Functional.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/juan/anaconda/envs/python2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import qutip as qt\n",
    "import os,sys,inspect\n",
    "currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n",
    "parentdir = os.path.dirname(currentdir)\n",
    "sys.path.insert(0,parentdir) \n",
    "%aimport analysis\n",
    "%aimport learning\n",
    "%aimport NV_generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig_dir='/Users/juan/Google Drive/Brown/2017S/Machine Learning Applied to Scientific Data  - ENGN2912Z/NV-Tomography/';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "B_field = 0.0403555 # Teslas\n",
    "gam_c = 67.262 * 10 ** 6 # Gyromagnetic ratio for a single c13 nucleus in rad s-1 T-1\n",
    "ms = 1\n",
    "omega_larmor = -1 * ms * gam_c * B_field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prime_sample = learning.load_obj_2('diamonds/diamond_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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7RmAjgJmNZTEzr5HaoYzQXuWceulQW5SzXY6nypmddiinmdWWBiJGmo7mcaB0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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112cf4b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(prime_sample['A']/analysis.mag,np.abs(prime_sample['B'])/analysis.mag)\n",
    "plt.xlim(-10,10)\n",
    "plt.ylim(0,20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functionalize the fitting algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def carbon_fit(diamond, guess_classifier, x_threshold):\n",
    "    #guess_scaler = learning.load_obj(\"classifiers/scaler_svm_rbf_di3220_29diamonds_cxABe\")#(\"classifiers/scaler_svm_rbfguess_limited_3220_C1_29diamonds\")scaler_svm_linear_di3220_29diamond\n",
    "    #guess_clf = learning.load_obj(\"classifiers/clf_svm_rbf_di3220_29diamonds_cxABe\")#(\"classifiers/clf_svm_rbfguess_limited_3220_C1_29diamonds\")\n",
    "    #Initialize results lists and others\n",
    "    num_correct_list, num_incorrect_list = [], []\n",
    "    As_list, Bs_list = [], []\n",
    "    guess_As_list, guess_Bs_list = [], []\n",
    "    dips_measured = []\n",
    "    \n",
    "    #print \"diamond_ind: \", diamond_ind\n",
    "    #diamond = diamonds[diamond_ind]\n",
    "    tau = diamond[\"tau\"] # This is an array with all the free evolution times.\n",
    "    data = diamond[\"data\"] # These are the corresping M(tau) values for N = 64\n",
    "    N = diamond[\"N\"]\n",
    "    dip_inds = diamond[\"dip_inds\"] # indices for the dips in data\n",
    "    windows = diamond[\"windows\"] # these indices determine the window around each dip\n",
    "    min_dip_ind = 3220\n",
    "    measure_dii = filter(lambda dii: dip_inds[dii] >= min_dip_ind, range(len(dip_inds))) # only times after 15 micros are taken\n",
    "    print \"dips measured: \", len(measure_dii)\n",
    "    dips_measured.append(len(measure_dii))\n",
    "    spin_dict = {}\n",
    "    for dii in measure_dii: # this loop iterates over all dips\n",
    "        if diamond[\"good_fits\"][dii]: # If this happens to be a good fit then\n",
    "            dip_ind = dip_inds[dii] \n",
    "            res_tau = tau[dip_ind]\n",
    "            for phi, x in zip(diamond[\"phis_list\"][dii], diamond[\"xs_list\"][dii]):\n",
    "                for omega_tilde, cosphi in analysis.calc_omega_tilde(phi, x, res_tau, omega_larmor):\n",
    "                    A, B = analysis.calc_A_B(cosphi, res_tau, omega_larmor, omega_tilde)\n",
    "                    if analysis.valid_A_B(A, B):\n",
    "                        if (dip_ind, phi, x) in spin_dict:\n",
    "                            spin_dict[(dip_ind, phi, x)] += [(A, B, cosphi)]\n",
    "                        else:\n",
    "                            spin_dict[(dip_ind, phi, x)] = [(A, B, cosphi)]\n",
    "    # replace spin_dict for a random guess\n",
    "#     spin_dict = {}\n",
    "#     for i in range(5000):\n",
    "#         spin_dict[(np.random.randint(1,5000), \n",
    "#                    np.random.uniform(0.,np.pi), \n",
    "#                    np.random.uniform(0.,2.))] = [(np.random.uniform(-150*analysis.mag,150*analysis.mag),np.random.uniform(0.,150*analysis.mag),np.random.uniform(-0.99,0.99))]\n",
    "    guess_As, guess_Bs, dataerrs, _, _, _, _ = analysis.choose_spin_guesses(spin_dict, N, omega_larmor, tau, data,\n",
    "                                                                guess_classifier, x_min = x_threshold, error_fun = analysis.squared_error)\n",
    "    cluster_As, cluster_Bs, cluster_dataerrs = analysis.cluster_spin_guesses(guess_As, guess_Bs, dataerrs, eps = .075, min_samples = 1)\n",
    "    As, Bs, num_remove = cluster_As, cluster_Bs, 1\n",
    "    A3 = np.copy(np.array(cluster_As));\n",
    "    B3 = np.copy(np.array(cluster_Bs));\n",
    "    while num_remove <= 2:\n",
    "        As, Bs, num_remove = analysis.remove_spins(As, Bs, N, omega_larmor, tau, data, num_remove = num_remove, error_fun = analysis.squared_error)\n",
    "    #As_list.append(As)\n",
    "    #Bs_list.append(Bs)\n",
    "    #guess_As_list.append(guess_As)\n",
    "    #guess_Bs_list.append(guess_Bs)\n",
    "    \n",
    "    print \"num_removed: \", len(cluster_As) - len(As)\n",
    "    num_correct = 0\n",
    "    spins = set(zip(diamond[\"A\"], np.abs(diamond[\"B\"])))\n",
    "    for A, B in zip(As, np.abs(Bs)):\n",
    "        for spin in spins:\n",
    "            if learning.is_fit(A, B, spin, A_acc = .5, B_acc = 2):\n",
    "                num_correct = num_correct + 1\n",
    "                spins.remove(spin)\n",
    "                break\n",
    "    num_incorrect = len(As) - num_correct\n",
    "    num_correct_list.append(num_correct)\n",
    "    num_incorrect_list.append(num_incorrect)\n",
    "    print \"correct: \", num_correct, \"incorrect: \", len(As) - num_correct\n",
    "    # grab the stage-1 fit values\n",
    "    A1=[]\n",
    "    B1=[]\n",
    "    for partial_set in spin_dictionary.itervalues():\n",
    "        for member in partial_set:\n",
    "            if len(member)>0:\n",
    "                A1.append(member[0]);\n",
    "                B1.append(member[1]);\n",
    "    A1 = np.array(A1);\n",
    "    B1 = np.array(B1);\n",
    "    A2 = np.array(guess_As);\n",
    "    B2 = np.array(guess_Bs);\n",
    "    A4 = As;\n",
    "    B4 = Bs;\n",
    "    return [[[A1, B1], [A2, B2], [A3, B3], [A4, B4]], num_correct, len(As) - num_correct]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "guess_scaler = learning.load_obj(\"classifiers/scaler_svm_rbf_di3220_29diamonds_cxABe\")#(\"classifiers/scaler_svm_rbfguess_limited_3220_C1_29diamonds\")scaler_svm_linear_di3220_29diamond\n",
    "guess_clf = learning.load_obj(\"classifiers/clf_svm_rbf_di3220_29diamonds_cxABe\")#(\"classifiers/clf_svm_rbfguess_limited_3220_C1_29diamonds\")\n",
    "the_pipe = (lambda features: guess_clf.predict(guess_scaler.transform([features])))\n",
    "#the_pipe = (lambda x: np.array([1.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prime_sample = learning.load_obj_2('diamonds/diamond_3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,\n",
       "        1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2])"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0.1,2.2,(2.2-0.1)/0.1+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num fits = 2814\n",
      "num fits = 23\n",
      "num fits = 12\n",
      "num fits = 11\n"
     ]
    }
   ],
   "source": [
    "for part in fit_sequence[0]:\n",
    "    print('num fits = '+str(len(part[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ETVcuJkDz9oOmXjciUw4MeuYpp8rmKL/jRK1Hu/j1QGLVNOpETVWz/d7UFklx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IkeN67ukqJbKssKVu6d2hHZqeANM4z7eh7u/uYdlLk8+G+Pq5GVvyJkRvy4dS/sbJCC8avmI8lb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mzvzDyMpRDyIhUQuYiITqxDo1mdcolalte3Ltv6cxtW1P5jVK5cQ6Ncu6aHtV1hHyMzJ4765H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\nIVMAExEREQnZ/w/kZqh+mXvBRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d9aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,20))\n",
    "stage_names = ['fit potential resonances + least squares',\n",
    "              'apply classifier and apply x threshold',\n",
    "              'cluster using DBSCAN',\n",
    "              'remove fits that increase overall error']\n",
