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<a href="netCDF4-module.html">Module netCDF4</a> ::
Class Variable
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class Variable</h1><p class="nomargin-top"></p>
<pre class="base-tree">
object --+
|
<strong class="uidshort">Variable</strong>
</pre>
<hr />
<p>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)</p>
<p>A netCDF <a href="netCDF4.Variable-class.html"
class="link">Variable</a> is used to read and write netCDF data. They
are analagous to numpy array objects.</p>
<p><a href="netCDF4.Variable-class.html" class="link">Variable</a>
instances should be created using the <a
href="netCDF4.Dataset-class.html#createVariable"
class="link">createVariable</a> method of a <a
href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
href="netCDF4.Group-class.html" class="link">Group</a> instance, not
using this class directly.</p>
<p><b>Parameters:</b></p>
<p><b><code>group</code></b> - <a href="netCDF4.Group-class.html"
class="link">Group</a> or <a href="netCDF4.Dataset-class.html"
class="link">Dataset</a> instance to associate with variable.</p>
<p><b><code>name</code></b> - Name of the variable.</p>
<p><b><code>datatype</code></b> - <a href="netCDF4.Variable-class.html"
class="link">Variable</a> data type. Can be specified by providing a
numpy dtype object, or a string that describes a numpy dtype object.
Supported values, corresponding to <code>str</code> attribute of numpy
dtype objects, include <code>'f4'</code> (32-bit floating point),
<code>'f8'</code> (64-bit floating point), <code>'i4'</code> (32-bit
signed integer), <code>'i2'</code> (16-bit signed integer),
<code>'i8'</code> (64-bit singed integer), <code>'i4'</code> (8-bit
singed integer), <code>'i1'</code> (8-bit signed integer),
<code>'u1'</code> (8-bit unsigned integer), <code>'u2'</code> (16-bit
unsigned integer), <code>'u4'</code> (32-bit unsigned integer),
<code>'u8'</code> (64-bit unsigned integer), or <code>'S1'</code>
(single-character string). From compatibility with Scientific.IO.NetCDF,
the old Numeric single character typecodes can also be used
(<code>'f'</code> instead of <code>'f4'</code>, <code>'d'</code> instead
of <code>'f8'</code>, <code>'h'</code> or <code>'s'</code> instead of
<code>'i2'</code>, <code>'b'</code> or <code>'B'</code> instead of
<code>'i1'</code>, <code>'c'</code> instead of <code>'S1'</code>, and
<code>'i'</code> or <code>'l'</code> instead of <code>'i4'</code>).
<code>datatype</code> can also be a <a
href="netCDF4.CompoundType-class.html" class="link">CompoundType</a>
instance (for a structured, or compound array), a <a
href="netCDF4.VLType-class.html" class="link">VLType</a> instance (for a
variable-length array), or the python <code>str</code> builtin (for a
variable-length string array).</p>
<p><b>Keywords:</b></p>
<p><b><code>dimensions</code></b> - a tuple containing the variable's
dimension names (defined previously with <code>createDimension</code>).
Default is an empty tuple which means the variable is a scalar (and
therefore has no dimensions).</p>
<p><b><code>zlib</code></b> - if <code>True</code>, data assigned to the
<a href="netCDF4.Variable-class.html" class="link">Variable</a> instance
is compressed on disk. Default <code>False</code>.</p>
<p><b><code>complevel</code></b> - 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 <code>zlib=False</code>.</p>
<p><b><code>shuffle</code></b> - if <code>True</code>, the HDF5 shuffle
filter is applied to improve compression. Default <code>True</code>.
Ignored if <code>zlib=False</code>.</p>
<p><b><code>fletcher32</code></b> - if <code>True</code> (default
<code>False</code>), the Fletcher32 checksum algorithm is used for error
detection.</p>
<p><b><code>contiguous</code></b> - if <code>True</code> (default
<code>False</code>), the variable data is stored contiguously on disk.
