format_HAR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "86ce8245-b923-42ba-b1cf-b0a8bdb804d4",
   "metadata": {},
   "source": [
    "# Notebook to format data from \"A public domain dataset for human activity recognition using smartphones\", Anguita et al (2013).\n",
    "\n",
    "Data can be found [here](https://doi.org/10.24432/C54S4K).\n",
    "\n",
    "[Leaderboard](https://paperswithcode.com/dataset/har)\n",
    "\n",
    "[Paper](https://www.esann.org/sites/default/files/proceedings/legacy/es2013-84.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594fc3f1-2e94-4188-9d49-9f307850211d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Outputs:\n",
    "- dataset_metadata (yaml)\n",
    "- clip_data (directory of csv files)\n",
    "- class_rep (yaml)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0272a66-c0c3-4a6b-8e2b-73356e7f176d",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec388900-f0ab-4e41-a56b-9597e4f4911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import h5py\n",
    "from matplotlib import pyplot as plt\n",
    "import glob\n",
    "import yaml\n",
    "import scipy.signal as signal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62862f57-b5c9-4569-bf16-8b5bc33e7fee",
   "metadata": {},
   "source": [
    "We first set up directories where datafiles and metadata will be stored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6bc728a2-a91e-4da2-abea-4b57ceb4e818",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify data filepaths, and set up directories where we will save files\n",
    "\n",
    "raw_data_dir = '/home/jupyter/behavior_data_local/data/raw/HAR' \n",
    "formatted_data_dir = '/home/jupyter/behavior_data_local/data/formatted/HAR'\n",
    "\n",
    "if not os.path.exists(formatted_data_dir):\n",
    "    os.makedirs(formatted_data_dir)\n",
    "    \n",
    "acc_fps = glob.glob(raw_data_dir + '/RawData/acc*.txt')\n",
    "gyr_fps = glob.glob(raw_data_dir + '/RawData/gyr*.txt')\n",
    "annotations_fp = os.path.join(raw_data_dir, 'RawData', 'labels.txt')\n",
    "\n",
    "clip_data_dir = os.path.join(formatted_data_dir, 'clip_data')\n",
    "if not os.path.exists(clip_data_dir):\n",
    "    os.makedirs(clip_data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe277a69-fbc9-4eab-8f04-c2062282d97d",
   "metadata": {},
   "source": [
    "Dataset metadata is kept in a dictionary for now. All entries in the dataset metadata are read by the main scripts in BEBE, and are required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98f244e7-ac03-48e2-868a-1b7f7dd15fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset metadata is tracked as a dictionary\n",
    "\n",
    "dataset_metadata = {}\n",
    "sr = 50 # sampling rate, in Hz\n",
    "dataset_metadata['sr'] = sr ## from dataset documentation\n",
    "dataset_metadata['dataset_name'] = 'HAR'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc07e2f1-eb44-43f6-9a75-a03c191ad6d2",
   "metadata": {},
   "source": [
    "Each contiguous chunk of data is a clip, and is associated with a unique clip_id. These clip ids will be used as filenames when saving formatted data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bbba4963-16ae-4884-bdc5-50c6bb2bf6d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we get clip_ids by drawing from the names of the pre-existing acc and gyr files.\n",
    "## eg '/home/jupyter/behavior_data_local/data/raw/HAR/RawData/acc_exp58_user29.txt' -> 'exp58_user29'\n",
    "\n",
    "acc_clip_ids = sorted(set([x.split('/')[-1].split('.')[0][4:] for x in acc_fps]))\n",
    "gyr_clip_ids = sorted(set([x.split('/')[-1].split('.')[0][5:] for x in gyr_fps]))\n",
    "\n",
    "assert acc_clip_ids == gyr_clip_ids\n",
    "\n",
    "clip_ids = acc_clip_ids\n",
    "dataset_metadata['clip_ids'] = clip_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44705825-f596-40b6-a2b6-f2be91f9444a",
   "metadata": {},
   "source": [
    "Each individual is given a number, beginning with 0 (skipped numbers are allowed but not preferred).\n",
