DEMO_c3d.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c76b635a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import dlc2kinematics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10209067",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the path to the .c3d file and the joints_dictionary\n",
    "\n",
    "sample_files_path = \"./examples/runway03.c3d\"\n",
    "\n",
    "joints_dict = {'left_hip_joint': ['lcrest', 'lhip', 'lknee'],\n",
    " 'left_knee_joint': ['lhip', 'lknee', 'lankle'],\n",
    " 'left_ankle_joint': ['lknee', 'lankle', 'lmtp'],\n",
    " 'right_hip_joint': ['rcrest', 'rhip', 'rknee'],\n",
    " 'right_knee_joint': ['rhip', 'rknee', 'rankle'],\n",
    " 'right_ankle_joint': ['rknee', 'rankle', 'rmtp']}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a509846d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the dataframe plus all the metadata from the .c3d file\n",
    "\n",
    "df, bodyparts, scorer, first_frame, last_frame, sample_rate = dlc2kinematics.preprocess_c3d.load_c3d_data(sample_files_path,\n",
    "                                                                                                         scorer=\"ELG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eeba52ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our file has spurious bodyparts called *12, *13, *14, *15 dropping those columns here.\n",
    "\n",
    "bodyparts_to_drop = [i[1] for i in df.columns.to_list()[::3] if i[1].startswith((\"*\"))]\n",
    "df = df.drop(bodyparts_to_drop, axis=1, level=1, inplace=False)\n",
    "bodyparts = [bp for bp in bodyparts if bp not in bodyparts_to_drop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e213a331",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing joint angles for left_hip_joint\n",
      "Computing joint angles for left_knee_joint\n",
      "Computing joint angles for left_ankle_joint\n",
      "Computing joint angles for right_hip_joint\n",
      "Computing joint angles for right_knee_joint\n",
      "Computing joint angles for right_ankle_joint\n",
      "Saving the joint angles as a pandas array in /Users/elisa/Documents/Repos/DLC2Kinematics\n",
      "Saving the joint angular velocity as a pandas array in /Users/elisa/Documents/Repos/DLC2Kinematics \n",
      "Saving the joint angular acceleration as a pandas array in /Users/elisa/Documents/Repos/DLC2Kinematics \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing the main functions \n",
    "\n",
    "df_vel = dlc2kinematics.compute_velocity(df, bodyparts=['all'])\n",
    "df_acc = dlc2kinematics.compute_acceleration(df, bodyparts=['all'])\n",
    "df_speed = dlc2kinematics.compute_speed(df,bodyparts=['all'])\n",
    "joint_angles = dlc2kinematics.compute_joint_angles(df,joints_dict)\n",
    "joint_vel = dlc2kinematics.compute_joint_velocity(joint_angles)\n",
    "joint_acc = dlc2kinematics.compute_joint_acceleration(joint_angles)\n",
    "corr = dlc2kinematics.compute_correlation(joint_vel, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b28cb3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = dlc2kinematics.compute_pca(joint_vel, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b354fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>scorer</th>\n",
       "      <th colspan=\"10\" halign=\"left\">ELG</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">scorer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bodyparts</th>\n",
       "      <th colspan=\"3\" halign=\"left\">lankle</th>\n",
       "      <th colspan=\"3\" halign=\"left\">lcrest</th>\n",
       "      <th colspan=\"3\" halign=\"left\">lhip</th>\n",
       "      <th>lknee</th>\n",
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       "      <th>lhip</th>\n",
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       "      <th>lshoulder</th>\n",
       "      <th>rankle</th>\n",
       "      <th>rcrest</th>\n",
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       "      <th>rmtp</th>\n",
       "      <th>rshoulder</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coords</th>\n",
       "      <th>x</th>\n",
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       "      <th>likelihood</th>\n",
       "      <th>likelihood</th>\n",
       "      <th>likelihood</th>\n",
       "      <th>likelihood</th>\n",
       "      <th>likelihood</th>\n",
       "      <th>likelihood</th>\n",
       "      <th>likelihood</th>\n",
       "      <th>likelihood</th>\n",
       "      <th>likelihood</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>-444.997559</td>\n",
       "      <td>52.688873</td>\n",
       "      <td>59.719612</td>\n",
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       "      <td>46.903336</td>\n",
       "      <td>-491.609192</td>\n",
       "      <td>25.788338</td>\n",
       "      <td>84.566307</td>\n",
       "      <td>-412.747345</td>\n",
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       "      <td>67.901657</td>\n",
       "      <td>-443.166595</td>\n",
       "      <td>53.146946</td>\n",
       "      <td>60.131798</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>-441.711517</td>\n",
       "      <td>53.564060</td>\n",
