analysis_ellipsoids_kappa_9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal components analysis for order parameter construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from analysis_tools.read_gsd import ReadGSD\n",
    "from analysis_tools.feature_creation import FrameToFeatures, TrajectoryToFeatures, TrajectoryToFeaturesBatch, TrajectoryToFeaturesBatchR\n",
    "from analysis_tools.feature_creation import FrameToFeaturesComposition, TrajectoryToFeaturesComposition\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import array, arange, floor, floor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hard disks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Kappa=9\n",
    "particle_inc = 104 #skip this many particles in generating features; #wp: because it selects index 0 be default, rounds up to integer if not a mulptiple of Ntot\n",
    "N_nn = 3120 #number of nearest nighbors for pca analysis #wp:Should not be bigger than Ntotal\n",
    "nn_inc = 10 #reduces the number of nearest neighbors to include as features\n",
    "split = 2 #chunks the data up so it can be processed by the pca tool if really large\n",
    "N_batch=1\n",
    "frame_skip=4\n",
    "\n",
    "#method = ['distance']\n",
    "method = ['orientation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#specify what data to read in and process\n",
    "#wp:there are 51 eta points given range below\n",
    "folder='ellipsoids/kappa_9.0_3N'; \n",
    "#file_data = [(arange(0.3100, 0.9000, 0.01), folder)]\n",
    "file_data = [(arange(0.3100, 0.4300, 0.01), folder),\n",
    "             (arange(0.4350, 0.4650, 0.005), folder),\n",
    "             (arange(0.4700, 0.9001, 0.01), folder)            \n",
    "            ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Density range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wp: the `_' variable is a dummy return variable like `~' in matlab\n",
    "#wp: Here, the for loop returns two arguments but only keeps the first that contains all the numbers\n",
    "etas = []\n",
    "[etas.extend(etas_) for etas_, _ in file_data]\n",
    "etas = array(etas)\n",
    "#print(etas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perfom PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import array_split\n",
    "from sklearn.decomposition import IncrementalPCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train on ideal gas structure first to identify trivial variance contributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#n_components_ig = N_nn/nn_inc-1  #for this the more the better, however, 50 is probably enough\n",
    "n_components_ig = N_batch*N_nn/nn_inc  #for this the more the better, however, 50 is probably enough\n",
    "#n_components_ig = N_nn/nn_inc*(N_batch+2) #for this the more the better, however, 50 is probably enough\n",
    "#print n_components_ig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from analysis_tools.feature_creation import FrameToFeatures, TrajectoryToFeatures\n",
    "\n",
    "###wp: Ideal gas pca structure\n",
    "incpca_ig = IncrementalPCA(n_components=n_components_ig, whiten=True)  #NOTICE: whitening is on here\n",
    "\n",
    "frames = None\n",
    "features = None\n",
    "split_features = None\n",
    "#split=1\n",
    "\n",
    "#features_partial_ig=[];\n",
    "\n",
    "for etas_, file_base in file_data:\n",
    "    for eta in etas_:\n",
    "        print 'eta={}'.format(eta)\n",
    "        filename = \"{}/trajectory_{:.4f}.gsd\".format(file_base, eta)\n",
    "        frames = ReadGSD(filename, shuffle_data=True, randomize=True)        \n",
    "        #print frames[0]['orientation'][0:5]\n",
    "        \n",
