[netcdf4-python] 01/07: Imported Upstream version 1.1.9
Sebastiaan Couwenberg
sebastic at moszumanska.debian.org
Thu Jul 30 23:30:11 UTC 2015
This is an automated email from the git hooks/post-receive script.
sebastic pushed a commit to branch master
in repository netcdf4-python.
commit 8cc7cc83924745ad31f2eed95ff75bad25bf3741
Author: Bas Couwenberg <sebastic at xs4all.nl>
Date: Fri Jul 31 01:08:23 2015 +0200
Imported Upstream version 1.1.9
---
.travis.yml | 1 +
Changelog | 23 +
MANIFEST.in | 4 +
README.gh-pages | 20 +-
README.md | 16 +-
README.wheels.md | 100 +
create_docs.sh | 7 +-
docs/api-objects.txt | 285 -
docs/class-tree.html | 151 -
docs/crarr.png | Bin 340 -> 0 bytes
docs/epydoc.css | 322 -
docs/epydoc.js | 293 -
docs/help.html | 272 -
docs/identifier-index.html | 698 --
docs/index.html | 1732 ----
docs/module-tree.html | 109 -
docs/netCDF4-module.html | 1660 ---
docs/netCDF4.CompoundType-class.html | 357 -
docs/netCDF4.Dataset-class.html | 1705 ----
docs/netCDF4.Dimension-class.html | 394 -
docs/netCDF4.Group-class.html | 387 -
docs/netCDF4.MFDataset-class.html | 528 -
docs/netCDF4.MFTime-class.html | 307 -
docs/netCDF4.VLType-class.html | 344 -
docs/netCDF4.Variable-class.html | 1464 ---
docs/netCDF4/index.html | 5584 ++++++++++
docs/redirect.html | 38 -
man/nc3tonc4.1 | 18 +-
man/nc4tonc3.1 | 6 +-
netCDF4/__init__.py | 8 +-
netCDF4/_netCDF4.c | 18150 +++++++++++++++++----------------
netCDF4/_netCDF4.pyx | 2593 ++---
netcdftime/_datetime.c | 482 +-
netcdftime/_datetime.pyx | 7 +
netcdftime/netcdftime.py | 29 +-
setup.py | 23 +-
test/run_all.py | 5 +
test/tst_netcdftime.py | 31 +-
test/tst_scaled.py | 45 +
test/tst_slicing.py | 12 +
40 files changed, 17003 insertions(+), 21207 deletions(-)
diff --git a/.travis.yml b/.travis.yml
index ec158e9..f0fad89 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,6 +2,7 @@ language: python
env:
global:
- DEPENDS="numpy cython"
+ - NO_NET=1
python:
- "2.7"
- "3.3"
diff --git a/Changelog b/Changelog
index 3595e0a..7952b63 100644
--- a/Changelog
+++ b/Changelog
@@ -1,3 +1,26 @@
+ version 1.1.9 (tag v1.1.9rel)
+ =============================
+ * fix for issue #391 (data is already byte-swapped to native
+ endian format by the HDF4 library).
+ * fix for issue #415 (copy.deepcopy does not work on
+ netcdftime datetime object).
+ * fix for issue #420 - len(v) where v is a scalar variable returned
+ unexpected IndexError, now returns "TypeError: len() on unsized object"
+ (same as numpy does for len() on a scalar array).
+ * translate docstrings from epydoc markup to markdown, so
+ pdoc can be used (epydoc is dead).
+ * add small offset in conversion to Julian date for numerical
+ stability (more accurate round trip calculations). This offset is removed
+ in back conversion only from microseconds. Pull request #433.
+ * add detection of unsigned integers to handling of automatic
+ packing (set_auto_scale and set_auto_maskandscale) when writing.
+ Pull request #435.
+ * use USE_SETUPCFG env var to over-ride use of setup.cfg. If USE_SETUPCFG
+ evaluates to false, setup.cfg will not be used and all configuration
+ variables can be set from environment variables. Useful when using 'pip
+ install' and nc-config is broken (issue #438).
+ * fix for integer overflow in date2index (issue #444).
+
version 1.1.8 (tag v1.1.8rel)
=============================
* v[...] now returns a numpy scalar array (not just a scalar) when
diff --git a/MANIFEST.in b/MANIFEST.in
index 0c02bd1..5db83eb 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -18,3 +18,7 @@ include netcdftime/__init__.py
include netcdftime/_datetime.pyx
include netcdftime/netcdftime.py
include netcdftime/_datetime.c
+include netCDF4/__init__.py
+include netCDF4/_netCDF4.pyx
+include netCDF4/utils.py
+include netCDF4/_netCDF4.c
diff --git a/README.gh-pages b/README.gh-pages
index e24650e..c9cef40 100644
--- a/README.gh-pages
+++ b/README.gh-pages
@@ -1,11 +1,15 @@
To update web docs at http://github.unidata.io/netcdf4-python:
-(in netcdf4-python github clone directory, after docs directory has been updated)
+First install fork of pdoc from https://github.com/jswhit/pdoc (requires mako,
+markdown and pygments).
-tar up contents of docs dir (cd docs; tar -cvf ../../html.tar *; cd ..)
-git checkout gh-pages
-tar -xvf ../html.tar
-git add *html
-git commit
-git push origin gh-pages
-git checkout master
+Then in netcdf4-python github clone directory (after building and
+installing github master),
+
+* generate docs (sh create_docs.sh)
+* copy docs/netCDF4/index.html up one level (cp docs/netCDF4/index.html ..)
+* git checkout gh-pages
+* cp ../index.html .
+* git commit index.html
+* git push origin gh-pages
+* git checkout master
diff --git a/README.md b/README.md
index a3ecee8..3b94b1b 100644
--- a/README.md
+++ b/README.md
@@ -7,17 +7,17 @@
## News
For the latest updates, see the [Changelog](https://github.com/Unidata/netcdf4-python/blob/master/Changelog).
-5/14/2015: Version 1.1.8 released. Unix-like paths can now be used in `createVariable` and `createGroup`.
-
- v = nc.createVariable('/path/to/var1',('xdim','ydim'),float)
-
+5/14/2015: Version [1.1.8](https://pypi.python.org/pypi/netCDF4/1.1.8) released. Unix-like paths can now be used in `createVariable` and `createGroup`.
+```python
+ v = nc.createVariable('/path/to/var1', ('xdim', 'ydim'), float)
+```
will create a variable named 'var1', while also creating the groups
'path' and 'path/to' if they do not already exist.
Similarly,
-
+```python
g = nc.createGroup('/path/to')
-
+```
now acts like `mkdir -p` in unix, creating groups 'path' and '/path/to',
if they don't already exist. Users who relied on `nc.createGroup(groupname)`
failing when the group already exists will have to modify their code, since
@@ -25,7 +25,7 @@ failing when the group already exists will have to modify their code, since
`Dataset.__getitem__` was also added. `nc['/path/to']`
now returns a group instance, and `nc['/path/to/var1']` now returns a variable instance.
-3/19/2015: Version 1.1.7 released. Global Interpreter Lock (GIL) now released when extension
+3/19/2015: Version [1.1.7](https://pypi.python.org/pypi/netCDF4/1.1.7) released. Global Interpreter Lock (GIL) now released when extension
module calls C library for read operations. This speeds up concurrent reads when using threads.
Users who wish to use netcdf4-python inside threads should read http://www.hdfgroup.org/hdf5-quest.html#gconc
regarding thread-safety in the HDF5 C library. Fixes to `setup.py` now ensure that `pip install netCDF4`
@@ -33,7 +33,7 @@ with `export USE_NCCONFIG=0` will use environment variables to find paths to lib
instead of relying exclusively on the nc-config utility.
## Quick Start
-* Clone GitHub repository (`git clone https://github.com/Unidata/netcdf4-python.git`), or get source tarball (or Windows binary installers) from [PyPI](https://pypi.python.org/pypi/netCDF4) (`pip install netCDF4`).
+* Clone GitHub repository (`git clone https://github.com/Unidata/netcdf4-python.git`), or get source tarball from [PyPI](https://pypi.python.org/pypi/netCDF4). Links to Windows and OS X precompiled binary packages are also available on [PyPI](https://pypi.python.org/pypi/netCDF4).
* Make sure [numpy](http://www.numpy.org/) (required) and [Cython](http://cython.org/) (recommended) are
installed and you have [Python](https://www.python.org) 2.5 or newer.
diff --git a/README.wheels.md b/README.wheels.md
new file mode 100644
index 0000000..d3a7fdd
--- /dev/null
+++ b/README.wheels.md
@@ -0,0 +1,100 @@
+# Building and uploading wheels
+
+## For OSX
+
+We automate OSX wheel building using a custom github repository that builds on
+the travis-ci OSX machines.
+
+The travis-ci interface for the builds is :
+https://travis-ci.org/MacPython/netcdf4-python-wheels
+
+The driving github repository is :
+https://github.com/MacPython/netcdf4-python-wheels
+
+### How it works
+
+The wheel-building repository:
+
+* does a fresh build of the required C / C++ libraries;
+* builds a netcdf4-python wheel, linking against these fresh builds;
+* processes the wheel using [delocate](https://pypi.python.org/pypi/delocate).
+ `delocate` copies the required dynamic libraries into the wheel and relinks
+ the extension modules against the copied libraries;
+* uploads the built wheel to http://wheels.scipy.org (a Rackspace container
+ kindly donated by Rackspace to scikit-learn).
+
+The resulting wheel is therefore self-contained and does not need any external
+dynamic libraries apart from those provided as standard by OSX.
+
+### Triggering a build
+
+You will need write permision to the github repository to trigger new builds
+on the travis-ci interface. Contact us on the mailing list if you need this.
+
+You can trigger a build by:
+
+* making a commit to the `netcdf4-python-wheels` repository (e.g. with `git
+ commit --allow-empty`); or
+* clicking on the circular arrow icon towards the top right of the travis-ci
+ page, to rerun the previous build.
+
+In general, it is better to trigger a build with a commit, because this makes
+a new set of build products and logs, keeping the old ones for reference.
+Keeping the old build logs helps us keep track of previous problems and
+successful builds.
+
+### Which netcdf4-python commit does the repository build?
+
+By default, the `netcd4-python-wheels` repository is usually set up to build
+the latest git tag. To check whether this is so have a look around line 5 of
+`.travis.yml` in the `netcdf4-python-wheels` repository. You should see
+something like:
+
+```
+- BUILD_COMMIT='latest-tag'
+```
+
+If this is commented out, then the repository is set up to build the current
+commit in the `netcdf4-python` submodule of the repository. If it is set to
+another value then it will be specifying a commit to build.
+
+You can therefore build any arbitrary commit by specificying the commit hash
+or branch name or tag name in this line of the `.travis.yml` file.
+
+### Uploading the built wheels to pypi
+
+Be careful, http://wheels.scipy.org points to a container on a distributed
+content delivery network. It can take up to 15 minutes for the new wheel file
+to get updated into the container at http://wheels.scipy.org.
+
+When the wheels are updated, you can of course just download them to your
+machine manually, and then upload them manually to pypi, or by using
+[twine][twine]. You can also use a script for doing this, housed at :
+https://github.com/MacPython/terryfy/blob/master/wheel-uploader
+
+You'll need [twine][twine] and [beautiful soup 4][bs4].
+
+You will typically have a directory on your machine where you store wheels,
+called a `wheelhouse`. The typical call for `wheel-uploader` would then
+be something like:
+
+```
+wheel-uploader -v -w ~/wheelhouse netCDF4 1.1.8
+```
+
+where:
+
+* `-v` means give verbose messages;
+* `-w ~/wheelhouse` means download the wheels from https://wheels.scipy.org to
+ the directory `~/wheelhouse`;
+* `netCDF4` is the root name of the wheel(s) to download / upload;
+* `1.1.8` is the version to download / upload.
+
+So, in this case, `wheel-uploader` will download all wheels starting with
+`netCDF4-1.1.8-` from http://wheels.scipy.org to `~/wheelhouse`, then upload
+them to pypi.
+
+Of course, you will need permissions to upload to pypi, for this to work.
+
+[twine]: https://pypi.python.org/pypi/twine
+[bs4]: https://pypi.python.org/pypi/beautifulsoup4
diff --git a/create_docs.sh b/create_docs.sh
index e2ae30d..45d94e1 100644
--- a/create_docs.sh
+++ b/create_docs.sh
@@ -1,2 +1,5 @@
-# svn propset svn:mime-type text/html docs/*html
-epydoc -v --no-frames --no-private --introspect-only --name netcdf4-python -o docs netCDF4._netCDF4
+# Uses pdoc (https://github.com/BurntSushi/pdoc)
+# to create html docs from docstrings in Cython source.
+# Use hacked version at https://github.com/jswhit/pdoc
+# which extracts cython method docstrings and function signatures.
+pdoc --html --html-no-source --overwrite --html-dir 'docs' netCDF4
diff --git a/docs/api-objects.txt b/docs/api-objects.txt
deleted file mode 100644
index be91bd3..0000000
--- a/docs/api-objects.txt
+++ /dev/null
@@ -1,285 +0,0 @@
-netCDF4._netCDF4 netCDF4._netCDF4-module.html
-netCDF4._netCDF4._set_default_format netCDF4._netCDF4-module.html#_set_default_format
-netCDF4._netCDF4.hr_units netCDF4._netCDF4-module.html#hr_units
-netCDF4._netCDF4.default_encoding netCDF4._netCDF4-module.html#default_encoding
-netCDF4._netCDF4.num2date netCDF4._netCDF4-module.html#num2date
-netCDF4._netCDF4.is_native_big netCDF4._netCDF4-module.html#is_native_big
-netCDF4._netCDF4.microsec_units netCDF4._netCDF4-module.html#microsec_units
-netCDF4._netCDF4._private_atts netCDF4._netCDF4-module.html#_private_atts
-netCDF4._netCDF4.day_units netCDF4._netCDF4-module.html#day_units
-netCDF4._netCDF4.MINYEAR netCDF4._netCDF4-module.html#MINYEAR
-netCDF4._netCDF4._gethdf5libversion netCDF4._netCDF4-module.html#_gethdf5libversion
-netCDF4._netCDF4.__has_nc_inq_format_extended__ netCDF4._netCDF4-module.html#__has_nc_inq_format_extended__
-netCDF4._netCDF4.__package__ netCDF4._netCDF4-module.html#__package__
-netCDF4._netCDF4.stringtochar netCDF4._netCDF4-module.html#stringtochar
-netCDF4._netCDF4.chartostring netCDF4._netCDF4-module.html#chartostring
-netCDF4._netCDF4.getlibversion netCDF4._netCDF4-module.html#getlibversion
-netCDF4._netCDF4.__netcdf4libversion__ netCDF4._netCDF4-module.html#__netcdf4libversion__
-netCDF4._netCDF4.sec_units netCDF4._netCDF4-module.html#sec_units
-netCDF4._netCDF4.__has_nc_inq_path__ netCDF4._netCDF4-module.html#__has_nc_inq_path__
-netCDF4._netCDF4.date2num netCDF4._netCDF4-module.html#date2num
-netCDF4._netCDF4.millisec_units netCDF4._netCDF4-module.html#millisec_units
-netCDF4._netCDF4.stringtoarr netCDF4._netCDF4-module.html#stringtoarr
-netCDF4._netCDF4._value netCDF4._netCDF4-module.html#_value
-netCDF4._netCDF4._nptonctype netCDF4._netCDF4-module.html#_nptonctype
-netCDF4._netCDF4.date2index netCDF4._netCDF4-module.html#date2index
-netCDF4._netCDF4.gregorian netCDF4._netCDF4-module.html#gregorian
-netCDF4._netCDF4.is_native_little netCDF4._netCDF4-module.html#is_native_little
-netCDF4._netCDF4.python3 netCDF4._netCDF4-module.html#python3
-netCDF4._netCDF4._dateparse netCDF4._netCDF4-module.html#_dateparse
-netCDF4._netCDF4._supportedtypes netCDF4._netCDF4-module.html#_supportedtypes
-netCDF4._netCDF4.NC_DISKLESS netCDF4._netCDF4-module.html#NC_DISKLESS
-netCDF4._netCDF4._key netCDF4._netCDF4-module.html#_key
-netCDF4._netCDF4.default_fillvals netCDF4._netCDF4-module.html#default_fillvals
-netCDF4._netCDF4._nctonptype netCDF4._netCDF4-module.html#_nctonptype
-netCDF4._netCDF4.__has_rename_grp__ netCDF4._netCDF4-module.html#__has_rename_grp__
-netCDF4._netCDF4._npversion netCDF4._netCDF4-module.html#_npversion
-netCDF4._netCDF4.__hdf5libversion__ netCDF4._netCDF4-module.html#__hdf5libversion__
-netCDF4._netCDF4.min_units netCDF4._netCDF4-module.html#min_units
-netCDF4._netCDF4.unicode_error netCDF4._netCDF4-module.html#unicode_error
-netCDF4._netCDF4.CompoundType netCDF4._netCDF4.CompoundType-class.html
-netCDF4._netCDF4.CompoundType.__new__ netCDF4._netCDF4.CompoundType-class.html#__new__
-netCDF4._netCDF4.CompoundType.dtype netCDF4._netCDF4.CompoundType-class.html#dtype
-netCDF4._netCDF4.CompoundType._nc_type netCDF4._netCDF4.CompoundType-class.html#_nc_type
-netCDF4._netCDF4.CompoundType.__repr__ netCDF4._netCDF4.CompoundType-class.html#__repr__
-netCDF4._netCDF4.CompoundType.__init__ netCDF4._netCDF4.CompoundType-class.html#__init__
-netCDF4._netCDF4.CompoundType.__unicode__ netCDF4._netCDF4.CompoundType-class.html#__unicode__
-netCDF4._netCDF4.CompoundType.name netCDF4._netCDF4.CompoundType-class.html#name
-netCDF4._netCDF4.Dataset netCDF4._netCDF4.Dataset-class.html
-netCDF4._netCDF4.Dataset._enddef netCDF4._netCDF4.Dataset-class.html#_enddef
-netCDF4._netCDF4.Dataset.variables netCDF4._netCDF4.Dataset-class.html#variables
-netCDF4._netCDF4.Dataset.__getattribute__ netCDF4._netCDF4.Dataset-class.html#__getattribute__
-netCDF4._netCDF4.Dataset.sync netCDF4._netCDF4.Dataset-class.html#sync
-netCDF4._netCDF4.Dataset.createCompoundType netCDF4._netCDF4.Dataset-class.html#createCompoundType
-netCDF4._netCDF4.Dataset.set_auto_maskandscale netCDF4._netCDF4.Dataset-class.html#set_auto_maskandscale
-netCDF4._netCDF4.Dataset.close netCDF4._netCDF4.Dataset-class.html#close
-netCDF4._netCDF4.Dataset._isopen netCDF4._netCDF4.Dataset-class.html#_isopen
-netCDF4._netCDF4.Dataset.getncattr netCDF4._netCDF4.Dataset-class.html#getncattr
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-netCDF4._netCDF4.Dataset.__setattr__ netCDF4._netCDF4.Dataset-class.html#__setattr__
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-netCDF4._netCDF4.Dataset.createVariable netCDF4._netCDF4.Dataset-class.html#createVariable
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-netCDF4._netCDF4.Dataset._redef netCDF4._netCDF4.Dataset-class.html#_redef
-netCDF4._netCDF4.Dataset.setncatts netCDF4._netCDF4.Dataset-class.html#setncatts
-netCDF4._netCDF4.Dataset.setncattr netCDF4._netCDF4.Dataset-class.html#setncattr
-netCDF4._netCDF4.Dataset.cmptypes netCDF4._netCDF4.Dataset-class.html#cmptypes
-netCDF4._netCDF4.Dataset.set_fill_off netCDF4._netCDF4.Dataset-class.html#set_fill_off
-netCDF4._netCDF4.Dataset.renameGroup netCDF4._netCDF4.Dataset-class.html#renameGroup
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-netCDF4._netCDF4.Dataset.ncattrs netCDF4._netCDF4.Dataset-class.html#ncattrs
-netCDF4._netCDF4.Dataset.__exit__ netCDF4._netCDF4.Dataset-class.html#__exit__
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-netCDF4._netCDF4.Dataset.parent netCDF4._netCDF4.Dataset-class.html#parent
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-netCDF4._netCDF4.Dataset.renameDimension netCDF4._netCDF4.Dataset-class.html#renameDimension
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-netCDF4._netCDF4.Dataset.__delattr__ netCDF4._netCDF4.Dataset-class.html#__delattr__
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-netCDF4._netCDF4.Dataset.__orthogonal_indexing__ netCDF4._netCDF4.Dataset-class.html#__orthogonal_indexing__
-netCDF4._netCDF4.Dimension netCDF4._netCDF4.Dimension-class.html
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diff --git a/docs/class-tree.html b/docs/class-tree.html
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--- a/docs/class-tree.html
+++ /dev/null
@@ -1,151 +0,0 @@
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- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>Class Hierarchy</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
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- <!-- Home link -->
- <th> <a
- href="netCDF4._netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th bgcolor="#70b0f0" class="navbar-select"
- > Trees </th>
-
- <!-- Index link -->
- <th> <a
- href="identifier-index.html">Indices</a> </th>
-
- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
-
- <!-- Project homepage -->
- <th class="navbar" align="right" width="100%">
- <table border="0" cellpadding="0" cellspacing="0">
- <tr><th class="navbar" align="center"
- >netcdf4-python</th>
- </tr></table></th>
- </tr>
-</table>
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- <tr valign="top">
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- </tr>
-</table>
-<center><b>
- [ <a href="module-tree.html">Module Hierarchy</a>
- | <a href="class-tree.html">Class Hierarchy</a> ]
-</b></center><br />
-<h1 class="epydoc">Class Hierarchy</h1>
-<ul class="nomargin-top">
- <li> <strong class="uidlink">object</strong>:
- <em class="summary">The most base type</em>
- <ul>
- <li> <strong class="uidlink"><a href="netCDF4._netCDF4.CompoundType-class.html">netCDF4._netCDF4.CompoundType</a></strong>:
- <em class="summary">A <a href="netCDF4._netCDF4.CompoundType-class.html"
- class="link">CompoundType</a> instance is used to describe a
- compound data type.</em>
- </li>
- <li> <strong class="uidlink"><a href="netCDF4._netCDF4.Dataset-class.html">netCDF4._netCDF4.Dataset</a></strong>:
- <em class="summary">Dataset(self, filename, mode="r", clobber=True,
- diskless=False, persist=False, keepweakref=False, format='NETCDF4')</em>
- <ul>
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diff --git a/docs/help.html b/docs/help.html
deleted file mode 100644
index 2f4f00c..0000000
--- a/docs/help.html
+++ /dev/null
@@ -1,272 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>Help</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
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- href="netCDF4._netCDF4-module.html">Home</a> </th>
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- href="module-tree.html">Trees</a> </th>
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- href="identifier-index.html">Indices</a> </th>
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- > Help </th>
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- <table border="0" cellpadding="0" cellspacing="0">
- <tr><th class="navbar" align="center"
- >netcdf4-python</th>
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-<table width="100%" cellpadding="0" cellspacing="0">
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- <!-- hide/show private -->
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- </td>
- </tr>
-</table>
-
-<h1 class="epydoc"> API Documentation </h1>
-
-<p> This document contains the API (Application Programming Interface)
-documentation for netcdf4-python. Documentation for the Python
-objects defined by the project is divided into separate pages for each
-package, module, and class. The API documentation also includes two
-pages containing information about the project as a whole: a trees
-page, and an index page. </p>
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-<h2> Object Documentation </h2>
-
- <p>Each <strong>Package Documentation</strong> page contains: </p>
- <ul>
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- <li> A list of the modules and sub-packages contained by the
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- <li> A detailed description of each variable defined by the
- package. </li>
- </ul>
-
- <p>Each <strong>Module Documentation</strong> page contains:</p>
- <ul>
- <li> A description of the module. </li>
- <li> A summary of the classes defined by the module. </li>
- <li> A summary of the functions defined by the module. </li>
- <li> A summary of the variables defined by the module. </li>
- <li> A detailed description of each function defined by the
- module. </li>
- <li> A detailed description of each variable defined by the
- module. </li>
- </ul>
-
- <p>Each <strong>Class Documentation</strong> page contains: </p>
- <ul>
- <li> A class inheritance diagram. </li>
- <li> A list of known subclasses. </li>
- <li> A description of the class. </li>
- <li> A summary of the methods defined by the class. </li>
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- <li> A detailed description of each method defined by the
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- <li> A detailed description of each instance variable defined by the
- class. </li>
- <li> A detailed description of each class (static) variable defined
- by the class. </li>
- </ul>
-
-<h2> Project Documentation </h2>
-
- <p> The <strong>Trees</strong> page contains the module and class hierarchies: </p>
- <ul>
- <li> The <em>module hierarchy</em> lists every package and module, with
- modules grouped into packages. At the top level, and within each
- package, modules and sub-packages are listed alphabetically. </li>
- <li> The <em>class hierarchy</em> lists every class, grouped by base
- class. If a class has more than one base class, then it will be
- listed under each base class. At the top level, and under each base
- class, classes are listed alphabetically. </li>
- </ul>
-
- <p> The <strong>Index</strong> page contains indices of terms and
- identifiers: </p>
- <ul>
- <li> The <em>term index</em> lists every term indexed by any object's
- documentation. For each term, the index provides links to each
- place where the term is indexed. </li>
- <li> The <em>identifier index</em> lists the (short) name of every package,
- module, class, method, function, variable, and parameter. For each
- identifier, the index provides a short description, and a link to
- its documentation. </li>
- </ul>
-
-<h2> The Table of Contents </h2>
-
-<p> The table of contents occupies the two frames on the left side of
-the window. The upper-left frame displays the <em>project
-contents</em>, and the lower-left frame displays the <em>module
-contents</em>: </p>
-
-<table class="help summary" border="1" cellspacing="0" cellpadding="3">
- <tr style="height: 30%">
- <td align="center" style="font-size: small">
- Project<br />Contents<hr />...</td>
- <td align="center" style="font-size: small" rowspan="2" width="70%">
- API<br />Documentation<br />Frame<br /><br /><br />
- </td>
- </tr>
- <tr>
- <td align="center" style="font-size: small">
- Module<br />Contents<hr /> <br />...<br />
- </td>
- </tr>
-</table><br />
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-<p> The <strong>project contents frame</strong> contains a list of all packages
-and modules that are defined by the project. Clicking on an entry
-will display its contents in the module contents frame. Clicking on a
-special entry, labeled "Everything," will display the contents of
-the entire project. </p>
-
-<p> The <strong>module contents frame</strong> contains a list of every
-submodule, class, type, exception, function, and variable defined by a
-module or package. Clicking on an entry will display its
-documentation in the API documentation frame. Clicking on the name of
-the module, at the top of the frame, will display the documentation
-for the module itself. </p>
-
-<p> The "<strong>frames</strong>" and "<strong>no frames</strong>" buttons below the top
-navigation bar can be used to control whether the table of contents is
-displayed or not. </p>
-
-<h2> The Navigation Bar </h2>
-
-<p> A navigation bar is located at the top and bottom of every page.
-It indicates what type of page you are currently viewing, and allows
-you to go to related pages. The following table describes the labels
-on the navigation bar. Note that not some labels (such as
-[Parent]) are not displayed on all pages. </p>
-
-<table class="summary" border="1" cellspacing="0" cellpadding="3" width="100%">
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- <th>Label</th>
- <th>Highlighted when...</th>
- <th>Links to...</th>
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- <td valign="top">viewing the help page</td>
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-</table>
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-<p> The "<strong>show private</strong>" and "<strong>hide private</strong>" buttons below
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-"<code>__init__</code>", and "<code>type_</code>" are not. However,
-if a module defines the "<code>__all__</code>" variable, then its
-contents are used to decide which objects are private. </p>
-
-<p> A timestamp below the bottom navigation bar indicates when each
-page was last updated. </p>
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th> <a
- href="netCDF4._netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
- <!-- Index link -->
- <th> <a
- href="identifier-index.html">Indices</a> </th>
-
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- <th bgcolor="#70b0f0" class="navbar-select"
- > Help </th>
-
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- <th class="navbar" align="right" width="100%">
- <table border="0" cellpadding="0" cellspacing="0">
- <tr><th class="navbar" align="center"
- >netcdf4-python</th>
- </tr></table></th>
- </tr>
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- <tr>
- <td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Tue May 12 15:08:44 2015
- </td>
- <td align="right" class="footer">
- <a target="mainFrame" href="http://epydoc.sourceforge.net"
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diff --git a/docs/identifier-index.html b/docs/identifier-index.html
deleted file mode 100644
index 9348af3..0000000
--- a/docs/identifier-index.html
+++ /dev/null
@@ -1,698 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>Identifier Index</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th> <a
- href="netCDF4._netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
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- <th bgcolor="#70b0f0" class="navbar-select"
- > Indices </th>
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- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
-
- <!-- Project homepage -->
- <th class="navbar" align="right" width="100%">
- <table border="0" cellpadding="0" cellspacing="0">
- <tr><th class="navbar" align="center"
- >netcdf4-python</th>
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- <tr valign="top">
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-<tr valign="bottom"><td>
-<h1 class="epydoc">Identifier Index</h1>
-</td><td>
-[
- <a href="#A">A</a>
- B
- <a href="#C">C</a>
- <a href="#D">D</a>
- <a href="#E">E</a>
- <a href="#F">F</a>
- <a href="#G">G</a>
- <a href="#H">H</a>
- <a href="#I">I</a>
- J
- <a href="#K">K</a>
- L
- <a href="#M">M</a>
- <a href="#N">N</a>
- O
- <a href="#P">P</a>
- Q
- <a href="#R">R</a>
- <a href="#S">S</a>
- T
- <a href="#U">U</a>
- <a href="#V">V</a>
- W
- X
- Y
- Z
- <a href="#_">_</a>
-]
-</td></table>
-<table border="0" width="100%">
-<tr valign="top"><td valign="top" width="1%"><h2 class="epydoc"><a name="A">A</a></h2></td>
-<td valign="top">
-<table class="link-index" width="100%" border="1">
-<tr>
-<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#assignValue">assignValue()</a><br />
-<span class="index-where">(in <a href="netCDF4._netCDF4.Variable-class.html">Variable</a>)</span></td>
-<td width="33%" class="link-index"> </td>
-<td width="33%" class="link-index"> </td>
-</tr>
-<tr><td class="link-index"> </td><td class="link-index"> </td><td class="link-index"> </td></tr>
-</table>
-</td></tr>
-<tr valign="top"><td valign="top" width="1%"><h2 class="epydoc"><a name="C">C</a></h2></td>
-<td valign="top">
-<table class="link-index" width="100%" border="1">
-<tr>
-<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#chartostring">chartostring()</a><br />
-<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
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-</tr>
-<tr>
-<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#chunking">chunking()</a><br />
-<span class="index-where">(in <a href="netCDF4._netCDF4.Variable-class.html">Variable</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#cmptypes">cmptypes</a><br />
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-<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#createGroup">createGroup()</a><br />
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-<tr>
-<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#close">close()</a><br />
-<span class="index-where">(in <a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4._netCDF4.CompoundType-class.html">CompoundType</a><br />
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-<tr>
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- Module _netCDF4
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-<!-- ==================== MODULE DESCRIPTION ==================== -->
-<h1 class="epydoc">Module _netCDF4</h1><p class="nomargin-top"></p>
-<h1 class="heading">Introduction</h1>
- <p>Python interface to the netCDF version 4 library. <a
- href="http://www.unidata.ucar.edu/software/netcdf/netcdf-4"
- target="_top">netCDF version 4</a> has many features not found in
- earlier versions of the library and is implemented on top of <a
- href="http://www.hdfgroup.org/HDF5" target="_top">HDF5</a>. This module
- can read and write files in both the new netCDF 4 and the old netCDF 3
- format, and can create files that are readable by HDF5 clients. 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.</p>
- <p>Most new features of netCDF 4 are implemented, such as multiple
- unlimited dimensions, groups and zlib data compression. All the new
- numeric data types (such as 64 bit and unsigned integer types) are
- implemented. Compound and variable length (vlen) data types are
- supported, but the enum and opaque data types are not. Mixtures of
- compound and vlen data types (compound types containing vlens, and
- vlens containing compound types) are not supported.</p>
- <h1 class="heading">Download</h1>
- <ul>
- <li>
- Latest bleeding-edge code from the <a
- href="http://github.com/Unidata/netcdf4-python"
- target="_top">github repository</a>.
- </li>
- <li>
- Latest <a href="https://pypi.python.org/pypi/netCDF4"
- target="_top">releases</a> (source code and windows installers).
- </li>
- </ul>
- <h1 class="heading">Requires</h1>
- <ul>
- <li>
- Python 2.5 or later (python 3 works too).
- </li>
- <li>
- numpy array module <a href="http://numpy.scipy.org"
- target="_top">http://numpy.scipy.org</a>, version 1.7.0 or later.
- </li>
- <li>
- <a href="http://cython.org" target="_top">Cython</a>, version 0.19
- or later, is optional - if it is installed setup.py will use it to
- recompile the Cython source code into C, using conditional
- compilation to enable features in the netCDF API that have been
- added since version 4.1.1. If Cython is not installed, these
- features (such as the ability to rename Group objects) will be
- disabled to preserve backward compatibility with older versions of
- the netCDF library.
- </li>
- <li>
- For python < 2.7, the ordereddict module <a
- href="http://python.org/pypi/ordereddict"
- target="_top">http://python.org/pypi/ordereddict</a>.
- </li>
- <li>
- The HDF5 C library version 1.8.4-patch1 or higher (1.8.8 or higher
- recommended) from <a href="ftp://ftp.hdfgroup.org/HDF5/current/src"
- target="_top">ftp://ftp.hdfgroup.org/HDF5/current/src</a>. Be sure
- to build with '<code>--enable-hl --enable-shared</code>'.
- </li>
- <li>
- <a href="http://curl.haxx.se/libcurl/" target="_top">Libcurl</a>,
- if you want <a href="http://opendap.org/" target="_top">OPeNDAP</a>
- support.
- </li>
- <li>
- <a href="http://www.hdfgroup.org/products/hdf4/"
- target="_top">HDF4</a>, if you want to be able to read HDF4
- "Scientific Dataset" (SD) files.
- </li>
- <li>
- The netCDF-4 C library from <a
- href="ftp://ftp.unidata.ucar.edu/pub/netcdf"
- target="_top">ftp://ftp.unidata.ucar.edu/pub/netcdf</a>. Version
- 4.1.1 or higher is required (4.2 or higher recommended). Be sure to
- build with '<code>--enable-netcdf-4 --enable-shared</code>', and
- set <code>CPPFLAGS="-I $HDF5_DIR/include"</code> and
- <code>LDFLAGS="-L $HDF5_DIR/lib"</code>, where
- <code>$HDF5_DIR</code> is the directory where HDF5 was installed.
- If you want <a href="http://opendap.org/" target="_top">OPeNDAP</a>
- support, add '<code>--enable-dap</code>'. If you want HDF4 SD
- support, add '<code>--enable-hdf4</code>' and add the location of
- the HDF4 headers and library to <code>CPPFLAGS</code> and
- <code>LDFLAGS</code>.
- </li>
- </ul>
- <h1 class="heading">Install</h1>
- <ul>
- <li>
- install the requisite python modules and C libraries (see above).
- It's easiest if all the C libs are built as shared libraries.
- </li>
- <li>
- By default, the utility <code>nc-config</code>, installed with
- netcdf 4.1.2 or higher, will be run used to determine where all the
- dependencies live.
- </li>
- <li>
- If <code>nc-config</code> is not in your default <code>PATH</code>,
- rename the file <code>setup.cfg.template</code> to
- <code>setup.cfg</code>, then edit in a text editor (follow the
- instructions in the comments). In addition to specifying the path
- to <code>nc-config</code>, you can manually set the paths to all
- the libraries and their include files (in case
- <code>nc-config</code> does not do the right thing).
- </li>
- <li>
- run <code>python setup.py build</code>, then <code>python setup.py
- install</code> (as root if necessary).
- </li>
- <li>
- run the tests in the 'test' directory by running <code>python
- run_all.py</code>.
- </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="netCDF4._netCDF4.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, groups, 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.
- <code>NETCDF4_CLASSIC</code> files use the version 4 disk format
- (HDF5), but do not use any features not found in the version 3 API.
- They can be read by netCDF 3 clients only if they have been relinked
- against the netCDF 4 library. They can also be read by HDF5 clients.
- <code>NETCDF4</code> files use the version 4 disk format (HDF5) and
- use the new features of the version 4 API. The <code>netCDF4</code>
- module can read and write files in any of these formats. When
- creating a new file, the format may be specified using the
- <code>format</code> keyword in the <code>Dataset</code> constructor.
- The default format is <code>NETCDF4</code>. To see how a given file
- is formatted, you can examine the <code>data_model</code> <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- attribute. Closing the netCDF file is accomplished via the <a
- href="netCDF4._netCDF4.Dataset-class.html#close"
- class="link">close</a> method of the <a
- href="netCDF4._netCDF4.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">from</span> netCDF4 <span class="py-keyword">import</span> Dataset
-<span class="py-prompt">>>> </span>rootgrp = Dataset(<span class="py-string">'test.nc'</span>, <span class="py-string">'w'</span>, format=<span class="py-string">'NETCDF4'</span>)
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> rootgrp.data_model
-<span class="py-output">NETCDF4</span>
-<span class="py-output"></span><span class="py-prompt">>>></span>
-<span class="py-prompt">>>> </span>rootgrp.close()</pre>
- <p>Remote <a href="http://opendap.org"
- target="_top">OPeNDAP</a>-hosted datasets can be accessed for reading
- over http if a URL is provided to the <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- constructor instead of a filename. However, this requires that the
- netCDF library be built with OPenDAP support, via the
- <code>--enable-dap</code> configure option (added in version
- 4.0.1).</p>
- <h2 class="heading">2) Groups in a netCDF file</h2>
- <p>netCDF version 4 added support for organizing data in hierarchical
- groups, which are analagous to directories in a filesystem. Groups
- serve as containers for variables, dimensions and attributes, as well
- as other groups. A <code>netCDF4.Dataset</code> defines creates a
- special group, called the 'root group', which is similar to the root
- directory in a unix filesystem. To create <a
- href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- instances, use the <a
- href="netCDF4._netCDF4.Dataset-class.html#createGroup"
- class="link">createGroup</a> method of a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- or <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- instance. <a href="netCDF4._netCDF4.Dataset-class.html#createGroup"
- class="link">createGroup</a> takes a single argument, a python string
- containing the name of the new group. The new <a
- href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- instances contained within the root group can be accessed by name
- using the <code>groups</code> dictionary attribute of the <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- instance. Only <code>NETCDF4</code> formatted files support Groups,
- if you try to create a Group in a netCDF 3 file you will get an error
- message.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>rootgrp = Dataset(<span class="py-string">'test.nc'</span>, <span class="py-string">'a'</span>)
-<span class="py-prompt">>>> </span>fcstgrp = rootgrp.createGroup(<span class="py-string">'forecasts'</span>)
-<span class="py-prompt">>>> </span>analgrp = rootgrp.createGroup(<span class="py-string">'analyses'</span>)
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> rootgrp.groups
-<span class="py-output">OrderedDict([('forecasts', <netCDF4.Group object at 0x1b4b7b0>),</span>
-<span class="py-output"> ('analyses', <netCDF4.Group object at 0x1b4b970>)])</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Groups can exist within groups in a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>,
- just as directories exist within directories in a unix filesystem.
- Each <a href="netCDF4._netCDF4.Group-class.html"
- class="link">Group</a> instance has a <code>'groups'</code> attribute
- dictionary containing all of the group instances contained within
- that group. Each <a href="netCDF4._netCDF4.Group-class.html"
- class="link">Group</a> instance also has a <code>'path'</code>
- attribute that contains a simulated unix directory path to that
- group. To simplify the creation of nested groups, you can use a
- unix-like path as an argument to <a
- href="netCDF4._netCDF4.Dataset-class.html#createGroup"
- class="link">createGroup</a>.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>fcstgrp1 = rootgrp.createGroup(<span class="py-string">'/forecasts/model1'</span>)
-<span class="py-prompt">>>> </span>fcstgrp2 = rootgrp.createGroup(<span class="py-string">'/forecasts/model2'</span>)</pre>
- <p>If any of the intermediate elements of the path do not exist, they
- are created, just as with the unix command <code>'mkdir -p'</code>.
- If you try to create a group that already exists, no error will be
- raised, and the existing group will be returned.</p>
- <p>Here's an example that shows how to navigate all the groups in a
- <a href="netCDF4._netCDF4.Dataset-class.html"
- class="link">Dataset</a>. The function <code>walktree</code> is a
- Python generator that is used to walk the directory tree. Note that
- printing the <a href="netCDF4._netCDF4.Dataset-class.html"
- class="link">Dataset</a> or <a
- href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- object yields summary information about it's contents.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">def</span> <span class="py-defname">walktree</span>(top):
-<span class="py-prompt">>>> </span> <span class="py-builtin">values</span> = top.groups.values()
-<span class="py-prompt">>>> </span> yield <span class="py-builtin">values</span>
-<span class="py-prompt">>>> </span> <span class="py-keyword">for</span> value <span class="py-keyword">in</span> top.groups.values():
-<span class="py-prompt">>>> </span> <span class="py-keyword">for</span> children <span class="py-keyword">in</span> walktree(value):
-<span class="py-prompt">>>> </span> yield children
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> rootgrp
-<span class="py-prompt">>>> </span><span class="py-keyword">for</span> children <span class="py-keyword">in</span> walktree(rootgrp):
-<span class="py-prompt">>>> </span> <span class="py-keyword">for</span> child <span class="py-keyword">in</span> children:
-<span class="py-prompt">>>> </span> <span class="py-keyword">print</span> child
-<span class="py-output"><type 'netCDF4.Dataset'></span>
-<span class="py-output">root group (NETCDF4 file format):</span>
-<span class="py-output"> dimensions:</span>
-<span class="py-output"> variables:</span>
-<span class="py-output"> groups: forecasts, analyses</span>
-<span class="py-output"><type 'netCDF4.Group'></span>
-<span class="py-output">group /forecasts:</span>
-<span class="py-output"> dimensions:</span>
-<span class="py-output"> variables:</span>
-<span class="py-output"> groups: model1, model2</span>
-<span class="py-output"><type 'netCDF4.Group'></span>
-<span class="py-output">group /analyses:</span>
-<span class="py-output"> dimensions:</span>
-<span class="py-output"> variables:</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"><type 'netCDF4.Group'></span>
-<span class="py-output">group /forecasts/model1:</span>
-<span class="py-output"> dimensions:</span>
-<span class="py-output"> variables:</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"><type 'netCDF4.Group'></span>
-<span class="py-output">group /forecasts/model2:</span>
-<span class="py-output"> dimensions:</span>
-<span class="py-output"> variables:</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <h2 class="heading">3) 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="netCDF4._netCDF4.Dataset-class.html#createDimension"
- class="link">createDimension</a> method of a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- or <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</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. In this example, there both the
- <code>time</code> and <code>level</code> dimensions are unlimited.
- Having more than one unlimited dimension is a new netCDF 4 feature,
- in netCDF 3 files there may be only one, and it must be the first
- (leftmost) dimension of the variable.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>level = rootgrp.createDimension(<span class="py-string">'level'</span>, None)
-<span class="py-prompt">>>> </span>time = rootgrp.createDimension(<span class="py-string">'time'</span>, None)
-<span class="py-prompt">>>> </span>lat = rootgrp.createDimension(<span class="py-string">'lat'</span>, 73)
-<span class="py-prompt">>>> </span>lon = rootgrp.createDimension(<span class="py-string">'lon'</span>, 144)</pre>
- <p>All of the <a href="netCDF4._netCDF4.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> rootgrp.dimensions
-<span class="py-output">OrderedDict([('level', <netCDF4.Dimension object at 0x1b48030>),</span>
-<span class="py-output"> ('time', <netCDF4.Dimension object at 0x1b481c0>),</span>
-<span class="py-output"> ('lat', <netCDF4.Dimension object at 0x1b480f8>),</span>
-<span class="py-output"> ('lon', <netCDF4.Dimension object at 0x1b48a08>)])</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Calling the python <code>len</code> function with a <a
- href="netCDF4._netCDF4.Dimension-class.html"
- class="link">Dimension</a> instance returns the current size of that
- dimension. The <a
- href="netCDF4._netCDF4.Dimension-class.html#isunlimited"
- class="link">isunlimited</a> method of a <a
- href="netCDF4._netCDF4.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">print</span> len(lon)
-<span class="py-output">144</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> len.is_unlimited()
-<span class="py-output">False</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> time.is_unlimited()
-<span class="py-output">True</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Printing the <a href="netCDF4._netCDF4.Dimension-class.html"
- class="link">Dimension</a> object provides useful summary info,
- including the name and length of the dimension, and whether it is
- unlimited.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">for</span> dimobj <span class="py-keyword">in</span> rootgrp.dimensions.values():
-<span class="py-prompt">>>> </span> <span class="py-keyword">print</span> dimobj
-<span class="py-output"><type 'netCDF4.Dimension'> (unlimited): name = 'level', size = 0</span>
-<span class="py-output"><type 'netCDF4.Dimension'> (unlimited): name = 'time', size = 0</span>
-<span class="py-output"><type 'netCDF4.Dimension'>: name = 'lat', size = 73</span>
-<span class="py-output"><type 'netCDF4.Dimension'>: name = 'lon', size = 144</span>
-<span class="py-output"><type 'netCDF4.Dimension'> (unlimited): name = 'time', size = 0</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p><a href="netCDF4._netCDF4.Dimension-class.html"
- class="link">Dimension</a> names can be changed using the <a
- href="netCDF4._netCDF4.Dataset-class.html#renameDimension"
- class="link">renameDimension</a> method of a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- or <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- instance.</p>
- <h2 class="heading">4) 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, netCDF4
- variables can be appended to along one or more 'unlimited'
- dimensions. To create a netCDF variable, use the <a
- href="netCDF4._netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a> method of a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- or <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- instance. The <a
- href="netCDF4._netCDF4.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="netCDF4._netCDF4.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>'i8'</code> (64-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). 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. The unsigned integer types and the 64-bit integer
- type can only be used if the file format is <code>NETCDF4</code>.</p>
- <p>The dimensions themselves are usually also defined as variables,
- called coordinate variables. The <a
- href="netCDF4._netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a> method returns an instance of the <a
- href="netCDF4._netCDF4.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 = rootgrp.createVariable(<span class="py-string">'time'</span>,<span class="py-string">'f8'</span>,(<span class="py-string">'time'</span>,))
-<span class="py-prompt">>>> </span>levels = rootgrp.createVariable(<span class="py-string">'level'</span>,<span class="py-string">'i4'</span>,(<span class="py-string">'level'</span>,))
-<span class="py-prompt">>>> </span>latitudes = rootgrp.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 = rootgrp.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 = rootgrp.createVariable(<span class="py-string">'temp'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'time'</span>,<span class="py-string">'level'</span>,<span class="py-string">'lat'</span>,<span class="py-string">'lon'</span>,))</pre>
- <p>To get summary info on a <a
- href="netCDF4._netCDF4.Variable-class.html" class="link">Variable</a>
- instance in an interactive session, just print it.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> temp
-<span class="py-output"><type 'netCDF4.Variable'></span>
-<span class="py-output">float32 temp(time, level, lat, lon)</span>
-<span class="py-output"> least_significant_digit: 3</span>
-<span class="py-output"> units: K</span>
-<span class="py-output">unlimited dimensions: time, level</span>
-<span class="py-output">current shape = (0, 0, 73, 144)</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>You can use a path to create a Variable inside a hierarchy of
- groups.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>ftemp = rootgrp.createVariable(<span class="py-string">'/forecasts/model1/temp'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'time'</span>,<span class="py-string">'level'</span>,<span class="py-string">'lat'</span>,<span class="py-string">'lon'</span>,))</pre>
- <p>If the intermediate groups do not yet exist, they will be
- created.</p>
- <p>You can also query a <a href="netCDF4._netCDF4.Dataset-class.html"
- class="link">Dataset</a> or <a
- href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- instance directly to obtain <a
- href="netCDF4._netCDF4.Group-class.html" class="link">Group</a> or <a
- href="netCDF4._netCDF4.Variable-class.html" class="link">Variable</a>
- instances using paths.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> rootgrp[<span class="py-string">'/forecasts/model1'</span>] <span class="py-comment"># a Group instance</span>
-<span class="py-output"><type 'netCDF4._netCDF4.Group'></span>
-<span class="py-output">group /forecasts/model1:</span>
-<span class="py-output"> dimensions(sizes):</span>
-<span class="py-output"> variables(dimensions): float32 temp(time,level,lat,lon)</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> rootgrp[<span class="py-string">'/forecasts/model1/temp'</span>] <span class="py-comment"># a Variable instance</span>
-<span class="py-output"><type 'netCDF4._netCDF4.Variable'></span>
-<span class="py-output">float32 temp(time, level, lat, lon)</span>
-<span class="py-output">path = /forecasts/model1</span>
-<span class="py-output">unlimited dimensions: time, level</span>
-<span class="py-output">current shape = (0, 0, 73, 144)</span>
-<span class="py-output">filling on, default _FillValue of 9.96920996839e+36 used</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>All of the variables in the <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- or <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</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> rootgrp.variables
-<span class="py-output">OrderedDict([('time', <netCDF4.Variable object at 0x1b4ba70>),</span>
-<span class="py-output"> ('level', <netCDF4.Variable object at 0x1b4bab0>),</span>
-<span class="py-output"> ('latitude', <netCDF4.Variable object at 0x1b4baf0>),</span>
-<span class="py-output"> ('longitude', <netCDF4.Variable object at 0x1b4bb30>),</span>
-<span class="py-output"> ('temp', <netCDF4.Variable object at 0x1b4bb70>)])</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p><a href="netCDF4._netCDF4.Variable-class.html"
- class="link">Variable</a> names can be changed using the <a
- href="netCDF4._netCDF4.Dataset-class.html#renameVariable"
- class="link">renameVariable</a> method of a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- instance.</p>
- <h2 class="heading">5) 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 group, or the
- entire dataset, as a whole. <a
- href="netCDF4._netCDF4.Variable-class.html" class="link">Variable</a>
- attributes provide information about one of the variables in a group.
- Global attributes are set by assigning values to <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- or <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- instance variables. <a href="netCDF4._netCDF4.Variable-class.html"
- class="link">Variable</a> attributes are set by assigning values to
- <a href="netCDF4._netCDF4.Variable-class.html"
- class="link">Variable</a> instances 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>rootgrp.description = <span class="py-string">'bogus example script'</span>
-<span class="py-prompt">>>> </span>rootgrp.history = <span class="py-string">'Created '</span> + time.ctime(time.time())
-<span class="py-prompt">>>> </span>rootgrp.source = <span class="py-string">'netCDF4 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>levels.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="netCDF4._netCDF4.Dataset-class.html#ncattrs"
- class="link">ncattrs</a> method of a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>,
- <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</a> or
- <a href="netCDF4._netCDF4.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> rootgrp.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(rootgrp,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 = netCDF4 python module tutorial</span></pre>
- <p>The <code>__dict__</code> attribute of a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>,
- <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</a> or
- <a href="netCDF4._netCDF4.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> rootgrp.__dict__
-<span class="py-output">OrderedDict([(u'description', u'bogus example script'),</span>
-<span class="py-output"> (u'history', u'Created Thu Mar 3 19:30:33 2011'),</span>
-<span class="py-output"> (u'source', u'netCDF4 python module tutorial')])</span></pre>
- <p>Attributes can be deleted from a netCDF <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>,
- <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</a> or
- <a href="netCDF4._netCDF4.Variable-class.html"
- class="link">Variable</a> using the python <code>del</code> statement
- (i.e. <code>del grp.foo</code> removes the attribute <code>foo</code>
- the the group <code>grp</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="netCDF4._netCDF4.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>lats = numpy.arange(-90,91,2.5)
-<span class="py-prompt">>>> </span>lons = numpy.arange(-180,180,2.5)
-<span class="py-prompt">>>> </span>latitudes[:] = lats
-<span class="py-prompt">>>> </span>longitudes[:] = lons
-<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></pre>
- <p>Unlike NumPy's array objects, netCDF <a
- href="netCDF4._netCDF4.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(rootgrp.dimensions[<span class="py-string">'lat'</span>])
-<span class="py-prompt">>>> </span>nlons = len(rootgrp.dimensions[<span class="py-string">'lon'</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, 0, 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 <span class="py-keyword">import</span> uniform
-<span class="py-prompt">>>> </span>temp[0:5,0:10,:,:] = uniform(size=(5,10,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 = (6, 10, 73, 144)</span>
-<span class="py-output"></span><span class="py-prompt">>>></span>
-<span class="py-prompt">>>> </span><span class="py-comment"># levels have grown, but no values yet assigned.</span>
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'levels shape after adding pressure data = '</span>,levels.shape
-<span class="py-output">levels shape after adding pressure data = (10,)</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Note that the size of the levels variable grows when data is
- appended along the <code>level</code> dimension of the variable
- <code>temp</code>, even though no data has yet been assigned to
- levels.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-comment"># now, assign data to levels dimension variable.</span>
-<span class="py-prompt">>>> </span>levels[:] = [1000.,850.,700.,500.,300.,250.,200.,150.,100.,50.]</pre>
- <p>However, that there are some differences between NumPy and netCDF
- variable slicing rules. Slices behave as usual, being specified as a
- <code>start:stop:step</code> triplet. Using a scalar integer index
- <code>i</code> takes the ith element and reduces the rank of the
- output array by one. Boolean array and integer sequence indexing
- behaves differently for netCDF variables than for numpy arrays. Only
- 1-d boolean arrays and integer sequences are allowed, and these
- indices work independently along each dimension (similar to the way
- vector subscripts work in fortran). This means that</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>temp[0, 0, [0,1,2,3], [0,1,2,3]]</pre>
- <p>returns an array of shape (4,4) when slicing a netCDF variable,
- but for a numpy array it returns an array of shape (4,). Similarly, a
- netCDF variable of shape <code>(2,3,4,5)</code> indexed with
- <code>[0, array([True, False, True]), array([False, True, True,
- True]), :]</code> would return a <code>(2, 3, 5)</code> array. In
- NumPy, this would raise an error since it would be equivalent to
- <code>[0, [0,1], [1,2,3], :]</code>. When slicing with integer
- sequences, the indices must be sorted in increasing order and contain
- no duplicates. While this behaviour may cause some confusion for
- those used to NumPy's 'fancy indexing' rules, it provides a very
- powerful way to extract data from multidimensional netCDF variables
- by using logical operations on the dimension arrays to create
- slices.</p>
- <p>For example,</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>tempdat = temp[::2, [1,3,6], lats>0, lons>0]</pre>
- <p>will extract time indices 0,2 and 4, pressure levels 850, 500 and
- 200 hPa, all Northern Hemisphere latitudes and Eastern Hemisphere
- longitudes, resulting in a numpy array of shape (3, 3, 36, 71).</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'shape of fancy temp slice = '</span>,tempdat.shape
-<span class="py-output">shape of fancy temp slice = (3, 3, 36, 71)</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p><b>Special note for scalar variables</b>: To extract data from a
- scalar variable <code>v</code> with no associated dimensions, use an
- Ellipsis slice (<code>v[...]</code>). The result will be a numpy
- scalar object.</p>
- <h2 class="heading">7) Dealing with time coordinates</h2>
- <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="netCDF4._netCDF4-module.html#num2date"
- class="link">num2date</a> and <a
- href="netCDF4._netCDF4-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> netCDF4 <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="netCDF4._netCDF4-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="netCDF4._netCDF4-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="netCDF4._netCDF4-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>
- <h2 class="heading">8) 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="netCDF4._netCDF4.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="netCDF4._netCDF4.Dataset-class.html"
- class="link">Dataset</a> instance, create a <a
- href="netCDF4._netCDF4.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). The files must in be
- in <code>NETCDF3_64BIT</code>, <code>NETCDF3_CLASSIC</code> or
- <code>NETCDF4_CLASSIC format</code> (<code>NETCDF4</code> formatted
- multi-file datasets are not supported).</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>,format=<span class="py-string">'NETCDF4_CLASSIC'</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="netCDF4._netCDF4.MFDataset-class.html"
- class="link">MFDataset</a></p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">from</span> netCDF4 <span class="py-keyword">import</span> MFDataset
-<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>
- <h2 class="heading">9) Efficient compression of netCDF variables</h2>
- <p>Data stored in netCDF 4 <a
- href="netCDF4._netCDF4.Variable-class.html" class="link">Variable</a>
- objects can be compressed and decompressed on the fly. The parameters
- for the compression are determined by the <code>zlib</code>,
- <code>complevel</code> and <code>shuffle</code> keyword arguments to
- the <a href="netCDF4._netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a> method. To turn on compression, set
- <code>zlib=True</code>. The <code>complevel</code> keyword regulates
- the speed and efficiency of the compression (1 being fastest, but
- lowest compression ratio, 9 being slowest but best compression
- ratio). The default value of <code>complevel</code> is 4. Setting
- <code>shuffle=False</code> will turn off the HDF5 shuffle filter,
- which de-interlaces a block of data before compression by reordering
- the bytes. The shuffle filter can significantly improve compression
- ratios, and is on by default. Setting <code>fletcher32</code>
- keyword argument to <a
- href="netCDF4._netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a> to <code>True</code> (it's
- <code>False</code> by default) enables the Fletcher32 checksum
- algorithm for error detection. It's also possible to set the HDF5
- chunking parameters and endian-ness of the binary data stored in the
- HDF5 file with the <code>chunksizes</code> and <code>endian</code>
- keyword arguments to <a
- href="netCDF4._netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a>. These keyword arguments only are
- relevant for <code>NETCDF4</code> and <code>NETCDF4_CLASSIC</code>
- files (where the underlying file format is HDF5) and are silently
- ignored if the file format is <code>NETCDF3_CLASSIC</code> or
- <code>NETCDF3_64BIT</code>,</p>
- <p>If your data only has a certain number of digits of precision (say
- for example, it is temperature data that was measured with a
- precision of 0.1 degrees), you can dramatically improve zlib
- compression by quantizing (or truncating) the data using the
- <code>least_significant_digit</code> keyword argument to <a
- href="netCDF4._netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a>. The least significant digit is the
- power of ten of the smallest decimal place in the data that is a
- reliable value. For example if the data has a precision of 0.1, then
- setting <code>least_significant_digit=1</code> will cause data the
- data to be quantized using
- <code>numpy.around(scale*data)/scale</code>, where scale = 2**bits,
- and bits is determined so that a precision of 0.1 is retained (in
- this case bits=4). Effectively, this makes the compression 'lossy'
- instead of 'lossless', that is some precision in the data is
- sacrificed for the sake of disk space.</p>
- <p>In our example, try replacing the line</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>temp = rootgrp.createVariable(<span class="py-string">'temp'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'time'</span>,<span class="py-string">'level'</span>,<span class="py-string">'lat'</span>,<span class="py-string">'lon'</span>,))</pre>
- <p>with</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>temp = dataset.createVariable(<span class="py-string">'temp'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'time'</span>,<span class="py-string">'level'</span>,<span class="py-string">'lat'</span>,<span class="py-string">'lon'</span>,),zlib=True)</pre>
- <p>and then</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>temp = dataset.createVariable(<span class="py-string">'temp'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'time'</span>,<span class="py-string">'level'</span>,<span class="py-string">'lat'</span>,<span class="py-string">'lon'</span>,),zlib=True,least_significant_digit=3)</pre>
- <p>and see how much smaller the resulting files are.</p>
- <h2 class="heading">10) Beyond homogenous arrays of a fixed type - compound data types</h2>
- <p>Compound data types map directly to numpy structured (a.k.a
- 'record' arrays). Structured arrays are akin to C structs, or
- derived types in Fortran. They allow for the construction of
- table-like structures composed of combinations of other data types,
- including other compound types. Compound types might be useful for
- representing multiple parameter values at each point on a grid, or at
- each time and space location for scattered (point) data. You can then
- access all the information for a point by reading one variable,
- instead of reading different parameters from different variables.
- Compound data types are created from the corresponding numpy data
- type using the <a
- href="netCDF4._netCDF4.Dataset-class.html#createCompoundType"
- class="link">createCompoundType</a> method of a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- or <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- instance. Since there is no native complex data type in netcdf,
- compound types are handy for storing numpy complex arrays. Here's an
- example:</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>f = Dataset(<span class="py-string">'complex.nc'</span>,<span class="py-string">'w'</span>)
-<span class="py-prompt">>>> </span>size = 3 <span class="py-comment"># length of 1-d complex array</span>
-<span class="py-prompt">>>> </span><span class="py-comment"># create sample complex data.</span>
-<span class="py-prompt">>>> </span>datac = numpy.exp(1j*(1.+numpy.linspace(0, numpy.pi, size)))
-<span class="py-prompt">>>> </span><span class="py-comment"># create complex128 compound data type.</span>
-<span class="py-prompt">>>> </span>complex128 = numpy.dtype([(<span class="py-string">'real'</span>,numpy.float64),(<span class="py-string">'imag'</span>,numpy.float64)])
-<span class="py-prompt">>>> </span>complex128_t = f.createCompoundType(complex128,<span class="py-string">'complex128'</span>)
-<span class="py-prompt">>>> </span><span class="py-comment"># create a variable with this data type, write some data to it.</span>
-<span class="py-prompt">>>> </span>f.createDimension(<span class="py-string">'x_dim'</span>,None)
-<span class="py-prompt">>>> </span>v = f.createVariable(<span class="py-string">'cmplx_var'</span>,complex128_t,<span class="py-string">'x_dim'</span>)
-<span class="py-prompt">>>> </span>data = numpy.empty(size,complex128) <span class="py-comment"># numpy structured array</span>
-<span class="py-prompt">>>> </span>data[<span class="py-string">'real'</span>] = datac.real; data[<span class="py-string">'imag'</span>] = datac.imag
-<span class="py-prompt">>>> </span>v[:] = data <span class="py-comment"># write numpy structured array to netcdf compound var</span>
-<span class="py-prompt">>>> </span><span class="py-comment"># close and reopen the file, check the contents.</span>
-<span class="py-prompt">>>> </span>f.close(); f = Dataset(<span class="py-string">'complex.nc'</span>)
-<span class="py-prompt">>>> </span>v = f.variables[<span class="py-string">'cmplx_var'</span>]
-<span class="py-prompt">>>> </span>datain = v[:] <span class="py-comment"># read in all the data into a numpy structured array</span>
-<span class="py-prompt">>>> </span><span class="py-comment"># create an empty numpy complex array</span>
-<span class="py-prompt">>>> </span>datac2 = numpy.empty(datain.shape,numpy.complex128)
-<span class="py-prompt">>>> </span><span class="py-comment"># .. fill it with contents of structured array.</span>
-<span class="py-prompt">>>> </span>datac2.real = datain[<span class="py-string">'real'</span>]; datac2.imag = datain[<span class="py-string">'imag'</span>]
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> datac.dtype,datac <span class="py-comment"># original data</span>
-<span class="py-output">complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j]</span>
-<span class="py-output"></span><span class="py-prompt">>>></span>
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> datac2.dtype,datac2 <span class="py-comment"># data from file</span>
-<span class="py-output">complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j]</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Compound types can be nested, but you must create the 'inner' ones
- first. All of the compound types defined for a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- or <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- are stored in a Python dictionary, just like variables and
- dimensions. As always, printing objects gives useful summary
- information in an interactive session:</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> f
-<span class="py-output"><type 'netCDF4.Dataset'></span>
-<span class="py-output">root group (NETCDF4 file format):</span>
-<span class="py-output"> dimensions: x_dim</span>
-<span class="py-output"> variables: cmplx_var</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"><type 'netCDF4.Variable'></span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.variables[<span class="py-string">'cmplx_var'</span>]
-<span class="py-output">compound cmplx_var(x_dim)</span>
-<span class="py-output">compound data type: [('real', '<f8'), ('imag', '<f8')]</span>
-<span class="py-output">unlimited dimensions: x_dim</span>
-<span class="py-output">current shape = (3,)</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.cmptypes
-<span class="py-output">OrderedDict([('complex128', <netCDF4.CompoundType object at 0x1029eb7e8>)])</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.cmptypes[<span class="py-string">'complex128'</span>]
-<span class="py-output"><type 'netCDF4.CompoundType'>: name = 'complex128', numpy dtype = [(u'real','<f8'), (u'imag', '<f8')]</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <h2 class="heading">11) Variable-length (vlen) data types</h2>
- <p>NetCDF 4 has support for variable-length or "ragged"
- arrays. These are arrays of variable length sequences having the
- same type. To create a variable-length data type, use the <a
- href="netCDF4._netCDF4.Dataset-class.html#createVLType"
- class="link">createVLType</a> method method of a <a
- href="netCDF4._netCDF4.Dataset-class.html" class="link">Dataset</a>
- or <a href="netCDF4._netCDF4.Group-class.html" class="link">Group</a>
- instance.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>f = Dataset(<span class="py-string">'tst_vlen.nc'</span>,<span class="py-string">'w'</span>)
-<span class="py-prompt">>>> </span>vlen_t = f.createVLType(numpy.int32, <span class="py-string">'phony_vlen'</span>)</pre>
- <p>The numpy datatype of the variable-length sequences and the name
- of the new datatype must be specified. Any of the primitive datatypes
- can be used (signed and unsigned integers, 32 and 64 bit floats, and
- characters), but compound data types cannot. A new variable can then
- be created using this datatype.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>x = f.createDimension(<span class="py-string">'x'</span>,3)
-<span class="py-prompt">>>> </span>y = f.createDimension(<span class="py-string">'y'</span>,4)
-<span class="py-prompt">>>> </span>vlvar = f.createVariable(<span class="py-string">'phony_vlen_var'</span>, vlen_t, (<span class="py-string">'y'</span>,<span class="py-string">'x'</span>))</pre>
- <p>Since there is no native vlen datatype in numpy, vlen arrays are
- represented in python as object arrays (arrays of dtype
- <code>object</code>). These are arrays whose elements are Python
- object pointers, and can contain any type of python object. For this
- application, they must contain 1-D numpy arrays all of the same type
- but of varying length. In this case, they contain 1-D numpy
- <code>int32</code> arrays of random length betwee 1 and 10.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">import</span> random
-<span class="py-prompt">>>> </span>data = numpy.empty(len(y)*len(x),object)
-<span class="py-prompt">>>> </span><span class="py-keyword">for</span> n <span class="py-keyword">in</span> range(len(y)*len(x)):
-<span class="py-prompt">>>> </span> data[n] = numpy.arange(random.randint(1,10),dtype=<span class="py-string">'int32'</span>)+1
-<span class="py-prompt">>>> </span>data = numpy.reshape(data,(len(y),len(x)))
-<span class="py-prompt">>>> </span>vlvar[:] = data
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'vlen variable =\n'</span>,vlvar[:]
-<span class="py-output">vlen variable =</span>
-<span class="py-output">[[[ 1 2 3 4 5 6 7 8 9 10] [1 2 3 4 5] [1 2 3 4 5 6 7 8]]</span>
-<span class="py-output"> [[1 2 3 4 5 6 7] [1 2 3 4 5 6] [1 2 3 4 5]]</span>
-<span class="py-output"> [[1 2 3 4 5] [1 2 3 4] [1]]</span>
-<span class="py-output"> [[ 1 2 3 4 5 6 7 8 9 10] [ 1 2 3 4 5 6 7 8 9 10]</span>
-<span class="py-output"> [1 2 3 4 5 6 7 8]]]</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f
-<span class="py-output"><type 'netCDF4.Dataset'></span>
-<span class="py-output">root group (NETCDF4 file format):</span>
-<span class="py-output"> dimensions: x, y</span>
-<span class="py-output"> variables: phony_vlen_var</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.variables[<span class="py-string">'phony_vlen_var'</span>]
-<span class="py-output"><type 'netCDF4.Variable'></span>
-<span class="py-output">vlen phony_vlen_var(y, x)</span>
-<span class="py-output">vlen data type: int32</span>
-<span class="py-output">unlimited dimensions:</span>
-<span class="py-output">current shape = (4, 3)</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.VLtypes[<span class="py-string">'phony_vlen'</span>]
-<span class="py-output"><type 'netCDF4.VLType'>: name = 'phony_vlen', numpy dtype = int32</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Numpy object arrays containing python strings can also be written
- as vlen variables, For vlen strings, you don't need to create a vlen
- data type. Instead, simply use the python <code>str</code> builtin
- (or a numpy string datatype with fixed length greater than 1) when
- calling the <a
- href="netCDF4._netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a> method.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>z = f.createDimension(<span class="py-string">'z'</span>,10)
-<span class="py-prompt">>>> </span>strvar = rootgrp.createVariable(<span class="py-string">'strvar'</span>, str, <span class="py-string">'z'</span>)</pre>
- <p>In this example, an object array is filled with random python
- strings with random lengths between 2 and 12 characters, and the data
- in the object array is assigned to the vlen string variable.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>chars = <span class="py-string">'1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'</span>
-<span class="py-prompt">>>> </span>data = numpy.empty(10,<span class="py-string">'O'</span>)
-<span class="py-prompt">>>> </span><span class="py-keyword">for</span> n <span class="py-keyword">in</span> range(10):
-<span class="py-prompt">>>> </span> stringlen = random.randint(2,12)
-<span class="py-prompt">>>> </span> data[n] = <span class="py-string">''</span>.join([random.choice(chars) <span class="py-keyword">for</span> i <span class="py-keyword">in</span> range(stringlen)])
-<span class="py-prompt">>>> </span>strvar[:] = data
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'variable-length string variable:\n'</span>,strvar[:]
-<span class="py-output">variable-length string variable:</span>
-<span class="py-output">[aDy29jPt jd7aplD b8t4RM jHh8hq KtaPWF9cQj Q1hHN5WoXSiT MMxsVeq td LUzvVTzj</span>
-<span class="py-output"> 5DS9X8S]</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f
-<span class="py-output"><type 'netCDF4.Dataset'></span>
-<span class="py-output">root group (NETCDF4 file format):</span>
-<span class="py-output"> dimensions: x, y, z</span>
-<span class="py-output"> variables: phony_vlen_var, strvar</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.variables[<span class="py-string">'strvar'</span>]
-<span class="py-output"><type 'netCDF4.Variable'></span>
-<span class="py-output">vlen strvar(z)</span>
-<span class="py-output">vlen data type: <type 'str'></span>
-<span class="py-output">unlimited dimensions:</span>
-<span class="py-output">current size = (10,)</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>It is also possible to set contents of vlen string variables with
- numpy arrays of any string or unicode data type. Note, however, that
- accessing the contents of such variables will always return numpy
- arrays with dtype <code>object</code>.</p>
- <p>All of the code in this tutorial is available in
- <code>examples/tutorial.py</code>, Unit tests are in the
- <code>test</code> directory.</p>
-
-<hr />
-<div class="fields"> <p><strong>Contact:</strong>
- Jeffrey Whitaker <jeffrey.s.whitaker at 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>
- 1.1.8
- </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="netCDF4._netCDF4.CompoundType-class.html" class="summary-name">CompoundType</a><br />
- A <a href="netCDF4._netCDF4.CompoundType-class.html"
- class="link">CompoundType</a> instance is used to describe a
- compound 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._netCDF4.Dataset-class.html" class="summary-name">Dataset</a><br />
- Dataset(self, filename, mode="r", clobber=True,
- diskless=False, persist=False, keepweakref=False, format='NETCDF4')
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4._netCDF4.Dimension-class.html" class="summary-name">Dimension</a><br />
- Dimension(self, group, 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="netCDF4._netCDF4.Group-class.html" class="summary-name">Group</a><br />
- Group(self, parent, name)
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4._netCDF4.MFDataset-class.html" class="summary-name">MFDataset</a><br />
- MFDataset(self, files, check=False, aggdim=None, exclude=[])
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4._netCDF4.MFTime-class.html" class="summary-name">MFTime</a><br />
- MFTime(self, time, units=None)
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4._netCDF4.VLType-class.html" class="summary-name">VLType</a><br />
- A <a href="netCDF4._netCDF4.VLType-class.html"
- class="link">VLType</a> instance is used to describe a variable
- length (VLEN) 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._netCDF4.Variable-class.html" class="summary-name">Variable</a><br />
- 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)
- </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="netCDF4._netCDF4-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="netCDF4._netCDF4-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="netCDF4._netCDF4-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="netCDF4._netCDF4-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="netCDF4._netCDF4-module.html#stringtoarr" class="summary-sig-name">stringtoarr</a>(<span class="summary-sig-arg">a</span>,
- <span class="summary-sig-arg">NUMCHARS</span>,
- <span class="summary-sig-arg">dtype</span>=<span class="summary-sig-default">'S'</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="netCDF4._netCDF4-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="MINYEAR"></a><span class="summary-name">MINYEAR</span> = <code title="1">1</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="NC_DISKLESS"></a><span class="summary-name">NC_DISKLESS</span> = <code title="8">8</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="__has_nc_inq_format_extended__"></a><span class="summary-name">__has_nc_inq_format_extended__</span> = <code title="1">1</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="__has_nc_inq_path__"></a><span class="summary-name">__has_nc_inq_path__</span> = <code title="1">1</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="__has_rename_grp__"></a><span class="summary-name">__has_rename_grp__</span> = <code title="1">1</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="__hdf5libversion__"></a><span class="summary-name">__hdf5libversion__</span> = <code title="'1.8.14'"><code class="variable-quote">'</code><code class="variable-string">1.8.14</code><code class="variable-quote">'</code></code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="__netcdf4libversion__"></a><span class="summary-name">__netcdf4libversion__</span> = <code title="u'4.3.3-rc2'"><code class="variable-quote">u'</code><code class="variable-string">4.3.3-rc2</code><code class="variable-quote">'</code></code>
- </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="'netCDF4'"><code class="variable-quote">'</code><code class="variable-string">netCDF4</code><code class="variable-quote">'</code></code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="day_units"></a><span class="summary-name">day_units</span> = <code title="['day', 'days', 'd']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">day</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">days</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="default_encoding"></a><span class="summary-name">default_encoding</span> = <code title="'utf-8'"><code class="variable-quote">'</code><code class="variable-string">utf-8</code><code class="variable-quote">'</code></code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4._netCDF4-module.html#default_fillvals" class="summary-name">default_fillvals</a> = <code title="{'S1': '\x00',
- 'U1': '\x00',
- 'f4': 9.96920996839e+36,
- 'f8': 9.96920996839e+36,
- 'i1': -127,
- 'i2': -32767,
- 'i4': -2147483647,
- 'i8': -9223372036854775806,
-..."><code class="variable-group">{</code><code class="variable-quote">'</code><code class="variable-string">S1</code><code class="variable-quote">'</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">\x00</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">U1</code><code class="variable-quote">'</code><code class="variable-op">: </code><cod [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="gregorian"></a><span class="summary-name">gregorian</span> = <code title="datetime.datetime(1582, 10, 15, 0, 0)">datetime.datetime(1582, 10, 15, 0, 0)</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="hr_units"></a><span class="summary-name">hr_units</span> = <code title="['hour', 'hours', 'hr', 'hrs', 'h']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">hour</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">hours</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote" [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="is_native_big"></a><span class="summary-name">is_native_big</span> = <code title="False">False</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="is_native_little"></a><span class="summary-name">is_native_little</span> = <code title="True">True</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4._netCDF4-module.html#microsec_units" class="summary-name">microsec_units</a> = <code title="['microseconds', 'microsecond', 'microsec', 'microsecs']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">microseconds</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">microsecond</code><code class="variable-quote">'</code> [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4._netCDF4-module.html#millisec_units" class="summary-name">millisec_units</a> = <code title="['milliseconds', 'millisecond', 'millisec', 'millisecs']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">milliseconds</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">millisecond</code><code class="variable-quote">'</code> [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="min_units"></a><span class="summary-name">min_units</span> = <code title="['minute', 'minutes', 'min', 'mins']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">minute</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">minutes</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="python3"></a><span class="summary-name">python3</span> = <code title="False">False</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="sec_units"></a><span class="summary-name">sec_units</span> = <code title="['second', 'seconds', 'sec', 'secs', 's']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">second</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">seconds</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="var [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="unicode_error"></a><span class="summary-name">unicode_error</span> = <code title="'replace'"><code class="variable-quote">'</code><code class="variable-string">replace</code><code class="variable-quote">'</code></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' or 'U1'). 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' or 'UN' 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>
- <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, seconds,
- milliseconds or microseconds. <b><code>reference time</code></b>
- is the time origin. Accuracy is somewhere between a millisecond
- and a microsecond, depending on the time interval and the
- calendar used.</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.</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>
- <dl class="fields">
- <dt>Parameters:</dt>
- <dd><ul class="nomargin-top">
- <li><strong class="pname"><code>times</code></strong> - numeric time values.</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, seconds,
- milliseconds or microseconds. <b><code>reference time</code></b>
- is the time origin. Accuracy is somewhere between a millisecond
- and a microsecond, depending on the time interval and the
- calendar used.</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 <code>calendar='proleptic_gregorian'</code>, or
- <code>calendar = 'standard'</code> or <code>'gregorian'</code>
- and the date is after the breakpoint between the Julian and
- Gregorian calendars (1582-10-15). Otherwise, they are 'phony'
- datetime objects which support some but not all the methods of
- 'real' python datetime objects. 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 class="sig-arg">dtype</span>=<span class="sig-default">'S'</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>
- <li><strong class="pname"><code>dtype</code></strong> - type of numpy array to return. Default is 'S', which means an
- array of dtype 'S1' will be returned. If dtype='U', a unicode
- array (dtype = 'U1') will be returned.</li>
- </ul></dd>
- <dt>Returns:</dt>
- <dd>A rank 1 numpy character array of length NUMCHARS with datatype
- 'S1' (default) or 'U1' (if dtype='U')</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' or 'UN', where
- N is the number of characters in each string. Will be converted
- to an array of characters (datatype 'S1' or 'U1') of shape
- a.shape + (N,).</li>
- </ul></dd>
- <dt>Returns:</dt>
- <dd>A numpy character array with datatype 'S1' or 'U1' and shape
- a.shape + (N,), where N is the length of each string in a.</dd>
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== VARIABLES DETAILS ==================== -->
-<a name="section-VariablesDetails"></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">Variables Details</span></td>
-</tr>
-</table>
-<a name="default_fillvals"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">default_fillvals</h3>
-
- <dl class="fields">
- </dl>
- <dl class="fields">
- <dt>Value:</dt>
- <dd><table><tr><td><pre class="variable">
-<code class="variable-group">{</code><code class="variable-quote">'</code><code class="variable-string">S1</code><code class="variable-quote">'</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">\x00</code><code class="variable-quote">'</code><code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">U1</code><code class="variable-quote">'</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">\x00</code><code class="variable-quote">'</code><code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">f4</code><code class="variable-quote">'</code><code class="variable-op">: </code>9.96920996839e+36<code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">f8</code><code class="variable-quote">'</code><code class="variable-op">: </code>9.96920996839e+36<code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">i1</code><code class="variable-quote">'</code><code class="variable-op">: </code>-127<code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">i2</code><code class="variable-quote">'</code><code class="variable-op">: </code>-32767<code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">i4</code><code class="variable-quote">'</code><code class="variable-op">: </code>-2147483647<code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">i8</code><code class="variable-quote">'</code><code class="variable-op">: </code>-9223372036854775806<code class="variable-op">,</code>
-<code class="variable-ellipsis">...</code>
-</pre></td></tr></table>
-</dd>
- </dl>
-</td></tr></table>
-</div>
-<a name="microsec_units"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">microsec_units</h3>
-
- <dl class="fields">
- </dl>
- <dl class="fields">
- <dt>Value:</dt>
- <dd><table><tr><td><pre class="variable">
-<code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">microseconds</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">microsecond</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">microsec</code><code class="variable-quote">'</code><code class="variable [...]
-</pre></td></tr></table>
-</dd>
- </dl>
-</td></tr></table>
-</div>
-<a name="millisec_units"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">millisec_units</h3>
-
- <dl class="fields">
- </dl>
- <dl class="fields">
- <dt>Value:</dt>
- <dd><table><tr><td><pre class="variable">
-<code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">milliseconds</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">millisecond</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">millisec</code><code class="variable-quote">'</code><code class="variable [...]
-</pre></td></tr></table>
-</dd>
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
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diff --git a/docs/module-tree.html b/docs/module-tree.html
deleted file mode 100644
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--- a/docs/module-tree.html
+++ /dev/null
@@ -1,109 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
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-<center><b>
- [ <a href="module-tree.html">Module Hierarchy</a>
- | <a href="class-tree.html">Class Hierarchy</a> ]
-</b></center><br />
-<h1 class="epydoc">Module Hierarchy</h1>
-<ul class="nomargin-top">
- <li class="private"> <strong class="uidlink"><a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a></strong>: <em class="summary">Python interface to the netCDF version 4 library.</em> </li>
-</ul>
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diff --git a/docs/netCDF4-module.html b/docs/netCDF4-module.html
deleted file mode 100644
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+++ /dev/null
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- "DTD/xhtml1-transitional.dtd">
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- <span class="breadcrumbs">
- Module netCDF4
- </span>
- </td>
- <td>
- <table cellpadding="0" cellspacing="0">
- <!-- hide/show private -->
- </table>
- </td>
- </tr>
-</table>
-<!-- ==================== MODULE DESCRIPTION ==================== -->
-<h1 class="epydoc">Module netCDF4</h1><p class="nomargin-top"><span class="codelink"><a href="netCDF4-pysrc.html">source code</a></span></p>
-<h1 class="heading">Introduction</h1>
- <p>Python interface to the netCDF version 4 library. <a
- href="http://www.unidata.ucar.edu/software/netcdf/netcdf-4"
- target="_top">netCDF version 4</a> has many features not found in
- earlier versions of the library and is implemented on top of <a
- href="http://www.hdfgroup.org/HDF5" target="_top">HDF5</a>. This module
- can read and write files in both the new netCDF 4 and the old netCDF 3
- format, and can create files that are readable by HDF5 clients. 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.</p>
- <p>Most new features of netCDF 4 are implemented, such as multiple
- unlimited dimensions, groups and zlib data compression. All the new
- numeric data types (such as 64 bit and unsigned integer types) are
- implemented. Compound and variable length (vlen) data types are
- supported, but the enum and opaque data types are not. Mixtures of
- compound and vlen data types (compound types containing vlens, and
- vlens containing compound types) are not supported.</p>
- <h1 class="heading">Download</h1>
- <ul>
- <li>
- Latest bleeding-edge code from the <a
- href="http://github.com/Unidata/netcdf4-python"
- target="_top">github repository</a>.
- </li>
- <li>
- Latest <a href="https://pypi.python.org/pypi/netCDF4"
- target="_top">releases</a> (source code and windows installers).
- </li>
- </ul>
- <h1 class="heading">Requires</h1>
- <ul>
- <li>
- Python 2.5 or later (python 3 works too).
- </li>
- <li>
- numpy array module <a href="http://numpy.scipy.org"
- target="_top">http://numpy.scipy.org</a>, version 1.7.0 or later.
- </li>
- <li>
- <a href="http://cython.org" target="_top">Cython</a>, version 0.19
- or later, is optional - if it is installed setup.py will use it to
- recompile the Cython source code into C, using conditional
- compilation to enable features in the netCDF API that have been
- added since version 4.1.1. If Cython is not installed, these
- features (such as the ability to rename Group objects) will be
- disabled to preserve backward compatibility with older versions of
- the netCDF library.
- </li>
- <li>
- For python < 2.7, the ordereddict module <a
- href="http://python.org/pypi/ordereddict"
- target="_top">http://python.org/pypi/ordereddict</a>.
- </li>
- <li>
- The HDF5 C library version 1.8.4-patch1 or higher (1.8.8 or higher
- recommended) from <a href="ftp://ftp.hdfgroup.org/HDF5/current/src"
- target="_top">ftp://ftp.hdfgroup.org/HDF5/current/src</a>. Be sure
- to build with '<code>--enable-hl --enable-shared</code>'.
- </li>
- <li>
- <a href="http://curl.haxx.se/libcurl/" target="_top">Libcurl</a>,
- if you want <a href="http://opendap.org/" target="_top">OPeNDAP</a>
- support.
- </li>
- <li>
- <a href="http://www.hdfgroup.org/products/hdf4/"
- target="_top">HDF4</a>, if you want to be able to read HDF4
- "Scientific Dataset" (SD) files.
- </li>
- <li>
- The netCDF-4 C library from <a
- href="ftp://ftp.unidata.ucar.edu/pub/netcdf"
- target="_top">ftp://ftp.unidata.ucar.edu/pub/netcdf</a>. Version
- 4.1.1 or higher is required (4.2 or higher recommended). Be sure to
- build with '<code>--enable-netcdf-4 --enable-shared</code>', and
- set <code>CPPFLAGS="-I $HDF5_DIR/include"</code> and
- <code>LDFLAGS="-L $HDF5_DIR/lib"</code>, where
- <code>$HDF5_DIR</code> is the directory where HDF5 was installed.
- If you want <a href="http://opendap.org/" target="_top">OPeNDAP</a>
- support, add '<code>--enable-dap</code>'. If you want HDF4 SD
- support, add '<code>--enable-hdf4</code>' and add the location of
- the HDF4 headers and library to <code>CPPFLAGS</code> and
- <code>LDFLAGS</code>.
- </li>
- </ul>
- <h1 class="heading">Install</h1>
- <ul>
- <li>
- install the requisite python modules and C libraries (see above).
- It's easiest if all the C libs are built as shared libraries.
- </li>
- <li>
- By default, the utility <code>nc-config</code>, installed with
- netcdf 4.1.2 or higher, will be run used to determine where all the
- dependencies live.
- </li>
- <li>
- If <code>nc-config</code> is not in your default <code>PATH</code>,
- rename the file <code>setup.cfg.template</code> to
- <code>setup.cfg</code>, then edit in a text editor (follow the
- instructions in the comments). In addition to specifying the path
- to <code>nc-config</code>, you can manually set the paths to all
- the libraries and their include files (in case
- <code>nc-config</code> does not do the right thing).
- </li>
- <li>
- run <code>python setup.py build</code>, then <code>python setup.py
- install</code> (as root if necessary).
- </li>
- <li>
- run the tests in the 'test' directory by running <code>python
- run_all.py</code>.
- </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="netCDF4.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, groups, 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.
- <code>NETCDF4_CLASSIC</code> files use the version 4 disk format
- (HDF5), but do not use any features not found in the version 3 API.
- They can be read by netCDF 3 clients only if they have been relinked
- against the netCDF 4 library. They can also be read by HDF5 clients.
- <code>NETCDF4</code> files use the version 4 disk format (HDF5) and
- use the new features of the version 4 API. The <code>netCDF4</code>
- module can read and write files in any of these formats. When
- creating a new file, the format may be specified using the
- <code>format</code> keyword in the <code>Dataset</code> constructor.
- The default format is <code>NETCDF4</code>. To see how a given file
- is formatted, you can examine the <code>data_model</code> <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> attribute.
- Closing the netCDF file is accomplished via the <a
- href="netCDF4.Dataset-class.html#close" class="link">close</a> method
- of the <a href="netCDF4.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">from</span> netCDF4 <span class="py-keyword">import</span> Dataset
-<span class="py-prompt">>>> </span>rootgrp = Dataset(<span class="py-string">'test.nc'</span>, <span class="py-string">'w'</span>, format=<span class="py-string">'NETCDF4'</span>)
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> rootgrp.data_model
-<span class="py-output">NETCDF4</span>
-<span class="py-output"></span><span class="py-prompt">>>></span>
-<span class="py-prompt">>>> </span>rootgrp.close()</pre>
- <p>Remote <a href="http://opendap.org"
- target="_top">OPeNDAP</a>-hosted datasets can be accessed for reading
- over http if a URL is provided to the <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a>
- constructor instead of a filename. However, this requires that the
- netCDF library be built with OPenDAP support, via the
- <code>--enable-dap</code> configure option (added in version
- 4.0.1).</p>
- <h2 class="heading">2) Groups in a netCDF file</h2>
- <p>netCDF version 4 added support for organizing data in hierarchical
- groups, which are analagous to directories in a filesystem. Groups
- serve as containers for variables, dimensions and attributes, as well
- as other groups. A <code>netCDF4.Dataset</code> defines creates a
- special group, called the 'root group', which is similar to the root
- directory in a unix filesystem. To create <a
- href="netCDF4.Group-class.html" class="link">Group</a> instances, use
- the <a href="netCDF4.Dataset-class.html#createGroup"
- class="link">createGroup</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. <a
- href="netCDF4.Dataset-class.html#createGroup"
- class="link">createGroup</a> takes a single argument, a python string
- containing the name of the new group. The new <a
- href="netCDF4.Group-class.html" class="link">Group</a> instances
- contained within the root group can be accessed by name using the
- <code>groups</code> dictionary attribute of the <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> instance.
- Only <code>NETCDF4</code> formatted files support Groups, if you try
- to create a Group in a netCDF 3 file you will get an error
- message.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>rootgrp = Dataset(<span class="py-string">'test.nc'</span>, <span class="py-string">'a'</span>)
-<span class="py-prompt">>>> </span>fcstgrp = rootgrp.createGroup(<span class="py-string">'forecasts'</span>)
-<span class="py-prompt">>>> </span>analgrp = rootgrp.createGroup(<span class="py-string">'analyses'</span>)
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> rootgrp.groups
-<span class="py-output">OrderedDict([('forecasts', <netCDF4.Group object at 0x1b4b7b0>),</span>
-<span class="py-output"> ('analyses', <netCDF4.Group object at 0x1b4b970>)])</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Groups can exist within groups in a <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a>, just as
- directories exist within directories in a unix filesystem. Each <a
- href="netCDF4.Group-class.html" class="link">Group</a> instance has a
- <code>'groups'</code> attribute dictionary containing all of the
- group instances contained within that group. Each <a
- href="netCDF4.Group-class.html" class="link">Group</a> instance also
- has a <code>'path'</code> attribute that contains a simulated unix
- directory path to that group.</p>
- <p>Here's an example that shows how to navigate all the groups in a
- <a href="netCDF4.Dataset-class.html" class="link">Dataset</a>. The
- function <code>walktree</code> is a Python generator that is used to
- walk the directory tree. Note that printing the <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a> object yields
- summary information about it's contents.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>fcstgrp1 = fcstgrp.createGroup(<span class="py-string">'model1'</span>)
-<span class="py-prompt">>>> </span>fcstgrp2 = fcstgrp.createGroup(<span class="py-string">'model2'</span>)
-<span class="py-prompt">>>> </span><span class="py-keyword">def</span> <span class="py-defname">walktree</span>(top):
-<span class="py-prompt">>>> </span> <span class="py-builtin">values</span> = top.groups.values()
-<span class="py-prompt">>>> </span> yield <span class="py-builtin">values</span>
-<span class="py-prompt">>>> </span> <span class="py-keyword">for</span> value <span class="py-keyword">in</span> top.groups.values():
-<span class="py-prompt">>>> </span> <span class="py-keyword">for</span> children <span class="py-keyword">in</span> walktree(value):
-<span class="py-prompt">>>> </span> yield children
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> rootgrp
-<span class="py-prompt">>>> </span><span class="py-keyword">for</span> children <span class="py-keyword">in</span> walktree(rootgrp):
-<span class="py-prompt">>>> </span> <span class="py-keyword">for</span> child <span class="py-keyword">in</span> children:
-<span class="py-prompt">>>> </span> <span class="py-keyword">print</span> child
-<span class="py-output"><type 'netCDF4.Dataset'></span>
-<span class="py-output">root group (NETCDF4 file format):</span>
-<span class="py-output"> dimensions:</span>
-<span class="py-output"> variables:</span>
-<span class="py-output"> groups: forecasts, analyses</span>
-<span class="py-output"><type 'netCDF4.Group'></span>
-<span class="py-output">group /forecasts:</span>
-<span class="py-output"> dimensions:</span>
-<span class="py-output"> variables:</span>
-<span class="py-output"> groups: model1, model2</span>
-<span class="py-output"><type 'netCDF4.Group'></span>
-<span class="py-output">group /analyses:</span>
-<span class="py-output"> dimensions:</span>
-<span class="py-output"> variables:</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"><type 'netCDF4.Group'></span>
-<span class="py-output">group /forecasts/model1:</span>
-<span class="py-output"> dimensions:</span>
-<span class="py-output"> variables:</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"><type 'netCDF4.Group'></span>
-<span class="py-output">group /forecasts/model2:</span>
-<span class="py-output"> dimensions:</span>
-<span class="py-output"> variables:</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <h2 class="heading">3) 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="netCDF4.Dataset-class.html#createDimension"
- class="link">createDimension</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. 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. In this example, there both the
- <code>time</code> and <code>level</code> dimensions are unlimited.
- Having more than one unlimited dimension is a new netCDF 4 feature,
- in netCDF 3 files there may be only one, and it must be the first
- (leftmost) dimension of the variable.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>level = rootgrp.createDimension(<span class="py-string">'level'</span>, None)
-<span class="py-prompt">>>> </span>time = rootgrp.createDimension(<span class="py-string">'time'</span>, None)
-<span class="py-prompt">>>> </span>lat = rootgrp.createDimension(<span class="py-string">'lat'</span>, 73)
-<span class="py-prompt">>>> </span>lon = rootgrp.createDimension(<span class="py-string">'lon'</span>, 144)</pre>
- <p>All of the <a href="netCDF4.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> rootgrp.dimensions
-<span class="py-output">OrderedDict([('level', <netCDF4.Dimension object at 0x1b48030>),</span>
-<span class="py-output"> ('time', <netCDF4.Dimension object at 0x1b481c0>),</span>
-<span class="py-output"> ('lat', <netCDF4.Dimension object at 0x1b480f8>),</span>
-<span class="py-output"> ('lon', <netCDF4.Dimension object at 0x1b48a08>)])</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Calling the python <code>len</code> function with a <a
- href="netCDF4.Dimension-class.html" class="link">Dimension</a>
- instance returns the current size of that dimension. The <a
- href="netCDF4.Dimension-class.html#isunlimited"
- class="link">isunlimited</a> method of a <a
- href="netCDF4.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">print</span> len(lon)
-<span class="py-output">144</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> len.is_unlimited()
-<span class="py-output">False</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> time.is_unlimited()
-<span class="py-output">True</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Printing the <a href="netCDF4.Dimension-class.html"
- class="link">Dimension</a> object provides useful summary info,
- including the name and length of the dimension, and whether it is
- unlimited.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">for</span> dimobj <span class="py-keyword">in</span> rootgrp.dimensions.values():
-<span class="py-prompt">>>> </span> <span class="py-keyword">print</span> dimobj
-<span class="py-output"><type 'netCDF4.Dimension'> (unlimited): name = 'level', size = 0</span>
-<span class="py-output"><type 'netCDF4.Dimension'> (unlimited): name = 'time', size = 0</span>
-<span class="py-output"><type 'netCDF4.Dimension'>: name = 'lat', size = 73</span>
-<span class="py-output"><type 'netCDF4.Dimension'>: name = 'lon', size = 144</span>
-<span class="py-output"><type 'netCDF4.Dimension'> (unlimited): name = 'time', size = 0</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p><a href="netCDF4.Dimension-class.html" class="link">Dimension</a>
- names can be changed using the <a
- href="netCDF4.Dataset-class.html#renameDimension"
- class="link">renameDimension</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.</p>
- <h2 class="heading">4) 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, netCDF4
- variables can be appended to along one or more 'unlimited'
- dimensions. To create a netCDF variable, use 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. The
- <a href="netCDF4.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="netCDF4.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>'i8'</code> (64-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). 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. The unsigned integer types and the 64-bit integer
- type can only be used if the file format is <code>NETCDF4</code>.</p>
- <p>The dimensions themselves are usually also defined as variables,
- called coordinate variables. The <a
- href="netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a> method returns an instance of the <a
- href="netCDF4.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 = rootgrp.createVariable(<span class="py-string">'time'</span>,<span class="py-string">'f8'</span>,(<span class="py-string">'time'</span>,))
-<span class="py-prompt">>>> </span>levels = rootgrp.createVariable(<span class="py-string">'level'</span>,<span class="py-string">'i4'</span>,(<span class="py-string">'level'</span>,))
-<span class="py-prompt">>>> </span>latitudes = rootgrp.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 = rootgrp.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 = rootgrp.createVariable(<span class="py-string">'temp'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'time'</span>,<span class="py-string">'level'</span>,<span class="py-string">'lat'</span>,<span class="py-string">'lon'</span>,))</pre>
- <p>All of the variables in the <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> or <a href="netCDF4.Group-class.html"
- class="link">Group</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> rootgrp.variables
-<span class="py-output">OrderedDict([('time', <netCDF4.Variable object at 0x1b4ba70>),</span>
-<span class="py-output"> ('level', <netCDF4.Variable object at 0x1b4bab0>),</span>
-<span class="py-output"> ('latitude', <netCDF4.Variable object at 0x1b4baf0>),</span>
-<span class="py-output"> ('longitude', <netCDF4.Variable object at 0x1b4bb30>),</span>
-<span class="py-output"> ('temp', <netCDF4.Variable object at 0x1b4bb70>)])</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>To get summary info on a <a href="netCDF4.Variable-class.html"
- class="link">Variable</a> instance in an interactive session, just
- print it.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> rootgrp.variables[<span class="py-string">'temp'</span>]
-<span class="py-output"><type 'netCDF4.Variable'></span>
-<span class="py-output">float32 temp(time, level, lat, lon)</span>
-<span class="py-output"> least_significant_digit: 3</span>
-<span class="py-output"> units: K</span>
-<span class="py-output">unlimited dimensions: time, level</span>
-<span class="py-output">current shape = (0, 0, 73, 144)</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p><a href="netCDF4.Variable-class.html" class="link">Variable</a>
- names can be changed using the <a
- href="netCDF4.Dataset-class.html#renameVariable"
- class="link">renameVariable</a> method of a <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a>
- instance.</p>
- <h2 class="heading">5) 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 group, or the
- entire dataset, as a whole. <a href="netCDF4.Variable-class.html"
- class="link">Variable</a> attributes provide information about one of
- the variables in a group. Global attributes are set by assigning
- values to <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> or <a href="netCDF4.Group-class.html"
- class="link">Group</a> instance variables. <a
- href="netCDF4.Variable-class.html" class="link">Variable</a>
- attributes are set by assigning values to <a
- href="netCDF4.Variable-class.html" class="link">Variable</a>
- instances 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>rootgrp.description = <span class="py-string">'bogus example script'</span>
-<span class="py-prompt">>>> </span>rootgrp.history = <span class="py-string">'Created '</span> + time.ctime(time.time())
-<span class="py-prompt">>>> </span>rootgrp.source = <span class="py-string">'netCDF4 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>levels.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="netCDF4.Dataset-class.html#ncattrs"
- class="link">ncattrs</a> method of a <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a>, <a
- href="netCDF4.Group-class.html" class="link">Group</a> or <a
- href="netCDF4.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> rootgrp.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(rootgrp,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 = netCDF4 python module tutorial</span></pre>
- <p>The <code>__dict__</code> attribute of a <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a>, <a
- href="netCDF4.Group-class.html" class="link">Group</a> or <a
- href="netCDF4.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> rootgrp.__dict__
-<span class="py-output">OrderedDict([(u'description', u'bogus example script'),</span>
-<span class="py-output"> (u'history', u'Created Thu Mar 3 19:30:33 2011'),</span>
-<span class="py-output"> (u'source', u'netCDF4 python module tutorial')])</span></pre>
- <p>Attributes can be deleted from a netCDF <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a>, <a
- href="netCDF4.Group-class.html" class="link">Group</a> or <a
- href="netCDF4.Variable-class.html" class="link">Variable</a> using
- the python <code>del</code> statement (i.e. <code>del grp.foo</code>
- removes the attribute <code>foo</code> the the group
- <code>grp</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="netCDF4.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>lats = numpy.arange(-90,91,2.5)
-<span class="py-prompt">>>> </span>lons = numpy.arange(-180,180,2.5)
-<span class="py-prompt">>>> </span>latitudes[:] = lats
-<span class="py-prompt">>>> </span>longitudes[:] = lons
-<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></pre>
- <p>Unlike NumPy's array objects, netCDF <a
- href="netCDF4.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(rootgrp.dimensions[<span class="py-string">'lat'</span>])
-<span class="py-prompt">>>> </span>nlons = len(rootgrp.dimensions[<span class="py-string">'lon'</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, 0, 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 <span class="py-keyword">import</span> uniform
-<span class="py-prompt">>>> </span>temp[0:5,0:10,:,:] = uniform(size=(5,10,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 = (6, 10, 73, 144)</span>
-<span class="py-output"></span><span class="py-prompt">>>></span>
-<span class="py-prompt">>>> </span><span class="py-comment"># levels have grown, but no values yet assigned.</span>
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'levels shape after adding pressure data = '</span>,levels.shape
-<span class="py-output">levels shape after adding pressure data = (10,)</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Note that the size of the levels variable grows when data is
- appended along the <code>level</code> dimension of the variable
- <code>temp</code>, even though no data has yet been assigned to
- levels.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-comment"># now, assign data to levels dimension variable.</span>
-<span class="py-prompt">>>> </span>levels[:] = [1000.,850.,700.,500.,300.,250.,200.,150.,100.,50.]</pre>
- <p>However, that there are some differences between NumPy and netCDF
- variable slicing rules. Slices behave as usual, being specified as a
- <code>start:stop:step</code> triplet. Using a scalar integer index
- <code>i</code> takes the ith element and reduces the rank of the
- output array by one. Boolean array and integer sequence indexing
- behaves differently for netCDF variables than for numpy arrays. Only
- 1-d boolean arrays and integer sequences are allowed, and these
- indices work independently along each dimension (similar to the way
- vector subscripts work in fortran). This means that</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>temp[0, 0, [0,1,2,3], [0,1,2,3]]</pre>
- <p>returns an array of shape (4,4) when slicing a netCDF variable,
- but for a numpy array it returns an array of shape (4,). Similarly, a
- netCDF variable of shape <code>(2,3,4,5)</code> indexed with
- <code>[0, array([True, False, True]), array([False, True, True,
- True]), :]</code> would return a <code>(2, 3, 5)</code> array. In
- NumPy, this would raise an error since it would be equivalent to
- <code>[0, [0,1], [1,2,3], :]</code>. When slicing with integer
- sequences, the indices must be sorted in increasing order and contain
- no duplicates. While this behaviour may cause some confusion for
- those used to NumPy's 'fancy indexing' rules, it provides a very
- powerful way to extract data from multidimensional netCDF variables
- by using logical operations on the dimension arrays to create
- slices.</p>
- <p>For example,</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>tempdat = temp[::2, [1,3,6], lats>0, lons>0]</pre>
- <p>will extract time indices 0,2 and 4, pressure levels 850, 500 and
- 200 hPa, all Northern Hemisphere latitudes and Eastern Hemisphere
- longitudes, resulting in a numpy array of shape (3, 3, 36, 71).</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'shape of fancy temp slice = '</span>,tempdat.shape
-<span class="py-output">shape of fancy temp slice = (3, 3, 36, 71)</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="netCDF4-module.html#num2date" class="link">num2date</a> and <a
- href="netCDF4-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> netCDF4 <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="netCDF4-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="netCDF4-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="netCDF4-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>
- <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="netCDF4.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="netCDF4.Dataset-class.html"
- class="link">Dataset</a> instance, create a <a
- href="netCDF4.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). The files must in be in <code>NETCDF3_64BIT</code>,
- <code>NETCDF3_CLASSIC</code> or <code>NETCDF4_CLASSIC format</code>
- (<code>NETCDF4</code> formatted multi-file datasets are not
- supported).</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>,format=<span class="py-string">'NETCDF4_CLASSIC'</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="netCDF4.MFDataset-class.html" class="link">MFDataset</a></p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">from</span> netCDF4 <span class="py-keyword">import</span> MFDataset
-<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>
- <h2 class="heading">8) Efficient compression of netCDF variables</h2>
- <p>Data stored in netCDF 4 <a href="netCDF4.Variable-class.html"
- class="link">Variable</a> objects can be compressed and decompressed
- on the fly. The parameters for the compression are determined by the
- <code>zlib</code>, <code>complevel</code> and <code>shuffle</code>
- keyword arguments to the <a
- href="netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a> method. To turn on compression, set
- <code>zlib=True</code>. The <code>complevel</code> keyword regulates
- the speed and efficiency of the compression (1 being fastest, but
- lowest compression ratio, 9 being slowest but best compression
- ratio). The default value of <code>complevel</code> is 4. Setting
- <code>shuffle=False</code> will turn off the HDF5 shuffle filter,
- which de-interlaces a block of data before compression by reordering
- the bytes. The shuffle filter can significantly improve compression
- ratios, and is on by default. Setting <code>fletcher32</code>
- keyword argument to <a
- href="netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a> to <code>True</code> (it's
- <code>False</code> by default) enables the Fletcher32 checksum
- algorithm for error detection. It's also possible to set the HDF5
- chunking parameters and endian-ness of the binary data stored in the
- HDF5 file with the <code>chunksizes</code> and <code>endian</code>
- keyword arguments to <a
- href="netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a>. These keyword arguments only are
- relevant for <code>NETCDF4</code> and <code>NETCDF4_CLASSIC</code>
- files (where the underlying file format is HDF5) and are silently
- ignored if the file format is <code>NETCDF3_CLASSIC</code> or
- <code>NETCDF3_64BIT</code>,</p>
- <p>If your data only has a certain number of digits of precision (say
- for example, it is temperature data that was measured with a
- precision of 0.1 degrees), you can dramatically improve zlib
- compression by quantizing (or truncating) the data using the
- <code>least_significant_digit</code> keyword argument to <a
- href="netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a>. The least significant digit is the
- power of ten of the smallest decimal place in the data that is a
- reliable value. For example if the data has a precision of 0.1, then
- setting <code>least_significant_digit=1</code> will cause data the
- data to be quantized using
- <code>numpy.around(scale*data)/scale</code>, where scale = 2**bits,
- and bits is determined so that a precision of 0.1 is retained (in
- this case bits=4). Effectively, this makes the compression 'lossy'
- instead of 'lossless', that is some precision in the data is
- sacrificed for the sake of disk space.</p>
- <p>In our example, try replacing the line</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>temp = rootgrp.createVariable(<span class="py-string">'temp'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'time'</span>,<span class="py-string">'level'</span>,<span class="py-string">'lat'</span>,<span class="py-string">'lon'</span>,))</pre>
- <p>with</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>temp = dataset.createVariable(<span class="py-string">'temp'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'time'</span>,<span class="py-string">'level'</span>,<span class="py-string">'lat'</span>,<span class="py-string">'lon'</span>,),zlib=True)</pre>
- <p>and then</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>temp = dataset.createVariable(<span class="py-string">'temp'</span>,<span class="py-string">'f4'</span>,(<span class="py-string">'time'</span>,<span class="py-string">'level'</span>,<span class="py-string">'lat'</span>,<span class="py-string">'lon'</span>,),zlib=True,least_significant_digit=3)</pre>
- <p>and see how much smaller the resulting files are.</p>
- <h2 class="heading">9) Beyond homogenous arrays of a fixed type - compound data types</h2>
- <p>Compound data types map directly to numpy structured (a.k.a
- 'record' arrays). Structured arrays are akin to C structs, or
- derived types in Fortran. They allow for the construction of
- table-like structures composed of combinations of other data types,
- including other compound types. Compound types might be useful for
- representing multiple parameter values at each point on a grid, or at
- each time and space location for scattered (point) data. You can then
- access all the information for a point by reading one variable,
- instead of reading different parameters from different variables.
