LSST Applications g063fba187b+cac8b7c890,g0f08755f38+6aee506743,g1653933729+a8ce1bb630,g168dd56ebc+a8ce1bb630,g1a2382251a+b4475c5878,g1dcb35cd9c+8f9bc1652e,g20f6ffc8e0+6aee506743,g217e2c1bcf+73dee94bd0,g28da252d5a+1f19c529b9,g2bbee38e9b+3f2625acfc,g2bc492864f+3f2625acfc,g3156d2b45e+6e55a43351,g32e5bea42b+1bb94961c2,g347aa1857d+3f2625acfc,g35bb328faa+a8ce1bb630,g3a166c0a6a+3f2625acfc,g3e281a1b8c+c5dd892a6c,g3e8969e208+a8ce1bb630,g414038480c+5927e1bc1e,g41af890bb2+8a9e676b2a,g7af13505b9+809c143d88,g80478fca09+6ef8b1810f,g82479be7b0+f568feb641,g858d7b2824+6aee506743,g89c8672015+f4add4ffd5,g9125e01d80+a8ce1bb630,ga5288a1d22+2903d499ea,gb58c049af0+d64f4d3760,gc28159a63d+3f2625acfc,gcab2d0539d+b12535109e,gcf0d15dbbd+46a3f46ba9,gda6a2b7d83+46a3f46ba9,gdaeeff99f8+1711a396fd,ge79ae78c31+3f2625acfc,gef2f8181fd+0a71e47438,gf0baf85859+c1f95f4921,gfa517265be+6aee506743,gfa999e8aa5+17cd334064,w.2024.51
LSST Data Management Base Package
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Classes | Functions | Variables
lsst.scarlet.lite.display Namespace Reference

Classes

class  AsinhPercentileNorm
 
class  LinearPercentileNorm
 

Functions

np.ndarray channels_to_rgb (int channels)
 
np.ndarray img_to_3channel (np.ndarray img, np.ndarray|None channel_map=None, float fill_value=0)
 
np.ndarray 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 show_likelihood (Blend blend, tuple[float, float]|None figsize=None, **kwargs)
 
 _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)
 
 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 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] get_extent (Box bbox)
 
matplotlib.pyplot.Figure 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 compare_spectra (bool use_flux=True, bool use_template=True, **list[Source] all_sources)
 

Variables

float panel_size = 4.0
 

Function Documentation

◆ _add_markers()

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 )
protected 
Add markers to a plot.

Parameters
----------
src:
    The source to mark on the plot.
extent:
    The extent of the source.
ax:
    The axis of the plot.
add_markers:
    Whether or not to add an "x" at the center of the source.
add_boxes:
    Whether or not to draw a box around the entire source.
marker_kwargs:
    Any kwargs to pass to the ``ax.plot`` when drawing the marker.
box_kwargs:
    Any kwargs to pass to `~matplotlib.patches.Rectangle` when creating
    the source box.

Definition at line 286 of file display.py.

294):
295 """Add markers to a plot.
296
297 Parameters
298 ----------
299 src:
300 The source to mark on the plot.
301 extent:
302 The extent of the source.
303 ax:
304 The axis of the plot.
305 add_markers:
306 Whether or not to add an "x" at the center of the source.
307 add_boxes:
308 Whether or not to draw a box around the entire source.
309 marker_kwargs:
310 Any kwargs to pass to the ``ax.plot`` when drawing the marker.
311 box_kwargs:
312 Any kwargs to pass to `~matplotlib.patches.Rectangle` when creating
313 the source box.
314 """
315 if add_markers and hasattr(src, "center") and src.center is not None:
316 center = np.array(src.center)[::-1]
317 ax.plot(*center, "wx", **marker_kwargs)
318
319 if add_boxes:
320 rect = Rectangle(
321 (extent[0], extent[2]),
322 extent[1] - extent[0],
323 extent[3] - extent[2],
324 **box_kwargs,
325 )
326 ax.add_artist(rect)
327
328

◆ channels_to_rgb()

np.ndarray lsst.scarlet.lite.display.channels_to_rgb ( int channels) 
Get the linear mapping of multiple channels to RGB channels
The mapping created here assumes the channels are ordered in wavelength
direction, starting with the shortest wavelength.
The mapping seeks to produce a relatively even weights for across
all channels. It does not consider e.g.
signal-to-noise variations across channels or human perception.
Parameters
----------
channels:
    Number of channels (in range(0,7)).
Returns
-------
channel_map:
    Array (3, `channels`) to map onto RGB.

