# Licensed under a 3-clause BSD style license - see LICENSE.rst import pytest import numpy as np from numpy.testing import assert_array_equal, assert_array_almost_equal from astropy.nddata.nduncertainty import (StdDevUncertainty, VarianceUncertainty, InverseVariance, UnknownUncertainty, IncompatibleUncertaintiesException) from astropy.nddata import NDDataRef from astropy.nddata.nddata import NDData from astropy.units import UnitsError, Quantity from astropy import units as u # Alias NDDataAllMixins in case this will be renamed ... :-) NDDataArithmetic = NDDataRef class StdDevUncertaintyUncorrelated(StdDevUncertainty): @property def supports_correlated(self): return False # Test with Data covers: # scalars, 1D, 2D and 3D # broadcasting between them @pytest.mark.parametrize(('data1', 'data2'), [ (np.array(5), np.array(10)), (np.array(5), np.arange(10)), (np.array(5), np.arange(10).reshape(2, 5)), (np.arange(10), np.ones(10) * 2), (np.arange(10), np.ones((10, 10)) * 2), (np.arange(10).reshape(2, 5), np.ones((2, 5)) * 3), (np.arange(1000).reshape(20, 5, 10), np.ones((20, 5, 10)) * 3) ]) def test_arithmetics_data(data1, data2): nd1 = NDDataArithmetic(data1) nd2 = NDDataArithmetic(data2) # Addition nd3 = nd1.add(nd2) assert_array_equal(data1+data2, nd3.data) # Subtraction nd4 = nd1.subtract(nd2) assert_array_equal(data1-data2, nd4.data) # Multiplication nd5 = nd1.multiply(nd2) assert_array_equal(data1*data2, nd5.data) # Division nd6 = nd1.divide(nd2) assert_array_equal(data1/data2, nd6.data) for nd in [nd3, nd4, nd5, nd6]: # Check that broadcasting worked as expected if data1.ndim > data2.ndim: assert data1.shape == nd.data.shape else: assert data2.shape == nd.data.shape # Check all other attributes are not set assert nd.unit is None assert nd.uncertainty is None assert nd.mask is None assert len(nd.meta) == 0 assert nd.wcs is None # Invalid arithmetic operations for data covering: # not broadcastable data def test_arithmetics_data_invalid(): nd1 = NDDataArithmetic([1, 2, 3]) nd2 = NDDataArithmetic([1, 2]) with pytest.raises(ValueError): nd1.add(nd2) # Test with Data and unit and covers: # identical units (even dimensionless unscaled vs. no unit), # equivalent units (such as meter and kilometer) # equivalent composite units (such as m/s and km/h) @pytest.mark.parametrize(('data1', 'data2'), [ (np.array(5) * u.s, np.array(10) * u.s), (np.array(5) * u.s, np.arange(10) * u.h), (np.array(5) * u.s, np.arange(10).reshape(2, 5) * u.min), (np.arange(10) * u.m / u.s, np.ones(10) * 2 * u.km / u.s), (np.arange(10) * u.m / u.s, np.ones((10, 10)) * 2 * u.m / u.h), (np.arange(10).reshape(2, 5) * u.m / u.s, np.ones((2, 5)) * 3 * u.km / u.h), (np.arange(1000).reshape(20, 5, 10), np.ones((20, 5, 10)) * 3 * u.dimensionless_unscaled), (np.array(5), np.array(10) * u.s / u.h), ]) def test_arithmetics_data_unit_identical(data1, data2): nd1 = NDDataArithmetic(data1) nd2 = NDDataArithmetic(data2) # Addition nd3 = nd1.add(nd2) ref = data1 + data2 ref_unit, ref_data = ref.unit, ref.value assert_array_equal(ref_data, nd3.data) assert nd3.unit == ref_unit # Subtraction nd4 = nd1.subtract(nd2) ref = data1 - data2 ref_unit, ref_data = ref.unit, ref.value assert_array_equal(ref_data, nd4.data) assert nd4.unit == ref_unit # Multiplication nd5 = nd1.multiply(nd2) ref = data1 * data2 ref_unit, ref_data = ref.unit, ref.value assert_array_equal(ref_data, nd5.data) assert nd5.unit == ref_unit # Division nd6 = nd1.divide(nd2) ref = data1 / data2 ref_unit, ref_data = ref.unit, ref.value assert_array_equal(ref_data, nd6.data) assert nd6.unit == ref_unit for nd in [nd3, nd4, nd5, nd6]: # Check that broadcasting worked as expected if data1.ndim > data2.ndim: assert data1.shape == nd.data.shape else: assert data2.shape == nd.data.shape # Check all other attributes are not set assert nd.uncertainty is None assert nd.mask is None assert len(nd.meta) == 0 assert nd.wcs is None # Test with Data and unit and covers: # not identical not convertible units # one with unit (which is not dimensionless) and one without @pytest.mark.parametrize(('data1', 'data2'), [ (np.array(5) * u.s, np.array(10) * u.m), (np.array(5) * u.Mpc, np.array(10) * u.km / u.s), (np.array(5) * u.Mpc, np.array(10)), (np.array(5), np.array(10) * u.s), ]) def test_arithmetics_data_unit_not_identical(data1, data2): nd1 = NDDataArithmetic(data1) nd2 = NDDataArithmetic(data2) # Addition should not be possible with pytest.raises(UnitsError): nd1.add(nd2) # Subtraction should not be possible with pytest.raises(UnitsError): nd1.subtract(nd2) # Multiplication is possible nd3 = nd1.multiply(nd2) ref = data1 * data2 ref_unit, ref_data = ref.unit, ref.value assert_array_equal(ref_data, nd3.data) assert nd3.unit == ref_unit # Division is possible nd4 = nd1.divide(nd2) ref = data1 / data2 ref_unit, ref_data = ref.unit, ref.value assert_array_equal(ref_data, nd4.data) assert nd4.unit == ref_unit for nd in [nd3, nd4]: # Check all other attributes are not set assert nd.uncertainty is None assert nd.mask is None assert len(nd.meta) == 0 assert nd.wcs is None # Tests with wcs (not very sensible because there is no operation between them # covering: # both set and identical/not identical # one set # None set @pytest.mark.parametrize(('wcs1', 'wcs2'), [ (None, None), (None, 5), (5, None), (5, 5), (7, 5), ]) def test_arithmetics_data_wcs(wcs1, wcs2): nd1 = NDDataArithmetic(1, wcs=wcs1) nd2 = NDDataArithmetic(1, wcs=wcs2) if wcs1 is None and wcs2 is None: ref_wcs = None elif wcs1 is None: ref_wcs = wcs2 elif wcs2 is None: ref_wcs = wcs1 else: ref_wcs = wcs1 # Addition nd3 = nd1.add(nd2) assert ref_wcs == nd3.wcs # Subtraction nd4 = nd1.subtract(nd2) assert ref_wcs == nd3.wcs # Multiplication nd5 = nd1.multiply(nd2) assert ref_wcs == nd3.wcs # Division nd6 = nd1.divide(nd2) assert ref_wcs == nd3.wcs for nd in [nd3, nd4, nd5, nd6]: # Check all other attributes are not set assert nd.unit is None assert nd.uncertainty is None assert len(nd.meta) == 0 assert nd.mask is None # Masks are completely separated in the NDArithmetics from the data so we need # no correlated tests but covering: # masks 1D, 2D and mixed cases with broadcasting @pytest.mark.parametrize(('mask1', 'mask2'), [ (None, None), (None, False), (True, None), (False, False), (True, False), (False, True), (True, True), (np.array(False), np.array(True)), (np.array(False), np.array([0, 1, 0, 1, 1], dtype=np.bool_)), (np.array(True), np.array([[0, 1, 0, 1, 1], [1, 1, 0, 1, 1]], dtype=np.bool_)), (np.array([0, 1, 0, 1, 1], dtype=np.bool_), np.array([1, 1, 0, 0, 1], dtype=np.bool_)), (np.array([0, 1, 0, 1, 1], dtype=np.bool_), np.array([[0, 1, 0, 1, 1], [1, 0, 0, 1, 1]], dtype=np.bool_)), (np.array([[0, 1, 0, 1, 1], [1, 0, 0, 1, 1]], dtype=np.bool_), np.array([[0, 1, 0, 1, 1], [1, 1, 0, 1, 1]], dtype=np.bool_)), ]) def test_arithmetics_data_masks(mask1, mask2): nd1 = NDDataArithmetic(1, mask=mask1) nd2 = NDDataArithmetic(1, mask=mask2) if mask1 is None and mask2 is None: ref_mask = None elif mask1 is None: ref_mask = mask2 elif mask2 is None: ref_mask = mask1 else: ref_mask = mask1 | mask2 # Addition nd3 = nd1.add(nd2) assert_array_equal(ref_mask, nd3.mask) # Subtraction nd4 = nd1.subtract(nd2) assert_array_equal(ref_mask, nd4.mask) # Multiplication nd5 = nd1.multiply(nd2) assert_array_equal(ref_mask, nd5.mask) # Division nd6 = nd1.divide(nd2) assert_array_equal(ref_mask, nd6.mask) for nd in [nd3, nd4, nd5, nd6]: # Check all other attributes are not set assert nd.unit is None assert nd.uncertainty is None assert len(nd.meta) == 0 assert nd.wcs is None # One additional case which can not be easily incorporated in the test above # what happens if the masks are numpy ndarrays are not broadcastable def test_arithmetics_data_masks_invalid(): nd1 = NDDataArithmetic(1, mask=np.array([1, 0], dtype=np.bool_)) nd2 = NDDataArithmetic(1, mask=np.array([1, 0, 1], dtype=np.bool_)) with pytest.raises(ValueError): nd1.add(nd2) with pytest.raises(ValueError): nd1.multiply(nd2) with pytest.raises(ValueError): nd1.subtract(nd2) with pytest.raises(ValueError): nd1.divide(nd2) # Covering: # both have uncertainties (data and uncertainty without unit) # tested against manually determined resulting uncertainties to verify the # implemented formulas # this test only works as long as data1 and data2 do not contain any 0 def test_arithmetics_stddevuncertainty_basic(): nd1 = NDDataArithmetic([1, 2, 3], uncertainty=StdDevUncertainty([1, 1, 3])) nd2 = NDDataArithmetic([2, 2, 2], uncertainty=StdDevUncertainty([2, 2, 2])) nd3 = nd1.add(nd2) nd4 = nd2.add(nd1) # Inverse operation should result in the same uncertainty assert_array_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = np.sqrt(np.array([1, 1, 3])**2 + np.array([2, 2, 2])**2) assert_array_equal(nd3.uncertainty.array, ref_uncertainty) nd3 = nd1.subtract(nd2) nd4 = nd2.subtract(nd1) # Inverse operation should result in the same uncertainty assert_array_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty (same as for add) assert_array_equal(nd3.uncertainty.array, ref_uncertainty) # Multiplication and Division only work with almost equal array comparisons # since the formula implemented and the formula used as reference are # slightly different. nd3 = nd1.multiply(nd2) nd4 = nd2.multiply(nd1) # Inverse operation should result in the same uncertainty assert_array_almost_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = np.abs(np.array([2, 4, 6])) * np.sqrt( (np.array([1, 1, 3]) / np.array([1, 2, 3]))**2 + (np.array([2, 2, 