[netcdf4-python] 01/13: Imported Upstream version 1.1.8
Sebastiaan Couwenberg
sebastic at moszumanska.debian.org
Fri Jun 5 00:00:57 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 ed72e39c0e8a3a041c7ae5a89c33192ca28b65d5
Author: Bas Couwenberg <sebastic at xs4all.nl>
Date: Thu Jun 4 23:12:23 2015 +0200
Imported Upstream version 1.1.8
---
.gitignore | 1 -
.travis.yml | 15 +-
Changelog | 48 +
MANIFEST.in | 11 +-
README.macosx | 18 -
README.md | 70 +-
create_docs.sh | 2 +-
docs/api-objects.txt | 557 +-
docs/class-tree.html | 45 +-
docs/help.html | 22 +-
docs/identifier-index.html | 673 +-
docs/index.html | 551 +-
docs/module-tree.html | 22 +-
docs/netCDF4-module.html | 129 +-
docs/netCDF4.CompoundType-class.html | 18 +-
docs/netCDF4.Dataset-class.html | 117 +-
docs/netCDF4.Dimension-class.html | 24 +-
docs/netCDF4.Group-class.html | 17 +-
docs/netCDF4.MFDataset-class.html | 29 +-
docs/netCDF4.MFTime-class.html | 14 +-
docs/netCDF4.VLType-class.html | 18 +-
docs/netCDF4.Variable-class.html | 122 +-
docs/redirect.html | 2 +-
examples/README.md | 13 +
examples/bench.py | 4 +-
examples/bench_compress.py | 2 +-
examples/bench_compress2.py | 2 +-
examples/bench_compress3.py | 2 +-
examples/bench_diskless.py | 6 +-
examples/data/prmsl.2000.nc | Bin 8985332 -> 0 bytes
examples/data/prmsl.2001.nc | Bin 8968789 -> 0 bytes
examples/data/prmsl.2002.nc | Bin 8972796 -> 0 bytes
examples/data/prmsl.2003.nc | Bin 8974435 -> 0 bytes
examples/data/prmsl.2004.nc | Bin 8997438 -> 0 bytes
examples/data/prmsl.2005.nc | Bin 8976678 -> 0 bytes
examples/data/prmsl.2006.nc | Bin 8969714 -> 0 bytes
examples/data/prmsl.2007.nc | Bin 8974360 -> 0 bytes
examples/data/prmsl.2008.nc | Bin 8994260 -> 0 bytes
examples/data/prmsl.2009.nc | Bin 8974678 -> 0 bytes
examples/data/prmsl.2010.nc | Bin 8970732 -> 0 bytes
examples/data/prmsl.2011.nc | Bin 8976285 -> 0 bytes
examples/data/rtofs_glo_3dz_f006_6hrly_reg3.nc | Bin 30027329 -> 0 bytes
examples/json_att.py | 24 +
examples/subset.py | 15 +
examples/test_stringarr.py | 2 +-
examples/threaded_read.py | 2 +-
examples/tutorial.py | 17 +-
constants.pyx => include/constants.pyx | 0
netCDF4.pxi => include/netCDF4.pxi | 1 +
man/nc3tonc4.1 | 78 +
man/nc4tonc3.1 | 45 +
man/ncinfo.1 | 44 +
netCDF4/__init__.py | 8 +
netCDF4.c => netCDF4/_netCDF4.c | 105288 +++++++++++-----------
netCDF4.pyx => netCDF4/_netCDF4.pyx | 1712 +-
netCDF4/utils.py | 911 +
netCDF4_utils.py | 404 -
netcdftime/__init__.py | 2 +
netcdftime/_datetime.c | 18 +-
netcdftime/netcdftime.py | 40 +-
ports/README | 14 -
ports/python/py-netcdf4/Portfile | 28 -
setup.cfg | 2 +
setup.cfg.template | 2 +
setup.py | 72 +-
test/tst_atts.py | 2 +-
test/tst_compoundvar.py | 2 +-
test/tst_compression.py | 3 +-
test/tst_dims.py | 4 +-
test/tst_diskless.py | 4 +-
test/tst_grps2.py | 44 +
test/tst_masked.py | 6 +
test/tst_masked2.py | 8 +-
test/tst_slicing.py | 1 +
test/tst_unlimdim.py | 6 +-
test/tst_utils.py | 72 +-
test/tst_vars.py | 6 +-
test/tst_vlen.py | 4 +-
utils.pyx | 815 -
utils/nc3tonc4 | 280 +-
utils/nc4tonc3 | 127 +-
utils/ncinfo | 82 +-
82 files changed, 57367 insertions(+), 55382 deletions(-)
diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index 567609b..0000000
--- a/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-build/
diff --git a/.travis.yml b/.travis.yml
index 232a5a5..ec158e9 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,13 +1,26 @@
language: python
+env:
+ global:
+ - DEPENDS="numpy cython"
python:
- "2.7"
- "3.3"
- "3.4"
+matrix:
+ include:
+ # Absolute minimum dependencies
+ - python: 2.7
+ env:
+ - DEPENDS="numpy==1.7.0 cython==0.19"
+ # test without Cython installed
+ - python: 2.7
+ env:
+ - DEPENDS="numpy"
notifications:
email: false
before_install:
- sudo apt-get install libhdf5-serial-dev netcdf-bin libnetcdf-dev
- - pip install numpy cython python-dateutil
+ - pip install $DEPENDS
install:
- python setup.py build
- python setup.py install
diff --git a/Changelog b/Changelog
index 7b087aa..3595e0a 100644
--- a/Changelog
+++ b/Changelog
@@ -1,3 +1,51 @@
+ version 1.1.8 (tag v1.1.8rel)
+ =============================
+ * v[...] now returns a numpy scalar array (not just a scalar) when
+ v is a scalar netcdf variable (issue #413).
+ * unix-like paths can now be used in createVariable and createGroup.
+ 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, g = nc.createGroup('/path/to') acts like 'mkdir -p' in unix, creating
+ the 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 nc.createGroup
+ will now return the existing group instance.
+ Dataset.__getitem__ also added. nc['/path/to'] returns
+ a Group instance, and nc['/path/to/var1'] returns a Variable
+ instance.
+ * change minimum required numpy to 1.7.0, fix so all tests pass with 1.7.0.
+ Added travis tests for minimum required cython, numpy (issue #404).
+ * enable abbreviations to time units specification, as allowed in CF (issue
+ #402). Now, instead of just 'seconds' and 'seconds', 'secs', 'sec' and 's'
+ are also allowed (similar to minutes, days and hours).
+ * install utility scripts in utils directory with setuptools entry points
+ (pull request #392 from @mindw). Code for utilities moved
+ to netCDF4_utils.py - makes utilities more windows-friendly.
+ * make sure booleans are treated correctly in setup.cfg. Add
+ use_cython (default True) to setup.cfg. If set to False, then
+ cython will not be used to compile netCDF4.pyx (existing netCDF4.c
+ will be used instead).
+ * use "from Cython.Build import cythonize" instead of
+ "from Cython.Distutils import build_ext" in setup.py (issue #393)
+ to conform to new cython build mechanism (CEP 201, described at
+ https://github.com/cython/cython/wiki/enhancements-distutils_preprocessing).
+ * unicode attributes now written as strings, not bytes (using
+ nc_put_att_string instead of nc_put_att_text, issue #388).
+ * add __orthogonal_indexing__ attribute to Variable, Dataset and Group (issue #385) to
+ denote that Variable objects do not follow numpy indexing semantics for integer and
+ boolean array indices.
+ * make sure application of scale_factor and add_offset works correctly when
+ scale_factor not given (issue #381).
+ * add man pages for nc3tonc4, nc4tonc3, ncinfo in man directory.
+ Not installed by setup.py (contributed by Ross Gammon, issue #383).
+ * replace tabs with spaces by running reindent.py on all *.py and *.pyx files
+ (issue #378).
+ * refactor netCDF4_utils and netCDF4 module into netCDF4 package.
+ Refactoring effectively removes netCDF4 utils private attributes from
+ netCDF4 namespace, so has the potential to break code using private
+ attributes (issue #409).
+
version 1.1.7 (tag v1.1.7rel)
=============================
* check to make sure cython >= 0.19 is available before trying
diff --git a/MANIFEST.in b/MANIFEST.in
index 954750f..0c02bd1 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,20 +1,15 @@
recursive-include docs *
-recursive-include ports *
+recursive-include man *
+recursive-include include *
include MANIFEST.in
include README.md
-include README.macosx
include COPYING
include Changelog
include setup.cfg
include setup.cfg.template
-include netCDF4.pyx
-include netCDF4_utils.py
-include netCDF4.pxi
-include netCDF4.c
-include utils.pyx
-include constants.pyx
include examples/*py
include examples/*ipynb
+include examples/README.md
include test/*py
include utils/nc3tonc4
include utils/nc4tonc3
diff --git a/README.macosx b/README.macosx
deleted file mode 100644
index 34e443e..0000000
--- a/README.macosx
+++ /dev/null
@@ -1,18 +0,0 @@
-netcdf4-python is now available in macports (py-netcdf4 package), so the
-instructions below are obsolete (unless you want to keep up to date with the
-latest netcdf4-python and let macports manage the installation).
-
---------------------
-
-Although netcdf4-python is not yet part of MacPorts (http://www.macports.org), you
-can use the included Portfile to install it using a local Portfile repository.
-See ports/README for more details. Once macports is installed, and your local
-Portfile repo is set up, all you have to do to install netcdf4-python and it's
-dependencies is run "sudo port install py27-netcdf4".
-
-This will install netcdf4-python from svn HEAD. To periodically upgrade, just do
- - sudo port uninstall py27-netcdf4
- - sudo port install py27-netcdf4 (this will re-download from svn and rebuild)
-
-google "macports local portfile" for more information on creating a local Portfile
-repository.
diff --git a/README.md b/README.md
index 0349931..a3ecee8 100644
--- a/README.md
+++ b/README.md
@@ -1,53 +1,53 @@
-netcdf4-python
-==============
-
-[python](http://python.org)/[numpy](http://numpy.org) interface to the netCDF [C library](https://github.com/Unidata/netcdf-c).
+# netcdf4-python
+[Python](http://python.org)/[numpy](http://numpy.org) interface to the netCDF [C library](https://github.com/Unidata/netcdf-c).
[](https://travis-ci.org/Unidata/netcdf4-python)
[](http://python.org/pypi/netCDF4)
-News
-----
-
+## 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)
+
+will create a variable named 'var1', while also creating the groups
+'path' and 'path/to' if they do not already exist.
+
+Similarly,
+
+ 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
+`nc.createGroup` will now return the existing group instance.
+`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
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`
-with `export USE_NCCONFIG=1` will use environment variables to find paths to libraries and include files,
+regarding thread-safety in the HDF5 C library. Fixes to `setup.py` now ensure that `pip install netCDF4`
+with `export USE_NCCONFIG=0` will use environment variables to find paths to libraries and include files,
instead of relying exclusively on the nc-config utility.
-3/8/2015: Version 1.1.6 released. Minor bug fixes for regressions introduced in 1.1.5,
-including incorrect handling of UTC offsets in units string by date2num/num2date.
-Datetime instances returned by num2date are now time-zone naive, so python-dateutil
-is no longer required.
-
-3/1/2015: Version 1.1.5 released. Significant improvements to netcdftime and
-num2date/date2num - accuracy is now between a millisecond and a microsecond depending
-on the time interval and calendar used. `use_ncconfig=True` is now the default
-in setup.py, so the utility nc-config is used to find the library and
-include file paths.
-
-Quick Start
------------
-
-* clone github repository, or get source tarball (or Windows binary installers) from
- [PyPI](https://pypi.python.org/pypi/netCDF4).
+## 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`).
-* make sure numpy and (required) and Cython (recommended) are
- installed and you have python 2.5 or newer.
+* 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.
-* make sure HDF5 and netcdf-4 are installed, and the nc-config utility
- is in your Unix PATH. If setup.cfg does not exist, copy setup.cfg.template
- to setup.cfg, and make sure the line with `use_ncconfig=True` is
- un-commented.
+* Make sure [HDF5](http://www.h5py.org/) and netcdf-4 are installed, and the `nc-config` utility
+ is in your Unix PATH. If `setup.cfg` does not exist, copy `setup.cfg.template`
+ to `setup.cfg`, and make sure the line with `use_ncconfig=True` is un-commented.
-* run `python setup.py build`, then `python setup.py install` (with sudo
- if necessary).
+* Run `python setup.py build`, then `python setup.py install` (with `sudo` if necessary).
-* To run all the tests, execute `cd test; python run_all.py`.
+* To run all the tests, execute `cd test && python run_all.py`.
+## Documentation
See the online [docs](http://unidata.github.io/netcdf4-python) for more details.
-Sample ipython notebooks available in the examples directory on [reading](http://nbviewer.ipython.org/github/Unidata/netcdf4-python/blob/master/examples/reading_netCDF.ipynb) and [writing](http://nbviewer.ipython.org/github/Unidata/netcdf4-python/blob/master/examples/writing_netCDF.ipynb) netcdf data with python.
+## Usage
+###### Sample [iPython](http://ipython.org/) notebooks available in the examples directory on [reading](http://nbviewer.ipython.org/github/Unidata/netcdf4-python/blob/master/examples/reading_netCDF.ipynb) and [writing](http://nbviewer.ipython.org/github/Unidata/netcdf4-python/blob/master/examples/writing_netCDF.ipynb) netCDF data with Python.
diff --git a/create_docs.sh b/create_docs.sh
index 9fa1ae1..e2ae30d 100644
--- a/create_docs.sh
+++ b/create_docs.sh
@@ -1,2 +1,2 @@
# svn propset svn:mime-type text/html docs/*html
-epydoc -v --no-frames --no-private --introspect-only -o docs netCDF4
+epydoc -v --no-frames --no-private --introspect-only --name netcdf4-python -o docs netCDF4._netCDF4
diff --git a/docs/api-objects.txt b/docs/api-objects.txt
index 598004f..be91bd3 100644
--- a/docs/api-objects.txt
+++ b/docs/api-objects.txt
@@ -1,272 +1,285 @@
-netCDF4 netCDF4-module.html
-netCDF4._set_default_format netCDF4-module.html#_set_default_format
-netCDF4.default_encoding netCDF4-module.html#default_encoding
-netCDF4.num2date netCDF4-module.html#num2date
-netCDF4.is_native_big netCDF4-module.html#is_native_big
-netCDF4._private_atts netCDF4-module.html#_private_atts
-netCDF4.MINYEAR netCDF4-module.html#MINYEAR
-netCDF4._gethdf5libversion netCDF4-module.html#_gethdf5libversion
-netCDF4.__has_nc_inq_format_extended__ netCDF4-module.html#__has_nc_inq_format_extended__
-netCDF4.__package__ netCDF4-module.html#__package__
-netCDF4.stringtochar netCDF4-module.html#stringtochar
-netCDF4.chartostring netCDF4-module.html#chartostring
-netCDF4.getlibversion netCDF4-module.html#getlibversion
-netCDF4.__netcdf4libversion__ netCDF4-module.html#__netcdf4libversion__
-netCDF4.__has_nc_inq_path__ netCDF4-module.html#__has_nc_inq_path__
-netCDF4.date2num netCDF4-module.html#date2num
-netCDF4.stringtoarr netCDF4-module.html#stringtoarr
-netCDF4._value netCDF4-module.html#_value
-netCDF4._nptonctype netCDF4-module.html#_nptonctype
-netCDF4.date2index netCDF4-module.html#date2index
-netCDF4.gregorian netCDF4-module.html#gregorian
-netCDF4.is_native_little netCDF4-module.html#is_native_little
-netCDF4.python3 netCDF4-module.html#python3
-netCDF4._dateparse netCDF4-module.html#_dateparse
-netCDF4._supportedtypes netCDF4-module.html#_supportedtypes
-netCDF4.NC_DISKLESS netCDF4-module.html#NC_DISKLESS
-netCDF4._key netCDF4-module.html#_key
-netCDF4.default_fillvals netCDF4-module.html#default_fillvals
-netCDF4._nctonptype netCDF4-module.html#_nctonptype
-netCDF4.__has_rename_grp__ netCDF4-module.html#__has_rename_grp__
-netCDF4._npversion netCDF4-module.html#_npversion
-netCDF4.__hdf5libversion__ netCDF4-module.html#__hdf5libversion__
-netCDF4.unicode_error netCDF4-module.html#unicode_error
-netCDF4.CompoundType netCDF4.CompoundType-class.html
-netCDF4.CompoundType.__new__ netCDF4.CompoundType-class.html#__new__
-netCDF4.CompoundType.dtype netCDF4.CompoundType-class.html#dtype
-netCDF4.CompoundType._nc_type netCDF4.CompoundType-class.html#_nc_type
-netCDF4.CompoundType.__repr__ netCDF4.CompoundType-class.html#__repr__
-netCDF4.CompoundType.__init__ netCDF4.CompoundType-class.html#__init__
-netCDF4.CompoundType.__unicode__ netCDF4.CompoundType-class.html#__unicode__
-netCDF4.CompoundType.name netCDF4.CompoundType-class.html#name
-netCDF4.Dataset netCDF4.Dataset-class.html
-netCDF4.Dataset._enddef netCDF4.Dataset-class.html#_enddef
-netCDF4.Dataset.variables netCDF4.Dataset-class.html#variables
-netCDF4.Dataset.__getattribute__ netCDF4.Dataset-class.html#__getattribute__
-netCDF4.Dataset.sync netCDF4.Dataset-class.html#sync
-netCDF4.Dataset.createCompoundType netCDF4.Dataset-class.html#createCompoundType
-netCDF4.Dataset.set_auto_maskandscale netCDF4.Dataset-class.html#set_auto_maskandscale
-netCDF4.Dataset.close netCDF4.Dataset-class.html#close
-netCDF4.Dataset._isopen netCDF4.Dataset-class.html#_isopen
-netCDF4.Dataset.getncattr netCDF4.Dataset-class.html#getncattr
-netCDF4.Dataset.__init__ netCDF4.Dataset-class.html#__init__
-netCDF4.Dataset.__setattr__ netCDF4.Dataset-class.html#__setattr__
-netCDF4.Dataset.__new__ netCDF4.Dataset-class.html#__new__
-netCDF4.Dataset.filepath netCDF4.Dataset-class.html#filepath
-netCDF4.Dataset.createVariable netCDF4.Dataset-class.html#createVariable
-netCDF4.Dataset.__enter__ netCDF4.Dataset-class.html#__enter__
-netCDF4.Dataset.disk_format netCDF4.Dataset-class.html#disk_format
-netCDF4.Dataset.__getattr__ netCDF4.Dataset-class.html#__getattr__
-netCDF4.Dataset.set_auto_mask netCDF4.Dataset-class.html#set_auto_mask
-netCDF4.Dataset.__unicode__ netCDF4.Dataset-class.html#__unicode__
-netCDF4.Dataset._redef netCDF4.Dataset-class.html#_redef
-netCDF4.Dataset.setncatts netCDF4.Dataset-class.html#setncatts
-netCDF4.Dataset.setncattr netCDF4.Dataset-class.html#setncattr
-netCDF4.Dataset.cmptypes netCDF4.Dataset-class.html#cmptypes
-netCDF4.Dataset.set_fill_off netCDF4.Dataset-class.html#set_fill_off
-netCDF4.Dataset.renameGroup netCDF4.Dataset-class.html#renameGroup
-netCDF4.Dataset.vltypes netCDF4.Dataset-class.html#vltypes
-netCDF4.Dataset.ncattrs netCDF4.Dataset-class.html#ncattrs
-netCDF4.Dataset.__exit__ netCDF4.Dataset-class.html#__exit__
-netCDF4.Dataset.renameAttribute netCDF4.Dataset-class.html#renameAttribute
-netCDF4.Dataset.parent netCDF4.Dataset-class.html#parent
-netCDF4.Dataset.createVLType netCDF4.Dataset-class.html#createVLType
-netCDF4.Dataset.keepweakref netCDF4.Dataset-class.html#keepweakref
-netCDF4.Dataset.renameDimension netCDF4.Dataset-class.html#renameDimension
-netCDF4.Dataset.groups netCDF4.Dataset-class.html#groups
-netCDF4.Dataset.path netCDF4.Dataset-class.html#path
-netCDF4.Dataset.delncattr netCDF4.Dataset-class.html#delncattr
-netCDF4.Dataset.dimensions netCDF4.Dataset-class.html#dimensions
-netCDF4.Dataset.set_auto_scale netCDF4.Dataset-class.html#set_auto_scale
-netCDF4.Dataset.file_format netCDF4.Dataset-class.html#file_format
-netCDF4.Dataset.renameVariable netCDF4.Dataset-class.html#renameVariable
-netCDF4.Dataset._grpid netCDF4.Dataset-class.html#_grpid
-netCDF4.Dataset.__delattr__ netCDF4.Dataset-class.html#__delattr__
-netCDF4.Dataset.set_fill_on netCDF4.Dataset-class.html#set_fill_on
-netCDF4.Dataset.data_model netCDF4.Dataset-class.html#data_model
-netCDF4.Dataset.__repr__ netCDF4.Dataset-class.html#__repr__
-netCDF4.Dataset.createGroup netCDF4.Dataset-class.html#createGroup
-netCDF4.Dataset.createDimension netCDF4.Dataset-class.html#createDimension
-netCDF4.Dimension netCDF4.Dimension-class.html
-netCDF4.Dimension.isunlimited netCDF4.Dimension-class.html#isunlimited
-netCDF4.Dimension.__init__ netCDF4.Dimension-class.html#__init__
-netCDF4.Dimension.group netCDF4.Dimension-class.html#group
-netCDF4.Dimension.__new__ netCDF4.Dimension-class.html#__new__
-netCDF4.Dimension.__len__ netCDF4.Dimension-class.html#__len__
-netCDF4.Dimension._dimid netCDF4.Dimension-class.html#_dimid
-netCDF4.Dimension.__unicode__ netCDF4.Dimension-class.html#__unicode__
-netCDF4.Dimension._grp netCDF4.Dimension-class.html#_grp
-netCDF4.Dimension.name netCDF4.Dimension-class.html#name
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diff --git a/docs/class-tree.html b/docs/class-tree.html
index 22e7bad..7bd668e 100644
--- a/docs/class-tree.html
+++ b/docs/class-tree.html
@@ -16,7 +16,7 @@
<tr valign="middle">
<!-- Home link -->
<th> <a
- href="netCDF4-module.html">Home</a> </th>
+ href="netCDF4._netCDF4-module.html">Home</a> </th>
<!-- Tree link -->
<th bgcolor="#70b0f0" class="navbar-select"
@@ -30,7 +30,12 @@
<th> <a
href="help.html">Help</a> </th>
- <th class="navbar" width="100%"></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>
<table width="100%" cellpadding="0" cellspacing="0">
@@ -52,39 +57,40 @@
<li> <strong class="uidlink">object</strong>:
<em class="summary">The most base type</em>
<ul>
- <li> <strong class="uidlink"><a href="netCDF4.CompoundType-class.html">netCDF4.CompoundType</a></strong>:
- <em class="summary">A <a href="netCDF4.CompoundType-class.html"
+ <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.Dataset-class.html">netCDF4.Dataset</a></strong>:
+ <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>
- <li> <strong class="uidlink"><a href="netCDF4.Group-class.html">netCDF4.Group</a></strong>:
+ <li> <strong class="uidlink"><a href="netCDF4._netCDF4.Group-class.html">netCDF4._netCDF4.Group</a></strong>:
<em class="summary">Group(self, parent, name)</em>
</li>
- <li> <strong class="uidlink"><a href="netCDF4.MFDataset-class.html">netCDF4.MFDataset</a></strong>:
+ <li> <strong class="uidlink"><a href="netCDF4._netCDF4.MFDataset-class.html">netCDF4._netCDF4.MFDataset</a></strong>:
<em class="summary">MFDataset(self, files, check=False, aggdim=None, exclude=[])</em>
</li>
</ul>
</li>
- <li> <strong class="uidlink"><a href="netCDF4.Dimension-class.html">netCDF4.Dimension</a></strong>:
+ <li> <strong class="uidlink"><a href="netCDF4._netCDF4.Dimension-class.html">netCDF4._netCDF4.Dimension</a></strong>:
<em class="summary">Dimension(self, group, name, size=None)</em>
</li>
- <li> <strong class="uidlink"><a href="netCDF4.VLType-class.html">netCDF4.VLType</a></strong>:
- <em class="summary">A <a href="netCDF4.VLType-class.html" class="link">VLType</a>
- instance is used to describe a variable length (VLEN) data type.</em>
+ <li> <strong class="uidlink"><a href="netCDF4._netCDF4.VLType-class.html">netCDF4._netCDF4.VLType</a></strong>:
+ <em class="summary">A <a href="netCDF4._netCDF4.VLType-class.html"
+ class="link">VLType</a> instance is used to describe a variable
+ length (VLEN) data type.</em>
</li>
- <li> <strong class="uidlink"><a href="netCDF4.Variable-class.html">netCDF4.Variable</a></strong>:
+ <li> <strong class="uidlink"><a href="netCDF4._netCDF4.Variable-class.html">netCDF4._netCDF4.Variable</a></strong>:
<em class="summary">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)</em>
</li>
- <li> <strong class="uidlink">netCDF4._Variable</strong>
+ <li> <strong class="uidlink">netCDF4._netCDF4._Variable</strong>
<ul>
- <li> <strong class="uidlink"><a href="netCDF4.MFTime-class.html">netCDF4.MFTime</a></strong>:
+ <li> <strong class="uidlink"><a href="netCDF4._netCDF4.MFTime-class.html">netCDF4._netCDF4.MFTime</a></strong>:
<em class="summary">MFTime(self, time, units=None)</em>
</li>
</ul>
@@ -98,7 +104,7 @@
<tr valign="middle">
<!-- Home link -->
<th> <a
- href="netCDF4-module.html">Home</a> </th>
+ href="netCDF4._netCDF4-module.html">Home</a> </th>
<!-- Tree link -->
<th bgcolor="#70b0f0" class="navbar-select"
@@ -112,13 +118,18 @@
<th> <a
href="help.html">Help</a> </th>
- <th class="navbar" width="100%"></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>
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/help.html b/docs/help.html
index 7f18c1f..2f4f00c 100644
--- a/docs/help.html
+++ b/docs/help.html
@@ -16,7 +16,7 @@
<tr valign="middle">
<!-- Home link -->
<th> <a
- href="netCDF4-module.html">Home</a> </th>
+ href="netCDF4._netCDF4-module.html">Home</a> </th>
<!-- Tree link -->
<th> <a
@@ -30,7 +30,12 @@
<th bgcolor="#70b0f0" class="navbar-select"
> Help </th>
- <th class="navbar" width="100%"></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>
<table width="100%" cellpadding="0" cellspacing="0">
@@ -47,7 +52,7 @@
<h1 class="epydoc"> API Documentation </h1>
<p> This document contains the API (Application Programming Interface)
-documentation for this project. Documentation for the Python
+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
@@ -220,7 +225,7 @@ page was last updated. </p>
<tr valign="middle">
<!-- Home link -->
<th> <a
- href="netCDF4-module.html">Home</a> </th>
+ href="netCDF4._netCDF4-module.html">Home</a> </th>
<!-- Tree link -->
<th> <a
@@ -234,13 +239,18 @@ page was last updated. </p>
<th bgcolor="#70b0f0" class="navbar-select"
> Help </th>
- <th class="navbar" width="100%"></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>
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/identifier-index.html b/docs/identifier-index.html
index 3f83b9f..9348af3 100644
--- a/docs/identifier-index.html
+++ b/docs/identifier-index.html
@@ -16,7 +16,7 @@
<tr valign="middle">
<!-- Home link -->
<th> <a
- href="netCDF4-module.html">Home</a> </th>
+ href="netCDF4._netCDF4-module.html">Home</a> </th>
<!-- Tree link -->
<th> <a
@@ -30,7 +30,12 @@
<th> <a
href="help.html">Help</a> </th>
- <th class="navbar" width="100%"></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>
<table width="100%" cellpadding="0" cellspacing="0">
@@ -55,7 +60,7 @@
<a href="#E">E</a>
<a href="#F">F</a>
<a href="#G">G</a>
- H
+ <a href="#H">H</a>
<a href="#I">I</a>
J
<a href="#K">K</a>
@@ -82,8 +87,8 @@
<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#assignValue">assignValue()</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
+<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>
@@ -94,36 +99,36 @@
<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#chartostring">chartostring()</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.MFDataset-class.html#close">close()</a><br />
-<span class="index-where">(in <a href="netCDF4.MFDataset-class.html">MFDataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#createDimension">createDimension()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
+<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>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.MFDataset-class.html#close">close()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.MFDataset-class.html">MFDataset</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#createDimension">createDimension()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#chunking">chunking()</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#cmptypes">cmptypes</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#createGroup">createGroup()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
+<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 />
+<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.Dataset-class.html#createGroup">createGroup()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#close">close()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.CompoundType-class.html">CompoundType</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#createVariable">createVariable()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
+<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 />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#createVariable">createVariable()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Group-class.html#close">close()</a><br />
-<span class="index-where">(in <a href="netCDF4.Group-class.html">Group</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#createCompoundType">createCompoundType()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#createVLType">createVLType()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Group-class.html#close">close()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Group-class.html">Group</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#createCompoundType">createCompoundType()</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.Dataset-class.html#createVLType">createVLType()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a>)</span></td>
</tr>
</table>
</td></tr>
@@ -131,49 +136,50 @@
<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#data_model">data_model</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#default_fillvals">default_fillvals</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#disk_format">disk_format</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#data_model">data_model</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-module.html#default_encoding">default_encoding</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#dimensions">dimensions</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Variable-class.html">Variable</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html">Dataset</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#delncattr">delncattr()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.CompoundType-class.html#dtype">dtype</a><br />
-<span class="index-where">(in <a href="netCDF4.CompoundType-class.html">CompoundType</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#default_fillvals">default_fillvals</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#disk_format">disk_format</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#datatype">datatype</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#delncattr">delncattr()</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.VLType-class.html#dtype">dtype</a><br />
-<span class="index-where">(in <a href="netCDF4.VLType-class.html">VLType</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#datatype">datatype</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#delncattr">delncattr()</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#dtype">dtype</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.CompoundType-class.html">CompoundType</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#date2index">date2index()</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dimension-class.html">Dimension</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#dtype">dtype</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#date2index">date2index()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#delncattr">delncattr()</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.VLType-class.html#dtype">dtype</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.VLType-class.html">VLType</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#date2num">date2num()</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#dimensions">dimensions</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"> </td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#date2num">date2num()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dimension-class.html">Dimension</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#dtype">dtype</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Variable-class.html">Variable</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#default_encoding">default_encoding</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#dimensions">dimensions</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#day_units">day_units</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#dimensions">dimensions</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a>)</span></td>
<td width="33%" class="link-index"> </td>
</tr>
</table>
@@ -182,8 +188,8 @@
<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#endian">endian()</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#endian">endian()</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>
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<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
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-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#filepath">filepath()</a><br />
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-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#filters">filters()</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#file_format">file_format</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.Dataset-class.html#filepath">filepath()</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.Variable-class.html#filters">filters()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Variable-class.html">Variable</a>)</span></td>
</tr>
<tr><td class="link-index"> </td><td class="link-index"> </td><td class="link-index"> </td></tr>
</table>
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<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
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-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#getValue">getValue()</a><br />
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-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#group">group()</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#get_var_chunk_cache">get_var_chunk_cache()</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.Variable-class.html#getValue">getValue()</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.Variable-class.html#group">group()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Variable-class.html">Variable</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#getlibversion">getlibversion()</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#gregorian">gregorian</a><br />
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-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#groups">groups</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#getlibversion">getlibversion()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#gregorian">gregorian</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#groups">groups</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a>)</span></td>
</tr>
<tr>
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-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Group-class.html">Group</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#getncattr">getncattr()</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.Group-class.html">Group</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
<td width="33%" class="link-index"> </td>
</tr>
<tr>
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-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dimension-class.html#group">group()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dimension-class.html">Dimension</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#getncattr">getncattr()</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.Dimension-class.html#group">group()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dimension-class.html">Dimension</a>)</span></td>
<td width="33%" class="link-index"> </td>
</tr>
</table>
</td></tr>
+<tr valign="top"><td valign="top" width="1%"><h2 class="epydoc"><a name="H">H</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#hr_units">hr_units</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</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="I">I</a></h2></td>
<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
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-<td width="33%" class="link-index"><a href="netCDF4-module.html#is_native_little">is_native_little</a><br />
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-<td width="33%" class="link-index"><a href="netCDF4.Dimension-class.html#isunlimited">isunlimited()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dimension-class.html">Dimension</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#is_native_big">is_native_big</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#is_native_little">is_native_little</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dimension-class.html#isunlimited">isunlimited()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dimension-class.html">Dimension</a>)</span></td>
</tr>
<tr><td class="link-index"> </td><td class="link-index"> </td><td class="link-index"> </td></tr>
</table>
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<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
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-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#keepweakref">keepweakref</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a>)</span></td>
<td width="33%" class="link-index"> </td>
<td width="33%" class="link-index"> </td>
</tr>
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<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#mask">mask</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.MFTime-class.html">MFTime</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#mask">mask</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-module.html#microsec_units">microsec_units</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#MINYEAR">MINYEAR</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+</tr>
+<tr>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.MFDataset-class.html">MFDataset</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#millisec_units">millisec_units</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
<td width="33%" class="link-index"> </td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.MFDataset-class.html">MFDataset</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#MINYEAR">MINYEAR</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.MFTime-class.html">MFTime</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#min_units">min_units</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
<td width="33%" class="link-index"> </td>
</tr>
</table>
@@ -288,35 +314,35 @@
<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.CompoundType-class.html#name">name</a><br />
-<span class="index-where">(in <a href="netCDF4.CompoundType-class.html">CompoundType</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#name">name</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#ncattrs">ncattrs()</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.CompoundType-class.html#name">name</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.CompoundType-class.html">CompoundType</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#name">name</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.Variable-class.html#ncattrs">ncattrs()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Variable-class.html">Variable</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Dimension-class.html#name">name</a><br />
-<span class="index-where">(in <a href="netCDF4.Dimension-class.html">Dimension</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#NC_DISKLESS">NC_DISKLESS</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#ndim">ndim</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dimension-class.html#name">name</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dimension-class.html">Dimension</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4-module.html#NC_DISKLESS">NC_DISKLESS</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Variable-class.html#ndim">ndim</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Variable-class.html">Variable</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Group-class.html#name">name</a><br />
-<span class="index-where">(in <a href="netCDF4.Group-class.html">Group</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#ncattrs">ncattrs()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4-module.html">netCDF4</a></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Group-class.html#name">name</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Group-class.html">Group</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#ncattrs">ncattrs()</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-module.html#num2date">num2date()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.VLType-class.html#name">name</a><br />
-<span class="index-where">(in <a href="netCDF4.VLType-class.html">VLType</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.MFDataset-class.html#ncattrs">ncattrs()</a><br />
-<span class="index-where">(in <a href="netCDF4.MFDataset-class.html">MFDataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#num2date">num2date()</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.VLType-class.html#name">name</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.VLType-class.html">VLType</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.MFDataset-class.html#ncattrs">ncattrs()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.MFDataset-class.html">MFDataset</a>)</span></td>
+<td width="33%" class="link-index"> </td>
</tr>
</table>
</td></tr>
@@ -324,12 +350,12 @@
<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
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-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#path">path</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4-module.html#python3">python3</a><br />
-<span class="index-where">(in <a href="netCDF4-module.html">netCDF4</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#parent">parent</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.Dataset-class.html#path">path</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-module.html#python3">python3</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4-module.html" onclick="show_private();">netCDF4._netCDF4</a>)</span></td>
</tr>
<tr><td class="link-index"> </td><td class="link-index"> </td><td class="link-index"> </td></tr>
</table>
@@ -338,18 +364,18 @@
<td valign="top">
<table class="link-index" width="100%" border="1">
<tr>
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-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#renameDimension">renameDimension()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
-<td width="33%" class="link-index"><a href="netCDF4.Dataset-class.html#renameVariable">renameVariable()</a><br />
-<span class="index-where">(in <a href="netCDF4.Dataset-class.html">Dataset</a>)</span></td>
+<td width="33%" class="link-index"><a href="netCDF4._netCDF4.Dataset-class.html#renameAttribute">renameAttribute()</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.Dataset-class.html#renameDimension">renameDimension()</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.Dataset-class.html#renameVariable">renameVariable()</a><br />
+<span class="index-where">(in <a href="netCDF4._netCDF4.Dataset-class.html">Dataset</a>)</span></td>
</tr>
<tr>
-<td width="33%" class="link-index"><a href="netCDF4.Variable-class.html#renameAttribute">renameAttribute()</a><br />
-<span class="index-where">(in <a href="netCDF4.Variable-class.html">Variable</a>)</span></td>
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diff --git a/docs/index.html b/docs/index.html
index 2b96e7f..93b58e4 100644
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+++ b/docs/index.html
@@ -3,7 +3,7 @@
"DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
- <title>netCDF4</title>
+ <title>netCDF4._netCDF4</title>
<link rel="stylesheet" href="epydoc.css" type="text/css" />
<script type="text/javascript" src="epydoc.js"></script>
</head>
@@ -30,14 +30,20 @@
<th> <a
href="help.html">Help</a> </th>
- <th class="navbar" width="100%"></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>
<table width="100%" cellpadding="0" cellspacing="0">
<tr valign="top">
<td width="100%">
<span class="breadcrumbs">
- Module netCDF4
+ Package netCDF4 ::
+ Module _netCDF4
</span>
</td>
<td>
@@ -48,7 +54,7 @@
</tr>
</table>
<!-- ==================== MODULE DESCRIPTION ==================== -->
-<h1 class="epydoc">Module netCDF4</h1><p class="nomargin-top"></p>
+<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"
@@ -87,8 +93,7 @@
</li>
<li>
numpy array module <a href="http://numpy.scipy.org"
- target="_top">http://numpy.scipy.org</a>, version 1.3.0 or later
- (1.5.1 or higher recommended, required if using python 3).
+ 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
@@ -169,7 +174,7 @@
<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>
+ 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
@@ -188,10 +193,11 @@
<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>
+ 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">
@@ -204,7 +210,7 @@
<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>
+ 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
@@ -216,20 +222,21 @@
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"
+ 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.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"
+ 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.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
+ 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>)
@@ -240,24 +247,34 @@
<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>
+ 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.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>
+ <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>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>
@@ -270,8 +287,8 @@
<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"> 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>
@@ -299,12 +316,13 @@
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"
+ using the <a
+ href="netCDF4._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
+ 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.
@@ -316,7 +334,7 @@
<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"
+ <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">
@@ -327,13 +345,14 @@
<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"
+ 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.Dimension-class.html" class="link">Dimension</a>
- instance can be used to determine if the dimensions is unlimited, or
- appendable.</p>
+ 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>
@@ -342,7 +361,7 @@
<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"
+ <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>
@@ -355,28 +374,30 @@
<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"
+ <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.Dataset-class.html" class="link">Dataset</a> or <a
- href="netCDF4.Group-class.html" class="link">Group</a> instance.</p>
+ 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.Dataset-class.html#createVariable"
+ href="netCDF4._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"
+ 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.Dataset-class.html#createDimension"
+ 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
@@ -398,11 +419,11 @@
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"
+ href="netCDF4._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>
+ 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>,))
@@ -410,23 +431,11 @@
<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>
+ <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> rootgrp.variables[<span class="py-string">'temp'</span>]
+<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>
@@ -434,26 +443,69 @@
<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"
+ <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.Dataset-class.html" class="link">Dataset</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.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>
+ 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>
@@ -465,16 +517,16 @@
<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"
+ <p>The <a href="netCDF4._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>
+ 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)
@@ -482,29 +534,30 @@
<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>
+ 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'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>
+ 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.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>
+ <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><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
@@ -520,9 +573,9 @@
<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>
+ 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>])
@@ -580,16 +633,22 @@
<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-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>
+ 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
@@ -597,7 +656,7 @@
<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">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)
@@ -606,35 +665,37 @@
<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
+ <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-module.html#date2index"
+ 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">7) Reading data from a multi-file netCDF dataset.</h2>
+ <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.MFDataset-class.html"
+ 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.Dataset-class.html"
+ create a <a href="netCDF4._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>
+ 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>)
@@ -643,7 +704,8 @@
<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>
+ 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>)
@@ -655,13 +717,13 @@
<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"
+ <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
@@ -672,14 +734,14 @@
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"
+ 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.Dataset-class.html#createVariable"
+ 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
@@ -690,7 +752,7 @@
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"
+ 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
@@ -711,7 +773,7 @@
<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>
+ <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
@@ -723,12 +785,13 @@
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"
+ href="netCDF4._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>
+ 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>
@@ -759,11 +822,11 @@
<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>
+ 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>
@@ -782,14 +845,15 @@
<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>
+ <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.Dataset-class.html#createVLType"
+ href="netCDF4._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>
+ 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>
@@ -842,7 +906,8 @@
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"
+ 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)
@@ -904,7 +969,7 @@
PERFORMANCE OF THIS SOFTWARE.
</p>
<p><strong>Version:</strong>
- 1.1.7
+ 1.1.8
</p>
</div><!-- ==================== CLASSES ==================== -->
<a name="section-Classes"></a>
@@ -918,8 +983,8 @@
<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"
+ <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>
@@ -928,7 +993,7 @@
<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 />
+ <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>
@@ -937,7 +1002,7 @@
<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 />
+ <a href="netCDF4._netCDF4.Dimension-class.html" class="summary-name">Dimension</a><br />
Dimension(self, group, name, size=None)
</td>
</tr>
@@ -945,7 +1010,7 @@
<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 />
+ <a href="netCDF4._netCDF4.Group-class.html" class="summary-name">Group</a><br />
Group(self, parent, name)
</td>
</tr>
@@ -953,7 +1018,7 @@
<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 />
+ <a href="netCDF4._netCDF4.MFDataset-class.html" class="summary-name">MFDataset</a><br />
MFDataset(self, files, check=False, aggdim=None, exclude=[])
</td>
</tr>
@@ -961,7 +1026,7 @@
<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 />
+ <a href="netCDF4._netCDF4.MFTime-class.html" class="summary-name">MFTime</a><br />
MFTime(self, time, units=None)
</td>
</tr>
@@ -969,16 +1034,17 @@
<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.
+ <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.Variable-class.html" class="summary-name">Variable</a><br />
+ <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',
@@ -1000,7 +1066,7 @@
</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 />
+ <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">
@@ -1017,7 +1083,7 @@
</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>,
+ <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 />
@@ -1038,7 +1104,7 @@
</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>,
+ <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>
@@ -1075,7 +1141,7 @@
</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>,
+ <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>
@@ -1094,7 +1160,7 @@
</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>,
+ <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>
@@ -1113,7 +1179,7 @@
</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 />
+ <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">
@@ -1186,7 +1252,14 @@
<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>
+ <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>
@@ -1200,7 +1273,7 @@
<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',
+ <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,
@@ -1222,6 +1295,13 @@
<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>
@@ -1236,6 +1316,27 @@
<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>
@@ -1243,6 +1344,13 @@
<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>
@@ -1534,6 +1642,42 @@
</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"
@@ -1555,13 +1699,18 @@
<th> <a
href="help.html">Help</a> </th>
- <th class="navbar" width="100%"></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>
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/module-tree.html b/docs/module-tree.html
index b26510f..450306a 100644
--- a/docs/module-tree.html
+++ b/docs/module-tree.html
@@ -16,7 +16,7 @@
<tr valign="middle">
<!-- Home link -->
<th> <a
- href="netCDF4-module.html">Home</a> </th>
+ href="netCDF4._netCDF4-module.html">Home</a> </th>
<!-- Tree link -->
<th bgcolor="#70b0f0" class="navbar-select"
@@ -30,7 +30,12 @@
<th> <a
href="help.html">Help</a> </th>
- <th class="navbar" width="100%"></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>
<table width="100%" cellpadding="0" cellspacing="0">
@@ -49,7 +54,7 @@
</b></center><br />
<h1 class="epydoc">Module Hierarchy</h1>
<ul class="nomargin-top">
- <li> <strong class="uidlink"><a href="netCDF4-module.html">netCDF4</a></strong>: <em class="summary">Python interface to the netCDF version 4 library.</em> </li>
+ <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>
<!-- ==================== NAVIGATION BAR ==================== -->
<table class="navbar" border="0" width="100%" cellpadding="0"
@@ -57,7 +62,7 @@
<tr valign="middle">
<!-- Home link -->
<th> <a
- href="netCDF4-module.html">Home</a> </th>
+ href="netCDF4._netCDF4-module.html">Home</a> </th>
<!-- Tree link -->
<th bgcolor="#70b0f0" class="navbar-select"
@@ -71,13 +76,18 @@
<th> <a
href="help.html">Help</a> </th>
- <th class="navbar" width="100%"></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>
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/netCDF4-module.html b/docs/netCDF4-module.html
index 2b96e7f..3e868e0 100644
--- a/docs/netCDF4-module.html
+++ b/docs/netCDF4-module.html
@@ -48,7 +48,7 @@
</tr>
</table>
<!-- ==================== MODULE DESCRIPTION ==================== -->
-<h1 class="epydoc">Module netCDF4</h1><p class="nomargin-top"></p>
+<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"
@@ -87,8 +87,7 @@
</li>
<li>
numpy array module <a href="http://numpy.scipy.org"
- target="_top">http://numpy.scipy.org</a>, version 1.3.0 or later
- (1.5.1 or higher recommended, required if using python 3).
+ 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
@@ -270,8 +269,8 @@
<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"> 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>
@@ -417,7 +416,7 @@
<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"> ('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>
@@ -490,7 +489,7 @@
<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'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
@@ -504,7 +503,7 @@
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><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
@@ -597,7 +596,7 @@
<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">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)
@@ -904,7 +903,7 @@
PERFORMANCE OF THIS SOFTWARE.
</p>
<p><strong>Version:</strong>
- 1.1.7
+ 1.1.8
</p>
</div><!-- ==================== CLASSES ==================== -->
<a name="section-Classes"></a>
@@ -1003,7 +1002,7 @@
<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>
@@ -1024,7 +1023,7 @@
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>
@@ -1043,7 +1042,7 @@
<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>
@@ -1061,7 +1060,7 @@
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>
@@ -1080,7 +1079,7 @@
<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>
@@ -1099,7 +1098,7 @@
<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>
@@ -1116,7 +1115,7 @@
<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>
@@ -1172,14 +1171,14 @@
<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>
+ <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'4.3.3-rc2'"><code class="variable-quote">u'</code><code class="variable-string">4.3.3-rc2</code><code class="variable-quote">'</code></code>
+ <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>
@@ -1193,6 +1192,13 @@
<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>
@@ -1222,6 +1228,13 @@
<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>
@@ -1236,6 +1249,27 @@
<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>
@@ -1243,6 +1277,13 @@
<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>
@@ -1266,7 +1307,7 @@
<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>
@@ -1298,7 +1339,7 @@
<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>
@@ -1344,7 +1385,7 @@
<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>
@@ -1392,7 +1433,7 @@
<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>
@@ -1447,7 +1488,7 @@
<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>
@@ -1478,7 +1519,7 @@
<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>
@@ -1534,6 +1575,42 @@
</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"
@@ -1561,7 +1638,7 @@
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/netCDF4.CompoundType-class.html b/docs/netCDF4.CompoundType-class.html
index b0ead53..a16a19d 100644
--- a/docs/netCDF4.CompoundType-class.html
+++ b/docs/netCDF4.CompoundType-class.html
@@ -49,7 +49,7 @@
</tr>
</table>
<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class CompoundType</h1><p class="nomargin-top"></p>
+<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 --+
|
@@ -111,7 +111,7 @@ object --+
<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>
@@ -129,7 +129,7 @@ object --+
<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>
@@ -146,7 +146,7 @@ object --+
<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>
@@ -162,7 +162,7 @@ object --+
<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>
@@ -248,7 +248,7 @@ object --+
<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>
@@ -272,7 +272,7 @@ object --+
<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>
@@ -296,7 +296,7 @@ object --+
<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>
@@ -335,7 +335,7 @@ object --+
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/netCDF4.Dataset-class.html b/docs/netCDF4.Dataset-class.html
index e498a83..bf9f227 100644
--- a/docs/netCDF4.Dataset-class.html
+++ b/docs/netCDF4.Dataset-class.html
@@ -49,7 +49,7 @@
</tr>
</table>
<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class Dataset</h1><p class="nomargin-top"></p>
+<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 --+
|
@@ -156,7 +156,7 @@ object --+
<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>
@@ -172,7 +172,7 @@ object --+
<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>
@@ -188,7 +188,7 @@ object --+
<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>
@@ -204,7 +204,7 @@ object --+
<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>
@@ -221,7 +221,7 @@ object --+
<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>
@@ -245,7 +245,7 @@ object --+
<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>
@@ -263,7 +263,7 @@ object --+
<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>
@@ -280,7 +280,7 @@ object --+
<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>
@@ -297,7 +297,7 @@ object --+
<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>
@@ -313,7 +313,7 @@ object --+
<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>
@@ -330,7 +330,7 @@ object --+
<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>
@@ -350,7 +350,7 @@ object --+
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>
@@ -370,7 +370,7 @@ object --+
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>
@@ -389,7 +389,7 @@ object --+
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>
@@ -409,7 +409,7 @@ object --+
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>
@@ -439,7 +439,7 @@ object --+
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>
@@ -458,7 +458,7 @@ object --+
<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>
@@ -476,7 +476,7 @@ object --+
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>
@@ -494,7 +494,7 @@ object --+
<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>
@@ -513,7 +513,7 @@ object --+
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>
@@ -535,7 +535,7 @@ object --+
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>
@@ -556,7 +556,7 @@ object --+
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>
@@ -577,7 +577,7 @@ object --+
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>
@@ -598,7 +598,7 @@ object --+
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>
@@ -620,7 +620,7 @@ object --+
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>
@@ -642,7 +642,7 @@ object --+
<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>
@@ -664,7 +664,7 @@ object --+
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>
@@ -682,7 +682,7 @@ object --+
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>
@@ -700,7 +700,7 @@ object --+
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>
@@ -719,7 +719,7 @@ object --+
<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>
@@ -738,7 +738,7 @@ object --+
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>
@@ -756,7 +756,7 @@ object --+
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>
@@ -914,6 +914,13 @@ object --+
<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>
@@ -944,7 +951,7 @@ object --+
<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>
@@ -966,7 +973,7 @@ object --+
<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>
@@ -996,7 +1003,7 @@ object --+
<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>
@@ -1020,7 +1027,7 @@ object --+
<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>
@@ -1044,7 +1051,7 @@ object --+
<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>
@@ -1066,7 +1073,7 @@ object --+
<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>
@@ -1090,7 +1097,7 @@ object --+
<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>
@@ -1122,7 +1129,7 @@ object --+
<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>
@@ -1154,7 +1161,7 @@ object --+
<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>
@@ -1178,7 +1185,7 @@ object --+
<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>
@@ -1212,7 +1219,7 @@ object --+
<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>
@@ -1348,7 +1355,7 @@ object --+
<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>
@@ -1369,7 +1376,7 @@ object --+
<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>
@@ -1390,7 +1397,7 @@ object --+
<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>
@@ -1412,7 +1419,7 @@ object --+
<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>
@@ -1443,7 +1450,7 @@ object --+
<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>
@@ -1475,7 +1482,7 @@ object --+
<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>
@@ -1505,7 +1512,7 @@ object --+
<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>
@@ -1528,7 +1535,7 @@ object --+
<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>
@@ -1558,7 +1565,7 @@ object --+
<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>
@@ -1580,7 +1587,7 @@ object --+
<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>
@@ -1676,7 +1683,7 @@ object --+
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/netCDF4.Dimension-class.html b/docs/netCDF4.Dimension-class.html
index f71ba38..3b7590e 100644
--- a/docs/netCDF4.Dimension-class.html
+++ b/docs/netCDF4.Dimension-class.html
@@ -49,7 +49,7 @@
</tr>
</table>
<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class Dimension</h1><p class="nomargin-top"></p>
+<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 --+
|
@@ -108,7 +108,7 @@ object --+
<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>
@@ -125,7 +125,7 @@ object --+
<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>
@@ -143,7 +143,7 @@ object --+
<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>
@@ -160,7 +160,7 @@ object --+
<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>
@@ -176,7 +176,7 @@ object --+
<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>
@@ -194,7 +194,7 @@ object --+
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>
@@ -213,7 +213,7 @@ object --+
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>
@@ -285,7 +285,7 @@ object --+
<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>
@@ -309,7 +309,7 @@ object --+
<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>
@@ -333,7 +333,7 @@ object --+
<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>
@@ -372,7 +372,7 @@ object --+
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/netCDF4.Group-class.html b/docs/netCDF4.Group-class.html
index 41122d8..55bef05 100644
--- a/docs/netCDF4.Group-class.html
+++ b/docs/netCDF4.Group-class.html
@@ -49,7 +49,7 @@
</tr>
</table>
<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class Group</h1><p class="nomargin-top"></p>
+<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 --+
|
@@ -110,7 +110,7 @@ object --+
<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>
@@ -128,7 +128,7 @@ object --+
<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>
@@ -148,7 +148,7 @@ object --+
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>
@@ -245,6 +245,7 @@ object --+
<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>:
@@ -275,7 +276,7 @@ object --+
<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>
@@ -299,7 +300,7 @@ object --+
<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>
@@ -322,7 +323,7 @@ object --+
<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>
@@ -364,7 +365,7 @@ object --+
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/netCDF4.MFDataset-class.html b/docs/netCDF4.MFDataset-class.html
index 119fc23..03b8962 100644
--- a/docs/netCDF4.MFDataset-class.html
+++ b/docs/netCDF4.MFDataset-class.html
@@ -49,7 +49,7 @@
</tr>
</table>
<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class MFDataset</h1><p class="nomargin-top"></p>
+<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 --+
|
@@ -103,7 +103,7 @@ object --+
<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>
@@ -121,7 +121,7 @@ object --+
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>
@@ -138,7 +138,7 @@ object --+
<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>
@@ -155,7 +155,7 @@ object --+
<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>
@@ -172,7 +172,7 @@ object --+
<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>
@@ -191,7 +191,7 @@ object --+
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>
@@ -293,6 +293,7 @@ object --+
<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>:
@@ -320,7 +321,7 @@ object --+
<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>
@@ -344,7 +345,7 @@ object --+
<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>
@@ -395,7 +396,7 @@ object --+
<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>
@@ -418,7 +419,7 @@ object --+
<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>
@@ -440,7 +441,7 @@ object --+
<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>
@@ -463,7 +464,7 @@ object --+
<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>
@@ -505,7 +506,7 @@ object --+
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/netCDF4.MFTime-class.html b/docs/netCDF4.MFTime-class.html
index e01c9d8..131ce36 100644
--- a/docs/netCDF4.MFTime-class.html
+++ b/docs/netCDF4.MFTime-class.html
@@ -49,7 +49,7 @@
</tr>
</table>
<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class MFTime</h1><p class="nomargin-top"></p>
+<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 --+
|
@@ -87,7 +87,7 @@ object --+
<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-prompt">>>> </span><span class="py-keyword">print</span> T[32]
<span class="py-output">32</span></pre>
<!-- ==================== INSTANCE METHODS ==================== -->
@@ -107,7 +107,7 @@ object --+
<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>
@@ -127,7 +127,7 @@ object --+
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>
@@ -213,7 +213,7 @@ object --+
<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>
@@ -239,7 +239,7 @@ object --+
<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>
@@ -285,7 +285,7 @@ object --+
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/netCDF4.VLType-class.html b/docs/netCDF4.VLType-class.html
index a3a3b9d..aa81b19 100644
--- a/docs/netCDF4.VLType-class.html
+++ b/docs/netCDF4.VLType-class.html
@@ -49,7 +49,7 @@
</tr>
</table>
<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class VLType</h1><p class="nomargin-top"></p>
+<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 --+
|
@@ -98,7 +98,7 @@ object --+
<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>
@@ -116,7 +116,7 @@ object --+
<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>
@@ -133,7 +133,7 @@ object --+
<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>
@@ -149,7 +149,7 @@ object --+
<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>
@@ -235,7 +235,7 @@ object --+
<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>
@@ -259,7 +259,7 @@ object --+
<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>
@@ -283,7 +283,7 @@ object --+
<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>
@@ -322,7 +322,7 @@ object --+
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/netCDF4.Variable-class.html b/docs/netCDF4.Variable-class.html
index f55bc33..473eb4c 100644
--- a/docs/netCDF4.Variable-class.html
+++ b/docs/netCDF4.Variable-class.html
@@ -49,7 +49,7 @@
</tr>
</table>
<!-- ==================== CLASS DESCRIPTION ==================== -->
-<h1 class="epydoc">Class Variable</h1><p class="nomargin-top"></p>
+<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 --+
|
@@ -189,7 +189,7 @@ object --+
<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>
@@ -206,7 +206,7 @@ object --+
<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>
@@ -224,7 +224,7 @@ object --+
<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>
@@ -240,7 +240,7 @@ object --+
<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>
@@ -257,7 +257,7 @@ object --+
<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>
@@ -275,7 +275,7 @@ object --+
<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>
@@ -305,7 +305,7 @@ object --+
<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>
@@ -322,7 +322,7 @@ object --+
<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>
@@ -340,7 +340,7 @@ object --+
<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>
@@ -357,7 +357,7 @@ object --+
<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>
@@ -374,7 +374,7 @@ object --+
<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>
@@ -393,7 +393,7 @@ object --+
<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>
@@ -409,7 +409,7 @@ object --+
<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>
@@ -427,7 +427,7 @@ object --+
<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>
@@ -444,7 +444,7 @@ object --+
<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>
@@ -463,7 +463,7 @@ object --+
<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>
@@ -481,7 +481,7 @@ object --+
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>
@@ -498,7 +498,7 @@ object --+
<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>
@@ -515,7 +515,7 @@ object --+
<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>
@@ -533,7 +533,7 @@ object --+
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>
@@ -551,7 +551,7 @@ object --+
<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>
@@ -569,7 +569,7 @@ object --+
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>
@@ -588,7 +588,7 @@ object --+
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>
@@ -609,7 +609,7 @@ object --+
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>
@@ -628,7 +628,7 @@ object --+
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>
@@ -648,7 +648,7 @@ object --+
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>
@@ -667,7 +667,7 @@ object --+
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>
@@ -687,7 +687,7 @@ object --+
<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>
@@ -706,7 +706,7 @@ object --+
<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>
@@ -725,7 +725,7 @@ object --+
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>
@@ -759,6 +759,14 @@ object --+
<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.
@@ -876,7 +884,7 @@ object --+
<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>
@@ -898,7 +906,7 @@ object --+
<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>
@@ -934,7 +942,7 @@ object --+
<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>
@@ -958,7 +966,7 @@ object --+
<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>
@@ -982,7 +990,7 @@ object --+
<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>
@@ -1004,7 +1012,7 @@ object --+
<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>
@@ -1027,7 +1035,7 @@ object --+
<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>
@@ -1047,7 +1055,7 @@ object --+
<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>
@@ -1071,7 +1079,7 @@ object --+
<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>
@@ -1092,7 +1100,7 @@ object --+
<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>
@@ -1112,7 +1120,7 @@ object --+
<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>
@@ -1134,7 +1142,7 @@ object --+
<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>
@@ -1156,7 +1164,7 @@ object --+
<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>
@@ -1188,7 +1196,7 @@ object --+
<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>
@@ -1238,7 +1246,7 @@ object --+
<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>
@@ -1280,7 +1288,7 @@ object --+
<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>
@@ -1302,7 +1310,7 @@ object --+
<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>
@@ -1324,7 +1332,7 @@ object --+
<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>
@@ -1346,6 +1354,20 @@ object --+
<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"
@@ -1420,7 +1442,7 @@ object --+
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
- Generated by Epydoc 3.0.1 on Wed Mar 18 10:55:13 2015
+ 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"
diff --git a/docs/redirect.html b/docs/redirect.html
index 747dc24..524c955 100644
--- a/docs/redirect.html
+++ b/docs/redirect.html
@@ -7,7 +7,7 @@
<body>
<script type="text/javascript">
<!--
-var pages = ["netCDF4.CompoundType-c", "netCDF4.Dimension-c", "netCDF4.MFDataset-c", "netCDF4.Variable-c", "netCDF4.Dataset-c", "netCDF4.MFTime-c", "netCDF4.VLType-c", "netCDF4.Group-c", "netCDF4-m"];
+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);
diff --git a/examples/README.md b/examples/README.md
new file mode 100644
index 0000000..318d7ec
--- /dev/null
+++ b/examples/README.md
@@ -0,0 +1,13 @@
+* `tutorial.py`: code from introduction section of documentation.
+* `json_att.py`: shows to to use json to serialize python objects, save them as
+ netcdf attributes, and then convert them back to python objects.
+* `subset.py`: shows how to use 'orthogonal indexing' to select geographic regions.
+* `reading_netcdf.ipynb`: ipython notebook from Unidata python workshop.
+* `writing_netcdf.ipynb`: ipython notebook from Unidata python workshop.
+* `threaded_read.py`: test script for concurrent threaded reads.
+* `bench.py`: benchmarks for reading/writing using different formats.
+* `bench_compress*.py``: benchmarks for reading/writing with compression.
+* `bench_diskless.py`: benchmarks for 'diskless' IO.
+* `test_stringarr.py`: test utilities for converting arrays of fixed-length strings
+ to arrays of characters (with an extra dimension), and vice-versa.
+ Useful since netcdf does not have a datatype for fixed-length string arrays.
diff --git a/examples/bench.py b/examples/bench.py
index 49122c1..f3ad752 100644
--- a/examples/bench.py
+++ b/examples/bench.py
@@ -6,7 +6,7 @@ from timeit import Timer
import os, sys
# create an n1dim by n2dim by n3dim random array.
-n1dim = 30
+n1dim = 30
n2dim = 15
n3dim = 73
n4dim = 144
@@ -31,7 +31,7 @@ def read_netcdf(filename):
for format in ['NETCDF3_CLASSIC','NETCDF3_64BIT','NETCDF4_CLASSIC','NETCDF4']:
sys.stdout.write('testing file format %s ...\n' % format)
- # writing, no compression.
+ # writing, no compression.
t = Timer("write_netcdf('test1.nc',format='%s')" % format,"from __main__ import write_netcdf")
sys.stdout.write('writing took %s seconds\n' %\
repr(sum(t.repeat(ntrials,1))/ntrials))
diff --git a/examples/bench_compress.py b/examples/bench_compress.py
index d06e2b1..9ce0018 100644
--- a/examples/bench_compress.py
+++ b/examples/bench_compress.py
@@ -6,7 +6,7 @@ from timeit import Timer
import os, sys
# create an n1dim by n2dim by n3dim random array.
-n1dim = 30
+n1dim = 30
n2dim = 15
n3dim = 73
n4dim = 144
diff --git a/examples/bench_compress2.py b/examples/bench_compress2.py
index 6ac3064..4f0b864 100644
--- a/examples/bench_compress2.py
+++ b/examples/bench_compress2.py
@@ -6,7 +6,7 @@ from timeit import Timer
import os, sys
# create an n1dim by n2dim by n3dim random array.
-n1dim = 30
+n1dim = 30
n2dim = 15
n3dim = 73
n4dim = 144
diff --git a/examples/bench_compress3.py b/examples/bench_compress3.py
index 0a35dee..38b36cd 100644
--- a/examples/bench_compress3.py
+++ b/examples/bench_compress3.py
@@ -10,7 +10,7 @@ URL="http://nomad1.ncep.noaa.gov:9090/dods/reanalyses/reanalysis-2/6hr/pgb/pgb"
nc = netCDF4.Dataset(URL)
# use real 500 hPa geopotential height data.
-n1dim = 100
+n1dim = 100
n3dim = 73
n4dim = 144
ntrials = 10
diff --git a/examples/bench_diskless.py b/examples/bench_diskless.py
index 4a8f377..dd7a783 100644
--- a/examples/bench_diskless.py
+++ b/examples/bench_diskless.py
@@ -6,7 +6,7 @@ from timeit import Timer
import os, sys
# create an n1dim by n2dim by n3dim random array.
-n1dim = 30
+n1dim = 30
n2dim = 15
n3dim = 73
n4dim = 144
@@ -27,7 +27,7 @@ def write_netcdf(filename,zlib=False,least_significant_digit=None,format='NETCDF
foo.testme2="hi I am an attribute"
foo.testme3="hi I am an attribute"
foo.testme4="hi I am an attribute"
- foo.testme5="hi I am an attribute"
+ foo.testme5="hi I am an attribute"
foo[:] = array
if closeit: file.close()
return file
@@ -37,7 +37,7 @@ def read_netcdf(ncfile):
for format in ['NETCDF4','NETCDF3_CLASSIC','NETCDF3_64BIT']:
sys.stdout.write('testing file format %s ...\n' % format)
- # writing, no compression.
