1123 lines
46 KiB
Plaintext
1123 lines
46 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
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"outputs": [],
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"source": [
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"from fn import op, _, F\n",
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"from operator import add, mul\n",
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"from itertools import repeat, chain\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\n",
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"import numpy as np\n",
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"from datetime import datetime, timedelta\n",
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"from decimal import Decimal, getcontext, ROUND_DOWN\n",
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"TWOPLACES = Decimal(10) ** -2 # same as Decimal('0.01')\n",
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"import pandas as pd\n",
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"import pprint\n",
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"# getcontext().prec=None\n",
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"# getcontext().rounding = ROUND_DOWN"
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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": 48,
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"metadata": {},
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"outputs": [],
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"source": [
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"# datetime_range('2018-10-01 01:00:00', '2018-10-01 02:00:00', timedelta(minutes=15))\n",
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"\n",
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"# # datetime(2018, 10, 1, 1, 0, 0) + timedelta(seconds=30)\n",
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"# # datetime.strptime('2018-10-01 15:16:24', '%Y-%m-%d %H:%M:%S')\n",
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"\n",
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"# time_step('2018-10-01 15:16:24', t_delta=timedelta(seconds=30))"
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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": 49,
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"metadata": {},
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"outputs": [],
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"source": [
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"flatten = lambda l: [item for sublist in l for item in sublist]\n",
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"\n",
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"def flatmap(f, items):\n",
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" return list(map(f, items))\n",
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" \n",
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"def datetime_range(start, end, delta, dt_format='%Y-%m-%d %H:%M:%S'):\n",
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" reverse_head = end\n",
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" [start, end] = [datetime.strptime(x, dt_format) for x in [start, end]]\n",
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" def _datetime_range(start, end, delta):\n",
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" current = start\n",
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" while current < end:\n",
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" yield current\n",
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" current += delta\n",
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" \n",
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" reverse_tail = [dt.strftime(dt_format) for dt in _datetime_range(start, end, delta)]\n",
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" return reverse_tail + [reverse_head]\n",
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" \n",
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"def last_index(l):\n",
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" return len(l)-1\n",
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"\n",
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"def round_down(x, fp=TWOPLACES):\n",
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" return x.quantize(TWOPLACES, rounding=ROUND_DOWN)\n",
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"\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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"# Stochastic Process\n",
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"# Input RNG Seed\n",
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"def bound_norm_random(rng, low, high):\n",
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" # Add RNG Seed\n",
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" res = rng.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(rng, low, high)\n",
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" return Decimal(res)\n",
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"\n",
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"def env_proc(trigger_step, update_f):\n",
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" def env_step_trigger(trigger_step, update_f, step):\n",
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" if step == trigger_step:\n",
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" return update_f\n",
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" else:\n",
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" return lambda x: x\n",
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" return partial(env_step_trigger, trigger_step, update_f)\n",
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"\n",
