misc.
This commit is contained in:
Joshua E. Jodesty
parent
a0266641f7
commit
e2752161c3
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@ -74,19 +74,27 @@ class Executor:
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# make env proc trigger field agnostic
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self.apply_env_proc(env_processes, last_in_copy, last_in_copy['timestamp']) # mutating last_in_copy
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print()
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print(last_in_copy)
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print()
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def set_sys_metrics(m_step, t_step, run):
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last_in_copy["mech_step"], last_in_copy["time_step"], last_in_copy['run'] = m_step, t_step, run
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def set_sys_metrics(state_dict, m_step, t_step, run):
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state_dict["mech_step"], state_dict["time_step"], state_dict['run'] = m_step, t_step, run
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if any(isinstance(x, list) for x in last_in_copy.values()):
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last_in_copies = flatten(last_in_copy)
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for last_in_copy in last_in_copies:
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set_sys_metrics(m_step, t_step, run)
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set_sys_metrics(last_in_copy, m_step, t_step, run)
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sL.append(last_in_copies)
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else:
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set_sys_metrics(m_step, t_step, run)
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set_sys_metrics(last_in_copy, m_step, t_step, run)
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sL.append(last_in_copy)
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print()
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pp.pprint(last_in_copies)
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print()
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del last_in_copy
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return sL
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@ -118,7 +126,7 @@ class Executor:
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last_states
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)
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print()
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pp.pprint(configs)
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# pp.pprint(configs)
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else:
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states_lists = self.mech_step(m_step, states_list, s_conf, b_conf, env_processes, t_step, run)
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@ -135,7 +143,7 @@ class Executor:
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def block_pipeline(self, states_list, configs, env_processes, time_seq, run):
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time_seq = [x + 1 for x in time_seq]
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simulation_list = [states_list]
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print(len(configs))
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# print(len(configs))
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for time_step in time_seq:
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# print(simulation_list)
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if len(simulation_list) == 1:
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@ -0,0 +1,576 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import _thread\n",
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"import time\n",
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"from fn.func import curried"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define a function for the thread\n",
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"def f(threadName, delay):\n",
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" count = 0\n",
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" print(count)\n",
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" # while count < 5:\n",
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" # time.sleep(delay)\n",
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" # count += 1\n",
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" # print(count)\n",
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" \n",
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"def pipe(x):\n",
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" print(x)\n",
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" return 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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"00\n",
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"\n"
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]
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}
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],
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"source": [
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"# Create two threads as follows\n",
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"try:\n",
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" _thread.start_new_thread( f, (\"Thread-1\", 2, ) )\n",
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" _thread.start_new_thread( f, (\"Thread-2\", 4, ) )\n",
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"except:\n",
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" print (\"Error: unable to start thread\")\n",
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"\n",
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"while 1:\n",
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" pass\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1, 2]\n",
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"('s2', <function fit_param.<locals>.<lambda> at 0x1099efae8>)\n",
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"('s2', <function fit_param.<locals>.<lambda> at 0x1099ef9d8>)\n"
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]
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}
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],
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"source": [
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"from SimCAD.engine.utils import sweep\n",
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"from SimCAD.utils import rename\n",
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"from SimCAD.configuration.utils import s_update\n",
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"\n",
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"# @curried\n",
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"def fit_param(param):\n",
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" return lambda x: x + param\n",
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"\n",
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"# xf = lambda param: lambda x: x + param\n",
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"\n",
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"def sweep(params, y, xf):\n",
