From e0796deec6cd09011458f3d149fe8144ab78bafc Mon Sep 17 00:00:00 2001 From: fa2338 Date: Thu, 5 Feb 2026 12:52:27 +0100 Subject: [PATCH] Push 05.02.2026 --- Berechnungen.py | 159 +- Campusnetz.ipynb | 37410 +++++++++++++++++++++++++++- Datenbank.py | 104 +- Datumsfestlegung.py | 73 +- Einheitenumrechnung.py | 85 + Import.py | 11 +- Koordinatentransformationen.py | 2 +- Netzqualitaet_Genauigkeit.py | 564 + Netzqualitaet_Zuverlaessigkeit.py | 828 + Netzqualität_Genauigkeit.py | 321 - Netzqualität_Zuverlässigkeit.py | 309 - Parameterschaetzung.py | 209 +- Proben.py | 40 +- Stochastisches_Modell.py | 34 +- Varianzkomponentenschaetzung.py | 2 +- 15 files changed, 38386 insertions(+), 1765 deletions(-) create mode 100644 Einheitenumrechnung.py create mode 100644 Netzqualitaet_Genauigkeit.py create mode 100644 Netzqualitaet_Zuverlaessigkeit.py delete mode 100644 Netzqualität_Genauigkeit.py delete mode 100644 Netzqualität_Zuverlässigkeit.py diff --git a/Berechnungen.py b/Berechnungen.py index 27e6a42..b4b8444 100644 --- a/Berechnungen.py +++ b/Berechnungen.py @@ -1,7 +1,4 @@ -from decimal import Decimal -import sympy as sp from typing import Any -import math import numpy as np import Datenbank @@ -383,92 +380,30 @@ class Berechnungen: return schraegdistanz_boden, zw_boden -class Einheitenumrechnung: - """Einheitenumrechnungen für Winkel- und Längeneinheiten. +class ENU: + """Transformationen zwischen XYZ und lokalem ENU-System. Die Klasse stellt Methoden zur Verfügung für: - - Umrechnung von Millibogensekunden (mas) in Radiant, - - Umrechnung von Millimetern (mm) in Meter, - - Umrechnung von Gon und Milligon (mgon) in Radiant (Decimal-basiert). + - Bestimmung eines ENU-Referenzpunkts über den Schwerpunkt gegebener XYZ-Koordinaten, + - Aufbau der lokalen Rotationsmatrix R0 (XYZ nach ENU) aus geodätischer Breite B und Länge L, + - Aufbau einer Rotationsmatrix R_ENU für einen gesamten Unbekanntenvektor, + - Transformation der Kovarianz-Matrix Qxx in das ENU-System, + - Transformation von Punktkoordinaten (XYZ) in lokale ENU-Koordinaten relativ zum Schwerpunkt. """ - def mas_to_rad(mas: float) -> float: - """Rechnet Millibogensekunden (mas) in Radiant um. - - Es gilt: rad = mas * (pi / (180 * 3600 * 1000)). - - :param mas: Winkel in Millibogensekunden (mas). - :type mas: float - :return: Winkel in Radiant. - :rtype: float - """ - umrechnungsfaktor = 1 / 1000 * 1 / 3600 * sp.pi / 180 - grad = mas * umrechnungsfaktor - return grad - - def mm_to_m(mm: float) -> float: - """Rechnet Millimeter in Meter um. - - Es gilt: m = mm / 1000. - - :param mm: Länge in Millimeter. - :type mm: float - :return: Länge in Meter. - :rtype: float - """ - m = mm / 1000 - return m - - def gon_to_rad_Decimal(gon: float) -> Decimal: - """Rechnet Gon in Radiant um (Decimal-basiert). - - Es gilt: 400 gon = 2*pi und damit rad = (gon / 200) * pi. - - :param gon: Winkel in Gon. - :type gon: float - :return: Winkel in Radiant als Decimal. - :rtype: Decimal - """ - gon = Decimal(gon) - pi = Decimal(str(math.pi)) - rad = (gon / Decimal(200)) * pi - return rad - - def mgon_to_rad_Decimal(gon: float) -> Decimal: - """Rechnet Milligon (mgon) in Radiant um (Decimal-basiert). - - Es gilt: 1 mgon = 0.001 gon und damit rad = (mgon / 200000) * pi. - - :param gon: Winkel in Milligon (mgon). - :type gon: float - :return: Winkel in Radiant als Decimal. - :rtype: Decimal - """ - gon = Decimal(gon) - pi = Decimal(str(math.pi)) - rad = (gon / Decimal(200000)) * pi - return rad - - def rad_to_gon_Decimal(rad: float) -> Decimal: - """Rechnet Radiant in Gon um (Decimal-basiert). - - Es gilt: 400 gon = 2*pi und damit rad = (gon / 200) * pi. - - :param rad: Winkel in Rad. - :type rad: float - :return: Winkel in Gon als Decimal. - :rtype: Decimal - """ - rad = Decimal(rad) - pi = Decimal(str(math.pi)) - gon = (rad / pi) * Decimal(200) - return gon - - -class ENU: @staticmethod def berechne_schwerpunkt_fuer_enu(berechnungen, dict_xyz): + """ + Berechnet die ENU-Referenz (B0, L0) aus dem Schwerpunkt gegebener XYZ-Koordinaten. + + :param berechnungen: Hilfsobjekt mit Methoden zur Berechnung von Breite B und Länge L. + :type berechnungen: Any + :param dict_xyz: Dictionary der Koordinaten. + :type dict_xyz: dict + :return: Tuple aus geodätischer Breite B0 und Länge L0 des Schwerpunktes. + :rtype: tuple[float, float] + """ XYZ = np.array(list(dict_xyz.values()), dtype=float) X0, Y0, Z0 = XYZ.mean(axis=0) B0 = float(berechnungen.B(X0, Y0, Z0)) @@ -478,6 +413,18 @@ class ENU: @staticmethod def berechne_R0_ENU(berechnungen, B, L): + """ + Erzeugt die 3×3-Rotationsmatrix R0 für die Transformation von XYZ nach ENU. + + :param berechnungen: Hilfsobjekt mit Methoden zur Berechnung der ENU-Komponenten. + :type berechnungen: Any + :param B: Geodätische Breite des ENU-Referenzpunkts. + :type B: float + :param L: Geodätische Länge des ENU-Referenzpunkts. + :type L: float + :return: Rotationsmatrix R0 (3×3) für XYZ nach ENU. + :rtype: numpy.ndarray + """ # East r11 = berechnungen.E(L, 1, 0) r12 = berechnungen.E(L, 0, 1) @@ -504,6 +451,20 @@ class ENU: @staticmethod def berechne_R_ENU(unbekannten_liste, R0): + """ + Erstellt eine Rotationsmatrix R für die Umrechnung eines Unbekanntenvektors ins ENU-System. + + Für jede Punkt-ID, die über Symbolnamen (z.B. X1, Y1, Z1) in unbekannten_liste erkannt wird, + wird die 3×3-Rotation R0 in die passende Position der Gesamtmatrix eingesetzt. + + :param unbekannten_liste: Liste der Unbekannten + :type unbekannten_liste: list + :param R0: Rotationsmatrix (3×3) für XYZ nach ENU. + :type R0: numpy.ndarray + :return: Rotationsmarix R (n×n) für den gesamten Unbekanntenvektor. + :rtype: numpy.ndarray + """ + names = [str(s) for s in unbekannten_liste] n = len(names) R = np.eye(n, dtype=float) @@ -526,6 +487,27 @@ class ENU: @staticmethod def transform_Qxx_zu_QxxENU(Qxx, unbekannten_liste, berechnungen, dict_xyz): + """ + Transformiert die Kofaktor-Matrix Qxx in das ENU-System. + + Es wird zunächst eine ENU-Referenz über den Schwerpunkt der Koordinaten bestimmt. + Anschließend wird R0 (XYZ nach ENU) und die Matrix R_ENU aufgebaut. Die Transformation + erfolgt über: + + Qxx_ENU = R_ENU · Qxx · R_ENUᵀ + + :param Qxx: Kofaktor-Matrix der Unbekannten im XYZ-System. + :type Qxx: numpy.ndarray + :param unbekannten_liste: Liste der Unbekannten. + :type unbekannten_liste: list + :param berechnungen: Hilfsobjekt für Breite/Länge und ENU-Komponenten. + :type berechnungen: Any + :param dict_xyz: Dictionary der Koordinaten. + :type dict_xyz: dict + :return: Qxx_ENU, (B0, L0), R0 mit Schwerpunkt-Referenz und lokaler Rotationsmatrix. + :rtype: tuple[numpy.ndarray, tuple[float, float], numpy.ndarray] + """ + B0, L0 = ENU.berechne_schwerpunkt_fuer_enu(berechnungen, dict_xyz) R0 = ENU.berechne_R0_ENU(berechnungen, B0, L0) R_ENU = ENU.berechne_R_ENU(unbekannten_liste, R0) @@ -538,6 +520,17 @@ class ENU: @staticmethod def transform_Koord_zu_KoordENU(dict_xyz, R0): + """ + Transformiert Punktkoordinaten (XYZ) in ENU-Koordinaten relativ zum Schwerpunkt. + + :param dict_xyz: Dictionary der Koordinaten. + :type dict_xyz: dict + :param R0: Rotationsmatrix (3×3) für XYZ nach ENU. + :type R0: numpy.ndarray + :return: Dictionary der ENU-Koordinaten. + :rtype: dict + """ + XYZ = np.asarray(list(dict_xyz.values()), dtype=float).reshape(-1, 3) XYZ0 = XYZ.mean(axis=0).reshape(3, ) @@ -546,4 +539,4 @@ class ENU: xyz = np.asarray(xyz, dtype=float).reshape(3, ) enu = (R0 @ (xyz - XYZ0)).reshape(3, ) Koord_ENU[str(pid)] = (float(enu[0]), float(enu[1]), float(enu[2])) - return Koord_ENU \ No newline at end of file + return Koord_ENU diff --git a/Campusnetz.ipynb b/Campusnetz.ipynb index 7a3032a..c7a6930 100644 --- a/Campusnetz.ipynb +++ b/Campusnetz.ipynb @@ -5,28 +5,30 @@ "id": "2bc687b1b4adb7bd", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:03.560447700Z", - "start_time": "2026-02-03T12:52:00.677288500Z" + "end_time": "2026-02-05T09:19:53.052875100Z", + "start_time": "2026-02-05T09:19:51.406470700Z" } }, "source": [ - "# Zelle 1: Import Pythonmodule\n", + "# Zelle 1: Import von Python-Modulen und Bibliotheken\n", "\n", "from datetime import datetime\n", "import importlib\n", "from IPython.display import HTML\n", "from IPython.display import display\n", - "import itables\n", - "from itables.widget import ITable\n", + "import numpy as np\n", "import pandas as pd\n", "\n", + "import Berechnungen\n", "import Datenbank\n", - "import Datumsfestlegung\n", + "import Export\n", "import Funktionales_Modell\n", "import Import\n", "import Koordinatentransformationen\n", + "import Netzqualitaet_Genauigkeit\n", + "import Netzqualitaet_Zuverlaessigkeit\n", "from Parameterschaetzung import Iterationen\n", - "import Stochastisches_Modell\n", + "import Proben\n", "import Varianzkomponentenschaetzung" ], "outputs": [], @@ -37,25 +39,28 @@ "id": "4f7efd7ba6ec18f9", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:03.598831700Z", - "start_time": "2026-02-03T12:52:03.583446600Z" + "end_time": "2026-02-05T09:19:53.693823Z", + "start_time": "2026-02-05T09:19:53.657303500Z" } }, "source": [ "# Zelle 2: Allgemeine Einstellungen\n", "\n", - "# Auswahl der Datumsdefinition (Aktuell ist lediglich die weiche Lagerung implementiert).\n", + "# Auswahl der Datumsdefinition (Aktuell ist lediglich die weiche Lagerung implementiert)\n", "datumfestlegung = \"weiche Lagerung\"\n", "\n", - "# Übergabe der großen- und kleinen Halbachse des jeweiligen Referenzellipsoids.\n", + "# Übergabe der großen und kleinen Halbachse des Referenzellipsoids (aktuell GRS80)\n", "a = 6378137.0 #m\n", "b = 6356752.314 #m\n", "\n", - "# Auswahl der SQLite-Datenbank oder anlegen einer neuen Datenbank, wenn diese nicht existiert.\n", + "# Pfad zur SQLite-Datenbank (wird angelegt, falls nicht vorhanden)\n", "pfad_datenbank = r\"Campusnetz.db\"\n", "\n", - "# Übergabe des Pfades zum GCG2016 des BKG:\n", - "pfad_tif_quasigeoidundolation = r\"Daten\\GCG2016v2023.tif\"" + "# Pfad zum Quasigeoid-Modell GCG 2016 des BKG\n", + "pfad_tif_quasigeoidundolation = r\"Daten\\GCG2016v2023.tif\"\n", + "\n", + "# Alle während jeder Iteration der Parameterschätzung erstellten Vektoren und Matrizenin einer CSV-Datei im Ordner Zwischenergebnisse speichern (Auf True setzen, wenn gewünscht)\n", + "speichern_in_csv = False" ], "outputs": [], "execution_count": 2 @@ -65,17 +70,17 @@ "id": "57fcd841405b7866", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:03.879842800Z", - "start_time": "2026-02-03T12:52:03.602829200Z" + "end_time": "2026-02-05T09:19:54.287687900Z", + "start_time": "2026-02-05T09:19:54.173295400Z" } }, "source": [ - "# Zelle 3: SQLite-Datenbank aufrufen oder anlegen und Klassen initialisieren\n", + "# Zelle 3: SQLite-Datenbank initialisieren und Klassen laden\n", "\n", - "# Aufrufen oder anlegen der SQLite-Datenbank\n", + "# Datenbank anlegen (falls nicht vorhanden)\n", "Datenbank.Datenbank_anlegen(pfad_datenbank)\n", "\n", - "# Klassen initialisieren\n", + "# Klassen aus eigenen Pythondateien initialisieren\n", "db_zugriff = Datenbank.Datenbankzugriff(pfad_datenbank)\n", "fm = Funktionales_Modell.FunktionalesModell(pfad_datenbank, a, b, pfad_tif_quasigeoidundolation)\n", "imp = Import.Import(pfad_datenbank, a, b)\n", @@ -89,71 +94,24 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:04.044124600Z", - "start_time": "2026-02-03T12:52:03.896928900Z" + "end_time": "2026-02-05T09:19:54.869776300Z", + "start_time": "2026-02-05T09:19:54.755537200Z" } }, "cell_type": "code", "source": [ "# Zelle 4: Ausgabe der bereits vorhandenen Instrumente\n", "\n", - "liste_instrumente_in_db = db_zugriff.get_alle_instrumente_liste()\n", - "\n", - "if isinstance(liste_instrumente_in_db, list) and len(liste_instrumente_in_db) > 0:\n", - " df_instrumente = pd.DataFrame(liste_instrumente_in_db, columns=['InstrumenteID', 'Typ', 'Bezeichnung'])\n", - " display(df_instrumente.style.hide(axis='index'))\n", - "\n", - "else:\n", - " print(\"Es wurden noch keine Instrumente angelegt. Bitte in der folgenden Zelle nachholen und diese Zelle erneut ausführen!\")" + "db_zugriff.tabelle_instrumente_aus_db()" ], "id": "2cf681b8ca7f268", "outputs": [ { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
InstrumenteIDTypBezeichnung
1TachymeterTrimble S9
2AnschlusspunktelA
3NivellierTrimble DiNi 0.3
4GNSSGNSS-Rover
\n" - ] - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } + "name": "stdout", + "output_type": "stream", + "text": [ + "Es wurden noch keine Instrumente angelegt. Bitte in der folgenden Zelle nachholen und diese Zelle erneut ausführen!\n" + ] } ], "execution_count": 4 @@ -161,13 +119,18 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:04.092732400Z", - "start_time": "2026-02-03T12:52:04.068125600Z" + "end_time": "2026-02-05T09:19:55.476972700Z", + "start_time": "2026-02-05T09:19:55.359017200Z" } }, "cell_type": "code", "source": [ - "# Zelle 4: Instrumente anlegen\n", + "# Zelle 5: Instrumente anlegen\n", + "\n", + "# Syntax: db_zugriff.set_instrument(Instrumententyp, Bezeichnung, [Beobachtungsgruppen])\n", + "# Hinweis: Alle GNSS-Rover sind als ein Instrument anzulegen!\n", + "# Sind Instrumente bereits in der Datenbank vorhanden, wird der Import übersprungen.\n", + "# Änderungen an bestehenden Instrumenten sind nicht möglich. In diesem Fall ist ein neues Instrument anlegen.\n", "\n", "db_zugriff.set_instrument(\n", " \"Tachymeter\", \"Trimble S9\", [\"Tachymeter_Streckenbeobachtungen\", \"Tachymeter_Richtungsbeobachtungen\", \"Tachymeter_Zenitwinkelbeobachtungen\"\n", @@ -185,12 +148,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Das Instrument Trimble S9 ist bereits in der Datenbank vorhanden.\n", - "Es hat die ID 1\n", - "Das Instrument Trimble DiNi 0.3 ist bereits in der Datenbank vorhanden.\n", - "Es hat die ID 3\n", - "Das Instrument GNSS-Rover ist bereits in der Datenbank vorhanden.\n", - "Es hat die ID 4\n" + "Das Instrument Trimble S9 wurde erfolgreich hinzugefügt.\n", + "Das Instrument Trimble DiNi 0.3 wurde erfolgreich hinzugefügt.\n", + "Das Instrument GNSS-Rover wurde erfolgreich hinzugefügt.\n" ] } ], @@ -199,80 +159,24 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:04.177819Z", - "start_time": "2026-02-03T12:52:04.109725700Z" + "end_time": "2026-02-05T09:19:56.162550500Z", + "start_time": "2026-02-05T09:19:56.081571800Z" } }, "cell_type": "code", "source": [ - "# Zelle 4: Ausgabe der bereits vorhandenen Genauigkeitsangaben\n", - "genauigkeiten_dict = db_zugriff.get_genauigkeiten_dict()\n", - "if genauigkeiten_dict == {}:\n", - " print(\"Es wurden noch keine apriori Genauigkeiten zu den Beobachtungsgruppen erfasst. Bitte in der folgenden Zelle nachholen und diese Zelle erneut ausführen.\")\n", - "else:\n", - " formatierte_daten = list(genauigkeiten_dict.values())\n", - " spalten = [\n", - " 'instrumenteID',\n", - " 'beobachtungsart',\n", - " 'stabw_apriori_konstant',\n", - " 'stabw_apriori_streckenprop'\n", - " ]\n", - " df = pd.DataFrame(formatierte_daten, columns=spalten)\n", - " display(df.style.hide(axis='index'))" + "# Zelle 6: Ausgabe der bereits vorhandenen Genauigkeitsangaben a-priori\n", + "\n", + "db_zugriff.tabelle_genauigkeiten_aus_db()" ], "id": "3a90f746c44a1ebb", "outputs": [ { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
instrumenteIDbeobachtungsartstabw_apriori_konstantstabw_apriori_streckenprop
1Tachymeter_Richtung0.000002nan
1Tachymeter_Strecke0.0008001.000000
1Tachymeter_Zenitwinkel0.000002nan
3Geometrisches_Nivellement0.0001000.300000
\n" - ] - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } + "name": "stdout", + "output_type": "stream", + "text": [ + "Es wurden noch keine apriori Genauigkeiten zu den Beobachtungsgruppen erfasst. Bitte in der folgenden Zelle nachholen und diese Zelle erneut ausführen.\n" + ] } ], "execution_count": 6 @@ -280,13 +184,25 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:04.261396100Z", - "start_time": "2026-02-03T12:52:04.216263400Z" + "end_time": "2026-02-05T09:19:56.976559400Z", + "start_time": "2026-02-05T09:19:56.846196100Z" } }, "cell_type": "code", "source": [ - "# Zelle 4: Genauigkeiten zu den Instrumenten erfassen\n", + "# Zelle 7: Genauigkeiten a-priori zu den Instrumenten erfassen\n", + "\n", + "# Syntax: db_zugriff.set_genauigkeiten(InstrumentenID, Beobachtungsgruppe, Standardabweichung_konstant, Standardabweichung_streckenproportional)\n", + "\n", + "# Einheit der Eingaben:\n", + "# - stabw_konstant:\n", + "# - Winkelbeobachtungen (Richtung, Zenitwinkel): Standardabweichung in mgon\n", + "# - Strecken: Standardabweichung in m (auf die Strecke bezogener absoluter Anteil)\n", + "# - Nivellement: Standardabweichung in m pro 1 km Doppelnivellement\n", + "# - stabw_streckenproportional (optional): streckenproportionaler Anteil in ppm\n", + "\n", + "# Sind Genauigkeitsangaben bereits in der Datenbank vorhanden, wird der Import übersprungen.\n", + "# Änderungen sind möglich.\n", "\n", "db_zugriff.set_genauigkeiten(1, \"Tachymeter_Richtung\", 0.15)\n", "db_zugriff.set_genauigkeiten(1, \"Tachymeter_Strecke\", 0.8, 1)\n", @@ -299,10 +215,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Die Genauigkeitsangabe ist bereits in der Datenbank vorhanden.\n", - "Die Genauigkeitsangabe ist bereits in der Datenbank vorhanden.\n", - "Die Genauigkeitsangabe ist bereits in der Datenbank vorhanden.\n", - "Die Genauigkeitsangabe ist bereits in der Datenbank vorhanden.\n" + "Die Genauigkeitsangabe für Geometrisches_Nivellement (Instrument: Trimble DiNi 0.3) wurde erfolgreich hinzugefügt.\n" ] } ], @@ -313,12 +226,13 @@ "id": "b28afe0c64aa59d6", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:04.524951100Z", - "start_time": "2026-02-03T12:52:04.281443800Z" + "end_time": "2026-02-05T09:19:57.950709500Z", + "start_time": "2026-02-05T09:19:57.622406200Z" } }, "source": [ - "# Zelle 5: Import Tachymeterdatensätze\n", + "# Zelle 8: Import Tachymeter-Datensätze\n", + "# Hinweis: Sind die Dateien bereits für den Import verwendet worden, wird der Import abgebrochen.\n", "\n", "# CSV-Datei mit den Koordinaten im Lokalen-Horizontsystem aus dem Tachymeterexport\n", "pfad_datei_tachymeter_koordinaten = r\"Daten\\campsnetz_koordinaten_plus_nachmessungen.csv\"\n", @@ -326,13 +240,13 @@ "# CSV-Datei mit den Beobachtungen aus dem Tachymeterexport\n", "pfad_datei_tachymeter_beobachtungen_csv = r\"Daten\\campsnetz_beobachtungen_plus_nachmessungen.csv\"\n", "\n", - "# JXL-Datei mit den Beobachtungen aus dem Tachymeterexport\n", + "# JXL-Datei mit den Beobachtungen aus dem Tachymeterexport (für die Korrektur der gerundeten Daten in der CSV-Datei)\n", "pfad_datei_tachymeter_beobachtungen_jxl = r\"Daten\\campusnetz_bereinigt_plus_nachmessung_neu.jxl\"\n", "\n", - "# Festlegen des Dateinamens der korrigierten und erweiterten CSV-Datei mit den Tachymeterbeobachtungen\n", + "# Dateiname der korrigierten und erweiterten CSV-Datei mit den Tachymeterbeobachtungen\n", "pfad_datei_tachymeterbeobachtungen_korrigiert = r\"Daten\\campsnetz_beobachtungen_plus_nachmessungen_korrigiert.csv\"\n", "\n", - "# InstrumentenID des Tachymeters zu den übergebenen Dateien übergeben\n", + "# InstrumentenID des Tachymeters (siehe Zelle 4)\n", "instrumentenID_Tachymeter = 1\n", "\n", "# Verarbeitung und Import der Tachymeterdatensätze\n", @@ -345,9 +259,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Korrektur erfolgreich abgeschlossen. Ausgabe: Daten\\campsnetz_beobachtungen_plus_nachmessungen_korrigiert.csv\n", - "Ersetzungen in der CSV-Datei (Rundung -> JXL volle Nachkommastellen): {'Hz': 1639, 'Z': 1838, 'SD': 747}\n", - "Der Import wurde abgebrochen, weil die Beobachtungen aus der Datei Daten\\campsnetz_beobachtungen_plus_nachmessungen_korrigiert.csv bereits in der Datenbank vorhanden sind.\n" + "Der Import der Datei Daten\\campsnetz_beobachtungen_plus_nachmessungen_korrigiert.csv wurde erfolgreich abgeschlossen.\n" ] } ], @@ -356,24 +268,25 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:04.583057500Z", - "start_time": "2026-02-03T12:52:04.526952800Z" + "end_time": "2026-02-05T09:19:59.775533Z", + "start_time": "2026-02-05T09:19:59.693878400Z" } }, "cell_type": "code", "source": [ - "# Zelle 6: Import der GNSS-Datensätze\n", + "# Zelle 9: Import GNSS-Datensätze\n", + "# Hinweis: Sind die Dateien bereits importiert, wird der Import übersprungen.\n", "\n", "# CSV-Datei mit den Koordinaten der statischen GNSS-Messung als export aus LeicaGeoOffice\n", "pfad_koordinaten_gnss = r\"Daten\\Koordinaten_ohne0648und10002.csv\"\n", "\n", - "# Txt-Datei mit den Basislinien und den Kovarianzen als export aus LeicaGeoOffice\n", + "# TXT-Datei mit den Basislinien und den Kovarianzen als Export aus LeicaGeoOffice\n", "pfad_basislinien_gnss = r\"Daten\\Basislinien_ohne0648und10002.asc.txt\"\n", "\n", - "# Standardabweichung apriori der Koordinaten der SAPOS-Referenzstationen in X, Y und Z für die weiche Lagerung\n", + "# Standardabweichung a priori der Koordinaten der SAPOS-Referenzstationen in X, Y und Z für die weiche Lagerung\n", "genauigkeit_sapos_referenzstationen = [0.005, 0.005, 0.008]\n", "\n", - "# InstrumentenID aller GNSS-Empfänger\n", + "# InstrumentenID aller GNSS-Empfänger (siehe Zelle 4)\n", "instrumentenID_GNSS_Empfaenger = 4\n", "\n", "# Import ausführen\n", @@ -386,7 +299,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Der Import wurde abgebrochen, weil die Beobachtungen aus der Datei Daten\\Basislinien_ohne0648und10002.asc.txt bereits in der Datenbank vorhanden sind.\n" + "Der Import der Datei Daten\\Basislinien_ohne0648und10002.asc.txt wurde erfolgreich abgeschlossen.