profile_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Dec 14 21:59:27 2015    results/cprofile4.txt\n",
      "\n",
      "         825703 function calls (724515 primitive calls) in 145.303 seconds\n",
      "\n",
      "   Ordered by: standard name\n",
      "\n",
      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-0>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-100>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-101>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-101>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-102>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-103>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-103>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-104>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-105>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-105>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-106>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-106>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-107>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-108>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-109>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-109>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-10>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-10>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-110>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-110>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-111>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-112>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-113>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-113>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-114>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-114>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-115>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-116>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-117>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-117>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-118>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-118>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-119>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-11>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-120>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-121>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-121>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-122>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-122>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-123>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-124>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-125>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-125>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-126>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-126>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-127>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-128>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-129>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-129>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-12>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-130>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-130>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-131>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-132>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-133>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-133>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-134>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-135>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-135>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-136>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-137>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-137>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-138>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-139>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-139>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-13>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-13>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-140>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-141>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-141>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-142>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-143>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-143>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-144>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-145>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-145>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-146>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-147>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-147>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-148>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-148>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-149>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-14>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-14>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-150>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-151>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-151>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-152>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-153>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-153>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-154>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-155>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-155>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-156>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-156>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-157>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-158>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-159>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-159>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-15>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-160>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-161>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-161>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-162>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-163>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-163>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-164>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-165>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-165>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-166>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-167>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-167>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-168>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-169>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-169>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-16>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-170>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-171>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-171>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-172>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-173>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-173>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-174>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-175>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-175>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-176>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-177>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-177>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-178>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-179>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-179>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-17>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-17>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-180>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-181>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-181>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-182>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-183>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-183>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-184>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-185>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-185>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-186>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-187>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-187>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-188>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-189>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-189>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-18>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-18>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-190>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-191>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-191>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-192>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-193>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-193>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-194>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-195>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-195>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-196>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-197>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-197>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-198>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-199>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-199>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-19>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-1>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-1>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-200>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-201>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-201>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-202>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-203>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-203>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-204>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-205>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-205>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-206>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-207>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-207>:1(_open_file)