feat(gen): add support for tuples and dataframes

midas/checker/frames.py

@@ -0,0 +1,133 @@
+from typing import Optional, TypeGuard, cast
+
+from midas.ast.location import Location
+from midas.checker.registry import TypesRegistry
+from midas.checker.reporter import FileReporter
+from midas.checker.types import ColumnType, DataFrameType, TupleType, Type, UnknownType
+
+import midas.ast.python as p
+
+
+def is_list_of_literals(exprs: list[p.Expr]) -> TypeGuard[list[p.LiteralExpr]]:
+    return all(isinstance(expr, p.LiteralExpr) for expr in exprs)
+
+
+class FrameManager:
+    def __init__(self, types: TypesRegistry) -> None:
+        self.types: TypesRegistry = types
+
+    def assign(
+        self,
+        reporter: FileReporter,
+        location: Location,
+        frame: DataFrameType,
+        index: p.Expr,
+        value_type: Type,
+    ) -> Type:
+        match index:
+            case p.LiteralExpr(value=str() as name):
+                return self.assign_column(reporter, location, frame, name, value_type)
+
+            case p.ListExpr(items=indices) if is_list_of_literals(indices) and all(
+                isinstance(idx, str) for idx in indices
+            ):
+                raise NotImplementedError
+
+            case _:
+                reporter.error(location, f"Invalid index type {index} on {frame}")
+                return UnknownType()
+
+    def assign_column(
+        self,
+        reporter: FileReporter,
+        location: Location,
+        frame: DataFrameType,
+        name: str,
+        type: Type,
+    ) -> Type:
+        if not isinstance(type, ColumnType):
+            reporter.error(
+                location,
+                f"Cannot assign {type} to dataframe column. Must be a ColumnType",
+            )
+            return frame
+        return self._set_column(frame, name, type)
+
+    def get(
+        self,
+        reporter: FileReporter,
+        location: Location,
+        frame: DataFrameType,
+        index: p.Expr,
+    ) -> Type:
+        match index:
+            case p.LiteralExpr(value=str() as name):
+                column: Optional[ColumnType] = FrameManager._get_column(frame, name)
+                if column is None:
+                    reporter.error(location, f"Unknown column '{name}' on {frame}")
+                    return UnknownType()
+                return column
+
+            case p.ListExpr(items=indices) if is_list_of_literals(indices) and all(
+                isinstance(index.value, str) for index in indices
+            ):
+                names: list[str] = [cast(str, index.value) for index in indices]
+                columns: list[ColumnType] = []
+                for name in names:
+                    column: Optional[ColumnType] = FrameManager._get_column(frame, name)
+                    if column is None:
+                        reporter.error(location, f"Unknown column '{name}' on {frame}")
+                        return UnknownType()
+                    columns.append(column)
+                return TupleType(items=tuple(columns))
+
+            case _:
+                reporter.error(location, f"Invalid index type {index} on {frame}")
+                return UnknownType()
+
+    @classmethod
+    def _set_column(
+        cls, frame: DataFrameType, name: str, column: ColumnType
+    ) -> DataFrameType:
+        new_columns: list[DataFrameType.Column] = []
+        index: int = len(frame.columns)
+        replace: bool = False
+        for i, col in enumerate(frame.columns):
+            if col.name == name:
+                index = i
+                replace = True
+                # TODO: check column type here to prevent changing it
+            new_columns.append(col)
+
+        new_col: DataFrameType.Column = DataFrameType.Column(
+            index=index,
+            name=name,
+            type=column,
+        )
+        if replace:
+            new_columns[index] = new_col
+        else:
+            new_columns.append(new_col)
+
+        return DataFrameType(columns=new_columns)
+
+    @classmethod
+    def _set_columns(
+        cls, frame: DataFrameType, names: list[str], columns: list[ColumnType]
+    ) -> DataFrameType:
+        for name, col in zip(names, columns):
+            frame = cls._set_column(frame, name, col)
+        return frame
+
+    @classmethod
+    def _get_column(cls, frame: DataFrameType, name: str) -> Optional[ColumnType]:
+        for col in frame.columns:
+            if col.name == name:
+                return col.type
+        return None
+
+    @classmethod
+    def _get_columns(
+        cls, frame: DataFrameType, names: list[str]
+    ) -> list[Optional[ColumnType]]:
+        return [cls._get_column(frame, name) for name in names]

