How to use the pynpoint.readwrite.nearreading.NearReadingModule function in pynpoint

def test_frame_type_missing(self) -> None:

        with fits.open(self.fitsfile) as hdulist:
            hdulist[10].header.remove('ESO DET FRAM TYPE')
            hdulist.writeto(self.fitsfile, overwrite=True)

        module = NearReadingModule(name_in='read6',
                                   input_dir=self.test_dir+'near',
                                   chopa_out_tag=self.positions[0],
                                   chopb_out_tag=self.positions[1])

        self.pipeline.add_module(module)

        with pytest.raises(ValueError) as error:
            self.pipeline.run_module('read6')

        assert str(error.value) == 'Frame type not found in the FITS header. Image number: 9.'

def test_near_median(self) -> None:

        module = NearReadingModule(name_in='read1c',
                                   input_dir=self.test_dir+'near',
                                   chopa_out_tag=self.positions[0],
                                   chopb_out_tag=self.positions[1],
                                   combine='median')

        self.pipeline.add_module(module)
        self.pipeline.run_module('read1c')

        data = self.pipeline.get_data(self.positions[0])
        assert np.mean(data) == pytest.approx(0.060582854, rel=self.limit, abs=0.)
        assert data.shape == (2, 10, 10)

        data = self.pipeline.get_data(self.positions[1])
        assert np.mean(data) == pytest.approx(0.060582854, rel=self.limit, abs=0.)
        assert data.shape == (2, 10, 10)

def test_check_header(self) -> None:

        with fits.open(self.fitsfile) as hdulist:
            hdulist[0].header['ESO DET CHOP ST'] = 'F'
            hdulist[0].header['ESO DET CHOP CYCSKIP'] = 1
            hdulist[0].header['ESO DET CHOP CYCSUM'] = 'T'
            hdulist.writeto(self.fitsfile, overwrite=True)

        module = NearReadingModule(name_in='read4',
                                   input_dir=self.test_dir+'near',
                                   chopa_out_tag=self.positions[0],
                                   chopb_out_tag=self.positions[1])

        self.pipeline.add_module(module)

        with pytest.warns(UserWarning) as warning:
            self.pipeline.run_module('read4')

        assert len(warning) == 3

        assert warning[0].message.args[0] == 'Dataset was obtained without chopping.'
        assert warning[1].message.args[0] == 'Chop cycles (1) have been skipped.'
        assert warning[2].message.args[0] == 'FITS file contains averaged images.'

        with fits.open(self.fitsfile) as hdulist:

def test_near_subtract_crop_mean(self) -> None:

        module = NearReadingModule(name_in='read1b',
                                   input_dir=self.test_dir+'near',
                                   chopa_out_tag=self.positions[0],
                                   chopb_out_tag=self.positions[1],
                                   subtract=True,
                                   crop=(None, None, 0.3),
                                   combine='mean')

        self.pipeline.add_module(module)
        self.pipeline.run_module('read1b')

        data = self.pipeline.get_data(self.positions[0])
        assert np.mean(data) == pytest.approx(0., rel=self.limit, abs=0.)
        assert data.shape == (2, 7, 7)

        data = self.pipeline.get_data(self.positions[1])
        assert np.mean(data) == pytest.approx(0., rel=self.limit, abs=0.)

with fits.open(self.fitsfile) as hdulist:

            with pytest.warns(UserWarning) as warning:
                hdulist[10].header['ESO DET FRAM TYPE'] = 'HCYCLE1'

            assert len(warning) == 1

            assert warning[0].message.args[0] == 'Keyword name \'ESO DET FRAM TYPE\' is greater ' \
                                                 'than 8 characters or contains characters not ' \
                                                 'allowed by the FITS standard; a HIERARCH card ' \
                                                 'will be created.'

            hdulist.writeto(self.fitsfile, overwrite=True)

        module = NearReadingModule(name_in='read7',
                                   input_dir=self.test_dir+'near',
                                   chopa_out_tag=self.positions[0],
                                   chopb_out_tag=self.positions[1])

        self.pipeline.add_module(module)

        with pytest.warns(UserWarning) as warning:
            self.pipeline.run_module('read7')

        assert len(warning) == 2

        assert warning[0].message.args[0] == 'Previous and current chop position (HCYCLE1) are ' \
                                             'the same. Skipping the current image.'

        assert warning[1].message.args[0] == 'The number of images is not equal for chop A and ' \
                                             'chop B.'

def test_frame_type_invalid(self) -> None:

        with fits.open(self.fitsfile) as hdulist:
            hdulist[10].header['ESO DET FRAM TYPE'] = 'Test'
            hdulist.writeto(self.fitsfile, overwrite=True)

        module = NearReadingModule(name_in='read5',
                                   input_dir=self.test_dir+'near',
                                   chopa_out_tag=self.positions[0],
                                   chopb_out_tag=self.positions[1])

        self.pipeline.add_module(module)

        with pytest.raises(ValueError) as error:
            self.pipeline.run_module('read5')

        assert str(error.value) == 'Frame type (Test) not a valid value. Expecting HCYCLE1 or ' \
                                   'HCYCLE2 as value for ESO DET FRAM TYPE.'

def test_near_read(self) -> None:

        module = NearReadingModule(name_in='read1a',
                                   input_dir=self.test_dir+'near',
                                   chopa_out_tag=self.positions[0],
                                   chopb_out_tag=self.positions[1])

        self.pipeline.add_module(module)
        self.pipeline.run_module('read1a')

        for item in self.positions:

            data = self.pipeline.get_data(item)
            assert np.mean(data) == pytest.approx(0.060582854, rel=self.limit, abs=0.)
            assert data.shape == (10, 10, 10)