Definition at line 41 of file display.py.

41def channels_to_rgb(channels: int) -> np.ndarray:
42 """Get the linear mapping of multiple channels to RGB channels
43 The mapping created here assumes the channels are ordered in wavelength
44 direction, starting with the shortest wavelength.
45 The mapping seeks to produce a relatively even weights for across
46 all channels. It does not consider e.g.
47 signal-to-noise variations across channels or human perception.
48 Parameters
49 ----------
50 channels:
51 Number of channels (in range(0,7)).
52 Returns
53 -------
54 channel_map:
55 Array (3, `channels`) to map onto RGB.
56 """
57 if channels not in range(0, 8):
58 msg = f"No mapping has been implemented for more than 8 channels, got {channels=}"
59 raise ValueError(msg)
60
61 channel_map = np.zeros((3, channels))
62 if channels == 1:
63 channel_map[0, 0] = channel_map[1, 0] = channel_map[2, 0] = 1
64 elif channels == 2:
65 channel_map[0, 1] = 0.667
66 channel_map[1, 1] = 0.333
67 channel_map[1, 0] = 0.333
68 channel_map[2, 0] = 0.667
69 channel_map /= 0.667
70 elif channels == 3:
71 channel_map[0, 2] = 1
72 channel_map[1, 1] = 1
73 channel_map[2, 0] = 1
74 elif channels == 4:
75 channel_map[0, 3] = 1
76 channel_map[0, 2] = 0.333
77 channel_map[1, 2] = 0.667
78 channel_map[1, 1] = 0.667
79 channel_map[2, 1] = 0.333
80 channel_map[2, 0] = 1
81 channel_map /= 1.333
82 elif channels == 5:
83 channel_map[0, 4] = 1
84 channel_map[0, 3] = 0.667
85 channel_map[1, 3] = 0.333
86 channel_map[1, 2] = 1
87 channel_map[1, 1] = 0.333
88 channel_map[2, 1] = 0.667
89 channel_map[2, 0] = 1
90 channel_map /= 1.667
91 elif channels == 6:
92 channel_map[0, 5] = 1
93 channel_map[0, 4] = 0.667
94 channel_map[0, 3] = 0.333
95 channel_map[1, 4] = 0.333
96 channel_map[1, 3] = 0.667
97 channel_map[1, 2] = 0.667
98 channel_map[1, 1] = 0.333
99 channel_map[2, 2] = 0.333
100 channel_map[2, 1] = 0.667
101 channel_map[2, 0] = 1
102 channel_map /= 2
103 elif channels == 7:
104 channel_map[:, 6] = 2 / 3.0
105 channel_map[0, 5] = 1
106 channel_map[0, 4] = 0.667
107 channel_map[0, 3] = 0.333
108 channel_map[1, 4] = 0.333
109 channel_map[1, 3] = 0.667
110 channel_map[1, 2] = 0.667
111 channel_map[1, 1] = 0.333
112 channel_map[2, 2] = 0.333
113 channel_map[2, 1] = 0.667
114 channel_map[2, 0] = 1
115 channel_map /= 2
116 return channel_map
117
118

◆ compare_spectra()

matplotlib.pyplot.Figure lsst.scarlet.lite.display.compare_spectra ( bool use_flux = True,
bool use_template = True,
**list[Source] all_sources ) 
Compare spectra from multiple different deblending results of the
same sources.

Parameters
----------
use_flux:
    Whether or not to show the re-distributed flux version of the model.
use_template:
    Whether or not to show the scarlet model templates.
all_sources:
    The list of sources for each different deblending model.

Definition at line 782 of file display.py.

