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Boolean expression to Python, SQL, SMT-LIB and LaTeX

Export a Boolean rule as a Python module, a PostgreSQL function, an SMT-LIB script for Z3 and a LaTeX formula, generated by the same exporters the Tangibool product ships and tests.

Everything runs in your browser; nothing is sent anywhere.

Write the rule and name its inputs the way your code names them (income_ok, credit_ok, guarantor); the exporter turns it into a Python function with typed Boolean parameters and a docstring, a PostgreSQL function with a documented non-null input contract, an SMT-LIB script that declares the inputs and asserts the rule so Z3 can check properties of it, and a LaTeX rendering with the usual symbols. Each identifier is mapped safely: reserved words and clashes are renamed and the map is listed.

These are the engine's own exporters. In the product's release checks the generated Python is executed with unittest, the PostgreSQL is run on a real database and the SMT-LIB on Z3, and all three must agree with the engine on every row of thousands of generated models. The code is never executed here; it is text for you to take away.

The exports describe the model, not the world: a Python function that returns the rule's value is as correct as the inputs you feed it, and the SQL contract says what happens with NULL (it is not handled; the input must be a real Boolean).

Limits

  • Truth tables up to 12 inputs (4096 rows).
  • Karnaugh maps up to 4 effective variables.
  • Normal forms up to 4096 terms; expressions up to 20000 characters and 64 inputs.

How to write expressions

Write inputs as names (member, A, credit_ok) and combine them with AND, OR, NOT, XOR, -> (implies), <-> (if and only if), NAND(a, b), NOR(a, b), XNOR(a, b), or the symbols &&, ||, !, ∧, ∨, ¬, ·, +, ′. Minterm and maxterm forms work too: Σm(A, B, C; 1, 3), ΠM(A, B; 0). Precedence: NOT, then AND, then XOR, then OR, then ->, then <->; use parentheses to be explicit. =, ==, ^, &, | and ~ are rejected with a repair.

Example

f := income_ok AND (credit_ok OR guarantor) AND NOT bankrupt · output

Exact checks

Always true (tautology)? No Counterexample: income_ok = F, credit_ok = F, guarantor = F, bankrupt = F · Exhaustively checked and solver cross-checked · 1 rows
Always false (contradiction)? No Counterexample: income_ok = T, credit_ok = F, guarantor = T, bankrupt = F · Exhaustively checked and solver cross-checked · 11 rows
Can be true? Yes Example assignment: income_ok = T, credit_ok = F, guarantor = T, bankrupt = F · Exhaustively checked and solver cross-checked · 11 rows

Truth table (16 rows)

Boolean expression to Python, SQL, SMT-LIB and LaTeX
#income_okcredit_okguarantorbankruptf
0FALSEFALSEFALSEFALSEFALSE
1FALSEFALSEFALSETRUEFALSE
2FALSEFALSETRUEFALSEFALSE
3FALSEFALSETRUETRUEFALSE
4FALSETRUEFALSEFALSEFALSE
5FALSETRUEFALSETRUEFALSE
6FALSETRUETRUEFALSEFALSE
7FALSETRUETRUETRUEFALSE
8TRUEFALSEFALSEFALSEFALSE
9TRUEFALSEFALSETRUEFALSE
10TRUEFALSETRUEFALSETRUE
11TRUEFALSETRUETRUEFALSE
12TRUETRUEFALSEFALSETRUE
13TRUETRUEFALSETRUEFALSE
14TRUETRUETRUEFALSETRUE
15TRUETRUETRUETRUEFALSE

Plain-language reading

Beginnerf is true exactly when all of the following hold:
1. income_ok is true
2. at least one of credit_ok or guarantor is true (both may be true)
3. bankrupt is false
In every other case, f is false.
About the operators:
- AND is true only when all of its parts are true.
- OR is inclusive: it is true when at least one part is true, including when several are.
- NOT reverses a value: true becomes false and false becomes true.
Intermediatef is true exactly when all of the following hold:
1. income_ok is true
2. at least one of credit_ok or guarantor is true (both may be true)
3. bankrupt is false
In every other case, f is false.
Advancedf is true exactly when all of the following hold [conjunction ∧]:
1. income_ok is true
2. at least one of credit_ok or guarantor is true (both may be true) [inclusive disjunction ∨]
3. bankrupt is false [negation ¬]
In every other case, f is false.
Formal notation: income_ok ∧ (credit_ok ∨ guarantor) ∧ ¬bankrupt

