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@DavidMStraub
Created September 28, 2016 07:35
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import flavio"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<flavio.classes.Parameter at 0x7f37c45db898>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flavio.Parameter('my parameter')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"param_symm = flavio.ParameterConstraints()\n",
"param_symm.set_constraint('my parameter', '1.0 +- 0.1')\n",
"param_asymm = flavio.ParameterConstraints()\n",
"param_asymm.set_constraint('my parameter', '1.0 + 0.3 - 0.05')\n",
"param_both = flavio.ParameterConstraints()\n",
"param_both.set_constraint('my parameter', '1.0 +- 0.1 + 0.3 - 0.05')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = np.linspace(0, 3, 10000)\n",
"y_s = [ np.exp(sum(param_symm.get_logprobability_all({'my parameter': X}).values()))\n",
" for X in x ]\n",
"y_a = [ np.exp(sum(param_asymm.get_logprobability_all({'my parameter': X}).values()))\n",
" for X in x ]\n",
"y_b = [ np.exp(sum(param_both.get_logprobability_all({'my parameter': X}).values()))\n",
" for X in x ]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f37923ce9e8>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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K8a8qVaoUuya2JCGi3KzVCgLSEiKEM7lw4UJo6aWEqDjpjhHlZs0kRMaECCGE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\nAO4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3792765358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, y_s, label='symmetric errors')\n",
"plt.plot(x, y_a, label='aymmetric errors')\n",
"plt.plot(x, y_b, label='combined errors')\n",
"plt.xlabel('my parameter')\n",
"plt.ylabel('likelihood')\n",
"plt.legend(bbox_to_anchor=(1.5,1))"
]
},
{
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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