784) -> matplotlib.pyplot.Figure:
785 """Compare spectra from multiple different deblending results of the
786 same sources.
787
788 Parameters
789 ----------
790 use_flux:
791 Whether or not to show the re-distributed flux version of the model.
792 use_template:
793 Whether or not to show the scarlet model templates.
794 all_sources:
795 The list of sources for each different deblending model.
796 """
797 first_key = next(iter(all_sources.keys()))
798 nbr_sources = len(all_sources[first_key])
799 for key, sources in all_sources.items():
800 if len(sources) != nbr_sources:
801 msg = (
802 "All source lists must have the same number of components."
803 f"Received {nbr_sources} sources for the list {first_key} and {len(sources)}"
804 f"for list {key}."
805 )
806 raise ValueError(msg)
807
808 columns = 4
809 rows = int(np.ceil(nbr_sources / columns))
810 fig, ax = plt.subplots(rows, columns, figsize=(15, 15 * rows / columns))
811 if rows == 1:
812 ax = [ax[0], ax[1]]
813
814 panel = 0
815 for k in range(nbr_sources):
816 row = panel // 4
817 column = panel - row * 4
818 ax[row][column].set_title(f"source {k}")
819 for key, sources in all_sources.items():
820 if sources[k].is_null:
821 continue
822 if use_template or not hasattr(sources[k], "flux"):
823 spectrum = np.sum(sources[k].get_model().data, axis=(1, 2))
824 ax[row][column].plot(spectrum, ".-", label=key + " model")
825 if use_flux and hasattr(sources[k], "flux"):
826 spectrum = np.sum(sources[k].get_model(use_flux=True).data, axis=(1, 2))
827 ax[row][column].plot(spectrum, ".--", label=key + " flux")
828 panel += 1
829 handles, labels = ax[0][0].get_legend_handles_labels()
830 fig.legend(handles, labels, loc="lower center", ncol=4)
831 return fig

◆ get_extent()

tuple[int, int, int, int] lsst.scarlet.lite.display.get_extent ( Box bbox) 
Convert a `Box` into a list of bounds used in matplotlib

Paramters
---------
bbox:
   The box to convert into an extent list.

Returns
-------
extent:
    Tuple of coordinates that matplotlib requires for the
    extent of an image in ``imshow``.

Definition at line 595 of file display.py.

595def get_extent(bbox: Box) -> tuple[int, int, int, int]:
596 """Convert a `Box` into a list of bounds used in matplotlib
597
598 Paramters
599 ---------
600 bbox:
601 The box to convert into an extent list.
602
603 Returns
604 -------
605 extent:
606 Tuple of coordinates that matplotlib requires for the
607 extent of an image in ``imshow``.
608 """
609 return bbox.start[-1], bbox.stop[-1], bbox.start[-2], bbox.stop[-2]
610
611

◆ img_to_3channel()

np.ndarray lsst.scarlet.lite.display.img_to_3channel ( np.ndarray img,
np.ndarray | None channel_map = None,
float fill_value = 0 ) 
Convert multi-band image cube into 3 RGB channels

Parameters
----------
img:
    This should be an array with dimensions (channels, height, width).
channel_map:
    Linear mapping with dimensions (3, channels)
fill_value:
    Value to use for any masked pixels.

Returns
-------
RGB:
    The input image converted into an RGB array that can be displayed
    with `matplotlib.imshow`.

Definition at line 166 of file display.py.

168) -> np.ndarray:
169 """Convert multi-band image cube into 3 RGB channels
170
171 Parameters
172 ----------
173 img:
174 This should be an array with dimensions (channels, height, width).
175 channel_map:
176 Linear mapping with dimensions (3, channels)
177 fill_value:
178 Value to use for any masked pixels.
179
180 Returns
181 -------
182 RGB:
183 The input image converted into an RGB array that can be displayed
184 with `matplotlib.imshow`.
185 """
186 # expand single img into cube
187 if img.ndim not in [2, 3]:
188 msg = f"The image must have 2 or 3 dimensions, got {img.ndim}"
189 raise ValueError(msg)
190
191 if len(img.shape) == 2:
192 ny, nx = img.shape
193 img_ = img.reshape((1, ny, nx))
194 elif len(img.shape) == 3:
195 img_ = img
196 else:
197 raise ValueError(f"Image must have either 2 or 3 dimensions, got {len(img.shape)}")
198 dimensions = len(img_)
199
200 # filterWeights: channel x band
201 if channel_map is None:
202 channel_map = channels_to_rgb(dimensions)
203 elif channel_map.shape != (3, len(img)):
204 raise ValueError("Invalid channel_map returned, something unexpected happened")
205
206 # map channels onto RGB channels
207 _, ny, nx = img_.shape
208 rgb = np.dot(channel_map, img_.reshape(dimensions, -1)).reshape((3, ny, nx))
209
210 if hasattr(rgb, "mask"):
211 rgb = rgb.filled(fill_value)
212
213 return rgb
214
215

