Add files via upload

ipynb/Bike Speed versus Grade.ipynb

@@ -28,7 +28,7 @@
     "\n",
     "The wandrer.earth site does a great job of showing my current status in all the places I've been, but I wanted to track my progress month-by-month, so I created the charts below.\n",
     "\n",
-    "The legend `\"94% ( 52/ 56 mi) 2.7 to 99% Atherton\"` means that I have ridden 52 of the 56 miles of roads (or 94%) in Atherton, and I need another 2.7 miles to reach the bonus scoring milestone of 99%. The companion notebook [**BikeCode.ipynb**](BikeCode.ipynb) has the implementation details."
+    "The legend `\"95% ( 53/56 mi) 2.3 to 99% Atherton\"` means that I have ridden 53 of the 56 miles of roads (or 95%) in Atherton, and I need another 2.3 miles to reach the bonus scoring milestone of 99%. The companion notebook [**BikeCode.ipynb**](BikeCode.ipynb) has the implementation details."
    ]
   },
   {
@@ -40,7 +40,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -52,7 +52,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -64,7 +64,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -76,7 +76,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -88,7 +88,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -100,7 +100,7 @@
     },
     {
      "data": {
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truncated
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truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -112,7 +112,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -124,7 +124,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -136,7 +136,19 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -175,7 +187,7 @@
     {
      "data": {
       "text/plain": [
-       "63"
+       "64"
       ]
      },
      "execution_count": 2,
@@ -191,7 +203,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**My Eddington Number is 63**: I've ridden at least 63 miles on at least 63 days (but not 64 miles on 64 days). My number is above [the mean for Strava](https://swinny.net/Cycling/-4687-Calculate-your-Eddington-Number), but not nearly as good as Eddington himself: his number was **84** when he died at age 62, and his roads, bicycles, and navigation aids were not nearly as nice as mine, so bravo zulu to him. I don't think I will reach 84, but how many more rides do I need to reach 65? or 70? I'll call the difference the Eddington gap:"
+    "**My Eddington Number is 64**: I've ridden at least 64 miles on at least 64 days (but not 65 miles on 65 days). My number is above [the mean for Strava](https://swinny.net/Cycling/-4687-Calculate-your-Eddington-Number), but not nearly as good as Eddington himself: his number was **84** when he died at age 62, and his roads, bicycles, and navigation aids were not nearly as nice as mine, so bravo zulu to him. I don't think I will reach 84, but how many more rides do I need to reach 65? or 70? I'll call the difference the Eddington gap:"
    ]
   },
   {
@@ -202,7 +214,7 @@
     {
      "data": {
       "text/plain": [
-       "{64: 3, 65: 7, 66: 11, 67: 19, 68: 23, 69: 34, 70: 40}"
+       "{65: 2, 66: 7, 67: 16, 68: 20, 69: 32, 70: 39}"
       ]
      },
      "execution_count": 3,
@@ -212,14 +224,14 @@
    ],
    "source": [
     "{mi: Ed_gap(rides['miles'], mi) \n",
-    " for mi in range(64, 71)}"
+    " for mi in range(65, 71)}"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I need just 7 more rides to reach 65, but 40 to reach 70.\n",
+    "I need just 2 more rides to reach 65, and 39 more to reach 70.\n",
     "\n",
     "Eddington was English, so he used miles, but we could also consider a **metric Eddington number**:"
    ]
@@ -232,7 +244,7 @@
     {
      "data": {
       "text/plain": [
-       "87"
+       "90"
       ]
      },
      "execution_count": 4,
@@ -252,7 +264,7 @@
     {
      "data": {
       "text/plain": [
-       "{88: 1, 90: 5, 92: 10, 94: 18, 96: 23, 98: 31, 100: 36}"
+       "{90: 0, 92: 5, 94: 13, 96: 18, 98: 26, 100: 31}"
       ]
      },
      "execution_count": 5,
@@ -262,14 +274,14 @@
    ],
    "source": [
     "{km: Ed_gap(rides['kms'], km) \n",
-    " for km in range(88, 101, 2)}"
+    " for km in range(90, 101, 2)}"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "My **metric Eddington Number is 87**, and my gap to 90 is 5 more rides, and to 100 is 36 more rides.\n",
+    "My **metric Eddington Number is 90**, and my gap to 100 is 31 more rides.\n",
     "\n",
     "Here are some properties of Eddington numbers:\n",
     "- Your Eddington number is monotonic: it can never decrease over time. \n",
@@ -367,8 +379,8 @@
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>2021</td>\n",
-       "      <td>87</td>\n",
-       "      <td>63</td>\n",
+       "      <td>90</td>\n",
+       "      <td>64</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -384,7 +396,7 @@
        "5  2018     76     53\n",
        "6  2019     79     55\n",
        "7  2020     86     61\n",
-       "8  2021     87     63"
+       "8  2021     90     64"
       ]
      },
      "execution_count": 6,
@@ -393,7 +405,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -480,7 +492,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -568,7 +580,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I see a wide spread of VAM numbers; at 7% to 8% I range from 1500 to 3000 ft/hour.  Champion cyclists sprint at over 6000 ft/hour, and can sustain [5400 ft/hour for 7 hours](https://www.strava.com/activities/4996833865]). As we can see in the segments listed below, I can sustain 3000 ft/hour for only a few minutes:"
+    "Champion cyclists sprint at over 6000 ft/hour and can sustain [over 4000 ft/hour for 7 hours](https://www.strava.com/activities/4996833865).  My VAM numbers range mostly from 1500 to 3000 ft/hour, biut I can sustain the higher numbers for only a couple of minutes:"
    ]
   },
   {
@@ -1302,7 +1314,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>218</th>\n",
+       "      <th>223</th>\n",
        "      <td>Sun, 11/29</td>\n",
        "      <td>2015</td>\n",
        "      <td>Mt. Hamilton</td>\n",
@@ -1316,7 +1328,7 @@
        "      <td>59.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>7</th>\n",
        "      <td>Fri, 4/2</td>\n",
        "      <td>2021</td>\n",
        "      <td>Everesting 5: climb 2×(OLH + WOLH)</td>\n",
@@ -1330,7 +1342,7 @@
        "      <td>50.65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>11</th>\n",
        "      <td>Mon, 3/29</td>\n",
        "      <td>2021</td>\n",
        "      <td>Everesting 1: Mt Diablo</td>\n",
@@ -1344,7 +1356,7 @@
        "      <td>35.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>10</th>\n",
        "      <td>Tue, 3/30</td>\n",
        "      <td>2021</td>\n",
        "      <td>Everesting 2: Kings + WOLH + OLH</td>\n",
@@ -1358,7 +1370,7 @@
        "      <td>57.91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>225</th>\n",
+       "      <th>230</th>\n",
        "      <td>Sat, 11/25</td>\n",
        "      <td>2017</td>\n",
        "      <td>Mt. Hamilton</td>\n",
@@ -1372,7 +1384,7 @@
        "      <td>58.97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>207</th>\n",
+       "      <th>212</th>\n",
        "      <td>Sun, 12/1</td>\n",
        "      <td>2013</td>\n",
        "      <td>Mt. Hamilton</td>\n",
@@ -1386,7 +1398,7 @@
        "      <td>60.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>185</th>\n",
+       "      <th>190</th>\n",
        "      <td>Fri, 10/30</td>\n",
        "      <td>2015</td>\n",
        "      <td>OLH / West Alpine</td>\n",
@@ -1400,7 +1412,7 @@
        "      <td>63.57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>84</th>\n",
+       "      <th>89</th>\n",
        "      <td>Sat, 4/26</td>\n",
        "      <td>2014</td>\n",
        "      <td>OLH / Tunitas Creek</td>\n",
@@ -1414,7 +1426,7 @@
        "      <td>94.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>75</th>\n",
+       "      <th>80</th>\n",
        "      <td>Sat, 4/18</td>\n",
        "      <td>2015</td>\n",
        "      <td>Tunitas + Lobitos Creeks</td>\n",
@@ -1428,7 +1440,7 @@
        "      <td>98.58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>59</th>\n",
+       "      <th>64</th>\n",
        "      <td>Wed, 10/14</td>\n",
        "      <td>2015</td>\n",
        "      <td>Half Moon Bay</td>\n",
@@ -1442,7 +1454,7 @@
        "      <td>117.41</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>65</th>\n",
+       "      <th>70</th>\n",
        "      <td>Sun, 6/4</td>\n",
        "      <td>2017</td>\n",
        "      <td>Sequoia Challenge</td>\n",
@@ -1456,7 +1468,7 @@
        "      <td>107.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>140</th>\n",
+       "      <th>145</th>\n",
        "      <td>Sat, 7/25</td>\n",
        "      <td>2015</td>\n",
        "      <td>Palo Alto, California</td>\n",
@@ -1470,7 +1482,7 @@
        "      <td>70.18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>86</th>\n",
