22634960315

Committed 03 Mar 2026 05:28PM UTC coverage: 59.552% (-0.03%) from 59.577%

Build # 22634960315

Build Type

push

github

Committed by

web-flow

Commit Message

 Experimental phase space coverage, covariance-aware clustering, and pipeline improvements (#168)

* Phase space test orbit generation — New phase_space package with HEALPix-based test orbit generation, recursive phase space splitting, coverage analysis across 6D orbital element bounds, and nearest-test-orbit proximity matching.

* Covariance propagation — Propagate observation and test orbit uncertainties through the gnomonic tangent plane, enabling Mahalanobis-distance-based observation filtering (TestOrbitMahalanobisObservationFilter) and covariance-derived adaptive clustering parameters.

* Cluster fitting — Replace simple clustering output with FittedClusters carrying polynomial motion models (position, velocity, acceleration, chi2). Adds fit_clusters(), per-observation residuals, and min_nights filtering.

* Pipeline changes — Add generate_ephemeris as an explicit pipeline stage (reusing ephemeris from filtering). Add stop_after_stage for early termination, yield_paths mode for reduced memory usage, and experimental link_test_orbits with recursive splitting.

* Analysis and plotting — New analysis.py (difi v2.0-powered linkage purity/completeness/findability metrics) and plotting.py (Plotly-based interactive visualization of observations and gnomonic-plane detections).

* Dependencies and cleanup — Upgrade to adam-core>=0.5.4, adam-assist>=0.3.6, rebound 4.6.0. Add plotly, healpy, difi. Remove pydantic (config uses dataclasses). Drop Python 3.10.

---------

Co-authored-by: Alec Koumjian <akoumjian@gmail.com>
Co-authored-by: Kathleen Kiker <kathleen@b612foundation.org>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

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352 existing lines in 16 files now uncovered.

