mirror of https://github.com/pyodide/pyodide.git
24 lines
680 B
Python
24 lines
680 B
Python
# setup: import numpy as np;lx,ly=(2**6,2**6);u=np.zeros([lx,ly],dtype=np.double);u[lx//2,ly//2]=1000.0;tempU=np.zeros([lx,ly],dtype=np.double) # noqa
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# run: diffusion(u,tempU,100)
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# pythran export diffusion(float [][], float [][], int)
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def diffusion(u, tempU, iterNum):
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"""
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Apply Numpy matrix for the Forward-Euler Approximation
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"""
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mu = 0.1
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for n in range(iterNum):
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tempU[1:-1, 1:-1] = u[1:-1, 1:-1] + mu * (
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u[2:, 1:-1]
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- 2 * u[1:-1, 1:-1]
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+ u[0:-2, 1:-1]
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+ u[1:-1, 2:]
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- 2 * u[1:-1, 1:-1]
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+ u[1:-1, 0:-2]
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)
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u[:, :] = tempU[:, :]
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tempU[:, :] = 0.0
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