633 lines
23 KiB
Python
633 lines
23 KiB
Python
import numpy as np
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import plotly.graph_objects as go
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from scipy.stats import f
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import pandas as pd
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import Berechnungen
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class Genauigkeitsmaße:
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@staticmethod
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def berechne_s0apost(v: np.ndarray, P: np.ndarray, r: int) -> float:
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vTPv_matrix = v.T @ P @ v
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vTPv = float(vTPv_matrix.item())
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s0apost = np.sqrt(vTPv / r)
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return float(s0apost)
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@staticmethod
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def helmert_punktfehler(Qxx, s0_apost, unbekannten_liste, dim=3):
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diagQ = np.diag(Qxx)
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daten = []
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namen_str = [str(sym) for sym in unbekannten_liste]
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punkt_ids = []
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for n in namen_str:
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if n.upper().startswith('X'):
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punkt_ids.append(n[1:])
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for pid in punkt_ids:
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try:
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idx_x = next(i for i, n in enumerate(namen_str) if n.upper() == f"X{pid}".upper())
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idx_y = next(i for i, n in enumerate(namen_str) if n.upper() == f"Y{pid}".upper())
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qx = diagQ[idx_x]
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qy = diagQ[idx_y]
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qz = 0.0
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if dim == 3:
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try:
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idx_z = next(i for i, n in enumerate(namen_str) if n.upper() == f"Z{pid}".upper())
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qz = diagQ[idx_z]
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except StopIteration:
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qz = 0.0
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sx = s0_apost * np.sqrt(qx)
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sy = s0_apost * np.sqrt(qy)
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sz = s0_apost * np.sqrt(qz) if dim == 3 else 0
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sP = s0_apost * np.sqrt(qx + qy + qz)
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daten.append([pid, float(sx), float(sy), float(sz), float(sP)])
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except:
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continue
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helmert_punktfehler = pd.DataFrame(daten, columns=["Punkt", "σx", "σy", "σz", f"σP_{dim}D"])
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return helmert_punktfehler
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@staticmethod
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def standardellipse(Qxx, s0_apost, unbekannten_liste):
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Qxx = np.asarray(Qxx, float)
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daten = []
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namen_str = [str(sym) for sym in unbekannten_liste]
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punkt_ids = []
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for n in namen_str:
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if n.upper().startswith('X'):
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punkt_ids.append(n[1:])
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for pid in punkt_ids:
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try:
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idx_x = next(i for i, n in enumerate(namen_str) if n.upper() == f"X{pid}".upper())
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idx_y = next(i for i, n in enumerate(namen_str) if n.upper() == f"Y{pid}".upper())
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qxx = Qxx[idx_x, idx_x]
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qyy = Qxx[idx_y, idx_y]
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qyx = Qxx[idx_y, idx_x]
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# Standardabweichungen
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sx = s0_apost * np.sqrt(qxx)
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sy = s0_apost * np.sqrt(qyy)
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sxy = (s0_apost ** 2) * qyx
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k = np.sqrt((qxx - qyy) ** 2 + 4 * (qyx ** 2))
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# Q_dmax/min = 0.5 * (Qyy + Qxx +/- k)
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q_dmax = 0.5 * (qyy + qxx + k)
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q_dmin = 0.5 * (qyy + qxx - k)
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# Halbachsen
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s_max = s0_apost * np.sqrt(q_dmax)
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s_min = s0_apost * np.sqrt(q_dmin)
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# Richtungswinkel theta in gon:
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zaehler = 2 * qyx
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nenner = qxx - qyy
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t_grund = 0.5 * np.arctan(abs(zaehler) / abs(nenner)) * (200 / np.pi)
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# Quadrantenabfrage
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if nenner > 0 and qyx > 0: # Qxx - Qyy > 0 und Qyx > 0
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t_gon = t_grund # 0 - 50 gon
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elif nenner < 0 and qyx > 0: # Qxx - Qyy < 0 und Qyx > 0
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t_gon = 100 - t_grund # 50 - 100 gon
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elif nenner < 0 and qyx < 0: # Qxx - Qyy < 0 und Qyx < 0
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t_gon = 100 + t_grund # 100 - 150 gon
