old_pulseTek2002B.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "TEKTRONIX,TDS 2002B,C101891,CF:91.1CT FV:v22.11\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "# coding=utf-8\n",
    "\n",
    "import tek3 # import tek3 as tek3 : utilidades de comunicación con Tek2002B\n",
    "import time # import time\n",
    "tds2002b = tek3.TekScope(\"/dev/usbtmc1\") # creo una instancia de tek3.TekScope\n",
    "\n",
    "# refresca lo importado de tek3 cada vez que se hace ejecución\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Registro automático de picos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def initPulse(inst=test):\n",
    "    inst.write('INITIATE_CLEAR')\n",
    "    inst.write('HEADER OFF')\n",
    "    inst.set_data_encoding('RIBinary')\n",
    "    inst.write('ACQuire:MODe SAMple') # test.write('ACQuire:MODe PEAKdetect') lo descarto pues manual dice que puede verse mas ruidoso  \n",
    "    inst.write('ACQUIRE:STOPAFTER SEQUENCE')\n",
    "    inst.write('ACQuire:STATE RUN')\n",
    "    inst.write('CH1:POSition 3.0')\n",
    "    inst.write('CH1:SCAle 2E-2')\n",
    "    inst.write('CH2:POSition -2.0')\n",
    "    inst.write('CH2:SCAle 2E-1')\n",
    "    inst.horizontal_main_position(1.0E-7)\n",
    "    inst.horizontal_main_scale(1.0E-7)\n",
    "    inst.trigger_source('CH1')\n",
    "    trgLevel= -0.08\n",
    "    test.trigger_level(trgLevel)\n",
    "    \n",
    "    \n",
    "def escalas(inst=test):\n",
    "    # obtener una vez los límites verticales del canal\n",
    "    pos1 = inst.get_channel_position(1)\n",
    "    scale1 = inst.get_channel_scale(1)\n",
    "    pos2 = inst.get_channel_position(2)\n",
    "    scale2 = inst.get_channel_scale(2)\n",
    "    return pos1, scale1, pos2, scale2\n",
    "\n",
    "\n",
    "def flName(camara, tipo, N):\n",
    "    filePath= './data/'\n",
    "    fileDate= time.strftime('%y%m%d')\n",
    "    fileCamara= camara # 'n' nuestra = Campbelliana\n",
    "    fileN= N\n",
    "    fileExt= '.npy'\n",
    "    fileType= tipo\n",
    "    fileName= filePath+ fileType+ fileDate + fileCamara+ ('{:02n}'.format(fileN))+ fileExt\n",
    "    return fileName\n",
    "\n",
    "\n",
    "def param():\n",
    "    '''\n",
    "    Grabo parámetros de la medición\n",
    "    '''\n",
    "    test.load_setup() # Obtiene parámetros del osciloscopio\n",
    "    sets= test.dico\n",
    "    [pos1, scale1, pos2, scale2]= escalas()\n",
    "    sets['POSITION1']= pos1\n",
    "    sets['SCALE1']= scale1\n",
    "    sets['POSITION2']= pos2\n",
    "    sets['SCALE2']= scale2\n",
    "    sets['ELECTRONICA']= 'Luis'\n",
    "    return sets\n",
    "\n",
    "\n",
    "def paramSave(N, camara='CFP-25'):\n",
    "    '''\n",
    "    Grabo parámetros de la medición\n",
    "    '''\n",
    "    sets= param(camara)\n",
    "    save(flName(camara, 'sets', N), sets)\n",
    "    \n",
    "    \n",
    "def eins(channel):\n",
    "    ''' \n",
    "    Returns single channel as horizontal array \n",
    "    '''\n",
    "    test.set_data_source(channel)\n",
    "    Y= float16(test.bin_read())\n",
    "    return Y\n",
    "\n",
    "\n",
    "def readBuff2(self):\n",
    "    try:\n",
    "        return self.ask('CURVE?')\n",
    "    except TimeoutError:\n",
    "        print('Probably requested channel has no data')\n",
    "        raise\n",
    "        \n",
    "        \n",
    "def zwei():\n",
    "    '''\n",
    "    Returns both channels as horizontal array\n",
    "    '''\n",
    "    Y1= eins(1)\n",
    "    Y2= eins(2)\n",
    "    Meas2= np.vstack([Y1, Y2])\n",
    "    return Meas2\n",
    "\n",
    "\n",
    "def single():\n",
    "    '''\n",
    "    Queries both channels, restarts acquistion\n",
    "    '''\n",
    "    test.start_acq()\n",
