:mod:`mdf_reader.read`
======================

.. py:module:: mdf_reader.read

.. autoapi-nested-parse::

   Manages the integral sequence in data file reading
   from a data model:
       - Access to data model
       - Data file import
       - Data file reading
       - Data validation
       - Output

   Contains the following functions:
       * ERV - does the actual extraction, read and validation of data input data
       * main - the main function of the script

   Can be run as a script with:
       python -m mdf_reader data_file **kwargs



Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   mdf_reader.read.ERV
   mdf_reader.read.validate_arg
   mdf_reader.read.validate_path
   mdf_reader.read.main



Attributes
~~~~~~~~~~

.. autoapisummary::

   mdf_reader.read.toolPath
   mdf_reader.read.schema_lib


.. data:: toolPath
   

   

.. data:: schema_lib
   

   

.. function:: ERV(TextParser, read_sections_list, schema, code_tables_path)

   Extracts, reads and validates data input data.


   :param TextParser: The data to extract and read
   :type TextParser: list or pandas.io.parsers.TextFileReader
   :param read_sections_list: List with subset of data model sections to output
   :type read_sections_list: list
   :param schema: Data model schema
   :type schema: dict
   :param code_tables_path: Path to data model code tables
   :type code_tables_path: str

   :returns: * **data** (*pandas.DataFrame, pandas.io.parsers.TextFileReader*) -- Contains the input data extracted and read
             * **valid** (*pandas.DataFrame, pandas.io.parsers.TextFileReader*) -- Contains the a boolean mask with the data validation output


.. function:: validate_arg(arg_name, arg_value, arg_type)

   Validates input argument is as expected type

   :param arg_name:
   :type arg_name: str
   :param arg_value:
   :type arg_value: arg_type
   :param arg_type:
   :type arg_type: python type

   :returns:
   :rtype: True,False


.. function:: validate_path(arg_name, arg_value)

   Validates input argument is an existing directory

   :param arg_name:
   :type arg_name: str
   :param arg_value:
   :type arg_value: str

   :returns:
   :rtype: True,False


.. function:: main(source, data_model=None, data_model_path=None, sections=None, chunksize=None, skiprows=None, out_path=None)

   Reads a data file to a pandas DataFrame using a pre-defined data model.
   Read data is validates against its data model producing a boolean mask
   on output.

   The data model needs to be input to the module as a named model
   (included in the module) or as the path to a valid data model.

   :param source: The file path to read
   :type source: str

   :keyword data_model: Name of internally available data model
   :kwtype data_model: str, optional
   :keyword data_model_path: Path to external data model
   :kwtype data_model_path: str, optional
   :keyword sections: List with subset of data model sections to outpu (default is
                      all)
   :kwtype sections: list, optional
   :keyword chunksize: Number of reports per chunk (default is
                       no chunking)
   :kwtype chunksize: int, optional
   :keyword skiprows: Number of initial rows to skip from file (default is 0)
   :kwtype skiprows: int, optional
   :keyword out_path: Path to output data, valid mask and attributes (default is
                      no output)
   :kwtype out_path: str, optional

   :returns: **output** -- Attributes data, mask and atts contain the corresponding
             information from the data file.
   :rtype: object

   .. note::

      This module can also be run as a script, with the keyword arguments
      as name_arg=arg