feasible_joint_reaction_loads/python/feasible_joint_reaction_loads.ipynb

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   "source": [
    "# Calculation of the Feasible Joint Reaction Loads"
   ]
  },
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   "cell_type": "markdown",
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    "The importance of evaluating the feasible muscle forces is demonstrated in the\n",
    "context of joint reaction analysis. An accurate estimation of the muscle forces\n",
    "is essential for the assessment of joint reaction loads. Consequently, the null\n",
    "space muscle forces can significantly alter the reaction forces without\n",
    "affecting the movement. The process is separated into four steps:\n",
    "\n",
    "1. Perform an inverse kinematics (IK), static optimization (SO) and joint\n",
    "reaction analysis (JRA) using OpenSim to generate the required data for the next\n",
    "steps.\n",
    "\n",
    "2. Calculate the feasible muscle forces that satisfy both the action and the\n",
    "physiological constraints of the muscles.\n",
    "\n",
    "3. For each distinct muscle force realization perform a joint reaction analysis.\n",
    "\n",
    "4. Post-process the reaction loads."
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# notebook general configuration\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import sympy as sp\n",
    "from IPython.display import display\n",
    "sp.interactive.printing.init_printing()\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (10.0, 6.0)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
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     "slide_type": "-"
    }
   },
   "source": [
    "## Subject Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Please provide the following information for the subject under study:\n",
    "\n",
    "- **.osim** the scaled OpenSim model (after scaling the model using a static\n",
    "    pose)\n",
    "\n",
    "- **.trc** the marker trajectories of the movement (based on OpenSim file\n",
    "    requirements)\n",
    "\n",
    "- **.mot** the ground reaction forces (based on the OpenSim file format)\n",
    "\n",
    "- **.xml** describing how to apply the ground reaction forces (based on OpenSim\n",
    "  force requirements)\n",
    "\n",
    "- **.xml** file with the reserve actuator for the SO (based on OpenSim requirements)\n",
    "\n",
    "- **subject mass** (used for scaling the reaction loads, and is not a\n",
    "    requirement)\n",
    "\n",
    "- **heel strike** and **toe off** events (used for annotation of the results,\n",
    "  and are not so important)\n",
    "\n",
    "\n",
    "The following calculations are performed for the *Gait10dof18musc* dataset. "
   ]
  },
  {
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   "source": [
    "# subject data\n",
    "mass = 72.6  # kg\n",
    "g = 9.8  # m/s^2\n",
    "body_weight = mass * g\n",
    "\n",
    "# time events of toe off and heel strike\n",
    "heel_strike_right = [0.65, 1.85]\n",
    "toe_off_right = [0.15, 1.4]\n",
    "heel_strike_left = [0.0, 1.25]\n",
    "toe_off_left = [0.8, 2]\n",
    "\n",
    "# required files and directories\n",
    "subject_dir = os.getcwd() + '/../dataset/Gait10dof18musc/'\n",
    "model_file = subject_dir + 'subject01.osim'\n",
    "trc_file = subject_dir + 'subject01_walk.trc'\n",
    "grf_file = subject_dir + 'subject01_walk_grf.mot'\n",
    "grf_xml_file = subject_dir + 'subject01_walk_grf.xml'\n",
    "reserve_actuators_file = subject_dir + 'reserve_actuators.xml'\n",
    "results_dir = subject_dir + 'notebook_results/'\n",
    "feasible_set_dir = results_dir + 'feasible_force_set/'\n",
    "jra_results_dir = results_dir + 'joint_reaction_analyses/'\n",
    "figures_dir = results_dir + 'fig/'\n",
    "\n",
    "if not (os.path.isfile(model_file) and\n",
    "        os.path.isfile(trc_file) and\n",
    "        os.path.isfile(grf_file) and\n",
