Upload stack_cubes_umi_1_franka dataset

plot_state_action_curves.py

@@ -0,0 +1,388 @@
+#!/usr/bin/env python3
+"""Plot state and action curves for every episode in this dataset."""
+
+from __future__ import annotations
+
+import csv
+import json
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pyarrow.parquet as pq
+
+
+DATASET_DIR = Path(__file__).resolve().parent
+OUTPUT_DIR = DATASET_DIR / "state_action_curves"
+EPISODE_DIR = OUTPUT_DIR / "episodes"
+
+STATE_COLOR = "#1f77b4"
+ACTION_COLOR = "#ff7f0e"
+
+
+def load_metadata() -> tuple[list[str], list[str], float]:
+    """Load feature names and frame rate from the dataset metadata."""
+    with (DATASET_DIR / "meta" / "info.json").open(encoding="utf-8") as file:
+        info = json.load(file)
+    return (
+        info["features"]["state"]["names"],
+        info["features"]["actions"]["names"],
+        float(info["fps"]),
+    )
+
+
+def load_episodes() -> list[dict[str, np.ndarray | int]]:
+    """Read state, action, and timestamp arrays from every episode."""
+    episode_files = sorted((DATASET_DIR / "data").glob("chunk-*/episode_*.parquet"))
+    episodes = []
+    for episode_file in episode_files:
+        table = pq.read_table(
+            episode_file,
+            columns=["state", "actions", "timestamp", "episode_index"],
+        )
+        episodes.append(
+            {
+                "index": int(table["episode_index"][0].as_py()),
+                "state": np.asarray(table["state"].to_pylist(), dtype=np.float64),
+                "action": np.asarray(table["actions"].to_pylist(), dtype=np.float64),
+                "time": np.asarray(table["timestamp"].to_pylist(), dtype=np.float64),
+            }
+        )
+    return episodes
+
+
+def add_figure_legend(fig: plt.Figure) -> None:
+    """Add the shared state/action legend to a figure."""
+    state_handle = plt.Line2D([], [], color=STATE_COLOR, label="state")
+    action_handle = plt.Line2D([], [], color=ACTION_COLOR, linestyle="--", label="action")
+    fig.legend(
+        handles=[state_handle, action_handle],
+        loc="upper right",
+        bbox_to_anchor=(0.99, 0.995),
+        ncol=2,
+        frameon=False,
+    )
+
+
+def plot_episode(
+    episode: dict[str, np.ndarray | int],
+    state_names: list[str],
+    action_names: list[str],
+) -> None:
+    """Plot all dimensions of one episode, overlaying state and action."""
+    state = episode["state"]
+    action = episode["action"]
+    time = episode["time"]
+    episode_index = episode["index"]
+    assert isinstance(state, np.ndarray)
+    assert isinstance(action, np.ndarray)
+    assert isinstance(time, np.ndarray)
+    assert isinstance(episode_index, int)
+
+    fig, axes = plt.subplots(5, 2, figsize=(16, 17), sharex=True)
+    for dim, axis in enumerate(axes.flat):
+        axis.plot(time, state[:, dim], color=STATE_COLOR, linewidth=1.5)
+        axis.plot(
+            time,
+            action[:, dim],
+            color=ACTION_COLOR,
+            linewidth=1.1,
+            linestyle="--",
+        )
+        title = state_names[dim]
+        if state_names[dim] != action_names[dim]:
+            title = f"{state_names[dim]} / {action_names[dim]}"
+        axis.set_title(f"dim {dim}: {title}")
+        axis.set_ylabel("value")
+        axis.grid(alpha=0.25)
+    for axis in axes[-1, :]:
+        axis.set_xlabel("time (s)")
+    duration = float(time[-1] - time[0]) if len(time) > 1 else 0.0
+    fig.suptitle(
+        f"Episode {episode_index:06d} — {len(time)} frames, {duration:.2f} s",
+        y=0.995,
+    )
+    add_figure_legend(fig)
+    fig.tight_layout(rect=(0, 0, 1, 0.975))
+    fig.savefig(
+        EPISODE_DIR / f"episode_{episode_index:06d}_state_action.png",
+        dpi=150,
+        bbox_inches="tight",
+    )
+    plt.close(fig)
+
+
+def plot_overview(
+    episodes: list[dict[str, np.ndarray | int]],
+    state_names: list[str],
+    action_names: list[str],
+) -> None:
+    """Overlay all episodes on a normalized time axis."""
