Tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Basic stuff\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from MTase.ActivityClassifier import ActivityClassifier, AverageRF\n",
    "from MTase.utils import train_test_split_data, evaluate_classifier_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook, tqdm_pandas\n",
    "from tqdm.auto import tqdm\n",
    "tqdm.pandas(tqdm_notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem import PandasTools\n",
    "from rdkit.Chem.rdmolops import RDKFingerprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = AverageRF(model_type='classifier', n_forests=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#active = pd.read_pickle(\"./active.pkl.bz2\")\n",
    "#inactive = pd.read_pickle(\"./inactive.pkl.bz2\")\n",
    "inconclusive = pd.read_pickle(\"./inconclusive.pkl.bz2\")\n",
    "#active['ActiveBit']   = True\n",
    "#inactive['ActiveBit'] = False\n",
    "#columns = ['PUBCHEM_SID', 'RDKit Fingerprints', 'ActiveBit']\n",
    "#data = pd.concat([active.filter(columns,axis=1),inactive],\n",
    "#                 axis=0,join='inner', ignore_index=True)\n",
    "#X_data  = np.array(list(data['RDKit Fingerprints'])).astype(int)\n",
    "#y_data = data['ActiveBit'].values\n",
    "#X_train, X_test, y_train, y_test = train_test_split_data(X_data, y_data, rate_inactives=0.90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gmseabra\\Miniconda3\\envs\\unc\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\gmseabra\\Miniconda3\\envs\\unc\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\gmseabra\\Miniconda3\\envs\\unc\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\gmseabra\\Miniconda3\\envs\\unc\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\gmseabra\\Miniconda3\\envs\\unc\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<MTase.ActivityClassifier.AverageRF at 0x168d257ca90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "803efa3d429840a3b13b25633d7c532a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gmseabra\\Miniconda3\\envs\\unc\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "predictor = ActivityClassifier(my_model,'rdkit')\n",
    "test = inconclusive[:10]\n",
    "test[\"ActiveBit\"] = test[\"SMILES\"].progress_apply(predictor.predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1510    0.26\n",
       "1763    0.14\n",
       "2164    0.60\n",
       "3318    0.22\n",
       "3418    0.14\n",
       "3444    0.30\n",
       "3599    0.20\n",
       "3600    0.28\n",
       "3710    0.18\n",
       "3853    0.10\n",