    "We also set up a dictionary `clip_id_to_individual_id` which is of the form `{clip_id : individual_id}`. This tells us which individual is represented in each clip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ea20959-639e-4f2c-8efa-945516302110",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Integer codes for individuals\n",
    "\n",
    "clip_id_to_individual_id = {clip_id : int(clip_id.split('_')[1][4:]) for clip_id in clip_ids}\n",
    "individuals = sorted({clip_id_to_individual_id[key] for key in clip_id_to_individual_id})\n",
    "\n",
    "dataset_metadata['individual_ids'] = individuals # There are 14 individuals but only 13 mentioned in the paper\n",
    "dataset_metadata['clip_id_to_individual_id'] = clip_id_to_individual_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdcde3b8-8190-4971-ab5b-0a9ea4b80646",
   "metadata": {},
   "source": [
    "We divide the individuals into five folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2bafb1c-3f7f-43af-82a5-acaf4c7c8b69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: [1, 6, 11, 16, 21, 26], 1: [2, 7, 12, 17, 22, 27], 2: [3, 8, 13, 18, 23, 28], 3: [4, 9, 14, 19, 24, 29], 4: [5, 10, 15, 20, 25, 30]}\n",
      "There are 30 individuals\n"
     ]
    }
   ],
   "source": [
    "# Set up data splits: divide individuals across folds for cross validation\n",
    "\n",
    "n_folds = 5\n",
    "dataset_metadata['n_folds'] = n_folds\n",
    "individuals_per_fold = {i : [] for i in range(n_folds)}\n",
    "\n",
    "for pointer, individual in enumerate(individuals):\n",
    "    individuals_per_fold[pointer % n_folds].append(individual)\n",
    "    \n",
    "dataset_metadata['individuals_per_fold'] = individuals_per_fold\n",
    "    \n",
    "print(individuals_per_fold)\n",
    "print(\"There are %d individuals\" % len(individuals))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0bcb729-1c92-440c-831f-23cd244c8332",
   "metadata": {},
   "source": [
    "We set up a dictionary `clip_ids_per_fold` which is of the form `{fold : list of clip_ids}`. It says in a given fold, which clip_ids are in that fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed6d55ef-3574-4052-9506-6c2d9af412cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up clip ids \n",
    "\n",
    "clip_ids_per_fold = {i : [] for i in range(n_folds)}\n",
    "for clip_id in clip_ids:\n",
    "    individual = clip_id_to_individual_id[clip_id]\n",
    "    for fold in range(n_folds):\n",
    "        if individual in individuals_per_fold[fold]:\n",
    "            clip_ids_per_fold[fold].append(clip_id)\n",
    "for fold in range(n_folds):\n",
    "    clip_ids_per_fold[fold] = sorted(clip_ids_per_fold[fold])\n",
    "\n",
    "dataset_metadata['clip_ids_per_fold'] = clip_ids_per_fold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c168a9ac-267f-4be8-ac36-229606913c24",
   "metadata": {},
   "source": [
    "We create a list of behavioral classes. By convention, the `unknown` class is always the first in this list, even if all data in the dataset has been annotated. The integer label associated with each behavior class name is equal to the index of that class name in this list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "444a3dc3-dfb0-4907-833e-8caf3eb4d88d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Integer codes for specific behaviors\n",
    "\n",
    "# list of behaviors, see activity_labels.txt\n",
    "beh_names = ['unknown', \n",
    "             'WALKING',\n",
    "             'WALKING_UPSTAIRS',\n",
    "             'WALKING_DOWNSTAIRS',\n",
    "             'SITTING',\n",
    "             'STANDING',\n",
    "             'LAYING',\n",
    "             # 'STAND_TO_SIT',\n",
    "             # 'SIT_TO_STAND',\n",
    "             # 'SIT_TO_LIE',\n",
    "             # 'LIE_TO_SIT',\n",
    "             # 'STAND_TO_LIE',\n",
    "             # 'LIE_TO_STAND',\n",
    "            ]\n",
    "\n",
    "dataset_metadata['label_names'] = beh_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43371d30-c242-465e-963a-3f1eb2e4fcb4",
   "metadata": {},
   "source": [
    "We name each data channel we will use. \n",
    "\n",