+ # writing, no compression.
t = Timer("write_netcdf('test1.nc',closeit=True,format='%s')" % format,"from __main__ import write_netcdf")
sys.stdout.write('writing took %s seconds\n' %\
repr(sum(t.repeat(ntrials,1))/ntrials))
diff --git a/examples/data/prmsl.2000.nc b/examples/data/prmsl.2000.nc
deleted file mode 100644
index eb3f888..0000000
Binary files a/examples/data/prmsl.2000.nc and /dev/null differ
diff --git a/examples/data/prmsl.2001.nc b/examples/data/prmsl.2001.nc
deleted file mode 100644
index 7a40439..0000000
Binary files a/examples/data/prmsl.2001.nc and /dev/null differ
diff --git a/examples/data/prmsl.2002.nc b/examples/data/prmsl.2002.nc
deleted file mode 100644
index 6adef02..0000000
Binary files a/examples/data/prmsl.2002.nc and /dev/null differ
diff --git a/examples/data/prmsl.2003.nc b/examples/data/prmsl.2003.nc
deleted file mode 100644
index 2f3ab1e..0000000
Binary files a/examples/data/prmsl.2003.nc and /dev/null differ
diff --git a/examples/data/prmsl.2004.nc b/examples/data/prmsl.2004.nc
deleted file mode 100644
index 2122363..0000000
Binary files a/examples/data/prmsl.2004.nc and /dev/null differ
diff --git a/examples/data/prmsl.2005.nc b/examples/data/prmsl.2005.nc
deleted file mode 100644
index 1bf5c7a..0000000
Binary files a/examples/data/prmsl.2005.nc and /dev/null differ
diff --git a/examples/data/prmsl.2006.nc b/examples/data/prmsl.2006.nc
deleted file mode 100644
index b613bc8..0000000
Binary files a/examples/data/prmsl.2006.nc and /dev/null differ
diff --git a/examples/data/prmsl.2007.nc b/examples/data/prmsl.2007.nc
deleted file mode 100644
index df779d4..0000000
Binary files a/examples/data/prmsl.2007.nc and /dev/null differ
diff --git a/examples/data/prmsl.2008.nc b/examples/data/prmsl.2008.nc
deleted file mode 100644
index da7fd8c..0000000
Binary files a/examples/data/prmsl.2008.nc and /dev/null differ
diff --git a/examples/data/prmsl.2009.nc b/examples/data/prmsl.2009.nc
deleted file mode 100644
index 2f36b7f..0000000
Binary files a/examples/data/prmsl.2009.nc and /dev/null differ
diff --git a/examples/data/prmsl.2010.nc b/examples/data/prmsl.2010.nc
deleted file mode 100644
index ec2129c..0000000
Binary files a/examples/data/prmsl.2010.nc and /dev/null differ
diff --git a/examples/data/prmsl.2011.nc b/examples/data/prmsl.2011.nc
deleted file mode 100644
index ff4d862..0000000
Binary files a/examples/data/prmsl.2011.nc and /dev/null differ
diff --git a/examples/data/rtofs_glo_3dz_f006_6hrly_reg3.nc b/examples/data/rtofs_glo_3dz_f006_6hrly_reg3.nc
deleted file mode 100644
index 70f1d6a..0000000
Binary files a/examples/data/rtofs_glo_3dz_f006_6hrly_reg3.nc and /dev/null differ
diff --git a/examples/json_att.py b/examples/json_att.py
new file mode 100644
index 0000000..7d95a7e
--- /dev/null
+++ b/examples/json_att.py
@@ -0,0 +1,24 @@
+from netCDF4 import Dataset
+import json
+# example showing how python objects (lists, dicts, None, True)
+# can be serialized as strings, saved as netCDF attributes,
+# and then converted back to python objects using json.
+ds = Dataset('json.nc', 'w')
+ds.pythonatt1 = json.dumps([u'foo', {u'bar': [u'baz', None, 1.0, 2]}])
+ds.pythonatt2 = "true" # converted to bool
+ds.pythonatt3 = "null" # converted to None
+print(ds)
+ds.close()
+ds = Dataset('json.nc')
+def convert_json(s):
+ try:
+ a = json.loads(s)
+ return a
+ except:
+ return s
+x = convert_json(ds.pythonatt1)
+print(type(x))
+print(x)
+print(convert_json(ds.pythonatt2))
+print(convert_json(ds.pythonatt3))
+ds.close()
diff --git a/examples/subset.py b/examples/subset.py
new file mode 100644
index 0000000..a8bd246
--- /dev/null
+++ b/examples/subset.py
@@ -0,0 +1,15 @@
+# use 'orthogonal indexing' feature to subselect data over CONUS.
+import netCDF4
+import numpy as np
+import matplotlib.pyplot as plt
+
+# use real data from CFS reanlysis.
+# note: we're reading GRIB2 data!
+URL="http://nomads.ncdc.noaa.gov/thredds/dodsC/modeldata/cmd_flxf/2010/201007/20100701/flxf00.gdas.2010070100.grb2"
+nc = netCDF4.Dataset(URL)
+lats = nc.variables['lat'][:]; lons = nc.variables['lon'][:]
+latselect = np.logical_and(lats>25,lats<50)
+lonselect = np.logical_and(lons>230,lons<305)
+data = nc.variables['Soil_moisture_content'][0,0,latselect,lonselect]
+plt.contourf(data[::-1]) # flip latitudes so they go south -> north
+plt.show()
diff --git a/examples/test_stringarr.py b/examples/test_stringarr.py
index 0d3ec74..758c4a7 100644
--- a/examples/test_stringarr.py
+++ b/examples/test_stringarr.py
@@ -32,7 +32,7 @@ for nrec in range(nrecs):
for n in range(n2):
data[n] = ''.join([random.choice(chars) for i in range(nchar)])
print(nrec,data)
- # convert data to array of characters with an extra dimension
+ # convert data to array of characters with an extra dimension
# (the number of characters per string) added to the right.
datac = stringtochar(data)
v[nrec] = datac
diff --git a/examples/threaded_read.py b/examples/threaded_read.py
index 8059400..c239a33 100644
--- a/examples/threaded_read.py
+++ b/examples/threaded_read.py
@@ -44,7 +44,7 @@ def get_data(serial=None):
#assert_array_almost_equal(data2,datal[int(fname[4])])
nc.close()
if serial is None:
- items.task_done()
+ items.task_done()
# Time it (no threading).
start = time.time()
diff --git a/examples/tutorial.py b/examples/tutorial.py
index 033500b..328ca17 100644
--- a/examples/tutorial.py
+++ b/examples/tutorial.py
@@ -11,8 +11,8 @@ rootgrp.close()
rootgrp = Dataset('test.nc', 'a')
fcstgrp = rootgrp.createGroup('forecasts')
analgrp = rootgrp.createGroup('analyses')
-fcstgrp1 = fcstgrp.createGroup('model1')
-fcstgrp2 = fcstgrp.createGroup('model2')
+fcstgrp1 = rootgrp.createGroup('/forecasts/model1')
+fcstgrp2 = rootgrp.createGroup('/forecasts/model2')
# walk the group tree using a Python generator.
def walktree(top):
@@ -23,8 +23,8 @@ def walktree(top):
yield children
print(rootgrp)
for children in walktree(rootgrp):
- for child in children:
- print(child)
+ for child in children:
+ print(child)
# dimensions.
level = rootgrp.createDimension('level', None)
@@ -53,6 +53,11 @@ longitudes = rootgrp.createVariable('longitude','f4',('lon',))
# this makes the compression 'lossy' (preserving a precision of 1/1000)
# try it and see how much smaller the file gets.
temp = rootgrp.createVariable('temp','f4',('time','level','lat','lon',),least_significant_digit=3)
+print(temp)
+# create variable in a group using a path.
+temp = rootgrp.createVariable('/forecasts/model1/temp','f4',('time','level','lat','lon',))
+print(rootgrp['/forecasts/model1']) # print the Group instance
+print(rootgrp['/forecasts/model1/temp']) # print the Variable instance
# attributes.
import time
@@ -75,8 +80,6 @@ print(rootgrp.__dict__)
print(rootgrp.variables)
-print(rootgrp.variables['temp'])
-
import numpy
# no unlimited dimension, just assign to slice.
lats = numpy.arange(-90,91,2.5)
@@ -201,7 +204,7 @@ data['surface_wind']['direction'] = 270
data['temp_sounding'] = (280.3,272.,270.,269.,266.,258.,254.1,250.,245.5,240.)
data['press_sounding'] = range(800,300,-50)
# variable-length string datatypes are not supported inside compound types, so
-# to store strings in a compound data type, each string must be
+# to store strings in a compound data type, each string must be
# stored as fixed-size (in this case 80) array of characters.
data['location_name'] = stringtoarr('Boulder, Colorado, USA',NUMCHARS)
# assign structured array to variable slice.
diff --git a/constants.pyx b/include/constants.pyx
similarity index 100%
rename from constants.pyx
rename to include/constants.pyx
diff --git a/netCDF4.pxi b/include/netCDF4.pxi
similarity index 99%
rename from netCDF4.pxi
rename to include/netCDF4.pxi
index fe3e956..102342d 100644
--- a/netCDF4.pxi
+++ b/include/netCDF4.pxi
@@ -288,6 +288,7 @@ cdef extern from "netcdf.h":
size_t len, void *op)
int nc_get_att(int ncid, int varid, char *name, void *ip) nogil
int nc_get_att_string(int ncid, int varid, char *name, char **ip) nogil
+ int nc_put_att_string(int ncid, int varid, char *name, size_t len, char **op) nogil
int nc_def_opaque(int ncid, size_t size, char *name, nc_type *xtypep)
int nc_inq_opaque(int ncid, nc_type xtype, char *name, size_t *sizep)
int nc_put_att_opaque(int ncid, int varid, char *name,
diff --git a/man/nc3tonc4.1 b/man/nc3tonc4.1
new file mode 100644
index 0000000..e6a3de5
--- /dev/null
+++ b/man/nc3tonc4.1
@@ -0,0 +1,78 @@
+.\" (C) Copyright 2015, Ross Gammon <rossgammon at mail.dk>,
+.\"
+.TH NC3TONC4 1 "22 Mar 2015"
+.\"
+.SH NAME
+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 ]
+.I netcdf3filename
+.I netcdf4filename
+.br
+.SH DESCRIPTION
+This manual page documents briefly the
+.B nc3tonc4
+command.
+.PP
+\fBnc3tonc4\fP is a program that converts a netCDF 3 file into netCDF 4 format, optionally unpacking variables packed as short integers (with scale_factor and add_offset) to floats, and adding zlib compression (with the HDF5 shuffle filter and fletcher32 checksum). Data may also be quantized (truncated) to a specified precision to improve compression.
+.SH OPTIONS
+These programs follow the usual GNU command line syntax, with long
+options starting with two dashes (`-').
+A summary of options is included below.
+.TP
+.B \-h
+Shows a summary of the available options.
+.TP
+.B \-o
+Overwite destination file (default is to raise an error if output file already exists).
+.TP
+.B \-\-vars
+A comma separated list of variable names to copy (default is to copy all variables).
+.TP
+.B \-\-classic=(0|1)
+Use NETCDF4_CLASSIC format instead of NETCDF4 (default = 1).
+.TP
+.B \-\-zlib=(0|1)
+Activate (or disable) zlib compression (the default is to activate).
+.TP
+.B \-\-complevel=(1-9)
+Set the zlib compression level (6 is default).
+.TP
+.B \-\-shuffle=(0|1)
+Activate (or disable) the shuffle filter (it is active by default).
+.TP
+.B \-\-fletcher32=(0|1)
+Activate (or disable) the fletcher32 checksum (it is not active by default).
+.TP
+.B \-\-unpackshort=(0|1)
+Unpack short integer variables to float variables using scale_factor and add_offset netCDF variable attributes (it is active by default).
+.TP
+.B \-\-quantize=(comma separated list of "variable name=integer" pairs)
+Truncate the data in the specified variables to a given decimal precision. For example, 'speed=2, height=-2, temp=0' will cause the variable 'speed' to be truncated to a precision of 0.01, 'height' to a precision of 100 and 'temp' to 1. This can significantly improve compression. The default is not to quantize any of the variables.
+.TP
+.B \-\-quiet=(0|1)
+If set to 1, don't print any diagnostic information.
+.TP
+.B \-\-chunk=(integer)
+The number of records along unlimited dimension to write at once. The default is 10. It is ignored if there is no unlimited dimension. If chunk=0, it means write all the data at once.
+.TP
+.B \-\-istart=(integer)
+The number of the record to start at along unlimited dimension. The default is 0. This option is ignored if there is no unlimited dimension.
+.TP
+.B \-\-istop=(integer)
+The number of the record to stop at along unlimited dimension. The default is 1. This option is ignored if there is no unlimited dimension.
+.SH SEE ALSO
+.BR ncinfo (1),
+.BR nc4tonc3 (1).
+.br
+.SH AUTHOR
+This manual page was written by Ross Gammon <rossgammon at mail.dk> based on the options displayed by nc3tonc4 \-h.
diff --git a/man/nc4tonc3.1 b/man/nc4tonc3.1
new file mode 100644
index 0000000..671f582
--- /dev/null
+++ b/man/nc4tonc3.1
@@ -0,0 +1,45 @@
+.\" (C) Copyright 2015, Ross Gammon <rossgammon at mail.dk>,
+.\"
+.TH NC4TONC3 1 "22 Mar 2015"
+.\"
+.SH NAME
+nc4tonc3 \- a program to convert a classic netCDF 4 file to netCDF 3 format
+.SH SYNOPSIS
+.B nc4tonc3
+.RB [\-h ]
+.RB [\-o ]
+.RB [\-\-chunk]
+.I netcdf4filename
+.I netcdf3filename
+.br
+.SH DESCRIPTION
+This manual page documents briefly the
+.B nc4tonc3
+command.
+.PP
+\fBnc4tonc3\fP is a program that converts a netCDF 4 file (in NETCDF4_CLASSIC format) to netCDF 3 format.
+.SH OPTIONS
+These programs follow the usual GNU command line syntax, with long
+options starting with two dashes (`-').
+A summary of options is included below.
+.TP
+.B \-h
+Shows a summary of the available options.
+.TP
+.B \-o
+Overwite destination file (default is to raise an error if output file already exists).
+.TP
+.B \-\-quiet=(0|1)
+If set to 1, don't print any diagnostic information.
+.TP
+.B \-\-format
+Choose the netcdf3 format to use. NETCDF3_64BIT is used by default, or it can be set to NETCDF3_CLASSIC.
+.TP
+.B \-\-chunk=(integer)
+The number of records along unlimited dimension to write at once. The default is 10. It is ignored if there is no unlimited dimension. If chunk=0, this means write all the data at once.
+.SH SEE ALSO
+.BR ncinfo (1),
+.BR nc3tonc4 (1).
+.br
+.SH AUTHOR
+This manual page was written by Ross Gammon <rossgammon at mail.dk> based on the options displayed by nc3tonc4 \-h.
diff --git a/man/ncinfo.1 b/man/ncinfo.1
new file mode 100644
index 0000000..c219e82
--- /dev/null
+++ b/man/ncinfo.1
@@ -0,0 +1,44 @@
+.\" (C) Copyright 2015, Ross Gammon <rossgammon at mail.dk>,
+.\"
+.TH NCINFO 1 "22 Mar 2015"
+.\"
+.SH NAME
+ncinfo \- a program to print summary information about a netCDF file
+.SH SYNOPSIS
+.B ncinfo
+.RB [ \-h ]
+.RB [ \-g|\-\-group=\fIgrp\fR ]
+.RB [ \-v|\-\-variable=\fIvar\fR ]
+.RB [ \-d|\-\-dimension=\fIdim\fR ]
+.I filename
+.br
+.SH DESCRIPTION
+This manual page documents briefly the
+.B ncinfo
+command.
+.PP
+\fBncinfo\fP is a program that prints summary information about a netCDF file
+.SH OPTIONS
+These programs follow the usual GNU command line syntax, with long
+options starting with two dashes (`-').
+A summary of options is included below.
+.TP
+.B \-h
+Shows a summary of the available options.
+.TP
+.B \-g grp, \-\-group=grp
+Prints information for this group. The default group is the root group. Nested groups are specified using posix paths e.g. group1/group2/group3.
+.TP
+.B \-v <variable name>, \-\-variable=<variable name>
+Prints information for this variable.
+.TP
+.B \-d <dimension name>, \-\-dimension=<dimension name>
+Prints information for this dimension.
+.TP
+The filename of the netCDF file must be supplied as the last argument.
+.SH SEE ALSO
+.BR nc3tonc4 (1),
+.BR nc4tonc3 (1).
+.br
+.SH AUTHOR
+This manual page was written by Ross Gammon <rossgammon at mail.dk> based on the options displayed by ncinfo \-h.
diff --git a/netCDF4/__init__.py b/netCDF4/__init__.py
new file mode 100644
index 0000000..5e881d5
--- /dev/null
+++ b/netCDF4/__init__.py
@@ -0,0 +1,8 @@
+# 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 (__version__, __netcdf4libversion__, __hdf5libversion__,
+ __has_rename_grp__, __has_nc_inq_path__,
+ __has_nc_inq_format_extended__)
diff --git a/netCDF4.c b/netCDF4/_netCDF4.c
similarity index 74%
rename from netCDF4.c
rename to netCDF4/_netCDF4.c
index 1e2eb8d..c5bb266 100644
--- a/netCDF4.c
+++ b/netCDF4/_netCDF4.c
@@ -242,8 +242,8 @@ class __Pyx_FakeReference {
#define _USE_MATH_DEFINES
#endif
#include <math.h>
-#define __PYX_HAVE__netCDF4
-#define __PYX_HAVE_API__netCDF4
+#define __PYX_HAVE__netCDF4___netCDF4
+#define __PYX_HAVE_API__netCDF4___netCDF4
#include "stdlib.h"
#include "H5public.h"
#include "netcdf.h"
@@ -430,56 +430,55 @@ static const char *__pyx_filename;
static const char *__pyx_f[] = {
- "netCDF4.pyx",
- "utils.pyx",
- "netCDF4.pxi",
+ "netCDF4/_netCDF4.pyx",
+ "include/netCDF4.pxi",
};
/*--- Type declarations ---*/
-struct __pyx_obj_7netCDF4_Dataset;
-struct __pyx_obj_7netCDF4_Group;
-struct __pyx_obj_7netCDF4_Dimension;
-struct __pyx_obj_7netCDF4_Variable;
-struct __pyx_obj_7netCDF4_CompoundType;
-struct __pyx_obj_7netCDF4_VLType;
-struct __pyx_opt_args_7netCDF4__read_compound;
-struct __pyx_opt_args_7netCDF4__read_vlen;
-struct __pyx_opt_args_7netCDF4__strencode;
+struct __pyx_obj_7netCDF4_8_netCDF4_Dataset;
+struct __pyx_obj_7netCDF4_8_netCDF4_Group;
+struct __pyx_obj_7netCDF4_8_netCDF4_Dimension;
+struct __pyx_obj_7netCDF4_8_netCDF4_Variable;
+struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType;
+struct __pyx_obj_7netCDF4_8_netCDF4_VLType;
+struct __pyx_opt_args_7netCDF4_8_netCDF4__read_compound;
+struct __pyx_opt_args_7netCDF4_8_netCDF4__read_vlen;
+struct __pyx_opt_args_7netCDF4_8_netCDF4__strencode;
struct __pyx_defaults;
typedef struct __pyx_defaults __pyx_defaults;
-/* "netCDF4.pyx":4002
+/* "netCDF4/_netCDF4.pyx":4118
* return xtype
*
* cdef _read_compound(group, nc_type xtype, endian=None): # <<<<<<<<<<<<<<
* # read a compound data type id from an existing file,
* # construct a corresponding numpy dtype instance,
*/
-struct __pyx_opt_args_7netCDF4__read_compound {
+struct __pyx_opt_args_7netCDF4_8_netCDF4__read_compound {
int __pyx_n;
PyObject *endian;
};
-/* "netCDF4.pyx":4160
+/* "netCDF4/_netCDF4.pyx":4276
* return xtype, dt
*
* cdef _read_vlen(group, nc_type xtype, endian=None): # <<<<<<<<<<<<<<
* # read a VLEN data type id from an existing file,
* # construct a corresponding numpy dtype instance,
*/
-struct __pyx_opt_args_7netCDF4__read_vlen {
+struct __pyx_opt_args_7netCDF4_8_netCDF4__read_vlen {
int __pyx_n;
PyObject *endian;
};
-/* "netCDF4.pyx":4187
+/* "netCDF4/_netCDF4.pyx":4303
* return VLType(group, dt, name, typeid=xtype)
*
* cdef _strencode(pystr,encoding=None): # <<<<<<<<<<<<<<
* # encode a string into bytes. If already bytes, do nothing.
* # uses default_encoding module variable for default encoding.
*/
-struct __pyx_opt_args_7netCDF4__strencode {
+struct __pyx_opt_args_7netCDF4_8_netCDF4__strencode {
int __pyx_n;
PyObject *encoding;
};
@@ -487,14 +486,14 @@ struct __pyx_defaults {
PyObject *__pyx_arg_exclude;
};
-/* "netCDF4.pyx":1303
+/* "netCDF4/_netCDF4.pyx":1353
*
*
* cdef class Dataset: # <<<<<<<<<<<<<<
* """
* Dataset(self, filename, mode="r", clobber=True, diskless=False, persist=False, keepweakref=False, format='NETCDF4')
*/
-struct __pyx_obj_7netCDF4_Dataset {
+struct __pyx_obj_7netCDF4_8_netCDF4_Dataset {
PyObject_HEAD
PyObject *__weakref__;
int _grpid;
@@ -510,29 +509,30 @@ struct __pyx_obj_7netCDF4_Dataset {
PyObject *cmptypes;
PyObject *vltypes;
PyObject *keepweakref;
+ PyObject *__orthogonal_indexing__;
};
-/* "netCDF4.pyx":2090
+/* "netCDF4/_netCDF4.pyx":2197
*
*
* cdef class Group(Dataset): # <<<<<<<<<<<<<<
* """
* Group(self, parent, name)
*/
-struct __pyx_obj_7netCDF4_Group {
- struct __pyx_obj_7netCDF4_Dataset __pyx_base;
+struct __pyx_obj_7netCDF4_8_netCDF4_Group {
+ struct __pyx_obj_7netCDF4_8_netCDF4_Dataset __pyx_base;
};
-/* "netCDF4.pyx":2178
+/* "netCDF4/_netCDF4.pyx":2287
*
*
* cdef class Dimension: # <<<<<<<<<<<<<<
* """
* Dimension(self, group, name, size=None)
*/
-struct __pyx_obj_7netCDF4_Dimension {
+struct __pyx_obj_7netCDF4_8_netCDF4_Dimension {
PyObject_HEAD
int _dimid;
int _grpid;
@@ -542,14 +542,14 @@ struct __pyx_obj_7netCDF4_Dimension {
};
-/* "netCDF4.pyx":2322
+/* "netCDF4/_netCDF4.pyx":2431
* 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)
*/
-struct __pyx_obj_7netCDF4_Variable {
+struct __pyx_obj_7netCDF4_8_netCDF4_Variable {
PyObject_HEAD
int _varid;
int _grpid;
@@ -565,17 +565,18 @@ struct __pyx_obj_7netCDF4_Variable {
PyObject *_grp;
PyObject *_cmptype;
PyObject *_vltype;
+ PyObject *__orthogonal_indexing__;
};
-/* "netCDF4.pyx":3843
+/* "netCDF4/_netCDF4.pyx":3959
* # Compound datatype support.
*
* cdef class CompoundType: # <<<<<<<<<<<<<<
* """
* A L{CompoundType} instance is used to describe a compound data type.
*/
-struct __pyx_obj_7netCDF4_CompoundType {
+struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType {
PyObject_HEAD
nc_type _nc_type;
PyObject *dtype;
@@ -583,14 +584,14 @@ struct __pyx_obj_7netCDF4_CompoundType {
};
-/* "netCDF4.pyx":4073
+/* "netCDF4/_netCDF4.pyx":4189
* # VLEN datatype support.
*
* cdef class VLType: # <<<<<<<<<<<<<<
* """
* A L{VLType} instance is used to describe a variable length (VLEN) data type.
*/
-struct __pyx_obj_7netCDF4_VLType {
+struct __pyx_obj_7netCDF4_8_netCDF4_VLType {
PyObject_HEAD
nc_type _nc_type;
PyObject *dtype;
@@ -678,12 +679,54 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject
static PyObject *__Pyx_GetBuiltinName(PyObject *name);
+#include <string.h>
+
+static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
+ const char* cstring, Py_ssize_t start, Py_ssize_t stop,
+ const char* encoding, const char* errors,
+ PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));
+
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
#else
#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
#endif
+static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb);
+static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb);
+
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
+ PyListObject* L = (PyListObject*) list;
+ Py_ssize_t len = Py_SIZE(list);
+ if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
+ Py_INCREF(x);
+ PyList_SET_ITEM(list, len, x);
+ Py_SIZE(list) = len+1;
+ return 0;
+ }
+ return PyList_Append(list, x);
+}
+#else
+#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
+#endif
+
+static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name);
+
+static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
+
+static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
+
+static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
+
+#if PY_MAJOR_VERSION >= 3
+#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
+#else
+#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
+#endif
+
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
#endif
@@ -717,29 +760,46 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
int is_list, int wraparound, int boundscheck);
-#include <string.h>
-
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
+static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb);
+static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb);
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
-#else
-#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
-#endif
+static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);
-static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb);
-static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb);
+static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
+ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \
+ const char* function_name);
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
+static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
+ Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);
static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
int has_cstart, int has_cstop, int wraparound);
-static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name);
+static CYTHON_INLINE int __Pyx_PySequence_Contains(PyObject* item, PyObject* seq, int eq) {
+ int result = PySequence_Contains(seq, item);
+ return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
+}
+
+#if PY_MAJOR_VERSION < 3
+#define __Pyx_PyString_Join __Pyx_PyBytes_Join
+#define __Pyx_PyBaseString_Join(s, v) (PyUnicode_CheckExact(s) ? PyUnicode_Join(s, v) : __Pyx_PyBytes_Join(s, v))
+#else
+#define __Pyx_PyString_Join PyUnicode_Join
+#define __Pyx_PyBaseString_Join PyUnicode_Join
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+ #if PY_MAJOR_VERSION < 3
+ #define __Pyx_PyBytes_Join _PyString_Join
+ #else
+ #define __Pyx_PyBytes_Join _PyBytes_Join
+ #endif
+#else
+static CYTHON_INLINE PyObject* __Pyx_PyBytes_Join(PyObject* sep, PyObject* values);
+#endif
static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
@@ -749,39 +809,6 @@ static CYTHON_INLINE int __Pyx_IterFinish(void);
static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected);
-static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
- Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);
-
-static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);
-
-static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
- PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \
- const char* function_name);
-
-#define __Pyx_PyObject_DelSlice(obj, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound) \
- __Pyx_PyObject_SetSlice(obj, (PyObject*)NULL, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound)
-static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
- PyObject* obj, PyObject* value, Py_ssize_t cstart, Py_ssize_t cstop,
- PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
- int has_cstart, int has_cstop, int wraparound);
-
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o,n,NULL)
-static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) {
- PyTypeObject* tp = Py_TYPE(obj);
- if (likely(tp->tp_setattro))
- return tp->tp_setattro(obj, attr_name, value);
-#if PY_MAJOR_VERSION < 3
- if (likely(tp->tp_setattr))
- return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value);
-#endif
- return PyObject_SetAttr(obj, attr_name, value);
-}
-#else
-#define __Pyx_PyObject_DelAttrStr(o,n) PyObject_DelAttr(o,n)
-#define __Pyx_PyObject_SetAttrStr(o,n,v) PyObject_SetAttr(o,n,v)
-#endif
-
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
PyListObject* L = (PyListObject*) list;
@@ -798,47 +825,11 @@ static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
#endif
-static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb);
-static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb);
-
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
- PyListObject* L = (PyListObject*) list;
- Py_ssize_t len = Py_SIZE(list);
- if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
- Py_INCREF(x);
- PyList_SET_ITEM(list, len, x);
- Py_SIZE(list) = len+1;
- return 0;
- }
- return PyList_Append(list, x);
-}
-#else
-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
-#endif
-
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
-
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);
-
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);
-
-static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
- const char* cstring, Py_ssize_t start, Py_ssize_t stop,
- const char* encoding, const char* errors,
- PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));
-
-static CYTHON_INLINE int __Pyx_PySequence_Contains(PyObject* item, PyObject* seq, int eq) {
- int result = PySequence_Contains(seq, item);
+static CYTHON_INLINE int __Pyx_PyDict_Contains(PyObject* item, PyObject* dict, int eq) {
+ int result = PyDict_Contains(dict, item);
return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
}
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg);
-
-static CYTHON_INLINE PyObject* __Pyx_PyDict_Keys(PyObject* d);
-
#if PY_MAJOR_VERSION >= 3
static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
PyObject *value;
@@ -859,36 +850,50 @@ static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key)
#endif
-static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x);
+static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);
+
+static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o,n,NULL)
+static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) {
+ PyTypeObject* tp = Py_TYPE(obj);
+ if (likely(tp->tp_setattro))
+ return tp->tp_setattro(obj, attr_name, value);
#if PY_MAJOR_VERSION < 3
-#define __Pyx_PyString_Join __Pyx_PyBytes_Join
-#define __Pyx_PyBaseString_Join(s, v) (PyUnicode_CheckExact(s) ? PyUnicode_Join(s, v) : __Pyx_PyBytes_Join(s, v))
-#else
-#define __Pyx_PyString_Join PyUnicode_Join
-#define __Pyx_PyBaseString_Join PyUnicode_Join
+ if (likely(tp->tp_setattr))
+ return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value);
#endif
-#if CYTHON_COMPILING_IN_CPYTHON
- #if PY_MAJOR_VERSION < 3
- #define __Pyx_PyBytes_Join _PyString_Join
- #else
- #define __Pyx_PyBytes_Join _PyBytes_Join
- #endif
+ return PyObject_SetAttr(obj, attr_name, value);
+}
#else
-static CYTHON_INLINE PyObject* __Pyx_PyBytes_Join(PyObject* sep, PyObject* values);
+#define __Pyx_PyObject_DelAttrStr(o,n) PyObject_DelAttr(o,n)
+#define __Pyx_PyObject_SetAttrStr(o,n,v) PyObject_SetAttr(o,n,v)
#endif
-static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
-#if CYTHON_COMPILING_IN_CPYTHON
- PyObject* none = _PyList_Extend((PyListObject*)L, v);
- if (unlikely(!none))
- return -1;
- Py_DECREF(none);
- return 0;
+static double __Pyx__PyObject_AsDouble(PyObject* obj);
+#if CYTHON_COMPILING_IN_PYPY
+#define __Pyx_PyObject_AsDouble(obj) \
+(likely(PyFloat_CheckExact(obj)) ? PyFloat_AS_DOUBLE(obj) : \
+ likely(PyInt_CheckExact(obj)) ? \
+ PyFloat_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj))
#else
- return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
+#define __Pyx_PyObject_AsDouble(obj) \
+((likely(PyFloat_CheckExact(obj))) ? \
+ PyFloat_AS_DOUBLE(obj) : __Pyx__PyObject_AsDouble(obj))
#endif
-}
+
+static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
+
+#define __Pyx_PyObject_DelSlice(obj, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound) \
+ __Pyx_PyObject_SetSlice(obj, (PyObject*)NULL, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound)
+static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
+ PyObject* obj, PyObject* value, Py_ssize_t cstart, Py_ssize_t cstop,
+ PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
+ int has_cstart, int has_cstop, int wraparound);
+
+static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
+ const char *name, int exact);
#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
(__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
@@ -899,27 +904,23 @@ static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyOb
static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v,
int is_list, int wraparound, int boundscheck);
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
+static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg);
-static CYTHON_INLINE int __Pyx_PyDict_Contains(PyObject* item, PyObject* dict, int eq) {
- int result = PyDict_Contains(dict, item);
- return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
+static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x);
-static double __Pyx__PyObject_AsDouble(PyObject* obj);
-#if CYTHON_COMPILING_IN_PYPY
-#define __Pyx_PyObject_AsDouble(obj) \
-(likely(PyFloat_CheckExact(obj)) ? PyFloat_AS_DOUBLE(obj) : \
- likely(PyInt_CheckExact(obj)) ? \
- PyFloat_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj))
+static CYTHON_INLINE PyObject* __Pyx_PyDict_Keys(PyObject* d);
+
+static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
+#if CYTHON_COMPILING_IN_CPYTHON
+ PyObject* none = _PyList_Extend((PyListObject*)L, v);
+ if (unlikely(!none))
+ return -1;
+ Py_DECREF(none);
+ return 0;
#else
-#define __Pyx_PyObject_AsDouble(obj) \
-((likely(PyFloat_CheckExact(obj))) ? \
- PyFloat_AS_DOUBLE(obj) : __Pyx__PyObject_AsDouble(obj))
+ return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
#endif
-
-static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
- const char *name, int exact);
+}
static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
@@ -1051,283 +1052,291 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
/* Module declarations from 'numpy' */
-/* Module declarations from 'netCDF4' */
-static PyTypeObject *__pyx_ptype_7netCDF4_ndarray = 0;
-static PyTypeObject *__pyx_ptype_7netCDF4_Dataset = 0;
-static PyTypeObject *__pyx_ptype_7netCDF4_Group = 0;
-static PyTypeObject *__pyx_ptype_7netCDF4_Dimension = 0;
-static PyTypeObject *__pyx_ptype_7netCDF4_Variable = 0;
-static PyTypeObject *__pyx_ptype_7netCDF4_CompoundType = 0;
-static PyTypeObject *__pyx_ptype_7netCDF4_VLType = 0;
-static PyObject *__pyx_f_7netCDF4__get_att_names(int, int); /*proto*/
-static PyObject *__pyx_f_7netCDF4__get_att(PyObject *, int, PyObject *); /*proto*/
-static PyObject *__pyx_f_7netCDF4__get_format(int); /*proto*/
-static PyObject *__pyx_f_7netCDF4__get_full_format(int); /*proto*/
-static PyObject *__pyx_f_7netCDF4__set_att(PyObject *, int, PyObject *, PyObject *); /*proto*/
-static PyObject *__pyx_f_7netCDF4__get_types(PyObject *); /*proto*/
-static PyObject *__pyx_f_7netCDF4__get_dims(PyObject *); /*proto*/
-static PyObject *__pyx_f_7netCDF4__get_grps(PyObject *); /*proto*/
-static PyObject *__pyx_f_7netCDF4__get_vars(PyObject *); /*proto*/
-static PyObject *__pyx_f_7netCDF4__def_compound(PyObject *, PyObject *, PyObject *); /*proto*/
-static PyObject *__pyx_f_7netCDF4__find_cmptype(PyObject *, PyObject *); /*proto*/
-static PyObject *__pyx_f_7netCDF4__read_compound(PyObject *, nc_type, struct __pyx_opt_args_7netCDF4__read_compound *__pyx_optional_args); /*proto*/
-static PyObject *__pyx_f_7netCDF4__def_vlen(PyObject *, PyObject *, PyObject *); /*proto*/
-static PyObject *__pyx_f_7netCDF4__read_vlen(PyObject *, nc_type, struct __pyx_opt_args_7netCDF4__read_vlen *__pyx_optional_args); /*proto*/
-static PyObject *__pyx_f_7netCDF4__strencode(PyObject *, struct __pyx_opt_args_7netCDF4__strencode *__pyx_optional_args); /*proto*/
-#define __Pyx_MODULE_NAME "netCDF4"
-int __pyx_module_is_main_netCDF4 = 0;
-
-/* Implementation of 'netCDF4' */
+/* Module declarations from 'netCDF4._netCDF4' */
+static PyTypeObject *__pyx_ptype_7netCDF4_8_netCDF4_ndarray = 0;
+static PyTypeObject *__pyx_ptype_7netCDF4_8_netCDF4_Dataset = 0;
+static PyTypeObject *__pyx_ptype_7netCDF4_8_netCDF4_Group = 0;
+static PyTypeObject *__pyx_ptype_7netCDF4_8_netCDF4_Dimension = 0;
+static PyTypeObject *__pyx_ptype_7netCDF4_8_netCDF4_Variable = 0;
+static PyTypeObject *__pyx_ptype_7netCDF4_8_netCDF4_CompoundType = 0;
+static PyTypeObject *__pyx_ptype_7netCDF4_8_netCDF4_VLType = 0;
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_att_names(int, int); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_att(PyObject *, int, PyObject *); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_format(int); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_full_format(int); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__set_att(PyObject *, int, PyObject *, PyObject *); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_types(PyObject *); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_dims(PyObject *); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_grps(PyObject *); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__get_vars(PyObject *); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__def_compound(PyObject *, PyObject *, PyObject *); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__find_cmptype(PyObject *, PyObject *); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__read_compound(PyObject *, nc_type, struct __pyx_opt_args_7netCDF4_8_netCDF4__read_compound *__pyx_optional_args); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__def_vlen(PyObject *, PyObject *, PyObject *); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__read_vlen(PyObject *, nc_type, struct __pyx_opt_args_7netCDF4_8_netCDF4__read_vlen *__pyx_optional_args); /*proto*/
+static PyObject *__pyx_f_7netCDF4_8_netCDF4__strencode(PyObject *, struct __pyx_opt_args_7netCDF4_8_netCDF4__strencode *__pyx_optional_args); /*proto*/
+#define __Pyx_MODULE_NAME "netCDF4._netCDF4"
+int __pyx_module_is_main_netCDF4___netCDF4 = 0;
+
+/* Implementation of 'netCDF4._netCDF4' */
static PyObject *__pyx_builtin_ImportError;
static PyObject *__pyx_builtin_object;
-static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin_IOError;
-static PyObject *__pyx_builtin_sum;
-static PyObject *__pyx_builtin_AttributeError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_IndexError;
static PyObject *__pyx_builtin_RuntimeError;
+static PyObject *__pyx_builtin_AttributeError;
static PyObject *__pyx_builtin_KeyError;
+static PyObject *__pyx_builtin_ValueError;
static PyObject *__pyx_builtin_TypeError;
+static PyObject *__pyx_builtin_IndexError;
+static PyObject *__pyx_builtin_IOError;
+static PyObject *__pyx_builtin_enumerate;
+static PyObject *__pyx_builtin_sum;
+static PyObject *__pyx_builtin_range;
static PyObject *__pyx_builtin_UnicodeDecodeError;
-static PyObject *__pyx_pf_7netCDF4__dateparse(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_timestr); /* proto */
-static PyObject *__pyx_pf_7netCDF4_2stringtoarr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_string, PyObject *__pyx_v_NUMCHARS, PyObject *__pyx_v_dtype); /* proto */
-static PyObject *__pyx_pf_7netCDF4_4stringtochar(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_a); /* proto */
-static PyObject *__pyx_pf_7netCDF4_6chartostring(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_b); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8date2num(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_dates, PyObject *__pyx_v_units, PyObject *__pyx_v_calendar); /* proto */
-static PyObject *__pyx_pf_7netCDF4_10num2date(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_times, PyObject *__pyx_v_units, PyObject *__pyx_v_calendar); /* proto */
-static PyObject *__pyx_pf_7netCDF4_12date2index(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_dates, PyObject *__pyx_v_nctime, PyObject *__pyx_v_calendar, PyObject *__pyx_v_select); /* proto */
-static PyObject *__pyx_pf_7netCDF4_14getlibversion(CYTHON_UNUSED PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9MFDataset_12__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9MFDataset___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_files, PyObject *__pyx_v_check, PyObject *__pyx_v_aggdim, PyObject *__pyx_v_exclude); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9MFDataset_2__setattr__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9MFDataset_4__getattribute__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9MFDataset_6ncattrs(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9MFDataset_8close(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9MFDataset_10__repr__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_10_Dimension___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_dimname, CYTHON_UNUSED PyObject *__pyx_v_dim, PyObject *__pyx_v_dimlens, PyObject *__pyx_v_dimtotlen); /* proto */
-static PyObject *__pyx_pf_7netCDF4_10_Dimension_2__len__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_10_Dimension_4isunlimited(CYTHON_UNUSED PyObject *__pyx_self, CYTHON_UNUSED PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_10_Dimension_6__repr__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9_Variable___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_dset, PyObject *__pyx_v_varname, PyObject *__pyx_v_var, PyObject *__pyx_v_recdimname); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9_Variable_2typecode(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9_Variable_4ncattrs(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9_Variable_6__getattr__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9_Variable_8__repr__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9_Variable_10__len__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9_Variable_12_shape(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9_Variable_14set_auto_maskandscale(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_val); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9_Variable_16__getitem__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_elem); /* proto */
-static PyObject *__pyx_pf_7netCDF4_6MFTime___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_time, PyObject *__pyx_v_units); /* proto */
-static PyObject *__pyx_pf_7netCDF4_6MFTime_2__getitem__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_elem); /* proto */
-static PyObject *__pyx_pf_7netCDF4_16_gethdf5libversion(CYTHON_UNUSED PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_18_set_default_format(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_format); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset___init__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_filename, PyObject *__pyx_v_mode, PyObject *__pyx_v_clobber, PyObject *__pyx_v_format, PyObject *__pyx_v_diskless, PyObject *__pyx_v_persist, PyObject *__pyx_v_keepweakref, CYTHON_UNUSED PyObject *__pyx_v_kwargs); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_2__enter__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_4__exit__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_atype, CYTHON_UNUSED PyObject *__pyx_v_value, CYTHON_UNUSED PyObject *__pyx_v_traceback); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_6filepath(CYTHON_UNUSED struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_8__repr__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_10__unicode__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_12close(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static void __pyx_pf_7netCDF4_7Dataset_14__dealloc__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_16sync(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_18_redef(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_20_enddef(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_22set_fill_on(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_24set_fill_off(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_26createDimension(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_dimname, PyObject *__pyx_v_size); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_28renameDimension(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_oldname, PyObject *__pyx_v_newname); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_30createCompoundType(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_datatype, PyObject *__pyx_v_datatype_name); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_32createVLType(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_datatype, PyObject *__pyx_v_datatype_name); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_34createVariable(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_varname, PyObject *__pyx_v_datatype, PyObject *__pyx_v_dimensions, PyObject *__pyx_v_zlib, PyObject *__pyx_v_complevel, PyObject *__pyx_v_shuffle, PyObject *__pyx_v_fletcher32, PyObject *__pyx_v_contiguous, PyObject *__pyx_v_chunksizes, PyObject *__pyx_v_endian, PyObject *__pyx_v_least_significant_digit, PyObject *__pyx_v_fill_value, PyObject *__pyx_v_chunk_cach [...]
-static PyObject *__pyx_pf_7netCDF4_7Dataset_36renameVariable(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_oldname, PyObject *__pyx_v_newname); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_38createGroup(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_groupname); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_40ncattrs(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_42setncattr(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_44setncatts(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_attdict); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_46getncattr(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_48__delattr__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_50delncattr(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_52__setattr__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_54__getattr__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_56renameAttribute(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_oldname, PyObject *__pyx_v_newname); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_58renameGroup(CYTHON_UNUSED struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_oldname, CYTHON_UNUSED PyObject *__pyx_v_newname); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_60set_auto_maskandscale(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_62set_auto_mask(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_64set_auto_scale(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_6_grpid___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_6_grpid_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_7_isopen___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_7_isopen_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_6groups___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_6groups_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_6groups_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_10dimensions___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_10dimensions_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_10dimensions_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_9variables___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_9variables_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_9variables_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_11disk_format___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_11disk_format_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_11disk_format_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_4path___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_4path_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_4path_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_6parent___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_6parent_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_6parent_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_11file_format___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_11file_format_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_11file_format_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_10data_model___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_10data_model_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_10data_model_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_8cmptypes___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_8cmptypes_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_8cmptypes_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_7vltypes___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_7vltypes_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_7vltypes_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_7Dataset_11keepweakref___get__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_11keepweakref_2__set__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_7Dataset_11keepweakref_4__del__(struct __pyx_obj_7netCDF4_Dataset *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_5Group___init__(struct __pyx_obj_7netCDF4_Group *__pyx_v_self, PyObject *__pyx_v_parent, PyObject *__pyx_v_name, PyObject *__pyx_v_kwargs); /* proto */
-static PyObject *__pyx_pf_7netCDF4_5Group_2close(CYTHON_UNUSED struct __pyx_obj_7netCDF4_Group *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_5Group_4_getname(struct __pyx_obj_7netCDF4_Group *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_5Group_4name___get__(struct __pyx_obj_7netCDF4_Group *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_5Group_4name_2__set__(CYTHON_UNUSED struct __pyx_obj_7netCDF4_Group *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_9Dimension___init__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_grp, PyObject *__pyx_v_name, PyObject *__pyx_v_size, PyObject *__pyx_v_kwargs); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_2_getname(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_4name___get__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_9Dimension_4name_2__set__(CYTHON_UNUSED struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_4__repr__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_6__unicode__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_pf_7netCDF4_9Dimension_8__len__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_10group(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_12isunlimited(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_6_dimid___get__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_9Dimension_6_dimid_2__set__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_6_grpid___get__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_9Dimension_6_grpid_2__set__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_11_data_model___get__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_9Dimension_11_data_model_2__set__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_9Dimension_11_data_model_4__del__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_5_name___get__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_9Dimension_5_name_2__set__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_9Dimension_5_name_4__del__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_9Dimension_4_grp___get__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_9Dimension_4_grp_2__set__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_9Dimension_4_grp_4__del__(struct __pyx_obj_7netCDF4_Dimension *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable___init__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_grp, PyObject *__pyx_v_name, PyObject *__pyx_v_datatype, PyObject *__pyx_v_dimensions, PyObject *__pyx_v_zlib, PyObject *__pyx_v_complevel, PyObject *__pyx_v_shuffle, PyObject *__pyx_v_fletcher32, PyObject *__pyx_v_contiguous, PyObject *__pyx_v_chunksizes, PyObject *__pyx_v_endian, PyObject *__pyx_v_least_significant_digit, PyObject *__pyx_v_fill_value, PyObject *__pyx_v_ch [...]
-static PyObject *__pyx_pf_7netCDF4_8Variable_2__array__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_4__repr__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_6__unicode__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_8_getdims(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_10_getname(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_4name___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_4name_2__set__(CYTHON_UNUSED struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_8datatype___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_5shape___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_5shape_2__set__(CYTHON_UNUSED struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_4size___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_10dimensions___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_10dimensions_2__set__(CYTHON_UNUSED struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_12group(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_14ncattrs(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_16setncattr(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_18setncatts(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_attdict); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_20getncattr(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_22delncattr(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_24filters(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_26endian(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_28chunking(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_30get_var_chunk_cache(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_32set_var_chunk_cache(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_size, PyObject *__pyx_v_nelems, PyObject *__pyx_v_preemption); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_34__delattr__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_36__setattr__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_38__getattr__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_40renameAttribute(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_oldname, PyObject *__pyx_v_newname); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_42__getitem__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_elem); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_44_toma(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_data); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_46_assign_vlen(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_elem, PyObject *__pyx_v_data); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_48__setitem__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_elem, PyObject *__pyx_v_data); /* proto */
-static Py_ssize_t __pyx_pf_7netCDF4_8Variable_50__len__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_52assignValue(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_val); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_54getValue(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_56set_auto_maskandscale(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_maskandscale); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_58set_auto_scale(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_scale); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_60set_auto_mask(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_mask); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_62_put(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyArrayObject *__pyx_v_data, PyObject *__pyx_v_start, PyObject *__pyx_v_count, PyObject *__pyx_v_stride); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_64_get(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_start, PyObject *__pyx_v_count, PyObject *__pyx_v_stride); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_6_varid___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_6_varid_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_6_grpid___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_6_grpid_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_10_nunlimdim___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_10_nunlimdim_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_5_name___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_5_name_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_5_name_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_4ndim___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_4ndim_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_4ndim_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_5dtype___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_5dtype_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_5dtype_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_4mask___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_4mask_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_4mask_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_5scale___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_5scale_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_5scale_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_12_isprimitive___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_12_isprimitive_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_12_isprimitive_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_11_iscompound___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_11_iscompound_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_11_iscompound_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_7_isvlen___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_7_isvlen_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_7_isvlen_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_4_grp___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_4_grp_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_4_grp_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_8_cmptype___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_8_cmptype_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_8_cmptype_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_8Variable_7_vltype___get__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_7_vltype_2__set__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_8Variable_7_vltype_4__del__(struct __pyx_obj_7netCDF4_Variable *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_12CompoundType___init__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self, PyObject *__pyx_v_grp, PyObject *__pyx_v_dt, PyObject *__pyx_v_dtype_name, PyObject *__pyx_v_kwargs); /* proto */
-static PyObject *__pyx_pf_7netCDF4_12CompoundType_2__repr__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_12CompoundType_4__unicode__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_12CompoundType_8_nc_type___get__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_12CompoundType_8_nc_type_2__set__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_12CompoundType_5dtype___get__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_12CompoundType_5dtype_2__set__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_12CompoundType_5dtype_4__del__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_12CompoundType_4name___get__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_12CompoundType_4name_2__set__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_12CompoundType_4name_4__del__(struct __pyx_obj_7netCDF4_CompoundType *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_6VLType___init__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self, PyObject *__pyx_v_grp, PyObject *__pyx_v_dt, PyObject *__pyx_v_dtype_name, PyObject *__pyx_v_kwargs); /* proto */
-static PyObject *__pyx_pf_7netCDF4_6VLType_2__repr__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_6VLType_4__unicode__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_6VLType_8_nc_type___get__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_6VLType_8_nc_type_2__set__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf_7netCDF4_6VLType_5dtype___get__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_6VLType_5dtype_2__set__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_6VLType_5dtype_4__del__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_7netCDF4_6VLType_4name___get__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self); /* proto */
-static int __pyx_pf_7netCDF4_6VLType_4name_2__set__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
-static int __pyx_pf_7netCDF4_6VLType_4name_4__del__(struct __pyx_obj_7netCDF4_VLType *__pyx_v_self); /* proto */
-static PyObject *__pyx_tp_new_7netCDF4_Dataset(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_7netCDF4_Group(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_7netCDF4_Dimension(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_7netCDF4_Variable(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_7netCDF4_CompoundType(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_7netCDF4_VLType(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4__gethdf5libversion(CYTHON_UNUSED PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_2getlibversion(CYTHON_UNUSED PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_4_set_default_format(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_format); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset___init__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_filename, PyObject *__pyx_v_mode, PyObject *__pyx_v_clobber, PyObject *__pyx_v_format, PyObject *__pyx_v_diskless, PyObject *__pyx_v_persist, PyObject *__pyx_v_keepweakref, CYTHON_UNUSED PyObject *__pyx_v_kwargs); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_2__enter__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_4__exit__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_atype, CYTHON_UNUSED PyObject *__pyx_v_value, CYTHON_UNUSED PyObject *__pyx_v_traceback); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_6__getitem__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_elem); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_8filepath(CYTHON_UNUSED struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_10__repr__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_12__unicode__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_14close(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static void __pyx_pf_7netCDF4_8_netCDF4_7Dataset_16__dealloc__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_18sync(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_20_redef(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_22_enddef(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_24set_fill_on(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_26set_fill_off(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_28createDimension(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_dimname, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_30renameDimension(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_oldname, PyObject *__pyx_v_newname); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_32createCompoundType(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_datatype, PyObject *__pyx_v_datatype_name); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_34createVLType(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_datatype, PyObject *__pyx_v_datatype_name); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_36createVariable(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_varname, PyObject *__pyx_v_datatype, PyObject *__pyx_v_dimensions, PyObject *__pyx_v_zlib, PyObject *__pyx_v_complevel, PyObject *__pyx_v_shuffle, PyObject *__pyx_v_fletcher32, PyObject *__pyx_v_contiguous, PyObject *__pyx_v_chunksizes, PyObject *__pyx_v_endian, PyObject *__pyx_v_least_significant_digit, PyObject *__pyx_v_fill_value, PyObject [...]
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_38renameVariable(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_oldname, PyObject *__pyx_v_newname); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_40createGroup(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_groupname); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_42ncattrs(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_44setncattr(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_46setncatts(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_attdict); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_48getncattr(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_50__delattr__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_52delncattr(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_54__setattr__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_56__getattr__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_58renameAttribute(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_oldname, PyObject *__pyx_v_newname); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_60renameGroup(CYTHON_UNUSED struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_oldname, CYTHON_UNUSED PyObject *__pyx_v_newname); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_62set_auto_maskandscale(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_64set_auto_mask(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_66set_auto_scale(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_6_grpid___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_6_grpid_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_7_isopen___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_7_isopen_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_6groups___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_6groups_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_6groups_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_10dimensions___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_10dimensions_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_10dimensions_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_9variables___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_9variables_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_9variables_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_11disk_format___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_11disk_format_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_11disk_format_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_4path___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_4path_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_4path_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_6parent___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_6parent_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_6parent_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_11file_format___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_11file_format_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_11file_format_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_10data_model___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_10data_model_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_10data_model_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_7Dataset_8cmptypes___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_7Dataset_8cmptypes_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_8cmptypes_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *__pyx_v_self); /* proto */
+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 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 */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_5Group_4name___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Group *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_5Group_4name_2__set__(CYTHON_UNUSED struct __pyx_obj_7netCDF4_8_netCDF4_Group *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension___init__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_grp, PyObject *__pyx_v_name, PyObject *__pyx_v_size, PyObject *__pyx_v_kwargs); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_2_getname(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_4name___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_4name_2__set__(CYTHON_UNUSED struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_4__repr__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_6__unicode__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static Py_ssize_t __pyx_pf_7netCDF4_8_netCDF4_9Dimension_8__len__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_10group(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_12isunlimited(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_6_dimid___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_6_dimid_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_6_grpid___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_6_grpid_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_11_data_model___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_11_data_model_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_11_data_model_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_5_name___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_5_name_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_5_name_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9Dimension_4_grp___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_4_grp_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_9Dimension_4_grp_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable___init__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_grp, PyObject *__pyx_v_name, PyObject *__pyx_v_datatype, PyObject *__pyx_v_dimensions, PyObject *__pyx_v_zlib, PyObject *__pyx_v_complevel, PyObject *__pyx_v_shuffle, PyObject *__pyx_v_fletcher32, PyObject *__pyx_v_contiguous, PyObject *__pyx_v_chunksizes, PyObject *__pyx_v_endian, PyObject *__pyx_v_least_significant_digit, PyObject *__pyx_v_fill_value, [...]
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_2__array__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_4__repr__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_6__unicode__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_8_getdims(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_10_getname(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_4name___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_4name_2__set__(CYTHON_UNUSED struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_8datatype___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_5shape___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_5shape_2__set__(CYTHON_UNUSED struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_4size___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_10dimensions___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_10dimensions_2__set__(CYTHON_UNUSED struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_12group(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_14ncattrs(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_16setncattr(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_18setncatts(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_attdict); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_20getncattr(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_22delncattr(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_24filters(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_26endian(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_28chunking(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_30get_var_chunk_cache(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_32set_var_chunk_cache(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_size, PyObject *__pyx_v_nelems, PyObject *__pyx_v_preemption); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_34__delattr__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_36__setattr__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_38__getattr__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_40renameAttribute(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_oldname, PyObject *__pyx_v_newname); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_42__getitem__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_elem); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_44_toma(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_data); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_46_assign_vlen(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_elem, PyObject *__pyx_v_data); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_48__setitem__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_elem, PyObject *__pyx_v_data); /* proto */
+static Py_ssize_t __pyx_pf_7netCDF4_8_netCDF4_8Variable_50__len__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_52assignValue(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_val); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_54getValue(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_56set_auto_maskandscale(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_maskandscale); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_58set_auto_scale(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_scale); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_60set_auto_mask(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_mask); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_62_put(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyArrayObject *__pyx_v_data, PyObject *__pyx_v_start, PyObject *__pyx_v_count, PyObject *__pyx_v_stride); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_64_get(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_start, PyObject *__pyx_v_count, PyObject *__pyx_v_stride); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_6_varid___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_6_varid_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_6_grpid___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_6_grpid_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_10_nunlimdim___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_10_nunlimdim_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_5_name___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_5_name_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_5_name_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_4ndim___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_4ndim_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_4ndim_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_5dtype___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_5dtype_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_5dtype_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_4mask___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_4mask_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_4mask_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_5scale___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_5scale_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_5scale_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_12_isprimitive___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_12_isprimitive_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_12_isprimitive_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_11_iscompound___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_11_iscompound_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_11_iscompound_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_7_isvlen___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_7_isvlen_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_7_isvlen_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_4_grp___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_4_grp_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_4_grp_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_8_cmptype___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_8_cmptype_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_8_cmptype_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_7_vltype___get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_7_vltype_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_7_vltype_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8Variable_23__orthogonal_indexing_____get__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_23__orthogonal_indexing___2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_8Variable_23__orthogonal_indexing___4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_Variable *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_12CompoundType___init__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self, PyObject *__pyx_v_grp, PyObject *__pyx_v_dt, PyObject *__pyx_v_dtype_name, PyObject *__pyx_v_kwargs); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_12CompoundType_2__repr__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_12CompoundType_4__unicode__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_12CompoundType_8_nc_type___get__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_12CompoundType_8_nc_type_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_12CompoundType_5dtype___get__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_12CompoundType_5dtype_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_12CompoundType_5dtype_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_12CompoundType_4name___get__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_12CompoundType_4name_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_12CompoundType_4name_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_6VLType___init__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self, PyObject *__pyx_v_grp, PyObject *__pyx_v_dt, PyObject *__pyx_v_dtype_name, PyObject *__pyx_v_kwargs); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_6VLType_2__repr__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_6VLType_4__unicode__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_6VLType_8_nc_type___get__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_6VLType_8_nc_type_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_6VLType_5dtype___get__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_6VLType_5dtype_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_6VLType_5dtype_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_6VLType_4name___get__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_6VLType_4name_2__set__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
+static int __pyx_pf_7netCDF4_8_netCDF4_6VLType_4name_4__del__(struct __pyx_obj_7netCDF4_8_netCDF4_VLType *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_6_dateparse(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_timestr); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_8stringtoarr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_string, PyObject *__pyx_v_NUMCHARS, PyObject *__pyx_v_dtype); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_10stringtochar(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_a); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_12chartostring(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_b); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_14date2num(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_dates, PyObject *__pyx_v_units, PyObject *__pyx_v_calendar); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_16num2date(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_times, PyObject *__pyx_v_units, PyObject *__pyx_v_calendar); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_18date2index(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_dates, PyObject *__pyx_v_nctime, PyObject *__pyx_v_calendar, PyObject *__pyx_v_select); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9MFDataset_12__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9MFDataset___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_files, PyObject *__pyx_v_check, PyObject *__pyx_v_aggdim, PyObject *__pyx_v_exclude); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9MFDataset_2__setattr__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9MFDataset_4__getattribute__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9MFDataset_6ncattrs(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9MFDataset_8close(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9MFDataset_10__repr__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_10_Dimension___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_dimname, CYTHON_UNUSED PyObject *__pyx_v_dim, PyObject *__pyx_v_dimlens, PyObject *__pyx_v_dimtotlen); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_10_Dimension_2__len__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_10_Dimension_4isunlimited(CYTHON_UNUSED PyObject *__pyx_self, CYTHON_UNUSED PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_10_Dimension_6__repr__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9_Variable___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_dset, PyObject *__pyx_v_varname, PyObject *__pyx_v_var, PyObject *__pyx_v_recdimname); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9_Variable_2typecode(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9_Variable_4ncattrs(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9_Variable_6__getattr__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9_Variable_8__repr__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9_Variable_10__len__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9_Variable_12_shape(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9_Variable_14set_auto_maskandscale(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_val); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_9_Variable_16__getitem__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_elem); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_6MFTime___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_time, PyObject *__pyx_v_units); /* proto */
+static PyObject *__pyx_pf_7netCDF4_8_netCDF4_6MFTime_2__getitem__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_elem); /* proto */
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_Dataset(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_Group(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_Dimension(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_Variable(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_CompoundType(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_VLType(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static char __pyx_k_[] = "\000";
static char __pyx_k_1[] = "1";
static char __pyx_k_O[] = "O";
static char __pyx_k_S[] = "S";
@@ -1340,7 +1349,7 @@ static char __pyx_k_f[] = "f";
static char __pyx_k_i[] = "i";
static char __pyx_k_k[] = "k";
static char __pyx_k_n[] = "n";
-static char __pyx_k_r[] = "%r\n";
+static char __pyx_k_r[] = "r";
static char __pyx_k_s[] = "(%s)";
static char __pyx_k_t[] = "t";
static char __pyx_k_u[] = "u'";
@@ -1350,6 +1359,7 @@ static char __pyx_k_0m[] = "\033[0m";
static char __pyx_k_4m[] = " \033[4m";
static char __pyx_k_S1[] = "S1";
static char __pyx_k_U1[] = "U1";
+static char __pyx_k__2[] = "";
static char __pyx_k_as[] = "as";
static char __pyx_k_bs[] = "bs";
static char __pyx_k_dt[] = "dt";
@@ -1370,19 +1380,17 @@ static char __pyx_k_u2[] = "u2";
static char __pyx_k_u4[] = "u4";
static char __pyx_k_u8[] = "u8";
static char __pyx_k_ws[] = "ws";
+static char __pyx_k__12[] = "<";
+static char __pyx_k__13[] = ">";
static char __pyx_k__14[] = "/";
-static char __pyx_k__15[] = "";
-static char __pyx_k__24[] = "\000";
-static char __pyx_k__34[] = "<";
-static char __pyx_k__35[] = ">";
-static char __pyx_k__37[] = "'";
-static char __pyx_k__39[] = ", ";
-static char __pyx_k__40[] = ",";
-static char __pyx_k__42[] = ",)";
-static char __pyx_k__43[] = ")";
-static char __pyx_k__45[] = "__";
-static char __pyx_k__52[] = "=";
-static char __pyx_k__53[] = "|";
+static char __pyx_k__17[] = "'";
+static char __pyx_k__19[] = ", ";
+static char __pyx_k__20[] = ",";
+static char __pyx_k__22[] = ",)";
+static char __pyx_k__23[] = ")";
+static char __pyx_k__26[] = "__";
+static char __pyx_k__33[] = "=";
+static char __pyx_k__34[] = "|";
static char __pyx_k_all[] = "all";
static char __pyx_k_any[] = "any";
static char __pyx_k_arr[] = "arr";
@@ -1394,7 +1402,7 @@ static char __pyx_k_day[] = "day";
static char __pyx_k_dim[] = "dim";
static char __pyx_k_doc[] = "__doc__";
static char __pyx_k_get[] = "_get";
-static char __pyx_k_grp[] = "_grp";
+static char __pyx_k_grp[] = "grp";
static char __pyx_k_idx[] = "idx";
static char __pyx_k_ind[] = "ind";
static char __pyx_k_key[] = "_key";
@@ -1404,8 +1412,8 @@ static char __pyx_k_msg[] = "msg";
static char __pyx_k_num[] = "num";
static char __pyx_k_pop[] = "pop";
static char __pyx_k_put[] = "put";
-static char __pyx_k_r_2[] = "r";
-static char __pyx_k_r_3[] = "r+";
+static char __pyx_k_r_2[] = "r+";
+static char __pyx_k_r_3[] = "%r\n";
static char __pyx_k_r_s[] = "r+s";
static char __pyx_k_s_2[] = "s";
static char __pyx_k_s_s[] = " %s: %s\n";
@@ -1417,7 +1425,7 @@ 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__115[] = ".";
+static char __pyx_k__118[] = ".";
static char __pyx_k_bool[] = "bool";
static char __pyx_k_cdfm[] = "cdfm";
static char __pyx_k_char[] = "char";
@@ -1452,7 +1460,7 @@ static char __pyx_k_name[] = "name";
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_path[] = "path";
static char __pyx_k_prod[] = "prod";
static char __pyx_k_repr[] = "__repr__";
static char __pyx_k_secs[] = "secs";
@@ -1474,7 +1482,7 @@ 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_7[] = "1.1.7";
+static char __pyx_k_1_1_8[] = "1.1.8";
static char __pyx_k_4_2_1[] = "4.2.1";
static char __pyx_k_align[] = "align";
static char __pyx_k_array[] = "array";
@@ -1492,9 +1500,8 @@ static char __pyx_k_empty[] = "empty";
static char __pyx_k_exact[] = "exact";
static char __pyx_k_files[] = "files";
static char __pyx_k_float[] = "float";
-static char __pyx_k_grp_2[] = "grp";
+static char __pyx_k_grp_2[] = "_grp";
static char __pyx_k_grpid[] = "_grpid";
-static char __pyx_k_hours[] = "hours";
static char __pyx_k_isnan[] = "isnan";
static char __pyx_k_items[] = "items";
static char __pyx_k_lower[] = "lower";
@@ -1508,7 +1515,7 @@ static char __pyx_k_range[] = "range";
static char __pyx_k_ravel[] = "ravel";
static char __pyx_k_redef[] = "_redef";
static char __pyx_k_round[] = "round";
-static char __pyx_k_s_s_s[] = "%s %s%s\n";
+static char __pyx_k_s_s_s[] = "%s %s(%s)\n";
static char __pyx_k_scale[] = "scale";
static char __pyx_k_shape[] = "shape";
static char __pyx_k_since[] = "since";
@@ -1520,6 +1527,7 @@ static char __pyx_k_tostr[] = "_tostr";
static char __pyx_k_tsecs[] = "tsecs";
static char __pyx_k_units[] = "units";
static char __pyx_k_utf_8[] = "utf-8";
+static char __pyx_k_utils[] = "utils";
static char __pyx_k_utime[] = "utime";
static char __pyx_k_vInst[] = "vInst";
static char __pyx_k_vName[] = "vName";
@@ -1556,7 +1564,7 @@ static char __pyx_k_nctime[] = "nctime";
static char __pyx_k_nelems[] = "nelems";
static char __pyx_k_object[] = "object";
static char __pyx_k_parent[] = "parent";
-static char __pyx_k_path_2[] = "path";
+static char __pyx_k_path_2[] = "_path";
static char __pyx_k_path_s[] = "path = %s\n";
static char __pyx_k_recLen[] = "_recLen";
static char __pyx_k_recVar[] = "_recVar";
@@ -1592,10 +1600,8 @@ static char __pyx_k_getdims[] = "_getdims";
static char __pyx_k_getitem[] = "__getitem__";
static char __pyx_k_getname[] = "_getname";
static char __pyx_k_group_s[] = "group %s:\n";
-static char __pyx_k_minutes[] = "minutes";
static char __pyx_k_nc_type[] = "_nc_type";
static char __pyx_k_ncattrs[] = "ncattrs";
-static char __pyx_k_netCDF4[] = "netCDF4";
static char __pyx_k_newname[] = "newname";
static char __pyx_k_offsets[] = "offsets";
static char __pyx_k_oldname[] = "oldname";
@@ -1606,7 +1612,7 @@ static char __pyx_k_python3[] = "python3";
static char __pyx_k_ref_num[] = "ref_num";
static char __pyx_k_replace[] = "replace";
static char __pyx_k_reshape[] = "reshape";
-static char __pyx_k_s_s_s_2[] = "%s %s(%s)\n";
+static char __pyx_k_s_s_s_2[] = "%s %s%s\n";
static char __pyx_k_seconds[] = "seconds";
static char __pyx_k_setattr[] = "__setattr__";
static char __pyx_k_shape_2[] = "_shape";
@@ -1638,12 +1644,14 @@ static char __pyx_k_endswith[] = "endswith";
static char __pyx_k_extShape[] = "extShape";
static char __pyx_k_filename[] = "filename";
static char __pyx_k_find_dim[] = "_find_dim";
-static char __pyx_k_groups_s[] = " groups = %s\n";
+static char __pyx_k_groups_s[] = " groups: %s\n";
static char __pyx_k_grpnames[] = "grpnames";
+static char __pyx_k_hr_units[] = "hr_units";
static char __pyx_k_ismasked[] = "ismasked";
static char __pyx_k_isscalar[] = "isscalar";
static char __pyx_k_itemsize[] = "itemsize";
static char __pyx_k_newSlice[] = "newSlice";
+static char __pyx_k_normpath[] = "normpath";
static char __pyx_k_num2date[] = "num2date";
static char __pyx_k_qualname[] = "__qualname__";
static char __pyx_k_quantize[] = "_quantize";
@@ -1665,6 +1673,7 @@ static char __pyx_k_cdfVLen_2[] = "cdfVLen";
static char __pyx_k_complevel[] = "complevel";
static char __pyx_k_datashape[] = "datashape";
static char __pyx_k_dateparse[] = "_dateparse";
+static char __pyx_k_day_units[] = "day_units";
static char __pyx_k_delncattr[] = "delncattr";
static char __pyx_k_dimtotlen[] = "dimtotlen";
static char __pyx_k_enumerate[] = "enumerate";
@@ -1675,11 +1684,13 @@ static char __pyx_k_itertools[] = "itertools";
static char __pyx_k_majorvers[] = "majorvers";
static char __pyx_k_mastervar[] = "_mastervar";
static char __pyx_k_metaclass[] = "__metaclass__";
+static char __pyx_k_min_units[] = "min_units";
static char __pyx_k_minorvers[] = "minorvers";
static char __pyx_k_npversion[] = "_npversion";
static char __pyx_k_nunlimdim[] = "_nunlimdim";
static char __pyx_k_posixpath[] = "posixpath";
static char __pyx_k_recdimlen[] = "recdimlen";
+static char __pyx_k_sec_units[] = "sec_units";
static char __pyx_k_setncattr[] = "setncattr";
static char __pyx_k_timedelta[] = "timedelta";
static char __pyx_k_totaltime[] = "totaltime";
@@ -1693,14 +1704,14 @@ static char __pyx_k_add_offset[] = "add_offset";
static char __pyx_k_aggDimName[] = "aggDimName";
static char __pyx_k_chunksizes[] = "chunksizes";
static char __pyx_k_contiguous[] = "contiguous";
-static char __pyx_k_data_model[] = "_data_model";
+static char __pyx_k_data_model[] = "data_model";
static char __pyx_k_date2index[] = "date2index";
static char __pyx_k_dimensions[] = "dimensions";
static char __pyx_k_dtype_name[] = "dtype_name";
static char __pyx_k_fill_value[] = "fill_value";
static char __pyx_k_filling_on[] = "filling on";
static char __pyx_k_fletcher32[] = "fletcher32";
-static char __pyx_k_groups_s_2[] = " groups: %s\n";
+static char __pyx_k_groups_s_2[] = " groups = %s\n";
static char __pyx_k_iscompound[] = "_iscompound";
static char __pyx_k_masterDims[] = "masterDims";
static char __pyx_k_masterType[] = "masterType";
@@ -1723,15 +1734,14 @@ 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_file_format[] = "_file_format";
+static char __pyx_k_file_format[] = "file_format";
static char __pyx_k_filling_off[] = "filling off\n";
static char __pyx_k_isprimitive[] = "_isprimitive";
static char __pyx_k_isunlimited[] = "isunlimited";
static char __pyx_k_keepweakref[] = "keepweakref";
static char __pyx_k_masterShape[] = "masterShape";
-static char __pyx_k_microsecond[] = "microsecond";
-static char __pyx_k_millisecond[] = "millisecond";
static char __pyx_k_ordereddict[] = "ordereddict";
static char __pyx_k_patchstring[] = "patchstring";
static char __pyx_k_releasevers[] = "releasevers";
@@ -1741,13 +1751,12 @@ static char __pyx_k_variables_s[] = " variables = %s\n";
static char __pyx_k_MFTime__time[] = "_MFTime__time";
static char __pyx_k_RuntimeError[] = "RuntimeError";
static char __pyx_k_chartostring[] = "chartostring";
-static char __pyx_k_data_model_2[] = "data_model";
+static char __pyx_k_data_model_2[] = "_data_model";
static char __pyx_k_dimensions_s[] = " dimensions = %s\n";
static char __pyx_k_getattribute[] = "__getattribute__";
static char __pyx_k_masked_array[] = "masked_array";
static char __pyx_k_masterRecVar[] = "masterRecVar";
static char __pyx_k_microseconds[] = "microseconds";
-static char __pyx_k_milliseconds[] = "milliseconds";
static char __pyx_k_private_atts[] = "_private_atts";
static char __pyx_k_recdimname_2[] = "_recdimname";
static char __pyx_k_scale_factor[] = "scale_factor";
@@ -1759,12 +1768,11 @@ static char __pyx_k_MFTime__delta[] = "_MFTime__delta";
static char __pyx_k_NETCDF3_64BIT[] = "NETCDF3_64BIT";
static char __pyx_k_datatype_name[] = "datatype_name";
static char __pyx_k_disk_format_2[] = "disk_format";
-static char __pyx_k_file_format_2[] = "file_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";
static char __pyx_k_missing_value[] = "missing_value";
static char __pyx_k_name_s_size_s[] = ": name = '%s', size = %s\n";
-static char __pyx_k_netCDF4_utils[] = "netCDF4_utils";
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";
@@ -1773,6 +1781,8 @@ static char __pyx_k_Variable___len[] = "_Variable.__len__";
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__";
+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_Dimension___len[] = "_Dimension.__len__";
@@ -1795,6 +1805,8 @@ static char __pyx_k_Variable_ncattrs[] = "_Variable.ncattrs";
static char __pyx_k_default_encoding[] = "default_encoding";
static char __pyx_k_default_fillvals[] = "default_fillvals";
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_MFDataset_ncattrs[] = "MFDataset.ncattrs";
static char __pyx_k_Variable_typecode[] = "_Variable.typecode";
@@ -1807,20 +1819,22 @@ 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_MFDataset___setattr[] = "MFDataset.__setattr__";
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_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";
+static char __pyx_k_unlimited_dimensions_s[] = "unlimited dimensions: %s\n";
+static char __pyx_k_unsupported_time_units[] = "unsupported time units";
static char __pyx_k_variables_dimensions_s[] = " variables(dimensions): %s\n";
static char __pyx_k_least_significant_digit[] = "least_significant_digit";
static char __pyx_k_no_since_in_unit_string[] = "no 'since' in unit_string";
static char __pyx_k_shape_cannot_be_altered[] = "shape cannot be altered";
static char __pyx_k_unlimited_name_s_size_s[] = " (unlimited): name = '%s', size = %s\n";
static char __pyx_k_MFDataset___getattribute[] = "MFDataset.__getattribute__";
-static char __pyx_k_unlimited_dimensions_s_2[] = "unlimited dimensions: %s\n";
+static char __pyx_k_unlimited_dimensions_s_2[] = "unlimited dimensions = %s\n";
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";
@@ -1828,13 +1842,13 @@ static char __pyx_k_s_not_a_valid_dimension_name[] = "%s not a valid dimension n
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_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_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_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_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_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 HD [...]