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-208>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-209>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-209>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-20>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-210>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-211>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-211>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-212>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-213>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-213>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-214>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-214>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-215>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-216>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-217>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-217>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-218>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-219>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-219>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-21>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-21>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-220>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-221>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-221>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-222>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-222>:1(_not_implemented_for)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-223>:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 <decorator-gen-224>:1(<module>)\n",
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      "        3    0.000    0.000    0.000    0.000 result.py:12(failfast)\n",
      "        1    0.000    0.000    0.000    0.000 result.py:24(TestResult)\n",
      "        1    0.000    0.000    0.000    0.000 result_config.py:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 result_config.py:7(<dictcomp>)\n",
      "        1    0.000    0.000    0.000    0.000 richclub.py:2(<module>)\n",
      "        1    0.004    0.004  145.303  145.303 run_simulation.py:1(<module>)\n",
      "        1    0.004    0.004  144.916  144.916 run_simulation.py:13(main)\n",
      "        1    0.000    0.000    0.002    0.002 runner.py:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 runner.py:120(TextTestRunner)\n",
      "        1    0.000    0.000    0.000    0.000 runner.py:13(_WritelnDecorator)\n",
      "        1    0.000    0.000    0.000    0.000 runner.py:29(TextTestResult)\n",
      "        1    0.000    0.000    0.001    0.001 scanner.py:2(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 scimath.py:17(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 selectors.py:196(_BaseSelectorImpl)\n",
      "        1    0.000    0.000    0.000    0.000 selectors.py:281(SelectSelector)\n",
      "        1    0.000    0.000    0.000    0.000 selectors.py:334(PollSelector)\n",
      "        1    0.000    0.000    0.001    0.001 selectors.py:5(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 selectors.py:50(_SelectorMapping)\n",
      "        1    0.000    0.000    0.000    0.000 selectors.py:512(KqueueSelector)\n",
      "        1    0.000    0.000    0.000    0.000 selectors.py:70(BaseSelector)\n",
      "        1    0.000    0.000    0.000    0.000 semiconnected.py:4(<module>)\n",
      "        2    0.000    0.000    0.000    0.000 shape_base.py:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 shortestaugmentingpath.py:4(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 shutil.py:49(Error)\n",
      "        1    0.000    0.000    0.004    0.004 shutil.py:5(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 shutil.py:52(SameFileError)\n",
      "        1    0.000    0.000    0.000    0.000 shutil.py:55(SpecialFileError)\n",
      "        1    0.000    0.000    0.000    0.000 shutil.py:59(ExecError)\n",
      "        1    0.000    0.000    0.000    0.000 shutil.py:62(ReadError)\n",
      "        1    0.000    0.000    0.000    0.000 shutil.py:65(RegistryError)\n",
      "        1    0.000    0.000    0.002    0.002 signal.py:1(<module>)\n",
      "       67    0.000    0.000    0.000    0.000 signal.py:10(<lambda>)\n",
      "       68    0.000    0.000    0.000    0.000 signal.py:17(<lambda>)\n",
      "       69    0.000    0.000    0.000    0.000 signal.py:22(<lambda>)\n",
      "        1    0.000    0.000    0.002    0.002 signals.py:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 signals.py:9(_InterruptHandler)\n",
      "        1    0.000    0.000    0.000    0.000 simple_paths.py:268(PathBuffer)\n",
      "        1    0.000    0.000    0.000    0.000 simple_paths.py:6(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 six.py:1(<module>)\n",
      "        4    0.000    0.000    0.000    0.000 six.py:67(_add_doc)\n",
      "        1    0.000    0.000    0.000    0.000 small.py:5(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 smetric.py:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 social.py:3(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 socket.py:120(_GiveupOnSendfile)\n",
      "        1    0.000    0.000    0.000    0.000 socket.py:123(socket)\n",
      "        1    0.000    0.000    0.002    0.002 socket.py:47(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 socket.py:528(SocketIO)\n",
      "      214    0.000    0.000    0.000    0.000 socket.py:76(<lambda>)\n",
      "      215    0.000    0.000    0.000    0.000 socket.py:81(<lambda>)\n",
      "        1    0.000    0.000    0.002    0.002 sparse6.py:13(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 sparseprior.py:1(<module>)\n",
      "        1    0.000    0.000    0.008    0.008 sparseprior.py:16(log_sparse_graphset_prior)\n",
      "        1    0.000    0.000    0.002    0.002 sparseprior.py:17(<listcomp>)\n",
      "        1    0.000    0.000    0.005    0.005 sparseprior.py:18(<listcomp>)\n",
      "      304    0.002    0.000    0.005    0.000 sparseprior.py:8(log_sparse_1graph_prior_unnormed)\n",
      "        1    0.000    0.000    0.000    0.000 special_matrices.py:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 spectral.py:4(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 spectrum.py:3(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 spfun_stats.py:34(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 sputils.py:2(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 sputils.py:21(<listcomp>)\n",
      "        1    0.000    0.000    0.000    0.000 sputils.py:232(IndexMixin)\n",
      "      268    0.000    0.000    0.000    0.000 sre_compile.py:101(fixup)\n",
      "      289    0.001    0.000    0.004    0.000 sre_compile.py:221(_compile_charset)\n",
      "      289    0.003    0.000    0.004    0.000 sre_compile.py:248(_optimize_charset)\n",
      "       53    0.000    0.000    0.000    0.000 sre_compile.py:374(_mk_bitmap)\n",
      "       53    0.000    0.000    0.000    0.000 sre_compile.py:376(<listcomp>)\n",
      "        6    0.000    0.000    0.000    0.000 sre_compile.py:379(_bytes_to_codes)\n",
      "      318    0.000    0.000    0.001    0.000 sre_compile.py:386(_simple)\n",
      "        6    0.000    0.000    0.000    0.000 sre_compile.py:391(_generate_overlap_table)\n",
      "       55    0.000    0.000    0.002    0.000 sre_compile.py:412(_compile_info)\n",
      "      110    0.000    0.000    0.000    0.000 sre_compile.py:513(isstring)\n",
      "       55    0.000    0.000    0.012    0.000 sre_compile.py:516(_code)\n",
      "       55    0.000    0.000    0.026    0.000 sre_compile.py:531(compile)\n",
      "   730/55    0.003    0.000    0.009    0.000 sre_compile.py:64(_compile)\n",
      "      764    0.000    0.000    0.000    0.000 sre_parse.py:105(__init__)\n",
      "     1239    0.000    0.000    0.000    0.000 sre_parse.py:153(__len__)\n",
      "     3194    0.002    0.000    0.002    0.000 sre_parse.py:157(__getitem__)\n",
      "      330    0.000    0.000    0.000    0.000 sre_parse.py:161(__setitem__)\n",
      "     1137    0.000    0.000    0.001    0.000 sre_parse.py:165(append)\n",
      " 1044/373    0.001    0.000    0.002    0.000 sre_parse.py:167(getwidth)\n",
      "       57    0.000    0.000    0.000    0.000 sre_parse.py:217(__init__)\n",
      "     4014    0.002    0.000    0.002    0.000 sre_parse.py:226(__next)\n",
      "     1893    0.000    0.000    0.001    0.000 sre_parse.py:242(match)\n",
      "     3120    0.001    0.000    0.002    0.000 sre_parse.py:247(get)\n",
      "        4    0.000    0.000    0.000    0.000 sre_parse.py:251(getwhile)\n",
      "       37    0.000    0.000    0.000    0.000 sre_parse.py:260(getuntil)\n",
      "      990    0.000    0.000    0.000    0.000 sre_parse.py:276(tell)\n",
      "        1    0.000    0.000    0.000    0.000 sre_parse.py:278(seek)\n",
      "      116    0.000    0.000    0.000    0.000 sre_parse.py:312(_class_escape)\n",
      "      187    0.000    0.000    0.000    0.000 sre_parse.py:362(_escape)\n",
      "   240/57    0.001    0.000    0.014    0.000 sre_parse.py:429(_parse_sub)\n",
      "   366/62    0.005    0.000    0.013    0.000 sre_parse.py:491(_parse)\n",
      "       57    0.000    0.000    0.000    0.000 sre_parse.py:70(__init__)\n",
      "      380    0.000    0.000    0.000    0.000 sre_parse.py:75(groups)\n",
      "      134    0.000    0.000    0.000    0.000 sre_parse.py:78(opengroup)\n",
      "       57    0.000    0.000    0.000    0.000 sre_parse.py:797(fix_flags)\n",
      "    57/55    0.000    0.000    0.014    0.000 sre_parse.py:819(parse)\n",
      "      134    0.000    0.000    0.000    0.000 sre_parse.py:90(closegroup)\n",
      "        2    0.000    0.000    0.000    0.000 sre_parse.py:92(checkgroup)\n",
      "        2    0.000    0.000    0.000    0.000 sre_parse.py:95(checklookbehindgroup)\n",
      "        1    0.000    0.000    0.001    0.001 stochastic.py:4(<module>)\n",
      "        1    0.000    0.000    0.001    0.001 stoerwagner.py:4(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 stride_tricks.py:15(DummyArray)\n",
      "        1    0.000    0.000    0.000    0.000 stride_tricks.py:7(<module>)\n",
      "        1    0.000    0.000    0.002    0.002 string.py:15(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 string.py:170(Formatter)\n",
      "        1    0.000    0.000    0.000    0.000 string.py:51(_TemplateMetaclass)\n",
      "        1    0.000    0.000    0.002    0.002 string.py:61(__init__)\n",
      "        1    0.000    0.000    0.000    0.000 string.py:73(Template)\n",