    "    Meas2= zwei()\n",
    "    return Meas2\n",
    "\n",
    "\n",
    "def darstellungNurZwei(T, Meas2, sav= None):\n",
    "    '''\n",
    "    T: times vector\n",
    "    Meas2: Both channels 2D array\n",
    "    sav: plot filename (eps)\n",
    "    '''\n",
    "        \n",
    "    fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True)\n",
    "    fig.set_size_inches(10,8)\n",
    "    fig.subplots_adjust(hspace=0.000)\n",
    "\n",
    "    ax0.plot(T, Meas2[0,:], 'y')\n",
    "    # Make the y-axis label and tick labels match the line color.\n",
    "    ax0.set_ylabel('ch 1 [V]', color='y')\n",
    "    ax0.grid(True)\n",
    "\n",
    "    ax1.plot(T, Meas2[1,:], 'c')\n",
    "    ax1.set_ylabel('ch 2 [V]', color='c')\n",
    "    ax1.set_xlabel('tiempo [s]')\n",
    "    ax1.grid(True)\n",
    "    \n",
    "    if (sav):\n",
    "        plt.savefig('./data/'+ sav+ '.eps')\n",
    "    else:\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "def darstellungNurZwei(T, Meas2, sav= None):\n",
    "    '''\n",
    "    T: times vector\n",
    "    Meas2: Both channels 2D array\n",
    "    sav: plot filename (eps)\n",
    "    '''\n",
    "        \n",
    "    fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True)\n",
    "    fig.set_size_inches(10,8)\n",
    "    fig.subplots_adjust(hspace=0.000)\n",
    "\n",
    "    ax0.plot(T, Meas2[0,:], 'y')\n",
    "    # Make the y-axis label and tick labels match the line color.\n",
    "    ax0.set_ylabel('ch 1 [V]', color='y')\n",
    "    ax0.grid(True)\n",
    "\n",
    "    ax1.plot(T, Meas2[1,:], 'c')\n",
    "    ax1.set_ylabel('ch 2 [V]', color='c')\n",
    "    ax1.set_xlabel('tiempo [s]')\n",
    "    ax1.grid(True)\n",
    "    \n",
    "    if (sav):\n",
    "        plt.savefig('./data/'+ sav+ '.eps')\n",
    "    else:\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "def acqDartellung(nombre, inst=test, graf=False):\n",
    "    zeit= tek3.TekScope.Xaxis(inst)\n",
    "    data= zeit\n",
    "    print('\\n')\n",
    "    for count in range(nombre): #\n",
    "        while(test.textAsk('BUSY?')=='1'):\n",
    "            time.sleep(.5)\n",
    "        M2= single()\n",
    "        data= np.vstack([data,M2])\n",
    "        if (graf):\n",
    "            darstellungNurZwei(zeit, M2)\n",
    "        print('\\rN= '+ str(count+ 1), end=' ')\n",
    "    print('\\n')\n",
    "    return data\n",
    "\n",
    "\n",
    "def prom(data, prop):\n",
    "    '''\n",
    "    (array, float) -> array\n",
    "    \n",
    "    Obtiene el promedio de la primer fracción prop de ambos canales de todas las mediciones en data\n",
    "    grProm[0]: ch1, grProm[1]: ch2\n",
    "\n",
    "    '''\n",
    "    messungenZahl= data.shape\n",
    "    erstePunkten= int(messungenZahl[1]* prop)\n",
    "    prom= np.zeros([int((messungenZahl[0]- 1)/ 2), 2])\n",
    "    for i in range(1, messungenZahl[0], 2):\n",
    "        ix= int((i-1)/2)\n",
    "        prom[ix,0]= (data[i][0:erstePunkten-1]).mean()\n",
    "        prom[ix,1]= (data[i+1][0:erstePunkten-1]).mean()\n",
    "    grProm= np.array([ (prom[:,0]).mean(), (prom[:,1]).mean() ])\n",
    "    return grProm\n",
    "\n",
    "\n",
    "class archie:\n",
    "    \"\"\"Nombre archivo - NO FUNCA AUN, NO PUEDO VARIAR EXTERNAMENTE tipo\"\"\"\n",
    "\n",
    "    def __init__(self, tipo, N, camara):\n",
    "        \"\"\" (archie, int, str, str, str) -> NoneType\n",
    "\n",
    "        Un nombre de archivo con ruta, cámara, fecha y número \n",
    "\n",
    "        >>> archie = CashRegister(5, 5, 5, 5, 5)\n",
    "        >>> archie.N\n",
    "        '5'\n",
    "        >>> archie.camara\n",
    "        'n'\n",
    "        >>> archie.path\n",
    "        './data/'\n",
    "        >>> register.date\n",
