
    if root.tag == "nil-classes":
        return []
    elif root.get("type") == "array":
        return [_parse(child) for child in root]

    d = {}
    for child in root:
        type = child.get("type") or "string"

        if child.get("nil"):
            value = None
        elif type == "boolean":
            value = True if child.text.lower() == "true" else False
        elif type == "dateTime":
            value = iso8601.parse_date(child.text)
        elif type == "decimal":
            value = decimal.Decimal(child.text)
        elif type == "integer":
            value = int(child.text)
        else:
            value = child.text

        d[child.tag] = value
    return d
14058
private void parseArray(String fieldName) {
        int chr;
        int idx = 0;
        while (true) {
            sr.skipWhitespaceRead();
            sr.unreadLastCharacter();
            parseValue(fieldName + "[" + (idx++) + "]");
            
            chr = sr.skipWhitespaceRead();
            if (chr == END_ARRAY) {
                break;
            }
            
            if (chr != VALUE_SEPARATOR) {
                throw new IllegalArgumentException("Expected ',' or ']' inside array at position " + sr.getPosition());
            }
        }
    }
1202270
def decode_array(input_array):
    """"""
    codec, length, param, input_array = parse_header(input_array)
    return codec_dict[codec].decode(input_array, param)
780223
function parse_Array(blob, length, opts) {
	var ref = parse_Ref(blob, 6);
	/* TODO: fAlwaysCalc */
	switch(opts.biff) {
		case 2: blob.l ++; length -= 7; break;
		case 3: case 4: blob.l += 2; length -= 8; break;
		default: blob.l += 6; length -= 12;
	}
	return [ref, parse_ArrayParsedFormula(blob, length, opts, ref)];
}
117509
def read_ma_array(self, infile, var_name):
        """"""
        file_obj = self.read_cdf(infile)

        # .data is not backwards compatible to old scipy versions, [:] is
        data = file_obj.variables[var_name][:]

        # load numpy if available
        try:
            import numpy as np
        except Exception:
            raise ImportError("numpy is required to return masked arrays.")

        if hasattr(file_obj.variables[var_name], "_FillValue"):
            # return masked array
            fill_val = file_obj.variables[var_name]._FillValue
            retval = np.ma.masked_where(data == fill_val, data)
        else:
            # generate dummy mask which is always valid
            retval = np.ma.array(data)

        return retval