{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85\n",
      "63\n",
      "51\n",
      "26\n",
      "30\n",
      "4\n",
      "7\n",
      "1\n",
      "17\n",
      "81\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 2])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.randint(0.5, 3.5)\n",
    "p_remove = 0.9\n",
    "np.random.choice([True, False], p=[p_remove, 1-p_remove])\n",
    "rng = np.random.default_rng(0)\n",
    "for _ in range(10):\n",
    "    print(rng.integers(0,100))\n",
    "np.random.choice([1, 2, 3], 2, p=[0.4, 0.4, 0.2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "share = 0.8\n",
    "list_succ_firms = [1, 1]\n",
    "round(share * len(list_succ_firms)) if round(share * len(list_succ_firms)) > 0 else 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.17307692307692307, 0.19230769230769232, 0.20192307692307693, 0.21153846153846154, 0.22115384615384615]\n",
      "[0.14899116146026878, 0.1819782155490595, 0.20111703154812216, 0.22226869439668717, 0.24564489704586234]\n",
      "[0.10801741721030356, 0.16114305076975205, 0.19682056666851946, 0.2403971829915773, 0.29362178235984765]\n",
      "[0.07643198434626533, 0.13926815562848321, 0.18799234648997357, 0.25376312466637047, 0.34254438886890737]\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "size = [18,20,21,22,23]\n",
    "p = [s / sum(size) for s in size]\n",
    "print(p)\n",
    "for beta in [0.1, 0.2, 0.3]:\n",
    "    damp_size = [math.exp(beta*s) for s in size]\n",
    "    print([s / sum(damp_size) for s in damp_size])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.16666666666666666, 0.5, 0.6666666666666666, 0.8333333333333334, 1.0]\n",
      "[0.8359588020779368, 0.9330329915368074, 0.960264500792218, 0.9819330445619127, 1.0]\n",
      "[0.408248290463863, 0.7071067811865476, 0.816496580927726, 0.9128709291752769, 1.0]\n",
      "[0.23849484685087588, 0.5743491774985174, 0.7229811807984657, 0.8642810744472068, 1.0]\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "size = [18,20,21,22,23]\n",
    "p = [(s - min(size) + 1)/(max(size)-min(size)+1) for s in size]\n",
    "print(p)\n",
    "for beta in [0.1, 0.5, 0.8]:\n",
    "    p = [((s - min(size) + 1)/(max(size)-min(size)+1))**beta for s in size]\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    }
   ],
   "source": [
    "import multiprocess as mp\n",
    "\n",
    "print(mp.cpu_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71\n"
     ]
    }
   ],
   "source": [
    "from orm import engine\n",
    "import pandas as pd\n",
    "import pickle\n",
    "str_sql = \"select e_id, count, max_max_ts, dct_lst_init_remove_firm_prod from iiabmdb.without_exp_experiment as a \" \\\n",
    "\"inner join \" \\\n",
    "\"(select e_id, count(id) as count, max(max_ts) as max_max_ts from iiabmdb.without_exp_sample as a \" \\\n",
    "\"inner join (select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id) as b \" \\\n",
    "\"on a.id = b.s_id \" \\\n",
    "\"group by e_id) as b \" \\\n",
    "\"on a.id = b.e_id \" \\\n",
    "\"order by count desc;\"\n",
    "result = pd.read_sql(sql=str_sql, con=engine)\n",
    "result['dct_lst_init_remove_firm_prod'] = result['dct_lst_init_remove_firm_prod'].apply(lambda x: pickle.loads(x))\n",
    "# print(result)\n",
    "list_dct = result.loc[result['count']>=9, 'dct_lst_init_remove_firm_prod'].to_list()\n",
    "print(len(list_dct))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "probabilities do not sum to 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m np\u001b[39m.\u001b[39;49mrandom\u001b[39m.\u001b[39;49mchoice([\u001b[39m1\u001b[39;49m], p\u001b[39m=\u001b[39;49m[\u001b[39m0.9\u001b[39;49m])\n",
      "File \u001b[1;32mmtrand.pyx:933\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: probabilities do not sum to 1"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.choice([1], p=[0.9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004495606232695251"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_remove = 0\n",
    "prob_remove = np.random.uniform(\n",
    "    prob_remove - 0.1, prob_remove + 0.1)\n",
    "prob_remove = 1 if prob_remove > 1 else prob_remove\n",
    "prob_remove = 0 if prob_remove < 0 else prob_remove\n",
    "prob_remove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8]\n"
     ]
    }
   ],
   "source": [
    "nprandom = np.random.default_rng(0)\n",
    "lst_choose_firm = nprandom.choice(range(10),\n",
    "                                       1,\n",
    "                                       replace=False\n",
    "                                       )\n",
    "print(lst_choose_firm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot take a larger sample than population when replace is False",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m nprandom \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mdefault_rng(\u001b[39m0\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m lst_choose_firm \u001b[39m=\u001b[39m nprandom\u001b[39m.\u001b[39;49mchoice([\u001b[39m1\u001b[39;49m,\u001b[39m2\u001b[39;49m],\n\u001b[0;32m      3\u001b[0m                                   \u001b[39m3\u001b[39;49m,\n\u001b[0;32m      4\u001b[0m                                   replace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[0;32m      5\u001b[0m                                   )\n\u001b[0;32m      6\u001b[0m lst_choose_firm\n",