    "        frames_split = array_split(frames, split)\n",
    "        \n",
    "        #frames = None\n",
    "        count = 0\n",
    "        \n",
    "        for frames_partial in frames_split:\n",
    "        #for frames_partial in frames_split[0:-1:2]:\n",
    "            #features_partial = TrajectoryToFeatures(frames_partial, N_nn=N_nn, method=method, \n",
    "            #                                        particle_inc=particle_inc, nn_inc=nn_inc)\n",
    "            #wp: Rather than using all frames by default, allow setting to skip some\n",
    "            \n",
    "            #if eta > 0.8200:\n",
    "            #    N_nni=N_nn2; particle_inci=particle_inc2; nn_inci=nn_inc2;\n",
    "            #else:\n",
    "            N_nni=N_nn; particle_inci=particle_inc; nn_inci=nn_inc;\n",
    "                \n",
    "            frames_partial=frames_partial[::frame_skip]\n",
    "        \n",
    "            features_partial = TrajectoryToFeaturesBatch(frames_partial, N_nn=N_nni, method=method, \n",
    "                                                         particle_inc=particle_inci, nn_inc=nn_inci,N_batch=N_batch)\n",
    "            \n",
    "            #features_partial = TrajectoryToFeaturesBatchR(frames_partial, N_nn=N_nni, method=method, \n",
    "             #                                            particle_inc=particle_inci, nn_inc=nn_inci,N_batch=N_batch)\n",
    "\n",
    "\n",
    "            \n",
    "            print features_partial.shape\n",
    "            incpca_ig.partial_fit(features_partial)\n",
    "            count = count + 1\n",
    "            print count\n",
    "            \n",
    "        clear_output()\n",
    "        frames_split = None\n",
    "        #features_partial_ig.append(features_partial)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train on actual data with trivial data variance removed by the ideal gas model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#n_components = N_nn/nn_inc-1 #this number will not affect the quality of the result just the computation time and amount of info returned\n",
    "n_components = N_batch*N_nn/nn_inc  #for this the more the better, however, 50 is probably enough\n",
    "#n_components = 2*N_nn/nn_inc  #for this the more the better, however, 50 is probably enough\n",
    "#n_components = N_nn/nn_inc*(N_batch+2) #for this the more the better, however, 50 is probably enough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "incpca = IncrementalPCA(n_components=n_components , whiten=False)  #NOTE: not whitening here\n",
    "\n",
    "frames = None\n",
    "features = None\n",
    "split_features = None\n",
    "#split=1\n",
    "features_partial_ellipse=[]\n",
    "\n",
    "for etas_, file_base in file_data:\n",
    "    for eta in etas_:\n",
    "        print 'eta={}'.format(eta)\n",
    "        filename = \"{}/trajectory_{:.4f}.gsd\".format(file_base, eta)\n",
    "        frames = ReadGSD(filename, shuffle_data=True, randomize=False)\n",
    "        \n",
    "        frames_split = array_split(frames, split)\n",
    "        frames = None\n",
    "        count = 0\n",
    "        for frames_partial in frames_split:\n",
    "            #features_partial = TrajectoryToFeatures(frames_partial, \n",
    "            #                                        N_nn=N_nn, \n",
    "            #                                        method=method, \n",
    "            #                                        particle_inc=particle_inc, \n",
    "            #                                        nn_inc=nn_inc)\n",
    "            \n",
    "\n",
    "            #if eta > 0.8200:\n",