    "        '150430'\n",
    "        \"\"\"\n",
    "        self.tipo= tipo\n",
    "        self.N=('{:02n}'.format(N))\n",
    "        self.camara = camara\n",
    "        self.path = './data/'\n",
    "        self.date = time.strftime('%y%m%d')\n",
    "        self.ext= '.npy'\n",
    "        self.fileName= self.tipo+ self.date+ self.camara+ self.N+ self.ext"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lectura rápida, graba ints comprimidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ptsAdq():\n",
    "    # correr una vez para obtener parámetros del buffer\n",
    "    buff2 = readBuff2(test) # esta bien la herencia?\n",
    "    dataPointsCharacters= int(buff2[1:2])\n",
    "    dataOffset= 2+ dataPointsCharacters\n",
    "    return (int(buff2[2:2+ dataPointsCharacters])), dataOffset # número de lecturas en cada adquisición\n",
    "\n",
    "\n",
    "def rapi(dataCount, dataOffset):\n",
    "    test.start_acq()\n",
    "    while(test.textAsk('BUSY?')=='1'):\n",
    "        time.sleep(1E-3)\n",
    "    # return (np.frombuffer(readBuff2(test), dtype = np.dtype('int8').newbyteorder('<'), count= dataCount, offset= dataOffset) )  \n",
    "    buff2 = readBuff2(test) # esta bien la herencia?\n",
    "    return ( np.frombuffer(buff2, dtype = np.dtype('int8').newbyteorder('<'), count= dataCount, offset= dataOffset) )\n",
    "\n",
    "\n",
    "def lectura(adquisiciones, puntosAdquisicion, dataOffset, tiempoInicial):\n",
    "    # la lectura en sí\n",
    "    ch1= np.empty([adquisiciones,puntosAdquisicion], dtype ='int8')\n",
    "    ch2= np.empty([adquisiciones,puntosAdquisicion], dtype ='int8')\n",
    "    zeit= test.temps() # zeit\n",
    "    # zeit= tek3.TekScope.Xaxis(test) # zeit\n",
    "    for medicion in range(adquisiciones):\n",
    "        print('\\rmedición '+ str(medicion)+ ' de '+ str(adquisiciones), end=' ')\n",
    "        test.set_data_source(1) # canal 2\n",
    "        ch1[medicion]= rapi(puntosAdquisicion, dataOffset)\n",
    "        test.set_data_source(2) # canal 2\n",
    "        ch2[medicion]= rapi(puntosAdquisicion, dataOffset)\n",
    "    print('\\rmedición '+ str(medicion+ 1)+ ' de '+ str(adquisiciones))\n",
    "    print('Tiempo transcurrido= {:.1f} min'.format((time.time()- tiempoInicial)/60) ) # tiempo transcurrido en segundos\n",
    "    return zeit, ch1, ch2\n",
    "\n",
    "\n",
    "def adquiere(medicionDia, settings, adquisiciones, descripcionDetector, descripcionArchivo, puntosAdquisicion, dataOffset, trgLevel= -0.058):\n",
    "    '''\n",
    "    registra ambos canales en formato de multiples arrays\n",
    "    '''\n",
    "\n",
    "    tiempoInicial= time.time()\n",
    "\n",
    "    # ch2 =lectura(adquisiciones, puntosAdquisicion, dataOffset, tiempoInicial)\n",
    "    [zeit, ch1, ch2]= lectura(adquisiciones, puntosAdquisicion, dataOffset, tiempoInicial)\n",
    "    \n",
    "    # PULSER INVIERTO SIGNO CH1: Mantengo el ánalisis imperturbado\n",
    "    ch1*= -1\n",
    "\n",
    "    # graba    \n",
    "    np.savez_compressed(flName(descripcionDetector, descripcionArchivo, medicionDia), settings=settings, zeit=zeit, ch1=ch1, ch2=ch2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consulta al osciloscopio del estado actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.02, 3.0, 0.01, 0.0, '1.0E-7', '3.16E-7', -0.038]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[ch1S, ch1p, ch2s, ch2p, hS, hP, trig]= [tds2002b.get_channel_scale('CH1'), tds2002b.get_channel_position('CH1'), tds2002b.get_channel_scale('CH2'), tds2002b.get_channel_position('CH2'), tds2002b.horizontal_main_scale(), tds2002b.horizontal_main_position(), tds2002b.trigger_level()]\n",
