<jupyter_start><jupyter_text>bar  Import packages<jupyter_code>import porespy as ps
import matplotlib.pyplot as plt
ps.visualization.set_mpl_style()<jupyter_output><empty_output><jupyter_text>Create input:<jupyter_code>im = ps.generators.blobs([100, 100])
tup = ps.metrics.chord_length_distribution(im)<jupyter_output><empty_output><jupyter_text>Display image:<jupyter_code>fig, ax = plt.subplots(figsize=[7, 7]);
plt.axis(False);
ps.visualization.bar(tup)
plt.title("default, h = 'pdf'");<jupyter_output><empty_output><jupyter_text>h<jupyter_code>fig, ax = plt.subplots(figsize=[7, 7]);
plt.axis(False);
ps.visualization.bar(tup, h = 'cdf')
plt.title("h = 'cdf'");<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16258992805755396, 'fraction_numerical': 0.014388489208633094, 'mean_word_length': 10.796610169491526, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '56066', 'license': 'MIT', 'n_tokens_mistral': 304, 'n_tokens_neox': 287, 'n_words': 34, 'path': 'examples/visualization/howtos/bar.ipynb', 'repo_name': 'jamesobutler/porespy', 'size': '695'}