How to Align a Piano Roll (SUPRA)

End-to-end alignment: IIIF images, ATON, MIDI, Audio, and Score

How to Align a Piano Roll (SUPRA)

This tutorial applies everything we’ve learned to a real-world case study: aligning a scanned piano roll image with MIDI files, audio, and score annotations from the Stanford University Piano Roll Archive (SUPRA).

Learning Objectives: - Create a graphical timeline from ATON analysis data - Add C-Maps for physical unit conversion (pixels to inches/cm) - Use the child timeline API for hierarchical relationships - Load MIDI, audio, and score data with specialized loaders - Build a TimelineGroup connecting all representations - Transfer coordinates across the entire alignment chain using Timestamps

Prerequisites: - Notebook 04 (Timelines, hierarchies) - Notebook 05 (Timestamps) - Notebook 07 (Alignment Basics, TimelineGroup)

Data Source: - Roll: WM 990 (Welte-Mignon red roll, T-100) - Piece: Richard Wagner - Meistersinger von Nurnberg: Vorspiel (Prelude) - Performer: Myrtle Elvyn, piano (December 6, 1905) - SUPRA URL: https://supra.stanford.edu/ - DRUID: fd660zf8362

Gold Standard Reference Values

Per the ZERO TOLERANCE policy, all values use exact counts from the SUPRA analysis:

Parameter	Value	Description
IMAGE_WIDTH	4,096	Image width in pixels
IMAGE_HEIGHT	299,400	Image height in pixels
LENGTH_DPI	300.25	Scan resolution (pixels per inch)
PHYSICAL_LENGTH	997.17	Roll length in inches (25.33 m)
FIRST_HOLE	15,343	Pixel row of first musical hole
LAST_HOLE	293,119	Pixel row of last musical hole
MUSICAL_LENGTH	277,776	Pixels from first to last hole
MUSICAL_HOLES	30,092	Individual hole punches (raw MIDI events)
RAW_MIDI_NOTES	30,092	Raw MIDI note count (1 per hole)
EXP_MIDI_NOTES	6,380	Expressive MIDI note count (merged)
SCORE_NOTES	5,577	Notes in DCML score
SCORE_MEASURES	222	Measures in DCML score
SCORE_LENGTH	888	Total quarterbeats

Setup

from pathlib import Path

from timetoalign import IdCoordinate, TimeUnit
from timetoalign.alignment import TimelineGroup
from timetoalign.core import timestamp_table_to_dataframe
from timetoalign.loader.graphical.aton import ATONLoader
from timetoalign.loader.midi import PerformanceMidiLoader
from timetoalign.loader.physical import AudioLoader
from timetoalign.loader.score import TSVLoader
from timetoalign.maps import ScalarMap
from timetoalign.timelines import ContinuousPhysicalTimeline

_notebook_dir = Path(".").resolve()
DATA_DIR = _notebook_dir.parent.parent / "tests" / "data" / "supra"
assert DATA_DIR.is_dir(), f"SUPRA data directory not found: {DATA_DIR}"

ATON_FILE = DATA_DIR / "image" / "fd660zf8362_analysis.txt"
MIDI_RAW_PATH = DATA_DIR / "midi" / "fd660zf8362_raw.mid"
MIDI_EXP_PATH = DATA_DIR / "midi" / "fd660zf8362_exp.mid"
MP3_PATH = DATA_DIR / "midi" / "fd660zf8362.mp3"
DCML_DIR = DATA_DIR / "dcml"

{"Data directory": str(DATA_DIR), "ATON file": ATON_FILE.name}

{'Data directory': 'C:\\Users\\hentschel\\git\\tta\\timetoalign\\tests\\data\\supra',
 'ATON file': 'fd660zf8362_analysis.txt'}

---

Part A: Create the Image Timeline (DGT1) from ATON Loader

The ATON (Artistic Text-based Object Notation) file contains hole punch data from the Stanford SUPRA project’s piano roll analysis. The loader creates a timeline with all hole events already populated at their absolute pixel coordinates.