    "[ch1S, ch1p, ch2s, ch2p, hS, hP, trig]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adquisición con generador de pulsos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "param() takes 0 positional arguments but 1 was given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-37e9606c56ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0mtds2002b\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrigger_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.04\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# [V]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0msettings\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtds2002b\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpuntosAdquisicion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataOffset\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mptsAdq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtds2002b\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# puntos por cada adquisición, headbits en cada adquisición\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: param() takes 0 positional arguments but 1 was given"
     ]
    }
   ],
   "source": [
    "# Secuencia principal\n",
    "tds2002b.write('INITIATE_CLEAR')\n",
    "tds2002b.write('HEADER OFF')\n",
    "tds2002b.set_data_encoding('RIBinary')\n",
    "tds2002b.write('ACQuire:MODe SAMple') # test.write('ACQuire:MODe PEAKdetect') lo descarto pues manual dice que puede verse mas ruidoso  \n",
    "tds2002b.write('ACQUIRE:STOPAFTER SEQuence')\n",
    "tds2002b.write('ACQuire:STATE RUN')\n",
    "tds2002b.write('CH1:COUPling DC')\n",
    "tds2002b.write('CH2:COUPling DC')\n",
    "\n",
    "tds2002b.write('CH1:SCAle 0.02')\n",
    "tds2002b.write('CH1:POSition 3.0')\n",
    "tds2002b.write('CH2:SCAle 0.01')\n",
    "tds2002b.write('CH2:POSition 0.0')\n",
    "tds2002b.horizontal_main_scale(1.0E-7)\n",
    "tds2002b.horizontal_main_position(3.16E-7)\n",
    "tds2002b.trigger_source('CH1')\n",
    "tds2002b.trigger_level(-0.04) # [V]\n",
    "\n",
    "settings= param(tds2002b)\n",
    "[puntosAdquisicion, dataOffset] = ptsAdq(tds2002b) # puntos por cada adquisición, headbits en cada adquisición\n",
    "\n",
    "descripcionDetector= 'z'\n",
    "descripcionArchivo= 'pul'\n",
    "adquisiciones= 1*1280 # número de lecturas\n",
    "\n",
    "for medicionDia in np.arange(1,2): # medición 1 a 2 (dos mediciones)\n",
    "    adquiere(medicionDia, settings, adquisiciones, descripcionDetector, descripcionArchivo, puntosAdquisicion, dataOffset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensayos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test.write('HEADER OFF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lectura simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{':HARDCOPY:BUTTON': 'SAVESALL',\n",
       " ':MATH:FFT:VERTICAL:POSITION': '0.0E0',\n",
       " ':CURSOR:VBARS:UNITS': 'SECONDS',\n",
       " 'STOPAFTER': 'SEQUENCE',\n",
       " ':DISPLAY:FORMAT': 'YT',\n",
       " ':TRIGGER:MAIN:MODE': 'NORMAL',\n",
       " 'SOURCE': 'CH2',\n",
       " ':MEASUREMENT:MEAS5:TYPE': 'FREQUENCY',\n",
       " 'STATE': '0',\n",
       " 'INKSAVER': 'ON',\n",
       " 'COUPLING': 'DC',\n",
       " ':VERBOSE': '1',\n",
       " 'CURRENTPROBE': '1.0E1',\n",
       " 'IDPRINT': 'DEFLT',\n",
       " 'DATEPRINT': 'DEFLT',\n",
       " 'FORMAT': 'EPSIMAGE',\n",
       " ':CH1:PROBE': '1.0E0',\n",
       " 'LAYOUT': 'PORTRAIT',\n",
       " 'SOURCE1': 'CH1',\n",
       " 'DESTINATION': 'REFA',\n",
       " ':PICTBRIDGE:PAPERSIZE': 'DEFLT',\n",
       " ':HORIZONTAL:VIEW': 'MAIN',\n",
       " ':ACQUIRE:MODE': 'SAMPLE',\n",
       " ':AUTORANGE:SETTINGS': 'BOTH',\n",
       " 'BANDWIDTH': 'OFF',\n",
       " ':SAVE:IMAGE:FILEFORMAT': 'EPSIMAGE',\n",
       " ':LANGUAGE': 'SPANISH',\n",
       " 'WIDTH:POLARITY': 'POSITIVE',\n",
       " 'YUNIT': '\"V\"',\n",