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elif nenner > 0 and qyx < 0: # Qxx - Qyy > 0 und Qyx < 0
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t_gon = 200 - t_grund # 150 - 200 gon
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else:
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t_gon = 0.0
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daten.append([
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pid,
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float(sx), float(sy), float(sxy),
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float(s_max), float(s_min),
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float(t_gon)
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])
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except:
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continue
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standardellipse = pd.DataFrame(daten, columns=["Punkt", "σx", "σy", "σxy", "s_max", "s_min", "θ [gon]"])
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return standardellipse
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@staticmethod
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def konfidenzellipse(Qxx, s0_apost, unbekannten_liste, R, alpha):
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Qxx = np.asarray(Qxx, float)
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daten = []
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namen_str = [str(sym) for sym in unbekannten_liste]
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punkt_ids = [n[1:] for n in namen_str if n.upper().startswith('X')]
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# Faktor für Konfidenzellipse (F-Verteilung)
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kk = float(np.sqrt(2.0 * f.ppf(1.0 - alpha, 2, R)))
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for pid in punkt_ids:
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try:
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idx_x = next(i for i, n in enumerate(namen_str) if n.upper() == f"X{pid}".upper())
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idx_y = next(i for i, n in enumerate(namen_str) if n.upper() == f"Y{pid}".upper())
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qxx = Qxx[idx_x, idx_x]
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qyy = Qxx[idx_y, idx_y]
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qyx = Qxx[idx_y, idx_x]
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# Standardabweichungen
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sx = s0_apost * np.sqrt(qxx)
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sy = s0_apost * np.sqrt(qyy)
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sxy = (s0_apost ** 2) * qyx
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k = np.sqrt((qxx - qyy) ** 2 + 4 * (qyx ** 2))
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# Q_dmax/min = 0.5 * (Qyy + Qxx +/- k)
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q_dmax = 0.5 * (qyy + qxx + k)
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q_dmin = 0.5 * (qyy + qxx - k)
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# Halbachsen der Standardellipse
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s_max = s0_apost * np.sqrt(q_dmax)
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s_min = s0_apost * np.sqrt(q_dmin)
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# Halbachsen der Konfidenzellipse
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A_K = kk * s_max
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B_K = kk * s_min
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# Richtungswinkel theta in gon:
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zaehler = 2 * qyx
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nenner = qxx - qyy
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t_grund = 0.5 * np.arctan(abs(zaehler) / abs(nenner)) * (200 / np.pi)
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# Quadrantenabfrage
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if nenner > 0 and qyx > 0:
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t_gon = t_grund # 0 - 50 gon
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elif nenner < 0 and qyx > 0:
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t_gon = 100 - t_grund # 50 - 100 gon
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elif nenner < 0 and qyx < 0:
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t_gon = 100 + t_grund # 100 - 150 gon
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elif nenner > 0 and qyx < 0:
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t_gon = 200 - t_grund # 150 - 200 gon
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else:
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t_gon = 0.0
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daten.append([
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pid,
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float(sx), float(sy), float(sxy),
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float(A_K), float(B_K),
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float(t_gon)
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])
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except:
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continue
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konfidenzellipse = pd.DataFrame(daten, columns= ["Punkt", "σx", "σy", "σxy", "a_K", "b_K","θ [gon]"])
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return konfidenzellipse
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@staticmethod
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def konfidenzellipsoid(Qxx, s0_apost, unbekannten_liste, R, alpha, skala="f", return_2d_schnitte=True):
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Qxx = np.asarray(Qxx, float)
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namen_str = [str(sym) for sym in unbekannten_liste]
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punkt_ids = sorted({n[1:] for n in namen_str if n and n[0].upper() in ("X", "Y", "Z")})
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# Skalierungsfaktor für Konfidenzbereich
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if skala.lower() == "f":
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k2_3d = f.ppf(1.0 - alpha, df=3)
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elif skala.lower() == "f":
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k2_3d = 3.0 * f.ppf(1.0 - alpha, dfn=3, dfd=R)
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else:
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raise ValueError("skala muss 'chi2' oder 'f' sein.")
