edgeimpulse.data

Modules

edgeimpulse.data.sample_type

Functions

delete_all_samples

edgeimpulse.data.delete_all_samples(
	category: str | None = None,
	api_key: str | None = None,
	timeout_sec: float | None = None
) ‑> edgeimpulse_api.models.generic_api_response.GenericApiResponse | None

Delete all samples in a given category. If category is set to None, all samples in the project are deleted.

Parameters
`category`	`str \| None = None`
`api_key`	`str \| None = None`
`timeout_sec`	`float \| None = None`

Returns
`edgeimpulse_api.models.generic_api_response.GenericApiResponse \| None`

delete_sample_by_id

edgeimpulse.data.delete_sample_by_id(
	sample_id: int,
	api_key: str | None = None,
	timeout_sec: float | None = None
) ‑> edgeimpulse_api.models.generic_api_response.GenericApiResponse | None

Delete a particular sample from a project given the sample ID.

Parameters
`sample_id`	`int`
`api_key`	`str \| None = None`
`timeout_sec`	`float \| None = None`

Returns
`edgeimpulse_api.models.generic_api_response.GenericApiResponse \| None`

delete_samples_by_filename

edgeimpulse.data.delete_samples_by_filename(
	filename: str,
	category: str | None = None,
	api_key: str | None = None,
	timeout_sec: float | None = None
) ‑> Tuple[Any | None, ...]

Delete any samples from an Edge Impulse project that match the given filename. Note: the filename argument must not include the original extension. For example, if you uploaded a file named my-image.01.png, you must provide the filename as my-image.01.

Parameters
`filename`	`str`
`category`	`str \| None = None`
`api_key`	`str \| None = None`
`timeout_sec`	`float \| None = None`

Returns
`Tuple[Any \| None, ...]`

download_samples_by_ids

edgeimpulse.data.download_samples_by_ids(
	sample_ids: int | List[int],
	api_key: str | None = None,
	timeout_sec: float | None = None,
	max_workers: int | None = None,
	show_progress: bool | None = False,
	pool_maxsize: int | None = 20,
	pool_connections: int | None = 20
) ‑> List[edgeimpulse.data.sample_type.Sample]

Download samples by their associated IDs from an Edge Impulse project. Downloaded sample data is returned as a DownloadSample object, which contains the raw data in a BytesIO object along with associated metadata. Important! All time series data is returned as a JSON file (in BytesIO format) with a timestamp column. This includes files originally uploaded as CSV, JSON, and CBOR. Edge Impulse Studio removes the timestamp column from any uploaded CSV files and computes an estimated sample rate. The timestamps are computed based on the sample rate, will always start at 0, and will be in milliseconds. These timestamps may not be the same as the original timestamps in the uploaded file.

Parameters
`sample_ids`	`int \| List[int]`
`api_key`	`str \| None = None`
`timeout_sec`	`float \| None = None`
`max_workers`	`int \| None = None`
`show_progress`	`bool \| None = False`
`pool_maxsize`	`int \| None = 20`
`pool_connections`	`int \| None = 20`

Returns
`List[edgeimpulse.data.sample_type.Sample]`

get_filename_by_id

edgeimpulse.data.get_filename_by_id(
	sample_id: int,
	api_key: str | None = None,
	timeout_sec: float | None = None
) ‑> str | None

Given an ID for a sample in a project, return the filename associated with that sample. Note that while multiple samples can have the same filename, each sample has a unique sample ID that is provided by Studio when the sample is uploaded.

Parameters
`sample_id`	`int`
`api_key`	`str \| None = None`
`timeout_sec`	`float \| None = None`

Returns
`str \| None`

get_sample_ids

edgeimpulse.data.get_sample_ids(
	filename: str | None = None,
	category: str | None = None,
	labels: str | None = None,
	api_key: str | None = None,
	num_workers: int | None = 4,
	timeout_sec: float | None = None
) ‑> List[edgeimpulse.data.sample_type.SampleInfo]

Get the sample IDs and filenames for all samples in a project, filtered by category, labels, or filename. Note that filenames are given by the root of the filename when uploaded. For example, if you upload my-image.01.png, it will be stored in your project with a hash such as my-image.01.png.4f262n1b.json. To find the ID(s) that match this sample, you must provide the argument filename=my-image.01. Notice the lack of extension and hash. Because of the potential for multiple samples (i.e., different sample IDs) with the same filename, we recommend providing unique filenames for your samples when uploading.

