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7de9f6c72d
This allows (and changes the current examples) to exit with status 99 to
skip a test instead of outputting a special string ("NumPy missing").
This also fixes the eigen test, which currently fails when eigen
headers are available but NumPy is not, to skip instead of failing when
NumPy isn't available.
35 lines
1.1 KiB
Python
Executable File
35 lines
1.1 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import print_function
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import sys
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sys.path.append('.')
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import example
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try:
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import numpy as np
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except ImportError:
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# NumPy missing: skip test
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exit(99)
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from example import vectorized_func
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from example import vectorized_func2
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from example import vectorized_func3
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print(vectorized_func3(np.array(3+7j)))
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for f in [vectorized_func, vectorized_func2]:
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print(f(1, 2, 3))
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print(f(np.array(1), np.array(2), 3))
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print(f(np.array([1, 3]), np.array([2, 4]), 3))
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print(f(np.array([[1, 3, 5], [7, 9, 11]]), np.array([[2, 4, 6], [8, 10, 12]]), 3))
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print(np.array([[1, 3, 5], [7, 9, 11]])* np.array([[2, 4, 6], [8, 10, 12]])*3)
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print(f(np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2))
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print(np.array([[1, 2, 3], [4, 5, 6]])* np.array([2, 3, 4])* 2)
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print(f(np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2))
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print(np.array([[1, 2, 3], [4, 5, 6]])* np.array([[2], [3]])* 2)
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from example import selective_func
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selective_func(np.array([1], dtype=np.int32))
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selective_func(np.array([1.0], dtype=np.float32))
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selective_func(np.array([1.0j], dtype=np.complex64))
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