Data utils¶
Utility classes related to data handling.
DataSequencer¶
Data sequencer class to process data matrix into sequences (images). Sequences can overlap. Sequencing grid can be altered between calls.
DataSequencer(\*args, \*\*kwargs) |
Data sequencer |
DataSequencer.process(data) |
Process |
DataSequencer.increase_shifting([shift_step]) |
Increase temporal shifting |
DataProcessor¶
Data processor class to process raw features into data suitable for machine learning algorithms. Feature processing chain and data processing chain are defined during the class construction, and these processing chains are applied to the input data.
DataProcessor(\*args, \*\*kwargs) |
Data processors with feature and data processing chains |
DataProcessor.load(feature_filename_dict[, ...]) |
Load feature item |
DataProcessor.process(feature_data[, ...]) |
Process feature data |
DataProcessor.process_features(feature_data) |
Process feature data |
DataProcessor.process_activity_data(...) |
Process activity data |
DataProcessor.process_data(data[, metadata]) |
Process data |
DataProcessor.call_method(method_name[, ...]) |
Call class method in the processing chain items |
DataBuffer¶
Data buffering class, which can be used to store data and meta data associated to the item. Item data is accessed through item key. When internal buffer is filled, oldest item is replaced.
DataBuffer(\*args, \*\*kwargs) |
Data buffer (FIFO) |
DataBuffer.count() |
Buffer usage |
DataBuffer.full() |
Buffer full |
DataBuffer.key_exists(key) |
Check that key exists in the buffer |
DataBuffer.set(key[, data, meta]) |
Insert item to the buffer |
DataBuffer.get(key) |
Get item based on key |
DataBuffer.clear() |
Empty the buffer |
ProcessingChain¶
Data processing chain class, inherited from list.
ProcessingChain |
|
ProcessingChain.process(data) |
Process the data with processing chain |
ProcessingChain.call_method(method_name[, ...]) |
Call class method in the processing chain items |