Parameters
`filename`	`str \| None = None`
`category`	`str \| None = None`
`labels`	`str \| None = None`
`api_key`	`str \| None = None`
`num_workers`	`int \| None = 4`
`timeout_sec`	`float \| None = None`

Returns
`List[edgeimpulse.data.sample_type.SampleInfo]`

infer_from_filename

edgeimpulse.data.infer_from_filename(
	sample: edgeimpulse.data.sample_type.Sample,
	file: str
) ‑> None

Extract label and category information from the filename and assigns them to the sample object. Files should look like this my-dataset/training/wave.1.cbor where wave is the label and training is the category. It checks if there is training, testing or anomaly in the filename to determine the sample category.

Parameters
`sample`	`edgeimpulse.data.sample_type.Sample`
`file`	`str`

Returns
`None`

numpy_timeseries_to_sample

edgeimpulse.data.numpy_timeseries_to_sample(
	values,
	sensors: List[edgeimpulse.data.sample_type.Sensor],
	sample_rate_ms: int
) ‑> edgeimpulse.data.sample_type.Sample

Convert numpy values to a sample that can be uploaded to Edge Impulse.

Parameters
`values`
`sensors`	`List[edgeimpulse.data.sample_type.Sensor]`
`sample_rate_ms`	`int`

Returns
`edgeimpulse.data.sample_type.Sample`

pandas_dataframe_to_sample

edgeimpulse.data.pandas_dataframe_to_sample(
	df,
	sample_rate_ms: int | None = None,
	label: str | None = None,
	filename: str | None = None,
	axis_columns: List[str] | None = None,
	metadata: dict | None = None,
	label_col: str | None = None,
	category: Literal['training', 'testing', 'split'] = 'split'
) ‑> edgeimpulse.data.sample_type.Sample

Convert a dataframe to a single sample. Can handle both timeseries and non-timeseries data. In order to be inferred as timeseries it must have:

More than one row
A sample rate or an index from which the sample rate can be inferred
- Therefore must be monotonically increasing
- And int or a date

Parameters
`df`
`sample_rate_ms`	`int \| None = None`
`label`	`str \| None = None`
`filename`	`str \| None = None`
`axis_columns`	`List[str] \| None = None`
`metadata`	`dict \| None = None`
`label_col`	`str \| None = None`
`category`	`Literal['training', 'testing', 'split'] = 'split'`

Returns
`edgeimpulse.data.sample_type.Sample`

upload_directory

edgeimpulse.data.upload_directory(
	directory: str,
	category: str | None = None,
	label: str | None = None,
	metadata: dict | None = None,
	transform: <built-in function callable> | None = None,
	allow_duplicates: bool | None = False,
	show_progress: bool | None = False,
	batch_size: int | None = 1024
) ‑> edgeimpulse.data.sample_type.UploadSamplesResponse

Upload a directory of files to Edge Impulse. Tries to autodetect whether it’s an Edge Impulse exported dataset, or a standard directory. The files can be in CBOR, JSON, image, or WAV file formats. You can read more about the different file formats accepted by the Edge Impulse ingestion service here: https://docs.edgeimpulse.com/reference/ingestion-api

Parameters
`directory`	`str`
`category`	`str \| None = None`
`label`	`str \| None = None`
`metadata`	`dict \| None = None`
`transform`	`<built-in function callable> \| None = None`
`allow_duplicates`	`bool \| None = False`
`show_progress`	`bool \| None = False`
`batch_size`	`int \| None = 1024`

Returns
`edgeimpulse.data.sample_type.UploadSamplesResponse`

upload_exported_dataset

edgeimpulse.data.upload_exported_dataset(
	directory: str,
	transform: <built-in function callable> | None = None,
	allow_duplicates: bool | None = False,
	show_progress: bool | None = False,
	batch_size: int | None = 1024
) ‑> edgeimpulse.data.sample_type.UploadSamplesResponse

Upload samples from a downloaded Edge Impulse dataset and preserve the info.labels information. Use this when you’ve exported your data in the studio, via the export functionality.

Parameters
`directory`	`str`
`transform`	`<built-in function callable> \| None = None`
`allow_duplicates`	`bool \| None = False`
`show_progress`	`bool \| None = False`
`batch_size`	`int \| None = 1024`

Returns
`edgeimpulse.data.sample_type.UploadSamplesResponse`

upload_numpy

edgeimpulse.data.upload_numpy(
	data,
	labels: List[str],
	sensors: List[edgeimpulse.data.sample_type.Sensor],
	sample_rate_ms: int,
	metadata: dict | None = None,
	category: Literal['training', 'testing', 'split', 'anomaly'] = 'split'
) ‑> edgeimpulse.data.sample_type.UploadSamplesResponse

Upload numpy arrays as timeseries using the Edge Impulse data acquisition format.

