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@anetasie
Created January 20, 2015 00:11
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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Analysis of the MCMC draws in 2D models"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Assumed image model - 2d Gaussian convolved with the PSF\n",
"\n",
"Parameters of interests: \n",
" (xpos,ypos) - source position \n",
" ampl - source intensity \n",
" sigma_a,sigma_b - size of Gaussian sigma in each dimension\n",
" theta - rotation angle \n",
"\n",
"First the model is fit to the image data in Sherpa, then the MCMC using get_draws with MetropolisMH\n",
"and flat priors. Here we only show the analysis of the draws from the MCMC runs.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"from astropy.table import Table\n",
"from astropy.io import fits\n",
"import numpy as np\n",
"import pickle\n",
"import matplotlib.mlab as mlab\n",
"from matplotlib.colors import LogNorm\n",
"#from density_contour import density_contour\n",
"from scipy.stats import mstats\n",
"from sherpa.astro.ui import *\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"WARNING: imaging routines will not be available, \n",
"failed to import sherpa.image.ds9_backend due to \n",
"'RuntimeErr: DS9Win unusable: Could not find ds9 on your PATH'\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Read the data - fits files contain a table of simulated draws\n",
"# Read and filter on the accepted draws\n",
"#datadir = '/data/sherpa/Scripts/MLE/August2014/Test2_prior'\n",
"datadir = '/Users/aneta/Sherpa/MLE/Data'\n",
"filename = 'cohort_r0008h_ECF90REG_draws'\n",
"hdulist = fits.open(datadir+'/'+filename+'.fits')\n",
"dat = hdulist[1].data\n",
"ok = dat.field(0) == True\n",
"print len(dat.field(0)[ok]), len(dat.field(0))\n",
"xpos = dat['src1.xpos'][ok]\n",
"ypos = dat['src1.ypos'][ok]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"464 1001\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(xpos,ypos,'o')\n",
"plt.xlabel('X position')\n",
"plt.ylabel('Y position')\n",
"#plt.savefig(datadir+'/'+filename+'position2d_scatter.png')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<matplotlib.text.Text at 0x10d1323d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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+NdZlef/DAOwFwA1kNTVAfr6t53FOTr5xwLKloqIekZF9RM9Zrxg8MRsnz115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\njz/+uGg5X3z2gLz+O/38Xd6VlYnBYGCxsbHs7Nm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"text": [
"<matplotlib.figure.Figure at 0x102755550>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"How to determine 95% confidence level for the source position (xpos,ypos)?\n",