    "        os.path.isfile(grf_xml_file) and\n",
    "        os.path.isfile(reserve_actuators_file)):\n",
    "    raise RuntimeError('required files do not exist')\n",
    "    \n",
    "if not (os.path.isdir(results_dir) and \n",
    "        os.path.isdir(feasible_set_dir) and\n",
    "        os.path.isdir(jra_results_dir) and\n",
    "        os.path.isdir(figures_dir)):\n",
    "    raise RuntimeError('required folders do not exist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Step 1: Perform Required OpenSim Analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
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   "source": [
    "In order to perform the feasible muscle force calculations, the kinematics and\n",
    "kinetics that satisfy the experimental measured motion and ground reaction\n",
    "forces must be calculated. Furthermore, OpenSim JRA is performed so as to\n",
    "compare the feasible reaction loads in step 4. The following functions make use\n",
    "of the OpenSim IK, SO and RJA analyses."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "from opensim_utils import perform_ik, perform_so, perform_jra, plot_sto\n",
    "\n",
    "# perform OpenSim inverse kinematics\n",
    "ik_file = perform_ik(model_file, trc_file, results_dir)\n",
    "plot_sto(ik_file, 4, save=True)\n",
    "\n",
    "# perform OpenSim static optimization\n",
    "(so_force_file, so_activation_file) = perform_so(model_file, ik_file, grf_file, \n",
    "                                                 grf_xml_file, reserve_actuators_file, \n",
    "                                                 results_dir)\n",
    "plot_sto(so_force_file, 4, save=True)\n",
    "\n",
    "# perform OpenSim joint reaction analysis\n",
    "jra_file = perform_jra(model_file, ik_file, grf_file, grf_xml_file,\n",
    "                       reserve_actuators_file, so_force_file, results_dir)\n",
    "\n",
    "# store file names so that they can be loaded without running this section\n",
    "pickle.dump((ik_file, so_force_file, jra_file), file(results_dir + 'opensim_files.dat', 'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "autoscroll": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
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   "outputs": [],
   "source": [
    "# get file names from step 1\n",
    "(ik_file, so_force_file, jra_file) = pickle.load(file(results_dir + 'opensim_files.dat', 'r'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Step 2: Calculate the Feasible Muscle Forces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
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   "source": [
    "The feasible muscle forces are calculated below. Initially, the moment arm and\n",
    "maximum muscle force quantities are computed for each instance of the\n",
    "movement. Then the following inequality is formed assuming a linear muscle model\n",
    "\n",
    "\\begin{equation}\\label{equ:linear-muscle-null-space-inequality}\n",
    "  \\begin{gathered}\n",
    "    f_m = f_{max} \\circ a_m = f_m^{\\parallel} +\n",
    "    N_{R} f_{m0},\\; 0 \\preceq a_m \\preceq 1\n",
    "    \\rightarrow \\\\\n",
    "    \\begin{bmatrix}\n",
    "      - N_{R} \\\\\n",
    "      \\hdashline\n",
    "      N_{R}\n",
    "    \\end{bmatrix}\n",
    "    f_{m0} \\preceq\n",
    "    \\begin{bmatrix}\n",
    "      f_m^{\\parallel} \\\\\n",
    "      \\hdashline\n",
    "      f_{max} - f_m^{\\parallel}\n",
    "    \\end{bmatrix} \\\\\n",
    "    Z f_{m0} \\preceq \\beta\n",
    "  \\end{gathered}\n",
    "\\end{equation}\n",
    "\n",
    "where $a_m \\in \\Re^{m}$ represents a vector of muscle activations, $f_{max} \\in\n",
    "\\Re^{m}$ a vector specifying the maximum muscle forces, $\\circ$ the Hadamard\n",
    "(elementwise) product, $f_m^{\\parallel}$ the particular muscle force solution\n",
    "that satisfies the action, $N_{R}$ the moment arm null space and $f_{m0}$ the\n",
    "null space forces."
