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LSST Applications g0f08755f38+c89d42e150,g1635faa6d4+b6cf076a36,g1653933729+a8ce1bb630,g1a0ca8cf93+4c08b13bf7,g28da252d5a+f33f8200ef,g29321ee8c0+0187be18b1,g2bbee38e9b+9634bc57db,g2bc492864f+9634bc57db,g2cdde0e794+c2c89b37c4,g3156d2b45e+41e33cbcdc,g347aa1857d+9634bc57db,g35bb328faa+a8ce1bb630,g3a166c0a6a+9634bc57db,g3e281a1b8c+9f2c4e2fc3,g414038480c+077ccc18e7,g41af890bb2+e740673f1a,g5fbc88fb19+17cd334064,g7642f7d749+c89d42e150,g781aacb6e4+a8ce1bb630,g80478fca09+f8b2ab54e1,g82479be7b0+e2bd23ab8b,g858d7b2824+c89d42e150,g9125e01d80+a8ce1bb630,g9726552aa6+10f999ec6a,ga5288a1d22+065360aec4,gacf8899fa4+9553554aa7,gae0086650b+a8ce1bb630,gb58c049af0+d64f4d3760,gbd46683f8f+ac57cbb13d,gc28159a63d+9634bc57db,gcf0d15dbbd+e37acf7834,gda3e153d99+c89d42e150,gda6a2b7d83+e37acf7834,gdaeeff99f8+1711a396fd,ge2409df99d+cb1e6652d6,ge79ae78c31+9634bc57db,gf0baf85859+147a0692ba,gf3967379c6+02b11634a5,w.2024.45
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