Default <code>False</code>. Setting to <code>True</code> for a variable
with an unlimited dimension will trigger an error.</p>
<p><b><code>chunksizes</code></b> - 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 <a
href="http://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.html"
target="_top">here</a>. 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. <code>chunksizes</code> cannot be set if
<code>contiguous=True</code>.</p>
<p><b><code>endian</code></b> - Can be used to control whether the data
is stored in little or big endian format on disk. Possible values are
<code>little, big</code> or <code>native</code> (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.</p>
<p>The <code>zlib, complevel, shuffle, fletcher32, contiguous,
chunksizes</code> and <code>endian</code> keywords are silently ignored
for netCDF 3 files that do not use HDF5.</p>
<p><b><code>least_significant_digit</code></b> - If specified, variable
data will be truncated (quantized). In conjunction with
<code>zlib=True</code> this produces 'lossy', but significantly more
efficient compression. For example, if
<code>least_significant_digit=1</code>, 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
<code>None</code>, or no quantization.</p>
<p><b><code>fill_value</code></b> - If specified, the default netCDF
<code>_FillValue</code> (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 <code>False</code>, then the variable is not
pre-filled. The default netCDF fill values can be found in
netCDF4.default_fillvals.</p>
<p><b>Returns:</b></p>
<p>a <a href="netCDF4.Variable-class.html" class="link">Variable</a>
instance. All further operations on the netCDF Variable are accomplised
via <a href="netCDF4.Variable-class.html" class="link">Variable</a>
instance methods.</p>
<p>A list of attribute names corresponding to netCDF attributes defined
for the variable can be obtained with the <code>ncattrs()</code> method.
These attributes can be created by assigning to an attribute of the <a
href="netCDF4.Variable-class.html" class="link">Variable</a> instance. A
dictionary containing all the netCDF attribute name/value pairs is
provided by the <code>__dict__</code> attribute of a <a
href="netCDF4.Variable-class.html" class="link">Variable</a>
instance.</p>
<p>The instance variables <code>dimensions, dtype, ndim, shape</code> and
<code>least_significant_digit</code> are read-only (and should not be
modified by the user).</p>
<!-- ==================== INSTANCE METHODS ==================== -->
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#__delattr__" class="summary-sig-name">__delattr__</a>(<span class="summary-sig-arg">...</span>)</span><br />
x.__delattr__('name') <==> del x.name</td>
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<td><span class="summary-sig"><a name="__delitem__"></a><span class="summary-sig-name">__delitem__</span>(<span class="summary-sig-arg">x</span>,
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del x[y]</td>
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<td><span class="summary-sig"><a name="__getattr__"></a><span class="summary-sig-name">__getattr__</span>(<span class="summary-sig-arg">...</span>)</span></td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#__getattribute__" class="summary-sig-name">__getattribute__</a>(<span class="summary-sig-arg">...</span>)</span><br />
x.__getattribute__('name') <==> x.name</td>
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<td><span class="summary-sig"><a name="__getitem__"></a><span class="summary-sig-name">__getitem__</span>(<span class="summary-sig-arg">x</span>,
<span class="summary-sig-arg">y</span>)</span><br />
x[y]</td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">group</span>,
<span class="summary-sig-arg">name</span>,
<span class="summary-sig-arg">datatype</span>,
<span class="summary-sig-arg">dimensions</span>=<span class="summary-sig-default">()</span>,
<span class="summary-sig-arg">zlib</span>=<span class="summary-sig-default">False</span>,
<span class="summary-sig-arg">complevel</span>=<span class="summary-sig-default">4</span>,
<span class="summary-sig-arg">shuffle</span>=<span class="summary-sig-default">True</span>,
<span class="summary-sig-arg">fletcher32</span>=<span class="summary-sig-default">False</span>,
<span class="summary-sig-arg">contiguous</span>=<span class="summary-sig-default">False</span>,
<span class="summary-sig-arg">chunksizes</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">endian</span>=<span class="summary-sig-default">'native'</span>,
<span class="summary-sig-arg">least_significant_digit</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">fill_value</span>=<span class="summary-sig-default">None</span>)</span><br />
x.__init__(...) initializes x; see help(type(x)) for signature</td>
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<td><span class="summary-sig"><a name="__len__"></a><span class="summary-sig-name">__len__</span>(<span class="summary-sig-arg">x</span>)</span><br />
len(x)</td>
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<span class="summary-type">a new object with type S, a subtype of T</span>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#__new__" class="summary-sig-name">__new__</a>(<span class="summary-sig-arg">T</span>,
<span class="summary-sig-arg">S</span>,
<span class="summary-sig-arg">...</span>)</span></td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#__setattr__" class="summary-sig-name">__setattr__</a>(<span class="summary-sig-arg">...</span>)</span><br />
