GenerateCombinedDataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "988cec37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from mpl_toolkits import mplot3d\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a4df3aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to recorded dataset\n",
    "\n",
    "datasets_dir = \"./Data/RecordedData/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8abdc784",
   "metadata": {},
   "source": [
    "# Combine FT sensor data from ILoSA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "73bfa2c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_685372/3989766937.py:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  data_left_ilosa = np.array(np.load(data_file_left_ilosa, allow_pickle=True,))\n",
      "/tmp/ipykernel_685372/3989766937.py:7: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  data_right_ilosa = np.array(np.load(data_file_right_ilosa, allow_pickle=True,))\n",
      "/tmp/ipykernel_685372/3989766937.py:12: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  data_left_ilosa_1 = np.array(np.load(data_file_left_ilosa_1, allow_pickle=True,))\n",
      "/tmp/ipykernel_685372/3989766937.py:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  data_right_ilosa_1 = np.array(np.load(data_file_right_ilosa_1, allow_pickle=True,))\n",
      "/tmp/ipykernel_685372/3989766937.py:50: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter3D(force_x_left, force_y_left, force_z_left, cmap='Greens');\n",
      "/tmp/ipykernel_685372/3989766937.py:51: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter3D(force_x_right, force_y_right, force_z_right, cmap='red');\n",
      "/tmp/ipykernel_685372/3989766937.py:57: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.array([combined_data_ilosa[i][1] for i in range(len(combined_data_ilosa))])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of left dataset: 10, and 30\n",
      "Length of right dataset: 10, and 29\n",
      "Combined length of left dataset: 40\n",
      "Combined length of right dataset: 39\n",
      "Training dataset: (63, 63)\n",
      "Testing dataset: (16, 16)\n",
      "920\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load dataset\n",
    "\n",
    "# left\n",
    "data_file_left_ilosa = os.path.join(datasets_dir, 'record_wrench_data_left.pickle')\n",
    "data_file_right_ilosa = os.path.join(datasets_dir, 'record_wrench_data_right.pickle')\n",
    "data_left_ilosa = np.array(np.load(data_file_left_ilosa, allow_pickle=True,))\n",
    "data_right_ilosa = np.array(np.load(data_file_right_ilosa, allow_pickle=True,))\n",
    "\n",
    "# right\n",
    "data_file_left_ilosa_1 = os.path.join(datasets_dir, 'record_wrench_data_left_1.pickle')\n",
    "data_file_right_ilosa_1 = os.path.join(datasets_dir, 'record_wrench_data_right_1.pickle')\n",
    "data_left_ilosa_1 = np.array(np.load(data_file_left_ilosa_1, allow_pickle=True,))\n",
    "data_right_ilosa_1 = np.array(np.load(data_file_right_ilosa_1, allow_pickle=True,))\n",
    "\n",
    "print(f\"Length of left dataset: {len(data_left_ilosa)}, and {len(data_left_ilosa_1)}\")\n",
    "print(f\"Length of right dataset: {len(data_right_ilosa)}, and {len(data_right_ilosa_1)}\")\n",
    "\n",
    "# combine dataset\n",
    "combined_left_ilosa = np.concatenate((data_left_ilosa, data_left_ilosa_1), axis=0)\n",
    "combined_right_ilosa = np.concatenate((data_right_ilosa, data_right_ilosa_1), axis=0)\n",
    "\n",
    "print(f\"Combined length of left dataset: {len(combined_left_ilosa)}\")\n",
    "print(f\"Combined length of right dataset: {len(combined_right_ilosa)}\")\n",
    "\n",
    "# get wrench \n",
    "wrench_left = np.array([combined_left_ilosa[i][1] for i in range(len(combined_left_ilosa))], dtype=object)\n",
    "wrench_right = np.array([combined_right_ilosa[i][1] for i in range(len(combined_right_ilosa))], dtype=object)\n",
    "\n",
    "# get labels\n",
    "labels_left = np.array([combined_left_ilosa[i][0] for i in range(len(combined_left_ilosa))], dtype=object)\n",
