v.0.4.0-framework.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>v.0.4 FULL IMPLEMENTATION (NN MODEL AND TRADING APP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>NN PREDICTION MODEL</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pylab as plt\n",
    "import datetime\n",
    "import locale\n",
    "locale.setlocale(locale.LC_NUMERIC, \"\")\n",
    "\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers.recurrent import LSTM, GRU\n",
    "from keras.layers import Convolution1D, MaxPooling1D, AtrousConvolution1D, RepeatVector\n",
    "from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, CSVLogger\n",
    "from keras.layers.wrappers import Bidirectional\n",
    "from keras import regularizers\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.advanced_activations import *\n",
    "from keras.optimizers import RMSprop, Adam, SGD, Nadam\n",
    "from keras.initializers import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ASK_FNAME = \"rtf/eurusd002-ask.rtf\"\n",
    "BID_FNAME = \"rtf/eurusd002-BID.rtf\"\n",
    "WINDOW=90\n",
    "FORECAST=45\n",
    "EMB_SIZE=10\n",
    "STEP=1  #best is 75\n",
    "TRAIN_TEST_PERCENTAGE=0.9\n",
    "SAVE_NAME = \"classification_model.hdf5\"\n",
    "LOAD_NAME = \"hdf5/alg08-alfa0.495.hdf5\"\n",
    "ENABLE_CSV_OUTPUT = 1\n",
    "NAME_CSV = \"classification\"\n",
    "TRAINING = 1\n",
    "TESTING = 0\n",
    "NUMBER_EPOCHS = 10\n",
    "TRADING_DAYS = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ternary_tensor(Time,aO,aH,aL,aC,aV,bO,bH,bL,bC,bV):\n",
    "    count=0\n",
    "    X,Y = [],[]\n",
    "    i=0\n",
    "    try:\n",
    "        count=count+1\n",
    "        try:\n",
    "            #ask open, ask high.. bid close, bid volume\n",
    "            ao = aO[i:i+WINDOW]\n",
    "            ah = aH[i:i+WINDOW]\n",
    "            al = aL[i:i+WINDOW]\n",
    "            ac = aC[i:i+WINDOW]\n",
    "            av = aV[i:i+WINDOW]\n",
    "            #zscore on time window interval\n",
    "            ao = (np.array(ao) - np.mean(ao)) / np.std(ao)\n",
    "            ah = (np.array(ah) - np.mean(ah)) / np.std(ah)\n",
    "            al = (np.array(al) - np.mean(al)) / np.std(al)\n",
    "            ac = (np.array(ac) - np.mean(ac)) / np.std(ac)\n",
    "\n",
    "            \n",
    "            bo = bO[i:i+WINDOW]\n",
    "            bh = bH[i:i+WINDOW]\n",
    "            bl = bL[i:i+WINDOW]\n",
    "            bc = bC[i:i+WINDOW]\n",
    "            bv = bV[i:i+WINDOW]\n",
    "            #zscore on time window interval\n",
    "            bo = (np.array(bo) - np.mean(bo)) / np.std(bo)\n",
    "            bh = (np.array(bh) - np.mean(bh)) / np.std(bh)\n",
    "            bl = (np.array(bl) - np.mean(bl)) / np.std(bl)\n",
    "            bc = (np.array(bc) - np.mean(bc)) / np.std(bc)        \n",
    "\n",
    "            x_i = np.column_stack((ao,ah,al,ac,av,bo,bh,bl,bc,bv))\n",
    "    \n",
    "                \n",
    "        except Exception as e:\n",
    "            print e\n",
    "            pass\n",
    "\n",
    "    except Exception as e:\n",
    "        print e\n",
    "        pass\n",
    "    return x_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def format_data(df,ternary=1,binary=0):\n",
    "    Time = df.Datetime\n",
    "    aO = df.Open_x.tolist()\n",
    "    aH = df.High_x.tolist()\n",
    "    aL = df.Low_x.tolist()\n",
    "    aC = df.Close_x.tolist()\n",
    "    aV = df.Volume_x.tolist()\n",
    "    bO = df.Open_y.tolist()\n",
    "    bH = df.High_y.tolist()\n",
    "    bL = df.Low_y.tolist()\n",
    "    bC = df.Close_y.tolist()\n",
    "    bV = df.Volume_y.tolist()\n",
    "    \n",
    "    #print(Time,aO,aH,aL,aC,aV,bO,bH,bL,bC)\n",
    "    \n",
    "    if(ternary==1):\n",
    "        return ternary_tensor(Time,aO,aH,aL,aC,aV,bO,bH,bL,bC,bV)\n",
    "\n",
    "    elif(binary==1):\n",
    "        return binary_tensor(Time,aO,aH,aL,aC,aV,bO,bH,bL,bC,bV)\n",
    "\n",
    "\n",
    "def spread(Y):\n",
    "    still = 0\n",
    "    for vec in Y:\n",
    "        if vec[2]==1:\n",
    "            still=still+1\n",
    "    spread =still*100/len(Y)\n",
    "    print spread,\"%\"\n",
    "    return spread\n",
    "\n",
    "def shuffle_in_unison(a, b):\n",
    "    # courtsey http://stackoverflow.com/users/190280/josh-bleecher-snyder\n",
    "    # shuffling of training data\n",
    "    assert len(a) == len(b)\n",
    "    shuffled_a = np.empty(a.shape, dtype=a.dtype)\n",
    "    shuffled_b = np.empty(b.shape, dtype=b.dtype)\n",
    "    permutation = np.random.permutation(len(a))\n",
    "    for old_index, new_index in enumerate(permutation):\n",
    "        shuffled_a[new_index] = a[old_index]\n",
    "        shuffled_b[new_index] = b[old_index]\n",
    "    return shuffled_a, shuffled_b\n",
    "\n",
    "def remove_nan_examples(data):\n",
    "    #some basic util functions\n",
    "    newX = []\n",
    "    for i in range(len(data)):\n",
    "        if np.isnan(data[i]).any() == False:\n",
    "            newX.append(data[i])\n",
    "    return newX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initializing model..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alex/tensorflow/lib/python2.7/site-packages/ipykernel_launcher.py:7: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(input_shape=(90, 10), padding=\"same\", filters=16, kernel_size=4)`\n",
