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February 22, 2019 21:05
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| Timer unit: 1e-06 s | |
| Total time: 0.265183 s | |
| File: /mnt/c/Users/thomasaar/Documents/atomap/atomap/atom_finding_refining.py | |
| Function: _make_model_from_atom_list at line 740 | |
| Line # Hits Time Per Hit % Time Line Contents | |
| ============================================================== | |
| 740 def _make_model_from_atom_list( | |
| 741 atom_list, | |
| 742 image_data, | |
| 743 percent_to_nn=0.40, | |
| 744 mask_radius=None): | |
| 745 """ | |
| 746 Make a HyperSpy model from a list of Atom_Position objects and | |
| 747 an image. | |
| 748 | |
| 749 Parameters | |
| 750 ---------- | |
| 751 atom_list : list of Atom_Position objects | |
| 752 List of atoms to be included in the model. | |
| 753 image_data : NumPy 2D array | |
| 754 percent_to_nn : float, optional | |
| 755 mask_radius : float, optional | |
| 756 Radius of the mask around each atom. If this is not set, | |
| 757 the radius will be the distance to the nearest atom in the | |
| 758 same sublattice times the `percent_to_nn` value. | |
| 759 | |
| 760 Returns | |
| 761 ------- | |
| 762 model : HyperSpy model | |
| 763 Model where the atoms are added as gaussian components. | |
| 764 mask : NumPy 2D array | |
| 765 The mask from _make_mask_from_positions() | |
| 766 | |
| 767 See also | |
| 768 -------- | |
| 769 _fit_atom_positions_with_gaussian_model | |
| 770 fit_atom_positions_gaussian | |
| 771 | |
| 772 Examples | |
| 773 -------- | |
| 774 >>> import numpy as np | |
| 775 >>> from atomap.atom_position import Atom_Position | |
| 776 >>> from atomap.atom_finding_refining import _make_model_from_atom_list | |
| 777 >>> atom_list = [Atom_Position(2, 2), Atom_Position(4, 4)] | |
| 778 >>> image = np.random.random((100, 100)) | |
| 779 >>> m, mask = _make_model_from_atom_list( | |
| 780 ... atom_list=atom_list, image_data=image, mask_radius=3) | |
| 781 >>> m.fit() | |
| 782 | |
| 783 """ | |
| 784 1 37211.0 37211.0 14.0 image_data = image_data.astype('float64') | |
| 785 1 50082.0 50082.0 18.9 mask = np.zeros_like(image_data) | |
| 786 | |
| 787 1 4.0 4.0 0.0 position_list, radius_list = [], [] | |
| 788 2 5.0 2.5 0.0 for atom in atom_list: | |
| 789 1 6.0 6.0 0.0 position_list.append((atom.pixel_y, atom.pixel_x)) | |
| 790 1 1.0 1.0 0.0 if mask_radius is None: | |
| 791 1 34.0 34.0 0.0 mask_radius = atom.get_closest_neighbor() * percent_to_nn | |
| 792 1 2.0 2.0 0.0 radius_list.append(mask_radius) | |
| 793 1 3.0 3.0 0.0 mask = _make_mask_from_positions( | |
| 794 1 65850.0 65850.0 24.8 position_list, radius_list, image_data.shape) | |
| 795 1 50197.0 50197.0 18.9 x0, x1, y0, y1 = _crop_mask_slice_indices(mask) | |
| 796 1 15.0 15.0 0.0 mask_crop = mask[x0:x1, y0:y1].astype('bool') | |
| 797 1 44049.0 44049.0 16.6 data_mask_crop = (image_data*mask)[x0:x1, y0:y1] | |
| 798 | |
| 799 1 4.0 4.0 0.0 upper_value = _find_median_upper_percentile( | |
| 800 1 400.0 400.0 0.2 data_mask_crop[mask_crop], upper_percentile=0.03) | |
| 801 1 2.0 2.0 0.0 lower_value = _find_background_value( | |
| 802 1 110.0 110.0 0.0 data_mask_crop[mask_crop], lowest_percentile=0.03) | |
| 803 1 19.0 19.0 0.0 data_mask_crop -= lower_value | |
| 804 1 22.0 22.0 0.0 data_mask_crop[data_mask_crop < 0] = 0. | |
| 805 | |
| 806 1 3736.0 3736.0 1.4 s = Signal2D(data_mask_crop) | |
| 807 1 2.0 2.0 0.0 gaussian_list = [] | |
| 808 2 4.0 2.0 0.0 for atom in atom_list: | |
| 809 1 1688.0 1688.0 0.6 gaussian = _atom_to_gaussian_component(atom) | |
| 810 1 3.0 3.0 0.0 if atom._gaussian_fitted: | |
| 811 1 16.0 16.0 0.0 gaussian.A.value = atom.amplitude_gaussian | |
| 812 else: | |
| 813 gaussian.A.value = upper_value*10 | |
| 814 1 2.0 2.0 0.0 gaussian_list.append(gaussian) | |
| 815 | |
| 816 1 206.0 206.0 0.1 s.axes_manager[0].offset = y0 | |
| 817 1 122.0 122.0 0.0 s.axes_manager[1].offset = x0 | |
| 818 1 11246.0 11246.0 4.2 m = s.create_model() | |
| 819 1 140.0 140.0 0.1 m.extend(gaussian_list) | |
| 820 1 2.0 2.0 0.0 return(m, mask) |
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