   ]
  },
  {
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   },
   "outputs": [],
   "source": [
    "# since this section of code may be called independently after step 1\n",
    "# ensure that cells 2, 5 and 9 are evaluated\n",
    "from IPython.display import Javascript\n",
    "Javascript(\"Jupyter.notebook.execute_cells([2, 5, 9])\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "from opensim_utils import calculate_muscle_data\n",
    "from util import null_space, construct_muscle_space_inequality, \\\n",
    "    convex_bounded_vertex_enumeration, readMotionFile, \\\n",
    "    index_containing_substring, write_as_sto_file\n",
    "    \n",
    "# calculate the moment arm and maximum muscle forces assuming a linear muscle\n",
    "# model (in a future version this can calculate the properties for a nonlinear\n",
    "# muscle model)\n",
    "moment_arm, max_force = calculate_muscle_data(model_file, ik_file)\n",
    "\n",
    "# read SO results\n",
    "so_header, so_labels, so_data = readMotionFile(so_force_file)\n",
    "so_data = np.array(so_data)\n",
    "coordinates = moment_arm[0].shape[0]\n",
    "muscles = moment_arm[0].shape[1]\n",
    "time = so_data[:, 0]\n",
    "entries = time.shape[0]\n",
    "np.\n",
    "# collect quantities for computing the feasible muscle forces\n",
    "NR = []\n",
    "Z = []\n",
    "b = []\n",
    "fm_par = []\n",
    "for t in tqdm(range(0, entries)):\n",
    "    # get tau, R, Fmax\n",
    "    fm = so_data[t, 1:(muscles + 1)]  # time the first column\n",
    "    RT_temp = moment_arm[t, :, :]\n",
    "    fmax_temp = max_force[t, :]\n",
    "\n",
    "    # calculate the reduced rank (independent columns) null space to avoid\n",
    "    # singularities\n",
    "    NR_temp = null_space(RT_temp)\n",
    "    \n",
    "    # fm_par = fm is used instead of fm_par = -RBarT * tau because the\n",
    "    # muscle may not be able to satisfy the action. In OpenSim reserve\n",
    "    # actuators are used to ensure that Static Optimization can satisfy the\n",
    "    # action. In this case, we ignore the residual forces and assume that fm\n",
    "    # is the minimum effort solution. If the model is able to satisfy the\n",
    "    # action without needing reserve forces then we can use fm_par = -RBarT\n",
    "    # * tau as obtained form Inverse Dynamics.\n",
    "    fm_par_temp = fm\n",
    "\n",
    "    Z_temp, b_temp = construct_muscle_space_inequality(NR_temp,\n",
    "                                                       fm_par_temp,\n",
    "                                                       fmax_temp)\n",
    "\n",
    "    # append results\n",
    "    NR.append(NR_temp)\n",
    "    Z.append(Z_temp)\n",
    "    b.append(b_temp)\n",
    "    fm_par.append(fm_par_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The next step is to sample the inequality $Z f_{m0} \\leq \\beta$. This is the\n",
    "bottleneck of the analysis. The *convex_bounded_vertex_enumeration* uses the\n",
    "lsr method, which is a vertex enumeration algorithm for finding the vertices\n",
    "of a polytope in $O(v m^3)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
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   },
   "outputs": [],
   "source": [
    "# calculate the feasible muscle force set by sampling the inequality\n",
    "f_set = []\n",
    "for t in tqdm(range(0, entries)):\n",
    "    try:\n",
    "        fs = convex_bounded_vertex_enumeration(Z[t], b[t][:, 0], 0, method='lrs')\n",
    "    except:\n",
    "        print('inequlity is infeasible thus append previous iteration')\n",
    "        f_set.append(f_set[-1])\n",
    "        continue\n",
    "        \n",
    "    temp = []\n",
    "    for i in range(0, fs.shape[0]):\n",
    "        temp.append(fm_par[t] + NR[t].dot(fs[i, :]))\n",
    "\n",
    "    f_set.append(temp)\n",
    "\n",
    "# serialization f_set -> [time x feasible force set set x muscles] so as to\n",
    "# avoid recomputing it\n",
    "pickle.dump(f_set, file(feasible_set_dir + 'f_set.dat', 'w'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Finally, store the feasible muscle force set into multiple .sto files that can\n",
