Getting Started with Baseband

This quickstart tutorial is meant to help the reader hit the ground running with Baseband. For more detail, including writing to files, see Using Baseband.

For installation instructions, please see Installing Baseband.

When using Baseband, we typically will also use numpy, the astropy.units module, and Time from the astropy.time module. Let’s import all of these:

>>> import baseband
>>> import numpy as np
>>> import astropy.units as u
>>> from astropy.time import Time

Opening Files

For this tutorial, we’ll use two sample files:

>>> from import SAMPLE_VDIF, SAMPLE_MARK5B

The first file is a VDIF one created from EVN/VLBA observations of Black Widow pulsar PSR B1957+20, while the second is a Mark 5B from EVN/WSRT observations of the same pulsar.

To open the VDIF file:

>>> fh_vdif =

Opening the Mark 5B file is slightly more involved, as not all required metadata is stored in the file itself:

>>> fh_m5b =, nchan=8, sample_rate=32*u.MHz,
...                        ref_time=Time('2014-06-13 12:00:00'))

Here, we’ve manually passed in as keywords the number of channels, the sample rate (number of samples per channel per second) as an astropy.units.Quantity, and a reference time within 500 days of the start of the observation as an astropy.time.Time. That last keyword is needed to properly read timestamps from the Mark 5B file. tries to open files using all available formats, returning whichever is successful. If you know the format of your file, you can pass its name with the format keyword, or directly use its format opener (for VDIF, it is Also, the baseband.file_info function can help determine the format and any missing information needed by - see Inspecting Files.

Do you have a sequence of files you want to read in? You can pass a list of filenames to, and it will open them up as if they were a single file! See Reading or Writing to a Sequence of Files.

Reading Files

Radio baseband files are generally composed of blocks of binary data, or payloads, stored alongside corresponding metadata, or headers. Each header and payload combination is known as a data frame, and most formats feature files composed of a long series of frames.

Baseband file objects are frame-reading wrappers around Python file objects, and have the same interface, including seek for seeking to different parts of the file, tell for reporting the file pointer’s current position, and read for reading data. The main difference is that Baseband file objects read and navigate in units of samples.

Let’s read some samples from the VDIF file:

>>> data =
>>> data  
array([[-1.      ,  1.      ,  1.      , -1.      , -1.      , -1.      ,
         3.316505,  3.316505],
       [-1.      ,  1.      , -1.      ,  1.      ,  1.      ,  1.      ,
         3.316505,  3.316505],
       [ 3.316505,  1.      , -1.      , -1.      ,  1.      ,  3.316505,
        -3.316505,  3.316505]], dtype=float32)
>>> data.shape
(3, 8)

Baseband decodes binary data into ndarray objects. Notice we input 3, and received an array of shape (3, 8); this is because there are 8 VDIF threads. Threads and channels represent different components of the data such as polarizations or frequency sub-bands, and the collection of all components at one point in time is referred to as a complete sample. Baseband reads in units of complete samples, and works with sample rates in units of complete samples per second (including with the Mark 5B example above). Like an ndarray, calling fh_vdif.shape returns the shape of the entire dataset:

>>> fh_vdif.shape
(40000, 8)

The first axis represents time, and all additional axes represent the shape of a complete sample. A labelled version of the complete sample shape is given by:

>>> fh_vdif.sample_shape

Baseband extracts basic properties and header metadata from opened files. Notably, the start and end times of the file are given by:

>>> fh_vdif.start_time
<Time object: scale='utc' format='isot' value=2014-06-16T05:56:07.000000000>
>>> fh_vdif.stop_time
<Time object: scale='utc' format='isot' value=2014-06-16T05:56:07.001250000>

For an overview of the file, we can either print fh_vdif itself, or use the info method:

>>> fh_vdif
<VDIFStreamReader name=... offset=3
    sample_rate=32.0 MHz, samples_per_frame=20000,
    bps=2, complex_data=False, edv=3, station=65532,
VDIFStream information:
start_time = 2014-06-16T05:56:07.000000000
stop_time = 2014-06-16T05:56:07.001250000
sample_rate = 32.0 MHz
shape = (40000, 8)
format = vdif
bps = 2
complex_data = False
verify = fix
readable = True

checks:  decodable: True
         continuous: no obvious gaps

VDIFFile information:
edv = 3
number_of_frames = 16
thread_ids = [0, 1, 2, 3, 4, 5, 6, 7]
number_of_framesets = 2
frame_rate = 1600.0 Hz
samples_per_frame = 20000
sample_shape = (8, 1)

Seeking is also done in units of complete samples, which is equivalent to seeking in timesteps. Let’s move forward 100 complete samples:


Seeking from the end or current position is also possible, using the same syntax as for typical file objects. It is also possible to seek in units of time:

>>>, 2)    # Seek 1000 samples from end.
>>>*, 1)    # Seek 10 us from current position.

fh_vdif.tell returns the current offset in samples or in time:

>>> fh_vdif.tell()
>>> fh_vdif.tell(    # Time since start of file.
<Quantity 1228.75 us>
>>> fh_vdif.tell(unit='time')
<Time object: scale='utc' format='isot' value=2014-06-16T05:56:07.001228750>

Finally, we close both files:

>>> fh_vdif.close()
>>> fh_m5b.close()