+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/utils.pyx";
+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_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";
@@ -1895,13 +1909,13 @@ 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_Users_jsw_python_netcdf4_python_2[] = "/Users/jsw/python/netcdf4-python.git/netCDF4.pyx";
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 PyObject *__pyx_kp_s_;
static PyObject *__pyx_kp_s_0m;
static PyObject *__pyx_kp_s_1;
-static PyObject *__pyx_kp_s_1_1_7;
+static PyObject *__pyx_kp_s_1_1_8;
static PyObject *__pyx_kp_s_4_2_1;
static PyObject *__pyx_kp_s_4m;
static PyObject *__pyx_n_s_AttributeError;
@@ -1956,7 +1970,6 @@ 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_kp_s_Users_jsw_python_netcdf4_python_2;
static PyObject *__pyx_n_s_V;
static PyObject *__pyx_n_s_ValueError;
static PyObject *__pyx_n_s_Variable;
@@ -1971,20 +1984,19 @@ static PyObject *__pyx_n_s_Variable_ncattrs;
static PyObject *__pyx_kp_s_Variable_object_no_longer_valid;
static PyObject *__pyx_n_s_Variable_set_auto_maskandscale;
static PyObject *__pyx_n_s_Variable_typecode;
-static PyObject *__pyx_kp_s__115;
+static PyObject *__pyx_kp_s__118;
+static PyObject *__pyx_kp_s__12;
+static PyObject *__pyx_kp_s__13;
static PyObject *__pyx_kp_s__14;
-static PyObject *__pyx_kp_s__15;
-static PyObject *__pyx_kp_s__24;
+static PyObject *__pyx_kp_s__17;
+static PyObject *__pyx_kp_s__19;
+static PyObject *__pyx_kp_s__2;
+static PyObject *__pyx_kp_s__20;
+static PyObject *__pyx_kp_s__22;
+static PyObject *__pyx_kp_s__23;
+static PyObject *__pyx_n_s__26;
+static PyObject *__pyx_kp_s__33;
static PyObject *__pyx_kp_s__34;
-static PyObject *__pyx_kp_s__35;
-static PyObject *__pyx_kp_s__37;
-static PyObject *__pyx_kp_s__39;
-static PyObject *__pyx_kp_s__40;
-static PyObject *__pyx_kp_s__42;
-static PyObject *__pyx_kp_s__43;
-static PyObject *__pyx_n_s__45;
-static PyObject *__pyx_kp_s__52;
-static PyObject *__pyx_kp_s__53;
static PyObject *__pyx_n_s_a;
static PyObject *__pyx_n_s_add_offset;
static PyObject *__pyx_n_s_aggDimId;
@@ -2049,6 +2061,7 @@ static PyObject *__pyx_n_s_concatenate;
static PyObject *__pyx_n_s_contiguous;
static PyObject *__pyx_n_s_copy;
static PyObject *__pyx_n_s_count;
+static PyObject *__pyx_n_s_createGroup;
static PyObject *__pyx_kp_s_created_using_fill_value_keyword;
static PyObject *__pyx_kp_s_current_shape_s;
static PyObject *__pyx_kp_s_current_size_s;
@@ -2070,6 +2083,7 @@ static PyObject *__pyx_n_s_dateparse;
static PyObject *__pyx_n_s_dates;
static PyObject *__pyx_n_s_datetime;
static PyObject *__pyx_n_s_day;
+static PyObject *__pyx_n_s_day_units;
static PyObject *__pyx_n_s_days;
static PyObject *__pyx_n_s_decode;
static PyObject *__pyx_n_s_default_encoding;
@@ -2167,7 +2181,7 @@ static PyObject *__pyx_n_s_has_nc_inq_path;
static PyObject *__pyx_n_s_has_rename_grp;
static PyObject *__pyx_n_s_hdf5libversion;
static PyObject *__pyx_n_s_hour;
-static PyObject *__pyx_n_s_hours;
+static PyObject *__pyx_n_s_hr_units;
static PyObject *__pyx_n_s_http;
static PyObject *__pyx_n_s_i;
static PyObject *__pyx_n_s_i0;
@@ -2226,13 +2240,12 @@ static PyObject *__pyx_kp_s_master_dataset_s_does_not_have_a;
static PyObject *__pyx_kp_s_master_dataset_s_does_not_have_a_2;
static PyObject *__pyx_n_s_mastervar;
static PyObject *__pyx_n_s_metaclass;
-static PyObject *__pyx_n_s_microsecond;
+static PyObject *__pyx_n_s_microsec_units;
static PyObject *__pyx_n_s_microseconds;
-static PyObject *__pyx_n_s_millisecond;
-static PyObject *__pyx_n_s_milliseconds;
+static PyObject *__pyx_n_s_millisec_units;
+static PyObject *__pyx_n_s_min_units;
static PyObject *__pyx_n_s_minorvers;
static PyObject *__pyx_n_s_minute;
-static PyObject *__pyx_n_s_minutes;
static PyObject *__pyx_n_s_missing_value;
static PyObject *__pyx_n_s_mode;
static PyObject *__pyx_kp_s_mode_must_be_w_r_a_or_r_got_s;
@@ -2259,13 +2272,13 @@ static PyObject *__pyx_n_s_nctonptype;
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;
-static PyObject *__pyx_n_s_netCDF4_utils;
+static PyObject *__pyx_n_s_netCDF4__netCDF4;
static PyObject *__pyx_n_s_netcdf4libversion;
static PyObject *__pyx_n_s_netcdftime;
static PyObject *__pyx_n_s_newSlice;
static PyObject *__pyx_n_s_newname;
static PyObject *__pyx_kp_s_no_since_in_unit_string;
+static PyObject *__pyx_n_s_normpath;
static PyObject *__pyx_n_s_nptonctype;
static PyObject *__pyx_n_s_npversion;
static PyObject *__pyx_n_s_num;
@@ -2280,6 +2293,7 @@ static PyObject *__pyx_n_s_oldname;
static PyObject *__pyx_kp_s_only_endian_native_allowed_for_N;
static PyObject *__pyx_kp_s_only_numpy_string_unicode_or_obj;
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_n_s_parse_date;
@@ -2304,8 +2318,8 @@ static PyObject *__pyx_n_s_put_ind;
static PyObject *__pyx_n_s_python3;
static PyObject *__pyx_n_s_qualname;
static PyObject *__pyx_n_s_quantize;
-static PyObject *__pyx_kp_s_r;
-static PyObject *__pyx_n_s_r_2;
+static PyObject *__pyx_n_s_r;
+static PyObject *__pyx_kp_s_r_2;
static PyObject *__pyx_kp_s_r_3;
static PyObject *__pyx_kp_s_r_s;
static PyObject *__pyx_n_s_range;
@@ -2332,11 +2346,13 @@ static PyObject *__pyx_kp_s_s_is_one_of_the_reserved_attrib;
static PyObject *__pyx_kp_s_s_is_one_of_the_reserved_attrib_2;
static PyObject *__pyx_kp_s_s_not_a_valid_dimension_name;
static PyObject *__pyx_kp_s_s_not_a_valid_variable_name;
+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_n_s_scale;
static PyObject *__pyx_n_s_scale_factor;
+static PyObject *__pyx_n_s_sec_units;
static PyObject *__pyx_n_s_second;
static PyObject *__pyx_n_s_seconds;
static PyObject *__pyx_n_s_secs;
@@ -2418,8 +2434,10 @@ static PyObject *__pyx_kp_s_unlimited_dimensions_s_2;
static PyObject *__pyx_kp_s_unlimited_name_s_size_s;
static PyObject *__pyx_kp_s_unsupported_component_type_for_V;
static PyObject *__pyx_kp_s_unsupported_datatype_specified_f;
+static PyObject *__pyx_kp_s_unsupported_time_units;
static PyObject *__pyx_n_s_utc_offset;
static PyObject *__pyx_kp_s_utf_8;
+static PyObject *__pyx_n_s_utils;
static PyObject *__pyx_n_s_utime;
static PyObject *__pyx_n_s_v;
static PyObject *__pyx_n_s_vInst;
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static PyObject *__pyx_int_1900;
static PyObject *__pyx_int_3600;
static PyObject *__pyx_int_neg_1;
-static PyObject *__pyx_tuple_;
-static PyObject *__pyx_slice__6;
-static PyObject *__pyx_slice__8;
+static PyObject *__pyx_slice__7;
static PyObject *__pyx_slice__9;
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static PyObject *__pyx_tuple__3;
static PyObject *__pyx_tuple__4;
static PyObject *__pyx_tuple__5;
-static PyObject *__pyx_tuple__7;
+static PyObject *__pyx_tuple__6;
+static PyObject *__pyx_tuple__8;
static PyObject *__pyx_slice__10;
static PyObject *__pyx_slice__11;
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-static PyObject *__pyx_slice__29;
-static PyObject *__pyx_slice__31;
-static PyObject *__pyx_slice__32;
-static PyObject *__pyx_slice__33;
+static PyObject *__pyx_slice__36;
+static PyObject *__pyx_slice__37;
+static PyObject *__pyx_slice__42;
+static PyObject *__pyx_slice__43;
+static PyObject *__pyx_slice__50;
+static PyObject *__pyx_slice__51;
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-static PyObject *__pyx_slice__69;
-static PyObject *__pyx_slice__70;
-static PyObject *__pyx_slice__73;
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+static PyObject *__pyx_slice__66;
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+static PyObject *__pyx_slice__77;
static PyObject *__pyx_slice__80;
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-static PyObject *__pyx_slice__96;
-static PyObject *__pyx_slice__99;
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+static PyObject *__pyx_slice__92;
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static PyObject *__pyx_tuple__25;
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static PyObject *__pyx_tuple__27;
static PyObject *__pyx_tuple__28;
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static PyObject *__pyx_tuple__38;
+static PyObject *__pyx_tuple__39;
+static PyObject *__pyx_tuple__40;
static PyObject *__pyx_tuple__41;
static PyObject *__pyx_tuple__44;
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static PyObject *__pyx_tuple__46;
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static PyObject *__pyx_tuple__49;
-static PyObject *__pyx_tuple__50;
-static PyObject *__pyx_tuple__51;
-static PyObject *__pyx_tuple__54;
+static PyObject *__pyx_tuple__52;
+static PyObject *__pyx_tuple__53;
+static PyObject *__pyx_tuple__56;
static PyObject *__pyx_tuple__57;
static PyObject *__pyx_tuple__58;
static PyObject *__pyx_tuple__59;
static PyObject *__pyx_tuple__60;
-static PyObject *__pyx_tuple__63;
+static PyObject *__pyx_tuple__62;
static PyObject *__pyx_tuple__64;
-static PyObject *__pyx_tuple__65;
-static PyObject *__pyx_tuple__66;
-static PyObject *__pyx_tuple__67;
static PyObject *__pyx_tuple__68;
+static PyObject *__pyx_tuple__69;
+static PyObject *__pyx_tuple__70;
static PyObject *__pyx_tuple__71;
static PyObject *__pyx_tuple__72;
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static PyObject *__pyx_tuple__78;
static PyObject *__pyx_tuple__79;
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static PyObject *__pyx_tuple__83;
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static PyObject *__pyx_tuple__87;
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static PyObject *__pyx_tuple__93;
static PyObject *__pyx_tuple__94;
static PyObject *__pyx_tuple__95;
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static PyObject *__pyx_tuple__97;
static PyObject *__pyx_tuple__98;
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static PyObject *__pyx_slice__100;
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static PyObject *__pyx_slice__105;
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static PyObject *__pyx_codeobj__178;
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static PyObject *__pyx_codeobj__182;
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*/
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- PyObject *__pyx_v_utc_offset = NULL;
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+ __Pyx_AddTraceback("netCDF4._netCDF4.MFTime.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+ __pyx_r = NULL;
__pyx_L0:;
- __Pyx_XDECREF(__pyx_v_encoding);
__Pyx_XGIVEREF(__pyx_r);
__Pyx_RefNannyFinishContext();
return __pyx_r;
}
-static PyObject *__pyx_tp_new_7netCDF4_Dataset(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
- struct __pyx_obj_7netCDF4_Dataset *p;
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_Dataset(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *p;
PyObject *o;
if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
o = (*t->tp_alloc)(t, 0);
@@ -59403,7 +60425,7 @@ static PyObject *__pyx_tp_new_7netCDF4_Dataset(PyTypeObject *t, CYTHON_UNUSED Py
o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
}
if (unlikely(!o)) return 0;
- p = ((struct __pyx_obj_7netCDF4_Dataset *)o);
+ p = ((struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *)o);
p->groups = Py_None; Py_INCREF(Py_None);
p->dimensions = Py_None; Py_INCREF(Py_None);
p->variables = Py_None; Py_INCREF(Py_None);
@@ -59415,11 +60437,12 @@ static PyObject *__pyx_tp_new_7netCDF4_Dataset(PyTypeObject *t, CYTHON_UNUSED Py
p->cmptypes = Py_None; Py_INCREF(Py_None);
p->vltypes = Py_None; Py_INCREF(Py_None);
p->keepweakref = Py_None; Py_INCREF(Py_None);
+ p->__orthogonal_indexing__ = Py_None; Py_INCREF(Py_None);
return o;
}
-static void __pyx_tp_dealloc_7netCDF4_Dataset(PyObject *o) {
- struct __pyx_obj_7netCDF4_Dataset *p = (struct __pyx_obj_7netCDF4_Dataset *)o;
+static void __pyx_tp_dealloc_7netCDF4_8_netCDF4_Dataset(PyObject *o) {
+ struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *p = (struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *)o;
#if PY_VERSION_HEX >= 0x030400a1
if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
if (PyObject_CallFinalizerFromDealloc(o)) return;
@@ -59430,7 +60453,7 @@ static void __pyx_tp_dealloc_7netCDF4_Dataset(PyObject *o) {
PyObject *etype, *eval, *etb;
PyErr_Fetch(&etype, &eval, &etb);
++Py_REFCNT(o);
- __pyx_pw_7netCDF4_7Dataset_15__dealloc__(o);
+ __pyx_pw_7netCDF4_8_netCDF4_7Dataset_17__dealloc__(o);
--Py_REFCNT(o);
PyErr_Restore(etype, eval, etb);
}
@@ -59446,12 +60469,13 @@ static void __pyx_tp_dealloc_7netCDF4_Dataset(PyObject *o) {
Py_CLEAR(p->cmptypes);
Py_CLEAR(p->vltypes);
Py_CLEAR(p->keepweakref);
+ Py_CLEAR(p->__orthogonal_indexing__);
(*Py_TYPE(o)->tp_free)(o);
}
-static int __pyx_tp_traverse_7netCDF4_Dataset(PyObject *o, visitproc v, void *a) {
+static int __pyx_tp_traverse_7netCDF4_8_netCDF4_Dataset(PyObject *o, visitproc v, void *a) {
int e;
- struct __pyx_obj_7netCDF4_Dataset *p = (struct __pyx_obj_7netCDF4_Dataset *)o;
+ struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *p = (struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *)o;
if (p->groups) {
e = (*v)(p->groups, a); if (e) return e;
}
@@ -59485,12 +60509,15 @@ static int __pyx_tp_traverse_7netCDF4_Dataset(PyObject *o, visitproc v, void *a)
if (p->keepweakref) {
e = (*v)(p->keepweakref, a); if (e) return e;
}
+ if (p->__orthogonal_indexing__) {
+ e = (*v)(p->__orthogonal_indexing__, a); if (e) return e;
+ }
return 0;
}
-static int __pyx_tp_clear_7netCDF4_Dataset(PyObject *o) {
+static int __pyx_tp_clear_7netCDF4_8_netCDF4_Dataset(PyObject *o) {
PyObject* tmp;
- struct __pyx_obj_7netCDF4_Dataset *p = (struct __pyx_obj_7netCDF4_Dataset *)o;
+ struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *p = (struct __pyx_obj_7netCDF4_8_netCDF4_Dataset *)o;
tmp = ((PyObject*)p->groups);
p->groups = Py_None; Py_INCREF(Py_None);
Py_XDECREF(tmp);
@@ -59524,34 +60551,44 @@ static int __pyx_tp_clear_7netCDF4_Dataset(PyObject *o) {
tmp = ((PyObject*)p->keepweakref);
p->keepweakref = Py_None; Py_INCREF(Py_None);
Py_XDECREF(tmp);
+ tmp = ((PyObject*)p->__orthogonal_indexing__);
+ p->__orthogonal_indexing__ = Py_None; Py_INCREF(Py_None);
+ Py_XDECREF(tmp);
return 0;
}
+static PyObject *__pyx_sq_item_7netCDF4_8_netCDF4_Dataset(PyObject *o, Py_ssize_t i) {
+ PyObject *r;
+ PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
+ r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
+ Py_DECREF(x);
+ return r;
+}
-static PyObject *__pyx_tp_getattro_7netCDF4_Dataset(PyObject *o, PyObject *n) {
+static PyObject *__pyx_tp_getattro_7netCDF4_8_netCDF4_Dataset(PyObject *o, PyObject *n) {
PyObject *v = PyObject_GenericGetAttr(o, n);
if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) {
PyErr_Clear();
- v = __pyx_pw_7netCDF4_7Dataset_55__getattr__(o, n);
+ v = __pyx_pw_7netCDF4_8_netCDF4_7Dataset_57__getattr__(o, n);
}
return v;
}
-static int __pyx_tp_setattro_7netCDF4_Dataset(PyObject *o, PyObject *n, PyObject *v) {
+static int __pyx_tp_setattro_7netCDF4_8_netCDF4_Dataset(PyObject *o, PyObject *n, PyObject *v) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_53__setattr__(o, n, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_55__setattr__(o, n, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_49__delattr__(o, n);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_51__delattr__(o, n);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset__grpid(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_6_grpid_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset__grpid(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_6_grpid_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset__grpid(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset__grpid(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_6_grpid_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_6_grpid_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -59559,13 +60596,13 @@ static int __pyx_setprop_7netCDF4_7Dataset__grpid(PyObject *o, PyObject *v, CYTH
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset__isopen(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_7_isopen_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset__isopen(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_7_isopen_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset__isopen(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset__isopen(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_7_isopen_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_7_isopen_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -59573,204 +60610,237 @@ static int __pyx_setprop_7netCDF4_7Dataset__isopen(PyObject *o, PyObject *v, CYT
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_groups(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_6groups_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_groups(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_6groups_1__get__(o);
+}
+
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_groups(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+ if (v) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_6groups_3__set__(o, v);
+ }
+ else {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_6groups_5__del__(o);
+ }
+}
+
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_dimensions(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_10dimensions_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset_groups(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_dimensions(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_6groups_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_10dimensions_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_6groups_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_10dimensions_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_dimensions(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_10dimensions_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_variables(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_9variables_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset_dimensions(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_variables(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_10dimensions_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_9variables_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_10dimensions_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_9variables_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_variables(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_9variables_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_disk_format(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11disk_format_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset_variables(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_disk_format(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_9variables_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11disk_format_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_9variables_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11disk_format_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_disk_format(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_11disk_format_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_path(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_4path_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset_disk_format(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_path(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_11disk_format_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_4path_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_11disk_format_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_4path_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_path(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_4path_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_parent(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_6parent_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset_path(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_parent(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_4path_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_6parent_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_4path_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_6parent_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_parent(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_6parent_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_file_format(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11file_format_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset_parent(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_file_format(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_6parent_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11file_format_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_6parent_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11file_format_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_file_format(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_11file_format_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_data_model(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_10data_model_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset_file_format(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_data_model(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_11file_format_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_10data_model_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_11file_format_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_10data_model_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_data_model(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_10data_model_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_cmptypes(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_8cmptypes_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset_data_model(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_cmptypes(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_10data_model_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_8cmptypes_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_10data_model_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_8cmptypes_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_cmptypes(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_8cmptypes_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_7Dataset_vltypes(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_7vltypes_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_7Dataset_cmptypes(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_vltypes(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_8cmptypes_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_7vltypes_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_8cmptypes_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_7vltypes_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_vltypes(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_7vltypes_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_7Dataset_vltypes(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_7Dataset_7vltypes_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11keepweakref_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_7vltypes_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11keepweakref_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_7Dataset_keepweakref(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_7Dataset_11keepweakref_1__get__(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 int __pyx_setprop_7netCDF4_7Dataset_keepweakref(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_7Dataset___orthogonal_indexing__(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_7Dataset_11keepweakref_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_23__orthogonal_indexing___3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_7Dataset_11keepweakref_5__del__(o);
- }
-}
-
-static PyMethodDef __pyx_methods_7netCDF4_Dataset[] = {
- {"__enter__", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_3__enter__, METH_NOARGS, 0},
- {"__exit__", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_5__exit__, METH_VARARGS|METH_KEYWORDS, 0},
- {"filepath", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_7filepath, METH_NOARGS, __pyx_doc_7netCDF4_7Dataset_6filepath},
- {"__unicode__", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_11__unicode__, METH_NOARGS, 0},
- {"close", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_13close, METH_NOARGS, __pyx_doc_7netCDF4_7Dataset_12close},
- {"sync", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_17sync, METH_NOARGS, __pyx_doc_7netCDF4_7Dataset_16sync},
- {"_redef", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_19_redef, METH_NOARGS, 0},
- {"_enddef", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_21_enddef, METH_NOARGS, 0},
- {"set_fill_on", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_23set_fill_on, METH_NOARGS, __pyx_doc_7netCDF4_7Dataset_22set_fill_on},
- {"set_fill_off", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_25set_fill_off, METH_NOARGS, __pyx_doc_7netCDF4_7Dataset_24set_fill_off},
- {"createDimension", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_27createDimension, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_7Dataset_26createDimension},
- {"renameDimension", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_29renameDimension, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_7Dataset_28renameDimension},
- {"createCompoundType", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_31createCompoundType, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_7Dataset_30createCompoundType},
- {"createVLType", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_33createVLType, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_7Dataset_32createVLType},
- {"createVariable", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_35createVariable, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_7Dataset_34createVariable},
- {"renameVariable", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_37renameVariable, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_7Dataset_36renameVariable},
- {"createGroup", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_39createGroup, METH_O, __pyx_doc_7netCDF4_7Dataset_38createGroup},
- {"ncattrs", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_41ncattrs, METH_NOARGS, __pyx_doc_7netCDF4_7Dataset_40ncattrs},
- {"setncattr", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_43setncattr, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_7Dataset_42setncattr},
- {"setncatts", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_45setncatts, METH_O, __pyx_doc_7netCDF4_7Dataset_44setncatts},
- {"getncattr", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_47getncattr, METH_O, __pyx_doc_7netCDF4_7Dataset_46getncattr},
- {"delncattr", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_51delncattr, METH_O, __pyx_doc_7netCDF4_7Dataset_50delncattr},
- {"__getattr__", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_55__getattr__, METH_O|METH_COEXIST, 0},
- {"renameAttribute", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_57renameAttribute, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_7Dataset_56renameAttribute},
- {"renameGroup", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_59renameGroup, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_7Dataset_58renameGroup},
- {"set_auto_maskandscale", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_61set_auto_maskandscale, METH_O, __pyx_doc_7netCDF4_7Dataset_60set_auto_maskandscale},
- {"set_auto_mask", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_63set_auto_mask, METH_O, __pyx_doc_7netCDF4_7Dataset_62set_auto_mask},
- {"set_auto_scale", (PyCFunction)__pyx_pw_7netCDF4_7Dataset_65set_auto_scale, METH_O, __pyx_doc_7netCDF4_7Dataset_64set_auto_scale},
+ return __pyx_pw_7netCDF4_8_netCDF4_7Dataset_23__orthogonal_indexing___5__del__(o);
+ }
+}
+
+static PyMethodDef __pyx_methods_7netCDF4_8_netCDF4_Dataset[] = {
+ {"__enter__", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_3__enter__, METH_NOARGS, 0},
+ {"__exit__", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_5__exit__, METH_VARARGS|METH_KEYWORDS, 0},
+ {"filepath", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_9filepath, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_8filepath},
+ {"__unicode__", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_13__unicode__, METH_NOARGS, 0},
+ {"close", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_15close, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_14close},
+ {"sync", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_19sync, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_18sync},
+ {"_redef", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_21_redef, METH_NOARGS, 0},
+ {"_enddef", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_23_enddef, METH_NOARGS, 0},
+ {"set_fill_on", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_25set_fill_on, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_24set_fill_on},
+ {"set_fill_off", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_27set_fill_off, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_26set_fill_off},
+ {"createDimension", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_29createDimension, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_28createDimension},
+ {"renameDimension", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_31renameDimension, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_30renameDimension},
+ {"createCompoundType", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_33createCompoundType, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_32createCompoundType},
+ {"createVLType", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_35createVLType, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_34createVLType},
+ {"createVariable", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_37createVariable, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_36createVariable},
+ {"renameVariable", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_39renameVariable, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_38renameVariable},
+ {"createGroup", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_41createGroup, METH_O, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_40createGroup},
+ {"ncattrs", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_43ncattrs, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_42ncattrs},
+ {"setncattr", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_45setncattr, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_44setncattr},
+ {"setncatts", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_47setncatts, METH_O, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_46setncatts},
+ {"getncattr", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_49getncattr, METH_O, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_48getncattr},
+ {"delncattr", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_53delncattr, METH_O, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_52delncattr},
+ {"__getattr__", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_57__getattr__, METH_O|METH_COEXIST, 0},
+ {"renameAttribute", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_59renameAttribute, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_58renameAttribute},
+ {"renameGroup", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_61renameGroup, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_60renameGroup},
+ {"set_auto_maskandscale", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_63set_auto_maskandscale, METH_O, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_62set_auto_maskandscale},
+ {"set_auto_mask", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_65set_auto_mask, METH_O, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_64set_auto_mask},
+ {"set_auto_scale", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_7Dataset_67set_auto_scale, METH_O, __pyx_doc_7netCDF4_8_netCDF4_7Dataset_66set_auto_scale},
{0, 0, 0, 0}
};
-static struct PyGetSetDef __pyx_getsets_7netCDF4_Dataset[] = {
- {(char *)"_grpid", __pyx_getprop_7netCDF4_7Dataset__grpid, __pyx_setprop_7netCDF4_7Dataset__grpid, 0, 0},
- {(char *)"_isopen", __pyx_getprop_7netCDF4_7Dataset__isopen, __pyx_setprop_7netCDF4_7Dataset__isopen, 0, 0},
- {(char *)"groups", __pyx_getprop_7netCDF4_7Dataset_groups, __pyx_setprop_7netCDF4_7Dataset_groups, 0, 0},
- {(char *)"dimensions", __pyx_getprop_7netCDF4_7Dataset_dimensions, __pyx_setprop_7netCDF4_7Dataset_dimensions, 0, 0},
- {(char *)"variables", __pyx_getprop_7netCDF4_7Dataset_variables, __pyx_setprop_7netCDF4_7Dataset_variables, 0, 0},
- {(char *)"disk_format", __pyx_getprop_7netCDF4_7Dataset_disk_format, __pyx_setprop_7netCDF4_7Dataset_disk_format, 0, 0},
- {(char *)"path", __pyx_getprop_7netCDF4_7Dataset_path, __pyx_setprop_7netCDF4_7Dataset_path, 0, 0},
- {(char *)"parent", __pyx_getprop_7netCDF4_7Dataset_parent, __pyx_setprop_7netCDF4_7Dataset_parent, 0, 0},
- {(char *)"file_format", __pyx_getprop_7netCDF4_7Dataset_file_format, __pyx_setprop_7netCDF4_7Dataset_file_format, 0, 0},
- {(char *)"data_model", __pyx_getprop_7netCDF4_7Dataset_data_model, __pyx_setprop_7netCDF4_7Dataset_data_model, 0, 0},
- {(char *)"cmptypes", __pyx_getprop_7netCDF4_7Dataset_cmptypes, __pyx_setprop_7netCDF4_7Dataset_cmptypes, 0, 0},
- {(char *)"vltypes", __pyx_getprop_7netCDF4_7Dataset_vltypes, __pyx_setprop_7netCDF4_7Dataset_vltypes, 0, 0},
- {(char *)"keepweakref", __pyx_getprop_7netCDF4_7Dataset_keepweakref, __pyx_setprop_7netCDF4_7Dataset_keepweakref, 0, 0},
+static struct PyGetSetDef __pyx_getsets_7netCDF4_8_netCDF4_Dataset[] = {
+ {(char *)"_grpid", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset__grpid, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset__grpid, 0, 0},
+ {(char *)"_isopen", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset__isopen, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset__isopen, 0, 0},
+ {(char *)"groups", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_groups, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_groups, 0, 0},
+ {(char *)"dimensions", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_dimensions, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_dimensions, 0, 0},
+ {(char *)"variables", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_variables, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_variables, 0, 0},
+ {(char *)"disk_format", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_disk_format, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_disk_format, 0, 0},
+ {(char *)"path", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_path, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_path, 0, 0},
+ {(char *)"parent", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_parent, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_parent, 0, 0},
+ {(char *)"file_format", __pyx_getprop_7netCDF4_8_netCDF4_7Dataset_file_format, __pyx_setprop_7netCDF4_8_netCDF4_7Dataset_file_format, 0, 0},
+ {(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},
{0, 0, 0, 0, 0}
};
-static PyTypeObject __pyx_type_7netCDF4_Dataset = {
+static PySequenceMethods __pyx_tp_as_sequence_Dataset = {
+ 0, /*sq_length*/
+ 0, /*sq_concat*/
+ 0, /*sq_repeat*/
+ __pyx_sq_item_7netCDF4_8_netCDF4_Dataset, /*sq_item*/
+ 0, /*sq_slice*/
+ 0, /*sq_ass_item*/
+ 0, /*sq_ass_slice*/
+ 0, /*sq_contains*/
+ 0, /*sq_inplace_concat*/
+ 0, /*sq_inplace_repeat*/
+};
+
+static PyMappingMethods __pyx_tp_as_mapping_Dataset = {
+ 0, /*mp_length*/
+ __pyx_pw_7netCDF4_8_netCDF4_7Dataset_7__getitem__, /*mp_subscript*/
+ 0, /*mp_ass_subscript*/
+};
+
+static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_Dataset = {
PyVarObject_HEAD_INIT(0, 0)
- "netCDF4.Dataset", /*tp_name*/
- sizeof(struct __pyx_obj_7netCDF4_Dataset), /*tp_basicsize*/
+ "netCDF4._netCDF4.Dataset", /*tp_name*/
+ sizeof(struct __pyx_obj_7netCDF4_8_netCDF4_Dataset), /*tp_basicsize*/
0, /*tp_itemsize*/
- __pyx_tp_dealloc_7netCDF4_Dataset, /*tp_dealloc*/
+ __pyx_tp_dealloc_7netCDF4_8_netCDF4_Dataset, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
@@ -59779,35 +60849,35 @@ static PyTypeObject __pyx_type_7netCDF4_Dataset = {
#else
0, /*reserved*/
#endif
- __pyx_pw_7netCDF4_7Dataset_9__repr__, /*tp_repr*/
+ __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11__repr__, /*tp_repr*/
0, /*tp_as_number*/
- 0, /*tp_as_sequence*/
- 0, /*tp_as_mapping*/
+ &__pyx_tp_as_sequence_Dataset, /*tp_as_sequence*/
+ &__pyx_tp_as_mapping_Dataset, /*tp_as_mapping*/
0, /*tp_hash*/
0, /*tp_call*/
0, /*tp_str*/
- __pyx_tp_getattro_7netCDF4_Dataset, /*tp_getattro*/
- __pyx_tp_setattro_7netCDF4_Dataset, /*tp_setattro*/
+ __pyx_tp_getattro_7netCDF4_8_netCDF4_Dataset, /*tp_getattro*/
+ __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} mean [...]
- __pyx_tp_traverse_7netCDF4_Dataset, /*tp_traverse*/
- __pyx_tp_clear_7netCDF4_Dataset, /*tp_clear*/
+ "\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 [...]
+ __pyx_tp_traverse_7netCDF4_8_netCDF4_Dataset, /*tp_traverse*/
+ __pyx_tp_clear_7netCDF4_8_netCDF4_Dataset, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
0, /*tp_iternext*/
- __pyx_methods_7netCDF4_Dataset, /*tp_methods*/
+ __pyx_methods_7netCDF4_8_netCDF4_Dataset, /*tp_methods*/
0, /*tp_members*/
- __pyx_getsets_7netCDF4_Dataset, /*tp_getset*/
+ __pyx_getsets_7netCDF4_8_netCDF4_Dataset, /*tp_getset*/
0, /*tp_base*/
0, /*tp_dict*/
0, /*tp_descr_get*/
0, /*tp_descr_set*/
0, /*tp_dictoffset*/
- __pyx_pw_7netCDF4_7Dataset_1__init__, /*tp_init*/
+ __pyx_pw_7netCDF4_8_netCDF4_7Dataset_1__init__, /*tp_init*/
0, /*tp_alloc*/
- __pyx_tp_new_7netCDF4_Dataset, /*tp_new*/
+ __pyx_tp_new_7netCDF4_8_netCDF4_Dataset, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
@@ -59822,19 +60892,19 @@ static PyTypeObject __pyx_type_7netCDF4_Dataset = {
#endif
};
-static PyObject *__pyx_tp_new_7netCDF4_Group(PyTypeObject *t, PyObject *a, PyObject *k) {
- PyObject *o = __pyx_tp_new_7netCDF4_Dataset(t, a, k);
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_Group(PyTypeObject *t, PyObject *a, PyObject *k) {
+ PyObject *o = __pyx_tp_new_7netCDF4_8_netCDF4_Dataset(t, a, k);
if (unlikely(!o)) return 0;
return o;
}
-static PyObject *__pyx_getprop_7netCDF4_5Group_name(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_5Group_4name_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_5Group_name(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_5Group_4name_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_5Group_name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_5Group_name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_5Group_4name_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_5Group_4name_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -59842,23 +60912,23 @@ static int __pyx_setprop_7netCDF4_5Group_name(PyObject *o, PyObject *v, CYTHON_U
}
}
-static PyMethodDef __pyx_methods_7netCDF4_Group[] = {
- {"close", (PyCFunction)__pyx_pw_7netCDF4_5Group_3close, METH_NOARGS, __pyx_doc_7netCDF4_5Group_2close},
- {"_getname", (PyCFunction)__pyx_pw_7netCDF4_5Group_5_getname, METH_NOARGS, 0},
+static PyMethodDef __pyx_methods_7netCDF4_8_netCDF4_Group[] = {
+ {"close", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_5Group_3close, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_5Group_2close},
+ {"_getname", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_5Group_5_getname, METH_NOARGS, 0},
{0, 0, 0, 0}
};
-static struct PyGetSetDef __pyx_getsets_7netCDF4_Group[] = {
- {(char *)"name", __pyx_getprop_7netCDF4_5Group_name, __pyx_setprop_7netCDF4_5Group_name, __pyx_k_string_name_of_Group_instance, 0},
+static struct PyGetSetDef __pyx_getsets_7netCDF4_8_netCDF4_Group[] = {
+ {(char *)"name", __pyx_getprop_7netCDF4_8_netCDF4_5Group_name, __pyx_setprop_7netCDF4_8_netCDF4_5Group_name, __pyx_k_string_name_of_Group_instance, 0},
{0, 0, 0, 0, 0}
};
-static PyTypeObject __pyx_type_7netCDF4_Group = {
+static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_Group = {
PyVarObject_HEAD_INIT(0, 0)
- "netCDF4.Group", /*tp_name*/
- sizeof(struct __pyx_obj_7netCDF4_Group), /*tp_basicsize*/
+ "netCDF4._netCDF4.Group", /*tp_name*/
+ sizeof(struct __pyx_obj_7netCDF4_8_netCDF4_Group), /*tp_basicsize*/
0, /*tp_itemsize*/
- __pyx_tp_dealloc_7netCDF4_Dataset, /*tp_dealloc*/
+ __pyx_tp_dealloc_7netCDF4_8_netCDF4_Dataset, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
@@ -59868,7 +60938,7 @@ static PyTypeObject __pyx_type_7netCDF4_Group = {
0, /*reserved*/
#endif
#if CYTHON_COMPILING_IN_PYPY
- __pyx_pw_7netCDF4_7Dataset_9__repr__, /*tp_repr*/
+ __pyx_pw_7netCDF4_8_netCDF4_7Dataset_11__repr__, /*tp_repr*/
#else
0, /*tp_repr*/
#endif
@@ -59882,24 +60952,24 @@ static PyTypeObject __pyx_type_7netCDF4_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{Paramet [...]
- __pyx_tp_traverse_7netCDF4_Dataset, /*tp_traverse*/
- __pyx_tp_clear_7netCDF4_Dataset, /*tp_clear*/
+ "\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: [...]
+ __pyx_tp_traverse_7netCDF4_8_netCDF4_Dataset, /*tp_traverse*/
+ __pyx_tp_clear_7netCDF4_8_netCDF4_Dataset, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
0, /*tp_iternext*/
- __pyx_methods_7netCDF4_Group, /*tp_methods*/
+ __pyx_methods_7netCDF4_8_netCDF4_Group, /*tp_methods*/
0, /*tp_members*/
- __pyx_getsets_7netCDF4_Group, /*tp_getset*/
+ __pyx_getsets_7netCDF4_8_netCDF4_Group, /*tp_getset*/
0, /*tp_base*/
0, /*tp_dict*/
0, /*tp_descr_get*/
0, /*tp_descr_set*/
0, /*tp_dictoffset*/
- __pyx_pw_7netCDF4_5Group_1__init__, /*tp_init*/
+ __pyx_pw_7netCDF4_8_netCDF4_5Group_1__init__, /*tp_init*/
0, /*tp_alloc*/
- __pyx_tp_new_7netCDF4_Group, /*tp_new*/
+ __pyx_tp_new_7netCDF4_8_netCDF4_Group, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
@@ -59914,8 +60984,8 @@ static PyTypeObject __pyx_type_7netCDF4_Group = {
#endif
};
-static PyObject *__pyx_tp_new_7netCDF4_Dimension(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
- struct __pyx_obj_7netCDF4_Dimension *p;
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_Dimension(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *p;
PyObject *o;
if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
o = (*t->tp_alloc)(t, 0);
@@ -59923,15 +60993,15 @@ static PyObject *__pyx_tp_new_7netCDF4_Dimension(PyTypeObject *t, CYTHON_UNUSED
o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
}
if (unlikely(!o)) return 0;
- p = ((struct __pyx_obj_7netCDF4_Dimension *)o);
+ p = ((struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *)o);
p->_data_model = Py_None; Py_INCREF(Py_None);
p->_name = Py_None; Py_INCREF(Py_None);
p->_grp = Py_None; Py_INCREF(Py_None);
return o;
}
-static void __pyx_tp_dealloc_7netCDF4_Dimension(PyObject *o) {
- struct __pyx_obj_7netCDF4_Dimension *p = (struct __pyx_obj_7netCDF4_Dimension *)o;
+static void __pyx_tp_dealloc_7netCDF4_8_netCDF4_Dimension(PyObject *o) {
+ struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *p = (struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *)o;
#if PY_VERSION_HEX >= 0x030400a1
if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
if (PyObject_CallFinalizerFromDealloc(o)) return;
@@ -59944,9 +61014,9 @@ static void __pyx_tp_dealloc_7netCDF4_Dimension(PyObject *o) {
(*Py_TYPE(o)->tp_free)(o);
}
-static int __pyx_tp_traverse_7netCDF4_Dimension(PyObject *o, visitproc v, void *a) {
+static int __pyx_tp_traverse_7netCDF4_8_netCDF4_Dimension(PyObject *o, visitproc v, void *a) {
int e;
- struct __pyx_obj_7netCDF4_Dimension *p = (struct __pyx_obj_7netCDF4_Dimension *)o;
+ struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *p = (struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *)o;
if (p->_data_model) {
e = (*v)(p->_data_model, a); if (e) return e;
}
@@ -59959,9 +61029,9 @@ static int __pyx_tp_traverse_7netCDF4_Dimension(PyObject *o, visitproc v, void *
return 0;
}
-static int __pyx_tp_clear_7netCDF4_Dimension(PyObject *o) {
+static int __pyx_tp_clear_7netCDF4_8_netCDF4_Dimension(PyObject *o) {
PyObject* tmp;
- struct __pyx_obj_7netCDF4_Dimension *p = (struct __pyx_obj_7netCDF4_Dimension *)o;
+ struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *p = (struct __pyx_obj_7netCDF4_8_netCDF4_Dimension *)o;
tmp = ((PyObject*)p->_data_model);
p->_data_model = Py_None; Py_INCREF(Py_None);
Py_XDECREF(tmp);
@@ -59974,13 +61044,13 @@ static int __pyx_tp_clear_7netCDF4_Dimension(PyObject *o) {
return 0;
}
-static PyObject *__pyx_getprop_7netCDF4_9Dimension_name(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_9Dimension_4name_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_9Dimension_name(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_4name_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_9Dimension_name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_9Dimension_name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_9Dimension_4name_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_4name_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -59988,13 +61058,13 @@ static int __pyx_setprop_7netCDF4_9Dimension_name(PyObject *o, PyObject *v, CYTH
}
}
-static PyObject *__pyx_getprop_7netCDF4_9Dimension__dimid(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_9Dimension_6_dimid_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_9Dimension__dimid(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_6_dimid_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_9Dimension__dimid(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_9Dimension__dimid(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_9Dimension_6_dimid_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_6_dimid_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -60002,13 +61072,13 @@ static int __pyx_setprop_7netCDF4_9Dimension__dimid(PyObject *o, PyObject *v, CY
}
}
-static PyObject *__pyx_getprop_7netCDF4_9Dimension__grpid(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_9Dimension_6_grpid_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_9Dimension__grpid(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_6_grpid_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_9Dimension__grpid(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_9Dimension__grpid(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_9Dimension_6_grpid_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_6_grpid_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -60016,65 +61086,65 @@ static int __pyx_setprop_7netCDF4_9Dimension__grpid(PyObject *o, PyObject *v, CY
}
}
-static PyObject *__pyx_getprop_7netCDF4_9Dimension__data_model(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_9Dimension_11_data_model_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_9Dimension__data_model(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_11_data_model_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_9Dimension__data_model(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_9Dimension__data_model(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_9Dimension_11_data_model_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_11_data_model_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_9Dimension_11_data_model_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_11_data_model_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_9Dimension__name(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_9Dimension_5_name_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_9Dimension__name(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_5_name_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_9Dimension__name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_9Dimension__name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_9Dimension_5_name_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_5_name_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_9Dimension_5_name_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_5_name_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_9Dimension__grp(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_9Dimension_4_grp_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_9Dimension__grp(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_4_grp_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_9Dimension__grp(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_9Dimension__grp(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_9Dimension_4_grp_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_4_grp_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_9Dimension_4_grp_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_9Dimension_4_grp_5__del__(o);
}
}
-static PyMethodDef __pyx_methods_7netCDF4_Dimension[] = {
- {"_getname", (PyCFunction)__pyx_pw_7netCDF4_9Dimension_3_getname, METH_NOARGS, 0},
- {"__unicode__", (PyCFunction)__pyx_pw_7netCDF4_9Dimension_7__unicode__, METH_NOARGS, 0},
- {"group", (PyCFunction)__pyx_pw_7netCDF4_9Dimension_11group, METH_NOARGS, __pyx_doc_7netCDF4_9Dimension_10group},
- {"isunlimited", (PyCFunction)__pyx_pw_7netCDF4_9Dimension_13isunlimited, METH_NOARGS, __pyx_doc_7netCDF4_9Dimension_12isunlimited},
+static PyMethodDef __pyx_methods_7netCDF4_8_netCDF4_Dimension[] = {
+ {"_getname", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_9Dimension_3_getname, METH_NOARGS, 0},
+ {"__unicode__", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_9Dimension_7__unicode__, METH_NOARGS, 0},
+ {"group", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_9Dimension_11group, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_9Dimension_10group},
+ {"isunlimited", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_9Dimension_13isunlimited, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_9Dimension_12isunlimited},
{0, 0, 0, 0}
};
-static struct PyGetSetDef __pyx_getsets_7netCDF4_Dimension[] = {
- {(char *)"name", __pyx_getprop_7netCDF4_9Dimension_name, __pyx_setprop_7netCDF4_9Dimension_name, __pyx_k_string_name_of_Dimension_instanc, 0},
- {(char *)"_dimid", __pyx_getprop_7netCDF4_9Dimension__dimid, __pyx_setprop_7netCDF4_9Dimension__dimid, 0, 0},
- {(char *)"_grpid", __pyx_getprop_7netCDF4_9Dimension__grpid, __pyx_setprop_7netCDF4_9Dimension__grpid, 0, 0},
- {(char *)"_data_model", __pyx_getprop_7netCDF4_9Dimension__data_model, __pyx_setprop_7netCDF4_9Dimension__data_model, 0, 0},
- {(char *)"_name", __pyx_getprop_7netCDF4_9Dimension__name, __pyx_setprop_7netCDF4_9Dimension__name, 0, 0},
- {(char *)"_grp", __pyx_getprop_7netCDF4_9Dimension__grp, __pyx_setprop_7netCDF4_9Dimension__grp, 0, 0},
+static struct PyGetSetDef __pyx_getsets_7netCDF4_8_netCDF4_Dimension[] = {
+ {(char *)"name", __pyx_getprop_7netCDF4_8_netCDF4_9Dimension_name, __pyx_setprop_7netCDF4_8_netCDF4_9Dimension_name, __pyx_k_string_name_of_Dimension_instanc, 0},
+ {(char *)"_dimid", __pyx_getprop_7netCDF4_8_netCDF4_9Dimension__dimid, __pyx_setprop_7netCDF4_8_netCDF4_9Dimension__dimid, 0, 0},
+ {(char *)"_grpid", __pyx_getprop_7netCDF4_8_netCDF4_9Dimension__grpid, __pyx_setprop_7netCDF4_8_netCDF4_9Dimension__grpid, 0, 0},
+ {(char *)"_data_model", __pyx_getprop_7netCDF4_8_netCDF4_9Dimension__data_model, __pyx_setprop_7netCDF4_8_netCDF4_9Dimension__data_model, 0, 0},
+ {(char *)"_name", __pyx_getprop_7netCDF4_8_netCDF4_9Dimension__name, __pyx_setprop_7netCDF4_8_netCDF4_9Dimension__name, 0, 0},
+ {(char *)"_grp", __pyx_getprop_7netCDF4_8_netCDF4_9Dimension__grp, __pyx_setprop_7netCDF4_8_netCDF4_9Dimension__grp, 0, 0},
{0, 0, 0, 0, 0}
};
static PySequenceMethods __pyx_tp_as_sequence_Dimension = {
- __pyx_pw_7netCDF4_9Dimension_9__len__, /*sq_length*/
+ __pyx_pw_7netCDF4_8_netCDF4_9Dimension_9__len__, /*sq_length*/
0, /*sq_concat*/
0, /*sq_repeat*/
0, /*sq_item*/
@@ -60087,17 +61157,17 @@ static PySequenceMethods __pyx_tp_as_sequence_Dimension = {
};
static PyMappingMethods __pyx_tp_as_mapping_Dimension = {
- __pyx_pw_7netCDF4_9Dimension_9__len__, /*mp_length*/
+ __pyx_pw_7netCDF4_8_netCDF4_9Dimension_9__len__, /*mp_length*/
0, /*mp_subscript*/
0, /*mp_ass_subscript*/
};
-static PyTypeObject __pyx_type_7netCDF4_Dimension = {
+static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_Dimension = {
PyVarObject_HEAD_INIT(0, 0)
- "netCDF4.Dimension", /*tp_name*/
- sizeof(struct __pyx_obj_7netCDF4_Dimension), /*tp_basicsize*/
+ "netCDF4._netCDF4.Dimension", /*tp_name*/
+ sizeof(struct __pyx_obj_7netCDF4_8_netCDF4_Dimension), /*tp_basicsize*/
0, /*tp_itemsize*/
- __pyx_tp_dealloc_7netCDF4_Dimension, /*tp_dealloc*/
+ __pyx_tp_dealloc_7netCDF4_8_netCDF4_Dimension, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
@@ -60106,7 +61176,7 @@ static PyTypeObject __pyx_type_7netCDF4_Dimension = {
#else
0, /*reserved*/
#endif
- __pyx_pw_7netCDF4_9Dimension_5__repr__, /*tp_repr*/
+ __pyx_pw_7netCDF4_8_netCDF4_9Dimension_5__repr__, /*tp_repr*/
0, /*tp_as_number*/
&__pyx_tp_as_sequence_Dimension, /*tp_as_sequence*/
&__pyx_tp_as_mapping_Dimension, /*tp_as_mapping*/
@@ -60117,24 +61187,24 @@ static PyTypeObject __pyx_type_7netCDF4_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 [...]
- __pyx_tp_traverse_7netCDF4_Dimension, /*tp_traverse*/
- __pyx_tp_clear_7netCDF4_Dimension, /*tp_clear*/
+ "\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 [...]