      "File \u001b[1;32m_generator.pyx:753\u001b[0m, in \u001b[0;36mnumpy.random._generator.Generator.choice\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Cannot take a larger sample than population when replace is False"
     ]
    }
   ],
   "source": [
    "nprandom = np.random.default_rng(0)\n",
    "lst_choose_firm = nprandom.choice([1,2],\n",
    "                                  3,\n",
    "                                  replace=False\n",
    "                                  )\n",
    "lst_choose_firm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n",
      "0 1\n",
      "0 2\n",
      "1 0\n",
      "1 1\n",
      "break\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for j in range(3):\n",
    "    for k in range(3):\n",
    "        print(j, k)\n",
    "        if j == k == 1:\n",
    "            print('break')\n",
    "            break\n",
    "    else:\n",
    "        continue\n",
    "    break\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.25\n",
      "2.25\n"
     ]
    }
   ],
   "source": [
    "print(27 / (4 * 3))\n",
    "print(27 / 4 / 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1,1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = list(range(1,11))\n",
    "print(lst)\n",
    "\n",
    "lst[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>e_id</th>\n",
       "      <th>n_disrupt_sample</th>\n",
       "      <th>total_n_disrupt_firm_prod_experiment</th>\n",
       "      <th>dct_lst_init_disrupt_firm_prod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>383</td>\n",
       "      <td>50</td>\n",
       "      <td>300.0</td>\n",
       "      <td>b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>227</td>\n",
       "      <td>50</td>\n",
       "      <td>250.0</td>\n",
       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83</td>\n",
       "      <td>50</td>\n",
       "      <td>200.0</td>\n",
       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135</td>\n",
       "      <td>50</td>\n",
       "      <td>200.0</td>\n",
       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>179</td>\n",
       "      <td>50</td>\n",
       "      <td>200.0</td>\n",
       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>56.0</td>\n",
       "      <td>b'\\x80\\x05\\x95\\x14\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>89</td>\n",
       "      <td>24</td>\n",
       "      <td>54.0</td>\n",
       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>90</td>\n",
       "      <td>24</td>\n",
       "      <td>54.0</td>\n",
       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>335</td>\n",
       "      <td>24</td>\n",
       "      <td>54.0</td>\n",
       "      <td>b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>449</td>\n",
       "      <td>24</td>\n",
       "      <td>53.0</td>\n",
       "      <td>b'\\x80\\x05\\x95\\x15\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    e_id  n_disrupt_sample  total_n_disrupt_firm_prod_experiment  \\\n",
       "0    383                50                                 300.0   \n",
       "1    227                50                                 250.0   \n",
       "2     83                50                                 200.0   \n",
       "3    135                50                                 200.0   \n",
       "4    179                50                                 200.0   \n",
       "..   ...               ...                                   ...   \n",
       "90    76                24                                  56.0   \n",
       "91    89                24                                  54.0   \n",
       "92    90                24                                  54.0   \n",
       "93   335                24                                  54.0   \n",
       "94   449                24                                  53.0   \n",
       "\n",
       "                       dct_lst_init_disrupt_firm_prod  \n",
       "0   b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
       "1   b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
       "2   b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
       "3   b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
       "4   b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
       "..                                                ...  \n",
       "90  b'\\x80\\x05\\x95\\x14\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
       "91  b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
       "92  b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
       "93  b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
       "94  b'\\x80\\x05\\x95\\x15\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
       "\n",
       "[95 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('SQL_export_high_risk_setting.sql', 'r') as f:\n",
    "    contents = f.read()\n",
    "\n",
    "import pandas as pd\n",
    "from orm import engine\n",
    "result = pd.read_sql(sql=contents, con=engine)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "\n",
    "G = nx.MultiDiGraph()\n",
    "G.add_edge(1, 2)\n",
    "nx.draw(G)\n",
    "G.has_edge(1, 2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "bcdafc093860683ffb58d6956591562b7f8ed5d58147d17d71a5d4d6605a08df"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}