    "            #    N_nni=N_nn2; particle_inci=particle_inc2; nn_inci=nn_inc2;\n",
    "            #else:\n",
    "            N_nni=N_nn; particle_inci=particle_inc; nn_inci=nn_inc;\n",
    "                \n",
    "            frames_partial=frames_partial[::frame_skip]\n",
    "               \n",
    "            features_partial = TrajectoryToFeaturesBatch(frames_partial, N_nn=N_nni, method=method, \n",
    "                                                         particle_inc=particle_inci, nn_inc=nn_inci,N_batch=N_batch)\n",
    "            \n",
    "            #wp:project *hard disk* features onto ig eigenvectors\n",
    "            incpca.partial_fit(incpca_ig.transform(features_partial))\n",
    "            #count = count + 1\n",
    "            #print count\n",
    "        clear_output()\n",
    "        frames_split = None\n",
    "        #features_partial_ellipse.append(features_partial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### System train without ideal gas correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "########wp: System fit *without ideal gas*\n",
    "incpca = IncrementalPCA(n_components=n_components , whiten=False)\n",
    "\n",
    "frames = None\n",
    "features = None\n",
    "split_features = None\n",
    "\n",
    "split=1\n",
    "for etas_, file_base in file_data:\n",
    "    for eta in etas_:\n",
    "        print 'eta={}'.format(eta)\n",
    "        filename = \"{}/trajectory_{:.4f}.gsd\".format(file_base, eta)\n",
    "        frames = ReadGSD(filename, shuffle_data=True, randomize=False)\n",
    "        \n",
    "        frames_split = array_split(frames, split)\n",
    "        frames = None\n",
    "        count = 0\n",
    "        for frames_partial in frames_split:\n",
    "            #features_partial = TrajectoryToFeatures(frames_partial, \n",
    "            #                                        N_nn=N_nn, \n",
    "            #                                        method=method, \n",
    "            #                                        particle_inc=particle_inc, \n",
    "            #                                        nn_inc=nn_inc)\n",
    "            \n",
    "            frames_partial=frames_partial[::frame_skip]\n",
    "            features_partial = TrajectoryToFeaturesBatchR(frames_partial, \n",
    "                                                    N_nn=N_nn, \n",
    "                                                   method=method, \n",
    "                                                    particle_inc=particle_inc, \n",
    "                                                    nn_inc=nn_inc,N_batch=N_batch)\n",
    "            \n",
    "            #wp: fits pure system features (no idea gas reduction)\n",
    "            incpca.partial_fit(features_partial)\n",
    "            count = count + 1\n",
    "            print \"Frame split: \", count\n",
    "        #wp:resets frame split\n",
    "        clear_output()\n",
    "        frames_split = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving trained models to files and reloading "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saves trained model to avoid retraining every time as well as memory issues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['trained_models/kappa_9.0.pca']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "folder_model='trained_models'\n",
    "model_ig=folder_model+'/kappa_9.0_ig.pca'; model=folder_model+'/kappa_9.0.pca';\n",
    "#wp: Saves model as file\n",
    "#ideal gas trained model\n",
    "joblib.dump(incpca_ig,model_ig)\n",
    "#full trained model\n",
    "joblib.dump(incpca,model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loads saved models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib\n",