\n" ] } ], @@ -397,12 +310,12 @@ "id": "2d8a0533726304a8", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:22.907935800Z", - "start_time": "2026-02-03T12:52:04.608054100Z" + "end_time": "2026-02-05T09:20:03.554566100Z", + "start_time": "2026-02-05T09:20:00.666135900Z" } }, "source": [ - "# Zelle 7: Helmerttransformation\n", + "# Zelle 10: Helmerttransformation\n", "\n", "# Transformationsparameter zwischen lokalem-Horizontsystem aus dem Tachymeterexport und ETRS89 / DREF 91 (Realisierung 2025) berechnen\n", "transformationsparameter = trafos.Helmerttransformation_Euler_Transformationsparameter_berechnen()\n", @@ -418,80 +331,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "l_berechnet_final:\n", + "Iteration Nr.6 abgeschlossen\n", + "Koordinaten berechnet aus Helmerttransformation:\n", "10001: 3794901.521, 546745.584, 5080065.755\n", - "10002: 3794867.011, 546729.617, 5080092.695\n", - "10003: 3794841.061, 546735.120, 5080111.557\n", - "10004: 3794803.469, 546714.145, 5080141.394\n", - "10005: 3794793.850, 546722.325, 5080147.942\n", - "10006: 3794766.364, 546707.643, 5080169.743\n", - "10007: 3794831.055, 546758.730, 5080116.675\n", "10008: 3794783.870, 546746.646, 5080152.758\n", - "10009: 3794767.479, 546740.091, 5080165.961\n", - "10010: 3794758.643, 546767.670, 5080169.471\n", - "10011: 3794894.930, 546833.119, 5080061.164\n", - "10012: 3794853.608, 546805.240, 5080094.900\n", - "10013: 3794849.615, 546826.872, 5080095.440\n", "10014: 3794838.746, 546812.367, 5080105.181\n", - "10015: 3794839.472, 546793.520, 5080106.782\n", - "10016: 3794826.666, 546788.731, 5080116.878\n", - "10017: 3794825.022, 546831.703, 5080113.383\n", - "10018: 3794762.253, 546797.694, 5080163.986\n", - "10019: 3794800.100, 546833.327, 5080131.731\n", - "10020: 3794782.615, 546834.473, 5080145.041\n", - "10021: 3794776.034, 546833.743, 5080150.018\n", - "10022: 3794778.341, 546841.753, 5080147.280\n", - "10023: 3794780.799, 546848.104, 5080144.929\n", - "10024: 3794772.819, 546857.098, 5080149.838\n", - "10025: 3794774.211, 546871.813, 5080147.362\n", "10026: 3794753.858, 546827.445, 5080167.092\n", - "10027: 3794757.593, 546874.333, 5080159.319\n", "10028: 3794889.671, 546908.764, 5080056.920\n", - "10029: 3794845.029, 546914.918, 5080089.105\n", - "10030: 3794845.357, 546901.029, 5080090.362\n", - "10031: 3794821.763, 546877.550, 5080110.752\n", - "10032: 3794807.851, 546888.488, 5080119.750\n", - "10033: 3794800.019, 546874.654, 5080127.209\n", - "10034: 3794886.107, 546965.700, 5080053.411\n", - "10035: 3794845.950, 546961.514, 5080084.091\n", - "10036: 3794815.055, 546969.597, 5080106.065\n", "10037: 3794800.626, 546960.749, 5080117.709\n", - "10038: 3794806.325, 546929.732, 5080116.901\n", - "10039: 3794804.164, 546914.733, 5080120.142\n", - "10040: 3794780.721, 546956.425, 5080133.161\n", - "10041: 3794778.154, 546925.879, 5080138.723\n", - "10042: 3794758.957, 546937.061, 5080151.609\n", - "10043: 3794747.274, 546919.151, 5080162.149\n", "10044: 3794752.686, 546958.324, 5080154.237\n", - "10045: 3794881.901, 547019.784, 5080050.718\n", - "10046: 3794846.580, 547012.997, 5080077.441\n", - "10047: 3794831.534, 547018.239, 5080088.124\n", - "10048: 3794809.105, 547017.302, 5080105.013\n", - "10049: 3794786.888, 547021.076, 5080121.441\n", - "10050: 3794766.769, 547012.526, 5080137.481\n", - "10051: 3794767.056, 546988.699, 5080139.995\n", - "10052: 3794743.624, 546984.416, 5080157.827\n", - "10053: 3794748.143, 547017.574, 5080150.924\n", "10054: 3794889.163, 547086.949, 5080038.116\n", - "10055: 3794838.849, 547081.903, 5080075.695\n", - "10056: 3794825.037, 547094.810, 5080084.484\n", - "10057: 3794800.815, 547078.670, 5080104.567\n", - "10058: 3794766.103, 547091.752, 5080129.113\n", "10059: 3794736.830, 547079.447, 5080152.362\n", - "666: 3794868.128, 547082.279, 5080054.295\n", - "812: 3794850.535, 547010.934, 5080075.089\n", - "816: 3794817.790, 547004.825, 5080100.040\n", - "FH11: 3794853.321, 546973.225, 5080077.224\n", - "FH13: 3794832.559, 546754.704, 5080116.651\n", - "FH14: 3794853.863, 546972.809, 5080076.935\n", - "FH15: 3794794.213, 546870.696, 5080132.914\n", - "FH3: 3794810.977, 547013.441, 5080105.068\n", - "FH4: 3794773.597, 546985.341, 5080135.930\n", "Streckendifferenzen zwischen Näherungskoordinate aus statischer GNSS-Messung und ergebnis der Helmerttransformation:\n", - "[0.027793, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025879, 0.0, 0.0, 0.0, 0.0, 0.0, 0.016527, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01604, 0.0, 0.066139, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07164, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025138, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.125881, 0.0, 0.0, 0.0, 0.0, 0.142477, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n", + "[0.027793, 0.025879, 0.016527, 0.01604, 0.066139, 0.07164, 0.025138, 0.125881, 0.142477]\n", "\n", "Differenz Schwerpunkt zwischen Näherungskoordinate aus statischer GNSS-Messung und ergebnis der Helmerttransformation::\n", - "Matrix([[-2.59e-10], [6.85e-12], [-2.32e-10]])\n", + "Matrix([[3.10e-10], [1.16e-10], [1.03e-10]])\n", "Betrag der Schwerpunkt-Differenz zwischen Näherungskoordinate aus statischer GNSS-Messung und ergebnis der Helmerttransformation::\n", "0.000m\n" ] @@ -504,12 +359,13 @@ "id": "ed9be38e35cfc619", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:23.040551100Z", - "start_time": "2026-02-03T12:52:22.972731300Z" + "end_time": "2026-02-05T09:20:04.917175200Z", + "start_time": "2026-02-05T09:20:04.875851600Z" } }, "source": [ - "# Zelle 8: Anschlusspunke für die weiche Lagerung festlegen\n", + "# Zelle 11: Anschlusspunkte für die weiche Lagerung festlegen\n", + "# Definieren Sie hier die Punktnummern, die als Anschlusspunkte für die weiche Lagerung verwendet werden sollen.\n", "\n", "# X-Koordinate der Punkte verwenden\n", "liste_koordinaten_x = []\n", @@ -534,12 +390,13 @@ "id": "2d2156381d974d94", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:23.074077800Z", - "start_time": "2026-02-03T12:52:23.053565800Z" + "end_time": "2026-02-05T09:20:05.699043500Z", + "start_time": "2026-02-05T09:20:05.656188800Z" } }, "source": [ - "# Zelle 9: Anschlusspunkte für die weiche Lagerung entfenen\n", + "# Zelle 12: Anschlusspunkte für die weiche Lagerung entfernen\n", + "# Definieren Sie hier die Punktnummern, die aus den Anschlusspunkten entfernt werden sollen.\n", "\n", "# X-Koordinate der Punkte entfernen\n", "liste_koordinaten_x = []\n", @@ -564,17 +421,18 @@ "id": "c2db29680c53f8c4", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T12:52:23.139970Z", - "start_time": "2026-02-03T12:52:23.077028300Z" + "end_time": "2026-02-05T09:20:07.171376500Z", + "start_time": "2026-02-05T09:20:06.989817400Z" } }, "source": [ - "# Zelle 10: Nivellement-Beobachtungen Importieren\n", + "# Zelle 13: Nivellement-Beobachtungen importieren\n", + "# Hinweis: Ist die Datei bereits importiert, wird der Import übersprungen.\n", "\n", "# CSV-Datei mit den Beobachtungen des geometrischen Nivellements\n", "pfad_datei_nivellement = r\"Daten\\Niv_bereinigt.DAT.csv\"\n", "\n", - "# InstrumentenID des Nivelliers zu den importierenden Datensätzen\n", + "# InstrumentenID des Nivelliers (siehe Zelle 4)\n", "instrumentenID_Nivellier = 3\n", "\n", "# Datenvorverarbeitung und Import in die Datenbank ausführen\n", @@ -583,12 +441,14 @@ ], "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Der Import wurde abgebrochen, weil die Beobachtungen aus der Datei Daten\\Niv_bereinigt.DAT.csv bereits in der Datenbank vorhanden sind.\n", - "Der Import wurde abgebrochen, weil die Beobachtungen aus der Datei Daten\\Niv_bereinigt.DAT.csv bereits in der Datenbank vorhanden sind.\n" - ] + "data": { + "text/plain": [ + "'Die Beobachtungen aus der Datei Daten\\\\Niv_bereinigt.DAT.csv wurden erfolgreich importiert.'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" } ], "execution_count": 13 @@ -596,17 +456,14 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:11:53.649255300Z", - "start_time": "2026-02-03T12:52:23.147167600Z" + "end_time": "2026-02-05T09:40:21.622429600Z", + "start_time": "2026-02-05T09:20:07.835998100Z" } }, "cell_type": "code", "source": [ - "# Zelle 11: Parameterschätzung iterativ berechnen\n", - "# Hinweis: Die Ausführung dauert je nach Rechner zwischen 13 und 15 Minuten\n", - "\n", - "# Alle Matrizen für jede Iteration in einer CSV-Datei speichern.\n", - "speichern_in_csv = False\n", + "# Zelle 14: Parameterschätzung iterativ berechnen\n", + "# Hinweis: Die Ausführung dauert je nach Rechenkapazität zwischen 13 und 20 Minuten.\n", "\n", "# Berechnung durchführen\n", "A_matrix_numerisch, Jacobimatrix_symbolisch_liste_unbekannte, Jacobimatrix_symbolisch_liste_beobachtungsvektor, x, dx, dl_k, ausgabe_parameterschaetzung = iterat.iterationen(datumfestlegung, speichern_in_csv)\n", @@ -631,15 +488,13 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:11:54.246976300Z", - "start_time": "2026-02-03T13:11:53.855303300Z" + "end_time": "2026-02-05T09:40:21.839256800Z", + "start_time": "2026-02-05T09:40:21.633438400Z" } }, "cell_type": "code", "source": [ - "# Proben\n", - "import Proben\n", - "importlib.reload(Proben)\n", + "# Zelle 15: Proben\n", "\n", "# Rechenprobe (ATPV-Probe)\n", "Proben.atpv_probe(A_matrix_numerisch, ausgabe_parameterschaetzung[\"P\"], ausgabe_parameterschaetzung[\"v\"])\n", @@ -647,7 +502,7 @@ "# Hauptprobe\n", "Proben.hauptprobe(A_matrix_numerisch, dx, dl_k, ausgabe_parameterschaetzung[\"v\"])" ], - "id": "cd87680d3baf6c8e", + "id": "de8f624059005fdd", "outputs": [ { "name": "stdout", @@ -661,69 +516,31 @@ "execution_count": 15 }, { - "cell_type": "code", - "id": "eb8342d747a577db", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:11:54.298534800Z", - "start_time": "2026-02-03T13:11:54.253957500Z" + "end_time": "2026-02-05T09:40:21.883828300Z", + "start_time": "2026-02-05T09:40:21.852785300Z" } }, - "source": [ - "# ------------------------------- Netzqualitätsmaße -------------------------------" - ], + "cell_type": "code", + "source": "# ------------------------------- Netzqualitätsmaße -------------------------------", + "id": "7d53274224166103", "outputs": [], "execution_count": 16 }, - { - "cell_type": "code", - "id": "7117d4492b8784da", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-03T13:11:54.497387500Z", - "start_time": "2026-02-03T13:11:54.306525400Z" - } - }, - "source": [ - "# Import\n", - "from Netzqualität_Zuverlässigkeit import Zuverlaessigkeit\n", - "import Netzqualität_Zuverlässigkeit\n", - "importlib.reload(Netzqualität_Zuverlässigkeit)\n", - "from Netzqualität_Genauigkeit import Genauigkeitsmaße\n", - "import Netzqualität_Genauigkeit\n", - "importlib.reload(Netzqualität_Genauigkeit)\n", - "import Export\n", - "importlib.reload(Export)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 17 - }, { "cell_type": "code", "id": "84075bea1f2c44d7", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:11:54.606315800Z", - "start_time": "2026-02-03T13:11:54.547296700Z" + "end_time": "2026-02-05T09:40:21.941799700Z", + "start_time": "2026-02-05T09:40:21.898344100Z" } }, "source": [ - "# Gesamtredundanz r\n", - "n = A_matrix_numerisch.shape[0]\n", - "u = A_matrix_numerisch.shape[1]\n", - "r_gesamt = Zuverlaessigkeit.gesamtredundanz(n, u)\n", - "print(f\"Die Gesamtredundanz des Netzes beträgt: {r_gesamt}\")" + "# Zelle 16: Gesamtredundanz r\n", + "\n", + "r_gesamt = Netzqualitaet_Zuverlaessigkeit.Zuverlaessigkeit.gesamtredundanz(A_matrix_numerisch.shape[0], A_matrix_numerisch.shape[1])" ], "outputs": [ { @@ -734,58 +551,46 @@ ] } ], - "execution_count": 18 + "execution_count": 17 }, { "cell_type": "code", "id": "1797726c5b3af9bf", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:11:54.678508400Z", - "start_time": "2026-02-03T13:11:54.616273200Z" + "end_time": "2026-02-05T09:40:22.003786800Z", + "start_time": "2026-02-05T09:40:21.956317800Z" } }, "source": [ - "# s0 aposteriori\n", - "s0_aposteriori = Genauigkeitsmaße.berechne_s0apost(ausgabe_parameterschaetzung[\"v\"], ausgabe_parameterschaetzung[\"P\"], r_gesamt)\n", - "print(f\"s0 aposteriori beträgt: {s0_aposteriori:.4f}\")" + "# Zelle 17: s0 a posteriori\n", + "\n", + "s0_aposteriori = Netzqualitaet_Genauigkeit.Genauigkeitsmaße.berechne_s0apost(ausgabe_parameterschaetzung[\"v\"], ausgabe_parameterschaetzung[\"P\"], r_gesamt)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "s0 aposteriori beträgt: 15.6493\n" + "s0 a posteriori beträgt: 15.6493\n" ] } ], - "execution_count": 19 + "execution_count": 18 }, { "cell_type": "code", "id": "a2fe23d9a19ac2f9", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:11:57.429548800Z", - "start_time": "2026-02-03T13:11:54.681581300Z" + "end_time": "2026-02-05T09:40:25.737177800Z", + "start_time": "2026-02-05T09:40:22.016774100Z" } }, "source": [ - "import numpy as np\n", + "# Zelle 18: Redundanzanteile ri\n", "\n", - "# Redundanzanteile ri\n", - "R = Zuverlaessigkeit.berechne_R(ausgabe_parameterschaetzung[\"Q_vv\"], ausgabe_parameterschaetzung[\"P\"])\n", - "ri, EVi = Zuverlaessigkeit.berechne_ri(R)\n", - "ri = np.asarray(ri).reshape(-1)\n", - "EVi = np.asarray(EVi).reshape(-1)\n", - "\n", - "labels = [str(s) for s in Jacobimatrix_symbolisch_liste_beobachtungsvektor]\n", - "klassen = [Zuverlaessigkeit.klassifiziere_ri(r) for r in ri]\n", - "Redundanzanteile = pd.DataFrame({\"Beobachtung\": labels, \"r_i\": ri, \"EV_i [%]\": EVi,\n", - " \"Klassifikation\": klassen,})\n", - "display(HTML(Redundanzanteile.to_html(index=False)))\n", - "\n", - "Redundanzanteile.to_excel(r\"Zwischenergebnisse\\Redundanzanteile.xlsx\", index=False)" + "R, ri, EVi, Redundanzanteile = Netzqualitaet_Zuverlaessigkeit.Zuverlaessigkeit.redundanzanteile_ri(ausgabe_parameterschaetzung[\"Q_vv\"], ausgabe_parameterschaetzung[\"P\"], Jacobimatrix_symbolisch_liste_beobachtungsvektor)" ], "outputs": [ { @@ -20747,42 +20552,21 @@ } } ], - "execution_count": 20 + "execution_count": 19 }, { "cell_type": "code", "id": "14db90a3d4d9118b", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:12:02.949194500Z", - "start_time": "2026-02-03T13:11:57.966965200Z" + "end_time": "2026-02-05T09:40:30.364997100Z", + "start_time": "2026-02-05T09:40:25.878584400Z" } }, "source": [ - "# Globaltest des Ausgleichungsmodells\n", - "sigma0_apriori = 1.0\n", + "# Zelle 19: Globaltest des Ausgleichungsmodells\n", "\n", - "alpha_input = input(\"Irrtumswahrscheinlichkeit α wählen (z.B. 0.05, 0.01) [Standard=0.001]: \").strip()\n", - "alpha_ds = 0.001 if alpha_input == \"\" else float(alpha_input)\n", - "\n", - "gt = Netzqualität_Zuverlässigkeit.Zuverlaessigkeit.globaltest(\n", - " r_gesamt=r_gesamt,\n", - " sigma0_apost=s0_aposteriori,\n", - " sigma0_apriori=sigma0_apriori,\n", - " alpha=alpha_ds\n", - ")\n", - "\n", - "df_globaltest = pd.DataFrame([\n", - " [\"Freiheitsgrad\", gt[\"r_gesamt\"]],\n", - " [\"σ̂₀ a posteriori\", gt[\"sigma0_apost\"]],\n", - " [\"σ₀ a priori\", gt[\"sigma0_apriori\"]],\n", - " [\"Signifikanzniveau α\", gt[\"alpha\"],],\n", - " [\"Testgröße T_G\", gt[\"T_G\"],],\n", - " [\"Kritischer Wert Fₖ\", gt[\"F_krit\"]],\n", - " [\"Nullhypothese H₀\", \"angenommen\" if gt[\"H0_angenommen\"] else \"verworfen\"],], columns=[\"Größe\", \"Wert\"])\n", - "\n", - "display(HTML(df_globaltest.to_html(index=False)))\n", - "print(gt[\"Interpretation\"])\n" + "globaltest = Netzqualitaet_Zuverlaessigkeit.Zuverlaessigkeit.globaltest(r_gesamt=r_gesamt, sigma0_apost=s0_aposteriori, sigma0_apriori=1)" ], "outputs": [ { @@ -20796,228 +20580,33 @@ ] } ], - "execution_count": 21 + "execution_count": 20 }, { "cell_type": "code", "id": "1a84fbfb3db101c9", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:12:08.875328300Z", - "start_time": "2026-02-03T13:12:03.011484700Z" + "end_time": "2026-02-05T11:12:52.115038200Z", + "start_time": "2026-02-05T11:12:47.181075700Z" } }, "source": [ - "# Lokaltest und innere Zuverlässigkeit\n", - "import ipywidgets as widgets\n", - "from IPython.display import display, clear_output\n", + "# Zelle 20: Lokaltest und innere Zuverlässigkeit\n", + "# Bei der Auswahl einer GNSS-Komponente, wie z.B. bx werden automatisch die anderen beiden Kompontenen aus-, bzw. abgewählt. Bitte warten, bis dies automatisch durchführt wurde.\n", "\n", - "itables.init_notebook_mode()\n", - "labels = [str(s) for s in Jacobimatrix_symbolisch_liste_beobachtungsvektor]\n", - "\n", - "alpha_ds = 0.001 if alpha_input == \"\" else float(alpha_input)\n", - "\n", - "beta_input = input(\"Macht des Tests (1-β) wählen [Standard: 80 % -> 0.80]: \").strip()\n", - "beta_ds = 0.80 if beta_input == \"\" else float(beta_input)\n", - "\n", - "Lokaltest = Netzqualität_Zuverlässigkeit.Zuverlaessigkeit.lokaltest_innere_Zuverlaessigkeit(\n", - " v=ausgabe_parameterschaetzung[\"v\"],\n", - " Q_vv=ausgabe_parameterschaetzung[\"Q_vv\"],\n", - " ri=ri,\n", - " labels=labels,\n", - " s0_apost=s0_aposteriori,\n", - " alpha=alpha_ds,\n", - " beta=beta_ds\n", - ")\n", - "\n", - "if \"v_i\" in Lokaltest.columns:\n", - " Lokaltest[\"v_i\"] = Lokaltest[\"v_i\"].round(6)\n", - "if \"r_i\" in Lokaltest.columns:\n", - " Lokaltest[\"r_i\"] = Lokaltest[\"r_i\"].round(4)\n", - "if \"s_vi\" in Lokaltest.columns:\n", - " Lokaltest[\"s_vi\"] = Lokaltest[\"s_vi\"].round(6)\n", - "if \"GF_i\" in Lokaltest.columns:\n", - " Lokaltest[\"GF_i\"] = Lokaltest[\"GF_i\"].round(6)\n", - "if \"GRZW_i\" in Lokaltest.columns:\n", - " Lokaltest[\"GRZW_i\"] = Lokaltest[\"GRZW_i\"].round(6)\n", - "\n", - "df = Lokaltest.copy()\n", - "\n", - "if \"Beobachtung\" not in df.columns:\n", - " if df.index.name == \"Beobachtung\":\n", - " df = df.reset_index()\n", - " else:\n", - " df = df.reset_index().rename(columns={\"index\": \"Beobachtung\"})\n", - "\n", - "if \"Beobachtung_ausschalten\" not in df.columns:\n", - " df.insert(0, \"Beobachtung_ausschalten\", \"\")\n", - "else:\n", - " col = df.pop(\"Beobachtung_ausschalten\")\n", - " df.insert(0, \"Beobachtung_ausschalten\", col)\n", - "\n", - "tabelle = ITable(\n", - " df,\n", - " maxBytes=5 * 1024 * 1024, # 5 MB\n", - " columnDefs=[\n", - " {\"targets\": 0, \"orderable\": False, \"className\": \"select-checkbox\", \"width\": \"26px\"},\n", - " ],\n", - " select={\"style\": \"multi\", \"selector\": \"td:first-child\"},\n", - " order=[[1, \"asc\"]],\n", - ")\n", - "\n", - "beob_list = tabelle.df[\"Beobachtung\"].astype(str).tolist()\n", - "dict_gnss_key_zu_index = {}\n", - "\n", - "for i, beob in enumerate(beob_list):\n", - " beob = str(beob).strip()\n", - "\n", - " if \"_gnssbx_\" in beob:\n", - " key = beob.split(\"_gnssbx_\", 1)[1].strip()\n", - " if key not in dict_gnss_key_zu_index:\n", - " dict_gnss_key_zu_index[key] = {}\n", - " dict_gnss_key_zu_index[key][\"bx\"] = i\n", - "\n", - " elif \"_gnssby_\" in beob:\n", - " key = beob.split(\"_gnssby_\", 1)[1].strip()\n", - " if key not in dict_gnss_key_zu_index:\n", - " dict_gnss_key_zu_index[key] = {}\n", - " dict_gnss_key_zu_index[key][\"by\"] = i\n", - "\n", - " elif \"_gnssbz_\" in beob:\n", - " key = beob.split(\"_gnssbz_\", 1)[1].strip()\n", - " if key not in dict_gnss_key_zu_index:\n", - " dict_gnss_key_zu_index[key] = {}\n", - " dict_gnss_key_zu_index[key][\"bz\"] = i\n", - "\n", - "\n", - "auswahl_zeilen_vorher = set(tabelle.selected_rows or [])\n", - "update_durch_code = False\n", - "\n", - "def gnss_komponente_und_key_aus_beobachtung(beob: str):\n", - " \"\"\"Extrahiert Komponente und einen eindeutigen Key für das Trio.\"\"\"\n", - " beob = str(beob).strip()\n", - " for komp in [\"bx\", \"by\", \"bz\"]:\n", - " marker = f\"_gnss{komp}_\"\n", - " if marker in beob:\n", - " # Ersetzt z.B. '_gnssbx_' durch '_gnss_'.\n", - " # So haben '39_gnssbx_...' und '39_gnssby_...' den identischen Key.\n", - " key = beob.replace(marker, \"_gnss_\")\n", - " return komp, key\n", - " return None, None\n", - "\n", - "# 1. Eindeutiges Dictionary aufbauen\n", - "dict_gnss_key_zu_alle_indizes = {}\n", - "for idx, row in df.iterrows():\n", - " komp, key = gnss_komponente_und_key_aus_beobachtung(row[\"Beobachtung\"])\n", - " if key:\n", - " if key not in dict_gnss_key_zu_alle_indizes:\n", - " dict_gnss_key_zu_alle_indizes[key] = []\n", - " dict_gnss_key_zu_alle_indizes[key].append(idx)\n", - "\n", - "def on_selected_rows_change(change):\n", - " global auswahl_zeilen_vorher, update_durch_code\n", - " if update_durch_code:\n", - " return\n", - "\n", - " # Aktuelle Auswahl vom Widget (DF-Indizes)\n", - " auswahl_aktuell = set(change[\"new\"] or [])\n", - "\n", - " hinzu = auswahl_aktuell - auswahl_zeilen_vorher\n", - " entfernt = auswahl_zeilen_vorher - auswahl_aktuell\n", - " auswahl_final = set(auswahl_aktuell)\n", - "\n", - " # LOGIK: Hinzufügen (Trio vervollständigen)\n", - " for idx in hinzu:\n", - " # Sicherstellen, dass der Index im DF existiert\n", - " beob_name = str(df.loc[idx, \"Beobachtung\"])\n", - " _, key = gnss_komponente_und_key_aus_beobachtung(beob_name)\n", - " if key in dict_gnss_key_zu_alle_indizes:\n", - " for p_idx in dict_gnss_key_zu_alle_indizes[key]:\n", - " auswahl_final.add(p_idx)\n", - "\n", - " # LOGIK: Abwählen (Ganzes Trio entfernen)\n", - " for idx in entfernt:\n", - " beob_name = str(df.loc[idx, \"Beobachtung\"])\n", - " _, key = gnss_komponente_und_key_aus_beobachtung(beob_name)\n", - " if key in dict_gnss_key_zu_alle_indizes:\n", - " for p_idx in dict_gnss_key_zu_alle_indizes[key]:\n", - " if p_idx in auswahl_final:\n", - " auswahl_final.remove(p_idx)\n", - "\n", - " # Nur bei Änderungen das Widget-Update triggern\n", - " if auswahl_final != auswahl_aktuell:\n", - " update_durch_code = True\n", - " tabelle.selected_rows = sorted(list(auswahl_final))\n", - " update_durch_code = False\n", - "\n", - " auswahl_zeilen_vorher = set(tabelle.selected_rows or [])\n", - " refresh_panel()\n", - "\n", - "def export_ausschalten_dict(Eintrag_Auswahl=\"beobachtung_ausschalten\", Wert_nicht_ausgewaehlt=\"\"):\n", - " auswahl = set(tabelle.selected_rows or [])\n", - " beob_list = tabelle.df[\"Beobachtung\"].astype(str).tolist()\n", - "\n", - " return {\n", - " beob_list[i]: (Eintrag_Auswahl if i in auswahl else Wert_nicht_ausgewaehlt)\n", - " for i in range(len(beob_list))\n", - " }\n", - "\n", - "ausschalten_dict = export_ausschalten_dict()\n", - "\n", - "out = widgets.Output()\n", - "btn_export = widgets.Button(description=\"Auswahl speichern\", icon=\"download\")\n", - "btn_reset = widgets.Button(description=\"Rückgängig\", icon=\"refresh\")\n", - "\n", - "def refresh_panel(_=None):\n", - " global ausschalten_dict\n", - " ausschalten_dict = export_ausschalten_dict()\n", - "\n", - " with out:\n", - " clear_output(wait=True)\n", - " auswahl = tabelle.selected_rows or []\n", - " print(f\"AUSGESCHALTET: {len(auswahl)}\")\n", - "\n", - " #Vorschau\n", - " if len(auswahl) > 0:\n", - " cols = [c for c in [\"Beobachtung\", \"v_i\", \"r_i\", \"auffaellig\", \"GF_i\", \"GRZW_i\"] if c in tabelle.df.columns]\n", - " display(tabelle.df.iloc[auswahl][cols].head(30))\n", - "\n", - "def exportieren(_):\n", - " global ausschalten_dict\n", - " ausschalten_dict = export_ausschalten_dict()\n", - "\n", - " with out:\n", - " clear_output(wait=True)\n", - " print(\"ausschalten_dict ist aktualisiert.