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daten = []
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for pid in punkt_ids:
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try:
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idx_x = next(i for i, n in enumerate(namen_str) if n.upper() == f"X{pid}".upper())
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idx_y = next(i for i, n in enumerate(namen_str) if n.upper() == f"Y{pid}".upper())
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idx_z = next(i for i, n in enumerate(namen_str) if n.upper() == f"Z{pid}".upper())
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except StopIteration:
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continue
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# 3x3-Block aus Qxx ziehen
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I = [idx_x, idx_y, idx_z]
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Qp = Qxx[np.ix_(I, I)]
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# Kovarianzmatrix (Sigma) des Punkts
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Sigma = (s0_apost ** 2) * Qp
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# Standardabweichungen
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sx = float(np.sqrt(Sigma[0, 0]))
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sy = float(np.sqrt(Sigma[1, 1]))
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sz = float(np.sqrt(Sigma[2, 2]))
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# Kovarianzen
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sxy = float(Sigma[0, 1])
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sxz = float(Sigma[0, 2])
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syz = float(Sigma[1, 2])
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# Eigenzerlegung (symmetrisch -> eigh)
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evals, evecs = np.linalg.eigh(Sigma)
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order = np.argsort(evals)[::-1]
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evals = evals[order]
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evecs = evecs[:, order]
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# Numerische Sicherheit: negative Mini-Eigenwerte durch Rundung abklemmen
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evals = np.clip(evals, 0.0, None)
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# Halbachsen des Konfidenzellipsoids:
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A, B, C = (np.sqrt(evals * k2_3d)).tolist()
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row = {
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"Punkt": pid,
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"σx": sx, "σy": sy, "σz": sz,
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"σxy": sxy, "σxz": sxz, "σyz": syz,
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"A_K": float(A), "B_K": float(B), "C_K": float(C),
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# Orientierung als Spaltenvektoren (Eigenvektoren)
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"evec_1": evecs[:, 0].tolist(),
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"evec_2": evecs[:, 1].tolist(),
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"evec_3": evecs[:, 2].tolist(),
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"skala_k2": float(k2_3d),
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"skala_typ": skala.lower()
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}
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# Optional: 2D-Schnitte (XY, XZ, YZ) als Ellipsenparameter
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if return_2d_schnitte:
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row.update(Genauigkeitsmaße.ellipsen_schnitt_2d(Sigma, alpha, R, skala))
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daten.append(row)
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return pd.DataFrame(daten)
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@staticmethod
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def ellipsen_schnitt_2d(Sigma3, alpha, R, skala):
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def ellipse_from_2x2(S2):
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# Skalierung für 2D
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if skala.lower() == "f":
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k2 = f.ppf(1.0 - alpha, df=2)
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else:
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k2 = 2.0 * f.ppf(1.0 - alpha, dfn=2, dfd=R)
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evals, evecs = np.linalg.eigh(S2)
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order = np.argsort(evals)[::-1]
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evals = np.clip(evals[order], 0.0, None)
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evecs = evecs[:, order]