      "        1    0.000    0.000    0.002    0.002 strongly_connected.py:3(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 subgraph_functions.py:1(<module>)\n",
      "        1    0.000    0.000    0.002    0.002 subprocess.py:356(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 subprocess.py:371(SubprocessError)\n",
      "        1    0.000    0.000    0.000    0.000 subprocess.py:374(CalledProcessError)\n",
      "        1    0.000    0.000    0.000    0.000 subprocess.py:401(TimeoutExpired)\n",
      "        1    0.000    0.000    0.000    0.000 subprocess.py:632(CompletedProcess)\n",
      "        1    0.000    0.000    0.000    0.000 subprocess.py:833(Popen)\n",
      "        1    0.000    0.000    0.000    0.000 suite.py:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 suite.py:16(BaseTestSuite)\n",
      "        1    0.000    0.000    0.000    0.000 suite.py:270(_ErrorHolder)\n",
      "        1    0.000    0.000    0.000    0.000 suite.py:317(_DebugResult)\n",
      "        1    0.000    0.000    0.000    0.000 suite.py:92(TestSuite)\n",
      "        1    0.000    0.000    0.000    0.000 swap.py:3(<module>)\n",
      "        1    0.000    0.000    0.001    0.001 synchronize.py:11(<module>)\n",
      "        5    0.000    0.000    0.000    0.000 synchronize.py:115(_make_name)\n",
      "        1    0.000    0.000    0.000    0.000 synchronize.py:124(Semaphore)\n",
      "        1    0.000    0.000    0.000    0.000 synchronize.py:126(__init__)\n",
      "        1    0.000    0.000    0.000    0.000 synchronize.py:143(BoundedSemaphore)\n",
      "        1    0.000    0.000    0.000    0.000 synchronize.py:160(Lock)\n",
      "        4    0.000    0.000    0.000    0.000 synchronize.py:162(__init__)\n",
      "        1    0.000    0.000    0.000    0.000 synchronize.py:185(RLock)\n",
      "        1    0.000    0.000    0.000    0.000 synchronize.py:211(Condition)\n",
      "        1    0.000    0.000    0.000    0.000 synchronize.py:333(Event)\n",
      "        1    0.000    0.000    0.000    0.000 synchronize.py:372(Barrier)\n",
      "        1    0.000    0.000    0.000    0.000 synchronize.py:47(SemLock)\n",
      "        5    0.000    0.000    0.000    0.000 synchronize.py:51(__init__)\n",
      "        5    0.000    0.000    0.000    0.000 synchronize.py:91(_make_methods)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:1380(TarFile)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:2406(TarIter)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:267(TarError)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:270(ExtractError)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:273(ReadError)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:276(CompressionError)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:279(StreamError)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:282(HeaderError)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:285(EmptyHeaderError)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:288(TruncatedHeaderError)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:291(EOFHeaderError)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:294(InvalidHeaderError)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:297(SubsequentHeaderError)\n",
      "        1    0.000    0.000    0.001    0.001 tarfile.py:30(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:304(_LowLevelFile)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:328(_Stream)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:578(_StreamProxy)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:608(_FileInFile)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:712(ExFileObject)\n",
      "        1    0.000    0.000    0.000    0.000 tarfile.py:723(TarInfo)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:118(_sanitize_params)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:136(_RandomNameSequence)\n",
      "        7    0.000    0.000    0.001    0.000 tempfile.py:146(rng)\n",
      "        7    0.000    0.000    0.001    0.000 tempfile.py:157(__next__)\n",
      "        7    0.000    0.000    0.000    0.000 tempfile.py:160(<listcomp>)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:163(_candidate_tempdir_list)\n",
      "        1    0.000    0.000    0.001    0.001 tempfile.py:188(_get_default_tempdir)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:235(_get_candidate_names)\n",
      "        1    0.000    0.000    0.004    0.004 tempfile.py:24(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:249(_mkstemp_inner)\n",
      "        2    0.000    0.000    0.001    0.001 tempfile.py:289(gettempdir)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:305(mkstemp)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:415(_TemporaryFileCloser)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:458(_TemporaryFileWrapper)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:619(SpooledTemporaryFile)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:767(TemporaryDirectory)\n",
      "        1    0.000    0.000    0.000    0.000 tempfile.py:97(_infer_return_type)\n",
      "        1    0.000    0.000    0.000    0.000 test.py:2(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:1(<module>)\n",
      "        3    0.000    0.000    0.000    0.000 threading.py:1031(join)\n",
      "        5    0.000    0.000    0.000    0.000 threading.py:1069(_wait_for_tstate_lock)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:1083(name)\n",
      "        2    0.000    0.000    0.000    0.000 threading.py:1111(is_alive)\n",
      "        3    0.000    0.000    0.000    0.000 threading.py:1127(daemon)\n",
      "        3    0.000    0.000    0.000    0.000 threading.py:1143(daemon)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:1165(Timer)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:1195(_MainThread)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:1197(__init__)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:1214(_DummyThread)\n",
      "       10    0.000    0.000    0.000    0.000 threading.py:1233(current_thread)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:201(Condition)\n",
      "       23    0.000    0.000    0.000    0.000 threading.py:213(__init__)\n",
      "       16    0.000    0.000    0.000    0.000 threading.py:237(__enter__)\n",
      "       16    0.000    0.000    0.000    0.000 threading.py:240(__exit__)\n",
      "      255    0.000    0.000    0.000    0.000 threading.py:246(_release_save)\n",
      "      255    0.000    0.000    0.001    0.000 threading.py:249(_acquire_restore)\n",
      "      272    0.000    0.000    0.000    0.000 threading.py:252(_is_owned)\n",
      "      255    0.003    0.000  144.400    0.566 threading.py:261(wait)\n",
      "       17    0.000    0.000    0.000    0.000 threading.py:332(notify)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:355(notify_all)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:367(Semaphore)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:447(BoundedSemaphore)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:485(Event)\n",
      "       20    0.000    0.000    0.000    0.000 threading.py:496(__init__)\n",
      "      315    0.000    0.000    0.000    0.000 threading.py:504(is_set)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:510(set)\n",
      "      307    0.002    0.000  144.402    0.470 threading.py:537(wait)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:573(Barrier)\n",
      "        3    0.000    0.000    0.000    0.000 threading.py:72(RLock)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:727(BrokenBarrierError)\n",
      "        3    0.000    0.000    0.000    0.000 threading.py:734(_newname)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:745(Thread)\n",
      "        4    0.000    0.000    0.000    0.000 threading.py:764(__init__)\n",
      "        3    0.000    0.000    0.001    0.000 threading.py:835(start)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:85(_RLock)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:897(_set_ident)\n",
      "        1    0.000    0.000    0.000    0.000 threading.py:900(_set_tstate_lock)\n",
      "        3    0.000    0.000    0.000    0.000 threading.py:973(_stop)\n",
      "        1    0.000    0.000    0.000    0.000 threshold.py:3(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 token.py:1(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 token.py:3(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 token.py:74(<dictcomp>)\n",
      "       19    0.000    0.000    0.000    0.000 tokenize.py:111(group)\n",
      "        1    0.000    0.000    0.000    0.000 tokenize.py:112(any)\n",
      "        2    0.000    0.000    0.000    0.000 tokenize.py:113(maybe)\n",
      "        1    0.000    0.000    0.000    0.000 tokenize.py:150(TokenError)\n",
      "        1    0.000    0.000    0.000    0.000 tokenize.py:152(StopTokenizing)\n",
      "       52    0.000    0.000    0.004    0.000 tokenize.py:172(_compile)\n",
      "        1    0.000    0.000    0.000    0.000 tokenize.py:183(Untokenizer)\n",
      "        1    0.000    0.000    0.002    0.002 tokenize.py:21(<module>)\n",
      "        1    0.000    0.000    0.000    0.000 tokenize.py:219(TokenError)\n",
      "        1    0.000    0.000    0.000    0.000 tokenize.py:221(StopTokenizing)\n",
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      "\n",
      "\n",
      "Mon Dec 14 21:59:27 2015    results/cprofile4.txt\n",
      "\n",
      "         825703 function calls (724515 primitive calls) in 145.303 seconds\n",
      "\n",
      "   Ordered by: cumulative time\n",
      "   List reduced from 2362 to 100 due to restriction <100>\n",
      "\n",
      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
      "    737/1    0.011    0.000  145.303  145.303 {built-in method builtins.exec}\n",
      "        1    0.004    0.004  145.303  145.303 run_simulation.py:1(<module>)\n",
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      "    600/9    0.003    0.000    0.391    0.043 <frozen importlib._bootstrap>:966(_find_and_load)\n",
      "    600/9    0.002    0.000    0.391    0.043 <frozen importlib._bootstrap>:939(_find_and_load_unlocked)\n",
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      "     1536    0.002    0.000    0.014    0.000 unweighted.py:104(bidirectional_shortest_path)\n",
      "   240/57    0.001    0.000    0.014    0.000 sre_parse.py:429(_parse_sub)\n",
      "   366/62    0.005    0.000    0.013    0.000 sre_parse.py:491(_parse)\n",
      "       23    0.001    0.000    0.013    0.001 __init__.py:349(namedtuple)\n",