       " 'TYPE': 'EDGE',\n",
       " ':MEASUREMENT:MEAS1:TYPE': 'PK2PK',\n",
       " 'SYNC': 'FIELD',\n",
       " 'STYLE': 'VECTORS',\n",
       " 'CH2': '1',\n",
       " 'WIDTH': '1.0E-3',\n",
       " 'STOP': '2500',\n",
       " 'PERSISTENCE': '0',\n",
       " ':MEASUREMENT:MEAS2:TYPE': 'PERIOD',\n",
       " 'STANDARD': 'NTSC',\n",
       " ':CURSOR:HBARS:POSITION1': '-1.0E-1',\n",
       " 'PRINTQUAL': 'DEFLT',\n",
       " 'SCALE1': 0.02,\n",
       " ':TRIGGER:MAIN:VIDEO:SOURCE': 'CH1',\n",
       " 'REFA': '0',\n",
       " ':DATA:ENCDG': 'RIBINARY',\n",
       " ':MEASUREMENT:MEAS4:TYPE': 'PK2PK',\n",
       " ':TRIGGER:MAIN:PULSE:SOURCE': 'CH1',\n",
       " 'SLOPE': 'RISE',\n",
       " 'SCALE2': 0.5,\n",
       " ':TRIGGER:MAIN:EDGE:SOURCE': 'CH1',\n",
       " 'POSITION2': -2.0,\n",
       " 'POSITION': '4.75E-7',\n",
       " ':MEASUREMENT:METHOD': 'AUTO',\n",
       " 'HOLDOFF:VALUE': '5.0E-7',\n",
       " 'SCALE': '1.0E0',\n",
       " 'VERTICAL:POSITION': '0.0E0',\n",
       " ':LOCK': 'NONE',\n",
       " 'REFB': '0',\n",
       " 'MATH': '0',\n",
       " 'POLARITY': 'NORMAL',\n",
       " 'WHEN': 'EQUAL',\n",
       " ':TRIGGER:MAIN:LEVEL': '-4.96E-2',\n",
       " 'POSITION1': 2.0,\n",
       " 'NUMAVG': '128',\n",
       " 'START': '1',\n",
       " ':MEASUREMENT:IMMED:TYPE': 'PERIOD',\n",
       " 'CONTRAST': '54',\n",
       " 'PORT': 'USB',\n",
       " ':CH2:PROBE': '1.0E0',\n",
       " ':SELECT:CH1': '1',\n",
       " 'LINE': '1',\n",
       " 'SELECT:SOURCE': 'CH1',\n",
       " ':CURSOR:FUNCTION': 'OFF',\n",
       " 'IMAGESIZE': 'DEFLT',\n",
       " 'PAPERTYPE': 'DEFLT',\n",
       " 'ELECTRONICA': 'Luis',\n",
       " 'MAIN:SCALE': '1.0E-7',\n",
       " ':MEASUREMENT:MEAS3:TYPE': 'NWIDTH',\n",
       " ':HORIZONTAL:DELAY:SCALE': '5.0E-9',\n",
       " ':MATH:DEFINE': '\"CH1-CH2\"',\n",
       " ':MATH:FFT:HORIZONTAL:POSITION': '5.0E1',\n",
       " 'INVERT': 'OFF'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "settings= param()\n",
    "settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'settings' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-819ddd13abdf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'POSITION1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'settings' is not defined"
     ]
    }
   ],
   "source": [
    "np.float(settings['POSITION1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: b'C'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-362ec988f134>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mpuntosAdquisicion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataOffset\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mptsAdq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# puntos por cada adquisición, headbits en cada adquisición\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mzeit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mch1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mch2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlectura\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpuntosAdquisicion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataOffset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-3-aee0265d6495>\u001b[0m in \u001b[0;36mptsAdq\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;31m# correr una vez para obtener parámetros del buffer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mbuff2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreadBuff2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# esta bien la herencia?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mdataPointsCharacters\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuff2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mdataOffset\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mdataPointsCharacters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuff2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mdataPointsCharacters\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataOffset\u001b[0m \u001b[0;31m# número de lecturas en cada adquisición\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: b'C'"