+       "      <th>91</th>\n",
        "      <td>Sat, 10/11</td>\n",
        "      <td>2014</td>\n",
        "      <td>OLH / Tunitas</td>\n",
@@ -1484,7 +1496,7 @@
        "      <td>93.79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>95</th>\n",
+       "      <th>100</th>\n",
        "      <td>Sat, 8/13</td>\n",
        "      <td>2016</td>\n",
        "      <td>Petaluma / Point Reyes</td>\n",
@@ -1498,7 +1510,7 @@
        "      <td>88.09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>67</th>\n",
+       "      <th>72</th>\n",
        "      <td>Fri, 8/28</td>\n",
        "      <td>2015</td>\n",
        "      <td>Pescadaro via OLH</td>\n",
@@ -1512,7 +1524,7 @@
        "      <td>106.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>5</th>\n",
        "      <td>Sun, 4/4</td>\n",
        "      <td>2021</td>\n",
        "      <td>Everesting 7: Mill Creek / Morrison Canyon</td>\n",
@@ -1526,7 +1538,7 @@
        "      <td>47.27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>88</th>\n",
+       "      <th>93</th>\n",
        "      <td>Wed, 6/18</td>\n",
        "      <td>2014</td>\n",
        "      <td>Sierra to the Sea Day 4</td>\n",
@@ -1540,7 +1552,7 @@
        "      <td>92.74</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>69</th>\n",
+       "      <th>74</th>\n",
        "      <td>Sun, 6/3</td>\n",
        "      <td>2018</td>\n",
        "      <td>The Sequoia</td>\n",
@@ -1554,7 +1566,7 @@
        "      <td>104.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>281</th>\n",
+       "      <th>286</th>\n",
        "      <td>Sat, 5/9</td>\n",
        "      <td>2015</td>\n",
        "      <td>OLH</td>\n",
@@ -1568,7 +1580,7 @@
        "      <td>52.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>200</th>\n",
+       "      <th>205</th>\n",
        "      <td>Sat, 9/12</td>\n",
        "      <td>2015</td>\n",
        "      <td>Morning Ride</td>\n",
@@ -1587,48 +1599,48 @@
       ],
       "text/plain": [
        "           date  year                                       title   hours  \\\n",
-       "218  Sun, 11/29  2015                                Mt. Hamilton  3.6800   \n",
-       "2      Fri, 4/2  2021          Everesting 5: climb 2×(OLH + WOLH)  3.2750   \n",
-       "6     Mon, 3/29  2021                     Everesting 1: Mt Diablo  2.5964   \n",
-       "5     Tue, 3/30  2021            Everesting 2: Kings + WOLH + OLH  3.3414   \n",
-       "225  Sat, 11/25  2017                                Mt. Hamilton  3.6894   \n",
-       "207   Sun, 12/1  2013                                Mt. Hamilton  3.7836   \n",
-       "185  Fri, 10/30  2015                           OLH / West Alpine  3.4794   \n",
-       "84    Sat, 4/26  2014                         OLH / Tunitas Creek  5.2572   \n",
-       "75    Sat, 4/18  2015                    Tunitas + Lobitos Creeks  5.2439   \n",
-       "59   Wed, 10/14  2015                               Half Moon Bay  6.1319   \n",
-       "65     Sun, 6/4  2017                           Sequoia Challenge  6.2903   \n",
-       "140   Sat, 7/25  2015                       Palo Alto, California  4.0408   \n",
-       "86   Sat, 10/11  2014                               OLH / Tunitas  5.0897   \n",
-       "95    Sat, 8/13  2016                      Petaluma / Point Reyes  4.5033   \n",
-       "67    Fri, 8/28  2015                           Pescadaro via OLH  5.3142   \n",
-       "0      Sun, 4/4  2021  Everesting 7: Mill Creek / Morrison Canyon  3.0794   \n",
-       "88    Wed, 6/18  2014                     Sierra to the Sea Day 4  4.9647   \n",
-       "69     Sun, 6/3  2018                                 The Sequoia  5.9708   \n",
-       "281    Sat, 5/9  2015                                         OLH  2.5047   \n",
-       "200   Sat, 9/12  2015                                Morning Ride  3.1008   \n",
+       "223  Sun, 11/29  2015                                Mt. Hamilton  3.6800   \n",
+       "7      Fri, 4/2  2021          Everesting 5: climb 2×(OLH + WOLH)  3.2750   \n",
+       "11    Mon, 3/29  2021                     Everesting 1: Mt Diablo  2.5964   \n",
+       "10    Tue, 3/30  2021            Everesting 2: Kings + WOLH + OLH  3.3414   \n",
+       "230  Sat, 11/25  2017                                Mt. Hamilton  3.6894   \n",
+       "212   Sun, 12/1  2013                                Mt. Hamilton  3.7836   \n",
+       "190  Fri, 10/30  2015                           OLH / West Alpine  3.4794   \n",
+       "89    Sat, 4/26  2014                         OLH / Tunitas Creek  5.2572   \n",
+       "80    Sat, 4/18  2015                    Tunitas + Lobitos Creeks  5.2439   \n",
+       "64   Wed, 10/14  2015                               Half Moon Bay  6.1319   \n",
+       "70     Sun, 6/4  2017                           Sequoia Challenge  6.2903   \n",
+       "145   Sat, 7/25  2015                       Palo Alto, California  4.0408   \n",
+       "91   Sat, 10/11  2014                               OLH / Tunitas  5.0897   \n",
+       "100   Sat, 8/13  2016                      Petaluma / Point Reyes  4.5033   \n",
+       "72    Fri, 8/28  2015                           Pescadaro via OLH  5.3142   \n",
+       "5      Sun, 4/4  2021  Everesting 7: Mill Creek / Morrison Canyon  3.0794   \n",
+       "93    Wed, 6/18  2014                     Sierra to the Sea Day 4  4.9647   \n",
+       "74     Sun, 6/3  2018                                 The Sequoia  5.9708   \n",
+       "286    Sat, 5/9  2015                                         OLH  2.5047   \n",
+       "205   Sat, 9/12  2015                                Morning Ride  3.1008   \n",
        "\n",
        "     miles  feet    mph     vam    fpm   pct     kms  \n",
-       "218  37.00  4902  10.05  1332.0  132.0  2.51   59.53  \n",
-       "2    31.48  4344   9.61  1326.0  138.0  2.61   50.65  \n",
-       "6    22.22  3406   8.56  1312.0  153.0  2.90   35.75  \n",
-       "5    35.99  4377  10.77  1310.0  122.0  2.30   57.91  \n",
-       "225  36.65  4806   9.93  1303.0  131.0  2.48   58.97  \n",
-       "207  37.56  4921   9.93  1301.0  131.0  2.48   60.43  \n",
-       "185  39.51  4505  11.36  1295.0  114.0  2.16   63.57  \n",
-       "84   58.69  6742  11.16  1282.0  115.0  2.18   94.43  \n",
-       "75   61.27  6611  11.68  1261.0  108.0  2.04   98.58  \n",
-       "59   72.97  7644  11.90  1247.0  105.0  1.98  117.41  \n",
-       "65   66.52  7520  10.58  1195.0  113.0  2.14  107.03  \n",
-       "140  43.62  4819  10.79  1193.0  110.0  2.09   70.18  \n",
-       "86   58.29  6044  11.45  1187.0  104.0  1.96   93.79  \n",
-       "95   54.75  5286  12.16  1174.0   97.0  1.83   88.09  \n",
-       "67   66.01  6137  12.42  1155.0   93.0  1.76  106.21  \n",
-       "0    29.38  3517   9.54  1142.0  120.0  2.27   47.27  \n",
-       "88   57.64  5561  11.61  1120.0   96.0  1.83   92.74  \n",
-       "69   64.92  6677  10.87  1118.0  103.0  1.95  104.46  \n",
-       "281  32.33  2788  12.91  1113.0   86.0  1.63   52.02  \n",
-       "200  38.20  3342  12.32  1078.0   87.0  1.66   61.46  "
+       "223  37.00  4902  10.05  1332.0  132.0  2.51   59.53  \n",
+       "7    31.48  4344   9.61  1326.0  138.0  2.61   50.65  \n",
+       "11   22.22  3406   8.56  1312.0  153.0  2.90   35.75  \n",
+       "10   35.99  4377  10.77  1310.0  122.0  2.30   57.91  \n",
+       "230  36.65  4806   9.93  1303.0  131.0  2.48   58.97  \n",
+       "212  37.56  4921   9.93  1301.0  131.0  2.48   60.43  \n",
+       "190  39.51  4505  11.36  1295.0  114.0  2.16   63.57  \n",
+       "89   58.69  6742  11.16  1282.0  115.0  2.18   94.43  \n",
+       "80   61.27  6611  11.68  1261.0  108.0  2.04   98.58  \n",
+       "64   72.97  7644  11.90  1247.0  105.0  1.98  117.41  \n",
+       "70   66.52  7520  10.58  1195.0  113.0  2.14  107.03  \n",
+       "145  43.62  4819  10.79  1193.0  110.0  2.09   70.18  \n",
+       "91   58.29  6044  11.45  1187.0  104.0  1.96   93.79  \n",
+       "100  54.75  5286  12.16  1174.0   97.0  1.83   88.09  \n",
+       "72   66.01  6137  12.42  1155.0   93.0  1.76  106.21  \n",
+       "5    29.38  3517   9.54  1142.0  120.0  2.27   47.27  \n",
+       "93   57.64  5561  11.61  1120.0   96.0  1.83   92.74  \n",
+       "74   64.92  6677  10.87  1118.0  103.0  1.95  104.46  \n",
+       "286  32.33  2788  12.91  1113.0   86.0  1.63   52.02  \n",
+       "205  38.20  3342  12.32  1078.0   87.0  1.66   61.46  "
       ]
      },
      "execution_count": 13,
@@ -1646,7 +1658,7 @@
    "source": [
     "# Exploring the Data: `rides` and `segments`\n",
     "\n",
-    "Some more poking around with the data:\n"
+    "\n"
    ]
   },
   {
@@ -1689,39 +1701,39 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
-       "      <td>486.000000</td>\n",
-       "      <td>486.000000</td>\n",
-       "      <td>486.000000</td>\n",
-       "      <td>486.000000</td>\n",
-       "      <td>486.000000</td>\n",
