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')
