# Licensed under a 3-clause BSD style license - see LICENSE.rst """Test `astropy.utils.timer`. .. note:: The tests only compare rough estimates as performance is machine-dependent. """ # STDLIB import time # THIRD-PARTY import pytest import numpy as np # LOCAL from astropy.utils.exceptions import AstropyUserWarning from astropy.utils.timer import RunTimePredictor from astropy.modeling.fitting import ModelsError def func_to_time(x): """This sleeps for y seconds for use with timing tests. .. math:: y = 5 * x - 10 """ y = 5.0 * np.asarray(x) - 10 time.sleep(y) return y def test_timer(): """Test function timer.""" p = RunTimePredictor(func_to_time) # --- These must run before data points are introduced. --- with pytest.raises(ValueError): p.do_fit() with pytest.raises(RuntimeError): p.predict_time(100) # --- These must run next to set up data points. --- with pytest.warns(AstropyUserWarning, match="ufunc 'multiply' did not " "contain a loop with signature matching types"): p.time_func([2.02, 2.04, 2.1, 'a', 2.3]) p.time_func(2.2) # Test OrderedDict assert p._funcname == 'func_to_time' assert p._cache_bad == ['a'] k = list(p.results.keys()) v = list(p.results.values()) np.testing.assert_array_equal(k, [2.02, 2.04, 2.1, 2.3, 2.2]) np.testing.assert_allclose(v, [0.1, 0.2, 0.5, 1.5, 1.0]) # --- These should only run once baseline is established. --- with pytest.raises(ModelsError): a = p.do_fit(model='foo') with pytest.raises(ModelsError): a = p.do_fit(fitter='foo') a = p.do_fit() assert p._power == 1 # Perfect slope is 5, with 10% uncertainty assert 4.5 <= a[1] <= 5.5 # Perfect intercept is -10, with 1-sec uncertainty assert -11 <= a[0] <= -9 # --- These should only run once fitting is completed. --- # Perfect answer is 490, with 10% uncertainty t = p.predict_time(100) assert 441 <= t <= 539 # Repeated call to access cached run time t2 = p.predict_time(100) assert t == t2