       "Name: ActiveBit, dtype: float64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.ActiveBit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4d9e70e73ac4635b4884eff566d9c01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Generating predictions:', max=10, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = predictor.predict(test.SMILES.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mol is None!!\n"
     ]
    },
    {
     "ename": "ArgumentError",
     "evalue": "Python argument types in\n    rdkit.Chem.rdmolops.RDKFingerprint(NoneType)\ndid not match C++ signature:\n    RDKFingerprint(class RDKit::ROMol mol, unsigned int minPath=1, unsigned int maxPath=7, unsigned int fpSize=2048, unsigned int nBitsPerHash=2, bool useHs=True, double tgtDensity=0.0, unsigned int minSize=128, bool branchedPaths=True, bool useBondOrder=True, class boost::python::api::object atomInvariants=0, class boost::python::api::object fromAtoms=0, class boost::python::api::object atomBits=None, class boost::python::api::object bitInfo=None)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-151-9908f7776b85>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmol\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Mol is None!!\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRDKFingerprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mToBitString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mArgumentError\u001b[0m: Python argument types in\n    rdkit.Chem.rdmolops.RDKFingerprint(NoneType)\ndid not match C++ signature:\n    RDKFingerprint(class RDKit::ROMol mol, unsigned int minPath=1, unsigned int maxPath=7, unsigned int fpSize=2048, unsigned int nBitsPerHash=2, bool useHs=True, double tgtDensity=0.0, unsigned int minSize=128, bool branchedPaths=True, bool useBondOrder=True, class boost::python::api::object atomInvariants=0, class boost::python::api::object fromAtoms=0, class boost::python::api::object atomBits=None, class boost::python::api::object bitInfo=None)"
     ]
    }
   ],
   "source": [
    "test_smiles = \"CC1=C(SC=C1)/C=C\\2/C(=O)C3=C(O2)C=C(C=C3)O\"\n",
    "mol = Chem.MolFromSmiles(test_smiles)\n",
    "if mol is None:\n",
    "    print(\"Mol is None!!\")\n",
    "fp = RDKFingerprint(mol)\n",
    "fp = np.array(list(map(int,fp.ToBitString())))\n",
    "fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.86, 0.14]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_model.predict_proba([fp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = predictor.classify_one(test_smiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1510                                        CC1=C(SC=C1)/C=C\\2/C(=O)C3=C(O2)C=C(C=C3)O\n",
       "1763    CC(C)OC1=CC=CC=C1/C=C\\2/C(=O)N(/C(=C(/C#N)\\C(=O)NC3=CC=C(C=C3)C(=O)O)/S2)CCCOC\n",
