package scipy

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type tag = [
  1. | `Netcdf_file
]
type t = [ `Netcdf_file | `Object ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val create : ?mode:[ `R | `W | `A ] -> ?mmap:bool -> ?version:[ `Two | `One ] -> ?maskandscale:bool -> filename:[ `File_like of Py.Object.t | `S of string ] -> unit -> t

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 :

, 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'

val close : [> tag ] Obj.t -> Py.Object.t

Closes the NetCDF file.

val createDimension : name:string -> length:int -> [> tag ] Obj.t -> Py.Object.t

Adds a dimension to the Dimension section of the NetCDF data structure.

Note that this function merely adds a new dimension that the variables can reference. The values for the dimension, if desired, should be added as a variable using `createVariable`, referring to this dimension.

Parameters ---------- name : str Name of the dimension (Eg, 'lat' or 'time'). length : int Length of the dimension.

See Also -------- createVariable

val createVariable : name:string -> type_:[ `S of string | `Dtype of Np.Dtype.t ] -> dimensions:Py.Object.t -> [> tag ] Obj.t -> Py.Object.t

Create an empty variable for the `netcdf_file` object, specifying its data type and the dimensions it uses.

Parameters ---------- name : str Name of the new variable. type : dtype or str Data type of the variable. dimensions : sequence of str List of the dimension names used by the variable, in the desired order.

Returns ------- variable : netcdf_variable The newly created ``netcdf_variable`` object. This object has also been added to the `netcdf_file` object as well.

See Also -------- createDimension

Notes ----- Any dimensions to be used by the variable should already exist in the NetCDF data structure or should be created by `createDimension` prior to creating the NetCDF variable.

val flush : [> tag ] Obj.t -> Py.Object.t

Perform a sync-to-disk flush if the `netcdf_file` object is in write mode.

See Also -------- sync : Identical function

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.

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