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import os
import h5py
import pytest
import numpy as np
from pynpoint.core.pypeline import Pypeline
from pynpoint.processing.darkflat import DarkCalibrationModule, FlatCalibrationModule
from pynpoint.util.tests import create_config, remove_test_data
class TestDarkFlat:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
np.random.seed(1)
images = np.random.normal(loc=0, scale=2e-4, size=(5, 11, 11))
dark = np.random.normal(loc=0, scale=2e-4, size=(5, 11, 11))
flat = np.random.normal(loc=0, scale=2e-4, size=(5, 11, 11))
crop = np.random.normal(loc=0, scale=2e-4, size=(5, 7, 7))
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'w') as hdf_file:
hdf_file.create_dataset('images', data=images)
hdf_file.create_dataset('dark', data=dark)
hdf_file.create_dataset('flat', data=flat)
hdf_file.create_dataset('crop', data=crop)
create_config(self.test_dir+'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self) -> None:
remove_test_data(self.test_dir)
def test_input_data(self) -> None:
data = self.pipeline.get_data('dark')
assert np.sum(data) == pytest.approx(-2.0262345764957305e-05, rel=self.limit, abs=0.)
assert data.shape == (5, 11, 11)
data = self.pipeline.get_data('flat')
assert np.sum(data) == pytest.approx(0.0076413379497053, rel=self.limit, abs=0.)
assert data.shape == (5, 11, 11)
def test_dark_calibration(self) -> None:
module = DarkCalibrationModule(name_in='dark',
image_in_tag='images',
dark_in_tag='dark',
image_out_tag='dark_cal')
self.pipeline.add_module(module)
self.pipeline.run_module('dark')
data = self.pipeline.get_data('dark_cal')
assert np.sum(data) == pytest.approx(0.00717386583629883, rel=self.limit, abs=0.)
assert data.shape == (5, 11, 11)
def test_flat_calibration(self) -> None:
module = FlatCalibrationModule(name_in='flat',
image_in_tag='dark_cal',
flat_in_tag='flat',
image_out_tag='flat_cal')
self.pipeline.add_module(module)
self.pipeline.run_module('flat')
data = self.pipeline.get_data('flat_cal')
assert np.sum(data) == pytest.approx(0.00717439711853594, rel=self.limit, abs=0.)
assert data.shape == (5, 11, 11)
def test_flat_crop(self) -> None:
module = FlatCalibrationModule(name_in='flat_crop',
image_in_tag='crop',
flat_in_tag='flat',
image_out_tag='flat_crop')
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('flat_crop')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The calibration images were cropped around their ' \
'center to match the shape of the science images.'
data = self.pipeline.get_data('flat_crop')
assert np.sum(data) == pytest.approx(-0.003242901413605404, rel=self.limit, abs=0.)
assert data.shape == (5, 7, 7)
def test_flat_too_small(self) -> None:
module = FlatCalibrationModule(name_in='flat_small',
image_in_tag='flat',
flat_in_tag='crop',
image_out_tag='flat_small')
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('flat_small')
assert str(error.value) == 'Shape of the calibration images is smaller than the ' \
'science images.'
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