       "2164                C1OC2=C(O1)C=C(C=C2)/C=C\\3/C(=O)N(C(=S)S3)CCC(=O)N(CCO)C4=CC=CC=C4\n",
       "3318    CCN\\1C2=CC=CC=C2C(/C1=C\\C=C\\C3=[N+](C4=CC=CC=C4C3(C)C)CC)(C)C.[O-]Cl(=O)(=O)=O\n",
       "3418                                     C1=CSC(=C1)C=C(C#N)C2=NC(=CS2)C3=CC=C(C=C3)Cl\n",
       "Name: SMILES, dtype: object"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.SMILES.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=300x300 at 0x1695700E4E0>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Draw.MolToImage(Chem.MolFromSmiles(\"C1=CSC(=C1)C=C(C#N)C2=NC(=CS2)C3=CC=C(C=C3)Cl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    COC1=CC(=O)C(=C(C1=O)CCCCCCC/C=C/CCCCCCCC2=C(C(=O)C=C(C2=O)OC)O)O\n",
       "1                    C1=CC2=C(C=C1[O-])OC3=CC(=O)C=CC3=[N+]2[O-].[Na+]\n",
       "2        CC1=CC=C(O1)C(=O)C2=C(C(=O)N(C2C3=CC(=CC=C3)OCC=C)C4=NC=CS4)O\n",
       "3                                  CC1=NC2=C(C=C1)C(=O)C(=C(C2=O)Br)OC\n",
       "4                                     COC1=C(C(=O)C2=C(C1=O)C=CC=N2)Br\n",
       "Name: SMILES, dtype: object"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active.SMILES.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True,  True, False,  True, False,  True, False,\n",
       "       False])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(test) > 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.26, 0.6 , 0.22, 0.3 , 0.28])"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[x>=0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 10*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=float64)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[x>=0.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'>' not supported between instances of 'list' and 'float'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-197-705f90575d45>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtest\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: '>' not supported between instances of 'list' and 'float'"
     ]
    }
   ],
   "source": [
    "np.array(test)[test > 0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x[x>=0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "smi = active.SMILES.head(10).values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['COC1=CC(=O)C(=C(C1=O)CCCCCCC/C=C/CCCCCCCC2=C(C(=O)C=C(C2=O)OC)O)O',\n",
       " 'C1=CC2=C(C=C1[O-])OC3=CC(=O)C=CC3=[N+]2[O-].[Na+]',\n",
       " 'CC1=CC=C(O1)C(=O)C2=C(C(=O)N(C2C3=CC(=CC=C3)OCC=C)C4=NC=CS4)O',\n",
       " 'CC1=NC2=C(C=C1)C(=O)C(=C(C2=O)Br)OC',\n",
       " 'COC1=C(C(=O)C2=C(C1=O)C=CC=N2)Br',\n",
       " 'C1=CC=C(C=C1)C(=O)NC2=C(C(=O)C(=C(C2=O)Cl)NC(=O)C3=CC=CC=C3)Cl',\n",
       " 'CCC(=O)NC1=C(C(=O)C(=C(C1=O)Cl)NC(=O)CC)Cl',\n",
