002_TrainNN_BigData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Callin Switzer\n",
    "## train NN with Jorge's new data\n",
    "## 23 Aug 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\calli\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow successfully installed.\n",
      "The installed version of TensorFlow includes GPU support.\n",
      "3.6.4 |Anaconda custom (64-bit)| (default, Jan 16 2018, 10:22:32) [MSC v.1900 64 bit (AMD64)] \n",
      "\n",
      "last run on 2018-10-04 15:32:50.402434\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import time\n",
    "from datetime import datetime\n",
    "import sys\n",
    "import csv\n",
    "import seaborn as sns\n",
    "import scipy.io\n",
    "import subprocess\n",
    "import csv\n",
    "\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "\n",
    "import tensorflow as tf\n",
    "print(\"TensorFlow successfully installed.\")\n",
    "if tf.test.is_built_with_cuda():\n",
    "    print(\"The installed version of TensorFlow includes GPU support.\")\n",
    "\n",
    "print(sys.version, \"\\n\")\n",
    "\n",
    "now = datetime.now()\n",
    "print(\"last run on \" + str(now))\n",
    "\n",
    "#%qtconsole\n",
    "\n",
    "# define directories\n",
    "baseDir = os.getcwd()\n",
    "dataDir = r'D:\\MothSimulations\\11c-AggressiveManeuver\\Qstore\\hws_am_con'\n",
    "figDir = r'D:\\Dropbox\\mothMachineLearning_dataAndFigs\\Figs'\n",
    "dataOutput = r'D:\\Dropbox\\mothMachineLearning_dataAndFigs\\DataOutput'\n",
    "savedModels = r'D:\\Dropbox\\mothMachineLearning_dataAndFigs\\savedModels'\n",
    "if not os.path.exists(dataOutput):\n",
    "    os.mkdir(dataOutput)\n",
    "\n",
    "if not os.path.exists(savedModels):\n",
    "    os.mkdir(savedModels)\n",
    "    \n",
    "# open file explorer\n",
    "# aa = r'explorer /select,\"'\n",
    "# subprocess.Popen(str(aa + figDir + '\\\"'))\n",
    "# subprocess.Popen(str(aa + dataDir + '\\\"'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow successfully installed.\n",
      "The installed version of TensorFlow includes GPU support.\n",
      "3.6.4 |Anaconda custom (64-bit)| (default, Jan 16 2018, 10:22:32) [MSC v.1900 64 bit (AMD64)] \n",
      "\n",
      "last run on 2018-10-04 15:32:50.418364\n"
     ]
    }
   ],
   "source": [
    "# import custom Utils functions\n",
    "from DeepLearningUtils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_10_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_11_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_12_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_13_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_14_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_15_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_16_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_17_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_18_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_19_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_1_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_20_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_21_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_22_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_23_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_24_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_25_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_26_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_27_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_28_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_29_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_2_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_30_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_31_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_32_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_33_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_34_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_35_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_36_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_37_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_38_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_39_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_3_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_40_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_4_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_5_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_6_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_7_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_8_hws_am_con.hdf5',\n",
       " 'D:\\\\Dropbox\\\\mothMachineLearning_dataAndFigs\\\\DataOutput\\\\Qstore_9_hws_am_con.hdf5']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataDir2 = r\"D:\\Dropbox\\mothMachineLearning_dataAndFigs\\DataOutput\"\n",
    "dataFiles = [x for x in listdir_fullpath(dataDir2) if  x.endswith(\"con.hdf5\")]\n",
    "print(len(dataFiles))\n",
    "dataFiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dfList = []\n",
    "\n",
    "# for dataFile in dataFiles:\n",
    "#     bf2 = pd.read_hdf(dataFile, \"data\")\n",
    "#     bf2[['simNum', 'timestep']] = bf2[['simNum', 'timestep']].astype(int)\n",
    "#     bf2['count'] = bf2.groupby([\"simNum\", \"timestep\"]).cumcount()\n",
    "#     sdf = bf2.loc[(bf2[\"count\"] == 0) | (bf2[\"count\"] == 99), :]\n",
    "#     ss = sdf.pivot_table(index=['simNum', 'timestep'], columns = \"count\",aggfunc='first')\n",
    "#     ss.columns = [f'{i}_{j}' if j != '' else f'{i}' for i,j in ss.columns]\n",
    "#     ss.reset_index(inplace=True) \n",