- Compound data types are created from the corresponding numpy data
- type using the <a
- href="netCDF4.Dataset-class.html#createCompoundType"
- class="link">createCompoundType</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.
- Since there is no native complex data type in netcdf, compound types
- are handy for storing numpy complex arrays. Here's an example:</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>f = Dataset(<span class="py-string">'complex.nc'</span>,<span class="py-string">'w'</span>)
-<span class="py-prompt">>>> </span>size = 3 <span class="py-comment"># length of 1-d complex array</span>
-<span class="py-prompt">>>> </span><span class="py-comment"># create sample complex data.</span>
-<span class="py-prompt">>>> </span>datac = numpy.exp(1j*(1.+numpy.linspace(0, numpy.pi, size)))
-<span class="py-prompt">>>> </span><span class="py-comment"># create complex128 compound data type.</span>
-<span class="py-prompt">>>> </span>complex128 = numpy.dtype([(<span class="py-string">'real'</span>,numpy.float64),(<span class="py-string">'imag'</span>,numpy.float64)])
-<span class="py-prompt">>>> </span>complex128_t = f.createCompoundType(complex128,<span class="py-string">'complex128'</span>)
-<span class="py-prompt">>>> </span><span class="py-comment"># create a variable with this data type, write some data to it.</span>
-<span class="py-prompt">>>> </span>f.createDimension(<span class="py-string">'x_dim'</span>,None)
-<span class="py-prompt">>>> </span>v = f.createVariable(<span class="py-string">'cmplx_var'</span>,complex128_t,<span class="py-string">'x_dim'</span>)
-<span class="py-prompt">>>> </span>data = numpy.empty(size,complex128) <span class="py-comment"># numpy structured array</span>
-<span class="py-prompt">>>> </span>data[<span class="py-string">'real'</span>] = datac.real; data[<span class="py-string">'imag'</span>] = datac.imag
-<span class="py-prompt">>>> </span>v[:] = data <span class="py-comment"># write numpy structured array to netcdf compound var</span>
-<span class="py-prompt">>>> </span><span class="py-comment"># close and reopen the file, check the contents.</span>
-<span class="py-prompt">>>> </span>f.close(); f = Dataset(<span class="py-string">'complex.nc'</span>)
-<span class="py-prompt">>>> </span>v = f.variables[<span class="py-string">'cmplx_var'</span>]
-<span class="py-prompt">>>> </span>datain = v[:] <span class="py-comment"># read in all the data into a numpy structured array</span>
-<span class="py-prompt">>>> </span><span class="py-comment"># create an empty numpy complex array</span>
-<span class="py-prompt">>>> </span>datac2 = numpy.empty(datain.shape,numpy.complex128)
-<span class="py-prompt">>>> </span><span class="py-comment"># .. fill it with contents of structured array.</span>
-<span class="py-prompt">>>> </span>datac2.real = datain[<span class="py-string">'real'</span>]; datac2.imag = datain[<span class="py-string">'imag'</span>]
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> datac.dtype,datac <span class="py-comment"># original data</span>
-<span class="py-output">complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j]</span>
-<span class="py-output"></span><span class="py-prompt">>>></span>
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> datac2.dtype,datac2 <span class="py-comment"># data from file</span>
-<span class="py-output">complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j]</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Compound types can be nested, but you must create the 'inner' ones
- first. All of the compound types defined for a <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a> are stored in
- a Python dictionary, just like variables and dimensions. As always,
- printing objects gives useful summary information in an interactive
- session:</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> f
-<span class="py-output"><type 'netCDF4.Dataset'></span>
-<span class="py-output">root group (NETCDF4 file format):</span>
-<span class="py-output"> dimensions: x_dim</span>
-<span class="py-output"> variables: cmplx_var</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"><type 'netCDF4.Variable'></span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.variables[<span class="py-string">'cmplx_var'</span>]
-<span class="py-output">compound cmplx_var(x_dim)</span>
-<span class="py-output">compound data type: [('real', '<f8'), ('imag', '<f8')]</span>
-<span class="py-output">unlimited dimensions: x_dim</span>
-<span class="py-output">current shape = (3,)</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.cmptypes
-<span class="py-output">OrderedDict([('complex128', <netCDF4.CompoundType object at 0x1029eb7e8>)])</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.cmptypes[<span class="py-string">'complex128'</span>]
-<span class="py-output"><type 'netCDF4.CompoundType'>: name = 'complex128', numpy dtype = [(u'real','<f8'), (u'imag', '<f8')]</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <h2 class="heading">10) Variable-length (vlen) data types.</h2>
- <p>NetCDF 4 has support for variable-length or "ragged"
- arrays. These are arrays of variable length sequences having the
- same type. To create a variable-length data type, use the <a
- href="netCDF4.Dataset-class.html#createVLType"
- class="link">createVLType</a> method 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.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>f = Dataset(<span class="py-string">'tst_vlen.nc'</span>,<span class="py-string">'w'</span>)
-<span class="py-prompt">>>> </span>vlen_t = f.createVLType(numpy.int32, <span class="py-string">'phony_vlen'</span>)</pre>
- <p>The numpy datatype of the variable-length sequences and the name
- of the new datatype must be specified. Any of the primitive datatypes
- can be used (signed and unsigned integers, 32 and 64 bit floats, and
- characters), but compound data types cannot. A new variable can then
- be created using this datatype.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>x = f.createDimension(<span class="py-string">'x'</span>,3)
-<span class="py-prompt">>>> </span>y = f.createDimension(<span class="py-string">'y'</span>,4)
-<span class="py-prompt">>>> </span>vlvar = f.createVariable(<span class="py-string">'phony_vlen_var'</span>, vlen_t, (<span class="py-string">'y'</span>,<span class="py-string">'x'</span>))</pre>
- <p>Since there is no native vlen datatype in numpy, vlen arrays are
- represented in python as object arrays (arrays of dtype
- <code>object</code>). These are arrays whose elements are Python
- object pointers, and can contain any type of python object. For this
- application, they must contain 1-D numpy arrays all of the same type
- but of varying length. In this case, they contain 1-D numpy
- <code>int32</code> arrays of random length betwee 1 and 10.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">import</span> random
-<span class="py-prompt">>>> </span>data = numpy.empty(len(y)*len(x),object)
-<span class="py-prompt">>>> </span><span class="py-keyword">for</span> n <span class="py-keyword">in</span> range(len(y)*len(x)):
-<span class="py-prompt">>>> </span> data[n] = numpy.arange(random.randint(1,10),dtype=<span class="py-string">'int32'</span>)+1
-<span class="py-prompt">>>> </span>data = numpy.reshape(data,(len(y),len(x)))
-<span class="py-prompt">>>> </span>vlvar[:] = data
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'vlen variable =\n'</span>,vlvar[:]
-<span class="py-output">vlen variable =</span>
-<span class="py-output">[[[ 1 2 3 4 5 6 7 8 9 10] [1 2 3 4 5] [1 2 3 4 5 6 7 8]]</span>
-<span class="py-output"> [[1 2 3 4 5 6 7] [1 2 3 4 5 6] [1 2 3 4 5]]</span>
-<span class="py-output"> [[1 2 3 4 5] [1 2 3 4] [1]]</span>
-<span class="py-output"> [[ 1 2 3 4 5 6 7 8 9 10] [ 1 2 3 4 5 6 7 8 9 10]</span>
-<span class="py-output"> [1 2 3 4 5 6 7 8]]]</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f
-<span class="py-output"><type 'netCDF4.Dataset'></span>
-<span class="py-output">root group (NETCDF4 file format):</span>
-<span class="py-output"> dimensions: x, y</span>
-<span class="py-output"> variables: phony_vlen_var</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.variables[<span class="py-string">'phony_vlen_var'</span>]
-<span class="py-output"><type 'netCDF4.Variable'></span>
-<span class="py-output">vlen phony_vlen_var(y, x)</span>
-<span class="py-output">vlen data type: int32</span>
-<span class="py-output">unlimited dimensions:</span>
-<span class="py-output">current shape = (4, 3)</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.VLtypes[<span class="py-string">'phony_vlen'</span>]
-<span class="py-output"><type 'netCDF4.VLType'>: name = 'phony_vlen', numpy dtype = int32</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>Numpy object arrays containing python strings can also be written
- as vlen variables, For vlen strings, you don't need to create a vlen
- data type. Instead, simply use the python <code>str</code> builtin
- (or a numpy string datatype with fixed length greater than 1) when
- calling the <a href="netCDF4.Dataset-class.html#createVariable"
- class="link">createVariable</a> method.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>z = f.createDimension(<span class="py-string">'z'</span>,10)
-<span class="py-prompt">>>> </span>strvar = rootgrp.createVariable(<span class="py-string">'strvar'</span>, str, <span class="py-string">'z'</span>)</pre>
- <p>In this example, an object array is filled with random python
- strings with random lengths between 2 and 12 characters, and the data
- in the object array is assigned to the vlen string variable.</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span>chars = <span class="py-string">'1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'</span>
-<span class="py-prompt">>>> </span>data = numpy.empty(10,<span class="py-string">'O'</span>)
-<span class="py-prompt">>>> </span><span class="py-keyword">for</span> n <span class="py-keyword">in</span> range(10):
-<span class="py-prompt">>>> </span> stringlen = random.randint(2,12)
-<span class="py-prompt">>>> </span> data[n] = <span class="py-string">''</span>.join([random.choice(chars) <span class="py-keyword">for</span> i <span class="py-keyword">in</span> range(stringlen)])
-<span class="py-prompt">>>> </span>strvar[:] = data
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> <span class="py-string">'variable-length string variable:\n'</span>,strvar[:]
-<span class="py-output">variable-length string variable:</span>
-<span class="py-output">[aDy29jPt jd7aplD b8t4RM jHh8hq KtaPWF9cQj Q1hHN5WoXSiT MMxsVeq td LUzvVTzj</span>
-<span class="py-output"> 5DS9X8S]</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f
-<span class="py-output"><type 'netCDF4.Dataset'></span>
-<span class="py-output">root group (NETCDF4 file format):</span>
-<span class="py-output"> dimensions: x, y, z</span>
-<span class="py-output"> variables: phony_vlen_var, strvar</span>
-<span class="py-output"> groups:</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> f.variables[<span class="py-string">'strvar'</span>]
-<span class="py-output"><type 'netCDF4.Variable'></span>
-<span class="py-output">vlen strvar(z)</span>
-<span class="py-output">vlen data type: <type 'str'></span>
-<span class="py-output">unlimited dimensions:</span>
-<span class="py-output">current size = (10,)</span>
-<span class="py-output"></span><span class="py-prompt">>>></span></pre>
- <p>It is also possible to set contents of vlen string variables with
- numpy arrays of any string or unicode data type. Note, however, that
- accessing the contents of such variables will always return numpy
- arrays with dtype <code>object</code>.</p>
- <p>All of the code in this tutorial is available in
- <code>examples/tutorial.py</code>, Unit tests are in the
- <code>test</code> directory.</p>
-
-<hr />
-<div class="fields"> <p><strong>Contact:</strong>
- Jeffrey Whitaker <jeffrey.s.whitaker at 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>
- 1.1.8
- </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="netCDF4.CompoundType-class.html" class="summary-name">CompoundType</a><br />
- A <a href="netCDF4.CompoundType-class.html"
- class="link">CompoundType</a> instance is used to describe a
- compound 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.Dataset-class.html" class="summary-name">Dataset</a><br />
- Dataset(self, filename, mode="r", clobber=True,
- diskless=False, persist=False, keepweakref=False, format='NETCDF4')
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4.Dimension-class.html" class="summary-name">Dimension</a><br />
- Dimension(self, group, 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="netCDF4.Group-class.html" class="summary-name">Group</a><br />
- Group(self, parent, name)
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4.MFDataset-class.html" class="summary-name">MFDataset</a><br />
- MFDataset(self, files, check=False, aggdim=None, exclude=[])
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4.MFTime-class.html" class="summary-name">MFTime</a><br />
- MFTime(self, time, units=None)
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4.VLType-class.html" class="summary-name">VLType</a><br />
- A <a href="netCDF4.VLType-class.html" class="link">VLType</a>
- instance is used to describe a variable length (VLEN) 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" class="summary-name">Variable</a><br />
- 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)
- </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="netCDF4-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">
- <span class="codelink"><a href="netCDF4-pysrc.html#chartostring">source code</a></span>
-
- </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-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">
- <span class="codelink"><a href="netCDF4-pysrc.html#date2index">source code</a></span>
-
- </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-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">
- <span class="codelink"><a href="netCDF4-pysrc.html#date2num">source code</a></span>
-
- </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">
- <span class="codelink"><a href="netCDF4-pysrc.html#getlibversion">source code</a></span>
-
- </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-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">
- <span class="codelink"><a href="netCDF4-pysrc.html#num2date">source code</a></span>
-
- </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-module.html#stringtoarr" class="summary-sig-name">stringtoarr</a>(<span class="summary-sig-arg">a</span>,
- <span class="summary-sig-arg">NUMCHARS</span>,
- <span class="summary-sig-arg">dtype</span>=<span class="summary-sig-default">'S'</span>)</span><br />
- convert a string to a character array of length NUMCHARS</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#stringtoarr">source code</a></span>
-
- </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-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">
- <span class="codelink"><a href="netCDF4-pysrc.html#stringtochar">source code</a></span>
-
- </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="MINYEAR"></a><span class="summary-name">MINYEAR</span> = <code title="1">1</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="NC_DISKLESS"></a><span class="summary-name">NC_DISKLESS</span> = <code title="8">8</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="__has_nc_inq_format_extended__"></a><span class="summary-name">__has_nc_inq_format_extended__</span> = <code title="1">1</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="__has_nc_inq_path__"></a><span class="summary-name">__has_nc_inq_path__</span> = <code title="1">1</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="__has_rename_grp__"></a><span class="summary-name">__has_rename_grp__</span> = <code title="1">1</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="__hdf5libversion__"></a><span class="summary-name">__hdf5libversion__</span> = <code title="'1.8.13'"><code class="variable-quote">'</code><code class="variable-string">1.8.13</code><code class="variable-quote">'</code></code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="__netcdf4libversion__"></a><span class="summary-name">__netcdf4libversion__</span> = <code title="u'4.3.2'"><code class="variable-quote">u'</code><code class="variable-string">4.3.2</code><code class="variable-quote">'</code></code>
- </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>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="day_units"></a><span class="summary-name">day_units</span> = <code title="['day', 'days', 'd']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">day</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">days</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="default_encoding"></a><span class="summary-name">default_encoding</span> = <code title="'utf-8'"><code class="variable-quote">'</code><code class="variable-string">utf-8</code><code class="variable-quote">'</code></code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4-module.html#default_fillvals" class="summary-name">default_fillvals</a> = <code title="{'S1': '\x00',
- 'U1': '\x00',
- 'f4': 9.96920996839e+36,
- 'f8': 9.96920996839e+36,
- 'i1': -127,
- 'i2': -32767,
- 'i4': -2147483647,
- 'i8': -9223372036854775806,
-..."><code class="variable-group">{</code><code class="variable-quote">'</code><code class="variable-string">S1</code><code class="variable-quote">'</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">\x00</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">U1</code><code class="variable-quote">'</code><code class="variable-op">: </code><cod [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="gregorian"></a><span class="summary-name">gregorian</span> = <code title="datetime.datetime(1582, 10, 15, 0, 0)">datetime.datetime(1582, 10, 15, 0, 0)</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="hr_units"></a><span class="summary-name">hr_units</span> = <code title="['hour', 'hours', 'hr', 'hrs', 'h']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">hour</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">hours</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote" [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="is_native_big"></a><span class="summary-name">is_native_big</span> = <code title="False">False</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="is_native_little"></a><span class="summary-name">is_native_little</span> = <code title="True">True</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4-module.html#microsec_units" class="summary-name">microsec_units</a> = <code title="['microseconds', 'microsecond', 'microsec', 'microsecs']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">microseconds</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">microsecond</code><code class="variable-quote">'</code><code cla [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4-module.html#millisec_units" class="summary-name">millisec_units</a> = <code title="['milliseconds', 'millisecond', 'millisec', 'millisecs']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">milliseconds</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">millisecond</code><code class="variable-quote">'</code><code cla [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="min_units"></a><span class="summary-name">min_units</span> = <code title="['minute', 'minutes', 'min', 'mins']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">minute</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">minutes</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="python3"></a><span class="summary-name">python3</span> = <code title="False">False</code>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="sec_units"></a><span class="summary-name">sec_units</span> = <code title="['second', 'seconds', 'sec', 'secs', 's']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">second</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">seconds</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="var [...]
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="unicode_error"></a><span class="summary-name">unicode_error</span> = <code title="'replace'"><code class="variable-quote">'</code><code class="variable-string">replace</code><code class="variable-quote">'</code></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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#chartostring">source code</a></span>
- </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' or 'U1'). 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' or 'UN' 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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#date2index">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#date2num">source code</a></span>
- </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>
- <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, seconds,
- milliseconds or microseconds. <b><code>reference time</code></b>
- is the time origin. Accuracy is somewhere between a millisecond
- and a microsecond, depending on the time interval and the
- calendar used.</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.</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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#num2date">source code</a></span>
- </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>
- <dl class="fields">
- <dt>Parameters:</dt>
- <dd><ul class="nomargin-top">
- <li><strong class="pname"><code>times</code></strong> - numeric time values.</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, seconds,
- milliseconds or microseconds. <b><code>reference time</code></b>
- is the time origin. Accuracy is somewhere between a millisecond
- and a microsecond, depending on the time interval and the
- calendar used.</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 <code>calendar='proleptic_gregorian'</code>, or
- <code>calendar = 'standard'</code> or <code>'gregorian'</code>
- and the date is after the breakpoint between the Julian and
- Gregorian calendars (1582-10-15). Otherwise, they are 'phony'
- datetime objects which support some but not all the methods of
- 'real' python datetime objects. 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 class="sig-arg">dtype</span>=<span class="sig-default">'S'</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#stringtoarr">source code</a></span>
- </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>
- <li><strong class="pname"><code>dtype</code></strong> - type of numpy array to return. Default is 'S', which means an
- array of dtype 'S1' will be returned. If dtype='U', a unicode
- array (dtype = 'U1') will be returned.</li>
- </ul></dd>
- <dt>Returns:</dt>
- <dd>A rank 1 numpy character array of length NUMCHARS with datatype
- 'S1' (default) or 'U1' (if dtype='U')</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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#stringtochar">source code</a></span>
- </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' or 'UN', where
- N is the number of characters in each string. Will be converted
- to an array of characters (datatype 'S1' or 'U1') of shape
- a.shape + (N,).</li>
- </ul></dd>
- <dt>Returns:</dt>
- <dd>A numpy character array with datatype 'S1' or 'U1' and shape
- a.shape + (N,), where N is the length of each string in a.</dd>
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== VARIABLES DETAILS ==================== -->
-<a name="section-VariablesDetails"></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">Variables Details</span></td>
-</tr>
-</table>
-<a name="default_fillvals"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">default_fillvals</h3>
-
- <dl class="fields">
- </dl>
- <dl class="fields">
- <dt>Value:</dt>
- <dd><table><tr><td><pre class="variable">
-<code class="variable-group">{</code><code class="variable-quote">'</code><code class="variable-string">S1</code><code class="variable-quote">'</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">\x00</code><code class="variable-quote">'</code><code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">U1</code><code class="variable-quote">'</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">\x00</code><code class="variable-quote">'</code><code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">f4</code><code class="variable-quote">'</code><code class="variable-op">: </code>9.96920996839e+36<code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">f8</code><code class="variable-quote">'</code><code class="variable-op">: </code>9.96920996839e+36<code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">i1</code><code class="variable-quote">'</code><code class="variable-op">: </code>-127<code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">i2</code><code class="variable-quote">'</code><code class="variable-op">: </code>-32767<code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">i4</code><code class="variable-quote">'</code><code class="variable-op">: </code>-2147483647<code class="variable-op">,</code>
- <code class="variable-quote">'</code><code class="variable-string">i8</code><code class="variable-quote">'</code><code class="variable-op">: </code>-9223372036854775806<code class="variable-op">,</code>
-<code class="variable-ellipsis">...</code>
-</pre></td></tr></table>
-</dd>
- </dl>
-</td></tr></table>
-</div>
-<a name="microsec_units"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">microsec_units</h3>
-
- <dl class="fields">
- </dl>
- <dl class="fields">
- <dt>Value:</dt>
- <dd><table><tr><td><pre class="variable">
-<code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">microseconds</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">microsecond</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">microsec</code><code class="variable-quote">'</code><code class="variable [...]
-</pre></td></tr></table>
-</dd>
- </dl>
-</td></tr></table>
-</div>
-<a name="millisec_units"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">millisec_units</h3>
-
- <dl class="fields">
- </dl>
- <dl class="fields">
- <dt>Value:</dt>
- <dd><table><tr><td><pre class="variable">
-<code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">milliseconds</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">millisecond</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">millisec</code><code class="variable-quote">'</code><code class="variable [...]
-</pre></td></tr></table>
-</dd>
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th bgcolor="#70b0f0" class="navbar-select"
- > Home </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
- <!-- Index link -->
- <th> <a
- href="identifier-index.html">Indices</a> </th>
-
- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
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- <th class="navbar" width="100%"></th>
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-</table>
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- <tr>
- <td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Sun May 3 20:05:24 2015
- </td>
- <td align="right" class="footer">
- <a target="mainFrame" href="http://epydoc.sourceforge.net"
- >http://epydoc.sourceforge.net</a>
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- <!--
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- // them unless we have a cookie that says to show them.
- checkCookie();
- // -->
-</script>
-</body>
-</html>
diff --git a/docs/netCDF4.CompoundType-class.html b/docs/netCDF4.CompoundType-class.html
deleted file mode 100644
index a16a19d..0000000
--- a/docs/netCDF4.CompoundType-class.html
+++ /dev/null
@@ -1,357 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>netCDF4.CompoundType</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
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- href="netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
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- href="module-tree.html">Trees</a> </th>
-
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- href="identifier-index.html">Indices</a> </th>
-
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- href="help.html">Help</a> </th>
-
- <th class="navbar" width="100%"></th>
- </tr>
-</table>
-<table width="100%" cellpadding="0" cellspacing="0">
- <tr valign="top">
- <td width="100%">
- <span class="breadcrumbs">
- <a href="netCDF4-module.html">Module netCDF4</a> ::
- Class CompoundType
- </span>
- </td>
- <td>
- <table cellpadding="0" cellspacing="0">
- <!-- hide/show private -->
- </table>
- </td>
- </tr>
-</table>
-<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class CompoundType</h1><p class="nomargin-top"><span class="codelink"><a href="netCDF4-pysrc.html#CompoundType">source code</a></span></p>
-<pre class="base-tree">
-object --+
- |
- <strong class="uidshort">CompoundType</strong>
-</pre>
-
-<hr />
-<p>A <a href="netCDF4.CompoundType-class.html"
- class="link">CompoundType</a> instance is used to describe a compound
- data type.</p>
- <p>Constructor: <code>CompoundType(group, datatype,
- datatype_name)</code></p>
-
-<hr />
-<div class="fields"> <p><strong>Attention:</strong>
- When creating nested compound data types, the inner compound data
- types must already be associated with CompoundType instances (so
- create CompoundType instances for the innermost structures first).
- <p><a href="netCDF4.CompoundType-class.html"
- class="link">CompoundType</a> instances should be created using the
- <a href="netCDF4.Dataset-class.html#createCompoundType"
- class="link">createCompoundType</a> method of a Dataset 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> instance to associate with the compound
- datatype.</p>
- <p><b><code>datatype</code></b> - A numpy dtype object describing a
- structured (a.k.a record) array. Can be composed of homogeneous
- numeric or character data types, or other structured array data
- types.</p>
- <p><b><code>datatype_name</code></b> - a Python string containing a
- description of the compound data type.</p>
- <p><b>Returns:</b></p>
- <p>a <a href="netCDF4.CompoundType-class.html"
- class="link">CompoundType</a> instance, which can be passed to the
- <code>createVariable</code> 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.</p>
- <p>The instance variables <code>dtype</code> and <code>name</code>
- should not be modified by the user.</p>
- </p>
-</div><!-- ==================== INSTANCE METHODS ==================== -->
-<a name="section-InstanceMethods"></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 Methods</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="netCDF4.CompoundType-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- x.__init__(...) initializes x; see help(type(x)) for signature</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#CompoundType.__init__">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type">a new object with type S, a subtype of T</span>
- </td><td class="summary">
- <table width="100%" cellpadding="0" cellspacing="0" border="0">
- <tr>
- <td><span class="summary-sig"><a href="netCDF4.CompoundType-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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#CompoundType.__new__">source code</a></span>
-
- </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.CompoundType-class.html#__repr__" class="summary-sig-name">__repr__</a>(<span class="summary-sig-arg">x</span>)</span><br />
- repr(x)</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#CompoundType.__repr__">source code</a></span>
-
- </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="__unicode__"></a><span class="summary-sig-name">__unicode__</span>(<span class="summary-sig-arg">...</span>)</span></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#CompoundType.__unicode__">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
- <tr>
- <td colspan="2" class="summary">
- <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
- <code>__delattr__</code>,
- <code>__format__</code>,
- <code>__getattribute__</code>,
- <code>__hash__</code>,
- <code>__reduce__</code>,
- <code>__reduce_ex__</code>,
- <code>__setattr__</code>,
- <code>__sizeof__</code>,
- <code>__str__</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="dtype"></a><span class="summary-name">dtype</span><br />
- A numpy dtype object describing the compound data type.
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="name"></a><span class="summary-name">name</span><br />
- A python string describing the compound type.
- </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 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="__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">...</span>)</span>
- <br /><em class="fname">(Constructor)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#CompoundType.__init__">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#CompoundType.__new__">source code</a></span>
- </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="__repr__"></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">__repr__</span>(<span class="sig-arg">x</span>)</span>
- <br /><em class="fname">(Representation operator)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#CompoundType.__repr__">source code</a></span>
- </td>
- </tr></table>
-
- <p>repr(x)</p>
- <dl class="fields">
- <dt>Overrides:
- object.__repr__
- </dt>
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
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- href="netCDF4-module.html">Home</a> </th>
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- Generated by Epydoc 3.0.1 on Sun May 3 20:05:24 2015
- </td>
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diff --git a/docs/netCDF4.Dataset-class.html b/docs/netCDF4.Dataset-class.html
deleted file mode 100644
index bf9f227..0000000
--- a/docs/netCDF4.Dataset-class.html
+++ /dev/null
@@ -1,1705 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>netCDF4.Dataset</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
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- href="help.html">Help</a> </th>
-
- <th class="navbar" width="100%"></th>
- </tr>
-</table>
-<table width="100%" cellpadding="0" cellspacing="0">
- <tr valign="top">
- <td width="100%">
- <span class="breadcrumbs">
- <a href="netCDF4-module.html">Module netCDF4</a> ::
- Class Dataset
- </span>
- </td>
- <td>
- <table cellpadding="0" cellspacing="0">
- <!-- hide/show private -->
- </table>
- </td>
- </tr>
-</table>
-<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class Dataset</h1><p class="nomargin-top"><span class="codelink"><a href="netCDF4-pysrc.html#Dataset">source code</a></span></p>
-<pre class="base-tree">
-object --+
- |
- <strong class="uidshort">Dataset</strong>
-</pre>
-
-<dl><dt>Known Subclasses:</dt>
-<dd>
- <ul class="subclass-list">
-<li><a href="netCDF4.Group-class.html">Group</a></li><li>, <a href="netCDF4.MFDataset-class.html">MFDataset</a></li> </ul>
-</dd></dl>
-
-<hr />
-<p>Dataset(self, filename, mode="r", clobber=True,
- diskless=False, persist=False, keepweakref=False, format='NETCDF4')</p>
- <p>A netCDF <a href="netCDF4.Dataset-class.html" class="link">Dataset</a>
- is a collection of dimensions, groups, variables and attributes. Together
- they describe the meaning of data and relations among data fields stored
- in a netCDF file.</p>
- <p><b>Parameters:</b></p>
- <p><b><code>filename</code></b> - Name of netCDF file to hold
- dataset.</p>
- <p><b>Keywords</b>:</p>
- <p><b><code>mode</code></b> - access mode. <code>r</code> means
- read-only; no data can be modified. <code>w</code> means write; a new
- file is created, an existing file with the same name is deleted.
- <code>a</code> and <code>r+</code> mean append (in analogy with serial
- files); an existing file is opened for reading and writing. Appending
- <code>s</code> to modes <code>w</code>, <code>r+</code> or <code>a</code>
- will enable unbuffered shared access to <code>NETCDF3_CLASSIC</code> or
- <code>NETCDF3_64BIT</code> formatted files. Unbuffered acesss may be
- useful even if you don't need shared access, since it may be faster for
- programs that don't access data sequentially. This option is ignored for
- <code>NETCDF4</code> and <code>NETCDF4_CLASSIC</code> formatted
- files.</p>
- <p><b><code>clobber</code></b> - if <code>True</code> (default), opening
- a file with <code>mode='w'</code> will clobber an existing file with the
- same name. if <code>False</code>, an exception will be raised if a file
- with the same name already exists.</p>
- <p><b><code>format</code></b> - underlying file format (one of
- <code>'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_CLASSIC'</code> or
- <code>'NETCDF3_64BIT'</code>. Only relevant if <code>mode = 'w'</code>
- (if <code>mode = 'r','a'</code> or <code>'r+'</code> the file format is
- automatically detected). Default <code>'NETCDF4'</code>, which means the
- data is stored in an HDF5 file, using netCDF 4 API features. Setting
- <code>format='NETCDF4_CLASSIC'</code> will create an HDF5 file, using
- only netCDF 3 compatibile API features. netCDF 3 clients must be
- recompiled and linked against the netCDF 4 library to read files in
- <code>NETCDF4_CLASSIC</code> format. <code>'NETCDF3_CLASSIC'</code> is
- the classic netCDF 3 file format that does not handle 2+ Gb files very
- well. <code>'NETCDF3_64BIT'</code> is the 64-bit offset version of the
- netCDF 3 file format, which fully supports 2+ GB files, but is only
- compatible with clients linked against netCDF version 3.6.0 or later.</p>
- <p><code>diskless</code> - create diskless (in memory) file. This is an
- experimental feature added to the C library after the netcdf-4.2
- release.</p>
- <p><code>persist</code> - if diskless=True, persist file to disk when
- closed (default False).</p>
- <p><code>keepweakref</code> - if keepweakref=True, child Dimension and
- Variable instances will keep weak references to the parent Dataset or
- Group object. Default is False, which means strong references will be
- kept. Having Dimension and Variable instances keep a strong reference to
- the parent Dataset instance, which in turn keeps a reference to child
- Dimension and Variable instances, creates circular references. Circular
- references complicate garbage collection, which may mean increased memory
- usage for programs that create may Dataset instances with lots of
- Variables. Setting keepweakref to True allows Dataset instances to be
- garbage collected as soon as they go out of scope, potential reducing
- memory usage. However, in most cases this is not desirable, since the
- associated Variable instances may still be needed, but are rendered
- unusable when the parent Dataset instance is garbage collected.</p>
- <p><b>Returns:</b></p>
- <p>a <a href="netCDF4.Dataset-class.html" class="link">Dataset</a>
- instance. All further operations on the netCDF Dataset are accomplised
- via <a href="netCDF4.Dataset-class.html" class="link">Dataset</a>
- instance methods.</p>
- <p>A list of attribute names corresponding to global netCDF attributes
- defined for the <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> can be obtained with the <a
- href="netCDF4.Dataset-class.html#ncattrs" class="link">ncattrs()</a>
- method. These attributes can be created by assigning to an attribute of
- the <a href="netCDF4.Dataset-class.html" class="link">Dataset</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.Dataset-class.html" class="link">Dataset</a> instance.</p>
- <p>The instance variables <code>dimensions, variables, groups, cmptypes,
- data_model, disk_format</code> and <code>path</code> are read-only (and
- should not be modified by the user).</p>
-
-<!-- ==================== INSTANCE METHODS ==================== -->
-<a name="section-InstanceMethods"></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 Methods</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="netCDF4.Dataset-class.html#__delattr__" class="summary-sig-name">__delattr__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- x.__delattr__('name') <==> del x.name</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__delattr__">source code</a></span>
-
- </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="__enter__"></a><span class="summary-sig-name">__enter__</span>(<span class="summary-sig-arg">...</span>)</span></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__enter__">source code</a></span>
-
- </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="__exit__"></a><span class="summary-sig-name">__exit__</span>(<span class="summary-sig-arg">...</span>)</span></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__exit__">source code</a></span>
-
- </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="__getattr__"></a><span class="summary-sig-name">__getattr__</span>(<span class="summary-sig-arg">...</span>)</span></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__getattr__">source code</a></span>
-
- </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.Dataset-class.html#__getattribute__" class="summary-sig-name">__getattribute__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- x.__getattribute__('name') <==> x.name</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__getattribute__">source code</a></span>
-
- </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.Dataset-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">filename</span>,
- <span class="summary-sig-arg">mode</span>=<span class="summary-sig-default">"r"</span>,
- <span class="summary-sig-arg">clobber</span>=<span class="summary-sig-default">True</span>,
- <span class="summary-sig-arg">diskless</span>=<span class="summary-sig-default">False</span>,
- <span class="summary-sig-arg">persist</span>=<span class="summary-sig-default">False</span>,
- <span class="summary-sig-arg">keepweakref</span>=<span class="summary-sig-default">False</span>,
- <span class="summary-sig-arg">format</span>=<span class="summary-sig-default">'NETCDF4'</span>)</span><br />
- x.__init__(...) initializes x; see help(type(x)) for signature</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__init__">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type">a new object with type S, a subtype of T</span>
- </td><td class="summary">
- <table width="100%" cellpadding="0" cellspacing="0" border="0">
- <tr>
- <td><span class="summary-sig"><a href="netCDF4.Dataset-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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__new__">source code</a></span>
-
- </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.Dataset-class.html#__repr__" class="summary-sig-name">__repr__</a>(<span class="summary-sig-arg">x</span>)</span><br />
- repr(x)</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__repr__">source code</a></span>
-
- </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.Dataset-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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__setattr__">source code</a></span>
-
- </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="__unicode__"></a><span class="summary-sig-name">__unicode__</span>(<span class="summary-sig-arg">...</span>)</span></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__unicode__">source code</a></span>
-
- </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="close"></a><span class="summary-sig-name">close</span>(<span class="summary-sig-arg">self</span>)</span><br />
- Close the Dataset.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.close">source code</a></span>
-
- </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.Dataset-class.html#createCompoundType" class="summary-sig-name">createCompoundType</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">datatype</span>,
- <span class="summary-sig-arg">datatype_name</span>)</span><br />
- Creates a new compound data type named <code>datatype_name</code>
- from the numpy dtype object <code>datatype</code>.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.createCompoundType">source code</a></span>
-
- </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.Dataset-class.html#createDimension" class="summary-sig-name">createDimension</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">dimname</span>,
- <span class="summary-sig-arg">size</span>=<span class="summary-sig-default">None</span>)</span><br />
- Creates a new dimension with the given <code>dimname</code> and
- <code>size</code>.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.createDimension">source code</a></span>
-
- </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.Dataset-class.html#createGroup" class="summary-sig-name">createGroup</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">groupname</span>)</span><br />
- Creates a new <a href="netCDF4.Group-class.html"
- class="link">Group</a> with the given <code>groupname</code>.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.createGroup">source code</a></span>
-
- </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.Dataset-class.html#createVLType" class="summary-sig-name">createVLType</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">datatype</span>,
- <span class="summary-sig-arg">datatype_name</span>)</span><br />
- Creates a new VLEN data type named <code>datatype_name</code> from a
- numpy dtype object <code>datatype</code>.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.createVLType">source code</a></span>
-
- </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.Dataset-class.html#createVariable" class="summary-sig-name">createVariable</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">varname</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 />
- Creates a new variable with the given <code>varname</code>,
- <code>datatype</code>, and <code>dimensions</code>.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.createVariable">source code</a></span>
-
- </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.Dataset-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 dataset or group attribute.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.delncattr">source code</a></span>
-
- </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.Dataset-class.html#filepath" class="summary-sig-name">filepath</a>(<span class="summary-sig-arg">self</span>)</span><br />
- Get the file system path (or the opendap URL) which was used to
- open/create the Dataset.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.filepath">source code</a></span>
-
- </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.Dataset-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 dataset or group attribute.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.getncattr">source code</a></span>
-
- </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="ncattrs"></a><span class="summary-sig-name">ncattrs</span>(<span class="summary-sig-arg">self</span>)</span><br />
- return netCDF global attribute names for this <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a> in a list.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.ncattrs">source code</a></span>
-
- </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="renameAttribute"></a><span class="summary-sig-name">renameAttribute</span>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">oldname</span>,
- <span class="summary-sig-arg">newname</span>)</span><br />
- rename a <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> or <a href="netCDF4.Group-class.html"
- class="link">Group</a> attribute named <code>oldname</code> to
- <code>newname</code>.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.renameAttribute">source code</a></span>
-
- </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="renameDimension"></a><span class="summary-sig-name">renameDimension</span>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">oldname</span>,
- <span class="summary-sig-arg">newname</span>)</span><br />
- rename a <a href="netCDF4.Dimension-class.html"
- class="link">Dimension</a> named <code>oldname</code> to
- <code>newname</code>.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.renameDimension">source code</a></span>
-
- </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="renameGroup"></a><span class="summary-sig-name">renameGroup</span>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">oldname</span>,
- <span class="summary-sig-arg">newname</span>)</span><br />
- rename a <a href="netCDF4.Group-class.html" class="link">Group</a>
- named <code>oldname</code> to <code>newname</code> (requires netcdf
- >= 4.3.1).</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.renameGroup">source code</a></span>
-
- </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="renameVariable"></a><span class="summary-sig-name">renameVariable</span>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">oldname</span>,
- <span class="summary-sig-arg">newname</span>)</span><br />
- rename a <a href="netCDF4.Variable-class.html"
- class="link">Variable</a> named <code>oldname</code> to
- <code>newname</code></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.renameVariable">source code</a></span>
-
- </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.Dataset-class.html#set_auto_mask" class="summary-sig-name">set_auto_mask</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">True_or_False</span>)</span><br />
- Call <a href="netCDF4.Dataset-class.html#set_auto_mask"
- class="link">set_auto_mask</a> for all variables contained in this <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a>, as well as
- for all variables in all its subgroups.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.set_auto_mask">source code</a></span>
-
- </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.Dataset-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">True_or_False</span>)</span><br />
- Call <a href="netCDF4.Dataset-class.html#set_auto_maskandscale"
- class="link">set_auto_maskandscale</a> for all variables contained in
- this <a href="netCDF4.Dataset-class.html" class="link">Dataset</a> or
- <a href="netCDF4.Group-class.html" class="link">Group</a>, as well as
- for all variables in all its subgroups.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.set_auto_maskandscale">source code</a></span>
-
- </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.Dataset-class.html#set_auto_scale" class="summary-sig-name">set_auto_scale</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">True_or_False</span>)</span><br />
- Call <a href="netCDF4.Dataset-class.html#set_auto_scale"
- class="link">set_auto_scale</a> for all variables contained in this
- <a href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a>, as well as
- for all variables in all its subgroups.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.set_auto_scale">source code</a></span>
-
- </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.Dataset-class.html#set_fill_off" class="summary-sig-name">set_fill_off</a>(<span class="summary-sig-arg">self</span>)</span><br />
- Sets the fill mode for a <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> open for writing to <code>off</code>.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.set_fill_off">source code</a></span>
-
- </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.Dataset-class.html#set_fill_on" class="summary-sig-name">set_fill_on</a>(<span class="summary-sig-arg">self</span>)</span><br />
- Sets the fill mode for a <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> open for writing to <code>on</code>.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.set_fill_on">source code</a></span>
-
- </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.Dataset-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 dataset or group attribute using name,value pair.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.setncattr">source code</a></span>
-
- </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.Dataset-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 dataset or group attributes at once using a
- python dictionary.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.setncatts">source code</a></span>
-
- </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="sync"></a><span class="summary-sig-name">sync</span>(<span class="summary-sig-arg">self</span>)</span><br />
- Writes all buffered data in the <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> to the disk file.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dataset.sync">source code</a></span>
-
- </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>__sizeof__</code>,
- <code>__str__</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="cmptypes"></a><span class="summary-name">cmptypes</span><br />
- The <code>cmptypes</code> dictionary maps the names of compound types
- defined for the <a href="netCDF4.Group-class.html"
- class="link">Group</a> or <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> to instances of the <a
- href="netCDF4.CompoundType-class.html" class="link">CompoundType</a>
- class.
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="data_model"></a><span class="summary-name">data_model</span><br />
- The <code>data_model</code> attribute describes the netCDF data model
- version, one of <code>NETCDF3_CLASSIC</code>, <code>NETCDF4</code>,
- <code>NETCDF4_CLASSIC</code> or <code>NETCDF3_64BIT</code>.
- </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 />
- The <code>dimensions</code> dictionary maps the names of dimensions
- defined for the <a href="netCDF4.Group-class.html"
- class="link">Group</a> or <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> to instances of the <a
- href="netCDF4.Dimension-class.html" class="link">Dimension</a> class.
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4.Dataset-class.html#disk_format" class="summary-name">disk_format</a><br />
- The <code>disk_format</code> attribute describes the underlying file
- format, one of <code>NETCDF3</code>, <code>HDF5</code>,
- <code>HDF4</code>, <code>PNETCDF</code>, <code>DAP2</code>,
- <code>DAP4</code> or <code>UNDEFINED</code>.
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="file_format"></a><span class="summary-name">file_format</span><br />
- same as <code>data_model</code>, retained for backwards
- compatibility.
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="groups"></a><span class="summary-name">groups</span><br />
- The groups dictionary maps the names of groups created for this <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a> to instances
- of the <a href="netCDF4.Group-class.html" class="link">Group</a>
- class (the <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> class is simply a special case of the <a
- href="netCDF4.Group-class.html" class="link">Group</a> class which
- describes the root group in the netCDF file).
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4.Dataset-class.html#parent" class="summary-name">parent</a><br />
- The <code>parent</code> attribute is a reference to the parent <a
- href="netCDF4.Group-class.html" class="link">Group</a> instance.
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a href="netCDF4.Dataset-class.html#path" class="summary-name">path</a><br />
- The <code>path</code> attribute shows the location of the <a
- href="netCDF4.Group-class.html" class="link">Group</a> in the <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> in a unix
- directory format (the names of groups in the hierarchy separated by
- backslashes).
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="variables"></a><span class="summary-name">variables</span><br />
- The <code>variables</code> dictionary maps the names of variables
- defined for this <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> or <a href="netCDF4.Group-class.html"
- class="link">Group</a> to instances of the <a
- href="netCDF4.Variable-class.html" class="link">Variable</a> class.
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="vltypes"></a><span class="summary-name">vltypes</span><br />
- The <code>vltypes</code> dictionary maps the names of variable-length
- types defined for the <a href="netCDF4.Group-class.html"
- class="link">Group</a> or <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> to instances of the <a
- href="netCDF4.VLType-class.html" class="link">VLType</a> class.
- </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="__orthogonal_indexing__"></a><span class="summary-name">__orthogonal_indexing__</span>
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="keepweakref"></a><span class="summary-name">keepweakref</span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__delattr__">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__getattribute__">source code</a></span>
- </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">filename</span>,
- <span class="sig-arg">mode</span>=<span class="sig-default">"r"</span>,
- <span class="sig-arg">clobber</span>=<span class="sig-default">True</span>,
- <span class="sig-arg">diskless</span>=<span class="sig-default">False</span>,
- <span class="sig-arg">persist</span>=<span class="sig-default">False</span>,
- <span class="sig-arg">keepweakref</span>=<span class="sig-default">False</span>,
- <span class="sig-arg">format</span>=<span class="sig-default">'NETCDF4'</span>)</span>
- <br /><em class="fname">(Constructor)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__init__">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__new__">source code</a></span>
- </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="__repr__"></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">__repr__</span>(<span class="sig-arg">x</span>)</span>
- <br /><em class="fname">(Representation operator)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__repr__">source code</a></span>
- </td>
- </tr></table>
-
- <p>repr(x)</p>
- <dl class="fields">
- <dt>Overrides:
- object.__repr__
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.__setattr__">source code</a></span>
- </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="createCompoundType"></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">createCompoundType</span>(<span class="sig-arg">self</span>,
- <span class="sig-arg">datatype</span>,
- <span class="sig-arg">datatype_name</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.createCompoundType">source code</a></span>
- </td>
- </tr></table>
-
- <p>Creates a new compound data type named <code>datatype_name</code> from
- the numpy dtype object <code>datatype</code>.</p>
- <dl class="fields">
- </dl>
-<div class="fields"> <p><strong>Attention:</strong>
- If the new compound data type contains other compound data types
- (i.e. it is a 'nested' compound type, where not all of the elements
- are homogenous numeric data types), then the 'inner' compound types
- <b>must</b> be created first.
- <p>The return value is the <a
- href="netCDF4.CompoundType-class.html"
- class="link">CompoundType</a> class instance describing the new
- datatype.</p>
- </p>
-</div></td></tr></table>
-</div>
-<a name="createDimension"></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">createDimension</span>(<span class="sig-arg">self</span>,
- <span class="sig-arg">dimname</span>,
- <span class="sig-arg">size</span>=<span class="sig-default">None</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.createDimension">source code</a></span>
- </td>
- </tr></table>
-
- <p>Creates a new dimension with the given <code>dimname</code> and
- <code>size</code>.</p>
- <p><code>size</code> must be a positive integer or <code>None</code>,
- which stands for "unlimited" (default is <code>None</code>).
- Specifying a size of 0 also results in an unlimited dimension. The return
- value is the <a href="netCDF4.Dimension-class.html"
- class="link">Dimension</a> class instance describing the new dimension.
- To determine the current maximum size of the dimension, use the
- <code>len</code> function on the <a href="netCDF4.Dimension-class.html"
- class="link">Dimension</a> instance. To determine if a dimension is
- 'unlimited', use the <code>isunlimited()</code> method of the <a
- href="netCDF4.Dimension-class.html" class="link">Dimension</a>
- instance.</p>
- <dl class="fields">
- </dl>
-</td></tr></table>
-</div>
-<a name="createGroup"></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">createGroup</span>(<span class="sig-arg">self</span>,
- <span class="sig-arg">groupname</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.createGroup">source code</a></span>
- </td>
- </tr></table>
-
- <p>Creates a new <a href="netCDF4.Group-class.html"
- class="link">Group</a> with the given <code>groupname</code>.</p>
- <p>The return value is a <a href="netCDF4.Group-class.html"
- class="link">Group</a> class instance describing the new group.</p>
- <dl class="fields">
- </dl>
-</td></tr></table>
-</div>
-<a name="createVLType"></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">createVLType</span>(<span class="sig-arg">self</span>,
- <span class="sig-arg">datatype</span>,
- <span class="sig-arg">datatype_name</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.createVLType">source code</a></span>
- </td>
- </tr></table>
-
- <p>Creates a new VLEN data type named <code>datatype_name</code> from a
- numpy dtype object <code>datatype</code>.</p>
- <p>The return value is the <a href="netCDF4.VLType-class.html"
- class="link">VLType</a> class instance describing the new datatype.</p>
- <dl class="fields">
- </dl>
-</td></tr></table>
-</div>
-<a name="createVariable"></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">createVariable</span>(<span class="sig-arg">self</span>,
- <span class="sig-arg">varname</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>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.createVariable">source code</a></span>
- </td>
- </tr></table>
-
- <p>Creates a new variable with the given <code>varname</code>,
- <code>datatype</code>, and <code>dimensions</code>. If dimensions are not
- given, the variable is assumed to be a scalar.</p>
- <p>The <code>datatype</code> can be a numpy datatype object, or a string
- that describes a numpy dtype object (like the <code>dtype.str</code>
- attribue of a numpy array). Supported specifiers include: <code>'S1' or
- 'c' (NC_CHAR), 'i1' or 'b' or 'B' (NC_BYTE), 'u1' (NC_UBYTE), 'i2' or 'h'
- or 's' (NC_SHORT), 'u2' (NC_USHORT), 'i4' or 'i' or 'l' (NC_INT), 'u4'
- (NC_UINT), 'i8' (NC_INT64), 'u8' (NC_UINT64), 'f4' or 'f' (NC_FLOAT),
- 'f8' or 'd' (NC_DOUBLE)</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). Numpy string and unicode datatypes with
- length greater than one are aliases for <code>str</code>.</p>
- <p>Data from netCDF variables is presented to python as numpy arrays with
- the corresponding data type.</p>
- <p><code>dimensions</code> must be a tuple containing dimension names
- (strings) that have been defined previously using
- <code>createDimension</code>. The default value is an empty tuple, which
- means the variable is a scalar.</p>
- <p>If the optional keyword <code>zlib</code> is <code>True</code>, the
- data will be compressed in the netCDF file using gzip compression
- (default <code>False</code>).</p>
- <p>The optional keyword <code>complevel</code> is an integer between 1
- and 9 describing the level of compression desired (default 4). Ignored if
- <code>zlib=False</code>.</p>
- <p>If the optional keyword <code>shuffle</code> is <code>True</code>, the
- HDF5 shuffle filter will be applied before compressing the data (default
- <code>True</code>). This significantly improves compression. Default is
- <code>True</code>. Ignored if <code>zlib=False</code>.</p>
- <p>If the optional keyword <code>fletcher32</code> is <code>True</code>,
- the Fletcher32 HDF5 checksum algorithm is activated to detect errors.
- Default <code>False</code>.</p>
- <p>If the optional keyword <code>contiguous</code> is <code>True</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>The optional keyword <code>chunksizes</code> can be used to manually
- 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>The optional keyword <code>endian</code> 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>The optional keyword <code>fill_value</code> can be used to override
- the default netCDF <code>_FillValue</code> (the value that the variable
- gets filled with before any data is written to it, defaults given in
- netCDF4.default_fillvals). If fill_value is set to <code>False</code>,
- then the variable is not pre-filled.</p>
- <p>If the optional keyword parameter <code>least_significant_digit</code>
- is 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
- <code>numpy.around(scale*data)/scale</code>, where scale = 2**bits, and
- bits is determined so that a precision of 0.1 is retained (in this case
- bits=4). From <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>:
- "least_significant_digit -- power of ten of the smallest decimal
- place in unpacked data that is a reliable value." Default is
- <code>None</code>, or no quantization, or 'lossless' compression.</p>
- <p>When creating variables in a <code>NETCDF4</code> or
- <code>NETCDF4_CLASSIC</code> formatted file, HDF5 creates something
- called a 'chunk cache' for each variable. The default size of the chunk
- cache may be large enough to completely fill available memory when
- creating thousands of variables. The optional keyword
- <code>chunk_cache</code> allows you to reduce (or increase) the size of
- the default chunk cache when creating a variable. The setting only
- persists as long as the Dataset is open - you can use the
- set_var_chunk_cache method to change it the next time the Dataset is
- opened. Warning - messing with this parameter can seriously degrade
- performance.</p>
- <p>The return value is the <a href="netCDF4.Variable-class.html"
- class="link">Variable</a> class instance describing the new variable.</p>
- <p>A list of names corresponding to netCDF variable attributes can be
- obtained with the <a href="netCDF4.Variable-class.html"
- class="link">Variable</a> method <code>ncattrs()</code>. 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><a href="netCDF4.Variable-class.html" class="link">Variable</a>
- instances behave much like array objects. Data can be assigned to or
- retrieved from a variable with indexing and slicing operations on the <a
- href="netCDF4.Variable-class.html" class="link">Variable</a> instance. A
- <a href="netCDF4.Variable-class.html" class="link">Variable</a> instance
- has six Dataset standard attributes: <code>dimensions, dtype, shape,
- ndim, name</code> and <code>least_significant_digit</code>. Application
- programs should never modify these attributes. The
- <code>dimensions</code> attribute is a tuple containing the names of the
- dimensions associated with this variable. The <code>dtype</code>
- attribute is a string describing the variable's data type (<code>i4, f8,
- S1,</code> etc). The <code>shape</code> attribute is a tuple describing
- the current sizes of all the variable's dimensions. The <code>name</code>
- attribute is a string containing the name of the Variable instance. The
- <code>least_significant_digit</code> attributes describes the power of
- ten of the smallest decimal place in the data the contains a reliable
- value. assigned to the <a href="netCDF4.Variable-class.html"
- class="link">Variable</a> instance. If <code>None</code>, the data is not
- truncated. The <code>ndim</code> attribute is the number of variable
- dimensions.</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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.delncattr">source code</a></span>
- </td>
- </tr></table>
-
- <p>delete a netCDF dataset or group 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="filepath"></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">filepath</span>(<span class="sig-arg">self</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.filepath">source code</a></span>
- </td>
- </tr></table>
-
- <p>Get the file system path (or the opendap URL) which was used to
- open/create the Dataset. Requires netcdf >= 4.1.2</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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.getncattr">source code</a></span>
- </td>
- </tr></table>
-
- <p>retrievel a netCDF dataset or group 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_mask"></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_mask</span>(<span class="sig-arg">self</span>,
- <span class="sig-arg">True_or_False</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.set_auto_mask">source code</a></span>
- </td>
- </tr></table>
-
- <p>Call <a href="netCDF4.Dataset-class.html#set_auto_mask"
- class="link">set_auto_mask</a> for all variables contained in this <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a>, as well as for
- all variables in all its subgroups.</p>
- <p><b>Parameters</b>:</p>
- <p><b><code>True_or_False</code></b> - Boolean determining if automatic
- conversion to masked arrays shall be applied for all variables.</p>
- <p><b>Notes</b>:</p>
- <p>Calling this function only affects existing variables. Variables
- created after calling this function will follow the default
- behaviour.</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">True_or_False</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.set_auto_maskandscale">source code</a></span>
- </td>
- </tr></table>
-
- <p>Call <a href="netCDF4.Dataset-class.html#set_auto_maskandscale"
- class="link">set_auto_maskandscale</a> for all variables contained in
- this <a href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a>, as well as for
- all variables in all its subgroups.</p>
- <p><b>Parameters</b>:</p>
- <p><b><code>True_or_False</code></b> - Boolean determining if automatic
- conversion to masked arrays and variable scaling shall be applied for all
- variables.</p>
- <p><b>Notes</b>:</p>
- <p>Calling this function only affects existing variables. Variables
- created after calling this function will follow the default
- behaviour.</p>
- <dl class="fields">
- </dl>
-</td></tr></table>
-</div>
-<a name="set_auto_scale"></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_scale</span>(<span class="sig-arg">self</span>,
- <span class="sig-arg">True_or_False</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.set_auto_scale">source code</a></span>
- </td>
- </tr></table>
-
- <p>Call <a href="netCDF4.Dataset-class.html#set_auto_scale"
- class="link">set_auto_scale</a> for all variables contained in this <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a>, as well as for
- all variables in all its subgroups.</p>
- <p><b>Parameters</b>:</p>
- <p><b><code>True_or_False</code></b> - Boolean determining if automatic
- variable scaling shall be applied for all variables.</p>
- <p><b>Notes</b>:</p>
- <p>Calling this function only affects existing variables. Variables
- created after calling this function will follow the default
- behaviour.</p>
- <dl class="fields">
- </dl>
-</td></tr></table>
-</div>
-<a name="set_fill_off"></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_fill_off</span>(<span class="sig-arg">self</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.set_fill_off">source code</a></span>
- </td>
- </tr></table>
-
- <p>Sets the fill mode for a <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> open for writing to <code>off</code>.</p>
- <p>This will prevent the data from being pre-filled with fill values,
- which may result in some performance improvements. However, you must then
- make sure the data is actually written before being read.</p>
- <dl class="fields">
- </dl>
-</td></tr></table>
-</div>
-<a name="set_fill_on"></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_fill_on</span>(<span class="sig-arg">self</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.set_fill_on">source code</a></span>
- </td>
- </tr></table>
-
- <p>Sets the fill mode for a <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> open for writing to <code>on</code>.</p>
- <p>This causes data to be pre-filled with fill values. The fill values
- can be controlled by the variable's <code>_Fill_Value</code> attribute,
- but is usually sufficient to the use the netCDF default
- <code>_Fill_Value</code> (defined separately for each variable type). The
- default behavior of the netCDF library correspongs to
- <code>set_fill_on</code>. Data which are equal to the
- <code>_Fill_Value</code> indicate that the variable was created, but
- never written to.</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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.setncattr">source code</a></span>
- </td>
- </tr></table>
-
- <p>set a netCDF dataset or group 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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dataset.setncatts">source code</a></span>
- </td>
- </tr></table>
-
- <p>set a bunch of netCDF dataset or group 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="disk_format"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">disk_format</h3>
- The <code>disk_format</code> attribute describes the underlying file
- format, one of <code>NETCDF3</code>, <code>HDF5</code>,
- <code>HDF4</code>, <code>PNETCDF</code>, <code>DAP2</code>,
- <code>DAP4</code> or <code>UNDEFINED</code>. Only available if using
- netcdf C library version >= 4.3.1, otherwise will always return
- <code>UNDEFINED</code>.
- <dl class="fields">
- </dl>
-</td></tr></table>
-</div>
-<a name="parent"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">parent</h3>
- The <code>parent</code> attribute is a reference to the parent <a
- href="netCDF4.Group-class.html" class="link">Group</a> instance.
- <code>None</code> for a the root group or <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> instance
- <dl class="fields">
- </dl>
-</td></tr></table>
-</div>
-<a name="path"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">path</h3>
- The <code>path</code> attribute shows the location of the <a
- href="netCDF4.Group-class.html" class="link">Group</a> in the <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> in a unix
- directory format (the names of groups in the hierarchy separated by
- backslashes). A <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> instance is the root group, so the path is
- simply <code>'/'</code>.
- <dl class="fields">
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th> <a
- href="netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
- <!-- Index link -->
- <th> <a
- href="identifier-index.html">Indices</a> </th>
-
- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
-
- <th class="navbar" width="100%"></th>
- </tr>
-</table>
-<table border="0" cellpadding="0" cellspacing="0" width="100%%">
- <tr>
- <td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Sun May 3 20:05:24 2015
- </td>
- <td align="right" class="footer">
- <a target="mainFrame" href="http://epydoc.sourceforge.net"
- >http://epydoc.sourceforge.net</a>
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-</table>
-
-<script type="text/javascript">
- <!--
- // Private objects are initially displayed (because if
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- // visible); but by default, we want to hide them. So hide
- // them unless we have a cookie that says to show them.
- checkCookie();
- // -->
-</script>
-</body>
-</html>
diff --git a/docs/netCDF4.Dimension-class.html b/docs/netCDF4.Dimension-class.html
deleted file mode 100644
index 3b7590e..0000000
--- a/docs/netCDF4.Dimension-class.html
+++ /dev/null
@@ -1,394 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>netCDF4.Dimension</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th> <a
- href="netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
- <!-- Index link -->
- <th> <a
- href="identifier-index.html">Indices</a> </th>
-
- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
-
- <th class="navbar" width="100%"></th>
- </tr>
-</table>
-<table width="100%" cellpadding="0" cellspacing="0">
- <tr valign="top">
- <td width="100%">
- <span class="breadcrumbs">
- <a href="netCDF4-module.html">Module netCDF4</a> ::
- Class Dimension
- </span>
- </td>
- <td>
- <table cellpadding="0" cellspacing="0">
- <!-- hide/show private -->
- </table>
- </td>
- </tr>
-</table>
-<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class Dimension</h1><p class="nomargin-top"><span class="codelink"><a href="netCDF4-pysrc.html#Dimension">source code</a></span></p>
-<pre class="base-tree">
-object --+
- |
- <strong class="uidshort">Dimension</strong>
-</pre>
-
-<hr />
-<p>Dimension(self, group, name, size=None)</p>
- <p>A netCDF <a href="netCDF4.Dimension-class.html"
- class="link">Dimension</a> is used to describe the coordinates of a <a
- href="netCDF4.Variable-class.html" class="link">Variable</a>.</p>
- <p><a href="netCDF4.Dimension-class.html" class="link">Dimension</a>
- instances should be created using the <a
- href="netCDF4.Dataset-class.html#createDimension"
- class="link">createDimension</a> method of a <a
- href="netCDF4.Group-class.html" class="link">Group</a> or <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</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> instance to associate with dimension.</p>
- <p><b><code>name</code></b> - Name of the dimension.</p>
- <p><b>Keywords:</b></p>
- <p><b><code>size</code></b> - Size of the dimension. <code>None</code>
- or 0 means unlimited. (Default <code>None</code>).</p>
- <p><b>Returns:</b></p>
- <p>a <a href="netCDF4.Dimension-class.html" class="link">Dimension</a>
- instance. All further operations on the netCDF Dimension are accomplised
- via <a href="netCDF4.Dimension-class.html" class="link">Dimension</a>
- instance methods.</p>
- <p>The current maximum size of a <a href="netCDF4.Dimension-class.html"
- class="link">Dimension</a> instance can be obtained by calling the python
- <code>len</code> function on the <a href="netCDF4.Dimension-class.html"
- class="link">Dimension</a> instance. The <code>isunlimited()</code>
- method of a <a href="netCDF4.Dimension-class.html"
- class="link">Dimension</a> instance can be used to determine if the
- dimension is unlimited</p>
-
-<!-- ==================== INSTANCE METHODS ==================== -->
-<a name="section-InstanceMethods"></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 Methods</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="netCDF4.Dimension-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">size</span>=<span class="summary-sig-default">None</span>)</span><br />
- x.__init__(...) initializes x; see help(type(x)) for signature</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dimension.__init__">source code</a></span>
-
- </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="__len__"></a><span class="summary-sig-name">__len__</span>(<span class="summary-sig-arg">x</span>)</span><br />
- len(x)</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dimension.__len__">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type">a new object with type S, a subtype of T</span>
- </td><td class="summary">
- <table width="100%" cellpadding="0" cellspacing="0" border="0">
- <tr>
- <td><span class="summary-sig"><a href="netCDF4.Dimension-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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dimension.__new__">source code</a></span>
-
- </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.Dimension-class.html#__repr__" class="summary-sig-name">__repr__</a>(<span class="summary-sig-arg">x</span>)</span><br />
- repr(x)</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dimension.__repr__">source code</a></span>
-
- </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="__unicode__"></a><span class="summary-sig-name">__unicode__</span>(<span class="summary-sig-arg">...</span>)</span></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dimension.__unicode__">source code</a></span>
-
- </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="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.Dimension-class.html"
- class="link">Dimension</a> is a member of.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dimension.group">source code</a></span>
-
- </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="isunlimited"></a><span class="summary-sig-name">isunlimited</span>(<span class="summary-sig-arg">self</span>)</span><br />
- returns <code>True</code> if the <a
- href="netCDF4.Dimension-class.html" class="link">Dimension</a>
- instance is unlimited, <code>False</code> otherwise.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Dimension.isunlimited">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
- <tr>
- <td colspan="2" class="summary">
- <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
- <code>__delattr__</code>,
- <code>__format__</code>,
- <code>__getattribute__</code>,
- <code>__hash__</code>,
- <code>__reduce__</code>,
- <code>__reduce_ex__</code>,
- <code>__setattr__</code>,
- <code>__sizeof__</code>,
- <code>__str__</code>,
- <code>__subclasshook__</code>
- </p>
- </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="name"></a><span class="summary-name">name</span><br />
- string name of Dimension instance
- </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="__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">size</span>=<span class="sig-default">None</span>)</span>
- <br /><em class="fname">(Constructor)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dimension.__init__">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dimension.__new__">source code</a></span>
- </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="__repr__"></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">__repr__</span>(<span class="sig-arg">x</span>)</span>
- <br /><em class="fname">(Representation operator)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Dimension.__repr__">source code</a></span>
- </td>
- </tr></table>
-
- <p>repr(x)</p>
- <dl class="fields">
- <dt>Overrides:
- object.__repr__
- </dt>
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th> <a
- href="netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
- <!-- Index link -->
- <th> <a
- href="identifier-index.html">Indices</a> </th>
-
- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
-
- <th class="navbar" width="100%"></th>
- </tr>
-</table>
-<table border="0" cellpadding="0" cellspacing="0" width="100%%">
- <tr>
- <td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Sun May 3 20:05:24 2015
- </td>
- <td align="right" class="footer">
- <a target="mainFrame" href="http://epydoc.sourceforge.net"
- >http://epydoc.sourceforge.net</a>
- </td>
- </tr>
-</table>
-
-<script type="text/javascript">
- <!--
- // Private objects are initially displayed (because if
- // javascript is turned off then we want them to be
- // visible); but by default, we want to hide them. So hide
- // them unless we have a cookie that says to show them.
- checkCookie();
- // -->
-</script>
-</body>
-</html>
diff --git a/docs/netCDF4.Group-class.html b/docs/netCDF4.Group-class.html
deleted file mode 100644
index 55bef05..0000000
--- a/docs/netCDF4.Group-class.html
+++ /dev/null
@@ -1,387 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>netCDF4.Group</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th> <a
- href="netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
- <!-- Index link -->
- <th> <a
- href="identifier-index.html">Indices</a> </th>
-
- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
-
- <th class="navbar" width="100%"></th>
- </tr>
-</table>
-<table width="100%" cellpadding="0" cellspacing="0">
- <tr valign="top">
- <td width="100%">
- <span class="breadcrumbs">
- <a href="netCDF4-module.html">Module netCDF4</a> ::
- Class Group
- </span>
- </td>
- <td>
- <table cellpadding="0" cellspacing="0">
- <!-- hide/show private -->
- </table>
- </td>
- </tr>
-</table>
-<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class Group</h1><p class="nomargin-top"><span class="codelink"><a href="netCDF4-pysrc.html#Group">source code</a></span></p>
-<pre class="base-tree">
-object --+
- |
- <a href="netCDF4.Dataset-class.html">Dataset</a> --+
- |
- <strong class="uidshort">Group</strong>
-</pre>
-
-<hr />
-<p>Group(self, parent, name)</p>
- <p>Groups define a hierarchical namespace within a netCDF file. They are
- analagous to directories in a unix filesystem. Each <a
- href="netCDF4.Group-class.html" class="link">Group</a> behaves like a <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> within a
- Dataset, and can contain it's own variables, dimensions and attributes
- (and other Groups).</p>
- <p><a href="netCDF4.Group-class.html" class="link">Group</a> instances
- should be created using the <a
- href="netCDF4.Dataset-class.html#createGroup"
- class="link">createGroup</a> method of a <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> instance, or
- another <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>parent</code></b> - <a href="netCDF4.Group-class.html"
- class="link">Group</a> instance for the parent group. If being created
- in the root group, use a <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> instance.</p>
- <p><b><code>name</code></b> - Name of the group.</p>
- <p><b>Returns:</b></p>
- <p>a <a href="netCDF4.Group-class.html" class="link">Group</a> instance.
- All further operations on the netCDF Group are accomplished via <a
- href="netCDF4.Group-class.html" class="link">Group</a> instance
- methods.</p>
- <p><a href="netCDF4.Group-class.html" class="link">Group</a> inherits
- from <a href="netCDF4.Dataset-class.html" class="link">Dataset</a>, so
- all the <a href="netCDF4.Dataset-class.html" class="link">Dataset</a>
- class methods and variables are available to a <a
- href="netCDF4.Group-class.html" class="link">Group</a> instance (except
- the <code>close</code> method).</p>
-
-<!-- ==================== INSTANCE METHODS ==================== -->
-<a name="section-InstanceMethods"></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 Methods</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="netCDF4.Group-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">parent</span>,
- <span class="summary-sig-arg">name</span>)</span><br />
- x.__init__(...) initializes x; see help(type(x)) for signature</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Group.__init__">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type">a new object with type S, a subtype of T</span>
- </td><td class="summary">
- <table width="100%" cellpadding="0" cellspacing="0" border="0">
- <tr>
- <td><span class="summary-sig"><a href="netCDF4.Group-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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Group.__new__">source code</a></span>
-
- </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.Group-class.html#close" class="summary-sig-name">close</a>(<span class="summary-sig-arg">self</span>)</span><br />
- overrides <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> close method which does not apply to <a
- href="netCDF4.Group-class.html" class="link">Group</a> instances,
- raises IOError.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Group.close">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
- <tr>
- <td colspan="2" class="summary">
- <p class="indent-wrapped-lines"><b>Inherited from <code><a href="netCDF4.Dataset-class.html">Dataset</a></code></b>:
- <code><a href="netCDF4.Dataset-class.html#__delattr__">__delattr__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__enter__">__enter__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__exit__">__exit__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__getattr__">__getattr__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__getattribute__">__getattribute__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__repr__">__repr__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__setattr__">__setattr__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__unicode__">__unicode__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#createCompoundType">createCompoundType</a></code>,
- <code><a href="netCDF4.Dataset-class.html#createDimension">createDimension</a></code>,
- <code><a href="netCDF4.Dataset-class.html#createGroup">createGroup</a></code>,
- <code><a href="netCDF4.Dataset-class.html#createVLType">createVLType</a></code>,
- <code><a href="netCDF4.Dataset-class.html#createVariable">createVariable</a></code>,
- <code><a href="netCDF4.Dataset-class.html#delncattr">delncattr</a></code>,
- <code><a href="netCDF4.Dataset-class.html#filepath">filepath</a></code>,
- <code><a href="netCDF4.Dataset-class.html#getncattr">getncattr</a></code>,
- <code><a href="netCDF4.Dataset-class.html#ncattrs">ncattrs</a></code>,
- <code><a href="netCDF4.Dataset-class.html#renameAttribute">renameAttribute</a></code>,
- <code><a href="netCDF4.Dataset-class.html#renameDimension">renameDimension</a></code>,
- <code><a href="netCDF4.Dataset-class.html#renameGroup">renameGroup</a></code>,
- <code><a href="netCDF4.Dataset-class.html#renameVariable">renameVariable</a></code>,
- <code><a href="netCDF4.Dataset-class.html#set_auto_mask">set_auto_mask</a></code>,
- <code><a href="netCDF4.Dataset-class.html#set_auto_maskandscale">set_auto_maskandscale</a></code>,
- <code><a href="netCDF4.Dataset-class.html#set_auto_scale">set_auto_scale</a></code>,
- <code><a href="netCDF4.Dataset-class.html#set_fill_off">set_fill_off</a></code>,
- <code><a href="netCDF4.Dataset-class.html#set_fill_on">set_fill_on</a></code>,
- <code><a href="netCDF4.Dataset-class.html#setncattr">setncattr</a></code>,
- <code><a href="netCDF4.Dataset-class.html#setncatts">setncatts</a></code>,
- <code><a href="netCDF4.Dataset-class.html#sync">sync</a></code>
- </p>
- <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>__sizeof__</code>,
- <code>__str__</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 colspan="2" class="summary">
- <p class="indent-wrapped-lines"><b>Inherited from <code><a href="netCDF4.Dataset-class.html">Dataset</a></code></b>:
- <code><a href="netCDF4.Dataset-class.html#cmptypes">cmptypes</a></code>,
- <code><a href="netCDF4.Dataset-class.html#data_model">data_model</a></code>,
- <code><a href="netCDF4.Dataset-class.html#dimensions">dimensions</a></code>,
- <code><a href="netCDF4.Dataset-class.html#disk_format">disk_format</a></code>,
- <code><a href="netCDF4.Dataset-class.html#file_format">file_format</a></code>,
- <code><a href="netCDF4.Dataset-class.html#groups">groups</a></code>,
- <code><a href="netCDF4.Dataset-class.html#parent">parent</a></code>,
- <code><a href="netCDF4.Dataset-class.html#path">path</a></code>,
- <code><a href="netCDF4.Dataset-class.html#variables">variables</a></code>,
- <code><a href="netCDF4.Dataset-class.html#vltypes">vltypes</a></code>
- </p>
- </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="name"></a><span class="summary-name">name</span><br />
- string name of Group instance
- </td>
- </tr>
- <tr>
- <td colspan="2" class="summary">
- <p class="indent-wrapped-lines"><b>Inherited from <code><a href="netCDF4.Dataset-class.html">Dataset</a></code></b>:
- <code><a href="netCDF4.Dataset-class.html#__orthogonal_indexing__">__orthogonal_indexing__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#keepweakref">keepweakref</a></code>
- </p>
- <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="__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">parent</span>,
- <span class="sig-arg">name</span>)</span>
- <br /><em class="fname">(Constructor)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Group.__init__">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Group.__new__">source code</a></span>
- </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="close"></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">close</span>(<span class="sig-arg">self</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Group.close">source code</a></span>
- </td>
- </tr></table>
-
- <p>overrides <a href="netCDF4.Dataset-class.html"
- class="link">Dataset</a> close method which does not apply to <a
- href="netCDF4.Group-class.html" class="link">Group</a> instances, raises
- IOError.</p>
- <dl class="fields">
- <dt>Overrides:
- <a href="netCDF4.Dataset-class.html#close">Dataset.close</a>
- </dt>
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== NAVIGATION BAR ==================== -->
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diff --git a/docs/netCDF4.MFDataset-class.html b/docs/netCDF4.MFDataset-class.html
deleted file mode 100644
index 03b8962..0000000
--- a/docs/netCDF4.MFDataset-class.html
+++ /dev/null
@@ -1,528 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>netCDF4.MFDataset</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
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- <tr valign="top">
- <td width="100%">
- <span class="breadcrumbs">
- <a href="netCDF4-module.html">Module netCDF4</a> ::
- Class MFDataset
- </span>
- </td>
- <td>
- <table cellpadding="0" cellspacing="0">
- <!-- hide/show private -->
- </table>
- </td>
- </tr>
-</table>
-<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class MFDataset</h1><p class="nomargin-top"><span class="codelink"><a href="netCDF4-pysrc.html#MFDataset">source code</a></span></p>
-<pre class="base-tree">
-object --+
- |
- <a href="netCDF4.Dataset-class.html">Dataset</a> --+
- |
- <strong class="uidshort">MFDataset</strong>
-</pre>
-
-<hr />
-<p>MFDataset(self, files, check=False, aggdim=None, exclude=[])</p>
- <p>Class for reading multi-file netCDF Datasets, making variables
- spanning multiple files appear as if they were in one file.</p>
- <p>Datasets must be in <code>NETCDF4_CLASSIC, NETCDF3_CLASSIC or
- NETCDF3_64BIT</code> format (<code>NETCDF4</code> Datasets won't
- work).</p>
- <p>Adapted from <a href="http://pysclint.sourceforge.net/pycdf"
- target="_top">pycdf</a> by Andre Gosselin.</p>
- <p>Example usage:</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">import</span> numpy
-<span class="py-prompt">>>> </span><span class="py-comment"># create a series of netCDF files with a variable sharing</span>
-<span class="py-prompt">>>> </span><span class="py-comment"># the same unlimited dimension.</span>
-<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()
-<span class="py-prompt">>>> </span><span class="py-comment"># now read all those files in at once, in one Dataset.</span>
-<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></pre>
-
-<!-- ==================== INSTANCE METHODS ==================== -->
-<a name="section-InstanceMethods"></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 Methods</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="netCDF4.MFDataset-class.html#__getattribute__" class="summary-sig-name">__getattribute__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- x.__getattribute__('name') <==> x.name</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.__getattribute__">source code</a></span>
-
- </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.MFDataset-class.html#__init__" class="summary-sig-name">__init__</a>()</span><br />
- Open a Dataset spanning multiple files, making it look as if it was a
- single file.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.__init__">source code</a></span>
-
- </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.MFDataset-class.html#__repr__" class="summary-sig-name">__repr__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- repr(x)</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.__repr__">source code</a></span>
-
- </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.MFDataset-class.html#__setattr__" class="summary-sig-name">__setattr__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- override base class attribute creation</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.__setattr__">source code</a></span>
-
- </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.MFDataset-class.html#close" class="summary-sig-name">close</a>(<span class="summary-sig-arg">...</span>)</span><br />
- Close the Dataset.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.close">source code</a></span>
-
- </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.MFDataset-class.html#ncattrs" class="summary-sig-name">ncattrs</a>(<span class="summary-sig-arg">...</span>)</span><br />
- return netCDF global attribute names for this <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a> in a list.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.ncattrs">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
- <tr>
- <td colspan="2" class="summary">
- <p class="indent-wrapped-lines"><b>Inherited from <code><a href="netCDF4.Dataset-class.html">Dataset</a></code></b>:
- <code><a href="netCDF4.Dataset-class.html#__delattr__">__delattr__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__enter__">__enter__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__exit__">__exit__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__getattr__">__getattr__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__new__">__new__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#__unicode__">__unicode__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#createCompoundType">createCompoundType</a></code>,
- <code><a href="netCDF4.Dataset-class.html#createDimension">createDimension</a></code>,
- <code><a href="netCDF4.Dataset-class.html#createGroup">createGroup</a></code>,
- <code><a href="netCDF4.Dataset-class.html#createVLType">createVLType</a></code>,
- <code><a href="netCDF4.Dataset-class.html#createVariable">createVariable</a></code>,
- <code><a href="netCDF4.Dataset-class.html#delncattr">delncattr</a></code>,
- <code><a href="netCDF4.Dataset-class.html#filepath">filepath</a></code>,
- <code><a href="netCDF4.Dataset-class.html#getncattr">getncattr</a></code>,
- <code><a href="netCDF4.Dataset-class.html#renameAttribute">renameAttribute</a></code>,
- <code><a href="netCDF4.Dataset-class.html#renameDimension">renameDimension</a></code>,
- <code><a href="netCDF4.Dataset-class.html#renameGroup">renameGroup</a></code>,
- <code><a href="netCDF4.Dataset-class.html#renameVariable">renameVariable</a></code>,
- <code><a href="netCDF4.Dataset-class.html#set_auto_mask">set_auto_mask</a></code>,
- <code><a href="netCDF4.Dataset-class.html#set_auto_maskandscale">set_auto_maskandscale</a></code>,
- <code><a href="netCDF4.Dataset-class.html#set_auto_scale">set_auto_scale</a></code>,
- <code><a href="netCDF4.Dataset-class.html#set_fill_off">set_fill_off</a></code>,
- <code><a href="netCDF4.Dataset-class.html#set_fill_on">set_fill_on</a></code>,
- <code><a href="netCDF4.Dataset-class.html#setncattr">setncattr</a></code>,
- <code><a href="netCDF4.Dataset-class.html#setncatts">setncatts</a></code>,
- <code><a href="netCDF4.Dataset-class.html#sync">sync</a></code>
- </p>
- <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>__sizeof__</code>,
- <code>__str__</code>,
- <code>__subclasshook__</code>
- </p>
- </td>
- </tr>
-</table>
-<!-- ==================== CLASS VARIABLES ==================== -->
-<a name="section-ClassVariables"></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">Class 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="__qualname__"></a><span class="summary-name">__qualname__</span> = <code title="'MFDataset'"><code class="variable-quote">'</code><code class="variable-string">MFDataset</code><code class="variable-quote">'</code></code>
- </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 colspan="2" class="summary">
- <p class="indent-wrapped-lines"><b>Inherited from <code><a href="netCDF4.Dataset-class.html">Dataset</a></code></b>:
- <code><a href="netCDF4.Dataset-class.html#cmptypes">cmptypes</a></code>,
- <code><a href="netCDF4.Dataset-class.html#data_model">data_model</a></code>,
- <code><a href="netCDF4.Dataset-class.html#dimensions">dimensions</a></code>,
- <code><a href="netCDF4.Dataset-class.html#disk_format">disk_format</a></code>,
- <code><a href="netCDF4.Dataset-class.html#file_format">file_format</a></code>,
- <code><a href="netCDF4.Dataset-class.html#groups">groups</a></code>,
- <code><a href="netCDF4.Dataset-class.html#parent">parent</a></code>,
- <code><a href="netCDF4.Dataset-class.html#path">path</a></code>,
- <code><a href="netCDF4.Dataset-class.html#variables">variables</a></code>,
- <code><a href="netCDF4.Dataset-class.html#vltypes">vltypes</a></code>
- </p>
- </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 colspan="2" class="summary">
- <p class="indent-wrapped-lines"><b>Inherited from <code><a href="netCDF4.Dataset-class.html">Dataset</a></code></b>:
- <code><a href="netCDF4.Dataset-class.html#__orthogonal_indexing__">__orthogonal_indexing__</a></code>,
- <code><a href="netCDF4.Dataset-class.html#keepweakref">keepweakref</a></code>
- </p>
- <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="__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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.__getattribute__">source code</a></span>
- </td>
- </tr></table>
-
- <p>x.__getattribute__('name') <==> x.name</p>
- <dl class="fields">
- <dt>Overrides:
- object.__getattribute__
- <dd><em class="note">(inherited documentation)</em></dd>
- </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>
- <br /><em class="fname">(Constructor)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.__init__">source code</a></span>
- </td>
- </tr></table>
-
- <p>Open a Dataset spanning multiple files, making it look as if it was a
- single file. Variables in the list of files that share the same dimension
- (specified with the keyword <code>aggdim</code>) are aggregated. If
- <code>aggdim</code> is not specified, the unlimited is aggregated.
- Currently, <code>aggdim</code> must be the leftmost (slowest varying)
- dimension of each of the variables to be aggregated.</p>
- <p>Adapted from <a href="http://pysclint.sourceforge.net/pycdf"
- target="_top">pycdf</a> by Andre Gosselin.</p>
- <p>Usage:</p>
- <p>nc = MFDataset(files, check=False, aggdim=None, exclude=[])</p>
- <dl class="fields">
- <dt>Parameters:</dt>
- <dd><ul class="nomargin-top">
- <li><strong class="pname"><code>files</code></strong> - either a sequence of netCDF files or a string with a wildcard
- (converted to a sorted list of files using glob) The first file
- in the list will become the "master" file, defining all
- the variables with an aggregation dimension which may span
- subsequent files. Attribute access returns attributes only from
- "master" file. The files are always opened in read-only
- mode.</li>
- <li><strong class="pname"><code>check</code></strong> - True if you want to do consistency checking to ensure the correct
- variables structure for all of the netcdf files. Checking makes
- the initialization of the MFDataset instance much slower. Default
- is False.</li>
- <li><strong class="pname"><code>aggdim</code></strong> - The name of the dimension to aggregate over (must be the leftmost
- dimension of each of the variables to be aggregated). If None
- (default), aggregate over the unlimited dimension.</li>
- <li><strong class="pname"><code>exclude</code></strong> - A list of variable names to exclude from aggregation. Default is
- an empty list.</li>
- </ul></dd>
- <dt>Overrides:
- object.__init__
- </dt>
- </dl>
-</td></tr></table>
-</div>
-<a name="__repr__"></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">__repr__</span>(<span class="sig-arg">...</span>)</span>
- <br /><em class="fname">(Representation operator)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.__repr__">source code</a></span>
- </td>
- </tr></table>
-
- <p>repr(x)</p>
- <dl class="fields">
- <dt>Overrides:
- object.__repr__
- <dd><em class="note">(inherited documentation)</em></dd>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.__setattr__">source code</a></span>
- </td>
- </tr></table>
-
- <p>override base class attribute creation</p>
- <dl class="fields">
- <dt>Overrides:
- object.__setattr__
- </dt>
- </dl>
-</td></tr></table>
-</div>
-<a name="close"></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">close</span>(<span class="sig-arg">...</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.close">source code</a></span>
- </td>
- </tr></table>
-
- <p>Close the Dataset.</p>
- <dl class="fields">
- <dt>Overrides:
- <a href="netCDF4.Dataset-class.html#close">Dataset.close</a>
- <dd><em class="note">(inherited documentation)</em></dd>
- </dt>
- </dl>
-</td></tr></table>
-</div>
-<a name="ncattrs"></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">ncattrs</span>(<span class="sig-arg">...</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#MFDataset.ncattrs">source code</a></span>
- </td>
- </tr></table>
-
- <p>return netCDF global attribute names for this <a
- href="netCDF4.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a> in a list.</p>
- <dl class="fields">
- <dt>Overrides:
- <a href="netCDF4.Dataset-class.html#ncattrs">Dataset.ncattrs</a>
- <dd><em class="note">(inherited documentation)</em></dd>
- </dt>
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th> <a
- href="netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
- <!-- Index link -->
- <th> <a
- href="identifier-index.html">Indices</a> </th>
-
- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
-
- <th class="navbar" width="100%"></th>
- </tr>
-</table>
-<table border="0" cellpadding="0" cellspacing="0" width="100%%">
- <tr>
- <td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Sun May 3 20:05:24 2015
- </td>
- <td align="right" class="footer">
- <a target="mainFrame" href="http://epydoc.sourceforge.net"
- >http://epydoc.sourceforge.net</a>
- </td>
- </tr>
-</table>
-
-<script type="text/javascript">
- <!--
- // Private objects are initially displayed (because if
- // javascript is turned off then we want them to be
- // visible); but by default, we want to hide them. So hide
- // them unless we have a cookie that says to show them.
- checkCookie();
- // -->
-</script>
-</body>
-</html>
diff --git a/docs/netCDF4.MFTime-class.html b/docs/netCDF4.MFTime-class.html
deleted file mode 100644
index 131ce36..0000000
--- a/docs/netCDF4.MFTime-class.html
+++ /dev/null
@@ -1,307 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>netCDF4.MFTime</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th> <a
- href="netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
- <!-- Index link -->
- <th> <a
- href="identifier-index.html">Indices</a> </th>
-
- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
-
- <th class="navbar" width="100%"></th>
- </tr>
-</table>
-<table width="100%" cellpadding="0" cellspacing="0">
- <tr valign="top">
- <td width="100%">
- <span class="breadcrumbs">
- <a href="netCDF4-module.html">Module netCDF4</a> ::
- Class MFTime
- </span>
- </td>
- <td>
- <table cellpadding="0" cellspacing="0">
- <!-- hide/show private -->
- </table>
- </td>
- </tr>
-</table>
-<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class MFTime</h1><p class="nomargin-top"><span class="codelink"><a href="netCDF4-pysrc.html#MFTime">source code</a></span></p>
-<pre class="base-tree">
-object --+
- |
- _Variable --+
- |
- <strong class="uidshort">MFTime</strong>
-</pre>
-
-<hr />
-<p>MFTime(self, time, units=None)</p>
- <p>Class providing an interface to a MFDataset time Variable by imposing
- a unique common time unit to all files.</p>
- <p>Example usage:</p>
-<pre class="py-doctest">
-<span class="py-prompt">>>> </span><span class="py-keyword">import</span> numpy
-<span class="py-prompt">>>> </span>f1 = Dataset(<span class="py-string">'mftest_1.nc'</span>,<span class="py-string">'w'</span>, format=<span class="py-string">'NETCDF4_CLASSIC'</span>)
-<span class="py-prompt">>>> </span>f2 = Dataset(<span class="py-string">'mftest_2.nc'</span>,<span class="py-string">'w'</span>, format=<span class="py-string">'NETCDF4_CLASSIC'</span>)
-<span class="py-prompt">>>> </span>f1.createDimension(<span class="py-string">'time'</span>,None)
-<span class="py-prompt">>>> </span>f2.createDimension(<span class="py-string">'time'</span>,None)
-<span class="py-prompt">>>> </span>t1 = f1.createVariable(<span class="py-string">'time'</span>,<span class="py-string">'i'</span>,(<span class="py-string">'time'</span>,))
-<span class="py-prompt">>>> </span>t2 = f2.createVariable(<span class="py-string">'time'</span>,<span class="py-string">'i'</span>,(<span class="py-string">'time'</span>,))
-<span class="py-prompt">>>> </span>t1.units = <span class="py-string">'days since 2000-01-01'</span>
-<span class="py-prompt">>>> </span>t2.units = <span class="py-string">'days since 2000-02-01'</span>
-<span class="py-prompt">>>> </span>t1.calendar = <span class="py-string">'standard'</span>
-<span class="py-prompt">>>> </span>t2.calendar = <span class="py-string">'standard'</span>
-<span class="py-prompt">>>> </span>t1[:] = numpy.arange(31)
-<span class="py-prompt">>>> </span>t2[:] = numpy.arange(30)
-<span class="py-prompt">>>> </span>f1.close()
-<span class="py-prompt">>>> </span>f2.close()
-<span class="py-prompt">>>> </span><span class="py-comment"># Read the two files in at once, in one Dataset.</span>
-<span class="py-prompt">>>> </span>f = MFDataset(<span class="py-string">'mftest*nc'</span>)
-<span class="py-prompt">>>> </span>t = f.variables[<span class="py-string">'time'</span>]
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> t.units
-<span class="py-output">days since 2000-01-01</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span><span class="py-keyword">print</span> t[32] <span class="py-comment"># The value written in the file, inconsistent with the MF time units.</span>
-<span class="py-output">1</span>
-<span class="py-output"></span><span class="py-prompt">>>> </span>T = MFTime(t)
-<span class="py-prompt">>>> </span><span class="py-keyword">print</span> T[32]
-<span class="py-output">32</span></pre>
-
-<!-- ==================== INSTANCE METHODS ==================== -->
-<a name="section-InstanceMethods"></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 Methods</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="netCDF4.MFTime-class.html#__getitem__" class="summary-sig-name">__getitem__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- Get records from a concatenated set of variables.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#MFTime.__getitem__">source code</a></span>
-
- </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.MFTime-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">time</span>,
- <span class="summary-sig-arg">units</span>=<span class="summary-sig-default">None</span>)</span><br />
- Create a time Variable with units consistent across a multifile
- dataset.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#MFTime.__init__">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
- <tr>
- <td colspan="2" class="summary">
- <p class="indent-wrapped-lines"><b>Inherited from <code>_Variable</code></b>:
- <code>__getattr__</code>,
- <code>__len__</code>,
- <code>__repr__</code>,
- <code>ncattrs</code>,
- <code>set_auto_maskandscale</code>,
- <code>typecode</code>
- </p>
- <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
- <code>__delattr__</code>,
- <code>__format__</code>,
- <code>__getattribute__</code>,
- <code>__hash__</code>,
- <code>__new__</code>,
- <code>__reduce__</code>,
- <code>__reduce_ex__</code>,
- <code>__setattr__</code>,
- <code>__sizeof__</code>,
- <code>__str__</code>,
- <code>__subclasshook__</code>
- </p>
- </td>
- </tr>
-</table>
-<!-- ==================== CLASS VARIABLES ==================== -->
-<a name="section-ClassVariables"></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">Class 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="__qualname__"></a><span class="summary-name">__qualname__</span> = <code title="'MFTime'"><code class="variable-quote">'</code><code class="variable-string">MFTime</code><code class="variable-quote">'</code></code>
- </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 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="__getitem__"></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">__getitem__</span>(<span class="sig-arg">...</span>)</span>
- <br /><em class="fname">(Indexing operator)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#MFTime.__getitem__">source code</a></span>
- </td>
- </tr></table>
-
- <p>Get records from a concatenated set of variables.</p>
- <dl class="fields">
- <dt>Overrides:
- _Variable.__getitem__
- <dd><em class="note">(inherited documentation)</em></dd>
- </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">time</span>,
- <span class="sig-arg">units</span>=<span class="sig-default">None</span>)</span>
- <br /><em class="fname">(Constructor)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#MFTime.__init__">source code</a></span>
- </td>
- </tr></table>
-
- <p>Create a time Variable with units consistent across a multifile
- dataset.</p>
- <dl class="fields">
- <dt>Parameters:</dt>
- <dd><ul class="nomargin-top">
- <li><strong class="pname"><code>time</code></strong> - Time variable from a MFDataset.</li>
- <li><strong class="pname"><code>units</code></strong> - Time units, for example, 'days since 1979-01-01'. If None, use
- the units from the master variable.</li>
- </ul></dd>
- <dt>Overrides:
- object.__init__
- </dt>
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th> <a
- href="netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
- <!-- Index link -->
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- href="identifier-index.html">Indices</a> </th>
-
- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
-
- <th class="navbar" width="100%"></th>
- </tr>
-</table>
-<table border="0" cellpadding="0" cellspacing="0" width="100%%">
- <tr>
- <td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Sun May 3 20:05:24 2015
- </td>
- <td align="right" class="footer">
- <a target="mainFrame" href="http://epydoc.sourceforge.net"
- >http://epydoc.sourceforge.net</a>
- </td>
- </tr>
-</table>
-
-<script type="text/javascript">
- <!--
- // Private objects are initially displayed (because if
- // javascript is turned off then we want them to be
- // visible); but by default, we want to hide them. So hide
- // them unless we have a cookie that says to show them.
- checkCookie();
- // -->
-</script>
-</body>
-</html>
diff --git a/docs/netCDF4.VLType-class.html b/docs/netCDF4.VLType-class.html
deleted file mode 100644
index aa81b19..0000000
--- a/docs/netCDF4.VLType-class.html
+++ /dev/null
@@ -1,344 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>netCDF4.VLType</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
- <tr valign="middle">
- <!-- Home link -->
- <th> <a
- href="netCDF4-module.html">Home</a> </th>
-
- <!-- Tree link -->
- <th> <a
- href="module-tree.html">Trees</a> </th>
-
- <!-- Index link -->
- <th> <a
- href="identifier-index.html">Indices</a> </th>
-
- <!-- Help link -->
- <th> <a
- href="help.html">Help</a> </th>
-
- <th class="navbar" width="100%"></th>
- </tr>
-</table>
-<table width="100%" cellpadding="0" cellspacing="0">
- <tr valign="top">
- <td width="100%">
- <span class="breadcrumbs">
- <a href="netCDF4-module.html">Module netCDF4</a> ::
- Class VLType
- </span>
- </td>
- <td>
- <table cellpadding="0" cellspacing="0">
- <!-- hide/show private -->
- </table>
- </td>
- </tr>
-</table>
-<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class VLType</h1><p class="nomargin-top"><span class="codelink"><a href="netCDF4-pysrc.html#VLType">source code</a></span></p>
-<pre class="base-tree">
-object --+
- |
- <strong class="uidshort">VLType</strong>
-</pre>
-
-<hr />
-<p>A <a href="netCDF4.VLType-class.html" class="link">VLType</a> instance
- is used to describe a variable length (VLEN) data type.</p>
- <p>Constructor: <code>VLType(group, datatype, datatype_name)</code></p>
- <p><a href="netCDF4.VLType-class.html" class="link">VLType</a> instances
- should be created using the <a
- href="netCDF4.Dataset-class.html#createVLType"
- class="link">createVLType</a> method of a Dataset 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> instance to associate with the VLEN datatype.</p>
- <p><b><code>datatype</code></b> - An numpy dtype object describing a the
- component type for the variable length array.</p>
- <p><b><code>datatype_name</code></b> - a Python string containing a
- description of the VLEN data type.</p>
- <p><b>Returns:</b></p>
- <p>a <a href="netCDF4.VLType-class.html" class="link">VLType</a>
- instance, which can be passed to the <code>createVariable</code> 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.</p>
- <p>The instance variables <code>dtype</code> and <code>name</code> should
- not be modified by the user.</p>
-
-<!-- ==================== INSTANCE METHODS ==================== -->
-<a name="section-InstanceMethods"></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 Methods</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="netCDF4.VLType-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- x.__init__(...) initializes x; see help(type(x)) for signature</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#VLType.__init__">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type">a new object with type S, a subtype of T</span>
- </td><td class="summary">
- <table width="100%" cellpadding="0" cellspacing="0" border="0">
- <tr>
- <td><span class="summary-sig"><a href="netCDF4.VLType-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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#VLType.__new__">source code</a></span>
-
- </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.VLType-class.html#__repr__" class="summary-sig-name">__repr__</a>(<span class="summary-sig-arg">x</span>)</span><br />
- repr(x)</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#VLType.__repr__">source code</a></span>
-
- </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="__unicode__"></a><span class="summary-sig-name">__unicode__</span>(<span class="summary-sig-arg">...</span>)</span></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#VLType.__unicode__">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
- <tr>
- <td colspan="2" class="summary">
- <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
- <code>__delattr__</code>,
- <code>__format__</code>,
- <code>__getattribute__</code>,
- <code>__hash__</code>,
- <code>__reduce__</code>,
- <code>__reduce_ex__</code>,
- <code>__setattr__</code>,
- <code>__sizeof__</code>,
- <code>__str__</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="dtype"></a><span class="summary-name">dtype</span><br />
- An object describing the VLEN type.
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="name"></a><span class="summary-name">name</span><br />
- A python string describing the VLEN type.
- </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 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="__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">...</span>)</span>
- <br /><em class="fname">(Constructor)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#VLType.__init__">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#VLType.__new__">source code</a></span>
- </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="__repr__"></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">__repr__</span>(<span class="sig-arg">x</span>)</span>
- <br /><em class="fname">(Representation operator)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#VLType.__repr__">source code</a></span>
- </td>
- </tr></table>
-
- <p>repr(x)</p>
- <dl class="fields">
- <dt>Overrides:
- object.__repr__
- </dt>
- </dl>
-</td></tr></table>
-</div>
-<br />
-<!-- ==================== NAVIGATION BAR ==================== -->
-<table class="navbar" border="0" width="100%" cellpadding="0"
- bgcolor="#a0c0ff" cellspacing="0">
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diff --git a/docs/netCDF4.Variable-class.html b/docs/netCDF4.Variable-class.html
deleted file mode 100644
index 473eb4c..0000000
--- a/docs/netCDF4.Variable-class.html
+++ /dev/null
@@ -1,1464 +0,0 @@
-<?xml version="1.0" encoding="ascii"?>
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
- "DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-<head>
- <title>netCDF4.Variable</title>
- <link rel="stylesheet" href="epydoc.css" type="text/css" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-
-<body bgcolor="white" text="black" link="blue" vlink="#204080"
- alink="#204080">
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-<table class="navbar" border="0" width="100%" cellpadding="0"
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- href="help.html">Help</a> </th>
-
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- <tr valign="top">
- <td width="100%">
- <span class="breadcrumbs">
- <a href="netCDF4-module.html">Module netCDF4</a> ::
- Class Variable
- </span>
- </td>
- <td>
- <table cellpadding="0" cellspacing="0">
- <!-- hide/show private -->
- </table>
- </td>
- </tr>
-</table>
-<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class Variable</h1><p class="nomargin-top"><span class="codelink"><a href="netCDF4-pysrc.html#Variable">source code</a></span></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). Numpy string and unicode datatypes with
- length greater than one are aliases for <code>str</code>.</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. For netCDF 3 files
- (that don't use HDF5), only <code>endian='native'</code> is allowed.</p>
- <p>The <code>zlib, complevel, shuffle, fletcher32, contiguous</code> and
- {chunksizes} 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 ==================== -->
-<a name="section-InstanceMethods"></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 Methods</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 name="__array__"></a><span class="summary-sig-name">__array__</span>(<span class="summary-sig-arg">...</span>)</span></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__array__">source code</a></span>
-
- </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#__delattr__" class="summary-sig-name">__delattr__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- x.__delattr__('name') <==> del x.name</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__delattr__">source code</a></span>
-
- </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="__delitem__"></a><span class="summary-sig-name">__delitem__</span>(<span class="summary-sig-arg">x</span>,
- <span class="summary-sig-arg">y</span>)</span><br />
- del x[y]</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__delitem__">source code</a></span>
-
- </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="__getattr__"></a><span class="summary-sig-name">__getattr__</span>(<span class="summary-sig-arg">...</span>)</span></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__getattr__">source code</a></span>
-
- </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#__getattribute__" class="summary-sig-name">__getattribute__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- x.__getattribute__('name') <==> x.name</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__getattribute__">source code</a></span>
-
- </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="__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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__getitem__">source code</a></span>
-
- </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#__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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__init__">source code</a></span>
-
- </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="__len__"></a><span class="summary-sig-name">__len__</span>(<span class="summary-sig-arg">x</span>)</span><br />
- len(x)</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__len__">source code</a></span>
-
- </td>
- </tr>
- </table>
-
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type">a new object with type S, a subtype of T</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#__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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__new__">source code</a></span>
-
- </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#__repr__" class="summary-sig-name">__repr__</a>(<span class="summary-sig-arg">x</span>)</span><br />
- repr(x)</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__repr__">source code</a></span>
-
- </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#__setattr__" class="summary-sig-name">__setattr__</a>(<span class="summary-sig-arg">...</span>)</span><br />
- x.__setattr__('name', value) <==> x.name = value</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__setattr__">source code</a></span>
-
- </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="__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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__setitem__">source code</a></span>
-
- </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="__unicode__"></a><span class="summary-sig-name">__unicode__</span>(<span class="summary-sig-arg">...</span>)</span></td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.__unicode__">source code</a></span>
-
- </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#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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.assignValue">source code</a></span>
-
- </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#chunking" class="summary-sig-name">chunking</a>(<span class="summary-sig-arg">self</span>)</span><br />
- return variable chunking information.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.chunking">source code</a></span>
-
- </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#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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.delncattr">source code</a></span>
-
- </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="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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.endian">source code</a></span>
-
- </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="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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.filters">source code</a></span>
-
- </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#getValue" class="summary-sig-name">getValue</a>(<span class="summary-sig-arg">self</span>)</span><br />
- get the value of a scalar variable.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.getValue">source code</a></span>
-
- </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#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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.get_var_chunk_cache">source code</a></span>
-
- </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#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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.getncattr">source code</a></span>
-
- </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="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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.group">source code</a></span>
-
- </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="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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.ncattrs">source code</a></span>
-
- </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="renameAttribute"></a><span class="summary-sig-name">renameAttribute</span>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">oldname</span>,
- <span class="summary-sig-arg">newname</span>)</span><br />
- rename a <a href="netCDF4.Variable-class.html"
- class="link">Variable</a> attribute named <code>oldname</code> to
- <code>newname</code>.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.renameAttribute">source code</a></span>
-
- </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#set_auto_mask" class="summary-sig-name">set_auto_mask</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">mask</span>)</span><br />
- turn on or off automatic conversion of variable data to and from
- masked arrays .</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.set_auto_mask">source code</a></span>
-
- </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#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>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.set_auto_maskandscale">source code</a></span>
-
- </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#set_auto_scale" class="summary-sig-name">set_auto_scale</a>(<span class="summary-sig-arg">self</span>,
- <span class="summary-sig-arg">scale</span>)</span><br />
- turn on or off automatic packing/unpacking of variable data using
- <code>scale_factor</code> and <code>add_offset</code> attributes.</td>
- <td align="right" valign="top">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.set_auto_scale">source code</a></span>
-
- </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#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">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.set_var_chunk_cache">source code</a></span>
-
- </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">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.setncattr">source code</a></span>
-
- </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">
- <span class="codelink"><a href="netCDF4-pysrc.html#Variable.setncatts">source code</a></span>
-
- </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>__sizeof__</code>,
- <code>__str__</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 href="netCDF4.Variable-class.html#__orthogonal_indexing__" class="summary-name">__orthogonal_indexing__</a><br />
- Always <code>True</code>.
- </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 href="netCDF4.Variable-class.html#mask" class="summary-name">mask</a><br />
- if True, data is automatically converted to/from masked arrays when
- missing values or fill values are present.
- </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 href="netCDF4.Variable-class.html#scale" class="summary-name">scale</a><br />
- if True, <code>scale_factor</code> and <code>add_offset</code> are
- automatically applied.