       " 'COC1=C2C(=C(C=C1)OC)C(=O)C(=C(C2=O)Cl)Cl',\n",
       " 'C1=CC=C(C=C1)OC2=C(C(=O)C3=CC=CC=C3C2=O)Cl',\n",
       " 'C1=CC=C(C(=C1)C2=C3C=CC(=O)C(=C3OC4=C2C=CC(=C4O)O)O)S(=O)(=O)O']"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "smi = np.array(smi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel = smi[x>=.2].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'values'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-240-600abc2c5249>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'values'"
     ]
    }
   ],
   "source": [
    "x.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<COC1=CC(=O)C(=C(C1=O)CCCCCCC/C=C/CCCCCCCC2=C(C(=O)C=C(C2=O)OC)O)O>',\n",
       " '<CC1=CC=C(O1)C(=O)C2=C(C(=O)N(C2C3=CC(=CC=C3)OCC=C)C4=NC=CS4)O>',\n",
       " '<CC1=NC2=C(C=C1)C(=O)C(=C(C2=O)Br)OC>',\n",
       " '<C1=CC=C(C=C1)C(=O)NC2=C(C(=O)C(=C(C2=O)Cl)NC(=O)C3=CC=CC=C3)Cl>',\n",
       " '<COC1=C2C(=C(C=C1)OC)C(=O)C(=C(C2=O)Cl)Cl>']"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel = []\n",
    "[sel.append('<'+s+'>') for s in smi[x>=.2]]\n",
    "#for s in smi[x>=.2]:\n",
    "#    s = '<'+s+'>'\n",
    "#    sel.append(s)\n",
    "sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object <genexpr> at 0x0000016957B3E8B8>\n"
     ]
    }
   ],
   "source": [
    "for s in sel:\n",
    "    print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<COC1=CC(=O)C(=C(C1=O)CCCCCCC/C=C/CCCCCCCC2=C(C(=O)C=C(C2=O)OC)O)O>',\n",
       " '<CC1=CC=C(O1)C(=O)C2=C(C(=O)N(C2C3=CC(=CC=C3)OCC=C)C4=NC=CS4)O>',\n",
       " '<CC1=NC2=C(C=C1)C(=O)C(=C(C2=O)Br)OC>',\n",
       " '<C1=CC=C(C=C1)C(=O)NC2=C(C(=O)C(=C(C2=O)Cl)NC(=O)C3=CC=CC=C3)Cl>',\n",
       " '<COC1=C2C(=C(C=C1)OC)C(=O)C(=C(C2=O)Cl)Cl>']"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<COC1=CC(=O)C(=C(C1=O)CCCCCCC/C=C/CCCCCCCC2=C(C(=O)C=C(C2=O)OC)O)O>\n",
      "<CC1=CC=C(O1)C(=O)C2=C(C(=O)N(C2C3=CC(=CC=C3)OCC=C)C4=NC=CS4)O>\n",
      "<CC1=NC2=C(C=C1)C(=O)C(=C(C2=O)Br)OC>\n",
      "<C1=CC=C(C=C1)C(=O)NC2=C(C(=O)C(=C(C2=O)Cl)NC(=O)C3=CC=CC=C3)Cl>\n",
      "<COC1=C2C(=C(C=C1)OC)C(=O)C(=C(C2=O)Cl)Cl>\n"
     ]
    }
   ],
   "source": [
    "for s,smi in enumerate(sel):\n",
    "    print(sel[s])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuple_list = []\n",
    "for i in range(10):\n",
