Data transformation

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Overview

Data transformation overview

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Transformation jobs

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Create a transformation job

• From the Data transformation page by selecting the Create job tab.
• From the Custom blocks->Transformation page by selecting the ”⋮” action button and selecting Run job.
• From the Data page:

• Default dataset
• Clinical dataset

Transform data from Clinical dataset view

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Run a transformation job

Additional parameters

• Default dataset
• Clinical dataset

Run a transformation job - Default dataset

InputAfter selecting your Input dataset, you can filter which files or directory you want to transform.In default dataset formats, we use wildcard filters (in a similar format to wildcards in git). This enable you to specify patterns that match multiple files or directories within your dataset:

• Asterisk ( * ): Represents any number of characters (including zero characters) in a filename or directory name. It is commonly used to match files of a certain type or files whose names follow a pattern. Example: /folder/*.png matches all PNG files in the /folder directory. Example: /data/*/results.csv matches any results.csv file in a subdirectory under /data.
• Double Asterisk ( ** ): Used to match any number of directories, including nested ones. This is particularly useful when the structure of directories is complex or not uniformly organized. Example: /data/**/experiment-* matches all files or directories starting with experiment- in any subdirectory under /data.

OutputWhen you work with default datasets in Edge Impulse, you have the flexibility to define how the output from your transformation jobs is structured. There are three main rules to choose from:

1. No Subfolders: This rule places all transformed files directly into your specified output directory, without creating any subfolders. For example, if you transform .txt files in /data and choose /output as your output directory, all transformed files will be saved directly in /output.
2. Subfolder per Input Item: Here, a new subfolder is created in the output directory for each input file or folder. This keeps the output from each item organized and separate. For instance, if your input includes folders like /data/2020, /data/2021, and /data/2022, and you apply this rule with /transformed as your output directory, you will get subfolders like /transformed/2020, /transformed/2021, and /transformed/2022, each containing the transformed data from the corresponding input year.
3. Use Full Path: This rule mirrors the entire input path when creating new sub-folders in the output directory. It’s especially useful for maintaining a clear trace of where each piece of output data originated, which is important in complex directory structures. For example, if you’re transforming files in /project/data/experiments, and you choose /results as your output directory, the output will follow the full input path, resulting in transformed data being stored in /results/project/data/experiments.

Note: For the transformation blocks operating on files when selecting the Subfolder or Full Path option, we will use the file name without extension to create the base folder. e.g. /activity-detection/Accelerometer.csv will be uploaded to /activity-detection-output/Accelerometer/.