LSST Applications g04e9c324dd+8c5ae1fdc5,g134cb467dc+b203dec576,g18429d2f64+358861cd2c,g199a45376c+0ba108daf9,g1fd858c14a+dd066899e3,g262e1987ae+ebfced1d55,g29ae962dfc+72fd90588e,g2cef7863aa+aef1011c0b,g35bb328faa+8c5ae1fdc5,g3fd5ace14f+b668f15bc5,g4595892280+3897dae354,g47891489e3+abcf9c3559,g4d44eb3520+fb4ddce128,g53246c7159+8c5ae1fdc5,g67b6fd64d1+abcf9c3559,g67fd3c3899+1f72b5a9f7,g74acd417e5+cb6b47f07b,g786e29fd12+668abc6043,g87389fa792+8856018cbb,g89139ef638+abcf9c3559,g8d7436a09f+bcf525d20c,g8ea07a8fe4+9f5ccc88ac,g90f42f885a+6054cc57f1,g97be763408+06f794da49,g9dd6db0277+1f72b5a9f7,ga681d05dcb+7e36ad54cd,gabf8522325+735880ea63,gac2eed3f23+abcf9c3559,gb89ab40317+abcf9c3559,gbf99507273+8c5ae1fdc5,gd8ff7fe66e+1f72b5a9f7,gdab6d2f7ff+cb6b47f07b,gdc713202bf+1f72b5a9f7,gdfd2d52018+8225f2b331,ge365c994fd+375fc21c71,ge410e46f29+abcf9c3559,geaed405ab2+562b3308c0,gf9a733ac38+8c5ae1fdc5,w.2025.35
LSST Data Management Base Package
|
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 |