osciloskop-hystogram-i-raw-vs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots of oscilloscope records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "280.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1e3*56e6/200e6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read waveforms from the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ./data/data00_1563119172-CHAN1.h5\n",
      "NORMAL\n",
      "  XINC: 2e-06    YINC: 0.0004    FRAMES: 1    TRIG: 0.0154    XORIGIN: -0.0014\n",
      "   YORIGIN: 0.0\n",
      "   TIME:    None\n",
      "RAW\n",
      "  XINC: 2e-08    YINC: 0.0004    FRAMES: 1    TRIG: 0.0154    XORIGIN: -0.0014\n",
      "   YORIGIN: 0.0\n",
      "   TIME:    None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Amplitude [V]')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import glob\n",
    "import numpy as np\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [18, 5]\n",
    "\n",
    "for f in glob.iglob(\"./data/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f\n",
    "    break\n",
    "\n",
    "#with h5py.File('./data/data11.h5','r') as hf:\n",
    "with h5py.File(f,'r') as hf:\n",
    "    #print('List of arrays in this file: \\n', hf.keys())\n",
    "    print 'NORMAL'\n",
    "    print '  XINC:', np.array(hf.get('XINC')), '   YINC:', np.array(hf.get('YINC')), \n",
    "    print '   FRAMES:', np.array(hf.get('FRAMES')), '   TRIG:', np.array(hf.get('TRIG')), \n",
    "    print '   XORIGIN:', np.array(hf.get('XORIGIN'))\n",
    "    print '   YORIGIN:', np.array(hf.get('YORIGIN'))\n",
    "    print '   TIME:   ', np.array(hf.get('TIME'))\n",
    "\n",
    "    print 'RAW'\n",
    "    print '  XINC:', np.array(hf.get('XINCR')), '   YINC:', np.array(hf.get('YINCR')), \n",
    "    print '   FRAMES:', np.array(hf.get('FRAMES')), '   TRIG:', np.array(hf.get('TRIGR')), \n",
    "    print '   XORIGIN:', np.array(hf.get('XORIGINR'))\n",
    "    print '   YORIGIN:', np.array(hf.get('YORIGINR'))\n",
    "    print '   TIME:   ', np.array(hf.get('TIME'))\n",
    "\n",
    "    \n",
    "    frames = np.array(hf.get('FRAMES'))\n",
    "    for n in range(1,frames+1):\n",
    "        data = hf.get(str(n))\n",
    "        np_data = 1.0 * np.array(data)\n",
    "        #print 'np_data:', max(np_data)\n",
    "        np_data = np_data - 128 - np.array(hf.get('YORIGIN'))\n",
    "        np_data = np.array(hf.get('YINC')) * np_data    # to Volts\n",
    "        np_time = range(0,1400)\n",
    "        np_time *= np.array(hf.get('XINC'))\n",
    "        np_time *= 1e6   # to microseconds\n",
    "        #np_time -= 10    # trigger time offset\n",
    "        np_time += 1.0e6*np.array(hf.get('XORIGIN'))\n",
    "        #np_time *= 1.0e-3 # milliseconds are more fun\n",
    "                \n",
    "        # Process the raw data\n",
    "        rawdata = hf.get(str(n)+'R')\n",
    "        np_rawdata = 1.0 * np.array(rawdata)\n",
    "\n",
    "        np_rawdata = np_rawdata - 128 - np.array(hf.get('YORIGINR'))\n",
    "        np_rawdata = np.array(hf.get('YINCR')) * np_rawdata    # to Volts\n",
    "        np_rawtime = range(0,len(np_rawdata))\n",
    "        np_rawtime *= np.array(hf.get('XINCR'))\n",
    "        np_rawtime *= 1e6   # to microseconds\n",
    "        #np_rawtime -= 10    # trigger time offset\n",
    "        np_rawtime += 1.0e6*np.array(hf.get('XORIGINR'))\n",
    "        #np_rawtime *= 1.0e-3 # milliseconds are more fun\n",
    "        plt.plot(np_rawtime,np_rawdata)\n",
    "        plt.plot(np_time, np_data)\n",
    "        \n",
    "plt.xlabel('Time [us]')\n",
    "plt.ylabel('Amplitude [V]')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7febf7b0ef10>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febf7b0ef50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmax=2\n",
    "ymax=0.12\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [18, 10]\n",
    "plt.xlim(-xmax,xmax)\n",
    "#plt.ylim(-ymax,ymax)\n",
    "\n",
    "plt.xlabel('Time [us]')\n",
    "plt.ylabel('Amplitude [V]')\n",
    "\n",
    "# The trigger comes 660 ns after signal start \n",
    "tshift=0.66 #us\n",
    "tshift = 0\n",
    "\n",
    "# Video memory data are further \n",
    "vmshift = 0.05 #us\n",
    "\n",
    "plt.plot((np_rawtime+tshift),np_rawdata)\n",
    "#plt.plot(vmshift+np_time+tshift, np_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse all data files in current directory\n",
    "Read in all the data files in current directory and process them into a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ./compare/data01_1548186723-CHAN1.h5\n",
      "XINC: 1e-09  YINC: 0.02  FRAMES: 18335  XORIGIN: -1.7e-07  YORIGIN: -77.0  TRIG: 0.0716\n",
      "CAPTURING: 229.66\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        xorigin = np.array(hf.get('XORIGIN'))\n",
    "        trig = np.array(hf.get('TRIG'))\n",
    "        capturing = np.array(hf.get('CAPTURING'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' XORIGIN:', xorigin, ' YORIGIN:', yorigin, ' TRIG:', trig        \n",
    "        print 'CAPTURING:', capturing         \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            if (maximum < 256):\n",
    "            #if (maximum < 254):\n",
    "                x.append(maximum)\n",
    "                #x.append(maximum-np_data[2])\n",
    "                #x.append(maximum-np_data[30])\n",
    "                #y.append(suma/maximum)\n",
    "                y.append(suma)\n",
    "                z.append(minimum)\n",
    "     \n",
    "for f in glob.iglob(\"./compare/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "for f in glob.iglob(\"./compare/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "    \n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(200,128), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "#ble = plt.hist2d(y, x, bins=(500,200),  cmap=plt.cm.jet)\n",
    "plt.xlabel('Integral of Pulse [square of the step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "#cb = plt.colorbar()\n",
    "#cb.set_label('Count')\n",
    "\n",
    "#plt.plot([0,10000],[100,100],ls='-',c='r',marker='o')\n",
    "#plt.plot([0,100000],[100,1000],c='r',lw=3,zorder=99)\n",
