112 lines
4.2 KiB
Plaintext
112 lines
4.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'N': 3, 'T': range(0, 182), 'M': {'block_time': 30, 'i_storage': 500, 'i_in_bandwidth': 500, 'i_out_bandwidth': 140, 'j_storage': 500, 'j_in_bandwidth': 400, 'j_out_bandwidth': 100, 'p_storage': 4000, 'p_in_bandwidth': 400, 'p_out_bandwidth': 600, 'k_storage': 8000, 'k_in_bandwidth': 400, 'k_out_bandwidth': 80, 'j_avail_to_i': 0.9, 'k_avail_to_i': 0.8, 'j_avail_to_k': 0.7, 'p_avail_to_i': 0.6, 'depth': 15}}, {'N': 3, 'T': range(0, 182), 'M': {'block_time': 30, 'i_storage': 500, 'i_in_bandwidth': 1234, 'i_out_bandwidth': 140, 'j_storage': 500, 'j_in_bandwidth': 400, 'j_out_bandwidth': 100, 'p_storage': 4000, 'p_in_bandwidth': 400, 'p_out_bandwidth': 600, 'k_storage': 8000, 'k_in_bandwidth': 400, 'k_out_bandwidth': 80, 'j_avail_to_i': 0.9, 'k_avail_to_i': 0.8, 'j_avail_to_k': 0.7, 'p_avail_to_i': 0.6, 'depth': 15}}]\n",
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"[{'N': 3, 'T': range(0, 182), 'M': {'block_time': 30, 'i_storage': 500, 'i_in_bandwidth': 500, 'i_out_bandwidth': 140, 'j_storage': 500, 'j_in_bandwidth': 400, 'j_out_bandwidth': 100, 'p_storage': 4000, 'p_in_bandwidth': 400, 'p_out_bandwidth': 600, 'k_storage': 8000, 'k_in_bandwidth': 400, 'k_out_bandwidth': 80, 'j_avail_to_i': 0.9, 'k_avail_to_i': 0.8, 'j_avail_to_k': 0.7, 'p_avail_to_i': 0.6, 'depth': 15}}, {'N': 3, 'T': range(0, 182), 'M': {'block_time': 30, 'i_storage': 500, 'i_in_bandwidth': 1234, 'i_out_bandwidth': 140, 'j_storage': 500, 'j_in_bandwidth': 400, 'j_out_bandwidth': 100, 'p_storage': 4000, 'p_in_bandwidth': 400, 'p_out_bandwidth': 600, 'k_storage': 8000, 'k_in_bandwidth': 400, 'k_out_bandwidth': 80, 'j_avail_to_i': 0.9, 'k_avail_to_i': 0.8, 'j_avail_to_k': 0.7, 'p_avail_to_i': 0.6, 'depth': 15}}]\n"
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]
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}
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],
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"source": [
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"from src.sim import run\n",
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"import matplotlib.pyplot as plt\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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" __________ ____ \n",
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" ________ __ _____/ ____/ | / __ \\\n",
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" / ___/ __` / __ / / / /| | / / / /\n",
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" / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
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" \\___/\\__,_/\\__,_/\\____/_/ |_/_____/ \n",
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" by BlockScience\n",
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" \n",
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"Execution Mode: multi_proc: [<cadCAD.configuration.Configuration object at 0x00000255875CDA58>, <cadCAD.configuration.Configuration object at 0x00000255875CDA90>]\n",
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"Configurations: [<cadCAD.configuration.Configuration object at 0x00000255875CDA58>, <cadCAD.configuration.Configuration object at 0x00000255875CDA90>]\n"
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]
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}
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],
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"source": [
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"experiments = run.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'src.model.utils.Event'>: {'sending_node': 'i', 'receiving_node': 'j', 'hash_file': 2, 'size': 3, 'block_init': 4, 'reward': 5, 'block_end': None, 'storing_node': None, 'proving_node': None, 'time': None}\n"
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]
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}
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],
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"source": [
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"print(experiments.dataset[0]['event'][4])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'src.model.utils.History'>: {'active': [<src.model.utils.Event object at 0x000002558AF62E10>], 'routed': [], 'solved': []}\n"
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]
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}
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],
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"source": [
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"print(experiments.dataset[0]['history'][4])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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