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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,94 @@ | ||
| from pyccel.decorators import pure | ||
| from numpy import shape | ||
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| @pure | ||
| def matmul(a: 'float[:,:]', b: 'float[:,:]', c: 'float[:,:]'): | ||
| """ | ||
| Performs the matrix-matrix product a*b and writes the result into c. | ||
| Parameters | ||
| ---------- | ||
| a : array[float] | ||
| The first input array (matrix). | ||
| b : array[float] | ||
| The second input array (matrix). | ||
| c : array[float] | ||
| The output array (matrix) which is the result of the matrix-matrix product a.dot(b). | ||
| """ | ||
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| c[:, :] = 0. | ||
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| sh_a = shape(a) | ||
| sh_b = shape(b) | ||
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| for i in range(sh_a[0]): | ||
| for j in range(sh_b[1]): | ||
| for k in range(sh_a[1]): | ||
| c[i, j] += a[i, k] * b[k, j] | ||
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| @pure | ||
| def sum_vec(a: 'float[:]') -> float: | ||
| """ | ||
| Sum the elements of a 1D vector. | ||
| Parameters | ||
| ---------- | ||
| a : array[float] | ||
| The 1d vector. | ||
| """ | ||
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| out = 0. | ||
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| sh_a = shape(a) | ||
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| for i in range(sh_a[0]): | ||
| out += a[i] | ||
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| return out | ||
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| @pure | ||
| def min_vec(a: 'float[:]') -> float: | ||
| """ | ||
| Compute the minimum a 1D vector. | ||
| Parameters | ||
| ---------- | ||
| a : array[float] | ||
| The 1D vector. | ||
| """ | ||
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| out = a[0] | ||
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| sh_a = shape(a) | ||
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| for i in range(sh_a[0]): | ||
| if a[i] < out: | ||
| out = a[i] | ||
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| return out | ||
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| @pure | ||
| def max_vec(a: 'float[:]') -> float: | ||
| """ | ||
| Compute the maximum a 1D vector. | ||
| Parameters | ||
| ---------- | ||
| a : array[float] | ||
| The 1D vector. | ||
| """ | ||
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| out = a[0] | ||
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| sh_a = shape(a) | ||
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| for i in range(sh_a[0]): | ||
| if a[i] > out: | ||
| out = a[i] | ||
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| return out |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,25 @@ | ||
| import numpy as np | ||
| import pytest | ||
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| from psydac.core.arrays import matmul, sum_vec, min_vec, max_vec | ||
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| @pytest.mark.parametrize('seed', [123, 6, 49573]) | ||
| def test_arrays(seed): | ||
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| np.random.seed(seed) | ||
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| a = np.random.rand(30, 20) | ||
| b = np.random.rand(20, 40) | ||
| c = np.zeros((30, 40)) | ||
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| matmul(a, b, c) | ||
| assert np.allclose(np.matmul(a, b), c) | ||
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| v = np.random.rand(32) | ||
| assert np.isclose(np.sum(v), sum_vec(v)) | ||
| assert np.isclose(np.min(v), min_vec(v)) | ||
| assert np.isclose(np.max(v), max_vec(v)) | ||
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| if __name__ == '__main__': | ||
| test_arrays(459) |
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Definitely needed in Struphy. Martin wrote an issue about it, since Psydac also at some point wants to enable C compilation.