Reorganizing tools

Author: zhyan0603

tools/Analysis_and_Processing/README.md

@@ -0,0 +1,15 @@
+
+
+
+| Folder               | Creator      | Email                      | Brief Description                                            |
+| -------------------- | ------------ | -------------------------- | ------------------------------------------------------------ |
+| add_groups           | Yuwen Zhang  | [email protected]           | Generate grouping method(s) for `model.xyz`.                 |
+| get_max_rmse_xyz     | Ke Xu        | [email protected]               | Identify structures with the largest errors.                 |
+| pbc_mol              | Zherui Chen  | [email protected] | It processes XYZ trajectory files of molecular simulations to ensure that molecules remain intact across periodic boundaries in the simulation box. |
+| pca_sampling         | Penghua Ying | [email protected]          | Farthest-point sampling based on `calorine`.                 |
+| perturbed2poscar     | Who?         |                            | What?                                                        |
+| rdf_adf              | Ke Xu        | [email protected]               | Calculate RDF and ADF using `OVITO`.                         |
+| select_xyz_frames    | Zherui Chen  | [email protected] | Select frames from the `exyz`  file.                         |
+| shift_energy_to_zero | Nan Xu       | [email protected]           | Shift the average energy of each species to zero for a dataset. |
+| split_xyz            | Yong Wang    | [email protected]        | Some functionalities for training/test data.                 |
+

tools/add_groups/Readme.md renamed to tools/Analysis_and_Processing/add_groups/Readme.md

