Module « scipy.io »

Classe « netcdf_file »

Informations générales

Héritage

builtins.object
    netcdf_file

Définition

class netcdf_file(builtins.object):

Description _{[extrait de netcdf_file.__doc__]}

    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://www.unidata.ucar.edu/software/netcdf/docs/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
    >>> f = netcdf.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
    >>> f = netcdf.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[:]
    >>> data.base.base
    <mmap.mmap object at 0x7fe753763180>

    If the data is to be processed after the file is closed, it needs
    to be copied to main memory:

    >>> data = time[:].copy()
    >>> f.close()
    >>> data.mean()
    4.5

    A NetCDF file can also be used as context manager:

    >>> from scipy.io import netcdf
    >>> with netcdf.netcdf_file('simple.nc', 'r') as f:
    ...     print(f.history)
    b'Created for a test'

    

Constructeur(s)

Signature du constructeur	Description
__init__(self, filename, mode='r', mmap=None, version=1, maskandscale=False)	Initialize netcdf_file from fileobj (str or file-like). [extrait de __init__.__doc__]

Liste des opérateurs

Opérateurs hérités de la classe object

__eq__, __ge__, __gt__, __le__, __lt__, __ne__

Méthodes héritées de la classe object

__delattr__, __dir__, __format__, __getattribute__, __hash__, __init_subclass__, __reduce__, __reduce_ex__, __repr__, __sizeof__, __str__, __subclasshook__