Supported formats
The TSL is composed of an expandible set of file formats whose design is pretty straighforward. An abstract Format class can be inherited and requires just two basic read and write methods. The data stored in a file must be used to create a Pandas DataFrame (which is used by the application for all its needs) and viceversa.
Here's a list of the currently supported formats.
CSV (Comma Separated Values)
The time series can be stored in comma-separated values (CSV) spreadsheets, which have a structure similar to the one reported below.Timestamp,Series #1,Series #2
2019-08-01 10:00:00,1,2
2019-08-01 10:05:00,1,5
2019-08-01 10:10:00,2,0
2019-08-01 10:15:00,3,8
2019-08-01 10:20:00,5,1
2019-08-01 10:25:00,8,8
The Timestamp column is optional, but can become really useful for accurate series processing: date/time strings like those above are generally accepted.
The CSV files can be obtained through common spreadsheet programs (like Microsoft Excel or Libreoffice Calc), but their management is also suitable for text editors.
Table of contents
- Project home
- Getting started
- Basic functionalities
- Configuration files
- Supported formats
- View on GitHub