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" op = [rename('sweep', s_update(y, xf(param))) for param in params]\n",
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" print(params)\n",
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" # print()\n",
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" return op\n",
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"\n",
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"for f in sweep([1,2], 's2', fit_param):\n",
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" print(f(1,2,3,4))\n",
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"# sweep([1,2], 's2', xf)"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1, 64, 2187, 65536]\n"
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]
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}
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],
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"source": [
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"# instantiate and configure the worker pool\n",
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"from pathos.threading import ThreadPool\n",
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"pool = ThreadPool(nodes=4)\n",
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"\n",
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"# do a blocking map on the chosen function\n",
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"print(pool.map(pow, [1,2,3,4], [5,6,7,8]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"ename": "SyntaxError",
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"evalue": "invalid syntax (<ipython-input-2-6e999d313015>, line 3)",
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"traceback": [
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"\u001b[0;36m File \u001b[0;32m\"<ipython-input-2-6e999d313015>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m [for f in fs]\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
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],
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"output_type": "error"
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}
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],
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"source": [
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"with Pool(len(configs)) as p:\n",
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" results = p.map(lambda t: t[0](t[1], t[2], t[3], t[4], t[5]), l)\n",
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" \n",
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"\n",
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"def state_multithreading(self, fs, m_step, sL, last_in_obj, _input):\n",
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" if type(fs) == 'list':\n",
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" pool.map(f(m_step, sL, last_in_obj, _input), fs)\n",
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" else:\n",
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" f(m_step, sL, last_in_obj, _input)"
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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": 12,
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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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"[('s2', [11, 23])]"
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]
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},
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"execution_count": 12,
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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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"from itertools import groupby\n",
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"l = [('s2', 11), ('s2', 23)]\n",
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"l.sort(key = lambda i : i[0])\n",
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"[(key, [i[1] for i in values]) for key, values in groupby(l, lambda i: i[0])]"
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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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"def groupByKV(l):\n",
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" l.sort(key = lambda i : i[0])\n",
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" return [(key, [i[1] for i in values]) for key, values in groupby(l, lambda i: i[0])]"
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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": 18,
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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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"[('s2', [11, 23])]"
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]
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},
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"execution_count": 18,
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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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"groupByKV(l)"
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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": 20,
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"metadata": {},
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"outputs": [
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{
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"ename": "SyntaxError",
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"evalue": "invalid syntax (<ipython-input-20-fada0ccd8d2a>, line 2)",
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"traceback": [
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"\u001b[0;36m File \u001b[0;32m\"<ipython-input-20-fada0ccd8d2a>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m collect = lambda tuplist: reduce(lambda acc, (k,v): acc[k].append(v) or acc,tuplist, defaultdict(list))\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
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],
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"output_type": "error"
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}
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],
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"source": [
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"from collections import defaultdict \n",
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"collect = lambda tuplist: reduce(lambda acc, (k,v): acc[k].append(v) or acc,tuplist, defaultdict(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": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import defaultdict\n",
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"d = defaultdict(list)\n",
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"for key, value in [('s2', 11), ('s2', 23)]:\n",
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" d[key].append(value)"
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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": 25,
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"metadata": {
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"collapsed": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Help on defaultdict object:\n",
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"\n",
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"class defaultdict(builtins.dict)\n",
