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 "cells": [
  {
   "cell_type": "markdown",
   "id": "dab9797a-d695-430d-9345-801697b584db",
   "metadata": {},
   "source": [
    "# Dynex SDK - Hello World "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5c69b7",
   "metadata": {},
   "source": [
    "Please copy \"dynex.ini\" (your configuration file) in the working directory before using this example! The file can be downloaded in your market place, section \"INSTALLATON\" - \"STEP 2\" - button \"DOWNLOAD\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce9c9138-5168-49a6-953b-69dd0c2cd297",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dynex\n",
    "import dimod\n",
    "from pyqubo import Array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d50bf1-174a-49a2-8e4a-4b38d755e3b9",
   "metadata": {},
   "source": [
    "## Verify Version of Dynex SDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf95e0fb-fd5e-42ef-a802-eaf70fb5eee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dynex.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8795b4-bfd8-486e-b2e0-d98785b2aa49",
   "metadata": {},
   "source": [
    "## Test your configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e763f4f-d24f-4ca7-93a3-cdab70950a0a",
   "metadata": {},
   "source": [
    "Successful run is required to start sampling with the Dynex SDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "442e7b70-7213-430c-82ef-5b165d1f382a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dynex.test()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fced7cc0-66eb-4269-b7b4-b284516dcb3e",
   "metadata": {},
   "source": [
    "## View your API acccount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31e09dcd-a9bb-4d7a-80e0-f343faa3f3d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dynex.account_status()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e49bfb6-7181-46e1-85a2-af8c051d9139",
   "metadata": {},
   "source": [
    "## Create a simple QUBO formulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aebae30f-3fd8-4f3a-9cd2-bbc9281be89a",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 15\n",
    "K = 3\n",
    "numbers = [4.8097315016016315, 4.325157567810298, 2.9877429101815127,\n",
    "           3.199880179616316, 0.5787939511978596, 1.2520928214246918,\n",
    "           2.262867466401502, 1.2300003067401255, 2.1601079352817925,\n",
    "           3.63753899583021, 4.598232793833491, 2.6215815162575646,\n",
    "           3.4227134835783364, 0.28254151584552023, 4.2548151473817075]\n",
    "\n",
    "q = Array.create('q', N, 'BINARY')\n",
    "H = sum(numbers[i] * q[i] for i in range(N)) + 5.0 * (sum(q) - K)**2\n",
    "model = H.compile()\n",
    "Q, offset = model.to_qubo(index_label=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3f7fb3c-69f0-4078-92d7-1910533744b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BQM from QUBO:\n",
    "bqm = dimod.BinaryQuadraticModel.from_qubo(Q, offset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82490d8-17f9-47c5-a0a2-84bcc6611939",
   "metadata": {},
   "source": [
    "## Sample on Dynex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c390424-e2ed-4c12-8d3c-12ce9a86d4af",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = dynex.BQM(bqm);\n",
    "sampler = dynex.DynexSampler(model,  mainnet=False, description='Dynex SDK test');\n",
    "sampleset = sampler.sample(num_reads=5000, annealing_time = 200, debugging=False);\n",
    "print('Result:')\n",
    "print(sampleset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "171a368e-bf05-46f7-aaaf-122034f23dcd",
   "metadata": {},
   "source": [
    "## Draw Sampleset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb8bd278-3162-4b72-9315-a1c64313e7bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d64d678e-62a4-42a5-90fd-d50d2e493905",
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate data:\n",
    "data = np.zeros(len(sampleset.variables)) \n",
    "for s in sampleset.record: #s[0] samples [1] energy [2] num_oc\n",
    "    for i in range(0,len(s[0])):\n",
    "        data[i] += ( s[0][i] * s[2] ); # weighted by num_oc\n",
    "\n",
    "plt.bar(sampleset.variables, data)\n",
    "plt.bar(sampleset.variables, sampleset.first.sample.values())\n",
    "plt.xlabel(\"Variables\")\n",
    "plt.ylabel(\"Occurences (positive)\")\n",
    "plt.title(\"Sampleset (orange: lowest ground state)\")\n",
    "plt.show() "
   ]
  }
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