CsvFileMultiplexer

CsvFileMultiplexer is a dialog box (GUI) written in Python 3 using the PyQt5 module. It was created to make it easy to simultaneously read multiple CSV files, with varying columns, into a single data model.

Please Note

In order to use CsvFileMultiplexer in your code, you must have PyQt5 installed.

License

CSV File Multiplexer is licensed under the Boost Software License - Version 1.0 - August 17th, 2003

Sample Code and Usage

To use CsvFileMultiplexer in your code, simply include CsvFileMultiplexer.py in the appropriate subdirectory of your project. Then include the appropriate import CsvFileMultiplexer statement in your code file(s).

Step 1: Subclass the CsvFileMultiplexer.AbstractRowConverter class and implement the fieldnames and convert methods.

class MyRowConverter(CsvFileMultiplexer.AbstractRowConverter):

    @staticmethod
    def fieldnames() -> set:
        return set([
            'header name 1',
            'header name 2',
            'header name 3'
        ])

    def convert(self, csv_row: dict) -> object:
        # use csv_row to create a new object
        return newOject

Step 2: Subclass the CsvFileMultiplexer.AbstractDataModel class and implement the add method.

class MyDataModel(CsvFileMultiplexer.AbstractDataModel):

    def __init__(self):
        self._data = []

    def add(self, data: object):
        # add the object to your data model
        self._data.append(data)

Step 3: Instantiate your data model and pass it to CsvFileMultiplexer.Dialog.load().

class MyApplication:

    # other attributes, methods, and members here...

    @QtCore.pyqtSlot()
    def testCSVFileLoader(self):

        # create an instance of your data model
        dataModel = MyDataModel()

        # open the dialog box
        # pass the instance of your data model and a list of subclasses of the CsvFileMultiplexer.AbstractRowConverter class
        success, errors = CsvFileMultiplexer.Dialog.load(self, dataModel, [MyRowConverter])
        
        if success:
            # do something with your data model
        else:
            # do something with errors