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"def time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', t_delta=timedelta(seconds=1)):\n",
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" dt = datetime.strptime(dt_str, dt_format)\n",
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" t = dt + t_delta #(days=days, minutes=minutes, seconds=seconds)\n",
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" return t.strftime(dt_format)\n",
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"\n",
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"def ep_time_step(s, dt_str, dt_format='%Y-%m-%d %H:%M:%S', t_delta=timedelta(seconds=1)):\n",
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" if s['mech_step'] == 0:\n",
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" return time_step(dt_str, dt_format, t_delta)\n",
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" else: \n",
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" return dt_str\n",
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" \n",
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"def shift_time(time_seq, t_delta, dt_format='%Y-%m-%d %H:%M:%S'):\n",
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" time_seq = [\n",
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" time_step(time,dt_format,t_delta) for time in time_seq\n",
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" ]\n",
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" time_seq = time_seq[:-1]\n",
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"# x = (datetime.strptime(time_seq[0], dt_format) - t_delta).strftime(dt_format)\n",
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" return time_seq"
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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": 50,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"\n",
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"# esceptions:\n",
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"# point to line giving problems and pass a sentinel"
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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": 51,
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"metadata": {},
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"outputs": [],
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"source": [
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"seed = {\n",
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" 'z': np.random.RandomState(1),\n",
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" 'a': np.random.RandomState(2),\n",
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" 'b': np.random.RandomState(3),\n",
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" 'c': np.random.RandomState(3)\n",
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"}\n",
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"\n",
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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'] + 1\n",
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"def b2m1(step, sL, s):\n",
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" return s['s1'] + 1\n",
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"\n",
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"def b1m2(step, sL, s):\n",
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" return s['s1'] + 1\n",
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"def b2m2(step, sL, s):\n",
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" return s['s1'] + 1\n",
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"\n",
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"def b1m3(step, sL, s):\n",
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" return s['s1'] + 1\n",
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"def b2m3(step, sL, s):\n",
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" return s['s2'] + 1\n",
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"\n",
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"# UI Internal States per Mechanism\n",
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"def s1m1(step, sL, s, _input):\n",
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" s['s1'] = s['s1'] + _input\n",
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"def s2m1(step, sL, s, _input):\n",
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" s['s2'] = s['s2'] + _input\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'] + _input\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'] + _input\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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"# UI Exogenous States #per Mechanism\n",
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"proc_one_coef_A = 0.7\n",
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"proc_one_coef_B = 1.3\n",
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"def es3p1(step, sL, s, _input):\n",
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" s['s3'] = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)\n",
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"def es4p2(step, sL, s, _input):\n",
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" s['s4'] = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B) \n",
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"def es5p2(step, sL, s, _input):\n",
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" s['timestamp'] = ep_time_step(s, s['timestamp'], t_delta=timedelta(seconds=1))\n",
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"\n",
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"\n",
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"# add env process f(s) read from time es\n",
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"# funcs execute in order\n",