@@ -1,87 +1,87 @@
-"""
-    Purpose:
-        Select structures from orginal large reference dataset based on principal component Analysis (PCA) of 
-        descriptor space using farthest point sampling. We use the PbTe as a toy example to show how this script
-        works, one need to modify the path of reference dataset, nep model, and selected frame number case by case.
-
-    Ref:
-        calorine: https://calorine.materialsmodeling.org/tutorials/visualize_descriptor_space_with_pca.html
-        https://github.com/bigd4/PyNEP/blob/master/examples/plot_select_structure.py
-
-    Author:
-        Penghua Ying <hityingph(at)163.com>
-"""
-
-from ase.io import read, write
-from pylab import *
-from calorine.nep import get_descriptors
-from sklearn.decomposition import PCA
-from tqdm import tqdm
-from scipy.spatial.distance import cdist
-
-# Farthest Point Sampling
-def farthest_point_sampling(points, n_samples):
-    n_points = points.shape[0]
-    selected_indices = [np.random.randint(n_points)]
-    for _ in range(1, n_samples):
-        distances = cdist(points, points[selected_indices])
-        min_distances = np.min(distances, axis=1)
-        next_index = np.argmax(min_distances)
-        selected_indices.append(next_index)
-    return selected_indices
-
-aw = 2
-fs = 16
-lw = 2
-font = {'size'   : fs}
-matplotlib.rc('font', **font)
-matplotlib.rc('axes' , linewidth=aw)
-
-def set_fig_properties(ax_list):
-    tl = 8
-    tw = 2
-    tlm = 4
-    
-    for ax in ax_list:
-        ax.tick_params(which='major', length=tl, width=tw)
-        ax.tick_params(which='minor', length=tlm, width=tw)
-        ax.tick_params(which='both', axis='both', direction='out', right=False, top=False)
-        
-        
-                         
-tol = read("../../examples/11_NEP_potential_PbTe/test.xyz", ":") # read orginal larger reference.xyz
-
-
-descriptors = []
-for i, t in tqdm(enumerate(tol)):
-    d = get_descriptors(t, model_filename='../../examples/11_NEP_potential_PbTe/nep.txt')  # get descriptors using the pre-trained nep model
-    d_mean = np.mean(d, axis=0) # Use average of each atomic descriptors to get structure descriptors
-    descriptors.append(d_mean)
-    
-descriptors = np.array(descriptors)
-print(f'Total frame of structures in dataset: {descriptors.shape[0]}')
-print(f'Number of descriptor components:  {descriptors.shape[1]}')
-pca = PCA(n_components=2)
-pc = pca.fit_transform(descriptors)
-p0 = pca.explained_variance_ratio_[0]
-p1 = pca.explained_variance_ratio_[1]
-print(f'Explained variance for component 0: {p0:.2f}')
-print(f'Explained variance for component 1: {p1:.2f}')
-
-# Select 25 structures using FPS
-n_samples = 25
-selected_indices = farthest_point_sampling(pc, n_samples)
-selected_structures = [tol[i] for i in selected_indices]
-unselected_structures = [t for i, t in enumerate(tol) if i not in selected_indices]
-
-# Save the selected and unselected structures
-write('selected_structures.xyz', selected_structures)
-
-figure(figsize=(10, 8))
-set_fig_properties([gca()])
-scatter(pc[:, 0], pc[:, 1], alpha=0.5, c="C0", label='All structures')
-scatter(pc[selected_indices, 0], pc[selected_indices, 1], s=8, color='C1', label='Selected structures')
-xlabel('PC1')
-ylabel('PC2')
-legend()
+"""
+    Purpose:
+        Select structures from orginal large reference dataset based on principal component Analysis (PCA) of 
+        descriptor space using farthest point sampling. We use the PbTe as a toy example to show how this script
+        works, one need to modify the path of reference dataset, nep model, and selected frame number case by case.
+
+    Ref:
+        calorine: https://calorine.materialsmodeling.org/tutorials/visualize_descriptor_space_with_pca.html
+        https://github.com/bigd4/PyNEP/blob/master/examples/plot_select_structure.py
+
+    Author:
+        Penghua Ying <hityingph(at)163.com>
+"""
+
+from ase.io import read, write
+from pylab import *
+from calorine.nep import get_descriptors
+from sklearn.decomposition import PCA
+from tqdm import tqdm
+from scipy.spatial.distance import cdist
+
+# Farthest Point Sampling
+def farthest_point_sampling(points, n_samples):
+    n_points = points.shape[0]
+    selected_indices = [np.random.randint(n_points)]
+    for _ in range(1, n_samples):
+        distances = cdist(points, points[selected_indices])
+        min_distances = np.min(distances, axis=1)
+        next_index = np.argmax(min_distances)
+        selected_indices.append(next_index)
+    return selected_indices
+
+aw = 2
+fs = 16
+lw = 2
+font = {'size'   : fs}
+matplotlib.rc('font', **font)
+matplotlib.rc('axes' , linewidth=aw)
+
+def set_fig_properties(ax_list):
+    tl = 8
+    tw = 2
+    tlm = 4
+    
+    for ax in ax_list:
+        ax.tick_params(which='major', length=tl, width=tw)
+        ax.tick_params(which='minor', length=tlm, width=tw)
+        ax.tick_params(which='both', axis='both', direction='out', right=False, top=False)
+        
+        
+                         
+tol = read("../../examples/11_NEP_potential_PbTe/test.xyz", ":") # read orginal larger reference.xyz
+
+
+descriptors = []
+for i, t in tqdm(enumerate(tol)):
+    d = get_descriptors(t, model_filename='../../examples/11_NEP_potential_PbTe/nep.txt')  # get descriptors using the pre-trained nep model
+    d_mean = np.mean(d, axis=0) # Use average of each atomic descriptors to get structure descriptors
+    descriptors.append(d_mean)
+    
+descriptors = np.array(descriptors)
+print(f'Total frame of structures in dataset: {descriptors.shape[0]}')
+print(f'Number of descriptor components:  {descriptors.shape[1]}')
+pca = PCA(n_components=2)
+pc = pca.fit_transform(descriptors)
+p0 = pca.explained_variance_ratio_[0]
+p1 = pca.explained_variance_ratio_[1]
+print(f'Explained variance for component 0: {p0:.2f}')
+print(f'Explained variance for component 1: {p1:.2f}')
+
+# Select 25 structures using FPS
+n_samples = 25
+selected_indices = farthest_point_sampling(pc, n_samples)
+selected_structures = [tol[i] for i in selected_indices]
+unselected_structures = [t for i, t in enumerate(tol) if i not in selected_indices]
+
+# Save the selected and unselected structures
+write('selected_structures.xyz', selected_structures)
+
+figure(figsize=(10, 8))
+set_fig_properties([gca()])
+scatter(pc[:, 0], pc[:, 1], alpha=0.5, c="C0", label='All structures')
+scatter(pc[selected_indices, 0], pc[selected_indices, 1], s=8, color='C1', label='Selected structures')
+xlabel('PC1')
+ylabel('PC2')
+legend()
 savefig('FPS.png', bbox_inches='tight')