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0.0

/src/thor/plotting.py

NEW 1	from typing import Optional	×
2
NEW 3	import numpy as np	×
NEW 4	import plotly.graph_objects as go	×
NEW 5	import pyarrow.compute as pc	×
6
NEW 7	from .analysis import ObservationLabels	×
NEW 8	from .observations import Observations	×
NEW 9	from .range_and_transform import TransformedDetections	×
10
NEW 11	__all__ = ["plot_transformed_detections"]	×
12
13
NEW 14	COLOR_PALETTE = [	×
15	"blue",
16	"green",
17	"purple",
18	"orange",
19	"cyan",
20	"magenta",
21	"lime",
22	"navy",
23	"teal",
24	"aqua",
25	"fuchsia",
26	"gold",
27	"indigo",
28	"turquoise",
29	"violet",
30	"lavender",
31	"chartreuse",
32	"steelblue",
33	"slateblue",
34	"mediumblue",
35	"dodgerblue",
36	"deepskyblue",
37	"lightskyblue",
38	"cadetblue",
39	"darkturquoise",
40	"mediumturquoise",
41	"darkseagreen",
42	"mediumseagreen",
43	"seagreen",
44	"forestgreen",
45	]
46
47
NEW 48	def plot_observations(	×
49	observations: Observations,
50	labels: Optional[ObservationLabels] = None,
51	include_unlabeled: bool = False,
52	connect_by_time: bool = True,
53	labeled_marker_size: int = 2,
54	labeled_line_width: int = 2,
55	unlabeled_marker_size: int = 1,
56	include_time: bool = True,
57	) -> go.Figure:
58	"""
59	Plots observations (RA/Dec) and labels.
60
61	Parameters
62	----------
63	observations : Observations
64	The observations to plot.
65	labels : ObservationLabels, optional
66	The labels to plot, can be empty if no labels are available.
67	include_unlabeled : bool, optional
68	Whether to include the unlabeled observations.
69	connect_by_time : bool, optional
70	Whether to connect the observations by time.
71	labeled_marker_size : int, optional
72	The size of the labeled markers.
73	labeled_line_width : int, optional
74	The width of the labeled lines.
75	unlabeled_marker_size : int, optional
76	The size of the unlabeled markers.
77	include_time : bool, optional
78	Whether to include the time axis.
79
80	Returns
81	-------
82	fig : go.Figure
83	The figure containing the plotted observations and labels.
84	"""
NEW 85	if labels is not None:	×
NEW 86	labels = labels.apply_mask(pc.is_in(labels.obs_id, observations.id))	×
87
NEW 88	null_labels = labels.apply_mask(pc.is_null(labels.object_id))	×
NEW 89	mask = pc.is_in(observations.id, null_labels.obs_id)	×
90
NEW 91	observations_unlabeled = observations.apply_mask(mask)	×
92
NEW 93	object_ids = labels.apply_mask(pc.invert(pc.is_null(labels.object_id))).object_id.unique()	×
NEW 94	observations_labeled = observations.apply_mask(pc.invert(mask))	×
95	else:
NEW 96	observations_unlabeled = observations	×
NEW 97	observations_labeled = observations	×
NEW 98	object_ids = []	×
99
NEW 100	fig = go.Figure()	×
101
NEW 102	if len(object_ids) > 0:	×
NEW 103	for idx, object_id in enumerate(object_ids.to_pylist()):	×
104
NEW 105	obs_ids = labels.select("object_id", object_id).obs_id	×
NEW 106	object_observations = observations_labeled.apply_mask(pc.is_in(observations_labeled.id, obs_ids))	×
NEW 107	object_observations = object_observations.sort_by(	×
108	["coordinates.time.days", "coordinates.time.nanos"]
109	)
110
NEW 111	o = np.full(len(object_observations), object_id)	×
NEW 112	t = object_observations.coordinates.time.mjd().to_numpy(zero_copy_only=False)	×
NEW 113	ra = object_observations.coordinates.lon.to_numpy(zero_copy_only=False)	×
NEW 114	dec = object_observations.coordinates.lat.to_numpy(zero_copy_only=False)	×
NEW 115	i = object_observations.id.to_numpy(zero_copy_only=False)	×
116
NEW 117	customdata = np.stack([o, i, t], axis=1)	×
NEW 118	hovertemplate = (	×
119	"object_id=%{customdata[0]}<br>"
120	"obs_id=%{customdata[1]}<br>"
121	"mjd=%{customdata[2]:.5f}<br>"
122	"RA=%{x:.6f}°<br>"