    "    tuple_list.append((i, 10*i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E5DDmr9vJ6CkZLN60h4v7teHei3vSvL6aqknpollsfYOZPQKsBfKAWSVCvyZwHfCLUh6ijpmlE1yofaK7vxZpkJmNAkYBpKWllemHEElGeQeK+OM7S3ny45WkNqjNkz8McG7PlvEuSxJANFM9TYBhQEdgJ/CqmV3r7i+EhvwN+MjdPy7lIdLcPdvMOgHvmVmmu68oOcjdJwGTILjY+hH8LCJJ4/OV2xgzJYPV23K5akB7xlzQg0Z11VRNohPNVM85wCp3zwEws6nAIOAFM7sXSAVuLm1nd88OXa40sw+A44DvBL+IHN6e/AImvrmYF+esJa1pCi+NHMigzmqqJmUTTfCvBU4ysxSCUz2DgXQzGwmcDwx29+JIO4Z+W8h19/1m1hw4BXg4NqWLJJf3Fm9m3LQsNu/OZ+SpHfnVeV1JqaVPZEvZRTPHP8fMJgPzCM7Tf0VwSmYfsAb4LPTJganufp+ZBYBb3H0k0AN4wsyKgWoE5/gXls+PIlI1bdu7n/tmLmT619l0bVmfv10ziOPS1FRNjpy5V77p9EAg4Onp6fEuQySu3J3/ZGxk/IwF7Mkv4CdndubWszpTq4baLch3mdlcdw9EM1a/J4pUQpt25XPXa5m8s2gL/do14qErBtK9lZqqSWwo+EUqEXfn5S/X8cDriygoLmbc0B7ceGpHqqvdgsSQgl+kkli9dR9jp2by2cptnNSpKRMv60uH5vXiXZZUQQp+kTgrKnae+e8qfj97CTWrVePBy/pw5Ynt1W5Byo2CXySOlmzawx2T5zN//S7O6dGCCZf2oVWjOvEuS6o4Bb9IHBwoLOax95fztw+W06BOTf581XFc3Le1zvKlQij4RSrY1+t2csfk+SzdvJdh/dtw78W9aFqvVrzLkiSi4BepILkHCvnDrKU888kqWjSow9MjAgzuoaZqUvEU/CIV4NPlWxkzNZO123O5ZmAaoy/oTsM6aqom8aHgFylHu/IKePCNRbz85To6NEvh5VEncVKnZvEuS5Kcgl+knMxeuJm7XsskZ89+bj69E788pyt1a1WPd1kiCn6RWNu6dz/jZyxgZsZGurdqwJM/DNC3XeN4lyXyDQW/SIy4O9O/zuY3/1nA3v2F/OrcrtxyxrFqqiaVjoJfJAayd+Yxblom7y/JoX/7xjx8RV+6tmwQ77JEIlLwixyF4mLnxS/W8tCbiykqdu6+qCfXD+qgpmpSqUUV/GZ2GzAScCATuAFoDbwMNCW4SMt17n4gwr5jgZuAIuDn7v52bEoXia9VW/cxekoGX6zazimdm/Hg8L6kNUuJd1kihxXNYuttgZ8DPd09z8xeAa4EhgKPuvvLZvY4wXD/e4l9e4bG9gLaAO+YWVd3L4rxzyFSYQqLinnqv6t4dPZSatWoxsOX9+V7gXZqtyAJI9qpnhpAXTMrAFKAjcDZwNWh+58DxlMi+IFhwMvuvh9YZWbLgQHAZ0dZt0hcLMzezegpGWRu2MW5PVsy4dLetGyopmqSWKJZc3eDmT1CcNH1PGAWMBfY6e6FoWHrgbYRdm8LfB52u7RxIpXa/sIi/vrecv7+wQoap9TksauPZ2ifVjrLl4QUzVRPE4Jn7h2BncCrwAURhkZavDfSqyLiIr9mNgoYBZCWlna4skQqzNw1Oxg9JYPlW/Yy/Li23HNRT5qoqZoksGimes4BVrl7DoCZTQUGAY3NrEborL8dkB1h3/VA+7DbpY3D3ScBkyC42HrUP4FIOdm3v5BHZi3hH5+upnXDOjx7w4mc1a1FvMsSOWrRBP9a4CQzSyE41TMYSAfeB64g+MmeEcD0CPvOAF4ysz8QfHO3C/BFDOoWKVcfL8th7NRM1u/I44cnH8MdQ7pTv7Y+/SxVQzRz/HPMbDLBj2wWAl8RPDN/HXjZzCaEtj0NYGaXAAF3v8fdF4Q+BbQwtO+t+kSPVGa7cgv47RsLeSV9PR2b1+OVm09mQMem8S5LJKbMvfLNqgQCAU9PT493GZJk3sraxN3Ts9i+7wCjTu/ELwZ3oU5NNVWTxGBmc909EM1Y/e4qSS9nT7Cp2uuZG+nRuiHPjDiRPu0axbsskXKj4Jek5e5MnbeB+2YuJO9AEbef341Rp3eiZnU1VZOqTcEvSWn9jlzunJbFR0tzOOGYJjx0eV86t6gf77JEKoSCX5JKcbHzwpw1PPTmYhwYf3FPfnhyB6qpqZokEQW/JI0VOXsZMyWDL1fv4LQuzXlgeB/aN1VTNUk+Cn6p8gqKinny45X88Z1l1KlRjd9d0ZcrTlBTNUleCn6p0rI27GL0lAwWZO9mSK9W3HdpL1o0UFM1SW4KfqmS8guK+PO7y3jio5U0SanF3685ngv6tI53WSKVgoJfqpz01du5Y0oGK3P2ccUJ7bjrwh40TlFTNZGDFPxSZezdX8jv3lrM85+voU2jujx/4wBO75oa77JEKh0Fv1QJHy7N4c6pmWTvymPEyR24/fxu1FNTNZGI9MqQhLYz9wD3z1zElHnr6ZRaj1dvPplABzVVEzkUBb8krDcyN3LP9Cx25BZw61nH8rOz1VRNJBoKfkk4W3bnc8/0Bby1YBO92jTkuRsH0KuNmqqJREvBLwnD3Xl17nomzFxIfmExo4d050endaSGmqqJlImCXxLCuu253Dktk4+XbeXEDk2YeHlfjk1VUzWRIxHNYuvdgH+HbeoE3AOcDHQLbWsM7HT3/hH2Xw3sAYqAwmgXChABKCp2nv9sNb97ewkG3D+sF9cMPEZN1USOQjRLLy4B+gOYWXVgAzDN3f94cIyZ/R7YdYiHOcvdtx5lrZJklm/Zwx2TM5i3didndE3lt8N7066JmqqJHK2yTvUMBla4+5qDGyzY6er7wNmxLEySV0FRMU98uII/v7uclNrV+cP3+zH8uLZqqiYSI2UN/iuBf5XYdhqw2d2XlbKPA7PMzIEn3H1SGb+nJJHM9bu4ffJ8Fm/aw4V9WzP+4l6kNqgd77JEqpSog9/MagGXAGNL3HUV3/3PINwp7p5tZi2A2Wa22N0/ivD4o4BRAGlpadGWJVVEfkERf3xnGU9+vJKm9WrxxHUncH6vVvEuS6RKKssZ/wXAPHfffHCDmdUALgNOKG0nd88OXW4xs2nAAOA7wR/6TWASQCAQ8DLUJQluzsptjJmayaqt+/hBoD13Du1Bo5Sa8S5LpMoqS/BHOrM/B1js7usj7WBm9YBq7r4ndP084L4jqlSqnD35BTz81hL++fka2jWpyws3DeTULs3jXZZIlRdV8JtZCnAucHOJu74z529mbYCn3H0o0BKYFnpTrgbwkru/dbRFS+J7f/EWxk3LZOPufG48pSO/Pr8rKbX0ZyUiFSGqV5q75wLNImy/PsK2bGBo6PpKoN/RlShVyfZ9B7h/5kKmfbWBLi3qM/mWQZxwTJN4lyWSVHSKJRXC3Xk9cyP3Tl/ArrwCfn52Z249uzO1a6ipmkhFU/BLudu8O5+7Xsti9sLN9GnbiBdGDqRH64bxLkskaSn4pdy4O6+kr2PC64s4UFjM2Au6c9OpaqomEm8KfikXa7flMmZqBp+u2MaAjk156PK+dGxeL95liQgKfomxomLn2U9W8ftZS6lezZhwaW+uHpCmpmoilYiCX2Jm6eZgU7Wv1+3k7O4tmHBpb9o0rhvvskSkBAW/HLUDhcX8/YMV/PX9ZdSvXYM/XdmfS/q1UVM1kUpKwS9HZf66nYyeksHiTXu4uF8bxl/ck2b11VRNpDJT8MsRyTtQxKPvLOWpj1eS2qA2T/4wwLk9W8a7LBGJgoJfyuyzFdsYOzWD1dtyuWpAe8YO7UHDOmqqJpIoFPwStd35BUx8czEvzVlLWtMUXho5kEGd1VRNJNEo+CUq7y7azLhpWWzZk8+PTuvIr87tRt1aarcgkogU/HJI2/bu5zf/WciM+dl0a9mAx687gf7tG8e7LBE5Cgp+icjdmTE/m9/8ZyF78gv45Tld+MmZnalVQ+0WRBKdgl++Y+OuPO6alsW7i7fQr31jHr68L91aNYh3WSISIwp++UZxsfPyl+t48I1FFBQXc9eFPbjhlI5UV7sFkSrlsMFvZt2Af4dt6gTcAzQGfgTkhLbf6e5vRNh/CPAnoDrBlbkmHm3REnurt+5jzNQMPl+5nZM7NWPi5X04ppmaqolURYcNfndfAvQHMLPqwAZgGnAD8Ki7P1LavqHxjxFctnE98KWZzXD3hTGoXWKgsKiYZ0JN1WpVr8bEy/rwgxPbq92CSBVW1qmewcAKd18TZTAMAJaHlmDEzF4GhgEK/kpg8abdjJ6cwfz1uzinRwsmXNqHVo3qxLssESlnZQ3+kour/9TMfgikA//n7jtKjG8LrAu7vR4YGOmBzWwUMAogLS2tjGVJWewvLOKx91fwt/eX06huTf5y1XFc1Le1zvJFkkTUn80zs1rAJcCroU1/B44lOA20Efh9pN0ibPNIj+/uk9w94O6B1NTUaMuSMvpq7Q4u/st/+fO7y7iob2tm/+oMLlYnTZGkUpYz/guAee6+GeDgJYCZPQnMjLDPeqB92O12QPYR1ClHKfdAIb+ftZRnPllFq4Z1eOb6AGd3V1M1kWRUluC/irBpHjNr7e4bQzeHA1kR9vkS6GJmHQm+KXwlcPUR1ipH6JPlWxkzNYN12/O4ZmAaYy7oTgM1VRNJWlEFv5mlEPxkzs1hmx82s/4Ep25WH7zPzNoQ/NjmUHcvNLOfAm8T/DjnM+6+IIb1yyHsyivgwTcW8fKX6+jQLIWXR53ESZ2axbssEYmzqILf3XOBZiW2XVfK2GxgaNjtN4DvfL5fytesBZu467Ustu7dz81ndOK2c7pSp6aaqomI/nK3ytm6dz/jZyxgZsZGurdqwFMjAvRtp6ZqIvI/Cv4qwt157esN/OY/C8ndX8T/nduVm884Vk3VROQ7FPxVQPbOPMZNy+T9JTkclxZsqtalpZqqiUhkCv4EVlzsvPjFWia+sYhih3su6smIQR3UVE1EDknBn6BW5uxlzJRMvli9nVM7N+fBy/rQvmlKvMsSkQSg4E8whUXFPPXfVTw6eym1alTj4cv78r1AO/3lrYhETcGfQBZm7+aOKfPJ2rCb83q25P5Le9OyoZqqiUjZKPgTwP7CIv763nL+/sEKGqfU5LGrj2don1Y6yxeRI6Lgr+TmrtnO6CmZLN+yl8uOb8vdF/akSb1a8S5LRBKYgr+S2re/kN+9vYTnPltNm0Z1+ccNJ3JmtxbxLktEqgAFfyX08bIcxk7NZP2OPH548jHcMaQ79Wvrn0pEYkNpUonsyi1gwusLeXXuejo1r8crN5/MgI5N412WiFQxCv5K4q2sTdw9PYvt+w7w4zOP5ReDu6ipmoiUCwV/nG3Zk8/4GQt4I3MTPVs35NnrT6R320bxLktEqjAFf5y4O1PmbeD+mQvJKyji9vO7Mer0TtSsrqZqIlK+FPxxsH5HLndOy+KjpTmccEwTHrq8L51b1I93WSKSJA4b/GbWDfh32KZOwD1AW+Bi4ACwArjB3XdG2H81sAcoAgrdPXD0ZSem4mLnn5+v4aG3FgPwm0t6cd1Jx1BNTdVEpAIdNvjdfQnQH8DMqhNcO3ca0A0YG1pe8SFgLDC6lIc5y923xqbkxLQiZy+jJ2eQvmYHp3VpzgPD1VRNROKjrFM9g4EV7r4GWBO2/XPgiphVVYUUFBUz6aOV/OndZdStWZ1HvtePy49vq3YLIhI3ZQ3+K4