Note: We use the from_file() constructor for one-line instantiation.

aton_loader = ATONLoader.from_file(ATON_FILE)

assert aton_loader.musical_holes == 30092
assert aton_loader.musical_notes == 8718
assert aton_loader.first_hole.value == 15343
assert aton_loader.last_hole.value == 293119
assert aton_loader.musical_length.value == 277776
assert aton_loader.image_dimensions["height"] == 299400

{
    "Image height": f"{aton_loader.image_dimensions['height']:,} pixels",
    "Musical holes": f"{aton_loader.musical_holes:,}",
    "First hole": aton_loader.first_hole,
    "Last hole": aton_loader.last_hole,
    "Musical length": aton_loader.musical_length,
    "Verification": "PASSED",
}

{'Image height': '299,400 pixels',
 'Musical holes': '30,092',
 'First hole': Coordinate(15343, pixels),
 'Last hole': Coordinate(293119, pixels),
 'Musical length': Coordinate(277776, pixels),
 'Verification': 'PASSED'}

The loader’s create_timeline() method creates a timeline spanning the full image with holes at absolute coordinates.

dgt1 = aton_loader.create_timeline(uid="dgt1", name="Piano Roll Image (WM 990)")
dgt1

Timeline[dgt1] (30092 events)
                       0 =============================== 299400 pixels

---

Part B: Add Physical Unit C-Maps

The piano roll was scanned at 300.25 DPI (dots per inch). We attach ConversionMaps to convert pixel coordinates to physical units.

Important: We specify name= for human-readable column headers in timestamp tables. Without it, columns would show as “map:ScalarMap_1”.

Calculation: 299,400 pixels / 300.25 DPI = 997.17 inches = 25.33 meters

LENGTH_DPI = 300.25

# Note: name= provides readable column headers (defaults to "source_to_target")
dgt1.add_conversion_map(
    ScalarMap(
        scalar=1 / LENGTH_DPI,
        source_unit="pixels",
        target_unit="inches",
        name="pixels_to_inches",  # Human-readable name for timestamp columns
    )
)

dgt1.add_conversion_map(
    ScalarMap(
        scalar=2.54 / LENGTH_DPI,
        source_unit="pixels",
        target_unit="cm",
        name="pixels_to_cm",  # Human-readable name for timestamp columns
    )
)

The convert_to method returns proper Coordinate objects with units.

image_length_inches = dgt1.convert_to(dgt1.length, "inches")
image_length_cm = dgt1.convert_to(dgt1.length, "cm")

{
    "Image length": dgt1.length,
    "Image length (inches)": image_length_inches,
    "Image length (cm)": image_length_cm,
    "Image length (meters)": f"{image_length_cm.value / 100:.2f} meters",
}

{'Image length': Coordinate(299400, pixels),
 'Image length (inches)': Coordinate(997.1690258118235, inches),
 'Image length (cm)': Coordinate(2532.8093255620315, centimeters),
 'Image length (meters)': '25.33 meters'}

---

Part C: Create Child Timeline for Relative Coordinates

The musical content doesn’t span the entire image - holes start at pixel 15,343. We create a child timeline to provide a relative coordinate view where the first hole = 0 pixels.

Key insight: The child has no events of its own. It’s purely a coordinate transformation. When we get timestamps, we see event coordinates in BOTH the parent (absolute) and child (relative) coordinate systems - for free!

dgt_holes = dgt1.create_child(
    length=aton_loader.musical_length,
    offset=aton_loader.first_hole,
    uid="dgt_holes",
    name="Musical Holes Region",
)
dgt1

Timeline[dgt1] (30092 events, 1 children, 2 cmaps)
                       0 =============================== 299400 pixels
  └─ Musical H...  15343  =============================  293119

---

Part D: Demonstrate Timestamps

With the parent-child hierarchy established, we can generate timestamps that show coordinates in both the parent (full image) and child (holes region) coordinate systems simultaneously.

Note: The to_dataframe() method provides column names with units appended (e.g., “pixels_to_inches (inches)”) and proper integer types. This is the recommended way to get timestamp data for display.

timestamps_df = dgt1.to_dataframe()

{"Total timestamps": len(timestamps_df), "Columns": list(timestamps_df.columns)}

{'Total timestamps': 20680,
 'Columns': ['axis (pixels)',
  'dgt1 (pixels)',
  'dgt_holes (pixels)',
  'pixels_to_inches (inches)',
  'pixels_to_cm (centimeters)']}

Why ~20,680 timestamps?

