init packaging

engine/configProcessor.py

@@ -0,0 +1,13 @@
+# if beh list empty, repeat 0 x n_states in list
+def generate_config(mechanisms, exogenous_states):
+    es_funcs = [exogenous_states[state] for state in list(exogenous_states.keys())]
+    config = list(
+        map(
+            lambda m: (
+                list(mechanisms[m]["states"].values()) + es_funcs,
+                list(mechanisms[m]["behaviors"].values())
+            ),
+            list(mechanisms.keys())
+        )
+    )
+    return config

engine/mechanismExecutor.py

@@ -0,0 +1,79 @@
+from copy import deepcopy
+from fn import op, _
+
+def getColResults(step, sL, s, funcs):
+    return list(map(lambda f: f(step, sL, s), funcs))
+
+
+def getBehaviorInput(step, sL, s, funcs):
+    return op.foldr(_ + _)(getColResults(step, sL, s, funcs))
+
+
+def apply_env_proc(env_processes, state_dict, step):
+    for state in state_dict.keys():
+        if state in list(env_processes.keys()):
+            state_dict[state] = env_processes[state](step)(state_dict[state])
+
+
+def mech_step(m_step, sL, state_funcs, behavior_funcs, env_processes, t_step):
+    in_copy, mutatable_copy, out_copy = deepcopy(sL), deepcopy(sL), deepcopy(sL)
+    last_in_obj, last_mut_obj = in_copy[-1], mutatable_copy[-1]
+
+    _input = getBehaviorInput(m_step, sL, last_in_obj, behavior_funcs)
+
+    for f in state_funcs:
+        f(m_step, sL, last_mut_obj, _input)
+
+    apply_env_proc(env_processes, last_mut_obj, last_mut_obj['timestamp'])
+
+    last_mut_obj["mech_step"], last_mut_obj["time_step"] = m_step, t_step
+    out_copy.append(last_mut_obj)
+
+    del last_in_obj, last_mut_obj, in_copy, mutatable_copy,
+
+    return out_copy
+
+
+def block_gen(states_list, configs, env_processes, t_step):
+    m_step = 0
+    states_list_copy = deepcopy(states_list)
+    genesis_states = states_list_copy[-1]
+    genesis_states['mech_step'], genesis_states['time_step'] = m_step, t_step
+    states_list = [genesis_states]
+
+    m_step += 1
+    for config in configs:
+        s_conf, b_conf = config[0], config[1]
+        states_list = mech_step(m_step, states_list, s_conf, b_conf, env_processes, t_step)
+        m_step += 1
+
+    t_step += 1
+
+    return states_list
+
+
+def pipeline(states_list, configs, env_processes, time_seq):
+    time_seq = [x + 1 for x in time_seq]
+    simulation_list = [states_list]
+    for time_step in time_seq:
+        pipeline_run = block_gen(simulation_list[-1], configs, env_processes, time_step)
+        head, *pipeline_run = pipeline_run
+        simulation_list.append(pipeline_run)
+
+    return simulation_list
+
+
+def simulation(states_list, configs, env_processes, time_seq, runs):
+    pipeline_run = []
+    for run in range(runs):
+        if run == 0:
+            head, *tail = pipeline(states_list, configs, env_processes, time_seq)
+            head[-1]['mech_step'], head[-1]['time_step'] = 0, 0
+            simulation_list = [head] + tail
+            pipeline_run += simulation_list
+        else:
+            transient_states_list = [pipeline_run[-1][-1]]
+            head, *tail = pipeline(transient_states_list, configs, env_processes, time_seq)
+            pipeline_run += tail
+
+    return pipeline_run

engine/run.py

@@ -0,0 +1,16 @@
+from ui.config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
+from engine.configProcessor import generate_config
+from engine.mechanismExecutor import simulation
+from engine.utils import flatten
+
+from tabulate import tabulate
+import pandas as pd
+
+def main():
+    states_list = [state_dict]
+    configs = generate_config(mechanisms, exogenous_states)
+    # p = pipeline(states_list, configs, env_processes, range(10))
+    N = sim_config['N']
+    s = simulation(states_list, configs, env_processes, range(5), N)
+    result = pd.DataFrame(flatten(s))
+    print(tabulate(result, headers='keys', tablefmt='psql'))

