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chore(colors): removing unreliable colors
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@@ -62,7 +62,6 @@ local$ rerun rerun+http://IP:GRPC_PORT/proxy
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"""
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import argparse
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import colorsys
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import gc
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import logging
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import time
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@@ -91,16 +90,6 @@ def get_feature_names(dataset: LeRobotDataset, key: str) -> list[str]:
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return [f"{key}_{d}" for d in range(feature["shape"][-1])]
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def get_sequential_colors(num_dims: int) -> list[list[int]]:
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"""Return a deterministic list of distinct RGB colors, one per dimension."""
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colors = []
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for d in range(num_dims):
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hue = d / max(num_dims, 1)
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r, g, b = colorsys.hsv_to_rgb(hue, 0.7, 0.9)
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colors.append([int(r * 255), int(g * 255), int(b * 255)])
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return colors
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def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
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assert chw_float32_torch.dtype == torch.float32
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assert chw_float32_torch.ndim == 3
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@@ -114,7 +103,7 @@ def build_blueprint_from_dataset(dataset: LeRobotDataset):
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"""Build a Rerun blueprint laying out camera images and time series for the given dataset.
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Camera images and scalar signals (action, state, reward, done, success) are arranged in a grid.
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The per-dimension series names and colors for ``action`` and ``state`` are applied directly
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The per-dimension series names for ``action`` and ``state`` are applied directly
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via blueprint overrides.
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"""
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import rerun as rr
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@@ -122,11 +111,11 @@ def build_blueprint_from_dataset(dataset: LeRobotDataset):
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views = [rrb.Spatial2DView(origin=key, name=key) for key in dataset.meta.camera_keys]
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# Style multi-dimensional signals (action, state) with per-dimension names and colors.
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# Style multi-dimensional signals (action, state) with per-dimension names.
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for origin, key in ((ACTION, ACTION), ("state", OBS_STATE)):
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if key in dataset.features:
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names = get_feature_names(dataset, key)
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styling = rr.SeriesLines(names=names, colors=get_sequential_colors(len(names)))
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styling = rr.SeriesLines(names=names)
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views.append(rrb.TimeSeriesView(origin=origin, name=origin, overrides={origin: styling}))
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for key in (DONE, REWARD, "next.success"):
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if key in dataset.features:
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