\n",
"\n",
"# Bicycling Statistics\n",
"\n",
"During a pandemic, bicycling is a great way to (1) spend some time, (2) get some exercise, (3) stay outside and be safe, and (4) track how you are progressing against your goals. My data comes from [Strava](https://www.strava.com/athletes/575579). At various times I've had various goals:\n",
"- **Distance**: I do about 6,000 miles a year.\n",
- "- **Elevation**: In 2022, for the first time I set an elevation goal: **space** (100 km of climbing).\n",
- "- **Wandering**: In 2020, I started using [Wandrer.earth](https://wandrer.earth/athletes/3534/) to track what \n",
- "new roads I have ridden.\n",
- "- **Eddington**: I've done 66 miles or more on 66 different days. So 66 is my Eddington Number.\n",
- "- **Speed**: I'm not trying to go fast, but I am interested in understanding how my speed varies with the steepness of the hill.\n",
+ "- **Climbing**: In 2022, I climbed to *space* (100 km of total elevation gain).\n",
+ "- **Explorer Tiles**: In 2022, I started tracking the 1-mile-square [explorer tiles](https://rideeverytile.com/) I have visited.\n",
+ "- **Wandering**: In 2020, I started using [Wandrer.earth](https://wandrer.earth/athletes/3534/) to track what new roads I have ridden.\n",
+ "- **Eddington Number**: I've done 67 miles or more on 67 different days. So 67 is my Eddington Number.\n",
+ "- **Speed**: I'm not going particularly fast, but I am interested in understanding how my speed varies with the steepness of the hill.\n",
"\n",
"This notebook is mostly for my own benefit, but if you're a cyclist you're welcome to adapt it to your own data, and if you're a data scientist, you might find it an interesting example of exploratory data analysis. The companion notebook [**BikeCode.ipynb**](BikeCode.ipynb) has the implementation details."
]
@@ -25,12 +25,12 @@
"source": [
"# Yearly Totals\n",
"\n",
- "Here are my overall stats for each year since I started keeping track in mid-2014. I have done 6,000 miles per year since 2016, except for 2020 when an injury kept me sidelined for two months. (*Note*: The column **vam** is vertical meters ascended per hour, **fpm** is feet climbed per mile, and **pct** is the average grade of the elevation, in percent.)\n"
+ "Here are my overall stats for each year since I started keeping track in mid-2014. I have done 6,000 miles per year since 2016, except for 2020 when an injury kept me sidelined for two months. The columns keep track of speed/distance (**mph** is the speed, computed as the quotient of **miles* and **hours**; **kms** is the metric conversion of **miles**) and climbing (**feet** is the total height climbed (or **km_up** in metric), **vam** is vertical meters ascended per hour, **fpm** is average feet ascended per mile and **pct** is average grade of climbing (**fpm** × 100 / 5280).\n"
]
},
{
"cell_type": "code",
- "execution_count": 447,
+ "execution_count": 92,
"metadata": {},
"outputs": [
{
@@ -63,20 +63,22 @@
"
fpm
\n",
"
pct
\n",
"
kms
\n",
+ "
km_up
\n",
" \n",
" \n",
" \n",
"
\n",
"
\n",
"
2022
\n",
- "
398.80
\n",
- "
4382
\n",
- "
292575
\n",
- "
10.99
\n",
- "
224.0
\n",
- "
67.0
\n",
- "
1.26
\n",
- "
7050.64
\n",
+ "
532.93
\n",
+ "
6028
\n",
+ "
362323
\n",
+ "
11.31
\n",
+ "
207.0
\n",
+ "
60.0
\n",
+ "
1.14
\n",
+ "
9699.05
\n",
+ "
110.4
\n",
"
\n",
"
\n",
"
\n",
@@ -89,6 +91,7 @@
"
32.0
\n",
"
0.61
\n",
"
9756.98
\n",
+ "
59.9
\n",
"
\n",
"
\n",
"
\n",
@@ -101,6 +104,7 @@