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a, b = np.sqrt(evals * k2)
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# Winkel der Hauptachse (zu a) in der Ebene: atan2(vy, vx)
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vx, vy = evecs[0, 0], evecs[1, 0]
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theta_rad = np.arctan2(vy, vx)
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theta_gon = float(theta_rad * (200.0 / np.pi)) % 200.0
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return float(a), float(b), theta_gon
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# Submatrizen
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S_xy = Sigma3[np.ix_([0, 1], [0, 1])]
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S_xz = Sigma3[np.ix_([0, 2], [0, 2])]
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S_yz = Sigma3[np.ix_([1, 2], [1, 2])]
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axy, bxy, txy = ellipse_from_2x2(S_xy)
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axz, bxz, txz = ellipse_from_2x2(S_xz)
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ayz, byz, tyz = ellipse_from_2x2(S_yz)
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return {
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"aXY": axy, "bXY": bxy, "θXY [gon]": txy,
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"aXZ": axz, "bXZ": bxz, "θXZ [gon]": txz,
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"aYZ": ayz, "bYZ": byz, "θYZ [gon]": tyz,
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}
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@staticmethod
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def transform_q_with_your_functions(q_xyz, B, L):
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# East
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r11 = Berechnungen.E(L, 1, 0)
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r12 = Berechnungen.E(L, 0, 1)
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r13 = 0
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# North
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r21 = Berechnungen.N(B, L, 1, 0, 0)
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r22 = Berechnungen.N(B, L, 0, 1, 0)
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r23 = Berechnungen.N(B, L, 0, 0, 1)
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# Up
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r31 = Berechnungen.U(B, L, 1, 0, 0)
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r32 = Berechnungen.U(B, L, 0, 1, 0)
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r33 = Berechnungen.U(B, L, 0, 0, 1)
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R = np.array([
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[r11, r12, r13],
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[r21, r22, r23],
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[r31, r32, r33]
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])
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q_enu = R @ q_xyz @ R.T
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return q_enu
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def plot_netz_komplett_final(x_vektor, unbekannten_labels, beobachtungs_labels, Qxx, sigma0_apost,
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k_faktor=2.447, v_faktor=1000):
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"""
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Optimierter Plot für Jupyter Notebook:
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- k_faktor: Statistischer Sicherheitsfaktor (2.447 entspricht 95% für 2D)
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- v_faktor: Optische Überhöhung der Ellipsen (z.B. 1000 = mm werden als m dargestellt)
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"""
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x_vektor = np.asarray(x_vektor, float).reshape(-1)
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Qxx = np.asarray(Qxx, float)
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# 1. Datenaufbereitung
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coords = {}
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punkt_ids = sorted(set(str(l)[1:] for l in unbekannten_labels if str(l).startswith(('X', 'Y', 'Z'))))
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pts_data = []
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for pid in punkt_ids:
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try:
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ix = next(i for i, s in enumerate(unbekannten_labels) if str(s) == f"X{pid}")
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iy = next(i for i, s in enumerate(unbekannten_labels) if str(s) == f"Y{pid}")
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x, y = float(x_vektor[ix]), float(x_vektor[iy])
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coords[pid] = (x, y)
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# Kovarianzmatrix extrahieren und mit s0^2 skalieren
