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Warning, /RecoTracker/PixelLowPtUtilities/test/pcsfVerify.ipynb is written in an unsupported language. File is not indexed. 

0001 {
0002  "cells": [
0003   {
0004    "cell_type": "code",
0005    "execution_count": 27,
0006    "metadata": {
0007     "collapsed": false
0008    },
0009    "outputs": [],
0010    "source": [
0011     "import numpy as np\n",
0012     "import matplotlib.pyplot as plt\n",
0013     "%matplotlib inline\n",
0014     "import math"
0015    ]
0016   },
0017   {
0018    "cell_type": "code",
0019    "execution_count": 28,
0020    "metadata": {
0021     "collapsed": false
0022    },
0023    "outputs": [
0024     {
0025      "data": {
0026       "application/javascript": [
0027        "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
0028        "    return false;\n",
0029        "}"
0030       ],
0031       "text/plain": [
0032        "<IPython.core.display.Javascript object>"
0033       ]
0034      },
0035      "metadata": {},
0036      "output_type": "display_data"
0037     }
0038    ],
0039    "source": [
0040     "%%javascript\n",
0041     "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
0042     "    return false;\n",
0043     "}"
0044    ]
0045   },
0046   {
0047    "cell_type": "code",
0048    "execution_count": 29,
0049    "metadata": {
0050     "collapsed": false
0051    },
0052    "outputs": [],
0053    "source": [
0054     "def split(row) :\n",
0055     "    r = []\n",
0056     "    for i in range(3,10) : r.append(row[i])\n",
0057     "    return (int(row[2])+100*(int(row[1])+10*int(row[0])), np.array(r),[int(row[11]),int(row[13])])\n",
0058     "types = []\n",
0059     "types += 3*[int] +8*[float] + 2*[int,float]\n",
0060     "f90 = open(\"../data/pixelShapePhase1_all.par\", \"r\").readlines()\n",
0061     "pcsf = np.genfromtxt(f90)\n",
0062     "\n",
0063     "\n",
0064     "f451 = open(\"../data/pixelShapePhase1_all.par\", \"r\").readlines()\n",
0065     "pcsf1 = np.genfromtxt(f451) #,dtype=types)\n",
0066     "map1 = {}\n",
0067     "for r in pcsf1 :\n",
0068     "    i,c,n = split(r)\n",
0069     "    map1[i]=(c,n)\n",
0070     "#print map1[111]\n",
0071     "\n",
0072     "\n",
0073     "f452 = open(\"../data/pixelShapePhase1_noL1.par\", \"r\").readlines()\n",
0074     "map2 = {}\n",
0075     "pcsf2 = np.genfromtxt(f452) #,dtype=types)\n",
0076     "for r in pcsf2 :\n",
0077     "    i,c,n = split(r)\n",
0078     "    map2[i]=(c,n)\n",
0079     "#xy1 = np.genfromtxt(f451, usecols=(3,4,5,6,7,8,9,10))\n",
0080     "#xy2 = np.genfromtxt(f452, usecols=(3,4,5,6,7,8,9,10))\n",
0081     "#for r in pcsf :\n",
0082     "#    if r[2]==0 : print r"
0083    ]
0084   },
0085   {
0086    "cell_type": "code",
0087    "execution_count": 30,
0088    "metadata": {
0089     "collapsed": true
0090    },
0091    "outputs": [],