      "  import sys\n",
      "/Users/alex/tensorflow/lib/python2.7/site-packages/ipykernel_launcher.py:14: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(padding=\"same\", kernel_size=4, filters=8)`\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#MODEL DEFINITION\n",
    "print 'initializing model..'\n",
    "model = Sequential()\n",
    "model.add(Convolution1D(input_shape = (WINDOW, EMB_SIZE),\n",
    "                        nb_filter=16,\n",
    "                        filter_length=4,\n",
    "                        border_mode='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Convolution1D(nb_filter=8,\n",
    "                        filter_length=4,\n",
    "                        border_mode='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(64))\n",
    "model.add(BatchNormalization())\n",
    "model.add(LeakyReLU())\n",
    "\n",
    "\n",
    "model.add(Dense(3))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "opt = Nadam(lr=0.002)\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.9, patience=30, min_lr=0.000001, verbose=1)\n",
    "checkpointer = ModelCheckpoint(filepath=SAVE_NAME, verbose=1, save_best_only=True)\n",
    "\n",
    "model.compile(optimizer=opt, \n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>TRADING APPLICATION</h2> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import httplib\n",
    "import json\n",
    "from Tkinter import *\n",
    "import datetime\n",
    "import time\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.select import Select\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "from pymouse import PyMouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "CHROME_PATH= '/Users/alex/Desktop/Coding/WWW/chromedriver'\n",
    "URL = \"https://demo-login.dukascopy.com/binary/?_ga=2.86701976.1253522851.1502011850-1942799576.1501349679\"\n",
    "USR = \"Leipert75EU\"\n",
    "PSW = \"913724d5\"\n",
    "\n",
    "class DukascopyBinary(object):\n",
    "    \"\"\"docstring for ClassName\"\"\"\n",
    "    def __init__(self, URL):\n",
    "        self.URL = URL\n",
    "        self.driver = webdriver.Chrome(CHROME_PATH)\n",
    "        self.driver.get(URL)\n",
    "        #datafeed values\n",
    "        self.minute=60\n",
    "        self.vec=[]\n",
    "        self.data=[]\n",
    "        \n",
    "    def set_type(self, ASK=0, BID=1):\n",
    "        global USR\n",
    "        global PSW\n",
    "        \n",
    "        \n",
    "        time.sleep(10)\n",
    "        self.login(USR,PSW)\n",
    "        time.sleep(15)\n",
    "        self.driver.find_elements_by_css_selector(\".S-T-U.a-rk-sk-tk-bb.a-rk-sk-tk-bb-cb.a-rk-sk-tk-bb-W\")[0].click()\n",
    "        time.sleep(0.5)\n",
    "        self.driver.find_element_by_id(\":1u\").click()\n",
    "        time.sleep(0.5)\n",
    "        self.driver.find_elements_by_css_selector(\".S-T-U.a-rk-sk-tk-bb.a-rk-sk-tk-bb-cb.a-rk-sk-tk-bb-W\")[2].click()\n",
    "        time.sleep(0.5)\n",
    "        self.driver.find_element_by_id(\":4f\").click()\n",
    "        time.sleep(0.5)\n",
    "        \n",
    "        if(ASK):\n",
    "            self.type = \"ASK\"\n",
    "            self.driver.find_element_by_id(\":2e\").click()\n",
    "            time.sleep(0.5)\n",
    "            self.driver.find_element_by_id(\":0\").click()\n",
    "            \n",
    "        elif(BID):\n",
    "            self.type = \"BID\"\n",
    "        \n",
    "        for i in xrange(100):\n",
    "            self.driver.find_element_by_css_selector(\".S-T-U.a-pb-ek-O\").click()\n",
    "            \n",
    "    def set_mouse(self,m):\n",
    "        print 'put mouse in',self.type,' position.. (5s)'\n",
    "        time.sleep(5)\n",
    "        self.x,self.y=m.position()\n",
    "        print '-> position captured'\n",
    "        \n",
    "    \n",
    "    def login(self,USR,PSW):\n",
    "        self.driver.find_element_by_id(\"textfield-1020-inputEl\").send_keys(USR)\n",
    "        self.driver.find_element_by_id(\"textfield-1021-inputEl\").send_keys(PSW)\n",
    "        self.driver.find_element_by_id(\"button-1035-btnEl\").click()\n",
    "    \n",
    "    def call(self):\n",
    "        self.driver.find_element_by_class_name(\"call\").click()\n",
    "        time.sleep(0.2)\n",
    "        self.driver.find_element_by_id(\"button-1014\").click()\n",
    "    \n",
    "    def put(self):\n",
    "        self.driver.find_element_by_class_name(\"put\").click()\n",
    "        time.sleep(0.2)\n",
    "        self.driver.find_element_by_id(\"button-1014\").click()\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class App(object):\n",
    "    def __init__(self):\n",
    "        global model\n",
    "        model.load_weights(LOAD_NAME)\n",
    "        \n",
    "        global URL\n",
    "        self.ask = DukascopyBinary(URL)\n",
    "        self.ask.set_type(ASK=1,BID=0)\n",
    "        self.bid = DukascopyBinary(URL)\n",
    "        self.bid.set_type(ASK=0,BID=1)\n",
    "        self.running = False\n",
    "        self.m = PyMouse()\n",
    "        \n",
    "        self.data = []\n",
    "        self.temp = \"\"\n",
    "        \n",
    "        self.datetime1,self.datetime2 = \"\",\"\"\n",
    "        \n",
    "        self.aOpen,self.aHigh,self.aLow,self.aClose,self.aVolume=0,0,0,0,0\n",
