DavidMStraub/gist:7238e5b688ceeb366948256c7480d12e

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import flavio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.interpolate\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "q2_fine = np.linspace(1, 6, 100)\n",
    "q2_coarse = np.linspace(1, 6, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.56 s, sys: 4 ms, total: 1.56 s\n",
      "Wall time: 1.56 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "P5p_central = [flavio.sm_prediction('P5p(B0->K*mumu)', q2) for q2 in q2_fine]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 48.9 s, sys: 38.9 s, total: 1min 27s\n",
      "Wall time: 45.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "P5p_rel_err = [flavio.sm_uncertainty('P5p(B0->K*mumu)', q2)/abs(flavio.sm_prediction('P5p(B0->K*mumu)', q2)) for q2 in q2_coarse]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "P5p_rel_err_interp = scipy.interpolate.interp1d(q2_coarse, P5p_rel_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "P5p_err = np.array([P5p_rel_err_interp(q2) for q2 in q2_fine])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc865279fd0>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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n0+Haa+GKK2LT+F12idXRu+wSHxCZvEtSwBeRtPnuu1i8Nnly3AG8805MDW23XTwE/dGPokZ+z55R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\nEAr4IiIFQgFfRKRAKOCLiBSI/w9D1dutAwf+PAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc86526f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(q2_fine, P5p_central, c='b')\n",
    "plt.plot(q2_fine, P5p_central + P5p_err, c='b', ls='--')\n",
    "plt.plot(q2_fine, P5p_central - P5p_err, c='b', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 40,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import flavio\n",
	"import matplotlib.pyplot as plt\n",
	"import numpy as np\n",
	"import scipy.interpolate\n",
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 42,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"q2_fine = np.linspace(1, 6, 100)\n",
	"q2_coarse = np.linspace(1, 6, 3)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 47,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"CPU times: user 1.56 s, sys: 4 ms, total: 1.56 s\n",
	"Wall time: 1.56 s\n"
	]
	}
	],
	"source": [
	"%%time\n",
	"P5p_central = [flavio.sm_prediction('P5p(B0->K*mumu)', q2) for q2 in q2_fine]"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 60,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"CPU times: user 48.9 s, sys: 38.9 s, total: 1min 27s\n",
	"Wall time: 45.1 s\n"
	]
	}
	],
	"source": [
	"%%time\n",
	"P5p_rel_err = [flavio.sm_uncertainty('P5p(B0->Kmumu)', q2)/abs(flavio.sm_prediction('P5p(B0->Kmumu)', q2)) for q2 in q2_coarse]"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 61,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"P5p_rel_err_interp = scipy.interpolate.interp1d(q2_coarse, P5p_rel_err)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 62,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"P5p_err = np.array([P5p_rel_err_interp(q2) for q2 in q2_fine])"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 63,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"[<matplotlib.lines.Line2D at 0x7fc865279fd0>]"
	]
	},
	"execution_count": 63,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
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	"text/plain": [
	"<matplotlib.figure.Figure at 0x7fc86526f4e0>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.plot(q2_fine, P5p_central, c='b')\n",
	"plt.plot(q2_fine, P5p_central + P5p_err, c='b', ls='--')\n",
	"plt.plot(q2_fine, P5p_central - P5p_err, c='b', ls='--')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3",
	"language": "python",
	"name": "python3"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.4.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}