dfnosorio/gemusexmlreader

Python class for reading GE MUSE 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.

GEMuseXMLReader

     Python class for reading GE® MUSE® 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 GEMuseXMLReader 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 GEMuseXMLReader.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 GEMuseXMLReader 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 GEMuseXMLReader

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

Header

     The header is structured as follows:

• Header

  • PatientInfo

    • Given_Name
    • Family_Name
    • ID
    • Gender
    • Race
    • Pacemaker

  • DeviceInfo

    • DeviceModel
    • DeviceName
    • DeviceSerial

  • AcquisitionInfo

    • AcquisitionDate
    • LeadAmplitudeUnitsPerBit
    • LeadAmplitudeUnits
    • Resolution
    • Filters
      • HighPass
      • LowPass
      • Algorithms*
        • Name
        • Purpose
    • SampleRate
    • LeadsInformation
      • LeadsNames
      • LeadsLabels
      • NumberOfSamples
    • HeaderString

• Could be more than one.