     ]
    }
   ],
   "source": [
    "[puntosAdquisicion, dataOffset] = ptsAdq() # puntos por cada adquisición, headbits en cada adquisición\n",
    "[zeit, ch1, ch2]=lectura(1, puntosAdquisicion, dataOffset, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cosaCh1= ((4/100)* ch1[0]- np.float(settings['POSITION1']) )* np.float(settings['SCALE1'])\n",
    "cosaCh2= ((4/100)* ch2[0]- np.float(settings['POSITION2']) )* np.float(settings['SCALE2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9353f5ada0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEACAYAAABRQBpkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9353f5a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cosaCh1)\n",
    "plt.plot(cosaCh2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0c75995cc0>]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEACAYAAABGYoqtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiJZKSRERCQrhYSIiGSlkBARkaz+D0roSNacnV+RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c75a1a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ch1_nega= ch1[0]* (-1)\n",
    "cosa_nega= ((4/100)* ch1_nega+ np.float(settings['POSITION1']) )* np.float(settings['SCALE1'])\n",
    "plt.plot(cosa_nega)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.02, 2.0, 0.5, -2.0, '1.0E-7', '2.56E-7', -0.0496)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.get_channel_scale('CH1'), test.get_channel_position('CH1'), test.get_channel_scale('CH2'), test.get_channel_position('CH2'), test.horizontal_main_scale(), test.horizontal_main_position(), test.trigger_level()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "settings= param()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0E-7'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.horizontal_main_scale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0E-7'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.horizontal_main_position()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test.horizontal_main_position(2.0E-7)\n",
    "test.horizontal_main_scale(1.0E-7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test.write('INITIATE_CLEAR')\n",
    "test.set_data_encoding('RIBinary')\n",
    "test.write('ACQuire:MODe SAMple') # test.write('ACQuire:MODe PEAKdetect') lo descarto pues manual dice que puede verse mas ruidoso  \n",
    "test.write('ACQUIRE:STOPAFTER SEQUENCE')\n",
    "test.write('ACQuire:STATE RUN')\n",
    "test.write('CH1:POSition 3.0')\n",
    "test.write('CH1:SCAle 5E-2')\n",
    "test.write('CH2:POSition -3.0')\n",
    "test.write('CH2:SCAle 2E-1')\n",
    "test.horizontal_main_position(2.0E-7)\n",
    "test.horizontal_main_scale(1.0E-7)\n",
    "test.trigger_source('CH1')\n",
    "trgLevel= -0.1\n",
    "test.trigger_level(trgLevel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "[puntosAdquisicion, dataOffset] = ptsAdq() # puntos por cada adquisición, headbits en cada adquisición"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.02, 3.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.get_channel_scale('CH1'), test.get_channel_position('CH1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-13"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aou=13\n",
    "aou*=-1\n",
    "aou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test.write('CH1:POSition 4.0')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "medición 1 de 1\n",
      "Tiempo transcurrido= 0.0 min\n"
     ]
    }
   ],
   "source": [