Logic diagram

Boolean expression to Python, SQL, SMT-LIB and LaTeX Boolean expression to Python, SQL, SMT-LIB and LaTeX income_ok credit_ok guarantor bankrupt f AND OR NOT
Boolean expression to Python, SQL, SMT-LIB and LaTeX Boolean expression to Python, SQL, SMT-LIB and LaTeX income_ok credit_ok guarantor bankrupt f AND OR NOT

Simplification, step by step

The engine found no law that shortens this expression; it is already as short as its rewriting laws can make it.

income_ok AND (credit_ok OR guarantor) AND NOT bankrupt

Karnaugh map

income_okcredit_ok \ guarantorbankrupt
00
01
11
10
00
0m0
0m1
0m3
0m2
01
0m4
0m5
0m7
0m6
11
1m12
0m13
0m15
1m14
10
0m8
0m9
0m11
1m10
  1. cells m12, m14income_ok AND credit_ok AND NOT bankrupt
  2. cells m10, m14income_ok AND guarantor AND NOT bankrupt
(income_ok AND credit_ok AND NOT bankrupt) OR (income_ok AND guarantor AND NOT bankrupt) · Derived (income_ok AND credit_ok AND NOT bankrupt) OR (income_ok AND guarantor AND NOT bankrupt). Exactly equivalent on every assignment.

Minterms and maxterms

f(income_ok, credit_ok, guarantor, bankrupt) = Σm(10, 12, 14)f(income_ok, credit_ok, guarantor, bankrupt) = ΠM(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 15)

Variable order: income_ok, credit_ok, guarantor, bankrupt · 16 rows

Normal forms

Conjunctive normal form (CNF)

income_ok AND (credit_ok OR guarantor) AND NOT bankrupt

3 clauses, 4 literals · complete

Disjunctive normal form (DNF)

(income_ok AND credit_ok AND NOT bankrupt) OR (income_ok AND guarantor AND NOT bankrupt)

2 terms, 6 literals · complete · Distribution ×2

NAND-only and NOR-only

NAND-only form

NAND(NAND(NAND(NAND(income_ok, NAND(NAND(credit_ok, credit_ok), NAND(guarantor, guarantor))), NAND(income_ok, NAND(NAND(credit_ok, credit_ok), NAND(guarantor, guarantor)))), NAND(bankrupt, bankrupt)), NAND(NAND(NAND(income_ok, NAND(NAND(credit_ok, credit_ok), NAND(guarantor, guarantor))), NAND(income_ok, NAND(NAND(credit_ok, credit_ok), NAND(guarantor, guarantor)))), NAND(bankrupt, bankrupt)))

23 gates · verified equivalent on every row

NOR-only form

NOR(NOR(NOR(NOR(income_ok, income_ok), NOR(credit_ok, guarantor)), NOR(NOR(income_ok, income_ok), NOR(credit_ok, guarantor))), bankrupt)

8 gates · verified equivalent on every row

The same rule as code

Python
# -*- coding: utf-8 -*-
# Generated by Tangibool (tangibool.python/1). Do not edit by hand; regenerate from the source revision.
# Model: not stated
# Source revision: reference
# Interpretation status: not stated
"""Tangibool evaluator (decision model).

Generated deterministically from the validated Boolean model; it evaluates exactly that
model. It does not establish that the model is a complete, legal, or safe statement of a
real-world rule, and evaluating examples does not verify the rule.

Source revision: reference
Interpretation status: not stated
Exporter: 1.0.0 (format tangibool.python/1; engine 1.0.0; semantics tangibool-semantics/1)
Logic digest: 97292c213b559106ccbe12422435b6470559626c9e8add8ade4903d416c7c44f
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78

Inputs (dict keys, declared order) and generated parameter names:
    'income_ok' -> input_income_ok
    'credit_ok' -> input_credit_ok
    'guarantor' -> input_guarantor
    'bankrupt' -> input_bankrupt

Outputs (declared order) and their functions:
    'f' -> output_f(inputs)

Assumptions (the declared scope, checked by in_scope(inputs)):
    none; every complete assignment is in scope.

