funml

FunML

A collection of utilities to help write python as though it were an ML-kind of functional language like OCaml

The API is still unstable. Use at your own risk.

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Documentation: https://sopherapps.github.io/funml

Source Code: https://github.com/sopherapps/funml

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Most Notable Features are:

1. Immutable data structures like enums, records, lists
2. Piping outputs of one function to another as inputs. That's how bigger functions are created from smaller ones.
3. Pattern matching for declarative conditional control of flow instead of using 'if's
4. Error handling using the Result monad, courtesy of rust. Instead of using try-except all over the place, functions return a Result which has the right data when successful and an exception if unsuccessful. The result is then pattern-matched to retrieve the data or react to the exception.
5. No None. Instead, we use the Option monad, courtesy of rust. When an Option has data, it is Option.SOME, or else it is Option.NONE. Pattern matching helps handle both scenarios.

Dependencies

• python 3.7+

Getting Started

• Ensure you have python 3.7 and above installed.
• Install FunML

pip install funml

• Add the following code in main.py

from copy import copy
from datetime import date

import funml as ml


class Date(ml.Enum):
    January = date
    February = date
    March = date
    April = date
    May = date
    June = date
    July = date
    August = date
    September = date
    October = date
    November = date
    December = date


@ml.record
class Color:
    r: int
    g: int
    b: int
    a: int


def main():
    """Main program"""

    """
    Primitive Expressions
    """
    unit = ml.val(lambda v: v)
    is_even = ml.val(lambda v: v % 2 == 0)
    mul = ml.val(lambda args: args[0] * args[1])
    superscript = ml.val(lambda num, power=1: num**power)
    get_month = ml.val(lambda value: value.month)
    is_num = ml.val(lambda v: isinstance(v, (int, float)))
    is_exp = ml.val(lambda v: isinstance(v, BaseException))
    if_else = lambda check=unit, do=unit, else_do=unit: ml.val(
        lambda *args, **kwargs: (
            ml.match(check(*args, **kwargs))
            .case(True, do=lambda: do(*args, **kwargs))
            .case(False, do=lambda: else_do(*args, **kwargs))
        )()
    )

    """
    High Order Expressions
    """
    factorial = lambda v, accum=1: (
        ml.match(v <= 0)
        .case(True, do=ml.val(accum))
        .case(False, do=lambda num, ac=0: factorial(num - 1, accum=num * ac)())
    )
    # currying expressions is possible
    cube = superscript(power=3)
    get_item_types = ml.ireduce(lambda x, y: f"{type(x)}, {type(y)}")
    nums_type_err = ml.val(
        lambda args: TypeError(f"expected numbers, got {get_item_types(args)}")
    )
    is_seq_of_nums = ml.ireduce(lambda x, y: x and is_num(y), True)
    to_result = ml.val(lambda v: ml.Result.ERR(v) if is_exp(v) else ml.Result.OK(v))

    try_multiply = (
        if_else(check=is_seq_of_nums, do=mul, else_do=nums_type_err) >> to_result
    )

    result_to_option = ml.if_ok(ml.Option.SOME, strict=False) >> ml.if_err(
        lambda *args: ml.Option.NONE, strict=False
    )
    to_date_enum = ml.val(
        lambda v: (
            ml.match(v.month)
            .case(1, do=ml.val(Date.January(v)))
            .case(2, do=ml.val(Date.February(v)))
            .case(3, do=ml.val(Date.March(v)))
            .case(4, do=ml.val(Date.April(v)))
            .case(5, do=ml.val(Date.May(v)))
            .case(6, do=ml.val(Date.June(v)))
            .case(7, do=ml.val(Date.July(v)))
            .case(8, do=ml.val(Date.August(v)))
            .case(9, do=ml.val(Date.September(v)))
            .case(10, do=ml.val(Date.October(v)))
            .case(11, do=ml.val(Date.November(v)))
            .case(12, do=ml.val(Date.December(v)))
        )()
    )
    get_month_str = get_month >> (
        ml.match()
        .case(1, do=ml.val("JAN"))
        .case(2, do=ml.val("FEB"))
        .case(3, do=ml.val("MAR"))
        .case(4, do=ml.val("APR"))
        .case(5, do=ml.val("MAY"))
        .case(6, do=ml.val("JUN"))
        .case(7, do=ml.val("JUL"))
        .case(8, do=ml.val("AUG"))
        .case(9, do=ml.val("SEP"))
        .case(10, do=ml.val("OCT"))
        .case(11, do=ml.val("NOV"))
        .case(12, do=ml.val("DEC"))
    )