      "       89    0.000    0.000    0.013    0.000 decorators.py:15(not_implemented_for)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pstats.Stats at 0x1033ee668>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pstats\n",
    "\n",
    "p =pstats.Stats('results/cprofile4.txt')\n",
    "p.strip_dirs().sort_stats(-1).print_stats()\n",
    "\n",
    "p.sort_stats('cumtime').print_stats(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import line_profiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timer unit: 1e-06 s\n",
      "\n",
      "Total time: 6.30322 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/graph_local_classes.py\n",
      "Function: sample_edge_first_event_only at line 242\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "   242                                               @profile\n",
      "   243                                               def sample_edge_first_event_only(self, edge, time):\n",
      "   244    662928       809192      1.2     12.8          index = self.structure.edges.index(edge)\n",
      "   245                                                   \n",
      "   246                                                   # does it occur?\n",
      "   247    662928       858155      1.3     13.6          occurs = np.random.rand() < self.params.p\n",
      "   248    662928       442454      0.7      7.0          if not occurs:\n",
      "   249    132539        84030      0.6      1.3              return None\n",
      "   250                                                   \n",
      "   251                                                   # how many events?\n",
      "   252    530389      1242264      2.3     19.7          num_events = np.random.poisson(lam=self.params.mu[index])\n",
      "   253    530389       385026      0.7      6.1          if num_events == 0:\n",
      "   254    223834       139018      0.6      2.2              return None\n",
      "   255                                                   \n",
      "   256                                                   # when do those events occur?\n",
      "   257    306555      2064801      6.7     32.8          event_time = time + np.random.exponential(scale=self.params.r[index]/num_events, size=1)\n",
      "   258                                                   # event_times.sort()\n",
      "   259                                                   # return [(t, edge[1]) for t in event_times]\n",
      "   260                                                   # import ipdb; ipdb.set_trace()\n",
      "   261    306555       278283      0.9      4.4          return (event_time, edge[1])\n",
      "\n",
      "Total time: 24.5669 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/graph_local_classes.py\n",
      "Function: _sample_solely_first_events at line 318\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "   318                                               @profile\n",
      "   319                                               def _sample_solely_first_events(self, max_time=4.0):\n",
      "   320    243200       826251      3.4      3.4          self._first_events = {node: np.inf for node in self.structure.nodes}\n",
      "   321    243200       236795      1.0      1.0          pending = []\n",
      "   322    243200       363229      1.5      1.5          heapq.heappush(pending,(self.init_time, self.init_node))\n",
      "   323                                                   # pending = [(self.init_time, self.init_node)]\n",
      "   324                                                   # self._all_events = []\n",
      "   325                                                   # short_circuit = False\n",
      "   326                                                   # import ipdb; ipdb.set_trace()\n",
      "   327    243200       288813      1.2      1.2          processed_nodes = set()\n",
      "   328    243200       370313      1.5      1.5          structure_nodes = set(self.structure.nodes)\n",
      "   329    698328       801954      1.1      3.3          while len(pending) > 0:\n",
      "   330                                                       # time, node = pending.pop(0)\n",
      "   331                                                       # import ipdb; ipdb.set_trace()\n",
      "   332    532302       742801      1.4      3.0              time, node = heapq.heappop(pending)\n",
      "   333    532302      1009195      1.9      4.1              if time >= max_time:\n",
      "   334     41936        42652      1.0      0.2                  break\n",
      "   335                                           \n",
      "   336                                                       #self._all_events.append((time, node))\n",
      "   337                                                       # if time < self._first_events[node]:\n",
      "   338                                                           # self._first_events[node] = time\n",
      "   339                                           \n",
      "   340    490366       510111      1.0      2.1              if node in processed_nodes:\n",
      "   341      3222         3016      0.9      0.0                  continue\n",
      "   342                                                       else:\n",
      "   343    487144       553304      1.1      2.3                  processed_nodes.add(node)\n",
      "   344    487144       556176      1.1      2.3                  self._first_events[node] = time\n",
      "   345    487144       498255      1.0      2.0              if processed_nodes==structure_nodes:\n",
      "   346                                                           # this only works if it is first event only\n",
      "   347     35238        35760      1.0      0.1                  break\n",
      "   348                                           \n",
      "   349    451906       539176      1.2      2.2              children_edges = self.structure.child_edges[node]\n",
      "   350                                           # this goes to each of the children_edges of the node and initiates events along that edge\n",
      "   351                                                       # import ipdb; ipdb.set_trace()\n",
      "   352   1154284      1257037      1.1      5.1              for edge in children_edges:\n",
      "   353    702378       783621      1.1      3.2                  if edge[1] in processed_nodes:\n",
      "   354     39450        36793      0.9      0.1                      continue\n",
      "   355                                                           else:\n",
      "   356    662928     12043391     18.2     49.0                      child_events = self.sample_edge_first_event_only(edge, time)\n",
      "   357                                                               # import ipdb; ipdb.set_trace()\n",
      "   358    662928       691683      1.0      2.8                      if not child_events:\n",
      "   359    356373       347327      1.0      1.4                          continue\n",
      "   360                                                               # import ipdb; ipdb.set_trace()\n",
      "   361    306555       592697      1.9      2.4                      heapq.heappush(pending,child_events)\n",
      "   362                                                           # import ipdb; ipdb.set_trace()\n",
      "   363                                                           # pending.sort()\n",
      "   364                                                       \n",
      "   365                                                   #self._all_events.sort()\n",
      "   366                                                   #self._compute_first_events()\n",
      "   367                                                   #return np.array(self._first_events)\n",
      "   368    243200      1436562      5.9      5.8          return np.array([self._first_events[node] for node in self.structure.nodes])\n",
      "\n",
      "Total time: 0 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/graph_local_classes.py\n",
      "Function: _sample_solely_first_events_better at line 370\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "   370                                               @profile\n",
      "   371                                               def _sample_solely_first_events_better(self, max_time=4.0):\n",
      "   372                                                   pending = []\n",
      "   373                                                   heapq.heappush(pending,(self.init_time, self.init_node))\n",
      "   374                                                   # pending = [(self.init_time, self.init_node)]\n",
      "   375                                                   self._first_events = {node: np.inf for node in self.structure.nodes}\n",
      "   376                                                   processed_nodes = set()\n",
      "   377                                                   structure_nodes = set(self.structure.nodes)\n",
      "   378                                                   while len(pending) > 0:\n",
      "   379                                                       # time, node = pending.pop(0)\n",
      "   380                                                       # import ipdb; ipdb.set_trace()\n",
      "   381                                                       time, node = heapq.heappop(pending)\n",
      "   382                                                       \n",
      "   383                                                       if processed_nodes==structure_nodes or time >= max_time:\n",
      "   384                                                           # this only works if it is first event only\n",
      "   385                                                           break\n",
      "   386                                           \n",
      "   387                                                       if node in processed_nodes:\n",
      "   388                                                           continue\n",
      "   389                                                       else:\n",
      "   390                                                           self._first_events[node] = time\n",
      "   391                                                           processed_nodes.add(node)\n",
      "   392                                           \n",
      "   393                                                       \n",
      "   394                                                       #self._all_events.append((time, node))\n",
