def test_fit_results_dict_exponential_min_max(): a = st.FitResults(params=[2, 2], errs=[3, 4], type='expon') d = st.fit_results_to_dict(a, min_bound=-100, max_bound=100) nt.assert_equal(d['lambda'], 1. / 2) nt.assert_equal(d['min'], -100) nt.assert_equal(d['max'], 100) nt.assert_equal(d['type'], 'exponential')
def test_fit_results_dict_exponential_min_max(): a = st.FitResults(params=[2, 2], errs=[3, 4], type='expon') d = st.fit_results_to_dict(a, min_bound=-100, max_bound=100) assert d['lambda'] == 1. / 2 assert d['min'] == -100 assert d['max'] == 100 assert d['type'] == 'exponential'
def test_fit_results_dict_normal_min_max(): a = st.FitResults(params=[1, 2], errs=[3, 4], type='norm') d = st.fit_results_to_dict(a, min_bound=-100, max_bound=100) assert d['mu'] == 1 assert d['sigma'] == 2 assert d['min'] == -100 assert d['max'] == 100 assert d['type'] == 'normal'
def test_fit_results_dict_normal_min_max(): a = st.FitResults(params=[1, 2], errs=[3, 4], type='norm') d = st.fit_results_to_dict(a, min_bound=-100, max_bound=100) nt.assert_equal(d['mu'], 1) nt.assert_equal(d['sigma'], 2) nt.assert_equal(d['min'], -100) nt.assert_equal(d['max'], 100) nt.assert_equal(d['type'], 'normal')
def test_fit_results_dict_uniform(): a = st.FitResults(params=[1, 2], errs=[3, 4], type='uniform') d = st.fit_results_to_dict(a) assert d['min'] == 1 assert d['max'] == 3 assert d['type'] == 'uniform'
def test_fit_results_dict_exponential(): a = st.FitResults(params=[2, 2], errs=[3, 4], type='expon') d = st.fit_results_to_dict(a) assert d['lambda'] == 1. / 2 assert d['type'] == 'exponential'
def test_fit_results_dict_normal(): a = st.FitResults(params=[1, 2], errs=[3, 4], type='norm') d = st.fit_results_to_dict(a) assert d['mu'] == 1 assert d['sigma'] == 2 assert d['type'] == 'normal'
def test_fit_results_dict_uniform_min_max(): a = st.FitResults(params=[1, 2], errs=[3, 4], type='uniform') d = st.fit_results_to_dict(a, min_bound=-100, max_bound=100) assert d['min'] == 1 assert d['max'] == 3 assert d['type'] == 'uniform'
def test_fit_results_dict_uniform(): a = st.FitResults(params=[1, 2], errs=[3, 4], type='uniform') d = st.fit_results_to_dict(a) nt.assert_equal(d['min'], 1) nt.assert_equal(d['max'], 3) nt.assert_equal(d['type'], 'uniform')
def test_fit_results_dict_exponential(): a = st.FitResults(params=[2, 2], errs=[3, 4], type='expon') d = st.fit_results_to_dict(a) nt.assert_equal(d['lambda'], 1. / 2) nt.assert_equal(d['type'], 'exponential')
def test_fit_results_dict_normal(): a = st.FitResults(params=[1, 2], errs=[3, 4], type='norm') d = st.fit_results_to_dict(a) nt.assert_equal(d['mu'], 1) nt.assert_equal(d['sigma'], 2) nt.assert_equal(d['type'], 'normal')
def test_fit_results_dict_uniform_min_max(): a = st.FitResults(params=[1, 2], errs=[3, 4], type='uniform') d = st.fit_results_to_dict(a, min_bound=-100, max_bound=100) nt.assert_equal(d['min'], 1) nt.assert_equal(d['max'], 3) nt.assert_equal(d['type'], 'uniform')