bernardraust / s5000f_message_reader

Hello world tutorial

S5000FmessageReader

     Python class for reading S5000F® message XML files. Returns a header with most of the file configurations and the lead's data is available as a Numpy array or a Pandas data frame.

Dependencies

     This reader needs the following libraries to parse the XML files:

• xmltodict (To read and create a dictionary from the XML file)
• numpy (To save the data from the leads into an array)
• pandas (To save the data from the leads into a structured data frame)
• xlwt (To save the lead data in a Excel file)

Usage

     The GEMuseXMLReader can be used in two different approaches:

• as a converter
• as a python class for accessing the data

---

     Converting XML file

     The S5000FmessageReader can be called in the command line to convert the XML into a CSV, JSON, Excel, Numpy object, or all. Paired with the converted file is a JSON with the header information.

python3 S5000FmessageReader.py 'filename.XML' [arguments]

               Arguments

• [-csv [CSV]] - Convert the XML to a CSV. Output filename is optional.
• [-pcsv [PCSV]] - Convert the XML to a CSV (Pandas formated). Output filename is optional.
• [-ops [OPS]] - Convert the XML to a txt compatible with PLUX's opensignals. Output filename is optional.
• [-x [EXCEL]] - Convert the XML to a Excel. Output filename is optional.
• [-np [NUMPY]] - Convert the XML to a Numpy. Output filename is optional.
• [-json [JSON]] - Convert the XML to a JSON. Output filename is optional.
• [-all [ALL]] - Convert the XML to a CSV, Excel, Numpy and JSON. Output filename is optional.

---

     Python class

     The S5000FmessageReader can be also be imported by another python script and used to converted the XML files, providing the data in either a Numpy array or a Panda data frame.

import S5000FmessageReader

GEMuseData = GEMuseXMLReader('filename.XML')

GEMuseData.header ## Header containing the patient, device and acquisition session parameters

GEMuseData.dataObject ## Dictionary containing the data separated by lead

GEMuseData.dataFrame ## Panda's data frame containg the acquisition data

GEMuseData.dataArray ## Numpy matrix containing the acquisition data

S5000F message

     The S5000F message is structured as follows:

• Message

  • Header

    • message identifier
    • message date
    • message time
    • message status
    • message type

  • Content

    • to be completed

  • Trailer

    • Message sender
    • Message receiver
    • Message context
    • Message remarks
    • Message classification

• Could be more than one.