    "for stage, counter in zip(stage_names,range(len(fit_sequence[0]))):\n",
    "    plt.subplot(len(fit_sequence[0]),1,counter+1)\n",
    "    this_pair = fit_sequence[0][counter]\n",
    "    plt.scatter(this_pair[0]/analysis.mag,this_pair[1]/analysis.mag,label=stage)\n",
    "    plt.scatter(prime_sample['A']/analysis.mag,np.abs(prime_sample['B'])/analysis.mag, \n",
    "                s=100, lw=1, c='r',marker='x',\n",
    "                label='ground truth')\n",
    "    plt.xlim(-150,150)\n",
    "    plt.ylim(0,100)\n",
    "    plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n",
      "dips measured:  382\n",
      "num_removed:  4\n",
      "correct:  15 incorrect:  5\n",
      "0.205882352941\n",
      "dips measured:  382\n",
      "num_removed:  4\n",
      "correct:  15 incorrect:  5\n",
      "0.311764705882\n",
      "dips measured:  382\n",
      "num_removed:  3\n",
      "correct:  15 incorrect:  5\n",
      "0.417647058824\n",
      "dips measured:  382\n",
      "num_removed:  2\n",
      "correct:  16 incorrect:  4\n",
      "0.523529411765\n",
      "dips measured:  382\n",
      "num_removed:  1\n",
      "correct:  16 incorrect:  5\n",
      "0.629411764706\n",
      "dips measured:  382\n",
      "num_removed:  1\n",
      "correct:  16 incorrect:  3\n",
      "0.735294117647\n",
      "dips measured:  382\n",
      "num_removed:  1\n",
      "correct:  17 incorrect:  1\n",
      "0.841176470588\n",
      "dips measured:  382\n",
      "num_removed:  1\n",
      "correct:  16 incorrect:  1\n",
      "0.947058823529\n",
      "dips measured:  382\n",
      "num_removed:  1\n",
      "correct:  15 incorrect:  1\n",
      "1.05294117647\n",
      "dips measured:  382\n",
      "num_removed:  1\n",
      "correct:  14 incorrect:  2\n",
      "1.15882352941\n",
      "dips measured:  382\n",
      "num_removed:  0\n",
      "correct:  12 incorrect:  1\n",
      "1.26470588235\n",
      "dips measured:  382\n",
      "num_removed:  1\n",
      "correct:  11 incorrect:  0\n",
      "1.37058823529\n",
      "dips measured:  382\n",
      "num_removed:  1\n",
      "correct:  11 incorrect:  0\n",
      "1.47647058824\n",
      "dips measured:  382\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  0\n",
      "1.58235294118\n",
      "dips measured:  382\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  0\n",
      "1.68823529412\n",
      "dips measured:  382\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  0\n",
      "1.79411764706\n",
      "dips measured:  382\n",
      "num_removed:  0\n",
      "correct:  9 incorrect:  0\n",
      "1.9\n",
      "dips measured:  382\n",
      "num_removed:  0\n",
      "correct:  5 incorrect:  0\n"
     ]
    }
   ],
   "source": [
    "x_threshold_analysis = []\n",
    "for x_t in np.linspace(0.1,1.9,(1.9-0.1)/0.1+1):\n",
    "    print(x_t)\n",
    "    fit_sequence = carbon_fit(prime_sample, the_pipe, x_t)\n",
    "    x_threshold_analysis.append([x_t,fit_sequence[-2],fit_sequence[-1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_threshold_analysis=np.array(x_threshold_analysis);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PQDbwSVHrWGtHWmtTrLUp8fHx/ktORCSAJMR6Cl0ebgzjluwkJ9f6OSMR8Qe/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\najwohwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ce0550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(x_threshold_analysis[:,0],x_threshold_analysis[:,1])\n",
    "plt.plot(x_threshold_analysis[:,0],x_threshold_analysis[:,1],label='correct')\n",
    "plt.scatter(x_threshold_analysis[:,0],x_threshold_analysis[:,2])\n",
    "plt.plot(x_threshold_analysis[:,0],x_threshold_analysis[:,2],label='incorrect')\n",
    "#plt.plot(x_threshold_analysis[:,0],x_threshold_analysis[:,2]/x_threshold_analysis[:,1],label='incorrect')\n",
    "plt.xlabel('$x_{threshold}$')\n",
    "plt.ylabel('#')\n",
    "plt.title('Effect of the $x$ threshold on the final fits')\n",
    "plt.ylim(0,max(x_threshold_analysis[:,1]))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "0.1\n",
      "dips measured:  420\n",
      "num_removed:  6\n",
      "correct:  14 incorrect:  8\n",
      "0.205882352941\n",
      "dips measured:  420\n",
      "num_removed:  6\n",
      "correct:  14 incorrect:  8\n",
      "0.311764705882\n",
      "dips measured:  420\n",
      "num_removed:  4\n",
      "correct:  14 incorrect:  8\n",
      "0.417647058824\n",
      "dips measured:  420\n",
      "num_removed:  4\n",
      "correct:  15 incorrect:  8\n",
      "0.523529411765\n",
      "dips measured:  420\n",
      "num_removed:  3\n",
      "correct:  14 incorrect:  8\n",
      "0.629411764706\n",
      "dips measured:  420\n",
      "num_removed:  2\n",
      "correct:  14 incorrect:  8\n",
      "0.735294117647\n",
      "dips measured:  420\n",
      "num_removed:  2\n",
      "correct:  14 incorrect:  7\n",
      "0.841176470588\n",
      "dips measured:  420\n",
      "num_removed:  2\n",
      "correct:  14 incorrect:  7\n",
      "0.947058823529\n",
      "dips measured:  420\n",
      "num_removed:  2\n",
      "correct:  13 incorrect:  6\n",
      "1.05294117647\n",
      "dips measured:  420\n",
      "num_removed:  1\n",
      "correct:  12 incorrect:  3\n",
      "1.15882352941\n",
      "dips measured:  420\n",
      "num_removed:  1\n",
      "correct:  10 incorrect:  1\n",
      "1.26470588235\n",
      "dips measured:  420\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  0\n",
      "1.37058823529\n",
      "dips measured:  420\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  0\n",
      "1.47647058824\n",
      "dips measured:  420\n",
      "num_removed:  0\n",
      "correct:  9 incorrect:  0\n",
      "1.58235294118\n",
      "dips measured:  420\n",
      "num_removed:  0\n",
      "correct:  9 incorrect:  0\n",
      "1.68823529412\n",
      "dips measured:  420\n",
      "num_removed:  0\n",
      "correct:  9 incorrect:  0\n",
      "1.79411764706\n",
      "dips measured:  420\n",
      "num_removed:  0\n",
      "correct:  8 incorrect:  0\n",
      "1.9\n",
      "dips measured:  420\n",
      "num_removed:  0\n",
      "correct:  6 incorrect:  0\n",
      "7\n",
      "0.1\n",
      "dips measured:  365\n",
      "num_removed:  2\n",
      "correct:  15 incorrect:  6\n",
      "0.205882352941\n",
      "dips measured:  365\n",
      "num_removed:  2\n",
      "correct:  15 incorrect:  6\n",
      "0.311764705882\n",
      "dips measured:  365\n",
      "num_removed:  2\n",
      "correct:  15 incorrect:  6\n",
      "0.417647058824\n",
      "dips measured:  365\n",
      "num_removed:  2\n",
      "correct:  15 incorrect:  5\n",
      "0.523529411765\n",
      "dips measured:  365\n",
      "num_removed:  1\n",
      "correct:  15 incorrect:  5\n",
      "0.629411764706\n",
      "dips measured:  365\n",
      "num_removed:  1\n",
      "correct:  14 incorrect:  5\n",
      "0.735294117647\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  13 incorrect:  4\n",
      "0.841176470588\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  12 incorrect:  3\n",
      "0.947058823529\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  12 incorrect:  2\n",
      "1.05294117647\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  13 incorrect:  1\n",
      "1.15882352941\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  12 incorrect:  1\n",
      "1.26470588235\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  12 incorrect:  0\n",
      "1.37058823529\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  9 incorrect:  0\n",
      "1.47647058824\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  7 incorrect:  0\n",
      "1.58235294118\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  7 incorrect:  0\n",
      "1.68823529412\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  6 incorrect:  0\n",
      "1.79411764706\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  4 incorrect:  0\n",
      "1.9\n",
      "dips measured:  365\n",
      "num_removed:  0\n",
      "correct:  3 incorrect:  0\n",
      "8\n",
      "0.1\n",
      "dips measured:  379\n",
      "num_removed:  6\n",
      "correct:  13 incorrect:  8\n",
      "0.205882352941\n",
      "dips measured:  379\n",
      "num_removed:  6\n",
      "correct:  13 incorrect:  8\n",
      "0.311764705882\n",
      "dips measured:  379\n",
      "num_removed:  4\n",
