<jupyter_start><jupyter_text>Detect landmarks<jupyter_code>mc = aly.MomentumCorrector(image=bandaug)
mc.slice = mc.image
mc.featureExtract(image=mc.image, direction='ccw', method='daofind', sigma=15, fwhm=30)
mc.view(mc.image, points=mc.features, annotated=True, cmap='viridis')
mc.pouter_ord[0,:] - mc.pouter_ord[3,:]<jupyter_output><empty_output><jupyter_text>Synthesize artificial MPES data within the first Brillouin zone<jupyter_code>hm, margins = fuller.generator.hexmask(hexside=170, imside=256, padded=True, pad_left=43, pad_top=43, ret='all')
plt.imshow(hm[:,ofs:-ofs]*bandaug)
plt.scatter(mc.pouter_ord[:,1], mc.pouter_ord[:,0])
bandcut = cut_margins(bandaug, margins, offsetx=ofs)
hmcut = cut_margins(hm, margins)
bandhm = bandcut*hmcut
plt.imshow(bandhm)
bandhm_dmean = np.nan_to_num(bandhm - np.nanmean(bandhm))
bcf = fuller.generator.decomposition_hex2d(bandhm_dmean, nterms=400)
plt.plot(bcf)
dg = fuller.generator.MPESDataGenerator()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1511746680286006, 'fraction_numerical': 0.02247191011235955, 'mean_word_length': 9.76923076923077, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '476592', 'license': 'MIT', 'n_tokens_mistral': 426, 'n_tokens_neox': 398, 'n_words': 53, 'path': 'examples/synthetic_mpes_data_generation_I (from calculation).ipynb', 'repo_name': 'mpes-kit/fuller', 'size': '979'}