    """
    Data
    """
    dates = [
        date(200, 3, 4),
        date(2009, 1, 16),
        date(1993, 12, 29),
        date(2004, 10, 13),
        date(2020, 9, 5),
        date(2004, 5, 7),
        date(1228, 8, 18),
    ]
    dates = ml.val(dates)
    nums = ml.val(ml.l(12, 3, 45, 7, 8, 6, 3))
    data = ml.l((2, 3), ("hey", 7), (5, "y"), (8.1, 6))
    blue = Color(r=0, g=0, b=255, a=1)

    """
    Pipeline Creation and Execution
    """
    dates_as_enums = dates >> ml.imap(to_date_enum) >> ml.execute()
    print(f"\ndates as enums: {dates_as_enums}")

    print(f"\nfirst date enum: {dates_as_enums[0]}")

    months_as_str = dates >> ml.imap(get_month_str) >> ml.execute()
    print(f"\nmonths of dates as str:\n{months_as_str}")

    print(f"\ncube of 5: {cube(5)}")

    even_nums_pipeline = nums >> ml.ifilter(is_even)
    # here `even_nums_pipeline` is a `Pipeline` instance
    print(even_nums_pipeline)

    factorials_list = (
        copy(even_nums_pipeline)
        >> ml.imap(lambda v: f"factorial for {v}: {factorial(v)}")
        >> ml.execute()
    )
    # we created a new pipeline by coping the previous one
    # otherwise we would be mutating the old pipeline.
    # Calling ml.execute(), we get an actual iterable of strings
    print(factorials_list)

    factorials_str = (
        even_nums_pipeline
        >> ml.imap(lambda v: f"factorial for {v}: {factorial(v)}")
        >> ml.ireduce(lambda x, y: f"{x}\n{y}")
        >> ml.execute()
    )
    # here after calling ml.execute(), we get one string as output
    print(factorials_str)

    print(f"blue: {blue}")

    data = ml.val(data) >> ml.imap(try_multiply) >> ml.execute()
    print(f"\nafter multiplication:\n{data}")

    data_as_options = ml.val(data) >> ml.imap(result_to_option) >> ml.execute()
    print(f"\ndata as options: {data_as_options}")

    data_as_actual_values = (
        ml.val(data) >> ml.ifilter(ml.is_ok) >> ml.imap(ml.if_ok(unit)) >> ml.execute()
    )
    print(f"\ndata as actual values: {data_as_actual_values}")


if __name__ == "__main__":
    main()

• Run the script

python main.py

• For more details, visit the docs

Contributing

Contributions are welcome. The docs have to maintained, the code has to be made cleaner, more idiomatic and faster, and there might be need for someone else to take over this repo in case I move on to other things. It happens!

Please look at the CONTRIBUTIONS GUIDELINES

License

Licensed under both the MIT License

Copyright (c) 2023 Martin Ahindura

Gratitude

"...and His (the Father's) incomparably great power for us who believe. That power is the same as the mighty strength He exerted when He raised Christ from the dead and seated Him at His right hand in the heavenly realms, far above all rule and authority, power and dominion, and every name that is invoked, not only in the present age but also in the one to come."

-- Ephesians 1: 19-21

All glory be to God.