- </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="datatype"></a><span class="summary-name">datatype</span><br />
- numpy data type (for primitive data types) or VLType/CompoundType
- instance (for compound or vlen data types)
- </td>
- </tr>
-<tr>
- <td width="15%" align="right" valign="top" class="summary">
- <span class="summary-type"> </span>
- </td><td class="summary">
- <a name="name"></a><span class="summary-name">name</span><br />
- string name of Variable instance
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.__delattr__">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.__getattribute__">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.__init__">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.__new__">source code</a></span>
- </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="__repr__"></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">__repr__</span>(<span class="sig-arg">x</span>)</span>
- <br /><em class="fname">(Representation operator)</em>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.__repr__">source code</a></span>
- </td>
- </tr></table>
-
- <p>repr(x)</p>
- <dl class="fields">
- <dt>Overrides:
- object.__repr__
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.__setattr__">source code</a></span>
- </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="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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.assignValue">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.chunking">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.delncattr">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.getValue">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.get_var_chunk_cache">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.getncattr">source code</a></span>
- </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_mask"></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_mask</span>(<span class="sig-arg">self</span>,
- <span class="sig-arg">mask</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.set_auto_mask">source code</a></span>
- </td>
- </tr></table>
-
- <p>turn on or off automatic conversion of variable data to and from
- masked arrays .</p>
- <p>If <code>mask</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>The default value of <code>mask</code> is <code>True</code> (automatic
- conversions are performed).</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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.set_auto_maskandscale">source code</a></span>
- </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_auto_scale"></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_scale</span>(<span class="sig-arg">self</span>,
- <span class="sig-arg">scale</span>)</span>
- </h3>
- </td><td align="right" valign="top"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.set_auto_scale">source code</a></span>
- </td>
- </tr></table>
-
- <p>turn on or off automatic packing/unpacking of variable data using
- <code>scale_factor</code> and <code>add_offset</code> attributes.</p>
- <p>If <code>scale</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>scale</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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.set_var_chunk_cache">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.setncattr">source code</a></span>
- </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"
- ><span class="codelink"><a href="netCDF4-pysrc.html#Variable.setncatts">source code</a></span>
- </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="__orthogonal_indexing__"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">__orthogonal_indexing__</h3>
- Always <code>True</code>. Indicates to client code that the object
- supports "orthogonal indexing", which means that slices that
- are 1d arrays or lists slice along each dimension independently. This
- behavior is similar to Fortran or Matlab, but different than numpy.
- <dl class="fields">
- </dl>
-</td></tr></table>
-</div>
-<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>
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-<a name="mask"></a>
-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">mask</h3>
- if True, data is automatically converted to/from masked arrays when
- missing values or fill values are present. Default is <code>True</code>,
- can be reset using <a href="netCDF4.Variable-class.html#set_auto_mask"
- class="link">set_auto_mask</a> and <a
- href="netCDF4.Variable-class.html#set_auto_maskandscale"
- class="link">set_auto_maskandscale</a> methods.
- <dl class="fields">
- </dl>
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-<div>
-<table class="details" border="1" cellpadding="3"
- cellspacing="0" width="100%" bgcolor="white">
-<tr><td>
- <h3 class="epydoc">scale</h3>
- if True, <code>scale_factor</code> and <code>add_offset</code> are
- automatically applied. Default is <code>True</code>, can be reset using
- <a href="netCDF4.Variable-class.html#set_auto_scale"
- class="link">set_auto_scale</a> and <a
- href="netCDF4.Variable-class.html#set_auto_maskandscale"
- class="link">set_auto_maskandscale</a> methods.
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- </dl>
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- <!-- Home link -->
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-
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- <th> <a
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- </td>
- <td align="right" class="footer">
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diff --git a/docs/netCDF4/index.html b/docs/netCDF4/index.html
new file mode 100644
index 0000000..782220e
--- /dev/null
+++ b/docs/netCDF4/index.html
@@ -0,0 +1,5584 @@
+<!doctype html>
+<head>
+ <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+ <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
+
+ <title>netCDF4 API documentation</title>
+ <meta name="description" content="Introduction
+============
+
+netcdf4-python is a Python interface to the netCDF C library.
+
+[netCDF ..." />
+
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+
+ <style type="text/css">
+
+/* ==========================================================================
+ EXAMPLE Media Queries for Responsive Design.
+ These examples override the primary ('mobile first') styles.
+ Modify as content requires.
+ ========================================================================== */
+
+ at media only screen and (min-width: 35em) {
+ /* Style adjustments for viewports that meet the condition */
+}
+
+ at media print,
+ (-o-min-device-pixel-ratio: 5/4),
+ (-webkit-min-device-pixel-ratio: 1.25),
+ (min-resolution: 120dpi) {
+ /* Style adjustments for high resolution devices */
+}
+
+/* ==========================================================================
+ Print styles.
+ Inlined to avoid required HTTP connection: h5bp.com/r
+ ========================================================================== */
+
+ at media print {
+ * {
+ background: transparent !important;
+ color: #000 !important; /* Black prints faster: h5bp.com/s */
+ box-shadow: none !important;
+ text-shadow: none !important;
+ }
+
+ a,
+ a:visited {
+ text-decoration: underline;
+ }
+
+ a[href]:after {
+ content: " (" attr(href) ")";
+ }
+
+ abbr[title]:after {
+ content: " (" attr(title) ")";
+ }
+
+ /*
+ * Don't show links for images, or javascript/internal links
+ */
+
+ .ir a:after,
+ a[href^="javascript:"]:after,
+ a[href^="#"]:after {
+ content: "";
+ }
+
+ pre,
+ blockquote {
+ border: 1px solid #999;
+ page-break-inside: avoid;
+ }
+
+ thead {
+ display: table-header-group; /* h5bp.com/t */
+ }
+
+ tr,
+ img {
+ page-break-inside: avoid;
+ }
+
+ img {
+ max-width: 100% !important;
+ }
+
+ @page {
+ margin: 0.5cm;
+ }
+
+ p,
+ h2,
+ h3 {
+ orphans: 3;
+ widows: 3;
+ }
+
+ h2,
+ h3 {
+ page-break-after: avoid;
+ }
+}
+
+ </style>
+
+ <script type="text/javascript">
+ function toggle(id, $link) {
+ $node = document.getElementById(id);
+ if (!$node)
+ return;
+ if (!$node.style.display || $node.style.display == 'none') {
+ $node.style.display = 'block';
+ $link.innerHTML = 'Hide source ≢';
+ } else {
+ $node.style.display = 'none';
+ $link.innerHTML = 'Show source ≡';
+ }
+ }
+ </script>
+</head>
+<body>
+<a href="#" id="top">Top</a>
+
+<div id="container">
+
+
+ <div id="sidebar">
+ <h1>Index</h1>
+ <ul id="index">
+
+ <li class="set"><h3><a href="#header-functions">Functions</a></h3>
+
+ <ul>
+ <li class="mono"><a href="#netCDF4.chartostring">chartostring</a></li>
+ <li class="mono"><a href="#netCDF4.date2index">date2index</a></li>
+ <li class="mono"><a href="#netCDF4.date2num">date2num</a></li>
+ <li class="mono"><a href="#netCDF4.getlibversion">getlibversion</a></li>
+ <li class="mono"><a href="#netCDF4.num2date">num2date</a></li>
+ <li class="mono"><a href="#netCDF4.stringtoarr">stringtoarr</a></li>
+ <li class="mono"><a href="#netCDF4.stringtochar">stringtochar</a></li>
+ </ul>
+
+ </li>
+
+ <li class="set"><h3><a href="#header-classes">Classes</a></h3>
+ <ul>
+ <li class="mono">
+ <span class="class_name"><a href="#netCDF4.CompoundType">CompoundType</a></span>
+
+
+ <ul>
+ <li class="mono"><a href="#netCDF4.CompoundType.__init__">__init__</a></li>
+ </ul>
+
+ </li>
+ <li class="mono">
+ <span class="class_name"><a href="#netCDF4.Dataset">Dataset</a></span>
+
+
+ <ul>
+ <li class="mono"><a href="#netCDF4.Dataset.__init__">__init__</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.close">close</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.createCompoundType">createCompoundType</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.createDimension">createDimension</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.createGroup">createGroup</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.createVLType">createVLType</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.createVariable">createVariable</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.delncattr">delncattr</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.filepath">filepath</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.getncattr">getncattr</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.ncattrs">ncattrs</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.renameAttribute">renameAttribute</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.renameDimension">renameDimension</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.renameGroup">renameGroup</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.renameVariable">renameVariable</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.set_auto_mask">set_auto_mask</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.set_auto_maskandscale">set_auto_maskandscale</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.set_auto_scale">set_auto_scale</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.set_fill_off">set_fill_off</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.set_fill_on">set_fill_on</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.setncattr">setncattr</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.setncatts">setncatts</a></li>
+ <li class="mono"><a href="#netCDF4.Dataset.sync">sync</a></li>
+ </ul>
+
+ </li>
+ <li class="mono">
+ <span class="class_name"><a href="#netCDF4.Dimension">Dimension</a></span>
+
+
+ <ul>
+ <li class="mono"><a href="#netCDF4.Dimension.__init__">__init__</a></li>
+ <li class="mono"><a href="#netCDF4.Dimension.group">group</a></li>
+ <li class="mono"><a href="#netCDF4.Dimension.isunlimited">isunlimited</a></li>
+ </ul>
+
+ </li>
+ <li class="mono">
+ <span class="class_name"><a href="#netCDF4.Group">Group</a></span>
+
+
+ <ul>
+ <li class="mono"><a href="#netCDF4.Group.__init__">__init__</a></li>
+ <li class="mono"><a href="#netCDF4.Group.close">close</a></li>
+ <li class="mono"><a href="#netCDF4.Group.createCompoundType">createCompoundType</a></li>
+ <li class="mono"><a href="#netCDF4.Group.createDimension">createDimension</a></li>
+ <li class="mono"><a href="#netCDF4.Group.createGroup">createGroup</a></li>
+ <li class="mono"><a href="#netCDF4.Group.createVLType">createVLType</a></li>
+ <li class="mono"><a href="#netCDF4.Group.createVariable">createVariable</a></li>
+ <li class="mono"><a href="#netCDF4.Group.delncattr">delncattr</a></li>
+ <li class="mono"><a href="#netCDF4.Group.filepath">filepath</a></li>
+ <li class="mono"><a href="#netCDF4.Group.getncattr">getncattr</a></li>
+ <li class="mono"><a href="#netCDF4.Group.ncattrs">ncattrs</a></li>
+ <li class="mono"><a href="#netCDF4.Group.renameAttribute">renameAttribute</a></li>
+ <li class="mono"><a href="#netCDF4.Group.renameDimension">renameDimension</a></li>
+ <li class="mono"><a href="#netCDF4.Group.renameGroup">renameGroup</a></li>
+ <li class="mono"><a href="#netCDF4.Group.renameVariable">renameVariable</a></li>
+ <li class="mono"><a href="#netCDF4.Group.set_auto_mask">set_auto_mask</a></li>
+ <li class="mono"><a href="#netCDF4.Group.set_auto_maskandscale">set_auto_maskandscale</a></li>
+ <li class="mono"><a href="#netCDF4.Group.set_auto_scale">set_auto_scale</a></li>
+ <li class="mono"><a href="#netCDF4.Group.set_fill_off">set_fill_off</a></li>
+ <li class="mono"><a href="#netCDF4.Group.set_fill_on">set_fill_on</a></li>
+ <li class="mono"><a href="#netCDF4.Group.setncattr">setncattr</a></li>
+ <li class="mono"><a href="#netCDF4.Group.setncatts">setncatts</a></li>
+ <li class="mono"><a href="#netCDF4.Group.sync">sync</a></li>
+ </ul>
+
+ </li>
+ <li class="mono">
+ <span class="class_name"><a href="#netCDF4.MFDataset">MFDataset</a></span>
+
+
+ <ul>
+ <li class="mono"><a href="#netCDF4.MFDataset.createCompoundType">createCompoundType</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.createDimension">createDimension</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.createGroup">createGroup</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.createVLType">createVLType</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.createVariable">createVariable</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.delncattr">delncattr</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.filepath">filepath</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.getncattr">getncattr</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.renameAttribute">renameAttribute</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.renameDimension">renameDimension</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.renameGroup">renameGroup</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.renameVariable">renameVariable</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.set_auto_mask">set_auto_mask</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.set_auto_maskandscale">set_auto_maskandscale</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.set_auto_scale">set_auto_scale</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.set_fill_off">set_fill_off</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.set_fill_on">set_fill_on</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.setncattr">setncattr</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.setncatts">setncatts</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.sync">sync</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.__init__">__init__</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.close">close</a></li>
+ <li class="mono"><a href="#netCDF4.MFDataset.ncattrs">ncattrs</a></li>
+ </ul>
+
+ </li>
+ <li class="mono">
+ <span class="class_name"><a href="#netCDF4.MFTime">MFTime</a></span>
+
+
+ <ul>
+ <li class="mono"><a href="#netCDF4.MFTime.__init__">__init__</a></li>
+ <li class="mono"><a href="#netCDF4.MFTime.ncattrs">ncattrs</a></li>
+ <li class="mono"><a href="#netCDF4.MFTime.set_auto_maskandscale">set_auto_maskandscale</a></li>
+ <li class="mono"><a href="#netCDF4.MFTime.typecode">typecode</a></li>
+ </ul>
+
+ </li>
+ <li class="mono">
+ <span class="class_name"><a href="#netCDF4.VLType">VLType</a></span>
+
+
+ <ul>
+ <li class="mono"><a href="#netCDF4.VLType.__init__">__init__</a></li>
+ </ul>
+
+ </li>
+ <li class="mono">
+ <span class="class_name"><a href="#netCDF4.Variable">Variable</a></span>
+
+
+ <ul>
+ <li class="mono"><a href="#netCDF4.Variable.__init__">__init__</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.assignValue">assignValue</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.chunking">chunking</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.delncattr">delncattr</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.endian">endian</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.filters">filters</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.getValue">getValue</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.get_var_chunk_cache">get_var_chunk_cache</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.getncattr">getncattr</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.group">group</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.ncattrs">ncattrs</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.renameAttribute">renameAttribute</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.set_auto_mask">set_auto_mask</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.set_auto_maskandscale">set_auto_maskandscale</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.set_auto_scale">set_auto_scale</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.set_var_chunk_cache">set_var_chunk_cache</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.setncattr">setncattr</a></li>
+ <li class="mono"><a href="#netCDF4.Variable.setncatts">setncatts</a></li>
+ </ul>
+
+ </li>
+ </ul>
+ </li>
+
+ </ul>
+ </div>
+
+ <article id="content">
+
+
+
+
+
+
+ <header id="section-intro">
+ <h1 class="title"><span class="name">netCDF4</span> module</h1>
+ <h1>Introduction</h1>
+<p>netcdf4-python is a Python interface to the netCDF C library. </p>
+<p><a href="http://www.unidata.ucar.edu/software/netcdf/netcdf-4">netCDF version 4</a> has many features
+not found in earlier versions of the library and is implemented on top of
+<a href="http://www.hdfgroup.org/HDF5">HDF5</a>. This module can read and write
+files in both the new netCDF 4 and the old netCDF 3 format, and can create
+files that are readable by HDF5 clients. The API modelled after
+<a href="http://dirac.cnrs-orleans.fr/plone/software/scientificpython/">Scientific.IO.NetCDF</a>,
+and should be familiar to users of that module.</p>
+<p>Most new features of netCDF 4 are implemented, such as multiple
+unlimited dimensions, groups and zlib data compression. All the new
+numeric data types (such as 64 bit and unsigned integer types) are
+implemented. Compound (struct) and variable length (vlen) data types are supported,
+but the enum and opaque data types are not. Mixtures of compound and vlen
+data types (compound types containing vlens, and vlens containing compound
+types) are not supported.</p>
+<h1>Download</h1>
+<ul>
+<li>Latest bleeding-edge code from the
+ <a href="http://github.com/Unidata/netcdf4-python">github repository</a>.</li>
+<li>Latest <a href="https://pypi.python.org/pypi/netCDF4">releases</a>
+ (source code and windows installers).</li>
+</ul>
+<h1>Requires</h1>
+<ul>
+<li>Python 2.5 or later (python 3 works too).</li>
+<li><a href="http://numpy.scipy.org">numpy array module</a>, version 1.7.0 or later.</li>
+<li><a href="http://cython.org">Cython</a>, version 0.19 or later, is optional - if it is installed setup.py will
+ use it to recompile the Cython source code into C, using conditional compilation
+ to enable features in the netCDF API that have been added since version 4.1.1. If
+ Cython is not installed, these features (such as the ability to rename Group objects)
+ will be disabled to preserve backward compatibility with older versions of the netCDF
+ library.</li>
+<li>For python < 2.7, the <a href="http://python.org/pypi/ordereddict">ordereddict module</a>.</li>
+<li>The HDF5 C library version 1.8.4-patch1 or higher (1.8.8 or higher
+ recommended) from <a href="ftp://ftp.hdfgroup.org/HDF5/current/src"></a>.
+ Be sure to build with <code>--enable-hl --enable-shared</code>.</li>
+<li><a href="http://curl.haxx.se/libcurl">Libcurl</a>, if you want
+ <a href="http://opendap.org">OPeNDAP</a> support.</li>
+<li><a href="http://www.hdfgroup.org/products/hdf4">HDF4</a>, if you want
+ to be able to read HDF4 "Scientific Dataset" (SD) files.</li>
+<li>The netCDF-4 C library from
+ <a href="ftp://ftp.unidata.ucar.edu/pub/netcdf">ftp://ftp.unidata.ucar.edu/pub/netcdf</a>.
+ Version 4.1.1 or higher is required (4.2 or higher recommended).
+ Be sure to build with <code>--enable-netcdf-4 --enable-shared</code>, and set
+ <code>CPPFLAGS="-I $HDF5_DIR/include"</code> and <code>LDFLAGS="-L $HDF5_DIR/lib"</code>,
+ where <code>$HDF5_DIR</code> is the directory where HDF5 was installed.
+ If you want <a href="http://opendap.org">OPeNDAP</a> support, add <code>--enable-dap</code>.
+ If you want HDF4 SD support, add <code>--enable-hdf4</code> and add
+ the location of the HDF4 headers and library to <code>$CPPFLAGS</code> and <code>$LDFLAGS</code>.</li>
+</ul>
+<h1>Install</h1>
+<ul>
+<li>install the requisite python modules and C libraries (see above). It's
+ easiest if all the C libs are built as shared libraries.</li>
+<li>By default, the utility <code>nc-config</code>, installed with netcdf 4.1.2 or higher,
+ will be run used to determine where all the dependencies live.</li>
+<li>If <code>nc-config</code> is not in your default <code>$PATH</code>, rename the
+ file <code>setup.cfg.template</code> to <code>setup.cfg</code>, then edit
+ in a text editor (follow the instructions in the comments).
+ In addition to specifying the path to <code>nc-config</code>,
+ you can manually set the paths to all the libraries and their include files
+ (in case <code>nc-config</code> does not do the right thing).</li>
+<li>run <code>python setup.py build</code>, then <code>python setup.py install</code> (as root if
+ necessary).</li>
+<li>run the tests in the 'test' directory by running <code>python run_all.py</code>.</li>
+</ul>
+<h1>Tutorial</h1>
+<ol>
+<li><a href="#section1">Creating/Opening/Closing a netCDF file.</a></li>
+<li><a href="#section2">Groups in a netCDF file.</a></li>
+<li><a href="#section3">Dimensions in a netCDF file.</a></li>
+<li><a href="#section4">Variables in a netCDF file.</a></li>
+<li><a href="#section5">Attributes in a netCDF file.</a></li>
+<li><a href="#section6">Writing data to and retrieving data from a netCDF variable.</a></li>
+<li><a href="#section7">Dealing with time coordinates.</a></li>
+<li><a href="#section8">Reading data from a multi-file netCDF dataset.</a></li>
+<li><a href="#section9">Efficient compression of netCDF variables.</a></li>
+<li><a href="#section10">Beyond homogenous arrays of a fixed type - compound data types.</a></li>
+<li><a href="#section11">Variable-length (vlen) data types.</a></li>
+</ol>
+<h2><div id='section1'>1) Creating/Opening/Closing a netCDF file.</h2>
+<p>To create a netCDF file from python, you simply call the <a href="#netCDF4.Dataset"><code>Dataset</code></a>
+constructor. This is also the method used to open an existing netCDF
+file. If the file is open for write access (<code>mode='w', 'r+'</code> or <code>'a'</code>), you may
+write any type of data including new dimensions, groups, 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. <code>NETCDF4_CLASSIC</code>
+files use the version 4 disk format (HDF5), but do not use any features
+not found in the version 3 API. They can be read by netCDF 3 clients
+only if they have been relinked against the netCDF 4 library. They can
+also be read by HDF5 clients. <code>NETCDF4</code> files use the version 4 disk
+format (HDF5) and use the new features of the version 4 API. The
+<code>netCDF4</code> module can read and write files in any of these formats. When
+creating a new file, the format may be specified using the <code>format</code>
+keyword in the <code>Dataset</code> constructor. The default format is
+<code>NETCDF4</code>. To see how a given file is formatted, you can examine the
+<code>data_model</code> attribute. Closing the netCDF file is
+accomplished via the <a href="#netCDF4.Dataset.close"><code>close</code></a> method of the <a href="#netCDF4.Dataset"><code>Dataset</code></a>
+instance.</p>
+<p>Here's an example:</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="kn">from</span> <span class="nn">netCDF4</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="o">>>></span> <span class="n">rootgrp</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="s">"test.nc"</span><span class="p">,</span> <span class="s">"w"</span><span class="p">,</span> <span class="n">format</span><span class="o">=</span><span class="s">"NETCDF4"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">data_model</span>
+<span class="n">NETCDF4</span>
+<span class="o">>>></span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+</pre></div>
+
+
+<p>Remote <a href="http://opendap.org">OPeNDAP</a>-hosted datasets can be accessed for
+reading over http if a URL is provided to the <a href="#netCDF4.Dataset"><code>Dataset</code></a> constructor instead of a
+filename. However, this requires that the netCDF library be built with
+OPenDAP support, via the <code>--enable-dap</code> configure option (added in
+version 4.0.1).</p>
+<h2><div id='section2'>2) Groups in a netCDF file.</h2>
+<p>netCDF version 4 added support for organizing data in hierarchical
+groups, which are analagous to directories in a filesystem. Groups serve
+as containers for variables, dimensions and attributes, as well as other
+groups. A <a href="#netCDF4.Dataset"><code>Dataset</code></a> defines creates a special group, called
+the 'root group', which is similar to the root directory in a unix
+filesystem. To create <a href="#netCDF4.Group"><code>Group</code></a> instances, use the
+<a href="#netCDF4.Dataset.createGroup"><code>createGroup</code></a> method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a>
+instance. <a href="#netCDF4.Dataset.createGroup"><code>createGroup</code></a> takes a single argument, a
+python string containing the name of the new group. The new <a href="#netCDF4.Group"><code>Group</code></a>
+instances contained within the root group can be accessed by name using
+the <code>groups</code> dictionary attribute of the <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance. Only
+<code>NETCDF4</code> formatted files support Groups, if you try to create a Group
+in a netCDF 3 file you will get an error message.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">rootgrp</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="s">"test.nc"</span><span class="p">,</span> <span class="s">"a"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">fcstgrp</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createGroup</span><span class="p">(</span><span class="s">"forecasts"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">analgrp</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createGroup</span><span class="p">(</span><span class="s">"analyses"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">groups</span>
+<span class="n">OrderedDict</span><span class="p">([(</span><span class="s">"forecasts"</span><span class="p">,</span>
+ <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">_netCDF4</span><span class="o">.</span><span class="n">Group</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b4b7b0</span><span class="o">></span><span class="p">),</span>
+ <span class="p">(</span><span class="s">"analyses"</span><span class="p">,</span>
+ <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">_netCDF4</span><span class="o">.</span><span class="n">Group</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b4b970</span><span class="o">></span><span class="p">)])</span>
+</pre></div>
+
+
+<p>Groups can exist within groups in a <a href="#netCDF4.Dataset"><code>Dataset</code></a>, just as directories
+exist within directories in a unix filesystem. Each <a href="#netCDF4.Group"><code>Group</code></a> instance
+has a <code>groups</code> attribute dictionary containing all of the group
+instances contained within that group. Each <a href="#netCDF4.Group"><code>Group</code></a> instance also has a
+<code>path</code> attribute that contains a simulated unix directory path to
+that group. To simplify the creation of nested groups, you can
+use a unix-like path as an argument to <a href="#netCDF4.Dataset.createGroup"><code>createGroup</code></a>.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">fcstgrp1</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createGroup</span><span class="p">(</span><span class="s">"/forecasts/model1"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">fcstgrp2</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createGroup</span><span class="p">(</span><span class="s">"/forecasts/model2"</span><span class="p">)</span>
+</pre></div>
+
+
+<p>If any of the intermediate elements of the path do not exist, they are created,
+just as with the unix command <code>'mkdir -p'</code>. If you try to create a group
+that already exists, no error will be raised, and the existing group will be
+returned.</p>
+<p>Here's an example that shows how to navigate all the groups in a
+<a href="#netCDF4.Dataset"><code>Dataset</code></a>. The function <code>walktree</code> is a Python generator that is used
+to walk the directory tree. Note that printing the <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a>
+object yields summary information about it's contents.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">def</span> <span class="nf">walktree</span><span class="p">(</span><span class="n">top</span><span class="p">):</span>
+<span class="o">>>></span> <span class="n">values</span> <span class="o">=</span> <span class="n">top</span><span class="o">.</span><span class="n">groups</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
+<span class="o">>>></span> <span class="k">yield</span> <span class="n">values</span>
+<span class="o">>>></span> <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">top</span><span class="o">.</span><span class="n">groups</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
+<span class="o">>>></span> <span class="k">for</span> <span class="n">children</span> <span class="ow">in</span> <span class="n">walktree</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
+<span class="o">>>></span> <span class="k">yield</span> <span class="n">children</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">rootgrp</span>
+<span class="o">>>></span> <span class="k">for</span> <span class="n">children</span> <span class="ow">in</span> <span class="n">walktree</span><span class="p">(</span><span class="n">rootgrp</span><span class="p">):</span>
+<span class="o">>>></span> <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">children</span><span class="p">:</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">child</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Dataset"</span><span class="o">></span>
+<span class="n">root</span> <span class="n">group</span> <span class="p">(</span><span class="n">NETCDF4</span> <span class="nb">file</span> <span class="n">format</span><span class="p">):</span>
+ <span class="n">dimensions</span><span class="p">:</span>
+ <span class="n">variables</span><span class="p">:</span>
+ <span class="n">groups</span><span class="p">:</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">analyses</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Group"</span><span class="o">></span>
+<span class="n">group</span> <span class="o">/</span><span class="n">forecasts</span><span class="p">:</span>
+ <span class="n">dimensions</span><span class="p">:</span>
+ <span class="n">variables</span><span class="p">:</span>
+ <span class="n">groups</span><span class="p">:</span> <span class="n">model1</span><span class="p">,</span> <span class="n">model2</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Group"</span><span class="o">></span>
+<span class="n">group</span> <span class="o">/</span><span class="n">analyses</span><span class="p">:</span>
+ <span class="n">dimensions</span><span class="p">:</span>
+ <span class="n">variables</span><span class="p">:</span>
+ <span class="n">groups</span><span class="p">:</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Group"</span><span class="o">></span>
+<span class="n">group</span> <span class="o">/</span><span class="n">forecasts</span><span class="o">/</span><span class="n">model1</span><span class="p">:</span>
+ <span class="n">dimensions</span><span class="p">:</span>
+ <span class="n">variables</span><span class="p">:</span>
+ <span class="n">groups</span><span class="p">:</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Group"</span><span class="o">></span>
+<span class="n">group</span> <span class="o">/</span><span class="n">forecasts</span><span class="o">/</span><span class="n">model2</span><span class="p">:</span>
+ <span class="n">dimensions</span><span class="p">:</span>
+ <span class="n">variables</span><span class="p">:</span>
+ <span class="n">groups</span><span class="p">:</span>
+</pre></div>
+
+
+<h2><div id='section3'>3) 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="#netCDF4.Dataset.createDimension"><code>createDimension</code></a> method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a>
+or <a href="#netCDF4.Group"><code>Group</code></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. In this example, there both the <code>time</code> and
+<code>level</code> dimensions are unlimited. Having more than one unlimited
+dimension is a new netCDF 4 feature, in netCDF 3 files there may be only
+one, and it must be the first (leftmost) dimension of the variable.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">level</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"level"</span><span class="p">,</span> <span class="bp">None</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">time</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"time"</span><span class="p">,</span> <span class="bp">None</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">lat</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"lat"</span><span class="p">,</span> <span class="mi">73</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">lon</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"lon"</span><span class="p">,</span> <span class="mi">144</span><span class="p">)</span>
+</pre></div>
+
+
+<p>All of the <a href="#netCDF4.Dimension"><code>Dimension</code></a> instances are stored in a python dictionary.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">print</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">dimensions</span>
+<span class="n">OrderedDict</span><span class="p">([(</span><span class="s">"level"</span><span class="p">,</span> <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">_netCDF4</span><span class="o">.</span><span class="n">Dimension</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b48030</span><span class="o">></span><span class="p">),</span>
+ <span class="p">(</span><span class="s">"time"</span><span class="p">,</span> <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">_netCDF4</span><span class="o">.</span><span class="n">Dimension</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b481c0</span><span class="o">></span><span class="p">),</span>
+ <span class="p">(</span><span class="s">"lat"</span><span class="p">,</span> <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">_netCDF4</span><span class="o">.</span><span class="n">Dimension</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b480f8</span><span class="o">></span><span class="p">),</span>
+ <span class="p">(</span><span class="s">"lon"</span><span class="p">,</span> <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">_netCDF4</span><span class="o">.</span><span class="n">Dimension</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b48a08</span><span class="o">></span><span class="p">)])</span>
+</pre></div>
+
+
+<p>Calling the python <code>len</code> function with a <a href="#netCDF4.Dimension"><code>Dimension</code></a> instance returns
+the current size of that dimension.
+The <a href="#netCDF4.Dimension.isunlimited"><code>isunlimited</code></a> method of a <a href="#netCDF4.Dimension"><code>Dimension</code></a> instance
+can be used to determine if the dimensions is unlimited, or appendable.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">print</span> <span class="nb">len</span><span class="p">(</span><span class="n">lon</span><span class="p">)</span>
+<span class="mi">144</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="nb">len</span><span class="o">.</span><span class="n">is_unlimited</span><span class="p">()</span>
+<span class="bp">False</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">time</span><span class="o">.</span><span class="n">is_unlimited</span><span class="p">()</span>
+<span class="bp">True</span>
+</pre></div>
+
+
+<p>Printing the <a href="#netCDF4.Dimension"><code>Dimension</code></a> object
+provides useful summary info, including the name and length of the dimension,
+and whether it is unlimited.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">for</span> <span class="n">dimobj</span> <span class="ow">in</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">dimensions</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">dimobj</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Dimension"</span><span class="o">></span> <span class="p">(</span><span class="n">unlimited</span><span class="p">):</span> <span class="n">name</span> <span class="o">=</span> <span class="s">"level"</span><span class="p">,</span> <span class="n">size</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Dimension"</span><span class="o">></span> <span class="p">(</span><span class="n">unlimited</span><span class="p">):</span> <span class="n">name</span> <span class="o">=</span> <span class="s">"time"</span><span class="p">,</span> <span class="n">size</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Dimension"</span><span class="o">></span><span class="p">:</span> <span class="n">name</span> <span class="o">=</span> <span class="s">"lat"</span><span class="p">,</span> <span class="n">size</span> <span class="o">=</span> <span class="mi">73</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Dimension"</span><span class="o">></span><span class="p">:</span> <span class="n">name</span> <span class="o">=</span> <span class="s">"lon"</span><span class="p">,</span> <span class="n">size</span> <span class="o">=</span> <span class="mi">144</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Dimension"</span><span class="o">></span> <span class="p">(</span><span class="n">unlimited</span><span class="p">):</span> <span class="n">name</span> <span class="o">=</span> <span class="s">"time"</span><span class="p">,</span> <span class="n">size</span> <span class="o">=</span> <span class="mi">0</span>
+</pre></div>
+
+
+<p><a href="#netCDF4.Dimension"><code>Dimension</code></a> names can be changed using the
+<code>netCDF4.Datatset.renameDimension</code> method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a> instance.</p>
+<h2><div id='section4'>4) 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">numpy module</a>. However,
+unlike numpy arrays, netCDF4 variables can be appended to along one or
+more 'unlimited' dimensions. To create a netCDF variable, use the
+<a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a> method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a> instance. The <a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></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="#netCDF4.Dataset.createDimension"><code>createDimension</code></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>'i8'</code> (64-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). 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. The unsigned integer types and the 64-bit integer type
+can only be used if the file format is <code>NETCDF4</code>.</p>
+<p>The dimensions themselves are usually also defined as variables, called
+coordinate variables. The <a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a>
+method returns an instance of the <a href="#netCDF4.Variable"><code>Variable</code></a> class whose methods can be
+used later to access and set variable data and attributes.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">times</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"time"</span><span class="p">,</span><span class="s">"f8"</span><span class="p">,(</span><span class="s">"time"</span><span class="p">,))</span>
+<span class="o">>>></span> <span class="n">levels</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"level"</span><span class="p">,</span><span class="s">"i4"</span><span class="p">,(</span><span class="s">"level"</span><span class="p">,))</span>
+<span class="o">>>></span> <span class="n">latitudes</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"latitude"</span><span class="p">,</span><span class="s">"f4"</span><span class="p">,(</span><span class="s">"lat"</span><span class="p">,))</span>
+<span class="o">>>></span> <span class="n">longitudes</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"longitude"</span><span class="p">,</span><span class="s">"f4"</span><span class="p">,(</span><span class="s">"lon"</span><span class="p">,))</span>
+<span class="o">>>></span> <span class="c"># two dimensions unlimited</span>
+<span class="o">>>></span> <span class="n">temp</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"temp"</span><span class="p">,</span><span class="s">"f4"</span><span class="p">,(</span><span class="s">"time"</span><span class="p">,</span><span class="s">"level"</span><span class="p">,</span><span class="s">"lat"</spa [...]
+</pre></div>
+
+
+<p>To get summary info on a <a href="#netCDF4.Variable"><code>Variable</code></a> instance in an interactive session, just print it.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">print</span> <span class="n">temp</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Variable"</span><span class="o">></span>
+<span class="n">float32</span> <span class="n">temp</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">)</span>
+ <span class="n">least_significant_digit</span><span class="p">:</span> <span class="mi">3</span>
+ <span class="n">units</span><span class="p">:</span> <span class="n">K</span>
+<span class="n">unlimited</span> <span class="n">dimensions</span><span class="p">:</span> <span class="n">time</span><span class="p">,</span> <span class="n">level</span>
+<span class="n">current</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">73</span><span class="p">,</span> <span class="mi">144</span><span class="p">)</span>
+</pre></div>
+
+
+<p>You can use a path to create a Variable inside a hierarchy of groups.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">ftemp</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"/forecasts/model1/temp"</span><span class="p">,</span><span class="s">"f4"</span><span class="p">,(</span><span class="s">"time"</span><span class="p">,</span><span class="s">"level"</span><span class= [...]
+</pre></div>
+
+
+<p>If the intermediate groups do not yet exist, they will be created.</p>
+<p>You can also query a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> instance directly to obtain <a href="#netCDF4.Group"><code>Group</code></a> or
+<a href="#netCDF4.Variable"><code>Variable</code></a> instances using paths.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">print</span> <span class="n">rootgrp</span><span class="p">[</span><span class="s">"/forecasts/model1"</span><span class="p">]</span> <span class="c"># a Group instance</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Group"</span><span class="o">></span>
+<span class="n">group</span> <span class="o">/</span><span class="n">forecasts</span><span class="o">/</span><span class="n">model1</span><span class="p">:</span>
+ <span class="n">dimensions</span><span class="p">(</span><span class="n">sizes</span><span class="p">):</span>
+ <span class="n">variables</span><span class="p">(</span><span class="n">dimensions</span><span class="p">):</span> <span class="n">float32</span> <span class="n">temp</span><span class="p">(</span><span class="n">time</span><span class="p">,</span><span class="n">level</span><span class="p">,</span><span class="n">lat</span><span class="p">,</span><span class="n">lon</span><span class="p">)</span>
+ <span class="n">groups</span><span class="p">:</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">rootgrp</span><span class="p">[</span><span class="s">"/forecasts/model1/temp"</span><span class="p">]</span> <span class="c"># a Variable instance</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Variable"</span><span class="o">></span>
+<span class="n">float32</span> <span class="n">temp</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">)</span>
+<span class="n">path</span> <span class="o">=</span> <span class="o">/</span><span class="n">forecasts</span><span class="o">/</span><span class="n">model1</span>
+<span class="n">unlimited</span> <span class="n">dimensions</span><span class="p">:</span> <span class="n">time</span><span class="p">,</span> <span class="n">level</span>
+<span class="n">current</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">73</span><span class="p">,</span> <span class="mi">144</span><span class="p">)</span>
+<span class="n">filling</span> <span class="n">on</span><span class="p">,</span> <span class="n">default</span> <span class="n">_FillValue</span> <span class="n">of</span> <span class="mf">9.96920996839e+36</span> <span class="n">used</span>
+</pre></div>
+
+
+<p>All of the variables in the <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> are stored in a
+Python dictionary, in the same way as the dimensions:</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">print</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">variables</span>
+<span class="n">OrderedDict</span><span class="p">([(</span><span class="s">"time"</span><span class="p">,</span> <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">Variable</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b4ba70</span><span class="o">></span><span class="p">),</span>
+ <span class="p">(</span><span class="s">"level"</span><span class="p">,</span> <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">Variable</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b4bab0</span><span class="o">></span><span class="p">),</span>
+ <span class="p">(</span><span class="s">"latitude"</span><span class="p">,</span> <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">Variable</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b4baf0</span><span class="o">></span><span class="p">),</span>
+ <span class="p">(</span><span class="s">"longitude"</span><span class="p">,</span> <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">Variable</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b4bb30</span><span class="o">></span><span class="p">),</span>
+ <span class="p">(</span><span class="s">"temp"</span><span class="p">,</span> <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">Variable</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1b4bb70</span><span class="o">></span><span class="p">)])</span>
+</pre></div>
+
+
+<p><a href="#netCDF4.Variable"><code>Variable</code></a> names can be changed using the
+<a href="#netCDF4.Dataset.renameVariable"><code>renameVariable</code></a> method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a>
+instance.</p>
+<h2><div id='section5'>5) 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 group, or the entire
+dataset, as a whole. <a href="#netCDF4.Variable"><code>Variable</code></a> attributes provide information about
+one of the variables in a group. Global attributes are set by assigning
+values to <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> instance variables. <a href="#netCDF4.Variable"><code>Variable</code></a>
+attributes are set by assigning values to <a href="#netCDF4.Variable"><code>Variable</code></a> instances
+variables. Attributes can be strings, numbers or sequences. Returning to
+our example,</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="kn">import</span> <span class="nn">time</span>
+<span class="o">>>></span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">description</span> <span class="o">=</span> <span class="s">"bogus example script"</span>
+<span class="o">>>></span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">history</span> <span class="o">=</span> <span class="s">"Created "</span> <span class="o">+</span> <span class="n">time</span><span class="o">.</span><span class="n">ctime</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">())</span>
+<span class="o">>>></span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">source</span> <span class="o">=</span> <span class="s">"netCDF4 python module tutorial"</span>
+<span class="o">>>></span> <span class="n">latitudes</span><span class="o">.</span><span class="n">units</span> <span class="o">=</span> <span class="s">"degrees north"</span>
+<span class="o">>>></span> <span class="n">longitudes</span><span class="o">.</span><span class="n">units</span> <span class="o">=</span> <span class="s">"degrees east"</span>
+<span class="o">>>></span> <span class="n">levels</span><span class="o">.</span><span class="n">units</span> <span class="o">=</span> <span class="s">"hPa"</span>
+<span class="o">>>></span> <span class="n">temp</span><span class="o">.</span><span class="n">units</span> <span class="o">=</span> <span class="s">"K"</span>
+<span class="o">>>></span> <span class="n">times</span><span class="o">.</span><span class="n">units</span> <span class="o">=</span> <span class="s">"hours since 0001-01-01 00:00:00.0"</span>
+<span class="o">>>></span> <span class="n">times</span><span class="o">.</span><span class="n">calendar</span> <span class="o">=</span> <span class="s">"gregorian"</span>
+</pre></div>
+
+
+<p>The <a href="#netCDF4.Dataset.ncattrs"><code>ncattrs</code></a> method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a>, <a href="#netCDF4.Group"><code>Group</code></a> or
+<a href="#netCDF4.Variable"><code>Variable</code></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>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">ncattrs</span><span class="p">():</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="s">"Global attr"</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="s">"="</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">rootgrp</span><span class="p">,</span><span class="n">name</span><span class="p">)</span>
+<span class="n">Global</span> <span class="n">attr</span> <span class="n">description</span> <span class="o">=</span> <span class="n">bogus</span> <span class="n">example</span> <span class="n">script</span>
+<span class="n">Global</span> <span class="n">attr</span> <span class="n">history</span> <span class="o">=</span> <span class="n">Created</span> <span class="n">Mon</span> <span class="n">Nov</span> <span class="mi">7</span> <span class="mf">10.30</span><span class="p">:</span><span class="mi">56</span> <span class="mi">2005</span>
+<span class="n">Global</span> <span class="n">attr</span> <span class="n">source</span> <span class="o">=</span> <span class="n">netCDF4</span> <span class="n">python</span> <span class="n">module</span> <span class="n">tutorial</span>
+</pre></div>
+
+
+<p>The <code>__dict__</code> attribute of a <a href="#netCDF4.Dataset"><code>Dataset</code></a>, <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Variable"><code>Variable</code></a>
+instance provides all the netCDF attribute name/value pairs in a python
+dictionary:</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">print</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">__dict__</span>
+<span class="n">OrderedDict</span><span class="p">([(</span><span class="s">u"description"</span><span class="p">,</span> <span class="s">u"bogus example script"</span><span class="p">),</span>
+ <span class="p">(</span><span class="s">u"history"</span><span class="p">,</span> <span class="s">u"Created Thu Mar 3 19:30:33 2011"</span><span class="p">),</span>
+ <span class="p">(</span><span class="s">u"source"</span><span class="p">,</span> <span class="s">u"netCDF4 python module tutorial"</span><span class="p">)])</span>
+</pre></div>
+
+
+<p>Attributes can be deleted from a netCDF <a href="#netCDF4.Dataset"><code>Dataset</code></a>, <a href="#netCDF4.Group"><code>Group</code></a> or
+<a href="#netCDF4.Variable"><code>Variable</code></a> using the python <code>del</code> statement (i.e. <code>del grp.foo</code>
+removes the attribute <code>foo</code> the the group <code>grp</code>).</p>
+<h2><div id='section6'>6) Writing data to and retrieving data from a netCDF variable.</h2>
+<p>Now that you have a netCDF <a href="#netCDF4.Variable"><code>Variable</code></a> instance, how do you put data
+into it? You can just treat it like an array and assign data to a slice.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="kn">import</span> <span class="nn">numpy</span>
+<span class="o">>>></span> <span class="n">lats</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">90</span><span class="p">,</span><span class="mi">91</span><span class="p">,</span><span class="mf">2.5</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">lons</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="mi">180</span><span class="p">,</span><span class="mf">2.5</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">latitudes</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">lats</span>
+<span class="o">>>></span> <span class="n">longitudes</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">lons</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="s">"latitudes =</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span><span class="n">latitudes</span><span class="p">[:]</span>
+<span class="n">latitudes</span> <span class="o">=</span>
+<span class="p">[</span><span class="o">-</span><span class="mf">90.</span> <span class="o">-</span><span class="mf">87.5</span> <span class="o">-</span><span class="mf">85.</span> <span class="o">-</span><span class="mf">82.5</span> <span class="o">-</span><span class="mf">80.</span> <span class="o">-</span><span class="mf">77.5</span> <span class="o">-</span><span class="mf">75.</span> <span class="o">-</span><span class="mf">72.5</span> <span class="o">-</span><span class="mf">70. [...]
+ <span class="o">-</span><span class="mf">60.</span> <span class="o">-</span><span class="mf">57.5</span> <span class="o">-</span><span class="mf">55.</span> <span class="o">-</span><span class="mf">52.5</span> <span class="o">-</span><span class="mf">50.</span> <span class="o">-</span><span class="mf">47.5</span> <span class="o">-</span><span class="mf">45.</span> <span class="o">-</span><span class="mf">42.5</span> <span class="o">-</span><span class="mf">40.</span> <span class="o [...]
+ <span class="o">-</span><span class="mf">30.</span> <span class="o">-</span><span class="mf">27.5</span> <span class="o">-</span><span class="mf">25.</span> <span class="o">-</span><span class="mf">22.5</span> <span class="o">-</span><span class="mf">20.</span> <span class="o">-</span><span class="mf">17.5</span> <span class="o">-</span><span class="mf">15.</span> <span class="o">-</span><span class="mf">12.5</span> <span class="o">-</span><span class="mf">10.</span> <span class=" [...]
+ <span class="mf">0.</span> <span class="mf">2.5</span> <span class="mf">5.</span> <span class="mf">7.5</span> <span class="mf">10.</span> <span class="mf">12.5</span> <span class="mf">15.</span> <span class="mf">17.5</span> <span class="mf">20.</span> <span class="mf">22.5</span> <span class="mf">25.</span> <span class="mf">27.5</span>
+ <span class="mf">30.</span> <span class="mf">32.5</span> <span class="mf">35.</span> <span class="mf">37.5</span> <span class="mf">40.</span> <span class="mf">42.5</span> <span class="mf">45.</span> <span class="mf">47.5</span> <span class="mf">50.</span> <span class="mf">52.5</span> <span class="mf">55.</span> <span class="mf">57.5</span>
+ <span class="mf">60.</span> <span class="mf">62.5</span> <span class="mf">65.</span> <span class="mf">67.5</span> <span class="mf">70.</span> <span class="mf">72.5</span> <span class="mf">75.</span> <span class="mf">77.5</span> <span class="mf">80.</span> <span class="mf">82.5</span> <span class="mf">85.</span> <span class="mf">87.5</span>
+ <span class="mf">90.</span> <span class="p">]</span>
+</pre></div>
+
+
+<p>Unlike NumPy's array objects, netCDF <a href="#netCDF4.Variable"><code>Variable</code></a>
+objects with unlimited dimensions will grow along those dimensions if you
+assign data outside the currently defined range of indices.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="c"># append along two unlimited dimensions by assigning to slice.</span>
+<span class="o">>>></span> <span class="n">nlats</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">rootgrp</span><span class="o">.</span><span class="n">dimensions</span><span class="p">[</span><span class="s">"lat"</span><span class="p">])</span>
+<span class="o">>>></span> <span class="n">nlons</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">rootgrp</span><span class="o">.</span><span class="n">dimensions</span><span class="p">[</span><span class="s">"lon"</span><span class="p">])</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="s">"temp shape before adding data = "</span><span class="p">,</span><span class="n">temp</span><span class="o">.</span><span class="n">shape</span>
+<span class="n">temp</span> <span class="n">shape</span> <span class="n">before</span> <span class="n">adding</span> <span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">73</span><span class="p">,</span> <span class="mi">144</span><span class="p">)</span>
+<span class="o">>>></span>
+<span class="o">>>></span> <span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">uniform</span>
+<span class="o">>>></span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> [...]
+<span class="o">>>></span> <span class="k">print</span> <span class="s">"temp shape after adding data = "</span><span class="p">,</span><span class="n">temp</span><span class="o">.</span><span class="n">shape</span>
+<span class="n">temp</span> <span class="n">shape</span> <span class="n">after</span> <span class="n">adding</span> <span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">73</span><span class="p">,</span> <span class="mi">144</span><span class="p">)</span>
+<span class="o">>>></span>
+<span class="o">>>></span> <span class="c"># levels have grown, but no values yet assigned.</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="s">"levels shape after adding pressure data = "</span><span class="p">,</span><span class="n">levels</span><span class="o">.</span><span class="n">shape</span>
+<span class="n">levels</span> <span class="n">shape</span> <span class="n">after</span> <span class="n">adding</span> <span class="n">pressure</span> <span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,)</span>
+</pre></div>
+
+
+<p>Note that the size of the levels variable grows when data is appended
+along the <code>level</code> dimension of the variable <code>temp</code>, even though no
+data has yet been assigned to levels.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="c"># now, assign data to levels dimension variable.</span>
+<span class="o">>>></span> <span class="n">levels</span><span class="p">[:]</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1000.</span><span class="p">,</span><span class="mf">850.</span><span class="p">,</span><span class="mf">700.</span><span class="p">,</span><span class="mf">500.</span><span class="p">,</span><span class="mf">300.</span><span class="p">,</span><span class="mf">250.</span><span class="p">,</span><span class="mf">200.</span><span cla [...]
+</pre></div>
+
+
+<p>However, that there are some differences between NumPy and netCDF
+variable slicing rules. Slices behave as usual, being specified as a
+<code>start:stop:step</code> triplet. Using a scalar integer index <code>i</code> takes the ith
+element and reduces the rank of the output array by one. Boolean array and
+integer sequence indexing behaves differently for netCDF variables
+than for numpy arrays. Only 1-d boolean arrays and integer sequences are
+allowed, and these indices work independently along each dimension (similar
+to the way vector subscripts work in fortran). This means that</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+</pre></div>
+
+
+<p>returns an array of shape (4,4) when slicing a netCDF variable, but for a
+numpy array it returns an array of shape (4,).
+Similarly, a netCDF variable of shape <code>(2,3,4,5)</code> indexed
+with <code>[0, array([True, False, True]), array([False, True, True, True]), :]</code>
+would return a <code>(2, 3, 5)</code> array. In NumPy, this would raise an error since
+it would be equivalent to <code>[0, [0,1], [1,2,3], :]</code>. When slicing with integer
+sequences, the indices must be sorted in increasing order and contain no duplicates.
+While this behaviour may cause some confusion for those used to NumPy's 'fancy indexing' rules,
+it provides a very powerful way to extract data from multidimensional netCDF
+variables by using logical operations on the dimension arrays to create slices.</p>
+<p>For example,</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">tempdat</span> <span class="o">=</span> <span class="n">temp</span><span class="p">[::</span><span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span> <span class="n">lats</span><span class="o">></span><span class="mi">0</span><span class="p"> [...]
+</pre></div>
+
+
+<p>will extract time indices 0,2 and 4, pressure levels
+850, 500 and 200 hPa, all Northern Hemisphere latitudes and Eastern
+Hemisphere longitudes, resulting in a numpy array of shape (3, 3, 36, 71).</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">print</span> <span class="s">"shape of fancy temp slice = "</span><span class="p">,</span><span class="n">tempdat</span><span class="o">.</span><span class="n">shape</span>
+<span class="n">shape</span> <span class="n">of</span> <span class="n">fancy</span> <span class="n">temp</span> <span class="nb">slice</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">36</span><span class="p">,</span> <span class="mi">71</span><span class="p">)</span>
+</pre></div>
+
+
+<p><strong><em>Special note for scalar variables</em></strong>: To extract data from a scalar variable
+<code>v</code> with no associated dimensions, use <code>np.asarray(v)</code> or <code>v[...]</code>. The result
+will be a numpy scalar array.</p>
+<h2><div id='section7'>7) Dealing with time coordinates.</h2>
+<p>Time coordinate values pose a special challenge to netCDF users. Most
+metadata standards (such as CF) 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="#netCDF4.num2date"><code>num2date</code></a> and <a href="#netCDF4.date2num"><code>date2num</code></a> are
+provided with this package to do just that. Here's an example of how they
+can be used:</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="c"># fill in times.</span>
+<span class="o">>>></span> <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
+<span class="o">>>></span> <span class="kn">from</span> <span class="nn">netCDF4</span> <span class="kn">import</span> <span class="n">num2date</span><span class="p">,</span> <span class="n">date2num</span>
+<span class="o">>>></span> <span class="n">dates</span> <span class="o">=</span> <span class="p">[</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2001</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span><span class="o">+</span><span class="n">n</span><span class="o">*</span><span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">= [...]
+<span class="o">>>></span> <span class="n">times</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">date2num</span><span class="p">(</span><span class="n">dates</span><span class="p">,</span><span class="n">units</span><span class="o">=</span><span class="n">times</span><span class="o">.</span><span class="n">units</span><span class="p">,</span><span class="n">calendar</span><span class="o">=</span><span class="n">times</span><span class="o">.</span><span [...]
+<span class="o">>>></span> <span class="k">print</span> <span class="s">"time values (in units </span><span class="si">%s</span><span class="s">): "</span> <span class="o">%</span> <span class="n">times</span><span class="o">.</span><span class="n">units</span><span class="o">+</span><span class="s">"</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span><span class="n">times</span><span class="p">[:]</span>
+<span class="n">time</span> <span class="n">values</span> <span class="p">(</span><span class="ow">in</span> <span class="n">units</span> <span class="n">hours</span> <span class="n">since</span> <span class="n">January</span> <span class="mi">1</span><span class="p">,</span> <span class="mo">0001</span><span class="p">):</span>
+<span class="p">[</span> <span class="mf">17533056.</span> <span class="mf">17533068.</span> <span class="mf">17533080.</span> <span class="mf">17533092.</span> <span class="mf">17533104.</span><span class="p">]</span>
+<span class="o">>>></span> <span class="n">dates</span> <span class="o">=</span> <span class="n">num2date</span><span class="p">(</span><span class="n">times</span><span class="p">[:],</span><span class="n">units</span><span class="o">=</span><span class="n">times</span><span class="o">.</span><span class="n">units</span><span class="p">,</span><span class="n">calendar</span><span class="o">=</span><span class="n">times</span><span class="o">.</span><span class="n">calendar</spa [...]
+<span class="o">>>></span> <span class="k">print</span> <span class="s">"dates corresponding to time values:</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span><span class="n">dates</span>
+<span class="n">dates</span> <span class="n">corresponding</span> <span class="n">to</span> <span class="n">time</span> <span class="n">values</span><span class="p">:</span>
+<span class="p">[</span><span class="mi">2001</span><span class="o">-</span><span class="mo">03</span><span class="o">-</span><span class="mo">01</span> <span class="mo">00</span><span class="p">:</span><span class="mo">00</span><span class="p">:</span><span class="mo">00</span> <span class="mi">2001</span><span class="o">-</span><span class="mo">03</span><span class="o">-</span><span class="mo">01</span> <span class="mi">12</span><span class="p">:</span><span class="mo">00</span><span c [...]
+ <span class="mi">2001</span><span class="o">-</span><span class="mo">03</span><span class="o">-</span><span class="mo">02</span> <span class="mi">12</span><span class="p">:</span><span class="mo">00</span><span class="p">:</span><span class="mo">00</span> <span class="mi">2001</span><span class="o">-</span><span class="mo">03</span><span class="o">-</span><span class="mo">03</span> <span class="mo">00</span><span class="p">:</span><span class="mo">00</span><span class="p">:</span><span [...]
+</pre></div>
+
+
+<p><a href="#netCDF4.num2date"><code>num2date</code></a> converts numeric values of time in the specified <code>units</code>
+and <code>calendar</code> to datetime objects, and <a href="#netCDF4.date2num"><code>date2num</code></a> does the reverse.
+All the calendars currently defined in the
+<a href="http://cf-pcmdi.llnl.gov/documents/cf-conventions/">CF metadata convention</a> are supported.
+A function called <a href="#netCDF4.date2index"><code>date2index</code></a> is also provided which returns the indices
+of a netCDF time variable corresponding to a sequence of datetime instances.</p>
+<h2><div id='section8'>8) 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="#netCDF4.MFDataset"><code>MFDataset</code></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="#netCDF4.Dataset"><code>Dataset</code></a> instance, create a <a href="#netCDF4.MFDataset"><code>MFDataset</code></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). The files
+must in be in <code>NETCDF3_64BIT</code>, <code>NETCDF3_CLASSIC</code> or
+<code>NETCDF4_CLASSIC format</code> (<code>NETCDF4</code> formatted multi-file
+datasets are not supported).</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">for</span> <span class="n">nf</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+<span class="o">>>></span> <span class="n">f</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="s">"mftest</span><span class="si">%s</span><span class="s">.nc"</span> <span class="o">%</span> <span class="n">nf</span><span class="p">,</span><span class="s">"w"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">f</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"x"</span><span class="p">,</span><span class="bp">None</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">x</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"x"</span><span class="p">,</span><span class="s">"i"</span><span class="p">,(</span><span class="s">"x"</span><span class="p">,))</span>
+<span class="o">>>></span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">nf</span><span class="o">*</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="o">*</span><span class="p">(</span>< [...]
+<span class="o">>>></span> <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+</pre></div>
+
+
+<p>Now read all the files back in at once with <a href="#netCDF4.MFDataset"><code>MFDataset</code></a></p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="kn">from</span> <span class="nn">netCDF4</span> <span class="kn">import</span> <span class="n">MFDataset</span>
+<span class="o">>>></span> <span class="n">f</span> <span class="o">=</span> <span class="n">MFDataset</span><span class="p">(</span><span class="s">"mftest*nc"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">f</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="s">"x"</span><span class="p">][:]</span>
+<span class="p">[</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span> <span class="mi">8</span> <span class="mi">9</span> <span class="mi">10</span> <span class="mi">11</span> <span class="mi">12</span> <span class="mi">13</span> <span class="mi">14</span> <span class="mi">15</span> <span class="mi">16</span> <span cla [...]
+ <span class="mi">25</span> <span class="mi">26</span> <span class="mi">27</span> <span class="mi">28</span> <span class="mi">29</span> <span class="mi">30</span> <span class="mi">31</span> <span class="mi">32</span> <span class="mi">33</span> <span class="mi">34</span> <span class="mi">35</span> <span class="mi">36</span> <span class="mi">37</span> <span class="mi">38</span> <span class="mi">39</span> <span class="mi">40</span> <span class="mi">41</span> <span class="mi">42</span> <span [...]
+ <span class="mi">50</span> <span class="mi">51</span> <span class="mi">52</span> <span class="mi">53</span> <span class="mi">54</span> <span class="mi">55</span> <span class="mi">56</span> <span class="mi">57</span> <span class="mi">58</span> <span class="mi">59</span> <span class="mi">60</span> <span class="mi">61</span> <span class="mi">62</span> <span class="mi">63</span> <span class="mi">64</span> <span class="mi">65</span> <span class="mi">66</span> <span class="mi">67</span> <span [...]
+ <span class="mi">75</span> <span class="mi">76</span> <span class="mi">77</span> <span class="mi">78</span> <span class="mi">79</span> <span class="mi">80</span> <span class="mi">81</span> <span class="mi">82</span> <span class="mi">83</span> <span class="mi">84</span> <span class="mi">85</span> <span class="mi">86</span> <span class="mi">87</span> <span class="mi">88</span> <span class="mi">89</span> <span class="mi">90</span> <span class="mi">91</span> <span class="mi">92</span> <span [...]
+</pre></div>
+
+
+<p>Note that <a href="#netCDF4.MFDataset"><code>MFDataset</code></a> can only be used to read, not write, multi-file
+datasets.</p>
+<h2><div id='section9'>9) Efficient compression of netCDF variables.</h2>
+<p>Data stored in netCDF 4 <a href="#netCDF4.Variable"><code>Variable</code></a> objects can be compressed and
+decompressed on the fly. The parameters for the compression are
+determined by the <code>zlib</code>, <code>complevel</code> and <code>shuffle</code> keyword arguments
+to the <a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a> method. To turn on
+compression, set <code>zlib=True</code>. The <code>complevel</code> keyword regulates the
+speed and efficiency of the compression (1 being fastest, but lowest
+compression ratio, 9 being slowest but best compression ratio). The
+default value of <code>complevel</code> is 4. Setting <code>shuffle=False</code> will turn
+off the HDF5 shuffle filter, which de-interlaces a block of data before
+compression by reordering the bytes. The shuffle filter can
+significantly improve compression ratios, and is on by default. Setting
+<code>fletcher32</code> keyword argument to
+<a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a> to <code>True</code> (it's <code>False</code> by
+default) enables the Fletcher32 checksum algorithm for error detection.
+It's also possible to set the HDF5 chunking parameters and endian-ness
+of the binary data stored in the HDF5 file with the <code>chunksizes</code>
+and <code>endian</code> keyword arguments to
+<a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a>. These keyword arguments only
+are relevant for <code>NETCDF4</code> and <code>NETCDF4_CLASSIC</code> files (where the
+underlying file format is HDF5) and are silently ignored if the file
+format is <code>NETCDF3_CLASSIC</code> or <code>NETCDF3_64BIT</code>,</p>
+<p>If your data only has a certain number of digits of precision (say for
+example, it is temperature data that was measured with a precision of
+0.1 degrees), you can dramatically improve zlib compression by
+quantizing (or truncating) the data using the <code>least_significant_digit</code>
+keyword argument to <a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a>. The least
+significant digit is the power of ten of the smallest decimal place in
+the data that is a reliable value. For example if the data has a
+precision of 0.1, then setting <code>least_significant_digit=1</code> will cause
+data the data to be quantized using <code>numpy.around(scale*data)/scale</code>, where
+scale = 2**bits, and bits is determined so that a precision of 0.1 is
+retained (in this case bits=4). Effectively, this makes the compression
+'lossy' instead of 'lossless', that is some precision in the data is
+sacrificed for the sake of disk space.</p>
+<p>In our example, try replacing the line</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">temp</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"temp"</span><span class="p">,</span><span class="s">"f4"</span><span class="p">,(</span><span class="s">"time"</span><span class="p">,</span><span class="s">"level"</span><span class="p">,</span><span c [...]
+</pre></div>
+
+
+<p>with</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">temp</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"temp"</span><span class="p">,</span><span class="s">"f4"</span><span class="p">,(</span><span class="s">"time"</span><span class="p">,</span><span class="s">"level"</span><span class="p">,</span><span c [...]
+</pre></div>
+
+
+<p>and then</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">temp</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"temp"</span><span class="p">,</span><span class="s">"f4"</span><span class="p">,(</span><span class="s">"time"</span><span class="p">,</span><span class="s">"level"</span><span class="p">,</span><span c [...]
+</pre></div>
+
+
+<p>and see how much smaller the resulting files are.</p>
+<h2><div id='section10'>10) Beyond homogenous arrays of a fixed type - compound data types.</h2>
+<p>Compound data types map directly to numpy structured (a.k.a 'record'
+arrays). Structured arrays are akin to C structs, or derived types
+in Fortran. They allow for the construction of table-like structures
+composed of combinations of other data types, including other
+compound types. Compound types might be useful for representing multiple
+parameter values at each point on a grid, or at each time and space
+location for scattered (point) data. You can then access all the
+information for a point by reading one variable, instead of reading
+different parameters from different variables. Compound data types
+are created from the corresponding numpy data type using the
+<a href="#netCDF4.Dataset.createCompoundType"><code>createCompoundType</code></a> method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> instance.
+Since there is no native complex data type in netcdf, compound types are handy
+for storing numpy complex arrays. Here's an example:</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">f</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="s">"complex.nc"</span><span class="p">,</span><span class="s">"w"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">size</span> <span class="o">=</span> <span class="mi">3</span> <span class="c"># length of 1-d complex array</span>
+<span class="o">>>></span> <span class="c"># create sample complex data.</span>
+<span class="o">>>></span> <span class="n">datac</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mi">1j</span><span class="o">*</span><span class="p">(</span><span class="mf">1.</span><span class="o">+</span><span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num [...]
+<span class="o">>>></span> <span class="c"># create complex128 compound data type.</span>
+<span class="o">>>></span> <span class="n">complex128</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dtype</span><span class="p">([(</span><span class="s">"real"</span><span class="p">,</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">),(</span><span class="s">"imag"</span><span class="p">,</span><span class="n">numpy</span><span class="o">.</span><span [...]
+<span class="o">>>></span> <span class="n">complex128_t</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">createCompoundType</span><span class="p">(</span><span class="n">complex128</span><span class="p">,</span><span class="s">"complex128"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="c"># create a variable with this data type, write some data to it.</span>
+<span class="o">>>></span> <span class="n">f</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"x_dim"</span><span class="p">,</span><span class="bp">None</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">v</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"cmplx_var"</span><span class="p">,</span><span class="n">complex128_t</span><span class="p">,</span><span class="s">"x_dim"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">size</span><span class="p">,</span><span class="n">complex128</span><span class="p">)</span> <span class="c"># numpy structured array</span>
+<span class="o">>>></span> <span class="n">data</span><span class="p">[</span><span class="s">"real"</span><span class="p">]</span> <span class="o">=</span> <span class="n">datac</span><span class="o">.</span><span class="n">real</span><span class="p">;</span> <span class="n">data</span><span class="p">[</span><span class="s">"imag"</span><span class="p">]</span> <span class="o">=</span> <span class="n">datac</span><span class="o">.</span><span class="n">imag</span>
+<span class="o">>>></span> <span class="n">v</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">data</span> <span class="c"># write numpy structured array to netcdf compound var</span>
+<span class="o">>>></span> <span class="c"># close and reopen the file, check the contents.</span>
+<span class="o">>>></span> <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">();</span> <span class="n">f</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="s">"complex.nc"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">v</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="s">"cmplx_var"</span><span class="p">]</span>
+<span class="o">>>></span> <span class="n">datain</span> <span class="o">=</span> <span class="n">v</span><span class="p">[:]</span> <span class="c"># read in all the data into a numpy structured array</span>
+<span class="o">>>></span> <span class="c"># create an empty numpy complex array</span>
+<span class="o">>>></span> <span class="n">datac2</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">datain</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">numpy</span><span class="o">.</span><span class="n">complex128</span><span class="p">)</span>
+<span class="o">>>></span> <span class="c"># .. fill it with contents of structured array.</span>
+<span class="o">>>></span> <span class="n">datac2</span><span class="o">.</span><span class="n">real</span> <span class="o">=</span> <span class="n">datain</span><span class="p">[</span><span class="s">"real"</span><span class="p">];</span> <span class="n">datac2</span><span class="o">.</span><span class="n">imag</span> <span class="o">=</span> <span class="n">datain</span><span class="p">[</span><span class="s">"imag"</span><span class="p">]</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">datac</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span><span class="n">datac</span> <span class="c"># original data</span>
+<span class="n">complex128</span> <span class="p">[</span> <span class="mf">0.54030231</span><span class="o">+</span><span class="mf">0.84147098j</span> <span class="o">-</span><span class="mf">0.84147098</span><span class="o">+</span><span class="mf">0.54030231j</span> <span class="o">-</span><span class="mf">0.54030231</span><span class="o">-</span><span class="mf">0.84147098j</span><span class="p">]</span>
+<span class="o">>>></span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">datac2</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span><span class="n">datac2</span> <span class="c"># data from file</span>
+<span class="n">complex128</span> <span class="p">[</span> <span class="mf">0.54030231</span><span class="o">+</span><span class="mf">0.84147098j</span> <span class="o">-</span><span class="mf">0.84147098</span><span class="o">+</span><span class="mf">0.54030231j</span> <span class="o">-</span><span class="mf">0.54030231</span><span class="o">-</span><span class="mf">0.84147098j</span><span class="p">]</span>
+</pre></div>
+
+
+<p>Compound types can be nested, but you must create the 'inner'
+ones first. All of the compound types defined for a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> are stored in a
+Python dictionary, just like variables and dimensions. As always, printing
+objects gives useful summary information in an interactive session:</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="k">print</span> <span class="n">f</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Dataset"</span><span class="o">></span>
+<span class="n">root</span> <span class="n">group</span> <span class="p">(</span><span class="n">NETCDF4</span> <span class="nb">file</span> <span class="n">format</span><span class="p">):</span>
+ <span class="n">dimensions</span><span class="p">:</span> <span class="n">x_dim</span>
+ <span class="n">variables</span><span class="p">:</span> <span class="n">cmplx_var</span>
+ <span class="n">groups</span><span class="p">:</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Variable"</span><span class="o">></span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">f</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="s">"cmplx_var"</span><span class="p">]</span>
+<span class="n">compound</span> <span class="n">cmplx_var</span><span class="p">(</span><span class="n">x_dim</span><span class="p">)</span>
+<span class="n">compound</span> <span class="n">data</span> <span class="nb">type</span><span class="p">:</span> <span class="p">[(</span><span class="s">"real"</span><span class="p">,</span> <span class="s">"<f8"</span><span class="p">),</span> <span class="p">(</span><span class="s">"imag"</span><span class="p">,</span> <span class="s">"<f8"</span><span class="p">)]</span>
+<span class="n">unlimited</span> <span class="n">dimensions</span><span class="p">:</span> <span class="n">x_dim</span>
+<span class="n">current</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,)</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">f</span><span class="o">.</span><span class="n">cmptypes</span>
+<span class="n">OrderedDict</span><span class="p">([(</span><span class="s">"complex128"</span><span class="p">,</span> <span class="o"><</span><span class="n">netCDF4</span><span class="o">.</span><span class="n">CompoundType</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x1029eb7e8</span><span class="o">></span><span class="p">)])</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">f</span><span class="o">.</span><span class="n">cmptypes</span><span class="p">[</span><span class="s">"complex128"</span><span class="p">]</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.CompoundType"</span><span class="o">></span><span class="p">:</span> <span class="n">name</span> <span class="o">=</span> <span class="s">"complex128"</span><span class="p">,</span> <span class="n">numpy</span> <span class="n">dtype</span> <span class="o">=</span> <span class="p">[(</span><span class="s">u"real"</span><span class="p">,</span><span class="s">"< [...]
+</pre></div>
+
+
+<h2><div id='section11'>11) Variable-length (vlen) data types.</h2>
+<p>NetCDF 4 has support for variable-length or "ragged" arrays. These are arrays
+of variable length sequences having the same type. To create a variable-length
+data type, use the <a href="#netCDF4.Dataset.createVLType"><code>createVLType</code></a> method
+method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> instance.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">f</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="s">"tst_vlen.nc"</span><span class="p">,</span><span class="s">"w"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">vlen_t</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">createVLType</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="s">"phony_vlen"</span><span class="p">)</span>
+</pre></div>
+
+
+<p>The numpy datatype of the variable-length sequences and the name of the
+new datatype must be specified. Any of the primitive datatypes can be
+used (signed and unsigned integers, 32 and 64 bit floats, and characters),
+but compound data types cannot.
+A new variable can then be created using this datatype.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">x</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"x"</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">y</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"y"</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">vlvar</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"phony_vlen_var"</span><span class="p">,</span> <span class="n">vlen_t</span><span class="p">,</span> <span class="p">(</span><span class="s">"y"</span><span class="p">,</span><span class="s">"x"</span><span class="p">))</span>
+</pre></div>
+
+
+<p>Since there is no native vlen datatype in numpy, vlen arrays are represented
+in python as object arrays (arrays of dtype <code>object</code>). These are arrays whose
+elements are Python object pointers, and can contain any type of python object.
+For this application, they must contain 1-D numpy arrays all of the same type
+but of varying length.
+In this case, they contain 1-D numpy <code>int32</code> arrays of random length betwee
+1 and 10.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="kn">import</span> <span class="nn">random</span>
+<span class="o">>>></span> <span class="n">data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span><span class="nb">object</span><span class="p">)</span>
+<span class="o">>>></span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)):</span>
+<span class="o">>>></span> <span class="n">data</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span><span class="n"> [...]
+<span class="o">>>></span> <span class="n">data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">data</span><span class="p">,(</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">),</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
+<span class="o">>>></span> <span class="n">vlvar</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">data</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="s">"vlen variable =</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span><span class="n">vlvar</span><span class="p">[:]</span>
+<span class="n">vlen</span> <span class="n">variable</span> <span class="o">=</span>
+<span class="p">[[[</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span> <span class="mi">8</span> <span class="mi">9</span> <span class="mi">10</span><span class="p">]</span> <span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]</ [...]
+ <span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span><span class="p">]</span> <span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span><span class="p">]</span> <span class="p">[</span><span class="mi">1</span> <span [...]
+ <span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]</span> <span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span><span class="p">]</span> <span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+ <span class="p">[[</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span> <span class="mi">8</span> <span class="mi">9</span> <span class="mi">10</span><span class="p">]</span> <span class="p">[</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class= [...]
+ <span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span> <span class="mi">8</span><span class="p">]]]</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">f</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Dataset"</span><span class="o">></span>
+<span class="n">root</span> <span class="n">group</span> <span class="p">(</span><span class="n">NETCDF4</span> <span class="nb">file</span> <span class="n">format</span><span class="p">):</span>
+ <span class="n">dimensions</span><span class="p">:</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span>
+ <span class="n">variables</span><span class="p">:</span> <span class="n">phony_vlen_var</span>
+ <span class="n">groups</span><span class="p">:</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">f</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="s">"phony_vlen_var"</span><span class="p">]</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Variable"</span><span class="o">></span>
+<span class="n">vlen</span> <span class="n">phony_vlen_var</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+<span class="n">vlen</span> <span class="n">data</span> <span class="nb">type</span><span class="p">:</span> <span class="n">int32</span>
+<span class="n">unlimited</span> <span class="n">dimensions</span><span class="p">:</span>
+<span class="n">current</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">f</span><span class="o">.</span><span class="n">VLtypes</span><span class="p">[</span><span class="s">"phony_vlen"</span><span class="p">]</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.VLType"</span><span class="o">></span><span class="p">:</span> <span class="n">name</span> <span class="o">=</span> <span class="s">"phony_vlen"</span><span class="p">,</span> <span class="n">numpy</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">int32</span>
+</pre></div>
+
+
+<p>Numpy object arrays containing python strings can also be written as vlen
+variables, For vlen strings, you don't need to create a vlen data type.
+Instead, simply use the python <code>str</code> builtin (or a numpy string datatype
+with fixed length greater than 1) when calling the
+<a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a> method.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">z</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"z"</span><span class="p">,</span><span class="mi">10</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">strvar</span> <span class="o">=</span> <span class="n">rootgrp</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"strvar"</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="s">"z"</span><span class="p">)</span>
+</pre></div>
+
+
+<p>In this example, an object array is filled with random python strings with
+random lengths between 2 and 12 characters, and the data in the object
+array is assigned to the vlen string variable.</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="n">chars</span> <span class="o">=</span> <span class="s">"1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"</span>
+<span class="o">>>></span> <span class="n">data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="s">"O"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+<span class="o">>>></span> <span class="n">stringlen</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">data</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="s">""</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">chars</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span [...]
+<span class="o">>>></span> <span class="n">strvar</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">data</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="s">"variable-length string variable:</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span><span class="n">strvar</span><span class="p">[:]</span>
+<span class="n">variable</span><span class="o">-</span><span class="n">length</span> <span class="n">string</span> <span class="n">variable</span><span class="p">:</span>
+<span class="p">[</span><span class="n">aDy29jPt</span> <span class="mi">5</span><span class="n">DS9X8</span> <span class="n">jd7aplD</span> <span class="n">b8t4RM</span> <span class="n">jHh8hq</span> <span class="n">KtaPWF9cQj</span> <span class="n">Q1hHN5WoXSiT</span> <span class="n">MMxsVeq</span> <span class="n">tdLUzvVTzj</span><span class="p">]</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">f</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Dataset"</span><span class="o">></span>
+<span class="n">root</span> <span class="n">group</span> <span class="p">(</span><span class="n">NETCDF4</span> <span class="nb">file</span> <span class="n">format</span><span class="p">):</span>
+ <span class="n">dimensions</span><span class="p">:</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span>
+ <span class="n">variables</span><span class="p">:</span> <span class="n">phony_vlen_var</span><span class="p">,</span> <span class="n">strvar</span>
+ <span class="n">groups</span><span class="p">:</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">f</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="s">"strvar"</span><span class="p">]</span>
+<span class="o"><</span><span class="nb">type</span> <span class="s">"netCDF4._netCDF4.Variable"</span><span class="o">></span>
+<span class="n">vlen</span> <span class="n">strvar</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
+<span class="n">vlen</span> <span class="n">data</span> <span class="nb">type</span><span class="p">:</span> <span class="o"><</span><span class="nb">type</span> <span class="s">"str"</span><span class="o">></span>
+<span class="n">unlimited</span> <span class="n">dimensions</span><span class="p">:</span>
+<span class="n">current</span> <span class="n">size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,)</span>
+</pre></div>
+
+
+<p>It is also possible to set contents of vlen string variables with numpy arrays
+of any string or unicode data type. Note, however, that accessing the contents
+of such variables will always return numpy arrays with dtype <code>object</code>.</p>
+<p>All of the code in this tutorial is available in <code>examples/tutorial.py</code>,
+Unit tests are in the <code>test</code> directory.</p>
+<p><strong>contact</strong>: Jeffrey Whitaker <a href="mailto:jeffrey.s.whitaker@noaa.gov">jeffrey.s.whitaker@noaa.gov</a></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>
+
+
+ </header>
+
+ <section id="section-items">
+
+ <h2 class="section-title" id="header-functions">Functions</h2>
+
+ <div class="item">
+ <div class="name def" id="netCDF4.chartostring">
+ <p>def <span class="ident">chartostring</span>(</p><p>b)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>convert a character array to a string array with one less dimension.</p>
+<p><strong><code>b</code></strong>: Input character array (numpy datatype <code>'S1'</code> or <code>'U1'</code>).
+Will be converted to a array of strings, where each string has a fixed
+length of <code>b.shape[-1]</code> characters.</p>
+<p>returns a numpy string array with datatype <code>'SN'</code> or <code>'UN'</code> and shape
+<code>b.shape[:-1]</code> where where <code>N=b.shape[-1]</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.date2index">
+ <p>def <span class="ident">date2index</span>(</p><p>dates, nctime, calendar=None, select='exact')</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Return indices of a netCDF time variable corresponding to the given dates.</p>
+<p><strong><code>dates</code></strong>: A datetime object or a sequence of datetime objects.
+The datetime objects should not include a time-zone offset.</p>
+<p><strong><code>nctime</code></strong>: A netCDF time variable object. The nctime object must have a
+<code>units</code> attribute.</p>
+<p><strong><code>calendar</code></strong>: describes the calendar used in the time calculations.
+All the values currently defined in the
+<a href="http://cfconventions.org">CF metadata convention</a>
+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.</p>
+<p><strong><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.</p>
+<p>returns an index (indices) of the netCDF time variable corresponding
+to the given datetime object(s).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.date2num">
+ <p>def <span class="ident">date2num</span>(</p><p>dates,units,calendar='standard')</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Return numeric time values given datetime objects. The units
+of the numeric time values are described by the <code>netCDF4.units</code> argument
+and the <code>netCDF4.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><strong><code>dates</code></strong>: A datetime object or a sequence of datetime objects.
+The datetime objects should not include a time-zone offset.</p>
+<p><strong><code>units</code></strong>: a string of the form <code><time units> since <reference time></code>
+describing the time units. <code><time units></code> can be days, hours, minutes,
+seconds, milliseconds or microseconds. <code><reference time></code> is the time
+origin. Accuracy is somewhere between a millisecond and a microsecond,
+depending on the time interval and the calendar used.</p>
+<p><strong><code>calendar</code></strong>: describes the calendar used in the time calculations.
+All the values currently defined in the
+<a href="http://cfconventions.org">CF metadata convention</a>
+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.</p>
+<p>returns a numeric time value, or an array of numeric time values.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.getlibversion">
+ <p>def <span class="ident">getlibversion</span>(</p><p>)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>returns a string describing the version of the netcdf library
+used to build the module, and when it was built.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.num2date">
+ <p>def <span class="ident">num2date</span>(</p><p>times,units,calendar='standard')</p>
+ </div>
+
+
+
+
+ <div class="desc"><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><strong><code>times</code></strong>: numeric time values.</p>
+<p><strong><code>units</code></strong>: a string of the form <code><time units> since <reference time></code>
+describing the time units. <code><time units></code> can be days, hours, minutes,
+seconds, milliseconds or microseconds. <code><reference time></code> is the time
+origin. Accuracy is somewhere between a millisecond and a microsecond,
+depending on the time interval and the calendar used.</p>
+<p><strong><code>calendar</code></strong>: describes the calendar used in the time calculations.
+All the values currently defined in the
+<a href="http://cfconventions.org">CF metadata convention</a>
+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.</p>
+<p>returns a datetime instance, or an array of datetime instances.</p>
+<p><strong><em>Note</em></strong>: The datetime instances returned are 'real' python datetime
+objects if <code>calendar='proleptic_gregorian'</code>, or
+<code>calendar='standard'</code> or <code>'gregorian'</code>
+and the date is after the breakpoint between the Julian and
+Gregorian calendars (1582-10-15). Otherwise, they are 'phony' datetime
+objects which support some but not all the methods of 'real' python
+datetime objects. The datetime instances
+do not contain a time-zone offset, even if the specified <code>units</code>
+contains one.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.stringtoarr">
+ <p>def <span class="ident">stringtoarr</span>(</p><p>a, NUMCHARS,dtype='S')</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>convert a string to a character array of length <code>NUMCHARS</code></p>
+<p><strong><code>a</code></strong>: Input python string.</p>
+<p><strong><code>NUMCHARS</code></strong>: number of characters used to represent string
+(if len(a) < <code>NUMCHARS</code>, it will be padded on the right with blanks).</p>
+<p><strong><code>dtype</code></strong>: type of numpy array to return. Default is <code>'S'</code>, which
+means an array of dtype <code>'S1'</code> will be returned. If dtype=<code>'U'</code>, a
+unicode array (dtype = <code>'U1'</code>) will be returned.</p>
+<p>returns a rank 1 numpy character array of length NUMCHARS with datatype <code>'S1'</code>
+(default) or <code>'U1'</code> (if dtype=<code>'U'</code>)</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.stringtochar">
+ <p>def <span class="ident">stringtochar</span>(</p><p>a)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>convert a string array to a character array with one extra dimension</p>
+<p><strong><code>a</code></strong>: Input numpy string array with numpy datatype <code>'SN'</code> or <code>'UN'</code>, where N
+is the number of characters in each string. Will be converted to
+an array of characters (datatype <code>'S1'</code> or <code>'U1'</code>) of shape <code>a.shape + (N,)</code>.</p>
+<p>returns a numpy character array with datatype <code>'S1'</code> or <code>'U1'</code>
+and shape <code>a.shape + (N,)</code>, where N is the length of each string in a.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <h2 class="section-title" id="header-classes">Classes</h2>
+
+ <div class="item">
+ <p id="netCDF4.CompoundType" class="name">class <span class="ident">CompoundType</span></p>
+
+
+ <div class="desc"><p>A <a href="#netCDF4.CompoundType"><code>CompoundType</code></a> instance is used to describe a compound data
+type, and can be passed to the the <a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a> method of
+a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> instance.
+Compound data types map to numpy structured arrays.
+See <a href="#netCDF4.CompoundType.__init__"><code>__init__</code></a> for more details.</p>
+<p>The instance variables <code>dtype</code> and <code>name</code> should not be modified by
+the user.</p></div>
+ <div class="source_cont">
+</div>
+
+
+ <div class="class">
+ <h3>Ancestors (in MRO)</h3>
+ <ul class="class_list">
+ <li><a href="#netCDF4.CompoundType">CompoundType</a></li>
+ <li>__builtin__.object</li>
+ </ul>
+ <h3>Class variables</h3>
+ <div class="item">
+ <p id="netCDF4.CompoundType.dtype" class="name">var <span class="ident">dtype</span></p>
+
+
+
+
+ <div class="desc"><p>A numpy dtype object describing the compound data type.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.CompoundType.name" class="name">var <span class="ident">name</span></p>
+
+
+
+
+ <div class="desc"><p>String name.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <h3>Static methods</h3>
+
+ <div class="item">
+ <div class="name def" id="netCDF4.CompoundType.__init__">
+ <p>def <span class="ident">__init__</span>(</p><p>group, datatype, datatype_name)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>CompoundType constructor.</p>
+<p><strong><code>group</code></strong>: <a href="#netCDF4.Group"><code>Group</code></a> instance to associate with the compound datatype.</p>
+<p><strong><code>datatype</code></strong>: A numpy dtype object describing a structured (a.k.a record)
+array. Can be composed of homogeneous numeric or character data types, or
+other structured array data types.</p>
+<p><strong><code>datatype_name</code></strong>: a Python string containing a description of the
+compound data type.</p>
+<p><strong><em>Note 1</em></strong>: When creating nested compound data types,
+the inner compound data types must already be associated with CompoundType
+instances (so create CompoundType instances for the innermost structures
+first).</p>
+<p><strong><em>Note 2</em></strong>: <a href="#netCDF4.CompoundType"><code>CompoundType</code></a> instances should be created using the
+<a href="#netCDF4.Dataset.createCompoundType"><code>createCompoundType</code></a>
+method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> instance, not using this class directly.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+ </div>
+ </div>
+
+ <div class="item">
+ <p id="netCDF4.Dataset" class="name">class <span class="ident">Dataset</span></p>
+
+
+ <div class="desc"><p>A netCDF <a href="#netCDF4.Dataset"><code>Dataset</code></a> is a collection of dimensions, groups, variables and
+attributes. Together they describe the meaning of data and relations among
+data fields stored in a netCDF file. See <a href="#netCDF4.Dataset.__init__"><code>__init__</code></a> for more
+details.</p>
+<p>A list of attribute names corresponding to global netCDF attributes
+defined for the <a href="#netCDF4.Dataset"><code>Dataset</code></a> can be obtained with the
+<a href="#netCDF4.Dataset.ncattrs"><code>ncattrs</code></a> method.
+These attributes can be created by assigning to an attribute of the
+<a href="#netCDF4.Dataset"><code>Dataset</code></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.Dataset"><code>Dataset</code></a> instance.</p>
+<p>The following class variables are read-only and should not be
+modified by the user.</p>
+<p><strong><code>dimensions</code></strong>: The <code>dimensions</code> dictionary maps the names of
+dimensions defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.Dimension"><code>Dimension</code></a> class.</p>
+<p><strong><code>variables</code></strong>: The <code>variables</code> dictionary maps the names of variables
+defined for this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> to instances of the
+<a href="#netCDF4.Variable"><code>Variable</code></a> class.</p>
+<p><strong><code>groups</code></strong>: The groups dictionary maps the names of groups created for
+this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> to instances of the <a href="#netCDF4.Group"><code>Group</code></a> class (the
+<a href="#netCDF4.Dataset"><code>Dataset</code></a> class is simply a special case of the <a href="#netCDF4.Group"><code>Group</code></a> class which
+describes the root group in the netCDF4 file).</p>
+<p><strong><code>cmptypes</code></strong>: The <code>cmptypes</code> dictionary maps the names of
+compound types defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.CompoundType"><code>CompoundType</code></a> class.</p>
+<p><strong><code>vltypes</code></strong>: The <code>vltypes</code> dictionary maps the names of
+variable-length types defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances
+of the <a href="#netCDF4.VLType"><code>VLType</code></a> class.</p>
+<p><strong><code>data_model</code></strong>: <code>data_model</code> describes the netCDF
+data model version, one of <code>NETCDF3_CLASSIC</code>, <code>NETCDF4</code>,
+<code>NETCDF4_CLASSIC</code> or <code>NETCDF3_64BIT</code>.</p>
+<p><strong><code>file_format</code></strong>: same as <code>data_model</code>, retained for backwards compatibility.</p>
+<p><strong><code>disk_format</code></strong>: <code>disk_format</code> describes the underlying
+file format, one of <code>NETCDF3</code>, <code>HDF5</code>, <code>HDF4</code>,
+<code>PNETCDF</code>, <code>DAP2</code>, <code>DAP4</code> or <code>UNDEFINED</code>. Only available if using
+netcdf C library version >= 4.3.1, otherwise will always return
+<code>UNDEFINED</code>.</p>
+<p><strong><code>parent</code></strong>: <code>parent</code> is a reference to the parent
+<a href="#netCDF4.Group"><code>Group</code></a> instance. <code>None</code> for a the root group or <a href="#netCDF4.Dataset"><code>Dataset</code></a>
+instance.</p>
+<p><strong><code>path</code></strong>: <code>path</code> shows the location of the <a href="#netCDF4.Group"><code>Group</code></a> in
+the <a href="#netCDF4.Dataset"><code>Dataset</code></a> in a unix directory format (the names of groups in the
+hierarchy separated by backslashes). A <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance is the root
+group, so the path is simply <code>'/'</code>.</p>
+<p><strong><code>keepweakref</code></strong>: If <code>True</code>, child Dimension and Variables objects only keep weak
+references to the parent Dataset or Group.</p></div>
+ <div class="source_cont">
+</div>
+
+
+ <div class="class">
+ <h3>Ancestors (in MRO)</h3>
+ <ul class="class_list">
+ <li><a href="#netCDF4.Dataset">Dataset</a></li>
+ <li>__builtin__.object</li>
+ </ul>
+ <h3>Class variables</h3>
+ <div class="item">
+ <p id="netCDF4.Dataset.cmptypes" class="name">var <span class="ident">cmptypes</span></p>
+
+
+
+
+ <div class="desc"><p>The <code>cmptypes</code> dictionary maps the names of
+compound types defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.CompoundType"><code>CompoundType</code></a> class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Dataset.data_model" class="name">var <span class="ident">data_model</span></p>
+
+
+
+
+ <div class="desc"><p><code>data_model</code> describes the netCDF
+data model version, one of <code>NETCDF3_CLASSIC</code>, <code>NETCDF4</code>,
+<code>NETCDF4_CLASSIC</code> or <code>NETCDF3_64BIT</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Dataset.dimensions" class="name">var <span class="ident">dimensions</span></p>
+
+
+
+
+ <div class="desc"><p>The <code>dimensions</code> dictionary maps the names of
+dimensions defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.Dimension"><code>Dimension</code></a> class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Dataset.disk_format" class="name">var <span class="ident">disk_format</span></p>
+
+
+
+
+ <div class="desc"><p><code>disk_format</code> describes the underlying
+file format, one of <code>NETCDF3</code>, <code>HDF5</code>, <code>HDF4</code>,
+<code>PNETCDF</code>, <code>DAP2</code>, <code>DAP4</code> or <code>UNDEFINED</code>. Only available if using
+netcdf C library version >= 4.3.1, otherwise will always return
+<code>UNDEFINED</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Dataset.file_format" class="name">var <span class="ident">file_format</span></p>
+
+
+
+
+ <div class="desc"><p>same as <code>data_model</code>, retained for backwards compatibility.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Dataset.groups" class="name">var <span class="ident">groups</span></p>
+
+
+
+
+ <div class="desc"><p>The groups dictionary maps the names of groups created for
+this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> to instances of the <a href="#netCDF4.Group"><code>Group</code></a> class (the
+<a href="#netCDF4.Dataset"><code>Dataset</code></a> class is simply a special case of the <a href="#netCDF4.Group"><code>Group</code></a> class which
+describes the root group in the netCDF4 file).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Dataset.keepweakref" class="name">var <span class="ident">keepweakref</span></p>
+
+
+
+
+ <div class="desc"><p>If <code>True</code>, child Dimension and Variables objects only keep weak references to
+the parent Dataset or Group.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Dataset.parent" class="name">var <span class="ident">parent</span></p>
+
+
+
+
+ <div class="desc"><p><code>parent</code> is a reference to the parent
+<a href="#netCDF4.Group"><code>Group</code></a> instance. <code>None</code> for a the root group or <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Dataset.path" class="name">var <span class="ident">path</span></p>
+
+
+
+
+ <div class="desc"><p><code>path</code> shows the location of the <a href="#netCDF4.Group"><code>Group</code></a> in
+the <a href="#netCDF4.Dataset"><code>Dataset</code></a> in a unix directory format (the names of groups in the
+hierarchy separated by backslashes). A <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance is the root
+group, so the path is simply <code>'/'</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Dataset.variables" class="name">var <span class="ident">variables</span></p>
+
+
+
+
+ <div class="desc"><p>The <code>variables</code> dictionary maps the names of variables
+defined for this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> to instances of the <a href="#netCDF4.Variable"><code>Variable</code></a>
+class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Dataset.vltypes" class="name">var <span class="ident">vltypes</span></p>
+
+
+
+
+ <div class="desc"><p>The <code>vltypes</code> dictionary maps the names of
+variable-length types defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.VLType"><code>VLType</code></a> class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <h3>Static methods</h3>
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.__init__">
+ <p>def <span class="ident">__init__</span>(</p><p>self, filename, mode="r", clobber=True, diskless=False, persist=False, weakref=False, format='NETCDF4')</p>
+ </div>
+
+
+
+
+ <div class="desc"><p><a href="#netCDF4.Dataset"><code>Dataset</code></a> constructor.</p>
+<p><strong><code>filename</code></strong>: Name of netCDF file to hold dataset.</p>
+<p><strong><code>mode</code></strong>: access mode. <code>r</code> means read-only; no data can be
+modified. <code>w</code> means write; a new file is created, an existing file with
+the same name is deleted. <code>a</code> and <code>r+</code> mean append (in analogy with
+serial files); an existing file is opened for reading and writing.
+Appending <code>s</code> to modes <code>w</code>, <code>r+</code> or <code>a</code> will enable unbuffered shared
+access to <code>NETCDF3_CLASSIC</code> or <code>NETCDF3_64BIT</code> formatted files.
+Unbuffered acesss may be useful even if you don't need shared
+access, since it may be faster for programs that don't access data
+sequentially. This option is ignored for <code>NETCDF4</code> and <code>NETCDF4_CLASSIC</code>
+formatted files.</p>
+<p><strong><code>clobber</code></strong>: if <code>True</code> (default), opening a file with <code>mode='w'</code>
+will clobber an existing file with the same name. if <code>False</code>, an
+exception will be raised if a file with the same name already exists.</p>
+<p><strong><code>format</code></strong>: underlying file format (one of <code>'NETCDF4',
+'NETCDF4_CLASSIC', 'NETCDF3_CLASSIC'</code> or <code>'NETCDF3_64BIT'</code>. Only
+relevant if <code>mode = 'w'</code> (if <code>mode = 'r','a'</code> or <code>'r+'</code> the file format
+is automatically detected). Default <code>'NETCDF4'</code>, which means the data is
+stored in an HDF5 file, using netCDF 4 API features. Setting
+<code>format='NETCDF4_CLASSIC'</code> will create an HDF5 file, using only netCDF 3
+compatibile API features. netCDF 3 clients must be recompiled and linked
+against the netCDF 4 library to read files in <code>NETCDF4_CLASSIC</code> format.
+<code>'NETCDF3_CLASSIC'</code> is the classic netCDF 3 file format that does not
+handle 2+ Gb files very well. <code>'NETCDF3_64BIT'</code> is the 64-bit offset
+version of the netCDF 3 file format, which fully supports 2+ GB files, but
+is only compatible with clients linked against netCDF version 3.6.0 or
+later.</p>
+<p><strong><code>diskless</code></strong>: If <code>True</code>, create diskless (in memory) file.<br />
+This is an experimental feature added to the C library after the
+netcdf-4.2 release.</p>
+<p><strong><code>persist</code></strong>: if <code>diskless=True</code>, persist file to disk when closed
+(default <code>False</code>).</p>
+<p><strong><code>keepweakref</code></strong>: if <code>True</code>, child Dimension and Variable instances will keep weak
+references to the parent Dataset or Group object. Default is <code>False</code>, which
+means strong references will be kept. Having Dimension and Variable instances
+keep a strong reference to the parent Dataset instance, which in turn keeps a
+reference to child Dimension and Variable instances, creates circular references.
+Circular references complicate garbage collection, which may mean increased
+memory usage for programs that create may Dataset instances with lots of
+Variables. Setting <code>keepweakref=True</code> allows Dataset instances to be
+garbage collected as soon as they go out of scope, potential reducing memory
+usage. However, in most cases this is not desirable, since the associated
+Variable instances may still be needed, but are rendered unusable when the
+parent Dataset instance is garbage collected.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.close">
+ <p>def <span class="ident">close</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Close the Dataset.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.createCompoundType">
+ <p>def <span class="ident">createCompoundType</span>(</p><p>self, datatype, datatype_name)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Creates a new compound data type named <code>datatype_name</code> from the numpy
+dtype object <code>datatype</code>.</p>
+<p><strong><em>Note</em></strong>: If the new compound data type contains other compound data types
+(i.e. it is a 'nested' compound type, where not all of the elements
+are homogenous numeric data types), then the 'inner' compound types <strong>must</strong> be
+created first.</p>
+<p>The return value is the <a href="#netCDF4.CompoundType"><code>CompoundType</code></a> class instance describing the new
+datatype.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.createDimension">
+ <p>def <span class="ident">createDimension</span>(</p><p>self, dimname, size=None)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Creates a new dimension with the given <code>dimname</code> and <code>size</code>.</p>
+<p><code>size</code> must be a positive integer or <code>None</code>, which stands for
+"unlimited" (default is <code>None</code>). Specifying a size of 0 also
+results in an unlimited dimension. The return value is the <a href="#netCDF4.Dimension"><code>Dimension</code></a>
+class instance describing the new dimension. To determine the current
+maximum size of the dimension, use the <code>len</code> function on the <a href="#netCDF4.Dimension"><code>Dimension</code></a>
+instance. To determine if a dimension is 'unlimited', use the
+<a href="#netCDF4.Dimension.isunlimited"><code>isunlimited</code></a> method of the <a href="#netCDF4.Dimension"><code>Dimension</code></a> instance.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.createGroup">
+ <p>def <span class="ident">createGroup</span>(</p><p>self, groupname)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Creates a new <a href="#netCDF4.Group"><code>Group</code></a> with the given <code>groupname</code>.</p>
+<p>If <code>groupname</code> is specified as a path, using forward slashes as in unix to
+separate components, then intermediate groups will be created as necessary
+(analagous to <code>mkdir -p</code> in unix). For example,
+<code>createGroup('/GroupA/GroupB/GroupC')</code> will create <code>GroupA</code>,
+<code>GroupA/GroupB</code>, and <code>GroupA/GroupB/GroupC</code>, if they don't already exist.
+If the specified path describes a group that already exists, no error is
+raised.</p>
+<p>The return value is a <a href="#netCDF4.Group"><code>Group</code></a> class instance.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.createVLType">
+ <p>def <span class="ident">createVLType</span>(</p><p>self, datatype, datatype_name)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Creates a new VLEN data type named <code>datatype_name</code> from a numpy
+dtype object <code>datatype</code>.</p>
+<p>The return value is the <a href="#netCDF4.VLType"><code>VLType</code></a> class instance describing the new
+datatype.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.createVariable">
+ <p>def <span class="ident">createVariable</span>(</p><p>self, varname, datatype, dimensions=(), zlib=False, complevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Creates a new variable with the given <code>varname</code>, <code>datatype</code>, and
+<code>dimensions</code>. If dimensions are not given, the variable is assumed to be
+a scalar.</p>
+<p>If <code>varname</code> is specified as a path, using forward slashes as in unix to
+separate components, then intermediate groups will be created as necessary
+For example, <code>createVariable('/GroupA/GroupB/VarC'),('x','y'),float)</code> will create groups <code>GroupA</code>
+and <code>GroupA/GroupB</code>, plus the variable <code>GroupA/GroupB/VarC</code>, if the preceding
+groups don't already exist.</p>
+<p>The <code>datatype</code> can be a numpy datatype object, or a string that describes
+a numpy dtype object (like the <code>dtype.str</code> attribue of a numpy array).
+Supported specifiers include: <code>'S1' or 'c' (NC_CHAR), 'i1' or 'b' or 'B'
+(NC_BYTE), 'u1' (NC_UBYTE), 'i2' or 'h' or 's' (NC_SHORT), 'u2'
+(NC_USHORT), 'i4' or 'i' or 'l' (NC_INT), 'u4' (NC_UINT), 'i8' (NC_INT64),
+'u8' (NC_UINT64), 'f4' or 'f' (NC_FLOAT), 'f8' or 'd' (NC_DOUBLE)</code>.
+<code>datatype</code> can also be a <a href="#netCDF4.CompoundType"><code>CompoundType</code></a> instance
+(for a structured, or compound array), a <a href="#netCDF4.VLType"><code>VLType</code></a> instance
+(for a variable-length array), or the python <code>str</code> builtin
+(for a variable-length string array). Numpy string and unicode datatypes with
+length greater than one are aliases for <code>str</code>.</p>
+<p>Data from netCDF variables is presented to python as numpy arrays with
+the corresponding data type.</p>
+<p><code>dimensions</code> must be a tuple containing dimension names (strings) that
+have been defined previously using <a href="#netCDF4.Dataset.createDimension"><code>createDimension</code></a>. The default value
+is an empty tuple, which means the variable is a scalar.</p>
+<p>If the optional keyword <code>zlib</code> is <code>True</code>, the data will be compressed in
+the netCDF file using gzip compression (default <code>False</code>).</p>
+<p>The optional keyword <code>complevel</code> is an integer between 1 and 9 describing
+the level of compression desired (default 4). Ignored if <code>zlib=False</code>.</p>
+<p>If the optional keyword <code>shuffle</code> is <code>True</code>, the HDF5 shuffle filter
+will be applied before compressing the data (default <code>True</code>). This
+significantly improves compression. Default is <code>True</code>. Ignored if
+<code>zlib=False</code>.</p>
+<p>If the optional keyword <code>fletcher32</code> is <code>True</code>, the Fletcher32 HDF5
+checksum algorithm is activated to detect errors. Default <code>False</code>.</p>
+<p>If the optional keyword <code>contiguous</code> is <code>True</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>The optional keyword <code>chunksizes</code> can be used to manually 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">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>The optional keyword <code>endian</code> 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>The optional keyword <code>fill_value</code> can be used to override the default
+netCDF <code>_FillValue</code> (the value that the variable gets filled with before
+any data is written to it, defaults given in <code>netCDF4.default_fillvals</code>).
+If fill_value is set to <code>False</code>, then the variable is not pre-filled.</p>
+<p>If the optional keyword parameter <code>least_significant_digit</code> is
+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 <code>numpy.around(scale*data)/scale</code>, where
+scale = 2**bits, and bits is determined so that a precision of 0.1 is
+retained (in this case bits=4). From the
+<a href="http://www.esrl.noaa.gov/psd/data/gridded/conventions/cdc_netcdf_standard.shtml">PSD metadata conventions</a>:
+"least_significant_digit -- power of ten of the smallest decimal place
+in unpacked data that is a reliable value." Default is <code>None</code>, or no
+quantization, or 'lossless' compression.</p>
+<p>When creating variables in a <code>NETCDF4</code> or <code>NETCDF4_CLASSIC</code> formatted file,
+HDF5 creates something called a 'chunk cache' for each variable. The
+default size of the chunk cache may be large enough to completely fill
+available memory when creating thousands of variables. The optional
+keyword <code>chunk_cache</code> allows you to reduce (or increase) the size of
+the default chunk cache when creating a variable. The setting only
+persists as long as the Dataset is open - you can use the set_var_chunk_cache
+method to change it the next time the Dataset is opened.
+Warning - messing with this parameter can seriously degrade performance.</p>
+<p>The return value is the <a href="#netCDF4.Variable"><code>Variable</code></a> class instance describing the new
+variable.</p>
+<p>A list of names corresponding to netCDF variable attributes can be
+obtained with the <a href="#netCDF4.Variable"><code>Variable</code></a> method <a href="#netCDF4.Variable.ncattrs"><code>ncattrs</code></a>. A dictionary
+containing all the netCDF attribute name/value pairs is provided by
+the <code>__dict__</code> attribute of a <a href="#netCDF4.Variable"><code>Variable</code></a> instance.</p>
+<p><a href="#netCDF4.Variable"><code>Variable</code></a> instances behave much like array objects. Data can be
+assigned to or retrieved from a variable with indexing and slicing
+operations on the <a href="#netCDF4.Variable"><code>Variable</code></a> instance. A <a href="#netCDF4.Variable"><code>Variable</code></a> instance has six
+Dataset standard attributes: <code>dimensions, dtype, shape, ndim, name</code> and
+<code>least_significant_digit</code>. Application programs should never modify
+these attributes. The <code>dimensions</code> attribute is a tuple containing the
+names of the dimensions associated with this variable. The <code>dtype</code>
+attribute is a string describing the variable's data type (<code>i4, f8,
+S1,</code> etc). The <code>shape</code> attribute is a tuple describing the current
+sizes of all the variable's dimensions. The <code>name</code> attribute is a
+string containing the name of the Variable instance.
+The <code>least_significant_digit</code>
+attributes describes the power of ten of the smallest decimal place in
+the data the contains a reliable value. assigned to the <a href="#netCDF4.Variable"><code>Variable</code></a>
+instance. If <code>None</code>, the data is not truncated. The <code>ndim</code> attribute
+is the number of variable dimensions.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.delncattr">
+ <p>def <span class="ident">delncattr</span>(</p><p>self,name,value)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>delete a netCDF dataset or group attribute. Use if you need to delete a
+netCDF attribute with the same name as one of the reserved python
+attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.filepath">
+ <p>def <span class="ident">filepath</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Get the file system path (or the opendap URL) which was used to
+open/create the Dataset. Requires netcdf >= 4.1.2</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.getncattr">
+ <p>def <span class="ident">getncattr</span>(</p><p>self,name)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>retrievel a netCDF dataset or group attribute.