    "#paths to where models are located\n",
    "folder_model='trained_models'\n",
    "model_ig=folder_model+'/kappa_9.0_ig.pca'; \n",
    "model=folder_model+'/kappa_9.0.pca';\n",
    "\n",
    "incpca_ig=joblib.load(model_ig);\n",
    "incpca=joblib.load(model);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting of models and analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute the order parameters based on two nested models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = None\n",
    "features = None\n",
    "raw_OPs = []\n",
    "#split=1\n",
    "\n",
    "for etas_, file_base in file_data:\n",
    "    for eta in etas_:\n",
    "        print 'eta={}'.format(eta)\n",
    "        filename = \"{}/trajectory_{:.4f}.gsd\".format(file_base, eta)\n",
    "        #wp: randomize undefined here?\n",
    "        frames = ReadGSD(filename, shuffle_data=True, randomize=False)\n",
    "        \n",
    "        frames_split = array_split(frames, split)\n",
    "        frames = None\n",
    "        count = 0\n",
    "        means = []\n",
    "        raw_OPs_single = []\n",
    "        for frames_partial in frames_split:\n",
    "            #features_partial = TrajectoryToFeatures(frames_partial, \n",
    "            #                                        N_nn=N_nn, \n",
    "            #                                        method=method, \n",
    "            #                                        particle_inc=particle_inc,            \n",
    "            #                                       nn_inc=nn_inc)\n",
    "            \n",
    "            N_nni=N_nn; particle_inci=particle_inc; nn_inci=nn_inc;\n",
    "                \n",
    "            frames_partial=frames_partial[::frame_skip]\n",
    "            #frames_partial=frames_partial[0:50]\n",
    "        \n",
    "            features_partial = TrajectoryToFeaturesBatch(frames_partial, N_nn=N_nni, method=method, \n",
    "                                                         particle_inc=particle_inci, nn_inc=nn_inci,N_batch=N_batch)\n",
    "            \n",
    "            #wp: This returns the 'Z=X*U' matrix with mxn dimensions, where m=samples and n=feature vectors\n",
    "            transformed_features_partial = incpca.transform(incpca_ig.transform(features_partial))\n",
    "            #transformed_features_partial = incpca_ig.transform(features_partial)\n",
    "            #wp:No ideal gas correction\n",
    "            #transformed_features_partial = incpca.transform(features_partial)\n",
    "            raw_OPs_single.append(transformed_features_partial)\n",
    "            #count = count + 1\n",
    "            #print count\n",
    "        clear_output()\n",
    "        raw_OPs.append(raw_OPs_single)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute order parameter stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import array, mean, std, abs, asarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "OPs = []\n",
    "OPs_std = []\n",
    "OPs_abs = []\n",
    "\n",
    "for single_eta in raw_OPs:\n",
    "    merged_data = []\n",
    "    \n",
    "    #wp: here single_eta is just the feature vector of samples; the for loop below extracts it as array 'split'\n",
    "    for split_eta in single_eta:\n",
    "        merged_data.extend(split_eta)\n",
    "\n",
    "    #wp: averages over all samples m\n",
    "    OPs.append(mean((split_eta), axis=0))\n",
    "    OPs_std.append(std(split_eta, axis=0))\n",
    "    OPs_abs.append(mean(abs(split_eta), axis=0))\n",
    "\n",
    "OPs = array(OPs)\n",
    "OPs_std = array(OPs_std)\n",