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" | defaultdict(default_factory[, ...]) --> dict with default factory\n",
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" | \n",
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" | The default factory is called without arguments to produce\n",
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" | a new value when a key is not present, in __getitem__ only.\n",
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" | A defaultdict compares equal to a dict with the same items.\n",
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" | All remaining arguments are treated the same as if they were\n",
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" | passed to the dict constructor, including keyword arguments.\n",
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" | \n",
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" | Method resolution order:\n",
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" | defaultdict\n",
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" | builtins.dict\n",
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" | builtins.object\n",
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" | \n",
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" | Methods defined here:\n",
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" | \n",
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" | __copy__(...)\n",
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" | D.copy() -> a shallow copy of D.\n",
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" | \n",
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" | __getattribute__(self, name, /)\n",
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" | Return getattr(self, name).\n",
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" | \n",
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" | __init__(self, /, *args, **kwargs)\n",
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" | Initialize self. See help(type(self)) for accurate signature.\n",
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" | \n",
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" | __missing__(...)\n",
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" | __missing__(key) # Called by __getitem__ for missing key; pseudo-code:\n",
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" | if self.default_factory is None: raise KeyError((key,))\n",
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" | self[key] = value = self.default_factory()\n",
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" | return value\n",
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" | \n",
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" | __reduce__(...)\n",
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" | Return state information for pickling.\n",
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" | \n",
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" | __repr__(self, /)\n",
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" | Return repr(self).\n",
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" | \n",
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" | copy(...)\n",
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" | D.copy() -> a shallow copy of D.\n",
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" | \n",
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" | ----------------------------------------------------------------------\n",
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" | Data descriptors defined here:\n",
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" | \n",
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" | default_factory\n",
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" | Factory for default value called by __missing__().\n",
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" | \n",
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" | ----------------------------------------------------------------------\n",
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" | Methods inherited from builtins.dict:\n",
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" | \n",
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" | __contains__(self, key, /)\n",
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" | True if D has a key k, else False.\n",
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" | \n",
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" | __delitem__(self, key, /)\n",
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" | Delete self[key].\n",
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" | \n",
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" | __eq__(self, value, /)\n",
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" | Return self==value.\n",
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" | \n",
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" | __ge__(self, value, /)\n",
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" | Return self>=value.\n",
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" | \n",
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" | __getitem__(...)\n",
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" | x.__getitem__(y) <==> x[y]\n",
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" | \n",
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" | __gt__(self, value, /)\n",
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" | Return self>value.\n",
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" | \n",
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" | __iter__(self, /)\n",
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" | Implement iter(self).\n",
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" | \n",
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" | __le__(self, value, /)\n",
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" | Return self<=value.\n",
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" | \n",
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" | __len__(self, /)\n",
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" | Return len(self).\n",
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" | \n",
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" | __lt__(self, value, /)\n",
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" | Return self<value.\n",
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" | \n",
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" | __ne__(self, value, /)\n",
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" | Return self!=value.\n",
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" | \n",
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" | __new__(*args, **kwargs) from builtins.type\n",
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" | Create and return a new object. See help(type) for accurate signature.\n",
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" | \n",
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" | __setitem__(self, key, value, /)\n",
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" | Set self[key] to value.\n",
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" | \n",
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" | __sizeof__(...)\n",
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" | D.__sizeof__() -> size of D in memory, in bytes\n",
