From 54ca2a00659a0339a956d62c93920cbbcb15c20b Mon Sep 17 00:00:00 2001 From: jswhit Date: Sun, 11 Sep 2022 16:23:57 -0600 Subject: [PATCH] add set_alignment/get_alignment to __all__, update docs --- docs/index.html | 581 ++++++++++++++++++++++++++-------------- src/netCDF4/__init__.py | 2 +- 2 files changed, 376 insertions(+), 207 deletions(-) diff --git a/docs/index.html b/docs/index.html index 55793dd5b..6fc81dc20 100644 --- a/docs/index.html +++ b/docs/index.html @@ -3,25 +3,25 @@ - + netCDF4 API documentation - - - - - - -

netCDF4

-

Version 1.6.0

+

Version 1.6.1

Introduction

@@ -582,7 +601,7 @@

Creating/Opening/Closing a netCDF

Here's an example:

-
>>> from netCDF4 import Dataset
+
>>> from netCDF4 import Dataset
 >>> rootgrp = Dataset("test.nc", "w", format="NETCDF4")
 >>> print(rootgrp.data_model)
 NETCDF4
@@ -611,7 +630,7 @@ 
Groups in a netCDF file

 NETCDF4 formatted files support Groups, if you try to create a Group
 in a netCDF 3 file you will get an error message.

 
-
>>> rootgrp = Dataset("test.nc", "a")
+
>>> rootgrp = Dataset("test.nc", "a")
 >>> fcstgrp = rootgrp.createGroup("forecasts")
 >>> analgrp = rootgrp.createGroup("analyses")
 >>> print(rootgrp.groups)
@@ -635,7 +654,7 @@ 
Groups in a netCDF file

 that group.  To simplify the creation of nested groups, you can
 use a unix-like path as an argument to Dataset.createGroup.

 
-
>>> fcstgrp1 = rootgrp.createGroup("/forecasts/model1")
+
>>> fcstgrp1 = rootgrp.createGroup("/forecasts/model1")
 >>> fcstgrp2 = rootgrp.createGroup("/forecasts/model2")
 

 
@@ -649,7 +668,7 @@ 
Groups in a netCDF file

 to walk the directory tree. Note that printing the Dataset or Group
 object yields summary information about it's contents.

 
-
>>> def walktree(top):
+
>>> def walktree(top):
 ...     yield top.groups.values()
 ...     for value in top.groups.values():
 ...         yield from walktree(value)
@@ -699,7 +718,7 @@ 
Dimensions in a netCDF file

 dimension is a new netCDF 4 feature, in netCDF 3 files there may be only
 one, and it must be the first (leftmost) dimension of the variable.

 
-
>>> level = rootgrp.createDimension("level", None)
+
>>> level = rootgrp.createDimension("level", None)
 >>> time = rootgrp.createDimension("time", None)
 >>> lat = rootgrp.createDimension("lat", 73)
 >>> lon = rootgrp.createDimension("lon", 144)
@@ -707,7 +726,7 @@ 
Dimensions in a netCDF file

 
 
All of the Dimension instances are stored in a python dictionary.

 
-
>>> print(rootgrp.dimensions)
+
>>> print(rootgrp.dimensions)
 {'level': <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'level', size = 0, 'time': <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time', size = 0, 'lat': <class 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 73, 'lon': <class 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 144}
 

 
@@ -716,7 +735,7 @@ 
Dimensions in a netCDF file

 Dimension.isunlimited method of a Dimension instance
 be used to determine if the dimensions is unlimited, or appendable.

 
-
>>> print(len(lon))
+
>>> print(len(lon))
 144
 >>> print(lon.isunlimited())
 False
@@ -728,7 +747,7 @@ 
Dimensions in a netCDF file

 provides useful summary info, including the name and length of the dimension,
 and whether it is unlimited.

 
-
>>> for dimobj in rootgrp.dimensions.values():
+
>>> for dimobj in rootgrp.dimensions.values():
 ...     print(dimobj)
 <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'level', size = 0
 <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time', size = 0
@@ -773,7 +792,7 @@ 
Variables in a netCDF file

 method returns an instance of the Variable class whose methods can be
 used later to access and set variable data and attributes.

 
-
>>> times = rootgrp.createVariable("time","f8",("time",))
+
>>> times = rootgrp.createVariable("time","f8",("time",))
 >>> levels = rootgrp.createVariable("level","i4",("level",))
 >>> latitudes = rootgrp.createVariable("lat","f4",("lat",))
 >>> longitudes = rootgrp.createVariable("lon","f4",("lon",))
@@ -785,7 +804,7 @@ 
Variables in a netCDF file

 
To get summary info on a Variable instance in an interactive session,
 just print it.

 
-
>>> print(temp)
+
>>> print(temp)
 <class 'netCDF4._netCDF4.Variable'>
 float32 temp(time, level, lat, lon)
     units: K
@@ -796,7 +815,7 @@ 
Variables in a netCDF file

 
 
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",))
+
>>> ftemp = rootgrp.createVariable("/forecasts/model1/temp","f4",("time","level","lat","lon",))
 

 
 
If the intermediate groups do not yet exist, they will be created.

@@ -804,7 +823,7 @@ 
Variables in a netCDF file

 
You can also query a Dataset or Group instance directly to obtain Group or
 Variable instances using paths.

 
-
>>> print(rootgrp["/forecasts/model1"])  # a Group instance
+
>>> print(rootgrp["/forecasts/model1"])  # a Group instance
 <class 'netCDF4._netCDF4.Group'>
 group /forecasts/model1:
     dimensions(sizes): 
@@ -822,7 +841,7 @@ 
Variables in a netCDF file

 
All of the variables in the Dataset or Group are stored in a
 Python dictionary, in the same way as the dimensions:

 
-
>>> print(rootgrp.variables)
+
>>> print(rootgrp.variables)
 {'time': <class 'netCDF4._netCDF4.Variable'>
 float64 time(time)
 unlimited dimensions: time
@@ -865,7 +884,7 @@ 
Attributes in a netCDF file

 variables. Attributes can be strings, numbers or sequences. Returning to
 our example,

 
-
>>> import time
+
>>> import time
 >>> rootgrp.description = "bogus example script"
 >>> rootgrp.history = "Created " + time.ctime(time.time())
 >>> rootgrp.source = "netCDF4 python module tutorial"
@@ -883,7 +902,7 @@ 
Attributes in a netCDF file

 built-in dir Python function will return a bunch of private methods
 and attributes that cannot (or should not) be modified by the user.