Input contract:
    Every required input must be exactly True or False (type(value) is bool). Missing
    inputs, None, strings, integers (including 0 and 1), and keys that are not declared
    inputs raise InputError before anything is evaluated, including inputs that a
    short-circuiting operator would never read. Results are always bool.

Scope:
    Decision functions raise OutOfScopeError when the inputs violate an assumption; an
    out-of-scope assignment is never turned into a False result. Pass check_scope=False
    to evaluate without the scope check, and use in_scope(inputs) to test it separately.

Usage:

    import tangibool_model as model

    model.evaluate({'income_ok': False, 'credit_ok': False, 'guarantor': False, 'bankrupt': False})
    # -> {'f': False}
    model.output_f({'income_ok': False, 'credit_ok': False, 'guarantor': False, 'bankrupt': False})
    # -> False
    model.evaluate({'income_ok': 1, 'credit_ok': False, 'guarantor': False, 'bankrupt': False})
    # raises InputError: 1 is an int, not a bool
"""

import collections.abc as _abc
from typing import Dict, Mapping, Tuple

__all__ = [
    "MODEL_INFO",
    "IDENTIFIER_MAP",
    "INPUT_NAMES",
    "OUTPUT_NAMES",
    "ASSUMPTIONS",
    "InputError",
    "OutOfScopeError",
    "minterm_index",
    "in_scope",
    "evaluate",
    "output_f",
]

MODEL_INFO = {
    "format": "tangibool.python/1",
    "mode": "decision",
    "model": None,
    "revision": "reference",
    "interpretation": None,
    "exporter": "1.0.0",
    "engine_version": "1.0.0",
    "semantics_version": "tangibool-semantics/1",
    "logic_digest": "97292c213b559106ccbe12422435b6470559626c9e8add8ade4903d416c7c44f",
    "assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
    "primary_output": "f",
    "unused_inputs": (),
    "subcircuits": (),
}

# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
    "v_5af74db8cffa": {"kind": "input", "name": "income_ok", "identifier": "input_income_ok"},
    "v_daa090d435c6": {"kind": "input", "name": "credit_ok", "identifier": "input_credit_ok"},
    "v_c4b7115caa02": {"kind": "input", "name": "guarantor", "identifier": "input_guarantor"},
    "v_ea8b6c536b87": {"kind": "input", "name": "bankrupt", "identifier": "input_bankrupt"},
    "f_18446ace12a6": {"kind": "output", "name": "f", "identifier": "output_f"},
}

INPUT_NAMES = ("income_ok", "credit_ok", "guarantor", "bankrupt")
OUTPUT_NAMES = ("f",)
ASSUMPTIONS = ()

_DECLARED_INPUTS = frozenset(INPUT_NAMES)
_SCOPE_INPUTS = ()
_ASSUMPTION_FORMULAS = {entry["name"]: entry["formula"] for entry in ASSUMPTIONS}


class InputError(ValueError):
    """Raised when inputs are missing, undeclared, or not exactly True/False.

    Every required input must satisfy type(value) is bool. Missing inputs, None, strings,
    integers (including 0 and 1), and keys that are not declared inputs are rejected before
    anything is evaluated. A missing input is never treated as False. The individual problems
    are listed in the problems attribute.
    """

    def __init__(self, problems):
        self.problems = tuple(problems)
        super().__init__("Invalid inputs: " + "; ".join(self.problems))


class OutOfScopeError(ValueError):
    """Raised when the inputs violate the model's assumptions (its declared scope).

    Outside its assumptions the rule has no decision: this error is never a False result.
    The names of the violated assumptions are in the violated attribute.
    """

    def __init__(self, violated):
        self.violated = tuple(violated)
        details = ", ".join(name + " (" + _ASSUMPTION_FORMULAS.get(name, "?") + ")" for name in self.violated)
        super().__init__(
            "Out of scope: the inputs violate "
            + ("assumption " if len(self.violated) == 1 else "assumptions ")
            + details
            + ". The rule has no decision outside its assumptions; this is not a False result."
        )


def _short_repr(value: object) -> str:
    text = repr(value)
    return text if len(text) <= 60 else text[:57] + "..."