    "By convention, channels with the string `Acc` in their name will be pre-processed to separate static and dynamic acceleration. This uses a Butterworth filter, and the cutoff frequency of this filter is a hyperparameter specified during training.\n",
    "\n",
    "By convention, the last two data channels must be `individual_id` (which is constant within a single clip) and `label`, respectively. These are both integer-valued."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f438d48f-0596-4a58-8669-358c9a4cec2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## clip_data column names\n",
    "\n",
    "clip_column_names = ['AccX', 'AccY', 'AccZ', 'GyrX', 'GyrY', 'GyrZ', 'individual_id', 'label']\n",
    "dataset_metadata['clip_column_names'] = clip_column_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c709f2-08c7-45fc-a29a-f034eb0031fe",
   "metadata": {},
   "source": [
    "We save the metadata dictionary as a `.yaml` file. However, we will still need to reload it and edit it at the very end of this notebook when we can compute average annotation duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a560ecfd-2ff8-4aff-a58c-76bb31200b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Save off dataset level metadata\n",
    "\n",
    "dataset_metadata_fp = os.path.join(formatted_data_dir, 'dataset_metadata.yaml')\n",
    "\n",
    "with open(dataset_metadata_fp, 'w') as file:\n",
    "    yaml.dump(dataset_metadata, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f964b042-2a2b-4690-bdd1-1685991ca77f",
   "metadata": {},
   "source": [
    "## Format Clip Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d816ae-831b-48bf-87ea-cc61eb9cd428",
   "metadata": {},
   "source": [
    "Now we are ready to format the data for BEBE. For each clip, we save the data from that clip as `clip_id.csv`. This file is a csv array without header. Its shape is `[num_sampled_time_steps, num_channels]`, where the `num_channels` is equal to the length of `dataset_metadata['clip_column_names']`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "431f04f7-b376-4f31-84a4-799d997482b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load up annotations\n",
    "\n",
    "annotations_fp = os.path.join(raw_data_dir, 'RawData', 'labels.txt')\n",
    "annotations = pd.read_csv(annotations_fp, delimiter = ' ', header = None, names = ['exp_id','ind_id','label','start_sample','end_sample'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "226d618a-e1ac-4b91-993b-2c1b50e1f7a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For each clip, create and save the data in the correct format\n",
    "\n",
    "total_dur_samples = 0\n",
    "total_annotation_dur_samples = 0\n",
    "\n",
    "for clip_id in clip_ids:\n",
    "    # load features\n",
    "    acc_fp = os.path.join(raw_data_dir, 'RawData', 'acc_' + clip_id + '.txt')\n",
    "    gyr_fp = os.path.join(raw_data_dir, 'RawData', 'gyro_' + clip_id + '.txt')\n",
    "    \n",
    "    acc_data = pd.read_csv(acc_fp, delimiter = ' ', header = None)\n",
    "    gyr_data = pd.read_csv(gyr_fp, delimiter = ' ', header = None)\n",
    "    \n",
    "    clip_dur_samples = len(acc_data)\n",
    "    total_dur_samples += clip_dur_samples\n",
    "        \n",
    "    acc_data = np.array(acc_data)\n",
    "    gyr_data = np.array(gyr_data)\n",
    "    \n",
    "    exp_id = int(clip_id[3:5])\n",
    "    annotations_sub = annotations[annotations['exp_id'] == exp_id]\n",
    "    \n",
    "    # reformat annotations\n",
    "    per_frame_annotations = np.zeros([clip_dur_samples, 1]) # populate with 0's, aka \"unknown\"\n",
    "    for i, row in annotations_sub.iterrows():\n",
    "        obs_int_label = row['label']\n",
    "        int_label = obs_int_label if obs_int_label < 7 else 0 # Treat transitional states as unknown\n",
    "        start_sample = row['start_sample']\n",
    "        end_sample = row['end_sample']\n",
    "        per_frame_annotations[start_sample: end_sample, :] = int_label\n",
    "\n",
    "    # insert individual id\n",
    "    individual_id = clip_id_to_individual_id[clip_id]\n",
