test_mGFPHLarge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "857a9c6e-8b84-488e-bf71-eab93994ef50",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run stdPackages.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c1efa0-f21b-48ae-9c6a-19a1b6b4e319",
   "metadata": {},
   "source": [
    "# Read database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f8e2e94-9aaf-4d90-a510-8f34f1006b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyDbs import read, readSets, broadcast\n",
    "kwargs = {'variables': ['Fundamentals', 'LoadVariables', 'TransmissionLines', 'GeneratorsVariables'],\n",
    "        'maps': ['LoadMaps','GeneratorsMaps'],\n",
    "        'variable2D': ['LoadVariation_E','LoadVariation_H','HourlyVariation'],\n",
    "        'scalars': ['Scalars']}\n",
    "db = read.dbFromWB(os.path.join(d['data'],'mGFPH.xlsx'), kwargs)\n",
    "readSets(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f2a0856-aa41-41c1-ba13-fd8fe6a48a30",
   "metadata": {},
   "source": [
    "1000 hours:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0db69a92-568b-4390-b0f8-1be0a1674aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "[db.__setitem__(k,adj.rc_pd(db[k], db['h'][0:1000])) for k in db.variableDomains('h', types=['set','variable'])];"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb85832-2dc8-42bf-9810-2df8bd2e32e4",
   "metadata": {},
   "source": [
    "Time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "206b8e2c-b560-49b3-b978-26ba83d86bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'setup': 0.11709332466125488,\n",
       " 'initBlocks': 1.9159445762634277,\n",
       " 'compileParameters': 1.0341877937316895,\n",
       " 'settingsFromCompiled': 0.0010001659393310547,\n",
       " 'inferGlobalDomains': 0.21129441261291504,\n",
       " 'getDenseArgs': 0.651397705078125,\n",
       " 'lp_args': 0.385509729385376,\n",
       " 'sol': 2.093674421310425,\n",
       " 'postSolve': 0.5073814392089844}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "times = {}\n",
    "t0 = time.time()\n",
    "# Initialize model:\n",
    "m = mGFPH.mSimple(db) # initialize using the database 'db' from above.\n",
    "times['setup'] = time.time()-t0\n",
    "# Initialize blocks:\n",
    "m.preSolve()\n",
    "m.initBlocks()\n",
    "times['initBlocks'] = time.time()-t0-sum(times.values())\n",
    "# Compile model:\n",
    "m.blocks.compileParameters()\n",
    "times['compileParameters'] = time.time()-t0-sum(times.values())\n",
    "m.blocks.settingsFromCompiled()\n",
    "times['settingsFromCompiled'] = time.time()-t0-sum(times.values())\n",
    "m.blocks.inferGlobalDomains()\n",
    "times['inferGlobalDomains'] = time.time()-t0-sum(times.values())\n",
    "m.blocks.getDenseArgs()\n",
    "times['getDenseArgs'] = time.time()-t0-sum(times.values())\n",
    "args = m.blocks.lp_args\n",
    "times['lp_args'] = time.time()-t0-sum(times.values())\n",
    "sol = optimize.linprog(method = m.method, **args)\n",
    "times['sol'] = time.time()-t0-sum(times.values())\n",
    "m.postSolve(sol)\n",
    "times['postSolve'] = time.time()-t0-sum(times.values())\n",
    "times"
   ]
  }
 ],
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