engine/utils.py

@@ -0,0 +1,54 @@
+from datetime import datetime, timedelta
+from decimal import Decimal
+from functools import partial
+
+flatten = lambda l: [item for sublist in l for item in sublist]
+
+def flatmap(f, items):
+        return list(map(f, items))
+
+
+def datetime_range(start, end, delta, dt_format='%Y-%m-%d %H:%M:%S'):
+    reverse_head = end
+    [start, end] = [datetime.strptime(x, dt_format) for x in [start, end]]
+
+    def _datetime_range(start, end, delta):
+        current = start
+        while current < end:
+            yield current
+            current += delta
+
+    reverse_tail = [dt.strftime(dt_format) for dt in _datetime_range(start, end, delta)]
+    return reverse_tail + [reverse_head]
+
+def last_index(l):
+    return len(l)-1
+
+def retrieve_state(l, offset):
+    return l[last_index(l) + offset + 1]
+
+def bound_norm_random(rng, low, high):
+    # Add RNG Seed
+    res = rng.normal((high+low)/2,(high-low)/6)
+    if (res<low or res>high):
+        res = bound_norm_random(rng, low, high)
+    return Decimal(res)
+
+def env_proc(trigger_step, update_f):
+    def env_step_trigger(trigger_step, update_f, step):
+        if step == trigger_step:
+            return update_f
+        else:
+            return lambda x: x
+    return partial(env_step_trigger, trigger_step, update_f)
+
+def time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', days=0, minutes=0, seconds=30):
+    dt = datetime.strptime(dt_str, dt_format)
+    t = dt + timedelta(days=days, minutes=minutes, seconds=seconds)
+    return t.strftime(dt_format)
+
+def ep_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', days=0, minutes=0, seconds=1):
+    if s['mech_step'] == 0:
+        return time_step(dt_str, fromat_str, days, minutes, seconds)
+    else:
+        return dt_str