+    fig, axes = plt.subplots(5, 2, figsize=(16, 17), sharex=True)
+    normalized_grid = np.linspace(0.0, 1.0, 300)
+
+    for dim, axis in enumerate(axes.flat):
+        interpolated_states = []
+        interpolated_actions = []
+        for episode in episodes:
+            state = episode["state"]
+            action = episode["action"]
+            assert isinstance(state, np.ndarray)
+            assert isinstance(action, np.ndarray)
+            progress = np.linspace(0.0, 1.0, len(state))
+            axis.plot(
+                progress,
+                state[:, dim],
+                color=STATE_COLOR,
+                linewidth=0.6,
+                alpha=0.13,
+            )
+            axis.plot(
+                progress,
+                action[:, dim],
+                color=ACTION_COLOR,
+                linewidth=0.6,
+                alpha=0.13,
+                linestyle="--",
+            )
+            interpolated_states.append(
+                np.interp(normalized_grid, progress, state[:, dim])
+            )
+            interpolated_actions.append(
+                np.interp(normalized_grid, progress, action[:, dim])
+            )
+
+        axis.plot(
+            normalized_grid,
+            np.median(interpolated_states, axis=0),
+            color=STATE_COLOR,
+            linewidth=2.2,
+        )
+        axis.plot(
+            normalized_grid,
+            np.median(interpolated_actions, axis=0),
+            color=ACTION_COLOR,
+            linewidth=1.8,
+            linestyle="--",
+        )
+        title = state_names[dim]
+        if state_names[dim] != action_names[dim]:
+            title = f"{state_names[dim]} / {action_names[dim]}"
+        axis.set_title(f"dim {dim}: {title}")
+        axis.set_ylabel("value")
+        axis.grid(alpha=0.25)
+
+    for axis in axes[-1, :]:
+        axis.set_xlabel("normalized episode progress")
+    fig.suptitle(
+        f"All {len(episodes)} episodes — faint: individual, bold: median",
+        y=0.995,
+    )
+    add_figure_legend(fig)
+    fig.tight_layout(rect=(0, 0, 1, 0.975))
+    fig.savefig(OUTPUT_DIR / "all_episodes_overview.png", dpi=180, bbox_inches="tight")
+    plt.close(fig)
+
+
+def plot_differences(
+    episodes: list[dict[str, np.ndarray | int]],
+    state_names: list[str],
+    action_names: list[str],
+) -> None:
+    """Plot action minus state to make small discrepancies visible."""
+    fig, axes = plt.subplots(5, 2, figsize=(16, 17), sharex=True)
+    for dim, axis in enumerate(axes.flat):
+        for episode in episodes:
+            state = episode["state"]
+            action = episode["action"]
+            assert isinstance(state, np.ndarray)
+            assert isinstance(action, np.ndarray)
+            progress = np.linspace(0.0, 1.0, len(state))
+            axis.plot(
+                progress,
+                action[:, dim] - state[:, dim],
+                linewidth=0.7,
+                alpha=0.38,
+            )
+        title = state_names[dim]
+        if state_names[dim] != action_names[dim]:
+            title = f"{action_names[dim]} − {state_names[dim]}"
+        axis.set_title(f"dim {dim}: {title}")
+        axis.set_ylabel("action − state")
+        axis.axhline(0.0, color="black", linewidth=0.7)
+        axis.grid(alpha=0.25)
+    for axis in axes[-1, :]:
+        axis.set_xlabel("normalized episode progress")
+    fig.suptitle(f"Action − state for all {len(episodes)} episodes", y=0.995)
+    fig.tight_layout(rect=(0, 0, 1, 0.975))
+    fig.savefig(
+        OUTPUT_DIR / "all_episodes_action_minus_state.png",
+        dpi=180,
+        bbox_inches="tight",
+    )
+    plt.close(fig)
+
+
+def write_diagnostics(
+    episodes: list[dict[str, np.ndarray | int]],
+    state_names: list[str],
+    action_names: list[str],
+    fps: float,
+) -> None:
+    """Write dimension-level range, mismatch, and jump diagnostics."""