def _checked_inputs(inputs: Mapping[str, bool], required: Tuple[str, ...], check_scope: bool) -> Dict[str, bool]:
    """Validate inputs strictly before anything is evaluated and return a plain copy."""
    if type(check_scope) is not bool:
        raise TypeError("check_scope must be True or False")
    if not isinstance(inputs, _abc.Mapping):
        raise InputError(["inputs must be a mapping from input names to True/False, got " + type(inputs).__name__])
    problems = []
    for key in inputs:
        if type(key) is not str or key not in _DECLARED_INPUTS:
            declared = ", ".join(repr(name) for name in INPUT_NAMES) or "none"
            problems.append("undeclared input " + _short_repr(key) + " (declared inputs: " + declared + ")")
    needed = tuple(required) + (_SCOPE_INPUTS if check_scope else ())
    for name in needed:
        if name not in inputs:
            problems.append("missing required input " + repr(name))
    for key, value in inputs.items():
        if type(key) is str and key in _DECLARED_INPUTS and type(value) is not bool:
            problems.append(
                "input " + repr(key) + " must be True or False (type bool), got "
                + type(value).__name__ + " " + _short_repr(value)
            )
    if problems:
        raise InputError(list(dict.fromkeys(problems)))
    return dict(inputs.items())


def _require_bool_args(function: str, pairs: Tuple[Tuple[str, object], ...]) -> None:
    problems = [
        "argument " + repr(name) + " of " + function + " must be True or False (type bool), got "
        + type(value).__name__ + " " + _short_repr(value)
        for name, value in pairs
        if type(value) is not bool
    ]
    if problems:
        raise InputError(problems)


def minterm_index(*values: bool) -> int:
    """Row index of an assignment over a declared variable order.

    The first value is the most significant bit, so all-False is row 0. Used for indexed
    minterm (sum) and maxterm (product) forms.
    """
    index = 0
    for value in values:
        if type(value) is not bool:
            raise InputError(["minterm_index() takes True/False values, got " + type(value).__name__ + " " + _short_repr(value)])
        index = index * 2 + (1 if value else 0)
    return index


def _scope_values(values: Dict[str, bool]) -> Tuple[Tuple[str, bool], ...]:
    """Value of every enabled, confirmed assumption for validated inputs, in declared order."""
    return ()


def _require_in_scope(values: Dict[str, bool]) -> None:
    violated = tuple(name for name, holds in _scope_values(values) if not holds)
    if violated:
        raise OutOfScopeError(violated)


def in_scope(inputs: Mapping[str, bool]) -> bool:
    """True when the inputs satisfy every assumption (the model's declared scope).

    Requires the inputs the assumptions read; raises InputError for invalid inputs.
    """
    values = _checked_inputs(inputs, (), True)
    return all(holds for _, holds in _scope_values(values))


# ---- Formula bodies (inputs are validated by the public functions below) ----


def _compute_output_f(input_income_ok: bool, input_credit_ok: bool, input_guarantor: bool, input_bankrupt: bool) -> bool:
    """Decision output 'f': income_ok AND (credit_ok OR guarantor) AND NOT bankrupt"""
    return input_income_ok and (input_credit_ok or input_guarantor) and (not input_bankrupt)


# ---- Public API ----


def output_f(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
    """Decision output 'f' (stable ID f_18446ace12a6).

    'f' := income_ok AND (credit_ok OR guarantor) AND NOT bankrupt

    Required inputs: 'income_ok', 'credit_ok', 'guarantor', 'bankrupt'.
    Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
    when check_scope is True and the inputs violate an assumption.
    """
    values = _checked_inputs(inputs, ("income_ok", "credit_ok", "guarantor", "bankrupt"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_f_value = _compute_output_f(values["income_ok"], values["credit_ok"], values["guarantor"], values["bankrupt"])
    return output_f_value


def evaluate(inputs: Mapping[str, bool], *, check_scope: bool = True) -> Dict[str, bool]:
    """Evaluate every output (declared order); each output is computed once.