    "#plt.xlim(60000,160000)\n",
    "#plt.ylim(50,150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.2204956677864809"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ./data/data07_1559318603-CHAN1.h5    ./data/data03_1559318400-CHAN1.h5    ./data/data09_1559318704-CHAN1.h5    ./data/data02_1559318349-CHAN1.h5    ./data/data04_1559318451-CHAN1.h5    ./data/data08_1559318653-CHAN1.h5    ./data/data10_1559318754-CHAN1.h5    ./data/data01_1559318299-CHAN1.h5    ./data/data05_1559318502-CHAN1.h5    ./data/data06_1559318552-CHAN1.h5"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        xorigin = np.array(hf.get('XORIGIN'))\n",
    "        trig = np.array(hf.get('TRIG'))\n",
    "        capturing = np.array(hf.get('CAPTURING'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        #print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' XORIGIN:', xorigin, ' YORIGIN:', yorigin, ' TRIG:', trig        \n",
    "        #print 'CAPTURING:', capturing         \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            if (maximum < 256):\n",
    "            #if (maximum < 254):\n",
    "                x.append(maximum)\n",
    "                #y.append(suma/maximum)\n",
    "                y.append(suma)\n",
    "                z.append((maximum))\n",
    "     \n",
    "for f in glob.iglob(\"./data/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "for f in glob.iglob(\"./data/**/xdata*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,129), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "#ble = plt.hist2d(y, x, bins=(500,200),  cmap=plt.cm.jet)\n",
    "plt.xlabel('Integral of Pulse [square of the step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "#cb = plt.colorbar()\n",
    "#cb.set_label('Count')\n",
    "\n",
    "#plt.plot([0,10000],[100,100],ls='-',c='r',marker='o')\n",
    "#plt.plot([0,100000],[100,1000],c='r',lw=3,zorder=99)\n",
    "#plt.xlim(60000,160000)\n",
    "#plt.ylim(50,150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simply replot the current x, y in a nicer way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.tight_layout()\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,203), range= [[25000, 60000], [48,250]], norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "plt.xlabel('Integral of Pulse [steps of ADC multiple by sample time]')\n",
    "plt.ylabel('Amplitude of Pulse [steps of ADC]')\n",
    "#plt.xlabel('Plocha pulzu [krok pulzu krat vzorkovaci cas]')\n",
    "#plt.ylabel('Amplituda pulzu [kroky ADC]')\n",
    "plt.title('PCRD, V_bias 3 V, step of ADC = ' + str(yinc) + ' [V], step of time = ' + str(xinc) + ' [s], frames = '+ str(all_frames))\n",
    "cb = plt.colorbar()\n",
    "cb.set_label('Counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flat histogram (only X or Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Surface')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "ble = plt.hist(y, bins=250)\n",
    "plt.yscale('log')\n",
    "#plt.xscale('log')\n",
    "#plt.ylim(0,200)\n",
    "#plt.xlim(1000,140000)\n",
    "plt.xlabel('Integral')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Surface')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Amplitude')"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 18})\n",
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "ble = plt.hist(x, bins=256-40-1, range=(40,256), histtype='step' )\n",
    "plt.yscale('log')\n",
    "#plt.xscale('log')\n",
    "#plt.ylim(0,200)\n",
    "plt.xlim(40,256)\n",
    "plt.xlabel('Ch.')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Amplitude')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cut Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/Na/data04_1559143613-CHAN1.h5 XINC: 1e-09  YINC: 0.0015625  FRAMES: 10090  YORIGIN: -119.0\n",
      "./data/Co/data02_1559142413-CHAN1.h5 XINC: 1e-09  YINC: 0.0015625  FRAMES: 3757  YORIGIN: -119.0\n",
      "./data/Co/data01_1559141460-CHAN1.h5 XINC: 1e-09  YINC: 0.0015625  FRAMES: 4844  YORIGIN: -119.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' YORIGIN:', yorigin \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            \n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            x.append(maximum)\n",
    "            #y.append(suma/maximum)\n",
    "            y.append(suma)\n",
    "            z.append((maximum))\n",
    "            \n",
    "\n",
    "def Cut(maxmax,minmin,colour):\n",
    "    for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "        try:\n",
    "            with h5py.File(f,'r') as hf:\n",
    "                frames = np.array(hf.get('FRAMES'))\n",
    "                for n in range(1,frames+1):\n",
    "                    data = hf.get(str(n))\n",
    "                    np_data = 1.0 * np.array(data)\n",
    "\n",
    "                    maximum = 0\n",
    "                    minimum = 255\n",
    "                    for i in range(1400):\n",
    "                        if np_data[i] > maximum:\n",
    "                            maximum = np_data[i]\n",
    "                        if np_data[i] < minimum:\n",
    "                            minimum = np_data[i]\n",
    "\n",
    "                    if (maximum <= maxmax) and (maximum >= minmin):\n",
    "                        plt.plot(np_data, c=colour)\n",
    "        except:        \n",
    "            pass\n",
    "\n",
    "     \n",
    "for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "    \n",
    "plt.figure(1,figsize=(20,10))\n",
    "matplotlib.rc('axes',edgecolor='black')\n",
    "\n",
    "#ble = plt.hist2d(y, x, bins=(500,128), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "ble = plt.hist2d(y, x, bins=(500,128),  cmap=plt.cm.jet)\n",
    "#plt.xlim(60000,125000)\n",
    "#plt.ylim(50,225)\n",
    "plt.title('CANDY')\n",
    "plt.xlabel('Integral of Pulse [square of step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "cb = plt.colorbar()\n",
    "\n",
    "maxmax1 = 100\n",
    "maxmax2 = 100\n",
    "maxmax3 = 100\n",
    "maxmax4 = 46\n",
    "maxmax5 = 35\n",
    "width = 1\n",
    "\n",
    "minmin1 = maxmax1-width\n",
    "plt.plot([0,150000],[maxmax1,maxmax1],c='r',lw=1,zorder=99)\n",