+    rows = []
+    for episode in episodes:
+        episode_index = episode["index"]
+        state = episode["state"]
+        action = episode["action"]
+        assert isinstance(episode_index, int)
+        assert isinstance(state, np.ndarray)
+        assert isinstance(action, np.ndarray)
+        for dim in range(state.shape[1]):
+            diff = action[:, dim] - state[:, dim]
+            state_jump = np.abs(np.diff(state[:, dim]))
+            action_jump = np.abs(np.diff(action[:, dim]))
+            rows.append(
+                {
+                    "episode": episode_index,
+                    "dimension": dim,
+                    "state_name": state_names[dim],
+                    "action_name": action_names[dim],
+                    "frames": len(state),
+                    "duration_s": (len(state) - 1) / fps,
+                    "state_min": np.min(state[:, dim]),
+                    "state_max": np.max(state[:, dim]),
+                    "action_min": np.min(action[:, dim]),
+                    "action_max": np.max(action[:, dim]),
+                    "max_abs_action_minus_state": np.max(np.abs(diff)),
+                    "mean_abs_action_minus_state": np.mean(np.abs(diff)),
+                    "max_abs_state_step": np.max(state_jump, initial=0.0),
+                    "max_abs_action_step": np.max(action_jump, initial=0.0),
+                }
+            )
+
+    with (OUTPUT_DIR / "diagnostics.csv").open("w", newline="", encoding="utf-8") as file:
+        writer = csv.DictWriter(file, fieldnames=rows[0].keys())
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def write_summary(
+    episodes: list[dict[str, np.ndarray | int]],
+    state_names: list[str],
+    action_names: list[str],
+    fps: float,
+) -> None:
+    """Write a compact human-readable diagnostic summary."""
+    all_states = np.concatenate([episode["state"] for episode in episodes], axis=0)
+    all_actions = np.concatenate([episode["action"] for episode in episodes], axis=0)
+    lengths = np.asarray([len(episode["state"]) for episode in episodes])
+    state_nonfinite = int(np.size(all_states) - np.isfinite(all_states).sum())
+    action_nonfinite = int(np.size(all_actions) - np.isfinite(all_actions).sum())
+
+    lines = [
+        "State/action curve diagnostics",
+        "==============================",
+        f"Episodes: {len(episodes)}",
+        f"Frames: {len(all_states)}",
+        (
+            "Frames per episode: "
+            f"min={lengths.min()}, median={np.median(lengths):.0f}, max={lengths.max()}"
+        ),
+        (
+            "Duration per episode (s): "
+            f"min={(lengths.min() - 1) / fps:.2f}, "
+            f"median={(np.median(lengths) - 1) / fps:.2f}, "
+            f"max={(lengths.max() - 1) / fps:.2f}"
+        ),
+        f"Non-finite state values: {state_nonfinite}",
+        f"Non-finite action values: {action_nonfinite}",
+        "",
+        "Per-dimension diagnostics:",
+    ]
+
+    for dim, state_name in enumerate(state_names):
+        max_state_step = (-1.0, -1, -1)
+        max_action_step = (-1.0, -1, -1)
+        for episode in episodes:
+            episode_index = episode["index"]
+            state = episode["state"]
+            action = episode["action"]
+            assert isinstance(episode_index, int)
+            assert isinstance(state, np.ndarray)
+            assert isinstance(action, np.ndarray)
+            if len(state) < 2:
+                continue
+            state_steps = np.abs(np.diff(state[:, dim]))
+            action_steps = np.abs(np.diff(action[:, dim]))
+            state_frame = int(np.argmax(state_steps))
+            action_frame = int(np.argmax(action_steps))
+            if state_steps[state_frame] > max_state_step[0]:
+                max_state_step = (
+                    float(state_steps[state_frame]),
+                    episode_index,
+                    state_frame,
+                )
+            if action_steps[action_frame] > max_action_step[0]:
+                max_action_step = (
+                    float(action_steps[action_frame]),
+                    episode_index,
+                    action_frame,
+                )
+
+        difference = np.abs(all_actions[:, dim] - all_states[:, dim])
+        lines.extend(
+            [
+                f"  dim {dim}: {state_name} / {action_names[dim]}",
+                (
+                    f"    state range: [{all_states[:, dim].min():.9g}, "
+                    f"{all_states[:, dim].max():.9g}]"
+                ),
+                (
+                    f"    action range: [{all_actions[:, dim].min():.9g}, "
+                    f"{all_actions[:, dim].max():.9g}]"
+                ),
+                (
+                    f"    |action-state|: mean={difference.mean():.9g}, "
+                    f"max={difference.max():.9g}"
+                ),
+                (
+                    f"    max state step: {max_state_step[0]:.9g} "
+                    f"(episode {max_state_step[1]:06d}, "
+                    f"frame {max_state_step[2]}->{max_state_step[2] + 1})"
+                ),
+                (
+                    f"    max action step: {max_action_step[0]:.9g} "
+                    f"(episode {max_action_step[1]:06d}, "
+                    f"frame {max_action_step[2]}->{max_action_step[2] + 1})"
+                ),
+            ]
+        )
+
+    (OUTPUT_DIR / "summary.txt").write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def main() -> None:
+    """Generate all curve plots and diagnostics."""
