Home | Trees | Indices | Help |
---|
|
Introduction ============ netcdf4-python is a Python interface to the netCDF C library. [netCDF version 4](http://www.unidata.ucar.edu/software/netcdf/netcdf-4) has many features not found in earlier versions of the library and is implemented on top of [HDF5](http://www.hdfgroup.org/HDF5). This module can read and write files in both the new netCDF 4 and the old netCDF 3 format, and can create files that are readable by HDF5 clients. The API modelled after [Scientific.IO.NetCDF](http://dirac.cnrs-orleans.fr/plone/software/scientificpython/), and should be familiar to users of that module. Most new features of netCDF 4 are implemented, such as multiple unlimited dimensions, groups and zlib data compression. All the new numeric data types (such as 64 bit and unsigned integer types) are implemented. Compound (struct) and variable length (vlen) data types are supported, but the enum and opaque data types are not. Mixtures of compound and vlen data types (compound types containing vlens, and vlens containing compound types) are not supported. Download ======== - Latest bleeding-edge code from the [github repository](http://github.com/Unidata/netcdf4-python). - Latest [releases](https://pypi.python.org/pypi/netCDF4) (source code and windows installers). Requires ======== - Python 2.5 or later (python 3 works too). - [numpy array module](http://numpy.scipy.org), version 1.7.0 or later. - [Cython](http://cython.org), version 0.19 or later, is optional - if it is installed setup.py will use it to recompile the Cython source code into C, using conditional compilation to enable features in the netCDF API that have been added since version 4.1.1. If Cython is not installed, these features (such as the ability to rename Group objects) will be disabled to preserve backward compatibility with older versions of the netCDF library. - For python < 2.7, the [ordereddict module](http://python.org/pypi/ordereddict). - The HDF5 C library version 1.8.4-patch1 or higher (1.8.8 or higher recommended) from [](ftp://ftp.hdfgroup.org/HDF5/current/src). Be sure to build with `--enable-hl --enable-shared`. - [Libcurl](http://curl.haxx.se/libcurl), if you want [OPeNDAP](http://opendap.org) support. - [HDF4](http://www.hdfgroup.org/products/hdf4), if you want to be able to read HDF4 "Scientific Dataset" (SD) files. - The netCDF-4 C library from [ftp://ftp.unidata.ucar.edu/pub/netcdf](ftp://ftp.unidata.ucar.edu/pub/netcdf). Version 4.1.1 or higher is required (4.2 or higher recommended). Be sure to build with `--enable-netcdf-4 --enable-shared`, and set `CPPFLAGS="-I $HDF5_DIR/include"` and `LDFLAGS="-L $HDF5_DIR/lib"`, where `$HDF5_DIR` is the directory where HDF5 was installed. If you want [OPeNDAP](http://opendap.org) support, add `--enable-dap`. If you want HDF4 SD support, add `--enable-hdf4` and add the location of the HDF4 headers and library to `$CPPFLAGS` and `$LDFLAGS`. Install ======= - 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`](https://pip.pypa.io/en/latest/reference/pip_install.html) 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 ======== 1. [Creating/Opening/Closing a netCDF file.](#section1) 2. [Groups in a netCDF file.](#section2) 3. [Dimensions in a netCDF file.](#section3) 4. [Variables in a netCDF file.](#section4) 5. [Attributes in a netCDF file.](#section5) 6. [Writing data to and retrieving data from a netCDF variable.](#section6) 7. [Dealing with time coordinates.](#section7) 8. [Reading data from a multi-file netCDF dataset.](#section8) 9. [Efficient compression of netCDF variables.](#section9) 10. [Beyond homogenous arrays of a fixed type - compound data types.](#section10) 11. [Variable-length (vlen) data types.](#section11) ## <div id='section1'>1) Creating/Opening/Closing a netCDF file. To create a netCDF file from python, you simply call the `netCDF4.