    "plt.plot([0,150000],[minmin1,minmin1],c='r',lw=1,zorder=99)\n",
    "\n",
    "minmin2 = maxmax2-width\n",
    "plt.plot([0,150000],[maxmax2,maxmax2],c='g',lw=1,zorder=99)\n",
    "plt.plot([0,150000],[minmin2,minmin2],c='g',lw=1,zorder=99)\n",
    "\n",
    "minmin3 = maxmax3-width\n",
    "plt.plot([0,150000],[maxmax3,maxmax3],c='purple',lw=1,zorder=99)\n",
    "plt.plot([0,150000],[minmin3,minmin3],c='purple',lw=1,zorder=99)\n",
    "\n",
    "minmin4 = maxmax4-width\n",
    "plt.plot([0,150000],[maxmax4,maxmax4],c='orange',lw=1,zorder=99)\n",
    "plt.plot([0,150000],[minmin4,minmin4],c='orange',lw=1,zorder=99)\n",
    "\n",
    "minmin5 = maxmax5-width\n",
    "plt.plot([0,150000],[maxmax5,maxmax5],c='grey',lw=1,zorder=99)\n",
    "plt.plot([0,150000],[minmin5,minmin5],c='grey',lw=1,zorder=99)\n",
    "\n",
    "plt.figure(2,figsize=(7,7))\n",
    "#matplotlib.rc('axes',edgecolor='r')\n",
    "Cut(maxmax1,minmin1,'r')\n",
    "Cut(maxmax2,minmin2,'g')\n",
    "Cut(maxmax3,minmin3,'purple')\n",
    "Cut(maxmax4,minmin4,'orange')\n",
    "Cut(maxmax5,minmin5,'grey')\n",
    "\n",
    "plt.xlabel('Sample Number ['+ str(xinc*1e9)+' ns per sample]')\n",
    "plt.ylabel('Amplitude [step of A/D converter, '+ str(yinc*1e3) + ' mV per step]')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Data has no positive values, and therefore can not be log-scaled.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-ccc0108c4e8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Integral of Pulse [square of step of A/D converter]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Amplitude of Pulse [step of A/D converter]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\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    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0mmaxmax1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m190\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mcolorbar\u001b[0;34m(mappable, cax, ax, **kw)\u001b[0m\n\u001b[1;32m   2235\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2237\u001b[0;31m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmappable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2238\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2239\u001b[0m \u001b[0mcolorbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar_doc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mcolorbar\u001b[0;34m(self, mappable, cax, ax, use_gridspec, **kw)\u001b[0m\n\u001b[1;32m   1593\u001b[0m                 \u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1594\u001b[0m         \u001b[0mcax\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1595\u001b[0;31m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1597\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_ax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36mcolorbar_factory\u001b[0;34m(cax, mappable, **kwargs)\u001b[0m\n\u001b[1;32m   1328\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mColorbarPatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1329\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1330\u001b[0;31m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mColorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1332\u001b[0m     \u001b[0mcid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacksSM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'changed'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_mappable_changed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ax, mappable, **kw)\u001b[0m\n\u001b[1;32m    903\u001b[0m                 \u001b[0mkw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'alpha'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 905\u001b[0;31m             \u001b[0mColorbarBase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    906\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_mappable_changed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ax, cmap, norm, alpha, values, boundaries, orientation, ticklocation, extend, spacing, ticks, format, drawedges, filled, extendfrac, extendrect, label)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0;31m# The rest is in a method so we can recalculate when clim changes.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_all\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    324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_extend_lower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    346\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mesh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_config_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    349\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_solids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m_config_axes\u001b[0;34m(self, X, Y)\u001b[0m\n\u001b[1;32m    442\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_artist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_ticks\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    445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36mupdate_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    371\u001b[0m         \"\"\"\n\u001b[1;32m    372\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m         \u001b[0mticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticklabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ticker\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    374\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morientation\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'vertical'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m_ticker\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    592\u001b[0m         \u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mintv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 594\u001b[0;31m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\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    595\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLogLocator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    596\u001b[0m             \u001b[0meps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e-10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1536\u001b[0m         \u001b[0;34m'Return the locations of the ticks'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1537\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1538\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1554\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1556\u001b[0;31m                     \u001b[0;34m\"Data has no positive values, and therefore can not be \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1557\u001b[0m                     \"log-scaled.