The timestamp table has one row per unique event coordinate in the hierarchy: - Parent timeline (dgt1) contains 30,092 hole events at absolute pixel coordinates - But many holes share the same pixel row (multiple notes at one time position) - After de-duplication: ~20,676 unique coordinates plus 4 boundary coordinates

Each row shows the coordinate in all coordinate systems simultaneously: - axis (pixels): The root (parent) coordinate in pixels - dgt1 (pixels): Same as axis (this IS the root timeline) - dgt_holes (pixels): Relative coordinate in child timeline (first hole = 0, NaN if outside) - C-Map columns: Physical units with names like “pixels_to_inches (inches)”

timestamps_df.head(10)

	axis (pixels)	dgt1 (pixels)	dgt_holes (pixels)	pixels_to_inches (inches)	pixels_to_cm (centimeters)
0	15343.0	15343.0	0.0	51.100749	129.795903
1	15391.0	15391.0	48.0	51.260616	130.201965
2	15617.0	15617.0	274.0	52.013322	132.113838
3	15626.0	15626.0	283.0	52.043297	132.189975
4	15638.0	15638.0	295.0	52.083264	132.291490
5	15655.0	15655.0	312.0	52.139883	132.435304
6	15675.0	15675.0	332.0	52.206495	132.604496
7	15683.0	15683.0	340.0	52.233139	132.672173
8	15684.0	15684.0	341.0	52.236470	132.680633
9	15703.0	15703.0	360.0	52.299750	132.841366

Query a specific coordinate: at pixel 100,000 in the image, what’s the local coordinate in the holes region?

ts = dgt1.get_timestamp(100000.0)

{
    "Query (parent coord)": 100000.0,
    "Child coord (dgt_holes)": ts["dgt_holes"],
    "Calculation": f"100000 - {aton_loader.first_hole.value} = {100000 - aton_loader.first_hole.value}",
}

{'Query (parent coord)': 100000.0,
 'Child coord (dgt_holes)': 84657.0,
 'Calculation': '100000 - 15343 = 84657'}

Boundary table shows where child timelines start and end (with C-Maps). Use timestamp_table_to_dataframe() for column names with units.

boundary_table = dgt1.get_boundary_table(conversion_maps=True)
boundary_df = timestamp_table_to_dataframe(boundary_table)
boundary_df

	axis (pixels)	dgt1 (pixels)	dgt_holes (pixels)	pixels_to_inches (inches)	pixels_to_cm (centimeters)
0	0.0	0.0	NaN	0.000000	0.000000
1	15343.0	15343.0	0.0	51.100749	129.795903
2	293119.0	293119.0	277776.0	976.249792	2479.674471
3	299400.0	299400.0	NaN	997.169026	2532.809326

---

Part E: Load External Data Files

Now we load the MIDI files, audio, and score annotations. Each becomes a separate timeline that we’ll connect via the TimelineGroup.

All loaders use the from_file() constructor for clean one-line loading.

E.1: DLT1 - Raw MIDI (one event per hole)

# One-line loading with from_file()
midi_raw_loader = PerformanceMidiLoader.from_file(MIDI_RAW_PATH)

# Create timeline directly from loader - no need to access store
dlt1_raw = midi_raw_loader.create_timeline(uid="dlt1_raw")
raw_note_count = len(midi_raw_loader.store.notes)

dlt1_raw

DiscreteLogicalTimeline[dlt1_raw] (30096 events, 2 children)
                       0 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 277776 ticks
  ├─ notes             0 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 277776 (30092 events)
  └─ controls          0 ,                                0 (4 events)

{
    "DLT1 (MIDI Raw)": dlt1_raw.id,
    "Length": f"{dlt1_raw.length.value:,} ticks",
    "Note events": f"{raw_note_count:,}",
}

{'DLT1 (MIDI Raw)': 'dlt1_raw',
 'Length': '277,776.0 ticks',
 'Note events': '30,092'}

E.2: DLT2 - Expressive MIDI (merged notes + dynamics)

midi_exp_loader = PerformanceMidiLoader.from_file(MIDI_EXP_PATH)
dlt2_exp = midi_exp_loader.create_timeline(uid="dlt2_exp")
exp_note_count = len(midi_exp_loader.store.notes)

dlt2_exp

DiscreteLogicalTimeline[dlt2_exp] (7876 events, 2 children)
                       0 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 274318 ticks
  ├─ notes             0 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,  273249 (6380 events)
  └─ controls          0 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 274318 (1496 events)

{
    "DLT2 (MIDI Expressive)": dlt2_exp.id,
    "Length": f"{dlt2_exp.length.value:,} ticks",
    "Note events": f"{exp_note_count:,}",
}

{'DLT2 (MIDI Expressive)': 'dlt2_exp',
 'Length': '274,318.0 ticks',
 'Note events': '6,380'}

E.3: DPT1 - Audio (MP3)

Note: MP3 loading requires mutagen or soundfile. If not installed, we use a mock timeline with the known duration from the README.

