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from .netcdf.write import write as netcdf_write
def write(fields, filename, fmt='NETCDF3_CLASSIC', overwrite=True,
verbose=False, cfa_options=None, mode='w',
least_significant_digit=None, endian='native', compress=0,
fletcher32=False, no_shuffle=False, datatype=None,
single=False, double=False, reference_datetime=None,
variable_attributes=None, HDF_chunksizes=None,
unlimited=None):
'''Write fields to a CF-netCDF or CFA-netCDF file.
NetCDF dimension and variable names will be taken from variables'
`~Variable.ncvar` attributes and the domain attribute
`~Domain.nc_dimensions` if present, otherwise they are inferred from
standard names or set to defaults. NetCDF names may be automatically
given a numerical suffix to avoid duplication.
Output netCDF file global properties are those which occur in the set
of CF global properties and non-standard data variable properties and
which have equal values across all input fields.
Logically identical field components are only written to the file
once, apart from when they need to fulfil both dimension coordinate
and auxiliary coordinate roles for different data variables.
.. seealso:: `cf.read`
:Parameters:
fields: (arbitrarily nested sequence of) `cf.Field` or `cf.FieldList`
The field or fields to write to the file.
filename: `str`
The output netCDF file. Various type of expansion are applied
to the file names:
==================== ======================================
Expansion Description
==================== ======================================
Tilde An initial component of ``~`` or
``~user`` is replaced by that *user*'s
home directory.
Environment variable Substrings of the form ``$name`` or
``${name}`` are replaced by the value
of environment variable *name*.
==================== ======================================
Where more than one type of expansion is used in the same
string, they are applied in the order given in the above
table.
Example: If the environment variable *MYSELF* has been set
to the "david", then ``'~$MYSELF/out.nc'`` is equivalent to
``'~david/out.nc'``.
fmt: `str`, optional
The format of the output file. One of:
===================== ================================================
fmt Description
===================== ================================================
``'NETCDF3_CLASSIC'`` Output to a CF-netCDF3 classic format file
``'NETCDF3_64BIT'`` Output to a CF-netCDF3 64-bit offset format file
``'NETCDF4_CLASSIC'`` Output to a CF-netCDF4 classic format file
``'NETCDF4'`` Output to a CF-netCDF4 format file
``'CFA3'`` Output to a CFA-netCDF3 classic format file
``'CFA4'`` Output to a CFA-netCDF4 format file
===================== ================================================
By default the *fmt* is ``'NETCDF3_CLASSIC'``. Note that the
netCDF3 formats may be slower than any of the other options.
overwrite: `bool`, optional
If False then raise an exception if the output file
pre-exists. By default a pre-existing output file is over
written.
verbose: `bool`, optional
If True then print one-line summaries of each field written.
cfa_options: `dict`, optional
A dictionary giving parameters for configuring the output
CFA-netCDF file:
========== ===============================================
Key Value
========== ===============================================
``'base'`` * If ``None`` (the default) then file names
within CFA-netCDF files are stored with
absolute paths.
* If set to an empty string then file names
within CFA-netCDF files are given relative to
the directory or URL base containing the
output CFA-netCDF file.
* If set to a string then file names within
CFA-netCDF files are given relative to the
directory or URL base described by the
value. For example: ``'../archive'``.
========== ===============================================
By default no parameters are specified.
mode: `str`, optional
Specify the mode of write access for the output file. One of:
======= =====================================================
mode Description
======= =====================================================
``'w'`` Open a new file for writing to. If it exists and
*overwrite* is True then the file is deleted prior to
being recreated.
======= =====================================================
By default the file is opened with write access mode ``'w'``.
endian: `str`, optional
The endian-ness of the output file. Valid values are
``'little'``, ``'big'`` or ``'native'``. By default the output
is native endian.
compress: `int`, optional
Regulate the speed and efficiency of compression. Must be an
integer between ``0`` and ``9``. ``0`` means no compression;
``1`` is the fastest, but has the lowest compression ratio;
``9`` is the slowest but best compression ratio. The default
value is ``0``. An exception is raised if compression is
requested for a netCDF3 output file format.
least_significant_digit: `int`, optional
Truncate the input field data arrays. For a positive integer,
N the precision that is retained in the compressed data is '10
to the power -N'. For example, a value of ``2`` will retain a
precision of 0.01. In conjunction with the *compress*
parameter this produces 'lossy', but significantly more
efficient compression.
fletcher32: `bool`, optional
If True then the Fletcher-32 HDF5 checksum algorithm is
activated to detect compression errors. Ignored if *compress*
is ``0``.
no_shuffle: `bool`, optional
If True then the HDF5 shuffle filter (which de-interlaces a
block of data before compression by reordering the bytes by
storing the first byte of all of a variable's values in the
chunk contiguously, followed by all the second bytes, and so
on) is turned off. By default the filter is applied because if
the data array values are not all wildly different, using the
filter can make the data more easily compressible. Ignored if
*compress* is ``0``.
datatype: `dict`, optional
Specify data type conversions to be applied prior to writing
data to disk. Arrays with data types which are not specified
remain unchanged. By default, input data types are
preserved. Data types defined by `numpy.dtype` objects in a
dictionary whose are input data types with values of output
data types.