    "        self.bOpen,self.bHigh,self.bLow,self.bClose,self.bVolume=0,0,0,0,0\n",
    "    \n",
    "    def mouse_config(self):\n",
    "        self.ask.set_mouse(self.m)\n",
    "        self.bid.set_mouse(self.m)\n",
    "        \n",
    "    def start(self):\n",
    "        self.running = True\n",
    "\n",
    "    def stop(self):\n",
    "        self.running = False\n",
    "    \n",
    "    def __tensor__(self):\n",
    "        print 'checking tensor'\n",
    "        if(len(self.data)>WINDOW):\n",
    "            df = pd.DataFrame(self.data[-WINDOW:])\n",
    "            df = df.rename(columns={ df.columns[0] : 'Datetime', df.columns[1] : 'Open_x', df.columns[2] : 'High_x', df.columns[3] : 'Low_x', df.columns[4]: 'Close_x', df.columns[5] : 'Volume_x', df.columns[6] : 'Open_y', df.columns[7] : 'High_y', df.columns[8] : 'Low_y', df.columns[9]: 'Close_y', df.columns[10] : 'Volume_y' })\n",
    "            self.X = format_data(df,1,0)\n",
    "            self.X = np.array(self.X)\n",
    "            return 1\n",
    "        else: \n",
    "            return 0\n",
    "        \n",
    "    def __alg08__(self,pred,alfa):\n",
    "        if pred[0][0]-pred[0][2]>alfa:\n",
    "            self.ask.call()\n",
    "            print 'put executed'\n",
    "        \n",
    "        elif pred[0][1]-pred[0][2]>alfa:\n",
    "            self.ask.put()\n",
    "            print 'call executed'\n",
    "        \n",
    "        else:\n",
    "            print 'no signal'\n",
    "    \n",
    "    def trading(self):\n",
    "        if self.running:\n",
    "            #print 'RUNNING'\n",
    "\n",
    "            try:\n",
    "                \n",
    "                now = app.ask.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[0].get_attribute(\"innerHTML\")+\" \"+app.ask.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[1].get_attribute(\"innerHTML\")\n",
    "        \n",
    "                if now!=self.temp:\n",
    "                    #print 'NEW VALUE'\n",
    "\n",
    "                    self.ask.driver.find_element_by_css_selector(\".S-T-U.a-pb-ek-O\").click()\n",
    "                    self.bid.driver.find_element_by_css_selector(\".S-T-U.a-pb-ek-O\").click()\n",
    "                    self.m.move(self.ask.x,self.ask.y)\n",
    "                    self.m.move(self.bid.x,self.bid.y)\n",
    "            \n",
    "                    try:\n",
    "                        print self.aOpen,self.aHigh,self.aLow,self.aClose,self.aVolume,self.bOpen,self.bHigh,self.bLow,self.bClose,self.bVolume\n",
    "                        print 'NEW VALUE APPENDED',self.aClose\n",
    "                        \n",
    "                        _time = datetime.datetime.strptime(app.datetime1+\" \"+app.datetime2,\"%Y-%m-%d %H:%M:%S.%f\") \n",
    "                        \n",
    "                        self.data.append([_time,float(self.aOpen),float(self.aHigh),float(self.aLow),float(self.aClose),float(self.aVolume),float(self.bOpen),float(self.bHigh),float(self.bLow),float(self.bClose),float(self.bVolume)])\n",
    "                        self.temp=now\n",
    "                        \n",
    "                        if(self.__tensor__()):\n",
    "                            pred =model.predict(np.reshape(self.X, (1,self.X.shape[0],self.X.shape[1])))\n",
    "                            self.text.insert(1.0, pred)\n",
    "                            self.__alg08__(pred, 0.495)\n",
    "                        \n",
    "                        \n",
    "                    except Exception,e:\n",
    "                        print 'inner loop exception:',str(e)\n",
    "                        pass\n",
    "                    \n",
    "                    \n",
    "        \n",
    "                #print 'START SCANNING', self.aClose\n",
    "                self.datetime1 = self.ask.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[0].get_attribute(\"innerHTML\")\n",
    "                self.datetime2= self.ask.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[1].get_attribute(\"innerHTML\")\n",
    "                self.aOpen = self.ask.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[2].get_attribute(\"innerHTML\")\n",
    "                self.aHigh = self.ask.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[3].get_attribute(\"innerHTML\")\n",
    "                self.aLow = self.ask.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[4].get_attribute(\"innerHTML\")\n",
    "                self.aClose = self.ask.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[5].get_attribute(\"innerHTML\")\n",
    "                self.aVolume = self.ask.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[6].get_attribute(\"innerHTML\")\n",
    "                self.bOpen = self.bid.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[2].get_attribute(\"innerHTML\")\n",
    "                self.bHigh = self.bid.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[3].get_attribute(\"innerHTML\")\n",
    "                self.bLow = self.bid.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[4].get_attribute(\"innerHTML\")\n",
    "                self.bClose = self.bid.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[5].get_attribute(\"innerHTML\")\n",
    "                self.bVolume = self.bid.driver.find_elements_by_class_name(\"a-b-G-Li-Mi\")[6].get_attribute(\"innerHTML\")\n",
