The NetCDF file format is increasingly more common. NetCDF files are binary files with a lot of flexibility for describing dimensioned variables.
In this class, we will use the netCDF4 library to interface with NetCDF files.
The netCDF4 library does not come standard with Python, so you will have to install it yourself. The standard installation procedure for installing the dependencies from source can be found at:
I found this link better for downloading and installing the Python library itself:
Consider installing Anaconda instead. Anaconda is Python packaged with hundreds of tools and libraries that you will want (This includes NetCDF4 and nearly everything else we will use in this course.)
To read or write NetCDF files, use the Dataset constructor.
>>> from netCDF4 import Dataset
>>> root = Dataset("write_test.nc", "w", format="NETCDF4")
>>> root.close()
The netCDF4 library supports four different NetCDF formats: NETCDF3_CLASSIC, NETCDF3_64BIT, NETCDF4_CLASSIC, and the default is NETCDF4. When reading a file, netCDF4 will attempt to auto-detect the file type.
>>> from netCDF4 import Dataset
>>> data = Dataset("read_test.nc", "r")
>>> data.close()
You can open an existing NetCDF file to write more date to it by using r+.
>>> from netCDF4 import Dataset
>>> data = Dataset("read_test.nc", "r+")
>>> # add more data
>>> data.close()
Groups are a powerful new ability in NetCDF version 4. They are much akin to the folder/directory structure in your computer. Each group can contain everything that a NetCDF file can contain (dimensions, variables, and attributes), but also other groups. Every NetCDF version 4 file starts out with one root group that basically contains the entire file.
Use createGroup to create a new group:
>>> from netCDF4 import Dataset
>>> root = Dataset("heat_map.nc", "w", format="NETCDF4")
>>> test_grp = root.createGroup("testing_data")
>>> print(root.groups)
OrderedDict([('testing_data', <netCDF4.Group object at 0x1588e50>)])
I have not seen a lot of people using groups in the wild, and so I will not focus on them in this lecture. But you should know they exist.
All variables in NetCDF files are defined in terms of a set of dimensions. For instance, meteorological variables might be defined along four dimensions: latitude, longitude, altitude, and time. But surface elevation data might only have two dimensions: latitude and longitude. Obviously, before we create any variables, we need to define the dimensions in the file:
>>> time = root.createDimension("time", None)
>>> layer = root.createDimension("layer", 11)
>>> lat = root.createDimension("lat", 321)
>>> lon = root.createDimension("lon", 291)
Here we pass createDimension two things: the first is a string name for the dimension and the next is the length of the dimension. By putting None for the length of the "time" dimension, we are allowing the time dimension to be unlimited.
To get an ordered dictionary of the dimensions, simply do:
>>> print(root.dimensions)
OrderedDict([('time', <netCDF4.Dimension object at 0x15b5910>),
('lat', <netCDF4.Dimension object at 0x15b5960>),
('lon', <netCDF4.Dimension object at 0x15b59b0>),
('layer', <netCDF4.Dimension object at 0x15b5a00>)])
You can also ask for the length of a dimension, or if the dimension is unlimited:
>>> len(layer)
11
>>> layer.isunlimited()
False
>>> time.isunlimited()
True
Variables are where we hold the meat of the data in our NetCDF files. Variables have names, data types, and exist along the dimensions we described earlier. As the name suggests, adding data along an unlimited dimension will just grow the dimension of the file in that direction.
>>> temp = root.createVariable("temp", "f4", ("time","layer","lat","lon"))
>>> rh = root.createVariable("rh", "f", ("time","layer","lat","lon"), least_significant_digit=3)
Here we see createVariable takes three things: the variable name, its data type, and a tuple describing the dimensions of the variable. There are several data types supported by NetCDF files:
| Data Type | Technical Specifier | Shorthand Specifier |
|---|---|---|
| 32-bit floating point | f4 | f |
| 64-bit floating point | f8 | d |
| 32-bit signed integer | i4 | i |
| 16-bit signed integer | i2 | h |
| 64-bit singed integer | i8 | |
| 8-bit signed integer | i1 | b |
| 8-bit unsigned integer | u1 | |
| 16-bit unsigned integer | u2 | |
| 32-bit unsigned integer | u4 | |
| 64-bit unsigned integer | u8 | |
| single-character string | S1 | c |
Also notice the use of the least_significant_digit keyword. We frequently don't need all the digits of precision that the 32-bit float f4 provides. In this case we are saying that we only really need 3 decimal places of precision to be stored.