try:
    audio_loader = AudioLoader.from_file(MP3_PATH)
    dpt1_audio = audio_loader.create_timeline(uid="dpt1_audio")
    audio_duration = audio_loader.duration_seconds
    audio_info = {
        "DPT1 (Audio)": dpt1_audio.id,
        "Sample rate": f"{audio_loader.sample_rate:,} Hz",
        "Duration": f"{audio_duration:.2f} seconds",
    }
except ValueError as e:
    print(f"Note: MP3 loading unavailable ({e}). Using mock timeline.")
    audio_duration = 573.0
    dpt1_audio = ContinuousPhysicalTimeline(
        length=audio_duration,
        unit=TimeUnit.seconds,
        uid="dpt1_audio",
    )
    audio_info = {
        "DPT1 (Audio)": dpt1_audio.id,
        "Duration": f"{audio_duration:.2f} seconds (from README)",
    }

dpt1_audio

DiscretePhysicalTimeline[dpt1_audio] (1 cmaps)
                         0 ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 19670082 samples

audio_info

{'DPT1 (Audio)': 'dpt1_audio',
 'Sample rate': '44,100 Hz',
 'Duration': '446.03 seconds'}

E.4: CLT1 - Score Annotations (DCML TSV files)

The DCML corpus provides score data in TSV format. We load all four files (notes, measures, harmonies, chords) at once using from_file() with glob.

Important: TimeToAlign! properly loads: - .harmonies.tsv as annotations (Roman numeral analysis) - .chords.tsv as control events (chord symbols)

SCORE_BASE = "WWV096-Meistersinger_01_Vorspiel-Prelude_SchottKleinmichel"
score_tsv_files = sorted(DCML_DIR.glob(f"{SCORE_BASE}.*.tsv"))

[f.name for f in score_tsv_files]

['WWV096-Meistersinger_01_Vorspiel-Prelude_SchottKleinmichel.chords.tsv',
 'WWV096-Meistersinger_01_Vorspiel-Prelude_SchottKleinmichel.harmonies.tsv',
 'WWV096-Meistersinger_01_Vorspiel-Prelude_SchottKleinmichel.measures.tsv',
 'WWV096-Meistersinger_01_Vorspiel-Prelude_SchottKleinmichel.notes.tsv']

Load all TSV files at once using from_file() with * unpacking - one line.

score_loader = TSVLoader.from_file(*score_tsv_files)
score_loader.store.summary()

{'notes_count': 5577,
 'measures_count': 222,
 'controls_count': 4378,
 'annotations_count': 1074,
 'has_rests': False,
 'format': 'tsv',
 'parser': 'ms3',
 'source': 'C:\\Users\\hentschel\\git\\tta\\timetoalign\\tests\\data\\supra\\dcml\\WWV096-Meistersinger_01_Vorspiel-Prelude_SchottKleinmichel.notes.tsv',
 'control_type': 'Chord',
 'n_controls': 4378,
 'annotation_type': 'Harmony',
 'n_annotations': 1074,
 'n_measures': 222,
 'flow_control': {'total_measures': 222,
  'has_repeats': False,
  'has_voltas': False,
  'has_breaks': False,
  'repeat_starts': 0,
  'repeat_ends': 0,
  'voltas': set(),
  'breaks': set()}}

Verify against gold standard (ZERO TOLERANCE).

notes_count = len(score_loader.store.notes)
measures_count = len(score_loader.store.measures)
annotations_count = len(score_loader.store.annotations)  # Harmonies
controls_count = len(score_loader.store.controls)  # Chords

assert notes_count == 5577, f"Notes mismatch: {notes_count} != 5577"
assert measures_count == 222, f"Measures mismatch: {measures_count} != 222"

{
    "Notes": notes_count,
    "Measures": measures_count,
    "Annotations (harmonies)": annotations_count,
    "Controls (chords)": controls_count,
    "Verification": "PASSED",
}

{'Notes': 5577,
 'Measures': 222,
 'Annotations (harmonies)': 1074,
 'Controls (chords)': 4378,
 'Verification': 'PASSED'}

Create the score timeline using create_timeline() directly from the loader.

clt1_score = score_loader.create_timeline(uid="clt1_score")
clt1_score

ContinuousLogicalTimeline[clt1_score] (11251 events, 4 children, 2 cmaps)
                      0 _________________________________ 888 quarters
  ├─ notes            0 ________________________________  886 (5577 events)
  ├─ measures         0 _________________________________ 888 (222 events)
  ├─ controls         0 ________________________________  886 (4378 events)
  └─ annotations      0 _________________________________ 888 (1074 events)

E.5: Inspect Harmony Annotations and Chord Controls

• Harmonies are loaded as annotations (Roman numeral analysis)
• Chords are loaded as control events (chord symbols)

Let’s examine the harmonies - we’ll use a specific label for coordinate transfer!