+    OUTPUT_DIR.mkdir(exist_ok=True)
+    EPISODE_DIR.mkdir(exist_ok=True)
+    state_names, action_names, fps = load_metadata()
+    episodes = load_episodes()
+    if not episodes:
+        raise RuntimeError(f"No episode parquet files found in {DATASET_DIR / 'data'}")
+
+    for episode in episodes:
+        plot_episode(episode, state_names, action_names)
+    plot_overview(episodes, state_names, action_names)
+    plot_differences(episodes, state_names, action_names)
+    write_diagnostics(episodes, state_names, action_names, fps)
+    write_summary(episodes, state_names, action_names, fps)
+    print(f"Wrote plots for {len(episodes)} episodes to {OUTPUT_DIR}")
+
+
+if __name__ == "__main__":
+    main()

state_action_curves/diagnostics.csv

@@ -0,0 +1,451 @@
+episode,dimension,state_name,action_name,frames,duration_s,state_min,state_max,action_min,action_max,max_abs_action_minus_state,mean_abs_action_minus_state,max_abs_state_step,max_abs_action_step
+0,0,x,x,350,11.633333333333333,0.23776200413703918,0.5994840264320374,0.23776200413703918,0.5994840264320374,0.0,0.0,0.010455995798110962,0.010455995798110962
+0,1,y,y,350,11.633333333333333,-0.2906140089035034,-0.13001500070095062,-0.2906140089035034,-0.13001500070095062,0.0,0.0,0.007366001605987549,0.007366001605987549
+0,2,z,z,350,11.633333333333333,-1.0751700401306152,-0.840362012386322,-1.0751700401306152,-0.840362012386322,0.0,0.0,0.008671998977661133,0.008671998977661133
+0,3,rot6d_0,rot6d_0,350,11.633333333333333,-0.4014439880847931,-0.046230901032686234,-0.4014439880847931,-0.046230901032686234,0.0,0.0,0.014374002814292908,0.014374002814292908
+0,4,rot6d_1,rot6d_1,350,11.633333333333333,0.16916699707508087,0.3257170021533966,0.16916699707508087,0.3257170021533966,0.0,0.0,0.018655002117156982,0.018655002117156982
+0,5,rot6d_2,rot6d_2,350,11.633333333333333,-0.9782590270042419,-0.8815039992332458,-0.9782590270042419,-0.8815039992332458,0.0,0.0,0.009171009063720703,0.009171009063720703
+0,6,rot6d_3,rot6d_3,350,11.633333333333333,-0.41907799243927,0.5935230255126953,-0.41907799243927,0.5935230255126953,0.0,0.0,0.039122000336647034,0.039122000336647034
+0,7,rot6d_4,rot6d_4,350,11.633333333333333,0.801925003528595,0.9845290184020996,0.801925003528595,0.9845290184020996,0.0,0.0,0.010154962539672852,0.010154962539672852
+0,8,rot6d_5,rot6d_5,350,11.633333333333333,0.06690389662981033,0.4138759970664978,0.06690389662981033,0.4138759970664978,0.0,0.0,0.023563995957374573,0.023563995957374573
+0,9,gripper_pos,gripper_cmd_pos_pct,350,11.633333333333333,0.2823529541492462,1.0,0.0,1.0,0.7176470458507538,0.10938706169170993,0.08627450466156006,0.44468899071216583
+1,0,x,x,270,8.966666666666667,0.1509840041399002,0.32864201068878174,0.1509840041399002,0.32864201068878174,0.0,0.0,0.006255000829696655,0.006255000829696655
+1,1,y,y,270,8.966666666666667,-0.3600510060787201,-0.12476500123739243,-0.3600510060787201,-0.12476500123739243,0.0,0.0,0.006219983100891113,0.006219983100891113
+1,2,z,z,270,8.966666666666667,-0.9267070293426514,-0.7700290083885193,-0.9267070293426514,-0.7700290083885193,0.0,0.0,0.006464958190917969,0.006464958190917969
+1,3,rot6d_0,rot6d_0,270,8.966666666666667,-0.24375900626182556,0.08883470296859741,-0.24375900626182556,0.08883470296859741,0.0,0.0,0.0247420072555542,0.0247420072555542
+1,4,rot6d_1,rot6d_1,270,8.966666666666667,0.1418360024690628,0.26575401425361633,0.1418360024690628,0.26575401425361633,0.0,0.0,0.021405011415481567,0.021405011415481567
+1,5,rot6d_2,rot6d_2,270,8.966666666666667,-0.9788010120391846,-0.9464669823646545,-0.9788010120391846,-0.9464669823646545,0.0,0.0,0.006970047950744629,0.006970047950744629
+1,6,rot6d_3,rot6d_3,270,8.966666666666667,-0.42378300428390503,-0.16544899344444275,-0.42378300428390503,-0.16544899344444275,0.0,0.0,0.019345998764038086,0.019345998764038086
+1,7,rot6d_4,rot6d_4,270,8.966666666666667,0.8598319888114929,0.9655259847640991,0.8598319888114929,0.9655259847640991,0.0,0.0,0.009026050567626953,0.009026050567626953
+1,8,rot6d_5,rot6d_5,270,8.966666666666667,0.19189800322055817,0.2988649904727936,0.19189800322055817,0.2988649904727936,0.0,0.0,0.021144986152648926,0.021144986152648926
+1,9,gripper_pos,gripper_cmd_pos_pct,270,8.966666666666667,0.2666666805744171,1.0,0.0,1.0,0.7333333194255829,0.12236736017813975,0.07450982928276062,0.549450971186161
+2,0,x,x,297,9.866666666666667,0.13224099576473236,0.2750999927520752,0.13224099576473236,0.2750999927520752,0.0,0.0,0.004563003778457642,0.004563003778457642
+2,1,y,y,297,9.866666666666667,-0.27234798669815063,-0.09370540082454681,-0.27234798669815063,-0.09370540082454681,0.0,0.0,0.00583098828792572,0.00583098828792572
+2,2,z,z,297,9.866666666666667,-0.9476630091667175,-0.8068689703941345,-0.9476630091667175,-0.8068689703941345,0.0,0.0,0.0062729716300964355,0.0062729716300964355
+2,3,rot6d_0,rot6d_0,297,9.866666666666667,-0.01010920014232397,0.21770599484443665,-0.01010920014232397,0.21770599484443665,0.0,0.0,0.02130700647830963,0.02130700647830963
+2,4,rot6d_1,rot6d_1,297,9.866666666666667,0.18894100189208984,0.2630710005760193,0.18894100189208984,0.2630710005760193,0.0,0.0,0.016651004552841187,0.016651004552841187
+2,5,rot6d_2,rot6d_2,297,9.866666666666667,-0.979423999786377,-0.9491429924964905,-0.979423999786377,-0.9491429924964905,0.0,0.0,0.0032439827919006348,0.0032439827919006348
+2,6,rot6d_3,rot6d_3,297,9.866666666666667,-0.4707689881324768,-0.22415100038051605,-0.4707689881324768,-0.22415100038051605,0.0,0.0,0.011218011379241943,0.011218011379241943
+2,7,rot6d_4,rot6d_4,297,9.866666666666667,0.8617050051689148,0.9584490060806274,0.8617050051689148,0.9584490060806274,0.0,0.0,0.005832016468048096,0.005832016468048096
+2,8,rot6d_5,rot6d_5,297,9.866666666666667,0.14217700064182281,0.22386999428272247,0.14217700064182281,0.22386999428272247,0.0,0.0,0.018057003617286682,0.018057003617286682
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state_action_curves/summary.txt

@@ -0,0 +1,70 @@
+State/action curve diagnostics
+==============================
+Episodes: 45
+Frames: 12939