x.__setattr__('name', value) <==> x.name = value</td>
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<td><span class="summary-sig"><a name="__setitem__"></a><span class="summary-sig-name">__setitem__</span>(<span class="summary-sig-arg">x</span>,
<span class="summary-sig-arg">i</span>,
<span class="summary-sig-arg">y</span>)</span><br />
x[i]=y</td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#__str__" class="summary-sig-name">__str__</a>(<span class="summary-sig-arg">x</span>)</span><br />
str(x)</td>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="__unicode__"></a><span class="summary-sig-name">__unicode__</span>(<span class="summary-sig-arg">...</span>)</span></td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#assignValue" class="summary-sig-name">assignValue</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">val</span>)</span><br />
assign a value to a scalar variable.</td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#chunking" class="summary-sig-name">chunking</a>(<span class="summary-sig-arg">self</span>)</span><br />
return variable chunking information.</td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#delncattr" class="summary-sig-name">delncattr</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">name</span>,
<span class="summary-sig-arg">value</span>)</span><br />
delete a netCDF variable attribute.</td>
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<td><span class="summary-sig"><a name="endian"></a><span class="summary-sig-name">endian</span>(<span class="summary-sig-arg">self</span>)</span><br />
return endian-ness (little,big,native) of variable (as stored in HDF5
file).</td>
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<td><span class="summary-sig"><a name="filters"></a><span class="summary-sig-name">filters</span>(<span class="summary-sig-arg">self</span>)</span><br />
return dictionary containing HDF5 filter parameters.</td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#getValue" class="summary-sig-name">getValue</a>(<span class="summary-sig-arg">self</span>)</span><br />
get the value of a scalar variable.</td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#get_var_chunk_cache" class="summary-sig-name">get_var_chunk_cache</a>(<span class="summary-sig-arg">self</span>)</span><br />
return variable chunk cache information in a tuple
(size,nelems,preemption).</td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#getncattr" class="summary-sig-name">getncattr</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">name</span>)</span><br />
retrievel a netCDF variable attribute.</td>
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<td><span class="summary-sig"><a name="group"></a><span class="summary-sig-name">group</span>(<span class="summary-sig-arg">self</span>)</span><br />
return the group that this <a href="netCDF4.Variable-class.html"
class="link">Variable</a> is a member of.</td>
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<td><span class="summary-sig"><a name="ncattrs"></a><span class="summary-sig-name">ncattrs</span>(<span class="summary-sig-arg">self</span>)</span><br />
return netCDF attribute names for this <a
href="netCDF4.Variable-class.html" class="link">Variable</a> in a
list.</td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#set_auto_maskandscale" class="summary-sig-name">set_auto_maskandscale</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">maskandscale</span>)</span><br />
turn on or off automatic conversion of variable data to and from
masked arrays and automatic packing/unpacking of variable data using
<code>scale_factor</code> and <code>add_offset</code> attributes.</td>
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<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#set_var_chunk_cache" class="summary-sig-name">set_var_chunk_cache</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">size</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">nelems</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">preemption</span>=<span class="summary-sig-default">None</span>)</span><br />
change variable chunk cache settings.</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#setncattr" class="summary-sig-name">setncattr</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">name</span>,
<span class="summary-sig-arg">value</span>)</span><br />
set a netCDF variable attribute using name,value pair.</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="netCDF4.Variable-class.html#setncatts" class="summary-sig-name">setncatts</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">attdict</span>)</span><br />
set a bunch of netCDF variable attributes at once using a python
dictionary.</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td colspan="2" class="summary">
<p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
<code>__format__</code>,
<code>__hash__</code>,
<code>__reduce__</code>,
<code>__reduce_ex__</code>,
<code>__repr__</code>,
<code>__sizeof__</code>,
<code>__subclasshook__</code>
</p>
</td>
</tr>
</table>
<!-- ==================== INSTANCE VARIABLES ==================== -->
<a name="section-InstanceVariables"></a>
<table class="summary" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
<td align="left" colspan="2" class="table-header">
<span class="table-header">Instance Variables</span></td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a name="dimensions"></a><span class="summary-name">dimensions</span><br />
A tuple containing the names of the dimensions associated with this
variable.