      "correct:  15 incorrect:  8\n",
      "0.417647058824\n",
      "dips measured:  379\n",
      "num_removed:  4\n",
      "correct:  16 incorrect:  7\n",
      "0.523529411765\n",
      "dips measured:  379\n",
      "num_removed:  3\n",
      "correct:  16 incorrect:  3\n",
      "0.629411764706\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  15 incorrect:  4\n",
      "0.735294117647\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  12 incorrect:  3\n",
      "0.841176470588\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  16 incorrect:  2\n",
      "0.947058823529\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  14 incorrect:  2\n",
      "1.05294117647\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  11 incorrect:  1\n",
      "1.15882352941\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  1\n",
      "1.26470588235\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  1\n",
      "1.37058823529\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  1\n",
      "1.47647058824\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  1\n",
      "1.58235294118\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  1\n",
      "1.68823529412\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  1\n",
      "1.79411764706\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  10 incorrect:  1\n",
      "1.9\n",
      "dips measured:  379\n",
      "num_removed:  0\n",
      "correct:  7 incorrect:  1\n",
      "9\n",
      "0.1\n",
      "dips measured:  373\n",
      "num_removed:  1\n",
      "correct:  9 incorrect:  3\n",
      "0.205882352941\n",
      "dips measured:  373\n",
      "num_removed:  1\n",
      "correct:  9 incorrect:  3\n",
      "0.311764705882\n",
      "dips measured:  373\n",
      "num_removed:  1\n",
      "correct:  9 incorrect:  3\n",
      "0.417647058824\n",
      "dips measured:  373\n",
      "num_removed:  1\n",
      "correct:  9 incorrect:  3\n",
      "0.523529411765\n",
      "dips measured:  373\n",
      "num_removed:  1\n",
      "correct:  9 incorrect:  3\n",
      "0.629411764706\n",
      "dips measured:  373\n",
      "num_removed:  1\n",
      "correct:  9 incorrect:  3\n",
      "0.735294117647\n",
      "dips measured:  373\n",
      "num_removed:  1\n",
      "correct:  8 incorrect:  3\n",
      "0.841176470588\n",
      "dips measured:  373\n",
      "num_removed:  1\n",
      "correct:  7 incorrect:  3\n",
      "0.947058823529\n",
      "dips measured:  373\n",
      "num_removed:  0\n",
      "correct:  7 incorrect:  3\n",
      "1.05294117647\n",
      "dips measured:  373\n",
      "num_removed:  0\n",
      "correct:  7 incorrect:  2\n",
      "1.15882352941\n",
      "dips measured:  373\n",
      "num_removed:  0\n",
      "correct:  7 incorrect:  2\n",
      "1.26470588235\n",
      "dips measured:  373\n",
      "num_removed:  0\n",
      "correct:  7 incorrect:  2\n",
      "1.37058823529\n",
      "dips measured:  373\n",
      "num_removed:  0\n",
      "correct:  7 incorrect:  2\n",
      "1.47647058824\n",
      "dips measured:  373\n",
      "num_removed:  0\n",
      "correct:  7 incorrect:  1\n",
      "1.58235294118\n",
      "dips measured:  373\n",
      "num_removed:  0\n",
      "correct:  6 incorrect:  1\n",
      "1.68823529412\n",
      "dips measured:  373\n",
      "num_removed:  0\n",
      "correct:  6 incorrect:  1\n",
      "1.79411764706\n",
      "dips measured:  373\n",
      "num_removed:  0\n",
      "correct:  6 incorrect:  0\n",
      "1.9\n",
      "dips measured:  373\n",
      "num_removed:  0\n",
      "correct:  5 incorrect:  0\n"
     ]
    }
   ],
   "source": [
    "# repeat the previous analysis but using an ensemble of samples\n",
    "#all_threshold_analysis = []\n",
    "for counter_1 in range(6,10):\n",
    "    print(counter_1)\n",
    "    prime_sample = learning.load_obj_2('diamonds/diamond_'+str(counter_1))\n",
    "    x_threshold_analysis = []\n",
    "    for x_t in np.linspace(0.1,1.9,(1.9-0.1)/0.1+1):\n",
    "        print(x_t)\n",
    "        fit_sequence = carbon_fit(prime_sample, the_pipe, x_t)\n",
    "        x_threshold_analysis.append([x_t,fit_sequence[-2],fit_sequence[-1]])\n",
    "    all_threshold_analysis.append(x_threshold_analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pickle.dump(all_threshold_analysis,open('threshold_analysis.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BPe3h+mnjZW/u9YyNj+LeS/TbCuO9SOU91oOj4cRWOLIWmoZDz/ng4lG82KLSOnLkCAEB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\n/wdpQvStL9EKJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c2ba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(1,2,1)\n",
    "for x_threshold_analysis_list, counter_1 in zip(all_threshold_analysis,range(len(all_threshold_analysis))):\n",
    "    x_threshold_analysis=np.array(x_threshold_analysis_list)\n",
    "    plt.scatter(x_threshold_analysis[:,0],x_threshold_analysis[:,1])\n",
    "    plt.plot(x_threshold_analysis[:,0],x_threshold_analysis[:,1],label='sample '+str(counter_1))\n",
    "    #plt.scatter(x_threshold_analysis[:,0],x_threshold_analysis[:,2])\n",
    "    #plt.plot(x_threshold_analysis[:,0],x_threshold_analysis[:,2],label='incorrect')\n",
    "    #plt.plot(x_threshold_analysis[:,0],x_threshold_analysis[:,2]/x_threshold_analysis[:,1],label='incorrect')\n",
    "    plt.xlabel('$x_{threshold}$')\n",
    "    plt.ylabel('#')\n",
    "    plt.title('Effect of the $x$ threshold on the final fits -- correct fits')\n",
    "plt.ylim(0,20)\n",
    "plt.legend()\n",
    "plt.subplot(1,2,2)\n",
    "for x_threshold_analysis_list, counter_1 in zip(all_threshold_analysis,range(len(all_threshold_analysis))):\n",
    "    x_threshold_analysis=np.array(x_threshold_analysis_list)\n",
    "    #plt.scatter(x_threshold_analysis[:,0],x_threshold_analysis[:,1])\n",
    "    #plt.plot(x_threshold_analysis[:,0],x_threshold_analysis[:,1],label='sample '+str(counter_1))\n",
    "    plt.scatter(x_threshold_analysis[:,0],x_threshold_analysis[:,2])\n",
    "    plt.plot(x_threshold_analysis[:,0],x_threshold_analysis[:,2],label='sample '+str(counter_1))\n",
    "    #plt.plot(x_threshold_analysis[:,0],x_threshold_analysis[:,2]/x_threshold_analysis[:,1],label='incorrect')\n",
    "    plt.xlabel('$x_{threshold}$')\n",
    "    plt.ylabel('#')\n",
    "    plt.title('Effect of the $x$ threshold on the final fits -- incorrect fits')\n",
    "plt.ylim(0,20)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(fig_dir+'x_threshold_analysis.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SmLELTmyHA5ZJ+hzcoGNfCBpgKZ4RCy7ezRe7EEIIIUQTGTJkCC+99BKxsbXLfT766CPm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\n6WqDBQtgJd3FUMASERERERHHGY8Lf3OQuMtFCZCyl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116fd1f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "#plt.subplot(1,2,1)\n",
    "for x_threshold_analysis_list, counter_1 in zip(all_threshold_analysis,range(len(all_threshold_analysis))):\n",
    "    x_threshold_analysis=np.array(x_threshold_analysis_list)\n",
    "    plt.scatter(x_threshold_analysis[:,0],x_threshold_analysis[:,1]+x_threshold_analysis[:,2])\n",
    "    plt.plot(x_threshold_analysis[:,0],x_threshold_analysis[:,1]+x_threshold_analysis[:,2],label='sample '+str(counter_1))\n",
    "    plt.xlabel('$x_{threshold}$')\n",
    "    plt.ylabel('#')\n",
    "    plt.title('Effect of the $x$ threshold on the final fits -- total final fits')\n",
    "plt.ylim(0,30)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(fig_dir+'x_threshold_analysis_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why can increasing the threshold, which reduces the amount of fits, increase the amount of correct fits?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/juan/anaconda/envs/python2/lib/python2.7/site-packages/ipykernel/__main__.py:13: RuntimeWarning: invalid value encountered in log\n"
     ]
    },
    {
     "data": {
      "image/png": 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k8R/nDwu3tLDtKq3iNwvXcPHYfozsV9+C2bljOmOO69rMno1buHIb//viJxjw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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c94a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,20))\n",
    "# plot the ground truth values\n",
    "plt.subplot(6,1,1)\n",
    "plt.scatter(prime_sample['A']/analysis.mag,np.abs(prime_sample['B'])/analysis.mag, label='ground truth')\n",