\")\n", - " ausgeschaltet = [k for k, v in ausschalten_dict.items() if v == \"X\"]\n", - " print(f\"Nur ausgeschaltete Beobachtungen ({len(ausgeschaltet)}):\")\n", - " display(ausgeschaltet[:300])\n", - "\n", - "def on_reset(_):\n", - " tabelle.selected_rows = []\n", - " refresh_panel()\n", - "\n", - "btn_export.on_click(exportieren)\n", - "btn_reset.on_click(on_reset)\n", - "\n", - "tabelle.observe(on_selected_rows_change, names=\"selected_rows\")\n", - "\n", - "\n", - "display(widgets.VBox([tabelle, widgets.HBox([btn_export, btn_reset]), out]))\n", - "refresh_panel()\n", - "\n", - "Lokaltest.to_excel(r\"Zwischenergebnisse\\Lokaltest_innere_Zuverlaessugkeit.xlsx\", index=False)\n" + "lokaltest, beta = Netzqualitaet_Zuverlaessigkeit.Zuverlaessigkeit.aufruf_lokaltest(Jacobimatrix_symbolisch_liste_beobachtungsvektor, globaltest[\"alpha\"], ausgabe_parameterschaetzung, ri, s0_aposteriori)\n" ], "outputs": [ { "data": { "text/plain": [ - "VBox(children=(, HBox(children=(Button(description='Auswah…" + "VBox(children=(, HBox(children=(Button(description='Auswah…" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "256898123de346839535b0ddd8d70ee8" + "model_id": "226872320799487eb9dcd3bd95e54514" } }, "metadata": {}, @@ -21027,21 +20616,21 @@ } } ], - "execution_count": 22 + "execution_count": 46 }, { "cell_type": "code", "id": "ebf2c9ef0a1899dc", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:12:09.075031Z", - "start_time": "2026-02-03T13:12:08.943230800Z" + "end_time": "2026-02-05T10:42:17.691148400Z", + "start_time": "2026-02-05T10:42:17.607048600Z" } }, "source": [ - "# Zelle XX: Ausgewählte Beobachtungen für weitere Iterationen ausschalten\n", + "# Zelle 21: Ausgewählte Beobachtungen für weitere Iterationen ausschalten\n", "\n", - "db_zugriff.set_beobachtung_ausschalten(ausschalten_dict)" + "db_zugriff.set_beobachtung_ausschalten(lokaltest)" ], "outputs": [ { @@ -21052,18 +20641,18 @@ ] } ], - "execution_count": 23 + "execution_count": 38 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:12:09.242962400Z", - "start_time": "2026-02-03T13:12:09.088708700Z" + "end_time": "2026-02-05T10:16:17.632511900Z", + "start_time": "2026-02-05T10:16:17.358271500Z" } }, "cell_type": "code", "source": [ - "# Varianzkomponentenschätzung\n", + "# Zelle 22: Varianzkomponentenschätzung\n", "\n", "vks.varianzkomponten_berechnen(Jacobimatrix_symbolisch_liste_beobachtungsvektor, ausgabe_parameterschaetzung, R)" ], @@ -21073,37 +20662,39 @@ "name": "stdout", "output_type": "stream", "text": [ + "s0 a posteriori beträgt: 3.0070\n", "s0 aposteriori der Beobachtungsgruppe SD beträgt: 3.0070\n", "Varianz aposteriori der Beobachtungsgruppe SD beträgt: 9.0422\n", + "s0 a posteriori beträgt: 3.7396\n", "s0 aposteriori der Beobachtungsgruppe R beträgt: 3.7396\n", "Varianz aposteriori der Beobachtungsgruppe R beträgt: 13.9844\n", + "s0 a posteriori beträgt: 8.2830\n", "s0 aposteriori der Beobachtungsgruppe ZW beträgt: 8.2830\n", "Varianz aposteriori der Beobachtungsgruppe ZW beträgt: 68.6088\n", + "s0 a posteriori beträgt: 65.8360\n", "s0 aposteriori der Beobachtungsgruppe gnss beträgt: 65.8360\n", "Varianz aposteriori der Beobachtungsgruppe gnss beträgt: 4334.3734\n", + "s0 a posteriori beträgt: 33.6965\n", "s0 aposteriori der Beobachtungsgruppe niv beträgt: 33.6965\n", "Varianz aposteriori der Beobachtungsgruppe niv beträgt: 1135.4575\n", + "s0 a posteriori beträgt: 1.5121\n", "s0 aposteriori der Beobachtungsgruppe lA beträgt: 1.5121\n", "Varianz aposteriori der Beobachtungsgruppe lA beträgt: 2.2865\n" ] } ], - "execution_count": 24 + "execution_count": 33 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:12:09.491393600Z", - "start_time": "2026-02-03T13:12:09.247224700Z" + "end_time": "2026-02-05T10:16:18.663132300Z", + "start_time": "2026-02-05T10:16:18.301299800Z" } }, "cell_type": "code", "source": [ - "# Varianzkomponten anpassen\n", - "\n", - "import Varianzkomponentenschaetzung\n", - "importlib.reload(Varianzkomponentenschaetzung)\n", - "vks = Varianzkomponentenschaetzung.VKS(pfad_datenbank)\n", + "# Zelle 23: Varianzkomponten anpassen\n", "\n", "vks.vks_ausfuehren()\n", "vks.zeige_vks_tabelle()" @@ -21118,7 +20709,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "14ba5b395c9a4bb29a269910823a04f9" + "model_id": "becccb3afdec4128b0905ff233a14b5f" } }, "metadata": {}, @@ -21128,33 +20719,45 @@ } } ], - "execution_count": 25 + "execution_count": 34 }, { "cell_type": "code", "id": "256f4a0805b69e14", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:12:11.770972900Z", - "start_time": "2026-02-03T13:12:09.497422600Z" + "end_time": "2026-02-05T11:14:24.072553600Z", + "start_time": "2026-02-05T11:14:07.254502900Z" } }, "source": [ - "# Äußere Zuverlässigkeit: Einfluss auf die Punktlage (EP)\n", - "from Netzqualität_Zuverlässigkeit import Zuverlaessigkeit\n", - "import Netzqualität_Zuverlässigkeit\n", - "importlib.reload(Netzqualität_Zuverlässigkeit)\n", - "labels = [str(s) for s in Jacobimatrix_symbolisch_liste_beobachtungsvektor]\n", + "# Zelle 24: Äußere Zuverlässigkeit\n", + "importlib.reload(Netzqualitaet_Zuverlaessigkeit)\n", "\n", - "Aussen = Netzqualität_Zuverlässigkeit.Zuverlaessigkeit.aeussere_zuverlaessigkeit_excelstyle_3d(\n", - " Lokaltest=Lokaltest,\n", - " labels=labels,\n", + "bezeichnungen = [str(s) for s in Jacobimatrix_symbolisch_liste_beobachtungsvektor]\n", + "\n", + "bezeichnungen = [str(s) for s in Jacobimatrix_symbolisch_liste_beobachtungsvektor]\n", + "\n", + "lokaltest_ergebnisse = Netzqualitaet_Zuverlaessigkeit.Zuverlaessigkeit.lokaltest_innere_Zuverlaessigkeit(\n", + " ausgabe_parameterschaetzung[\"v\"],\n", + " ausgabe_parameterschaetzung[\"Q_vv\"],\n", + " ri,\n", + " bezeichnungen,\n", + " s0_aposteriori,\n", + " globaltest[\"alpha\"],\n", + " beta\n", + ")\n", + "\n", + "Aussen = Netzqualitaet_Zuverlaessigkeit.Zuverlaessigkeit.aeussere_zuverlaessigkeit(\n", + " Lokaltest=lokaltest_ergebnisse,\n", + " bezeichnung=bezeichnungen,\n", " Qxx=ausgabe_parameterschaetzung[\"Q_xx\"],\n", " A=A_matrix_numerisch,\n", " P=ausgabe_parameterschaetzung[\"P\"],\n", " s0_apost=s0_aposteriori,\n", " unbekannten_liste=Jacobimatrix_symbolisch_liste_unbekannte,\n", - " x=x\n", + " x=x,\n", + " ausschliessen=\"lA\"\n", ")\n", "\n", "display(HTML(Aussen.to_html(index=False)))\n", @@ -21162,42 +20765,36395 @@ ], "outputs": [ { - "ename": "AttributeError", - "evalue": "type object 'Zuverlaessigkeit' has no attribute 'aeussere_zuverlaessigkeit_excelstyle_3d'", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mAttributeError\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[26]\u001B[39m\u001B[32m, line 7\u001B[39m\n\u001B[32m 4\u001B[39m importlib.reload(Netzqualität_Zuverlässigkeit)\n\u001B[32m 5\u001B[39m labels = [\u001B[38;5;28mstr\u001B[39m(s) \u001B[38;5;28;01mfor\u001B[39;00m s \u001B[38;5;129;01min\u001B[39;00m Jacobimatrix_symbolisch_liste_beobachtungsvektor]\n\u001B[32m----> \u001B[39m\u001B[32m7\u001B[39m Aussen = \u001B[43mNetzqualität_Zuverlässigkeit\u001B[49m\u001B[43m.\u001B[49m\u001B[43mZuverlaessigkeit\u001B[49m\u001B[43m.\u001B[49m\u001B[43maeussere_zuverlaessigkeit_excelstyle_3d\u001B[49m(\n\u001B[32m 8\u001B[39m Lokaltest=Lokaltest,\n\u001B[32m 9\u001B[39m labels=labels,\n\u001B[32m 10\u001B[39m Qxx=ausgabe_parameterschaetzung[\u001B[33m\"\u001B[39m\u001B[33mQ_xx\u001B[39m\u001B[33m\"\u001B[39m],\n\u001B[32m 11\u001B[39m A=A_matrix_numerisch,\n\u001B[32m 12\u001B[39m P=ausgabe_parameterschaetzung[\u001B[33m\"\u001B[39m\u001B[33mP\u001B[39m\u001B[33m\"\u001B[39m],\n\u001B[32m 13\u001B[39m s0_apost=s0_aposteriori,\n\u001B[32m 14\u001B[39m unbekannten_liste=Jacobimatrix_symbolisch_liste_unbekannte,\n\u001B[32m 15\u001B[39m x=x\n\u001B[32m 16\u001B[39m )\n\u001B[32m 18\u001B[39m display(HTML(Aussen.to_html(index=\u001B[38;5;28;01mFalse\u001B[39;00m)))\n\u001B[32m 19\u001B[39m Aussen.to_excel(\u001B[33mr\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mZwischenergebnisse\u001B[39m\u001B[33m\\\u001B[39m\u001B[33mAeussere_Zuverlaessigkeit.xlsx\u001B[39m\u001B[33m\"\u001B[39m, index=\u001B[38;5;28;01mFalse\u001B[39;00m)\n", - "\u001B[31mAttributeError\u001B[39m: type object 'Zuverlaessigkeit' has no attribute 'aeussere_zuverlaessigkeit_excelstyle_3d'" - ] + "data": { + 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BeobachtungStand-PktZiel-Pktr_iEP_GF [mm]EP_grzw [mm]EFSP_3D [mm]EF*SP_3D [mm]
1_SD_1_10009_1000610009100060.9276340.0070952.3055110.68399448.60053433.242473
2_SD_1_10009_1001010009100100.9357400.2718342.0380320.64175148.58911031.182097
3_SD_1_10009_1001810009100180.9353200.1340772.0558090.64398948.58911031.290853
4_SD_1_10009_1000810009100080.9276540.2653072.3037930.68389148.58911033.229645
5_SD_1_10009_1000610009100060.9276340.0163002.3055110.68399448.60053433.242472
6_SD_1_10009_1001010009100100.9357400.2479372.0380320.64175148.58911031.182098
7_SD_1_10009_1001810009100180.9353200.1271762.0558090.64398948.58911031.290854
8_SD_1_10009_1000810009100080.9276540.2691462.3037930.68389148.58911033.229645
9_SD_1_10009_1000610009100060.9276340.0305132.3055110.68399448.60053433.242473
10_SD_1_10009_1001010009100100.9357400.2447752.0380320.64175148.58911031.182098
11_SD_1_10009_1001810009100180.9353200.1375402.0558090.64398948.58911031.290853
12_SD_1_10009_1000810009100080.9276540.2886362.3037930.68389148.58911033.229646
13_SD_2_10006_1000910006100090.9276340.1193472.3055110.68399448.60053433.242475
14_SD_2_10006_1000510006100050.9433170.0013711.7912940.60029948.60053429.174860
15_SD_2_10006_1000310006100030.9437530.0154401.7885460.59785048.60053429.055847
16_SD_2_10006_1000410006100040.9326910.0522722.1404800.65786848.63818231.997486
17_SD_2_10006_1001010006100100.9289550.3171452.2663610.67723648.60053432.914028
18_SD_2_10006_1001810006100180.9225990.0589222.4862790.70931648.60053434.473158
19_SD_2_10006_1000910006100090.9276340.1279622.3055110.68399448.60053433.242476
20_SD_2_10006_1000510006100050.9433170.0254001.7912940.60029948.60053429.174861
21_SD_2_10006_1000310006100030.9437530.0097831.7885460.59785048.60053429.055847
22_SD_2_10006_1000410006100040.9326910.0033142.1404800.65786848.63818231.997488
23_SD_2_10006_1001010006100100.9289550.3247232.2663610.67723648.60053432.914028
24_SD_2_10006_1001810006100180.9225990.0379672.4862790.70931648.60053434.473159
25_SD_2_10006_1000910006100090.9276340.1240862.3055110.68399448.60053433.242475
26_SD_2_10006_1000510006100050.9433170.0043551.7912940.60029948.60053429.174861
27_SD_2_10006_1000310006100030.9437530.0021471.7885460.59785048.60053429.055848
28_SD_2_10006_1000410006100040.9326910.0367312.1404800.65786848.63818231.997486
29_SD_2_10006_1001010006100100.9289550.3170832.2663610.67723648.60053432.914028
30_SD_2_10006_1001810006100180.9225990.0379432.4862790.70931648.60053434.473159
31_SD_3_10008_1000910008100090.9276540.3828142.3037930.68389148.58911033.229647
32_SD_3_10008_1000710008100070.9546490.1612931.4271240.53375548.51486625.895061
33_SD_3_10008_1000510008100050.9333090.0216892.1176510.65462748.54831931.781016
34_SD_3_10008_1000910008100090.9276540.3910612.3037930.68389148.58911033.229647
35_SD_3_10008_1000710008100070.9546490.1660541.4271240.53375548.51486625.895061
36_SD_3_10008_1000510008100050.9333090.0122882.1176510.65462748.54831931.781016
37_SD_3_10008_1000910008100090.9276540.3638522.3037930.68389148.58911033.229647
38_SD_3_10008_1000710008100070.9546490.1708181.4271240.53375548.51486625.895061
39_SD_3_10008_1000510008100050.9333090.0178272.1176510.65462748.54831931.781016
40_SD_4_10005_1000810005100080.9333090.0162452.1176500.65462648.54831931.781016
41_SD_4_10005_1000310005100030.9522910.0132791.5032550.54813748.54831926.611129
42_SD_4_10005_1000110005100010.9529280.2469901.5000510.54428148.54831926.423944
43_SD_4_10005_1000210005100020.9448680.1095381.7503690.59154848.61217328.756454
44_SD_4_10005_1000410005100040.9353530.0117702.0496650.64380948.63818231.313705
45_SD_4_10005_1000610005100060.9433170.0506891.7912940.60029948.60053429.174859
46_SD_4_10005_1000810005100080.9333090.0126692.1176500.65462648.54831931.781016
47_SD_4_10005_1000310005100030.9522910.0232801.5032550.54813748.54831926.611129
48_SD_4_10005_1000110005100010.9529280.2396431.5000510.54428148.54831926.423945
49_SD_4_10005_1000210005100020.9448680.1037751.7503690.59154848.61217328.756455
50_SD_4_10005_1000410005100040.9353530.0065532.0496650.64380948.63818231.313705
51_SD_4_10005_1000610005100060.9433170.0416801.7912940.60029948.60053429.174859
52_SD_4_10005_1000810005100080.9333090.0376842.1176500.65462648.54831931.781015
53_SD_4_10005_1000310005100030.9522910.0106961.5032550.54813748.54831926.611129
54_SD_4_10005_1000110005100010.9529280.2346731.5000510.54428148.54831926.423945
55_SD_4_10005_1000210005100020.9448680.1411491.7503680.59154848.61217328.756452
56_SD_4_10005_1000410005100040.9353530.0131462.0496650.64380948.63818231.313705
57_SD_4_10005_1000610005100060.9433170.0476801.7912940.60029948.60053429.174859
58_SD_5_10004_1000510004100050.9353530.0434452.0496650.64380948.63818231.313705
59_SD_5_10004_1000310004100030.9397060.0407341.9110090.62031648.63818230.171026
60_SD_5_10004_1000210004100020.9346100.0868292.0844860.64775948.63818231.505834
61_SD_5_10004_1000610004100060.9326910.0731072.1404800.65786848.63818231.997485
62_SD_5_10004_1000510004100050.9353530.0454982.0496650.64380948.63818231.313706
63_SD_5_10004_1000310004100030.9397060.0279101.9110090.62031648.63818230.171025
64_SD_5_10004_1000210004100020.9346100.0920772.0844860.64775948.63818231.505834
65_SD_5_10004_1000610004100060.9326910.0803852.1404800.65786848.63818231.997485
66_SD_5_10004_1000510004100050.9353530.0454832.0496650.64380948.63818231.313706
67_SD_5_10004_1000310004100030.9397060.0279271.9110090.62031648.63818230.171025
68_SD_5_10004_1000210004100020.9346100.0868322.0844860.64775948.63818231.505834
69_SD_5_10004_1000610004100060.9326910.0731072.1404800.65786848.63818231.997485
70_SD_6_10003_1000410003100040.9397060.0097751.9110080.62031648.63818230.171024
71_SD_6_10003_1000610003100060.9437530.0412781.7885460.59785048.60053429.055845
72_SD_6_10003_1000510003100050.9522910.0144281.5032550.54813748.54831926.611129
73_SD_6_10003_1000710003100070.9365160.0627122.0123090.63759748.51486630.932946
74_SD_6_10003_1001610003100160.9399040.0028371.9050790.61923248.51134630.039762
75_SD_6_10003_1000110003100010.9540890.2566991.4476410.53720148.51134626.060348
76_SD_6_10003_1000210003100020.9438740.1006541.7726570.59717048.61217329.029747
77_SD_6_10003_1000410003100040.9397060.0161931.9110080.62031648.63818230.171025
78_SD_6_10003_1000610003100060.9437530.0740611.7885450.59785048.60053429.055842
79_SD_6_10003_1000510003100050.9522910.0244181.5032550.54813748.54831926.611129
80_SD_6_10003_1000710003100070.9365160.0637452.0123090.63759748.51486630.932946
81_SD_6_10003_1001610003100160.9399040.0156441.9050790.61923248.51134630.039763
82_SD_6_10003_1000110003100010.9540890.2708901.4476410.53720148.51134626.060347
83_SD_6_10003_1000210003100020.9438740.1125391.7726570.59717048.61217329.029747
84_SD_6_10003_1000410003100040.9397060.0001571.9110080.62031648.63818230.171024
85_SD_6_10003_1000610003100060.9437530.0501801.7885450.59785048.60053429.055844
86_SD_6_10003_1000510003100050.9522910.0018181.5032550.54813748.54831926.611128
87_SD_6_10003_1000710003100070.9365160.0851072.0123090.63759748.51486630.932946
88_SD_6_10003_1001610003100160.9399040.0124331.9050790.61923248.51134630.039763
89_SD_6_10003_1000110003100010.9540890.2441681.4476410.53720148.51134626.060349
90_SD_6_10003_1000210003100020.9438740.1095651.7726570.59717048.61217329.029747
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1204_niv_10059_10057100570.7038073.5809872.2320430.48288548.58807823.462465
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" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], - "execution_count": 26 + "execution_count": 50 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:12:36.848843200Z", - "start_time": "2026-02-03T13:12:34.078930100Z" + "end_time": "2026-02-05T11:14:38.454212700Z", + "start_time": "2026-02-05T11:14:34.674370700Z" } }, "cell_type": "code", "source": [ - "importlib.reload(Netzqualität_Genauigkeit)\n", + "# Zelle 25: Standardabweichungen der einzelnen Punkte und Helmert'scher Punktfehler (3D)\n", "\n", - "# Standardabweichungen der einzelnen Punkte und Helmert'scher Punktfehler (3D)\n", - "dim = int(input(\"Helmertscher Punktfehler (2 = 2D, 3 = 3D): \"))\n", - "\n", - "Helmertscher_Punktfehler = Netzqualität_Genauigkeit.Genauigkeitsmaße.helmert_punktfehler(\n", - " Qxx=ausgabe_parameterschaetzung[\"Q_xx\"],\n", - " s0_apost=s0_aposteriori,\n", - " unbekannten_liste=Jacobimatrix_symbolisch_liste_unbekannte,\n", - " dim=dim)\n", - "\n", - "display(HTML(Helmertscher_Punktfehler.to_html(index=False)))\n", - "Helmertscher_Punktfehler.to_excel(r\"Zwischenergebnisse\\Standardabweichungen_Helmertscher_Punktfehler.xlsx\", index=False)\n", - "#print(res[\"Q_xx\"])" + "Helmertscher_Punktfehler = Netzqualitaet_Genauigkeit.Genauigkeitsmaße.helmert_punktfehler(ausgabe_parameterschaetzung[\"Q_xx\"], s0_aposteriori,Jacobimatrix_symbolisch_liste_unbekannte)" ], "id": "9e6624251a23847d", "outputs": [ @@ -21747,33 +57703,21 @@ } } ], - "execution_count": 27 + "execution_count": 51 }, { "cell_type": "code", "id": "89fbcfe85fb6e4bb", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:12:40.456060900Z", - "start_time": "2026-02-03T13:12:40.089996Z" + "end_time": "2026-02-05T11:14:53.385748800Z", + "start_time": "2026-02-05T11:14:53.000207900Z" } }, "source": [ - "# Standardellipse bzw. Helmert'sche Fehlerellipsen\n", - "Standardellipse = Netzqualität_Genauigkeit.Genauigkeitsmaße.standardellipse(\n", - " Qxx=ausgabe_parameterschaetzung[\"Q_xx\"],\n", - " s0_apost=s0_aposteriori,\n", - " unbekannten_liste=Jacobimatrix_symbolisch_liste_unbekannte)\n", + "# Zelle 26: Standardellipse bzw. Helmert'sche Fehlerellipsen\n", "\n", - "Standardellipse[\"σx [m]\"] = Standardellipse[\"σx [m]\"].astype(float).round(4)\n", - "Standardellipse[\"σy [m]\"] = Standardellipse[\"σy [m]\"].astype(float).round(4)\n", - "Standardellipse[\"Große Halbachse [m]\"] = Standardellipse[\"Große Halbachse [m]\"].astype(float).round(4)\n", - "Standardellipse[\"Kleine Halbachse [m]\"] = Standardellipse[\"Kleine Halbachse [m]\"].astype(float).round(4)\n", - "Standardellipse[\"θ [gon]\"] = Standardellipse[\"θ [gon]\"].astype(float).round(3)\n", - "\n", - "display(HTML(Standardellipse.to_html(index=False)))\n", - "\n", - "Standardellipse.to_excel(r\"Zwischenergebnisse\\Standardellipse.xlsx\", index=False)" + "Standardellipse = Netzqualitaet_Genauigkeit.Genauigkeitsmaße.standardellipse(ausgabe_parameterschaetzung[\"Q_xx\"], s0_aposteriori, Jacobimatrix_symbolisch_liste_unbekannte)\n" ], "outputs": [ { @@ -22472,40 +58416,21 @@ } } ], - "execution_count": 28 + "execution_count": 52 }, { "cell_type": "code", "id": "7de561d7eaebb1c2", "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:12:52.079782Z", - "start_time": "2026-02-03T13:12:42.641492200Z" + "end_time": "2026-02-05T11:14:58.095964500Z", + "start_time": "2026-02-05T11:14:54.031420600Z" } }, "source": [ - "# Konfidenzellipsen\n", - "alpha_input = input(\"Konfidenzniveau wählen (z.B. 0.05 für 95%, 0.01 für 99%) [Standard=0.05]: \")\n", - "if alpha_input.strip() == \"\":\n", - " alpha = 0.05\n", - "else:\n", - " alpha = float(alpha_input)\n", - "print(f\"→ Verwende alpha = {alpha} (Konfidenz = {(1-alpha)*100:.1f}%)\")\n", + "# Zelle 27: Konfidenzellipsen\n", "\n", - "Konfidenzellipse = Netzqualität_Genauigkeit.Genauigkeitsmaße.konfidenzellipse(\n", - " Qxx=ausgabe_parameterschaetzung[\"Q_xx\"],\n", - " s0_apost=s0_aposteriori,\n", - " unbekannten_liste=Jacobimatrix_symbolisch_liste_unbekannte,\n", - " R=r_gesamt,\n", - " alpha=alpha)\n", - "\n", - "Konfidenzellipse[\"Große Halbachse [m]\"] = Konfidenzellipse[\"Große Halbachse [m]\"].round(4)\n", - "Konfidenzellipse[\"Kleine Halbachse [m]\"] = Konfidenzellipse[\"Kleine Halbachse [m]\"].round(4)\n", - "Konfidenzellipse[\"θ [gon]\"] = Konfidenzellipse[\"θ [gon]\"].round(3)\n", - "\n", - "display(HTML(Konfidenzellipse.to_html(index=False)))\n", - "\n", - "Konfidenzellipse.to_excel(r\"Zwischenergebnisse\\Konfidenzellipse.xlsx\", index=False)" + "Konfidenzellipse = Netzqualitaet_Genauigkeit.Genauigkeitsmaße.konfidenzellipse(ausgabe_parameterschaetzung[\"Q_xx\"], s0_aposteriori, Jacobimatrix_symbolisch_liste_unbekannte, r_gesamt, False)" ], "outputs": [ { @@ -23204,70 +59129,20 @@ } } ], - "execution_count": 29 + "execution_count": 53 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-02-03T13:12:59.141788800Z", - "start_time": "2026-02-03T13:12:54.893953800Z" + "end_time": "2026-02-05T11:14:59.708037200Z", + "start_time": "2026-02-05T11:14:59.425364700Z" } }, "cell_type": "code", "source": [ - "# Konfidenzellipsen im ENU-System\n", - "import Netzqualität_Genauigkeit\n", - "importlib.reload(Netzqualität_Genauigkeit)\n", - "from IPython.display import display, HTML\n", - "from Berechnungen import Berechnungen\n", - "from Berechnungen import ENU, Einheitenumrechnung\n", + "# Zelle 28: Konfidenzellipsen im ENU-System\n", "\n", - "berechnungen = Berechnungen(a, b)\n", - "alpha_input = input(\"Konfidenzniveau wählen (z.B. 0.05 für 95%, 0.01 für 99%) [Standard=0.05]: \")\n", - "if alpha_input.strip() == \"\":\n", - " alpha = 0.05\n", - "else:\n", - " alpha = float(alpha_input)\n", - "print(f\"→ Verwende alpha = {alpha} (Konfidenz = {(1-alpha)*100:.1f}%)\")\n", - "\n", - "# 1) Qxx ins ENU-System transformieren\n", - "Qxx_enu, (B0, L0), R0 = ENU.transform_Qxx_zu_QxxENU(\n", - " Qxx=ausgabe_parameterschaetzung[\"Q_xx\"],\n", - " unbekannten_liste=Jacobimatrix_symbolisch_liste_unbekannte,\n", - " berechnungen=berechnungen,\n", - " dict_xyz=dict_koordinaten_ausgleichungsergebnis\n", - ")\n", - "\n", - "print(f\"ENU-Referenz (Schwerpunkt): B0={Einheitenumrechnung.rad_to_gon_Decimal(B0):.8f} rad, L0={Einheitenumrechnung.rad_to_gon_Decimal(L0):.8f} rad\")\n", - "\n", - "# 2) Konfidenzellipse im ENU-System\n", - "Konfidenzellipse_ENU = Netzqualität_Genauigkeit.Genauigkeitsmaße.konfidenzellipse(\n", - " Qxx=Qxx_enu,\n", - " s0_apost=s0_aposteriori,\n", - " unbekannten_liste=Jacobimatrix_symbolisch_liste_unbekannte,\n", - " R=r_gesamt,\n", - " alpha=alpha\n", - ")\n", - "\n", - "# 3) Spaltennamen anpassen\n", - "Konfidenzellipse_ENU = Konfidenzellipse_ENU.rename(columns={\n", - " \"σx [m]\": \"σE [m]\",\n", - " \"σy [m]\": \"σN [m]\",\n", - " \"σxy [m]\": \"σEN [m]\",\n", - " \"θ [gon]\": \"θ_EN [gon]\"\n", - "})\n", - "\n", - "# 4) Runden & Anzeigen\n", - "Konfidenzellipse_ENU[\"σE [m]\"] = Konfidenzellipse_ENU[\"σE [m]\"].round(4)\n", - "Konfidenzellipse_ENU[\"σN [m]\"] = Konfidenzellipse_ENU[\"σN [m]\"].round(4)\n", - "Konfidenzellipse_ENU[\"Große Halbachse [m]\"] = Konfidenzellipse_ENU[\"Große Halbachse [m]\"].round(4)\n", - "Konfidenzellipse_ENU[\"Kleine Halbachse [m]\"] = Konfidenzellipse_ENU[\"Kleine Halbachse [m]\"].round(4)\n", - "Konfidenzellipse_ENU[\"θ_EN [gon]\"] = Konfidenzellipse_ENU[\"θ_EN [gon]\"].round(4)\n", - "\n", - "display(HTML(Konfidenzellipse_ENU.to_html(index=False)))\n", - "\n", - "# 5) Export\n", - "Konfidenzellipse_ENU.to_excel(r\"Zwischenergebnisse\\Konfidenzellipse_ENU.xlsx\", index=False)" + "Konfidenzellipse_ENU, R0 = Netzqualitaet_Genauigkeit.Genauigkeitsmaße.konfidenzellipsen_enu(a, b, ausgabe_parameterschaetzung, Jacobimatrix_symbolisch_liste_unbekannte, dict_koordinaten_ausgleichungsergebnis, s0_aposteriori, r_gesamt)" ], "id": "ddc157c0ff21c93e", "outputs": [ @@ -23278,58 +59153,38 @@ "traceback": [ "\u001B[31m---------------------------------------------------------------------------\u001B[39m", "\u001B[31mTypeError\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[30]\u001B[39m\u001B[32m, line 17\u001B[39m\n\u001B[32m 14\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m→ Verwende alpha = \u001B[39m\u001B[38;5;132;01m{\u001B[39;00malpha\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m (Konfidenz = \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m(\u001B[32m1\u001B[39m-alpha)*\u001B[32m100\u001B[39m\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.1f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m%)\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 16\u001B[39m \u001B[38;5;66;03m# 1) Qxx ins ENU-System transformieren\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m17\u001B[39m Qxx_enu, (B0, L0), R0 = \u001B[43mENU\u001B[49m\u001B[43m.