"
18.0
\n",
"
0.34
\n",
"
8593.67
\n",
+ "
28.9
\n",
"
\n",
"
\n",
"
\n",
@@ -113,6 +117,7 @@
"
25.0
\n",
"
0.47
\n",
"
9679.74
\n",
+ "
45.7
\n",
"
\n",
"
\n",
"
\n",
@@ -125,6 +130,7 @@
"
26.0
\n",
"
0.49
\n",
"
9816.51
\n",
+ "
48.4
\n",
"
\n",
"
\n",
"
\n",
@@ -137,6 +143,7 @@
"
27.0
\n",
"
0.52
\n",
"
11835.80
\n",
+ "
61.6
\n",
"
\n",
"
\n",
"
\n",
@@ -149,6 +156,7 @@
"
32.0
\n",
"
0.60
\n",
"
10199.45
\n",
+ "
61.4
\n",
"
\n",
"
\n",
"
\n",
@@ -161,6 +169,7 @@
"
38.0
\n",
"
0.73
\n",
"
8772.27
\n",
+ "
64.0
\n",
"
\n",
"
\n",
"
\n",
@@ -173,25 +182,26 @@
"
48.0
\n",
"
0.91
\n",
"
3972.62
\n",
+ "
36.1
\n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " year hours miles feet mph vam fpm pct kms\n",
- " 2022 398.80 4382 292575 10.99 224.0 67.0 1.26 7050.64\n",
- " 2021 490.53 6064 196634 12.36 122.0 32.0 0.61 9756.98\n",
- " 2020 438.88 5341 94777 12.17 66.0 18.0 0.34 8593.67\n",
- " 2019 476.32 6016 149797 12.63 96.0 25.0 0.47 9679.74\n",
- " 2018 475.93 6101 158642 12.82 102.0 26.0 0.49 9816.51\n",
- " 2017 567.33 7356 202096 12.97 109.0 27.0 0.52 11835.80\n",
- " 2016 486.38 6339 201453 13.03 126.0 32.0 0.60 10199.45\n",
- " 2015 419.95 5452 209859 12.98 152.0 38.0 0.73 8772.27\n",
- " 2014 191.03 2469 118481 12.92 189.0 48.0 0.91 3972.62"
+ " year hours miles feet mph vam fpm pct kms km_up\n",
+ " 2022 532.93 6028 362323 11.31 207.0 60.0 1.14 9699.05 110.4\n",
+ " 2021 490.53 6064 196634 12.36 122.0 32.0 0.61 9756.98 59.9\n",
+ " 2020 438.88 5341 94777 12.17 66.0 18.0 0.34 8593.67 28.9\n",
+ " 2019 476.32 6016 149797 12.63 96.0 25.0 0.47 9679.74 45.7\n",
+ " 2018 475.93 6101 158642 12.82 102.0 26.0 0.49 9816.51 48.4\n",
+ " 2017 567.33 7356 202096 12.97 109.0 27.0 0.52 11835.80 61.6\n",
+ " 2016 486.38 6339 201453 13.03 126.0 32.0 0.60 10199.45 61.4\n",
+ " 2015 419.95 5452 209859 12.98 152.0 38.0 0.73 8772.27 64.0\n",
+ " 2014 191.03 2469 118481 12.92 189.0 48.0 0.91 3972.62 36.1"
]
},
- "execution_count": 447,
+ "execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
@@ -206,202 +216,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Elevation\n",
- "\n",
- "My friend [A. J. Jacobs](https://ajjacobs.com/) has an incling treadmill, and for 2022 set the goal of **walking to space**: climbing a total elevation equal to the distance from the Earth's surface to the top of the atmoshere. [A group](https://www.facebook.com/groups/260966686136038) of about 40 of us joined the quest. The boundary of \"space\" is vague, but could reassonably be reckoned as any of the following:\n",
- "\n",
- "|Units|In Feet|Description|\n",
- "|-- --|--|--|\n",
- "|100 km|328,084|The [Kármán line](https://en.wikipedia.org/wiki/K%C3%A1rm%C3%A1n_line)|\n",
- "|300 kilofeet|300,000|A nice round number|\n",
- "|10 Everests|290,320|A classic benchmark|\n",
- "|50 miles|264,000|[NASA's edge of space](https://science.nasa.gov/edge-space)|\n",
- "\n",
- "\n",
- "Here's how I'm progressing towards space (I took some time off in June, and made up for it in July):"
+ "And here's the same data on a daily basis, assuming 350 days of riding in a year:"
]
},
{
"cell_type": "code",
- "execution_count": 448,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "climbing()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Wandering \n",
- "\n",