-       "      <td>486.000000</td>\n",
-       "      <td>486.000000</td>\n",
-       "      <td>486.000000</td>\n",
-       "      <td>486.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>491.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>2016.462963</td>\n",
-       "      <td>3.115474</td>\n",
-       "      <td>40.070844</td>\n",
-       "      <td>1669.654321</td>\n",
-       "      <td>13.033807</td>\n",
-       "      <td>512.502058</td>\n",
-       "      <td>40.997942</td>\n",
-       "      <td>0.776317</td>\n",
-       "      <td>64.473909</td>\n",
+       "      <td>2016.509165</td>\n",
+       "      <td>3.140550</td>\n",
+       "      <td>40.354990</td>\n",
+       "      <td>1672.439919</td>\n",
+       "      <td>13.025703</td>\n",
+       "      <td>510.706721</td>\n",
+       "      <td>40.873727</td>\n",
+       "      <td>0.773951</td>\n",
+       "      <td>64.931079</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>1.904327</td>\n",
-       "      <td>1.293698</td>\n",
-       "      <td>15.782804</td>\n",
-       "      <td>1370.266537</td>\n",
-       "      <td>1.282601</td>\n",
-       "      <td>295.230078</td>\n",
-       "      <td>27.304809</td>\n",
-       "      <td>0.517015</td>\n",
-       "      <td>25.394528</td>\n",
+       "      <td>1.948683</td>\n",
+       "      <td>1.311238</td>\n",
+       "      <td>15.951748</td>\n",
+       "      <td>1370.960494</td>\n",
+       "      <td>1.281094</td>\n",
+       "      <td>295.110833</td>\n",
+       "      <td>27.278315</td>\n",
+       "      <td>0.516556</td>\n",
+       "      <td>25.666323</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
@@ -1738,38 +1750,38 @@
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>2015.000000</td>\n",
-       "      <td>2.145225</td>\n",
-       "      <td>28.092500</td>\n",
-       "      <td>666.000000</td>\n",
-       "      <td>12.290000</td>\n",
-       "      <td>264.750000</td>\n",
-       "      <td>19.250000</td>\n",
+       "      <td>2.151700</td>\n",
+       "      <td>28.135000</td>\n",
+       "      <td>674.000000</td>\n",
+       "      <td>12.275000</td>\n",
+       "      <td>260.500000</td>\n",
+       "      <td>19.000000</td>\n",
        "      <td>0.370000</td>\n",
-       "      <td>45.202500</td>\n",
+       "      <td>45.270000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>2016.000000</td>\n",
-       "      <td>2.685950</td>\n",
-       "      <td>34.560000</td>\n",
-       "      <td>1294.500000</td>\n",
-       "      <td>13.220000</td>\n",
-       "      <td>495.000000</td>\n",
+       "      <td>2.690000</td>\n",
+       "      <td>35.180000</td>\n",
+       "      <td>1293.000000</td>\n",
+       "      <td>13.200000</td>\n",
+       "      <td>488.000000</td>\n",
        "      <td>36.000000</td>\n",
        "      <td>0.680000</td>\n",
-       "      <td>55.605000</td>\n",
+       "      <td>56.600000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>2018.000000</td>\n",
-       "      <td>3.639925</td>\n",
-       "      <td>45.502500</td>\n",
-       "      <td>2141.250000</td>\n",
-       "      <td>13.807500</td>\n",
-       "      <td>688.750000</td>\n",
+       "      <td>3.672350</td>\n",
+       "      <td>45.750000</td>\n",
+       "      <td>2139.500000</td>\n",
+       "      <td>13.800000</td>\n",
+       "      <td>688.500000</td>\n",
        "      <td>54.000000</td>\n",
        "      <td>1.030000</td>\n",
-       "      <td>73.212500</td>\n",
+       "      <td>73.610000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
@@ -1789,23 +1801,23 @@
       ],
       "text/plain": [
        "              year       hours       miles         feet         mph  \\\n",
-       "count   486.000000  486.000000  486.000000   486.000000  486.000000   \n",
-       "mean   2016.462963    3.115474   40.070844  1669.654321   13.033807   \n",
-       "std       1.904327    1.293698   15.782804  1370.266537    1.282601   \n",
+       "count   491.000000  491.000000  491.000000   491.000000  491.000000   \n",
+       "mean   2016.509165    3.140550   40.354990  1672.439919   13.025703   \n",
+       "std       1.948683    1.311238   15.951748  1370.960494    1.281094   \n",
        "min    2012.000000    1.540000   20.960000    68.000000    8.560000   \n",
-       "25%    2015.000000    2.145225   28.092500   666.000000   12.290000   \n",
-       "50%    2016.000000    2.685950   34.560000  1294.500000   13.220000   \n",
-       "75%    2018.000000    3.639925   45.502500  2141.250000   13.807500   \n",
+       "25%    2015.000000    2.151700   28.135000   674.000000   12.275000   \n",
+       "50%    2016.000000    2.690000   35.180000  1293.000000   13.200000   \n",
+       "75%    2018.000000    3.672350   45.750000  2139.500000   13.800000   \n",
        "max    2021.000000    8.137500  101.000000  7644.000000   16.770000   \n",
        "\n",
        "               vam         fpm         pct         kms  \n",
-       "count   486.000000  486.000000  486.000000  486.000000  \n",
-       "mean    512.502058   40.997942    0.776317   64.473909  \n",
-       "std     295.230078   27.304809    0.517015   25.394528  \n",
+       "count   491.000000  491.000000  491.000000  491.000000  \n",
+       "mean    510.706721   40.873727    0.773951   64.931079  \n",
+       "std     295.110833   27.278315    0.516556   25.666323  \n",
        "min      33.000000    3.000000    0.050000   33.720000  \n",
-       "25%     264.750000   19.250000    0.370000   45.202500  \n",
-       "50%     495.000000   36.000000    0.680000   55.605000  \n",
-       "75%     688.750000   54.000000    1.030000   73.212500  \n",
+       "25%     260.500000   19.000000    0.370000   45.270000  \n",
+       "50%     488.000000   36.000000    0.680000   56.600000  \n",
+       "75%     688.500000   54.000000    1.030000   73.610000  \n",
        "max    1332.000000  153.000000    2.900000  162.510000  "
       ]
      },
@@ -2019,7 +2031,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>224</th>\n",
+       "      <th>229</th>\n",
        "      <td>Sun, 5/22</td>\n",
        "      <td>2016</td>\n",
        "      <td>Canada</td>\n",
@@ -2033,7 +2045,7 @@
        "      <td>59.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>248</th>\n",
+       "      <th>253</th>\n",
        "      <td>Wed, 9/13</td>\n",
        "      <td>2017</td>\n",
        "      <td>Healdburg / Jimtown</td>\n",
@@ -2047,7 +2059,7 @@
        "      <td>55.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>469</th>\n",
+       "      <th>474</th>\n",
        "      <td>Sat, 1/25</td>\n",
        "      <td>2014</td>\n",
        "      <td>Woodside</td>\n",
@@ -2061,7 +2073,7 @@
        "      <td>40.35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>478</th>\n",
+       "      <th>483</th>\n",
        "      <td>Sat, 4/11</td>\n",
        "      <td>2015</td>\n",
        "      <td>Woodside</td>\n",
@@ -2075,7 +2087,7 @@
        "      <td>39.79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>429</th>\n",
+       "      <th>434</th>\n",
        "      <td>Sun, 1/18</td>\n",
        "      <td>2015</td>\n",
        "      <td>Woodside</td>\n",
@@ -2089,7 +2101,7 @@
        "      <td>41.87</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>467</th>\n",
+       "      <th>472</th>\n",
        "      <td>Fri, 6/24</td>\n",
        "      <td>2016</td>\n",
        "      <td>Foothill Expway</td>\n",
@@ -2103,7 +2115,7 @@
        "      <td>40.40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>268</th>\n",
+       "      <th>273</th>\n",
        "      <td>Sun, 1/26</td>\n",
        "      <td>2014</td>\n",
        "      <td>Canada Rd</td>\n",
@@ -2117,7 +2129,7 @@
        "      <td>53.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>292</th>\n",
+       "      <th>297</th>\n",
        "      <td>Sun, 4/12</td>\n",
        "      <td>2015</td>\n",
        "      <td>Palo Alto Cycling</td>\n",
@@ -2131,7 +2143,7 @@
        "      <td>51.10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>134</th>\n",
+       "      <th>139</th>\n",
        "      <td>Sun, 10/15</td>\n",
        "      <td>2017</td>\n",
        "      <td>Los Gatos</td>\n",
@@ -2145,7 +2157,7 @@
        "      <td>71.94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>391</th>\n",
+       "      <th>396</th>\n",
        "      <td>Sun, 2/28</td>\n",
        "      <td>2016</td>\n",
        "      <td>Woodside Loop</td>\n",
@@ -2159,7 +2171,7 @@
        "      <td>43.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>94</th>\n",
+       "      <th>99</th>\n",
        "      <td>Sun, 8/5</td>\n",
        "      <td>2018</td>\n",
        "      <td>Bike Ride Northwest Day 1</td>\n",
@@ -2173,7 +2185,7 @@
        "      <td>89.73</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>109</th>\n",
+       "      <th>114</th>\n",
        "      <td>Sun, 6/26</td>\n",