# Get harmony annotations (from .harmonies.tsv)
harmonies = score_loader.store.annotations.filter(subtype="Harmony")
harmonies_df = harmonies.to_pandas()[["name", "text", "start", "mc", "mn"]].head(20)
harmonies_df

	name	text	start	mc	mn
0	C.I	C.I	0	1	1
1	IM2	IM2	2	1	1
2	vi7	vi7	5	2	2
3	V7/ii	V7/ii	7	2	2
4	V2(6)/ii	V2(6)/ii	8	3	3
5	V2	V2	9	3	3
6	I6	I6	10	3	3
7	V43	V43	11	3	3
8	I	I	12	4	4
9	IV64	IV64	13	4	4
10	IV6	IV6	27/2	4	4
11	vii%7(9)	vii%7(9)	14	4	4
12	I	I	29/2	4	4
13	V43	V43	15	4	4
14	I6	I6	31/2	4	4
15	ii65	ii65	16	5	5
16	vii%65	vii%65	17	5	5
17	viio6	viio6	37/2	5	5
18	I6	I6	20	6	6
19	I64	I64	21	6	6

# Get chord control events (from .chords.tsv)
chords = score_loader.store.controls.filter(subtype="Chord")
if len(chords) > 0:
    chords_df = chords.to_pandas()[["name", "text", "start", "mc", "mn"]].head(10)
    chords_df
else:
    {"Chord controls": "No chords loaded (file may not exist)"}

---

Part F: Create the TimelineGroup

Now we bring everything together in a TimelineGroup. This establishes commensurability between all timelines, enabling coordinate transfer.

Important: A TimelineGroup does NOT have a root timeline - all timelines are peers. We create it from a list of timelines.

Alignment structure:

dgt_holes (pixels)
    |
    +-- dlt1_raw (MIDI ticks)
    |
    +-- dlt2_exp (MIDI ticks)
    |
    +-- dpt1_audio (seconds)
    |
    +-- clt1_score (quarterbeats)

group = TimelineGroup(
    id="supra_alignment",
    name="SUPRA Piano Roll Alignment",
    timelines=[dgt_holes, dlt1_raw, dlt2_exp, dpt1_audio, clt1_score],
)

group

TimelineGroup[supra_alignment] (5 timelines, 2 timestamps)
┌─────────────────────────────────────────────────────────────────────────────┐
│ Timeline[dgt_holes]                                                         │
│                        0 =========================== 277776 pixels          │
│                                                                             │
│ DiscreteLogicalTimeline[dlt1_raw] (30096 events, 2 children)                │
│                        0 ,,,,,,,,,,,,,,,,,,,,,,,,,,,, 277776 ticks          │
│   ├─ notes             0 ,,,,,,,,,,,,,,,,,,,,,,,,,,,, 277776 (30092 events) │
│   └─ controls          0 ,                            0 (4 events)          │
│                                                                             │
│ DiscreteLogicalTimeline[dlt2_exp] (7876 events, 2 children)                 │
│                        0 ,,,,,,,,,,,,,,,,,,,,,,,,,,,, 274318 ticks          │
│   ├─ notes             0 ,,,,,,,,,,,,,,,,,,,,,,,,,,,  273249 (6380 events)  │
│   └─ controls          0 ,,,,,,,,,,,,,,,,,,,,,,,,,,,, 274318 (1496 events)  │
│                                                                             │
│ DiscretePhysicalTimeline[dpt1_audio] (1 cmaps)                              │
│                          0 ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 19670082 samples          │
│                                                                             │
│ ContinuousLogicalTimeline[clt1_score] (11251 events, 4 children, 2 cmaps)   │
│                       0 _____________________________ 888 quarters          │
│   ├─ notes            0 ____________________________  886 (5577 events)     │
│   ├─ measures         0 _____________________________ 888 (222 events)      │
│   ├─ controls         0 ____________________________  886 (4378 events)     │
│   └─ annotations      0 _____________________________ 888 (1074 events)     │
└─────────────────────────────────────────────────────────────────────────────┘
Timestamps: 2

{"Group": group.name, "Timelines": group.timeline_ids, "Count": group.n_timelines}

{'Group': 'SUPRA Piano Roll Alignment',
 'Timelines': ['dgt_holes',
  'dlt1_raw',
  'dlt2_exp',
  'dpt1_audio',
  'clt1_score'],
 'Count': 5}

---

Part G: Group Timestamps - The Heart of Alignment

This is the key feature! The TimelineGroup maintains a timestamp table where each row represents a synchronized point across ALL timelines. Coordinate transfer uses these timestamps via interpolation.

