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Module netCDF3
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<!-- ==================== MODULE DESCRIPTION ==================== -->
<h1 class="epydoc">Module netCDF3</h1><p class="nomargin-top"></p>
<h1 class="heading">Introduction</h1>
<p>Python interface to the netCDF version 3 library. The API modelled
after <a
href="http://dirac.cnrs-orleans.fr/plone/software/scientificpython/"
target="_top">Scientific.IO.NetCDF</a>, and should be familiar to users
of that module. Some new features not found in
Scientific.IO.NetCDF:</p>
<ul>
<li>
ability to read multi-file netCDF Datasets, making variables
spanning multiple files appear as if they were in one file (see <a
href="netCDF3.MFDataset-class.html" class="link">MFDataset</a> for
more details).
</li>
<li>
support for masked arrays, automatic packing and unpacking of
packed integer data (see <a
href="netCDF3.Variable-class.html#set_auto_maskandscale"
class="link">Variable.set_auto_maskandscale</a> for details).
</li>
<li>
supports more complicated slicing (including numpy 'fancy
indexing').
</li>
<li>
includes convenience functions for converting to and from datetime
objects to numeric time values (see <a
href="netCDF3-module.html#num2date" class="link">num2date</a> and
<a href="netCDF3-module.html#date2num" class="link">date2num</a>),
using all the calendars in the CF standard.
</li>
<li>
convenience functions for converting arrays of characters to arrays
of strings, and vice-versa (<a
href="netCDF3-module.html#stringtochar"
class="link">stringtochar</a> and <a
href="netCDF3-module.html#chartostring"
class="link">chartostring</a>).
</li>
<li>
can use numpy dtype objects to specify netCDF variable datatype.
</li>
</ul>
<h1 class="heading">Download</h1>
<ul>
<li>
<a href="http://code.google.com/p/netcdf4-python/"
target="_top">Project page</a>.
</li>
<li>
<a href="http://code.google.com/p/netcdf4-python/source"
target="_top">Subversion repository</a>.
</li>
<li>
<a href="http://code.google.com/p/netcdf4-python/downloads/list"
target="_top">Source tar.gz</a>.
</li>
</ul>
<h1 class="heading">Requires</h1>
<ul>
<li>
numpy array module <a href="http://numpy.scipy.org"
target="_top">http://numpy.scipy.org</a>, version 1.2.1 or later.
</li>
<li>
The netCDF-3 C library (version 3.6 or later), available at <a
href="ftp://ftp.unidata.ucar.edu/pub/netcdf"
target="_top">ftp://ftp.unidata.ucar.edu/pub/netcdf</a>.
</li>
</ul>
<h1 class="heading">Install</h1>
<ul>
<li>
install the requisite python modules and C libraries (see above).
Optionally, set the <code>NETCDF3_DIR</code> environment variable
to point to where the netCDF version 3 library and headers are
installed. If <code>NETCDF3_DIR</code> is not set, some standard
locations will be searched.
</li>
<li>
run 'python setup-nc3.py install'
</li>
<li>
run some of the tests in the 'test3' directory.
</li>
</ul>
<h1 class="heading">Tutorial</h1>
<h2 class="heading">1) Creating/Opening/Closing a netCDF file</h2>
<p>To create a netCDF file from python, you simply call the <a
href="netCDF3.Dataset-class.html" class="link">Dataset</a>
constructor. This is also the method used to open an existing netCDF
file. If the file is open for write access (<code>w, r+</code> or
<code>a</code>), you may write any type of data including new
dimensions, variables and attributes. netCDF files come in several
flavors (<code>NETCDF3_CLASSIC, NETCDF3_64BIT,
NETCDF4_CLASSIC</code>, and <code>NETCDF4</code>). The first two
flavors are supported by version 3 of the netCDF library, and are
supported in this module. To read or write <code>NETCDF4</code> and
<code>NETCDF4_CLASSIC</code> files use the companion <a
href="netCDF4-module.html" class="link">netCDF4</a> python module.
The default format <code>NETCDF3_64BIT</code>. To see how a given
file is formatted, you can examine the <code>file_format</code> <a
href="netCDF3.Dataset-class.html" class="link">Dataset</a> attribute.
