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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Float value 0.123 was truncated converting to int64
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in cast_array_to_feature
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in <listcomp>
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2116, in cast_array_to_feature
return array_cast(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1963, in array_cast
return array.cast(pa_type)
File "pyarrow/array.pxi", line 996, in pyarrow.lib.Array.cast
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/compute.py", line 404, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 590, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 385, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Float value 0.123 was truncated converting to int64
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1524, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
@odata.context
string | frequency
int64 | dataX
sequence | data
list | segmentsData
dict |
|---|---|---|---|---|
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi | 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[0.010238697599939272,0.00902639363836164,0.00883441650601308,0.0079670175968(...TRUNCATED) | {"pqAverageValue":0,"qtAverageValue":0,"stAverageValue":0,"pqIntervals":[],"qtIntervals":[],"stSegme(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi | 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[-0.19204919820271385,-0.16168700097028402,-0.13357405875984235,-0.1087178621(...TRUNCATED) | {"pqAverageValue":0,"qtAverageValue":0,"stAverageValue":0,"pqIntervals":[],"qtIntervals":[],"stSegme(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi | 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[-0.06444768114603353,-0.06460485273116512,-0.05416165717299959,-0.0322357479(...TRUNCATED) | {"pqAverageValue":0.123,"qtAverageValue":0.3539,"stAverageValue":0.1244,"pqIntervals":[{"indexOfFirs(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi | 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[0.10450544410468408,0.10260305300709255,0.09595574124681122,0.08463284439636(...TRUNCATED) | {"pqAverageValue":0.0967,"qtAverageValue":0.365,"stAverageValue":0.1195,"pqIntervals":[{"indexOfFirs(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi | 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[0.014993661467586974,0.008662880801621389,0.004641376230735654,0.00414176094(...TRUNCATED) | {"pqAverageValue":0.0874,"qtAverageValue":0.3602,"stAverageValue":0.0858,"pqIntervals":[{"indexOfFir(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi | 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[-0.048094163254367536,-0.04386362830406088,-0.040033151275657666,-0.03640048(...TRUNCATED) | {"pqAverageValue":0.0825,"qtAverageValue":0.3671,"stAverageValue":0.0785,"pqIntervals":[{"indexOfFir(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi | 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[-0.1720298608457035,-0.1381471928533835,-0.06007278530980501,0.0688884425330(...TRUNCATED) | {"pqAverageValue":0,"qtAverageValue":0,"stAverageValue":0,"pqIntervals":[],"qtIntervals":[],"stSegme(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi | 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[0.1131532094846479,0.11222595037012538,0.11013556269575805,0.106072310579041(...TRUNCATED) | {"pqAverageValue":0,"qtAverageValue":0,"stAverageValue":0,"pqIntervals":[],"qtIntervals":[],"stSegme(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi | 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[-0.1901804469911864,-0.14616334658338984,-0.0879028916014203,-0.020309804042(...TRUNCATED) | {"pqAverageValue":0.0658,"qtAverageValue":0.3796,"stAverageValue":0.1139,"pqIntervals":[{"indexOfFir(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi | 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[0.21822900584593816,0.2105110505712715,0.20564538459138082,0.200052671310298(...TRUNCATED) | {"pqAverageValue":0.0563,"qtAverageValue":0.3772,"stAverageValue":0.11,"pqIntervals":[{"indexOfFirst(...TRUNCATED) |
End of preview.
Multi-Camera Dataset for rPPG
The progress in remote photoplethysmography (rPPG) is limited by the key issues of existing publicly available datasets, namely, small size, privacy concerns with facial videos, and single-camera setups. To address these limitations, this paper introduces a comprehensive large-scale multi-view video dataset for rPPG and health parameter estimation. Our dataset includes 3600 video recordings from 600 subjects, captured in both resting states and post-physical activity, using three different web and smartphone cameras at various angles. Each recording is synchronized with a 100 Hz PPG signal and includes additional health metrics, such as arterial pressure, temperature, oxygen saturation, respiratory rate, stress level, and blood test results.
Read more in the paper: TO BE ADDED
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