 
-
>>> for name in rootgrp.ncattrs():
+
>>> for name in rootgrp.ncattrs():
 ...     print("Global attr {} = {}".format(name, getattr(rootgrp, name)))
 Global attr description = bogus example script
 Global attr history = Created Mon Jul  8 14:19:41 2019
@@ -894,7 +913,7 @@ 
Attributes in a netCDF file

 instance provides all the netCDF attribute name/value pairs in a python
 dictionary:

 
-
>>> print(rootgrp.__dict__)
+
>>> print(rootgrp.__dict__)
 {'description': 'bogus example script', 'history': 'Created Mon Jul  8 14:19:41 2019', 'source': 'netCDF4 python module tutorial'}
 

 
@@ -907,7 +926,7 @@ 
Writing data
 
Now that you have a netCDF 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 as np
+
>>> import numpy as np
 >>> lats =  np.arange(-90,91,2.5)
 >>> lons =  np.arange(-180,180,2.5)
 >>> latitudes[:] = lats
@@ -927,7 +946,7 @@ 
Writing data
 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.
+
>>> # append along two unlimited dimensions by assigning to slice.
 >>> nlats = len(rootgrp.dimensions["lat"])
 >>> nlons = len(rootgrp.dimensions["lon"])
 >>> print("temp shape before adding data = {}".format(temp.shape))
@@ -947,7 +966,7 @@ 
Writing data
 along the level dimension of the variable temp, even though no
 data has yet been assigned to levels.

 
-
>>> # now, assign data to levels dimension variable.
+
>>> # now, assign data to levels dimension variable.
 >>> levels[:] =  [1000.,850.,700.,500.,300.,250.,200.,150.,100.,50.]
 

 
@@ -960,7 +979,7 @@ 
Writing data
 allowed, and these indices work independently along each dimension (similar
 to the way vector subscripts work in fortran).  This means that

 
-
>>> temp[0, 0, [0,1,2,3], [0,1,2,3]].shape
+
>>> temp[0, 0, [0,1,2,3], [0,1,2,3]].shape
 (4, 4)
 

 
@@ -978,14 +997,14 @@ 
Writing data
 
 
For example,

 
-
>>> tempdat = temp[::2, [1,3,6], lats>0, lons>0]
+
>>> tempdat = temp[::2, [1,3,6], lats>0, lons>0]
 

 
 
will extract time indices 0,2 and 4, pressure levels
 850, 500 and 200 hPa, all Northern Hemisphere latitudes and Eastern
 Hemisphere longitudes, resulting in a numpy array of shape  (3, 3, 36, 71).

 
-
>>> print("shape of fancy temp slice = {}".format(tempdat.shape))
+
>>> print("shape of fancy temp slice = {}".format(tempdat.shape))
 shape of fancy temp slice = (3, 3, 36, 71)
 

 
@@ -1018,7 +1037,7 @@ 
Dealing with time coordinates

 provided by cftime to do just that.
 Here's an example of how they can be used:

 
-
>>> # fill in times.
+
>>> # fill in times.
 >>> from datetime import datetime, timedelta
 >>> from cftime import num2date, date2num
 >>> dates = [datetime(2001,3,1)+n*timedelta(hours=12) for n in range(temp.shape[0])]
@@ -1058,7 +1077,7 @@ 
Reading data from a multi
 NETCDF4_CLASSIC format (NETCDF4 formatted multi-file
 datasets are not supported).

 
-
>>> for nf in range(10):
+
>>> for nf in range(10):
 ...     with Dataset("mftest%s.nc" % nf, "w", format="NETCDF4_CLASSIC") as f:
 ...         _ = f.createDimension("x",None)
 ...         x = f.createVariable("x","i",("x",))
@@ -1067,7 +1086,7 @@ 
Reading data from a multi
 
 
Now read all the files back in at once with MFDataset

 
-
>>> from netCDF4 import MFDataset
+
>>> from netCDF4 import MFDataset
 >>> f = MFDataset("mftest*nc")
 >>> print(f.variables["x"][:])
 [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
@@ -1134,22 +1153,22 @@ 
Efficient compression of netC
 
 
In our example, try replacing the line

 
-
>>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",))
+
>>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",))
 

 
 
with

 
-
>>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",),compression='zlib')
+
>>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",),compression='zlib')
 

 
 
and then

 
-
>>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",),compression='zlib',least_significant_digit=3)
+
>>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",),compression='zlib',least_significant_digit=3)
 

 
 
or with netcdf-c >= 4.9.0

 
-
>>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",),compression='zlib',significant_digits=4)
+
>>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",),compression='zlib',significant_digits=4)
 

 
 
and see how much smaller the resulting files are.

@@ -1170,7 +1189,7 @@ 
Beyond ho
 Since there is no native complex data type in netcdf, compound types are handy
 for storing numpy complex arrays.  Here's an example:

 
-
>>> f = Dataset("complex.nc","w")
+
>>> f = Dataset("complex.nc","w")
 >>> size = 3 # length of 1-d complex array
 >>> # create sample complex data.
 >>> datac = np.exp(1j*(1.+np.linspace(0, np.pi, size)))
@@ -1206,7 +1225,7 @@ 
Beyond ho
 in a Python dictionary, just like variables and dimensions. As always, printing
 objects gives useful summary information in an interactive session:

 
-
>>> print(f)
+
>>> print(f)
 <class 'netCDF4._netCDF4.Dataset'>
 root group (NETCDF4 data model, file format HDF5):
     dimensions(sizes): x_dim(3)
@@ -1231,7 +1250,7 @@ 
Variable-length (vlen) data types

 data type, use the Dataset.createVLType method
 method of a Dataset or Group instance.

 
-
>>> f = Dataset("tst_vlen.nc","w")
+
>>> f = Dataset("tst_vlen.nc","w")
 >>> vlen_t = f.createVLType(np.int32, "phony_vlen")
 

 
@@ -1241,7 +1260,7 @@ 
Variable-length (vlen) data types

 but compound data types cannot.
 A new variable can then be created using this datatype.

 
-
>>> x = f.createDimension("x",3)
+
>>> x = f.createDimension("x",3)
 >>> y = f.createDimension("y",4)
 >>> vlvar = f.createVariable("phony_vlen_var", vlen_t, ("y","x"))
 

@@ -1254,7 +1273,7 @@ 
Variable-length (vlen) data types

 In this case, they contain 1-D numpy int32 arrays of random length between
 1 and 10.

 
-
>>> import random
+
>>> import random
 >>> random.seed(54321)
 >>> data = np.empty(len(y)*len(x),object)
 >>> for n in range(len(y)*len(x)):
@@ -1294,7 +1313,7 @@ 
Variable-length (vlen) data types

 with fixed length greater than 1) when calling the
 Dataset.createVariable method.

