<jupyter_start><jupyter_text>Sample visualization- https://www.youtube.com/watch?v=4pYvVtO1WxI Specifications - http://coursera.cs.princeton.edu/algs4/assignments/percolation.html<jupyter_code>PercolationExperiment= PercolationStats()
PercolationExperiment.run_simulations(n = 5, t = 100)
print_title(" 2 x 2 grid, 10,000 trials")
_ = PercolationExperiment.run_simulations(n = 2, t = 10000, print_stats = True)

print_title(" 2 x 2 grid, 100,000 trials")
_ = PercolationExperiment.run_simulations(n = 2, t = 100000, print_stats = True)

print_spacer()

print_title(" 5 x 5 grid, 200 trials")
_ = PercolationExperiment.run_simulations(n = 5, t = 200, print_stats = True)

print_title(" 5 x 5 grid, 500 trials")
_ = PercolationExperiment.run_simulations(n = 5, t = 500, print_stats = True)

print_spacer()
print_title(" 20 x 20 grid, 50 trials")
_ = PercolationExperiment.run_simulations(n = 20, t = 50, print_stats = True)

print_title(" 20 x 20 grid, 100 trials")
_ = PercolationExperiment.run_simulations(n = 20, t = 100, print_stats = True)

print_title(" 20 x 20 grid, 200 trials")
_ = PercolationExperiment.run_simulations(n = 20, t = 200, print_stats = True)

print_spacer()

print_title(" 50 x 50 grid, 50 trials")
_ = PercolationExperiment.run_simulations(n = 50, t = 50, print_stats = True)

print_title(" 50 x 50 grid, 50 trials")
_ = PercolationExperiment.run_simulations(n = 50, t = 50, print_stats = True)

print_title(" 50 x 50 grid, 100 trials")
_ = PercolationExperiment.run_simulations(n = 50, t = 100, print_stats = True)

print_title(" 50 x 50 grid, 100 trials")
_ = PercolationExperiment.run_simulations(n = 50, t = 100, print_stats = True)

print_title(" 50 x 50 grid, 200 trials")
_ = PercolationExperiment.run_simulations(n = 50, t = 200, print_stats = True)

print_title(" 50 x 50 grid, 200 trials")
_ = PercolationExperiment.run_simulations(n = 50, t = 200, print_stats = True)

print_spacer()

print_title(" 100 x 100 grid, 50 trials")
_ = PercolationExperiment.run_simulations(n = 100, t = 50, print_stats = True)

print_title(" 100 x 100 grid, 50 trials")
_ = PercolationExperiment.run_simulations(n = 100, t = 50, print_stats = True)

print_title(" 100 x 100 grid, 100 trials")
_ = PercolationExperiment.run_simulations(n = 100, t = 100, print_stats = True)

print_title(" 100 x 100 grid, 100 trials")
_ = PercolationExperiment.run_simulations(n = 100, t = 100, print_stats = True)

print_title(" 100 x 100 grid, 200 trials")
_ = PercolationExperiment.run_simulations(n = 100, t = 200, print_stats = True)

print_title(" 100 x 100 grid, 200 trials")
_ = PercolationExperiment.run_simulations(n = 100, t = 200, print_stats = True)


print_spacer()

print_title(" 200 x 200 grid, 50 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 50, print_stats = True)

print_title(" 200 x 200 grid, 50 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 50, print_stats = True)

print_title(" 200 x 200 grid, 100 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 100, print_stats = True)

print_title(" 200 x 200 grid, 100 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 100, print_stats = True)

print_title(" 200 x 200 grid, 200 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 200, print_stats = True)

print_title(" 200 x 200 grid, 200 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 200, print_stats = True)

print_spacer()

print_title(" 200 x 200 grid, 50 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 50, print_stats = True)

print_title(" 200 x 200 grid, 50 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 50, print_stats = True)

print_title(" 200 x 200 grid, 100 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 100, print_stats = True)

print_title(" 200 x 200 grid, 100 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 100, print_stats = True)

print_title(" 200 x 200 grid, 200 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 200, print_stats = True)

print_title(" 200 x 200 grid, 200 trials")
_ = PercolationExperiment.run_simulations(n = 200, t = 200, print_stats = True)<jupyter_output>----------------------------
 20 x 20 grid, 50 trials
----------------------------
mean : 0.5901000000000001
stdev : 0.04765136998266147
low95 : 0.576891714115753
high95 : 0.6033082858842471
----------------------------
 20 x 20 grid, 100 trials
----------------------------
mean : 0.5975750000000001
stdev : 0.04579468756407133
low95 : 0.5885992412374421
high95 : 0.606550758762558
----------------------------
 20 x 20 grid, 200 trials
----------------------------
mean : 0.58715
stdev : 0.046809675587886915
low95 : 0.5806625099494659
high95 : 0.593637490050534

............................................

----------------------------
 50 x 50 grid, 50 trials
----------------------------
mean : 0.598168
stdev : 0.024744782505095694
low95 : 0.5913090962247309
high95 : 0.6050269037752691
----------------------------
 50 x 50 grid, 50 trials
----------------------------
mean : 0.589744
stdev : 0.025673715296833398
low95 : 0.5826276091916197
high95 : 0.5968603908083804
----------------------[...]
{'fraction_non_alphanumeric': 0.1697168175688692, 'fraction_numerical': 0.15006742438836448, 'mean_word_length': 3.562390158172232, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 2, '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': '657875', 'license': 'MIT', 'n_tokens_mistral': 2413, 'n_tokens_neox': 1760, 'n_words': 489, 'path': 'princeton-course/L2-percolation/n1-percolation-simulations.ipynb', 'repo_name': 'sravyapulavarthi/algorithm-playground', 'size': '5191'}