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LSST Applications g0f08755f38+51082c0d4d,g1653933729+a905cd61c3,g168dd56ebc+a905cd61c3,g1a2382251a+29afb38aec,g20f6ffc8e0+51082c0d4d,g217e2c1bcf+ab9ba0d5ca,g28da252d5a+81b6c2f226,g2bbee38e9b+cc7bbd92cc,g2bc492864f+cc7bbd92cc,g32e5bea42b+4321971e9a,g347aa1857d+cc7bbd92cc,g35bb328faa+a905cd61c3,g3a166c0a6a+cc7bbd92cc,g3bd4b5ce2c+e795d60641,g3e281a1b8c+2bff41ced5,g414038480c+4de324692b,g41af890bb2+f80cf72528,g43bc871e57+a73212ffc0,g78460c75b0+4ae99bb757,g80478fca09+dbf4c199e3,g82479be7b0+84a80b86d5,g8365541083+a905cd61c3,g858d7b2824+51082c0d4d,g9125e01d80+a905cd61c3,ga5288a1d22+379478ca77,gb58c049af0+84d1b6ec45,gc28159a63d+cc7bbd92cc,gc5452a3dca+b82ec7cc4c,gcab2d0539d+4a1e53d2eb,gcf0d15dbbd+a702646d8b,gda6a2b7d83+a702646d8b,gdaeeff99f8+686ef0dd99,ge79ae78c31+cc7bbd92cc,gef2f8181fd+c1889b0e42,gf0baf85859+f9edac6842,gf1e97e5484+bcd3814849,gfa517265be+51082c0d4d,gfa999e8aa5+d85414070d,w.2025.01
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