 
-
>>> z = f.createDimension("z",10)
+
>>> z = f.createDimension("z",10)
 >>> strvar = f.createVariable("strvar", str, "z")
 

 
@@ -1302,7 +1321,7 @@ 
Variable-length (vlen) data types

 random lengths between 2 and 12 characters, and the data in the object
 array is assigned to the vlen string variable.

 
-
>>> chars = "1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
+
>>> chars = "1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
 >>> data = np.empty(10,"O")
 >>> for n in range(10):
 ...     stringlen = random.randint(2,12)
@@ -1341,7 +1360,7 @@ 
Enum data type

 values and their names are used to define an Enum data type using
 Dataset.createEnumType.

 
-
>>> nc = Dataset('clouds.nc','w')
+
>>> nc = Dataset('clouds.nc','w')
 >>> # python dict with allowed values and their names.
 >>> enum_dict = {'Altocumulus': 7, 'Missing': 255,
 ... 'Stratus': 2, 'Clear': 0,
@@ -1359,7 +1378,7 @@ 
Enum data type

 is made to write an integer value not associated with one of the
 specified names.

 
-
>>> time = nc.createDimension('time',None)
+
>>> time = nc.createDimension('time',None)
 >>> # create a 1d variable of type 'cloud_type'.
 >>> # The fill_value is set to the 'Missing' named value.
 >>> cloud_var = nc.createVariable('primary_cloud',cloud_type,'time',
@@ -1396,7 +1415,7 @@ 
Parallel IO

 available. To use parallel IO, your program must be running in an MPI
 environment using mpi4py.

 
-
>>> from mpi4py import MPI
+
>>> from mpi4py import MPI
 >>> import numpy as np
 >>> from netCDF4 import Dataset
 >>> rank = MPI.COMM_WORLD.rank  # The process ID (integer 0-3 for 4-process run)
@@ -1408,7 +1427,7 @@ 
Parallel IO

 when a new dataset is created or an existing dataset is opened,
 use the parallel keyword to enable parallel access.

 
-
>>> nc = Dataset('parallel_test.nc','w',parallel=True)
+
>>> nc = Dataset('parallel_test.nc','w',parallel=True)
 

 
 
The optional comm keyword may be used to specify a particular
@@ -1416,7 +1435,7 @@ 
Parallel IO

 can now write to the file indepedently.  In this example the process rank is
 written to a different variable index on each task

 
-
>>> d = nc.createDimension('dim',4)
+
>>> d = nc.createDimension('dim',4)
 >>> v = nc.createVariable('var', np.int64, 'dim')
 >>> v[rank] = rank
 >>> nc.close()
@@ -1483,7 +1502,7 @@ 
Dealing with strings

 stringtochar is used to convert the numpy string array to an array of
 characters with one more dimension. For example,

 
-
>>> from netCDF4 import stringtochar
+
>>> from netCDF4 import stringtochar
 >>> nc = Dataset('stringtest.nc','w',format='NETCDF4_CLASSIC')
 >>> _ = nc.createDimension('nchars',3)
 >>> _ = nc.createDimension('nstrings',None)
@@ -1516,7 +1535,7 @@ 
Dealing with strings

 character array dtype under the hood when creating the netcdf compound type.
 Here's an example:

 
-
>>> nc = Dataset('compoundstring_example.nc','w')
+
>>> nc = Dataset('compoundstring_example.nc','w')
 >>> dtype = np.dtype([('observation', 'f4'),
 ...                      ('station_name','S10')])
 >>> station_data_t = nc.createCompoundType(dtype,'station_data')
@@ -1561,7 +1580,7 @@ 
In-memory (diskless) Datasets

 object representing the Dataset. Below are examples illustrating both
 approaches.

 
-
>>> # create a diskless (in-memory) Dataset,
+
>>> # create a diskless (in-memory) Dataset,
 >>> # and persist the file to disk when it is closed.
 >>> nc = Dataset('diskless_example.nc','w',diskless=True,persist=True)
 >>> d = nc.createDimension('x',None)
@@ -1623,7 +1642,7 @@ 
In-memory (diskless) Datasets

 the parallel IO example, which is in examples/mpi_example.py.
 Unit tests are in the test directory.

 
-
contact: Jeffrey Whitaker jeffrey.s.whitaker@noaa.gov

+
contact: Jeffrey Whitaker jeffrey.s.whitaker@noaa.gov

 
 
copyright: 2008 by Jeffrey Whitaker.

 
@@ -1636,7 +1655,7 @@ 
In-memory (diskless) Datasets

 
                         

             View Source
-            
# init for netCDF4. package
+            
# init for netCDF4. package
 # Docstring comes from extension module _netCDF4.
 from ._netCDF4 import *
 # Need explicit imports for names beginning with underscores
@@ -1650,12 +1669,13 @@ 
In-memory (diskless) Datasets

                        __has_bzip2_support__, __has_blosc_support__, __has_szip_support__)
 import os
 __all__ =\
-['Dataset','Variable','Dimension','Group','MFDataset','MFTime','CompoundType','VLType','date2num','num2date','date2index','stringtochar','chartostring','stringtoarr','getlibversion','EnumType','get_chunk_cache','set_chunk_cache']
+['Dataset','Variable','Dimension','Group','MFDataset','MFTime','CompoundType','VLType','date2num','num2date','date2index','stringtochar','chartostring','stringtoarr','getlibversion','EnumType','get_chunk_cache','set_chunk_cache','set_alignment','get_alignment']
 # if HDF5_PLUGIN_PATH not set, point to package path if plugins live there
+pluginpath = os.path.join(__path__[0],'plugins')
 if 'HDF5_PLUGIN_PATH' not in os.environ and\
-    (os.path.exists(os.path.join(__path__[0],'lib__nczhdf5filters.so')) or\
-    os.path.exists(os.path.join(__path__[0],'lib__nczhdf5filters.dylib'))):
-    os.environ['HDF5_PLUGIN_PATH']=__path__[0]
+    (os.path.exists(os.path.join(pluginpath,'lib__nczhdf5filters.so')) or\
+     os.path.exists(os.path.join(pluginpath,'lib__nczhdf5filters.dylib'))):
+    os.environ['HDF5_PLUGIN_PATH']=pluginpath
 

 
         

@@ -1670,7 +1690,7 @@ 
In-memory (diskless) Datasets

         Dataset:
     

 
-        
+    
             
A netCDF 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. See Dataset.__init__ for more
@@ -1748,7 +1768,7 @@ 
In-memory (diskless) Datasets

             Dataset()
     

 
-        
+    
             
__init__(self, filename, mode="r", clobber=True, diskless=False,
 persist=False, keepweakref=False, memory=None, encoding=None,
 parallel=False, comm=None, info=None, format='NETCDF4')

@@ -1854,7 +1874,7 @@ 
In-memory (diskless) Datasets

             filepath(unknown):
     

 
-        
+    
             
filepath(self,encoding=None)

 
 
Get the file system path (or the opendap URL) which was used to
@@ -1873,7 +1893,7 @@ 
In-memory (diskless) Datasets

             close(unknown):
     

 
-        
+    
             
close(self)

 
 
Close the Dataset.