Closing the netCDF file is accomplished via the <a
href="netCDF3.Dataset-class.html#close" class="link">close</a> method
of the <a href="netCDF3.Dataset-class.html" class="link">Dataset</a>
instance.</p>
<p>Here's an example:</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">import</span> netCDF3
<span class="py-prompt">>>> </span>ncfile = netCDF3.Dataset(<span class="py-string">'test.nc'</span>, <span class="py-string">'w'</span>)
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> ncfile.file_format
<span class="py-output">NETCDF3_64BIT</span>
<span class="py-output"></span><span class="py-prompt">>>></span>
<span class="py-prompt">>>> </span>ncfile.close()</pre>
<h2 class="heading">2) Dimensions in a netCDF file</h2>
<p>netCDF defines the sizes of all variables in terms of dimensions,
so before any variables can be created the dimensions they use must
be created first. A special case, not often used in practice, is that
of a scalar variable, which has no dimensions. A dimension is created
using the <a href="netCDF3.Dataset-class.html#createDimension"
class="link">createDimension</a> method of a <a
href="netCDF3.Dataset-class.html" class="link">Dataset</a> instance.
A Python string is used to set the name of the dimension, and an
integer value is used to set the size. To create an unlimited
dimension (a dimension that can be appended to), the size value is
set to <code>None</code> or 0. netCDF 3 files can only have one
unlimited dimension, and it must be the first (leftmost) dimension of
the variable.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>ncfile.createDimension(<span class="py-string">'press'</span>, 10)
<span class="py-prompt">>>> </span>ncfile.createDimension(<span class="py-string">'time'</span>, None)
<span class="py-prompt">>>> </span>ncfile.createDimension(<span class="py-string">'lat'</span>, 73)
<span class="py-prompt">>>> </span>ncfile.createDimension(<span class="py-string">'lon'</span>, 144)</pre>
<p>All of the <a href="netCDF3.Dimension-class.html"
class="link">Dimension</a> instances are stored in a python
dictionary.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> ncfile.dimensions
<span class="py-output">{'lat': <netCDF3.Dimension object at 0x24a5f7b0>, </span>
<span class="py-output"> 'time': <netCDF3.Dimension object at 0x24a5f788>, </span>
<span class="py-output"> 'lon': <netCDF3.Dimension object at 0x24a5f7d8>, </span>
<span class="py-output"> 'press': <netCDF3.Dimension object at 0x24a5f760>}</span>
<span class="py-output"></span><span class="py-prompt">>>></span></pre>
<p>Calling the python <code>len</code> function with a <a
href="netCDF3.Dimension-class.html" class="link">Dimension</a>
instance returns the current size of that dimension. The <a
href="netCDF3.Dimension-class.html#isunlimited"
class="link">isunlimited</a> method of a <a
href="netCDF3.Dimension-class.html" class="link">Dimension</a>
instance can be used to determine if the dimensions is unlimited, or
appendable.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">for</span> dimname, dimobj <span class="py-keyword">in</span> ncfile.dimensions.iteritems():
<span class="py-prompt">>>> </span> <span class="py-keyword">print</span> dimname, len(dimobj), dimobj.isunlimited()
<span class="py-output">lat 73 False</span>
<span class="py-output">time 0 True</span>
<span class="py-output">lon 144 False</span>
<span class="py-output">press 10 False</span>
<span class="py-output"></span><span class="py-prompt">>>></span></pre>
<p><a href="netCDF3.Dimension-class.html" class="link">Dimension</a>
names can be changed using the <a
href="netCDF3.Dataset-class.html#renameDimension"
class="link">renameDimension</a> method of a <a
href="netCDF3.Dataset-class.html" class="link">Dataset</a>
instance.</p>
<h2 class="heading">3) Variables in a netCDF file</h2>
<p>netCDF variables behave much like python multidimensional array
objects supplied by the <a href="http://numpy.scipy.org"
target="_top">numpy module</a>. However, unlike numpy arrays, netCDF3
variables can be appended to along one 'unlimited' dimension. To
create a netCDF variable, use the <a
href="netCDF3.Dataset-class.html#createVariable"
class="link">createVariable</a> method of a <a
href="netCDF3.Dataset-class.html" class="link">Dataset</a> instance.