    "OPs_abs = array(OPs_abs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The feature matrix input for each eta is of dimensions 15000 312\n",
      "Number of rows:  63\n",
      "Array size for each eta:  (15000, 312)\n",
      "[-1.47287654e+01 -1.31125787e+00  2.78582815e-03 -1.34386247e+00\n",
      "  2.16875182e-01  1.67629723e+00  6.49059893e-01 -2.08129230e+00\n",
      "  2.02065722e+00 -3.35130799e-01  5.29325068e-01 -1.69646773e-01\n",
      "  1.35520610e-01  6.89021354e-01 -7.40861877e-03  1.50262997e+00\n",
      " -1.05664024e+00 -1.75329549e+00  2.20237448e-01 -1.23051116e+00\n",
      " -1.06423212e+00  1.58033450e+00  5.56026186e-01 -1.73666184e-01\n",
      "  1.16371352e+00 -1.78719437e-01 -5.70496843e-01  6.05475090e-01\n",
      "  1.73556935e-01  1.52209752e-01 -1.21442931e+00  7.05307500e-01\n",
      "  1.75894276e+00  1.98821961e+00  3.56085503e-01  1.15452109e+00\n",
      "  1.28047813e+00  1.28295950e+00 -2.07658402e+00 -5.42048995e-01\n",
      " -2.18863502e+00 -1.36472943e+00  1.21965523e+00 -1.83505309e+00\n",
      " -4.70673863e-01  9.72524464e-01 -8.36309711e-02 -2.12231224e-01\n",
      " -2.13209358e-01  3.89823130e-01  1.91076942e+00 -3.71174900e-01\n",
      " -9.32576108e-01  4.82509163e-01  2.50286937e-01  7.65097973e-01\n",
      " -1.14849338e+00 -1.72292397e+00  9.28800493e-01 -1.44833871e+00\n",
      " -1.87777713e+00 -6.62015610e-01  1.03233251e+00  1.33559297e+00\n",
      "  6.81563730e-01  7.09982295e-01 -3.70108232e-01  1.88848908e-03\n",
      " -9.87379838e-02  6.39680677e-02 -1.77430051e-01 -5.85150472e-01\n",
      "  1.02064483e+00 -1.57055014e+00 -1.09037774e+00 -1.54066532e+00\n",
      "  4.78495457e-01 -1.07005170e+00 -1.26388436e+00 -9.89507238e-01\n",
      " -6.28808015e-01  7.57937387e-02  1.38719028e+00 -1.08383707e+00\n",
      " -9.00443910e-01  1.62681624e+00  2.18490967e+00  3.59361901e-01\n",
      "  1.67026263e+00 -1.99125959e-01 -2.96977875e-01  7.89026991e-01\n",
      " -1.82391808e+00 -1.07355647e+00 -2.19576949e-01 -1.73328086e+00\n",
      "  1.53559715e+00 -7.02694812e-01  7.12478849e-01  1.20384523e+00\n",
      "  3.01521680e-02 -1.64953086e+00  3.80658330e-01  7.61333054e-01\n",
      " -2.74879020e-01  1.22208774e+00 -3.00259078e-01  8.19075465e-01\n",
      " -4.34380305e-01 -8.67690832e-02  4.10633338e-01  6.81231994e-01\n",
      " -6.11444914e-01  1.84204900e+00 -1.77686073e+00  2.47232319e-03\n",
      "  2.30341760e+00 -2.26772162e-01 -8.73820424e-01  1.23282460e+00\n",
      "  1.96326375e+00 -1.29265015e+00 -1.59871559e+00 -1.23690067e+00\n",
      "  1.24145072e+00 -1.06310458e+00 -4.04760087e-01  5.71056263e-02\n",
      "  2.43330725e-01 -4.48220614e-01 -1.20693374e+00 -2.35230067e-01\n",
      " -2.03371017e-01  2.05153434e-01 -7.20008133e-01 -8.42405583e-01\n",
      "  3.83033629e-01  9.74085836e-01 -3.66955029e-01 -1.04513344e+00\n",
      "  2.28437550e-01  5.42104282e-01 -1.24670602e+00 -9.05892192e-01\n",
      "  4.74578757e-01  2.16500724e-01 -2.48365193e-01  4.52952869e-01\n",
      "  3.31679473e-01 -9.97873853e-01  1.68994495e-01  1.01239454e+00\n",
      "  9.08758676e-01  9.23383962e-01  8.58225548e-01  5.03835102e-01\n",
      "  3.46953113e-01 -3.62811565e-01 -1.04601456e+00 -1.79953328e+00\n",
      "  2.44194467e-01 -1.42295888e-01 -5.40663298e-01  2.97405118e-01\n",
      " -1.75081321e+00  6.09805749e-01 -1.55743334e-01 -2.11590392e-01\n",
      " -1.22459479e+00  1.18828483e+00  2.16340264e+00  2.10183288e+00\n",
      " -4.64970243e-01 -1.62076705e+00 -3.03617215e-01 -3.16304478e+00\n",
      " -4.87720374e-02 -2.30109531e-01 -1.33879637e-01 -1.17015666e+00\n",
      " -1.80445909e+00  1.08169208e-01  9.30487314e-01  8.39111907e-01\n",