    "                #print 'FINISH SCANNING', self.aClose\n",
    "                \n",
    "            except Exception,e:\n",
    "                \n",
    "                #print 'outer loop exception:',str(e)\n",
    "                pass\n",
    "            \n",
    "        self.root.after(100, self.trading)\n",
    "        \n",
    "        \n",
    "    def run(self):\n",
    "        self.root = Tk()\n",
    "        self.root.title(\"v.0.4.0-framework\")\n",
    "        self.root.geometry(\"300x100\")\n",
    "        \n",
    "        self.root2 = Tk()\n",
    "        self.text=Text(self.root2, width = 40, height=4, font=(\"Helvetica\",32))\n",
    "        self.text.pack()\n",
    "        self.text.insert(1.0, \"PREDIZIONI:\")\n",
    "\n",
    "        \n",
    "\n",
    "        app = Frame(self.root)\n",
    "        app.grid()\n",
    "\n",
    "        start = Button(app, text=\"Start\", command=self.start)\n",
    "        stop = Button(app, text=\"Stop\", command=self.stop)\n",
    "        start.grid(row=0, column=0, padx=(40,40), pady=(40,40))\n",
    "        stop.grid(row=0, column=1, padx=(40,40), pady=(40,40))\n",
    "\n",
    "        start.grid()\n",
    "        stop.grid()\n",
    "\n",
    "        self.root.after(1000, self.trading)  # After 1 second, call scanning\n",
    "        self.root.mainloop()\n",
    "        self.root2.mainloop()\n",
    "        \n",
    "\n",
    "    \n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to solve:\n",
    "\n",
    "sync between ask and bid windows (when update time is not sync)\n",
    "increase frequency of scanning?\n",
    "ALWAYS double check with official DUKASCOPY data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "app = App()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "put mouse in ASK  position.. (5s)\n",
      "-> position captured\n",
      "put mouse in BID  position.. (5s)\n",
      "-> position captured\n"
     ]
    }
   ],
   "source": [
    "app.mouse_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0 0 0 0 0 0 0 0\n",
      "NEW VALUE APPENDED 0\n",
      "inner loop exception: time data ' ' does not match format '%Y-%m-%d %H:%M:%S.%f'\n",
      "1.17516 1.17516 1.17512 1.17513 45.54 1.17515 1.17515 1.17509 1.17510 30.2\n",
      "NEW VALUE APPENDED 1.17513\n",
      "checking tensor\n",
      "1.17516 1.17531 1.17511 1.17521 246.04 1.17515 1.17519 1.17519 1.17519 1.5\n",
      "NEW VALUE APPENDED 1.17521\n",
      "checking tensor\n",
      "1.17521 1.17521 1.17504 1.17507 209.64 1.17519 1.17519 1.17502 1.17503 119.71\n",
      "NEW VALUE APPENDED 1.17507\n",
      "checking tensor\n",
      "1.17506 1.17530 1.17506 1.17516 177.11 1.17505 1.17528 1.17505 1.17514 99.39\n",
      "NEW VALUE APPENDED 1.17516\n",
      "checking tensor\n",
      "1.17516 1.17516 1.17507 1.17507 154 1.17514 1.17514 1.17505 1.17505 106.69\n",
      "NEW VALUE APPENDED 1.17507\n",
      "checking tensor\n",
      "1.17507 1.17509 1.17506 1.17507 216.07 1.17506 1.17507 1.17504 1.17505 70.73\n",
      "NEW VALUE APPENDED 1.17507\n",
      "checking tensor\n",
      "1.17507 1.17518 1.17518 1.17518 1.5 1.17516 1.17516 1.17516 1.17516 1\n",
      "NEW VALUE APPENDED 1.17518\n",
      "checking tensor\n",
      "1.17518 1.17520 1.17517 1.17518 146.29 1.17516 1.17517 1.17514 1.17516 84.57\n",
      "NEW VALUE APPENDED 1.17518\n",
      "checking tensor\n",
      "1.17517 1.17523 1.17512 1.17521 194.72 1.17516 1.17522 1.17509 1.17520 143.24\n",
      "NEW VALUE APPENDED 1.17521\n",
      "checking tensor\n",
      "1.17521 1.17521 1.17509 1.17516 140.45 1.17518 1.17519 1.17507 1.17514 81.75\n",
      "NEW VALUE APPENDED 1.17516\n",
      "checking tensor\n",
      "1.17521 1.17521 1.17521 1.17521 4.59 1.17519 1.17519 1.17519 1.17519 1.25\n",
      "NEW VALUE APPENDED 1.17521\n",
      "checking tensor\n",
      "1.17521 1.17535 1.17521 1.17525 231.68 1.17519 1.17521 1.17521 1.17521 1.31\n",
      "NEW VALUE APPENDED 1.17525\n",
      "checking tensor\n",
      "1.17524 1.17543 1.17521 1.17534 267.14 1.17521 1.17540 1.17520 1.17529 226.43\n",
      "NEW VALUE APPENDED 1.17534\n",
      "checking tensor\n",
      "1.17534 1.17540 1.17530 1.17531 143.14 1.17531 1.17539 1.17527 1.17529 103.39\n",
      "NEW VALUE APPENDED 1.17531\n",
      "checking tensor\n",
      "1.17531 1.17531 1.17521 1.17521 101.27 1.17529 1.17529 1.17518 1.17520 52.84\n",
      "NEW VALUE APPENDED 1.17521\n",
      "checking tensor\n",
      "1.17521 1.17537 1.17521 1.17537 173.4 1.17519 1.17536 1.17519 1.17536 46.95\n",
      "NEW VALUE APPENDED 1.17537\n",
      "checking tensor\n",
      "1.17538 1.17541 1.17531 1.17535 159.62 1.17537 1.17539 1.17529 1.17531 101.74\n",
      "NEW VALUE APPENDED 1.17535\n",
      "checking tensor\n",
      "1.17535 1.17552 1.17531 1.17552 130.77 1.17533 1.17551 1.17530 1.17546 70.19\n",
      "NEW VALUE APPENDED 1.17552\n",
      "checking tensor\n",
      "1.17554 1.17542 1.17542 1.17542 1 1.17541 1.17541 1.17539 1.17541 122.65\n",
      "NEW VALUE APPENDED 1.17542\n",
      "checking tensor\n",
      "1.17542 1.17544 1.17541 1.17542 82.8 1.17541 1.17543 1.17540 1.17541 66.5\n",
      "NEW VALUE APPENDED 1.17542\n",
      "checking tensor\n",
      "1.17542 1.17542 1.17522 1.17526 157.68 1.17541 1.17541 1.17519 1.17525 88.75\n",
      "NEW VALUE APPENDED 1.17526\n",
      "checking tensor\n",