    "be used by the JRA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
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   },
   "outputs": [],
   "source": [
    "# keep only muscle forces\n",
    "idx = index_containing_substring(so_labels, 'FX')[0]\n",
    "labels = so_labels[:idx]\n",
    "\n",
    "# find largest feasible set\n",
    "S = len(f_set[0])\n",
    "for fs in f_set:\n",
    "    if len(fs) > S:\n",
    "        S = len(fs)\n",
    "\n",
    "# export muscle force realizations\n",
    "for j in tqdm(range(0, S)):\n",
    "    data_temp = []\n",
    "    for i in range(0, so_data.shape[0]):\n",
    "        data_temp.append(f_set[i][j % len(f_set[i])])\n",
    "\n",
    "    write_as_sto_file(feasible_set_dir + str(j) + '.sto', labels,\n",
    "                      time, np.array(data_temp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Step 3: Perform Joint Reaction Analyses on Feasible Muscle Forces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "For each distinct muscle force realization computed in the previous step\n",
    "initiate a JRA so as to calculate the influence of the muscle forces on the\n",
    "joint reaction loads. Due to memory leaks in OpenSim JRA implementation this\n",
    "process must be restarted since it can cause problems with the RAM usage. The if\n",
    "statement is used so as to continue from the last performed analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# since this section of code may be called independently after step 2\n",
    "# ensure that cells 2, 5 and 9 are evaluated\n",
    "from IPython.display import Javascript\n",
    "Javascript(\"Jupyter.notebook.execute_cells([2, 5, 9])\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from opensim_utils import perform_jra\n",
    "\n",
    "# get all files in the directory\n",
    "feasible_force_files = os.listdir(feasible_set_dir)\n",
    "\n",
    "# remove files that are not .sto\n",
    "feasible_force_files = [e for e in feasible_force_files if '.sto' in e]\n",
    "\n",
    "# perform joint reaction analyses\n",
    "previous_iteration = 0\n",
    "for i, force_file in enumerate(tqdm(feasible_force_files)):\n",
    "    # change the previous_iteration variable\n",
    "    if i < previous_iteration:\n",
    "         continue\n",
    "\n",
    "    if i > previous_iteration + 200:\n",
    "        print('please shutdown this notebook and reopen (RAM usage problem)')\n",
    "        print('set previous_iteration=' + str(previous_iteration + 200))\n",
    "        break\n",
    "        \n",
    "    perform_jra(model_file, ik_file, grf_file, grf_xml_file,\n",
    "                reserve_actuators_file, feasible_set_dir + force_file,\n",
    "                jra_results_dir, prefix=str(i) + '_')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Step 4: Postprocess Joint Reaction Loads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "This section collects the joint reaction loads calculated in the previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "Jupyter.notebook.execute_cells([2, 5, 9])"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# since this section of code may be called independently after step 3\n",
    "# ensure that cells 2, 5 and 9 are evaluated\n",
    "from IPython.display import Javascript\n",
    "Javascript(\"Jupyter.notebook.execute_cells([2, 5, 9])\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "autoscroll": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16524/16524 [01:59<00:00, 138.28it/s]\n"
     ]
    }
   ],
   "source": [
    "from util import readMotionFile, index_containing_substring\n",
    "\n",
    "# select the joint of interest\n",
    "joint = 'hip_r'\n",
    "\n",
    "# load OpenSim JRA results (step 1)\n",
    "os_header, os_labels, os_data = readMotionFile(jra_file)\n",
    "os_data = np.array(os_data)\n",
    "joint_index = index_containing_substring(os_labels, joint)\n",
    "assert(joint_index != -1)\n",
    "\n",
    "# get all files from the JRA batch simulation (step 3)\n",
