from dataclasses import dataclass
import numpy as np
from scipy import stats
from scipy.stats import norm
import pandas as pd

@dataclass
class Zuverlaessigkeit:

    @staticmethod
    def gesamtredundanz(n, u):
        r = n - u
        return r


    @staticmethod
    def berechne_R(Q_vv, P):
        R = Q_vv @ P
        return R       #Redundanzmatrix


    @staticmethod
    def berechne_ri(R):
        ri = np.diag(R)
        EVi = 100.0 * ri
        return ri, EVi      #Redundanzanteile


    @staticmethod
    def klassifiziere_ri(ri):       #Klassifizierung der Redundanzanteile
        if ri < 0.01:
            return "nicht kontrollierbar"
        elif ri < 0.10:
            return "schlecht kontrollierbar"
        elif ri < 0.30:
            return "ausreichend kontrollierbar"
        elif ri < 0.70:
            return "gut kontrollierbar"
        else:
            return "nahezu vollständig redundant"

    @staticmethod
    def globaltest(r_gesamt, sigma0_apost, sigma0_apriori, alpha):
        T_G = (sigma0_apost ** 2) / (sigma0_apriori ** 2)
        F_krit = stats.f.ppf(1 - alpha, r_gesamt, 10 ** 9)
        H0 = T_G < F_krit

        if H0:
            interpretation = (
                "Nullhypothese H₀ angenommen.\n"
            )
        else:
            interpretation = (
                "Nullhypothese H₀ verworfen!\n"
                "Dies kann folgende Gründe haben:\n"
                "→ Es befinden sich grobe Fehler im Datenmaterial. Bitte Lokaltest durchführen und ggf. grobe Fehler im Datenmaterial entfernen.\n"
                "→ Das stochastische Modell ist zu optimistisch. Bitte Gewichte überprüfen und ggf. anpassen."
            )

        return {
            "r_gesamt": r_gesamt,
            "sigma0_apost": sigma0_apost,
            "sigma0_apriori": sigma0_apriori,
            "alpha": alpha,
            "T_G": T_G,
            "F_krit": F_krit,
            "H0_angenommen": H0,
            "Interpretation": interpretation,
        }


    def lokaltest_innere_Zuverlaessigkeit(v, Q_vv, ri, labels, s0_apost, alpha, beta):
        v = np.asarray(v, float).reshape(-1)
        Q_vv = np.asarray(Q_vv, float)
        ri = np.asarray(ri, float).reshape(-1)
        labels = list(labels)

        # Grobfehlerabschätzung:
        ri_ = np.where(ri == 0, np.nan, ri)
        GF = -v / ri_

        # Standardabweichungen der Residuen
        qv = np.diag(Q_vv).astype(float)
        s_vi = float(s0_apost) * np.sqrt(qv)

        # Normierte Verbesserung NV
        NV = np.abs(v) / s_vi

        # Quantile k und kA (zweiseitig),
        k = float(norm.ppf(1 - alpha / 2))
        kA = float(norm.ppf(1 - beta))    # (Testmacht 1-β)

        # Nichtzentralitätsparameter δ0
        nzp = k + kA

        # Grenzwert für die Aufdeckbarkeit eines GF (GRZW)
        GRZW_i = (s_vi / ri_) * nzp

        auffaellig = NV > nzp

        Lokaltest_innere_Zuv = pd.DataFrame({
            "Beobachtung": labels,
            "v_i": v,
            "r_i": ri,
            "s_vi": s_vi,
            "k": k,
            "NV_i": NV,
            "auffaellig": auffaellig,
            "GF_i": GF,
            "GRZW_i": GRZW_i,
            "alpha": alpha,
            "beta": beta,
            "kA": kA,
            "δ0": nzp,
        })
        return Lokaltest_innere_Zuv


    def aeussere_zuverlaessigkeit_EF_EP1(Lokaltest, labels, Qxx, A, P, s0_apost, unbekannten_liste, x):
        df = Lokaltest.copy()
        labels = list(labels)
        Qxx = np.asarray(Qxx, float)
        A = np.asarray(A, float)
        P = np.asarray(P, float)
        x = np.asarray(x, float).reshape(-1)
        ri = df["r_i"].astype(float).to_numpy()
        GF = df["GF_i"].astype(float).to_numpy()
        s_vi = df["s_vi"].astype(float).to_numpy()
        GRZW = df["GRZW_i"].astype(float).to_numpy()
        nzp = df["δ0"].astype(float).to_numpy()
        n = A.shape[0]  # Anzahl Beobachtungen
        u = A.shape[1]  # Anzahl Unbekannte