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 several flavors (`NETCDF3_CLASSIC, NETCDF3_64BIT, NETCDF4_CLASSIC`, and `NETCDF4`). The first two flavors are supported by version 3 of the netCDF library. `NETCDF4_CLASSIC` files use the version 4 disk format (HDF5), but do not use any features not found in the version 3 API. They can be read by netCDF 3 clients only if they have been relinked against the netCDF 4 library. They can also be read by HDF5 clients. `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 `netCDF4.Dataset.close` method of the `netCDF4.Dataset` instance. Here's an example: :::python >>> from netCDF4 import Dataset >>> rootgrp = Dataset("test.nc", "w", format="NETCDF4") >>> print rootgrp.data_model NETCDF4 >>> rootgrp.close() Remote [OPeNDAP](http://opendap.org)-hosted datasets can be accessed for reading over http if a URL is provided to the `netCDF4.Dataset` constructor instead of a filename. However, this requires that the netCDF library be built with OPenDAP support, via the `--enable-dap` configure option (added in version 4.0.1). ## <div id='section2'>2) Groups in a netCDF file. netCDF version 4 added support for organizing data in hierarchical groups, which are analagous to directories in a filesystem. Groups serve as containers for variables, dimensions and attributes, as well as other groups. A `netCDF4.Dataset` defines creates a special group, called the 'root group', which is similar to the root directory in a unix filesystem. To create `netCDF4.Group` instances, use the `netCDF4.Dataset.createGroup` method of a `netCDF4.Dataset` or `netCDF4.Group` instance. `netCDF4.Dataset.createGroup` takes a single argument, a python string containing the name of the new group. The new `netCDF4.Group` instances contained within the root group can be accessed by name using the `groups` dictionary attribute of the `netCDF4.Dataset` instance. Only `NETCDF4` formatted files support Groups, if you try to create a Group in a netCDF 3 file you will get an error message. :::python >>> rootgrp = Dataset("test.nc", "a") >>> fcstgrp = rootgrp.createGroup("forecasts") >>> analgrp = rootgrp.createGroup("analyses") >>> print rootgrp.groups OrderedDict([("forecasts", <netCDF4._netCDF4.Group object at 0x1b4b7b0>), ("analyses", <netCDF4._netCDF4.Group object at 0x1b4b970>)]) Groups can exist within groups in a `netCDF4.Dataset`, just as directories exist within directories in a unix filesystem. Each `netCDF4.Group` instance has a `groups` attribute dictionary containing all of the group instances contained within that group. Each `netCDF4.Group` instance also has a `path` attribute that contains a simulated unix directory path to that group. To simplify the creation of nested groups, you can use a unix-like path as an argument to `netCDF4.Dataset.createGroup`. :::python >>> fcstgrp1 = rootgrp.createGroup("/forecasts/model1") >>> fcstgrp2 = rootgrp.createGroup("/forecasts/model2") If any of the intermediate elements of the path do not exist, they are created, just as with the unix command `'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 `netCDF4.Dataset`. The function `walktree` is a Python generator that is used to walk the directory tree. Note that printing the `netCDF4.Dataset` or `netCDF4.Group` object yields summary information about it's contents. :::python >>> def walktree(top): >>> values = top.groups.values() >>> yield values >>> for value in top.groups.values(): >>> for children in walktree(value): >>> yield children >>> print rootgrp >>> for children in walktree(rootgrp): >>> for child in children: >>> print child <type "netCDF4._netCDF4.Dataset"> root group (NETCDF4 file format): dimensions: variables: groups: forecasts, analyses <type "netCDF4._netCDF4.Group"> group /forecasts: dimensions: variables: groups: model1, model2 <type "netCDF4._netCDF4.Group"> group /analyses: dimensions: variables: groups: <type "netCDF4._netCDF4.Group"> group /forecasts/model1: dimensions: variables: groups: <type "netCDF4._netCDF4.Group"> group /forecasts/model2: dimensions: variables: groups: ## <div id='section3'>3) Dimensions in a netCDF file. netCDF defines the sizes of all variables in terms of dimensions, so before any variables can be created the dimensions they use must be created first. A special case, not often used in practice, is that of a scalar variable, which has no dimensions. A dimension is created using the `netCDF4.Dataset.createDimension` method of a `netCDF4.Dataset` or `netCDF4.Group` instance. A Python string is used to set the name of the dimension, and an integer value is used to set the size. To create an unlimited dimension (a dimension that can be appended to), the size value is set to `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. :::python >>> level = rootgrp.createDimension("level", None) >>> time = rootgrp.createDimension("time", None) >>> lat = rootgrp.createDimension("lat", 73) >>> lon = rootgrp.createDimension("lon", 