    "labels_right = np.array([combined_right_ilosa[i][0] for i in range(len(combined_right_ilosa))], dtype=object)\n",
    "\n",
    "# plot\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "force_left = [wrench_left[i][j][:3] for i in range(len(wrench_left)) for j in range(len(wrench_left[i]))]\n",
    "force_right = [wrench_right[i][j][:3] for i in range(len(wrench_right)) for j in range(len(wrench_right[i]))]\n",
    "\n",
    "force_x_left = [force_left[i][0] for i in range(len(force_left))]\n",
    "force_y_left = [force_left[i][1] for i in range(len(force_left))]\n",
    "force_z_left = [force_left[i][2] for i in range(len(force_left))]\n",
    "\n",
    "force_x_right = [force_right[i][0] for i in range(len(force_right))]\n",
    "force_y_right = [force_right[i][1] for i in range(len(force_right))]\n",
    "force_z_right = [force_right[i][2] for i in range(len(force_right))]\n",
    "force_x_left\n",
    "\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter3D(force_x_left, force_y_left, force_z_left, cmap='Greens');\n",
    "ax.scatter3D(force_x_right, force_y_right, force_z_right, cmap='red');\n",
    "\n",
    "\n",
    "combined_data_ilosa = np.concatenate((combined_left_ilosa, combined_right_ilosa), axis=0)\n",
    "\n",
    "\n",
    "X = np.array([combined_data_ilosa[i][1] for i in range(len(combined_data_ilosa))])\n",
    "y = np.array([combined_data_ilosa[i][0] for i in range(len(combined_data_ilosa))])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "print(f\"Training dataset: {X_train.shape[0], y_train.shape[0]}\")\n",
    "print(f\"Testing dataset: {X_test.shape[0], y_test.shape[0]}\")\n",
    "\n",
    "combined_data_training_ilosa = [(y_train[i], X_train[i]) for i in range(len(X_train))]\n",
    "\n",
    "with open('./Data/CombinedData/combined_data_training_ilosa.pickle', 'wb') as handle:\n",
    "    pickle.dump(combined_data_training_ilosa, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "combined_data_testing_ilosa = [(y_test[i] ,X_test[i]) for i in range(len(X_test))]\n",
    "\n",
    "with open('./Data/CombinedData/combined_data_testing_ilosa.pickle', 'wb') as handle:\n",
    "    pickle.dump(combined_data_testing_ilosa, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9c01ab",
   "metadata": {},
   "source": [
    "# Combine FT sensor data from panda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c6f5ec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_685372/1760960297.py:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  data_left_panda = np.array(np.load(data_file_left_panda, allow_pickle=True,))\n",
      "/tmp/ipykernel_685372/1760960297.py:7: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  data_right_panda = np.array(np.load(data_file_right_panda, allow_pickle=True,))\n",
      "/tmp/ipykernel_685372/1760960297.py:12: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  data_left_panda_1 = np.array(np.load(data_file_left_panda_1, allow_pickle=True,))\n",
      "/tmp/ipykernel_685372/1760960297.py:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  data_right_panda_1 = np.array(np.load(data_file_right_panda_1, allow_pickle=True,))\n",
      "/tmp/ipykernel_685372/1760960297.py:50: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter3D(force_x_left, force_y_left, force_z_left, cmap='Greens');\n",
      "/tmp/ipykernel_685372/1760960297.py:51: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter3D(force_x_right, force_y_right, force_z_right, cmap='red');\n",
      "/tmp/ipykernel_685372/1760960297.py:57: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.array([combined_data_panda[i][1] for i in range(len(combined_data_panda))])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of left dataset: 10, and 30\n",
      "Length of right dataset: 10, and 29\n",
      "Combined length of left dataset: 40\n",
      "Combined length of right dataset: 39\n",
      "Training dataset: (63, 63)\n",
      "Testing dataset: (16, 16)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load dataset\n",
    "\n",
    "# left\n",