    "plt.xlim(-150,150)\n",
    "plt.ylim(0,100)\n",
    "plt.legend()\n",
    "plt.subplot(6,1,2)\n",
    "# plot stage 1 fit values\n",
    "plt.scatter(A1/analysis.mag,\n",
    "            B1/analysis.mag,\n",
    "            label='all potential resonances',\n",
    "            s=np.log(1./E1))\n",
    "plt.xlim(-150,150)\n",
    "plt.ylim(0,100)\n",
    "plt.legend()\n",
    "plt.subplot(6,1,3)\n",
    "# plot the final fit values\n",
    "plt.scatter(prime_sample['A']/analysis.mag,np.abs(prime_sample['B'])/analysis.mag, label='ground truth')\n",
    "\n",
    "plt.scatter(As/analysis.mag,Bs/analysis.mag,marker='x',s=100)\n",
    "# and also number them\n",
    "for this_A, this_B, counter in zip(As,Bs,range(len(As))):\n",
    "    plt.text(this_A/analysis.mag, \n",
    "             this_B/analysis.mag, \n",
    "             str(counter+1),\n",
    "             ha='center',\n",
    "             va='bottom')\n",
    "#plt.xlim(-10,10)\n",
    "#plt.ylim(0,20)\n",
    "plt.margins(0.1)\n",
    "plt.xlim(-150,150)\n",
    "plt.ylim(0,100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trashcan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %load analysis.py\n",
    "# NV-spin-tomography Michael Scheer mgscheer@gmail.com\n",
    "\n",
    "# the main code for the analysis\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import scipy.optimize as opt\n",
    "from scipy import special, fftpack, signal\n",
    "import operator, heapq, itertools, warnings\n",
    "import sklearn as sk\n",
    "import learning\n",
    "\n",
    "# frequency scale -- turns kHz into radial Hz. Used for printing paramaters nicely\n",
    "mag = 2 * np.pi * 1e3\n",
    "\n",
    "# a special error class that is thrown in this code when a fit does not work\n",
    "class FitError(Exception):\n",
    "\tdef __init__(self, value):\n",
    "\t\tself.value = value\n",
    "\tdef __str__(self):\n",
    "\t\treturn repr(self.value)\n",
    "\n",
    "# create a nice looking plot\n",
    "def initialize_data_plot(figsize = (10,5), xlims = None, ylims = [-1.05,1.05]):\n",
    "\tfig,ax = plt.subplots(figsize=figsize)\n",
    "\tax.set_ylim(ylims)\n",
    "\tif xlims is not None:\n",
    "\t\tax.set_xlim(xlims)\n",
    "\treturn fig, ax\n",
    "\n",
    "# calculates the M function for a single spin A, B\n",
    "def calc_M_single(A,B,N,omega_larmor,tau):\n",
    "\ttau = np.array(tau)\n",
    "\tomega_tilde = np.sqrt((A + omega_larmor) ** 2 + B ** 2)\n",
    "\tmx = B/omega_tilde\n",
    "\tmz = (A+omega_larmor)/omega_tilde\n",
    "\talpha = omega_tilde * tau\n",
    "\tbeta = omega_larmor * tau\n",
    "\tcos_phi = np.cos(alpha) * np.cos(beta)- mz * np.sin(alpha) * np.sin(beta)\n",
    "\tvec_num = (mx ** 2) * (1-np.cos(alpha)) * (1-np.cos(beta))\n",
    "\tvec_denom = 1.0 + cos_phi\n",
    "\tfor i in np.where(vec_denom == 0.0)[0]:\n",
    "\t\tvec_denom[i] = .0001\n",
    "\tvec_term = vec_num/vec_denom\n",
    "\tangle_term = np.sin(N * np.arccos(cos_phi) / 2.0) ** 2\n",
    "\treturn 1 - (vec_term * angle_term)\n",
    "\n",
    "# calculates the M function corresponding to many spins given by\n",
    "# A_list, B_list\n",
    "# equivalent to np.array([calc_M_single(A, B, N, omega_larmor, tau) for A, B in zip(A_list, B_list)]).prod()\n",
    "def calc_M(A_list, B_list, N, omega_larmor, tau):\n",
    "\t'''\n",
    "\tcalc_M(A_list, B_list, N, omega_larmor, tau)\n",
    "\tgives back M\n",
    "\t'''\n",
    "\treturn reduce(lambda accum, next: accum * calc_M_single(next[0], next[1], N, omega_larmor, tau), zip(A_list, B_list), 1.0)\n",
    "\n",
    "# calculates what A and B must be given the value of cos(phi) as well as omega_tilde\n",
    "def calc_A_B(cosphi, res_tau, omega_larmor, omega_tilde):\n",
    "\talpha = omega_tilde * res_tau\n",
    "\tbeta = omega_larmor * res_tau\n",
    "\tmz = (np.cos(alpha) * np.cos(beta) - cosphi)/(np.sin(alpha) * np.sin(beta))\n",
    "\tA = mz * omega_tilde - omega_larmor\n",
    "\tB = np.sqrt((1 - mz ** 2)) * omega_tilde\n",
    "\treturn A, np.abs(B)\n",
    "\n",
    "# Firm bounds on omega_tilde arising from an upper bound on hyperfine couplings\n",
    "def omega_bounds(omega_larmor, coupling_bound = 150 * mag):\n",
    "\tupper_omega_bound = omega_larmor + coupling_bound\n",
    "\tlower_omega_bound = omega_larmor - coupling_bound\n",
    "\treturn lower_omega_bound, upper_omega_bound\n",
    "\n",
    "# checks if the A, B pair has a coupling larger than coupling_bound\n",
    "def valid_A_B(A, B, coupling_bound = 150 * mag):\n",
    "\treturn A ** 2 + B ** 2 <= coupling_bound ** 2\n",
    "\n",
    "# for a real sequence y indexed by a sequence x,\n",
    "# writes y = a0 + a1 * cos(omega_0 * x) + b1 * sin(omega_0 * x) + ...\n",
    "# with omega_0 = 2 * np.pi/len(x)\n",
    "# as a \"fourier series\" (though for discrete y not continuous) and finds (very approximately)\n",
    "# the a and b coefficients.\n",
    "# coefs.real = a0, a1, a2, ...\n",
    "# coefs.imag = 0, -b1, -b2, ... (notice the negative signs)\n",
    "# see http://www.feynmanlectures.caltech.edu/I_50.html for fourier series and\n",
    "# http://docs.scipy.org/doc/scipy-0.16.1/reference/generated/scipy.fftpack.fft.html for the formula used for FFT.\n",
    "# notice the 2/N difference between the two formulas for all but the 0 frequency term.\n",
    "def sinusoid_coefs_fft(x, y):\n",
    "\tft = fftpack.fft(y)\n",
    "\tfreqs = fftpack.fftfreq(len(x), d = (x[1]-x[0])/(2*np.pi))\n",
    "\t# get rid of negative frequencies\n",
    "\tft, freqs = map(np.array, zip(*filter(lambda p: p[1] >= 0, zip(ft, freqs))))\n",
    "\tcoefs = 2.0 * np.array(ft)/len(x)\n",
    "\tcoefs[0] = .5 * coefs[0]\n",
    "\treturn coefs, freqs\n",
    "\n",
    "# given the coefs and freqs from sinusoid_coefs_fft, finds the most likely frequencies\n",
    "# present in the signal. It looks for peaks in both the real and imaginary part of the coefficients.\n",
    "# it returns a list of frequencies in decreasing order of likelihood, based on the absolute value of the\n",
    "# fourier spectrum (coefs) at that point.\n",
    "def find_peaks_from_fourier(coefs, freqs):\n",
    "\textremal_inds = signal.argrelextrema(coefs.real, np.greater)[0]\n",
    "\textremal_inds = np.r_[extremal_inds, signal.argrelextrema(coefs.real, np.less)[0]]\n",
    "\textremal_inds = np.r_[extremal_inds, signal.argrelextrema(coefs.imag, np.greater)[0]]\n",
    "\textremal_inds = np.r_[extremal_inds, signal.argrelextrema(coefs.imag, np.less)[0]]\n",
    "\textremal_inds = np.unique(extremal_inds)\n",
    "\tfreq_inds = sorted(extremal_inds, key=lambda ind: -np.abs(coefs[ind]))\n",
    "\treturn freqs[freq_inds]\n",
    "\n",
    "# takes a list of frequencies that are peaks in a fourier spectrum, either of the real\n",
    "# or imaginary part as well as a number of spins to look for. It then approximates the frequencies\n",
    "# of the underlying product cosine functions that were in the signal. See appendix C of thesis.\n",
    "def gen_freqs_from_fourier(freqs, num_spins):\n",
    "\t# sort them in decreasing order\n",
    "\tfreqs = sorted(freqs, key=lambda x:-x)\n",
    "\tif num_spins == 1: # requires 1 freq\n",
    "\t\treturn [freqs[0]]\n",
    "\telif num_spins == 2: # requires 2 freqs\n",
    "\t\tf0, f1 = freqs[:2]\n",
    "\t\treturn [.5 * (f0 + f1), .5 * (f0 - f1)]\n",
    "\telif num_spins == 3: # requires 3 freqs\n",
    "\t\tf0, f1, f2 = freqs[:3]\n",
    "\t\treturn [.5 * (f1 + f2), .5 * (f0 - f2), .5 * (f0 - f1)]\n",
    "\telif num_spins == 4: # requires 5 freqs\n",
    "\t\tphi01sum, phi2, phi3 = gen_freqs_from_fourier(freqs[:4], 3)\n",
    "\t\tf3, f4 = freqs[3:5]        \n",
    "\t\tphi0 = .5 * (f3 + f4)\n",
    "\t\tphi1 = phi01sum - phi0\n",
    "\t\treturn [phi0, phi1, phi2, phi3]\n",
    "\telse:\n",
    "\t\treturn gen_freqs_from_fourier(freqs, 4) # change this if you figure out how to do 5\n",
    "\n",
    "# the form of the signal as a function of N for a fixed tau\n",
    "def spin_fit_fun_single(N, phi, x):\n",
    "\treturn 1 - x * (np.sin(N * phi/2.0) ** 2)\n",
    "\n",
    "# the form of the signal as a function of N for a fixed tau with several spins\n",
    "# there can be any number of spins passed, in the form [phi0, phi1, ..., phin, x0, x1, ..., xn]\n",
    "# where the function for a single spin is 1 - x * (np.sin(N * phi/2.0) ** 2)\n",
