307 lines
7.8 KiB
Plaintext
307 lines
7.8 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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"source": [
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"from fn import op, _\n",
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"from itertools import repeat\n",
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"from functools import reduce\n",
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"# from objproxies import LazyProxy\n",
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"import json\n",
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"from copy import deepcopy, copy\n",
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"from pipetools import pipe\n",
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"from functools import partial"
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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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"source": [
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"def last_index(l):\n",
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" return len(l)-1\n",
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"def retrieve_state(l, offset):\n",
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" return l[last_index(l) + offset + 1]\n",
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"def bound_norm_random(low, high):\n",
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" res = np.random.normal((high+low)/2,(high-low)/6)\n",
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" if (res<low or res>high):\n",
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" res = bound_norm_random(low, high)\n",
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" return res"
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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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"source": [
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"# def pipeline(*steps):\n",
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"# return reduce(lambda x, y: y(x), list(steps))\n",
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"# def compose(*funcs):\n",
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"# return lambda x: reduce(lambda f, g: g(f), list(funcs), x)"
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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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"source": [
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"# ToDo:\n",
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"# Handle case where Mechanisms have no input. Perhaps the sentinel value of 0\n",
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"# Mock Simulation SimCADUI Lucid chart "
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# UI Behaviors per Mechanism\n",
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"def b1m1(step, sL, s):\n",
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" return s['s1']\n",
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"def b2m1(step, sL, s):\n",
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" return s['s1'] * s['s2']\n",
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"\n",
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"def b1m2(step, sL, s):\n",
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" return s['s1']\n",
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"def b2m2(step, sL, s):\n",
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" return s['s1'] / s['s2']\n",
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"\n",
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"def b1m3(step, sL, s):\n",
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" return s['s1']\n",
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"def b2m3(step, sL, s):\n",
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" ps = retrieve_state(sL, -3)\n",
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" return ps['s2']\n",
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"\n",
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"# UI States per Mechanism\n",
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"def s1m1(step, sL, s, _input):\n",
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" s['s1'] = s['s1']**2 + _input\n",
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"def s2m1(step, sL, s, _input):\n",
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" s['s2'] = s['s2'] + 1 + _input\n",
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"\n",
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"\n",
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"def s3m1(step, sL, s, _input):\n",
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" s['s3'] = s['s3']\n",
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"\n",
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"def s1m2(step, sL, s, _input):\n",
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" s['s1'] = s['s1'] + _input\n",
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"def s2m2(step, sL, s, _input):\n",
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" s['s2'] = s['s2']\n",
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" \n",
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"def s3m2(step, sL, s, _input):\n",
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" s['s3'] = s['s3'] + 1\n",
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" \n",
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"def s1m3(step, sL, s, _input):\n",
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" s['s1'] = s['s1']\n",
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"def s2m3(step, sL, s, _input):\n",
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" s['s2'] = s['s2'] + _input\n",
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" \n",
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"state_dict = {\n",
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" 's1': 2,\n",
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" 's2': 4,\n",
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" 's3': 300\n",
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"}\n",
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" \n",
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"j = {\n",
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" \"m1\": {\n",
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" \"behaviors\": {\n",
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" \"b1\": b1m1,\n",
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" \"b2\": b2m1\n",
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" },\n",
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" \"states\": {\n",
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" \"s1\": s1m1,\n",
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" \"s2\": s2m1,\n",
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" \"s3\": s3m1\n",
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" }\n",
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" },\n",
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" \"m2\": {\n",
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" \"behaviors\": {\n",
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" \"b1\": b1m2,\n",
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" \"b2\": b2m2\n",
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" },\n",
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" \"states\": {\n",
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" \"s1\": s1m2,\n",
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" \"s2\": s2m2,\n",
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" \"s3\": s3m2\n",
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" }\n",
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" },\n",
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" \"m3\": {\n",
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" \"behaviors\": {\n",
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" \"b1\": b1m3,\n",
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" \"b2\": b2m3\n",
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" },\n",
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" \"states\": {\n",
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" \"s1\": s1m3,\n",
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" \"s2\": s2m3\n",
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" }\n",
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" }\n",
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"}"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def generate_configs(j):\n",
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" return list(\n",
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" map(\n",
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" lambda x: (\n",
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" list(j[x][\"states\"].values()),\n",
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" list(j[x][\"behaviors\"].values())\n",
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" ), \n",
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" j.keys()\n",
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" )\n",
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" )\n",
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"\n",
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"# partials = list(map(lambda f: partial(f, step, sL), funcs))\n",
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"def getColResults(step, sL, s, funcs):\n",
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" return list(map(lambda f: f(step, sL, s), funcs))\n",
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"\n",
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"def getBehaviorInput(step, sL, s, funcs): \n",
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" return op.foldr(_ + _)(getColResults(step, sL, s, funcs))\n",
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"\n",
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"def mech_step(step, sL, state_funcs, behavior_funcs):\n",
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" # Purge Memory accociated with certain objs\n",
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" # Truncate reffs\n",
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" in_copy, mutatable_copy, out_copy = deepcopy(sL), deepcopy(sL), deepcopy(sL)\n",
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" last_in_obj, last_mut_obj = in_copy[-1], mutatable_copy[-1]\n",
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" \n",
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" _input = getBehaviorInput(step, sL, last_in_obj, behavior_funcs)\n",
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"\n",
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" for f in state_funcs:\n",
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" f(step, sL, last_mut_obj, _input)\n",
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" \n",
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" last_mut_obj[\"step\"] = step\n",
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" out_copy.append(last_mut_obj) \n",
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" return out_copy\n",
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"\n",
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"# isolate ALL step logic within 'step_func'\n",
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"def pipeline(states_list, configs, step_func):\n",
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" step = 0\n",
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" genesis_states = states_list[-1]\n",
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" genesis_states['step'] = step\n",
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" states_list = [genesis_states]\n",
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" \n",
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" step = step_func(step, 1)\n",
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" for config in configs:\n",
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" s_conf, b_conf = config[0], config[1]\n",
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" states_list = mech_step(step, states_list, s_conf, b_conf)\n",
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" \n",
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" step = step_func(step, 1)\n",
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" return states_list"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'s1': 2, 's2': 4, 's3': 300}]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"states_list = [state_dict]\n",
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"states_list"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'s1': 2, 's2': 4, 's3': 300, 'step': 0},\n",
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" {'s1': 14, 's2': 15, 's3': 300, 'step': 1},\n",
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" {'s1': 28.933333333333334, 's2': 15, 's3': 301, 'step': 2},\n",
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" {'s1': 28.933333333333334, 's2': 47.93333333333334, 's3': 301, 'step': 3}]"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"configs = generate_configs(j)\n",
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"def step_func(x, y):\n",
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" return x + y\n",
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"pipeline(states_list, configs, step_func)\n",
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"# categorize by simulation"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"47.93333333333334"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"15 + 28.933333333333334 + 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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# def multiply(x,y,z):\n",
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"# return x * y * z\n",
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"\n",
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"# # create a new function that multiplies by 2\n",
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"# dbl = partial(partial(multiply,2), multiply, 2)\n",
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"# print(dbl(2))"
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]
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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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