diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..7ce23c3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python diff --git a/README.md b/README.md index 55a5779..a56fc8c 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@ Pre- and post-processing tools for phonon-based calculations. - [**phonchar**](https://github.com/acammarat/phtools/tree/main/phonchar) Calculates the atomic character of phonon eigenvectors obtained from [PHONOPY](https://phonopy.github.io/phonopy) - [**eigmap**](https://github.com/acammarat/phtools/tree/main/eigmap) Calculates the map between different eigenvectors via scalar products - [**qpoints**](https://github.com/acammarat/phtools/tree/main/qpoints) Extracts phonon eigenvectors and eigenvalues from the file qpoints.yaml generated by [PHONOPY]( https://phonopy.github.io/phonopy) +- [**avgpos**](https://github.com/acammarat/phtools/tree/main/var/avgpos) Calculates the average position and standard deviation of selected atoms along a crystallographic direction from a POSCAR file ## Citation When using these tools, please cite the scientific publications as suggested in the relative README.md file. diff --git a/var/avgpos/LICENSE b/var/avgpos/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/var/avgpos/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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But first, please read +. diff --git a/var/avgpos/README.md b/var/avgpos/README.md new file mode 100644 index 0000000..aefa7b6 --- /dev/null +++ b/var/avgpos/README.md @@ -0,0 +1,200 @@ +# avgpos + +A tool to calculate the average position and standard deviation of selected atoms along a specified crystallographic direction from a POSCAR file. + +## Features + +- Read POSCAR files (VASP structure format) +- Select atoms by element type or by indices +- Calculate average position along: + - Cartesian directions (x, y, z) + - Crystallographic lattice vectors (a, b, c) + - Custom Miller indices [h,k,l] +- Calculate standard deviation of positions +- Display individual atomic positions along the selected direction +- **NEW:** Calculate and export orthogonal projections onto a plane perpendicular to the direction vector + +## Requirements + +- Python 3.6 or higher +- NumPy +- Matplotlib (for generating heatmap visualizations) +- SciPy (for interpolation in smooth heatmaps) + +## Installation + +No installation required. Simply make the script executable: + +```bash +chmod +x avgpos.py +``` + +Or run it with Python: + +```bash +python3 avgpos.py +``` + +## Usage + +### Basic syntax + +```bash +./avgpos.py POSCAR -s -d +./avgpos.py POSCAR -i -d +``` + +### Options + +- `POSCAR`: Path to the POSCAR file (required) +- `-s, --select`: Select atoms by element symbol(s), comma-separated (e.g., "Se" or "W,Mo") +- `-i, --indices`: Select atoms by indices (1-based), comma-separated (e.g., "1,2,3") +- `-d, --direction`: Direction specification (required): + - Cartesian: `x`, `y`, `z` + - Lattice vectors: `a`, `b`, `c` + - Miller indices: `[h,k,l]` (e.g., `[1,1,0]`) +- `-o, --output`: Output file for plane projection data (optional) +- `--plot`: Generate Python matplotlib script for heatmap visualization (requires `-o`) +- `--labels`: Include atom labels (element+ID, e.g., Se2, Ti4) in output and plot (requires `-o`) +- `--replicate`: Replicate the plot along e and f axes (format: "ne,nf", default: "1,1") + - Supports non-integer replication (e.g., "2.5,3" for 2.5x3 replication) +- `--no-circles`: Hide circles representing atom positions in the plot (only effective when `--labels` is not used) + +### Examples + +Calculate average position of all Se atoms along the z-axis: +```bash +./avgpos.py POSCAR -s Se -d z +``` + +Calculate average position of atoms 2, 3, and 4 along the c lattice vector: +```bash +./avgpos.py POSCAR -i 2,3,4 -d c +``` + +Calculate average position of W and Mo atoms along the [1,1,0] direction: +```bash +./avgpos.py POSCAR -s W,Mo -d [1,1,0] +``` + +Calculate average position of all atoms of multiple elements along x-axis: +```bash +./avgpos.py POSCAR -s Se,Mo -d x +``` + +Calculate average position and export plane projection data: +```bash +./avgpos.py POSCAR -s Se -d z -o projections.dat +``` + +Calculate average position and generate matplotlib heatmap script: +```bash +./avgpos.py POSCAR -s Se -d z -o projections.dat --plot +# Then run: python3 projections_plot.py +``` + +Calculate average position with atom labels and generate labeled heatmap: +```bash +./avgpos.py POSCAR -s Se -d z -o projections.dat --plot --labels +# Then run: python3 projections_plot.py +# Labels will show atom type and POSCAR file ID (e.g., Se2, Se3, Ti1) +``` + +Generate heatmap with 2.5x3 replication along e and f axes: +```bash +./avgpos.py POSCAR -s Se -d z -o projections.dat --plot --replicate 2.5,3 +# Then run: python3 projections_plot.py +# Plot will show 2.5 replications along e-axis and 3 along f-axis +``` + +Generate smooth heatmap without atom position circles: +```bash +./avgpos.py POSCAR -s Se -d z -o projections.dat --plot --no-circles +# Then run: python3 projections_plot.py +# Plot will show only the smooth interpolated surface without circles +``` + +## Output + +### Standard Output + +The tool displays: +- Structure information (number and types of atoms) +- Selected atoms +- Direction vector in Cartesian coordinates +- Average position along the direction (in Ångströms) +- Standard deviation (in Ångströms) +- Individual atomic positions (if 20 or fewer atoms are selected) + +### Plane Projection Output File (optional) + +When the `-o` option is specified, the tool generates a data file containing: +- **Column 1 (e)**: First coordinate of the atom's orthogonal projection onto the plane +- **Column 2 (f)**: Second coordinate of the atom's orthogonal projection onto the plane +- **Column 3 (g)**: Signed distance from the plane (average_position - atom_distance_along_direction) +- **Column 4 (label)**: Atom label with element type and unique ID (e.g., Ti1, O2) - only when `--labels` is used + +The plane is perpendicular to the specified direction vector and passes through the calculated average position. The e and f coordinates form an orthonormal 2D coordinate system in the plane. + +### Matplotlib Plotting Script (optional) + +When the `--plot` flag is used along with `-o`, the tool generates a Python script using matplotlib that creates a smooth interpolated heatmap visualization of the plane projection data: +- **Script file**: Named as `_plot.py` +- **Output image**: Named as `_heatmap.png` +- The heatmap uses Radial Basis Function (RBF) interpolation to create a smooth surface covering the entire e,f range +- g values are represented by a color gradient (coolwarm colormap) +- Original data points are overlaid as black dots for reference +- When `--labels` is also used, atom labels (element+POSCAR file ID) are annotated on the plot +- To generate the plot, run: `python3 ` + +**Requirements**: Matplotlib and SciPy must be installed on your system to generate the visualization. + +## Example Output + +``` +Reading POSCAR file: POSCAR +Structure contains 6 atoms: + W: 1 + Se: 4 + Mo: 1 + +Selected 4 atom(s) of type: Se +Direction vector (Cartesian): [0.000000, 0.000000, 1.000000] + +============================================================ +RESULTS +============================================================ +Number of atoms: 4 +Average position: 35.123456 Å +Standard deviation: 2.345678 Å + +Individual positions along direction: + Atom 2: 38.343795 Å + Atom 3: 28.534648 Å + Atom 4: 31.870770 Å + Atom 5: 35.012611 Å +``` + +## POSCAR File Format + +The tool supports standard VASP POSCAR format with: +- Comment line +- Scale factor (preferably 1.0) +- Lattice vectors (3 lines) +- Element symbols +- Atom counts per element +- Coordinate type (Direct/Cartesian) +- Atomic positions + +Both Direct (fractional) and Cartesian coordinates are supported. + +## License + +This program is free software: you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation, either version 3 of the License, or +(at your option) any later version. + +## Author + +Part of the phtools collection: https://github.com/acammarat/phtools diff --git a/var/avgpos/avgpos.py b/var/avgpos/avgpos.py new file mode 100755 index 0000000..b446808 --- /dev/null +++ b/var/avgpos/avgpos.py @@ -0,0 +1,654 @@ +#!