144) All of the `netCDF4.Dimension` instances are stored in a python dictionary. :::python >>> print rootgrp.dimensions OrderedDict([("level", <netCDF4._netCDF4.Dimension object at 0x1b48030>), ("time", <netCDF4._netCDF4.Dimension object at 0x1b481c0>), ("lat", <netCDF4._netCDF4.Dimension object at 0x1b480f8>), ("lon", <netCDF4._netCDF4.Dimension object at 0x1b48a08>)]) Calling the python `len` function with a `netCDF4.Dimension` instance returns the current size of that dimension. The `netCDF4.Dimension.isunlimited` method of a `netCDF4.Dimension` instance can be used to determine if the dimensions is unlimited, or appendable. :::python >>> print len(lon) 144 >>> print len.is_unlimited() False >>> print time.is_unlimited() True Printing the `netCDF4.Dimension` object provides useful summary info, including the name and length of the dimension, and whether it is unlimited. :::python >>> for dimobj in rootgrp.dimensions.values(): >>> print dimobj <type "netCDF4._netCDF4.Dimension"> (unlimited): name = "level", size = 0 <type "netCDF4._netCDF4.Dimension"> (unlimited): name = "time", size = 0 <type "netCDF4._netCDF4.Dimension">: name = "lat", size = 73 <type "netCDF4._netCDF4.Dimension">: name = "lon", size = 144 <type "netCDF4._netCDF4.Dimension"> (unlimited): name = "time", size = 0 `netCDF4.Dimension` names can be changed using the `netCDF4.Datatset.renameDimension` method of a `netCDF4.Dataset` or `netCDF4.Group` instance. ## <div id='section4'>4) Variables in a netCDF file. netCDF variables behave much like python multidimensional array objects supplied by the [numpy module](http://numpy.scipy.org). However, unlike numpy arrays, netCDF4 variables can be appended to along one or more 'unlimited' dimensions. To create a netCDF variable, use the `netCDF4.Dataset.createVariable` method of a `netCDF4.Dataset` or `netCDF4.Group` instance. The `netCDF4.Dataset.createVariable` method has two mandatory arguments, the variable name (a Python string), and the variable datatype. The variable's dimensions are given by a tuple containing the dimension names (defined previously with `netCDF4.Dataset.createDimension`). To create a scalar variable, simply leave out the dimensions keyword. The variable primitive datatypes correspond to the dtype attribute of a numpy array. You can specify the datatype as a numpy dtype object, or anything that can be converted to a numpy dtype object. Valid datatype specifiers include: `'f4'` (32-bit floating point), `'f8'` (64-bit floating point), `'i4'` (32-bit signed integer), `'i2'` (16-bit signed integer), `'i8'` (64-bit singed integer), `'i1'` (8-bit signed integer), `'u1'` (8-bit unsigned integer), `'u2'` (16-bit unsigned integer), `'u4'` (32-bit unsigned integer), `'u8'` (64-bit unsigned integer), or `'S1'` (single-character string). The old Numeric single-character typecodes (`'f'`,`'d'`,`'h'`, `'s'`,`'b'`,`'B'`,`'c'`,`'i'`,`'l'`), corresponding to (`'f4'`,`'f8'`,`'i2'`,`'i2'`,`'i1'`,`'i1'`,`'S1'`,`'i4'`,`'i4'`), will also work. The unsigned integer types and the 64-bit integer type can only be used if the file format is `NETCDF4`. The dimensions themselves are usually also defined as variables, called coordinate variables. The `netCDF4.Dataset.createVariable` method returns an instance of the `netCDF4.Variable` class whose methods can be used later to access and set variable data and attributes. :::python >>> times = rootgrp.createVariable("time","f8",("time",)) >>> levels = rootgrp.createVariable("level","i4",("level",)) >>> latitudes = rootgrp.createVariable("latitude","f4",("lat",)) >>> longitudes = rootgrp.createVariable("longitude","f4",("lon",)) >>> # two dimensions unlimited >>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",)) To get summary info on a `netCDF4.Variable` instance in an interactive session, just print it. :::python >>> print temp <type "netCDF4._netCDF4.Variable"> float32 temp(time, level, lat, lon) least_significant_digit: 3 units: K unlimited dimensions: time, level current shape = (0, 0, 73, 144) You can use a path to create a Variable inside a hierarchy of groups. :::python >>> ftemp = rootgrp.createVariable("/forecasts/model1/temp","f4",("time","level","lat","lon",)) If the intermediate groups do not yet exist, they will be created. You can also query a `netCDF4.Dataset` or `netCDF4.Group` instance directly to obtain `netCDF4.Group` or `netCDF4.Variable` instances