    "#     dfList.append(ss)\n",
    "#     print(dataFile)\n",
    "\n",
    "# trainDF = pd.concat(dfList)\n",
    "\n",
    "# trainDF = trainDF.drop(columns=['F_99', 'alpha_99', \n",
    "#                                'tau_99'])\n",
    "# trainDF=trainDF.rename(columns = {'F_0':'F', \n",
    "#                                  \"alpha_0\": \"alpha\", \n",
    "#                                  \"tau_0\": \"tau\"})\n",
    "\n",
    "# trainDF.reset_index(inplace = True, drop = True)\n",
    "# print(trainDF.shape)\n",
    "# trainDF.head()\n",
    "\n",
    "# trainDF.sort_values([\"timestep\", \"simNum\"], ascending = [True, True]).head()\n",
    "\n",
    "# trainDF.columns\n",
    "\n",
    "# # convert to fx and fy\n",
    "# trainDF[\"Fx\"] = trainDF.F * np.cos(trainDF.alpha)\n",
    "# trainDF[\"Fy\"] = trainDF.F * np.sin(trainDF.alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trainDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # save training dataset, so it's faster to read in next time\n",
    "# trainDF.to_csv(os.path.join(dataDir2, \"smallDF.csv\"), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>simNum</th>\n",
       "      <th>timestep</th>\n",
       "      <th>F</th>\n",
       "      <th>alpha</th>\n",
       "      <th>phi_0</th>\n",
       "      <th>phi_99</th>\n",
       "      <th>phi_dot_0</th>\n",
       "      <th>phi_dot_99</th>\n",
       "      <th>tau</th>\n",
       "      <th>theta_0</th>\n",
       "      <th>...</th>\n",
       "      <th>x_0</th>\n",
       "      <th>x_99</th>\n",
       "      <th>x_dot_0</th>\n",
       "      <th>x_dot_99</th>\n",
       "      <th>y_0</th>\n",
       "      <th>y_99</th>\n",
       "      <th>y_dot_0</th>\n",
       "      <th>y_dot_99</th>\n",
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       "      <td>683.734561</td>\n",
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       "      <td>-1.298536</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>-133.752363</td>\n",
       "      <td>22501.211072</td>\n",
       "      <td>-30998.792903</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>34264.536249</td>\n",
       "      <td>2.465501</td>\n",
       "      <td>3.948428</td>\n",
       "      <td>4.019544</td>\n",
       "      <td>2.009397</td>\n",
       "      <td>5.425513</td>\n",
       "      <td>72580.767201</td>\n",
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       "      <td>...</td>\n",
       "      <td>-0.019322</td>\n",
       "      <td>-6.079672</td>\n",
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       "      <td>2.520087</td>\n",
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       "      <td>12045.791872</td>\n",
       "      <td>4.573819</td>\n",
       "      <td>3.966608</td>\n",
       "      <td>3.933017</td>\n",
       "      <td>-0.124272</td>\n",
       "      <td>-2.775517</td>\n",
       "      <td>93435.855346</td>\n",
       "      <td>0.768515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509507</td>\n",
       "      <td>2.986736</td>\n",
       "      <td>52.490438</td>\n",
       "      <td>193.914740</td>\n",
       "      <td>4.834518</td>\n",
       "      <td>2.470847</td>\n",
       "      <td>-18.848146</td>\n",
       "      <td>-211.050011</td>\n",
       "      <td>-1663.845687</td>\n",
       "      <td>-11930.327714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35562.854879</td>\n",
       "      <td>0.767089</td>\n",
       "      <td>4.000007</td>\n",
       "      <td>4.111777</td>\n",
       "      <td>3.434103</td>\n",
       "      <td>7.246049</td>\n",
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       "      <td>0.773977</td>\n",
       "      <td>...</td>\n",
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       "      <td>25602.909376</td>\n",
       "      <td>24682.132781</td>\n",
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       "      <th>4</th>\n",
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       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   simNum  timestep             F     alpha     phi_0    phi_99  phi_dot_0  \\\n",
       "0       0         0  38304.433962  5.340270  3.926991  3.869604   0.000000   \n",
       "1       0         1  34264.536249  2.465501  3.948428  4.019544   2.009397   \n",
       "2       0         2  12045.791872  4.573819  3.966608  3.933017  -0.124272   \n",
       "3       0         3  35562.854879  0.767089  4.000007  4.111777   3.434103   \n",
       "4       0         4  20856.636628  2.424378  4.009324  3.995061  -2.309667   \n",
       "\n",
       "   phi_dot_99           tau   theta_0      ...            x_0      x_99  \\\n",
       "0   -5.674295  10963.268558  0.785398      ...       0.000000  6.853314   \n",
       "1    5.425513  72580.767201  0.768478      ...      -0.019322 -6.079672   \n",
       "2   -2.775517  93435.855346  0.768515      ...       0.509507  2.986736   \n",
       "3    7.246049 -96088.338473  0.773977      ...      -0.836633 -4.334782   \n",
       "4    0.686804 -27142.805375  0.815904      ...       0.429770  2.895126   \n",
       "\n",
       "      x_dot_0    x_dot_99       y_0       y_99     y_dot_0    y_dot_99  \\\n",
       "0    0.000100  683.734561  0.000000  -1.298536    0.000100 -133.752363   \n",
       "1   -0.145396 -606.129200  2.520087   6.366770  250.908304  120.273149   \n",
       "2   52.490438  193.914740  4.834518   2.470847  -18.848146 -211.050011   \n",
       "3 -187.046694 -157.064454  6.838940  17.508792  218.755312  844.589134   \n",
       "4  313.730247  -65.350386  8.392823   6.771320  -58.222983 -102.458917   \n",