    "# equivalent to np.array([single_spin_fit_fun(N, phi, x) for phi, x in zip(phis, xs)]).prod()\n",
    "def spin_fit_fun(N, *args):\n",
    "\tnum_spins = len(args)/2\n",
    "\tphis, xs = args[:num_spins], args[num_spins:2*num_spins]\n",
    "\treturn reduce(lambda accum, next: accum * spin_fit_fun_single(N, next[0], next[1]), zip(phis, xs), 1.0)\n",
    "\n",
    "# for two arrays l1 and l2 returns the squared error between them\n",
    "def squared_error(l1, l2):\n",
    "\treturn sum((l1 - l2) ** 2)\n",
    "\n",
    "# Use scipy.optimize.curve_fit to fit N_data to several spins using fit_fun. Requires a guess of the spin\n",
    "# parameters (phi's and x's) as param_guess\n",
    "def spin_fit_guess(N_vals, N_data, num_spins, param_guess, error_fun = squared_error, fit_fun = spin_fit_fun):\n",
    "\ttry:\n",
    "\t\twith warnings.catch_warnings():\n",
    "\t\t\twarnings.simplefilter(\"ignore\")\n",
    "\t\t\tpopts, pcovs = opt.curve_fit(fit_fun, N_vals, N_data, p0 = param_guess)\n",
    "\t\txs = popts[num_spins:2*num_spins]\n",
    "\t\terror = error_fun(fit_fun(N_vals, *popts), N_data)\n",
    "\t\treturn popts, error, np.all((xs > 0) & (xs <= 2))\n",
    "\texcept (RuntimeError, TypeError):\n",
    "\t\traise FitError(\"Failed to fit spins with N data.\")\n",
    "\n",
    "# fit possibly several spins using a fit_fun to N_data\n",
    "# for reference, see http://stackoverflow.com/questions/13405053/scipy-leastsq-fit-to-a-sine-wave-failing\n",
    "# and appendix C of thesis.\n",
    "def spin_fit(N_vals, N_data, error_fun = squared_error, verbose = True, plots = True, fit_fun = spin_fit_fun, extra_params = []):\n",
    "\tcoefs, freqs = sinusoid_coefs_fft(N_vals, N_data)\n",
    "\tlikely_freqs = find_peaks_from_fourier(coefs, freqs)\n",
    "\tparams, errors, spins_found = [], [], []\n",
    "\t# the higest number of spins that can be fit with l frequencies\n",
    "\tdef highest_spin(l):\n",
    "\t\tif l <= 3:\n",
    "\t\t\treturn l\n",
    "\t\telif l == 4:\n",
    "\t\t\treturn 3\n",
    "\t\telse:\n",
    "\t\t\treturn 4 # make this 5 if you figure out how to do 5\n",
    "\tfor num_spins in range(1,1 + highest_spin(len(likely_freqs))):\n",
    "\t\tapprox_phis = gen_freqs_from_fourier(likely_freqs, num_spins)\n",
    "\t\tapprox_xs = num_spins * [2.0]\n",
    "\t\tp0s = approx_phis + approx_xs + extra_params[:num_spins]\n",
    "\t\ttry:\n",
    "\t\t\tpopts, error, valid = spin_fit_guess(N_vals, N_data, num_spins, p0s, error_fun = error_fun, fit_fun = fit_fun)\n",
    "\t\t\tif valid:\n",
    "\t\t\t\tparams.append(popts)\n",
    "\t\t\t\terrors.append(error)\n",
    "\t\t\t\tspins_found.append(num_spins)\n",
    "\t\texcept FitError:\n",
    "\t\t\tpass\n",
    "\tif not params:\n",
    "\t\traise FitError(\"Failed to fit spins with N data.\")\n",
    "\telse:\n",
    "\t\tind = np.argmin(errors)\n",
    "\t\tif plots:\n",
    "\t\t\tplt.figure(figsize=(10,10))\n",
    "\t\t\tplt.plot(freqs, coefs.real, label = 'real part')\n",
    "\t\t\tplt.plot(freqs, coefs.imag, label = 'imag part')\n",
    "\t\t\tplt.title('Fourier spectrum')\n",
    "\t\t\tplt.xlabel('phi')\n",
    "\t\t\tplt.ylabel('Fourier coeficient')\n",
    "\t\t\tplt.legend()\n",
    "\t\t\tplt.show()\n",
    "\t\t\tplt.figure(figsize=(10,10))\n",
    "\t\t\tplt.plot(N_vals, N_data, '.-k', lw=0.4, label = 'data')\n",
    "\t\t\tplt.plot(N_vals, fit_fun(N_vals, *params[ind]), label = 'fit')\n",
    "\t\t\tplt.title('Fitted N data')\n",
    "\t\t\tplt.xlabel('N')\n",
    "\t\t\tplt.ylabel('M')\n",
    "\t\t\tplt.legend()\n",
    "\t\t\tplt.show()\n",
    "\t\treturn params[ind], spins_found[ind]\n",
    "\n",
    "# repeated attempts to fit N_vals and N_data using several spins. It tries subsets of the data of increasing length\n",
    "# and then uses the best result as a guess for one final fit of the whole data. See appendix C of thesis for more detail.\n",
    "def repeated_spin_fit(N_vals, N_data, error_tol, error_fun = squared_error, num_subsets = 4,\n",
    "\tfit_fun = spin_fit_fun, extra_params = [], verbose = True, plots = True):\n",
    "\tspin_fits, scaled_errors = [], []\n",
    "\tfor subset in [np.arange(int(len(N_vals) * r)) for r in np.linspace(0, 1, 1 + num_subsets)[1:]]:\n",
    "\t\ttry:\n",
    "\t\t\tspin_ans = spin_fit(N_vals[subset], N_data[subset], error_fun = error_fun, fit_fun = fit_fun,\n",
    "\t\t\t\textra_params = extra_params, verbose = verbose, plots = plots)\n",
    "\t\t\tspin_fits.append(spin_ans)\n",
    "\t\t\tscaled_errors.append(1.0 * error_fun(fit_fun(N_vals, *spin_ans[0]), N_data)/len(N_vals))\n",
    "\t\texcept FitError:\n",
    "\t\t\tpass\n",
    "\tif not spin_fits:\n",
    "\t\traise FitError(\"Failed to fit spins with N data.\")\n",
    "\tmin_ind = np.argmin(scaled_errors)\n",
    "\tpopt_spin, num_spins = spin_fits[min_ind]\n",
    "\tif verbose:\n",
    "\t\tprint \"best before final pass: \", min_ind\n",
    "\t\tphis, xs = np.array(popt_spin[:num_spins]) % np.pi, np.array(popt_spin[num_spins:2*num_spins])\n",
    "\t\tphis, xs = map(np.array, zip(*sorted(zip(phis, xs), key = lambda x:-x[1])))# sort phis in decreasing order by xs\n",
    "\t\tprint \"answer before final pass: \", xs, phis\n",
    "\ttry:\n",
    "\t\tpopts, error, valid = spin_fit_guess(N_vals, N_data, num_spins, popt_spin, error_fun = error_fun, fit_fun = fit_fun)\n",
    "\t\tif valid:\n",
    "\t\t\tspin_fits.append((popts, num_spins))\n",
    "\t\t\tscaled_errors.append(1.0 * error_fun(fit_fun(N_vals, *popts), N_data)/len(N_vals))\n",
    "\t\t\tmin_ind = np.argmin(scaled_errors)\n",
    "\t\t\tpopt_spin, num_spins = spin_fits[min_ind]\n",
    "\t\tif plots:\n",
    "\t\t\tplt.figure(figsize = (10,10))\n",
    "\t\t\tplt.plot(N_vals, N_data, '.-k', lw=0.4, label = 'data')\n",
    "\t\t\tplt.plot(N_vals, fit_fun(N_vals, *popts), label = 'fit')\n",
    "\t\t\tplt.title('Fitted N data')\n",
    "\t\t\tplt.xlabel('N')\n",
    "\t\t\tplt.ylabel('M')\n",
    "\t\t\tplt.legend()\n",
    "\t\t\tplt.show()\n",
    "\texcept FitError:\n",
    "\t\tpass\n",
    "\tif verbose:\n",
    "\t\tprint \"best overall: \", min_ind\n",
    "\t\tprint scaled_errors\n",
    "\tscaled_error = scaled_errors[min_ind]\n",
    "\tphis, xs = np.array(popt_spin[:num_spins]) % np.pi, np.array(popt_spin[num_spins:2*num_spins])\n",
    "\t#phis, xs = map(np.array, zip(*sorted(zip(phis, xs), key = lambda x:-x[1])))# sort phis in decreasing order by xs\n",
    "\treturn phis, xs, scaled_error    \n",
    "\n",
    "# creates a tau vector that matches the tau from real data from Tim and Julia.\n",
    "# should be filled in with realistic tau vectors for other N in the future.\n",
    "def choose_tau_params(N, num_samples = 5100):\n",
    "\tif N != 64:\n",
    "\t\traise FitError(\"N should be 64\")\n",
    "\telse:\n",
    "\t\tmin_tau = 3.0000000000000001e-06\n",
    "\t\tmax_tau = 2.1999999999999999e-05\n",
    "\t\tlength = num_samples\n",
    "\t\ttau = np.linspace(min_tau, max_tau, length)\n",
    "\t\treturn tau\n",
    "\n",
    "# finds local minima in the vector data as well as the intervals in which they are minima.\n",
    "# only finds dips below fit_dips_below and less wide than max_width if the parameters are not None.\n",
    "def find_resonances(data, fit_dips_below = None, max_width = None):\n",
    "\tmin_inds = (signal.argrelextrema(data, np.less)[0]).astype(int)\n",
    "\tmax_inds = (signal.argrelextrema(data, np.greater)[0]).astype(int)\n",
    "\twidths = max_inds[1:] - max_inds[:-1]\n",
    "\tdip_inds = min_inds[1:] if min_inds[0] < max_inds[0] else min_inds[:-1]\n",
    "\twindows = [(max_inds[i], max_inds[i+1]) for i in range(len(dip_inds)-1)] # should this be 3?\n",
    "\t# a dip must go below fit_dips_below, unless fit_dips_below = None and it's width should be at most max_width unless max_width = None\n",
    "\tfilter_fun = lambda p: ((data[p[0]] < fit_dips_below) if (fit_dips_below != None) else True) and (p[1] <= max_width if max_width != None else True)\n",
    "\tdip_inds, widths, windows = map(np.array, zip(*filter(filter_fun, zip(dip_inds, widths, windows))))\n",
    "\t# sort by width\n",
    "\t# sort_inds = np.argsort(-1 * widths)\n",
    "\t# return dip_inds[sort_inds], windows[sort_inds]\n",
    "\treturn dip_inds, windows\n",
    "\n",
    "# Calculates all possible omega_tilde values for a given phi and x at a given res_tau.\n",
    "# See chapter 4 of thesis.\n",
    "def calc_omega_tilde(phi, x, res_tau, omega_larmor):\n",