# Get the full timestamp table
group_timestamps = group.get_timestamps_df()
group_timestamps

	dgt_holes (pixels)	dlt1_raw (ticks)	dlt2_exp (ticks)	dpt1_audio (samples)	clt1_score (quarters)	samples_to_seconds (seconds)	quarters_to_ticks (ticks)	quarters_to_measures (measures)
0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	1.0
1	277776.0	277776.0	274318.0	19670082.0	888.0	446.033605	426240.0	223.0

Each row shows the same musical moment in all coordinate systems. Column names include units (e.g., “dgt_holes (pixels)”, “dpt1_audio (seconds)”).

---

Part H: Coordinate Transfer Using Timestamps

The right way to transfer coordinates is through timestamps! Instead of manually calling group.convert(), we use get_timestamp_at() which returns a full cross-section through all timelines.

H.1: Transfer a Specific Harmony Label Across All Timelines

Let’s take the first occurrence of a I chord (C major) and find its position in every timeline.

# Find the first I chord (tonic) harmony
i_chords = score_loader.store.annotations.filter(text="I")
if len(i_chords) > 0:
    first_i = i_chords.to_pandas().iloc[0]
    i_chord_qb = float(first_i["start"])
    i_chord_label = first_i["text"]
    i_chord_mc = first_i["mc"]
else:
    # Fallback if no I chord
    first_harmony = score_loader.store.annotations.to_pandas().iloc[0]
    i_chord_qb = float(first_harmony["start"])
    i_chord_label = first_harmony["text"]
    i_chord_mc = first_harmony["mc"]

{
    "Harmony label": i_chord_label,
    "Position (quarterbeats)": i_chord_qb,
    "Measure": i_chord_mc,
}

{'Harmony label': 'I', 'Position (quarterbeats)': 12.0, 'Measure': 4}

Now get the timestamp at this position - one call gives us ALL coordinates!

# Get timestamp at the harmony position in the score timeline
harmony_ts = group.get_timestamp_at(i_chord_qb, "clt1_score")
harmony_ts

TimeStamp interpolated

ID	Coordinate	Type
clt1_score	12 quarters	axis
dgt_holes	3753 pixels	child
dlt1_raw	3754 ticks	child
dlt2_exp	3707 ticks	child
dpt1_audio	265812 samples	child
seconds	6.027483 seconds	cmap
ticks	5760 ticks	cmap
measures	4 measures	cmap

H.2: Full Image Coordinates + Physical Units via IdCoordinate

The group timestamp gives us dgt_holes (relative pixels). To get the absolute image position AND physical units (C-Maps), we use IdCoordinate - it carries the child’s timeline_id, so the parent automatically applies the offset!

if harmony_ts["dgt_holes"] is not None:
    # Create IdCoordinate from child timeline - parent auto-offsets!
    child_coord = IdCoordinate(harmony_ts["dgt_holes"], TimeUnit.pixels, "dgt_holes")

    # Parent's get_timestamps() recognizes the child_id and applies offset
    image_ts = dgt1.get_timestamps(coordinates=[child_coord])
    image_ts

H.3: Multiple Harmony Labels - Batch Transfer

For batch transfers, use group.get_timestamps_at() - the DEAD-SIMPLE API: pass coordinates, get a DataFrame with all timelines and units in column names.

# Get first 10 harmonies
first_10_harmonies = (
    score_loader.store.annotations.filter(subtype="Harmony").to_pandas().head(10)
)