The <a href="netCDF3.Dataset-class.html#createVariable"
class="link">createVariable</a> method has two mandatory arguments,
the variable name (a Python string), and the variable datatype. The
variable's dimensions are given by a tuple containing the dimension
names (defined previously with <a
href="netCDF3.Dataset-class.html#createDimension"
class="link">createDimension</a>). To create a scalar variable,
simply leave out the dimensions keyword. The variable primitive
datatypes correspond to the dtype attribute of a numpy array. You can
specify the datatype as a numpy dtype object, or anything that can be
converted to a numpy dtype object. Valid datatype specifiers
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>'i1'</code> (8-bit
signed integer), or <code>'S1'</code> (single-character string) The
old Numeric single-character typecodes
(<code>'f'</code>,<code>'d'</code>,<code>'h'</code>,
<code>'s'</code>,<code>'b'</code>,<code>'B'</code>,<code>'c'</code>,<code>'i'</code>,<code>'l'</code>),
corresponding to
(<code>'f4'</code>,<code>'f8'</code>,<code>'i2'</code>,<code>'i2'</code>,<code>'i1'</code>,<code>'i1'</code>,<code>'S1'</code>,<code>'i4'</code>,<code>'i4'</code>),
will also work.</p>
<p>The dimensions themselves are usually also defined as variables,
called coordinate variables. The <a
href="netCDF3.Dataset-class.html#createVariable"
class="link">createVariable</a> method returns an instance of the <a
href="netCDF3.Variable-class.html" class="link">Variable</a> class
whose methods can be used later to access and set variable data and
attributes.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>times = ncfile.createVariable(<span class="py-string">'time'</span>,<span class="py-string">'f8'</span>,(<span class="py-string">'time'</span>,))
<span class="py-prompt">>>> </span>pressure = ncfile.createVariable(<span class="py-string">'press'</span>,<span class="py-string">'i4'</span>,(<span class="py-string">'press'</span>,))
<span class="py-prompt">>>> </span>latitudes = ncfile.createVariable(<span class="py-string">'latitude'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'lat'</span>,))
<span class="py-prompt">>>> </span>longitudes = ncfile.createVariable(<span class="py-string">'longitude'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'lon'</span>,))
<span class="py-prompt">>>> </span><span class="py-comment"># two dimensions unlimited.</span>
<span class="py-prompt">>>> </span>temp = ncfile.createVariable(<span class="py-string">'temp'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'time'</span>,<span class="py-string">'press'</span>,<span class="py-string">'lat'</span>,<span class="py-string">'lon'</span>,))</pre>
<p>All of the variables in the <a href="netCDF3.Dataset-class.html"
class="link">Dataset</a> are stored in a Python dictionary, in the
same way as the dimensions:</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> ncfile.variables
<span class="py-output">{'temp': <netCDF3.Variable object at 0x24a61068>,</span>
<span class="py-output"> 'pressure': <netCDF3.Variable object at 0.35f0f80>, </span>
<span class="py-output"> 'longitude': <netCDF3.Variable object at 0x24a61030>,</span>
<span class="py-output"> 'pressure': <netCDF3.Variable object at 0x24a610a0>, </span>
<span class="py-output"> 'time': <netCDF3.Variable object at 02x45f0.4.58>, </span>
<span class="py-output"> 'latitude': <netCDF3.Variable object at 0.3f0fb8>}</span>
<span class="py-output"></span><span class="py-prompt">>>></span></pre>
<p><a href="netCDF3.Variable-class.html" class="link">Variable</a>
names can be changed using the <a
href="netCDF3.Dataset-class.html#renameVariable"
class="link">renameVariable</a> method of a <a
href="netCDF3.Dataset-class.html" class="link">Dataset</a>
instance.</p>
<h2 class="heading">4) Attributes in a netCDF file</h2>
<p>There are two types of attributes in a netCDF file, global and
variable. Global attributes provide information about a dataset as a
whole. <a href="netCDF3.Variable-class.html"
class="link">Variable</a> attributes provide information about one of
the variables in a dataset. Global attributes are set by assigning
values to <a href="netCDF3.Dataset-class.html"
class="link">Dataset</a> instance variables. <a
href="netCDF3.Variable-class.html" class="link">Variable</a>
attributes are set by assigning values to <a
href="netCDF3.Variable-class.html" class="link">Variable</a> instance
variables. Attributes can be strings, numbers or sequences. Returning
to our example,</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">import</span> time
<span class="py-prompt">>>> </span>ncfile.description = <span class="py-string">'bogus example script'</span>
<span class="py-prompt">>>> </span>ncfile.history = <span class="py-string">'Created '</span> + time.ctime(time.time())
<span class="py-prompt">>>> </span>ncfile.source = <span class="py-string">'netCDF3 python module tutorial'</span>
<span class="py-prompt">>>> </span>latitudes.units = <span class="py-string">'degrees north'</span>
<span class="py-prompt">>>> </span>longitudes.units = <span class="py-string">'degrees east'</span>
<span class="py-prompt">>>> </span>pressure.units = <span class="py-string">'hPa'</span>