       "\n",
       "             Fx            Fy  \n",
       "0  22501.211072 -30998.792903  \n",
       "1 -26727.166452  21441.012519  \n",
       "2  -1663.845687 -11930.327714  \n",
       "3  25602.909376  24682.132781  \n",
       "4 -15718.389244  13708.812166  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainDF2 = pd.read_csv(os.path.join(dataDir2, \"smallDF.csv\"))\n",
    "trainDF2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make dataset\n",
    "\n",
    "# refref: add dots as output\n",
    "\n",
    "X = trainDF2.loc[:, [\"x_0\", \"y_0\", \"phi_0\", \"theta_0\", \n",
    "                    \"x_99\", \"y_99\", \"phi_99\", \"theta_99\", \n",
    "                   \"x_dot_0\", \"y_dot_0\", \"phi_dot_0\", \"theta_dot_0\"]]\n",
    "\n",
    "Y = trainDF2.loc[:, [\"Fx\", \"Fy\", \"tau\", \"x_dot_99\", \"y_dot_99\", \n",
    "                    \"phi_dot_99\", \"theta_dot_99\"] ]\n",
    "\n",
    "# # # subtract x_0 and y_0 from everything\n",
    "# X.x_99 = X.x_99 - X.x_0\n",
    "# X.y_99 = X.y_99 - X.y_0\n",
    "\n",
    "# X.x_0 = X.y_0 = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>x_0</th>\n",
       "      <th>y_0</th>\n",
       "      <th>phi_0</th>\n",
       "      <th>theta_0</th>\n",
       "      <th>x_99</th>\n",
       "      <th>y_99</th>\n",
       "      <th>phi_99</th>\n",
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       "      <th>x_dot_0</th>\n",
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       "      <th>phi_dot_0</th>\n",
       "      <th>theta_dot_0</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.926991</td>\n",
       "      <td>0.785398</td>\n",
       "      <td>6.853314</td>\n",
       "      <td>-1.298536</td>\n",
       "      <td>3.869604</td>\n",
       "      <td>0.746382</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.019322</td>\n",
       "      <td>2.520087</td>\n",
       "      <td>3.948428</td>\n",
       "      <td>0.768478</td>\n",
       "      <td>-6.079672</td>\n",
       "      <td>6.366770</td>\n",
       "      <td>4.019544</td>\n",
       "      <td>0.940422</td>\n",
       "      <td>-0.145396</td>\n",
       "      <td>250.908304</td>\n",
       "      <td>2.009397</td>\n",
       "      <td>0.087406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.509507</td>\n",
       "      <td>4.834518</td>\n",
       "      <td>3.966608</td>\n",
       "      <td>0.768515</td>\n",
       "      <td>2.986736</td>\n",
       "      <td>2.470847</td>\n",
       "      <td>3.933017</td>\n",
       "      <td>0.868402</td>\n",
       "      <td>52.490438</td>\n",
       "      <td>-18.848146</td>\n",
       "      <td>-0.124272</td>\n",
       "      <td>-1.029996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.836633</td>\n",
       "      <td>6.838940</td>\n",
       "      <td>4.000007</td>\n",
       "      <td>0.773977</td>\n",
       "      <td>-4.334782</td>\n",
       "      <td>17.508792</td>\n",
       "      <td>4.111777</td>\n",
       "      <td>0.747358</td>\n",
       "      <td>-187.046694</td>\n",
       "      <td>218.755312</td>\n",
       "      <td>3.434103</td>\n",
       "      <td>2.037211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.429770</td>\n",
       "      <td>8.392823</td>\n",
       "      <td>4.009324</td>\n",
       "      <td>0.815904</td>\n",
       "      <td>2.895126</td>\n",
       "      <td>6.771320</td>\n",
       "      <td>3.995061</td>\n",
       "      <td>0.761962</td>\n",
       "      <td>313.730247</td>\n",
       "      <td>-58.222983</td>\n",
       "      <td>-2.309667</td>\n",
       "      <td>-0.673328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        x_0       y_0     phi_0   theta_0      x_99       y_99    phi_99  \\\n",
       "0  0.000000  0.000000  3.926991  0.785398  6.853314  -1.298536  3.869604   \n",
       "1 -0.019322  2.520087  3.948428  0.768478 -6.079672   6.366770  4.019544   \n",
       "2  0.509507  4.834518  3.966608  0.768515  2.986736   2.470847  3.933017   \n",
       "3 -0.836633  6.838940  4.000007  0.773977 -4.334782  17.508792  4.111777   \n",
       "4  0.429770  8.392823  4.009324  0.815904  2.895126   6.771320  3.995061   \n",
       "\n",
       "   theta_99     x_dot_0     y_dot_0  phi_dot_0  theta_dot_0  \n",
       "0  0.746382    0.000100    0.000100   0.000000     0.000000  \n",
       "1  0.940422   -0.145396  250.908304   2.009397     0.087406  \n",
       "2  0.868402   52.490438  -18.848146  -0.124272    -1.029996  \n",
       "3  0.747358 -187.046694  218.755312   3.434103     2.037211  \n",
       "4  0.761962  313.730247  -58.222983  -2.309667    -0.673328  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Fx</th>\n",
       "      <th>Fy</th>\n",
       "      <th>tau</th>\n",
       "      <th>x_dot_99</th>\n",
       "      <th>y_dot_99</th>\n",
       "      <th>phi_dot_99</th>\n",
       "      <th>theta_dot_99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22501.211072</td>\n",
       "      <td>-30998.792903</td>\n",
       "      <td>10963.268558</td>\n",
       "      <td>683.734561</td>\n",
       "      <td>-133.752363</td>\n",
       "      <td>-5.674295</td>\n",
       "      <td>-4.770986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-26727.166452</td>\n",
       "      <td>21441.012519</td>\n",
       "      <td>72580.767201</td>\n",
       "      <td>-606.129200</td>\n",
       "      <td>120.273149</td>\n",
       "      <td>5.425513</td>\n",
       "      <td>10.479995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1663.845687</td>\n",
