def _applycal(self, array, apply_correction):
"""Calibrate `array` with `apply_correction` and return all factors."""
array_dask = da.from_array(array, chunks=(10, 4, 6))
final_cal_products, correction = calc_correction(
array_dask.chunks, self.cache, CORRPRODS, CAL_PRODUCTS, FREQS, {'cal': CAL_FREQS})
corrected = da.core.elemwise(apply_correction, array_dask, correction,
dtype=array_dask.dtype)
return corrected.compute(), correction.compute()
def test_skip_missing_products(self):
dump = 15
channels = np.s_[22:38]
shape = (N_DUMPS, N_CHANS, N_CORRPRODS)
chunks = da.core.normalize_chunks((10, 5, -1), shape)
final_cal_products, corrections = calc_correction(
chunks, self.cache, CORRPRODS, [], FREQS, {'cal': CAL_FREQS})
assert_equal(final_cal_products, [])
assert_equal(corrections, None)
with assert_raises(ValueError):
calc_correction(chunks, self.cache, CORRPRODS, ['INVALID'], FREQS,
{'cal': CAL_FREQS})
unknown = CAL_STREAM + '.UNKNOWN'
final_cal_products, corrections = calc_correction(
chunks, self.cache, CORRPRODS, [unknown], FREQS, {'cal': CAL_FREQS},
skip_missing_products=True)
assert_equal(final_cal_products, [])
assert_equal(corrections, None)
cal_products = CAL_PRODUCTS + [unknown]
with assert_raises(KeyError):
calc_correction(chunks, self.cache, CORRPRODS, cal_products, FREQS,
{'cal': CAL_FREQS}, skip_missing_products=False)
final_cal_products, corrections = calc_correction(
chunks, self.cache, CORRPRODS, cal_products, FREQS, {'cal': CAL_FREQS},
skip_missing_products=True)
assert_equal(set(final_cal_products), set(CAL_PRODUCTS))
corrections = corrections[dump:dump+1, channels].compute()
expected_corrections = corrections_per_corrprod([dump], channels,
final_cal_products)
assert_array_equal(corrections, expected_corrections)
def test_calc_correction(self):
dump = 15
channels = np.s_[22:38]
shape = (N_DUMPS, N_CHANS, N_CORRPRODS)
chunks = da.core.normalize_chunks((10, 5, -1), shape)
final_cal_products, corrections = calc_correction(
chunks, self.cache, CORRPRODS, CAL_PRODUCTS, FREQS, {'cal': CAL_FREQS})
assert_equal(set(final_cal_products), set(CAL_PRODUCTS))
corrections = corrections[dump:dump+1, channels].compute()
expected_corrections = corrections_per_corrprod([dump], channels,
final_cal_products)
assert_array_equal(corrections, expected_corrections)