      "   395                                                       # if time < self._first_events[node]:\n",
      "   396                                                       #     self._first_events[node] = time\n",
      "   397                                           \n",
      "   398                                           \n",
      "   399                                                       # if processed_nodes==structure_nodes:\n",
      "   400                                                           # this only works if it is first event only\n",
      "   401                                                           # break\n",
      "   402                                           \n",
      "   403                                                       children_edges = self.structure.child_edges[node]\n",
      "   404                                           # this goes to each of the children_edges of the node and initiates events along that edge\n",
      "   405                                                       # import ipdb; ipdb.set_trace()\n",
      "   406                                                       for edge in children_edges:\n",
      "   407                                                           if edge[1] in processed_nodes:\n",
      "   408                                                               continue\n",
      "   409                                                           else:\n",
      "   410                                                               child_events = self.sample_edge_first_event_only(edge, time)\n",
      "   411                                                               # import ipdb; ipdb.set_trace()\n",
      "   412                                                               if not child_events:\n",
      "   413                                                                   continue\n",
      "   414                                                               # import ipdb; ipdb.set_trace()\n",
      "   415                                                               heapq.heappush(pending,child_events)\n",
      "   416                                                           # import ipdb; ipdb.set_trace()\n",
      "   417                                                           # pending.sort()\n",
      "   418                                                       \n",
      "   419                                                   #self._all_events.sort()\n",
      "   420                                                   #self._compute_first_events()\n",
      "   421                                                   #return np.array(self._first_events)\n",
      "   422                                                   return np.array([self._first_events[node] for node in self.structure.nodes])\n",
      "\n",
      "Total time: 37.3733 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/graph_local_classes.py\n",
      "Function: sample_solely_first_events at line 435\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "   435                                               @profile\n",
      "   436                                               def sample_solely_first_events(self, k=1, first_only=True, max_time=4.0):\n",
      "   437      1216         4761      3.9      0.0          first_events = np.zeros((k, len(self.structure.nodes)))\n",
      "   438    244416       166828      0.7      0.4          for i in range(k):\n",
      "   439                                                       # import ipdb; ipdb.set_trace()\n",
      "   440    243200     37200977    153.0     99.5              first_events[i] = self._sample_solely_first_events(max_time=max_time)\n",
      "   441                                                       # self._first_events = {node: np.inf for node in self.structure.nodes}\n",
      "   442                                           \n",
      "   443      1216          761      0.6      0.0          return first_events\n",
      "\n",
      "Total time: 40.7774 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
      "Function: p_graph_given_d at line 39\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "    39                                               @profile\n",
      "    40                                               def p_graph_given_d(self,graphs,options):\n",
      "    41                                                   # sets a catch for all numerical warnings to be treated as errors\n",
      "    42                                                   # np.seterr(all='raise')\n",
      "    43                                                   # np.seterr(under='raise')\n",
      "    44         4           75     18.8      0.0          np.seterr(over='raise')\n",
      "    45                                                   \"\"\"\n",
      "    46                                                   options includes \n",
      "    47                                                   sparsity: the sparsity argument for the prior \n",
      "    48                                                   param_sample_size: the number of parameters to be sampled\n",
      "    49                                                   stigma_sample_size: the number of internal states to be sampled\n",
      "    50                                                   data_sets: the different data_sets to be evaluated as likelihoods\n",
      "    51                                                   data_p: the probability of each data_set in data_sets\n",
      "    52                                                   num_data_samps: number of samples of observed data to be considered in the liklihood\n",
      "    53                                                   \"\"\"\n",
      "    54         4            8      2.0      0.0          self.graphs = graphs\n",
      "    55                                           \n",
      "    56                                                   # Note: some day this will need to change if graphs is made to be an iterator and not a list\n",
      "    57         4            8      2.0      0.0          self.max_graph = self.graphs[0]\n",
      "    58         4            7      1.8      0.0          self.options = options\n",
      "    59         4            8      2.0      0.0          num_graphs = len(graphs)\n",
      "    60                                           \n",
      "    61                                                   # loglikelihood = np.empty(len(self.graphs))\n",
      "    62         4            6      1.5      0.0          num_params= options[\"param_sample_size\"]\n",
      "    63                                           \n",
      "    64                                                   # generate 1 complete graph with many data structures shared beneath it\n",
      "    65         4           11      2.8      0.0          self.gs_out = GraphStructure.from_networkx(sub_graph_from_edge_type(self.max_graph,\n",
      "    66         4         4769   1192.2      0.0              edge_types=[\"observed\"]))\n",
      "    67                                                   \n",
      "    68         4            8      2.0      0.0          self.gs_in = [GraphStructure.from_networkx(sub_graph_from_edge_type(graph,\n",
      "    69         4       920419 230104.8      2.3              edge_types=[\"hidden_sample\"])) for graph in graphs]\n",
      "    70                                           \n",
      "    71         4          405    101.2      0.0          max_graph_params = GraphParams.from_networkx(self.max_graph)\n",
      "    72                                                   \n",
      "    73         4          693    173.2      0.0          self.param_list = [max_graph_params.sample() for x in range(num_params)]\n",
      "    74                                           \n",
      "    75         4           12      3.0      0.0          if self.options[\"parallel\"]:            \n",
      "    76                                                       loglikelihood_by_param = np.array(Parallel(n_jobs = -1, \n",
      "    77                                                           backend = \"multiprocessing\", verbose = 20)(\n",
      "    78                                                           delayed(self.subgraph_cross_entropy)(\n",
      "    79                                                               max_graph_params.from_dict(params)) for params in self.param_list))\n",
      "    80                                                   else:\n",
      "    81         4            8      2.0      0.0              loglikelihood_by_param = np.array([self.subgraph_cross_entropy(max_graph_params.from_dict(params)) \n",
      "    82         4     39757180 9939295.0     97.5                  for params in self.param_list])\n",
      "    83                                                   \n",
      "    84         4          360     90.0      0.0          loglikelihood = logmeanexp(loglikelihood_by_param,axis=0)\n",
      "    85                                                   \n",
      "    86         4            9      2.2      0.0          sparsity = options[\"sparsity\"]\n",
      "    87         4        93411  23352.8      0.2          logposterior = self.logposterior_from_loglik_logsparseprior(loglikelihood,sparsity=sparsity)\n",
      "    88                                           \n",
      "    89         4           31      7.8      0.0          return graphs,np.exp(logposterior),loglikelihood,self.options,self.param_list\n",
      "\n",
      "Total time: 39.757 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
      "Function: subgraph_cross_entropy at line 91\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "    91                                               @profile\n",
      "    92                                               def subgraph_cross_entropy(self,max_graph_params):\n",
      "    93         4            4      1.0      0.0          n = self.options[\"num_data_samps\"]\n",
      "    94         4           18      4.5      0.0          q = np.array(self.options[\"data_probs\"])\n",
      "    95         4           38      9.5      0.0          δ = np.array(self.options[\"data_sets\"])\n",
      "    96         4         2553    638.2      0.0          gp_out = max_graph_params.subgraph_copy(self.gs_out.edges)\n",
      "    97                                           \n",
      "    98                                           \n",
      "    99                                                   # note that q*approx_loglik_from_hidden_states should be vector)\n",
