Glossary¶
- cut-flow
- A series of “cuts” which remove events from the processing.
- data-space
- The current set of variables known to fast-carpenter and passed between stages. Stages can modify the data-space which will affect what subequent stages see. Before any stages have been run, the data-space contains only those variables given in the input datasets.
- processing config
- A YAML-based description of the way the input data should be processed.
- processing stage
- A single step in the processing chain, which can modify the data-space or produce new outputs.
- dataset config
- A YAML-based description of the input files which form the datasets to be processed.
- expression
- A string representing some mathematical manipulation of variables in the data-space.
- dataframe
- A programmatic interface to a table-like representation of data.
In the context of fast-carpenter, “dataframe” will usually refer to the
pandas.DataFrameimplementation. - jagged array
- A generalisation of a multi-dimensional numpy array where the length of each sub-array in the second (and third, and fourth, and so on) dimension can vary. For example, if each event contains a list of particles produced in that event, this would be represented by a jagged array, since there can be different numbers of particles in each event. Typically for fast-carpenter, a jagged array refers to the specific implementation from the awkward-array package