0092    "source": [
0093     "def ylim(row) :\n",
0094     "    return (min(row[5],-row[10]),max(row[6],-row[9]))\n",
0095     "def xlim(row) :\n",
0096     "    return (min(row[3],-row[8]),max(row[4],-row[7]))\n"
0097    ]
0098   },
0099   {
0100    "cell_type": "code",
0101    "execution_count": 31,
0102    "metadata": {
0103     "collapsed": false
0104    },
0105    "outputs": [],
0106    "source": [
0107     "#for r in pcsf :\n",
0108     "# print r[2],ylim(r)"
0109    ]
0110   },
0111   {
0112    "cell_type": "code",
0113    "execution_count": 32,
0114    "metadata": {
0115     "collapsed": false
0116    },
0117    "outputs": [],
0118    "source": [
0119     "def ploty(ax,pcsf,col='b',barrel=0) :\n",
0120     "    for r in pcsf :\n",
0121     "        if (r[0]!=barrel) : continue\n",
0122     "        x = []\n",
0123     "        y = []\n",
0124     "        x.append(r[2])\n",
0125     "        x.append(r[2])\n",
0126     "        x.append(r[2]+1)\n",
0127     "        x.append(r[2]+1)\n",
0128     "        l = ylim(r)\n",
0129     "        y.append(l[0])\n",
0130     "        y.append(l[1])\n",
0131     "        y.append(l[1])\n",
0132     "        y.append(l[0])\n",
0133     "        ax.fill(y, x, col,alpha=0.2)\n",
0134     "    ax.set_xlabel('predicted y size',fontsize=14)\n",
0135     "    ax.set_ylabel('measured x size',fontsize=14)\n",
0136     "\n",
0137     "def plotx(ax,pcsf,col='b',barrel=0) :\n",
0138     "    for r in pcsf :\n",
0139     "        if (r[0]!=barrel) : continue\n",
0140     "        x = []\n",
0141     "        y = []\n",
0142     "        x.append(r[1])\n",
0143     "        x.append(r[1])\n",
0144     "        x.append(r[1]+1)\n",
0145     "        x.append(r[1]+1)\n",
0146     "        l = xlim(r)\n",
0147     "        y.append(l[0])\n",
0148     "        y.append(l[1])\n",
0149     "        y.append(l[1])\n",
0150     "        y.append(l[0])\n",
0151     "        ax.fill(y, x, col,alpha=0.2)\n",
0152     "    ax.set_xlabel('predicted x size',fontsize=14)\n",
0153     "    ax.set_ylabel('measured x size',fontsize=14)\n"
0154    ]
0155   },
0156   {
0157    "cell_type": "code",
0158    "execution_count": 33,
0159    "metadata": {
0160     "collapsed": false
0161    },
0162    "outputs": [
0163     {
0164      "data": {
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LSR406oIAAIDuGD+Zjavq8UkekeTtrbUDVTWZ5K7W2vwS1tROtHL79u13L09NTWVqamoJ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\nRK5CYII=\n",
0166       "text/plain": [
0167        "<matplotlib.figure.Figure at 0x7f2b9a275050>"
0168       ]
0169      },
0170      "metadata": {},
0171      "output_type": "display_data"
0172     }
0173    ],
0174    "source": [
0175     "fig, ax = plt.subplots(figsize=(14, 8))\n",
0176     "ploty(ax,pcsf,'b',0)\n",
0177     "ax.set_title('Barrel Y')\n",
0178     "plt.show()"
0179    ]
0180   },