2]) / np.array([2, 2, 2]))**2) assert_array_almost_equal(nd3.uncertainty.array, ref_uncertainty) nd3 = nd1.divide(nd2) nd4 = nd2.divide(nd1) # Inverse operation gives a different uncertainty! # Compare it to the theoretical uncertainty ref_uncertainty_1 = np.abs(np.array([1/2, 2/2, 3/2])) * np.sqrt( (np.array([1, 1, 3]) / np.array([1, 2, 3]))**2 + (np.array([2, 2, 2]) / np.array([2, 2, 2]))**2) assert_array_almost_equal(nd3.uncertainty.array, ref_uncertainty_1) ref_uncertainty_2 = np.abs(np.array([2, 1, 2/3])) * np.sqrt( (np.array([1, 1, 3]) / np.array([1, 2, 3]))**2 + (np.array([2, 2, 2]) / np.array([2, 2, 2]))**2) assert_array_almost_equal(nd4.uncertainty.array, ref_uncertainty_2) # Tests for correlation, covering # correlation between -1 and 1 with correlation term being positive / negative # also with one data being once positive and once completely negative # The point of this test is to compare the used formula to the theoretical one. # TODO: Maybe covering units too but I think that should work because of # the next tests. Also this may be reduced somehow. @pytest.mark.parametrize(('cor', 'uncert1', 'data2'), [ (-1, [1, 1, 3], [2, 2, 7]), (-0.5, [1, 1, 3], [2, 2, 7]), (-0.25, [1, 1, 3], [2, 2, 7]), (0, [1, 1, 3], [2, 2, 7]), (0.25, [1, 1, 3], [2, 2, 7]), (0.5, [1, 1, 3], [2, 2, 7]), (1, [1, 1, 3], [2, 2, 7]), (-1, [-1, -1, -3], [2, 2, 7]), (-0.5, [-1, -1, -3], [2, 2, 7]), (-0.25, [-1, -1, -3], [2, 2, 7]), (0, [-1, -1, -3], [2, 2, 7]), (0.25, [-1, -1, -3], [2, 2, 7]), (0.5, [-1, -1, -3], [2, 2, 7]), (1, [-1, -1, -3], [2, 2, 7]), (-1, [1, 1, 3], [-2, -3, -2]), (-0.5, [1, 1, 3], [-2, -3, -2]), (-0.25, [1, 1, 3], [-2, -3, -2]), (0, [1, 1, 3], [-2, -3, -2]), (0.25, [1, 1, 3], [-2, -3, -2]), (0.5, [1, 1, 3], [-2, -3, -2]), (1, [1, 1, 3], [-2, -3, -2]), (-1, [-1, -1, -3], [-2, -3, -2]), (-0.5, [-1, -1, -3], [-2, -3, -2]), (-0.25, [-1, -1, -3], [-2, -3, -2]), (0, [-1, -1, -3], [-2, -3, -2]), (0.25, [-1, -1, -3], [-2, -3, -2]), (0.5, [-1, -1, -3], [-2, -3, -2]), (1, [-1, -1, -3], [-2, -3, -2]), ]) def test_arithmetics_stddevuncertainty_basic_with_correlation( cor, uncert1, data2): data1 = np.array([1, 2, 3]) data2 = np.array(data2) uncert1 = np.array(uncert1) uncert2 = np.array([2, 2, 2]) nd1 = NDDataArithmetic(data1, uncertainty=StdDevUncertainty(uncert1)) nd2 = NDDataArithmetic(data2, uncertainty=StdDevUncertainty(uncert2)) nd3 = nd1.add(nd2, uncertainty_correlation=cor) nd4 = nd2.add(nd1, uncertainty_correlation=cor) # Inverse operation should result in the same uncertainty assert_array_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = np.sqrt(uncert1**2 + uncert2**2 + 2 * cor * np.abs(uncert1 * uncert2)) assert_array_equal(nd3.uncertainty.array, ref_uncertainty) nd3 = nd1.subtract(nd2, uncertainty_correlation=cor) nd4 = nd2.subtract(nd1, uncertainty_correlation=cor) # Inverse operation should result in the same uncertainty assert_array_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = np.sqrt(uncert1**2 + uncert2**2 - 2 * cor * np.abs(uncert1 * uncert2)) assert_array_equal(nd3.uncertainty.array, ref_uncertainty) # Multiplication and Division only work with almost equal array comparisons # since the formula implemented and the formula used as reference are # slightly different. nd3 = nd1.multiply(nd2, uncertainty_correlation=cor) nd4 = nd2.multiply(nd1, uncertainty_correlation=cor) # Inverse operation should result in the same uncertainty assert_array_almost_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = (np.abs(data1 * data2)) * np.sqrt( (uncert1 / data1)**2 + (uncert2 / data2)**2 + (2 * cor * np.abs(uncert1 * uncert2) / (data1 * data2))) assert_array_almost_equal(nd3.uncertainty.array, ref_uncertainty) nd3 = nd1.divide(nd2, uncertainty_correlation=cor) nd4 = nd2.divide(nd1, uncertainty_correlation=cor) # Inverse operation gives a different uncertainty! # Compare it to the theoretical uncertainty ref_uncertainty_1 = (np.abs(data1 / data2)) * np.sqrt( (uncert1 / data1)**2 + (uncert2 / data2)**2 - (2 * cor * np.abs(uncert1 * uncert2) / (data1 * data2))) assert_array_almost_equal(nd3.uncertainty.array, ref_uncertainty_1) ref_uncertainty_2 = (np.abs(data2 / data1)) * np.sqrt( (uncert1 / data1)**2 + (uncert2 / data2)**2 - (2 * cor * np.abs(uncert1 * uncert2) / (data1 * data2))) assert_array_almost_equal(nd4.uncertainty.array, ref_uncertainty_2) # Tests for correlation, covering # correlation between -1 and 1 with correlation term being positive / negative # also with one data being once positive and once completely negative # The point of this test is to compare the used formula to the theoretical one. # TODO: Maybe covering units too but I think that should work because of # the next tests. Also this may be reduced somehow. @pytest.mark.parametrize(('cor', 