        "      <td>2016</td>\n",
        "      <td>Los Gatos</td>\n",
@@ -2187,7 +2199,7 @@
        "      <td>81.71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>475</th>\n",
+       "      <th>480</th>\n",
        "      <td>Sun, 1/19</td>\n",
        "      <td>2014</td>\n",
        "      <td>Palo Alto, CA</td>\n",
@@ -2201,7 +2213,7 @@
        "      <td>40.24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>126</th>\n",
+       "      <th>131</th>\n",
        "      <td>Mon, 1/19</td>\n",
        "      <td>2015</td>\n",
        "      <td>Canada Rd, etc.</td>\n",
@@ -2215,7 +2227,7 @@
        "      <td>73.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>247</th>\n",
+       "      <th>252</th>\n",
        "      <td>Sun, 12/6</td>\n",
        "      <td>2015</td>\n",
        "      <td>Canada Rd</td>\n",
@@ -2229,7 +2241,7 @@
        "      <td>55.78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>298</th>\n",
+       "      <th>303</th>\n",
        "      <td>Tue, 6/18</td>\n",
        "      <td>2013</td>\n",
        "      <td>work etc (headwinds)</td>\n",
@@ -2243,7 +2255,7 @@
        "      <td>50.65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>139</th>\n",
+       "      <th>144</th>\n",
        "      <td>Fri, 9/23</td>\n",
        "      <td>2016</td>\n",
        "      <td>Los Gatos</td>\n",
@@ -2257,7 +2269,7 @@
        "      <td>70.68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>87</th>\n",
+       "      <th>92</th>\n",
        "      <td>Sat, 7/9</td>\n",
        "      <td>2016</td>\n",
        "      <td>Santa Cruz</td>\n",
@@ -2271,7 +2283,7 @@
        "      <td>93.69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>156</th>\n",
+       "      <th>161</th>\n",
        "      <td>Sun, 8/7</td>\n",
        "      <td>2016</td>\n",
        "      <td>Canada</td>\n",
@@ -2285,7 +2297,7 @@
        "      <td>66.27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>387</th>\n",
+       "      <th>392</th>\n",
        "      <td>Mon, 3/30</td>\n",
        "      <td>2015</td>\n",
        "      <td>Woodside Loop</td>\n",
@@ -2304,48 +2316,48 @@
       ],
       "text/plain": [
        "           date  year                      title   hours  miles  feet    mph  \\\n",
-       "224   Sun, 5/22  2016                     Canada  2.1867  36.68  1332  16.77   \n",
-       "248   Wed, 9/13  2017        Healdburg / Jimtown  2.1342  34.45   912  16.14   \n",
-       "469   Sat, 1/25  2014                   Woodside  1.5606  25.08  1243  16.07   \n",
-       "478   Sat, 4/11  2015                   Woodside  1.5400  24.73  1035  16.06   \n",
-       "429   Sun, 1/18  2015                   Woodside  1.6378  26.02  1257  15.89   \n",
-       "467   Fri, 6/24  2016            Foothill Expway  1.5903  25.11   623  15.79   \n",
-       "268   Sun, 1/26  2014                  Canada Rd  2.0997  33.12  1446  15.77   \n",
-       "292   Sun, 4/12  2015          Palo Alto Cycling  2.0303  31.76  1210  15.64   \n",
-       "134  Sun, 10/15  2017                  Los Gatos  2.8647  44.71  1437  15.61   \n",
-       "391   Sun, 2/28  2016              Woodside Loop  1.7261  26.93   843  15.60   \n",
-       "94     Sun, 8/5  2018  Bike Ride Northwest Day 1  3.5783  55.77  1824  15.59   \n",
-       "109   Sun, 6/26  2016                  Los Gatos  3.2758  50.78  1181  15.50   \n",
-       "475   Sun, 1/19  2014              Palo Alto, CA  1.6153  25.01  1201  15.48   \n",
-       "126   Mon, 1/19  2015            Canada Rd, etc.  2.9539  45.64  1836  15.45   \n",
-       "247   Sun, 12/6  2015                  Canada Rd  2.2514  34.67  1237  15.40   \n",
-       "298   Tue, 6/18  2013       work etc (headwinds)  2.0594  31.48   809  15.29   \n",
-       "139   Fri, 9/23  2016                  Los Gatos  2.8894  43.93  1339  15.20   \n",
-       "87     Sat, 7/9  2016                 Santa Cruz  3.8397  58.23  4042  15.17   \n",
-       "156    Sun, 8/7  2016                     Canada  2.7161  41.19  1526  15.17   \n",
-       "387   Mon, 3/30  2015              Woodside Loop  1.7961  27.22  1081  15.16   \n",
+       "229   Sun, 5/22  2016                     Canada  2.1867  36.68  1332  16.77   \n",
+       "253   Wed, 9/13  2017        Healdburg / Jimtown  2.1342  34.45   912  16.14   \n",
+       "474   Sat, 1/25  2014                   Woodside  1.5606  25.08  1243  16.07   \n",
+       "483   Sat, 4/11  2015                   Woodside  1.5400  24.73  1035  16.06   \n",
+       "434   Sun, 1/18  2015                   Woodside  1.6378  26.02  1257  15.89   \n",
+       "472   Fri, 6/24  2016            Foothill Expway  1.5903  25.11   623  15.79   \n",
+       "273   Sun, 1/26  2014                  Canada Rd  2.0997  33.12  1446  15.77   \n",
+       "297   Sun, 4/12  2015          Palo Alto Cycling  2.0303  31.76  1210  15.64   \n",
+       "139  Sun, 10/15  2017                  Los Gatos  2.8647  44.71  1437  15.61   \n",
+       "396   Sun, 2/28  2016              Woodside Loop  1.7261  26.93   843  15.60   \n",
+       "99     Sun, 8/5  2018  Bike Ride Northwest Day 1  3.5783  55.77  1824  15.59   \n",
+       "114   Sun, 6/26  2016                  Los Gatos  3.2758  50.78  1181  15.50   \n",
+       "480   Sun, 1/19  2014              Palo Alto, CA  1.6153  25.01  1201  15.48   \n",
+       "131   Mon, 1/19  2015            Canada Rd, etc.  2.9539  45.64  1836  15.45   \n",
+       "252   Sun, 12/6  2015                  Canada Rd  2.2514  34.67  1237  15.40   \n",
+       "303   Tue, 6/18  2013       work etc (headwinds)  2.0594  31.48   809  15.29   \n",
+       "144   Fri, 9/23  2016                  Los Gatos  2.8894  43.93  1339  15.20   \n",
+       "92     Sat, 7/9  2016                 Santa Cruz  3.8397  58.23  4042  15.17   \n",
+       "161    Sun, 8/7  2016                     Canada  2.7161  41.19  1526  15.17   \n",
+       "392   Mon, 3/30  2015              Woodside Loop  1.7961  27.22  1081  15.16   \n",
        "\n",
        "        vam   fpm   pct    kms  \n",
-       "224   609.0  36.0  0.69  59.02  \n",
-       "248   427.0  26.0  0.50  55.43  \n",
-       "469   796.0  50.0  0.94  40.35  \n",
-       "478   672.0  42.0  0.79  39.79  \n",
-       "429   767.0  48.0  0.91  41.87  \n",
-       "467   392.0  25.0  0.47  40.40  \n",
-       "268   689.0  44.0  0.83  53.29  \n",
-       "292   596.0  38.0  0.72  51.10  \n",
-       "134   502.0  32.0  0.61  71.94  \n",
-       "391   488.0  31.0  0.59  43.33  \n",
-       "94    510.0  33.0  0.62  89.73  \n",
-       "109   361.0  23.0  0.44  81.71  \n",
-       "475   744.0  48.0  0.91  40.24  \n",
-       "126   622.0  40.0  0.76  73.43  \n",
-       "247   549.0  36.0  0.68  55.78  \n",
-       "298   393.0  26.0  0.49  50.65  \n",
-       "139   463.0  30.0  0.58  70.68  \n",
-       "87   1053.0  69.0  1.31  93.69  \n",
-       "156   562.0  37.0  0.70  66.27  \n",
-       "387   602.0  40.0  0.75  43.80  "
+       "229   609.0  36.0  0.69  59.02  \n",
+       "253   427.0  26.0  0.50  55.43  \n",
+       "474   796.0  50.0  0.94  40.35  \n",
+       "483   672.0  42.0  0.79  39.79  \n",
+       "434   767.0  48.0  0.91  41.87  \n",
+       "472   392.0  25.0  0.47  40.40  \n",
+       "273   689.0  44.0  0.83  53.29  \n",
+       "297   596.0  38.0  0.72  51.10  \n",
+       "139   502.0  32.0  0.61  71.94  \n",
+       "396   488.0  31.0  0.59  43.33  \n",
+       "99    510.0  33.0  0.62  89.73  \n",
+       "114   361.0  23.0  0.44  81.71  \n",
+       "480   744.0  48.0  0.91  40.24  \n",
+       "131   622.0  40.0  0.76  73.43  \n",
+       "252   549.0  36.0  0.68  55.78  \n",
+       "303   393.0  26.0  0.49  50.65  \n",
+       "144   463.0  30.0  0.58  70.68  \n",
+       "92   1053.0  69.0  1.31  93.69  \n",
+       "161   562.0  37.0  0.70  66.27  \n",
+       "392   602.0  40.0  0.75  43.80  "
       ]
      },
      "execution_count": 16,
@@ -2354,7 +2366,7 @@
     }
    ],
    "source": [
-    "top(rides, 'mph') # Fastest rides"
+    "top(rides, 'mph') # Fastest rides (of more than 20 miles)"
    ]
   },
   {
@@ -3028,7 +3040,7 @@
     }
    ],
    "source": [
-    "top(segments, 'feet') # Biggest climbs"
+    "top(segments, 'feet') # Biggest climbing segments"
    ]
   },
   {
@@ -3367,6 +3379,15 @@
    "source": [
     "top(segments, 'pct') # Steepest climbs"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given an array of boxes, m rows, each row has n boxes. In each box, there is a known number of coins, c[i][j]. Start from the first row, you could choose coins from one box. Then move to the second row and get all coins from another box on the second row. Keep moving row by row and collecting coins. But if you choose x-th column from previous row, but a different y-th column for the current row, you have to pay abs(x - y) amount of changing fee.\n",
+    "\n",
+    "Please find a way to maximize the final coins you collect after paying changing fees."
+   ]
   }
  ],
  "metadata": {