0181   {
0182    "cell_type": "code",
0183    "execution_count": 34,
0184    "metadata": {
0185     "collapsed": false
0186    },
0187    "outputs": [
0188     {
0189      "data": {
0190       "image/png": 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j4wc/rru/vWRiYnH/DFXN5j/+Y+Vhl4+NtaxcefjlwzQ+nmzbtjJVG++y78qV\nlWuuuWLR9r169VjWrj130bYHsJxs3LgxGzfe9XvrkdRy+MBRVf8zyY8lmUmyKsm9k/xDa+25C/q1\n5VAvHEsuuWQy69atT5JcdtlkTj55/YFlV1wxma985b5Zs+bsbN06mVWr1h+0/o03fjUrV56aJLn5\n5h1ZseKkJaz+2HH77Vtyv/vd8aFz377JnHbawc/lbbdN5owz7mzftWtLzjmnvx9Ud+68Ouedd/aS\n7nPbtsmcf/7Bv5ulMjU1mQsuGN3+AfqgqtJaO6pvHZfF6XOttV9vrZ3RWntIkguTvG9hIAIAABiG\nZRGKAAAARmVZXFM0X2vtA0k+MOo6AACAfnCkCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWh\nCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA\n6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWh\nCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6DWhCAAA6LWJURcAx5NNmzbn\n8su3ZPfuuVx33fXZunUq09N1UL8dO3Zkx47bMjs72Hb37p3O3Fy7WzVNT9+e1q06MzOd1u78LmR2\ndjqzs2NpLZmbm05ywkHrt9YOrJ/cvRoWz53PZVVSVamaTdVE1zaTsbET5vVpGR+fONB/YuLr3/Kq\nkhUrDn7Md8fY2Fx27lyVJBkfn82uXfc+qM/ExGz27n37gfkVK5KqUxZl/8eilSvHc801py3pPlev\nHsvU1JLu8uusXbtidDsH4LCEIlhE27dPZ2LioTnrrPXZsWMyp5ySrFmz/qB+1167Jfe618qsXHn2\nQNu9+eYdWbHipCFUfOyYnt6RU0658znYu/eWPOAB982ePVty5pnnJkl27ZrMOefc+Xzv3DmZ8867\nY37btsmcf/7X/y6mpiZzwQUH/34AgH5x+hwAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEA\nANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBr\nQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEA\nANBrQhHdEtvgAAAO6klEQVQAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBrQhEAANBryyIUVdWD\nq+p9VfWZqtpcVS8ZdU0AAEA/TIy6gM5Mkl9orX2qqtYkmayqS1trnxt1YQAAwPFtWRwpaq19pbX2\nqW56V5ItSR406roAAIDj37IIRfNV1VlJHpPkI6OuBQAAOP4tl9PnkjsC0Zokb0/ys90Ro4NcfPHF\nB6Y3bNiQDRs2LGWJLHObNm3O9u3TB+a3bPlidu+eS5J87GOfya5dMwets2PHrszMzB3Uvnv3bZmd\nbUe1/717p7N373RaS2ZmZjIzszetnZC5uZY2b1OtzWRuLmlt0O8ljq6OQ6tUJVU1+BoD9K+aydjY\nCd30XMbHTzji9iYmJlKVrFhx+H6HMjZWufXWFQfmx8cru3evysREy759JyVJVqxoqdp4oM+qVck1\n11yVJFm9eixTU1+/zbVrVwQAOLZt3LgxGzduHKDn4VVri/Fh656rqokk70ryf1prrz5Mn7Zc6mV5\nuuSSyaxbt/7A/GWXTebkk++Y/6d/ujzr1n3rQetce+1Xc+KJpx7UfuONt2TlyvsuSl0337wjK1ac\ntCjbujump3fklFNOyt69t+QBDxj8Me3Z89WceebBz818u3ZN5pxz7niOd+6czHnnrT9s323bJnP+\n+eszNTWZCy44fD8AgLurqtJaG/xb4GV2+txfJfns4QIRAADAMCyLUFRVT0ryo0m+vao+WVWfqKoL\nRl0XAABw/FsW1xS11j6cZHzUdQAAAP2zLI4UAQAAjIpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQ\nBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA\n9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQ\nBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9JpQBAAA9NrEqAug\nfzZt2pzt26ezZcsXs3v3XLZuvSF797YDyz/xicns2XOvu7XtvXtvy9zcnVl/enpPWjuhW7Y3rdVB\n68zNtbR2UHNaO3T73TE3N3cP1p5NkoyN3fE4qlomJsa76WR8fPxAz7Gx5MQTVx20hbGxyq23rsj4\neGX37oOXH87ExFj27VtzxD4rVrRUbUySrFqVXHPNVYftu3r1WKamkrVrVwxcAwDAsAlFLLnt26ez\nbt36XHVVctZZ67Njx5acfvq5