netCDF4 module

Version 1.2.7

Introduction

netcdf4-python is a Python interface to the netCDF C library.

netCDF version 4 has many features not found in earlier versions of the library and is implemented on top of 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 Scientific.IO.NetCDF, 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 (struct), variable length (vlen) and enumerated (enum) data types are supported, but not the opaque data type. Mixtures of compound, vlen and enum data types (such as compound types containing enums, or vlens containing compound types) are not supported.

Download

Latest bleeding-edge code from the github repository.
Latest releases (source code and windows installers).

Requires

Python 2.7 or later (python 3 works too).
numpy array module, version 1.7.0 or later.
Cython, version 0.19 or later, is optional - if it is installed setup.py will use it to recompile the Cython source code into C, using conditional compilation to enable features in the netCDF API that have been added since version 4.1.1. If Cython is not installed, these features (such as the ability to rename Group objects) will be disabled to preserve backward compatibility with older versions of the netCDF library.
The HDF5 C library version 1.8.4-patch1 or higher (1.8.x recommended) from . netCDF version 4.4.1 or higher is recommended if using HDF5 1.10.x - otherwise resulting files may be unreadable by clients using earlier versions of HDF5. For netCDF < 4.4.1, HDF5 version 1.8.x is recommended. Be sure to build with --enable-hl --enable-shared.
Libcurl, if you want OPeNDAP support.
HDF4, if you want to be able to read HDF4 "Scientific Dataset" (SD) files.
The netCDF-4 C library from the github releases page. Version 4.1.1 or higher is required (4.2 or higher recommended). Be sure to build with --enable-netcdf-4 --enable-shared, and set CPPFLAGS="-I $HDF5_DIR/include" and LDFLAGS="-L $HDF5_DIR/lib", where $HDF5_DIR is the directory where HDF5 was installed. If you want OPeNDAP support, add --enable-dap. If you want HDF4 SD support, add --enable-hdf4 and add the location of the HDF4 headers and library to $CPPFLAGS and $LDFLAGS.

Install

install the requisite python modules and C libraries (see above). It's easiest if all the C libs are built as shared libraries.
By default, the utility nc-config, installed with netcdf 4.1.2 or higher, will be run used to determine where all the dependencies live.
If nc-config is not in your default $PATH, rename the file setup.cfg.template to setup.cfg, then edit in a text editor (follow the instructions in the comments). In addition to specifying the path to nc-config, you can manually set the paths to all the libraries and their include files (in case nc-config does not do the right thing).
run python setup.py build, then python setup.py install (as root if necessary).
pip install can also be used, with library paths set with environment variables. To make this work, the USE_SETUPCFG environment variable must be used to tell setup.py not to use setup.cfg. For example, USE_SETUPCFG=0 HDF5_INCDIR=/usr/include/hdf5/serial HDF5_LIBDIR=/usr/lib/x86_64-linux-gnu/hdf5/serial pip install has been shown to work on an Ubuntu/Debian linux system. Similarly, environment variables (all capitalized) can be used to set the include and library paths for hdf5, netCDF4, hdf4, szip, jpeg, curl and zlib. If the libraries are installed in standard places (e.g. /usr or /usr/local), the environment variables do not need to be set.
run the tests in the 'test' directory by running python run_all.py.

Tutorial

Creating/Opening/Closing a netCDF file.
Groups in a netCDF file.
Dimensions in a netCDF file.
Variables in a netCDF file.
Attributes in a netCDF file.
Writing data to and retrieving data from a netCDF variable.
Dealing with time coordinates.
Reading data from a multi-file netCDF dataset.
Efficient compression of netCDF variables.
Beyond homogeneous arrays of a fixed type - compound data types.
Variable-length (vlen) data types.
Enum data type.

1) Creating/Opening/Closing a netCDF file.

To create a netCDF file from python, you simply call the Dataset constructor. This is also the method used to open an existing netCDF file. If the file is open for write access (mode='w', 'r+' or 'a'), you may write any type of data including new dimensions, groups, variables and attributes. netCDF files come in five flavors (NETCDF3_CLASSIC, NETCDF3_64BIT_OFFSET, NETCDF3_64BIT_DATA, NETCDF4_CLASSIC, and NETCDF4). NETCDF3_CLASSIC was the original netcdf binary format, and was limited to file sizes less than 2 Gb. NETCDF3_64BIT_OFFSET was introduced in version 3.6.0 of the library, and extended the original binary format to allow for file sizes greater than 2 Gb. NETCDF3_64BIT_DATA is a new format that requires version 4.4.0 of the C library - it extends the NETCDF3_64BIT_OFFSET binary format to allow for unsigned/64 bit integer data types and 64-bit dimension sizes. NETCDF3_64BIT is an alias for NETCDF3_64BIT_OFFSET. NETCDF4_CLASSIC files use the version 4 disk format (HDF5), but omits features not found in the version 3 API. They can be read by netCDF 3 clients only if they have been relinked against the netCDF 4 library. They can also be read by HDF5 clients. NETCDF4 files use the version 4 disk format (HDF5) and use the new features of the version 4 API. The netCDF4 module can read and write files in any of these formats. When creating a new file, the format may be specified using the format keyword in the Dataset constructor. The default format is NETCDF4. To see how a given file is formatted, you can examine the data_model attribute. Closing the netCDF file is accomplished via the close method of the Dataset instance.

Here's an example:

>>> from netCDF4 import Dataset
>>> rootgrp = Dataset("test.nc", "w", format="NETCDF4")
>>> print rootgrp.data_model
NETCDF4
>>> rootgrp.close()

Remote OPeNDAP-hosted datasets can be accessed for reading over http if a URL is provided to the Dataset constructor instead of a filename. However, this requires that the netCDF library be built with OPenDAP support, via the --enable-dap configure option (added in version 4.0.1).

2) Groups in a netCDF file.

netCDF version 4 added support for organizing data in hierarchical groups, which are analogous to directories in a filesystem. Groups serve as containers for variables, dimensions and attributes, as well as other groups. A Dataset creates a special group, called the 'root group', which is similar to the root directory in a unix filesystem. To create Group instances, use the createGroup method of a Dataset or Group instance. createGroup takes a single argument, a python string containing the name of the new group. The new Group instances contained within the root group can be accessed by name using the groups dictionary attribute of the Dataset instance. Only NETCDF4 formatted files support Groups, if you try to create a Group in a netCDF 3 file you will get an error message.

>>> rootgrp = Dataset("test.nc", "a")
>>> fcstgrp = rootgrp.createGroup("forecasts")
>>> analgrp = rootgrp.createGroup("analyses")
>>> print rootgrp.groups
OrderedDict([("forecasts", 
              <netCDF4._netCDF4.Group object at 0x1b4b7b0>),
             ("analyses", 
              <netCDF4._netCDF4.Group object at 0x1b4b970>)])

Groups can exist within groups in a Dataset, just as directories exist within directories in a unix filesystem. Each Group instance has a groups attribute dictionary containing all of the group instances contained within that group. Each Group instance also has a path attribute that contains a simulated unix directory path to that group. To simplify the creation of nested groups, you can use a unix-like path as an argument to 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 '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 Dataset. The function walktree is a Python generator that is used to walk the directory tree. Note that printing the Dataset or Group object yields summary information about it's contents.