@@ -1889,7 +1909,7 @@ 
In-memory (diskless) Datasets

             isopen(unknown):
     

 
-        
+    
             
isopen(self)

 
 
Is the Dataset open or closed?

@@ -1905,7 +1925,7 @@ 
In-memory (diskless) Datasets

             sync(unknown):
     

 
-        
+    
             
sync(self)

 
 
Writes all buffered data in the Dataset to the disk file.

@@ -1921,7 +1941,7 @@ 
In-memory (diskless) Datasets

             set_fill_on(unknown):
     

 
-        
+    
             
set_fill_on(self)

 
 
Sets the fill mode for a Dataset open for writing to on.

@@ -1945,7 +1965,7 @@ 
In-memory (diskless) Datasets

             set_fill_off(unknown):
     

 
-        
+    
             
set_fill_off(self)

 
 
Sets the fill mode for a Dataset open for writing to off.

@@ -1965,7 +1985,7 @@ 
In-memory (diskless) Datasets

             createDimension(unknown):
     

 
-        
+    
             
createDimension(self, dimname, size=None)

 
 
Creates a new dimension with the given dimname and size.

@@ -1989,7 +2009,7 @@ 
In-memory (diskless) Datasets

             renameDimension(unknown):
     

 
-        
+    
             
renameDimension(self, oldname, newname)

 
 
rename a Dimension named oldname to newname.

@@ -2005,7 +2025,7 @@ 
In-memory (diskless) Datasets

             createCompoundType(unknown):
     

 
-        
+    
             
createCompoundType(self, datatype, datatype_name)

 
 
Creates a new compound data type named datatype_name from the numpy
@@ -2030,7 +2050,7 @@ 
In-memory (diskless) Datasets

             createVLType(unknown):
     

 
-        
+    
             
createVLType(self, datatype, datatype_name)

 
 
Creates a new VLEN data type named datatype_name from a numpy
@@ -2050,7 +2070,7 @@ 
In-memory (diskless) Datasets

             createEnumType(unknown):
     

 
-        
+    
             
createEnumType(self, datatype, datatype_name, enum_dict)

 
 
Creates a new Enum data type named datatype_name from a numpy
@@ -2071,7 +2091,7 @@ 
In-memory (diskless) Datasets

             createVariable(unknown):
     

 
-        
+    
             
createVariable(self, varname, datatype, dimensions=(), compression=None, zlib=False,
 complevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None,
 szip_coding='nn', szip_pixels_per_block=8, blosc_shuffle=1,
@@ -2166,7 +2186,7 @@ 
In-memory (diskless) Datasets

 
 
The optional keyword fill_value can be used to override the default
 netCDF _FillValue (the value that the variable gets filled with before
-any data is written to it, defaults given in the dict netCDF4.default_fillvals).
+any data is written to it, defaults given in the dict netCDF4.default_fillvals).
 If fill_value is set to False, then the variable is not pre-filled.

 
 
If the optional keyword parameters least_significant_digit or significant_digits are
@@ -2234,7 +2254,7 @@ 
In-memory (diskless) Datasets

             renameVariable(unknown):
     

 
-        
+    
             
renameVariable(self, oldname, newname)

 
 
rename a Variable named oldname to newname

@@ -2250,7 +2270,7 @@ 
In-memory (diskless) Datasets

             createGroup(unknown):
     

 
-        
+    
             
createGroup(self, groupname)

 
 
Creates a new Group with the given groupname.

@@ -2276,7 +2296,7 @@ 
In-memory (diskless) Datasets

             ncattrs(unknown):
     

 
-        
+    
             
ncattrs(self)

 
 
return netCDF global attribute names for this Dataset or Group in a list.

@@ -2292,7 +2312,7 @@ 
In-memory (diskless) Datasets

             setncattr(unknown):
     

 
-        
+    
             
setncattr(self,name,value)

 
 
set a netCDF dataset or group attribute using name,value pair.
@@ -2310,7 +2330,7 @@ 
In-memory (diskless) Datasets

             setncattr_string(unknown):
     

 
-        
+    
             
setncattr_string(self,name,value)

 
 
set a netCDF dataset or group string attribute using name,value pair.
@@ -2328,7 +2348,7 @@ 
In-memory (diskless) Datasets

             setncatts(unknown):
     

 
-        
+    
             
setncatts(self,attdict)

 
 
set a bunch of netCDF dataset or group attributes at once using a python dictionary.
@@ -2347,7 +2367,7 @@ 
In-memory (diskless) Datasets

             getncattr(unknown):
     

 
-        
+    
             
getncattr(self,name)

 
 
retrieve a netCDF dataset or group attribute.
@@ -2368,7 +2388,7 @@ 
In-memory (diskless) Datasets

             delncattr(unknown):
     

 
-        
+    
             
delncattr(self,name,value)

 
 
delete a netCDF dataset or group attribute.  Use if you need to delete a
@@ -2386,7 +2406,7 @@ 
In-memory (diskless) Datasets

             renameAttribute(unknown):
     

 
-        
+    
             
renameAttribute(self, oldname, newname)

 
 
rename a Dataset or Group attribute named oldname to newname.

@@ -2402,7 +2422,7 @@ 
In-memory (diskless) Datasets

             renameGroup(unknown):
     

 
-        
+    
             
renameGroup(self, oldname, newname)

 
 
rename a Group named oldname to newname (requires netcdf >= 4.3.1).