+Frames per episode: min=225, median=279, max=382
+Duration per episode (s): min=7.47, median=9.27, max=12.70
+Non-finite state values: 0
+Non-finite action values: 0
+
+Per-dimension diagnostics:
+  dim 0: x / x
+    state range: [0.0337026007, 0.599484026]
+    action range: [0.0337026007, 0.599484026]
+    |action-state|: mean=0, max=0
+    max state step: 0.0136409998 (episode 000012, frame 43->44)
+    max action step: 0.0136409998 (episode 000012, frame 43->44)
+  dim 1: y / y
+    state range: [-0.408670992, -0.0533262007]
+    action range: [-0.408670992, -0.0533262007]
+    |action-state|: mean=0, max=0
+    max state step: 0.0102030039 (episode 000011, frame 96->97)
+    max action step: 0.0102030039 (episode 000011, frame 96->97)
+  dim 2: z / z
+    state range: [-1.07517004, -0.755661011]
+    action range: [-1.07517004, -0.755661011]
+    |action-state|: mean=0, max=0
+    max state step: 0.0252610445 (episode 000024, frame 69->70)
+    max action step: 0.0252610445 (episode 000024, frame 69->70)
+  dim 3: rot6d_0 / rot6d_0
+    state range: [-0.470118999, 0.556989014]
+    action range: [-0.470118999, 0.556989014]
+    |action-state|: mean=0, max=0
+    max state step: 0.0378140211 (episode 000012, frame 48->49)
+    max action step: 0.0378140211 (episode 000012, frame 48->49)
+  dim 4: rot6d_1 / rot6d_1
+    state range: [-0.0323692001, 0.325717002]
+    action range: [-0.0323692001, 0.325717002]
+    |action-state|: mean=0, max=0
+    max state step: 0.0391891003 (episode 000013, frame 262->263)
+    max action step: 0.0391891003 (episode 000013, frame 262->263)
+  dim 5: rot6d_2 / rot6d_2
+    state range: [-0.999855995, -0.830295026]
+    action range: [-0.999855995, -0.830295026]
+    |action-state|: mean=0, max=0
+    max state step: 0.0135160089 (episode 000012, frame 48->49)
+    max action step: 0.0135160089 (episode 000012, frame 48->49)
+  dim 6: rot6d_3 / rot6d_3
+    state range: [-0.492462009, 0.593523026]
+    action range: [-0.492462009, 0.593523026]
+    |action-state|: mean=0, max=0
+    max state step: 0.0453729928 (episode 000017, frame 287->288)
+    max action step: 0.0453729928 (episode 000017, frame 287->288)
+  dim 7: rot6d_4 / rot6d_4
+    state range: [0.801925004, 0.999834001]
+    action range: [0.801925004, 0.999834001]
+    |action-state|: mean=0, max=0
+    max state step: 0.0154519677 (episode 000024, frame 0->1)
+    max action step: 0.0154519677 (episode 000024, frame 0->1)
+  dim 8: rot6d_5 / rot6d_5
+    state range: [-0.0775778964, 0.413875997]
+    action range: [-0.0775778964, 0.413875997]
+    |action-state|: mean=0, max=0
+    max state step: 0.0485715047 (episode 000017, frame 287->288)
+    max action step: 0.0485715047 (episode 000017, frame 287->288)
+  dim 9: gripper_pos / gripper_cmd_pos_pct
+    state range: [0.239215687, 1]
+    action range: [0, 1]
+    |action-state|: mean=0.105356062, max=0.827666517
+    max state step: 0.0901961029 (episode 000010, frame 194->195)
+    max action step: 0.575580001 (episode 000003, frame 164->165)