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Merge pull request #362 from LCOGT/feature/dark-temp-scaling
Feature/dark temp scaling
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import pytest | ||
import numpy as np | ||
import mock | ||
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from banzai.dark import DarkSubtractor | ||
from banzai.tests.utils import FakeCCDData, FakeLCOObservationFrame, FakeContext | ||
from banzai.lco import LCOCalibrationFrame | ||
from banzai.data import CCDData | ||
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pytestmark = pytest.mark.dark_subtractor | ||
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@pytest.fixture(scope='module') | ||
def set_random_seed(): | ||
np.random.seed(7298374) | ||
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def test_null_input_images(): | ||
normalizer = DarkSubtractor(None) | ||
image = normalizer.run(None) | ||
assert image is None | ||
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@mock.patch('banzai.lco.LCOFrameFactory.open') | ||
@mock.patch('banzai.calibrations.CalibrationUser.get_calibration_file_info', return_value='test.fits') | ||
def test_reasonable_dark_subtraction(mock_super_cal_name, mock_super_frame): | ||
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mock_super_cal_name.return_value = {'filename': 'test.fits'} | ||
mock_super_frame.return_value = LCOCalibrationFrame(hdu_list=[CCDData(data=0.5*np.ones((100,100)), | ||
meta={'EXPTIME': 1.0, | ||
'SATURATE': 35000, | ||
'GAIN': 1.0, | ||
'MAXLIN': 35000, | ||
'ISMASTER': True, | ||
'CCDATEMP': -100})], file_path='/tmp') | ||
image = FakeLCOObservationFrame(hdu_list=[CCDData(data=4*np.ones((100,100)), meta={'EXPTIME': 2.0, | ||
'SATURATE': 35000, | ||
'GAIN': 1.0, | ||
'MAXLIN': 35000, | ||
'CCDATEMP': -100})]) | ||
subtractor = DarkSubtractor(FakeContext()) | ||
image = subtractor.do_stage(image) | ||
np.testing.assert_allclose(image.data, 3*np.ones((100,100))) | ||
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@mock.patch('banzai.lco.LCOFrameFactory.open') | ||
@mock.patch('banzai.calibrations.CalibrationUser.get_calibration_file_info', return_value='test.fits') | ||
def test_reasonable_dark_subtraction_with_scaling(mock_super_cal_name, mock_super_frame): | ||
dark_temperature_coefficient = 0.5 | ||
image_measured_temp = 0 | ||
super_measured_temp = -5 | ||
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mock_super_cal_name.return_value = {'filename': 'test.fits'} | ||
mock_super_frame.return_value = LCOCalibrationFrame(hdu_list=[CCDData(data=0.5*np.ones((100,100)), | ||
meta={'EXPTIME': 1.0, | ||
'SATURATE': 35000, | ||
'GAIN': 1.0, | ||
'MAXLIN': 35000, | ||
'ISMASTER': True, | ||
'DRKTCOEF': dark_temperature_coefficient, | ||
'CCDATEMP': -5})], file_path='/tmp') | ||
image = FakeLCOObservationFrame(hdu_list=[CCDData(data=4*np.ones((100,100)), | ||
meta={'EXPTIME': 2.0, | ||
'SATURATE': 35000, | ||
'GAIN': 1.0, | ||
'MAXLIN': 35000, | ||
'CCDATEMP': 0})]) | ||
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dark_scaling_factor = np.exp(dark_temperature_coefficient * (image_measured_temp - super_measured_temp)) | ||
subtracted_data = image.data - np.ones((100,100)) * dark_scaling_factor | ||
subtractor = DarkSubtractor(FakeContext()) | ||
image = subtractor.do_stage(image) | ||
np.testing.assert_allclose(image.data, subtracted_data) |