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Merge branch 'develop' into documentation
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fjosw
commit
822ce887c1
8 changed files with 597 additions and 24 deletions
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@ -108,24 +108,6 @@ def test_prior_fit_num_grad():
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auto = pe.fits.least_squares(x, y, lambda a, x: anp.exp(a[0] * x) + a[1], num_grad=False, piors=y[:2])
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def test_least_squares_num_grad():
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x = []
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y = []
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for i in range(2, 5):
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x.append(i * 0.01)
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y.append(pe.pseudo_Obs(i * 0.01, 0.0001, "ens"))
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num = pe.fits.least_squares(x, y, lambda a, x: np.exp(a[0] * x) + a[1], num_grad=True)
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auto = pe.fits.least_squares(x, y, lambda a, x: anp.exp(a[0] * x) + a[1], num_grad=False)
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assert(num[0] == auto[0])
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assert(num[1] == auto[1])
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assert(num[0] == auto[0])
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assert(num[1] == auto[1])
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def test_total_least_squares_num_grad():
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x = []
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y = []
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@ -608,6 +590,249 @@ def test_ks_test():
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pe.fits.ks_test(fit_res)
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def test_combined_fit_list_v_array():
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res = []
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for y_test in [{'a': [pe.Obs([np.random.normal(i, 0.5, 1000)], ['ensemble1']) for i in range(1, 7)]},
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{'a': np.array([pe.Obs([np.random.normal(i, 0.5, 1000)], ['ensemble1']) for i in range(1, 7)])}]:
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for x_test in [{'a': [0, 1, 2, 3, 4, 5]}, {'a': np.arange(6)}]:
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for key in y_test.keys():
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[item.gamma_method() for item in y_test[key]]
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def func_a(a, x):
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return a[1] * x + a[0]
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funcs_test = {"a": func_a}
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res.append(pe.fits.least_squares(x_test, y_test, funcs_test))
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assert (res[0][0] - res[1][0]).is_zero(atol=1e-8)
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assert (res[0][1] - res[1][1]).is_zero(atol=1e-8)
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def test_combined_fit_vs_standard_fit():
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x_const = {'a':[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'b':np.arange(10, 20)}
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y_const = {'a':[pe.Obs([np.random.normal(1, val, 1000)], ['ensemble1'])
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for val in [0.25, 0.3, 0.01, 0.2, 0.5, 1.3, 0.26, 0.4, 0.1, 1.0]],
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'b':[pe.Obs([np.random.normal(1, val, 1000)], ['ensemble1'])
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for val in [0.5, 1.12, 0.26, 0.25, 0.3, 0.01, 0.2, 1.0, 0.38, 0.1]]}
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for key in y_const.keys():
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[item.gamma_method() for item in y_const[key]]
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y_const_ls = np.concatenate([np.array(o) for o in y_const.values()])
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x_const_ls = np.arange(0, 20)
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def func_const(a,x):
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return 0 * x + a[0]
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funcs_const = {"a": func_const,"b": func_const}
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for method_kw in ['Levenberg-Marquardt', 'migrad', 'Powell', 'Nelder-Mead']:
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res = []
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res.append(pe.fits.least_squares(x_const, y_const, funcs_const, method = method_kw, expected_chisquare=True))
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res.append(pe.fits.least_squares(x_const_ls, y_const_ls, func_const, method = method_kw, expected_chisquare=True))
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[item.gamma_method for item in res]
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assert np.isclose(0.0, (res[0].chisquare_by_dof - res[1].chisquare_by_dof), 1e-14, 1e-8)
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assert np.isclose(0.0, (res[0].chisquare_by_expected_chisquare - res[1].chisquare_by_expected_chisquare), 1e-14, 1e-8)
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assert np.isclose(0.0, (res[0].p_value - res[1].p_value), 1e-14, 1e-8)
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assert (res[0][0] - res[1][0]).is_zero(atol=1e-8)
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def test_combined_fit_no_autograd():
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def func_exp1(x):
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return 0.3*np.exp(0.5*x)
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def func_exp2(x):
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return 0.3*np.exp(0.8*x)
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xvals_b = np.arange(0,6)
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xvals_a = np.arange(0,8)
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def func_a(a,x):
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return a[0]*np.exp(a[1]*x)