+ __pyx_tp_traverse_7netCDF4_8_netCDF4_Dimension, /*tp_traverse*/
+ __pyx_tp_clear_7netCDF4_8_netCDF4_Dimension, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
0, /*tp_iternext*/
- __pyx_methods_7netCDF4_Dimension, /*tp_methods*/
+ __pyx_methods_7netCDF4_8_netCDF4_Dimension, /*tp_methods*/
0, /*tp_members*/
- __pyx_getsets_7netCDF4_Dimension, /*tp_getset*/
+ __pyx_getsets_7netCDF4_8_netCDF4_Dimension, /*tp_getset*/
0, /*tp_base*/
0, /*tp_dict*/
0, /*tp_descr_get*/
0, /*tp_descr_set*/
0, /*tp_dictoffset*/
- __pyx_pw_7netCDF4_9Dimension_1__init__, /*tp_init*/
+ __pyx_pw_7netCDF4_8_netCDF4_9Dimension_1__init__, /*tp_init*/
0, /*tp_alloc*/
- __pyx_tp_new_7netCDF4_Dimension, /*tp_new*/
+ __pyx_tp_new_7netCDF4_8_netCDF4_Dimension, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
@@ -60149,8 +61219,8 @@ static PyTypeObject __pyx_type_7netCDF4_Dimension = {
#endif
};
-static PyObject *__pyx_tp_new_7netCDF4_Variable(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
- struct __pyx_obj_7netCDF4_Variable *p;
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_Variable(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ struct __pyx_obj_7netCDF4_8_netCDF4_Variable *p;
PyObject *o;
if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
o = (*t->tp_alloc)(t, 0);
@@ -60158,7 +61228,7 @@ static PyObject *__pyx_tp_new_7netCDF4_Variable(PyTypeObject *t, CYTHON_UNUSED P
o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
}
if (unlikely(!o)) return 0;
- p = ((struct __pyx_obj_7netCDF4_Variable *)o);
+ p = ((struct __pyx_obj_7netCDF4_8_netCDF4_Variable *)o);
p->_name = Py_None; Py_INCREF(Py_None);
p->ndim = Py_None; Py_INCREF(Py_None);
p->dtype = Py_None; Py_INCREF(Py_None);
@@ -60170,11 +61240,12 @@ static PyObject *__pyx_tp_new_7netCDF4_Variable(PyTypeObject *t, CYTHON_UNUSED P
p->_grp = Py_None; Py_INCREF(Py_None);
p->_cmptype = Py_None; Py_INCREF(Py_None);
p->_vltype = Py_None; Py_INCREF(Py_None);
+ p->__orthogonal_indexing__ = Py_None; Py_INCREF(Py_None);
return o;
}
-static void __pyx_tp_dealloc_7netCDF4_Variable(PyObject *o) {
- struct __pyx_obj_7netCDF4_Variable *p = (struct __pyx_obj_7netCDF4_Variable *)o;
+static void __pyx_tp_dealloc_7netCDF4_8_netCDF4_Variable(PyObject *o) {
+ struct __pyx_obj_7netCDF4_8_netCDF4_Variable *p = (struct __pyx_obj_7netCDF4_8_netCDF4_Variable *)o;
#if PY_VERSION_HEX >= 0x030400a1
if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
if (PyObject_CallFinalizerFromDealloc(o)) return;
@@ -60192,12 +61263,13 @@ static void __pyx_tp_dealloc_7netCDF4_Variable(PyObject *o) {
Py_CLEAR(p->_grp);
Py_CLEAR(p->_cmptype);
Py_CLEAR(p->_vltype);
+ Py_CLEAR(p->__orthogonal_indexing__);
(*Py_TYPE(o)->tp_free)(o);
}
-static int __pyx_tp_traverse_7netCDF4_Variable(PyObject *o, visitproc v, void *a) {
+static int __pyx_tp_traverse_7netCDF4_8_netCDF4_Variable(PyObject *o, visitproc v, void *a) {
int e;
- struct __pyx_obj_7netCDF4_Variable *p = (struct __pyx_obj_7netCDF4_Variable *)o;
+ struct __pyx_obj_7netCDF4_8_netCDF4_Variable *p = (struct __pyx_obj_7netCDF4_8_netCDF4_Variable *)o;
if (p->_name) {
e = (*v)(p->_name, a); if (e) return e;
}
@@ -60231,12 +61303,15 @@ static int __pyx_tp_traverse_7netCDF4_Variable(PyObject *o, visitproc v, void *a
if (p->_vltype) {
e = (*v)(p->_vltype, a); if (e) return e;
}
+ if (p->__orthogonal_indexing__) {
+ e = (*v)(p->__orthogonal_indexing__, a); if (e) return e;
+ }
return 0;
}
-static int __pyx_tp_clear_7netCDF4_Variable(PyObject *o) {
+static int __pyx_tp_clear_7netCDF4_8_netCDF4_Variable(PyObject *o) {
PyObject* tmp;
- struct __pyx_obj_7netCDF4_Variable *p = (struct __pyx_obj_7netCDF4_Variable *)o;
+ struct __pyx_obj_7netCDF4_8_netCDF4_Variable *p = (struct __pyx_obj_7netCDF4_8_netCDF4_Variable *)o;
tmp = ((PyObject*)p->_name);
p->_name = Py_None; Py_INCREF(Py_None);
Py_XDECREF(tmp);
@@ -60270,9 +61345,12 @@ static int __pyx_tp_clear_7netCDF4_Variable(PyObject *o) {
tmp = ((PyObject*)p->_vltype);
p->_vltype = Py_None; Py_INCREF(Py_None);
Py_XDECREF(tmp);
+ tmp = ((PyObject*)p->__orthogonal_indexing__);
+ p->__orthogonal_indexing__ = Py_None; Py_INCREF(Py_None);
+ Py_XDECREF(tmp);
return 0;
}
-static PyObject *__pyx_sq_item_7netCDF4_Variable(PyObject *o, Py_ssize_t i) {
+static PyObject *__pyx_sq_item_7netCDF4_8_netCDF4_Variable(PyObject *o, Py_ssize_t i) {
PyObject *r;
PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
@@ -60280,9 +61358,9 @@ static PyObject *__pyx_sq_item_7netCDF4_Variable(PyObject *o, Py_ssize_t i) {
return r;
}
-static int __pyx_mp_ass_subscript_7netCDF4_Variable(PyObject *o, PyObject *i, PyObject *v) {
+static int __pyx_mp_ass_subscript_7netCDF4_8_netCDF4_Variable(PyObject *o, PyObject *i, PyObject *v) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_49__setitem__(o, i, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_49__setitem__(o, i, v);
}
else {
PyErr_Format(PyExc_NotImplementedError,
@@ -60291,31 +61369,31 @@ static int __pyx_mp_ass_subscript_7netCDF4_Variable(PyObject *o, PyObject *i, Py
}
}
-static PyObject *__pyx_tp_getattro_7netCDF4_Variable(PyObject *o, PyObject *n) {
+static PyObject *__pyx_tp_getattro_7netCDF4_8_netCDF4_Variable(PyObject *o, PyObject *n) {
PyObject *v = PyObject_GenericGetAttr(o, n);
if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) {
PyErr_Clear();
- v = __pyx_pw_7netCDF4_8Variable_39__getattr__(o, n);
+ v = __pyx_pw_7netCDF4_8_netCDF4_8Variable_39__getattr__(o, n);
}
return v;
}
-static int __pyx_tp_setattro_7netCDF4_Variable(PyObject *o, PyObject *n, PyObject *v) {
+static int __pyx_tp_setattro_7netCDF4_8_netCDF4_Variable(PyObject *o, PyObject *n, PyObject *v) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_37__setattr__(o, n, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_37__setattr__(o, n, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_35__delattr__(o, n);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_35__delattr__(o, n);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable_name(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_4name_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable_name(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4name_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable_name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable_name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_4name_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4name_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -60323,17 +61401,17 @@ static int __pyx_setprop_7netCDF4_8Variable_name(PyObject *o, PyObject *v, CYTHO
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable_datatype(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_8datatype_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable_datatype(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_8datatype_1__get__(o);
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable_shape(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_5shape_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable_shape(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5shape_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable_shape(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable_shape(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_5shape_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5shape_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -60341,17 +61419,17 @@ static int __pyx_setprop_7netCDF4_8Variable_shape(PyObject *o, PyObject *v, CYTH
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable_size(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_4size_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable_size(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4size_1__get__(o);
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable_dimensions(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_10dimensions_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable_dimensions(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_10dimensions_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable_dimensions(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable_dimensions(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_10dimensions_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_10dimensions_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -60359,13 +61437,13 @@ static int __pyx_setprop_7netCDF4_8Variable_dimensions(PyObject *o, PyObject *v,
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable__varid(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_6_varid_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable__varid(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_6_varid_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable__varid(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable__varid(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_6_varid_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_6_varid_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -60373,13 +61451,13 @@ static int __pyx_setprop_7netCDF4_8Variable__varid(PyObject *o, PyObject *v, CYT
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable__grpid(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_6_grpid_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable__grpid(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_6_grpid_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable__grpid(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable__grpid(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_6_grpid_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_6_grpid_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -60387,13 +61465,13 @@ static int __pyx_setprop_7netCDF4_8Variable__grpid(PyObject *o, PyObject *v, CYT
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable__nunlimdim(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_10_nunlimdim_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable__nunlimdim(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_10_nunlimdim_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable__nunlimdim(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable__nunlimdim(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_10_nunlimdim_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_10_nunlimdim_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -60401,207 +61479,221 @@ static int __pyx_setprop_7netCDF4_8Variable__nunlimdim(PyObject *o, PyObject *v,
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable__name(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_5_name_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable__name(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5_name_1__get__(o);
+}
+
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable__name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+ if (v) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5_name_3__set__(o, v);
+ }
+ else {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5_name_5__del__(o);
+ }
+}
+
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable_ndim(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4ndim_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable__name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable_ndim(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_5_name_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4ndim_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_5_name_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4ndim_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable_ndim(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_4ndim_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable_dtype(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5dtype_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable_ndim(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable_dtype(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_4ndim_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5dtype_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_4ndim_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5dtype_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable_dtype(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_5dtype_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable_mask(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4mask_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable_dtype(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable_mask(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_5dtype_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4mask_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_5dtype_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4mask_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable_mask(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_4mask_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable_scale(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5scale_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable_mask(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable_scale(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_4mask_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5scale_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_4mask_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_5scale_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable_scale(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_5scale_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable__isprimitive(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_12_isprimitive_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable_scale(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable__isprimitive(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_5scale_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_12_isprimitive_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_5scale_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_12_isprimitive_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable__isprimitive(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_12_isprimitive_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable__iscompound(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_11_iscompound_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable__isprimitive(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable__iscompound(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_12_isprimitive_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_11_iscompound_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_12_isprimitive_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_11_iscompound_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable__iscompound(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_11_iscompound_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable__isvlen(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_7_isvlen_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable__iscompound(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable__isvlen(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_11_iscompound_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_7_isvlen_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_11_iscompound_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_7_isvlen_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable__isvlen(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_7_isvlen_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable__grp(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4_grp_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable__isvlen(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable__grp(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_7_isvlen_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4_grp_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_7_isvlen_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_4_grp_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable__grp(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_4_grp_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable__cmptype(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_8_cmptype_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable__grp(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable__cmptype(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_4_grp_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_8_cmptype_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_4_grp_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_8_cmptype_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable__cmptype(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_8_cmptype_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable__vltype(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_7_vltype_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable__cmptype(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable__vltype(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_8_cmptype_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_7_vltype_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_8_cmptype_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_7_vltype_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_8Variable__vltype(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_8Variable_7_vltype_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_8Variable___orthogonal_indexing__(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_23__orthogonal_indexing___1__get__(o);
}
-static int __pyx_setprop_7netCDF4_8Variable__vltype(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_8Variable___orthogonal_indexing__(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_8Variable_7_vltype_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_23__orthogonal_indexing___3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_8Variable_7_vltype_5__del__(o);
- }
-}
-
-static PyMethodDef __pyx_methods_7netCDF4_Variable[] = {
- {"__array__", (PyCFunction)__pyx_pw_7netCDF4_8Variable_3__array__, METH_NOARGS, 0},
- {"__unicode__", (PyCFunction)__pyx_pw_7netCDF4_8Variable_7__unicode__, METH_NOARGS, 0},
- {"_getdims", (PyCFunction)__pyx_pw_7netCDF4_8Variable_9_getdims, METH_NOARGS, 0},
- {"_getname", (PyCFunction)__pyx_pw_7netCDF4_8Variable_11_getname, METH_NOARGS, 0},
- {"group", (PyCFunction)__pyx_pw_7netCDF4_8Variable_13group, METH_NOARGS, __pyx_doc_7netCDF4_8Variable_12group},
- {"ncattrs", (PyCFunction)__pyx_pw_7netCDF4_8Variable_15ncattrs, METH_NOARGS, __pyx_doc_7netCDF4_8Variable_14ncattrs},
- {"setncattr", (PyCFunction)__pyx_pw_7netCDF4_8Variable_17setncattr, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8Variable_16setncattr},
- {"setncatts", (PyCFunction)__pyx_pw_7netCDF4_8Variable_19setncatts, METH_O, __pyx_doc_7netCDF4_8Variable_18setncatts},
- {"getncattr", (PyCFunction)__pyx_pw_7netCDF4_8Variable_21getncattr, METH_O, __pyx_doc_7netCDF4_8Variable_20getncattr},
- {"delncattr", (PyCFunction)__pyx_pw_7netCDF4_8Variable_23delncattr, METH_O, __pyx_doc_7netCDF4_8Variable_22delncattr},
- {"filters", (PyCFunction)__pyx_pw_7netCDF4_8Variable_25filters, METH_NOARGS, __pyx_doc_7netCDF4_8Variable_24filters},
- {"endian", (PyCFunction)__pyx_pw_7netCDF4_8Variable_27endian, METH_NOARGS, __pyx_doc_7netCDF4_8Variable_26endian},
- {"chunking", (PyCFunction)__pyx_pw_7netCDF4_8Variable_29chunking, METH_NOARGS, __pyx_doc_7netCDF4_8Variable_28chunking},
- {"get_var_chunk_cache", (PyCFunction)__pyx_pw_7netCDF4_8Variable_31get_var_chunk_cache, METH_NOARGS, __pyx_doc_7netCDF4_8Variable_30get_var_chunk_cache},
- {"set_var_chunk_cache", (PyCFunction)__pyx_pw_7netCDF4_8Variable_33set_var_chunk_cache, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8Variable_32set_var_chunk_cache},
- {"__getattr__", (PyCFunction)__pyx_pw_7netCDF4_8Variable_39__getattr__, METH_O|METH_COEXIST, 0},
- {"renameAttribute", (PyCFunction)__pyx_pw_7netCDF4_8Variable_41renameAttribute, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8Variable_40renameAttribute},
- {"_toma", (PyCFunction)__pyx_pw_7netCDF4_8Variable_45_toma, METH_O, 0},
- {"_assign_vlen", (PyCFunction)__pyx_pw_7netCDF4_8Variable_47_assign_vlen, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8Variable_46_assign_vlen},
- {"assignValue", (PyCFunction)__pyx_pw_7netCDF4_8Variable_53assignValue, METH_O, __pyx_doc_7netCDF4_8Variable_52assignValue},
- {"getValue", (PyCFunction)__pyx_pw_7netCDF4_8Variable_55getValue, METH_NOARGS, __pyx_doc_7netCDF4_8Variable_54getValue},
- {"set_auto_maskandscale", (PyCFunction)__pyx_pw_7netCDF4_8Variable_57set_auto_maskandscale, METH_O, __pyx_doc_7netCDF4_8Variable_56set_auto_maskandscale},
- {"set_auto_scale", (PyCFunction)__pyx_pw_7netCDF4_8Variable_59set_auto_scale, METH_O, __pyx_doc_7netCDF4_8Variable_58set_auto_scale},
- {"set_auto_mask", (PyCFunction)__pyx_pw_7netCDF4_8Variable_61set_auto_mask, METH_O, __pyx_doc_7netCDF4_8Variable_60set_auto_mask},
- {"_put", (PyCFunction)__pyx_pw_7netCDF4_8Variable_63_put, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8Variable_62_put},
- {"_get", (PyCFunction)__pyx_pw_7netCDF4_8Variable_65_get, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8Variable_64_get},
+ return __pyx_pw_7netCDF4_8_netCDF4_8Variable_23__orthogonal_indexing___5__del__(o);
+ }
+}
+
+static PyMethodDef __pyx_methods_7netCDF4_8_netCDF4_Variable[] = {
+ {"__array__", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_3__array__, METH_NOARGS, 0},
+ {"__unicode__", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_7__unicode__, METH_NOARGS, 0},
+ {"_getdims", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_9_getdims, METH_NOARGS, 0},
+ {"_getname", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_11_getname, METH_NOARGS, 0},
+ {"group", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_13group, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_12group},
+ {"ncattrs", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_15ncattrs, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_14ncattrs},
+ {"setncattr", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_17setncattr, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_16setncattr},
+ {"setncatts", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_19setncatts, METH_O, __pyx_doc_7netCDF4_8_netCDF4_8Variable_18setncatts},
+ {"getncattr", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_21getncattr, METH_O, __pyx_doc_7netCDF4_8_netCDF4_8Variable_20getncattr},
+ {"delncattr", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_23delncattr, METH_O, __pyx_doc_7netCDF4_8_netCDF4_8Variable_22delncattr},
+ {"filters", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_25filters, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_24filters},
+ {"endian", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_27endian, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_26endian},
+ {"chunking", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_29chunking, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_28chunking},
+ {"get_var_chunk_cache", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_31get_var_chunk_cache, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_30get_var_chunk_cache},
+ {"set_var_chunk_cache", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_33set_var_chunk_cache, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_32set_var_chunk_cache},
+ {"__getattr__", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_39__getattr__, METH_O|METH_COEXIST, 0},
+ {"renameAttribute", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_41renameAttribute, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_40renameAttribute},
+ {"_toma", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_45_toma, METH_O, 0},
+ {"_assign_vlen", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_47_assign_vlen, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_46_assign_vlen},
+ {"assignValue", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_53assignValue, METH_O, __pyx_doc_7netCDF4_8_netCDF4_8Variable_52assignValue},
+ {"getValue", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_55getValue, METH_NOARGS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_54getValue},
+ {"set_auto_maskandscale", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_57set_auto_maskandscale, METH_O, __pyx_doc_7netCDF4_8_netCDF4_8Variable_56set_auto_maskandscale},
+ {"set_auto_scale", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_59set_auto_scale, METH_O, __pyx_doc_7netCDF4_8_netCDF4_8Variable_58set_auto_scale},
+ {"set_auto_mask", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_61set_auto_mask, METH_O, __pyx_doc_7netCDF4_8_netCDF4_8Variable_60set_auto_mask},
+ {"_put", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_63_put, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_62_put},
+ {"_get", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_8Variable_65_get, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7netCDF4_8_netCDF4_8Variable_64_get},
{0, 0, 0, 0}
};
-static struct PyGetSetDef __pyx_getsets_7netCDF4_Variable[] = {
- {(char *)"name", __pyx_getprop_7netCDF4_8Variable_name, __pyx_setprop_7netCDF4_8Variable_name, __pyx_k_string_name_of_Variable_instance, 0},
- {(char *)"datatype", __pyx_getprop_7netCDF4_8Variable_datatype, 0, __pyx_k_numpy_data_type_for_primitive_da, 0},
- {(char *)"shape", __pyx_getprop_7netCDF4_8Variable_shape, __pyx_setprop_7netCDF4_8Variable_shape, __pyx_k_find_current_sizes_of_all_variab, 0},
- {(char *)"size", __pyx_getprop_7netCDF4_8Variable_size, 0, __pyx_k_Return_the_number_of_stored_elem, 0},
- {(char *)"dimensions", __pyx_getprop_7netCDF4_8Variable_dimensions, __pyx_setprop_7netCDF4_8Variable_dimensions, __pyx_k_get_variables_s_dimension_names, 0},
- {(char *)"_varid", __pyx_getprop_7netCDF4_8Variable__varid, __pyx_setprop_7netCDF4_8Variable__varid, 0, 0},
- {(char *)"_grpid", __pyx_getprop_7netCDF4_8Variable__grpid, __pyx_setprop_7netCDF4_8Variable__grpid, 0, 0},
- {(char *)"_nunlimdim", __pyx_getprop_7netCDF4_8Variable__nunlimdim, __pyx_setprop_7netCDF4_8Variable__nunlimdim, 0, 0},
- {(char *)"_name", __pyx_getprop_7netCDF4_8Variable__name, __pyx_setprop_7netCDF4_8Variable__name, 0, 0},
- {(char *)"ndim", __pyx_getprop_7netCDF4_8Variable_ndim, __pyx_setprop_7netCDF4_8Variable_ndim, 0, 0},
- {(char *)"dtype", __pyx_getprop_7netCDF4_8Variable_dtype, __pyx_setprop_7netCDF4_8Variable_dtype, 0, 0},
- {(char *)"mask", __pyx_getprop_7netCDF4_8Variable_mask, __pyx_setprop_7netCDF4_8Variable_mask, 0, 0},
- {(char *)"scale", __pyx_getprop_7netCDF4_8Variable_scale, __pyx_setprop_7netCDF4_8Variable_scale, 0, 0},
- {(char *)"_isprimitive", __pyx_getprop_7netCDF4_8Variable__isprimitive, __pyx_setprop_7netCDF4_8Variable__isprimitive, 0, 0},
- {(char *)"_iscompound", __pyx_getprop_7netCDF4_8Variable__iscompound, __pyx_setprop_7netCDF4_8Variable__iscompound, 0, 0},
- {(char *)"_isvlen", __pyx_getprop_7netCDF4_8Variable__isvlen, __pyx_setprop_7netCDF4_8Variable__isvlen, 0, 0},
- {(char *)"_grp", __pyx_getprop_7netCDF4_8Variable__grp, __pyx_setprop_7netCDF4_8Variable__grp, 0, 0},
- {(char *)"_cmptype", __pyx_getprop_7netCDF4_8Variable__cmptype, __pyx_setprop_7netCDF4_8Variable__cmptype, 0, 0},
- {(char *)"_vltype", __pyx_getprop_7netCDF4_8Variable__vltype, __pyx_setprop_7netCDF4_8Variable__vltype, 0, 0},
+static struct PyGetSetDef __pyx_getsets_7netCDF4_8_netCDF4_Variable[] = {
+ {(char *)"name", __pyx_getprop_7netCDF4_8_netCDF4_8Variable_name, __pyx_setprop_7netCDF4_8_netCDF4_8Variable_name, __pyx_k_string_name_of_Variable_instance, 0},
+ {(char *)"datatype", __pyx_getprop_7netCDF4_8_netCDF4_8Variable_datatype, 0, __pyx_k_numpy_data_type_for_primitive_da, 0},
+ {(char *)"shape", __pyx_getprop_7netCDF4_8_netCDF4_8Variable_shape, __pyx_setprop_7netCDF4_8_netCDF4_8Variable_shape, __pyx_k_find_current_sizes_of_all_variab, 0},
+ {(char *)"size", __pyx_getprop_7netCDF4_8_netCDF4_8Variable_size, 0, __pyx_k_Return_the_number_of_stored_elem, 0},
+ {(char *)"dimensions", __pyx_getprop_7netCDF4_8_netCDF4_8Variable_dimensions, __pyx_setprop_7netCDF4_8_netCDF4_8Variable_dimensions, __pyx_k_get_variables_s_dimension_names, 0},
+ {(char *)"_varid", __pyx_getprop_7netCDF4_8_netCDF4_8Variable__varid, __pyx_setprop_7netCDF4_8_netCDF4_8Variable__varid, 0, 0},
+ {(char *)"_grpid", __pyx_getprop_7netCDF4_8_netCDF4_8Variable__grpid, __pyx_setprop_7netCDF4_8_netCDF4_8Variable__grpid, 0, 0},
+ {(char *)"_nunlimdim", __pyx_getprop_7netCDF4_8_netCDF4_8Variable__nunlimdim, __pyx_setprop_7netCDF4_8_netCDF4_8Variable__nunlimdim, 0, 0},
+ {(char *)"_name", __pyx_getprop_7netCDF4_8_netCDF4_8Variable__name, __pyx_setprop_7netCDF4_8_netCDF4_8Variable__name, 0, 0},
+ {(char *)"ndim", __pyx_getprop_7netCDF4_8_netCDF4_8Variable_ndim, __pyx_setprop_7netCDF4_8_netCDF4_8Variable_ndim, 0, 0},
+ {(char *)"dtype", __pyx_getprop_7netCDF4_8_netCDF4_8Variable_dtype, __pyx_setprop_7netCDF4_8_netCDF4_8Variable_dtype, 0, 0},
+ {(char *)"mask", __pyx_getprop_7netCDF4_8_netCDF4_8Variable_mask, __pyx_setprop_7netCDF4_8_netCDF4_8Variable_mask, 0, 0},
+ {(char *)"scale", __pyx_getprop_7netCDF4_8_netCDF4_8Variable_scale, __pyx_setprop_7netCDF4_8_netCDF4_8Variable_scale, 0, 0},
+ {(char *)"_isprimitive", __pyx_getprop_7netCDF4_8_netCDF4_8Variable__isprimitive, __pyx_setprop_7netCDF4_8_netCDF4_8Variable__isprimitive, 0, 0},
+ {(char *)"_iscompound", __pyx_getprop_7netCDF4_8_netCDF4_8Variable__iscompound, __pyx_setprop_7netCDF4_8_netCDF4_8Variable__iscompound, 0, 0},
+ {(char *)"_isvlen", __pyx_getprop_7netCDF4_8_netCDF4_8Variable__isvlen, __pyx_setprop_7netCDF4_8_netCDF4_8Variable__isvlen, 0, 0},
+ {(char *)"_grp", __pyx_getprop_7netCDF4_8_netCDF4_8Variable__grp, __pyx_setprop_7netCDF4_8_netCDF4_8Variable__grp, 0, 0},
+ {(char *)"_cmptype", __pyx_getprop_7netCDF4_8_netCDF4_8Variable__cmptype, __pyx_setprop_7netCDF4_8_netCDF4_8Variable__cmptype, 0, 0},
+ {(char *)"_vltype", __pyx_getprop_7netCDF4_8_netCDF4_8Variable__vltype, __pyx_setprop_7netCDF4_8_netCDF4_8Variable__vltype, 0, 0},
+ {(char *)"__orthogonal_indexing__", __pyx_getprop_7netCDF4_8_netCDF4_8Variable___orthogonal_indexing__, __pyx_setprop_7netCDF4_8_netCDF4_8Variable___orthogonal_indexing__, 0, 0},
{0, 0, 0, 0, 0}
};
static PySequenceMethods __pyx_tp_as_sequence_Variable = {
- __pyx_pw_7netCDF4_8Variable_51__len__, /*sq_length*/
+ __pyx_pw_7netCDF4_8_netCDF4_8Variable_51__len__, /*sq_length*/
0, /*sq_concat*/
0, /*sq_repeat*/
- __pyx_sq_item_7netCDF4_Variable, /*sq_item*/
+ __pyx_sq_item_7netCDF4_8_netCDF4_Variable, /*sq_item*/
0, /*sq_slice*/
0, /*sq_ass_item*/
0, /*sq_ass_slice*/
@@ -60611,17 +61703,17 @@ static PySequenceMethods __pyx_tp_as_sequence_Variable = {
};
static PyMappingMethods __pyx_tp_as_mapping_Variable = {
- __pyx_pw_7netCDF4_8Variable_51__len__, /*mp_length*/
- __pyx_pw_7netCDF4_8Variable_43__getitem__, /*mp_subscript*/
- __pyx_mp_ass_subscript_7netCDF4_Variable, /*mp_ass_subscript*/
+ __pyx_pw_7netCDF4_8_netCDF4_8Variable_51__len__, /*mp_length*/
+ __pyx_pw_7netCDF4_8_netCDF4_8Variable_43__getitem__, /*mp_subscript*/
+ __pyx_mp_ass_subscript_7netCDF4_8_netCDF4_Variable, /*mp_ass_subscript*/
};
-static PyTypeObject __pyx_type_7netCDF4_Variable = {
+static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_Variable = {
PyVarObject_HEAD_INIT(0, 0)
- "netCDF4.Variable", /*tp_name*/
- sizeof(struct __pyx_obj_7netCDF4_Variable), /*tp_basicsize*/
+ "netCDF4._netCDF4.Variable", /*tp_name*/
+ sizeof(struct __pyx_obj_7netCDF4_8_netCDF4_Variable), /*tp_basicsize*/
0, /*tp_itemsize*/
- __pyx_tp_dealloc_7netCDF4_Variable, /*tp_dealloc*/
+ __pyx_tp_dealloc_7netCDF4_8_netCDF4_Variable, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
@@ -60630,35 +61722,35 @@ static PyTypeObject __pyx_type_7netCDF4_Variable = {
#else
0, /*reserved*/
#endif
- __pyx_pw_7netCDF4_8Variable_5__repr__, /*tp_repr*/
+ __pyx_pw_7netCDF4_8_netCDF4_8Variable_5__repr__, /*tp_repr*/
0, /*tp_as_number*/
&__pyx_tp_as_sequence_Variable, /*tp_as_sequence*/
&__pyx_tp_as_mapping_Variable, /*tp_as_mapping*/
0, /*tp_hash*/
0, /*tp_call*/
0, /*tp_str*/
- __pyx_tp_getattro_7netCDF4_Variable, /*tp_getattro*/
- __pyx_tp_setattro_7netCDF4_Variable, /*tp_setattro*/
+ __pyx_tp_getattro_7netCDF4_8_netCDF4_Variable, /*tp_getattro*/
+ __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 directl [...]
- __pyx_tp_traverse_7netCDF4_Variable, /*tp_traverse*/
- __pyx_tp_clear_7netCDF4_Variable, /*tp_clear*/
+ "\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 [...]
+ __pyx_tp_traverse_7netCDF4_8_netCDF4_Variable, /*tp_traverse*/
+ __pyx_tp_clear_7netCDF4_8_netCDF4_Variable, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
0, /*tp_iternext*/
- __pyx_methods_7netCDF4_Variable, /*tp_methods*/
+ __pyx_methods_7netCDF4_8_netCDF4_Variable, /*tp_methods*/
0, /*tp_members*/
- __pyx_getsets_7netCDF4_Variable, /*tp_getset*/
+ __pyx_getsets_7netCDF4_8_netCDF4_Variable, /*tp_getset*/
0, /*tp_base*/
0, /*tp_dict*/
0, /*tp_descr_get*/
0, /*tp_descr_set*/
0, /*tp_dictoffset*/
- __pyx_pw_7netCDF4_8Variable_1__init__, /*tp_init*/
+ __pyx_pw_7netCDF4_8_netCDF4_8Variable_1__init__, /*tp_init*/
0, /*tp_alloc*/
- __pyx_tp_new_7netCDF4_Variable, /*tp_new*/
+ __pyx_tp_new_7netCDF4_8_netCDF4_Variable, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
@@ -60673,8 +61765,8 @@ static PyTypeObject __pyx_type_7netCDF4_Variable = {
#endif
};
-static PyObject *__pyx_tp_new_7netCDF4_CompoundType(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
- struct __pyx_obj_7netCDF4_CompoundType *p;
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_CompoundType(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *p;
PyObject *o;
if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
o = (*t->tp_alloc)(t, 0);
@@ -60682,14 +61774,14 @@ static PyObject *__pyx_tp_new_7netCDF4_CompoundType(PyTypeObject *t, CYTHON_UNUS
o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
}
if (unlikely(!o)) return 0;
- p = ((struct __pyx_obj_7netCDF4_CompoundType *)o);
+ p = ((struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *)o);
p->dtype = Py_None; Py_INCREF(Py_None);
p->name = Py_None; Py_INCREF(Py_None);
return o;
}
-static void __pyx_tp_dealloc_7netCDF4_CompoundType(PyObject *o) {
- struct __pyx_obj_7netCDF4_CompoundType *p = (struct __pyx_obj_7netCDF4_CompoundType *)o;
+static void __pyx_tp_dealloc_7netCDF4_8_netCDF4_CompoundType(PyObject *o) {
+ struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *p = (struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *)o;
#if PY_VERSION_HEX >= 0x030400a1
if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
if (PyObject_CallFinalizerFromDealloc(o)) return;
@@ -60701,9 +61793,9 @@ static void __pyx_tp_dealloc_7netCDF4_CompoundType(PyObject *o) {
(*Py_TYPE(o)->tp_free)(o);
}
-static int __pyx_tp_traverse_7netCDF4_CompoundType(PyObject *o, visitproc v, void *a) {
+static int __pyx_tp_traverse_7netCDF4_8_netCDF4_CompoundType(PyObject *o, visitproc v, void *a) {
int e;
- struct __pyx_obj_7netCDF4_CompoundType *p = (struct __pyx_obj_7netCDF4_CompoundType *)o;
+ struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *p = (struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *)o;
if (p->dtype) {
e = (*v)(p->dtype, a); if (e) return e;
}
@@ -60713,9 +61805,9 @@ static int __pyx_tp_traverse_7netCDF4_CompoundType(PyObject *o, visitproc v, voi
return 0;
}
-static int __pyx_tp_clear_7netCDF4_CompoundType(PyObject *o) {
+static int __pyx_tp_clear_7netCDF4_8_netCDF4_CompoundType(PyObject *o) {
PyObject* tmp;
- struct __pyx_obj_7netCDF4_CompoundType *p = (struct __pyx_obj_7netCDF4_CompoundType *)o;
+ struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *p = (struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType *)o;
tmp = ((PyObject*)p->dtype);
p->dtype = Py_None; Py_INCREF(Py_None);
Py_XDECREF(tmp);
@@ -60725,13 +61817,13 @@ static int __pyx_tp_clear_7netCDF4_CompoundType(PyObject *o) {
return 0;
}
-static PyObject *__pyx_getprop_7netCDF4_12CompoundType__nc_type(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_12CompoundType_8_nc_type_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_12CompoundType__nc_type(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_12CompoundType_8_nc_type_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_12CompoundType__nc_type(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_12CompoundType__nc_type(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_12CompoundType_8_nc_type_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_12CompoundType_8_nc_type_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -60739,50 +61831,50 @@ static int __pyx_setprop_7netCDF4_12CompoundType__nc_type(PyObject *o, PyObject
}
}
-static PyObject *__pyx_getprop_7netCDF4_12CompoundType_dtype(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_12CompoundType_5dtype_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_12CompoundType_dtype(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_12CompoundType_5dtype_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_12CompoundType_dtype(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_12CompoundType_dtype(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_12CompoundType_5dtype_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_12CompoundType_5dtype_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_12CompoundType_5dtype_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_12CompoundType_5dtype_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_12CompoundType_name(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_12CompoundType_4name_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_12CompoundType_name(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_12CompoundType_4name_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_12CompoundType_name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_12CompoundType_name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_12CompoundType_4name_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_12CompoundType_4name_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_12CompoundType_4name_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_12CompoundType_4name_5__del__(o);
}
}
-static PyMethodDef __pyx_methods_7netCDF4_CompoundType[] = {
- {"__unicode__", (PyCFunction)__pyx_pw_7netCDF4_12CompoundType_5__unicode__, METH_NOARGS, 0},
+static PyMethodDef __pyx_methods_7netCDF4_8_netCDF4_CompoundType[] = {
+ {"__unicode__", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_12CompoundType_5__unicode__, METH_NOARGS, 0},
{0, 0, 0, 0}
};
-static struct PyGetSetDef __pyx_getsets_7netCDF4_CompoundType[] = {
- {(char *)"_nc_type", __pyx_getprop_7netCDF4_12CompoundType__nc_type, __pyx_setprop_7netCDF4_12CompoundType__nc_type, 0, 0},
- {(char *)"dtype", __pyx_getprop_7netCDF4_12CompoundType_dtype, __pyx_setprop_7netCDF4_12CompoundType_dtype, 0, 0},
- {(char *)"name", __pyx_getprop_7netCDF4_12CompoundType_name, __pyx_setprop_7netCDF4_12CompoundType_name, 0, 0},
+static struct PyGetSetDef __pyx_getsets_7netCDF4_8_netCDF4_CompoundType[] = {
+ {(char *)"_nc_type", __pyx_getprop_7netCDF4_8_netCDF4_12CompoundType__nc_type, __pyx_setprop_7netCDF4_8_netCDF4_12CompoundType__nc_type, 0, 0},
+ {(char *)"dtype", __pyx_getprop_7netCDF4_8_netCDF4_12CompoundType_dtype, __pyx_setprop_7netCDF4_8_netCDF4_12CompoundType_dtype, 0, 0},
+ {(char *)"name", __pyx_getprop_7netCDF4_8_netCDF4_12CompoundType_name, __pyx_setprop_7netCDF4_8_netCDF4_12CompoundType_name, 0, 0},
{0, 0, 0, 0, 0}
};
-static PyTypeObject __pyx_type_7netCDF4_CompoundType = {
+static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_CompoundType = {
PyVarObject_HEAD_INIT(0, 0)
- "netCDF4.CompoundType", /*tp_name*/
- sizeof(struct __pyx_obj_7netCDF4_CompoundType), /*tp_basicsize*/
+ "netCDF4._netCDF4.CompoundType", /*tp_name*/
+ sizeof(struct __pyx_obj_7netCDF4_8_netCDF4_CompoundType), /*tp_basicsize*/
0, /*tp_itemsize*/
- __pyx_tp_dealloc_7netCDF4_CompoundType, /*tp_dealloc*/
+ __pyx_tp_dealloc_7netCDF4_8_netCDF4_CompoundType, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
@@ -60791,7 +61883,7 @@ static PyTypeObject __pyx_type_7netCDF4_CompoundType = {
#else
0, /*reserved*/
#endif
- __pyx_pw_7netCDF4_12CompoundType_3__repr__, /*tp_repr*/
+ __pyx_pw_7netCDF4_8_netCDF4_12CompoundType_3__repr__, /*tp_repr*/
0, /*tp_as_number*/
0, /*tp_as_sequence*/
0, /*tp_as_mapping*/
@@ -60802,24 +61894,24 @@ static PyTypeObject __pyx_type_7netCDF4_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 [...]
- __pyx_tp_traverse_7netCDF4_CompoundType, /*tp_traverse*/
- __pyx_tp_clear_7netCDF4_CompoundType, /*tp_clear*/
+ "\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 [...]
+ __pyx_tp_traverse_7netCDF4_8_netCDF4_CompoundType, /*tp_traverse*/
+ __pyx_tp_clear_7netCDF4_8_netCDF4_CompoundType, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
0, /*tp_iternext*/
- __pyx_methods_7netCDF4_CompoundType, /*tp_methods*/
+ __pyx_methods_7netCDF4_8_netCDF4_CompoundType, /*tp_methods*/
0, /*tp_members*/
- __pyx_getsets_7netCDF4_CompoundType, /*tp_getset*/
+ __pyx_getsets_7netCDF4_8_netCDF4_CompoundType, /*tp_getset*/
0, /*tp_base*/
0, /*tp_dict*/
0, /*tp_descr_get*/
0, /*tp_descr_set*/
0, /*tp_dictoffset*/
- __pyx_pw_7netCDF4_12CompoundType_1__init__, /*tp_init*/
+ __pyx_pw_7netCDF4_8_netCDF4_12CompoundType_1__init__, /*tp_init*/
0, /*tp_alloc*/
- __pyx_tp_new_7netCDF4_CompoundType, /*tp_new*/
+ __pyx_tp_new_7netCDF4_8_netCDF4_CompoundType, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
@@ -60834,8 +61926,8 @@ static PyTypeObject __pyx_type_7netCDF4_CompoundType = {
#endif
};
-static PyObject *__pyx_tp_new_7netCDF4_VLType(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
- struct __pyx_obj_7netCDF4_VLType *p;
+static PyObject *__pyx_tp_new_7netCDF4_8_netCDF4_VLType(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ struct __pyx_obj_7netCDF4_8_netCDF4_VLType *p;
PyObject *o;
if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
o = (*t->tp_alloc)(t, 0);
@@ -60843,14 +61935,14 @@ static PyObject *__pyx_tp_new_7netCDF4_VLType(PyTypeObject *t, CYTHON_UNUSED PyO
o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
}
if (unlikely(!o)) return 0;
- p = ((struct __pyx_obj_7netCDF4_VLType *)o);
+ p = ((struct __pyx_obj_7netCDF4_8_netCDF4_VLType *)o);
p->dtype = Py_None; Py_INCREF(Py_None);
p->name = Py_None; Py_INCREF(Py_None);
return o;
}
-static void __pyx_tp_dealloc_7netCDF4_VLType(PyObject *o) {
- struct __pyx_obj_7netCDF4_VLType *p = (struct __pyx_obj_7netCDF4_VLType *)o;
+static void __pyx_tp_dealloc_7netCDF4_8_netCDF4_VLType(PyObject *o) {
+ struct __pyx_obj_7netCDF4_8_netCDF4_VLType *p = (struct __pyx_obj_7netCDF4_8_netCDF4_VLType *)o;
#if PY_VERSION_HEX >= 0x030400a1
if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
if (PyObject_CallFinalizerFromDealloc(o)) return;
@@ -60862,9 +61954,9 @@ static void __pyx_tp_dealloc_7netCDF4_VLType(PyObject *o) {
(*Py_TYPE(o)->tp_free)(o);
}
-static int __pyx_tp_traverse_7netCDF4_VLType(PyObject *o, visitproc v, void *a) {
+static int __pyx_tp_traverse_7netCDF4_8_netCDF4_VLType(PyObject *o, visitproc v, void *a) {
int e;
- struct __pyx_obj_7netCDF4_VLType *p = (struct __pyx_obj_7netCDF4_VLType *)o;
+ struct __pyx_obj_7netCDF4_8_netCDF4_VLType *p = (struct __pyx_obj_7netCDF4_8_netCDF4_VLType *)o;
if (p->dtype) {
e = (*v)(p->dtype, a); if (e) return e;
}
@@ -60874,9 +61966,9 @@ static int __pyx_tp_traverse_7netCDF4_VLType(PyObject *o, visitproc v, void *a)
return 0;
}
-static int __pyx_tp_clear_7netCDF4_VLType(PyObject *o) {
+static int __pyx_tp_clear_7netCDF4_8_netCDF4_VLType(PyObject *o) {
PyObject* tmp;
- struct __pyx_obj_7netCDF4_VLType *p = (struct __pyx_obj_7netCDF4_VLType *)o;
+ struct __pyx_obj_7netCDF4_8_netCDF4_VLType *p = (struct __pyx_obj_7netCDF4_8_netCDF4_VLType *)o;
tmp = ((PyObject*)p->dtype);
p->dtype = Py_None; Py_INCREF(Py_None);
Py_XDECREF(tmp);
@@ -60886,13 +61978,13 @@ static int __pyx_tp_clear_7netCDF4_VLType(PyObject *o) {
return 0;
}
-static PyObject *__pyx_getprop_7netCDF4_6VLType__nc_type(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_6VLType_8_nc_type_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_6VLType__nc_type(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_6VLType_8_nc_type_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_6VLType__nc_type(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_6VLType__nc_type(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_6VLType_8_nc_type_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_6VLType_8_nc_type_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
@@ -60900,50 +61992,50 @@ static int __pyx_setprop_7netCDF4_6VLType__nc_type(PyObject *o, PyObject *v, CYT
}
}
-static PyObject *__pyx_getprop_7netCDF4_6VLType_dtype(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_6VLType_5dtype_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_6VLType_dtype(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_6VLType_5dtype_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_6VLType_dtype(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_6VLType_dtype(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_6VLType_5dtype_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_6VLType_5dtype_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_6VLType_5dtype_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_6VLType_5dtype_5__del__(o);
}
}
-static PyObject *__pyx_getprop_7netCDF4_6VLType_name(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_7netCDF4_6VLType_4name_1__get__(o);
+static PyObject *__pyx_getprop_7netCDF4_8_netCDF4_6VLType_name(PyObject *o, CYTHON_UNUSED void *x) {
+ return __pyx_pw_7netCDF4_8_netCDF4_6VLType_4name_1__get__(o);
}
-static int __pyx_setprop_7netCDF4_6VLType_name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
+static int __pyx_setprop_7netCDF4_8_netCDF4_6VLType_name(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
if (v) {
- return __pyx_pw_7netCDF4_6VLType_4name_3__set__(o, v);
+ return __pyx_pw_7netCDF4_8_netCDF4_6VLType_4name_3__set__(o, v);
}
else {
- return __pyx_pw_7netCDF4_6VLType_4name_5__del__(o);
+ return __pyx_pw_7netCDF4_8_netCDF4_6VLType_4name_5__del__(o);
}
}
-static PyMethodDef __pyx_methods_7netCDF4_VLType[] = {
- {"__unicode__", (PyCFunction)__pyx_pw_7netCDF4_6VLType_5__unicode__, METH_NOARGS, 0},
+static PyMethodDef __pyx_methods_7netCDF4_8_netCDF4_VLType[] = {
+ {"__unicode__", (PyCFunction)__pyx_pw_7netCDF4_8_netCDF4_6VLType_5__unicode__, METH_NOARGS, 0},
{0, 0, 0, 0}
};
-static struct PyGetSetDef __pyx_getsets_7netCDF4_VLType[] = {
- {(char *)"_nc_type", __pyx_getprop_7netCDF4_6VLType__nc_type, __pyx_setprop_7netCDF4_6VLType__nc_type, 0, 0},
- {(char *)"dtype", __pyx_getprop_7netCDF4_6VLType_dtype, __pyx_setprop_7netCDF4_6VLType_dtype, 0, 0},
- {(char *)"name", __pyx_getprop_7netCDF4_6VLType_name, __pyx_setprop_7netCDF4_6VLType_name, 0, 0},
+static struct PyGetSetDef __pyx_getsets_7netCDF4_8_netCDF4_VLType[] = {
+ {(char *)"_nc_type", __pyx_getprop_7netCDF4_8_netCDF4_6VLType__nc_type, __pyx_setprop_7netCDF4_8_netCDF4_6VLType__nc_type, 0, 0},
+ {(char *)"dtype", __pyx_getprop_7netCDF4_8_netCDF4_6VLType_dtype, __pyx_setprop_7netCDF4_8_netCDF4_6VLType_dtype, 0, 0},
+ {(char *)"name", __pyx_getprop_7netCDF4_8_netCDF4_6VLType_name, __pyx_setprop_7netCDF4_8_netCDF4_6VLType_name, 0, 0},
{0, 0, 0, 0, 0}
};
-static PyTypeObject __pyx_type_7netCDF4_VLType = {
+static PyTypeObject __pyx_type_7netCDF4_8_netCDF4_VLType = {
PyVarObject_HEAD_INIT(0, 0)
- "netCDF4.VLType", /*tp_name*/
- sizeof(struct __pyx_obj_7netCDF4_VLType), /*tp_basicsize*/
+ "netCDF4._netCDF4.VLType", /*tp_name*/
+ sizeof(struct __pyx_obj_7netCDF4_8_netCDF4_VLType), /*tp_basicsize*/
0, /*tp_itemsize*/
- __pyx_tp_dealloc_7netCDF4_VLType, /*tp_dealloc*/
+ __pyx_tp_dealloc_7netCDF4_8_netCDF4_VLType, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
@@ -60952,7 +62044,7 @@ static PyTypeObject __pyx_type_7netCDF4_VLType = {
#else
0, /*reserved*/
#endif
- __pyx_pw_7netCDF4_6VLType_3__repr__, /*tp_repr*/
+ __pyx_pw_7netCDF4_8_netCDF4_6VLType_3__repr__, /*tp_repr*/
0, /*tp_as_number*/
0, /*tp_as_sequence*/
0, /*tp_as_mapping*/
@@ -60963,24 +62055,24 @@ static PyTypeObject __pyx_type_7netCDF4_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 lengt [...]
- __pyx_tp_traverse_7netCDF4_VLType, /*tp_traverse*/
- __pyx_tp_clear_7netCDF4_VLType, /*tp_clear*/
+ "\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 [...]
+ __pyx_tp_traverse_7netCDF4_8_netCDF4_VLType, /*tp_traverse*/
+ __pyx_tp_clear_7netCDF4_8_netCDF4_VLType, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
0, /*tp_iternext*/
- __pyx_methods_7netCDF4_VLType, /*tp_methods*/
+ __pyx_methods_7netCDF4_8_netCDF4_VLType, /*tp_methods*/
0, /*tp_members*/
- __pyx_getsets_7netCDF4_VLType, /*tp_getset*/
+ __pyx_getsets_7netCDF4_8_netCDF4_VLType, /*tp_getset*/
0, /*tp_base*/
0, /*tp_dict*/
0, /*tp_descr_get*/
0, /*tp_descr_set*/
0, /*tp_dictoffset*/
- __pyx_pw_7netCDF4_6VLType_1__init__, /*tp_init*/
+ __pyx_pw_7netCDF4_8_netCDF4_6VLType_1__init__, /*tp_init*/
0, /*tp_alloc*/
- __pyx_tp_new_7netCDF4_VLType, /*tp_new*/
+ __pyx_tp_new_7netCDF4_8_netCDF4_VLType, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
@@ -61006,7 +62098,7 @@ static struct PyModuleDef __pyx_moduledef = {
#else
PyModuleDef_HEAD_INIT,
#endif
- "netCDF4",
+ "_netCDF4",
__pyx_k_Introduction_Python_interface_t, /* m_doc */
-1, /* m_size */
__pyx_methods /* m_methods */,
@@ -61018,9 +62110,10 @@ static struct PyModuleDef __pyx_moduledef = {
#endif
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_7, __pyx_k_1_1_7, sizeof(__pyx_k_1_1_7), 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_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_n_s_AttributeError, __pyx_k_AttributeError, sizeof(__pyx_k_AttributeError), 0, 0, 1, 1},
@@ -61075,7 +62168,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__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_kp_s_Users_jsw_python_netcdf4_python_2, __pyx_k_Users_jsw_python_netcdf4_python_2, sizeof(__pyx_k_Users_jsw_python_netcdf4_python_2), 0, 0, 1, 0},
{&__pyx_n_s_V, __pyx_k_V, sizeof(__pyx_k_V), 0, 0, 1, 1},
{&__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},
@@ -61090,20 +62182,19 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__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_n_s_Variable_set_auto_maskandscale, __pyx_k_Variable_set_auto_maskandscale, sizeof(__pyx_k_Variable_set_auto_maskandscale), 0, 0, 1, 1},
{&__pyx_n_s_Variable_typecode, __pyx_k_Variable_typecode, sizeof(__pyx_k_Variable_typecode), 0, 0, 1, 1},
- {&__pyx_kp_s__115, __pyx_k__115, sizeof(__pyx_k__115), 0, 0, 1, 0},
+ {&__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__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__15, __pyx_k__15, sizeof(__pyx_k__15), 0, 0, 1, 0},
- {&__pyx_kp_s__24, __pyx_k__24, sizeof(__pyx_k__24), 0, 0, 1, 0},
+ {&__pyx_kp_s__17, __pyx_k__17, sizeof(__pyx_k__17), 0, 0, 1, 0},
+ {&__pyx_kp_s__19, __pyx_k__19, sizeof(__pyx_k__19), 0, 0, 1, 0},
+ {&__pyx_kp_s__2, __pyx_k__2, sizeof(__pyx_k__2), 0, 0, 1, 0},
+ {&__pyx_kp_s__20, __pyx_k__20, sizeof(__pyx_k__20), 0, 0, 1, 0},
+ {&__pyx_kp_s__22, __pyx_k__22, sizeof(__pyx_k__22), 0, 0, 1, 0},
+ {&__pyx_kp_s__23, __pyx_k__23, sizeof(__pyx_k__23), 0, 0, 1, 0},
+ {&__pyx_n_s__26, __pyx_k__26, sizeof(__pyx_k__26), 0, 0, 1, 1},
+ {&__pyx_kp_s__33, __pyx_k__33, sizeof(__pyx_k__33), 0, 0, 1, 0},
{&__pyx_kp_s__34, __pyx_k__34, sizeof(__pyx_k__34), 0, 0, 1, 0},
- {&__pyx_kp_s__35, __pyx_k__35, sizeof(__pyx_k__35), 0, 0, 1, 0},
- {&__pyx_kp_s__37, __pyx_k__37, sizeof(__pyx_k__37), 0, 0, 1, 0},
- {&__pyx_kp_s__39, __pyx_k__39, sizeof(__pyx_k__39), 0, 0, 1, 0},
- {&__pyx_kp_s__40, __pyx_k__40, sizeof(__pyx_k__40), 0, 0, 1, 0},
- {&__pyx_kp_s__42, __pyx_k__42, sizeof(__pyx_k__42), 0, 0, 1, 0},
- {&__pyx_kp_s__43, __pyx_k__43, sizeof(__pyx_k__43), 0, 0, 1, 0},
- {&__pyx_n_s__45, __pyx_k__45, sizeof(__pyx_k__45), 0, 0, 1, 1},
- {&__pyx_kp_s__52, __pyx_k__52, sizeof(__pyx_k__52), 0, 0, 1, 0},
- {&__pyx_kp_s__53, __pyx_k__53, sizeof(__pyx_k__53), 0, 0, 1, 0},
{&__pyx_n_s_a, __pyx_k_a, sizeof(__pyx_k_a), 0, 0, 1, 1},
{&__pyx_n_s_add_offset, __pyx_k_add_offset, sizeof(__pyx_k_add_offset), 0, 0, 1, 1},
{&__pyx_n_s_aggDimId, __pyx_k_aggDimId, sizeof(__pyx_k_aggDimId), 0, 0, 1, 1},
@@ -61168,6 +62259,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_contiguous, __pyx_k_contiguous, sizeof(__pyx_k_contiguous), 0, 0, 1, 1},
{&__pyx_n_s_copy, __pyx_k_copy, sizeof(__pyx_k_copy), 0, 0, 1, 1},
{&__pyx_n_s_count, __pyx_k_count, sizeof(__pyx_k_count), 0, 0, 1, 1},
+ {&__pyx_n_s_createGroup, __pyx_k_createGroup, sizeof(__pyx_k_createGroup), 0, 0, 1, 1},
{&__pyx_kp_s_created_using_fill_value_keyword, __pyx_k_created_using_fill_value_keyword, sizeof(__pyx_k_created_using_fill_value_keyword), 0, 0, 1, 0},
{&__pyx_kp_s_current_shape_s, __pyx_k_current_shape_s, sizeof(__pyx_k_current_shape_s), 0, 0, 1, 0},
{&__pyx_kp_s_current_size_s, __pyx_k_current_size_s, sizeof(__pyx_k_current_size_s), 0, 0, 1, 0},
@@ -61189,6 +62281,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_dates, __pyx_k_dates, sizeof(__pyx_k_dates), 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},
+ {&__pyx_n_s_day_units, __pyx_k_day_units, sizeof(__pyx_k_day_units), 0, 0, 1, 1},
{&__pyx_n_s_days, __pyx_k_days, sizeof(__pyx_k_days), 0, 0, 1, 1},
{&__pyx_n_s_decode, __pyx_k_decode, sizeof(__pyx_k_decode), 0, 0, 1, 1},
{&__pyx_n_s_default_encoding, __pyx_k_default_encoding, sizeof(__pyx_k_default_encoding), 0, 0, 1, 1},
@@ -61286,7 +62379,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_has_rename_grp, __pyx_k_has_rename_grp, sizeof(__pyx_k_has_rename_grp), 0, 0, 1, 1},
{&__pyx_n_s_hdf5libversion, __pyx_k_hdf5libversion, sizeof(__pyx_k_hdf5libversion), 0, 0, 1, 1},
{&__pyx_n_s_hour, __pyx_k_hour, sizeof(__pyx_k_hour), 0, 0, 1, 1},
- {&__pyx_n_s_hours, __pyx_k_hours, sizeof(__pyx_k_hours), 0, 0, 1, 1},
+ {&__pyx_n_s_hr_units, __pyx_k_hr_units, sizeof(__pyx_k_hr_units), 0, 0, 1, 1},
{&__pyx_n_s_http, __pyx_k_http, sizeof(__pyx_k_http), 0, 0, 1, 1},
{&__pyx_n_s_i, __pyx_k_i, sizeof(__pyx_k_i), 0, 0, 1, 1},
{&__pyx_n_s_i0, __pyx_k_i0, sizeof(__pyx_k_i0), 0, 0, 1, 1},
@@ -61345,13 +62438,12 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_kp_s_master_dataset_s_does_not_have_a_2, __pyx_k_master_dataset_s_does_not_have_a_2, sizeof(__pyx_k_master_dataset_s_does_not_have_a_2), 0, 0, 1, 0},
{&__pyx_n_s_mastervar, __pyx_k_mastervar, sizeof(__pyx_k_mastervar), 0, 0, 1, 1},
{&__pyx_n_s_metaclass, __pyx_k_metaclass, sizeof(__pyx_k_metaclass), 0, 0, 1, 1},
- {&__pyx_n_s_microsecond, __pyx_k_microsecond, sizeof(__pyx_k_microsecond), 0, 0, 1, 1},
+ {&__pyx_n_s_microsec_units, __pyx_k_microsec_units, sizeof(__pyx_k_microsec_units), 0, 0, 1, 1},
{&__pyx_n_s_microseconds, __pyx_k_microseconds, sizeof(__pyx_k_microseconds), 0, 0, 1, 1},
- {&__pyx_n_s_millisecond, __pyx_k_millisecond, sizeof(__pyx_k_millisecond), 0, 0, 1, 1},
- {&__pyx_n_s_milliseconds, __pyx_k_milliseconds, sizeof(__pyx_k_milliseconds), 0, 0, 1, 1},
+ {&__pyx_n_s_millisec_units, __pyx_k_millisec_units, sizeof(__pyx_k_millisec_units), 0, 0, 1, 1},
+ {&__pyx_n_s_min_units, __pyx_k_min_units, sizeof(__pyx_k_min_units), 0, 0, 1, 1},
{&__pyx_n_s_minorvers, __pyx_k_minorvers, sizeof(__pyx_k_minorvers), 0, 0, 1, 1},
{&__pyx_n_s_minute, __pyx_k_minute, sizeof(__pyx_k_minute), 0, 0, 1, 1},
- {&__pyx_n_s_minutes, __pyx_k_minutes, sizeof(__pyx_k_minutes), 0, 0, 1, 1},
{&__pyx_n_s_missing_value, __pyx_k_missing_value, sizeof(__pyx_k_missing_value), 0, 0, 1, 1},
{&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1},
{&__pyx_kp_s_mode_must_be_w_r_a_or_r_got_s, __pyx_k_mode_must_be_w_r_a_or_r_got_s, sizeof(__pyx_k_mode_must_be_w_r_a_or_r_got_s), 0, 0, 1, 0},
@@ -61378,13 +62470,13 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1},
{&__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, __pyx_k_netCDF4, sizeof(__pyx_k_netCDF4), 0, 0, 1, 1},
- {&__pyx_n_s_netCDF4_utils, __pyx_k_netCDF4_utils, sizeof(__pyx_k_netCDF4_utils), 0, 0, 1, 1},
+ {&__pyx_n_s_netCDF4__netCDF4, __pyx_k_netCDF4__netCDF4, sizeof(__pyx_k_netCDF4__netCDF4), 0, 0, 1, 1},
{&__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},
{&__pyx_n_s_newname, __pyx_k_newname, sizeof(__pyx_k_newname), 0, 0, 1, 1},
{&__pyx_kp_s_no_since_in_unit_string, __pyx_k_no_since_in_unit_string, sizeof(__pyx_k_no_since_in_unit_string), 0, 0, 1, 0},
+ {&__pyx_n_s_normpath, __pyx_k_normpath, sizeof(__pyx_k_normpath), 0, 0, 1, 1},
{&__pyx_n_s_nptonctype, __pyx_k_nptonctype, sizeof(__pyx_k_nptonctype), 0, 0, 1, 1},
{&__pyx_n_s_npversion, __pyx_k_npversion, sizeof(__pyx_k_npversion), 0, 0, 1, 1},
{&__pyx_n_s_num, __pyx_k_num, sizeof(__pyx_k_num), 0, 0, 1, 1},
@@ -61399,6 +62491,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_kp_s_only_endian_native_allowed_for_N, __pyx_k_only_endian_native_allowed_for_N, sizeof(__pyx_k_only_endian_native_allowed_for_N), 0, 0, 1, 0},
{&__pyx_kp_s_only_numpy_string_unicode_or_obj, __pyx_k_only_numpy_string_unicode_or_obj, sizeof(__pyx_k_only_numpy_string_unicode_or_obj), 0, 0, 1, 0},
{&__pyx_n_s_ordereddict, __pyx_k_ordereddict, sizeof(__pyx_k_ordereddict), 0, 0, 1, 1},
+ {&__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_n_s_parse_date, __pyx_k_parse_date, sizeof(__pyx_k_parse_date), 0, 0, 1, 1},
@@ -61423,8 +62516,8 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_python3, __pyx_k_python3, sizeof(__pyx_k_python3), 0, 0, 1, 1},
{&__pyx_n_s_qualname, __pyx_k_qualname, sizeof(__pyx_k_qualname), 0, 0, 1, 1},
{&__pyx_n_s_quantize, __pyx_k_quantize, sizeof(__pyx_k_quantize), 0, 0, 1, 1},
- {&__pyx_kp_s_r, __pyx_k_r, sizeof(__pyx_k_r), 0, 0, 1, 0},
- {&__pyx_n_s_r_2, __pyx_k_r_2, sizeof(__pyx_k_r_2), 0, 0, 1, 1},
+ {&__pyx_n_s_r, __pyx_k_r, sizeof(__pyx_k_r), 0, 0, 1, 1},
+ {&__pyx_kp_s_r_2, __pyx_k_r_2, sizeof(__pyx_k_r_2), 0, 0, 1, 0},
{&__pyx_kp_s_r_3, __pyx_k_r_3, sizeof(__pyx_k_r_3), 0, 0, 1, 0},
{&__pyx_kp_s_r_s, __pyx_k_r_s, sizeof(__pyx_k_r_s), 0, 0, 1, 0},
{&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1},
@@ -61451,11 +62544,13 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_kp_s_s_is_one_of_the_reserved_attrib_2, __pyx_k_s_is_one_of_the_reserved_attrib_2, sizeof(__pyx_k_s_is_one_of_the_reserved_attrib_2), 0, 0, 1, 0},
{&__pyx_kp_s_s_not_a_valid_dimension_name, __pyx_k_s_not_a_valid_dimension_name, sizeof(__pyx_k_s_not_a_valid_dimension_name), 0, 0, 1, 0},
{&__pyx_kp_s_s_not_a_valid_variable_name, __pyx_k_s_not_a_valid_variable_name, sizeof(__pyx_k_s_not_a_valid_variable_name), 0, 0, 1, 0},
+ {&__pyx_kp_s_s_not_found_in_s, __pyx_k_s_not_found_in_s, sizeof(__pyx_k_s_not_found_in_s), 0, 0, 1, 0},
{&__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_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},
{&__pyx_n_s_second, __pyx_k_second, sizeof(__pyx_k_second), 0, 0, 1, 1},
{&__pyx_n_s_seconds, __pyx_k_seconds, sizeof(__pyx_k_seconds), 0, 0, 1, 1},
{&__pyx_n_s_secs, __pyx_k_secs, sizeof(__pyx_k_secs), 0, 0, 1, 1},
@@ -61473,1651 +62568,1686 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_shuffle, __pyx_k_shuffle, sizeof(__pyx_k_shuffle), 0, 0, 1, 1},
{&__pyx_n_s_since, __pyx_k_since, sizeof(__pyx_k_since), 0, 0, 1, 1},
{&__pyx_kp_s_single_element_VLEN_slices_must, __pyx_k_single_element_VLEN_slices_must, sizeof(__pyx_k_single_element_VLEN_slices_must), 0, 0, 1, 0},
- {&__pyx_n_s_size, __pyx_k_size, sizeof(__pyx_k_size), 0, 0, 1, 1},
- {&__pyx_kp_s_size_of_data_array_does_not_conf, __pyx_k_size_of_data_array_does_not_conf, sizeof(__pyx_k_size_of_data_array_does_not_conf), 0, 0, 1, 0},
- {&__pyx_n_s_slen, __pyx_k_slen, sizeof(__pyx_k_slen), 0, 0, 1, 1},
- {&__pyx_n_s_sortbylist, __pyx_k_sortbylist, sizeof(__pyx_k_sortbylist), 0, 0, 1, 1},
- {&__pyx_n_s_split, __pyx_k_split, sizeof(__pyx_k_split), 0, 0, 1, 1},
- {&__pyx_n_s_squeeze, __pyx_k_squeeze, sizeof(__pyx_k_squeeze), 0, 0, 1, 1},
- {&__pyx_n_s_sta, __pyx_k_sta, sizeof(__pyx_k_sta), 0, 0, 1, 1},
- {&__pyx_n_s_standard, __pyx_k_standard, sizeof(__pyx_k_standard), 0, 0, 1, 1},
- {&__pyx_n_s_start, __pyx_k_start, sizeof(__pyx_k_start), 0, 0, 1, 1},
- {&__pyx_n_s_startswith, __pyx_k_startswith, sizeof(__pyx_k_startswith), 0, 0, 1, 1},
- {&__pyx_n_s_step, __pyx_k_step, sizeof(__pyx_k_step), 0, 0, 1, 1},
- {&__pyx_n_s_stop, __pyx_k_stop, sizeof(__pyx_k_stop), 0, 0, 1, 1},
- {&__pyx_n_s_str, __pyx_k_str, sizeof(__pyx_k_str), 0, 0, 1, 1},
- {&__pyx_n_s_strd, __pyx_k_strd, sizeof(__pyx_k_strd), 0, 0, 1, 1},
- {&__pyx_n_s_stride, __pyx_k_stride, sizeof(__pyx_k_stride), 0, 0, 1, 1},
- {&__pyx_kp_s_strides_must_all_be_1_for_string, __pyx_k_strides_must_all_be_1_for_string, sizeof(__pyx_k_strides_must_all_be_1_for_string), 0, 0, 1, 0},
- {&__pyx_kp_s_strides_must_all_be_1_for_vlen_v, __pyx_k_strides_must_all_be_1_for_vlen_v, sizeof(__pyx_k_strides_must_all_be_1_for_vlen_v), 0, 0, 1, 0},
- {&__pyx_n_s_string, __pyx_k_string, sizeof(__pyx_k_string), 0, 0, 1, 1},
- {&__pyx_kp_s_string_type, __pyx_k_string_type, sizeof(__pyx_k_string_type), 0, 0, 1, 0},
- {&__pyx_n_s_stringtoarr, __pyx_k_stringtoarr, sizeof(__pyx_k_stringtoarr), 0, 0, 1, 1},
- {&__pyx_n_s_stringtochar, __pyx_k_stringtochar, sizeof(__pyx_k_stringtochar), 0, 0, 1, 1},
- {&__pyx_n_s_strip, __pyx_k_strip, sizeof(__pyx_k_strip), 0, 0, 1, 1},
- {&__pyx_n_s_strt, __pyx_k_strt, sizeof(__pyx_k_strt), 0, 0, 1, 1},
- {&__pyx_n_s_subdtype, __pyx_k_subdtype, sizeof(__pyx_k_subdtype), 0, 0, 1, 1},
- {&__pyx_n_s_sum, __pyx_k_sum, sizeof(__pyx_k_sum), 0, 0, 1, 1},
- {&__pyx_n_s_supportedtypes, __pyx_k_supportedtypes, sizeof(__pyx_k_supportedtypes), 0, 0, 1, 1},
- {&__pyx_n_s_sys, __pyx_k_sys, sizeof(__pyx_k_sys), 0, 0, 1, 1},
- {&__pyx_n_s_t, __pyx_k_t, sizeof(__pyx_k_t), 0, 0, 1, 1},
- {&__pyx_n_s_td, __pyx_k_td, sizeof(__pyx_k_td), 0, 0, 1, 1},
- {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1},
- {&__pyx_n_s_tile, __pyx_k_tile, sizeof(__pyx_k_tile), 0, 0, 1, 1},
- {&__pyx_n_s_time, __pyx_k_time, sizeof(__pyx_k_time), 0, 0, 1, 1},
- {&__pyx_n_s_time2index, __pyx_k_time2index, sizeof(__pyx_k_time2index), 0, 0, 1, 1},
- {&__pyx_n_s_timedelta, __pyx_k_timedelta, sizeof(__pyx_k_timedelta), 0, 0, 1, 1},
- {&__pyx_n_s_times, __pyx_k_times, sizeof(__pyx_k_times), 0, 0, 1, 1},
- {&__pyx_n_s_timestr, __pyx_k_timestr, sizeof(__pyx_k_timestr), 0, 0, 1, 1},
- {&__pyx_n_s_timestr_split, __pyx_k_timestr_split, sizeof(__pyx_k_timestr_split), 0, 0, 1, 1},
- {&__pyx_kp_s_to_assign_values_to_a_non_scalar, __pyx_k_to_assign_values_to_a_non_scalar, sizeof(__pyx_k_to_assign_values_to_a_non_scalar), 0, 0, 1, 0},
- {&__pyx_kp_s_to_retrieve_values_from_a_non_sc, __pyx_k_to_retrieve_values_from_a_non_sc, sizeof(__pyx_k_to_retrieve_values_from_a_non_sc), 0, 0, 1, 0},
- {&__pyx_n_s_tolist, __pyx_k_tolist, sizeof(__pyx_k_tolist), 0, 0, 1, 1},
- {&__pyx_n_s_toma, __pyx_k_toma, sizeof(__pyx_k_toma), 0, 0, 1, 1},
- {&__pyx_n_s_tostr, __pyx_k_tostr, sizeof(__pyx_k_tostr), 0, 0, 1, 1},
- {&__pyx_n_s_tostring, __pyx_k_tostring, sizeof(__pyx_k_tostring), 0, 0, 1, 1},
- {&__pyx_n_s_totaltime, __pyx_k_totaltime, sizeof(__pyx_k_totaltime), 0, 0, 1, 1},
- {&__pyx_n_s_traceback, __pyx_k_traceback, sizeof(__pyx_k_traceback), 0, 0, 1, 1},
- {&__pyx_n_s_tsecs, __pyx_k_tsecs, sizeof(__pyx_k_tsecs), 0, 0, 1, 1},
- {&__pyx_kp_s_type_must_string_or_unicode_S_or, __pyx_k_type_must_string_or_unicode_S_or, sizeof(__pyx_k_type_must_string_or_unicode_S_or), 0, 0, 1, 0},
- {&__pyx_n_s_typecode, __pyx_k_typecode, sizeof(__pyx_k_typecode), 0, 0, 1, 1},
- {&__pyx_n_s_typeid, __pyx_k_typeid, sizeof(__pyx_k_typeid), 0, 0, 1, 1},
- {&__pyx_kp_s_u, __pyx_k_u, sizeof(__pyx_k_u), 0, 0, 1, 0},
- {&__pyx_n_s_u1, __pyx_k_u1, sizeof(__pyx_k_u1), 0, 0, 1, 1},
- {&__pyx_n_s_u2, __pyx_k_u2, sizeof(__pyx_k_u2), 0, 0, 1, 1},
- {&__pyx_n_s_u4, __pyx_k_u4, sizeof(__pyx_k_u4), 0, 0, 1, 1},
- {&__pyx_n_s_u8, __pyx_k_u8, sizeof(__pyx_k_u8), 0, 0, 1, 1},
- {&__pyx_n_s_unicode, __pyx_k_unicode, sizeof(__pyx_k_unicode), 0, 0, 1, 1},
- {&__pyx_n_s_unicode_error, __pyx_k_unicode_error, sizeof(__pyx_k_unicode_error), 0, 0, 1, 1},
- {&__pyx_n_s_unit, __pyx_k_unit, sizeof(__pyx_k_unit), 0, 0, 1, 1},
- {&__pyx_n_s_units, __pyx_k_units, sizeof(__pyx_k_units), 0, 0, 1, 1},
- {&__pyx_n_s_unlimdims, __pyx_k_unlimdims, sizeof(__pyx_k_unlimdims), 0, 0, 1, 1},
- {&__pyx_kp_s_unlimited_dimensions_s, __pyx_k_unlimited_dimensions_s, sizeof(__pyx_k_unlimited_dimensions_s), 0, 0, 1, 0},
- {&__pyx_kp_s_unlimited_dimensions_s_2, __pyx_k_unlimited_dimensions_s_2, sizeof(__pyx_k_unlimited_dimensions_s_2), 0, 0, 1, 0},
- {&__pyx_kp_s_unlimited_name_s_size_s, __pyx_k_unlimited_name_s_size_s, sizeof(__pyx_k_unlimited_name_s_size_s), 0, 0, 1, 0},
- {&__pyx_kp_s_unsupported_component_type_for_V, __pyx_k_unsupported_component_type_for_V, sizeof(__pyx_k_unsupported_component_type_for_V), 0, 0, 1, 0},
- {&__pyx_kp_s_unsupported_datatype_specified_f, __pyx_k_unsupported_datatype_specified_f, sizeof(__pyx_k_unsupported_datatype_specified_f), 0, 0, 1, 0},
- {&__pyx_n_s_utc_offset, __pyx_k_utc_offset, sizeof(__pyx_k_utc_offset), 0, 0, 1, 1},
- {&__pyx_kp_s_utf_8, __pyx_k_utf_8, sizeof(__pyx_k_utf_8), 0, 0, 1, 0},
- {&__pyx_n_s_utime, __pyx_k_utime, sizeof(__pyx_k_utime), 0, 0, 1, 1},
- {&__pyx_n_s_v, __pyx_k_v, sizeof(__pyx_k_v), 0, 0, 1, 1},
- {&__pyx_n_s_vInst, __pyx_k_vInst, sizeof(__pyx_k_vInst), 0, 0, 1, 1},
- {&__pyx_n_s_vName, __pyx_k_vName, sizeof(__pyx_k_vName), 0, 0, 1, 1},
- {&__pyx_n_s_val, __pyx_k_val, sizeof(__pyx_k_val), 0, 0, 1, 1},
- {&__pyx_n_s_value, __pyx_k_value, sizeof(__pyx_k_value), 0, 0, 1, 1},
- {&__pyx_n_s_value_2, __pyx_k_value_2, sizeof(__pyx_k_value_2), 0, 0, 1, 1},
- {&__pyx_n_s_values, __pyx_k_values, sizeof(__pyx_k_values), 0, 0, 1, 1},
- {&__pyx_n_s_var, __pyx_k_var, sizeof(__pyx_k_var), 0, 0, 1, 1},
- {&__pyx_n_s_varInfo, __pyx_k_varInfo, sizeof(__pyx_k_varInfo), 0, 0, 1, 1},
- {&__pyx_kp_s_variable_s_data_type_mismatch_be, __pyx_k_variable_s_data_type_mismatch_be, sizeof(__pyx_k_variable_s_data_type_mismatch_be), 0, 0, 1, 0},
- {&__pyx_kp_s_variable_s_dimensions_mismatch_b, __pyx_k_variable_s_dimensions_mismatch_b, sizeof(__pyx_k_variable_s_dimensions_mismatch_b), 0, 0, 1, 0},
- {&__pyx_kp_s_variable_s_rank_mismatch_between, __pyx_k_variable_s_rank_mismatch_between, sizeof(__pyx_k_variable_s_rank_mismatch_between), 0, 0, 1, 0},
- {&__pyx_kp_s_variable_s_shape_mismatch_betwee, __pyx_k_variable_s_shape_mismatch_betwee, sizeof(__pyx_k_variable_s_shape_mismatch_betwee), 0, 0, 1, 0},
- {&__pyx_n_s_variables, __pyx_k_variables, sizeof(__pyx_k_variables), 0, 0, 1, 1},
- {&__pyx_kp_s_variables_dimensions_s, __pyx_k_variables_dimensions_s, sizeof(__pyx_k_variables_dimensions_s), 0, 0, 1, 0},
- {&__pyx_kp_s_variables_s, __pyx_k_variables_s, sizeof(__pyx_k_variables_s), 0, 0, 1, 0},
- {&__pyx_n_s_varid, __pyx_k_varid, sizeof(__pyx_k_varid), 0, 0, 1, 1},
- {&__pyx_n_s_varname, __pyx_k_varname, sizeof(__pyx_k_varname), 0, 0, 1, 1},
- {&__pyx_n_s_varnames, __pyx_k_varnames, sizeof(__pyx_k_varnames), 0, 0, 1, 1},
- {&__pyx_n_s_vars, __pyx_k_vars, sizeof(__pyx_k_vars), 0, 0, 1, 1},
- {&__pyx_n_s_version, __pyx_k_version, sizeof(__pyx_k_version), 0, 0, 1, 1},
- {&__pyx_n_s_version_info, __pyx_k_version_info, sizeof(__pyx_k_version_info), 0, 0, 1, 1},
- {&__pyx_n_s_vid, __pyx_k_vid, sizeof(__pyx_k_vid), 0, 0, 1, 1},
- {&__pyx_n_s_view, __pyx_k_view, sizeof(__pyx_k_view), 0, 0, 1, 1},
- {&__pyx_n_s_vlen, __pyx_k_vlen, sizeof(__pyx_k_vlen), 0, 0, 1, 1},
- {&__pyx_kp_s_vlen_data_type_s, __pyx_k_vlen_data_type_s, sizeof(__pyx_k_vlen_data_type_s), 0, 0, 1, 0},
- {&__pyx_kp_s_vlen_string_array_attributes_not, __pyx_k_vlen_string_array_attributes_not, sizeof(__pyx_k_vlen_string_array_attributes_not), 0, 0, 1, 0},
- {&__pyx_n_s_vltype, __pyx_k_vltype, sizeof(__pyx_k_vltype), 0, 0, 1, 1},
- {&__pyx_n_s_vltypes, __pyx_k_vltypes, sizeof(__pyx_k_vltypes), 0, 0, 1, 1},
- {&__pyx_n_s_w, __pyx_k_w, sizeof(__pyx_k_w), 0, 0, 1, 1},
- {&__pyx_n_s_walk_grps, __pyx_k_walk_grps, sizeof(__pyx_k_walk_grps), 0, 0, 1, 1},
- {&__pyx_n_s_warn, __pyx_k_warn, sizeof(__pyx_k_warn), 0, 0, 1, 1},
- {&__pyx_n_s_warnings, __pyx_k_warnings, sizeof(__pyx_k_warnings), 0, 0, 1, 1},
- {&__pyx_n_s_weakref, __pyx_k_weakref, sizeof(__pyx_k_weakref), 0, 0, 1, 1},
- {&__pyx_kp_s_wrong_data_type_in_object_array, __pyx_k_wrong_data_type_in_object_array, sizeof(__pyx_k_wrong_data_type_in_object_array), 0, 0, 1, 0},
- {&__pyx_kp_s_wrong_data_type_should_be_s_got, __pyx_k_wrong_data_type_should_be_s_got, sizeof(__pyx_k_wrong_data_type_should_be_s_got), 0, 0, 1, 0},
- {&__pyx_n_s_ws, __pyx_k_ws, sizeof(__pyx_k_ws), 0, 0, 1, 1},
- {&__pyx_n_s_year, __pyx_k_year, sizeof(__pyx_k_year), 0, 0, 1, 1},
- {&__pyx_n_s_zeros, __pyx_k_zeros, sizeof(__pyx_k_zeros), 0, 0, 1, 1},
- {&__pyx_n_s_zip, __pyx_k_zip, sizeof(__pyx_k_zip), 0, 0, 1, 1},
- {&__pyx_n_s_zlib, __pyx_k_zlib, sizeof(__pyx_k_zlib), 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 = 777; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_object = __Pyx_GetBuiltinName(__pyx_n_s_object); if (!__pyx_builtin_object) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 564; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 15; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 87; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_IOError = __Pyx_GetBuiltinName(__pyx_n_s_IOError); if (!__pyx_builtin_IOError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 410; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_sum = __Pyx_GetBuiltinName(__pyx_n_s_sum); if (!__pyx_builtin_sum) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 499; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_AttributeError = __Pyx_GetBuiltinName(__pyx_n_s_AttributeError); if (!__pyx_builtin_AttributeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 603; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 646; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_builtin_IndexError = __Pyx_GetBuiltinName(__pyx_n_s_IndexError); if (!__pyx_builtin_IndexError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 663; __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 = 893; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __Pyx_GOTREF(__pyx_tuple__91);
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- __Pyx_GOTREF(__pyx_tuple__92);
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- __Pyx_GOTREF(__pyx_tuple__95);
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- __Pyx_GOTREF(__pyx_slice__96);
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- __Pyx_GOTREF(__pyx_tuple__97);
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- __Pyx_GOTREF(__pyx_tuple__98);
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+ __Pyx_GIVEREF(__pyx_tuple__90);
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+ /* "netCDF4/_netCDF4.pyx":4265
* namstring = bytestr
* dt = numpy.dtype(dt) # convert to numpy datatype.