@@ -2418,7 +2438,7 @@ 
In-memory (diskless) Datasets

             set_auto_chartostring(unknown):
     

 
-        
+    
             
set_auto_chartostring(self, True_or_False)

 
 
Call Variable.set_auto_chartostring for all variables contained in this Dataset or
@@ -2443,7 +2463,7 @@ 
In-memory (diskless) Datasets

             set_auto_maskandscale(unknown):
     

 
-        
+    
             
set_auto_maskandscale(self, True_or_False)

 
 
Call Variable.set_auto_maskandscale for all variables contained in this Dataset or
@@ -2466,7 +2486,7 @@ 
In-memory (diskless) Datasets

             set_auto_mask(unknown):
     

 
-        
+    
             
set_auto_mask(self, True_or_False)

 
 
Call Variable.set_auto_mask for all variables contained in this Dataset or
@@ -2490,7 +2510,7 @@ 
In-memory (diskless) Datasets

             set_auto_scale(unknown):
     

 
-        
+    
             
set_auto_scale(self, True_or_False)

 
 
Call Variable.set_auto_scale for all variables contained in this Dataset or
@@ -2513,7 +2533,7 @@ 
In-memory (diskless) Datasets

             set_always_mask(unknown):
     

 
-        
+    
             
set_always_mask(self, True_or_False)

 
 
Call Variable.set_always_mask for all variables contained in
@@ -2541,7 +2561,7 @@ 
In-memory (diskless) Datasets

             set_ncstring_attrs(unknown):
     

 
-        
+    
             
set_ncstring_attrs(self, True_or_False)

 
 
Call Variable.set_ncstring_attrs for all variables contained in
@@ -2566,7 +2586,7 @@ 
In-memory (diskless) Datasets

             get_variables_by_attributes(unknown):
     

 
-        
+    
             
get_variables_by_attribute(self, **kwargs)

 
 
Returns a list of variables that match specific conditions.

@@ -2574,7 +2594,7 @@ 
In-memory (diskless) Datasets

 
Can pass in key=value parameters and variables are returned that
 contain all of the matches. For example,

 
-
>>> # Get variables with x-axis attribute.
+
>>> # Get variables with x-axis attribute.
 >>> vs = nc.get_variables_by_attributes(axis='X')
 >>> # Get variables with matching "standard_name" attribute
 >>> vs = nc.get_variables_by_attributes(standard_name='northward_sea_water_velocity')
@@ -2585,7 +2605,7 @@ 
In-memory (diskless) Datasets

 the attribute value.  None is given as the attribute value when the
 attribute does not exist on the variable. For example,

 
-
>>> # Get Axis variables
+
>>> # Get Axis variables
 >>> vs = nc.get_variables_by_attributes(axis=lambda v: v in ['X', 'Y', 'Z', 'T'])
 >>> # Get variables that don't have an "axis" attribute
 >>> vs = nc.get_variables_by_attributes(axis=lambda v: v is None)
@@ -2604,7 +2624,7 @@ 
In-memory (diskless) Datasets

             fromcdl(unknown):
     

 
-        
+    
             
fromcdl(cdlfilename, ncfilename=None, mode='a',format='NETCDF4')

 
 
call ncgen via subprocess to create Dataset from CDL
@@ -2634,7 +2654,7 @@ 
In-memory (diskless) Datasets

             tocdl(unknown):
     

 
-        
+    
             
tocdl(self, coordvars=False, data=False, outfile=None)

 
 
call ncdump via subprocess to create CDL
@@ -2649,13 +2669,74 @@ 
In-memory (diskless) Datasets

 

 
 
+                            

+                            

+                                        
#  
+
+        
+            def
+            has_blosc_filter(unknown):
+    

+
+    
+            
has_blosc_filter(self)
+returns True if blosc compression filter is available

+

+
+
+                            

+                            

+                                        
#  
+
+        
+            def
+            has_zstd_filter(unknown):
+    

+
+    
+            
has_zstd_filter(self)
+returns True if zstd compression filter is available

+

+
+
+                            

+                            

+                                        
#  
+
+        
+            def
+            has_bzip2_filter(unknown):
+    

+
+    
+            
has_bzip2_filter(self)
+returns True if bzip2 compression filter is available

+

+
+
+                            

+                            

+                                        
#  
+
+        
+            def
+            has_szip_filter(unknown):
+    

+
+    
+            
has_szip_filter(self)
+returns True if szip compression filter is available

+

+
+
                             

                             

                                             
#  
 
-        name = <attribute 'name' of 'netCDF4._netCDF4.Dataset' objects>
+        name
     

 
+    
             
string name of Group instance

 

 
@@ -2664,109 +2745,121 @@ 
In-memory (diskless) Datasets

                             

                                             
#  
 
-        groups = <attribute 'groups' of 'netCDF4._netCDF4.Dataset' objects>
+        groups
     

 
     
+    
 
                             

                             

                                             
#  
 
-        dimensions = <attribute 'dimensions' of 'netCDF4._netCDF4.Dataset' objects>
+        dimensions
     

 
     
+    
 
                             

                             

                                             
#  
 
-        variables = <attribute 'variables' of 'netCDF4._netCDF4.Dataset' objects>
+        variables
     

 
     
+    
 
                             

                             

                                             
#  
 
-        disk_format = <attribute 'disk_format' of 'netCDF4._netCDF4.Dataset' objects>
+        disk_format
     

 
     
+    
 
                             

                             

                                             
#  
 
-        path = <attribute 'path' of 'netCDF4._netCDF4.Dataset' objects>
+        path
     

 
     
+    
 
                             

                             

                                             
#  
 
-        parent = <attribute 'parent' of 'netCDF4._netCDF4.Dataset' objects>
+        parent
     

 
     
+    
 
                             

                             

                                             
#  
 
-        file_format = <attribute 'file_format' of 'netCDF4._netCDF4.Dataset' objects>
+        file_format
     

 
     
+    
 
                             

                             

                                             
#  
 
-        data_model = <attribute 'data_model' of 'netCDF4._netCDF4.Dataset' objects>
+        data_model
     

 
     
+    
 
                             

                             

                                             
#  
 
-        cmptypes = <attribute 'cmptypes' of 'netCDF4._netCDF4.Dataset' objects>
+        cmptypes
     

 
     
+    
 
                             

                             

                                             
#  
 
-        vltypes = <attribute 'vltypes' of 'netCDF4._netCDF4.Dataset' objects>
+        vltypes
     

 
     
+    
 
                             

                             

                                             
#  
 
-        enumtypes = <attribute 'enumtypes' of 'netCDF4._netCDF4.Dataset' objects>
+        enumtypes
     

 
     
+    
 
                             

                             

                                             
#  
 
-        keepweakref = <attribute 'keepweakref' of 'netCDF4._netCDF4.Dataset' objects>
+        keepweakref
     

 
     
+

@@ -2779,7 +2872,7 @@

In-memory (diskless) Datasets

Variable: - +

A netCDF Variable is used to read and write netCDF data. They are analogous to numpy array objects. See Variable.__init__ for more details.

