format_rattlesnakes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "86ce8245-b923-42ba-b1cf-b0a8bdb804d4",
   "metadata": {},
   "source": [
    "# Notebook to format data from \"Integrative Framework for Long-Term Activity Monitoring of Small and Secretive Animals: Validation With a Cryptic Pitviper\", DeSantis et al. (2020).\n",
    "\n",
    "Paper can be found [here](https://www.frontiersin.org/articles/10.3389/fevo.2020.00169/full).\n",
    "\n",
    "Data was shared over email"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77461baa-2d89-4ddf-b8b9-283f19b3313e",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9cd11e9-258b-4304-a21d-f826f276b73b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outputs:\n",
    "# formatted dataset directory\n",
    "# dataset_metadata (yaml), includes\n",
    "## sample_rate\n",
    "## dataset_label_names (list)\n",
    "## dataset_feature_names (list)\n",
    "## clip_ids (list)\n",
    "# clip_data (directory), includes\n",
    "## data_files (multiple files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7470e077-e978-495a-b349-27cc5a6b2a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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\n",
    "import datetime\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6bc728a2-a91e-4da2-abea-4b57ceb4e818",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify data filepaths\n",
    "\n",
    "raw_data_dir = '/home/jupyter/behavior_data_local/data/raw/desantis_rattlesnakes'\n",
    "formatted_data_dir = '/home/jupyter/behavior_data_local/data/formatted/desantis_rattlesnakes'\n",
    "\n",
    "if not os.path.exists(formatted_data_dir):\n",
    "    os.makedirs(formatted_data_dir)\n",
    "    \n",
    "data_fp = os.path.join(raw_data_dir, 'CRAT_ACT_TrainingDataset_2016-2018IMRS.csv')\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": "code",
   "execution_count": 5,
   "id": "98f244e7-ac03-48e2-868a-1b7f7dd15fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset metadata\n",
    "dataset_metadata = {}\n",
    "sr = 1\n",
    "dataset_metadata['sr'] = sr ## from dataset documentation\n",
    "dataset_metadata['dataset_name'] = 'desantis_rattlesnakes'\n",
    "dataset_metadata['dynamic_acc_only'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b1891a7-1ff4-4df0-9c00-ecf634c3870a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data = pd.read_csv(data_fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a0a16c-d965-4a15-8fa1-79b9487778b6",
   "metadata": {},
   "source": [
    "\"Note that there are two tagIDs included but not reflected in the paper (RT-ACT13 and 14), because the logger batteries failed almost immediately after deployment (design/waterproofing flaw), but the initial releases were still captured for inclusion in the validation dataset. Additionally, RT-ACT12 is not included in the validation dataset because the individual was not on the surface during any validation observations within the brief monitoring window, and the video of the release was lost. \" -Dominic DeSantis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a14e119-3b7e-44d4-9da2-90f74a6bd077",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RT-ACT9     34261\n",
       "RT-ACT11    25207\n",
       "RT-ACT5     12356\n",
       "RT-ACT2     10451\n",
       "RT-ACT7      6744\n",
       "RT-ACT4      5984\n",
       "RT-ACT6      4746\n",
       "RT-ACT1      4478\n",
       "RT-ACT8      3962\n",
       "RT-ACT13     2836\n",
       "RT-ACT10     1612\n",
       "RT-ACT3       766\n",
       "RT-ACT14      607\n",
       "Name: TagID, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['TagID'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "792a4a04-9c63-45d6-ba91-9bea7b58eec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interpret date and time\n",
    "\n",
    "data['DateTime'] = pd.to_datetime(data['Date'] + ' ' + data['Time'], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7fa104a6-5093-484f-978e-1cb0d65243ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract integer labels for individuals\n",
    "\n",
