76 lines
1.8 KiB
Python
76 lines
1.8 KiB
Python
import numpy as np
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import pandas as pd
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import random, os
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def valores_objetivo(archivo, material):
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carga = pd.read_excel(archivo, sheet_name='carga', engine='openpyxl').dropna()
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E = np.mean(carga.loc[carga['material'] == material, ['V1', 'V2']].values, axis=1)
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I = np.mean(carga.loc[carga['material'] == material, ['I1', 'I2']].values, axis=1)
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f = np.array(carga['f'][carga['material'] == material])
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return E, I, f
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def generar_curva(offset, ruta):
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muVacio = 4 * np.pi * 1e-7
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h = np.arange(0,30000,1000)
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b = muVacio * h + offset
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with open(ruta, "w") as f:
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f.write('[PLACOND]\n')
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f.write('Type=Fixed_Solid\n')
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f.write('Solid Type=Steel\n')
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f.write('Thermal Conductivity=28\n')
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f.write('Specific Heat=460\n')
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f.write('Density=7800\n')
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f.write('Notes=\n')
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f.write('ElectricalResistivity=5.2E-7\n')
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f.write('TempCoefElectricalResistivity=0\n')
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f.write('PoissonsRatio=0\n')
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f.write('YoungsCoefficient=0\n')
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f.write('YieldStress=0\n')
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for i in np.arange(len(h)):
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f.write(f'BValue[{i}]={b[i]}\n')
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f.write(f'HValue[{i}]={h[i]}\n')
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def logica_offset(offset, error, errorObjetivo):
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'''
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Coge el offset, error actual y lo analiza.
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devuelve flag y offset nuevo
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si se cumple la condicion devuelve
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un True y None para offset
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'''
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limSup = 1 + errorObjetivo
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limInf = 1 - errorObjetivo
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delta = random.random()/100
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if error <= limSup and error >= limInf: return True, None
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if error > 1: offset = offset - delta
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elif error < 1: offset = offset + delta
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return False, offset
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if __name__ == '__main__':
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directorio = ''
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nombre = "curva.mdb"
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ruta = f'{directorio}{nombre}'
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generar_curva(1,ruta)
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