'uncert1', 'data2'), [ (-1, [1, 1, 3], [2, 2, 7]), (-0.5, [1, 1, 3], [2, 2, 7]), (-0.25, [1, 1, 3], [2, 2, 7]), (0, [1, 1, 3], [2, 2, 7]), (0.25, [1, 1, 3], [2, 2, 7]), (0.5, [1, 1, 3], [2, 2, 7]), (1, [1, 1, 3], [2, 2, 7]), (-1, [-1, -1, -3], [2, 2, 7]), (-0.5, [-1, -1, -3], [2, 2, 7]), (-0.25, [-1, -1, -3], [2, 2, 7]), (0, [-1, -1, -3], [2, 2, 7]), (0.25, [-1, -1, -3], [2, 2, 7]), (0.5, [-1, -1, -3], [2, 2, 7]), (1, [-1, -1, -3], [2, 2, 7]), (-1, [1, 1, 3], [-2, -3, -2]), (-0.5, [1, 1, 3], [-2, -3, -2]), (-0.25, [1, 1, 3], [-2, -3, -2]), (0, [1, 1, 3], [-2, -3, -2]), (0.25, [1, 1, 3], [-2, -3, -2]), (0.5, [1, 1, 3], [-2, -3, -2]), (1, [1, 1, 3], [-2, -3, -2]), (-1, [-1, -1, -3], [-2, -3, -2]), (-0.5, [-1, -1, -3], [-2, -3, -2]), (-0.25, [-1, -1, -3], [-2, -3, -2]), (0, [-1, -1, -3], [-2, -3, -2]), (0.25, [-1, -1, -3], [-2, -3, -2]), (0.5, [-1, -1, -3], [-2, -3, -2]), (1, [-1, -1, -3], [-2, -3, -2]), ]) def test_arithmetics_varianceuncertainty_basic_with_correlation( cor, uncert1, data2): data1 = np.array([1, 2, 3]) data2 = np.array(data2) uncert1 = np.array(uncert1)**2 uncert2 = np.array([2, 2, 2])**2 nd1 = NDDataArithmetic(data1, uncertainty=VarianceUncertainty(uncert1)) nd2 = NDDataArithmetic(data2, uncertainty=VarianceUncertainty(uncert2)) nd3 = nd1.add(nd2, uncertainty_correlation=cor) nd4 = nd2.add(nd1, uncertainty_correlation=cor) # Inverse operation should result in the same uncertainty assert_array_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = (uncert1 + uncert2 + 2 * cor * np.sqrt(uncert1 * uncert2)) assert_array_equal(nd3.uncertainty.array, ref_uncertainty) nd3 = nd1.subtract(nd2, uncertainty_correlation=cor) nd4 = nd2.subtract(nd1, uncertainty_correlation=cor) # Inverse operation should result in the same uncertainty assert_array_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = (uncert1 + uncert2 - 2 * cor * np.sqrt(uncert1 * uncert2)) assert_array_equal(nd3.uncertainty.array, ref_uncertainty) # Multiplication and Division only work with almost equal array comparisons # since the formula implemented and the formula used as reference are # slightly different. nd3 = nd1.multiply(nd2, uncertainty_correlation=cor) nd4 = nd2.multiply(nd1, uncertainty_correlation=cor) # Inverse operation should result in the same uncertainty assert_array_almost_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = (data1 * data2)**2 * ( uncert1 / data1**2 + uncert2 / data2**2 + (2 * cor * np.sqrt(uncert1 * uncert2) / (data1 * data2))) assert_array_almost_equal(nd3.uncertainty.array, ref_uncertainty) nd3 = nd1.divide(nd2, uncertainty_correlation=cor) nd4 = nd2.divide(nd1, uncertainty_correlation=cor) # Inverse operation gives a different uncertainty because of the # prefactor nd1/nd2 vs nd2/nd1. Howeveare, a large chunk is the same. ref_common = ( uncert1 / data1**2 + uncert2 / data2**2 - (2 * cor * np.sqrt(uncert1 * uncert2) / (data1 * data2))) # Compare it to the theoretical uncertainty ref_uncertainty_1 = (data1 / data2)**2 * ref_common assert_array_almost_equal(nd3.uncertainty.array, ref_uncertainty_1) ref_uncertainty_2 = (data2 / data1)**2 * ref_common assert_array_almost_equal(nd4.uncertainty.array, ref_uncertainty_2) # Tests for correlation, covering # correlation between -1 and 1 with correlation term being positive / negative # also with one data being once positive and once completely negative # The point of this test is to compare the used formula to the theoretical one. # TODO: Maybe covering units too but I think that should work because of # the next tests. Also this may be reduced somehow. @pytest.mark.parametrize(('cor', 'uncert1', 'data2'), [ (-1, [1, 1, 3], [2, 2, 7]), (-0.5, [1, 1, 3], [2, 2, 7]), (-0.25, [1, 1, 3], [2, 2, 7]), (0, [1, 1, 3], [2, 2, 7]), (0.25, [1, 1, 3], [2, 2, 7]), (0.5, [1, 1, 3], [2, 2, 7]), (1, [1, 1, 3], [2, 2, 7]), (-1, [-1, -1, -3], [2, 2, 7]), (-0.5, [-1, -1, -3], [2, 2, 7]), (-0.25, [-1, -1, -3], [2, 2, 7]), (0, [-1, -1, -3], [2, 2, 7]), (0.25, [-1, -1, -3], [2, 2, 7]), (0.5, [-1, -1, -3], [2, 2, 7]), (1, [-1, -1, -3], [2, 2, 7]), (-1, [1, 1, 3], [-2, -3, -2]), (-0.5, [1, 1, 3], [-2, -3, -2]), (-0.25, [1, 1, 3], [-2, -3, -2]), (0, [1, 1, 3], [-2, -3, -2]), (0.25, [1, 1, 3], [-2, -3, -2]), (0.5, [1, 1, 3], [-2, -3, -2]), (1, [1, 1, 3], [-2, -3, -2]), (-1, [-1, -1, -3], [-2, -3, -2]), (-0.5, [-1, -1, -3], [-2, -3, -2]), (-0.25, [-1, -1, -3], [-2, -3, -2]), (0, [-1, -1, -3], [-2, -3, -2]), (0.25, [-1, -1, -3], [-2, -3, -2]), (0.5, [-1, -1, -3], [-2, -3, -2]), (1, [-1, -1, -3], [-2, -3, -2]), ]) def test_arithmetics_inversevarianceuncertainty_basic_with_correlation( cor, uncert1, data2): data1 = np.array([1, 2, 3]) data2 = np.array(data2) uncert1 = 1 / np.array(uncert1)**2 uncert2 = 1 / np.array([2, 2, 2])**2 