    Required inputs: 'income_ok', 'credit_ok', 'guarantor', 'bankrupt'.
    Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
    when check_scope is True and the inputs violate an assumption.
    """
    values = _checked_inputs(inputs, ("income_ok", "credit_ok", "guarantor", "bankrupt"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_f_value = _compute_output_f(values["income_ok"], values["credit_ok"], values["guarantor"], values["bankrupt"])
    return {
        "f": output_f_value,
    }
PostgreSQL
-- Tangibool PostgreSQL export (tangibool.postgresql/1)
-- Target: PostgreSQL 16 or later with typed boolean inputs; tested with PostgreSQL 18.3 (PGlite 0.5.8).
-- Not a claim of compatibility with other SQL dialects.
-- Model: not stated
-- Source revision: reference
-- Interpretation status: not stated
-- Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1)
-- Logic digest: 97292c213b559106ccbe12422435b6470559626c9e8add8ade4903d416c7c44f
-- Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
-- Mode: decision
--
-- Read-only: this file contains only WITH / VALUES / SELECT statements. No DDL, DML,
-- procedures, credentials, network access, or database changes. The expressions are pure
-- Boolean expressions, so their result does not depend on SQL evaluation order.
--
-- NON-NULL INPUT PRECONDITION: NULL is not a truth value of this rule. Every fragment below
-- is defined only when every input column it reads is a non-NULL boolean. The guard template
-- (section 2) marks any NULL input as 'invalid_input' and any row that violates the
-- assumptions as 'out_of_scope', and fills output columns only for 'ok' rows; a NULL in an
-- output column of another row is an envelope marker, not a third truth value. Never wrap
-- the fragments in COALESCE(..., FALSE) or otherwise choose a meaning for unknown inputs.
-- This evaluates the supplied model; it does not show that the model is a complete, legal,
-- or safe statement of a real-world rule.
--
-- Input columns (stable ID: name -> column):
--   v_5af74db8cffa: income_ok -> "in_income_ok"
--   v_daa090d435c6: credit_ok -> "in_credit_ok"
--   v_c4b7115caa02: guarantor -> "in_guarantor"
--   v_ea8b6c536b87: bankrupt -> "in_bankrupt"
-- Output columns:
--   f_18446ace12a6: f -> "out_f"
-- Assumptions (scope K; exported separately and never folded into an output):
--   none; every complete input row is in scope.

-- ----------------------------------------------------------------------------------------------
-- 1. Expression fragments (parenthesised; non-NULL boolean inputs required)
-- ----------------------------------------------------------------------------------------------
--
-- [out_f] output f (stable ID f_18446ace12a6)
--   f := income_ok AND (credit_ok OR guarantor) AND NOT bankrupt
--   PRECONDITION: "in_income_ok", "in_credit_ok", "in_guarantor", "in_bankrupt" are non-NULL booleans.
--   ("in_income_ok" AND ("in_credit_ok" OR "in_guarantor") AND (NOT "in_bankrupt"))
--