       "      <td>-11930.327714</td>\n",
       "      <td>93435.855346</td>\n",
       "      <td>193.914740</td>\n",
       "      <td>-211.050011</td>\n",
       "      <td>-2.775517</td>\n",
       "      <td>3.897680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25602.909376</td>\n",
       "      <td>24682.132781</td>\n",
       "      <td>-96088.338473</td>\n",
       "      <td>-157.064454</td>\n",
       "      <td>844.589134</td>\n",
       "      <td>7.246049</td>\n",
       "      <td>0.318982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-15718.389244</td>\n",
       "      <td>13708.812166</td>\n",
       "      <td>-27142.805375</td>\n",
       "      <td>-65.350386</td>\n",
       "      <td>-102.458917</td>\n",
       "      <td>0.686804</td>\n",
       "      <td>-1.304712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fx            Fy           tau    x_dot_99    y_dot_99  \\\n",
       "0  22501.211072 -30998.792903  10963.268558  683.734561 -133.752363   \n",
       "1 -26727.166452  21441.012519  72580.767201 -606.129200  120.273149   \n",
       "2  -1663.845687 -11930.327714  93435.855346  193.914740 -211.050011   \n",
       "3  25602.909376  24682.132781 -96088.338473 -157.064454  844.589134   \n",
       "4 -15718.389244  13708.812166 -27142.805375  -65.350386 -102.458917   \n",
       "\n",
       "   phi_dot_99  theta_dot_99  \n",
       "0   -5.674295     -4.770986  \n",
       "1    5.425513     10.479995  \n",
       "2   -2.775517      3.897680  \n",
       "3    7.246049      0.318982  \n",
       "4    0.686804     -1.304712  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.2, random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler  \n",
    "scalerX = StandardScaler()  \n",
    "scalerY = StandardScaler()  \n",
    "\n",
    "# Don't cheat - fit only on training data\n",
    "scalerX.fit(Xtrain)  \n",
    "scalerY.fit(Ytrain) \n",
    "\n",
    "Xtrain_scaled = scalerX.transform(Xtrain)  \n",
    "Ytrain_scaled = scalerY.transform(Ytrain)  \n",
    "\n",
    "# apply same transformation to test data\n",
    "Xtest_scaled = scalerX.transform(Xtest)\n",
    "Ytest_scaled = scalerY.transform(Ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4921.410286</td>\n",
       "      <td>-29472.860520</td>\n",
       "      <td>-43474.926597</td>\n",
       "      <td>567.926777</td>\n",
       "      <td>-88.040935</td>\n",
       "      <td>-4.418419</td>\n",
       "      <td>-7.368523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11498.075007</td>\n",
       "      <td>2931.787592</td>\n",
       "      <td>30830.482492</td>\n",
       "      <td>444.337367</td>\n",
       "      <td>2.489686</td>\n",
       "      <td>-2.655246</td>\n",
       "      <td>-0.479400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4137.565172</td>\n",
       "      <td>-4224.449916</td>\n",
       "      <td>90265.867970</td>\n",
       "      <td>193.859357</td>\n",
       "      <td>217.524718</td>\n",
       "      <td>-0.423825</td>\n",
       "      <td>5.993808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-34932.224981</td>\n",
       "      <td>-25717.453464</td>\n",
       "      <td>38308.396523</td>\n",
       "      <td>12.065647</td>\n",
       "      <td>-910.281258</td>\n",
       "      <td>-3.621573</td>\n",
       "      <td>-0.800979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-10521.307892</td>\n",
       "      <td>31032.192025</td>\n",
       "      <td>-79689.073081</td>\n",
       "      <td>-853.024376</td>\n",
       "      <td>208.355764</td>\n",
       "      <td>6.069526</td>\n",
       "      <td>0.281335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0             1             2           3           4         5  \\\n",
       "0   4921.410286 -29472.860520 -43474.926597  567.926777  -88.040935 -4.418419   \n",
       "1  11498.075007   2931.787592  30830.482492  444.337367    2.489686 -2.655246   \n",
       "2   4137.565172  -4224.449916  90265.867970  193.859357  217.524718 -0.423825   \n",
       "3 -34932.224981 -25717.453464  38308.396523   12.065647 -910.281258 -3.621573   \n",
       "4 -10521.307892  31032.192025 -79689.073081 -853.024376  208.355764  6.069526   \n",
       "\n",
       "          6  \n",
       "0 -7.368523  \n",
       "1 -0.479400  \n",
       "2  5.993808  \n",
       "3 -0.800979  \n",
       "4  0.281335  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(scalerY.inverse_transform(Ytrain_scaled)).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Fx</th>\n",
       "      <th>Fy</th>\n",
       "      <th>tau</th>\n",
       "      <th>x_dot_99</th>\n",
       "      <th>y_dot_99</th>\n",
       "      <th>phi_dot_99</th>\n",
       "      <th>theta_dot_99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4921.410286</td>\n",
       "      <td>-29472.860520</td>\n",
       "      <td>-43474.926597</td>\n",
       "      <td>567.926777</td>\n",
       "      <td>-88.040935</td>\n",
       "      <td>-4.418419</td>\n",
       "      <td>-7.368523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11498.075007</td>\n",
       "      <td>2931.787592</td>\n",
       "      <td>30830.482492</td>\n",
       "      <td>444.337367</td>\n",
       "      <td>2.489686</td>\n",
       "      <td>-2.655246</td>\n",
       "      <td>-0.479400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4137.565172</td>\n",
       "      <td>-4224.449916</td>\n",
       "      <td>90265.867970</td>\n",
       "      <td>193.859357</td>\n",
       "      <td>217.524718</td>\n",
       "      <td>-0.423825</td>\n",