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"def env_a(x): \n",
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" return 10\n",
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"def env_b(x): \n",
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" return 10\n",
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" \n",
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"def what_ever(x): \n",
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" return x + 1\n",
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"\n",
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"# Genesis States \n",
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"state_dict = {\n",
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" 's1': Decimal(0.0),\n",
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" 's2': Decimal(0.0),\n",
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" 's3': Decimal(1.0),\n",
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" 's4': Decimal(1.0),\n",
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" 'timestamp': '2018-10-01 15:16:24'\n",
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"}\n",
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"\n",
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"# simulation = {\n",
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"# \"s3\": es3p1\n",
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"# }\n",
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"\n",
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"exogenous_states = {\n",
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" \"s3\": es3p1,\n",
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" \"s4\": es4p2,\n",
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" \"timestamp\": es5p2\n",
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"}\n",
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"\n",
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"# Update time at the end of each pipeline (once per 'block')\n",
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"# Enable the acceptance of user defined time\n",
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"env_processes = {\n",
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" \"s3\": env_proc('2018-10-01 15:16:25', env_a),\n",
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" \"s4\": env_proc('2018-10-01 15:16:25', env_b)\n",
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"}\n",
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" \n",
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"mechanisms = {\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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" }\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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" }\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": 52,
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"metadata": {},
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"outputs": [],
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"source": [
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"# if beh list empty, repeat 0 x n_states in list \n",
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"def generate_config(mechanisms, exogenous_states):\n",
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" es_funcs = [exogenous_states[state] for state in list(exogenous_states.keys())]\n",
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" config = list(\n",
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" map(\n",
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" lambda m: (\n",
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" list(mechanisms[m][\"states\"].values()) + es_funcs,\n",
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" list(mechanisms[m][\"behaviors\"].values())\n",
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" ), \n",
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" list(mechanisms.keys())\n",
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" )\n",
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" )\n",
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" return config\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 apply_env_proc(env_processes, state_dict, step):\n",
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" for state in state_dict.keys(): \n",
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" if state in list(env_processes.keys()):\n",
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" state_dict[state] = env_processes[state](step)(state_dict[state])\n",
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"# return state_dict\n",
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" \n",
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"def mech_step(m_step, sL, state_funcs, behavior_funcs, env_processes, t_step):\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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" # * force eval of _input withing state functions\n",
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" _input = getBehaviorInput(m_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(m_step, sL, last_mut_obj, _input)\n",
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" \n",
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" apply_env_proc(env_processes, last_mut_obj, last_mut_obj['timestamp'])\n",
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" \n",
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" last_mut_obj[\"mech_step\"], last_mut_obj[\"time_step\"] = m_step, t_step\n",
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" out_copy.append(last_mut_obj)\n",
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" \n",
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" del last_in_obj, last_mut_obj, in_copy, mutatable_copy,\n",