    "    per_frame_individual_id = np.full_like(per_frame_annotations, individual_id)\n",
    "    \n",
    "    clip_data = np.concatenate([acc_data, gyr_data, per_frame_individual_id, per_frame_annotations], axis = 1)\n",
    "    \n",
    "    clip_data_fp = os.path.join(clip_data_dir, clip_id + '.csv')\n",
    "    np.savetxt(clip_data_fp, clip_data, delimiter=\",\")\n",
    "    \n",
    "    clip_annotation_dur_samples = sum(per_frame_annotations > 0)\n",
    "    total_annotation_dur_samples += clip_annotation_dur_samples\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5047bcb7-34c4-41e1-8c58-6b6e5eb49a46",
   "metadata": {},
   "source": [
    "Summary statistics computing during processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70b1f763-a89c-4e3b-aa50-d36ed1a697da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total duration is 22455.440000 seconds, i.e. 6.237622 hours\n",
      "total annotation duration is 14951.000000 seconds, i.e. 4.153056 hours\n"
     ]
    }
   ],
   "source": [
    "total_dur_sec = total_dur_samples / sr\n",
    "total_dur_hours = total_dur_sec / 3600\n",
    "print(\"total duration is %f seconds, i.e. %f hours\" % (total_dur_sec, total_dur_hours))\n",
    "\n",
    "total_annotation_dur_sec = total_annotation_dur_samples / sr\n",
    "total_annotation_dur_hours = total_annotation_dur_sec / 3600\n",
    "print(\"total annotation duration is %f seconds, i.e. %f hours\" % (total_annotation_dur_sec, total_annotation_dur_hours))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9844300-e520-49a9-b975-210caa20a21f",
   "metadata": {},
   "source": [
    "## Check it all looks ok"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d63f3d9-407b-467a-862f-fe9abe795d10",
   "metadata": {},
   "source": [
    "These are just some simple visualizations, they do not affect anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "717812e0-af4e-4bd0-96db-5ce83ece61c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "clip_id = clip_ids[12]\n",
    "clip_data_fp = os.path.join(clip_data_dir, clip_id + '.csv')\n",
    "data = np.genfromtxt(clip_data_fp, delimiter = ',')\n",
    "#data = np.load(clip_data_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77164f71-4257-43b1-ae76-49f1438c836e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17195, 8)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1d243b7-ec2e-4bc5-8239-400cc7241953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(np.shape(data)[1]):\n",
    "    plt.plot(data[:, i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ff4e9c59-9f68-4ee9-9cfd-0d1ca21b5900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0a14bdab50>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee76f97f-7d96-48fc-a9c2-f3ec377837a6",
   "metadata": {},
   "source": [
    "## Plot Dataset Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb918d8-f2a3-42ba-9617-a83a3c79c914",
   "metadata": {},
   "source": [
    "The cells in this section should be able to run without editing. They just provide visualizations of some dataset properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc4d237a-2684-4db3-9a08-7b81652d9371",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count class representation\n",
    "\n",
    "beh_names = dataset_metadata['label_names']\n",
    "label_count = {fold : {name : 0 for name in beh_names} for fold in range(n_folds)}\n",
    "\n",
    "for clip_id in clip_ids:\n",
    "    clip_data_fp = os.path.join(clip_data_dir, clip_id + '.csv')\n",
    "    data = pd.read_csv(clip_data_fp, delimiter = ',', header = None)\n",
    "    counts = data[data.keys()[-1]].astype(int).value_counts()\n",
    "    for key in counts.keys():\n",
    "        name = beh_names[key]\n",
    "        for fold in range(n_folds):\n",
    "            if clip_id in dataset_metadata['clip_ids_per_fold'][fold]:\n",
    "                label_count[fold][name] += counts[key]\n",
    "                \n",
    "label_perc = {fold : {name : 0 for name in beh_names[1:]} for fold in range(n_folds)}\n",
    "\n",
    "for name in beh_names[1:]:\n",
