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Functions

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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_namestr | None = 'label'
ts_col_namestr | 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
filenamestr | None = None
categorystr | None = None
labelsstr | None = None
max_workers=None

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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