ipynb/BikeCode.ipynb

@@ -141,7 +141,8 @@
     "\n",
     "def wandrer(category, entries, start=start):\n",
     "    \"\"\"Plot Wandrer.earth data.\"\"\"\n",
-    "    fig, ax = plt.figure(), plt.subplot(111); plt.plot()\n",
+    "    fig, ax = plt.figure(), plt.subplot(111); \n",
+    "    plt.plot()\n",
     "    for (place, miles, pcts), marker in zip(entries, '^v><osdhxDHPX*1234'):\n",
     "        N = len(pcts)\n",
     "        dates = [Month(start + i) for i in range(N)]\n",
@@ -209,6 +210,27 @@
     "places = parse_places(open('bikeplaces.txt'))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_places(filename='bikeplaces.txt'):\n",
+    "    return ''.join(map(update_line, open(filename)))\n",
+    "\n",
+    "def update_line(line):\n",
+    "    words = line.split()\n",
+    "    if not words or words[0].startswith(':'):\n",
+    "        pass\n",
+    "    elif '*' in words[-1]:\n",
+    "        m, d = words[-1].split('*')\n",
+    "        words[-1] = m + '*' + str(int(d) + 1)\n",
+    "    else:\n",
+    "        words[-1] += '*2'\n",
+    "    return ' '.join(words) + '\\n'"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -218,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [

ipynb/StableMatching.ipynb

@@ -13,7 +13,7 @@
     "- Hospitals in turn state which students they prefer. \n",
     "- An algorithm finds a stable matching.\n",
     "\n",
-    "Each actor has **preferences** for who they would prefer to be matched with. In the default way of stating the problem, preferences are expressed as an **ordering**: each actor rates the possible matches on the other side from most preferred to least preferred. But we will go beyond that, allowing each actor to say more: to express their preference for each possible match as a **utility**: a number between 0 and 1. For example actor $A$ on one side could say that they would like to be paired with actor $β$ on the other side with utility 0.9 (meaning a very desireable match) and with actor $γ$ on the other side with utility 0.1 (meaning an undesireable match). The algorithm we present actually pays attention only to the ordering of preferences, but we will use the utilities to analyze how well each side does, on average.\n",
+    "Each actor has **preferences** for who they would prefer to be matched with. In the default way of stating the problem, preferences are expressed as an **ordering**: each actor rates the possible matches on the other side from most preferred to least preferred. But we will go beyond that, allowing each actor to say more: to express their preference for each possible match as a **utility**: a number between 0 and 1. For example actor $A$ on one side could say that they would like to be paired with actor β on the other side with utility 0.9 (meaning a very desireable match) and with actor γ on the other side with utility 0.1 (meaning an undesireable match). The algorithm we present actually pays attention only to the ordering of preferences, but we will use the utilities to analyze how well each side does, on average.\n",
     "\n",
     "A matching is **stable** if it is **not** the case that there is an actor from one side and an actor from the other side who both have a higher preference for each other than they have for who they are currently matched with. \n",
     "\n",
@@ -40,9 +40,16 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from statistics import mean, stdev\n",
+    "from typing import *\n",
     "import random\n",
+    "import itertools \n",
+    "flatten = itertools.chain.from_iterable\n",
     "\n",
-    "def stable_matching(P, A) -> set:\n",
+    "\n",
+    "ID = int\n",
+    "Match = Tuple[ID, ID]\n",
+    "\n",
+    "def stable_matching(P, A) -> Set[Match]:\n",
     "    \"\"\"Compute a stable match, a set of (p, a) pairs.\n",
     "    P and A are square preference arrays: P[p][a] is how much p likes a;\n",
     "    A[a][p] is how much a likes p. Stable means there is no (p, a)\n",
@@ -50,7 +57,8 @@
     "    ids = range(len(P))           # ID numbers of all actors on (either) side\n",
     "    unmatched = set(ids)          # Members of P that are not yet matched to anyone\n",
     "    matched = {}                  # {a: p} mapping of who acceptors are matched with\n",
-    "    proposals = orderings(P, ids) # proposals[p] is an ordered list of who p should propose to\n",
+    "    proposals = [sorted(ids, key=lambda a: P[p][a]) \n",
+    "                 for p in ids]    # proposals[p] is an ordered list of who p should propose to\n",
     "    while unmatched:\n",
     "        p = next(iter(unmatched)) # p is an arbitrary unmatched Proposer\n",
     "        a = proposals[p].pop()    # a is p's most preferred remaining acceptor\n",
@@ -61,9 +69,7 @@
     "            unmatched.add(matched[a])\n",
     "            unmatched.remove(p)\n",
     "            matched[a] = p\n",
-    "    return {(p, a) for (a, p) in matched.items()}\n",
-    "\n",
-    "def orderings(P, ids): return [sorted(ids, key=lambda a: P[p][a]) for p in ids]"
+    "    return {(p, a) for (a, p) in matched.items()}"
    ]
   },
   {
@@ -80,7 +86,7 @@
     "\n",
     "# Preference Arrays\n",
     "\n",
-    "Let's define some preference arrays. `I` is the identity matrix: it says that every proposer number $i$ likes acceptor $i$ best, and dislikes the others equally. `X` is the same as the identity matrix for indexes 0, 1, and 2, but it says that 3 prefers 4 and 4 prefers 3."
+    "Let's define some preference arrays. `I` is the identity matrix: it says that every proposer number $i$ likes acceptor $i$ best, and dislikes the others equally. `X` is the same as the identity matrix for indexes 0, 1, and 2, but it says that the actor with index 3 would be happy with any of 2, 3, or 4, and actor 4 prefers 3."
    ]
   },
   {
@@ -98,71 +104,32 @@
     "X = [[1, 0, 0, 0, 0],\n",
     "     [0, 1, 0, 0, 0],\n",
     "     [0, 0, 1, 0, 0],\n",
-    "     [0, 0, 0, 0, 1],\n",
+    "     [0, 0, 1, 0, 0],\n",
     "     [0, 0, 0, 1, 0]]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see what happens when `I` is the proposing side, and when `X` is the proposing side:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "stable_matching(I, X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{(0, 0), (1, 1), (2, 2), (3, 4), (4, 3)}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stable_matching(X, I)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When `I` is the proposing side, every actor in `I` gets their first-choice match.\n",
+    "I = [[.9, .4, .3, .2, .1],\n",
+    "     [.1, .9, .4, .3, .2],\n",
+    "     [.2, .1, .9, .4, .3],\n",
+    "     [.3, .2, .1, .9, .4],\n",
+    "     [.4, .3, .2, .1, .9]]\n",
     "\n",
-    "Likewise, when `X` is the proposer, every actor in `X` gets their first-choice match.\n",
-    "\n",
-    "We can measure the average utility to each side for any matching:"
+    "M = [[.9, .4, .3, .2, .1],\n",
+    "     [.1, .9, .4, .3, .2],\n",
+    "     [.2, .1, .9, .4, .3],\n",
+    "     [.1, .2, .3, .4, .9],\n",
+    "     [.9, .4, .3, .2, .1]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,18 +141,45 @@
     "            mean(A[a][p] for (p, a) in matching))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see what happens when `I` is the proposing side, and when `X` is the proposing side:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1, 0.6)"
+       "{(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stable_matching(I, X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.9, 0.64)"
+      ]
+     },
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -196,16 +190,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1, 0.6)"
+       "{(0, 0), (1, 1), (2, 2), (3, 4), (4, 3)}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stable_matching(X, I)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.76, 0.64)"
+      ]
+     },
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,6 +228,17 @@
     "mean_utilities(X, I)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When `I` is the proposing side, every actor in `I` gets their first-choice match.\n",
+    "\n",
+    "Likewise, when `X` is the proposer, every actor in `X` gets a first-choice match.\n",
+    "\n",
+    "We can measure the average utility to each side for any matching:"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -230,8 +255,8 @@
     "# Preferences with Common and Private Values\n",
     "\n",
     "I will create a bunch of randomized preference arrays and get a feeling for how they perform. But I don't want them to be completely random; I want them to reflect, in a very abstract way, some properties of the real world:\n",
-    "- Some choices have intrinsic properties that make them widely popular (or unpopular). For example, Massachusetts General Hospital is considered an excellent choice by the vast majority of aspiring residents. The amount of utility that is commonly agreed upon is called the **common value** of a choice.\n",
-    "- Some choices have ideosyncratic properties that appeal only to specific choosers. For example, you might really like your hometown hospital, even if it is not highly-regarded by others. We call this the **private value** of a choice.\n",
+    "- Some choices have intrinsic properties that make them widely popular (or unpopular). For example, Massachusetts General Hospital is considered an excellent choice by many aspiring residents. The amount of utility that is commonly agreed upon is called the **common value** of a choice.\n",
+    "- Some choices have idiosyncratic properties that appeal only to specific choosers. For example, you might really want to be a resident at your hometown hospital, even if it is not highly-regarded by others. This is the **private value** of a choice.\n",
     "- In real world situations there is usually a mix of common and private value.\n",
     "\n",
     "The function call `preferences(N, 0.75)`, for example, creates an NxN array of preferences, where each preference is 75% common value and 25% individual value. I implement individual value as being proportional to the ID number (`a` in the code):"
@@ -353,7 +378,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `preferences` function has been designed so that the average utility value is close to 0.5:"
+    "The `preferences` function has been designed so that the average utility value is close to 0.5, for all values of `c`:"
    ]
   },
   {
@@ -364,7 +389,7 @@
     {
      "data": {
       "text/plain": [
-       "0.5000443999999999"
+       "0.5000444"
       ]
      },
      "execution_count": 12,
@@ -373,7 +398,27 @@
     }
    ],
    "source": [
-    "mean(map(mean, preferences(100)))"
+    "mean(flatten(preferences(100)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.50186629"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean(flatten(preferences(100, c=0.25)))"
    ]
   },
   {
@@ -385,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -396,37 +441,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[([[0.5238, 0.6412, 0.7176, 0.733],\n",
-       "   [0.4411, 0.358, 0.7271, 0.6545],\n",
-       "   [0.2763, 0.5521, 0.6802, 0.4661],\n",
-       "   [0.1171, 0.3816, 0.6731, 0.7765]],\n",
-       "  [[0.4918, 0.6565, 0.5443, 0.7238],\n",
-       "   [0.3467, 0.447, 0.3938, 0.6281],\n",
-       "   [0.4353, 0.6498, 0.3784, 0.4451],\n",
-       "   [0.4623, 0.663, 0.3553, 0.5625]]),\n",
-       " ([[0.3339, 0.3563, 0.5098, 0.7453],\n",
-       "   [0.2433, 0.5171, 0.7037, 0.8426],\n",
-       "   [0.3665, 0.2497, 0.5967, 0.598],\n",
-       "   [0.1948, 0.2769, 0.5439, 0.6575]],\n",
-       "  [[0.2957, 0.6704, 0.5526, 0.8597],\n",
-       "   [0.1714, 0.2168, 0.7819, 0.5757],\n",
-       "   [0.3677, 0.3445, 0.3445, 0.5274],\n",
-       "   [0.4455, 0.3256, 0.747, 0.5011]])]"
+       "[([[0.1692, 0.6815, 0.6931],\n",
+       "   [0.2352, 0.7386, 0.7107],\n",
+       "   [0.2157, 0.6245, 0.7099]],\n",
+       "  [[0.095, 0.6114, 0.6695],\n",
+       "   [0.3564, 0.4007, 0.4181],\n",
+       "   [0.4574, 0.7469, 0.7934]]),\n",
+       " ([[0.5066, 0.2609, 0.439],\n",
+       "   [0.0884, 0.6074, 0.4683],\n",
+       "   [0.5632, 0.6722, 0.7142]],\n",
+       "  [[0.1663, 0.6358, 0.8687],\n",
+       "   [0.4716, 0.7222, 0.8577],\n",
+       "   [0.2024, 0.2663, 0.5462]])]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "examples(N=4, repeat=2)"
+    "examples(N=3, repeat=2)"
    ]
   },
   {
@@ -440,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,9 +490,9 @@
     "    pr, ac = transpose(mean_utilities(P, A) for (P, A) in examples(N, c, repeat))\n",
     "    plt.hist(pr, bins=bins, alpha=0.5)\n",
     "    plt.hist(ac, bins=bins, alpha=0.5);\n",
-    "    print(f'''{repeat:,d} examples with N = {N} actors, common factor c = {c}\n",
-    "    Acceptors: {mean(ac):4.3f} ± {stdev(ac):4.3f}\n",
-    "    Proposers: {mean(pr):4.3f} ± {stdev(pr):4.3f}''')\n",
+    "    print(f'''{repeat:,d} examples with N = {N} actors, common value ratio c = {c}\n",
+    "    Acceptors: {mean(ac):.3f} ± {stdev(ac):.3f}\n",
+    "    Proposers: {mean(pr):.3f} ± {stdev(pr):.3f}''')\n",
     "    \n",
     "def transpose(matrix): return list(zip(*matrix))"
    ]
@@ -465,21 +506,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10,000 examples with N = 25 actors, common factor c = 0.0\n",
-      "    Acceptors: 0.730 ± 0.064\n",
+      "10,000 examples with N = 25 actors, common value ratio c = 0.0\n",
+      "    Acceptors: 0.729 ± 0.063\n",
       "    Proposers: 0.870 ± 0.035\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -505,21 +546,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10,000 examples with N = 25 actors, common factor c = 0.5\n",
-      "    Acceptors: 0.621 ± 0.022\n",
+      "10,000 examples with N = 25 actors, common value ratio c = 0.5\n",
+      "    Acceptors: 0.621 ± 0.021\n",
       "    Proposers: 0.638 ± 0.019\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -536,21 +577,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10,000 examples with N = 50 actors, common factor c = 0.5\n",
-      "    Acceptors: 0.647 ± 0.014\n",
-      "    Proposers: 0.661 ± 0.012\n"
+      "10,000 examples with N = 50 actors, common value ratio c = 0.5\n",
+      "    Acceptors: 0.647 ± 0.013\n",
+      "    Proposers: 0.661 ± 0.011\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -571,26 +612,26 @@
    "source": [
     "We see that the gap between proposer and acceptor has been greatly reduced (but not eliminated). With more actors, the variance is smaller (the histogram is not as wide). \n",
     "\n",
-    "What happens with 90% common value?"
+    "What happens with 90% common value? How aboout 99%?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10,000 examples with N = 25 actors, common factor c = 0.9\n",
+      "10,000 examples with N = 25 actors, common value ratio c = 0.9\n",
       "    Acceptors: 0.509 ± 0.005\n",
       "    Proposers: 0.510 ± 0.005\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -605,6 +646,37 @@
     "show(c=0.9)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10,000 examples with N = 25 actors, common value ratio c = 0.99\n",
+      "    Acceptors: 0.500 ± 0.001\n",
+      "    Proposers: 0.500 ± 0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show(c=0.99)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