@@ -2863,7 +2956,7 @@

In-memory (diskless) Datasets

Variable()
- +

__init__(self, group, name, datatype, dimensions=(), compression=None, zlib=False, complevel=4, shuffle=True, szip_coding='nn', szip_pixels_per_block=8, blosc_shuffle=1, fletcher32=False, contiguous=False, @@ -2984,7 +3077,7 @@

In-memory (diskless) Datasets

value that the variable gets filled with before any data is written to it) is replaced with this value. If fill_value is set to False, then the variable is not pre-filled. The default netCDF fill values can be found -in the dictionary netCDF4.default_fillvals.

+in the dictionary netCDF4.default_fillvals.

chunk_cache: If specified, sets the chunk cache size for this variable. Persists as long as Dataset is open. Use set_var_chunk_cache to @@ -3005,7 +3098,7 @@

In-memory (diskless) Datasets

group(unknown):
- +

group(self)

return the group that this Variable is a member of.

@@ -3021,7 +3114,7 @@

In-memory (diskless) Datasets

ncattrs(unknown):
- +

ncattrs(self)

return netCDF attribute names for this Variable in a list.

@@ -3037,7 +3130,7 @@

In-memory (diskless) Datasets

setncattr(unknown):
- +

setncattr(self,name,value)

set a netCDF variable attribute using name,value pair. Use if you need to set a @@ -3055,7 +3148,7 @@

In-memory (diskless) Datasets

setncattr_string(unknown):
- +

setncattr_string(self,name,value)

set a netCDF variable string attribute using name,value pair. @@ -3074,7 +3167,7 @@

In-memory (diskless) Datasets

setncatts(unknown):
- +

setncatts(self,attdict)

set a bunch of netCDF variable attributes at once using a python dictionary. @@ -3093,7 +3186,7 @@

In-memory (diskless) Datasets

getncattr(unknown):
- +

getncattr(self,name)

retrieve a netCDF variable attribute. Use if you need to set a @@ -3114,7 +3207,7 @@

In-memory (diskless) Datasets

delncattr(unknown):
- +

delncattr(self,name,value)

delete a netCDF variable attribute. Use if you need to delete a @@ -3132,7 +3225,7 @@

In-memory (diskless) Datasets

filters(unknown):
- +

filters(self)

return dictionary containing HDF5 filter parameters.

@@ -3148,7 +3241,7 @@

In-memory (diskless) Datasets

quantization(unknown):
- +

quantization(self)

return number of significant digits and the algorithm used in quantization. @@ -3165,7 +3258,7 @@

In-memory (diskless) Datasets

endian(unknown):
- +

endian(self)

return endian-ness (little,big,native) of variable (as stored in HDF5 file).

@@ -3181,7 +3274,7 @@

In-memory (diskless) Datasets

chunking(unknown):
- +

chunking(self)

return variable chunking information. If the dataset is @@ -3200,7 +3293,7 @@

In-memory (diskless) Datasets

get_var_chunk_cache(unknown):
- +

get_var_chunk_cache(self)

return variable chunk cache information in a tuple (size,nelems,preemption). @@ -3218,7 +3311,7 @@

In-memory (diskless) Datasets

set_var_chunk_cache(unknown):
- +

set_var_chunk_cache(self,size=None,nelems=None,preemption=None)

change variable chunk cache settings. @@ -3236,7 +3329,7 @@

In-memory (diskless) Datasets

renameAttribute(unknown):
- +

renameAttribute(self, oldname, newname)

rename a Variable attribute named oldname to newname.

@@ -3252,7 +3345,7 @@

In-memory (diskless) Datasets

assignValue(unknown):
- +

assignValue(self, val)

assign a value to a scalar variable. Provided for compatibility with @@ -3269,7 +3362,7 @@

In-memory (diskless) Datasets

getValue(unknown):
- +

getValue(self)

get the value of a scalar variable. Provided for compatibility with @@ -3286,7 +3379,7 @@

In-memory (diskless) Datasets

set_auto_chartostring(unknown):
- +

set_auto_chartostring(self,chartostring)

turn on or off automatic conversion of character variable data to and @@ -3317,7 +3410,7 @@

In-memory (diskless) Datasets

use_nc_get_vars(unknown):
- +

use_nc_get_vars(self,_use_get_vars)

enable the use of netcdf library routine nc_get_vars @@ -3337,7 +3430,7 @@

In-memory (diskless) Datasets

set_auto_maskandscale(unknown):
- +

set_auto_maskandscale(self,maskandscale)

turn on or off automatic conversion of variable data to and @@ -3401,7 +3494,7 @@

In-memory (diskless) Datasets

set_auto_scale(unknown):
- +

set_auto_scale(self,scale)

turn on or off automatic packing/unpacking of variable @@ -3450,7 +3543,7 @@

In-memory (diskless) Datasets

set_auto_mask(unknown):
- +

set_auto_mask(self,mask)

turn on or off automatic conversion of variable data to and @@ -3485,7 +3578,7 @@

In-memory (diskless) Datasets

set_always_mask(unknown):
- +

set_always_mask(self,always_mask)

turn on or off conversion of data without missing values to regular @@ -3508,7 +3601,7 @@

In-memory (diskless) Datasets

set_ncstring_attrs(unknown):
- +

set_always_mask(self,ncstring_attrs)

turn on or off creating NC_STRING string attributes.

@@ -3530,7 +3623,7 @@

In-memory (diskless) Datasets

set_collective(unknown):
- +

set_collective(self,True_or_False)

turn on or off collective parallel IO access. Ignored if file is not @@ -3547,7 +3640,7 @@

In-memory (diskless) Datasets

get_dims(unknown):
- +

get_dims(self)

return a tuple of Dimension instances associated with this @@ -3559,9 +3652,10 @@

In-memory (diskless) Datasets

#   - name = <attribute 'name' of 'netCDF4._netCDF4.Variable' objects> + name
+

string name of Variable instance

@@ -3570,9 +3664,10 @@

In-memory (diskless) Datasets

#   - datatype = <attribute 'datatype' of 'netCDF4._netCDF4.Variable' objects> + datatype
+

numpy data type (for primitive data types) or VLType/CompoundType/EnumType instance (for compound, vlen or enum data types)

@@ -3583,9 +3678,10 @@

In-memory (diskless) Datasets

#   - shape = <attribute 'shape' of 'netCDF4._netCDF4.Variable' objects> + shape
+

find current sizes of all variable dimensions

@@ -3594,9 +3690,10 @@

In-memory (diskless) Datasets

#   - size = <attribute 'size' of 'netCDF4._netCDF4.Variable' objects> + size
+

Return the number of stored elements.