\u001B[49m\u001B[43mtransform_Qxx_zu_QxxENU\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 18\u001B[39m \u001B[43m \u001B[49m\u001B[43mQxx\u001B[49m\u001B[43m=\u001B[49m\u001B[43mausgabe_parameterschaetzung\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mQ_xx\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 19\u001B[39m \u001B[43m \u001B[49m\u001B[43munbekannten_liste\u001B[49m\u001B[43m=\u001B[49m\u001B[43mJacobimatrix_symbolisch_liste_unbekannte\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 20\u001B[39m \u001B[43m \u001B[49m\u001B[43mberechnungen\u001B[49m\u001B[43m=\u001B[49m\u001B[43mberechnungen\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 21\u001B[39m \u001B[43m \u001B[49m\u001B[43mdict_xyz\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdict_koordinaten_ausgleichungsergebnis\u001B[49m\n\u001B[32m 22\u001B[39m \u001B[43m)\u001B[49m\n\u001B[32m 24\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mENU-Referenz (Schwerpunkt): B0=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mEinheitenumrechnung.rad_to_gon_Decimal(B0)\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.8f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m rad, L0=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mEinheitenumrechnung.rad_to_gon_Decimal(L0)\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.8f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m rad\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 26\u001B[39m \u001B[38;5;66;03m# 2) Konfidenzellipse im ENU-System\u001B[39;00m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\Masterprojekt_V3\\Berechnungen.py:529\u001B[39m, in \u001B[36mENU.transform_Qxx_zu_QxxENU\u001B[39m\u001B[34m(Qxx, unbekannten_liste, berechnungen, dict_xyz)\u001B[39m\n\u001B[32m 527\u001B[39m \u001B[38;5;129m@staticmethod\u001B[39m\n\u001B[32m 528\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mtransform_Qxx_zu_QxxENU\u001B[39m(Qxx, unbekannten_liste, berechnungen, dict_xyz):\n\u001B[32m--> \u001B[39m\u001B[32m529\u001B[39m B0, L0 = \u001B[43mENU\u001B[49m\u001B[43m.\u001B[49m\u001B[43mberechne_schwerpunkt_fuer_enu\u001B[49m\u001B[43m(\u001B[49m\u001B[43mberechnungen\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdict_xyz\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 530\u001B[39m R0 = ENU.berechne_R0_ENU(berechnungen, B0, L0)\n\u001B[32m 531\u001B[39m R_ENU = ENU.berechne_R_ENU(unbekannten_liste, R0)\n", - "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\Masterprojekt_V3\\Berechnungen.py:474\u001B[39m, in \u001B[36mENU.berechne_schwerpunkt_fuer_enu\u001B[39m\u001B[34m(berechnungen, dict_xyz)\u001B[39m\n\u001B[32m 472\u001B[39m XYZ = np.array(\u001B[38;5;28mlist\u001B[39m(dict_xyz.values()), dtype=\u001B[38;5;28mfloat\u001B[39m)\n\u001B[32m 473\u001B[39m X0, Y0, Z0 = XYZ.mean(axis=\u001B[32m0\u001B[39m)\n\u001B[32m--> \u001B[39m\u001B[32m474\u001B[39m B0 = \u001B[38;5;28;43mfloat\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mberechnungen\u001B[49m\u001B[43m.\u001B[49m\u001B[43mB\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mY0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mZ0\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 475\u001B[39m L0 = \u001B[38;5;28mfloat\u001B[39m(berechnungen.L(X0, Y0))\n\u001B[32m 476\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m B0, L0\n", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[54]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m 1\u001B[39m \u001B[38;5;66;03m# Zelle 28: Konfidenzellipsen im ENU-System\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m Konfidenzellipse_ENU, R0 = \u001B[43mNetzqualitaet_Genauigkeit\u001B[49m\u001B[43m.\u001B[49m\u001B[43mGenauigkeitsmaße\u001B[49m\u001B[43m.\u001B[49m\u001B[43mkonfidenzellipsen_enu\u001B[49m\u001B[43m(\u001B[49m\u001B[43ma\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mb\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mausgabe_parameterschaetzung\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mJacobimatrix_symbolisch_liste_unbekannte\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdict_koordinaten_ausgleichungsergebnis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43ms0_aposteriori\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mr_gesamt\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\Masterprojekt_V3\\Netzqualitaet_Genauigkeit.py:354\u001B[39m, in \u001B[36mGenauigkeitsmaße.konfidenzellipsen_enu\u001B[39m\u001B[34m(a, b, ausgabe_parameterschaetzung, liste_unbekannte, ausgleichungsergebnis, s0apost, r_gesamt)\u001B[39m\n\u001B[32m 351\u001B[39m berechnungen = Berechnungen.Berechnungen(a, b)\n\u001B[32m 353\u001B[39m \u001B[38;5;66;03m# 1) Qxx ins ENU-System transformieren\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m354\u001B[39m Qxx_enu, (B0, L0), R0 = \u001B[43mBerechnungen\u001B[49m\u001B[43m.\u001B[49m\u001B[43mENU\u001B[49m\u001B[43m.\u001B[49m\u001B[43mtransform_Qxx_zu_QxxENU\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 355\u001B[39m \u001B[43m \u001B[49m\u001B[43mQxx\u001B[49m\u001B[43m=\u001B[49m\u001B[43mausgabe_parameterschaetzung\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mQ_xx\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 356\u001B[39m \u001B[43m \u001B[49m\u001B[43munbekannten_liste\u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mliste_unbekannte\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 357\u001B[39m \u001B[43m \u001B[49m\u001B[43mberechnungen\u001B[49m\u001B[43m=\u001B[49m\u001B[43mberechnungen\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 358\u001B[39m \u001B[43m \u001B[49m\u001B[43mdict_xyz\u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mausgleichungsergebnis\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 359\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 361\u001B[39m \u001B[38;5;28mprint\u001B[39m(\n\u001B[32m 362\u001B[39m \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mENU-Referenz (Schwerpunkt): B0=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mEinheitenumrechnung.rad_to_gon_Decimal(B0)\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.8f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m rad, L0=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mEinheitenumrechnung.rad_to_gon_Decimal(L0)\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.8f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m rad\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 364\u001B[39m \u001B[38;5;66;03m# 2) Konfidenzellipse im ENU-System\u001B[39;00m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\Masterprojekt_V3\\Berechnungen.py:511\u001B[39m, in \u001B[36mENU.transform_Qxx_zu_QxxENU\u001B[39m\u001B[34m(Qxx, unbekannten_liste, berechnungen, dict_xyz)\u001B[39m\n\u001B[32m 488\u001B[39m \u001B[38;5;129m@staticmethod\u001B[39m\n\u001B[32m 489\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mtransform_Qxx_zu_QxxENU\u001B[39m(Qxx, unbekannten_liste, berechnungen, dict_xyz):\n\u001B[32m 490\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 491\u001B[39m \u001B[33;03m Transformiert die Kofaktor-Matrix Qxx in das ENU-System.\u001B[39;00m\n\u001B[32m 492\u001B[39m \n\u001B[32m (...)\u001B[39m\u001B[32m 508\u001B[39m \u001B[33;03m :rtype: tuple[numpy.ndarray, tuple[float, float], numpy.ndarray]\u001B[39;00m\n\u001B[32m 509\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m511\u001B[39m B0, L0 = \u001B[43mENU\u001B[49m\u001B[43m.\u001B[49m\u001B[43mberechne_schwerpunkt_fuer_enu\u001B[49m\u001B[43m(\u001B[49m\u001B[43mberechnungen\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdict_xyz\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 512\u001B[39m R0 = ENU.berechne_R0_ENU(berechnungen, B0, L0)\n\u001B[32m 513\u001B[39m R_ENU = ENU.berechne_R_ENU(unbekannten_liste, R0)\n", + "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\Masterprojekt_V3\\Berechnungen.py:409\u001B[39m, in \u001B[36mENU.berechne_schwerpunkt_fuer_enu\u001B[39m\u001B[34m(berechnungen, dict_xyz)\u001B[39m\n\u001B[32m 407\u001B[39m XYZ = np.array(\u001B[38;5;28mlist\u001B[39m(dict_xyz.values()), dtype=\u001B[38;5;28mfloat\u001B[39m)\n\u001B[32m 408\u001B[39m X0, Y0, Z0 = XYZ.mean(axis=\u001B[32m0\u001B[39m)\n\u001B[32m--> \u001B[39m\u001B[32m409\u001B[39m B0 = \u001B[38;5;28;43mfloat\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mberechnungen\u001B[49m\u001B[43m.\u001B[49m\u001B[43mB\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mY0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mZ0\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 410\u001B[39m L0 = \u001B[38;5;28mfloat\u001B[39m(berechnungen.L(X0, Y0))\n\u001B[32m 411\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m B0, L0\n", "\u001B[31mTypeError\u001B[39m: only 0-dimensional arrays can be converted to Python scalars" ] } ], - "execution_count": 30 + "execution_count": 54 }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-03T13:13:02.614020Z", - "start_time": "2026-02-03T13:13:02.405517200Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ - "# Netzplot mit Konfidenzellipsen (im ENU-System)\n", + "# Zelle 29: Netzplot mit Konfidenzellipsen (im ENU-System)\n", + "\n", "Koord_ENU = Berechnungen.ENU.transform_Koord_zu_KoordENU(dict_koordinaten_ausgleichungsergebnis, R0)\n", "\n", - "Netzqualität_Genauigkeit.Plot.netzplot_ellipsen(\n", + "Netzqualitaet_Genauigkeit.Plot.netzplot_ellipsen(\n", " Koord_ENU=Koord_ENU,\n", " unbekannten_labels=Jacobimatrix_symbolisch_liste_unbekannte,\n", " beobachtungs_labels=Jacobimatrix_symbolisch_liste_beobachtungsvektor,\n", " df_konf_ellipsen_enu=Konfidenzellipse_ENU,\n", - " v_faktor=1000\n", + " skalierung=1000\n", ")" ], "id": "94a42bd0a62875ea", - "outputs": [ - { - "ename": "AttributeError", - "evalue": "type object 'Berechnungen' has no attribute 'ENU'", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mAttributeError\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[31]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m 1\u001B[39m \u001B[38;5;66;03m# Netzplot mit Konfidenzellipsen (im ENU-System)\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m Koord_ENU = \u001B[43mBerechnungen\u001B[49m\u001B[43m.\u001B[49m\u001B[43mENU\u001B[49m.transform_Koord_zu_KoordENU(dict_koordinaten_ausgleichungsergebnis, R0)\n\u001B[32m 4\u001B[39m Netzqualität_Genauigkeit.Plot.netzplot_ellipsen(\n\u001B[32m 5\u001B[39m Koord_ENU=Koord_ENU,\n\u001B[32m 6\u001B[39m unbekannten_labels=Jacobimatrix_symbolisch_liste_unbekannte,\n\u001B[32m (...)\u001B[39m\u001B[32m 9\u001B[39m v_faktor=\u001B[32m1000\u001B[39m\n\u001B[32m 10\u001B[39m )\n", - "\u001B[31mAttributeError\u001B[39m: type object 'Berechnungen' has no attribute 'ENU'" - ] - } - ], - "execution_count": 31 + "outputs": [], + "execution_count": null }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-03T13:13:03.893917600Z", - "start_time": "2026-02-03T13:13:03.688483900Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "from Koordinatentransformationen import Transformationen\n", @@ -23342,29 +59197,13 @@ "print(koordinaten_utm)" ], "id": "844b818e7a3db233", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'10009': (446396.94591415, 5888520.08094115, 4.10777948), '10006': (446365.07795161, 5888527.29471266, 3.68753525), '10010': (446425.55518804, 5888525.71849433, 4.02860182), '10018': (446454.64203767, 5888515.81613257, 4.35595289), '10008': (446400.85623403, 5888498.39118974, 3.83790962), '10005': (446375.27772687, 5888490.66897582, 3.82648859), '10003': (446380.52421907, 5888429.95115165, 3.8529721), '10004': (446365.69248356, 5888480.16793507, 3.5985052), '10007': (446405.40317179, 5888437.96757, 4.02274657), '10001': (446381.39590079, 5888353.41623005, 4.00323709), '10002': (446371.02972399, 5888398.83046288, 3.69653835), '10016': (446435.71592037, 5888437.80141373, 4.16115611), '10011': (446468.86084665, 5888344.89390017, 3.9174367), '10026': (446485.32535014, 5888520.58998356, 4.4035858), '10027': (446531.04386139, 5888507.11704915, 4.42319958), '10043': (446576.91395582, 5888511.32739251, 4.40898907), '10044': (446614.75130838, 5888497.45038381, 4.63721451), '10021': (446488.0821543, 5888492.04452579, 4.45110673), '10020': (446487.77510911, 5888483.77074699, 4.4378151), '10024': (446511.64604705, 5888491.55881958, 4.39859429), '10025': (446525.95955805, 5888487.13347816, 4.5088213), '10022': (446495.63071223, 5888487.58229049, 4.31719027), '10023': (446501.51875394, 5888483.43541364, 4.43933837), '10019': (446483.91095313, 5888462.1176305, 4.0752408), '10033': (446524.73307044, 5888454.30930261, 3.94778669), '10017': (446478.41840933, 5888431.63144741, 4.06622892), '10052': (446641.92418297, 5888503.40657669, 4.37573547), '10042': (446592.76072499, 5888493.52780231, 4.44240179), '10053': (446673.96732362, 5888491.64964562, 4.36509241), '10037': (446609.64849096, 5888437.37043924, 4.06232796), '10040': (446608.49168081, 5888462.90173678, 4.2481142), '10041': (446578.70429569, 5888472.04410218, 4.57175855), '10038': (446578.07871709, 5888436.22245014, 4.1638404), '10051': (446642.49473498, 5888473.76062772, 4.37116014), '10036': (446616.1343385, 5888417.88256718, 4.08725292), '10035': (446603.32840613, 5888381.30511103, 4.16332113), '10039': (446563.65893334, 5888441.78088632, 4.17670297), '10059': (446736.81535087, 5888493.54038755, 4.12309814), '10050': (446666.03617945, 5888469.38709553, 4.25158838), '10013': (446469.78561984, 5888402.03001289, 3.89765242), '10028': (446544.37927185, 5888337.01975024, 3.88573211), '10012': (446447.80973603, 5888401.28535222, 3.99511551), '10014': (446457.17348428, 5888418.29895374, 4.00186169), '10031': (446524.19629859, 5888426.87870755, 3.95237936), '10015': (446438.44013302, 5888421.0303462, 4.08843899), '10032': (446537.1704124, 5888441.92463292, 3.81368352), '10030': (446543.68653156, 5888393.07040129, 3.66348687), '10029': (446557.42655447, 5888390.86074423, 3.63381599), '10034': (446601.18909472, 5888330.65768456, 3.82487788), '10045': (446655.25351484, 5888325.64587865, 3.80730286), '10049': (446671.37261868, 5888442.92049073, 4.08292411), '10047': (446661.71527487, 5888388.00083862, 3.68339891), '10046': (446654.22623754, 5888370.34650451, 3.6210586), '10048': (446663.87505206, 5888415.88348224, 3.87491694), '10057': (446725.75936279, 5888414.19231216, 3.84941408), '10055': (446723.50475259, 5888367.43152201, 3.52940691), '10054': (446720.45736308, 5888304.00591895, 3.7704292), '10058': (446744.28745728, 5888454.91618515, 3.94966702), '10056': (446737.89430548, 5888381.23008845, 3.50050671), '812': (446651.57375319, 5888365.88825536, 3.9760202), '816': (446650.27834962, 5888407.48620417, 3.97318405), 'FH3': (446659.79452216, 5888414.88157291, 4.68859948), '666': (446718.30526609, 5888330.8390722, 3.8529037), 'FH11': (446613.73349477, 5888369.89570942, 4.04198709), 'FH14': (446613.2501924, 5888369.35341691, 4.09744053), 'FH4': (446638.16113668, 5888466.57489975, 4.70290668), 'FH13': (446401.20354881, 5888437.26710489, 4.53884451), 'FH15': (446521.73637568, 5888462.81253098, 4.71286213), '0645': (396176.37475573, 5898983.77551295, 25.018148), '0656': (446638.03765746, 5888402.34474123, 28.6470742), '0995': (487705.36134043, 5880834.409109, 32.63163491), '1675': (462469.68758776, 5861557.08510576, 32.28560465), 'ESTE': (404322.14103353, 5868460.70378741, 29.63519866), 'GNA2': (501255.689642, 5912256.2931415, 18.31637386)}\n" - ] - } - ], - "execution_count": 32 + "outputs": [], + "execution_count": null }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-03T13:13:05.605376700Z", - "start_time": "2026-02-03T13:13:05.470130900Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", "# 1. Wir nutzen dein existierendes 'dict_koordinaten'\n", "# Da stehen nur die echten Punkte drin (74 Stück)\n", "namen = list(dict_koordinaten_ausgleichungsergebnis.keys())\n", @@ -23393,7 +59232,7 @@ "\n", "# 5. Export-Dictionary\n", "ergebnisse = {\n", - " \"df_globaltest\": df_globaltest,\n", + " \"df_globaltest\": globaltest,\n", " \"df_redundanz\": Redundanzanteile,\n", " \"df_ellipsen\": Standardellipse,\n", " \"df_konfidenzellipsen\": Konfidenzellipse,\n", @@ -23409,19 +59248,14 @@ ], "id": "343b70a659e335f8", "outputs": [], - "execution_count": 33 + "execution_count": null }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-03T13:13:15.406894700Z", - "start_time": "2026-02-03T13:13:06.566061200Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "importlib.reload(Export)\n", - "# Erzeugung eines Protokolls der hybriden Netzausgleichung\n", + "# Zelle XX: Erzeugung eines Protokolls der hybriden Netzausgleichung\n", "\n", "# Input (später: an den Anfang des Notebooks schieben)\n", "bearbeiter = input(\"Bearbeiter: \")\n", @@ -23435,7 +59269,7 @@ "\n", "\n", "ergebnisse = {\n", - " \"df_globaltest\": df_globaltest,\n", + " \"df_globaltest\": globaltest,\n", " \"df_redundanz\": Redundanzanteile,\n", " \"df_ellipsen\": Standardellipse,\n", " \"df_konfidenzellipsen\": Konfidenzellipse,\n", @@ -23447,22 +59281,6 @@ "Export.Export.speichere_html_protokoll(metadaten, ergebnisse)" ], "id": "23aa13721f563c90", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Protokoll wurde gespeichert unter: C:\\Users\\fabia\\Desktop\\Masterprojekt_V3\\Protokolle\\Protokoll_Campusnetz.html\n" - ] - } - ], - "execution_count": 34 - }, - { - "metadata": {}, - "cell_type": "code", - "source": "", - "id": "c796d53a07f85ee8", "outputs": [], "execution_count": null } diff --git a/Datenbank.py b/Datenbank.py index 5b29d01..daeb570 100644 --- a/Datenbank.py +++ b/Datenbank.py @@ -1,11 +1,12 @@ from decimal import Decimal import os +import pandas as pd import sqlite3 import sympy as sp from sympy import MutableDenseMatrix from typing import Any -from Berechnungen import Einheitenumrechnung +from Einheitenumrechnung import Einheitenumrechnung class Datenbank_anlegen: """Legt die SQLite-Datenbank für die Ausgleichungsrechnung an. @@ -147,6 +148,7 @@ class Datenbankzugriff: - Schreiben in die Datenbank durch alle set_* Methoden - Lesen aus der Datenbank durch alle get_* Methoden + - Darstellen der aus der Datenbenak gelesenen Daten in tabellarischer Form durch alle tabelle_* Methoden :ivar pfad_datenbank: Pfad zur SQLite-Datenbankdatei. :vartype pfad_datenbank: str @@ -241,7 +243,7 @@ class Datenbankzugriff: else: id_instrument = cursor.execute( "SELECT instrumenteID FROM Instrumente WHERE typ = ? AND name =?", (typ, name)) - print(f"Das Instrument {name} ist bereits in der Datenbank vorhanden.