* if dt.str[1:] in _supportedtypes: # <<<<<<<<<<<<<<
* # find netCDF primitive data type corresponding to
* # specified numpy data type.
*/
- __pyx_slice__110 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__110)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4149; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__110);
- __Pyx_GIVEREF(__pyx_slice__110);
+ __pyx_slice__91 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__91)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4265; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__91);
+ __Pyx_GIVEREF(__pyx_slice__91);
- /* "netCDF4.pyx":4152
+ /* "netCDF4/_netCDF4.pyx":4268
* # find netCDF primitive data type corresponding to
* # specified numpy data type.
* xtype_tmp = _nptonctype[dt.str[1:]] # <<<<<<<<<<<<<<
* ierr = nc_def_vlen(grp._grpid, namstring, xtype_tmp, &xtype);
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*/
- __pyx_slice__111 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__111)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4152; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_slice__111);
- __Pyx_GIVEREF(__pyx_slice__111);
+ __pyx_slice__92 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__92)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4268; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_slice__92);
+ __Pyx_GIVEREF(__pyx_slice__92);
- /* "netCDF4.pyx":4157
- * raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
+ /* "netCDF4/_netCDF4.pyx":4273
+ * raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
* else:
* raise KeyError("unsupported datatype specified for VLEN") # <<<<<<<<<<<<<<
* return xtype, dt
*
*/
- __pyx_tuple__112 = PyTuple_Pack(1, __pyx_kp_s_unsupported_datatype_specified_f); if (unlikely(!__pyx_tuple__112)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4157; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__112);
- __Pyx_GIVEREF(__pyx_tuple__112);
+ __pyx_tuple__93 = PyTuple_Pack(1, __pyx_kp_s_unsupported_datatype_specified_f); if (unlikely(!__pyx_tuple__93)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4273; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__93);
+ __Pyx_GIVEREF(__pyx_tuple__93);
- /* "netCDF4.pyx":4184
+ /* "netCDF4/_netCDF4.pyx":4300
* dt = numpy.dtype(datatype) # see if it is a primitive type
* except KeyError:
* raise KeyError("unsupported component type for VLEN") # <<<<<<<<<<<<<<
* return VLType(group, dt, name, typeid=xtype)
*
*/
- __pyx_tuple__113 = PyTuple_Pack(1, __pyx_kp_s_unsupported_component_type_for_V); if (unlikely(!__pyx_tuple__113)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4184; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__113);
- __Pyx_GIVEREF(__pyx_tuple__113);
+ __pyx_tuple__94 = PyTuple_Pack(1, __pyx_kp_s_unsupported_component_type_for_V); if (unlikely(!__pyx_tuple__94)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4300; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__94);
+ __Pyx_GIVEREF(__pyx_tuple__94);
+
+ /* "netCDF4/_netCDF4.pyx":4331
+ * units = timestr_split[0].lower()
+ * if timestr_split[1].lower() != 'since':
+ * raise ValueError("no 'since' in unit_string") # <<<<<<<<<<<<<<
+ * # parse the date string.
+ * n = timestr.find('since')+6
+ */
+ __pyx_tuple__95 = PyTuple_Pack(1, __pyx_kp_s_no_since_in_unit_string); if (unlikely(!__pyx_tuple__95)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4331; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__95);
+ __Pyx_GIVEREF(__pyx_tuple__95);
+
+ /* "netCDF4/_netCDF4.pyx":4333
+ * raise ValueError("no 'since' in unit_string")
+ * # parse the date string.
+ * n = timestr.find('since')+6 # <<<<<<<<<<<<<<
+ * isostring = timestr[n:]
+ * year, month, day, hour, minute, second, utc_offset =\
+ */
+ __pyx_tuple__96 = PyTuple_Pack(1, __pyx_n_s_since); if (unlikely(!__pyx_tuple__96)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4333; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__96);
+ __Pyx_GIVEREF(__pyx_tuple__96);
+
+ /* "netCDF4/_netCDF4.pyx":4360
+ * (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')
+ * arr[0:len(string)] = tuple(string)
+ */
+ __pyx_tuple__97 = PyTuple_Pack(1, __pyx_kp_s_dtype_must_string_or_unicode_S_o); if (unlikely(!__pyx_tuple__97)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__97);
+ __Pyx_GIVEREF(__pyx_tuple__97);
+
+ /* "netCDF4/_netCDF4.pyx":4379
+ * dtype = a.dtype.kind
+ * if dtype not in ["S","U"]:
+ * raise ValueError("type must string or unicode ('S' or 'U')") # <<<<<<<<<<<<<<
+ * b = numpy.array(tuple(a.tostring().decode(default_encoding)),dtype+'1')
+ * b.shape = a.shape + (a.itemsize,)
+ */
+ __pyx_tuple__98 = PyTuple_Pack(1, __pyx_kp_s_type_must_string_or_unicode_S_or); if (unlikely(!__pyx_tuple__98)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4379; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__98);
+ __Pyx_GIVEREF(__pyx_tuple__98);
+
+ /* "netCDF4/_netCDF4.pyx":4398
+ * dtype = b.dtype.kind
+ * if dtype not in ["S","U"]:
+ * raise ValueError("type must string or unicode ('S' or 'U')") # <<<<<<<<<<<<<<
+ * bs = b.tostring().decode(default_encoding)
+ * slen = int(b.shape[-1])
+ */
+ __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 = 4398; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__99);
+ __Pyx_GIVEREF(__pyx_tuple__99);
+
+ /* "netCDF4/_netCDF4.pyx":4402
+ * slen = int(b.shape[-1])
+ * a = numpy.array([bs[n1:n1+slen] for n1 in range(0,len(bs),slen)],dtype+repr(slen))
+ * a.shape = b.shape[:-1] # <<<<<<<<<<<<<<
+ * return a
+ *
+ */
+ __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);
+
+ /* "netCDF4/_netCDF4.pyx":4475
+ * times.append(totaltime/1.e6/3600./24.)
+ * else:
+ * raise ValueError('unsupported time units') # <<<<<<<<<<<<<<
+ * if isscalar:
+ * return times[0]
+ */
+ __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);
+
+ /* "netCDF4/_netCDF4.pyx":4561
+ * 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);
+
+ /* "netCDF4/_netCDF4.pyx":4691
+ * # 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);
+
+ /* "netCDF4/_netCDF4.pyx":4753
+ * # 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:]: # <<<<<<<<<<<<<<
+ * part = Dataset(f)
+ * varInfo = part.variables
+ */
+ __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);
+
+ /* "netCDF4/_netCDF4.pyx":4764
+ * #if not vInst.dimensions[0] != aggDimName:
+ *
+ * masterDims, masterShape, masterType = masterRecVar[v][:3] # <<<<<<<<<<<<<<
+ * extDims = varInfo[v].dimensions
+ * extShape = varInfo[v].shape
+ */
+ __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);
+
+ /* "netCDF4/_netCDF4.pyx":4781
+ * "master %s (%s) and extension %s (%s)" %
+ * (v, master, len(masterShape), f, len(extShape)))
+ * if masterShape[1:] != extShape[1:]: # <<<<<<<<<<<<<<
+ * raise IOError("variable %s : shape mismatch between "
+ * "master %s (%s) and extension %s (%s)" %
+ */
+ __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_GOTREF(__pyx_slice__107);
+ __Pyx_GIVEREF(__pyx_slice__107);
+
+ /* "netCDF4/_netCDF4.pyx":4824
+ * 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)
+ * self._data_model.append(dset.data_model)
+ */
+ __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);
+
+ /* "netCDF4/_netCDF4.pyx":4932
+ * def _shape(self):
+ * recdimlen = len(self._dset.dimensions[self._recdimname])
+ * return (recdimlen,) + self._mastervar.shape[1:] # <<<<<<<<<<<<<<
+ * def set_auto_maskandscale(self,val):
+ * for v in self._recVar:
+ */
+ __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);
+
+ /* "netCDF4/_netCDF4.pyx":4953
+ * # 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);
+
+ /* "netCDF4/_netCDF4.pyx":4954
+ * # 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);
+
+ /* "netCDF4/_netCDF4.pyx":4971
+ * 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);
+
+ /* "netCDF4/_netCDF4.pyx":4995
+ *
+ * # 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);
+
+ /* "netCDF4/_netCDF4.pyx":5096
+ * 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);
+
+ /* "netCDF4/_netCDF4.pyx":5100
+ * # 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);
+
+ /* "netCDF4/_netCDF4.pyx":5106
+ *
+ * # 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);
- /* "netCDF4.pyx":781
+ /* "netCDF4/_netCDF4.pyx":820
* 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__114 = PyTuple_Pack(1, __pyx_kp_s_please_install_ordereddict_https); if (unlikely(!__pyx_tuple__114)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 781; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__114);
- __Pyx_GIVEREF(__pyx_tuple__114);
+ __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);
- /* "netCDF4.pyx":800
+ /* "netCDF4/_netCDF4.pyx":839
* 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__116 = PyTuple_Pack(1, __pyx_kp_s__115); if (unlikely(!__pyx_tuple__116)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 800; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__116);
- __Pyx_GIVEREF(__pyx_tuple__116);
+ __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);
+
+ /* "netCDF4/_netCDF4.pyx":840
+ * 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);
+
+ /* "netCDF4/_netCDF4.pyx":847
+ * # check for required version of netcdf-4 and hdf5.
+ *
+ * def _gethdf5libversion(): # <<<<<<<<<<<<<<
+ * majorvers = H5_VERS_MAJOR
+ * minorvers = H5_VERS_MINOR
+ */
+ __pyx_tuple__121 = PyTuple_Pack(4, __pyx_n_s_majorvers, __pyx_n_s_minorvers, __pyx_n_s_releasevers, __pyx_n_s_patchstring); if (unlikely(!__pyx_tuple__121)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 847; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__121);
+ __Pyx_GIVEREF(__pyx_tuple__121);
+ __pyx_codeobj__122 = (PyObject*)__Pyx_PyCode_New(0, 0, 4, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__121, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_jsw_python_netcdf4_python, __pyx_n_s_gethdf5libversion, 847, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__122)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 847; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+ /* "netCDF4/_netCDF4.pyx":857
+ * return '%d.%d.%d-%s' % (majorvers,minorvers,releasevers,patchstring)
+ *
+ * def getlibversion(): # <<<<<<<<<<<<<<
+ * """
+ * getlibversion()
+ */
+ __pyx_codeobj__123 = (PyObject*)__Pyx_PyCode_New(0, 0, 0, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_jsw_python_netcdf4_python, __pyx_n_s_getlibversion, 857, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__123)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 857; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+ /* "netCDF4/_netCDF4.pyx":902
+ * 'f8':NC_FILL_DOUBLE}
+ *
+ * is_native_little = numpy.dtype('<f4').byteorder == '=' # <<<<<<<<<<<<<<
+ * is_native_big = numpy.dtype('>f4').byteorder == '='
+ *
+ */
+ __pyx_tuple__124 = PyTuple_Pack(1, __pyx_kp_s_f4_2); if (unlikely(!__pyx_tuple__124)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 902; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__124);
+ __Pyx_GIVEREF(__pyx_tuple__124);
- /* "netCDF4.pyx":801
- * 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"
+ /* "netCDF4/_netCDF4.pyx":903
+ *
+ * is_native_little = numpy.dtype('<f4').byteorder == '='
+ * is_native_big = numpy.dtype('>f4').byteorder == '=' # <<<<<<<<<<<<<<
+ *
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*/
- __pyx_tuple__117 = PyTuple_Pack(1, __pyx_kp_s_requires_numpy_version_1_0rc1_or); if (unlikely(!__pyx_tuple__117)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 801; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __Pyx_GOTREF(__pyx_tuple__117);
- __Pyx_GIVEREF(__pyx_tuple__117);
+ __pyx_tuple__125 = PyTuple_Pack(1, __pyx_kp_s_f4_3); if (unlikely(!__pyx_tuple__125)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 903; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__125);
+ __Pyx_GIVEREF(__pyx_tuple__125);
- /* "utils.pyx":5
+ /* "netCDF4/_netCDF4.pyx":1011
+ * return value_arr
+ *
+ * def _set_default_format(object format='NETCDF4'): # <<<<<<<<<<<<<<
+ * # Private function to set the netCDF file format
+ * if format == 'NETCDF4':
+ */
+ __pyx_tuple__126 = PyTuple_Pack(1, __pyx_n_s_format); if (unlikely(!__pyx_tuple__126)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__126);
+ __Pyx_GIVEREF(__pyx_tuple__126);
+ __pyx_codeobj__127 = (PyObject*)__Pyx_PyCode_New(1, 0, 1, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__126, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_jsw_python_netcdf4_python, __pyx_n_s_set_default_format, 1011, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__127)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+ /* "netCDF4/_netCDF4.pyx":4321
*
* # start of the gregorian calendar
* gregorian = datetime(1582,10,15) # <<<<<<<<<<<<<<
*
* def _dateparse(timestr):
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- __Pyx_GOTREF(__pyx_tuple__118);
- __Pyx_GIVEREF(__pyx_tuple__118);
+ __pyx_tuple__128 = PyTuple_Pack(3, __pyx_int_1582, __pyx_int_10, __pyx_int_15); if (unlikely(!__pyx_tuple__128)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4321; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_tuple__128);
+ __Pyx_GIVEREF(__pyx_tuple__128);
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+ /* "netCDF4/_netCDF4.pyx":4323
* gregorian = datetime(1582,10,15)
*
* def _dateparse(timestr): # <<<<<<<<<<<<<<
* """parse a string of the form time-units since yyyy-mm-dd hh:mm:ss,
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*/
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- /* "utils.pyx":28
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* def stringtoarr(string,NUMCHARS,dtype='S'): # <<<<<<<<<<<<<<
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- /* "utils.pyx":51
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* return arr
*
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- /* "utils.pyx":70
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*
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- /* "utils.pyx":91
+ /* "netCDF4/_netCDF4.pyx":4405
* return a
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- /* "utils.pyx":168
+ /* "netCDF4/_netCDF4.pyx":4484
* return cdftime.date2num(dates)
*
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* """
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- /* "utils.pyx":261
+ /* "netCDF4/_netCDF4.pyx":4579
* return cdftime.num2date(times)
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-PyMODINIT_FUNC initnetCDF4(void); /*proto*/
-PyMODINIT_FUNC initnetCDF4(void)
+PyMODINIT_FUNC init_netCDF4(void); /*proto*/
+PyMODINIT_FUNC init_netCDF4(void)
#else
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-PyMODINIT_FUNC PyInit_netCDF4(void)
+PyMODINIT_FUNC PyInit__netCDF4(void); /*proto*/
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{
PyObject *__pyx_t_1 = NULL;
@@ -63195,7 +64325,7 @@ PyMODINIT_FUNC PyInit_netCDF4(void)
Py_FatalError("failed to import 'refnanny' module");
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/*--- Type import code ---*/
/*--- Variable import code ---*/
/*--- Function import code ---*/
/*--- Execution code ---*/
- /* "netCDF4.pyx":766
+ /* "netCDF4/_netCDF4.pyx":805
* OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
* PERFORMANCE OF THIS SOFTWARE."""
* __test__ = None # <<<<<<<<<<<<<<
* del __test__ # hack so epydoc doesn't show __test__
*
*/
- if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, Py_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 766; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, Py_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 805; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- /* "netCDF4.pyx":767
+ /* "netCDF4/_netCDF4.pyx":806
* PERFORMANCE OF THIS SOFTWARE."""
* __test__ = None
* del __test__ # hack so epydoc doesn't show __test__ # <<<<<<<<<<<<<<
*
* # Make changes to this file, not the c-wrappers that Pyrex generates.
*/
- if (__Pyx_PyObject_DelAttrStr(__pyx_m, __pyx_n_s_test) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 767; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (__Pyx_PyObject_DelAttrStr(__pyx_m, __pyx_n_s_test) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 806; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- /* "netCDF4.pyx":772
+ /* "netCDF4/_netCDF4.pyx":811
*
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- * from netCDF4_utils import _StartCountStride, _quantize, _find_dim, _walk_grps, \ # <<<<<<<<<<<<<<
- * _out_array_shape, _sortbylist, _tostr
+ * from .utils import (_StartCountStride, _quantize, _find_dim, _walk_grps, # <<<<<<<<<<<<<<
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*/
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__Pyx_INCREF(__pyx_n_s_tostr);
PyList_SET_ITEM(__pyx_t_1, 6, __pyx_n_s_tostr);
__Pyx_GIVEREF(__pyx_n_s_tostr);
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__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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__Pyx_XGOTREF(__pyx_t_5);
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__Pyx_GIVEREF(__pyx_n_s_OrderedDict);
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__Pyx_XGOTREF(__pyx_t_10);
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__pyx_L29_try_end:;
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Py_DECREF(__pyx_m); __pyx_m = 0;
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- self = PyCFunction_GET_SELF(func);
- if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
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- result = cfunc(self, arg);
- Py_LeaveRecursiveCall();
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- return __Pyx__PyObject_CallOneArg(func, arg);
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- PyObject* args = PyTuple_Pack(1, arg);
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- int wraparound, int boundscheck) {
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- PyObject *r = PyList_GET_ITEM(o, i);
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- return r;
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- return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
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- return PySequence_GetItem(o, i);
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- int wraparound, int boundscheck) {
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- if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
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- PyObject *r = PyTuple_GET_ITEM(o, i);
- Py_INCREF(r);
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-#endif
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-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
- int is_list, int wraparound, int boundscheck) {
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- PyObject *r = PyList_GET_ITEM(o, n);
- Py_INCREF(r);
- return r;
- }
- }
- else if (PyTuple_CheckExact(o)) {
- Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
- if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
- PyObject *r = PyTuple_GET_ITEM(o, n);
- Py_INCREF(r);
- return r;
- }
- } else {
- PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
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- if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
- Py_ssize_t l = m->sq_length(o);
- if (likely(l >= 0)) {
- i += l;
- } else {
- if (PyErr_ExceptionMatches(PyExc_OverflowError))
- PyErr_Clear();
- else
- return NULL;
- }
- }
- return m->sq_item(o, i);
+static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
+ const char* cstring, Py_ssize_t start, Py_ssize_t stop,
+ const char* encoding, const char* errors,
+ PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
+ Py_ssize_t length;
+ if (unlikely((start < 0) | (stop < 0))) {
+ length = strlen(cstring);
+ if (start < 0) {
+ start += length;
+ if (start < 0)
+ start = 0;
}
+ if (stop < 0)
+ stop += length;
}
-#else
- if (is_list || PySequence_Check(o)) {
- return PySequence_GetItem(o, i);
- }
-#endif
- return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
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-
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
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- return (equals == Py_NE);
- } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
- return (equals == Py_NE);
+ length = stop - start;
+ if (unlikely(length <= 0))
+ return PyUnicode_FromUnicode(NULL, 0);
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+ if (decode_func) {
+ return decode_func(cstring, length, errors);
} else {
- int result;
- PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
- if (!py_result)
- return -1;
- result = __Pyx_PyObject_IsTrue(py_result);
- Py_DECREF(py_result);
- return result;
+ return PyUnicode_Decode(cstring, length, encoding, errors);
}
-#endif
}
-
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
- return PyObject_RichCompareBool(s1, s2, equals);
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- PyObject* owned_ref = NULL;
-#endif
- int s1_is_unicode, s2_is_unicode;
- if (s1 == s2) {
- goto return_eq;
- }
- s1_is_unicode = PyUnicode_CheckExact(s1);
- s2_is_unicode = PyUnicode_CheckExact(s2);
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- s1 = owned_ref;
- s1_is_unicode = 1;
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- return __Pyx_PyBytes_Equals(s1, s2, equals);
- }
-#endif
- if (s1_is_unicode & s2_is_unicode) {
- Py_ssize_t length;
- int kind;
- void *data1, *data2;
- if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
- return -1;
- length = __Pyx_PyUnicode_GET_LENGTH(s1);
- if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
- goto return_ne;
- }
- kind = __Pyx_PyUnicode_KIND(s1);
- if (kind != __Pyx_PyUnicode_KIND(s2)) {
- goto return_ne;
- }
- data1 = __Pyx_PyUnicode_DATA(s1);
- data2 = __Pyx_PyUnicode_DATA(s2);
- if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
- goto return_ne;
- } else if (length == 1) {
- goto return_eq;
- } else {
- int result = memcmp(data1, data2, (size_t)(length * kind));
- #if PY_MAJOR_VERSION < 3
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- } else if ((s2 == Py_None) & s1_is_unicode) {
- goto return_ne;
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- int result;
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- if (!py_result)
- return -1;
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- Py_DECREF(py_result);
- return result;
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
+ PyObject *result;
+ ternaryfunc call = func->ob_type->tp_call;
+ if (unlikely(!call))
+ return PyObject_Call(func, arg, kw);
+ if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
+ return NULL;
+ result = (*call)(func, arg, kw);
+ Py_LeaveRecursiveCall();
+ if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
+ PyErr_SetString(
+ PyExc_SystemError,
+ "NULL result without error in PyObject_Call");
}
-return_eq:
- #if PY_MAJOR_VERSION < 3
- Py_XDECREF(owned_ref);
- #endif
- return (equals == Py_EQ);
-return_ne:
- #if PY_MAJOR_VERSION < 3
- Py_XDECREF(owned_ref);
- #endif
- return (equals == Py_NE);
-#endif
+ return result;
}
+#endif
static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) {
#if CYTHON_COMPILING_IN_CPYTHON
@@ -65684,103 +66618,6 @@ bad:
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-#if CYTHON_COMPILING_IN_CPYTHON
- PyMappingMethods* mp;
-#if PY_MAJOR_VERSION < 3
- PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
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- if (_py_start && (*_py_start != Py_None)) {
- cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
- if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
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- if (!has_cstop) {
- if (_py_stop && (*_py_stop != Py_None)) {
- cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
- if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
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- cstop = PY_SSIZE_T_MAX;
- }
- if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
- Py_ssize_t l = ms->sq_length(obj);
- if (likely(l >= 0)) {
- if (cstop < 0) {
- cstop += l;
- if (cstop < 0) cstop = 0;
- }
- if (cstart < 0) {
- cstart += l;
- if (cstart < 0) cstart = 0;
- }
- } else {
- if (PyErr_ExceptionMatches(PyExc_OverflowError))
- PyErr_Clear();
- else
- goto bad;
- }
- }
- return ms->sq_slice(obj, cstart, cstop);
- }
-#endif
- mp = Py_TYPE(obj)->tp_as_mapping;
- if (likely(mp && mp->mp_subscript))
-#endif
- {
- PyObject* result;
- PyObject *py_slice, *py_start, *py_stop;
- if (_py_slice) {
- py_slice = *_py_slice;
- } else {
- PyObject* owned_start = NULL;
- PyObject* owned_stop = NULL;
- if (_py_start) {
- py_start = *_py_start;
- } else {
- if (has_cstart) {
- owned_start = py_start = PyInt_FromSsize_t(cstart);
- if (unlikely(!py_start)) goto bad;
- } else
- py_start = Py_None;
- }
- if (_py_stop) {
- py_stop = *_py_stop;
- } else {
- if (has_cstop) {
- owned_stop = py_stop = PyInt_FromSsize_t(cstop);
- if (unlikely(!py_stop)) {
- Py_XDECREF(owned_start);
- goto bad;
- }
- } else
- py_stop = Py_None;
- }
- py_slice = PySlice_New(py_start, py_stop, Py_None);
- Py_XDECREF(owned_start);
- Py_XDECREF(owned_stop);
- if (unlikely(!py_slice)) goto bad;
- }
-#if CYTHON_COMPILING_IN_CPYTHON
- result = mp->mp_subscript(obj, py_slice);
-#else
- result = PyObject_GetItem(obj, py_slice);
-#endif
- if (!_py_slice) {
- Py_DECREF(py_slice);
- }
- return result;
- }
- PyErr_Format(PyExc_TypeError,
- "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name);
-bad:
- return NULL;
-}
-
static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {
PyObject *result;
#if CYTHON_COMPILING_IN_CPYTHON
@@ -65798,85 +66635,366 @@ static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {
return result;
}
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
- PyErr_Format(PyExc_ValueError,
- "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
- PyErr_Format(PyExc_ValueError,
- "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
- index, (index == 1) ? "" : "s");
+static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
+ if (unlikely(!type)) {
+ PyErr_SetString(PyExc_SystemError, "Missing type object");
+ return 0;
+ }
+ if (likely(PyObject_TypeCheck(obj, type)))
+ return 1;
+ PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
+ Py_TYPE(obj)->tp_name, type->tp_name);
+ return 0;
}
-static CYTHON_INLINE int __Pyx_IterFinish(void) {
-#if CYTHON_COMPILING_IN_CPYTHON
- PyThreadState *tstate = PyThreadState_GET();
- PyObject* exc_type = tstate->curexc_type;
- if (unlikely(exc_type)) {
- if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) {
- PyObject *exc_value, *exc_tb;
- exc_value = tstate->curexc_value;
- exc_tb = tstate->curexc_traceback;
- tstate->curexc_type = 0;
- tstate->curexc_value = 0;
- tstate->curexc_traceback = 0;
- Py_DECREF(exc_type);
- Py_XDECREF(exc_value);
- Py_XDECREF(exc_tb);
- return 0;
+static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
+#if CYTHON_COMPILING_IN_PYPY
+ return PyObject_RichCompareBool(s1, s2, equals);
+#else
+ if (s1 == s2) {
+ return (equals == Py_EQ);
+ } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
+ const char *ps1, *ps2;
+ Py_ssize_t length = PyBytes_GET_SIZE(s1);
+ if (length != PyBytes_GET_SIZE(s2))
+ return (equals == Py_NE);
+ ps1 = PyBytes_AS_STRING(s1);
+ ps2 = PyBytes_AS_STRING(s2);
+ if (ps1[0] != ps2[0]) {
+ return (equals == Py_NE);
+ } else if (length == 1) {
+ return (equals == Py_EQ);
} else {
- return -1;
+ int result = memcmp(ps1, ps2, (size_t)length);
+ return (equals == Py_EQ) ? (result == 0) : (result != 0);
}
+ } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
+ return (equals == Py_NE);
+ } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
+ return (equals == Py_NE);
+ } else {
+ int result;
+ PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+ if (!py_result)
+ return -1;
+ result = __Pyx_PyObject_IsTrue(py_result);
+ Py_DECREF(py_result);
+ return result;
}
- return 0;
+#endif
+}
+
+static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
+#if CYTHON_COMPILING_IN_PYPY
+ return PyObject_RichCompareBool(s1, s2, equals);
#else
- if (unlikely(PyErr_Occurred())) {
- if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
- PyErr_Clear();
- return 0;
- } else {
+#if PY_MAJOR_VERSION < 3
+ PyObject* owned_ref = NULL;
+#endif
+ int s1_is_unicode, s2_is_unicode;
+ if (s1 == s2) {
+ goto return_eq;
+ }
+ s1_is_unicode = PyUnicode_CheckExact(s1);
+ s2_is_unicode = PyUnicode_CheckExact(s2);
+#if PY_MAJOR_VERSION < 3
+ if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
+ owned_ref = PyUnicode_FromObject(s2);
+ if (unlikely(!owned_ref))
+ return -1;
+ s2 = owned_ref;
+ s2_is_unicode = 1;
+ } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
+ owned_ref = PyUnicode_FromObject(s1);
+ if (unlikely(!owned_ref))
+ return -1;
+ s1 = owned_ref;
+ s1_is_unicode = 1;
+ } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
+ return __Pyx_PyBytes_Equals(s1, s2, equals);
+ }
+#endif
+ if (s1_is_unicode & s2_is_unicode) {
+ Py_ssize_t length;
+ int kind;
+ void *data1, *data2;
+ if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
return -1;
+ length = __Pyx_PyUnicode_GET_LENGTH(s1);
+ if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
+ goto return_ne;
+ }
+ kind = __Pyx_PyUnicode_KIND(s1);
+ if (kind != __Pyx_PyUnicode_KIND(s2)) {
+ goto return_ne;
+ }
+ data1 = __Pyx_PyUnicode_DATA(s1);
+ data2 = __Pyx_PyUnicode_DATA(s2);
+ if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
+ goto return_ne;
+ } else if (length == 1) {
+ goto return_eq;
+ } else {
+ int result = memcmp(data1, data2, (size_t)(length * kind));
+ #if PY_MAJOR_VERSION < 3
+ Py_XDECREF(owned_ref);
+ #endif
+ return (equals == Py_EQ) ? (result == 0) : (result != 0);
}
+ } else if ((s1 == Py_None) & s2_is_unicode) {
+ goto return_ne;
+ } else if ((s2 == Py_None) & s1_is_unicode) {
+ goto return_ne;
+ } else {
+ int result;
+ PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+ if (!py_result)
+ return -1;
+ result = __Pyx_PyObject_IsTrue(py_result);
+ Py_DECREF(py_result);
+ return result;
}
- return 0;
+return_eq:
+ #if PY_MAJOR_VERSION < 3
+ Py_XDECREF(owned_ref);
+ #endif
+ return (equals == Py_EQ);
+return_ne:
+ #if PY_MAJOR_VERSION < 3
+ Py_XDECREF(owned_ref);
+ #endif
+ return (equals == Py_NE);
#endif
}
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
- if (unlikely(retval)) {
- Py_DECREF(retval);
- __Pyx_RaiseTooManyValuesError(expected);
- return -1;
- } else {
- return __Pyx_IterFinish();
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
+ PyObject *self, *result;
+ PyCFunction cfunc;
+ cfunc = PyCFunction_GET_FUNCTION(func);
+ self = PyCFunction_GET_SELF(func);
+ if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
+ return NULL;
+ result = cfunc(self, arg);
+ Py_LeaveRecursiveCall();
+ if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
+ PyErr_SetString(
+ PyExc_SystemError,
+ "NULL result without error in PyObject_Call");
}
- return 0;
+ return result;
}
+#endif
-static void __Pyx_RaiseArgtupleInvalid(
- const char* func_name,
- int exact,
- Py_ssize_t num_min,
- Py_ssize_t num_max,
- Py_ssize_t num_found)
-{
- Py_ssize_t num_expected;
- const char *more_or_less;
- if (num_found < num_min) {
- num_expected = num_min;
- more_or_less = "at least";
+#if CYTHON_COMPILING_IN_CPYTHON
+static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
+ PyObject *result;
+ PyObject *args = PyTuple_New(1);
+ if (unlikely(!args)) return NULL;
+ Py_INCREF(arg);
+ PyTuple_SET_ITEM(args, 0, arg);
+ result = __Pyx_PyObject_Call(func, args, NULL);
+ Py_DECREF(args);
+ return result;
+}
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
+#ifdef __Pyx_CyFunction_USED
+ if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
+#else
+ if (likely(PyCFunction_Check(func))) {
+#endif
+ if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
+ return __Pyx_PyObject_CallMethO(func, arg);
+ }
+ }
+ return __Pyx__PyObject_CallOneArg(func, arg);
+}
+#else
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
+ PyObject* args = PyTuple_Pack(1, arg);
+ return (likely(args)) ? __Pyx_PyObject_Call(func, args, NULL) : NULL;
+}
+#endif
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
+#ifdef __Pyx_CyFunction_USED
+ if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
+#else
+ if (likely(PyCFunction_Check(func))) {
+#endif
+ if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
+ return __Pyx_PyObject_CallMethO(func, NULL);
+ }
+ }
+ return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);
+}
+#endif
+
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
+ PyObject *r;
+ if (!j) return NULL;
+ r = PyObject_GetItem(o, j);
+ Py_DECREF(j);
+ return r;
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+ int wraparound, int boundscheck) {
+#if CYTHON_COMPILING_IN_CPYTHON
+ if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o);
+ if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
+ PyObject *r = PyList_GET_ITEM(o, i);
+ Py_INCREF(r);
+ return r;
+ }
+ return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+ return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+ int wraparound, int boundscheck) {
+#if CYTHON_COMPILING_IN_CPYTHON
+ if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
+ if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
+ PyObject *r = PyTuple_GET_ITEM(o, i);
+ Py_INCREF(r);
+ return r;
+ }
+ return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+ return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
+ int is_list, int wraparound, int boundscheck) {
+#if CYTHON_COMPILING_IN_CPYTHON
+ if (is_list || PyList_CheckExact(o)) {
+ Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
+ if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
+ PyObject *r = PyList_GET_ITEM(o, n);
+ Py_INCREF(r);
+ return r;
+ }
+ }
+ else if (PyTuple_CheckExact(o)) {
+ Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
+ if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
+ PyObject *r = PyTuple_GET_ITEM(o, n);
+ Py_INCREF(r);
+ return r;
+ }
} else {
- num_expected = num_max;
- more_or_less = "at most";
+ PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
+ if (likely(m && m->sq_item)) {
+ if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
+ Py_ssize_t l = m->sq_length(o);
+ if (likely(l >= 0)) {
+ i += l;
+ } else {
+ if (PyErr_ExceptionMatches(PyExc_OverflowError))
+ PyErr_Clear();
+ else
+ return NULL;
+ }
+ }
+ return m->sq_item(o, i);
+ }
}
- if (exact) {
- more_or_less = "exactly";
+#else
+ if (is_list || PySequence_Check(o)) {
+ return PySequence_GetItem(o, i);
}
- PyErr_Format(PyExc_TypeError,
- "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
- func_name, more_or_less, num_expected,
- (num_expected == 1) ? "" : "s", num_found);
+#endif
+ return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+}
+
+static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
+ PyThreadState *tstate = PyThreadState_GET();
+ *type = tstate->exc_type;
+ *value = tstate->exc_value;
+ *tb = tstate->exc_traceback;
+ Py_XINCREF(*type);
+ Py_XINCREF(*value);
+ Py_XINCREF(*tb);
+#else
+ PyErr_GetExcInfo(type, value, tb);
+#endif
+}
+static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
+ PyObject *tmp_type, *tmp_value, *tmp_tb;
+ PyThreadState *tstate = PyThreadState_GET();
+ tmp_type = tstate->exc_type;
+ tmp_value = tstate->exc_value;
+ tmp_tb = tstate->exc_traceback;
+ tstate->exc_type = type;
+ tstate->exc_value = value;
+ tstate->exc_traceback = tb;
+ Py_XDECREF(tmp_type);
+ Py_XDECREF(tmp_value);
+ Py_XDECREF(tmp_tb);
+#else
+ PyErr_SetExcInfo(type, value, tb);
+#endif
+}
+
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
+ PyObject *local_type, *local_value, *local_tb;
+#if CYTHON_COMPILING_IN_CPYTHON
+ PyObject *tmp_type, *tmp_value, *tmp_tb;
+ PyThreadState *tstate = PyThreadState_GET();
+ local_type = tstate->curexc_type;
+ local_value = tstate->curexc_value;
+ local_tb = tstate->curexc_traceback;
+ tstate->curexc_type = 0;
+ tstate->curexc_value = 0;
+ tstate->curexc_traceback = 0;
+#else
+ PyErr_Fetch(&local_type, &local_value, &local_tb);
+#endif
+ PyErr_NormalizeException(&local_type, &local_value, &local_tb);
+#if CYTHON_COMPILING_IN_CPYTHON
+ if (unlikely(tstate->curexc_type))
+#else
+ if (unlikely(PyErr_Occurred()))
+#endif
+ goto bad;
+ #if PY_MAJOR_VERSION >= 3
+ if (local_tb) {
+ if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
+ goto bad;
+ }
+ #endif
+ Py_XINCREF(local_tb);
+ Py_XINCREF(local_type);
+ Py_XINCREF(local_value);
+ *type = local_type;
+ *value = local_value;
+ *tb = local_tb;
+#if CYTHON_COMPILING_IN_CPYTHON
+ tmp_type = tstate->exc_type;
+ tmp_value = tstate->exc_value;
+ tmp_tb = tstate->exc_traceback;
+ tstate->exc_type = local_type;
+ tstate->exc_value = local_value;
+ tstate->exc_traceback = local_tb;
+ Py_XDECREF(tmp_type);
+ Py_XDECREF(tmp_value);
+ Py_XDECREF(tmp_tb);
+#else
+ PyErr_SetExcInfo(local_type, local_value, local_tb);
+#endif
+ return 0;
+bad:
+ *type = 0;
+ *value = 0;
+ *tb = 0;
+ Py_XDECREF(local_type);
+ Py_XDECREF(local_value);
+ Py_XDECREF(local_tb);
+ return -1;
}
static void __Pyx_RaiseDoubleKeywordsError(
@@ -65993,15 +67111,40 @@ bad:
return -1;
}
-static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
- PyObject* obj, PyObject* value, Py_ssize_t cstart, Py_ssize_t cstop,
+static void __Pyx_RaiseArgtupleInvalid(
+ const char* func_name,
+ int exact,
+ Py_ssize_t num_min,
+ Py_ssize_t num_max,
+ Py_ssize_t num_found)
+{
+ Py_ssize_t num_expected;
+ const char *more_or_less;
+ if (num_found < num_min) {
+ num_expected = num_min;
+ more_or_less = "at least";
+ } else {
+ num_expected = num_max;
+ more_or_less = "at most";
+ }
+ if (exact) {
+ more_or_less = "exactly";
+ }
+ PyErr_Format(PyExc_TypeError,
+ "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
+ func_name, more_or_less, num_expected,
+ (num_expected == 1) ? "" : "s", num_found);
+}
+
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
+ PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) {
#if CYTHON_COMPILING_IN_CPYTHON
PyMappingMethods* mp;
#if PY_MAJOR_VERSION < 3
PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
- if (likely(ms && ms->sq_ass_slice)) {
+ if (likely(ms && ms->sq_slice)) {
if (!has_cstart) {
if (_py_start && (*_py_start != Py_None)) {
cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
@@ -66034,14 +67177,14 @@ static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
goto bad;
}
}
- return ms->sq_ass_slice(obj, cstart, cstop, value);
+ return ms->sq_slice(obj, cstart, cstop);
}
#endif
mp = Py_TYPE(obj)->tp_as_mapping;
- if (likely(mp && mp->mp_ass_subscript))
+ if (likely(mp && mp->mp_subscript))
#endif
{
- int result;
+ PyObject* result;
PyObject *py_slice, *py_start, *py_stop;
if (_py_slice) {
py_slice = *_py_slice;
@@ -66075,9 +67218,9 @@ static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
if (unlikely(!py_slice)) goto bad;
}
#if CYTHON_COMPILING_IN_CPYTHON
- result = mp->mp_ass_subscript(obj, py_slice, value);
+ result = mp->mp_subscript(obj, py_slice);
#else
- result = value ? PyObject_SetItem(obj, py_slice, value) : PyObject_DelItem(obj, py_slice);
+ result = PyObject_GetItem(obj, py_slice);
#endif
if (!_py_slice) {
Py_DECREF(py_slice);
@@ -66085,102 +67228,71 @@ static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
return result;
}
PyErr_Format(PyExc_TypeError,
- "'%.200s' object does not support slice %.10s",
- Py_TYPE(obj)->tp_name, value ? "assignment" : "deletion");
+ "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name);
bad:
- return -1;
+ return NULL;
}
-static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
-#if CYTHON_COMPILING_IN_CPYTHON
- PyThreadState *tstate = PyThreadState_GET();
- *type = tstate->exc_type;
- *value = tstate->exc_value;
- *tb = tstate->exc_traceback;
- Py_XINCREF(*type);
- Py_XINCREF(*value);
- Py_XINCREF(*tb);
-#else
- PyErr_GetExcInfo(type, value, tb);
-#endif
+#if !CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyBytes_Join(PyObject* sep, PyObject* values) {
+ return PyObject_CallMethodObjArgs(sep, __pyx_n_s_join, values, NULL);
}
-static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
-#if CYTHON_COMPILING_IN_CPYTHON
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- PyThreadState *tstate = PyThreadState_GET();
- tmp_type = tstate->exc_type;
- tmp_value = tstate->exc_value;
- tmp_tb = tstate->exc_traceback;
- tstate->exc_type = type;
- tstate->exc_value = value;
- tstate->exc_traceback = tb;
- Py_XDECREF(tmp_type);
- Py_XDECREF(tmp_value);
- Py_XDECREF(tmp_tb);
-#else
- PyErr_SetExcInfo(type, value, tb);
#endif
+
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
+ PyErr_Format(PyExc_ValueError,
+ "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
}
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
- PyObject *local_type, *local_value, *local_tb;
+static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
+ PyErr_Format(PyExc_ValueError,
+ "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
+ index, (index == 1) ? "" : "s");
+}
+
+static CYTHON_INLINE int __Pyx_IterFinish(void) {
#if CYTHON_COMPILING_IN_CPYTHON
- PyObject *tmp_type, *tmp_value, *tmp_tb;
PyThreadState *tstate = PyThreadState_GET();
- local_type = tstate->curexc_type;
- local_value = tstate->curexc_value;
- local_tb = tstate->curexc_traceback;
- tstate->curexc_type = 0;
- tstate->curexc_value = 0;
- tstate->curexc_traceback = 0;
-#else
- PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
- PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_COMPILING_IN_CPYTHON
- if (unlikely(tstate->curexc_type))
-#else
- if (unlikely(PyErr_Occurred()))
-#endif
- goto bad;
- #if PY_MAJOR_VERSION >= 3
- if (local_tb) {
- if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
- goto bad;
+ PyObject* exc_type = tstate->curexc_type;
+ if (unlikely(exc_type)) {
+ if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) {
+ PyObject *exc_value, *exc_tb;
+ exc_value = tstate->curexc_value;
+ exc_tb = tstate->curexc_traceback;
+ tstate->curexc_type = 0;
+ tstate->curexc_value = 0;
+ tstate->curexc_traceback = 0;
+ Py_DECREF(exc_type);
+ Py_XDECREF(exc_value);
+ Py_XDECREF(exc_tb);
+ return 0;
+ } else {
+ return -1;
+ }
}
- #endif
- Py_XINCREF(local_tb);
- Py_XINCREF(local_type);
- Py_XINCREF(local_value);
- *type = local_type;
- *value = local_value;
- *tb = local_tb;
-#if CYTHON_COMPILING_IN_CPYTHON
- tmp_type = tstate->exc_type;
- tmp_value = tstate->exc_value;
- tmp_tb = tstate->exc_traceback;
- tstate->exc_type = local_type;
- tstate->exc_value = local_value;
- tstate->exc_traceback = local_tb;
- Py_XDECREF(tmp_type);
- Py_XDECREF(tmp_value);
- Py_XDECREF(tmp_tb);
+ return 0;
#else
- PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
+ if (unlikely(PyErr_Occurred())) {
+ if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
+ PyErr_Clear();
+ return 0;
+ } else {
+ return -1;
+ }
+ }
return 0;
-bad:
- *type = 0;
- *value = 0;
- *tb = 0;
- Py_XDECREF(local_type);
- Py_XDECREF(local_value);
- Py_XDECREF(local_tb);
- return -1;
+#endif
}
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
- PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
+static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
+ if (unlikely(retval)) {
+ Py_DECREF(retval);
+ __Pyx_RaiseTooManyValuesError(expected);
+ return -1;
+ } else {
+ return __Pyx_IterFinish();
+ }
+ return 0;
}
static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
@@ -66209,88 +67321,173 @@ bad:
return NULL;
}
-static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
- const char* cstring, Py_ssize_t start, Py_ssize_t stop,
- const char* encoding, const char* errors,
- PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
- Py_ssize_t length;
- if (unlikely((start < 0) | (stop < 0))) {
- length = strlen(cstring);
- if (start < 0) {
- start += length;
- if (start < 0)
- start = 0;
+static double __Pyx__PyObject_AsDouble(PyObject* obj) {
+ PyObject* float_value;
+#if CYTHON_COMPILING_IN_PYPY
+ float_value = PyNumber_Float(obj);
+#else
+ PyNumberMethods *nb = Py_TYPE(obj)->tp_as_number;
+ if (likely(nb) && likely(nb->nb_float)) {
+ float_value = nb->nb_float(obj);
+ if (likely(float_value) && unlikely(!PyFloat_Check(float_value))) {
+ PyErr_Format(PyExc_TypeError,
+ "__float__ returned non-float (type %.200s)",
+ Py_TYPE(float_value)->tp_name);
+ Py_DECREF(float_value);
+ goto bad;
}
- if (stop < 0)
- stop += length;
- }
- length = stop - start;
- if (unlikely(length <= 0))
- return PyUnicode_FromUnicode(NULL, 0);
- cstring += start;
- if (decode_func) {
- return decode_func(cstring, length, errors);
+ } else if (PyUnicode_CheckExact(obj) || PyBytes_CheckExact(obj)) {
+#if PY_MAJOR_VERSION >= 3
+ float_value = PyFloat_FromString(obj);
+#else
+ float_value = PyFloat_FromString(obj, 0);
+#endif
} else {
- return PyUnicode_Decode(cstring, length, encoding, errors);
+ PyObject* args = PyTuple_New(1);
+ if (unlikely(!args)) goto bad;
+ PyTuple_SET_ITEM(args, 0, obj);
+ float_value = PyObject_Call((PyObject*)&PyFloat_Type, args, 0);
+ PyTuple_SET_ITEM(args, 0, 0);
+ Py_DECREF(args);
+ }
+#endif
+ if (likely(float_value)) {
+ double value = PyFloat_AS_DOUBLE(float_value);
+ Py_DECREF(float_value);
+ return value;
}
+bad:
+ return (double)-1;
}
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) {
- PyObject *method, *result = NULL;
- method = __Pyx_PyObject_GetAttrStr(obj, method_name);
- if (unlikely(!method)) goto bad;
+static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
+ PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
+}
+
+static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
+ PyObject* obj, PyObject* value, Py_ssize_t cstart, Py_ssize_t cstop,
+ PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
+ int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) {
#if CYTHON_COMPILING_IN_CPYTHON
- if (likely(PyMethod_Check(method))) {
- PyObject *self = PyMethod_GET_SELF(method);
- if (likely(self)) {
- PyObject *args;
- PyObject *function = PyMethod_GET_FUNCTION(method);
- args = PyTuple_New(2);
- if (unlikely(!args)) goto bad;
- Py_INCREF(self);
- PyTuple_SET_ITEM(args, 0, self);
- Py_INCREF(arg);
- PyTuple_SET_ITEM(args, 1, arg);
- Py_INCREF(function);
- Py_DECREF(method); method = NULL;
- result = __Pyx_PyObject_Call(function, args, NULL);
- Py_DECREF(args);
- Py_DECREF(function);
- return result;
+ PyMappingMethods* mp;
+#if PY_MAJOR_VERSION < 3
+ PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
+ if (likely(ms && ms->sq_ass_slice)) {
+ if (!has_cstart) {
+ if (_py_start && (*_py_start != Py_None)) {
+ cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
+ if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
+ } else
+ cstart = 0;
+ }
+ if (!has_cstop) {
+ if (_py_stop && (*_py_stop != Py_None)) {
+ cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
+ if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
+ } else
+ cstop = PY_SSIZE_T_MAX;
+ }
+ if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
+ Py_ssize_t l = ms->sq_length(obj);
+ if (likely(l >= 0)) {
+ if (cstop < 0) {
+ cstop += l;
+ if (cstop < 0) cstop = 0;
+ }
+ if (cstart < 0) {
+ cstart += l;
+ if (cstart < 0) cstart = 0;
+ }
+ } else {
+ if (PyErr_ExceptionMatches(PyExc_OverflowError))
+ PyErr_Clear();
+ else
+ goto bad;
+ }
}
+ return ms->sq_ass_slice(obj, cstart, cstop, value);
}
#endif
- result = __Pyx_PyObject_CallOneArg(method, arg);
+ mp = Py_TYPE(obj)->tp_as_mapping;
+ if (likely(mp && mp->mp_ass_subscript))
+#endif
+ {
+ int result;
+ PyObject *py_slice, *py_start, *py_stop;
+ if (_py_slice) {
+ py_slice = *_py_slice;
+ } else {
+ PyObject* owned_start = NULL;
+ PyObject* owned_stop = NULL;
+ if (_py_start) {
+ py_start = *_py_start;
+ } else {
+ if (has_cstart) {
+ owned_start = py_start = PyInt_FromSsize_t(cstart);
+ if (unlikely(!py_start)) goto bad;
+ } else
+ py_start = Py_None;
+ }
+ if (_py_stop) {
+ py_stop = *_py_stop;
+ } else {
+ if (has_cstop) {
+ owned_stop = py_stop = PyInt_FromSsize_t(cstop);
+ if (unlikely(!py_stop)) {
+ Py_XDECREF(owned_start);
+ goto bad;
+ }
+ } else
+ py_stop = Py_None;
+ }
+ py_slice = PySlice_New(py_start, py_stop, Py_None);
+ Py_XDECREF(owned_start);
+ Py_XDECREF(owned_stop);
+ if (unlikely(!py_slice)) goto bad;
+ }
+#if CYTHON_COMPILING_IN_CPYTHON
+ result = mp->mp_ass_subscript(obj, py_slice, value);
+#else
+ result = value ? PyObject_SetItem(obj, py_slice, value) : PyObject_DelItem(obj, py_slice);
+#endif
+ if (!_py_slice) {
+ Py_DECREF(py_slice);
+ }
+ return result;
+ }
+ PyErr_Format(PyExc_TypeError,
+ "'%.200s' object does not support slice %.10s",
+ Py_TYPE(obj)->tp_name, value ? "assignment" : "deletion");
bad:
- Py_XDECREF(method);
- return result;
+ return -1;
}
-static CYTHON_INLINE PyObject* __Pyx_PyDict_Keys(PyObject* d) {
- if (PY_MAJOR_VERSION >= 3)
- return __Pyx_PyObject_CallMethod1((PyObject*)&PyDict_Type, __pyx_n_s_keys, d);
- else
- return PyDict_Keys(d);
+static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) {
+ PyErr_Format(PyExc_TypeError,
+ "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
+ name, type->tp_name, Py_TYPE(obj)->tp_name);
}
-
-static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x) {
- if (likely(PyList_CheckExact(L))) {
- if (unlikely(__Pyx_PyList_Append(L, x) < 0)) return -1;
- } else {
- PyObject* retval = __Pyx_PyObject_CallMethod1(L, __pyx_n_s_append, x);
- if (unlikely(!retval))
- return -1;
- Py_DECREF(retval);
+static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
+ const char *name, int exact)
+{
+ if (unlikely(!type)) {
+ PyErr_SetString(PyExc_SystemError, "Missing type object");
+ return 0;
+ }
+ if (none_allowed && obj == Py_None) return 1;
+ else if (exact) {
+ if (likely(Py_TYPE(obj) == type)) return 1;
+ #if PY_MAJOR_VERSION == 2
+ else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
+ #endif
+ }
+ else {
+ if (likely(PyObject_TypeCheck(obj, type))) return 1;
}
+ __Pyx_RaiseArgumentTypeInvalid(name, obj, type);
return 0;
}
-#if !CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyBytes_Join(PyObject* sep, PyObject* values) {
- return PyObject_CallMethodObjArgs(sep, __pyx_n_s_join, values, NULL);
-}
-#endif
-
static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) {
int r;
if (!j) return -1;
@@ -66339,83 +67536,56 @@ static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObje
return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v);
}
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
- if (unlikely(!type)) {
- PyErr_SetString(PyExc_SystemError, "Missing type object");
- return 0;
- }
- if (likely(PyObject_TypeCheck(obj, type)))
- return 1;
- PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
- Py_TYPE(obj)->tp_name, type->tp_name);
- return 0;
-}
-
-static double __Pyx__PyObject_AsDouble(PyObject* obj) {
- PyObject* float_value;
-#if CYTHON_COMPILING_IN_PYPY
- float_value = PyNumber_Float(obj);
-#else
- PyNumberMethods *nb = Py_TYPE(obj)->tp_as_number;
- if (likely(nb) && likely(nb->nb_float)) {
- float_value = nb->nb_float(obj);
- if (likely(float_value) && unlikely(!PyFloat_Check(float_value))) {
- PyErr_Format(PyExc_TypeError,
- "__float__ returned non-float (type %.200s)",
- Py_TYPE(float_value)->tp_name);
- Py_DECREF(float_value);
- goto bad;
+static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) {
+ PyObject *method, *result = NULL;
+ method = __Pyx_PyObject_GetAttrStr(obj, method_name);
+ if (unlikely(!method)) goto bad;
+#if CYTHON_COMPILING_IN_CPYTHON
+ if (likely(PyMethod_Check(method))) {
+ PyObject *self = PyMethod_GET_SELF(method);
+ if (likely(self)) {
+ PyObject *args;
+ PyObject *function = PyMethod_GET_FUNCTION(method);
+ args = PyTuple_New(2);
+ if (unlikely(!args)) goto bad;
+ Py_INCREF(self);
+ PyTuple_SET_ITEM(args, 0, self);
+ Py_INCREF(arg);
+ PyTuple_SET_ITEM(args, 1, arg);
+ Py_INCREF(function);
+ Py_DECREF(method); method = NULL;
+ result = __Pyx_PyObject_Call(function, args, NULL);
+ Py_DECREF(args);
+ Py_DECREF(function);
+ return result;
}
- } else if (PyUnicode_CheckExact(obj) || PyBytes_CheckExact(obj)) {
-#if PY_MAJOR_VERSION >= 3
- float_value = PyFloat_FromString(obj);
-#else
- float_value = PyFloat_FromString(obj, 0);
-#endif
- } else {
- PyObject* args = PyTuple_New(1);
- if (unlikely(!args)) goto bad;
- PyTuple_SET_ITEM(args, 0, obj);
- float_value = PyObject_Call((PyObject*)&PyFloat_Type, args, 0);
- PyTuple_SET_ITEM(args, 0, 0);
- Py_DECREF(args);
}
#endif
- if (likely(float_value)) {
- double value = PyFloat_AS_DOUBLE(float_value);
- Py_DECREF(float_value);
- return value;
- }
+ result = __Pyx_PyObject_CallOneArg(method, arg);
bad:
- return (double)-1;
+ Py_XDECREF(method);
+ return result;
}
-static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) {
- PyErr_Format(PyExc_TypeError,
- "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
- name, type->tp_name, Py_TYPE(obj)->tp_name);
-}
-static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
- const char *name, int exact)
-{
- if (unlikely(!type)) {
- PyErr_SetString(PyExc_SystemError, "Missing type object");
- return 0;
- }
- if (none_allowed && obj == Py_None) return 1;
- else if (exact) {
- if (likely(Py_TYPE(obj) == type)) return 1;
- #if PY_MAJOR_VERSION == 2
- else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
- #endif
- }
- else {
- if (likely(PyObject_TypeCheck(obj, type))) return 1;
+static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x) {
+ if (likely(PyList_CheckExact(L))) {
+ if (unlikely(__Pyx_PyList_Append(L, x) < 0)) return -1;
+ } else {
+ PyObject* retval = __Pyx_PyObject_CallMethod1(L, __pyx_n_s_append, x);
+ if (unlikely(!retval))
+ return -1;
+ Py_DECREF(retval);
}
- __Pyx_RaiseArgumentTypeInvalid(name, obj, type);
return 0;
}
+static CYTHON_INLINE PyObject* __Pyx_PyDict_Keys(PyObject* d) {
+ if (PY_MAJOR_VERSION >= 3)
+ return __Pyx_PyObject_CallMethod1((PyObject*)&PyDict_Type, __pyx_n_s_keys, d);
+ else
+ return PyDict_Keys(d);
+}
+
static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
diff --git a/netCDF4.pyx b/netCDF4/_netCDF4.pyx
similarity index 76%
rename from netCDF4.pyx
rename to netCDF4/_netCDF4.pyx
index 90b30b4..5f92d16 100644
--- a/netCDF4.pyx
+++ b/netCDF4/_netCDF4.pyx
@@ -2,25 +2,25 @@
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
-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
-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
+Python interface to the netCDF version 4 library. U{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
+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.
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 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
+data types (compound types containing vlens, and vlens containing compound
types) are not supported.
-Download
+Download
========
- Latest bleeding-edge code from the U{github repository
@@ -28,13 +28,12 @@ Download
- Latest U{releases <https://pypi.python.org/pypi/netCDF4>}
(source code and windows installers).
-Requires
-========
+Requires
+========
- Python 2.5 or later (python 3 works too).
- - numpy array module U{http://numpy.scipy.org}, version 1.3.0 or later (1.5.1
- or higher recommended, required if using python 3).
- - U{Cython <http://cython.org>}, version 0.19 or later, is optional - if it is installed setup.py will
+ - 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
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)
@@ -66,8 +65,8 @@ Install
- By default, the utility C{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
- in a text editor (follow the instructions in the comments).
+ file C{setup.cfg.template} to C{setup.cfg}, then edit
+ in a text editor (follow the instructions in the comments).
In addition to specifying the path to C{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).
@@ -111,11 +110,11 @@ 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
+reading over http if a URL is provided to the L{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
version 4.0.1).
-
+
2) Groups in a netCDF file
--------------------------
@@ -147,15 +146,22 @@ 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
-that group.
+that group. To simplify the creation of nested groups, you can
+use a unix-like path as an argument to L{createGroup<Dataset.createGroup>}.
+
+>>> 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
+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}
object yields summary information about it's contents.
->>> fcstgrp1 = fcstgrp.createGroup('model1')
->>> fcstgrp2 = fcstgrp.createGroup('model2')
>>> def walktree(top):
>>> values = top.groups.values()
>>> yield values
@@ -168,8 +174,8 @@ object yields summary information about it's contents.
>>> print child
<type 'netCDF4.Dataset'>
root group (NETCDF4 file format):
- dimensions:
- variables:
+ dimensions:
+ variables:
groups: forecasts, analyses
<type 'netCDF4.Group'>
group /forecasts:
@@ -213,7 +219,7 @@ one, and it must be the first (leftmost) dimension of the variable.
>>> 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.
@@ -253,7 +259,7 @@ and whether it is unlimited.
L{Dimension} names can be changed using the
L{renameDimension<Dataset.renameDimension>} method of a L{Dataset} or
L{Group} instance.
-
+
4) Variables in a netCDF file
-----------------------------
@@ -268,7 +274,7 @@ 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
variable, simply leave out the dimensions keyword. The variable
-primitive datatypes correspond to the dtype attribute of a numpy array.
+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
@@ -295,20 +301,9 @@ used later to access and set variable data and attributes.
>>> # two dimensions unlimited.
