fixedwidthpy

Fixed Width Files are a NIGHTMARE!! So, I took my time to make the BEST library for dealing with them!

License

Copyright 2024 https://github.com/VoxLight

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

   /README.md

Installation

You can install the package using pip:

pip install git+https://github.com/VoxLight/fixedwidthpy

Usage

Basic Usage

Here is a basic example of how to use fixedwidthpy to define and use columns, data rows, and the file handler.

from fixedwidthpy.datarow import DataRow, mark_as_column
from fixedwidthpy.fwf_handler import FixedWidthFileHandler

# Define a simple record with some data and column specifications
class SimpleRecord(DataRow):

    @mark_as_column(header="Name", width=20, order=1, fill=' ', align='left')
    def fetch_name(self):
        return "Alice"

    @mark_as_column(header="Age", width=3, order=2, fill='0', align='right')
    def fetch_age(self):
        return 30

# Create an instance of the record
record = SimpleRecord()
record.fetch_data()

# Create a file handler and add the record
handler = FixedWidthFileHandler()
handler.add_row(record)
handler.export_to_fw_file('simple_data.txt')

Advanced Usage with Business Logic

The fixedwidthpy library was createdout of frustration with the clashing of business logic and formatting and adadada... I wasn't alone, however, This often arises when working with fixed-width files. However, take a look. This example shows how you can define business logic within your data rows and invalidate rows based on specific conditions. This helps us parse data and get so much more hands on with the data (like we have always wanted) without a messy codebase.

You can call invalidate() on the instance of your record in the event that you no longer want to process the record. This will prevent the record from being written to the file, but save the rest of the data. This is useful when you want to skip invalid records but still process the rest of the data.

from fixedwidthpy.column import ColumnSpec, Column
from fixedwidthpy.datarow import DataRow, mark_as_column
from fixedwidthpy.fwf_handler import FixedWidthFileHandler

class AdvancedRecord(DataRow):

    def __init__(self, name, age):
        super().__init__()
        self.name = name
        self.age = age

    @mark_as_column(header="Name", width=20, order=1, fill=' ', align='left')
    def fetch_name(self):
        if not self.name:
            self.invalidate("Name cannot be empty")
        return self.name

    @mark_as_column(header="Age", width=3, order=2, fill='0', align='right')
    def fetch_age(self):
        if not (0 <= self.age <= 120):
            self.invalidate("Age must be between 0 and 120")
        return self.age

# Create an instance of the record
records = [
    AdvancedRecord("Alice", 30),
    AdvancedRecord("", 25),         # This record will be invalidated
    AdvancedRecord("Bob", 200)      # This record will be invalidated
]

# Create a file handler and add the valid records
handler = FixedWidthFileHandler()
for record in records:
    record.fetch_data()
    if record.is_valid:
        handler.add_row(record)
    else:
        print(f"Invalid record: {record.invalid_reason}")

handler.export_to_fw_file('advanced_data.txt')

Why fixedwidthpy?

When working with fixed-width files, there is often a complex interaction between business logic and the formatting of the data. Many existing libraries either focus on the formatting aspect or the data validation aspect but not both. fixedwidthpy was created to address this gap.

By using fixedwidthpy, you can:

• Define columns with specific formatting rules.
• Embed business logic within your data-fetching methods.
• Invalidate rows based on custom business rules, ensuring only valid data is written to your fixed-width files.

This approach not only simplifies the handling of fixed-width files but also ensures that your data processing logic remains clean and maintainable.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.