test(pyargus): add tests comparing semantics against metric-temporal-logic

noxfile.py

@@ -136,7 +136,7 @@ def mypy(session: nox.Session):
 def tests(session: nox.Session):
     session.conda_install("pytest", "hypothesis", "lark")
     session.env.update(ENV)
-    session.install("./pyargus")
+    session.install("./pyargus[test]")
     try:
         session.run(
             "cargo", "test", "--workspace", "--exclude", "pyargus", external=True
@@ -181,6 +181,8 @@ def coverage(session: nox.Session):
         "develop",
         "-m",
         "./pyargus/Cargo.toml",
+        "-E",
+        "test",
         silent=True,
     )
     try:

pyargus/argus/test_utils/signals_gen.py

@@ -35,24 +35,34 @@ def gen_samples(
     min_size: int,
     max_size: int,
     dtype_: Union[Type[AllowedDtype], dtype],
-) -> List[Tuple[float, AllowedDtype]]:
+    n_lists: int = 1,
+) -> Union[List[Tuple[float, AllowedDtype]], List[List[Tuple[float, AllowedDtype]]]]:
     """
     Generate arbitrary samples for a signal where the time stamps are strictly
     monotonically increasing
+
+    :param n_lists: used to generate multiple sample lists with the same time domain. This is used for testing against
+                    `metric-temporal-logic` as it doesn't check for non-overlapping domains.
     """
-    elements = gen_element_fn(dtype_)
-    values = draw(st.lists(elements, min_size=min_size, max_size=max_size))
-    xs = draw(
+    n_lists = max(1, n_lists)
+    timestamps = draw(
         st.lists(
             st.integers(min_value=0, max_value=2**32 - 1),
             unique=True,
-            min_size=len(values),
-            max_size=len(values),
+            min_size=min_size,
+            max_size=max_size,
+        ).map(lambda t: list(map(float, sorted(set(t)))))
     )
-        .map(lambda t: map(float, sorted(set(t))))
-        .map(lambda t: list(zip(t, values)))
-    )
-    return xs
+    elements = gen_element_fn(dtype_)
+
+    sample_lists = [
+        draw(st.lists(elements, min_size=len(timestamps), max_size=len(timestamps)).map(lambda xs: list(zip(timestamps, xs))))
+        for _ in range(n_lists)
+    ]
+    if n_lists == 1:
+        return sample_lists[0]
+    else:
+        return sample_lists
 
 
 def empty_signal(dtype_: Union[Type[AllowedDtype], dtype]) -> SearchStrategy[argus.Signal]:

pyargus/pyproject.toml

@@ -35,7 +35,13 @@ dev = [
   "black",
 ]
 
-test = ["pytest", "coverage", "hypothesis[lark]"]
+test = [
+  "pytest",
+  "coverage",
+  "hypothesis[lark]",
+  "metric-temporal-logic",
+  "rtamt",
+]
 
 [build-system]
 requires = ["maturin>=1.0,<2.0"]
@@ -54,6 +60,11 @@ packages = ["argus"]
 # ignore_missing_imports = true
 show_error_codes = true
 
+[[tool.mypy.overrides]]
+module = "mtl"
+ignore_missing_imports = true
+
+
 [tool.ruff]
 line-length = 127
 select = ["E", "F", "W", "N", "B", "ANN", "PYI"]

pyargus/src/lib.rs

@@ -4,11 +4,12 @@ mod signals;
 
 use argus::Error as ArgusError;
 use ariadne::Source;
-use expr::PyExpr;
 use pyo3::exceptions::{PyKeyError, PyRuntimeError, PyTypeError, PyValueError};
 use pyo3::prelude::*;
 use pyo3::types::{PyBool, PyFloat, PyInt, PyType};
 
+use crate::expr::{PyBoolExpr, PyNumExpr};
+
 #[derive(derive_more::From)]
 struct PyArgusError(ArgusError);
 
@@ -73,10 +74,14 @@ impl DType {
 /// Parse a string expression into a concrete Argus expression.
 #[pyfunction]
 fn parse_expr(expr_str: &str) -> PyResult<PyObject> {
+    use argus::expr::Expr;
     use ariadne::{Color, Label, Report, ReportKind};
 
     match argus::parse_str(expr_str) {
-        Ok(expr) => Python::with_gil(|py| PyExpr::from_expr(py, expr)),
+        Ok(expr) => Python::with_gil(|py| match expr {
+            Expr::Bool(e) => PyBoolExpr::from_expr(py, e),
+            Expr::Num(e) => PyNumExpr::from_expr(py, e),
+        }),
         Err(errs) => {
             let mut buf = Vec::new();
             {