midas/checker/python.py

@@ -6,6 +6,7 @@ from typing import Optional
 import midas.ast.python as p
 from midas.ast.location import Location
 from midas.checker.environment import Environment
+from midas.checker.frames import FrameManager
 from midas.checker.operators import (
     PY_COMPARATOR_METHODS,
     PY_OPERATOR_METHODS,
@@ -18,9 +19,12 @@ from midas.checker.resolver import Resolver
 from midas.checker.types import (
     AliasType,
     AppliedType,
+    ColumnType,
+    DataFrameType,
     Function,
     GenericType,
     OverloadedFunction,
+    TupleType,
     Type,
     TypeVar,
     UnitType,
@@ -67,6 +71,7 @@ class PythonTyper(
         self.logger: logging.Logger = logging.getLogger("PythonTyper")
         self.reporter: FileReporter = reporter.for_file(None)
         self.types: TypesRegistry = types
+        self.frame_mgr: FrameManager = FrameManager(self.types)
         self.global_env: Environment = Preamble(self.types)
         self.env: Environment = self.global_env
         self.locals: dict[p.Expr, int] = {}
@@ -309,9 +314,15 @@ class PythonTyper(
             case p.VariableExpr():
                 self._assign_var(location, target, value_type)
 
+            # Allow any kind of object because we disallow creating new attributes
             case p.GetExpr(object=object, name=name):
                 self._assign_attr(location, object, name, value_type)
 
+            # Only support variable expressions because modifying
+            # the underlying value would require reference types
+            case p.SubscriptExpr(object=p.VariableExpr() as var, index=index):
+                self._assign_sub(location, var, index, value_type)
+
             case _:
                 if not isinstance(target, p.VariableExpr):
                     self.logger.warning(f"Unsupported assignment to {target}")
@@ -350,6 +361,27 @@ class PythonTyper(
                 f"Cannot assign {value_type} to member '{object_type}.{name}' of type {member}",
             )
 
+    def _assign_sub(
+        self,
+        location: Location,
+        var: p.VariableExpr,
+        index: p.Expr,
+        value_type: Type,
+    ):
+        var_type: Type = self.type_of(var)
+        # TODO: what happens if type is an alias of a dataframe type
+        match var_type:
+            case DataFrameType() as frame:
+                new_type: Type = self.frame_mgr.assign(
+                    self.reporter, location, frame, index, value_type
+                )
+                self.env.assign(var.name, new_type)
+            case _:
+                self.reporter.error(
+                    location,
+                    f"Cannot assign {value_type} to index {index} of {var_type}",
+                )
+
     def visit_return_stmt(self, stmt: p.ReturnStmt) -> None:
         type: Type = self.type_of(stmt.value) if stmt.value is not None else UnitType()
         self.env.return_types.append(type)
@@ -622,6 +654,13 @@ class PythonTyper(
 
     def visit_subscript_expr(self, expr: p.SubscriptExpr) -> Type:
         object: Type = self.type_of(expr.object)
+        unfolded: Type = unfold_type(object)
+        match unfolded:
+            case TupleType():
+                return self._visit_tuple_subscript(unfolded, expr)
+            case DataFrameType():
+                return self._visit_frame_subscript(unfolded, expr)
+
         operation: Optional[Type] = self.types.lookup_member(object, "__getitem__")
         if operation is None:
             self.reporter.error(
@@ -659,13 +698,26 @@ class PythonTyper(
         self.reporter.warning(node.location, "ConstraintType not yet supported")
         return UnknownType()
 
-    def visit_frame_column(self, node: p.FrameColumn) -> Type:
-        self.reporter.warning(node.location, "FrameColumn not yet supported")
-        return UnknownType()
+    def visit_frame_column(self, node: p.FrameColumn) -> ColumnType:
+        return ColumnType(
+            type=(
+                self.resolve_type_expr(node.type)
+                if node.type is not None
+                else UnknownType()
+            )
+        )
 
     def visit_frame_type(self, node: p.FrameType) -> Type:
-        self.reporter.warning(node.location, "FrameType not yet supported")
-        return UnknownType()
+        return DataFrameType(
+            columns=[
+                DataFrameType.Column(
+                    index=i,
+                    name=column.name,
+                    type=self.visit_frame_column(column),
+                )
+                for i, column in enumerate(node.columns)
+            ]
+        )
 
