|
13 | 13 | from astropy.io import fits |
14 | 14 | from spectral_cube import SpectralCube |
15 | 15 |
|
16 | | -from pipelineVersion import version, tableversion |
17 | | -from scNoiseRoutines import mad_zero_centered |
| 16 | +from .pipelineVersion import version, tableversion |
| 17 | +from .scNoiseRoutines import mad_zero_centered |
18 | 18 |
|
19 | 19 | np.seterr(divide='ignore', invalid='ignore') |
20 | 20 |
|
@@ -729,13 +729,13 @@ def recipe_phangs_broad_mask( |
729 | 729 | mask = join_masks(mask, other_mask, operation='sum' |
730 | 730 | , order='fast_nearest_neighbor') |
731 | 731 |
|
732 | | - if recipe is 'anyscale': |
| 732 | + if recipe == 'anyscale': |
733 | 733 | mask_values = mask.filled_data[:].value > 0 |
734 | 734 | mask = SpectralCube(mask_values*1.0, wcs=mask.wcs, header=mask.header |
735 | 735 | , meta={'BUNIT': ' ', 'BTYPE': 'Mask'}) |
736 | 736 | mask.allow_huge_operations = True |
737 | 737 |
|
738 | | - if recipe is 'somescales': |
| 738 | + if recipe == 'somescales': |
739 | 739 | mask_values = mask.filled_data[:].value > (fraction_of_scales |
740 | 740 | * len(list_of_masks)) |
741 | 741 | mask = SpectralCube(mask_values*1.0, wcs=mask.wcs, header=mask.header |
|
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