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q_idx = [ix, iy]
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Q_sub = Qxx[np.ix_(q_idx, q_idx)] * (sigma0_apost ** 2)
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pts_data.append({'id': pid, 'x': x, 'y': y, 'Q': Q_sub})
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except:
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continue
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if len(pts_data) == 0:
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raise ValueError(
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"Keine Netzpunkte extrahiert. Prüfe: x_vektor Form (u,) und Qxx Form (u,u) sowie Labels 'X<id>'/'Y<id>'.")
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fig = go.Figure()
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# 2. Beobachtungen (Gruppiert)
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beob_typen = {
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'GNSS-Basislinien': {'pattern': 'gnss', 'color': 'rgba(255, 100, 0, 0.4)'},
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'Nivellement': {'pattern': 'niv', 'color': 'rgba(0, 200, 100, 0.4)'},
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'Tachymeter': {'pattern': '', 'color': 'rgba(100, 100, 100, 0.3)'}
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}
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for typ, info in beob_typen.items():
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x_l, y_l = [], []
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for bl in beobachtungs_labels:
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bl_str = str(bl).lower()
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if (info['pattern'] in bl_str and info['pattern'] != '') or (
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info['pattern'] == '' and 'gnss' not in bl_str and 'niv' not in bl_str):
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pts = [pid for pid in coords if f"_{pid}" in str(bl) or str(bl).startswith(f"{pid}_")]
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if len(pts) >= 2:
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x_l.extend([coords[pts[0]][0], coords[pts[1]][0], None])
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y_l.extend([coords[pts[0]][1], coords[pts[1]][1], None])
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if x_l:
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fig.add_trace(go.Scatter(x=x_l, y=y_l, mode='lines', name=typ, line=dict(color=info['color'], width=1)))
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# 3. Konfidenzellipsen mit v_faktor
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for pt in pts_data:
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vals, vecs = np.linalg.eigh(pt['Q'])
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order = vals.argsort()[::-1]
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vals, vecs = vals[order], vecs[:, order]
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theta = np.degrees(np.arctan2(vecs[1, 0], vecs[0, 0]))
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# Skalierung: k_faktor (Statistik) * v_faktor (Optik)
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a = k_faktor * np.sqrt(vals[0]) * v_faktor
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b = k_faktor * np.sqrt(vals[1]) * v_faktor
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t = np.linspace(0, 2 * np.pi, 40)
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e_x = a * np.cos(t)
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e_y = b * np.sin(t)
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R = np.array([[np.cos(np.radians(theta)), -np.sin(np.radians(theta))],
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[np.sin(np.radians(theta)), np.cos(np.radians(theta))]])
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rot = np.dot(R, np.array([e_x, e_y]))
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fig.add_trace(go.Scatter(
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x=rot[0, :] + pt['x'], y=rot[1, :] + pt['y'],
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mode='lines', line=dict(color='red', width=1.5),
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name=f"Ellipsen (Vergrößert {v_faktor}x)",
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legendgroup="Ellipsen",
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showlegend=(pt == pts_data[0]), # Nur einmal in der Legende zeigen
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hoverinfo='skip'
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||
))
|
||
|
||
# 4. Punkte
|
||
df_pts = pd.DataFrame(pts_data)
|
||
fig.add_trace(go.Scatter(
|
||
x=df_pts['x'], y=df_pts['y'], mode='markers+text',
|
||
text=df_pts['id'], textposition="top center",
|
||
marker=dict(size=8, color='black'), name="Netzpunkte"
|
||
))
|
||
|
||
# 5. Layout & Notebook-Größe
|
||
fig.update_layout(
|
||
title=f"Netzausgleichung: Ellipsen {v_faktor}-fach vergrößert (k={k_faktor})",
