Load data array and metadata from file on disk.
971209
def get_dataset(self, dataset_id, ds_info, xslice=slice(None), yslice=slice(None)):
        """"""
        var_path = ds_info.get('file_key', '{}'.format(dataset_id.name))
        metadata = self.get_metadata(dataset_id, ds_info)
        shape = metadata['shape']
        file_shape = self[var_path + '/shape']
        if isinstance(shape, tuple) and len(shape) == 2:
            # 2D array
            if xslice.start is not None:
                shape = (shape[0], xslice.stop - xslice.start)
            if yslice.start is not None:
                shape = (yslice.stop - yslice.start, shape[1])
        elif isinstance(shape, tuple) and len(shape) == 1 and yslice.start is not None:
            shape = ((yslice.stop - yslice.start) / yslice.step,)
        metadata['shape'] = shape

        valid_min = self[var_path + '/attr/valid_min']
        valid_max = self[var_path + '/attr/valid_max']
        # no need to check fill value since we are using valid min/max
        scale_factor = self.get(var_path + '/attr/scale_factor')
        add_offset = self.get(var_path + '/attr/add_offset')

        if isinstance(file_shape, tuple) and len(file_shape) == 3:
            data = self[var_path][0, yslice, xslice]
        elif isinstance(file_shape, tuple) and len(file_shape) == 2:
            data = self[var_path][yslice, xslice]
        elif isinstance(file_shape, tuple) and len(file_shape) == 1:
            data = self[var_path][yslice]
        else:
            data = self[var_path]
        data = data.where((data >= valid_min) & (data <= valid_max))
        if scale_factor is not None:
            data = data * scale_factor + add_offset

        if ds_info.get('cloud_clear', False):
            # clear-sky if bit 15-16 are 00
            clear_sky_mask = (self['l2p_flags'][0] & 0b1100000000000000) != 0
            data = data.where(~clear_sky_mask)

        data.attrs.update(metadata)
        return data
247653
def load_data_batch(self, data_batch):
        """"""
        _load_data(data_batch, self.data_arrays)
        _load_label(data_batch, self.label_arrays)
961577
private void loadFiles() {
		m_filesMap = new HashMap<String, FileInfo>();
		List<ColumnValue> list = m_helper.get(m_storeName, m_row, "File/");
		for(ColumnValue v : list) {
			FileInfo fi = new FileInfo(v.columnName, v.getString());
			m_filesMap.put(fi.getName(), fi); 
		}
	}
440173
def _load_metadata(self):
        """"""
        if self._synchronizer is None:
            self._load_metadata_nosync()
        else:
            mkey = self._key_prefix + array_meta_key
            with self._synchronizer[mkey]:
                self._load_metadata_nosync()
38992
def load_json(file):
    """"""
    here = os.path.dirname(os.path.abspath(__file__))
    with open(os.path.join(here, file)) as jfile:
        data = json.load(jfile)
    return data
430080
def load_data(self, filename, *args, **kwargs):
        
        # header keys
        header_param = self.parameters.get('header')  # default is None
        # data keys
        data_param = self.parameters['data']  # raises KeyError if no 'data'
        dtype = data_param['dtype']  # raises KeyError if no 'dtype'
        # convert to tuple and normal ASCII
        _utf8_list_to_ascii_tuple(dtype) if dtype else None  # -> tuple of str
        delimiter = data_param.get('delimiter')  # default is None
        skiprows = data_param.get('skiprows')  # default is None
        data_units = data_param.get('units', {})  # default is an empty dict
        data = {}  # a dictionary for data
        # open file for reading
        with open(filename, 'r') as fid:
            # read header
            if header_param:
                data.update(_read_header(fid, header_param))
                fid.seek(0)  # move cursor back to beginning
            # read data
            data_data = np.loadtxt(fid, dtype, delimiter=delimiter,
                                   skiprows=skiprows)
        # apply units
        data.update(_apply_units(data_data, data_units, fid.name))
        return data
848906
def load_files(files):
    """"""