nd1 = NDDataArithmetic(data1, uncertainty=InverseVariance(uncert1)) nd2 = NDDataArithmetic(data2, uncertainty=InverseVariance(uncert2)) nd3 = nd1.add(nd2, uncertainty_correlation=cor) nd4 = nd2.add(nd1, uncertainty_correlation=cor) # Inverse operation should result in the same uncertainty assert_array_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = 1/ (1 / uncert1 + 1 / uncert2 + 2 * cor / np.sqrt(uncert1 * uncert2)) assert_array_equal(nd3.uncertainty.array, ref_uncertainty) nd3 = nd1.subtract(nd2, uncertainty_correlation=cor) nd4 = nd2.subtract(nd1, uncertainty_correlation=cor) # Inverse operation should result in the same uncertainty assert_array_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = 1 / (1 / uncert1 + 1 / uncert2 - 2 * cor / np.sqrt(uncert1 * uncert2)) assert_array_equal(nd3.uncertainty.array, ref_uncertainty) # Multiplication and Division only work with almost equal array comparisons # since the formula implemented and the formula used as reference are # slightly different. nd3 = nd1.multiply(nd2, uncertainty_correlation=cor) nd4 = nd2.multiply(nd1, uncertainty_correlation=cor) # Inverse operation should result in the same uncertainty assert_array_almost_equal(nd3.uncertainty.array, nd4.uncertainty.array) # Compare it to the theoretical uncertainty ref_uncertainty = 1 / ((data1 * data2)**2 * ( 1 / uncert1 / data1**2 + 1 / uncert2 / data2**2 + (2 * cor / np.sqrt(uncert1 * uncert2) / (data1 * data2)))) assert_array_almost_equal(nd3.uncertainty.array, ref_uncertainty) nd3 = nd1.divide(nd2, uncertainty_correlation=cor) nd4 = nd2.divide(nd1, uncertainty_correlation=cor) # Inverse operation gives a different uncertainty because of the # prefactor nd1/nd2 vs nd2/nd1. Howeveare, a large chunk is the same. ref_common = ( 1 / uncert1 / data1**2 + 1 / uncert2 / data2**2 - (2 * cor / np.sqrt(uncert1 * uncert2) / (data1 * data2))) # Compare it to the theoretical uncertainty ref_uncertainty_1 = 1 / ((data1 / data2)**2 * ref_common) assert_array_almost_equal(nd3.uncertainty.array, ref_uncertainty_1) ref_uncertainty_2 = 1 / ((data2 / data1)**2 * ref_common) assert_array_almost_equal(nd4.uncertainty.array, ref_uncertainty_2) # Covering: # just an example that a np.ndarray works as correlation, no checks for # the right result since these were basically done in the function above. def test_arithmetics_stddevuncertainty_basic_with_correlation_array(): data1 = np.array([1, 2, 3]) data2 = np.array([1, 1, 1]) uncert1 = np.array([1, 1, 1]) uncert2 = np.array([2, 2, 2]) cor = np.array([0, 0.25, 0]) nd1 = NDDataArithmetic(data1, uncertainty=StdDevUncertainty(uncert1)) nd2 = NDDataArithmetic(data2, uncertainty=StdDevUncertainty(uncert2)) nd1.add(nd2, uncertainty_correlation=cor) # Covering: # That propagate throws an exception when correlation is given but the # uncertainty does not support correlation. def test_arithmetics_with_correlation_unsupported(): data1 = np.array([1, 2, 3]) data2 = np.array([1, 1, 1]) uncert1 = np.array([1, 1, 1]) uncert2 = np.array([2, 2, 2]) cor = 3 nd1 = NDDataArithmetic(data1, uncertainty=StdDevUncertaintyUncorrelated(uncert1)) nd2 = NDDataArithmetic(data2, uncertainty=StdDevUncertaintyUncorrelated(uncert2)) with pytest.raises(ValueError): nd1.add(nd2, uncertainty_correlation=cor) # Covering: # only one has an uncertainty (data and uncertainty without unit) # tested against the case where the other one has zero uncertainty. (this case # must be correct because we tested it in the last case) # Also verify that if the result of the data has negative values the resulting # uncertainty has no negative values. def test_arithmetics_stddevuncertainty_one_missing(): nd1 = NDDataArithmetic([1, -2, 3]) nd1_ref = NDDataArithmetic([1, -2, 3], uncertainty=StdDevUncertainty([0, 0, 0])) nd2 = NDDataArithmetic([2, 2, -2], uncertainty=StdDevUncertainty([2, 2, 2])) # Addition nd3 = nd1.add(nd2) nd3_ref = nd1_ref.add(nd2) assert_array_equal(nd3.uncertainty.array, nd3_ref.uncertainty.array) assert_array_equal(np.abs(nd3.uncertainty.array), nd3.uncertainty.array) nd3 = nd2.add(nd1) nd3_ref = nd2.add(nd1_ref) assert_array_equal(nd3.uncertainty.array, nd3_ref.uncertainty.array) assert_array_equal(np.abs(nd3.uncertainty.array), nd3.uncertainty.array) # Subtraction nd3 = nd1.subtract(nd2) nd3_ref = nd1_ref.subtract(nd2) assert_array_equal(nd3.uncertainty.array, nd3_ref.uncertainty.array) assert_array_equal(np.abs(nd3.uncertainty.array), nd3.uncertainty.array) nd3 = nd2.subtract(nd1) nd3_ref = nd2.subtract(nd1_ref) assert_array_equal(nd3.uncertainty.array, nd3_ref.uncertainty.array) assert_array_equal(np.abs(nd3.uncertainty.array), nd3.uncertainty.array) # Multiplication nd3 = nd1.multiply(nd2) nd3_ref = nd1_ref.multiply(nd2) assert_array_equal(nd3.uncertainty.array, nd3_ref.uncertainty.array) assert_array_equal(np.abs(nd3.uncertainty.array), nd3.uncertainty.array) nd3 = nd2.multiply(nd1) nd3_ref = nd2.multiply(nd1_ref) assert_array_equal(nd3.uncertainty.array, nd3_ref.uncertainty.array) assert_array_equal(np.abs(nd3.uncertainty.array), nd3.uncertainty.array) # Division nd3 = nd1.divide(nd2) nd3_ref = nd1_ref.divide(nd2) assert_array_equal(nd3.uncertainty.array, nd3_ref.uncertainty.array) assert_array_equal(np.abs(nd3.uncertainty.array), nd3.uncertainty.array) nd3 = nd2.divide(nd1) nd3_ref = nd2.divide(nd1_ref) assert_array_equal(nd3.uncertainty.array, nd3_ref.uncertainty.array) assert_array_equal(np.abs(nd3.uncertainty.array), nd3.uncertainty.array) # Covering: # data with unit and uncertainty with unit (but equivalent units) # compared against correctly scaled NDDatas @pytest.mark.parametrize(('uncert1', 'uncert2'), [ (np.array([1, 2, 3]) * u.m, None), (np.array([1, 2, 3]) * u.cm, None), (None, np.array([1, 2, 3]) * u.m), (None, np.array([1, 2, 3]) * u.cm), (np.array([1, 2, 3]), np.array([2, 3, 4])), (np.array([1, 2, 3]) * u.m, np.array([2, 3, 4])), (np.array([1, 2, 3]), np.array([2, 3, 4])) * u.m, (np.array([1, 2, 3]) * u.m, np.array([2, 3, 4])) * u.m, (np.array([1, 2, 3]) * u.cm, np.array([2, 3, 4])), (np.array([1, 2, 3]), np.array([2, 3, 4])) * u.cm, (np.array([1, 2, 3]) * u.cm, np.array([2, 3, 4])) * u.cm, (np.array([1, 2, 3]) * u.km, np.array([2, 3, 4])) * u.cm, ]) def test_arithmetics_stddevuncertainty_with_units(uncert1, uncert2): # Data has same units data1 = np.array([1, 2, 3]) * u.m data2 = np.array([-4, 7, 0]) * u.m if uncert1 is not None: uncert1 = StdDevUncertainty(uncert1) if isinstance(uncert1, Quantity): uncert1_ref = uncert1.to_value(data1.unit) else: uncert1_ref = uncert1 uncert_ref1 = StdDevUncertainty(uncert1_ref, copy=True) else: uncert1 = None uncert_ref1 = None if uncert2 is not None: uncert2 = StdDevUncertainty(uncert2) if isinstance(uncert2, Quantity): uncert2_ref = uncert2.to_value(data2.unit) else: uncert2_ref = uncert2 uncert_ref2 = StdDevUncertainty(uncert2_ref, copy=True) else: uncert2 = None uncert_ref2 = None nd1 = NDDataArithmetic(data1, uncertainty=uncert1) nd2 = NDDataArithmetic(data2, uncertainty=uncert2) nd1_ref = NDDataArithmetic(data1, uncertainty=uncert_ref1) nd2_ref = NDDataArithmetic(data2, uncertainty=uncert_ref2) # Let's start the tests # Addition nd3 = nd1.add(nd2) nd3_ref = nd1_ref.add(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.add(nd1) nd3_ref = nd2_ref.add(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Subtraction nd3 = nd1.subtract(nd2) nd3_ref = nd1_ref.subtract(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.subtract(nd1) nd3_ref = nd2_ref.subtract(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Multiplication nd3 = nd1.multiply(nd2) nd3_ref = nd1_ref.multiply(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.multiply(nd1) nd3_ref = nd2_ref.multiply(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Division nd3 = nd1.divide(nd2) nd3_ref = nd1_ref.divide(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.divide(nd1) nd3_ref = nd2_ref.divide(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Covering: # data with unit and uncertainty with unit (but equivalent units) # compared against correctly scaled NDDatas @pytest.mark.parametrize(('uncert1', 'uncert2'), [ (np.array([1, 2, 3]) * u.m, None), (np.array([1, 2, 3]) * u.cm, None), (None, np.array([1, 2, 3]) * u.m), (None, np.array([1, 2, 3]) * u.cm), (np.array([1, 2, 3]), np.array([2, 3, 4])), (np.array([1, 2, 3]) * u.m, np.array([2, 3, 4])), (np.array([1, 2, 3]), np.array([2, 3, 4])) * u.m, (np.array([1, 2, 3]) * u.m, np.array([2, 3, 4])) * u.m, (np.array([1, 2, 3]) * u.cm, np.array([2, 3, 4])), (np.array([1, 2, 3]), np.array([2, 3, 4])) * u.cm, (np.array([1, 2, 3]) * u.cm, np.array([2, 3, 4])) * u.cm, (np.array([1, 2, 3]) * u.km, np.array([2, 3, 4])) * u.cm, ]) def test_arithmetics_varianceuncertainty_with_units(uncert1, uncert2): # Data has same units data1 = np.array([1, 2, 3]) * u.m data2 = np.array([-4, 7, 0]) * u.m if uncert1 is not None: uncert1 = VarianceUncertainty(uncert1**2) if isinstance(uncert1, Quantity): uncert1_ref = uncert1.to_value(data1.unit**2) else: uncert1_ref = uncert1 uncert_ref1 = VarianceUncertainty(uncert1_ref, copy=True) else: uncert1 = None uncert_ref1 = None if uncert2 is not None: uncert2 = VarianceUncertainty(uncert2**2) if isinstance(uncert2, Quantity): uncert2_ref = uncert2.to_value(data2.unit**2) else: uncert2_ref = uncert2 uncert_ref2 = VarianceUncertainty(uncert2_ref, copy=True) else: uncert2 = None uncert_ref2 = None nd1 = NDDataArithmetic(data1, uncertainty=uncert1) nd2 = NDDataArithmetic(data2, uncertainty=uncert2) nd1_ref = NDDataArithmetic(data1, uncertainty=uncert_ref1) nd2_ref = NDDataArithmetic(data2, uncertainty=uncert_ref2) # Let's start the tests # Addition nd3 = nd1.add(nd2) nd3_ref = nd1_ref.add(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.add(nd1) nd3_ref = nd2_ref.add(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Subtraction nd3 = nd1.subtract(nd2) nd3_ref = nd1_ref.subtract(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.subtract(nd1) nd3_ref = nd2_ref.subtract(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Multiplication nd3 = nd1.multiply(nd2) nd3_ref = nd1_ref.multiply(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.multiply(nd1) nd3_ref = nd2_ref.multiply(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Division nd3 = nd1.divide(nd2) nd3_ref = nd1_ref.divide(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.divide(nd1) nd3_ref = nd2_ref.divide(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Covering: # data with unit and uncertainty with unit (but equivalent units) # compared against correctly scaled NDDatas @pytest.mark.parametrize(('uncert1', 'uncert2'), [ (np.array([1, 2, 3]) * u.m, None), (np.array([1, 2, 3]) * u.cm, None), (None, np.array([1, 2, 3]) * u.m), (None, np.array([1, 2, 3]) * u.cm), (np.array([1, 2, 3]), np.array([2, 3, 4])), (np.array([1, 2, 3]) * u.m, np.array([2, 3, 4])), (np.array([1, 2, 3]), np.array([2, 3, 4])) * u.m, (np.array([1, 2, 3]) * u.m, np.array([2, 3, 4])) * u.m, (np.array([1, 2, 3]) * u.cm, np.array([2, 3, 4])), (np.array([1, 2, 3]), np.array([2, 3, 4])) * u.cm, (np.array([1, 2, 3]) * u.cm, np.array([2, 3, 4])) * u.cm, (np.array([1, 2, 3]) * u.km, np.array([2, 3, 4])) * u.cm, ]) def test_arithmetics_inversevarianceuncertainty_with_units(uncert1, uncert2): # Data has same units data1 = np.array([1, 2, 3]) * u.m data2 = np.array([-4, 7, 0]) * u.m if uncert1 is not None: uncert1 = InverseVariance(1 / uncert1**2) if isinstance(uncert1, Quantity): uncert1_ref = uncert1.to_value(1 / data1.unit**2) else: uncert1_ref = uncert1 uncert_ref1 = InverseVariance(uncert1_ref, copy=True) else: uncert1 = None uncert_ref1 = None if uncert2 is not None: uncert2 = InverseVariance(1 / uncert2**2) if isinstance(uncert2, Quantity): uncert2_ref = uncert2.to_value(1 / data2.unit**2) else: uncert2_ref = uncert2 uncert_ref2 = InverseVariance(uncert2_ref, copy=True) else: uncert2 = None uncert_ref2 = None nd1 = NDDataArithmetic(data1, uncertainty=uncert1) nd2 = NDDataArithmetic(data2, uncertainty=uncert2) nd1_ref = NDDataArithmetic(data1, uncertainty=uncert_ref1) nd2_ref = NDDataArithmetic(data2, uncertainty=uncert_ref2) # Let's start the tests # Addition nd3 = nd1.add(nd2) nd3_ref = nd1_ref.add(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.add(nd1) nd3_ref = nd2_ref.add(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Subtraction nd3 = nd1.subtract(nd2) nd3_ref = nd1_ref.subtract(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.subtract(nd1) nd3_ref = nd2_ref.subtract(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Multiplication nd3 = nd1.multiply(nd2) nd3_ref = nd1_ref.multiply(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.multiply(nd1) nd3_ref = nd2_ref.multiply(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Division nd3 = nd1.divide(nd2) nd3_ref = nd1_ref.divide(nd2_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) nd3 = nd2.divide(nd1) nd3_ref = nd2_ref.divide(nd1_ref) assert nd3.unit == nd3_ref.unit assert nd3.uncertainty.unit == nd3_ref.uncertainty.unit assert_array_equal(nd3.uncertainty.array, nd3.uncertainty.array) # Test abbreviation and long name for taking the first found meta, mask, wcs @pytest.mark.parametrize(('use_abbreviation'), ['ff', 'first_found']) def test_arithmetics_handle_switches(use_abbreviation): meta1 = {'a': 1} meta2 = {'b': 2} mask1 = True mask2 = False uncertainty1 = StdDevUncertainty([1, 2, 3]) uncertainty2 = StdDevUncertainty([1, 2, 3]) wcs1 = 5 wcs2 = 100 data1 = [1, 1, 1] data2 = [1, 1, 1] nd1 = NDDataArithmetic(data1, meta=meta1, mask=mask1, wcs=wcs1, uncertainty=uncertainty1) nd2 = NDDataArithmetic(data2, meta=meta2, mask=mask2, wcs=wcs2, uncertainty=uncertainty2) nd3 = NDDataArithmetic(data1) # Both have the attributes but option None is chosen nd_ = nd1.add(nd2, propagate_uncertainties=None, handle_meta=None, handle_mask=None, compare_wcs=None) assert nd_.wcs is None assert len(nd_.meta) == 0 assert nd_.mask is None assert nd_.uncertainty is None # Only second has attributes and False is chosen nd_ = nd3.add(nd2, propagate_uncertainties=False, handle_meta=use_abbreviation, handle_mask=use_abbreviation, compare_wcs=use_abbreviation) assert nd_.wcs == wcs2 assert nd_.meta == meta2 assert nd_.mask == mask2 