      "1.17526 1.17544 1.17525 1.17537 359.47 1.17524 1.17543 1.17522 1.17539 90.1\n",
      "NEW VALUE APPENDED 1.17537\n",
      "checking tensor\n",
      "1.17538 1.17542 1.17526 1.17527 152.59 1.17535 1.17540 1.17525 1.17529 90.19\n",
      "NEW VALUE APPENDED 1.17527\n",
      "checking tensor\n",
      "1.17527 1.17532 1.17526 1.17530 243.79 1.17526 1.17530 1.17524 1.17526 99.78\n",
      "NEW VALUE APPENDED 1.17530\n",
      "checking tensor\n",
      "1.17530 1.17539 1.17527 1.17538 224.2 1.17526 1.17536 1.17524 1.17536 64.44\n",
      "NEW VALUE APPENDED 1.17538\n",
      "checking tensor\n",
      "1.17538 1.17551 1.17538 1.17546 199.01 1.17536 1.17549 1.17536 1.17544 32.26\n",
      "NEW VALUE APPENDED 1.17546\n",
      "checking tensor\n",
      "1.17547 1.17551 1.17536 1.17537 162.63 1.17544 1.17549 1.17533 1.17534 70.74\n",
      "NEW VALUE APPENDED 1.17537\n",
      "checking tensor\n",
      "1.17534 1.17537 1.17527 1.17528 193.65 1.17533 1.17535 1.17524 1.17526 109.59\n",
      "NEW VALUE APPENDED 1.17528\n",
      "checking tensor\n",
      "1.17527 1.17539 1.17526 1.17531 102.62 1.17526 1.17528 1.17528 1.17529 85.25\n",
      "NEW VALUE APPENDED 1.17531\n",
      "checking tensor\n",
      "1.17531 1.17540 1.17528 1.17539 161.55 1.17528 1.17538 1.17526 1.17535 80.44\n",
      "NEW VALUE APPENDED 1.17539\n",
      "checking tensor\n",
      "1.17538 1.17543 1.17537 1.17542 120.82 1.17535 1.17541 1.17534 1.17540 106.32\n",
      "NEW VALUE APPENDED 1.17542\n",
      "checking tensor\n",
      "1.17542 1.17547 1.17538 1.17544 99.58 1.17541 1.17546 1.17536 1.17541 75.68\n",
      "NEW VALUE APPENDED 1.17544\n",
      "checking tensor\n",
      "1.17545 1.17551 1.17542 1.17542 103.05 1.17544 1.17549 1.17540 1.17540 72.85\n",
      "NEW VALUE APPENDED 1.17542\n",
      "checking tensor\n",
      "1.17541 1.17542 1.17530 1.17532 106.54 1.17540 1.17540 1.17529 1.17531 128.21\n",
      "NEW VALUE APPENDED 1.17532\n",
      "checking tensor\n",
      "1.17531 1.17531 1.17528 1.17528 5.01 1.17529 1.17533 1.17525 1.17528 105.67\n",
      "NEW VALUE APPENDED 1.17528\n",
      "checking tensor\n",
      "1.17531 1.17534 1.17528 1.17531 114.05 1.17529 1.17533 1.17524 1.17529 146.11\n",
      "NEW VALUE APPENDED 1.17531\n",
      "checking tensor\n",
      "1.17532 1.17533 1.17526 1.17530 125.78 1.17529 1.17530 1.17524 1.17530 134.87\n",
      "NEW VALUE APPENDED 1.17530\n",
      "checking tensor\n",
      "1.17531 1.17533 1.17526 1.17529 56.99 1.17529 1.17530 1.17525 1.17527 107.2\n",
      "NEW VALUE APPENDED 1.17529\n",
      "checking tensor\n",
      "1.17529 1.17543 1.17527 1.17538 99.33 1.17527 1.17541 1.17526 1.17536 96.49\n",
      "NEW VALUE APPENDED 1.17538\n",
      "checking tensor\n",
      "1.17537 1.17542 1.17537 1.17542 59.98 1.17536 1.17540 1.17535 1.17536 35.53\n",
      "NEW VALUE APPENDED 1.17542\n",
      "checking tensor\n",
      "1.17541 1.17541 1.17519 1.17523 128.83 1.17539 1.17540 1.17517 1.17520 88.92\n",
      "NEW VALUE APPENDED 1.17523\n",
      "checking tensor\n",
      "1.17524 1.17538 1.17524 1.17529 119.71 1.17521 1.17535 1.17521 1.17527 121.82\n",
      "NEW VALUE APPENDED 1.17529\n",
      "checking tensor\n",
      "1.17527 1.17534 1.17521 1.17532 80.28 1.17526 1.17531 1.17519 1.17531 146.91\n",
      "NEW VALUE APPENDED 1.17532\n",
      "checking tensor\n",
      "1.17535 1.17560 1.17535 1.17549 113.97 1.17533 1.17558 1.17533 1.17547 149.99\n",
      "NEW VALUE APPENDED 1.17549\n",
      "checking tensor\n",
      "1.17549 1.17556 1.17548 1.17554 64.41 1.17547 1.17551 1.17546 1.17551 106.83\n",
      "NEW VALUE APPENDED 1.17554\n",
      "checking tensor\n",
      "1.17554 1.17559 1.17542 1.17552 123.68 1.17550 1.17556 1.17541 1.17550 26.43\n",
      "NEW VALUE APPENDED 1.17552\n",
      "checking tensor\n",
      "1.17552 1.17569 1.17552 1.17568 123.52 1.17550 1.17567 1.17549 1.17562 132.42\n",
      "NEW VALUE APPENDED 1.17568\n",
      "checking tensor\n",
      "1.17568 1.17584 1.17562 1.17584 127.44 1.17564 1.17582 1.17560 1.17582 117.41\n",
      "NEW VALUE APPENDED 1.17584\n",
      "checking tensor\n",
      "1.17585 1.17588 1.17575 1.17575 73.44 1.17582 1.17584 1.17571 1.17571 86.02\n",
      "NEW VALUE APPENDED 1.17575\n",
      "checking tensor\n",
      "1.17575 1.17579 1.17573 1.17574 146.74 1.17571 1.17575 1.17568 1.17571 62\n",
      "NEW VALUE APPENDED 1.17574\n",
      "checking tensor\n",
      "1.17573 1.17581 1.17571 1.17579 188.12 1.17570 1.17578 1.17568 1.17575 104.84\n",
      "NEW VALUE APPENDED 1.17579\n",
      "checking tensor\n",
      "1.17577 1.17578 1.17562 1.17578 202.14 1.17574 1.17574 1.17558 1.17569 127.83\n",
      "NEW VALUE APPENDED 1.17578\n",
      "checking tensor\n",
      "1.17578 1.17582 1.17566 1.17568 230.23 1.17574 1.17579 1.17564 1.17564 39.69\n",
      "NEW VALUE APPENDED 1.17568\n",
      "checking tensor\n",
      "1.17568 1.17609 1.17566 1.17593 156.61 1.17564 1.17608 1.17563 1.17595 43.55\n",
      "NEW VALUE APPENDED 1.17593\n",
      "checking tensor\n",
      "1.17594 1.17594 1.17579 1.17586 134.71 1.17591 1.17592 1.17576 1.17583 157.82\n",
      "NEW VALUE APPENDED 1.17586\n",
      "checking tensor\n",
      "1.17585 1.17593 1.17582 1.17592 93.22 1.17584 1.17591 1.17579 1.17590 94.72\n",
      "NEW VALUE APPENDED 1.17592\n",
      "checking tensor\n",
      "1.17593 1.17593 1.17577 1.17577 153.59 1.17589 1.17591 1.17573 1.17573 20.05\n",
