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import os
import h5py
import pytest
import numpy as np
from pynpoint.core.pypeline import Pypeline
from pynpoint.readwrite.fitsreading import FitsReadingModule
from pynpoint.processing.resizing import AddLinesModule
from pynpoint.processing.timedenoising import CwtWaveletConfiguration, DwtWaveletConfiguration, \
WaveletTimeDenoisingModule, TimeNormalizationModule
from pynpoint.util.tests import create_config, remove_test_data, create_star_data
class TestTimeDenoising:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(self.test_dir+'images')
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=['images'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='images',
input_dir=self.test_dir+'images',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('images')
assert np.sum(data) == pytest.approx(105.54278879805277, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_wavelet_denoising_cwt_dog(self) -> None:
cwt_config = CwtWaveletConfiguration(wavelet='dog',
wavelet_order=2,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'dog'
assert cwt_config.m_wavelet_order == 2
assert not cwt_config.m_keep_mean
assert cwt_config.m_resolution == 0.5
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_cwt_dog',
image_in_tag='images',
image_out_tag='wavelet_cwt_dog',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_cwt_dog')
data = self.pipeline.get_data('wavelet_cwt_dog')
assert np.sum(data) == pytest.approx(105.1035789572968, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
self.pipeline.run_module('wavelet_cwt_dog')
data_multi = self.pipeline.get_data('wavelet_cwt_dog')
assert data == pytest.approx(data_multi, rel=self.limit, abs=0.)
assert data.shape == data_multi.shape
def test_wavelet_denoising_cwt_morlet(self) -> None:
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
cwt_config = CwtWaveletConfiguration(wavelet='morlet',
wavelet_order=5,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'morlet'
assert cwt_config.m_wavelet_order == 5
assert not cwt_config.m_keep_mean
assert cwt_config.m_resolution == 0.5
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_cwt_morlet',
image_in_tag='images',
image_out_tag='wavelet_cwt_morlet',
padding='mirror',
median_filter=False,
threshold_function='hard')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_cwt_morlet')
data = self.pipeline.get_data('wavelet_cwt_morlet')
assert np.sum(data) == pytest.approx(104.86262840716438, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
data = self.pipeline.get_attribute('wavelet_cwt_morlet', 'NFRAMES', static=False)
assert data[0] == data[1] == 5
def test_wavelet_denoising_dwt(self) -> None:
dwt_config = DwtWaveletConfiguration(wavelet='db8')
assert dwt_config.m_wavelet == 'db8'
module = WaveletTimeDenoisingModule(wavelet_configuration=dwt_config,
name_in='wavelet_dwt',
image_in_tag='images',
image_out_tag='wavelet_dwt',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_dwt')
data = self.pipeline.get_data('wavelet_dwt')
assert np.sum(data) == pytest.approx(105.54278879805277, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_time_normalization(self) -> None:
module = TimeNormalizationModule(name_in='timenorm',
image_in_tag='images',
image_out_tag='timenorm')
self.pipeline.add_module(module)
self.pipeline.run_module('timenorm')
data = self.pipeline.get_data('timenorm')
assert np.sum(data) == pytest.approx(56.443663773873, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_wavelet_denoising_even_size(self) -> None:
module = AddLinesModule(name_in='add',
image_in_tag='images',
image_out_tag='images_even',
lines=(1, 0, 1, 0))
self.pipeline.add_module(module)
self.pipeline.run_module('add')
data = self.pipeline.get_data('images_even')
assert np.sum(data) == pytest.approx(105.54278879805275, rel=self.limit, abs=0.)
assert data.shape == (10, 12, 12)
cwt_config = CwtWaveletConfiguration(wavelet='dog',
wavelet_order=2,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'dog'
assert cwt_config.m_wavelet_order == 2
assert not cwt_config.m_keep_mean
assert cwt_config.m_resolution == 0.5
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_even_1',
image_in_tag='images_even',
image_out_tag='wavelet_even_1',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_even_1')
data = self.pipeline.get_data('wavelet_even_1')
assert np.sum(data) == pytest.approx(105.1035789572968, rel=self.limit, abs=0.)
assert data.shape == (10, 12, 12)
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_even_2',
image_in_tag='images_even',
image_out_tag='wavelet_even_2',
padding='mirror',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_even_2')
data = self.pipeline.get_data('wavelet_even_2')
assert np.sum(data) == pytest.approx(105.06809820408587, rel=self.limit, abs=0.)
assert data.shape == (10, 12, 12)
data = self.pipeline.get_attribute('images', 'NFRAMES', static=False)
assert data == pytest.approx([5, 5], rel=self.limit, abs=0.)
data = self.pipeline.get_attribute('wavelet_even_1', 'NFRAMES', static=False)
assert data == pytest.approx([5, 5], rel=self.limit, abs=0.)
data = self.pipeline.get_attribute('wavelet_even_2', 'NFRAMES', static=False)
assert data == pytest.approx([5, 5], rel=self.limit, abs=0.)
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