\")\n\u001b[1;32m   1558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Data has no positive values, and therefore can not be log-scaled."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function post_execute at 0x7f9fa8f44848> (for post_execute):\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "values must all be positive",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m                     \u001b[0mdraw_all\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    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;31m# IPython >= 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/_pylab_helpers.pyc\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m                 \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\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    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[0matexit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestroy_all\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2024\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2025\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2026\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2028\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_cursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, magnification)\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdyintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],\n\u001b[0;32m--> 624\u001b[0;31m                                       transformed_viewLim)\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m         \u001b[0mfc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_get_unsampled_image\u001b[0;34m(self, A, image_extents, viewlim)\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rgbacache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m                     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m                     \u001b[0;31m# Avoid side effects: to_rgba can return its argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                     \u001b[0;31m# unchanged.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/cm.pyc\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(self, x, alpha, bytes)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;31m# This is the normal case, mapping a scalar array:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colors.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, value, clip)\u001b[0m\n\u001b[1;32m   1002\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"minvalue must be less than or equal to maxvalue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\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-> 1004\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"values must all be positive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\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;31mValueError\u001b[0m: values must all be positive"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "values must all be positive",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    248\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2178\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2179\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2180\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2181\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2182\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 527\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    528\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    529\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, magnification)\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdyintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],\n\u001b[0;32m--> 624\u001b[0;31m                                       transformed_viewLim)\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m         \u001b[0mfc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_get_unsampled_image\u001b[0;34m(self, A, image_extents, viewlim)\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rgbacache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m                     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m                     \u001b[0;31m# Avoid side effects: to_rgba can return its argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                     \u001b[0;31m# unchanged.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/cm.pyc\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(self, x, alpha, bytes)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;31m# This is the normal case, mapping a scalar array:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colors.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, value, clip)\u001b[0m\n\u001b[1;32m   1002\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"minvalue must be less than or equal to maxvalue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\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-> 1004\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"values must all be positive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\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;31mValueError\u001b[0m: values must all be positive"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9e7fbb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' YORIGIN:', yorigin \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            \n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            x.append(maximum)\n",
    "            #y.append(suma/maximum)\n",
    "            y.append(suma)\n",
    "            z.append((maximum))\n",
    "            \n",
    "\n",
    "def Cut(maxmax,minmin,colour):\n",
