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zusammenfügen 30.1.

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mi2847
2026-01-30 18:41:46 +01:00
parent 1561eb242e
commit d069b7ca81
10 changed files with 65708 additions and 35899 deletions
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195
Netzqualität_Zuverlässigkeit.py
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@@ -116,7 +116,103 @@ class Zuverlaessigkeit:
return Lokaltest_innere_Zuv
def aeussere_zuverlaessigkeit_EF_EP1(Lokaltest, labels, Qxx, A, P, s0_apost, unbekannten_liste, x):
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 ""
# SD, GNSS, Niv: direkt Wegfehler
if "_SD_" in lbl or "gnss" in lbl.lower() or "niv" in lbl.lower():
EP_m[i] = (1.0 - ri[i]) * 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] = (1.0 - ri[i]) * (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
})
def aeussere_zuverlaessigkeit_EF_EP(Lokaltest, labels, Qxx, A, P, s0_apost, unbekannten_liste, x):
df = Lokaltest.copy()
labels = list(labels)
Qxx = np.asarray(Qxx, float)
@@ -216,99 +312,4 @@ class Zuverlaessigkeit:
"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
})
return out