123	"Dec=%{y:.6f}°<extra></extra>"
124	)
125
126	# Assign color from palette, cycling if needed
NEW 127	color = COLOR_PALETTE[idx % len(COLOR_PALETTE)]	×
128
NEW 129	if include_time:	×
NEW 130	fig.add_trace(	×
131	go.Scatter3d(
132	x=ra,
133	y=dec,
134	z=t,
135	mode="markers+lines" if connect_by_time else "markers",
136	name=str(object_id),
137	marker=dict(size=labeled_marker_size, color=color),
138	line=dict(width=labeled_line_width, color=color),
139	customdata=customdata,
140	hovertemplate=hovertemplate,
141	)
142	)
143	else:
NEW 144	fig.add_trace(	×
145	go.Scattergl(
146	x=ra,
147	y=dec,
148	mode="markers+lines" if connect_by_time else "markers",
149	name=str(object_id),
150	marker=dict(size=labeled_marker_size, color=color),
151	line=dict(width=labeled_line_width, color=color),
152	customdata=customdata,
153	hovertemplate=hovertemplate,
154	)
155	)
156
NEW 157	if include_unlabeled:	×
158
NEW 159	ra = observations_unlabeled.coordinates.lon.to_numpy(zero_copy_only=False)	×
NEW 160	dec = observations_unlabeled.coordinates.lat.to_numpy(zero_copy_only=False)	×
NEW 161	t = observations_unlabeled.coordinates.time.mjd().to_numpy(zero_copy_only=False)	×
NEW 162	i = observations_unlabeled.id.to_numpy(zero_copy_only=False)	×
163
NEW 164	customdata = np.stack([i, t], axis=1)	×
NEW 165	hovertemplate = (	×
166	"obs_id=%{customdata[0]}<br>"
167	"mjd=%{customdata[1]:.5f}<br>"
168	"RA=%{x:.6f}°<br>"
169	"Dec=%{y:.6f}°<extra></extra>"
170	)
171
NEW 172	if include_time:	×
NEW 173	fig.add_trace(	×
174	go.Scatter3d(
175	x=ra,
176	y=dec,
177	z=t,
178	mode="markers",
179	marker=dict(size=unlabeled_marker_size, color="lightcoral"),
180	customdata=customdata,
181	hovertemplate=hovertemplate,
182	name="Unknown",
183	)
184	)
185	else:
NEW 186	fig.add_trace(	×
187	go.Scattergl(
188	x=ra,
189	y=dec,
190	mode="markers",
191	marker=dict(size=unlabeled_marker_size, color="lightcoral"),
192	customdata=customdata,
193	hovertemplate=hovertemplate,
194	name="Unknown",
195	)
196	)
197
NEW 198	title = "Observations (RA, Dec)"	×
199
NEW 200	if include_time:	×
NEW 201	fig.update_layout(	×
202	title=title,
203	paper_bgcolor="black",
204	width=1000,
205	height=1000,
206	title_font=dict(color="white"),
207	legend=dict(font=dict(color="white"), bgcolor="rgba(0,0,0,0)"),
208	scene=dict(
209	bgcolor="black",
210	xaxis=dict(
211	title=dict(text="RA [deg]"),
212	showbackground=False,
213	showgrid=True,
214	gridcolor="rgba(255,255,255,0.10)",
215	zerolinecolor="white",
216	linecolor="white",
217	color="white",
218	title_font=dict(color="white"),
219	),
220	yaxis=dict(
221	title=dict(text="Dec [deg]"),
222	showbackground=False,
223	showgrid=True,
224	gridcolor="rgba(255,255,255,0.10)",
225	zerolinecolor="white",
226	linecolor="white",
227	color="white",
228	title_font=dict(color="white"),
229	),
230	zaxis=dict(
231	title="Time [MJD]",
232	showbackground=False,
233	showgrid=True,
234	gridcolor="rgba(255,255,255,0.10)",
235	zerolinecolor="white",
236	linecolor="white",
237	color="white",
238	title_font=dict(color="white"),
239	),
240	),
241	)
242	else:
NEW 243	fig.update_layout(	×
244	title=title,
245	paper_bgcolor="black",
246	plot_bgcolor="black",
247	width=1000,
248	height=1000,
249	title_font=dict(color="white"),
250	legend=dict(font=dict(color="white"), bgcolor="rgba(0,0,0,0)"),
251	xaxis=dict(
252	title=dict(text="RA [deg]"),
253	showgrid=True,
254	gridcolor="rgba(255,255,255,0.15)",
255	zeroline=False,
256	linecolor="white",
257	color="white",
258	title_font=dict(color="white"),
259	tickfont=dict(color="white"),
260	mirror=True,
261	),
262	yaxis=dict(
263	title=dict(text="Dec [deg]"),
264	showgrid=True,
265	gridcolor="rgba(255,255,255,0.15)",
266	zeroline=False,
267	linecolor="white",
268	color="white",