       "      <td>5.993808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-34932.224981</td>\n",
       "      <td>-25717.453464</td>\n",
       "      <td>38308.396523</td>\n",
       "      <td>12.065647</td>\n",
       "      <td>-910.281258</td>\n",
       "      <td>-3.621573</td>\n",
       "      <td>-0.800979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-10521.307892</td>\n",
       "      <td>31032.192025</td>\n",
       "      <td>-79689.073081</td>\n",
       "      <td>-853.024376</td>\n",
       "      <td>208.355764</td>\n",
       "      <td>6.069526</td>\n",
       "      <td>0.281335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fx            Fy           tau    x_dot_99    y_dot_99  \\\n",
       "0   4921.410286 -29472.860520 -43474.926597  567.926777  -88.040935   \n",
       "1  11498.075007   2931.787592  30830.482492  444.337367    2.489686   \n",
       "2   4137.565172  -4224.449916  90265.867970  193.859357  217.524718   \n",
       "3 -34932.224981 -25717.453464  38308.396523   12.065647 -910.281258   \n",
       "4 -10521.307892  31032.192025 -79689.073081 -853.024376  208.355764   \n",
       "\n",
       "   phi_dot_99  theta_dot_99  \n",
       "0   -4.418419     -7.368523  \n",
       "1   -2.655246     -0.479400  \n",
       "2   -0.423825      5.993808  \n",
       "3   -3.621573     -0.800979  \n",
       "4    6.069526      0.281335  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Ytrain).reset_index(drop = True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import MinMaxScaler  \n",
    "# scalerX = MinMaxScaler()  \n",
    "# scalerY = MinMaxScaler()  \n",
    "\n",
    "# # Don't cheat - fit only on training data\n",
    "# scalerX.fit(Xtrain)  \n",
    "# scalerY.fit(Ytrain) \n",
    "\n",
    "# Xtrain_scaled = scalerX.transform(Xtrain)  \n",
    "# Ytrain_scaled = scalerY.transform(Ytrain)  \n",
    "\n",
    "# # apply same transformation to test data\n",
    "# Xtest_scaled = scalerX.transform(Xtest)\n",
    "# Ytest_scaled = scalerY.transform(Ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# build Keras model with functional API, instead of Sequential\n",
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "\n",
    "# callcacks\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.callbacks import ReduceLROnPlateau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input(shape=(Xtrain_scaled.shape[1],))\n",
    "\n",
    "x = Dense(400, activation='tanh')(inputs)\n",
    "x = Dense(400, activation='tanh')(x)\n",
    "x = Dense(400, activation='tanh')(x)\n",
    "x = Dense(16, activation='tanh')(x)\n",
    "predictions = Dense(Ytrain_scaled.shape[1], activation='linear')(x)\n",
    "\n",
    "# This creates a model that includes\n",
    "# the Input layer and three Dense layers\n",
    "model = Model(inputs=inputs, outputs=predictions)\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics = ['mse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystop = EarlyStopping(monitor='val_mean_squared_error', patience=50, \n",
    "                          verbose=1, mode='auto', min_delta = 0.001)\n",
    "\n",
    "history = model.fit(Xtrain_scaled, Ytrain_scaled, validation_split = 0.3, \n",
    "                    verbose = 2, batch_size=2**14, epochs = 1000, \n",
    "                    callbacks = [earlystop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model_history(model_history, saveFig = False):\n",
    "    fig, axs = plt.subplots(1,1,figsize=(10,5))\n",
    "    # summarize history for accuracy\n",
    "    axs.plot(range(1,len(model_history.history['mean_squared_error'])+1),\n",
    "             model_history.history['mean_squared_error'])\n",
    "    axs.plot(range(1,len(model_history.history['val_mean_squared_error'])+1),\n",
    "             model_history.history['val_mean_squared_error'])\n",
    "    axs.set_title('Model MSE')\n",
    "    axs.set_ylabel('mean_squared_error')\n",
    "    axs.set_xlabel('Epoch')\n",
    "    axs.set_xticks(np.arange(1,len(model_history.history['val_mean_squared_error'])+1),\n",
    "                   len(model_history.history['val_mean_squared_error'])/10)\n",
    "    axs.legend(['train', 'val'], loc='best')\n",
    "    if saveFig:\n",
    "        fig.savefig(os.path.join(figDir, \"ModelTraining.png\"), dpi = 120, bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model_history(history, saveFig = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# load model\n",
    "from keras.models import load_model\n",
    "model = load_model(os.path.join(savedModels, 'my_model.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "nnpreds = model.predict(Xtest_scaled[ :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "# model.save(os.path.join(savedModels, 'my_model.h5'))\n",
    "# aa = r'explorer /select,\"'\n",
    "# subprocess.Popen(str(aa + savedModels + '\\\"'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rescale\n",
    "nnpreds_unscaled = scalerY.inverse_transform(nnpreds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ad77adb828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close(\"all\")\n",
    "fig, axs = plt.subplots(1,7, figsize=np.array((20, 4)) / 1.1, facecolor='w', edgecolor='k')\n",
    "fig.subplots_adjust(hspace = 0.3, wspace=0.3)\n",
    "#fig.suptitle('Predicted vs. acutal ', fontsize=14, fontweight='bold')\n",
    "\n",
    "axs = axs.ravel()\n",
    "\n",
    "nms = Y.columns\n",
    "nms2 = [nms[ii].replace(\"dot\", \"d\") for ii in range(len(nms))]\n",
    "nms2\n",
    "\n",
    "# replace lightest colors with white\n",
    "import matplotlib.colors\n",
    "\n",