+Use if you need to get a netCDF attribute with the same
+name as one of the reserved python attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.ncattrs">
+ <p>def <span class="ident">ncattrs</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>return netCDF global attribute names for this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> in a list.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.renameAttribute">
+ <p>def <span class="ident">renameAttribute</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>rename a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> attribute named <code>oldname</code> to <code>newname</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.renameDimension">
+ <p>def <span class="ident">renameDimension</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>rename a <a href="#netCDF4.Dimension"><code>Dimension</code></a> named <code>oldname</code> to <code>newname</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.renameGroup">
+ <p>def <span class="ident">renameGroup</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>rename a <a href="#netCDF4.Group"><code>Group</code></a> named <code>oldname</code> to <code>newname</code> (requires netcdf >= 4.3.1).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.renameVariable">
+ <p>def <span class="ident">renameVariable</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>rename a <a href="#netCDF4.Variable"><code>Variable</code></a> named <code>oldname</code> to <code>newname</code></p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.set_auto_mask">
+ <p>def <span class="ident">set_auto_mask</span>(</p><p>self, True_or_False)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Call <a href="#netCDF4.Variable.set_auto_mask"><code>set_auto_mask</code></a> for all variables contained in this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a>, as well as for all variables in all its subgroups.</p>
+<p><strong><code>True_or_False</code></strong>: Boolean determining if automatic conversion to masked arrays
+shall be applied for all variables.</p>
+<p><strong><em>Note</em></strong>: Calling this function only affects existing variables. Variables created
+after calling this function will follow the default behaviour.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.set_auto_maskandscale">
+ <p>def <span class="ident">set_auto_maskandscale</span>(</p><p>self, True_or_False)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Call <a href="#netCDF4.Variable.set_auto_maskandscale"><code>set_auto_maskandscale</code></a> for all variables contained in this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a>, as well as for all variables in all its subgroups.</p>
+<p><strong><code>True_or_False</code></strong>: Boolean determining if automatic conversion to masked arrays
+and variable scaling shall be applied for all variables.</p>
+<p><strong><em>Note</em></strong>: Calling this function only affects existing variables. Variables created
+after calling this function will follow the default behaviour.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.set_auto_scale">
+ <p>def <span class="ident">set_auto_scale</span>(</p><p>self, True_or_False)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Call <a href="#netCDF4.Variable.set_auto_scale"><code>set_auto_scale</code></a> for all variables contained in this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a>, as well as for all variables in all its subgroups.</p>
+<p><strong><code>True_or_False</code></strong>: Boolean determining if automatic variable scaling
+shall be applied for all variables.</p>
+<p><strong><em>Note</em></strong>: Calling this function only affects existing variables. Variables created
+after calling this function will follow the default behaviour.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.set_fill_off">
+ <p>def <span class="ident">set_fill_off</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Sets the fill mode for a <a href="#netCDF4.Dataset"><code>Dataset</code></a> open for writing to <code>off</code>.</p>
+<p>This will prevent the data from being pre-filled with fill values, which
+may result in some performance improvements. However, you must then make
+sure the data is actually written before being read.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.set_fill_on">
+ <p>def <span class="ident">set_fill_on</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Sets the fill mode for a <a href="#netCDF4.Dataset"><code>Dataset</code></a> open for writing to <code>on</code>.</p>
+<p>This causes data to be pre-filled with fill values. The fill values can be
+controlled by the variable's <code>_Fill_Value</code> attribute, but is usually
+sufficient to the use the netCDF default <code>_Fill_Value</code> (defined
+separately for each variable type). The default behavior of the netCDF
+library correspongs to <code>set_fill_on</code>. Data which are equal to the
+<code>_Fill_Value</code> indicate that the variable was created, but never written
+to.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.setncattr">
+ <p>def <span class="ident">setncattr</span>(</p><p>self,name,value)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>set a netCDF dataset or group attribute using name,value pair.
+Use if you need to set a netCDF attribute with the
+with the same name as one of the reserved python attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.setncatts">
+ <p>def <span class="ident">setncatts</span>(</p><p>self,attdict)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>set a bunch of netCDF dataset or group attributes at once using a python dictionary.
+This may be faster when setting a lot of attributes for a <code>NETCDF3</code>
+formatted file, since nc_redef/nc_enddef is not called in between setting
+each attribute</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dataset.sync">
+ <p>def <span class="ident">sync</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Writes all buffered data in the <a href="#netCDF4.Dataset"><code>Dataset</code></a> to the disk file.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+ </div>
+ </div>
+
+ <div class="item">
+ <p id="netCDF4.Dimension" class="name">class <span class="ident">Dimension</span></p>
+
+
+ <div class="desc"><p>A netCDF <a href="#netCDF4.Dimension"><code>Dimension</code></a> is used to describe the coordinates of a <a href="#netCDF4.Variable"><code>Variable</code></a>.
+See <a href="#netCDF4.Dimension.__init__"><code>__init__</code></a> for more details.</p>
+<p>The current maximum size of a <a href="#netCDF4.Dimension"><code>Dimension</code></a> instance can be obtained by
+calling the python <code>len</code> function on the <a href="#netCDF4.Dimension"><code>Dimension</code></a> instance. The
+<a href="#netCDF4.Dimension.isunlimited"><code>isunlimited</code></a> method of a <a href="#netCDF4.Dimension"><code>Dimension</code></a> instance can be used to
+determine if the dimension is unlimited.</p>
+<p>Read-only class variables:</p>
+<p><strong><code>name</code></strong>: String name, used when creating a <a href="#netCDF4.Variable"><code>Variable</code></a> with
+<a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a>.</p></div>
+ <div class="source_cont">
+</div>
+
+
+ <div class="class">
+ <h3>Ancestors (in MRO)</h3>
+ <ul class="class_list">
+ <li><a href="#netCDF4.Dimension">Dimension</a></li>
+ <li>__builtin__.object</li>
+ </ul>
+ <h3>Class variables</h3>
+ <div class="item">
+ <p id="netCDF4.Dimension.name" class="name">var <span class="ident">name</span></p>
+
+
+
+
+ <div class="desc"><p>A string describing the name of the <a href="#netCDF4.Dimension"><code>Dimension</code></a> - used when creating a
+<a href="#netCDF4.Variable"><code>Variable</code></a> instance with <a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <h3>Static methods</h3>
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dimension.__init__">
+ <p>def <span class="ident">__init__</span>(</p><p>self, group, name, size=None)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p><a href="#netCDF4.Dimension"><code>Dimension</code></a> constructor.</p>
+<p><strong><code>group</code></strong>: <a href="#netCDF4.Group"><code>Group</code></a> instance to associate with dimension.</p>
+<p><strong><code>name</code></strong>: Name of the dimension.</p>
+<p><strong><code>size</code></strong>: Size of the dimension. <code>None</code> or 0 means unlimited. (Default <code>None</code>).</p>
+<p><strong><em>Note</em></strong>: <a href="#netCDF4.Dimension"><code>Dimension</code></a> instances should be created using the
+<a href="#netCDF4.Dataset.createDimension"><code>createDimension</code></a> method of a <a href="#netCDF4.Group"><code>Group</code></a> or
+<a href="#netCDF4.Dataset"><code>Dataset</code></a> instance, not using <a href="#netCDF4.Dimension.__init__"><code>__init__</code></a> directly.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dimension.group">
+ <p>def <span class="ident">group</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>return the group that this <a href="#netCDF4.Dimension"><code>Dimension</code></a> is a member of.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Dimension.isunlimited">
+ <p>def <span class="ident">isunlimited</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>returns <code>True</code> if the <a href="#netCDF4.Dimension"><code>Dimension</code></a> instance is unlimited, <code>False</code> otherwise.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+ </div>
+ </div>
+
+ <div class="item">
+ <p id="netCDF4.Group" class="name">class <span class="ident">Group</span></p>
+
+
+ <div class="desc"><p>Groups define a hierarchical namespace within a netCDF file. They are
+analagous to directories in a unix filesystem. Each <a href="#netCDF4.Group"><code>Group</code></a> behaves like
+a <a href="#netCDF4.Dataset"><code>Dataset</code></a> within a Dataset, and can contain it's own variables,
+dimensions and attributes (and other Groups). See <a href="#netCDF4.Group.__init__"><code>__init__</code></a>
+for more details.</p>
+<p><a href="#netCDF4.Group"><code>Group</code></a> inherits from <a href="#netCDF4.Dataset"><code>Dataset</code></a>, so all the
+<a href="#netCDF4.Dataset"><code>Dataset</code></a> class methods and variables are available
+to a <a href="#netCDF4.Group"><code>Group</code></a> instance (except the <code>close</code> method).</p>
+<p>Additional read-only class variables:</p>
+<p><strong><code>name</code></strong>: String describing the group name.</p></div>
+ <div class="source_cont">
+</div>
+
+
+ <div class="class">
+ <h3>Ancestors (in MRO)</h3>
+ <ul class="class_list">
+ <li><a href="#netCDF4.Group">Group</a></li>
+ <li><a href="#netCDF4.Dataset">Dataset</a></li>
+ <li>__builtin__.object</li>
+ </ul>
+ <h3>Class variables</h3>
+ <div class="item">
+ <p id="netCDF4.Group.cmptypes" class="name">var <span class="ident">cmptypes</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.cmptypes">cmptypes</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>The <code>cmptypes</code> dictionary maps the names of
+compound types defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.CompoundType"><code>CompoundType</code></a> class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.data_model" class="name">var <span class="ident">data_model</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.data_model">data_model</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p><code>data_model</code> describes the netCDF
+data model version, one of <code>NETCDF3_CLASSIC</code>, <code>NETCDF4</code>,
+<code>NETCDF4_CLASSIC</code> or <code>NETCDF3_64BIT</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.dimensions" class="name">var <span class="ident">dimensions</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.dimensions">dimensions</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>The <code>dimensions</code> dictionary maps the names of
+dimensions defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.Dimension"><code>Dimension</code></a> class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.disk_format" class="name">var <span class="ident">disk_format</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.disk_format">disk_format</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p><code>disk_format</code> describes the underlying
+file format, one of <code>NETCDF3</code>, <code>HDF5</code>, <code>HDF4</code>,
+<code>PNETCDF</code>, <code>DAP2</code>, <code>DAP4</code> or <code>UNDEFINED</code>. Only available if using
+netcdf C library version >= 4.3.1, otherwise will always return
+<code>UNDEFINED</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.file_format" class="name">var <span class="ident">file_format</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.file_format">file_format</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>same as <code>data_model</code>, retained for backwards compatibility.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.groups" class="name">var <span class="ident">groups</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.groups">groups</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>The groups dictionary maps the names of groups created for
+this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> to instances of the <a href="#netCDF4.Group"><code>Group</code></a> class (the
+<a href="#netCDF4.Dataset"><code>Dataset</code></a> class is simply a special case of the <a href="#netCDF4.Group"><code>Group</code></a> class which
+describes the root group in the netCDF4 file).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.keepweakref" class="name">var <span class="ident">keepweakref</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.keepweakref">keepweakref</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>If <code>True</code>, child Dimension and Variables objects only keep weak references to
+the parent Dataset or Group.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.name" class="name">var <span class="ident">name</span></p>
+
+
+
+
+ <div class="desc"><p>A string describing the name of the <a href="#netCDF4.Group"><code>Group</code></a>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.parent" class="name">var <span class="ident">parent</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.parent">parent</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p><code>parent</code> is a reference to the parent
+<a href="#netCDF4.Group"><code>Group</code></a> instance. <code>None</code> for a the root group or <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.path" class="name">var <span class="ident">path</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.path">path</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p><code>path</code> shows the location of the <a href="#netCDF4.Group"><code>Group</code></a> in
+the <a href="#netCDF4.Dataset"><code>Dataset</code></a> in a unix directory format (the names of groups in the
+hierarchy separated by backslashes). A <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance is the root
+group, so the path is simply <code>'/'</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.variables" class="name">var <span class="ident">variables</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.variables">variables</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>The <code>variables</code> dictionary maps the names of variables
+defined for this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> to instances of the <a href="#netCDF4.Variable"><code>Variable</code></a>
+class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Group.vltypes" class="name">var <span class="ident">vltypes</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.vltypes">vltypes</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>The <code>vltypes</code> dictionary maps the names of
+variable-length types defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.VLType"><code>VLType</code></a> class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <h3>Static methods</h3>
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.__init__">
+ <p>def <span class="ident">__init__</span>(</p><p>self, parent, name)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.__init__">__init__</a></code>
+ </p>
+
+
+
+ <div class="desc"><p><a href="#netCDF4.Group"><code>Group</code></a> constructor.</p>
+<p><strong><code>parent</code></strong>: <a href="#netCDF4.Group"><code>Group</code></a> instance for the parent group. If being created
+in the root group, use a <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance.</p>
+<p><strong><code>name</code></strong>: - Name of the group.</p>
+<p><strong><em>Note</em></strong>: <a href="#netCDF4.Group"><code>Group</code></a> instances should be created using the
+<a href="#netCDF4.Dataset.createGroup"><code>createGroup</code></a> method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance, or
+another <a href="#netCDF4.Group"><code>Group</code></a> instance, not using this class directly.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.close">
+ <p>def <span class="ident">close</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.close">close</a></code>
+ </p>
+
+
+
+ <div class="desc"><p>overrides <a href="#netCDF4.Dataset"><code>Dataset</code></a> close method which does not apply to <a href="#netCDF4.Group"><code>Group</code></a>
+instances, raises IOError.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.createCompoundType">
+ <p>def <span class="ident">createCompoundType</span>(</p><p>self, datatype, datatype_name)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.createCompoundType">createCompoundType</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Creates a new compound data type named <code>datatype_name</code> from the numpy
+dtype object <code>datatype</code>.</p>
+<p><strong><em>Note</em></strong>: If the new compound data type contains other compound data types
+(i.e. it is a 'nested' compound type, where not all of the elements
+are homogenous numeric data types), then the 'inner' compound types <strong>must</strong> be
+created first.</p>
+<p>The return value is the <a href="#netCDF4.CompoundType"><code>CompoundType</code></a> class instance describing the new
+datatype.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.createDimension">
+ <p>def <span class="ident">createDimension</span>(</p><p>self, dimname, size=None)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.createDimension">createDimension</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Creates a new dimension with the given <code>dimname</code> and <code>size</code>.</p>
+<p><code>size</code> must be a positive integer or <code>None</code>, which stands for
+"unlimited" (default is <code>None</code>). Specifying a size of 0 also
+results in an unlimited dimension. The return value is the <a href="#netCDF4.Dimension"><code>Dimension</code></a>
+class instance describing the new dimension. To determine the current
+maximum size of the dimension, use the <code>len</code> function on the <a href="#netCDF4.Dimension"><code>Dimension</code></a>
+instance. To determine if a dimension is 'unlimited', use the
+<a href="#netCDF4.Dimension.isunlimited"><code>isunlimited</code></a> method of the <a href="#netCDF4.Dimension"><code>Dimension</code></a> instance.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.createGroup">
+ <p>def <span class="ident">createGroup</span>(</p><p>self, groupname)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.createGroup">createGroup</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Creates a new <a href="#netCDF4.Group"><code>Group</code></a> with the given <code>groupname</code>.</p>
+<p>If <code>groupname</code> is specified as a path, using forward slashes as in unix to
+separate components, then intermediate groups will be created as necessary
+(analagous to <code>mkdir -p</code> in unix). For example,
+<code>createGroup('/GroupA/GroupB/GroupC')</code> will create <code>GroupA</code>,
+<code>GroupA/GroupB</code>, and <code>GroupA/GroupB/GroupC</code>, if they don't already exist.
+If the specified path describes a group that already exists, no error is
+raised.</p>
+<p>The return value is a <a href="#netCDF4.Group"><code>Group</code></a> class instance.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.createVLType">
+ <p>def <span class="ident">createVLType</span>(</p><p>self, datatype, datatype_name)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.createVLType">createVLType</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Creates a new VLEN data type named <code>datatype_name</code> from a numpy
+dtype object <code>datatype</code>.</p>
+<p>The return value is the <a href="#netCDF4.VLType"><code>VLType</code></a> class instance describing the new
+datatype.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.createVariable">
+ <p>def <span class="ident">createVariable</span>(</p><p>self, varname, datatype, dimensions=(), zlib=False, complevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.createVariable">createVariable</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Creates a new variable with the given <code>varname</code>, <code>datatype</code>, and
+<code>dimensions</code>. If dimensions are not given, the variable is assumed to be
+a scalar.</p>
+<p>If <code>varname</code> is specified as a path, using forward slashes as in unix to
+separate components, then intermediate groups will be created as necessary
+For example, <code>createVariable('/GroupA/GroupB/VarC'),('x','y'),float)</code> will create groups <code>GroupA</code>
+and <code>GroupA/GroupB</code>, plus the variable <code>GroupA/GroupB/VarC</code>, if the preceding
+groups don't already exist.</p>
+<p>The <code>datatype</code> can be a numpy datatype object, or a string that describes
+a numpy dtype object (like the <code>dtype.str</code> attribue of a numpy array).
+Supported specifiers include: <code>'S1' or 'c' (NC_CHAR), 'i1' or 'b' or 'B'
+(NC_BYTE), 'u1' (NC_UBYTE), 'i2' or 'h' or 's' (NC_SHORT), 'u2'
+(NC_USHORT), 'i4' or 'i' or 'l' (NC_INT), 'u4' (NC_UINT), 'i8' (NC_INT64),
+'u8' (NC_UINT64), 'f4' or 'f' (NC_FLOAT), 'f8' or 'd' (NC_DOUBLE)</code>.
+<code>datatype</code> can also be a <a href="#netCDF4.CompoundType"><code>CompoundType</code></a> instance
+(for a structured, or compound array), a <a href="#netCDF4.VLType"><code>VLType</code></a> instance
+(for a variable-length array), or the python <code>str</code> builtin
+(for a variable-length string array). Numpy string and unicode datatypes with
+length greater than one are aliases for <code>str</code>.</p>
+<p>Data from netCDF variables is presented to python as numpy arrays with
+the corresponding data type.</p>
+<p><code>dimensions</code> must be a tuple containing dimension names (strings) that
+have been defined previously using <a href="#netCDF4.Dataset.createDimension"><code>createDimension</code></a>. The default value
+is an empty tuple, which means the variable is a scalar.</p>
+<p>If the optional keyword <code>zlib</code> is <code>True</code>, the data will be compressed in
+the netCDF file using gzip compression (default <code>False</code>).</p>
+<p>The optional keyword <code>complevel</code> is an integer between 1 and 9 describing
+the level of compression desired (default 4). Ignored if <code>zlib=False</code>.</p>
+<p>If the optional keyword <code>shuffle</code> is <code>True</code>, the HDF5 shuffle filter
+will be applied before compressing the data (default <code>True</code>). This
+significantly improves compression. Default is <code>True</code>. Ignored if
+<code>zlib=False</code>.</p>
+<p>If the optional keyword <code>fletcher32</code> is <code>True</code>, the Fletcher32 HDF5
+checksum algorithm is activated to detect errors. Default <code>False</code>.</p>
+<p>If the optional keyword <code>contiguous</code> is <code>True</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>The optional keyword <code>chunksizes</code> can be used to manually 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">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>The optional keyword <code>endian</code> 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>The optional keyword <code>fill_value</code> can be used to override the default
+netCDF <code>_FillValue</code> (the value that the variable gets filled with before
+any data is written to it, defaults given in <code>netCDF4.default_fillvals</code>).
+If fill_value is set to <code>False</code>, then the variable is not pre-filled.</p>
+<p>If the optional keyword parameter <code>least_significant_digit</code> is
+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 <code>numpy.around(scale*data)/scale</code>, where
+scale = 2**bits, and bits is determined so that a precision of 0.1 is
+retained (in this case bits=4). From the
+<a href="http://www.esrl.noaa.gov/psd/data/gridded/conventions/cdc_netcdf_standard.shtml">PSD metadata conventions</a>:
+"least_significant_digit -- power of ten of the smallest decimal place
+in unpacked data that is a reliable value." Default is <code>None</code>, or no
+quantization, or 'lossless' compression.</p>
+<p>When creating variables in a <code>NETCDF4</code> or <code>NETCDF4_CLASSIC</code> formatted file,
+HDF5 creates something called a 'chunk cache' for each variable. The
+default size of the chunk cache may be large enough to completely fill
+available memory when creating thousands of variables. The optional
+keyword <code>chunk_cache</code> allows you to reduce (or increase) the size of
+the default chunk cache when creating a variable. The setting only
+persists as long as the Dataset is open - you can use the set_var_chunk_cache
+method to change it the next time the Dataset is opened.
+Warning - messing with this parameter can seriously degrade performance.</p>
+<p>The return value is the <a href="#netCDF4.Variable"><code>Variable</code></a> class instance describing the new
+variable.</p>
+<p>A list of names corresponding to netCDF variable attributes can be
+obtained with the <a href="#netCDF4.Variable"><code>Variable</code></a> method <a href="#netCDF4.Variable.ncattrs"><code>ncattrs</code></a>. A dictionary
+containing all the netCDF attribute name/value pairs is provided by
+the <code>__dict__</code> attribute of a <a href="#netCDF4.Variable"><code>Variable</code></a> instance.</p>
+<p><a href="#netCDF4.Variable"><code>Variable</code></a> instances behave much like array objects. Data can be
+assigned to or retrieved from a variable with indexing and slicing
+operations on the <a href="#netCDF4.Variable"><code>Variable</code></a> instance. A <a href="#netCDF4.Variable"><code>Variable</code></a> instance has six
+Dataset standard attributes: <code>dimensions, dtype, shape, ndim, name</code> and
+<code>least_significant_digit</code>. Application programs should never modify
+these attributes. The <code>dimensions</code> attribute is a tuple containing the
+names of the dimensions associated with this variable. The <code>dtype</code>
+attribute is a string describing the variable's data type (<code>i4, f8,
+S1,</code> etc). The <code>shape</code> attribute is a tuple describing the current
+sizes of all the variable's dimensions. The <code>name</code> attribute is a
+string containing the name of the Variable instance.
+The <code>least_significant_digit</code>
+attributes describes the power of ten of the smallest decimal place in
+the data the contains a reliable value. assigned to the <a href="#netCDF4.Variable"><code>Variable</code></a>
+instance. If <code>None</code>, the data is not truncated. The <code>ndim</code> attribute
+is the number of variable dimensions.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.delncattr">
+ <p>def <span class="ident">delncattr</span>(</p><p>self,name,value)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.delncattr">delncattr</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>delete a netCDF dataset or group attribute. Use if you need to delete a
+netCDF attribute with the same name as one of the reserved python
+attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.filepath">
+ <p>def <span class="ident">filepath</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.filepath">filepath</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Get the file system path (or the opendap URL) which was used to
+open/create the Dataset. Requires netcdf >= 4.1.2</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.getncattr">
+ <p>def <span class="ident">getncattr</span>(</p><p>self,name)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.getncattr">getncattr</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>retrievel a netCDF dataset or group attribute.
+Use if you need to get a netCDF attribute with the same
+name as one of the reserved python attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.ncattrs">
+ <p>def <span class="ident">ncattrs</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.ncattrs">ncattrs</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>return netCDF global attribute names for this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> in a list.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.renameAttribute">
+ <p>def <span class="ident">renameAttribute</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.renameAttribute">renameAttribute</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>rename a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> attribute named <code>oldname</code> to <code>newname</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.renameDimension">
+ <p>def <span class="ident">renameDimension</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.renameDimension">renameDimension</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>rename a <a href="#netCDF4.Dimension"><code>Dimension</code></a> named <code>oldname</code> to <code>newname</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.renameGroup">
+ <p>def <span class="ident">renameGroup</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.renameGroup">renameGroup</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>rename a <a href="#netCDF4.Group"><code>Group</code></a> named <code>oldname</code> to <code>newname</code> (requires netcdf >= 4.3.1).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.renameVariable">
+ <p>def <span class="ident">renameVariable</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.renameVariable">renameVariable</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>rename a <a href="#netCDF4.Variable"><code>Variable</code></a> named <code>oldname</code> to <code>newname</code></p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.set_auto_mask">
+ <p>def <span class="ident">set_auto_mask</span>(</p><p>self, True_or_False)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.set_auto_mask">set_auto_mask</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Call <a href="#netCDF4.Variable.set_auto_mask"><code>set_auto_mask</code></a> for all variables contained in this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a>, as well as for all variables in all its subgroups.</p>
+<p><strong><code>True_or_False</code></strong>: Boolean determining if automatic conversion to masked arrays
+shall be applied for all variables.</p>
+<p><strong><em>Note</em></strong>: Calling this function only affects existing variables. Variables created
+after calling this function will follow the default behaviour.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.set_auto_maskandscale">
+ <p>def <span class="ident">set_auto_maskandscale</span>(</p><p>self, True_or_False)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.set_auto_maskandscale">set_auto_maskandscale</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Call <a href="#netCDF4.Variable.set_auto_maskandscale"><code>set_auto_maskandscale</code></a> for all variables contained in this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a>, as well as for all variables in all its subgroups.</p>
+<p><strong><code>True_or_False</code></strong>: Boolean determining if automatic conversion to masked arrays
+and variable scaling shall be applied for all variables.</p>
+<p><strong><em>Note</em></strong>: Calling this function only affects existing variables. Variables created
+after calling this function will follow the default behaviour.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.set_auto_scale">
+ <p>def <span class="ident">set_auto_scale</span>(</p><p>self, True_or_False)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.set_auto_scale">set_auto_scale</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Call <a href="#netCDF4.Variable.set_auto_scale"><code>set_auto_scale</code></a> for all variables contained in this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a>, as well as for all variables in all its subgroups.</p>
+<p><strong><code>True_or_False</code></strong>: Boolean determining if automatic variable scaling
+shall be applied for all variables.</p>
+<p><strong><em>Note</em></strong>: Calling this function only affects existing variables. Variables created
+after calling this function will follow the default behaviour.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.set_fill_off">
+ <p>def <span class="ident">set_fill_off</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.set_fill_off">set_fill_off</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Sets the fill mode for a <a href="#netCDF4.Dataset"><code>Dataset</code></a> open for writing to <code>off</code>.</p>
+<p>This will prevent the data from being pre-filled with fill values, which
+may result in some performance improvements. However, you must then make
+sure the data is actually written before being read.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.set_fill_on">
+ <p>def <span class="ident">set_fill_on</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.set_fill_on">set_fill_on</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Sets the fill mode for a <a href="#netCDF4.Dataset"><code>Dataset</code></a> open for writing to <code>on</code>.</p>
+<p>This causes data to be pre-filled with fill values. The fill values can be
+controlled by the variable's <code>_Fill_Value</code> attribute, but is usually
+sufficient to the use the netCDF default <code>_Fill_Value</code> (defined
+separately for each variable type). The default behavior of the netCDF
+library correspongs to <code>set_fill_on</code>. Data which are equal to the
+<code>_Fill_Value</code> indicate that the variable was created, but never written
+to.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.setncattr">
+ <p>def <span class="ident">setncattr</span>(</p><p>self,name,value)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.setncattr">setncattr</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>set a netCDF dataset or group attribute using name,value pair.
+Use if you need to set a netCDF attribute with the
+with the same name as one of the reserved python attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.setncatts">
+ <p>def <span class="ident">setncatts</span>(</p><p>self,attdict)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.setncatts">setncatts</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>set a bunch of netCDF dataset or group attributes at once using a python dictionary.
+This may be faster when setting a lot of attributes for a <code>NETCDF3</code>
+formatted file, since nc_redef/nc_enddef is not called in between setting
+each attribute</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Group.sync">
+ <p>def <span class="ident">sync</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.sync">sync</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Writes all buffered data in the <a href="#netCDF4.Dataset"><code>Dataset</code></a> to the disk file.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+ </div>
+ </div>
+
+ <div class="item">
+ <p id="netCDF4.MFDataset" class="name">class <span class="ident">MFDataset</span></p>
+
+
+ <div class="desc"><p>Class for reading multi-file netCDF Datasets, making variables
+spanning multiple files appear as if they were in one file.
+Datasets must be in <code>NETCDF4_CLASSIC, NETCDF3_CLASSIC or NETCDF3_64BIT</code>
+format (<code>NETCDF4</code> Datasets won't work).</p>
+<p>Adapted from <a href="http://pysclint.sourceforge.net/pycdf">pycdf</a> by Andre Gosselin.</p>
+<p>Example usage (See <a href="#netCDF4.MFDataset.__init__"><code>__init__</code></a> for more details):</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="kn">import</span> <span class="nn">numpy</span>
+<span class="o">>>></span> <span class="c"># create a series of netCDF files with a variable sharing</span>
+<span class="o">>>></span> <span class="c"># the same unlimited dimension.</span>
+<span class="o">>>></span> <span class="k">for</span> <span class="n">nf</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+<span class="o">>>></span> <span class="n">f</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="s">"mftest</span><span class="si">%s</span><span class="s">.nc"</span> <span class="o">%</span> <span class="n">nf</span><span class="p">,</span><span class="s">"w"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">f</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"x"</span><span class="p">,</span><span class="bp">None</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">x</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"x"</span><span class="p">,</span><span class="s">"i"</span><span class="p">,(</span><span class="s">"x"</span><span class="p">,))</span>
+<span class="o">>>></span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">nf</span><span class="o">*</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="o">*</span><span class="p">(</span>< [...]
+<span class="o">>>></span> <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+<span class="o">>>></span> <span class="c"># now read all those files in at once, in one Dataset.</span>
+<span class="o">>>></span> <span class="n">f</span> <span class="o">=</span> <span class="n">MFDataset</span><span class="p">(</span><span class="s">"mftest*nc"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">f</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="s">"x"</span><span class="p">][:]</span>
+<span class="p">[</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span> <span class="mi">8</span> <span class="mi">9</span> <span class="mi">10</span> <span class="mi">11</span> <span class="mi">12</span> <span class="mi">13</span> <span class="mi">14</span> <span class="mi">15</span> <span class="mi">16</span> <span cla [...]
+ <span class="mi">25</span> <span class="mi">26</span> <span class="mi">27</span> <span class="mi">28</span> <span class="mi">29</span> <span class="mi">30</span> <span class="mi">31</span> <span class="mi">32</span> <span class="mi">33</span> <span class="mi">34</span> <span class="mi">35</span> <span class="mi">36</span> <span class="mi">37</span> <span class="mi">38</span> <span class="mi">39</span> <span class="mi">40</span> <span class="mi">41</span> <span class="mi">42</span> <span [...]
+ <span class="mi">50</span> <span class="mi">51</span> <span class="mi">52</span> <span class="mi">53</span> <span class="mi">54</span> <span class="mi">55</span> <span class="mi">56</span> <span class="mi">57</span> <span class="mi">58</span> <span class="mi">59</span> <span class="mi">60</span> <span class="mi">61</span> <span class="mi">62</span> <span class="mi">63</span> <span class="mi">64</span> <span class="mi">65</span> <span class="mi">66</span> <span class="mi">67</span> <span [...]
+ <span class="mi">75</span> <span class="mi">76</span> <span class="mi">77</span> <span class="mi">78</span> <span class="mi">79</span> <span class="mi">80</span> <span class="mi">81</span> <span class="mi">82</span> <span class="mi">83</span> <span class="mi">84</span> <span class="mi">85</span> <span class="mi">86</span> <span class="mi">87</span> <span class="mi">88</span> <span class="mi">89</span> <span class="mi">90</span> <span class="mi">91</span> <span class="mi">92</span> <span [...]
+</pre></div></div>
+ <div class="source_cont">
+</div>
+
+
+ <div class="class">
+ <h3>Ancestors (in MRO)</h3>
+ <ul class="class_list">
+ <li><a href="#netCDF4.MFDataset">MFDataset</a></li>
+ <li><a href="#netCDF4.Dataset">Dataset</a></li>
+ <li>__builtin__.object</li>
+ </ul>
+ <h3>Class variables</h3>
+ <div class="item">
+ <p id="netCDF4.MFDataset.cmptypes" class="name">var <span class="ident">cmptypes</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.cmptypes">cmptypes</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>The <code>cmptypes</code> dictionary maps the names of
+compound types defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.CompoundType"><code>CompoundType</code></a> class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.MFDataset.data_model" class="name">var <span class="ident">data_model</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.data_model">data_model</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p><code>data_model</code> describes the netCDF
+data model version, one of <code>NETCDF3_CLASSIC</code>, <code>NETCDF4</code>,
+<code>NETCDF4_CLASSIC</code> or <code>NETCDF3_64BIT</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.MFDataset.dimensions" class="name">var <span class="ident">dimensions</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.dimensions">dimensions</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>The <code>dimensions</code> dictionary maps the names of
+dimensions defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.Dimension"><code>Dimension</code></a> class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.MFDataset.disk_format" class="name">var <span class="ident">disk_format</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.disk_format">disk_format</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p><code>disk_format</code> describes the underlying
+file format, one of <code>NETCDF3</code>, <code>HDF5</code>, <code>HDF4</code>,
+<code>PNETCDF</code>, <code>DAP2</code>, <code>DAP4</code> or <code>UNDEFINED</code>. Only available if using
+netcdf C library version >= 4.3.1, otherwise will always return
+<code>UNDEFINED</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.MFDataset.file_format" class="name">var <span class="ident">file_format</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.file_format">file_format</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>same as <code>data_model</code>, retained for backwards compatibility.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.MFDataset.groups" class="name">var <span class="ident">groups</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.groups">groups</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>The groups dictionary maps the names of groups created for
+this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> to instances of the <a href="#netCDF4.Group"><code>Group</code></a> class (the
+<a href="#netCDF4.Dataset"><code>Dataset</code></a> class is simply a special case of the <a href="#netCDF4.Group"><code>Group</code></a> class which
+describes the root group in the netCDF4 file).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.MFDataset.keepweakref" class="name">var <span class="ident">keepweakref</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.keepweakref">keepweakref</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>If <code>True</code>, child Dimension and Variables objects only keep weak references to
+the parent Dataset or Group.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.MFDataset.parent" class="name">var <span class="ident">parent</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.parent">parent</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p><code>parent</code> is a reference to the parent
+<a href="#netCDF4.Group"><code>Group</code></a> instance. <code>None</code> for a the root group or <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.MFDataset.path" class="name">var <span class="ident">path</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.path">path</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p><code>path</code> shows the location of the <a href="#netCDF4.Group"><code>Group</code></a> in
+the <a href="#netCDF4.Dataset"><code>Dataset</code></a> in a unix directory format (the names of groups in the
+hierarchy separated by backslashes). A <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance is the root
+group, so the path is simply <code>'/'</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.MFDataset.variables" class="name">var <span class="ident">variables</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.variables">variables</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>The <code>variables</code> dictionary maps the names of variables
+defined for this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> to instances of the <a href="#netCDF4.Variable"><code>Variable</code></a>
+class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.MFDataset.vltypes" class="name">var <span class="ident">vltypes</span></p>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.vltypes">vltypes</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>The <code>vltypes</code> dictionary maps the names of
+variable-length types defined for the <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> to instances of the
+<a href="#netCDF4.VLType"><code>VLType</code></a> class.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <h3>Static methods</h3>
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.createCompoundType">
+ <p>def <span class="ident">createCompoundType</span>(</p><p>self, datatype, datatype_name)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.createCompoundType">createCompoundType</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Creates a new compound data type named <code>datatype_name</code> from the numpy
+dtype object <code>datatype</code>.</p>
+<p><strong><em>Note</em></strong>: If the new compound data type contains other compound data types
+(i.e. it is a 'nested' compound type, where not all of the elements
+are homogenous numeric data types), then the 'inner' compound types <strong>must</strong> be
+created first.</p>
+<p>The return value is the <a href="#netCDF4.CompoundType"><code>CompoundType</code></a> class instance describing the new
+datatype.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.createDimension">
+ <p>def <span class="ident">createDimension</span>(</p><p>self, dimname, size=None)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.createDimension">createDimension</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Creates a new dimension with the given <code>dimname</code> and <code>size</code>.</p>
+<p><code>size</code> must be a positive integer or <code>None</code>, which stands for
+"unlimited" (default is <code>None</code>). Specifying a size of 0 also
+results in an unlimited dimension. The return value is the <a href="#netCDF4.Dimension"><code>Dimension</code></a>
+class instance describing the new dimension. To determine the current
+maximum size of the dimension, use the <code>len</code> function on the <a href="#netCDF4.Dimension"><code>Dimension</code></a>
+instance. To determine if a dimension is 'unlimited', use the
+<a href="#netCDF4.Dimension.isunlimited"><code>isunlimited</code></a> method of the <a href="#netCDF4.Dimension"><code>Dimension</code></a> instance.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.createGroup">
+ <p>def <span class="ident">createGroup</span>(</p><p>self, groupname)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.createGroup">createGroup</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Creates a new <a href="#netCDF4.Group"><code>Group</code></a> with the given <code>groupname</code>.</p>
+<p>If <code>groupname</code> is specified as a path, using forward slashes as in unix to
+separate components, then intermediate groups will be created as necessary
+(analagous to <code>mkdir -p</code> in unix). For example,
+<code>createGroup('/GroupA/GroupB/GroupC')</code> will create <code>GroupA</code>,
+<code>GroupA/GroupB</code>, and <code>GroupA/GroupB/GroupC</code>, if they don't already exist.
+If the specified path describes a group that already exists, no error is
+raised.</p>
+<p>The return value is a <a href="#netCDF4.Group"><code>Group</code></a> class instance.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.createVLType">
+ <p>def <span class="ident">createVLType</span>(</p><p>self, datatype, datatype_name)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.createVLType">createVLType</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Creates a new VLEN data type named <code>datatype_name</code> from a numpy
+dtype object <code>datatype</code>.</p>
+<p>The return value is the <a href="#netCDF4.VLType"><code>VLType</code></a> class instance describing the new
+datatype.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.createVariable">
+ <p>def <span class="ident">createVariable</span>(</p><p>self, varname, datatype, dimensions=(), zlib=False, complevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.createVariable">createVariable</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Creates a new variable with the given <code>varname</code>, <code>datatype</code>, and
+<code>dimensions</code>. If dimensions are not given, the variable is assumed to be
+a scalar.</p>
+<p>If <code>varname</code> is specified as a path, using forward slashes as in unix to
+separate components, then intermediate groups will be created as necessary
+For example, <code>createVariable('/GroupA/GroupB/VarC'),('x','y'),float)</code> will create groups <code>GroupA</code>
+and <code>GroupA/GroupB</code>, plus the variable <code>GroupA/GroupB/VarC</code>, if the preceding
+groups don't already exist.</p>
+<p>The <code>datatype</code> can be a numpy datatype object, or a string that describes
+a numpy dtype object (like the <code>dtype.str</code> attribue of a numpy array).
+Supported specifiers include: <code>'S1' or 'c' (NC_CHAR), 'i1' or 'b' or 'B'
+(NC_BYTE), 'u1' (NC_UBYTE), 'i2' or 'h' or 's' (NC_SHORT), 'u2'
+(NC_USHORT), 'i4' or 'i' or 'l' (NC_INT), 'u4' (NC_UINT), 'i8' (NC_INT64),
+'u8' (NC_UINT64), 'f4' or 'f' (NC_FLOAT), 'f8' or 'd' (NC_DOUBLE)</code>.
+<code>datatype</code> can also be a <a href="#netCDF4.CompoundType"><code>CompoundType</code></a> instance
+(for a structured, or compound array), a <a href="#netCDF4.VLType"><code>VLType</code></a> instance
+(for a variable-length array), or the python <code>str</code> builtin
+(for a variable-length string array). Numpy string and unicode datatypes with
+length greater than one are aliases for <code>str</code>.</p>
+<p>Data from netCDF variables is presented to python as numpy arrays with
+the corresponding data type.</p>
+<p><code>dimensions</code> must be a tuple containing dimension names (strings) that
+have been defined previously using <a href="#netCDF4.Dataset.createDimension"><code>createDimension</code></a>. The default value
+is an empty tuple, which means the variable is a scalar.</p>
+<p>If the optional keyword <code>zlib</code> is <code>True</code>, the data will be compressed in
+the netCDF file using gzip compression (default <code>False</code>).</p>
+<p>The optional keyword <code>complevel</code> is an integer between 1 and 9 describing
+the level of compression desired (default 4). Ignored if <code>zlib=False</code>.</p>
+<p>If the optional keyword <code>shuffle</code> is <code>True</code>, the HDF5 shuffle filter
+will be applied before compressing the data (default <code>True</code>). This
+significantly improves compression. Default is <code>True</code>. Ignored if
+<code>zlib=False</code>.</p>
+<p>If the optional keyword <code>fletcher32</code> is <code>True</code>, the Fletcher32 HDF5
+checksum algorithm is activated to detect errors. Default <code>False</code>.</p>
+<p>If the optional keyword <code>contiguous</code> is <code>True</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>The optional keyword <code>chunksizes</code> can be used to manually 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">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>The optional keyword <code>endian</code> 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>The optional keyword <code>fill_value</code> can be used to override the default
+netCDF <code>_FillValue</code> (the value that the variable gets filled with before
+any data is written to it, defaults given in <code>netCDF4.default_fillvals</code>).
+If fill_value is set to <code>False</code>, then the variable is not pre-filled.</p>
+<p>If the optional keyword parameter <code>least_significant_digit</code> is
+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 <code>numpy.around(scale*data)/scale</code>, where
+scale = 2**bits, and bits is determined so that a precision of 0.1 is
+retained (in this case bits=4). From the
+<a href="http://www.esrl.noaa.gov/psd/data/gridded/conventions/cdc_netcdf_standard.shtml">PSD metadata conventions</a>:
+"least_significant_digit -- power of ten of the smallest decimal place
+in unpacked data that is a reliable value." Default is <code>None</code>, or no
+quantization, or 'lossless' compression.</p>
+<p>When creating variables in a <code>NETCDF4</code> or <code>NETCDF4_CLASSIC</code> formatted file,
+HDF5 creates something called a 'chunk cache' for each variable. The
+default size of the chunk cache may be large enough to completely fill
+available memory when creating thousands of variables. The optional
+keyword <code>chunk_cache</code> allows you to reduce (or increase) the size of
+the default chunk cache when creating a variable. The setting only
+persists as long as the Dataset is open - you can use the set_var_chunk_cache
+method to change it the next time the Dataset is opened.
+Warning - messing with this parameter can seriously degrade performance.</p>
+<p>The return value is the <a href="#netCDF4.Variable"><code>Variable</code></a> class instance describing the new
+variable.</p>
+<p>A list of names corresponding to netCDF variable attributes can be
+obtained with the <a href="#netCDF4.Variable"><code>Variable</code></a> method <a href="#netCDF4.Variable.ncattrs"><code>ncattrs</code></a>. A dictionary
+containing all the netCDF attribute name/value pairs is provided by
+the <code>__dict__</code> attribute of a <a href="#netCDF4.Variable"><code>Variable</code></a> instance.</p>
+<p><a href="#netCDF4.Variable"><code>Variable</code></a> instances behave much like array objects. Data can be
+assigned to or retrieved from a variable with indexing and slicing
+operations on the <a href="#netCDF4.Variable"><code>Variable</code></a> instance. A <a href="#netCDF4.Variable"><code>Variable</code></a> instance has six
+Dataset standard attributes: <code>dimensions, dtype, shape, ndim, name</code> and
+<code>least_significant_digit</code>. Application programs should never modify
+these attributes. The <code>dimensions</code> attribute is a tuple containing the
+names of the dimensions associated with this variable. The <code>dtype</code>
+attribute is a string describing the variable's data type (<code>i4, f8,
+S1,</code> etc). The <code>shape</code> attribute is a tuple describing the current
+sizes of all the variable's dimensions. The <code>name</code> attribute is a
+string containing the name of the Variable instance.
+The <code>least_significant_digit</code>
+attributes describes the power of ten of the smallest decimal place in
+the data the contains a reliable value. assigned to the <a href="#netCDF4.Variable"><code>Variable</code></a>
+instance. If <code>None</code>, the data is not truncated. The <code>ndim</code> attribute
+is the number of variable dimensions.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.delncattr">
+ <p>def <span class="ident">delncattr</span>(</p><p>self,name,value)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.delncattr">delncattr</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>delete a netCDF dataset or group attribute. Use if you need to delete a
+netCDF attribute with the same name as one of the reserved python
+attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.filepath">
+ <p>def <span class="ident">filepath</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.filepath">filepath</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Get the file system path (or the opendap URL) which was used to
+open/create the Dataset. Requires netcdf >= 4.1.2</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.getncattr">
+ <p>def <span class="ident">getncattr</span>(</p><p>self,name)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.getncattr">getncattr</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>retrievel a netCDF dataset or group attribute.
+Use if you need to get a netCDF attribute with the same
+name as one of the reserved python attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.renameAttribute">
+ <p>def <span class="ident">renameAttribute</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.renameAttribute">renameAttribute</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>rename a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> attribute named <code>oldname</code> to <code>newname</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.renameDimension">
+ <p>def <span class="ident">renameDimension</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.renameDimension">renameDimension</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>rename a <a href="#netCDF4.Dimension"><code>Dimension</code></a> named <code>oldname</code> to <code>newname</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.renameGroup">
+ <p>def <span class="ident">renameGroup</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.renameGroup">renameGroup</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>rename a <a href="#netCDF4.Group"><code>Group</code></a> named <code>oldname</code> to <code>newname</code> (requires netcdf >= 4.3.1).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.renameVariable">
+ <p>def <span class="ident">renameVariable</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.renameVariable">renameVariable</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>rename a <a href="#netCDF4.Variable"><code>Variable</code></a> named <code>oldname</code> to <code>newname</code></p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.set_auto_mask">
+ <p>def <span class="ident">set_auto_mask</span>(</p><p>self, True_or_False)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.set_auto_mask">set_auto_mask</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Call <a href="#netCDF4.Variable.set_auto_mask"><code>set_auto_mask</code></a> for all variables contained in this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a>, as well as for all variables in all its subgroups.</p>
+<p><strong><code>True_or_False</code></strong>: Boolean determining if automatic conversion to masked arrays
+shall be applied for all variables.</p>
+<p><strong><em>Note</em></strong>: Calling this function only affects existing variables. Variables created
+after calling this function will follow the default behaviour.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.set_auto_maskandscale">
+ <p>def <span class="ident">set_auto_maskandscale</span>(</p><p>self, True_or_False)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.set_auto_maskandscale">set_auto_maskandscale</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Call <a href="#netCDF4.Variable.set_auto_maskandscale"><code>set_auto_maskandscale</code></a> for all variables contained in this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a>, as well as for all variables in all its subgroups.</p>
+<p><strong><code>True_or_False</code></strong>: Boolean determining if automatic conversion to masked arrays
+and variable scaling shall be applied for all variables.</p>
+<p><strong><em>Note</em></strong>: Calling this function only affects existing variables. Variables created
+after calling this function will follow the default behaviour.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.set_auto_scale">
+ <p>def <span class="ident">set_auto_scale</span>(</p><p>self, True_or_False)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.set_auto_scale">set_auto_scale</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Call <a href="#netCDF4.Variable.set_auto_scale"><code>set_auto_scale</code></a> for all variables contained in this <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a>, as well as for all variables in all its subgroups.</p>
+<p><strong><code>True_or_False</code></strong>: Boolean determining if automatic variable scaling
+shall be applied for all variables.</p>
+<p><strong><em>Note</em></strong>: Calling this function only affects existing variables. Variables created
+after calling this function will follow the default behaviour.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.set_fill_off">
+ <p>def <span class="ident">set_fill_off</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.set_fill_off">set_fill_off</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Sets the fill mode for a <a href="#netCDF4.Dataset"><code>Dataset</code></a> open for writing to <code>off</code>.</p>
+<p>This will prevent the data from being pre-filled with fill values, which
+may result in some performance improvements. However, you must then make
+sure the data is actually written before being read.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.set_fill_on">
+ <p>def <span class="ident">set_fill_on</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.set_fill_on">set_fill_on</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Sets the fill mode for a <a href="#netCDF4.Dataset"><code>Dataset</code></a> open for writing to <code>on</code>.</p>
+<p>This causes data to be pre-filled with fill values. The fill values can be
+controlled by the variable's <code>_Fill_Value</code> attribute, but is usually
+sufficient to the use the netCDF default <code>_Fill_Value</code> (defined
+separately for each variable type). The default behavior of the netCDF
+library correspongs to <code>set_fill_on</code>. Data which are equal to the
+<code>_Fill_Value</code> indicate that the variable was created, but never written
+to.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.setncattr">
+ <p>def <span class="ident">setncattr</span>(</p><p>self,name,value)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.setncattr">setncattr</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>set a netCDF dataset or group attribute using name,value pair.
+Use if you need to set a netCDF attribute with the
+with the same name as one of the reserved python attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.setncatts">
+ <p>def <span class="ident">setncatts</span>(</p><p>self,attdict)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.setncatts">setncatts</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>set a bunch of netCDF dataset or group attributes at once using a python dictionary.
+This may be faster when setting a lot of attributes for a <code>NETCDF3</code>
+formatted file, since nc_redef/nc_enddef is not called in between setting
+each attribute</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.sync">
+ <p>def <span class="ident">sync</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.sync">sync</a></code>
+ </p>
+
+
+
+ <div class="desc inherited"><p>Writes all buffered data in the <a href="#netCDF4.Dataset"><code>Dataset</code></a> to the disk file.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+ <h3>Methods</h3>
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.__init__">
+ <p>def <span class="ident">__init__</span>(</p><p>self, files, check=False, aggdim=None, exclude=[])</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.__init__">__init__</a></code>
+ </p>
+
+
+
+ <div class="desc"><p>Open a Dataset spanning multiple files, making it look as if it was a
+single file. Variables in the list of files that share the same
+dimension (specified with the keyword <code>aggdim</code>) are aggregated. If
+<code>aggdim</code> is not specified, the unlimited is aggregated. Currently,
+<code>aggdim</code> must be the leftmost (slowest varying) dimension of each
+of the variables to be aggregated.</p>
+<p><strong><code>files</code></strong>: either a sequence of netCDF files or a string with a
+wildcard (converted to a sorted list of files using glob) The first file
+in the list will become the "master" file, defining all the
+variables with an aggregation dimension which may span
+subsequent files. Attribute access returns attributes only from "master"
+file. The files are always opened in read-only mode.</p>
+<p><strong><code>check</code></strong>: True if you want to do consistency checking to ensure the
+correct variables structure for all of the netcdf files. Checking makes
+the initialization of the MFDataset instance much slower. Default is
+False.</p>
+<p><strong><code>aggdim</code></strong>: The name of the dimension to aggregate over (must
+be the leftmost dimension of each of the variables to be aggregated).
+If None (default), aggregate over the unlimited dimension.</p>
+<p><strong><code>exclude</code></strong>: A list of variable names to exclude from aggregation.
+Default is an empty list.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.close">
+ <p>def <span class="ident">close</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.close">close</a></code>
+ </p>
+
+
+
+ <div class="desc"><p>close all the open files.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFDataset.ncattrs">
+ <p>def <span class="ident">ncattrs</span>(</p><p>self)</p>
+ </div>
+
+ <p class="inheritance">
+ <strong>Inheritance:</strong>
+ <code><a href="#netCDF4.Dataset">Dataset</a></code>.<code><a href="#netCDF4.Dataset.ncattrs">ncattrs</a></code>
+ </p>
+
+
+
+ <div class="desc"><p>return the netcdf attribute names from the master file.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+ </div>
+ </div>
+
+ <div class="item">
+ <p id="netCDF4.MFTime" class="name">class <span class="ident">MFTime</span></p>
+
+
+ <div class="desc"><p>Class providing an interface to a MFDataset time Variable by imposing a unique common
+time unit to all files. </p>
+<p>Example usage (See <a href="#netCDF4.MFTime.__init__"><code>__init__</code></a> for more details):</p>
+<div class="codehilite"><pre><span class="o">>>></span> <span class="kn">import</span> <span class="nn">numpy</span>
+<span class="o">>>></span> <span class="n">f1</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="s">"mftest_1.nc"</span><span class="p">,</span><span class="s">"w"</span><span class="p">,</span> <span class="n">format</span><span class="o">=</span><span class="s">"NETCDF4_CLASSIC"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">f2</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="s">"mftest_2.nc"</span><span class="p">,</span><span class="s">"w"</span><span class="p">,</span> <span class="n">format</span><span class="o">=</span><span class="s">"NETCDF4_CLASSIC"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">f1</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"time"</span><span class="p">,</span><span class="bp">None</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">f2</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="s">"time"</span><span class="p">,</span><span class="bp">None</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">t1</span> <span class="o">=</span> <span class="n">f1</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"time"</span><span class="p">,</span><span class="s">"i"</span><span class="p">,(</span><span class="s">"time"</span><span class="p">,))</span>
+<span class="o">>>></span> <span class="n">t2</span> <span class="o">=</span> <span class="n">f2</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="s">"time"</span><span class="p">,</span><span class="s">"i"</span><span class="p">,(</span><span class="s">"time"</span><span class="p">,))</span>
+<span class="o">>>></span> <span class="n">t1</span><span class="o">.</span><span class="n">units</span> <span class="o">=</span> <span class="s">"days since 2000-01-01"</span>
+<span class="o">>>></span> <span class="n">t2</span><span class="o">.</span><span class="n">units</span> <span class="o">=</span> <span class="s">"days since 2000-02-01"</span>
+<span class="o">>>></span> <span class="n">t1</span><span class="o">.</span><span class="n">calendar</span> <span class="o">=</span> <span class="s">"standard"</span>
+<span class="o">>>></span> <span class="n">t2</span><span class="o">.</span><span class="n">calendar</span> <span class="o">=</span> <span class="s">"standard"</span>
+<span class="o">>>></span> <span class="n">t1</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">31</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">t2</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">f1</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+<span class="o">>>></span> <span class="n">f2</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+<span class="o">>>></span> <span class="c"># Read the two files in at once, in one Dataset.</span>
+<span class="o">>>></span> <span class="n">f</span> <span class="o">=</span> <span class="n">MFDataset</span><span class="p">(</span><span class="s">"mftest*nc"</span><span class="p">)</span>
+<span class="o">>>></span> <span class="n">t</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="s">"time"</span><span class="p">]</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">t</span><span class="o">.</span><span class="n">units</span>
+<span class="n">days</span> <span class="n">since</span> <span class="mi">2000</span><span class="o">-</span><span class="mo">01</span><span class="o">-</span><span class="mo">01</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">t</span><span class="p">[</span><span class="mi">32</span><span class="p">]</span> <span class="c"># The value written in the file, inconsistent with the MF time units.</span>
+<span class="mi">1</span>
+<span class="o">>>></span> <span class="n">T</span> <span class="o">=</span> <span class="n">MFTime</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+<span class="o">>>></span> <span class="k">print</span> <span class="n">T</span><span class="p">[</span><span class="mi">32</span><span class="p">]</span>
+<span class="mi">32</span>
+</pre></div></div>
+ <div class="source_cont">
+</div>
+
+
+ <div class="class">
+ <h3>Ancestors (in MRO)</h3>
+ <ul class="class_list">
+ <li><a href="#netCDF4.MFTime">MFTime</a></li>
+ <li>netCDF4._netCDF4._Variable</li>
+ <li>__builtin__.object</li>
+ </ul>
+ <h3>Methods</h3>
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFTime.__init__">
+ <p>def <span class="ident">__init__</span>(</p><p>self, time, units=None)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>Create a time Variable with units consistent across a multifile
+dataset.</p>
+<p><strong><code>time</code></strong>: Time variable from a <a href="#netCDF4.MFDataset"><code>MFDataset</code></a>.</p>
+<p><strong><code>units</code></strong>: Time units, for example, <code>days since 1979-01-01</code>. If None, use
+the units from the master variable.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFTime.ncattrs">
+ <p>def <span class="ident">ncattrs</span>(</p><p>...)</p>
+ </div>
+
+
+
+
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFTime.set_auto_maskandscale">
+ <p>def <span class="ident">set_auto_maskandscale</span>(</p><p>...)</p>
+ </div>
+
+
+
+
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.MFTime.typecode">
+ <p>def <span class="ident">typecode</span>(</p><p>...)</p>
+ </div>
+
+
+
+
+ <div class="source_cont">
+</div>
+
+ </div>
+
+ </div>
+ </div>
+
+ <div class="item">
+ <p id="netCDF4.VLType" class="name">class <span class="ident">VLType</span></p>
+
+
+ <div class="desc"><p>A <a href="#netCDF4.VLType"><code>VLType</code></a> instance is used to describe a variable length (VLEN) data
+type, and can be passed to the the <a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a> method of
+a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> instance. See
+<a href="#netCDF4.VLType.__init__"><code>__init__</code></a> for more details.</p>
+<p>The instance variables <code>dtype</code> and <code>name</code> should not be modified by
+the user.</p></div>
+ <div class="source_cont">
+</div>
+
+
+ <div class="class">
+ <h3>Ancestors (in MRO)</h3>
+ <ul class="class_list">
+ <li><a href="#netCDF4.VLType">VLType</a></li>
+ <li>__builtin__.object</li>
+ </ul>
+ <h3>Class variables</h3>
+ <div class="item">
+ <p id="netCDF4.VLType.dtype" class="name">var <span class="ident">dtype</span></p>
+
+
+
+
+ <div class="desc"><p>A numpy dtype object describing the component type for the VLEN.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.VLType.name" class="name">var <span class="ident">name</span></p>
+
+
+
+
+ <div class="desc"><p>String name.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <h3>Static methods</h3>
+
+ <div class="item">
+ <div class="name def" id="netCDF4.VLType.__init__">
+ <p>def <span class="ident">__init__</span>(</p><p>group, datatype, datatype_name)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>VLType constructor.</p>
+<p><strong><code>group</code></strong>: <a href="#netCDF4.Group"><code>Group</code></a> instance to associate with the VLEN datatype.</p>
+<p><strong><code>datatype</code></strong>: An numpy dtype object describing a the component type for the
+variable length array.</p>
+<p><strong><code>datatype_name</code></strong>: a Python string containing a description of the
+VLEN data type.</p>
+<p><strong><em><code>Note</code></em></strong>: <a href="#netCDF4.VLType"><code>VLType</code></a> instances should be created using the
+<a href="#netCDF4.Dataset.createVLType"><code>createVLType</code></a>
+method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or <a href="#netCDF4.Group"><code>Group</code></a> instance, not using this class directly.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+ </div>
+ </div>
+
+ <div class="item">
+ <p id="netCDF4.Variable" class="name">class <span class="ident">Variable</span></p>
+
+
+ <div class="desc"><p>A netCDF <a href="#netCDF4.Variable"><code>Variable</code></a> is used to read and write netCDF data. They are
+analagous to numpy array objects. See <a href="#netCDF4.Variable.__init__"><code>__init__</code></a> for more
+details.</p>
+<p>A list of attribute names corresponding to netCDF attributes defined for
+the variable can be obtained with the <a href="#netCDF4.Variable.ncattrs"><code>ncattrs</code></a> method. These
+attributes can be created by assigning to an attribute of the
+<a href="#netCDF4.Variable"><code>Variable</code></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"><code>Variable</code></a> instance.</p>
+<p>The following class variables are read-only:</p>
+<p><strong><code>dimensions</code></strong>: A tuple containing the names of the
+dimensions associated with this variable.</p>
+<p><strong><code>dtype</code></strong>: A numpy dtype object describing the
+variable's data type.</p>
+<p><strong><code>ndim</code></strong>: The number of variable dimensions.</p>
+<p><strong><code>shape</code></strong>: A tuple with the current shape (length of all dimensions).</p>
+<p><strong><code>scale</code></strong>: If True, <code>scale_factor</code> and <code>add_offset</code> are
+applied. Default is <code>True</code>, can be reset using <a href="#netCDF4.Variable.set_auto_scale"><code>set_auto_scale</code></a> and
+<a href="#netCDF4.Variable.set_auto_maskandscale"><code>set_auto_maskandscale</code></a> methods.</p>
+<p><strong><code>mask</code></strong>: If True, data is automatically converted to/from masked
+arrays when missing values or fill values are present. Default is <code>True</code>, can be
+reset using <a href="#netCDF4.Variable.set_auto_mask"><code>set_auto_mask</code></a> and <a href="#netCDF4.Variable.set_auto_maskandscale"><code>set_auto_maskandscale</code></a>
+methods.</p>
+<p><strong><code>least_significant_digit</code></strong>: 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"><code>Variable</code></a>
+instance. If <code>None</code>, the data is not truncated.</p>
+<p><strong><code>__orthogonal_indexing__</code></strong>: Always <code>True</code>. Indicates to client code
+that the object supports 'orthogonal indexing', which means that slices
+that are 1d arrays or lists slice along each dimension independently. This
+behavior is similar to Fortran or Matlab, but different than numpy.</p>
+<p><strong><code>datatype</code></strong>: numpy data type (for primitive data types) or VLType/CompoundType
+ instance (for compound or vlen data types).</p>
+<p><strong><code>name</code></strong>: String name.</p>
+<p><strong><code>size</code></strong>: The number of stored elements.</p></div>
+ <div class="source_cont">
+</div>
+
+
+ <div class="class">
+ <h3>Ancestors (in MRO)</h3>
+ <ul class="class_list">
+ <li><a href="#netCDF4.Variable">Variable</a></li>
+ <li>__builtin__.object</li>
+ </ul>
+ <h3>Class variables</h3>
+ <div class="item">
+ <p id="netCDF4.Variable.datatype" class="name">var <span class="ident">datatype</span></p>
+
+
+
+
+ <div class="desc"><p>numpy data type (for primitive data types) or VLType/CompoundType
+instance (for compound or vlen data types).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Variable.dimensions" class="name">var <span class="ident">dimensions</span></p>
+
+
+
+
+ <div class="desc"><p>A tuple containing the names of the
+dimensions associated with this variable.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Variable.dtype" class="name">var <span class="ident">dtype</span></p>
+
+
+
+
+ <div class="desc"><p>A numpy dtype object describing the
+variable's data type.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Variable.mask" class="name">var <span class="ident">mask</span></p>
+
+
+
+
+ <div class="desc"><p>If True, data is automatically converted to/from masked
+arrays when missing values or fill values are present. Default is <code>True</code>, can be
+reset using <a href="#netCDF4.Variable.set_auto_mask"><code>set_auto_mask</code></a> and <a href="#netCDF4.Variable.set_auto_maskandscale"><code>set_auto_maskandscale</code></a>
+methods.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Variable.name" class="name">var <span class="ident">name</span></p>
+
+
+
+
+ <div class="desc"><p>String name.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Variable.ndim" class="name">var <span class="ident">ndim</span></p>
+
+
+
+
+ <div class="desc"><p>The number of variable dimensions.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Variable.scale" class="name">var <span class="ident">scale</span></p>
+
+
+
+
+ <div class="desc"><p>if True, <code>scale_factor</code> and <code>add_offset</code> are
+applied. Default is <code>True</code>, can be reset using <a href="#netCDF4.Variable.set_auto_scale"><code>set_auto_scale</code></a> and
+<a href="#netCDF4.Variable.set_auto_maskandscale"><code>set_auto_maskandscale</code></a> methods.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Variable.shape" class="name">var <span class="ident">shape</span></p>
+
+
+
+
+ <div class="desc"><p>A tuple with the current shape (length of all dimensions).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <div class="item">
+ <p id="netCDF4.Variable.size" class="name">var <span class="ident">size</span></p>
+
+
+
+
+ <div class="desc"><p>The number of stored elements.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+ <h3>Static methods</h3>
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.__init__">
+ <p>def <span class="ident">__init__</span>(</p><p>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>
+ </div>
+
+
+
+
+ <div class="desc"><p><a href="#netCDF4.Variable"><code>Variable</code></a> constructor.</p>
+<p><strong><code>group</code></strong>: <a href="#netCDF4.Group"><code>Group</code></a> or <a href="#netCDF4.Dataset"><code>Dataset</code></a> instance to associate with variable.</p>
+<p><strong><code>name</code></strong>: Name of the variable.</p>
+<p><strong><code>datatype</code></strong>: <a href="#netCDF4.Variable"><code>Variable</code></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"><code>CompoundType</code></a> instance
+(for a structured, or compound array), a <a href="#netCDF4.VLType"><code>VLType</code></a> instance
+(for a variable-length array), or the python <code>str</code> builtin
+(for a variable-length string array). Numpy string and unicode datatypes with
+length greater than one are aliases for <code>str</code>.</p>
+<p><strong><code>dimensions</code></strong>: 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><strong><code>zlib</code></strong>: if <code>True</code>, data assigned to the <a href="#netCDF4.Variable"><code>Variable</code></a>
+instance is compressed on disk. Default <code>False</code>.</p>
+<p><strong><code>complevel</code></strong>: 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><strong><code>shuffle</code></strong>: 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><strong><code>fletcher32</code></strong>: if <code>True</code> (default <code>False</code>), the Fletcher32 checksum
+algorithm is used for error detection.</p>
+<p><strong><code>contiguous</code></strong>: 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><strong><code>chunksizes</code></strong>: 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">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><strong><code>endian</code></strong>: 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.
+For netCDF 3 files (that don't use HDF5), only <code>endian='native'</code> is allowed.</p>
+<p>The <code>zlib, complevel, shuffle, fletcher32, contiguous</code> and <code>chunksizes</code>
+keywords are silently ignored for netCDF 3 files that do not use HDF5.</p>
+<p><strong><code>least_significant_digit</code></strong>: 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<em>data)/scale, where scale = 2</em>*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><strong><code>fill_value</code></strong>: 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 <code>netCDF4.default_fillvals</code>.</p>
+<p><strong><em>Note</em></strong>: <a href="#netCDF4.Variable"><code>Variable</code></a> instances should be created using the
+<a href="#netCDF4.Dataset.createVariable"><code>createVariable</code></a> method of a <a href="#netCDF4.Dataset"><code>Dataset</code></a> or
+<a href="#netCDF4.Group"><code>Group</code></a> instance, not using this class directly.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.assignValue">
+ <p>def <span class="ident">assignValue</span>(</p><p>self, val)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>assign a value to a scalar variable. Provided for compatibility with
+Scientific.IO.NetCDF, can also be done by assigning to an Ellipsis slice ([...]).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.chunking">
+ <p>def <span class="ident">chunking</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><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></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.delncattr">
+ <p>def <span class="ident">delncattr</span>(</p><p>self,name,value)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>delete a netCDF variable attribute. Use if you need to delete a
+netCDF attribute with the same name as one of the reserved python
+attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.endian">
+ <p>def <span class="ident">endian</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>return endian-ness (<code>little,big,native</code>) of variable (as stored in HDF5 file).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.filters">
+ <p>def <span class="ident">filters</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>return dictionary containing HDF5 filter parameters.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.getValue">
+ <p>def <span class="ident">getValue</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>get the value of a scalar variable. Provided for compatibility with
+Scientific.IO.NetCDF, can also be done by slicing with an Ellipsis ([...]).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.get_var_chunk_cache">
+ <p>def <span class="ident">get_var_chunk_cache</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><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></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.getncattr">
+ <p>def <span class="ident">getncattr</span>(</p><p>self,name)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>retrievel a netCDF variable attribute. Use if you need to set a
+netCDF attribute with the same name as one of the reserved python
+attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.group">
+ <p>def <span class="ident">group</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>return the group that this <a href="#netCDF4.Variable"><code>Variable</code></a> is a member of.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.ncattrs">
+ <p>def <span class="ident">ncattrs</span>(</p><p>self)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>return netCDF attribute names for this <a href="#netCDF4.Variable"><code>Variable</code></a> in a list.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.renameAttribute">
+ <p>def <span class="ident">renameAttribute</span>(</p><p>self, oldname, newname)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>rename a <a href="#netCDF4.Variable"><code>Variable</code></a> attribute named <code>oldname</code> to <code>newname</code>.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.set_auto_mask">
+ <p>def <span class="ident">set_auto_mask</span>(</p><p>self,mask)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>turn on or off automatic conversion of variable data to and
+from masked arrays .</p>
+<p>If <code>mask</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>The default value of <code>mask</code> is <code>True</code>
+(automatic conversions are performed).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.set_auto_maskandscale">
+ <p>def <span class="ident">set_auto_maskandscale</span>(</p><p>self,maskandscale)</p>
+ </div>
+
+
+
+
+ <div class="desc"><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>
+<div class="codehilite"><pre>data = self.scale_factor*data + self.add_offset
+</pre></div>
+
+
+<p>When data is written to a variable it is packed using::</p>
+<div class="codehilite"><pre>data = (data - self.add_offset)/self.scale_factor
+</pre></div>
+
+
+<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 the
+<a href="http://www.esrl.noaa.gov/psd/data/gridded/conventions/cdc_netcdf_standard.shtml">PSD metadata conventions</a>.</p>
+<p>The default value of <code>maskandscale</code> is <code>True</code>
+(automatic conversions are performed).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.set_auto_scale">
+ <p>def <span class="ident">set_auto_scale</span>(</p><p>self,scale)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>turn on or off automatic packing/unpacking of variable
+data using <code>scale_factor</code> and <code>add_offset</code> attributes.</p>
+<p>If <code>scale</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>
+<div class="codehilite"><pre>data = self.scale_factor*data + self.add_offset
+</pre></div>
+
+
+<p>When data is written to a variable it is packed using::</p>
+<div class="codehilite"><pre>data = (data - self.add_offset)/self.scale_factor
+</pre></div>
+
+
+<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 the
+<a href="http://www.esrl.noaa.gov/psd/data/gridded/conventions/cdc_netcdf_standard.shtml">PSD metadata conventions</a>.</p>
+<p>The default value of <code>scale</code> is <code>True</code>
+(automatic conversions are performed).</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.set_var_chunk_cache">
+ <p>def <span class="ident">set_var_chunk_cache</span>(</p><p>self,size=None,nelems=None,preemption=None)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>change variable chunk cache settings.
+See netcdf C library documentation for <code>nc_set_var_chunk_cache</code> for
+details.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.setncattr">
+ <p>def <span class="ident">setncattr</span>(</p><p>self,name,value)</p>
+ </div>
+
+
+
+
+ <div class="desc"><p>set a netCDF variable attribute using name,value pair. Use if you need to set a
+netCDF attribute with the same name as one of the reserved python
+attributes.</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+
+ <div class="item">
+ <div class="name def" id="netCDF4.Variable.setncatts">
+ <p>def <span class="ident">setncatts</span>(</p><p>self,attdict)</p>
+ </div>
+
+
+
+
+ <div class="desc"><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 <code>NETCDF3</code>
+formatted file, since nc_redef/nc_enddef is not called in between setting
+each attribute</p></div>
+ <div class="source_cont">
+</div>
+
+ </div>
+
+ </div>
+ </div>
+
+ </section>
+
+ </article>
+ <div class="clear"> </div>
+ <footer id="footer">
+ <p>
+ Documentation generated by
+ <a href="https://github.com/BurntSushi/pdoc">pdoc 0.3.1</a>
+ </p>
+
+ <p>pdoc is in the public domain with the
+ <a href="http://unlicense.org">UNLICENSE</a></p>
+
+ <p>Design by <a href="http://nadh.in">Kailash Nadh</a></p>
+ </footer>
+</div>
+</body>
+</html>
diff --git a/docs/redirect.html b/docs/redirect.html
deleted file mode 100644
index 524c955..0000000
--- a/docs/redirect.html
+++ /dev/null
@@ -1,38 +0,0 @@
-<html><head><title>Epydoc Redirect Page</title>
-<meta http-equiv="cache-control" content="no-cache" />
-<meta http-equiv="expires" content="0" />
-<meta http-equiv="pragma" content="no-cache" />
- <script type="text/javascript" src="epydoc.js"></script>
-</head>
-<body>
-<script type="text/javascript">
-<!--
-var pages = ["netCDF4._netCDF4.CompoundType-c", "netCDF4._netCDF4.Dimension-c", "netCDF4._netCDF4.MFDataset-c", "netCDF4._netCDF4.Variable-c", "netCDF4._netCDF4.Dataset-c", "netCDF4._netCDF4.MFTime-c", "netCDF4._netCDF4.VLType-c", "netCDF4._netCDF4.Group-c", "netCDF4._netCDF4-m"];
-var dottedName = get_anchor();
-if (dottedName) {
- var target = redirect_url(dottedName);
- if (target) window.location.replace(target);
-}
-// -->
-</script>
-
-<h3>Epydoc Auto-redirect page</h3>
-
-<p>When javascript is enabled, this page will redirect URLs of
-the form <tt>redirect.html#<i>dotted.name</i></tt> to the
-documentation for the object with the given fully-qualified
-dotted name.</p>
-<p><a id="message"> </a></p>
-
-<script type="text/javascript">
-<!--
-if (dottedName) {
- var msg = document.getElementById("message");
- msg.innerHTML = "No documentation found for <tt>"+
- dottedName+"</tt>";
-}
-// -->
-</script>
-
-</body>
-</html>
diff --git a/man/nc3tonc4.1 b/man/nc3tonc4.1
index e6a3de5..cfccf59 100644
--- a/man/nc3tonc4.1
+++ b/man/nc3tonc4.1
@@ -6,15 +6,15 @@
nc3tonc4 \- a program to convert netCDF 3 files to netCDF 4 format files
.SH SYNOPSIS
.B nc3tonc4
-.RB [\-h ]
-.RB [\-o ]
-.RB [\-\-vars=\fIvar1,var2,..\fR ]
-.RB [\-\-zlib=\fI(0|1)\fR ]
-.RB [\-\-complevel=\fI(1\-9)\fR ]
-.RB [\-\-shuffle=\fI(0|1)\fR ]
-.RB [\-\-fletcher32=\fI(0|1)\fR ]
-.RB [\-\-unpackshort=\fI(0|1)\fR ]
-.RB [\-\-quantize=\fIvar1=n1,var2=n2,..\fR ]
+.RB [ \-h ]
+.RB [ \-o ]
+.RB [ \-\-vars=\fIvar1,var2,..\fR ]
+.RB [ \-\-zlib=\fI(0|1)\fR ]
+.RB [ \-\-complevel=\fI(1\-9)\fR ]
+.RB [ \-\-shuffle=\fI(0|1)\fR ]
+.RB [ \-\-fletcher32=\fI(0|1)\fR ]
+.RB [ \-\-unpackshort=\fI(0|1)\fR ]
+.RB [ \-\-quantize=\fIvar1=n1,var2=n2,..\fR ]
.I netcdf3filename
.I netcdf4filename
.br
diff --git a/man/nc4tonc3.1 b/man/nc4tonc3.1
index 671f582..02154f4 100644
--- a/man/nc4tonc3.1
+++ b/man/nc4tonc3.1
@@ -6,9 +6,9 @@
nc4tonc3 \- a program to convert a classic netCDF 4 file to netCDF 3 format
.SH SYNOPSIS
.B nc4tonc3
-.RB [\-h ]
-.RB [\-o ]
-.RB [\-\-chunk]
+.RB [ \-h ]
+.RB [ \-o ]
+.RB [ \-\-chunk ]
.I netcdf4filename
.I netcdf3filename
.br
diff --git a/netCDF4/__init__.py b/netCDF4/__init__.py
index 5e881d5..cd68eda 100644
--- a/netCDF4/__init__.py
+++ b/netCDF4/__init__.py
@@ -1,8 +1,10 @@
-# init for netCDF4 package
-# Docstring comes from extension module _netCDF4
+# init for netCDF4. package
+# Docstring comes from extension module _netCDF4.
from ._netCDF4 import *
# Need explicit imports for names beginning with underscores
-from ._netCDF4 import __doc__
+from ._netCDF4 import __doc__, __pdoc__
from ._netCDF4 import (__version__, __netcdf4libversion__, __hdf5libversion__,
__has_rename_grp__, __has_nc_inq_path__,
__has_nc_inq_format_extended__)
+__all__ =\
+['Dataset','Variable','Dimension','Group','MFDataset','MFTime','CompoundType','VLType','date2num','num2date','date2index','stringtochar','chartostring','stringtoarr','getlibversion']
diff --git a/netCDF4/_netCDF4.c b/netCDF4/_netCDF4.c
index c5bb266..883c44c 100644
--- a/netCDF4/_netCDF4.c
+++ b/netCDF4/_netCDF4.c
@@ -447,7 +447,7 @@ struct __pyx_opt_args_7netCDF4_8_netCDF4__strencode;
struct __pyx_defaults;
typedef struct __pyx_defaults __pyx_defaults;
-/* "netCDF4/_netCDF4.pyx":4118
+/* "netCDF4/_netCDF4.pyx":4230
* return xtype
*
* cdef _read_compound(group, nc_type xtype, endian=None): # <<<<<<<<<<<<<<
@@ -459,7 +459,7 @@ struct __pyx_opt_args_7netCDF4_8_netCDF4__read_compound {
PyObject *endian;
};
-/* "netCDF4/_netCDF4.pyx":4276
+/* "netCDF4/_netCDF4.pyx":4387
* return xtype, dt
*
* cdef _read_vlen(group, nc_type xtype, endian=None): # <<<<<<<<<<<<<<
@@ -471,7 +471,7 @@ struct __pyx_opt_args_7netCDF4_8_netCDF4__read_vlen {
PyObject *endian;
};
-/* "netCDF4/_netCDF4.pyx":4303
+/* "netCDF4/_netCDF4.pyx":4414
* return VLType(group, dt, name, typeid=xtype)
*
* cdef _strencode(pystr,encoding=None): # <<<<<<<<<<<<<<
@@ -486,12 +486,12 @@ struct __pyx_defaults {
PyObject *__pyx_arg_exclude;
};
-/* "netCDF4/_netCDF4.pyx":1353
- *
+/* "netCDF4/_netCDF4.pyx":1374
+ * __pdoc__ = {}
*
* cdef class Dataset: # <<<<<<<<<<<<<<
* """
- * Dataset(self, filename, mode="r", clobber=True, diskless=False, persist=False, keepweakref=False, format='NETCDF4')
+ * A netCDF `netCDF4.Dataset` is a collection of dimensions, groups, variables and
*/
struct __pyx_obj_7netCDF4_8_netCDF4_Dataset {
PyObject_HEAD
@@ -508,29 +508,29 @@ struct __pyx_obj_7netCDF4_8_netCDF4_Dataset {
PyObject *data_model;
PyObject *cmptypes;
PyObject *vltypes;
- PyObject *keepweakref;
PyObject *__orthogonal_indexing__;
+ PyObject *keepweakref;
};
-/* "netCDF4/_netCDF4.pyx":2197
+/* "netCDF4/_netCDF4.pyx":2257
*
*
* cdef class Group(Dataset): # <<<<<<<<<<<<<<
* """
- * Group(self, parent, name)
+ * Groups define a hierarchical namespace within a netCDF file. They are
*/
struct __pyx_obj_7netCDF4_8_netCDF4_Group {
struct __pyx_obj_7netCDF4_8_netCDF4_Dataset __pyx_base;
};
-/* "netCDF4/_netCDF4.pyx":2287
+/* "netCDF4/_netCDF4.pyx":2351
*
*
* cdef class Dimension: # <<<<<<<<<<<<<<
* """
- * Dimension(self, group, name, size=None)
+ * A netCDF `netCDF4.Dimension` is used to describe the coordinates of a `netCDF4.Variable`.
*/
struct __pyx_obj_7netCDF4_8_netCDF4_Dimension {
PyObject_HEAD
@@ -542,12 +542,12 @@ struct __pyx_obj_7netCDF4_8_netCDF4_Dimension {
};
-/* "netCDF4/_netCDF4.pyx":2431
+/* "netCDF4/_netCDF4.pyx":2500
* return False
*
* cdef class 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 `netCDF4.Variable` is used to read and write netCDF data. They are
*/
struct __pyx_obj_7netCDF4_8_netCDF4_Variable {
PyObject_HEAD
@@ -569,12 +569,12 @@ struct __pyx_obj_7netCDF4_8_netCDF4_Variable {
};
-/* "netCDF4/_netCDF4.pyx":3959
+/* "netCDF4/_netCDF4.pyx":4071
* # Compound datatype support.
*
* cdef class CompoundType: # <<<<<<<<<<<<<<
* """
- * A L{CompoundType} instance is used to describe a compound data type.
+ * A `netCDF4.CompoundType` instance is used to describe a compound data
*/
struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType {
PyObject_HEAD
@@ -584,12 +584,12 @@ struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType {
};
-/* "netCDF4/_netCDF4.pyx":4189
+/* "netCDF4/_netCDF4.pyx":4301
* # VLEN datatype support.
*
* cdef class VLType: # <<<<<<<<<<<<<<
* """
- * A L{VLType} instance is used to describe a variable length (VLEN) data type.
+ * A `netCDF4.VLType` instance is used to describe a variable length (VLEN) data
*/
struct __pyx_obj_7netCDF4_8_netCDF4_VLType {
PyObject_HEAD
@@ -1163,12 +1163,12 @@ static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_8cmptypes_4__del__(struct __pyx_
static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_7vltypes___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_7vltypes_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_7vltypes_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_11keepweakref___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_11keepweakref_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_11keepweakref_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_23__orthogonal_indexing_____get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_23__orthogonal_indexing___2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_23__orthogonal_indexing___4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_11keepweakref___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_11keepweakref_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_11keepweakref_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
static int __pyx_pf_7netCDF4_8_netCDF4_5Group___init__(struct __pyx_obj_7netCDF4_8_netCDF4_Group *__pyx_v_self, PyObject *__pyx_v_parent, PyObject *__pyx_v_name, PyObject *__pyx_v_kwargs); /* proto */
static PyObject *__pyx_pf_7netCDF4_8_netCDF4_5Group_2close(CYTHON_UNUSED struct __pyx_obj_7netCDF4_8_netCDF4_Group *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7netCDF4_8_netCDF4_5Group_4_getname(struct __pyx_obj_7netCDF4_8_netCDF4_Group *__pyx_v_self); /* proto */
@@ -1371,6 +1371,7 @@ static char __pyx_k_i2[] = "i2";
static char __pyx_k_i4[] = "i4";
static char __pyx_k_i8[] = "i8";
static char __pyx_k_id[] = "id";
+static char __pyx_k_iu[] = "iu";
static char __pyx_k_ma[] = "ma";
static char __pyx_k_n1[] = "n1";
static char __pyx_k_nv[] = "nv";
@@ -1425,7 +1426,8 @@ static char __pyx_k_val[] = "val";
static char __pyx_k_var[] = "var";
static char __pyx_k_vid[] = "vid";
static char __pyx_k_zip[] = "zip";
-static char __pyx_k__118[] = ".";
+static char __pyx_k_HDF4[] = "HDF4";
+static char __pyx_k__120[] = ".";
static char __pyx_k_bool[] = "bool";
static char __pyx_k_cdfm[] = "cdfm";
static char __pyx_k_char[] = "char";
@@ -1461,6 +1463,7 @@ static char __pyx_k_ndim[] = "ndim";
static char __pyx_k_numv[] = "numv";
static char __pyx_k_part[] = "part";
static char __pyx_k_path[] = "path";
+static char __pyx_k_pdoc[] = "__pdoc__";
static char __pyx_k_prod[] = "prod";
static char __pyx_k_repr[] = "__repr__";
static char __pyx_k_secs[] = "secs";
@@ -1482,7 +1485,7 @@ static char __pyx_k_vlen[] = "vlen";
static char __pyx_k_warn[] = "warn";
static char __pyx_k_year[] = "year";
static char __pyx_k_zlib[] = "zlib";
-static char __pyx_k_1_1_8[] = "1.1.8";
+static char __pyx_k_1_1_9[] = "1.1.9";
static char __pyx_k_4_2_1[] = "4.2.1";
static char __pyx_k_align[] = "align";
static char __pyx_k_array[] = "array";
@@ -1698,6 +1701,7 @@ static char __pyx_k_traceback[] = "traceback";
static char __pyx_k_unlimdims[] = "unlimdims";
static char __pyx_k_variables[] = "variables";
static char __pyx_k_walk_grps[] = "_walk_grps";
+static char __pyx_k_Group_name[] = "Group.name";
static char __pyx_k_IndexError[] = "IndexError";
static char __pyx_k_ValueError[] = "ValueError";
static char __pyx_k_add_offset[] = "add_offset";
@@ -1729,13 +1733,15 @@ static char __pyx_k_utc_offset[] = "utc_offset";
static char __pyx_k_ImportError[] = "ImportError";
static char __pyx_k_NC_DISKLESS[] = "NC_DISKLESS";
static char __pyx_k_OrderedDict[] = "OrderedDict";
+static char __pyx_k_String_name[] = "String name.";
+static char __pyx_k_VLType_name[] = "VLType.name";
static char __pyx_k_assign_vlen[] = "_assign_vlen";
static char __pyx_k_cdfRecVar_2[] = "cdfRecVar";
static char __pyx_k_chunk_cache[] = "chunk_cache";
static char __pyx_k_collections[] = "collections";
static char __pyx_k_concatenate[] = "concatenate";
static char __pyx_k_createGroup[] = "createGroup";
-static char __pyx_k_disk_format[] = "_disk_format";
+static char __pyx_k_disk_format[] = "disk_format";
static char __pyx_k_file_format[] = "file_format";
static char __pyx_k_filling_off[] = "filling off\n";
static char __pyx_k_isprimitive[] = "_isprimitive";
@@ -1748,8 +1754,10 @@ static char __pyx_k_releasevers[] = "releasevers";
static char __pyx_k_string_type[] = ": string type";
static char __pyx_k_stringtoarr[] = "stringtoarr";
static char __pyx_k_variables_s[] = " variables = %s\n";
+static char __pyx_k_Dataset_path[] = "Dataset.path";
static char __pyx_k_MFTime__time[] = "_MFTime__time";
static char __pyx_k_RuntimeError[] = "RuntimeError";
+static char __pyx_k_VLType_dtype[] = "VLType.dtype";
static char __pyx_k_chartostring[] = "chartostring";
static char __pyx_k_data_model_2[] = "_data_model";
static char __pyx_k_dimensions_s[] = " dimensions = %s\n";
@@ -1766,8 +1774,12 @@ static char __pyx_k_Illegal_index[] = "Illegal index";
static char __pyx_k_MFTime___init[] = "MFTime.__init__";
static char __pyx_k_MFTime__delta[] = "_MFTime__delta";
static char __pyx_k_NETCDF3_64BIT[] = "NETCDF3_64BIT";
+static char __pyx_k_Variable_mask[] = "Variable.mask";
+static char __pyx_k_Variable_name[] = "Variable.name";
+static char __pyx_k_Variable_ndim[] = "Variable.ndim";
+static char __pyx_k_Variable_size[] = "Variable.size";
static char __pyx_k_datatype_name[] = "datatype_name";
-static char __pyx_k_disk_format_2[] = "disk_format";
+static char __pyx_k_disk_format_2[] = "_disk_format";
static char __pyx_k_file_format_2[] = "_file_format";
static char __pyx_k_getlibversion[] = "getlibversion";
static char __pyx_k_is_native_big[] = "is_native_big";
@@ -1777,7 +1789,13 @@ static char __pyx_k_set_auto_mask[] = "set_auto_mask";
static char __pyx_k_timestr_split[] = "timestr_split";
static char __pyx_k_unicode_error[] = "unicode_error";
static char __pyx_k_AttributeError[] = "AttributeError";
+static char __pyx_k_Dataset_groups[] = "Dataset.groups";
+static char __pyx_k_Dataset_parent[] = "Dataset.parent";
+static char __pyx_k_Dimension_name[] = "Dimension.name";
static char __pyx_k_Variable___len[] = "_Variable.__len__";
+static char __pyx_k_Variable_dtype[] = "Variable.dtype";
+static char __pyx_k_Variable_scale[] = "Variable.scale";
+static char __pyx_k_Variable_shape[] = "Variable.shape";
static char __pyx_k_current_size_s[] = "current size = %s\n";
static char __pyx_k_has_rename_grp[] = "__has_rename_grp__";
static char __pyx_k_hdf5libversion[] = "__hdf5libversion__";
@@ -1785,6 +1803,7 @@ static char __pyx_k_microsec_units[] = "microsec_units";
static char __pyx_k_millisec_units[] = "millisec_units";
static char __pyx_k_set_auto_scale[] = "set_auto_scale";
static char __pyx_k_supportedtypes[] = "_supportedtypes";
+static char __pyx_k_Dataset_vltypes[] = "Dataset.vltypes";
static char __pyx_k_Dimension___len[] = "_Dimension.__len__";
static char __pyx_k_MFDataset_close[] = "MFDataset.close";
static char __pyx_k_NETCDF3_CLASSIC[] = "NETCDF3_CLASSIC";
@@ -1795,6 +1814,7 @@ static char __pyx_k_Variable__shape[] = "_Variable._shape";
static char __pyx_k_current_shape_s[] = "current shape = %s\n";
static char __pyx_k_has_nc_inq_path[] = "__has_nc_inq_path__";
static char __pyx_k_out_array_shape[] = "_out_array_shape";
+static char __pyx_k_Dataset_cmptypes[] = "Dataset.cmptypes";
static char __pyx_k_Dimension___init[] = "_Dimension.__init__";
static char __pyx_k_Dimension___repr[] = "_Dimension.__repr__";
static char __pyx_k_MFDataset___init[] = "MFDataset.__init__";
@@ -1808,22 +1828,33 @@ static char __pyx_k_is_native_little[] = "is_native_little";
static char __pyx_k_netCDF4__netCDF4[] = "netCDF4._netCDF4";
static char __pyx_k_s_not_found_in_s[] = "%s not found in %s";
static char __pyx_k_vlen_data_type_s[] = "vlen data type: %s\n";
+static char __pyx_k_CompoundType_name[] = "CompoundType.name";
+static char __pyx_k_Dataset_variables[] = "Dataset.variables";
static char __pyx_k_MFDataset_ncattrs[] = "MFDataset.ncattrs";
+static char __pyx_k_Variable_datatype[] = "Variable.datatype";
static char __pyx_k_Variable_typecode[] = "_Variable.typecode";
static char __pyx_k_gethdf5libversion[] = "_gethdf5libversion";
static char __pyx_k_netcdf4libversion[] = "__netcdf4libversion__";
+static char __pyx_k_CompoundType_dtype[] = "CompoundType.dtype";
+static char __pyx_k_Dataset_data_model[] = "Dataset.data_model";
+static char __pyx_k_Dataset_dimensions[] = "Dataset.dimensions";
static char __pyx_k_UnicodeDecodeError[] = "UnicodeDecodeError";
static char __pyx_k_Variable___getattr[] = "_Variable.__getattr__";
static char __pyx_k_Variable___getitem[] = "_Variable.__getitem__";
static char __pyx_k_dimensions_sizes_s[] = " dimensions(sizes): %s\n";
static char __pyx_k_set_default_format[] = "_set_default_format";
+static char __pyx_k_Dataset_disk_format[] = "Dataset.disk_format";
+static char __pyx_k_Dataset_file_format[] = "Dataset.file_format";
+static char __pyx_k_Dataset_keepweakref[] = "Dataset.keepweakref";
static char __pyx_k_MFDataset___setattr[] = "MFDataset.__setattr__";
+static char __pyx_k_Variable_dimensions[] = "Variable.dimensions";
static char __pyx_k_get_var_chunk_cache[] = "get_var_chunk_cache";
static char __pyx_k_orthogoral_indexing[] = "__orthogoral_indexing__";
static char __pyx_k_proleptic_gregorian[] = "proleptic_gregorian";
static char __pyx_k_compound_data_type_s[] = "compound data type: %s\n";
static char __pyx_k_name_s_numpy_dtype_s[] = ": name = '%s', numpy dtype = %s\n";
static char __pyx_k_Dimension_isunlimited[] = "_Dimension.isunlimited";
+static char __pyx_k_len_of_unsized_object[] = "len() of unsized object";
static char __pyx_k_set_auto_maskandscale[] = "set_auto_maskandscale";
static char __pyx_k_name_cannot_be_altered[] = "name cannot be altered";
static char __pyx_k_unlimited_dimensions_s[] = "unlimited dimensions: %s\n";
@@ -1839,39 +1870,61 @@ static char __pyx_k_has_nc_inq_format_extended[] = "__has_nc_inq_format_extended
static char __pyx_k_s_not_a_valid_variable_name[] = "%s not a valid variable name";
static char __pyx_k_dimensions_cannot_be_altered[] = "dimensions cannot be altered";
static char __pyx_k_s_not_a_valid_dimension_name[] = "%s not a valid dimension name";
+static char __pyx_k_The_number_of_stored_elements[] = "The number of stored elements.";
static char __pyx_k_mode_must_be_w_r_a_or_r_got_s[] = "mode must be 'w', 'r', 'a' or 'r+', got '%s'";
static char __pyx_k_string_name_of_Group_instance[] = "string name of Group instance";
+static char __pyx_k_Introduction_netcdf4_python_is[] = "\nIntroduction\n============\n\nnetcdf4-python is a Python interface to the netCDF C library. \n\n[netCDF version 4](http://www.unidata.ucar.edu/software/netcdf/netcdf-4) has many features\nnot found in earlier versions of the library and is implemented on top of\n[HDF5](http://www.hdfgroup.org/HDF5). This module can read and write\nfiles in both the new netCDF 4 and the old netCDF 3 format, and can create\nfiles that are readable [...]
+static char __pyx_k_Variable___orthogonal_indexing[] = "Variable.__orthogonal_indexing__";
static char __pyx_k_Variable_set_auto_maskandscale[] = "_Variable.set_auto_maskandscale";
static char __pyx_k_filepath_method_not_enabled_To[] = "\nfilepath method not enabled. To enable, install Cython, make sure you have\nversion 4.1.2 or higher of the netcdf C lib, and rebuild netcdf4-python.";
+static char __pyx_k_path_shows_the_location_of_the[] = "`path` shows the location of the `netCDF4.Group` in\n the `netCDF4.Dataset` in a unix directory format (the names of groups in the\n hierarchy separated by backslashes). A `netCDF4.Dataset` instance is the root\n group, so the path is simply `'/'`.";
static char __pyx_k_renameGroup_method_not_enabled[] = "\nrenameGroup method not enabled. To enable, install Cython, make sure you have\nversion 4.3.1 or higher of the netcdf C lib, and rebuild netcdf4-python.";
+static char __pyx_k_A_numpy_dtype_object_describing[] = "A numpy dtype object describing the\n variable's data type.";
+static char __pyx_k_A_string_describing_the_name_of[] = "A string describing the name of the `netCDF4.Group`.";
+static char __pyx_k_A_tuple_containing_the_names_of[] = "A tuple containing the names of the\n dimensions associated with this variable.";
+static char __pyx_k_Always_True_Indicates_to_client[] = "Always `True`. Indicates to client code\n that the object supports 'orthogonal indexing', which means that slices\n that are 1d arrays or lists slice along each dimension independently. This\n behavior is similar to Fortran or Matlab, but different than numpy.";
+static char __pyx_k_Class_for_reading_multi_file_ne[] = "\nClass for reading multi-file netCDF Datasets, making variables\nspanning multiple files appear as if they were in one file.\nDatasets must be in `NETCDF4_CLASSIC, NETCDF3_CLASSIC or NETCDF3_64BIT`\nformat (`NETCDF4` Datasets won't work).\n\nAdapted from [pycdf](http://pysclint.sourceforge.net/pycdf) by Andre Gosselin.\n\nExample usage (See `netCDF4.MFDataset.__init__` for more details):\n\n :::python\n >>> import numpy\n [...]
+static char __pyx_k_Class_providing_an_interface_to[] = "\nClass providing an interface to a MFDataset time Variable by imposing a unique common\ntime unit to all files. \n\nExample usage (See `netCDF4.MFTime.__init__` for more details):\n\n :::python\n >>> import numpy\n >>> f1 = Dataset(\"mftest_1.nc\",\"w\", format=\"NETCDF4_CLASSIC\")\n >>> f2 = Dataset(\"mftest_2.nc\",\"w\", format=\"NETCDF4_CLASSIC\")\n >>> f1.createDimension(\"time\",None)\n >>> f2.createDimensio [...]
static char __pyx_k_FillValue_attribute_must_be_set[] = "_FillValue attribute must be set when variable is ";
-static char __pyx_k_Introduction_Python_interface_t[] = "\nIntroduction\n============\n\nPython interface to the netCDF version 4 library. U{netCDF version 4\n<http://www.unidata.ucar.edu/software/netcdf/netcdf-4>} has many features\nnot found in earlier versions of the library and is implemented on top of\nU{HDF5 <http://www.hdfgroup.org/HDF5>}. This module can read and write\nfiles in both the new netCDF 4 and the old netCDF 3 format, and can create\nfiles that are readable by HDF5 cl [...]
-static char __pyx_k_MFDataset_self_files_check_Fals[] = "\nMFDataset(self, files, check=False, aggdim=None, exclude=[])\n\nClass for reading multi-file netCDF Datasets, making variables\nspanning multiple files appear as if they were in one file.\n\nDatasets must be in C{NETCDF4_CLASSIC, NETCDF3_CLASSIC or NETCDF3_64BIT}\nformat (C{NETCDF4} Datasets won't work).\n\nAdapted from U{pycdf <http://pysclint.sourceforge.net/pycdf>} by Andre Gosselin.\n\nExample usage:\n\n>>> import numpy\n>>> [...]
-static char __pyx_k_MFTime_self_time_units_None_Cla[] = "\nMFTime(self, time, units=None)\n\nClass providing an interface to a MFDataset time Variable by imposing a unique common\ntime unit to all files.\n\nExample usage:\n\n>>> import numpy\n>>> f1 = Dataset('mftest_1.nc','w', format='NETCDF4_CLASSIC')\n>>> f2 = Dataset('mftest_2.nc','w', format='NETCDF4_CLASSIC')\n>>> f1.createDimension('time',None)\n>>> f2.createDimension('time',None)\n>>> t1 = f1.createVariable('time','i',('time',))\ [...]
