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import pytest
import sys
import aoflagger
import numpy
# Append current directory to system path in order to import testconfig
sys.path.append(".")
import testconfig as config
"""
Use AOFlagger's Python interface to perform a simple flagging of Gaussian data.
This test does not use an initial mask.
"""
def test_without_initial_mask():
n_channels = 256
n_times = 400
n_polarizations = 4
n_images_per_polarization = 2 # Real and imaginary
n_images = n_polarizations * n_images_per_polarization
flagger = aoflagger.AOFlagger()
path = flagger.find_strategy_file(aoflagger.TelescopeId.Generic)
strategy = flagger.load_strategy_file(path)
data = flagger.make_image_set(n_times, n_channels, n_images)
for imgindex in range(n_images):
# Initialize data with random numbers
values = numpy.random.normal(0, 1, [n_channels, n_times])
data.set_image_buffer(imgindex, values)
flags = strategy.run(data)
flagvalues = flags.get_buffer()
ratio = float(sum(sum(flagvalues))) / (n_channels * n_times)
# This tests the false positive ratio, which should be around 0.5%.
assert ratio < 0.02
# With so many samples, always at least a few samples should get flagged:
assert ratio > 0
def test_set_mask():
flagger = aoflagger.AOFlagger()
n_channels = 100
n_times = 50
flag_mask = flagger.make_flag_mask(n_channels, n_times, False)
buf = flag_mask.get_buffer()
assert not buf.any()
buf[10, 20] = True
flag_mask.set_buffer(buf)
buf = flag_mask.get_buffer()
assert sum(sum(buf)) == 1
assert buf[10, 20] == True
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