Parameters
`data`
`labels`	`List[str]`
`sensors`	`List[edgeimpulse.data.sample_type.Sensor]`
`sample_rate_ms`	`int`
`metadata`	`dict \| None = None`
`category`	`Literal['training', 'testing', 'split', 'anomaly'] = 'split'`

Returns
`edgeimpulse.data.sample_type.UploadSamplesResponse`

upload_pandas_dataframe

edgeimpulse.data.upload_pandas_dataframe(
	df,
	feature_cols: List[str],
	label_col: str | None = None,
	category_col: str | None = None,
	metadata_cols: List[str] | None = None
) ‑> edgeimpulse.data.sample_type.UploadSamplesResponse

Upload non-timeseries data to Edge Impulse where each dataframe row becomes a sample.

Parameters
`df`
`feature_cols`	`List[str]`
`label_col`	`str \| None = None`
`category_col`	`str \| None = None`
`metadata_cols`	`List[str] \| None = None`

Returns
`edgeimpulse.data.sample_type.UploadSamplesResponse`

upload_pandas_dataframe_wide

edgeimpulse.data.upload_pandas_dataframe_wide(
	df,
	sample_rate_ms: int,
	data_col_start: int | None = None,
	label_col: str | None = None,
	category_col: str | None = None,
	metadata_cols: List[str] | None = None,
	data_col_length: int | None = None,
	data_axis_cols: List[str] | None = None
) ‑> edgeimpulse.data.sample_type.UploadSamplesResponse

Upload a dataframe to Edge Impulse where each column represents a value in the timeseries data and the rows become the individual samples.

Parameters
`df`
`sample_rate_ms`	`int`
`data_col_start`	`int \| None = None`
`label_col`	`str \| None = None`
`category_col`	`str \| None = None`
`metadata_cols`	`List[str] \| None = None`
`data_col_length`	`int \| None = None`
`data_axis_cols`	`List[str] \| None = None`

Returns
`edgeimpulse.data.sample_type.UploadSamplesResponse`

upload_pandas_dataframe_with_group

edgeimpulse.data.upload_pandas_dataframe_with_group(
	df,
	timestamp_col: str,
	group_by: str,
	feature_cols: List[str],
	label_col: str | None = None,
	category_col: str | None = None,
	metadata_cols: List[str] | None = None
) ‑> edgeimpulse.data.sample_type.UploadSamplesResponse

Upload a dataframe where the rows contain multiple samples and timeseries data for those samples. It uses a group_by in order to detect what timeseries value belongs to which sample.

Parameters
`df`
`timestamp_col`	`str`
`group_by`	`str`
`feature_cols`	`List[str]`
`label_col`	`str \| None = None`
`category_col`	`str \| None = None`
`metadata_cols`	`List[str] \| None = None`

Returns
`edgeimpulse.data.sample_type.UploadSamplesResponse`

upload_pandas_sample

edgeimpulse.data.upload_pandas_sample(
	df,
	label: str | None = None,
	sample_rate_ms: int | None = None,
	filename: str | None = None,
	axis_columns: List[str] | None = None,
	metadata: dict | None = None,
	label_col: str | None = None,
	category: Literal['training', 'testing', 'split'] = 'split'
) ‑> edgeimpulse.data.sample_type.UploadSamplesResponse

Upload a single dataframe sample. Upload a single dataframe sample to Edge Impulse.

Parameters
`df`
`label`	`str \| None = None`
`sample_rate_ms`	`int \| None = None`
`filename`	`str \| None = None`
`axis_columns`	`List[str] \| None = None`
`metadata`	`dict \| None = None`
`label_col`	`str \| None = None`
`category`	`Literal['training', 'testing', 'split'] = 'split'`

Returns
`edgeimpulse.data.sample_type.UploadSamplesResponse`

upload_plain_directory

edgeimpulse.data.upload_plain_directory(
	directory: str,
	category: str | None = None,
	label: str | None = None,
	metadata: dict | None = None,
	transform: <built-in function callable> | None = None,
	allow_duplicates: bool | None = False,
	show_progress: bool | None = False,
	batch_size: int | None = 1024
) ‑> edgeimpulse.data.sample_type.UploadSamplesResponse

Upload a directory of files to Edge Impulse. The samples can be in CBOR, JSON, image, or WAV file formats.

