{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "24a21909-a3ad-46b4-a78f-3ea9b493962f",
   "metadata": {},
   "source": [
    "# EK381 Video Playlist\n",
    "\n",
    "Use this notebook to select and play the EK381 videos.\n",
    "\n",
    "Run all cells, then use the select to select the video you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d1e34b6d-c5db-44a6-902d-a839621c7923",
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython.display as disp\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ffce1e5-5570-4adb-9aef-6eec8db68bac",
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   "source": [
    "video_info={'id': {0: 'IOOYbtuoXOY',\n",
    "  1: 'paHSf35tBoA',\n",
    "  2: 'HfsIf-RDMAE',\n",
    "  3: 'tCwy7jfvkzw',\n",
    "  4: 'x_Kne2Oj3Us',\n",
    "  5: 'PhaI5x2GTCQ',\n",
    "  6: '0S3nZjZuAxo',\n",
    "  7: 'zQOt2UzrK2k',\n",
    "  8: 'aXJvLKrCA5w',\n",
    "  9: 'J4H_IhqS6ck',\n",
    "  10: 'mbC7N-DFglM',\n",
    "  11: 'fzFXRARNk4U',\n",
    "  12: 'T8LOEKmUqOA',\n",
    "  13: 'rfKhYTMoxBI',\n",
    "  14: 'BG9jOwKDrxM',\n",
    "  15: 'fwSEuzgJfl4',\n",
    "  16: 'sDGUHdrryT0',\n",
    "  17: '-jYcHkgSPK0',\n",
    "  18: 'TNpjC_H9aDE',\n",
    "  19: 'i81WuxrU_b0',\n",
    "  20: 'GZ0v2ahlzPE',\n",
    "  21: 'Mc4eVJnNN34',\n",
    "  22: '86m-cizvK14',\n",
    "  23: 'GfNGkvMyFLQ',\n",
    "  24: 'YsU_iDGrTKI',\n",
    "  25: 'twomug5upzM',\n",
    "  26: 'xADJo0wH7-A',\n",
    "  27: 'yQdWLJSWa2o',\n",
    "  28: '4SxMT7uxa3Q',\n",
    "  29: 'P1dKyLee9e0',\n",
    "  30: '6t_2WRGE91o',\n",
    "  31: 'zdxy1gt9LlA',\n",
    "  32: 'KLgyrEqc2ho',\n",
    "  33: 'HfaKrIKf5SM',\n",
    "  34: 'h_Mhtsa_IJo',\n",
    "  35: 'FVkwTzyaCO8',\n",
    "  36: 'EKnDV-iBiUY',\n",
    "  37: '6QJc8RaFwnA',\n",
    "  38: 'RuFBYGo84Hs',\n",
    "  39: 'HTRERPW0Tx8',\n",
    "  40: 'o1tzq-rJtDk',\n",
    "  41: 'Kvc-c2g9dKs',\n",
    "  42: 'Jja88G4X2XQ',\n",
    "  43: 'gRUufpAMmHM',\n",
    "  44: 'YsEiMTvwuJs',\n",
    "  45: 'lhdWusphZRg',\n",
    "  46: 'nTs4SV5_I8k',\n",
    "  47: 'yYiIMXa4m2E',\n",
    "  48: 'p_sNQqCIV90'},\n",
    " 'title': {0: 'Probability 1.1 Set Theory (2022)',\n",
    "  1: 'Probability 1.2 Probability Axioms (2022)',\n",
    "  2: 'Probability 1.3 Conditional Probability Concepts (2022)',\n",
    "  3: 'Probability 1.4 Conditional Probability Examples (2022)',\n",
    "  4: 'Probability 1.5 Independence (2022)',\n",
    "  5: 'Probability 1.6 Counting (2022)',\n",
    "  6: 'Probability 2.1 Discrete Random Variables (2022)',\n",
    "  7: 'Probability 2.2 Discrete Random Variables: Cumulative Distribution Function (2022)',\n",
    "  8: 'Probability 2.3 Expectation for Discrete Random Variables (2022)',\n",
    "  9: 'Probability 2.4 Variance for Discrete Random Variables (2022)',\n",
    "  10: 'Probability 2.5 Important Families of Discrete Random Variables (2022)',\n",
    "  11: 'Probability 2.6 Conditioning for Discrete Random Variables (2022)',\n",
    "  12: 'Probability 3.1 Continuous Random Variables (2022)',\n",
    "  13: 'Probability 3.2 Expectation and Variance for Continuous Random Variables (2022)',\n",
    "  14: 'Probability 3.3 Important Families of Continuous Random Variables (2022)',\n",
    "  15: 'Probability 3.4 Conditioning for Continuous Random Variables (2022)',\n",
    "  16: 'Probability 4.1 Pairs of Discrete Random Variables (2022)',\n",
    "  17: 'Probability 4.2 Conditional PMF (2022)',\n",
    "  18: 'Probability 4.3 Pairs of Continuous Random Variables (2022)',\n",