     def _get_call_result(
         self,
@@ -1108,3 +1160,23 @@ class PythonTyper(
                 return p.BaseType(location=location, base=name, param=None)
             case _:
                 raise NotImplementedError
+
+    def _visit_tuple_subscript(self, tup: TupleType, expr: p.SubscriptExpr) -> Type:
+        match expr.index:
+            case p.LiteralExpr(value=int() as index):
+                if index < 0 or index >= len(tup.items):
+                    self.reporter.error(
+                        expr.location, f"Index {index} out of range for tuple {tup}"
+                    )
+                    return UnknownType()
+                return tup.items[index]
+            case _:
+                self.reporter.error(
+                    expr.location, f"Invalid index type {expr.index} on {tup}"
+                )
+                return UnknownType()
+
+    def _visit_frame_subscript(
+        self, frame: DataFrameType, expr: p.SubscriptExpr
+    ) -> Type:
+        return self.frame_mgr.get(self.reporter, expr.location, frame, expr.index)

midas/checker/resolver.py

@@ -128,6 +128,10 @@ class Resolver(p.Stmt.Visitor[None], p.Expr.Visitor[None]):
 
             case p.GetExpr():
                 target.accept(self)
+
+            case p.SubscriptExpr():
+                target.accept(self)
+
             case _:
                 raise Exception(f"Unsupported assignment to {target}")
 

midas/checker/types.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 from dataclasses import dataclass, field
 from enum import StrEnum
-from typing import Optional, assert_never
+from typing import Optional, assert_never, cast
 
 import midas.ast.midas as m
 from midas.ast.printer import MidasPrinter
@@ -156,6 +156,37 @@ class ConstraintType:
         return f"{self.type} where {printer.print(self.constraint)}"
 
 
+@dataclass(frozen=True, kw_only=True)
+class TupleType:
+    items: tuple[Type, ...]
+
+    def __str__(self) -> str:
+        return f"({', '.join(map(str, self.items))})"
+
+
+@dataclass(frozen=True, kw_only=True)
+class ColumnType:
+    type: Type
+
+    def __str__(self) -> str:
+        return f"Column[{self.type}]"
+
+
+@dataclass(frozen=True, kw_only=True)
+class DataFrameType:
+    columns: list[Column]
+
+    def __str__(self) -> str:
+        schema: list[str] = [f"{col.name}: {col.type}" for col in self.columns]
+        return f"Frame[{', '.join(schema)}]"
+
+    @dataclass(frozen=True, kw_only=True)
+    class Column:
+        index: int
+        name: Optional[str]
+        type: ColumnType
+
+
 def substitute_typevars(type: Type, substitutions: dict[str, Type]) -> Type:
     def sub_argument(arg: Function.Argument):
         return Function.Argument(
@@ -165,6 +196,13 @@ def substitute_typevars(type: Type, substitutions: dict[str, Type]) -> Type:
             required=arg.required,
         )
 
+    def sub_column(col: DataFrameType.Column):
+        return DataFrameType.Column(
+            index=col.index,
+            name=col.name,
+            type=cast(ColumnType, substitute_typevars(col.type, substitutions)),
+        )
+
     match type:
         case TopType():
             return type
@@ -250,10 +288,26 @@ def substitute_typevars(type: Type, substitutions: dict[str, Type]) -> Type:
                 body=substitute_typevars(body, substitutions),
             )
 
+        case TupleType(items=items):
+            return TupleType(
+                items=tuple(substitute_typevars(item, substitutions) for item in items),
+            )
+
+        case ColumnType(type=items_type):
+            return ColumnType(
+                type=substitute_typevars(items_type, substitutions),
+            )
+
+        case DataFrameType(columns=columns):
+            return DataFrameType(
+                columns=list(map(sub_column, columns)),
+            )
+
         case UnknownType() | UnitType():
             return type
 
         case TopType() | GenericType():
+
             raise NotImplementedError(f"Unsupported type {type}")
 
         # Ensure exhaustiveness
@@ -317,6 +371,15 @@ def to_annotation(type: Type) -> str:
         case ConstraintType():
             return str(type)
 
+        case TupleType(items=items):
+            return f"Tuple[{', '.join(map(to_annotation, items))}]"
+
+        case ColumnType():
+            return "pd.Series"
+
+        case DataFrameType():
+            return "pd.DataFrame"
+
         case _:
             assert_never(type)
 
