verb 0.1.0

Easy make mini-languages to do python things.

verb

Easy make mini-languages to do python things.

To install: pip install verb

A quick intro to Command

from verb import *

In a nutshell, you make a str-to-func mapping (or use the default)

import operator as o

func_of_op_str = {  # Note: Order represents precedence!
    '-': o.sub,
    '+': o.add,
    '*': o.mul,
    '/': o.truediv,
}

# You give it a command string

command_str = '1 + 2 - 3 * 4 / 8'
command = Command(command_str, func_of_op_str)

# You execute the command
command()

1.5

You give it a command string

command_str = '1 + 2 - 3 * 4 / 8'
command = Command(command_str, func_of_op_str)

You execute the command

command()

1.5

It may be useful to see what the operation structure looks like

d = command.to_dict()
d

{'-': ({'+': (1, 2)}, {'*': (3, {'/': (4, 8)})})}

# Or if you read better with indents

from functools import partial
import json
from lined import Pipe

print_jdict = Pipe(partial(json.dumps, indent=2), print)  # Note: Only works if your dict is JSON-izable. 

print_jdict(d)

{
  "-": [
    {
      "+": [
        1,
        2
      ]
    },
    {
      "*": [
        3,
        {
          "/": [
            4,
            8
          ]
        }
      ]
    }
  ]
}

That same dict can be used as a parameter to make the same command

command = Command(d, func_of_op_str)
command()

1.5

Example: Table Selector Mini Language

import operator as o
from typing import Callable, Mapping
from functools import partial

import pandas as pd
from lined import Pipe
from verb import str_to_basic_pyobj, Command


dflt_func_of_op_str_for_table_selection = {  # Note: Order represents precedence!
    '&': o.__and__,
    '==': o.__eq__,
    '<=': o.__le__,
    '>=': o.__ge__,
    '<': o.__lt__,
    '>': o.__gt__,
}


def mk_table_selector(
    table: pd.DataFrame,
    func_of_op_str: Mapping[str, Callable] = dflt_func_of_op_str_for_table_selection
):

    def leaf_processor(x):
        x = str_to_basic_pyobj(x)
        if x in df:
            return df[x]
        return x

    run_command = Pipe(
        partial(
            Command.from_string,
            func_of_op_str=func_of_op_str,
            leaf_processor=leaf_processor
        ),
        lambda f: f(),
        lambda idx: df[idx],
    )

    return run_command

import pandas as pd

df = pd.DataFrame(
    [{'source': 'audio', 'bt': 5, 'tt': 7, 'annot': 'cat'},
     {'source': 'audio',
        'bt': 6,
        'tt': 9,
        'annot': 'dog',
        'comments': 'barks and chases cat away'},
        {'source': 'visual', 'bt': 5, 'tt': 8, 'annot': 'cat'},
        {'source': 'visual',
         'bt': 6,
         'tt': 15,
         'annot': 'dog',
         'comments': 'dog remains in view after bark ceases'}]
)
df

	source	bt	tt	annot	comments
0	audio	5	7	cat	NaN
1	audio	6	9	dog	barks and chases cat away
2	visual	5	8	cat	NaN
3	visual	6	15	dog	dog remains in view after bark ceases

run_command = mk_table_selector(df)

run_command('source == audio')

	source	bt	tt	annot	comments
0	audio	5	7	cat	NaN
1	audio	6	9	dog	barks and chases cat away

run_command('tt <= 8')

	source	bt	tt	annot	comments
0	audio	5	7	cat	NaN
2	visual	5	8	cat	NaN

run_command('source == audio & tt <= 8')

	source	bt	tt	annot	comments
0	audio	5	7	cat	NaN