    "    for fold in range(n_folds):\n",
    "        label_perc[fold][name] = float(label_count[fold][name] / sum([label_count[fold][j] for j in beh_names[1:]]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9c515788-245b-42f2-95ef-c439c9fe121d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot class representation\n",
    "\n",
    "# https://gist.github.com/thriveth/8560036#file-cbcolors-py\n",
    "CB_color_cycle = ['#377eb8', '#ff7f00', '#4daf4a',\n",
    "                  '#f781bf', '#a65628', '#984ea3',\n",
    "                  '#999999', '#e41a1c', '#dede00']\n",
    "\n",
    "keys = np.arange(len(beh_names))[1:]\n",
    "offset = 0.1\n",
    "width = 0.1\n",
    "plt.figure(figsize=(8, 6), dpi=80)\n",
    "\n",
    "for fold in range(n_folds):\n",
    "    current_offset = -(fold - n_folds // 2)*offset\n",
    "    plt.barh(keys +current_offset, [label_perc[fold][beh_names[key]] for key in keys], height=width, align='center', label = fold, color = CB_color_cycle[fold])\n",
    "    \n",
    "plt.title(\"Class representation across folds\")\n",
    "plt.yticks(keys, beh_names[1:], fontsize = 7, rotation = 60)\n",
    "plt.xlabel('Percent of known labels')\n",
    "plt.legend()\n",
    "target_fp = os.path.join(formatted_data_dir, 'class_rep.svg')\n",
    "\n",
    "plt.savefig(target_fp, format = 'svg')\n",
    "plt.show()\n",
    "\n",
    "numerical_target_fp = os.path.join(formatted_data_dir, 'class_rep.yaml')\n",
    "with open(numerical_target_fp, 'w') as file:\n",
    "    yaml.dump(label_perc, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2375043d-2ff7-4cfa-a509-4fc6d5fe2c7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart, of known vs unknown samples \n",
    "\n",
    "labels = 'Known', 'Unknown'\n",
    "sizes = {fold : [sum([label_count[fold][j] for j in beh_names[1:]]), label_count[fold]['unknown']] for fold in range(n_folds)}\n",
    "# train_sizes = [sum([train_label_count[j] for j in beh_names[1:]]), train_label_count['unknown']]\n",
    "# val_sizes = [sum([val_label_count[j] for j in beh_names[1:]]), val_label_count['unknown']]\n",
    "# test_sizes = [sum([test_label_count[j] for j in beh_names[1:]]), test_label_count['unknown']]\n",
    "\n",
    "fig, ax = plt.subplots(1,n_folds, figsize = (3*n_folds,2))\n",
    "\n",
    "for fold in range(n_folds):\n",
    "    ax[fold].pie(sizes[fold], labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "    ax[fold].axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "    ax[fold].set_title(fold)\n",
    "\n",
    "target_fp = os.path.join(formatted_data_dir, 'perc_known.svg')\n",
    "\n",
    "plt.savefig(target_fp, format='svg')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e1fee4-6542-4f3f-8f7c-5609baddb75b",
   "metadata": {},
   "source": [
    "## Temporal Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5bdbfc-5a3d-4b4d-84f0-b49b8be8dd19",
   "metadata": {},
   "source": [
    "This cell calculates the average duration of an annotation. It should run without needing to be edited, with the exception of the variable `infill_max_dur_sec`. This is only relevant in the situation where the annotations have regular gaps that should be filled in for the purpose of computing the average annotation duration. In the paper, this was only relevant for the Polar Bear dataset, because the annotations had regular gaps due to the video recording being duty-cycled (i.e. the camera turned off regularly to save battery). If this is not relevant for your dataset, leave `infill_max_dur_sec=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5c827143-891f-43d1-8e0f-9d401e61cfdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 61/61 [00:02<00:00, 23.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean overall label duration is 17.466 seconds\n",
      "Re-saving metadata to /home/jupyter/behavior_data_local/data/formatted/HAR/dataset_metadata.yaml\n"
     ]
    }
   ],
   "source": [
    "####################################\n",
    "infill_max_dur_sec = 0 # If annotations are nnnnn0000000nnnnn, where 0=unknown, do we fill in 0's with n's?\n",
    "####################################\n",