\nEs hat die ID {id_instrument.fetchone()[0]}") + print(f"Das Instrument {name} ist bereits in der Datenbank vorhanden.") con.commit() cursor.close() con.close() @@ -252,7 +254,8 @@ class Datenbankzugriff: Prüft, ob instrumenteID existiert und ob mindestens eine Genauigkeitsangabe übergeben wurde. Je nach Beobachtungsart werden Einheitenumrechnungen durchgeführt (z. B. mgon → rad bzw. mm → m). - Der Eintrag wird nur ergänzt, wenn in Genauigkeiten kein identischer Datensatz vorhanden ist. + Der Eintrag wird neu hinzugefügt, wenn in Genauigkeiten kein identischer Datensatz vorhanden ist. + Ansonsten wird der bestehende Datensatz in der Datenbank aktualisiert. :param instrumenteID: ID des Instruments in der Tabelle Instrumente. :type instrumenteID: int @@ -300,51 +303,36 @@ class Datenbankzugriff: if isinstance(stabw_apriori_streckenprop, Decimal): stabw_apriori_streckenprop = float(stabw_apriori_streckenprop) - # Überprüfen, ob die Genauigkeitsinformation für die jeweilige Beobachtungsart des Instruments bereits vorhanden ist. Wenn nein, wird die Benutzereingabe in die Datenbank gespeichert. - sql = "SELECT 1 FROM Genauigkeiten WHERE instrumenteID = ? AND beobachtungsart = ?" - params = [instrumenteID, beobachtungsart] + # Prüfen, ob für dieses Instrument und diese Art bereits ein Eintrag existiert + vorhanden = cursor.execute("SELECT 1 FROM Genauigkeiten WHERE instrumenteID = ? AND beobachtungsart = ?", + (instrumenteID, beobachtungsart)).fetchone() - if stabw_apriori_konstant is None: - sql += " AND stabw_apriori_konstant IS NULL" + if vorhanden: + # Bestehenden Datensatz aktualisieren + cursor.execute( + "UPDATE Genauigkeiten SET stabw_apriori_konstant = ?, stabw_apriori_streckenprop = ? WHERE instrumenteID = ? AND beobachtungsart = ?", + (stabw_apriori_konstant, stabw_apriori_streckenprop, instrumenteID, beobachtungsart) + ) + print(f"Die Genauigkeitsangabe für {beobachtungsart} (Instrument: {instrumentenname}) wurde aktualisiert.") else: - sql += " AND stabw_apriori_konstant = ?" - params.append(stabw_apriori_konstant) - - if stabw_apriori_streckenprop is None: - sql += " AND stabw_apriori_streckenprop IS NULL" - else: - sql += " AND stabw_apriori_streckenprop = ?" - params.append(stabw_apriori_streckenprop) - - liste_genauigkeiten = cursor.execute(sql, tuple(params)).fetchall() - - if liste_genauigkeiten == []: + # Neuen Datensatz anlegen if stabw_apriori_konstant is not None and stabw_apriori_streckenprop is not None: cursor.execute( "INSERT INTO Genauigkeiten (instrumenteID, beobachtungsart, stabw_apriori_konstant, stabw_apriori_streckenprop) VALUES (?, ?, ?, ?)", (instrumenteID, beobachtungsart, stabw_apriori_konstant, stabw_apriori_streckenprop) ) - print( - f"Die Genauigkeitsangabe für die Beobachtungsart {beobachtungsart} des Instrumentes {instrumentenname} wurde erfolgreich hinzugefügt.") - - elif stabw_apriori_konstant is None and stabw_apriori_streckenprop is not None: + elif stabw_apriori_konstant is None: cursor.execute( "INSERT INTO Genauigkeiten (instrumenteID, beobachtungsart, stabw_apriori_streckenprop) VALUES (?, ?, ?)", (instrumenteID, beobachtungsart, stabw_apriori_streckenprop) ) - print( - f"Die Genauigkeitsangabe für die Beobachtungsart {beobachtungsart} des Instrumentes {instrumentenname} wurde erfolgreich hinzugefügt.") - - elif stabw_apriori_streckenprop is None and stabw_apriori_konstant is not None: + else: cursor.execute( "INSERT INTO Genauigkeiten (instrumenteID, beobachtungsart, stabw_apriori_konstant) VALUES (?, ?, ?)", (instrumenteID, beobachtungsart, stabw_apriori_konstant) ) - print( - f"Die Genauigkeitsangabe für die Beobachtungsart {beobachtungsart} des Instrumentes {instrumentenname} wurde erfolgreich hinzugefügt.") - - else: - print("Die Genauigkeitsangabe ist bereits in der Datenbank vorhanden.") + print( + f"Die Genauigkeitsangabe für {beobachtungsart} (Instrument: {instrumentenname}) wurde erfolgreich hinzugefügt.") con.commit() cursor.close() @@ -711,7 +699,7 @@ class Datenbankzugriff: return liste_instrumente def get_alle_instrumente_liste(self: str) -> list: - """Liest alle Instrumente aus der Tabelle Instrumente. + """Liest alle Instrumente aus der Tabelle Instrumente auf. Gibt eine Liste der gefundenen Instrumente zurück. Falls keine Instrumente vorhanden sind, wird eine Textausgabe mit verfügbaren Typen zurückgegeben. @@ -977,4 +965,52 @@ class Datenbankzugriff: con.close() return liste_varianzkomponenten + def tabelle_instrumente_aus_db(self) -> None: + """Stellt die in der Datenbank gespeicherten Instrumente tabellarisch dar. + Die Methode liest alle Datensätze aus der Tabelle Instrumente über die + zugehörige Datenbankzugriffsmethode und erzeugt daraus einen pandas-DataFrame + zur Anzeige im Notebook. Sind keine Instrumente vorhanden, wird ein Hinweistext + ausgegeben, dass die Instrumente zuerst anzulegen sind. + + :return: None + :rtype: None + """ + liste_instrumente_in_db = self.get_alle_instrumente_liste() + + # Prüfen, ob Datensätze in der Tabelle Instrumente enthalten sind + if isinstance(liste_instrumente_in_db, list) and len(liste_instrumente_in_db) > 0: + df_instrumente = pd.DataFrame(liste_instrumente_in_db, columns=['InstrumenteID', 'Typ', 'Bezeichnung']) + display(df_instrumente.style.hide(axis='index')) + + else: + print( + "Es wurden noch keine Instrumente angelegt. Bitte in der folgenden Zelle nachholen und diese Zelle erneut ausführen!") + + def tabelle_genauigkeiten_aus_db(self) -> None: + """Stellt die a-priori Genauigkeiten der Beobachtungsgruppen tabellarisch dar. + + Die Methode liest alle Einträge aus der Tabelle Genauigkeiten über die + zugehörige Datenbankzugriffsmethode und erstellt eine + übersichtliche Anzeige im Notebook. Liegen keine Genauigkeitsangaben vor, + wird ein Hinweistext ausgegeben, dass die a-priori Genauigkeiten zunächst zu + erfassen sind. + + :return: None + :rtype: None + """ + genauigkeiten_dict = self.get_genauigkeiten_dict() + # Prüfen, ob Datensätze in der Tabelle Instrumente enthalten sind + if genauigkeiten_dict == {}: + print( + "Es wurden noch keine apriori Genauigkeiten zu den Beobachtungsgruppen erfasst. Bitte in der folgenden Zelle nachholen und diese Zelle erneut ausführen.") + else: + formatierte_daten = list(genauigkeiten_dict.values()) + spalten = [ + 'instrumenteID', + 'beobachtungsart', + 'stabw_apriori_konstant', + 'stabw_apriori_streckenprop' + ] + df = pd.DataFrame(formatierte_daten, columns=spalten) + display(df.style.hide(axis='index')) diff --git a/Datumsfestlegung.py b/Datumsfestlegung.py index 350fcc9..3161c0b 100644 --- a/Datumsfestlegung.py +++ b/Datumsfestlegung.py @@ -9,6 +9,32 @@ class Datumsfestlegung: @staticmethod def weiches_datum(Q_ll: np.ndarray, Q_AA: np.ndarray, varianzkompontenschaetzung_erfolgt: bool, dict_indizes_beobachtungsgruppen: dict) -> np.ndarray: + """ + Erstellt die erweiterte Kofaktor- und Gewichtsmatrix für eine weiche Datumsfestlegung. + + Aus den Kofaktormatrizen der Beobachtungen Q_ll und der Kofaktormatrix der Anschlusspunkte Q_AA + wird eine erweiterte Kofaktormatrix Q_ext aufgebaut. Zusätzlich wird die zugehörige Gewichtsmatrix P erzeugt. + + Falls keine Varianzkomponentenschätzung durchgeführt wurde, wird P als Blockmatrix aus den + Inversen (Gewichten) von Q_ll und Q_AA aufgebaut. + + Falls eine Varianzkomponentenschätzung durchgeführt wurde, wird Q_ext entsprechend den in definierten + Beobachtungsgruppen in Teilblöcke zerlegt (z.B. SD, R, ZW, gnss, niv, lA). Für jeden Block wird die Gewichtsmatrix + berechnet und anschließend zu einer Gesamtgewichtsmatrix zusammengesetzt. + + :param Q_ll: a-priori Kofaktormatrix der Beobachtungen. + :type Q_ll: numpy.ndarray + :param Q_AA: a-priori Kofaktormatrix der Anschlusspunkte. + :type Q_AA: numpy.ndarray + :param varianzkompontenschaetzung_erfolgt: Kennzeichen, ob eine Varianzkomponentenschätzung berücksichtigt werden soll. + :type varianzkompontenschaetzung_erfolgt: bool + :param dict_indizes_beobachtungsgruppen: Dictionary mit Indexbereichen je Beobachtungsgruppe zur Blockzerlegung. + :type dict_indizes_beobachtungsgruppen: dict + :return: Tuple aus erweiterter Kofaktormatrix Q_ext und zugehöriger Gewichtsmatrix P. + :rtype: tuple[numpy.ndarray, numpy.ndarray] + :raises ValueError: Wenn Q_ll oder Q_AA keine quadratische Matrix ist. + """ + if Stochastisches_Modell.StochastischesModell.berechne_P(Q_ll).ndim != 2 or \ Stochastisches_Modell.StochastischesModell.berechne_P(Q_ll).shape[0] != \ Stochastisches_Modell.StochastischesModell.berechne_P(Q_ll).shape[1]: @@ -95,21 +121,37 @@ class Datumsfestlegung: Z(aufgeteilt_lA_invertiert, aufgeteilt_gnss_invertiert), Z(aufgeteilt_lA_invertiert, aufgeteilt_niv_invertiert), aufgeteilt_lA_invertiert] ]) - - # print(aufgeteilt) - # print(beobachtungsgruppe, indizes) - # Export.matrix_to_csv( - # fr"Zwischenergebnisse\_{beobachtungsgruppe}.csv", - # [""], - # labels, - # aufgeteilt, - # f"{beobachtungsgruppe}" - # ) - return Q_ext, P @staticmethod def indizes_beobachtungsvektor_nach_beobachtungsgruppe(Jacobimatrix_symbolisch_liste_beobachtungsvektor): + """ + Ermittelt Indexbereiche des Beobachtungsvektors getrennt nach Beobachtungsgruppen. + + Die Funktion analysiert die Bezeichner des symbolischen Beobachtungsvektors (z.B. + aus der Jacobi-Matrix) und ordnet jede Beobachtung anhand ihres Kennzeichens + (Beobachtungsart) einer Gruppe zu. Für jede Beobachtungsgruppe wird anschließend + der minimale und maximale Index im Beobachtungsvektor bestimmt. + + Unterstützte Beobachtungsgruppen sind u.a.: + + - SD : Tachymeter-Strecken, + - R : Tachymeter-Richtungen, + - ZW : Tachymeter-Zenitwinkel, + - gnss: GNSS-Basislinienkomponenten (bx/by/bz), + - niv : Geometrisches Nivellement, + - lA : Pseudobeobachtungen. + + Die zurückgegebenen Indexbereiche dienen insbesondere zur Blockzerlegung von + Kofaktor- oder Gewichtsmatrizen (z.B. bei Varianzkomponentenschätzung oder + weicher Datumsfestlegung). + + :param Jacobimatrix_symbolisch_liste_beobachtungsvektor: Liste der Beobachtungen. + :type Jacobimatrix_symbolisch_liste_beobachtungsvektor: list + :return: Dictionary mit Indexbereichen je Beobachtungsgruppe. + :rtype: dict + :raises ValueError: Wenn für eine Beobachtungsgruppe keine Indizes ermittelt werden können. + """ liste_strecken_indizes = [] liste_richtungen_indizes = [] liste_zenitwinkel_indizes = [] @@ -156,7 +198,7 @@ class Datumsfestlegung: return names, {n: i for i, n in enumerate(names)} @staticmethod - def build_G_from_names(x0, unbekannten_liste, liste_punktnummern=None, mit_massstab=True): + def erstelle_G(x0, unbekannten_liste, liste_punktnummern=None, mit_massstab=True): """ Baut G (u x d) in den vollen Unbekanntenraum. Wenn liste_punktnummern=None, werden alle Punkt-IDs aus unbekannten_liste @@ -235,11 +277,8 @@ class Datumsfestlegung: return E - def loese_geraendert_mit_Qxx(N, n, G): - """ - löst [N G; G^T 0] [dx;k] = [n;0] - und liefert zusätzlich Q_xx als oberen linken Block von inv(K). - """ + def berechne_dx(N, n, G): + N = np.asarray(N, float) n = np.asarray(n, float).reshape(-1, 1) G = np.asarray(G, float) diff --git a/Einheitenumrechnung.py b/Einheitenumrechnung.py new file mode 100644 index 0000000..ef9de6c --- /dev/null +++ b/Einheitenumrechnung.py @@ -0,0 +1,85 @@ +from decimal import Decimal +import sympy as sp +import math + +class Einheitenumrechnung: + """Einheitenumrechnungen für Winkel- und Längeneinheiten. + + Die Klasse stellt Methoden zur Verfügung für: + + - Umrechnung von Millibogensekunden (mas) in Radiant, + - Umrechnung von Millimetern (mm) in Meter, + - Umrechnung von Gon und Milligon (mgon) in Radiant (Decimal-basiert). + """ + + def mas_to_rad(mas: float) -> float: + """Rechnet Millibogensekunden (mas) in Radiant um. + + Es gilt: rad = mas * (pi / (180 * 3600 * 1000)). + + :param mas: Winkel in Millibogensekunden (mas). + :type mas: float + :return: Winkel in Radiant. + :rtype: float + """ + umrechnungsfaktor = 1 / 1000 * 1 / 3600 * sp.pi / 180 + grad = mas * umrechnungsfaktor + return grad + + def mm_to_m(mm: float) -> float: + """Rechnet Millimeter in Meter um. + + Es gilt: m = mm / 1000. + + :param mm: Länge in Millimeter. + :type mm: float + :return: Länge in Meter. + :rtype: float + """ + m = mm / 1000 + return m + + def gon_to_rad_Decimal(gon: float) -> Decimal: + """Rechnet Gon in Radiant um (Decimal-basiert). + + Es gilt: 400 gon = 2*pi und damit rad = (gon / 200) * pi. + + :param gon: Winkel in Gon. + :type gon: float + :return: Winkel in Radiant als Decimal. + :rtype: Decimal + """ + gon = Decimal(gon) + pi = Decimal(str(math.pi)) + rad = (gon / Decimal(200)) * pi + return rad + + def mgon_to_rad_Decimal(gon: float) -> Decimal: + """Rechnet Milligon (mgon) in Radiant um (Decimal-basiert). + + Es gilt: 1 mgon = 0.001 gon und damit rad = (mgon / 200000) * pi. + + :param gon: Winkel in Milligon (mgon). + :type gon: float + :return: Winkel in Radiant als Decimal. + :rtype: Decimal + """ + gon = Decimal(gon) + pi = Decimal(str(math.pi)) + rad = (gon / Decimal(200000)) * pi + return rad + + def rad_to_gon_Decimal(rad: float) -> Decimal: + """Rechnet Radiant in Gon um (Decimal-basiert). + + Es gilt: 400 gon = 2*pi und damit rad = (gon / 200) * pi. + + :param rad: Winkel in Rad. + :type rad: float + :return: Winkel in Gon als Decimal. + :rtype: Decimal + """ + rad = Decimal(rad) + pi = Decimal(str(math.pi)) + gon = (rad / pi) * Decimal(200) + return gon \ No newline at end of file diff --git a/Import.py b/Import.py index bba96a5..9456f43 100644 --- a/Import.py +++ b/Import.py @@ -6,6 +6,7 @@ import xml.etree.ElementTree as ET from Berechnungen import Berechnungen import Berechnungen +from Einheitenumrechnung import Einheitenumrechnung class Import: @@ -625,7 +626,7 @@ class Import: if Import_fortsetzen: nummer_zielpunkt = 0 - # Abfragen der aktuell höschten Nummer im Attribut beobachtungsgruppeID der Tabelle Beobachtungen + # Abfragen der aktuell höchsten Nummer im Attribut beobachtungsgruppeID der Tabelle Beobachtungen try: nummer_beobachtungsgruppeID = max(liste_beobachtungsgruppeID) except: @@ -741,7 +742,7 @@ class Import: richtung2 = self.string_to_decimal(liste[5]) - Decimal(200) zenitwinkel_vollsatz_gon = (self.string_to_decimal(liste_aktueller_zielpunkt[6]) - self.string_to_decimal( liste[6]) + 400) / 2 - zenitwinkel_vollsatz_rad = Berechnungen.Einheitenumrechnung.gon_to_rad_Decimal(zenitwinkel_vollsatz_gon) + zenitwinkel_vollsatz_rad = Einheitenumrechnung.gon_to_rad_Decimal(zenitwinkel_vollsatz_gon) distanz_vollsatz = (self.string_to_decimal(liste_aktueller_zielpunkt[7]) + self.string_to_decimal( liste[7])) / 2 if richtung2 < 0 and richtung1 != Decimal(0): @@ -749,7 +750,7 @@ class Import: elif richtung2 > 400: richtung2 -= Decimal(400) richtung_vollsatz_gon = (richtung1 + richtung2) / 2 - richtung_vollsatz_rad = Berechnungen.Einheitenumrechnung.gon_to_rad_Decimal(richtung_vollsatz_gon) + richtung_vollsatz_rad = Einheitenumrechnung.gon_to_rad_Decimal(richtung_vollsatz_gon) if liste_aktueller_zielpunkt[8] == liste[8]: prismenhoehe = liste_aktueller_zielpunkt[8] @@ -891,7 +892,7 @@ class Import: # Es werden nur Höhendifferenzen für Punkte berechnet, für die Näherungskoordinaten in der Datenbank vorliegen. if Import_fortsetzen: - print(f"Für folgende Nivellementpunkte werden die Höhen in der Ausgleichung berechnet: {liste_punktnummern_in_db}\nFür folgende Punkte wird aktuell keine Höhe in der Ausgleichung berechnet: {liste_punktnummern_nicht_in_db}. Bei Bedarf im folgenden Schritt ändern!") + print(f"Folgende Stand- und Zielpunkte des geometrischen Nivellements werden für die Beobachtungsgruppe ausgeglichen: {liste_punktnummern_in_db}\nFür folgende Punkte wird aktuell keine Höhe in der Ausgleichung berechnet: {liste_punktnummern_nicht_in_db}. Bei Bedarf im folgenden Schritt ändern!") return dict_punkt_mittelwert_punkthoehen, liste_punktnummern_in_db def import_beobachtungen_nivellement_naeherung_punkthoehen(self, dict_punkt_mittelwert_punkthoehen: dict, @@ -1173,7 +1174,7 @@ class Import: return f"Die Beobachtungen aus der Datei {pfad_datei} wurden erfolgreich importiert." else: - print(f"Anzahl nicht RVVR durch 4 teilbar. Bitte die Datei {pfad_datei} überprüfen! Der Import wurde abgebrochen.") + print(f"Anzahl RVVR durch 4 teilbar. Bitte die Datei {pfad_datei} überprüfen! Der Import wurde abgebrochen.") Import_fortsetzen = False def import_koordinaten_gnss(self, pfad_datei: str, liste_sapos_stationen_genauigkeiten: list) -> str: diff --git a/Koordinatentransformationen.py b/Koordinatentransformationen.py index 9d029e4..c0fb9f8 100644 --- a/Koordinatentransformationen.py +++ b/Koordinatentransformationen.py @@ -270,7 +270,7 @@ class Transformationen: Zi = l_berechnet_final[3 * i + 2] liste_l_berechnet_final.append(sp.Matrix([Xi, Yi, Zi])) - print("l_berechnet_final:") + print("Koordinaten berechnet aus Helmerttransformation:") for punktnummer, l_fin in zip(gemeinsame_punktnummern, liste_l_berechnet_final): print(f"{punktnummer}: {float(l_fin[0]):.3f}, {float(l_fin[1]):.3f}, {float(l_fin[2]):.3f}") diff --git a/Netzqualitaet_Genauigkeit.py b/Netzqualitaet_Genauigkeit.py new file mode 100644 index 0000000..110a44f --- /dev/null +++ b/Netzqualitaet_Genauigkeit.py @@ -0,0 +1,564 @@ +import numpy as np +import plotly.graph_objects as go +from scipy.stats import f +import pandas as pd +from IPython.display import HTML +from IPython.display import display, clear_output +import Berechnungen +import Einheitenumrechnung + + +class Genauigkeitsmaße: + """Berechnung von Genauigkeitsmaße zur Bewertung der erreichten Netzqualität. + + Die Klasse stellt Methoden zur Verfügung für: + + - Berechnung der a-posteriori Standardabweichung der Gewichtseinheit s₀ + - Berechnung des Helmertschen Punktfehlers (2D/3D), + - Berechnung der Standardellipse (Helmertschen Fehlerellipse), + - Berechnung der Konfidenzellipse auf Basis eines Konfidenzniveaus (alpha) mit Skalierung über die F-Verteilung, + - Berechnung von Konfidenzellipsen im lokalen ENU-System durch Transformation von Qxx → Qxx_ENU, + inkl. Ausgabe/Export tabellarischer Ergebnisse. + """ + + + @staticmethod + def berechne_s0apost(v: np.ndarray, P: np.ndarray, r: int) -> float: + """ + Berechnet die a-posteriori Standardabweichung der Gewichtseinheit s₀. + + Die a-posteriori Standardabweichung dient als Qualitätsmaß für die + Ausgleichung nach der Methode der kleinsten Quadrate. Dabei beschreibt + r die Redundanz (Freiheitsgrade). + + :param v: Residuenvektor der Beobachtungen. + :type v: numpy.ndarray + :param P: Gewichtsmatrix der Beobachtungen. + :type P: numpy.ndarray + :param r: Redundanz bzw. Anzahl der Freiheitsgrade der Ausgleichung. + :type r: int + :return: a-posteriori Standardabweichung der Gewichtseinheit s₀. + :rtype: float + """ + + vTPv_matrix = v.T @ P @ v + vTPv = vTPv_matrix.item() + s0apost = np.sqrt(vTPv / r) + print(f"s0 a posteriori beträgt: {s0apost:.4f}") + return s0apost + + + @staticmethod + def helmert_punktfehler(Qxx, s0_apost, unbekannten_liste): + """ + Berechnet den Helmertschen Punktfehler (2D/3D) anhand der Standardabweichungen der Koordinaten der Punkte. + + Aus der Kofaktor-Matrix der Unbekannten Qxx werden die Kofaktoren punktweise ausgelesen. Durch Multiplikation + mit der a-posteriori Standardabweichung der Gewichtseinheit s₀ werden die Standardabweichungen je Koordinate + (σx, σy, σz) sowie der Helmertsche Punktfehler σP berechnet: + + Die Punktzuordnung erfolgt über die Symbolnamen der Unbekanntenliste (z.B. X1, Y1, Z1). + Die Dimension (2D/3D) wird interaktiv per Eingabe abgefragt. Zusätzlich werden die + Ergebnisse als Tabelle ausgegeben und in eine Excel-Datei exportiert. + + :param Qxx: Kofaktor-Matrix der Unbekannten. + :type Qxx: numpy.ndarray + :param s0_apost: a-posteriori Standardabweichung der Gewichtseinheit s₀. + :type s0_apost: float + :param unbekannten_liste: Liste der Unbekannten. + :type unbekannten_liste: list + :return: Tabelle mit Standardabweichungen und Helmertschem Punktfehler je Punkt. + :rtype: pandas.DataFrame + :raises ValueError: Wenn eine ungültige Dimension (nicht 2 oder 3) eingegeben wird. + """ + dim = int(input("Helmertscher Punktfehler (2 = 2D, 3 = 3D): ")) + diagQ = np.diag(Qxx) + daten = [] + namen_str = [str(sym) for sym in unbekannten_liste] + + punkt_ids = [] + for n in namen_str: + if n.upper().startswith('X'): + punkt_ids.append(n[1:]) + + for pid in punkt_ids: + try: + idx_x = next(i for i, n in enumerate(namen_str) if n.upper() == f"X{pid}".upper()) + idx_y = next(i for i, n in enumerate(namen_str) if n.upper() == f"Y{pid}".upper()) + + qx = diagQ[idx_x] + qy = diagQ[idx_y] + qz = 0.0 + + if dim == 3: + try: + idx_z = next(i for i, n in enumerate(namen_str) if n.upper() == f"Z{pid}".upper()) + qz = diagQ[idx_z] + except StopIteration: + qz = 0.0 + + sx = s0_apost * np.sqrt(qx) + sy = s0_apost * np.sqrt(qy) + sz = s0_apost * np.sqrt(qz) if dim == 3 else 0 + + sP = s0_apost * np.sqrt(qx + qy + qz) + + daten.append([pid, float(sx), float(sy), float(sz), float(sP)]) + except: + continue + + helmert_punktfehler = pd.DataFrame(daten, columns=["Punkt", "σx", "σy", "σz", f"σP_{dim}D"]) + display(HTML(helmert_punktfehler.to_html(index=False))) + helmert_punktfehler.to_excel(r"Zwischenergebnisse\Standardabweichungen_Helmertscher_Punktfehler.xlsx",index=False) + return helmert_punktfehler + + + + @staticmethod + def standardellipse(Qxx, s0_apost, unbekannten_liste): + """ + Berechnet die Standardellipse (Helmertsche Fehlerellipse) für die Punkte aus Qxx und s₀ a posteriori. + + Für jeden Punkt werden aus der Kofaktor-Matrix der Unbekannten Qxx die + Kofaktoren von X und Y ausgelesen (qxx, qyy, qyx). + Daraus werden Standardabweichungen σx, σy sowie die Kovarianz σxy bestimmt und + anschließend die Parameter der Standardellipse berechnet: + + - Große und kleine Halbachse der Standardellipse, + - Richtungswinkel θ der großen Halbachse in gon. + + Die Punktzuordnung erfolgt über die Symbolnamen der Unbekanntenliste (z.B. X1, Y1). + Zusätzlich werden die Ergebnisse tabellarisch ausgegeben und in eine Excel-Datei expoertiert. + + :param Qxx: Kofaktor-Matrix der Unbekannten. + :type Qxx: numpy.ndarray + :param s0_apost: a-posteriori Standardabweichung der Gewichtseinheit s₀. + :type s0_apost: float + :param unbekannten_liste: Liste der Unbekannten. + :type unbekannten_liste: list + :return: Tabelle mit Standardabweichungen und Parametern der Standardellipse je Punkt. + :rtype: pandas.DataFrame + """ + Qxx = np.asarray(Qxx, float) + daten = [] + namen_str = [str(sym) for sym in unbekannten_liste] + + punkt_ids = [] + for n in namen_str: + if n.upper().startswith('X'): + punkt_ids.append(n[1:]) + + for pid in punkt_ids: + try: + idx_x = next(i for i, n in enumerate(namen_str) if n.upper() == f"X{pid}".upper()) + idx_y = next(i for i, n in enumerate(namen_str) if n.upper() == f"Y{pid}".upper()) + + qxx = Qxx[idx_x, idx_x] + qyy = Qxx[idx_y, idx_y] + qyx = Qxx[idx_y, idx_x] + + # Standardabweichungen + sx = s0_apost * np.sqrt(qxx) + sy = s0_apost * np.sqrt(qyy) + sxy = (s0_apost ** 2) * qyx + + k = np.sqrt((qxx - qyy) ** 2 + 4 * (qyx ** 2)) + + # Q_dmax/min = 0.5 * (Qyy + Qxx +/- k) + q_dmax = 0.5 * (qyy + qxx + k) + q_dmin = 0.5 * (qyy + qxx - k) + + # Halbachsen + s_max = s0_apost * np.sqrt(q_dmax) + s_min = s0_apost * np.sqrt(q_dmin) + + # Richtungswinkel theta in gon: + zaehler = 2 * qyx + nenner = qxx - qyy + t_grund = 0.5 * np.arctan(abs(zaehler) / abs(nenner)) * (200 / np.pi) + + # Quadrantenabfrage + if nenner > 0 and qyx > 0: # Qxx - Qyy > 0 und Qyx > 0 + t_gon = t_grund # 0 - 50 gon + elif nenner < 0 and qyx > 0: # Qxx - Qyy < 0 und Qyx > 0 + t_gon = 100 - t_grund # 50 - 100 gon + elif nenner < 0 and qyx < 0: # Qxx - Qyy < 0 und Qyx < 0 + t_gon = 100 + t_grund # 100 - 150 gon + elif nenner > 0 and qyx < 0: # Qxx - Qyy > 0 und Qyx < 0 + t_gon = 200 - t_grund # 150 - 200 gon + else: + t_gon = 0.0 + + daten.append([ + pid, + float(sx), float(sy), float(sxy), + float(s_max), float(s_min), + float(t_gon) + ]) + except: + continue + standardellipse = pd.DataFrame(daten, columns=["Punkt", "σx [m]", "σy [m]", "σxy [m]", "Große Halbachse [m]", "Kleine Halbachse [m]", "θ [gon]"]) + standardellipse["σx [m]"] = standardellipse["σx [m]"].astype(float).round(4) + standardellipse["σy [m]"] = standardellipse["σy [m]"].astype(float).round(4) + standardellipse["Große Halbachse [m]"] = standardellipse["Große Halbachse [m]"].astype(float).round(4) + standardellipse["Kleine Halbachse [m]"] = standardellipse["Kleine Halbachse [m]"].astype(float).round(4) + standardellipse["θ [gon]"] = standardellipse["θ [gon]"].astype(float).round(3) + display(HTML(standardellipse.to_html(index=False))) + standardellipse.to_excel(r"Zwischenergebnisse\Standardellipse.xlsx", index=False) + return standardellipse + + + + @staticmethod + def konfidenzellipse(Qxx, s0_apost, unbekannten_liste, R, ausgabe_erfolgt): + """ + Berechnet die Konfidenzellipse für Punkte aus Qxx und einem Konfidenzniveau. + + Auf Basis der Kovarianz-Matrix der Unbekannten Qxx und der a-posteriori + Standardabweichung der Gewichtseinheit s₀ werden für jeden Punkt die Parameter + der Konfidenzellipse berechnet. Das Konfidenzniveau wird mittels einer Eingabe + über alpha gewählt (Standard: 0.05 für 95%). + + Die Punktzuordnung erfolgt über die Symbolnamen der Unbekanntenliste (z.B. X1, Y1). + Optional wird die Tabelle ausgegeben und als Excel-Datei exportiert, abhängig von + ausgabe_erfolgt. + + :param Qxx: Kofaktor-Matrix der geschätzten Unbekannten. + :type Qxx: numpy.ndarray + :param s0_apost: a-posteriori Standardabweichung der Gewichtseinheit s₀. + :type s0_apost: float + :param unbekannten_liste: Liste der Unbekannten. + :type unbekannten_liste: list + :param R: Redundanz (Freiheitsgrade) für die F-Verteilung. + :type R: int + :param ausgabe_erfolgt: Steuert, ob eine Ausgabe/Dateischreibung erfolgen soll (False = Ausgabe). + :type ausgabe_erfolgt: bool + :return: Tabelle der Konfidenzellipse je Punkt, verwendetes alpha. + :rtype: tuple[pandas.DataFrame, float] + :raises ValueError: Wenn alpha nicht in (0, 1) liegt oder nicht in float umgewandelt werden kann. + """ + alpha_input = input("Konfidenzniveau wählen (z.B. 0.05 für 95%, 0.01 für 99%) [Standard=0.05]: ") + if alpha_input.strip() == "": + alpha = 0.05 + else: + alpha = float(alpha_input) + print(f"→ Verwende alpha = {alpha} (Konfidenz = {(1 - alpha) * 100:.1f}%)") + Qxx = np.asarray(Qxx, float) + daten = [] + namen_str = [str(sym) for sym in unbekannten_liste] + + punkt_ids = [n[1:] for n in namen_str if n.upper().startswith('X')] + + # Faktor für Konfidenzellipse (F-Verteilung) + kk = float(np.sqrt(2.0 * f.ppf(1.0 - alpha, 2, R))) + + for pid in punkt_ids: + try: + idx_x = next(i for i, n in enumerate(namen_str) if n.upper() == f"X{pid}".upper()) + idx_y = next(i for i, n in enumerate(namen_str) if n.upper() == f"Y{pid}".upper()) + + qxx = Qxx[idx_x, idx_x] + qyy = Qxx[idx_y, idx_y] + qyx = Qxx[idx_y, idx_x] + + # Standardabweichungen + sx = s0_apost * np.sqrt(qxx) + sy = s0_apost * np.sqrt(qyy) + sxy = (s0_apost ** 2) * qyx + + k = np.sqrt((qxx - qyy) ** 2 + 4 * (qyx ** 2)) + + # Q_dmax/min = 0.5 * (Qyy + Qxx +/- k) + q_dmax = 0.5 * (qyy + qxx + k) + q_dmin = 0.5 * (qyy + qxx - k) + + # Halbachsen der Standardellipse + s_max = s0_apost * np.sqrt(q_dmax) + s_min = s0_apost * np.sqrt(q_dmin) + + # Halbachsen der Konfidenzellipse + A_K = kk * s_max + B_K = kk * s_min + + # Richtungswinkel theta in gon: + zaehler = 2 * qyx + nenner = qxx - qyy + t_grund = 0.5 * np.arctan(abs(zaehler) / abs(nenner)) * (200 / np.pi) + + # Quadrantenabfrage + if nenner > 0 and qyx > 0: + t_gon = t_grund # 0 - 50 gon + elif nenner < 0 and qyx > 0: + t_gon = 100 - t_grund # 50 - 100 gon + elif nenner < 0 and qyx < 0: + t_gon = 100 + t_grund # 100 - 150 gon + elif nenner > 0 and qyx < 0: + t_gon = 200 - t_grund # 150 - 200 gon + else: + t_gon = 0.0 + + daten.append([ + pid, + float(sx), float(sy), float(sxy), + float(A_K), float(B_K), + float(t_gon) + ]) + + except: + continue + + konfidenzellipse = pd.DataFrame(daten, columns=["Punkt", "σx [m]", "σy [m]", "σxy [m]", "Große Halbachse [m]", "Kleine Halbachse [m]", "θ [gon]"]) + konfidenzellipse["Große Halbachse [m]"] = konfidenzellipse["Große Halbachse [m]"].round(4) + konfidenzellipse["Kleine Halbachse [m]"] = konfidenzellipse["Kleine Halbachse [m]"].round(4) + konfidenzellipse["θ [gon]"] = konfidenzellipse["θ [gon]"].round(3) + if ausgabe_erfolgt == False: + display(HTML(konfidenzellipse.to_html(index=False))) + konfidenzellipse.to_excel(r"Zwischenergebnisse\Konfidenzellipse.xlsx", index=False) + return konfidenzellipse, alpha + + + @staticmethod + def konfidenzellipsen_enu(a, b, ausgabe_parameterschaetzung, liste_unbekannte, ausgleichungsergebnis, s0apost, r_gesamt): + """ + Berechnet Konfidenzellipsen im lokalen ENU-System aus einer ins ENU-System transformierten Qxx-Matrix. + + Die Funktion transformiert zunächst die Kofaktor-Matrix der Unbekannten Qxx + in ein East-North-Up-System (ENU) bezogen auf den Schwerpunkt der verwendeten + Punkte (B0, L0). Anschließend wird auf Basis der transformierten Matrix die + Konfidenzellipse über die Funktion "konfidenzellipse" bestimmt. + Zum Schluss werden Spaltennamen an die ENU-Notation angepasst, Werte gerundet, + tabellarisch ausgegeben und als Excel-Datei exportiert. + + :param a: Große Halbachse a des Referenzellipsoids (z.B. WGS84/GRS80) in Metern. + :type a: float + :param b: Große Halbachse b des Referenzellipsoids (z.B. WGS84/GRS80) in Metern. + :type b: float + :param ausgabe_parameterschaetzung: Dictonary der Ergebnisse der Parameterschätzung, muss "Q_xx" enthalten. + :type ausgabe_parameterschaetzung: dict + :param liste_unbekannte: Liste der Unbekannten. + :type liste_unbekannte: list + :param ausgleichungsergebnis: Dictionary der geschätzten Punktkoordinaten (XYZ) zur ENU-Referenzbildung. + :type ausgleichungsergebnis: dict + :param s0apost: a-posteriori Standardabweichung der Gewichtseinheit s₀. + :type s0apost: float + :param r_gesamt: Redundanz (Freiheitsgrade) für die Konfidenzberechnung. + :type r_gesamt: int + :return: Tabelle der Konfidenzellipse im ENU-System, Rotationsmatrix R0 der ENU-Transformation. + :rtype: tuple[pandas.DataFrame, numpy.ndarray] + :raises KeyError: Wenn ``ausgabe_parameterschaetzung`` keinen Eintrag ``"Q_xx"`` enthält. + """ + + berechnungen = Berechnungen.Berechnungen(a, b) + + # 1) Qxx ins ENU-System transformieren + Qxx_enu, (B0, L0), R0 = Berechnungen.ENU.transform_Qxx_zu_QxxENU( + Qxx=ausgabe_parameterschaetzung["Q_xx"], + unbekannten_liste= liste_unbekannte, + berechnungen=berechnungen, + dict_xyz= ausgleichungsergebnis, + ) + + print( + f"ENU-Referenz (Schwerpunkt): B0={Einheitenumrechnung.rad_to_gon_Decimal(B0):.8f} rad, L0={Einheitenumrechnung.rad_to_gon_Decimal(L0):.8f} rad") + + # 2) Konfidenzellipse im ENU-System + Konfidenzellipse_ENU, alpha = Genauigkeitsmaße.konfidenzellipse( + Qxx_enu, + s0apost, + liste_unbekannte, + r_gesamt, + ausgabe_erfolgt = True + ) + + # 3) Spaltennamen anpassen + Konfidenzellipse_ENU = Konfidenzellipse_ENU.rename(columns={ + "σx [m]": "σE [m]", + "σy [m]": "σN [m]", + "σxy [m]": "σEN [m]", + "θ [gon]": "θ_EN [gon]" + }) + + # 4) Runden und Anzeigen + Konfidenzellipse_ENU["σE [m]"] = Konfidenzellipse_ENU["σE [m]"].round(4) + Konfidenzellipse_ENU["σN [m]"] = Konfidenzellipse_ENU["σN [m]"].round(4) + Konfidenzellipse_ENU["Große Halbachse [m]"] = Konfidenzellipse_ENU["Große Halbachse [m]"].round(4) + Konfidenzellipse_ENU["Kleine Halbachse [m]"] = Konfidenzellipse_ENU["Kleine Halbachse [m]"].round(4) + Konfidenzellipse_ENU["θ_EN [gon]"] = Konfidenzellipse_ENU["θ_EN [gon]"].round(4) + + display(HTML(Konfidenzellipse_ENU.to_html(index=False))) + + # 5) Export + Konfidenzellipse_ENU.to_excel(r"Zwischenergebnisse\Konfidenzellipse_ENU.xlsx", index=False) + return Konfidenzellipse_ENU, R0 + + + +class Plot: + """Visualisierung geodätischer Netze und Genauigkeitsmaße. + + Die Klasse stellt Methoden zur Verfügung für: + + - grafische Darstellung von geodätischen Netzen im lokalen ENU-System, + - Visualisierung von Beobachtungen als Verbindungslinien, + - Darstellung von Konfidenzellipsen, + - interaktive Netzdarstellung mit Plotly inklusive Hover-Informationen, + - Skalierung und Layout-Anpassung zur anschaulichen Präsentation von + Lagegenauigkeiten. + + Die Klasse dient ausschließlich der Ergebnisvisualisierung und nimmt keine + numerischen Berechnungen vor. + """ + + @staticmethod + def netzplot_ellipsen( + Koord_ENU, + unbekannten_labels, + beobachtungs_labels, + df_konf_ellipsen_enu, + skalierung=1000, + n_ellipse_pts=60, + titel="Netzplot im ENU-System mit Konfidenzellipsen" + ): + """ + Erstellt einen Netzplot im ENU-System inklusive Konfidenzellipsen, Netzpunkten und Beobachtungslinien. + + Die Funktion visualisiert das geodätische Netz im East-North-Up-System (ENU) + mit Plotly. Dabei werden: + + - Beobachtungen als Verbindungslinien zwischen Punkten dargestellt, deren Ansicht aus- und eingeschaltet werden kann, + - Konfidenzellipsen je Punkt (Halbachsen und Richtungswinkel), + - Netzpunkte mit Punkt-ID und Koordinaten im Hover-Text angezeigt. + + Die Ellipsen werden zur besseren Sichtbarkeit mit einem Faktor "skalierung" vergrößert. Dieser kann angepasst werden. + Der Richtungswinkel wird in gon erwartet und intern nach Radiant umgerechnet. + + :param Koord_ENU: Dictionary der Punktkoordinaten im ENU-System. + :type Koord_ENU: dict + :param unbekannten_labels: Liste der Unbekannten zur Ableitung der Punkt-IDs (z.B. X1, Y1, Z1). + :type unbekannten_labels: list + :param beobachtungs_labels: Liste der Beobachtungen zur Ableitung von Verbindungslinien. + :type beobachtungs_labels: list + :param df_konf_ellipsen_enu: DataFrame mit Konfidenzellipsenparametern je Punkt. + :type df_konf_ellipsen_enu: pandas.DataFrame + :param skalierung: Faktor zur visuellen Vergrößerung der Ellipsen im Plot. + :type skalierung: float + :param n_ellipse_pts: Anzahl der Stützpunkte zur Approximation der Ellipse. + :type n_ellipse_pts: int + :param titel: Titel des Plots. + :type titel: str + :return: None + :rtype: None + :raises ValueError: Wenn weder "θ_EN [gon]" noch "θ [gon]" im DataFrame vorhanden ist. + """ + + names = [str(s).strip() for s in unbekannten_labels] + + if "θ_EN [gon]" in df_konf_ellipsen_enu.columns: + theta_col = "θ_EN [gon]" + elif "θ [gon]" in df_konf_ellipsen_enu.columns: + theta_col = "θ [gon]" + else: + raise ValueError("Spalte 'θ_EN [gon]' oder 'θ [gon]' fehlt im DataFrame.") + + punkt_ids = sorted({nm[1:] for nm in names if nm and nm[0].upper() in ("X", "Y", "Z")}) + + fig = go.Figure() + + # 1) Darstellungen der Beobachtungen + beob_typen = { + 'GNSS-Basislinien': {'pattern': 'gnss', 'color': 'rgba(255, 100, 0, 0.4)'}, + 'Tachymeter-Beob': {'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() + is_typ = ((info['pattern'] in bl_str and info['pattern'] != '') or + (info['pattern'] == '' and 'gnss' not in bl_str and 'niv' not in bl_str)) + if not is_typ: + continue + + bl_raw = str(bl) + pts = [] + for pid in punkt_ids: + if (f"_{pid}" in bl_raw) or bl_raw.startswith(f"{pid}_"): + if pid in Koord_ENU: + pts.append(pid) + + if len(pts) >= 2: + p1, p2 = pts[0], pts[1] + x_l.extend([Koord_ENU[p1][0], Koord_ENU[p2][0], None]) # E + y_l.extend([Koord_ENU[p1][1], Koord_ENU[p2][1], None]) # N + + 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))) + + # 2) Darstellung der Konfidenzellipsen + t = np.linspace(0, 2 * np.pi, n_ellipse_pts) + first = True + for _, row in df_konf_ellipsen_enu.iterrows(): + pid = str(row["Punkt"]) + if pid not in Koord_ENU: + continue + + a = float(row["Große Halbachse [m]"]) * skalierung + b = float(row["Kleine Halbachse [m]"]) * skalierung + theta = float(row[theta_col]) * np.pi / 200.0 # gon->rad + + ex = a * np.cos(t) + ey = b * np.sin(t) + + c, s = np.cos(theta), np.sin(theta) + xr = c * ex - s * ey + yr = s * ex + c * ey + + E0, N0, _ = Koord_ENU[pid] + + fig.add_trace(go.Scatter( + x=E0 + xr, y=N0 + yr, + mode="lines", + line=dict(color="red", width=1.5), + name=f"Ellipsen (×{skalierung})", + legendgroup="Ellipsen", + showlegend=first, + hoverinfo="skip" + )) + first = False + + # 3) Darstellung der Punkte + xs, ys, texts, hovers = [], [], [], [] + for pid in punkt_ids: + if pid not in Koord_ENU: + continue + E, N, U = Koord_ENU[pid] + xs.append(E); + ys.append(N); + texts.append(pid) + hovers.append(f"Punkt {pid}
E={E:.4f} m
N={N:.4f} m
U={U:.4f} m") + + fig.add_trace(go.Scatter( + x=xs, y=ys, mode="markers+text", + text=texts, textposition="top center", + marker=dict(size=8, color="black"), + name="Netzpunkte", + hovertext=hovers, hoverinfo="text" + )) + + fig.update_layout( + title=f"{titel} (Ellipsen ×{skalierung})", + xaxis=dict(title="E [m]", scaleanchor="y", scaleratio=1, showgrid=True, gridcolor="lightgrey"), + yaxis=dict(title="N [m]", showgrid=True, gridcolor="lightgrey"), + width=1100, height=900, + template="plotly_white", + plot_bgcolor="white" + ) + + fig.add_annotation( + text=f"Maßstab Ellipsen:
Dargestellte Größe = Konfidenzellipse × {skalierung}", + 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}) \ No newline at end of file diff --git a/Netzqualitaet_Zuverlaessigkeit.py b/Netzqualitaet_Zuverlaessigkeit.py new file mode 100644 index 0000000..e35a6f9 --- /dev/null +++ b/Netzqualitaet_Zuverlaessigkeit.py @@ -0,0 +1,828 @@ +from dataclasses import dataclass +import numpy as np +from scipy import stats +from scipy.stats import norm +import pandas as pd +from IPython.display import HTML +from IPython.display import display, clear_output +import ipywidgets as widgets +import itables +from itables.widget import ITable + + +@dataclass +class Zuverlaessigkeit: + + @staticmethod + def gesamtredundanz(n, u): + """ + Berechnet die Gesamtredundanz des Netzes. + + Die Gesamtredundanz ergibt sich aus der Differenz zwischen der Anzahl der + Beobachtungen n und der Anzahl der Unbekannten u. Sie entspricht der Anzahl + der Freiheitsgrade. + + :param n: Anzahl der Beobachtungen. + :type n: int + :param u: Anzahl der Unbekannten. + :type u: int + :return: Gesamtredundanz des Netzes. + :rtype: int + """ + r_gesamt = n - u + print(f"Die Gesamtredundanz des Netzes beträgt: {r_gesamt}") + return r_gesamt + + + @staticmethod + def berechne_R(Q_vv, P): + """ + Berechnet die Redundanzmatrix R aus Qvv und der Gewichtsmatrix P. + + Die Redundanzmatrix wird definiert als: + R = Qvv · P + + :param Q_vv: Kofaktor-Matrix der Residuen. + :type Q_vv: numpy.ndarray + :param P: Gewichtsmatrix der Beobachtungen. + :type P: numpy.ndarray + :return: Redundanzmatrix R. + :rtype: numpy.ndarray + """ + R = Q_vv @ P + return R + + + @staticmethod + def berechne_ri(R): + """ + Berechnet die Redundanzanteile einzelner Beobachtungen. + + Die Redundanzanteile rᵢ ergeben sich aus den Diagonalelementen der Redundanzmatrix R. + Zusätzlich werden die effektiven Redundanzanteile EVi in Prozent berechnet: + + EVi = 100 · rᵢ + + :param R: Redundanzmatrix R. + :type R: numpy.ndarray + :return: Tuple aus Redundanzanteilen rᵢ und effektiven Redundanzanteilen EVi in Prozent. + :rtype: tuple[numpy.ndarray, numpy.ndarray] + """ + ri = np.diag(R) + EVi = 100.0 * ri + return ri, EVi + + + @staticmethod + def klassifiziere_ri(ri): + """ + Klassifiziert einen Redundanzanteil rᵢ nach seiner Kontrollierbarkeit. + + Der Redundanzanteil wird anhand üblicher geodätischer Schwellenwerte + qualitativ bewertet. + + :param ri: Redundanzanteil einer einzelnen Beobachtung. + :type ri: float + :return: Qualitative Bewertung der Kontrollierbarkeit. + :rtype: str + """ + 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 redundanzanteile_ri(Qvv, P, liste_beob): + """ + Berechnet und dokumentiert Redundanzanteile rᵢ und EVᵢ für alle Beobachtungen. + + Die Ergebnisse werden als DataFrame ausgegeben, als HTML-Tabelle angezeigt und + als Excel-Datei exportiert. + + :param Qvv: Kofaktor-Matrix der Residuen. + :type Qvv: numpy.ndarray + :param P: Gewichtsmatrix der Beobachtungen. + :type P: numpy.ndarray + :param liste_beob: Liste der Beobachtungslabels (Zeilenbeschriftungen) zur Zuordnung der Ergebnisse. + :type liste_beob: list + :return: Redundanzmatrix R, Redundanzanteile rᵢ, effektive Redundanzanteile EVᵢ, Ergebnistabelle als DataFrame. + :rtype: tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, pandas.DataFrame] + """ + R = Zuverlaessigkeit.berechne_R(Qvv, P) + ri, EVi = Zuverlaessigkeit.berechne_ri(R) + ri = np.asarray(ri).reshape(-1) + EVi = np.asarray(EVi).reshape(-1) + + labels = [str(s) for s in liste_beob] + klassen = [Zuverlaessigkeit.klassifiziere_ri(r) for r in ri] + Redundanzanteile = pd.DataFrame({"Beobachtung": labels, "r_i": ri, "EV_i [%]": EVi, "Klassifikation": klassen, }) + display(HTML(Redundanzanteile.to_html(index=False))) + Redundanzanteile.to_excel(r"Zwischenergebnisse\Redundanzanteile.xlsx", index=False) + return R, ri, EVi, Redundanzanteile + + + @staticmethod + def globaltest(r_gesamt, sigma0_apost, sigma0_apriori=1): + """ + Führt den Globaltest zur Prüfung des Ausgleichungsmodells durch. + + Der Globaltest überprüft, ob die a-posteriori Standardabweichung der Gewichtseinheit σ̂₀ + mit der a-priori Annahme σ₀ vereinbar ist. Als Testgröße wird verwendet: + + T_G = (σ̂₀²) / (σ₀²) + + Die Entscheidung erfolgt über die F-Verteilung. Das Signifikanzniveau alpha wird interaktiv abgefragt + (Standard: 0.001). Zusätzlich wird eine Ergebnis-Tabelle und eine Interpretation ausgegeben. + + :param r_gesamt: Gesamtredundanz bzw. Freiheitsgrade. + :type r_gesamt: int + :param sigma0_apost: a-posteriori Standardabweichung der Gewichtseinheit σ̂₀. + :type sigma0_apost: float + :param sigma0_apriori: a-priori Standardabweichung der Gewichtseinheit σ₀ (Standard=1). + :type sigma0_apriori: float + :return: Dictionary mit Testparametern, Testergebnis (H₀ angenommen/verworfen) und Interpretation. + :rtype: dict[str, Any] + :raises ValueError: Wenn alpha nicht in (0, 1) liegt oder nicht in float umgewandelt werden kann. + """ + + alpha_input = input("Irrtumswahrscheinlichkeit α wählen (z.B. 0.05, 0.01) [Standard=0.001]: ").strip() + alpha = 0.001 if alpha_input == "" else float(alpha_input) + 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." + ) + globaltest = pd.DataFrame([ + ["Freiheitsgrad", r_gesamt], + ["σ̂₀ a posteriori", sigma0_apost], + ["σ₀ a priori", sigma0_apriori], + ["Signifikanzniveau α", alpha, ], + ["Testgröße T_G", T_G, ], + ["Kritischer Wert Fₖ", F_krit], + ["Nullhypothese H₀", "angenommen" if H0 else "verworfen"], ], columns=["Größe", "Wert"]) + + display(HTML(globaltest.to_html(index=False))) + print(interpretation) + + 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): + """ + Führt den Lokaltest zur Grobfehlerdetektion je Beobachtung durch. + + Auf Basis der Residuen v, der Kofaktor-Matrix der Residuen Qvv und der Redundanzanteile rᵢ + werden für jede Beobachtung statistische Kennwerte zur Detektion grober Fehler berechnet. Dazu zählen: + + - Grobfehlerabschätzung: GFᵢ = − vᵢ / rᵢ + - Standardabweichung der Residuen: s_vᵢ = s₀ · √q_vᵢ (mit q_vᵢ = diag(Qvv)) + - Normierte Verbesserung: NVᵢ = |vᵢ| / s_vᵢ + - Nichtzentralitätsparameter: δ₀ = k + k_A + mit k aus dem zweiseitigen Normalquantil (α) und k_A aus der Testmacht (1−β) + - Grenzwert der Aufdeckbarkeit (Minimal detektierbarer Grobfehler): GRZWᵢ = (s_vᵢ / rᵢ) · δ₀ + + Beobachtungen werden als auffällig markiert, wenn NVᵢ > δ₀. Für rᵢ = 0 wird die Grobfehlerabschätzung + und der Grenzwert als NaN gesetzt. + + :param v: Residuenvektor der Beobachtungen. + :type v: array_like + :param Q_vv: Kofaktor-Matrix der Residuen. + :type Q_vv: array_like + :param ri: Redundanzanteile der Beobachtungen. + :type ri: array_like + :param labels: Liste der Beobachtungen zur Zuordnung in der Ergebnistabelle. + :type labels: list + :param s0_apost: a-posteriori Standardabweichung der Gewichtseinheit s₀. + :type s0_apost: float + :param alpha: Irrtumswahrscheinlichkeit α (Signifikanzniveau, zweiseitiger Test). + :type alpha: float + :param beta: Wahrscheinlichkeit β für einen Fehler 2. Art (Testmacht = 1−β). + :type beta: float + :return: DataFrame mit NVᵢ, Auffälligkeit, Grobfehlerabschätzung GFᵢ und Grenzwert GRZWᵢ je Beobachtung. + :rtype: pandas.DataFrame + :raises ValueError: Wenn alpha oder beta nicht im Intervall (0, 1) liegen. + """ + 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 aufruf_lokaltest(liste_beob, alpha, ausgabe_parameterschaetzung, ri, s0_aposteriori): + """Startet den Lokaltest und erzeugt die interaktive Tabelle. + + :param liste_beob: Liste der Beobachtungslabels. + :type liste_beob: list + :param alpha: Signifikanzniveau. + :type alpha: float + :param ausgabe_parameterschaetzung: Dictionary mit den Ergebnissen der letzten Iteration der Parameterschätzung. + :type ausgabe_parameterschaetzung: dict + :param ri: Redundanz. + :type ri: Any + :param s0_aposteriori: a-posteriori Standardabweichung. + :type s0_aposteriori: float + :return: ausschalten_dict + :rtype: dict + """ + + # Initialisieren einer interaktiven Tabelle für die Benutzereingaben + itables.init_notebook_mode() + labels = [str(s) for s in liste_beob] + + # Benutzereingabe von β + beta_input = input("Macht des Tests (1-β) wählen [Standard: 80 % -> 0.80]: ").strip() + beta = 0.80 if beta_input == "" else float(beta_input) + + # Berechnungen für den Lokaltest + Lokaltest = Zuverlaessigkeit.lokaltest_innere_Zuverlaessigkeit( + v=ausgabe_parameterschaetzung["v"], + Q_vv=ausgabe_parameterschaetzung["Q_vv"], + ri=ri, + labels=labels, + s0_apost=s0_aposteriori, + alpha=alpha, + beta=beta + ) + + if "v_i" in Lokaltest.columns: + Lokaltest["v_i"] = Lokaltest["v_i"].round(6) + if "r_i" in