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       "      <td>-487.328644</td>\n",
       "      <td>27.819424</td>\n",
       "      <td>85.803703</td>\n",
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       "      <td>72.257660</td>\n",
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       "      <td>-440.174835</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>-484.629700</td>\n",
       "      <td>29.323593</td>\n",
       "      <td>86.512001</td>\n",
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       "      <td>68.933228</td>\n",
       "      <td>-438.631714</td>\n",
       "      <td>54.380566</td>\n",
       "      <td>61.266098</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>56.716793</td>\n",
       "      <td>445.170441</td>\n",
       "      <td>18.133451</td>\n",
       "      <td>87.442825</td>\n",
       "      <td>510.867004</td>\n",
       "      <td>67.838043</td>\n",
       "      <td>66.164879</td>\n",
       "      <td>484.880341</td>\n",
       "      <td>45.812061</td>\n",
       "      <td>66.019554</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>56.791084</td>\n",
       "      <td>445.615295</td>\n",
       "      <td>18.260849</td>\n",
       "      <td>88.109924</td>\n",
       "      <td>511.333649</td>\n",
       "      <td>67.774216</td>\n",
       "      <td>66.589836</td>\n",
       "      <td>485.486023</td>\n",
       "      <td>45.872482</td>\n",
       "      <td>66.510834</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>56.691242</td>\n",
       "      <td>446.014771</td>\n",
       "      <td>18.417027</td>\n",
       "      <td>88.573105</td>\n",
       "      <td>511.901520</td>\n",
       "      <td>67.663422</td>\n",
       "      <td>66.856987</td>\n",
       "      <td>486.089569</td>\n",
       "      <td>45.935753</td>\n",
       "      <td>66.898468</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>56.792355</td>\n",
       "      <td>446.489868</td>\n",
       "      <td>18.682011</td>\n",
       "      <td>89.119133</td>\n",
       "      <td>512.372131</td>\n",
       "      <td>67.560394</td>\n",
       "      <td>67.202644</td>\n",
       "      <td>486.617615</td>\n",
       "      <td>45.952614</td>\n",
       "      <td>67.192963</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>57.553661</td>\n",
       "      <td>447.285370</td>\n",
       "      <td>18.786667</td>\n",
       "      <td>89.723595</td>\n",
       "      <td>512.871460</td>\n",
       "      <td>67.441116</td>\n",
       "      <td>67.579010</td>\n",
       "      <td>487.180145</td>\n",
       "      <td>46.003773</td>\n",
       "      <td>67.575958</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>379 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "scorer           ELG                                                           \\\n",
       "bodyparts     lankle                            lcrest                          \n",
       "coords             x           y          z          x           y          z   \n",
       "0          47.007511 -493.187439  25.222166  83.864677 -414.261658  72.140793   \n",
       "1          46.903336 -491.609192  25.788338  84.566307 -412.747345  72.182121   \n",
       "2          46.915806 -489.708313  26.647650  85.242775 -411.256561  72.216339   \n",
       "3          47.232620 -487.328644  27.819424  85.803703 -409.719360  72.257660   \n",
       "4          47.903484 -484.629700  29.323593  86.512001 -408.312714  72.297142   \n",
       "..               ...         ...        ...        ...         ...        ...   \n",
       "374        56.716793  445.170441  18.133451  87.442825  510.867004  67.838043   \n",
       "375        56.791084  445.615295  18.260849  88.109924  511.333649  67.774216   \n",
       "376        56.691242  446.014771  18.417027  88.573105  511.901520  67.663422   \n",
       "377        56.792355  446.489868  18.682011  89.119133  512.372131  67.560394   \n",
       "378        57.553661  447.285370  18.786667  89.723595  512.871460  67.441116   \n",
       "\n",
       "scorer                                                  ...     scorer  \\\n",
       "bodyparts       lhip                             lknee  ...       lhip   \n",
       "coords             x           y          z          x  ... likelihood   \n",
       "0          67.525536 -444.997559  52.688873  59.719612  ...        1.0   \n",
       "1          67.901657 -443.166595  53.146946  60.131798  ...        1.0   \n",
       "2          68.268326 -441.711517  53.564060  60.487247  ...        1.0   \n",
       "3          68.668564 -440.174835  53.945728  60.855938  ...        1.0   \n",
       "4          68.933228 -438.631714  54.380566  61.266098  ...        1.0   \n",
       "..               ...         ...        ...        ...  ...        ...   \n",
       "374        66.164879  484.880341  45.812061  66.019554  ...        1.0   \n",
       "375        66.589836  485.486023  45.872482  66.510834  ...        1.0   \n",
       "376        66.856987  486.089569  45.935753  66.898468  ...        1.0   \n",
       "377        67.202644  486.617615  45.952614  67.192963  ...        1.0   \n",