    "\tbeta = omega_larmor * res_tau\n",
    "\tcosbeta, cosphi_0 = np.cos(beta), np.cos(phi)\n",
    "\tlower_omega_bound, upper_omega_bound = omega_bounds(omega_larmor)\n",
    "\tomega_cosphi = []\n",
    "\tfor cosphi in [cosphi_0, -cosphi_0]:\n",
    "\t\tprod = x * (1+cosbeta) * (1+cosphi)\n",
    "\t\tcoeff = [1, prod - 2 * cosbeta * cosphi, prod + cosbeta ** 2 + cosphi ** 2 - 1]\n",
    "\t\tif coeff[1] ** 2 - 4 * coeff[0] * coeff[2] >= 0: # discriminant is nonnegative\n",
    "\t\t\tcosalphas = np.roots(coeff)\n",
    "\t\t\tfor cosalpha in filter(lambda x: np.abs(x) <= 1, cosalphas):\n",
    "\t\t\t\talpha_0_pi = np.arccos(cosalpha)\n",
    "\t\t\t\tfor alpha_base in [alpha_0_pi, 2 * np.pi - alpha_0_pi]:\n",
    "\t\t\t\t\tlower_int = int(np.ceil((lower_omega_bound * res_tau - alpha_base)/(2 * np.pi)))\n",
    "\t\t\t\t\tupper_int = int(np.floor((upper_omega_bound * res_tau - alpha_base)/(2 * np.pi)))\n",
    "\t\t\t\t\tfor n in range(lower_int, upper_int + 1):\n",
    "\t\t\t\t\t\tomega_cosphi.append(((alpha_base + 2 * np.pi * n)/res_tau, cosphi))\n",
    "\treturn omega_cosphi\n",
    "\n",
    "# for a given dip_ind, measures N data and fits it using repeated_spin_fit. If the fit error is less than error_tol,\n",
    "# it finds all possible A and B values for each fit spin. Each possible A and B value that is within the bounds (is_valid)\n",
    "# is added to spin_dict.\n",
    "def analyze_dip(dip_ind, tau, data_func, omega_larmor, spin_dict = {}, error_tol = .1/64,\n",
    "\t\t\t\tN_vals = np.arange(0,256,2), error_fun = squared_error, fit_fun = spin_fit_fun, extra_params = [],\n",
    "\t\t\t\tnum_subsets = 4, verbose = False, plots = False):\n",
    "\tres_tau = tau[dip_ind]\n",
    "\tN_data = data_func(N_vals, res_tau)\n",
    "\ttry:\n",
    "\t\tphis, xs, scaled_error = repeated_spin_fit(N_vals, N_data, error_fun = error_fun, fit_fun = fit_fun, extra_params = extra_params,\n",
    "\t\t\tnum_subsets = num_subsets, verbose = verbose, plots = plots)\n",
    "\texcept FitError:\n",
    "\t\tif verbose:\n",
    "\t\t\tprint \"Failed to fit spins with N data.\"\n",
    "\t\treturn spin_dict\n",
    "\tif verbose:\n",
    "\t\tprint \"res_tau: \", res_tau\n",
    "\t\tprint \"scaled_error <= error_tol: \", scaled_error <= error_tol\n",
    "\t\tprint \"scaled_error: \", scaled_error\n",
    "\t\tphis, xs = map(np.array, zip(*sorted(zip(phis, xs), key = lambda x:-x[1])))# sort phis in decreasing order by xs\n",
    "\t\tprint \"xs, phis, scaled_error: \", xs, phis, scaled_error\n",
    "\tif scaled_error > error_tol:\n",
    "\t\treturn spin_dict\n",
    "\tfor phi, x in zip(phis, xs):\n",
    "\t\tfor omega_tilde, cosphi in calc_omega_tilde(phi, x, res_tau, omega_larmor): # all the possible omegas with their associated cosphis\n",
    "\t\t\tA, B = calc_A_B(cosphi, res_tau, omega_larmor, omega_tilde)\n",
    "\t\t\tif valid_A_B(A, B):\n",
    "\t\t\t\tif (dip_ind, phi, x) in spin_dict:\n",
    "\t\t\t\t\tspin_dict[(dip_ind, phi, x)] += [(A, B, cosphi)]\n",
    "\t\t\t\telse:\n",
    "\t\t\t\t\tspin_dict[(dip_ind, phi, x)] = [(A, B, cosphi)]\n",
    "\treturn spin_dict\n",
    "\n",
    "# takes in a dict of the type returned by analyze_dip and for each entry which represents a particular spin found at a particular dip\n",
    "# it chooses the A, B value that minimizes the error_fun from the tau data. It returns these A and B guesses along with the calculated errors.\n",
    "def choose_spin_guesses(spin_dict, N, omega_larmor, tau, data, classifier, x_min = 1, error_fun = squared_error):\n",
    "\tguess_As, guess_Bs, dataerrs = [], [], []\n",
    "\tall_guess_As, all_guess_Bs, select_As, select_Bs = [], [], [], []\n",
    "\tfor k in spin_dict.keys():\n",
    "\t\terr = []\n",
    "\t\tfor A, B, _ in spin_dict[k]:\n",
    "\t\t\tall_guess_As.append(A)\n",
    "\t\t\tall_guess_Bs.append(B)\n",
    "\t\t\terr.append(error_fun(calc_M_single(A, B, N, omega_larmor, tau), data))\n",
    "\t\tmin_err_ind = np.argmin(err)\n",
    "\t\tbest_A, best_B, best_cosphi = spin_dict[k][min_err_ind]\n",
    "\t\tselect_As.append(best_A)\n",
    "\t\tselect_Bs.append(best_B)\n",
    "\t\tbest_err = err[min_err_ind]\n",
    "\t\t_, _, x = k\n",
    "\t\tfeatures = [best_cosphi, x, best_A, best_B, best_err]\n",
    "\t\tif classifier(features) and x >= x_min:\n",
    "\t\t\tguess_As.append(best_A)\n",
    "\t\t\tguess_Bs.append(best_B)\n",
    "\t\t\tdataerrs.append(best_err)\n",
    "\treturn (np.array(guess_As), np.array(guess_Bs), np.array(dataerrs),\n",
    "\t\tnp.array(all_guess_As), np.array(all_guess_Bs), np.array(select_As), np.array(select_Bs))\n",
    "\n",
    "# given guesses for As and Bs, clusters these guesses using the DBSCAN algorithm.\n",
    "# for info about DBSCAN see http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html\n",
    "# for info about clustering with sklearn see http://scikit-learn.org/stable/modules/clustering.html#clustering\n",
    "def cluster_spin_guesses(guess_As, guess_Bs, dataerrs, eps = .075, min_samples = 1):\n",
    "\tX = sk.preprocessing.StandardScaler().fit_transform(zip(guess_As, guess_Bs)) # possibly need to be lists?\n",
    "\tdb = sk.cluster.DBSCAN(eps = eps, min_samples = min_samples).fit(X)\n",
    "\tlabels = db.labels_\n",
    "\tcluster_As, cluster_Bs, cluster_dataerrs = [], [], []\n",
    "\tfor k in np.unique(labels):\n",
    "\t\tif k >= 0:\n",
    "\t\t\tlabel_inds = np.where(labels == k)[0]\n",
    "\t\t\tbest_guess = np.argmin(dataerrs[label_inds])\n",
    "\t\t\tcluster_As.append(guess_As[label_inds][best_guess])\n",
    "\t\t\tcluster_Bs.append(guess_Bs[label_inds][best_guess])\n",
    "\t\t\tcluster_dataerrs.append(dataerrs[label_inds][best_guess])\n",
    "\treturn np.array(cluster_As), np.array(cluster_Bs), np.array(cluster_dataerrs)\n",
    "\n",
    "# an approximation of the background due to the weakly coupled spins\n",
    "#A_background = 4 * mag * (np.random.rand(400) - .5)\n",
    "#B_background = 2 * mag * (np.random.rand(400))\n",
    "#background_dict = {\"A_background\" : A_background, \"B_background\" : B_background}\n",
    "#learning.store_obj(background_dict, \"background_A_B\")\n",
    "background_dict = learning.load_obj(\"background_A_B\")\n",
    "A_background, B_background = background_dict[\"A_background\"], background_dict[\"B_background\"]\n",
    "\n",
    "# given guess_As and guess_Bs, this function considers all ways of removing num_remove spins from the guess list\n",
    "# it compares all of these possibilities along with not taking anything out in terms of the error from this subset to the data\n",
    "# the error function considers the spins along with 400 weakly coupled spins which approximates the background.\n",
    "# it returns the optimal subset as well as a number to remove next time: the same number if a spin was removed, otherwise the number\n",
    "# considered for removal this time plus one (considering taking several out at the same time)\n",
    "def remove_spins(guess_As, guess_Bs, N, omega_larmor, tau, data, num_remove = 1, error_fun = squared_error, verbose = False):\n",
    "\t# creates all subsets of s with size between lower and upper\n",
    "\tdef subset_size_range(s, lower, upper):\n",
    "\t\tans = []\n",
    "\t\tfor i in range(lower, upper+1):\n",
    "\t\t\tfor j in itertools.combinations(s, i):\n",
    "\t\t\t\tans.append(j)\n",
    "\t\treturn ans\n",
    "\tguess_As, guess_Bs = np.array(guess_As), np.array(guess_Bs)\n",
    "\tM_background = calc_M(A_background, B_background, N, omega_larmor, tau)\n",
    "\terr, As, Bs = [], [], []\n",
    "\tfor subset in subset_size_range(range(len(guess_As)), len(guess_As) - num_remove, len(guess_As) - 1):\n",
    "\t\tsubset = np.array(subset)\n",
    "\t\terr.append(error_fun(M_background * calc_M(guess_As[subset], guess_Bs[subset], N, omega_larmor, tau), data))\n",
    "\t\tAs.append(guess_As[subset])\n",
    "\t\tBs.append(guess_Bs[subset])\n",
    "\tbest_ind = np.argmin(err)\n",
    "\torig_error = error_fun(M_background * calc_M(guess_As, guess_Bs, N, omega_larmor, tau), data)\n",
    "\tif verbose:\n",
    "\t\tprint \"new error, old error: \", err[best_ind], orig_error\n",
    "\tif orig_error > err[best_ind]:\n",
    "\t\treturn As[best_ind], Bs[best_ind], num_remove\n",
    "\telse:\n",
    "\t\treturn guess_As, guess_Bs, num_remove + 1\n",
    "\n",
