class_docstr: A file object for NetCDF data.
  
  A `netcdf_file` object has two standard attributes: `dimensions` and
  `variables`. The values of both are dictionaries, mapping dimension
  names to their associated lengths and variable names to variables,
  respectively. Application programs should never modify these
  dictionaries.
  
  All other attributes correspond to global attributes defined in the
  NetCDF file. Global file attributes are created by assigning to an
  attribute of the `netcdf_file` object.
  
  Parameters
  ----------
  filename : string or file-like
      string -> filename
  mode : {'r', 'w', 'a'}, optional
      read-write-append mode, default is 'r'
  mmap : None or bool, optional
      Whether to mmap `filename` when reading.  Default is True
      when `filename` is a file name, False when `filename` is a
      file-like object. Note that when mmap is in use, data arrays
      returned refer directly to the mmapped data on disk, and the
      file cannot be closed as long as references to it exist.
  version : {1, 2}, optional
      version of netcdf to read / write, where 1 means *Classic
      format* and 2 means *64-bit offset format*.  Default is 1.  See
      `here <https://docs.unidata.ucar.edu/nug/current/netcdf_introduction.html#select_format>`__
      for more info.
  maskandscale : bool, optional
      Whether to automatically scale and/or mask data based on attributes.
      Default is False.
  
  Notes
  -----
  The major advantage of this module over other modules is that it doesn't
  require the code to be linked to the NetCDF libraries. This module is
  derived from `pupynere <https://bitbucket.org/robertodealmeida/pupynere/>`_.
  
  NetCDF files are a self-describing binary data format. The file contains
  metadata that describes the dimensions and variables in the file. More
  details about NetCDF files can be found `here
  <https://www.unidata.ucar.edu/software/netcdf/guide_toc.html>`__. There
  are three main sections to a NetCDF data structure:
  
  1. Dimensions
  2. Variables
  3. Attributes
  
  The dimensions section records the name and length of each dimension used
  by the variables. The variables would then indicate which dimensions it
  uses and any attributes such as data units, along with containing the data
  values for the variable. It is good practice to include a
  variable that is the same name as a dimension to provide the values for
  that axes. Lastly, the attributes section would contain additional
  information such as the name of the file creator or the instrument used to
  collect the data.
  
  When writing data to a NetCDF file, there is often the need to indicate the
  'record dimension'. A record dimension is the unbounded dimension for a
  variable. For example, a temperature variable may have dimensions of
  latitude, longitude and time. If one wants to add more temperature data to
  the NetCDF file as time progresses, then the temperature variable should
  have the time dimension flagged as the record dimension.
  
  In addition, the NetCDF file header contains the position of the data in
  the file, so access can be done in an efficient manner without loading
  unnecessary data into memory. It uses the ``mmap`` module to create
  Numpy arrays mapped to the data on disk, for the same purpose.
  
  Note that when `netcdf_file` is used to open a file with mmap=True
  (default for read-only), arrays returned by it refer to data
  directly on the disk. The file should not be closed, and cannot be cleanly
  closed when asked, if such arrays are alive. You may want to copy data arrays
  obtained from mmapped Netcdf file if they are to be processed after the file
  is closed, see the example below.
  
  Examples
  --------
  To create a NetCDF file:
  
  >>> from scipy.io import netcdf_file
  >>> import numpy as np
  >>> f = netcdf_file('simple.nc', 'w')
  >>> f.history = 'Created for a test'
  >>> f.createDimension('time', 10)
  >>> time = f.createVariable('time', 'i', ('time',))
  >>> time[:] = np.arange(10)
  >>> time.units = 'days since 2008-01-01'
  >>> f.close()
  
  Note the assignment of ``arange(10)`` to ``time[:]``.  Exposing the slice
  of the time variable allows for the data to be set in the object, rather
  than letting ``arange(10)`` overwrite the ``time`` variable.
  
  To read the NetCDF file we just created:
  
  >>> from scipy.io import netcdf_file
  >>> f = netcdf_file('simple.nc', 'r')
  >>> print(f.history)
  b'Created for a test'
  >>> time = f.variables['time']
  >>> print(time.units)
  b'days since 2008-01-01'
  >>> print(time.shape)
  (10,)
  >>> print(time[-1])
  9
  
  NetCDF files, when opened read-only, return arrays that refer
  directly to memory-mapped data on disk:
  
  >>> data = time[:]
  
  If the data is to be processed after the file is closed, it needs
  to be copied to main memory:
  
  >>> data = time[:].copy()
  >>> del time
  >>> f.close()
  >>> data.mean()
  4.5
  
  A NetCDF file can also be used as context manager:
  
  >>> from scipy.io import netcdf_file
  >>> with netcdf_file('simple.nc', 'r') as f:
  ...     print(f.history)
  b'Created for a test'
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docstr: Perform a sync-to-disk flush if the `netcdf_file` object is in write mode.
  
  See Also
  --------
  sync : Identical function
docstr_tok: ["Perform","a","sync-to-disk","flush","if","the","`","netcdf_file","`","object","is","in","write","mode",".","See","Also","--","--","--","--","sync",":","Identical","function"]
code: def flush(self):
      """
      Perform a sync-to-disk flush if the `netcdf_file` object is in write mode.
  
      See Also
      --------
      sync : Identical function
  
      """
      if hasattr(self, "mode") and self.mode in "wa":
          self._write()
  
imports: import warnings
  import weakref
  from operator import mul
  from platform import python_implementation
  import mmap
  import numpy
  from numpy import frombuffer, dtype, empty, array, asarray
  from numpy import little_endian
  from functools import reduce
label_desc: Use image node_id 8 for calling the netcdf_file obj's underlying member method code with example usage: obj.flush() without return types