Lokaltest.columns: + Lokaltest["r_i"] = Lokaltest["r_i"].round(4) + if "s_vi" in Lokaltest.columns: + Lokaltest["s_vi"] = Lokaltest["s_vi"].round(6) + if "GF_i" in Lokaltest.columns: + Lokaltest["GF_i"] = Lokaltest["GF_i"].round(6) + if "GRZW_i" in Lokaltest.columns: + Lokaltest["GRZW_i"] = Lokaltest["GRZW_i"].round(6) + + # Anlegen des Dataframes + df = Lokaltest.copy() + + if "Beobachtung" not in df.columns: + if df.index.name == "Beobachtung": + df = df.reset_index() + else: + df = df.reset_index().rename(columns={"index": "Beobachtung"}) + + if "Beobachtung_ausschalten" not in df.columns: + df.insert(0, "Beobachtung_ausschalten", "") + else: + zeile = df.pop("Beobachtung_ausschalten") + df.insert(0, "Beobachtung_ausschalten", zeile) + + gui = LokaltestInnereZuverlaessigkeitGUI(df) + gui.ausgabe_erstellen() + gui.zeige_tabelle() + + Lokaltest.to_excel(r"Zwischenergebnisse\Lokaltest_innere_Zuverlaessugkeit.xlsx", index=False) + return gui.ausschalten_dict, beta + + + + def aeussere_zuverlaessigkeit( + Lokaltest, bezeichnung, Qxx, A, P, s0_apost, unbekannten_liste, x, + ausschliessen=("lA_",), + ): + """ + Berechnet Parameter der äußeren Zuverlässigkeit (EP/EF) je Beobachtung. + + Auf Basis der Ergebnisse des Lokaltests werden für jede Beobachtung Maße der äußeren + Zuverlässigkeit bestimmt. Dazu zählen: + + - Einfluss auf die (relative) Punktlage EP: + - aus geschätzter Modellstörung: EP_GF,i = |(1 - r_i) · GF_i| + - aus Grenzwert der nicht mehr aufdeckbaren Modellstörung: EP_GRZW,i = |(1 - r_i) · GRZW_i| + Für Winkelbeobachtungen (R/ZW) wird EP in eine äquivalente Querabweichung (in m) umgerechnet: q = EP · s + wobei EP als Winkelstörung im Bogenmaß (rad) und s als räumliche Strecke zwischen Stand- und + Zielpunkt verwendet wird. + - Einflussfaktor / Netzverzerrung EF (Worst-Case-Einfluss einer nicht detektierten Störung): + Es wird eine Einzelstörung Δl_i = GRZW_i angesetzt (alle anderen Δl_j = 0) und in den + Unbekanntenraum übertragen: Δx = Q_xx · A^T · P · Δl + Der Einflussfaktor wird lokal (nur für die von der Beobachtung berührten Punktkoordinaten, + i.d.R. Stand- und Zielpunkt) über die gewichtete Norm berechnet: EF_i^2 = (Δx_loc^T · Q_loc^{-1} · Δx_loc) / s0^2 + mit s0 = a posteriori Standardabweichung der Gewichtseinheit. + - Punktstreuungsmaß SP_3D und maximale Verfälschung EF·SP: + Für die berührten Punkte wird je Punkt der 3×3-Block aus Q_xx betrachtet: als Maß wird die maximale Spur + verwendet: SP_3D,loc = s0 · sqrt( max( tr(Q_P) ) ) + und daraus: (EF·SP)_i = EF_i · SP_3D,loc + + Pseudobeobachtungen (z.B. Lagerungs-/Anschlussgleichungen) können über Präfixe in + "ausschliessen" aus der Auswertung entfernt werden. Es wird geprüft, ob die Anzahl + der Bezeichnungen und die Zeilenanzahl des Lokaltests zur Beobachtungsanzahl von A passen. + + :param Lokaltest: DataFrame des Lokaltests`. + :type Lokaltest: pandas.DataFrame + :param bezeichnung: Bezeichnungen der Beobachtungen. + :type bezeichnung: list + :param Qxx: Kofaktor-Matrix der Unbekannten. + :type Qxx: numpy.ndarray + :param A: Jacobi-Matrix (A-Matrix). + :type A: numpy.ndarray + :param P: Gewichtsmatrix der Beobachtungen. + :type P: numpy.ndarray + :param s0_apost: a-posteriori Standardabweichung der Gewichtseinheit s₀. + :type s0_apost: float + :param unbekannten_liste: Liste der Unbekannten. + :type unbekannten_liste: list + :param x: Unbekanntenvektor. + :type x: array_like + :param ausschliessen: Präfixe von Beobachtungsbezeichnungen, die aus der Auswertung entfernt werden sollen + (Standard: ("lA_",) für Lagerungs-/Pseudobeobachtungen). + :type ausschliessen: tuple + :return: DataFrame mit Stand/Zielpunkt, Redundanzanteil rᵢ, EP (aus GF und GRZW), EF sowie SP_3D und EF·SP_3D. + :rtype: pandas.DataFrame + :raises ValueError: Wenn die Anzahl der Bezeichnungen oder die Zeilenanzahl des Lokaltests nicht zu A passt. + """ + + lokaltest_daten = Lokaltest.copy() + bezeichnung = [str(l) for l in list(bezeichnung)] + + Qxx = np.asarray(Qxx, float) + A = np.asarray(A, float) + P = np.asarray(P, float) + x = np.asarray(x, float).reshape(-1) + + namen_str = [str(sym) for sym in unbekannten_liste] + + # Konsistenzprüfung + n = A.shape[0] + if len(bezeichnung) != n: + raise ValueError(f"len(labels)={len(bezeichnung)} passt nicht zu A.shape[0]={n}.") + if len(lokaltest_daten) != n: + raise ValueError(f"Lokaltest hat {len(lokaltest_daten)} Zeilen, A hat {n} Beobachtungen.") + + # Pseudobeobachtungen lA rausfiltern + beobachtungen = np.ones(n, dtype=bool) + if ausschliessen: + for i, bez in enumerate(bezeichnung): + if any(bez.startswith(pref) for pref in ausschliessen): + beobachtungen[i] = False + + lokaltest_daten = lokaltest_daten.loc[beobachtungen].reset_index(drop=True) + bezeichnung = [bez for (bez, k) in zip(bezeichnung, beobachtungen) if k] + A = A[beobachtungen, :] + P = P[np.ix_(beobachtungen, beobachtungen)] + n = A.shape[0] + + # Daten aus dem Lokaltest + ri = lokaltest_daten["r_i"].astype(float).to_numpy() + GF = lokaltest_daten["GF_i"].astype(float).to_numpy() + GRZW = lokaltest_daten["GRZW_i"].astype(float).to_numpy() + s0 = float(s0_apost) + + # Punktkoordinaten + koordinaten = {} + punkt_ids = sorted({name[1:] for name in namen_str + if name[:1].upper() in ("X", "Y", "Z") and len(name) > 1}) + 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}") + koordinaten[pid] = (x[ix], x[iy], x[iz]) + except ValueError: + continue + + # Standpunkt/Zielpunkt + standpunkte = [""] * n + zielpunkte = [""] * n + for i, bez in enumerate(bezeichnung): + parts = bez.split("_") + sp, zp = None, None + + if any(k in bez for k in ["_SD_", "_R_", "_ZW_"]): + if len(parts) >= 5: + sp, zp = parts[3].strip(), parts[4].strip() + elif "gnss" in bez.lower(): + if len(parts) >= 2: + sp, zp = parts[-2].strip(), parts[-1].strip() + elif "niv" in bez.lower(): + if len(parts) >= 4: + sp = parts[3].strip() + if len(parts) >= 5: + zp = parts[4].strip() + else: + sp = parts[-1].strip() + standpunkte[i] = sp or "" + zielpunkte[i] = zp or "" + + # Berechnung des EP + EP_GF = np.abs((1.0 - ri) * GF) + EP_grzw = np.abs((1.0 - ri) * GRZW) + + EP_hat_m = np.full(n, np.nan, float) + EP_grzw_m = np.full(n, np.nan, float) + + for i, bez in enumerate(bezeichnung): + sp = standpunkte[i] + zp = zielpunkte[i] + + wenn_winkel = ("_R_" in bez) or ("_ZW_" in bez) + if not wenn_winkel: + EP_hat_m[i] = EP_GF[i] + EP_grzw_m[i] = EP_grzw[i] + continue + + # Wenn Winkel: Querabweichung = Winkel * Strecke (3D) + if sp in koordinaten and zp in koordinaten: + X1, Y1, Z1 = koordinaten[sp] + X2, Y2, Z2 = koordinaten[zp] + s = np.sqrt((X2 - X1) ** 2 + (Y2 - Y1) ** 2 + (Z2 - Z1) ** 2) + EP_hat_m[i] = EP_GF[i] * s + EP_grzw_m[i] = EP_grzw[i] * s + + # Berechnung von EF + EF = np.full(n, np.nan, float) + SP_m = np.full(n, np.nan, float) + EF_SP_m = np.full(n, np.nan, float) + + for i in range(n): + sp = standpunkte[i] + zp = zielpunkte[i] + bloecke = [] + idx = [] + try: + if sp: + b = [ + namen_str.index(f"X{sp}"), + namen_str.index(f"Y{sp}"), + namen_str.index(f"Z{sp}") + ] + bloecke.append(b) + idx += b + if zp: + b = [ + namen_str.index(f"X{zp}"), + namen_str.index(f"Y{zp}"), + namen_str.index(f"Z{zp}") + ] + bloecke.append(b) + idx += b + except ValueError: + continue + if not bloecke: + continue + idx = list(dict.fromkeys(idx)) + + dl = np.zeros((n, 1)) # dl ungestört + dl[i, 0] = GRZW[i] # dl gestört durch GRZW + dx = Qxx @ (A.T @ (P @ dl)) # dx mit Störung + + dx_pkt = dx[idx, :] + Q_pkt = Qxx[np.ix_(idx, idx)] + + # EF + EF2 = (dx_pkt.T @ np.linalg.solve(Q_pkt, dx_pkt)).item() / (s0 ** 2) + EF[i] = np.sqrt(max(0.0, EF2)) + + # SP 3D: Spur der 3x3 Matrizen in einer Liste + spur_matrix_liste = [np.trace(Qxx[np.ix_(b, b)]) for b in bloecke] + if not spur_matrix_liste: + continue + + # SP des schlechtesten Punktes bestimmen + sigma_max = s0 * np.sqrt(max(spur_matrix_liste)) + SP_m[i] = sigma_max + + EF_SP_m[i] = EF[i] * sigma_max + + aeussere_zuverlaessigkeit = pd.DataFrame({ + "Beobachtung": bezeichnung, + "Stand-Pkt": standpunkte, + "Ziel-Pkt": zielpunkte, + "r_i": ri, + "EP_GF [mm]": EP_hat_m * 1000.0, + "EP_grzw [mm]": EP_grzw_m * 1000.0, + "EF": EF, + "SP_3D [mm]": SP_m * 1000.0, + "EF*SP_3D [mm]": EF_SP_m * 1000.0, + }) + return aeussere_zuverlaessigkeit + +class LokaltestInnereZuverlaessigkeitGUI: + """Interaktive Auswahloberfläche für den Lokaltest (innere Zuverlässigkeit). + + Die Klasse erzeugt eine ITable-Tabelle auf Basis des Lokaltest-DataFrames und stellt + eine Mehrfachauswahl bereit. Für GNSS-Basislinien wird sichergestellt, dass bei Auswahl + einer Komponente (bx/by/bz) automatisch das gesamte Trio gewählt bzw. abgewählt wird. + """ + + def __init__(self, df): + """Initialisiert die GUI-Objekte. + + :param df: DataFrame des Lokaltests (inkl. Spalte "Beobachtung"). + :type df: pandas.DataFrame + :return: None + :rtype: None + """ + self.df = df + try: + if not (self.df.index.equals(pd.RangeIndex(start=0, stop=len(self.df), step=1))): + self.df = self.df.reset_index(drop=True) + except: + self.df = self.df.reset_index(drop=True) + + self.tabelle = None + + self.dict_gnss = {} + self.dict_gnss_erweitert = {} + + self.auswahl_zeilen_vorher = set() + self.update_durch_code = False + + self.ausschalten_dict = {} + + self.output = widgets.Output() + self.btn_auswahl_speichern = widgets.Button(description="Auswahl speichern", icon="download") + self.btn_auswahl_zuruecksetzen = widgets.Button(description="Rückgängig", icon="refresh") + + @staticmethod + def gnss_komponenten_extrahieren(beobachtung: str): + """Extrahiert GNSS-Komponente und einen eindeutigen Key für bx/by/bz-Trio. + + :param beobachtung: Text aus Spalte "Beobachtung". + :type beobachtung: str + :return: (komponente, key) oder (None, None) + :rtype: tuple[str | None, str | None] + """ + beobachtung = str(beobachtung).strip() + for gnss_komponente in ["bx", "by", "bz"]: + bezeichnung = f"_gnss{gnss_komponente}_" + if bezeichnung in beobachtung: + key = beobachtung.replace(bezeichnung, "_gnss_") + return gnss_komponente, key + return None, None + + def gnss_dictionary_erstellen(self) -> None: + """Exportiert die Tabelleneinträge in ein Dictionary auf Basis der Tabellenzeilen. + + :return: None + :rtype: None + """ + liste_beobachtungen = self.tabelle.df["Beobachtung"].astype(str).tolist() + # Als Instanzvariable speichern + self.dict_gnss = {} + + for i, beobachtung in enumerate(liste_beobachtungen): + beobachtung = str(beobachtung).strip() + + if "_gnssbx_" in beobachtung: + key = beobachtung.split("_gnssbx_", 1)[1].strip() + if key not in self.dict_gnss: + self.dict_gnss[key] = {} + self.dict_gnss[key]["bx"] = i + + if "_gnssby_" in beobachtung: + key = beobachtung.split("_gnssby_", 1)[1].strip() + if key not in self.dict_gnss: + self.dict_gnss[key] = {} + self.dict_gnss[key]["by"] = i + + if "_gnssbz_" in beobachtung: + key = beobachtung.split("_gnssbz_", 1)[1].strip() + if key not in self.dict_gnss: + self.dict_gnss[key] = {} + self.dict_gnss[key]["bz"] = i + + def gnss_dictionary_erweitert_erstellen(self) -> None: + """Baut ein Dictionary mit alles GNSS-Komponten auf. + + :return: None + :rtype: None + """ + self.dict_gnss_erweitert = {} + + for idx, row in self.df.iterrows(): + value, key = self.gnss_komponenten_extrahieren(row["Beobachtung"]) + if key: + if key not in self.dict_gnss_erweitert: + self.dict_gnss_erweitert[key] = [] + self.dict_gnss_erweitert[key].append(idx) + + def export_ausschalten_dict(self, Eintrag_Auswahl: str = "beobachtung_ausschalten", Wert_nicht_ausgewaehlt: str = "") -> dict: + """Exportiert die aktuelle Auswahl in ein Dictionary. + + :param Eintrag_Auswahl: Wert für ausgewählte Beobachtungen (beobachtung_ausschalten). + :type Eintrag_Auswahl: str + :param Wert_nicht_ausgewaehlt: Wert für nicht ausgewählte Beobachtungen (""). + :type Wert_nicht_ausgewaehlt: str + :return: Dict {Beobachtung: "beobachtung_ausschalten" oder ""} + :rtype: dict + """ + auswahl = set(self.tabelle.selected_rows or []) + liste_beobachtungen = self.tabelle.df["Beobachtung"].astype(str).tolist() + + # Zurückgabe des Ergebnisdictionarys für die Weiterverarbeitung + return { + liste_beobachtungen[i]: (Eintrag_Auswahl if i in auswahl else Wert_nicht_ausgewaehlt) + for i in range(len(liste_beobachtungen)) + } + + def aktualisiere_ausschalten_dict(self) -> None: + """Aktualisiert das ausschalten_dict in der Instanzvariablen. + + :return: None + :rtype: None + """ + neu = self.export_ausschalten_dict() + self.ausschalten_dict.clear() + self.ausschalten_dict.update(neu) + + def ausgabe_aktualisieren(self) -> None: + """Aktualisiert Ausgabe und schreibt self.ausschalten_dict neu. + + :return: None + :rtype: None + """ + self.aktualisiere_ausschalten_dict() + + with self.output: + clear_output(wait=True) + auswahl = self.tabelle.selected_rows or [] + print(f"AUSGESCHALTET: {len(auswahl)}") + + if len(auswahl) > 0: + zeilen = [c for c in ["Beobachtung", "v_i", "r_i", "auffaellig", "GF_i", "GRZW_i"] if c in self.tabelle.df.columns] + display(self.tabelle.df.iloc[auswahl][zeilen].head(30)) + + def auswahl_exportieren(self, _=None) -> None: + """Button-Callback: Ausgabe der ausgeschalteten Beobachtungen. + + :param _: Button-Event + :type _: Any + :return: None + :rtype: None + """ + self.aktualisiere_ausschalten_dict() + + with self.output: + clear_output(wait=True) + print("ausschalten_dict ist aktualisiert.") + ausgeschaltet = [k for k, v in self.ausschalten_dict.items() if v == "X"] + print(f"Nur ausgeschaltete Beobachtungen ({len(ausgeschaltet)}):") + display(ausgeschaltet[:300]) + + def auswahl_zuruecksetzen(self, _=None) -> None: + """Button-aktion: setzt Auswahl zurück. + + :param _: Button-Event + :type _: Any + :return: None + :rtype: None + """ + self.tabelle.selected_rows = [] + self.ausgabe_aktualisieren() + + def gnss_auswahl_synchronisieren(self, aenderungen: dict) -> None: + """Synchronisiert die GNSS-bx/by/bz Auswahl. + + :param aenderungen: Dictionary mit Änderungen. + :type aenderungen: dict + :return: None + :rtype: None + """ + if self.update_durch_code: + return + + auswahl_aktuell = set(aenderungen["new"] or []) + + hinzufuegen = auswahl_aktuell - self.auswahl_zeilen_vorher + entfernt = self.auswahl_zeilen_vorher - auswahl_aktuell + auswahl_final = set(auswahl_aktuell) + + # Hinzufügen -> Alle Komponten auswählen + for index in hinzufuegen: + if index not in self.df.index: + continue + + beob_name = str(self.df.loc[index, "Beobachtung"]) + value, key = self.gnss_komponenten_extrahieren(beob_name) + + if key in self.dict_gnss_erweitert: + for p_idx in self.dict_gnss_erweitert[key]: + auswahl_final.add(p_idx) + + # Entfernen -> alle Komponenten abwählen + for index in entfernt: + if index not in self.df.index: + continue + + beob_name = str(self.df.loc[index, "Beobachtung"]) + value, key = self.gnss_komponenten_extrahieren(beob_name) + + if key in self.dict_gnss_erweitert: + for p_idx in self.dict_gnss_erweitert[key]: + if p_idx in auswahl_final: + auswahl_final.remove(p_idx) + + # Nur bei Änderungen zurück in die Ausgabe schreiben + if auswahl_final != auswahl_aktuell: + self.update_durch_code = True + self.tabelle.selected_rows = sorted(list(auswahl_final)) + self.update_durch_code = False + + self.auswahl_zeilen_vorher = set(self.tabelle.selected_rows or []) + self.ausgabe_aktualisieren() + + + def ausgabe_erstellen(self) -> None: + """Erstellt die Tabelle und verbindet die Buttons. + + :return: None + :rtype: None + """ + self.tabelle = ITable( + self.df, + maxBytes=5 * 1024 * 1024, # 5 MB + columnDefs=[ + {"targets": 0, "orderable": False, "className": "select-checkbox", "width": "26px"}, + ], + select={"style": "multi", "selector": "td:first-child"}, + order=[[1, "asc"]], + ) + + self.gnss_dictionary_erstellen() + self.gnss_dictionary_erweitert_erstellen() + + self.auswahl_zeilen_vorher = set(self.tabelle.selected_rows or []) + self.aktualisiere_ausschalten_dict() + + self.btn_auswahl_speichern.on_click(self.auswahl_exportieren) + self.btn_auswahl_zuruecksetzen.on_click(self.auswahl_zuruecksetzen) + + self.tabelle.observe(self.gnss_auswahl_synchronisieren, names="selected_rows") + + def zeige_tabelle(self) -> None: + """Zeigt die Tabelle an. + + :return: None + :rtype: None + """ + display(widgets.VBox([self.tabelle, widgets.HBox([self.btn_auswahl_speichern, self.btn_auswahl_zuruecksetzen]), self.output])) + self.ausgabe_aktualisieren() \ No newline at end of file diff --git a/Netzqualität_Genauigkeit.py b/Netzqualität_Genauigkeit.py deleted file mode 100644 index 4c861f0..0000000 --- a/Netzqualität_Genauigkeit.py +++ /dev/null @@ -1,321 +0,0 @@ -import numpy as np -import plotly.graph_objects as go -from scipy.stats import f -import pandas as pd -import Berechnungen - - -class Genauigkeitsmaße: - - - @staticmethod - def berechne_s0apost(v: np.ndarray, P: np.ndarray, r: int) -> float: - vTPv_matrix = v.T @ P @ v - vTPv = float(vTPv_matrix.item()) - s0apost = np.sqrt(vTPv / r) - return float(s0apost) - - - @staticmethod - def helmert_punktfehler(Qxx, s0_apost, unbekannten_liste, dim=3): - diagQ = np.diag(Qxx) - daten = [] - namen_str = [str(sym) for sym in unbekannten_liste] - - punkt_ids = [] - for n in namen_str: - if n.upper().startswith('X'): - punkt_ids.append(n[1:]) - - for pid in punkt_ids: - try: - idx_x = next(i for i, n in enumerate(namen_str) if n.upper() == f"X{pid}".upper()) - idx_y = next(i for i, n in enumerate(namen_str) if n.upper() == f"Y{pid}".upper()) - - qx = diagQ[idx_x] - qy = diagQ[idx_y] - qz = 0.0 - - if dim == 3: - try: - idx_z = next(i for i, n in enumerate(namen_str) if n.upper() == f"Z{pid}".upper()) - qz = diagQ[idx_z] - except StopIteration: - qz = 0.0 - - sx = s0_apost * np.sqrt(qx) - sy = s0_apost * np.sqrt(qy) - sz = s0_apost * np.sqrt(qz) if dim == 3 else 0 - - sP = s0_apost * np.sqrt(qx + qy + qz) - - daten.append([pid, float(sx), float(sy), float(sz), float(sP)]) - except: - continue - - helmert_punktfehler = pd.DataFrame(daten, columns=["Punkt", "σx", "σy", "σz", f"σP_{dim}D"]) - return helmert_punktfehler - - - - @staticmethod - def standardellipse(Qxx, s0_apost, unbekannten_liste): - Qxx = np.asarray(Qxx, float) - daten = [] - namen_str = [str(sym) for sym in unbekannten_liste] - - punkt_ids = [] - for n in namen_str: - if n.upper().startswith('X'): - punkt_ids.append(n[1:]) - - for pid in punkt_ids: - try: - idx_x = next(i for i, n in enumerate(namen_str) if n.upper() == f"X{pid}".upper()) - idx_y = next(i for i, n in enumerate(namen_str) if n.upper() == f"Y{pid}".upper()) - - qxx = Qxx[idx_x, idx_x] - qyy = Qxx[idx_y, idx_y] - qyx = Qxx[idx_y, idx_x] - - # Standardabweichungen - sx = s0_apost * np.sqrt(qxx) - sy = s0_apost * np.sqrt(qyy) - sxy = (s0_apost ** 2) * qyx - - k = np.sqrt((qxx - qyy) ** 2 + 4 * (qyx ** 2)) - - # Q_dmax/min = 0.5 * (Qyy + Qxx +/- k) - q_dmax = 0.5 * (qyy + qxx + k) - q_dmin = 0.5 * (qyy + qxx - k) - - # Halbachsen - s_max = s0_apost * np.sqrt(q_dmax) - s_min = s0_apost * np.sqrt(q_dmin) - - # Richtungswinkel theta in gon: - zaehler = 2 * qyx - nenner = qxx - qyy - t_grund = 0.5 * np.arctan(abs(zaehler) / abs(nenner)) * (200 / np.pi) - - # Quadrantenabfrage - if nenner > 0 and qyx > 0: # Qxx - Qyy > 0 und Qyx > 0 - t_gon = t_grund # 0 - 50 gon - elif nenner < 0 and qyx > 0: # Qxx - Qyy < 0 und Qyx > 0 - t_gon = 100 - t_grund # 50 - 100 gon - elif nenner < 0 and qyx < 0: # Qxx - Qyy < 0 und Qyx < 0 - t_gon = 100 + t_grund # 100 - 150 gon - elif nenner > 0 and qyx < 0: # Qxx - Qyy > 0 und Qyx < 0 - t_gon = 200 - t_grund # 150 - 200 gon - else: - t_gon = 0.0 - - daten.append([ - pid, - float(sx), float(sy), float(sxy), - float(s_max), float(s_min), - float(t_gon) - ]) - except: - continue - standardellipse = pd.DataFrame(daten, columns=["Punkt", "σx [m]", "σy [m]", "σxy [m]", "Große Halbachse [m]", "Kleine Halbachse [m]", "θ [gon]"]) - return standardellipse - - - - @staticmethod - def konfidenzellipse(Qxx, s0_apost, unbekannten_liste, R, alpha): - Qxx = np.asarray(Qxx, float) - daten = [] - namen_str = [str(sym) for sym in unbekannten_liste] - - punkt_ids = [n[1:] for n in namen_str if n.upper().startswith('X')] - - # Faktor für Konfidenzellipse (F-Verteilung) - kk = float(np.sqrt(2.0 * f.ppf(1.0 - alpha, 2, R))) - - for pid in punkt_ids: - try: - idx_x = next(i for i, n in enumerate(namen_str) if n.upper() == f"X{pid}".upper()) - idx_y = next(i for i, n in enumerate(namen_str) if n.upper() == f"Y{pid}".upper()) - - qxx = Qxx[idx_x, idx_x] - qyy = Qxx[idx_y, idx_y] - qyx = Qxx[idx_y, idx_x] - - # Standardabweichungen - sx = s0_apost * np.sqrt(qxx) - sy = s0_apost * np.sqrt(qyy) - sxy = (s0_apost ** 2) * qyx - - k = np.sqrt((qxx - qyy) ** 2 + 4 * (qyx ** 2)) - - # Q_dmax/min = 0.5 * (Qyy + Qxx +/- k) - q_dmax = 0.5 * (qyy + qxx + k) - q_dmin = 0.5 * (qyy + qxx - k) - - # Halbachsen der Standardellipse - s_max = s0_apost * np.sqrt(q_dmax) - s_min = s0_apost * np.sqrt(q_dmin) - - # Halbachsen der Konfidenzellipse - A_K = kk * s_max - B_K = kk * s_min - - # Richtungswinkel theta in gon: - zaehler = 2 * qyx - nenner = qxx - qyy - t_grund = 0.5 * np.arctan(abs(zaehler) / abs(nenner)) * (200 / np.pi) - - # Quadrantenabfrage - if nenner > 0 and qyx > 0: - t_gon = t_grund # 0 - 50 gon - elif nenner < 0 and qyx > 0: - t_gon = 100 - t_grund # 50 - 100 gon - elif nenner < 0 and qyx < 0: - t_gon = 100 + t_grund # 100 - 150 gon - elif nenner > 0 and qyx < 0: - t_gon = 200 - t_grund # 150 - 200 gon - else: - t_gon = 0.0 - - daten.append([ - pid, - float(sx), float(sy), float(sxy), - float(A_K), float(B_K), - float(t_gon) - ]) - - except: - continue - - konfidenzellipse = pd.DataFrame(daten, columns=["Punkt", "σx [m]", "σy [m]", "σxy [m]", "Große Halbachse [m]", - "Kleine Halbachse [m]", "θ [gon]"]) - return konfidenzellipse - - - -class Plot: - - - @staticmethod - def netzplot_ellipsen( - Koord_ENU, - unbekannten_labels, - beobachtungs_labels, - df_konf_ellipsen_enu, - v_faktor=1000, - n_ellipse_pts=60, - title="Netzplot im ENU-System mit Konfidenzellipsen" - ): - names = [str(s).strip() for s in unbekannten_labels] - - if "θ_EN [gon]" in df_konf_ellipsen_enu.columns: - theta_col = "θ_EN [gon]" - elif "θ [gon]" in df_konf_ellipsen_enu.columns: - theta_col = "θ [gon]" - else: - raise ValueError("Spalte 'θ_EN [gon]' oder 'θ [gon]' fehlt im DataFrame.") - - punkt_ids = sorted({nm[1:] for nm in names if nm and nm[0].upper() in ("X", "Y", "Z")}) - - fig = go.Figure() - - # 1) Darstellungen der Beobachtungen - beob_typen = { - 'GNSS-Basislinien': {'pattern': 'gnss', 'color': 'rgba(255, 100, 0, 0.4)'}, - 'Tachymeter-Beob': {'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() - is_typ = ((info['pattern'] in bl_str and info['pattern'] != '') or - (info['pattern'] == '' and 'gnss' not in bl_str and 'niv' not in bl_str)) - if not is_typ: - continue - - bl_raw = str(bl) - pts = [] - for pid in punkt_ids: - if (f"_{pid}" in bl_raw) or bl_raw.startswith(f"{pid}_"): - if pid in Koord_ENU: - pts.append(pid) - - if len(pts) >= 2: - p1, p2 = pts[0], pts[1] - x_l.extend([Koord_ENU[p1][0], Koord_ENU[p2][0], None]) # E - y_l.extend([Koord_ENU[p1][1], Koord_ENU[p2][1], None]) # N - - 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))) - - # 2) Darstellung der Konfidenzellipsen - t = np.linspace(0, 2 * np.pi, n_ellipse_pts) - first = True - for _, row in df_konf_ellipsen_enu.iterrows(): - pid = str(row["Punkt"]) - if pid not in Koord_ENU: - continue - - a = float(row["a_K"]) * v_faktor - b = float(row["b_K"]) * v_faktor - theta = float(row[theta_col]) * np.pi / 200.0 # gon->rad - - ex = a * np.cos(t) - ey = b * np.sin(t) - - c, s = np.cos(theta), np.sin(theta) - xr = c * ex - s * ey - yr = s * ex + c * ey - - E0, N0, _ = Koord_ENU[pid] - - fig.add_trace(go.Scatter( - x=E0 + xr, y=N0 + yr, - mode="lines", - line=dict(color="red", width=1.5), - name=f"Ellipsen (×{v_faktor})", - legendgroup="Ellipsen", - showlegend=first, - hoverinfo="skip" - )) - first = False - - # 3) Darstellung der Punkte - xs, ys, texts, hovers = [], [], [], [] - for pid in punkt_ids: - if pid not in Koord_ENU: - continue - E, N, U = Koord_ENU[pid] - xs.append(E); - ys.append(N); - texts.append(pid) - hovers.append(f"Punkt {pid}