- "The website [**Wandrer.earth**](https://wandrer.earth) tracks the distinct roads a user has biked on. It provides a fun incentive to get out and explore new roads. The site is gamified in a way that the biggest rewards are for first reaching 25% of the road-miles in each city or neighborhood, and then 90%. (You get no credit for repeating a road you've already been on.) \n",
- "\n",
- "For the most part, your scores can only go up, as you cover more roads over time. But the [underlying map](https://www.openstreetmap.org/) is periodically updated, and sometimes roads are added or removed from a city's map, so your mileage may vary. \n",
- "\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",
- "Each line in the legend gives the percentage of roads ridden (miles ridden / total miles), miles to go to the next percentage reward, and the name of the town or area. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 449,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "wandering(places)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# More Kinds of Wandering \n",
- "\n",
- "As part of my wandering, in April 2022 I was able to get to 25% of every city that rings the San Francisco Bay and is below San Francisco or Oakland (see map [with](ring2.jpeg) or [without](ring1.jpeg) roads traveled; as soon as you get 25% of a city, it lights up with a color).\n",
- "\n",
- "The OpenStreetMap world is divided into **tiles** of approximately 1 mile square. [Tile hunting](https://www.statshunters.com/heatmap/tiles) records how many of these tiles you have passed through on. It is gamified to track (1) the total number of tiles visited (all the colored squares on [my map](tilehunter.jpg)), (2) the largest square of visited tiles (the blue square on my map), and (3) the largest cluster of contiguous tiles, each of which is bordered on all four sides by a visited tile (green plus blue on my map). I like the idea of tile hunting, in that I prefer to visit a lot of places (whereas others want to ride every road in fewer places). But since I live on a penninsula, it is not easy for me to form a large square. Still, I'll start tracking my tiles; here's the first entry:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 450,
+ "execution_count": 93,
"metadata": {},
"outputs": [
{
@@ -425,30 +245,267 @@
" \n",
"
\n",
"
\n",
- "
date
\n",
- "
tiles
\n",
- "
square
\n",
- "
cluster
\n",
+ "
year
\n",
+ "
hours
\n",
+ "
miles
\n",
+ "
feet
\n",
+ "
mph
\n",
+ "
vam
\n",
+ "
fpm
\n",
+ "
pct
\n",
+ "
kms
\n",
+ "
km_up
\n",
"
\n",
" \n",
" \n",
"
\n",
"
\n",
- "
Sep 2022
\n",
- "
2481
\n",
- "
11x11
\n",
- "
295
\n",
+ "
2022
\n",
+ "
1.5
\n",
+ "
17.2
\n",
+ "
1035.2
\n",
+ "
11.31
\n",
+ "
207.0
\n",
+ "
60.0
\n",
+ "
1.14
\n",
+ "
27.7
\n",
+ "
0.315
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
2021
\n",
+ "
1.4
\n",
+ "
17.3
\n",
+ "
561.8
\n",
+ "
12.36
\n",
+ "
122.0
\n",
+ "
32.0
\n",
+ "
0.61
\n",
+ "
27.9
\n",
+ "
0.171
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
2020
\n",
+ "
1.3
\n",
+ "
15.3
\n",
+ "
270.8
\n",
+ "
12.17
\n",
+ "
66.0
\n",
+ "
18.0
\n",
+ "
0.34
\n",
+ "
24.6
\n",
+ "
0.083
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
2019
\n",
+ "
1.4
\n",
+ "
17.2
\n",
+ "
428.0
\n",
+ "
12.63
\n",
+ "
96.0
\n",
+ "
25.0
\n",
+ "
0.47
\n",
+ "
27.7
\n",
+ "
0.131
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
2018
\n",
+ "
1.4
\n",
+ "
17.4
\n",
+ "
453.3
\n",
+ "
12.82
\n",
+ "
102.0
\n",
+ "
26.0
\n",
+ "
0.49
\n",
+ "
28.0
\n",
+ "
0.138
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
2017
\n",
+ "
1.6
\n",
+ "
21.0
