Python interface

The external Python API mirrors the external C++ API, and only differs in that it follows the common Python naming conventions. For a reference of the functions and a general overview of what can be done with the Python interface, see the C++ interface. A reference of the Python API can also be obtained by running help(aoflagger) in (i)python:

In [1]: import aoflagger

In [2]: help(aoflagger)
Help on module aoflagger:

    aoflagger - AOFlagger module for detection of radio-frequency interference


    class AOFlagger(pybind11_builtins.pybind11_object)
     |  Main class that gives access to the aoflagger functions.

The rest of this chapter discusses a few practical topics related to the Python interface.


The AOFlagger Python module is compiled into an object library. It is compiled along when you run make as described on the Installation chapter. Currently, make compiles the following library on my computer:


The normal/proper way of installing this library into its correct location, is by running make install. On my computer, this copies that file to /install-prefix/lib. For Python to find this library, the path needs to be in your Python search path, which is normally set with the PYTHONPATH environment variable, e.g.:

export PYTHONPATH=/install-prefix/lib:${PYTHONPATH}

There’s no need to run a for AOFlagger. Also, AOFlagger can’t be installed via pip. When installing AOFlagger via a Debian/Ubuntu package, the library should be installed and found without any manual user tweaking. Be aware that the Python interface and binding tool was improved in version 3.0, and it is therefore recommended that the latest release of aoflagger (>= 3.0) is used.

Using the Python interface

The aoflagger module can be included in Python using a standard import command:

import aoflagger

A few examples are given in the data directory. The following is an example to calculate the false-positives ratio of the default strategy:

import aoflagger
import numpy

nch = 256
ntimes = 1000
count = 50       # number of trials in the false-positives test

flagger = aoflagger.AOFlagger()
path = flagger.find_strategy_file(aoflagger.TelescopeId.Generic)
strategy = flagger.load_strategy_file(path)
data = flagger.make_image_set(ntimes, nch, 8)

ratiosum = 0.0
ratiosumsq = 0.0
for repeat in range(count):
    for imgindex in range(8):
        # Initialize data with random numbers
        values = numpy.random.normal(0, 1, [nch, ntimes])
        data.set_image_buffer(imgindex, values)

    flags =
    flagvalues = flags.get_buffer()
    ratio = float(sum(sum(flagvalues))) / (nch*ntimes)
    ratiosum += ratio
    ratiosumsq += ratio*ratio

print("Percentage flags (false-positive rate) on Gaussian data: " +
    str(ratiosum * 100.0 / count) + "% +/- " +
        (ratiosumsq/count - ratiosum*ratiosum / (count*count) )
        ) * 100.0) )

This takes about 10 seconds to run on my computer.