    "data_file_left_panda = os.path.join(datasets_dir, 'record_wrench_data1_left.pickle')\n",
    "data_file_right_panda = os.path.join(datasets_dir, 'record_wrench_data1_right.pickle')\n",
    "data_left_panda = np.array(np.load(data_file_left_panda, allow_pickle=True,))\n",
    "data_right_panda = np.array(np.load(data_file_right_panda, allow_pickle=True,))\n",
    "\n",
    "# right\n",
    "data_file_left_panda_1 = os.path.join(datasets_dir, 'record_wrench_data1_left_1.pickle')\n",
    "data_file_right_panda_1 = os.path.join(datasets_dir, 'record_wrench_data1_right_1.pickle')\n",
    "data_left_panda_1 = np.array(np.load(data_file_left_panda_1, allow_pickle=True,))\n",
    "data_right_panda_1 = np.array(np.load(data_file_right_panda_1, allow_pickle=True,))\n",
    "\n",
    "print(f\"Length of left dataset: {len(data_left_panda)}, and {len(data_left_panda_1)}\")\n",
    "print(f\"Length of right dataset: {len(data_right_panda)}, and {len(data_right_panda_1)}\")\n",
    "\n",
    "# combine_dataset\n",
    "combined_left_panda = np.concatenate((data_left_panda, data_left_panda_1), axis=0)\n",
    "combined_right_panda = np.concatenate((data_right_panda, data_right_panda_1), axis=0)\n",
    "\n",
    "print(f\"Combined length of left dataset: {len(combined_left_panda)}\")\n",
    "print(f\"Combined length of right dataset: {len(combined_right_panda)}\")\n",
    "\n",
    "# get wrench\n",
    "wrench_left = np.array([combined_left_panda[i][1] for i in range(len(combined_left_panda))], dtype=object)\n",
    "wrench_right = np.array([combined_right_panda[i][1] for i in range(len(combined_right_panda))], dtype=object)\n",
    "\n",
    "# get labels\n",
    "labels_left = np.array([combined_left_panda[i][0] for i in range(len(combined_left_panda))], dtype=object)\n",
    "labels_right = np.array([combined_right_panda[i][0] for i in range(len(combined_right_panda))], dtype=object)\n",
    "\n",
    "# plot\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "force_left = [wrench_left[i][j][:3] for i in range(len(wrench_left)) for j in range(len(wrench_left[i]))]\n",
    "force_right = [wrench_right[i][j][:3] for i in range(len(wrench_right)) for j in range(len(wrench_right[i]))]\n",
    "\n",
    "force_x_left = [force_left[i][0] for i in range(len(force_left))]\n",
    "force_y_left = [force_left[i][1] for i in range(len(force_left))]\n",
    "force_z_left = [force_left[i][2] for i in range(len(force_left))]\n",
    "\n",
    "force_x_right = [force_right[i][0] for i in range(len(force_right))]\n",
    "force_y_right = [force_right[i][1] for i in range(len(force_right))]\n",
    "force_z_right = [force_right[i][2] for i in range(len(force_right))]\n",
    "force_x_left\n",
    "\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter3D(force_x_left, force_y_left, force_z_left, cmap='Greens');\n",
    "ax.scatter3D(force_x_right, force_y_right, force_z_right, cmap='red');\n",
    "\n",
    "\n",
    "combined_data_panda = np.concatenate((combined_left_panda, combined_right_panda), axis=0)\n",
    "\n",
    "\n",
    "X = np.array([combined_data_panda[i][1] for i in range(len(combined_data_panda))])\n",
    "y = np.array([combined_data_panda[i][0] for i in range(len(combined_data_panda))])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "print(f\"Training dataset: {X_train.shape[0], y_train.shape[0]}\")\n",
    "print(f\"Testing dataset: {X_test.shape[0], y_test.shape[0]}\")\n",
    "\n",
    "combined_data_training_panda = [(y_train[i], X_train[i]) for i in range(len(X_train))]\n",
    "\n",
    "with open('./Data/CombinedData/combined_data_training_panda.pickle', 'wb') as handle:\n",
    "    pickle.dump(combined_data_training_panda, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "combined_data_testing_panda = [(y_test[i] ,X_test[i]) for i in range(len(X_test))]\n",
    "\n",
    "with open('./Data/CombinedData/combined_data_testing_panda.pickle', 'wb') as handle:\n",
    "    pickle.dump(combined_data_testing_panda, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
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