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LSST Applications g1653933729+34a971ddd9,g1a997c3884+34a971ddd9,g28da252d5a+32b12a4112,g2bbee38e9b+ffc559ee46,g2bc492864f+ffc559ee46,g2cdde0e794+704103fe75,g3156d2b45e+6e87dc994a,g347aa1857d+ffc559ee46,g35bb328faa+34a971ddd9,g3a166c0a6a+ffc559ee46,g3e281a1b8c+8ec26ec694,g4005a62e65+ba0306790b,g414038480c+9f5be647b3,g41af890bb2+686ab7cd58,g4e1a3235cc+1fd785b14f,g7b55021d1b+da0d0eec6b,g80478fca09+6e21f9dc20,g82479be7b0+6fa35d7547,g858d7b2824+356eaf3287,g9125e01d80+34a971ddd9,ga5288a1d22+ba1f9bb3ef,gae0086650b+34a971ddd9,gb4748cc0e8+fd26a265a0,gb4ec7eb0ab+356eaf3287,gb58c049af0+ace264a4f2,gc081298178+30f12d1e69,gc28159a63d+ffc559ee46,gcf0d15dbbd+4adb3139bd,gd6b7c0dfd1+aae9efebf5,gda3e153d99+356eaf3287,gda6a2b7d83+4adb3139bd,gdaeeff99f8+7774323b41,gdd5a9049c5+ea78b51778,ge2409df99d+fa478de0b0,ge33fd446bb+356eaf3287,ge79ae78c31+ffc559ee46,gf0baf85859+890af219f9,gf5289d68f6+e11c78aeea,w.2024.41
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 |
1.10.0