    "  19: 'Probability 4.4 Conditional PDF (2022)',\n",
    "  20: 'Probability 4.5 Independence of Random Variables (2022)',\n",
    "  21: 'Probability 4.6 Expected Value of a Function of Random Variables (2022)',\n",
    "  22: 'Probability 4.7 Conditional Expectation (2022)',\n",
    "  23: 'Probability 5.1 Covariance (2022)',\n",
    "  24: 'Probability 5.2 Correlation Coefficient (2022)',\n",
    "  25: 'Probability 5.3 Jointly Gaussian Random Variables - Concepts (2022)',\n",
    "  26: 'Probability 5.4 Jointly Gaussian Random Variables - Examples (2022)',\n",
    "  27: 'Probability 5.5 Random Vectors (2022)',\n",
    "  28: 'Probability 5.6 Gaussian Vectors (2022)',\n",
    "  29: 'Probability 6.1 Binary Hypothesis Testing - Concepts (2022)',\n",
    "  30: 'Probability 6.2 Binary Hypothesis Testing - Examples (2022)',\n",
    "  31: 'Probability 6.3 Likelihood Ratio (2022)',\n",
    "  32: 'Probability 6.4 Detection with Vector Observations (2022)',\n",
    "  33: 'Probability 7.1 MMSE Estimation (2022)',\n",
    "  34: 'Probability 7.2 Linear Estimation (2022)',\n",
    "  35: 'Probability 7.3 Vector Estimation (2022)',\n",
    "  36: 'Probability 8.1 Sums of Random Variables (2022)',\n",
    "  37: 'Probability 8.2 Limits of Random Variables (2022)',\n",
    "  38: 'Probability 9.1 Confidence Intervals - Concepts (2022)',\n",
    "  39: 'Probability 9.2 Confidence Intervals - Examples (2022)',\n",
    "  40: 'Probability 9.3 Significance Testing - Concepts (2022)',\n",
    "  41: 'Probability 9.4 Significance Testing - Examples (2022)',\n",
    "  42: 'Probability 10.1 Machine Learning Overview (2022)',\n",
    "  43: 'Probability 10.2 Binary Classification (2022)',\n",
    "  44: 'Probability 10.3 Dimensionality Reduction (2022)',\n",
    "  45: 'Probability 11.1 Markov Chains (2022)',\n",
    "  46: 'Probability 11.2 State Vector and Transition Matrix (2022)',\n",
    "  47: 'Probability 11.3 State Classification (2022)',\n",
    "  48: 'Probability 11.4 Unique Limiting State Vector (2022)'}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "581705ad-37ea-4ba0-b62d-7febcd6fe0e8",
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "066c3990-11d4-472f-acca-3608f8bcaa4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "d422b9097b0a4959af57c72da988c4ca",
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      "text/plain": [
       "Dropdown(layout=Layout(height='40px', width='80%'), options=(('Probability 1.1 Set Theory (2022)', 0), ('Proba…"
      ]
     },
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     "output_type": "display_data"
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output=widgets.Output()\n",
    "out_vid=widgets.Output()\n",
    "def on_value_change(change):\n",
    "    with output:\n",
    "        vidid = video_info['id'][change['new']]\n",
    "        prob_video = disp.YouTubeVideo(vidid, width=800, height=600,allowautoplay=True)\n",
    "        display(prob_video,out_vid,clear=True)\n",
    "\n",
    "infolist=list(zip(video_info['title'].values(),range(49)))\n",
    "playlist = widgets.Dropdown(options=infolist,\n",
    "                               layout=widgets.Layout(width='80%', height='40px'))\n",
    "playlist.observe(on_value_change, names='value')\n",
    "display(playlist,output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18a81d01-e816-4d79-98e2-e5fdb739d3c3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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