assert_array_equal(nd_.uncertainty.array, uncertainty2.array) # Only first has attributes and False is chosen nd_ = nd1.add(nd3, propagate_uncertainties=False, handle_meta=use_abbreviation, handle_mask=use_abbreviation, compare_wcs=use_abbreviation) assert nd_.wcs == wcs1 assert nd_.meta == meta1 assert nd_.mask == mask1 assert_array_equal(nd_.uncertainty.array, uncertainty1.array) def test_arithmetics_meta_func(): def meta_fun_func(meta1, meta2, take='first'): if take == 'first': return meta1 else: return meta2 meta1 = {'a': 1} meta2 = {'a': 3, 'b': 2} mask1 = True mask2 = False uncertainty1 = StdDevUncertainty([1, 2, 3]) uncertainty2 = StdDevUncertainty([1, 2, 3]) wcs1 = 5 wcs2 = 100 data1 = [1, 1, 1] data2 = [1, 1, 1] nd1 = NDDataArithmetic(data1, meta=meta1, mask=mask1, wcs=wcs1, uncertainty=uncertainty1) nd2 = NDDataArithmetic(data2, meta=meta2, mask=mask2, wcs=wcs2, uncertainty=uncertainty2) nd3 = nd1.add(nd2, handle_meta=meta_fun_func) assert nd3.meta['a'] == 1 assert 'b' not in nd3.meta nd4 = nd1.add(nd2, handle_meta=meta_fun_func, meta_take='second') assert nd4.meta['a'] == 3 assert nd4.meta['b'] == 2 with pytest.raises(KeyError): nd1.add(nd2, handle_meta=meta_fun_func, take='second') def test_arithmetics_wcs_func(): def wcs_comp_func(wcs1, wcs2, tolerance=0.1): if abs(wcs1 - wcs2) <= tolerance: return True else: return False meta1 = {'a': 1} meta2 = {'a': 3, 'b': 2} mask1 = True mask2 = False uncertainty1 = StdDevUncertainty([1, 2, 3]) uncertainty2 = StdDevUncertainty([1, 2, 3]) wcs1 = 99.99 wcs2 = 100 data1 = [1, 1, 1] data2 = [1, 1, 1] nd1 = NDDataArithmetic(data1, meta=meta1, mask=mask1, wcs=wcs1, uncertainty=uncertainty1) nd2 = NDDataArithmetic(data2, meta=meta2, mask=mask2, wcs=wcs2, uncertainty=uncertainty2) nd3 = nd1.add(nd2, compare_wcs=wcs_comp_func) assert nd3.wcs == 99.99 with pytest.raises(ValueError): nd1.add(nd2, compare_wcs=wcs_comp_func, wcs_tolerance=0.00001) with pytest.raises(KeyError): nd1.add(nd2, compare_wcs=wcs_comp_func, tolerance=1) def test_arithmetics_mask_func(): def mask_sad_func(mask1, mask2, fun=0): if fun > 0.5: return mask2 else: return mask1 meta1 = {'a': 1} meta2 = {'a': 3, 'b': 2} mask1 = [True, False, True] mask2 = [True, False, False] uncertainty1 = StdDevUncertainty([1, 2, 3]) uncertainty2 = StdDevUncertainty([1, 2, 3]) wcs1 = 99.99 wcs2 = 100 data1 = [1, 1, 1] data2 = [1, 1, 1] nd1 = NDDataArithmetic(data1, meta=meta1, mask=mask1, wcs=wcs1, uncertainty=uncertainty1) nd2 = NDDataArithmetic(data2, meta=meta2, mask=mask2, wcs=wcs2, uncertainty=uncertainty2) nd3 = nd1.add(nd2, handle_mask=mask_sad_func) assert_array_equal(nd3.mask, nd1.mask) nd4 = nd1.add(nd2, handle_mask=mask_sad_func, mask_fun=1) assert_array_equal(nd4.mask, nd2.mask) with pytest.raises(KeyError): nd1.add(nd2, handle_mask=mask_sad_func, fun=1) @pytest.mark.parametrize('meth', ['add', 'subtract', 'divide', 'multiply']) def test_two_argument_useage(meth): ndd1 = NDDataArithmetic(np.ones((3, 3))) ndd2 = NDDataArithmetic(np.ones((3, 3))) # Call add on the class (not the instance) and compare it with already # tested useage: ndd3 = getattr(NDDataArithmetic, meth)(ndd1, ndd2) ndd4 = getattr(ndd1, meth)(ndd2) np.testing.assert_array_equal(ndd3.data, ndd4.data) # And the same done on an unrelated instance... ndd3 = getattr(NDDataArithmetic(-100), meth)(ndd1, ndd2) ndd4 = getattr(ndd1, meth)(ndd2) np.testing.assert_array_equal(ndd3.data, ndd4.data) @pytest.mark.parametrize('meth', ['add', 'subtract', 'divide', 'multiply']) def test_two_argument_useage_non_nddata_first_arg(meth): data1 = 50 data2 = 100 # Call add on the class (not the instance) ndd3 = getattr(NDDataArithmetic, meth)(data1, data2) # Compare it with the instance-useage and two identical NDData-like # classes: ndd1 = NDDataArithmetic(data1) ndd2 = NDDataArithmetic(data2) ndd4 = getattr(ndd1, meth)(ndd2) np.testing.assert_array_equal(ndd3.data, ndd4.data) # and check it's also working when called on an instance ndd3 = getattr(NDDataArithmetic(-100), meth)(data1, data2) ndd4 = getattr(ndd1, meth)(ndd2) np.testing.assert_array_equal(ndd3.data, ndd4.data) def test_arithmetics_unknown_uncertainties(): # Not giving any uncertainty class means it is saved as UnknownUncertainty ndd1 = NDDataArithmetic(np.ones((3, 3)), uncertainty=UnknownUncertainty(np.ones((3, 3)))) ndd2 = NDDataArithmetic(np.ones((3, 3)), uncertainty=UnknownUncertainty(np.ones((3, 3))*2)) # There is no way to propagate uncertainties: with pytest.raises(IncompatibleUncertaintiesException): ndd1.add(ndd2) # But it should be possible without propagation ndd3 = ndd1.add(ndd2, propagate_uncertainties=False) np.testing.assert_array_equal(ndd1.uncertainty.array, ndd3.uncertainty.array) ndd4 = ndd1.add(ndd2, propagate_uncertainties=None) assert ndd4.uncertainty is None