    "cmap = plt.cm.magma_r\n",
    "cmaplist = np.array([cmap(i) for i in range(cmap.N)])\n",
    "#cmaplist[0:2] = np.repeat((0.0,0.0,0.0,0.0), 2).reshape(-1,4)\n",
    "cmaplist[:,0:3] = np.divide(cmaplist[:, 0:3], 1.1)\n",
    "#cmaplist[:,0:3] = np.transpose(np.vstack([np.linspace(1, 0, 256), np.linspace(1, 0, 256), np.linspace(1, 0, 256)]))\n",
    "cmaplist[0] = (1,1,1,0.5)\n",
    "cmap = matplotlib.colors.LinearSegmentedColormap.from_list('mcm',cmaplist, cmap.N)\n",
    "\n",
    "for ii in range(len(Y.columns)):\n",
    "    # read in image\n",
    "    \n",
    "    try:\n",
    "        axs[ii].hexbin(y = Ytest.iloc[:,ii],x = nnpreds_unscaled[:,ii], gridsize = 150, cmap = cmap)\n",
    "        axs[ii].set_xlabel(\"Predicted Value\")\n",
    "        axs[ii].xaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "        axs[ii].yaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "        axs[ii].ticklabel_format(style='sci',  axis='both', scilimits=(3,4))\n",
    "        t = axs[ii].xaxis.get_offset_text()\n",
    "        t.set_x(1.1)\n",
    "        t.set_y(0.5)\n",
    "        if(ii == 0):\n",
    "            axs[ii].set_ylabel(\"Actual Value\")\n",
    "        axs[ii].set_title(nms2[ii])\n",
    "        axs[ii].plot(Ytest.iloc[0:1000,ii], Ytest.iloc[0:1000,ii], 'grey', linewidth = 1, linestyle  = \"--\")\n",
    "    except:\n",
    "        pass\n",
    "plt.tight_layout()\n",
    "\n",
    "fig.savefig(os.path.join(figDir, \"NNPreds_newData_control_bigbatch_FxFy_hexbin_loadedModel.png\"), dpi = 500, bbox_inches='tight')\n",
    "# fig.savefig(os.path.join(figDir, \"NNPreds_newData_control_bigbatch_FxFy_hexbin_loadedModel.svg\"),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# residual plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ad787d3978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close(\"all\")\n",
    "fig, axs = plt.subplots(1,7, figsize=np.array((20, 4)) / 1.1, facecolor='w', edgecolor='k')\n",
    "fig.subplots_adjust(hspace = 0.3, wspace=0.3)\n",
    "#fig.suptitle('Predicted vs. acutal ', fontsize=14, fontweight='bold')\n",
    "\n",
    "axs = axs.ravel()\n",
    "\n",
    "nms = Y.columns\n",
    "nms2 = [nms[ii].replace(\"dot\", \"d\") for ii in range(len(nms))]\n",
    "nms2\n",
    "\n",
    "\n",
    "for ii in range(len(Y.columns)):\n",
    "    # read in image\n",
    "    \n",
    "    try:\n",
    "        axs[ii].hexbin(y = Ytest.iloc[:,ii] - nnpreds_unscaled[:,ii],x = nnpreds_unscaled[:,ii], gridsize = 150, cmap = cmap)\n",
    "        axs[ii].set_xlabel(\"Predicted Value\")\n",
    "        axs[ii].xaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "        axs[ii].yaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "#         axs[ii].ticklabel_format(style='sci',  axis='both', scilimits=(3,4))\n",
    "#         t = axs[ii].xaxis.get_offset_text()\n",
    "#         t.set_x(1.1)\n",
    "#         t.set_y(0.5)\n",
    "        if(ii == 0):\n",
    "            axs[ii].set_ylabel(\"Actual - Predicted\")\n",
    "        axs[ii].set_title(nms2[ii])\n",
    "        #axs[ii].plot(Ytest.iloc[0:1000,ii], Ytest.iloc[0:1000,ii], 'grey', linewidth = 1, linestyle  = \"--\")\n",
    "    except:\n",
    "        pass\n",
    "plt.tight_layout()\n",
    "\n",
    "fig.savefig(os.path.join(figDir, \"NNPreds_resids.png\"), dpi = 500, bbox_inches='tight')\n",
    "# fig.savefig(os.path.join(figDir, \"NNPreds_newData_control_bigbatch_FxFy_hexbin_loadedModel.svg\"),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['     Fx',\n",
       " '     Fy',\n",
       " '     tau',\n",
       " '     xd9',\n",
       " '     yd9',\n",
       " '     phid9',\n",
       " '     thetad9']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nms = Y.columns\n",
    "nms2 = [nms[ii].replace(\"dot\", \"d\") for ii in range(len(nms))]\n",
    "nms2 = [nms2[ii].replace(\"_\", \"\") for ii in range(len(nms2))]\n",
    "nms2 = [nms2[ii].replace(\"99\", \"9\") for ii in range(len(nms2))]\n",
    "nms2 = [\"     \" + nms2[ii] for ii in range(len(nms2))]\n",
    "nms2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YES\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1635c1e63c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combine residual and regular plots\n",
    "plt.close(\"all\")\n",
    "fig, axs = plt.subplots(2,7, figsize=np.array((30, 8)) / 1.7, facecolor='w', edgecolor='k')\n",
    "# fig.subplots_adjust(hspace = 0.3, wspace=0.3)\n",
    "fig.subplots_adjust(hspace = 0.1, wspace=0.5)\n",
    "#fig.suptitle('Predicted vs. acutal ', fontsize=14, fontweight='bold')\n",
    "\n",
    "axs = axs.ravel()\n",
    "\n",
    "\n",
    "# replace lightest colors with white\n",
    "import matplotlib.colors\n",
    "\n",
    "cmap = plt.cm.magma_r\n",
    "cmaplist = np.array([cmap(i) for i in range(cmap.N)])\n",
    "#cmaplist[0:2] = np.repeat((0.0,0.0,0.0,0.0), 2).reshape(-1,4)\n",
    "cmaplist[:,0:3] = np.divide(cmaplist[:, 0:3], 1.1)\n",
    "#cmaplist[:,0:3] = np.transpose(np.vstack([np.linspace(1, 0, 256), np.linspace(1, 0, 256), np.linspace(1, 0, 256)]))\n",
    "cmaplist[0] = (1,1,1,0.5)\n",
    "cmap = matplotlib.colors.LinearSegmentedColormap.from_list('mcm',cmaplist, cmap.N)\n",
    "\n",
    "for ii in range(len(Y.columns)):\n",
    "    # read in image\n",
    "    \n",
    "    try:\n",
    "        axs[ii].hexbin(y = Ytest.iloc[:,ii],x = nnpreds_unscaled[:,ii], gridsize = 150, cmap = cmap)\n",
    "#         axs[ii].set_xlabel(\"Predicted Value\")\n",
    "#         axs[ii].xaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "        axs[ii].yaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "        axs[ii].ticklabel_format(style='sci',  axis='y', scilimits=(3,4))\n",