<span class="py-prompt">>>> </span>temp.units = <span class="py-string">'K'</span>
<span class="py-prompt">>>> </span>times.units = <span class="py-string">'hours since 0001-01-01 00:00:00.0'</span>
<span class="py-prompt">>>> </span>times.calendar = <span class="py-string">'gregorian'</span></pre>
<p>The <a href="netCDF3.Dataset-class.html#ncattrs"
class="link">ncattrs</a> method of a <a
href="netCDF3.Dataset-class.html" class="link">Dataset</a> or <a
href="netCDF3.Variable-class.html" class="link">Variable</a> instance
can be used to retrieve the names of all the netCDF attributes. This
method is provided as a convenience, since using the built-in
<code>dir</code> Python function will return a bunch of private
methods and attributes that cannot (or should not) be modified by the
user.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">for</span> name <span class="py-keyword">in</span> ncfile.ncattrs():
<span class="py-prompt">>>> </span> <span class="py-keyword">print</span> <span class="py-string">'Global attr'</span>, name, <span class="py-string">'='</span>, getattr(ncfile,name)
<span class="py-output">Global attr description = bogus example script</span>
<span class="py-output">Global attr history = Created Mon Nov 7 10.30:56 2005</span>
<span class="py-output">Global attr source = netCDF3 python module tutorial</span></pre>
<p>The <code>__dict__</code> attribute of a <a
href="netCDF3.Dataset-class.html" class="link">Dataset</a> or <a
href="netCDF3.Variable-class.html" class="link">Variable</a> instance
provides all the netCDF attribute name/value pairs in a python
dictionary:</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> ncfile.__dict__
<span class="py-output">{'source': 'netCDF3 python module tutorial',</span>
<span class="py-output">'description': 'bogus example script',</span>
<span class="py-output">'history': 'Created Mon Nov 7 10.30:56 2005'}</span></pre>
<p>Attributes can be deleted from a netCDF <a
href="netCDF3.Dataset-class.html" class="link">Dataset</a> or <a
href="netCDF3.Variable-class.html" class="link">Variable</a> using
the python <code>del</code> statement (i.e. <code>del var.foo</code>
removes the attribute <code>foo</code> the the variable
<code>var</code>).</p>
<h2 class="heading">6) Writing data to and retrieving data from a netCDF variable</h2>
<p>Now that you have a netCDF <a href="netCDF3.Variable-class.html"
class="link">Variable</a> instance, how do you put data into it? You
can just treat it like an array and assign data to a slice.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">import</span> numpy
<span class="py-prompt">>>> </span>latitudes[:] = numpy.arange(-90,91,2.5)
<span class="py-prompt">>>> </span>pressure[:] = numpy.arange(1000,90,-100)
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'latitudes =\n'</span>,latitudes[:]
<span class="py-output">latitudes =</span>
<span class="py-output">[-90. -87.5 -85. -82.5 -80. -77.5 -75. -72.5 -70. -67.5 -65. -62.5</span>
<span class="py-output"> -60. -57.5 -55. -52.5 -50. -47.5 -45. -42.5 -40. -37.5 -35. -32.5</span>
<span class="py-output"> -30. -27.5 -25. -22.5 -20. -17.5 -15. -12.5 -10. -7.5 -5. -2.5</span>
<span class="py-output"> 0. 2.5 5. 7.5 10. 12.5 15. 17.5 20. 22.5 25. 27.5</span>
<span class="py-output"> 30. 32.5 35. 37.5 40. 42.5 45. 47.5 50. 52.5 55. 57.5</span>
<span class="py-output"> 60. 62.5 65. 67.5 70. 72.5 75. 77.5 80. 82.5 85. 87.5</span>
<span class="py-output"> 90. ]</span>
<span class="py-output"></span><span class="py-prompt">>>></span>
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'pressure levels =\n'</span>,pressure[:]
<span class="py-output">[1000 900 800 700 600 500 400 300 200 100]</span>
<span class="py-output"></span><span class="py-prompt">>>></span></pre>
<p>Unlike numpy array objects, netCDF <a
href="netCDF3.Variable-class.html" class="link">Variable</a> objects
with unlimited dimensions will grow along those dimensions if you
assign data outside the currently defined range of indices.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-comment"># append along two unlimited dimensions by assigning to slice.</span>
<span class="py-prompt">>>> </span>nlats = len(ncfile.dimensions[<span class="py-string">'lat'</span>])
<span class="py-prompt">>>> </span>nlons = len(ncfile.dimensions[<span class="py-string">'lon'</span>])
<span class="py-prompt">>>> </span>nlevs = len(ncfile.dimensions[<span class="py-string">'press'</span>])
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'temp shape before adding data = '</span>,temp.shape
<span class="py-output">temp shape before adding data = (0, 10, 73, 144)</span>
<span class="py-output"></span><span class="py-prompt">>>></span>
<span class="py-prompt">>>> </span><span class="py-keyword">from</span> numpy.random.mtrand <span class="py-keyword">import</span> uniform
<span class="py-prompt">>>> </span>temp[0:5,:,:,:] = uniform(size=(5,nlevs,nlats,nlons))
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'temp shape after adding data = '</span>,temp.shape
<span class="py-output">temp shape after adding data = (5, 16, 73, 144)</span>
<span class="py-output"></span><span class="py-prompt">>>></span></pre>
<p>Time coordinate values pose a special challenge to netCDF users.