pyargus/tests/test_semantics.py

@@ -0,0 +1,89 @@
+from typing import List, Tuple
+
+import hypothesis.strategies as st
+import mtl
+from hypothesis import given
+
+import argus
+from argus.test_utils.signals_gen import gen_samples
+
+
+@given(
+    sample_lists=gen_samples(min_size=3, max_size=50, dtype_=bool, n_lists=2),
+    spec=st.one_of(
+        [
+            st.just(spec)
+            for spec in [
+                "a",
+                "~a",
+                "(a & b)",
+                "(a | b)",
+                "(a -> b)",
+                "(a <-> b)",
+                "(a ^ b)",
+            ]
+        ]
+    ),
+)
+def test_boolean_propositional_expr(
+    sample_lists: List[List[Tuple[float, bool]]],
+    spec: str,
+) -> None:
+    mtl_spec = mtl.parse(spec)
+    argus_spec = argus.parse_expr(spec)
+    assert isinstance(argus_spec, argus.BoolExpr)
+
+    a, b = sample_lists
+    mtl_data = dict(a=a, b=b)
+    argus_data = argus.Trace(
+        dict(
+            a=argus.BoolSignal.from_samples(a, interpolation_method="constant"),
+            b=argus.BoolSignal.from_samples(b, interpolation_method="constant"),
+        )
+    )
+
+    mtl_rob = mtl_spec(mtl_data, quantitative=False)
+    argus_rob = argus.eval_bool_semantics(argus_spec, argus_data)
+
+    assert mtl_rob == argus_rob.at(0), f"{argus_rob=}"
+
+
+@given(
+    sample_lists=gen_samples(min_size=3, max_size=50, dtype_=bool, n_lists=2),
+    spec=st.one_of(
+        [
+            st.just(spec)
+            for spec in [
+                "F a",
+                "G b",
+                "(G(a & b))",
+                "(F(a | b))",
+                "G(a -> F[0,2] b)",
+                "(a U b)",
+                "(a U[0,2] b)",
+            ]
+        ]
+    ),
+)
+def test_boolean_temporal_expr(
+    sample_lists: List[List[Tuple[float, bool]]],
+    spec: str,
+) -> None:
+    mtl_spec = mtl.parse(spec)
+    argus_spec = argus.parse_expr(spec)
+    assert isinstance(argus_spec, argus.BoolExpr)
+
+    a = sample_lists[0]
+    b = sample_lists[1]
+    mtl_data = dict(a=a, b=b)
+    argus_data = argus.Trace(
+        dict(
+            a=argus.BoolSignal.from_samples(a, interpolation_method="constant"),
+            b=argus.BoolSignal.from_samples(b, interpolation_method="constant"),
+        )
+    )
+
+    mtl_rob = mtl_spec(mtl_data, quantitative=False)
+    argus_rob = argus.eval_bool_semantics(argus_spec, argus_data)
+
+    assert mtl_rob == argus_rob.at(0), f"{argus_rob=}"

pyargus/tests/test_signals.py

@@ -29,7 +29,7 @@ def test_correct_constant_signals(data: st.DataObject) -> None:
 def test_correctly_create_signals(data: st.DataObject) -> None:
     dtype_ = data.draw(gen_dtype())
     xs = data.draw(gen_samples(min_size=0, max_size=100, dtype_=dtype_))
-    signal = sampled_signal(xs, dtype_)
+    signal = sampled_signal(xs, dtype_)  # type: ignore
     assert isinstance(signal, argus.Signal)
     if len(xs) > 0:
         expected_start_time = xs[0][0]
@@ -46,7 +46,7 @@ def test_correctly_create_signals(data: st.DataObject) -> None:
         a = data.draw(draw_index(xs))
         assert a < len(xs)
         at, expected_val = xs[a]
-        actual_val = signal.at(at)
+        actual_val = signal.at(at)  # type: ignore
 
         assert actual_val is not None
         assert actual_val == expected_val
@@ -79,10 +79,10 @@ def test_signal_create_should_fail(data: st.DataObject) -> None:
     assert a < len(xs)
     assert b < len(xs)
     # Swap two indices in the samples
-    xs[b], xs[a] = xs[a], xs[b]
+    xs[b], xs[a] = xs[a], xs[b]  # type: ignore
 
     with pytest.raises(RuntimeError, match=r"trying to create a non-monotonically signal.+"):
-        _ = sampled_signal(xs, dtype_)
+        _ = sampled_signal(xs, dtype_)  # type: ignore
 
 
 @given(st.data())