A[i][i-1] = -aW[i+1]
if i+2 <= self.__N-1:
A[i][i+2] = -aEE[i+1]
A[-1][-1] = aP[-2]
A[-1][-2] = -aW[-2]
if __name__ == '__main__':
a = Matrix(6)
print('-' * 20)
print(a.mat())
print('-' * 20)
from Diffusion import Diffusion1D
df1 = Diffusion1D(6, 1, 0.25)
df1.alloc(6)
df1.calcCoef()
df1.source(100)
print(df1.aP(), df1.aE(), df1.aW(), df1.Su(), sep = '\n')
print('-' * 20)
df1.bcDirichlet('LEFT_WALL', 2)
df1.bcDirichlet('RIGHT_WALL', 1)
print(df1.aP(), df1.aE(), df1.aW(), df1.Su(), sep = '\n')
print('-' * 20)
a.build(df1)
print(a.mat())
print('-' * 20)
def printFrame(d):
"""
Función que imprime el error calculado
"""
# Calculo el error porcentual y agrego al DataFrame
# una columna con esos datos llamada 'Error %'
d['Error %'] = d['Error'] / d['Analytic']
print(DataFrame(d))
print('.'+ '-'*70 + '.')
def calcError(phiA, phiN):
"""
Función que calcula el error entre la aproximación calculada y el valor real de la solución.
@return: error
"""
return np.absolute(phiA - phiN)
if __name__ == '__main__':
Coefficients.alloc(5)
m = Mesh(nodes = 5)
d = Diffusion1D(m.volumes())
ma = Matrix(m.volumes())
a = Advection1D(m.volumes())
print(m.delta(), d.aP(), a.aP(), ma.mat(), sep='\n')
printData(nvx =5, nx = 6, longitud = 1.3)
4 + i)
# Construye el objeto CSR
Asp = csr_matrix((data, indices.astype(int), indptr.astype(int)))
return Asp
if __name__ == '__main__':
n = 9
a = Matrix(n)
print('-' * 20)
print(a.mat())
print('-' * 20)
from Diffusion import Diffusion1D
df1 = Diffusion1D(n, 1, 0.25)
df1.alloc(n)
df1.calcCoef()
df1.setSu(100)
print(df1.aP(), df1.aE(), df1.aW(), df1.Su(), sep='\n')
print('-' * 20)
df1.bcDirichlet('LEFT_WALL', 2)
df1.bcDirichlet('RIGHT_WALL', 1)
print(df1.aP(), df1.aE(), df1.aW(), df1.Su(), sep='\n')
print('-' * 20)
a.build(df1)
print(a.mat())
print('-' * 20)