269	title_font=dict(color="white"),
270	tickfont=dict(color="white"),
271	mirror=True,
272	scaleanchor="x",
273	scaleratio=1,
274	),
275	)
NEW 276	return fig	×
277
278
NEW 279	def plot_transformed_detections(	×
280	transformed_detections: TransformedDetections,
281	labels: Optional[ObservationLabels] = None,
282	include_unlabeled: bool = False,
283	connect_by_time: bool = True,
284	labeled_marker_size: int = 2,
285	labeled_line_width: int = 2,
286	unlabeled_marker_size: int = 1,
287	include_time: bool = True,
288	) -> go.Figure:
289	"""
290	Plots the transformed detections and labels.
291
292	Parameters
293	----------
294	transformed_detections : TransformedDetections
295	The transformed detections to plot.
296	labels : ObservationLabels, optional
297	The labels to plot, can be empty if no labels are available.
298	include_unlabeled : bool, optional
299	Whether to include the unlabeled detections.
300	connect_by_time : bool, optional
301	Whether to connect the detections by time.
302	labeled_marker_size : int, optional
303	The size of the labeled markers.
304	labeled_line_width : int, optional
305	The width of the labeled lines.
306	unlabeled_marker_size : int, optional
307	The size of the unlabeled markers.
308	include_time : bool, optional
309	Whether to include the time axis.
310
311	Returns
312	-------
313	fig : go.Figure
314	The figure containing the plotted detections and labels.
315	"""
NEW 316	if labels is not None:	×
NEW 317	labels = labels.apply_mask(pc.is_in(labels.obs_id, transformed_detections.id))	×
318
NEW 319	null_labels = labels.apply_mask(pc.is_null(labels.object_id))	×
NEW 320	mask = pc.is_in(transformed_detections.id, null_labels.obs_id)	×
321
NEW 322	transformed_detections_unlabeled = transformed_detections.apply_mask(mask)	×
323
NEW 324	object_ids = labels.apply_mask(pc.invert(pc.is_null(labels.object_id))).object_id.unique()	×
NEW 325	transformed_detections_labeled = transformed_detections.apply_mask(pc.invert(mask))	×
326	else:
NEW 327	transformed_detections_unlabeled = transformed_detections	×
NEW 328	transformed_detections_labeled = transformed_detections	×
NEW 329	object_ids = []	×
330
NEW 331	fig = go.Figure()	×
332
NEW 333	if len(object_ids) > 0:	×
NEW 334	for idx, object_id in enumerate(object_ids.to_pylist()):	×
335
NEW 336	obs_ids = labels.select("object_id", object_id).obs_id	×
NEW 337	object_detections = transformed_detections_labeled.apply_mask(	×
338	pc.is_in(transformed_detections_labeled.id, obs_ids)
339	)
NEW 340	object_detections = object_detections.sort_by(["coordinates.time.days", "coordinates.time.nanos"])	×
341
NEW 342	o = np.full(len(object_detections), object_id)	×
NEW 343	t = object_detections.coordinates.time.mjd().to_numpy(zero_copy_only=False)	×
NEW 344	x = object_detections.coordinates.theta_x.to_numpy(zero_copy_only=False)	×
NEW 345	y = object_detections.coordinates.theta_y.to_numpy(zero_copy_only=False)	×
NEW 346	n = object_detections.night.to_numpy(zero_copy_only=False)	×
NEW 347	i = object_detections.id.to_numpy(zero_copy_only=False)	×
348
NEW 349	customdata = np.stack([o, i, t, n], axis=1)	×
NEW 350	hovertemplate = (	×
351	"object_id=%{customdata[0]}<br>"
352	"obs_id=%{customdata[1]}<br>"
353	"mjd=%{customdata[2]:.5f}<br>"
354	"night=%{customdata[3]}<br>"
355	"θx=%{x:.6f}°<br>"
356	"θy=%{y:.6f}°<extra></extra>"
357	)
358
359	# Assign color from palette, cycling if needed
NEW 360	color = COLOR_PALETTE[idx % len(COLOR_PALETTE)]	×
361
NEW 362	if include_time:	×
NEW 363	fig.add_trace(	×
364	go.Scatter3d(
365	x=x,
366	y=y,
367	z=t,
368	mode="markers+lines" if connect_by_time else "markers",
369	name=str(object_id),
370	marker=dict(size=labeled_marker_size, color=color),
371	line=dict(width=labeled_line_width, color=color),
