autotui 0.1.2

helpers for creating TUIs with persistent typed data

autotui

This uses type hints to convert NamedTuple's to JSON, and back to python objects.

It also wraps prompt_toolkit to prompt the user and validate the input for common types, and is extendible to whatever types you want.

This has built-ins to prompt, validate and serialize:

• int
• float
• bool
• str
• datetime
• Optional[<type>]
• List[<type>]
• Set[<type>]

Note: Doesn't support all of these recursively, see below for more info.

I wrote this so that I don't have to repeatedly write boilerplate-y python code to validate/serialize/deserialize data.

As an example, if I want to log whenever I drink water to a file:

from datetime import datetime
from typing import NamedTuple

from autotui.shortcuts import load_prompt_and_writeback

class Water(NamedTuple):
    at: datetime
    glass_count: float

if __name__ == "__main__":
    load_prompt_and_writeback(Water, "~/.local/share/water.json")

Which, after running a few times, would create:

~/.local/share/water.json

[
    {
        "at": 1598856786,
        "glass_count": 2.0
    },
    {
        "at": 1598856800,
        "glass_count": 1.0
    }
]

(datetimes are serialized into epoch time)

If I want to load the values back into python, its just:

from autotui.shortcuts import load_from

class Water(NamedTuple):
    #... (same as above)

if __name__ == "__main__":
    print(load_from(Water, "~/.local/share/water.json"))

#[Water(at=datetime.datetime(2020, 8, 31, 6, 53, 6, tzinfo=datetime.timezone.utc), glass_count=2.0),
# Water(at=datetime.datetime(2020, 8, 31, 6, 53, 20, tzinfo=datetime.timezone.utc), glass_count=1.0)]

Installation

This requires python3.8+, specifically for modern typing support.

To install with pip, run:

pip3 install autotui
pip3 install 'autotui[optional]'  # to install dateparser, for parsing human-readable times

Custom Types

If your algebraic data type is getting too complicated and autotui can't parse it, you can always specify another NamedTuple or type, and pass a type_validators, and type_[de]serializer to handle the validation, serialization, deserialization for that type/attribute name.

As a more complicated example, heres a validator for timedelta (duration of time), being entered as MM:SS, and the corresponding serializers.

# see examples/timedelta_serializer.py for imports

# handle validating the user input
# can throw a ValueError
def _timedelta(user_input: str) -> timedelta:
    if len(user_input.strip()) == 0:
        raise ValueError("Not enough input!")
    minutes, _, seconds = user_input.partition(":")
    # could throw ValueError
    return timedelta(minutes=float(minutes), seconds=float(seconds))

 
# serializer for timedelta, converts to JSON-compatible integer
def to_seconds(t: timedelta) -> int:
    return int(t.total_seconds())


# deserializer from integer to timedelta
def from_seconds(seconds: int) -> timedelta:
    return timedelta(seconds=seconds)


# The data we want to persist to the file
class Action(NamedTuple):
    name: str
    duration: timedelta


# AutoHandler describes what function to use to validate
# user input, and which errors to wrap while validating
timedelta_handler = AutoHandler(
    func=_timedelta,  # accepts the string the user is typing as input
    catch_errors=[ValueError],
)

# Note: validators are of type
# Dict[Type, AutoHandler]
# serializer/deserializers are
# Dict[Type, Callable]
# the Callable accepts one argument,
# which is either the type being serialized
# or deserialized

# use the validator to prompt the user for the NamedTuple data
# name: str automatically uses a generic string prompt
# duration: timedelta gets handled by the type_validator
a = prompt_namedtuple(
    Action,
    type_validators={
        timedelta: timedelta_handler,
    },
)


# Note: this specifies timedelta as the type,
# not int. It uses what the NamedTuple
# specifies as the type for that field, not
# the type of the value thats loaded from JSON

# dump to JSON
a_str: str = namedtuple_sequence_dumps(
    [a],
    type_serializers={
        timedelta: to_seconds,
    },
    indent=None,
)

# load from JSON
a_load = namedtuple_sequence_loads(
    a_str,
    to=Action,
    type_deserializers={
        timedelta: from_seconds,
    },
)[0]

# can also specify with attributes instead of types
a_load2 = namedtuple_sequence_loads(
    a_str,
    to=Action,
    attr_deserializers={
        "duration": from_seconds,
    },
)[0]

print(a)
print(a_str)
print(a_load)
print(a_load2)

Output:

$ python3 ./examples/timedelta_serializer.py
'name' (str) > on the bus
'duration' (_timedelta) > 30:00
Action(name='on the bus', duration=datetime.timedelta(seconds=1800))
[{"name": "on the bus", "duration": 1800}]
Action(name='on the bus', duration=datetime.timedelta(seconds=1800))
Action(name='on the bus', duration=datetime.timedelta(seconds=1800))

The general philosophy I've taken for serialization and deserialization is send a warning if the types aren't what the NamedTuple expects, but load the values anyways. If serialization can't serialize something, it warns, and if simplejson.dump doesn't have a way to handle it, it throws an error. When deserializing, all values are loaded from their JSON primitives, and then converted into their corresponding python equivalents; If the value doesn't exist, it warns and sets it to None, if theres a deserializer supplied, it uses that. This is meant to help facilitate quick TUIs, I don't want to have to fight with it.

Theres lots of examples on how this is handled/edge-cases in the tests.

You can also take a look at the examples

Tests

pip3 install 'autotui[testing]'
pytest  # in the root directory
pytest --doctest-modules ./autotui