-static char __pyx_k_Users_jsw_python_netcdf4_python[] = "/Users/jsw/python/netcdf4-python.git/netCDF4/_netCDF4.pyx";
+static char __pyx_k_The_vltypes_dictionary_maps_the[] = "The `vltypes` dictionary maps the names of\n variable-length types defined for the `netCDF4.Group` or `netCDF4.Dataset` to instances of the\n `netCDF4.VLType` class.";
static char __pyx_k_Variable_object_no_longer_valid[] = "Variable object no longer valid";
static char __pyx_k_assign_vlen_method_only_for_use[] = "_assign_vlen method only for use with VLEN variables";
static char __pyx_k_cannot_set__FillValue_attribute[] = "cannot set _FillValue attribute for VLEN or compound variable";
static char __pyx_k_cannot_specify_chunksizes_for_a[] = "cannot specify chunksizes for a contiguous dataset";
+static char __pyx_k_data_model_describes_the_netCDF[] = "`data_model` describes the netCDF\n data model version, one of `NETCDF3_CLASSIC`, `NETCDF4`,\n `NETCDF4_CLASSIC` or `NETCDF3_64BIT`.";
+static char __pyx_k_disk_format_describes_the_under[] = "`disk_format` describes the underlying\n file format, one of `NETCDF3`, `HDF5`, `HDF4`,\n `PNETCDF`, `DAP2`, `DAP4` or `UNDEFINED`. Only available if using\n netcdf C library version >= 4.3.1, otherwise will always return\n `UNDEFINED`.";
static char __pyx_k_endian_keyword_argument_must_be[] = "'endian' keyword argument must be 'little','big' or 'native', got '%s'";
static char __pyx_k_endian_ness_of_dtype_and_endian[] = "endian-ness of dtype and endian kwarg do not match, using endian kwarg";
static char __pyx_k_get_variables_s_dimension_names[] = "get variables's dimension names";
static char __pyx_k_illegal_data_type_for_attribute[] = "illegal data type for attribute, must be one of %s, got %s";
+static char __pyx_k_net_bio_c2sm_hvwaldow_git_netcd[] = "/net/bio/c2sm/hvwaldow/git/netcdf4-python/netCDF4/_netCDF4.pyx";
+static char __pyx_k_parent_is_a_reference_to_the_pa[] = "`parent` is a reference to the parent\n `netCDF4.Group` instance. `None` for a the root group or `netCDF4.Dataset` instance";
static char __pyx_k_s_is_one_of_the_reserved_attrib[] = "'%s' is one of the reserved attributes %s, cannot delete. Use delncattr instead.";
+static char __pyx_k_same_as_data_model_retained_for[] = "same as `data_model`, retained for backwards compatibility.";
static char __pyx_k_single_element_VLEN_slices_must[] = "single element VLEN slices must be specified by integers only";
static char __pyx_k_wrong_data_type_in_object_array[] = "wrong data type in object array: should be %s, got %s";
static char __pyx_k_wrong_data_type_should_be_s_got[] = "wrong data type: should be %s, got %s";
+static char __pyx_k_A_tuple_with_the_current_shape_l[] = "A tuple with the current shape (length of all dimensions).";
+static char __pyx_k_Describes_the_power_of_ten_of_th[] = "Describes the power of ten of the \n smallest decimal place in the data the contains a reliable value. Data is\n truncated to this decimal place when it is assigned to the `netCDF4.Variable`\n instance. If `None`, the data is not truncated.";
static char __pyx_k_Dimension_object_no_longer_valid[] = "Dimension object no longer valid";
+static char __pyx_k_If_True_child_Dimension_and_Vari[] = "If `True`, child Dimension and Variables objects only keep weak references to\n the parent Dataset or Group.";
+static char __pyx_k_If_True_data_is_automatically_co[] = "If True, data is automatically converted to/from masked \n arrays when missing values or fill values are present. Default is `True`, can be\n reset using `netCDF4.Variable.set_auto_mask` and `netCDF4.Variable.set_auto_maskandscale`\n methods.";
static char __pyx_k_MFNetCDF4_only_works_with_NETCDF[] = "MFNetCDF4 only works with NETCDF3_CLASSIC, NETCDF3_64BIT and NETCDF4_CLASSIC formatted files, not NETCDF4";
static char __pyx_k_MFTime_requires_that_the_same_ti[] = "MFTime requires that the same time calendar is used by all files.";
static char __pyx_k_MFTime_requires_that_the_time_va[] = "MFTime requires that the time variable in all files have a calendar attribute";
static char __pyx_k_Return_the_number_of_stored_elem[] = "Return the number of stored elements.";
+static char __pyx_k_The_cmptypes_dictionary_maps_the[] = "The `cmptypes` dictionary maps the names of\n compound types defined for the `netCDF4.Group` or `netCDF4.Dataset` to instances of the\n `netCDF4.CompoundType` class.";
+static char __pyx_k_The_dimensions_dictionary_maps_t[] = "The `dimensions` dictionary maps the names of\n dimensions defined for the `netCDF4.Group` or `netCDF4.Dataset` to instances of the\n `netCDF4.Dimension` class.";
+static char __pyx_k_The_groups_dictionary_maps_the_n[] = "The groups dictionary maps the names of groups created for\n this `netCDF4.Dataset` or `netCDF4.Group` to instances of the `netCDF4.Group` class (the\n `netCDF4.Dataset` class is simply a special case of the `netCDF4.Group` class which\n describes the root group in the netCDF4 file).";
+static char __pyx_k_The_number_of_variable_dimension[] = "The number of variable dimensions.";
+static char __pyx_k_The_variables_dictionary_maps_th[] = "The `variables` dictionary maps the names of variables\n defined for this `netCDF4.Dataset` or `netCDF4.Group` to instances of the `netCDF4.Variable`\n class.";
static char __pyx_k_Unsupported_compound_type_elemen[] = "Unsupported compound type element";
+static char __pyx_k_Variable_least_significant_digit[] = "Variable.least_significant_digit";
static char __pyx_k_Variable_length_strings_are_only[] = "Variable length strings are only supported for the NETCDF4 format. For other formats, consider using netCDF4.stringtochar to convert string arrays into character arrays with an additional dimension.";
static char __pyx_k_aggregation_variable_s_not_defin[] = "aggregation variable %s not defined in %s";
static char __pyx_k_attribute_s_has_unsupported_data[] = "attribute %s has unsupported datatype";
static char __pyx_k_cannot_assign_fill_value_for_mas[] = "cannot assign fill_value for masked array when missing_value attribute is not a scalar";
-static char __pyx_k_cannot_close_a_L_Group_only_appl[] = "cannot close a L{Group} (only applies to Dataset)";
+static char __pyx_k_cannot_close_a_netCDF4_Group_onl[] = "cannot close a `netCDF4.Group` (only applies to Dataset)";
static char __pyx_k_cannot_find_compound_type_in_thi[] = "cannot find compound type in this group or parent groups";
static char __pyx_k_cannot_using_file_globbing_for_r[] = "cannot using file globbing for remote (OPeNDAP) datasets";
static char __pyx_k_chunksizes_must_be_a_sequence_wi[] = "chunksizes must be a sequence with the same length as dimensions";
@@ -1884,6 +1937,7 @@ static char __pyx_k_dtype_must_string_or_unicode_S_o[] = "dtype must string or u
static char __pyx_k_filling_on_default__FillValue_of[] = "filling on, default _FillValue of %s ignored\n";
static char __pyx_k_find_current_sizes_of_all_variab[] = "find current sizes of all variable dimensions";
static char __pyx_k_format_must_be_NETCDF4_NETCDF4_C[] = "format must be 'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT', or 'NETCDF3_CLASSIC', got '%s'";
+static char __pyx_k_if_True_scale_factor_and_add_off[] = "if True, `scale_factor` and `add_offset` are\n applied. Default is `True`, can be reset using `netCDF4.Variable.set_auto_scale` and\n `netCDF4.Variable.set_auto_maskandscale` methods.";
static char __pyx_k_illegal_primitive_data_type_must[] = "illegal primitive data type, must be one of %s, got %s";
static char __pyx_k_invalid_scale_factor_or_add_offs[] = "invalid scale_factor or add_offset attribute, no unpacking done...";
static char __pyx_k_master_dataset_s_does_not_have_a[] = "master dataset %s does not have a aggregation dimension";
@@ -1909,25 +1963,58 @@ static char __pyx_k_variable_s_dimensions_mismatch_b[] = "variable %s : dimensio
static char __pyx_k_variable_s_rank_mismatch_between[] = "variable %s : rank mismatch between master %s (%s) and extension %s (%s)";
static char __pyx_k_variable_s_shape_mismatch_betwee[] = "variable %s : shape mismatch between master %s (%s) and extension %s (%s)";
static char __pyx_k_vlen_string_array_attributes_not[] = "vlen string array attributes not supported";
+static char __pyx_k_A_numpy_dtype_object_describing_2[] = "A numpy dtype object describing the compound data type.";
+static char __pyx_k_A_numpy_dtype_object_describing_3[] = "A numpy dtype object describing the component type for the VLEN.";
+static char __pyx_k_A_string_describing_the_name_of_2[] = "A string describing the name of the `netCDF4.Dimension` - used when creating a\n `netCDF4.Variable` instance with `netCDF4.Dataset.createVariable`.";
static char __pyx_k_s_is_one_of_the_reserved_attrib_2[] = "'%s' is one of the reserved attributes %s, cannot rebind. Use setncattr instead.";
static char __pyx_k_filling_on_default__FillValue_of_2[] = "filling on, default _FillValue of %s used\n";
static char __pyx_k_master_dataset_s_does_not_have_a_2[] = "master dataset %s does not have any variables to aggregate";
+static char __pyx_k_numpy_data_type_for_primitive_da_2[] = "numpy data type (for primitive data types) or VLType/CompoundType\n instance (for compound or vlen data types).";
static PyObject *__pyx_kp_s_;
static PyObject *__pyx_kp_s_0m;
static PyObject *__pyx_kp_s_1;
-static PyObject *__pyx_kp_s_1_1_8;
+static PyObject *__pyx_kp_s_1_1_9;
static PyObject *__pyx_kp_s_4_2_1;
static PyObject *__pyx_kp_s_4m;
+static PyObject *__pyx_kp_s_A_numpy_dtype_object_describing;
+static PyObject *__pyx_kp_s_A_numpy_dtype_object_describing_2;
+static PyObject *__pyx_kp_s_A_numpy_dtype_object_describing_3;
+static PyObject *__pyx_kp_s_A_string_describing_the_name_of;
+static PyObject *__pyx_kp_s_A_string_describing_the_name_of_2;
+static PyObject *__pyx_kp_s_A_tuple_containing_the_names_of;
+static PyObject *__pyx_kp_s_A_tuple_with_the_current_shape_l;
+static PyObject *__pyx_kp_s_Always_True_Indicates_to_client;
static PyObject *__pyx_n_s_AttributeError;
+static PyObject *__pyx_kp_s_Class_for_reading_multi_file_ne;
+static PyObject *__pyx_kp_s_Class_providing_an_interface_to;
+static PyObject *__pyx_kp_s_CompoundType_dtype;
+static PyObject *__pyx_kp_s_CompoundType_name;
+static PyObject *__pyx_kp_s_Dataset_cmptypes;
+static PyObject *__pyx_kp_s_Dataset_data_model;
+static PyObject *__pyx_kp_s_Dataset_dimensions;
+static PyObject *__pyx_kp_s_Dataset_disk_format;
+static PyObject *__pyx_kp_s_Dataset_file_format;
+static PyObject *__pyx_kp_s_Dataset_groups;
+static PyObject *__pyx_kp_s_Dataset_keepweakref;
+static PyObject *__pyx_kp_s_Dataset_parent;
+static PyObject *__pyx_kp_s_Dataset_path;
+static PyObject *__pyx_kp_s_Dataset_variables;
+static PyObject *__pyx_kp_s_Dataset_vltypes;
+static PyObject *__pyx_kp_s_Describes_the_power_of_ten_of_th;
static PyObject *__pyx_n_s_Dimension;
static PyObject *__pyx_n_s_Dimension___init;
static PyObject *__pyx_n_s_Dimension___len;
static PyObject *__pyx_n_s_Dimension___repr;
static PyObject *__pyx_n_s_Dimension_isunlimited;
+static PyObject *__pyx_kp_s_Dimension_name;
static PyObject *__pyx_kp_s_Dimension_object_no_longer_valid;
static PyObject *__pyx_n_s_FillValue;
static PyObject *__pyx_kp_s_FillValue_attribute_must_be_set;
+static PyObject *__pyx_kp_s_Group_name;
+static PyObject *__pyx_n_s_HDF4;
static PyObject *__pyx_n_s_IOError;
+static PyObject *__pyx_kp_s_If_True_child_Dimension_and_Vari;
+static PyObject *__pyx_kp_s_If_True_data_is_automatically_co;
static PyObject *__pyx_kp_s_Illegal_index;
static PyObject *__pyx_n_s_ImportError;
static PyObject *__pyx_n_s_IndexError;
@@ -1939,7 +2026,6 @@ static PyObject *__pyx_n_s_MFDataset___repr;
static PyObject *__pyx_n_s_MFDataset___setattr;
static PyObject *__pyx_n_s_MFDataset_close;
static PyObject *__pyx_n_s_MFDataset_ncattrs;
-static PyObject *__pyx_kp_s_MFDataset_self_files_check_Fals;
static PyObject *__pyx_kp_s_MFNetCDF4_only_works_with_NETCDF;
static PyObject *__pyx_n_s_MFTime;
static PyObject *__pyx_n_s_MFTime___getitem;
@@ -1948,7 +2034,6 @@ static PyObject *__pyx_n_s_MFTime__delta;
static PyObject *__pyx_n_s_MFTime__time;
static PyObject *__pyx_kp_s_MFTime_requires_that_the_same_ti;
static PyObject *__pyx_kp_s_MFTime_requires_that_the_time_va;
-static PyObject *__pyx_kp_s_MFTime_self_time_units_None_Cla;
static PyObject *__pyx_n_s_MINYEAR;
static PyObject *__pyx_n_s_NC_DISKLESS;
static PyObject *__pyx_n_s_NETCDF3;
@@ -1963,29 +2048,49 @@ static PyObject *__pyx_n_s_RuntimeError;
static PyObject *__pyx_n_s_S;
static PyObject *__pyx_n_s_S1;
static PyObject *__pyx_n_s_StartCountStride;
+static PyObject *__pyx_kp_s_String_name;
+static PyObject *__pyx_kp_s_The_cmptypes_dictionary_maps_the;
+static PyObject *__pyx_kp_s_The_dimensions_dictionary_maps_t;
+static PyObject *__pyx_kp_s_The_groups_dictionary_maps_the_n;
+static PyObject *__pyx_kp_s_The_number_of_stored_elements;
+static PyObject *__pyx_kp_s_The_number_of_variable_dimension;
+static PyObject *__pyx_kp_s_The_variables_dictionary_maps_th;
+static PyObject *__pyx_kp_s_The_vltypes_dictionary_maps_the;
static PyObject *__pyx_n_s_TypeError;
static PyObject *__pyx_n_s_U;
static PyObject *__pyx_n_s_U1;
static PyObject *__pyx_n_s_UNDEFINED;
static PyObject *__pyx_n_s_UnicodeDecodeError;
static PyObject *__pyx_kp_s_Unsupported_compound_type_elemen;
-static PyObject *__pyx_kp_s_Users_jsw_python_netcdf4_python;
static PyObject *__pyx_n_s_V;
+static PyObject *__pyx_kp_s_VLType_dtype;
+static PyObject *__pyx_kp_s_VLType_name;
static PyObject *__pyx_n_s_ValueError;
static PyObject *__pyx_n_s_Variable;
static PyObject *__pyx_n_s_Variable___getattr;
static PyObject *__pyx_n_s_Variable___getitem;
static PyObject *__pyx_n_s_Variable___init;
static PyObject *__pyx_n_s_Variable___len;
+static PyObject *__pyx_kp_s_Variable___orthogonal_indexing;
static PyObject *__pyx_n_s_Variable___repr;
static PyObject *__pyx_n_s_Variable__shape;
+static PyObject *__pyx_kp_s_Variable_datatype;
+static PyObject *__pyx_kp_s_Variable_dimensions;
+static PyObject *__pyx_kp_s_Variable_dtype;
+static PyObject *__pyx_kp_s_Variable_least_significant_digit;
static PyObject *__pyx_kp_s_Variable_length_strings_are_only;
+static PyObject *__pyx_kp_s_Variable_mask;
+static PyObject *__pyx_kp_s_Variable_name;
static PyObject *__pyx_n_s_Variable_ncattrs;
+static PyObject *__pyx_kp_s_Variable_ndim;
static PyObject *__pyx_kp_s_Variable_object_no_longer_valid;
+static PyObject *__pyx_kp_s_Variable_scale;
static PyObject *__pyx_n_s_Variable_set_auto_maskandscale;
+static PyObject *__pyx_kp_s_Variable_shape;
+static PyObject *__pyx_kp_s_Variable_size;
static PyObject *__pyx_n_s_Variable_typecode;
-static PyObject *__pyx_kp_s__118;
static PyObject *__pyx_kp_s__12;
+static PyObject *__pyx_kp_s__120;
static PyObject *__pyx_kp_s__13;
static PyObject *__pyx_kp_s__14;
static PyObject *__pyx_kp_s__17;
@@ -2027,7 +2132,7 @@ static PyObject *__pyx_n_s_byteorder;
static PyObject *__pyx_n_s_byteswap;
static PyObject *__pyx_n_s_calendar;
static PyObject *__pyx_kp_s_cannot_assign_fill_value_for_mas;
-static PyObject *__pyx_kp_s_cannot_close_a_L_Group_only_appl;
+static PyObject *__pyx_kp_s_cannot_close_a_netCDF4_Group_onl;
static PyObject *__pyx_kp_s_cannot_find_compound_type_in_thi;
static PyObject *__pyx_kp_s_cannot_set__FillValue_attribute;
static PyObject *__pyx_kp_s_cannot_specify_chunksizes_for_a;
@@ -2072,6 +2177,7 @@ static PyObject *__pyx_n_s_dat;
static PyObject *__pyx_n_s_data;
static PyObject *__pyx_n_s_data_model;
static PyObject *__pyx_n_s_data_model_2;
+static PyObject *__pyx_kp_s_data_model_describes_the_netCDF;
static PyObject *__pyx_kp_s_data_to_put_in_string_variable_m;
static PyObject *__pyx_n_s_datashape;
static PyObject *__pyx_n_s_datatype;
@@ -2105,6 +2211,7 @@ static PyObject *__pyx_n_s_dims_2;
static PyObject *__pyx_n_s_dimtotlen;
static PyObject *__pyx_n_s_disk_format;
static PyObject *__pyx_n_s_disk_format_2;
+static PyObject *__pyx_kp_s_disk_format_describes_the_under;
static PyObject *__pyx_n_s_diskless;
static PyObject *__pyx_kp_s_diskless_mode_requires_netcdf_li;
static PyObject *__pyx_n_s_doc;
@@ -2191,6 +2298,7 @@ static PyObject *__pyx_n_s_i4;
static PyObject *__pyx_n_s_i8;
static PyObject *__pyx_n_s_id;
static PyObject *__pyx_n_s_idx;
+static PyObject *__pyx_kp_s_if_True_scale_factor_and_add_off;
static PyObject *__pyx_kp_s_illegal_data_type_for_attribute;
static PyObject *__pyx_kp_s_illegal_primitive_data_type_must;
static PyObject *__pyx_n_s_import;
@@ -2213,6 +2321,7 @@ static PyObject *__pyx_n_s_item;
static PyObject *__pyx_n_s_items;
static PyObject *__pyx_n_s_itemsize;
static PyObject *__pyx_n_s_itertools;
+static PyObject *__pyx_n_s_iu;
static PyObject *__pyx_n_s_izip;
static PyObject *__pyx_n_s_join;
static PyObject *__pyx_n_s_k;
@@ -2222,6 +2331,7 @@ static PyObject *__pyx_n_s_keys;
static PyObject *__pyx_n_s_kind;
static PyObject *__pyx_n_s_least_significant_digit;
static PyObject *__pyx_n_s_len;
+static PyObject *__pyx_kp_s_len_of_unsized_object;
static PyObject *__pyx_n_s_little;
static PyObject *__pyx_n_s_lower;
static PyObject *__pyx_n_s_lst;
@@ -2273,6 +2383,7 @@ static PyObject *__pyx_n_s_ndim;
static PyObject *__pyx_kp_s_negative_strides_not_allowed_whe;
static PyObject *__pyx_n_s_nelems;
static PyObject *__pyx_n_s_netCDF4__netCDF4;
+static PyObject *__pyx_kp_s_net_bio_c2sm_hvwaldow_git_netcd;
static PyObject *__pyx_n_s_netcdf4libversion;
static PyObject *__pyx_n_s_netcdftime;
static PyObject *__pyx_n_s_newSlice;
@@ -2284,6 +2395,7 @@ static PyObject *__pyx_n_s_npversion;
static PyObject *__pyx_n_s_num;
static PyObject *__pyx_n_s_num2date;
static PyObject *__pyx_n_s_numpy;
+static PyObject *__pyx_kp_s_numpy_data_type_for_primitive_da_2;
static PyObject *__pyx_n_s_numv;
static PyObject *__pyx_n_s_nunlimdim;
static PyObject *__pyx_n_s_nv;
@@ -2296,12 +2408,15 @@ static PyObject *__pyx_n_s_ordereddict;
static PyObject *__pyx_n_s_orthogoral_indexing;
static PyObject *__pyx_n_s_out_array_shape;
static PyObject *__pyx_n_s_parent;
+static PyObject *__pyx_kp_s_parent_is_a_reference_to_the_pa;
static PyObject *__pyx_n_s_parse_date;
static PyObject *__pyx_n_s_part;
static PyObject *__pyx_n_s_patchstring;
static PyObject *__pyx_n_s_path;
static PyObject *__pyx_n_s_path_2;
static PyObject *__pyx_kp_s_path_s;
+static PyObject *__pyx_kp_s_path_shows_the_location_of_the;
+static PyObject *__pyx_n_s_pdoc;
static PyObject *__pyx_n_s_persist;
static PyObject *__pyx_kp_s_please_install_ordereddict_https;
static PyObject *__pyx_n_s_pop;
@@ -2350,6 +2465,7 @@ static PyObject *__pyx_kp_s_s_not_found_in_s;
static PyObject *__pyx_kp_s_s_s;
static PyObject *__pyx_kp_s_s_s_s;
static PyObject *__pyx_kp_s_s_s_s_2;
+static PyObject *__pyx_kp_s_same_as_data_model_retained_for;
static PyObject *__pyx_n_s_scale;
static PyObject *__pyx_n_s_scale_factor;
static PyObject *__pyx_n_s_sec_units;
@@ -2522,17 +2638,17 @@ static PyObject *__pyx_slice__51;
static PyObject *__pyx_slice__54;
static PyObject *__pyx_slice__55;
static PyObject *__pyx_slice__61;
-static PyObject *__pyx_slice__63;
-static PyObject *__pyx_slice__65;
+static PyObject *__pyx_slice__64;
static PyObject *__pyx_slice__66;
static PyObject *__pyx_slice__67;
-static PyObject *__pyx_slice__77;
-static PyObject *__pyx_slice__80;
+static PyObject *__pyx_slice__68;
+static PyObject *__pyx_slice__78;
static PyObject *__pyx_slice__81;
-static PyObject *__pyx_slice__84;
-static PyObject *__pyx_slice__86;
-static PyObject *__pyx_slice__91;
+static PyObject *__pyx_slice__82;
+static PyObject *__pyx_slice__85;
+static PyObject *__pyx_slice__87;
static PyObject *__pyx_slice__92;
+static PyObject *__pyx_slice__93;
static PyObject *__pyx_tuple__15;
static PyObject *__pyx_tuple__16;
static PyObject *__pyx_tuple__18;
@@ -2564,8 +2680,8 @@ static PyObject *__pyx_tuple__58;
static PyObject *__pyx_tuple__59;
static PyObject *__pyx_tuple__60;
static PyObject *__pyx_tuple__62;
-static PyObject *__pyx_tuple__64;
-static PyObject *__pyx_tuple__68;
+static PyObject *__pyx_tuple__63;
+static PyObject *__pyx_tuple__65;
static PyObject *__pyx_tuple__69;
static PyObject *__pyx_tuple__70;
static PyObject *__pyx_tuple__71;
@@ -2574,48 +2690,49 @@ static PyObject *__pyx_tuple__73;
static PyObject *__pyx_tuple__74;
static PyObject *__pyx_tuple__75;
static PyObject *__pyx_tuple__76;
-static PyObject *__pyx_tuple__78;
+static PyObject *__pyx_tuple__77;
static PyObject *__pyx_tuple__79;
-static PyObject *__pyx_tuple__82;
+static PyObject *__pyx_tuple__80;
static PyObject *__pyx_tuple__83;
-static PyObject *__pyx_tuple__85;
-static PyObject *__pyx_tuple__87;
+static PyObject *__pyx_tuple__84;
+static PyObject *__pyx_tuple__86;
static PyObject *__pyx_tuple__88;
static PyObject *__pyx_tuple__89;
static PyObject *__pyx_tuple__90;
-static PyObject *__pyx_tuple__93;
+static PyObject *__pyx_tuple__91;
static PyObject *__pyx_tuple__94;
static PyObject *__pyx_tuple__95;
static PyObject *__pyx_tuple__96;
static PyObject *__pyx_tuple__97;
static PyObject *__pyx_tuple__98;
static PyObject *__pyx_tuple__99;
-static PyObject *__pyx_slice__100;
-static PyObject *__pyx_slice__104;
+static PyObject *__pyx_slice__101;
static PyObject *__pyx_slice__105;
static PyObject *__pyx_slice__106;
static PyObject *__pyx_slice__107;
-static PyObject *__pyx_slice__109;
-static PyObject *__pyx_slice__110;
+static PyObject *__pyx_slice__108;
static PyObject *__pyx_slice__111;
+static PyObject *__pyx_slice__112;
static PyObject *__pyx_slice__113;
-static PyObject *__pyx_tuple__101;
+static PyObject *__pyx_slice__115;
+static PyObject *__pyx_tuple__100;
static PyObject *__pyx_tuple__102;
static PyObject *__pyx_tuple__103;
-static PyObject *__pyx_tuple__108;
-static PyObject *__pyx_tuple__112;
+static PyObject *__pyx_tuple__104;
+static PyObject *__pyx_tuple__109;
+static PyObject *__pyx_tuple__110;
static PyObject *__pyx_tuple__114;
-static PyObject *__pyx_tuple__115;
static PyObject *__pyx_tuple__116;
static PyObject *__pyx_tuple__117;
+static PyObject *__pyx_tuple__118;
static PyObject *__pyx_tuple__119;
-static PyObject *__pyx_tuple__120;
static PyObject *__pyx_tuple__121;
-static PyObject *__pyx_tuple__124;
-static PyObject *__pyx_tuple__125;
+static PyObject *__pyx_tuple__122;
+static PyObject *__pyx_tuple__123;
static PyObject *__pyx_tuple__126;
+static PyObject *__pyx_tuple__127;
static PyObject *__pyx_tuple__128;
-static PyObject *__pyx_tuple__129;
+static PyObject *__pyx_tuple__130;
static PyObject *__pyx_tuple__131;
static PyObject *__pyx_tuple__133;
static PyObject *__pyx_tuple__135;
@@ -2643,11 +2760,11 @@ static PyObject *__pyx_tuple__177;
static PyObject *__pyx_tuple__179;
static PyObject *__pyx_tuple__181;
static PyObject *__pyx_tuple__183;
-static PyObject *__pyx_tuple__184;
-static PyObject *__pyx_codeobj__122;
-static PyObject *__pyx_codeobj__123;
-static PyObject *__pyx_codeobj__127;
-static PyObject *__pyx_codeobj__130;
+static PyObject *__pyx_tuple__185;
+static PyObject *__pyx_tuple__186;
+static PyObject *__pyx_codeobj__124;
+static PyObject *__pyx_codeobj__125;
+static PyObject *__pyx_codeobj__129;
static PyObject *__pyx_codeobj__132;
static PyObject *__pyx_codeobj__134;
static PyObject *__pyx_codeobj__136;
@@ -2674,9 +2791,10 @@ static PyObject *__pyx_codeobj__176;
static PyObject *__pyx_codeobj__178;
static PyObject *__pyx_codeobj__180;
static PyObject *__pyx_codeobj__182;
-static PyObject *__pyx_codeobj__185;
+static PyObject *__pyx_codeobj__184;
+static PyObject *__pyx_codeobj__187;
-/* "netCDF4/_netCDF4.pyx":847
+/* "netCDF4/_netCDF4.pyx":865
* # check for required version of netcdf-4 and hdf5.
*
* def _gethdf5libversion(): # <<<<<<<<<<<<<<
@@ -2716,7 +2834,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4__gethdf5libversion(CYTHON_UNUSED Py
int __pyx_clineno = 0;
__Pyx_RefNannySetupContext("_gethdf5libversion", 0);
- /* "netCDF4/_netCDF4.pyx":848
+ /* "netCDF4/_netCDF4.pyx":866
*
* def _gethdf5libversion():
* majorvers = H5_VERS_MAJOR # <<<<<<<<<<<<<<
@@ -2725,7 +2843,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4__gethdf5libversion(CYTHON_UNUSED Py
*/
__pyx_v_majorvers = H5_VERS_MAJOR;
- /* "netCDF4/_netCDF4.pyx":849
+ /* "netCDF4/_netCDF4.pyx":867
* def _gethdf5libversion():
* majorvers = H5_VERS_MAJOR
* minorvers = H5_VERS_MINOR # <<<<<<<<<<<<<<
@@ -2734,7 +2852,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4__gethdf5libversion(CYTHON_UNUSED Py
*/
__pyx_v_minorvers = H5_VERS_MINOR;
- /* "netCDF4/_netCDF4.pyx":850
+ /* "netCDF4/_netCDF4.pyx":868
* majorvers = H5_VERS_MAJOR
* minorvers = H5_VERS_MINOR
* releasevers = H5_VERS_RELEASE # <<<<<<<<<<<<<<
@@ -2743,30 +2861,30 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4__gethdf5libversion(CYTHON_UNUSED Py
*/
__pyx_v_releasevers = H5_VERS_RELEASE;
- /* "netCDF4/_netCDF4.pyx":851
+ /* "netCDF4/_netCDF4.pyx":869
* minorvers = H5_VERS_MINOR
* releasevers = H5_VERS_RELEASE
* patchstring = H5_VERS_SUBRELEASE.decode('ascii') # <<<<<<<<<<<<<<
* if not patchstring:
* return '%d.%d.%d' % (majorvers,minorvers,releasevers)
*/
- __pyx_t_1 = __Pyx_decode_c_string(H5_VERS_SUBRELEASE, 0, strlen(H5_VERS_SUBRELEASE), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 851; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_decode_c_string(H5_VERS_SUBRELEASE, 0, strlen(H5_VERS_SUBRELEASE), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 869; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__pyx_v_patchstring = __pyx_t_1;
__pyx_t_1 = 0;
- /* "netCDF4/_netCDF4.pyx":852
+ /* "netCDF4/_netCDF4.pyx":870
* releasevers = H5_VERS_RELEASE
* patchstring = H5_VERS_SUBRELEASE.decode('ascii')
* if not patchstring: # <<<<<<<<<<<<<<
* return '%d.%d.%d' % (majorvers,minorvers,releasevers)
* else:
*/
- __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_patchstring); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 852; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_patchstring); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 870; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_t_3 = ((!__pyx_t_2) != 0);
if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":853
+ /* "netCDF4/_netCDF4.pyx":871
* patchstring = H5_VERS_SUBRELEASE.decode('ascii')
* if not patchstring:
* return '%d.%d.%d' % (majorvers,minorvers,releasevers) # <<<<<<<<<<<<<<
@@ -2774,13 +2892,13 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4__gethdf5libversion(CYTHON_UNUSED Py
* return '%d.%d.%d-%s' % (majorvers,minorvers,releasevers,patchstring)
*/
__Pyx_XDECREF(__pyx_r);
- __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_majorvers); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 853; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_majorvers); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 871; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
- __pyx_t_4 = __Pyx_PyInt_From_int(__pyx_v_minorvers); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 853; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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*/
switch (__pyx_v_att_type) {
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__Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
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goto __pyx_L0;
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#endif
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__pyx_v_ierr = nc_inq_format(__pyx_v_grpid, (&__pyx_v_formatp));
}
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}
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goto __pyx_L0;
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goto __pyx_L0;
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goto __pyx_L0;
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default: break;
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}
- /* "netCDF4/_netCDF4.pyx":1130
+ /* "netCDF4/_netCDF4.pyx":1148
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
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__pyx_t_1 = 0;
- /* "netCDF4/_netCDF4.pyx":1131
+ /* "netCDF4/_netCDF4.pyx":1149
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+ __pyx_t_5 = __Pyx_GetModuleGlobalName(__pyx_n_s_OrderedDict); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1149; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ /* "netCDF4/_netCDF4.pyx":1150
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__pyx_t_3 = ((__pyx_v_ntypes > 0) != 0);
if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":1133
+ /* "netCDF4/_netCDF4.pyx":1151
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for (__pyx_v_n = 0; __pyx_v_n < __pyx_t_2; __pyx_v_n++) {
- /* "netCDF4/_netCDF4.pyx":1134
+ /* "netCDF4/_netCDF4.pyx":1152
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- /* "netCDF4/_netCDF4.pyx":1135
+ /* "netCDF4/_netCDF4.pyx":1153
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#endif
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+ /* "netCDF4/_netCDF4.pyx":1154
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}
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+ /* "netCDF4/_netCDF4.pyx":1153
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+ /* "netCDF4/_netCDF4.pyx":1156
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- /* "netCDF4/_netCDF4.pyx":1139
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- /* "netCDF4/_netCDF4.pyx":1150
+ /* "netCDF4/_netCDF4.pyx":1168
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*/
switch (__pyx_v_classp) {
- /* "netCDF4/_netCDF4.pyx":1140
+ /* "netCDF4/_netCDF4.pyx":1158
* if ierr != NC_NOERR:
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- /* "netCDF4/_netCDF4.pyx":1141
+ /* "netCDF4/_netCDF4.pyx":1159
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+ __pyx_t_5 = __Pyx_GetModuleGlobalName(__pyx_n_s_default_encoding); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1159; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_7 = __Pyx_GetModuleGlobalName(__pyx_n_s_unicode_error); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1159; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -5773,14 +5891,14 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_types(PyObject *__pyx_v_group)
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+ /* "netCDF4/_netCDF4.pyx":1166
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* #print 'WARNING: unsupported compound type, skipping...'
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__pyx_L23_try_end:;
}
- /* "netCDF4/_netCDF4.pyx":1149
+ /* "netCDF4/_netCDF4.pyx":1167
* #print 'WARNING: unsupported compound type, skipping...'
* continue
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*/
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+ if (unlikely(PyObject_SetItem(__pyx_v_cmptypes, __pyx_v_name, __pyx_v_cmptype) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1167; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
break;
- /* "netCDF4/_netCDF4.pyx":1150
+ /* "netCDF4/_netCDF4.pyx":1168
* continue
* cmptypes[name] = cmptype
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@@ -5882,21 +6000,21 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_types(PyObject *__pyx_v_group)
*/
case NC_VLEN:
- /* "netCDF4/_netCDF4.pyx":1151
+ /* "netCDF4/_netCDF4.pyx":1169
* cmptypes[name] = cmptype
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*/
- __pyx_t_6 = __Pyx_PyObject_FromString(__pyx_v_namstring); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_6 = __Pyx_PyObject_FromString(__pyx_v_namstring); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1169; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_6 = __Pyx_GetModuleGlobalName(__pyx_n_s_default_encoding); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1169; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_7 = __Pyx_GetModuleGlobalName(__pyx_n_s_unicode_error); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1169; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_t_9 = 1;
}
}
- __pyx_t_8 = PyTuple_New(2+__pyx_t_9); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_8 = PyTuple_New(2+__pyx_t_9); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1169; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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if (__pyx_t_5) {
PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_5); __pyx_t_5 = NULL;
@@ -5921,14 +6039,14 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_types(PyObject *__pyx_v_group)
__Pyx_GIVEREF(__pyx_t_7);
__pyx_t_6 = 0;
__pyx_t_7 = 0;
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__Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
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__Pyx_XDECREF_SET(__pyx_v_name, __pyx_t_10);
__pyx_t_10 = 0;
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+ /* "netCDF4/_netCDF4.pyx":1172
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__Pyx_XGOTREF(__pyx_t_11);
/*try:*/ {
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+ /* "netCDF4/_netCDF4.pyx":1173
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*/
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+ __pyx_t_10 = __pyx_f_7netCDF4_8_netCDF4__read_vlen(__pyx_v_group, __pyx_v_xtype, NULL); if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1173; __pyx_clineno = __LINE__; goto __pyx_L26_error;}
__Pyx_GOTREF(__pyx_t_10);
__Pyx_XDECREF_SET(__pyx_v_vltype, __pyx_t_10);
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__Pyx_XDECREF(__pyx_t_10); __pyx_t_10 = 0;
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+ /* "netCDF4/_netCDF4.pyx":1174
* try:
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if (__pyx_t_14) {
__Pyx_AddTraceback("netCDF4._netCDF4._get_types", __pyx_clineno, __pyx_lineno, __pyx_filename);
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+ if (__Pyx_GetException(&__pyx_t_10, &__pyx_t_1, &__pyx_t_8) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1174; __pyx_clineno = __LINE__; goto __pyx_L28_except_error;}
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__Pyx_GOTREF(__pyx_t_1);
__Pyx_GOTREF(__pyx_t_8);
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+ /* "netCDF4/_netCDF4.pyx":1176
* except KeyError:
* #print 'WARNING: unsupported VLEN type, skipping...'
* continue # <<<<<<<<<<<<<<
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__pyx_L33_try_end:;
}
- /* "netCDF4/_netCDF4.pyx":1159
+ /* "netCDF4/_netCDF4.pyx":1177
* #print 'WARNING: unsupported VLEN type, skipping...'
* continue
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+ if (unlikely(PyObject_SetItem(__pyx_v_vltypes, __pyx_v_name, __pyx_v_vltype) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1177; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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}
__pyx_L7:;
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+ /* "netCDF4/_netCDF4.pyx":1179
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* cdef _get_dims(group):
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+ __pyx_t_8 = PyTuple_New(2); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1179; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_8);
__Pyx_INCREF(__pyx_v_cmptypes);
PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_v_cmptypes);
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__pyx_t_8 = 0;
goto __pyx_L0;
- /* "netCDF4/_netCDF4.pyx":1116
+ /* "netCDF4/_netCDF4.pyx":1134
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*
* cdef _get_types(group): # <<<<<<<<<<<<<<
- * # Private function to create L{CompoundType} or L{VLType} instances for all the
- * # compound or VLEN types in a L{Group} or L{Dataset}.
+ * # Private function to create `netCDF4.CompoundType` or `netCDF4.VLType` instances for all the
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*/
/* function exit code */
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return __pyx_r;
}
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*
* cdef _get_dims(group): # <<<<<<<<<<<<<<
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- * # dimensions in a L{Group} or Dataset
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+ * # dimensions in a `netCDF4.Group` or Dataset
*/
static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_dims(PyObject *__pyx_v_group) {
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+ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_group, __pyx_n_s_grpid); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1188; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_2 = __Pyx_PyInt_As_int(__pyx_t_1); if (unlikely((__pyx_t_2 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1188; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_v__grpid = __pyx_t_2;
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#endif
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+ /* "netCDF4/_netCDF4.pyx":1190
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__pyx_v_ierr = nc_inq_ndims(__pyx_v__grpid, (&__pyx_v_numdims));
}
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+ /* "netCDF4/_netCDF4.pyx":1189
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+ /* "netCDF4/_netCDF4.pyx":1191
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if (__pyx_t_3) {
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+ /* "netCDF4/_netCDF4.pyx":1192
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+ __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1192; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_1);
__Pyx_GIVEREF(__pyx_t_1);
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+ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_t_5, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1192; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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}
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if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":1178
+ /* "netCDF4/_netCDF4.pyx":1196
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+ /* "netCDF4/_netCDF4.pyx":1197
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#endif
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- /* "netCDF4/_netCDF4.pyx":1180
+ /* "netCDF4/_netCDF4.pyx":1198
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}
- /* "netCDF4/_netCDF4.pyx":1179
+ /* "netCDF4/_netCDF4.pyx":1197
* if numdims > 0:
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+ /* "netCDF4/_netCDF4.pyx":1199
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if (__pyx_t_3) {
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for (__pyx_v_n = 0; __pyx_v_n < __pyx_t_2; __pyx_v_n++) {
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for (__pyx_v_n = 0; __pyx_v_n < __pyx_t_2; __pyx_v_n++) {
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- /* "netCDF4/_netCDF4.pyx":1188
+ /* "netCDF4/_netCDF4.pyx":1206
* for n from 0 <= n < numdims:
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+ /* "netCDF4/_netCDF4.pyx":1205
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+ /* "netCDF4/_netCDF4.pyx":1207
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+ __pyx_t_1 = __Pyx_decode_c_string(__pyx_t_4, 0, strlen(__pyx_t_4), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1190; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_INCREF(__pyx_t_1);
PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_1);
__Pyx_GIVEREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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+ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_t_5, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
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__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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__pyx_L7:;
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+ /* "netCDF4/_netCDF4.pyx":1211
* name = namstring.decode(default_encoding,unicode_error)
* dimensions[name] = Dimension(group, name, id=dimids[n])
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__pyx_r = __pyx_v_dimensions;
goto __pyx_L0;
- /* "netCDF4/_netCDF4.pyx":1163
+ /* "netCDF4/_netCDF4.pyx":1181
* return cmptypes, vltypes
*
* cdef _get_dims(group): # <<<<<<<<<<<<<<
- * # Private function to create L{Dimension} instances for all the
- * # dimensions in a L{Group} or Dataset
+ * # Private function to create `netCDF4.Dimension` instances for all the
+ * # dimensions in a `netCDF4.Group` or Dataset
*/
/* function exit code */
@@ -6556,12 +6674,12 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_dims(PyObject *__pyx_v_group) {
return __pyx_r;
}
-/* "netCDF4/_netCDF4.pyx":1195
+/* "netCDF4/_netCDF4.pyx":1213
* return dimensions
*
* cdef _get_grps(group): # <<<<<<<<<<<<<<
- * # Private function to create L{Group} instances for all the
- * # groups in a L{Group} or Dataset
+ * # Private function to create `netCDF4.Group` instances for all the
+ * # groups in a `netCDF4.Group` or Dataset
*/
static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_grps(PyObject *__pyx_v_group) {
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int __pyx_clineno = 0;
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+ /* "netCDF4/_netCDF4.pyx":1220
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* ierr = nc_inq_grps(_grpid, &numgrps, NULL)
*/
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+ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_group, __pyx_n_s_grpid); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_v__grpid = __pyx_t_2;
- /* "netCDF4/_netCDF4.pyx":1203
+ /* "netCDF4/_netCDF4.pyx":1221
* # get number of groups in this Group.
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#endif
/*try:*/ {
- /* "netCDF4/_netCDF4.pyx":1204
+ /* "netCDF4/_netCDF4.pyx":1222
* _grpid = group._grpid
* with nogil:
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}
- /* "netCDF4/_netCDF4.pyx":1203
+ /* "netCDF4/_netCDF4.pyx":1221
* # get number of groups in this Group.
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}
}
- /* "netCDF4/_netCDF4.pyx":1205
+ /* "netCDF4/_netCDF4.pyx":1223
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* if ierr != NC_NOERR: # <<<<<<<<<<<<<<
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
- * # create dictionary containing L{Group} instances for groups in this group
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*/
__pyx_t_3 = ((__pyx_v_ierr != NC_NOERR) != 0);
if (__pyx_t_3) {
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+ /* "netCDF4/_netCDF4.pyx":1224
* ierr = nc_inq_grps(_grpid, &numgrps, NULL)
* if ierr != NC_NOERR:
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii')) # <<<<<<<<<<<<<<
- * # create dictionary containing L{Group} instances for groups in this group
+ * # create dictionary containing `netCDF4.Group` instances for groups in this group
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__pyx_t_4 = ((char *)nc_strerror(__pyx_v_ierr));
- __pyx_t_1 = __Pyx_decode_c_string(__pyx_t_4, 0, strlen(__pyx_t_4), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1206; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_decode_c_string(__pyx_t_4, 0, strlen(__pyx_t_4), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1224; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1206; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1224; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_1);
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+ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_t_5, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1224; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1206; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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}
- /* "netCDF4/_netCDF4.pyx":1208
+ /* "netCDF4/_netCDF4.pyx":1226
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
- * # create dictionary containing L{Group} instances for groups in this group
+ * # create dictionary containing `netCDF4.Group` instances for groups in this group
* groups = OrderedDict() # <<<<<<<<<<<<<<
* if numgrps > 0:
* grpids = <int *>malloc(sizeof(int) * numgrps)
*/
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+ __pyx_t_5 = __Pyx_GetModuleGlobalName(__pyx_n_s_OrderedDict); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1226; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -6699,18 +6817,18 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_grps(PyObject *__pyx_v_group) {
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- __pyx_t_1 = __Pyx_PyObject_CallNoArg(__pyx_t_5); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_PyObject_CallNoArg(__pyx_t_5); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1226; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
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__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
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__pyx_t_1 = 0;
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- * # create dictionary containing L{Group} instances for groups in this group
+ /* "netCDF4/_netCDF4.pyx":1227
+ * # create dictionary containing `netCDF4.Group` instances for groups in this group
* groups = OrderedDict()
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* grpids = <int *>malloc(sizeof(int) * numgrps)
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__pyx_t_3 = ((__pyx_v_numgrps > 0) != 0);
if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":1210
+ /* "netCDF4/_netCDF4.pyx":1228
* groups = OrderedDict()
* if numgrps > 0:
* grpids = <int *>malloc(sizeof(int) * numgrps) # <<<<<<<<<<<<<<
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*/
__pyx_v_grpids = ((int *)malloc(((sizeof(int)) * __pyx_v_numgrps)));
- /* "netCDF4/_netCDF4.pyx":1211
+ /* "netCDF4/_netCDF4.pyx":1229
* if numgrps > 0:
* grpids = <int *>malloc(sizeof(int) * numgrps)
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@@ -6742,7 +6860,7 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_grps(PyObject *__pyx_v_group) {
#endif
/*try:*/ {
- /* "netCDF4/_netCDF4.pyx":1212
+ /* "netCDF4/_netCDF4.pyx":1230
* grpids = <int *>malloc(sizeof(int) * numgrps)
* with nogil:
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__pyx_v_ierr = nc_inq_grps(__pyx_v__grpid, NULL, __pyx_v_grpids);
}
- /* "netCDF4/_netCDF4.pyx":1211
+ /* "netCDF4/_netCDF4.pyx":1229
* if numgrps > 0:
* grpids = <int *>malloc(sizeof(int) * numgrps)
* with nogil: # <<<<<<<<<<<<<<
@@ -6770,7 +6888,7 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_grps(PyObject *__pyx_v_group) {
}
}
- /* "netCDF4/_netCDF4.pyx":1213
+ /* "netCDF4/_netCDF4.pyx":1231
* with nogil:
* ierr = nc_inq_grps(_grpid, NULL, grpids)
* if ierr != NC_NOERR: # <<<<<<<<<<<<<<
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__pyx_t_3 = ((__pyx_v_ierr != NC_NOERR) != 0);
if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":1214
+ /* "netCDF4/_netCDF4.pyx":1232
* ierr = nc_inq_grps(_grpid, NULL, grpids)
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for (__pyx_v_n = 0; __pyx_v_n < __pyx_t_2; __pyx_v_n++) {
- /* "netCDF4/_netCDF4.pyx":1216
+ /* "netCDF4/_netCDF4.pyx":1234
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- /* "netCDF4/_netCDF4.pyx":1217
+ /* "netCDF4/_netCDF4.pyx":1235
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}
- /* "netCDF4/_netCDF4.pyx":1216
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/* function exit code */
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*/
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#endif
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- /* "netCDF4/_netCDF4.pyx":1248
+ /* "netCDF4/_netCDF4.pyx":1266
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- /* "netCDF4/_netCDF4.pyx":1247
+ /* "netCDF4/_netCDF4.pyx":1265
* varids = <int *>malloc(sizeof(int) * numvars)
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- /* "netCDF4/_netCDF4.pyx":1249
+ /* "netCDF4/_netCDF4.pyx":1267
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+ /* "netCDF4/_netCDF4.pyx":1276
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+ /* "netCDF4/_netCDF4.pyx":1278
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if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":1264
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- /* "netCDF4/_netCDF4.pyx":1269
+ /* "netCDF4/_netCDF4.pyx":1287
* ierr = nc_inq_vartype(_grpid, varid, &xtype)
* if ierr != NC_NOERR:
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii')) # <<<<<<<<<<<<<<
@@ -7564,140 +7682,156 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_vars(PyObject *__pyx_v_group) {
* endianness = None
*/
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+ __pyx_t_1 = __Pyx_decode_c_string(__pyx_t_4, 0, strlen(__pyx_t_4), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1287; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
- __pyx_t_6 = PyTuple_New(1); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1269; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_6 = PyTuple_New(1); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1287; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_INCREF(__pyx_t_1);
PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_1);
__Pyx_GIVEREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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+ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_t_6, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1287; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
__Pyx_Raise(__pyx_t_1, 0, 0, 0);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1269; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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}
- /* "netCDF4/_netCDF4.pyx":1271
+ /* "netCDF4/_netCDF4.pyx":1289
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
* # get endian-ness of variable.
* endianness = None # <<<<<<<<<<<<<<
- * with nogil:
- * ierr = nc_inq_var_endian(_grpid, varid, &iendian)
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*/
__Pyx_INCREF(Py_None);
__Pyx_XDECREF_SET(__pyx_v_endianness, Py_None);
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- * with nogil: # <<<<<<<<<<<<<<
- * ierr = nc_inq_var_endian(_grpid, varid, &iendian)
- * if ierr == NC_NOERR:
+ /* "netCDF4/_netCDF4.pyx":1292
+ * # assume data returned by HDF4 lib has already been byte-swapped
+ * # (if needed) so that it is in native endian format (issue #391).
+ * if group.disk_format != 'HDF4': # <<<<<<<<<<<<<<
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+ * ierr = nc_inq_var_endian(_grpid, varid, &iendian)
*/
- {
- #ifdef WITH_THREAD
- PyThreadState *_save;
- Py_UNBLOCK_THREADS
- #endif
- /*try:*/ {
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+ __Pyx_GOTREF(__pyx_t_1);
+ __pyx_t_3 = (__Pyx_PyString_Equals(__pyx_t_1, __pyx_n_s_HDF4, Py_NE)); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1292; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+ if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":1273
- * endianness = None
- * with nogil:
- * ierr = nc_inq_var_endian(_grpid, varid, &iendian) # <<<<<<<<<<<<<<
- * if ierr == NC_NOERR:
- * if iendian == NC_ENDIAN_LITTLE:
+ /* "netCDF4/_netCDF4.pyx":1293
+ * # (if needed) so that it is in native endian format (issue #391).
+ * if group.disk_format != 'HDF4':
+ * with nogil: # <<<<<<<<<<<<<<
+ * ierr = nc_inq_var_endian(_grpid, varid, &iendian)
+ * if ierr == NC_NOERR:
*/
- __pyx_v_ierr = nc_inq_var_endian(__pyx_v__grpid, __pyx_v_varid, (&__pyx_v_iendian));
- }
+ {
+ #ifdef WITH_THREAD
+ PyThreadState *_save;
+ Py_UNBLOCK_THREADS
+ #endif
+ /*try:*/ {
- /* "netCDF4/_netCDF4.pyx":1272
- * # get endian-ness of variable.
- * endianness = None
- * with nogil: # <<<<<<<<<<<<<<
- * ierr = nc_inq_var_endian(_grpid, varid, &iendian)
- * if ierr == NC_NOERR:
+ /* "netCDF4/_netCDF4.pyx":1294
+ * if group.disk_format != 'HDF4':
+ * with nogil:
+ * ierr = nc_inq_var_endian(_grpid, varid, &iendian) # <<<<<<<<<<<<<<
+ * if ierr == NC_NOERR:
+ * if iendian == NC_ENDIAN_LITTLE:
*/
- /*finally:*/ {
- /*normal exit:*/{
- #ifdef WITH_THREAD
- Py_BLOCK_THREADS
- #endif
- goto __pyx_L34;
+ __pyx_v_ierr = nc_inq_var_endian(__pyx_v__grpid, __pyx_v_varid, (&__pyx_v_iendian));
}
- __pyx_L34:;
- }
- }
- /* "netCDF4/_netCDF4.pyx":1274
- * with nogil:
- * ierr = nc_inq_var_endian(_grpid, varid, &iendian)
- * if ierr == NC_NOERR: # <<<<<<<<<<<<<<
- * if iendian == NC_ENDIAN_LITTLE:
- * endianness = '<'
+ /* "netCDF4/_netCDF4.pyx":1293
+ * # (if needed) so that it is in native endian format (issue #391).
+ * if group.disk_format != 'HDF4':
+ * with nogil: # <<<<<<<<<<<<<<
+ * ierr = nc_inq_var_endian(_grpid, varid, &iendian)
+ * if ierr == NC_NOERR:
*/
- __pyx_t_3 = ((__pyx_v_ierr == NC_NOERR) != 0);
- if (__pyx_t_3) {
+ /*finally:*/ {
+ /*normal exit:*/{
+ #ifdef WITH_THREAD
+ Py_BLOCK_THREADS
+ #endif
+ goto __pyx_L35;
+ }
+ __pyx_L35:;
+ }
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+
+ /* "netCDF4/_netCDF4.pyx":1295
+ * with nogil:
+ * ierr = nc_inq_var_endian(_grpid, varid, &iendian)
+ * if ierr == NC_NOERR: # <<<<<<<<<<<<<<
+ * if iendian == NC_ENDIAN_LITTLE:
+ * endianness = '<'
+ */
+ __pyx_t_3 = ((__pyx_v_ierr == NC_NOERR) != 0);
+ if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":1277
- * if iendian == NC_ENDIAN_LITTLE:
- * endianness = '<'
- * elif iendian == NC_ENDIAN_BIG: # <<<<<<<<<<<<<<
- * endianness = '>'
+ /* "netCDF4/_netCDF4.pyx":1298
+ * if iendian == NC_ENDIAN_LITTLE:
+ * endianness = '<'
+ * elif iendian == NC_ENDIAN_BIG: # <<<<<<<<<<<<<<
+ * endianness = '>'
* # check to see if it is a supported user-defined type.
*/
- switch (__pyx_v_iendian) {
+ switch (__pyx_v_iendian) {
- /* "netCDF4/_netCDF4.pyx":1275
- * ierr = nc_inq_var_endian(_grpid, varid, &iendian)
- * if ierr == NC_NOERR:
- * if iendian == NC_ENDIAN_LITTLE: # <<<<<<<<<<<<<<
- * endianness = '<'
- * elif iendian == NC_ENDIAN_BIG:
+ /* "netCDF4/_netCDF4.pyx":1296
+ * ierr = nc_inq_var_endian(_grpid, varid, &iendian)
+ * if ierr == NC_NOERR:
+ * if iendian == NC_ENDIAN_LITTLE: # <<<<<<<<<<<<<<
+ * endianness = '<'
+ * elif iendian == NC_ENDIAN_BIG:
*/
- case NC_ENDIAN_LITTLE:
+ case NC_ENDIAN_LITTLE:
- /* "netCDF4/_netCDF4.pyx":1276
- * if ierr == NC_NOERR:
- * if iendian == NC_ENDIAN_LITTLE:
- * endianness = '<' # <<<<<<<<<<<<<<
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- __Pyx_DECREF_SET(__pyx_v_endianness, __pyx_kp_s__12);
- break;
+ /* "netCDF4/_netCDF4.pyx":1297
+ * if ierr == NC_NOERR:
+ * if iendian == NC_ENDIAN_LITTLE:
+ * endianness = '<' # <<<<<<<<<<<<<<
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+ __Pyx_DECREF_SET(__pyx_v_endianness, __pyx_kp_s__12);
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- /* "netCDF4/_netCDF4.pyx":1277
- * if iendian == NC_ENDIAN_LITTLE:
- * endianness = '<'
- * elif iendian == NC_ENDIAN_BIG: # <<<<<<<<<<<<<<
- * endianness = '>'
+ /* "netCDF4/_netCDF4.pyx":1298
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+ * endianness = '>'
* # check to see if it is a supported user-defined type.
*/
- case NC_ENDIAN_BIG:
+ case NC_ENDIAN_BIG:
- /* "netCDF4/_netCDF4.pyx":1278
- * endianness = '<'
- * elif iendian == NC_ENDIAN_BIG:
- * endianness = '>' # <<<<<<<<<<<<<<
+ /* "netCDF4/_netCDF4.pyx":1299
+ * endianness = '<'
+ * elif iendian == NC_ENDIAN_BIG:
+ * endianness = '>' # <<<<<<<<<<<<<<
* # check to see if it is a supported user-defined type.
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*/
- __Pyx_INCREF(__pyx_kp_s__13);
- __Pyx_DECREF_SET(__pyx_v_endianness, __pyx_kp_s__13);
- break;
- default: break;
+ __Pyx_INCREF(__pyx_kp_s__13);
+ __Pyx_DECREF_SET(__pyx_v_endianness, __pyx_kp_s__13);
+ break;
+ default: break;
+ }
+ goto __pyx_L36;
}
- goto __pyx_L35;
+ __pyx_L36:;
+ goto __pyx_L30;
}
- __pyx_L35:;
+ __pyx_L30:;
- /* "netCDF4/_netCDF4.pyx":1280
- * endianness = '>'
+ /* "netCDF4/_netCDF4.pyx":1301
+ * endianness = '>'
* # check to see if it is a supported user-defined type.
* try: # <<<<<<<<<<<<<<
* datatype = _nctonptype[xtype]
@@ -7710,22 +7844,22 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_vars(PyObject *__pyx_v_group) {
__Pyx_XGOTREF(__pyx_t_13);
/*try:*/ {
- /* "netCDF4/_netCDF4.pyx":1281
+ /* "netCDF4/_netCDF4.pyx":1302
* # check to see if it is a supported user-defined type.
* try:
* datatype = _nctonptype[xtype] # <<<<<<<<<<<<<<
* if endianness is not None:
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*/
- __pyx_t_1 = __Pyx_GetModuleGlobalName(__pyx_n_s_nctonptype); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1281; __pyx_clineno = __LINE__; goto __pyx_L36_error;}
+ __pyx_t_1 = __Pyx_GetModuleGlobalName(__pyx_n_s_nctonptype); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1302; __pyx_clineno = __LINE__; goto __pyx_L37_error;}
__Pyx_GOTREF(__pyx_t_1);
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+ __pyx_t_6 = __Pyx_GetItemInt(__pyx_t_1, __pyx_v_xtype, nc_type, 1, __Pyx_PyInt_From_nc_type, 0, 1, 1); if (unlikely(__pyx_t_6 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1302; __pyx_clineno = __LINE__; goto __pyx_L37_error;};
__Pyx_GOTREF(__pyx_t_6);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
__Pyx_XDECREF_SET(__pyx_v_datatype, __pyx_t_6);
__pyx_t_6 = 0;
- /* "netCDF4/_netCDF4.pyx":1282
+ /* "netCDF4/_netCDF4.pyx":1303
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__pyx_t_14 = (__pyx_t_3 != 0);
if (__pyx_t_14) {
- /* "netCDF4/_netCDF4.pyx":1283
+ /* "netCDF4/_netCDF4.pyx":1304
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*/
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+ __pyx_t_6 = PyNumber_Add(__pyx_v_endianness, __pyx_v_datatype); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1304; __pyx_clineno = __LINE__; goto __pyx_L37_error;}
__Pyx_GOTREF(__pyx_t_6);
__Pyx_DECREF_SET(__pyx_v_datatype, __pyx_t_6);
__pyx_t_6 = 0;
- goto __pyx_L44;
+ goto __pyx_L45;
}
- __pyx_L44:;
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}
__Pyx_XDECREF(__pyx_t_11); __pyx_t_11 = 0;
__Pyx_XDECREF(__pyx_t_12); __pyx_t_12 = 0;
__Pyx_XDECREF(__pyx_t_13); __pyx_t_13 = 0;
- goto __pyx_L43_try_end;
- __pyx_L36_error:;
+ goto __pyx_L44_try_end;
+ __pyx_L37_error:;
__Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0;
__Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
__Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0;
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__Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0;
__Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0;
- /* "netCDF4/_netCDF4.pyx":1284
+ /* "netCDF4/_netCDF4.pyx":1305
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__pyx_t_15 = PyErr_ExceptionMatches(__pyx_builtin_KeyError);
if (__pyx_t_15) {
__Pyx_AddTraceback("netCDF4._netCDF4._get_vars", __pyx_clineno, __pyx_lineno, __pyx_filename);
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+ if (__Pyx_GetException(&__pyx_t_6, &__pyx_t_1, &__pyx_t_10) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1305; __pyx_clineno = __LINE__; goto __pyx_L39_except_error;}
__Pyx_GOTREF(__pyx_t_6);
__Pyx_GOTREF(__pyx_t_1);
__Pyx_GOTREF(__pyx_t_10);
- /* "netCDF4/_netCDF4.pyx":1285
+ /* "netCDF4/_netCDF4.pyx":1306
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__pyx_t_14 = ((__pyx_v_xtype == NC_STRING) != 0);
if (__pyx_t_14) {
- /* "netCDF4/_netCDF4.pyx":1286
+ /* "netCDF4/_netCDF4.pyx":1307
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__Pyx_XDECREF_SET(__pyx_v_datatype, ((PyObject *)((PyObject*)(&PyString_Type))));
- goto __pyx_L47;
+ goto __pyx_L48;
}
/*else*/ {
- /* "netCDF4/_netCDF4.pyx":1288
+ /* "netCDF4/_netCDF4.pyx":1309
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* else:
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@@ -7815,7 +7949,7 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_vars(PyObject *__pyx_v_group) {
#endif
/*try:*/ {
- /* "netCDF4/_netCDF4.pyx":1289
+ /* "netCDF4/_netCDF4.pyx":1310
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__pyx_v_ierr = nc_inq_user_type(__pyx_v__grpid, __pyx_v_xtype, __pyx_v_namstring_cmp, NULL, NULL, NULL, (&__pyx_v_classp));
}
- /* "netCDF4/_netCDF4.pyx":1288
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* datatype = str
* else:
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#ifdef WITH_THREAD
Py_BLOCK_THREADS
#endif
- goto __pyx_L52;
+ goto __pyx_L53;
}
- __pyx_L52:;
+ __pyx_L53:;
}
}
- /* "netCDF4/_netCDF4.pyx":1298
+ /* "netCDF4/_netCDF4.pyx":1319
* #print "WARNING: variable '%s' has unsupported compound datatype, skipping .." % name
* continue
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*/
switch (__pyx_v_classp) {
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+ /* "netCDF4/_netCDF4.pyx":1312
* ierr = nc_inq_user_type(_grpid, xtype, namstring_cmp,
* NULL, NULL, NULL, &classp)
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*/
case NC_COMPOUND:
- /* "netCDF4/_netCDF4.pyx":1293
+ /* "netCDF4/_netCDF4.pyx":1314
* if classp == NC_COMPOUND: # a compound type
* # create CompoundType instance describing this compound type.
* try: # <<<<<<<<<<<<<<
@@ -7875,7 +8009,7 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_vars(PyObject *__pyx_v_group) {
__Pyx_XGOTREF(__pyx_t_18);
/*try:*/ {
- /* "netCDF4/_netCDF4.pyx":1294
+ /* "netCDF4/_netCDF4.pyx":1315
* # create CompoundType instance describing this compound type.
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*/
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__pyx_t_19.endian = __pyx_v_endianness;
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__Pyx_XDECREF(__pyx_t_17); __pyx_t_17 = 0;
__Pyx_XDECREF(__pyx_t_18); __pyx_t_18 = 0;
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- __pyx_L53_error:;
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__Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0;
__Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
__Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0;
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__Pyx_AddTraceback("netCDF4._netCDF4._get_vars", __pyx_clineno, __pyx_lineno, __pyx_filename);
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__Pyx_GOTREF(__pyx_t_5);
__Pyx_GOTREF(__pyx_t_8);
- /* "netCDF4/_netCDF4.pyx":1297
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*/
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- __pyx_L62_except_continue:;
+ goto __pyx_L63_except_continue;
+ __pyx_L63_except_continue:;
__Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
__Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
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__Pyx_XGIVEREF(__pyx_t_16);
__Pyx_XGIVEREF(__pyx_t_17);
__Pyx_XGIVEREF(__pyx_t_18);
__Pyx_ExceptionReset(__pyx_t_16, __pyx_t_17, __pyx_t_18);
- goto __pyx_L38_except_error;
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__Pyx_XGIVEREF(__pyx_t_17);
__Pyx_XGIVEREF(__pyx_t_18);
__Pyx_ExceptionReset(__pyx_t_16, __pyx_t_17, __pyx_t_18);
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__Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0;
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__Pyx_GOTREF(__pyx_t_5);
__Pyx_GOTREF(__pyx_t_7);
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* #print "WARNING: variable '%s' has unsupported datatype, skipping .." % name
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__Pyx_XGIVEREF(__pyx_t_18);
__Pyx_XGIVEREF(__pyx_t_17);
__Pyx_XGIVEREF(__pyx_t_16);
__Pyx_ExceptionReset(__pyx_t_18, __pyx_t_17, __pyx_t_16);
- goto __pyx_L38_except_error;
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__Pyx_XGIVEREF(__pyx_t_18);
__Pyx_XGIVEREF(__pyx_t_17);
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__Pyx_ExceptionReset(__pyx_t_18, __pyx_t_17, __pyx_t_16);
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*/
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- __pyx_L47:;
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__Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
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__Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
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__Pyx_XGIVEREF(__pyx_t_11);
__Pyx_XGIVEREF(__pyx_t_12);
__Pyx_XGIVEREF(__pyx_t_13);
__Pyx_ExceptionReset(__pyx_t_11, __pyx_t_12, __pyx_t_13);
goto __pyx_L1_error;
- __pyx_L42_try_continue:;
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__Pyx_XGIVEREF(__pyx_t_11);
__Pyx_XGIVEREF(__pyx_t_12);
__Pyx_XGIVEREF(__pyx_t_13);
__Pyx_ExceptionReset(__pyx_t_11, __pyx_t_12, __pyx_t_13);
goto __pyx_L15_continue;
- __pyx_L37_exception_handled:;
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__Pyx_XGIVEREF(__pyx_t_11);
__Pyx_XGIVEREF(__pyx_t_12);
__Pyx_XGIVEREF(__pyx_t_13);
__Pyx_ExceptionReset(__pyx_t_11, __pyx_t_12, __pyx_t_13);
- __pyx_L43_try_end:;
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#endif
/*try:*/ {
- /* "netCDF4/_netCDF4.pyx":1310
+ /* "netCDF4/_netCDF4.pyx":1331
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}
- /* "netCDF4/_netCDF4.pyx":1309
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#ifdef WITH_THREAD
Py_BLOCK_THREADS
#endif
- goto __pyx_L77;
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}
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+ /* "netCDF4/_netCDF4.pyx":1332
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if (__pyx_t_14) {
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PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_t_10);
__Pyx_GIVEREF(__pyx_t_10);
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#endif
/*try:*/ {
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- /* "netCDF4/_netCDF4.pyx":1314
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Py_BLOCK_THREADS
#endif
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goto __pyx_L0;
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return __pyx_r;
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static int __pyx_pw_7netCDF4_8_netCDF4_7Dataset_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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PyObject *__pyx_v_mode = 0;
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@@ -8796,12 +8934,12 @@ static int __pyx_pw_7netCDF4_8_netCDF4_7Dataset_1__init__(PyObject *__pyx_v_self
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- /* "netCDF4/_netCDF4.pyx":1477
- * __orthogonal_indexing__
+ /* "netCDF4/_netCDF4.pyx":1489
+ * the parent Dataset or Group."""
*
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* diskless=False, persist=False, keepweakref=False, **kwargs):
- * cdef int grpid, ierr, numgrps, numdims, numvars
+ * """
*/
/* function exit code */
@@ -8831,7 +8969,7 @@ static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset___init__(struct __pyx_obj_7netCD
int __pyx_clineno = 0;
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+ /* "netCDF4/_netCDF4.pyx":1552
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+ /* "netCDF4/_netCDF4.pyx":1556
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- /* "netCDF4/_netCDF4.pyx":1488
+ /* "netCDF4/_netCDF4.pyx":1557
* raise ValueError('diskless mode requires netcdf lib >= 4.2.1, you have %s' % __netcdf4libversion__)
* bytestr = _strencode(filename)
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__pyx_v_path = __pyx_t_5;
- /* "netCDF4/_netCDF4.pyx":1489
+ /* "netCDF4/_netCDF4.pyx":1558
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* path = bytestr
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}
/*else*/ {
- /* "netCDF4/_netCDF4.pyx":1543
+ /* "netCDF4/_netCDF4.pyx":1612
* ierr = nc_create(path, NC_SHARE | NC_NOCLOBBER | NC_DISKLESS , &grpid)
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}
/*else*/ {
- /* "netCDF4/_netCDF4.pyx":1545
+ /* "netCDF4/_netCDF4.pyx":1614
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PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_6);
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}
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+ /* "netCDF4/_netCDF4.pyx":1615
* else:
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__pyx_t_1 = ((__pyx_v_ierr != NC_NOERR) != 0);
if (__pyx_t_1) {
- /* "netCDF4/_netCDF4.pyx":1547
+ /* "netCDF4/_netCDF4.pyx":1616
* raise ValueError("mode must be 'w', 'r', 'a' or 'r+', got '%s'" % mode)
* if ierr != NC_NOERR:
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+ __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1616; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_6);
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__Pyx_Raise(__pyx_t_6, 0, 0, 0);
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}
- /* "netCDF4/_netCDF4.pyx":1549
+ /* "netCDF4/_netCDF4.pyx":1618
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
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* self.data_model = _get_format(grpid) # <<<<<<<<<<<<<<
* # data_model attribute used to be file_format (versions < 1.0.8), retain
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*/
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+ __pyx_t_6 = __pyx_f_7netCDF4_8_netCDF4__get_format(__pyx_v_grpid); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1618; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_6);
__Pyx_GIVEREF(__pyx_t_6);
__Pyx_GOTREF(__pyx_v_self->data_model);
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__pyx_v_self->data_model = __pyx_t_6;
__pyx_t_6 = 0;
- /* "netCDF4/_netCDF4.pyx":1552
+ /* "netCDF4/_netCDF4.pyx":1621
* # data_model attribute used to be file_format (versions < 1.0.8), retain
* # file_format for backwards compatibility.
* self.file_format = self.data_model # <<<<<<<<<<<<<<
@@ -9581,14 +9719,14 @@ static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset___init__(struct __pyx_obj_7netCD
__pyx_v_self->file_format = __pyx_t_6;
__pyx_t_6 = 0;
- /* "netCDF4/_netCDF4.pyx":1553
+ /* "netCDF4/_netCDF4.pyx":1622
* # file_format for backwards compatibility.
* self.file_format = self.data_model
* self.disk_format = _get_full_format(grpid) # <<<<<<<<<<<<<<
* # diskless read access only works with NETCDF_CLASSIC (for now)
* #ncopen = mode.startswith('a') or mode.startswith('r')
*/
- __pyx_t_6 = __pyx_f_7netCDF4_8_netCDF4__get_full_format(__pyx_v_grpid); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1553; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_6 = __pyx_f_7netCDF4_8_netCDF4__get_full_format(__pyx_v_grpid); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1622; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_6);
__Pyx_GIVEREF(__pyx_t_6);
__Pyx_GOTREF(__pyx_v_self->disk_format);
@@ -9596,7 +9734,7 @@ static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset___init__(struct __pyx_obj_7netCD
__pyx_v_self->disk_format = __pyx_t_6;
__pyx_t_6 = 0;
- /* "netCDF4/_netCDF4.pyx":1558
+ /* "netCDF4/_netCDF4.pyx":1627
* #if diskless and self.data_model != 'NETCDF3_CLASSIC' and ncopen:
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*/
__pyx_v_self->_grpid = __pyx_v_grpid;
- /* "netCDF4/_netCDF4.pyx":1559
+ /* "netCDF4/_netCDF4.pyx":1628
* # raise ValueError("diskless access only supported for NETCDF3_CLASSIC format")
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* self._isopen = 1 # <<<<<<<<<<<<<<
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*/
__pyx_v_self->_isopen = 1;
- /* "netCDF4/_netCDF4.pyx":1560
+ /* "netCDF4/_netCDF4.pyx":1629
* self._grpid = grpid
* self._isopen = 1
* self.path = '/' # <<<<<<<<<<<<<<
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__Pyx_DECREF(__pyx_v_self->path);
__pyx_v_self->path = __pyx_kp_s__14;
- /* "netCDF4/_netCDF4.pyx":1561
+ /* "netCDF4/_netCDF4.pyx":1630
* self._isopen = 1
* self.path = '/'
* self.parent = None # <<<<<<<<<<<<<<
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__Pyx_DECREF(__pyx_v_self->parent);
__pyx_v_self->parent = Py_None;
- /* "netCDF4/_netCDF4.pyx":1562
+ /* "netCDF4/_netCDF4.pyx":1631
* self.path = '/'
* self.parent = None
* self.keepweakref = keepweakref # <<<<<<<<<<<<<<
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__Pyx_DECREF(__pyx_v_self->keepweakref);
__pyx_v_self->keepweakref = __pyx_v_keepweakref;
- /* "netCDF4/_netCDF4.pyx":1564
+ /* "netCDF4/_netCDF4.pyx":1633
* self.keepweakref = keepweakref
* # get compound and vlen types in the root Group.
* self.cmptypes, self.vltypes = _get_types(self) # <<<<<<<<<<<<<<
* # get dimensions in the root group.
* self.dimensions = _get_dims(self)
*/
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+ __pyx_t_6 = __pyx_f_7netCDF4_8_netCDF4__get_types(((PyObject *)__pyx_v_self)); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1633; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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PyObject* sequence = __pyx_t_6;
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if (size > 2) __Pyx_RaiseTooManyValuesError(2);
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1564; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -9685,15 +9823,15 @@ static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset___init__(struct __pyx_obj_7netCD
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__Pyx_GOTREF(__pyx_t_3);
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+ __pyx_t_8 = PyObject_GetIter(__pyx_t_6); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1633; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_8);
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@@ -9701,7 +9839,7 @@ static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset___init__(struct __pyx_obj_7netCD
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+ if (__Pyx_IternextUnpackEndCheck(__pyx_t_9(__pyx_t_8), 2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1633; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_t_9 = NULL;
__Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1564; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_L26_unpacking_done:;
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__pyx_v_self->vltypes = __pyx_t_3;
__pyx_t_3 = 0;
- /* "netCDF4/_netCDF4.pyx":1566
+ /* "netCDF4/_netCDF4.pyx":1635
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* # get dimensions in the root group.
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*/
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+ __pyx_t_6 = __pyx_f_7netCDF4_8_netCDF4__get_dims(((PyObject *)__pyx_v_self)); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_6);
__Pyx_GIVEREF(__pyx_t_6);
__Pyx_GOTREF(__pyx_v_self->dimensions);
@@ -9738,14 +9876,14 @@ static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset___init__(struct __pyx_obj_7netCD
__pyx_v_self->dimensions = __pyx_t_6;
__pyx_t_6 = 0;
- /* "netCDF4/_netCDF4.pyx":1568
+ /* "netCDF4/_netCDF4.pyx":1637
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1771; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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*
* def set_fill_on(self): # <<<<<<<<<<<<<<
* """
- * set_fill_on(self)
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/* function exit code */
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return __pyx_r;
}
-/* "netCDF4/_netCDF4.pyx":1703
+/* "netCDF4/_netCDF4.pyx":1773
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
*
* def set_fill_off(self): # <<<<<<<<<<<<<<
* """
- * set_fill_off(self)
+ * **`set_fill_off(self)`**
*/
/* Python wrapper */
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_27set_fill_off(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
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+static char __pyx_doc_7netCDF4_8_netCDF4_7Dataset_26set_fill_off[] = "\n**`set_fill_off(self)`**\n\nSets the fill mode for a `netCDF4.Dataset` open for writing to `off`.\n\nThis will prevent the data from being pre-filled with fill values, which\nmay result in some performance improvements. However, you must then make\nsure the data is actually written before being read.";
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_27set_fill_off(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
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int __pyx_clineno = 0;
__Pyx_RefNannySetupContext("set_fill_off", 0);
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+ /* "netCDF4/_netCDF4.pyx":1783
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+ /* "netCDF4/_netCDF4.pyx":1784
* cdef int ierr, oldmode
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__pyx_t_1 = ((__pyx_v_ierr != NC_NOERR) != 0);
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+ /* "netCDF4/_netCDF4.pyx":1785
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__pyx_t_2 = ((char *)nc_strerror(__pyx_v_ierr));
- __pyx_t_3 = __Pyx_decode_c_string(__pyx_t_2, 0, strlen(__pyx_t_2), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1715; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_3 = __Pyx_decode_c_string(__pyx_t_2, 0, strlen(__pyx_t_2), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1785; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1785; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1785; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1715; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1785; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
- /* "netCDF4/_netCDF4.pyx":1703
+ /* "netCDF4/_netCDF4.pyx":1773
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
*
* def set_fill_off(self): # <<<<<<<<<<<<<<
* """
- * set_fill_off(self)
+ * **`set_fill_off(self)`**
*/
/* function exit code */
@@ -12156,17 +12294,17 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_26set_fill_off(struct __py
return __pyx_r;
}
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+/* "netCDF4/_netCDF4.pyx":1787
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*
* def createDimension(self, dimname, size=None): # <<<<<<<<<<<<<<
* """
- * createDimension(self, dimname, size=None)
+ * **`createDimension(self, dimname, size=None)`**
*/
/* Python wrapper */
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_29createDimension(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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PyObject *__pyx_v_dimname = 0;
PyObject *__pyx_v_size = 0;
@@ -12201,7 +12339,7 @@ static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_29createDimension(PyObject
}
}
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+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "createDimension") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1787; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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}
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+ __Pyx_RaiseArgtupleInvalid("createDimension", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1787; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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int __pyx_clineno = 0;
__Pyx_RefNannySetupContext("createDimension", 0);
- /* "netCDF4/_netCDF4.pyx":1730
+ /* "netCDF4/_netCDF4.pyx":1800
* instance. To determine if a dimension is 'unlimited', use the
- * C{isunlimited()} method of the L{Dimension} instance."""
+ * `netCDF4.Dimension.isunlimited` method of the `netCDF4.Dimension` instance."""
* self.dimensions[dimname] = Dimension(self, dimname, size=size) # <<<<<<<<<<<<<<
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*/
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+ __pyx_t_1 = PyTuple_New(2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1800; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -12255,36 +12393,36 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_28createDimension(struct _
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PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_v_dimname);
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+ __pyx_t_2 = PyDict_New(); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1800; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_size, __pyx_v_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1800; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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+ if (unlikely(PyObject_SetItem(__pyx_v_self->dimensions, __pyx_v_dimname, __pyx_t_3) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1800; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- /* "netCDF4/_netCDF4.pyx":1731
- * C{isunlimited()} method of the L{Dimension} instance."""
+ /* "netCDF4/_netCDF4.pyx":1801
+ * `netCDF4.Dimension.isunlimited` method of the `netCDF4.Dimension` instance."""
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* return self.dimensions[dimname] # <<<<<<<<<<<<<<
*
* def renameDimension(self, oldname, newname):
*/
__Pyx_XDECREF(__pyx_r);
- __pyx_t_3 = PyObject_GetItem(__pyx_v_self->dimensions, __pyx_v_dimname); if (unlikely(__pyx_t_3 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1731; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+ __pyx_t_3 = PyObject_GetItem(__pyx_v_self->dimensions, __pyx_v_dimname); if (unlikely(__pyx_t_3 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1801; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
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__pyx_r = __pyx_t_3;
__pyx_t_3 = 0;
goto __pyx_L0;
- /* "netCDF4/_netCDF4.pyx":1717
+ /* "netCDF4/_netCDF4.pyx":1787
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
*
* def createDimension(self, dimname, size=None): # <<<<<<<<<<<<<<
* """
- * createDimension(self, dimname, size=None)
+ * **`createDimension(self, dimname, size=None)`**
*/
/* function exit code */
@@ -12300,17 +12438,17 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_28createDimension(struct _
return __pyx_r;
}
-/* "netCDF4/_netCDF4.pyx":1733
+/* "netCDF4/_netCDF4.pyx":1803
* return self.dimensions[dimname]
*
* def renameDimension(self, oldname, newname): # <<<<<<<<<<<<<<
* """
- * renameDimension(self, oldname, newname)
+ * **`renameDimension(self, oldname, newname)`**
*/
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static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_31renameDimension(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_31renameDimension(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
PyObject *__pyx_v_oldname = 0;
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else {
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+ __Pyx_RaiseArgtupleInvalid("renameDimension", 1, 2, 2, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1803; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "renameDimension") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1803; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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goto __pyx_L5_argtuple_error;
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+ __Pyx_RaiseArgtupleInvalid("renameDimension", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1803; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_L3_error:;
__Pyx_AddTraceback("netCDF4._netCDF4.Dataset.renameDimension", __pyx_clineno, __pyx_lineno, __pyx_filename);
__Pyx_RefNannyFinishContext();
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+ /* "netCDF4/_netCDF4.pyx":1809
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+ __pyx_t_1 = __pyx_f_7netCDF4_8_netCDF4__strencode(__pyx_v_newname, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1809; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_t_1 = 0;
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+ /* "netCDF4/_netCDF4.pyx":1812
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- if (unlikely(PyObject_SetItem(__pyx_v_self->dimensions, __pyx_v_newname, __pyx_v_dim) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1753; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(PyObject_SetItem(__pyx_v_self->dimensions, __pyx_v_newname, __pyx_v_dim) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1823; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- /* "netCDF4/_netCDF4.pyx":1733
+ /* "netCDF4/_netCDF4.pyx":1803
* return self.dimensions[dimname]
*
* def renameDimension(self, oldname, newname): # <<<<<<<<<<<<<<
* """
- * renameDimension(self, oldname, newname)
+ * **`renameDimension(self, oldname, newname)`**
*/
/* function exit code */
@@ -12683,17 +12821,17 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_30renameDimension(struct _
return __pyx_r;
}
-/* "netCDF4/_netCDF4.pyx":1757
+/* "netCDF4/_netCDF4.pyx":1827
* # looks in the file, so no need to manually update.
*
* def createCompoundType(self, datatype, datatype_name): # <<<<<<<<<<<<<<
* """
- * createCompoundType(self, datatype, datatype_name)
+ * **`createCompoundType(self, datatype, datatype_name)`**
*/
/* Python wrapper */
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_33createCompoundType(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_7netCDF4_8_netCDF4_7Dataset_32createCompoundType[] = "\ncreateCompoundType(self, datatype, datatype_name)\n\nCreates a new compound data type named C{datatype_name} from the numpy\ndtype object C{datatype}.\n\n at attention: If the new compound data type contains other compound data types\n(i.e. it is a 'nested' compound type, where not all of the elements\nare homogenous numeric data types), then the 'inner' compound types B{must} be\ncreated first.\n\nThe return valu [...]
+static char __pyx_doc_7netCDF4_8_netCDF4_7Dataset_32createCompoundType[] = "\n**`createCompoundType(self, datatype, datatype_name)`**\n\nCreates a new compound data type named `datatype_name` from the numpy\ndtype object `datatype`.\n\n***Note***: If the new compound data type contains other compound data types\n(i.e. it is a 'nested' compound type, where not all of the elements\nare homogenous numeric data types), then the 'inner' compound types **must** be\ncreated first.\n\nThe return [...]
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_33createCompoundType(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
PyObject *__pyx_v_datatype = 0;
PyObject *__pyx_v_datatype_name = 0;
@@ -12723,11 +12861,11 @@ static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_33createCompoundType(PyObj
case 1:
if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_datatype_name)) != 0)) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("createCompoundType", 1, 2, 2, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1757; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("createCompoundType", 1, 2, 2, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1827; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
}
if (unlikely(kw_args > 0)) {
- if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "createCompoundType") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1757; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "createCompoundType") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1827; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
} else if (PyTuple_GET_SIZE(__pyx_args) != 2) {
goto __pyx_L5_argtuple_error;
@@ -12740,7 +12878,7 @@ static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_33createCompoundType(PyObj
}
goto __pyx_L4_argument_unpacking_done;
__pyx_L5_argtuple_error:;
- __Pyx_RaiseArgtupleInvalid("createCompoundType", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1757; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("createCompoundType", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1827; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_L3_error:;
__Pyx_AddTraceback("netCDF4._netCDF4.Dataset.createCompoundType", __pyx_clineno, __pyx_lineno, __pyx_filename);
__Pyx_RefNannyFinishContext();
@@ -12763,14 +12901,14 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_32createCompoundType(struc
int __pyx_clineno = 0;
__Pyx_RefNannySetupContext("createCompoundType", 0);
- /* "netCDF4/_netCDF4.pyx":1771
- * The return value is the L{CompoundType} class instance describing the new
+ /* "netCDF4/_netCDF4.pyx":1841
+ * The return value is the `netCDF4.CompoundType` class instance describing the new
* datatype."""
* self.cmptypes[datatype_name] = CompoundType(self, datatype,\ # <<<<<<<<<<<<<<
* datatype_name)
* return self.cmptypes[datatype_name]
*/
- __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1771; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1841; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_INCREF(((PyObject *)__pyx_v_self));
PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)__pyx_v_self));
@@ -12781,13 +12919,13 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_32createCompoundType(struc
__Pyx_INCREF(__pyx_v_datatype_name);
PyTuple_SET_ITEM(__pyx_t_1, 2, __pyx_v_datatype_name);
__Pyx_GIVEREF(__pyx_v_datatype_name);
- __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)__pyx_ptype_7netCDF4_8_netCDF4_CompoundType)), __pyx_t_1, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1771; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)__pyx_ptype_7netCDF4_8_netCDF4_CompoundType)), __pyx_t_1, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1841; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- if (unlikely(PyObject_SetItem(__pyx_v_self->cmptypes, __pyx_v_datatype_name, __pyx_t_2) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1771; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(PyObject_SetItem(__pyx_v_self->cmptypes, __pyx_v_datatype_name, __pyx_t_2) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1841; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- /* "netCDF4/_netCDF4.pyx":1773
+ /* "netCDF4/_netCDF4.pyx":1843
* self.cmptypes[datatype_name] = CompoundType(self, datatype,\
* datatype_name)
* return self.cmptypes[datatype_name] # <<<<<<<<<<<<<<
@@ -12795,18 +12933,18 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_32createCompoundType(struc
* def createVLType(self, datatype, datatype_name):
*/
__Pyx_XDECREF(__pyx_r);
- __pyx_t_2 = PyObject_GetItem(__pyx_v_self->cmptypes, __pyx_v_datatype_name); if (unlikely(__pyx_t_2 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1773; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+ __pyx_t_2 = PyObject_GetItem(__pyx_v_self->cmptypes, __pyx_v_datatype_name); if (unlikely(__pyx_t_2 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1843; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
__Pyx_GOTREF(__pyx_t_2);
__pyx_r = __pyx_t_2;
__pyx_t_2 = 0;
goto __pyx_L0;
- /* "netCDF4/_netCDF4.pyx":1757
+ /* "netCDF4/_netCDF4.pyx":1827
* # looks in the file, so no need to manually update.
*
* def createCompoundType(self, datatype, datatype_name): # <<<<<<<<<<<<<<
* """
- * createCompoundType(self, datatype, datatype_name)
+ * **`createCompoundType(self, datatype, datatype_name)`**
*/
/* function exit code */
@@ -12821,17 +12959,17 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_32createCompoundType(struc
return __pyx_r;
}
-/* "netCDF4/_netCDF4.pyx":1775
+/* "netCDF4/_netCDF4.pyx":1845
* return self.cmptypes[datatype_name]
*
* def createVLType(self, datatype, datatype_name): # <<<<<<<<<<<<<<
* """
- * createVLType(self, datatype, datatype_name)
+ * **`createVLType(self, datatype, datatype_name)`**
*/
/* Python wrapper */
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_35createVLType(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_7netCDF4_8_netCDF4_7Dataset_34createVLType[] = "\ncreateVLType(self, datatype, datatype_name)\n\nCreates a new VLEN data type named C{datatype_name} from a numpy\ndtype object C{datatype}.\n\nThe return value is the L{VLType} class instance describing the new\ndatatype.";
+static char __pyx_doc_7netCDF4_8_netCDF4_7Dataset_34createVLType[] = "\n**`createVLType(self, datatype, datatype_name)`**\n\nCreates a new VLEN data type named `datatype_name` from a numpy\ndtype object `datatype`.\n\nThe return value is the `netCDF4.VLType` class instance describing the new\ndatatype.";
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_35createVLType(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
PyObject *__pyx_v_datatype = 0;
PyObject *__pyx_v_datatype_name = 0;
@@ -12861,11 +12999,11 @@ static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_35createVLType(PyObject *_
case 1:
if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_datatype_name)) != 0)) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("createVLType", 1, 2, 2, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1775; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("createVLType", 1, 2, 2, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1845; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
}
if (unlikely(kw_args > 0)) {
- if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "createVLType") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1775; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "createVLType") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1845; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
} else if (PyTuple_GET_SIZE(__pyx_args) != 2) {
goto __pyx_L5_argtuple_error;
@@ -12878,7 +13016,7 @@ static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_35createVLType(PyObject *_
}
goto __pyx_L4_argument_unpacking_done;
__pyx_L5_argtuple_error:;
- __Pyx_RaiseArgtupleInvalid("createVLType", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1775; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("createVLType", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1845; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_L3_error:;
__Pyx_AddTraceback("netCDF4._netCDF4.Dataset.createVLType", __pyx_clineno, __pyx_lineno, __pyx_filename);
__Pyx_RefNannyFinishContext();
@@ -12901,14 +13039,14 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_34createVLType(struct __py
int __pyx_clineno = 0;
__Pyx_RefNannySetupContext("createVLType", 0);
- /* "netCDF4/_netCDF4.pyx":1784
- * The return value is the L{VLType} class instance describing the new
+ /* "netCDF4/_netCDF4.pyx":1854
+ * The return value is the `netCDF4.VLType` class instance describing the new
* datatype."""
* self.vltypes[datatype_name] = VLType(self, datatype, datatype_name) # <<<<<<<<<<<<<<
* return self.vltypes[datatype_name]
*
*/
- __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1784; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1854; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_INCREF(((PyObject *)__pyx_v_self));
PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)__pyx_v_self));
@@ -12919,13 +13057,13 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_34createVLType(struct __py
__Pyx_INCREF(__pyx_v_datatype_name);
PyTuple_SET_ITEM(__pyx_t_1, 2, __pyx_v_datatype_name);
__Pyx_GIVEREF(__pyx_v_datatype_name);
- __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)__pyx_ptype_7netCDF4_8_netCDF4_VLType)), __pyx_t_1, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1784; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)__pyx_ptype_7netCDF4_8_netCDF4_VLType)), __pyx_t_1, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1854; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- if (unlikely(PyObject_SetItem(__pyx_v_self->vltypes, __pyx_v_datatype_name, __pyx_t_2) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1784; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(PyObject_SetItem(__pyx_v_self->vltypes, __pyx_v_datatype_name, __pyx_t_2) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1854; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- /* "netCDF4/_netCDF4.pyx":1785
+ /* "netCDF4/_netCDF4.pyx":1855
* datatype."""
* self.vltypes[datatype_name] = VLType(self, datatype, datatype_name)
* return self.vltypes[datatype_name] # <<<<<<<<<<<<<<
@@ -12933,18 +13071,18 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_34createVLType(struct __py
* def createVariable(self, varname, datatype, dimensions=(), zlib=False,
*/
__Pyx_XDECREF(__pyx_r);
- __pyx_t_2 = PyObject_GetItem(__pyx_v_self->vltypes, __pyx_v_datatype_name); if (unlikely(__pyx_t_2 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1785; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+ __pyx_t_2 = PyObject_GetItem(__pyx_v_self->vltypes, __pyx_v_datatype_name); if (unlikely(__pyx_t_2 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1855; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
__Pyx_GOTREF(__pyx_t_2);
__pyx_r = __pyx_t_2;
__pyx_t_2 = 0;
goto __pyx_L0;
- /* "netCDF4/_netCDF4.pyx":1775
+ /* "netCDF4/_netCDF4.pyx":1845
* return self.cmptypes[datatype_name]
*
* def createVLType(self, datatype, datatype_name): # <<<<<<<<<<<<<<
* """
- * createVLType(self, datatype, datatype_name)
+ * **`createVLType(self, datatype, datatype_name)`**
*/
/* function exit code */
@@ -12959,7 +13097,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_34createVLType(struct __py
return __pyx_r;
}
-/* "netCDF4/_netCDF4.pyx":1787
+/* "netCDF4/_netCDF4.pyx":1857
* return self.vltypes[datatype_name]
*
* def createVariable(self, varname, datatype, dimensions=(), zlib=False, # <<<<<<<<<<<<<<
@@ -12969,7 +13107,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_34createVLType(struct __py
/* Python wrapper */
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_37createVariable(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_7netCDF4_8_netCDF4_7Dataset_36createVariable[] = "\ncreateVariable(self, varname, datatype, dimensions=(), zlib=False, complevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None)\n\nCreates a new variable with the given C{varname}, C{datatype}, and\nC{dimensions}. If dimensions are not given, the variable is assumed to be\na scalar.\n\nIf C{varname} is specified as a path, using forw [...]
+static char __pyx_doc_7netCDF4_8_netCDF4_7Dataset_36createVariable[] = "\n**`createVariable(self, varname, datatype, dimensions=(), zlib=False,\ncomplevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None,\nendian='native', least_significant_digit=None, fill_value=None)`**\n\nCreates a new variable with the given `varname`, `datatype`, and\n`dimensions`. If dimensions are not given, the variable is assumed to be\na scalar.\n\nIf `varname` is specified as a path, using [...]
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_37createVariable(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
PyObject *__pyx_v_varname = 0;
PyObject *__pyx_v_datatype = 0;
@@ -12997,7 +13135,7 @@ static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_37createVariable(PyObject
values[3] = ((PyObject *)Py_False);
values[4] = ((PyObject *)__pyx_int_4);
- /* "netCDF4/_netCDF4.pyx":1788
+ /* "netCDF4/_netCDF4.pyx":1858
*
* def createVariable(self, varname, datatype, dimensions=(), zlib=False,
* complevel=4, shuffle=True, fletcher32=False, contiguous=False, # <<<<<<<<<<<<<<
@@ -13008,7 +13146,7 @@ static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_37createVariable(PyObject
values[6] = ((PyObject *)Py_False);
values[7] = ((PyObject *)Py_False);
- /* "netCDF4/_netCDF4.pyx":1789
+ /* "netCDF4/_netCDF4.pyx":1859
* def createVariable(self, varname, datatype, dimensions=(), zlib=False,
* complevel=4, shuffle=True, fletcher32=False, contiguous=False,
* chunksizes=None, endian='native', least_significant_digit=None, # <<<<<<<<<<<<<<
@@ -13019,12 +13157,12 @@ static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_37createVariable(PyObject
values[9] = ((PyObject *)__pyx_n_s_native);
values[10] = ((PyObject *)Py_None);
- /* "netCDF4/_netCDF4.pyx":1790
+ /* "netCDF4/_netCDF4.pyx":1860
* complevel=4, shuffle=True, fletcher32=False, contiguous=False,
* chunksizes=None, endian='native', least_significant_digit=None,
* fill_value=None, chunk_cache=None): # <<<<<<<<<<<<<<
* """
- * createVariable(self, varname, datatype, dimensions=(), zlib=False, complevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None)
+ * **`createVariable(self, varname, datatype, dimensions=(), zlib=False,
*/
values[11] = ((PyObject *)Py_None);
values[12] = ((PyObject *)Py_None);
@@ -13056,7 +13194,7 @@ static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_37createVariable(PyObject
case 1:
if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_datatype)) != 0)) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("createVariable", 0, 2, 13, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1787; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("createVariable", 0, 2, 13, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1857; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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- if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_zlib, __pyx_v_zlib) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1920; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_complevel, __pyx_v_complevel) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1920; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_dimensions, __pyx_v_dimensions) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1992; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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* dimensions=dimensions, zlib=zlib, complevel=complevel, shuffle=shuffle,
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return __pyx_r;
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goto __pyx_L5_argtuple_error;
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@@ -13681,38 +13819,38 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_38renameVariable(struct __
__pyx_L10_try_end:;
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__Pyx_DECREF_SET(__pyx_v_groupname, __pyx_t_1);
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__pyx_v_group = ((PyObject *)__pyx_v_self);
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__pyx_t_1 = 0;
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goto __pyx_L3_continue;
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}
__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
- /* "netCDF4/_netCDF4.pyx":1977
+ /* "netCDF4/_netCDF4.pyx":2049
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goto __pyx_L0;
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*
* def createGroup(self, groupname): # <<<<<<<<<<<<<<
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*/
/* function exit code */
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return __pyx_r;
}
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* """
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*/
/* Python wrapper */
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_43ncattrs(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
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static PyObject *__pyx_pw_7netCDF4_8_netCDF4_7Dataset_43ncattrs(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
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+ __pyx_t_1 = __pyx_f_7netCDF4_8_netCDF4__get_att_names(__pyx_v_self->_grpid, NC_GLOBAL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2056; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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return __pyx_r;
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2068; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_1 = __pyx_f_7netCDF4_8_netCDF4__strencode(__pyx_v_oldname, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2154; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- /* "netCDF4/_netCDF4.pyx":2083
+ /* "netCDF4/_netCDF4.pyx":2155
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* bytestr = _strencode(newname)
* newnamec = bytestr
*/
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+ __pyx_t_2 = __Pyx_PyObject_AsString(__pyx_v_bytestr); if (unlikely((!__pyx_t_2) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2155; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_v_oldnamec = __pyx_t_2;
- /* "netCDF4/_netCDF4.pyx":2084
+ /* "netCDF4/_netCDF4.pyx":2156
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*/
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+ /* "netCDF4/_netCDF4.pyx":2159
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* """
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return __pyx_r;
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-/* "netCDF4/_netCDF4.pyx":2090
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*
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* """
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- /* "netCDF4/_netCDF4.pyx":2090
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*
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+ __pyx_r = __pyx_pf_7netCDF4_8_netCDF4_7Dataset_11keepweakref_4__del__(((struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *)__pyx_v_self));
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return __pyx_r;
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+/* "netCDF4/_netCDF4.pyx":2276
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* def __init__(self, parent, name, **kwargs): # <<<<<<<<<<<<<<
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- * cdef char *groupname
+ * """
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*/
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static int __pyx_pw_7netCDF4_8_netCDF4_5Group_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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+#endif
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__pyx_v_self->__pyx_base.data_model = __pyx_t_1;
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__pyx_v_self->__pyx_base.file_format = __pyx_t_1;
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__pyx_v_self->__pyx_base.parent = __pyx_v_parent;
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__pyx_v_self->__pyx_base.vltypes = __pyx_t_6;
__pyx_t_6 = 0;
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+ /* "netCDF4/_netCDF4.pyx":2308
* self.cmptypes, self.vltypes = _get_types(self)
* # get dimensions in this Group.
* self.dimensions = _get_dims(self) # <<<<<<<<<<<<<<
* # get variables in this Group.
* self.variables = _get_vars(self)
*/
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__Pyx_GIVEREF(__pyx_t_1);
__Pyx_GOTREF(__pyx_v_self->__pyx_base.dimensions);
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__pyx_v_self->__pyx_base.dimensions = __pyx_t_1;
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+ /* "netCDF4/_netCDF4.pyx":2310
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* # get groups in this Group.
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__Pyx_GIVEREF(__pyx_t_1);
__Pyx_GOTREF(__pyx_v_self->__pyx_base.variables);
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__pyx_v_self->__pyx_base.variables = __pyx_t_1;
__pyx_t_1 = 0;
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+ /* "netCDF4/_netCDF4.pyx":2312
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__Pyx_GIVEREF(__pyx_t_1);
__Pyx_GOTREF(__pyx_v_self->__pyx_base.groups);
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}
/*else*/ {
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+ /* "netCDF4/_netCDF4.pyx":2314
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+ /* "netCDF4/_netCDF4.pyx":2315
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__pyx_v_groupname = __pyx_t_11;
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+ /* "netCDF4/_netCDF4.pyx":2316
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+ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_parent, __pyx_n_s_grpid); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2316; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- /* "netCDF4/_netCDF4.pyx":2253
+ /* "netCDF4/_netCDF4.pyx":2317
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if (__pyx_t_8) {
- /* "netCDF4/_netCDF4.pyx":2254
+ /* "netCDF4/_netCDF4.pyx":2318
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+ __pyx_t_1 = __Pyx_decode_c_string(__pyx_t_11, 0, strlen(__pyx_t_11), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2318; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_6 = PyTuple_New(1); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2318; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_1);
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+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2318; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
- /* "netCDF4/_netCDF4.pyx":2255
+ /* "netCDF4/_netCDF4.pyx":2319
* if ierr != NC_NOERR:
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+ __pyx_t_6 = __Pyx_GetModuleGlobalName(__pyx_n_s_OrderedDict); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2319; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_1 = __Pyx_PyObject_CallOneArg(__pyx_t_6, __pyx_t_3); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2319; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_1 = __Pyx_PyObject_CallNoArg(__pyx_t_6); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2319; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
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__pyx_v_self->__pyx_base.cmptypes = __pyx_t_1;
__pyx_t_1 = 0;
- /* "netCDF4/_netCDF4.pyx":2256
+ /* "netCDF4/_netCDF4.pyx":2320
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__Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
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__pyx_v_self->__pyx_base.vltypes = __pyx_t_1;
__pyx_t_1 = 0;
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+ /* "netCDF4/_netCDF4.pyx":2321
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__pyx_v_self->__pyx_base.dimensions = __pyx_t_1;
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}
__pyx_L3:;
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* def __init__(self, parent, name, **kwargs): # <<<<<<<<<<<<<<
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- * cdef char *groupname
+ * """
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*/
/* function exit code */
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return __pyx_r;
}
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* self.groups = OrderedDict()
*
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*/
/* Python wrapper */
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*
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*/
/* function exit code */
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return __pyx_r;
}
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#endif
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+ /* "netCDF4/_netCDF4.pyx":2347
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return __pyx_r;
}
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*
* def __init__(self, grp, name, size=None, **kwargs): # <<<<<<<<<<<<<<
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- * cdef char *dimname
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*/
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+#if CYTHON_COMPILING_IN_CPYTHON
+struct wrapperbase __pyx_wrapperbase_7netCDF4_8_netCDF4_9Dimension___init__;
+#endif
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}
/*else*/ {
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+ /* "netCDF4/_netCDF4.pyx":2398
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__pyx_v_self->_data_model = __pyx_t_1;
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- /* "netCDF4/_netCDF4.pyx":2405
+ /* "netCDF4/_netCDF4.pyx":2474
* if self._data_model == 'NETCDF4':
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if (__pyx_t_1) {
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+ /* "netCDF4/_netCDF4.pyx":2475
* ierr = nc_inq_unlimdims(self._grpid, &numunlimdims, NULL)
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__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2406; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- /* "netCDF4/_netCDF4.pyx":2407
+ /* "netCDF4/_netCDF4.pyx":2476
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__pyx_t_1 = ((__pyx_v_numunlimdims == 0) != 0);
if (__pyx_t_1) {
- /* "netCDF4/_netCDF4.pyx":2408
+ /* "netCDF4/_netCDF4.pyx":2477
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
* if numunlimdims == 0:
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}
/*else*/ {
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+ /* "netCDF4/_netCDF4.pyx":2479
* return False
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__pyx_t_5 = __pyx_v_self->_dimid;
__pyx_v_dimid = __pyx_t_5;
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#endif
/*try:*/ {
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+ /* "netCDF4/_netCDF4.pyx":2481
* dimid = self._dimid
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}
- /* "netCDF4/_netCDF4.pyx":2411
+ /* "netCDF4/_netCDF4.pyx":2480
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}
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+ /* "netCDF4/_netCDF4.pyx":2482
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+ /* "netCDF4/_netCDF4.pyx":2483
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+ __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2483; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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}
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+ /* "netCDF4/_netCDF4.pyx":2494
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}
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@@ -21476,12 +21622,12 @@ static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_6_grpid_2__set__(struct __pyx_
return __pyx_r;
}
-/* "netCDF4/_netCDF4.pyx":2317
- * determine if the dimension is unlimited"""
+/* "netCDF4/_netCDF4.pyx":2367
+ * """
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* cdef public _data_model, _name, _grp # <<<<<<<<<<<<<<
- *
- * def __init__(self, grp, name, size=None, **kwargs):
+ * # Docstrings for class variables (used by pdoc).
+ * __pdoc__['Dimension.name']=\
*/
/* Python wrapper */
@@ -21745,8 +21891,8 @@ static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_4_grp_4__del__(struct __pyx_ob
return __pyx_r;
}
-/* "netCDF4/_netCDF4.pyx":2571
- * _isvlen, _grp, _cmptype, _vltype, __orthogonal_indexing__
+/* "netCDF4/_netCDF4.pyx":2592
+ * """The number of stored elements."""
*
* def __init__(self, grp, name, datatype, dimensions=(), zlib=False, # <<<<<<<<<<<<<<
* complevel=4, shuffle=True, fletcher32=False, contiguous=False,
@@ -21755,6 +21901,10 @@ static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_4_grp_4__del__(struct __pyx_ob
/* Python wrapper */
static int __pyx_pw_7netCDF4_8_netCDF4_8Variable_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_7netCDF4_8_netCDF4_8Variable___init__[] = "\n **`__init__(self, group, name, datatype, dimensions=(), zlib=False,\n complevel=4, shuffle=True, fletcher32=False, contiguous=False,\n chunksizes=None, endian='native',\n least_significant_digit=None,fill_value=None)`**\n\n `netCDF4.Variable` constructor.\n\n **`group`**: `netCDF4.Group` or `netCDF4.Dataset` instance to associate with variable.\n \n **`name`**: Name [...]
+#if CYTHON_COMPILING_IN_CPYTHON
+struct wrapperbase __pyx_wrapperbase_7netCDF4_8_netCDF4_8Variable___init__;
+#endif
static int __pyx_pw_7netCDF4_8_netCDF4_8Variable_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
PyObject *__pyx_v_grp = 0;
PyObject *__pyx_v_name = 0;
@@ -21786,7 +21936,7 @@ static int __pyx_pw_7netCDF4_8_netCDF4_8Variable_1__init__(PyObject *__pyx_v_sel
values[4] = ((PyObject *)Py_False);
values[5] = ((PyObject *)__pyx_int_4);
- /* "netCDF4/_netCDF4.pyx":2572
+ /* "netCDF4/_netCDF4.pyx":2593
*
* def __init__(self, grp, name, datatype, dimensions=(), zlib=False,
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@@ -21797,23 +21947,23 @@ static int __pyx_pw_7netCDF4_8_netCDF4_8Variable_1__init__(PyObject *__pyx_v_sel
values[7] = ((PyObject *)Py_False);
values[8] = ((PyObject *)Py_False);
- /* "netCDF4/_netCDF4.pyx":2573
+ /* "netCDF4/_netCDF4.pyx":2594
* def __init__(self, grp, name, datatype, dimensions=(), zlib=False,
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* chunksizes=None, endian='native', least_significant_digit=None, # <<<<<<<<<<<<<<
* fill_value=None, chunk_cache=None, **kwargs):
- * cdef int ierr, ndims, icontiguous, ideflate_level, numdims, _grpid
+ * """
*/
values[9] = ((PyObject *)Py_None);
values[10] = ((PyObject *)__pyx_n_s_native);
values[11] = ((PyObject *)Py_None);
- /* "netCDF4/_netCDF4.pyx":2574
+ /* "netCDF4/_netCDF4.pyx":2595
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* chunksizes=None, endian='native', least_significant_digit=None,
* fill_value=None, chunk_cache=None, **kwargs): # <<<<<<<<<<<<<<
- * cdef int ierr, ndims, icontiguous, ideflate_level, numdims, _grpid
- * cdef char *varname
+ * """
+ * **`__init__(self, group, name, datatype, dimensions=(), zlib=False,
*/
values[12] = ((PyObject *)Py_None);
values[13] = ((PyObject *)Py_None);
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case 1:
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+ __Pyx_RaiseArgtupleInvalid("__init__", 0, 3, 14, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2592; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __Pyx_RaiseArgtupleInvalid("__init__", 0, 3, 14, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2592; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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}
}
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+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, __pyx_v_kwargs, values, pos_args, "__init__") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2592; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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}
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+ __Pyx_RaiseArgtupleInvalid("__init__", 0, 3, 14, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2592; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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__pyx_L4_argument_unpacking_done:;
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- /* "netCDF4/_netCDF4.pyx":2571
- * _isvlen, _grp, _cmptype, _vltype, __orthogonal_indexing__
+ /* "netCDF4/_netCDF4.pyx":2592
+ * """The number of stored elements."""
*
* def __init__(self, grp, name, datatype, dimensions=(), zlib=False, # <<<<<<<<<<<<<<
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__Pyx_INCREF(__pyx_v_dimensions);
__Pyx_INCREF(__pyx_v_zlib);
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+ /* "netCDF4/_netCDF4.pyx":2694
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__pyx_v_self->__orthogonal_indexing__ = Py_True;
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* # if complevel is set to zero, set zlib to False.
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+ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_complevel); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2696; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_t_2 = ((!__pyx_t_1) != 0);
if (__pyx_t_2) {
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+ /* "netCDF4/_netCDF4.pyx":2697
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}
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}
__pyx_L4:;
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}
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__pyx_L30_bool_binop_done:;
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__pyx_v_self->_iscompound = Py_True;
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*/
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__pyx_t_8 = (__pyx_t_2 != 0);
if (__pyx_t_8) {
- /* "netCDF4/_netCDF4.pyx":2732
+ /* "netCDF4/_netCDF4.pyx":2843
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+ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__39, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2843; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_Raise(__pyx_t_3, 0, 0, 0);
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2732; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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goto __pyx_L68;
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+ /* "netCDF4/_netCDF4.pyx":2845
* raise ValueError('cannot specify chunksizes for a contiguous dataset')
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@@ -23732,7 +23882,7 @@ static int __pyx_pf_7netCDF4_8_netCDF4_8Variable___init__(struct __pyx_obj_7netC
}
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- /* "netCDF4/_netCDF4.pyx":2735
+ /* "netCDF4/_netCDF4.pyx":2846
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__pyx_t_2 = (__pyx_t_8 != 0);
if (__pyx_t_2) {
- /* "netCDF4/_netCDF4.pyx":2736
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}
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- /* "netCDF4/_netCDF4.pyx":2738
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+ __pyx_t_11 = PyObject_Length(__pyx_v_chunksizes); if (unlikely(__pyx_t_11 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2849; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_14 = PyObject_Length(__pyx_v_dimensions); if (unlikely(__pyx_t_14 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2849; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_t_2 = ((__pyx_t_11 != __pyx_t_14) != 0);
if (__pyx_t_2) {
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- __pyx_t_2 = (__Pyx_PyString_Equals(__pyx_t_3, __pyx_n_s_NETCDF4, Py_NE)); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2739; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -23792,10 +23942,10 @@ static int __pyx_pf_7netCDF4_8_netCDF4_8Variable___init__(struct __pyx_obj_7netC
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__Pyx_GOTREF(__pyx_t_3);
__Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
@@ -23804,21 +23954,21 @@ static int __pyx_pf_7netCDF4_8_netCDF4_8Variable___init__(struct __pyx_obj_7netC
}
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+ /* "netCDF4/_netCDF4.pyx":2851
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- __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__40, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2740; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_Raise(__pyx_t_3, 0, 0, 0);
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2740; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_v_chunksizesp = ((size_t *)malloc(((sizeof(size_t)) * __pyx_v_ndims)));
- /* "netCDF4/_netCDF4.pyx":2742
+ /* "netCDF4/_netCDF4.pyx":2853
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- /* "netCDF4/_netCDF4.pyx":2746
+ /* "netCDF4/_netCDF4.pyx":2857
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*/
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__pyx_t_2 = ((__pyx_v_ierr != NC_NOERR) != 0);
if (__pyx_t_2) {
- /* "netCDF4/_netCDF4.pyx":2748
+ /* "netCDF4/_netCDF4.pyx":2859
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+ __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_grp, __pyx_n_s_data_model); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2859; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -23925,10 +24075,10 @@ static int __pyx_pf_7netCDF4_8_netCDF4_8Variable___init__(struct __pyx_obj_7netC
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__Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
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+ __pyx_t_3 = __Pyx_decode_c_string(__pyx_t_10, 0, strlen(__pyx_t_10), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2860; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+static char __pyx_doc_7netCDF4_8_netCDF4_8Variable_12group[] = "\n**`group(self)`**\n\nreturn the group that this `netCDF4.Variable` is a member of.";
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_8Variable_13group(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
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__Pyx_RefNannySetupContext("group", 0);
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+ /* "netCDF4/_netCDF4.pyx":3069
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+ * return the group that this `netCDF4.Variable` is a member of."""