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" return out_copy\n",
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"\n",
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"def block_gen(states_list, configs, env_processes, t_step):\n",
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" m_step = 0\n",
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" states_list_copy = deepcopy(states_list)\n",
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" genesis_states = states_list_copy[-1]\n",
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" genesis_states['mech_step'], genesis_states['time_step'] = m_step, t_step\n",
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" states_list = [genesis_states]\n",
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" \n",
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" m_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(m_step, states_list, s_conf, b_conf, env_processes, t_step) \n",
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" m_step += 1 \n",
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" \n",
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" return states_list\n",
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"\n",
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"def pipeline(states_list, configs, env_processes, time_seq, shift_time):\n",
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" simulation_list = [states_list]\n",
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" for time_step in shift_time(time_seq):\n",
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" pipeline_run = block_gen(simulation_list[-1], configs, env_processes, time_step)\n",
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" head, *pipeline_run = pipeline_run\n",
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" simulation_list.append(pipeline_run)\n",
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" \n",
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" return simulation_list\n",
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"\n",
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"def simulation(states_list, configs, env_processes, time_seq, shift_time, runs):\n",
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" pipeline_run = []\n",
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" for run in range(runs):\n",
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" if run == 0:\n",
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" head, *tail = pipeline(states_list, configs, env_processes, time_seq, shift_time)\n",
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" head[-1]['mech_step'], head[-1]['time_step'] = 0, time_seq[0]\n",
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" simulation_list = [head] + tail\n",
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" pipeline_run += simulation_list\n",
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" else:\n",
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" transient_states_list = [pipeline_run[-1][-1]]\n",
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" head, *tail = pipeline(transient_states_list, configs, env_processes, time_seq, shift_time)\n",
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" pipeline_run += tail\n",
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" \n",
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" return pipeline_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": 53,
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"metadata": {},
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"outputs": [],
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"source": [
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"states_list = [state_dict]\n",
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"configs = generate_config(mechanisms, exogenous_states)\n",
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"t_delta = timedelta(seconds=1)\n",
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"dt_format = '%Y-%m-%d %H:%M:%S'\n",
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"time_seq = datetime_range('2018-10-01 15:16:25', '2018-10-01 15:16:30', t_delta)\n",
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"shift_time = partial(shift_time, t_delta=t_delta, dt_format=dt_format)\n",
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"# t_step_func = partial(time_step, dt_format='%Y-%m-%d %H:%M:%S', t_delta=timedelta(seconds=1))\n",
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"p = pipeline(states_list, configs, env_processes, time_seq, shift_time)\n",
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"s = simulation(states_list, configs, env_processes, time_seq, shift_time, 2)\n",
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"# x = flatten(s)\n",
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"# transient_state = p[-1][1]\n",
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"# transient_state['mech_step'], transient_state['time_step'] = 0, 0\n",
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"# head, *tail = pipeline([transient_state], configs, env_processes, range(1))\n",
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"# head\n",
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"# datetime_range('2018-10-01 15:16:24', '2018-10-01 15:16:34', timedelta(seconds=1))"
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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": 54,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
|
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"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
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|
" }\n",
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"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