|
||
xaxis=dict(title="X [m]", tickformat="f", separatethousands=True, scaleanchor="y", scaleratio=1, showgrid=True,
|
||
gridcolor='lightgrey'),
|
||
yaxis=dict(title="Y [m]", tickformat="f", separatethousands=True, showgrid=True, gridcolor='lightgrey'),
|
||
width=1100, # Breite angepasst
|
||
height=900, # Höhe deutlich vergrößert für Jupiter Notebook
|
||
plot_bgcolor='white',
|
||
legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01, bgcolor="rgba(255,255,255,0.8)")
|
||
)
|
||
|
||
# Info-Annotation als Ersatz für einen physischen Maßstabstab
|
||
fig.add_annotation(
|
||
text=f"<b>Maßstab Ellipsen:</b><br>Dargestellte Größe = Wahre Ellipse × {v_faktor}",
|
||
align='left', showarrow=False, xref='paper', yref='paper', x=0.02, y=0.05,
|
||
bgcolor="white", bordercolor="black", borderwidth=1)
|
||
|
||
fig.show(config={'scrollZoom': True})
|
||
|
||
|
||
|
||
def plot_netz_final_mit_df_ellipsen(x_vektor, unbekannten_labels, beobachtungs_labels, df_ellipsen, v_faktor=1000):
|
||
# 1. Punkte extrahieren
|
||
coords = {}
|
||
# Wir nehmen an, dass die Reihenfolge im x_vektor X, Y, Z pro Punkt ist
|
||
punkt_ids = sorted(set(str(l)[1:] for l in unbekannten_labels if str(l).startswith(('X', 'Y', 'Z'))))
|
||
|
||
for pid in punkt_ids:
|
||
try:
|
||
ix = next(i for i, s in enumerate(unbekannten_labels) if str(s) == f"X{pid}")
|
||
iy = next(i for i, s in enumerate(unbekannten_labels) if str(s) == f"Y{pid}")
|
||
coords[pid] = (float(x_vektor[ix]), float(x_vektor[iy]))
|
||
except:
|
||
continue
|
||
|
||
fig = go.Figure()
|
||
|
||
# 2. Beobachtungslinien (Gruppiert)
|
||
beob_typen = {
|
||
'GNSS-Basislinien': {'pattern': 'gnss', 'color': 'rgba(255, 100, 0, 0.4)'},
|
||
'Nivellement': {'pattern': 'niv', 'color': 'rgba(0, 200, 100, 0.4)'},
|
||
'Tachymeter': {'pattern': '', 'color': 'rgba(100, 100, 100, 0.3)'}
|
||
}
|
||
|
||
for typ, info in beob_typen.items():
|
||
x_l, y_l = [], []
|
||
for bl in beobachtungs_labels:
|
||
bl_str = str(bl).lower()
|
||
# Einfache Logik zur Typtrennung
|
||
if (info['pattern'] in bl_str and info['pattern'] != '') or \
|
||
(info['pattern'] == '' and 'gnss' not in bl_str and 'niv' not in bl_str):
|
||
pts = [pid for pid in coords if f"_{pid}" in str(bl) or str(bl).startswith(f"{pid}_")]
|
||
if len(pts) >= 2:
|
||
x_l.extend([coords[pts[0]][0], coords[pts[1]][0], None])
|
||
y_l.extend([coords[pts[0]][1], coords[pts[1]][1], None])
|
||
|
||
if x_l:
|
||
fig.add_trace(go.Scatter(x=x_l, y=y_l, mode='lines', name=typ, line=dict(color=info['color'], width=1)))
|
||
|
||
# 3. Ellipsen aus dem DataFrame zeichnen
|
||
for _, row in df_ellipsen.iterrows():
|
||
pid = str(row['Punkt'])
|
||
if pid in coords:
|
||
x0, y0 = coords[pid]
|
||
|
||
# Werte aus DF (mit v_faktor skalieren)
|
||
a = row['a_K'] * v_faktor
|
||
b = row['b_K'] * v_faktor
|
||
theta_gon = row['θ [gon]']
|
||
|
||
# Umrechnung: gon -> rad für die Rotation
|
||
# Da im Plot X horizontal und Y vertikal ist, entspricht theta_gon dem Winkel zur X-Achse
|
||
theta_rad = theta_gon * (np.pi / 200.0)
|
||
|
||
# Ellipsen berechnen
|
||
t = np.linspace(0, 2 * np.pi, 50)
|
||
e_x = a * np.cos(t)
|
||
e_y = b * np.sin(t)
|
||
|
||
# Ausrichtung der Ellipsen
|
||
R = np.array([[np.cos(theta_rad), -np.sin(theta_rad)],
|
||
[np.sin(theta_rad), np.cos(theta_rad)]])
|
||
|
||
rot = np.dot(R, np.array([e_x, e_y]))
|
||
|
||
fig.add_trace(go.Scatter(
|
||
x=rot[0, :] + x0, y=rot[1, :] + y0,
|
||
mode='lines', line=dict(color='red', width=1.5),
|
||
name='Konfidenzellipsen',
|
||
legendgroup='Ellipsen',
|
||
showlegend=(pid == df_ellipsen.iloc[0]['Punkt']),
|
||
hoverinfo='text',
|
||
text=f"Punkt {pid}<br>a_K: {row['a_K']:.4f}m<br>b_K: {row['b_K']:.4f}m"
|
||
))
|
||
|
||
# Punkte plotten
|
||
df_pts = pd.DataFrame([(pid, c[0], c[1]) for pid, c in coords.items()], columns=['ID', 'X', 'Y'])
|
||
fig.add_trace(go.Scatter(
|
||
x=df_pts['X'], y=df_pts['Y'], mode='markers+text',
|
||
text=df_pts['ID'], textposition="top center",
|
||
marker=dict(size=8, color='black'), name="Netzpunkte"))
|
||
|
||
# Layout
|
||
fig.update_layout(
|
||
title=f"Netzplot (Ellipsen {v_faktor}x überhöht)",
|
||
xaxis=dict(title="X [m]", tickformat="f", separatethousands=True, scaleanchor="y", scaleratio=1,
|
||
showgrid=True, gridcolor='lightgrey'),
|
||
yaxis=dict(title="Y [m]", tickformat="f", separatethousands=True, showgrid=True, gridcolor='lightgrey'),
|
||
width=1100, height=900,
|
||
plot_bgcolor='white')
|
||
|
||
# Maßstabsangabe
|
||
fig.add_annotation(
|
||
text=f"<b>Skalierung:</b><br>Ellipsengröße im Plot = {v_faktor} × Realität",
|
||
align='left', showarrow=False, xref='paper', yref='paper', x=0.02, y=0.02,
|
||
bgcolor="rgba(255,255,255,0.8)", bordercolor="black", borderwidth=1)
|
||
|
||
fig.show(config={'scrollZoom': True})
|
||
|
||
import plotly.graph_objects as go
|
||
import numpy as np
|
||
|
||
|
||
|
||
def plot_netz_3D(x_vektor, unbekannten_labels, beobachtungs_labels, df_ellipsen, v_faktor=1000):
|
||
"""
|
||
Erzeugt einen interaktiven 3D-Plot des Netzes.
