The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<battery_percent: int64, experiment: string, valid: bool, ema_packet_rows: int64, checks: struct<ema_csv_rows_match_packets: bool, ema_parameters_match_packets: bool, observation_bin_is_10_s: bool, ema_window_is_120_s: bool, raw_png_exists: bool, raw_svg_exists: bool, ema_png_exists: bool, ema_svg_exists: bool, raw_svg_has_14000_points: bool, ema_svg_has_14000_points: bool, raw_has_7_panels: bool, ema_has_7_panels: bool, all_x_axes_are_0_to_40: bool, all_panels_use_2000_points: bool>>
to
{'battery_percent': Value('int64'), 'analysis_directory': Value('string'), 'valid': Value('bool'), 'checks': {'packet_rows_match_manifest': Value('bool'), 'successful_equals_packets': Value('bool'), 'zero_failed_packets': Value('bool'), 'seven_device_fingerprints': Value('bool'), 'seven_phone_statistics_rows': Value('bool'), 'seven_y_range_rows': Value('bool'), 'mac_rows_match_manifest': Value('bool'), 'svg_is_valid_xml': Value('bool'), 'svg_point_count_limited': Value('bool')}, 'fingerprint_csv_rows': Value('int64'), 'svg_circle_count': Value('int64')}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<battery_percent: int64, experiment: string, valid: bool, ema_packet_rows: int64, checks: struct<ema_csv_rows_match_packets: bool, ema_parameters_match_packets: bool, observation_bin_is_10_s: bool, ema_window_is_120_s: bool, raw_png_exists: bool, raw_svg_exists: bool, ema_png_exists: bool, ema_svg_exists: bool, raw_svg_has_14000_points: bool, ema_svg_has_14000_points: bool, raw_has_7_panels: bool, ema_has_7_panels: bool, all_x_axes_are_0_to_40: bool, all_panels_use_2000_points: bool>>
to
{'battery_percent': Value('int64'), 'analysis_directory': Value('string'), 'valid': Value('bool'), 'checks': {'packet_rows_match_manifest': Value('bool'), 'successful_equals_packets': Value('bool'), 'zero_failed_packets': Value('bool'), 'seven_device_fingerprints': Value('bool'), 'seven_phone_statistics_rows': Value('bool'), 'seven_y_range_rows': Value('bool'), 'mac_rows_match_manifest': Value('bool'), 'svg_is_valid_xml': Value('bool'), 'svg_point_count_limited': Value('bool')}, 'fingerprint_csv_rows': Value('int64'), 'svg_circle_count': Value('int64')}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Battery-test CFO analysis
This folder contains the all-packet CFO fingerprints, statistics, and plots for
all ten Morty battery experiments (10% through 100%). Source H5 recordings were
read without modification. The estimator reuses
CSE237D_weyl\pipeline\parallel_h5_cfo_all_packets.py.
Start with summary\all_packets\ALL_PACKETS_RESULTS.md for the consolidated
index. Each experiment has:
all_packets_cfo\packet_cfo_all_fingerprints.csv— every assigned packet.all_packets_cfo\statistics— phone median, variance, MAD, percentiles, and statistical plots.all_packets_cfo\plots_segmented_mac— reference-style PNG and zoomable SVG.
The segmented plots use at most 2,000 evenly selected points per phone for
readability; all CSV summaries and statistics still use every packet. PNG and
SVG use the same sampled rows and similar small marker sizes. Every continuous
x-axis is fixed at 0--40 minutes, so shorter recordings leave blank space.
--shared-y means every phone panel has the same median-centered +/- X Hz range,
while preserving each phone's distinct CFO center.
To rerun levels 20–100 with the satellite Conda environment:
C:\Users\15255\anaconda3\envs\satellite\python.exe .\run_remaining_battery_all_packets.py
To rebuild and validate the consolidated index without recomputing CFO:
C:\Users\15255\anaconda3\envs\satellite\python.exe .\build_all_packet_battery_summary.py
The preferred fingerprint is relative_cfo_hz: raw packet CFO minus the
simultaneous cross-device median in a 120-second window. This reduces common
receiver-oscillator drift.
Raw and EMA-calibrated plots
summary\raw_ema\RAW_EMA_RESULTS.md indexes raw and EMA-calibrated PNG/SVG
plots for every level. The EMA workflow uses 10-second robust cross-device
center observations and a causal 120-second EMA span, avoiding the hard
two-minute boundaries in the original block calibration.
To regenerate raw and EMA outputs for all levels:
C:\Users\15255\anaconda3\envs\satellite\python.exe .\run_all_levels_raw_ema_plots.py
To validate and rebuild their consolidated index:
C:\Users\15255\anaconda3\envs\satellite\python.exe .\build_raw_ema_index.py
Smoother offline calibrations
summary\smoothers\SMOOTHER_RESULTS.md indexes centered 120-second moving
average and Gaussian (sigma=60 s, truncated at +/-3 sigma) calibration
plots for every level. These offline filters reduce the influence of a single
new observation compared with the causal EMA.
C:\Users\15255\anaconda3\envs\satellite\python.exe .\run_all_levels_smoother_plots.py
C:\Users\15255\anaconda3\envs\satellite\python.exe .\build_smoother_index.py
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