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LSST Applications g1653933729+b86e4b8053,g2303c004a3+a99d6d42bd,g28da252d5a+3e0fd49a16,g2bbee38e9b+db5ba6d06c,g2bc492864f+db5ba6d06c,g2cdde0e794+b03b09fb0a,g3156d2b45e+b0ccf75d80,g347aa1857d+db5ba6d06c,g35bb328faa+b86e4b8053,g3a166c0a6a+db5ba6d06c,g3e281a1b8c+c9a36f98fc,g414038480c+70d5606f74,g42360bd172+a99d6d42bd,g461a3dce89+b86e4b8053,g5c9cb89fa5+b7dae1cdc6,g7f102674c4+d27c5e2769,g7f343241b9+516b07f5d7,g80478fca09+ee10125a54,g82479be7b0+11c35b2845,g858d7b2824+a99d6d42bd,g8cd86fa7b1+cb4c24add2,g9125e01d80+b86e4b8053,g979bb04a14+30ae7a28c8,g9ddcbc5298+b4cb6f9276,ga44b09e51c+390e0c76f2,ga5288a1d22+c32cd41bfc,gae0086650b+b86e4b8053,gb58c049af0+2fe46b4317,gc28159a63d+db5ba6d06c,gcd45df26be+a99d6d42bd,gcf0d15dbbd+be93eff7c7,gda6a2b7d83+be93eff7c7,gdaeeff99f8+b9455e0a82,ge5cf12406b+e20940066e,ge79ae78c31+db5ba6d06c,ge7c99ddb59+26aa1bc97f,gf048a9a2f4+f6d4943a12,gf0baf85859+e0282cf37d,w.2024.32
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