372	customdata=customdata,
373	hovertemplate=hovertemplate,
374	)
375	)
376	else:
NEW 377	fig.add_trace(	×
378	go.Scattergl(
379	x=x,
380	y=y,
381	mode="markers+lines" if connect_by_time else "markers",
382	name=str(object_id),
383	marker=dict(size=labeled_marker_size, color=color),
384	line=dict(width=labeled_line_width, color=color),
385	customdata=customdata,
386	hovertemplate=hovertemplate,
387	)
388	)
389
NEW 390	if include_unlabeled:	×
391
NEW 392	x = transformed_detections_unlabeled.coordinates.theta_x.to_numpy(zero_copy_only=False)	×
NEW 393	y = transformed_detections_unlabeled.coordinates.theta_y.to_numpy(zero_copy_only=False)	×
NEW 394	t = transformed_detections_unlabeled.coordinates.time.mjd().to_numpy(zero_copy_only=False)	×
NEW 395	n = transformed_detections_unlabeled.night.to_numpy(zero_copy_only=False)	×
NEW 396	i = transformed_detections_unlabeled.id.to_numpy(zero_copy_only=False)	×
397
NEW 398	customdata = np.stack([i, t, n], axis=1)	×
NEW 399	hovertemplate = (	×
400	"obs_id=%{customdata[0]}<br>"
401	"mjd=%{customdata[1]:.5f}<br>"
402	"night=%{customdata[2]}<br>"
403	"θx=%{x:.6f}°<br>"
404	"θy=%{y:.6f}°<extra></extra>"
405	)
406
NEW 407	if include_time:	×
NEW 408	fig.add_trace(	×
409	go.Scatter3d(
410	x=x,
411	y=y,
412	z=t,
413	mode="markers",
414	marker=dict(size=unlabeled_marker_size, color="lightcoral"),
415	customdata=customdata,
416	hovertemplate=hovertemplate,
417	name="Unknown",
418	)
419	)
420	else:
NEW 421	fig.add_trace(	×
422	go.Scattergl(
423	x=x,
424	y=y,
425	mode="markers",
426	marker=dict(size=unlabeled_marker_size, color="lightcoral"),
427	customdata=customdata,
428	hovertemplate=hovertemplate,
429	name="Unknown",
430	)
431	)
432
NEW 433	title = r"Transformed Detections (θ<sub>X</sub>, θ<sub>Y</sub>)"	×
434
NEW 435	if include_time:	×
NEW 436	fig.update_layout(	×
437	title=title,
438	paper_bgcolor="black",
439	width=1000,
440	height=1000,
441	title_font=dict(color="white"),
442	legend=dict(font=dict(color="white"), bgcolor="rgba(0,0,0,0)"),
443	scene=dict(
444	bgcolor="black",
445	xaxis=dict(
446	title=dict(text="θ<sub>X</sub> [deg]"),
447	showbackground=False,
448	showgrid=True,
449	gridcolor="rgba(255,255,255,0.10)",
450	zerolinecolor="white",
451	linecolor="white",
452	color="white",
453	title_font=dict(color="white"),
454	),
455	yaxis=dict(
456	title=dict(text="θ<sub>Y</sub> [deg]"),
457	showbackground=False,
458	showgrid=True,
459	gridcolor="rgba(255,255,255,0.10)",
460	zerolinecolor="white",
461	linecolor="white",
462	color="white",
463	title_font=dict(color="white"),
464	),
465	zaxis=dict(
466	title="Time [MJD]",
467	showbackground=False,
468	showgrid=True,
469	gridcolor="rgba(255,255,255,0.10)",
470	zerolinecolor="white",
471	linecolor="white",
472	color="white",
473	title_font=dict(color="white"),
474	),
475	),
476	)
477	else:
NEW 478	fig.update_layout(	×
479	title=title,
480	paper_bgcolor="black",
481	plot_bgcolor="black",
482	width=1000,
483	height=1000,
484	title_font=dict(color="white"),
485	legend=dict(font=dict(color="white"), bgcolor="rgba(0,0,0,0)"),
486	xaxis=dict(
487	title=dict(text="θ<sub>X</sub> [deg]"),
488	showgrid=True,
489	gridcolor="rgba(255,255,255,0.15)",
490	zeroline=False,
491	linecolor="white",
492	color="white",
493	title_font=dict(color="white"),
494	tickfont=dict(color="white"),
495	mirror=True,
496	),
497	yaxis=dict(
498	title=dict(text="θ<sub>Y</sub> [deg]"),
499	showgrid=True,
500	gridcolor="rgba(255,255,255,0.15)",
501	zeroline=False,
502	linecolor="white",
503	color="white",
504	title_font=dict(color="white"),
505	tickfont=dict(color="white"),
506	mirror=True,
507	scaleanchor="x",
508	scaleratio=1,
509	),
510	)
NEW 511	return fig	×

moeyensj / thor / 22634960315

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