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LSST Applications g00d0e8bbd7+edbf708997,g03191d30f7+8a59b3663b,g199a45376c+5137f08352,g1a3ebadc4e+1992cd03cb,g1fd858c14a+e3a09fe978,g262e1987ae+03c1293f13,g29ae962dfc+c9ee637888,g2cef7863aa+73c82f25e4,g35bb328faa+edbf708997,g3fd5ace14f+0efc468a66,g47891489e3+6dc8069a4c,g53246c7159+edbf708997,g64539dfbff+ebfceef01a,g67b6fd64d1+6dc8069a4c,g74acd417e5+c9a090baa5,g786e29fd12+af89c03590,g7ac6eacf09+1a9ca7c200,g7ae74a0b1c+a25e60b391,g7aefaa3e3d+e16af44d2f,g7cc15d900a+721c000477,g87389fa792+a4172ec7da,g89139ef638+6dc8069a4c,g8d7436a09f+d270975941,g8ea07a8fe4+db21c37724,g92c671f44c+ebfceef01a,g98df359435+e42c3b5ef0,ga2180abaac+edbf708997,gac66b60396+966efe6077,gb88ae4c679+7d1e613a8d,gbaa8f7a6c5+38b34f4976,gbf99507273+edbf708997,gc24b5d6ed1+ebfceef01a,gca7fc764a6+6dc8069a4c,gd7ef33dd92+6dc8069a4c,gdab6d2f7ff+c9a090baa5,gdbb4c4dda9+ebfceef01a,ge410e46f29+6dc8069a4c,geaed405ab2+e194be0d2b,w.2025.47
LSST Data Management Base Package
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Go to the source code of this file.
Classes | |
| class | lsst.scarlet.lite.display.LinearPercentileNorm |
| class | lsst.scarlet.lite.display.AsinhPercentileNorm |
Namespaces | |
| namespace | lsst |
| namespace | lsst.scarlet |
| namespace | lsst.scarlet.lite |
| namespace | lsst.scarlet.lite.display |
Functions | |
| np.ndarray | lsst.scarlet.lite.display.channels_to_rgb (int channels) |
| np.ndarray | lsst.scarlet.lite.display.img_to_3channel (np.ndarray img, np.ndarray|None channel_map=None, float fill_value=0) |
| np.ndarray | lsst.scarlet.lite.display.img_to_rgb (np.ndarray|Image img, np.ndarray|None channel_map=None, float fill_value=0, Mapping|None norm=None, np.ndarray|None mask=None) |
| matplotlib.pyplot.Figure | lsst.scarlet.lite.display.show_likelihood (Blend blend, tuple[float, float]|None figsize=None, **kwargs) |
| lsst.scarlet.lite.display._add_markers (Source src, tuple[float, float, float, float] extent, matplotlib.pyplot.Axes ax, bool add_markers, bool add_boxes, dict marker_kwargs, dict box_kwargs) | |
| lsst.scarlet.lite.display.show_observation (Observation observation, Mapping|None norm=None, np.ndarray|None channel_map=None, Sequence|None centers=None, str|None psf_scaling=None, tuple[float, float]|None figsize=None) | |
| matplotlib.pyplot.Figure | lsst.scarlet.lite.display.show_scene (Blend blend, Mapping|None norm=None, np.ndarray|None channel_map=None, bool show_model=True, bool show_observed=False, bool show_rendered=False, bool show_residual=False, bool add_labels=True, bool add_boxes=False, tuple[float, float]|None figsize=None, bool linear=True, bool use_flux=False, dict|None box_kwargs=None) |
| tuple[int, int, int, int] | lsst.scarlet.lite.display.get_extent (Box bbox) |
| matplotlib.pyplot.Figure | lsst.scarlet.lite.display.show_sources (Blend blend, list[Source]|None sources=None, Mapping|None norm=None, np.ndarray|None channel_map=None, bool show_model=True, bool show_observed=False, bool show_rendered=False, bool show_spectrum=True, tuple[float, float]|None figsize=None, bool model_mask=True, bool add_markers=True, bool add_boxes=False, bool use_flux=False) |
| matplotlib.pyplot.Figure | lsst.scarlet.lite.display.compare_spectra (bool use_flux=True, bool use_template=True, **list[Source] all_sources) |
Variables | |
| float | lsst.scarlet.lite.display.panel_size = 4.0 |