    "test.write('INITIATE_CLEAR')\n",
    "test.set_data_encoding('RIBinary')\n",
    "test.write('ACQuire:MODe SAMple') # test.write('ACQuire:MODe PEAKdetect') lo descarto pues manual dice que puede verse mas ruidoso  \n",
    "test.write('ACQUIRE:STOPAFTER SEQUENCE')\n",
    "test.write('ACQuire:STATE RUN')\n",
    "test.write('CH1:SCAle 0.02')\n",
    "test.write('CH1:POSition 4.0')\n",
    "test.write('CH2:SCAle 0.5')\n",
    "test.write('CH2:POSition -2.0')\n",
    "test.horizontal_main_scale(1.0E-7)\n",
    "test.horizontal_main_position(0)\n",
    "trgLevel= -0.05\n",
    "test.trigger_source('CH1')\n",
    "test.trigger_level(trgLevel)\n",
    "\n",
    "# adquisiciones= 1280 # número de lecturas\n",
    "adquisiciones= 1 # número de lecturas\n",
    "\n",
    "descripcionDetector= 'z'\n",
    "descripcionArchivo= 'pul'\n",
    "\n",
    "settings= param()\n",
    "[puntosAdquisicion, dataOffset] = ptsAdq() # puntos por cada adquisición, headbits en cada adquisición\n",
    "\n",
    "for medicionDia in np.arange(1,2): # medición 1 a 2 (dos mediciones)\n",
    "    adquiere(medicionDia, settings, adquisiciones, descripcionDetector, descripcionArchivo, puntosAdquisicion, dataOffset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.96e-07"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.get_out_waveform_horizontal_zero()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test.horizontal_main_position('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0E0'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.horizontal_main_position()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0E-7'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.horizontal_main_scale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.0E-10'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xincr= test.textAsk('WFMPre:XINcr?')\n",
    "xincr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'-5.0E-7'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xzero= test.textAsk('WFMPre:XZEro?')\n",
    "xzero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5e-07"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(xzero)+ 2500*float(xincr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.996e-07"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(xzero)+ 1*float(xincr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.get_horizontal_record_length()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tiempos= [1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tiempos[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.9999999999999998e-07"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temps=float(xzero)+arange(2500)*float(xincr)\n",
    "temps[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.988000000000001e-07"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temps[2497]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.9960000000000016e-07"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temps[2499]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(temps[2499],3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%s\n"
     ]
    }
   ],
   "source": [
    "print('%s'.format(temps[2499]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.00e-07\n"
     ]
    }
   ],
   "source": [
    "print('{:4.2e}'.format(temps[2499]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def temps(self):\n",
    "    xincr= self.get_out_waveform_horizontal_sampling_interval()\n",
    "    xzero= self.get_out_waveform_horizontal_zero()\n",
    "    lesTemps= xzero+ np.arange(2500)* xincr\n",
    "    return lesTemps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4e-10"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.get_out_waveform_horizontal_sampling_interval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}