"""Synthetic vineyard / winery DataFrames.

Twelve generators spanning the whole operation -- vineyard block records, field
time-series, lab chemistry, cellar, soil, pest scouting, sensors, sensory panel,
logistics, sales, canopy management, and barrel aging.

WHY SO MANY: the student model must learn to READ the schema preview it is given
and reference those columns. With only a few generators it can instead memorise
one fixed column vocabulary ("vineyard plot" -> df['brix']) and still drive the
training loss to zero -- then fail on any unseen DataFrame. Distinct column names
per generator is what forces the model to actually read the preview.

SEEDING: every generator takes `seed` and builds its OWN Generator, so a call is
reproducible from (name, seed) alone. This matters because generate_raw.py builds
a df to write the preview, and execute.py later rebuilds it to validate the code
-- they must agree. A module-level shared RNG would advance between those two
calls and hand back different data.
"""
import numpy as np
import pandas as pd

BLOCKS    = ["North Slope", "River Bench", "Hilltop", "Old Vines", "Clay Flat", "East Terrace"]
VARIETIES = ["Cabernet Sauvignon", "Chardonnay", "Pinot Noir", "Syrah", "Merlot", "Sauvignon Blanc"]
ROOTSTOCK = ["101-14", "3309C", "SO4", "1103P", "Riparia Gloire"]
DISEASES  = ["Powdery Mildew", "Downy Mildew", "Botrytis", "None"]


def block_vintage_df(n=220, seed=0):
    """One row per block x vintage: climate, canopy, yield, and quality at harvest."""
    rng = np.random.default_rng(seed)
    gdd = rng.normal(1650, 220, n).round(0).clip(900, 2600)
    # warmer seasons ripen harder: brix tracks GDD, acid falls away as sugar climbs
    brix = (18 + (gdd - 900) / 1700 * 6 + rng.normal(0, 0.9, n)).round(1).clip(18, 30)
    return pd.DataFrame({
        "block":     rng.choice(BLOCKS, n),
        "variety":   rng.choice(VARIETIES, n),
        "rootstock": rng.choice(ROOTSTOCK, n),
        "vintage":   rng.integers(2012, 2025, n),
        "gdd":                gdd,                                                  # growing degree days
        "rainfall_mm":        rng.normal(520, 160, n).round(1).clip(120, 1100),
        "irrigation_mm":      rng.normal(180, 70, n).round(0).clip(0, 400),
        "yield_tonnes_per_acre": rng.normal(4.2, 1.6, n).round(2).clip(0.5, 9),
        "brix":               brix,                                                 # sugar
        "ph":                 rng.normal(3.55, 0.18, n).round(2).clip(3.0, 4.1),
        "titratable_acidity_gL": (13 - brix * 0.25 + rng.normal(0, 0.7, n)).round(2).clip(3, 11),
        "disease":            rng.choice(DISEASES, n, p=[0.28, 0.17, 0.15, 0.40]),
        "vine_age_years":     rng.integers(3, 60, n),
    })


def phenology_df(n=260, seed=0):
    """Time series across a growing season: berry weight, sugar, acid accumulation."""
    rng = np.random.default_rng(seed)
    dates = pd.date_range("2024-04-01", periods=n // 4 + 1, freq="W")
    reps = np.tile(dates, 4)[:n]
    # week index drives ripening: sugar up, acid down, veraison sigmoid
    wk = np.array([(d - dates[0]).days / 7 for d in reps])
    span = max(wk.max(), 1)
    return pd.DataFrame({
        "date":    reps,
        "block":   rng.choice(BLOCKS, n),
        "variety": rng.choice(VARIETIES, n),
        "berry_weight_g":  (0.4 + 1.4 * wk / span + rng.normal(0, 0.15, n)).round(3).clip(0.2, 2.6),
        "brix":            (6 + 18 * wk / span + rng.normal(0, 1.2, n)).round(1).clip(5, 28),
        "titratable_acidity_gL": (19 - 12 * wk / span + rng.normal(0, 1.0, n)).round(2).clip(3, 20),
        "veraison_pct":    (100 / (1 + np.exp(-(wk - span / 2) * 0.8)) + rng.normal(0, 6, n)).round(0).clip(0, 100),
    })