    "    for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "        try:\n",
    "            with h5py.File(f,'r') as hf:\n",
    "                frames = np.array(hf.get('FRAMES'))\n",
    "                for n in range(1,frames+1):\n",
    "                    data = hf.get(str(n))\n",
    "                    np_data = 1.0 * np.array(data)\n",
    "\n",
    "                    maximum = 0\n",
    "                    minimum = 255\n",
    "                    suma = 0\n",
    "                    colour = 'black'\n",
    "                    for i in range(1400):\n",
    "                        if np_data[i] > maximum:\n",
    "                            maximum = np_data[i]\n",
    "                        if np_data[i] < minimum:\n",
    "                            minimum = np_data[i]\n",
    "                        suma = suma + np_data[i]\n",
    "                        \n",
    "                    if (maximum <= maxmax) and (maximum >= minmin):\n",
    "                        if (suma>0) and (suma<73000):\n",
    "                            colour = 'red'\n",
    "                        if (suma>73000) and (suma<82000):\n",
    "                            colour = 'green'\n",
    "                        if (suma>82000) and (suma<100000):\n",
    "                            colour = 'purple'\n",
    "                        if (suma>100000) and (suma<150000):\n",
    "                            colour = 'orange'\n",
    "                        plt.plot(np_data, c=colour)\n",
    "        except:        \n",
    "            pass\n",
    "\n",
    "     \n",
    "for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "    \n",
    "plt.figure(1,figsize=(20,10))\n",
    "matplotlib.rc('axes',edgecolor='black')\n",
    "plt.xlim(42000,175000)\n",
    "plt.ylim(35,130)\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,128), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "plt.title('CANDY x CERF')\n",
    "plt.xlabel('Integral of Pulse [square of step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "cb = plt.colorbar()\n",
    "\n",
    "maxmax1 = 190\n",
    "maxmax2 = 167\n",
    "maxmax3 = 123\n",
    "maxmax4 = 105\n",
    "maxmax5 = 60\n",
    "width = 4\n",
    "\n",
    "minmin1 = maxmax1-width\n",
    "#plt.plot([0,175000],[maxmax1,maxmax1],c='r',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin1,minmin1],c='r',lw=1,zorder=99)\n",
    "\n",
    "minmin2 = maxmax2-width\n",
    "#plt.plot([0,175000],[maxmax2,maxmax2],c='g',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin2,minmin2],c='g',lw=1,zorder=99)\n",
    "\n",
    "minmin3 = maxmax3-width\n",
    "plt.plot([0,175000],[maxmax3,maxmax3],c='purple',lw=1,zorder=99)\n",
    "plt.plot([0,175000],[minmin3,minmin3],c='purple',lw=1,zorder=99)\n",
    "\n",
    "minmin4 = maxmax4-width\n",
    "#plt.plot([0,175000],[maxmax4,maxmax4],c='orange',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin4,minmin4],c='orange',lw=1,zorder=99)\n",
    "\n",
    "minmin5 = maxmax5-width\n",
    "#plt.plot([0,175000],[maxmax5,maxmax5],c='grey',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin5,minmin5],c='grey',lw=1,zorder=99)\n",
    "\n",
    "plt.figure(2,figsize=(7,7))\n",
    "#matplotlib.rc('axes',edgecolor='r')\n",
    "#Cut(maxmax1,minmin1,'r')\n",
    "#Cut(maxmax2,minmin2,'g')\n",
    "Cut(maxmax3,minmin3,'purple')\n",
    "#Cut(maxmax4,minmin4,'orange')\n",
    "#Cut(maxmax5,minmin5,'grey')\n",
    "\n",
    "plt.xlabel('Sample Number ['+ str(xinc*1e9)+' ns per sample]')\n",
    "plt.ylabel('Amplitude [step of A/D converter, '+ str(yinc*1e3) + ' mV per step]')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vertical Cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Data has no positive values, and therefore can not be log-scaled.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-61752287803a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Integral of Pulse [square of step of A/D converter]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Amplitude of Pulse [step of A/D converter]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\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    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[0mmaxmax1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m81000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mcolorbar\u001b[0;34m(mappable, cax, ax, **kw)\u001b[0m\n\u001b[1;32m   2235\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2237\u001b[0;31m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmappable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2238\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2239\u001b[0m \u001b[0mcolorbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar_doc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36mupdate_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    371\u001b[0m         \"\"\"\n\u001b[1;32m    372\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m         \u001b[0mticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticklabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ticker\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    374\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morientation\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'vertical'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m_ticker\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    592\u001b[0m         \u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mintv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 594\u001b[0;31m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\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    595\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLogLocator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    596\u001b[0m             \u001b[0meps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e-10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1536\u001b[0m         \u001b[0;34m'Return the locations of the ticks'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1537\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1538\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1554\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1556\u001b[0;31m                     \u001b[0;34m\"Data has no positive values, and therefore can not be \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1557\u001b[0m                     \"log-scaled.