netCDF4.Variable

Module netCDF4 :: Class Variable |

# Class Variable

    object --+
             |
            Variable

Variable(self, group, name, datatype, dimensions=(), zlib=False, complevel=4, 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). Numpy string and unicode datatypes with length greater than one are aliases for `str`.

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 4. 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. For netCDF 3 files (that don't use HDF5), only `endian='native'` is allowed.

The `zlib, complevel, shuffle, fletcher32, contiguous` and {chunksizes} 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
__array__(...) |
__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=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None) x.__init__(...) initializes x; see help(type(x)) 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) |
__unicode__(...) |
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. |
renameAttribute(self, oldname, newname) rename a Variable attribute named `oldname` to `newname`. |
set_auto_mask(self, mask) turn on or off automatic conversion of variable data to and from masked arrays . |
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_auto_scale(self, scale) turn on or off 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. |
setncatts(self, attdict) set a bunch of netCDF variable attributes at once using a python dictionary. |
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.
| mask if True, data is automatically converted to/from masked arrays when missing values or fill values are present.
| ndim The number of variable dimensions.
| scale if True, `scale_factor` and `add_offset` are automatically applied.
| shape a tuple describing the current size of all the variable's dimensions.

Properties
| datatype numpy data type (for primitive data types) or VLType/CompoundType instance (for compound or vlen data types)
| 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=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None) (Constructor) |
x.__init__(...) initializes x; see help(type(x)) 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_mask(self, mask) |
turn on or off automatic conversion of variable data to and from masked arrays . If `mask` 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. The default value of `mask` is `True` (automatic conversions are performed).

### 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_auto_scale(self, scale) |
turn on or off automatic packing/unpacking of variable data using `scale_factor` and `add_offset` attributes. If `scale` 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 `scale` 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.

### setncatts(self, attdict) |
set a bunch of netCDF variable attributes at once using a python dictionary. This may be faster when setting a lot of attributes for a NETCDF3 formatted file, since nc_redef/nc_enddef is not called in between setting each attribute

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.

### mask if True, data is automatically converted to/from masked arrays when missing values or fill values are present. Default is `True`, can be reset using set_auto_mask and set_auto_maskandscale methods.

### scale if True, `scale_factor` and `add_offset` are automatically applied. Default is `True`, can be reset using set_auto_scale and set_auto_maskandscale methods.