      " -1.53723177e+00  9.48127609e-01  1.35942362e+00  6.87724729e-01\n",
      "  1.87774596e+00 -2.20142196e-01 -4.80139076e-01  6.10135796e-01\n",
      " -8.75995202e-01  7.23474260e-01 -5.84814984e-02 -1.94735489e+00\n",
      " -1.13078712e+00 -1.12811478e+00  1.20558868e+00  2.65225144e-01\n",
      " -1.72570542e+00 -5.51685131e-02 -3.73849891e-01  3.62427986e-01\n",
      " -5.19940580e-01  4.90875825e-01 -1.31367219e+00 -4.30338413e-01\n",
      " -1.57989885e+00  5.23750769e-01 -9.75231780e-01  2.81590755e-01\n",
      " -8.14544596e-01  8.83712156e-01  2.07667706e-01  3.36358390e-02\n",
      " -5.49533217e-01  3.29933776e-02 -1.92259515e+00 -9.50157618e-01\n",
      " -3.48889290e-01  1.50745808e+00 -1.70350795e+00 -1.24175766e+00\n",
      " -8.22614261e-01  3.99763320e-01 -2.27475991e-01 -6.45016591e-01\n",
      "  3.92061860e-01 -2.20993225e-01 -1.03095947e+00  1.74769648e+00\n",
      " -1.31226469e+00  6.56954089e-01  7.08257897e-01  9.96983432e-01\n",
      "  4.04647649e-01  4.82712422e-01  1.49997690e+00  1.25525724e+00\n",
      " -1.56467988e-02  8.46149622e-03  2.24632725e-01 -9.62354669e-01\n",
      "  1.10148773e+00  9.83903566e-01 -6.17848662e-02 -6.31902128e-02\n",
      " -2.04758448e+00 -1.26541580e+00  1.42660873e+00 -5.18228362e-01\n",
      "  1.32183472e+00 -7.63912351e-01  1.67808328e+00 -1.77100028e+00\n",
      " -6.71956200e-01  1.43899142e+00  2.92869239e-01 -7.60221983e-01\n",
      "  6.64251116e-01 -8.60055918e-01  1.15188787e+00  1.22456849e+00\n",
      " -2.29408326e+00 -1.48173695e+00  4.28468026e-01 -6.02248399e-01\n",
      " -1.12698943e+00  4.59048823e-01  1.36691255e+00 -4.90073159e-01\n",
      " -1.91249633e-01  1.28364856e+00  1.12833312e+00  7.11942993e-01\n",
      "  1.22872250e-01  4.70902551e-01  3.68186676e-01  1.80967458e-01\n",
      " -8.87016077e-01  1.23184727e+00  3.49403959e-01 -1.34377808e+00\n",
      " -5.73585302e-01 -2.19529346e+00  7.77493140e-01 -5.62739496e-02\n",
      " -2.30741994e-01  1.32746377e+00  1.23802091e+00 -2.33417107e-01\n",
      "  2.10332541e-01 -1.48595606e-01 -1.75385444e+00  4.40432386e-01\n",
      " -1.13758455e+00 -1.84940447e+00  1.10276792e+00 -2.63928180e-01\n",
      "  6.67799957e-01  1.57582487e+00 -6.94686665e-01  1.45991412e-03\n",
      " -3.15390228e-01  1.32070116e+00  2.75383375e+00  4.32652978e-02\n",
      " -7.81355588e-01  2.33473912e-01  3.30703886e-01  1.05393510e-01]\n",
      "Shape of average order parameter (63, 312)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-c7c494c0fd0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_OP_samples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metas\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mraw_OP_samples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'o-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "#wp:figuring things out...\n",
    "print \"The feature matrix input for each eta is of dimensions\",len(features_partial),len(features_partial[0])\n",
    "\n",
    "eta_index=0\n",
    "print \"Number of rows: \", len(raw_OPs)\n",
    "print \"Array size for each eta: \", raw_OPs[eta_index][0].shape\n",
    "print raw_OPs[eta_index][0][0]\n",
    "\n",
    "raw_OP_sample=[]\n",
    "raw_OP_samples=[]\n",
    "samplenum=20\n",
    "for mi in range(samplenum):\n",
    "    for eta_index in range(len(raw_OPs)):\n",
    "        raw_OP_sample.append(raw_OPs[eta_index][0][mi][0])\n",
    "    raw_OP_samples.append(asarray(raw_OP_sample))\n",
    "    raw_OP_sample=[]\n",
    "\n",
    "#wp: for the averaged value\n",
    "print \"Shape of average order parameter\", OPs.shape\n",
    "\n",
    "for i in range(len(raw_OP_samples)):\n",