>>> temp = rootgrp.createVariable('temp','f4',('time','level','lat','lon',))
-All of the variables in the L{Dataset} or L{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>)])
->>>
-
To get summary info on a L{Variable} instance in an interactive session, just print it.
->>> print rootgrp.variables['temp']
+>>> print temp
<type 'netCDF4.Variable'>
float32 temp(time, level, lat, lon)
least_significant_digit: 3
@@ -317,15 +312,52 @@ 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',))
+
+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
+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>)])
+>>>
+
L{Variable} names can be changed using the
L{renameVariable<Dataset.renameVariable>} method of a L{Dataset}
instance.
-
+
5) Attributes in a netCDF file
------------------------------
-There are two types of attributes in a netCDF file, global and variable.
+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
one of the variables in a group. Global attributes are set by assigning
@@ -357,13 +389,13 @@ 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}
-instance provides all the netCDF attribute name/value pairs in a python
+The C{__dict__} attribute of a L{Dataset}, L{Group} or L{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'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
@@ -376,7 +408,7 @@ removes the attribute C{foo} the the group C{grp}).
Now that you have a netCDF L{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
+>>> import numpy
>>> lats = numpy.arange(-90,91,2.5)
>>> lons = numpy.arange(-180,180,2.5)
>>> latitudes[:] = lats
@@ -392,8 +424,8 @@ latitudes =
90. ]
>>>
-Unlike NumPy's array objects, netCDF L{Variable}
-objects with unlimited dimensions will grow along those dimensions if you
+Unlike NumPy's array objects, netCDF L{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.
@@ -419,9 +451,9 @@ 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.]
-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
+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
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
@@ -431,7 +463,7 @@ to the way vector subscripts work in fortran). This means that
>>> 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,).
+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
@@ -441,7 +473,7 @@ While this behaviour may cause some confusion for those used to NumPy's 'fancy i
it provides a very powerful way to extract data from multidimensional netCDF
variables by using logical operations on the dimension arrays to create slices.
-For example,
+For example,
>>> tempdat = temp[::2, [1,3,6], lats>0, lons>0]
@@ -453,6 +485,13 @@ Hemisphere longitudes, resulting in a numpy array of shape (3, 3, 36, 71).
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.
+
+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
measure relative to a fixed date using a certain calendar, with units
@@ -468,7 +507,7 @@ can be used:
>>> 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):
+time values (in units hours since January 1, 0001):
[ 17533056. 17533068. 17533080. 17533092. 17533104.]
>>>
>>> dates = num2date(times[:],units=times.units,calendar=times.calendar)
@@ -480,26 +519,26 @@ dates corresponding to time values:
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
+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
of a netCDF time variable corresponding to a sequence of datetime instances.
-7) Reading data from a multi-file netCDF dataset.
+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 L{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
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
+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
+must in be in C{NETCDF3_64BIT}, C{NETCDF3_CLASSIC} or
C{NETCDF4_CLASSIC format} (C{NETCDF4} formatted multi-file
datasets are not supported).
@@ -522,9 +561,9 @@ Now read all the files back in at once with L{MFDataset}
>>>
Note that MFDataset can only be used to read, not write, multi-file
-datasets.
+datasets.
-8) Efficient compression of netCDF variables
+9) Efficient compression of netCDF variables
--------------------------------------------
Data stored in netCDF 4 L{Variable} objects can be compressed and
@@ -577,19 +616,19 @@ and then
and see how much smaller the resulting files are.
-9) Beyond homogenous arrays of a fixed type - compound data types
------------------------------------------------------------------
+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
in Fortran. They allow for the construction of table-like structures
-composed of combinations of other data types, including other
+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
+are created from the corresponding numpy data type using the
L{createCompoundType<Dataset.createCompoundType>} method of a L{Dataset} or L{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:
@@ -645,19 +684,19 @@ OrderedDict([('complex128', <netCDF4.CompoundType object at 0x1029eb7e8>)])
<type 'netCDF4.CompoundType'>: name = 'complex128', numpy dtype = [(u'real','<f8'), (u'imag', '<f8')]
>>>
-10) Variable-length (vlen) data types.
---------------------------------------
+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
+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.
>>> 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
+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.
@@ -667,7 +706,7 @@ A new variable can then be created using this datatype.
>>> 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 C{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.
@@ -704,16 +743,16 @@ current shape = (4, 3)
>>>
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.
+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
with fixed length greater than 1) when calling the
-L{createVariable<Dataset.createVariable>} method.
+L{createVariable<Dataset.createVariable>} method.
>>> 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
+random lengths between 2 and 12 characters, and the data in the object
array is assigned to the vlen string variable.
>>> chars = '1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
@@ -769,8 +808,8 @@ del __test__ # hack so epydoc doesn't show __test__
# Make changes to this file, not the c-wrappers that Pyrex generates.
# pure python utilities
-from netCDF4_utils import _StartCountStride, _quantize, _find_dim, _walk_grps, \
- _out_array_shape, _sortbylist, _tostr
+from .utils import (_StartCountStride, _quantize, _find_dim, _walk_grps,
+ _out_array_shape, _sortbylist, _tostr)
# try to use built-in ordered dict in python >= 2.7
try:
from collections import OrderedDict
@@ -785,7 +824,7 @@ except ImportError:
# python3: zip is already python2's itertools.izip
pass
-__version__ = "1.1.7"
+__version__ = "1.1.8"
# Initialize numpy
import posixpath
@@ -801,10 +840,6 @@ if _npversion.split('.')[0] < '1':
raise ImportError('requires numpy version 1.0rc1 or later')
import_array()
include "netCDF4.pxi"
-# include pure python utility functions and MFDataset class.
-# (use include instead of importing them so docstrings
-# get included in C extension code).
-include "utils.pyx"
include "constants.pyx"
# check for required version of netcdf-4 and hdf5.
@@ -815,9 +850,18 @@ def _gethdf5libversion():
releasevers = H5_VERS_RELEASE
patchstring = H5_VERS_SUBRELEASE.decode('ascii')
if not patchstring:
- return '%d.%d.%d' % (majorvers,minorvers,releasevers)
+ return '%d.%d.%d' % (majorvers,minorvers,releasevers)
else:
- return '%d.%d.%d-%s' % (majorvers,minorvers,releasevers,patchstring)
+ return '%d.%d.%d-%s' % (majorvers,minorvers,releasevers,patchstring)
+
+def getlibversion():
+ """
+getlibversion()
+
+returns a string describing the version of the netcdf library
+used to build the module, and when it was built.
+ """
+ return (<char *>nc_inq_libvers()).decode('ascii')
__netcdf4libversion__ = getlibversion().split()[0]
__hdf5libversion__ = _gethdf5libversion()
@@ -841,7 +885,7 @@ _nptonctype = {'U1' : NC_CHAR,
'f4' : NC_FLOAT,
'f8' : NC_DOUBLE}
-default_fillvals = {#'S1':NC_FILL_CHAR,
+default_fillvals = {#'S1':NC_FILL_CHAR,
'U1':'\0',
'S1':'\0',
'i1':NC_FILL_BYTE,
@@ -1023,7 +1067,7 @@ cdef _set_att(grp, int varid, name, value):
cdef int i, ierr, lenarr, n
cdef char *attname
cdef char *datstring
- cdef ndarray value_arr
+ cdef ndarray value_arr
bytestr = _strencode(name)
attname = bytestr
# put attribute value into a numpy array.
@@ -1035,7 +1079,9 @@ cdef _set_att(grp, int varid, name, value):
if value_arr.dtype.str[1:] == 'i8' and ('i8' not in _supportedtypes or\
is_netcdf3):
value_arr = value_arr.astype('i4')
- # if array contains strings, write a text attribute.
+ # if array contains ascii strings, write a text attribute (stored as bytes).
+ # if array contains unicode strings, and data model is NETCDF4,
+ # write as a string. string arrays are concatenated into a single string.
if value_arr.dtype.char in ['S','U']:
if not value_arr.shape:
dats = _strencode(value_arr.item())
@@ -1047,7 +1093,11 @@ cdef _set_att(grp, int varid, name, value):
if lenarr == 0:
# write null byte
lenarr=1; datstring = '\x00'
- ierr = nc_put_att_text(grp._grpid, varid, attname, lenarr, datstring)
+ if value_arr.dtype.char == 'U' and not is_netcdf3:
+ # a unicode string, use put_att_string (if NETCDF4 file).
+ ierr = nc_put_att_string(grp._grpid, varid, attname, 1, &datstring)
+ else:
+ ierr = nc_put_att_text(grp._grpid, varid, attname, lenarr, datstring)
if ierr != NC_NOERR:
raise AttributeError((<char *>nc_strerror(ierr)).decode('ascii'))
# a 'regular' array type ('f4','i4','f8' etc)
@@ -1201,111 +1251,111 @@ cdef _get_vars(group):
else:
for n from 0 <= n < numvars:
varids[n] = n
- # loop over variables.
+ # loop over variables.
for n from 0 <= n < numvars:
- varid = varids[n]
- # get variable name.
- with nogil:
- ierr = nc_inq_varname(_grpid, varid, namstring)
- if ierr != NC_NOERR:
- raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
- name = namstring.decode(default_encoding,unicode_error)
- if ierr != NC_NOERR:
- raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
- # get variable type.
- with nogil:
- ierr = nc_inq_vartype(_grpid, varid, &xtype)
- if ierr != NC_NOERR:
- 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 = '>'
- # check to see if it is a supported user-defined type.
- try:
- datatype = _nctonptype[xtype]
- if endianness is not None:
- datatype = endianness + datatype
- except KeyError:
- if xtype == NC_STRING:
- datatype = str
- else:
- with nogil:
- ierr = nc_inq_user_type(_grpid, xtype, namstring_cmp,
- NULL, NULL, NULL, &classp)
- if classp == NC_COMPOUND: # a compound type
- # create CompoundType instance describing this compound type.
- try:
- datatype = _read_compound(group, xtype, endian=endianness)
- except KeyError:
- #print "WARNING: variable '%s' has unsupported compound datatype, skipping .." % name
- continue
- elif classp == NC_VLEN: # a compound type
- # create VLType instance describing this compound type.
- try:
- datatype = _read_vlen(group, xtype, endian=endianness)
- except KeyError:
- #print "WARNING: variable '%s' has unsupported VLEN datatype, skipping .." % name
- continue
- else:
- #print "WARNING: variable '%s' has unsupported datatype, skipping .." % name
- continue
- # get number of dimensions.
- with nogil:
- ierr = nc_inq_varndims(_grpid, varid, &numdims)
- if ierr != NC_NOERR:
- raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
- # get dimension ids.
- with nogil:
- ierr = nc_inq_vardimid(_grpid, varid, dimids)
- if ierr != NC_NOERR:
- raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
- # loop over dimensions, retrieve names.
- # if not found in current group, look in parents.
- # QUESTION: what if grp1 has a dimension named 'foo'
- # and so does it's parent - can a variable in grp1
- # use the 'foo' dimension from the parent?
- dimensions = []
- for nn from 0 <= nn < numdims:
- grp = group
- found = False
- while not found:
- for key, value in grp.dimensions.items():
- if value._dimid == dimids[nn]:
- dimensions.append(key)
- found = True
- break
- grp = grp.parent
- # create new variable instance.
- if endianness == '>':
- variables[name] = Variable(group, name, datatype, dimensions, id=varid, endian='big')
- elif endianness == '<':
- variables[name] = Variable(group, name, datatype, dimensions, id=varid, endian='little')
- else:
- variables[name] = Variable(group, name, datatype, dimensions, id=varid)
+ varid = varids[n]
+ # get variable name.
+ with nogil:
+ ierr = nc_inq_varname(_grpid, varid, namstring)
+ if ierr != NC_NOERR:
+ raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
+ name = namstring.decode(default_encoding,unicode_error)
+ if ierr != NC_NOERR:
+ raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
+ # get variable type.
+ with nogil:
+ ierr = nc_inq_vartype(_grpid, varid, &xtype)
+ if ierr != NC_NOERR:
+ 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 = '>'
+ # check to see if it is a supported user-defined type.
+ try:
+ datatype = _nctonptype[xtype]
+ if endianness is not None:
+ datatype = endianness + datatype
+ except KeyError:
+ if xtype == NC_STRING:
+ datatype = str
+ else:
+ with nogil:
+ ierr = nc_inq_user_type(_grpid, xtype, namstring_cmp,
+ NULL, NULL, NULL, &classp)
+ if classp == NC_COMPOUND: # a compound type
+ # create CompoundType instance describing this compound type.
+ try:
+ datatype = _read_compound(group, xtype, endian=endianness)
+ except KeyError:
+ #print "WARNING: variable '%s' has unsupported compound datatype, skipping .." % name
+ continue
+ elif classp == NC_VLEN: # a compound type
+ # create VLType instance describing this compound type.
+ try:
+ datatype = _read_vlen(group, xtype, endian=endianness)
+ except KeyError:
+ #print "WARNING: variable '%s' has unsupported VLEN datatype, skipping .." % name
+ continue
+ else:
+ #print "WARNING: variable '%s' has unsupported datatype, skipping .." % name
+ continue
+ # get number of dimensions.
+ with nogil:
+ ierr = nc_inq_varndims(_grpid, varid, &numdims)
+ if ierr != NC_NOERR:
+ raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
+ # get dimension ids.
+ with nogil:
+ ierr = nc_inq_vardimid(_grpid, varid, dimids)
+ if ierr != NC_NOERR:
+ raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
+ # loop over dimensions, retrieve names.
+ # if not found in current group, look in parents.
+ # QUESTION: what if grp1 has a dimension named 'foo'
+ # and so does it's parent - can a variable in grp1
+ # use the 'foo' dimension from the parent?
+ dimensions = []
+ for nn from 0 <= nn < numdims:
+ grp = group
+ found = False
+ while not found:
+ for key, value in grp.dimensions.items():
+ if value._dimid == dimids[nn]:
+ dimensions.append(key)
+ found = True
+ break
+ grp = grp.parent
+ # create new variable instance.
+ if endianness == '>':
+ variables[name] = Variable(group, name, datatype, dimensions, id=varid, endian='big')
+ elif endianness == '<':
+ variables[name] = Variable(group, name, datatype, dimensions, id=varid, endian='little')
+ else:
+ variables[name] = Variable(group, name, datatype, dimensions, id=varid)
free(varids) # free pointer holding variable ids.
return variables
-# these are class attributes that
+# these are class attributes that
# only exist at the python level (not in the netCDF file).
_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']
+ 'file_format','_isvlen','_iscompound','_cmptype','_vltype','keepweakref','name','__orthogoral_indexing__']
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
-attributes. Together they describe the meaning of data and relations among
+A netCDF L{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:}
@@ -1320,7 +1370,7 @@ 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
+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.
@@ -1329,21 +1379,21 @@ 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
+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
+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).
@@ -1355,7 +1405,7 @@ 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
+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
@@ -1366,41 +1416,41 @@ B{Returns:}
a L{Dataset} instance. All further operations on the netCDF
Dataset are accomplised via L{Dataset} instance methods.
-A list of attribute names corresponding to global netCDF attributes
-defined for the L{Dataset} can be obtained with the L{ncattrs()} method.
-These attributes can be created by assigning to an attribute of the
+A list of attribute names corresponding to global netCDF attributes
+defined for the L{Dataset} can be obtained with the L{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
+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
+ 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}
+ 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
+ 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
+ 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
+ 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.
@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}.
+C{NETCDF4_CLASSIC} or C{NETCDF3_64BIT}.
@ivar file_format: same as C{data_model}, retained for backwards
compatibility.
@@ -1421,13 +1471,16 @@ L{Group} instance. C{None} for a the root group or L{Dataset} instance"""
cdef public int _grpid
cdef public int _isopen
cdef public groups, dimensions, variables, disk_format, path, parent,\
- file_format, data_model, cmptypes, vltypes, keepweakref
+ file_format, data_model, cmptypes, vltypes, keepweakref, \
+ __orthogonal_indexing__
def __init__(self, filename, mode='r', clobber=True, format='NETCDF4',
diskless=False, persist=False, keepweakref=False, **kwargs):
cdef int grpid, ierr, numgrps, numdims, numvars
cdef char *path
cdef char namstring[NC_MAX_NAME+1]
+ # flag to indicate that Variables in this Dataset support orthogonal indexing.
+ self.__orthogonal_indexing__ = True
if diskless and __netcdf4libversion__ < '4.2.1':
#diskless = False # don't raise error, instead silently ignore
raise ValueError('diskless mode requires netcdf lib >= 4.2.1, you have %s' % __netcdf4libversion__)
@@ -1525,6 +1578,25 @@ L{Group} instance. C{None} for a the root group or L{Dataset} instance"""
def __exit__(self,atype,value,traceback):
self.close()
+ def __getitem__(self, elem):
+ # return variable or group defined in relative path.
+ # split out group names in unix path.
+ elem = posixpath.normpath(elem)
+ # last name in path, could be a variable or group
+ dirname, lastname = posixpath.split(elem)
+ nestedgroups = dirname.split('/')
+ group = self
+ # iterate over groups in path.
+ for g in nestedgroups:
+ if g: group = group.groups[g]
+ # return last one, either a group or a variable.
+ if lastname in group.groups:
+ return group.groups[lastname]
+ elif lastname in group.variables:
+ return group.variables[lastname]
+ else:
+ raise IndexError('%s not found in %s' % (lastname,group.path))
+
def filepath(self):
"""
filepath(self)
@@ -1540,15 +1612,15 @@ open/create the Dataset. Requires netcdf >= 4.1.2"""
return path.decode('ascii')
ELSE:
msg = """
-filepath method not enabled. To enable, install Cython, make sure you have
+filepath method not enabled. To enable, install Cython, make sure you have
version 4.1.2 or higher of the netcdf C lib, and rebuild netcdf4-python."""
raise ValueError(msg)
def __repr__(self):
if python3:
- return self.__unicode__()
+ return self.__unicode__()
else:
- return unicode(self).encode(default_encoding)
+ return unicode(self).encode(default_encoding)
def __unicode__(self):
ncdump = ['%r\n' % type(self)]
@@ -1580,7 +1652,7 @@ version 4.1.2 or higher of the netcdf C lib, and rebuild netcdf4-python."""
close(self)
Close the Dataset."""
- cdef int ierr
+ cdef int ierr
ierr = nc_close(self._grpid)
if ierr != NC_NOERR:
raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
@@ -1616,12 +1688,12 @@ set_fill_on(self)
Sets the fill mode for a L{Dataset} open for writing to C{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
-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
+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
+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
to."""
cdef int ierr, oldmode
ierr = nc_set_fill (self._grpid, NC_FILL, &oldmode)
@@ -1632,10 +1704,10 @@ to."""
"""
set_fill_off(self)
-Sets the fill mode for a L{Dataset} open for writing to C{off}.
+Sets the fill mode for a L{Dataset} open for writing to C{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
+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."""
cdef int ierr, oldmode
ierr = nc_set_fill (self._grpid, NC_NOFILL, &oldmode)
@@ -1646,14 +1718,14 @@ sure the data is actually written before being read."""
"""
createDimension(self, dimname, size=None)
-Creates a new dimension with the given C{dimname} and C{size}.
+Creates a new dimension with the given C{dimname} and C{size}.
-C{size} must be a positive integer or C{None}, which stands for
+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}
-class instance describing the new dimension. To determine the current
-maximum size of the dimension, use the C{len} function on the L{Dimension}
-instance. To determine if a dimension is 'unlimited', use the
+results in an unlimited dimension. The return value is the L{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}
+instance. To determine if a dimension is 'unlimited', use the
C{isunlimited()} method of the L{Dimension} instance."""
self.dimensions[dimname] = Dimension(self, dimname, size=size)
return self.dimensions[dimname]
@@ -1719,51 +1791,57 @@ datatype."""
"""
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 C{varname}, C{datatype}, and
+C{dimensions}. If dimensions are not given, the variable is assumed to be
a scalar.
-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'
-(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),
+If C{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
+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'
+(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
+(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}.
Data from netCDF variables is presented to python as numpy arrays with
-the corresponding data type.
+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
+C{dimensions} must be a tuple containing dimension names (strings) that
+have been defined previously using C{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
+If the optional keyword C{zlib} is C{True}, the data will be compressed in
the netCDF file using gzip compression (default C{False}).
-The optional keyword C{complevel} is an integer between 1 and 9 describing
+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}.
-If the optional keyword C{shuffle} is C{True}, the HDF5 shuffle filter
-will be applied before compressing the data (default C{True}). This
+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 C{fletcher32} is C{True}, the Fletcher32 HDF5
+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 C{contiguous} is C{True}, the variable data is
+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
a variable with an unlimited dimension will trigger an error.
The optional keyword C{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>}.
+<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}.
@@ -1779,8 +1857,8 @@ some performance advantage to be gained by setting the endian-ness.
The C{zlib, complevel, shuffle, fletcher32, contiguous, chunksizes} and C{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
+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.
@@ -1796,9 +1874,9 @@ U{http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml}:
in unpacked data that is a reliable value." Default is C{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 C{NETCDF4} or C{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
+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
the default chunk cache when creating a variable. The setting only
@@ -1806,10 +1884,10 @@ 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 L{Variable} class instance describing the new
variable.
-A list of names corresponding to netCDF variable attributes can be
+A list of names corresponding to netCDF variable attributes can be
obtained with the L{Variable} method C{ncattrs()}. A dictionary
containing all the netCDF attribute name/value pairs is provided by
the C{__dict__} attribute of a L{Variable} instance.
@@ -1830,12 +1908,21 @@ 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
is the number of variable dimensions."""
- self.variables[varname] = Variable(self, varname, datatype,
+ # if varname specified as a path, split out group names.
+ varname = posixpath.normpath(varname)
+ dirname, varname = posixpath.split(varname) # varname is last.
+ # create parent groups (like mkdir -p).
+ if not dirname:
+ group = self
+ else:
+ group = self.createGroup(dirname)
+ # create variable.
+ group.variables[varname] = Variable(group, varname, datatype,
dimensions=dimensions, zlib=zlib, complevel=complevel, shuffle=shuffle,
fletcher32=fletcher32, contiguous=contiguous, chunksizes=chunksizes,
endian=endian, least_significant_digit=least_significant_digit,
fill_value=fill_value, chunk_cache=chunk_cache)
- return self.variables[varname]
+ return group.variables[varname]
def renameVariable(self, oldname, newname):
"""
@@ -1865,10 +1952,30 @@ createGroup(self, groupname)
Creates a new L{Group} with the given C{groupname}.
-The return value is a L{Group} class instance describing the new group."""
- self.groups[groupname] = Group(self, groupname)
- return self.groups[groupname]
-
+If C{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.
+If the specified path describes a group that already exists, no error is
+raised.
+
+The return value is a L{Group} class instance."""
+ # if group specified as a path, split out group names
+ groupname = posixpath.normpath(groupname)
+ nestedgroups = groupname.split('/')
+ group = self
+ # loop over group names, create parent groups if they do not already
+ # exist.
+ for g in nestedgroups:
+ if not g: continue
+ if g not in group.groups:
+ group.groups[g] = Group(group, g)
+ group = group.groups[g]
+ # if group already exists, just return the group
+ # (prior to 1.1.8, this would have raised an error)
+ return group
+
def ncattrs(self):
"""
ncattrs(self)
@@ -1891,8 +1998,8 @@ attributes."""
"""
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
+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"""
if self.data_model != 'NETCDF4': self._redef()
@@ -1951,7 +2058,7 @@ attributes."""
# level and not in the netCDF file.
if name.startswith('__') and name.endswith('__'):
# if __dict__ requested, return a dict with netCDF attributes.
- if name == '__dict__':
+ if name == '__dict__':
names = self.ncattrs()
values = []
for name in names:
@@ -2003,7 +2110,7 @@ rename a L{Group} named C{oldname} to C{newname} (requires netcdf >= 4.3.1)."""
self.groups[newname] = grp
ELSE:
msg = """
-renameGroup method not enabled. To enable, install Cython, make sure you have
+renameGroup method not enabled. To enable, install Cython, make sure you have
version 4.3.1 or higher of the netcdf C lib, and rebuild netcdf4-python."""
raise ValueError(msg)
@@ -2089,11 +2196,11 @@ after calling this function will follow the default behaviour.
cdef class Group(Dataset):
"""
-Group(self, parent, name)
+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,
+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
@@ -2112,12 +2219,14 @@ B{Returns:}
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}
+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)."""
def __init__(self, parent, name, **kwargs):
cdef int ierr
cdef char *groupname
+ # flag to indicate that Variables in this Group support orthogonal indexing.
+ self.__orthogonal_indexing__ = True
# set data_model and file_format attributes.
self.data_model = parent.data_model
self.file_format = parent.file_format
@@ -2153,7 +2262,7 @@ method)."""
"""
close(self)
-overrides L{Dataset} close method which does not apply to L{Group}
+overrides L{Dataset} close method which does not apply to L{Group}
instances, raises IOError."""
raise IOError('cannot close a L{Group} (only applies to Dataset)')
@@ -2197,7 +2306,7 @@ B{C{size}} - Size of the dimension. C{None} or 0 means unlimited. (Default C{No
B{Returns:}
-a L{Dimension} instance. All further operations on the netCDF Dimension
+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
@@ -2255,9 +2364,9 @@ determine if the dimension is unlimited"""
def __repr__(self):
if python3:
- return self.__unicode__()
+ return self.__unicode__()
else:
- return unicode(self).encode(default_encoding)
+ return unicode(self).encode(default_encoding)
def __unicode__(self):
if not dir(self._grp):
@@ -2266,7 +2375,7 @@ determine if the dimension is unlimited"""
return repr(type(self))+" (unlimited): name = '%s', size = %s\n" % (self._name,len(self))
else:
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
cdef int ierr
@@ -2306,7 +2415,7 @@ returns C{True} if the L{Dimension} instance is unlimited, C{False} otherwise.""
unlimdim_ids = []
for n from 0 <= n < numunlimdims:
unlimdim_ids.append(unlimdimids[n])
- if dimid in unlimdim_ids:
+ if dimid in unlimdim_ids:
return True
else:
return False
@@ -2323,7 +2432,7 @@ 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
+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
@@ -2336,42 +2445,42 @@ 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
+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 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
+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}
+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,
+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}.
+Ignored if C{zlib=False}.
-B{C{shuffle}} - if C{True}, the HDF5 shuffle filter is applied
+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
+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
@@ -2381,7 +2490,7 @@ 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>}.
+<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}.
@@ -2406,15 +2515,15 @@ 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)
+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
+a L{Variable} instance. All further operations on the netCDF Variable are
accomplised via L{Variable} instance methods.
A list of attribute names corresponding to netCDF attributes defined for
@@ -2425,17 +2534,17 @@ name/value pairs is provided by the C{__dict__} attribute of a
L{Variable} instance.
The instance variables C{dimensions, dtype, ndim, shape}
-and C{least_significant_digit} are read-only (and
+and C{least_significant_digit} are read-only (and
should not be modified by the user).
- at ivar dimensions: A tuple containing the names of the dimensions
+ at ivar dimensions: A tuple containing the names of the dimensions
associated with this variable.
@ivar dtype: A numpy dtype object describing the variable's data type.
@ivar ndim: The number of variable dimensions.
- at ivar shape: a tuple describing the current size of all the variable's
+ at ivar shape: a tuple describing the current size of all the variable's
dimensions.
@ivar scale: if True, C{scale_factor} and C{add_offset} are automatically
@@ -2447,12 +2556,17 @@ 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.
@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. """
+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.
+
+ at ivar __orthogonal_indexing__: Always C{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."""
cdef public int _varid, _grpid, _nunlimdim
cdef public _name, ndim, dtype, mask, scale, _isprimitive, _iscompound,\
- _isvlen, _grp,_cmptype,_vltype
+ _isvlen, _grp, _cmptype, _vltype, __orthogonal_indexing__
def __init__(self, grp, name, datatype, dimensions=(), zlib=False,
complevel=4, shuffle=True, fletcher32=False, contiguous=False,
@@ -2465,6 +2579,8 @@ instance. If C{None}, the data is not truncated. """
cdef size_t sizep, nelemsp
cdef size_t *chunksizesp
cdef float preemptionp
+ # flag to indicate that orthogonal indexing is supported
+ self.__orthogonal_indexing__ = True
# if complevel is set to zero, set zlib to False.
if not complevel:
zlib = False
@@ -2486,13 +2602,13 @@ instance. If C{None}, the data is not truncated. """
datatype != str and \
type(datatype) != numpy.dtype:
datatype = numpy.dtype(datatype)
- # convert numpy string dtype with length > 1
+ # convert numpy string dtype with length > 1
# or any numpy unicode dtype into str
if (isinstance(datatype, numpy.dtype) and
((datatype.kind == 'S' and datatype.itemsize > 1) or
datatype.kind == 'U')):
datatype = str
- # check if endian keyword consistent with datatype specification.
+ # check if endian keyword consistent with datatype specification.
dtype_endian = getattr(datatype,'byteorder',None)
if dtype_endian == '=': dtype_endian='native'
if dtype_endian == '>': dtype_endian='big'
@@ -2514,11 +2630,11 @@ instance. If C{None}, the data is not truncated. """
if isinstance(datatype, CompoundType) or isinstance(datatype, VLType)\
or datatype == str:
if isinstance(datatype, CompoundType):
- self._iscompound = True
- self._cmptype = datatype
+ self._iscompound = True
+ self._cmptype = datatype
if isinstance(datatype, VLType) or datatype==str:
- self._isvlen = True
- self._vltype = datatype
+ self._isvlen = True
+ self._vltype = datatype
if datatype==str:
if grp.data_model != 'NETCDF4':
raise ValueError(
@@ -2533,7 +2649,7 @@ instance. If C{None}, the data is not truncated. """
self.dtype = datatype.dtype
elif datatype.str[1:] in _supportedtypes:
self._isprimitive = True
- # find netCDF primitive data type corresponding to
+ # find netCDF primitive data type corresponding to
# specified numpy data type.
xtype = _nptonctype[datatype.str[1:]]
# dtype variable attribute is a numpy datatype object.
@@ -2567,7 +2683,7 @@ instance. If C{None}, the data is not truncated. """
else: # a scalar variable.
ierr = nc_def_var(self._grpid, varname, xtype, ndims,
NULL, &self._varid)
- # set chunk cache size if desired
+ # set chunk cache size if desired
# default is 1mb per var, can cause problems when many (1000's)
# of vars are created. This change only lasts as long as file is
# open.
@@ -2648,7 +2764,7 @@ instance. If C{None}, the data is not truncated. """
msg="only endian='native' allowed for NETCDF3 files"
raise RuntimeError(msg)
# set a fill value for this variable if fill_value keyword
- # given. This avoids the HDF5 overhead of deleting and
+ # given. This avoids the HDF5 overhead of deleting and
# recreating the dataset if it is set later (after the enddef).
if fill_value is not None:
if not fill_value and isinstance(fill_value,bool):
@@ -2693,15 +2809,15 @@ instance. If C{None}, the data is not truncated. """
def __array__(self):
# numpy special method that returns a numpy array.
- # allows numpy ufuncs to work faster on Variable objects
- # (issue 216).
+ # allows numpy ufuncs to work faster on Variable objects
+ # (issue 216).
return self[...]
def __repr__(self):
if python3:
- return self.__unicode__()
+ return self.__unicode__()
else:
- return unicode(self).encode(default_encoding)
+ return unicode(self).encode(default_encoding)
def __unicode__(self):
cdef int ierr, no_fill
@@ -2863,8 +2979,8 @@ attributes."""
"""
setncatts(self,attdict)
-set a bunch of netCDF variable attributes at once using a python dictionary.
-This may be faster when setting a lot of attributes for a NETCDF3
+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"""
if self._grp.data_model != 'NETCDF4': self._grp._redef()
@@ -2928,7 +3044,7 @@ endian(self)
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']:
+ if self._grp.data_model not in ['NETCDF4_CLASSIC','NETCDF4']:
return 'native'
with nogil:
ierr = nc_inq_var_endian(self._grpid, self._varid, &iendian)
@@ -2945,7 +3061,7 @@ return endian-ness (little,big,native) of variable (as stored in HDF5 file)."""
"""
chunking(self)
-return variable chunking information. If the dataset is
+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."""
@@ -3053,7 +3169,7 @@ details."""
# level and not in the netCDF file.
if name.startswith('__') and name.endswith('__'):
# if __dict__ requested, return a dict with netCDF attributes.
- if name == '__dict__':
+ if name == '__dict__':
names = self.ncattrs()
values = []
for name in names:
@@ -3095,22 +3211,22 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
else:
data = numpy.empty(datashape, dtype=self.dtype)
- # Determine which dimensions need to be
+ # Determine which dimensions need to be
# squeezed (those for which elem is an integer scalar).
- # The convention used is that for those cases,
+ # 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:
squeeze[i] = 0
- # Reshape the arrays so we can iterate over them.
+ # Reshape the arrays so we can iterate over them.
start = start.reshape((-1, self.ndim or 1))
count = count.reshape((-1, self.ndim or 1))
stride = stride.reshape((-1, self.ndim or 1))
put_ind = put_ind.reshape((-1, self.ndim or 1))
- # Fill output array with data chunks.
+ # Fill output array with data chunks.
for (a,b,c,i) in zip(start, count, stride, put_ind):
datout = self._get(a,b,c)
if not hasattr(datout,'shape') or data.shape == datout.shape:
@@ -3119,13 +3235,13 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
shape = getattr(data[tuple(i)], 'shape', ())
data[tuple(i)] = datout.reshape(shape)
- # Remove extra singleton dimensions.
+ # Remove extra singleton dimensions.
if hasattr(data,'shape'):
data = data[tuple(squeeze)]
if hasattr(data,'ndim') and self.ndim == 0:
- # Make sure a numpy scalar is returned instead of a 1-d array of
+ # Make sure a numpy scalar array is returned instead of a 1-d array of
# length 1.
- if data.ndim != 0: data = data[0]
+ if data.ndim != 0: data = numpy.asarray(data[0])
# if auto_scale mode set to True, (through
# a call to set_auto_scale or set_auto_maskandscale),
@@ -3156,7 +3272,7 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
data = data*self.scale_factor
# else if variable has only add_offset attributes, rescale.
elif hasattr(self, 'add_offset') and self.add_offset != 0.0:
- data += self.add_offset
+ data = data + self.add_offset
return data
def _toma(self,data):
@@ -3171,7 +3287,7 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
(self.endian() == 'little' and is_native_big):
mval.byteswap(True)
if mval.shape == (): # mval a scalar.
- hasmval = data==mval
+ hasmval = data==mval
# is scalar missing value a NaN?
try:
mvalisnan = numpy.isnan(mval)
@@ -3201,7 +3317,7 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
# is _FillValue a NaN?
try:
fvalisnan = numpy.isnan(fval)
- except TypeError: # isnan fails on some dtypes (issue 202)
+ except: # isnan fails on some dtypes (issue 202)
fvalisnan = False
if fvalisnan:
mask = numpy.isnan(data)
@@ -3214,7 +3330,7 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
fill_value = fval
totalmask += mask
# issue 209: don't return masked array if variable filling
- # is disabled.
+ # is disabled.
else:
with nogil:
ierr = nc_inq_var_fill(self._grpid,self._varid,&no_fill,NULL)
@@ -3272,7 +3388,7 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
if isinstance(elem, int):
if ndims > 1:
raise IndexError(msg)
- if elem < 0:
+ if elem < 0:
if self.shape[0]+elem >= 0:
elem = self.shape[0]+elem
else:
@@ -3302,8 +3418,8 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
start = [elem]
count = [1]*ndims
for n from 0 <= n < ndims:
- startp[n] = start[n]
- countp[n] = count[n]
+ startp[n] = start[n]
+ countp[n] = count[n]
if self.dtype == str: # VLEN string
strdata = <char **>malloc(sizeof(char *))
bytestr = _strencode(data)
@@ -3357,8 +3473,8 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
# A numpy array is needed. Convert if necessary.
# 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 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 \
hasattr(self, 'scale_factor') or hasattr(self, 'add_offset'):
data = numpy.array(data,numpy.float)
@@ -3379,8 +3495,8 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
# create a view so shape in caller is not modified (issue 90)
data = data.view()
data.shape = tuple(datashape)
-
- # Reshape these arrays so we can iterate over them.
+
+ # Reshape these arrays so we can iterate over them.
start = start.reshape((-1, self.ndim or 1))
count = count.reshape((-1, self.ndim or 1))
stride = stride.reshape((-1, self.ndim or 1))
@@ -3438,12 +3554,12 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
fillval = default_fillvals[self.dtype.str[1:]]
data = data.filled(fill_value=fillval)
- # Fill output array with data chunks.
+ # Fill output array with data chunks.
for (a,b,c,i) in zip(start, count, stride, put_ind):
dataput = data[tuple(i)]
if dataput.size == 0: continue # nothing to write
# convert array scalar to regular array with one element.
- if dataput.shape == ():
+ if dataput.shape == ():
if self._isvlen:
dataput=numpy.array(dataput,'O')
else:
@@ -3459,7 +3575,7 @@ rename a L{Variable} attribute named C{oldname} to C{newname}."""
"""
assignValue(self, val)
-assign a value to a scalar variable. Provided for compatibility with
+assign a value to a scalar variable. Provided for compatibility with
Scientific.IO.NetCDF, can also be done by assigning to a slice ([:])."""
if len(self.dimensions):
raise IndexError('to assign values to a non-scalar variable, use a slice')
@@ -3469,7 +3585,7 @@ Scientific.IO.NetCDF, can also be done by assigning to a slice ([:])."""
"""
getValue(self)
-get the value of a scalar variable. Provided for compatibility with
+get the value of a scalar variable. Provided for compatibility with
Scientific.IO.NetCDF, can also be done by slicing ([:])."""
if len(self.dimensions):
raise IndexError('to retrieve values from a non-scalar variable, use slicing')
@@ -3485,7 +3601,7 @@ data using C{scale_factor} and C{add_offset} attributes.
If C{maskandscale} is set to C{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
+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
@@ -3498,7 +3614,7 @@ C{scale_factor} or an C{add_offset} attribute, then data read
from that variable is unpacked using::
data = self.scale_factor*data + self.add_offset
-
+
When data is written to a variable it is packed using::
data = (data - self.add_offset)/self.scale_factor
@@ -3506,7 +3622,7 @@ 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
+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>}.
@@ -3604,9 +3720,9 @@ The default value of C{mask} is C{True}
sl = []
for n from 0 <= n < ndims:
count[n] = abs(count[n]) # make -1 into +1
- countp[n] = count[n]
+ countp[n] = count[n]
# for neg strides, reverse order (then flip that axis after data read in)
- if stride[n] < 0:
+ if stride[n] < 0:
negstride = 1
stridep[n] = -stride[n]
startp[n] = start[n]+stride[n]*(count[n]-1)
@@ -3618,7 +3734,7 @@ The default value of C{mask} is C{True}
sl.append(slice(None,None, 1))
totelem = totelem*countp[n]
# check to see that size of data array is what is expected
- # for slice given.
+ # for slice given.
dataelem = PyArray_SIZE(data)
if totelem != dataelem:
raise IndexError('size of data array does not conform to slice')
@@ -3627,7 +3743,7 @@ The default value of C{mask} is C{True}
data = data[sl].copy() # make sure a copy is made.
if self._isprimitive or self._iscompound:
# primitive or compound data type.
- # if data type of array doesn't match variable,
+ # if data type of array doesn't match variable,
# try to cast the data.
if self.dtype != data.dtype:
data = data.astype(self.dtype) # cast data, if necessary.
@@ -3657,7 +3773,7 @@ The default value of C{mask} is C{True}
startp, countp, stridep, data.data)
if ierr != NC_NOERR:
raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
- elif self._isvlen:
+ elif self._isvlen:
if data.dtype.char !='O':
raise TypeError('data to put in string variable must be an object array containing Python strings')
# flatten data array.
@@ -3674,10 +3790,10 @@ The default value of C{mask} is C{True}
for i from 0<=i<totelem:
strdata[i] = data[i]
# strides all 1 or scalar variable, use put_vara (faster)
- if sum(stride) == ndims or ndims == 0:
+ if sum(stride) == ndims or ndims == 0:
ierr = nc_put_vara(self._grpid, self._varid,
startp, countp, strdata)
- else:
+ else:
raise IndexError('strides must all be 1 for string variables')
#ierr = nc_put_vars(self._grpid, self._varid,
# startp, countp, stridep, strdata)
@@ -3702,10 +3818,10 @@ The default value of C{mask} is C{True}
vldata[i].p = dataarr.data
databuff = databuff + data.strides[0]
# strides all 1 or scalar variable, use put_vara (faster)
- if sum(stride) == ndims or ndims == 0:
+ if sum(stride) == ndims or ndims == 0:
ierr = nc_put_vara(self._grpid, self._varid,
startp, countp, vldata)
- else:
+ else:
raise IndexError('strides must all be 1 for vlen variables')
#ierr = nc_put_vars(self._grpid, self._varid,
# startp, countp, stridep, vldata)
@@ -3742,9 +3858,9 @@ The default value of C{mask} is C{True}
sl = []
for n from 0 <= n < ndims:
count[n] = abs(count[n]) # make -1 into +1
- countp[n] = count[n]
+ countp[n] = count[n]
# for neg strides, reverse order (then flip that axis after data read in)
- if stride[n] < 0:
+ if stride[n] < 0:
negstride = 1
stridep[n] = -stride[n]
startp[n] = start[n]+stride[n]*(count[n]-1)
@@ -3757,7 +3873,7 @@ The default value of C{mask} is C{True}
if self._isprimitive or self._iscompound:
data = numpy.empty(shapeout, self.dtype)
# strides all 1 or scalar variable, use get_vara (faster)
- if sum(stride) == ndims or ndims == 0:
+ if sum(stride) == ndims or ndims == 0:
with nogil:
ierr = nc_get_vara(self._grpid, self._varid,
startp, countp, data.data)
@@ -3766,7 +3882,7 @@ The default value of C{mask} is C{True}
ierr = nc_get_vars(self._grpid, self._varid,
startp, countp, stridep, data.data)
if ierr == NC_EINVALCOORDS:
- raise IndexError
+ raise IndexError
elif ierr != NC_NOERR:
raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
elif self._isvlen:
@@ -3780,7 +3896,7 @@ The default value of C{mask} is C{True}
# allocate pointer array to hold string data.
strdata = <char **>malloc(sizeof(char *) * totelem)
# strides all 1 or scalar variable, use get_vara (faster)
- if sum(stride) == ndims or ndims == 0:
+ if sum(stride) == ndims or ndims == 0:
with nogil:
ierr = nc_get_vara(self._grpid, self._varid,
startp, countp, strdata)
@@ -3790,7 +3906,7 @@ The default value of C{mask} is C{True}
#ierr = nc_get_vars(self._grpid, self._varid,
# startp, countp, stridep, strdata)
if ierr == NC_EINVALCOORDS:
- raise IndexError
+ raise IndexError
elif ierr != NC_NOERR:
raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
# loop over elements of object array, fill array with
@@ -3805,7 +3921,7 @@ The default value of C{mask} is C{True}
# allocate struct array to hold vlen data.
vldata = <nc_vlen_t *>malloc(totelem*sizeof(nc_vlen_t))
# strides all 1 or scalar variable, use get_vara (faster)
- if sum(stride) == ndims or ndims == 0:
+ if sum(stride) == ndims or ndims == 0:
with nogil:
ierr = nc_get_vara(self._grpid, self._varid,
startp, countp, vldata)
@@ -3814,7 +3930,7 @@ The default value of C{mask} is C{True}
#ierr = nc_get_vars(self._grpid, self._varid,
# startp, countp, stridep, vldata)
if ierr == NC_EINVALCOORDS:
- raise IndexError
+ raise IndexError
elif ierr != NC_NOERR:
raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
# loop over elements of object array, fill array with
@@ -3831,8 +3947,8 @@ The default value of C{mask} is C{True}
if negstride:
# reverse data along axes with negative strides.
data = data[sl].copy() # make a copy so data is contiguous.
- if not self.dimensions:
- return data[0] # a scalar
+ if not self.dimensions:
+ return data[0] # a scalar
elif squeeze_out:
return numpy.squeeze(data)
else:
@@ -3860,15 +3976,15 @@ 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.
+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
+B{C{datatype_name}} - a Python string containing a description of the
compound data type.
B{Returns:}
-a L{CompoundType} instance, which can be passed to the C{createVariable}
+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
@@ -3893,9 +4009,9 @@ the user.
def __repr__(self):
if python3:
- return self.__unicode__()
+ return self.__unicode__()
else:
- return unicode(self).encode(default_encoding)
+ return unicode(self).encode(default_encoding)
def __unicode__(self):
return repr(type(self))+": name = '%s', numpy dtype = %s\n" %\
@@ -3939,7 +4055,7 @@ cdef _def_compound(grp, object dt, object dtype_name):
else:
if format.shape == (): # nested scalar compound type
# find this compound type in this group or it's parents.
- xtype_tmp = _find_cmptype(grp, format)
+ xtype_tmp = _find_cmptype(grp, format)
bytestr = _strencode(name)
nested_namstring = bytestr
ierr = nc_insert_compound(grp._grpid, xtype,\
@@ -3962,7 +4078,7 @@ cdef _def_compound(grp, object dt, object dtype_name):
raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
else: # nested array compound type.
# find this compound type in this group or it's parents.
- xtype_tmp = _find_cmptype(grp, format.subdtype[0])
+ xtype_tmp = _find_cmptype(grp, format.subdtype[0])
bytestr = _strencode(name)
nested_namstring = bytestr
ierr = nc_insert_array_compound(grp._grpid,xtype,\
@@ -3988,7 +4104,7 @@ cdef _find_cmptype(grp, dtype):
if names1==names2 and formats1==formats2:
match = True
break
- if not match:
+ if not match:
try:
parent_grp = grp.parent
except AttributeError:
@@ -4001,7 +4117,7 @@ cdef _find_cmptype(grp, dtype):
cdef _read_compound(group, nc_type xtype, endian=None):
# read a compound data type id from an existing file,
- # construct a corresponding numpy dtype instance,
+ # construct a corresponding numpy dtype instance,
# then use that to create a CompoundType instance.
# called by _get_vars, _get_types and _get_att.
# Calls itself recursively for nested compound types.
@@ -4043,7 +4159,7 @@ cdef _read_compound(group, nc_type xtype, endian=None):
for ndim from 0 <= ndim < numdims:
field_shape = field_shape + (dim_sizes[ndim],)
# check to see if this field is a nested compound type.
- try:
+ try:
field_type = _nctonptype[field_typeid]
if endian is not None:
format = endian + format
@@ -4076,7 +4192,7 @@ 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{VLType} instances should be created using the
L{createVLType<Dataset.createVLType>}
method of a Dataset or L{Group} instance, not using this class directly.
@@ -4085,14 +4201,14 @@ 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.
+variable length array.
-B{C{datatype_name}} - a Python string containing a description of the
+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}
+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
@@ -4119,9 +4235,9 @@ the user.
def __repr__(self):
if python3:
- return self.__unicode__()
+ return self.__unicode__()
else:
- return unicode(self).encode(default_encoding)
+ return unicode(self).encode(default_encoding)
def __unicode__(self):
if self.dtype == str:
@@ -4147,19 +4263,19 @@ cdef _def_vlen(grp, object dt, object dtype_name):
namstring = bytestr
dt = numpy.dtype(dt) # convert to numpy datatype.
if dt.str[1:] in _supportedtypes:
- # find netCDF primitive data type corresponding to
+ # find netCDF primitive data type corresponding to
# specified numpy data type.
xtype_tmp = _nptonctype[dt.str[1:]]
ierr = nc_def_vlen(grp._grpid, namstring, xtype_tmp, &xtype);
if ierr != NC_NOERR:
- raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
+ raise RuntimeError((<char *>nc_strerror(ierr)).decode('ascii'))
else:
raise KeyError("unsupported datatype specified for VLEN")
return xtype, dt
cdef _read_vlen(group, nc_type xtype, endian=None):
# read a VLEN data type id from an existing file,
- # construct a corresponding numpy dtype instance,
+ # construct a corresponding numpy dtype instance,
# then use that to create a VLType instance.
# called by _get_types, _get_vars.
cdef int ierr, _grpid
@@ -4178,7 +4294,7 @@ cdef _read_vlen(group, nc_type xtype, endian=None):
name = vl_namstring.decode(default_encoding,unicode_error)
try:
datatype = _nctonptype[base_xtype]
- if endian is not None: datatype = endian + datatype
+ if endian is not None: datatype = endian + datatype
dt = numpy.dtype(datatype) # see if it is a primitive type
except KeyError:
raise KeyError("unsupported component type for VLEN")
@@ -4193,3 +4309,815 @@ cdef _strencode(pystr,encoding=None):
return pystr.encode(encoding)
except (AttributeError, UnicodeDecodeError):
return pystr # already bytes or unicode?
+
+#----------------------------------------
+# extra utilities (formerly in utils.pyx)
+#----------------------------------------
+from datetime import timedelta, datetime, MINYEAR
+from netcdftime import _parse_date, microsec_units, millisec_units,\
+ sec_units, min_units, hr_units, day_units
+
+# start of the gregorian calendar
+gregorian = datetime(1582,10,15)
+
+def _dateparse(timestr):
+ """parse a string of the form time-units since yyyy-mm-dd hh:mm:ss,
+ return a datetime instance"""
+ # same as version in netcdftime, but returns a timezone naive
+ # python datetime instance with the utc_offset included.
+ timestr_split = timestr.split()
+ units = timestr_split[0].lower()
+ if timestr_split[1].lower() != 'since':
+ raise ValueError("no 'since' in unit_string")
+ # parse the date string.
+ n = timestr.find('since')+6
+ isostring = timestr[n:]
+ year, month, day, hour, minute, second, utc_offset =\
+ _parse_date( isostring.strip() )
+ basedate = datetime(year, month, day, hour, minute, second)
+ # add utc_offset to basedate time instance (which is timezone naive)
+ basedate += timedelta(days=utc_offset/1440.)
+ return basedate
+
+def stringtoarr(string,NUMCHARS,dtype='S'):
+ """
+stringtoarr(a, NUMCHARS,dtype='S')
+
+convert a string to a character array of length NUMCHARS
+
+ at param 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).
+
+ 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.
+
+ at return: 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')
+ arr[0:len(string)] = tuple(string)
+ return arr
+
+def 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
+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,).
+
+ 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."""
+ dtype = a.dtype.kind
+ if dtype not in ["S","U"]:
+ raise ValueError("type must string or unicode ('S' or 'U')")
+ b = numpy.array(tuple(a.tostring().decode(default_encoding)),dtype+'1')
+ b.shape = a.shape + (a.itemsize,)
+ return b
+
+def 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').
+Will be converted to a array of strings, where each string has a fixed
+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]."""
+ dtype = b.dtype.kind
+ if dtype not in ["S","U"]:
+ raise ValueError("type must string or unicode ('S' or 'U')")
+ bs = b.tostring().decode(default_encoding)
+ slen = int(b.shape[-1])
+ a = numpy.array([bs[n1:n1+slen] for n1 in range(0,len(bs),slen)],dtype+repr(slen))
+ a.shape = b.shape[:-1]
+ return a
+
+def 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
+be in UTC with no time-zone offset. If there is a
+time-zone offset in C{units}, it will be applied to the
+returned numeric values.
+
+ at param 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
+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.
+
+ at return: a numeric time value, or an array of numeric time values.
+ """
+ basedate = _dateparse(units)
+ unit = units.split()[0].lower()
+
+ if (calendar == 'proleptic_gregorian' and basedate.year >= MINYEAR) or \
+ (calendar in ['gregorian','standard'] and basedate > gregorian):
+ # use python datetime module,
+ isscalar = False
+ try:
+ dates[0]
+ except:
+ isscalar = True
+ if isscalar:
+ dates = numpy.array([dates])
+ else:
+ dates = numpy.array(dates)
+ shape = dates.shape
+ ismasked = False
+ if hasattr(dates,'mask'):
+ mask = dates.mask
+ ismasked = True
+ times = []
+ for date in dates.flat:
+ if ismasked and not date:
+ times.append(None)
+ else:
+ td = date - basedate
+ # total time in microseconds.
+ totaltime = td.microseconds + (td.seconds + td.days * 24 * 3600) * 1.e6
+ if unit in microsec_units:
+ times.append(totaltime)
+ elif unit in millisec_units:
+ times.append(totaltime/1.e3)
+ elif unit in sec_units:
+ times.append(totaltime/1.e6)
+ elif unit in min_units:
+ times.append(totaltime/1.e6/60)
+ elif unit in hr_units:
+ times.append(totaltime/1.e6/3600)
+ elif unit in day_units:
+ times.append(totaltime/1.e6/3600./24.)
+ else:
+ raise ValueError('unsupported time units')
+ if isscalar:
+ return times[0]
+ else:
+ return numpy.reshape(numpy.array(times), shape)
+ else: # use netcdftime module for other calendars
+ cdftime = netcdftime.utime(units,calendar=calendar)
+ return cdftime.date2num(dates)
+
+def 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
+UTC with no time-zone offset, even if the specified
+C{units} contain a time-zone offset.
+
+ at param 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
+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.
+
+ at return: 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'}
+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}
+contains one.
+ """
+ calendar = calendar.lower()
+ basedate = _dateparse(units)
+ unit = units.split()[0].lower()
+
+ if (calendar == 'proleptic_gregorian' and basedate.year >= MINYEAR) or \
+ (calendar in ['gregorian','standard'] and basedate > gregorian):
+ # use python datetime module,
+ isscalar = False
+ try:
+ times[0]
+ except:
+ isscalar = True
+ if isscalar:
+ times = numpy.array([times],dtype='d')
+ else:
+ times = numpy.array(times, dtype='d')
+ shape = times.shape
+ ismasked = False
+ if hasattr(times,'mask'):
+ mask = times.mask
+ ismasked = True
+ dates = []
+ for time in times.flat:
+ if ismasked and not time:
+ dates.append(None)
+ else:
+ # convert to total seconds
+ if unit in microsec_units:
+ tsecs = time/1.e6
+ elif unit in millisec_units:
+ tsecs = time/1.e3
+ elif unit in sec_units:
+ tsecs = time
+ elif unit in min_units:
+ tsecs = time*60.
+ elif unit in hr_units:
+ tsecs = time*3600.
+ elif unit in day_units:
+ tsecs = time*86400.
+ else:
+ raise ValueError('unsupported time units')
+ # compute time delta.
+ days = tsecs // 86400.
+ msecsd = tsecs*1.e6 - days*86400.*1.e6
+ secs = msecsd // 1.e6
+ msecs = numpy.round(msecsd - secs*1.e6)
+ td = timedelta(days=days,seconds=secs,microseconds=msecs)
+ # add time delta to base date.
+ date = basedate + td
+ dates.append(date)
+ if isscalar:
+ return dates[0]
+ else:
+ return numpy.reshape(numpy.array(dates), shape)
+ else: # use netcdftime for other calendars
+ cdftime = netcdftime.utime(units,calendar=calendar)
+ return cdftime.num2date(times)
+
+def 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.
+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
+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
+correspond to the closest dates.
+
+ at return: an index (indices) of the netCDF time variable corresponding
+to the given datetime object(s).
+ """
+ if calendar == None:
+ calendar = getattr(nctime, 'calendar', 'standard')
+ calendar = calendar.lower()
+ basedate = _dateparse(nctime.units)
+
+ if (calendar == 'proleptic_gregorian' and basedate.year >= MINYEAR) or \
+ (calendar in ['gregorian','standard'] and basedate > gregorian):
+ # use python datetime
+ times = date2num(dates,nctime.units,calendar=calendar)
+ return netcdftime.time2index(times, nctime, calendar, select)
+ else: # use netcdftime module for other cases
+ return netcdftime.date2index(dates, nctime, calendar, select)
+
+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]
+ """
+
+ 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.
+ """
+
+ # Open the master file in the base class, so that the CDFMF instance
+ # 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)
+ else:
+ files = sorted(glob(files))
+
+ master = files[0]
+
+ # Open the master again, this time as a classic CDF instance. This will avoid
+ # calling methods of the CDFMF subclass when querying the master file.
+ cdfm = Dataset(master)
+ # copy attributes from master.
+ for name, value in cdfm.__dict__.items():
+ self.__dict__[name] = value
+
+ # Make sure the master defines a dim with name aggdim,
+ # or an unlimited dimension.
+ aggDimId = None
+ for dimname,dim in cdfm.dimensions.items():
+ if aggdim is None:
+ if dim.isunlimited():
+ aggDimId = dim
+ aggDimName = dimname
+ else:
+ if dimname == aggdim:
+ aggDimId = dim
+ aggDimName = dimname
+ if aggDimId is None:
+ raise IOError("master dataset %s does not have a aggregation dimension" % master)
+
+ # Get info on all aggregation variables defined in the master.
+ # Make sure the master defines at least one aggregation variable.
+ masterRecVar = {}
+ for vName,v in cdfm.variables.items():
+ # skip variables specified in exclude list.
+ if vName in exclude: continue
+ dims = v.dimensions
+ shape = v.shape
+ dtype = v.dtype
+ # Be carefull: we may deal with a scalar (dimensionless) variable.
+ # Unlimited dimension always occupies index 0.
+ if (len(dims) > 0 and aggDimName == dims[0]):
+ masterRecVar[vName] = (dims, shape, dtype)
+ if len(masterRecVar) == 0:
+ raise IOError("master dataset %s does not have any variables to aggregate" % master)
+
+ # Create the following:
+ # cdf list of Dataset instances
+ # cdfVLen list unlimited dimension lengths in each CDF instance
+ # cdfRecVar dictionary indexed by the aggregation var names; each key holds
+ # a list of the corresponding Variable instance, one for each
+ # cdf file of the file set
+ cdf = [cdfm]
+ self._cdf = cdf # Store this now, because dim() method needs it
+ cdfVLen = [len(aggDimId)]
+ cdfRecVar = {}
+ for v in masterRecVar.keys():
+ cdfRecVar[v] = [cdfm.variables[v]]
+
+ # Open each remaining file in read-only mode.
+ # 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:]:
+ part = Dataset(f)
+ varInfo = part.variables
+ for v in masterRecVar.keys():
+ if check:
+ # Make sure master rec var is also defined here.
+ if v not in varInfo.keys():
+ raise IOError("aggregation variable %s not defined in %s" % (v, f))
+
+ #if not vInst.dimensions[0] != aggDimName:
+
+ masterDims, masterShape, masterType = masterRecVar[v][:3]
+ extDims = varInfo[v].dimensions
+ extShape = varInfo[v].shape
+ extType = varInfo[v].dtype
+ # Check that dimension names are identical.
+ if masterDims != extDims:
+ raise IOError("variable %s : dimensions mismatch between "
+ "master %s (%s) and extension %s (%s)" %
+ (v, master, masterDims, f, extDims))
+
+ # Check that the ranks are identical, and the dimension lengths are
+ # identical (except for that of the unlimited dimension, which of
+ # course may vary.
+ if len(masterShape) != len(extShape):
+ raise IOError("variable %s : rank mismatch between "
+ "master %s (%s) and extension %s (%s)" %
+ (v, master, len(masterShape), f, len(extShape)))
+ if masterShape[1:] != extShape[1:]:
+ raise IOError("variable %s : shape mismatch between "
+ "master %s (%s) and extension %s (%s)" %
+ (v, master, masterShape, f, extShape))
+
+ # Check that the data types are identical.
+ if masterType != extType:
+ raise IOError("variable %s : data type mismatch between "
+ "master %s (%s) and extension %s (%s)" %
+ (v, master, masterType, f, extType))
+
+ # Everythig ok.
+ vInst = part.variables[v]
+ cdfRecVar[v].append(vInst)
+ else:
+ # No making sure of anything -- assume this is ok..
+ vInst = part.variables[v]
+ cdfRecVar[v].append(vInst)
+
+ cdf.append(part)
+ cdfVLen.append(len(part.dimensions[aggDimName]))
+
+ # Attach attributes to the MFDataset instance.
+ # A local __setattr__() method is required for them.
+ self._files = files # list of cdf file names in the set
+ self._cdfVLen = cdfVLen # list of unlimited lengths
+ self._cdfTLen = sum(cdfVLen) # total length
+ self._cdfRecVar = cdfRecVar # dictionary of Variable instances for all
+ # the aggregation variables
+ self._dims = cdfm.dimensions
+ self._grps = cdfm.groups
+ for dimname, dim in self._dims.items():
+ if dimname == aggDimName:
+ self._dims[dimname] = _Dimension(dimname, dim, self._cdfVLen, self._cdfTLen)
+ self._vars = cdfm.variables
+ for varname,var in self._vars.items():
+ if varname in self._cdfRecVar.keys():
+ self._vars[varname] = _Variable(self, varname, var, aggDimName)
+ self._file_format = []
+ self._data_model = []
+ self._disk_format = []
+ 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)
+ self._data_model.append(dset.data_model)
+ self._disk_format.append(dset.disk_format)
+ self._path = '/'
+
+ def __setattr__(self, name, value):
+ """override base class attribute creation"""
+ self.__dict__[name] = value
+
+ def __getattribute__(self, name):
+ if name in ['variables','dimensions','file_format','groups',\
+ 'data_model','disk_format','path']:
+ if name == 'dimensions': return self._dims
+ if name == 'variables': return self._vars
+ if name == 'file_format': return self._file_format
+ if name == 'data_model': return self._data_model
+ if name == 'disk_format': return self._disk_format
+ if name == 'path': return self._path
+ if name == 'groups': return self._grps
+ else:
+ return Dataset.__getattribute__(self, name)
+
+ def ncattrs(self):
+ return self._cdf[0].__dict__.keys()
+
+ def close(self):
+ for dset in self._cdf:
+ dset.close()
+
+ def __repr__(self):
+ ncdump = ['%r\n' % type(self)]
+ dimnames = tuple([str(dimname) for dimname in self.dimensions.keys()])
+ varnames = tuple([str(varname) for varname in self.variables.keys()])
+ grpnames = ()
+ if self.path == '/':
+ ncdump.append('root group (%s data model, file format %s):\n' %
+ (self.data_model[0], self.disk_format[0]))
+ else:
+ ncdump.append('group %s:\n' % self.path)
+ attrs = [' %s: %s\n' % (name,self.__dict__[name]) for name in\
+ self.ncattrs()]
+ ncdump = ncdump + attrs
+ ncdump.append(' dimensions = %s\n' % str(dimnames))
+ ncdump.append(' variables = %s\n' % str(varnames))
+ ncdump.append(' groups = %s\n' % str(grpnames))
+ return ''.join(ncdump)
+
+class _Dimension(object):
+ def __init__(self, dimname, dim, dimlens, dimtotlen):
+ self.dimlens = dimlens
+ self.dimtotlen = dimtotlen
+ self._name = dimname
+ def __len__(self):
+ return self.dimtotlen
+ def isunlimited(self):
+ return True
+ def __repr__(self):
+ if self.isunlimited():
+ return repr(type(self))+" (unlimited): name = '%s', size = %s\n" % (self._name,len(self))
+ else:
+ return repr(type(self))+": name = '%s', size = %s\n" % (self._name,len(self))
+
+class _Variable(object):
+ def __init__(self, dset, varname, var, recdimname):
+ self.dimensions = var.dimensions
+ self._dset = dset
+ self._grp = dset
+ self._mastervar = var
+ self._recVar = dset._cdfRecVar[varname]
+ self._recdimname = recdimname
+ self._recLen = dset._cdfVLen
+ self.dtype = var.dtype
+ self._name = var._name
+ # copy attributes from master.
+ for name, value in var.__dict__.items():
+ self.__dict__[name] = value
+ def typecode(self):
+ return self.dtype
+ def ncattrs(self):
+ return self._mastervar.__dict__.keys()
+ def __getattr__(self,name):
+ if name == 'shape': return self._shape()
+ if name == 'ndim': return len(self._shape())
+ try:
+ return self.__dict__[name]
+ except:
+ raise AttributeError(name)
+ def __repr__(self):
+ ncdump_var = ['%r\n' % type(self)]
+ dimnames = tuple([str(dimname) for dimname in self.dimensions])
+ attrs = [' %s: %s\n' % (name,self.__dict__[name]) for name in\
+ self.ncattrs()]
+ ncdump_var.append('%s %s%s\n' %\
+ (self.dtype,self._name,dimnames))
+ ncdump_var = ncdump_var + attrs
+ unlimdims = []
+ for dimname in self.dimensions:
+ dim = _find_dim(self._grp, dimname)
+ if dim.isunlimited():
+ unlimdims.append(str(dimname))
+ ncdump_var.append('unlimited dimensions = %s\n' % repr(tuple(unlimdims)))
+ ncdump_var.append('current size = %s\n' % repr(self.shape))
+ return ''.join(ncdump_var)
+ def __len__(self):
+ return self._shape()[0]
+ def _shape(self):
+ recdimlen = len(self._dset.dimensions[self._recdimname])
+ return (recdimlen,) + self._mastervar.shape[1:]
+ def set_auto_maskandscale(self,val):
+ for v in self._recVar:
+ v.set_auto_maskandscale(val)
+ def __getitem__(self, elem):
+ """Get records from a concatenated set of variables."""
+
+ # This special method is used to index the netCDF variable
+ # using the "extended slice syntax". The extended slice syntax
+ # is a perfect match for the "start", "count" and "stride"
+ # arguments to the nc_get_var() function, and is much more easy
+ # to use.
+ start, count, stride, put_ind =\
+ _StartCountStride(elem, self.shape)
+ datashape = _out_array_shape(count)
+ data = ma.empty(datashape, dtype=self.dtype)
+
+ # Determine which dimensions need to be squeezed
+ # (those for which elem is an integer scalar).