>>> def walktree(top):
>>>     values = top.groups.values()
>>>     yield values
>>>     for value in top.groups.values():
>>>         for children in walktree(value):
>>>             yield children
>>> print rootgrp
>>> for children in walktree(rootgrp):
>>>      for child in children:
>>>          print child
<type "netCDF4._netCDF4.Dataset">
root group (NETCDF4 file format):
    dimensions:
    variables:
    groups: forecasts, analyses
<type "netCDF4._netCDF4.Group">
group /forecasts:
    dimensions:
    variables:
    groups: model1, model2
<type "netCDF4._netCDF4.Group">
group /analyses:
    dimensions:
    variables:
    groups:
<type "netCDF4._netCDF4.Group">
group /forecasts/model1:
    dimensions:
    variables:
    groups:
<type "netCDF4._netCDF4.Group">
group /forecasts/model2:
    dimensions:
    variables:
    groups:

3) Dimensions in a netCDF file.

netCDF defines the sizes of all variables in terms of dimensions, so before any variables can be created the dimensions they use must be created first. A special case, not often used in practice, is that of a scalar variable, which has no dimensions. A dimension is created using the createDimension method of a Dataset or Group instance. A Python string is used to set the name of the dimension, and an integer value is used to set the size. To create an unlimited dimension (a dimension that can be appended to), the size value is set to None or 0. In this example, there both the time and level dimensions are unlimited. Having more than one unlimited dimension is a new netCDF 4 feature, in netCDF 3 files there may be only one, and it must be the first (leftmost) dimension of the variable.

>>> level = rootgrp.createDimension("level", None)
>>> time = rootgrp.createDimension("time", None)
>>> lat = rootgrp.createDimension("lat", 73)
>>> lon = rootgrp.createDimension("lon", 144)

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

>>> print rootgrp.dimensions
OrderedDict([("level", <netCDF4._netCDF4.Dimension object at 0x1b48030>),
             ("time", <netCDF4._netCDF4.Dimension object at 0x1b481c0>),
             ("lat", <netCDF4._netCDF4.Dimension object at 0x1b480f8>),
             ("lon", <netCDF4._netCDF4.Dimension object at 0x1b48a08>)])

Calling the python len function with a Dimension instance returns the current size of that dimension. The isunlimited method of a Dimension instance can be used to determine if the dimensions is unlimited, or appendable.

>>> print len(lon)
144
>>> print lon.isunlimited()
False
>>> print time.isunlimited()
True

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

>>> for dimobj in rootgrp.dimensions.values():
>>>    print dimobj
<type "netCDF4._netCDF4.Dimension"> (unlimited): name = "level", size = 0
<type "netCDF4._netCDF4.Dimension"> (unlimited): name = "time", size = 0
<type "netCDF4._netCDF4.Dimension">: name = "lat", size = 73
<type "netCDF4._netCDF4.Dimension">: name = "lon", size = 144
<type "netCDF4._netCDF4.Dimension"> (unlimited): name = "time", size = 0

Dimension names can be changed using the netCDF4.Datatset.renameDimension method of a Dataset or Group instance.

4) Variables in a netCDF file.

netCDF variables behave much like python multidimensional array objects supplied by the numpy module. However, unlike numpy arrays, netCDF4 variables can be appended to along one or more 'unlimited' dimensions. To create a netCDF variable, use the createVariable method of a Dataset or Group instance. The createVariable method has two mandatory arguments, the variable name (a Python string), and the variable datatype. The variable's dimensions are given by a tuple containing the dimension names (defined previously with createDimension). To create a scalar variable, simply leave out the dimensions keyword. The variable primitive datatypes correspond to the dtype attribute of a numpy array. You can specify the datatype as a numpy dtype object, or anything that can be converted to a numpy dtype object. Valid datatype specifiers include: 'f4' (32-bit floating point), 'f8' (64-bit floating point), 'i4' (32-bit signed integer), 'i2' (16-bit signed integer), 'i8' (64-bit signed integer), 'i1' (8-bit signed integer), 'u1' (8-bit unsigned integer), 'u2' (16-bit unsigned integer), 'u4' (32-bit unsigned integer), 'u8' (64-bit unsigned integer), or 'S1' (single-character string). The old Numeric single-character typecodes ('f','d','h', 's','b','B','c','i','l'), corresponding to ('f4','f8','i2','i2','i1','i1','S1','i4','i4'), will also work. The unsigned integer types and the 64-bit integer type can only be used if the file format is NETCDF4.

The dimensions themselves are usually also defined as variables, called coordinate variables. The createVariable 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",))
>>> levels = rootgrp.createVariable("level","i4",("level",))
>>> latitudes = rootgrp.createVariable("latitude","f4",("lat",))
>>> longitudes = rootgrp.createVariable("longitude","f4",("lon",))
>>> # two dimensions unlimited
>>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",))

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

>>> print temp
<type "netCDF4._netCDF4.Variable">
float32 temp(time, level, lat, lon)
    least_significant_digit: 3
    units: K
unlimited dimensions: time, level
current shape = (0, 0, 73, 144)

You can use a path to create a Variable inside a hierarchy of groups.

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

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

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
<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 Dataset or 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>)])

Variable names can be changed using the renameVariable method of a Dataset instance.

5) Attributes in a netCDF file.

There are two types of attributes in a netCDF file, global and variable. Global attributes provide information about a group, or the entire dataset, as a whole. Variable attributes provide information about one of the variables in a group. Global attributes are set by assigning values to Dataset or Group instance variables. Variable attributes are set by assigning values to Variable instances variables. Attributes can be strings, numbers or sequences. Returning to our example,

>>> import time
>>> rootgrp.description = "bogus example script"
>>> rootgrp.history = "Created " + time.ctime(time.time())
>>> rootgrp.source = "netCDF4 python module tutorial"
>>> latitudes.units = "degrees north"
>>> longitudes.units = "degrees east"
>>> levels.units = "hPa"
>>> temp.units = "K"
>>> times.units = "hours since 0001-01-01 00:00:00.0"
>>> times.calendar = "gregorian"

The ncattrs method of a Dataset, Group or Variable instance can be used to retrieve the names of all the netCDF attributes. This method is provided as a convenience, since using the built-in dir Python function will return a bunch of private methods and attributes that cannot (or should not) be modified by the user.

>>> for name in rootgrp.ncattrs():
>>>     print "Global attr", name, "=", getattr(rootgrp,name)
Global attr description = bogus example script
Global attr history = Created Mon Nov  7 10.30:56 2005
Global attr source = netCDF4 python module tutorial

The __dict__ attribute of a Dataset, Group or Variable instance provides all the netCDF attribute name/value pairs in a python dictionary:

>>> print rootgrp.__dict__
OrderedDict([(u"description", u"bogus example script"),
             (u"history", u"Created Thu Mar  3 19:30:33 2011"),
             (u"source", u"netCDF4 python module tutorial")])

Attributes can be deleted from a netCDF Dataset, Group or Variable using the python del statement (i.e. del grp.foo removes the attribute foo the the group grp).