      "   100         4     39754402 9938600.5    100.0          return np.array([n*np.dot(q,self.approx_loglik_from_hidden_states(δ,graph,max_graph_params,gp_out,g_idx)) for g_idx,graph in enumerate(self.graphs)])\n",
      "\n",
      "Total time: 0 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
      "Function: gen_iter_simulations_first_only at line 102\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "   102                                               @profile\n",
      "   103                                               def gen_iter_simulations_first_only(self,gs_in,gp_in,K):\n",
      "   104                                                   # builds a simulation object and then samples returning an M lengthed generator\n",
      "   105                                                   inner_simul = InnerGraphSimulation(gs_in, gp_in)\n",
      "   106                                                   return inner_simul.sample_iter_solely_first_events(K)\n",
      "\n",
      "Total time: 37.7016 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
      "Function: gen_simulations_first_only at line 108\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "   108                                               @profile\n",
      "   109                                               def gen_simulations_first_only(self,gs_in,gp_in,K):\n",
      "   110                                                   # builds a simulation object and then samples returning an M lengthed generator\n",
      "   111      1216        11546      9.5      0.0          inner_simul = InnerGraphSimulation(gs_in, gp_in)\n",
      "   112      1216     37690016  30995.1    100.0          return inner_simul.sample_solely_first_events(K)\n",
      "\n",
      "Total time: 39.7275 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
      "Function: approx_loglik_from_hidden_states at line 114\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "   114                                               @profile\n",
      "   115                                               def approx_loglik_from_hidden_states(self,data_sets,graph,max_graph_params,gp_out,g_idx):\n",
      "   116      1216         1885      1.6      0.0          K = self.options[\"stigma_sample_size\"]\n",
      "   117                                           \n",
      "   118                                                   # gs_in = GraphStructure.from_networkx(sub_graph_from_edge_type(graph,\n",
      "   119                                                   #     edge_types=[\"hidden_sample\"]))\n",
      "   120      1216       821663    675.7      2.1          gp_in = max_graph_params.subgraph_copy(self.gs_in[g_idx].edges)\n",
      "   121                                           \n",
      "   122                                                   # hidden_states_iter = self.gen_iter_simulations_first_only(self.gs_in[g_idx],gp_in,K)\n",
      "   123      1216     37714690  31015.4     94.9          hidden_states_iter = self.gen_simulations_first_only(self.gs_in[g_idx],gp_in,K)\n",
      "   124                                           \n",
      "   125      1216         8572      7.0      0.0          temp_array = np.zeros(shape=(K,data_sets.shape[0]))\n",
      "   126                                                   # import ipdb; ipdb.set_trace()\n",
      "   127                                                   # for idx, hidden_state_sample in enumerate(hidden_states_iter):\n",
      "   128                                                   #     temp_array[idx,:] = np.array([self.loglik_with_hidden_states(data_set,hidden_state_sample,gp_out) for data_set in data_sets])\n",
      "   129                                                   # import ipdb; ipdb.set_trace()\n",
      "   130                                           \n",
      "   131                                                   # return value shape should give hidden_sample (first idx) by data_set (idx) summed loglik\n",
      "   132      1216      1025257    843.1      2.6          temp_array = self.loglik_with_hidden_states_vectorized(data_sets,hidden_states_iter,gp_out)\n",
      "   133                                                   # import IPython; IPython.embed()\n",
      "   134                                                   # import ipdb; ipdb.set_trace()\n",
      "   135      1216       155427    127.8      0.4          return logmeanexp(temp_array,axis=0)\n",
      "\n",
      "Total time: 1.01339 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
      "Function: loglik_with_hidden_states_vectorized at line 137\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "   137                                               @profile\n",
      "   138                                               def loglik_with_hidden_states_vectorized(self,data_sets,hidden_states,gp_out):\n",
      "   139                                           \n",
      "   140                                                   # K = hidden_states.shape[0]\n",
      "   141                                                   # temp_array = np.zeros(shape=(K,data_sets.shape[0]))\n",
      "   142                                           \n",
      "   143                                                   # creates new axis so that it broadcasts correctly\n",
      "   144                                           \n",
      "   145      1216         3814      3.1      0.4          h_states = hidden_states[:,np.newaxis,:]\n",
      "   146      1216         1945      1.6      0.2          d_sets = data_sets[np.newaxis,:,:]\n",
      "   147                                                   # import ipdb; ipdb.set_trace()\n",
      "   148                                                   # for i,data_set in enumerate(data_sets):\n",
      "   149                                                   #     # temp_array[:,i] = np.sum(self.multi_edge_multisample_loglik_vectorized(\n",
      "   150                                                   #     #     hidden_states,data_set,gp_out.psi,gp_out.r),axis=1)\n",
      "   151                                                   #     temp_array[:,i] = np.sum(self.multi_edge_loglik_vectorized(\n",
      "   152                                                   #         hidden_states,data_set,gp_out.psi,gp_out.r),axis=1)\n",
      "   153                                                   \n",
      "   154                                           \n",
      "   155      1216      1007627    828.6     99.4          return np.sum(self.multi_edge_loglik_vectorized(h_states,d_sets,gp_out.psi,gp_out.r),axis=-1)\n",
      "\n",
      "Total time: 0.931527 s\n",
      "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
      "Function: multi_edge_loglik_vectorized at line 157\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "   157                                               @profile\n",
      "   158                                               def multi_edge_loglik_vectorized(self, cause_time, effect_time, psi, r, T=4.0):\n",
      "   159      1216       152274    125.2     16.3          cause_time, effect_time, psi, r = np.broadcast_arrays(cause_time, effect_time, psi, r)\n",
      "   160      1216         8258      6.8      0.9          out = np.zeros(cause_time.shape)\n",
      "   161      1216        41391     34.0      4.4          cause_inf = np.isinf(cause_time)\n",
      "   162      1216         4295      3.5      0.5          cause_ok = ~cause_inf\n",
      "   163      1216        22176     18.2      2.4          effect_inf = np.isinf(effect_time)\n",
      "   164      1216         2848      2.3      0.3          effect_ok = ~effect_inf\n",
      "   165                                                   #out[(cause_time - effect_time) == 0] = 0\n",
      "   166                                                   #out[cause_inf & effect_inf] = 0\n",
      "   167      1216        23906     19.7      2.6          out[cause_inf & effect_ok] = -np.inf\n",
      "   168                                           \n",
      "   169      1216         3185      2.6      0.3          idx = cause_ok & effect_inf\n",
      "   170      1216        77480     63.7      8.3          exp_val = np.exp(-r[idx] * (T - cause_time[idx]))\n",
      "   171                                                   \n",
      "   172      1216        80570     66.3      8.6          out[idx] = -(psi[idx] / r[idx]) * (1 - exp_val)\n",
      "   173                                           \n",
      "   174                                                   # out[cause_ok & (effect_time[effect_ok] < cause_time[effect_ok])] = -np.inf\n",
      "   175      1216        46326     38.1      5.0          out[cause_ok & effect_ok & (effect_time < cause_time)] = -np.inf\n",
      "   176                                           \n",
      "   177      1216        30023     24.7      3.2          idx = cause_ok & (effect_time >= cause_time)\n",
      "   178      1216       136217    112.0     14.6          exp_val = np.exp(-r[idx] * (effect_time[idx] - cause_time[idx]))\n",
      "   179      1216       301410    247.9     32.4          out[idx] = np.log(psi[idx]) - (r[idx] * (effect_time[idx] - cause_time[idx])) - (psi[idx] / r[idx]) * (1 - exp_val)\n",
      "   180                                                   # import ipdb; ipdb.set_trace()\n",
      "   181      1216         1168      1.0      0.1          return out\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lstats = line_profiler.load_stats(\"run_simulation.py.lprof\")\n",
    "line_profiler.show_text(lstats.timings, lstats.unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Timer unit: 1e-06 s\n",
    "\n",
    "Total time: 2.90067 s\n",
    "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/graph_local_classes.py\n",
    "Function: sample_edge_first_event_only at line 241\n",
    "\n",
    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
    "==============================================================\n",
    "   241                                               @profile\n",
    "   242                                               def sample_edge_first_event_only(self, edge, time):\n",
    "   243    326725       381856      1.2     13.2          index = self.structure.edges.index(edge)\n",
    "   244                                                   \n",
    "   245                                                   # does it occur?\n",
    "   246    326725       398866      1.2     13.8          occurs = np.random.rand() < self.params.p\n",