Parameters
`directory`	`str`
`category`	`str \| None = None`
`label`	`str \| None = None`
`metadata`	`dict \| None = None`
`transform`	`<built-in function callable> \| None = None`
`allow_duplicates`	`bool \| None = False`
`show_progress`	`bool \| None = False`
`batch_size`	`int \| None = 1024`

Returns
`edgeimpulse.data.sample_type.UploadSamplesResponse`

upload_samples

edgeimpulse.data.upload_samples(
	samples: edgeimpulse.data.sample_type.Sample | List[edgeimpulse.data.sample_type.Sample],
	allow_duplicates: bool | None = False,
	api_key: str | None = None,
	timeout_sec: float | None = None,
	max_workers: int | None = None,
	show_progress: bool | None = False,
	pool_maxsize: int | None = 20,
	pool_connections: int | None = 20
) ‑> edgeimpulse.data.sample_type.UploadSamplesResponse

Upload one or more samples to an Edge Impulse project using the ingestion service. Each sample must be wrapped in a Sample object, which contains metadata about that sample. Give this function a single Sample or a List of Sample objects to upload to your project. The data field of the Sample must be a raw binary stream, such as a BufferedIOBase object (which you can create with the open(..., "rb") function).

Parameters
`samples`	`edgeimpulse.data.sample_type.Sample \| List[edgeimpulse.data.sample_type.Sample]`
`allow_duplicates`	`bool \| None = False`
`api_key`	`str \| None = None`
`timeout_sec`	`float \| None = None`
`max_workers`	`int \| None = None`
`show_progress`	`bool \| None = False`
`pool_maxsize`	`int \| None = 20`
`pool_connections`	`int \| None = 20`

Returns
`edgeimpulse.data.sample_type.UploadSamplesResponse`

Classes

Sample

edgeimpulse.data.Sample(
	data: io.BufferedIOBase | _io.StringIO | str,
	filename: str | None = None,
	category: Literal['training', 'testing', 'anomaly', 'split'] | None = 'split',
	label: str | None = None,
	bounding_boxes: List[dict] | None = None,
	metadata: dict | None = None,
	sample_id: int | None = None,
	structured_labels: List[dict] | None = None
)

Wrapper class for sample data, labels, and associated metadata. Sample data should be contained in a file or file-like object, for example, as the return from open(..., "rb"). The upload_samples() function expects Sample objects as input.

Parameters
`data`	`io.BufferedIOBase \| _io.StringIO \| str`
`filename`	`str \| None = None`
`category`	`Literal['training', 'testing', 'anomaly', 'split'] \| None = 'split'`
`label`	`str \| None = None`
`bounding_boxes`	`List[dict] \| None = None`
`metadata`	`dict \| None = None`
`sample_id`	`int \| None = None`
`structured_labels`	`List[dict] \| None = None`

Instance variables
`bounding_boxes`	`List[dict] \| None`
`category`	`Literal['training', 'testing', 'anomaly', 'split'] \| None`
`data`	`io.BufferedIOBase \| _io.StringIO \| str`
`filename`	`str \| None`
`label`	`str \| None`
`metadata`	`dict \| None`
`sample_id`	`int \| None`
`structured_labels`	`List[dict] \| None`

OVERVIEW

CLIs

LIBRARIES

SPECIFICATIONS

PROTOCOLS

Modules

Functions

delete_all_samples

delete_sample_by_id

delete_samples_by_filename

download_samples_by_ids

get_filename_by_id

get_sample_ids

infer_from_filename

numpy_timeseries_to_sample

pandas_dataframe_to_sample

upload_directory

upload_exported_dataset

upload_numpy

upload_pandas_dataframe

upload_pandas_dataframe_wide

upload_pandas_dataframe_with_group

upload_pandas_sample

upload_plain_directory

upload_samples

Classes

Sample

​Modules

​Functions

​delete_all_samples

​delete_sample_by_id

​delete_samples_by_filename

​download_samples_by_ids

​get_filename_by_id

​get_sample_ids

​infer_from_filename

​numpy_timeseries_to_sample

​pandas_dataframe_to_sample

​upload_directory

​upload_exported_dataset

​upload_numpy

​upload_pandas_dataframe

​upload_pandas_dataframe_wide

​upload_pandas_dataframe_with_group

​upload_pandas_sample

​upload_plain_directory

​upload_samples

​Classes

​Sample

Modules

Functions

delete_all_samples

delete_sample_by_id

delete_samples_by_filename

download_samples_by_ids

get_filename_by_id

get_sample_ids

infer_from_filename

numpy_timeseries_to_sample

pandas_dataframe_to_sample

upload_directory

upload_exported_dataset

upload_numpy

upload_pandas_dataframe

upload_pandas_dataframe_wide

upload_pandas_dataframe_with_group

upload_pandas_sample

upload_plain_directory

upload_samples

Classes

Sample