◆ img_to_rgb()

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 ) 
Convert images to normalized RGB.

If normalized values are outside of the range [0..255], they will be
truncated such as to preserve the corresponding color.

Parameters
----------
img:
    This should be an array with dimensions (channels, height, width).
channel_map:
    Linear mapping with dimensions (3, channels)
fill_value:
    Value to use for any masked pixels.
norm:
    Norm to use for mapping in the allowed range [0..255].
    If ``norm=None``, `LinearPercentileNorm` will be used.
mask:
    A [0,1] binary mask to apply over the top of the image,
    where pixels with mask==1 are masked out.

Returns
-------
rgb:
    RGB values with dimensions (3, height, width) and dtype uint8

Definition at line 216 of file display.py.

222) -> np.ndarray:
223 """Convert images to normalized RGB.
224
225 If normalized values are outside of the range [0..255], they will be
226 truncated such as to preserve the corresponding color.
227
228 Parameters
229 ----------
230 img:
231 This should be an array with dimensions (channels, height, width).
232 channel_map:
233 Linear mapping with dimensions (3, channels)
234 fill_value:
235 Value to use for any masked pixels.
236 norm:
237 Norm to use for mapping in the allowed range [0..255].
238 If ``norm=None``, `LinearPercentileNorm` will be used.
239 mask:
240 A [0,1] binary mask to apply over the top of the image,
241 where pixels with mask==1 are masked out.
242
243 Returns
244 -------
245 rgb:
246 RGB values with dimensions (3, height, width) and dtype uint8
247 """
248 if isinstance(img, Image):
249 img = img.data
250 _rgb = img_to_3channel(img, channel_map=channel_map, fill_value=fill_value)
251 if norm is None:
252 norm = LinearMapping(image=_rgb)
253 rgb = norm.make_rgb_image(*_rgb)
254 if mask is not None:
255 rgb = np.dstack([rgb, ~mask * 255])
256 return rgb
257
258

◆ show_likelihood()

matplotlib.pyplot.Figure lsst.scarlet.lite.display.show_likelihood ( Blend blend,
tuple[float, float] | None figsize = None,
** kwargs ) 
Display a plot of the likelihood in each iteration for a blend

Parameters
----------
blend:
    The blend to generate the likelihood plot for.
figsize:
    The size of the figure.
kwargs:
    Keyword arguements passed to `blend.log_likelihood`.

Returns
-------
fig:
    The figure containing the log-likelihood plot.

Definition at line 259 of file display.py.

261) -> matplotlib.pyplot.Figure:
262 """Display a plot of the likelihood in each iteration for a blend
263
264 Parameters
265 ----------
266 blend:
267 The blend to generate the likelihood plot for.
268 figsize:
269 The size of the figure.
270 kwargs:
271 Keyword arguements passed to `blend.log_likelihood`.
272
273 Returns
274 -------
275 fig:
276 The figure containing the log-likelihood plot.
277 """
278 fig, ax = plt.subplots(1, 1, figsize=figsize)
279 ax.plot(blend.log_likelihood, **kwargs)
280 ax.set_xlabel("Iteration")
281 ax.xaxis.set_major_locator(MaxNLocator(integer=True))
282 ax.set_ylabel("log-Likelihood")
283 return fig
284
285

◆ show_observation()

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 ) 
Plot observation in standardized form.