       "378        67.579010  487.180145  46.003773  67.575958  ...        1.0   \n",
       "\n",
       "scorer                                                                       \\\n",
       "bodyparts      lknee       lmtp  lshoulder     rankle     rcrest       rhip   \n",
       "coords    likelihood likelihood likelihood likelihood likelihood likelihood   \n",
       "0                1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "1                1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "2                1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "3                1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "4                1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "..               ...        ...        ...        ...        ...        ...   \n",
       "374              1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "375              1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "376              1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "377              1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "378              1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "\n",
       "scorer                                      \n",
       "bodyparts      rknee       rmtp  rshoulder  \n",
       "coords    likelihood likelihood likelihood  \n",
       "0                1.0        1.0        1.0  \n",
       "1                1.0        1.0        1.0  \n",
       "2                1.0        1.0        1.0  \n",
       "3                1.0        1.0        1.0  \n",
       "4                1.0        1.0        1.0  \n",
       "..               ...        ...        ...  \n",
       "374              1.0        1.0        1.0  \n",
       "375              1.0        1.0        1.0  \n",
       "376              1.0        1.0        1.0  \n",
       "377              1.0        1.0        1.0  \n",
       "378              1.0        1.0        1.0  \n",
       "\n",
       "[379 rows x 48 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding a mock column likelihood to be able to use compute_umap\n",
    "\n",
    "for bp in bodyparts:\n",
    "    df[\"scorer\", bp, \"likelihood\"] = np.ones(len(df))\n",
    "df.sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c70a7cc",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'umap' has no attribute 'UMAP'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xl/jtsrdg_x2q3ghmx00l1wr_pc0000gn/T/ipykernel_8843/3954133158.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mkeypoints\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'lknee'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rknee'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0membedding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransformed_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdlc2kinematics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_umap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeypoints\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeypoints\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_transform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"euclidean\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mdlc2kinematics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_umap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformed_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"indigo\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\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~/Documents/Repos/DLC2Kinematics/src/dlc2kinematics/mainfxns.py\u001b[0m in \u001b[0;36mcompute_umap\u001b[0;34m(df, keypoints, pcutoff, chunk_length, fit_transform, n_neighbors, n_components, metric, min_dist, metric_kwds, output_metric, output_metric_kwds, n_epochs, learning_rate, init, spread, low_memory, set_op_mix_ratio, local_connectivity, repulsion_strength, negative_sample_rate, transform_queue_size, a, b, random_state, angular_rp_forest, target_n_neighbors, target_metric, target_metric_kwds, target_weight, transform_seed, force_approximation_algorithm, verbose, unique)\u001b[0m\n\u001b[1;32m    330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m     \u001b[0;31m# Create embedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m     embedding = umap.UMAP(\n\u001b[0m\u001b[1;32m    333\u001b[0m         \u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m         \u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'umap' has no attribute 'UMAP'"
     ]
    }
   ],
   "source": [
    "# This fails to run on my end unless I modify the import in the backend, \n",
    "# from import umap to import umap.umap_ as umap but it probably is a problem of my setup\n",
    "\n",
    "keypoints = ['lknee', 'rknee']\n",
    "\n",
    "embedding, transformed_data = dlc2kinematics.compute_umap(df, keypoints=keypoints, chunk_length=30, fit_transform=True, n_neighbors=30, n_components=3, metric=\"euclidean\")\n",
    "dlc2kinematics.plot_umap(transformed_data, size=5, alpha=1, color=\"indigo\", figsize=(10, 6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e91ffd0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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