@@ -3605,9 +3702,10 @@

In-memory (diskless) Datasets

#   - dimensions = <attribute 'dimensions' of 'netCDF4._netCDF4.Variable' objects> + dimensions
+

get variables's dimension names

@@ -3616,55 +3714,61 @@

In-memory (diskless) Datasets

#   - ndim = <attribute 'ndim' of 'netCDF4._netCDF4.Variable' objects> + ndim
+
#   - dtype = <attribute 'dtype' of 'netCDF4._netCDF4.Variable' objects> + dtype
+
#   - mask = <attribute 'mask' of 'netCDF4._netCDF4.Variable' objects> + mask
+
#   - scale = <attribute 'scale' of 'netCDF4._netCDF4.Variable' objects> + scale
+
#   - always_mask = <attribute 'always_mask' of 'netCDF4._netCDF4.Variable' objects> + always_mask
+
#   - chartostring = <attribute 'chartostring' of 'netCDF4._netCDF4.Variable' objects> + chartostring
+
@@ -3677,7 +3781,7 @@

In-memory (diskless) Datasets

Dimension:
- +

A netCDF Dimension is used to describe the coordinates of a Variable. See Dimension.__init__ for more details.

@@ -3703,7 +3807,7 @@

In-memory (diskless) Datasets

Dimension()
- +

__init__(self, group, name, size=None)

Dimension constructor.

@@ -3729,7 +3833,7 @@

In-memory (diskless) Datasets

group(unknown):
- +

group(self)

return the group that this Dimension is a member of.

@@ -3745,7 +3849,7 @@

In-memory (diskless) Datasets

isunlimited(unknown):
- +

isunlimited(self)

returns True if the Dimension instance is unlimited, False otherwise.

@@ -3756,9 +3860,10 @@

In-memory (diskless) Datasets

#   - name = <attribute 'name' of 'netCDF4._netCDF4.Dimension' objects> + name
+

string name of Dimension instance

@@ -3767,9 +3872,10 @@

In-memory (diskless) Datasets

#   - size = <attribute 'size' of 'netCDF4._netCDF4.Dimension' objects> + size
+

current size of Dimension (calls len on Dimension instance)

@@ -3785,7 +3891,7 @@

In-memory (diskless) Datasets

Group(netCDF4.Dataset):
- +

Groups define a hierarchical namespace within a netCDF file. They are analogous to directories in a unix filesystem. Each Group behaves like a Dataset within a Dataset, and can contain it's own variables, @@ -3809,7 +3915,7 @@

In-memory (diskless) Datasets

Group()
- +

__init__(self, parent, name) Group constructor.

@@ -3833,7 +3939,7 @@

In-memory (diskless) Datasets

close(unknown):
- +

close(self)

overrides Dataset close method which does not apply to Group @@ -3876,6 +3982,10 @@

Inherited Members
get_variables_by_attributes
fromcdl
tocdl
+
has_blosc_filter
+
has_zstd_filter
+
has_bzip2_filter
+
has_szip_filter
name
groups
dimensions
@@ -3903,7 +4013,7 @@
Inherited Members
MFDataset(netCDF4.Dataset):
- +

Class for reading multi-file netCDF Datasets, making variables spanning multiple files appear as if they were in one file. Datasets must be in NETCDF4_CLASSIC, NETCDF3_CLASSIC, NETCDF3_64BIT_OFFSET @@ -3913,7 +4023,7 @@

Inherited Members

Example usage (See MFDataset.__init__ for more details):

-
>>> import numpy as np
+
>>> import numpy as np
 >>> # create a series of netCDF files with a variable sharing
 >>> # the same unlimited dimension.
 >>> for nf in range(10):
@@ -3940,7 +4050,7 @@ 
Inherited Members

             MFDataset(files, check=False, aggdim=None, exclude=[], master_file=None)
     

 
-        
+    
             
__init__(self, files, check=False, aggdim=None, exclude=[],
 master_file=None)

 
@@ -3985,7 +4095,7 @@ 
Inherited Members

             ncattrs(self):
     

 
-        
+    
             
ncattrs(self)

 
 
return the netcdf attribute names from the master file.

@@ -4001,7 +4111,7 @@ 
Inherited Members

             close(self):
     

 
-        
+    
             
close(self)

 
 
close all the open files.

@@ -4042,6 +4152,10 @@ 
Inherited Members

                 
get_variables_by_attributes

                 
fromcdl

                 
tocdl

+                
has_blosc_filter

+                
has_zstd_filter

+                
has_bzip2_filter

+                
has_szip_filter

                 
name

                 
groups

                 
dimensions

@@ -4069,13 +4183,13 @@ 
Inherited Members

         MFTime(netCDF4._netCDF4._Variable):
     

 
-        
+    
             
Class providing an interface to a MFDataset time Variable by imposing a unique common
 time unit and/or calendar to all files.

 
 
Example usage (See MFTime.__init__ for more details):

 
-
>>> import numpy as np
+
>>> import numpy as np
 >>> f1 = Dataset("mftest_1.nc","w", format="NETCDF4_CLASSIC")
 >>> f2 = Dataset("mftest_2.nc","w", format="NETCDF4_CLASSIC")
 >>> f1.createDimension("time",None)
@@ -4111,7 +4225,7 @@ 
Inherited Members

             MFTime(time, units=None, calendar=None)
     

 
-        
+    
             
__init__(self, time, units=None, calendar=None)

 
 
Create a time Variable with units consistent across a multifile
@@ -4155,7 +4269,7 @@ 
Inherited Members

         CompoundType:
     

 
-        
+    
             
A CompoundType instance is used to describe a compound data
 type, and can be passed to the the Dataset.createVariable method of
 a Dataset or Group instance.
@@ -4174,7 +4288,7 @@ 
Inherited Members

             CompoundType()
     

 
-        
+    
             
__init__(group, datatype, datatype_name)

 
 
CompoundType constructor.

@@ -4203,28 +4317,31 @@ 
Inherited Members

                             

                                             
#  
 
-        dtype = <attribute 'dtype' of 'netCDF4._netCDF4.CompoundType' objects>
+        dtype
     

 
     
+    
 
                             

                             

                                             
#  
 
-        dtype_view = <attribute 'dtype_view' of 'netCDF4._netCDF4.CompoundType' objects>
+        dtype_view
     

 
     
+    
 
                             

                             

                                             
#  
 
-        name = <attribute 'name' of 'netCDF4._netCDF4.CompoundType' objects>
+        name
     

 
     
+    
 
                             

                 
@@ -4237,7 +4354,7 @@ 
Inherited Members

         VLType:
     

 
-        
+    
             
A VLType instance is used to describe a variable length (VLEN) data
 type, and can be passed to the the Dataset.createVariable method of
 a Dataset or Group instance. See
@@ -4255,7 +4372,7 @@ 
Inherited Members

             VLType()
     

 
-        
+    
             
__init__(group, datatype, datatype_name)

 
 
VLType constructor.

