Create solver_standard_deviation.py

solver_standard_deviation.py

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+from __future__ import annotations
+
+import re
+from statistics import pstdev
+from typing import Optional, List
+
+from models import SolverResult
+
+
+def _nums(text: str) -> List[float]:
+    return [float(x) for x in re.findall(r"-?\d+(?:\.\d+)?", text)]
+
+
+def solve_standard_deviation(text: str) -> Optional[SolverResult]:
+    lower = (text or "").lower()
+
+    if "standard deviation" not in lower and "std dev" not in lower:
+        return None
+
+    nums = _nums(lower)
+
+    # Conceptual rule: adding same constant does not change SD
+    if any(p in lower for p in ["add the same", "increased by the same", "decreased by the same", "plus a constant"]):
+        result = "unchanged"
+        return SolverResult(
+            domain="quant",
+            solved=True,
+            topic="standard_deviation",
+            answer_value=result,
+            internal_answer=result,
+            steps=[
+                "Adding or subtracting the same constant shifts all values equally.",
+                "The spread does not change, so standard deviation is unchanged.",
+            ],
+        )
+
+    # Conceptual rule: multiplying all by a constant scales SD
+    if any(p in lower for p in ["multiplied by", "scaled by", "increase by the same percent", "decrease by the same percent"]):
+        m = re.search(r"(multiplied by|scaled by)\s*(-?\d+(?:\.\d+)?)", lower)
+        if m:
+            factor = abs(float(m.group(2)))
+            return SolverResult(
+                domain="quant",
+                solved=True,
+                topic="standard_deviation",
+                answer_value=f"multiplied by {factor:g}",
+                internal_answer=f"multiplied by {factor:g}",
+                steps=[
+                    "When every value is multiplied by a constant, standard deviation is multiplied by the absolute value of that constant.",
+                ],
+            )
+
+    # Numeric SD if list-like
+    if len(nums) >= 2:
+        result = pstdev(nums)
+        return SolverResult(
+            domain="quant",
+            solved=True,
+            topic="standard_deviation",
+            answer_value=f"{result:g}",
+            internal_answer=f"{result:g}",
+            steps=[
+                "Find the mean.",
+                "Compute squared deviations from the mean.",
+                "Average them and take the square root.",
+            ],
+        )
+
+    return None