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add_labels_to_dataframe

edgeimpulse.experimental.util.add_labels_to_dataframe(
		df,
		labels,
		label_col_name='label'
)
Adds labels to a DataFrame based on provided label information. Parameters
  • df
  • labels
  • label_col_name=‘label’

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convert_json_cbor_to_dataframe

edgeimpulse.experimental.util.convert_json_cbor_to_dataframe(
		data,
		ts_col_name=None
)
Converts JSON CBOR data to a pandas DataFrame. Parameters
  • data
  • ts_col_name=None

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convert_sample_to_dataframe

edgeimpulse.experimental.util.convert_sample_to_dataframe(
		sample,
		label_col_name: str | None = 'label',
		ts_col_name: str | None = None
)
Converts a sample to a DataFrame and adds labels if provided. Parameters
  • sample
  • label_col_name: str | None = ‘label’
  • ts_col_name: str | None = None

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fetch_samples

edgeimpulse.experimental.util.fetch_samples(
		filename: str | None = None,
		category: str | None = None,
		labels: str | None = None,
		max_workers=None
)
Fetch samples based on the provided parameters and stream them by their IDs. Parameters
  • filename: str | None = None
  • category: str | None = None
  • labels: str | None = None
  • max_workers=None

​
generate_labels_from_dataframe

edgeimpulse.experimental.util.generate_labels_from_dataframe(
		df,
		label_col='label',
		file_name=None
)
Generates structured labels from a DataFrame based on transitions in the specified label column. This function iterates over the rows of a pandas DataFrame and detects changes in the values of a specified label column. It groups consecutive rows with the same label value, and for each group, it returns a dictionary containing the start index, end index, and the label. Optionally, the result can be returned in a dictionary format, compatible with file saving, including the file name. Parameters
  • df
  • label_col=‘label’
  • file_name=None