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import os
import numpy as np
import pytest
from pynpoint.core.pypeline import Pypeline
from pynpoint.readwrite.fitsreading import FitsReadingModule
from pynpoint.processing.background import LineSubtractionModule, \
MeanBackgroundSubtractionModule, \
NoddingBackgroundModule, \
SimpleBackgroundSubtractionModule
from pynpoint.processing.pcabackground import DitheringBackgroundModule
from pynpoint.processing.stacksubset import StackCubesModule
from pynpoint.util.tests import create_config, create_dither_data, create_star_data, \
remove_test_data
class TestBackground:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_dither_data(self.test_dir+'dither')
create_star_data(self.test_dir+'science')
create_star_data(self.test_dir+'sky')
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, folders=['dither', 'science', 'sky'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read1',
image_tag='dither',
input_dir=self.test_dir+'dither')
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('dither')
assert np.sum(data) == pytest.approx(211.20534661914408, rel=self.limit, abs=0.)
assert data.shape == (20, 21, 21)
module = FitsReadingModule(name_in='read2',
image_tag='science',
input_dir=self.test_dir+'science')
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
data = self.pipeline.get_data('science')
assert np.sum(data) == pytest.approx(105.54278879805277, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
module = FitsReadingModule(name_in='read3',
image_tag='sky',
input_dir=self.test_dir+'sky')
self.pipeline.add_module(module)
self.pipeline.run_module('read3')
data = self.pipeline.get_data('sky')
assert np.sum(data) == pytest.approx(105.54278879805277, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_simple_background(self) -> None:
module = SimpleBackgroundSubtractionModule(shift=5,
name_in='simple',
image_in_tag='dither',
image_out_tag='simple')
self.pipeline.add_module(module)
self.pipeline.run_module('simple')
data = self.pipeline.get_data('simple')
# TODO Unclear why the absolute value is required
# Local the sum is 3.55 and on Github -3.55
assert np.abs(np.sum(data)) == pytest.approx(3.552713678800501e-15, rel=self.limit, abs=0.)
assert data.shape == (20, 21, 21)
def test_mean_background_shift(self) -> None:
module = MeanBackgroundSubtractionModule(shift=5,
cubes=1,
name_in='mean2',
image_in_tag='dither',
image_out_tag='mean2')
self.pipeline.add_module(module)
self.pipeline.run_module('mean2')
data = self.pipeline.get_data('mean2')
assert np.sum(data) == pytest.approx(2.473864361018551, rel=self.limit, abs=0.)
assert data.shape == (20, 21, 21)
def test_mean_background_nframes(self) -> None:
module = MeanBackgroundSubtractionModule(shift=None,
cubes=1,
name_in='mean1',
image_in_tag='dither',
image_out_tag='mean1')
self.pipeline.add_module(module)
self.pipeline.run_module('mean1')
data = self.pipeline.get_data('mean1')
assert np.sum(data) == pytest.approx(2.473864361018551, rel=self.limit, abs=0.)
assert data.shape == (20, 21, 21)
def test_dithering_attributes(self) -> None:
module = DitheringBackgroundModule(name_in='pca_dither1',
image_in_tag='dither',
image_out_tag='pca_dither1',
center=None,
cubes=None,
size=0.2,
gaussian=0.05,
subframe=0.1,
pca_number=1,
mask_star=0.05)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_dither1')
data = self.pipeline.get_data('dither_dither_crop1')
assert np.sum(data) == pytest.approx(54.62410860562912, rel=self.limit, abs=0.)
assert data.shape == (20, 9, 9)
data = self.pipeline.get_data('dither_dither_star1')
assert np.sum(data) == pytest.approx(54.873885838788595, rel=self.limit, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_mean1')
assert np.sum(data) == pytest.approx(54.204960755115245, rel=self.limit, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_background1')
assert np.sum(data) == pytest.approx(-0.24977723315947564, rel=self.limit, abs=0.)
assert data.shape == (15, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_fit1')
assert np.sum(data) == pytest.approx(-0.6816458444287745, rel=1e-5, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_res1')
assert np.sum(data) == pytest.approx(55.63879076093719, rel=1e-6, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_mask1')
assert np.sum(data) == pytest.approx(360.0, rel=self.limit, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('pca_dither1')
assert np.sum(data) == pytest.approx(208.24417329569593, rel=1e-6, abs=0.)
assert data.shape == (20, 9, 9)
attr = self.pipeline.get_attribute('dither_dither_pca_res1', 'STAR_POSITION', static=False)
assert np.sum(attr) == pytest.approx(40., rel=self.limit, abs=0.)
assert attr.shape == (5, 2)
def test_dithering_center(self) -> None:
module = DitheringBackgroundModule(name_in='pca_dither2',
image_in_tag='dither',
image_out_tag='pca_dither2',
center=[(5, 5), (5, 15), (15, 15), (15, 5)],
cubes=1,
size=0.2,
gaussian=0.05,
subframe=None,
pca_number=1,
mask_star=0.05)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_dither2')
data = self.pipeline.get_data('pca_dither2')
assert np.sum(data) == pytest.approx(208.24417332523367, rel=1e-6, abs=0.)
assert data.shape == (20, 9, 9)
def test_nodding_background(self) -> None:
module = StackCubesModule(name_in='mean',
image_in_tag='sky',
image_out_tag='mean',
combine='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('mean')
data = self.pipeline.get_data('mean')
assert np.sum(data) == pytest.approx(21.108557759610548, rel=self.limit, abs=0.)
assert data.shape == (2, 11, 11)
attr = self.pipeline.get_attribute('mean', 'INDEX', static=False)
assert np.sum(attr) == pytest.approx(1, rel=self.limit, abs=0.)
assert attr.shape == (2, )
attr = self.pipeline.get_attribute('mean', 'NFRAMES', static=False)
assert np.sum(attr) == pytest.approx(2, rel=self.limit, abs=0.)
assert attr.shape == (2, )
module = NoddingBackgroundModule(name_in='nodding',
sky_in_tag='mean',
science_in_tag='science',
image_out_tag='nodding',
mode='both')
self.pipeline.add_module(module)
self.pipeline.run_module('nodding')
data = self.pipeline.get_data('nodding')
assert np.sum(data) == pytest.approx(1.793466459074705, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_line_background_mean(self) -> None:
module = LineSubtractionModule(name_in='line1',
image_in_tag='science',
image_out_tag='science_line1',
combine='mean',
mask=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('line1')
data = self.pipeline.get_data('science_line1')
assert np.sum(data) == pytest.approx(104.55443019231085, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_line_background_median(self) -> None:
module = LineSubtractionModule(name_in='line2',
image_in_tag='science',
image_out_tag='science_line2',
combine='median',
mask=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('line2')
data = self.pipeline.get_data('science_line2')
assert np.sum(data) == pytest.approx(106.09825573198366, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
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