def berry_chem_df(n=200, seed=0):
    """Lab chemistry samples: phenolics, anthocyanins, tannin by variety/block."""
    rng = np.random.default_rng(seed)
    phenolics = rng.normal(55, 15, n).round(1).clip(10, 110)
    return pd.DataFrame({
        "sample_id": np.arange(1, n + 1),
        "block":     rng.choice(BLOCKS, n),
        "variety":   rng.choice(VARIETIES, n),
        # anthocyanin and tannin are both phenolic fractions -> they co-vary
        "anthocyanin_mg_g":  (phenolics * 0.022 + rng.normal(0, 0.22, n)).round(3).clip(0.1, 3.0),
        "total_phenolics_au": phenolics,
        "tannin_mg_g":       (phenolics * 0.032 + rng.normal(0, 0.35, n)).round(2).clip(0.3, 4.5),
        "ph":                rng.normal(3.55, 0.18, n).round(2).clip(3.0, 4.1),
        "brix":              rng.normal(24.5, 2.2, n).round(1).clip(18, 30),
    })


def cellar_ferment_df(n=240, seed=0):
    """Daily tank readings through alcoholic fermentation."""
    rng = np.random.default_rng(seed)
    day = rng.integers(0, 16, n)
    sugar = (230 * np.exp(-0.19 * day) + rng.normal(0, 6, n)).round(1).clip(0, 260)
    return pd.DataFrame({
        "tank_id":       rng.choice([f"T{i:02d}" for i in range(1, 19)], n),
        "yeast_strain":  rng.choice(["EC-1118", "RC-212", "D254", "BM4x4", "Native"], n),
        "day_of_ferment": day,
        "residual_sugar_gL": sugar,
        # every gram of sugar consumed becomes roughly 1/17 % alcohol
        "alcohol_pct":   ((230 - sugar) / 17.0 + rng.normal(0, 0.25, n)).round(2).clip(0, 16),
        "must_temp_c":   rng.normal(26, 3.5, n).round(1).clip(12, 35),
        "cap_temp_c":    rng.normal(29, 4.0, n).round(1).clip(14, 40),
        "free_so2_ppm":  rng.normal(28, 9, n).round(0).clip(0, 60),
        "volatile_acidity_gL": rng.normal(0.42, 0.14, n).round(3).clip(0.05, 1.2),
        "punchdowns_per_day": rng.integers(0, 4, n),
    })


def soil_survey_df(n=180, seed=0):
    """Soil pit descriptions and lab results by depth horizon."""
    rng = np.random.default_rng(seed)
    clay = rng.normal(28, 11, n).round(1).clip(3, 65)
    return pd.DataFrame({
        "pit_id":        rng.choice([f"P{i:03d}" for i in range(1, 41)], n),
        "soil_series":   rng.choice(["Bale Loam", "Pleasanton", "Sobrante", "Hambright", "Yolo Silt"], n),
        "drainage_class": rng.choice(["Well drained", "Moderately well", "Somewhat poor", "Excessive"], n),
        "horizon_depth_cm": rng.choice([15, 30, 45, 60, 90, 120], n),
        "clay_pct":      clay,
        "sand_pct":      (90 - clay + rng.normal(0, 7, n)).round(1).clip(5, 92),
        "organic_matter_pct": rng.normal(2.1, 0.8, n).round(2).clip(0.2, 6.0),
        # clay holds cations -> CEC rises with clay fraction
        "cec_meq_100g":  (clay * 0.42 + rng.normal(0, 2.2, n)).round(1).clip(2, 40),
        "soil_ph":       rng.normal(6.4, 0.6, n).round(2).clip(4.5, 8.4),
        "available_water_mm_m": rng.normal(135, 35, n).round(0).clip(40, 240),
    })


def pest_scouting_df(n=280, seed=0):
    """Weekly scouting walks: pest pressure per row."""
    rng = np.random.default_rng(seed)
    incidence = rng.gamma(2.0, 5.5, n).round(1).clip(0, 100)
    return pd.DataFrame({
        "scout_date":  np.tile(pd.date_range("2024-05-01", periods=n // 7 + 1, freq="W"), 7)[:n],
        "block":       rng.choice(BLOCKS, n),
        "row_number":  rng.integers(1, 61, n),
        "pest_name":   rng.choice(["Vine Mealybug", "Leafhopper", "Spider Mite",
                                   "European Grapevine Moth", "Thrips"], n),
        "incidence_pct": incidence,
        "severity_index": (incidence / 25 + rng.normal(0, 0.4, n)).round(2).clip(0, 5),
        "trap_count":   rng.poisson(6, n),
        "beneficials_count": rng.poisson(3, n),
        "threshold_exceeded": incidence > 20,
        "spray_applied": rng.random(n) < 0.3,
    })