    "# given a data function data_func(N, tau) that represents a diamond, returns an estimation\n",
    "# of the hyperfine couplings of many c13s. The function returns, in order\n",
    "# the final estimation, the total set of estimated parameters, the set remaining after using least squares,\n",
    "# the set remaining after applying the classifier, and then the set after applying clustering. See chapter 4 of thesis.\n",
    "def analyze_diamond(data_func, N, omega_larmor, kernel = \"rbf\", verbose = False, plots = False):\n",
    "\ttau = choose_tau_params(N)\n",
    "\tdata = data_func(N, tau)\n",
    "\tdip_inds, windows = find_resonances(data, fit_dips_below = None)\n",
    "\tlower_ind_cutoff = 3220 # corresponds to 15 microseconds\n",
    "\tif verbose:\n",
    "\t\tprint \"number of dips to measure: \", sum(dip_inds >= lower_ind_cutoff)\n",
    "\tspin_dict = {}\n",
    "\tfor dip_ind in dip_inds:\n",
    "\t\tif dip_ind >= lower_ind_cutoff: # 15 microseconds and on\n",
    "\t\t\tspin_dict = analyze_dip(dip_ind, tau, data_func, omega_larmor, spin_dict, N_vals = np.arange(0,256,2),\n",
    "\t\t\t\terror_tol = .1/64, verbose = verbose, plots = plots)\n",
    "\t# load the classifiers\n",
    "\tguess_scaler = learning.load_obj(\"classifiers/scaler_svm_\" + kernel + \"_di3220_29diamonds_cxABe\")\n",
    "\tguess_clf = learning.load_obj(\"classifiers/clf_svm_\" + kernel + \"_di3220_29diamonds_cxABe\")\n",
    "\tdef guess_classifier(features):\n",
    "\t\treturn guess_clf.predict(guess_scaler.transform([features]))\n",
    "\tguess_As, guess_Bs, dataerrs, all_guess_As, all_guess_Bs, select_As, select_Bs = choose_spin_guesses(spin_dict, N, omega_larmor, tau, data, guess_classifier, x_min = 1, error_fun = squared_error)\n",
    "\tcluster_As, cluster_Bs, cluster_dataerrs = cluster_spin_guesses(guess_As, guess_Bs, dataerrs, eps = .075, min_samples = 1)\n",
    "\tAs, Bs, num_remove = cluster_As, cluster_Bs, 1\n",
    "\twhile num_remove <= 2:\n",
    "\t\tAs, Bs, num_remove = remove_spins(As, Bs, N, omega_larmor, tau, data, num_remove = num_remove, error_fun = squared_error, verbose=verbose)\n",
    "\treturn As, Bs, all_guess_As, all_guess_Bs, select_As, select_Bs, guess_As, guess_Bs, cluster_As, cluster_Bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %load learning.py\n",
    "# NV-spin-tomography Michael Scheer mgscheer@gmail.com\n",
    "\n",
    "# Creating datasets and training classifiers, storing objects on disk\n",
    "\n",
    "import numpy as np\n",
    "import sklearn as sk\n",
    "from sklearn import svm, preprocessing, cross_validation, cluster, metrics\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.externals import joblib\n",
    "import analysis\n",
    "import NV_generator\n",
    "import pickle\n",
    "\n",
    "# store an object in a file\n",
    "def store_obj(obj, fname):\n",
    "\tjoblib.dump(obj, fname + \".pkl\")\n",
    "\n",
    "def store_obj_2(obj, fname):\n",
    "\tpickle.dump(obj, open(fname + \".pkl\",'wb'))\n",
    "\n",
    "# load an object from a file\n",
    "def load_obj(fname):\n",
    "\treturn joblib.load(fname + \".pkl\")\n",
    "\n",
    "def load_obj_2(fname):\n",
    "\treturn pickle.load(open(fname + \".pkl\",'rb'))\n",
    "\n",
    "# decides if a fitted A and B are close enough to a spin to be counted as a fit.\n",
    "# the B tends to be harder to fit so the accuracy demanded is less.\n",
    "def is_fit(fitted_A, fitted_B, spin, A_acc = .1, B_acc = .5):\n",
    "\tA_acc = analysis.mag * A_acc\n",
    "\tB_acc= analysis.mag * B_acc\n",
    "\treturn ((spin[0] >= fitted_A - A_acc) and (spin[0] <= fitted_A + A_acc) and\n",
    "\t\t(spin[1] >= fitted_B - B_acc) and (spin[1] <= fitted_B + B_acc))\n",
    "\n",
    "# creates a dataset of diamonds with N measurements already analyzed, saves it to disk\n",
    "def create_diamonds(diamond_num_list, omega_larmor, num_spins = 450):\n",
    "\tverbose, plots = False, False\n",
    "\tnum_subsets = 4\n",
    "\terror_fun = analysis.squared_error\n",
    "\terror_tol = .1/64\n",
    "\tN = 64\n",
    "\tN_vals = np.arange(0, 256, 2)\n",
    "\ttau = analysis.choose_tau_params(N)\n",
    "\tfor diamond_num in diamond_num_list:\n",
    "\t\tprint \"diamond_num: \", diamond_num\n",
    "\t\tA, B, r, costheta, sintheta = NV_generator.generate_spins(num_spins)\n",
    "\t\tdef data_func(N, tau, noise_level = .02):\n",
    "\t\t\tdata = analysis.calc_M(A, B, N, omega_larmor, tau)\n",
    "\t\t\tnoise = np.random.randn(len(data)) * noise_level\n",
    "\t\t\treturn data + noise\n",
    "\t\tdata = data_func(N, tau)\n",
    "\t\tdip_inds, windows = analysis.find_resonances(data, fit_dips_below = None)\n",
    "\t\tsuccessful_fits, good_fits, phis_list, xs_list, scaled_errors = [], [], [], [], []\n",
    "\t\tprint \"number of dips: \", len(dip_inds)\n",
    "\t\tfor dii in range(len(dip_inds)):\n",
    "\t\t\tif dii%50==0:\n",
    "\t\t\t\tprint \"dii: \", dii\n",
    "\t\t\tdip_ind = dip_inds[dii]\n",
    "\t\t\tres_tau = tau[dip_ind]\n",
    "\t\t\tN_data = data_func(N_vals, res_tau)\n",
    "\t\t\tsuccessful_fit, good_fit = True, True # did the fit succeed without throwing an error? was the error in the fit small enough?\n",
    "\t\t\ttry:\n",
    "\t\t\t\tphis, xs, scaled_error = analysis.repeated_spin_fit(N_vals, N_data, error_tol = error_tol, error_fun = error_fun, num_subsets = num_subsets, verbose = verbose, plots = plots)\n",
    "\t\t\t\tgood_fit = scaled_error <= error_tol\n",
    "\t\t\texcept analysis.FitError:\n",
    "\t\t\t\tsuccessful_fit, good_fit = False, False\n",
    "\t\t\t\tphis, xs = None, None\n",
    "\t\t\t\tscaled_error = None\n",
    "\t\t\tsuccessful_fits.append(successful_fit)\n",
    "\t\t\tgood_fits.append(good_fit)\n",
    "\t\t\tphis_list.append(phis)\n",
    "\t\t\txs_list.append(xs)\n",
    "\t\t\tscaled_errors.append(scaled_errors)\n",
    "\t\tprint \"create diamond_dict\"\n",
    "\t\tdiamond_dict = {\"A\" : A, \"B\" : B, \"r\" : r, \"costheta\" : costheta, \"sintheta\" : sintheta, \"N\" : N,\n",
    "\t\t\t\"tau\" : tau, \"data\" : data, \"N_vals\" : N_vals, \"N_data\" : N_data, \"dip_inds\" : dip_inds, \"windows\" : windows,\n",
    "\t\t\t\"successful_fits\" : successful_fits, \"good_fits\" : good_fits, \"error_tol\" : error_tol, \"num_subsets\" : num_subsets,\n",
    "\t\t\t\"phis_list\" : phis_list, \"xs_list\" : xs_list, \"scaled_errors\" : scaled_errors}\n",
    "\t\tprint \"store diamond_dict\"\n",
    "\t\tstore_obj_2(diamond_dict, \"diamonds/diamond_\" + str(diamond_num))\n",
    "\n",
    "# train and optionally save a support vector machine classifier\n",
    "# scaler preprocesses the data (scales it) and clf is the classifier\n",
    "def train_svm(train_X, train_Y, test_X, test_Y, kernel = 'rbf', C=1.0, verbose = False, pickle = False, suffix = \"\"):\n",
    "\tscaler = sk.preprocessing.StandardScaler().fit(train_X)\n",
    "\tclf = sk.svm.SVC(kernel = kernel, C=C)# svm with rbf kernel (default)\n",
    "\tclf.fit(scaler.transform(train_X), train_Y)\n",
    "\tif verbose:\n",
    "\t\tprint \"training score: \", clf.score(scaler.transform(train_X), train_Y)\n",
    "\t\tprint \"testing score: \", clf.score(scaler.transform(test_X), test_Y)\n",
    "\tif pickle:\n",
    "\t\tstore_obj(scaler, \"classifiers/scaler_svm_\" + kernel + suffix)\n",
    "\t\tstore_obj(clf, \"classifiers/clf_svm_\" + kernel + suffix)\n",
    "\treturn scaler, clf\n",
    "\n",
    "# train and optionally save an extra-trees classifier\n",
    "# scaler preprocesses the data (scales it) and clf is the classifier\n",
    "def train_trees(train_X, train_Y, test_X, test_Y, verbose = False, pickle = False, suffix = \"\"):\n",
    "\tscaler = sk.preprocessing.StandardScaler().fit(train_X)\n",
    "\tclf = sk.ensemble.ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=1, random_state=0)\n",
    "\tclf.fit(scaler.transform(train_X), train_Y)\n",
    "\tif verbose:\n",
    "\t\tprint \"training score: \", clf.score(scaler.transform(train_X), train_Y)\n",
    "\t\tprint \"testing score: \", clf.score(scaler.transform(test_X), test_Y)\n",
    "\tif pickle:\n",
    "\t\tstore_obj(scaler, \"classifiers/scaler_trees\" + suffix)\n",
    "\t\tstore_obj(clf, \"classifiers/clf_trees\" + suffix)\n",
    "\treturn scaler, clf\n",
    "\n",
    "# creates and optionally saves the dataset used to train the SVM which classifies guesses.\n",