Most metadata standards (such as CF and COARDS) specify that time
should be measure relative to a fixed date using a certain calendar,
with units specified like <code>hours since YY:MM:DD hh-mm-ss</code>.
These units can be awkward to deal with, without a utility to convert
the values to and from calendar dates. The functione called <a
href="netCDF3-module.html#num2date" class="link">num2date</a> and <a
href="netCDF3-module.html#date2num" class="link">date2num</a> are
provided with this package to do just that. Here's an example of how
they can be used:</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-comment"># fill in times.</span>
<span class="py-prompt">>>> </span><span class="py-keyword">from</span> datetime <span class="py-keyword">import</span> datetime, timedelta
<span class="py-prompt">>>> </span><span class="py-keyword">from</span> netCDF3 <span class="py-keyword">import</span> num2date, date2num
<span class="py-prompt">>>> </span>dates = [datetime(2001,3,1)+n*timedelta(hours=12) <span class="py-keyword">for</span> n <span class="py-keyword">in</span> range(temp.shape[0])]
<span class="py-prompt">>>> </span>times[:] = date2num(dates,units=times.units,calendar=times.calendar)
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'time values (in units %s): '</span> % times.units+<span class="py-string">'\n'</span>,times[:]
<span class="py-output">time values (in units hours since January 1, 0001): </span>
<span class="py-output">[ 17533056. 17533068. 17533080. 17533092. 17533104.]</span>
<span class="py-output"></span><span class="py-prompt">>>></span>
<span class="py-prompt">>>> </span>dates = num2date(times[:],units=times.units,calendar=times.calendar)
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'dates corresponding to time values:\n'</span>,dates
<span class="py-output">dates corresponding to time values:</span>
<span class="py-output">[2001-03-01 00:00:00 2001-03-01 12:00:00 2001-03-02 00:00:00</span>
<span class="py-output"> 2001-03-02 12:00:00 2001-03-03 00:00:00]</span>
<span class="py-output"></span><span class="py-prompt">>>></span></pre>
<p><a href="netCDF3-module.html#num2date" class="link">num2date</a>
converts numeric values of time in the specified <code>units</code>
and <code>calendar</code> to datetime objects, and <a
href="netCDF3-module.html#date2num" class="link">date2num</a> does
the reverse. All the calendars currently defined in the <a
href="http://cf-pcmdi.llnl.gov/documents/cf-conventions/"
target="_top">CF metadata convention</a> are supported. A function
called <a href="netCDF3-module.html#date2index"
class="link">date2index</a> is also provided which returns the
indices of a netCDF time variable corresponding to a sequence of
datetime instances.</p>
<p>All of the code in this tutorial is available in
<code>examples/tutorial-nc3.py</code>, Unit tests are in the
<code>test3</code> directory.</p>
<h2 class="heading">7) Reading data from a multi-file netCDF dataset.</h2>
<p>If you want to read data from a variable that spans multiple
netCDF files, you can use the <a href="netCDF3.MFDataset-class.html"
class="link">MFDataset</a> class to read the data as if it were
contained in a single file. Instead of using a single filename to
create a <a href="netCDF3.Dataset-class.html"
class="link">Dataset</a> instance, create a <a
href="netCDF3.MFDataset-class.html" class="link">MFDataset</a>
instance with either a list of filenames, or a string with a wildcard
(which is then converted to a sorted list of files using the python
glob module). Variables in the list of files that share the same
unlimited dimension are aggregated together, and can be sliced across
multiple files. To illustrate this, let's first create a bunch of
netCDF files with the same variable (with the same unlimited
dimension).</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">for</span> nfile <span class="py-keyword">in</span> range(10):
<span class="py-prompt">>>> </span> f = Dataset(<span class="py-string">'mftest'</span>+repr(nfile)+<span class="py-string">'.nc'</span>,<span class="py-string">'w'</span>)
<span class="py-prompt">>>> </span> f.createDimension(<span class="py-string">'x'</span>,None)
<span class="py-prompt">>>> </span> x = f.createVariable(<span class="py-string">'x'</span>,<span class="py-string">'i'</span>,(<span class="py-string">'x'</span>,))
<span class="py-prompt">>>> </span> x[0:10] = numpy.arange(nfile*10,10*(nfile+1))
<span class="py-prompt">>>> </span> f.close()</pre>
<p>Now read all the files back in at once with <a
href="netCDF3.MFDataset-class.html" class="link">MFDataset</a></p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>f = MFDataset(<span class="py-string">'mftest*nc'</span>)