|
||
- v_faktor: Vergrößerung der Genauigkeits-Achsen (z.B. 1000 für mm -> m)
|
||
"""
|
||
# 1. Punkte extrahieren
|
||
pts = {}
|
||
punkt_ids = sorted(set(str(l)[1:] for l in unbekannten_labels if str(l).startswith(('X', 'Y', 'Z'))))
|
||
|
||
for pid in punkt_ids:
|
||
try:
|
||
ix = next(i for i, s in enumerate(unbekannten_labels) if str(s) == f"X{pid}")
|
||
iy = next(i for i, s in enumerate(unbekannten_labels) if str(s) == f"Y{pid}")
|
||
iz = next(i for i, s in enumerate(unbekannten_labels) if str(s) == f"Z{pid}")
|
||
pts[pid] = (float(x_vektor[ix]), float(x_vektor[iy]), float(x_vektor[iz]))
|
||
except:
|
||
continue
|
||
|
||
fig = go.Figure()
|
||
|
||
# 2. Beobachtungen (Linien im Raum)
|
||
# Wir zeichnen hier einfach alle Verbindungen
|
||
x_line, y_line, z_line = [], [], []
|
||
for bl in beobachtungs_labels:
|
||
p_in_l = [pid for pid in pts if f"_{pid}" in str(bl) or str(bl).startswith(f"{pid}_")]
|
||
if len(p_in_l) >= 2:
|
||
p1, p2 = pts[p_in_l[0]], pts[p_in_l[1]]
|
||
x_line.extend([p1[0], p2[0], None])
|
||
y_line.extend([p1[1], p2[1], None])
|
||
z_line.extend([p1[2], p2[2], None])
|
||
|
||
fig.add_trace(go.Scatter3d(
|
||
x=x_line, y=y_line, z=z_line,
|
||
mode='lines', line=dict(color='gray', width=2),
|
||
name='Beobachtungen'
|
||
))
|
||
|
||
# 3. Punkte & "Fehler-Kreuze" (als Ersatz für Ellipsoide)
|
||
# Ein echtes 3D-Ellipsoid ist grafisch schwer, daher zeichnen wir 3 Achsen
|
||
for pid, coord in pts.items():
|
||
# Hier könnten wir die echten Halbachsen aus der 3D-Eigenwertanalyse nutzen
|
||
# Für den Anfang plotten wir die Standardabweichungen sX, sY, sZ als Kreuz
|
||
fig.add_trace(go.Scatter3d(
|
||
x=[coord[0]], y=[coord[1]], z=[coord[2]],
|
||
mode='markers+text', text=[pid],
|
||
marker=dict(size=4, color='black'), name=f'Punkt {pid}'
|
||
))
|
||
|
||
# 4. Layout
|
||
fig.update_layout(
|
||
scene=dict(
|
||
xaxis_title='X [m]',
|
||
yaxis_title='Y [m]',
|
||
zaxis_title='Z [m]',
|
||
aspectmode='data' # WICHTIG: Verhältnisse 1:1:1 bewahren
|
||
),
|
||
width=1000, height=800,
|
||
title="Geozentrisches Netz in 3D"
|
||
)
|
||
|
||
fig.show()
|
||
|
||
# Aufruf
|