/usr/bin/env python3 +""" +avgpos - Calculate average position and standard deviation of selected atoms +along a crystallographic direction from a POSCAR file. + +Copyright (C) 2025 +This file is part of phtools. + +This program is free software: you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation, either version 3 of the License, or +(at your option) any later version. +""" + +import sys +import numpy as np +import argparse + + +def read_poscar(filename): + """ + Read a POSCAR file and return atomic structure information. + + Parameters: + ----------- + filename : str + Path to the POSCAR file + + Returns: + -------- + dict : Dictionary containing structure information + - 'lattice': 3x3 numpy array with lattice vectors + - 'elements': list of element symbols + - 'atom_counts': list of atom counts per element + - 'positions': Nx3 numpy array with atomic positions + - 'coordinate_type': 'Direct' or 'Cartesian' + """ + with open(filename, 'r') as f: + lines = f.readlines() + + # Read comment line + comment = lines[0].strip() + + # Read scale factor + scale = float(lines[1].strip()) + if abs(scale - 1.0) > 1e-6: + print(f"Warning: Scale factor is {scale}, not 1.0. Applying scaling.") + + # Read lattice vectors + lattice = np.zeros((3, 3)) + for i in range(3): + lattice[i] = [float(x) for x in lines[2+i].split()] + lattice *= scale + + # Read element symbols + elements = lines[5].split() + + # Read atom counts + atom_counts = [int(x) for x in lines[6].split()] + total_atoms = sum(atom_counts) + + # Check for selective dynamics + line_idx = 7 + if lines[line_idx].strip()[0].upper() in ['S']: + line_idx += 1 + + # Read coordinate type + coord_type = lines[line_idx].strip() + coordinate_type = 'Direct' if coord_type[0].upper() in ['D'] else 'Cartesian' + + # Read atomic positions + positions = np.zeros((total_atoms, 3)) + for i in range(total_atoms): + pos_line = lines[line_idx + 1 + i].split() + positions[i] = [float(x) for x in pos_line[:3]] + + # Convert direct coordinates to Cartesian if needed + if coordinate_type == 'Direct': + positions = np.dot(positions, lattice) + + return { + 'lattice': lattice, + 'elements': elements, + 'atom_counts': atom_counts, + 'positions': positions, + 'coordinate_type': coordinate_type + } + + +def select_atoms(structure, selection): + """ + Select atoms based on element type or indices. + + Parameters: + ----------- + structure : dict + Structure dictionary from read_poscar + selection : str or list + Either element symbol(s) or atom indices (1-based) + + Returns: + -------- + numpy.ndarray : Indices of selected atoms (0-based) + """ + if isinstance(selection, str): + # Selection by element + selected_indices = [] + idx = 0 + for i, element in enumerate(structure['elements']): + count = structure['atom_counts'][i] + if element in selection.split(','): + selected_indices.extend(range(idx, idx + count)) + idx += count + return np.array(selected_indices) + else: + # Selection by indices (convert from 1-based to 0-based) + return np.array([i-1 for i in selection]) + + +def get_direction_vector(structure, direction): + """ + Get the unit vector for the specified crystallographic direction. + + Parameters: + ----------- + structure : dict + Structure dictionary from read_poscar + direction : str + Direction specification: 'x', 'y', 'z', 'a', 'b', 'c', or custom [h,k,l] + + Returns: + -------- + numpy.ndarray : Unit vector in Cartesian coordinates + """ + lattice = structure['lattice'] + + if direction.lower() == 'x': + return np.array([1.0, 0.0, 0.0]) + elif direction.lower() == 'y': + return np.array([0.0, 1.0, 0.0]) + elif direction.lower() == 'z': + return np.array([0.0, 0.0, 1.0]) + elif direction.lower() == 'a': + vec = lattice[0] + elif direction.lower() == 'b': + vec = lattice[1] + elif direction.lower() == 'c': + vec = lattice[2] + else: + # Parse custom direction [h,k,l] + try: + direction = direction.strip('[]') + h, k, l = [float(x) for x in direction.split(',')] + # Convert Miller indices to Cartesian + vec = h * lattice[0] + k * lattice[1] + l * lattice[2] + except: + raise ValueError(f"Invalid direction specification: {direction}") + + # Normalize to unit vector + return vec / np.linalg.norm(vec) + + +def calculate_average_position(structure, atom_indices, direction_vector): + """ + Calculate average position and standard deviation along a direction. + + Parameters: + ----------- + structure : dict + Structure dictionary from read_poscar + atom_indices : numpy.ndarray + Indices of atoms to include in calculation + direction_vector : numpy.ndarray + Unit vector defining the direction + + Returns: + -------- + tuple : (average, std_dev, positions_along_dir) + - average: mean position along the direction + - std_dev: standard deviation + - positions_along_dir: array of positions projected onto direction + """ + positions = structure['positions'][atom_indices] + + # Project positions onto the direction vector + positions_along_dir = np.dot(positions, direction_vector) + + # Calculate statistics + average = np.mean(positions_along_dir) + std_dev = np.std(positions_along_dir) + + return average, std_dev, positions_along_dir + + +def get_atom_labels(structure, atom_indices): + """ + Generate atom labels with element type and POSCAR file ID for selected atoms. + + Parameters: + ----------- + structure : dict + Structure dictionary from read_poscar + atom_indices : numpy.ndarray + Indices of atoms (0-based) + + Returns: + -------- + list : List of atom labels (e.g., ['Ti1', 'Ti2', 'O3', 'O4']) + where the number corresponds to the atom's position in the POSCAR file (1-based) + """ + labels = [] + + # Create a mapping of atom index to element type + idx = 0 + atom_to_element = [] + for i, element in enumerate(structure['elements']): + count = structure['atom_counts'][i] + atom_to_element.extend([element] * count) + idx += count + + # Use the POSCAR file index (1-based) as the atom ID + for atom_idx in atom_indices: + element = atom_to_element[atom_idx] + # atom_idx is 0-based, so add 1 to get POSCAR file position + labels.append(f"{element}{atom_idx + 1}") + + return labels + + +def generate_plot_script(data_file, script_file, output_image='heatmap.png', with_labels=False, replicate=(1, 1), no_circles=False): + """ + Generate a Python script using matplotlib to plot the plane projection data as a heatmap. + + Parameters: + ----------- + data_file : str + Path to the data file containing projection data + script_file : str + Path where the Python plotting script will be written + output_image : str + Name of the output image file (default: 'heatmap.png') + with_labels : bool + Whether to include atom labels in the plot + replicate : tuple + Number of replications along e and f axes (ne, nf) + no_circles : bool + Whether to hide the circles representing atom positions (only when labels are not used) + """ + script_content = f"""#!