using paths. :::python >>> print rootgrp["/forecasts/model1"] # a Group instance <type "netCDF4._netCDF4.Group"> group /forecasts/model1: dimensions(sizes): variables(dimensions): float32 temp(time,level,lat,lon) groups: >>> print rootgrp["/forecasts/model1/temp"] # a Variable instance <type "netCDF4._netCDF4.Variable"> float32 temp(time, level, lat, lon) path = /forecasts/model1 unlimited dimensions: time, level current shape = (0, 0, 73, 144) filling on, default _FillValue of 9.96920996839e+36 used All of the variables in the `netCDF4.Dataset` or `netCDF4.Group` are stored in a Python dictionary, in the same way as the dimensions: :::python >>> print rootgrp.variables OrderedDict([("time", <netCDF4.Variable object at 0x1b4ba70>), ("level", <netCDF4.Variable object at 0x1b4bab0>), ("latitude", <netCDF4.Variable object at 0x1b4baf0>), ("longitude", <netCDF4.Variable object at 0x1b4bb30>), ("temp", <netCDF4.Variable object at 0x1b4bb70>)]) `netCDF4.Variable` names can be changed using the `netCDF4.Dataset.renameVariable` method of a `netCDF4.Dataset` instance. ## <div id='section5'>5) Attributes in a netCDF file. There are two types of attributes in a netCDF file, global and variable. Global attributes provide information about a group, or the entire dataset, as a whole. `netCDF4.Variable` attributes provide information about one of the variables in a group. Global attributes are set by assigning values to `netCDF4.Dataset` or `netCDF4.Group` instance variables. `netCDF4.Variable` attributes are set by assigning values to `netCDF4.Variable` instances variables. Attributes can be strings, numbers or sequences. Returning to our example, :::python >>> import time >>> rootgrp.description = "bogus example script" >>> rootgrp.history = "Created " + time.ctime(time.time()) >>> rootgrp.source = "netCDF4 python module tutorial" >>> latitudes.units = "degrees north" >>> longitudes.units = "degrees east" >>> levels.units = "hPa" >>> temp.units = "K" >>> times.units = "hours since 0001-01-01 00:00:00.0" >>> times.calendar = "gregorian" The `netCDF4.Dataset.ncattrs` method of a `netCDF4.Dataset`, `netCDF4.Group` or `netCDF4.Variable` instance can be used to retrieve the names of all the netCDF attributes. This method is provided as a convenience, since using the built-in `dir` Python function will return a bunch of private methods and attributes that cannot (or should not) be modified by the user. :::python >>> for name in rootgrp.ncattrs(): >>> print "Global attr", name, "=", getattr(rootgrp,name) Global attr description = bogus example script Global attr history = Created Mon Nov 7 10.30:56 2005 Global attr source = netCDF4 python module tutorial The `__dict__` attribute of a `netCDF4.Dataset`, `netCDF4.Group` or `netCDF4.Variable` instance provides all the netCDF attribute name/value pairs in a python dictionary: :::python >>> print rootgrp.__dict__ OrderedDict([(u"description", u"bogus example script"), (u"history", u"Created Thu Mar 3 19:30:33 2011"), (u"source", u"netCDF4 python module tutorial")]) Attributes can be deleted from a netCDF `netCDF4.Dataset`, `netCDF4.Group` or `netCDF4.Variable` using the python `del` statement (i.e. `del grp.foo` removes the attribute `foo` the the group `grp`). ## <div id='section6'>6) Writing data to and retrieving data from a netCDF variable. Now that you have a netCDF `netCDF4.Variable` instance, how do you put data into it? You can just treat it like an array and assign data to a slice. :::python >>> import numpy >>> lats = numpy.arange(-90,91,2.5) >>> lons = numpy.arange(-180,180,2.5) >>> latitudes[:] = lats >>> longitudes[:] = lons >>> print "latitudes =\n",latitudes[:] latitudes = [-90. -87.5 -85. -82.5 -80. -77.5 -75. -72.5 -70. -67.5 -65. -62.5 -60. -57.5 -55. -52.5 -50. -47.5 -45. -42.5 -40. -37.5 -35. -32.5 -30. -27.5 -25. -22.5 -20. -17.5 -15. -12.5 -10. -7.5 -5. -2.5 0. 2.5 5. 7.5 10. 12.5 15. 17.5 20. 22.5 25. 27.5 30. 32.5 35. 37.5 40. 42.5 45. 47.5 50. 52.5 55. 57.5 60. 62.5 65. 67.5 70. 72.5 75. 77.5 80. 82.5 85. 