Initial addition of ipf reader.

        parse(ell)

    return json.dumps(stream)

def read_ipfs(input_data_list):

    """

    Read a socet ipf file into a pandas data frame

    Parameters

    ----------

    input_data_list : list

                 list of paths to the a set of input data files

    Returns

    -------

    df : pd.DataFrame

         containing the ipf data with appropriate column names and indices

    """

    default_columns = np.genfromtxt(input_data_list[0], skip_header=2, dtype='unicode',

                                    max_rows = 1, delimiter = ',')

    columns = []

    for column in default_columns:

        if '(' in column and ')' in column:

            column_name ,suffix = column.split('(')

            num = int(suffix.split(')')[0])

            for column_num in range(int(num)):

                new_column = '{}{}'.format(column_name, column_num)

                columns.append(new_column);

        else:

            columns.append(column)

    d_total = []

    for input_file in input_data_list:

        d = read_ipf(input_file)

        for point in d:

            d_total.append(point)

    df = pd.DataFrame(d_total, columns=columns)

    # Soft conversion of numeric types to numerics, allows str in first col for point_id

    df = df.apply(pd.to_numeric, errors='ignore')

    # Validate the read data with the header point count

    # assert int(cnt) == len(df), 'Dataframe length {} does not match point length {}.'.format(int(cnt), len(df))

    return df

def read_ipf(input_data):

    """

    Read a socet ipf file into a pandas data frame

    Parameters

    ----------

    input_data : str

                 path to the an input data file

    Returns

    -------

    df : pd.DataFrame

         containing the ipf data with appropriate column names and indices

    """

    # Check that the number of rows is matching the expected number

    with open(input_data, 'r') as f:

        for i, l in enumerate(f):

            if i == 1:

                cnt = int(l)

            elif i == 2:

                col = l

                break

    # default_columns = np.genfromtxt(input_data, skip_header=2, dtype='unicode',

    #                                 max_rows = 1, delimiter = ',')

    #

    # columns = []

    #

    # for column in default_columns:

    #

    #     if '(' in column and ')' in column:

    #         column_name ,suffix = column.split('(')

    #         num = int(suffix.split(')')[0])

    #

    #         for column_num in range(int(num)):

    #             new_column = '{}{}'.format(column_name, column_num)

    #             columns.append(new_column);

    #

    #     else:

    #         columns.append(column)

    # TODO: Add unicode conversion

    d = [line.split() for line in open(input_data, 'r')]

    d = np.hstack(np.array(d[3:]))

    d = d.reshape(-1, 12)

    assert int(cnt) == len(d), 'Dataframe length {} does not match point length {}.'.format(int(cnt), len(df))

    return d

    # df = pd.DataFrame(d, columns=columns)

    #

    # # Soft conversion of numeric types to numerics, allows str in first col for point_id

    # df = df.apply(pd.to_numeric, errors='ignore')

    #

    # # Validate the read data with the header point count

    # assert int(cnt) == len(df), 'Dataframe length {} does not match point length {}.'.format(int(cnt), len(df))

    #

    # return df

def read_gpf(input_data):

    """

    Read a socet gpf file into a pandas data frame

                col = l

                break

    default_columns = np.genfromtxt(input_data, skip_header=2, dtype='unicode',

                                    max_rows = 1, delimiter = ',')

    columns = []

    for column in default_columns:

        if '(' in column and ')' in column:

            column_name ,suffix = column.split('(')

            num = int(suffix.split(')')[0])

            for column_num in range(int(num)):

                new_column = '{}{}'.format(column_name, column_num)

                columns.append(new_column);

        else:

            columns.append(column)

    # Mixed types requires read as unicode - let pandas soft convert

    d = np.genfromtxt(input_data, skip_header=3, dtype='unicode')

    d = d.reshape(-1, 12)

    #TODO: cols should be used to dynamically generate the column names

    df = pd.DataFrame(d, columns=['point_id', 'stat', 'known',

                              'lat_Y_North', 'long_X_East','ht',

                              'sigma0', 'sigma1', 'sigma2',

                              'res0', 'res1', 'res2'])

    df = pd.DataFrame(d, columns=columns)

    # Soft conversion of numeric types to numerics, allows str in first col for point_id

    df = df.apply(pd.to_numeric, errors='ignore')

    # Validate the read data with the header point count

    assert int(cnt) == len(df)

    assert int(cnt) == len(df), 'Dataframe length {} does not match point length {}.'.format(int(cnt), len(df))

    return df