    "        axs[ii].axes.xaxis.set_ticklabels([])\n",
    "        if(ii == 0):\n",
    "            axs[ii].set_ylabel(\"Actual Value\")\n",
    "        axs[ii].set_title(nms2[ii])\n",
    "        axs[ii].plot(Ytest.iloc[0:1000,ii], Ytest.iloc[0:1000,ii], 'grey', linewidth = 1, linestyle  = \"--\")\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "for ii in range(len(Y.columns)):\n",
    "    jj = ii + len(Y.columns)\n",
    "    \n",
    "    try:\n",
    "        axs[jj].hexbin(y = Ytest.iloc[:,ii] - nnpreds_unscaled[:,ii],x = nnpreds_unscaled[:,ii], gridsize = 150, cmap = cmap)\n",
    "        axs[jj].set_xlabel(\"Predicted Value\")\n",
    "        axs[jj].xaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "        axs[jj].yaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "        mmin = np.min(Ytest.iloc[:,ii] - nnpreds_unscaled[:,ii])\n",
    "        mmax = np.max(Ytest.iloc[:,ii] - nnpreds_unscaled[:,ii])\n",
    "        \n",
    "        upper = np.max([np.abs(mmin), np.abs(mmax)])\n",
    "        axs[jj].set_ylim(-upper, upper)\n",
    "\n",
    "        if(ii == 0):\n",
    "            print(\"YES\")\n",
    "            axs[jj].set_ylabel(\"Actual - Predicted\")\n",
    "        #axs[jj].set_title(nms2[ii])\n",
    "        axs[jj].hlines(y = 0, xmin = np.min(nnpreds_unscaled[:,ii]), \n",
    "                       xmax = np.max(nnpreds_unscaled[:,ii]), linestyle =  \"--\", linewidth = 1)\n",
    "    except:\n",
    "        pass\n",
    "#plt.tight_layout()\n",
    "fig.savefig(os.path.join(figDir, \"NNPreds_resids_combined.png\"), dpi = 500, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b06dff3ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert Fx and Fy back to F and alpha\n",
    "\n",
    "fig, axs = plt.subplots(1,2, figsize=(20, 4), facecolor='w', edgecolor='k')\n",
    "fig.subplots_adjust(hspace = 0.3, wspace=0.3)\n",
    "ii = 0\n",
    "axs[ii].hexbin(y = Ytest.iloc[:,ii],x = nnpreds_unscaled[:,ii], gridsize = 150, cmap = cmap)\n",
    "axs[ii].set_xlabel(\"Predicted Value\")\n",
    "axs[ii].xaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "axs[ii].yaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "axs[ii].ticklabel_format(style='sci',  axis='both', scilimits=(0,0))\n",
    "t = axs[ii].xaxis.get_offset_text()\n",
    "t.set_x(1.1)\n",
    "t.set_y(500)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Fx', 'Fy', 'tau', 'x_d_99', 'y_d_99', 'phi_d_99', 'theta_d_99']"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1.1,21,'$\\\\times\\\\mathdefault{10^{1}}\\\\mathdefault{}$')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mean squared error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999506847353872 Fx\n",
      "0.9999627147331593 Fy\n",
      "0.9999438044472624 tau\n",
      "0.9999599609986949 x_dot_99\n",
      "0.9999467703504513 y_dot_99\n",
      "0.9999202305164062 phi_dot_99\n",
      "0.9999309651201447 theta_dot_99\n"
     ]
    }
   ],
   "source": [
    "for ii in range(len(nms)):\n",
    "    #np.sqrt(mean_squared_error(Ytest.iloc[0:1000,ii], nnpreds_unscaled[:,ii]))\n",
    "    print(r2_score(Ytest.iloc[0:1000,ii], nnpreds_unscaled[:,ii]), nms[ii])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2d9de6ac908>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d9de3d9048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter( nnpreds_unscaled[:,ii],Ytest.iloc[0:1000,ii]-  nnpreds_unscaled[:,ii])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPreds = pd.DataFrame(nnpreds_unscaled, columns = Y.columns)\n",
    "TestPreds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestInput = pd.DataFrame(Xtest, columns = X.columns).reset_index(drop = True)\n",
    "TestInput.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# F will be the same in any quadrant\n",
    "\n",
    "TestPreds[\"F\"] = np.sqrt(TestPreds[\"Fx\"]**2 + TestPreds[\"Fy\"]**2)\n",
    "\n",
    "# trainDF[\"Fx\"] = trainDF.F * np.cos(trainDF.alpha)\n",
    "# trainDF[\"Fy\"] = trainDF.F * np.sin(trainDF.alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPreds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate alpha\n",
    "def quadrant(Fx, Fy):\n",
    "    if (Fx >= 0) & (Fy >= 0):\n",
    "        q = 1\n",
    "    elif (Fx < 0) & (Fy >= 0):\n",
    "        q = 2\n",
    "    elif (Fx < 0) & (Fy < 0):\n",
    "        q = 3\n",
    "    elif (Fx >= 0) & (Fy < 0):\n",
    "        q = 4\n",
    "    else:\n",
    "        q = 999999\n",
    "    return(q)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPreds[\"quadrant\"] = [quadrant(fx, fy) for fx,fy in zip(TestPreds[\"Fx\"], TestPreds[\"Fy\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPreds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def angleCalc(Fx, Fy, q):\n",
    "    fx = np.abs(Fx)\n",
    "    fy = np.abs(Fy)\n",
    "    \n",
    "    if q == 1:\n",
    "        alpha = np.arctan(fy/fx)\n",
    "    elif q == 2:\n",
    "        alpha = np.pi - np.arctan(fy/fx)\n",
    "    elif q == 3: \n",
    "        alpha = np.pi + np.arctan(fy/fx)\n",
    "    elif q == 4:\n",
    "        alpha = (2*np.pi) - np.arctan(fy/fx)\n",