    "individuals = sorted(data['TagID'].unique())\n",
    "def individuals_to_int(string):\n",
    "    return int(string.split('-')[1][3:])\n",
    "\n",
    "data['individual'] = data['TagID'].map(individuals_to_int)\n",
    "\n",
    "individuals = sorted(data['individual'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8952a2d7-61c1-4c4e-b37a-221745bb82f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 114009/114009 [01:15<00:00, 1508.87it/s]\n"
     ]
    }
   ],
   "source": [
    "# Split into clips\n",
    "# Keep track of which individual is associated to which clip_id\n",
    "# This is written very inefficiently, but it's fine for this small dataset\n",
    "\n",
    "data['clip_id'] = 0\n",
    "\n",
    "current_clip_id = 0\n",
    "clip_ids = []\n",
    "\n",
    "clip_id_to_individual_id = {}\n",
    "\n",
    "clip_id_to_individual_id['clip_0'] = int(data.loc[0, 'individual'])\n",
    "for index in tqdm.tqdm(data.index[:-1]):\n",
    "\n",
    "    data.loc[index, 'clip_id'] = current_clip_id\n",
    "    if data.loc[index+1, 'individual'] != data.loc[index, 'individual']:\n",
    "        current_clip_id += 1\n",
    "        clip_id_to_individual_id['clip_' + str(current_clip_id)] = int(data.loc[index+1, 'individual'])\n",
    "    elif (data.loc[index+1, 'DateTime'] - data.loc[index, 'DateTime']).total_seconds() != 1.0:\n",
    "        current_clip_id += 1\n",
    "        clip_id_to_individual_id['clip_' + str(current_clip_id)] = int(data.loc[index+1, 'individual'])\n",
    "\n",
    "data.loc[index + 1, 'clip_id'] = current_clip_id\n",
    "data['clip_id'] = data['clip_id'].map(lambda x : 'clip_' + str(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f83a4773-6288-43fc-82d0-569f936fc859",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Omit short clips (fewer than 150 seconds)\n",
    "# Recreate list of individuals, just in case any were dropped in this process\n",
    "\n",
    "long_clips = list(data['clip_id'].value_counts()[data['clip_id'].value_counts() >= 150].keys())\n",
    "data = data[data['clip_id'].isin(long_clips)].copy()\n",
    "\n",
    "individuals = sorted(data['individual'].unique())\n",
    "clip_ids = sorted(data['clip_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ea20959-639e-4f2c-8efa-945516302110",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Codify integer codes for individuals\n",
    "\n",
    "dataset_metadata['individual_ids'] = [int(x) for x in individuals]\n",
    "dataset_metadata['clip_id_to_individual_id'] = clip_id_to_individual_id\n",
    "dataset_metadata['clip_ids'] = clip_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b719e122-9b2d-4fb1-a519-36f58a545c0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: [1, 6, 11], 1: [2, 7, 13], 2: [3, 8, 14], 3: [4, 9], 4: [5, 10]}\n",
      "There are 13 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(int(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": "code",
   "execution_count": 14,
   "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": "3fca914b-f4d0-4265-b18c-71cd7aca2dc5",
   "metadata": {},
   "source": [
    "## Create dataset-level metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b9343516-5a19-4882-9e32-1ef628a49667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Moving', 'Not Moving', 'Moving ', 'Not moving', 'Not Moving '],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Behavior'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ac34de9-3853-44e0-ae8c-2b7bed24f72e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean up label names\n",
    "\n",
    "obs_beh_to_beh_name = {\n",
    "    'Moving' : 'Moving',\n",
    "    'Not Moving' : 'Not_Moving',\n",
    "    'Moving ' : 'Moving',\n",
    "    'Not moving' : 'Not_Moving',\n",
    "    'Not Moving ' : 'Not_Moving'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "444a3dc3-dfb0-4907-833e-8caf3eb4d88d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['unknown', 'Moving', 'Not_Moving']\n"
     ]
    }
   ],
   "source": [
    "## Integer codes for behaviors\n",
    "\n",
    "beh_names = sorted(set([obs_beh_to_beh_name[k] for k in obs_beh_to_beh_name]))\n",