+ # 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:
+ squeeze[i] = 0
+
+ # Reshape the arrays so we can iterate over them.
+ strt = start.reshape((-1, self.ndim or 1))
+ cnt = count.reshape((-1, self.ndim or 1))
+ strd = stride.reshape((-1, self.ndim or 1))
+ put_ind = put_ind.reshape((-1, self.ndim or 1))
+
+ # Fill output array with data chunks.
+ # Number of variables making up the MFVariable.Variable.
+ nv = len(self._recLen)
+ for (start,count,stride,ind) in zip(strt, cnt, strd, put_ind):
+ # make sure count=-1 becomes count=1
+ 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.
+ sta = start[0]
+ step = stride[0]
+ stop = sta + count[0] * step
+
+ # Build a list representing the concatenated list of all records in
+ # the MFVariable variable set. The list is composed of 2-elem lists
+ # each holding:
+ # the record index inside the variables, from 0 to n
+ # the index of the Variable instance to which each record belongs
+ idx = [] # list of record indices
+ vid = [] # list of Variable indices
+ for n in range(nv):
+ k = self._recLen[n] # number of records in this variable
+ idx.extend(range(k))
+ vid.extend([n] * k)
+
+ # Merge the two lists to get a list of 2-elem lists.
+ # Slice this list along the first dimension.
+ lst = list(zip(idx, vid)).__getitem__(slice(sta, stop, step))
+
+ # 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])
+ newSlice.append(s)
+
+ # Apply the slicing expression to each var in turn, extracting records
+ # in a list of arrays.
+ lstArr = []
+ ismasked = False
+ for n in range(nv):
+ # Get the list of indices for variable 'n'.
+ idx = [i for i,numv in lst if numv == n]
+ if idx:
+ # Rebuild slicing expression for dimension 0.
+ newSlice[0] = slice(idx[0], idx[-1] + 1, step)
+ # Extract records from the var, and append them to a list
+ # of arrays.
+ dat = Variable.__getitem__(self._recVar[n],tuple(newSlice))
+ if ma.isMA(dat) and not ismasked:
+ ismasked=True
+ fill_value = dat.fill_value
+ lstArr.append(dat)
+ if ismasked:
+ lstArr = ma.concatenate(lstArr)
+ else:
+ lstArr = numpy.concatenate(lstArr)
+ if lstArr.dtype != data.dtype: data = data.astype(lstArr.dtype)
+ # sometimes there are legitimate singleton dimensions, in which
+ # case the array shapes won't conform. If so, a ValueError will
+ # result, and no squeeze will be done.
+ try:
+ data[tuple(ind)] = lstArr.squeeze()
+ except ValueError:
+ data[tuple(ind)] = lstArr
+
+ # Remove extra singleton dimensions.
+ data = data[tuple(squeeze)]
+
+ # if no masked elements, return numpy array.
+ if ma.isMA(data) and not data.mask.any():
+ data = data.filled()
+
+ return data
+
+
+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
+ """
+
+ 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.
+ """
+ import datetime
+ self.__time = time
+
+ # copy attributes from master time variable.
+ for name, value in time.__dict__.items():
+ self.__dict__[name] = value
+
+ # make sure calendar attribute present in all files.
+ 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.
+ 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.
+ self.units = units or time.units
+
+ # 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)
+
+ # Create delta vector: delta = ref_num(ref_date) - num(ref_date)
+ # So that ref_num(date) = num(date) + delta
+ self.__delta = numpy.empty(len(self), time.dtype)
+
+ i0 = 0; i1 = 0
+ for i,v in enumerate(self._recVar):
+ n = self._recLen[i] # Length of time vector.
+ num = date2num(ref_date, v.units, self.calendar)
+ i1 += n
+ self.__delta[i0:i1] = ref_num - num
+ i0 += n
+
+
+ def __getitem__(self, elem):
+ return self.__time[elem] + self.__delta[elem]
diff --git a/netCDF4/utils.py b/netCDF4/utils.py
new file mode 100644
index 0000000..ca49917
--- /dev/null
+++ b/netCDF4/utils.py
@@ -0,0 +1,911 @@
+from __future__ import print_function
+
+import sys
+import numpy as np
+from numpy import ma
+import warnings
+import getopt
+import os
+
+python3 = sys.version_info[0] > 2
+if python3:
+ # no unicode type in python 3, use bytes instead when testing
+ # for a string-like object
+ unicode = str
+try:
+ bytes
+except NameError:
+ # no bytes type in python < 2.6
+ bytes = str
+
+
+def _sortbylist(A,B):
+ # sort one list (A) using the values from another list (B)
+ return [A[i] for i in sorted(range(len(A)), key=B.__getitem__)]
+
+def _find_dim(grp, dimname):
+ # find Dimension instance given group and name.
+ # look in current group, and parents.
+ group = grp
+ dim = None
+ while 1:
+ try:
+ dim = group.dimensions[dimname]
+ break
+ except:
+ try:
+ group = group.parent
+ except:
+ raise ValueError("cannot find dimension %s in this group or parent groups" % dimname)
+ return dim
+
+def _walk_grps(topgrp):
+ """Iterate through all (sub-) groups of topgrp, similar to os.walktree.
+
+ """
+ grps = topgrp.groups.values()
+ yield grps
+ for grp in topgrp.groups.values():
+ for children in _walk_grps(grp):
+ yield children
+
+def _quantize(data,least_significant_digit):
+ """
+quantize data to improve compression. data is quantized using
+around(scale*data)/scale, where scale is 2**bits, and bits is determined
+from the least_significant_digit. For example, if
+least_significant_digit=1, bits will be 4.
+ """
+ precision = pow(10.,-least_significant_digit)
+ exp = np.log10(precision)
+ if exp < 0:
+ exp = int(np.floor(exp))
+ else:
+ exp = int(np.ceil(exp))
+ bits = np.ceil(np.log2(pow(10.,-exp)))
+ scale = pow(2.,bits)
+ datout = np.around(scale*data)/scale
+ if ma.isMA(datout):
+ datout.set_fill_value(data.fill_value)
+ return datout
+ else:
+ return datout
+
+def _StartCountStride(elem, shape, dimensions=None, grp=None, datashape=None,\
+ put=False):
+ """Return start, count, stride and indices needed to store/extract data
+ into/from a netCDF variable.
+
+ This function is used to convert a slicing expression into a form that is
+ compatible with the nc_get_vars function. Specifically, it needs
+ to interpret integers, slices, Ellipses, and 1-d sequences of integers
+ and booleans.
+
+ Numpy uses "broadcasting indexing" to handle array-valued indices.
+ "Broadcasting indexing" (a.k.a "fancy indexing") treats all multi-valued
+ indices together to allow arbitrary points to be extracted. The index
+ arrays can be multidimensional, and more than one can be specified in a
+ slice, as long as they can be "broadcast" against each other.
+ This style of indexing can be very powerful, but it is very hard
+ to understand, explain, and implement (and can lead to hard to find bugs).
+ Most other python packages and array processing
+ languages (such as netcdf4-python, xray, biggus, matlab and fortran)
+ use "orthogonal indexing" which only allows for 1-d index arrays and
+ treats these arrays of indices independently along each dimension.
+
+ The implementation of "orthogonal indexing" used here requires that
+ index arrays be 1-d boolean or integer. If integer arrays are used,
+ the index values must be sorted and contain no duplicates.
+
+ In summary, slicing netcdf4-python variable objects with 1-d integer or
+ boolean arrays is allowed, but may give a different result than slicing a
+ numpy array.
+
+ Numpy also supports slicing an array with a boolean array of the same
+ shape. For example x[x>0] returns a 1-d array with all the positive values of x.
+ This is also not supported in netcdf4-python, if x.ndim > 1.
+
+ Orthogonal indexing can be used in to select netcdf variable slices
+ using the dimension variables. For example, you can use v[lat>60,lon<180]
+ to fetch the elements of v obeying conditions on latitude and longitude.
+ Allow for this sort of simple variable subsetting is the reason we decided to
+ deviate from numpy's slicing rules.
+
+ This function is used both by the __setitem__ and __getitem__ method of
+ the Variable class.
+
+ Parameters
+ ----------
+ elem : tuple of integer, slice, ellipsis or 1-d boolean or integer
+ sequences used to slice the netCDF Variable (Variable[elem]).
+ shape : tuple containing the current shape of the netCDF variable.
+ dimensions : sequence
+ The name of the dimensions. This is only useful to find out
+ whether or not some dimensions are unlimited. Only needed within
+ __setitem__.
+ grp : netCDF Group
+ The netCDF group to which the variable being set belongs to.
+ Only needed within __setitem__.
+ datashape : sequence
+ The shape of the data that is being stored. Only needed by __setitime__
+ put : True|False (default False). If called from __setitem__, put is True.
+
+ Returns
+ -------
+ start : ndarray (..., n)
+ A starting indices array of dimension n+1. The first n
+ dimensions identify different independent data chunks. The last dimension
+ can be read as the starting indices.
+ count : ndarray (..., n)
+ An array of dimension (n+1) storing the number of elements to get.
+ stride : ndarray (..., n)
+ An array of dimension (n+1) storing the steps between each datum.
+ indices : ndarray (..., n)
+ An array storing the indices describing the location of the
+ data chunk in the target/source array (__getitem__/__setitem__).
+
+ Notes:
+
+ netCDF data is accessed via the function:
+ nc_get_vars(grpid, varid, start, count, stride, data)
+
+ Assume that the variable has dimension n, then
+
+ start is a n-tuple that contains the indices at the beginning of data chunk.
+ count is a n-tuple that contains the number of elements to be accessed.
+ stride is a n-tuple that contains the step length between each element.
+
+ """
+ # Adapted from pycdf (http://pysclint.sourceforge.net/pycdf)
+ # by Andre Gosselin..
+ # Modified by David Huard to handle efficiently fancy indexing with
+ # sequences of integers or booleans.
+
+ nDims = len(shape)
+ if nDims == 0:
+ nDims = 1
+ shape = (1,)
+
+ # When a single array or (non-tuple) sequence of integers is given
+ # as a slice, assume it applies to the first dimension,
+ # and use ellipsis for remaining dimensions.
+ if np.iterable(elem):
+ if type(elem) == np.ndarray or (type(elem) != tuple and \
+ np.array([_is_int(e) for e in elem]).all()):
+ elem = [elem]
+ for n in range(len(elem)+1,nDims+1):
+ elem.append(slice(None,None,None))
+ else: # Convert single index to sequence
+ elem = [elem]
+
+ # replace sequence of integer indices with boolean arrays
+ newElem = []
+ IndexErrorMsg=\
+ "only integers, slices (`:`), ellipsis (`...`), and 1-d integer or boolean arrays are valid indices"
+ for i, e in enumerate(elem):
+ # string-like object try to cast to int
+ # needs to be done first, since strings are iterable and
+ # hard to distinguish from something castable to an iterable numpy array.
+ if type(e) in [str,bytes,unicode]:
+ try:
+ e = int(e)
+ except:
+ raise IndexError(IndexErrorMsg)
+ ea = np.asarray(e)
+ # Raise error if multidimensional indexing is used.
+ if ea.ndim > 1:
+ raise IndexError("Index cannot be multidimensional")
+ # set unlim to True if dimension is unlimited and put==True
+ # (called from __setitem__)
+ if put and (dimensions is not None and grp is not None) and len(dimensions):
+ try:
+ dimname = dimensions[i]
+ # is this dimension unlimited?
+ # look in current group, and parents for dim.
+ dim = _find_dim(grp, dimname)
+ unlim = dim.isunlimited()
+ except IndexError: # more slices than dimensions (issue 371)
+ unlim = False
+ else:
+ unlim = False
+ # an iterable (non-scalar) integer array.
+ if np.iterable(ea) and ea.dtype.kind == 'i':
+ # convert negative indices in 1d array to positive ones.
+ ea = np.where(ea < 0, ea + shape[i], ea)
+ if np.any(ea < 0):
+ raise IndexErro("integer index out of range")
+ if not np.all(np.diff(ea) > 0): # same but cheaper than np.all(np.unique(ea) == ea)
+ # raise an error when new indexing behavior is different
+ # (i.e. when integer sequence not sorted, or there are
+ # duplicate indices in the sequence)
+ msg = "integer sequences in slices must be sorted and cannot have duplicates"
+ raise IndexError(msg)
+ # convert to boolean array.
+ # if unlim, let boolean array be longer than current dimension
+ # length.
+ elen = shape[i]
+ if unlim:
+ elen = max(ea.max()+1,elen)
+ else:
+ if ea.max()+1 > elen:
+ msg="integer index exceeds dimension size"
+ raise IndexError(msg)
+ eb = np.zeros(elen,np.bool)
+ eb[ea] = True
+ newElem.append(eb)
+ # an iterable (non-scalar) boolean array
+ elif np.iterable(ea) and ea.dtype.kind =='b':
+ # check that boolen array not too long
+ if not unlim and shape[i] != len(ea):
+ msg="""
+Boolean array must have the same shape as the data along this dimension."""
+ raise IndexError(msg)
+ newElem.append(ea)
+ # integer scalar
+ elif ea.dtype.kind == 'i':
+ newElem.append(e)
+ # slice or ellipsis object
+ elif type(e) == slice or type(e) == type(Ellipsis):
+ newElem.append(e)
+ else: # castable to a scalar int, otherwise invalid
+ try:
+ e = int(e)
+ newElem.append(e)
+ except:
+ raise IndexError(IndexErrorMsg)
+ elem = newElem
+
+ # replace Ellipsis and boolean arrays with slice objects, if possible.
+ hasEllipsis = False
+ newElem = []
+ for e in elem:
+ ea = np.asarray(e)
+ # Replace ellipsis with slices.
+ if type(e) == type(Ellipsis):
+ if hasEllipsis:
+ raise IndexError("At most one ellipsis allowed in a slicing expression")
+ # The ellipsis stands for the missing dimensions.
+ newElem.extend((slice(None, None, None),) * (nDims - len(elem) + 1))
+ hasEllipsis = True
+ # Replace boolean array with slice object if possible.
+ elif ea.dtype.kind == 'b':
+ if ea.any():
+ indices = np.flatnonzero(ea)
+ start = indices[0]
+ stop = indices[-1] + 1
+ if len(indices) >= 2:
+ step = indices[1] - indices[0]
+ else:
+ step = None
+ if np.array_equal(indices, np.arange(start, stop, step)):
+ newElem.append(slice(start, stop, step))
+ else:
+ newElem.append(ea)
+ else:
+ newElem.append(slice(0, 0))
+ else:
+ newElem.append(e)
+ elem = newElem
+
+ # If slice doesn't cover all dims, assume ellipsis for rest of dims.
+ if len(elem) < nDims:
+ for n in range(len(elem)+1,nDims+1):
+ elem.append(slice(None,None,None))
+
+ # make sure there are not too many dimensions in slice.
+ if len(elem) > nDims:
+ raise ValueError("slicing expression exceeds the number of dimensions of the variable")
+
+ # Compute the dimensions of the start, count, stride and indices arrays.
+ # The number of elements in the first n dimensions corresponds to the
+ # number of times the _get method will be called.
+ sdim = []
+ for i, e in enumerate(elem):
+ # at this stage e is a slice, a scalar integer, or a 1d boolean array.
+ # Booleans --- _get call for each True value
+ if np.asarray(e).dtype.kind == 'b':
+ sdim.append(e.sum())
+ # Scalar int or slice, just a single _get call
+ else:
+ sdim.append(1)
+
+ # Create the start, count, stride and indices arrays.
+
+ sdim.append(max(nDims, 1))
+ start = np.empty(sdim, dtype=int)
+ count = np.empty(sdim, dtype=int)
+ stride = np.empty(sdim, dtype=int)
+ indices = np.empty(sdim, dtype=object)
+
+ for i, e in enumerate(elem):
+
+ ea = np.asarray(e)
+
+ # set unlim to True if dimension is unlimited and put==True
+ # (called from __setitem__). Note: grp and dimensions must be set.
+ if put and (dimensions is not None and grp is not None) and len(dimensions):
+ dimname = dimensions[i]
+ # is this dimension unlimited?
+ # look in current group, and parents for dim.
+ dim = _find_dim(grp, dimname)
+ unlim = dim.isunlimited()
+ else:
+ unlim = False
+
+ # SLICE #
+ if type(e) == slice:
+
+ # determine length parameter for slice.indices.
+
+ # shape[i] can be zero for unlim dim that hasn't been written to
+ # yet.
+ # length of slice may be longer than current shape
+ # if dimension is unlimited (and we are writing, not reading).
+ if unlim and e.stop is not None and e.stop > shape[i]:
+ length = e.stop
+ elif unlim and e.stop is None and datashape != ():
+ if e.start is None:
+ length = datashape[i]
+ else:
+ length = e.start+datashape[i]
+ else:
+ if unlim and datashape == () and len(dim) == 0:
+ # writing scalar along unlimited dimension using slicing
+ # syntax (var[:] = 1, when var.shape = ())
+ length = 1
+ else:
+ length = shape[i]
+
+ beg, end, inc = e.indices(length)
+ n = len(range(beg,end,inc))
+
+ start[...,i] = beg
+ count[...,i] = n
+ stride[...,i] = inc
+ indices[...,i] = slice(None)
+
+ # BOOLEAN ITERABLE #
+ elif ea.dtype.kind == 'b':
+ e = np.arange(len(e))[e] # convert to integer array
+ start[...,i] = np.apply_along_axis(lambda x: e*x, i, np.ones(sdim[:-1]))
+ indices[...,i] = np.apply_along_axis(lambda x: np.arange(sdim[i])*x, i, np.ones(sdim[:-1], int))
+
+ count[...,i] = 1
+ stride[...,i] = 1
+
+
+ # all that's left is SCALAR INTEGER #
+ else:
+ if e >= 0:
+ start[...,i] = e
+ elif e < 0 and (-e <= shape[i]) :
+ start[...,i] = e+shape[i]
+ else:
+ raise IndexError("Index out of range")
+
+ count[...,i] = 1
+ stride[...,i] = 1
+ indices[...,i] = -1 # Use -1 instead of 0 to indicate that
+ # this dimension shall be squeezed.
+
+ return start, count, stride, indices#, out_shape
+
+def _out_array_shape(count):
+ """Return the output array shape given the count array created by getStartCountStride"""
+
+ s = list(count.shape[:-1])
+ out = []
+
+ for i, n in enumerate(s):
+ if n == 1:
+ c = count[..., i].ravel()[0] # All elements should be identical.
+ out.append(c)
+ else:
+ out.append(n)
+ return out
+
+def _is_container(a):
+ # is object container-like? (can test for
+ # membership with "is in", but not a string)
+ try: 1 in a
+ except: return False
+ if type(a) == type(basestring): return False
+ return True
+
+def _is_int(a):
+ try:
+ return int(a) == a
+ except:
+ return False
+
+def _tostr(s):
+ try:
+ ss = str(s)
+ except:
+ ss = s
+ return ss
+
+
+def _getgrp(g,p):
+ import posixpath
+ grps = p.split("/")
+ for gname in grps:
+ if gname == "": continue
+ g = g.groups[gname]
+ return g
+
+def ncinfo():
+
+ from netCDF4 import Dataset
+
+
+ usage = """
+ Print summary information about a netCDF file.
+
+ usage: %s [-h] [-g grp or --group=grp] [-v var or --variable=var] [-d dim or --dimension=dim] filename
+
+ -h -- Print usage message.
+ -g <group name> or --group=<group name> -- Print info for this group
+ (default is root group). Nested groups specified
+ using posix paths ("group1/group2/group3").
+ -v <variable name> or --variable=<variable name> -- Print info for this variable.
+ -d <dimension name> or --dimension=<dimension name> -- Print info for this dimension.
+
+ netcdf filename must be last argument.
+\n""" % os.path.basename(sys.argv[0])
+
+ try:
+ opts, pargs = getopt.getopt(sys.argv[1:],'hv:g:d:',
+ ['group=',
+ 'variable=',
+ 'dimension='])
+ except:
+ (type, value, traceback) = sys.exc_info()
+ sys.stdout.write("Error parsing the options. The error was: %s\n" % value)
+ sys.stderr.write(usage)
+ sys.exit(0)
+
+ # Get the options
+ group = None; var = None; dim=None
+ for option in opts:
+ if option[0] == '-h':
+ sys.stderr.write(usage)
+ sys.exit(0)
+ elif option[0] == '--group' or option[0] == '-g':
+ group = option[1]
+ elif option[0] == '--variable' or option[0] == '-v':
+ var = option[1]
+ elif option[0] == '--dimension' or option[0] == '-d':
+ dim = option[1]
+ else:
+ sys.stdout.write("%s: Unrecognized option\n" % option[0])
+ sys.stderr.write(usage)
+ sys.exit(0)
+
+ # filename passed as last argumenbt
+ filename = pargs[-1]
+
+ f = Dataset(filename)
+ if group is None:
+ if var is None and dim is None:
+ print(f)
+ else:
+ if var is not None:
+ print(f.variables[var])
+ if dim is not None:
+ print(f.dimensions[dim])
+ else:
+ if var is None and dim is None:
+ print(getgrp(f,group))
+ else:
+ g = getgrp(f,group)
+ if var is not None:
+ print(g.variables[var])
+ if dim is not None:
+ print(g.dimensions[var])
+ f.close()
+
+def _nc4tonc3(filename4,filename3,clobber=False,nchunk=10,quiet=False,format='NETCDF3_64BIT'):
+ """convert a netcdf 4 file (filename4) in NETCDF4_CLASSIC format
+ to a netcdf 3 file (filename3) in NETCDF3_64BIT format."""
+ ncfile4 = Dataset(filename4,'r')
+ if ncfile4.file_format != 'NETCDF4_CLASSIC':
+ raise IOError('input file must be in NETCDF4_CLASSIC format')
+ ncfile3 = Dataset(filename3,'w',clobber=clobber,format=format)
+ # create dimensions. Check for unlimited dim.
+ unlimdimname = False
+ unlimdim = None
+ # create global attributes.
+ if not quiet: sys.stdout.write('copying global attributes ..\n')
+ #for attname in ncfile4.ncattrs():
+ # setattr(ncfile3,attname,getattr(ncfile4,attname))
+ ncfile3.setncatts(ncfile4.__dict__)
+ if not quiet: sys.stdout.write('copying dimensions ..\n')
+ for dimname,dim in ncfile4.dimensions.items():
+ if dim.isunlimited():
+ unlimdimname = dimname
+ unlimdim = dim
+ ncfile3.createDimension(dimname,None)
+ else:
+ ncfile3.createDimension(dimname,len(dim))
+ # create variables.
+ for varname,ncvar in ncfile4.variables.items():
+ if not quiet:
+ sys.stdout.write('copying variable %s\n' % varname)
+ # is there an unlimited dimension?
+ if unlimdimname and unlimdimname in ncvar.dimensions:
+ hasunlimdim = True
+ else:
+ hasunlimdim = False
+ if hasattr(ncvar, '_FillValue'):
+ FillValue = ncvar._FillValue
+ else:
+ FillValue = None
+ var = ncfile3.createVariable(varname,ncvar.dtype,ncvar.dimensions,fill_value=FillValue)
+ # fill variable attributes.
+ attdict = ncvar.__dict__
+ if '_FillValue' in attdict:
+ del attdict['_FillValue']
+ var.setncatts(attdict)
+ #for attname in ncvar.ncattrs():
+ # if attname == '_FillValue': continue
+ # setattr(var,attname,getattr(ncvar,attname))
+ # fill variables with data.
+ if hasunlimdim: # has an unlim dim, loop over unlim dim index.
+ # range to copy
+ if nchunk:
+ start = 0; stop = len(unlimdim); step = nchunk
+ if step < 1:
+ step = 1
+ for n in range(start, stop, step):
+ nmax = n+nchunk
+ if nmax > len(unlimdim):
+ nmax=len(unlimdim)
+ var[n:nmax] = ncvar[n:nmax]
+ else:
+ var[0:len(unlimdim)] = ncvar[:]
+ else: # no unlim dim or 1-d variable, just copy all data at once.
+ var[:] = ncvar[:]
+ ncfile3.sync() # flush data to disk
+ # close files.
+ ncfile3.close()
+ ncfile4.close()
+
+def nc4tonc3():
+ usage = """
+ Convert a netCDF 4 file (in NETCDF4_CLASSIC format) to netCDF 3 format.
+
+ usage: %s [-h] [-o] [--chunk] netcdf4filename netcdf3filename
+ -h -- Print usage message.
+ -o -- Overwite destination file (default is to raise an error if output file already exists).
+ --quiet=(0|1) -- if 1, don't print diagnostic information.
+ --format -- netcdf3 format to use (NETCDF3_64BIT by default, can be set to NETCDF3_CLASSIC)
+ --chunk=(integer) -- number of records along unlimited dimension to
+ write at once. Default 10. Ignored if there is no unlimited
+ dimension. chunk=0 means write all the data at once.
+\n""" % os.path.basename(sys.argv[0])
+
+ try:
+ opts, pargs = getopt.getopt(sys.argv[1:], 'ho',
+ ['format=','chunk=','quiet='])
+ except:
+ (type, value, traceback) = sys.exc_info()
+ sys.stdout.write("Error parsing the options. The error was: %s\n" % value)
+ sys.stderr.write(usage)
+ sys.exit(0)
+
+ # default options
+ quiet = 0
+ chunk = 1000
+ format = 'NETCDF3_64BIT'
+ overwritefile = 0
+
+ # Get the options
+ for option in opts:
+ if option[0] == '-h':
+ sys.stderr.write(usage)
+ sys.exit(0)
+ elif option[0] == '-o':
+ overwritefile = 1
+ elif option[0] == '--quiet':
+ quiet = int(option[1])
+ elif option[0] == '--format':
+ format = option[1]
+ elif option[0] == '--chunk':
+ chunk = int(option[1])
+ else:
+ sys.stdout.write("%s : Unrecognized option\n" % options[0])
+ sys.stderr.write(usage)
+ sys.exit(0)
+
+ # if we pass a number of files different from 2, abort
+ if len(pargs) < 2 or len(pargs) > 2:
+ sys.stdout.write("You need to pass both source and destination!\n.")
+ sys.stderr.write(usage)
+ sys.exit(0)
+
+ # Catch the files passed as the last arguments
+ filename4 = pargs[0]
+ filename3 = pargs[1]
+
+ # copy the data from filename4 to filename3.
+ _nc4tonc3(filename4,filename3,clobber=overwritefile,quiet=quiet,format=format)
+
+
+def _nc3tonc4(filename3,filename4,unpackshort=True,
+ zlib=True,complevel=6,shuffle=True,fletcher32=False,
+ clobber=False,lsd_dict=None,nchunk=10,quiet=False,classic=0,
+ vars=None,istart=0,istop=-1):
+ """convert a netcdf 3 file (filename3) to a netcdf 4 file
+ The default format is 'NETCDF4', but can be set
+ to NETCDF4_CLASSIC if classic=1.
+ If unpackshort=True, variables stored as short
+ integers with a scale and offset are unpacked to floats.
+ in the netcdf 4 file. If the lsd_dict is not None, variable names
+ corresponding to the keys of the dict will be truncated to the decimal place
+ specified by the values of the dict. This improves compression by
+ making it 'lossy'..
+ If vars is not None, only variable names in the list
+ will be copied (plus all the dimension variables).
+ The zlib, complevel and shuffle keywords control
+ how the compression is done."""
+
+ from netCDF4 import Dataset
+
+ ncfile3 = Dataset(filename3,'r')
+ if classic:
+ ncfile4 = Dataset(filename4,'w',clobber=clobber,format='NETCDF4_CLASSIC')
+ else:
+ ncfile4 = Dataset(filename4,'w',clobber=clobber,format='NETCDF4')
+ mval = 1.e30 # missing value if unpackshort=True
+ # create dimensions. Check for unlimited dim.
+ unlimdimname = False
+ unlimdim = None
+ # create global attributes.
+ if not quiet: sys.stdout.write('copying global attributes ..\n')
+ #for attname in ncfile3.ncattrs():
+ # setattr(ncfile4,attname,getattr(ncfile3,attname))
+ ncfile4.setncatts(ncfile3.__dict__)
+ if not quiet: sys.stdout.write('copying dimensions ..\n')
+ for dimname,dim in ncfile3.dimensions.items():
+ if dim.isunlimited():
+ unlimdimname = dimname
+ unlimdim = dim
+ ncfile4.createDimension(dimname,None)
+ if istop == -1: istop=len(unlimdim)
+ else:
+ ncfile4.createDimension(dimname,len(dim))
+ # create variables.
+ if vars is None:
+ varnames = ncfile3.variables.keys()
+ else:
+ # variables to copy specified
+ varnames = vars
+ # add dimension variables
+ for dimname in ncfile3.dimensions.keys():
+ if dimname in ncfile3.variables.keys() and\
+ dimname not in varnames:
+ varnames.append(dimname)
+ for varname in varnames:
+ ncvar = ncfile3.variables[varname]
+ if not quiet: sys.stdout.write('copying variable %s\n' % varname)
+ # quantize data?
+ if lsd_dict is not None and lsd_dict.has_key(varname):
+ lsd = lsd_dict[varname]
+ if not quiet: sys.stdout.write('truncating to least_significant_digit = %d\n'%lsd)
+ else:
+ lsd = None # no quantization.
+ # unpack short integers to floats?
+ if unpackshort and hasattr(ncvar,'scale_factor') and hasattr(ncvar,'add_offset'):
+ dounpackshort = True
+ datatype = 'f4'
+ else:
+ dounpackshort = False
+ datatype = ncvar.dtype
+ # is there an unlimited dimension?
+ if unlimdimname and unlimdimname in ncvar.dimensions:
+ hasunlimdim = True
+ else:
+ hasunlimdim = False
+ if dounpackshort:
+ if not quiet: sys.stdout.write('unpacking short integers to floats ...\n')
+ sys.stdout.write('')
+ if hasattr(ncvar, '_FillValue'):
+ FillValue = ncvar._FillValue
+ else:
+ FillValue = None
+ var = ncfile4.createVariable(varname,datatype,ncvar.dimensions, fill_value=FillValue, least_significant_digit=lsd,zlib=zlib,complevel=complevel,shuffle=shuffle,fletcher32=fletcher32)
+ # fill variable attributes.
+ attdict = ncvar.__dict__
+ if '_FillValue' in attdict: del attdict['_FillValue']
+ if dounpackshort and 'add_offset' in attdict:
+ del attdict['add_offset']
+ if dounpackshort and 'scale_factor' in attdict:
+ del attdict['scale_factor']
+ if dounpackshort and 'missing_value' in attdict:
+ attdict['missing_value']=mval
+ var.setncatts(attdict)
+ #for attname in ncvar.ncattrs():
+ # if attname == '_FillValue': continue
+ # if dounpackshort and attname in ['add_offset','scale_factor']: continue
+ # if dounpackshort and attname == 'missing_value':
+ # setattr(var,attname,mval)
+ # else:
+ # setattr(var,attname,getattr(ncvar,attname))
+ # fill variables with data.
+ if hasunlimdim: # has an unlim dim, loop over unlim dim index.
+ # range to copy
+ if nchunk:
+ start = istart; stop = istop; step = nchunk
+ if step < 1: step = 1
+ for n in range(start, stop, step):
+ nmax = n+nchunk
+ if nmax > istop: nmax=istop
+ idata = ncvar[n:nmax]
+ if dounpackshort:
+ tmpdata = (ncvar.scale_factor*idata.astype('f')+ncvar.add_offset).astype('f')
+ if hasattr(ncvar,'missing_value'):
+ tmpdata = numpy.where(idata == ncvar.missing_value, mval, tmpdata)
+ else:
+ tmpdata = idata
+ var[n-istart:nmax-istart] = tmpdata
+ else:
+ idata = ncvar[:]
+ if dounpackshort:
+ tmpdata = (ncvar.scale_factor*idata.astype('f')+ncvar.add_offset).astype('f')
+ if hasattr(ncvar,'missing_value'):
+ tmpdata = numpy.where(idata == ncvar.missing_value, mval, tmpdata)
+ else:
+ tmpdata = idata
+ var[0:len(unlimdim)] = tmpdata
+ else: # no unlim dim or 1-d variable, just copy all data at once.
+ idata = ncvar[:]
+ if dounpackshort:
+ tmpdata = (ncvar.scale_factor*idata.astype('f')+ncvar.add_offset).astype('f')
+ if hasattr(ncvar,'missing_value'):
+ tmpdata = numpy.where(idata == ncvar.missing_value, mval, tmpdata)
+ else:
+ tmpdata = idata
+ var[:] = tmpdata
+ ncfile4.sync() # flush data to disk
+ # close files.
+ ncfile3.close()
+ ncfile4.close()
+
+
+def nc3tonc4():
+ usage = """
+ Convert a netCDF 3 file to netCDF 4 format, optionally
+ unpacking variables packed as short integers (with scale_factor and add_offset)
+ to floats, and adding zlib compression (with the HDF5 shuffle filter and fletcher32 checksum).
+ Data may also be quantized (truncated) to a specified precision to improve compression.
+
+ usage: %s [-h] [-o] [--vars=var1,var2,..] [--zlib=(0|1)] [--complevel=(1-9)] [--shuffle=(0|1)] [--fletcher32=(0|1)] [--unpackshort=(0|1)] [--quantize=var1=n1,var2=n2,..] netcdf3filename netcdf4filename
+ -h -- Print usage message.
+ -o -- Overwite destination file (default is to raise an error if output file already exists).
+ --vars -- comma separated list of variable names to copy (default is to copy
+ all variables)
+ --classic=(0|1) -- use NETCDF4_CLASSIC format instead of NETCDF4 (default 1)
+ --zlib=(0|1) -- Activate (or disable) zlib compression (default is activate).
+ --complevel=(1-9) -- Set zlib compression level (6 is default).
+ --shuffle=(0|1) -- Activate (or disable) the shuffle filter (active by default).
+ --fletcher32=(0|1) -- Activate (or disable) the fletcher32 checksum (not
+ active by default).
+ --unpackshort=(0|1) -- Unpack short integer variables to float variables
+ using scale_factor and add_offset netCDF variable attributes (active by default).
+ --quantize=(comma separated list of "variable name=integer" pairs) --
+ Truncate the data in the specified variables to a given decimal precision.
+ For example, 'speed=2, height=-2, temp=0' will cause the variable
+ 'speed' to be truncated to a precision of 0.01, 'height' to a precision of 100
+ and 'temp' to 1. This can significantly improve compression. The default
+ is not to quantize any of the variables.
+ --quiet=(0|1) -- if 1, don't print diagnostic information.
+ --chunk=(integer) -- number of records along unlimited dimension to
+ write at once. Default 10. Ignored if there is no unlimited
+ dimension. chunk=0 means write all the data at once.
+ --istart=(integer) -- number of record to start at along unlimited dimension.
+ Default 0. Ignored if there is no unlimited dimension.
+ --istop=(integer) -- number of record to stop at along unlimited dimension.
+ Default -1. Ignored if there is no unlimited dimension.
+\n""" % os.path.basename(sys.argv[0])
+
+ try:
+ opts, pargs = getopt.getopt(sys.argv[1:], 'ho',
+ ['classic=',
+ 'vars=',
+ 'zlib=',
+ 'quiet=',
+ 'complevel=',
+ 'shuffle=',
+ 'fletcher32=',
+ 'unpackshort=',
+ 'quantize=',
+ 'chunk=',
+ 'istart=',
+ 'istop='])
+ except:
+ (type, value, traceback) = sys.exc_info()
+ sys.stdout.write("Error parsing the options. The error was: %s\n" % value)
+ sys.stderr.write(usage)
+ sys.exit(0)
+
+ # default options
+ overwritefile = 0
+ complevel = 6
+ classic = 1
+ zlib = 1
+ shuffle = 1
+ fletcher32 = 0
+ unpackshort = 1
+ vars = None
+ quantize = None
+ quiet = 0
+ chunk = 1000
+ istart = 0
+ istop = -1
+
+ # Get the options
+ for option in opts:
+ if option[0] == '-h':
+ sys.stderr.write(usage)
+ sys.exit(0)
+ elif option[0] == '-o':
+ overwritefile = 1
+ elif option[0] == '--classic':
+ classic = int(option[1])
+ elif option[0] == '--zlib':
+ zlib = int(option[1])
+ elif option[0] == '--quiet':
+ quiet = int(option[1])
+ elif option[0] == '--complevel':
+ complevel = int(option[1])
+ elif option[0] == '--shuffle':
+ shuffle = int(option[1])
+ elif option[0] == '--fletcher32':
+ fletcher32 = int(option[1])
+ elif option[0] == '--unpackshort':
+ unpackshort = int(option[1])
+ elif option[0] == '--chunk':
+ chunk = int(option[1])
+ elif option[0] == '--vars':
+ vars = option[1]
+ elif option[0] == '--quantize':
+ quantize = option[1]
+ elif option[0] == '--istart':
+ istart = int(option[1])
+ elif option[0] == '--istop':
+ istop = int(option[1])
+ else:
+ sys.stdout.write("%s: Unrecognized option\n" % option[0])
+ sys.stderr.write(usage)
+ sys.exit(0)
+
+ # if we pass a number of files different from 2, abort
+ if len(pargs) < 2 or len(pargs) > 2:
+ sys.stdout.write("You need to pass both source and destination!.\n")
+ sys.stderr.write(usage)
+ sys.exit(0)
+
+ # Catch the files passed as the last arguments
+ filename3 = pargs[0]
+ filename4 = pargs[1]
+
+ # Parse the quantize option, create a dictionary from key/value pairs.
+ if quantize is not None:
+ lsd_dict = {}
+ for p in quantize.split(','):
+ kv = p.split('=')
+ lsd_dict[kv[0]] = int(kv[1])
+ else:
+ lsd_dict=None
+
+ # Parse the vars option, create a list of variable names.
+ if vars is not None:
+ vars = vars.split(',')
+
+ # copy the data from filename3 to filename4.
+ _nc3tonc4(filename3,filename4,unpackshort=unpackshort,
+ zlib=zlib,complevel=complevel,shuffle=shuffle,
+ fletcher32=fletcher32,clobber=overwritefile,lsd_dict=lsd_dict,
+ nchunk=chunk,quiet=quiet,vars=vars,classic=classic,
+ istart=istart,istop=istop)
diff --git a/netCDF4_utils.py b/netCDF4_utils.py
deleted file mode 100644
index f19ebef..0000000
--- a/netCDF4_utils.py
+++ /dev/null
@@ -1,404 +0,0 @@
-import numpy as np
-from numpy import ma
-import sys
-
-python3 = sys.version_info[0] > 2
-if python3:
- # no unicode type in python 3, use bytes instead when testing
- # for a string-like object
- unicode = str
-try:
- bytes
-except NameError:
- # no bytes type in python < 2.6
- bytes = str
-
-
-def _sortbylist(A,B):
- # sort one list (A) using the values from another list (B)
- return [A[i] for i in sorted(range(len(A)), key=B.__getitem__)]
-
-def _find_dim(grp, dimname):
- # find Dimension instance given group and name.
- # look in current group, and parents.
- group = grp
- dim = None
- while 1:
- try:
- dim = group.dimensions[dimname]
- break
- except:
- try:
- group = group.parent
- except:
- raise ValueError("cannot find dimension %s in this group or parent groups" % dimname)
- return dim
-
-def _walk_grps(topgrp):
- """Iterate through all (sub-) groups of topgrp, similar to os.walktree.
-
- """
- grps = topgrp.groups.values()
- yield grps
- for grp in topgrp.groups.values():
- for children in _walk_grps(grp):
- yield children
-
-def _quantize(data,least_significant_digit):
- """
-quantize data to improve compression. data is quantized using
-around(scale*data)/scale, where scale is 2**bits, and bits is determined
-from the least_significant_digit. For example, if
-least_significant_digit=1, bits will be 4.
- """
- precision = pow(10.,-least_significant_digit)
- exp = np.log10(precision)
- if exp < 0:
- exp = int(np.floor(exp))
- else:
- exp = int(np.ceil(exp))
- bits = np.ceil(np.log2(pow(10.,-exp)))
- scale = pow(2.,bits)
- datout = np.around(scale*data)/scale
- if ma.isMA(datout):
- datout.set_fill_value(data.fill_value)
- return datout
- else:
- return datout
-
-def _StartCountStride(elem, shape, dimensions=None, grp=None, datashape=None,\
- put=False):
- """Return start, count, stride and indices needed to store/extract data
- into/from a netCDF variable.
-
- This function is used to convert a NumPy index into a form that is
- compatible with the nc_get_vars function. Specifically, it needs
- to interpret slices, ellipses, sequences of integers as well as
- sequences of booleans.
-
- Note that all the fancy indexing tricks
- implemented in NumPy are not supported. In particular, multidimensional
- indexing is not supported and will raise an IndexError. Note also that
- boolean indexing does not work as in NumPy. In NumPy, booleans arrays
- behave identically to integer indices. For netCDF variables, we thought
- it would be useful to use a different logic, namely dimension independence.
- What this means is that you can do:
- >>> v[lat>60, lon<180, :]
- to fetch the elements of v obeying conditions on latitude and longitude.
-
- This function is used both by the __setitem__ and __getitem__ method of
- the Variable class. Although the behavior is similar in both cases, there
- are some differences to be noted.
-
- Parameters
- ----------
- elem : tuple of integer, slice, ellipsis or sequence of integers.
- The indexing information for the netCDF Variable: Variable[elem]
- shape : tuple
- The current shape of the netCDF variable.
- dimensions : sequence
- The name of the dimensions. This is only useful to find out
- whether or not some dimensions are unlimited. Only needed within
- __setitem__.
- grp : netCDF Group
- The netCDF group to which the variable being set belongs to.
- Only needed within __setitem__.
- datashape : sequence
- The shape of the data that is being stored. Only needed within
- __setitem__.
- put : True|False (default False). If called from __setitem__, put is True.
-
- Returns
- -------
- start : ndarray (..., n)
- A starting indices array of dimension n+1. The first n
- dimensions identify different independent data chunks. The last dimension
- can be read as the starting indices.
- count : ndarray (..., n)
- An array of dimension (n+1) storing the number of elements to get.
- stride : ndarray (..., n)
- An array of dimension (n+1) storing the steps between each datum.
- indices : ndarray (..., n)
- An array storing the indices describing the location of the
- data chunk in the target/source array (__getitem__/__setitem__).
-
- Notes:
-
- netCDF data is accessed via the function:
- nc_get_vars(grpid, varid, start, count, stride, data)
-
- Assume that the variable has dimension n, then
-
- start is a n-tuple that contains the indices at the beginning of data chunk.
- count is a n-tuple that contains the number of elements to be accessed.
- stride is a n-tuple that contains the step length between each element.
-
- """
- # Adapted from pycdf (http://pysclint.sourceforge.net/pycdf)
- # by Andre Gosselin..
- # Modified by David Huard to handle efficiently fancy indexing with
- # sequences of integers or booleans.
-
- nDims = len(shape)
- if nDims == 0:
- nDims = 1
- shape = (1,)
-
- # When a single array or (non-tuple) sequence of integers is given
- # as a slice, assume it applies to the first dimension,
- # and use ellipsis for remaining dimensions.
- if np.iterable(elem):
- if type(elem) == np.ndarray or (type(elem) != tuple and \
- np.array([_is_int(e) for e in elem]).all()):
- elem = [elem]
- for n in range(len(elem)+1,nDims+1):
- elem.append(slice(None,None,None))
- else: # Convert single index to sequence
- elem = [elem]
-
- # replace sequence of integer indices with boolean arrays
- newElem = []
- IndexErrorMsg=\
- "only integers, slices (`:`), ellipsis (`...`), and 1-d integer or boolean arrays are valid indices"
- for i, e in enumerate(elem):
- # string-like object try to cast to int
- # needs to be done first, since strings are iterable and
- # hard to distinguish from something castable to an iterable numpy array.
- if type(e) in [str,bytes,unicode]:
- try:
- e = int(e)
- except:
- raise IndexError(IndexErrorMsg)
- ea = np.asarray(e)
- # Raise error if multidimensional indexing is used.
- if ea.ndim > 1:
- raise IndexError("Index cannot be multidimensional")
- # set unlim to True if dimension is unlimited and put==True
- # (called from __setitem__)
- if put and (dimensions is not None and grp is not None) and len(dimensions):
- try:
- dimname = dimensions[i]
- # is this dimension unlimited?
- # look in current group, and parents for dim.
- dim = _find_dim(grp, dimname)
- unlim = dim.isunlimited()
- except IndexError: # more slices than dimensions (issue 371)
- unlim = False
- else:
- unlim = False
- # an iterable (non-scalar) integer array.
- if np.iterable(ea) and ea.dtype.kind == 'i':
- # convert negative indices in 1d array to positive ones.
- ea = np.where(ea < 0, ea + shape[i], ea)
- if np.any(ea < 0):
- raise IndexErro("integer index out of range")
- if not np.all(np.diff(ea) > 0): # same but cheaper than np.all(np.unique(ea) == ea)
- # raise an error when new indexing behavior is different
- # (i.e. when integer sequence not sorted, or there are
- # duplicate indices in the sequence)
- msg = "integer sequences in slices must be sorted and cannot have duplicates"
- raise IndexError(msg)
- # convert to boolean array.
- # if unlim, let boolean array be longer than current dimension
- # length.
- elen = shape[i]
- if unlim:
- elen = max(ea.max()+1,elen)
- else:
- if ea.max()+1 > elen:
- msg="integer index exceeds dimension size"
- raise IndexError(msg)
- eb = np.zeros(elen,np.bool)
- eb[ea] = True
- newElem.append(eb)
- # an iterable (non-scalar) boolean array
- elif np.iterable(ea) and ea.dtype.kind =='b':
- # check that boolen array not too long
- if not unlim and shape[i] != len(ea):
- msg="""
-Boolean array must have the same shape as the data along this dimension."""
- raise IndexError(msg)
- newElem.append(ea)
- # integer scalar
- elif ea.dtype.kind == 'i':
- newElem.append(e)
- # slice or ellipsis object
- elif type(e) == slice or type(e) == type(Ellipsis):
- newElem.append(e)
- else: # castable to a scalar int, otherwise invalid
- try:
- e = int(e)
- newElem.append(e)
- except:
- raise IndexError(IndexErrorMsg)
- elem = newElem
-
- # replace Ellipsis and boolean arrays with slice objects, if possible.
- hasEllipsis = False
- newElem = []
- for e in elem:
- ea = np.asarray(e)
- # Replace ellipsis with slices.
- if type(e) == type(Ellipsis):
- if hasEllipsis:
- raise IndexError("At most one ellipsis allowed in a slicing expression")
- # The ellipsis stands for the missing dimensions.
- newElem.extend((slice(None, None, None),) * (nDims - len(elem) + 1))
- hasEllipsis = True
- # Replace boolean array with slice object if possible.
- elif ea.dtype.kind == 'b':
- if ea.any():
- indices = np.flatnonzero(ea)
- start = indices[0]
- stop = indices[-1] + 1
- if len(indices) >= 2:
- step = indices[1] - indices[0]
- else:
- step = None
- if np.array_equal(indices, np.arange(start, stop, step)):
- newElem.append(slice(start, stop, step))
- else:
- newElem.append(ea)
- else:
- newElem.append(slice(0, 0))
- else:
- newElem.append(e)
- elem = newElem
-
- # If slice doesn't cover all dims, assume ellipsis for rest of dims.
- if len(elem) < nDims:
- for n in range(len(elem)+1,nDims+1):
- elem.append(slice(None,None,None))
-
- # make sure there are not too many dimensions in slice.
- if len(elem) > nDims:
- raise ValueError("slicing expression exceeds the number of dimensions of the variable")
-
- # Compute the dimensions of the start, count, stride and indices arrays.
- # The number of elements in the first n dimensions corresponds to the
- # number of times the _get method will be called.
- sdim = []
- for i, e in enumerate(elem):
- # at this stage e is a slice, a scalar integer, or a 1d boolean array.
- # Booleans --- _get call for each True value
- if np.asarray(e).dtype.kind == 'b':
- sdim.append(e.sum())
- # Scalar int or slice, just a single _get call
- else:
- sdim.append(1)
-
- # Create the start, count, stride and indices arrays.
-
- sdim.append(max(nDims, 1))
- start = np.empty(sdim, dtype=int)
- count = np.empty(sdim, dtype=int)
- stride = np.empty(sdim, dtype=int)
- indices = np.empty(sdim, dtype=object)
-
- for i, e in enumerate(elem):
-
- ea = np.asarray(e)
-
- # set unlim to True if dimension is unlimited and put==True
- # (called from __setitem__). Note: grp and dimensions must be set.
- if put and (dimensions is not None and grp is not None) and len(dimensions):
- dimname = dimensions[i]
- # is this dimension unlimited?
- # look in current group, and parents for dim.
- dim = _find_dim(grp, dimname)
- unlim = dim.isunlimited()
- else:
- unlim = False
-
- # SLICE #
- if type(e) == slice:
-
- # determine length parameter for slice.indices.
-
- # shape[i] can be zero for unlim dim that hasn't been written to
- # yet.
- # length of slice may be longer than current shape
- # if dimension is unlimited (and we are writing, not reading).
- if unlim and e.stop is not None and e.stop > shape[i]:
- length = e.stop
- elif unlim and e.stop is None and datashape != ():
- if e.start is None:
- length = datashape[i]
- else:
- length = e.start+datashape[i]
- else:
- if unlim and datashape == () and len(dim) == 0:
- # writing scalar along unlimited dimension using slicing
- # syntax (var[:] = 1, when var.shape = ())
- length = 1
- else:
- length = shape[i]
-
- beg, end, inc = e.indices(length)
- n = len(range(beg,end,inc))
-
- start[...,i] = beg
- count[...,i] = n
- stride[...,i] = inc
- indices[...,i] = slice(None)
-
- # BOOLEAN ITERABLE #
- elif ea.dtype.kind == 'b':
- e = np.arange(len(e))[e] # convert to integer array
- start[...,i] = np.apply_along_axis(lambda x: e*x, i, np.ones(sdim[:-1]))
- indices[...,i] = np.apply_along_axis(lambda x: np.arange(sdim[i])*x, i, np.ones(sdim[:-1], int))
-
- count[...,i] = 1
- stride[...,i] = 1
-
-
- # all that's left is SCALAR INTEGER #
- else:
- if e >= 0:
- start[...,i] = e
- elif e < 0 and (-e <= shape[i]) :
- start[...,i] = e+shape[i]
- else:
- raise IndexError("Index out of range")
-
- count[...,i] = 1
- stride[...,i] = 1
- indices[...,i] = -1 # Use -1 instead of 0 to indicate that
- # this dimension shall be squeezed.
-
- return start, count, stride, indices#, out_shape
-
-def _out_array_shape(count):
- """Return the output array shape given the count array created by getStartCountStride"""
-
- s = list(count.shape[:-1])
- out = []
-
- for i, n in enumerate(s):
- if n == 1:
- c = count[..., i].ravel()[0] # All elements should be identical.
- out.append(c)
- else:
- out.append(n)
- return out
-
-def _is_container(a):
- # is object container-like? (can test for
- # membership with "is in", but not a string)
- try: 1 in a
- except: return False
- if type(a) == type(basestring): return False
- return True
-
-def _is_int(a):
- try:
- return int(a) == a
- except:
- return False
-
-def _tostr(s):
- try:
- ss = str(s)
- except:
- ss = s
- return ss
diff --git a/netcdftime/__init__.py b/netcdftime/__init__.py
index d38514c..a7d1f4a 100644
--- a/netcdftime/__init__.py
+++ b/netcdftime/__init__.py
@@ -1,2 +1,4 @@
from .netcdftime import utime, JulianDayFromDate, DateFromJulianDay
from .netcdftime import datetime, _parse_date, date2index, time2index
+from .netcdftime import microsec_units, millisec_units, \
+ sec_units, hr_units, day_units, min_units
diff --git a/netcdftime/_datetime.c b/netcdftime/_datetime.c
index 8d9eddd..ae575c2 100644
--- a/netcdftime/_datetime.c
+++ b/netcdftime/_datetime.c
@@ -1,5 +1,13 @@
/* Generated by Cython 0.22 */
+/* BEGIN: Cython Metadata
+{
+ "distutils": {
+ "depends": []
+ }
+}
+END: Cython Metadata */
+
#define PY_SSIZE_T_CLEAN
#ifndef CYTHON_USE_PYLONG_INTERNALS
#ifdef PYLONG_BITS_IN_DIGIT
@@ -721,13 +729,12 @@ 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_Users_jsw_python_netcdf4_python[] = "/Users/jsw/python/netcdf4-python.git/netcdftime/_datetime.pyx";
+static char __pyx_k_home_mb312_dev_trees_netcdf4_py[] = "/home/mb312/dev_trees/netcdf4-python/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;
static PyObject *__pyx_n_s_compile;
@@ -741,6 +748,7 @@ static PyObject *__pyx_n_s_find;
static PyObject *__pyx_n_s_findall;
static PyObject *__pyx_n_s_fmt;
static PyObject *__pyx_n_s_format;
+static PyObject *__pyx_kp_s_home_mb312_dev_trees_netcdf4_py;
static PyObject *__pyx_n_s_hour;
static PyObject *__pyx_n_s_i;
static PyObject *__pyx_n_s_illegal_s;
@@ -3452,7 +3460,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_NotImplemented, __pyx_k_NotImplemented, sizeof(__pyx_k_NotImplemented), 0, 0, 1, 1},
{&__pyx_kp_s_This_strftime_implementation_doe, __pyx_k_This_strftime_implementation_doe, sizeof(__pyx_k_This_strftime_implementation_doe), 0, 0, 1, 0},
{&__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_compile, __pyx_k_compile, sizeof(__pyx_k_compile), 0, 0, 1, 1},
@@ -3466,6 +3473,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__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},
@@ -3591,7 +3599,7 @@ static int __Pyx_InitCachedConstants(void) {
__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_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_Users_jsw_python_netcdf4_python, __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_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;}
/* "netcdftime/_datetime.pyx":97
*
@@ -3603,7 +3611,7 @@ static int __Pyx_InitCachedConstants(void) {
__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_GOTREF(__pyx_tuple__9);
__Pyx_GIVEREF(__pyx_tuple__9);
- __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, 97, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 97; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __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_home_mb312_dev_trees_netcdf4_py, __pyx_n_s_strftime, 97, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 97; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_RefNannyFinishContext();
return 0;
__pyx_L1_error:;
diff --git a/netcdftime/netcdftime.py b/netcdftime/netcdftime.py
index b0fb173..69a8359 100644
--- a/netcdftime/netcdftime.py
+++ b/netcdftime/netcdftime.py
@@ -13,9 +13,13 @@ except ImportError: # python 3.x
from ._datetime import datetime
-_units = ['days', 'hours', 'minutes', 'seconds',
- 'day', 'hour', 'minute', 'second',
- 'milliseconds','millisecond','microseconds','microsecond']
+microsec_units = ['microseconds','microsecond', 'microsec', 'microsecs']
+millisec_units = ['milliseconds', 'millisecond', 'millisec', 'millisecs']
+sec_units = ['second', 'seconds', 'sec', 'secs', 's']
+min_units = ['minute', 'minutes', 'min', 'mins']
+hr_units = ['hour', 'hours', 'hr', 'hrs', 'h']
+day_units = ['day', 'days', 'd']
+_units = microsec_units+millisec_units+sec_units+min_units+hr_units+day_units
_calendars = ['standard', 'gregorian', 'proleptic_gregorian',
'noleap', 'julian', 'all_leap', '365_day', '366_day', '360_day']
@@ -299,7 +303,7 @@ def DateFromJulianDay(JD, calendar='standard'):
year[year <= 0] = year[year <= 0] - 1
# a leap year?
- leap = np.zeros(len(year))
+ leap = np.zeros(len(year),dtype=dayofyr.dtype)
leap[year % 4 == 0] = 1
if calendar == 'proleptic_gregorian':
leap[(year % 100 == 0) & (year % 400 != 0)] = 0
@@ -736,18 +740,20 @@ units to datetime objects.
if not isscalar:
jdelta = numpy.array(jdelta)
# convert to desired units, subtract time zone offset.
- if self.units in ['microseconds','microsecond']:
+ if self.units in microsec_units:
jdelta = jdelta * 86400. * 1.e6 - self.tzoffset * 60. * 1.e6
- elif self.units in ['milliseconds', 'millisecond']:
+ elif self.units in millisec_units:
jdelta = jdelta * 86400. * 1.e3 - self.tzoffset * 60. * 1.e3
- elif self.units in ['second', 'seconds']:
+ elif self.units in sec_units:
jdelta = jdelta * 86400. - self.tzoffset * 60.
- elif self.units in ['minute', 'minutes']:
+ elif self.units in min_units:
jdelta = jdelta * 1440. - self.tzoffset
- elif self.units in ['hour', 'hours']:
+ elif self.units in hr_units:
jdelta = jdelta * 24. - self.tzoffset / 60.
- elif self.units in ['day', 'days']:
+ elif self.units in day_units:
jdelta = jdelta - self.tzoffset / 1440.
+ else:
+ raise ValueError('unsupported time units')
if isscalar:
return jdelta
else:
@@ -788,18 +794,20 @@ units to datetime objects.
time_value = numpy.array(time_value, dtype='d')
shape = time_value.shape
# convert to desired units, add time zone offset.
- if self.units in ['microseconds','microsecond']:
+ if self.units in microsec_units:
jdelta = time_value / 86400000000. + self.tzoffset / 1440.
- elif self.units in ['milliseconds', 'millisecond']:
+ elif self.units in millisec_units:
jdelta = time_value / 86400000. + self.tzoffset / 1440.
- elif self.units in ['second', 'seconds']:
+ elif self.units in sec_units:
jdelta = time_value / 86400. + self.tzoffset / 1440.
- elif self.units in ['minute', 'minutes']:
+ elif self.units in min_units:
jdelta = time_value / 1440. + self.tzoffset / 1440.
- elif self.units in ['hour', 'hours']:
+ elif self.units in hr_units:
jdelta = time_value / 24. + self.tzoffset / 1440.
- elif self.units in ['day', 'days']:
+ elif self.units in day_units:
jdelta = time_value + self.tzoffset / 1440.
+ else:
+ raise ValueError('unsupported time units')
jd = self._jd0 + jdelta
if self.calendar in ['julian', 'standard', 'gregorian', 'proleptic_gregorian']:
if not isscalar:
diff --git a/ports/README b/ports/README
deleted file mode 100644
index f92baef..0000000
--- a/ports/README
+++ /dev/null
@@ -1,14 +0,0 @@
-* copy this directory to your home directory.
-(cd ..; tar -cvf $HOME/ports.tar ports; cd $HOME; tar -xvf ports.tar; rm ports.tar)
-
-* sudo vi /opt/local/etc/macports/sources.conf, add the line
-file:///Users/<myusername>/ports
- just *before* the line
-rsync://rsync.macports.org/release/ports [default]
-
-* cd /Users/<myusername>/ports; sudo portindex
-
-* sudo port install py27-netcdf4
-This will install netcdf4-python from svn HEAD. To periodically upgrade, just do
- - sudo port uninstall py-netcdf4
- - sudo port install py-netcdf4 (this will re-download and rebuild)
diff --git a/ports/python/py-netcdf4/Portfile b/ports/python/py-netcdf4/Portfile
deleted file mode 100644
index 54aef5b..0000000
--- a/ports/python/py-netcdf4/Portfile
+++ /dev/null
@@ -1,28 +0,0 @@
-# -*- coding: utf-8; mode: tcl; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- vim:fenc=utf-8:ft=tcl:et:sw=4:ts=4:sts=4
-# $Id$
-PortSystem 1.0
-PortGroup python 1.0
-
-name py-netcdf4
-version svnhead
-categories-append science
-platforms darwin
-maintainers openmaintainer
-description Python interface to the netCDF version 4 library.
-long_description ${description}
-homepage http://code.google.com/p/netcdf4-python/
-master_sites http://netcdf4-python.googlecode.com/files/
-license MIT
-
-distname trunk
-
-fetch.type svn
-svn.url http://netcdf4-python.googlecode.com/svn/${distname}
-
-depends_lib-append port:netcdf port:py${python.version}-numpy
-
-python.default_version 27
-python.versions 26 27
-
-build.env-append USE_NCCONFIG=1
-destroot.env USE_NCCONFIG=1
diff --git a/setup.cfg b/setup.cfg
index d4f522a..d95d728 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -5,6 +5,8 @@
# will be used to determine the locations of required libraries.
# Usually, nothing else is needed.
use_ncconfig=True
+# use cython to compile netCDF4.pyx (if cython is available).
+use_cython=True
# path to nc-config script (use if not found in unix PATH).
#ncconfig=/usr/local/bin/nc-config
[directories]
diff --git a/setup.cfg.template b/setup.cfg.template
index d4f522a..d95d728 100644
--- a/setup.cfg.template
+++ b/setup.cfg.template
@@ -5,6 +5,8 @@
# will be used to determine the locations of required libraries.
# Usually, nothing else is needed.
use_ncconfig=True
+# use cython to compile netCDF4.pyx (if cython is available).
+use_cython=True
# path to nc-config script (use if not found in unix PATH).
#ncconfig=/usr/local/bin/nc-config
[directories]
diff --git a/setup.py b/setup.py
index 2568fe4..bc2dd96 100644
--- a/setup.py
+++ b/setup.py
@@ -1,13 +1,27 @@
-import os, sys, subprocess, shutil
+import os, sys, subprocess
+import os.path as osp
+
try:
from setuptools import setup, Extension
- setuptools_extra_kwargs = {"install_requires":["numpy>=1.3"]}
+ setuptools_extra_kwargs = {
+ "install_requires": ["numpy>=1.7"],
+ "entry_points": {
+ 'console_scripts': [
+ 'ncinfo = netCDF4.utils:ncinfo',
+ 'nc4tonc3 = netCDF4.utils:nc4tonc3',
+ 'nc3tonc4 = netCDF4.utils:nc3tonc4',
+ ]
+ },
+ }
except ImportError:
from distutils.core import setup, Extension
- setuptools_extra_kwargs = {}
+ setuptools_extra_kwargs = {
+ "scripts": ['utils/nc3tonc4','utils/nc4tonc3','utils/ncinfo']
+ }
from distutils.dist import Distribution
+
try:
- from Cython.Distutils import build_ext
+ from Cython.Build import cythonize
from Cython import __version__ as cython_version
if cython_version >= '0.19':
has_cython = True
@@ -140,6 +154,7 @@ setup_cfg = 'setup.cfg'
# contents of setup.cfg will override env vars.
ncconfig = None
use_ncconfig = None
+use_cython = True
if os.path.exists(setup_cfg):
sys.stdout.write('reading from setup.cfg...\n')
config = configparser.SafeConfigParser()
@@ -180,15 +195,21 @@ if os.path.exists(setup_cfg):
except: pass
try: curl_incdir = config.get("directories", "curl_incdir")
except: pass
- try: use_ncconfig = config.get("options", "use_ncconfig")
+ try: use_ncconfig = config.getboolean("options", "use_ncconfig")
except: pass
try: ncconfig = config.get("options", "ncconfig")
except: pass
+ try: use_cython = config.getboolean("options", "use_cython")
+ except: pass
+
+# turn off cython compilation if desired
+if has_cython and not use_cython:
+ has_cython = False
# make sure USE_NCCONFIG from environment takes
# precendence over use_ncconfig from setup.cfg (issue #341).
if USE_NCCONFIG is None and use_ncconfig is not None:
- USE_NCCONFIG = bool(use_ncconfig)
+ USE_NCCONFIG = use_ncconfig
elif USE_NCCONFIG is None:
USE_NCCONFIG = False
@@ -235,7 +256,7 @@ HDF5_DIR environment variable not set, checking some standard locations ..\n""")
raise ValueError('did not find HDF5 headers')
else:
if HDF5_incdir is None:
- HDF5_incdir = os.path.join(HDF5_dir, 'include')
+ HDF5_incdir = os.path.join(HDF5_dir, 'include')
hdf5_version = check_hdf5version(HDF5_incdir)
if hdf5_version is None:
raise ValueError('did not find HDF5 headers in %s' % HDF5_incdir)
@@ -329,19 +350,26 @@ if netcdf_lib_version is None:
else:
sys.stdout.write('using netcdf library version %s\n' % netcdf_lib_version)
+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:]:
sys.stdout.write('using Cython to compile netCDF4.pyx...\n')
- # recompile netCDF4.pyx
- extensions = [Extension("netCDF4",["netCDF4.pyx"],libraries=libs,library_dirs=lib_dirs,include_dirs=inc_dirs,runtime_library_dirs=lib_dirs),
+ extensions = [Extension("netCDF4._netCDF4",
+ [netcdf4_src_root + '.pyx'],
+ libraries=libs,
+ library_dirs=lib_dirs,
+ include_dirs=inc_dirs,
+ runtime_library_dirs=lib_dirs),
Extension('netcdftime._datetime', ['netcdftime/_datetime.pyx'])]
# remove netCDF4.c file if it exists, so cython will recompile netCDF4.pyx.
# run for build *and* install (issue #263). Otherwise 'pip install' will
# not regenerate netCDF4.c, even if the C lib supports the new features.
- if len(sys.argv) >= 2 and os.path.exists('netCDF4.c'):
- os.remove('netCDF4.c')
+ if len(sys.argv) >= 2 and os.path.exists(netcdf4_src_c):
+ os.remove(netcdf4_src_c)
# this determines whether renameGroup and filepath methods will work.
has_rename_grp, has_nc_inq_path, has_nc_inq_format_extended = check_api(inc_dirs)
- f = open('constants.pyx','w')
+ f = open(osp.join('include', 'constants.pyx'),'w')
if has_rename_grp:
sys.stdout.write('netcdf lib has group rename capability\n')
f.write('DEF HAS_RENAME_GRP = 1\n')
@@ -361,22 +389,25 @@ if has_cython and 'sdist' not in sys.argv[1:]:
sys.stdout.write('netcdf lib does not have nc_inq_format_extended function\n')
f.write('DEF HAS_NC_INQ_FORMAT_EXTENDED = 0\n')
f.close()
- cmdclass = {'build_ext': build_ext}
+ ext_modules = cythonize(extensions, include_path=['include'])
else:
- # use existing netCDF4.c, don't need cython.