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a name="dtype"></a><span class="summary-name">dtype</span><br />
A numpy dtype object describing the variable's data type.
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a href="netCDF4.Variable-class.html#least_significant_digit" class="summary-name">least_significant_digit</a><br />
Describes the power of ten of the smallest decimal place in the data
the contains a reliable value.
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a name="ndim"></a><span class="summary-name">ndim</span><br />
The number of variable dimensions.
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a name="shape"></a><span class="summary-name">shape</span><br />
a tuple describing the current size of all the variable's dimensions.
</td>
</tr>
</table>
<!-- ==================== PROPERTIES ==================== -->
<a name="section-Properties"></a>
<table class="summary" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
<td align="left" colspan="2" class="table-header">
<span class="table-header">Properties</span></td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a name="maskandscale"></a><span class="summary-name">maskandscale</span>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a name="size"></a><span class="summary-name">size</span><br />
Return the number of stored elements.
</td>
</tr>
<tr>
<td colspan="2" class="summary">
<p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
<code>__class__</code>
</p>
</td>
</tr>
</table>
<!-- ==================== METHOD DETAILS ==================== -->
<a name="section-MethodDetails"></a>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
<td align="left" colspan="2" class="table-header">
<span class="table-header">Method Details</span></td>
</tr>
</table>
<a name="__delattr__"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">__delattr__</span>(<span class="sig-arg">...</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>x.__delattr__('name') <==> del x.name</p>
<dl class="fields">
<dt>Overrides:
object.__delattr__
</dt>
</dl>
</td></tr></table>
</div>
<a name="__getattribute__"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">__getattribute__</span>(<span class="sig-arg">...</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>x.__getattribute__('name') <==> x.name</p>
<dl class="fields">
<dt>Overrides:
object.__getattribute__
</dt>
</dl>
</td></tr></table>
</div>
<a name="__init__"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">group</span>,
<span class="sig-arg">name</span>,
<span class="sig-arg">datatype</span>,
<span class="sig-arg">dimensions</span>=<span class="sig-default">()</span>,
<span class="sig-arg">zlib</span>=<span class="sig-default">False</span>,
<span class="sig-arg">complevel</span>=<span class="sig-default">4</span>,
<span class="sig-arg">shuffle</span>=<span class="sig-default">True</span>,
<span class="sig-arg">fletcher32</span>=<span class="sig-default">False</span>,
<span class="sig-arg">contiguous</span>=<span class="sig-default">False</span>,
<span class="sig-arg">chunksizes</span>=<span class="sig-default">None</span>,
<span class="sig-arg">endian</span>=<span class="sig-default">'native'</span>,
<span class="sig-arg">least_significant_digit</span>=<span class="sig-default">None</span>,
<span class="sig-arg">fill_value</span>=<span class="sig-default">None</span>)</span>
<br /><em class="fname">(Constructor)</em>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>x.__init__(...) initializes x; see help(type(x)) for signature</p>
<dl class="fields">
<dt>Overrides:
object.__init__
</dt>
</dl>
</td></tr></table>
</div>
<a name="__new__"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">__new__</span>(<span class="sig-arg">T</span>,
<span class="sig-arg">S</span>,
<span class="sig-arg">...</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<dl class="fields">
<dt>Returns: a new object with type S, a subtype of T</dt>
<dt>Overrides:
object.__new__
</dt>
</dl>
</td></tr></table>
</div>
<a name="__setattr__"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">__setattr__</span>(<span class="sig-arg">...</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>x.__setattr__('name', value) <==> x.name = value</p>
<dl class="fields">
<dt>Overrides:
object.__setattr__
</dt>
</dl>
</td></tr></table>
</div>
<a name="__str__"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">__str__</span>(<span class="sig-arg">x</span>)</span>
<br /><em class="fname">(Informal representation operator)</em>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>str(x)</p>
<dl class="fields">
<dt>Overrides:
object.__str__
</dt>
</dl>
</td></tr></table>
</div>
<a name="assignValue"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">assignValue</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">val</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>assign a value to a scalar variable. Provided for compatibility with