    "    plt.plot(etas,raw_OP_samples[i], 'o-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Check out the results of PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from random import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f36e4f5c050>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "####wp: Comparing the component variance for the hard disk and ideal gas system\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.plot(incpca.explained_variance_ratio_,marker='o')\n",
    "plt.plot(incpca_ig.explained_variance_ratio_,marker='^')\n",
    "\n",
    "#plt.ylim(-0.1,0.1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explained variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pineros/programs/local/miniconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:1339: UserWarning: findfont: Font family [u'arial'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the principal components\n",
    "colors = 200*['green', 'purple', 'orange', 'teal', 'black', 'red']\n",
    "\n",
    "#plt.plot(incpca.explained_variance_ratio_, marker='o', linewidth=1.0, markersize=12, color = 'red', markerfacecolor='None') \n",
    "plt.plot(incpca.explained_variance_ratio_, marker='o', linewidth=1.0, markersize=5) \n",
    "\n",
    "\n",
    "#backround grid details\n",
    "axes = plt.gca()\n",
    "#axes.grid(b = True, which = 'both', axis = 'both', color = 'gray', linestyle = '-', alpha = 0.5, linewidth = 0.5) \n",
    "#axes.set_axis_bgcolor('white')  \n",
    "\n",
    "#font scpecifications\n",
    "title_font = {'family' : 'arial', 'color'  : 'black', 'weight' : 'heavy','size': 20}\n",
    "axis_label_font = {'family' : 'arial', 'color'  : 'black', 'weight' : 'normal','size': 20}                                                   \n",
    "\n",
    "#figure size and tick style\n",
    "plt.rcParams[\"figure.figsize\"] = [12,12]\n",
    "plt.rc('axes',edgecolor='black',linewidth=1)\n",
    "plt.tick_params(which='both', axis='both', color='black', length=4, width=0.5)\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "#plt.yscale('log')\n",
    "#!!!wp: Why eta? #Shouldn't be features?\n",
    "plt.xlabel(r'component', y=3, fontsize=40, fontdict = axis_label_font)\n",
    "plt.ylabel(r'Explained variance', fontsize=40, fontdict = axis_label_font)\n",
    "\n",
    "#title and axis labels\n",
    "plt.tick_params(axis='both', labelsize=30)\n",
    "plt.tight_layout()\n",
    "#plt.title('Explained variance for principal components', y=1.05, fontdict = title_font)\n",
    "\n",
    "#saves\n",
    "#plt.savefig('figures/OP_variance_orientation_4_3N.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Principal component weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import dot\n",
    "components = dot(incpca.components_, incpca_ig.components_)\n",
    "components_ig = incpca_ig.components_\n",
    "#components = incpca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the principal components\n",
    "#colors = 200*['green', 'purple', 'orange', 'teal', 'black', 'red']\n",
    "for i in range(0,1):\n",
    "    p_data = components[i]\n",
    "    #plt.plot(range(len(p_data)), p_data, marker='o', linewidth = 1.0, markersize=5, color = colors[i])\n",
    "    plt.plot(range(len(p_data)), p_data, marker='o', linewidth = 1.0, markersize=5)\n",
    "    #p_data = components_ig[i]\n",
    "    #plt.plot(range(len(p_data)), p_data, marker='^', linewidth = 1.0, markersize=5, color = colors[i])  \n",
    "  \n",
    "\n",
    "#backround grid details\n",
    "axes = plt.gca()\n",
    "#axes.grid(b = True, which = 'both', axis = 'both', color = 'gray', linestyle = '-', alpha = 0.5, linewidth = 0.5) \n",