\")\n\u001b[1;32m   1558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Data has no positive values, and therefore can not be log-scaled."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function post_execute at 0x7f9fa8f44848> (for post_execute):\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "values must all be positive",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m                     \u001b[0mdraw_all\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    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;31m# IPython >= 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/_pylab_helpers.pyc\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m                 \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\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    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[0matexit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestroy_all\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2024\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2025\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2026\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2028\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_cursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1157\u001b[0m         \u001b[0mdsu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mitemgetter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1159\u001b[0;31m             \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2323\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2324\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2326\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m                               \" correctly with this backend.\")\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m             \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_image_magnification\u001b[0m\u001b[0;34m(\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    390\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, magnification)\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdyintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],\n\u001b[0;32m--> 624\u001b[0;31m                                       transformed_viewLim)\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m         \u001b[0mfc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_get_unsampled_image\u001b[0;34m(self, A, image_extents, viewlim)\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rgbacache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m                     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m                     \u001b[0;31m# Avoid side effects: to_rgba can return its argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                     \u001b[0;31m# unchanged.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/cm.pyc\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(self, x, alpha, bytes)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;31m# This is the normal case, mapping a scalar array:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colors.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, value, clip)\u001b[0m\n\u001b[1;32m   1002\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"minvalue must be less than or equal to maxvalue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\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-> 1004\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"values must all be positive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\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;31mValueError\u001b[0m: values must all be positive"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "values must all be positive",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
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      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2323\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2324\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2326\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m                               \" correctly with this backend.\")\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m             \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_image_magnification\u001b[0m\u001b[0;34m(\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    390\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, magnification)\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdyintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],\n\u001b[0;32m--> 624\u001b[0;31m                                       transformed_viewLim)\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m         \u001b[0mfc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_get_unsampled_image\u001b[0;34m(self, A, image_extents, viewlim)\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rgbacache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m                     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m                     \u001b[0;31m# Avoid side effects: to_rgba can return its argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                     \u001b[0;31m# unchanged.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/cm.pyc\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(self, x, alpha, bytes)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;31m# This is the normal case, mapping a scalar array:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colors.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, value, clip)\u001b[0m\n\u001b[1;32m   1002\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"minvalue must be less than or equal to maxvalue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\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-> 1004\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"values must all be positive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\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;31mValueError\u001b[0m: values must all be positive"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9e7c9510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' YORIGIN:', yorigin \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            \n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            x.append(maximum)\n",