        # Einflussfaktor EF berechnen
        EF = np.zeros(n, dtype=float)

        for i in range(n):
            # 1) ∇l_i aufstellen
            nabla_l = np.zeros((n, 1))
            nabla_l[i, 0] = GRZW[i]

            # 2) ∇x_i = Qxx * A^T * P * ∇l_i
            nabla_x = Qxx @ (A.T @ (P @ nabla_l))

            # 3) EF_i^2 = (∇x_i^T * Qxx^{-1} * ∇x_i) / s0^2
            Qxx_inv_nabla_x = np.linalg.solve(Qxx, nabla_x)  # = Qxx^{-1} ∇x_i
            #EF2 = float((nabla_x.T @ Qxx_inv_nabla_x) / (float(s0_apost) ** 2)).item()
            EF2 = ((nabla_x.T @ Qxx_inv_nabla_x) / (float(s0_apost) ** 2)).item()

            EF[i] = np.sqrt(EF2)

        # Koordinaten-Dict aus x
        coords = {}
        j = 0
        while j < len(unbekannten_liste):
            name = str(unbekannten_liste[j])
            if name.startswith("X"):
                pn = name[1:]
                coords[pn] = (x[j], x[j + 1], x[j + 2])
                j += 3
            else:
                j += 1

        # EP + Standpunkte
        EP_m = np.full(len(labels), np.nan, dtype=float)
        standpunkte = [""] * len(labels)

        for i, lbl in enumerate(labels):
            parts = lbl.split("_")
            sp = None
            zp = None

            # Tachymeter: ID_SD_GRP_SP_ZP / ID_R_GRP_SP_ZP / ID_ZW_GRP_SP_ZP
            if ("_SD_" in lbl) or ("_R_" in lbl) or ("_ZW_" in lbl):
                if len(parts) >= 5:
                    sp = parts[3].strip()
                    zp = parts[4].strip()

            # GNSS: *_gnssbx_SP_ZP etc.
            if ("gnss" in lbl) and (len(parts) >= 4):
                sp = parts[-2].strip()
                zp = parts[-1].strip()

            standpunkte[i] = sp if sp is not None else ""
            one_minus_r = (1.0 - ri[i])

            # SD + GNSS: direkt in m
            if ("_SD_" in lbl) or ("gnss" in lbl):
                EP_m[i] = one_minus_r * GF[i]

            # R / ZW: Winkel -> Streckenäquivalent über s
            elif ("_R_" in lbl) or ("_ZW_" in lbl):
                if sp and zp and (sp in coords) and (zp in coords):
                    X1, Y1, Z1 = coords[sp]
                    X2, Y2, Z2 = coords[zp]
                    s = float(np.sqrt((X2 - X1) ** 2 + (Y2 - Y1) ** 2 + (Z2 - Z1) ** 2))
                    EP_m[i] = one_minus_r * ((GF[i]) * s)

        # SP am Standpunkt (2D)
        diagQ = np.diag(Qxx)
        SP_cache_mm = {}

        for sp in set([s for s in standpunkte if s]):
            idx_x = [k for k, sym in enumerate(unbekannten_liste) if str(sym) == f"X{sp}"][0]
            qx = diagQ[idx_x]
            qy = diagQ[idx_x + 1]
            SP_cache_mm[sp] = float(s0_apost) * np.sqrt(qx + qy) * 1000.0

        SP_mm = np.array([SP_cache_mm.get(sp, np.nan) for sp in standpunkte], dtype=float)

        out = pd.DataFrame({
            "Beobachtung": labels,
            "Stand-Pkt": standpunkte,
            "EF": EF,
            "EP [mm]": EP_m * 1000.0,
            "SP [mm]": SP_mm,
            "EF*SP [mm]": EF * SP_mm,
        })
        return out


    def aeussere_zuverlaessigkeit_EF_EP_stabil(Lokaltest, labels, Qxx, A, P, s0_apost, unbekannten_liste, x):
        df = Lokaltest.copy()
        labels = list(labels)
        Qxx = np.asarray(Qxx, float)
        A = np.asarray(A, float)
        P = np.asarray(P, float)
        x = np.asarray(x, float).reshape(-1)
        ri = df["r_i"].astype(float).to_numpy()
        GF = df["GF_i"].astype(float).to_numpy()
        GRZW = df["GRZW_i"].astype(float).to_numpy()
        n = A.shape[0]

