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		<properties node_id="16" format="literal">{'controlAreaVisible': True, 'currentScriptIndex': 1, 'savedWidgetGeometry': b'\x01\xd9\xd0\xcb\x00\x03\x00\x00\xff\xff\xff\xff\xff\xff\xff\xf9\x00\x00\x06\x00\x00\x00\x030\x00\x00\x00\x00\x00\x00\x00\x17\x00\x00\x05\xff\x00\x00\x03/\x00\x00\x00\x00\x02\x00\x00\x00\x06\x00\x00\x00\x00\x00\x00\x00\x00\x17\x00\x00\x05\xff\x00\x00\x03/', 'scriptLibrary': [{'name': 'Beispiel', 'script': 'import numpy as np\nfrom Orange.data import Table, Domain, ContinuousVariable, DiscreteVariable\n\ndomain = Domain([ContinuousVariable("age"),\n                 ContinuousVariable("height"),\n                 DiscreteVariable("gender", values=("M", "F"))])\narr = np.array([\n  [25, 186, 0],\n  [30, 164, 1]])\nout_data = Table.from_numpy(domain, arr)\n', 'filename': None}, {'name': 'Matplotlib_CurveFitPlot', 'script': "import numpy as np\nfrom scipy.optimize import curve_fit\nimport matplotlib.pyplot as plt\n\nprint(in_data)\n\nM_raw = in_data[: ,0]\neta_raw = in_data[: ,1]\n\nM = []\neta = []\n\nfor x in M_raw:\n        for v in x:\n            M.append(v)\nprint(M)\n\nfor y in eta_raw:\n        for w in y:\n            eta.append(w)\nprint(eta)\n\n# Funktion für die Funktionsgleichung\ndef func(x, a, b, d):\n    return (a * x**2 + b * x) / (b * x + d)\n    #Alte Gleichung - Paramter c macht komische Sachen\n    #return (a * x**2 + b * x + c) / (b * x + d)\n\n# Gewichte für die Datenpunkte\nweights = np.ones(len(eta))  # Standardgewichte (alle Punkte gleich gewichtet)\n\n# Setze Gewichte für Ausreißer oder bestimmte Intervalle auf niedrige Werte\n# Beispiel: Gewichte 0.3 für die angegebenen Datenpunkte\n\n#weight=0.9\n\n#weights[22] = weight\n#weights[24] = weight\n#weights[25] = weight\n#weights[26] = weight\n#weights[28] = weight\n#weights[29] = weight\n#weights[30] = weight\n#weights[31] = weight\n#weights[32] = weight\n\n\n# Fit der Datenpunkte an die Funktionsgleichung\nparams, _ = curve_fit(func, M, eta, sigma=1/weights)\n\n# Extrahiere die Fit-Parameter a, b, c und d\na = params[0]\nb = params[1]\n#c = params[2]\nd = params[2]\n\n# Erstelle ein Array von x-Werten für die Funktionsgleichung\nx_fit = np.linspace(min(M), max(M), 100)\n\n# Berechne die entsprechenden y-Werte der Funktion\ny_fit = func(x_fit, a, b, d)\n\n# Plot der Datenpunkte und der angepassten Funktion\nplt.scatter(M, eta, label='Datenpunkte')\nplt.plot(x_fit, y_fit, 'r', label='Wirkungsgrad über M_L')\nplt.xlabel('Drehmoment (M) [Nm]')\nplt.ylabel('Wirkungsgrad (η)')\nplt.title('Wirkungsgrad über Drehmoment')\nplt.legend()\nplt.grid(True)\nplt.show()\n\nprint('Fit-Parameter:')\nprint('a =', a)\nprint('b =', b)\n#print('c =', c)\nprint('d =', d)", 'filename': None}], 'scriptText': "import numpy as np\nfrom scipy.optimize import curve_fit\nimport matplotlib.pyplot as plt\n\nprint(in_data)\n\nM_raw = in_data[: ,0]\neta_raw = in_data[: ,1]\n\nM = []\neta = []\n\nfor x in M_raw:\n        for v in x:\n            M.append(v)\nprint(M)\n\nfor y in eta_raw:\n        for w in y:\n            eta.append(w)\nprint(eta)\n\n# Funktion für die Funktionsgleichung\ndef func(x, a, b, d):\n    return (a * x**2 + b * x) / (b * x + d)\n    #Alte Gleichung - Paramter c macht komische Sachen\n    #return (a * x**2 + b * x + c) / (b * x + d)\n\n# Gewichte für die Datenpunkte\nweights = np.ones(len(eta))  # Standardgewichte (alle Punkte gleich gewichtet)\n\n# Setze Gewichte für Ausreißer oder bestimmte Intervalle auf niedrige Werte\n# Beispiel: Gewichte 0.3 für die angegebenen Datenpunkte\n\n#weight=0.9\n\n#weights[22] = weight\n#weights[24] = weight\n#weights[25] = weight\n#weights[26] = weight\n#weights[28] = weight\n#weights[29] = weight\n#weights[30] = weight\n#weights[31] = weight\n#weights[32] = weight\n\n\n# Fit der Datenpunkte an die Funktionsgleichung\nparams, _ = curve_fit(func, M, eta, sigma=1/weights)\n\n# Extrahiere die Fit-Parameter a, b, c und d\na = params[0]\nb = params[1]\n#c = params[2]\nd = params[2]\n\n# Erstelle ein Array von x-Werten für die Funktionsgleichung\nx_fit = np.linspace(min(M), max(M), 100)\n\n# Berechne die entsprechenden y-Werte der Funktion\ny_fit = func(x_fit, a, b, d)\n\n# Plot der Datenpunkte und der angepassten Funktion\nplt.scatter(M, eta, label='Datenpunkte')\nplt.plot(x_fit, y_fit, 'r', label='Wirkungsgrad über M_L')\nplt.xlabel('Drehmoment (M) [Nm]')\nplt.ylabel('Wirkungsgrad (η)')\nplt.title('Wirkungsgrad über Drehmoment')\nplt.legend()\nplt.grid(True)\nplt.show()\n\nprint('Fit-Parameter:')\nprint('a =', a)\nprint('b =', b)\n#print('c =', c)\nprint('d =', d)", 'splitterState': b'\x00\x00\x00\xff\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\xa0\x00\x00\x00^\x01\xff\xff\xff\xff\x01\x00\x00\x00\x02\x00', 'vimModeEnabled': False, '__version__': 2}</properties>
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