@@ -342,4 +405,7 @@ Type = (
     | GenericType
     | AppliedType
     | ConstraintType
+    | TupleType
+    | ColumnType
+    | DataFrameType
 )

midas/generator/generator.py

@@ -1,4 +1,5 @@
 import ast
+import logging
 import shutil
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -13,13 +14,16 @@ from midas.checker.types import (
     AliasType,
     AppliedType,
     BaseType,
+    ColumnType,
     ComplexType,
     ConstraintType,
+    DataFrameType,
     ExtensionType,
     Function,
     GenericType,
     OverloadedFunction,
     TopType,
+    TupleType,
     Type,
     TypeVar,
     UnitType,
@@ -40,6 +44,7 @@ class Generator(p.Stmt.Visitor[ast.stmt], p.Expr.Visitor[ast.expr]):
         self.workdir: Path = workdir.resolve()
         self.build_dir: Path = self.workdir / "build" / "midas"
         self.rel_src_path: Path = Path()
+        self.logger: logging.Logger = logging.getLogger("Generator")
 
         self._typed_ast: TypedAST = TypedAST(
             stmts=[],
@@ -327,6 +332,19 @@ class Generator(p.Stmt.Visitor[ast.stmt], p.Expr.Visitor[ast.expr]):
                 if bound is not None:
                     self._make_cast_asserts(src_location, expr, bound)
 
+            case TupleType(items=items):
+                self._add_assert(
+                    ast.Call(
+                        func=ast.Name(id="isinstance"),
+                        args=[expr, ast.Name(id="tuple")],
+                        keywords=[],
+                    ),
+                    self._make_cast_assert_message(src_location, expr, type),
+                )
+                assert isinstance(expr, ast.Tuple)
+                for item, item_type in zip(expr.elts, items):
+                    self._make_cast_asserts(src_location, item, item_type)
+
             case (
                 TopType()
                 | Function()
@@ -334,8 +352,10 @@ class Generator(p.Stmt.Visitor[ast.stmt], p.Expr.Visitor[ast.expr]):
                 | ComplexType()
                 | ExtensionType()
                 | GenericType()
+                | ColumnType()
+                | DataFrameType()
             ):
-                raise NotImplementedError(f"Can't make assertion for type {type}")
+                self.logger.warning(f"Can't make assertion for type {type}")
 
             # Ensure exhaustiveness
             case _:

midas/generator/stubs.py

@@ -7,13 +7,16 @@ from midas.checker.types import (
     AliasType,
     AppliedType,
     BaseType,
+    ColumnType,
     ComplexType,
     ConstraintType,
+    DataFrameType,
     ExtensionType,
     Function,
     GenericType,
     OverloadedFunction,
     TopType,
+    TupleType,
     Type,
     TypeVar,
     UnitType,
@@ -30,6 +33,7 @@ class StubsGenerator:
         self.types: TypesRegistry = types
         self.stubs: list[ast.stmt] = []
         self.typing_imports: set[str] = set()
+        self.import_pandas: bool = False
         self.protocol_idx: int = 0
         self.stub_idx: int = 0
         self.type_var_idx: int = 0
@@ -38,6 +42,7 @@ class StubsGenerator:
     def generate_stubs(self) -> ast.Module:
         self.stubs = []
         self.typing_imports = set()
+        self.import_pandas = False
         for name, type in self.types._types.items():
             # Skip builtin types, not just based on name so the user can override
             # TODO: check if added members on builtin type
@@ -53,7 +58,7 @@ class StubsGenerator:
                     continue
             self.generate_stub(name, type)
 
-        imports = [
+        imports: list[ast.stmt] = [
             ast.ImportFrom(
                 module="__future__",
                 names=[ast.alias(name="annotations")],
@@ -70,6 +75,17 @@ class StubsGenerator:
                     level=0,
                 )
             )
+        if self.import_pandas:
+            imports.append(
+                ast.Import(
+                    names=[
+                        ast.alias(
+                            name="pandas",
+                            asname="pd",
+                        )
+                    ],
+                )
+            )
         return ast.Module(body=imports + self.stubs, type_ignores=[])
 
     def generate_stub(self, name: str, type: Type):
@@ -231,6 +247,31 @@ class StubsGenerator:
             case ConstraintType():
                 return self.dump_type(type.type)
 
+            case TupleType(items=items):
+                return ast.Subscript(
+                    value=ast.Name(id="tuple"),
+                    slice=ast.Tuple(
+                        elts=[self.dump_type(item) for item in items],
+                    ),
+                )
+
+            case ColumnType(type=inner):
+                self.import_pandas = True
+                return ast.Subscript(
+                    value=ast.Attribute(
+                        value=ast.Name(id="pd"),
+                        attr="Series",
+                    ),
+                    slice=self.dump_type(inner),
+                )
+
+            case DataFrameType():
+                self.import_pandas = True
+                return ast.Attribute(
+                    value=ast.Name(id="pd"),
+                    attr="DataFrame",
+                )
+
             case _:
                 assert_never(type)