Scientific.IO.NetCDF, can also be done by assigning to a slice ([:]).</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="chunking"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">chunking</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>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.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="delncattr"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">delncattr</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">name</span>,
<span class="sig-arg">value</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>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.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="getValue"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">getValue</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>get the value of a scalar variable. Provided for compatibility with
Scientific.IO.NetCDF, can also be done by slicing ([:]).</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="get_var_chunk_cache"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">get_var_chunk_cache</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>return variable chunk cache information in a tuple
(size,nelems,preemption). See netcdf C library documentation for
<code>nc_get_var_chunk_cache</code> for details.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="getncattr"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">getncattr</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">name</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>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.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="set_auto_maskandscale"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">set_auto_maskandscale</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">maskandscale</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>turn on or off automatic conversion of variable data to and from
masked arrays and automatic packing/unpacking of variable data using
<code>scale_factor</code> and <code>add_offset</code> attributes.</p>
<p>If <code>maskandscale</code> is set to <code>True</code>, 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.</p>
<p>If <code>maskandscale</code> is set to <code>True</code>, and the
variable has a <code>scale_factor</code> or an <code>add_offset</code>
attribute, then data read from that variable is unpacked using:</p>
<pre class="literalblock">
data = self.scale_factor*data + self.add_offset
</pre>
<p>When data is written to a variable it is packed using:</p>
<pre class="literalblock">
data = (data - self.add_offset)/self.scale_factor
</pre>
<p>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 <code>scale_factor</code> and <code>add_offset</code> can be used to
provide simple compression, see <a
href="http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml"
target="_top">http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml</a>.</p>
<p>The default value of <code>maskandscale</code> is <code>True</code>
(automatic conversions are performed).</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="set_var_chunk_cache"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">set_var_chunk_cache</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">size</span>=<span class="sig-default">None</span>,
<span class="sig-arg">nelems</span>=<span class="sig-default">None</span>,
<span class="sig-arg">preemption</span>=<span class="sig-default">None</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>change variable chunk cache settings. See netcdf C library
documentation for <code>nc_set_var_chunk_cache</code> for details.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="setncattr"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">setncattr</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">name</span>,
<span class="sig-arg">value</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>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.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="setncatts"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">setncatts</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">attdict</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>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</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<br />
<!-- ==================== INSTANCE VARIABLE DETAILS ==================== -->
<a name="section-InstanceVariableDetails"></a>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
<td align="left" colspan="2" class="table-header">
<span class="table-header">Instance Variable Details</span></td>
</tr>
</table>
<a name="least_significant_digit"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<h3 class="epydoc">least_significant_digit</h3>
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 <a href="netCDF4.Variable-class.html"
class="link">Variable</a> instance. If <code>None</code>, the data is not
truncated.
<dl class="fields">
</dl>
</td></tr></table>
</div>
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