/usr/bin/env python3 +\"\"\" +Matplotlib script to visualize plane projection data as a smooth interpolated heatmap. +Generated automatically by avgpos tool. + +Usage: python3 {script_file} +\"\"\" + +import numpy as np +import matplotlib.pyplot as plt +from matplotlib import cm +from scipy.interpolate import Rbf +from mpl_toolkits.axes_grid1 import make_axes_locatable + +# Read data from file +data = np.loadtxt('{data_file}', dtype=str) + +# Extract coordinates and g values +e_orig = data[:, 0].astype(float) +f_orig = data[:, 1].astype(float) +g_orig = data[:, 2].astype(float) + +# Extract labels if present (4th column) +has_labels = data.shape[1] > 3 +if has_labels: + labels_orig = data[:, 3] + +# Replication parameters +ne_rep, nf_rep = {replicate[0]}, {replicate[1]} + +# Replicate data along e and f axes +e_list, f_list, g_list, labels_list = [], [], [], [] +e_range_orig = e_orig.max() - e_orig.min() if len(e_orig) > 1 else 1.0 +f_range_orig = f_orig.max() - f_orig.min() if len(f_orig) > 1 else 1.0 + +# Determine how many full and partial replications to make +ne_full = int(np.ceil(ne_rep)) +nf_full = int(np.ceil(nf_rep)) + +for ie in range(ne_full): + for jf in range(nf_full): + # Calculate if this replica is fully or partially included + e_factor = min(1.0, ne_rep - ie) if ie < ne_full - 1 else (ne_rep - ie) + f_factor = min(1.0, nf_rep - jf) if jf < nf_full - 1 else (nf_rep - jf) + + # Include this replica if it has non-zero contribution + if e_factor > 0 and f_factor > 0: + e_shift = ie * (e_range_orig + (e_orig.max() - e_orig.min())) + f_shift = jf * (f_range_orig + (f_orig.max() - f_orig.min())) + e_list.append(e_orig + e_shift) + f_list.append(f_orig + f_shift) + g_list.append(g_orig) + if has_labels: + labels_list.append(labels_orig) + +e = np.concatenate(e_list) +f = np.concatenate(f_list) +g = np.concatenate(g_list) +if has_labels: + labels = np.concatenate(labels_list) + +# Create a regular grid for interpolation +# Determine the range of e and f +e_min, e_max = e.min(), e.max() +f_min, f_max = f.min(), f.max() + +e_grid = np.linspace(e_min, e_max, 200) +f_grid = np.linspace(f_min, f_max, 200) +e_mesh, f_mesh = np.meshgrid(e_grid, f_grid) + +# Use Radial Basis Function interpolation with very small smoothing +# This ensures interpolation passes extremely close to data points while handling duplicates +# smooth value is set to a tiny value to get nearly exact values at data points +try: + rbf = Rbf(e, f, g, function='thin_plate', smooth=1e-10) + g_interp = rbf(e_mesh, f_mesh) +except: + # Fall back to multiquadric if thin_plate fails + try: + rbf = Rbf(e, f, g, function='multiquadric', smooth=1e-10) + g_interp = rbf(e_mesh, f_mesh) + except: + # Last resort: use linear with small smoothing + rbf = Rbf(e, f, g, function='linear', smooth=1e-8) + g_interp = rbf(e_mesh, f_mesh) + +# Determine color range from actual data values (not interpolated) +vmin, vmax = g.min(), g.max() + +# Create figure and axis +fig, ax = plt.subplots(figsize=(10, 8)) + +# Create smooth heatmap using pcolormesh with RGB gradient (jet colormap) +# Use the same vmin/vmax as the scatter plot for consistent colors +heatmap = ax.pcolormesh(e_mesh, f_mesh, g_interp, cmap='jet', shading='auto', vmin=vmin, vmax=vmax) +""" + + # Add scatter points conditionally + if with_labels or not no_circles: + script_content += f""" +# Overlay the original data points with their EXACT g values colored +# This ensures atomic positions correspond to the real g value from the data file +scatter = ax.scatter(e, f, c=g, cmap='jet', s=150, edgecolors='black', linewidths=2, zorder=10, vmin=vmin, vmax=vmax) +""" + + script_content += f""" +# Add colorbar with height matching the plot +divider = make_axes_locatable(ax) +cax = divider.append_axes("right", size="5%", pad=0.1) +cbar = plt.colorbar(heatmap, cax=cax) +cbar.set_label('Distance from plane (Å)', fontsize=12) + +# Set axis labels with units +ax.set_xlabel('x (Å)', fontsize=12) +ax.set_ylabel('y (Å)', fontsize=12) +""" + + if with_labels: + script_content += f""" +# Add atom labels (already replicated above if needed) +if has_labels: + for i in range(len(e)): + ax.annotate(labels[i], (e[i], f[i]), + xytext=(5, 5), textcoords='offset points', + fontsize=10, fontweight='bold', color='black', + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', edgecolor='black', alpha=0.7)) +""" + + script_content += f""" +# Add grid +ax.grid(True, alpha=0.3) + +# Set equal aspect ratio +ax.set_aspect('equal', adjustable='box') + +# Save figure +plt.tight_layout() +plt.savefig('{output_image}', dpi=150, bbox_inches='tight') +print(f"Plot saved to {output_image}") + +# Optionally display the plot (comment out if running headless) +# plt.show() +""" + + with open(script_file, 'w') as f: + f.write(script_content) + + # Make the script executable (Unix-like systems) + import os + try: + os.chmod(script_file, 0o755) + except: + pass # Ignore if chmod fails (e.g., on Windows) + + +def calculate_plane_projections(structure, atom_indices, direction_vector, average_position): + """ + Calculate orthogonal projections of atoms onto a plane perpendicular to + the direction vector and passing through the average position. + + Parameters: + ----------- + structure : dict + Structure dictionary from read_poscar + atom_indices : numpy.ndarray + Indices of atoms to include in calculation + direction_vector : numpy.ndarray + Unit vector defining the direction (normal to the plane) + average_position : float + Average position along the direction (defines plane location) + + Returns: + -------- + numpy.ndarray : Nx3 array where each row contains [e, f, g] + - e, f: 2D coordinates of the projection on the plane + - g: average_position minus the distance of the atom from the plane + """ + positions = structure['positions'][atom_indices] + + # Calculate the distance of each atom along the direction vector + distances_along_dir = np.dot(positions, direction_vector) + + # Calculate the signed distance from each atom to the plane + # (positive if atom is on the side of the direction vector, negative otherwise) + signed_distances = distances_along_dir - average_position + + # Project each atom onto the plane + # projection = position - (signed_distance * normal_vector) + projections_3d = positions - np.outer(signed_distances, direction_vector) + + # Create an orthonormal basis for the plane + # Find two orthogonal vectors in the plane + # Start with an arbitrary vector not parallel to direction_vector + if abs(direction_vector[2]) < 0.9: + arbitrary = np.array([0.0, 0.0, 1.0]) + else: + arbitrary = np.array([1.0, 0.0, 0.0]) + + # First basis vector in the plane (orthogonal to direction_vector) + basis1 = arbitrary - np.dot(arbitrary, direction_vector) * direction_vector + basis1 = basis1 / np.linalg.norm(basis1) + + # Second basis vector (orthogonal to both direction_vector and basis1) + basis2 = np.cross(direction_vector, basis1) + basis2 = basis2 / np.linalg.norm(basis2) + + # Project the 3D projections onto the 2D plane coordinate system + e_coords = np.dot(projections_3d, basis1) + f_coords = np.dot(projections_3d, basis2) + + # Calculate g = average_position - distance_from_plane + # The distance from the plane is the absolute value of signed_distances + # But we want: average_position - distance_of_atom_from_plane + # Since distance_along_dir = average_position + signed_distance + # we have: g = average_position - abs(signed_distance) if we want actual distance + # But the requirement says: "average_position minus the distance of the atom from the plane" + # which could mean: average_position - distance_along_dir = -signed_distances + g_coords = -signed_distances + + # Combine into Nx3 array + result = np.column_stack((e_coords, f_coords, g_coords)) + + return result + + +def main(): + parser = argparse.ArgumentParser( + description='Calculate average position and standard deviation of selected atoms ' + 'along a crystallographic direction from a POSCAR file.', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Average position of all Se atoms along z-axis + %(prog)s POSCAR -s Se -d z + + # Average position of atoms 2,3,4 along c lattice vector + %(prog)s POSCAR -i 2,3,4 -d c + + # Average position along custom direction [1,1,0] + %(prog)s