<jupyter_start><jupyter_text>Parameters Black Scholes    - T = 1    - r = 0     Network Parameters    - N = [15, 25, 50, 75, 100, 150, 200, 300]    -  = n*p = [0 .. 1.8]    - M => ER(N,p)<jupyter_code>dview.block=False
in_0_1 = [(15,0.1, el) for el in pList(15,8)]#, (25,0.1,pList(25,8)), (50,0.1,pList(50,6)), (75, 0.1, pList(75,6))]#, (100, 0.1, pList(100,4)), (200, 0.1, pList(200,2))]
async_res = []
ids = []
for el in in_0_1:
    b_re = dview.apply_async(run_sim, *el)
    async_res.append(b_re)
    ids.extend(b_re.msg_ids)

#re_test[0].get(1)

a = rc.get_result(ids, owner=False).wait_interactive()
b_re.get_dict()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.21505376344086022, 'fraction_numerical': 0.11827956989247312, 'mean_word_length': 3.9022556390977443, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 6, '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': '349638', 'license': 'MIT', 'n_tokens_mistral': 358, 'n_tokens_neox': 312, 'n_words': 57, 'path': 'scripts/.ipynb_checkpoints/RunER-checkpoint.ipynb', 'repo_name': 'Atomtomate/sys_risk', 'size': '651'}