    "            #y.append(suma/maximum)\n",
    "            y.append(suma)\n",
    "            z.append((maximum))\n",
    "            \n",
    "\n",
    "def Cut(maxmax,minmin,colour):\n",
    "    for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "        try:\n",
    "            with h5py.File(f,'r') as hf:\n",
    "                frames = np.array(hf.get('FRAMES'))\n",
    "                for n in range(1,frames+1):\n",
    "                    data = hf.get(str(n))\n",
    "                    np_data = 1.0 * np.array(data)\n",
    "\n",
    "                    maximum = 0\n",
    "                    minimum = 255\n",
    "                    suma = 0\n",
    "                    colour = 'black'\n",
    "                    for i in range(1400):\n",
    "                        if np_data[i] > maximum:\n",
    "                            maximum = np_data[i]\n",
    "                        if np_data[i] < minimum:\n",
    "                            minimum = np_data[i]\n",
    "                        suma = suma + np_data[i]\n",
    "                        \n",
    "                    if (maximum <= maxmax) and (maximum >= minmin):\n",
    "                        if (suma>0) and (suma<73000):\n",
    "                            colour = 'purple'\n",
    "                        if (suma>73000) and (suma<82000):\n",
    "                            print suma, \".\"\n",
    "                            colour = 'red'\n",
    "                        if (suma>82000) and (suma<100000):\n",
    "                            print suma\n",
    "                            colour = 'green'\n",
    "                        if (suma>100000) and (suma<150000):\n",
    "                            colour = 'orange'\n",
    "                        plt.plot(np_data, c=colour)\n",
    "        except:        \n",
    "            pass\n",
    "\n",
    "def VCut(maxmax,minmin,colour):\n",
    "    for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "        try:\n",
    "            with h5py.File(f,'r') as hf:\n",
    "                frames = np.array(hf.get('FRAMES'))\n",
    "                for n in range(1,frames+1):\n",
    "                    data = hf.get(str(n))\n",
    "                    np_data = 1.0 * np.array(data)\n",
    "\n",
    "                    maximum = 0\n",
    "                    minimum = 255\n",
    "                    suma = 0\n",
    "                    for i in range(1400):\n",
    "                        if np_data[i] > maximum:\n",
    "                            maximum = np_data[i]\n",
    "                        if np_data[i] < minimum:\n",
    "                            minimum = np_data[i]\n",
    "                        suma = suma + np_data[i]\n",
    "                        \n",
    "                    if (suma<=maxmax) and (suma>=minmin):\n",
    "                        #print maximum\n",
    "                        colour = 'green'\n",
    "                        if (maximum>70):\n",
    "                            colour = 'red'\n",
    "                            \n",
    "                        plt.plot(np_data, c=colour)\n",
    "        except:        \n",
    "            pass\n",
    "\n",
    "     \n",
    "for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "    \n",
    "plt.figure(1,figsize=(20,10))\n",
    "matplotlib.rc('axes',edgecolor='black')\n",
    "plt.xlim(42000,175000)\n",
    "plt.ylim(35,130)\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,128), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "plt.title('Protons X Photons')\n",
    "plt.xlabel('Integral of Pulse [square of step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "cb = plt.colorbar()\n",
    "\n",
    "maxmax1 = 81000\n",
    "maxmax2 = 167\n",
    "maxmax3 = 80\n",
    "maxmax4 = 105\n",
    "maxmax5 = 60\n",
    "width = 1\n",
    "\n",
    "minmin1 = maxmax1-1000\n",
    "plt.plot([maxmax1,maxmax1],[0,250],c='r',lw=1,zorder=99)\n",
    "plt.plot([minmin1,minmin1],[0,250],c='r',lw=1,zorder=99)\n",
    "\n",
    "minmin2 = maxmax2-width\n",
    "#plt.plot([0,175000],[maxmax2,maxmax2],c='g',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin2,minmin2],c='g',lw=1,zorder=99)\n",
    "\n",
    "minmin3 = maxmax3-width\n",
    "plt.plot([0,175000],[maxmax3,maxmax3],c='purple',lw=1,zorder=99)\n",
    "plt.plot([0,175000],[minmin3,minmin3],c='purple',lw=1,zorder=99)\n",
    "\n",
    "minmin4 = maxmax4-width\n",
    "#plt.plot([0,175000],[maxmax4,maxmax4],c='orange',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin4,minmin4],c='orange',lw=1,zorder=99)\n",
    "\n",
    "minmin5 = maxmax5-width\n",
    "#plt.plot([0,175000],[maxmax5,maxmax5],c='grey',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin5,minmin5],c='grey',lw=1,zorder=99)\n",
    "\n",
    "plt.figure(2,figsize=(7,7))\n",
    "#matplotlib.rc('axes',edgecolor='r')\n",
    "#Cut(maxmax1,minmin1,'r')\n",
    "#Cut(maxmax2,minmin2,'g')\n",
    "Cut(maxmax3,minmin3,'purple')\n",
    "#Cut(maxmax4,minmin4,'orange')\n",
    "#Cut(maxmax5,minmin5,'grey')\n",
    "\n",
    "plt.xlabel('Sample Number ['+ str(xinc*1e9)+' ns per sample]')\n",
    "plt.ylabel('Amplitude [step of A/D converter, '+ str(yinc*1e3) + ' mV per step]')\n",
    "\n",
    "plt.figure(3,figsize=(7,7))\n",
    "#matplotlib.rc('axes',edgecolor='r')\n",
    "#Cut(maxmax1,minmin1,'r')\n",
    "#Cut(maxmax2,minmin2,'g')\n",
    "VCut(maxmax1,minmin1,'purple')\n",
    "#Cut(maxmax4,minmin4,'orange')\n",
    "#Cut(maxmax5,minmin5,'grey')\n",
    "\n",
    "plt.xlabel('Sample Number ['+ str(xinc*1e9)+' ns per sample]')\n",