    "#axes.set_axis_bgcolor('white')  \n",
    "\n",
    "#font scpecifications\n",
    "title_font = {'family' : 'arial', 'color'  : 'black', 'weight' : 'heavy','size': 20}\n",
    "axis_label_font = {'family' : 'arial', 'color'  : 'black', 'weight' : 'normal','size': 20}                                                   \n",
    "\n",
    "#figure size and tick style\n",
    "plt.rcParams[\"figure.figsize\"] = [12,12]\n",
    "plt.rc('axes',edgecolor='black',linewidth=1)\n",
    "plt.tick_params(which='both', axis='both', color='black', length=4, width=0.5)\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "\n",
    "#axis range and labels (also specify if log or not)\n",
    "#plt.xlim(0.1,100)\n",
    "\n",
    "#plt.yscale('log')\n",
    "plt.xlabel(r'$n$th component', y=3, fontsize=40, fontdict = axis_label_font)\n",
    "plt.ylabel(r'$P_{1}$ weight', fontsize=40, fontdict = axis_label_font)\n",
    "\n",
    "#title and axis labels\n",
    "plt.tick_params(axis='both', labelsize=25)\n",
    "#plt.title('PCA feature weights', y=1.05, fontdict = title_font)\n",
    "\n",
    "#save figure\n",
    "fig=plt.figure(1)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('figures/OP_orientation_batch_kappa_4_weights.svg',dpi=fig.dpi)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Order parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the principal components\n",
    "colors = 200*['green', 'purple', 'orange', 'teal', 'black', 'red']\n",
    "for i in range(0,1): #12,13\n",
    "    #p_data = (OPs[:,i]-OPs[0,i])/max(OPs[:,i]-OPs[0,i])\n",
    "    p_data = OPs[:,i]\n",
    "    #plt.plot(etas, (p_data), marker='o', linewidth = 1.0, markersize=10, color = colors[i], markerfacecolor='None')\n",
    "    #p_data = OPs_std[:,i]/max(OPs_std[:,i])\n",
    "    #plt.plot(etas, (p_data), marker='^', linewidth = 1.0, markersize=12, color = colors[i])\n",
    "    #p_data = OPs_abs[:,i]\n",
    "    plt.plot(etas, (p_data), marker='.', linewidth = 1.0, markersize=10, color = colors[i])\n",
    "\n",
    "#backround grid details\n",
    "axes = plt.gca()\n",
    "axes.grid(b = True, which = 'both', axis = 'both', color = 'gray', linestyle = '-', alpha = 0.5, linewidth = 0.5) \n",
    "\n",
    "\n",
    "#figure size and tick style\n",
    "plt.rcParams[\"figure.figsize\"] = [12,8]\n",
    "plt.rc('axes',edgecolor='black',linewidth=1)\n",
    "plt.tick_params(which='both', axis='both', color='black', length=4, width=0.5)\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "#axis range and labels (also specify if log or not)\n",
    "#plt.xlim(0.60, 0.84)\n",
    "\n",
    "#plt.yscale('log')\n",
    "plt.xlabel(r'$\\eta$', y=3, fontsize=20, fontdict = axis_label_font)\n",
    "plt.ylabel(r'$\\langle P_{i} \\rangle$ and $\\langle |P_{i}| \\rangle$', fontsize=20, fontdict = axis_label_font)\n",
    "\n",
    "#title and axis labels\n",
    "plt.tick_params(axis='both', labelsize=20)\n",
    "plt.title('PCA ellipsoids', y=1.05, fontdict = title_font)\n",
    "plt.minorticks_on()\n",
    "\n",
    "#save figure\n",
    "fig=plt.figure(1)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('figures/OP_orientation_batch_kappa_4_ave_std.svg',dpi=fig.dpi)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save OPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Append eta ranges\n",
    "etas2=etas.reshape(etas.shape[0],1)\n",
    "\n",
    "#etas2=etas[...,None] #wp: works just as fine\n",
    "OPsave=np.concatenate((etas2,OPs),1)\n",
    "\n",
    "#save to txt file\n",
    "np.savetxt('OPs_kappa9_30k_300_3N.dat',OPsave)\n",
    "np.savetxt('OPs_kappa9_30k_300_std_3N.dat',OPs_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}