def test_process_bpm():
data = numpy.zeros((10, 10), dtype='float32') + 3.0
mask = numpy.zeros((10, 10), dtype='int32')
mask[3,3] = 1
result = process_bpm_median(data, mask)
assert result[3,3] == 3.0
def run(self, img):
import numina.array.bpm as bpm
imgid = self.get_imgid(img)
_logger.debug('correcting bad pixel mask in %s', imgid)
data = self.datamodel.get_data(img)
newdata = bpm.process_bpm_median(data, self.bpm, hwin=2, wwin=2, fill=0)
# newdata = array.fixpix(data, self.bpm)
# FIXME: this breaks datamodel abstraction
img['primary'].data = newdata
hdr = img['primary'].header
hdr['NUM-BPM'] = self.calibid
hdr['history'] = 'BPM correction with {}'.format(self.calibid)
hdr['history'] = 'BPM correction time {}'.format(datetime.datetime.utcnow().isoformat())
return img