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__pyx_r = __pyx_v_self->_grp;
goto __pyx_L0;
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+ /* "netCDF4/_netCDF4.pyx":3064
*
*
* def group(self): # <<<<<<<<<<<<<<
* """
- * group(self)
+ * **`group(self)`**
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/* function exit code */
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return __pyx_r;
}
-/* "netCDF4/_netCDF4.pyx":2960
+/* "netCDF4/_netCDF4.pyx":3071
* return self._grp
*
* def ncattrs(self): # <<<<<<<<<<<<<<
* """
- * ncattrs(self)
+ * **`ncattrs(self)`**
*/
/* Python wrapper */
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_8Variable_15ncattrs(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
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+static char __pyx_doc_7netCDF4_8_netCDF4_8Variable_14ncattrs[] = "\n**`ncattrs(self)`**\n\nreturn netCDF attribute names for this `netCDF4.Variable` in a list.";
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+ * return netCDF attribute names for this `netCDF4.Variable` in a list."""
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* def setncattr(self,name,value):
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__pyx_r = __pyx_t_1;
__pyx_t_1 = 0;
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*
* def ncattrs(self): # <<<<<<<<<<<<<<
* """
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+ * **`ncattrs(self)`**
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/* function exit code */
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return __pyx_r;
}
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*
* def setncattr(self,name,value): # <<<<<<<<<<<<<<
* """
- * setncattr(self,name,value)
+ * **`setncattr(self,name,value)`**
*/
/* Python wrapper */
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+static char __pyx_doc_7netCDF4_8_netCDF4_8Variable_16setncattr[] = "\n**`setncattr(self,name,value)`**\n\nset a netCDF variable attribute using name,value pair. Use if you need to set a\nnetCDF attribute with the same name as one of the reserved python\nattributes.";
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+ __Pyx_RaiseArgtupleInvalid("setncattr", 1, 2, 2, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3078; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "setncattr") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3078; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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goto __pyx_L5_argtuple_error;
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+ __Pyx_RaiseArgtupleInvalid("setncattr", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3078; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_t_1 = __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3085; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_3 = __pyx_f_7netCDF4_8_netCDF4__set_att(__pyx_t_1, __pyx_v_self->_varid, __pyx_v_name, __pyx_v_value); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3086; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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/* function exit code */
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return __pyx_r;
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* """
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__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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+ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_self->_grp, __pyx_n_s_data_model); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_3 = (__Pyx_PyString_Equals(__pyx_t_1, __pyx_n_s_NETCDF4_CLASSIC, Py_NE)); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__pyx_t_3) {
} else {
__pyx_t_2 = __pyx_t_3;
goto __pyx_L4_bool_binop_done;
}
- __pyx_t_3 = (__Pyx_PyString_Equals(__pyx_t_1, __pyx_n_s_NETCDF4, Py_NE)); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3023; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_3 = (__Pyx_PyString_Equals(__pyx_t_1, __pyx_n_s_NETCDF4, Py_NE)); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_t_2 = __pyx_t_3;
__pyx_L4_bool_binop_done:;
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
@@ -28372,7 +28522,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_ob
goto __pyx_L0;
}
- /* "netCDF4/_netCDF4.pyx":3024
+ /* "netCDF4/_netCDF4.pyx":3135
* filtdict = {'zlib':False,'shuffle':False,'complevel':0,'fletcher32':False}
* if self._grp.data_model not in ['NETCDF4_CLASSIC','NETCDF4']: return
* with nogil: # <<<<<<<<<<<<<<
@@ -28386,7 +28536,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_ob
#endif
/*try:*/ {
- /* "netCDF4/_netCDF4.pyx":3025
+ /* "netCDF4/_netCDF4.pyx":3136
* if self._grp.data_model not in ['NETCDF4_CLASSIC','NETCDF4']: return
* with nogil:
* ierr = nc_inq_var_deflate(self._grpid, self._varid, &ishuffle, &ideflate, &ideflate_level) # <<<<<<<<<<<<<<
@@ -28396,7 +28546,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_ob
__pyx_v_ierr = nc_inq_var_deflate(__pyx_v_self->_grpid, __pyx_v_self->_varid, (&__pyx_v_ishuffle), (&__pyx_v_ideflate), (&__pyx_v_ideflate_level));
}
- /* "netCDF4/_netCDF4.pyx":3024
+ /* "netCDF4/_netCDF4.pyx":3135
* filtdict = {'zlib':False,'shuffle':False,'complevel':0,'fletcher32':False}
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* with nogil: # <<<<<<<<<<<<<<
@@ -28414,7 +28564,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_ob
}
}
- /* "netCDF4/_netCDF4.pyx":3026
+ /* "netCDF4/_netCDF4.pyx":3137
* with nogil:
* ierr = nc_inq_var_deflate(self._grpid, self._varid, &ishuffle, &ideflate, &ideflate_level)
* if ierr != NC_NOERR: # <<<<<<<<<<<<<<
@@ -28424,7 +28574,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_ob
__pyx_t_3 = ((__pyx_v_ierr != NC_NOERR) != 0);
if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":3027
+ /* "netCDF4/_netCDF4.pyx":3138
* ierr = nc_inq_var_deflate(self._grpid, self._varid, &ishuffle, &ideflate, &ideflate_level)
* if ierr != NC_NOERR:
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@@ -28432,23 +28582,23 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_ob
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*/
__pyx_t_4 = ((char *)nc_strerror(__pyx_v_ierr));
- __pyx_t_1 = __Pyx_decode_c_string(__pyx_t_4, 0, strlen(__pyx_t_4), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3027; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_decode_c_string(__pyx_t_4, 0, strlen(__pyx_t_4), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3138; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3027; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3138; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_5);
__Pyx_INCREF(__pyx_t_1);
PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_1);
__Pyx_GIVEREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_t_5, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3027; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_t_5, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3138; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
__Pyx_Raise(__pyx_t_1, 0, 0, 0);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3027; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3138; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
- /* "netCDF4/_netCDF4.pyx":3028
+ /* "netCDF4/_netCDF4.pyx":3139
* if ierr != NC_NOERR:
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
* with nogil: # <<<<<<<<<<<<<<
@@ -28462,7 +28612,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_ob
#endif
/*try:*/ {
- /* "netCDF4/_netCDF4.pyx":3029
+ /* "netCDF4/_netCDF4.pyx":3140
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
* with nogil:
* ierr = nc_inq_var_fletcher32(self._grpid, self._varid, &ifletcher32) # <<<<<<<<<<<<<<
@@ -28472,7 +28622,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_ob
__pyx_v_ierr = nc_inq_var_fletcher32(__pyx_v_self->_grpid, __pyx_v_self->_varid, (&__pyx_v_ifletcher32));
}
- /* "netCDF4/_netCDF4.pyx":3028
+ /* "netCDF4/_netCDF4.pyx":3139
* if ierr != NC_NOERR:
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
* with nogil: # <<<<<<<<<<<<<<
@@ -28490,7 +28640,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_ob
}
}
- /* "netCDF4/_netCDF4.pyx":3030
+ /* "netCDF4/_netCDF4.pyx":3141
* with nogil:
* ierr = nc_inq_var_fletcher32(self._grpid, self._varid, &ifletcher32)
* if ierr != NC_NOERR: # <<<<<<<<<<<<<<
@@ -28500,7 +28650,7 @@ static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_ob
__pyx_t_3 = ((__pyx_v_ierr != NC_NOERR) != 0);
if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":3031
+ /* "netCDF4/_netCDF4.pyx":3142
* ierr = nc_inq_var_fletcher32(self._grpid, self._varid, &ifletcher32)
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*/
__pyx_t_4 = ((char *)nc_strerror(__pyx_v_ierr));
- __pyx_t_1 = __Pyx_decode_c_string(__pyx_t_4, 0, strlen(__pyx_t_4), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3031; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_decode_c_string(__pyx_t_4, 0, strlen(__pyx_t_4), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
- __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3031; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_5);
__Pyx_INCREF(__pyx_t_1);
PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_1);
__Pyx_GIVEREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_t_5, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3031; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_t_5, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
__Pyx_Raise(__pyx_t_1, 0, 0, 0);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3031; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
- /* "netCDF4/_netCDF4.pyx":3032
+ /* "netCDF4/_netCDF4.pyx":3143
* if ierr != NC_NOERR:
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* if ideflate: # <<<<<<<<<<<<<<
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__pyx_t_3 = (__pyx_v_ideflate != 0);
if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":3033
+ /* "netCDF4/_netCDF4.pyx":3144
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* if ishuffle:
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+ if (unlikely(PyDict_SetItem(__pyx_v_filtdict, __pyx_n_s_zlib, Py_True) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3144; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_ideflate_level); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3145; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ if (unlikely(PyDict_SetItem(__pyx_v_filtdict, __pyx_n_s_complevel, __pyx_t_1) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3145; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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* if ishuffle: # <<<<<<<<<<<<<<
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__pyx_t_3 = (__pyx_v_ishuffle != 0);
if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":3036
+ /* "netCDF4/_netCDF4.pyx":3147
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*/
- if (unlikely(PyDict_SetItem(__pyx_v_filtdict, __pyx_n_s_shuffle, Py_True) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3036; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(PyDict_SetItem(__pyx_v_filtdict, __pyx_n_s_shuffle, Py_True) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
goto __pyx_L15;
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__pyx_L15:;
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__pyx_t_3 = (__pyx_v_ifletcher32 != 0);
if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":3038
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*/
- if (unlikely(PyDict_SetItem(__pyx_v_filtdict, __pyx_n_s_fletcher32, Py_True) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3038; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(PyDict_SetItem(__pyx_v_filtdict, __pyx_n_s_fletcher32, Py_True) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3149; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
goto __pyx_L16;
}
__pyx_L16:;
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goto __pyx_L0;
- /* "netCDF4/_netCDF4.pyx":3016
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*
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*/
/* function exit code */
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return __pyx_r;
}
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*
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*/
/* Python wrapper */
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_8Variable_27endian(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
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static PyObject *__pyx_pw_7netCDF4_8_netCDF4_8Variable_27endian(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
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*/
- __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_self->_grp, __pyx_n_s_data_model); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3047; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_self->_grp, __pyx_n_s_data_model); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3158; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
- __pyx_t_3 = (__Pyx_PyString_Equals(__pyx_t_1, __pyx_n_s_NETCDF4_CLASSIC, Py_NE)); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3047; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_3 = (__Pyx_PyString_Equals(__pyx_t_1, __pyx_n_s_NETCDF4_CLASSIC, Py_NE)); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3158; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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goto __pyx_L4_bool_binop_done;
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+ /* "netCDF4/_netCDF4.pyx":3159
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goto __pyx_L0;
}
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+ /* "netCDF4/_netCDF4.pyx":3160
* if self._grp.data_model not in ['NETCDF4_CLASSIC','NETCDF4']:
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#endif
/*try:*/ {
- /* "netCDF4/_netCDF4.pyx":3050
+ /* "netCDF4/_netCDF4.pyx":3161
* return 'native'
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}
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+ /* "netCDF4/_netCDF4.pyx":3160
* if self._grp.data_model not in ['NETCDF4_CLASSIC','NETCDF4']:
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}
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*/
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- /* "netCDF4/_netCDF4.pyx":3053
+ /* "netCDF4/_netCDF4.pyx":3164
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*/
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+ /* "netCDF4/_netCDF4.pyx":3165
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goto __pyx_L0;
break;
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*/
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break;
default:
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+ /* "netCDF4/_netCDF4.pyx":3169
* return 'big'
* else:
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break;
}
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+ /* "netCDF4/_netCDF4.pyx":3152
* return filtdict
*
* def endian(self): # <<<<<<<<<<<<<<
* """
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+ * **`endian(self)`**
*/
/* function exit code */
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return __pyx_r;
}
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* return 'native'
*
* def chunking(self): # <<<<<<<<<<<<<<
* """
- * chunking(self)
+ * **`chunking(self)`**
*/
/* Python wrapper */
static PyObject *__pyx_pw_7netCDF4_8_netCDF4_8Variable_29chunking(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
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+ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_self->_grp, __pyx_n_s_data_model); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3181; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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#endif
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+ /* "netCDF4/_netCDF4.pyx":3185
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}
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*/
free(__pyx_v_chunksizesp);
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__pyx_t_3 = (__pyx_v_icontiguous != 0);
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}
/*else*/ {
- /* "netCDF4/_netCDF4.pyx":3084
+ /* "netCDF4/_netCDF4.pyx":3195
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goto __pyx_L0;
}
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+ /* "netCDF4/_netCDF4.pyx":3171
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*
* def chunking(self): # <<<<<<<<<<<<<<
* """
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*/
/* function exit code */
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return __pyx_r;
}
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*
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int __pyx_clineno = 0;
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#endif
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+ /* "netCDF4/_netCDF4.pyx":3208
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*
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/* function exit code */
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return __pyx_r;
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static PyObject *__pyx_pw_7netCDF4_8_netCDF4_8Variable_33set_var_chunk_cache(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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__pyx_v_preemption_orig = __pyx_t_4;
__pyx_t_4 = 0;
- /* "netCDF4/_netCDF4.pyx":3116
+ /* "netCDF4/_netCDF4.pyx":3227
* # reset chunk cache size, leave other parameters unchanged.
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__pyx_t_8 = (__pyx_t_7 != 0);
if (__pyx_t_8) {
- /* "netCDF4/_netCDF4.pyx":3117
+ /* "netCDF4/_netCDF4.pyx":3228
* size_orig, nelems_orig, preemption_orig = self.get_var_chunk_cache()
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__pyx_v_sizep = __pyx_t_9;
goto __pyx_L5;
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/*else*/ {
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+ /* "netCDF4/_netCDF4.pyx":3230
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__pyx_L5:;
- /* "netCDF4/_netCDF4.pyx":3120
+ /* "netCDF4/_netCDF4.pyx":3231
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__pyx_t_7 = (__pyx_t_8 != 0);
if (__pyx_t_7) {
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+ /* "netCDF4/_netCDF4.pyx":3232
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__pyx_v_nelemsp = __pyx_t_9;
goto __pyx_L6;
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/*else*/ {
- /* "netCDF4/_netCDF4.pyx":3123
+ /* "netCDF4/_netCDF4.pyx":3234
* nelemsp = nelems
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* nelemsp = nelems_orig # <<<<<<<<<<<<<<
* if preemption is not None:
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+ /* "netCDF4/_netCDF4.pyx":3235
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__pyx_t_8 = (__pyx_t_7 != 0);
if (__pyx_t_8) {
- /* "netCDF4/_netCDF4.pyx":3125
+ /* "netCDF4/_netCDF4.pyx":3236
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goto __pyx_L7;
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/*else*/ {
- /* "netCDF4/_netCDF4.pyx":3127
+ /* "netCDF4/_netCDF4.pyx":3238
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+ /* "netCDF4/_netCDF4.pyx":3239
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__Pyx_INCREF(__pyx_t_1);
PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1);
__Pyx_GIVEREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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+ /* "netCDF4/_netCDF4.pyx":3215
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return __pyx_r;
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-/* "netCDF4/_netCDF4.pyx":3133
+/* "netCDF4/_netCDF4.pyx":3244
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__Pyx_RefNannySetupContext("__delattr__", 0);
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3179; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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if (__pyx_t_11) {
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__pyx_t_14 = __pyx_t_11;
goto __pyx_L48_bool_binop_done;
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goto __pyx_L52_bool_binop_done;
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__pyx_L50_bool_binop_done:;
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PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __pyx_t_3 = NULL;
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__Pyx_GIVEREF(__pyx_v_self->dtype);
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__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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__pyx_L49_bool_binop_done:;
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}
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}
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__pyx_v_databuff = __pyx_t_14;
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+ /* "netCDF4/_netCDF4.pyx":3923
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PyTuple_SET_ITEM(__pyx_t_1, 2+__pyx_t_9, __pyx_v_offsets);
__Pyx_GIVEREF(__pyx_v_offsets);
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+ __pyx_t_10 = Py_TYPE(__pyx_t_6)->tp_iternext; if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4155; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_6, __pyx_t_9); __Pyx_INCREF(__pyx_t_7); __pyx_t_9++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4155; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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if (exc_type) {
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- else {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4043; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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if (size > 3) __Pyx_RaiseTooManyValuesError(3);
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4043; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_t_8 = 0;
__pyx_v_offset = __pyx_t_3;
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__Pyx_DECREF_SET(__pyx_v_bytestr, __pyx_t_7);
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__Pyx_XDECREF(__pyx_t_12); __pyx_t_12 = 0;
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__Pyx_GOTREF(__pyx_t_12);
__Pyx_GOTREF(__pyx_t_8);
__Pyx_GOTREF(__pyx_t_7);
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__Pyx_Raise(__pyx_t_1, 0, 0, 0);
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4050; __pyx_clineno = __LINE__; goto __pyx_L15_except_error;}
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__Pyx_RefNannySetupContext("_find_cmptype", 0);
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__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4097; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_2 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4209; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_GOTREF(__pyx_t_7);
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*/
__pyx_v_match = 1;
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__Pyx_GOTREF(__pyx_t_1);
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+ __pyx_t_1 = __pyx_f_7netCDF4_8_netCDF4__find_cmptype(__pyx_v_parent_grp, __pyx_v_dtype); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4227; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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}
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/*try:*/ {
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}
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}
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if (__pyx_t_3) {
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__Pyx_XDECREF_SET(__pyx_v_field_shape, __pyx_empty_tuple);
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__Pyx_GOTREF(__pyx_t_1);
__Pyx_GOTREF(__pyx_t_8);
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- /* "netCDF4/_netCDF4.pyx":4271
+ /* "netCDF4/_netCDF4.pyx":4382
* ierr = nc_def_vlen(grp._grpid, namstring, xtype_tmp, &xtype);
* if ierr != NC_NOERR:
* raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii')) # <<<<<<<<<<<<<<
@@ -46568,43 +46762,43 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__def_vlen(PyObject *__pyx_v_grp, PyO
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- /* "netCDF4/_netCDF4.pyx":4273
+ /* "netCDF4/_netCDF4.pyx":4384
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*
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__Pyx_Raise(__pyx_t_5, 0, 0, 0);
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+ /* "netCDF4/_netCDF4.pyx":4385
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PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_5);
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__pyx_t_6 = 0;
goto __pyx_L0;
- /* "netCDF4/_netCDF4.pyx":4249
+ /* "netCDF4/_netCDF4.pyx":4360
* (self.name, self.dtype)
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return __pyx_r;
}
-/* "netCDF4/_netCDF4.pyx":4276
+/* "netCDF4/_netCDF4.pyx":4387
* return xtype, dt
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}
}
- /* "netCDF4/_netCDF4.pyx":4285
+ /* "netCDF4/_netCDF4.pyx":4396
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+ /* "netCDF4/_netCDF4.pyx":4397
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__pyx_t_3 = ((__pyx_v_xtype == NC_STRING) != 0);
if (__pyx_t_3) {
- /* "netCDF4/_netCDF4.pyx":4287
+ /* "netCDF4/_netCDF4.pyx":4398
* _grpid = group._grpid
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* dt = str # <<<<<<<<<<<<<<
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__pyx_v_dt = ((PyObject *)((PyObject*)(&PyString_Type)));
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+ /* "netCDF4/_netCDF4.pyx":4399
* if xtype == NC_STRING:
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}
/*else*/ {
- /* "netCDF4/_netCDF4.pyx":4290
+ /* "netCDF4/_netCDF4.pyx":4401
* name = None
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@@ -46754,7 +46948,7 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__read_vlen(PyObject *__pyx_v_group,
#endif
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+ /* "netCDF4/_netCDF4.pyx":4402
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}
- /* "netCDF4/_netCDF4.pyx":4290
+ /* "netCDF4/_netCDF4.pyx":4401
* name = None
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}
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+ /* "netCDF4/_netCDF4.pyx":4403
* with nogil:
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+ /* "netCDF4/_netCDF4.pyx":4404
* ierr = nc_inq_vlen(_grpid, xtype, vl_namstring, &vlsize, &base_xtype)
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@@ -46844,7 +47038,7 @@ static PyObject *__pyx_f_7netCDF4_8_netCDF4__read_vlen(PyObject *__pyx_v_group,
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__pyx_t_1 = 0;
- /* "netCDF4/_netCDF4.pyx":4295
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PyTuple_SET_ITEM(__pyx_t_7, 0+1, __pyx_v_datatype);
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__Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0;
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__Pyx_GOTREF(__pyx_t_7);
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__pyx_L3:;
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return __pyx_r;
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__Pyx_DECREF_SET(__pyx_v_encoding, __pyx_t_3);
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PyTuple_SET_ITEM(__pyx_t_9, 0+1, __pyx_v_encoding);
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__Pyx_DECREF_SET(__pyx_v_dates, __pyx_t_1);
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+ __pyx_t_5 = __Pyx_PyObject_IsTrue(__pyx_v_time); if (unlikely(__pyx_t_5 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4655; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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/*else*/ {
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+ /* "netCDF4/_netCDF4.pyx":4688
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goto __pyx_L0;
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return __pyx_r;
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*/
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__Pyx_XDECREF_SET(__pyx_v_value, __pyx_t_7);
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if (size > 2) __Pyx_RaiseTooManyValuesError(2);
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+ __pyx_t_8 = __Pyx_PyObject_Append(__pyx_t_6, __pyx_t_4); if (unlikely(__pyx_t_8 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4934; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_XDECREF_SET(__pyx_v_value, __pyx_t_6);
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__Pyx_XDECREF_SET(__pyx_v_t, __pyx_t_3);
__pyx_t_3 = 0;
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- __pyx_t_9 = PyObject_HasAttr(__pyx_v_t, __pyx_n_s_calendar); if (unlikely(__pyx_t_9 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5095; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_9 = PyObject_HasAttr(__pyx_v_t, __pyx_n_s_calendar); if (unlikely(__pyx_t_9 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5216; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_t_10 = ((!(__pyx_t_9 != 0)) != 0);
if (__pyx_t_10) {
- /* "netCDF4/_netCDF4.pyx":5096
+ /* "netCDF4/_netCDF4.pyx":5217
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__Pyx_Raise(__pyx_t_3, 0, 0, 0);
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5096; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ /* "netCDF4/_netCDF4.pyx":5215
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* # make sure calendar attribute present in all files.
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+ __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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* """
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* """
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/* function exit code */
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return __pyx_r;
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- p->keepweakref = Py_None; Py_INCREF(Py_None);
p->__orthogonal_indexing__ = Py_None; Py_INCREF(Py_None);
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Py_CLEAR(p->cmptypes);
Py_CLEAR(p->vltypes);
- Py_CLEAR(p->keepweakref);
Py_CLEAR(p->__orthogonal_indexing__);
+ Py_CLEAR(p->keepweakref);
(*Py_TYPE(o)->tp_free)(o);
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- }
if (p->__orthogonal_indexing__) {
e = (*v)(p->__orthogonal_indexing__, a); if (e) return e;
}
+ if (p->keepweakref) {
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+ }
return 0;
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tmp = ((PyObject*)p->__orthogonal_indexing__);
p->__orthogonal_indexing__ = Py_None; Py_INCREF(Py_None);
Py_XDECREF(tmp);
+ tmp = ((PyObject*)p->keepweakref);
+ p->keepweakref = Py_None; Py_INCREF(Py_None);
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}
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if (v) {
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}
else {
- return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11keepweakref_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_23__orthogonal_indexing___5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset___orthogonal_indexing__(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_23__orthogonal_indexing___1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_keepweakref(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11keepweakref_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset___orthogonal_indexing__(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_keepweakref(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_23__orthogonal_indexing___3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11keepweakref_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_23__orthogonal_indexing___5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11keepweakref_5__del__(o);
}
}
@@ -60811,8 +61039,8 @@ static struct PyGetSetDef __pyx_getsets_7netCDF4_8_netCDF4_Dataset[] = {
{(char *)"data_model", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_data_model, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_data_model, 0, 0},
{(char *)"cmptypes", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_cmptypes, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_cmptypes, 0, 0},
{(char *)"vltypes", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_vltypes, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_vltypes, 0, 0},
- {(char *)"keepweakref", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_keepweakref, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_keepweakref, 0, 0},
{(char *)"__orthogonal_indexing__", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset___orthogonal_indexing__, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset___orthogonal_indexing__, 0, 0},
+ {(char *)"keepweakref", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_keepweakref, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_keepweakref, 0, 0},
{0, 0, 0, 0, 0}
};
@@ -60860,7 +61088,7 @@ static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_Dataset = {
__pyx_tp_setattro_7netCDF4_8_netCDF4_Dataset, /*tp_setattro*/
0, /*tp_as_buffer*/
Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
- "\nDataset(self, filename, mode=\"r\", clobber=True, diskless=False, persist=False, keepweakref=False, format='NETCDF4')\n\nA netCDF L{Dataset} is a collection of dimensions, groups, variables and\nattributes. Together they describe the meaning of data and relations among\ndata fields stored in a netCDF file.\n\nB{Parameters:}\n\nB{C{filename}} - Name of netCDF file to hold dataset.\n\nB{Keywords}:\n\nB{C{mode}} - access mode. C{r} means read-only; no data can be\nmodified. C{w} means [...]
+ "\nA netCDF `netCDF4.Dataset` is a collection of dimensions, groups, variables and\nattributes. Together they describe the meaning of data and relations among\ndata fields stored in a netCDF file. See `netCDF4.Dataset.__init__` for more\ndetails.\n\nA list of attribute names corresponding to global netCDF attributes\ndefined for the `netCDF4.Dataset` can be obtained with the\n`netCDF4.Dataset.ncattrs` method.\nThese attributes can be created by assigning to an attribute of the\n`netCDF [...]
__pyx_tp_traverse_7netCDF4_8_netCDF4_Dataset, /*tp_traverse*/
__pyx_tp_clear_7netCDF4_8_netCDF4_Dataset, /*tp_clear*/
0, /*tp_richcompare*/
@@ -60952,7 +61180,7 @@ static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_Group = {
0, /*tp_setattro*/
0, /*tp_as_buffer*/
Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
- "\nGroup(self, parent, name)\n\nGroups define a hierarchical namespace within a netCDF file. They are\nanalagous to directories in a unix filesystem. Each L{Group} behaves like\na L{Dataset} within a Dataset, and can contain it's own variables,\ndimensions and attributes (and other Groups).\n\nL{Group} instances should be created using the\nL{createGroup<Dataset.createGroup>} method of a L{Dataset} instance, or\nanother L{Group} instance, not using this class directly.\n\nB{Parameters: [...]
+ "\nGroups define a hierarchical namespace within a netCDF file. They are\nanalagous to directories in a unix filesystem. Each `netCDF4.Group` behaves like\na `netCDF4.Dataset` within a Dataset, and can contain it's own variables,\ndimensions and attributes (and other Groups). See `netCDF4.Group.__init__`\nfor more details.\n\n`netCDF4.Group` inherits from `netCDF4.Dataset`, so all the \n`netCDF4.Dataset` class methods and variables are available\nto a `netCDF4.Group` instance (except t [...]
__pyx_tp_traverse_7netCDF4_8_netCDF4_Dataset, /*tp_traverse*/
__pyx_tp_clear_7netCDF4_8_netCDF4_Dataset, /*tp_clear*/
0, /*tp_richcompare*/
@@ -61187,7 +61415,7 @@ static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_Dimension = {
0, /*tp_setattro*/
0, /*tp_as_buffer*/
Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
- "\nDimension(self, group, name, size=None)\n\nA netCDF L{Dimension} is used to describe the coordinates of a L{Variable}.\n\nL{Dimension} instances should be created using the\nL{createDimension<Dataset.createDimension>} method of a L{Group} or\nL{Dataset} instance, not using this class directly.\n\nB{Parameters:}\n\nB{C{group}} - L{Group} instance to associate with dimension.\n\nB{C{name}} - Name of the dimension.\n\nB{Keywords:}\n\nB{C{size}} - Size of the dimension. C{None} or 0 m [...]
+ "\nA netCDF `netCDF4.Dimension` is used to describe the coordinates of a `netCDF4.Variable`.\nSee `netCDF4.Dimension.__init__` for more details.\n\nThe current maximum size of a `netCDF4.Dimension` instance can be obtained by\ncalling the python `len` function on the `netCDF4.Dimension` instance. The\n`netCDF4.Dimension.isunlimited` method of a `netCDF4.Dimension` instance can be used to\ndetermine if the dimension is unlimited.\n\nRead-only class variables:\n\n**`name`**: String name, [...]
__pyx_tp_traverse_7netCDF4_8_netCDF4_Dimension, /*tp_traverse*/
__pyx_tp_clear_7netCDF4_8_netCDF4_Dimension, /*tp_clear*/
0, /*tp_richcompare*/
@@ -61733,7 +61961,7 @@ static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_Variable = {
__pyx_tp_setattro_7netCDF4_8_netCDF4_Variable, /*tp_setattro*/
0, /*tp_as_buffer*/
Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
- "\nVariable(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)\n\nA netCDF L{Variable} is used to read and write netCDF data. They are\nanalagous to numpy array objects.\n\nL{Variable} instances should be created using the\nL{createVariable<Dataset.createVariable>} method of a L{Dataset} or\nL{Group} instance, not using this class directly [...]
+ "\nA netCDF `netCDF4.Variable` is used to read and write netCDF data. They are\nanalagous to numpy array objects. See `netCDF4.Variable.__init__` for more\ndetails.\n\nA list of attribute names corresponding to netCDF attributes defined for\nthe variable can be obtained with the `netCDF4.Variable.ncattrs` method. These\nattributes can be created by assigning to an attribute of the\n`netCDF4.Variable` instance. A dictionary containing all the netCDF attribute\nname/value pairs is provi [...]
__pyx_tp_traverse_7netCDF4_8_netCDF4_Variable, /*tp_traverse*/
__pyx_tp_clear_7netCDF4_8_netCDF4_Variable, /*tp_clear*/
0, /*tp_richcompare*/
@@ -61894,7 +62122,7 @@ static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_CompoundType = {
0, /*tp_setattro*/
0, /*tp_as_buffer*/
Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
- "\nA L{CompoundType} instance is used to describe a compound data type.\n\nConstructor: C{CompoundType(group, datatype, datatype_name)}\n\n at attention: When creating nested compound data types,\nthe inner compound data types must already be associated with CompoundType\ninstances (so create CompoundType instances for the innermost structures\nfirst).\n\nL{CompoundType} instances should be created using the\nL{createCompoundType<Dataset.createCompoundType>}\nmethod of a Dataset or L{Grou [...]
+ "\nA `netCDF4.CompoundType` instance is used to describe a compound data\ntype, and can be passed to the the `netCDF4.Dataset.createVariable` method of\na `netCDF4.Dataset` or `netCDF4.Group` instance. \nCompound data types map to numpy structured arrays.\nSee `netCDF4.CompoundType.__init__` for more details.\n\nThe instance variables `dtype` and `name` should not be modified by\nthe user.\n", /*tp_doc*/
__pyx_tp_traverse_7netCDF4_8_netCDF4_CompoundType, /*tp_traverse*/
__pyx_tp_clear_7netCDF4_8_netCDF4_CompoundType, /*tp_clear*/
0, /*tp_richcompare*/
@@ -62055,7 +62283,7 @@ static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_VLType = {
0, /*tp_setattro*/
0, /*tp_as_buffer*/
Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
- "\nA L{VLType} instance is used to describe a variable length (VLEN) data type.\n\nConstructor: C{VLType(group, datatype, datatype_name)}\n\nL{VLType} instances should be created using the\nL{createVLType<Dataset.createVLType>}\nmethod of a Dataset or L{Group} instance, not using this class directly.\n\nB{Parameters:}\n\nB{C{group}} - L{Group} instance to associate with the VLEN datatype.\n\nB{C{datatype}} - An numpy dtype object describing a the component type for the\nvariable length [...]
+ "\nA `netCDF4.VLType` instance is used to describe a variable length (VLEN) data\ntype, and can be passed to the the `netCDF4.Dataset.createVariable` method of\na `netCDF4.Dataset` or `netCDF4.Group` instance. See \n`netCDF4.VLType.__init__` for more details.\n\nThe instance variables `dtype` and `name` should not be modified by\nthe user.\n", /*tp_doc*/
__pyx_tp_traverse_7netCDF4_8_netCDF4_VLType, /*tp_traverse*/
__pyx_tp_clear_7netCDF4_8_netCDF4_VLType, /*tp_clear*/
0, /*tp_richcompare*/
@@ -62099,7 +62327,7 @@ static struct PyModuleDef __pyx_moduledef = {
PyModuleDef_HEAD_INIT,
#endif
"_netCDF4",
- __pyx_k_Introduction_Python_interface_t, /* m_doc */
+ __pyx_k_Introduction_netcdf4_python_is, /* m_doc */
-1, /* m_size */
__pyx_methods /* m_methods */,
NULL, /* m_reload */
@@ -62113,19 +62341,48 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_kp_s_, __pyx_k_, sizeof(__pyx_k_), 0, 0, 1, 0},
{&__pyx_kp_s_0m, __pyx_k_0m, sizeof(__pyx_k_0m), 0, 0, 1, 0},
{&__pyx_kp_s_1, __pyx_k_1, sizeof(__pyx_k_1), 0, 0, 1, 0},
- {&__pyx_kp_s_1_1_8, __pyx_k_1_1_8, sizeof(__pyx_k_1_1_8), 0, 0, 1, 0},
+ {&__pyx_kp_s_1_1_9, __pyx_k_1_1_9, sizeof(__pyx_k_1_1_9), 0, 0, 1, 0},
{&__pyx_kp_s_4_2_1, __pyx_k_4_2_1, sizeof(__pyx_k_4_2_1), 0, 0, 1, 0},
{&__pyx_kp_s_4m, __pyx_k_4m, sizeof(__pyx_k_4m), 0, 0, 1, 0},
+ {&__pyx_kp_s_A_numpy_dtype_object_describing, __pyx_k_A_numpy_dtype_object_describing, sizeof(__pyx_k_A_numpy_dtype_object_describing), 0, 0, 1, 0},
+ {&__pyx_kp_s_A_numpy_dtype_object_describing_2, __pyx_k_A_numpy_dtype_object_describing_2, sizeof(__pyx_k_A_numpy_dtype_object_describing_2), 0, 0, 1, 0},
+ {&__pyx_kp_s_A_numpy_dtype_object_describing_3, __pyx_k_A_numpy_dtype_object_describing_3, sizeof(__pyx_k_A_numpy_dtype_object_describing_3), 0, 0, 1, 0},
+ {&__pyx_kp_s_A_string_describing_the_name_of, __pyx_k_A_string_describing_the_name_of, sizeof(__pyx_k_A_string_describing_the_name_of), 0, 0, 1, 0},
+ {&__pyx_kp_s_A_string_describing_the_name_of_2, __pyx_k_A_string_describing_the_name_of_2, sizeof(__pyx_k_A_string_describing_the_name_of_2), 0, 0, 1, 0},
+ {&__pyx_kp_s_A_tuple_containing_the_names_of, __pyx_k_A_tuple_containing_the_names_of, sizeof(__pyx_k_A_tuple_containing_the_names_of), 0, 0, 1, 0},
+ {&__pyx_kp_s_A_tuple_with_the_current_shape_l, __pyx_k_A_tuple_with_the_current_shape_l, sizeof(__pyx_k_A_tuple_with_the_current_shape_l), 0, 0, 1, 0},
+ {&__pyx_kp_s_Always_True_Indicates_to_client, __pyx_k_Always_True_Indicates_to_client, sizeof(__pyx_k_Always_True_Indicates_to_client), 0, 0, 1, 0},
{&__pyx_n_s_AttributeError, __pyx_k_AttributeError, sizeof(__pyx_k_AttributeError), 0, 0, 1, 1},
+ {&__pyx_kp_s_Class_for_reading_multi_file_ne, __pyx_k_Class_for_reading_multi_file_ne, sizeof(__pyx_k_Class_for_reading_multi_file_ne), 0, 0, 1, 0},
+ {&__pyx_kp_s_Class_providing_an_interface_to, __pyx_k_Class_providing_an_interface_to, sizeof(__pyx_k_Class_providing_an_interface_to), 0, 0, 1, 0},
+ {&__pyx_kp_s_CompoundType_dtype, __pyx_k_CompoundType_dtype, sizeof(__pyx_k_CompoundType_dtype), 0, 0, 1, 0},
+ {&__pyx_kp_s_CompoundType_name, __pyx_k_CompoundType_name, sizeof(__pyx_k_CompoundType_name), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_cmptypes, __pyx_k_Dataset_cmptypes, sizeof(__pyx_k_Dataset_cmptypes), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_data_model, __pyx_k_Dataset_data_model, sizeof(__pyx_k_Dataset_data_model), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_dimensions, __pyx_k_Dataset_dimensions, sizeof(__pyx_k_Dataset_dimensions), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_disk_format, __pyx_k_Dataset_disk_format, sizeof(__pyx_k_Dataset_disk_format), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_file_format, __pyx_k_Dataset_file_format, sizeof(__pyx_k_Dataset_file_format), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_groups, __pyx_k_Dataset_groups, sizeof(__pyx_k_Dataset_groups), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_keepweakref, __pyx_k_Dataset_keepweakref, sizeof(__pyx_k_Dataset_keepweakref), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_parent, __pyx_k_Dataset_parent, sizeof(__pyx_k_Dataset_parent), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_path, __pyx_k_Dataset_path, sizeof(__pyx_k_Dataset_path), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_variables, __pyx_k_Dataset_variables, sizeof(__pyx_k_Dataset_variables), 0, 0, 1, 0},
+ {&__pyx_kp_s_Dataset_vltypes, __pyx_k_Dataset_vltypes, sizeof(__pyx_k_Dataset_vltypes), 0, 0, 1, 0},
+ {&__pyx_kp_s_Describes_the_power_of_ten_of_th, __pyx_k_Describes_the_power_of_ten_of_th, sizeof(__pyx_k_Describes_the_power_of_ten_of_th), 0, 0, 1, 0},
{&__pyx_n_s_Dimension, __pyx_k_Dimension, sizeof(__pyx_k_Dimension), 0, 0, 1, 1},
{&__pyx_n_s_Dimension___init, __pyx_k_Dimension___init, sizeof(__pyx_k_Dimension___init), 0, 0, 1, 1},
{&__pyx_n_s_Dimension___len, __pyx_k_Dimension___len, sizeof(__pyx_k_Dimension___len), 0, 0, 1, 1},
{&__pyx_n_s_Dimension___repr, __pyx_k_Dimension___repr, sizeof(__pyx_k_Dimension___repr), 0, 0, 1, 1},
{&__pyx_n_s_Dimension_isunlimited, __pyx_k_Dimension_isunlimited, sizeof(__pyx_k_Dimension_isunlimited), 0, 0, 1, 1},
+ {&__pyx_kp_s_Dimension_name, __pyx_k_Dimension_name, sizeof(__pyx_k_Dimension_name), 0, 0, 1, 0},
{&__pyx_kp_s_Dimension_object_no_longer_valid, __pyx_k_Dimension_object_no_longer_valid, sizeof(__pyx_k_Dimension_object_no_longer_valid), 0, 0, 1, 0},
{&__pyx_n_s_FillValue, __pyx_k_FillValue, sizeof(__pyx_k_FillValue), 0, 0, 1, 1},
{&__pyx_kp_s_FillValue_attribute_must_be_set, __pyx_k_FillValue_attribute_must_be_set, sizeof(__pyx_k_FillValue_attribute_must_be_set), 0, 0, 1, 0},
+ {&__pyx_kp_s_Group_name, __pyx_k_Group_name, sizeof(__pyx_k_Group_name), 0, 0, 1, 0},
+ {&__pyx_n_s_HDF4, __pyx_k_HDF4, sizeof(__pyx_k_HDF4), 0, 0, 1, 1},
{&__pyx_n_s_IOError, __pyx_k_IOError, sizeof(__pyx_k_IOError), 0, 0, 1, 1},
+ {&__pyx_kp_s_If_True_child_Dimension_and_Vari, __pyx_k_If_True_child_Dimension_and_Vari, sizeof(__pyx_k_If_True_child_Dimension_and_Vari), 0, 0, 1, 0},
+ {&__pyx_kp_s_If_True_data_is_automatically_co, __pyx_k_If_True_data_is_automatically_co, sizeof(__pyx_k_If_True_data_is_automatically_co), 0, 0, 1, 0},
{&__pyx_kp_s_Illegal_index, __pyx_k_Illegal_index, sizeof(__pyx_k_Illegal_index), 0, 0, 1, 0},
{&__pyx_n_s_ImportError, __pyx_k_ImportError, sizeof(__pyx_k_ImportError), 0, 0, 1, 1},
{&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1},
@@ -62137,7 +62394,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_MFDataset___setattr, __pyx_k_MFDataset___setattr, sizeof(__pyx_k_MFDataset___setattr), 0, 0, 1, 1},
{&__pyx_n_s_MFDataset_close, __pyx_k_MFDataset_close, sizeof(__pyx_k_MFDataset_close), 0, 0, 1, 1},
{&__pyx_n_s_MFDataset_ncattrs, __pyx_k_MFDataset_ncattrs, sizeof(__pyx_k_MFDataset_ncattrs), 0, 0, 1, 1},
- {&__pyx_kp_s_MFDataset_self_files_check_Fals, __pyx_k_MFDataset_self_files_check_Fals, sizeof(__pyx_k_MFDataset_self_files_check_Fals), 0, 0, 1, 0},
{&__pyx_kp_s_MFNetCDF4_only_works_with_NETCDF, __pyx_k_MFNetCDF4_only_works_with_NETCDF, sizeof(__pyx_k_MFNetCDF4_only_works_with_NETCDF), 0, 0, 1, 0},
{&__pyx_n_s_MFTime, __pyx_k_MFTime, sizeof(__pyx_k_MFTime), 0, 0, 1, 1},
{&__pyx_n_s_MFTime___getitem, __pyx_k_MFTime___getitem, sizeof(__pyx_k_MFTime___getitem), 0, 0, 1, 1},
@@ -62146,7 +62402,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_MFTime__time, __pyx_k_MFTime__time, sizeof(__pyx_k_MFTime__time), 0, 0, 1, 1},
{&__pyx_kp_s_MFTime_requires_that_the_same_ti, __pyx_k_MFTime_requires_that_the_same_ti, sizeof(__pyx_k_MFTime_requires_that_the_same_ti), 0, 0, 1, 0},
{&__pyx_kp_s_MFTime_requires_that_the_time_va, __pyx_k_MFTime_requires_that_the_time_va, sizeof(__pyx_k_MFTime_requires_that_the_time_va), 0, 0, 1, 0},
- {&__pyx_kp_s_MFTime_self_time_units_None_Cla, __pyx_k_MFTime_self_time_units_None_Cla, sizeof(__pyx_k_MFTime_self_time_units_None_Cla), 0, 0, 1, 0},
{&__pyx_n_s_MINYEAR, __pyx_k_MINYEAR, sizeof(__pyx_k_MINYEAR), 0, 0, 1, 1},
{&__pyx_n_s_NC_DISKLESS, __pyx_k_NC_DISKLESS, sizeof(__pyx_k_NC_DISKLESS), 0, 0, 1, 1},
{&__pyx_n_s_NETCDF3, __pyx_k_NETCDF3, sizeof(__pyx_k_NETCDF3), 0, 0, 1, 1},
@@ -62161,29 +62416,49 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_S, __pyx_k_S, sizeof(__pyx_k_S), 0, 0, 1, 1},
{&__pyx_n_s_S1, __pyx_k_S1, sizeof(__pyx_k_S1), 0, 0, 1, 1},
{&__pyx_n_s_StartCountStride, __pyx_k_StartCountStride, sizeof(__pyx_k_StartCountStride), 0, 0, 1, 1},
+ {&__pyx_kp_s_String_name, __pyx_k_String_name, sizeof(__pyx_k_String_name), 0, 0, 1, 0},
+ {&__pyx_kp_s_The_cmptypes_dictionary_maps_the, __pyx_k_The_cmptypes_dictionary_maps_the, sizeof(__pyx_k_The_cmptypes_dictionary_maps_the), 0, 0, 1, 0},
+ {&__pyx_kp_s_The_dimensions_dictionary_maps_t, __pyx_k_The_dimensions_dictionary_maps_t, sizeof(__pyx_k_The_dimensions_dictionary_maps_t), 0, 0, 1, 0},
+ {&__pyx_kp_s_The_groups_dictionary_maps_the_n, __pyx_k_The_groups_dictionary_maps_the_n, sizeof(__pyx_k_The_groups_dictionary_maps_the_n), 0, 0, 1, 0},
+ {&__pyx_kp_s_The_number_of_stored_elements, __pyx_k_The_number_of_stored_elements, sizeof(__pyx_k_The_number_of_stored_elements), 0, 0, 1, 0},
+ {&__pyx_kp_s_The_number_of_variable_dimension, __pyx_k_The_number_of_variable_dimension, sizeof(__pyx_k_The_number_of_variable_dimension), 0, 0, 1, 0},
+ {&__pyx_kp_s_The_variables_dictionary_maps_th, __pyx_k_The_variables_dictionary_maps_th, sizeof(__pyx_k_The_variables_dictionary_maps_th), 0, 0, 1, 0},
+ {&__pyx_kp_s_The_vltypes_dictionary_maps_the, __pyx_k_The_vltypes_dictionary_maps_the, sizeof(__pyx_k_The_vltypes_dictionary_maps_the), 0, 0, 1, 0},
{&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1},
{&__pyx_n_s_U, __pyx_k_U, sizeof(__pyx_k_U), 0, 0, 1, 1},
{&__pyx_n_s_U1, __pyx_k_U1, sizeof(__pyx_k_U1), 0, 0, 1, 1},
{&__pyx_n_s_UNDEFINED, __pyx_k_UNDEFINED, sizeof(__pyx_k_UNDEFINED), 0, 0, 1, 1},
{&__pyx_n_s_UnicodeDecodeError, __pyx_k_UnicodeDecodeError, sizeof(__pyx_k_UnicodeDecodeError), 0, 0, 1, 1},
{&__pyx_kp_s_Unsupported_compound_type_elemen, __pyx_k_Unsupported_compound_type_elemen, sizeof(__pyx_k_Unsupported_compound_type_elemen), 0, 0, 1, 0},
- {&__pyx_kp_s_Users_jsw_python_netcdf4_python, __pyx_k_Users_jsw_python_netcdf4_python, sizeof(__pyx_k_Users_jsw_python_netcdf4_python), 0, 0, 1, 0},
{&__pyx_n_s_V, __pyx_k_V, sizeof(__pyx_k_V), 0, 0, 1, 1},
+ {&__pyx_kp_s_VLType_dtype, __pyx_k_VLType_dtype, sizeof(__pyx_k_VLType_dtype), 0, 0, 1, 0},
+ {&__pyx_kp_s_VLType_name, __pyx_k_VLType_name, sizeof(__pyx_k_VLType_name), 0, 0, 1, 0},
{&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1},
{&__pyx_n_s_Variable, __pyx_k_Variable, sizeof(__pyx_k_Variable), 0, 0, 1, 1},
{&__pyx_n_s_Variable___getattr, __pyx_k_Variable___getattr, sizeof(__pyx_k_Variable___getattr), 0, 0, 1, 1},
{&__pyx_n_s_Variable___getitem, __pyx_k_Variable___getitem, sizeof(__pyx_k_Variable___getitem), 0, 0, 1, 1},
{&__pyx_n_s_Variable___init, __pyx_k_Variable___init, sizeof(__pyx_k_Variable___init), 0, 0, 1, 1},
{&__pyx_n_s_Variable___len, __pyx_k_Variable___len, sizeof(__pyx_k_Variable___len), 0, 0, 1, 1},
+ {&__pyx_kp_s_Variable___orthogonal_indexing, __pyx_k_Variable___orthogonal_indexing, sizeof(__pyx_k_Variable___orthogonal_indexing), 0, 0, 1, 0},
{&__pyx_n_s_Variable___repr, __pyx_k_Variable___repr, sizeof(__pyx_k_Variable___repr), 0, 0, 1, 1},
{&__pyx_n_s_Variable__shape, __pyx_k_Variable__shape, sizeof(__pyx_k_Variable__shape), 0, 0, 1, 1},
+ {&__pyx_kp_s_Variable_datatype, __pyx_k_Variable_datatype, sizeof(__pyx_k_Variable_datatype), 0, 0, 1, 0},
+ {&__pyx_kp_s_Variable_dimensions, __pyx_k_Variable_dimensions, sizeof(__pyx_k_Variable_dimensions), 0, 0, 1, 0},
+ {&__pyx_kp_s_Variable_dtype, __pyx_k_Variable_dtype, sizeof(__pyx_k_Variable_dtype), 0, 0, 1, 0},
+ {&__pyx_kp_s_Variable_least_significant_digit, __pyx_k_Variable_least_significant_digit, sizeof(__pyx_k_Variable_least_significant_digit), 0, 0, 1, 0},
{&__pyx_kp_s_Variable_length_strings_are_only, __pyx_k_Variable_length_strings_are_only, sizeof(__pyx_k_Variable_length_strings_are_only), 0, 0, 1, 0},
+ {&__pyx_kp_s_Variable_mask, __pyx_k_Variable_mask, sizeof(__pyx_k_Variable_mask), 0, 0, 1, 0},
+ {&__pyx_kp_s_Variable_name, __pyx_k_Variable_name, sizeof(__pyx_k_Variable_name), 0, 0, 1, 0},
{&__pyx_n_s_Variable_ncattrs, __pyx_k_Variable_ncattrs, sizeof(__pyx_k_Variable_ncattrs), 0, 0, 1, 1},
+ {&__pyx_kp_s_Variable_ndim, __pyx_k_Variable_ndim, sizeof(__pyx_k_Variable_ndim), 0, 0, 1, 0},
{&__pyx_kp_s_Variable_object_no_longer_valid, __pyx_k_Variable_object_no_longer_valid, sizeof(__pyx_k_Variable_object_no_longer_valid), 0, 0, 1, 0},
+ {&__pyx_kp_s_Variable_scale, __pyx_k_Variable_scale, sizeof(__pyx_k_Variable_scale), 0, 0, 1, 0},
{&__pyx_n_s_Variable_set_auto_maskandscale, __pyx_k_Variable_set_auto_maskandscale, sizeof(__pyx_k_Variable_set_auto_maskandscale), 0, 0, 1, 1},
+ {&__pyx_kp_s_Variable_shape, __pyx_k_Variable_shape, sizeof(__pyx_k_Variable_shape), 0, 0, 1, 0},
+ {&__pyx_kp_s_Variable_size, __pyx_k_Variable_size, sizeof(__pyx_k_Variable_size), 0, 0, 1, 0},
{&__pyx_n_s_Variable_typecode, __pyx_k_Variable_typecode, sizeof(__pyx_k_Variable_typecode), 0, 0, 1, 1},
- {&__pyx_kp_s__118, __pyx_k__118, sizeof(__pyx_k__118), 0, 0, 1, 0},
{&__pyx_kp_s__12, __pyx_k__12, sizeof(__pyx_k__12), 0, 0, 1, 0},
+ {&__pyx_kp_s__120, __pyx_k__120, sizeof(__pyx_k__120), 0, 0, 1, 0},
{&__pyx_kp_s__13, __pyx_k__13, sizeof(__pyx_k__13), 0, 0, 1, 0},
{&__pyx_kp_s__14, __pyx_k__14, sizeof(__pyx_k__14), 0, 0, 1, 0},
{&__pyx_kp_s__17, __pyx_k__17, sizeof(__pyx_k__17), 0, 0, 1, 0},
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{&__pyx_n_s_byteswap, __pyx_k_byteswap, sizeof(__pyx_k_byteswap), 0, 0, 1, 1},
{&__pyx_n_s_calendar, __pyx_k_calendar, sizeof(__pyx_k_calendar), 0, 0, 1, 1},
{&__pyx_kp_s_cannot_assign_fill_value_for_mas, __pyx_k_cannot_assign_fill_value_for_mas, sizeof(__pyx_k_cannot_assign_fill_value_for_mas), 0, 0, 1, 0},
- {&__pyx_kp_s_cannot_close_a_L_Group_only_appl, __pyx_k_cannot_close_a_L_Group_only_appl, sizeof(__pyx_k_cannot_close_a_L_Group_only_appl), 0, 0, 1, 0},
+ {&__pyx_kp_s_cannot_close_a_netCDF4_Group_onl, __pyx_k_cannot_close_a_netCDF4_Group_onl, sizeof(__pyx_k_cannot_close_a_netCDF4_Group_onl), 0, 0, 1, 0},
{&__pyx_kp_s_cannot_find_compound_type_in_thi, __pyx_k_cannot_find_compound_type_in_thi, sizeof(__pyx_k_cannot_find_compound_type_in_thi), 0, 0, 1, 0},
{&__pyx_kp_s_cannot_set__FillValue_attribute, __pyx_k_cannot_set__FillValue_attribute, sizeof(__pyx_k_cannot_set__FillValue_attribute), 0, 0, 1, 0},
{&__pyx_kp_s_cannot_specify_chunksizes_for_a, __pyx_k_cannot_specify_chunksizes_for_a, sizeof(__pyx_k_cannot_specify_chunksizes_for_a), 0, 0, 1, 0},
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{&__pyx_n_s_data, __pyx_k_data, sizeof(__pyx_k_data), 0, 0, 1, 1},
{&__pyx_n_s_data_model, __pyx_k_data_model, sizeof(__pyx_k_data_model), 0, 0, 1, 1},
{&__pyx_n_s_data_model_2, __pyx_k_data_model_2, sizeof(__pyx_k_data_model_2), 0, 0, 1, 1},
+ {&__pyx_kp_s_data_model_describes_the_netCDF, __pyx_k_data_model_describes_the_netCDF, sizeof(__pyx_k_data_model_describes_the_netCDF), 0, 0, 1, 0},
{&__pyx_kp_s_data_to_put_in_string_variable_m, __pyx_k_data_to_put_in_string_variable_m, sizeof(__pyx_k_data_to_put_in_string_variable_m), 0, 0, 1, 0},
{&__pyx_n_s_datashape, __pyx_k_datashape, sizeof(__pyx_k_datashape), 0, 0, 1, 1},
{&__pyx_n_s_datatype, __pyx_k_datatype, sizeof(__pyx_k_datatype), 0, 0, 1, 1},
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{&__pyx_n_s_dimtotlen, __pyx_k_dimtotlen, sizeof(__pyx_k_dimtotlen), 0, 0, 1, 1},
{&__pyx_n_s_disk_format, __pyx_k_disk_format, sizeof(__pyx_k_disk_format), 0, 0, 1, 1},
{&__pyx_n_s_disk_format_2, __pyx_k_disk_format_2, sizeof(__pyx_k_disk_format_2), 0, 0, 1, 1},
+ {&__pyx_kp_s_disk_format_describes_the_under, __pyx_k_disk_format_describes_the_under, sizeof(__pyx_k_disk_format_describes_the_under), 0, 0, 1, 0},
{&__pyx_n_s_diskless, __pyx_k_diskless, sizeof(__pyx_k_diskless), 0, 0, 1, 1},
{&__pyx_kp_s_diskless_mode_requires_netcdf_li, __pyx_k_diskless_mode_requires_netcdf_li, sizeof(__pyx_k_diskless_mode_requires_netcdf_li), 0, 0, 1, 0},
{&__pyx_n_s_doc, __pyx_k_doc, sizeof(__pyx_k_doc), 0, 0, 1, 1},
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{&__pyx_n_s_i8, __pyx_k_i8, sizeof(__pyx_k_i8), 0, 0, 1, 1},
{&__pyx_n_s_id, __pyx_k_id, sizeof(__pyx_k_id), 0, 0, 1, 1},
{&__pyx_n_s_idx, __pyx_k_idx, sizeof(__pyx_k_idx), 0, 0, 1, 1},
+ {&__pyx_kp_s_if_True_scale_factor_and_add_off, __pyx_k_if_True_scale_factor_and_add_off, sizeof(__pyx_k_if_True_scale_factor_and_add_off), 0, 0, 1, 0},
{&__pyx_kp_s_illegal_data_type_for_attribute, __pyx_k_illegal_data_type_for_attribute, sizeof(__pyx_k_illegal_data_type_for_attribute), 0, 0, 1, 0},
{&__pyx_kp_s_illegal_primitive_data_type_must, __pyx_k_illegal_primitive_data_type_must, sizeof(__pyx_k_illegal_primitive_data_type_must), 0, 0, 1, 0},
{&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1},
@@ -62411,6 +62689,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_items, __pyx_k_items, sizeof(__pyx_k_items), 0, 0, 1, 1},
{&__pyx_n_s_itemsize, __pyx_k_itemsize, sizeof(__pyx_k_itemsize), 0, 0, 1, 1},
{&__pyx_n_s_itertools, __pyx_k_itertools, sizeof(__pyx_k_itertools), 0, 0, 1, 1},
+ {&__pyx_n_s_iu, __pyx_k_iu, sizeof(__pyx_k_iu), 0, 0, 1, 1},
{&__pyx_n_s_izip, __pyx_k_izip, sizeof(__pyx_k_izip), 0, 0, 1, 1},
{&__pyx_n_s_join, __pyx_k_join, sizeof(__pyx_k_join), 0, 0, 1, 1},
{&__pyx_n_s_k, __pyx_k_k, sizeof(__pyx_k_k), 0, 0, 1, 1},
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{&__pyx_n_s_kind, __pyx_k_kind, sizeof(__pyx_k_kind), 0, 0, 1, 1},
{&__pyx_n_s_least_significant_digit, __pyx_k_least_significant_digit, sizeof(__pyx_k_least_significant_digit), 0, 0, 1, 1},
{&__pyx_n_s_len, __pyx_k_len, sizeof(__pyx_k_len), 0, 0, 1, 1},
+ {&__pyx_kp_s_len_of_unsized_object, __pyx_k_len_of_unsized_object, sizeof(__pyx_k_len_of_unsized_object), 0, 0, 1, 0},
{&__pyx_n_s_little, __pyx_k_little, sizeof(__pyx_k_little), 0, 0, 1, 1},
{&__pyx_n_s_lower, __pyx_k_lower, sizeof(__pyx_k_lower), 0, 0, 1, 1},
{&__pyx_n_s_lst, __pyx_k_lst, sizeof(__pyx_k_lst), 0, 0, 1, 1},
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{&__pyx_kp_s_negative_strides_not_allowed_whe, __pyx_k_negative_strides_not_allowed_whe, sizeof(__pyx_k_negative_strides_not_allowed_whe), 0, 0, 1, 0},
{&__pyx_n_s_nelems, __pyx_k_nelems, sizeof(__pyx_k_nelems), 0, 0, 1, 1},
{&__pyx_n_s_netCDF4__netCDF4, __pyx_k_netCDF4__netCDF4, sizeof(__pyx_k_netCDF4__netCDF4), 0, 0, 1, 1},
+ {&__pyx_kp_s_net_bio_c2sm_hvwaldow_git_netcd, __pyx_k_net_bio_c2sm_hvwaldow_git_netcd, sizeof(__pyx_k_net_bio_c2sm_hvwaldow_git_netcd), 0, 0, 1, 0},
{&__pyx_n_s_netcdf4libversion, __pyx_k_netcdf4libversion, sizeof(__pyx_k_netcdf4libversion), 0, 0, 1, 1},
{&__pyx_n_s_netcdftime, __pyx_k_netcdftime, sizeof(__pyx_k_netcdftime), 0, 0, 1, 1},
{&__pyx_n_s_newSlice, __pyx_k_newSlice, sizeof(__pyx_k_newSlice), 0, 0, 1, 1},
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{&__pyx_n_s_num, __pyx_k_num, sizeof(__pyx_k_num), 0, 0, 1, 1},
{&__pyx_n_s_num2date, __pyx_k_num2date, sizeof(__pyx_k_num2date), 0, 0, 1, 1},
{&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1},
+ {&__pyx_kp_s_numpy_data_type_for_primitive_da_2, __pyx_k_numpy_data_type_for_primitive_da_2, sizeof(__pyx_k_numpy_data_type_for_primitive_da_2), 0, 0, 1, 0},
{&__pyx_n_s_numv, __pyx_k_numv, sizeof(__pyx_k_numv), 0, 0, 1, 1},
{&__pyx_n_s_nunlimdim, __pyx_k_nunlimdim, sizeof(__pyx_k_nunlimdim), 0, 0, 1, 1},
{&__pyx_n_s_nv, __pyx_k_nv, sizeof(__pyx_k_nv), 0, 0, 1, 1},
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{&__pyx_n_s_orthogoral_indexing, __pyx_k_orthogoral_indexing, sizeof(__pyx_k_orthogoral_indexing), 0, 0, 1, 1},
{&__pyx_n_s_out_array_shape, __pyx_k_out_array_shape, sizeof(__pyx_k_out_array_shape), 0, 0, 1, 1},
{&__pyx_n_s_parent, __pyx_k_parent, sizeof(__pyx_k_parent), 0, 0, 1, 1},
+ {&__pyx_kp_s_parent_is_a_reference_to_the_pa, __pyx_k_parent_is_a_reference_to_the_pa, sizeof(__pyx_k_parent_is_a_reference_to_the_pa), 0, 0, 1, 0},
{&__pyx_n_s_parse_date, __pyx_k_parse_date, sizeof(__pyx_k_parse_date), 0, 0, 1, 1},
{&__pyx_n_s_part, __pyx_k_part, sizeof(__pyx_k_part), 0, 0, 1, 1},
{&__pyx_n_s_patchstring, __pyx_k_patchstring, sizeof(__pyx_k_patchstring), 0, 0, 1, 1},
{&__pyx_n_s_path, __pyx_k_path, sizeof(__pyx_k_path), 0, 0, 1, 1},
{&__pyx_n_s_path_2, __pyx_k_path_2, sizeof(__pyx_k_path_2), 0, 0, 1, 1},
{&__pyx_kp_s_path_s, __pyx_k_path_s, sizeof(__pyx_k_path_s), 0, 0, 1, 0},
+ {&__pyx_kp_s_path_shows_the_location_of_the, __pyx_k_path_shows_the_location_of_the, sizeof(__pyx_k_path_shows_the_location_of_the), 0, 0, 1, 0},
+ {&__pyx_n_s_pdoc, __pyx_k_pdoc, sizeof(__pyx_k_pdoc), 0, 0, 1, 1},
{&__pyx_n_s_persist, __pyx_k_persist, sizeof(__pyx_k_persist), 0, 0, 1, 1},
{&__pyx_kp_s_please_install_ordereddict_https, __pyx_k_please_install_ordereddict_https, sizeof(__pyx_k_please_install_ordereddict_https), 0, 0, 1, 0},
{&__pyx_n_s_pop, __pyx_k_pop, sizeof(__pyx_k_pop), 0, 0, 1, 1},
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{&__pyx_kp_s_s_s, __pyx_k_s_s, sizeof(__pyx_k_s_s), 0, 0, 1, 0},
{&__pyx_kp_s_s_s_s, __pyx_k_s_s_s, sizeof(__pyx_k_s_s_s), 0, 0, 1, 0},
{&__pyx_kp_s_s_s_s_2, __pyx_k_s_s_s_2, sizeof(__pyx_k_s_s_s_2), 0, 0, 1, 0},
+ {&__pyx_kp_s_same_as_data_model_retained_for, __pyx_k_same_as_data_model_retained_for, sizeof(__pyx_k_same_as_data_model_retained_for), 0, 0, 1, 0},
{&__pyx_n_s_scale, __pyx_k_scale, sizeof(__pyx_k_scale), 0, 0, 1, 1},
{&__pyx_n_s_scale_factor, __pyx_k_scale_factor, sizeof(__pyx_k_scale_factor), 0, 0, 1, 1},
{&__pyx_n_s_sec_units, __pyx_k_sec_units, sizeof(__pyx_k_sec_units), 0, 0, 1, 1},
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static int __Pyx_InitCachedBuiltins(void) {
- __pyx_builtin_ImportError = __Pyx_GetBuiltinName(__pyx_n_s_ImportError); if (!__pyx_builtin_ImportError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 816; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_object = __Pyx_GetBuiltinName(__pyx_n_s_object); if (!__pyx_builtin_object) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4872; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_RuntimeError = __Pyx_GetBuiltinName(__pyx_n_s_RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 937; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_AttributeError = __Pyx_GetBuiltinName(__pyx_n_s_AttributeError); if (!__pyx_builtin_AttributeError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 961; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_KeyError = __Pyx_GetBuiltinName(__pyx_n_s_KeyError); if (!__pyx_builtin_KeyError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 984; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1022; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1108; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_IndexError = __Pyx_GetBuiltinName(__pyx_n_s_IndexError); if (!__pyx_builtin_IndexError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1598; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_IOError = __Pyx_GetBuiltinName(__pyx_n_s_IOError); if (!__pyx_builtin_IOError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2267; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_sum = __Pyx_GetBuiltinName(__pyx_n_s_sum); if (!__pyx_builtin_sum) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3768; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3783; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_GOTREF(__pyx_tuple__4);
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__Pyx_GOTREF(__pyx_tuple__5);
__Pyx_GIVEREF(__pyx_tuple__5);
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+ /* "netCDF4/_netCDF4.pyx":1096
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__Pyx_GOTREF(__pyx_tuple__6);
__Pyx_GIVEREF(__pyx_tuple__6);
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+ __pyx_slice__7 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1097; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__7);
__Pyx_GIVEREF(__pyx_slice__7);
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__Pyx_GOTREF(__pyx_tuple__8);
__Pyx_GIVEREF(__pyx_tuple__8);
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+ /* "netCDF4/_netCDF4.pyx":1125
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+ __pyx_slice__9 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1125; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__9);
__Pyx_GIVEREF(__pyx_slice__9);
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+ /* "netCDF4/_netCDF4.pyx":1126
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__Pyx_GOTREF(__pyx_slice__10);
__Pyx_GIVEREF(__pyx_slice__10);
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__Pyx_GIVEREF(__pyx_slice__11);
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+ /* "netCDF4/_netCDF4.pyx":1656
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+ __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_s__14); if (unlikely(!__pyx_tuple__15)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1656; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__15);
__Pyx_GIVEREF(__pyx_tuple__15);
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__Pyx_GOTREF(__pyx_tuple__16);
__Pyx_GIVEREF(__pyx_tuple__16);
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__Pyx_GOTREF(__pyx_tuple__18);
__Pyx_GIVEREF(__pyx_tuple__18);
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__Pyx_GOTREF(__pyx_tuple__21);
__Pyx_GIVEREF(__pyx_tuple__21);
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__Pyx_GOTREF(__pyx_tuple__24);
__Pyx_GIVEREF(__pyx_tuple__24);
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+ /* "netCDF4/_netCDF4.pyx":2038
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__Pyx_GOTREF(__pyx_tuple__25);
__Pyx_GIVEREF(__pyx_tuple__25);
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+ __pyx_tuple__27 = PyTuple_Pack(1, __pyx_n_s__26); if (unlikely(!__pyx_tuple__27)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2121; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__27);
__Pyx_GIVEREF(__pyx_tuple__27);
- /* "netCDF4/_netCDF4.pyx":2059
+ /* "netCDF4/_netCDF4.pyx":2131
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__Pyx_GOTREF(__pyx_tuple__28);
__Pyx_GIVEREF(__pyx_tuple__28);
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__Pyx_GOTREF(__pyx_tuple__29);
__Pyx_GIVEREF(__pyx_tuple__29);
- /* "netCDF4/_netCDF4.pyx":2267
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+ /* "netCDF4/_netCDF4.pyx":2331
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__Pyx_GOTREF(__pyx_tuple__30);
__Pyx_GIVEREF(__pyx_tuple__30);
- /* "netCDF4/_netCDF4.pyx":2284
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*
*/
- __pyx_tuple__31 = PyTuple_Pack(1, __pyx_kp_s_name_cannot_be_altered); if (unlikely(!__pyx_tuple__31)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2284; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__31 = PyTuple_Pack(1, __pyx_kp_s_name_cannot_be_altered); if (unlikely(!__pyx_tuple__31)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2348; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__31);
__Pyx_GIVEREF(__pyx_tuple__31);
- /* "netCDF4/_netCDF4.pyx":2363
+ /* "netCDF4/_netCDF4.pyx":2432
* return self._getname()
* def __set__(self,value):
* raise AttributeError("name cannot be altered") # <<<<<<<<<<<<<<
*
* def __repr__(self):
*/
- __pyx_tuple__32 = PyTuple_Pack(1, __pyx_kp_s_name_cannot_be_altered); if (unlikely(!__pyx_tuple__32)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2363; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__32 = PyTuple_Pack(1, __pyx_kp_s_name_cannot_be_altered); if (unlikely(!__pyx_tuple__32)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2432; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__32);
__Pyx_GIVEREF(__pyx_tuple__32);
- /* "netCDF4/_netCDF4.pyx":2640
+ /* "netCDF4/_netCDF4.pyx":2751
* if datatype==str:
* if grp.data_model != 'NETCDF4':
* raise ValueError( # <<<<<<<<<<<<<<
* 'Variable length strings are only supported for the '
* 'NETCDF4 format. For other formats, consider using '
*/
- __pyx_tuple__35 = PyTuple_Pack(1, __pyx_kp_s_Variable_length_strings_are_only); if (unlikely(!__pyx_tuple__35)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2640; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__35 = PyTuple_Pack(1, __pyx_kp_s_Variable_length_strings_are_only); if (unlikely(!__pyx_tuple__35)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2751; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__35);
__Pyx_GIVEREF(__pyx_tuple__35);
- /* "netCDF4/_netCDF4.pyx":2650
+ /* "netCDF4/_netCDF4.pyx":2761
* # dtype variable attribute is a numpy datatype object.
* self.dtype = datatype.dtype
* elif datatype.str[1:] in _supportedtypes: # <<<<<<<<<<<<<<
* self._isprimitive = True
* # find netCDF primitive data type corresponding to
*/
- __pyx_slice__36 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2650; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__36 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2761; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__36);
__Pyx_GIVEREF(__pyx_slice__36);
- /* "netCDF4/_netCDF4.pyx":2654
+ /* "netCDF4/_netCDF4.pyx":2765
* # find netCDF primitive data type corresponding to
* # specified numpy data type.
* xtype = _nptonctype[datatype.str[1:]] # <<<<<<<<<<<<<<
* # dtype variable attribute is a numpy datatype object.
* self.dtype = datatype
*/
- __pyx_slice__37 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__37)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__37 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__37)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2765; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__37);
__Pyx_GIVEREF(__pyx_slice__37);
- /* "netCDF4/_netCDF4.pyx":2690
+ /* "netCDF4/_netCDF4.pyx":2801
* # of vars are created. This change only lasts as long as file is
* # open.
* if grp.data_model.startswith('NETCDF4') and chunk_cache is not None: # <<<<<<<<<<<<<<
* ierr = nc_get_var_chunk_cache(self._grpid, self._varid, &sizep,
* &nelemsp, &preemptionp)
*/
- __pyx_tuple__38 = PyTuple_Pack(1, __pyx_n_s_NETCDF4); if (unlikely(!__pyx_tuple__38)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__38 = PyTuple_Pack(1, __pyx_n_s_NETCDF4); if (unlikely(!__pyx_tuple__38)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2801; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__38);
__Pyx_GIVEREF(__pyx_tuple__38);
- /* "netCDF4/_netCDF4.pyx":2732
+ /* "netCDF4/_netCDF4.pyx":2843
* icontiguous = NC_CONTIGUOUS
* if chunksizes is not None:
* raise ValueError('cannot specify chunksizes for a contiguous dataset') # <<<<<<<<<<<<<<
* else:
* icontiguous = NC_CHUNKED
*/
- __pyx_tuple__39 = PyTuple_Pack(1, __pyx_kp_s_cannot_specify_chunksizes_for_a); if (unlikely(!__pyx_tuple__39)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2732; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__39 = PyTuple_Pack(1, __pyx_kp_s_cannot_specify_chunksizes_for_a); if (unlikely(!__pyx_tuple__39)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2843; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__39);
__Pyx_GIVEREF(__pyx_tuple__39);
- /* "netCDF4/_netCDF4.pyx":2740
+ /* "netCDF4/_netCDF4.pyx":2851
* if len(chunksizes) != len(dimensions):
* if grp.data_model != 'NETCDF4': grp._enddef()
* raise ValueError('chunksizes must be a sequence with the same length as dimensions') # <<<<<<<<<<<<<<
* chunksizesp = <size_t *>malloc(sizeof(size_t) * ndims)
* for n from 0 <= n < ndims:
*/
- __pyx_tuple__40 = PyTuple_Pack(1, __pyx_kp_s_chunksizes_must_be_a_sequence_wi); if (unlikely(!__pyx_tuple__40)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2740; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__40 = PyTuple_Pack(1, __pyx_kp_s_chunksizes_must_be_a_sequence_wi); if (unlikely(!__pyx_tuple__40)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2851; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__40);
__Pyx_GIVEREF(__pyx_tuple__40);
- /* "netCDF4/_netCDF4.pyx":2787
+ /* "netCDF4/_netCDF4.pyx":2898
* _set_att(self._grp, self._varid, '_FillValue', fillval)
* else:
* raise AttributeError("cannot set _FillValue attribute for VLEN or compound variable") # <<<<<<<<<<<<<<
* if least_significant_digit is not None:
* self.least_significant_digit = least_significant_digit
*/
- __pyx_tuple__41 = PyTuple_Pack(1, __pyx_kp_s_cannot_set__FillValue_attribute); if (unlikely(!__pyx_tuple__41)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2787; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__41 = PyTuple_Pack(1, __pyx_kp_s_cannot_set__FillValue_attribute); if (unlikely(!__pyx_tuple__41)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2898; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__41);
__Pyx_GIVEREF(__pyx_tuple__41);
- /* "netCDF4/_netCDF4.pyx":2862
+ /* "netCDF4/_netCDF4.pyx":2973
* msg = 'filling on'
* except AttributeError:
* fillval = default_fillvals[self.dtype.str[1:]] # <<<<<<<<<<<<<<
* if self.dtype.str[1:] in ['u1','i1']:
* msg = 'filling on, default _FillValue of %s ignored\n' % fillval
*/
- __pyx_slice__42 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__42)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2862; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__42 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__42)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2973; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__42);
__Pyx_GIVEREF(__pyx_slice__42);
- /* "netCDF4/_netCDF4.pyx":2863
+ /* "netCDF4/_netCDF4.pyx":2974
* except AttributeError:
* fillval = default_fillvals[self.dtype.str[1:]]
* if self.dtype.str[1:] in ['u1','i1']: # <<<<<<<<<<<<<<
* msg = 'filling on, default _FillValue of %s ignored\n' % fillval
* else:
*/
- __pyx_slice__43 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__43)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2863; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__43 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__43)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2974; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__43);
__Pyx_GIVEREF(__pyx_slice__43);
- /* "netCDF4/_netCDF4.pyx":2916
+ /* "netCDF4/_netCDF4.pyx":3027
* return self._getname()
* def __set__(self,value):
* raise AttributeError("name cannot be altered") # <<<<<<<<<<<<<<
*
* property datatype:
*/
- __pyx_tuple__44 = PyTuple_Pack(1, __pyx_kp_s_name_cannot_be_altered); if (unlikely(!__pyx_tuple__44)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2916; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__44 = PyTuple_Pack(1, __pyx_kp_s_name_cannot_be_altered); if (unlikely(!__pyx_tuple__44)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3027; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__44);
__Pyx_GIVEREF(__pyx_tuple__44);
- /* "netCDF4/_netCDF4.pyx":2938
+ /* "netCDF4/_netCDF4.pyx":3049
* return shape
* def __set__(self,value):
* raise AttributeError("shape cannot be altered") # <<<<<<<<<<<<<<
*
* property size:
*/
- __pyx_tuple__45 = PyTuple_Pack(1, __pyx_kp_s_shape_cannot_be_altered); if (unlikely(!__pyx_tuple__45)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2938; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__45 = PyTuple_Pack(1, __pyx_kp_s_shape_cannot_be_altered); if (unlikely(!__pyx_tuple__45)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3049; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__45);
__Pyx_GIVEREF(__pyx_tuple__45);
- /* "netCDF4/_netCDF4.pyx":2950
+ /* "netCDF4/_netCDF4.pyx":3061
* return self._getdims()
* def __set__(self,value):
* raise AttributeError("dimensions cannot be altered") # <<<<<<<<<<<<<<
*
*
*/
- __pyx_tuple__46 = PyTuple_Pack(1, __pyx_kp_s_dimensions_cannot_be_altered); if (unlikely(!__pyx_tuple__46)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2950; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__46 = PyTuple_Pack(1, __pyx_kp_s_dimensions_cannot_be_altered); if (unlikely(!__pyx_tuple__46)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3061; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__46);
__Pyx_GIVEREF(__pyx_tuple__46);
- /* "netCDF4/_netCDF4.pyx":3160
+ /* "netCDF4/_netCDF4.pyx":3271
* value = numpy.array(value, self.dtype)
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* elif not name.endswith('__'): # <<<<<<<<<<<<<<
* if hasattr(self,name):
* raise AttributeError(
*/
- __pyx_tuple__47 = PyTuple_Pack(1, __pyx_n_s__26); if (unlikely(!__pyx_tuple__47)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3160; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__47 = PyTuple_Pack(1, __pyx_n_s__26); if (unlikely(!__pyx_tuple__47)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3271; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__47);
__Pyx_GIVEREF(__pyx_tuple__47);
- /* "netCDF4/_netCDF4.pyx":3170
+ /* "netCDF4/_netCDF4.pyx":3281
* # if name in _private_atts, it is stored at the python
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* if name.startswith('__') and name.endswith('__'): # <<<<<<<<<<<<<<
* # if __dict__ requested, return a dict with netCDF attributes.
* if name == '__dict__':
*/
- __pyx_tuple__48 = PyTuple_Pack(1, __pyx_n_s__26); if (unlikely(!__pyx_tuple__48)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3170; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__48 = PyTuple_Pack(1, __pyx_n_s__26); if (unlikely(!__pyx_tuple__48)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3281; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__48);
__Pyx_GIVEREF(__pyx_tuple__48);
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+ __pyx_tuple__49 = PyTuple_Pack(1, __pyx_n_s__26); if (unlikely(!__pyx_tuple__49)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3281; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__49);
__Pyx_GIVEREF(__pyx_tuple__49);
- /* "netCDF4/_netCDF4.pyx":3218
+ /* "netCDF4/_netCDF4.pyx":3329
* # The convention used is that for those cases,
* # put_ind for this dimension is set to -1 by _StartCountStride.
* squeeze = data.ndim * [slice(None),] # <<<<<<<<<<<<<<
* for i,n in enumerate(put_ind.shape[:-1]):
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*/
- __pyx_slice__50 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__50)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3218; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__50 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__50)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3329; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__50);
__Pyx_GIVEREF(__pyx_slice__50);
- /* "netCDF4/_netCDF4.pyx":3219
+ /* "netCDF4/_netCDF4.pyx":3330
* # put_ind for this dimension is set to -1 by _StartCountStride.
* squeeze = data.ndim * [slice(None),]
* for i,n in enumerate(put_ind.shape[:-1]): # <<<<<<<<<<<<<<
* if n == 1 and put_ind[...,i].ravel()[0] == -1:
* squeeze[i] = 0
*/
- __pyx_slice__51 = PySlice_New(Py_None, __pyx_int_neg_1, Py_None); if (unlikely(!__pyx_slice__51)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__51 = PySlice_New(Py_None, __pyx_int_neg_1, Py_None); if (unlikely(!__pyx_slice__51)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3330; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__51);
__Pyx_GIVEREF(__pyx_slice__51);
- /* "netCDF4/_netCDF4.pyx":3288
+ /* "netCDF4/_netCDF4.pyx":3399
* if (self.endian() == 'big' and is_native_little) or\
* (self.endian() == 'little' and is_native_big):
* mval.byteswap(True) # <<<<<<<<<<<<<<
* if mval.shape == (): # mval a scalar.
* hasmval = data==mval
*/
- __pyx_tuple__52 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__52)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3288; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__52 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__52)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3399; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__52);
__Pyx_GIVEREF(__pyx_tuple__52);
- /* "netCDF4/_netCDF4.pyx":3316
+ /* "netCDF4/_netCDF4.pyx":3427
* if (self.endian() == 'big' and is_native_little) or\
* (self.endian() == 'little' and is_native_big):
* fval.byteswap(True) # <<<<<<<<<<<<<<
* # is _FillValue a NaN?
* try:
*/
- __pyx_tuple__53 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__53)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3316; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__53 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__53)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__53);
__Pyx_GIVEREF(__pyx_tuple__53);
- /* "netCDF4/_netCDF4.pyx":3351
+ /* "netCDF4/_netCDF4.pyx":3462
* # small to assume one of the values should appear as a missing
* # value unless a _FillValue attribute is set explicitly."
* if no_fill != 1 and self.dtype.str[1:] not in ['u1','i1']: # <<<<<<<<<<<<<<
* fillval = numpy.array(default_fillvals[self.dtype.str[1:]],self.dtype)
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*/
- __pyx_slice__54 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__54)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__54 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__54)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__54);
__Pyx_GIVEREF(__pyx_slice__54);
- /* "netCDF4/_netCDF4.pyx":3352
+ /* "netCDF4/_netCDF4.pyx":3463
* # value unless a _FillValue attribute is set explicitly."
* if no_fill != 1 and self.dtype.str[1:] not in ['u1','i1']:
* fillval = numpy.array(default_fillvals[self.dtype.str[1:]],self.dtype) # <<<<<<<<<<<<<<
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* # is not native.
*/
- __pyx_slice__55 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__55)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3352; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__55 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__55)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3463; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__55);
__Pyx_GIVEREF(__pyx_slice__55);
- /* "netCDF4/_netCDF4.pyx":3357
+ /* "netCDF4/_netCDF4.pyx":3468
* if (self.endian() == 'big' and is_native_little) or\
* (self.endian() == 'little' and is_native_big):
* fillval.byteswap(True) # <<<<<<<<<<<<<<
* has_fillval = data == fillval
* # if data is an array scalar, has_fillval will be a boolean.
*/
- __pyx_tuple__56 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__56)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3357; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__56 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__56)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3468; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__56);
__Pyx_GIVEREF(__pyx_tuple__56);
- /* "netCDF4/_netCDF4.pyx":3383
+ /* "netCDF4/_netCDF4.pyx":3494
* cdef ndarray data2
* if not self._isvlen:
* raise TypeError('_assign_vlen method only for use with VLEN variables') # <<<<<<<<<<<<<<
* ndims = self.ndim
* msg="single element VLEN slices must be specified by integers only"
*/
- __pyx_tuple__57 = PyTuple_Pack(1, __pyx_kp_s_assign_vlen_method_only_for_use); if (unlikely(!__pyx_tuple__57)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3383; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__57 = PyTuple_Pack(1, __pyx_kp_s_assign_vlen_method_only_for_use); if (unlikely(!__pyx_tuple__57)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3494; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__57);
__Pyx_GIVEREF(__pyx_tuple__57);
- /* "netCDF4/_netCDF4.pyx":3395
+ /* "netCDF4/_netCDF4.pyx":3506
* elem = self.shape[0]+elem
* else:
* raise IndexError("Illegal index") # <<<<<<<<<<<<<<
* elif isinstance(elem, tuple):
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- __pyx_tuple__58 = PyTuple_Pack(1, __pyx_kp_s_Illegal_index); if (unlikely(!__pyx_tuple__58)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3395; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__58 = PyTuple_Pack(1, __pyx_kp_s_Illegal_index); if (unlikely(!__pyx_tuple__58)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3506; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__58);
__Pyx_GIVEREF(__pyx_tuple__58);
- /* "netCDF4/_netCDF4.pyx":3398
+ /* "netCDF4/_netCDF4.pyx":3509
* elif isinstance(elem, tuple):
* if len(elem) != ndims:
* raise IndexError("Illegal index") # <<<<<<<<<<<<<<
* elemnew = []
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*/
- __pyx_tuple__59 = PyTuple_Pack(1, __pyx_kp_s_Illegal_index); if (unlikely(!__pyx_tuple__59)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3398; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__59 = PyTuple_Pack(1, __pyx_kp_s_Illegal_index); if (unlikely(!__pyx_tuple__59)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3509; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__59);
__Pyx_GIVEREF(__pyx_tuple__59);
- /* "netCDF4/_netCDF4.pyx":3406
+ /* "netCDF4/_netCDF4.pyx":3517
* enew = self.shape[n]+e
* if enew < 0:
* raise IndexError("Illegal index") # <<<<<<<<<<<<<<
* else:
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- __pyx_tuple__60 = PyTuple_Pack(1, __pyx_kp_s_Illegal_index); if (unlikely(!__pyx_tuple__60)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3406; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__60 = PyTuple_Pack(1, __pyx_kp_s_Illegal_index); if (unlikely(!__pyx_tuple__60)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3517; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__60);
__Pyx_GIVEREF(__pyx_tuple__60);
- /* "netCDF4/_netCDF4.pyx":3554
+ /* "netCDF4/_netCDF4.pyx":3665
* fillval = self._FillValue
* else:
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* data = data.filled(fill_value=fillval)
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*/
- __pyx_slice__61 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__61)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__61 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__61)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3665; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__61);
__Pyx_GIVEREF(__pyx_slice__61);
- /* "netCDF4/_netCDF4.pyx":3581
- * Scientific.IO.NetCDF, can also be done by assigning to a slice ([:])."""
+ /* "netCDF4/_netCDF4.pyx":3683
+ * def __len__(self):
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+ * raise TypeError('len() of unsized object') # <<<<<<<<<<<<<<
+ * else:
+ * return self.shape[0]
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+ __pyx_tuple__62 = PyTuple_Pack(1, __pyx_kp_s_len_of_unsized_object); if (unlikely(!__pyx_tuple__62)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3683; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__62);
+ __Pyx_GIVEREF(__pyx_tuple__62);
+
+ /* "netCDF4/_netCDF4.pyx":3695
+ * Scientific.IO.NetCDF, can also be done by assigning to an Ellipsis slice ([...])."""
* if len(self.dimensions):
* raise IndexError('to assign values to a non-scalar variable, use a slice') # <<<<<<<<<<<<<<
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*
*/
- __pyx_tuple__62 = PyTuple_Pack(1, __pyx_kp_s_to_assign_values_to_a_non_scalar); if (unlikely(!__pyx_tuple__62)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3581; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__62);
- __Pyx_GIVEREF(__pyx_tuple__62);
+ __pyx_tuple__63 = PyTuple_Pack(1, __pyx_kp_s_to_assign_values_to_a_non_scalar); if (unlikely(!__pyx_tuple__63)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3695; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__63);
+ __Pyx_GIVEREF(__pyx_tuple__63);
- /* "netCDF4/_netCDF4.pyx":3582
+ /* "netCDF4/_netCDF4.pyx":3696
* if len(self.dimensions):
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* self[:]=val # <<<<<<<<<<<<<<
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- __pyx_slice__63 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__63)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3582; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__63);
- __Pyx_GIVEREF(__pyx_slice__63);
+ __pyx_slice__64 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__64)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3696; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__64);
+ __Pyx_GIVEREF(__pyx_slice__64);
- /* "netCDF4/_netCDF4.pyx":3591
- * Scientific.IO.NetCDF, can also be done by slicing ([:])."""
+ /* "netCDF4/_netCDF4.pyx":3705
+ * Scientific.IO.NetCDF, can also be done by slicing with an Ellipsis ([...])."""
* if len(self.dimensions):
* raise IndexError('to retrieve values from a non-scalar variable, use slicing') # <<<<<<<<<<<<<<
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*/
- __pyx_tuple__64 = PyTuple_Pack(1, __pyx_kp_s_to_retrieve_values_from_a_non_sc); if (unlikely(!__pyx_tuple__64)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3591; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__64);
- __Pyx_GIVEREF(__pyx_tuple__64);
+ __pyx_tuple__65 = PyTuple_Pack(1, __pyx_kp_s_to_retrieve_values_from_a_non_sc); if (unlikely(!__pyx_tuple__65)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3705; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__65);
+ __Pyx_GIVEREF(__pyx_tuple__65);
- /* "netCDF4/_netCDF4.pyx":3592
+ /* "netCDF4/_netCDF4.pyx":3706
* if len(self.dimensions):
* raise IndexError('to retrieve values from a non-scalar variable, use slicing')
* return self[slice(None)] # <<<<<<<<<<<<<<
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* def set_auto_maskandscale(self,maskandscale):
*/
- __pyx_slice__65 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__65)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3592; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__65);
- __Pyx_GIVEREF(__pyx_slice__65);
+ __pyx_slice__66 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__66)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3706; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__66);
+ __Pyx_GIVEREF(__pyx_slice__66);
- /* "netCDF4/_netCDF4.pyx":3730
+ /* "netCDF4/_netCDF4.pyx":3842
* startp[n] = start[n]+stride[n]*(count[n]-1)
* stride[n] = -stride[n]
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- __pyx_slice__66 = PySlice_New(Py_None, Py_None, __pyx_int_neg_1); if (unlikely(!__pyx_slice__66)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3730; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__66);
- __Pyx_GIVEREF(__pyx_slice__66);
+ __pyx_slice__67 = PySlice_New(Py_None, Py_None, __pyx_int_neg_1); if (unlikely(!__pyx_slice__67)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3842; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__67);
+ __Pyx_GIVEREF(__pyx_slice__67);
- /* "netCDF4/_netCDF4.pyx":3734
+ /* "netCDF4/_netCDF4.pyx":3846
* startp[n] = start[n]
* stridep[n] = stride[n]
* sl.append(slice(None,None, 1)) # <<<<<<<<<<<<<<
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*/
- __pyx_slice__67 = PySlice_New(Py_None, Py_None, __pyx_int_1); if (unlikely(!__pyx_slice__67)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3734; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__67);
- __Pyx_GIVEREF(__pyx_slice__67);
+ __pyx_slice__68 = PySlice_New(Py_None, Py_None, __pyx_int_1); if (unlikely(!__pyx_slice__68)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3846; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__68);
+ __Pyx_GIVEREF(__pyx_slice__68);
- /* "netCDF4/_netCDF4.pyx":3740
+ /* "netCDF4/_netCDF4.pyx":3852
* dataelem = PyArray_SIZE(data)
* if totelem != dataelem:
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- __pyx_tuple__68 = PyTuple_Pack(1, __pyx_kp_s_size_of_data_array_does_not_conf); if (unlikely(!__pyx_tuple__68)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3740; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__68);
- __Pyx_GIVEREF(__pyx_tuple__68);
+ __pyx_tuple__69 = PyTuple_Pack(1, __pyx_kp_s_size_of_data_array_does_not_conf); if (unlikely(!__pyx_tuple__69)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3852; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__69);
+ __Pyx_GIVEREF(__pyx_tuple__69);
- /* "netCDF4/_netCDF4.pyx":3754
+ /* "netCDF4/_netCDF4.pyx":3866
* if self.endian() == 'native':
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- __Pyx_GOTREF(__pyx_tuple__69);
- __Pyx_GIVEREF(__pyx_tuple__69);
+ __pyx_tuple__70 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__70)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3866; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__70);
+ __Pyx_GIVEREF(__pyx_tuple__70);
- /* "netCDF4/_netCDF4.pyx":3756
+ /* "netCDF4/_netCDF4.pyx":3868
* data.byteswap(True)
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* data.byteswap(True) # <<<<<<<<<<<<<<
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- __Pyx_GOTREF(__pyx_tuple__70);
- __Pyx_GIVEREF(__pyx_tuple__70);
+ __pyx_tuple__71 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__71)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3868; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__71);
+ __Pyx_GIVEREF(__pyx_tuple__71);
- /* "netCDF4/_netCDF4.pyx":3759
+ /* "netCDF4/_netCDF4.pyx":3871
* if self.endian() == 'big':
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* data.byteswap(True) # <<<<<<<<<<<<<<
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- __pyx_tuple__71 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__71)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3759; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__71);
- __Pyx_GIVEREF(__pyx_tuple__71);
+ __pyx_tuple__72 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__72)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3871; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__72);
+ __Pyx_GIVEREF(__pyx_tuple__72);
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+ /* "netCDF4/_netCDF4.pyx":3873
* data.byteswap(True)
* if is_native_little and data.dtype.byteorder == '=':
* data.byteswap(True) # <<<<<<<<<<<<<<
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- __Pyx_GOTREF(__pyx_tuple__72);
- __Pyx_GIVEREF(__pyx_tuple__72);
+ __pyx_tuple__73 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__73)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3873; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__73);
+ __Pyx_GIVEREF(__pyx_tuple__73);
- /* "netCDF4/_netCDF4.pyx":3764
+ /* "netCDF4/_netCDF4.pyx":3876
* if self.endian() == 'little':
* if is_native_little and data.dtype.byteorder not in ['=','|']:
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* if is_native_big and data.dtype.byteorder == '=':
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- __Pyx_GOTREF(__pyx_tuple__73);
- __Pyx_GIVEREF(__pyx_tuple__73);
+ __pyx_tuple__74 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__74)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3876; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__74);
+ __Pyx_GIVEREF(__pyx_tuple__74);
- /* "netCDF4/_netCDF4.pyx":3766
+ /* "netCDF4/_netCDF4.pyx":3878
* data.byteswap(True)
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* data.byteswap(True) # <<<<<<<<<<<<<<
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- __pyx_tuple__74 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__74)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3766; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__74);
- __Pyx_GIVEREF(__pyx_tuple__74);
+ __pyx_tuple__75 = PyTuple_Pack(1, Py_True); if (unlikely(!__pyx_tuple__75)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3878; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__75);
+ __Pyx_GIVEREF(__pyx_tuple__75);
- /* "netCDF4/_netCDF4.pyx":3778
+ /* "netCDF4/_netCDF4.pyx":3890
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- __Pyx_GOTREF(__pyx_tuple__75);
- __Pyx_GIVEREF(__pyx_tuple__75);
+ __pyx_tuple__76 = PyTuple_Pack(1, __pyx_kp_s_data_to_put_in_string_variable_m); if (unlikely(!__pyx_tuple__76)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3890; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__76);
+ __Pyx_GIVEREF(__pyx_tuple__76);
- /* "netCDF4/_netCDF4.pyx":3797
+ /* "netCDF4/_netCDF4.pyx":3909
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* else:
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- __Pyx_GOTREF(__pyx_tuple__76);
- __Pyx_GIVEREF(__pyx_tuple__76);
+ __pyx_tuple__77 = PyTuple_Pack(1, __pyx_kp_s_strides_must_all_be_1_for_string); if (unlikely(!__pyx_tuple__77)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3909; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__77);
+ __Pyx_GIVEREF(__pyx_tuple__77);
- /* "netCDF4/_netCDF4.pyx":3813
+ /* "netCDF4/_netCDF4.pyx":3925
* elptr = (<void**>databuff)[0]
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- __Pyx_GOTREF(__pyx_slice__77);
- __Pyx_GIVEREF(__pyx_slice__77);
+ __pyx_slice__78 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__78)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3925; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__78);
+ __Pyx_GIVEREF(__pyx_slice__78);
- /* "netCDF4/_netCDF4.pyx":3825
+ /* "netCDF4/_netCDF4.pyx":3937
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- __pyx_tuple__78 = PyTuple_Pack(1, __pyx_kp_s_strides_must_all_be_1_for_vlen_v); if (unlikely(!__pyx_tuple__78)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3825; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__78);
- __Pyx_GIVEREF(__pyx_tuple__78);
+ __pyx_tuple__79 = PyTuple_Pack(1, __pyx_kp_s_strides_must_all_be_1_for_vlen_v); if (unlikely(!__pyx_tuple__79)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3937; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__79);
+ __Pyx_GIVEREF(__pyx_tuple__79);
- /* "netCDF4/_netCDF4.pyx":3850
+ /* "netCDF4/_netCDF4.pyx":3962
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- __pyx_tuple__79 = PyTuple_Pack(1, __pyx_int_1); if (unlikely(!__pyx_tuple__79)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3850; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__79);
- __Pyx_GIVEREF(__pyx_tuple__79);
+ __pyx_tuple__80 = PyTuple_Pack(1, __pyx_int_1); if (unlikely(!__pyx_tuple__80)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3962; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__80);
+ __Pyx_GIVEREF(__pyx_tuple__80);
- /* "netCDF4/_netCDF4.pyx":3868
+ /* "netCDF4/_netCDF4.pyx":3980
* startp[n] = start[n]+stride[n]*(count[n]-1)
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- __Pyx_GOTREF(__pyx_slice__80);
- __Pyx_GIVEREF(__pyx_slice__80);
+ __pyx_slice__81 = PySlice_New(Py_None, Py_None, __pyx_int_neg_1); if (unlikely(!__pyx_slice__81)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3980; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__81);
+ __Pyx_GIVEREF(__pyx_slice__81);
- /* "netCDF4/_netCDF4.pyx":3872
+ /* "netCDF4/_netCDF4.pyx":3984
* startp[n] = start[n]
* stridep[n] = stride[n]
* sl.append(slice(None,None, 1)) # <<<<<<<<<<<<<<
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- __pyx_slice__81 = PySlice_New(Py_None, Py_None, __pyx_int_1); if (unlikely(!__pyx_slice__81)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3872; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__81);
- __Pyx_GIVEREF(__pyx_slice__81);
+ __pyx_slice__82 = PySlice_New(Py_None, Py_None, __pyx_int_1); if (unlikely(!__pyx_slice__82)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3984; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__82);
+ __Pyx_GIVEREF(__pyx_slice__82);
- /* "netCDF4/_netCDF4.pyx":3905
+ /* "netCDF4/_netCDF4.pyx":4017
* else:
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- __pyx_tuple__82 = PyTuple_Pack(1, __pyx_kp_s_strides_must_all_be_1_for_string); if (unlikely(!__pyx_tuple__82)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3905; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__82);
- __Pyx_GIVEREF(__pyx_tuple__82);
+ __pyx_tuple__83 = PyTuple_Pack(1, __pyx_kp_s_strides_must_all_be_1_for_string); if (unlikely(!__pyx_tuple__83)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4017; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__83);
+ __Pyx_GIVEREF(__pyx_tuple__83);
- /* "netCDF4/_netCDF4.pyx":3929
+ /* "netCDF4/_netCDF4.pyx":4041
* startp, countp, vldata)
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- __pyx_tuple__83 = PyTuple_Pack(1, __pyx_kp_s_strides_must_all_be_1_for_vlen_v); if (unlikely(!__pyx_tuple__83)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3929; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__83);
- __Pyx_GIVEREF(__pyx_tuple__83);
+ __pyx_tuple__84 = PyTuple_Pack(1, __pyx_kp_s_strides_must_all_be_1_for_vlen_v); if (unlikely(!__pyx_tuple__84)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4041; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__84);
+ __Pyx_GIVEREF(__pyx_tuple__84);
- /* "netCDF4/_netCDF4.pyx":4048
+ /* "netCDF4/_netCDF4.pyx":4160
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- __pyx_slice__84 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__84)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4048; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__84);
- __Pyx_GIVEREF(__pyx_slice__84);
+ __pyx_slice__85 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__85)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4160; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__85);
+ __Pyx_GIVEREF(__pyx_slice__85);
- /* "netCDF4/_netCDF4.pyx":4050
+ /* "netCDF4/_netCDF4.pyx":4162
* xtype_tmp = _nptonctype[format.str[1:]]
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- __pyx_tuple__85 = PyTuple_Pack(1, __pyx_kp_s_Unsupported_compound_type_elemen); if (unlikely(!__pyx_tuple__85)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4050; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__85);
- __Pyx_GIVEREF(__pyx_tuple__85);
+ __pyx_tuple__86 = PyTuple_Pack(1, __pyx_kp_s_Unsupported_compound_type_elemen); if (unlikely(!__pyx_tuple__86)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4162; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__86);
+ __Pyx_GIVEREF(__pyx_tuple__86);
- /* "netCDF4/_netCDF4.pyx":4072
+ /* "netCDF4/_netCDF4.pyx":4184
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- __pyx_slice__86 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__86)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4072; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__86);
- __Pyx_GIVEREF(__pyx_slice__86);
+ __pyx_slice__87 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__87)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4184; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__87);
+ __Pyx_GIVEREF(__pyx_slice__87);
- /* "netCDF4/_netCDF4.pyx":4074
+ /* "netCDF4/_netCDF4.pyx":4186
* xtype_tmp = _nptonctype[format.subdtype[0].str[1:]]
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- __pyx_tuple__87 = PyTuple_Pack(1, __pyx_kp_s_Unsupported_compound_type_elemen); if (unlikely(!__pyx_tuple__87)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4074; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__87);
- __Pyx_GIVEREF(__pyx_tuple__87);
+ __pyx_tuple__88 = PyTuple_Pack(1, __pyx_kp_s_Unsupported_compound_type_elemen); if (unlikely(!__pyx_tuple__88)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__88);
+ __Pyx_GIVEREF(__pyx_tuple__88);
- /* "netCDF4/_netCDF4.pyx":4111
+ /* "netCDF4/_netCDF4.pyx":4223
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- __pyx_tuple__88 = PyTuple_Pack(1, __pyx_kp_s_cannot_find_compound_type_in_thi); if (unlikely(!__pyx_tuple__88)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4111; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__88);
- __Pyx_GIVEREF(__pyx_tuple__88);
+ __pyx_tuple__89 = PyTuple_Pack(1, __pyx_kp_s_cannot_find_compound_type_in_thi); if (unlikely(!__pyx_tuple__89)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__89);
+ __Pyx_GIVEREF(__pyx_tuple__89);
- /* "netCDF4/_netCDF4.pyx":4113
+ /* "netCDF4/_netCDF4.pyx":4225
* raise ValueError("cannot find compound type in this group or parent groups")
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* raise ValueError("cannot find compound type in this group or parent groups") # <<<<<<<<<<<<<<
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- __Pyx_GOTREF(__pyx_tuple__89);
- __Pyx_GIVEREF(__pyx_tuple__89);
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+ __Pyx_GOTREF(__pyx_tuple__90);
+ __Pyx_GIVEREF(__pyx_tuple__90);
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+ /* "netCDF4/_netCDF4.pyx":4286
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- __Pyx_GOTREF(__pyx_tuple__90);
- __Pyx_GIVEREF(__pyx_tuple__90);
+ __pyx_tuple__91 = PyTuple_Pack(1, __pyx_kp_s_compound_field_of_an_unsupported); if (unlikely(!__pyx_tuple__91)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4286; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __Pyx_GOTREF(__pyx_slice__91);
- __Pyx_GIVEREF(__pyx_slice__91);
+ __pyx_slice__92 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__92)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4376; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__92);
+ __Pyx_GIVEREF(__pyx_slice__92);
- /* "netCDF4/_netCDF4.pyx":4268
+ /* "netCDF4/_netCDF4.pyx":4379
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- __Pyx_GOTREF(__pyx_slice__92);
- __Pyx_GIVEREF(__pyx_slice__92);
+ __pyx_slice__93 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__93)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4379; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__93);
+ __Pyx_GIVEREF(__pyx_slice__93);
- /* "netCDF4/_netCDF4.pyx":4273
+ /* "netCDF4/_netCDF4.pyx":4384
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- __Pyx_GOTREF(__pyx_tuple__93);
- __Pyx_GIVEREF(__pyx_tuple__93);
+ __pyx_tuple__94 = PyTuple_Pack(1, __pyx_kp_s_unsupported_datatype_specified_f); if (unlikely(!__pyx_tuple__94)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4384; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __Pyx_GOTREF(__pyx_tuple__94);
- __Pyx_GIVEREF(__pyx_tuple__94);
+ __pyx_tuple__95 = PyTuple_Pack(1, __pyx_kp_s_unsupported_component_type_for_V); if (unlikely(!__pyx_tuple__95)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4411; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__95);
+ __Pyx_GIVEREF(__pyx_tuple__95);
- /* "netCDF4/_netCDF4.pyx":4331
+ /* "netCDF4/_netCDF4.pyx":4442
* units = timestr_split[0].lower()
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- __Pyx_GOTREF(__pyx_tuple__95);
- __Pyx_GIVEREF(__pyx_tuple__95);
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+ __Pyx_GOTREF(__pyx_tuple__96);
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- /* "netCDF4/_netCDF4.pyx":4333
+ /* "netCDF4/_netCDF4.pyx":4444
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- __Pyx_GOTREF(__pyx_tuple__96);
- __Pyx_GIVEREF(__pyx_tuple__96);
+ __pyx_tuple__97 = PyTuple_Pack(1, __pyx_n_s_since); if (unlikely(!__pyx_tuple__97)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4444; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__97);
+ __Pyx_GIVEREF(__pyx_tuple__97);
- /* "netCDF4/_netCDF4.pyx":4360
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- __Pyx_GOTREF(__pyx_tuple__97);
- __Pyx_GIVEREF(__pyx_tuple__97);
+ __pyx_tuple__98 = PyTuple_Pack(1, __pyx_kp_s_dtype_must_string_or_unicode_S_o); if (unlikely(!__pyx_tuple__98)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4471; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__98);
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- /* "netCDF4/_netCDF4.pyx":4379
+ /* "netCDF4/_netCDF4.pyx":4490
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- __Pyx_GOTREF(__pyx_tuple__98);
- __Pyx_GIVEREF(__pyx_tuple__98);
+ __pyx_tuple__99 = PyTuple_Pack(1, __pyx_kp_s_type_must_string_or_unicode_S_or); if (unlikely(!__pyx_tuple__99)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4490; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__99);
+ __Pyx_GIVEREF(__pyx_tuple__99);
- /* "netCDF4/_netCDF4.pyx":4398
+ /* "netCDF4/_netCDF4.pyx":4509
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- __Pyx_GOTREF(__pyx_tuple__99);
- __Pyx_GIVEREF(__pyx_tuple__99);
+ __pyx_tuple__100 = PyTuple_Pack(1, __pyx_kp_s_type_must_string_or_unicode_S_or); if (unlikely(!__pyx_tuple__100)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4509; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__100);
+ __Pyx_GIVEREF(__pyx_tuple__100);
- /* "netCDF4/_netCDF4.pyx":4402
+ /* "netCDF4/_netCDF4.pyx":4513
* slen = int(b.shape[-1])
* a = numpy.array([bs[n1:n1+slen] for n1 in range(0,len(bs),slen)],dtype+repr(slen))
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*
*/
- __pyx_slice__100 = PySlice_New(Py_None, __pyx_int_neg_1, Py_None); if (unlikely(!__pyx_slice__100)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4402; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__100);
- __Pyx_GIVEREF(__pyx_slice__100);
+ __pyx_slice__101 = PySlice_New(Py_None, __pyx_int_neg_1, Py_None); if (unlikely(!__pyx_slice__101)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4513; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__101);
+ __Pyx_GIVEREF(__pyx_slice__101);
- /* "netCDF4/_netCDF4.pyx":4475
+ /* "netCDF4/_netCDF4.pyx":4586
* times.append(totaltime/1.e6/3600./24.)