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" | \n",
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" | clear(...)\n",
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" | D.clear() -> None. Remove all items from D.\n",
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" | \n",
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" | fromkeys(iterable, value=None, /) from builtins.type\n",
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" | Returns a new dict with keys from iterable and values equal to value.\n",
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" | \n",
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" | get(...)\n",
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" | D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.\n",
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" | \n",
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" | items(...)\n",
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" | D.items() -> a set-like object providing a view on D's items\n",
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" | \n",
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" | keys(...)\n",
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" | D.keys() -> a set-like object providing a view on D's keys\n",
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" | \n",
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" | pop(...)\n",
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" | D.pop(k[,d]) -> v, remove specified key and return the corresponding value.\n",
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" | If key is not found, d is returned if given, otherwise KeyError is raised\n",
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" | \n",
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" | popitem(...)\n",
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" | D.popitem() -> (k, v), remove and return some (key, value) pair as a\n",
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" | 2-tuple; but raise KeyError if D is empty.\n",
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" | \n",
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" | setdefault(...)\n",
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" | D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D\n",
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" | \n",
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" | update(...)\n",
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" | D.update([E, ]**F) -> None. Update D from dict/iterable E and F.\n",
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" | If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]\n",
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" | If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v\n",
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" | In either case, this is followed by: for k in F: D[k] = F[k]\n",
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" | \n",
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" | values(...)\n",
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" | D.values() -> an object providing a view on D's values\n",
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" | \n",
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" | ----------------------------------------------------------------------\n",
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" | Data and other attributes inherited from builtins.dict:\n",
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" | \n",
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" | __hash__ = None\n",
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"\n"
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]
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}
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],
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"source": [
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"help(d)"
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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": 27,
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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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"{'s2': [11, 23]}"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dict(d)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def groupByKey(l):\n",
|
||||
" d = defaultdict(list)\n",
|
||||
" for key, value in l:\n",
|
||||
" d[key].append(value)\n",
|
||||
" return list(dict(d).items()).pop()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"('s2', [11, 23])"
|
||||
]
|
||||
},
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"r = groupByKey([('s2', 11), ('s2', 23)])\n",
|
||||
"r"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# xf = lambda param: 1.0 + param\n",
|
||||
"# def xf(y, param, s):\n",
|
||||
"# return s[y] + param\n",
|
||||
"\n",
|
||||
"# def fit_param(param):\n",
|
||||
"# y = 's2'\n",
|
||||
"# x = 1 + param\n",
|
||||
"# return lambda step, sL, s, _input: (y, x)\n",
|
||||
"#\n",
|
||||
"# def fit_param(param):\n",
|
||||
"# return lambda step, sL, s, _input: (\n",
|
||||
"# 's2',\n",
|
||||
"# s['s2'] + param\n",
|
||||
"# )\n",
|
||||
"#\n",
|
||||
"# s2m1 = sweep(\n",
|
||||
"# params = [Decimal(11.0), Decimal(22.0)],\n",
|
||||
"# sweep_f = fit_param\n",
|
||||
"# )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from decimal import Decimal\n",
|
||||
"from itertools import product\n",
|
||||
"\n",
|
||||
"# def \n",
|
||||
"\n",
|
||||
"l = {\n",
|
||||
" 's1': 1, \n",
|
||||
" 's2': [Decimal('11'), Decimal('22')], \n",
|
||||
" 's3': [Decimal('12'), Decimal('23')], \n",
|
||||
" 's4': 10, \n",
|
||||
" 'timestamp': '2018-10-01 15:16:25', \n",
|
||||
" 'mech_step': 0, \n",
|
||||
" 'time_step': 1\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"def flattenDict(l):\n",
|
||||
" def tupalize(k, vs):\n",
|
||||
" l = []\n",
|
||||
" if isinstance(vs, list):\n",
|
||||
" for v in vs:\n",
|
||||
" l.append((k, v))\n",
|
||||
" else:\n",
|
||||
" l.append((k, vs))\n",
|
||||
" return l\n",
|
||||
"\n",
|
||||
" flat_list = [tupalize(k, vs) for k, vs in l.items()]\n",
|
||||
" flat_dict = [dict(items) for items in product(*flat_list)]\n",
|
||||
" return flat_dict"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[{'s1': 1,\n",
|
||||
" 's2': Decimal('11'),\n",
|
||||
" 's3': Decimal('12'),\n",
|
||||
" 's4': 10,\n",
|
||||
" 'timestamp': '2018-10-01 15:16:25',\n",
|
||||
" 'mech_step': 0,\n",
|
||||
" 'time_step': 1},\n",
|
||||
" {'s1': 1,\n",
|
||||
" 's2': Decimal('11'),\n",
|
||||
" 's3': Decimal('23'),\n",
|
||||
" 's4': 10,\n",
|
||||
" 'timestamp': '2018-10-01 15:16:25',\n",
|
||||
" 'mech_step': 0,\n",
|
||||
" 'time_step': 1},\n",
|
||||
" {'s1': 1,\n",
|
||||
" 's2': Decimal('22'),\n",
|
||||
" 's3': Decimal('12'),\n",
|
||||
" 's4': 10,\n",
|
||||
" 'timestamp': '2018-10-01 15:16:25',\n",
|
||||
" 'mech_step': 0,\n",
|
||||
" 'time_step': 1},\n",
|
||||
" {'s1': 1,\n",
|
||||
" 's2': Decimal('22'),\n",
|
||||
" 's3': Decimal('23'),\n",
|
||||
" 's4': 10,\n",
|
||||
" 'timestamp': '2018-10-01 15:16:25',\n",
|
||||
" 'mech_step': 0,\n",
|
||||
" 'time_step': 1}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"flattenDict(l)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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": 1
|
||||
}
|
||||
Loading…
Reference in New Issue