F/Rdh+I9+eDgrnwCwzc+AJd58UaZCZjQJGAaSlpZWxrMopa8MuRk/JYEH2bob2acX4S3rRooGaqolIfEUd/GZWC7iE4JRO+PZxQCHwYim7nuLu2WbWAphtZovd/aOSg0L/IUwCCAQCHm1dlVF+QRF/fncZT3y0kiYptXj82uMZ0rt1vMsSEQHKdsZ/ATDP3Tcf3GBmI4CLgMHuHjGs3T07dLnFzKYBA4DvBH9V8eXq7YyenMHKrfv43gntuOvCnjRKqRnvskREvlGW4L+KsGkeMxtC8M3cM9w9N9IOZlYPqObue0LXzwPuO4p6K629+wt5+K3FPP/ZGto2rsvzNw7g9K6p8S5LROQ7ogp+M0sBzgVuDtv8V6A2wekbgM/d/RYzawM85e401ZBnAAAKHUlEQVRDgZbAtND9NYCX3P2tGNZfKXy4NIc7p2aSvSuP6wd14Pbzu1FPTdVEpJKKKp1CZ/TNSmzrXMrYbGBo6PpKoN9R1lhp7cw9wH0zFzJ13gaOTa3HqzefTKCDmqqJSOWm09Ij4O68mbWJe6ZnsTO3gJ+e1Zmfnt1ZTdVEJCEo+Mtoy+587p6exdsLNtO7bUOeu3EAvdqoqZqIJA4Ff5TcnVfnrmfCzIXkFxYzekh3fnRaR2qoqZqIJBgFfxTWbc9l7NRM/rt8KwM6NGXi5X3olKqmaiKSmBT8h1BU7Dz/2WoefmsJ1QzuH9aLawaqqZqIJDYFfymWb9nDHZMzmLd2J2d0TeWBy/rQtnHdeJclInLUFPwlFBQV8/gHK/jLe8tJqV2dR3/Qj0v7q6maiFQdCv4wmet3cfvk+SzetIcL+7bmN5f0onn92vEuS0QkphT8BJuqPfrOUp78aCXN69fmietO4PxereJdlohIuUj64J+zchtjpmayaus+fhBoz50X9qBRXTVVE5GqK2mDf09+AQ+9tZgXPl9L+6Z1eXHkQE7p3DzeZYmIlLukDP73F29h3LRMNu7O56ZTO/J/53UlpVZSHgoRSUJJlXbb9x3g/pkLmfbVBrq0qM+UHw/i+LQm8S5LRKRCJUXwuzszMzYyfsYCduUV8PPBXbj1rGOpXUNN1UQk+VT54N+8O59x07J4Z9Fm+rZrxAsjB9KjdcN4lyUiEjeH7TBmZt3M7Ouwr91m9ksza2pms81sWegy4pyJmY0IjVkWWqqxQrg7L3+xlnP+8CEfL8vhzqHdmfrjQQp9EUl6hz3jd/clQH8AM6sObACmAWOAd919opmNCd0eHb6vmTUF7gUCgANzzWyGu++I6U9RwtptuYyZmsGnK7YxsGNTHrq8Lx2a1yvPbykikjDKOtUzGFjh7mvMbBhwZmj7c8AHlAh+4HxgtrtvBzCz2cAQwtbujaWiYufZT1bxyKwl1KhWjd8O781VJ6apqZqISJiyBv+V/C+0W7r7RgB332hmLSKMbwusC7u9PrQt5nblFjDi2S/4et1Ozu7egt8O703rRmqqJiJSUtTBb2a1gEuAsWV4/Ein2l7K448CRgGkpaWV4VsENaxbg2OapXDDKR24pF8bNVUTESlFWZaPugCY5+6bQ7c3m1lrgNDllgj7rAfah91uB2RHenB3n+TuAXcPpKamlqGsIDPjT1cexzB10hQROaSyBP9VfHtufgZw8FM6I4DpEfZ5GzjPzJqEPvVzXmibiIjESVTBb2YpwLnA1LDNE4FzzWxZ6L6JobEBM3sKIPSm7v3Al6Gv+w6+0SsiIvFh7hGn3OMqEAh4enp6vMsQEUkYZjbX3QPRjC3LVI+IiFQBCn4RkSSj4BcRSTIKfhGRJKPgFxFJMpXyUz1mlgOsOcLdmwNbY1hOrKiuslFdZaO6yqYq1nWMu0f116+VMviPhpmlR/uRpoqkuspGdZWN6iqbZK9LUz0iIklGwS8ikmSqYvBPincBpVBdZaO6ykZ1lU1S11Xl5vhFROTQquIZv4iIHELCBH9pi76XGGNm9mczW25mGWZ2fNh9MV/0PcqargnVkmFmn5pZv7D7VptZZmjfmHali7K2M81sV9iYe8LuG2JmS0LHckwF13V72P1ZZlYUWr+5vI/ZbWa2IPQ9/2VmdUrcX9vM/h06JnPMrEPYfWND25eY2fkVXNevzGxh6Dn2rpkdE3ZfUdixnFHBdV1vZjlh339k2H0xfz2Woa5Hw2paamY7w+4rz+P1i1BNC0o+50P3V1x+uXvCfQHVgU0EP7cavn0o8CbBlb9OAuaEtjcFVoYum4SuN6mgmgYd/F4EF7OZE3bfaqB5HI/XmcDMUsavADoBtYD5QM+KqqvEmIuB98r7mBFcEnQVUDd0+xXg+hJjfgI8Hrp+JfDv0PWeoWNUG+gYOnbVK7Cus4CU0PUfH6wrdHtvOT2noqnreuCvEfYtt9djNHWVGP8z4JkKOF69gSwgheDKh+8AXUqMqbD8Spgz/hK+WfS9xPZhwPMe9DnQ2IKrg32z6Lu77wAOLvpe7jW5+6eh7wnwOcFVyCpaacerNAOA5e6+0t0PAC8TPLbxqKvkAkDlqQZQ18xqEHyBllwtbhjwXOj6ZGCwmVlo+8vuvt/dVwHLCR7DCqnL3d9399zQzYp8jh3ueJWmvF+PZamrop5fPYDP3T3X3QuBD4HhJcZUWH4lavCHL/oerrTF3Sti0ffSagp3E8H/0Q9yYJaZzbXgmsPl5VC1nWxm883sTTPrFdpWEcfrcHUdXABoCDAlbHO5HDN33wA8AqwFNgK73H1WiWHfHJfQi3cX0IxyPF5R1hWu5HOsjpmlm9nnZnZpLGoqY12Xh6YtJpvZwWVYK8XxCk2JdQTeC9tcLseL4Nn+6WbWLPS8Hsq3l6WFCsyvhAt++9+i769GujvCNj/E9oqo6eCYswi+KEeHbT7F3Y8nOAV0q5mdHquaoqxtHsFpln7AX4DXDu4WYWxMP/4VzTEjOM3ziX971bZyOWYWXBp0GMEgaAPUM7NrSw6LsGu5Pr+irOvg2GuBAPC7sM1pHvxL0KuBP5rZsRVY13+ADu7el+DUxsHflirF8SJ44jHZ3YvCtpXL8XL3RcBDBM/W3yI4NVhYsvxIux5i+xFLuODnu4u+hyttcfeoF30vh5ows77AU8Awd992cLu7Z4cutwDTiO30wGFrc/fd7r43dP0NoKaZNaf8j9ch6wrznd8IyvGYnQOscvccdy8guMzooBJjvjkuoWmERsB2yvd4RVMXZnYOMA64xN33H9wedrxWAh8Ax1VUXe6+LayWJ4ETQtfjfrxCDvX8ivXxwt2fdvfj3f10gs+bZSWGVFx+lccbGeX5RXC++YZS7ruQb7858oX/782RVQTfGGkSut60gmpKIzjnO6jE9npAg7DrnwJDKvh4teJ/f8sxgOCvx0ZwjnQlwbOmg2/u9qqoukL3HwzVehVxzICBwAKCc8JG8Oz0ZyXG3Mq339x9JXS9F99+c3clsXtzN5q6jiP4hnLJNwubALVD15sTDJqYvEkfZV2tw64PJzjHXa6vx2jqCo3rRvCDAlYRxyv0mC1Cl2nAYkq8QUsF5ldMfqCK+gr9Y24DGoVtuwW4JXTdgMdCL4JMIBA27kaCAbz8UIFTDjU9BewAvg59pYe2dwqFxfzQE3VcHI7XT0Pfez7BNwUHhY0bCiwNHcuY1na4ukK3ryf4hmn4fuV6zIDfhF6QWcA/CQb5fQTPogHqEJyaWg58AXQK23dc6FgtAS6o4LreATaHPcdmhLYPCr0O5ocub6rguh4Me369D3QP27dcXo/R1BUaMx6YWGK/8j5eHwMLQ48/uOTzngrML/3lrohIkknEOX4RETkKCn4RkSSj4BcRSTIKfhGRJKPgFxFJMgp+EZEko+AXEUkyCn4RkSTz/7zQEcWiQr29AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = zip(*tuple_list)\n",
    "plt.plot(x,y)\n",
    "lines = []\n",
    "for line in lines:\n",
    "    plt.axvline(x=line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(7, 70), (8, 80), (9, 90)]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple_list[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(7, 70), (8, 80), (9, 90)]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple_list = tuple_list[-3:]\n",
    "tuple_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tuple_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold = 12.58884%\n"
     ]
    }
   ],
   "source": [
    "t = 0.12588840000000004\n",
    "print(f\"Threshold = {t:.5%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3,-1,3,4,-1,43,524355,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = []\n",
    "l.append(sum (x == -1))\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'len'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-283-c1edb20bc1af>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'len'"
     ]
    }
   ],
   "source": [
    "x.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(1.0,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.4355"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(x[8]/10000, 3435)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YESY\n"
     ]
    }
   ],
   "source": [
    "if (2 < x < 8 ):\n",
    "    print(\"YESY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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