<jupyter_start><jupyter_text>Diffractem examples...welcome!  So let's go<jupyter_code>from diffractem import dataset, proc2d, peakfinder8_extension, pre_proc_opts
from tifffile import imread
opts = pre_proc_opts
ds = dataset.Dataset.from_files('/Users/antonie/data/GV_final_edmond/data/data/GV_S11_001_00000.nxs', chunking=10)
pxmask = imread('/Users/antonie/data/GV_final_edmond/data/Ref12_pxmask.tif')
img = ds.stacks['raw_counts'][41,...].compute()
import matplotlib.pyplot as plt
import numpy as np
plt.imshow(img, vmin=0, vmax=10)
from collections import OrderedDict
dict([('X', 1), ('Y', 2)])
peakinfo = proc2d.get_peaks(img, 850, 380, pxmask=None, extended_info=True)
peakinfo<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13464235624123422, 'fraction_numerical': 0.04207573632538569, 'mean_word_length': 7.5, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '112107', 'license': 'MIT', 'n_tokens_mistral': 298, 'n_tokens_neox': 273, 'n_words': 50, 'path': 'diffractem/notebooks/overview.ipynb', 'repo_name': 'mahabul123/diffractem', 'size': '713'}