Parameters
----------
observation:
    The observation to show.
norm:
    An ``astropy.visualization.lupton_rgb.Mapping`` to map the colors.
channel_map:
    A mapping to convert the multiband image into an RGB image.
centers:
    A list of source centers to mark on the plot.
    If `centers` is ``None`` then no markers are added.
psf_scaling:
    Scaling to use to display the PSF.
    If `psf_scaling` is ``None`` then the PSF is not displayed.
    If `psf_scaling` is "native",
    then the PSF is displayed with no scaling.
    If `psf_scaling` is "same", then the PSF is normalzied using the
    brightest pixel in each band.
figsize:
    The size of the output figure.
    If not size is specified then the figsize is calculated automatically
    based on the number of objects shown.

Definition at line 329 of file display.py.

336):
337 """Plot observation in standardized form.
338
339 Parameters
340 ----------
341 observation:
342 The observation to show.
343 norm:
344 An ``astropy.visualization.lupton_rgb.Mapping`` to map the colors.
345 channel_map:
346 A mapping to convert the multiband image into an RGB image.
347 centers:
348 A list of source centers to mark on the plot.
349 If `centers` is ``None`` then no markers are added.
350 psf_scaling:
351 Scaling to use to display the PSF.
352 If `psf_scaling` is ``None`` then the PSF is not displayed.
353 If `psf_scaling` is "native",
354 then the PSF is displayed with no scaling.
355 If `psf_scaling` is "same", then the PSF is normalzied using the
356 brightest pixel in each band.
357 figsize:
358 The size of the output figure.
359 If not size is specified then the figsize is calculated automatically
360 based on the number of objects shown.
361 """
362 if psf_scaling is None:
363 panels = 1
364 else:
365 panels = 2
366 if psf_scaling not in ["native", "same"]:
367 raise ValueError(f"psf_scaling must be either 'same' or 'native', got {psf_scaling}")
368 if figsize is None:
369 figsize = (panel_size * panels, panel_size)
370 fig, ax = plt.subplots(1, panels, figsize=figsize)
371 if not hasattr(ax, "__iter__"):
372 ax = (ax,)
373
374 # Mask any pixels with zero weight in all bands
375 mask = np.sum(observation.weights.data, axis=0) == 0
376 # if there are no masked pixels, do not use a mask
377 if np.all(mask == 0):
378 mask = None
379
380 panel = 0
381 extent = get_extent(observation.bbox)
382 ax[panel].imshow(
383 img_to_rgb(observation.images, norm=norm, channel_map=channel_map, mask=mask),
384 extent=extent,
385 origin="lower",
386 )
387 ax[panel].set_title("Observation")
388
389 if centers is not None:
390 for k, center in enumerate(centers):
391 # If the image is multi-band, use a white label,
392 # otherwise the image with be black and white so use red.
393 color = "w" if observation.images.shape[0] > 1 else "r"
394 ax[panel].text(*center[::-1], k, color=color, ha="center", va="center")
395
396 panel += 1
397 if psf_scaling is not None:
398 psf_image = np.zeros(observation.images.shape)
399
400 if observation.model_psf is not None:
401 psf_model = observation.psfs
402 # make PSF as bright as the brightest pixel of the observation
403 psf_model *= np.max(np.mean(observation.images.data, axis=0)) / np.max(np.mean(psf_model, axis=0))
404 if psf_scaling == "native":
405 psf_image = psf_model
406 else:
407 psf_image = np.zeros(observation.images.shape)
408 height = psf_model.shape[1]
409 width = psf_model.shape[2]
410 height_diff = observation.images.shape[1] - height
411 width_diff = observation.images.shape[2] - width
412 y0 = height_diff // 2
413 x0 = width_diff // 2
414 yf = y0 + height
415 xf = x0 + width
416 psf_image[:, y0:yf, x0:xf] = psf_model
417 ax[panel].imshow(img_to_rgb(psf_image, norm=norm), origin="lower")
418 ax[panel].set_title("PSF")
419
420 fig.tight_layout()
421 return fig
422
423

◆ show_scene()

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 ) 
Plot all sources to recreate the scene.

The functions provides a fast way of evaluating the quality
of the entire model,
i.e. the combination of all sources that seek to fit the observation.