    "\n",
    "# This cell runs almost as a stand alone, but requires the first few cells of this notebook to be run first\n",
    "\n",
    "import tqdm\n",
    "from pathlib import Path\n",
    "\n",
    "dataset_metadata_fp = os.path.join(formatted_data_dir, 'dataset_metadata.yaml')\n",
    "\n",
    "with open(dataset_metadata_fp, 'r') as file:\n",
    "    metadata = yaml.safe_load(file)\n",
    "\n",
    "clip_fps = list(Path(clip_data_dir).glob('*.csv'))\n",
    "\n",
    "def create_list_of_durations(x, infill_max_dur_sec, samplerate, unknown_value = 0):\n",
    "    # First pass: create a list of label durations, together with a list of the associated classes\n",
    "    current_value = 0\n",
    "    current_dur = 0\n",
    "    list_of_durs = []\n",
    "    list_of_labels = []\n",
    "    for i in x:\n",
    "        if i == current_value:\n",
    "            current_dur += 1\n",
    "        elif i != current_value:\n",
    "            list_of_durs.append(current_dur / samplerate)\n",
    "            list_of_labels.append(current_value)\n",
    "            current_dur = 1\n",
    "            current_value = i\n",
    "            \n",
    "    list_of_durs.append(current_dur)\n",
    "    list_of_labels.append(current_value)\n",
    "            \n",
    "    # repeatedly merge similar labels if they are seperated by unknowns of short duration\n",
    "    # This is mainly to deal with duty cycling of observations\n",
    "    infill_max_dur_samples = int(infill_max_dur_sec * samplerate)\n",
    "    \n",
    "    if list_of_labels[0] == unknown_value:\n",
    "        del list_of_labels[0]\n",
    "        del list_of_durs[0]\n",
    "        \n",
    "    if list_of_labels[-1] == unknown_value:\n",
    "        del list_of_labels[-1]\n",
    "        del list_of_durs[-1]\n",
    "    \n",
    "    j = 1\n",
    "    while j<len(list_of_labels)-1:\n",
    "        if list_of_labels[j] != unknown_value:\n",
    "            j+= 1\n",
    "        elif list_of_labels[j-1] == list_of_labels[j+1] and list_of_durs[j] < infill_max_dur_samples:\n",
    "            # merge if it's a short interval of unknowns between two of the same label\n",
    "            list_of_durs[j-1] += list_of_durs[j+1]\n",
    "            del list_of_durs[j+1]\n",
    "            del list_of_durs[j]\n",
    "            del list_of_labels[j+1]\n",
    "            del list_of_labels[j]\n",
    "        else:\n",
    "            # otherwise, drop the unknown segment\n",
    "            del list_of_durs[j]\n",
    "            del list_of_labels[j]\n",
    "\n",
    "    return list_of_labels, list_of_durs # list of annotation durations, in samples.\n",
    "                             \n",
    "durs_sec_by_individual = {i : [] for i in metadata['individual_ids']}\n",
    "overall_durs_sec = []\n",
    "    \n",
    "for fp in tqdm.tqdm(clip_fps):\n",
    "    clip_id = fp.stem\n",
    "    individual_id = metadata['clip_id_to_individual_id'][clip_id]\n",
    "    clip_annotations = list(pd.read_csv(fp, header = None).values[:, -1])\n",
    "    l, d = create_list_of_durations(clip_annotations, infill_max_dur_sec, metadata['sr'])\n",
    "    durs_sec_by_individual[individual_id].extend(d)\n",
    "    overall_durs_sec.extend(d)\n",
    "    \n",
    "mean_dur_sec_by_individual = {i : float(np.mean(durs_sec_by_individual[i])) for i in durs_sec_by_individual}\n",
    "mean_overall_dur_sec = float(np.mean(overall_durs_sec))\n",
    "\n",
    "metadata['mean_dur_sec_by_individual'] = mean_dur_sec_by_individual\n",
    "metadata['mean_overall_dur_sec'] = mean_overall_dur_sec\n",
    "\n",
    "print(\"Mean overall label duration is %0.3f seconds\" % mean_overall_dur_sec)\n",
    "\n",
    "print(\"Re-saving metadata to %s\" % str(dataset_metadata_fp))\n",
    "\n",
    "with open(dataset_metadata_fp, 'w') as file:\n",
    "    yaml.dump(metadata, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d123044c-799d-40c7-86ff-1bf37e40669b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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