* else:
* raise ValueError('unsupported time units') # <<<<<<<<<<<<<<
* if isscalar:
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- __pyx_tuple__101 = PyTuple_Pack(1, __pyx_kp_s_unsupported_time_units); if (unlikely(!__pyx_tuple__101)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4475; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__101);
- __Pyx_GIVEREF(__pyx_tuple__101);
+ __pyx_tuple__102 = PyTuple_Pack(1, __pyx_kp_s_unsupported_time_units); if (unlikely(!__pyx_tuple__102)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4586; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__102);
+ __Pyx_GIVEREF(__pyx_tuple__102);
- /* "netCDF4/_netCDF4.pyx":4561
+ /* "netCDF4/_netCDF4.pyx":4672
* tsecs = time*86400.
* else:
* raise ValueError('unsupported time units') # <<<<<<<<<<<<<<
* # compute time delta.
* days = tsecs // 86400.
*/
- __pyx_tuple__102 = PyTuple_Pack(1, __pyx_kp_s_unsupported_time_units); if (unlikely(!__pyx_tuple__102)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4561; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__102);
- __Pyx_GIVEREF(__pyx_tuple__102);
+ __pyx_tuple__103 = PyTuple_Pack(1, __pyx_kp_s_unsupported_time_units); if (unlikely(!__pyx_tuple__103)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4672; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__103);
+ __Pyx_GIVEREF(__pyx_tuple__103);
- /* "netCDF4/_netCDF4.pyx":4691
+ /* "netCDF4/_netCDF4.pyx":4798
* # can be used like a CDF instance.
* if isinstance(files, str):
* if files.startswith('http'): # <<<<<<<<<<<<<<
* msg='cannot using file globbing for remote (OPeNDAP) datasets'
* raise ValueError(msg)
*/
- __pyx_tuple__103 = PyTuple_Pack(1, __pyx_n_s_http); if (unlikely(!__pyx_tuple__103)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4691; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__103);
- __Pyx_GIVEREF(__pyx_tuple__103);
+ __pyx_tuple__104 = PyTuple_Pack(1, __pyx_n_s_http); if (unlikely(!__pyx_tuple__104)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4798; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__104);
+ __Pyx_GIVEREF(__pyx_tuple__104);
- /* "netCDF4/_netCDF4.pyx":4753
+ /* "netCDF4/_netCDF4.pyx":4860
* # Make sure each file defines the same aggregation variables as the master
* # and that the variables are defined in the same way (name, shape and type)
* for f in files[1:]: # <<<<<<<<<<<<<<
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- __pyx_slice__104 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__104)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4753; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__104);
- __Pyx_GIVEREF(__pyx_slice__104);
+ __pyx_slice__105 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__105)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4860; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__105);
+ __Pyx_GIVEREF(__pyx_slice__105);
- /* "netCDF4/_netCDF4.pyx":4764
+ /* "netCDF4/_netCDF4.pyx":4871
* #if not vInst.dimensions[0] != aggDimName:
*
* masterDims, masterShape, masterType = masterRecVar[v][:3] # <<<<<<<<<<<<<<
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- __pyx_slice__105 = PySlice_New(Py_None, __pyx_int_3, Py_None); if (unlikely(!__pyx_slice__105)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4764; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__105);
- __Pyx_GIVEREF(__pyx_slice__105);
+ __pyx_slice__106 = PySlice_New(Py_None, __pyx_int_3, Py_None); if (unlikely(!__pyx_slice__106)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4871; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__106);
+ __Pyx_GIVEREF(__pyx_slice__106);
- /* "netCDF4/_netCDF4.pyx":4781
+ /* "netCDF4/_netCDF4.pyx":4888
* "master %s (%s) and extension %s (%s)" %
* (v, master, len(masterShape), f, len(extShape)))
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*/
- __pyx_slice__106 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__106)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4781; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__106);
- __Pyx_GIVEREF(__pyx_slice__106);
- __pyx_slice__107 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__107)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4781; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__107 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__107)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__107);
__Pyx_GIVEREF(__pyx_slice__107);
+ __pyx_slice__108 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__108)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__108);
+ __Pyx_GIVEREF(__pyx_slice__108);
- /* "netCDF4/_netCDF4.pyx":4824
+ /* "netCDF4/_netCDF4.pyx":4931
* for dset in self._cdf:
* if dset.file_format == 'NETCDF4' or dset.data_model == 'NETCDF4':
* raise ValueError('MFNetCDF4 only works with NETCDF3_CLASSIC, NETCDF3_64BIT and NETCDF4_CLASSIC formatted files, not NETCDF4') # <<<<<<<<<<<<<<
* self._file_format.append(dset.file_format)
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- __pyx_tuple__108 = PyTuple_Pack(1, __pyx_kp_s_MFNetCDF4_only_works_with_NETCDF); if (unlikely(!__pyx_tuple__108)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4824; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__108);
- __Pyx_GIVEREF(__pyx_tuple__108);
+ __pyx_tuple__109 = PyTuple_Pack(1, __pyx_kp_s_MFNetCDF4_only_works_with_NETCDF); if (unlikely(!__pyx_tuple__109)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4931; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__109);
+ __Pyx_GIVEREF(__pyx_tuple__109);
+
+ /* "netCDF4/_netCDF4.pyx":5047
+ * def __len__(self):
+ * if not self._shape:
+ * raise TypeError('len() of unsized object') # <<<<<<<<<<<<<<
+ * else:
+ * return self._shape()[0]
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+ __pyx_tuple__110 = PyTuple_Pack(1, __pyx_kp_s_len_of_unsized_object); if (unlikely(!__pyx_tuple__110)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5047; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__110);
+ __Pyx_GIVEREF(__pyx_tuple__110);
- /* "netCDF4/_netCDF4.pyx":4932
+ /* "netCDF4/_netCDF4.pyx":5052
* def _shape(self):
* recdimlen = len(self._dset.dimensions[self._recdimname])
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* def set_auto_maskandscale(self,val):
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*/
- __pyx_slice__109 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__109)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4932; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__109);
- __Pyx_GIVEREF(__pyx_slice__109);
+ __pyx_slice__111 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__111)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5052; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__111);
+ __Pyx_GIVEREF(__pyx_slice__111);
- /* "netCDF4/_netCDF4.pyx":4953
+ /* "netCDF4/_netCDF4.pyx":5073
* # The convention used is that for those cases,
* # put_ind for this dimension is set to -1 by _StartCountStride.
* squeeze = data.ndim * [slice(None),] # <<<<<<<<<<<<<<
* for i,n in enumerate(put_ind.shape[:-1]):
* if n == 1 and put_ind[...,i].ravel()[0] == -1:
*/
- __pyx_slice__110 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__110)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4953; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__110);
- __Pyx_GIVEREF(__pyx_slice__110);
+ __pyx_slice__112 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__112)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5073; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__112);
+ __Pyx_GIVEREF(__pyx_slice__112);
- /* "netCDF4/_netCDF4.pyx":4954
+ /* "netCDF4/_netCDF4.pyx":5074
* # put_ind for this dimension is set to -1 by _StartCountStride.
* squeeze = data.ndim * [slice(None),]
* for i,n in enumerate(put_ind.shape[:-1]): # <<<<<<<<<<<<<<
* if n == 1 and put_ind[...,i].ravel()[0] == -1:
* squeeze[i] = 0
*/
- __pyx_slice__111 = PySlice_New(Py_None, __pyx_int_neg_1, Py_None); if (unlikely(!__pyx_slice__111)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4954; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__111);
- __Pyx_GIVEREF(__pyx_slice__111);
+ __pyx_slice__113 = PySlice_New(Py_None, __pyx_int_neg_1, Py_None); if (unlikely(!__pyx_slice__113)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5074; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__113);
+ __Pyx_GIVEREF(__pyx_slice__113);
- /* "netCDF4/_netCDF4.pyx":4971
+ /* "netCDF4/_netCDF4.pyx":5091
* count = [abs(cnt) for cnt in count]
* if (numpy.array(stride) < 0).any():
* raise IndexError('negative strides not allowed when slicing MFVariable Variable instance') # <<<<<<<<<<<<<<
* # Start, stop and step along 1st dimension, eg the unlimited
* # dimension.
*/
- __pyx_tuple__112 = PyTuple_Pack(1, __pyx_kp_s_negative_strides_not_allowed_whe); if (unlikely(!__pyx_tuple__112)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4971; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__112);
- __Pyx_GIVEREF(__pyx_tuple__112);
+ __pyx_tuple__114 = PyTuple_Pack(1, __pyx_kp_s_negative_strides_not_allowed_whe); if (unlikely(!__pyx_tuple__114)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5091; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__114);
+ __Pyx_GIVEREF(__pyx_tuple__114);
- /* "netCDF4/_netCDF4.pyx":4995
+ /* "netCDF4/_netCDF4.pyx":5115
*
* # Rebuild the slicing expression for dimensions 1 and ssq.
* newSlice = [slice(None, None, None)] # <<<<<<<<<<<<<<
* for n in range(1, len(start)): # skip dimension 0
* s = slice(start[n],start[n] + count[n] * stride[n], stride[n])
*/
- __pyx_slice__113 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__113)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4995; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__113);
- __Pyx_GIVEREF(__pyx_slice__113);
+ __pyx_slice__115 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__115)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5115; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__115);
+ __Pyx_GIVEREF(__pyx_slice__115);
- /* "netCDF4/_netCDF4.pyx":5096
+ /* "netCDF4/_netCDF4.pyx":5217
* for t in self._recVar:
* if not hasattr(t,'calendar'):
* raise ValueError('MFTime requires that the time variable in all files have a calendar attribute') # <<<<<<<<<<<<<<
*
* # Check that calendar is the same in all files.
*/
- __pyx_tuple__114 = PyTuple_Pack(1, __pyx_kp_s_MFTime_requires_that_the_time_va); if (unlikely(!__pyx_tuple__114)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5096; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__114);
- __Pyx_GIVEREF(__pyx_tuple__114);
+ __pyx_tuple__116 = PyTuple_Pack(1, __pyx_kp_s_MFTime_requires_that_the_time_va); if (unlikely(!__pyx_tuple__116)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__116);
+ __Pyx_GIVEREF(__pyx_tuple__116);
- /* "netCDF4/_netCDF4.pyx":5100
+ /* "netCDF4/_netCDF4.pyx":5221
* # Check that calendar is the same in all files.
* if len(set([t.calendar for t in self._recVar])) > 1:
* raise ValueError('MFTime requires that the same time calendar is used by all files.') # <<<<<<<<<<<<<<
*
* # Override units if units is specified.
*/
- __pyx_tuple__115 = PyTuple_Pack(1, __pyx_kp_s_MFTime_requires_that_the_same_ti); if (unlikely(!__pyx_tuple__115)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5100; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__115);
- __Pyx_GIVEREF(__pyx_tuple__115);
+ __pyx_tuple__117 = PyTuple_Pack(1, __pyx_kp_s_MFTime_requires_that_the_same_ti); if (unlikely(!__pyx_tuple__117)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5221; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__117);
+ __Pyx_GIVEREF(__pyx_tuple__117);
- /* "netCDF4/_netCDF4.pyx":5106
+ /* "netCDF4/_netCDF4.pyx":5227
*
* # Reference date to compute the difference between different time units.
* ref_date = datetime.datetime(1900,1,1) # <<<<<<<<<<<<<<
* ref_num = date2num(ref_date, self.units, self.calendar)
*
*/
- __pyx_tuple__116 = PyTuple_Pack(3, __pyx_int_1900, __pyx_int_1, __pyx_int_1); if (unlikely(!__pyx_tuple__116)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5106; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__116);
- __Pyx_GIVEREF(__pyx_tuple__116);
+ __pyx_tuple__118 = PyTuple_Pack(3, __pyx_int_1900, __pyx_int_1, __pyx_int_1); if (unlikely(!__pyx_tuple__118)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5227; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__118);
+ __Pyx_GIVEREF(__pyx_tuple__118);
- /* "netCDF4/_netCDF4.pyx":820
+ /* "netCDF4/_netCDF4.pyx":838
* from ordereddict import OrderedDict
* except ImportError:
* raise ImportError('please install ordereddict (https://pypi.python.org/pypi/ordereddict)') # <<<<<<<<<<<<<<
* try:
* from itertools import izip as zip
*/
- __pyx_tuple__117 = PyTuple_Pack(1, __pyx_kp_s_please_install_ordereddict_https); if (unlikely(!__pyx_tuple__117)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 820; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__117);
- __Pyx_GIVEREF(__pyx_tuple__117);
+ __pyx_tuple__119 = PyTuple_Pack(1, __pyx_kp_s_please_install_ordereddict_https); if (unlikely(!__pyx_tuple__119)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 838; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__119);
+ __Pyx_GIVEREF(__pyx_tuple__119);
- /* "netCDF4/_netCDF4.pyx":839
+ /* "netCDF4/_netCDF4.pyx":857
* from numpy import ma
* from numpy import __version__ as _npversion
* if _npversion.split('.')[0] < '1': # <<<<<<<<<<<<<<
* raise ImportError('requires numpy version 1.0rc1 or later')
* import_array()
*/
- __pyx_tuple__119 = PyTuple_Pack(1, __pyx_kp_s__118); if (unlikely(!__pyx_tuple__119)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 839; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__119);
- __Pyx_GIVEREF(__pyx_tuple__119);
+ __pyx_tuple__121 = PyTuple_Pack(1, __pyx_kp_s__120); if (unlikely(!__pyx_tuple__121)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 857; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__121);
+ __Pyx_GIVEREF(__pyx_tuple__121);
- /* "netCDF4/_netCDF4.pyx":840
+ /* "netCDF4/_netCDF4.pyx":858
* from numpy import __version__ as _npversion
* if _npversion.split('.')[0] < '1':
* raise ImportError('requires numpy version 1.0rc1 or later') # <<<<<<<<<<<<<<
* import_array()
* include "netCDF4.pxi"
*/
- __pyx_tuple__120 = PyTuple_Pack(1, __pyx_kp_s_requires_numpy_version_1_0rc1_or); if (unlikely(!__pyx_tuple__120)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 840; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__120);
- __Pyx_GIVEREF(__pyx_tuple__120);
+ __pyx_tuple__122 = PyTuple_Pack(1, __pyx_kp_s_requires_numpy_version_1_0rc1_or); if (unlikely(!__pyx_tuple__122)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 858; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__122);
+ __Pyx_GIVEREF(__pyx_tuple__122);
- /* "netCDF4/_netCDF4.pyx":847
+ /* "netCDF4/_netCDF4.pyx":865
* # check for required version of netcdf-4 and hdf5.
*
* def _gethdf5libversion(): # <<<<<<<<<<<<<<
* majorvers = H5_VERS_MAJOR
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* return arr
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* return b
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* return a
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* def date2num(dates,units,calendar='standard'): # <<<<<<<<<<<<<<
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+ __Pyx_GOTREF(__pyx_tuple__139);
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- /* "netCDF4/_netCDF4.pyx":4484
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* """
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- /* "netCDF4/_netCDF4.pyx":5076
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* """
*
* def __init__(self, time, units=None): # <<<<<<<<<<<<<<
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__Pyx_GOTREF(__pyx_tuple__183);
__Pyx_GIVEREF(__pyx_tuple__183);
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@@ -64347,7 +64655,7 @@ PyMODINIT_FUNC PyInit__netCDF4(void)
#endif
/*--- Module creation code ---*/
#if PY_MAJOR_VERSION < 3
- __pyx_m = Py_InitModule4("_netCDF4", __pyx_methods, __pyx_k_Introduction_Python_interface_t, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m);
+ __pyx_m = Py_InitModule4("_netCDF4", __pyx_methods, __pyx_k_Introduction_netcdf4_python_is, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m);
#else
__pyx_m = PyModule_Create(&__pyx_moduledef);
#endif
@@ -64384,64 +64692,124 @@ PyMODINIT_FUNC PyInit__netCDF4(void)
/*--- Function export code ---*/
/*--- Type init code ---*/
__pyx_ptype_7netCDF4_8_netCDF4_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject), 0); if (unlikely(!__pyx_ptype_7netCDF4_8_netCDF4_ndarray)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 706; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- if (PyType_Ready(&__pyx_type_7netCDF4_8_netCDF4_Dataset) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1353; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyType_Ready(&__pyx_type_7netCDF4_8_netCDF4_Dataset) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1374; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7netCDF4_8_netCDF4_Dataset.tp_print = 0;
- if (PyObject_SetAttrString(__pyx_m, "Dataset", (PyObject *)&__pyx_type_7netCDF4_8_netCDF4_Dataset) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1353; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #if CYTHON_COMPILING_IN_CPYTHON
+ {
+ PyObject *wrapper = PyObject_GetAttrString((PyObject *)&__pyx_type_7netCDF4_8_netCDF4_Dataset, "__init__"); if (unlikely(!wrapper)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1374; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (Py_TYPE(wrapper) == &PyWrapperDescr_Type) {
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_7Dataset___init__ = *((PyWrapperDescrObject *)wrapper)->d_base;
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_7Dataset___init__.doc = __pyx_doc_7netCDF4_8_netCDF4_7Dataset___init__;
+ ((PyWrapperDescrObject *)wrapper)->d_base = &__pyx_wrapperbase_7netCDF4_8_netCDF4_7Dataset___init__;
+ }
+ }
+ #endif
+ if (PyObject_SetAttrString(__pyx_m, "Dataset", (PyObject *)&__pyx_type_7netCDF4_8_netCDF4_Dataset) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1374; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__pyx_type_7netCDF4_8_netCDF4_Dataset.tp_weaklistoffset == 0) __pyx_type_7netCDF4_8_netCDF4_Dataset.tp_weaklistoffset = offsetof(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset, __weakref__);
__pyx_ptype_7netCDF4_8_netCDF4_Dataset = &__pyx_type_7netCDF4_8_netCDF4_Dataset;
__pyx_type_7netCDF4_8_netCDF4_Group.tp_base = __pyx_ptype_7netCDF4_8_netCDF4_Dataset;
- if (PyType_Ready(&__pyx_type_7netCDF4_8_netCDF4_Group) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2197; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyType_Ready(&__pyx_type_7netCDF4_8_netCDF4_Group) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2257; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7netCDF4_8_netCDF4_Group.tp_print = 0;
- if (PyObject_SetAttrString(__pyx_m, "Group", (PyObject *)&__pyx_type_7netCDF4_8_netCDF4_Group) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2197; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #if CYTHON_COMPILING_IN_CPYTHON
+ {
+ PyObject *wrapper = PyObject_GetAttrString((PyObject *)&__pyx_type_7netCDF4_8_netCDF4_Group, "__init__"); if (unlikely(!wrapper)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2257; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (Py_TYPE(wrapper) == &PyWrapperDescr_Type) {
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_5Group___init__ = *((PyWrapperDescrObject *)wrapper)->d_base;
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_5Group___init__.doc = __pyx_doc_7netCDF4_8_netCDF4_5Group___init__;
+ ((PyWrapperDescrObject *)wrapper)->d_base = &__pyx_wrapperbase_7netCDF4_8_netCDF4_5Group___init__;
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if (__pyx_type_7netCDF4_8_netCDF4_Group.tp_weaklistoffset == 0) __pyx_type_7netCDF4_8_netCDF4_Group.tp_weaklistoffset = offsetof(struct __pyx_obj_7netCDF4_8_netCDF4_Group, __pyx_base.__weakref__);
__pyx_ptype_7netCDF4_8_netCDF4_Group = &__pyx_type_7netCDF4_8_netCDF4_Group;
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+ if (PyType_Ready(&__pyx_type_7netCDF4_8_netCDF4_Dimension) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7netCDF4_8_netCDF4_Dimension.tp_print = 0;
- if (PyObject_SetAttrString(__pyx_m, "Dimension", (PyObject *)&__pyx_type_7netCDF4_8_netCDF4_Dimension) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2287; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #if CYTHON_COMPILING_IN_CPYTHON
+ {
+ PyObject *wrapper = PyObject_GetAttrString((PyObject *)&__pyx_type_7netCDF4_8_netCDF4_Dimension, "__init__"); if (unlikely(!wrapper)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (Py_TYPE(wrapper) == &PyWrapperDescr_Type) {
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_9Dimension___init__ = *((PyWrapperDescrObject *)wrapper)->d_base;
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_9Dimension___init__.doc = __pyx_doc_7netCDF4_8_netCDF4_9Dimension___init__;
+ ((PyWrapperDescrObject *)wrapper)->d_base = &__pyx_wrapperbase_7netCDF4_8_netCDF4_9Dimension___init__;
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+ }
+ #endif
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__pyx_ptype_7netCDF4_8_netCDF4_Dimension = &__pyx_type_7netCDF4_8_netCDF4_Dimension;
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+ if (PyType_Ready(&__pyx_type_7netCDF4_8_netCDF4_Variable) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2500; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- if (PyObject_SetAttrString(__pyx_m, "Variable", (PyObject *)&__pyx_type_7netCDF4_8_netCDF4_Variable) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2431; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #if CYTHON_COMPILING_IN_CPYTHON
+ {
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+ if (Py_TYPE(wrapper) == &PyWrapperDescr_Type) {
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_8Variable___init__ = *((PyWrapperDescrObject *)wrapper)->d_base;
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_8Variable___init__.doc = __pyx_doc_7netCDF4_8_netCDF4_8Variable___init__;
+ ((PyWrapperDescrObject *)wrapper)->d_base = &__pyx_wrapperbase_7netCDF4_8_netCDF4_8Variable___init__;
+ }
+ }
+ #endif
+ if (PyObject_SetAttrString(__pyx_m, "Variable", (PyObject *)&__pyx_type_7netCDF4_8_netCDF4_Variable) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2500; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7netCDF4_8_netCDF4_Variable = &__pyx_type_7netCDF4_8_netCDF4_Variable;
- if (PyType_Ready(&__pyx_type_7netCDF4_8_netCDF4_CompoundType) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3959; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyType_Ready(&__pyx_type_7netCDF4_8_netCDF4_CompoundType) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4071; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7netCDF4_8_netCDF4_CompoundType.tp_print = 0;
- if (PyObject_SetAttrString(__pyx_m, "CompoundType", (PyObject *)&__pyx_type_7netCDF4_8_netCDF4_CompoundType) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3959; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #if CYTHON_COMPILING_IN_CPYTHON
+ {
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+ if (Py_TYPE(wrapper) == &PyWrapperDescr_Type) {
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_12CompoundType___init__ = *((PyWrapperDescrObject *)wrapper)->d_base;
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_12CompoundType___init__.doc = __pyx_doc_7netCDF4_8_netCDF4_12CompoundType___init__;
+ ((PyWrapperDescrObject *)wrapper)->d_base = &__pyx_wrapperbase_7netCDF4_8_netCDF4_12CompoundType___init__;
+ }
+ }
+ #endif
+ if (PyObject_SetAttrString(__pyx_m, "CompoundType", (PyObject *)&__pyx_type_7netCDF4_8_netCDF4_CompoundType) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4071; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7netCDF4_8_netCDF4_CompoundType = &__pyx_type_7netCDF4_8_netCDF4_CompoundType;
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+ if (PyType_Ready(&__pyx_type_7netCDF4_8_netCDF4_VLType) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4301; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7netCDF4_8_netCDF4_VLType.tp_print = 0;
- if (PyObject_SetAttrString(__pyx_m, "VLType", (PyObject *)&__pyx_type_7netCDF4_8_netCDF4_VLType) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4189; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #if CYTHON_COMPILING_IN_CPYTHON
+ {
+ PyObject *wrapper = PyObject_GetAttrString((PyObject *)&__pyx_type_7netCDF4_8_netCDF4_VLType, "__init__"); if (unlikely(!wrapper)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4301; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (Py_TYPE(wrapper) == &PyWrapperDescr_Type) {
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_6VLType___init__ = *((PyWrapperDescrObject *)wrapper)->d_base;
+ __pyx_wrapperbase_7netCDF4_8_netCDF4_6VLType___init__.doc = __pyx_doc_7netCDF4_8_netCDF4_6VLType___init__;
+ ((PyWrapperDescrObject *)wrapper)->d_base = &__pyx_wrapperbase_7netCDF4_8_netCDF4_6VLType___init__;
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+ }
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/*--- Type import code ---*/
/*--- Variable import code ---*/
/*--- Function import code ---*/
/*--- Execution code ---*/
- /* "netCDF4/_netCDF4.pyx":805
+ /* "netCDF4/_netCDF4.pyx":823
* OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
* PERFORMANCE OF THIS SOFTWARE."""
* __test__ = None # <<<<<<<<<<<<<<
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- /* "netCDF4/_netCDF4.pyx":806
+ /* "netCDF4/_netCDF4.pyx":824
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* del __test__ # hack so epydoc doesn't show __test__ # <<<<<<<<<<<<<<
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*/
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+ __pyx_t_1 = __Pyx_ImportFrom(__pyx_t_2, __pyx_n_s_tostr); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 829; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_XGOTREF(__pyx_t_5);
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+ /* "netCDF4/_netCDF4.pyx":833
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__Pyx_GIVEREF(__pyx_n_s_OrderedDict);
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__Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0;
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__Pyx_GOTREF(__pyx_t_2);
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__Pyx_XGOTREF(__pyx_t_10);
/*try:*/ {
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__Pyx_GIVEREF(__pyx_n_s_OrderedDict);
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__Pyx_XDECREF(__pyx_t_11); __pyx_t_11 = 0;
__Pyx_XDECREF(__pyx_t_12); __pyx_t_12 = 0;
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+ if (__Pyx_GetException(&__pyx_t_12, &__pyx_t_11, &__pyx_t_13) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 837; __pyx_clineno = __LINE__; goto __pyx_L14_except_error;}
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@@ -64655,7 +65023,7 @@ PyMODINIT_FUNC PyInit__netCDF4(void)
__pyx_L9_try_end:;
}
- /* "netCDF4/_netCDF4.pyx":821
+ /* "netCDF4/_netCDF4.pyx":839
* except ImportError:
* raise ImportError('please install ordereddict (https://pypi.python.org/pypi/ordereddict)')
* try: # <<<<<<<<<<<<<<
@@ -64669,24 +65037,24 @@ PyMODINIT_FUNC PyInit__netCDF4(void)
__Pyx_XGOTREF(__pyx_t_3);
/*try:*/ {
- /* "netCDF4/_netCDF4.pyx":822
+ /* "netCDF4/_netCDF4.pyx":840
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+ __pyx_t_2 = __Pyx_Import(__pyx_n_s_itertools, __pyx_t_7, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 840; __pyx_clineno = __LINE__; goto __pyx_L22_error;}
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+ __pyx_t_7 = __Pyx_ImportFrom(__pyx_t_2, __pyx_n_s_izip); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 840; __pyx_clineno = __LINE__; goto __pyx_L22_error;}
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__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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@@ -64703,7 +65071,7 @@ PyMODINIT_FUNC PyInit__netCDF4(void)
__Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0;
__Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0;
- /* "netCDF4/_netCDF4.pyx":823
+ /* "netCDF4/_netCDF4.pyx":841
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* except ImportError: # <<<<<<<<<<<<<<
@@ -64730,189 +65098,189 @@ PyMODINIT_FUNC PyInit__netCDF4(void)
__pyx_L29_try_end:;
}
- /* "netCDF4/_netCDF4.pyx":827
+ /* "netCDF4/_netCDF4.pyx":845
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- /* "netCDF4/_netCDF4.pyx":830
+ /* "netCDF4/_netCDF4.pyx":848
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* import numpy
*/
- __pyx_t_2 = __Pyx_Import(__pyx_n_s_posixpath, 0, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 830; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __Pyx_Import(__pyx_n_s_posixpath, 0, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 848; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
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+ if (PyDict_SetItem(__pyx_d, __pyx_n_s_posixpath, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 848; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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+ /* "netCDF4/_netCDF4.pyx":849
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* import numpy
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*/
- __pyx_t_2 = __Pyx_Import(__pyx_n_s_netcdftime, 0, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 831; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __Pyx_Import(__pyx_n_s_netcdftime, 0, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 849; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
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+ if (PyDict_SetItem(__pyx_d, __pyx_n_s_netcdftime, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 849; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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*/
- __pyx_t_2 = __Pyx_Import(__pyx_n_s_numpy, 0, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 832; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __Pyx_Import(__pyx_n_s_numpy, 0, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 850; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
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+ if (PyDict_SetItem(__pyx_d, __pyx_n_s_numpy, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 850; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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+ /* "netCDF4/_netCDF4.pyx":851
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* import weakref # <<<<<<<<<<<<<<
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*/
- __pyx_t_2 = __Pyx_Import(__pyx_n_s_weakref, 0, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 833; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __Pyx_Import(__pyx_n_s_weakref, 0, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 851; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ if (PyDict_SetItem(__pyx_d, __pyx_n_s_weakref, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 851; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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- __pyx_t_2 = __Pyx_Import(__pyx_n_s_sys, 0, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 834; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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*/
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* """
*
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*/
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* def __getitem__(self, elem): # <<<<<<<<<<<<<<
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*/
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*
* class MFTime(_Variable): # <<<<<<<<<<<<<<
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diff --git a/netCDF4/_netCDF4.pyx b/netCDF4/_netCDF4.pyx
index 5f92d16..492cadc 100644
--- a/netCDF4/_netCDF4.pyx
+++ b/netCDF4/_netCDF4.pyx
@@ -2,20 +2,20 @@
Introduction
============
-Python interface to the netCDF version 4 library. U{netCDF version 4
-<http://www.unidata.ucar.edu/software/netcdf/netcdf-4>} has many features
+netcdf4-python is a Python interface to the netCDF C library.
+
+[netCDF version 4](http://www.unidata.ucar.edu/software/netcdf/netcdf-4) has many features
not found in earlier versions of the library and is implemented on top of
-U{HDF5 <http://www.hdfgroup.org/HDF5>}. This module can read and write
+[HDF5](http://www.hdfgroup.org/HDF5). This module can read and write
files in both the new netCDF 4 and the old netCDF 3 format, and can create
files that are readable by HDF5 clients. The API modelled after
-U{Scientific.IO.NetCDF
-<http://dirac.cnrs-orleans.fr/plone/software/scientificpython/>}, and should be
-familiar to users of that module.
+[Scientific.IO.NetCDF](http://dirac.cnrs-orleans.fr/plone/software/scientificpython/),
+and should be familiar to users of that module.
Most new features of netCDF 4 are implemented, such as multiple
unlimited dimensions, groups and zlib data compression. All the new
numeric data types (such as 64 bit and unsigned integer types) are
-implemented. Compound and variable length (vlen) data types are supported,
+implemented. Compound (struct) and variable length (vlen) data types are supported,
but the enum and opaque data types are not. Mixtures of compound and vlen
data types (compound types containing vlens, and vlens containing compound
types) are not supported.
@@ -23,38 +23,39 @@ types) are not supported.
Download
========
- - Latest bleeding-edge code from the U{github repository
- <http://github.com/Unidata/netcdf4-python>}.
- - Latest U{releases <https://pypi.python.org/pypi/netCDF4>}
+ - Latest bleeding-edge code from the
+ [github repository](http://github.com/Unidata/netcdf4-python).
+ - Latest [releases](https://pypi.python.org/pypi/netCDF4)
(source code and windows installers).
Requires
========
- Python 2.5 or later (python 3 works too).
- - numpy array module U{http://numpy.scipy.org}, version 1.7.0 or later.
- - U{Cython <http://cython.org>}, version 0.19 or later, is optional - if it is installed setup.py will
+ - [numpy array module](http://numpy.scipy.org), version 1.7.0 or later.
+ - [Cython](http://cython.org), version 0.19 or later, is optional - if it is installed setup.py will
use it to recompile the Cython source code into C, using conditional compilation
to enable features in the netCDF API that have been added since version 4.1.1. If
Cython is not installed, these features (such as the ability to rename Group objects)
will be disabled to preserve backward compatibility with older versions of the netCDF
library.
- - For python < 2.7, the ordereddict module U{http://python.org/pypi/ordereddict}.
+ - For python < 2.7, the [ordereddict module](http://python.org/pypi/ordereddict).
- The HDF5 C library version 1.8.4-patch1 or higher (1.8.8 or higher
- recommended) from U{ftp://ftp.hdfgroup.org/HDF5/current/src}.
- Be sure to build with 'C{--enable-hl --enable-shared}'.
- - U{Libcurl <http://curl.haxx.se/libcurl/>}, if you want
- U{OPeNDAP<http://opendap.org/>} support.
- - U{HDF4 <http://www.hdfgroup.org/products/hdf4/>}, if you want
+ recommended) from [](ftp://ftp.hdfgroup.org/HDF5/current/src).
+ Be sure to build with `--enable-hl --enable-shared`.
+ - [Libcurl](http://curl.haxx.se/libcurl), if you want
+ [OPeNDAP](http://opendap.org) support.
+ - [HDF4](http://www.hdfgroup.org/products/hdf4), if you want
to be able to read HDF4 "Scientific Dataset" (SD) files.
- - The netCDF-4 C library from U{ftp://ftp.unidata.ucar.edu/pub/netcdf}.
+ - The netCDF-4 C library from
+ [ftp://ftp.unidata.ucar.edu/pub/netcdf](ftp://ftp.unidata.ucar.edu/pub/netcdf).
Version 4.1.1 or higher is required (4.2 or higher recommended).
- Be sure to build with 'C{--enable-netcdf-4 --enable-shared}', and set
- C{CPPFLAGS="-I $HDF5_DIR/include"} and C{LDFLAGS="-L $HDF5_DIR/lib"},
- where C{$HDF5_DIR} is the directory where HDF5 was installed.
- If you want U{OPeNDAP<http://opendap.org/>} support, add 'C{--enable-dap}'.
- If you want HDF4 SD support, add 'C{--enable-hdf4}' and add
- the location of the HDF4 headers and library to C{CPPFLAGS} and C{LDFLAGS}.
+ Be sure to build with `--enable-netcdf-4 --enable-shared`, and set
+ `CPPFLAGS="-I $HDF5_DIR/include"` and `LDFLAGS="-L $HDF5_DIR/lib"`,
+ where `$HDF5_DIR` is the directory where HDF5 was installed.
+ If you want [OPeNDAP](http://opendap.org) support, add `--enable-dap`.
+ If you want HDF4 SD support, add `--enable-hdf4` and add
+ the location of the HDF4 headers and library to `$CPPFLAGS` and `$LDFLAGS`.
Install
@@ -62,412 +63,428 @@ Install
- install the requisite python modules and C libraries (see above). It's
easiest if all the C libs are built as shared libraries.
- - By default, the utility C{nc-config}, installed with netcdf 4.1.2 or higher,
+ - By default, the utility `nc-config`, installed with netcdf 4.1.2 or higher,
will be run used to determine where all the dependencies live.
- - If C{nc-config} is not in your default C{PATH}, rename the
- file C{setup.cfg.template} to C{setup.cfg}, then edit
+ - If `nc-config` is not in your default `$PATH`, rename the
+ file `setup.cfg.template` to `setup.cfg`, then edit
in a text editor (follow the instructions in the comments).
- In addition to specifying the path to C{nc-config},
+ In addition to specifying the path to `nc-config`,
you can manually set the paths to all the libraries and their include files
- (in case C{nc-config} does not do the right thing).
- - run C{python setup.py build}, then C{python setup.py install} (as root if
+ (in case `nc-config` does not do the right thing).
+ - run `python setup.py build`, then `python setup.py install` (as root if
necessary).
- - run the tests in the 'test' directory by running C{python run_all.py}.
+ - run the tests in the 'test' directory by running `python run_all.py`.
Tutorial
========
-1) Creating/Opening/Closing a netCDF file
------------------------------------------
+1. [Creating/Opening/Closing a netCDF file.](#section1)
+2. [Groups in a netCDF file.](#section2)
+3. [Dimensions in a netCDF file.](#section3)
+4. [Variables in a netCDF file.](#section4)
+5. [Attributes in a netCDF file.](#section5)
+6. [Writing data to and retrieving data from a netCDF variable.](#section6)
+7. [Dealing with time coordinates.](#section7)
+8. [Reading data from a multi-file netCDF dataset.](#section8)
+9. [Efficient compression of netCDF variables.](#section9)
+10. [Beyond homogenous arrays of a fixed type - compound data types.](#section10)
+11. [Variable-length (vlen) data types.](#section11)
+
+
+## <div id='section1'>1) Creating/Opening/Closing a netCDF file.
-To create a netCDF file from python, you simply call the L{Dataset}
+To create a netCDF file from python, you simply call the `netCDF4.Dataset`
constructor. This is also the method used to open an existing netCDF
-file. If the file is open for write access (C{w, r+} or C{a}), you may
+file. If the file is open for write access (`mode='w', 'r+'` or `'a'`), you may
write any type of data including new dimensions, groups, variables and
-attributes. netCDF files come in several flavors (C{NETCDF3_CLASSIC,
-NETCDF3_64BIT, NETCDF4_CLASSIC}, and C{NETCDF4}). The first two flavors
-are supported by version 3 of the netCDF library. C{NETCDF4_CLASSIC}
+attributes. netCDF files come in several flavors (`NETCDF3_CLASSIC,
+NETCDF3_64BIT, NETCDF4_CLASSIC`, and `NETCDF4`). The first two flavors
+are supported by version 3 of the netCDF library. `NETCDF4_CLASSIC`
files use the version 4 disk format (HDF5), but do not use any features
not found in the version 3 API. They can be read by netCDF 3 clients
only if they have been relinked against the netCDF 4 library. They can
-also be read by HDF5 clients. C{NETCDF4} files use the version 4 disk
+also be read by HDF5 clients. `NETCDF4` files use the version 4 disk
format (HDF5) and use the new features of the version 4 API. The
-C{netCDF4} module can read and write files in any of these formats. When
-creating a new file, the format may be specified using the C{format}
-keyword in the C{Dataset} constructor. The default format is
-C{NETCDF4}. To see how a given file is formatted, you can examine the
-C{data_model} L{Dataset} attribute. Closing the netCDF file is
-accomplished via the L{close<Dataset.close>} method of the L{Dataset}
+`netCDF4` module can read and write files in any of these formats. When
+creating a new file, the format may be specified using the `format`
+keyword in the `Dataset` constructor. The default format is
+`NETCDF4`. To see how a given file is formatted, you can examine the
+`data_model` attribute. Closing the netCDF file is
+accomplished via the `netCDF4.Dataset.close` method of the `netCDF4.Dataset`
instance.
Here's an example:
->>> from netCDF4 import Dataset
->>> rootgrp = Dataset('test.nc', 'w', format='NETCDF4')
->>> print rootgrp.data_model
-NETCDF4
->>>
->>> rootgrp.close()
+ :::python
+ >>> from netCDF4 import Dataset
+ >>> rootgrp = Dataset("test.nc", "w", format="NETCDF4")
+ >>> print rootgrp.data_model
+ NETCDF4
+ >>> rootgrp.close()
-Remote U{OPeNDAP<http://opendap.org>}-hosted datasets can be accessed for
-reading over http if a URL is provided to the L{Dataset} constructor instead of a
+Remote [OPeNDAP](http://opendap.org)-hosted datasets can be accessed for
+reading over http if a URL is provided to the `netCDF4.Dataset` constructor instead of a
filename. However, this requires that the netCDF library be built with
-OPenDAP support, via the C{--enable-dap} configure option (added in
+OPenDAP support, via the `--enable-dap` configure option (added in
version 4.0.1).
-2) Groups in a netCDF file
---------------------------
+## <div id='section2'>2) Groups in a netCDF file.
netCDF version 4 added support for organizing data in hierarchical
groups, which are analagous to directories in a filesystem. Groups serve
as containers for variables, dimensions and attributes, as well as other
-groups. A C{netCDF4.Dataset} defines creates a special group, called
+groups. A `netCDF4.Dataset` defines creates a special group, called
the 'root group', which is similar to the root directory in a unix
-filesystem. To create L{Group} instances, use the
-L{createGroup<Dataset.createGroup>} method of a L{Dataset} or L{Group}
-instance. L{createGroup<Dataset.createGroup>} takes a single argument, a
-python string containing the name of the new group. The new L{Group}
+filesystem. To create `netCDF4.Group` instances, use the
+`netCDF4.Dataset.createGroup` method of a `netCDF4.Dataset` or `netCDF4.Group`
+instance. `netCDF4.Dataset.createGroup` takes a single argument, a
+python string containing the name of the new group. The new `netCDF4.Group`
instances contained within the root group can be accessed by name using
-the C{groups} dictionary attribute of the L{Dataset} instance. Only
-C{NETCDF4} formatted files support Groups, if you try to create a Group
+the `groups` dictionary attribute of the `netCDF4.Dataset` instance. Only
+`NETCDF4` formatted files support Groups, if you try to create a Group
in a netCDF 3 file you will get an error message.
->>> rootgrp = Dataset('test.nc', 'a')
->>> fcstgrp = rootgrp.createGroup('forecasts')
->>> analgrp = rootgrp.createGroup('analyses')
->>> print rootgrp.groups
-OrderedDict([('forecasts', <netCDF4.Group object at 0x1b4b7b0>),
- ('analyses', <netCDF4.Group object at 0x1b4b970>)])
->>>
-
-Groups can exist within groups in a L{Dataset}, just as directories
-exist within directories in a unix filesystem. Each L{Group} instance
-has a C{'groups'} attribute dictionary containing all of the group
-instances contained within that group. Each L{Group} instance also has a
-C{'path'} attribute that contains a simulated unix directory path to
+ :::python
+ >>> rootgrp = Dataset("test.nc", "a")
+ >>> fcstgrp = rootgrp.createGroup("forecasts")
+ >>> analgrp = rootgrp.createGroup("analyses")
+ >>> print rootgrp.groups
+ OrderedDict([("forecasts",
+ <netCDF4._netCDF4.Group object at 0x1b4b7b0>),
+ ("analyses",
+ <netCDF4._netCDF4.Group object at 0x1b4b970>)])
+
+Groups can exist within groups in a `netCDF4.Dataset`, just as directories
+exist within directories in a unix filesystem. Each `netCDF4.Group` instance
+has a `groups` attribute dictionary containing all of the group
+instances contained within that group. Each `netCDF4.Group` instance also has a
+`path` attribute that contains a simulated unix directory path to
that group. To simplify the creation of nested groups, you can
-use a unix-like path as an argument to L{createGroup<Dataset.createGroup>}.
+use a unix-like path as an argument to `netCDF4.Dataset.createGroup`.
->>> fcstgrp1 = rootgrp.createGroup('/forecasts/model1')
->>> fcstgrp2 = rootgrp.createGroup('/forecasts/model2')
+ :::python
+ >>> fcstgrp1 = rootgrp.createGroup("/forecasts/model1")
+ >>> fcstgrp2 = rootgrp.createGroup("/forecasts/model2")
If any of the intermediate elements of the path do not exist, they are created,
-just as with the unix command C{'mkdir -p'}. If you try to create a group
+just as with the unix command `'mkdir -p'`. If you try to create a group
that already exists, no error will be raised, and the existing group will be
returned.
Here's an example that shows how to navigate all the groups in a
-L{Dataset}. The function C{walktree} is a Python generator that is used
-to walk the directory tree. Note that printing the L{Dataset} or L{Group}
+`netCDF4.Dataset`. The function `walktree` is a Python generator that is used
+to walk the directory tree. Note that printing the `netCDF4.Dataset` or `netCDF4.Group`
object yields summary information about it's contents.
->>> def walktree(top):
->>> values = top.groups.values()
->>> yield values
->>> for value in top.groups.values():
->>> for children in walktree(value):
->>> yield children
->>> print rootgrp
->>> for children in walktree(rootgrp):
->>> for child in children:
->>> print child
-<type 'netCDF4.Dataset'>
-root group (NETCDF4 file format):
- dimensions:
- variables:
+ :::python
+ >>> def walktree(top):
+ >>> values = top.groups.values()
+ >>> yield values
+ >>> for value in top.groups.values():
+ >>> for children in walktree(value):
+ >>> yield children
+ >>> print rootgrp
+ >>> for children in walktree(rootgrp):
+ >>> for child in children:
+ >>> print child
+ <type "netCDF4._netCDF4.Dataset">
+ root group (NETCDF4 file format):
+ dimensions:
+ variables:
groups: forecasts, analyses
-<type 'netCDF4.Group'>
-group /forecasts:
- dimensions:
- variables:
- groups: model1, model2
-<type 'netCDF4.Group'>
-group /analyses:
- dimensions:
- variables:
- groups:
-<type 'netCDF4.Group'>
-group /forecasts/model1:
- dimensions:
- variables:
- groups:
-<type 'netCDF4.Group'>
-group /forecasts/model2:
- dimensions:
- variables:
- groups:
->>>
-
-3) Dimensions in a netCDF file
-------------------------------
+ <type "netCDF4._netCDF4.Group">
+ group /forecasts:
+ dimensions:
+ variables:
+ groups: model1, model2
+ <type "netCDF4._netCDF4.Group">
+ group /analyses:
+ dimensions:
+ variables:
+ groups:
+ <type "netCDF4._netCDF4.Group">
+ group /forecasts/model1:
+ dimensions:
+ variables:
+ groups:
+ <type "netCDF4._netCDF4.Group">
+ group /forecasts/model2:
+ dimensions:
+ variables:
+ groups:
+
+## <div id='section3'>3) Dimensions in a netCDF file.
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 L{createDimension<Dataset.createDimension>} method of a L{Dataset}
-or L{Group} instance. A Python string is used to set the name of the
+the `netCDF4.Dataset.createDimension` method of a `netCDF4.Dataset`
+or `netCDF4.Group` 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 C{None} or 0. In this example, there both the C{time} and
-C{level} dimensions are unlimited. Having more than one unlimited
+value is set to `None` or 0. In this example, there both the `time` and
+`level` dimensions are unlimited. Having more than one unlimited
dimension is a new netCDF 4 feature, in netCDF 3 files there may be only
one, and it must be the first (leftmost) dimension of the variable.
->>> level = rootgrp.createDimension('level', None)
->>> time = rootgrp.createDimension('time', None)
->>> lat = rootgrp.createDimension('lat', 73)
->>> lon = rootgrp.createDimension('lon', 144)
+ :::python
+ >>> level = rootgrp.createDimension("level", None)
+ >>> time = rootgrp.createDimension("time", None)
+ >>> lat = rootgrp.createDimension("lat", 73)
+ >>> lon = rootgrp.createDimension("lon", 144)
-All of the L{Dimension} instances are stored in a python dictionary.
+All of the `netCDF4.Dimension` instances are stored in a python dictionary.
->>> print rootgrp.dimensions
-OrderedDict([('level', <netCDF4.Dimension object at 0x1b48030>),
- ('time', <netCDF4.Dimension object at 0x1b481c0>),
- ('lat', <netCDF4.Dimension object at 0x1b480f8>),
- ('lon', <netCDF4.Dimension object at 0x1b48a08>)])
->>>
+ :::python
+ >>> print rootgrp.dimensions
+ OrderedDict([("level", <netCDF4._netCDF4.Dimension object at 0x1b48030>),
+ ("time", <netCDF4._netCDF4.Dimension object at 0x1b481c0>),
+ ("lat", <netCDF4._netCDF4.Dimension object at 0x1b480f8>),
+ ("lon", <netCDF4._netCDF4.Dimension object at 0x1b48a08>)])
-Calling the python C{len} function with a L{Dimension} instance returns
+Calling the python `len` function with a `netCDF4.Dimension` instance returns
the current size of that dimension.
-The L{isunlimited<Dimension.isunlimited>} method of a L{Dimension} instance
+The `netCDF4.Dimension.isunlimited` method of a `netCDF4.Dimension` instance
can be used to determine if the dimensions is unlimited, or appendable.
->>> print len(lon)
-144
->>> print len.is_unlimited()
-False
->>> print time.is_unlimited()
-True
->>>
+ :::python
+ >>> print len(lon)
+ 144
+ >>> print len.is_unlimited()
+ False
+ >>> print time.is_unlimited()
+ True
-Printing the L{Dimension} object
+Printing the `netCDF4.Dimension` object
provides useful summary info, including the name and length of the dimension,
and whether it is unlimited.
->>> for dimobj in rootgrp.dimensions.values():
->>> print dimobj
-<type 'netCDF4.Dimension'> (unlimited): name = 'level', size = 0
-<type 'netCDF4.Dimension'> (unlimited): name = 'time', size = 0
-<type 'netCDF4.Dimension'>: name = 'lat', size = 73
-<type 'netCDF4.Dimension'>: name = 'lon', size = 144
-<type 'netCDF4.Dimension'> (unlimited): name = 'time', size = 0
->>>
+ :::python
+ >>> for dimobj in rootgrp.dimensions.values():
+ >>> print dimobj
+ <type "netCDF4._netCDF4.Dimension"> (unlimited): name = "level", size = 0
+ <type "netCDF4._netCDF4.Dimension"> (unlimited): name = "time", size = 0
+ <type "netCDF4._netCDF4.Dimension">: name = "lat", size = 73
+ <type "netCDF4._netCDF4.Dimension">: name = "lon", size = 144
+ <type "netCDF4._netCDF4.Dimension"> (unlimited): name = "time", size = 0
-L{Dimension} names can be changed using the
-L{renameDimension<Dataset.renameDimension>} method of a L{Dataset} or
-L{Group} instance.
+`netCDF4.Dimension` names can be changed using the
+`netCDF4.Datatset.renameDimension` method of a `netCDF4.Dataset` or
+`netCDF4.Group` instance.
-4) Variables in a netCDF file
------------------------------
+## <div id='section4'>4) Variables in a netCDF file.
netCDF variables behave much like python multidimensional array objects
-supplied by the U{numpy module <http://numpy.scipy.org>}. However,
+supplied by the [numpy module](http://numpy.scipy.org). However,
unlike numpy arrays, netCDF4 variables can be appended to along one or
more 'unlimited' dimensions. To create a netCDF variable, use the
-L{createVariable<Dataset.createVariable>} method of a L{Dataset} or
-L{Group} instance. The L{createVariable<Dataset.createVariable>} method
+`netCDF4.Dataset.createVariable` method of a `netCDF4.Dataset` or
+`netCDF4.Group` instance. The `netCDF4.Dataset.createVariable` 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
-L{createDimension<Dataset.createDimension>}). To create a scalar
+`netCDF4.Dataset.createDimension`). 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: C{'f4'} (32-bit floating point), C{'f8'} (64-bit floating
-point), C{'i4'} (32-bit signed integer), C{'i2'} (16-bit signed
-integer), C{'i8'} (64-bit singed integer), C{'i1'} (8-bit signed
-integer), C{'u1'} (8-bit unsigned integer), C{'u2'} (16-bit unsigned
-integer), C{'u4'} (32-bit unsigned integer), C{'u8'} (64-bit unsigned
-integer), or C{'S1'} (single-character string). The old Numeric
-single-character typecodes (C{'f'},C{'d'},C{'h'},
-C{'s'},C{'b'},C{'B'},C{'c'},C{'i'},C{'l'}), corresponding to
-(C{'f4'},C{'f8'},C{'i2'},C{'i2'},C{'i1'},C{'i1'},C{'S1'},C{'i4'},C{'i4'}),
+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), `'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). The old Numeric
+single-character typecodes (`'f'`,`'d'`,`'h'`,
+`'s'`,`'b'`,`'B'`,`'c'`,`'i'`,`'l'`), corresponding to
+(`'f4'`,`'f8'`,`'i2'`,`'i2'`,`'i1'`,`'i1'`,`'S1'`,`'i4'`,`'i4'`),
will also work. The unsigned integer types and the 64-bit integer type
-can only be used if the file format is C{NETCDF4}.
+can only be used if the file format is `NETCDF4`.
The dimensions themselves are usually also defined as variables, called
-coordinate variables. The L{createVariable<Dataset.createVariable>}
-method returns an instance of the L{Variable} class whose methods can be
+coordinate variables. The `netCDF4.Dataset.createVariable`
+method returns an instance of the `netCDF4.Variable` class whose methods can be
used later to access and set variable data and attributes.
->>> times = rootgrp.createVariable('time','f8',('time',))
->>> levels = rootgrp.createVariable('level','i4',('level',))
->>> latitudes = rootgrp.createVariable('latitude','f4',('lat',))
->>> longitudes = rootgrp.createVariable('longitude','f4',('lon',))
->>> # two dimensions unlimited.
->>> temp = rootgrp.createVariable('temp','f4',('time','level','lat','lon',))
-
-To get summary info on a L{Variable} instance in an interactive session, just print it.
-
->>> print temp
-<type 'netCDF4.Variable'>
-float32 temp(time, level, lat, lon)
- least_significant_digit: 3
- units: K
-unlimited dimensions: time, level
-current shape = (0, 0, 73, 144)
->>>
+ :::python
+ >>> times = rootgrp.createVariable("time","f8",("time",))
+ >>> levels = rootgrp.createVariable("level","i4",("level",))
+ >>> latitudes = rootgrp.createVariable("latitude","f4",("lat",))
+ >>> longitudes = rootgrp.createVariable("longitude","f4",("lon",))
+ >>> # two dimensions unlimited
+ >>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",))
+
+To get summary info on a `netCDF4.Variable` instance in an interactive session, just print it.
+
+ :::python
+ >>> print temp
+ <type "netCDF4._netCDF4.Variable">
+ float32 temp(time, level, lat, lon)
+ least_significant_digit: 3
+ units: K
+ unlimited dimensions: time, level
+ current shape = (0, 0, 73, 144)
You can use a path to create a Variable inside a hierarchy of groups.
->>> ftemp = rootgrp.createVariable('/forecasts/model1/temp','f4',('time','level','lat','lon',))
+ :::python
+ >>> ftemp = rootgrp.createVariable("/forecasts/model1/temp","f4",("time","level","lat","lon",))
If the intermediate groups do not yet exist, they will be created.
-You can also query a L{Dataset} or L{Group} instance directly to obtain L{Group} or
-L{Variable} instances using paths.
-
->>> print rootgrp['/forecasts/model1'] # a Group instance
-<type 'netCDF4._netCDF4.Group'>
-group /forecasts/model1:
- dimensions(sizes):
- variables(dimensions): float32 temp(time,level,lat,lon)
- groups:
->>>
-
->>> print rootgrp['/forecasts/model1/temp'] # a Variable instance
-<type 'netCDF4._netCDF4.Variable'>
-float32 temp(time, level, lat, lon)
-path = /forecasts/model1
-unlimited dimensions: time, level
-current shape = (0, 0, 73, 144)
-filling on, default _FillValue of 9.96920996839e+36 used
->>>
-
-All of the variables in the L{Dataset} or L{Group} are stored in a
+You can also query a `netCDF4.Dataset` or `netCDF4.Group` instance directly to obtain `netCDF4.Group` or
+`netCDF4.Variable` instances using paths.
+
+ :::python
+ >>> print rootgrp["/forecasts/model1"] # a Group instance
+ <type "netCDF4._netCDF4.Group">
+ group /forecasts/model1:
+ dimensions(sizes):
+ variables(dimensions): float32 temp(time,level,lat,lon)
+ groups:
+ >>> print rootgrp["/forecasts/model1/temp"] # a Variable instance
+ <type "netCDF4._netCDF4.Variable">
+ float32 temp(time, level, lat, lon)
+ path = /forecasts/model1
+ unlimited dimensions: time, level
+ current shape = (0, 0, 73, 144)
+ filling on, default _FillValue of 9.96920996839e+36 used
+
+All of the variables in the `netCDF4.Dataset` or `netCDF4.Group` are stored in a
Python dictionary, in the same way as the dimensions:
->>> print rootgrp.variables
-OrderedDict([('time', <netCDF4.Variable object at 0x1b4ba70>),
- ('level', <netCDF4.Variable object at 0x1b4bab0>),
- ('latitude', <netCDF4.Variable object at 0x1b4baf0>),
- ('longitude', <netCDF4.Variable object at 0x1b4bb30>),
- ('temp', <netCDF4.Variable object at 0x1b4bb70>)])
->>>
+ :::python
+ >>> print rootgrp.variables
+ OrderedDict([("time", <netCDF4.Variable object at 0x1b4ba70>),
+ ("level", <netCDF4.Variable object at 0x1b4bab0>),
+ ("latitude", <netCDF4.Variable object at 0x1b4baf0>),
+ ("longitude", <netCDF4.Variable object at 0x1b4bb30>),
+ ("temp", <netCDF4.Variable object at 0x1b4bb70>)])
-L{Variable} names can be changed using the
-L{renameVariable<Dataset.renameVariable>} method of a L{Dataset}
+`netCDF4.Variable` names can be changed using the
+`netCDF4.Dataset.renameVariable` method of a `netCDF4.Dataset`
instance.
-5) Attributes in a netCDF file
-------------------------------
+## <div id='section5'>5) Attributes in a netCDF file.
There are two types of attributes in a netCDF file, global and variable.
Global attributes provide information about a group, or the entire
-dataset, as a whole. L{Variable} attributes provide information about
+dataset, as a whole. `netCDF4.Variable` attributes provide information about
one of the variables in a group. Global attributes are set by assigning
-values to L{Dataset} or L{Group} instance variables. L{Variable}
-attributes are set by assigning values to L{Variable} instances
+values to `netCDF4.Dataset` or `netCDF4.Group` instance variables. `netCDF4.Variable`
+attributes are set by assigning values to `netCDF4.Variable` instances
variables. Attributes can be strings, numbers or sequences. Returning to
our example,
->>> import time
->>> rootgrp.description = 'bogus example script'
->>> rootgrp.history = 'Created ' + time.ctime(time.time())
->>> rootgrp.source = 'netCDF4 python module tutorial'
->>> latitudes.units = 'degrees north'
->>> longitudes.units = 'degrees east'
->>> levels.units = 'hPa'
->>> temp.units = 'K'
->>> times.units = 'hours since 0001-01-01 00:00:00.0'
->>> times.calendar = 'gregorian'
-
-The L{ncattrs<Dataset.ncattrs>} method of a L{Dataset}, L{Group} or
-L{Variable} instance can be used to retrieve the names of all the netCDF
+ :::python
+ >>> import time
+ >>> rootgrp.description = "bogus example script"
+ >>> rootgrp.history = "Created " + time.ctime(time.time())
+ >>> rootgrp.source = "netCDF4 python module tutorial"
+ >>> latitudes.units = "degrees north"
+ >>> longitudes.units = "degrees east"
+ >>> levels.units = "hPa"
+ >>> temp.units = "K"
+ >>> times.units = "hours since 0001-01-01 00:00:00.0"
+ >>> times.calendar = "gregorian"
+
+The `netCDF4.Dataset.ncattrs` method of a `netCDF4.Dataset`, `netCDF4.Group` or
+`netCDF4.Variable` 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 C{dir} Python function will return a bunch of private methods
+built-in `dir` Python function will return a bunch of private methods
and attributes that cannot (or should not) be modified by the user.
->>> for name in rootgrp.ncattrs():
->>> print 'Global attr', name, '=', getattr(rootgrp,name)
-Global attr description = bogus example script
-Global attr history = Created Mon Nov 7 10.30:56 2005
-Global attr source = netCDF4 python module tutorial
+ :::python
+ >>> for name in rootgrp.ncattrs():
+ >>> print "Global attr", name, "=", getattr(rootgrp,name)
+ Global attr description = bogus example script
+ Global attr history = Created Mon Nov 7 10.30:56 2005
+ Global attr source = netCDF4 python module tutorial
-The C{__dict__} attribute of a L{Dataset}, L{Group} or L{Variable}
+The `__dict__` attribute of a `netCDF4.Dataset`, `netCDF4.Group` or `netCDF4.Variable`
instance provides all the netCDF attribute name/value pairs in a python
dictionary:
->>> print rootgrp.__dict__
-OrderedDict([(u'description', u'bogus example script'),
- (u'history', u'Created Thu Mar 3 19:30:33 2011'),
- (u'source', u'netCDF4 python module tutorial')])
+ :::python
+ >>> print rootgrp.__dict__
+ OrderedDict([(u"description", u"bogus example script"),
+ (u"history", u"Created Thu Mar 3 19:30:33 2011"),
+ (u"source", u"netCDF4 python module tutorial")])
-Attributes can be deleted from a netCDF L{Dataset}, L{Group} or
-L{Variable} using the python C{del} statement (i.e. C{del grp.foo}
-removes the attribute C{foo} the the group C{grp}).
+Attributes can be deleted from a netCDF `netCDF4.Dataset`, `netCDF4.Group` or
+`netCDF4.Variable` using the python `del` statement (i.e. `del grp.foo`
+removes the attribute `foo` the the group `grp`).
-6) Writing data to and retrieving data from a netCDF variable
--------------------------------------------------------------
+## <div id='section6'>6) Writing data to and retrieving data from a netCDF variable.
-Now that you have a netCDF L{Variable} instance, how do you put data
+Now that you have a netCDF `netCDF4.Variable` instance, how do you put data
into it? You can just treat it like an array and assign data to a slice.
->>> import numpy
->>> lats = numpy.arange(-90,91,2.5)
->>> lons = numpy.arange(-180,180,2.5)
->>> latitudes[:] = lats
->>> longitudes[:] = lons
->>> print 'latitudes =\\n',latitudes[:]
-latitudes =
-[-90. -87.5 -85. -82.5 -80. -77.5 -75. -72.5 -70. -67.5 -65. -62.5
- -60. -57.5 -55. -52.5 -50. -47.5 -45. -42.5 -40. -37.5 -35. -32.5
- -30. -27.5 -25. -22.5 -20. -17.5 -15. -12.5 -10. -7.5 -5. -2.5
- 0. 2.5 5. 7.5 10. 12.5 15. 17.5 20. 22.5 25. 27.5
- 30. 32.5 35. 37.5 40. 42.5 45. 47.5 50. 52.5 55. 57.5
- 60. 62.5 65. 67.5 70. 72.5 75. 77.5 80. 82.5 85. 87.5
- 90. ]
->>>
-
-Unlike NumPy's array objects, netCDF L{Variable}
+ :::python
+ >>> import numpy
+ >>> lats = numpy.arange(-90,91,2.5)
+ >>> lons = numpy.arange(-180,180,2.5)
+ >>> latitudes[:] = lats
+ >>> longitudes[:] = lons
+ >>> print "latitudes =\\n",latitudes[:]
+ latitudes =
+ [-90. -87.5 -85. -82.5 -80. -77.5 -75. -72.5 -70. -67.5 -65. -62.5
+ -60. -57.5 -55. -52.5 -50. -47.5 -45. -42.5 -40. -37.5 -35. -32.5
+ -30. -27.5 -25. -22.5 -20. -17.5 -15. -12.5 -10. -7.5 -5. -2.5
+ 0. 2.5 5. 7.5 10. 12.5 15. 17.5 20. 22.5 25. 27.5
+ 30. 32.5 35. 37.5 40. 42.5 45. 47.5 50. 52.5 55. 57.5
+ 60. 62.5 65. 67.5 70. 72.5 75. 77.5 80. 82.5 85. 87.5
+ 90. ]
+
+Unlike NumPy's array objects, netCDF `netCDF4.Variable`
objects with unlimited dimensions will grow along those dimensions if you
assign data outside the currently defined range of indices.
->>> # append along two unlimited dimensions by assigning to slice.
->>> nlats = len(rootgrp.dimensions['lat'])
->>> nlons = len(rootgrp.dimensions['lon'])
->>> print 'temp shape before adding data = ',temp.shape
-temp shape before adding data = (0, 0, 73, 144)
->>>
->>> from numpy.random import uniform
->>> temp[0:5,0:10,:,:] = uniform(size=(5,10,nlats,nlons))
->>> print 'temp shape after adding data = ',temp.shape
-temp shape after adding data = (6, 10, 73, 144)
->>>
->>> # levels have grown, but no values yet assigned.
->>> print 'levels shape after adding pressure data = ',levels.shape
-levels shape after adding pressure data = (10,)
->>>
+ :::python
+ >>> # append along two unlimited dimensions by assigning to slice.
+ >>> nlats = len(rootgrp.dimensions["lat"])
+ >>> nlons = len(rootgrp.dimensions["lon"])
+ >>> print "temp shape before adding data = ",temp.shape
+ temp shape before adding data = (0, 0, 73, 144)
+ >>>
+ >>> from numpy.random import uniform
+ >>> temp[0:5,0:10,:,:] = uniform(size=(5,10,nlats,nlons))
+ >>> print "temp shape after adding data = ",temp.shape
+ temp shape after adding data = (6, 10, 73, 144)
+ >>>
+ >>> # levels have grown, but no values yet assigned.
+ >>> print "levels shape after adding pressure data = ",levels.shape
+ levels shape after adding pressure data = (10,)
Note that the size of the levels variable grows when data is appended
-along the C{level} dimension of the variable C{temp}, even though no
+along the `level` dimension of the variable `temp`, even though no
data has yet been assigned to levels.
->>> # now, assign data to levels dimension variable.
->>> levels[:] = [1000.,850.,700.,500.,300.,250.,200.,150.,100.,50.]
+ :::python
+ >>> # now, assign data to levels dimension variable.
+ >>> levels[:] = [1000.,850.,700.,500.,300.,250.,200.,150.,100.,50.]
However, that there are some differences between NumPy and netCDF
variable slicing rules. Slices behave as usual, being specified as a
-C{start:stop:step} triplet. Using a scalar integer index C{i} takes the ith
+`start:stop:step` triplet. Using a scalar integer index `i` takes the ith
element and reduces the rank of the output array by one. Boolean array and
integer sequence indexing behaves differently for netCDF variables
than for numpy arrays. Only 1-d boolean arrays and integer sequences are
allowed, and these indices work independently along each dimension (similar
to the way vector subscripts work in fortran). This means that
->>> temp[0, 0, [0,1,2,3], [0,1,2,3]]
+ :::python
+ >>> temp[0, 0, [0,1,2,3], [0,1,2,3]]
returns an array of shape (4,4) when slicing a netCDF variable, but for a
numpy array it returns an array of shape (4,).
-Similarly, a netCDF variable of shape C{(2,3,4,5)} indexed
-with C{[0, array([True, False, True]), array([False, True, True, True]), :]}
-would return a C{(2, 3, 5)} array. In NumPy, this would raise an error since
-it would be equivalent to C{[0, [0,1], [1,2,3], :]}. When slicing with integer
+Similarly, a netCDF variable of shape `(2,3,4,5)` indexed
+with `[0, array([True, False, True]), array([False, True, True, True]), :]`
+would return a `(2, 3, 5)` array. In NumPy, this would raise an error since
+it would be equivalent to `[0, [0,1], [1,2,3], :]`. When slicing with integer
sequences, the indices must be sorted in increasing order and contain no duplicates.
While this behaviour may cause some confusion for those used to NumPy's 'fancy indexing' rules,
it provides a very powerful way to extract data from multidimensional netCDF
@@ -475,128 +492,126 @@ variables by using logical operations on the dimension arrays to create slices.
For example,
->>> tempdat = temp[::2, [1,3,6], lats>0, lons>0]
+ :::python
+ >>> tempdat = temp[::2, [1,3,6], lats>0, lons>0]
will extract time indices 0,2 and 4, pressure levels
850, 500 and 200 hPa, all Northern Hemisphere latitudes and Eastern
Hemisphere longitudes, resulting in a numpy array of shape (3, 3, 36, 71).
->>> print 'shape of fancy temp slice = ',tempdat.shape
-shape of fancy temp slice = (3, 3, 36, 71)
->>>
+ :::python
+ >>> print "shape of fancy temp slice = ",tempdat.shape
+ shape of fancy temp slice = (3, 3, 36, 71)
-B{Special note for scalar variables}: To extract data from a scalar variable
-C{v} with no associated dimensions, use an Ellipsis slice (C{v[...]}). The result
-will be a numpy scalar object.
+***Special note for scalar variables***: To extract data from a scalar variable
+`v` with no associated dimensions, use `np.asarray(v)` or `v[...]`. The result
+will be a numpy scalar array.
-7) Dealing with time coordinates
---------------------------------
+## <div id='section7'>7) Dealing with time coordinates.
Time coordinate values pose a special challenge to netCDF users. Most
-metadata standards (such as CF and COARDS) specify that time should be
+metadata standards (such as CF) specify that time should be
measure relative to a fixed date using a certain calendar, with units
-specified like C{hours since YY:MM:DD hh-mm-ss}. These units can be
+specified like `hours since YY:MM:DD hh-mm-ss`. These units can be
awkward to deal with, without a utility to convert the values to and
-from calendar dates. The functione called L{num2date} and L{date2num} are
+from calendar dates. The functione called `netCDF4.num2date` and `netCDF4.date2num` are
provided with this package to do just that. Here's an example of how they
can be used:
->>> # fill in times.
->>> from datetime import datetime, timedelta
->>> from netCDF4 import num2date, date2num
->>> dates = [datetime(2001,3,1)+n*timedelta(hours=12) for n in range(temp.shape[0])]
->>> times[:] = date2num(dates,units=times.units,calendar=times.calendar)
->>> print 'time values (in units %s): ' % times.units+'\\n',times[:]
-time values (in units hours since January 1, 0001):
-[ 17533056. 17533068. 17533080. 17533092. 17533104.]
->>>
->>> dates = num2date(times[:],units=times.units,calendar=times.calendar)
->>> print 'dates corresponding to time values:\\n',dates
-dates corresponding to time values:
-[2001-03-01 00:00:00 2001-03-01 12:00:00 2001-03-02 00:00:00
- 2001-03-02 12:00:00 2001-03-03 00:00:00]
->>>
-
-L{num2date} converts numeric values of time in the specified C{units}
-and C{calendar} to datetime objects, and L{date2num} does the reverse.
-All the calendars currently defined in the U{CF metadata convention
-<http://cf-pcmdi.llnl.gov/documents/cf-conventions/>} are supported.
-A function called L{date2index} is also provided which returns the indices
+ :::python
+ >>> # fill in times.
+ >>> from datetime import datetime, timedelta
+ >>> from netCDF4 import num2date, date2num
+ >>> dates = [datetime(2001,3,1)+n*timedelta(hours=12) for n in range(temp.shape[0])]
+ >>> times[:] = date2num(dates,units=times.units,calendar=times.calendar)
+ >>> print "time values (in units %s): " % times.units+"\\n",times[:]
+ time values (in units hours since January 1, 0001):
+ [ 17533056. 17533068. 17533080. 17533092. 17533104.]
+ >>> dates = num2date(times[:],units=times.units,calendar=times.calendar)
+ >>> print "dates corresponding to time values:\\n",dates
+ dates corresponding to time values:
+ [2001-03-01 00:00:00 2001-03-01 12:00:00 2001-03-02 00:00:00
+ 2001-03-02 12:00:00 2001-03-03 00:00:00]
+
+`netCDF4.num2date` converts numeric values of time in the specified `units`
+and `calendar` to datetime objects, and `netCDF4.date2num` does the reverse.
+All the calendars currently defined in the
+[CF metadata convention](http://cf-pcmdi.llnl.gov/documents/cf-conventions/) are supported.
+A function called `netCDF4.date2index` is also provided which returns the indices
of a netCDF time variable corresponding to a sequence of datetime instances.
-8) Reading data from a multi-file netCDF dataset.
--------------------------------------------------
+## <div id='section8'>8) Reading data from a multi-file netCDF dataset.
If you want to read data from a variable that spans multiple netCDF files,
-you can use the L{MFDataset} class to read the data as if it were
+you can use the `netCDF4.MFDataset` class to read the data as if it were
contained in a single file. Instead of using a single filename to create
-a L{Dataset} instance, create a L{MFDataset} instance with either a list
+a `netCDF4.Dataset` instance, create a `netCDF4.MFDataset` 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). The files
-must in be in C{NETCDF3_64BIT}, C{NETCDF3_CLASSIC} or
-C{NETCDF4_CLASSIC format} (C{NETCDF4} formatted multi-file
+must in be in `NETCDF3_64BIT`, `NETCDF3_CLASSIC` or
+`NETCDF4_CLASSIC format` (`NETCDF4` formatted multi-file
datasets are not supported).
->>> for nfile in range(10):
->>> f = Dataset('mftest'+repr(nfile)+'.nc','w',format='NETCDF4_CLASSIC')
->>> f.createDimension('x',None)
->>> x = f.createVariable('x','i',('x',))
->>> x[0:10] = numpy.arange(nfile*10,10*(nfile+1))
->>> f.close()
-
-Now read all the files back in at once with L{MFDataset}
-
->>> from netCDF4 import MFDataset
->>> f = MFDataset('mftest*nc')
->>> print f.variables['x'][:]
-[ 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
- 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
- 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
- 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]
->>>
-
-Note that MFDataset can only be used to read, not write, multi-file
+ :::python
+ >>> for nf in range(10):
+ >>> f = Dataset("mftest%s.nc" % nf,"w")
+ >>> f.createDimension("x",None)
+ >>> x = f.createVariable("x","i",("x",))
+ >>> x[0:10] = numpy.arange(nf*10,10*(nf+1))
+ >>> f.close()
+
+Now read all the files back in at once with `netCDF4.MFDataset`
+
+ :::python
+ >>> from netCDF4 import MFDataset
+ >>> f = MFDataset("mftest*nc")
+ >>> print f.variables["x"][:]
+ [ 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
+ 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
+ 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
+ 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]
+
+Note that `netCDF4.MFDataset` can only be used to read, not write, multi-file
datasets.
-9) Efficient compression of netCDF variables
---------------------------------------------
+## <div id='section9'>9) Efficient compression of netCDF variables.
-Data stored in netCDF 4 L{Variable} objects can be compressed and
+Data stored in netCDF 4 `netCDF4.Variable` objects can be compressed and
decompressed on the fly. The parameters for the compression are
-determined by the C{zlib}, C{complevel} and C{shuffle} keyword arguments
-to the L{createVariable<Dataset.createVariable>} method. To turn on
-compression, set C{zlib=True}. The C{complevel} keyword regulates the
+determined by the `zlib`, `complevel` and `shuffle` keyword arguments
+to the `netCDF4.Dataset.createVariable` method. To turn on
+compression, set `zlib=True`. The `complevel` keyword regulates the
speed and efficiency of the compression (1 being fastest, but lowest
compression ratio, 9 being slowest but best compression ratio). The
-default value of C{complevel} is 4. Setting C{shuffle=False} will turn
+default value of `complevel` is 4. Setting `shuffle=False` will turn
off the HDF5 shuffle filter, which de-interlaces a block of data before
compression by reordering the bytes. The shuffle filter can
significantly improve compression ratios, and is on by default. Setting
-C{fletcher32} keyword argument to
-L{createVariable<Dataset.createVariable>} to C{True} (it's C{False} by
+`fletcher32` keyword argument to
+`netCDF4.Dataset.createVariable` to `True` (it's `False` by
default) enables the Fletcher32 checksum algorithm for error detection.
It's also possible to set the HDF5 chunking parameters and endian-ness
-of the binary data stored in the HDF5 file with the C{chunksizes}
-and C{endian} keyword arguments to
-L{createVariable<Dataset.createVariable>}. These keyword arguments only
-are relevant for C{NETCDF4} and C{NETCDF4_CLASSIC} files (where the
+of the binary data stored in the HDF5 file with the `chunksizes`
+and `endian` keyword arguments to
+`netCDF4.Dataset.createVariable`. These keyword arguments only
+are relevant for `NETCDF4` and `NETCDF4_CLASSIC` files (where the
underlying file format is HDF5) and are silently ignored if the file
-format is C{NETCDF3_CLASSIC} or C{NETCDF3_64BIT},
+format is `NETCDF3_CLASSIC` or `NETCDF3_64BIT`,
If your data only has a certain number of digits of precision (say for
example, it is temperature data that was measured with a precision of
0.1 degrees), you can dramatically improve zlib compression by
-quantizing (or truncating) the data using the C{least_significant_digit}
-keyword argument to L{createVariable<Dataset.createVariable>}. The least
+quantizing (or truncating) the data using the `least_significant_digit`
+keyword argument to `netCDF4.Dataset.createVariable`. The least
significant digit is the power of ten of the smallest decimal place in
the data that is a reliable value. For example if the data has a
-precision of 0.1, then setting C{least_significant_digit=1} will cause
-data the data to be quantized using C{numpy.around(scale*data)/scale}, where
+precision of 0.1, then setting `least_significant_digit=1` will cause
+data the data to be quantized using `numpy.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). Effectively, this makes the compression
'lossy' instead of 'lossless', that is some precision in the data is
@@ -604,20 +619,22 @@ sacrificed for the sake of disk space.
In our example, try replacing the line
->>> temp = rootgrp.createVariable('temp','f4',('time','level','lat','lon',))
+ :::python
+ >>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",))
with
->>> temp = dataset.createVariable('temp','f4',('time','level','lat','lon',),zlib=True)
+ :::python
+ >>> temp = dataset.createVariable("temp","f4",("time","level","lat","lon",),zlib=True)
and then
->>> temp = dataset.createVariable('temp','f4',('time','level','lat','lon',),zlib=True,least_significant_digit=3)
+ :::python
+ >>> temp = dataset.createVariable("temp","f4",("time","level","lat","lon",),zlib=True,least_significant_digit=3)
and see how much smaller the resulting files are.
-10) Beyond homogenous arrays of a fixed type - compound data types
-------------------------------------------------------------------
+## <div id='section10'>10) Beyond homogenous arrays of a fixed type - compound data types.
Compound data types map directly to numpy structured (a.k.a 'record'
arrays). Structured arrays are akin to C structs, or derived types
@@ -629,71 +646,71 @@ location for scattered (point) data. You can then access all the
information for a point by reading one variable, instead of reading
different parameters from different variables. Compound data types
are created from the corresponding numpy data type using the
-L{createCompoundType<Dataset.createCompoundType>} method of a L{Dataset} or L{Group} instance.
+`netCDF4.Dataset.createCompoundType` method of a `netCDF4.Dataset` or `netCDF4.Group` instance.
Since there is no native complex data type in netcdf, compound types are handy
for storing numpy complex arrays. Here's an example:
->>> f = Dataset('complex.nc','w')
->>> size = 3 # length of 1-d complex array
->>> # create sample complex data.
->>> datac = numpy.exp(1j*(1.+numpy.linspace(0, numpy.pi, size)))
->>> # create complex128 compound data type.
->>> complex128 = numpy.dtype([('real',numpy.float64),('imag',numpy.float64)])
->>> complex128_t = f.createCompoundType(complex128,'complex128')
->>> # create a variable with this data type, write some data to it.
->>> f.createDimension('x_dim',None)
->>> v = f.createVariable('cmplx_var',complex128_t,'x_dim')
->>> data = numpy.empty(size,complex128) # numpy structured array
->>> data['real'] = datac.real; data['imag'] = datac.imag
->>> v[:] = data # write numpy structured array to netcdf compound var
->>> # close and reopen the file, check the contents.
->>> f.close(); f = Dataset('complex.nc')
->>> v = f.variables['cmplx_var']
->>> datain = v[:] # read in all the data into a numpy structured array
->>> # create an empty numpy complex array
->>> datac2 = numpy.empty(datain.shape,numpy.complex128)
->>> # .. fill it with contents of structured array.
->>> datac2.real = datain['real']; datac2.imag = datain['imag']
->>> print datac.dtype,datac # original data
-complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j]
->>>
->>> print datac2.dtype,datac2 # data from file
-complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j]
->>>
+ :::python
+ >>> f = Dataset("complex.nc","w")
+ >>> size = 3 # length of 1-d complex array
+ >>> # create sample complex data.
+ >>> datac = numpy.exp(1j*(1.+numpy.linspace(0, numpy.pi, size)))
+ >>> # create complex128 compound data type.
+ >>> complex128 = numpy.dtype([("real",numpy.float64),("imag",numpy.float64)])
+ >>> complex128_t = f.createCompoundType(complex128,"complex128")
+ >>> # create a variable with this data type, write some data to it.
+ >>> f.createDimension("x_dim",None)
+ >>> v = f.createVariable("cmplx_var",complex128_t,"x_dim")
+ >>> data = numpy.empty(size,complex128) # numpy structured array
+ >>> data["real"] = datac.real; data["imag"] = datac.imag
+ >>> v[:] = data # write numpy structured array to netcdf compound var
+ >>> # close and reopen the file, check the contents.
+ >>> f.close(); f = Dataset("complex.nc")
+ >>> v = f.variables["cmplx_var"]
+ >>> datain = v[:] # read in all the data into a numpy structured array
+ >>> # create an empty numpy complex array
+ >>> datac2 = numpy.empty(datain.shape,numpy.complex128)
+ >>> # .. fill it with contents of structured array.
+ >>> datac2.real = datain["real"]; datac2.imag = datain["imag"]
+ >>> print datac.dtype,datac # original data
+ complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j]
+ >>>
+ >>> print datac2.dtype,datac2 # data from file
+ complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j]
Compound types can be nested, but you must create the 'inner'
-ones first. All of the compound types defined for a L{Dataset} or L{Group} are stored in a
+ones first. All of the compound types defined for a `netCDF4.Dataset` or `netCDF4.Group` are stored in a
Python dictionary, just like variables and dimensions. As always, printing
objects gives useful summary information in an interactive session:
->>> print f
-<type 'netCDF4.Dataset'>
-root group (NETCDF4 file format):
- dimensions: x_dim
- variables: cmplx_var
- groups:
-<type 'netCDF4.Variable'>
->>> print f.variables['cmplx_var']
-compound cmplx_var(x_dim)
-compound data type: [('real', '<f8'), ('imag', '<f8')]
-unlimited dimensions: x_dim
-current shape = (3,)
->>> print f.cmptypes
-OrderedDict([('complex128', <netCDF4.CompoundType object at 0x1029eb7e8>)])
->>> print f.cmptypes['complex128']
-<type 'netCDF4.CompoundType'>: name = 'complex128', numpy dtype = [(u'real','<f8'), (u'imag', '<f8')]
->>>
-
-11) Variable-length (vlen) data types
--------------------------------------
+ :::python
+ >>> print f
+ <type "netCDF4._netCDF4.Dataset">
+ root group (NETCDF4 file format):
+ dimensions: x_dim
+ variables: cmplx_var
+ groups:
+ <type "netCDF4._netCDF4.Variable">
+ >>> print f.variables["cmplx_var"]
+ compound cmplx_var(x_dim)
+ compound data type: [("real", "<f8"), ("imag", "<f8")]
+ unlimited dimensions: x_dim
+ current shape = (3,)
+ >>> print f.cmptypes
+ OrderedDict([("complex128", <netCDF4.CompoundType object at 0x1029eb7e8>)])
+ >>> print f.cmptypes["complex128"]
+ <type "netCDF4._netCDF4.CompoundType">: name = "complex128", numpy dtype = [(u"real","<f8"), (u"imag", "<f8")]
+
+## <div id='section11'>11) Variable-length (vlen) data types.
NetCDF 4 has support for variable-length or "ragged" arrays. These are arrays
of variable length sequences having the same type. To create a variable-length
-data type, use the L{createVLType<Dataset.createVLType>} method
-method of a L{Dataset} or L{Group} instance.
+data type, use the `netCDF4.Dataset.createVLType` method
+method of a `netCDF4.Dataset` or `netCDF4.Group` instance.
->>> f = Dataset('tst_vlen.nc','w')
->>> vlen_t = f.createVLType(numpy.int32, 'phony_vlen')
+ :::python
+ >>> f = Dataset("tst_vlen.nc","w")
+ >>> vlen_t = f.createVLType(numpy.int32, "phony_vlen")
The numpy datatype of the variable-length sequences and the name of the
new datatype must be specified. Any of the primitive datatypes can be
@@ -701,96 +718,97 @@ used (signed and unsigned integers, 32 and 64 bit floats, and characters),
but compound data types cannot.
A new variable can then be created using this datatype.
->>> x = f.createDimension('x',3)
->>> y = f.createDimension('y',4)
->>> vlvar = f.createVariable('phony_vlen_var', vlen_t, ('y','x'))
+ :::python
+ >>> x = f.createDimension("x",3)
+ >>> y = f.createDimension("y",4)
+ >>> vlvar = f.createVariable("phony_vlen_var", vlen_t, ("y","x"))
Since there is no native vlen datatype in numpy, vlen arrays are represented
-in python as object arrays (arrays of dtype C{object}). These are arrays whose
+in python as object arrays (arrays of dtype `object`). These are arrays whose
elements are Python object pointers, and can contain any type of python object.
For this application, they must contain 1-D numpy arrays all of the same type
but of varying length.
-In this case, they contain 1-D numpy C{int32} arrays of random length betwee
+In this case, they contain 1-D numpy `int32` arrays of random length betwee
1 and 10.
->>> import random
->>> data = numpy.empty(len(y)*len(x),object)
->>> for n in range(len(y)*len(x)):
->>> data[n] = numpy.arange(random.randint(1,10),dtype='int32')+1
->>> data = numpy.reshape(data,(len(y),len(x)))
->>> vlvar[:] = data
->>> print 'vlen variable =\\n',vlvar[:]
-vlen variable =
-[[[ 1 2 3 4 5 6 7 8 9 10] [1 2 3 4 5] [1 2 3 4 5 6 7 8]]
- [[1 2 3 4 5 6 7] [1 2 3 4 5 6] [1 2 3 4 5]]
- [[1 2 3 4 5] [1 2 3 4] [1]]
- [[ 1 2 3 4 5 6 7 8 9 10] [ 1 2 3 4 5 6 7 8 9 10]
- [1 2 3 4 5 6 7 8]]]
->>> print f
-<type 'netCDF4.Dataset'>
-root group (NETCDF4 file format):
- dimensions: x, y
- variables: phony_vlen_var
- groups:
->>> print f.variables['phony_vlen_var']
-<type 'netCDF4.Variable'>
-vlen phony_vlen_var(y, x)
-vlen data type: int32
-unlimited dimensions:
-current shape = (4, 3)
->>> print f.VLtypes['phony_vlen']
-<type 'netCDF4.VLType'>: name = 'phony_vlen', numpy dtype = int32
->>>
+ :::python
+ >>> import random
+ >>> data = numpy.empty(len(y)*len(x),object)
+ >>> for n in range(len(y)*len(x)):
+ >>> data[n] = numpy.arange(random.randint(1,10),dtype="int32")+1
+ >>> data = numpy.reshape(data,(len(y),len(x)))
+ >>> vlvar[:] = data
+ >>> print "vlen variable =\\n",vlvar[:]
+ vlen variable =
+ [[[ 1 2 3 4 5 6 7 8 9 10] [1 2 3 4 5] [1 2 3 4 5 6 7 8]]
+ [[1 2 3 4 5 6 7] [1 2 3 4 5 6] [1 2 3 4 5]]
+ [[1 2 3 4 5] [1 2 3 4] [1]]
+ [[ 1 2 3 4 5 6 7 8 9 10] [ 1 2 3 4 5 6 7 8 9 10]
+ [1 2 3 4 5 6 7 8]]]
+ >>> print f
+ <type "netCDF4._netCDF4.Dataset">
+ root group (NETCDF4 file format):
+ dimensions: x, y
+ variables: phony_vlen_var
+ groups:
+ >>> print f.variables["phony_vlen_var"]
+ <type "netCDF4._netCDF4.Variable">
+ vlen phony_vlen_var(y, x)
+ vlen data type: int32
+ unlimited dimensions:
+ current shape = (4, 3)
+ >>> print f.VLtypes["phony_vlen"]
+ <type "netCDF4._netCDF4.VLType">: name = "phony_vlen", numpy dtype = int32
Numpy object arrays containing python strings can also be written as vlen
variables, For vlen strings, you don't need to create a vlen data type.
-Instead, simply use the python C{str} builtin (or a numpy string datatype
+Instead, simply use the python `str` builtin (or a numpy string datatype
with fixed length greater than 1) when calling the
-L{createVariable<Dataset.createVariable>} method.
+`netCDF4.Dataset.createVariable` method.
->>> z = f.createDimension('z',10)
->>> strvar = rootgrp.createVariable('strvar', str, 'z')
+ :::python
+ >>> z = f.createDimension("z",10)
+ >>> strvar = rootgrp.createVariable("strvar", str, "z")
In this example, an object array is filled with random python strings with
random lengths between 2 and 12 characters, and the data in the object
array is assigned to the vlen string variable.
->>> chars = '1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
->>> data = numpy.empty(10,'O')
->>> for n in range(10):
->>> stringlen = random.randint(2,12)
->>> data[n] = ''.join([random.choice(chars) for i in range(stringlen)])
->>> strvar[:] = data
->>> print 'variable-length string variable:\\n',strvar[:]
-variable-length string variable:
-[aDy29jPt jd7aplD b8t4RM jHh8hq KtaPWF9cQj Q1hHN5WoXSiT MMxsVeq td LUzvVTzj
- 5DS9X8S]
->>> print f
-<type 'netCDF4.Dataset'>
-root group (NETCDF4 file format):
- dimensions: x, y, z
- variables: phony_vlen_var, strvar
- groups:
->>> print f.variables['strvar']
-<type 'netCDF4.Variable'>
-vlen strvar(z)
-vlen data type: <type 'str'>
-unlimited dimensions:
-current size = (10,)
->>>
+ :::python
+ >>> chars = "1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
+ >>> data = numpy.empty(10,"O")
+ >>> for n in range(10):
+ >>> stringlen = random.randint(2,12)
+ >>> data[n] = "".join([random.choice(chars) for i in range(stringlen)])
+ >>> strvar[:] = data
+ >>> print "variable-length string variable:\\n",strvar[:]
+ variable-length string variable:
+ [aDy29jPt 5DS9X8 jd7aplD b8t4RM jHh8hq KtaPWF9cQj Q1hHN5WoXSiT MMxsVeq tdLUzvVTzj]
+ >>> print f
+ <type "netCDF4._netCDF4.Dataset">
+ root group (NETCDF4 file format):
+ dimensions: x, y, z
+ variables: phony_vlen_var, strvar
+ groups:
+ >>> print f.variables["strvar"]
+ <type "netCDF4._netCDF4.Variable">
+ vlen strvar(z)
+ vlen data type: <type "str">
+ unlimited dimensions:
+ current size = (10,)
It is also possible to set contents of vlen string variables with numpy arrays
of any string or unicode data type. Note, however, that accessing the contents
-of such variables will always return numpy arrays with dtype C{object}.
+of such variables will always return numpy arrays with dtype `object`.
-All of the code in this tutorial is available in C{examples/tutorial.py},
-Unit tests are in the C{test} directory.
+All of the code in this tutorial is available in `examples/tutorial.py`,
+Unit tests are in the `test` directory.
- at contact: Jeffrey Whitaker <jeffrey.s.whitaker at noaa.gov>
+**contact**: Jeffrey Whitaker <jeffrey.s.whitaker at noaa.gov>
- at copyright: 2008 by Jeffrey Whitaker.
+**copyright**: 2008 by Jeffrey Whitaker.
- at license: Permission to use, copy, modify, and distribute this software and
+**license**: 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
@@ -824,7 +842,7 @@ except ImportError:
# python3: zip is already python2's itertools.izip
pass
-__version__ = "1.1.8"
+__version__ = "1.1.9"
# Initialize numpy
import posixpath
@@ -856,7 +874,7 @@ def _gethdf5libversion():
def getlibversion():
"""
-getlibversion()
+**`getlibversion()`**
returns a string describing the version of the netcdf library
used to build the module, and when it was built.
@@ -1114,8 +1132,8 @@ cdef _set_att(grp, int varid, name, value):
raise AttributeError((<char *>nc_strerror(ierr)).decode('ascii'))
cdef _get_types(group):
- # Private function to create L{CompoundType} or L{VLType} instances for all the
- # compound or VLEN types in a L{Group} or L{Dataset}.
+ # Private function to create `netCDF4.CompoundType` or `netCDF4.VLType` instances for all the
+ # compound or VLEN types in a `netCDF4.Group` or `netCDF4.Dataset`.
cdef int ierr, ntypes, classp, n, _grpid
cdef nc_type xtype
cdef nc_type typeids[NC_MAX_VARS]
@@ -1161,8 +1179,8 @@ cdef _get_types(group):
return cmptypes, vltypes
cdef _get_dims(group):
- # Private function to create L{Dimension} instances for all the
- # dimensions in a L{Group} or Dataset
+ # Private function to create `netCDF4.Dimension` instances for all the
+ # dimensions in a `netCDF4.Group` or Dataset
cdef int ierr, numdims, n, _grpid
cdef int dimids[NC_MAX_DIMS]
cdef char namstring[NC_MAX_NAME+1]
@@ -1193,8 +1211,8 @@ cdef _get_dims(group):
return dimensions
cdef _get_grps(group):
- # Private function to create L{Group} instances for all the
- # groups in a L{Group} or Dataset
+ # Private function to create `netCDF4.Group` instances for all the
+ # groups in a `netCDF4.Group` or Dataset
cdef int ierr, numgrps, n, _grpid
cdef int *grpids
cdef char namstring[NC_MAX_NAME+1]
@@ -1204,7 +1222,7 @@ cdef _get_grps(group):
ierr = nc_inq_grps(_grpid, &numgrps, NULL)
if ierr != NC_NOERR:
raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
- # create dictionary containing L{Group} instances for groups in this group
+ # create dictionary containing `netCDF4.Group` instances for groups in this group
groups = OrderedDict()
if numgrps > 0:
grpids = <int *>malloc(sizeof(int) * numgrps)
@@ -1223,8 +1241,8 @@ cdef _get_grps(group):
return groups
cdef _get_vars(group):
- # Private function to create L{Variable} instances for all the
- # variables in a L{Group} or Dataset
+ # Private function to create `netCDF4.Variable` instances for all the
+ # variables in a `netCDF4.Group` or Dataset
cdef int ierr, numvars, n, nn, numdims, varid, classp, iendian, _grpid
cdef int *varids
cdef int dim_sizes[NC_MAX_DIMS]
@@ -1269,13 +1287,16 @@ cdef _get_vars(group):
raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
# get endian-ness of variable.
endianness = None
- with nogil:
- ierr = nc_inq_var_endian(_grpid, varid, &iendian)
- if ierr == NC_NOERR:
- if iendian == NC_ENDIAN_LITTLE:
- endianness = '<'
- elif iendian == NC_ENDIAN_BIG:
- endianness = '>'
+ # assume data returned by HDF4 lib has already been byte-swapped
+ # (if needed) so that it is in native endian format (issue #391).
+ if group.disk_format != 'HDF4':
+ with nogil:
+ ierr = nc_inq_var_endian(_grpid, varid, &iendian)
+ if ierr == NC_NOERR:
+ if iendian == NC_ENDIAN_LITTLE:
+ endianness = '<'
+ elif iendian == NC_ENDIAN_BIG:
+ endianness = '>'
# check to see if it is a supported user-defined type.
try:
datatype = _nctonptype[xtype]
@@ -1347,135 +1368,183 @@ cdef _get_vars(group):
_private_atts =\
['_grpid','_grp','_varid','groups','dimensions','variables','dtype','data_model','disk_format',
'_nunlimdim','path','parent','ndim','mask','scale','cmptypes','vltypes','_isprimitive',
- 'file_format','_isvlen','_iscompound','_cmptype','_vltype','keepweakref','name','__orthogoral_indexing__']
-
+ 'file_format','_isvlen','_iscompound','_cmptype','_vltype','name','__orthogoral_indexing__','keepweakref']
+__pdoc__ = {}
cdef class Dataset:
"""
-Dataset(self, filename, mode="r", clobber=True, diskless=False, persist=False, keepweakref=False, format='NETCDF4')
-
-A netCDF L{Dataset} is a collection of dimensions, groups, variables and
+A netCDF `netCDF4.Dataset` is a collection of dimensions, groups, variables and
attributes. Together they describe the meaning of data and relations among
-data fields stored in a netCDF file.
-
-B{Parameters:}
-
-B{C{filename}} - Name of netCDF file to hold dataset.
-
-B{Keywords}:
-
-B{C{mode}} - access mode. C{r} means read-only; no data can be
-modified. C{w} means write; a new file is created, an existing file with
-the same name is deleted. C{a} and C{r+} mean append (in analogy with
-serial files); an existing file is opened for reading and writing.
-Appending C{s} to modes C{w}, C{r+} or C{a} will enable unbuffered shared
-access to C{NETCDF3_CLASSIC} or C{NETCDF3_64BIT} formatted files.
-Unbuffered acesss may be useful even if you don't need shared
-access, since it may be faster for programs that don't access data
-sequentially. This option is ignored for C{NETCDF4} and C{NETCDF4_CLASSIC}
-formatted files.
-
-B{C{clobber}} - if C{True} (default), opening a file with C{mode='w'}
-will clobber an existing file with the same name. if C{False}, an
-exception will be raised if a file with the same name already exists.
-
-B{C{format}} - underlying file format (one of C{'NETCDF4',
-'NETCDF4_CLASSIC', 'NETCDF3_CLASSIC'} or C{'NETCDF3_64BIT'}. Only
-relevant if C{mode = 'w'} (if C{mode = 'r','a'} or C{'r+'} the file format
-is automatically detected). Default C{'NETCDF4'}, which means the data is
-stored in an HDF5 file, using netCDF 4 API features. Setting
-C{format='NETCDF4_CLASSIC'} will create an HDF5 file, using only netCDF 3
-compatibile API features. netCDF 3 clients must be recompiled and linked
-against the netCDF 4 library to read files in C{NETCDF4_CLASSIC} format.
-C{'NETCDF3_CLASSIC'} is the classic netCDF 3 file format that does not
-handle 2+ Gb files very well. C{'NETCDF3_64BIT'} is the 64-bit offset
-version of the netCDF 3 file format, which fully supports 2+ GB files, but
-is only compatible with clients linked against netCDF version 3.6.0 or
-later.
-
-C{diskless} - create diskless (in memory) file. This is an experimental
-feature added to the C library after the netcdf-4.2 release.
-
-C{persist} - if diskless=True, persist file to disk when closed (default False).
-
-C{keepweakref} - if keepweakref=True, child Dimension and Variable instances will keep weak
-references to the parent Dataset or Group object. Default is False, which
-means strong references will be kept. Having Dimension and Variable instances
-keep a strong reference to the parent Dataset instance, which in turn keeps a
-reference to child Dimension and Variable instances, creates circular references.
-Circular references complicate garbage collection, which may mean increased
-memory usage for programs that create may Dataset instances with lots of
-Variables. Setting keepweakref to True allows Dataset instances to be
-garbage collected as soon as they go out of scope, potential reducing memory
-usage. However, in most cases this is not desirable, since the associated
-Variable instances may still be needed, but are rendered unusable when the
-parent Dataset instance is garbage collected.
-
-B{Returns:}
-
-a L{Dataset} instance. All further operations on the netCDF
-Dataset are accomplised via L{Dataset} instance methods.
+data fields stored in a netCDF file. See `netCDF4.Dataset.__init__` for more
+details.
A list of attribute names corresponding to global netCDF attributes
-defined for the L{Dataset} can be obtained with the L{ncattrs()} method.
+defined for the `netCDF4.Dataset` can be obtained with the
+`netCDF4.Dataset.ncattrs` method.
These attributes can be created by assigning to an attribute of the
-L{Dataset} instance. A dictionary containing all the netCDF attribute
-name/value pairs is provided by the C{__dict__} attribute of a
-L{Dataset} instance.
-
-The instance variables C{dimensions, variables, groups,
-cmptypes, data_model, disk_format} and C{path} are read-only (and should not be modified by the
-user).
-
- at ivar dimensions: The C{dimensions} dictionary maps the names of
-dimensions defined for the L{Group} or L{Dataset} to instances of the
-L{Dimension} class.
-
- at ivar variables: The C{variables} dictionary maps the names of variables
-defined for this L{Dataset} or L{Group} to instances of the L{Variable}
-class.
-
- at ivar groups: The groups dictionary maps the names of groups created for
-this L{Dataset} or L{Group} to instances of the L{Group} class (the
-L{Dataset} class is simply a special case of the L{Group} class which
-describes the root group in the netCDF file).
-
- at ivar cmptypes: The C{cmptypes} dictionary maps the names of
-compound types defined for the L{Group} or L{Dataset} to instances of the
-L{CompoundType} class.
-
- at ivar vltypes: The C{vltypes} dictionary maps the names of
-variable-length types defined for the L{Group} or L{Dataset} to instances of the
-L{VLType} class.
-
- at ivar data_model: The C{data_model} attribute describes the netCDF
-data model version, one of C{NETCDF3_CLASSIC}, C{NETCDF4},
-C{NETCDF4_CLASSIC} or C{NETCDF3_64BIT}.
-
- at ivar file_format: same as C{data_model}, retained for backwards
-compatibility.
-
- at ivar disk_format: The C{disk_format} attribute describes the underlying
-file format, one of C{NETCDF3}, C{HDF5}, C{HDF4},
-C{PNETCDF}, C{DAP2}, C{DAP4} or C{UNDEFINED}. Only available if using
-netcdf C library version >= 4.3.1, otherwise will always return C{UNDEFINED}.
-
- at ivar path: The C{path} attribute shows the location of the L{Group} in
-the L{Dataset} in a unix directory format (the names of groups in the
-hierarchy separated by backslashes). A L{Dataset} instance is the root
-group, so the path is simply C{'/'}.
-
- at ivar parent: The C{parent} attribute is a reference to the parent
-L{Group} instance. C{None} for a the root group or L{Dataset} instance"""
+`netCDF4.Dataset` instance. A dictionary containing all the netCDF attribute
+name/value pairs is provided by the `__dict__` attribute of a
+`netCDF4.Dataset` instance.
+
+The following class variables are read-only and should not be
+modified by the user.
+
+**`dimensions`**: The `dimensions` dictionary maps the names of
+dimensions defined for the `netCDF4.Group` or `netCDF4.Dataset` to instances of the
+`netCDF4.Dimension` class.
+
+**`variables`**: The `variables` dictionary maps the names of variables
+defined for this `netCDF4.Dataset` or `netCDF4.Group` to instances of the
+`netCDF4.Variable` class.
+
+**`groups`**: The groups dictionary maps the names of groups created for
+this `netCDF4.Dataset` or `netCDF4.Group` to instances of the `netCDF4.Group` class (the
+`netCDF4.Dataset` class is simply a special case of the `netCDF4.Group` class which
+describes the root group in the netCDF4 file).
+
+**`cmptypes`**: The `cmptypes` dictionary maps the names of
+compound types defined for the `netCDF4.Group` or `netCDF4.Dataset` to instances of the
+`netCDF4.CompoundType` class.
+
+**`vltypes`**: The `vltypes` dictionary maps the names of
+variable-length types defined for the `netCDF4.Group` or `netCDF4.Dataset` to instances
+of the `netCDF4.VLType` class.
+
+**`data_model`**: `data_model` describes the netCDF
+data model version, one of `NETCDF3_CLASSIC`, `NETCDF4`,
+`NETCDF4_CLASSIC` or `NETCDF3_64BIT`.
+
+**`file_format`**: same as `data_model`, retained for backwards compatibility.
+
+**`disk_format`**: `disk_format` describes the underlying
+file format, one of `NETCDF3`, `HDF5`, `HDF4`,
+`PNETCDF`, `DAP2`, `DAP4` or `UNDEFINED`. Only available if using
+netcdf C library version >= 4.3.1, otherwise will always return
+`UNDEFINED`.
+
+**`parent`**: `parent` is a reference to the parent
+`netCDF4.Group` instance. `None` for a the root group or `netCDF4.Dataset`
+instance.
+
+**`path`**: `path` shows the location of the `netCDF4.Group` in
+the `netCDF4.Dataset` in a unix directory format (the names of groups in the
+hierarchy separated by backslashes). A `netCDF4.Dataset` instance is the root
+group, so the path is simply `'/'`.