Parameters
----------
blend:
    The blend containing the observatons and sources to plot.
norm:
    Norm to compress image intensity to the range [0,255].
channel_map:
    Linear mapping with dimensions (3, channels).
show_model:
    Whether the model is shown in the model frame.
show_observed:
    Whether the observation is shown.
show_rendered:
    Whether the model, rendered to match the observation, is shown.
show_residual:
    Whether the residuals between rendered model and observation is shown.
add_labels:
    Whether each source is labeled with its numerical
    index in the source list.
add_boxes:
    Whether each source box is shown.
figsize:
    Size of the final figure.
linear:
    Whether or not to display the scene in a single line (`True`) or
    on multiple lines (`False`).
use_flux:
    Whether to show the flux redistributed model (`source.flux`) or
    the model itself (`source.get_model()`) for each source.
box_kwargs:
    Keyword arguments to create boxes (`matplotlib.patches.Rectangle`)
    around sources, if `add_boxes == True`.

Returns
-------
fig:
    The figure that is generated based on the parameters.

Definition at line 424 of file display.py.

438) -> matplotlib.pyplot.Figure:
439 """Plot all sources to recreate the scene.
440
441 The functions provides a fast way of evaluating the quality
442 of the entire model,
443 i.e. the combination of all sources that seek to fit the observation.
444
445 Parameters
446 ----------
447 blend:
448 The blend containing the observatons and sources to plot.
449 norm:
450 Norm to compress image intensity to the range [0,255].
451 channel_map:
452 Linear mapping with dimensions (3, channels).
453 show_model:
454 Whether the model is shown in the model frame.
455 show_observed:
456 Whether the observation is shown.
457 show_rendered:
458 Whether the model, rendered to match the observation, is shown.
459 show_residual:
460 Whether the residuals between rendered model and observation is shown.
461 add_labels:
462 Whether each source is labeled with its numerical
463 index in the source list.
464 add_boxes:
465 Whether each source box is shown.
466 figsize:
467 Size of the final figure.
468 linear:
469 Whether or not to display the scene in a single line (`True`) or
470 on multiple lines (`False`).
471 use_flux:
472 Whether to show the flux redistributed model (`source.flux`) or
473 the model itself (`source.get_model()`) for each source.
474 box_kwargs:
475 Keyword arguments to create boxes (`matplotlib.patches.Rectangle`)
476 around sources, if `add_boxes == True`.
477
478 Returns
479 -------
480 fig:
481 The figure that is generated based on the parameters.
482 """
483 if box_kwargs is None:
484 box_kwargs = {"facecolor": "none", "edgecolor": "w", "lw": 0.5}
485
486 panels = sum((show_model, show_observed, show_rendered, show_residual))
487 if linear:
488 if figsize is None:
489 figsize = (panel_size * panels, panel_size)
490 fig, ax = plt.subplots(1, panels, figsize=figsize)
491 else:
492 columns = int(np.ceil(panels / 2))
493 if figsize is None:
494 figsize = (panel_size * columns, panel_size * 2)
495 fig = plt.figure(figsize=figsize)
496 ax = [fig.add_subplot(2, columns, n + 1) for n in range(panels)]
497 if not hasattr(ax, "__iter__"):
498 ax = (ax,)
499
500 observation = blend.observation
501 sources = blend.sources
502 model = blend.get_model(use_flux=use_flux)
503 bbox = blend.bbox
504
505 # Mask any pixels with zero weight in all bands
506 if observation is not None:
507 mask = np.sum(observation.weights.data, axis=0) == 0
508 # if there are no masked pixels, do not use a mask
509 if np.all(mask == 0):
510 mask = None
511 else:
512 mask = None
513
514 panel = 0
515 if show_model:
516 extent = get_extent(bbox)
517 ax[panel].imshow(
518 img_to_rgb(model.data, norm=norm, channel_map=channel_map, mask=mask),
519 extent=extent,
520 origin="lower",
521 )
522 ax[panel].set_title("Model")
523 panel += 1
524
525 if (show_rendered or show_residual) and not use_flux:
526 model = observation.convolve(model)
527 extent = get_extent(observation.bbox)
528
529 if show_rendered:
530 ax[panel].imshow(
531 img_to_rgb(model.data, norm=norm, channel_map=channel_map, mask=mask),
532 extent=extent,
533 origin="lower",
534 )
535 ax[panel].set_title("Model Rendered")
536 panel += 1
537
538 if show_observed:
539 ax[panel].imshow(
540 img_to_rgb(observation.images.data, norm=norm, channel_map=channel_map, mask=mask),
541 extent=extent,
542 origin="lower",
543 )
544 ax[panel].set_title("Observation")
545 panel += 1
546
547 if show_residual:
548 residual = observation.images - model
549 norm_ = LinearPercentileNorm(residual.data)
550 ax[panel].imshow(
551 img_to_rgb(residual.data, norm=norm_, channel_map=channel_map, mask=mask),
552 extent=extent,
553 origin="lower",
554 )
555 ax[panel].set_title("Residual")
556 panel += 1
557
558 for k, src in enumerate(sources):
559 if add_boxes:
560 panel = 0
561 extent = get_extent(src.bbox)
562 if show_model:
563 rect = Rectangle(
564 (extent[0], extent[2]),
565 extent[1] - extent[0],
566 extent[3] - extent[2],
567 **box_kwargs,
568 )
569 ax[panel].add_artist(rect)
570 panel = 1
571 if observation is not None:
572 for panel in range(panel, panels):
573 rect = Rectangle(
574 (extent[0], extent[2]),
575 extent[1] - extent[0],
576 extent[3] - extent[2],
577 **box_kwargs,
578 )
579 ax[panel].add_artist(rect)
580
581 if add_labels and hasattr(src, "center") and src.center is not None:
582 center = src.center
583 panel = 0
584 if show_model:
585 ax[panel].text(*center[::-1], k, color="w", ha="center", va="center")
586 panel = 1
587 if observation is not None:
588 for panel in range(panel, panels):
589 ax[panel].text(*center[::-1], k, color="w", ha="center", va="center")
590
591 fig.tight_layout()
592 return fig
593
594