notebooks/CAD_Engine.ipynb

@@ -29,59 +29,6 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['2018-10-01 01:00:00',\n",
-       " '2018-10-01 01:15:00',\n",
-       " '2018-10-01 01:30:00',\n",
-       " '2018-10-01 01:45:00',\n",
-       " '2018-10-01 02:00:00']"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def datetime_range(start, end, delta, dt_format='%Y-%m-%d %H:%M:%S'):\n",
-    "    reverse_head = end\n",
-    "    [start, end] = [datetime.strptime(x, dt_format) for x in [start, end]]\n",
-    "    def _datetime_range(start, end, delta):\n",
-    "        current = start\n",
-    "        while current < end:\n",
-    "            yield current\n",
-    "            current += delta\n",
-    "            \n",
-    "    reverse_tail = [dt.strftime(dt_format) for dt in _datetime_range(start, end, delta)]\n",
-    "    return reverse_tail + [reverse_head]\n",
-    "\n",
-    "\n",
-    "datetime_range('2018-10-01 01:00:00', '2018-10-01 02:00:00', timedelta(minutes=15))\n",
-    "\n",
-    "# # datetime(2018, 10, 1, 1, 0, 0) + timedelta(seconds=30)\n",
-    "# # datetime.strptime('2018-10-01 15:16:24', '%Y-%m-%d %H:%M:%S')\n",
-    "\n",
-    "# def time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', days=0, minutes=0, seconds=30):\n",
-    "#     dt = datetime.strptime(dt_str, dt_format)\n",
-    "#     t = dt + timedelta(days=days, minutes=minutes, seconds=seconds)\n",
-    "#     return t.strftime(dt_format)\n",
-    "\n",
-    "# def es_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', days=0, minutes=0, seconds=1):\n",
-    "#     if s['mech_step'] == 0:\n",
-    "#         return time_step(dt_str, fromat_str, days, minutes, seconds)\n",
-    "#     else: \n",
-    "#         return dt_str\n",
-    "\n",
-    "# time_step('2018-10-01 15:16:24')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
    "outputs": [],
    "source": [
     "flatten = lambda l: [item for sublist in l for item in sublist]\n",
@@ -106,8 +53,7 @@
     "\n",
     "def retrieve_state(l, offset):\n",
     "    return l[last_index(l) + offset + 1]\n",
-    "# Stochastic Process\n",
+    "\n",
-    "# Input RNG Seed\n",
     "def bound_norm_random(rng, low, high):\n",
     "    # Add RNG Seed\n",
     "    res = rng.normal((high+low)/2,(high-low)/6)\n",
@@ -118,10 +64,8 @@
     "def env_proc(trigger_step, update_f):\n",
     "    def env_step_trigger(trigger_step, update_f, step):\n",
     "        if step == trigger_step:\n",
-    "#             print('trigered: '+step)\n",
     "            return update_f\n",
     "        else:\n",
-    "#             print('NOT trigered: '+step)\n",
     "            return lambda x: x\n",
     "    return partial(env_step_trigger, trigger_step, update_f)\n",
     "\n",
@@ -137,28 +81,22 @@
     "        return dt_str\n",
     "\n",
     "def round_down(x, fp=TWOPLACES):\n",
-    "    return x.quantize(TWOPLACES, rounding=ROUND_DOWN)\n",
+    "    return x.quantize(TWOPLACES, rounding=ROUND_DOWN)"
-    "    \n",
-    "# def round_down(f, fp=TWOPLACES):\n",
-    "#     return (f).quantize(fp, ROUND_DOWN)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
-    "\n",
     "# esceptions:\n",
     "# point to line giving problems and pass a sentinel"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,14 +149,10 @@
     "def es5p2(step, sL, s, _input):\n",
     "    s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)\n",
     "\n",
-    "\n",
-    "# add env process f(s) read from time es\n",
-    "# funcs execute in order\n",
     "def env_a(x): \n",
     "    return 10\n",
     "def env_b(x): \n",
     "    return 10\n",
-    "    \n",
     "def what_ever(x): \n",
     "    return x + 1\n",
     "\n",
@@ -231,9 +165,9 @@
     "    'timestamp': '2018-10-01 15:16:24'\n",
     "}\n",
     "\n",
-    "# simulation = {\n",
+    "sim_config = {\n",
-    "#     \"s3\": es3p1\n",
+    "    \"N\": 2\n",
-    "# }\n",
+    "}\n",
     "\n",
     "exogenous_states = {\n",
     "    \"s3\": es3p1,\n",
@@ -241,8 +175,6 @@
     "    \"timestamp\": es5p2\n",
     "}\n",
     "\n",
-    "# Update time at the end of each pipeline (once per 'block')\n",
-    "# Enable the acceptance of user defined time\n",
     "env_processes = {\n",
     "    \"s3\": env_proc('2018-10-01 15:16:25', env_a),\n",
     "    \"s4\": env_proc('2018-10-01 15:16:25', env_b)\n",
@@ -284,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -316,16 +248,11 @@
     "#     return state_dict\n",
     "            \n",
     "def mech_step(m_step, sL, state_funcs, behavior_funcs, env_processes, t_step):\n",
-    "    # Truncate reffs\n",
     "    in_copy, mutatable_copy, out_copy = deepcopy(sL), deepcopy(sL), deepcopy(sL)\n",
     "    last_in_obj, last_mut_obj = in_copy[-1], mutatable_copy[-1]\n",
     "    \n",
-    "    # * force eval of _input withing state functions\n",
     "    _input = getBehaviorInput(m_step, sL, last_in_obj, behavior_funcs)\n",
     "\n",
-    "    \n",
-    "#     apply_env_proc(env_processes, last_mut_obj, t_step)\n",
-    "\n",
     "    for f in state_funcs:\n",
     "        f(m_step, sL, last_mut_obj, _input)\n",
     "        \n",
@@ -382,34 +309,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time_seq = datetime_range('2018-10-01 15:16:24', '2018-10-01 15:16:34', timedelta(seconds=1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "states_list = [state_dict]\n",
     "configs = generate_config(mechanisms, exogenous_states)\n",
     "p = pipeline(states_list, configs, env_processes, range(10))\n",
-    "s = simulation(states_list, configs, env_processes, range(5), 2)\n",
+    "N = sim_config['N']\n",
-    "x = flatten(s)\n",
+    "s = simulation(states_list, configs, env_processes, range(5), N)\n",
+    "# x = flatten(s)\n",
     "# transient_state = p[-1][1]\n",
     "# transient_state['mech_step'], transient_state['time_step'] = 0, 0\n",
     "# head, *tail = pipeline([transient_state], configs, env_processes, range(1))\n",
-    "# head\n",
+    "# head"
-    "datetime_range('2018-10-01 15:16:24', '2018-10-01 15:16:34', timedelta(seconds=1))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -858,7 +776,7 @@
        "30  2018-10-01 15:16:34  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -876,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -885,7 +803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -894,7 +812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -1343,7 +1261,7 @@
        "30  2018-10-01 15:16:34  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1357,7 +1275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1366,7 +1284,7 @@
        "[0, 1]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1377,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1385,9 +1303,9 @@
      "evalue": "name 'non_numeric' is not defined",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m--------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31m---------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                     Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-9786f8c4c4ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mstate_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m's1'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'3323'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m's2'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2572'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m's3'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2.657'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m's4'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'7.914'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'timestamp'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'2018-10-01 15:16:26'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mech_step'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'time_step'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mstate_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTWOPLACES\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mROUND_DOWN\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstate_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mstate_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_numeric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mstate_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-12-9786f8c4c4ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mstate_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m's1'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'3323'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m's2'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2572'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m's3'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2.657'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m's4'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'7.914'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'timestamp'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'2018-10-01 15:16:26'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mech_step'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'time_step'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mstate_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTWOPLACES\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mROUND_DOWN\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstate_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mstate_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_numeric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mstate_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'non_numeric' is not defined"
      ]
     }

