# Kitaptaki kod blokları; bu dosya içinde sırayla çalıştırılır.
# --- 1. Robot bölgesi, dönüşüm ve kesin karşılaştırma ---
import numpy as np
from sympy import Matrix
from agbook import (
    determinant_2d, orientation_diagnostics, polygon_area,
    polygon_signed_area, triangle_area,
)

boundary = np.array([
    [0., 0.], [6., 0.], [7., 3.], [4.5, 5.], [1., 4.]
])
tolerance = 1.0e-10

signed_area = polygon_signed_area(boundary)
area = polygon_area(boundary)
fan_area = sum(
    triangle_area(boundary[0], boundary[i], boundary[i + 1])
    for i in range(1, len(boundary) - 1)
)
first_turn = orientation_diagnostics(
    *boundary[:3], relative_tolerance=tolerance
)
reversed_area = polygon_signed_area(boundary[::-1])
translated_area = polygon_signed_area(
    boundary + np.array([1.0e9, -2.0e9])
)

linear_map = np.array([[1.2, 0.3], [-0.2, 0.8]])
mapped = boundary @ linear_map.T
scale = determinant_2d(linear_map[:, 0], linear_map[:, 1])
mapped_ratio = polygon_area(mapped) / area

near = np.array([
    [0., 0.], [1.0e8, 1.0e8], [2.0e8, 2.0e8 + 1.]
])
near_result = orientation_diagnostics(
    *near, relative_tolerance=1.0e-8
)
exact = Matrix([
    [100000000, 100000000],
    [200000000, 200000001],
]).det()

print(f"yonlu_alan_m2: {signed_area:.6f}")
print(f"alan_m2: {area:.6f}")
print(f"ilk_donus: {first_turn.classification}")
print(f"ucgen_yelpazesi_m2: {fan_area:.6f}")
print(f"ters_sira_yonlu_alan_m2: {reversed_area:.6f}")
print(f"oteleme_farki_m2: {translated_area-signed_area:.3e}")
print(f"donusum_determinanti: {scale:.6f}")
print(f"donusmus_alan_orani: {mapped_ratio:.6f}")
print(f"yakin_normalize_determinant: "
      f"{near_result.normalized_determinant:.6e}")
print(f"yakin_sinif: {near_result.classification}")
print(f"kesin_iki_kat_alan: {exact}")