Module netCDF4 :: Class Variable

Class Variable

object --+
         |
        Variable

Variable(self, group, name, datatype, dimensions=(), zlib=False, complevel=6, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None,fill_value=None)

A netCDF Variable is used to read and write netCDF data. They are analagous to numpy array objects.

Variable instances should be created using the createVariable method of a Dataset or Group instance, not using this class directly.

Parameters:

group - Group or Dataset instance to associate with variable.

name - Name of the variable.

datatype - Variable data type. Can be specified by providing a numpy dtype object, or a string that describes a numpy dtype object. Supported values, corresponding to str attribute of numpy dtype objects, include 'f4' (32-bit floating point), 'f8' (64-bit floating point), 'i4' (32-bit signed integer), 'i2' (16-bit signed integer), 'i8' (64-bit singed integer), 'i4' (8-bit singed integer), 'i1' (8-bit signed integer), 'u1' (8-bit unsigned integer), 'u2' (16-bit unsigned integer), 'u4' (32-bit unsigned integer), 'u8' (64-bit unsigned integer), or 'S1' (single-character string). From compatibility with Scientific.IO.NetCDF, the old Numeric single character typecodes can also be used ('f' instead of 'f4', 'd' instead of 'f8', 'h' or 's' instead of 'i2', 'b' or 'B' instead of 'i1', 'c' instead of 'S1', and 'i' or 'l' instead of 'i4'). datatype can also be a CompoundType instance (for a structured, or compound array), a VLType instance (for a variable-length array), or the python str builtin (for a variable-length string array).

Keywords:

dimensions - a tuple containing the variable's dimension names (defined previously with createDimension). Default is an empty tuple which means the variable is a scalar (and therefore has no dimensions).

zlib - if True, data assigned to the Variable instance is compressed on disk. Default False.

complevel - the level of zlib compression to use (1 is the fastest, but poorest compression, 9 is the slowest but best compression). Default 6. Ignored if zlib=False.

shuffle - if True, the HDF5 shuffle filter is applied to improve compression. Default True. Ignored if zlib=False.

fletcher32 - if True (default False), the Fletcher32 checksum algorithm is used for error detection.

contiguous - if True (default False), the variable data is stored contiguously on disk. Default False. Setting to True for a variable with an unlimited dimension will trigger an error.

chunksizes - Can be used to specify the HDF5 chunksizes for each dimension of the variable. A detailed discussion of HDF chunking and I/O performance is available here. Basically, you want the chunk size for each dimension to match as closely as possible the size of the data block that users will read from the file. chunksizes cannot be set if contiguous=True.

endian - Can be used to control whether the data is stored in little or big endian format on disk. Possible values are little, big or native (default). The library will automatically handle endian conversions when the data is read, but if the data is always going to be read on a computer with the opposite format as the one used to create the file, there may be some performance advantage to be gained by setting the endian-ness.

The zlib, complevel, shuffle, fletcher32, contiguous, chunksizes and endian keywords are silently ignored for netCDF 3 files that do not use HDF5.

least_significant_digit - If specified, variable data will be truncated (quantized). In conjunction with zlib=True this produces 'lossy', but significantly more efficient compression. For example, if least_significant_digit=1, data will be quantized using around(scale*data)/scale, where scale = 2**bits, and bits is determined so that a precision of 0.1 is retained (in this case bits=4). Default is None, or no quantization.

fill_value - If specified, the default netCDF _FillValue (the value that the variable gets filled with before any data is written to it) is replaced with this value. If fill_value is set to False, then the variable is not pre-filled. The default netCDF fill values can be found in netCDF4.default_fillvals.

Returns:

a Variable instance. All further operations on the netCDF Variable are accomplised via Variable instance methods.

A list of attribute names corresponding to netCDF attributes defined for the variable can be obtained with the ncattrs() method. These attributes can be created by assigning to an attribute of the Variable instance. A dictionary containing all the netCDF attribute name/value pairs is provided by the __dict__ attribute of a Variable instance.

The instance variables dimensions, dtype, ndim, shape and least_significant_digit are read-only (and should not be modified by the user).

Instance Methods
 
__delattr__(...)
x.__delattr__('name') <==> del x.name
 
__delitem__(x, y)
del x[y]
 
__getattr__(...)
 
__getattribute__(...)
x.__getattribute__('name') <==> x.name
 
__getitem__(x, y)
x[y]
 
__init__(self, group, name, datatype, dimensions=(), zlib=False, complevel=6, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None)
x.__init__(...) initializes x; see x.__class__.__doc__ for signature
 
__len__(x)
len(x)
a new object with type S, a subtype of T
__new__(T, S, ...)
 
__setattr__(...)
x.__setattr__('name', value) <==> x.name = value
 
__setitem__(x, i, y)
x[i]=y
 
__str__(x)
str(x)
 
assignValue(self, val)
assign a value to a scalar variable.
 
chunking(self)
return variable chunking information.
 
delncattr(self, name, value)
delete a netCDF variable attribute.
 