-- ----------------------------------------------------------------------------------------------
-- 2. Guard template (read-only SELECT over an inputs list)
-- ----------------------------------------------------------------------------------------------
-- Replace the example rows between the markers with your own rows, or replace the inputs
-- CTE with a SELECT that provides the boolean columns listed in the header. status is
-- 'invalid_input' when any input is NULL, 'out_of_scope' when the assumptions are false, and
-- 'ok' otherwise; output columns are filled only for 'ok' rows (NULL elsewhere is an
-- envelope marker). Assumption columns are shown for rows with complete inputs.
WITH inputs ("row_id", "in_income_ok", "in_credit_ok", "in_guarantor", "in_bankrupt") AS (
  -- tangibool:example-rows:begin
  VALUES
    (1, FALSE, FALSE, FALSE, FALSE),
    (2, FALSE, FALSE, FALSE, TRUE),
    (3, FALSE, FALSE, TRUE, FALSE),
    (4, FALSE, FALSE, TRUE, TRUE),
    (5, FALSE, TRUE, FALSE, FALSE),
    (6, FALSE, TRUE, FALSE, TRUE),
    (7, FALSE, TRUE, TRUE, FALSE),
    (8, FALSE, TRUE, TRUE, TRUE),
    (9, TRUE, FALSE, FALSE, FALSE),
    (10, TRUE, FALSE, FALSE, TRUE),
    (11, TRUE, FALSE, TRUE, FALSE),
    (12, TRUE, FALSE, TRUE, TRUE),
    (13, TRUE, TRUE, FALSE, FALSE),
    (14, TRUE, TRUE, FALSE, TRUE),
    (15, TRUE, TRUE, TRUE, FALSE),
    (16, TRUE, TRUE, TRUE, TRUE),
    (17, NULL::boolean, FALSE, FALSE, FALSE)
  -- tangibool:example-rows:end
),
checked AS (
  SELECT
    inputs.*,
    CASE
      WHEN "in_income_ok" IS NULL OR "in_credit_ok" IS NULL OR "in_guarantor" IS NULL OR "in_bankrupt" IS NULL THEN 'invalid_input'
      ELSE 'ok'
    END AS "status"
  FROM inputs
)
SELECT
  "row_id",
  "in_income_ok",
  "in_credit_ok",
  "in_guarantor",
  "in_bankrupt",
  "status",
  CASE WHEN "status" = 'ok' THEN ("in_income_ok" AND ("in_credit_ok" OR "in_guarantor") AND (NOT "in_bankrupt")) END AS "out_f"
FROM checked
ORDER BY "row_id";
SMT-LIB
; Tangibool SMT-LIB export (tangibool.smtlib/1)
; Profile: SMT-LIB 2.6 Boolean subset (QF_UF): Bool constants, define-fun, let, push/pop, assert, check-sat, echo.
; Tested with Z3 5.1.0 (z3-solver 5.2.0, WebAssembly build).
; Model: not stated
; Source revision: reference
; Interpretation status: not stated
; Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1)
; Logic digest: 97292c213b559106ccbe12422435b6470559626c9e8add8ade4903d416c7c44f
; Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
; Mode: decision
;
; Reading results: each query block states what sat and unsat mean for its question. A sat
; answer comes with a witness or counterexample (ask for it with (get-model) after sat only);
; unsat settles that scoped query and is solver-checked, not an independently checked proof
; certificate; unknown settles nothing. Blocks are scoped with push/pop, so assertions never
; accumulate across questions. This checks the supplied model, not whether it is a complete,
; legal, or safe statement of a real-world rule.
;
; Symbols (stable ID: name -> symbol):
;   v_5af74db8cffa: income_ok -> in_income_ok
;   v_daa090d435c6: credit_ok -> in_credit_ok
;   v_c4b7115caa02: guarantor -> in_guarantor
;   v_ea8b6c536b87: bankrupt -> in_bankrupt
;   f_18446ace12a6: f -> out_f

(set-option :produce-models true)
(set-info :smt-lib-version 2.6)
(set-logic QF_UF)

; ---- Inputs (declared order)
(declare-const in_income_ok Bool) ; income_ok
(declare-const in_credit_ok Bool) ; credit_ok
(declare-const in_guarantor Bool) ; guarantor
(declare-const in_bankrupt Bool) ; bankrupt

; ---- Outputs (dependencies first)
; output f (stable ID f_18446ace12a6): income_ok AND (credit_ok OR guarantor) AND NOT bankrupt
(define-fun out_f () Bool (and in_income_ok (or in_credit_ok in_guarantor) (not in_bankrupt)))

; ---- Assumptions (the scope K; never folded into an output)
; K: conjunction of the enabled, confirmed assumptions (true when there are none)
(define-fun K () Bool true)

; ---- Definitions only: nothing is asserted and no check is run.
; Add (assert ...) and (check-sat) inside (push 1) ... (pop 1) to ask a question.
LaTeX
f = \mathit{income\_ok} \cdot \left(\mathit{credit\_ok} + \mathit{guarantor}\right) \cdot \overline{\mathit{bankrupt}}

Questions

Why does the SQL function require non-null inputs?

SQL's three-valued logic means a OR NOT a is not TRUE when a is NULL, so a rule that is a tautology in Boolean logic is not one in SQL with nullable columns. The export documents a non-null input contract and gives a guard template rather than silently changing the logic.

Can I export the tests too?

In the app, yes: the Python export can include a unittest module built from your saved scenario and property tests, and the runner executes them in CI. This tool exports the rule only.

Related

Tools

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Generated from the Tangibool engine, semantics tangibool-semantics/1, engine 1.0.0, build . Every table, map, simplification and verdict on these pages was computed by the Tangibool engine when the site was built, and every claim was re-checked by the same exact checker the app uses. Limits are stated where they apply. © 2026 Foreranger LTD.