def irrigation_sensor_df(n=300, seed=0):
    """Hourly-to-daily sensor telemetry for irrigation scheduling."""
    rng = np.random.default_rng(seed)
    moisture = rng.normal(24, 6, n).round(2).clip(6, 42)
    return pd.DataFrame({
        "timestamp":   pd.date_range("2024-06-01", periods=n, freq="6h"),
        "sensor_id":   rng.choice([f"S-{i:02d}" for i in range(1, 13)], n),
        "block":       rng.choice(BLOCKS, n),
        "soil_moisture_vwc": moisture,
        # drier soil -> more negative (more stressed) stem water potential
        "stem_water_potential_bar": (-18 + moisture * 0.33 + rng.normal(0, 1.1, n)).round(2).clip(-20, -2),
        "canopy_temp_c": rng.normal(29, 5, n).round(1).clip(12, 46),
        "air_temp_c":    rng.normal(26, 6, n).round(1).clip(8, 44),
        "relative_humidity_pct": rng.normal(52, 16, n).round(0).clip(8, 99),
        "et0_mm":       rng.gamma(3, 1.6, n).round(2).clip(0.2, 14),
        "valve_open":   rng.random(n) < 0.22,
    })


def sensory_panel_df(n=260, seed=0):
    """Blind tasting panel scores, one row per taster x wine."""
    rng = np.random.default_rng(seed)
    fruit = rng.normal(6.4, 1.5, n).round(1).clip(1, 10)
    structure = rng.normal(6.0, 1.6, n).round(1).clip(1, 10)
    return pd.DataFrame({
        "wine_code":  rng.choice([f"W{i:03d}" for i in range(1, 25)], n),
        "taster_id":  rng.choice([f"J{i:02d}" for i in range(1, 13)], n),
        "flight":     rng.choice(["Flight A", "Flight B", "Flight C"], n),
        "aroma_intensity": rng.normal(6.1, 1.7, n).round(1).clip(1, 10),
        "fruit_score": fruit,
        "tannin_score": structure,
        "acidity_score": rng.normal(6.2, 1.4, n).round(1).clip(1, 10),
        "finish_seconds": rng.gamma(4, 3.2, n).round(0).clip(2, 60),
        # overall is mostly a blend of fruit and structure, plus taster noise
        "overall_rating": (fruit * 0.5 + structure * 0.4 + rng.normal(0, 0.6, n)).round(1).clip(1, 10),
        "would_purchase": rng.random(n) < 0.45,
    })


def harvest_logistics_df(n=240, seed=0):
    """Pick-day operations: crews, bins, transport to the crush pad."""
    rng = np.random.default_rng(seed)
    crew = rng.integers(6, 25, n)
    return pd.DataFrame({
        "pick_date":   np.tile(pd.date_range("2024-08-20", periods=n // 6 + 1, freq="D"), 6)[:n],
        "block":       rng.choice(BLOCKS, n),
        "crew_id":     rng.choice(["Crew Alpha", "Crew Bravo", "Crew Charlie", "Crew Delta"], n),
        "crew_size":   crew,
        "pick_method": rng.choice(["Hand", "Machine"], n, p=[0.72, 0.28]),
        # more pickers -> more bins, with diminishing returns and day-to-day noise
        "bins_filled": (crew * 1.9 + rng.normal(0, 4, n)).round(0).clip(2, 70),
        "kg_per_hour": rng.normal(410, 120, n).round(0).clip(80, 900),
        "transport_km": rng.normal(14, 7, n).round(1).clip(0.5, 45),
        "fruit_temp_c": rng.normal(17, 5, n).round(1).clip(4, 34),
        "wait_time_min": rng.gamma(2.5, 14, n).round(0).clip(0, 180),
        "mog_pct":     rng.normal(2.4, 1.3, n).round(2).clip(0, 12),   # material other than grapes
    })


