Python builtins.map方法代碼示例

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def is_instance_factory(_type):
    """

    Parameters
    ----------
    `_type` - the type to be checked against

    Returns
    -------
    validator - a function of a single argument x , which raises
                ValueError if x is not an instance of `_type`

    """
    if isinstance(_type, (tuple, list)):
        _type = tuple(_type)
        type_repr = "|".join(map(lambda x: x.__name__, _type))
    else:
        type_repr = "'{typ}'".format(typ=_type.__name__)

    def inner(x):
        if not isinstance(x, _type):
            msg = "Value must be an instance of {type_repr}"
            raise ValueError(msg.format(type_repr=type_repr))

    return inner 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def get_ast(self):
        fields = [get_field_ast(f) for f in self.fields]
        methods = []
        for m in self.methods:
            if isinstance(m, DvMethod) and m.ast:
                methods.append(m.get_ast())
        isInterface = 'interface' in self.access
        return {
            'rawname': self.thisclass[1:-1],
            'name': parse_descriptor(self.thisclass),
            'super': parse_descriptor(self.superclass),
            'flags': self.access,
            'isInterface': isInterface,
            'interfaces': list(map(parse_descriptor, self.interfaces)),
            'fields': fields,
            'methods': methods,
        } 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def anything_beetween(opener_and_closer):
    """Builds a (pyparsing) parser for the content inside delimiters.

    Args:
    opener_and_closer: a string containing two elements: opener and closer

    Returns:
      A (pyparsing) parser for the content inside delimiters.
    """
    opener = pyparsing.Literal(opener_and_closer[0])
    closer = pyparsing.Literal(opener_and_closer[1])
    char_removal_mapping = dict.fromkeys(list(map(ord, opener_and_closer)))
    other_chars = str(string.printable).translate(char_removal_mapping)
    word_without_delimiters = pyparsing.Word(other_chars).setName(
        "other_chars")
    anything = pyparsing.Forward()
    delimited_block = opener + anything + closer
    # pylint: disable=expression-not-assigned
    anything << pyparsing.ZeroOrMore(
        word_without_delimiters.setName("word_without_delimiters")
        | delimited_block.setName("delimited_block")
    )

    # Combine all the parts into a single string.
    return pyparsing.Combine(anything) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def _update_column_metadata(self, row, header=False):
        if self._style == Styles.HORIZONTAL:
            for i in range(len(self._column_names)):
                if not header:
                    cell_format, cell = row[i]
                else:
                    cell = row[i]
                    # TODO - never used cell_format = self._no_alert_style

                if header:
                    max_length = max(list(map(len, cell.split(' '))))
                else:
                    max_length = len(cell)
                if self._column_widths[i] < max_length:
                    self._column_widths[i] = max_length
                if not header:
                    if not filesize.isfilesize(cell):
                        if cell != self._no_entry:
                            self._column_types[i] = "string"
        elif self._style == Styles.VERTICAL:
            length = max(
                [len(r[1]) if type(r) is tuple else len(r) for r in row])
            self._column_widths.append(length)
        else:
            raise ValueError("Style must be either HORIZONAL or VERTICAL") 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def info_namespace_statistics(self, namespace):
        """
        Get statistics for a namespace.

        Returns:
        dict -- {stat_name : stat_value, ...}
        """

        ns_stat = util.info_to_dict(self.info("namespace/%s" % namespace))

        # Due to new server feature namespace add/remove with rolling restart,
        # there is possibility that different nodes will have different namespaces.
        # type = unknown means namespace is not available on this node, so just return empty map.
        if ns_stat and not isinstance(ns_stat, Exception) and "type" in ns_stat and ns_stat["type"] == "unknown":
            ns_stat = {}
        return ns_stat 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def filter_file_list(file_list):
  """Filters file list by removing duplicates, non-existent files
  and directories."""
  filtered_file_list = []
  for file_path in file_list:
    if not os.path.exists(file_path):
      continue