6) Writing data to and retrieving data from a netCDF variable.

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
>>> lats =  numpy.arange(-90,91,2.5)
>>> lons =  numpy.arange(-180,180,2.5)
>>> latitudes[:] = lats
>>> longitudes[:] = lons
>>> print "latitudes =\n",latitudes[:]
latitudes =
[-90.  -87.5 -85.  -82.5 -80.  -77.5 -75.  -72.5 -70.  -67.5 -65.  -62.5
 -60.  -57.5 -55.  -52.5 -50.  -47.5 -45.  -42.5 -40.  -37.5 -35.  -32.5
 -30.  -27.5 -25.  -22.5 -20.  -17.5 -15.  -12.5 -10.   -7.5  -5.   -2.5
   0.    2.5   5.    7.5  10.   12.5  15.   17.5  20.   22.5  25.   27.5
  30.   32.5  35.   37.5  40.   42.5  45.   47.5  50.   52.5  55.   57.5
  60.   62.5  65.   67.5  70.   72.5  75.   77.5  80.   82.5  85.   87.5
  90. ]

Unlike NumPy's array objects, netCDF Variable objects with unlimited dimensions will grow along those dimensions if you assign data outside the currently defined range of indices.

>>> # append along two unlimited dimensions by assigning to slice.
>>> nlats = len(rootgrp.dimensions["lat"])
>>> nlons = len(rootgrp.dimensions["lon"])
>>> print "temp shape before adding data = ",temp.shape
temp shape before adding data =  (0, 0, 73, 144)
>>>
>>> from numpy.random import uniform
>>> temp[0:5,0:10,:,:] = uniform(size=(5,10,nlats,nlons))
>>> print "temp shape after adding data = ",temp.shape
temp shape after adding data =  (6, 10, 73, 144)
>>>
>>> # levels have grown, but no values yet assigned.
>>> print "levels shape after adding pressure data = ",levels.shape
levels shape after adding pressure data =  (10,)

Note that the size of the levels variable grows when data is appended along the level dimension of the variable temp, even though no data has yet been assigned to levels.

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

However, that there are some differences between NumPy and netCDF variable slicing rules. Slices behave as usual, being specified as a start:stop:step triplet. Using a scalar integer index i takes the ith element and reduces the rank of the output array by one. Boolean array and integer sequence indexing behaves differently for netCDF variables than for numpy arrays. Only 1-d boolean arrays and integer sequences are allowed, and these indices work independently along each dimension (similar to the way vector subscripts work in fortran). This means that

>>> temp[0, 0, [0,1,2,3], [0,1,2,3]]

returns an array of shape (4,4) when slicing a netCDF variable, but for a numpy array it returns an array of shape (4,). Similarly, a netCDF variable of shape (2,3,4,5) indexed with [0, array([True, False, True]), array([False, True, True, True]), :] would return a (2, 3, 5) array. In NumPy, this would raise an error since it would be equivalent to [0, [0,1], [1,2,3], :]. When slicing with integer sequences, the indices need not be sorted and may contain duplicates (both of these are new features in version 1.2.1). While this behaviour may cause some confusion for those used to NumPy's 'fancy indexing' rules, it provides a very powerful way to extract data from multidimensional netCDF variables by using logical operations on the dimension arrays to create slices.

For example,

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

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

>>> print "shape of fancy temp slice = ",tempdat.shape
shape of fancy temp slice =  (3, 3, 36, 71)

Special note for scalar variables: To extract data from a scalar variable v with no associated dimensions, use np.asarray(v) or v[...]. The result will be a numpy scalar array.

7) Dealing with time coordinates.

Time coordinate values pose a special challenge to netCDF users. Most metadata standards (such as CF) specify that time should be measure relative to a fixed date using a certain calendar, with units specified like hours since YY:MM:DD hh-mm-ss. These units can be awkward to deal with, without a utility to convert the values to and from calendar dates. The function called num2date and date2num are provided with this package to do just that. Here's an example of how they can be used:

>>> # fill in times.
>>> from datetime import datetime, timedelta
>>> from netCDF4 import num2date, date2num
>>> dates = [datetime(2001,3,1)+n*timedelta(hours=12) for n in range(temp.shape[0])]
>>> times[:] = date2num(dates,units=times.units,calendar=times.calendar)
>>> print "time values (in units %s): " % times.units+"\n",times[:]
time values (in units hours since January 1, 0001):
[ 17533056.  17533068.  17533080.  17533092.  17533104.]
>>> dates = num2date(times[:],units=times.units,calendar=times.calendar)
>>> print "dates corresponding to time values:\n",dates
dates corresponding to time values:
[2001-03-01 00:00:00 2001-03-01 12:00:00 2001-03-02 00:00:00
 2001-03-02 12:00:00 2001-03-03 00:00:00]

num2date converts numeric values of time in the specified units and calendar to datetime objects, and date2num does the reverse. All the calendars currently defined in the CF metadata convention are supported. A function called date2index is also provided which returns the indices of a netCDF time variable corresponding to a sequence of datetime instances.

8) Reading data from a multi-file netCDF dataset.

If you want to read data from a variable that spans multiple netCDF files, you can use the MFDataset class to read the data as if it were contained in a single file. Instead of using a single filename to create a Dataset instance, create a MFDataset instance with either a list of filenames, or a string with a wildcard (which is then converted to a sorted list of files using the python glob module). Variables in the list of files that share the same unlimited dimension are aggregated together, and can be sliced across multiple files. To illustrate this, let's first create a bunch of netCDF files with the same variable (with the same unlimited dimension). The files must in be in NETCDF3_64BIT_OFFSET, NETCDF3_64BIT_DATA, NETCDF3_CLASSIC or NETCDF4_CLASSIC format (NETCDF4 formatted multi-file datasets are not supported).

>>> for nf in range(10):
>>>     f = Dataset("mftest%s.nc" % nf,"w")
>>>     f.createDimension("x",None)
>>>     x = f.createVariable("x","i",("x",))
>>>     x[0:10] = numpy.arange(nf*10,10*(nf+1))
>>>     f.close()

Now read all the files back in at once with MFDataset

>>> from netCDF4 import MFDataset
>>> f = MFDataset("mftest*nc")
>>> print f.variables["x"][:]
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99]

Note that MFDataset can only be used to read, not write, multi-file datasets.

9) Efficient compression of netCDF variables.

Data stored in netCDF 4 Variable objects can be compressed and decompressed on the fly. The parameters for the compression are determined by the zlib, complevel and shuffle keyword arguments to the createVariable method. To turn on compression, set zlib=True. The complevel keyword regulates the speed and efficiency of the compression (1 being fastest, but lowest compression ratio, 9 being slowest but best compression ratio). The default value of complevel is 4. Setting shuffle=False will turn off the HDF5 shuffle filter, which de-interlaces a block of data before compression by reordering the bytes. The shuffle filter can significantly improve compression ratios, and is on by default. Setting fletcher32 keyword argument to createVariable to True (it's False by default) enables the Fletcher32 checksum algorithm for error detection. It's also possible to set the HDF5 chunking parameters and endian-ness of the binary data stored in the HDF5 file with the chunksizes and endian keyword arguments to createVariable. These keyword arguments only are relevant for NETCDF4 and NETCDF4_CLASSIC files (where the underlying file format is HDF5) and are silently ignored if the file format is NETCDF3_CLASSIC, NETCDF3_64BIT_OFFSET or NETCDF3_64BIT_DATA.