B5ZfccUf5uyzf3GkNS62m2/ekRUrTrpb695++5asWbMnp522Pkmy\nbdu7s2HDM5IkO3duyXnn3fncXXPNW/Kbv3nhIlUNANAPTp8DAAB6TSgCAAB6TSgCAAB6TSgCAAB6\nTSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgC\nAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6\nTSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6TSgCAAB6bdmEoqq6oKo+\nV1VfqKqXjroe2LZt46hLoEc2bvR6Y2l5zbGUvN5Y7pZFKKqqsSR/muQZSR6Z5Ier6hGjrot+E4pY\nSj4wsNS85lhKXm8sd8siFCV5QpKrWmvXttZuT/KWJM8edVEAAMDxb7mEogcluW7e/Je7NgAAgKGq\n1tqoa0hVPSfJM1prP9nN/1iSJ7TWXrKg3+iLBQAAlrXWWh1N/4nhlXJUrk9yxrz5B3dtX+doHxwA\nAMBdWS6nz30sycOq6syqWpHkwiTvHHVRAADA8W9ZHClqrc1W1c8kubQLaq9rrW0ZdV0AAMDxb1lc\nUwQAADAqy+X0uYFV1UVV9eWq+kT3c8Goa+L442bCLKWquqaqrqiqT1bVR0ddD8eXqnpdVd1UVVfO\nazu5qi6tqs9X1bur6qTRVsnx5DCvOZ/fGIqqenBVva+qPlNVm6vqJbkb73PH3JGiqrooya2ttT8a\ndS0cn7qbCX8hydOT3NBd83Zha+1zo66N41NVfSnJ+tbatlHXwvGnqp6cZFeSN7bWHt21/X6Sr7XW\nXtF98XNya+1XR10rx4fDvOZ8fmMoquq0JKe11j5VVWuSTHb3O33+0bzPHXNHijpGoWOY3EyYpVbH\n8Psxy1xr7UNJFgbuZyd5Qzf9hiTfO4LSOE4d5jUXn98YhtbaV1prn+qmdyXZ0o1kfVTvc8fqH+Gf\nqapPVdVfOuTPELiZMEutJXlPVX2sql406mLohVNbazel+0CR5NRRF0Qv+PzGUFXVWUkek2RTkvsf\nzfvcsgxFVfWeqrpy3s/m7t9nJfmzJA9prT0myVeSOAwLHOue1Fp7bJLvSvLfu1NPYCkdW+fScyzy\n+Y2h6k6de3uSn+2OGC18Xzvi+9yyGJJ7odbadwzY9S+S/POQy6F/BrqZMCyW1tqN3b9TVfWP3Smc\nHxp1XRzXbqqq+7fWburOx//qqAvi+NZam5o36/Mbi6qqJrpA9DettXd0zUf1PrcsjxQdSfeg9vv+\nJJ8eYTkcn9xMmCVTVSd2326lqlYn+U7vawxBLbie451Jfrybfl6SdxxmPbi7vu415/MbQ/ZXST7b\nWnv1vLajep87Fkefe2N3ruBckmuSvHj/+YKwWLqhQl8972bCvzfqmjg+VdXZSf6xO6w/keRNXm8s\npqp6c5INSe6X5KYkFyX5pyRvS3J6kmuT/GBrbfuoa+X4cJjX3NN8fmMYqupJST6YZHP3t7Ql+fUk\nH03y1kHf5465UAQAALCYjrnT5wAAABaTUAQAAPSaUAQAAPSaUAQAAPSaUAQAAPSaUAQAAPSaUATA\noquqX6yqq+fNX1RVV46ols1V9VtLuL+nVtVsVd13qfYJwD0jFAEwLPNvhPfKJE8ddMWqmquq7x9O\nWUP34SQPaK3dMupCABjMxKgLAGB5qqoTWmu3L8a2Wmt7kuxZjG0td621mSRfHXUdAAzOkSKAHqiq\n91fV/1dVf1xVt3Q/r1jQ5+ruNLfXVdW2JH/btT+wqt4yb713VdXDFqz7K1V1Y1XtrKq/TrJmwfKL\nqmrzgrbnVdWVVbW3qr5SVa/fX0d3lOnt3RGjL81b51lV9fGquq2qvlhVv1NVJ8xbvq6q3lFVe7rH\n8/y7eF7O7E51e+yC9hdV1VRVHfLLw6p6SlVdXlW3VtX2qtpUVd+YO0+fm9t/+lxXx1z3Mztv+oxu\n+X2q6rVVdVP3/L2/qtYf8RcKwKISigD640eSVJInJvnJJD9ZVT+3oM/PJ9mSZH2SX6+qVUnen2R3\nkm/r1r0hyXuramXu+FD/g0l+O8n/SPLYJF9I8guH2P+B0+mq6sVJ/jzJ65J8U5JnJNl/zdHjuzp/\nIslp3Xyq6hldUPuTJOcmeUGS5yT53Xn7eEOShyT59iTfm+S5Sc483BPSWrs2yaXdtuZ7fpI3dEd9\nvk5VjSf5pyQfTPKoJE9I8sdJZg/1WJM8rnscpyV5QJJ3Jflskpu65f/aLfuuJI/ptvtvVXX/w9UN\nwOKq1toA3QA4llXV+7vrXB4xr+03kry4tbb/iMXVSa5srT17Xp8XJHlpa+0b5rWNdx/of6q19vaq\n+nCSza21n5rX5z1JHtpae0g3f1GS57TWHt3NX5fkja213zhMvXNJfqC19g/z2j6Q5NLW2u/Oa3t2\nkr9trd27qs5J8rkk57fWNnXLz0jypSQvb629/DD7ek6S13bPz3RVnZvk00m+qbW25RD9T05yc5IN\nrbV/P8TypyZ5X5J1C68rqqqXJvnFJE9orV1TVd/eBax1rbV98/p9MsmbWmt/cJhfKQCLyJEigP7Y\ntGD+8iQPqqr5p7p9fEGfxyZ5SHea2K1VdWuS7UnWJnlo1+fcw2z7kKpqXZIHdcHhaKxP8hsLanlz\nklXdUZVHdEdrPrZ/hdba1u7I1pG8I8ntSfYP7PCCJB89VCDqtrmtOyJ1aXcq4c9X1el3VXxVPSvJ\nRUm+v7V2Tdf82CSrk9y84HE9ct7zC8CQGWgBgPl2L5