It is also very common to create a variable for each dimension:
>>> times = root.createVariable("time", "f8", ("time"))
>>> layers = root.createVariable("layer", "i4", ("layer"))
>>> latitudes = root.createVariable("latitude", "f4", ("lat"))
>>> longitudes = root.createVariable("longitude", "f4", ("lon"))
It is also possible to create "scalar" variables, that have no dimensions and are just simple numbers. But these are not very common. Just leave off the dimensions:
>>> num_fires = root.createVariable("num_fires", 'i')
FUN FACT: Variables and Dimensions can not be deleted from a NetCDF file. This is not a bug in netCDF4, but a feature in the underlying C API. Your only option is to copy the file bit-by-bit, and just skip the variable/dimension in question.
Attributes are extra information you attach to a group or a variable. They can be strings, numbers, or sequences. Some really common examples are putting attributes on the root group:
>>> root.description = 'Heat map in California during the final days.'
>>> root.problem = "It's hot. So hot."
Also, variables frequently need attributes:
>>> latitudes.units = "degrees north"
>>> longitudes.units = "degrees east"
>>> layers.units = "hPa"
>>> rh.units = 'percent'
>>> temp.units = "F"
>>> times.units = "hours since 0001-01-01 00:00:00.0"
>>> times.calendar = "gregorian"
If you want to see what attributes a group or variable have, use `.ncattrs() :
>>> root.ncattrs()
[u'description', u'problem']
>>>
>>> for name in root.ncattrs():
... print(name)
... print(getattr(root, name))
...
description
Heat map in California during the final days.
problem
It's hot. So hot.
Of course, this also works for variables:
>>> temp.ncattrs()
[u'units']
>>>
>>> temp.units
u'Celcius'
Reading and writing data to/from NetCDF files is pretty easy.
The first thing we know is that temp is a 4D variable, where one of the dimensions is time. So we will create a four-dimensional array, and store the values in temp:
>>> import numpy as np
>>> temp[0:1,0:11,:,:] = 100.0 * np.random.random(size=(1, 11, 321, 291)) + 70.0
At this point, we could even address an individual data point directly:
>>> temp[0][0][0][0] = -24.5
Or we could use the list slicing syntax to set several locations at once:
>>> import numpy as np
>>> temp[0][10][34][56:78] = 2.0 * np.arange(56, 78)
It is important to note though that until you initialize all of the values of a variable, you can't set individual values:
>>> rh[0][0][0][0] = 123.4
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "netCDF4.pyx", line 2778, in netCDF4.Variable.__getitem__ (netCDF4.c:34626)
File "netCDF4.pyx", line 3319, in netCDF4.Variable._get (netCDF4.c:41263)
IndexError
Similarly, once a variable has values, we can read locations using the standard list syntax:
>>> temp[0][0][0][0]
-24.5
>>> temp[0][0][50][50]
83.317664206502371
>>> temp[0][0][79][25:29]
array([141.61345365, 129.34104319, 121.31416574, 80.44523185])
Keep in mind that this won't work if the variable is unset:
>>> rh[0][0][0][0]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "netCDF4.pyx", line 2778, in netCDF4.Variable.__getitem__ (netCDF4.c:34626)
File "netCDF4.pyx", line 3319, in netCDF4.Variable._get (netCDF4.c:41263)
IndexError
The NumPy data structures used in netCDF4 are faster and designed to make use of the standard Python syntax for reading and writing data elements in lists. This makes netCDF4 easier to learn and will speed your work.
Working with the time dimension could be a little unweidly because the units are frequnetly things like: "hours since midnight, January 1st, 1970.". To help with this, netCDF4 comes with two tools to convert between datetime objects, and the integers representing the number of hours since some datetime: date2num and num2date.
>>> from datetime import datetime, timedelta
>>> from netCDF4 import date2num, num2date
First, let's create a list of datetimes for our time dimension and use date2num to load them into place:
>>> dates = [datetime(2015, 6, 21) + n*timedelta(hours=12) for n in range(temp.shape[0])]
>>> times[:] = date2num(dates,units=times.units,calendar=times.calendar)
>>> print(times[:])
[ 17658504. 17658516. 17658528. 17658540. 17658552. 17658564.