    for py_file in files:
        LOG.debug("exec %s", py_file)
        execfile(py_file, globals(), locals())
1211367
def from_file(self, fname):
        """"""
        f = open(fname, "rb")
        data = f.read()
        self.update(data)
        f.close()
385752
def load_obsdata(self, idx: int) -> None:
        """"""
        if self._obs_ramflag:
            self.obs[0] = self._obs_array[idx]
        elif self._obs_diskflag:
            raw = self._obs_file.read(8)
            self.obs[0] = struct.unpack('d', raw)
1066211
def load_many(self, fobjs=None):
        """"""
        if fobjs is not None:
            # tolerance
            if not hasattr(fobjs, "__iter__"):
                fobjs = [fobjs]

            for index, (fobj, page) in enumerate(zip(fobjs, self.pages)):
                if fobj is None:
                    continue
                elif isinstance(fobj, ft.DataFile):
                    self.load(fobj, index)
                elif isinstance(fobj, str):
                    self.load_filename(fobj, index)
                else:
                    raise TypeError("Invalid object of class '{}'".format(fobj.__class__.__name__))
174361
def file(ctx, data_dir, data_file):
    """"""
    if not ctx.file:
        ctx.data_file = data_file

    if not ctx.data_dir:
        ctx.data_dir = data_dir

    ctx.type = 'file'
860823
def _load(self, data_file):
        

       # Load the data from a file.
        try:
            data = Schema().load(data_file)
        except (Exception, IOError, ValueError) as e:
            raise e

        return data
777371
def _load_from_file(self, filename):
    """"""
    if not tf.gfile.Exists(filename):
      raise ValueError("File %s not found" % filename)
    with tf.gfile.Open(filename) as f:
      self._load_from_file_object(f)
1220050
def load(self, dtype_out_time, dtype_out_vert=False, region=False,
             plot_units=False, mask_unphysical=False):
        """"""
        msg = ("Loading data from disk for object={0}, dtype_out_time={1}, "
               "dtype_out_vert={2}, and region="
               "{3}".format(self, dtype_out_time, dtype_out_vert, region))
        logging.info(msg + ' ({})'.format(ctime()))
        # Grab from the object if its there.
        try:
            data = self.data_out[dtype_out_time]
        except (AttributeError, KeyError):
            # Otherwise get from disk.  Try scratch first, then archive.
            try:
                data = self._load_from_disk(dtype_out_time, dtype_out_vert,
                                            region=region)
            except IOError:
                data = self._load_from_tar(dtype_out_time, dtype_out_vert)
        # Copy the array to self.data_out for ease of future access.
        self._update_data_out(data, dtype_out_time)
        # Apply desired plotting/cleanup methods.
        if mask_unphysical:
            data = self.var.mask_unphysical(data)
        if plot_units:
            data = self.var.to_plot_units(data, dtype_vert=dtype_out_vert)
        return data
968934
def load_data(verbose=False):
    ""

    df = pd.read_csv(STOPS_PATH, usecols=['stop_id', 'stop_name'])
    if verbose:
        print('- Loaded %d entries from "%s".' % (len(df), STOPS_PATH))
    return df
1325229
def load(self, filename=None, ignore_discard=False, ignore_expires=False):
        """"""
        if filename is None:
            if self.filename is not None: filename = self.filename
            else: raise ValueError(MISSING_FILENAME_TEXT)

        f = open(filename)
        try:
            self._really_load(f, filename, ignore_discard, ignore_expires)
        finally:
            f.close()
1224244
def recursive_load(search_root):
    """"""
    for root, dirs, files in os.walk(search_root):
        dir_name = os.path.basename(root)
        if dir_name == 'fixtures':
            for file_name in files:
                fixture_path = os.path.join(root, file_name)
                execute_manage('loaddata', fixture_path)