◆ show_sources()

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 ) 
Plot individual source models

The functions provides a fast way of evaluating the quality of
individual sources.

Parameters
----------
blend:
    The blend that contains the sources.
sources:
    The list of sources to plot.
    If `sources` is `None` then all of the sources in `blend` are
    displayed.
norm:
    Norm to compress image intensity to the range [0,255].
channel_map:
    Linear mapping with dimensions (3, channels).
show_model:
    Whether the model is shown in the model frame.
show_observed:
    Whether the observation is shown.
show_rendered:
    Whether the model, rendered to match the observation, is shown.
show_spectrum:
    Whether or not to show a plot for the spectrum of each component
    in each source.
figsize:
    Size of the final figure.
model_mask:
    Whether pixels with no flux in a model are masked.
add_markers:
    Whether all of the sources are marked in each plot.
add_boxes:
    Whether each source box is shown.
use_flux:
    Whether to show the flux redistributed model (`source.flux`) or
    the model itself (`source.get_model()`) for each source.

Returns
-------
fig:
    The figure that is generated based on the parameters.

Definition at line 612 of file display.py.

626) -> matplotlib.pyplot.Figure:
627 """Plot individual source models
628
629 The functions provides a fast way of evaluating the quality of
630 individual sources.
631
632 Parameters
633 ----------
634 blend:
635 The blend that contains the sources.
636 sources:
637 The list of sources to plot.
638 If `sources` is `None` then all of the sources in `blend` are
639 displayed.
640 norm:
641 Norm to compress image intensity to the range [0,255].
642 channel_map:
643 Linear mapping with dimensions (3, channels).
644 show_model:
645 Whether the model is shown in the model frame.
646 show_observed:
647 Whether the observation is shown.
648 show_rendered:
649 Whether the model, rendered to match the observation, is shown.
650 show_spectrum:
651 Whether or not to show a plot for the spectrum of each component
652 in each source.
653 figsize:
654 Size of the final figure.
655 model_mask:
656 Whether pixels with no flux in a model are masked.
657 add_markers:
658 Whether all of the sources are marked in each plot.
659 add_boxes:
660 Whether each source box is shown.
661 use_flux:
662 Whether to show the flux redistributed model (`source.flux`) or
663 the model itself (`source.get_model()`) for each source.
664
665 Returns
666 -------
667 fig:
668 The figure that is generated based on the parameters.
669 """
670 observation = blend.observation
671 if sources is None:
672 sources = blend.sources
673 panels = sum((show_model, show_observed, show_rendered, show_spectrum))
674 n_sources = len([src for src in sources if not src.is_null])
675 if figsize is None:
676 figsize = (panel_size * panels, panel_size * n_sources)
677