87.5 90. ] Unlike NumPy's array objects, netCDF `netCDF4.Variable` objects with unlimited dimensions will grow along those dimensions if you assign data outside the currently defined range of indices. :::python >>> # append along two unlimited dimensions by assigning to slice. >>> nlats = len(rootgrp.dimensions["lat"]) >>> nlons = len(rootgrp.dimensions["lon"]) >>> print "temp shape before adding data = ",temp.shape temp shape before adding data = (0, 0, 73, 144) >>> >>> from numpy.random import uniform >>> temp[0:5,0:10,:,:] = uniform(size=(5,10,nlats,nlons)) >>> print "temp shape after adding data = ",temp.shape temp shape after adding data = (6, 10, 73, 144) >>> >>> # levels have grown, but no values yet assigned. >>> print "levels shape after adding pressure data = ",levels.shape levels shape after adding pressure data = (10,) Note that the size of the levels variable grows when data is appended along the `level` dimension of the variable `temp`, even though no data has yet been assigned to levels. :::python >>> # now, assign data to levels dimension variable. >>> levels[:] = [1000.,850.,700.,500.,300.,250.,200.,150.,100.,50.] However, that there are some differences between NumPy and netCDF variable slicing rules. Slices behave as usual, being specified as a `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 :::python >>> temp[0, 0, [0,1,2,3], [0,1,2,3]] returns an array of shape (4,4) when slicing a netCDF variable, but for a numpy array it returns an array of shape (4,). Similarly, a netCDF variable of shape `(2,3,4,5)` indexed with `[0, array([True, False, True]), array([False, True, True, True]), :]` would return a `(2, 3, 5)` array. In NumPy, this would raise an error since it would be equivalent to `[0, [0,1], [1,2,3], :]`. When slicing with integer sequences, the indices must be sorted in increasing order and contain no duplicates. While this behaviour may cause some confusion for those used to NumPy's 'fancy indexing' rules, it provides a very powerful way to extract data from multidimensional netCDF variables by using logical operations on the dimension arrays to create slices. For example, :::python >>> tempdat = temp[::2, [1,3,6], lats>0, lons>0] will extract time indices 0,2 and 4, pressure levels 850, 500 and 200 hPa, all Northern Hemisphere latitudes and Eastern Hemisphere longitudes, resulting in a numpy array of shape (3, 3, 36, 71). :::python >>> 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. ## <div id='section7'>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 functione called `netCDF4.num2date` and `netCDF4.date2num` are provided with this package to do just that. Here's an example of how they can be used: :::python >>> # fill in times. >>> from datetime import datetime, timedelta >>> from netCDF4 import num2date, date2num >>> dates = [datetime(2001,3,1)+n*timedelta(hours=12) for n in range(temp.shape[0])] >>> times[:] = date2num(dates,units=times.units,calendar=times.calendar) >>> print "time values (in units %s): " % times.units+"\n",times[:] time values (in units hours since January 1, 0001): [ 17533056. 17533068. 17533080. 17533092. 17533104.] >>> dates = num2date(times[:],units=times.units,calendar=times.calendar) >>> print "dates corresponding to time values:\n",dates dates corresponding to time values: [2001-03-01 00:00:00 2001-03-01 12:00:00 2001-03-02 00:00:00 2001-03-02 12:00:00 2001-03-03 00:00:00] `netCDF4.num2date` converts numeric values of time in the specified `units` and `calendar` to datetime objects, and `netCDF4.date2num` does the reverse. All the calendars currently defined in the [CF metadata convention](http://cf-pcmdi.llnl.gov/documents/cf-conventions/) are supported. A function called `netCDF4.date2index` is also provided which returns the indices of a netCDF time variable corresponding to a sequence of datetime instances. ## <div id='section8'>8) Reading data from a multi-file netCDF dataset. If you want to read data from a variable that spans multiple netCDF files, you can use the `netCDF4.MFDataset` class to read the data as if it were contained in a single file. Instead of using a single filename to create a `netCDF4.Dataset` instance, create a `netCDF4.MFDataset` instance with either a list of filenames, or a string with a wildcard (which is then converted to a sorted list of files using the python glob module). Variables in the list of files that share the same unlimited dimension are aggregated together, and can be sliced across multiple files. To illustrate this, let's first create a bunch of netCDF files with the same variable (with the same unlimited dimension). The files must in be in `NETCDF3_64BIT`, `NETCDF3_CLASSIC` or `NETCDF4_CLASSIC format` (`NETCDF4` formatted multi-file datasets are not supported). :::python >>> for nf