If your data only has a certain number of digits of precision (say for example, it is temperature data that was measured with a precision of 0.1 degrees), you can dramatically improve zlib compression by quantizing (or truncating) the data using the least_significant_digit keyword argument to createVariable. The least significant digit is the power of ten of the smallest decimal place in the data that is a reliable value. For example if the data has a precision of 0.1, then setting least_significant_digit=1 will cause data the data to be quantized using numpy.around(scale*data)/scale, where scale = 2**bits, and bits is determined so that a precision of 0.1 is retained (in this case bits=4). Effectively, this makes the compression 'lossy' instead of 'lossless', that is some precision in the data is sacrificed for the sake of disk space.

In our example, try replacing the line

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

with

>>> temp = dataset.createVariable("temp","f4",("time","level","lat","lon",),zlib=True)

and then

>>> temp = dataset.createVariable("temp","f4",("time","level","lat","lon",),zlib=True,least_significant_digit=3)

and see how much smaller the resulting files are.

10) Beyond homogeneous 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 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 createCompoundType method of a Dataset or Group instance. Since there is no native complex data type in netcdf, compound types are handy for storing numpy complex arrays. Here's an example:

>>> f = Dataset("complex.nc","w")
>>> size = 3 # length of 1-d complex array
>>> # create sample complex data.
>>> datac = numpy.exp(1j*(1.+numpy.linspace(0, numpy.pi, size)))
>>> # create complex128 compound data type.
>>> complex128 = numpy.dtype([("real",numpy.float64),("imag",numpy.float64)])
>>> complex128_t = f.createCompoundType(complex128,"complex128")
>>> # create a variable with this data type, write some data to it.
>>> f.createDimension("x_dim",None)
>>> v = f.createVariable("cmplx_var",complex128_t,"x_dim")
>>> data = numpy.empty(size,complex128) # numpy structured array
>>> data["real"] = datac.real; data["imag"] = datac.imag
>>> v[:] = data # write numpy structured array to netcdf compound var
>>> # close and reopen the file, check the contents.
>>> f.close(); f = Dataset("complex.nc")
>>> v = f.variables["cmplx_var"]
>>> datain = v[:] # read in all the data into a numpy structured array
>>> # create an empty numpy complex array
>>> datac2 = numpy.empty(datain.shape,numpy.complex128)
>>> # .. fill it with contents of structured array.
>>> datac2.real = datain["real"]; datac2.imag = datain["imag"]
>>> print datac.dtype,datac # original data
complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j  -0.54030231-0.84147098j]
>>>
>>> print datac2.dtype,datac2 # data from file
complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j  -0.54030231-0.84147098j]

Compound types can be nested, but you must create the 'inner' ones first. All of the compound types defined for a Dataset or Group are stored in a Python dictionary, just like variables and dimensions. As always, printing objects gives useful summary information in an interactive session:

>>> print f
<type "netCDF4._netCDF4.Dataset">
root group (NETCDF4 file format):
    dimensions: x_dim
    variables: cmplx_var
    groups:
<type "netCDF4._netCDF4.Variable">
>>> print f.variables["cmplx_var"]
compound cmplx_var(x_dim)
compound data type: [("real", "<f8"), ("imag", "<f8")]
unlimited dimensions: x_dim
current shape = (3,)
>>> print f.cmptypes
OrderedDict([("complex128", <netCDF4.CompoundType object at 0x1029eb7e8>)])
>>> print f.cmptypes["complex128"]
<type "netCDF4._netCDF4.CompoundType">: name = "complex128", numpy dtype = [(u"real","<f8"), (u"imag", "<f8")]

11) Variable-length (vlen) data types.

NetCDF 4 has support for variable-length or "ragged" arrays. These are arrays of variable length sequences having the same type. To create a variable-length data type, use the createVLType method method of a Dataset or 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 used (signed and unsigned integers, 32 and 64 bit floats, and characters), but compound data types cannot. A new variable can then be created using this datatype.

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

Since there is no native vlen datatype in numpy, vlen arrays are represented in python as object arrays (arrays of dtype object). These are arrays whose elements are Python object pointers, and can contain any type of python object. For this application, they must contain 1-D numpy arrays all of the same type but of varying length. In this case, they contain 1-D numpy int32 arrays of random length between 1 and 10.

>>> import random
>>> data = numpy.empty(len(y)*len(x),object)
>>> for n in range(len(y)*len(x)):
>>>    data[n] = numpy.arange(random.randint(1,10),dtype="int32")+1
>>> data = numpy.reshape(data,(len(y),len(x)))
>>> vlvar[:] = data
>>> print "vlen variable =\n",vlvar[:]
vlen variable =
[[[ 1  2  3  4  5  6  7  8  9 10] [1 2 3 4 5] [1 2 3 4 5 6 7 8]]
 [[1 2 3 4 5 6 7] [1 2 3 4 5 6] [1 2 3 4 5]]
 [[1 2 3 4 5] [1 2 3 4] [1]]
 [[ 1  2  3  4  5  6  7  8  9 10] [ 1  2  3  4  5  6  7  8  9 10]
  [1 2 3 4 5 6 7 8]]]
>>> print f
<type "netCDF4._netCDF4.Dataset">
root group (NETCDF4 file format):
    dimensions: x, y
    variables: phony_vlen_var
    groups:
>>> print f.variables["phony_vlen_var"]
<type "netCDF4._netCDF4.Variable">
vlen phony_vlen_var(y, x)
vlen data type: int32
unlimited dimensions:
current shape = (4, 3)
>>> print f.VLtypes["phony_vlen"]
<type "netCDF4._netCDF4.VLType">: name = "phony_vlen", numpy dtype = int32

Numpy object arrays containing python strings can also be written as vlen variables, For vlen strings, you don't need to create a vlen data type. Instead, simply use the python str builtin (or a numpy string datatype with fixed length greater than 1) when calling the 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 array is assigned to the vlen string variable.

>>> chars = "1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
>>> data = numpy.empty(10,"O")
>>> for n in range(10):
>>>     stringlen = random.randint(2,12)
>>>     data[n] = "".join([random.choice(chars) for i in range(stringlen)])
>>> strvar[:] = data
>>> print "variable-length string variable:\n",strvar[:]
variable-length string variable:
[aDy29jPt 5DS9X8 jd7aplD b8t4RM jHh8hq KtaPWF9cQj Q1hHN5WoXSiT MMxsVeq tdLUzvVTzj]
>>> print f
<type "netCDF4._netCDF4.Dataset">
root group (NETCDF4 file format):
    dimensions: x, y, z
    variables: phony_vlen_var, strvar
    groups:
>>> print f.variables["strvar"]
<type "netCDF4._netCDF4.Variable">
vlen strvar(z)
vlen data type: <type "str">
unlimited dimensions:
current size = (10,)

It is also possible to set contents of vlen string variables with numpy arrays of any string or unicode data type. Note, however, that accessing the contents of such variables will always return numpy arrays with dtype object.

12) Enum data type.

netCDF4 has an enumerated data type, which is an integer datatype that is restricted to certain named values. Since Enums don't map directly to a numpy data type, they are read and written as integer arrays.

Here's an example of using an Enum type to hold cloud type data. The base integer data type and a python dictionary describing the allowed values and their names are used to define an Enum data type using createEnumType.

>>> nc = Dataset('clouds.nc','w')
>>> # python dict with allowed values and their names.
>>> enum_dict = {u'Altocumulus': 7, u'Missing': 255, 
>>> u'Stratus': 2, u'Clear': 0,
>>> u'Nimbostratus': 6, u'Cumulus': 4, u'Altostratus': 5,
>>> u'Cumulonimbus': 1, u'Stratocumulus': 3}
>>> # create the Enum type called 'cloud_t'.
>>> cloud_type = nc.createEnumType(numpy.uint8,'cloud_t',enum_dict)
>>> print cloud_type
<type 'netCDF4._netCDF4.EnumType'>: name = 'cloud_t',
numpy dtype = uint8, fields/values ={u'Cumulus': 4,
u'Altocumulus': 7, u'Missing': 255,
u'Stratus': 2, u'Clear': 0,
u'Cumulonimbus': 1, u'Stratocumulus': 3,
u'Nimbostratus': 6, u'Altostratus': 5}

A new variable can be created in the usual way using this data type. Integer data is written to the variable that represents the named cloud types in enum_dict. A ValueError will be raised if an attempt is made to write an integer value not associated with one of the specified names.

>>> 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',
>>> fill_value=enum_dict['Missing'])
>>> # write some data to the variable.
>>> cloud_var[:] = [enum_dict['Clear'],enum_dict['Stratus'],
>>> enum_dict['Cumulus'],enum_dict['Missing'],
>>> enum_dict['Cumulonimbus']]
>>> nc.close()
>>> # reopen the file, read the data.
>>> nc = Dataset('clouds.nc')
>>> cloud_var = nc.variables['primary_cloud']
>>> print cloud_var
<type 'netCDF4._netCDF4.Variable'>
enum primary_cloud(time)
    _FillValue: 255
enum data type: uint8
unlimited dimensions: time
current shape = (5,)
>>> print cloud_var.datatype.enum_dict
{u'Altocumulus': 7, u'Missing': 255, u'Stratus': 2,
u'Clear': 0, u'Nimbostratus': 6, u'Cumulus': 4,
u'Altostratus': 5, u'Cumulonimbus': 1,
u'Stratocumulus': 3}
>>> print cloud_var[:]
[0 2 4 -- 1]
>>> nc.close()

All of the code in this tutorial is available in examples/tutorial.py, Unit tests are in the test directory.

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

copyright: 2008 by Jeffrey Whitaker.

license: Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both the copyright notice and this permission notice appear in supporting documentation. THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

Functions

def chartostring(

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

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.

returns a numpy string array with datatype 'SN' or 'UN' and shape b.shape[:-1] where where N=b.shape[-1].

def date2index(

dates, nctime, calendar=None, select='exact')

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

dates: A datetime object or a sequence of datetime objects. The datetime objects should not include a time-zone offset.

nctime: A netCDF time variable object. The nctime object must have a units attribute.

calendar: describes the calendar used in the time calculations. All the values currently defined in the CF metadata convention Valid calendars 'standard', 'gregorian', 'proleptic_gregorian' 'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'. Default is 'standard', which is a mixed Julian/Gregorian calendar. If calendar is None, its value is given by nctime.calendar or standard if no such attribute exists.

select: 'exact', 'before', 'after', 'nearest' The index selection method. exact will return the indices perfectly matching the dates given. before and after will return the indices corresponding to the dates just before or just after the given dates if an exact match cannot be found. nearest will return the indices that correspond to the closest dates.

returns an index (indices) of the netCDF time variable corresponding to the given datetime object(s).

def date2num(

dates,units,calendar='standard')

Return numeric time values given datetime objects. The units of the numeric time values are described by the netCDF4.units argument and the netCDF4.calendar keyword. The datetime objects must be in UTC with no time-zone offset. If there is a time-zone offset in units, it will be applied to the returned numeric values.

dates: A datetime object or a sequence of datetime objects. The datetime objects should not include a time-zone offset.

units: a string of the form <time units> since <reference time> describing the time units. <time units> can be days, hours, minutes, seconds, milliseconds or microseconds. <reference time> is the time origin.

returns a numeric time value, or an array of numeric time values with approximately millisecond accuracy.

def getlibversion(

)

returns a string describing the version of the netcdf library used to build the module, and when it was built.

def num2date(

times,units,calendar='standard')

Return datetime objects given numeric time values. The units of the numeric time values are described by the units argument and the calendar keyword. The returned datetime objects represent UTC with no time-zone offset, even if the specified units contain a time-zone offset.

times: numeric time values.

returns a datetime instance, or an array of datetime instances with approximately millisecond accuracy.

Note: The datetime instances returned are 'real' python datetime objects if calendar='proleptic_gregorian', or calendar='standard' or 'gregorian' and the date is after the breakpoint between the Julian and Gregorian calendars (1582-10-15). Otherwise, they are 'phony' datetime objects which support some but not all the methods of 'real' python datetime objects. The datetime instances do not contain a time-zone offset, even if the specified units contains one.

def stringtoarr(

a, NUMCHARS,dtype='S')

convert a string to a character array of length NUMCHARS

a: Input python string.

NUMCHARS: number of characters used to represent string (if len(a) < NUMCHARS, it will be padded on the right with blanks).

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.

returns a rank 1 numpy character array of length NUMCHARS with datatype 'S1' (default) or 'U1' (if dtype='U')

def stringtochar(

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

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,).

returns a numpy character array with datatype 'S1' or 'U1' and shape a.shape + (N,), where N is the length of each string in a.

Classes

class CompoundType

A CompoundType instance is used to describe a compound data type, and can be passed to the the createVariable method of a Dataset or Group instance. Compound data types map to numpy structured arrays. See __init__ for more details.

The instance variables dtype and name should not be modified by the user.

Ancestors (in MRO)

CompoundType
__builtin__.object

Class variables

var dtype

A numpy dtype object describing the compound data type.

var name

String name.

Static methods

def __init__(

group, datatype, datatype_name)

CompoundType constructor.

group: Group instance to associate with the compound datatype.

datatype: A numpy dtype object describing a structured (a.k.a record) array. Can be composed of homogeneous numeric or character data types, or other structured array data types.

datatype_name: a Python string containing a description of the compound data type.

Note 1: When creating nested compound data types, the inner compound data types must already be associated with CompoundType instances (so create CompoundType instances for the innermost structures first).

Note 2: CompoundType instances should be created using the createCompoundType method of a Dataset or Group instance, not using this class directly.

class 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 __init__ for more details.

A list of attribute names corresponding to global netCDF attributes defined for the Dataset can be obtained with the ncattrs method. These attributes can be created by assigning to an attribute of the Dataset instance. A dictionary containing all the netCDF attribute name/value pairs is provided by the __dict__ attribute of a Dataset instance.

The following class variables are read-only and should not be modified by the user.

dimensions: The dimensions dictionary maps the names of dimensions defined for the Group or Dataset to instances of the Dimension class.

variables: The variables dictionary maps the names of variables defined for this Dataset or Group to instances of the Variable class.

groups: The groups dictionary maps the names of groups created for this Dataset or Group to instances of the Group class (the Dataset class is simply a special case of the Group class which describes the root group in the netCDF4 file).

cmptypes: The cmptypes dictionary maps the names of compound types defined for the Group or Dataset to instances of the CompoundType class.

vltypes: The vltypes dictionary maps the names of variable-length types defined for the Group or Dataset to instances of the VLType class.

enumtypes: The enumtypes dictionary maps the names of Enum types defined for the Group or Dataset to instances of the EnumType class.

data_model: data_model describes the netCDF data model version, one of NETCDF3_CLASSIC, NETCDF4, NETCDF4_CLASSIC, NETCDF3_64BIT_OFFSET or NETCDF3_64BIT_DATA.

file_format: same as data_model, retained for backwards compatibility.

disk_format: disk_format describes the underlying file format, one of NETCDF3, HDF5, HDF4, PNETCDF, DAP2, DAP4 or UNDEFINED. Only available if using netcdf C library version >= 4.3.1, otherwise will always return UNDEFINED.

parent: parent is a reference to the parent Group instance. None for the root group or Dataset instance.

path: path shows the location of the Group in the Dataset in a unix directory format (the names of groups in the hierarchy separated by backslashes). A Dataset instance is the root group, so the path is simply '/'.

keepweakref: If True, child Dimension and Variables objects only keep weak references to the parent Dataset or Group.

Ancestors (in MRO)

Dataset
__builtin__.object

Class variables

var cmptypes

The cmptypes dictionary maps the names of compound types defined for the Group or Dataset to instances of the CompoundType class.

var data_model

data_model describes the netCDF data model version, one of NETCDF3_CLASSIC, NETCDF4, NETCDF4_CLASSIC, NETCDF3_64BIT_OFFSET or NETCDF3_64BIT_DATA.

var dimensions

The dimensions dictionary maps the names of dimensions defined for the Group or Dataset to instances of the Dimension class.

var disk_format

disk_format describes the underlying file format, one of NETCDF3, HDF5, HDF4, PNETCDF, DAP2, DAP4 or UNDEFINED. Only available if using netcdf C library version >= 4.3.1, otherwise will always return UNDEFINED.

var enumtypes

The enumtypes dictionary maps the names of Enum types defined for the Group or Dataset to instances of the EnumType class.

var file_format

same as data_model, retained for backwards compatibility.

var groups

The groups dictionary maps the names of groups created for this Dataset or Group to instances of the Group class (the Dataset class is simply a special case of the Group class which describes the root group in the netCDF4 file).

var keepweakref

If True, child Dimension and Variables objects only keep weak references to the parent Dataset or Group.

var parent

parent is a reference to the parent Group instance. None for the root group or Dataset instance

var path

path shows the location of the Group in the Dataset in a unix directory format (the names of groups in the hierarchy separated by backslashes). A Dataset instance is the root group, so the path is simply '/'.

var variables

The variables dictionary maps the names of variables defined for this Dataset or Group to instances of the Variable class.

var vltypes

The vltypes dictionary maps the names of variable-length types defined for the Group or Dataset to instances of the VLType class.

Static methods

def __init__(

self, filename, mode="r", clobber=True, diskless=False, persist=False, keepweakref=False, format='NETCDF4')

Dataset constructor.

filename: Name of netCDF file to hold dataset. Can also be a python 3 pathlib instance or the URL of an OpenDAP dataset.

mode: access mode. r means read-only; no data can be modified. w means write; a new file is created, an existing file with the same name is deleted. a and r+ mean append (in analogy with serial files); an existing file is opened for reading and writing. Appending s to modes w, r+ or a will enable unbuffered shared access to NETCDF3_CLASSIC, NETCDF3_64BIT_OFFSET or NETCDF3_64BIT_DATA formatted files. Unbuffered access may be useful even if you don't need shared access, since it may be faster for programs that don't access data sequentially. This option is ignored for NETCDF4 and NETCDF4_CLASSIC formatted files.

clobber: if True (default), opening a file with mode='w' will clobber an existing file with the same name. if False, an exception will be raised if a file with the same name already exists.

format: underlying file format (one of 'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_CLASSIC', 'NETCDF3_64BIT_OFFSET' or 'NETCDF3_64BIT_DATA'. Only relevant if mode = 'w' (if mode = 'r','a' or 'r+' the file format is automatically detected). Default 'NETCDF4', which means the data is stored in an HDF5 file, using netCDF 4 API features. Setting format='NETCDF4_CLASSIC' will create an HDF5 file, using only netCDF 3 compatible API features. netCDF 3 clients must be recompiled and linked against the netCDF 4 library to read files in NETCDF4_CLASSIC format. 'NETCDF3_CLASSIC' is the classic netCDF 3 file format that does not handle 2+ Gb files. 'NETCDF3_64BIT_OFFSET' 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. 'NETCDF3_64BIT_DATA' is the 64-bit data version of the netCDF 3 file format, which supports 64-bit dimension sizes plus unsigned and 64 bit integer data types, but is only compatible with clients linked against netCDF version 4.4.0 or later.

diskless: If True, create diskless (in memory) file.
This is an experimental feature added to the C library after the netcdf-4.2 release.

persist: if diskless=True, persist file to disk when closed (default False).

keepweakref: if True, child Dimension and Variable instances will keep weak references to the parent Dataset or Group object. Default is False, which means strong references will be kept. Having Dimension and Variable instances keep a strong reference to the parent Dataset instance, which in turn keeps a reference to child Dimension and Variable instances, creates circular references. Circular references complicate garbage collection, which may mean increased memory usage for programs that create may Dataset instances with lots of Variables. It also will result in the Dataset object never being deleted, which means it may keep open files alive as well. Setting keepweakref=True allows Dataset instances to be garbage collected as soon as they go out of scope, potentially reducing memory usage and open file handles. However, in many cases this is not desirable, since the associated Variable instances may still be needed, but are rendered unusable when the parent Dataset instance is garbage collected.

def close(

self)

Close the Dataset.

def createCompoundType(

self, datatype, datatype_name)

Creates a new compound data type named datatype_name from the numpy dtype object datatype.

Note: If the new compound data type contains other compound data types (i.e. it is a 'nested' compound type, where not all of the elements are homogeneous numeric data types), then the 'inner' compound types must be created first.