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|
" }\n",
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"\n",
|
|
" .dataframe thead th {\n",
|
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
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" <th>mech_step</th>\n",
|
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" <th>s1</th>\n",
|
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" <th>s2</th>\n",
|
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" <th>s3</th>\n",
|
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" <th>s4</th>\n",
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" <th>time_step</th>\n",
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" <th>timestamp</th>\n",
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
|
|
" <th>0</th>\n",
|
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" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
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" <td>0</td>\n",
|
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>2018-10-01 15:16:25</td>\n",
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" <td>2018-10-01 15:16:24</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
|
" <th>1</th>\n",
|
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" <td>1</td>\n",
|
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" <td>2</td>\n",
|
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" <td>2</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>2018-10-01 15:16:26</td>\n",
|
|
" <td>2018-10-01 15:16:25</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>8</td>\n",
|
|
" <td>8</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>2018-10-01 15:16:26</td>\n",
|
|
" <td>2018-10-01 15:16:25</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>2018-10-01 15:16:26</td>\n",
|
|
" <td>2018-10-01 15:16:25</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>80</td>\n",
|
|
" <td>80</td>\n",
|
|
" <td>9.252129050706137114445937186</td>\n",
|
|
" <td>10.98236743425816008823403536</td>\n",
|
|
" <td>2018-10-01 15:16:27</td>\n",
|
|
" <td>2018-10-01 15:16:26</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>242</td>\n",
|
|
" <td>242</td>\n",
|
|
" <td>9.260479329378158177289328465</td>\n",
|
|
" <td>9.773134505872951353663717959</td>\n",
|
|
" <td>2018-10-01 15:16:27</td>\n",
|
|
" <td>2018-10-01 15:16:26</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>728</td>\n",
|
|
" <td>728</td>\n",
|
|
" <td>8.447309329946559323070338635</td>\n",
|
|
" <td>8.614972595444376133636202387</td>\n",
|
|
" <td>2018-10-01 15:16:27</td>\n",
|
|
" <td>2018-10-01 15:16:26</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2186</td>\n",
|
|
" <td>2186</td>\n",
|
|
" <td>8.315164547245310978888053714</td>\n",
|
|
" <td>8.437805770666267959046252347</td>\n",
|
|
" <td>2018-10-01 15:16:28</td>\n",
|
|
" <td>2018-10-01 15:16:27</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>6560</td>\n",
|
|
" <td>6560</td>\n",
|
|
" <td>8.528507099880499769035782570</td>\n",
|
|
" <td>9.691788887317723858642586596</td>\n",
|
|
" <td>2018-10-01 15:16:28</td>\n",
|
|
" <td>2018-10-01 15:16:27</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>19682</td>\n",
|
|
" <td>19682</td>\n",
|
|
" <td>7.685226186116018598361218396</td>\n",
|
|
" <td>9.921209296133436640888106066</td>\n",
|
|
" <td>2018-10-01 15:16:28</td>\n",
|
|
" <td>2018-10-01 15:16:27</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>59048</td>\n",
|
|
" <td>59048</td>\n",
|
|
" <td>7.424833841540128989990233434</td>\n",
|
|
" <td>8.905490631385261513421888970</td>\n",
|
|
" <td>2018-10-01 15:16:29</td>\n",
|
|
" <td>2018-10-01 15:16:28</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>177146</td>\n",
|
|
" <td>177146</td>\n",
|
|
" <td>7.249471123029554104994307320</td>\n",
|
|
" <td>8.270535205001335858385142896</td>\n",
|
|
" <td>2018-10-01 15:16:29</td>\n",
|
|
" <td>2018-10-01 15:16:28</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>531440</td>\n",
|
|
" <td>531440</td>\n",
|
|
" <td>6.787204963083554624043069103</td>\n",
|
|
" <td>8.787646255441291389289396948</td>\n",
|
|
" <td>2018-10-01 15:16:29</td>\n",
|
|
" <td>2018-10-01 15:16:28</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1594322</td>\n",
|
|
" <td>1594322</td>\n",
|
|
" <td>5.981148162646233553536647982</td>\n",
|
|
" <td>8.646592789945505515808943440</td>\n",
|
|
" <td>2018-10-01 15:16:30</td>\n",
|
|
" <td>2018-10-01 15:16:29</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>4782968</td>\n",
|
|
" <td>4782968</td>\n",
|
|
" <td>5.131097081865108301139205944</td>\n",
|
|
" <td>7.981811305613678677662030625</td>\n",
|
|
" <td>2018-10-01 15:16:30</td>\n",
|
|
" <td>2018-10-01 15:16:29</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>14348906</td>\n",
|
|
" <td>14348906</td>\n",
|
|
" <td>5.052337206803277494008920518</td>\n",
|
|
" <td>7.798205123642025674557723581</td>\n",
|
|
" <td>2018-10-01 15:16:30</td>\n",
|
|
" <td>2018-10-01 15:16:29</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>43046720</td>\n",
|
|
" <td>43046720</td>\n",
|
|
" <td>4.916400557718402271697528026</td>\n",
|
|
" <td>8.379215283050421575080212185</td>\n",
|
|
" <td>2018-10-01 15:16:26</td>\n",
|
|
" <td>2018-10-01 15:16:30</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>129140162</td>\n",
|
|
" <td>129140162</td>\n",
|
|
" <td>6.013429850254044301398955256</td>\n",
|
|
" <td>10.03504105054768761482285059</td>\n",
|
|
" <td>2018-10-01 15:16:26</td>\n",
|
|
" <td>2018-10-01 15:16:30</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>387420488</td>\n",
|
|
" <td>387420488</td>\n",
|
|
" <td>4.549299447945809819406984995</td>\n",
|
|
" <td>8.786558182746000129903289727</td>\n",
|
|
" <td>2018-10-01 15:16:26</td>\n",
|
|
" <td>2018-10-01 15:16:30</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1162261466</td>\n",