17658576. 17658588. 17658600. 17658612. 17658624. 17658636.
17658648. 17658660. 17658672. 17658684. 17658696. 17658708.]
By contrast, you can use num2date to convert these numbers back into something more human-readable:
>>> dates = num2date(times[:],units=times.units,calendar=times.calendar)
>>> print(dates)
[datetime.datetime(2015, 6, 21, 0, 0) datetime.datetime(2015, 6, 21, 12, 0)
datetime.datetime(2015, 6, 22, 0, 0) datetime.datetime(2015, 6, 22, 12, 0)
datetime.datetime(2015, 6, 23, 0, 0) datetime.datetime(2015, 6, 23, 12, 0)
datetime.datetime(2015, 6, 24, 0, 0) datetime.datetime(2015, 6, 24, 12, 0)
datetime.datetime(2015, 6, 25, 0, 0) datetime.datetime(2015, 6, 25, 12, 0)
datetime.datetime(2015, 6, 26, 0, 0) datetime.datetime(2015, 6, 26, 12, 0)
datetime.datetime(2015, 6, 27, 0, 0) datetime.datetime(2015, 6, 27, 12, 0)
datetime.datetime(2015, 6, 28, 0, 0) datetime.datetime(2015, 6, 28, 12, 0)
datetime.datetime(2015, 6, 29, 0, 0) datetime.datetime(2015, 6, 29, 12, 0)]
Working with unlimited dimensions is actually pretty easy. For instance, if we print the shape of temp, we would get:
>>> temp.shape
(1, 11, 321, 291)
But the time dimension will automatically extend if we simply add more data:
>>> import numpy as np
>>> temp[0:5,0:11,:,:] = np.random.random(size=(5, 11, 321, 291))
>>> print(temp.shape)
(5, 11, 321, 291)
All we had to do above was add data into one of our variables and the time variable was automatically grown to match:
>>> print(len(time))
5
When creating a variable, you can opt to have it compressed inside the NetCDF file:
>>> pressure = root.createVariable("pressure",
"f4",
("time", "layer", "lat", "lon"),
zlib=True,
least_significant_digit=3,
complevel=9)
We did three things to improve the compression of the variable. First and foremost, we set zlib=True, this is what defines pressure as a compressed variable. Second, we set least_significant_digit to our lowest tolerable level, reducing the number of decimal places we have to store and compress. Lastly, we set the optional "compression level" parameter complevel to 9. The complevel parameter can go from 1 (fastest, but least compression) to 9 (slowest, but most compression), 4 is the default.
Many programs that use NetCDF files will require them to be in the IOAPI format. These IOAPI-formatted NetCDF files will be read/written by netCDF4 like any other NetCDF files, but will have several restrictions which define them. There are 8 different types of IOAPI file currently supported, and for the sake of brevity, we will only discuss the GRIDDED IOAPI format used for representing 2D or 3D time-dependent variables.