gfS/LJJD/UndI231KPrjaW5GVJ3naIZVPz\npo/qFIjW2kxVvTHJC6rqbUl+LMlv3sU6L6iqVyW5IMn3JPndqnp2a+09h+pfVd/Unfr331prH1rw\nmL6S5MmHeH53Hs3jAODuE4oA+uNbFsx/a5IbWmu7jrDOJ5JcmORrrbXDfUjf0l1r9NcLtn1IrbWp\nqro+ydOT/Nthut2eZPwQtTyitfalQ61QVZ/rQsYT9h+56k6fe+ARHt9+f9ld5/PfukEi/u6uVmit\nbU6yOckrq+pfkzwvyUGhqKpOSfLOJK9prf31gsWfSHL/OzbXrl64LgBLw+lzAP3xwKp6VVWdU1U/\nkOSXkvzRXazzpu76oXd0I66d1f37B1W1//SuVyd5XlW9sKoeVlW/1gWTI/ndJD9XVT9XVQ+vqsdU\n1fzBGa5J8vSqun9Vre3aXp7kR6rqZVX1yKr6hqp6TlX9fu5IFV9I8u4kr6mqJ1bVY5K8fpChwLt1\nP9TdT+ltRwqK3XPw/1bVt1bVGVX1tCSPTvKZ+d3mTf99ki8neVX3ePb/VGvtvd19jd5RVRd02/7W\nqrq4qp50V3UDsDiEIoD+eFN39OUjSV6T5C+6UdP2O+i0s9babUme0g1W8NbuqNDru2uKtnV93prk\n4iS/0x35eGSSPzxSIa21P0/y35O8sDva8q9JvnFel19M8rQkW7ttprV2aZJnJtnQPYaPJHlpkmvn\nrfe8JFd3R6De0T3maw6u4JBel+SE7t8j2ZPknO75+Hz3fPxNkvlDnM9/Lr8tyZO6YHRDkhu7f/df\nh/Rd3fVVr+0GinhLt/27uhYKgEVi9DmAHuhGn9vcWnvJqGtZrrqR4Z4/f4Q+APrBNUUA9FpVrU5y\nVpKXdPdbAqBnnD4H0A9OCzi8P+2GIv/37hQ2AHrG6XMAAECvOVIEAAD0mlAEAAD0mlAEAAD0mlAE\nAAD0mlAEAAD02v8FiP1V+HLpHSwAAAAASUVORK5CYII=\n",
0191       "text/plain": [
0192        "<matplotlib.figure.Figure at 0x7f2b98f12050>"
0193       ]
0194      },
0195      "metadata": {},
0196      "output_type": "display_data"
0197     }
0198    ],
0199    "source": [
0200     "fig, ax = plt.subplots(figsize=(14, 8))\n",
0201     "ploty(ax,pcsf,'b',1)\n",
0202     "ax.set_title('Forward Y')\n",
0203     "plt.show()"
0204    ]
0205   },
0206   {
0207    "cell_type": "code",
0208    "execution_count": 35,
0209    "metadata": {
0210     "collapsed": false
0211    },
0212    "outputs": [
0213     {
0214      "data": {
0215       "image/png": 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ckORlST5cVU8db4kAAADjM2ooelCSDw3TP53kyiTHJ/mVJM8dY30AAABjNWooOirJ9mH6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\n6Nr/A8aV3BRt3cI9AAAAAElFTkSuQmCC\n",
0216       "text/plain": [
0217        "<matplotlib.figure.Figure at 0x7f2b9a36ab10>"
0218       ]
0219      },
0220      "metadata": {},
0221      "output_type": "display_data"
0222     }
0223    ],
0224    "source": [
0225     "fig, ax = plt.subplots(figsize=(14, 8))\n",
0226     "plotx(ax,pcsf,'b',0)\n",
0227     "ax.set_title('Barrel X')\n",
0228     "plt.show()"
0229    ]
0230   },
0231   {
0232    "cell_type": "code",
0233    "execution_count": 36,
0234    "metadata": {
0235     "collapsed": false,
0236     "scrolled": true
0237    },
0238    "outputs": [
0239     {
0240      "data": {
0241       "image/png": 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uJI9NcmlV3W1a+0SSM4dUGwAAwLwZNPy0JD+cZGOSz1TV9w25LgAAgHk1aPipJJuSPLG7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\nY2ifutOrAAAAAElFTkSuQmCC\n",
0242       "text/plain": [
0243        "<matplotlib.figure.Figure at 0x7f2b98868b50>"
0244       ]
0245      },
0246      "metadata": {},
0247      "output_type": "display_data"
0248     }
0249    ],