in range(10): >>> f = Dataset("mftest%s.nc" % nf,"w") >>> f.createDimension("x",None) >>> x = f.createVariable("x","i",("x",)) >>> x[0:10] = numpy.arange(nf*10,10*(nf+1)) >>> f.close() Now read all the files back in at once with `netCDF4.MFDataset` :::python >>> from netCDF4 import MFDataset >>> f = MFDataset("mftest*nc") >>> print f.variables["x"][:] [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99] Note that `netCDF4.MFDataset` can only be used to read, not write, multi-file datasets. ## <div id='section9'>9) Efficient compression of netCDF variables. Data stored in netCDF 4 `netCDF4.Variable` objects can be compressed and decompressed on the fly. The parameters for the compression are determined by the `zlib`, `complevel` and `shuffle` keyword arguments to the `netCDF4.Dataset.createVariable` method. To turn on compression, set `zlib=True`. The `complevel` keyword regulates the speed and efficiency of the compression (1 being fastest, but lowest compression ratio, 9 being slowest but best compression ratio). The default value of `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 `netCDF4.Dataset.createVariable` to `True` (it's `False` by default) enables the Fletcher32 checksum algorithm for error detection. It's also possible to set the HDF5 chunking parameters and endian-ness of the binary data stored in the HDF5 file with the `chunksizes` and `endian` keyword arguments to `netCDF4.Dataset.createVariable`. These keyword arguments only are relevant for `NETCDF4` and `NETCDF4_CLASSIC` files (where the underlying file format is HDF5) and are silently ignored if the file format is `NETCDF3_CLASSIC` or `NETCDF3_64BIT`, If your data only has a certain number of digits of precision (say for example, it is temperature data that was measured with a precision of 0.1 degrees), you can dramatically improve zlib compression by quantizing (or truncating) the data using the `least_significant_digit` keyword argument to `netCDF4.Dataset.createVariable`. The least significant digit is the power of ten of the smallest decimal place in the data that is a reliable value. For example if the data has a precision of 0.1, then setting `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 :::python >>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",)) with :::python >>> temp = dataset.createVariable("temp","f4",("time","level","lat","lon",),zlib=True) and then :::python >>> temp = dataset.createVariable("temp","f4",("time","level","lat","lon",),zlib=True,least_significant_digit=3) and see how much smaller the resulting files are. ## <div id='section10'>10) Beyond homogenous arrays of a fixed type - compound data types. Compound data types map directly to numpy structured (a.k.a 'record' arrays). Structured arrays are akin to C structs, or derived types 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 `netCDF4.Dataset.createCompoundType` method of a `netCDF4.Dataset` or `netCDF4.Group` instance. Since there is no native complex data type in netcdf, compound types are handy for storing numpy complex arrays. Here's an example: :::python >>> f = Dataset("complex.nc","w") >>> size = 3 # length of 1-d complex array >>> # create sample complex data. >>> datac = numpy.exp(1j*(1.+numpy.linspace(0, numpy.pi, size))) >>> # create complex128 compound data type. >>> complex128 = numpy.dtype([("real",numpy.float64),("imag",numpy.float64)]) >>> complex128_t = f.createCompoundType(complex128,"complex128") >>> # create a variable with this data type, write some data to it. >>> f.createDimension("x_dim",None) >>> v = f.createVariable("cmplx_var",complex128_t,"x_dim") >>> data = numpy.empty(size,complex128) # numpy structured array >>> data["real"] = datac.real; data["imag"] = datac.imag >>> v[:] = data # write numpy structured array to netcdf compound var >>> # close and reopen the file, check the contents. >>> f.close(); f = Dataset("complex.nc") >>> v = f.variables["cmplx_var"] >>> datain = v[:] # read in all the data into a numpy structured array >>> # create an empty numpy complex array >>> datac2 = numpy.empty(datain.shape,numpy.complex128) >>> # .. fill it with contents of structured array. >>> datac2.real = datain["real"]; datac2.imag = datain["imag"] >>> print datac.dtype,datac # original data complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j] >>> >>> print datac2.dtype,datac2 # data from file complex128 [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j] Compound types can be nested, but you must create the 'inner' ones first. All of the compound types defined for a `netCDF4.Dataset` or `netCDF4.Group` are stored in a Python dictionary, just like variables and dimensions. As always, printing objects gives useful summary information in an interactive session: :::python >>> print f <type "netCDF4._netCDF4.Dataset"> root group (NETCDF4 file format): dimensions: x_dim variables: cmplx_var groups: <type "netCDF4._netCDF4.Variable"> >>> print f.variables["cmplx_var"] compound cmplx_var(x_dim) compound data type: [("real", "<f8"), ("imag", "<f8")] unlimited dimensions: x_dim current shape = (3,) >>> print f.cmptypes OrderedDict([("complex128", <netCDF4.CompoundType object at 0x1029eb7e8>)]) >>> print f.cmptypes["complex128"] <type "netCDF4._netCDF4.CompoundType">: name = "complex128", numpy dtype = [(u"real","<f8"), (u"imag", "<f8")] ## <div id='section11'>11) Variable-length (vlen) data types. NetCDF 4 has support for variable-length or "ragged" arrays. These are arrays of variable length sequences having the same type. To create a variable-length data type, use the `netCDF4.Dataset.createVLType` method method of a `netCDF4.Dataset` or `netCDF4.Group` instance. :::python >>> f = Dataset("tst_vlen.nc","w") >>> vlen_t = f.createVLType(numpy.int32, "phony_vlen") The numpy datatype of the variable-length sequences and the name of the new datatype must be specified. Any of the primitive datatypes can be 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. :::python >>> x = f.createDimension("x",3) >>> y = f.createDimension("y",4) >>> vlvar = f.createVariable("phony_vlen_var", vlen_t, ("y","x")) Since there is no native vlen datatype in numpy, vlen arrays are represented in python as object arrays (arrays of dtype `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 betwee 1 and 10. :::python >>> import random >>> data = numpy.empty(len(y)*len(x),object) >>> for n in range(len(y)*len(x)): >>> data[n] = numpy.arange(random.randint(1,10),dtype="int32")+1 >>> data = numpy.reshape(data,(len(y),len(x))) >>> vlvar[:] = data >>> print "vlen variable =\n",vlvar[:] vlen variable = [[[ 1 2 3 4 5 6 7 8 9 10] [1 2 3 4 5] [1 2 3 4 5 6 7 8]] [[1 2 3 4 5 6 7] [1 2 3 4 5 6] [1 2 3 4 5]] [[1 2 3 4 5] [1 2 3 4] [1]] [[ 1 2 3 4 5 6 7 8 9 10] [ 1 2 3 4 5 6 7 8 9 10] [1 2 3 4 5 6 7 8]]] >>> print f <type "netCDF4._netCDF4.Dataset"> root group (NETCDF4 file format): dimensions: x, y variables: phony_vlen_var groups: >>> print f.variables["phony_vlen_var"] <type "netCDF4._netCDF4.Variable"> vlen phony_vlen_var(y, x) vlen data type: int32 unlimited dimensions: current shape = (4, 3) >>> print f.VLtypes["phony_vlen"] <type "netCDF4._netCDF4.VLType">: name = "phony_vlen", numpy dtype = int32 Numpy object arrays containing python strings can also be written as vlen variables, For vlen strings, you don't need to create a vlen data type. Instead, simply use the python `str` builtin (or a numpy string datatype with fixed length greater than 1) when calling the `netCDF4.Dataset.createVariable` method. :::python >>> z = f.createDimension("z",10) >>> strvar = rootgrp.createVariable("strvar", str, "z") In this example, an object array is filled with random python strings with random lengths between 2 and 12 characters, and the data in the object array is assigned to the vlen string variable. :::python >>> chars = "1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" >>> data = numpy.empty(10,"O") >>> for n in range(10): >>> stringlen = random.randint(2,12) >>> data[n] = "".join([random.choice(chars) for i in range(stringlen)]) >>> strvar[:] = data >>> print "variable-length string variable:\n",strvar[:] variable-length string variable: [aDy29jPt 5DS9X8 jd7aplD b8t4RM jHh8hq KtaPWF9cQj Q1hHN5WoXSiT MMxsVeq tdLUzvVTzj] >>> print f <type "netCDF4._netCDF4.Dataset"> root group (NETCDF4 file format): dimensions: x, y, z variables: phony_vlen_var, strvar groups: >>> print f.variables["strvar"] <type "netCDF4._netCDF4.Variable"> vlen strvar(z) vlen data type: <type "str"> unlimited dimensions: current size = (10,) It is also possible to set contents of vlen string variables with numpy arrays of any string or unicode data type. Note, however, that accessing the contents of such variables will always return numpy arrays with dtype `object`. 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.