    "plt.ylabel('Amplitude [step of A/D converter, '+ str(yinc*1e3) + ' mV per step]')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limited maximum histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/data40_1538561816-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data22_1538560556-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data15_1538559989-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data09_1538559561-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data14_1538559914-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data10_1538559633-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data23_1538560624-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data18_1538560275-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data13_1538559846-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data37_1538561604-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data32_1538561255-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data11_1538559705-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data42_1538561956-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data31_1538561188-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data30_1538561116-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data02_1538559068-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data01_1538559000-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data28_1538560976-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data03_1538559141-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data16_1538560062-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data41_1538561888-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data46_1538562237-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data38_1538561676-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data34_1538561398-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data29_1538561048-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data33_1538561325-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data08_1538559493-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data45_1538562166-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data05_1538559281-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data07_1538559421-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data43_1538562028-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data25_1538560764-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data06_1538559353-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data27_1538560908-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data19_1538560347-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data39_1538561748-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data21_1538560484-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data20_1538560415-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data17_1538560206-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data44_1538562098-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data36_1538561537-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data12_1538559774-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data26_1538560835-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data35_1538561465-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data04_1538559209-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data24_1538560696-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data47_1538562305-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9fd70f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9fd70310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' YORIGIN:', yorigin \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            if (maximum < 151):\n",
    "                x.append(maximum)\n",
    "                #y.append(suma/maximum)\n",
    "                y.append(suma)\n",
    "                z.append((maximum))\n",
    "     \n",
    "for f in glob.iglob(\"./data/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "\n",
    "    \n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,150), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "#ble = plt.hist2d(y, x, bins=(500,200),  cmap=plt.cm.jet)\n",
    "plt.xlabel('Integral of Pulse [square of the step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "#cb = plt.colorbar()\n",
    "#cb.set_label('Count')\n",
    "\n",
    "#plt.plot([0,10000],[100,100],ls='-',c='r',marker='o')\n",
    "#plt.plot([0,100000],[100,1000],c='r',lw=3,zorder=99)\n",
    "#plt.xlim(60000,160000)\n",
    "#plt.ylim(50,150)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram for oscilloscope B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9fcb58d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9fcb5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        xorigin = np.array(hf.get('XORIGIN'))\n",
    "        trig = np.array(hf.get('TRIG'))\n",
    "        capturing = np.array(hf.get('CAPTURING'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' XORIGIN:', xorigin, ' YORIGIN:', yorigin, ' TRIG:', trig        \n",
    "        print 'CAPTURING:', capturing         \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            if (maximum < 256):\n",
    "            #if (maximum < 254):\n",
    "                x.append(maximum)\n",
    "                #y.append(suma/maximum)\n",
    "                y.append(suma)\n",
    "                z.append((maximum))\n",
    "     \n",
    "for f in glob.iglob(\"./dataB/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "for f in glob.iglob(\"./dataB/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "\n",
    "    \n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,129), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "#ble = plt.hist2d(y, x, bins=(500,200),  cmap=plt.cm.jet)\n",
    "plt.xlabel('Integral of Pulse [square of the step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "#cb = plt.colorbar()\n",
    "#cb.set_label('Count')\n",
    "\n",
    "#plt.plot([0,10000],[100,100],ls='-',c='r',marker='o')\n",
    "#plt.plot([0,100000],[100,1000],c='r',lw=3,zorder=99)\n",
    "#plt.xlim(60000,160000)\n",
    "#plt.ylim(50,150)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "56e6*5e-10"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}