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def func_b(a,x):
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return a[0]*np.exp(a[2]*x)
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funcs = {'a':func_a, 'b':func_b}
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xs = {'a':xvals_a, 'b':xvals_b}
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ys = {'a':[pe.Obs([np.random.normal(item, item*1.5, 1000)],['ensemble1']) for item in func_exp1(xvals_a)],
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'b':[pe.Obs([np.random.normal(item, item*1.4, 1000)],['ensemble1']) for item in func_exp2(xvals_b)]}
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for key in funcs.keys():
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[item.gamma_method() for item in ys[key]]
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with pytest.raises(Exception):
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pe.least_squares(xs, ys, funcs)
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pe.least_squares(xs, ys, funcs, num_grad=True)
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def test_combined_fit_invalid_fit_functions():
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def func1(a, x):
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return a[0] + a[1] * x + a[2] * anp.sinh(x) + a[199]
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def func2(a, x, y):
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return a[0] + a[1] * x
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def func3(x):
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return x
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def func_valid(a,x):
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return a[0] + a[1] * x
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xvals =[]
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yvals =[]
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err = 0.1
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for x in range(1, 8, 2):
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xvals.append(x)
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yvals.append(pe.pseudo_Obs(x + np.random.normal(0.0, err), err, 'test1') + pe.pseudo_Obs(0, err / 100, 'test2', samples=87))
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[o.gamma_method() for o in yvals]
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for func in [func1, func2, func3]:
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with pytest.raises(Exception):
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pe.least_squares({'a':xvals}, {'a':yvals}, {'a':func})
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with pytest.raises(Exception):
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pe.least_squares({'a':xvals, 'b':xvals}, {'a':yvals, 'b':yvals}, {'a':func, 'b':func_valid})
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with pytest.raises(Exception):
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pe.least_squares({'a':xvals, 'b':xvals}, {'a':yvals, 'b':yvals}, {'a':func_valid, 'b':func})
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def test_combined_fit_invalid_input():
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xvals =[]
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yvals =[]
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err = 0.1
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def func_valid(a,x):
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return a[0] + a[1] * x
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for x in range(1, 8, 2):
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xvals.append(x)
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yvals.append(pe.pseudo_Obs(x + np.random.normal(0.0, err), err, 'test1') + pe.pseudo_Obs(0, err / 100, 'test2', samples=87))
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with pytest.raises(Exception):
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pe.least_squares({'a':xvals}, {'b':yvals}, {'a':func_valid})
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def test_combined_fit_no_autograd():
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def func_exp1(x):
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return 0.3*np.exp(0.5*x)
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def func_exp2(x):
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return 0.3*np.exp(0.8*x)
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xvals_b = np.arange(0,6)
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xvals_a = np.arange(0,8)
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def func_a(a,x):
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return a[0]*np.exp(a[1]*x)
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def func_b(a,x):
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return a[0]*np.exp(a[2]*x)
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funcs = {'a':func_a, 'b':func_b}
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xs = {'a':xvals_a, 'b':xvals_b}
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ys = {'a':[pe.Obs([np.random.normal(item, item*1.5, 1000)],['ensemble1']) for item in func_exp1(xvals_a)],
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'b':[pe.Obs([np.random.normal(item, item*1.4, 1000)],['ensemble1']) for item in func_exp2(xvals_b)]}
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for key in funcs.keys():
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[item.gamma_method() for item in ys[key]]
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with pytest.raises(Exception):
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pe.least_squares(xs, ys, funcs)
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pe.least_squares(xs, ys, funcs, num_grad=True)
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def test_combined_fit_num_grad():
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def func_exp1(x):
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return 0.3*np.exp(0.5*x)
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def func_exp2(x):
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return 0.3*np.exp(0.8*x)
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xvals_b = np.arange(0,6)
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xvals_a = np.arange(0,8)
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def func_num_a(a,x):
