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NetCDF reader/writer module.
This module is used to read and create NetCDF files. NetCDF files are
accessed through the `netcdf_file` object. Data written to and from NetCDF
files are contained in `netcdf_variable` objects. Attributes are given
as member variables of the `netcdf_file` and `netcdf_variable` objects.
This module implements the Scientific.IO.NetCDF API to read and create
NetCDF files. The same API is also used in the PyNIO and pynetcdf
modules, allowing these modules to be used interchangeably when working
with NetCDF files.
Only NetCDF3 is supported here; for NetCDF4 see
`netCDF4-python `__,
which has a similar API.
netcdf_filenetcdf_variable N)mul)python_implementation)
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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 `__
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 `_.
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
`__. 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'
rNr Fc C s" |dvrt dt|drL|| _d| _|du r4d}q|rt|dst dn6|| _|d kr^d
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Adds a dimension to the Dimension section of the NetCDF data structure.
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----------
name : str
Name of the dimension (Eg, 'lat' or 'time').
length : int
Length of the dimension.
See Also
--------
createVariable
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Create an empty variable for the `netcdf_file` object, specifying its data
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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.
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A data object for netcdf files.
`netcdf_variable` objects are constructed by calling the method
`netcdf_file.createVariable` on the `netcdf_file` object. `netcdf_variable`
objects behave much like array objects defined in numpy, except that their
data resides in a file. Data is read by indexing and written by assigning
to an indexed subset; the entire array can be accessed by the index ``[:]``
or (for scalars) by using the methods `getValue` and `assignValue`.
`netcdf_variable` objects also have attribute `shape` with the same meaning
as for arrays, but the shape cannot be modified. There is another read-only
attribute `dimensions`, whose value is the tuple of dimension names.
All other attributes correspond to variable attributes defined in
the NetCDF file. Variable attributes are created by assigning to an
attribute of the `netcdf_variable` object.
Parameters
----------
data : array_like
The data array that holds the values for the variable.
Typically, this is initialized as empty, but with the proper shape.
typecode : dtype character code
Desired data-type for the data array.
size : int
Desired element size for the data array.
shape : sequence of ints
The shape of the array. This should match the lengths of the
variable's dimensions.
dimensions : sequence of strings
The names of the dimensions used by the variable. Must be in the
same order of the dimension lengths given by `shape`.
attributes : dict, optional
Attribute values (any type) keyed by string names. These attributes
become attributes for the netcdf_variable object.
maskandscale : bool, optional
Whether to automatically scale and/or mask data based on attributes.
Default is False.
Attributes
----------
dimensions : list of str
List of names of dimensions used by the variable object.
isrec, shape
Properties
See also
--------
isrec, shape
NFc
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znetcdf_variable.__init__c C s0 z|| j |< W n ty Y n0 || j|< d S rG rH rK rD rD rE rN k s
znetcdf_variable.__setattr__c C s t | jjo| jd S )aD Returns whether the variable has a record dimension or not.
A record dimension is a dimension along which additional data could be
easily appended in the netcdf data structure without much rewriting of
the data file. This attribute is a read-only property of the
`netcdf_variable`.
r )boolrp rm r rY rD rD rE r~ t s znetcdf_variable.isrecc C s | j jS )zReturns the shape tuple of the data variable.
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)rp rm rY rD rD rE rm s znetcdf_variable.shapec C s
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Retrieve a scalar value from a `netcdf_variable` of length one.
Raises
------
ValueError
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this exception will be raised.
)rp itemrY rD rD rE getValue s znetcdf_variable.getValuec C s$ | j jjstd|| j dd< dS )a
Assign a scalar value to a `netcdf_variable` of length one.
Parameters
----------
value : scalar
Scalar value (of compatible type) to assign to a length-one netcdf
variable. This value will be written to file.
Raises
------
ValueError
If the input is not a scalar, or if the destination is not a length-one
netcdf variable.
zvariable is not writeableN)rp flagsZ writeable RuntimeErrorr rD rD rE assignValue s
znetcdf_variable.assignValuec C s | j S )z
Return the typecode of the variable.
Returns
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typecode : char
The character typecode of the variable (e.g., 'i' for int).
)r rY rD rD rE rn s
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Return the itemsize of the variable.
Returns
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itemsize : int
The element size of the variable (e.g., 8 for float64).
)r rY rD rD rE rk s
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The default encoded fill-value for this Variable's data type.
)rl rn rk FILLMAP)rB r rD rD rE _default_encoded_fill_value s z+netcdf_variable._default_encoded_fill_valuec C sP d| j v rDtj| j d | jjd }t|| kr:|S | S n| S dS )z
Returns the encoded fill value for this variable as bytes.
This is taken from either the _FillValue attribute, or the default fill
value for this variable's data type.
r r) N) r@ r? r rp r rw r rk r )rB r rD rD rE r s
z'netcdf_variable._get_encoded_fill_valuec C s4 d| j v r| j d }nd| j v r,| j d }nd}|S )aw
Returns the value denoting "no data" for this variable.
If this variable does not have a missing/fill value, returns None.
If both _FillValue and missing_value are given, give precedence to
_FillValue. The netCDF standard gives special meaning to _FillValue;
missing_value is just used for compatibility with old datasets.
r r N)r@ )rB r rD rD rE r s
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Applies the given missing value to the data array.
Returns a numpy.ma array, with any value equal to missing_value masked
out (unless missing_value is None, in which case the original array is
returned).
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