    "jra_files = os.listdir(jra_results_dir)\n",
    "\n",
    "# remove files that are not joint reactions\n",
    "jra_files = [e for e in jra_files if 'ReactionLoads' in e]\n",
    "        \n",
    "# allocate the necessary space to collect all results\n",
    "solutions_to_keep = len(jra_files)\n",
    "simulationData = np.empty([solutions_to_keep,\n",
    "                           os_data.shape[0],\n",
    "                           os_data.shape[1]],\n",
    "                          dtype=float)\n",
    "\n",
    "# collect reaction loads\n",
    "for i, f in enumerate(tqdm(jra_files)):\n",
    "    header, labels, data = readMotionFile(jra_results_dir + f)\n",
    "    simulationData[i, :, :] = np.array(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The following section of code compares the feasible and OpenSim reaction loads\n",
    "for the joint of interest. The reaction loads are normalized using the body\n",
    "weight of the subject. The heel strike and toe off events are annotated\n",
    "accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "autoscroll": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DNtkwU3rhDTT2HciQR+cz8pop7PrjEmon+eYZEh+vDBhzsnzrh5qOSTshjapK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\nRERERERElPUYwCAiIiIiIiKirPf/AS14U9xXOfLwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select the heel strike and toe off events\n",
    "if '_l' in joint:\n",
    "    heel_strike = heel_strike_left\n",
    "    toe_off = toe_off_left\n",
    "else:\n",
    "    heel_strike = heel_strike_right\n",
    "    toe_off = toe_off_right\n",
    "\n",
    "# plot data min/max reactions vs OpenSim JRA\n",
    "joints = 3\n",
    "fig, ax = plt.subplots(nrows=1, ncols=joints, figsize=(15, 5))\n",
    "for i in range(0, joints):\n",
    "    # plot feasible reaction loads\n",
    "    min_reaction = np.min(simulationData[1:, 1:, joint_index[i]] / body_weight, \n",
    "                          axis=0)\n",
    "    max_reaction = np.max(simulationData[1:, 1:, joint_index[i]] / body_weight, \n",
    "                          axis=0)\n",
    "    ax[i].fill_between(os_data[1:, 0], min_reaction, max_reaction, \n",
    "                       color='b', alpha=0.2, label='Feasible Reactions')\n",
    "    # plot OpenSim reaction loads\n",
    "    ax[i].plot(os_data[1:, 0], os_data[1:, joint_index[i]] / body_weight,\n",
    "               '-.r', label='OpenSim JRA')\n",
    "    # annotate the heel strike and toe off regions\n",
    "    min_min = np.min(min_reaction)\n",
    "    max_max = np.max(max_reaction)\n",
    "    ax[i].vlines(x=heel_strike, ymin=min_min, ymax=max_max,\n",
    "                 color='c', linestyle='--', label='HS')\n",
    "    ax[i].vlines(x=toe_off, ymin=min_min, ymax=max_max,\n",
    "                 color='m', linestyle=':', label='TO')\n",
    "    # figure settings\n",
    "    ax[i].set_title(os_labels[joint_index[i]])\n",
    "    ax[i].set_xlabel('time (s)')\n",
    "    ax[i].set_ylabel('reaction / body weight')\n",
    "    if i == joints - 1:\n",
    "        ax[i].legend()\n",
    "\n",
    "# export results\n",
    "fig.tight_layout()\n",
    "fig.savefig(figures_dir + joint + '.pdf',\n",
    "            format='pdf', dpi=300)\n",
    "fig.savefig(figures_dir + joint + '.png',\n",
    "            format='png', dpi=300)\n",
    "# fig.show()"
   ]
  },
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   "source": [
    "We observe that the results obtained from OpenSim joint reaction analysis\n",
    "predict low reaction load levels, since the minimum muscle effort criterion is\n",
    "used to compute the muscle forces, ignoring muscle co-contraction. On the\n",
    "contrary, it is possible to calculate the feasible reactions without making any\n",
    "prior assumption, which can limit the scope and extent of the analysis. Last and\n",
    "perhaps most importantly, the large range of possible values confirms that\n",
    "misinterpretation of the results is possible if the null space solutions are\n",
    "ignored."
   ]
  }
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