0250    "source": [
0251     "fig, ax = plt.subplots(figsize=(14, 8))\n",
0252     "plotx(ax,pcsf,'b',1)\n",
0253     "ax.set_title('Forward X')\n",
0254     "plt.show()"
0255    ]
0256   },
0257   {
0258    "cell_type": "code",
0259    "execution_count": 37,
0260    "metadata": {
0261     "collapsed": false
0262    },
0263    "outputs": [
0264     {
0265      "data": {
0266       "image/png": 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kSVKuWYokSZIk5ZqlSJIkSVKuWYokSZIk5ZqlSJIkSVKuRUqp0hlKFhFpd8orSZJGtuSW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\nADlIvEtcZfnDAAAAAElFTkSuQmCC\n",
0267       "text/plain": [
0268        "<matplotlib.figure.Figure at 0x7f2b9a0bab90>"
0269       ]
0270      },
0271      "metadata": {},
0272      "output_type": "display_data"
0273     }
0274    ],
0275    "source": [
0276     "fig, ax = plt.subplots(figsize=(14, 8))\n",
0277     "ploty(ax,pcsf1,'g',0)\n",
0278     "ploty(ax,pcsf2,'r',0)\n",
0279     "ax.set_title('Barrel Y')\n",
0280     "ax.text(0, 14, 'all clusters',color='g',fontsize=14)\n",
0281     "ax.text(0, 12, 'w/o BPIX1',color='r',fontsize=14)\n",
0282     "plt.show()"
0283    ]
0284   },
0285   {
0286    "cell_type": "code",
0287    "execution_count": null,
0288    "metadata": {
0289     "collapsed": false
0290    },
0291    "outputs": [
0292     {
0293      "data": {
0294       "image/png": 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sGwAAYE9GLUU/0LRaa2+pqq8lec8sZnlekrdV1aIklyf5lVncNgAAwC6NWoruluTa6QOt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\n",
0295       "text/plain": [
0296        "<matplotlib.figure.Figure at 0x7f2b9a0ee610>"
0297       ]
0298      },
0299      "metadata": {},
0300      "output_type": "display_data"
0301     }
0302    ],
0303    "source": [
0304     "fig, ax = plt.subplots(figsize=(14, 8))\n",
0305     "plotx(ax,pcsf1,'g',0)\n",
0306     "plotx(ax,pcsf2,'r',0)\n",
0307     "ax.set_title('Barrel X')\n",
0308     "plt.show()"
0309    ]
0310   },
0311   {
0312    "cell_type": "code",
0313    "execution_count": null,
0314    "metadata": {
0315     "collapsed": false
0316    },
0317    "outputs": [
0318     {
0319      "name": "stdout",
0320      "output_type": "stream",
0321      "text": [
0322       "3 [ 0.050461 -0.00588  -0.00144  -1.13216   0.058117 -0.02567   1.20359 ] [3112, 3293] [3296, 3346]\n",
0323       "3 [ 0.050461 -0.00588  -0.00144  -1.13216   0.058117 -0.02567   1.20359 ] [3112, 3293] [3296, 3346]\n",
0324       "4 [ 0.010162 -0.10886   0.00313  -3.08253  -0.044705 -0.18037   2.30214 ] [1942, 1638] [1498, 1526]\n",
0325       "4 [ 0.010162 -0.10886   0.00313  -3.08253  -0.044705 -0.18037   2.30214 ] [1942, 1638] [1498, 1526]\n",
0326       "5 [ 0.083755 -0.2295   -0.0794   -5.13733   0.111318 -0.1916    4.57214 ] [1160, 1333] [707, 807]\n",
0327       "5 [ 0.083755 -0.2295   -0.0794   -5.13733   0.111318 -0.1916    4.57214 ] [1160, 1333] [707, 807]\n",