    "   247    326725       208333      0.6      7.2          if not occurs:\n",
    "   248     65391        39652      0.6      1.4              return None\n",
    "   249                                                   \n",
    "   250                                                   # how many events?\n",
    "   251    261334       623035      2.4     21.5          num_events = np.random.poisson(lam=self.params.mu[index])\n",
    "   252    261334       187189      0.7      6.5          if num_events == 0:\n",
    "   253    130382        79183      0.6      2.7              return None\n",
    "   254                                                   \n",
    "   255                                                   # when do those events occur?\n",
    "   256    130952       870888      6.7     30.0          event_time = time + np.random.exponential(scale=self.params.r[index]/num_events, size=1)\n",
    "   257                                                   # event_times.sort()\n",
    "   258                                                   # return [(t, edge[1]) for t in event_times]\n",
    "   259                                                   # import ipdb; ipdb.set_trace()\n",
    "   260    130952       111672      0.9      3.8          return (event_time, edge[1])\n",
    "\n",
    "Total time: 12.1135 s\n",
    "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/graph_local_classes.py\n",
    "Function: _sample_solely_first_events at line 317\n",
    "\n",
    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
    "==============================================================\n",
    "   317                                               @profile\n",
    "   318                                               def _sample_solely_first_events(self, max_time=4.0):\n",
    "   319    121600       121897      1.0      1.0          pending = []\n",
    "   320    121600       187755      1.5      1.5          heapq.heappush(pending,(self.init_time, self.init_node))\n",
    "   321                                                   # pending = [(self.init_time, self.init_node)]\n",
    "   322    121600       140176      1.2      1.2          self._all_events = []\n",
    "   323    121600       386441      3.2      3.2          self._first_events = {node: np.inf for node in self.structure.nodes}\n",
    "   324    121600       111870      0.9      0.9          short_circuit = False\n",
    "   325    121600       144643      1.2      1.2          processed_nodes = set()\n",
    "   326    121600       178827      1.5      1.5          structure_nodes = set(self.structure.nodes)\n",
    "   327    342096       395455      1.2      3.3          while len(pending) > 0:\n",
    "   328                                                       # time, node = pending.pop(0)\n",
    "   329                                                       # import ipdb; ipdb.set_trace()\n",
    "   330    249445       329548      1.3      2.7              time, node = heapq.heappop(pending)\n",
    "   331    249445       452177      1.8      3.7              if time >= max_time:\n",
    "   332     14038        13882      1.0      0.1                  break\n",
    "   333                                           \n",
    "   334                                                       #self._all_events.append((time, node))\n",
    "   335    235407       392782      1.7      3.2              if time < self._first_events[node]:\n",
    "   336    234195       254233      1.1      2.1                  self._first_events[node] = time\n",
    "   337                                           \n",
    "   338    235407       238530      1.0      2.0              if node in processed_nodes:\n",
    "   339      1212         1117      0.9      0.0                  continue\n",
    "   340                                                       else:\n",
    "   341    234195       252685      1.1      2.1                  processed_nodes.add(node)\n",
    "   342    234195       237891      1.0      2.0              if processed_nodes==structure_nodes:\n",
    "   343                                                           # this only works if it is first event only\n",
    "   344     14911        15004      1.0      0.1                  break\n",
    "   345                                           \n",
    "   346    219284       251248      1.1      2.1              children_edges = self.structure.child_edges[node]\n",
    "   347                                           # this goes to each of the children_edges of the node and initiates events along that edge\n",
    "   348                                                       # import ipdb; ipdb.set_trace()\n",
    "   349    562930       602900      1.1      5.0              for edge in children_edges:\n",
    "   350    343646       380228      1.1      3.1                  if edge[1] in processed_nodes:\n",
    "   351     16921        15855      0.9      0.1                      continue\n",
    "   352                                                           else:\n",
    "   353    326725      5581106     17.1     46.1                      child_events = self.sample_edge_first_event_only(edge, time)\n",
    "   354                                                               # import ipdb; ipdb.set_trace()\n",
    "   355    326725       341669      1.0      2.8                      if not child_events:\n",
    "   356    195773       188015      1.0      1.6                          continue\n",
    "   357                                                               # import ipdb; ipdb.set_trace()\n",
    "   358    130952       236442      1.8      2.0                      heapq.heappush(pending,child_events)\n",
    "   359                                                           # import ipdb; ipdb.set_trace()\n",
    "   360                                                           # pending.sort()\n",
    "   361                                                       \n",
    "   362                                                   #self._all_events.sort()\n",
    "   363                                                   #self._compute_first_events()\n",
    "   364                                                   #return np.array(self._first_events)\n",
    "   365    121600       661143      5.4      5.5          return np.array([self._first_events[node] for node in self.structure.nodes])\n",
    "\n",
    "Total time: 21.2028 s\n",
    "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
    "Function: p_graph_given_d at line 39\n",
    "\n",
    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
    "==============================================================\n",
    "    39                                               @profile\n",
    "    40                                               def p_graph_given_d(self,graphs,options):\n",
    "    41                                                   # sets a catch for all numerical warnings to be treated as errors\n",
    "    42                                                   # np.seterr(all='raise')\n",
    "    43                                                   # np.seterr(under='raise')\n",
    "    44         4           69     17.2      0.0          np.seterr(over='raise')\n",
    "    45                                                   \"\"\"\n",
    "    46                                                   options includes \n",
    "    47                                                   sparsity: the sparsity argument for the prior \n",
    "    48                                                   param_sample_size: the number of parameters to be sampled\n",
    "    49                                                   stigma_sample_size: the number of internal states to be sampled\n",
    "    50                                                   data_sets: the different data_sets to be evaluated as likelihoods\n",
    "    51                                                   data_p: the probability of each data_set in data_sets\n",
    "    52                                                   num_data_samps: number of samples of observed data to be considered in the liklihood\n",
    "    53                                                   \"\"\"\n",
    "    54         4           10      2.5      0.0          self.graphs = graphs\n",
    "    55                                           \n",
    "    56                                                   # Note: some day this will need to change if graphs is made to be an iterator and not a list\n",
    "    57         4            7      1.8      0.0          self.max_graph = self.graphs[0]\n",
    "    58         4            8      2.0      0.0          self.options = options\n",
    "    59         4            6      1.5      0.0          num_graphs = len(graphs)\n",
    "    60                                           \n",
    "    61                                                   # loglikelihood = np.empty(len(self.graphs))\n",
    "    62         4            8      2.0      0.0          num_params= options[\"param_sample_size\"]\n",
    "    63                                           \n",
    "    64                                                   # generate 1 complete graph with many data structures shared beneath it\n",
    "    65         4           11      2.8      0.0          self.gs_out = GraphStructure.from_networkx(sub_graph_from_edge_type(self.max_graph,\n",
    "    66         4         4776   1194.0      0.0              edge_types=[\"observed\"]))\n",
    "    67                                                   \n",
    "    68         4            8      2.0      0.0          self.gs_in = [GraphStructure.from_networkx(sub_graph_from_edge_type(graph,\n",
    "    69         4       820287 205071.8      3.9              edge_types=[\"hidden_sample\"])) for graph in graphs]\n",
    "    70                                           \n",
    "    71         4          390     97.5      0.0          max_graph_params = GraphParams.from_networkx(self.max_graph)\n",
    "    72                                                   \n",
    "    73         4          668    167.0      0.0          self.param_list = [max_graph_params.sample() for x in range(num_params)]\n",
    "    74                                           \n",
    "    75         4            9      2.2      0.0          if self.options[\"parallel\"]:            \n",
    "    76                                                       loglikelihood_by_param = np.array(Parallel(n_jobs = -1, \n",