+
+**`keepweakref`**: If `True`, child Dimension and Variables objects only keep weak
+references to the parent Dataset or Group.
+"""
cdef object __weakref__
cdef public int _grpid
cdef public int _isopen
cdef public groups, dimensions, variables, disk_format, path, parent,\
- file_format, data_model, cmptypes, vltypes, keepweakref, \
- __orthogonal_indexing__
+ file_format, data_model, cmptypes, vltypes, __orthogonal_indexing__, \
+ keepweakref
+ # Docstrings for class variables (used by pdoc).
+ __pdoc__['Dataset.dimensions']=\
+ """The `dimensions` dictionary maps the names of
+ dimensions defined for the `netCDF4.Group` or `netCDF4.Dataset` to instances of the
+ `netCDF4.Dimension` class."""
+ __pdoc__['Dataset.variables']=\
+ """The `variables` dictionary maps the names of variables
+ defined for this `netCDF4.Dataset` or `netCDF4.Group` to instances of the `netCDF4.Variable`
+ class."""
+ __pdoc__['Dataset.groups']=\
+ """The groups dictionary maps the names of groups created for
+ this `netCDF4.Dataset` or `netCDF4.Group` to instances of the `netCDF4.Group` class (the
+ `netCDF4.Dataset` class is simply a special case of the `netCDF4.Group` class which
+ describes the root group in the netCDF4 file)."""
+ __pdoc__['Dataset.cmptypes']=\
+ """The `cmptypes` dictionary maps the names of
+ compound types defined for the `netCDF4.Group` or `netCDF4.Dataset` to instances of the
+ `netCDF4.CompoundType` class."""
+ __pdoc__['Dataset.vltypes']=\
+ """The `vltypes` dictionary maps the names of
+ variable-length types defined for the `netCDF4.Group` or `netCDF4.Dataset` to instances of the
+ `netCDF4.VLType` class."""
+ __pdoc__['Dataset.data_model']=\
+ """`data_model` describes the netCDF
+ data model version, one of `NETCDF3_CLASSIC`, `NETCDF4`,
+ `NETCDF4_CLASSIC` or `NETCDF3_64BIT`."""
+ __pdoc__['Dataset.file_format']=\
+ """same as `data_model`, retained for backwards compatibility."""
+ __pdoc__['Dataset.disk_format']=\
+ """`disk_format` describes the underlying
+ file format, one of `NETCDF3`, `HDF5`, `HDF4`,
+ `PNETCDF`, `DAP2`, `DAP4` or `UNDEFINED`. Only available if using
+ netcdf C library version >= 4.3.1, otherwise will always return
+ `UNDEFINED`."""
+ __pdoc__['Dataset.parent']=\
+ """`parent` is a reference to the parent
+ `netCDF4.Group` instance. `None` for a the root group or `netCDF4.Dataset` instance"""
+ __pdoc__['Dataset.path']=\
+ """`path` shows the location of the `netCDF4.Group` in
+ the `netCDF4.Dataset` in a unix directory format (the names of groups in the
+ hierarchy separated by backslashes). A `netCDF4.Dataset` instance is the root
+ group, so the path is simply `'/'`."""
+ __pdoc__['Dataset.keepweakref']=\
+ """If `True`, child Dimension and Variables objects only keep weak references to
+ the parent Dataset or Group."""
def __init__(self, filename, mode='r', clobber=True, format='NETCDF4',
diskless=False, persist=False, keepweakref=False, **kwargs):
+ """
+ **`__init__(self, filename, mode="r", clobber=True, diskless=False,
+ persist=False, weakref=False, format='NETCDF4')`**
+
+ `netCDF4.Dataset` constructor.
+
+ **`filename`**: Name of netCDF file to hold dataset.
+
+ **`mode`**: access mode. `r` means read-only; no data can be
+ modified. `w` means write; a new file is created, an existing file with
+ the same name is deleted. `a` and `r+` mean append (in analogy with
+ serial files); an existing file is opened for reading and writing.
+ Appending `s` to modes `w`, `r+` or `a` will enable unbuffered shared
+ access to `NETCDF3_CLASSIC` or `NETCDF3_64BIT` formatted files.
+ Unbuffered acesss may be useful even if you don't need shared
+ access, since it may be faster for programs that don't access data
+ sequentially. This option is ignored for `NETCDF4` and `NETCDF4_CLASSIC`
+ formatted files.
+
+ **`clobber`**: if `True` (default), opening a file with `mode='w'`
+ will clobber an existing file with the same name. if `False`, an
+ exception will be raised if a file with the same name already exists.
+
+ **`format`**: underlying file format (one of `'NETCDF4',
+ 'NETCDF4_CLASSIC', 'NETCDF3_CLASSIC'` or `'NETCDF3_64BIT'`. Only
+ relevant if `mode = 'w'` (if `mode = 'r','a'` or `'r+'` the file format
+ is automatically detected). Default `'NETCDF4'`, which means the data is
+ stored in an HDF5 file, using netCDF 4 API features. Setting
+ `format='NETCDF4_CLASSIC'` will create an HDF5 file, using only netCDF 3
+ compatibile API features. netCDF 3 clients must be recompiled and linked
+ against the netCDF 4 library to read files in `NETCDF4_CLASSIC` format.
+ `'NETCDF3_CLASSIC'` is the classic netCDF 3 file format that does not
+ handle 2+ Gb files very well. `'NETCDF3_64BIT'` is the 64-bit offset
+ version of the netCDF 3 file format, which fully supports 2+ GB files, but
+ is only compatible with clients linked against netCDF version 3.6.0 or
+ later.
+
+ **`diskless`**: If `True`, create diskless (in memory) file.
+ This is an experimental feature added to the C library after the
+ netcdf-4.2 release.
+
+ **`persist`**: if `diskless=True`, persist file to disk when closed
+ (default `False`).
+
+ **`keepweakref`**: if `True`, child Dimension and Variable instances will keep weak
+ references to the parent Dataset or Group object. Default is `False`, which
+ means strong references will be kept. Having Dimension and Variable instances
+ keep a strong reference to the parent Dataset instance, which in turn keeps a
+ reference to child Dimension and Variable instances, creates circular references.
+ Circular references complicate garbage collection, which may mean increased
+ memory usage for programs that create may Dataset instances with lots of
+ Variables. Setting `keepweakref=True` allows Dataset instances to be
+ garbage collected as soon as they go out of scope, potential reducing memory
+ usage. However, in most cases this is not desirable, since the associated
+ Variable instances may still be needed, but are rendered unusable when the
+ parent Dataset instance is garbage collected.
+ """
cdef int grpid, ierr, numgrps, numdims, numvars
cdef char *path
cdef char namstring[NC_MAX_NAME+1]
@@ -1599,7 +1668,7 @@ L{Group} instance. C{None} for a the root group or L{Dataset} instance"""
def filepath(self):
"""
-filepath(self)
+**`filepath(self)`**
Get the file system path (or the opendap URL) which was used to
open/create the Dataset. Requires netcdf >= 4.1.2"""
@@ -1649,9 +1718,10 @@ version 4.1.2 or higher of the netcdf C lib, and rebuild netcdf4-python."""
def close(self):
"""
-close(self)
+**`close(self)`**
-Close the Dataset."""
+Close the Dataset.
+ """
cdef int ierr
ierr = nc_close(self._grpid)
if ierr != NC_NOERR:
@@ -1666,9 +1736,9 @@ Close the Dataset."""
def sync(self):
"""
-sync(self)
+**`sync(self)`**
-Writes all buffered data in the L{Dataset} to the disk file."""
+Writes all buffered data in the `netCDF4.Dataset` to the disk file."""
cdef int ierr
ierr = nc_sync(self._grpid)
if ierr != NC_NOERR:
@@ -1684,16 +1754,16 @@ Writes all buffered data in the L{Dataset} to the disk file."""
def set_fill_on(self):
"""
-set_fill_on(self)
+**`set_fill_on(self)`**
-Sets the fill mode for a L{Dataset} open for writing to C{on}.
+Sets the fill mode for a `netCDF4.Dataset` open for writing to `on`.
This causes data to be pre-filled with fill values. The fill values can be
-controlled by the variable's C{_Fill_Value} attribute, but is usually
-sufficient to the use the netCDF default C{_Fill_Value} (defined
+controlled by the variable's `_Fill_Value` attribute, but is usually
+sufficient to the use the netCDF default `_Fill_Value` (defined
separately for each variable type). The default behavior of the netCDF
-library correspongs to C{set_fill_on}. Data which are equal to the
-C{_Fill_Value} indicate that the variable was created, but never written
+library correspongs to `set_fill_on`. Data which are equal to the
+`_Fill_Value` indicate that the variable was created, but never written
to."""
cdef int ierr, oldmode
ierr = nc_set_fill (self._grpid, NC_FILL, &oldmode)
@@ -1702,9 +1772,9 @@ to."""
def set_fill_off(self):
"""
-set_fill_off(self)
+**`set_fill_off(self)`**
-Sets the fill mode for a L{Dataset} open for writing to C{off}.
+Sets the fill mode for a `netCDF4.Dataset` open for writing to `off`.
This will prevent the data from being pre-filled with fill values, which
may result in some performance improvements. However, you must then make
@@ -1716,25 +1786,25 @@ sure the data is actually written before being read."""
def createDimension(self, dimname, size=None):
"""
-createDimension(self, dimname, size=None)
+**`createDimension(self, dimname, size=None)`**
-Creates a new dimension with the given C{dimname} and C{size}.
+Creates a new dimension with the given `dimname` and `size`.
-C{size} must be a positive integer or C{None}, which stands for
-"unlimited" (default is C{None}). Specifying a size of 0 also
-results in an unlimited dimension. The return value is the L{Dimension}
+`size` must be a positive integer or `None`, which stands for
+"unlimited" (default is `None`). Specifying a size of 0 also
+results in an unlimited dimension. The return value is the `netCDF4.Dimension`
class instance describing the new dimension. To determine the current
-maximum size of the dimension, use the C{len} function on the L{Dimension}
+maximum size of the dimension, use the `len` function on the `netCDF4.Dimension`
instance. To determine if a dimension is 'unlimited', use the
-C{isunlimited()} method of the L{Dimension} instance."""
+`netCDF4.Dimension.isunlimited` method of the `netCDF4.Dimension` instance."""
self.dimensions[dimname] = Dimension(self, dimname, size=size)
return self.dimensions[dimname]
def renameDimension(self, oldname, newname):
"""
-renameDimension(self, oldname, newname)
+**`renameDimension(self, oldname, newname)`**
-rename a L{Dimension} named C{oldname} to C{newname}."""
+rename a `netCDF4.Dimension` named `oldname` to `newname`."""
cdef char *namstring
bytestr = _strencode(newname)
namstring = bytestr
@@ -1756,17 +1826,17 @@ rename a L{Dimension} named C{oldname} to C{newname}."""
def createCompoundType(self, datatype, datatype_name):
"""
-createCompoundType(self, datatype, datatype_name)
+**`createCompoundType(self, datatype, datatype_name)`**
-Creates a new compound data type named C{datatype_name} from the numpy
-dtype object C{datatype}.
+Creates a new compound data type named `datatype_name` from the numpy
+dtype object `datatype`.
- at attention: If the new compound data type contains other compound data types
+***Note***: If the new compound data type contains other compound data types
(i.e. it is a 'nested' compound type, where not all of the elements
-are homogenous numeric data types), then the 'inner' compound types B{must} be
+are homogenous numeric data types), then the 'inner' compound types **must** be
created first.
-The return value is the L{CompoundType} class instance describing the new
+The return value is the `netCDF4.CompoundType` class instance describing the new
datatype."""
self.cmptypes[datatype_name] = CompoundType(self, datatype,\
datatype_name)
@@ -1774,12 +1844,12 @@ datatype."""
def createVLType(self, datatype, datatype_name):
"""
-createVLType(self, datatype, datatype_name)
+**`createVLType(self, datatype, datatype_name)`**
-Creates a new VLEN data type named C{datatype_name} from a numpy
-dtype object C{datatype}.
+Creates a new VLEN data type named `datatype_name` from a numpy
+dtype object `datatype`.
-The return value is the L{VLType} class instance describing the new
+The return value is the `netCDF4.VLType` class instance describing the new
datatype."""
self.vltypes[datatype_name] = VLType(self, datatype, datatype_name)
return self.vltypes[datatype_name]
@@ -1789,124 +1859,126 @@ datatype."""
chunksizes=None, endian='native', least_significant_digit=None,
fill_value=None, chunk_cache=None):
"""
-createVariable(self, varname, datatype, dimensions=(), zlib=False, complevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None)
+**`createVariable(self, varname, datatype, dimensions=(), zlib=False,
+complevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None,
+endian='native', least_significant_digit=None, fill_value=None)`**
-Creates a new variable with the given C{varname}, C{datatype}, and
-C{dimensions}. If dimensions are not given, the variable is assumed to be
+Creates a new variable with the given `varname`, `datatype`, and
+`dimensions`. If dimensions are not given, the variable is assumed to be
a scalar.
-If C{varname} is specified as a path, using forward slashes as in unix to
+If `varname` is specified as a path, using forward slashes as in unix to
separate components, then intermediate groups will be created as necessary
-For example, C{createVariable('/GroupA/GroupB/VarC'),('x','y'),float)} will create groups C{GroupA}
-and C{GroupA/GroupB}, plus the variable C{GroupA/GroupB/VarC}, if the preceding
+For example, `createVariable('/GroupA/GroupB/VarC'),('x','y'),float)` will create groups `GroupA`
+and `GroupA/GroupB`, plus the variable `GroupA/GroupB/VarC`, if the preceding
groups don't already exist.
-The C{datatype} can be a numpy datatype object, or a string that describes
-a numpy dtype object (like the C{dtype.str} attribue of a numpy array).
-Supported specifiers include: C{'S1' or 'c' (NC_CHAR), 'i1' or 'b' or 'B'
+The `datatype` can be a numpy datatype object, or a string that describes
+a numpy dtype object (like the `dtype.str` attribue of a numpy array).
+Supported specifiers include: `'S1' or 'c' (NC_CHAR), 'i1' or 'b' or 'B'
(NC_BYTE), 'u1' (NC_UBYTE), 'i2' or 'h' or 's' (NC_SHORT), 'u2'
(NC_USHORT), 'i4' or 'i' or 'l' (NC_INT), 'u4' (NC_UINT), 'i8' (NC_INT64),
-'u8' (NC_UINT64), 'f4' or 'f' (NC_FLOAT), 'f8' or 'd' (NC_DOUBLE)}.
-C{datatype} can also be a L{CompoundType} instance
-(for a structured, or compound array), a L{VLType} instance
-(for a variable-length array), or the python C{str} builtin
+'u8' (NC_UINT64), 'f4' or 'f' (NC_FLOAT), 'f8' or 'd' (NC_DOUBLE)`.
+`datatype` can also be a `netCDF4.CompoundType` instance
+(for a structured, or compound array), a `netCDF4.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 C{str}.
+length greater than one are aliases for `str`.
Data from netCDF variables is presented to python as numpy arrays with
the corresponding data type.
-C{dimensions} must be a tuple containing dimension names (strings) that
-have been defined previously using C{createDimension}. The default value
+`dimensions` must be a tuple containing dimension names (strings) that
+have been defined previously using `netCDF4.Dataset.createDimension`. The default value
is an empty tuple, which means the variable is a scalar.
-If the optional keyword C{zlib} is C{True}, the data will be compressed in
-the netCDF file using gzip compression (default C{False}).
+If the optional keyword `zlib` is `True`, the data will be compressed in
+the netCDF file using gzip compression (default `False`).
-The optional keyword C{complevel} is an integer between 1 and 9 describing
-the level of compression desired (default 4). Ignored if C{zlib=False}.
+The optional keyword `complevel` is an integer between 1 and 9 describing
+the level of compression desired (default 4). Ignored if `zlib=False`.
-If the optional keyword C{shuffle} is C{True}, the HDF5 shuffle filter
-will be applied before compressing the data (default C{True}). This
-significantly improves compression. Default is C{True}. Ignored if
-C{zlib=False}.
+If the optional keyword `shuffle` is `True`, the HDF5 shuffle filter
+will be applied before compressing the data (default `True`). This
+significantly improves compression. Default is `True`. Ignored if
+`zlib=False`.
-If the optional keyword C{fletcher32} is C{True}, the Fletcher32 HDF5
-checksum algorithm is activated to detect errors. Default C{False}.
+If the optional keyword `fletcher32` is `True`, the Fletcher32 HDF5
+checksum algorithm is activated to detect errors. Default `False`.
-If the optional keyword C{contiguous} is C{True}, the variable data is
-stored contiguously on disk. Default C{False}. Setting to C{True} for
+If the optional keyword `contiguous` is `True`, the variable data is
+stored contiguously on disk. Default `False`. Setting to `True` for
a variable with an unlimited dimension will trigger an error.
-The optional keyword C{chunksizes} can be used to manually specify the
+The optional keyword `chunksizes` can be used to manually specify the
HDF5 chunksizes for each dimension of the variable. A detailed
-discussion of HDF chunking and I/O performance is available U{here
-<http://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.html>}.
+discussion of HDF chunking and I/O performance is available
+[here](http://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.html).
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. C{chunksizes} cannot be set if C{contiguous=True}.
+from the file. `chunksizes` cannot be set if `contiguous=True`.
-The optional keyword C{endian} can be used to control whether the
+The optional keyword `endian` can be used to control whether the
data is stored in little or big endian format on disk. Possible
-values are C{little, big} or C{native} (default). The library
+values are `little, big` or `native` (default). The library
will automatically handle endian conversions when the data is read,
but if the data is always going to be read on a computer with the
opposite format as the one used to create the file, there may be
some performance advantage to be gained by setting the endian-ness.
-The C{zlib, complevel, shuffle, fletcher32, contiguous, chunksizes} and C{endian}
+The `zlib, complevel, shuffle, fletcher32, contiguous, chunksizes` and `endian`
keywords are silently ignored for netCDF 3 files that do not use HDF5.
-The optional keyword C{fill_value} can be used to override the default
-netCDF C{_FillValue} (the value that the variable gets filled with before
-any data is written to it, defaults given in netCDF4.default_fillvals).
-If fill_value is set to C{False}, then the variable is not pre-filled.
+The optional keyword `fill_value` can be used to override the default
+netCDF `_FillValue` (the value that the variable gets filled with before
+any data is written to it, defaults given in `netCDF4.default_fillvals`).
+If fill_value is set to `False`, then the variable is not pre-filled.
-If the optional keyword parameter C{least_significant_digit} is
+If the optional keyword parameter `least_significant_digit` is
specified, variable data will be truncated (quantized). In conjunction
-with C{zlib=True} this produces 'lossy', but significantly more
-efficient compression. For example, if C{least_significant_digit=1},
-data will be quantized using C{numpy.around(scale*data)/scale}, where
+with `zlib=True` this produces 'lossy', but significantly more
+efficient compression. For example, if `least_significant_digit=1`,
+data will be quantized using `numpy.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). From
-U{http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml}:
+retained (in this case bits=4). From the
+[PSD metadata conventions](http://www.esrl.noaa.gov/psd/data/gridded/conventions/cdc_netcdf_standard.shtml):
"least_significant_digit -- power of ten of the smallest decimal place
-in unpacked data that is a reliable value." Default is C{None}, or no
+in unpacked data that is a reliable value." Default is `None`, or no
quantization, or 'lossless' compression.
-When creating variables in a C{NETCDF4} or C{NETCDF4_CLASSIC} formatted file,
+When creating variables in a `NETCDF4` or `NETCDF4_CLASSIC` formatted file,
HDF5 creates something called a 'chunk cache' for each variable. The
default size of the chunk cache may be large enough to completely fill
available memory when creating thousands of variables. The optional
-keyword C{chunk_cache} allows you to reduce (or increase) the size of
+keyword `chunk_cache` allows you to reduce (or increase) the size of
the default chunk cache when creating a variable. The setting only
persists as long as the Dataset is open - you can use the set_var_chunk_cache
method to change it the next time the Dataset is opened.
Warning - messing with this parameter can seriously degrade performance.
-The return value is the L{Variable} class instance describing the new
+The return value is the `netCDF4.Variable` class instance describing the new
variable.
A list of names corresponding to netCDF variable attributes can be
-obtained with the L{Variable} method C{ncattrs()}. A dictionary
+obtained with the `netCDF4.Variable` method `netCDF4.Variable.ncattrs`. A dictionary
containing all the netCDF attribute name/value pairs is provided by
-the C{__dict__} attribute of a L{Variable} instance.
+the `__dict__` attribute of a `netCDF4.Variable` instance.
-L{Variable} instances behave much like array objects. Data can be
+`netCDF4.Variable` instances behave much like array objects. Data can be
assigned to or retrieved from a variable with indexing and slicing
-operations on the L{Variable} instance. A L{Variable} instance has six
-Dataset standard attributes: C{dimensions, dtype, shape, ndim, name} and
-C{least_significant_digit}. Application programs should never modify
-these attributes. The C{dimensions} attribute is a tuple containing the
-names of the dimensions associated with this variable. The C{dtype}
-attribute is a string describing the variable's data type (C{i4, f8,
-S1,} etc). The C{shape} attribute is a tuple describing the current
-sizes of all the variable's dimensions. The C{name} attribute is a
+operations on the `netCDF4.Variable` instance. A `netCDF4.Variable` instance has six
+Dataset standard attributes: `dimensions, dtype, shape, ndim, name` and
+`least_significant_digit`. Application programs should never modify
+these attributes. The `dimensions` attribute is a tuple containing the
+names of the dimensions associated with this variable. The `dtype`
+attribute is a string describing the variable's data type (`i4, f8,
+S1,` etc). The `shape` attribute is a tuple describing the current
+sizes of all the variable's dimensions. The `name` attribute is a
string containing the name of the Variable instance.
-The C{least_significant_digit}
+The `least_significant_digit`
attributes describes the power of ten of the smallest decimal place in
-the data the contains a reliable value. assigned to the L{Variable}
-instance. If C{None}, the data is not truncated. The C{ndim} attribute
+the data the contains a reliable value. assigned to the `netCDF4.Variable`
+instance. If `None`, the data is not truncated. The `ndim` attribute
is the number of variable dimensions."""
# if varname specified as a path, split out group names.
varname = posixpath.normpath(varname)
@@ -1926,9 +1998,9 @@ is the number of variable dimensions."""
def renameVariable(self, oldname, newname):
"""
-renameVariable(self, oldname, newname)
+**`renameVariable(self, oldname, newname)`**
-rename a L{Variable} named C{oldname} to C{newname}"""
+rename a `netCDF4.Variable` named `oldname` to `newname`"""
cdef char *namstring
try:
var = self.variables[oldname]
@@ -1948,19 +2020,19 @@ rename a L{Variable} named C{oldname} to C{newname}"""
def createGroup(self, groupname):
"""
-createGroup(self, groupname)
+**`createGroup(self, groupname)`**
-Creates a new L{Group} with the given C{groupname}.
+Creates a new `netCDF4.Group` with the given `groupname`.
-If C{groupname} is specified as a path, using forward slashes as in unix to
+If `groupname` is specified as a path, using forward slashes as in unix to
separate components, then intermediate groups will be created as necessary
-(analagous to C{mkdir -p} in unix). For example,
-C{createGroup('/GroupA/GroupB/GroupC')} will create C{GroupA},
-C{GroupA/GroupB}, and C{GroupA/GroupB/GroupC}, if they don't already exist.
+(analagous to `mkdir -p` in unix). For example,
+`createGroup('/GroupA/GroupB/GroupC')` will create `GroupA`,
+`GroupA/GroupB`, and `GroupA/GroupB/GroupC`, if they don't already exist.
If the specified path describes a group that already exists, no error is
raised.
-The return value is a L{Group} class instance."""
+The return value is a `netCDF4.Group` class instance."""
# if group specified as a path, split out group names
groupname = posixpath.normpath(groupname)
nestedgroups = groupname.split('/')
@@ -1978,28 +2050,28 @@ The return value is a L{Group} class instance."""
def ncattrs(self):
"""
-ncattrs(self)
+**`ncattrs(self)`**
-return netCDF global attribute names for this L{Dataset} or L{Group} in a list."""
+return netCDF global attribute names for this `netCDF4.Dataset` or `netCDF4.Group` in a list."""
return _get_att_names(self._grpid, NC_GLOBAL)
def setncattr(self,name,value):
"""
-setncattr(self,name,value)
+**`setncattr(self,name,value)`**
-set a netCDF dataset or group 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."""
+set a netCDF dataset or group attribute using name,value pair.
+Use if you need to set a netCDF attribute with the
+with the same name as one of the reserved python attributes."""
if self.data_model != 'NETCDF4': self._redef()
_set_att(self, NC_GLOBAL, name, value)
if self.data_model != 'NETCDF4': self._enddef()
def setncatts(self,attdict):
"""
-setncatts(self,attdict)
+**`setncatts(self,attdict)`**
set a bunch of netCDF dataset or group attributes at once using a python dictionary.
-This may be faster when setting a lot of attributes for a NETCDF3
+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"""
if self.data_model != 'NETCDF4': self._redef()
@@ -2009,11 +2081,11 @@ each attribute"""
def getncattr(self,name):
"""
-getncattr(self,name)
+**`getncattr(self,name)`**
-retrievel a netCDF dataset or group attribute. Only use if you need to set a
-netCDF attribute with the same name as one of the reserved python
-attributes."""
+retrievel a netCDF dataset or group attribute.
+Use if you need to get a netCDF attribute with the same
+name as one of the reserved python attributes."""
return _get_att(self, NC_GLOBAL, name)
def __delattr__(self,name):
@@ -2026,9 +2098,9 @@ attributes."""
def delncattr(self, name):
"""
-delncattr(self,name,value)
+**`delncattr(self,name,value)`**
-delete a netCDF dataset or group attribute. Only use if you need to delete a
+delete a netCDF dataset or group attribute. Use if you need to delete a
netCDF attribute with the same name as one of the reserved python
attributes."""
cdef char *attname
@@ -2073,9 +2145,9 @@ attributes."""
def renameAttribute(self, oldname, newname):
"""
-renameAttribute(self, oldname, newname)
+**`renameAttribute(self, oldname, newname)`**
-rename a L{Dataset} or L{Group} attribute named C{oldname} to C{newname}."""
+rename a `netCDF4.Dataset` or `netCDF4.Group` attribute named `oldname` to `newname`."""
cdef int ierr
cdef char *oldnamec
cdef char *newnamec
@@ -2089,9 +2161,9 @@ rename a L{Dataset} or L{Group} attribute named C{oldname} to C{newname}."""
def renameGroup(self, oldname, newname):
"""
-renameGroup(self, oldname, newname)
+**`renameGroup(self, oldname, newname)`**
-rename a L{Group} named C{oldname} to C{newname} (requires netcdf >= 4.3.1)."""
+rename a `netCDF4.Group` named `oldname` to `newname` (requires netcdf >= 4.3.1)."""
cdef int ierr
cdef char *newnamec
IF HAS_RENAME_GRP:
@@ -2116,19 +2188,15 @@ version 4.3.1 or higher of the netcdf C lib, and rebuild netcdf4-python."""
def set_auto_maskandscale(self, value):
"""
-set_auto_maskandscale(self, True_or_False)
-
-Call L{set_auto_maskandscale} for all variables contained in this L{Dataset} or
-L{Group}, as well as for all variables in all its subgroups.
+**`set_auto_maskandscale(self, True_or_False)`**
-B{Parameters}:
+Call `netCDF4.Variable.set_auto_maskandscale` for all variables contained in this `netCDF4.Dataset` or
+`netCDF4.Group`, as well as for all variables in all its subgroups.
-B{C{True_or_False}} - Boolean determining if automatic conversion to masked arrays
+**`True_or_False`**: Boolean determining if automatic conversion to masked arrays
and variable scaling shall be applied for all variables.
-B{Notes}:
-
-Calling this function only affects existing variables. Variables created
+***Note***: Calling this function only affects existing variables. Variables created
after calling this function will follow the default behaviour.
"""
@@ -2143,19 +2211,15 @@ after calling this function will follow the default behaviour.
def set_auto_mask(self, value):
"""
-set_auto_mask(self, True_or_False)
-
-Call L{set_auto_mask} for all variables contained in this L{Dataset} or
-L{Group}, as well as for all variables in all its subgroups.
+**`set_auto_mask(self, True_or_False)`**
-B{Parameters}:
+Call `netCDF4.Variable.set_auto_mask` for all variables contained in this `netCDF4.Dataset` or
+`netCDF4.Group`, as well as for all variables in all its subgroups.
-B{C{True_or_False}} - Boolean determining if automatic conversion to masked arrays
+**`True_or_False`**: Boolean determining if automatic conversion to masked arrays
shall be applied for all variables.
-B{Notes}:
-
-Calling this function only affects existing variables. Variables created
+***Note***: Calling this function only affects existing variables. Variables created
after calling this function will follow the default behaviour.
"""
@@ -2169,19 +2233,15 @@ after calling this function will follow the default behaviour.
def set_auto_scale(self, value):
"""
-set_auto_scale(self, True_or_False)
-
-Call L{set_auto_scale} for all variables contained in this L{Dataset} or
-L{Group}, as well as for all variables in all its subgroups.
+**`set_auto_scale(self, True_or_False)`**
-B{Parameters}:
+Call `netCDF4.Variable.set_auto_scale` for all variables contained in this `netCDF4.Dataset` or
+`netCDF4.Group`, as well as for all variables in all its subgroups.
-B{C{True_or_False}} - Boolean determining if automatic variable scaling
+**`True_or_False`**: Boolean determining if automatic variable scaling
shall be applied for all variables.
-B{Notes}:
-
-Calling this function only affects existing variables. Variables created
+***Note***: Calling this function only affects existing variables. Variables created
after calling this function will follow the default behaviour.
"""
@@ -2196,33 +2256,37 @@ after calling this function will follow the default behaviour.
cdef class Group(Dataset):
"""
-Group(self, parent, name)
-
Groups define a hierarchical namespace within a netCDF file. They are
-analagous to directories in a unix filesystem. Each L{Group} behaves like
-a L{Dataset} within a Dataset, and can contain it's own variables,
-dimensions and attributes (and other Groups).
-
-L{Group} instances should be created using the
-L{createGroup<Dataset.createGroup>} method of a L{Dataset} instance, or
-another L{Group} instance, not using this class directly.
-
-B{Parameters:}
-
-B{C{parent}} - L{Group} instance for the parent group. If being created
-in the root group, use a L{Dataset} instance.
+analagous to directories in a unix filesystem. Each `netCDF4.Group` behaves like
+a `netCDF4.Dataset` within a Dataset, and can contain it's own variables,
+dimensions and attributes (and other Groups). See `netCDF4.Group.__init__`
+for more details.
-B{C{name}} - Name of the group.
+`netCDF4.Group` inherits from `netCDF4.Dataset`, so all the
+`netCDF4.Dataset` class methods and variables are available
+to a `netCDF4.Group` instance (except the `close` method).
-B{Returns:}
+Additional read-only class variables:
-a L{Group} instance. All further operations on the netCDF
-Group are accomplished via L{Group} instance methods.
-
-L{Group} inherits from L{Dataset}, so all the L{Dataset} class methods and
-variables are available to a L{Group} instance (except the C{close}
-method)."""
+**`name`**: String describing the group name.
+"""
+ # Docstrings for class variables (used by pdoc).
+ __pdoc__['Group.name']=\
+ """A string describing the name of the `netCDF4.Group`."""
def __init__(self, parent, name, **kwargs):
+ """
+ **`__init__(self, parent, name)`**
+ `netCDF4.Group` constructor.
+
+ **`parent`**: `netCDF4.Group` instance for the parent group. If being created
+ in the root group, use a `netCDF4.Dataset` instance.
+
+ **`name`**: - Name of the group.
+
+ ***Note***: `netCDF4.Group` instances should be created using the
+ `netCDF4.Dataset.createGroup` method of a `netCDF4.Dataset` instance, or
+ another `netCDF4.Group` instance, not using this class directly.
+ """
cdef int ierr
cdef char *groupname
# flag to indicate that Variables in this Group support orthogonal indexing.
@@ -2260,11 +2324,11 @@ method)."""
def close(self):
"""
-close(self)
+**`close(self)`**
-overrides L{Dataset} close method which does not apply to L{Group}
+overrides `netCDF4.Dataset` close method which does not apply to `netCDF4.Group`
instances, raises IOError."""
- raise IOError('cannot close a L{Group} (only applies to Dataset)')
+ raise IOError('cannot close a `netCDF4.Group` (only applies to Dataset)')
def _getname(self):
# private method to get name associated with instance.
@@ -2286,37 +2350,42 @@ instances, raises IOError."""
cdef class Dimension:
"""
-Dimension(self, group, name, size=None)
+A netCDF `netCDF4.Dimension` is used to describe the coordinates of a `netCDF4.Variable`.
+See `netCDF4.Dimension.__init__` for more details.
-A netCDF L{Dimension} is used to describe the coordinates of a L{Variable}.
+The current maximum size of a `netCDF4.Dimension` instance can be obtained by
+calling the python `len` function on the `netCDF4.Dimension` instance. The
+`netCDF4.Dimension.isunlimited` method of a `netCDF4.Dimension` instance can be used to
+determine if the dimension is unlimited.
-L{Dimension} instances should be created using the
-L{createDimension<Dataset.createDimension>} method of a L{Group} or
-L{Dataset} instance, not using this class directly.
+Read-only class variables:
-B{Parameters:}
-
-B{C{group}} - L{Group} instance to associate with dimension.
-
-B{C{name}} - Name of the dimension.
-
-B{Keywords:}
-
-B{C{size}} - Size of the dimension. C{None} or 0 means unlimited. (Default C{None}).
-
-B{Returns:}
-
-a L{Dimension} instance. All further operations on the netCDF Dimension
-are accomplised via L{Dimension} instance methods.
-
-The current maximum size of a L{Dimension} instance can be obtained by
-calling the python C{len} function on the L{Dimension} instance. The
-C{isunlimited()} method of a L{Dimension} instance can be used to
-determine if the dimension is unlimited"""
+**`name`**: String name, used when creating a `netCDF4.Variable` with
+`netCDF4.Dataset.createVariable`.
+"""
cdef public int _dimid, _grpid
cdef public _data_model, _name, _grp
+ # Docstrings for class variables (used by pdoc).
+ __pdoc__['Dimension.name']=\
+ """A string describing the name of the `netCDF4.Dimension` - used when creating a
+ `netCDF4.Variable` instance with `netCDF4.Dataset.createVariable`."""
def __init__(self, grp, name, size=None, **kwargs):
+ """
+ **`__init__(self, group, name, size=None)`**
+
+ `netCDF4.Dimension` constructor.
+
+ **`group`**: `netCDF4.Group` instance to associate with dimension.
+
+ **`name`**: Name of the dimension.
+
+ **`size`**: Size of the dimension. `None` or 0 means unlimited. (Default `None`).
+
+ ***Note***: `netCDF4.Dimension` instances should be created using the
+ `netCDF4.Dataset.createDimension` method of a `netCDF4.Group` or
+ `netCDF4.Dataset` instance, not using `netCDF4.Dimension.__init__` directly.
+ """
cdef int ierr
cdef char *dimname
cdef size_t lendim
@@ -2377,7 +2446,7 @@ determine if the dimension is unlimited"""
return repr(type(self))+": name = '%s', size = %s\n" % (self._name,len(self))
def __len__(self):
- # len(L{Dimension} instance) returns current size of dimension
+ # len(`netCDF4.Dimension` instance) returns current size of dimension
cdef int ierr
cdef size_t lengthp
with nogil:
@@ -2388,16 +2457,16 @@ determine if the dimension is unlimited"""
def group(self):
"""
-group(self)
+**`group(self)`**
-return the group that this L{Dimension} is a member of."""
+return the group that this `netCDF4.Dimension` is a member of."""
return self._grp
def isunlimited(self):
"""
-isunlimited(self)
+**`isunlimited(self)`**
-returns C{True} if the L{Dimension} instance is unlimited, C{False} otherwise."""
+returns `True` if the `netCDF4.Dimension` instance is unlimited, `False` otherwise."""
cdef int ierr, n, numunlimdims, ndims, nvars, ngatts, xdimid
cdef int unlimdimids[NC_MAX_DIMS]
if self._data_model == 'NETCDF4':
@@ -2430,148 +2499,190 @@ returns C{True} if the L{Dimension} instance is unlimited, C{False} otherwise.""
cdef class 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 L{Variable} is used to read and write netCDF data. They are
-analagous to numpy array objects.
-
-L{Variable} instances should be created using the
-L{createVariable<Dataset.createVariable>} method of a L{Dataset} or
-L{Group} instance, not using this class directly.
-
-B{Parameters:}
-
-B{C{group}} - L{Group} or L{Dataset} instance to associate with variable.
-
-B{C{name}} - Name of the variable.
-
-B{C{datatype}} - L{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 C{str} attribute of numpy dtype
-objects, include C{'f4'} (32-bit floating point), C{'f8'} (64-bit floating
-point), C{'i4'} (32-bit signed integer), C{'i2'} (16-bit signed integer),
-C{'i8'} (64-bit singed integer), C{'i4'} (8-bit singed integer), C{'i1'}
-(8-bit signed integer), C{'u1'} (8-bit unsigned integer), C{'u2'} (16-bit
-unsigned integer), C{'u4'} (32-bit unsigned integer), C{'u8'} (64-bit
-unsigned integer), or C{'S1'} (single-character string). From
-compatibility with Scientific.IO.NetCDF, the old Numeric single character
-typecodes can also be used (C{'f'} instead of C{'f4'}, C{'d'} instead of
-C{'f8'}, C{'h'} or C{'s'} instead of C{'i2'}, C{'b'} or C{'B'} instead of
-C{'i1'}, C{'c'} instead of C{'S1'}, and C{'i'} or C{'l'} instead of
-C{'i4'}). C{datatype} can also be a L{CompoundType} instance
-(for a structured, or compound array), a L{VLType} instance
-(for a variable-length array), or the python C{str} builtin
-(for a variable-length string array). Numpy string and unicode datatypes with
-length greater than one are aliases for C{str}.
-
-B{Keywords:}
-
-B{C{dimensions}} - a tuple containing the variable's dimension names
-(defined previously with C{createDimension}). Default is an empty tuple
-which means the variable is a scalar (and therefore has no dimensions).
-
-B{C{zlib}} - if C{True}, data assigned to the L{Variable}
-instance is compressed on disk. Default C{False}.
-
-B{C{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 C{zlib=False}.
-
-B{C{shuffle}} - if C{True}, the HDF5 shuffle filter is applied
-to improve compression. Default C{True}. Ignored if C{zlib=False}.
-
-B{C{fletcher32}} - if C{True} (default C{False}), the Fletcher32 checksum
-algorithm is used for error detection.
-
-B{C{contiguous}} - if C{True} (default C{False}), the variable data is
-stored contiguously on disk. Default C{False}. Setting to C{True} for
-a variable with an unlimited dimension will trigger an error.
-
-B{C{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 U{here
-<http://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.html>}.
-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. C{chunksizes} cannot be set if C{contiguous=True}.
-
-B{C{endian}} - Can be used to control whether the
-data is stored in little or big endian format on disk. Possible
-values are C{little, big} or C{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 C{endian='native'} is allowed.
-
-The C{zlib, complevel, shuffle, fletcher32, contiguous} and {chunksizes}
-keywords are silently ignored for netCDF 3 files that do not use HDF5.
-
-B{C{least_significant_digit}} - If specified, variable data will be
-truncated (quantized). In conjunction with C{zlib=True} this produces
-'lossy', but significantly more efficient compression. For example, if
-C{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
-C{None}, or no quantization.
-
-B{C{fill_value}} - If specified, the default netCDF C{_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 C{False}, then
-the variable is not pre-filled. The default netCDF fill values can be found
-in netCDF4.default_fillvals.
-
-B{Returns:}
-
-a L{Variable} instance. All further operations on the netCDF Variable are
-accomplised via L{Variable} instance methods.
+A netCDF `netCDF4.Variable` is used to read and write netCDF data. They are
+analagous to numpy array objects. See `netCDF4.Variable.__init__` for more
+details.
A list of attribute names corresponding to netCDF attributes defined for
-the variable can be obtained with the C{ncattrs()} method. These
+the variable can be obtained with the `netCDF4.Variable.ncattrs` method. These
attributes can be created by assigning to an attribute of the
-L{Variable} instance. A dictionary containing all the netCDF attribute
-name/value pairs is provided by the C{__dict__} attribute of a
-L{Variable} instance.
+`netCDF4.Variable` instance. A dictionary containing all the netCDF attribute
+name/value pairs is provided by the `__dict__` attribute of a
+`netCDF4.Variable` instance.
-The instance variables C{dimensions, dtype, ndim, shape}
-and C{least_significant_digit} are read-only (and
-should not be modified by the user).
+The following class variables are read-only:
- at ivar dimensions: A tuple containing the names of the dimensions
-associated with this variable.
+**`dimensions`**: A tuple containing the names of the
+dimensions associated with this variable.
- at ivar dtype: A numpy dtype object describing the variable's data type.
+**`dtype`**: A numpy dtype object describing the
+variable's data type.
- at ivar ndim: The number of variable dimensions.
+**`ndim`**: The number of variable dimensions.
- at ivar shape: a tuple describing the current size of all the variable's
-dimensions.
+**`shape`**: A tuple with the current shape (length of all dimensions).
- at ivar scale: if True, C{scale_factor} and C{add_offset} are automatically
-applied. Default is C{True}, can be reset using L{set_auto_scale} and
-L{set_auto_maskandscale} methods.
+**`scale`**: If True, `scale_factor` and `add_offset` are
+applied. Default is `True`, can be reset using `netCDF4.Variable.set_auto_scale` and
+`netCDF4.Variable.set_auto_maskandscale` methods.
- at ivar mask: if True, data is automatically converted to/from masked arrays
-when missing values or fill values are present. Default is C{True}, can be
-reset using L{set_auto_mask} and L{set_auto_maskandscale} methods.
+**`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 `netCDF4.Variable.set_auto_mask` and `netCDF4.Variable.set_auto_maskandscale`
+methods.
- at ivar 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 L{Variable}
-instance. If C{None}, the data is not truncated.
+**`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 `netCDF4.Variable`
+instance. If `None`, the data is not truncated.
- at ivar __orthogonal_indexing__: Always C{True}. Indicates to client code
-that the object supports "orthogonal indexing", which means that slices
+**`__orthogonal_indexing__`**: Always `True`. Indicates to client code
+that the object supports 'orthogonal indexing', which means that slices
that are 1d arrays or lists slice along each dimension independently. This
-behavior is similar to Fortran or Matlab, but different than numpy."""
+behavior is similar to Fortran or Matlab, but different than numpy.
+
+**`datatype`**: numpy data type (for primitive data types) or VLType/CompoundType
+ instance (for compound or vlen data types).
+
+**`name`**: String name.
+
+**`size`**: The number of stored elements.
+ """
cdef public int _varid, _grpid, _nunlimdim
cdef public _name, ndim, dtype, mask, scale, _isprimitive, _iscompound,\
_isvlen, _grp, _cmptype, _vltype, __orthogonal_indexing__
+ # Docstrings for class variables (used by pdoc).
+ __pdoc__['Variable.dimensions'] = \
+ """A tuple containing the names of the
+ dimensions associated with this variable."""
+ __pdoc__['Variable.dtype'] = \
+ """A numpy dtype object describing the
+ variable's data type."""
+ __pdoc__['Variable.ndim'] = \
+ """The number of variable dimensions."""
+ __pdoc__['Variable.scale'] = \
+ """if True, `scale_factor` and `add_offset` are
+ applied. Default is `True`, can be reset using `netCDF4.Variable.set_auto_scale` and
+ `netCDF4.Variable.set_auto_maskandscale` methods."""
+ __pdoc__['Variable.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 `netCDF4.Variable.set_auto_mask` and `netCDF4.Variable.set_auto_maskandscale`
+ methods."""
+ __pdoc__['Variable.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 `netCDF4.Variable`
+ instance. If `None`, the data is not truncated."""
+ __pdoc__['Variable.__orthogonal_indexing__'] = \
+ """Always `True`. Indicates to client code
+ that the object supports 'orthogonal indexing', which means that slices
+ that are 1d arrays or lists slice along each dimension independently. This
+ behavior is similar to Fortran or Matlab, but different than numpy."""
+ __pdoc__['Variable.datatype'] = \
+ """numpy data type (for primitive data types) or VLType/CompoundType
+ instance (for compound or vlen data types)."""
+ __pdoc__['Variable.name'] = \
+ """String name."""
+ __pdoc__['Variable.shape'] = \
+ """A tuple with the current shape (length of all dimensions)."""
+ __pdoc__['Variable.size'] = \
+ """The number of stored elements."""
def __init__(self, grp, name, datatype, dimensions=(), zlib=False,
complevel=4, shuffle=True, fletcher32=False, contiguous=False,
chunksizes=None, endian='native', least_significant_digit=None,
fill_value=None, chunk_cache=None, **kwargs):
+ """
+ **`__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)`**
+
+ `netCDF4.Variable` constructor.
+
+ **`group`**: `netCDF4.Group` or `netCDF4.Dataset` instance to associate with variable.
+
+ **`name`**: Name of the variable.
+
+ **`datatype`**: `netCDF4.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 `netCDF4.CompoundType` instance
+ (for a structured, or compound array), a `netCDF4.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`.
+
+ **`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 `netCDF4.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](http://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.html).
+ 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`.
+
+ ***Note***: `netCDF4.Variable` instances should be created using the
+ `netCDF4.Dataset.createVariable` method of a `netCDF4.Dataset` or
+ `netCDF4.Group` instance, not using this class directly.
+ """
cdef int ierr, ndims, icontiguous, ideflate_level, numdims, _grpid
cdef char *varname
cdef nc_type xtype
@@ -2952,23 +3063,23 @@ behavior is similar to Fortran or Matlab, but different than numpy."""
def group(self):
"""
-group(self)
+**`group(self)`**
-return the group that this L{Variable} is a member of."""
+return the group that this `netCDF4.Variable` is a member of."""
return self._grp
def ncattrs(self):
"""
-ncattrs(self)
+**`ncattrs(self)`**
-return netCDF attribute names for this L{Variable} in a list."""
+return netCDF attribute names for this `netCDF4.Variable` in a list."""
return _get_att_names(self._grpid, self._varid)
def setncattr(self,name,value):
"""
-setncattr(self,name,value)
+**`setncattr(self,name,value)`**
-set a netCDF variable attribute using name,value pair. Only use if you need to set a
+set a netCDF variable attribute using name,value pair. Use if you need to set a
netCDF attribute with the same name as one of the reserved python
attributes."""
if self._grp.data_model != 'NETCDF4': self._grp._redef()
@@ -2977,10 +3088,10 @@ attributes."""
def setncatts(self,attdict):
"""
-setncatts(self,attdict)
+**`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
+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"""
if self._grp.data_model != 'NETCDF4': self._grp._redef()
@@ -2990,18 +3101,18 @@ each attribute"""
def getncattr(self,name):
"""
-getncattr(self,name)
+**`getncattr(self,name)`**
-retrievel a netCDF variable attribute. Only use if you need to set a
+retrievel a netCDF variable attribute. Use if you need to set a
netCDF attribute with the same name as one of the reserved python
attributes."""
return _get_att(self._grp, self._varid, name)
def delncattr(self, name):
"""
-delncattr(self,name,value)
+**`delncattr(self,name,value)`**
-delete a netCDF variable attribute. Only use if you need to delete a
+delete a netCDF variable attribute. Use if you need to delete a
netCDF attribute with the same name as one of the reserved python
attributes."""
cdef char *attname
@@ -3015,7 +3126,7 @@ attributes."""
def filters(self):
"""
-filters(self)
+**`filters(self)`**
return dictionary containing HDF5 filter parameters."""
cdef int ierr,ideflate,ishuffle,ideflate_level,ifletcher32
@@ -3040,9 +3151,9 @@ return dictionary containing HDF5 filter parameters."""
def endian(self):
"""
-endian(self)
+**`endian(self)`**
-return endian-ness (little,big,native) of variable (as stored in HDF5 file)."""
+return endian-ness (`little,big,native`) of variable (as stored in HDF5 file)."""
cdef int ierr, iendian
if self._grp.data_model not in ['NETCDF4_CLASSIC','NETCDF4']:
return 'native'
@@ -3059,7 +3170,7 @@ return endian-ness (little,big,native) of variable (as stored in HDF5 file)."""
def chunking(self):
"""
-chunking(self)
+**`chunking(self)`**
return variable chunking information. If the dataset is
defined to be contiguous (and hence there is no chunking) the word 'contiguous'
@@ -3085,10 +3196,10 @@ each dimension is returned."""
def get_var_chunk_cache(self):
"""
-get_var_chunk_cache(self)
+**`get_var_chunk_cache(self)`**
return variable chunk cache information in a tuple (size,nelems,preemption).
-See netcdf C library documentation for C{nc_get_var_chunk_cache} for
+See netcdf C library documentation for `nc_get_var_chunk_cache` for
details."""
cdef int ierr
cdef size_t sizep, nelemsp
@@ -3103,10 +3214,10 @@ details."""
def set_var_chunk_cache(self,size=None,nelems=None,preemption=None):
"""
-set_var_chunk_cache(self,size=None,nelems=None,preemption=None)
+**`set_var_chunk_cache(self,size=None,nelems=None,preemption=None)`**
change variable chunk cache settings.
-See netcdf C library documentation for C{nc_set_var_chunk_cache} for
+See netcdf C library documentation for `nc_set_var_chunk_cache` for
details."""
cdef int ierr
cdef size_t sizep, nelemsp
@@ -3184,9 +3295,9 @@ details."""
def renameAttribute(self, oldname, newname):
"""
-renameAttribute(self, oldname, newname)
+**`renameAttribute(self, oldname, newname)`**
-rename a L{Variable} attribute named C{oldname} to C{newname}."""
+rename a `netCDF4.Variable` attribute named `oldname` to `newname`."""
cdef int ierr
cdef char *oldnamec
cdef char *newnamec
@@ -3475,7 +3586,7 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
# assume it's a numpy or masked array if it has an 'ndim' attribute.
if not hasattr(data,'ndim'):
# if auto scaling is to be done, don't cast to an integer yet.
- if self.scale and self.dtype.kind == 'i' and \
+ if self.scale and self.dtype.kind in 'iu' and \
hasattr(self, 'scale_factor') or hasattr(self, 'add_offset'):
data = numpy.array(data,numpy.float)
else:
@@ -3522,13 +3633,13 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
# pack non-masked values using scale_factor and add_offset
if hasattr(self, 'scale_factor') and hasattr(self, 'add_offset'):
data = (data - self.add_offset)/self.scale_factor
- if self.dtype.kind == 'i': data = numpy.around(data)
+ if self.dtype.kind in 'iu': data = numpy.around(data)
elif hasattr(self, 'scale_factor'):
data = data/self.scale_factor
- if self.dtype.kind == 'i': data = numpy.around(data)
+ if self.dtype.kind in 'iu': data = numpy.around(data)
elif hasattr(self, 'add_offset'):
data = data - self.add_offset
- if self.dtype.kind == 'i': data = numpy.around(data)
+ if self.dtype.kind in 'iu': data = numpy.around(data)
if ma.isMA(data):
# if underlying data in masked regions of masked array
# corresponds to missing values, don't fill masked array -
@@ -3568,38 +3679,41 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
def __len__(self):
- return self.shape[0]
+ if not self.shape:
+ raise TypeError('len() of unsized object')
+ else:
+ return self.shape[0]
def assignValue(self,val):
"""
-assignValue(self, val)
+**`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 ([:])."""
+Scientific.IO.NetCDF, can also be done by assigning to an Ellipsis slice ([...])."""
if len(self.dimensions):
raise IndexError('to assign values to a non-scalar variable, use a slice')
self[:]=val
def getValue(self):
"""
-getValue(self)
+**`getValue(self)`**
get the value of a scalar variable. Provided for compatibility with
-Scientific.IO.NetCDF, can also be done by slicing ([:])."""
+Scientific.IO.NetCDF, can also be done by slicing with an Ellipsis ([...])."""
if len(self.dimensions):
raise IndexError('to retrieve values from a non-scalar variable, use slicing')
return self[slice(None)]
def set_auto_maskandscale(self,maskandscale):
"""
-set_auto_maskandscale(self,maskandscale)
+**`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 C{scale_factor} and C{add_offset} attributes.
+data using `scale_factor` and `add_offset` attributes.
-If C{maskandscale} is set to C{True}, when data is read from a variable
+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
@@ -3609,8 +3723,8 @@ 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 C{maskandscale} is set to C{True}, and the variable has a
-C{scale_factor} or an C{add_offset} attribute, then data read
+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
@@ -3622,12 +3736,11 @@ When data is written to a variable it is packed using::
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 C{scale_factor} and C{add_offset} can be
-used to provide simple compression, see
-U{http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml
-<http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml>}.
+For more information on how `scale_factor` and `add_offset` can be
+used to provide simple compression, see the
+[PSD metadata conventions](http://www.esrl.noaa.gov/psd/data/gridded/conventions/cdc_netcdf_standard.shtml).
-The default value of C{maskandscale} is C{True}
+The default value of `maskandscale` is `True`
(automatic conversions are performed).
"""
if maskandscale:
@@ -3639,13 +3752,13 @@ The default value of C{maskandscale} is C{True}
def set_auto_scale(self,scale):
"""
-set_auto_scale(self,scale)
+**`set_auto_scale(self,scale)`**
turn on or off automatic packing/unpacking of variable
-data using C{scale_factor} and C{add_offset} attributes.
+data using `scale_factor` and `add_offset` attributes.
-If C{scale} is set to C{True}, and the variable has a
-C{scale_factor} or an C{add_offset} attribute, then data read
+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
@@ -3657,12 +3770,11 @@ When data is written to a variable it is packed using::
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 C{scale_factor} and C{add_offset} can be
-used to provide simple compression, see
-U{http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml
-<http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml>}.
+For more information on how `scale_factor` and `add_offset` can be
+used to provide simple compression, see the
+[PSD metadata conventions](http://www.esrl.noaa.gov/psd/data/gridded/conventions/cdc_netcdf_standard.shtml).
-The default value of C{scale} is C{True}
+The default value of `scale` is `True`
(automatic conversions are performed).
"""
if scale:
@@ -3672,12 +3784,12 @@ The default value of C{scale} is C{True}
def set_auto_mask(self,mask):
"""
-set_auto_mask(self,mask)
+**`set_auto_mask(self,mask)`**
turn on or off automatic conversion of variable data to and
from masked arrays .
-If C{mask} is set to C{True}, when data is read from a variable
+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
@@ -3687,7 +3799,7 @@ 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 C{mask} is C{True}
+The default value of `mask` is `True`
(automatic conversions are performed).
"""
if mask:
@@ -3958,45 +4070,45 @@ The default value of C{mask} is C{True}
cdef class CompoundType:
"""
-A L{CompoundType} instance is used to describe a compound data type.
-
-Constructor: C{CompoundType(group, datatype, datatype_name)}
-
- at attention: When creating nested compound data types,
-the inner compound data types must already be associated with CompoundType
-instances (so create CompoundType instances for the innermost structures
-first).
-
-L{CompoundType} instances should be created using the
-L{createCompoundType<Dataset.createCompoundType>}
-method of a Dataset or L{Group} instance, not using this class directly.
-
-B{Parameters:}
-
-B{C{group}} - L{Group} instance to associate with the compound datatype.
-
-B{C{datatype}} - A numpy dtype object describing a structured (a.k.a record)
-array. Can be composed of homogeneous numeric or character data types, or
-other structured array data types.
-
-B{C{datatype_name}} - a Python string containing a description of the
-compound data type.
+A `netCDF4.CompoundType` instance is used to describe a compound data
+type, and can be passed to the the `netCDF4.Dataset.createVariable` method of
+a `netCDF4.Dataset` or `netCDF4.Group` instance.
+Compound data types map to numpy structured arrays.
+See `netCDF4.CompoundType.__init__` for more details.
-B{Returns:}
-
-a L{CompoundType} instance, which can be passed to the C{createVariable}
-method of a L{Dataset} or L{Group} instance.
-
-The instance variables C{dtype} and C{name} should not be modified by
+The instance variables `dtype` and `name` should not be modified by
the user.
-
- at ivar dtype: A numpy dtype object describing the compound data type.
-
- at ivar name: A python string describing the compound type.
"""
cdef public nc_type _nc_type
cdef public dtype, name
+ __pdoc__['CompoundType.name'] = \
+ """String name."""
+ __pdoc__['CompoundType.dtype'] = \
+ """A numpy dtype object describing the compound data type."""
def __init__(self, grp, object dt, object dtype_name, **kwargs):
+ """
+ ***`__init__(group, datatype, datatype_name)`***
+
+ CompoundType constructor.
+
+ **`group`**: `netCDF4.Group` instance to associate with the compound datatype.
+
+ **`datatype`**: A numpy dtype object describing a structured (a.k.a record)
+ array. Can be composed of homogeneous numeric or character data types, or
+ other structured array data types.
+
+ **`datatype_name`**: a Python string containing a description of the
+ compound data type.
+
+ ***Note 1***: When creating nested compound data types,
+ the inner compound data types must already be associated with CompoundType
+ instances (so create CompoundType instances for the innermost structures
+ first).
+
+ ***Note 2***: `netCDF4.CompoundType` instances should be created using the
+ `netCDF4.Dataset.createCompoundType`
+ method of a `netCDF4.Dataset` or `netCDF4.Group` instance, not using this class directly.
+ """
cdef nc_type xtype
dt = numpy.dtype(dt,align=True)
if 'typeid' in kwargs:
@@ -4188,39 +4300,38 @@ cdef _read_compound(group, nc_type xtype, endian=None):
cdef class VLType:
"""
-A L{VLType} instance is used to describe a variable length (VLEN) data type.
-
-Constructor: C{VLType(group, datatype, datatype_name)}
-
-L{VLType} instances should be created using the
-L{createVLType<Dataset.createVLType>}
-method of a Dataset or L{Group} instance, not using this class directly.
+A `netCDF4.VLType` instance is used to describe a variable length (VLEN) data
+type, and can be passed to the the `netCDF4.Dataset.createVariable` method of
+a `netCDF4.Dataset` or `netCDF4.Group` instance. See
+`netCDF4.VLType.__init__` for more details.
-B{Parameters:}
-
-B{C{group}} - L{Group} instance to associate with the VLEN datatype.
-
-B{C{datatype}} - An numpy dtype object describing a the component type for the
-variable length array.
-
-B{C{datatype_name}} - a Python string containing a description of the
-VLEN data type.
-
-B{Returns:}
-
-a L{VLType} instance, which can be passed to the C{createVariable}
-method of a L{Dataset} or L{Group} instance.
-
-The instance variables C{dtype} and C{name} should not be modified by
+The instance variables `dtype` and `name` should not be modified by
the user.
-
- at ivar dtype: An object describing the VLEN type.
-
- at ivar name: A python string describing the VLEN type.
"""
cdef public nc_type _nc_type
cdef public dtype, name
+ __pdoc__['VLType.name'] = \
+ """String name."""
+ __pdoc__['VLType.dtype'] = \
+ """A numpy dtype object describing the component type for the VLEN."""
def __init__(self, grp, object dt, object dtype_name, **kwargs):
+ """
+ **`__init__(group, datatype, datatype_name)`**
+
+ VLType constructor.
+
+ **`group`**: `netCDF4.Group` instance to associate with the VLEN datatype.
+
+ **`datatype`**: An numpy dtype object describing a the component type for the
+ variable length array.
+
+ **`datatype_name`**: a Python string containing a description of the
+ VLEN data type.
+
+ ***`Note`***: `netCDF4.VLType` instances should be created using the
+ `netCDF4.Dataset.createVLType`
+ method of a `netCDF4.Dataset` or `netCDF4.Group` instance, not using this class directly.
+ """
cdef nc_type xtype
if 'typeid' in kwargs:
xtype = kwargs['typeid']
@@ -4341,21 +4452,21 @@ def _dateparse(timestr):
def stringtoarr(string,NUMCHARS,dtype='S'):
"""
-stringtoarr(a, NUMCHARS,dtype='S')
+**`stringtoarr(a, NUMCHARS,dtype='S')`**
-convert a string to a character array of length NUMCHARS
+convert a string to a character array of length `NUMCHARS`
- at param a: Input python string.
+**`a`**: Input python string.
- at param NUMCHARS: number of characters used to represent string
-(if len(a) < NUMCHARS, it will be padded on the right with blanks).
+**`NUMCHARS`**: number of characters used to represent string
+(if len(a) < `NUMCHARS`, it will be padded on the right with blanks).
- at keyword dtype: type of numpy array to return. Default is 'S', which
-means an array of dtype 'S1' will be returned. If dtype='U', a
-unicode array (dtype = 'U1') will be returned.
+**`dtype`**: type of numpy array to return. Default is `'S'`, which
+means an array of dtype `'S1'` will be returned. If dtype=`'U'`, a
+unicode array (dtype = `'U1'`) will be returned.
- at return: A rank 1 numpy character array of length NUMCHARS with datatype 'S1'
-(default) or 'U1' (if dtype='U')"""
+returns a rank 1 numpy character array of length NUMCHARS with datatype `'S1'`
+(default) or `'U1'` (if dtype=`'U'`)"""
if dtype not in ["S","U"]:
raise ValueError("dtype must string or unicode ('S' or 'U')")
arr = numpy.zeros(NUMCHARS,dtype+'1')
@@ -4364,16 +4475,16 @@ unicode array (dtype = 'U1') will be returned.
def stringtochar(a):
"""
-stringtochar(a)
+**`stringtochar(a)`**
convert a string array to a character array with one extra dimension
- at param a: Input numpy string array with numpy datatype 'SN' or 'UN', where N
+**`a`**: Input numpy string array with numpy datatype `'SN'` or `'UN'`, where N
is the number of characters in each string. Will be converted to
-an array of characters (datatype 'S1' or 'U1') of shape a.shape + (N,).
+an array of characters (datatype `'S1'` or `'U1'`) of shape `a.shape + (N,)`.
- at return: A numpy character array with datatype 'S1' or 'U1'
-and shape a.shape + (N,), where N is the length of each string in a."""
+returns a numpy character array with datatype `'S1'` or `'U1'`
+and shape `a.shape + (N,)`, where N is the length of each string in a."""
dtype = a.dtype.kind
if dtype not in ["S","U"]:
raise ValueError("type must string or unicode ('S' or 'U')")
@@ -4383,16 +4494,16 @@ and shape a.shape + (N,), where N is the length of each string in a."""
def chartostring(b):
"""
-chartostring(b)
+**`chartostring(b)`**
convert a character array to a string array with one less dimension.
- at param b: Input character array (numpy datatype 'S1' or 'U1').
+**`b`**: Input character array (numpy datatype `'S1'` or `'U1'`).
Will be converted to a array of strings, where each string has a fixed
-length of b.shape[-1] characters.
+length of `b.shape[-1]` characters.
- at return: A numpy string array with datatype 'SN' or 'UN' and shape b.shape[:-1],
-where N=b.shape[-1]."""
+returns a numpy string array with datatype `'SN'` or `'UN'` and shape
+`b.shape[:-1]` where where `N=b.shape[-1]`."""
dtype = b.dtype.kind
if dtype not in ["S","U"]:
raise ValueError("type must string or unicode ('S' or 'U')")
@@ -4404,32 +4515,32 @@ where N=b.shape[-1]."""
def date2num(dates,units,calendar='standard'):
"""
-date2num(dates,units,calendar='standard')
+**`date2num(dates,units,calendar='standard')`**
Return numeric time values given datetime objects. The units
-of the numeric time values are described by the L{units} argument
-and the L{calendar} keyword. The datetime objects must
+of the numeric time values are described by the `netCDF4.units` argument
+and the `netCDF4.calendar` keyword. The datetime objects must
be in UTC with no time-zone offset. If there is a
-time-zone offset in C{units}, it will be applied to the
+time-zone offset in `units`, it will be applied to the
returned numeric values.
- at param dates: A datetime object or a sequence of datetime objects.
+**`dates`**: A datetime object or a sequence of datetime objects.
The datetime objects should not include a time-zone offset.
- at param units: a string of the form C{'B{time units} since B{reference time}}'
-describing the time units. B{C{time units}} can be days, hours, minutes,
-seconds, milliseconds or microseconds. B{C{reference time}} is the time
+**`units`**: a string of the form `<time units> since <reference time>`
+describing the time units. `<time units>` can be days, hours, minutes,
+seconds, milliseconds or microseconds. `<reference time>` is the time
origin. Accuracy is somewhere between a millisecond and a microsecond,
depending on the time interval and the calendar used.
- at param calendar: describes the calendar used in the time calculations.
-All the values currently defined in the U{CF metadata convention
-<http://cf-pcmdi.llnl.gov/documents/cf-conventions/>} are supported.
-Valid calendars C{'standard', 'gregorian', 'proleptic_gregorian'
-'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'}.
-Default is C{'standard'}, which is a mixed Julian/Gregorian calendar.
+**`calendar`**: describes the calendar used in the time calculations.
+All the values currently defined in the
+[CF metadata convention](http://cfconventions.org)
+Valid calendars `'standard', 'gregorian', 'proleptic_gregorian'
+'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'`.
+Default is `'standard'`, which is a mixed Julian/Gregorian calendar.
- at return: a numeric time value, or an array of numeric time values.
+returns a numeric time value, or an array of numeric time values.
"""
basedate = _dateparse(units)
unit = units.split()[0].lower()
@@ -4483,39 +4594,39 @@ Default is C{'standard'}, which is a mixed Julian/Gregorian calendar.
def num2date(times,units,calendar='standard'):
"""
-num2date(times,units,calendar='standard')
+**`num2date(times,units,calendar='standard')`**
Return datetime objects given numeric time values. The units
-of the numeric time values are described by the C{units} argument
-and the C{calendar} keyword. The returned datetime objects represent
+of the numeric time values are described by the `units` argument
+and the `calendar` keyword. The returned datetime objects represent
UTC with no time-zone offset, even if the specified
-C{units} contain a time-zone offset.
+`units` contain a time-zone offset.
- at param times: numeric time values.
+**`times`**: numeric time values.
- at param units: a string of the form C{'B{time units} since B{reference time}}'
-describing the time units. B{C{time units}} can be days, hours, minutes,
-seconds, milliseconds or microseconds. B{C{reference time}} is the time
+**`units`**: a string of the form `<time units> since <reference time>`
+describing the time units. `<time units>` can be days, hours, minutes,
+seconds, milliseconds or microseconds. `<reference time>` is the time
origin. Accuracy is somewhere between a millisecond and a microsecond,
depending on the time interval and the calendar used.
- at keyword calendar: describes the calendar used in the time calculations.
-All the values currently defined in the U{CF metadata convention
-<http://cf-pcmdi.llnl.gov/documents/cf-conventions/>} are supported.
-Valid calendars C{'standard', 'gregorian', 'proleptic_gregorian'
-'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'}.
-Default is C{'standard'}, which is a mixed Julian/Gregorian calendar.
+**`calendar`**: describes the calendar used in the time calculations.
+All the values currently defined in the
+[CF metadata convention](http://cfconventions.org)
+Valid calendars `'standard', 'gregorian', 'proleptic_gregorian'
+'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'`.
+Default is `'standard'`, which is a mixed Julian/Gregorian calendar.
- at return: a datetime instance, or an array of datetime instances.
+returns a datetime instance, or an array of datetime instances.
-The datetime instances returned are 'real' python datetime
-objects if C{calendar='proleptic_gregorian'}, or
-C{calendar = 'standard'} or C{'gregorian'}
+***Note***: The datetime instances returned are 'real' python datetime
+objects if `calendar='proleptic_gregorian'`, or
+`calendar='standard'` or `'gregorian'`
and the date is after the breakpoint between the Julian and
Gregorian calendars (1582-10-15). Otherwise, they are 'phony' datetime
objects which support some but not all the methods of 'real' python
datetime objects. The datetime instances
-do not contain a time-zone offset, even if the specified C{units}
+do not contain a time-zone offset, even if the specified `units`
contains one.
"""
calendar = calendar.lower()
@@ -4578,31 +4689,33 @@ contains one.
def date2index(dates, nctime, calendar=None, select='exact'):
"""
-date2index(dates, nctime, calendar=None, select='exact')
+**`date2index(dates, nctime, calendar=None, select='exact')`**
Return indices of a netCDF time variable corresponding to the given dates.
- at param dates: A datetime object or a sequence of datetime objects.
+**`dates`**: A datetime object or a sequence of datetime objects.
The datetime objects should not include a time-zone offset.
- at param nctime: A netCDF time variable object. The nctime object must have a
-C{units} attribute.
-
- at keyword calendar: Describes the calendar used in the time calculation.
-Valid calendars C{'standard', 'gregorian', 'proleptic_gregorian'
-'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'}.
-Default is C{'standard'}, which is a mixed Julian/Gregorian calendar
-If C{calendar} is None, its value is given by C{nctime.calendar} or
-C{standard} if no such attribute exists.
-
- at keyword select: C{'exact', 'before', 'after', 'nearest'}
-The index selection method. C{exact} will return the indices perfectly
-matching the dates given. C{before} and C{after} will return the indices
+**`nctime`**: A netCDF time variable object. The nctime object must have a
+`units` attribute.
+
+**`calendar`**: describes the calendar used in the time calculations.
+All the values currently defined in the
+[CF metadata convention](http://cfconventions.org)
+Valid calendars `'standard', 'gregorian', 'proleptic_gregorian'
+'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'`.
+Default is `'standard'`, which is a mixed Julian/Gregorian calendar.
+If `calendar` is None, its value is given by `nctime.calendar` or
+`standard` if no such attribute exists.
+
+**`select`**: `'exact', 'before', 'after', 'nearest'`
+The index selection method. `exact` will return the indices perfectly
+matching the dates given. `before` and `after` will return the indices
corresponding to the dates just before or just after the given dates if
-an exact match cannot be found. C{nearest} will return the indices that
+an exact match cannot be found. `nearest` will return the indices that
correspond to the closest dates.
- at return: an index (indices) of the netCDF time variable corresponding
+returns an index (indices) of the netCDF time variable corresponding
to the given datetime object(s).
"""
if calendar == None:
@@ -4620,69 +4733,63 @@ to the given datetime object(s).
class MFDataset(Dataset):
"""
-MFDataset(self, files, check=False, aggdim=None, exclude=[])
-
Class for reading multi-file netCDF Datasets, making variables
spanning multiple files appear as if they were in one file.
-
-Datasets must be in C{NETCDF4_CLASSIC, NETCDF3_CLASSIC or NETCDF3_64BIT}
-format (C{NETCDF4} Datasets won't work).
-
-Adapted from U{pycdf <http://pysclint.sourceforge.net/pycdf>} by Andre Gosselin.
-
-Example usage:
-
->>> import numpy
->>> # create a series of netCDF files with a variable sharing
->>> # the same unlimited dimension.
->>> for nfile in range(10):
->>> f = Dataset('mftest'+repr(nfile)+'.nc','w')
->>> f.createDimension('x',None)
->>> x = f.createVariable('x','i',('x',))
->>> x[0:10] = numpy.arange(nfile*10,10*(nfile+1))
->>> f.close()
->>> # now read all those files in at once, in one Dataset.
->>> f = MFDataset('mftest*nc')
->>> print f.variables['x'][:]
-[ 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
- 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
- 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
- 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]
+Datasets must be in `NETCDF4_CLASSIC, NETCDF3_CLASSIC or NETCDF3_64BIT`
+format (`NETCDF4` Datasets won't work).
+
+Adapted from [pycdf](http://pysclint.sourceforge.net/pycdf) by Andre Gosselin.
+
+Example usage (See `netCDF4.MFDataset.__init__` for more details):
+
+ :::python
+ >>> import numpy
+ >>> # create a series of netCDF files with a variable sharing
+ >>> # the same unlimited dimension.
+ >>> for nf in range(10):
+ >>> f = Dataset("mftest%s.nc" % nf,"w")
+ >>> f.createDimension("x",None)
+ >>> x = f.createVariable("x","i",("x",))
+ >>> x[0:10] = numpy.arange(nf*10,10*(nf+1))
+ >>> f.close()
+ >>> # now read all those files in at once, in one Dataset.
+ >>> f = MFDataset("mftest*nc")
+ >>> print f.variables["x"][:]
+ [ 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
+ 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
+ 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
+ 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]
"""
def __init__(self, files, check=False, aggdim=None, exclude=[]):
"""
-Open a Dataset spanning multiple files, making it look as if it was a
-single file. Variables in the list of files that share the same
-dimension (specified with the keyword C{aggdim}) are aggregated. If
-C{aggdim} is not specified, the unlimited is aggregated. Currently,
-C{aggdim} must be the leftmost (slowest varying) dimension of each
-of the variables to be aggregated.
-
-Adapted from U{pycdf <http://pysclint.sourceforge.net/pycdf>} by Andre Gosselin.
-
-Usage:
-
-nc = MFDataset(files, check=False, aggdim=None, exclude=[])
-
- at param files: either a sequence of netCDF files or a string with a
-wildcard (converted to a sorted list of files using glob) The first file
-in the list will become the "master" file, defining all the
-variables with an aggregation dimension which may span
-subsequent files. Attribute access returns attributes only from "master"
-file. The files are always opened in read-only mode.
-
- at keyword check: True if you want to do consistency checking to ensure the
-correct variables structure for all of the netcdf files. Checking makes
-the initialization of the MFDataset instance much slower. Default is
-False.
-
- at keyword aggdim: The name of the dimension to aggregate over (must
-be the leftmost dimension of each of the variables to be aggregated).
-If None (default), aggregate over the unlimited dimension.
-
- at keyword exclude: A list of variable names to exclude from aggregation.
-Default is an empty list.
+ **`__init__(self, files, check=False, aggdim=None, exclude=[])`**
+
+ Open a Dataset spanning multiple files, making it look as if it was a
+ single file. Variables in the list of files that share the same
+ dimension (specified with the keyword `aggdim`) are aggregated. If
+ `aggdim` is not specified, the unlimited is aggregated. Currently,
+ `aggdim` must be the leftmost (slowest varying) dimension of each
+ of the variables to be aggregated.
+
+ **`files`**: either a sequence of netCDF files or a string with a
+ wildcard (converted to a sorted list of files using glob) The first file
+ in the list will become the "master" file, defining all the
+ variables with an aggregation dimension which may span
+ subsequent files. Attribute access returns attributes only from "master"
+ file. The files are always opened in read-only mode.
+
+ **`check`**: True if you want to do consistency checking to ensure the
+ correct variables structure for all of the netcdf files. Checking makes
+ the initialization of the MFDataset instance much slower. Default is
+ False.
+
+ **`aggdim`**: The name of the dimension to aggregate over (must
+ be the leftmost dimension of each of the variables to be aggregated).
+ If None (default), aggregate over the unlimited dimension.
+
+ **`exclude`**: A list of variable names to exclude from aggregation.
+ Default is an empty list.
"""
# Open the master file in the base class, so that the CDFMF instance
@@ -4845,9 +4952,19 @@ Default is an empty list.
return Dataset.__getattribute__(self, name)
def ncattrs(self):
+ """
+ **`ncattrs(self)`**
+
+ return the netcdf attribute names from the master file.
+ """
return self._cdf[0].__dict__.keys()
def close(self):
+ """
+ **`close(self)`**
+
+ close all the open files.
+ """
for dset in self._cdf:
dset.close()
@@ -4926,7 +5043,10 @@ class _Variable(object):
ncdump_var.append('current size = %s\n' % repr(self.shape))
return ''.join(ncdump_var)
def __len__(self):
- return self._shape()[0]
+ if not self._shape:
+ raise TypeError('len() of unsized object')
+ else:
+ return self._shape()[0]
def _shape(self):
recdimlen = len(self._dset.dimensions[self._recdimname])
return (recdimlen,) + self._mastervar.shape[1:]
@@ -5039,49 +5159,50 @@ class _Variable(object):
class MFTime(_Variable):
"""
-MFTime(self, time, units=None)
-
Class providing an interface to a MFDataset time Variable by imposing a unique common
-time unit to all files.
-
-Example usage:
-
->>> import numpy
->>> f1 = Dataset('mftest_1.nc','w', format='NETCDF4_CLASSIC')
->>> f2 = Dataset('mftest_2.nc','w', format='NETCDF4_CLASSIC')
->>> f1.createDimension('time',None)
->>> f2.createDimension('time',None)
->>> t1 = f1.createVariable('time','i',('time',))
->>> t2 = f2.createVariable('time','i',('time',))
->>> t1.units = 'days since 2000-01-01'
->>> t2.units = 'days since 2000-02-01'
->>> t1.calendar = 'standard'
->>> t2.calendar = 'standard'
->>> t1[:] = numpy.arange(31)
->>> t2[:] = numpy.arange(30)
->>> f1.close()
->>> f2.close()
->>> # Read the two files in at once, in one Dataset.
->>> f = MFDataset('mftest*nc')
->>> t = f.variables['time']
->>> print t.units
-days since 2000-01-01
->>> print t[32] # The value written in the file, inconsistent with the MF time units.
-1
->>> T = MFTime(t)
->>> print T[32]
-32
+time unit to all files.
+
+Example usage (See `netCDF4.MFTime.__init__` for more details):
+
+ :::python
+ >>> import numpy
+ >>> f1 = Dataset("mftest_1.nc","w", format="NETCDF4_CLASSIC")
+ >>> f2 = Dataset("mftest_2.nc","w", format="NETCDF4_CLASSIC")
+ >>> f1.createDimension("time",None)
+ >>> f2.createDimension("time",None)
+ >>> t1 = f1.createVariable("time","i",("time",))
+ >>> t2 = f2.createVariable("time","i",("time",))
+ >>> t1.units = "days since 2000-01-01"
+ >>> t2.units = "days since 2000-02-01"
+ >>> t1.calendar = "standard"
+ >>> t2.calendar = "standard"
+ >>> t1[:] = numpy.arange(31)
+ >>> t2[:] = numpy.arange(30)
+ >>> f1.close()
+ >>> f2.close()
+ >>> # Read the two files in at once, in one Dataset.
+ >>> f = MFDataset("mftest*nc")
+ >>> t = f.variables["time"]
+ >>> print t.units
+ days since 2000-01-01
+ >>> print t[32] # The value written in the file, inconsistent with the MF time units.
+ 1
+ >>> T = MFTime(t)
+ >>> print T[32]
+ 32
"""
def __init__(self, time, units=None):
"""
-Create a time Variable with units consistent across a multifile
-dataset.
-
- at param time: Time variable from a MFDataset.
-
- at keyword units: Time units, for example, 'days since 1979-01-01'. If None, use
-the units from the master variable.
+ **`__init__(self, time, units=None)`**
+
+ Create a time Variable with units consistent across a multifile
+ dataset.
+
+ **`time`**: Time variable from a `netCDF4.MFDataset`.
+
+ **`units`**: Time units, for example, `days since 1979-01-01`. If None, use
+ the units from the master variable.
"""
import datetime
self.__time = time
diff --git a/netcdftime/_datetime.c b/netcdftime/_datetime.c
index ae575c2..8f133d4 100644
--- a/netcdftime/_datetime.c
+++ b/netcdftime/_datetime.c
@@ -1,13 +1,5 @@
/* Generated by Cython 0.22 */
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static Py_hash_t __pyx_pf_10netcdftime_9_datetime_8datetime_10__hash__(struct __pyx_obj_10netcdftime_9_datetime_datetime *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_10netcdftime_9_datetime_8datetime_12__richcmp__(PyObject *__pyx_v_self, PyObject *__pyx_v_other, int __pyx_v_op); /* proto */
+static PyObject *__pyx_pf_10netcdftime_9_datetime_8datetime_14__reduce__(struct __pyx_obj_10netcdftime_9_datetime_datetime *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_10netcdftime_9_datetime_8datetime_4year___get__(struct __pyx_obj_10netcdftime_9_datetime_datetime *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_10netcdftime_9_datetime_8datetime_5month___get__(struct __pyx_obj_10netcdftime_9_datetime_datetime *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_10netcdftime_9_datetime_8datetime_3day___get__(struct __pyx_obj_10netcdftime_9_datetime_datetime *__pyx_v_self); /* proto */
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static char __pyx_k_text[] = "text";
static char __pyx_k_time[] = "time";
static char __pyx_k_year[] = "year";
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static char __pyx_k_delta[] = "delta";
static char __pyx_k_month[] = "month";
static char __pyx_k_sites[] = "sites";
@@ -729,14 +723,16 @@ static char __pyx_k_real_datetime[] = "real_datetime";
static char __pyx_k_NotImplemented[] = "NotImplemented";
static char __pyx_k_to_real_datetime[] = "_to_real_datetime";
static char __pyx_k_netcdftime__datetime[] = "netcdftime._datetime";
-static char __pyx_k_home_mb312_dev_trees_netcdf4_py[] = "/home/mb312/dev_trees/netcdf4-python/netcdftime/_datetime.pyx";
+static char __pyx_k_Users_jsw_python_netcdf4_python[] = "/Users/jsw/python/netcdf4-python.git/netcdftime/_datetime.pyx";
static char __pyx_k_This_strftime_implementation_doe[] = "This strftime implementation does not handle %s";
static PyObject *__pyx_kp_s_4d;
static PyObject *__pyx_n_s_NotImplemented;
static PyObject *__pyx_kp_s_This_strftime_implementation_doe;
static PyObject *__pyx_n_s_TypeError;
+static PyObject *__pyx_kp_s_Users_jsw_python_netcdf4_python;
static PyObject *__pyx_n_s_ValueError;
static PyObject *__pyx_kp_s_Y_m_d_H_M_S;
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static PyObject *__pyx_n_s_compile;
static PyObject *__pyx_n_s_datetime;
static PyObject *__pyx_n_s_day;
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static PyObject *__pyx_n_s_findall;
static PyObject *__pyx_n_s_fmt;
static PyObject *__pyx_n_s_format;
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static PyObject *__pyx_n_s_hour;
static PyObject *__pyx_n_s_i;
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return __pyx_r;
}
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{&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1},
+ {&__pyx_kp_s_Users_jsw_python_netcdf4_python, __pyx_k_Users_jsw_python_netcdf4_python, sizeof(__pyx_k_Users_jsw_python_netcdf4_python), 0, 0, 1, 0},
{&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1},
{&__pyx_kp_s_Y_m_d_H_M_S, __pyx_k_Y_m_d_H_M_S, sizeof(__pyx_k_Y_m_d_H_M_S), 0, 0, 1, 0},
+ {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1},
{&__pyx_n_s_compile, __pyx_k_compile, sizeof(__pyx_k_compile), 0, 0, 1, 1},
{&__pyx_n_s_datetime, __pyx_k_datetime, sizeof(__pyx_k_datetime), 0, 0, 1, 1},
{&__pyx_n_s_day, __pyx_k_day, sizeof(__pyx_k_day), 0, 0, 1, 1},
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{&__pyx_n_s_findall, __pyx_k_findall, sizeof(__pyx_k_findall), 0, 0, 1, 1},
{&__pyx_n_s_fmt, __pyx_k_fmt, sizeof(__pyx_k_fmt), 0, 0, 1, 1},
{&__pyx_n_s_format, __pyx_k_format, sizeof(__pyx_k_format), 0, 0, 1, 1},
- {&__pyx_kp_s_home_mb312_dev_trees_netcdf4_py, __pyx_k_home_mb312_dev_trees_netcdf4_py, sizeof(__pyx_k_home_mb312_dev_trees_netcdf4_py), 0, 0, 1, 0},
{&__pyx_n_s_hour, __pyx_k_hour, sizeof(__pyx_k_hour), 0, 0, 1, 1},
{&__pyx_n_s_i, __pyx_k_i, sizeof(__pyx_k_i), 0, 0, 1, 1},
{&__pyx_n_s_illegal_s, __pyx_k_illegal_s, sizeof(__pyx_k_illegal_s), 0, 0, 1, 1},
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__pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_builtin_NotImplemented = __Pyx_GetBuiltinName(__pyx_n_s_NotImplemented); if (!__pyx_builtin_NotImplemented) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 99; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 106; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
return 0;
__pyx_L1_error:;
return -1;
@@ -3545,73 +3715,73 @@ static int __Pyx_InitCachedConstants(void) {
__Pyx_GOTREF(__pyx_tuple__2);
__Pyx_GIVEREF(__pyx_tuple__2);
- /* "netcdftime/_datetime.pyx":99
+ /* "netcdftime/_datetime.pyx":106
* def _strftime(dt, fmt):
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* raise TypeError("This strftime implementation does not handle %s") # <<<<<<<<<<<<<<
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* # if dt.year > 1900:
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__Pyx_GOTREF(__pyx_tuple__3);
__Pyx_GIVEREF(__pyx_tuple__3);
- /* "netcdftime/_datetime.pyx":114
+ /* "netcdftime/_datetime.pyx":121
* year = year + ((2000 - year) // 28) * 28
* timetuple = dt.timetuple()
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* sites1 = _findall(s1, str(year))
*
*/
- __pyx_slice__4 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 114; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__4 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 121; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__4);
__Pyx_GIVEREF(__pyx_slice__4);
- /* "netcdftime/_datetime.pyx":117
+ /* "netcdftime/_datetime.pyx":124
* sites1 = _findall(s1, str(year))
*
* s2 = time.strftime(fmt, (year + 28,) + timetuple[1:]) # <<<<<<<<<<<<<<
* sites2 = _findall(s2, str(year + 28))
*
*/
- __pyx_slice__5 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 117; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice__5 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 124; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice__5);
__Pyx_GIVEREF(__pyx_slice__5);
- /* "netcdftime/_datetime.pyx":77
+ /* "netcdftime/_datetime.pyx":84
*
*
* _illegal_s = re.compile(r"((^|[^%])(%%)*%s)") # <<<<<<<<<<<<<<
*
*
*/
- __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_s_s); if (unlikely(!__pyx_tuple__6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 77; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_s_s); if (unlikely(!__pyx_tuple__6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__6);
__Pyx_GIVEREF(__pyx_tuple__6);
- /* "netcdftime/_datetime.pyx":80
+ /* "netcdftime/_datetime.pyx":87
*
*
* def _findall(text, substr): # <<<<<<<<<<<<<<
* # Also finds overlaps
* sites = []
*/
- __pyx_tuple__7 = PyTuple_Pack(5, __pyx_n_s_text, __pyx_n_s_substr, __pyx_n_s_sites, __pyx_n_s_i, __pyx_n_s_j); if (unlikely(!__pyx_tuple__7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__7 = PyTuple_Pack(5, __pyx_n_s_text, __pyx_n_s_substr, __pyx_n_s_sites, __pyx_n_s_i, __pyx_n_s_j); if (unlikely(!__pyx_tuple__7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 87; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__7);
__Pyx_GIVEREF(__pyx_tuple__7);
- __pyx_codeobj__8 = (PyObject*)__Pyx_PyCode_New(2, 0, 5, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__7, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_home_mb312_dev_trees_netcdf4_py, __pyx_n_s_findall, 80, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_codeobj__8 = (PyObject*)__Pyx_PyCode_New(2, 0, 5, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__7, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_jsw_python_netcdf4_python, __pyx_n_s_findall, 87, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 87; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- /* "netcdftime/_datetime.pyx":97
+ /* "netcdftime/_datetime.pyx":104
*
*
* def _strftime(dt, fmt): # <<<<<<<<<<<<<<
* if _illegal_s.search(fmt):
* raise TypeError("This strftime implementation does not handle %s")
*/
- __pyx_tuple__9 = PyTuple_Pack(14, __pyx_n_s_dt, __pyx_n_s_fmt, __pyx_n_s_year, __pyx_n_s_delta, __pyx_n_s_off, __pyx_n_s_timetuple, __pyx_n_s_s1, __pyx_n_s_sites1, __pyx_n_s_s2, __pyx_n_s_sites2, __pyx_n_s_sites, __pyx_n_s_site, __pyx_n_s_s_2, __pyx_n_s_syear); if (unlikely(!__pyx_tuple__9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 97; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__9 = PyTuple_Pack(14, __pyx_n_s_dt, __pyx_n_s_fmt, __pyx_n_s_year, __pyx_n_s_delta, __pyx_n_s_off, __pyx_n_s_timetuple, __pyx_n_s_s1, __pyx_n_s_sites1, __pyx_n_s_s2, __pyx_n_s_sites2, __pyx_n_s_sites, __pyx_n_s_site, __pyx_n_s_s_2, __pyx_n_s_syear); if (unlikely(!__pyx_tuple__9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 104; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__9);
__Pyx_GIVEREF(__pyx_tuple__9);
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+ __pyx_codeobj__10 = (PyObject*)__Pyx_PyCode_New(2, 0, 14, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__9, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_jsw_python_netcdf4_python, __pyx_n_s_strftime, 104, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 104; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_RefNannyFinishContext();
return 0;
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@@ -3789,46 +3959,46 @@ PyMODINIT_FUNC PyInit__datetime(void)
if (PyDict_SetItem(__pyx_d, __pyx_n_s_time, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- /* "netcdftime/_datetime.pyx":77
+ /* "netcdftime/_datetime.pyx":84
*
*
* _illegal_s = re.compile(r"((^|[^%])(%%)*%s)") # <<<<<<<<<<<<<<
*
*
*/
- __pyx_t_2 = __Pyx_GetModuleGlobalName(__pyx_n_s_re); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 77; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __Pyx_GetModuleGlobalName(__pyx_n_s_re); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
- __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_compile); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 77; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_compile); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_tuple__6, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 77; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_tuple__6, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- if (PyDict_SetItem(__pyx_d, __pyx_n_s_illegal_s, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 77; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyDict_SetItem(__pyx_d, __pyx_n_s_illegal_s, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- /* "netcdftime/_datetime.pyx":80
+ /* "netcdftime/_datetime.pyx":87
*
*
* def _findall(text, substr): # <<<<<<<<<<<<<<
* # Also finds overlaps
* sites = []
*/
- __pyx_t_2 = PyCFunction_NewEx(&__pyx_mdef_10netcdftime_9_datetime_1_findall, NULL, __pyx_n_s_netcdftime__datetime); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = PyCFunction_NewEx(&__pyx_mdef_10netcdftime_9_datetime_1_findall, NULL, __pyx_n_s_netcdftime__datetime); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 87; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
- if (PyDict_SetItem(__pyx_d, __pyx_n_s_findall, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyDict_SetItem(__pyx_d, __pyx_n_s_findall, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 87; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- /* "netcdftime/_datetime.pyx":97
+ /* "netcdftime/_datetime.pyx":104
*
*
* def _strftime(dt, fmt): # <<<<<<<<<<<<<<
* if _illegal_s.search(fmt):
* raise TypeError("This strftime implementation does not handle %s")
*/
- __pyx_t_2 = PyCFunction_NewEx(&__pyx_mdef_10netcdftime_9_datetime_3_strftime, NULL, __pyx_n_s_netcdftime__datetime); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 97; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = PyCFunction_NewEx(&__pyx_mdef_10netcdftime_9_datetime_3_strftime, NULL, __pyx_n_s_netcdftime__datetime); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 104; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
- if (PyDict_SetItem(__pyx_d, __pyx_n_s_strftime, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 97; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyDict_SetItem(__pyx_d, __pyx_n_s_strftime, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 104; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
/* "netcdftime/_datetime.pyx":1
diff --git a/netcdftime/_datetime.pyx b/netcdftime/_datetime.pyx
index 9329f51..8ac1791 100644
--- a/netcdftime/_datetime.pyx
+++ b/netcdftime/_datetime.pyx
@@ -73,6 +73,13 @@ and format.
return PyObject_RichCompare(self_str, other_str, op)
return NotImplemented
+ def __reduce__(self):
+ """special method that allows instance to be pickled"""
+ args = (self.year,self.month,self.day,self.hour,
+ self.minute,self.second,self.microsecond,
+ self.dayofwk,self.dayofyr)
+ return (self.__class__,args)
+
_illegal_s = re.compile(r"((^|[^%])(%%)*%s)")
diff --git a/netcdftime/netcdftime.py b/netcdftime/netcdftime.py
index 69a8359..e2ac23d 100644
--- a/netcdftime/netcdftime.py
+++ b/netcdftime/netcdftime.py
@@ -33,7 +33,6 @@ ISO8601_REGEX = re.compile(r"(?P<year>[0-9]{1,4})(-(?P<month>[0-9]{1,2})(-(?P<da
TIMEZONE_REGEX = re.compile(
"(?P<prefix>[+-])(?P<hours>[0-9]{1,2}):(?P<minutes>[0-9]{1,2})")
-
def JulianDayFromDate(date, calendar='standard'):
"""
@@ -119,6 +118,13 @@ def JulianDayFromDate(date, calendar='standard'):
# adjust for Julian calendar if necessary
jd = jd + B
+ # Add a small offset (proportional to Julian date) for correct re-conversion.
+ # This is about 45 microseconds in 2000 for Julian date starting -4712.
+ # (pull request #433).
+ eps = np.finfo(float).eps
+ eps = np.maximum(eps*jd, eps)
+ jd += eps
+
if isscalar:
return jd[0]
else:
@@ -232,7 +238,6 @@ def DateFromJulianDay(JD, calendar='standard'):
Meeus, Jean (1998) Astronomical Algorithms (2nd Edition). Willmann-Bell,
Virginia. p. 63
-
"""
# based on redate.py by David Finlayson.
@@ -246,7 +251,7 @@ def DateFromJulianDay(JD, calendar='standard'):
# get the day (Z) and the fraction of the day (F)
# add 0.000005 which is 452 ms in case of jd being after
# second 23:59:59 of a day we want to round to the next day see issue #75
- Z = np.atleast_1d(np.int32(np.round(julian + 0.00005)))
+ Z = np.atleast_1d(np.int32(np.round(julian)))
F = np.atleast_1d(julian + 0.5 - Z).astype(np.float64)
if calendar in ['standard', 'gregorian']:
# MC
@@ -313,14 +318,18 @@ def DateFromJulianDay(JD, calendar='standard'):
inc_idx = np.where((leap == 1) & (month > 2))[0]
dayofyr[inc_idx] = dayofyr[inc_idx] + leap[inc_idx]
- eps = np.clip(
- (1e-12 * np.abs(Z)).astype(np.float64), np.float64(1e-12), None)
- hour = np.clip((F * 24. + eps).astype(np.int64), 0, 23)
- F -= hour / 24.
- minute = np.clip((F * 1440. + eps).astype(np.int64), 0, 59)
+ # Subtract the offset from JulianDayFromDate from the microseconds (pull
+ # request #433).
+ eps = np.finfo(float).eps
+ eps = np.maximum(eps*julian, eps)
+ hour = np.clip((F * 24.).astype(np.int64), 0, 23)
+ F -= hour / 24.
+ minute = np.clip((F * 1440.).astype(np.int64), 0, 59)
+ # this is an overestimation due to added offset in JulianDayFromDate
second = np.clip((F - minute / 1440.) * 86400., 0, None)
- # microseconds may not be accurate.
microsecond = (second % 1)*1.e6
+ # remove the offset from the microsecond calculation.
+ microsecond = np.clip(microsecond - eps*86400.*1e6, 0, 999999)
# convert year, month, day, hour, minute, second to int32
year = year.astype(np.int32)
@@ -1084,7 +1093,7 @@ def time2index(times, nctime, calendar=None, select='exact'):
elif select == 'nearest':
nearest_to_left = num[mismatch] < numpy.array(
- [nctime[i - 1] + nctime[i] for i in index[mismatch]]) / 2.
+ [float(nctime[i - 1]) + float(nctime[i]) for i in index[mismatch]]) / 2.
index[mismatch] = index[mismatch] - 1 * nearest_to_left
else:
diff --git a/setup.py b/setup.py
index bc2dd96..f2da400 100644
--- a/setup.py
+++ b/setup.py
@@ -149,13 +149,22 @@ curl_incdir = os.environ.get('CURL_INCDIR')
USE_NCCONFIG = os.environ.get('USE_NCCONFIG')
if USE_NCCONFIG is not None:
USE_NCCONFIG = bool(int(USE_NCCONFIG))
+USE_SETUPCFG = os.environ.get('USE_SETUPCFG')
+# override use of setup.cfg with env var.
+if USE_SETUPCFG is not None:
+ USE_SETUPCFG = bool(int(USE_SETUPCFG))
+else:
+ USE_SETUPCFG = True
+
setup_cfg = 'setup.cfg'
-# contents of setup.cfg will override env vars.
+# contents of setup.cfg will override env vars, unless
+# USE_SETUPCFG evaluates to True. Exception is use_ncconfig,
+# which does not take precedence ofver USE_NCCONFIG env var.
ncconfig = None
use_ncconfig = None
use_cython = True
-if os.path.exists(setup_cfg):
+if USE_SETUPCFG and os.path.exists(setup_cfg):
sys.stdout.write('reading from setup.cfg...\n')
config = configparser.SafeConfigParser()
config.read(setup_cfg)
@@ -292,8 +301,10 @@ NETCDF4_DIR environment variable not set, checking standard locations.. \n""")
netCDF4_libdir = os.path.join(netCDF4_dir, 'lib')
libs = ['netcdf','hdf5_hl','hdf5','z']
- lib_dirs = [netCDF4_libdir,HDF5_libdir]
- inc_dirs = [netCDF4_incdir,HDF5_incdir]
+ if netCDF4_libdir is not None: lib_dirs=[netCDF4_libdir]
+ if HDF5_libdir is not None: lib_dirs.append(HDF5_libdir)
+ if netCDF4_incdir is not None: inc_dirs=[netCDF4_incdir]
+ if HDF5_incdir is not None: inc_dirs.append(HDF5_incdir)
# add szip to link if desired.
if szip_libdir is None and szip_dir is not None:
@@ -353,7 +364,7 @@ else:
cmdclass = {}
netcdf4_src_root = osp.join('netCDF4', '_netCDF4')
netcdf4_src_c = netcdf4_src_root + '.c'
-if has_cython and 'sdist' not in sys.argv[1:]:
+if has_cython and 'sdist' not in sys.argv[1:] and 'clean' not in sys.argv[1:]:
sys.stdout.write('using Cython to compile netCDF4.pyx...\n')
extensions = [Extension("netCDF4._netCDF4",
[netcdf4_src_root + '.pyx'],
@@ -402,7 +413,7 @@ else:
setup(name = "netCDF4",
cmdclass = cmdclass,
- version = "1.1.8",
+ version = "1.1.9",
long_description = "netCDF version 4 has many features not found in earlier versions of the library, such as hierarchical groups, zlib compression, multiple unlimited dimensions, and new data types. It is implemented on top of HDF5. This module implements most of the new features, and can read and write netCDF files compatible with older versions of the library. The API is modelled after Scientific.IO.NetCDF, and should be familiar to users of that module.\n\nThis project has a `Sub [...]
author = "Jeff Whitaker",
author_email = "jeffrey.s.whitaker at noaa.gov",
diff --git a/test/run_all.py b/test/run_all.py
index 8dba27c..f2a9c0d 100755
--- a/test/run_all.py
+++ b/test/run_all.py
@@ -18,6 +18,11 @@ if __netcdf4libversion__ < '4.2.1':
test_files.remove('tst_diskless.py')
sys.stdout.write('not running tst_diskless.py ...\n')
+# Don't run tests that require network connectivity
+if os.getenv('NO_NET'):
+ test_files.remove('tst_dap.py');
+ sys.stdout.write('not running tst_dap.py ...\n')
+
# Build the test suite from the tests found in the test files.
testsuite = unittest.TestSuite()
for f in test_files:
diff --git a/test/tst_netcdftime.py b/test/tst_netcdftime.py
index 7e50d38..8da92f9 100644
--- a/test/tst_netcdftime.py
+++ b/test/tst_netcdftime.py
@@ -1,13 +1,14 @@
from netcdftime import utime, JulianDayFromDate, DateFromJulianDay
from netcdftime import datetime as datetimex
-from netCDF4 import Dataset, num2date, date2num, date2index
+from netCDF4 import Dataset, num2date, date2num, date2index, num2date
+import copy
import numpy
import random
import sys
import unittest
import os
import tempfile
-from datetime import datetime
+from datetime import datetime, timedelta
from numpy.testing import assert_almost_equal, assert_equal
# test netcdftime module for netCDF time <--> python datetime conversions.
@@ -207,7 +208,7 @@ class netcdftimeTestCase(unittest.TestCase):
assert_equal(numpy.around(t), 3600)
# test fix for issue 75 (seconds hit 60 at end of month,
# day goes out of range).
- t = 733498.999999
+ t = 733499.0
d = num2date(t, units='days since 0001-01-01 00:00:00')
dateformat = '%Y-%m-%d %H:%M:%S'
assert_equal(d.strftime(dateformat), '2009-04-01 00:00:00')
@@ -412,6 +413,9 @@ class netcdftimeTestCase(unittest.TestCase):
self.assertEqual(d1, d2)
self.assertEqual(num2date(d1, units, cap_cal),
num2date(d1, units, low_cal))
+ # issue 415
+ t = datetimex(2001, 12, 1, 2, 3, 4)
+ self.assertEqual(t, copy.deepcopy(t))
class TestDate2index(unittest.TestCase):
@@ -466,6 +470,16 @@ class TestDate2index(unittest.TestCase):
time2.units = 'days since 1901-01-01'
self.first_timestamp = datetime(2000, 1, 1)
time2[0] = date2num(self.first_timestamp, time2.units)
+ ntimes = 21
+ f.createDimension("record", ntimes)
+ time3 = f.createVariable("time3", numpy.int32, ("record", ))
+ time3.units = "seconds since 1970-01-01 00:00:00"
+ date = datetime(2037,1,1,0)
+ dates = [date]
+ for ndate in range(ntimes-1):
+ date += (ndate+1)*timedelta(hours=1)
+ dates.append(date)
+ time3[:] = date2num(dates,time3.units)
f.close()
def tearDown(self):
@@ -587,5 +601,16 @@ class TestDate2index(unittest.TestCase):
assert_equal(date, date2)
f.close()
+ def test_issue444(self):
+ # make sure integer overflow not causing error in
+ # calculation of nearest index when sum of adjacent
+ # time values won't fit in 32 bits.
+ ntimes = 20
+ f = Dataset(self.file, 'r')
+ query_time = datetime(2037, 1, 3, 21, 12)
+ index = date2index(query_time, f.variables['time3'], select='nearest')
+ assert(index == 11)
+ f.close()
+
if __name__ == '__main__':
unittest.main()
diff --git a/test/tst_scaled.py b/test/tst_scaled.py
index 3e9f688..230a193 100755
--- a/test/tst_scaled.py
+++ b/test/tst_scaled.py
@@ -135,6 +135,51 @@ class SetAutoScaleTrue(SetAutoScaleTestBase):
f.close()
+class WriteAutoScaleTest(SetAutoScaleTestBase):
+
+ def test_auto_scale_write(self):
+
+ """Testing automatic packing to all kinds of integer types"""
+
+ def packparams(dmax, dmin, dtyp):
+ kind = dtyp[0]
+ n = int(dtyp[1]) * 8
+ scale_factor = (dmax - dmin) / (2**n - 1)
+ if kind == 'i':
+ add_offset = dmin + 2**(n-1) * scale_factor
+ elif kind == 'u':
+ add_offset = dmin
+ else:
+ raise Exception
+ return((add_offset, scale_factor))
+
+ for dtyp in ['i1', 'i2', 'i4', 'u1', 'u2', 'u4']:
+ np.random.seed(456)
+ data = np.random.uniform(size=100)
+ f = Dataset(self.testfile, 'w')
+ f.createDimension('x')
+ #
+ # save auto_scaled
+ v = f.createVariable('v', dtyp, ('x',))
+ v.set_auto_scale(True) # redundant
+ v.add_offset, v.scale_factor = packparams(
+ np.max(data), np.min(data), dtyp)
+ v[:] = data
+ f.close()
+ #
+ # read back
+ f = Dataset(self.testfile, 'r')
+ v = f.variables['v']
+ v.set_auto_mask(False)
+ v.set_auto_scale(True) # redundant
+ vdata = v[:]
+ # error normalized by scale factor
+ maxerrnorm = np.max(np.abs((vdata - data) / v.scale_factor))
+ # 1e-5 accounts for floating point errors
+ assert(maxerrnorm < 0.5 + 1e-5)
+ f.close()
+
+
class GlobalSetAutoScaleTest(unittest.TestCase):
def setUp(self):
diff --git a/test/tst_slicing.py b/test/tst_slicing.py
index 61f4268..8011d36 100644
--- a/test/tst_slicing.py
+++ b/test/tst_slicing.py
@@ -181,5 +181,17 @@ class VariablesTestCase(unittest.TestCase):
assert_array_almost_equal(cc,a[-1,3:,:6])
f.close()
+ def test_retain_single_dims(self):
+ f = Dataset(self.file, 'r')
+ v = f.variables['data']
+ keys = ((0, 1, 2, 3, 4, 5, 6, 7, 8), (5,), (4,))
+ shape = (9, 1, 1)
+ data = v[keys]
+ assert_equal(data.shape, shape)
+ keys = ((0, 1, 2, 3, 4, 5, 6, 7, 8), 5, 4,)
+ shape = (9,)
+ data = v[keys]
+ assert_equal(data.shape, shape)
+
if __name__ == '__main__':
unittest.main()
--
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