# DEAD-SIMPLE: Get timestamps for all harmony coordinates in ONE CALL
harmony_coords = first_10_harmonies["start"].tolist()
group_df = group.get_timestamps_at(harmony_coords, "clt1_score")
group_df

	clt1_score (quarters)	dgt_holes (pixels)	dlt1_raw (ticks)	dlt2_exp (ticks)	dpt1_audio (samples)	seconds	ticks	measures
0	0.0	0.000000	0	0	0	0.000000	0.0	1.00
1	2.0	625.621622	626	618	44302	1.004580	960.0	1.50
2	5.0	1564.054054	1564	1545	110755	2.511451	2400.0	2.25
3	7.0	2189.675676	2190	2162	155057	3.516032	3360.0	2.75
4	8.0	2502.486486	2502	2471	177208	4.018322	3840.0	3.00
5	9.0	2815.297297	2815	2780	199359	4.520612	4320.0	3.25
6	10.0	3128.108108	3128	3089	221510	5.022902	4800.0	3.50
7	11.0	3440.918919	3441	3398	243661	5.525193	5280.0	3.75
8	12.0	3753.729730	3754	3707	265812	6.027483	5760.0	4.00
9	13.0	4066.540541	4067	4016	287963	6.529773	6240.0	4.25

To also get the parent’s C-Maps (inches, cm), pass IdCoordinates to the parent:

# Get dgt_holes coordinates from group timestamps as IdCoordinates
dgt_holes_col = (
    "dgt_holes (pixels)" if "dgt_holes (pixels)" in group_df.columns else "dgt_holes"
)
child_coords = [
    IdCoordinate(v, TimeUnit.pixels, "dgt_holes")
    for v in group_df[dgt_holes_col].dropna()
]

# Parent timeline auto-applies offset and returns timestamps with C-Maps
parent_df = dgt1.get_timestamps(coordinates=child_coords)
parent_df

	axis (pixels)	dgt1 (pixels)	dgt_holes (pixels)	pixels_to_inches (inches)	pixels_to_cm (centimeters)
0	15343.000000	15343.000000	0.000000	51.100749	129.795903
1	15968.621622	15968.621622	625.621622	53.184418	135.088423
2	16907.054054	16907.054054	1564.054054	56.309922	143.027202
3	17532.675676	17532.675676	2189.675676	58.393591	148.319721
4	17845.486486	17845.486486	2502.486486	59.435425	150.965981
5	18158.297297	18158.297297	2815.297297	60.477260	153.612240
6	18471.108108	18471.108108	3128.108108	61.519094	156.258500
7	18783.918919	18783.918919	3440.918919	62.560929	158.904760
8	19096.729730	19096.729730	3753.729730	63.602763	161.551019
9	19409.540541	19409.540541	4066.540541	64.644598	164.197279

---

Part I: Comprehensive Group Timestamps Demo

Let’s demonstrate all the capabilities of group timestamps.

I.1: Query from Different Timelines

We can query the group from ANY member timeline. Just display the timestamp!

# Query at 100 seconds in the audio - timestamp shows ALL peer timelines
audio_100s = group.get_timestamp_at(100.0, "dpt1_audio")
audio_100s

TimeStamp interpolated

ID	Coordinate	Type
dpt1_audio	100 samples	axis
dgt_holes	1 pixels	child
dlt1_raw	1 ticks	child
dlt2_exp	1 ticks	child
clt1_score	0.004514 quarters	child
seconds	0.002268 seconds	cmap
ticks	2 ticks	cmap
measures	1.001129 measures	cmap

# Query at 50,000 pixels in the holes region
holes_50k = group.get_timestamp_at(50000, "dgt_holes")
holes_50k

TimeStamp interpolated

ID	Coordinate	Type
dgt_holes	50000 pixels	axis
dlt1_raw	50000 ticks	child
dlt2_exp	49378 ticks	child
dpt1_audio	3540637 samples	child
clt1_score	159.841023 quarters	child
seconds	80.286553 seconds	cmap
ticks	76723 ticks	cmap
measures	40.960256 measures	cmap

I.2: Boundary Points

Check the alignment at the start and end of the musical content. Display timestamps directly to see all coordinate values:

# Start of music (coordinate 0 in dgt_holes)
start_ts = group.get_timestamp_at(0, "dgt_holes")
start_ts

TimeStamp

ID	Coordinate	Type
dgt_holes	0 pixels	axis
dlt1_raw	0 ticks	child
dlt2_exp	0 ticks	child
dpt1_audio	0 samples	child
clt1_score	0 quarters	child
seconds	0 seconds	cmap
ticks	0 ticks	cmap
measures	1 measures	cmap

# End of music
end_ts = group.get_timestamp_at(int(aton_loader.musical_length.value), "dgt_holes")
end_ts

TimeStamp

ID	Coordinate	Type
dgt_holes	277776 pixels	axis
dlt1_raw	277776 ticks	child
dlt2_exp	274318 ticks	child
dpt1_audio	19670082 samples	child
clt1_score	888 quarters	child
seconds	446.033605 seconds	cmap
ticks	426240 ticks	cmap
measures	223 measures	cmap