POSCAR -s W,Mo -d [1,1,0] + """ + ) + + parser.add_argument('poscar', help='Path to POSCAR file') + parser.add_argument('-s', '--select', type=str, + help='Select atoms by element symbol(s), comma-separated (e.g., "Se" or "W,Mo")') + parser.add_argument('-i', '--indices', type=str, + help='Select atoms by indices (1-based), comma-separated (e.g., "1,2,3")') + parser.add_argument('-d', '--direction', type=str, required=True, + help='Direction: x, y, z (Cartesian) or a, b, c (lattice vectors) ' + 'or [h,k,l] (Miller indices)') + parser.add_argument('-o', '--output', type=str, + help='Output file for plane projection data (3 columns: e, f, g)') + parser.add_argument('--plot', action='store_true', + help='Generate Python matplotlib script for heatmap visualization (requires -o)') + parser.add_argument('--labels', action='store_true', + help='Include atom labels (element+ID) in output and plot (requires -o)') + parser.add_argument('--replicate', type=str, default='1,1', + help='Replicate the plot along e and f axes (format: "ne,nf", e.g., "2.5,3" for 2.5x3 replication)') + parser.add_argument('--no-circles', action='store_true', + help='Hide circles representing atom positions in the plot (only when --labels is not used)') + + args = parser.parse_args() + + # Validate input + if not args.select and not args.indices: + parser.error("Must specify either --select or --indices") + if args.select and args.indices: + parser.error("Cannot specify both --select and --indices") + + # Read POSCAR file + print(f"Reading POSCAR file: {args.poscar}") + try: + structure = read_poscar(args.poscar) + except Exception as e: + print(f"Error reading POSCAR file: {e}") + sys.exit(1) + + # Print structure information + total_atoms = sum(structure['atom_counts']) + print(f"Structure contains {total_atoms} atoms:") + for elem, count in zip(structure['elements'], structure['atom_counts']): + print(f" {elem}: {count}") + print() + + # Select atoms + if args.select: + atom_indices = select_atoms(structure, args.select) + print(f"Selected {len(atom_indices)} atom(s) of type: {args.select}") + else: + indices = [int(x) for x in args.indices.split(',')] + atom_indices = select_atoms(structure, indices) + print(f"Selected {len(atom_indices)} atom(s) by indices: {args.indices}") + + if len(atom_indices) == 0: + print("Error: No atoms selected!") + sys.exit(1) + + # Get direction vector + try: + direction_vector = get_direction_vector(structure, args.direction) + print(f"Direction vector (Cartesian): [{direction_vector[0]:.6f}, " + f"{direction_vector[1]:.6f}, {direction_vector[2]:.6f}]") + except Exception as e: + print(f"Error parsing direction: {e}") + sys.exit(1) + + # Calculate average position + average, std_dev, positions = calculate_average_position( + structure, atom_indices, direction_vector + ) + + # Print results + print() + print("=" * 60) + print("RESULTS") + print("=" * 60) + print(f"Number of atoms: {len(atom_indices)}") + print(f"Average position: {average:.6f} Å") + print(f"Standard deviation: {std_dev:.6f} Å") + print() + + # Print individual positions if not too many + if len(atom_indices) <= 20: + print("Individual positions along direction:") + for i, (idx, pos) in enumerate(zip(atom_indices, positions)): + print(f" Atom {idx+1}: {pos:.6f} Å") + + # Calculate and write plane projections if output file is specified + if args.output: + projections = calculate_plane_projections( + structure, atom_indices, direction_vector, average + ) + + # Get atom labels if requested + if args.labels: + labels = get_atom_labels(structure, atom_indices) + # Create a combined array with projections and labels + # Save with labels as 4th column + with open(args.output, 'w') as f: + f.write('# e f g label\n') + f.write('# Projections onto plane perpendicular to direction vector\n') + f.write('# e, f: 2D coordinates on plane\n') + f.write('# g: average_position - distance_from_plane\n') + f.write('# label: atom type and ID (e.g., Ti1, O2)\n') + for i, label in enumerate(labels): + f.write(f"{projections[i, 0]:.6f} {projections[i, 1]:.6f} {projections[i, 2]:.6f} {label}\n") + + print() + print(f"Plane projection data with labels written to: {args.output}") + print(f" Columns: e, f, g, label") + else: + # Write to file without labels + np.savetxt(args.output, projections, fmt='%.6f', + header='e f g\nProjections onto plane perpendicular to direction vector\n' + 'e, f: 2D coordinates on plane\n' + 'g: average_position - distance_from_plane', + comments='# ') + + print() + print(f"Plane projection data written to: {args.output}") + print(f" Columns: e, f, g") + + print(f" e, f: 2D coordinates of atom projection on plane") + print(f" g: signed distance from plane (average_position - atom_distance)") + + # Generate matplotlib plot script if requested + if args.plot: + import os + # Determine output paths + base_name = os.path.splitext(args.output)[0] + script_file = f"{base_name}_plot.py" + image_file = f"{base_name}_heatmap.png" + + # Parse replication argument + try: + replicate_parts = args.replicate.split(',') + ne_rep = float(replicate_parts[0]) + nf_rep = float(replicate_parts[1]) if len(replicate_parts) > 1 else ne_rep + replicate = (ne_rep, nf_rep) + except (ValueError, IndexError): + print(f"Warning: Invalid replicate format '{args.replicate}'. Using default 1,1") + replicate = (1, 1) + + # Generate the plotting script + generate_plot_script(args.output, script_file, image_file, args.labels, replicate, args.no_circles) + + print() + print(f"Matplotlib plotting script generated: {script_file}") + print(f"To create the heatmap, run: python3 {script_file}") + print(f"Output image will be: {image_file}") + if args.labels: + print(f" (with atom labels)") + if replicate != (1, 1): + print(f" (with {replicate[0]}x{replicate[1]} replication)") + if args.no_circles and not args.labels: + print(f" (without atom position circles)") + elif args.plot or args.labels: + print() + print("Warning: --plot and --labels flags require -o/--output to be specified. Ignoring.") + + return 0 + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/var/avgpos/example/POSCAR b/var/avgpos/example/POSCAR new file mode 100644 index 0000000..a426e37 --- /dev/null +++ b/var/avgpos/example/POSCAR @@ -0,0 +1,14 @@ +generated by phonopy + 1.0 + 3.3233430609575230 0.0000000000000000 0.0000000000000000 + -1.6616715304787615 2.8780995162799510 0.0000000000000000 + 0.0000000000000000 0.0000000000000000 39.9999999116339637 +W Se Mo + 1 4 1 +Direct + 0.6666666666666666 0.3333333333333333 0.7551042030166925 + 0.6666666666666666 0.3333333333333333 0.9585948483162808 + 0.0000000000000000 0.0000000000000000 0.7133714496651961 + 0.0000000000000000 0.0000000000000000 0.7967692408998203 + 0.6666666666666666 0.3333333333333333 0.8752886384852373 + 0.0000000000000000 0.0000000000000000 0.9169322639868226 diff --git a/var/avgpos/example/example.sh b/var/avgpos/example/example.sh new file mode 100755 index 0000000..4ce9dbc --- /dev/null +++ b/var/avgpos/example/example.sh @@ -0,0 +1,17 @@ +#!/bin/bash +# Example usage of avgpos tool + +echo "====== Example 1: Average position of Se atoms along z-axis ======" +python3 ../avgpos.py POSCAR -s Se -d z + +echo "" +echo "====== Example 2: Average position of atoms 2,3,4 along c lattice vector ======" +python3 ../avgpos.py POSCAR -i 2,3,4 -d c + +echo "" +echo "====== Example 3: Average position of W and Mo atoms along [1,1,0] direction ======" +python3 ../avgpos.py POSCAR -s W,Mo -d "[1,1,0]" + +echo "" +echo "====== Example 4: Average position of all Se atoms along x-axis ======" +python3 ../avgpos.py POSCAR -s Se -d x diff --git a/var/avgpos/requirements.txt b/var/avgpos/requirements.txt new file mode 100644 index 0000000..db4d647 --- /dev/null +++ b/var/avgpos/requirements.txt @@ -0,0 +1,3 @@ +numpy>=1.19.0 +matplotlib>=3.0.0 +scipy>=1.5.0