endian(self)
return endian-ness (little,big,native) of variable (as stored in HDF5 file).
 
filters(self)
return dictionary containing HDF5 filter parameters.
 
getValue(self)
get the value of a scalar variable.
 
get_var_chunk_cache(self)
return variable chunk cache information in a tuple (size,nelems,preemption).
 
getncattr(self, name)
retrievel a netCDF variable attribute.
 
group(self)
return the group that this Variable is a member of.
 
ncattrs(self)
return netCDF attribute names for this Variable in a list.
 
set_auto_maskandscale(self, maskandscale)
turn on or off automatic conversion of variable data to and from masked arrays and automatic packing/unpacking of variable data using scale_factor and add_offset attributes.
 
set_var_chunk_cache(self, size=None, nelems=None, preemption=None)
change variable chunk cache settings.
 
setncattr(self, name, value)
set a netCDF variable attribute using name,value pair.

Inherited from object: __format__, __hash__, __reduce__, __reduce_ex__, __repr__, __sizeof__, __subclasshook__

Instance Variables
  dimensions
A tuple containing the names of the dimensions associated with this variable.
  dtype
A numpy dtype object describing the variable's data type.
  least_significant_digit
Describes the power of ten of the smallest decimal place in the data the contains a reliable value.
  ndim
The number of variable dimensions.
  shape
a tuple describing the current size of all the variable's dimensions.
Properties
  maskandscale
  size
Return the number of stored elements.

Inherited from object: __class__

Method Details

__delattr__(...)

 

x.__delattr__('name') <==> del x.name

Overrides: object.__delattr__

__getattribute__(...)

 

x.__getattribute__('name') <==> x.name

Overrides: object.__getattribute__

__init__(self, group, name, datatype, dimensions=(), zlib=False, complevel=6, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None)
(Constructor)

 

x.__init__(...) initializes x; see x.__class__.__doc__ for signature

Overrides: object.__init__

__new__(T, S, ...)

 
Returns: a new object with type S, a subtype of T
Overrides: object.__new__

__setattr__(...)

 

x.__setattr__('name', value) <==> x.name = value

Overrides: object.__setattr__

__str__(x)
(Informal representation operator)

 

str(x)

Overrides: object.__str__

assignValue(self, val)

 

assign a value to a scalar variable. Provided for compatibility with Scientific.IO.NetCDF, can also be done by assigning to a slice ([:]).

chunking(self)

 

return variable chunking information. If the dataset is defined to be contiguous (and hence there is no chunking) the word 'contiguous' is returned. Otherwise, a sequence with the chunksize for each dimension is returned.

delncattr(self, name, value)

 

delete a netCDF variable attribute. Only use if you need to delete a netCDF attribute with the same name as one of the reserved python attributes.

getValue(self)

 

get the value of a scalar variable. Provided for compatibility with Scientific.IO.NetCDF, can also be done by slicing ([:]).

get_var_chunk_cache(self)

 

return variable chunk cache information in a tuple (size,nelems,preemption). See netcdf C library documentation for nc_get_var_chunk_cache for details.

getncattr(self, name)

 

retrievel a netCDF variable attribute. Only use if you need to set a netCDF attribute with the same name as one of the reserved python attributes.

set_auto_maskandscale(self, maskandscale)

 

turn on or off automatic conversion of variable data to and from masked arrays and automatic packing/unpacking of variable data using scale_factor and add_offset attributes.

If maskandscale is set to True, when data is read from a variable it is converted to a masked array if any of the values are exactly equal to the either the netCDF _FillValue or the value specified by the missing_value variable attribute. The fill_value of the masked array is set to the missing_value attribute (if it exists), otherwise the netCDF _FillValue attribute (which has a default value for each data type). When data is written to a variable, the masked array is converted back to a regular numpy array by replacing all the masked values by the fill_value of the masked array.

If maskandscale is set to True, and the variable has a scale_factor or an add_offset attribute, then data read from that variable is unpacked using:

   data = self.scale_factor*data + self.add_offset

When data is written to a variable it is packed using:

   data = (data - self.add_offset)/self.scale_factor

If either scale_factor is present, but add_offset is missing, add_offset is assumed zero. If add_offset is present, but scale_factor is missing, scale_factor is assumed to be one. For more information on how scale_factor and add_offset can be used to provide simple compression, see http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml.

The default value of maskandscale is True (automatic conversions are performed).

set_var_chunk_cache(self, size=None, nelems=None, preemption=None)

 

change variable chunk cache settings. See netcdf C library documentation for nc_set_var_chunk_cache for details.

setncattr(self, name, value)

 

set a netCDF variable attribute using name,value pair. Only use if you need to set a netCDF attribute with the same name as one of the reserved python attributes.


Instance Variable Details

least_significant_digit

Describes the power of ten of the smallest decimal place in the data the contains a reliable value. Data is truncated to this decimal place when it is assigned to the Variable instance. If None, the data is not truncated.