|
|
" <td>1162261466</td>\n",
|
|
" <td>4.600582110558934131398650528</td>\n",
|
|
" <td>8.236153319064736461682698833</td>\n",
|
|
" <td>2018-10-01 15:16:27</td>\n",
|
|
" <td>2018-10-01 15:16:31</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3486784400</td>\n",
|
|
" <td>3486784400</td>\n",
|
|
" <td>4.771008161391651222975581842</td>\n",
|
|
" <td>7.574159240304879095669686928</td>\n",
|
|
" <td>2018-10-01 15:16:27</td>\n",
|
|
" <td>2018-10-01 15:16:31</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>21</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>10460353202</td>\n",
|
|
" <td>10460353202</td>\n",
|
|
" <td>5.419690586923192171796198305</td>\n",
|
|
" <td>5.741907123385151994511077932</td>\n",
|
|
" <td>2018-10-01 15:16:27</td>\n",
|
|
" <td>2018-10-01 15:16:31</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>22</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>31381059608</td>\n",
|
|
" <td>31381059608</td>\n",
|
|
" <td>5.691681664880417077015819078</td>\n",
|
|
" <td>5.211474324395010139047971223</td>\n",
|
|
" <td>2018-10-01 15:16:28</td>\n",
|
|
" <td>2018-10-01 15:16:32</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>23</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>94143178826</td>\n",
|
|
" <td>94143178826</td>\n",
|
|
" <td>5.211182098947496199476580803</td>\n",
|
|
" <td>4.677884100494724327605499943</td>\n",
|
|
" <td>2018-10-01 15:16:28</td>\n",
|
|
" <td>2018-10-01 15:16:32</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>24</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>282429536480</td>\n",
|
|
" <td>282429536480</td>\n",
|
|
" <td>5.211187185828099975453746034</td>\n",
|
|
" <td>5.203667962846090136827830871</td>\n",
|
|
" <td>2018-10-01 15:16:28</td>\n",
|
|
" <td>2018-10-01 15:16:32</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>847288609442</td>\n",
|
|
" <td>847288609442</td>\n",
|
|
" <td>5.493817263246493315310649359</td>\n",
|
|
" <td>5.135024176139143498787810469</td>\n",
|
|
" <td>2018-10-01 15:16:29</td>\n",
|
|
" <td>2018-10-01 15:16:33</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>26</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2541865828328</td>\n",
|
|
" <td>2541865828328</td>\n",
|
|
" <td>5.321581588765326386518278255</td>\n",
|
|
" <td>4.301463175225263107024383580</td>\n",
|
|
" <td>2018-10-01 15:16:29</td>\n",
|
|
" <td>2018-10-01 15:16:33</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>27</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>7625597484986</td>\n",
|
|
" <td>7625597484986</td>\n",
|
|
" <td>5.731881775768539828310868603</td>\n",
|
|
" <td>4.579628239651492432231803488</td>\n",
|
|
" <td>2018-10-01 15:16:29</td>\n",
|
|
" <td>2018-10-01 15:16:33</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>28</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>22876792454960</td>\n",
|
|
" <td>22876792454960</td>\n",
|
|
" <td>4.661114297930784123663525967</td>\n",
|
|
" <td>4.416469476559052658960219284</td>\n",
|
|
" <td>2018-10-01 15:16:30</td>\n",
|
|
" <td>2018-10-01 15:16:34</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>29</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>68630377364882</td>\n",
|
|
" <td>68630377364882</td>\n",
|
|
" <td>5.468039257826792700899090619</td>\n",
|
|
" <td>3.646616558254807724187351899</td>\n",
|
|
" <td>2018-10-01 15:16:30</td>\n",
|
|
" <td>2018-10-01 15:16:34</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>30</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>205891132094648</td>\n",
|
|
" <td>205891132094648</td>\n",
|
|
" <td>6.270571355793247137773251590</td>\n",
|
|
" <td>3.429041311970567806939288123</td>\n",
|
|
" <td>2018-10-01 15:16:30</td>\n",
|
|
" <td>2018-10-01 15:16:34</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" mech_step s1 s2 \\\n",
|
|
"0 0 0 0 \n",
|
|
"1 1 2 2 \n",
|
|
"2 2 8 8 \n",
|
|
"3 3 26 26 \n",
|
|
"4 1 80 80 \n",
|
|
"5 2 242 242 \n",
|
|
"6 3 728 728 \n",
|
|
"7 1 2186 2186 \n",
|
|
"8 2 6560 6560 \n",
|
|
"9 3 19682 19682 \n",
|
|
"10 1 59048 59048 \n",
|
|
"11 2 177146 177146 \n",
|
|
"12 3 531440 531440 \n",
|
|
"13 1 1594322 1594322 \n",
|
|
"14 2 4782968 4782968 \n",
|
|
"15 3 14348906 14348906 \n",
|
|
"16 1 43046720 43046720 \n",
|
|
"17 2 129140162 129140162 \n",
|
|
"18 3 387420488 387420488 \n",
|
|
"19 1 1162261466 1162261466 \n",
|
|
"20 2 3486784400 3486784400 \n",
|
|
"21 3 10460353202 10460353202 \n",
|
|
"22 1 31381059608 31381059608 \n",
|
|
"23 2 94143178826 94143178826 \n",
|
|
"24 3 282429536480 282429536480 \n",
|
|
"25 1 847288609442 847288609442 \n",
|
|
"26 2 2541865828328 2541865828328 \n",
|
|
"27 3 7625597484986 7625597484986 \n",
|
|
"28 1 22876792454960 22876792454960 \n",
|
|
"29 2 68630377364882 68630377364882 \n",
|
|
"30 3 205891132094648 205891132094648 \n",
|
|
"\n",
|
|
" s3 s4 \\\n",
|
|
"0 1 1 \n",
|
|
"1 10 10 \n",
|
|
"2 10 10 \n",
|
|
"3 10 10 \n",
|
|
"4 9.252129050706137114445937186 10.98236743425816008823403536 \n",
|
|
"5 9.260479329378158177289328465 9.773134505872951353663717959 \n",
|
|
"6 8.447309329946559323070338635 8.614972595444376133636202387 \n",
|
|
"7 8.315164547245310978888053714 8.437805770666267959046252347 \n",
|
|
"8 8.528507099880499769035782570 9.691788887317723858642586596 \n",
|
|
"9 7.685226186116018598361218396 9.921209296133436640888106066 \n",
|
|
"10 7.424833841540128989990233434 8.905490631385261513421888970 \n",
|
|
"11 7.249471123029554104994307320 8.270535205001335858385142896 \n",
|
|
"12 6.787204963083554624043069103 8.787646255441291389289396948 \n",
|
|
"13 5.981148162646233553536647982 8.646592789945505515808943440 \n",
|
|
"14 5.131097081865108301139205944 7.981811305613678677662030625 \n",
|
|
"15 5.052337206803277494008920518 7.798205123642025674557723581 \n",
|
|
"16 4.916400557718402271697528026 8.379215283050421575080212185 \n",