I.3: Round-Trip Verification

Verify that coordinate transfer is reversible. We show the full timestamps at each step so you can see all coordinates involved.

test_coord = 100000  # pixels in holes region

# Step 1: Get timestamp at our test coordinate
ts1 = group.get_timestamp_at(test_coord, "dgt_holes")
ts1

TimeStamp interpolated

ID	Coordinate	Type
dgt_holes	100000 pixels	axis
dlt1_raw	100000 ticks	child
dlt2_exp	98755 ticks	child
dpt1_audio	7081275 samples	child
clt1_score	319.682046 quarters	child
seconds	160.573129 seconds	cmap
ticks	153447 ticks	cmap
measures	80.920511 measures	cmap

# Step 2: Transfer to audio, then back to holes
ts2 = group.get_timestamp_at(ts1["dpt1_audio"], "dpt1_audio")
ts2

TimeStamp interpolated

ID	Coordinate	Type
dpt1_audio	7081275 samples	axis
dgt_holes	100000 pixels	child
dlt1_raw	100000 ticks	child
dlt2_exp	98755 ticks	child
clt1_score	319.682053 quarters	child
seconds	160.573129 seconds	cmap
ticks	153447 ticks	cmap
measures	80.920513 measures	cmap

# Step 3: Transfer to score, then back to holes
ts3 = group.get_timestamp_at(ts1["clt1_score"], "clt1_score")
ts3

TimeStamp interpolated

ID	Coordinate	Type
clt1_score	319.682046 quarters	axis
dgt_holes	100000 pixels	child
dlt1_raw	100000 ticks	child
dlt2_exp	98755 ticks	child
dpt1_audio	7081275 samples	child
seconds	160.573129 seconds	cmap
ticks	153447 ticks	cmap
measures	80.920511 measures	cmap

# Verify round-trip precision by examining coordinates from each timestamp
print("Round-trip verification:")
print(f"  Original:   {test_coord} pixels")
print(f"  Via audio:  {ts2['dgt_holes']} pixels")
print(f"  Via score:  {ts3['dgt_holes']} pixels")
print(f"  Audio diff: {abs(ts2['dgt_holes'] - test_coord):.6f}")
print(f"  Score diff: {abs(ts3['dgt_holes'] - test_coord):.6f}")

Round-trip verification:
  Original:   100000 pixels
  Via audio:  100000.00225723513 pixels
  Via score:  100000.00000000001 pixels
  Audio diff: 0.002257
  Score diff: 0.000000

---

Summary

In this tutorial, we demonstrated a complete alignment workflow:

1. Image Timeline (DGT1): Created from ATON analysis with holes as events
2. Physical C-Maps: Attached with human-readable names (name=)
3. Child Timeline (dgt_holes): Modeled the musical region as a child
4. Timestamps: Showed cross-section views through the hierarchy
5. External Data: Loaded MIDI, audio, and score with harmonies + chords
6. TimelineGroup: Connected all timelines (no root - all peers)
7. Coordinate Transfer via Timestamps: The RIGHT way to transfer coordinates!

Key Patterns

Pattern	Usage
One-line loading	ATONLoader.from_file(path)
Direct timeline creation	loader.create_timeline(uid=...)
Named C-Maps	ScalarMap(..., name="pixels_to_inches")
Coordinate transfer	group.get_timestamp_at(coord, timeline_id)
Timeline display	Just timeline (no print() needed)

Timeline Diagram

DGT1 (Full Image: 0 - 299,400 px)
  |-- pixels_to_inches (C-Map)
  |-- pixels_to_cm (C-Map)
  |
  +-- [15,343 px] -- dgt_holes (Musical Region: 0 - 277,776 px) -- [293,119 px]
                          |
                          | TimelineGroup (all peers)
                          |
                          +-- dlt1_raw (MIDI raw: ticks)
                          |
                          +-- dlt2_exp (MIDI expressive: ticks)
                          |
                          +-- dpt1_audio (Audio: seconds)
                          |
                          +-- clt1_score (Score: quarterbeats)
                                |-- notes (5,577 events)
                                |-- measures (222 events)
                                |-- annotations (harmonies from .harmonies.tsv)
                                +-- controls (chords from .chords.tsv)

Next Steps

• how01_beat_grids.ipynb: Work with BeatGrid, FloorMap, and RotationMap
• Advanced: Implement WarpMap for non-linear alignment (expressive timing)