0328       "6 [ 0.037792 -0.28313   0.06848  -5.74361   0.007536 -0.18186   5.06985 ] [850, 792] [487, 577]\n",
0329       "6 [ 0.037792 -0.28313   0.06848  -5.74361   0.007536 -0.18186   5.06985 ] [850, 792] [487, 577]\n",
0330       "7 [-0.063356 -0.26894   0.19705  -6.77911  -0.040049 -0.09972   6.23528 ] [528, 475] [332, 368]\n",
0331       "7 [-0.063356 -0.26894   0.19705  -6.77911  -0.040049 -0.09972   6.23528 ] [528, 475] [332, 368]\n",
0332       "8 [ -1.73400000e-03  -8.48000000e-03   1.01850000e-01  -5.88942000e+00\n",
0333       "  -2.72809000e-01  -2.26350000e-01   6.91315000e+00] [205, 215] [270, 247]\n",
0334       "8 [ -1.73400000e-03  -8.48000000e-03   1.01850000e-01  -5.88942000e+00\n",
0335       "  -2.72809000e-01  -2.26350000e-01   6.91315000e+00] [205, 215] [270, 247]\n",
0336       "9 [ -1.97307000e-01  -1.85224000e-01   1.66000000e-03  -5.73503000e+00\n",
0337       "  -2.28399000e-01  -3.16942000e-01   6.17870000e+00] [56, 66] [234, 211]\n",
0338       "9 [ -1.97307000e-01  -1.85224000e-01   1.66000000e-03  -5.73503000e+00\n",
0339       "  -2.28399000e-01  -3.16942000e-01   6.17870000e+00] [56, 66] [234, 211]\n",
0340       "600 [ 0.1156    3.27937   0.003578 -0.016258  0.00869  -0.06998   0.098931] [19, 11] [103, 23]\n",
0341       "101 [ 0.053555 -0.02515  -1.00218  -0.95669  -2.585218 -0.21082  -5.112611] [96611, 35] [61569, 93037]\n",
0342       "103 [ -9.16537000e-02  -6.27800000e-02  -2.48000000e-03  -1.12712000e+00\n",
0343       "   1.23600000e-02   3.60000000e-06   4.39500000e-02] [49823, 831] [51798, 819]\n",
0344       "104 [ -4.69200000e-03  -5.10000000e-04   3.00000000e-05  -1.64490000e+00\n",
0345       "   2.33200000e-02   5.60000000e-06   1.31050000e-01] [30885, 427] [30362, 400]\n",
0346       "105 [ -2.02020000e-02  -5.00000000e-05   4.80600000e-02  -3.12871000e+00\n",
0347       "  -3.22000000e-03  -1.12000000e-04  -9.65000000e-03] [18863, 126] [15548, 131]\n",
0348       "106 [ -9.98900000e-03  -3.19000000e-03   9.09000000e-02  -4.36547000e+00\n",
0349       "  -8.46000000e-03   5.40000000e-04   2.97639000e+00] [12847, 111] [9394, 52]\n",
0350       "106 [ -9.98900000e-03  -3.19000000e-03   9.09000000e-02  -4.36547000e+00\n",
0351       "  -8.46000000e-03   5.40000000e-04   2.97639000e+00] [12847, 111] [9394, 52]\n",
0352       "107 [ -1.27710000e-02  -2.11100000e-02   8.99300000e-02  -4.95216000e+00\n",
0353       "  -1.45000000e-03   9.41250000e-03  -4.13610000e-01] [7582, 70] [6232, 96]\n",
0354       "108 [-0.095001  -0.09991    0.09494   -5.46185    0.03305   -0.0554775  4.88906  ] [2821, 74] [4024, 24]\n",
0355       "108 [-0.095001  -0.09991    0.09494   -5.46185    0.03305   -0.0554775  4.88906  ] [2821, 74] [4024, 24]\n",
0356       "109 [-0.109348 -0.10023   0.05755  -4.6616    0.01065  -0.153057  1.4854  ] [546, 44] [3298, 32]\n",