@@ -4278,19 +4395,21 @@ 
Inherited Members

                             

                                             
#  
 
-        dtype = <attribute 'dtype' of 'netCDF4._netCDF4.VLType' objects>
+        dtype
     

 
     
+    
 
                             

                             

                                             
#  
 
-        name = <attribute 'name' of 'netCDF4._netCDF4.VLType' objects>
+        name
     

 
     
+    
 
                             

                 
@@ -4302,8 +4421,8 @@ 
Inherited Members

             date2num(unknown):
     

 
-        
-            
date2num(dates, units, calendar=None, has_year_zero=None)

+    
+            
date2num(dates, units, calendar=None, has_year_zero=None, longdouble=False)

 
 
Return numeric time values given datetime objects. The units
 of the numeric time values are described by the units argument
@@ -4348,6 +4467,12 @@ 
Inherited Members

 This kwarg is not needed to define calendar systems allowed by CF
 (the calendar-specific defaults do this).

 
+
longdouble: If set True, output is in the long double float type
+(numpy.float128) instead of float (numpy.float64), allowing microsecond
+accuracy when converting a time value to a date and back again. Otherwise
+this is only possible if the discretization of the time variable is an
+integer multiple of the units.

+
 
returns a numeric time value, or an array of numeric time values
 with approximately 1 microsecond accuracy.

 

@@ -4362,7 +4487,7 @@ 
Inherited Members

             num2date(unknown):
     

 
-        
+    
             
num2date(times, units, calendar=u'standard', only_use_cftime_datetimes=True, only_use_python_datetimes=False, has_year_zero=None)

 
 
Return datetime objects given numeric time values. The units
@@ -4434,7 +4559,7 @@ 
Inherited Members

             date2index(unknown):
     

 
-        
+    
             
date2index(dates, nctime, calendar=None, select=u'exact', has_year_zero=None)

 
 
Return indices of a netCDF time variable corresponding to the given dates.

@@ -4488,7 +4613,7 @@ 
Inherited Members

             stringtochar(unknown):
     

 
-        
+    
             
stringtochar(a,encoding='utf-8')

 
 
convert a string array to a character array with one extra dimension

@@ -4515,7 +4640,7 @@ 
Inherited Members

             chartostring(unknown):
     

 
-        
+    
             
chartostring(b,encoding='utf-8')

 
 
convert a character array to a string array with one less dimension.

@@ -4542,7 +4667,7 @@ 
Inherited Members

             stringtoarr(unknown):
     

 
-        
+    
             
stringtoarr(a, NUMCHARS,dtype='S')

 
 
convert a string to a character array of length NUMCHARS

@@ -4570,7 +4695,7 @@ 
Inherited Members

             getlibversion(unknown):
     

 
-        
+    
             
getlibversion()

 
 
returns a string describing the version of the netcdf library
@@ -4588,7 +4713,7 @@ 
Inherited Members

         EnumType:
     

 
-        
+    
             
A EnumType instance is used to describe an Enum data
 type, and can be passed to the the Dataset.createVariable method of
 a Dataset or Group instance. See
@@ -4606,7 +4731,7 @@ 
Inherited Members

             EnumType()
     

 
-        
+    
             
__init__(group, datatype, datatype_name, enum_dict)

 
 
EnumType constructor.

@@ -4632,28 +4757,31 @@ 
Inherited Members

                             

                                             
#  
 
-        dtype = <attribute 'dtype' of 'netCDF4._netCDF4.EnumType' objects>
+        dtype
     

 
     
+    
 
                             

                             

                                             
#  
 
-        name = <attribute 'name' of 'netCDF4._netCDF4.EnumType' objects>
+        name
     

 
     
+    
 
                             

                             

                                             
#  
 
-        enum_dict = <attribute 'enum_dict' of 'netCDF4._netCDF4.EnumType' objects>
+        enum_dict
     

 
     
+    
 
                             

                 
@@ -4665,7 +4793,7 @@ 
Inherited Members

             get_chunk_cache(unknown):
     

 
-        
+    
             
get_chunk_cache()

 
 
return current netCDF chunk cache information in a tuple (size,nelems,preemption).
@@ -4683,7 +4811,7 @@ 
Inherited Members

             set_chunk_cache(unknown):
     

 
-        
+    
             
set_chunk_cache(self,size=None,nelems=None,preemption=None)

 
 
change netCDF4 chunk cache settings.
@@ -4692,6 +4820,47 @@ 
Inherited Members

 

 
 
+                
+                

+                            
#  
+
+        
+            def
+            set_alignment(unknown):
+    

+
+    
+            
set_alignment(threshold,alignment)

+
+
Change the HDF5 file alignment.
+See netcdf C library documentation for nc_set_alignment for
+details.

+
+
This function was added in netcdf 4.9.0.

+

+
+
+                

+                

+                            
#  
+
+        
+            def
+            get_alignment(unknown):
+    

+
+    
+            
get_alignment()

+
+
return current netCDF alignment within HDF5 files in a tuple
+(threshold,alignment). See netcdf C library documentation for
+nc_get_alignment for details. Values can be reset with
+set_alignment.

+
+
This function was added in netcdf 4.9.0.

+

+
+
diff --git a/src/netCDF4/__init__.py b/src/netCDF4/__init__.py index e84607bab..9f0b10ec7 100644 --- a/src/netCDF4/__init__.py +++ b/src/netCDF4/__init__.py @@ -12,7 +12,7 @@ __has_bzip2_support__, __has_blosc_support__, __has_szip_support__) import os __all__ =\ -['Dataset','Variable','Dimension','Group','MFDataset','MFTime','CompoundType','VLType','date2num','num2date','date2index','stringtochar','chartostring','stringtoarr','getlibversion','EnumType','get_chunk_cache','set_chunk_cache'] +['Dataset','Variable','Dimension','Group','MFDataset','MFTime','CompoundType','VLType','date2num','num2date','date2index','stringtochar','chartostring','stringtoarr','getlibversion','EnumType','get_chunk_cache','set_chunk_cache','set_alignment','get_alignment'] # if HDF5_PLUGIN_PATH not set, point to package path if plugins live there pluginpath = os.path.join(__path__[0],'plugins') if 'HDF5_PLUGIN_PATH' not in os.environ and\