        # Namen als Strings für die Suche
        namen_str = [str(sym) for sym in unbekannten_liste]

        # 1) Einflussfaktor EF berechnen
        EF = np.zeros(n, dtype=float)
        for i in range(n):
            nabla_l = np.zeros((n, 1))
            nabla_l[i, 0] = GRZW[i]
            nabla_x = Qxx @ (A.T @ (P @ nabla_l))
            Qxx_inv_nabla_x = np.linalg.solve(Qxx, nabla_x)
            EF2 = ((nabla_x.T @ Qxx_inv_nabla_x) / (float(s0_apost) ** 2)).item()
            EF[i] = np.sqrt(max(0, EF2))

        # 2) Koordinaten-Dict
        coords = {}
        punkt_ids = [n[1:] for n in namen_str if n.upper().startswith("X")]

        for pid in punkt_ids:
            try:
                ix = namen_str.index(f"X{pid}")
                iy = namen_str.index(f"Y{pid}")
                iz = namen_str.index(f"Z{pid}")

                coords[pid] = (x[ix], x[iy], x[iz] if iz is not None else 0.0)
            except:
                continue

        # 3) EP + Standpunkte
        EP_m = np.full(len(labels), np.nan, dtype=float)
        standpunkte = [""] * len(labels)

        for i, lbl in enumerate(labels):
            parts = lbl.split("_")
            sp, zp = None, None

            if any(k in lbl for k in ["_SD_", "_R_", "_ZW_"]):
                if len(parts) >= 5: sp, zp = parts[3].strip(), parts[4].strip()
            elif "gnss" in lbl.lower():
                sp, zp = parts[-2].strip(), parts[-1].strip()
            elif "niv" in lbl.lower():
                if len(parts) >= 4:
                    sp = parts[3].strip()
                else:
                    sp = parts[-1].strip()

            standpunkte[i] = sp if sp is not None else ""
            one_minus_r = (1.0 - ri[i])

            # SD, GNSS, Niv: direkt Wegfehler
            if "_SD_" in lbl or "gnss" in lbl.lower() or "niv" in lbl.lower():
                EP_m[i] = one_minus_r * GF[i]
            # Winkel: Streckenäquivalent
            elif "_R_" in lbl or "_ZW_" in lbl:
                if sp in coords and zp in coords:
                    X1, Y1, _ = coords[sp]
                    X2, Y2, _ = coords[zp]
                    s = np.sqrt((X2 - X1) ** 2 + (Y2 - Y1) ** 2)
                    EP_m[i] = one_minus_r * (GF[i] * s)

        # 4) SP am Standpunkt (2D oder 1D)
        diagQ = np.diag(Qxx)
        SP_cache_mm = {}
        for sp in set([s for s in standpunkte if s]):
            try:
                ix = namen_str.index(f"X{sp}")
                iy = namen_str.index(f"Y{sp}")
                SP_cache_mm[sp] = float(s0_apost) * np.sqrt(diagQ[ix] + diagQ[iy]) * 1000.0
            except ValueError:
                # Falls keine Lage, prüfe Höhe (Nivellement)
                try:
                    iz = namen_str.index(f"Z{sp}")
                    SP_cache_mm[sp] = float(s0_apost) * np.sqrt(diagQ[iz]) * 1000.0
                except ValueError:
                    SP_cache_mm[sp] = 0.0

        SP_mm = np.array([SP_cache_mm.get(sp, np.nan) for sp in standpunkte], dtype=float)

        return pd.DataFrame({
            "Beobachtung": labels, "Stand-Pkt": standpunkte, "EF": EF,
            "EP [mm]": EP_m * 1000.0, "SP [mm]": SP_mm, "EF*SP [mm]": EF * SP_mm
        })