    if os.path.isdir(file_path):
      continue

    # Do a os specific case normalization before comparison.
    if (os.path.normcase(file_path) in list(
        map(os.path.normcase, filtered_file_list))):
      continue

    filtered_file_list.append(file_path)

  if len(filtered_file_list) != len(file_list):
    logs.log('Filtered file list (%s) from (%s).' % (str(filtered_file_list),
                                                     str(file_list)))

  return filtered_file_list 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def is_binary_file(file_path, bytes_to_read=1024):
  """Return true if the file looks like a binary file."""
  file_extension = os.path.splitext(file_path)[1].lower()
  if file_extension in BINARY_EXTENSIONS:
    return True
  if file_extension in TEXT_EXTENSIONS:
    return False

  text_characters = list(map(chr, list(range(32, 128)))) + ['\r', '\n', '\t']
  try:
    with open(file_path, 'rb') as file_handle:
      data = file_handle.read(bytes_to_read)
  except:
    logs.log_error('Could not read file %s in is_binary_file.' % file_path)
    return None

  binary_data = [char for char in data if char not in text_characters]

  return len(binary_data) > len(data) * 0.1 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def _assert_valid_axes_map(self):
        """
        Ensure that there are no axis which map to the same axis and raise a
        helpful error message.
        """
        duplicate_axis_names = self._duplicate_axis_names()

        # if there are duplicate_axis_names throw an exception
        if duplicate_axis_names:
            message = 'AxesMap was can not have duplicate names, but found:'
            for target_axis, source_axes in duplicate_axis_names.items():
                message += '\n    {} maps to {}'.format(
                    target_axis, ', '.join(source_axes)
                )

            raise DuplicateAxisNames(message, duplicate_axis_names) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def protobuf_scalar_to_python(val):
    assert isinstance(val, ops_pb.Scalar)
    scalar_key = val.WhichOneof('value')
    if scalar_key == 'uuid_val':
        raise ValueError("During deserialization, no attributes should reference UUIDs.")
    elif scalar_key == 'map_val':
        return pb_to_dict(val.map_val.map)
    elif scalar_key == 'null_val':
        return None
    elif scalar_key == 'slice_val':
        val = val.slice_val
        return slice(val.start.value if val.HasField('start') else None,
                     val.stop.value if val.HasField('stop') else None,
                     val.step.value if val.HasField('step') else None)
    elif scalar_key == 'dtype_val':
        return pb_to_dtype(val.dtype_val)
    elif scalar_key == 'axis':
        return pb_to_axis(val.axis)
    elif scalar_key == 'axes_map':
        return pb_to_axes_map(val.axes_map)
    return getattr(val, scalar_key) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def protobuf_attr_to_python(val):
    if val.HasField('scalar'):
        return protobuf_scalar_to_python(val.scalar)

    if val.HasField('tensor'):
        return pb_to_tensor(val.tensor)
    elif val.HasField('repeated_scalar'):
        if len(val.repeated_scalar.val) == 1 and \
                val.repeated_scalar.val[0].string_val == '_ngraph_iter_sentinel_':
            return ()
        else:
            return list(map(protobuf_scalar_to_python, val.repeated_scalar.val))
    elif val.HasField('axes'):
        return protobuf_to_axes(val.axes)
    else:
        raise ValueError("Cannot convert {} to python attribute value".format(val)) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def make_cphase_df(obs,band='unknown',polarization='unknown',mode='all',count='max',round_s=0.1,snrcut=0.,uv_min=False):

    """generate DataFrame of closure phases

    Args: 
        obs: ObsData object
        round_s: accuracy of datetime object in seconds

