import os from matminer.datasets import load_dataset from mp_api.client import MPRester from pymatgen.analysis.diffraction.xrd import XRDCalculator import pymatviz as pmv from pymatviz.enums import ElemColorScheme, Key from pymatgen.symmetry.analyzer import SpacegroupAnalyzer import matplotlib.pyplot as plt import numpy as np import pandas as pd from tqdm import tqdm MP_API_KEY = "" MID = ["mp-353", "mp-661", "mp-856", "mp-1000", "mp-1479", "mp-2284", "mp-2294", "mp-10044", "mp-10086", "mp-10910", "mp-18905", "mp-23231", "mp-36526", "mp-861883", "mp-862786"] xrd_calculator = XRDCalculator(wavelength='CuKa') patterns = {} for mid in tqdm(MID): # if os.path.exists(f"{mid}-xrd.png"): # print(f"{mid}-xrd.png already exists, skipping...") # continue with MPRester(MP_API_KEY) as mpr: structure = mpr.get_structure_by_material_id(mid) sga = SpacegroupAnalyzer(structure) conventional_structure = sga.get_conventional_standard_structure() xrd_pattern = xrd_calculator.get_pattern(conventional_structure, scaled=False) patterns[mid] = xrd_pattern plt.figure(figsize=(12, 6)) bar_width = 0.5 x = xrd_pattern.x y = xrd_pattern.y / np.max(xrd_pattern.y) * 100 plt.bar(x, y, width=bar_width, color='black') plt.xlabel('2 Theta (degrees)', fontsize=14) plt.ylabel('Intensity (a.u.)', fontsize=14) plt.xticks(fontsize=12) plt.yticks(fontsize=12) plt.grid(axis='y', linestyle='--', alpha=0.7) plt.gca().spines['top'].set_linewidth(0.5) plt.gca().spines['right'].set_linewidth(0.5) plt.tight_layout() plt.savefig(f"{mid}-xrd.png", dpi=300, bbox_inches='tight') plt.close() for _ in tqdm(range(5)): mids = np.random.choice(MID, 3, replace=False) mids = sorted(mids) combined_pattern = {} for mid in mids: pattern = patterns[mid] for two_theta, intensity in zip(pattern.x, pattern.y): if two_theta in combined_pattern: combined_pattern[two_theta] += intensity else: combined_pattern[two_theta] = intensity combined_pattern_list = [(k, v) for k, v in combined_pattern.items()] combined_pattern_list.sort(key=lambda x: x[0]) x = np.array([item[0] for item in combined_pattern_list]) y = np.array([item[1] for item in combined_pattern_list]) y = y / np.max(y) * 100 plt.figure(figsize=(12, 6)) bar_width = 0.5 plt.bar(x, y, width=bar_width, color='black') plt.xlabel('2 Theta (degrees)', fontsize=14) plt.ylabel('Intensity (a.u.)', fontsize=14) plt.xticks(fontsize=12) plt.yticks(fontsize=12) plt.grid(axis='y', linestyle='--', alpha=0.7) plt.gca().spines['top'].set_linewidth(0.5) plt.gca().spines['right'].set_linewidth(0.5) plt.tight_layout() plt.savefig(f"{'_'.join(mids)}-xrd.png", dpi=300, bbox_inches='tight') plt.close()