    "    return(alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPreds[\"alpha\"] = [angleCalc(fx, fy, q) for fx, fy, q in zip(TestPreds[\"Fx\"], TestPreds[\"Fy\"], TestPreds[\"quadrant\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPreds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPredsComb = pd.concat([TestPreds, TestInput], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPredsComb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPredsComb.rename(index=str, columns={\"F\": \"F_pred\", \"alpha\": \"alpha_pred\", \"tau\":\"tau_pred\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPredsComb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestPredsComb.drop(['Fx', 'Fy', \"quadrant\"], axis=1, inplace = True)\n",
    "TestPredsComb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols= ['x_0', 'y_0', 'phi_0', 'theta_0', \n",
    "       'x_dot_0', 'y_dot_0', 'phi_dot_0', 'theta_dot_0', \n",
    "       'F_pred', 'alpha_pred', 'tau_pred', \n",
    "      'x_99', 'y_99', 'phi_99', 'theta_99', \n",
    "      'x_dot_99', 'y_dot_99', 'phi_dot_99', 'theta_dot_99' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tc2 = TestPredsComb.loc[:, cols]\n",
    "tc2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tc2.rename(columns = {\"x_dot_99\": \"x_dot_99_pred\", \n",
    "                     \"y_dot_99\": \"y_dot_99_pred\", \n",
    "                     \"theta_dot_99\": \"theta_dot_99_pred\", \n",
    "                     \"phi_dot_99\": \"phi_dot_99_pred\"}, inplace = True)\n",
    "tc2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tc2.to_csv(os.path.join(dataOutput, \"NNpreds.csv\"), index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tc2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "tc_sm =  tc2.sample(n=1000, random_state = 902902)\n",
    "tc_sm.to_csv(os.path.join(dataOutput, \"NNpreds_small.csv\"), index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check alpha on trainDf\n",
    "trainDF2[\"quadrant\"] = [quadrant(fx, fy) for fx,fy in zip(trainDF2[\"Fx\"], trainDF2[\"Fy\"])]\n",
    "\n",
    "trainDF2[\"alphaCalc\"] = [angleCalc(fx, fy, q) for fx, fy, q in zip(trainDF2[\"Fx\"], trainDF2[\"Fy\"], trainDF2[\"quadrant\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainDF2.loc[:, [\"alpha\", \"alphaCalc\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close(\"all\")\n",
    "fig, axs = plt.subplots(2,int(len(Y.columns)/2), figsize=(15, 10), facecolor='w', edgecolor='k')\n",
    "fig.subplots_adjust(hspace = 0.1, wspace=0.3)\n",
    "fig.suptitle('Predicted vs. acutal ', fontsize=14, fontweight='bold')\n",
    "\n",
    "axs = axs.ravel()\n",
    "\n",
    "nms = Y.columns\n",
    "\n",
    "for ii in range(len(Y.columns)):\n",
    "    # read in image\n",
    "    \n",
    "    try:\n",
    "        axs[ii].scatter(y = Ytest.iloc[:,ii],x = nnpreds_unscaled[:,ii], s = 1, alpha = 0.1 )\n",
    "        axs[ii].set_xlabel(\"Predicted Value (un-scaled)\")\n",
    "        if(ii == 0):\n",
    "            axs[ii].set_ylabel(\"Actual Value\\n(un-scaled)\")\n",
    "        axs[ii].set_title(nms[ii])\n",
    "        axs[ii].plot(Ytest.iloc[:,ii], Ytest.iloc[:,ii], 'b')\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    \n",
    "fig.savefig(os.path.join(figDir, \"NNPreds_newData_control_bigbatch_FxFy.png\"), dpi = 120, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ytest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# refref: idea - predict whole paths, rather than just the endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close(\"all\")\n",
    "fig, axs = plt.subplots(1,3, figsize=(15, 5), facecolor='w', edgecolor='k')\n",
    "fig.subplots_adjust(hspace = 0.1, wspace=0.3)\n",
    "fig.suptitle('Predicted vs. acutal ', fontsize=14, fontweight='bold')\n",
    "\n",
    "axs = axs.ravel()\n",
    "\n",
    "nms = [\"Fx\", \"Fy\", \"tau\"]\n",
    "\n",
    "\n",
    "# replace lightest colors with white\n",
    "import matplotlib.colors\n",
    "\n",
    "cmap = plt.cm.magma_r\n",
    "cmaplist = np.array([cmap(i) for i in range(cmap.N)])\n",
    "#cmaplist[0:2] = np.repeat((0.0,0.0,0.0,0.0), 2).reshape(-1,4)\n",
    "cmaplist[:,0:3] = np.divide(cmaplist[:, 0:3], 1.1)\n",
    "#cmaplist[:,0:3] = np.transpose(np.vstack([np.linspace(1, 0, 256), np.linspace(1, 0, 256), np.linspace(1, 0, 256)]))\n",
    "cmaplist[0] = (1,1,1,0.5)\n",
    "cmap = matplotlib.colors.LinearSegmentedColormap.from_list('mcm',cmaplist, cmap.N)\n",
    "\n",
    "for ii in range(3):\n",
    "    # read in image\n",
    "    \n",
    "    try:\n",
    "        axs[ii].hexbin(y = Ytest_scaled[:,ii],x = nnpreds[:,ii], gridsize = 150, cmap = cmap)\n",
    "        axs[ii].set_xlabel(\"Predicted Value (scaled)\")\n",
    "        if(ii == 0):\n",
    "            axs[ii].set_ylabel(\"Actual Value\\n(scaled)\")\n",
    "        axs[ii].set_title(nms[ii])\n",
    "        axs[ii].plot(Ytest_scaled[:,ii], Ytest_scaled[:,ii], 'grey', linewidth = 1, linestyle  = \"--\")\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "fig.savefig(os.path.join(figDir, \"NNPreds_newData_control_bigbatch_FxFy_hexbin.png\"), dpi = 120, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bf2[['simNum', 'timestep']] = bf2[['simNum', 'timestep']].astype(int)\n",
    "\n",
    "bf2['count'] = bf2.groupby([\"simNum\", \"timestep\"]).cumcount()\n",
    "\n",
    "bf2.head()\n",
    "\n",
    "bf2.tail()\n",
    "\n",
    "sdf = bf2.loc[(bf2[\"count\"] == 0) | (bf2[\"count\"] == 99), :]\n",
    "sdf.head()\n",
    "\n",
    "ss = sdf.pivot_table(index='simNum', columns = \"count\",aggfunc='first')\n",
    "ss.columns = [f'{i}_{j}' if j != '' else f'{i}' for i,j in ss.columns]\n",
    "ss[\"simNum\"] = ss.index\n",
    "ss.index.name = None\n",
    "ss.head()\n",
    "\n",
    "np.array(ss).shape\n",
    "\n",
    "np.vstack([np.array(ss), np.array(ss)]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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