"The simulations represent the posterior density: Pr(params|data) = Likelihood(data|params)*prior(params)\n",
"\n",
"Cashstat = - 2* log(Likelihood)\n",
"like = exp(-cashstat/2.)\n",
"\n",
"We can use the CDF of likelihood to find the tail of the distribution. \n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cashstat = dat['cashstat'][ok]\n",
"# pvals are the likelihood values\n",
"pvals = np.exp(-cashstat/2.)\n",
"ptot = sum(pvals)\n",
"normpvals = pvals/ptot #normalized to 1\n",
"# calculate percentile \n",
"f95 = np.percentile(normpvals,5)\n",
"print f95\n",
"it = normpvals > f95\n",
"plt.clf()\n",
"plot_cdf(normpvals)\n",
"plt.xlabel('normpvals')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3.16552699335e-05\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"<matplotlib.text.Text at 0x10d1289d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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xcXG0adOGa665ho1eWqb09dfmZagbboBWrbzykSIifs3SjXsul4uoqCgyMzMJ\nCwsjPj6etLQ0Yk7ru7F69Wpat25N7dq1ycjIIDU1lezs7JIhPbj5xDDM/RZTpsCECXDffRAS4pG3\nFhHxKZ7euGfpKqmcnBwiIyNp1qwZAMnJyaSnp5coGAkJCe4/d+rUid27d1sZiTfeMPtFrVsHDoel\nHyUiElAsvSRVUFBAeHi4+7nD4aCgoOCcx//zn/+kZ8+eluUpLIQxY+Dtt1UsRETOl6UjjJDzuNaz\nfPlyZs6cycqVK0v9empqqvvPTqcT5wXc1X7kSLj1VoiNPe+Xioj4vKysLLIs3KFpacEICwsjPz/f\n/Tw/Px9HKb/ab9y4kfvvv5+MjAx+//vfl/pepxeMC7FqFSxcaN5FT0QkEJ35y/S4ceM8+v6WXpLq\n0KED27ZtIzc3lxMnTjB37lySkpJKHJOXl0e/fv145513iIyMtCzLzJnw+ONwjnokIiLlsHSEUbVq\nVaZNm0ZiYiIul4uUlBRiYmKYMWMGAEOHDuWZZ57h0KFDDBs2DIBq1aqRk5NT6c8+vX/Ou+9CRgY8\n+WSl39Y3qZeUlEG9pMRTAvZ+GKdOkqefhgEDzA16V1zh+Ww+QQVDyqCCEbx0P4zzUFQEEyeal6IC\ntliIiHhJQBeMvDyoVg0GD7Y7iYiI/wvY9uYnT8LevTB3LtSubXcaERH/F7AjjJ9/hksvhdM2kouI\nSCUE7AijTRu44w67U3iJJrulDJrsFk8JyFVSOTnQsyd8/z3UqmVhMBERH6ZVUhXw4ovmyigVCxER\nzwm4EcaqVZCUBFu2QL16FgcTEfFhGmGU4cQJ6NsX3nxTxUJExNMCqmC88IJ5u1ULO6SLiAStgCoY\na9aY7ctDQsx2CBfQAd0/ZTp/aw8icoagOhfEUgFTMNauhS+/hLvusjuJiEhgCpiC8eGHcMstUKOG\n3UlERAJTQGzcy8uDGTPMS1IiImKNgBhhfPUVdO4MzZrZnUREJHAFRMHIyoL27e1OISIS2Px+4973\n30PHjrBuHYSHezmYiIgP08a9/3G54IsvoFs3eOYZFQsREav55Qhj+3bzEpTDASNHwtChNoYTEfFR\nnh5h+GXBSEmBBg3M26+KiEjpPF0w/G5Z7bFjsGyZue9CRES8x+/mMObPh+bNoV07u5OIiAQXv7ok\ndeQIREeb3Wi7dSv7Nad65wTF3cZO9ZHSnfekFEF1LkgJQb1K6vnnoWvX8ouFiIh4nl8VjGXL4O67\n7U4hIhK8tLFlAAAK4ElEQVSc/KZgrF4N+flw7bV2JxERCU5+UzDGjYOnnoJLLrE7iYhIcPKbgrFy\nJfTvb3cKEZHg5TerpBo0MPjxR7uTiIj4j6BdJZWQYHcCEZHg5jcFo3NnuxOIiAQ3vykYERF2JxAR\nCW5+UzAaN7Y7gYhIcPObglG/vt0JRESCm98UjHr1zu94p/O3HjoBL9P5Wz8pkTME1bkglrK0YGRk\nZBAdHU3Lli2ZNGlSqceMHDmSli1bEhcXx7p16875XhphiIjYy7KC4XK5GDFiBBkZGXz77bekpaWx\nefPmEscsXryY7du3s23bNl577TWGDRt27qB+MxY6W5aftwn15/z+nB2U327+nt/TLPsxnJOTQ2Rk\nJM2aNaNatWokJyeTnp5e4pgFCxYwaNAgADp16sThw4fZt2+fVZFs4+//p/Pn/P6cHZTfbv6e39Ms\nKxgFBQWEh4e7nzscDgoKCso9Zvfu3VZFEhGRSrCsYISEhFTouDO3rVf0dSIi4mWGRVavXm0kJia6\nn0+YMMGYOHFiiWOGDh1qpKWluZ9HRUUZe/fuPeu9IiIiDEAPPfTQQ4/zeERERHj053pVLNKhQwe2\nbdtGbm4uTZo0Ye7cuaSlpZU4JikpiWnTppGcnEx2djZ16tShUaNGZ73X9u3brYopIiIVZFnBqFq1\nKtOmTSMxMRGXy0VKSgoxMTHMmDEDgKFDh9KzZ08WL15MZGQkNWrUYNasWVbFERGRSvKL9uYiImI/\nr+9uqMxmvnO99uDBg3Tv3p1WrVpx0003cfjwYb/K//7773PFFVdQpUoVvvrqK8uyW5X/scceIyYm\nhri4OPr168dPP/3kV/mffPJJ4uLiaNu2Ld26dSM/P9+v8p8yefJkQkNDOXjwoN9kT01NxeFw0K5d\nO9q1a0dGRoYl2a3KDzB16lRiYmKIjY1lzJgxfpU/OTnZ/b1v3rw57dq1KzuER2dEylFUVGREREQY\nO3fuNE6cOGHExcUZ3377bYljFi1aZNx8882GYRhGdna20alTp3Jf+9hjjxmTJk0yDMMwJk6caIwZ\nM8av8m/evNnYsmWL4XQ6jbVr11qS3cr8H3/8seFyuQzDMIwxY8b43fe/sLDQ/fopU6YYKSkpfpXf\nMAwjLy/PSExMNJo1a2YcOHDAb7KnpqYakydP9nheb+X/5JNPjBtvvNE4ceKEYRiG8eOPP/pV/tON\nHj3aGD9+fJk5vDrCuNDNfHv37i3ztae/ZtCgQfz73//2q/zR0dG0atXKkszeyN+9e3dC/7cVv1On\nTpbtpbEqf82aNd2vP3LkCPUt6kNjVX6AUaNG8fzzz1uS2+rshheuiluV/9VXX+XPf/4z1apVA6BB\ngwZ+lf8UwzCYN28eAwYMKDOHVwvGhW7mKygoYM+ePed87b59+9yrqxo1amTZbnGr8nuLN/LPnDmT\nnj17WpDe2vxjx46ladOmvPnmmzz++ON+lT89PR2Hw0GbNm0syW1ldjAv6cTFxZGSkmLZ5WSr8m/b\nto0VK1bQuXNnnE4na9as8av8p3z22Wc0atSIiHJuPOTVgnGhm/nOdUxp7xcSEmLZ5j9P5reD1fmf\nffZZLrroIu66664Len15rMz/7LPPkpeXx7333ssjjzxy3q+vCCvy//rrr0yYMIFx48Zd0Osryqrv\n/bBhw9i5cyfr16+ncePGjB49+kLilcuq/EVFRRw6dIjs7GxeeOEF7rjjjguJVy6rz920tLQKnbeW\nLastTVhYWIkJxfz8fBwOR5nH7N69G4fDwcmTJ8/6+7CwMMAcVezdu5fLLruMH374gYYNG/p8/tJe\nazUr88+ePZvFixezbNkyv8x/yl133WXZCMmK/Dt27CA3N5e4uDj38e3btycnJ8ej54FV3/vTM953\n33306dPHY5m9kd/hcNCvXz8A4uPjCQ0N5cCBA9Q73/sx2JQfzKI3f/78ii24qcQ8zHk7efKk0aJF\nC2Pnzp3G8ePHy524Wb16tXvipqzXPvbYY+5d5M8995xlk65W5T/F6XQaa9assSS7lfmXLFlitG7d\n2vjvf/9rWXYr82/dutX9+ilTphgDBw70q/yns2rS26rse/bscb/+xRdfNAYMGODx7Fbmnz59uvHU\nU08ZhmEYW7ZsMcLDw/0qv2GY56/T6axQDq8WDMMwjMWLFxutWrUyIiIijAkTJhiGYX7Tp0+f7j5m\n+PDhRkREhNGmTZsSq4ZKe61hGMaBAweMbt26GS1btjS6d+9uHDp0yK/yf/jhh4bD4TAuueQSo1Gj\nRkaPHj38Kn9kZKTRtGlTo23btkbbtm2NYcOG+VX+2267zYiNjTXi4uKMfv36Gfv27fOr/Kdr3ry5\nJQXDqux33323ceWVVxpt2rQx+vbtW2prIF/Of+LECWPgwIFGbGyscdVVVxnLly/3q/yGYRj33nuv\nMWPGjApl0MY9ERGpED++LZGIiHiTCoaIiFSICoaIiFSICoaIiFSICoaIiFSICoaIiFSICoaIj3A6\nnaxdu9buGCLnpIIhUgqXy+X1z7SyD5qIJ6hgSMDKzc0lJiaGBx54gNjYWBITEzl27Bjr16+nc+fO\n7hs+neqQ6nQ6eeSRR4iPj+ell17C6XQyatQo4uPjiYmJ4csvv+TWW2+lVatWPPnkk+7PiI6OZuDA\ngbRu3Zrbb7+dX3/9lYyMjBKN6LKystx9koYNG0Z8fDyxsbGkpqaelbu4uJh7772XK6+8kjZt2vCP\nf/zD+m+WSEVUer+6iI/auXOnUbVqVWPDhg2GYRjGHXfcYbzzzjtGmzZtjBUrVhiGYRhPPfWU8fDD\nDxuGYfbyGj58uPv1TqfTePzxxw3DMIyXXnrJaNy4sbF3717j+PHjhsPhMA4ePGjs3LnTCAkJMVat\nWmUYhmEMGTLE+Nvf/mYUFRUZTZs2NY4ePWoYhmE8+OCDxrvvvmsYhmEcPHjQMAzzxjZOp9PYuHGj\n+/PWrl1rrFmzxujevbs7x+HDhy37HomcD40wJKA1b97cfZ+I9u3bs2PHDg4fPsy1114LmDfcWrFi\nhfv4O++8s8Trk5KSAIiNjSU2NpZGjRpx0UUX0aJFC3cH0PDwcBISEgAYOHAgn3/+OVWqVKFHjx4s\nWLCAoqIiFi9eTN++fQGYO3cu7du356qrruKbb75h8+bNJT4zIiKC77//npEjR7J06VJq1aplwXdG\n5PypYEhAu/jii91/rlKlylk36DHOaKVWo0aNUl8fGhpa4r1CQ0MpKioCSt6rwDjtPi3JycnMmzeP\n5cuX06FDB2rUqMHOnTuZPHkyn3zyCRs2bKBXr14cO3asxGfWqVOHDRs24HQ6mT59Ovfdd9+F/vNF\nPEoFQ4JK7dq1qVu3Lp9//jkAb7/9Nk6n0/31MwtIReTl5ZGdnQ3AnDlz3KOX6667jq+++orXX3/d\nfevLwsJCatSoQa1atdi3bx9Lliwp8V6GYXDgwAFcLhf9+vVj/PjxFbtPgYgXePUGSiLeduaqo5CQ\nEGbPns2DDz7I0aNHiYiIYNasWec8/vS/P9fXoqKiePnllxkyZAhXXHEFw4YNA8wRTe/evXnzzTd5\n6623AIiLi6Ndu3ZER0cTHh5Oly5dzvqcgoICBg8eTHFxMQATJ068sH+8iIepvblIJeTm5tKnTx82\nbdpkdxQRy+mSlEglae+EBAuNMEREpEI0whARkQpRwRARkQpRwRARkQpRwRARkQpRwRARkQpRwRAR\nkQr5f+uhjaXcdjF5AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10d1f1990>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_pdf(normpvals)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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4nT5vxEg3YrcVEZnFbjL7x24rIiKyOYYHERHJxvAgIiLZGB5ERCQbw4OIiGTj\nVF0iMotTg+n3OFWXiHocTg22PE7VJSIim2N4EBGRbAwPIiKSjeFBRESyMTyIiEg2TtUloh7HmlOD\nOS24e2w2VVej0aB///5wdHSEs7MzioqKUFdXh5kzZ+LXX3+VnjI4cODAtgVzqi4RWVBvmRZ820zV\nFQQBBoMBhw8fRlFREQAgIyMDERERKCsrQ3h4ODIyMmxVHhH1EtdbOdZ6DRpk609sGTZreXh5eeHA\ngQMYPHiwtMzPzw/79u2DSqVCTU0N9Ho9jh071mY/tjyIyJ7ZqqVzW7U8Jk2ahJCQELz55psAgNra\nWqhUKgCASqVCbW2trcojIqKbsNmA+ddff42hQ4fi9OnTiIiIgJ+fX5v1giBA6GTELCUlRXqv1+uh\n1+sVrJSIyP4YDAYYDAbFjt8j7m2VmpoKFxcXvPnmmzAYDPDw8EB1dTUmTpzIbisiuq2w2+oWXLp0\nCRcuXAAAXLx4EYWFhRgzZgyio6ORnZ0NAMjOzsa0adNsUR4REZlhk5ZHeXk5HnvsMQBAS0sL/vzn\nP2PFihWoq6tDTEwMTp06xam6RHRbul1aHj2i20oOhgcR2bPbJTx4hTkRkRXdytXzPenvZrY8iIh6\ngdtiwJyIiOwbw4OIiGRjeBARkWwMDyIiko3hQUREsjE8iIhINoYHERHJxvAgIiLZGB5ERCQbw4OI\niGRjeBARkWwMDyIiko3hQUREsvW48CgoKICfnx98fX2xatUqW5dDREQd6FHh0draiqeffhoFBQUo\nLS3Ftm3bcPToUVuXZVFKPpDeGli/7dhz7QDrv930qPAoKiqCj48PNBoNnJ2dMWvWLOTm5tq6LIuy\n9/8BWb/t2HPtAOu/3fSo8DAajRg+fLj0s1qthtFotGFFRETUkR4VHkJ3n81IRETWJfYg+/fvF6Oi\noqSf09LSxIyMjDbbeHt7iwD44osvvviS8fL29rbo93WPeoZ5S0sLRo0ahU8//RTDhg1DaGgotm3b\nBn9/f1uXRkREN3CydQE3cnJywmuvvYaoqCi0trZi3rx5DA4ioh6oR7U8iIjIPth0wLwrFwQ+88wz\n8PX1RWBgIA4fPmx237q6OkRERGDkyJGIjIxEQ0ODXdX//vvvY/To0XB0dMShQ4cUq12p+pcsWQJ/\nf38EBgZi+vTpOHfunF3Vv3LlSgQGBiIoKAjh4eGoqKiwq/qvW7NmDRwcHFBXV2dX9aekpECtVkOn\n00Gn06FMgdq6AAAFlklEQVSgoMBuageAdevWwd/fHwEBAVi2bJkitStV/6xZs6R/dy8vL+h0upsX\nYdERFBlaWlpEb29vsby8XGxubhYDAwPF0tLSNtvs2bNHfOihh0RRFMVvv/1WDAsLM7vvkiVLxFWr\nVomiKIoZGRnismXL7Kr+o0ePij/99JOo1+vFgwcPKlK7kvUXFhaKra2toiiK4rJly+zu3//8+fPS\n/llZWeK8efPsqn5RFMVTp06JUVFRokajEc+ePWtX9aekpIhr1qxRpGala//ss8/ESZMmic3NzaIo\niuJvv/1mV/XfKDk5WXzxxRdvWofNWh5duSAwLy8PCQkJAICwsDA0NDSgpqbmpvveuE9CQgJ27txp\nV/X7+flh5MiRitRsjfojIiLg4OAg7VNZWWlX9bu6ukr7NzY24q677rKr+gEgKSkJ//znPxWp2xr1\niwr3pCtV+7/+9S+sWLECzs7OAIAhQ4bYVf3XiaKIHTt2IDY29qZ12Cw8unJBYGfbVFVVdbpvbW0t\nVCoVAEClUqG2ttau6rcWa9S/adMmTJkyRYHqla3/+eefx913343s7GwsX77crurPzc2FWq3G2LFj\nFalb6fqBa10/gYGBmDdvniLdzkrVfvz4cXzxxRcYP3489Ho9Dhw4YPHalaz/ui+//BIqlQre3t43\nrcNm4dHVCwK78leIKIodHk8QBMUuPLRk/bagdP0vv/wy/vCHPyAuLq5b+5ujZP0vv/wyTp06hblz\n52Lx4sWy9+8KJepvampCWloaUlNTu7W/HEr9+ycmJqK8vBwlJSUYOnQokpOTu1PeTSlVe0tLC+rr\n6/Htt9/ilVdeQUxMTHfKM0vp391t27Z16ffWZlN1PT092wxGVlRUQK1W33SbyspKqNVqXL16td1y\nT09PANdaGzU1NfDw8EB1dTXc3d17fP0d7as0Jet/5513kJ+fj08//dQu678uLi5OsZaTEvX/8ssv\nOHnyJAIDA6Xtg4ODUVRUZPHfA6X+/W+sc/78+Zg6dapF61aydrVajenTpwMA7rnnHjg4OODs2bMY\nPHiwXdQPXAvAjz76qGuTdW5h3OaWXL16VRwxYoRYXl4uXrlyxeygz/79+6VBn5vtu2TJEumq9PT0\ndMUGbJWq/zq9Xi8eOHBAkdqVrH/v3r2iVqsVT58+rVjtStZfVlYm7Z+VlSXOnj3bruq/kZID5krV\nX1VVJe3/6quvirGxsXZT+xtvvCG+8MILoiiK4k8//SQOHz7c4rUrWb8oXvv91ev1XarDprcnyc/P\nF0eOHCl6e3uLaWlpoihe+w/wxhtvSNv89a9/Fb29vcWxY8e2mX3U0b6iKIpnz54Vw8PDRV9fXzEi\nIkKsr6+3q/o//PBDUa1Wi3fccYeoUqnEyZMn21X9Pj4+4t133y0GBQWJQUFBYmJiol3VP2PGDDEg\nIEAMDAwUp0+fLtbW1tpV/Tfy8vJSLDyUqj8+Pl4cM2aMOHbsWPHRRx8Va2pq7Kb25uZmcfbs2WJA\nQIA4btw48fPPP1ekdqXqF0VRnDt3rrhhw4Yu1cCLBImISLYedVddIiKyDwwPIiKSjeFBRESyMTyI\niEg2hgcREcnG8CAiItkYHkREJBvDg4iIZGN4EN2i4uJiBAYG4sqVK7h48SICAgJQWlpq67KIFMUr\nzIksYOXKlbh8+TKampowfPhwRZ8iR9QTMDyILODq1asICQlB3759sX//fsUeBUDUU7DbisgCzpw5\ng4sXL6KxsRFNTU22LodIcWx5EFlAdHQ04uLicOLECVRXV2PdunW2LolIUTZ7GBTR7WLz5s3o06cP\nZs2aBZPJhHvvvRcGgwF6vd7WpREphi0PIiKSjWMeREQkG8ODiIhkY3gQEZFsDA8iIpKN4UFERLIx\nPIiISDaGBxERycbwICIi2f4P0rp0PlMUs2wAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10d204590>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Now if we just use cashstat values do we get the same result?\n",
"plot_cdf(cashstat)\n",
"fc95 = np.percentile(cashstat, 95)\n",
"ic = cashstat < fc95\n",
"plot_cdf(cashstat)\n",
"plt.xlabel('normpvals')\n",
"print len(xpos[it]), len(xpos[ic])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"440 440\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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417/M5ya3bm13EhER66ggVMF778G4cXp2sojUbSoI53HwIHz7rW5GE5G6T7OM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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10d630c10>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.clf()\n",
"plot_scatter(xpos,ypos,'o')\n",
"ic = cashstat < np.percentile(dat['cashstat'],95)\n",
"plot_scatter(xpos[it],ypos[it], overplot=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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A8N+4gnmPwUZbxSrceEV6hVBFXFRdrMew50dBJNd43dDkD7mTUR7hIo98cd85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\nXX9otwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d7a98d0>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Both - CDF based on likelihood or cashstat gives the same results in terms \n",