0357       "109 [-0.109348 -0.10023   0.05755  -4.6616    0.01065  -0.153057  1.4854  ] [546, 44] [3298, 32]\n",
0358       "110 [ -1.66653500e-01   2.76100000e-02   4.08700000e-02  -3.92690000e+00\n",
0359       "  -1.38600000e-02  -9.99982500e-02  -2.40000000e-03] [57, 9] [3181, 18]\n",
0360       "201 [ -5.70000000e-04   4.11600000e-02   6.84230000e-02   1.88300000e-02\n",
0361       "   1.46263000e+00   1.90000000e-04  -4.10000000e-04] [38259, 43] [54160, 46]\n",
0362       "207 [  1.50000000e-04   9.98800000e-02   4.91000000e-03  -3.57140000e-01\n",
0363       "   3.10000000e-03  -1.00000000e-05   2.69934000e+00] [4258, 13] [11294, 6]\n",
0364       "209 [  2.50000000e-04   2.17160000e-01   1.40880000e-01  -1.75330000e+00\n",
0365       "   3.02640000e-01   4.64000000e-03   6.50000000e-03] [361, 9] [4068, 8]\n",
0366       "210 [  3.90300000e-02   2.30910000e-01   9.07500000e-02  -1.65700000e+00\n",
0367       "   3.87000000e-03   4.84000000e-03  -1.00000000e-04] [43, 7] [3664, 7]\n",
0368       "300 [  1.81130000e-01  -9.80500000e-02  -1.52550000e-02   3.69950000e-02\n",
0369       "   1.66810000e+00  -3.96400000e-02  -3.07000000e-04] [353, 51] [471, 86]\n",
0370       "304 [  1.60000000e-04   5.86000000e-03   2.59000000e-03  -3.00000000e-05\n",
0371       "   1.09821000e+00  -2.90000000e-04   3.89950000e+00] [1200, 5] [1342, 1]\n",
0372       "400 [ -2.00000000e-04   2.38800000e-01   4.88410000e-02  -1.72840000e-02\n",
0373       "   1.10107000e+00  -1.37720000e-01  -7.12800000e-03] [55, 47] [99, 68]\n",
0374       "401 [  1.48700000e-02  -2.95384000e+00   9.95988158e-02   3.09400000e-02\n",
0375       "  -1.75000000e-03  -1.79350000e-01  -1.10100000e-02] [25, 7] [21, 13]\n"
0376      ]
0377     }
0378    ],
0379    "source": [
0380     "dxy = []\n",
0381     "for k in map1.keys() :\n",
0382     "    if k in map2.keys() :\n",
0383     "        d = map2[k][0]-map1[k][0]\n",
0384     "        if (max(d)<100 and min(d)>-100) :\n",
0385     "            dxy.append(d)\n",
0386     "            if max(d) >1 : print k,d,map2[k][1],map1[k][1]\n",
0387     "            if min(d) <-1 : print k,d,map2[k][1],map1[k][1]\n",
0388     "plt.hist(dxy,bins=50, normed=1)\n",
0389     "plt.show()"
0390    ]
0391   }
0392  ],
0393  "metadata": {
0394   "kernelspec": {
0395    "display_name": "Python 2",
0396    "language": "python",
0397    "name": "python2"
0398   },
0399   "language_info": {
0400    "codemirror_mode": {
0401     "name": "ipython",
0402     "version": 2
0403    },
0404    "file_extension": ".py",
0405    "mimetype": "text/x-python",
0406    "name": "python",
0407    "nbconvert_exporter": "python",
0408    "pygments_lexer": "ipython2",
0409    "version": "2.7.11"
0410   }
0411  },
0412  "nbformat": 4,
0413  "nbformat_minor": 1
0414 }
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