<span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.variables[<span class="py-string">'x'</span>][:]
<span class="py-output">[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24</span>
<span class="py-output"> 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49</span>
<span class="py-output"> 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74</span>
<span class="py-output"> 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99]</span>
<span class="py-output"></span><span class="py-prompt">>>></span></pre>
<p>Note that MFDataset can only be used to read, not write,
multi-file datasets.</p>
<hr />
<div class="fields"> <p><strong>Contact:</strong>
Jeffrey Whitaker <jeffrey.s.whitaker@noaa.gov>
</p>
<p><strong>Copyright:</strong>
2008 by Jeffrey Whitaker.
</p>
<p><strong>License:</strong>
Permission to use, copy, modify, and distribute this software and
its documentation for any purpose and without fee is hereby
granted, provided that the above copyright notice appear in all
copies and that both the copyright notice and this permission
notice appear in supporting documentation. THE AUTHOR DISCLAIMS ALL
WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE
AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES
OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR
PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER
TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
PERFORMANCE OF THIS SOFTWARE.
</p>
<p><strong>Version:</strong>
0.9.2
</p>
</div><!-- ==================== CLASSES ==================== -->
<a name="section-Classes"></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">Classes</span></td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a href="netCDF3.Dataset-class.html" class="summary-name">Dataset</a><br />
Dataset(self, filename, mode="r", clobber=True,
format='NETCDF3_64BIT')
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a href="netCDF3.Dimension-class.html" class="summary-name">Dimension</a><br />
Dimension(self, dset, name, size=None)
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a href="netCDF3.MFDataset-class.html" class="summary-name">MFDataset</a><br />
MFDataset(self, files, check=False, exclude=[])
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<a href="netCDF3.Variable-class.html" class="summary-name">Variable</a><br />
Variable(self, dset, name, datatype, dimensions=(),
fill_value=None)
</td>
</tr>
</table>
<!-- ==================== FUNCTIONS ==================== -->
<a name="section-Functions"></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">Functions</span></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="netCDF3-module.html#chartostring" class="summary-sig-name">chartostring</a>(<span class="summary-sig-arg">b</span>)</span><br />
convert a character array to a string array with one less dimension.</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="netCDF3-module.html#date2index" class="summary-sig-name">date2index</a>(<span class="summary-sig-arg">dates</span>,
<span class="summary-sig-arg">nctime</span>,
<span class="summary-sig-arg">calendar</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">select</span>=<span class="summary-sig-default">'exact'</span>)</span><br />
Return indices of a netCDF time variable corresponding to the given
dates.</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="netCDF3-module.html#date2num" class="summary-sig-name">date2num</a>(<span class="summary-sig-arg">dates</span>,
<span class="summary-sig-arg">units</span>,
<span class="summary-sig-arg">calendar</span>=<span class="summary-sig-default">'standard'</span>)</span><br />
Return numeric time values given datetime objects.</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 name="getlibversion"></a><span class="summary-sig-name">getlibversion</span>()</span><br />
returns a string describing the version of the netcdf library used to
build the module, and when it was built.</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="netCDF3-module.html#num2date" class="summary-sig-name">num2date</a>(<span class="summary-sig-arg">times</span>,
<span class="summary-sig-arg">units</span>,
<span class="summary-sig-arg">calendar</span>=<span class="summary-sig-default">'standard'</span>)</span><br />
Return datetime objects given numeric time values.</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="netCDF3-module.html#stringtoarr" class="summary-sig-name">stringtoarr</a>(<span class="summary-sig-arg">a</span>,
<span class="summary-sig-arg">NUMCHARS</span>)</span><br />