E={E:.4f} m
N={N:.4f} m
U={U:.4f} m") - - fig.add_trace(go.Scatter( - x=xs, y=ys, mode="markers+text", - text=texts, textposition="top center", - marker=dict(size=8, color="black"), - name="Netzpunkte", - hovertext=hovers, hoverinfo="text" - )) - - fig.update_layout( - title=f"{title} (Ellipsen ×{v_faktor})", - xaxis=dict(title="E [m]", scaleanchor="y", scaleratio=1, showgrid=True, gridcolor="lightgrey"), - yaxis=dict(title="N [m]", showgrid=True, gridcolor="lightgrey"), - width=1100, height=900, - template="plotly_white", - plot_bgcolor="white" - ) - - fig.add_annotation( - text=f"Maßstab Ellipsen:
Dargestellte Größe = Konfidenzellipse × {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}) \ No newline at end of file diff --git a/Netzqualität_Zuverlässigkeit.py b/Netzqualität_Zuverlässigkeit.py deleted file mode 100644 index 777f79a..0000000 --- a/Netzqualität_Zuverlässigkeit.py +++ /dev/null @@ -1,309 +0,0 @@ -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( - Lokaltest, labels, Qxx, A, P, s0_apost, unbekannten_liste, x, - angle_units="rad", - ep_use_abs=True, - exclude_prefixes=("lA_",), - ): - df = Lokaltest.copy() - labels = [str(l) for l in list(labels)] - - Qxx = np.asarray(Qxx, float) - A = np.asarray(A, float) - P = np.asarray(P, float) - x = np.asarray(x, float).reshape(-1) - - namen_str = [str(sym) for sym in unbekannten_liste] - - n = A.shape[0] - if len(labels) != n: - raise ValueError(f"len(labels)={len(labels)} passt nicht zu A.shape[0]={n}.") - if len(df) != n: - raise ValueError(f"Lokaltest hat {len(df)} Zeilen, A hat {n} Beobachtungen.") - - # Pseudobeobachtungen rausfiltern - keep = np.ones(n, dtype=bool) - if exclude_prefixes: - for i, lbl in enumerate(labels): - if any(lbl.startswith(pref) for pref in exclude_prefixes): - keep[i] = False - - # alles konsistent kürzen (wichtig: auch A & P!) - df = df.loc[keep].reset_index(drop=True) - labels = [lbl for (lbl, k) in zip(labels, keep) if k] - A = A[keep, :] - P = P[np.ix_(keep, keep)] - - # neue n - n = A.shape[0] - - # Daten aus dem Lokaltest - ri = df["r_i"].astype(float).to_numpy() - GF = df["GF_i"].astype(float).to_numpy() - GRZW = df["GRZW_i"].astype(float).to_numpy() - - s0 = float(s0_apost) - - def to_rad(val): - if angle_units == "rad": - return val - if angle_units == "gon": - return val * (np.pi / 200.0) - if angle_units == "deg": - return val * (np.pi / 180.0) - raise ValueError("angle_units muss 'rad', 'gon' oder 'deg' sein.") - - # Punktkoordinaten aus x (für Streckenäquivalent bei Winkel-EP) - coords = {} - punkt_ids = sorted({name[1:] for name in namen_str - if name[:1].upper() in ("X", "Y", "Z") and len(name) > 1}) - 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]) - except ValueError: - continue - - # Standpunkt/Zielpunkt - standpunkte = [""] * n - zielpunkte = [""] * n - 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(): - if len(parts) >= 2: - sp, zp = parts[-2].strip(), parts[-1].strip() - elif "niv" in lbl.lower(): - if len(parts) >= 4: - sp = parts[3].strip() - if len(parts) >= 5: - zp = parts[4].strip() - else: - sp = parts[-1].strip() - - standpunkte[i] = sp or "" - zielpunkte[i] = zp or "" - - # Berechnung des EPs - EP_GF = (1.0 - ri) * GF - EP_grzw = (1.0 - ri) * GRZW - if ep_use_abs: - EP_GF = np.abs(EP_GF) - EP_grzw = np.abs(EP_grzw) - - EP_hat_m = np.full(n, np.nan, float) - EP_grzw_m = np.full(n, np.nan, float) - - for i, lbl in enumerate(labels): - sp = standpunkte[i] - zp = zielpunkte[i] - - is_angle = ("_R_" in lbl) or ("_ZW_" in lbl) - if not is_angle: - EP_hat_m[i] = EP_GF[i] - EP_grzw_m[i] = EP_grzw[i] - continue - - # Winkel -> Querabweichung = Winkel(rad) * Strecke (3D) - if sp in coords and zp in coords: - X1, Y1, Z1 = coords[sp] - X2, Y2, Z2 = coords[zp] - s = np.sqrt((X2 - X1) ** 2 + (Y2 - Y1) ** 2 + (Z2 - Z1) ** 2) - - EP_hat_m[i] = to_rad(EP_GF[i]) * s - EP_grzw_m[i] = to_rad(EP_grzw[i]) * s - - # 3x3 Blöcke - def idx_xyz(pid): - return [ - namen_str.index(f"X{pid}"), - namen_str.index(f"Y{pid}"), - namen_str.index(f"Z{pid}") - ] - - # EF lokal + SP lokal (3D) - EF = np.full(n, np.nan, float) - SP_loc_m = np.full(n, np.nan, float) - EFSP_loc_m = np.full(n, np.nan, float) - - for i in range(n): - sp = standpunkte[i] - zp = zielpunkte[i] - - blocks = [] - idx = [] - - try: - if sp: - b = idx_xyz(sp) - blocks.append(b) - idx += b - if zp: - b = idx_xyz(zp) - blocks.append(b) - idx += b - except ValueError: - continue - - if not blocks: - continue - - idx = list(dict.fromkeys(idx)) # unique - - # Δx_i aus Grenzstörung - dl = np.zeros((n, 1)) - dl[i, 0] = GRZW[i] - dx = Qxx @ (A.T @ (P @ dl)) - - dx_loc = dx[idx, :] - Q_loc = Qxx[np.ix_(idx, idx)] - - # EF lokal - EF2 = (dx_loc.T @ np.linalg.solve(Q_loc, dx_loc)).item() / (s0 ** 2) - EF[i] = np.sqrt(max(0.0, EF2)) - - # SP lokal 3D: max trace der 3x3 Punktblöcke - tr_list = [np.trace(Qxx[np.ix_(b, b)]) for b in blocks] - if not tr_list: - continue - - sigmaPmax_loc = s0 * np.sqrt(max(tr_list)) - SP_loc_m[i] = sigmaPmax_loc - EFSP_loc_m[i] = EF[i] * sigmaPmax_loc - - ausgabe_zuv = pd.DataFrame({ - "Beobachtung": labels, - "Stand-Pkt": standpunkte, - "Ziel-Pkt": zielpunkte, - "r_i": ri, - "EP_GF [mm]": EP_hat_m * 1000.0, - "EP_grzw [mm]": EP_grzw_m * 1000.0, - "EF": EF, - "SP_loc_3D [mm]": SP_loc_m * 1000.0, - "EF*SP_loc_3D [mm]": EFSP_loc_m * 1000.0, - }) - return ausgabe_zuv diff --git a/Parameterschaetzung.py b/Parameterschaetzung.py index 7928d0e..e26387f 100644 --- a/Parameterschaetzung.py +++ b/Parameterschaetzung.py @@ -1,25 +1,43 @@ from Stochastisches_Modell import StochastischesModell -from Netzqualität_Genauigkeit import Genauigkeitsmaße + from Datumsfestlegung import Datumsfestlegung -import numpy as np -import Export -import importlib import Datenbank -import importlib -import Export from Export import Export as Exporter import Stochastisches_Modell import Funktionales_Modell -import Koordinatentransformationen import Parameterschaetzung -import Netzqualität_Genauigkeit import Datumsfestlegung import numpy as np -import sympy as sp -import pandas as pd def ausgleichung_global(A, dl, Q_ext, P): + """ + Führt eine Ausgleichung nach kleinsten Quadraten durch. + + Aus der Designmatrix A, dem Verbesserungsvektor dl und der Gewichtsmatrix P wird das Normalgleichungssystem + aufgestellt und gelöst. Anschließend werden Residuen sowie Kofaktor-Matrizen der Unbekannten und Beobachtungen berechnet. + + Es werden folgende Berechnungsschitte durchgeführt: + + 1) Normalgleichungsmatrix N = Aᵀ · P · A und Absolutglied n = Aᵀ · P · dl + 2) Lösung dx = N⁻¹ · n + 3) Residuen v = A · dx − dl + 4) Kofaktormatrix der Unbekannten Q_xx + 5) Kofaktormatrix der ausgeglichenen Beobachtungen Q_ll_dach + 6) Kofaktormatrix der Verbesserungen Q_vv + + :param A: Jacobi-Matrix (A-Matrix). + :type A: array_like + :param dl: Verbesserungsvektor bzw. Beobachtungsabweichungen. + :type dl: array_like + :param Q_ext: a-priori Kofaktormatrix der Beobachtungen. + :type Q_ext: array_like + :param P: Gewichtsmatrix der Beobachtungen. + :type P: array_like + :return: Dictionary mit Ausgleichungsergebnissen, Zuschlagsvektor dx. + :rtype: tuple[dict[str, Any], numpy.ndarray] + :raises numpy.linalg.LinAlgError: Wenn das Normalgleichungssystem singulär ist und nicht gelöst werden kann. + """ A=np.asarray(A, float) dl = np.asarray(dl, float).reshape(-1, 1) Q_ext = np.asarray(Q_ext, float) @@ -62,7 +80,7 @@ def ausgleichung_global(A, dl, Q_ext, P): -def ausgleichung_lokal1(A, dl, Q_ll): +def ausgleichung_lokal(A, dl, Q_ll): A = np.asarray(A, dtype=float) dl = np.asarray(dl, dtype=float).reshape(-1, 1) Q_ll = np.asarray(Q_ll, dtype=float) @@ -110,175 +128,6 @@ def ausgleichung_lokal1(A, dl, Q_ll): return dict_ausgleichung, dx - -def ausgleichung_lokal( - A, dl, Q_ll, - *, - datumfestlegung="hart", - x0=None, - unbekannten_liste=None, - liste_punktnummern=None, - datenbank=None, - mit_massstab=True -): - A = np.asarray(A, float) - dl = np.asarray(dl, float).reshape(-1, 1) - Q_ll = np.asarray(Q_ll, float) - - # 1) Gewichtsmatrix - P = np.linalg.inv(Q_ll) - - # 2) Normalgleichungen - N = A.T @ P @ A - n = A.T @ P @ dl - u = N.shape[0] - - # 3) Datumsfestlegung - if datumfestlegung == "weiche Lagerung": - # hier wurde Q_ll bereits extern erweitert → ganz normal lösen - dx = np.linalg.solve(N, n) - - elif datumfestlegung == "gesamtspur": - if x0 is None or unbekannten_liste is None or liste_punktnummern is None: - raise ValueError("gesamtspur benötigt x0, unbekannten_liste, liste_punktnummern") - - G = Datumsfestlegung.build_G_from_names( - x0, - unbekannten_liste, - liste_punktnummern, - mit_massstab=mit_massstab - ) - dx, k = Datumsfestlegung.berechne_dx_geraendert(N, n, G) - - elif datumfestlegung == "teilspur": - if x0 is None or unbekannten_liste is None or liste_punktnummern is None or datenbank is None: - raise ValueError("teilspur benötigt x0, unbekannten_liste, liste_punktnummern, datenbank") - - # G über alle Punkte - G = Datumsfestlegung.build_G_from_names( - x0, - unbekannten_liste, - liste_punktnummern, - mit_massstab=mit_massstab - ) - - # Auswahl aus DB - liste_datumskoordinaten = datenbank.get_datumskoordinate() - if not liste_datumskoordinaten: - raise ValueError("Teilspur gewählt, aber keine Datumskoordinaten in der DB gesetzt.") - - aktive = Datumsfestlegung.aktive_indices_from_selection( - [(s[1:], s[0]) for s in liste_datumskoordinaten], - unbekannten_liste - ) - - E = Datumsfestlegung.auswahlmatrix_E(u, aktive) - Gi = E @ G - - dx, k = Datumsfestlegung.berechne_dx_geraendert(N, n, Gi) - - else: - raise ValueError(f"Unbekannte Datumsfestlegung: {datumfestlegung}") - - # 4) Residuen - v = dl - A @ dx - - # 5) Kofaktormatrix der Unbekannten - Q_xx = StochastischesModell.berechne_Q_xx(N) - - # 6) Kofaktormatrix der Beobachtungen - Q_ll_dach = StochastischesModell.berechne_Q_ll_dach(A, Q_xx) - - # 7) Kofaktormatrix der Verbesserungen - Q_vv = StochastischesModell.berechne_Qvv(Q_ll, Q_ll_dach) - - dict_ausgleichung = { - "dx": dx, - "v": v, - "P": P, - "N": N, - "Q_xx": Q_xx, - "Q_ll_dach": Q_ll_dach, - "Q_vv": Q_vv, - "Q_ll": Q_ll, - } - - return dict_ausgleichung, dx - - -def ausgleichung_spurminimierung(A, dl, Q_ll, *, datumfestlegung, x0, unbekannten_liste, datenbank=None, mit_massstab=True): - """ - Freie Ausgleichung mit Gesamtspur- oder Teilspurminimierung. - Rückgabe-Layout wie ausgleichung_global. - """ - A = np.asarray(A, float) - dl = np.asarray(dl, float).reshape(-1, 1) - Q_ll = np.asarray(Q_ll, float) - - # 1) Gewichtsmatrix P - P = StochastischesModell.berechne_P(Q_ll) - - # 2) Normalgleichungen - N = A.T @ P @ A - n = A.T @ P @ dl - - # 3) G über ALLE Punkte aus unbekannten_liste (automatisch) - G = Datumsfestlegung.build_G_from_names( - x0=x0, - unbekannten_liste=unbekannten_liste, - liste_punktnummern=None, # <- wichtig: auto - mit_massstab=mit_massstab - ) - - # 4) Gesamtspur / Teilspur - if datumfestlegung == "gesamtspur": - Gi = G - k = None # wird unten überschrieben - - elif datumfestlegung == "teilspur": - if datenbank is None: - raise ValueError("teilspur benötigt datenbank mit get_datumskoordinate()") - liste_datumskoordinaten = datenbank.get_datumskoordinate() - if not liste_datumskoordinaten: - raise ValueError("Teilspur gewählt, aber keine Datumskoordinaten in der DB gesetzt.") - - # ["X10034","Y10034"] -> [("10034","X"),("10034","Y")] - auswahl = [(s[1:], s[0]) for s in liste_datumskoordinaten] - aktive = Datumsfestlegung.aktive_indices_from_selection(auswahl, unbekannten_liste) - - E = Datumsfestlegung.auswahlmatrix_E(N.shape[0], aktive) - Gi = E @ G - - else: - raise ValueError("datumfestlegung muss 'gesamtspur' oder 'teilspur' sein") - - # 5) Lösung per Ränderung + korrektes Q_xx - dx, k, Q_xx = Datumsfestlegung.loese_geraendert_mit_Qxx(N, n, Gi) - - # 6) Residuen (wie bei dir) - v = A @ dx - dl - - # 7) Kofaktormatrix der Beobachtungen - Q_ll_dach = StochastischesModell.berechne_Q_ll_dach(A, Q_xx) - - # 8) Kofaktormatrix der Verbesserungen - Q_vv = StochastischesModell.berechne_Qvv(Q_ll, Q_ll_dach) - - dict_ausgleichung = { - "dx": dx, - "v": v, - "P": P, - "N": N, - "n": n, - "k": k, - "Q_xx": Q_xx, - "Q_ll_dach": Q_ll_dach, - "Q_vv": Q_vv, - "Q_ll": Q_ll, - "datumfestlegung": datumfestlegung, - } - return dict_ausgleichung, dx - class Iterationen: """Iterative Ausgleichung auf Basis des funktionalen und stochastischen Modells. diff --git a/Proben.py b/Proben.py index 1d756a1..e889aa6 100644 --- a/Proben.py +++ b/Proben.py @@ -1,6 +1,23 @@ import numpy as np -# d + def atpv_probe(A, P, v, tol=1e-7): + """ + Führt die ATPv-Probe zur Kontrolle der Lösung des Normalgleichungssystems durch. + + Die Funktion überprüft, ob der Ausdruck Aᵀ · P · v näherungsweise Null ist. + Die Prüfung erfolgt unter Verwendung einer vorgegebenen Toleranz. + + :param A: Jacobi-Matrix (A-Matrix). + :type A: array_like + :param P: Gewichtsmatrix der Beobachtungen. + :type P: array_like + :param v: Residuenvektor der Beobachtungen. + :type v: array_like + :param tol: Absolute Toleranz für den Vergleich mit Null. + :type tol: float + :return: None + :rtype: None + """ A = np.asarray(A, float) P = np.asarray(P, float) v = np.asarray(v, float).reshape(-1, 1) @@ -14,6 +31,27 @@ def atpv_probe(A, P, v, tol=1e-7): def hauptprobe(A, x, l, v, tol=1e-7): + """ + Führt die Hauptprobe zur Überprüfung der berechneten Residuen durch. + + Die Hauptprobe kontrolliert, ob die Residuen v mit der Beziehung + v = A · x − l übereinstimmen. Stimmen der berechnete Residuenvektor + und der über das funktionale Modell rekonstruierte Residuenvektor + innerhalb der Toleranz überein, gilt die Ausgleichung als konsistent. + + :param A: Jacobi-Matrix (A-Matrix). + :type A: array_like + :param x: Lösungsvektor der Unbekannten. + :type x: array_like + :param l: Beobachtungsvektor. + :type l: array_like + :param v: Residuenvektor aus der Ausgleichung. + :type v: array_like + :param tol: Absolute Toleranz für den Vergleich der Residuen. + :type tol: float + :return: None + :rtype: None + """ A = np.asarray(A, float) x = np.asarray(x, float).reshape(-1, 1) l = np.asarray(l, float).reshape(-1, 1) diff --git a/Stochastisches_Modell.py b/Stochastisches_Modell.py index fec26f0..21fa6e1 100644 --- a/Stochastisches_Modell.py +++ b/Stochastisches_Modell.py @@ -44,7 +44,7 @@ class StochastischesModell: self.db_zugriff = Datenbankzugriff(self.pfad_datenbank) def Qll_symbolisch(self, liste_beobachtungen_symbolisch: list) -> sp.Matrix: - """Erstellt die symbolische Varianz-Kovarianz-Matrix Qll der Beobachtungen. + """Erstellt die symbolische Kofaktormatrix der Beobachtungen. Aus den symbolischen Beobachtungskennungen wird die Beobachtungsart abgeleitet (Tachymeter: SD/R/ZW, GNSS: gnssbx/gnssby/gnssbz, Geometrisches Nivellement: niv). Für jede Beobachtung wird eine symbolische Varianzgleichung @@ -61,7 +61,7 @@ class StochastischesModell: :param liste_beobachtungen_symbolisch: Liste der symbolischen Beobachtungskennungen. :type liste_beobachtungen_symbolisch: list - :return: Symbolische Varianz-Kovarianz-Matrix Qll. + :return: Symbolische Kofaktormatrix Qll. :rtype: sp.Matrix """ liste_standardabweichungen_symbole = [] @@ -204,7 +204,7 @@ class StochastischesModell: return Qll def Qll_numerisch(self, Qll_Matrix_Symbolisch: sp.Matrix, liste_beobachtungen_symbolisch: list) -> np.Matrix: - """Erstellt eine numerische Varianz-Kovarianz-Matrix aus einer symbolischen Qll-Matrix. + """Erstellt eine numerische Kofaktormatrix der Beobachtungen aus einer symbolischen Qll-Matrix. Es werden die zur Substitution benötigten Werte aus der Datenbank abgefragt und den in Qll vorkommenden Symbolen zugeordnet, u. a.: @@ -221,11 +221,11 @@ class StochastischesModell: Die numerische Matrix wird als CSV-Datei in Zwischenergebnisse\\Qll_Numerisch.csv exportiert. - :param Qll_Matrix_Symbolisch: Symbolische Varianz-Kovarianz-Matrix Qll. + :param Qll_Matrix_Symbolisch: Symbolische Kofaktormatrix der Beobachtungen Qll. :type Qll_Matrix_Symbolisch: sp.Matrix :param liste_beobachtungen_symbolisch: Liste der symbolischen Beobachtungskennungen. :type liste_beobachtungen_symbolisch: list - :return: Numerische Varianz-Kovarianz-Matrix Qll als Numpy-Array. + :return: Numerische Kofaktormatrix Qll als Numpy-Array. :rtype: np.Matrix :raises ValueError: Falls Symbole in Qll_Matrix_Symbolisch enthalten sind, für die keine Substitutionen vorhanden sind. """ @@ -380,7 +380,7 @@ class StochastischesModell: return Qll_numerisch def QAA_symbolisch(self, liste_beobachtungen_symbolisch: list) -> np.Matrix: - """Erstellt die symbolische Varianz-Kovarianz-Matrix QAA der Anschlusspunkte (weiche Lagerung). + """Erstellt die symbolische Kofaktormatrix der Anschlusspunkte (weiche Lagerung). Es werden ausschließlich Beobachtungen berücksichtigt, deren Kennung mit "lA_" beginnt. Für jede Anschlussbedingung wird eine (symbolische) Standardabweichung StabwAA_* angesetzt und mit der Varianzkomponente der Beobachtungsgruppe @@ -390,7 +390,7 @@ class StochastischesModell: :param liste_beobachtungen_symbolisch: Liste der symbolischen Beobachtungskennungen. :type liste_beobachtungen_symbolisch: list - :return: Symbolische Varianz-Kovarianz-Matrix QAA. + :return: Symbolische Kofaktormatrix QAA. :rtype: sp.Matrix """ liste_standardabweichungen_symbole = [] @@ -428,11 +428,11 @@ class StochastischesModell: Die numerische Matrix wird als CSV-Datei in Zwischenergebnisse\\QAA_Numerisch.csv exportiert. - :param QAA_Matrix_Symbolisch: Symbolische Varianz-Kovarianz-Matrix QAA. + :param QAA_Matrix_Symbolisch: Symbolische Kofaktormatrix QAA. :type QAA_Matrix_Symbolisch: sp.Matrix :param liste_beobachtungen_symbolisch: Liste der symbolischen Beobachtungskennungen. :type liste_beobachtungen_symbolisch: list - :return: Numerische Varianz-Kovarianz-Matrix QAA als Numpy-Array. + :return: Numerische VKofaktormatrix QAA als Numpy-Array. :rtype: np.Matrix """ # Symbolische Listen @@ -483,11 +483,11 @@ class StochastischesModell: @staticmethod def berechne_P(Q_ll: np.ndarray) -> np.ndarray: - """Berechnet die Gewichtsmatrix P aus einer Varianz-Kovarianz-Matrix Qll. + """Berechnet die Gewichtsmatrix P aus der Kofaktormatrix. Die Gewichtsmatrix wird als Inverse von Qll gebildet: P = inv(Qll). - :param Q_ll: Varianz-Kovarianz-Matrix der Beobachtungen. + :param Q_ll: Kofaktormatrix der Beobachtungen. :type Q_ll: np.ndarray :return: Gewichtsmatrix P. :rtype: np.ndarray @@ -515,7 +515,7 @@ class StochastischesModell: @staticmethod def berechne_Q_ll_dach(A: np.ndarray, Q_xx: np.ndarray) -> np.ndarray: - """Berechnet die (geschätzte) Varianz-Kovarianz-Matrix der Beobachtungen Qll_dach. + """Berechnet die (geschätzte) Kofaktormatrix der Beobachtungen Qll_dach. Die Matrix wird gemäß Qll_dach = A @ Qxx @ A.T gebildet. @@ -523,7 +523,7 @@ class StochastischesModell: :type A: np.ndarray :param Q_xx: Kofaktormatrix der Unbekannten. :type Q_xx: np.ndarray - :return: Geschätzte Varianz-Kovarianz-Matrix der Beobachtungen Qll_dach. + :return: Geschätzte VKofaktormatrix der Beobachtungen Qll_dach. :rtype: np.ndarray """ Q_ll_dach = A @ Q_xx @ A.T @@ -531,16 +531,16 @@ class StochastischesModell: @staticmethod def berechne_Qvv(Q_ll: np.ndarray, Q_ll_dach: np.ndarray) -> np.ndarray: - """Berechnet die Varianz-Kovarianz-Matrix der Residuen Qvv. + """Berechnet die Kofaktormatrix der Residuen Qvv. Die Residuenkovarianz wird als Differenz aus Beobachtungs-Kovarianz und dem durch das Modell erklärten Anteil gebildet: Qvv = Qll - Qll_dach. - :param Q_ll: Varianz-Kovarianz-Matrix der Beobachtungen. + :param Q_ll: Kofaktormatrix der Beobachtungen. :type Q_ll: np.ndarray - :param Q_ll_dach: Geschätzte Varianz-Kovarianz-Matrix der Beobachtungen. + :param Q_ll_dach: Geschätzte Kofaktormatrix der Beobachtungen. :type Q_ll_dach: np.ndarray - :return: Varianz-Kovarianz-Matrix der Residuen Qvv. + :return: Kofaktormatrix der Residuen Qvv. :rtype: np.ndarray """ Q_vv = Q_ll - Q_ll_dach diff --git a/Varianzkomponentenschaetzung.py b/Varianzkomponentenschaetzung.py index 3c6b4b6..704d745 100644 --- a/Varianzkomponentenschaetzung.py +++ b/Varianzkomponentenschaetzung.py @@ -5,7 +5,7 @@ import pandas as pd import Datenbank import Datumsfestlegung -from Netzqualität_Genauigkeit import Genauigkeitsmaße +from Netzqualitaet_Genauigkeit import Genauigkeitsmaße class VKS: """Varianzkomponentenschätzung (VKS) mit Anpassung durch Benutzereingaben.