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import os
from urllib.request import urlretrieve
import h5py
import pytest
import numpy as np
from pynpoint.core.pypeline import Pypeline
from pynpoint.readwrite.fitsreading import FitsReadingModule
from pynpoint.processing.limits import ContrastCurveModule, MassLimitsModule
from pynpoint.processing.psfpreparation import AngleInterpolationModule
from pynpoint.util.tests import create_config, create_star_data, remove_test_data
class TestLimits:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(self.test_dir+'self.limits', npix=21, pos_star=10.)
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(path=self.test_dir,
folders=['self.limits'],
files=['model.AMES-Cond-2000.M-0.0.NaCo.Vega'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='read',
input_dir=self.test_dir+'self.limits')
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('read')
assert np.sum(data) == pytest.approx(108.43655133957289, rel=self.limit, abs=0.)
assert data.shape == (10, 21, 21)
def test_angle_interpolation(self) -> None:
module = AngleInterpolationModule(name_in='angle',
data_tag='read')
self.pipeline.add_module(module)
self.pipeline.run_module('angle')
attr = self.pipeline.get_attribute('read', 'PARANG', static=False)
assert np.sum(attr) == pytest.approx(900., rel=self.limit, abs=0.)
assert attr.shape == (10, )
def test_contrast_curve(self) -> None:
proc = ['single', 'multi']
for item in proc:
if item == 'multi':
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = ContrastCurveModule(name_in='contrast_'+item,
image_in_tag='read',
psf_in_tag='read',
contrast_out_tag='limits_'+item,
separation=(0.2, 0.3, 0.2),
angle=(0., 360., 180.),
threshold=('sigma', 5.),
psf_scaling=1.,
aperture=0.05,
pca_number=2,
cent_size=None,
edge_size=1.,
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('contrast_'+item)
data = self.pipeline.get_data('limits_'+item)
assert data[0, 0] == pytest.approx(0.2, rel=self.limit, abs=0.)
assert data[0, 1] == pytest.approx(2.580878183791224, rel=self.limit, abs=0.)
assert data[0, 2] == pytest.approx(0.0007097688120261913, rel=self.limit, abs=0.)
assert data[0, 3] == pytest.approx(0.00020126490906225968, rel=self.limit, abs=0.)
assert data.shape == (1, 4)
def test_contrast_curve_fpf(self) -> None:
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
module = ContrastCurveModule(name_in='contrast_fpf',
image_in_tag='read',
psf_in_tag='read',
contrast_out_tag='limits_fpf',
separation=(0.2, 0.3, 0.2),
angle=(0., 360., 180.),
threshold=('fpf', 1e-6),
psf_scaling=1.,
aperture=0.05,
pca_number=2,
cent_size=None,
edge_size=1.,
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('contrast_fpf')
data = self.pipeline.get_data('limits_fpf')
assert data[0, 0] == pytest.approx(0.2, rel=self.limit, abs=0.)
assert data[0, 1] == pytest.approx(1.9339430843041776, rel=self.limit, abs=0.)
assert data[0, 2] == pytest.approx(0.000709768812026221, rel=self.limit, abs=0.)
assert data[0, 3] == pytest.approx(1e-06, rel=self.limit, abs=0.)
assert data.shape == (1, 4)
def test_mass_limits(self) -> None:
separation = np.linspace(0.1, 1.0, 10)
contrast = -2.5*np.log10(1e-4/separation)
variance = 0.1*contrast
limits = np.zeros((10, 4))
limits[:, 0] = separation
limits[:, 1] = contrast
limits[:, 2] = variance
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['contrast_limits'] = limits
url = 'https://home.strw.leidenuniv.nl/~stolker/pynpoint/' \
'model.AMES-Cond-2000.M-0.0.NaCo.Vega'
filename = self.test_dir + 'model.AMES-Cond-2000.M-0.0.NaCo.Vega'
urlretrieve(url, filename)
module = MassLimitsModule(model_file=filename,
star_prop={'magnitude': 10., 'distance': 100., 'age': 20.},
name_in='mass',
contrast_in_tag='contrast_limits',
mass_out_tag='mass_limits',
instr_filter='L\'')
self.pipeline.add_module(module)
self.pipeline.run_module('mass')
data = self.pipeline.get_data('mass_limits')
assert np.mean(data[:, 0]) == pytest.approx(0.55, rel=self.limit, abs=0.)
assert np.mean(data[:, 1]) == pytest.approx(0.001891690765603738, rel=self.limit, abs=0.)
assert np.mean(data[:, 2]) == pytest.approx(0.000964309686441908, rel=self.limit, abs=0.)
assert np.mean(data[:, 3]) == pytest.approx(-0.000696402843279597, rel=self.limit, abs=0.)
assert data.shape == (10, 4)
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