    "#beh_names.remove('unknown')\n",
    "beh_names.insert(0, 'unknown')\n",
    "\n",
    "print(beh_names)\n",
    "\n",
    "dataset_metadata['label_names'] = beh_names\n",
    "\n",
    "beh_str_to_int = {name : i for i, name in enumerate(beh_names)}\n",
    "beh_int_to_str = {i : name for i, name in enumerate(beh_names)}\n",
    "\n",
    "data['Behavior'] = data['Behavior'].map(lambda x : obs_beh_to_beh_name[x])\n",
    "data['beh_label'] = data['Behavior'].map(lambda x : beh_str_to_int[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f438d48f-0596-4a58-8669-358c9a4cec2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## clip_data column names\n",
    "\n",
    "clip_column_names = ['AccX', 'AccY', 'AccZ', 'individual_id', 'label']\n",
    "dataset_metadata['clip_column_names'] = clip_column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": "code",
   "execution_count": 20,
   "id": "c7bd7e0b-68ed-4814-9977-284f8877a143",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 146/146 [00:02<00:00, 56.77it/s]\n"
     ]
    }
   ],
   "source": [
    "# Save off individual clips in csv files\n",
    "\n",
    "data['beh_int_label'] = 0\n",
    "\n",
    "total_dur_samples = 0\n",
    "total_annotation_dur_samples = 0\n",
    "\n",
    "for clip_id in tqdm.tqdm(clip_ids):\n",
    "    data_sub = data[data['clip_id'] == clip_id].copy()\n",
    "\n",
    "    features = np.array(data_sub[['X', 'Ydyn', 'Z']]) #data has already been preprocessed\n",
    "    clip_dur_samples = np.shape(features)[0]\n",
    "    \n",
    "    total_dur_samples += clip_dur_samples\n",
    "\n",
    "    per_frame_annotations = np.array(data_sub[['individual', 'beh_label']])\n",
    "\n",
    "    clip_data = np.concatenate([features, 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 = np.sum(clip_data[:,-1] > 0)\n",
    "    total_annotation_dur_samples += clip_annotation_dur_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2be201c7-8aaf-45f8-8910-8b65ab2c5ae8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total duration is 111155.000000 seconds, i.e. 30.876389 hours\n",
      "total annotation duration is 111155.000000 seconds, i.e. 30.876389 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": "code",
   "execution_count": 22,
   "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": 23,
   "id": "77164f71-4257-43b1-ae76-49f1438c836e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(379, 5)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b1d243b7-ec2e-4bc5-8239-400cc7241953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 25,
   "id": "ff4e9c59-9f68-4ee9-9cfd-0d1ca21b5900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f711e5ed0d0>,\n",
       " <matplotlib.lines.Line2D at 0x7f711e5edf10>,\n",
       " <matplotlib.lines.Line2D at 0x7f711e5fb290>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[:, :3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d010f266-9639-4890-adc3-169d3ae72a4b",
   "metadata": {},
   "source": [
    "## Plot Dataset Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "265e780d-d369-4350-a4eb-12089af5cd94",
   "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": 27,
   "id": "6b354cb8-5d01-4af4-9fbc-4883d9768173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 28,
   "id": "b8da2e9c-e13f-4234-8772-1dd49d8bf776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": "2326ffbf-cf7c-42e4-ac48-ed43238772ed",
   "metadata": {},
   "source": [
    "## Temporal Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3537cd48-28e1-47ca-a613-fca852cca54f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 146/146 [00:00<00:00, 424.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean overall label duration is 721.786 seconds\n",
      "Re-saving metadata to /home/jupyter/behavior_data_local/data/formatted/desantis_rattlesnakes/dataset_metadata.yaml\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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": "e1ac190c-d1fd-45e9-9170-3e20b2ec89af",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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