dev_T1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# math\n",
    "import numpy as np\n",
    "\n",
    "#plot\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Load module and reload it if it was modified during development\n",
    "from Quantitative_MRI import T1; import importlib; importlib.reload(T1);\n",
    "from Quantitative_MRI import models; import importlib; importlib.reload(models);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Quantitative_MRI.T1.T1_vtr_signal at 0x1075715f8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T1.T1_vtr_signal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# simulate data\n",
    "tr = np.linspace(0,10,20)\n",
    "t1 = 2.1\n",
    "Mz = 1.0\n",
    "s = models.T1_vTR(tr,t1,Mz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ChcAE59yGyJfZQsV/gx1F8NXr/K5ERCQmmmy5O+dqzewWYBGQCsx2zn1gZjeG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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9d7860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create object\n",
    "S =  T1.T1_vtr_signal(tr,s)\n",
    "\n",
    "# add noise\n",
    "S.yhat = T1._add_noise(S.yraw, 0.1)\n",
    "\n",
    "#plot\n",
    "plt.plot(S.x,S.yhat,'o',S.x, S.yraw); plt.legend(('Noise','Real'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.1,  1. ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T1.T1_vtr_signal(tr,s).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "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": 2
}