    "def guess_dataset(diamonds, omega_larmor, min_dip_ind = 3220, training_percent = .75, pickle = False, suffix = \"\"):\n",
    "\ttrain_X = [] # feature vectors\n",
    "\ttrain_Y = [] # classifications\n",
    "\ttest_X = []\n",
    "\ttest_Y = []\n",
    "\terror_fun = analysis.squared_error\n",
    "\tfor diamond_ind in range(len(diamonds)):\n",
    "\t\tprint \"diamond_ind: \", diamond_ind\n",
    "\t\tdiamond = diamonds[diamond_ind]\n",
    "\t\ttau = diamond[\"tau\"]\n",
    "\t\tdata = diamond[\"data\"]\n",
    "\t\tN = diamond[\"N\"]\n",
    "\t\tdip_inds = diamond[\"dip_inds\"] # the indices for the dips\n",
    "\t\twindows = diamond[\"windows\"] # where was this used?\n",
    "\t\tspin_dict = {} # The keys to the dictionary will be a tuple (dip_ind, phi, x)\n",
    "\t\tfor dii in range(len(dip_inds)):\n",
    "\t\t\tif diamond[\"good_fits\"][dii] and dip_inds[dii] >= min_dip_ind:\n",
    "\t\t\t\tdip_ind = dip_inds[dii]\n",
    "\t\t\t\tres_tau = tau[dip_ind]\n",
    "\t\t\t\tfor phi, x in zip(diamond[\"phis_list\"][dii], diamond[\"xs_list\"][dii]):\n",
    "\t\t\t\t\tfor omega_tilde, cosphi in analysis.calc_omega_tilde(phi, x, res_tau, omega_larmor):\n",
    "\t\t\t\t\t\tA, B = analysis.calc_A_B(cosphi, res_tau, omega_larmor, omega_tilde)\n",
    "\t\t\t\t\t\tif analysis.valid_A_B(A, B): # this checks for the upper bound imposed on (A,B)\n",
    "\t\t\t\t\t\t\tif (dip_ind, phi, x) in spin_dict: # if (dip_ind,phi,x) is already a key in the dictionary then add another value to the list that corresponds to it\n",
    "\t\t\t\t\t\t\t\tspin_dict[(dip_ind, phi, x)] += [(A, B, cosphi)]\n",
    "\t\t\t\t\t\t\telse: # if it is not, then simply initialize the list with the corresponding tuple\n",
    "\t\t\t\t\t\t\t\tspin_dict[(dip_ind, phi, x)] = [(A, B, cosphi)]\n",
    "\t\tfor k in spin_dict.keys(): #now iterate through they dictionary's keys\n",
    "\t\t\terr = []\n",
    "\t\t\tfor A, B, _ in spin_dict[k]:\n",
    "\t\t\t\terr.append(error_fun(analysis.calc_M_single(A, B, N, omega_larmor, tau), data)) # calculate the errors from assuming data only comes from a single spin\n",
    "\t\t\tmin_err_ind = np.argmin(err)\n",
    "\t\t\tbest_A, best_B, best_cosphi = spin_dict[k][min_err_ind]\n",
    "\t\t\tbest_err = err[min_err_ind]\n",
    "\t\t\t_, _, x = k \n",
    "\t\t\tfeatures = [best_cosphi, x, best_A, best_B, best_err] # this is the feature vector\n",
    "\t\t\ttag = 0\n",
    "\t\t\tfor spin in set(zip(diamond[\"A\"], np.abs(diamond[\"B\"]))): #iterate through the true spins in the diamond\n",
    "\t\t\t\tif is_fit(best_A, best_B, spin, A_acc = .5, B_acc = 2): # arbitrary thresholds over here\n",
    "\t\t\t\t\ttag = 1 # best_A and best_B have been found to correspond to a one of the true spins\n",
    "\t\t\t\t\tbreak\n",
    "\t\t\tif diamond_ind < training_percent * len(diamonds): # first diamonds for the training set\n",
    "\t\t\t\ttrain_X.append(features)\n",
    "\t\t\t\ttrain_Y.append(tag)\n",
    "\t\t\telse: # the other ones for the test set\n",
    "\t\t\t\ttest_X.append(features)\n",
    "\t\t\t\ttest_Y.append(tag)\n",
    "\tif pickle:\n",
    "\t\tprint \"create dataset\"\n",
    "\t\tdataset = {\"train_X\" : train_X, \"train_Y\" : train_Y, \"test_X\" : test_X, \"test_Y\" : test_Y}\n",
    "\t\tprint \"store dataset\"\n",
    "\t\tstore_obj(dataset, \"datasets/guess_dataset\" + suffix)\n",
    "\treturn train_X, train_Y, test_X, test_Y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %load NV_generator.py\n",
    "# NV-spin-tomography Michael Scheer mgscheer@gmail.com\n",
    "\n",
    "# generating random NV centers\n",
    "\n",
    "import numpy as np\n",
    "import itertools\n",
    "\n",
    "# For explanation of the formulas used here, see chapter 2 of thesis.\n",
    "\n",
    "# Constants\n",
    "# lattice constant of diamond\n",
    "a0 = 3.567 * 10 ** (-10) # in meters\n",
    "# Hyperfine coupling related constants\n",
    "gam_el = -1.760859 * 10 ** 11 # Gyromagnetic ratio for a single electron in rad s-1 T-1\n",
    "gam_n = 67.262 * 10 ** 6 # Gyromagnetic ratio for a single c13 nucleus in rad s-1 T-1\n",
    "hbar = 1.05457173 * 10 ** (-34)\n",
    "pi = np.pi\n",
    "h = hbar * 2 * pi\n",
    "mu0 = 4 * pi * 10 ** (-7)\n",
    "\n",
    "# Carbon Lattice Definition\n",
    "# Basis vectors\n",
    "# i, j, k are basis vectors of Face Centered Cubic Bravais lattice\n",
    "i = a0/2*np.array([0,1,1])\n",
    "j = a0/2*np.array([1,0,1])\n",
    "k = a0/2*np.array([1,1,0])\n",
    "# a and b are the relative positions of the nitrogen and vacancy\n",
    "a = np.array([0,0,0])\n",
    "b = a0/4*np.array([1,1,1])\n",
    "# rotate all the vectors so that the symmetry axis (b) of the NV center is along the z direction\n",
    "# rotation around z axis by pi/4 puts b in yz plane\n",
    "Rz = np.array([[np.cos(pi/4),-np.sin(pi/4),0],[np.sin(pi/4),np.cos(pi/4),0],[0,0,1]])\n",
    "sqrt2angle = np.arctan(np.sqrt(2))\n",
    "# rotation around x axis by sqrt2angle moves b from yz plane to z axis\n",
    "Rx = np.array([[1,0,0],[0,np.cos(sqrt2angle),-np.sin(sqrt2angle)],[0,np.sin(sqrt2angle),np.cos(sqrt2angle)]])\n",
    "b = Rx.dot(Rz).dot(b)\n",
    "i = Rx.dot(Rz).dot(i)\n",
    "j = Rx.dot(Rz).dot(j)\n",
    "k = Rx.dot(Rz).dot(k)\n",
    "\n",
    "hyperfine_prefactor = -1 * mu0 * gam_el * gam_n * hbar/(4 * pi) # Only one hbar instead of hbar ** 2 since we convert to angular frequency units in the hamiltonian.\n",
    "\n",
    "# Takes Carbon Concentration, and gridsize (N) as inputs\n",
    "# Returns a list of 2D numpy arrays with hyperfine couplings of c13 nuclei to the NV center in angular frequency Hz\n",
    "# If sphere is true the c13 nuclei inside a sphere are returned instead of those inside a cube.\t\n",
    "def generate_NV(c13_concentration=0.011,N=25,sphere = True):\n",
    "\t# define position of NV in middle of the grid\n",
    "\tcenter = round(N/2)\n",
    "\tNVPos = center * i + center * j + center * k\n",
    "\n",
    "\tspin_list = []\n",
    "\t#Calculate hyperfine strength for all gridpoints\n",
    "\tfor n, m, l in itertools.product(range(N), repeat = 3):\n",
    "\t\tif (n,m,l) != (center, center, center): # all lattice pairs except the nitrogen and vacancy\n",
    "\t\t\tlattice_position = n * i + m * j + l * k - NVPos # relative position of a lattice point to the NV center\n",
    "\t\t\tfor pos in [lattice_position, lattice_position + b]: # the two lattice positions at this grid location\n",
    "\t\t\t\tx, y, z = pos\n",
    "\t\t\t\tr = np.sqrt(pos.dot(pos))\n",
    "\t\t\t\tcostheta = z/r\n",
    "\t\t\t\tsintheta = np.sqrt(x ** 2 + y ** 2)/r\n",
    "\t\t\t\tA = hyperfine_prefactor * (3 * costheta ** 2 - 1)/(r ** 3)\n",
    "\t\t\t\tB = hyperfine_prefactor * 3 * costheta * sintheta /(r ** 3)\n",
    "\t\t\t\tspin_list.append((r, A, B, costheta, sintheta))\n",
    "\n",
    "\tif sphere:\n",
    "\t\tspin_list.sort() # sort by r value from smallest to largest\n",
    "\t\tspin_list = spin_list[:len(spin_list)/2] # keep the closest half of the spins\n",
    "\n",
    "\t# for each lattice point, let it be a c13 with probability c13_concentration \n",
    "\tc13_inds = np.where(np.random.rand(len(spin_list))<c13_concentration)[0]\n",
    "\tc13_spins = np.array(spin_list)[c13_inds]\n",
    "\tr, A, B, costheta, sintheta = zip(*c13_spins)\n",
    "\treturn np.array(A), np.array(B), np.array(r), np.array(costheta), np.array(sintheta)\n",
    "\n",
    "# Generates C13s near a NV center. The number of C13s is roughly num_spins if N==None.\n",
    "# If enforce_bound = True, the random generation will repeat until there are no couplings greater than coupling_bound.\n",
    "def generate_spins(num_spins, enforce_bound = True, coupling_bound = 150 * 2 * pi * 1e3, c13_concentration = 0.011, N = None, abs_val = False, verbose = False):\n",
    "\tif N == None:\n",
    "\t\tN = int(np.ceil(((num_spins*1.0)/c13_concentration) ** (1.0/3)))\n",
    "\tif verbose:\n",
    "\t\tprint \"N:\", N\n",
    "\tA, B, r, costheta, sintheta = generate_NV(N = N, c13_concentration = c13_concentration)\n",
    "\twhile enforce_bound and (np.any(A ** 2 + B ** 2 > (coupling_bound) ** 2)):\n",
    "\t\tA, B, r, costheta, sintheta = generate_NV(N = N, c13_concentration = c13_concentration)\n",
    "\tif abs_val:\n",
    "\t\tB = np.abs(B)\n",
    "\treturn A, B, r, costheta, sintheta\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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