    "    77                                                           backend = \"multiprocessing\", verbose = 20)(\n",
    "    78                                                           delayed(self.subgraph_cross_entropy)(\n",
    "    79                                                               max_graph_params.from_dict(params)) for params in self.param_list))\n",
    "    80                                                   else:\n",
    "    81         4            9      2.2      0.0              loglikelihood_by_param = np.array([self.subgraph_cross_entropy(max_graph_params.from_dict(params)) \n",
    "    82         4     20291329 5072832.2     95.7                  for params in self.param_list])\n",
    "    83                                                   \n",
    "    84         4          337     84.2      0.0          loglikelihood = logmeanexp(loglikelihood_by_param,axis=0)\n",
    "    85                                                   \n",
    "    86         4            8      2.0      0.0          sparsity = options[\"sparsity\"]\n",
    "    87         4        84847  21211.8      0.4          logposterior = self.logposterior_from_loglik_logsparseprior(loglikelihood,sparsity=sparsity)\n",
    "    88                                           \n",
    "    89         4           31      7.8      0.0          return graphs,np.exp(logposterior),loglikelihood,self.options,self.param_list\n",
    "\n",
    "Total time: 20.2912 s\n",
    "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
    "Function: subgraph_cross_entropy at line 91\n",
    "\n",
    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
    "==============================================================\n",
    "    91                                               @profile\n",
    "    92                                               def subgraph_cross_entropy(self,max_graph_params):\n",
    "    93         4            6      1.5      0.0          n = self.options[\"num_data_samps\"]\n",
    "    94         4           17      4.2      0.0          q = np.array(self.options[\"data_probs\"])\n",
    "    95         4           35      8.8      0.0          δ = np.array(self.options[\"data_sets\"])\n",
    "    96         4         2470    617.5      0.0          gp_out = max_graph_params.subgraph_copy(self.gs_out.edges)\n",
    "    97                                           \n",
    "    98                                           \n",
    "    99                                                   # note that q*approx_loglik_from_hidden_states should be vector)\n",
    "   100         4     20288646 5072161.5    100.0          return np.array([n*np.dot(q,self.approx_loglik_from_hidden_states(δ,graph,max_graph_params,gp_out,g_idx)) for g_idx,graph in enumerate(self.graphs)])\n",
    "\n",
    "Total time: 0 s\n",
    "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
    "Function: gen_iter_simulations_first_only at line 102\n",
    "\n",
    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
    "==============================================================\n",
    "   102                                               @profile\n",
    "   103                                               def gen_iter_simulations_first_only(self,gs_in,gp_in,K):\n",
    "   104                                                   # builds a simulation object and then samples returning an M lengthed generator\n",
    "   105                                                   inner_simul = InnerGraphSimulation(gs_in, gp_in)\n",
    "   106                                                   return inner_simul.sample_iter_solely_first_events(K)\n",
    "\n",
    "Total time: 18.7658 s\n",
    "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
    "Function: gen_simulations_first_only at line 108\n",
    "\n",
    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
    "==============================================================\n",
    "   108                                               @profile\n",
    "   109                                               def gen_simulations_first_only(self,gs_in,gp_in,K):\n",
    "   110                                                   # builds a simulation object and then samples returning an M lengthed generator\n",
    "   111      1216         6459      5.3      0.0          inner_simul = InnerGraphSimulation(gs_in, gp_in)\n",
    "   112      1216     18759302  15427.1    100.0          return inner_simul.sample_solely_first_events(K)\n",
    "\n",
    "Total time: 20.2659 s\n",
    "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
    "Function: approx_loglik_from_hidden_states at line 114\n",
    "\n",
    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
    "==============================================================\n",
    "   114                                               @profile\n",
    "   115                                               def approx_loglik_from_hidden_states(self,data_sets,graph,max_graph_params,gp_out,g_idx):\n",
    "   116      1216         1782      1.5      0.0          K = self.options[\"stigma_sample_size\"]\n",
    "   117                                           \n",
    "   118                                                   # gs_in = GraphStructure.from_networkx(sub_graph_from_edge_type(graph,\n",
    "   119                                                   #     edge_types=[\"hidden_sample\"]))\n",
    "   120      1216       762425    627.0      3.8          gp_in = max_graph_params.subgraph_copy(self.gs_in[g_idx].edges)\n",
    "   121                                           \n",
    "   122                                                   # hidden_states_iter = self.gen_iter_simulations_first_only(self.gs_in[g_idx],gp_in,K)\n",
    "   123      1216     18776918  15441.5     92.7          hidden_states_iter = self.gen_simulations_first_only(self.gs_in[g_idx],gp_in,K)\n",
    "   124                                           \n",
    "   125      1216         6244      5.1      0.0          temp_array = np.zeros(shape=(K,data_sets.shape[0]))\n",
    "   126                                                   # import ipdb; ipdb.set_trace()\n",
    "   127                                                   # for idx, hidden_state_sample in enumerate(hidden_states_iter):\n",
    "   128                                                   #     temp_array[idx,:] = np.array([self.loglik_with_hidden_states(data_set,hidden_state_sample,gp_out) for data_set in data_sets])\n",
    "   129                                                   # import ipdb; ipdb.set_trace()\n",
    "   130                                           \n",
    "   131                                                   # return value shape should give hidden_sample (first idx) by data_set (idx) summed loglik\n",
    "   132      1216       595016    489.3      2.9          temp_array = self.loglik_with_hidden_states_vectorized(data_sets,hidden_states_iter,gp_out)\n",
    "   133                                                   # import IPython; IPython.embed()\n",
    "   134                                                   # import ipdb; ipdb.set_trace()\n",
    "   135      1216       123515    101.6      0.6          return logmeanexp(temp_array,axis=0)\n",
    "\n",
    "Total time: 0.584242 s\n",
    "File: /Users/cocosci/Dropbox/Work/Berkeley/Projects/Time/hidden_structure_inference/lib/likelihood_calculations_shared_params.py\n",
    "Function: loglik_with_hidden_states_vectorized at line 137\n",
    "\n",
    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
    "==============================================================\n",
    "   137                                               @profile\n",
    "   138                                               def loglik_with_hidden_states_vectorized(self,data_sets,hidden_states,gp_out):\n",
    "   139                                           \n",
    "   140                                                   # K = hidden_states.shape[0]\n",
    "   141                                                   # temp_array = np.zeros(shape=(K,data_sets.shape[0]))\n",
    "   142                                           \n",
    "   143                                                   # creates new axis so that it broadcasts correctly\n",
    "   144                                           \n",
    "   145      1216         3349      2.8      0.6          h_states = hidden_states[:,np.newaxis,:]\n",
    "   146      1216         1804      1.5      0.3          d_sets = data_sets[np.newaxis,:,:]\n",
    "   147                                                   # import ipdb; ipdb.set_trace()\n",
    "   148                                                   # for i,data_set in enumerate(data_sets):\n",
    "   149                                                   #     # temp_array[:,i] = np.sum(self.multi_edge_multisample_loglik_vectorized(\n",
    "   150                                                   #     #     hidden_states,data_set,gp_out.psi,gp_out.r),axis=1)\n",
    "   151                                                   #     temp_array[:,i] = np.sum(self.multi_edge_loglik_vectorized(\n",
    "   152                                                   #         hidden_states,data_set,gp_out.psi,gp_out.r),axis=1)\n",
    "   153                                                   \n",
    "   154                                           \n",
    "   155      1216       579089    476.2     99.1          return np.sum(self.multi_edge_loglik_vectorized(h_states,d_sets,gp_out.psi,gp_out.r),axis=-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_line_profiler.LineStats at 0x1103d3a90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(a,b) for a,b in enumerate(range(5))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "test = np.array([0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile(test,[5,1])\n",
    "np.tile([0,1,2],[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[], [], [], [], [], [], [], [], [], []]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[]]*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "test = np.array([[1,2],[1,2]])\n",
    "np.random.poisson(lam=test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "test = np.array([x for x in range(16)])\n",
    "test.resize((2,8))\n",
    "test2 = np.array([print(i) for i,x in enumerate(test)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7]\n",
      "[ 8  9 10 11 12 13 14 15]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[print(x) for x in test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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