ui/config.py

@@ -0,0 +1,120 @@
+from engine.utils import bound_norm_random, ep_time_step, env_proc
+
+import numpy as np
+from decimal import Decimal
+
+seed = {
+    'z': np.random.RandomState(1),
+    'a': np.random.RandomState(2),
+    'b': np.random.RandomState(3),
+    'c': np.random.RandomState(3)
+}
+
+# Behaviors per Mechanism
+def b1m1(step, sL, s):
+    return s['s1'] + 1
+def b2m1(step, sL, s):
+    return s['s1'] + 1
+
+def b1m2(step, sL, s):
+    return s['s1'] + 1
+def b2m2(step, sL, s):
+    return s['s1'] + 1
+
+def b1m3(step, sL, s):
+    return s['s1'] + 1
+def b2m3(step, sL, s):
+    return s['s2'] + 1
+
+
+# Internal States per Mechanism
+def s1m1(step, sL, s, _input):
+    s['s1'] = s['s1'] + _input
+def s2m1(step, sL, s, _input):
+    s['s2'] = s['s2'] + _input
+
+
+def s1m2(step, sL, s, _input):
+    s['s1'] = s['s1'] + _input
+def s2m2(step, sL, s, _input):
+    s['s2'] = s['s2'] + _input
+
+def s1m3(step, sL, s, _input):
+    s['s1'] = s['s1'] + _input
+def s2m3(step, sL, s, _input):
+    s['s2'] = s['s2'] + _input
+
+# Exogenous States
+proc_one_coef_A = 0.7
+proc_one_coef_B = 1.3
+def es3p1(step, sL, s, _input):
+    s['s3'] = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
+def es4p2(step, sL, s, _input):
+    s['s4'] = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
+def es5p2(step, sL, s, _input):
+    s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)
+
+# Environment States
+def env_a(x):
+    return x
+def env_b(x):
+    return x
+def what_ever(x):
+    return x + 1
+
+# Genesis States
+state_dict = {
+    's1': Decimal(0.0),
+    's2': Decimal(0.0),
+    's3': Decimal(1.0),
+    's4': Decimal(1.0),
+    'timestamp': '2018-10-01 15:16:24'
+}
+
+exogenous_states = {
+    "s3": es3p1,
+    "s4": es4p2,
+    "timestamp": es5p2
+}
+
+env_processes = {
+    "s3": env_proc('2018-10-01 15:16:25', env_a),
+    "s4": env_proc('2018-10-01 15:16:25', env_b)
+}
+
+mechanisms = {
+    "m1": {
+        "behaviors": {
+            "b1": b1m1,
+            "b2": b2m1
+        },
+        "states": {
+            "s1": s1m1,
+            "s2": s2m1,
+        }
+    },
+    "m2": {
+        "behaviors": {
+            "b1": b1m2,
+            "b2": b2m2
+        },
+        "states": {
+            "s1": s1m2,
+            "s2": s2m2,
+        }
+    },
+    "m3": {
+        "behaviors": {
+            "b1": b1m3,
+            "b2": b2m3
+        },
+        "states": {
+            "s1": s1m3,
+            "s2": s2m3,
+        }
+    }
+}
+
+sim_config = {
+    "N": 2
+}

ui/test.py

@@ -0,0 +1,2 @@
+from engine import run
+run.main()