<jupyter_start><jupyter_text>d['logMmin'] = 13.108822003<jupyter_code>add_logMmin(d, cat)
cat.populate(d, min_ptcl = 100)
cat.model.mock._total_abundance
from scipy.stats import poisson
dist = poisson
dist.isf(np.linspace(0,1,10), 2)
?? dist.isf
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()
p = np.linspace(0,1,1000)
plt.plot(p, dist.isf(p, 2, loc = 0))
p = np.linspace(0,1,int(1e6))
d = dist.isf(p, 2)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15578947368421053, 'fraction_numerical': 0.06736842105263158, 'mean_word_length': 6.4375, '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': '528118', 'license': 'MIT', 'n_tokens_mistral': 235, 'n_tokens_neox': 218, 'n_words': 41, 'path': 'notebooks/Debug Corrab Bug.ipynb', 'repo_name': 'mclaughlin6464/pearce', 'size': '475'}