convert a string to a character array of length NUMCHARS</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="netCDF3-module.html#stringtochar" class="summary-sig-name">stringtochar</a>(<span class="summary-sig-arg">a</span>)</span><br />
convert a string array to a character array with one extra dimension</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
</table>
<!-- ==================== VARIABLES ==================== -->
<a name="section-Variables"></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">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="__package__"></a><span class="summary-name">__package__</span> = <code title="None">None</code>
</td>
</tr>
</table>
<!-- ==================== FUNCTION DETAILS ==================== -->
<a name="section-FunctionDetails"></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">Function Details</span></td>
</tr>
</table>
<a name="chartostring"></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">chartostring</span>(<span class="sig-arg">b</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>convert a character array to a string array with one less
dimension.</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>b</code></strong> - Input character array (numpy datatype 'S1'). Will be converted to
a array of strings, where each string has a fixed length of
b.shape[-1] characters.</li>
</ul></dd>
<dt>Returns:</dt>
<dd>A numpy string array with datatype 'SN' and shape b.shape[:-1],
where N=b.shape[-1].</dd>
</dl>
</td></tr></table>
</div>
<a name="date2index"></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">date2index</span>(<span class="sig-arg">dates</span>,
<span class="sig-arg">nctime</span>,
<span class="sig-arg">calendar</span>=<span class="sig-default">None</span>,
<span class="sig-arg">select</span>=<span class="sig-default">'exact'</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>Return indices of a netCDF time variable corresponding to the given
dates.</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>dates</code></strong> - A datetime object or a sequence of datetime objects. The datetime
objects should not include a time-zone offset.</li>
<li><strong class="pname"><code>nctime</code></strong> - A netCDF time variable object. The nctime object must have a
<code>units</code> attribute.</li>
<li><strong class="pname"><code>calendar</code></strong> - Describes the calendar used in the time calculation. Valid
calendars <code>'standard', 'gregorian', 'proleptic_gregorian'
'noleap', '365_day', '360_day', 'julian', 'all_leap',
'366_day'</code>. Default is <code>'standard'</code>, which is a
mixed Julian/Gregorian calendar If <code>calendar</code> is None,
its value is given by <code>nctime.calendar</code> or
<code>standard</code> if no such attribute exists.</li>
<li><strong class="pname"><code>select</code></strong> - <code>'exact', 'before', 'after', 'nearest'</code> The index
selection method. <code>exact</code> will return the indices
perfectly matching the dates given. <code>before</code> and
<code>after</code> will return the indices corresponding to the
dates just before or just after the given dates if an exact match
cannot be found. <code>nearest</code> will return the indices
that correspond to the closest dates.</li>
</ul></dd>
<dt>Returns:</dt>
<dd>an index (indices) of the netCDF time variable corresponding to
the given datetime object(s).</dd>
</dl>
</td></tr></table>
</div>
<a name="date2num"></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">date2num</span>(<span class="sig-arg">dates</span>,
<span class="sig-arg">units</span>,
<span class="sig-arg">calendar</span>=<span class="sig-default">'standard'</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>Return numeric time values given datetime objects. The units of the
numeric time values are described by the <code class="link">units</code>
argument and the <code class="link">calendar</code> keyword. The datetime
objects must be in UTC with no time-zone offset. If there is a time-zone
offset in <code>units</code>, it will be applied to the returned numeric
values.</p>
<p>Like the matplotlib <code>date2num</code> function, except that it
allows for different units and calendars. Behaves the same if
<code>units = 'days since 0001-01-01 00:00:00'</code> and <code>calendar
= 'proleptic_gregorian'</code>.</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>dates</code></strong> - A datetime object or a sequence of datetime objects. The datetime
objects should not include a time-zone offset.</li>
<li><strong class="pname"><code>units</code></strong> - a string of the form <code>'<b>time units</b> since <b>reference
time</b></code>' describing the time units. <b><code>time
units</code></b> can be days, hours, minutes or seconds.
