"""Extract centerline pressure lines from WingBody-junction surface .pvtu files.

DNS 1-6 coordinate convention (per kbwiki):
    x = streamwise (origin at airfoil root leading edge)
    y = normalwise (wall-normal; bottom wall at y=0)
    z = spanwise (symmetry at z=0; lateral boundaries at z/T = +-4)
    T = airfoil thickness = 71.7 mm (reference length)

Outputs (CSV with x/T, Cp = averaged_pressure / (rho_ref u_ref^2 / 2)):
    bottom_wall_centerline.csv  : z = 0 strip on the bottom wall
    wing_root_chord.csv         : y/T ~= 0.05 strip on the wing surface (near root)

The averaged_pressure stored in the .vtu is dimensionless: p / p_ref. We map to a
"reduced Cp" via:  Cp = (p/p_ref - 1) * (gamma * Ma^2)^{-1}  using gamma=1.4, Ma=0.078.

Usage: python extract_wbj_surface_lines.py
"""

import os
import pathlib
import sys
import numpy as np
import pandas as pd
import vtk
from vtk.util import numpy_support as ns

T = 1.0  # work in normalised x/T (data is already normalised in the DNS, so keep T=1)
Z_TOL = 0.01  # strip half-width in z/T
Y_TARGET = 0.05  # wing extraction height (near root)
Y_TOL = 0.01

GAMMA = 1.4
MACH = 0.078

DST = os.environ.get('WBJ_HIGHFIDELITY_DIR',
                     str(pathlib.Path(__file__).parent / 'ERCOFTAC_WingBodyJunction' / 'highfidelity'))
RAW = os.path.join(DST, 'raw_surface')


def load_pvtu(path):
    reader = vtk.vtkXMLPUnstructuredGridReader()
    reader.SetFileName(path)
    reader.Update()
    grid = reader.GetOutput()
    pts = ns.vtk_to_numpy(grid.GetPoints().GetData())  # shape (N, 3)
    pdata = grid.GetPointData()
    arr_names = [pdata.GetArrayName(i) for i in range(pdata.GetNumberOfArrays())]
    if 'averaged_pressure' not in arr_names:
        raise RuntimeError(f'No averaged_pressure in {path}: have {arr_names}')
    p = ns.vtk_to_numpy(pdata.GetArray('averaged_pressure'))
    return pts, p


def p_to_cp(p_over_pref):
    """Convert dimensionless p/p_ref to a Cp-like coefficient.
    For low Mach, Cp = (p - p_ref) / (0.5 rho_ref u_ref^2). Using p = (rho_ref/gamma) * (p/p_ref) * gamma:
    Equivalently, (p/p_ref - 1) / (0.5 gamma Ma^2).  At Ma=0.078, denom ~ 0.00426.
    """
    return (p_over_pref - 1.0) / (0.5 * GAMMA * MACH**2)


def extract_strip(pts, p, axis_to_filter, target, tol):
    mask = np.abs(pts[:, axis_to_filter] - target) < tol
    return pts[mask], p[mask]


def main():
    # --- bottom wall: z=0 strip ---
    pts, p = load_pvtu(os.path.join(RAW, 'bottom_wall_averaged_pressure.pvtu'))
    sub_pts, sub_p = extract_strip(pts, p, axis_to_filter=2, target=0.0, tol=Z_TOL)
    cp = p_to_cp(sub_p)
    df = pd.DataFrame({
        'x_over_T': sub_pts[:, 0] / T,
        'y_over_T': sub_pts[:, 1] / T,
        'z_over_T': sub_pts[:, 2] / T,
        'p_over_pref': sub_p,
        'Cp': cp,
    }).sort_values('x_over_T').reset_index(drop=True)
    out = os.path.join(DST, 'bottom_wall_centerline.csv')
    df.to_csv(out, index=False)
    print(f'  bottom_wall  -> {os.path.basename(out)} '
          f'({len(df)} rows; x range [{df.x_over_T.min():.2f}, {df.x_over_T.max():.2f}])')

    # --- wing: y/T ~= 0.05 strip (root chord) ---
    pts, p = load_pvtu(os.path.join(RAW, 'wing_averaged_pressure.pvtu'))
    sub_pts, sub_p = extract_strip(pts, p, axis_to_filter=1, target=Y_TARGET, tol=Y_TOL)
    cp = p_to_cp(sub_p)
    df = pd.DataFrame({
        'x_over_T': sub_pts[:, 0] / T,
        'y_over_T': sub_pts[:, 1] / T,
        'z_over_T': sub_pts[:, 2] / T,
        'p_over_pref': sub_p,
        'Cp': cp,
    }).sort_values('x_over_T').reset_index(drop=True)
    out = os.path.join(DST, 'wing_root_chord.csv')
    df.to_csv(out, index=False)
    print(f'  wing_root    -> {os.path.basename(out)} '
          f'({len(df)} rows; x range [{df.x_over_T.min():.2f}, {df.x_over_T.max():.2f}])')


if __name__ == '__main__':
    main()