      "NEW VALUE APPENDED 1.17577\n",
      "checking tensor\n",
      "1.17578 1.17578 1.17570 1.17577 153.64 1.17575 1.17575 1.17567 1.17575 95.71\n",
      "NEW VALUE APPENDED 1.17577\n",
      "checking tensor\n",
      "1.17578 1.17584 1.17572 1.17583 157.78 1.17575 1.17581 1.17569 1.17581 125.39\n",
      "NEW VALUE APPENDED 1.17583\n",
      "checking tensor\n",
      "1.17583 1.17594 1.17578 1.17582 142.42 1.17580 1.17592 1.17574 1.17578 234.88\n",
      "NEW VALUE APPENDED 1.17582\n",
      "checking tensor\n",
      "1.17582 1.17583 1.17577 1.17579 110.98 1.17577 1.17579 1.17575 1.17575 90.21\n",
      "NEW VALUE APPENDED 1.17579\n",
      "checking tensor\n",
      "1.17578 1.17579 1.17573 1.17574 111.12 1.17575 1.17576 1.17569 1.17569 65.23\n",
      "NEW VALUE APPENDED 1.17574\n",
      "checking tensor\n",
      "1.17574 1.17578 1.17569 1.17578 126 1.17569 1.17574 1.17565 1.17574 131.9\n",
      "NEW VALUE APPENDED 1.17578\n",
      "checking tensor\n",
      "1.17581 1.17581 1.17581 1.17581 6.84 1.17578 1.17578 1.17574 1.17579 74.29\n",
      "NEW VALUE APPENDED 1.17581\n",
      "checking tensor\n",
      "1.17581 1.17583 1.17569 1.17569 129.55 1.17578 1.17579 1.17564 1.17565 88.49\n",
      "NEW VALUE APPENDED 1.17569\n",
      "checking tensor\n",
      "1.17569 1.17581 1.17569 1.17580 69.54 1.17568 1.17579 1.17565 1.17575 62.28\n",
      "NEW VALUE APPENDED 1.17580\n",
      "checking tensor\n",
      "1.17579 1.17589 1.17577 1.17583 153.82 1.17576 1.17587 1.17574 1.17579 44.17\n",
      "NEW VALUE APPENDED 1.17583\n",
      "checking tensor\n",
      "1.17583 1.17584 1.17578 1.17578 94.24 1.17579 1.17582 1.17575 1.17576 65.31\n",
      "NEW VALUE APPENDED 1.17578\n",
      "checking tensor\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.17582 1.17582 1.17582 1.17582 1.69 1.17578 1.17578 1.17578 1.17578 88.04\n",
      "NEW VALUE APPENDED 1.17582\n",
      "checking tensor\n",
      "1.17582 1.17582 1.17577 1.17577 114.11 1.17578 1.17579 1.17574 1.17574 78.87\n",
      "NEW VALUE APPENDED 1.17577\n",
      "checking tensor\n",
      "1.17575 1.17577 1.17566 1.17576 104.86 1.17571 1.17574 1.17563 1.17570 60.84\n",
      "NEW VALUE APPENDED 1.17576\n",
      "checking tensor\n",
      "1.17577 1.17577 1.17568 1.17571 162.08 1.17574 1.17574 1.17564 1.17570 45.88\n",
      "NEW VALUE APPENDED 1.17571\n",
      "checking tensor\n",
      "1.17570 1.17603 1.17570 1.17589 262.81 1.17569 1.17599 1.17568 1.17590 159.44\n",
      "NEW VALUE APPENDED 1.17589\n",
      "checking tensor\n",
      "1.17590 1.17599 1.17587 1.17597 263.79 1.17588 1.17595 1.17583 1.17595 164.52\n",
      "NEW VALUE APPENDED 1.17597\n",
      "checking tensor\n",
      "1.17597 1.17618 1.17595 1.17617 180.96 1.17595 1.17614 1.17592 1.17614 171.13\n",
      "NEW VALUE APPENDED 1.17617\n",
      "checking tensor\n",
      "1.17617 1.17677 1.17617 1.17668 312.87 1.17613 1.17674 1.17613 1.17659 204\n",
      "NEW VALUE APPENDED 1.17668\n",
      "checking tensor\n",
      "1.17667 1.17671 1.17653 1.17653 172.76 1.17665 1.17668 1.17650 1.17655 170.94\n",
      "NEW VALUE APPENDED 1.17653\n",
      "checking tensor\n",
      "1.17653 1.17663 1.17645 1.17659 170.5 1.17650 1.17659 1.17643 1.17655 122.07\n",
      "NEW VALUE APPENDED 1.17659\n",
      "checking tensor\n",
      "1.17658 1.17664 1.17652 1.17658 217.73 1.17655 1.17659 1.17648 1.17653 190.43\n",
      "NEW VALUE APPENDED 1.17658\n",
      "checking tensor\n",
      "1.17658 1.17663 1.17657 1.17658 131.04 1.17653 1.17659 1.17653 1.17654 67.56\n",
      "NEW VALUE APPENDED 1.17658\n",
      "checking tensor\n",
      "1.17657 1.17662 1.17655 1.17657 190.96 1.17654 1.17659 1.17653 1.17654 85.98\n",
      "NEW VALUE APPENDED 1.17657\n",
      "checking tensor\n",
      "1.17658 1.17658 1.17651 1.17653 241.89 1.17648 1.17654 1.17648 1.17649 56.43\n",
      "NEW VALUE APPENDED 1.17653\n",
      "checking tensor\n",
      "1.17653 1.17662 1.17652 1.17657 158.01 1.17648 1.17659 1.17648 1.17653 74.73\n",
      "NEW VALUE APPENDED 1.17657\n",
      "checking tensor\n",
      "1.17657 1.17657 1.17647 1.17647 132.01 1.17653 1.17644 1.17643 1.17645 90.88\n",
      "NEW VALUE APPENDED 1.17647\n",
      "checking tensor\n",
      "1.17647 1.17657 1.17647 1.17655 84.11 1.17644 1.17654 1.17644 1.17651 69.79\n",
      "NEW VALUE APPENDED 1.17655\n",
      "checking tensor\n",
      "1.17656 1.17656 1.17648 1.17653 135.28 1.17652 1.17653 1.17644 1.17649 120.22\n",
      "NEW VALUE APPENDED 1.17653\n",
      "checking tensor\n",
      "1.17652 1.17662 1.17651 1.17661 193.02 1.17649 1.17659 1.17648 1.17657 145.51\n",
      "NEW VALUE APPENDED 1.17661\n",
      "checking tensor\n",
      "1.17662 1.17663 1.17655 1.17661 222.78 1.17658 1.17659 1.17654 1.17655 144.67\n",
      "NEW VALUE APPENDED 1.17661\n",
      "checking tensor\n",
      "1.17655 1.17655 1.17655 1.17655 1 1.17659 1.17659 1.17653 1.17653 84.44\n",
      "NEW VALUE APPENDED 1.17655\n",
      "checking tensor\n",
      "1.17655 1.17657 1.17647 1.17648 188.43 1.17654 1.17655 1.17644 1.17645 168.52\n",
      "NEW VALUE APPENDED 1.17648\n",
      "checking tensor\n",
      "1.17648 1.17654 1.17645 1.17652 172.1 1.17645 1.17653 1.17643 1.17649 139.53\n",
      "NEW VALUE APPENDED 1.17652\n",
      "checking tensor\n",
      "no signal\n",
      "1.17648 1.17648 1.17628 1.17631 183.45 1.17647 1.17647 1.17625 1.17628 109.27\n",