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return a[0]*np.exp(a[1]*x)
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def func_num_b(a,x):
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return a[0]*np.exp(a[2]*x)
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def func_auto_a(a,x):
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return a[0]*anp.exp(a[1]*x)
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def func_auto_b(a,x):
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return a[0]*anp.exp(a[2]*x)
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funcs_num = {'a':func_num_a, 'b':func_num_b}
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funcs_auto = {'a':func_auto_a, 'b':func_auto_b}
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xs = {'a':xvals_a, 'b':xvals_b}
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ys = {'a':[pe.Obs([np.random.normal(item, item*1.5, 1000)],['ensemble1']) for item in func_exp1(xvals_a)],
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'b':[pe.Obs([np.random.normal(item, item*1.4, 1000)],['ensemble1']) for item in func_exp2(xvals_b)]}
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for key in funcs_num.keys():
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[item.gamma_method() for item in ys[key]]
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num = pe.fits.least_squares(xs, ys, funcs_num, num_grad=True)
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auto = pe.fits.least_squares(xs, ys, funcs_auto, num_grad=False)
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assert(num[0] == auto[0])
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assert(num[1] == auto[1])
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def test_combined_fit_dictkeys_no_order():
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def func_exp1(x):
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return 0.3*np.exp(0.5*x)
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def func_exp2(x):
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return 0.3*np.exp(0.8*x)
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xvals_b = np.arange(0,6)
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xvals_a = np.arange(0,8)
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def func_num_a(a,x):
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return a[0]*np.exp(a[1]*x)
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def func_num_b(a,x):
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return a[0]*np.exp(a[2]*x)
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def func_auto_a(a,x):
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return a[0]*anp.exp(a[1]*x)
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def func_auto_b(a,x):
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return a[0]*anp.exp(a[2]*x)
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funcs = {'a':func_auto_a, 'b':func_auto_b}
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funcs_no_order = {'b':func_auto_b, 'a':func_auto_a}
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xs = {'a':xvals_a, 'b':xvals_b}
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xs_no_order = {'b':xvals_b, 'a':xvals_a}
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yobs_a = [pe.Obs([np.random.normal(item, item*1.5, 1000)],['ensemble1']) for item in func_exp1(xvals_a)]
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yobs_b = [pe.Obs([np.random.normal(item, item*1.4, 1000)],['ensemble1']) for item in func_exp2(xvals_b)]
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ys = {'a': yobs_a, 'b': yobs_b}
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ys_no_order = {'b': yobs_b, 'a': yobs_a}
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for key in funcs.keys():
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[item.gamma_method() for item in ys[key]]
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[item.gamma_method() for item in ys_no_order[key]]
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for method_kw in ['Levenberg-Marquardt', 'migrad', 'Powell', 'Nelder-Mead']:
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order = pe.fits.least_squares(xs, ys, funcs,method = method_kw)
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no_order_func = pe.fits.least_squares(xs, ys, funcs_no_order,method = method_kw)
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no_order_x = pe.fits.least_squares(xs_no_order, ys, funcs,method = method_kw)
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no_order_y = pe.fits.least_squares(xs, ys_no_order, funcs,method = method_kw)
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no_order_func_x = pe.fits.least_squares(xs_no_order, ys, funcs_no_order,method = method_kw)
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no_order_func_y = pe.fits.least_squares(xs, ys_no_order, funcs_no_order,method = method_kw)
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no_order_x_y = pe.fits.least_squares(xs_no_order, ys_no_order, funcs,method = method_kw)
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assert(no_order_func[0] == order[0])
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assert(no_order_func[1] == order[1])
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assert(no_order_x[0] == order[0])
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assert(no_order_x[1] == order[1])
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assert(no_order_y[0] == order[0])
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assert(no_order_y[1] == order[1])
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assert(no_order_func_x[0] == order[0])
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assert(no_order_func_x[1] == order[1])
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assert(no_order_func_y[0] == order[0])
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assert(no_order_func_y[1] == order[1])
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assert(no_order_x_y[0] == order[0])
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assert(no_order_x_y[1] == order[1])
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def fit_general(x, y, func, silent=False, **kwargs):
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"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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