IOAPI/NetCDF restrictions:
- must be formatted for NETCDF3_CLASSIC
- must have six dimensions:
- TSTEP = Unlimited dimension
- DATE-TIME = Julian Day and Time (YYYYJJJJ, HHMMSS)
- LAY = number of vertical layers
- VAR = number of variables
- ROW = number of rows
- COL = number of columns
- must have a TFLAG variable to map DATE-TIMES to each variable's TSTEP (TSTEP, VAR, DATE-TIME)
- variables
- names = 16 characters (with trailing spaces)
- must have "units" attribute = 16 characters (with trailing spaces)
- must have "var_desc" description attribute = 80 characters (with trailing spaces)
- requires a certain set of global attributes:
- IOAPI_VERSION version id string
- EXEC_ID value of environment variable EXECUTION_ID
- FTYPE data structure type for the variables in this file
- UPNAM name of last program writing to this file
- FILEDESC text describing the file
- HISTORY text history of file and alterations
- WDATE last-update date
- WTIME last-update time
- CDATE file-creation date
- CTIME file-creation time
- SDATE file start date YYYYDDD
- STIME file start time HHMMSS
- TSTEP file time step HHMMSS
- GDNAM grid name
- GDTYP horizontal coordinate system type, defined in PARMS3.EXT, 2 is LCC projection
- P_ALP first map projection descriptive parameter
- P_BET second map projection descriptive parameter
- P_GAM third map projection descriptive parameter
- XCENT longitude for coordinate system origin (where X = 0)
- YCENT latitude for coordinate system origin (where Y = 0)
- XORIG X-coordinate for lower-left (southwest) corner of the grid
- YORIG Y-coordinate for lower-left (southwest) corner of the grid
- XCELL X-coordinate grid cell size
- YCELL Y-coordinate grid cell size
- NCOLS number of horizontal grid columns
- NROWS number of horizontal grid rows
- NTHIK boundary perimeter thickness (number of cells)
- VGTYP vertical coordinate type
- VGTOP model-top, for sigma coord types
- NLAYS number of vertical grid layers
- VGLVLS array of vertical coordinate grid level values
- NVARS number of variables
- VAR-LIST string representing list of variable names
I find these restrictions are generally enough to worry about. Are you really going to need to worry about the fact that you can't have more than 1024 variables in a file? But there are many more restrictions that define an IOAPI NetCDF file, GRIDDED or otherwise. You can find these in the official documentation.
GRIDDED IOAPI restrictions:
- global attribute
NTHIKis ignored (NTHIK = 0)
The NETCDF3_CLASSIC restrictions:
- can only have one unlimited dimension
- do not support groups (only the root group is allowed)
For the following examples, imagine we are reading a GRIDDED-type IOAPI NetCDF file called "small_test.ncf".
Of course, all GRIDDED IOAPI files have the same dimensions:
>>> root.dimensions.keys()
[u'TSTEP', u'DATE-TIME', u'LAY', u'VAR', u'ROW', u'COL']
But what dimensions does this file have?
>>> root.variables.keys()
[u'TFLAG', u'PM25']
Well, 'PM25' may have been a surprise, but we already knew the file had to contain a TFLAG variable:
>>> print(root.variables['TFLAG'])
<type 'netCDF4.Variable'>
int32 TFLAG(TSTEP, VAR, DATE-TIME)
units: <YYYYDDD,HHMMSS>
long_name: TFLAG
var_desc: Timestep-valid flags: (1) YYYYDDD or (2) HHMMSS
unlimited dimensions: TSTEP
current shape = (1, 1, 2)
>>>
>>> print(root.variables['TFLAG'][:])
[[[2007335 0]]]
And all IOAPI-formatted NetCDF files have to contain the same list of attributes:
>>> sorted(root.ncattrs())
[u'CDATE', u'CTIME', u'EXEC_ID', u'FILEDESC', u'FTYPE', u'GDNAM', u'GDTYP',
u'HISTORY', u'IOAPI_VERSION', u'NCOLS', u'NLAYS', u'NROWS', u'NTHIK',
u'NVARS', u'P_ALP', u'P_BET', u'P_GAM', u'SDATE', u'STIME', u'TSTEP',
u'UPNAM', u'VAR-LIST', u'VGLVLS', u'VGTOP', u'VGTYP', u'WDATE', u'WTIME',
u'XCELL', u'XCENT', u'XORIG', u'YCELL', u'YCENT', u'YORIG']
And all of these attributes are easily accessible:
>>> root.IOAPI_VERSION
u'$Id: @(#) ioapi library version 3.0 $ '
>>> root.NVARS
1
>>> root.NLAYS
1
>>> root.NROWS
291
>>> root.NCOLS
321
Working with NetCDF files can take some time getting used to. So here are some short scripts to serve as examples of doing various small tasks.
- Script version of the non-IOAPI portion of this lecture.
- Script that averages two very similar NetCDF files.
- Script that will make a copy of a GRIDDED IOAPI NetCDF file with all empty data.
- Script that will extract the values at a single grid cell for a GRIDDED IOAPI NetCDF.
netCDF4 resources:
- Official API Docs
- Quick netCDF4 Intro
- Great GoogleCode Introduction
- Read & Plot a NetCDF File - Useful example, also plots using MatPlotLib
- netCDF4 Tutorial Module - This is just a Python module that uses all the netCDF4 features.
- Official netCDF4 GitHub Repo
General NetCDF resources:
IOAPI resources: