Update tests
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Jakob Lass
parent
03899fc52f
commit
c8728bba4d
@ -1,176 +1,88 @@
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import unittest
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import torch
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import numpy as np
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import os
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import sys
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maindir = os.getcwd()
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main_path = maindir[:maindir.find('ds4ms/code')]
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sys.path.append(main_path+"/ds4ms/code/src")
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from background import background
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from AMBER.background import background
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class TestBackground(unittest.TestCase):
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def setUp(self):
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self.bg = background(data_path='/mydata/ds4ms/victor/Data/', str_dataset='VanadiumOzide', str_option='6p9T')
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self.bg.load_data(verbose=True)
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self.bg.set_grid_volume(dqx=0.03, dqy=0.03, dE=0.08)
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self.bg.set_binned_data()
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self.bg.set_radial_bins(max_radius=6.0, n_bins=10)
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self.bg.Ygrid = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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def test_load_data(self):
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self.bg.load_data(verbose=True)
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self.assertIsNotNone(self.bg.ds)
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def test_set_dataset(self):
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ds = "dummy_dataset"
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self.bg.set_dataset(ds)
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self.assertEqual(self.bg.ds, ds)
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def test_mask_preprocessing(self):
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self.bg.mask_preprocessing()
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self.assertIsNotNone(self.bg.ds.mask)
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def test_set_grid_volume(self):
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self.bg.set_grid_volume(dqx=0.03, dqy=0.03, dE=0.08)
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self.assertEqual(self.bg.dqx, 0.03)
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self.assertEqual(self.bg.dqy, 0.03)
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self.assertEqual(self.bg.dE, 0.08)
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def test_set_binned_data(self):
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self.bg.set_binned_data()
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self.assertIsNotNone(self.bg.data)
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self.assertIsNotNone(self.bg.bins)
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def test_set_variables(self):
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self.bg.set_variables()
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self.assertIsNotNone(self.bg.X)
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self.assertIsNotNone(self.bg.b)
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self.assertIsNotNone(self.bg.b_grid)
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def test_R_operator(self):
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Y_r = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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b = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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result = self.bg.R_operator(Y_r, b)
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self.assertIsNotNone(result)
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def test_Rstar_operator(self):
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Y_r = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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X = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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result = self.bg.Rstar_operator(Y_r, X)
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self.assertIsNotNone(result)
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def test_mask_nans(self):
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x = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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result = self.bg.mask_nans(x)
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self.assertIsNotNone(result)
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def test_S_lambda(self):
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x = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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lambda_ = torch.tensor(np.random.rand(10), dtype=torch.float64)
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result = self.bg.S_lambda(x, lambda_)
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self.assertIsNotNone(result)
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def test_L_b(self):
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self.bg.set_radial_nans()
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result = self.bg.L_b(e_cut=0)
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self.assertIsNotNone(result)
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def test_gamma_matrix(self):
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self.bg.set_radial_nans()
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result = self.bg.gamma_matrix(e_cut=0)
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self.assertIsNotNone(result)
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def test_set_radial_nans(self):
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self.bg.set_radial_nans()
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self.assertIsNotNone(self.bg.u)
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def test_set_b_design_matrix(self):
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beta_ = torch.tensor(1.0, dtype=torch.float64)
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alpha_ = torch.tensor(np.random.rand(10), dtype=torch.float64)
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result = self.bg.set_b_design_matrix(beta_, alpha_, e_cut=0)
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self.assertIsNotNone(result)
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def test_compute_laplacian(self):
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Y_r = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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result = self.bg.compute_laplacian(Y_r)
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self.assertIsNotNone(result)
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def test_compute_all_laplacians(self):
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Y_r = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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self.bg.compute_all_laplacians(Y_r)
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self.assertIsNotNone(self.bg.L_list)
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def test_TV_denoising(self):
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gamma_ = torch.tensor(np.random.rand(10), dtype=torch.float64)
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result = self.bg.TV_denoising(gamma_, n_epochs=1, verbose=False)
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self.assertIsNotNone(result)
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def test_L_e(self):
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result = self.bg.L_e()
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self.assertIsNotNone(result)
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def test_set_e_design_matrix(self):
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result = self.bg.set_e_design_matrix(mu_=1.0)
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self.assertIsNotNone(result)
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def test_MAD_lambda(self):
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result = self.bg.MAD_lambda()
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self.assertIsNotNone(result)
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def test_mu_estimator(self):
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result = self.bg.mu_estimator()
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self.assertIsNotNone(result)
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def test_denoising(self):
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Y_r = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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lambda_ = torch.tensor(np.random.rand(10), dtype=torch.float64)
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beta_ = torch.tensor(np.random.rand(10), dtype=torch.float64)
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alpha_ = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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self.bg.denoising(Y_r, lambda_, beta_, alpha_, mu_=1.0, n_epochs=1, verbose=False)
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self.assertIsNotNone(self.bg.X)
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self.assertIsNotNone(self.bg.b)
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def test_applyBackground(self):
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self.bg.applyBackground(median=False)
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self.assertIsNotNone(self.bg.ds.backgroundModel)
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def test_median_bg(self):
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result = self.bg.median_bg(self.bg.Ygrid)
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self.assertIsNotNone(result)
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def test_compute_signal_to_noise(self):
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b = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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result = self.bg.compute_signal_to_noise(b)
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self.assertIsNotNone(result)
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def test_plot_snr(self):
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b = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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self.bg.plot_snr(b, e_cut=range(2, 4), fmin=0.0, fmax=0.1)
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def test_save_arrays(self):
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self.bg.save_arrays(median=True)
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self.assertTrue(os.path.exists('arrays'))
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def test_compute_signal_to_obs(self):
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b = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
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result = self.bg.compute_signal_to_obs(b)
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self.assertIsNotNone(result)
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"""Set up mock data for testing."""