<b><code>reference time</code></b> is the time origin. A valid
choice would be units=<code>'hours since 1800-01-01 00:00:00
-6:00'</code>.</li>
<li><strong class="pname"><code>calendar</code></strong> - describes the calendar used in the time calculations. All the
values currently defined in the <a
href="http://cf-pcmdi.llnl.gov/documents/cf-conventions/"
target="_top">CF metadata convention</a> are supported. Valid
calendars <code>'standard', 'gregorian', 'proleptic_gregorian'
'noleap', '365_day', '360_day', 'julian', 'all_leap',
'366_day'</code>. Default is <code>'standard'</code>, which is a
mixed Julian/Gregorian calendar.</li>
</ul></dd>
<dt>Returns:</dt>
<dd>a numeric time value, or an array of numeric time values.
<p>The maximum resolution of the numeric time values is 1
second.</p></dd>
</dl>
</td></tr></table>
</div>
<a name="num2date"></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">num2date</span>(<span class="sig-arg">times</span>,
<span class="sig-arg">units</span>,
<span class="sig-arg">calendar</span>=<span class="sig-default">'standard'</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>Return datetime objects given numeric time values. The units of the
numeric time values are described by the <code>units</code> argument and
the <code>calendar</code> keyword. The returned datetime objects
represent UTC with no time-zone offset, even if the specified
<code>units</code> contain a time-zone offset.</p>
<p>Like the matplotlib <code>num2date</code> function, except that it
allows for different units and calendars. Behaves the same if
<code>units = 'days since 001-01-01 00:00:00'</code> and <code>calendar =
'proleptic_gregorian'</code>.</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>times</code></strong> - numeric time values. Maximum resolution is 1 second.</li>
<li><strong class="pname"><code>units</code></strong> - a string of the form <code>'<b>time units</b> since <b>reference
time</b></code>' describing the time units. <b><code>time
units</code></b> can be days, hours, minutes or seconds.
<b><code>reference time</code></b> is the time origin. A valid
choice would be units=<code>'hours since 1800-01-01 00:00:00
-6:00'</code>.</li>
<li><strong class="pname"><code>calendar</code></strong> - describes the calendar used in the time calculations. All the
values currently defined in the <a
href="http://cf-pcmdi.llnl.gov/documents/cf-conventions/"
target="_top">CF metadata convention</a> are supported. Valid
calendars <code>'standard', 'gregorian', 'proleptic_gregorian'
'noleap', '365_day', '360_day', 'julian', 'all_leap',
'366_day'</code>. Default is <code>'standard'</code>, which is a
mixed Julian/Gregorian calendar.</li>
</ul></dd>
<dt>Returns:</dt>
<dd>a datetime instance, or an array of datetime instances.
<p>The datetime instances returned are 'real' python datetime
objects if the date falls in the Gregorian calendar (i.e.
<code>calendar='proleptic_gregorian'</code>, or <code>calendar =
'standard'</code> or <code>'gregorian'</code> and the date is
after 1582-10-15). Otherwise, they are 'phony' datetime objects
which support some but not all the methods of 'real' python
datetime objects. This is because the python datetime module
cannot the uses the <code>'proleptic_gregorian'</code> calendar,
even before the switch occured from the Julian calendar in 1582.
The datetime instances do not contain a time-zone offset, even if
the specified <code>units</code> contains one.</p></dd>
</dl>
</td></tr></table>
</div>
<a name="stringtoarr"></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">stringtoarr</span>(<span class="sig-arg">a</span>,
<span class="sig-arg">NUMCHARS</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>convert a string to a character array of length NUMCHARS</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>a</code></strong> - Input python string.</li>
<li><strong class="pname"><code>NUMCHARS</code></strong> - number of characters used to represent string (if len(a) <
NUMCHARS, it will be padded on the right with blanks).</li>
</ul></dd>
<dt>Returns:</dt>
<dd>A rank 1 numpy character array of length NUMCHARS with datatype
'S1'</dd>
</dl>
</td></tr></table>
</div>
<a name="stringtochar"></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">stringtochar</span>(<span class="sig-arg">a</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>convert a string array to a character array with one extra
dimension</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>a</code></strong> - Input numpy string array with numpy datatype 'SN', where N is the
number of characters in each string. Will be converted to an
array of characters (datatype 'S1') of shape a.shape + (N,).</li>
</ul></dd>
<dt>Returns:</dt>
<dd>A numpy character array with datatype 'S1' and shape a.shape +
(N,), where N is the length of each string in a.</dd>
</dl>
</td></tr></table>
</div>
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2fe194b2d4a4a955d2a305c2cd613c17
>
files
>
25
