"""F5-conf-04: VFI vs closed form for Brock-Mirman growth with log utility.
Run from workspace root: ~/wp29/venv/bin/python out/analysis.py
Reads only data/model.csv; deterministic (no randomness)."""
import json, os
import numpy as np
import pandas as pd

ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
df = pd.read_csv(os.path.join(ROOT, "data", "model.csv"))
p = dict(zip(df["parameter"], df["value"]))
alpha = float(p["alpha"]); beta = float(p["beta"])
y_min = float(p["y_min"]); y_max = float(p["y_max"]); N = int(float(p["grid_points"]))
spacing = str(p["grid_spacing"]).strip(); tol = float(p["vfi_tolerance"])
evals = [float(p["eval_y_1"]), float(p["eval_y_2"]), float(p["eval_y_3"])]
data_issues = []
assert spacing == "log"
assert 0 < alpha < 1 and 0 < beta < 1

# ---------------- closed form (guess V = A + B ln y, verified in report) ----------------
ab = alpha * beta
B = 1.0 / (1.0 - ab)
A = (np.log(1.0 - ab) + (ab / (1.0 - ab)) * np.log(ab)) / (1.0 - beta)
c_closed = lambda y: (1.0 - ab) * y
V_closed = lambda y: A + B * np.log(y)
savings_rate = ab  # (y - c)/y

# ---------------- VFI on the prescribed grid (discrete choice of next-period y on the grid) ----
grid = np.exp(np.linspace(np.log(y_min), np.log(y_max), N))
k_needed = grid ** (1.0 / alpha)           # investment needed to reach y' = grid[j]
C = grid[:, None] - k_needed[None, :]       # consumption for (y_i, y'_j)
feasible = C > 0
U = np.full(C.shape, -np.inf)
U[feasible] = np.log(C[feasible])
n_infeasible_pairs = int((~feasible).sum())

V = np.zeros(N)
n_iter = 0
sup_change = np.inf
max_iter = 100000
while sup_change >= tol and n_iter < max_iter:
    V_new = np.max(U + beta * V[None, :], axis=1)
    sup_change = float(np.max(np.abs(V_new - V)))
    V = V_new
    n_iter += 1
converged = sup_change < tol
pol_idx = np.argmax(U + beta * V[None, :], axis=1)
c_pol = grid - k_needed[pol_idx]
bound_hits = int(np.sum((pol_idx == 0) | (pol_idx == N - 1)))
if bound_hits:
    data_issues.append({"issue": f"policy hits grid bound at {bound_hits} nodes",
                        "handling": "reported; results at such nodes may be distorted by truncation"})

# interpolation at evaluation points: linear in ln y (grid is uniform in ln y)
lg = np.log(grid)
def interp(vals, y):
    assert y_min <= y <= y_max
    return float(np.interp(np.log(y), lg, vals))
on_node = [bool(np.min(np.abs(lg - np.log(y))) < 1e-12) for y in evals]

c_num = [interp(c_pol, y) for y in evals]
c_cf = [float(c_closed(y)) for y in evals]
max_err = float(max(abs(a - b) for a, b in zip(c_num, c_cf)))
V_num_1 = interp(V, 1.0)

# diagnostics (not required keys; printed only)
err_grid_c = float(np.max(np.abs(c_pol - c_closed(grid))))
err_grid_V = float(np.max(np.abs(V - V_closed(grid))))
print(f"grid step in ln y = {lg[1]-lg[0]:.6f}; eval points on nodes: {on_node}")
print(f"VFI iterations={n_iter}, final sup change={sup_change:.3e}, converged={converged}")
print(f"policy bound hits={bound_hits}; infeasible (y,y') pairs={n_infeasible_pairs}")
print(f"max |c_vfi - c_closed| over all grid nodes = {err_grid_c:.3e}; max |V_vfi - V_closed| = {err_grid_V:.3e}")

if not all(on_node):
    pass  # interpolation is allowed by TASK.md; documented in report

res = {
    "task_id": "F5-conf-04",
    "c_closed_y05": c_cf[0], "c_closed_y1": c_cf[1], "c_closed_y2": c_cf[2],
    "c_num_y05": c_num[0], "c_num_y1": c_num[1], "c_num_y2": c_num[2],
    "max_abs_error_policy": max_err,
    "value_closed_y1": float(V_closed(1.0)),
    "value_num_y1": V_num_1,
    "savings_rate_closed_form": float(savings_rate),
    "contraction_modulus": beta,
    "grid_points_used": int(N),
    "n_iterations": int(n_iter),
    "final_sup_change": float(sup_change),
    "feasibility": "complete" if converged else "partial",
    "feasibility_reason": None if converged else "VFI did not reach tolerance within max_iter",
    "data_issues": data_issues,
    "n_input_rows": {"model.csv": int(len(df))},
    "n_analysed": int(N),
    "random_seed": None,
    "diagnostics": {
        "grid_step_log_y": float(lg[1] - lg[0]),
        "eval_points_on_grid_nodes": on_node,
        "policy_grid_bound_hits": bound_hits,
        "max_abs_policy_error_all_nodes": err_grid_c,
        "max_abs_value_error_all_nodes": err_grid_V,
        "initial_guess": "V0 = 0",
        "choice_variable": "next-period output y' restricted to grid",
        "interpolation": "linear in ln y",
    },
}
os.makedirs(os.path.join(ROOT, "out"), exist_ok=True)
with open(os.path.join(ROOT, "out", "results.json"), "w") as f:
    json.dump(res, f, indent=2)
print(json.dumps({k: v for k, v in res.items() if k != "diagnostics"}, indent=2))