678 fig, ax = plt.subplots(n_sources, panels, figsize=figsize, squeeze=False)
679
680 marker_kwargs = {"mew": 1, "ms": 10}
681 box_kwargs = {"facecolor": "none", "edgecolor": "w", "lw": 0.5}
682
683 skipped = 0
684 for k, src in enumerate(sources):
685 if src.is_null:
686 skipped += 1
687 continue
688 if use_flux:
689 if src.flux_weighted_image is None:
690 raise ValueError(f"Flux has not been calculated for src {k}, rerun measure.conserve_flux")
691 src_box = src.flux_weighted_image.bbox
692 else:
693 src_box = src.bbox
694
695 extent = get_extent(src_box)
696
697 # model in its bbox
698 panel = 0
699 model = src.get_model(use_flux=use_flux)
700
701 if show_model:
702 if model_mask:
703 _model_mask = np.max(model.data, axis=0) <= 0
704 else:
705 _model_mask = None
706 # Show the unrendered model in it's bbox
707 ax[k - skipped][panel].imshow(
708 img_to_rgb(model.data, norm=norm, channel_map=channel_map, mask=_model_mask),
709 extent=extent,
710 origin="lower",
711 )
712 ax[k - skipped][panel].set_title("Model Source {}".format(k))
713 _add_markers(
714 src,
715 extent,
716 ax[k - skipped][panel],
717 add_markers,
718 False,
719 marker_kwargs,
720 box_kwargs,
721 )
722 panel += 1
723
724 # model in observation frame
725 if show_rendered:
726 # Center and show the rendered model
727 model_ = Image(np.zeros(observation.shape), bands=observation.bands)
728 model_.insert(src.get_model(use_flux=use_flux))
729 if not use_flux:
730 model_ = observation.convolve(model_)
731 ax[k - skipped][panel].imshow(
732 img_to_rgb(model_.data, norm=norm, channel_map=channel_map),
733 extent=get_extent(observation.bbox),
734 origin="lower",
735 )
736 ax[k - skipped][panel].set_title("Model Source {} Rendered".format(k))
737 _add_markers(
738 src,
739 extent,
740 ax[k - skipped][panel],
741 add_markers,
742 add_boxes,
743 marker_kwargs,
744 box_kwargs,
745 )
746 panel += 1
747
748 if show_observed:
749 # Center the observation on the source and display it
750 _images = observation.images
751 ax[k - skipped][panel].imshow(
752 img_to_rgb(_images.data, norm=norm, channel_map=channel_map),
753 extent=get_extent(observation.bbox),
754 origin="lower",
755 )
756 ax[k - skipped][panel].set_title(f"Observation {k}")
757 _add_markers(
758 src,
759 extent,
760 ax[k - skipped][panel],
761 add_markers,
762 add_boxes,
763 marker_kwargs,
764 box_kwargs,
765 )
766 panel += 1
767
768 if show_spectrum:
769 spectra = [np.sum(model.data, axis=(1, 2))]
770
771 for spectrum in spectra:
772 ax[k - skipped][panel].plot(spectrum)
773 ax[k - skipped][panel].set_xticks(range(len(spectra)))
774 ax[k - skipped][panel].set_title("Spectrum")
775 ax[k - skipped][panel].set_xlabel("Band")
776 ax[k - skipped][panel].set_ylabel("Intensity")
777
778 fig.tight_layout()
779 return fig
780
781

Variable Documentation

◆ panel_size

float lsst.scarlet.lite.display.panel_size = 4.0

Definition at line 38 of file display.py.

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