Version: 1.1.9
|
|||
|
|
|||
CompoundType A `netCDF4.CompoundType` instance is used to describe a compound data type, and can be passed to the the `netCDF4.Dataset.createVariable` method of a `netCDF4.Dataset` or `netCDF4.Group` instance. |
|||
Dataset A netCDF `netCDF4.Dataset` is a collection of dimensions, groups, variables and attributes. |
|||
Dimension A netCDF `netCDF4.Dimension` is used to describe the coordinates of a `netCDF4.Variable`. |
|||
Group Groups define a hierarchical namespace within a netCDF file. |
|||
MFDataset Class for reading multi-file netCDF Datasets, making variables spanning multiple files appear as if they were in one file. |
|||
MFTime Class providing an interface to a MFDataset time Variable by imposing a unique common time unit to all files. |
|||
VLType A `netCDF4.VLType` instance is used to describe a variable length (VLEN) data type, and can be passed to the the `netCDF4.Dataset.createVariable` method of a `netCDF4.Dataset` or `netCDF4.Group` instance. |
|||
Variable A netCDF `netCDF4.Variable` is used to read and write netCDF data. |
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|
|||
MINYEAR = 1
|
|||
NC_DISKLESS = 8
|
|||
__has_nc_inq_format_extended__ = 1
|
|||
__has_nc_inq_path__ = 1
|
|||
__has_rename_grp__ = 1
|
|||
__hdf5libversion__ =
|
|||
__netcdf4libversion__ =
|
|||
__package__ =
|
|||
__pdoc__ =
|
|||
day_units =
|
|||
default_encoding =
|
|||
default_fillvals =
|
|||
gregorian = datetime.datetime(1582, 10, 15, 0, 0)
|
|||
hr_units =
|
|||
is_native_big = False
|
|||
is_native_little = True
|
|||
microsec_units =
|
|||
millisec_units =
|
|||
min_units =
|
|||
python3 = False
|
|||
sec_units =
|
|||
unicode_error =
|
|
**`chartostring(b)`** 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]`. |
**`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](http://cfconventions.org) Valid calendars `'standard', 'gregorian', 'proleptic_gregorian' 'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'`. Default is `'standard'`, which is a mixed Julian/Gregorian calendar. If `calendar` is None, its value is given by `nctime.calendar` or `standard` if no such attribute exists. **`select`**: `'exact', 'before', 'after', 'nearest'` The index selection method. `exact` will return the indices perfectly matching the dates given. `before` and `after` will return the indices corresponding to the dates just before or just after the given dates if an exact match cannot be found. `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). |
**`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. Accuracy is somewhere between a millisecond and a microsecond, depending on the time interval and the calendar used. **`calendar`**: describes the calendar used in the time calculations. All the values currently defined in the [CF metadata convention](http://cfconventions.org) Valid calendars `'standard', 'gregorian', 'proleptic_gregorian' 'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'`. Default is `'standard'`, which is a mixed Julian/Gregorian calendar. returns a numeric time value, or an array of numeric time values. |
**`getlibversion()`** returns a string describing the version of the netcdf library used to build the module, and when it was built. |
**`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. **`units`**: a string of the form `<time units> since <reference time>` describing the time units. `<time units>` can be days, hours, minutes, seconds, milliseconds or microseconds. `<reference time>` is the time origin. Accuracy is somewhere between a millisecond and a microsecond, depending on the time interval and the calendar used. **`calendar`**: describes the calendar used in the time calculations. All the values currently defined in the [CF metadata convention](http://cfconventions.org) Valid calendars `'standard', 'gregorian', 'proleptic_gregorian' 'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'`. Default is `'standard'`, which is a mixed Julian/Gregorian calendar. returns a datetime instance, or an array of datetime instances. ***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. |
**`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'`) |
**`stringtochar(a)`** 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. |
|
__pdoc__
|
default_fillvals
|
microsec_units
|
millisec_units
|
Home | Trees | Indices | Help |
---|
Generated by Epydoc 3.0.1 on Sun Apr 3 06:23:31 2016 | http://epydoc.sourceforge.net |