    Returns:
        df: closure phase data in DataFrame format
    """

    data=obs.c_phases(mode=mode,count=count,snrcut=snrcut,uv_min=uv_min)
    sour=obs.source
    df = pd.DataFrame(data=data).copy()
    df['fmjd'] = df['time']/24.
    df['mjd'] = obs.mjd + df['fmjd']
    df['triangle'] = list(map(lambda x: x[0]+'-'+x[1]+'-'+x[2],zip(df['t1'],df['t2'],df['t3'])))
    df['datetime'] = Time(df['mjd'], format='mjd').datetime
    df['datetime'] =list(map(lambda x: round_time(x,round_s=round_s),df['datetime']))
    df['jd'] = Time(df['mjd'], format='mjd').jd
    df['polarization'] = polarization
    df['band'] = band
    df['source'] = sour
    return df 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def make_bsp_df(obs,band='unknown',polarization='unknown',mode='all',count='min',round_s=0.1,snrcut=0., uv_min=False):

    """generate DataFrame of bispectra

    Args: 
        obs: ObsData object
        round_s: accuracy of datetime object in seconds

    Returns:
        df: bispectra data in DataFrame format
    """

    data = obs.bispectra(mode=mode,count=count,snrcut=snrcut,uv_min=uv_min)
    sour=obs.source
    df = pd.DataFrame(data=data).copy()
    df['fmjd'] = df['time']/24.
    df['mjd'] = obs.mjd + df['fmjd']
    df['triangle'] = list(map(lambda x: x[0]+'-'+x[1]+'-'+x[2],zip(df['t1'],df['t2'],df['t3'])))
    df['datetime'] = Time(df['mjd'], format='mjd').datetime
    df['datetime'] =list(map(lambda x: round_time(x,round_s=round_s),df['datetime']))
    df['jd'] = Time(df['mjd'], format='mjd').jd
    df['polarization'] = polarization
    df['band'] = band
    df['source'] = sour
    return df 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def match_multiple_frames(frames, what_is_same, dt = 0,uniquely=True):

    if dt > 0:
        for frame in frames:
            frame['round_time'] = list(map(lambda x: np.round((x- datetime.datetime(2017,4,4)).total_seconds()/dt),frame['datetime']))
        what_is_same += ['round_time']
    
    frames_common = {}
    for frame in frames:
        frame['all_ind'] = list(zip(*[frame[x] for x in what_is_same]))   
        if frames_common != {}:
            frames_common = frames_common&set(frame['all_ind'])
        else:
            frames_common = set(frame['all_ind'])

    frames_out = []
    for frame in frames:
        frame = frame[list(map(lambda x: x in frames_common, frame.all_ind))].copy()
        if uniquely:
            frame.drop_duplicates(subset=['all_ind'], keep='first', inplace=True)

        frame = frame.sort_values('all_ind').reset_index(drop=True)
        frame.drop('all_ind', axis=1,inplace=True)
        frames_out.append(frame.copy())
    return frames_out 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def standardize(item):
    """This method attempts to return common python objects
       to a standard form. It is typically used for post-
       processing output from json.loads.
    """
    if type(item) == tuple or type(item) == list:
        return list(map(standardize, item))
    elif type(item) == str:
        return str(item)
    elif type(item) == np.ndarray:
        return standardize(list(item))
    elif type(item) == dict:
        replacement = {}

        for k, v in item.items():
            replacement[str(k)] = standardize(v)

        return replacement
    else:
        # attempt to coerce numpy scalars
        try: return float(item)
        except: pass

    return item 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import map [as 別名]
def get_broadcast_funcs(**kwargs):
    kw = Objectify(kwargs, conf={})
    pieces = kw.conf[kw.extract] if kw.extract else kw.conf
    no_conf = remove_keys(kwargs, 'conf')
    noop = partial(cast, _type='none')

    if kw.listize:
        listed = listize(pieces)
        piece_defs = map(DotDict, listed) if kw.pdictize else listed
        parser = partial(parse_conf, **no_conf)
        pfuncs = [partial(parser, conf=conf) for conf in piece_defs]
        get_pieces = lambda item: broadcast(item, *pfuncs)
    elif kw.ptype != 'none':
        conf = DotDict(pieces) if kw.pdictize and pieces else pieces
        get_pieces = partial(parse_conf, conf=conf, **no_conf)
    else:
        get_pieces = noop

    ffunc = noop if kw.ftype == 'none' else partial(get_field, **kwargs)
    return (ffunc, get_pieces)