The return value is the CompoundType class instance describing the new datatype.

def createDimension(

self, dimname, size=None)

Creates a new dimension with the given dimname and size.

size must be a positive integer or None, which stands for "unlimited" (default is None). Specifying a size of 0 also results in an unlimited dimension. The return value is the Dimension class instance describing the new dimension. To determine the current maximum size of the dimension, use the len function on the Dimension instance. To determine if a dimension is 'unlimited', use the isunlimited method of the Dimension instance.

def createEnumType(

self, datatype, datatype_name, enum_dict)

Creates a new Enum data type named datatype_name from a numpy integer dtype object datatype, and a python dictionary defining the enum fields and values.

The return value is the EnumType class instance describing the new datatype.

def createGroup(

self, groupname)

Creates a new Group with the given groupname.

If groupname is specified as a path, using forward slashes as in unix to separate components, then intermediate groups will be created as necessary (analogous to mkdir -p in unix). For example, createGroup('/GroupA/GroupB/GroupC') will create GroupA, GroupA/GroupB, and GroupA/GroupB/GroupC, if they don't already exist. If the specified path describes a group that already exists, no error is raised.

The return value is a Group class instance.

def createVLType(

self, datatype, datatype_name)

Creates a new VLEN data type named datatype_name from a numpy dtype object datatype.

The return value is the VLType class instance describing the new datatype.

def 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 varname, datatype, and dimensions. If dimensions are not given, the variable is assumed to be a scalar.

If varname is specified as a path, using forward slashes as in unix to separate components, then intermediate groups will be created as necessary For example, createVariable('/GroupA/GroupB/VarC', float, ('x','y')) will create groups GroupA and GroupA/GroupB, plus the variable GroupA/GroupB/VarC, if the preceding groups don't already exist.

The datatype can be a numpy datatype object, or a string that describes a numpy dtype object (like the dtype.str attribute of a numpy array). Supported specifiers include: 'S1' or 'c' (NC_CHAR), 'i1' or 'b' or 'B' (NC_BYTE), 'u1' (NC_UBYTE), 'i2' or 'h' or 's' (NC_SHORT), 'u2' (NC_USHORT), 'i4' or 'i' or 'l' (NC_INT), 'u4' (NC_UINT), 'i8' (NC_INT64), 'u8' (NC_UINT64), 'f4' or 'f' (NC_FLOAT), 'f8' or 'd' (NC_DOUBLE). datatype can also be a CompoundType instance (for a structured, or compound array), a VLType instance (for a variable-length array), or the python str builtin (for a variable-length string array). Numpy string and unicode datatypes with length greater than one are aliases for str.

Data from netCDF variables is presented to python as numpy arrays with the corresponding data type.

dimensions must be a tuple containing dimension names (strings) that have been defined previously using createDimension. The default value is an empty tuple, which means the variable is a scalar.

If the optional keyword zlib is True, the data will be compressed in the netCDF file using gzip compression (default False).

The optional keyword complevel is an integer between 1 and 9 describing the level of compression desired (default 4). Ignored if zlib=False.

If the optional keyword shuffle is True, the HDF5 shuffle filter will be applied before compressing the data (default True). This significantly improves compression. Default is True. Ignored if zlib=False.

If the optional keyword fletcher32 is True, the Fletcher32 HDF5 checksum algorithm is activated to detect errors. Default False.

If the optional keyword contiguous is True, the variable data is stored contiguously on disk. Default False. Setting to True for a variable with an unlimited dimension will trigger an error.

The optional keyword 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 here. Basically, you want the chunk size for each dimension to match as closely as possible the size of the data block that users will read from the file. chunksizes cannot be set if contiguous=True.

The optional keyword endian can be used to control whether the data is stored in little or big endian format on disk. Possible values are little, big or native (default). The library will automatically handle endian conversions when the data is read, but if the data is always going to be read on a computer with the opposite format as the one used to create the file, there may be some performance advantage to be gained by setting the endian-ness.

The zlib, complevel, shuffle, fletcher32, contiguous, chunksizes and endian keywords are silently ignored for netCDF 3 files that do not use HDF5.

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

If the optional keyword parameter least_significant_digit is specified, variable data will be truncated (quantized). In conjunction with zlib=True this produces 'lossy', but significantly more efficient compression. For example, if least_significant_digit=1, data will be quantized using numpy.around(scale*data)/scale, where scale = 2**bits, and bits is determined so that a precision of 0.1 is retained (in this case bits=4). From the PSD metadata conventions: "least_significant_digit -- power of ten of the smallest decimal place in unpacked data that is a reliable value." Default is None, or no quantization, or 'lossless' compression.

When creating variables in a NETCDF4 or NETCDF4_CLASSIC formatted file, HDF5 creates something called a 'chunk cache' for each variable. The default size of the chunk cache may be large enough to completely fill available memory when creating thousands of variables. The optional keyword chunk_cache allows you to reduce (or increase) the size of the default chunk cache when creating a variable. The setting only persists as long as the Dataset is open - you can use the set_var_chunk_cache method to change it the next time the Dataset is opened. Warning - messing with this parameter can seriously degrade performance.

The return value is the Variable class instance describing the new variable.

A list of names corresponding to netCDF variable attributes can be obtained with the Variable method ncattrs. A dictionary containing all the netCDF attribute name/value pairs is provided by the __dict__ attribute of a Variable instance.

Variable instances behave much like array objects. Data can be assigned to or retrieved from a variable with indexing and slicing operations on the Variable instance. A Variable instance has six Dataset standard attributes: dimensions, dtype, shape, ndim, name and least_significant_digit. Application programs should never modify these attributes. The dimensions attribute is a tuple containing the names of the dimensions associated with this variable. The dtype attribute is a string describing the variable's data type (i4, f8, S1, etc). The shape attribute is a tuple describing the current sizes of all the variable's dimensions. The name attribute is a string containing the name of the Variable instance. The least_significant_digit attributes describes the power of ten of the smallest decimal place in the data the contains a reliable value. assigned to the Variable instance. If None, the data is not truncated. The ndim attribute is the number of variable dimensions.

def delncattr(

self,name,value)

delete a netCDF dataset or group attribute. Use if you need to delete a netCDF attribute with the same name as one of the reserved python attributes.

def filepath(

self)

Get the file system path (or the opendap URL) which was used to open/create the Dataset. Requires netcdf >= 4.1.2

def get_variables_by_attributes(

...)

Returns a list of variables that match specific conditions.

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

>>> # 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')

Can pass in key=callable parameter and variables are returned if the callable returns True. The callable should accept a single parameter, the attribute value. None is given as the attribute value when the attribute does not exist on the variable. For example,

>>> # 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)
>>> # Get variables that have a "grid_mapping" attribute
>>> vs = nc.get_variables_by_attributes(grid_mapping=lambda v: v is not None)

def getncattr(

self,name)

retrieve a netCDF dataset or group attribute. Use if you need to get a netCDF attribute with the same name as one of the reserved python attributes.

def isopen(

...)

is the Dataset open or closed?

def ncattrs(

self)

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

def renameAttribute(

self, oldname, newname)

rename a Dataset or Group attribute named oldname to newname.

def renameDimension(

self, oldname, newname)

rename a Dimension named oldname to newname.

def renameGroup(

self, oldname, newname)

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

def renameVariable(

self, oldname, newname)

rename a Variable named oldname to newname

def set_auto_mask(

self, True_or_False)

Call set_auto_mask for all variables contained in this Dataset or Group, as well as for all variables in all its subgroups.