def wine_sales_df(n=320, seed=0):
    """Bottle sales by channel and region."""
    rng = np.random.default_rng(seed)
    price = rng.normal(38, 14, n).round(2).clip(9, 130)
    return pd.DataFrame({
        "order_date":  pd.date_range("2023-01-01", periods=n, freq="D"),
        "sku":         rng.choice([f"SKU-{i:03d}" for i in range(1, 19)], n),
        "region":      rng.choice(["Napa", "Sonoma", "Oregon", "Export EU", "Export Asia"], n),
        "channel":     rng.choice(["Tasting Room", "Wine Club", "Distributor", "Online"], n),
        "unit_price_usd": price,
        # cheaper bottles move in larger volumes
        "bottles_sold": (rng.gamma(3, 22, n) * (60 / price)).round(0).clip(1, 900),
        "discount_pct": rng.choice([0, 5, 10, 15, 20, 25], n, p=[.4, .16, .16, .12, .1, .06]),
        "shipping_cost_usd": rng.normal(22, 9, n).round(2).clip(0, 80),
        "club_member":  rng.random(n) < 0.38,
    })


def canopy_pruning_df(n=200, seed=0):
    """Dormant pruning and canopy architecture measurements per vine."""
    rng = np.random.default_rng(seed)
    shoots = rng.integers(12, 60, n)
    return pd.DataFrame({
        "vine_id":     np.arange(1000, 1000 + n),
        "block":       rng.choice(BLOCKS, n),
        "trellis_type": rng.choice(["VSP", "Lyre", "Head-trained", "Quadrilateral Cordon"], n),
        "pruning_method": rng.choice(["Spur", "Cane", "Minimal"], n),
        "shoot_count": shoots,
        "bud_count":   (shoots * 1.4 + rng.normal(0, 3, n)).round(0).clip(8, 100),
        # leaf area scales with how many shoots the vine carries
        "leaf_area_m2": (shoots * 0.14 + rng.normal(0, 0.6, n)).round(2).clip(0.5, 12),
        "cane_weight_kg": rng.normal(0.85, 0.3, n).round(3).clip(0.1, 2.5),
        "internode_length_cm": rng.normal(7.5, 2.0, n).round(1).clip(2, 16),
        "cluster_count": rng.integers(8, 55, n),
    })


def barrel_aging_df(n=220, seed=0):
    """Barrel inventory and extraction chemistry during elevage."""
    rng = np.random.default_rng(seed)
    months = rng.integers(0, 25, n)
    return pd.DataFrame({
        "barrel_id":   rng.choice([f"B{i:04d}" for i in range(1, 121)], n),
        "cooper":      rng.choice(["Taransaud", "Seguin Moreau", "Francois Freres",
                                   "Nadalie", "World Cooperage"], n),
        "oak_origin":  rng.choice(["French", "American", "Hungarian"], n),
        "toast_level": rng.choice(["Light", "Medium", "Medium Plus", "Heavy"], n),
        "months_in_barrel": months,
        "barrel_age_fills": rng.integers(1, 6, n),
        # oak compounds extract over time in barrel
        "vanillin_ppb": (months * 21 + rng.normal(0, 45, n)).round(0).clip(0, 700),
        "oak_lactone_ppb": (months * 14 + rng.normal(0, 38, n)).round(0).clip(0, 500),
        "color_intensity_au": rng.normal(11.5, 3.0, n).round(2).clip(2, 22),
        "topping_volume_l": rng.gamma(2, 0.9, n).round(2).clip(0, 9),
    })


GENERATORS = [
    block_vintage_df,
    phenology_df,
    berry_chem_df,
    cellar_ferment_df,
    soil_survey_df,
    pest_scouting_df,
    irrigation_sensor_df,
    sensory_panel_df,
    harvest_logistics_df,
    wine_sales_df,
    canopy_pruning_df,
    barrel_aging_df,
]


def df_preview(df, n=5):
    """The EXACT schema string the model sees at train AND inference time.

    Keep this the single source of truth -- any drift between train/infer
    formatting pushes the model out of distribution.
    """
    dtypes = ", ".join(f"{c} ({df[c].dtype})" for c in df.columns)
    return f"Columns and dtypes:\n  {dtypes}\nSample rows:\n{df.head(n).to_string(index=False)}"