- extensions = [Extension("netCDF4",["netCDF4.c"],libraries=libs,library_dirs=lib_dirs,include_dirs=inc_dirs,runtime_library_dirs=lib_dirs),
+ extensions = [Extension("netCDF4._netCDF4",
+ [netcdf4_src_c],
+ libraries=libs,
+ library_dirs=lib_dirs,
+ include_dirs=inc_dirs,
+ runtime_library_dirs=lib_dirs),
Extension('netcdftime._datetime', ['netcdftime/_datetime.c'])]
- cmdclass = {}
+ ext_modules = extensions
setup(name = "netCDF4",
cmdclass = cmdclass,
- version = "1.1.7",
+ version = "1.1.8",
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",
url = "http://github.com/Unidata/netcdf4-python",
download_url = "http://python.org/pypi/netCDF4",
- scripts = ['utils/nc3tonc4','utils/nc4tonc3','utils/ncinfo'],
platforms = ["any"],
license = "OSI Approved",
description = "Provides an object-oriented python interface to the netCDF version 4 library.",
@@ -387,7 +418,6 @@ setup(name = "netCDF4",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: System :: Archiving :: Compression",
"Operating System :: OS Independent"],
- py_modules = ["netCDF4_utils"],
- packages = ['netcdftime'],
- ext_modules = extensions,
+ packages = ['netcdftime', 'netCDF4'],
+ ext_modules = ext_modules,
**setuptools_extra_kwargs)
diff --git a/test/tst_atts.py b/test/tst_atts.py
index 65005ee..11b1bad 100644
--- a/test/tst_atts.py
+++ b/test/tst_atts.py
@@ -4,7 +4,7 @@ import unittest
import os
import tempfile
import numpy as NP
-from numpy.random.mtrand import uniform
+from numpy.random.mtrand import uniform
import netCDF4
try:
diff --git a/test/tst_compoundvar.py b/test/tst_compoundvar.py
index 99164a1..90176d2 100644
--- a/test/tst_compoundvar.py
+++ b/test/tst_compoundvar.py
@@ -19,7 +19,7 @@ TYPE_NAME2 = 'cmp2'
TYPE_NAME3 = 'cmp3'
TYPE_NAME4 = 'cmp4'
TYPE_NAME5 = 'cmp5'
-DIM_SIZE=3
+DIM_SIZE=3
dtype1=np.dtype([('i', 'i2'), ('j', 'i8')])
dtype2=np.dtype([('x', 'f4',), ('y', 'f8',(3,2))])
dtype3=np.dtype([('xx', dtype1), ('yy', dtype2)])
diff --git a/test/tst_compression.py b/test/tst_compression.py
index 7df8225..642a97c 100644
--- a/test/tst_compression.py
+++ b/test/tst_compression.py
@@ -1,5 +1,6 @@
from numpy.random.mtrand import uniform
-from netCDF4 import Dataset, _quantize
+from netCDF4 import Dataset
+from netCDF4.utils import _quantize
from numpy.testing import assert_almost_equal
import os, tempfile, unittest
diff --git a/test/tst_dims.py b/test/tst_dims.py
index c1aac2c..fc02d40 100644
--- a/test/tst_dims.py
+++ b/test/tst_dims.py
@@ -3,7 +3,7 @@ import unittest
import os
import tempfile
import numpy as NP
-from numpy.random.mtrand import uniform
+from numpy.random.mtrand import uniform
import netCDF4
FILE_NAME = tempfile.mktemp(".nc")
@@ -11,7 +11,7 @@ LAT_NAME="lat"
LAT_LEN = 25
LAT_LENG = 50
LON_NAME="lon"
-LON_LEN = 50
+LON_LEN = 50
LON_LENG = 100
LEVEL_NAME="level"
LEVEL_LEN = None
diff --git a/test/tst_diskless.py b/test/tst_diskless.py
index e0ae272..b7a55b8 100644
--- a/test/tst_diskless.py
+++ b/test/tst_diskless.py
@@ -1,6 +1,6 @@
import unittest, os, tempfile
import numpy as np
-from numpy.random.mtrand import uniform
+from numpy.random.mtrand import uniform
from numpy.testing import assert_array_equal, assert_array_almost_equal
import netCDF4
@@ -8,7 +8,7 @@ import netCDF4
# create an n1dim by n2dim by n3dim random array
n1dim = 10
-n2dim = 73
+n2dim = 73
n3dim = 144
ranarr = 100.*uniform(size=(n1dim,n2dim,n3dim))
ranarr2 = 100.*uniform(size=(n1dim,n2dim,n3dim))
diff --git a/test/tst_grps2.py b/test/tst_grps2.py
new file mode 100644
index 0000000..13c471c
--- /dev/null
+++ b/test/tst_grps2.py
@@ -0,0 +1,44 @@
+import sys
+import unittest
+import os
+import tempfile
+import numpy as NP
+import netCDF4
+
+# test implicit group creation by using unix-like paths
+# in createVariable and createGroups (added in 1.1.8).
+# also test Dataset.__getitem__, also added in 1.1.8.
+
+FILE_NAME = tempfile.mktemp(".nc")
+
+class Groups2TestCase(unittest.TestCase):
+
+ def setUp(self):
+ self.file = FILE_NAME
+ f = netCDF4.Dataset(self.file,'w')
+ x = f.createDimension('x',10)
+ # create groups in path if they don't already exist
+ v = f.createVariable('/grouped/data/v',float,('x',))
+ g = f.groups['grouped']
+ # create groups underneath 'grouped'
+ v2 = g.createVariable('./data/data2/v2',float,('x',))
+ f.close()
+
+ def tearDown(self):
+ # Remove the temporary files
+ os.remove(self.file)
+
+ def runTest(self):
+ """testing implicit group and creation and Dataset.__getitem__"""
+ f = netCDF4.Dataset(self.file, 'r')
+ v1 = f['/grouped/data/v']
+ v2 = ((f.groups['grouped']).groups['data']).variables['v']
+ g = f['/grouped/data']
+ v3 = g['data2/v2']
+ assert(v1 == v2)
+ assert(g == f.groups['grouped'].groups['data'])
+ assert(v3.name == 'v2')
+ f.close()
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/test/tst_masked.py b/test/tst_masked.py
index 654ed20..870c31a 100644
--- a/test/tst_masked.py
+++ b/test/tst_masked.py
@@ -61,6 +61,10 @@ class PrimitiveTypesTestCase(unittest.TestCase):
doh.scale_factor = 0.1
doh.add_offset = 0.
doh[0] = 1.1
+ # added to test fix to issue 381
+ doh2 = file.createVariable('packeddata3','i2','n')
+ doh2.add_offset = 1.
+ doh2[0] = 1.
file.close()
def tearDown(self):
@@ -75,6 +79,7 @@ class PrimitiveTypesTestCase(unittest.TestCase):
datamasked3 = file.variables['maskeddata3']
datapacked = file.variables['packeddata']
datapacked2 = file.variables['packeddata2']
+ datapacked3 = file.variables['packeddata3']
# check missing_value, scale_factor and add_offset attributes.
assert datamasked.missing_value == missing_value
assert datapacked.scale_factor == scale_factor
@@ -95,6 +100,7 @@ class PrimitiveTypesTestCase(unittest.TestCase):
assert_array_almost_equal(datamasked[:].filled(),ranarr)
assert_array_almost_equal(datamasked2[:].filled(),ranarr2)
assert_array_almost_equal(datapacked[:],packeddata,decimal=4)
+ assert(datapacked3[:].dtype == NP.float)
# added to test fix to issue 46 (result before r865 was 10)
assert_array_equal(datapacked2[0],11)
file.close()
diff --git a/test/tst_masked2.py b/test/tst_masked2.py
index 7892bd0..1dcca92 100755
--- a/test/tst_masked2.py
+++ b/test/tst_masked2.py
@@ -7,7 +7,7 @@ from numpy import ma, seterr
from numpy.testing import assert_array_equal, assert_array_almost_equal
from netCDF4 import Dataset, default_fillvals
-seterr(over='ignore') # don't print warning for overflow errors
+seterr(over='ignore') # don't print warning for overflow errors
# test automatic conversion of masked arrays, and
# packing/unpacking of short ints.
@@ -42,7 +42,7 @@ class PrimitiveTypesTestCase(unittest.TestCase):
v[2]=4000
v[3]=v.missing_value
f.close()
-
+
self.files.append(FILE_NAME2)
f = Dataset(FILE_NAME1,'r')
# create a new file, copy data, but change missing value and
@@ -56,7 +56,7 @@ class PrimitiveTypesTestCase(unittest.TestCase):
b[:] = f.variables['v'][:]
f.close()
f2.close()
-
+
self.files.append(FILE_NAME3)
f = Dataset(FILE_NAME3,'w')
x = f.createDimension('x',None)
@@ -75,7 +75,7 @@ class PrimitiveTypesTestCase(unittest.TestCase):
os.remove(f)
def runTest(self):
- """testing auto-conversion of masked arrays and packed integers"""
+ """testing auto-conversion of masked arrays and packed integers"""
f = Dataset(self.files[0])
data = f.variables['v'][:]
diff --git a/test/tst_slicing.py b/test/tst_slicing.py
index ee6fbc4..61f4268 100644
--- a/test/tst_slicing.py
+++ b/test/tst_slicing.py
@@ -94,6 +94,7 @@ class VariablesTestCase(unittest.TestCase):
v[...] = 10
assert_array_equal(v[...], 10)
assert_equal(v.shape, v[...].shape)
+ assert(type(v[...]) == np.ndarray)
f.close()
def test_issue259(self):
diff --git a/test/tst_unlimdim.py b/test/tst_unlimdim.py
index 0b42757..85bd89e 100644
--- a/test/tst_unlimdim.py
+++ b/test/tst_unlimdim.py
@@ -3,7 +3,7 @@ import unittest
import os
import tempfile
import numpy as NP
-from numpy.random.mtrand import uniform
+from numpy.random.mtrand import uniform
from numpy.testing import assert_array_equal, assert_array_almost_equal
import netCDF4
@@ -28,8 +28,8 @@ class UnlimdimTestCase(unittest.TestCase):
f.createDimension('n3', n3dim)
foo = f.createVariable('data1', ranarr.dtype.str[1:], ('n1','n2','n3'))
# write some data to it.
- #foo[:,0:n2dim,:] = ranarr
- foo[:] = ranarr
+ #foo[:,0:n2dim,:] = ranarr
+ foo[:] = ranarr
foo[:,n2dim:,:] = 2.*ranarr
# bar has 2 unlimited dimensions
f.createDimension('n4', None)
diff --git a/test/tst_utils.py b/test/tst_utils.py
index dcae845..7ce9d1f 100644
--- a/test/tst_utils.py
+++ b/test/tst_utils.py
@@ -1,5 +1,5 @@
from numpy.testing import assert_equal
-import netCDF4 as nc
+from netCDF4.utils import _StartCountStride, _out_array_shape
import unittest
import numpy as np
@@ -8,7 +8,7 @@ class TestgetStartCountStride(unittest.TestCase):
def test_basic(self):
# Basic usage
elem = [0, slice(None), slice(None)]
- start, count, stride, put_ind = nc._StartCountStride(elem, (50, 4, 10))
+ start, count, stride, put_ind = _StartCountStride(elem, (50, 4, 10))
assert_equal(start, 0)
assert_equal(count[..., 0], 1)
assert_equal(count[..., 1], 4)
@@ -17,12 +17,12 @@ class TestgetStartCountStride(unittest.TestCase):
assert_equal(put_ind[...,0], -1)
assert_equal(put_ind[...,1], slice(None))
assert_equal(put_ind[...,2], slice(None))
- assert_equal(nc._out_array_shape(count), (1, 4,10))
+ assert_equal(_out_array_shape(count), (1, 4,10))
def test_slice(self):
# start and stop slice
elem = [5, slice(None), slice(5, 8, 2)]
- start, count, stride, put_ind = nc._StartCountStride(elem, (50, 4, 10))
+ start, count, stride, put_ind = _StartCountStride(elem, (50, 4, 10))
assert_equal(start[..., 0], 5)
assert_equal(start[..., 1], 0)
assert_equal(start[..., 2], 5)
@@ -31,12 +31,12 @@ class TestgetStartCountStride(unittest.TestCase):
assert_equal(count[..., 2], 2)
assert_equal(stride[..., 2], 2)
- assert_equal(nc._out_array_shape(count), (1, 4,2))
+ assert_equal(_out_array_shape(count), (1, 4,2))
def test_fancy(self):
# Fancy indexing
elem = [slice(None), [1,2,3], 8]
- start, count, stride, put_ind = nc._StartCountStride(elem, (50, 4, 10))
+ start, count, stride, put_ind = _StartCountStride(elem, (50, 4, 10))
assert_equal(start[..., 0], 0)
assert_equal(start[..., 1].squeeze(), 1)
assert_equal(start[..., 2], 8)
@@ -45,30 +45,30 @@ class TestgetStartCountStride(unittest.TestCase):
assert_equal(count[...,2], 1)
assert_equal(put_ind[...,1].squeeze(), slice(None, None, None))
- assert_equal(nc._out_array_shape(count), (50, 3, 1))
+ assert_equal(_out_array_shape(count), (50, 3, 1))
i = np.array([2,5,7],'i4')
elem = [slice(None, -1,2),i,slice(None)]
- start, count, stride, put_ind = nc._StartCountStride(elem, (9,10,11))
+ start, count, stride, put_ind = _StartCountStride(elem, (9,10,11))
try:
elem = ( np.arange(6).reshape((3,2)), slice(None), slice(None) )
- start, count, stride, put_ind = nc._StartCountStride(elem, (3,4,5))
+ start, count, stride, put_ind = _StartCountStride(elem, (3,4,5))
except IndexError:
pass
# this one should be converted to a slice
elem = [slice(None), [1,3,5], 8]
- start, count, stride, put_ind = nc._StartCountStride(elem, (50, 6, 10))
+ start, count, stride, put_ind = _StartCountStride(elem, (50, 6, 10))
assert_equal(put_ind[...,1].squeeze(), slice(None,None,None))
def test_multiple_sequences(self):
elem = [[4,6,7], [1,2,3], slice(None)]
- start, count, stride, put_ind = nc._StartCountStride(elem, (50, 4, 10))
+ start, count, stride, put_ind = _StartCountStride(elem, (50, 4, 10))
- assert_equal(nc._out_array_shape(count), (3, 3, 10))
+ assert_equal(_out_array_shape(count), (3, 3, 10))
assert_equal(start[..., 0].squeeze(), [4,6,7])
assert_equal(start[..., 1].squeeze(), [1,1,1])
@@ -79,29 +79,29 @@ class TestgetStartCountStride(unittest.TestCase):
i = [1,3,4]
elem = (i, i, i)
- start, count, stride, put_ind = nc._StartCountStride(elem, (50, 5, 10))
- assert_equal(nc._out_array_shape(count), (3,3,3))
+ start, count, stride, put_ind = _StartCountStride(elem, (50, 5, 10))
+ assert_equal(_out_array_shape(count), (3,3,3))
def test_put_indices(self):
elem = (1, slice(None), slice(None))
- start, count, stride, put_ind = nc._StartCountStride(elem, (3,4,5))
+ start, count, stride, put_ind = _StartCountStride(elem, (3,4,5))
orig = np.arange(60).reshape((3,4,5))
- dest = np.empty(nc._out_array_shape(count))
+ dest = np.empty(_out_array_shape(count))
dest[tuple(put_ind[0,0,0])] = orig[tuple(elem)]
def test_boolean(self):
elem = (1, slice(None), np.array([True, True, False, False, True]))
- start, count, stride, put_ind = nc._StartCountStride(elem, (50, 4,5))
+ start, count, stride, put_ind = _StartCountStride(elem, (50, 4,5))
assert_equal(start[..., 2].squeeze(), [0,1,4])
assert_equal(count[...,2], 1)
- assert_equal(nc._out_array_shape(count), (1, 4, 3))
+ assert_equal(_out_array_shape(count), (1, 4, 3))
# Multiple booleans --- The behavior is different from NumPy in this case.
elem = (np.array([True, True, False]), np.array([True, True, False, True]), slice(None))
- start, count, stride, put_ind = nc._StartCountStride(elem, (3,4,5))
- assert_equal(nc._out_array_shape(count), (2,3,5))
+ start, count, stride, put_ind = _StartCountStride(elem, (3,4,5))
+ assert_equal(_out_array_shape(count), (2,3,5))
try:
elem = (np.array([True, True, False]), np.array([True, True, True, False]), slice(None))
@@ -113,14 +113,14 @@ class TestgetStartCountStride(unittest.TestCase):
def test_1d(self):
# Scalar
elem = (0)
- start, count, stride, put_ind = nc._StartCountStride(elem, (10,))
+ start, count, stride, put_ind = _StartCountStride(elem, (10,))
assert_equal(start, 0)
assert_equal(count, 1)
assert_equal(stride, 1)
assert_equal(put_ind, -1)
elem = (-1)
- start, count, stride, put_ind = nc._StartCountStride(elem, (10,))
+ start, count, stride, put_ind = _StartCountStride(elem, (10,))
assert_equal(start, 9)
assert_equal(count, 1)
assert_equal(stride, 1)
@@ -128,7 +128,7 @@ class TestgetStartCountStride(unittest.TestCase):
# test conversion of a integer index array to a slice
elem = (np.array([0]))
- start, count, stride, put_ind = nc._StartCountStride(elem, (10,))
+ start, count, stride, put_ind = _StartCountStride(elem, (10,))
assert_equal(start, 0)
assert_equal(count, 1)
assert_equal(stride, 1)
@@ -136,7 +136,7 @@ class TestgetStartCountStride(unittest.TestCase):
# Slice
elem = (slice(2,5,2))
- start, count, stride, put_ind = nc._StartCountStride(elem, (10,))
+ start, count, stride, put_ind = _StartCountStride(elem, (10,))
assert_equal(start, 2)
assert_equal(count, 2)
assert_equal(stride, 2)
@@ -144,7 +144,7 @@ class TestgetStartCountStride(unittest.TestCase):
# Integer sequence
elem = ([2,4,7])
- start, count, stride, put_ind = nc._StartCountStride(elem, (10,))
+ start, count, stride, put_ind = _StartCountStride(elem, (10,))
assert_equal(start.squeeze(), [2,4,7])
assert_equal(count, 1)
assert_equal(stride, 1)
@@ -152,7 +152,7 @@ class TestgetStartCountStride(unittest.TestCase):
# Boolean slicing
elem = (np.array([True, True, False, True, False]),)
- start, count, stride, put_ind = nc._StartCountStride(elem, (5,))
+ start, count, stride, put_ind = _StartCountStride(elem, (5,))
assert_equal(start.squeeze(), [0,1,3])
assert_equal(count, 1)
assert_equal(stride, 1)
@@ -160,7 +160,7 @@ class TestgetStartCountStride(unittest.TestCase):
# Integer sequence simplification
elem = ([2,3,4])
- start, count, stride, put_ind = nc._StartCountStride(elem, (10,))
+ start, count, stride, put_ind = _StartCountStride(elem, (10,))
assert_equal(start, 2)
assert_equal(count, 3)
assert_equal(stride, 1)
@@ -168,7 +168,7 @@ class TestgetStartCountStride(unittest.TestCase):
# Boolean indices simplification
elem = (np.array([False, True, True, True, False]))
- start, count, stride, put_ind = nc._StartCountStride(elem, (5,))
+ start, count, stride, put_ind = _StartCountStride(elem, (5,))
assert_equal(start, 1)
assert_equal(count, 3)
assert_equal(stride, 1)
@@ -176,10 +176,10 @@ class TestgetStartCountStride(unittest.TestCase):
# All False
elem = (np.array([False, False, False, False]))
- start, count, stride, put_ind = nc._StartCountStride(elem, (4,))
+ start, count, stride, put_ind = _StartCountStride(elem, (4,))
assert_equal(count, 0)
- assert_equal(nc._out_array_shape(count), (0,))
+ assert_equal(_out_array_shape(count), (0,))
class TestsetStartCountStride(unittest.TestCase):
@@ -189,13 +189,13 @@ class TestsetStartCountStride(unittest.TestCase):
grp = FakeGroup({'x':False, 'y':False, 'time':True})
elem=(slice(None), slice(None), 1)
- start, count, stride, take_ind = nc._StartCountStride(elem, (22, 25, 1), ['x', 'y', 'time'], grp, (22,25))
+ start, count, stride, take_ind = _StartCountStride(elem, (22, 25, 1), ['x', 'y', 'time'], grp, (22,25))
assert_equal(start[0][0][0], [0, 0, 1])
assert_equal(count[0][0][0], (22, 25, 1))
assert_equal(take_ind[0][0][0], (slice(None), slice(None), -1))
elem=(slice(None), slice(None), slice(1, 4))
- start, count, stride, take_ind = nc._StartCountStride(elem, (22,25,1),\
+ start, count, stride, take_ind = _StartCountStride(elem, (22,25,1),\
['x', 'y', 'time'], grp, (22,25,3), put=True)
assert_equal(start[0][0][0], [0, 0, 1])
assert_equal(count[0][0][0], (22, 25, 3))
@@ -205,7 +205,7 @@ class TestsetStartCountStride(unittest.TestCase):
grp = FakeGroup({'x':False, 'y':False})
elem=([0,4,5], slice(20, None))
- start, count, stride, take_ind = nc._StartCountStride(elem, (22, 25), ['x', 'y'], grp, (3,5))
+ start, count, stride, take_ind = _StartCountStride(elem, (22, 25), ['x', 'y'], grp, (3,5))
assert_equal(start[0][0], (0, 20))
assert_equal(start[1][0], (4, 20))
assert_equal(start[2][0], (5, 20))
@@ -220,7 +220,7 @@ class TestsetStartCountStride(unittest.TestCase):
grp = FakeGroup({'x':False, 'y':False, 'z':False})
elem=([0,4,5], np.array([False, True, False, True, True]), slice(None))
- start, count, stride, take_ind = nc._StartCountStride(elem, (10, 5, 12), ['x', 'y', 'z'], grp, (3, 3, 12))
+ start, count, stride, take_ind = _StartCountStride(elem, (10, 5, 12), ['x', 'y', 'z'], grp, (3, 3, 12))
assert_equal(start[0][0][0], (0, 1, 0))
assert_equal(start[1][0][0], (4, 1, 0))
assert_equal(start[2][0][0], (5, 1, 0))
@@ -235,7 +235,7 @@ class TestsetStartCountStride(unittest.TestCase):
grp = FakeGroup({'time':True,'x':False, 'y':False})
elem = ([0,2,5], slice(None), slice(None))
- start, count, stride, take_ind = nc._StartCountStride(elem, (0, 6, 7),\
+ start, count, stride, take_ind = _StartCountStride(elem, (0, 6, 7),\
['time', 'x', 'y'], grp, (3, 6, 7), put=True)
assert_equal(start[0][0][0], (0, 0, 0))
assert_equal(start[2][0][0], (5, 0, 0))
@@ -245,7 +245,7 @@ class TestsetStartCountStride(unittest.TestCase):
elem = (slice(None, None, 2), slice(None), slice(None))
- start, count, stride, take_ind = nc._StartCountStride(elem, (0, 6, 7),\
+ start, count, stride, take_ind = _StartCountStride(elem, (0, 6, 7),\
['time', 'x', 'y'], grp, (10, 6, 7),put=True)
assert_equal(start[0][0][0], (0,0,0))
assert_equal(count[0][0][0], (5, 6, 7))
diff --git a/test/tst_vars.py b/test/tst_vars.py
index 398c5a2..c88ff6a 100644
--- a/test/tst_vars.py
+++ b/test/tst_vars.py
@@ -3,7 +3,7 @@ import unittest
import os
import tempfile
import numpy as NP
-from numpy.random.mtrand import uniform
+from numpy.random.mtrand import uniform
from numpy.testing import assert_array_equal, assert_array_almost_equal
import netCDF4
@@ -66,14 +66,14 @@ class VariablesTestCase(unittest.TestCase):
assert v2.shape == (DIM2_LEN,DIM3_LEN)
assert v1.size == DIM1_LEN * DIM2_LEN * DIM3_LEN
assert len(v1) == DIM1_LEN
-
+
#assert NP.allclose(v1[:],randomdata)
assert_array_almost_equal(v1[:],randomdata)
# check variables in sub group.
g = f.groups[GROUP_NAME]
varnames = sorted(g.variables.keys())
v1 = g.variables[VAR_DOUBLE_NAME]
- # test iterating over variable (should stop when
+ # test iterating over variable (should stop when
# it gets to the end and raises IndexError, issue 121)
for v in v1:
pass
diff --git a/test/tst_vlen.py b/test/tst_vlen.py
index aea1d97..904394c 100644
--- a/test/tst_vlen.py
+++ b/test/tst_vlen.py
@@ -74,7 +74,7 @@ class TestInvalidDataType(unittest.TestCase):
f = Dataset(FILE_NAME, 'w', format='NETCDF3_CLASSIC')
f.createDimension('x', 1)
with self.assertRaisesRegexp(ValueError, 'strings are only supported'):
- f.createVariable('foo', str, ('x',))
+ f.createVariable('foo', str, ('x',))
f.close()
os.remove(FILE_NAME)
@@ -113,7 +113,7 @@ class TestObjectArrayIndexing(unittest.TestCase):
f = Dataset(self.file, 'r')
vs_alt = f.variables[VAR3_NAME]
unicode_strings = vs_alt[:]
- fancy_indexed = unicode_strings[0][[1,2,4]]
+ fancy_indexed = unicode_strings[0][[1,2,4]]
assert fancy_indexed[0] == 'abc'
assert fancy_indexed[1] == 'abcd'
assert fancy_indexed[2] == 'abcdef'
diff --git a/utils.pyx b/utils.pyx
deleted file mode 100644
index 6338265..0000000
--- a/utils.pyx
+++ /dev/null
@@ -1,815 +0,0 @@
-from datetime import timedelta, datetime, MINYEAR
-from netcdftime import _parse_date
-
-# start of the gregorian calendar
-gregorian = datetime(1582,10,15)
-
-def _dateparse(timestr):
- """parse a string of the form time-units since yyyy-mm-dd hh:mm:ss,
- return a datetime instance"""
- # same as version in netcdftime, but returns a timezone naive
- # python datetime instance with the utc_offset included.
- timestr_split = timestr.split()
- units = timestr_split[0].lower()
- if timestr_split[1].lower() != 'since':
- raise ValueError("no 'since' in unit_string")
- # parse the date string.
- n = timestr.find('since')+6
- isostring = timestr[n:]
- year, month, day, hour, minute, second, utc_offset =\
- _parse_date( isostring.strip() )
- basedate = datetime(year, month, day, hour, minute, second)
- # add utc_offset to basedate time instance (which is timezone naive)
- basedate += timedelta(days=utc_offset/1440.)
- return basedate
-
-# utility functions (visible from python).
-
-def stringtoarr(string,NUMCHARS,dtype='S'):
- """
-stringtoarr(a, NUMCHARS,dtype='S')
-
-convert a string to a character array of length NUMCHARS
-
- at param 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).
-
- 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.
-
- at return: 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')
- arr[0:len(string)] = tuple(string)
- return arr
-
-def 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
-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,).
-
- 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."""
- dtype = a.dtype.kind
- if dtype not in ["S","U"]:
- raise ValueError("type must string or unicode ('S' or 'U')")
- b = numpy.array(tuple(a.tostring().decode(default_encoding)),dtype+'1')
- b.shape = a.shape + (a.itemsize,)
- return b
-
-def 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').
-Will be converted to a array of strings, where each string has a fixed
-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]."""
- dtype = b.dtype.kind
- if dtype not in ["S","U"]:
- raise ValueError("type must string or unicode ('S' or 'U')")
- bs = b.tostring().decode(default_encoding)
- slen = int(b.shape[-1])
- a = numpy.array([bs[n1:n1+slen] for n1 in range(0,len(bs),slen)],dtype+repr(slen))
- a.shape = b.shape[:-1]
- return a
-
-def 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
-be in UTC with no time-zone offset. If there is a
-time-zone offset in C{units}, it will be applied to the
-returned numeric values.
-
- at param 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
-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.
-
- at return: a numeric time value, or an array of numeric time values.
- """
- basedate = _dateparse(units)
- unit = units.split()[0].lower()
-
- if (calendar == 'proleptic_gregorian' and basedate.year >= MINYEAR) or \
- (calendar in ['gregorian','standard'] and basedate > gregorian):
- # use python datetime module,
- isscalar = False
- try:
- dates[0]
- except:
- isscalar = True
- if isscalar:
- dates = numpy.array([dates])
- else:
- dates = numpy.array(dates)
- shape = dates.shape
- ismasked = False
- if hasattr(dates,'mask'):
- mask = dates.mask
- ismasked = True
- times = []
- for date in dates.flat:
- if ismasked and not date:
- times.append(None)
- else:
- td = date - basedate
- # total time in microseconds.
- totaltime = td.microseconds + (td.seconds + td.days * 24 * 3600) * 1.e6
- if unit == 'microseconds' or unit == 'microsecond':
- times.append(totaltime)
- elif unit == 'milliseconds' or unit == 'millisecond':
- times.append(totaltime/1.e3)
- elif unit == 'seconds' or unit == 'second':
- times.append(totaltime/1.e6)
- elif unit == 'minutes' or unit == 'minute':
- times.append(totaltime/1.e6/60)
- elif unit == 'hours' or unit == 'hour':
- times.append(totaltime/1.e6/3600)
- elif unit == 'days' or unit == 'day':
- times.append(totaltime/1.e6/3600./24.)
- if isscalar:
- return times[0]
- else:
- return numpy.reshape(numpy.array(times), shape)
- else: # use netcdftime module for other calendars
- cdftime = netcdftime.utime(units,calendar=calendar)
- return cdftime.date2num(dates)
-
-def 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
-UTC with no time-zone offset, even if the specified
-C{units} contain a time-zone offset.
-
- at param 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
-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.
-
- at return: 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'}
-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}
-contains one.
- """
- calendar = calendar.lower()
- basedate = _dateparse(units)
- unit = units.split()[0].lower()
-
- if (calendar == 'proleptic_gregorian' and basedate.year >= MINYEAR) or \
- (calendar in ['gregorian','standard'] and basedate > gregorian):
- # use python datetime module,
- isscalar = False
- try:
- times[0]
- except:
- isscalar = True
- if isscalar:
- times = numpy.array([times],dtype='d')
- else:
- times = numpy.array(times, dtype='d')
- shape = times.shape
- ismasked = False
- if hasattr(times,'mask'):
- mask = times.mask
- ismasked = True
- dates = []
- for time in times.flat:
- if ismasked and not time:
- dates.append(None)
- else:
- # convert to total seconds
- if unit == 'microseconds' or unit == 'microsecond':
- tsecs = time/1.e6
- elif unit == 'milliseconds' or unit == 'millisecond':
- tsecs = time/1.e3
- elif unit == 'seconds' or unit == 'second':
- tsecs = time
- elif unit == 'minutes' or unit == 'minute':
- tsecs = time*60.
- elif unit == 'hours' or unit == 'hour':
- tsecs = time*3600.
- elif unit == 'days' or unit == 'day':
- tsecs = time*86400.
- # compute time delta.
- days = tsecs // 86400.
- msecsd = tsecs*1.e6 - days*86400.*1.e6
- secs = msecsd // 1.e6
- msecs = numpy.round(msecsd - secs*1.e6)
- td = timedelta(days=days,seconds=secs,microseconds=msecs)
- # add time delta to base date.
- date = basedate + td
- dates.append(date)
- if isscalar:
- return dates[0]
- else:
- return numpy.reshape(numpy.array(dates), shape)
- else: # use netcdftime for other calendars
- cdftime = netcdftime.utime(units,calendar=calendar)
- return cdftime.num2date(times)
-
-def 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.
-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
-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
-correspond to the closest dates.
-
- at return: an index (indices) of the netCDF time variable corresponding
-to the given datetime object(s).
- """
- if calendar == None:
- calendar = getattr(nctime, 'calendar', 'standard')
- calendar = calendar.lower()
- basedate = _dateparse(nctime.units)
-
- if (calendar == 'proleptic_gregorian' and basedate.year >= MINYEAR) or \
- (calendar in ['gregorian','standard'] and basedate > gregorian):
- # use python datetime
- times = date2num(dates,nctime.units,calendar=calendar)
- return netcdftime.time2index(times, nctime, calendar, select)
- else: # use netcdftime module for other cases
- return netcdftime.date2index(dates, nctime, calendar, select)
-
-def getlibversion():
- """
-getlibversion()
-
-returns a string describing the version of the netcdf library
-used to build the module, and when it was built.
- """
- return (<char *>nc_inq_libvers()).decode('ascii')
-
-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]
- """
-
- 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.
- """
-
- # Open the master file in the base class, so that the CDFMF instance
- # 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)
- else:
- files = sorted(glob(files))
-
- master = files[0]
-
- # Open the master again, this time as a classic CDF instance. This will avoid
- # calling methods of the CDFMF subclass when querying the master file.
- cdfm = Dataset(master)
- # copy attributes from master.
- for name, value in cdfm.__dict__.items():
- self.__dict__[name] = value
-
- # Make sure the master defines a dim with name aggdim,
- # or an unlimited dimension.
- aggDimId = None
- for dimname,dim in cdfm.dimensions.items():
- if aggdim is None:
- if dim.isunlimited():
- aggDimId = dim
- aggDimName = dimname
- else:
- if dimname == aggdim:
- aggDimId = dim
- aggDimName = dimname
- if aggDimId is None:
- raise IOError("master dataset %s does not have a aggregation dimension" % master)
-
- # Get info on all aggregation variables defined in the master.
- # Make sure the master defines at least one aggregation variable.
- masterRecVar = {}
- for vName,v in cdfm.variables.items():
- # skip variables specified in exclude list.
- if vName in exclude: continue
- dims = v.dimensions
- shape = v.shape
- dtype = v.dtype
- # Be carefull: we may deal with a scalar (dimensionless) variable.
- # Unlimited dimension always occupies index 0.
- if (len(dims) > 0 and aggDimName == dims[0]):
- masterRecVar[vName] = (dims, shape, dtype)
- if len(masterRecVar) == 0:
- raise IOError("master dataset %s does not have any variables to aggregate" % master)
-
- # Create the following:
- # cdf list of Dataset instances
- # cdfVLen list unlimited dimension lengths in each CDF instance
- # cdfRecVar dictionary indexed by the aggregation var names; each key holds
- # a list of the corresponding Variable instance, one for each
- # cdf file of the file set
- cdf = [cdfm]
- self._cdf = cdf # Store this now, because dim() method needs it
- cdfVLen = [len(aggDimId)]
- cdfRecVar = {}
- for v in masterRecVar.keys():
- cdfRecVar[v] = [cdfm.variables[v]]
-
- # Open each remaining file in read-only mode.
- # 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:]:
- part = Dataset(f)
- varInfo = part.variables
- for v in masterRecVar.keys():
- if check:
- # Make sure master rec var is also defined here.
- if v not in varInfo.keys():
- raise IOError("aggregation variable %s not defined in %s" % (v, f))
-
- #if not vInst.dimensions[0] != aggDimName:
-
- masterDims, masterShape, masterType = masterRecVar[v][:3]
- extDims, extShape, extType = varInfo[v][:3]
- extDims = varInfo[v].dimensions
- extShape = varInfo[v].shape
- extType = varInfo[v].dtype
- # Check that dimension names are identical.
- if masterDims != extDims:
- raise IOError("variable %s : dimensions mismatch between "
- "master %s (%s) and extension %s (%s)" %
- (v, master, masterDims, f, extDims))
-
- # Check that the ranks are identical, and the dimension lengths are
- # identical (except for that of the unlimited dimension, which of
- # course may vary.
- if len(masterShape) != len(extShape):
- raise IOError("variable %s : rank mismatch between "
- "master %s (%s) and extension %s (%s)" %
- (v, master, len(masterShape), f, len(extShape)))
- if masterShape[1:] != extShape[1:]:
- raise IOError("variable %s : shape mismatch between "
- "master %s (%s) and extension %s (%s)" %
- (v, master, masterShape, f, extShape))
-
- # Check that the data types are identical.
- if masterType != extType:
- raise IOError("variable %s : data type mismatch between "
- "master %s (%s) and extension %s (%s)" %
- (v, master, masterType, f, extType))
-
- # Everythig ok.
- vInst = part.variables[v]
- cdfRecVar[v].append(vInst)
- else:
- # No making sure of anything -- assume this is ok..
- vInst = part.variables[v]
- cdfRecVar[v].append(vInst)
-
- cdf.append(part)
- cdfVLen.append(len(part.dimensions[aggDimName]))
-
- # Attach attributes to the MFDataset instance.
- # A local __setattr__() method is required for them.
- self._files = files # list of cdf file names in the set
- self._cdfVLen = cdfVLen # list of unlimited lengths
- self._cdfTLen = sum(cdfVLen) # total length
- self._cdfRecVar = cdfRecVar # dictionary of Variable instances for all
- # the aggregation variables
- self._dims = cdfm.dimensions
- self._grps = cdfm.groups
- for dimname, dim in self._dims.items():
- if dimname == aggDimName:
- self._dims[dimname] = _Dimension(dimname, dim, self._cdfVLen, self._cdfTLen)
- self._vars = cdfm.variables
- for varname,var in self._vars.items():
- if varname in self._cdfRecVar.keys():
- self._vars[varname] = _Variable(self, varname, var, aggDimName)
- self._file_format = []
- self._data_model = []
- self._disk_format = []
- 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)
- self._data_model.append(dset.data_model)
- self._disk_format.append(dset.disk_format)
- self._path = '/'
-
- def __setattr__(self, name, value):
- """override base class attribute creation"""
- self.__dict__[name] = value
-
- def __getattribute__(self, name):
- if name in ['variables','dimensions','file_format','groups',\
- 'data_model','disk_format','path']:
- if name == 'dimensions': return self._dims
- if name == 'variables': return self._vars
- if name == 'file_format': return self._file_format
- if name == 'data_model': return self._data_model
- if name == 'disk_format': return self._disk_format
- if name == 'path': return self._path
- if name == 'groups': return self._grps
- else:
- return Dataset.__getattribute__(self, name)
-
- def ncattrs(self):
- return self._cdf[0].__dict__.keys()
-
- def close(self):
- for dset in self._cdf:
- dset.close()
-
- def __repr__(self):
- ncdump = ['%r\n' % type(self)]
- dimnames = tuple([str(dimname) for dimname in self.dimensions.keys()])
- varnames = tuple([str(varname) for varname in self.variables.keys()])
- grpnames = ()
- if self.path == '/':
- ncdump.append('root group (%s data model, file format %s):\n' %
- (self.data_model[0], self.disk_format[0]))
- else:
- ncdump.append('group %s:\n' % self.path)
- attrs = [' %s: %s\n' % (name,self.__dict__[name]) for name in\
- self.ncattrs()]
- ncdump = ncdump + attrs
- ncdump.append(' dimensions = %s\n' % str(dimnames))
- ncdump.append(' variables = %s\n' % str(varnames))
- ncdump.append(' groups = %s\n' % str(grpnames))
- return ''.join(ncdump)
-
-class _Dimension(object):
- def __init__(self, dimname, dim, dimlens, dimtotlen):
- self.dimlens = dimlens
- self.dimtotlen = dimtotlen
- self._name = dimname
- def __len__(self):
- return self.dimtotlen
- def isunlimited(self):
- return True
- def __repr__(self):
- if self.isunlimited():
- return repr(type(self))+" (unlimited): name = '%s', size = %s\n" % (self._name,len(self))
- else:
- return repr(type(self))+": name = '%s', size = %s\n" % (self._name,len(self))
-
-class _Variable(object):
- def __init__(self, dset, varname, var, recdimname):
- self.dimensions = var.dimensions
- self._dset = dset
- self._grp = dset
- self._mastervar = var
- self._recVar = dset._cdfRecVar[varname]
- self._recdimname = recdimname
- self._recLen = dset._cdfVLen
- self.dtype = var.dtype
- self._name = var._name
- # copy attributes from master.
- for name, value in var.__dict__.items():
- self.__dict__[name] = value
- def typecode(self):
- return self.dtype
- def ncattrs(self):
- return self._mastervar.__dict__.keys()
- def __getattr__(self,name):
- if name == 'shape': return self._shape()
- if name == 'ndim': return len(self._shape())
- try:
- return self.__dict__[name]
- except:
- raise AttributeError(name)
- def __repr__(self):
- ncdump_var = ['%r\n' % type(self)]
- dimnames = tuple([str(dimname) for dimname in self.dimensions])
- attrs = [' %s: %s\n' % (name,self.__dict__[name]) for name in\
- self.ncattrs()]
- ncdump_var.append('%s %s%s\n' %\
- (self.dtype,self._name,dimnames))
- ncdump_var = ncdump_var + attrs
- unlimdims = []
- for dimname in self.dimensions:
- dim = _find_dim(self._grp, dimname)
- if dim.isunlimited():
- unlimdims.append(str(dimname))
- ncdump_var.append('unlimited dimensions = %s\n' % repr(tuple(unlimdims)))
- ncdump_var.append('current size = %s\n' % repr(self.shape))
- return ''.join(ncdump_var)
- def __len__(self):
- return self._shape()[0]
- def _shape(self):
- recdimlen = len(self._dset.dimensions[self._recdimname])
- return (recdimlen,) + self._mastervar.shape[1:]
- def set_auto_maskandscale(self,val):
- for v in self._recVar:
- v.set_auto_maskandscale(val)
- def __getitem__(self, elem):
- """Get records from a concatenated set of variables."""
-
- # This special method is used to index the netCDF variable
- # using the "extended slice syntax". The extended slice syntax
- # is a perfect match for the "start", "count" and "stride"
- # arguments to the nc_get_var() function, and is much more easy
- # to use.
- start, count, stride, put_ind =\
- _StartCountStride(elem, self.shape)
- datashape = _out_array_shape(count)
- data = ma.empty(datashape, dtype=self.dtype)
-
- # Determine which dimensions need to be squeezed
- # (those for which elem is an integer scalar).
- # 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:
- squeeze[i] = 0
-
- # Reshape the arrays so we can iterate over them.
- strt = start.reshape((-1, self.ndim or 1))
- cnt = count.reshape((-1, self.ndim or 1))
- strd = stride.reshape((-1, self.ndim or 1))
- put_ind = put_ind.reshape((-1, self.ndim or 1))
-
- # Fill output array with data chunks.
- # Number of variables making up the MFVariable.Variable.
- nv = len(self._recLen)
- for (start,count,stride,ind) in zip(strt, cnt, strd, put_ind):
- # make sure count=-1 becomes count=1
- 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.
- sta = start[0]
- step = stride[0]
- stop = sta + count[0] * step
-
- # Build a list representing the concatenated list of all records in
- # the MFVariable variable set. The list is composed of 2-elem lists
- # each holding:
- # the record index inside the variables, from 0 to n
- # the index of the Variable instance to which each record belongs
- idx = [] # list of record indices
- vid = [] # list of Variable indices
- for n in range(nv):
- k = self._recLen[n] # number of records in this variable
- idx.extend(range(k))
- vid.extend([n] * k)
-
- # Merge the two lists to get a list of 2-elem lists.
- # Slice this list along the first dimension.
- lst = list(zip(idx, vid)).__getitem__(slice(sta, stop, step))
-
- # 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])
- newSlice.append(s)
-
- # Apply the slicing expression to each var in turn, extracting records
- # in a list of arrays.
- lstArr = []
- ismasked = False
- for n in range(nv):
- # Get the list of indices for variable 'n'.
- idx = [i for i,numv in lst if numv == n]
- if idx:
- # Rebuild slicing expression for dimension 0.
- newSlice[0] = slice(idx[0], idx[-1] + 1, step)
- # Extract records from the var, and append them to a list
- # of arrays.
- dat = Variable.__getitem__(self._recVar[n],tuple(newSlice))
- if ma.isMA(dat) and not ismasked:
- ismasked=True
- fill_value = dat.fill_value
- lstArr.append(dat)
- if ismasked:
- lstArr = ma.concatenate(lstArr)
- else:
- lstArr = numpy.concatenate(lstArr)
- if lstArr.dtype != data.dtype: data = data.astype(lstArr.dtype)
- # sometimes there are legitimate singleton dimensions, in which
- # case the array shapes won't conform. If so, a ValueError will
- # result, and no squeeze will be done.
- try:
- data[tuple(ind)] = lstArr.squeeze()
- except ValueError:
- data[tuple(ind)] = lstArr
-
- # Remove extra singleton dimensions.
- data = data[tuple(squeeze)]
-
- # if no masked elements, return numpy array.
- if ma.isMA(data) and not data.mask.any():
- data = data.filled()
-
- return data
-
-
-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
- """
-
- 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.
- """
- import datetime
- self.__time = time
-
- # copy attributes from master time variable.
- for name, value in time.__dict__.items():
- self.__dict__[name] = value
-
- # make sure calendar attribute present in all files.
- 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.
- 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.
- self.units = units or time.units
-
- # 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)
-
- # Create delta vector: delta = ref_num(ref_date) - num(ref_date)
- # So that ref_num(date) = num(date) + delta
- self.__delta = numpy.empty(len(self), time.dtype)
-
- i0 = 0; i1 = 0
- for i,v in enumerate(self._recVar):
- n = self._recLen[i] # Length of time vector.
- num = date2num(ref_date, v.units, self.calendar)
- i1 += n
- self.__delta[i0:i1] = ref_num - num
- i0 += n
-
-
- def __getitem__(self, elem):
- return self.__time[elem] + self.__delta[elem]
diff --git a/utils/nc3tonc4 b/utils/nc3tonc4
index 23e1fbb..ed4964e 100755
--- a/utils/nc3tonc4
+++ b/utils/nc3tonc4
@@ -1,280 +1,4 @@
#!/usr/bin/env python
-from netCDF4 import Dataset
-import numpy as NP
-import sys
-
-def nc3tonc4(filename3,filename4,unpackshort=True,
- zlib=True,complevel=6,shuffle=True,fletcher32=False,
- clobber=False,lsd_dict=None,nchunk=10,quiet=False,classic=0,
- vars=None,istart=0,istop=-1):
- """convert a netcdf 3 file (filename3) to a netcdf 4 file
- The default format is 'NETCDF4', but can be set
- to NETCDF4_CLASSIC if classic=1.
- If unpackshort=True, variables stored as short
- integers with a scale and offset are unpacked to floats.
- in the netcdf 4 file. If the lsd_dict is not None, variable names
- corresponding to the keys of the dict will be truncated to the decimal place
- specified by the values of the dict. This improves compression by
- making it 'lossy'..
- If vars is not None, only variable names in the list
- will be copied (plus all the dimension variables).
- The zlib, complevel and shuffle keywords control
- how the compression is done."""
-
- ncfile3 = Dataset(filename3,'r')
- if classic:
- ncfile4 = Dataset(filename4,'w',clobber=clobber,format='NETCDF4_CLASSIC')
- else:
- ncfile4 = Dataset(filename4,'w',clobber=clobber,format='NETCDF4')
- mval = 1.e30 # missing value if unpackshort=True
- # create dimensions. Check for unlimited dim.
- unlimdimname = False
- unlimdim = None
- # create global attributes.
- if not quiet: sys.stdout.write('copying global attributes ..\n')
- #for attname in ncfile3.ncattrs():
- # setattr(ncfile4,attname,getattr(ncfile3,attname))
- ncfile4.setncatts(ncfile3.__dict__)
- if not quiet: sys.stdout.write('copying dimensions ..\n')
- for dimname,dim in ncfile3.dimensions.items():
- if dim.isunlimited():
- unlimdimname = dimname
- unlimdim = dim
- ncfile4.createDimension(dimname,None)
- if istop == -1: istop=len(unlimdim)
- else:
- ncfile4.createDimension(dimname,len(dim))
- # create variables.
- if vars is None:
- varnames = ncfile3.variables.keys()
- else:
- # variables to copy specified
- varnames = vars
- # add dimension variables
- for dimname in ncfile3.dimensions.keys():
- if dimname in ncfile3.variables.keys() and\
- dimname not in varnames:
- varnames.append(dimname)
- for varname in varnames:
- ncvar = ncfile3.variables[varname]
- if not quiet: sys.stdout.write('copying variable %s\n' % varname)
- # quantize data?
- if lsd_dict is not None and lsd_dict.has_key(varname):
- lsd = lsd_dict[varname]
- if not quiet: sys.stdout.write('truncating to least_significant_digit = %d\n'%lsd)
- else:
- lsd = None # no quantization.
- # unpack short integers to floats?
- if unpackshort and hasattr(ncvar,'scale_factor') and hasattr(ncvar,'add_offset'):
- dounpackshort = True
- datatype = 'f4'
- else:
- dounpackshort = False
- datatype = ncvar.dtype
- # is there an unlimited dimension?
- if unlimdimname and unlimdimname in ncvar.dimensions:
- hasunlimdim = True
- else:
- hasunlimdim = False
- if dounpackshort:
- if not quiet: sys.stdout.write('unpacking short integers to floats ...\n')
- sys.stdout.write('')
- if hasattr(ncvar, '_FillValue'):
- FillValue = ncvar._FillValue
- else:
- FillValue = None
- var = ncfile4.createVariable(varname,datatype,ncvar.dimensions, fill_value=FillValue, least_significant_digit=lsd,zlib=zlib,complevel=complevel,shuffle=shuffle,fletcher32=fletcher32)
- # fill variable attributes.
- attdict = ncvar.__dict__
- if '_FillValue' in attdict: del attdict['_FillValue']
- if dounpackshort and 'add_offset' in attdict: del attdict['add_offset']
- if dounpackshort and 'scale_factor' in attdict: del attdict['scale_factor']
- if dounpackshort and 'missing_value' in attdict: attdict['missing_value']=mval
- var.setncatts(attdict)
- #for attname in ncvar.ncattrs():
- # if attname == '_FillValue': continue
- # if dounpackshort and attname in ['add_offset','scale_factor']: continue
- # if dounpackshort and attname == 'missing_value':
- # setattr(var,attname,mval)
- # else:
- # setattr(var,attname,getattr(ncvar,attname))
- # fill variables with data.
- if hasunlimdim: # has an unlim dim, loop over unlim dim index.
- # range to copy
- if nchunk:
- start = istart; stop = istop; step = nchunk
- if step < 1: step = 1
- for n in range(start, stop, step):
- nmax = n+nchunk
- if nmax > istop: nmax=istop
- idata = ncvar[n:nmax]
- if dounpackshort:
- tmpdata = (ncvar.scale_factor*idata.astype('f')+ncvar.add_offset).astype('f')
- if hasattr(ncvar,'missing_value'):
- tmpdata = NP.where(idata == ncvar.missing_value, mval, tmpdata)
- else:
- tmpdata = idata
- var[n-istart:nmax-istart] = tmpdata
- else:
- idata = ncvar[:]
- if dounpackshort:
- tmpdata = (ncvar.scale_factor*idata.astype('f')+ncvar.add_offset).astype('f')
- if hasattr(ncvar,'missing_value'):
- tmpdata = NP.where(idata == ncvar.missing_value, mval, tmpdata)
- else:
- tmpdata = idata
- var[0:len(unlimdim)] = tmpdata
- else: # no unlim dim or 1-d variable, just copy all data at once.
- idata = ncvar[:]
- if dounpackshort:
- tmpdata = (ncvar.scale_factor*idata.astype('f')+ncvar.add_offset).astype('f')
- if hasattr(ncvar,'missing_value'):
- tmpdata = NP.where(idata == ncvar.missing_value, mval, tmpdata)
- else:
- tmpdata = idata
- var[:] = tmpdata
- ncfile4.sync() # flush data to disk
- # close files.
- ncfile3.close()
- ncfile4.close()
-
-if __name__ == '__main__':
-
- import getopt, os
-
- usage = """
- Convert a netCDF 3 file to netCDF 4 format, optionally
- unpacking variables packed as short integers (with scale_factor and add_offset)
- to floats, and adding zlib compression (with the HDF5 shuffle filter and fletcher32 checksum).
- Data may also be quantized (truncated) to a specified precision to improve compression.
-
- usage: %s [-h] [-o] [--vars=var1,var2,..] [--zlib=(0|1)] [--complevel=(1-9)] [--shuffle=(0|1)] [--fletcher32=(0|1)] [--unpackshort=(0|1)] [--quantize=var1=n1,var2=n2,..] netcdf3filename netcdf4filename
- -h -- Print usage message.
- -o -- Overwite destination file (default is to raise an error if output file already exists).
- --vars -- comma separated list of variable names to copy (default is to copy
- all variables)
- --classic=(0|1) -- use NETCDF4_CLASSIC format instead of NETCDF4 (default 1)
- --zlib=(0|1) -- Activate (or disable) zlib compression (default is activate).
- --complevel=(1-9) -- Set zlib compression level (6 is default).
- --shuffle=(0|1) -- Activate (or disable) the shuffle filter (active by default).
- --fletcher32=(0|1) -- Activate (or disable) the fletcher32 checksum (not
- active by default).
- --unpackshort=(0|1) -- Unpack short integer variables to float variables
- using scale_factor and add_offset netCDF variable attributes (active by default).
- --quantize=(comma separated list of "variable name=integer" pairs) --
- Truncate the data in the specified variables to a given decimal precision.
- For example, 'speed=2, height=-2, temp=0' will cause the variable
- 'speed' to be truncated to a precision of 0.01, 'height' to a precision of 100
- and 'temp' to 1. This can significantly improve compression. The default
- is not to quantize any of the variables.
- --quiet=(0|1) -- if 1, don't print diagnostic information.
- --chunk=(integer) -- number of records along unlimited dimension to
- write at once. Default 10. Ignored if there is no unlimited
- dimension. chunk=0 means write all the data at once.
- --istart=(integer) -- number of record to start at along unlimited dimension.
- Default 0. Ignored if there is no unlimited dimension.
- --istop=(integer) -- number of record to stop at along unlimited dimension.
- Default -1. Ignored if there is no unlimited dimension.
-\n""" % os.path.basename(sys.argv[0])
-
- try:
- opts, pargs = getopt.getopt(sys.argv[1:], 'ho',
- ['classic=',
- 'vars=',
- 'zlib=',
- 'quiet=',
- 'complevel=',
- 'shuffle=',
- 'fletcher32=',
- 'unpackshort=',
- 'quantize=',
- 'chunk=',
- 'istart=',
- 'istop='])
- except:
- (type, value, traceback) = sys.exc_info()
- sys.stdout.write("Error parsing the options. The error was: %s\n" % value)
- sys.stderr.write(usage)
- sys.exit(0)
-
- # default options
- overwritefile = 0
- complevel = 6
- classic = 1
- zlib = 1
- shuffle = 1
- fletcher32 = 0
- unpackshort = 1
- vars = None
- quantize = None
- quiet = 0
- chunk = 1000
- istart = 0
- istop = -1
-
- # Get the options
- for option in opts:
- if option[0] == '-h':
- sys.stderr.write(usage)
- sys.exit(0)
- elif option[0] == '-o':
- overwritefile = 1
- elif option[0] == '--classic':
- classic = int(option[1])
- elif option[0] == '--zlib':
- zlib = int(option[1])
- elif option[0] == '--quiet':
- quiet = int(option[1])
- elif option[0] == '--complevel':
- complevel = int(option[1])
- elif option[0] == '--shuffle':
- shuffle = int(option[1])
- elif option[0] == '--fletcher32':
- fletcher32 = int(option[1])
- elif option[0] == '--unpackshort':
- unpackshort = int(option[1])
- elif option[0] == '--chunk':
- chunk = int(option[1])
- elif option[0] == '--vars':
- vars = option[1]
- elif option[0] == '--quantize':
- quantize = option[1]
- elif option[0] == '--istart':
- istart = int(option[1])
- elif option[0] == '--istop':
- istop = int(option[1])
- else:
- sys.stdout.write("%s: Unrecognized option\n" % option[0])
- sys.stderr.write(usage)
- sys.exit(0)
-
- # if we pass a number of files different from 2, abort
- if len(pargs) < 2 or len(pargs) > 2:
- sys.stdout.write("You need to pass both source and destination!.\n")
- sys.stderr.write(usage)
- sys.exit(0)
-
- # Catch the files passed as the last arguments
- filename3 = pargs[0]
- filename4 = pargs[1]
-
- # Parse the quantize option, create a dictionary from key/value pairs.
- if quantize is not None:
- lsd_dict = {}
- for p in quantize.split(','):
- kv = p.split('=')
- lsd_dict[kv[0]] = int(kv[1])
- else:
- lsd_dict=None
-
- # Parse the vars option, create a list of variable names.
- if vars is not None:
- vars = vars.split(',')
-
- # copy the data from filename3 to filename4.
- nc3tonc4(filename3,filename4,unpackshort=unpackshort,
- zlib=zlib,complevel=complevel,shuffle=shuffle,
- fletcher32=fletcher32,clobber=overwritefile,lsd_dict=lsd_dict,
- nchunk=chunk,quiet=quiet,vars=vars,classic=classic,
- istart=istart,istop=istop)
+from netCDF4.utils import nc3tonc4
+nc3tonc4()
diff --git a/utils/nc4tonc3 b/utils/nc4tonc3
index 41eaf7b..efa12f6 100755
--- a/utils/nc4tonc3
+++ b/utils/nc4tonc3
@@ -1,127 +1,4 @@
#!/usr/bin/env python
-from netCDF4 import Dataset
-def nc4tonc3(filename4,filename3,clobber=False,nchunk=10,quiet=False,format='NETCDF3_64BIT'):
- """convert a netcdf 4 file (filename4) in NETCDF4_CLASSIC format
- to a netcdf 3 file (filename3) in NETCDF3_64BIT format."""
- ncfile4 = Dataset(filename4,'r')
- if ncfile4.file_format != 'NETCDF4_CLASSIC':
- raise IOError('input file must be in NETCDF4_CLASSIC format')
- ncfile3 = Dataset(filename3,'w',clobber=clobber,format=format)
- # create dimensions. Check for unlimited dim.
- unlimdimname = False
- unlimdim = None
- # create global attributes.
- if not quiet: sys.stdout.write('copying global attributes ..\n')
- #for attname in ncfile4.ncattrs():
- # setattr(ncfile3,attname,getattr(ncfile4,attname))
- ncfile3.setncatts(ncfile4.__dict__)
- if not quiet: sys.stdout.write('copying dimensions ..\n')
- for dimname,dim in ncfile4.dimensions.items():
- if dim.isunlimited():
- unlimdimname = dimname
- unlimdim = dim
- ncfile3.createDimension(dimname,None)
- else:
- ncfile3.createDimension(dimname,len(dim))
- # create variables.
- for varname,ncvar in ncfile4.variables.items():
- if not quiet: sys.stdout.write('copying variable %s\n' % varname)
- # is there an unlimited dimension?
- if unlimdimname and unlimdimname in ncvar.dimensions:
- hasunlimdim = True
- else:
- hasunlimdim = False
- if hasattr(ncvar, '_FillValue'):
- FillValue = ncvar._FillValue
- else:
- FillValue = None
- var = ncfile3.createVariable(varname,ncvar.dtype,ncvar.dimensions,fill_value=FillValue)
- # fill variable attributes.
- attdict = ncvar.__dict__
- if '_FillValue' in attdict: del attdict['_FillValue']
- var.setncatts(attdict)
- #for attname in ncvar.ncattrs():
- # if attname == '_FillValue': continue
- # setattr(var,attname,getattr(ncvar,attname))
- # fill variables with data.
- if hasunlimdim: # has an unlim dim, loop over unlim dim index.
- # range to copy
- if nchunk:
- start = 0; stop = len(unlimdim); step = nchunk
- if step < 1: step = 1
- for n in range(start, stop, step):
- nmax = n+nchunk
- if nmax > len(unlimdim): nmax=len(unlimdim)
- var[n:nmax] = ncvar[n:nmax]
- else:
- var[0:len(unlimdim)] = ncvar[:]
- else: # no unlim dim or 1-d variable, just copy all data at once.
- var[:] = ncvar[:]
- ncfile3.sync() # flush data to disk
- # close files.
- ncfile3.close()
- ncfile4.close()
-
-if __name__ == '__main__':
-
- import sys, getopt, os
-
- usage = """
- Convert a netCDF 4 file (in NETCDF4_CLASSIC format) to netCDF 3 format.
-
- usage: %s [-h] [-o] [--chunk] netcdf4filename netcdf3filename
- -h -- Print usage message.
- -o -- Overwite destination file (default is to raise an error if output file already exists).
- --quiet=(0|1) -- if 1, don't print diagnostic information.
- --format -- netcdf3 format to use (NETCDF3_64BIT by default, can be set to NETCDF3_CLASSIC)
- --chunk=(integer) -- number of records along unlimited dimension to
- write at once. Default 10. Ignored if there is no unlimited
- dimension. chunk=0 means write all the data at once.
-\n""" % os.path.basename(sys.argv[0])
-
- try:
- opts, pargs = getopt.getopt(sys.argv[1:], 'ho',
- ['format=','chunk=','quiet='])
- except:
- (type, value, traceback) = sys.exc_info()
- sys.stdout.write("Error parsing the options. The error was: %s\n" % value)
- sys.stderr.write(usage)
- sys.exit(0)
-
- # default options
- quiet = 0
- chunk = 1000
- format = 'NETCDF3_64BIT'
- overwritefile = 0
-
- # Get the options
- for option in opts:
- if option[0] == '-h':
- sys.stderr.write(usage)
- sys.exit(0)
- elif option[0] == '-o':
- overwritefile = 1
- elif option[0] == '--quiet':
- quiet = int(option[1])
- elif option[0] == '--format':
- format = option[1]
- elif option[0] == '--chunk':
- chunk = int(option[1])
- else:
- sys.stdout.write("%s : Unrecognized option\n" % options[0])
- sys.stderr.write(usage)
- sys.exit(0)
-
- # if we pass a number of files different from 2, abort
- if len(pargs) < 2 or len(pargs) > 2:
- sys.stdout.write("You need to pass both source and destination!\n.")
- sys.stderr.write(usage)
- sys.exit(0)
-
- # Catch the files passed as the last arguments
- filename4 = pargs[0]
- filename3 = pargs[1]
-
- # copy the data from filename4 to filename3.
- nc4tonc3(filename4,filename3,clobber=overwritefile,quiet=quiet,format=format)
+from netCDF4.utils import nc4tonc3
+nc4tonc3()
diff --git a/utils/ncinfo b/utils/ncinfo
index 101082c..3afff8f 100755
--- a/utils/ncinfo
+++ b/utils/ncinfo
@@ -1,82 +1,4 @@
#!/usr/bin/env python
-from __future__ import print_function
-from netCDF4 import Dataset
-import sys
-def getgrp(g,p):
- import posixpath
- grps = p.split("/")
- for gname in grps:
- if gname == "": continue
- g = g.groups[gname]
- return g
-
-if __name__ == '__main__':
-
- import getopt, os
-
- usage = """
- Print summary information about a netCDF file.
-
- usage: %s [-h] [-g grp or --group=grp] [-v var or --variable=var] [-d dim or --dimension=dim] filename
-
- -h -- Print usage message.
- -g <group name> or --group=<group name> -- Print info for this group
- (default is root group). Nested groups specified
- using posix paths ("group1/group2/group3").
- -v <variable name> or --variable=<variable name> -- Print info for this variable.
- -d <dimension name> or --dimension=<dimension name> -- Print info for this dimension.
-
- netcdf filename must be last argument.
-\n""" % os.path.basename(sys.argv[0])
-
- try:
- opts, pargs = getopt.getopt(sys.argv[1:],'hv:g:d:',
- ['group=',
- 'variable=',
- 'dimension='])
- except:
- (type, value, traceback) = sys.exc_info()
- sys.stdout.write("Error parsing the options. The error was: %s\n" % value)
- sys.stderr.write(usage)
- sys.exit(0)
-
- # Get the options
- group = None; var = None; dim=None
- for option in opts:
- if option[0] == '-h':
- sys.stderr.write(usage)
- sys.exit(0)
- elif option[0] == '--group' or option[0] == '-g':
- group = option[1]
- elif option[0] == '--variable' or option[0] == '-v':
- var = option[1]
- elif option[0] == '--dimension' or option[0] == '-d':
- dim = option[1]
- else:
- sys.stdout.write("%s: Unrecognized option\n" % option[0])
- sys.stderr.write(usage)
- sys.exit(0)
-
- # filename passed as last argumenbt
- filename = pargs[-1]
-
- f = Dataset(filename)
- if group is None:
- if var is None and dim is None:
- print(f)
- else:
- if var is not None:
- print(f.variables[var])
- if dim is not None:
- print(f.dimensions[dim])
- else:
- if var is None and dim is None:
- print(getgrp(f,group))
- else:
- g = getgrp(f,group)
- if var is not None:
- print(g.variables[var])
- if dim is not None:
- print(g.dimensions[var])
- f.close()
+from netCDF4.utils import ncinfo
+ncinfo()
--
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