|
|
"17 6.013429850254044301398955256 10.03504105054768761482285059 \n",
|
|
"18 4.549299447945809819406984995 8.786558182746000129903289727 \n",
|
|
"19 4.600582110558934131398650528 8.236153319064736461682698833 \n",
|
|
"20 4.771008161391651222975581842 7.574159240304879095669686928 \n",
|
|
"21 5.419690586923192171796198305 5.741907123385151994511077932 \n",
|
|
"22 5.691681664880417077015819078 5.211474324395010139047971223 \n",
|
|
"23 5.211182098947496199476580803 4.677884100494724327605499943 \n",
|
|
"24 5.211187185828099975453746034 5.203667962846090136827830871 \n",
|
|
"25 5.493817263246493315310649359 5.135024176139143498787810469 \n",
|
|
"26 5.321581588765326386518278255 4.301463175225263107024383580 \n",
|
|
"27 5.731881775768539828310868603 4.579628239651492432231803488 \n",
|
|
"28 4.661114297930784123663525967 4.416469476559052658960219284 \n",
|
|
"29 5.468039257826792700899090619 3.646616558254807724187351899 \n",
|
|
"30 6.270571355793247137773251590 3.429041311970567806939288123 \n",
|
|
"\n",
|
|
" time_step timestamp \n",
|
|
"0 2018-10-01 15:16:25 2018-10-01 15:16:24 \n",
|
|
"1 2018-10-01 15:16:26 2018-10-01 15:16:25 \n",
|
|
"2 2018-10-01 15:16:26 2018-10-01 15:16:25 \n",
|
|
"3 2018-10-01 15:16:26 2018-10-01 15:16:25 \n",
|
|
"4 2018-10-01 15:16:27 2018-10-01 15:16:26 \n",
|
|
"5 2018-10-01 15:16:27 2018-10-01 15:16:26 \n",
|
|
"6 2018-10-01 15:16:27 2018-10-01 15:16:26 \n",
|
|
"7 2018-10-01 15:16:28 2018-10-01 15:16:27 \n",
|
|
"8 2018-10-01 15:16:28 2018-10-01 15:16:27 \n",
|
|
"9 2018-10-01 15:16:28 2018-10-01 15:16:27 \n",
|
|
"10 2018-10-01 15:16:29 2018-10-01 15:16:28 \n",
|
|
"11 2018-10-01 15:16:29 2018-10-01 15:16:28 \n",
|
|
"12 2018-10-01 15:16:29 2018-10-01 15:16:28 \n",
|
|
"13 2018-10-01 15:16:30 2018-10-01 15:16:29 \n",
|
|
"14 2018-10-01 15:16:30 2018-10-01 15:16:29 \n",
|
|
"15 2018-10-01 15:16:30 2018-10-01 15:16:29 \n",
|
|
"16 2018-10-01 15:16:26 2018-10-01 15:16:30 \n",
|
|
"17 2018-10-01 15:16:26 2018-10-01 15:16:30 \n",
|
|
"18 2018-10-01 15:16:26 2018-10-01 15:16:30 \n",
|
|
"19 2018-10-01 15:16:27 2018-10-01 15:16:31 \n",
|
|
"20 2018-10-01 15:16:27 2018-10-01 15:16:31 \n",
|
|
"21 2018-10-01 15:16:27 2018-10-01 15:16:31 \n",
|
|
"22 2018-10-01 15:16:28 2018-10-01 15:16:32 \n",
|
|
"23 2018-10-01 15:16:28 2018-10-01 15:16:32 \n",
|
|
"24 2018-10-01 15:16:28 2018-10-01 15:16:32 \n",
|
|
"25 2018-10-01 15:16:29 2018-10-01 15:16:33 \n",
|
|
"26 2018-10-01 15:16:29 2018-10-01 15:16:33 \n",
|
|
"27 2018-10-01 15:16:29 2018-10-01 15:16:33 \n",
|
|
"28 2018-10-01 15:16:30 2018-10-01 15:16:34 \n",
|
|
"29 2018-10-01 15:16:30 2018-10-01 15:16:34 \n",
|
|
"30 2018-10-01 15:16:30 2018-10-01 15:16:34 "
|
|
]
|
|
},
|
|
"execution_count": 54,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pd.DataFrame(flatten(s))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# pd.DataFrame(flatten(p))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# flatten(pipeline(states_list, configs, env_processes, range(10)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "TypeError",
|
|
"evalue": "strptime() argument 1 must be str, not int",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m--------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m<ipython-input-11-67fd24ba5424>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mconfigs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmechanisms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexogenous_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# type(pipeline(states_list, configs, env_processes, range(10)))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstates_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
"\u001b[0;32m<ipython-input-6-392390d50a95>\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(states_list, configs, env_processes, time_seq)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0msimulation_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mstates_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0mshift_time_partial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshift_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_delta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mt_delta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%Y-%m-%d %H:%M:%S'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0mtime_seq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshift_time_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime_seq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtime_step\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtime_seq\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mpipeline_run\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblock_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m<ipython-input-3-53307d3b1d89>\u001b[0m in \u001b[0;36mshift_time\u001b[0;34m(time_seq, t_delta, dt_format)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshift_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_delta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%Y-%m-%d %H:%M:%S'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m time_seq = [\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0mtime_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdt_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtime_seq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 57\u001b[0m ]\n\u001b[1;32m 58\u001b[0m \u001b[0mtime_seq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime_seq\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m<ipython-input-3-53307d3b1d89>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshift_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_delta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%Y-%m-%d %H:%M:%S'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m time_seq = [\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0mtime_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdt_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtime_seq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 57\u001b[0m ]\n\u001b[1;32m 58\u001b[0m \u001b[0mtime_seq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime_seq\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m<ipython-input-3-53307d3b1d89>\u001b[0m in \u001b[0;36mtime_step\u001b[0;34m(dt_str, dt_format, t_delta)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtime_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%Y-%m-%d %H:%M:%S'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_delta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseconds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdt\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mt_delta\u001b[0m \u001b[0;31m#(days=days, minutes=minutes, seconds=seconds)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;31mTypeError\u001b[0m: strptime() argument 1 must be str, not int"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"states_list = [state_dict]\n",
|
|
"configs = generate_config(mechanisms, exogenous_states)\n",
|
|
"# type(pipeline(states_list, configs, env_processes, range(10)))\n",
|
|
"pd.DataFrame(flatten(pipeline(states_list, configs, env_processes, range(10))))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"list(range(2))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Decimal('240.0').quantize(Decimal('0.01'), ROUND_DOWN)\n",
|
|
"# round(Decimal('240.01'), 2)\n",
|
|
"state_dict = {'s1': Decimal('3323'), 's2': Decimal('2572'), 's3': Decimal('2.657'), 's4': Decimal('7.914'), 'timestamp': '2018-10-01 15:16:26', 'mech_step': 1, 'time_step': 2}\n",
|
|
"state_dict = {k: v.quantize(TWOPLACES, ROUND_DOWN) for k, v in state_dict.items() if type(v) == Decimal}\n",
|
|
"state_dict.update(non_numeric)\n",
|
|
"state_dict"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"states_list = [state_dict]\n",
|
|
"states_list\n",
|
|
"# {'s1': 8.742503764992283e+170,\n",
|
|
"# 's2': 1.119720780594405e+171,\n",
|
|
"# 's3': 4.251811100418906,\n",
|
|
"# 's4': 6.92728042985628,\n",
|
|
"# 'timestamp': '2018-10-01 15:16:31',\n",
|
|
"# 'mech_step': 3,\n",
|
|
"# 'time_step': 7}\n",
|
|
"# {'s1': Decimal('2.221384236472411425810660220E+330'),\n",
|
|
"# 's2': Decimal('2.845096899831187764579913612E+330'),\n",
|
|
"# 's3': Decimal('3.622519693611211445932083740'),\n",
|
|
"# 's4': Decimal('5.161487081026327858663750787'),\n",
|
|
"# 'timestamp': '2018-10-01 15:16:32',\n",
|
|
"# 'mech_step': 3,\n",
|
|
"# 'time_step': 8}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from decimal import Decimal, MAX_EMAX\n",
|
|
"# Decimal(2.5347751615574597e+167)**2\n",
|
|
"MAX_EMAX**5"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"1000 * 1000"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from datetime import datetime, timedelta\n",
|
|
"# from datetime import timedelta \n",
|
|
"# st = datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n",
|
|
"st = datetime.now()\n",
|
|
"print(st)\n",
|
|
"x = st + timedelta(seconds=60)\n",
|
|
"print(x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n",
|
|
"datetime_object = datetime.strptime(s, '%Y-%m-%d %H:%M:%S')\n",
|
|
"datetime_object"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"datetime.fromtimestamp()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from datetime import datetime, timedelta\n",
|
|
"\n",
|
|
"# def time_step_func(year, month, day, hour, minute, second):\n",
|
|
"a = datetime(2016, 11, 15, 9, 59, 25)#.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
|
|
"b = a + timedelta(seconds=60) # days, seconds, then other fields.\n",
|
|
"print(a)\n",
|
|
"print(b)\n",
|
|
"st = datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n",
|
|
"def time_step_func(dt_str, fromat_str='%Y-%m-%d %H:%M:%S', days=0, minutes=0, seconds=1):\n",
|
|
" dt = datetime.strptime(dt_str, fromat_str)\n",
|
|
" t = dt + timedelta(days=days, minutes=minutes, seconds=seconds)\n",
|
|
" return t.strftime(fromat_str)\n",
|
|
"\n",
|
|
"time_step_func('2018-10-01 15:16:24')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def time_step_func(x, y):\n",
|
|
" return x + y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for m in M: #M is the parameter sweep array\n",
|
|
" # for Pairwise, run below separately\n",
|
|
" for n in N: #N is the total number of Montecarlo runs\n",
|
|
" for t in T: #T[len(T)]-T[0] is the duration of each simulation\n",
|
|
" # Pipeline\n",
|
|
" for b in Behaviors:\n",
|
|
" for M in Mechanisms:\n",
|
|
" for s in States:\n",
|
|
" update(s)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"[{'s1': 2}] + [{'s1': 3}]\n",
|
|
"list(zip([block_gen] * 2, range(2)))\n",
|
|
"len([0,1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"states_list = [state_dict]\n",
|
|
"configs = generate_config(mechanisms, exogenous_states)\n",
|
|
"def step_func(x, y):\n",
|
|
" return x + y\n",
|
|
"pipeline(states_list, configs, env_processes, step_func)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# def s3step1(step, sL, s, _input):\n",
|
|
"# s['s3'] = s['s3'] \n",
|
|
"# # def s3step2(step, sL, s, _input):\n",
|
|
"# s['s3'] = s['s3'] + 1\n",
|
|
"# def s3step3(step, sL, s, _input):\n",
|
|
"# s['s3'] = s['s3'] * bound_norm_random(proc_one_coef_A, proc_one_coef_B)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# 15 + 28.933333333333334 + 4"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# def multiply(x,y,z):\n",
|
|
"# return x * y * z\n",
|
|
"\n",
|
|
"# # create a new function that multiplies by 2\n",
|
|
"# dbl = partial(partial(multiply,2), multiply, 2)\n",
|
|
"# print(dbl(2))"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.6.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|