" of the number of accepted positions\n",
"\n",
"But does this represent the posterior for the locations (xpos,ypos)?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.hist2d(xpos,ypos, bins=20 ,cmap='Greens', norm = LogNorm())\n",
"plt.gca()\n",
"plt.title('Position')\n",
"plt.colorbar()\n",
"plt.xlabel('X position')\n",
"plt.ylabel('Y position')\n",
"#plt.savefig(datadir+'/'+filename+'position2d.png')\n",
"#plt.plot(xpos[it],ypos[it])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"<matplotlib.text.Text at 0x10d7ba850>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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0olWrVggNDUVoaCjeeecdB0RZ+xSZOz3//PPo1q0bdDodjh07ZsfoqqsvXlna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\nIkzKREQSYVImIpLI/wcS4vjZ3YSZdQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d667350>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#x= dat['src2.ypos'][ok]\n",
"x = xpos\n",
"num_bins = 40\n",
"# the histogram of the data\n",
"n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n",
"plt.xlabel('Y Position')\n",
"plt.ylabel('Probability density')\n",
"plt.title(r'PDF')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"<matplotlib.text.Text at 0x10d7e0710>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ZM2Lv3r1CCCGqq6tF165d7erfvjn12/rf4PLly0IIIerq6kR4eLj4/vvvJdtt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\n",
"text": [
"<matplotlib.figure.Figure at 0x10d45c350>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_trace(dat['src1.xpos'])\n",
"plt.xlabel('Iteration Number')\n",
"plt.ylabel('X position')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<matplotlib.text.Text at 0x10d718250>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Fo2FBXnZTfbEo+Ipg1vUUaFm7d6e94nffpWX0JevJihgFIfT81QYTATQuwa4d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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10d1886d0>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_trace(dat['src1.ypos'])\n",
"plt.xlabel('Iteration Number')\n",
"plt.ylabel('Y Position')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.text.Text at 0x10dcb4a10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ba2UoL5drHestATPLxI47yM2YQIcOUggCtb5VSyApyTgmsGOHnBiSCEamll8R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\nvWGAHwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d789690>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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