ipynb/bikeplaces.txt

@@ -1,103 +1,116 @@
-:Around 90%:
-Los Altos: 138: 40.2 43.7 72.4 77.2 84.5 90.8      | 91.0*2 91.1
-Atherton: 55.7: 0*4 91.2 94.0                      | 95.2*2 94.2
-East Palo Alto: 46.5: 74.4 91.2 91.9*2 92.2 93.3   | 93.4*2 92.4
-Emerald Lake Hills: 24.4: 0.0 94.3*3 44.7 51.0     | 80.0 85.8 91.3
-Menlo Park: 136.4: 67.7 76.8 87.7 90.7 91.2 90.1   | 92.5*2 91.4
-North Fair Oaks: 26.8: 48.1 90.4 93.1 93.8 94.8 96.1 | 96.9*2 98.9
-Mountain View: 205: 53.0 59.9 63.0 63.6 72.9 77.1  | 91.1*3
-Palo Alto: 292: 63.0 73.6 85.4 85.7 87.1 87.6      | 88.1*2 90.3
+:Over 90%:
+Los Altos: 138: 40.2 43.7 72.4 77.2 84.5 90.8 | 91.0*2 91.1*3
+Atherton: 55.7: 0*4 91.2 94.0 | 95.2*2 94.2 94.9*2
+East Palo Alto: 46.5: 74.4 91.2 91.9*2 92.2 93.3 | 93.4*2 92.4*3
+Emerald Lake Hills: 24.4: 0.0 94.3*3 44.7 51.0 | 80.0 85.8 91.3*3
+Menlo Park: 136.4: 67.7 76.8 87.7 90.7 91.2 90.1 | 92.5*2 91.4 91.6*2
+North Fair Oaks: 26.8: 48.1 90.4 93.1 93.8 94.8 96.1 | 96.9*2 99.1*3
+Mountain View: 205: 53.0 59.9 63.0 63.6 72.9 77.1 | 91.1*5
+Palo Alto: 292: 63.0 73.6 85.4 85.7 87.1 87.6 | 88.1*2 90.3 90.4*2
+Loyola: 18.3: 0*4 60.8 62.1 | 62.1*4 88.6
 
-:Around 25%:
-Belmont: 98.1: 15.5 17.3 18.6 18.6 20.6*2 | 27.4*2 27.5
-Campbell: 119: 8.9 10.1 12.4*4          | 12.4 25.2*2
-Cupertino: 172: 22.1 23.9 26.2*3 26.3   | 26.4*3
-Hillsborough: 85.3: 3.3*6               | 3.6 24.5 25.3
-Los Gatos: 148: 7.5 8.6 8.8*4           | 8.8 26.1*2
-San Mateo: 256: 11.1*6                  | 11.3 25.5 27.8
-Saratoga: 180: 14.5 15.7 17.4*4         | 17.4 26.9*2
-Sunnyvale: 357: 19.4 19.9 22.2*4        | 25.1*3
-Union City: 207: 7*3 8*3                | 8.8*2 25.7
-Fremont: 771: 9*3 10*3                  | 11.7*2 27.5
-San Mateo Highlands: 18: 0*6                     | 18.0 29.2*2
+:Between 30% and 90%:
+Foster City: 150: 9.1*6 | 9.1 27.4 37.2*3
+Burlingame: 88.4: 9.4*6 | 9.4 31.5*4
+Los Altos Hills: 91: 48.4*2 49.0 55.1*2 55.4 | 55.8*5
+Millbrae: 65: 0*6 | 0 18.4 31.6*3
+Monte Sereno: 20.4: 20.5*6 | 20.5 39.8*4
+Portola Valley: 59: 0*4 57.3 59.8 | 59.8*2 61.0*3
+Redwood City: 235.1: 34.0 39.1 46.0 51.6 56.9 60.8 | 62.9*2 65.1*3
+San Carlos: 99: 22.2 26.0 32.9 32.9 37.2 39.0 | 40.5*2 41.4*3
+Woodside: 75.2: 51.9*2 52.3*3 54.0 | 56.1*5
+Newark: 147: 15*3 17*3 | 18.7 26.8 30.8*3
 
-:Between 30% and 80%:
-Foster City: 150: 9.1*6                          | 9.1 27.4 37.2
-Burlingame: 88.4: 9.4*6                          | 9.4 31.5*2
-Los Altos Hills: 91: 48.4*2 49.0 55.1*2 55.4     | 55.8*3
-Loyola: 18.3: 0*4 60.8 62.1                      | 62.1*3
-Millbrae: 65: 0*6                                | 0 18.4 31.6
-Monte Sereno: 20.4: 20.5*6                       | 20.5 39.8*2
-Portola Valley: 59: 0*4 57.3 59.8                | 59.8*2 61.0
-Redwood City: 235.1: 34.0 39.1 46.0 51.6 56.9 60.8 | 62.9*2 65.1
-San Carlos: 99: 22.2 26.0 32.9 32.9 37.2 39.0    | 40.5*2 41.4
-Woodside: 75.2: 51.9*2 52.3*3 54.0               | 56.1*3
-Newark: 145: 15*3 17*3                  | 18.7 26.8 30.8
+:Just Over 25%:
+Belmont: 98.1: 15.5 17.3 18.6 18.6 20.6*2 | 27.4*2 27.5*3
+Campbell: 119: 8.9 10.1 12.4*4 | 12.4 25.2*4
+Cupertino: 172: 22.1 23.9 26.2*3 26.3 | 26.4*5
+Hillsborough: 85.3: 3.3*6 | 3.6 24.5 25.3*3
+Los Gatos: 148: 7.5 8.6 8.8*4 | 8.8 26.1*4
+San Mateo: 256: 11.1*6 | 11.3 25.5 27.8*3
+Saratoga: 180: 14.5 15.7 17.4*4 | 17.4 26.9*4
+Sunnyvale: 357: 19.4 19.9 22.2*4 | 25.1*3 25.8*2
+Union City: 208.8: 7*3 8*3 | 8.8*2 25.7*2 26.7
+Fremont: 780.2: 9*3 10*3 | 11.7*2 27.5 28.1*2
+San Mateo Highlands: 18: 0*6 | 18.0 29.2*4
+Santa Clara: 348: 6.4*2 9.6*4 | 9.6*3 26.6*2
+San Bruno: 114: 0*6 | 0*3 25.4*2
+Milpitas: 224: 2.2*4 4.4*2 | 4.4*3 26.6*2
 
 :Just Getting Started:
-Santa Clara: 348: 6.4*2 9.6*4  | 9.6*2 13.2
-Half Moon Bay: 68: 0*6         | 8.9*3
-Emeryville: 28.1: 0*6          | 5.5*3
-Hayward: 446.3: 0*6            | 4.6*3
-Milpitas: 224: 2.2*4 4.4*2     | 4.4*3
-San Jose: 2543: 1.3 1.36 5.3*4 | 5.4*3
-Berkeley: 260: 5.0*3 7.0*3     | 7.0*3
-San Bruno: 114: 0*6            | 0*2 7.5
+Half Moon Bay: 68: 0*6 | 8.9*5
+Emeryville: 28.1: 0*6 | 5.5*4 7.0
+Hayward: 445.1: 0*6 | 4.6*4 5.1
+San Jose: 2543: 1.3 1.36 5.3*4 | 5.4*3 6.7 8.4
+Berkeley: 260.3: 5.0*3 7.0*3 | 7.0*5
+San Rafael: 260: 0*6 | 0*4 3.7
+Albany: 42.7: 0*6 | 0*4 6.6
+Corte Madera: 51: 0*6 | 0*4 13
+Mill Valley: 92.2: 0*6 | 0*4 5.1
+Sausalito: 32.7: 0*6 | 0*4 13.1
 