**Example:**
To convert 64-bit floats and 64-bit integers to their
32-bit counterparts: ``datatype={numpy.dtype('float64'):
numpy.dtype('float32'), numpy.dtype('int64'):
numpy.dtype('int32')}``.
single: `bool`, optional
Write 64-bit floats as 32-bit floats and 64-bit integers as
32-bit integers. By default, input data types are
preserved. Note that ``single=True`` is exactly equivalent to
``datatype={numpy.dtype('float64'): numpy.dtype('float32'),
numpy.dtype('int64'): numpy.dtype('int32')}``.
double: `bool`, optional
Write 32-bit floats as 64-bit floats and 32-bit integers as
64-bit integers. By default, input data types are
preserved. Note that ``double=True`` is exactly equivalent to
``datatype={numpy.dtype('float32'): numpy.dtype('float64'),
numpy.dtype('int32'): numpy.dtype('int64')}``.
HDF_chunksizes: `dict`, optional
Manually specify HDF5 chunks for the field data arrays.
Chunking refers to a storage layout where a data array is
partitioned into fixed-size multi-dimensional chunks when
written to a netCDF4 file on disk. Chunking is ignored if the
field is written to a netCDF3 format file.
A chunk has the same rank as the data array, but with fewer
(or no more) elements along each axes. The chunk is defined by
a dictionary whose keys identify axes with values of the
chunks size for those axes.
If a given chunk size for an axis is larger than the axis size
for any field, then the size of the axis at the time of
writing to disk will be used instead.
If chunk sizes have been specified for some but not all axes,
then the each unset chunk size is assumed to be the full size
of the axis for each field.
If no chunk sizes have been set for any axes then the netCDF
default chunk is used
(http://www.unidata.ucar.edu/software/netcdf/docs/netcdf_perf_chunking.html).
If any chunk sizes have already been set on a field with the
`cf.Field.HDF_chunks` method then these are used in instead.
A detailed discussion of HDF chunking and I/O performance is
available at
https://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.html and
http://www.unidata.ucar.edu/software/netcdf/workshops/2011/nc4chunking.
Basically, you want the chunks for each dimension to match as
closely as possible the size and shape of the data block that
users will read from the file.
unlimited: sequence of `str`, optional
Create a unlimited dimensions (dimensions that can be appended
to). A dimension is identified by either a standard name; one
of T, Z, Y, X denoting time, height or horixontal axes (as
defined by the CF conventions); or the value of an arbitrary
CF property preceeded by the property name and a colon. For
example:
Multiple unlimited axes may be defined by specifying more
than one --unlimited option. Note, however, that only netCDF4
formats support multiple unlimited dimensions. For example,
to set the time and Z dimensions to be unlimited you could
use --unlim- ited=time --unlimited=Z
An example of defining an axis by an arbitrary CF property could
be --unlimited=long_name:pseudo_level
:Returns:
`None`
:Raises:
IOError:
If *overwrite* is False and the output file pre-exists.
:Examples:
>>> f
[<CF Field: air_pressure(30, 24)>,
<CF Field: u_compnt_of_wind(19, 29, 24)>,
<CF Field: v_compnt_of_wind(19, 29, 24)>,
<CF Field: potential_temperature(19, 30, 24)>]
>>> write(f, 'file')
>>> type(f)
<class 'cf.field.FieldList'>
>>> type(g)
<class 'cf.field.Field'>
>>> cf.write([f, g], 'file.nc', verbose=True)
[<CF Field: air_pressure(30, 24)>,
<CF Field: u_compnt_of_wind(19, 29, 24)>,
<CF Field: v_compnt_of_wind(19, 29, 24)>,
<CF Field: potential_temperature(19, 30, 24)>]
'''
if fields:
netcdf_write(fields, filename, fmt=fmt, overwrite=overwrite,
verbose=verbose, cfa_options=cfa_options,
mode=mode,
least_significant_digit=least_significant_digit,
endian=endian, compress=compress,
no_shuffle=no_shuffle, fletcher32=fletcher32,
datatype=datatype, single=single, double=double,
reference_datetime=reference_datetime,
variable_attributes=variable_attributes,
HDF_chunks=HDF_chunksizes, unlimited=unlimited)
#--- End: def
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