      "NEW VALUE APPENDED 1.17631\n",
      "checking tensor\n",
      "no signal\n",
      "1.17630 1.17633 1.17628 1.17630 87.94 1.17628 1.17630 1.17624 1.17626 89.31\n",
      "NEW VALUE APPENDED 1.17630\n",
      "checking tensor\n",
      "no signal\n",
      "1.17630 1.17633 1.17623 1.17627 127.91 1.17629 1.17630 1.17619 1.17624 64.53\n",
      "NEW VALUE APPENDED 1.17627\n",
      "checking tensor\n",
      "no signal\n",
      "1.17627 1.17648 1.17627 1.17647 241.47 1.17623 1.17645 1.17623 1.17644 138.08\n",
      "NEW VALUE APPENDED 1.17647\n",
      "checking tensor\n",
      "no signal\n",
      "1.17647 1.17648 1.17631 1.17637 225.94 1.17644 1.17646 1.17628 1.17634 129.96\n",
      "NEW VALUE APPENDED 1.17637\n",
      "checking tensor\n",
      "no signal\n",
      "1.17636 1.17642 1.17632 1.17636 3.58 1.17634 1.17634 1.17630 1.17634 89.27\n",
      "NEW VALUE APPENDED 1.17636\n",
      "checking tensor\n",
      "no signal\n",
      "1.17635 1.17643 1.17635 1.17643 85.28 1.17634 1.17641 1.17641 1.17641 3.19\n",
      "NEW VALUE APPENDED 1.17643\n",
      "checking tensor\n",
      "no signal\n",
      "1.17644 1.17644 1.17643 1.17643 93.14 1.17641 1.17641 1.17638 1.17639 93.82\n",
      "NEW VALUE APPENDED 1.17643\n",
      "checking tensor\n",
      "no signal\n",
      "1.17652 1.17652 1.17652 1.17652 1.69 1.17649 1.17651 1.17639 1.17649 111.22\n",
      "NEW VALUE APPENDED 1.17652\n",
      "checking tensor\n",
      "no signal\n",
      "1.17652 1.17659 1.17650 1.17652 103.34 1.17649 1.17655 1.17649 1.17650 123.72\n",
      "NEW VALUE APPENDED 1.17652\n",
      "checking tensor\n",
      "no signal\n",
      "1.17651 1.17657 1.17641 1.17641 130.32 1.17649 1.17656 1.17639 1.17639 101.43\n",
      "NEW VALUE APPENDED 1.17641\n",
      "checking tensor\n",
      "no signal\n",
      "1.17642 1.17642 1.17640 1.17641 47.72 1.17639 1.17639 1.17638 1.17639 75.83\n",
      "NEW VALUE APPENDED 1.17641\n",
      "checking tensor\n",
      "no signal\n",
      "1.17640 1.17642 1.17634 1.17638 3.93 1.17639 1.17639 1.17633 1.17634 153.97\n",
      "NEW VALUE APPENDED 1.17638\n",
      "checking tensor\n",
      "no signal\n",
      "1.17637 1.17648 1.17635 1.17635 204.52 1.17634 1.17643 1.17633 1.17633 247.25\n",
      "NEW VALUE APPENDED 1.17635\n",
      "checking tensor\n",
      "no signal\n",
      "1.17636 1.17638 1.17632 1.17633 197.97 1.17633 1.17634 1.17630 1.17633 68.17\n",
      "NEW VALUE APPENDED 1.17633\n",
      "checking tensor\n",
      "no signal\n",
      "1.17632 1.17644 1.17632 1.17638 212.55 1.17630 1.17641 1.17630 1.17634 75.8\n",
      "NEW VALUE APPENDED 1.17638\n",
      "checking tensor\n",
      "no signal\n",
      "1.17637 1.17649 1.17637 1.17643 102.04 1.17634 1.17645 1.17634 1.17639 100.03\n",
      "NEW VALUE APPENDED 1.17643\n",
      "checking tensor\n",
      "no signal\n",
      "1.17643 1.17643 1.17622 1.17623 160.51 1.17639 1.17639 1.17618 1.17620 156.45\n",
      "NEW VALUE APPENDED 1.17623\n",
      "checking tensor\n",
      "no signal\n",
      "1.17623 1.17623 1.17617 1.17619 154.14 1.17620 1.17620 1.17613 1.17616 125.89\n",
      "NEW VALUE APPENDED 1.17619\n",
      "checking tensor\n",
      "no signal\n",
      "1.17620 1.17626 1.17620 1.17621 2 1.17618 1.17621 1.17617 1.17620 118.8\n",
      "NEW VALUE APPENDED 1.17621\n",
      "checking tensor\n",
      "no signal\n",
      "1.17620 1.17629 1.17618 1.17629 164.73 1.17618 1.17624 1.17614 1.17623 85.03\n",
      "NEW VALUE APPENDED 1.17629\n",
      "checking tensor\n",
      "no signal\n",
      "1.17625 1.17635 1.17623 1.17632 133.68 1.17622 1.17631 1.17618 1.17628 52.66\n",
      "NEW VALUE APPENDED 1.17632\n",
      "checking tensor\n",
      "no signal\n",
      "1.17632 1.17639 1.17637 1.17637 5.06 1.17633 1.17633 1.17633 1.17629 86.11\n",
      "NEW VALUE APPENDED 1.17637\n",
      "checking tensor\n",
      "no signal\n",
      "1.17637 1.17637 1.17626 1.17627 214.15 1.17633 1.17633 1.17622 1.17623 102.99\n",
      "NEW VALUE APPENDED 1.17627\n",
      "checking tensor\n",
      "no signal\n",
      "1.17628 1.17638 1.17628 1.17637 204.58 1.17622 1.17634 1.17622 1.17633 87.9\n",
      "NEW VALUE APPENDED 1.17637\n",
      "checking tensor\n",
      "no signal\n",
      "1.17637 1.17642 1.17630 1.17642 107.65 1.17634 1.17639 1.17628 1.17629 93.02\n",
      "NEW VALUE APPENDED 1.17642\n",
      "checking tensor\n",
      "no signal\n",
      "1.17644 1.17648 1.17643 1.17647 145.12 1.17642 1.17645 1.17640 1.17643 144.89\n",
      "NEW VALUE APPENDED 1.17647\n",
      "checking tensor\n",
      "no signal\n",
      "1.17647 1.17648 1.17641 1.17645 183.04 1.17644 1.17646 1.17637 1.17643 94.45\n",
      "NEW VALUE APPENDED 1.17645\n",
      "checking tensor\n",
      "no signal\n",
      "1.17643 1.17646 1.17639 1.17645 206.86 1.17641 1.17644 1.17637 1.17639 119.05\n",
      "NEW VALUE APPENDED 1.17645\n",
      "checking tensor\n",
      "no signal\n",
      "1.17646 1.17646 1.17638 1.17644 139.68 1.17643 1.17645 1.17636 1.17643 201.63\n",
      "NEW VALUE APPENDED 1.17644\n",
      "checking tensor\n",
      "no signal\n",
      "1.17643 1.17650 1.17642 1.17647 101.39 1.17642 1.17648 1.17639 1.17645 83.58\n",
      "NEW VALUE APPENDED 1.17647\n",
      "checking tensor\n",
      "no signal\n",
      "1.17647 1.17651 1.17646 1.17649 121.61 1.17645 1.17648 1.17643 1.17646 80.41\n",
