<jupyter_start><jupyter_text>What Does Exponential Growth Look Like?<jupyter_code>%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(context='talk', palette='deep', color_codes=True, style='darkgrid')
time = np.arange(0, 10, 0.01)<jupyter_output><empty_output><jupyter_text>Fiddle with this rate and see how things change. For large rates, we get the traditional-looking exponential curve. For small rates, things look linear over this small time scale.<jupyter_code>rate = 0.05
y = np.exp(rate * time)
plt.plot(time, y)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1018363939899833, 'fraction_numerical': 0.015025041736227046, 'mean_word_length': 6.894736842105263, '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': '547624', 'license': 'MIT', 'n_tokens_mistral': 210, 'n_tokens_neox': 199, 'n_words': 61, 'path': 'exponential_growth_explore.ipynb', 'repo_name': 'jedludlow/covid-19', 'size': '599'}