-:Small Neighborhoods (under 12 road miles):
-Burlingame Hills: 6: 0*6           | 0.8 34.5*2
-Kensington Square: 0.6: 86.9 100*5 | 100*3
-Ladera: 8.0: 0*4 30.5 29.8         | 29.8*2 47.6
-Los Trancos Woods: 5.4: 0*4 71.4*2 | 71.4*3
-Menlo Oaks: 3.6: 0*4 98.4 99.7     | 99.7*3
-Palomar Park: 3.9: 0*6             | 91.1*2 94.9
-Sequoia Tract: 11.6: 0*4 72.8 82.3 | 92.5*3
-Sky Londa: 11.8: 0*4 72.1*2        | 73.2*3
-West Menlo Park: 11.2: 0*4 97.5 98.1 | 98.1*3    
+:Tiny Neighborhoods (under 12 road miles):
+Burlingame Hills: 6: 0*6 | 0.8 34.5*4
+Kensington Square: 0.6: 86.9 100*5 | 100*5
+Ladera: 8.0: 0*4 30.5 29.8 | 29.8*2 47.6*3
+Los Trancos Woods: 5.4: 0*4 71.4*2 | 71.4*5
+Menlo Oaks: 3.6: 0*4 98.4 99.7 | 99.7*5
+Palomar Park: 3.9: 0*6 | 91.1*2 94.9*3
+Sequoia Tract: 11.6: 0*4 72.8 82.3 | 92.5*5
+Sky Londa: 11.8: 0*4 72.1*2 | 73.2*5
+West Menlo Park: 11.2: 0*4 97.5 98.1 | 98.1*3 99.3*2
 
 :San Francisco Neighborhoods:
-Balboa Terrace: 3.4: 0*6    | 18.2*3
-Cole Valley: 1.7: 0*6       | 19.6*3
-Forest Hill: 6.1: 0*6       | 15.7*3
-Golden Gate Park: 40.5: 0*6 | 25.8*3
-Lincoln Park: 3.8: 0*6      | 42.4*3
-Pacific Heights: 18: 0*6    | 10.8*3
-Ashbury Heights: 3.7: 0*6   | 12.9*3
-Clarendon Heights: 6: 0*6   | 14.3*3
-Cow Hollow: 12: 0*6         |5*3
-Financial District: 9.4: 0*6   |5.8*3
-Golden Gate Heights: 17.8: 0*6 | 10.7*3
-Polk Gulch: 4: 0*6          | 18.2*3
-Presidio Heights: 6.5: 0*6  | 15.1*3
-Presidio Park: 43.1: 0*6    | 21.1*3
-Presidio Terrace: 2.7: 0*6  | 37.7*3
-Seacliff: 4.1: 0*6          | 23.1*3
-South Beach: 4.8: 0*6       | 28.2*3
+Aquatic Park / Fort Mason: 6.4: 0*6 | 0*4 14.9
+Central Waterfront: 10.2: 0*6 | 0*4 6.7
+Northern Waterfront: 5.6: 0*6 | 0*4 16.4
+Dogpatch: 5.1: 0*6 | 0*4 12.4
+Fisherman's Wharf: 6.2: 0*6 | 0*4 14.2
+Balboa Terrace: 3.4: 0*6 | 18.2*5
+Cole Valley: 1.7: 0*6 | 19.6*5
+Forest Hill: 6.1: 0*6 | 15.7*5
+Golden Gate Park: 40.8: 0*6 | 25.6*5
+Lincoln Park: 3.7: 0*6 | 43*5
+Pacific Heights: 18: 0*6 | 10.8*5 
+Ashbury Heights: 3.7: 0*6 | 12.9*5
+Clarendon Heights: 6: 0*6 | 14.3*5
+Cow Hollow: 12: 0*6 |5*4 12.0
+Financial District: 9.4: 0*6 |5.8*4 9.7
+Golden Gate Heights: 17.8: 0*6 | 10.7*5
+Polk Gulch: 4: 0*6 | 18.2*5
+Presidio Heights: 6.5: 0*6 | 15.1*5
+Presidio National Park: 43.5: 0*6 | 21.1*4 24.4
+Presidio Terrace: 2.8: 0*6 | 37.0*5
+Seacliff: 4.1: 0*6 | 23.1*5
+South Beach: 4.8: 0*6 | 28.2*4 39.7
+Little Hollywood: 3.7: 0*6 | 0*4 15.4
 
 :Far Away (When will I return?):
-Mokelumne Hill, Calaveras: 14.7: 28.9*6| 28.9*3
-MIT, Cambridge, MA: 9.6: 37.2*6    | 37.2*3
-Barangaroo, NSW: 1.7: 49.9*6       | 49.9*3
-Dawes Point, NSW: 1.8: 29.2*6      | 29.2*3
-Millers Point, NSW: 3.2: 38.2*6    | 38.2*3   
-Stinson Beach, Marin: 11.2: 9.2*6  | 9.2*3
-Muir Beach, Marin: 4.6: 35.8*6     | 35.8*3
-Cambridge, MA: 180.8: 6.4*6        | 6.4*3
-Bodega Bay, Sonoma: 28.9: 18*6     | 18*3
-Guerneville, Sonoma: 22.7: 23.4*6  | 23.4*3
-Healdsburg, Sonoma: 53.7: 18.5*6   | 18.5*3
+Mokelumne Hill, Calaveras: 14.7: 28.9*6| 28.9*5
+MIT, Cambridge, MA: 9.6: 37.2*6 | 37.2*5
+Barangaroo, NSW: 1.7: 49.9*6 | 49.9*5
+Dawes Point, NSW: 1.8: 29.2*6 | 29.2*5
+Millers Point, NSW: 3.2: 38.2*6 | 38.2*5
+Stinson Beach, Marin: 11.2: 9.2*6 | 9.2*5
+Muir Beach, Marin: 4.6: 35.8*6 | 35.8*5
+Cambridge, MA: 180.8: 6.4*6 | 6.4*5
+Bodega Bay, Sonoma: 28.9: 18*6 | 18*5
+Guerneville, Sonoma: 22.7: 23.4*6 | 23.4*5
+Healdsburg, Sonoma: 53.7: 18.5*6 | 18.5*5
 
 :California Counties:
-San Mateo: 3248: 20.1 21.2 22.9 23.4 24.57 25.53   | 26.43 30.0 31.27
-Santa Clara: 7396: 12.7 13.6 15.4 15.6 16.04 16.29 | 16.78 17.73 18.15
-Alameda: 5704: 3.3*3 3.94*3 | 4.73*2 7.06
-Marin: 2322: 6.7*6          | 6.7*3
-Napa: 1524: 5.1*6           | 5.1*3
-Sonoma: 4556: 5.1*6         | 5.1*3
-San Francisco: 1183: 4.5*6  | 4.5*3
-Santa Cruz: 2767: 2.3*6     | 2.3*3
+San Mateo: 3248: 20.1 21.2 22.9 23.4 24.57 25.53 | 26.43 30.0 31.27 32.24 32.61
+Santa Clara: 7396: 12.7 13.6 15.4 15.6 16.04 16.29 | 16.78 17.73 18.15 20.0 20.68
+Alameda: 5704: 3.3*3 3.94*3 | 4.73*2 7.06 7.14 7.33
+Marin: 2322: 6.7*6 | 6.7*4 7.98
+Napa: 1524: 5.1*6 | 5.1*5
+Sonoma: 4556: 5.1*6 | 5.1*5
+San Francisco: 1183: 4.5*6 | 4.5*4 5.46
+Santa Cruz: 2767: 2.3*6 | 2.3*5
 
-:Large Places (over 300,000 road miles):
-California: 365188: .712 .811  .846  .867   .8875   .8873  | .9054   .9624   1.0289
-USA:       6317607: .048 .052  .055  .05589 .0571   .05749 | .05853  .06183  .06553
-Earth:    38609276: .008 .0089 .0091 .00936 .009535 .009426| .009597 .010136 .010561
+:California:
+California: 365188: .712 .811 .846 .867 .8875 .8873 | .9054 .9624 1.0289 1.0758 1.1052
+
+:USA and Earth
+USA: 6317607: .048 .052 .055 .05589 .0571 .05749 | .05853 .06183 .06553 .06824 0.06993
+Earth: 38609276: .008 .0089 .0091 .00936 .009535 .009536| .009597 .010136 .010561 .010997 .011269

ipynb/bikerides.tsv

@@ -1,5 +1,10 @@
 date	year	title	hours	miles	feet
 ##### 2019-2021: Mostly Eddington rides; most recent first
+Sun, 5/16	2021	Palo Alto / SF / San Rafael	5:41:52	70.33	2,073
+Sat, 5/15	2021	San Rafael to Palo Alto over 20 bridges	5:23:44	68.30	871
+Sat, 4/24	2021	Alviso / Niles / Dumbarton 100km with Juliet	5:44:24	69.79	1,230
+Sun, 4/11	2021	OLH / Tunitas	6:06:24	66.04	4,629 
+Sat, 4/10	2021	Milpitas	4:56:57	65.41	913
 Sun, 4/4	2021	Everesting 7: Mill Creek / Morrison Canyon	3:04:46	29.38	3,517
 Sat, 4/3	2021	Everesting 6: OLH / West Alpine	4:26:16	46.64	4,400
 Fri, 4/2	2021	Everesting 5: climb 2×(OLH + WOLH)	3:16:30	31.48	4,344