True_or_False: Boolean determining if automatic conversion to masked arrays shall be applied for all variables.

Note: Calling this function only affects existing variables. Variables created after calling this function will follow the default behaviour.

def set_auto_maskandscale(

self, True_or_False)

Call set_auto_maskandscale for all variables contained in this Dataset or Group, as well as for all variables in all its subgroups.

True_or_False: Boolean determining if automatic conversion to masked arrays and variable scaling shall be applied for all variables.

Note: Calling this function only affects existing variables. Variables created after calling this function will follow the default behaviour.

def set_auto_scale(

self, True_or_False)

Call set_auto_scale for all variables contained in this Dataset or Group, as well as for all variables in all its subgroups.

True_or_False: Boolean determining if automatic variable scaling shall be applied for all variables.

Note: Calling this function only affects existing variables. Variables created after calling this function will follow the default behaviour.

def set_fill_off(

self)

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

This will prevent the data from being pre-filled with fill values, which may result in some performance improvements. However, you must then make sure the data is actually written before being read.

def set_fill_on(

self)

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

This causes data to be pre-filled with fill values. The fill values can be controlled by the variable's _Fill_Value attribute, but is usually sufficient to the use the netCDF default _Fill_Value (defined separately for each variable type). The default behavior of the netCDF library corresponds to set_fill_on. Data which are equal to the _Fill_Value indicate that the variable was created, but never written to.

def setncattr(

self,name,value)

set a netCDF dataset or group attribute using name,value pair. Use if you need to set a netCDF attribute with the with the same name as one of the reserved python attributes.

def setncattr_string(

self,name,value)

set a netCDF dataset or group string attribute using name,value pair. Use if you need to ensure that a netCDF attribute is created with type NC_STRING if the file format is NETCDF4. Use if you need to set an attribute to an array of variable-length strings.

def 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 formatted file, since nc_redef/nc_enddef is not called in between setting each attribute

def sync(

self)

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

class Dimension

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

The current maximum size of a Dimension instance can be obtained by calling the python len function on the Dimension instance. The isunlimited method of a Dimension instance can be used to determine if the dimension is unlimited.

Read-only class variables:

name: String name, used when creating a Variable with createVariable.

size: Current Dimension size (same as len(d), where d is a Dimension instance).

Ancestors (in MRO)

Dimension
__builtin__.object

Class variables

var name

A string describing the name of the Dimension - used when creating a Variable instance with createVariable.

var size

Static methods

def __init__(

self, group, name, size=None)

Dimension constructor.

group: Group instance to associate with dimension.

name: Name of the dimension.

size: Size of the dimension. None or 0 means unlimited. (Default None).

Note: Dimension instances should be created using the createDimension method of a Group or Dataset instance, not using __init__ directly.

def group(

self)

return the group that this Dimension is a member of.

def isunlimited(

self)

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

class EnumType

A EnumType instance is used to describe an Enum data type, and can be passed to the the createVariable method of a Dataset or Group instance. See __init__ for more details.

The instance variables dtype, name and enum_dict should not be modified by the user.

Ancestors (in MRO)

EnumType
__builtin__.object

Class variables

var dtype

A numpy integer dtype object describing the base type for the Enum.

var enum_dict

A python dictionary describing the enum fields and values.

var name

String name.

Static methods

def __init__(

group, datatype, datatype_name, enum_dict)

EnumType constructor.

group: Group instance to associate with the VLEN datatype.

datatype: An numpy integer dtype object describing the base type for the Enum.

datatype_name: a Python string containing a description of the Enum data type.

enum_dict: a Python dictionary containing the Enum field/value pairs.

Note: EnumType instances should be created using the createEnumType method of a Dataset or Group instance, not using this class directly.

class Group

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, dimensions and attributes (and other Groups). See __init__ for more details.

Group inherits from Dataset, so all the Dataset class methods and variables are available to a Group instance (except the close method).

Additional read-only class variables:

name: String describing the group name.

Ancestors (in MRO)

Group
Dataset
__builtin__.object

Class variables

var cmptypes

Inheritance: Dataset.cmptypes

The cmptypes dictionary maps the names of compound types defined for the Group or Dataset to instances of the CompoundType class.

var data_model

Inheritance: Dataset.data_model

data_model describes the netCDF data model version, one of NETCDF3_CLASSIC, NETCDF4, NETCDF4_CLASSIC, NETCDF3_64BIT_OFFSET or NETCDF3_64BIT_DATA.

var dimensions

Inheritance: Dataset.dimensions

The dimensions dictionary maps the names of dimensions defined for the Group or Dataset to instances of the Dimension class.

var disk_format

Inheritance: Dataset.disk_format

var enumtypes

Inheritance: Dataset.enumtypes

The enumtypes dictionary maps the names of Enum types defined for the Group or Dataset to instances of the EnumType class.

var file_format

Inheritance: Dataset.file_format

same as data_model, retained for backwards compatibility.

var groups

Inheritance: Dataset.groups

var keepweakref

Inheritance: Dataset.keepweakref

If True, child Dimension and Variables objects only keep weak references to the parent Dataset or Group.

var name

A string describing the name of the Group.

var parent

Inheritance: Dataset.parent

parent is a reference to the parent Group instance. None for the root group or Dataset instance

var path

Inheritance: Dataset.path

var variables

Inheritance: Dataset.variables

The variables dictionary maps the names of variables defined for this Dataset or Group to instances of the Variable class.

var vltypes

Inheritance: Dataset.vltypes

The vltypes dictionary maps the names of variable-length types defined for the Group or Dataset to instances of the VLType class.

Static methods

def __init__(

self, parent, name)

Inheritance: Dataset.__init__

Group constructor.

parent: Group instance for the parent group. If being created in the root group, use a Dataset instance.

name: - Name of the group.

Note: Group instances should be created using the createGroup method of a Dataset instance, or another Group instance, not using this class directly.

def close(

self)

Inheritance: Dataset.close

overrides Dataset close method which does not apply to Group instances, raises IOError.

def createCompoundType(

self, datatype, datatype_name)

Inheritance: Dataset.createCompoundType

Creates a new compound data type named datatype_name from the numpy dtype object datatype.

The return value is the CompoundType class instance describing the new datatype.

def createDimension(

self, dimname, size=None)

Inheritance: Dataset.createDimension

Creates a new dimension with the given dimname and size.

def createEnumType(

self, datatype, datatype_name, enum_dict)

Inheritance: Dataset.createEnumType

Creates a new Enum data type named datatype_name from a numpy integer dtype object datatype, and a python dictionary defining the enum fields and values.

The return value is the EnumType class instance describing the new datatype.

def createGroup(

self, groupname)

Inheritance: Dataset.createGroup

Creates a new Group with the given groupname.

The return value is a Group class instance.

def createVLType(

self, datatype, datatype_name)

Inheritance: Dataset.createVLType

Creates a new VLEN data type named datatype_name from a numpy dtype object datatype.

The return value is the VLType class instance describing the new datatype.

def 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)

Inheritance: Dataset.createVariable