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self.bg = background(dtype=np.float32)
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self.bg.set_gridcell_size(dqx=0.03, dqy=0.03, dE=0.1)
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Qx = np.linspace(-1, 1, 10)
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Qy = np.linspace(-1, 1, 10)
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E = np.linspace(0, 1, 5)
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Int = np.random.rand(len(Qx), len(Qy), len(E))
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self.bg.set_binned_data(Qx, Qy, E, Int)
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self.bg.set_radial_bins(max_radius=3.0, n_bins=100)
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def test_cross_validation(self):
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lambda_range = torch.tensor([1.0])
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alpha_range = torch.tensor([1.0])
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beta_range = torch.tensor([1.0])
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mu_range = torch.tensor([1.0])
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result = self.bg.cross_validation(lambda_range=lambda_range, alpha_range=alpha_range,
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beta_range=beta_range, mu_range=mu_range, n_epochs=1, verbose=False)
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self.assertIsNotNone(result)
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"""Test the cross_validation method."""
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q = 0.75
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beta_range = np.array([1.0, 2.0])
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lambda_ = 0.01
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mu_ = 0.001
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n_epochs = 5
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rmse = self.bg.cross_validation(q, beta_range, lambda_, mu_, n_epochs, verbose=False)
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self.assertEqual(len(rmse), len(beta_range))
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self.assertTrue(np.all(rmse >= 0))
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def test_compute_mask(self):
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result = self.bg.compute_mask(q=0.75, e_cut=None)
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self.assertIsNotNone(result)
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def test_set_gridcell_size(self):
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"""Test set_gridcell_size method."""
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self.bg.set_gridcell_size(dqx=0.05, dqy=0.05, dE=0.2)
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self.assertEqual(self.bg.dqx, 0.05)
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self.assertEqual(self.bg.dqy, 0.05)
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self.assertEqual(self.bg.dE, 0.2)
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def test_set_binned_data(self):
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"""Test set_binned_data method."""
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Qx = np.linspace(-2, 2, 20)
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Qy = np.linspace(-2, 2, 20)
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E = np.linspace(0, 2, 10)
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Int = np.random.rand(len(Qx), len(Qy), len(E))
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self.bg.set_binned_data(Qx, Qy, E, Int)
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self.assertEqual(self.bg.Qx_size, len(Qx))
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self.assertEqual(self.bg.Qy_size, len(Qy))
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self.assertEqual(self.bg.E_size, len(E))
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def test_set_radial_bins(self):
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"""Test set_radial_bins method."""
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self.bg.set_radial_bins(max_radius=5.0, n_bins=50)
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self.assertEqual(self.bg.max_radius, 5.0)
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self.assertEqual(self.bg.n_bins, 50)
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self.assertEqual(len(self.bg.r_range), 51)
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def test_R_operator(self):
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"""Test R_operator method."""
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b = np.random.rand(self.bg.E_size, self.bg.n_bins)
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b_grid = self.bg.R_operator(b)
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self.assertEqual(b_grid.shape, (self.bg.E_size, self.bg.Qx_size * self.bg.Qy_size))
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def test_Rstar_operator(self):
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"""Test Rstar_operator method."""
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X = np.random.rand(self.bg.E_size, self.bg.Qx_size * self.bg.Qy_size)
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v_agg = self.bg.Rstar_operator(X)
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self.assertEqual(v_agg.shape, (self.bg.E_size, self.bg.n_bins))
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def test_gamma_matrix(self):
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"""Test gamma_matrix method."""
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gamma_mat = self.bg.gamma_matrix()
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self.assertEqual(gamma_mat.shape, (self.bg.n_bins, self.bg.n_bins))
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def test_mask_nans(self):
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"""Test mask_nans method."""
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x = np.random.rand(self.bg.E_size, self.bg.Qx_size * self.bg.Qy_size)
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x[0, 0] = np.nan
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masked_x = self.bg.mask_nans(x)
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self.assertTrue(np.isnan(masked_x[0, 0]))
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def test_S_lambda(self):
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"""Test S_lambda method."""
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x = np.array([1.0, -2.0, 0.5])
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lambda_ = 1.0
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result = self.bg.S_lambda(x, lambda_)
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expected = np.array([0.0, -1.0, 0.0])
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np.testing.assert_array_almost_equal(result, expected)
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if __name__ == '__main__':
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if __name__ == "__main__":
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unittest.main()
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@ -4,10 +4,10 @@ import torch
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from scipy.sparse import lil_matrix, csr_matrix, coo_matrix
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import os
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import sys
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maindir = os.getcwd()
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main_path = maindir[:maindir.find('ds4ms/code')]
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sys.path.append(main_path+"/ds4ms/code/src")
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from graph_laplacian import create_laplacian_matrix, delete_from_csr, \
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#maindir = os.getcwd()
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#main_path = maindir[:maindir.find('ds4ms/code')]
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#sys.path.append(main_path+"/ds4ms/code/src")
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from AMBER.graph_laplacian import create_laplacian_matrix, delete_from_csr, \
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remove_vertex, laplacian, unnormalized_laplacian, laplacian_chain, \
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unnormalized_laplacian_chain
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