      "NEW VALUE APPENDED 1.17649\n",
      "checking tensor\n",
      "no signal\n",
      "1.17649 1.17657 1.17647 1.17648 113.76 1.17646 1.17655 1.17643 1.17644 191.07\n",
      "NEW VALUE APPENDED 1.17648\n",
      "checking tensor\n",
      "no signal\n",
      "1.17649 1.17652 1.17642 1.17642 135.34 1.17644 1.17649 1.17638 1.17638 140.87\n",
      "NEW VALUE APPENDED 1.17642\n",
      "checking tensor\n",
      "no signal\n",
      "1.17642 1.17642 1.17622 1.17623 166.42 1.17637 1.17637 1.17618 1.17620 113.15\n",
      "NEW VALUE APPENDED 1.17623\n",
      "checking tensor\n",
      "no signal\n",
      "1.17621 1.17629 1.17620 1.17629 147.9 1.17619 1.17626 1.17617 1.17625 151.45\n",
      "NEW VALUE APPENDED 1.17629\n",
      "checking tensor\n",
      "no signal\n",
      "1.17628 1.17631 1.17623 1.17623 129.23 1.17624 1.17628 1.17619 1.17619 126.56\n",
      "NEW VALUE APPENDED 1.17623\n",
      "checking tensor\n",
      "no signal\n",
      "1.17624 1.17626 1.17621 1.17623 115.66 1.17620 1.17622 1.17616 1.17619 40.12\n",
      "NEW VALUE APPENDED 1.17623\n",
      "checking tensor\n",
      "no signal\n",
      "1.17623 1.17628 1.17623 1.17624 120.97 1.17619 1.17625 1.17618 1.17620 109.78\n",
      "NEW VALUE APPENDED 1.17624\n",
      "checking tensor\n",
      "no signal\n",
      "1.17624 1.17633 1.17621 1.17629 216.1 1.17621 1.17629 1.17617 1.17625 20.62\n",
      "NEW VALUE APPENDED 1.17629\n",
      "checking tensor\n",
      "no signal\n",
      "1.17629 1.17641 1.17628 1.17641 136.97 1.17625 1.17635 1.17625 1.17633 129.43\n",
      "NEW VALUE APPENDED 1.17641\n",
      "checking tensor\n",
      "no signal\n",
      "1.17643 1.17644 1.17632 1.17636 171.39 1.17639 1.17642 1.17629 1.17629 120.72\n",
      "NEW VALUE APPENDED 1.17636\n",
      "checking tensor\n",
      "no signal\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.17638 1.17638 1.17638 1.17638 5.06 1.17634 1.17637 1.17628 1.17636 137.86\n",
      "NEW VALUE APPENDED 1.17638\n",
      "checking tensor\n",
      "no signal\n",
      "1.17638 1.17645 1.17636 1.17639 107.91 1.17634 1.17640 1.17630 1.17635 108.87\n",
      "NEW VALUE APPENDED 1.17639\n",
      "checking tensor\n",
      "no signal\n",
      "1.17636 1.17638 1.17634 1.17636 50.68 1.17634 1.17634 1.17630 1.17632 74.21\n",
      "NEW VALUE APPENDED 1.17636\n",
      "checking tensor\n",
      "no signal\n",
      "1.17635 1.17635 1.17632 1.17634 39.95 1.17631 1.17631 1.17629 1.17629 32.99\n",
      "NEW VALUE APPENDED 1.17634\n",
      "checking tensor\n",
      "no signal\n",
      "1.17634 1.17635 1.17630 1.17633 41.63 1.17629 1.17630 1.17629 1.17630 62.95\n",
      "NEW VALUE APPENDED 1.17633\n",
      "checking tensor\n",
      "no signal\n",
      "1.17634 1.17640 1.17633 1.17640 76.95 1.17630 1.17636 1.17630 1.17635 117.13\n",
      "NEW VALUE APPENDED 1.17640\n",
      "checking tensor\n",
      "no signal\n",
      "1.17640 1.17649 1.17638 1.17649 54.15 1.17635 1.17645 1.17633 1.17643 88.93\n",
      "NEW VALUE APPENDED 1.17649\n",
      "checking tensor\n",
      "no signal\n",
      "1.17648 1.17653 1.17643 1.17644 66.99 1.17643 1.17648 1.17638 1.17640 78.11\n",
      "NEW VALUE APPENDED 1.17644\n",
      "checking tensor\n",
      "no signal\n",
      "1.17646 1.17652 1.17646 1.17648 73.21 1.17641 1.17648 1.17641 1.17643 68.18\n",
      "NEW VALUE APPENDED 1.17648\n",
      "checking tensor\n",
      "no signal\n",
      "1.17648 1.17648 1.17633 1.17633 72.31 1.17643 1.17643 1.17633 1.17633 39.5\n",
      "NEW VALUE APPENDED 1.17633\n",
      "checking tensor\n",
      "no signal\n",
      "1.17634 1.17641 1.17632 1.17641 80.63 1.17629 1.17635 1.17629 1.17635 41.68\n",
      "NEW VALUE APPENDED 1.17641\n",
      "checking tensor\n",
      "no signal\n",
      "1.17641 1.17643 1.17641 1.17642 51.83 1.17635 1.17638 1.17635 1.17638 3.88\n",
      "NEW VALUE APPENDED 1.17642\n",
      "checking tensor\n",
      "no signal\n",
      "1.17643 1.17643 1.17643 1.17643 15.63 1.17638 1.17638 1.17637 1.17638 7.06\n",
      "NEW VALUE APPENDED 1.17643\n",
      "checking tensor\n",
      "no signal\n",
      "1.17644 1.17644 1.17641 1.17644 53.88 1.17638 1.17640 1.17636 1.17639 76.01\n",
      "NEW VALUE APPENDED 1.17644\n",
      "checking tensor\n",
      "no signal\n",
      "1.17644 1.17645 1.17643 1.17644 84.78 1.17639 1.17640 1.17637 1.17638 47.73\n",
      "NEW VALUE APPENDED 1.17644\n",
      "checking tensor\n",
      "no signal\n",
      "1.17644 1.17645 1.17643 1.17645 32.78 1.17638 1.17639 1.17638 1.17639 33.58\n",
      "NEW VALUE APPENDED 1.17645\n",
      "checking tensor\n",
      "no signal\n",
      "1.17645 1.17649 1.17645 1.17648 80.25 1.17641 1.17645 1.17640 1.17643 119.41\n",
      "NEW VALUE APPENDED 1.17648\n",
      "checking tensor\n",
      "no signal\n",
      "1.17649 1.17649 1.17649 1.17649 3.19 1.17643 1.17647 1.17643 1.17647 25.35\n",
      "NEW VALUE APPENDED 1.17649\n",
      "checking tensor\n",
      "no signal\n",
      "1.17649 1.17650 1.17645 1.17649 79.1 1.17643 1.17646 1.17642 1.17643 95.88\n",
      "NEW VALUE APPENDED 1.17649\n",
      "checking tensor\n",
      "no signal\n"
     ]
    }
   ],
   "source": [
    "app.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  160.84,\n",
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       "  117.47],\n",
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