LSST Applications g04dff08e69+fafbcb10e2,g0d33ba9806+3d21495239,g0fba68d861+4a282a2c93,g1ec0fe41b4+f536777771,g1fd858c14a+ae46bc2a71,g35bb328faa+fcb1d3bbc8,g4af146b050+9c38a215af,g4d2262a081+36f1e108ba,g53246c7159+fcb1d3bbc8,g5a012ec0e7+b20b785ecb,g60b5630c4e+3d21495239,g6273192d42+d9e7b43dd0,g67b6fd64d1+4086c0989b,g78460c75b0+2f9a1b4bcd,g786e29fd12+cf7ec2a62a,g7b71ed6315+fcb1d3bbc8,g87b7deb4dc+2198278b84,g8852436030+54b48a5987,g89139ef638+4086c0989b,g9125e01d80+fcb1d3bbc8,g94187f82dc+3d21495239,g989de1cb63+4086c0989b,g9d31334357+3d21495239,g9f33ca652e+83205baa3c,gabe3b4be73+1e0a283bba,gabf8522325+fa80ff7197,gb1101e3267+85d1f90f4c,gb58c049af0+f03b321e39,gb89ab40317+4086c0989b,gc0bb628dac+d11454dffd,gcf25f946ba+54b48a5987,gd6cbbdb0b4+af3c3595f5,gd9a9a58781+fcb1d3bbc8,gde0f65d7ad+a74c3eaa38,ge278dab8ac+d65b3c2b70,ge410e46f29+4086c0989b,gf23fb2af72+b3e27b8ebc,gf67bdafdda+4086c0989b,v29.0.0.rc6
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 |