\n",
+ "
577.4
\n",
+ "
12.97
\n",
+ "
109.0
\n",
+ "
27.0
\n",
+ "
0.52
\n",
+ "
33.8
\n",
+ "
0.176
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
2016
\n",
+ "
1.4
\n",
+ "
18.1
\n",
+ "
575.6
\n",
+ "
13.03
\n",
+ "
126.0
\n",
+ "
32.0
\n",
+ "
0.60
\n",
+ "
29.1
\n",
+ "
0.175
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
2015
\n",
+ "
1.2
\n",
+ "
15.6
\n",
+ "
599.6
\n",
+ "
12.98
\n",
+ "
152.0
\n",
+ "
38.0
\n",
+ "
0.73
\n",
+ "
25.1
\n",
+ "
0.183
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
2014
\n",
+ "
0.5
\n",
+ "
7.1
\n",
+ "
338.5
\n",
+ "
12.92
\n",
+ "
189.0
\n",
+ "
48.0
\n",
+ "
0.91
\n",
+ "
11.4
\n",
+ "
0.103
\n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " date tiles square cluster\n",
- " Sep 2022 2481 11x11 295"
+ " year hours miles feet mph vam fpm pct kms km_up\n",
+ " 2022 1.5 17.2 1035.2 11.31 207.0 60.0 1.14 27.7 0.315\n",
+ " 2021 1.4 17.3 561.8 12.36 122.0 32.0 0.61 27.9 0.171\n",
+ " 2020 1.3 15.3 270.8 12.17 66.0 18.0 0.34 24.6 0.083\n",
+ " 2019 1.4 17.2 428.0 12.63 96.0 25.0 0.47 27.7 0.131\n",
+ " 2018 1.4 17.4 453.3 12.82 102.0 26.0 0.49 28.0 0.138\n",
+ " 2017 1.6 21.0 577.4 12.97 109.0 27.0 0.52 33.8 0.176\n",
+ " 2016 1.4 18.1 575.6 13.03 126.0 32.0 0.60 29.1 0.175\n",
+ " 2015 1.2 15.6 599.6 12.98 152.0 38.0 0.73 25.1 0.183\n",
+ " 2014 0.5 7.1 338.5 12.92 189.0 48.0 0.91 11.4 0.103"
]
},
- "execution_count": 450,
+ "execution_count": 93,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "daily"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Climbing \n",
+ "\n",
+ "In 2022 my friend [A. J. Jacobs](https://ajjacobs.com/) set a goal of **walking to space**: climbing a total elevation equal to the distance from the Earth's surface to the top of the atmoshere. [A group](https://www.facebook.com/groups/260966686136038) of about 40 of us joined the quest. The boundary of \"space\" is vague, but could is often reckoned as either 100 km (the [Kármán line](https://en.wikipedia.org/wiki/K%C3%A1rm%C3%A1n_line)) or [50 miles](https://science.nasa.gov/edge-space); in 2022 I surpassed 100 kilometers of climbing (over 1,000 feet per day), but most years I'm closer to 60 kilometers (about 600 feet per day)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Explorer Tiles\n",
+ "\n",
+ "\n",
+ "The [OpenStreetMap](https://www.openstreetmap.org/) world map is divided into **[explorer tiles](https://www.statshunters.com/faq-10-what-are-explorer-tiles)** of approximately 1 mile square. Sites like [Veloviewer](https://veloviewer.com) and [Statshunter](https://www.statshunters.com/) challenge bicyclist/hikers to record which tiles they have passed through. The process is gamified to highlight the following statistics:\n",
+ "- The largest **square** (an *n* × *n* array of visited ties). \n",
+ "- The maximum **cluster** (a set of contiguous interior visited tiles, where \"interior\" means surrounded by visited tiles).\n",
+ "- The **total** number of visited tiles.\n",
+ " \n",
+ "\n",
+ "Since I live on a penninsula, it is not easy for me to form a large square, and I sometimes have to work hard to connect different parts of my map (such as connecting San Francisco and Marin). I have a [separrate page]() documenting my explorations, but here are a few key points along the way:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
@@ -461,12 +518,186 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "I live in Santa Clara County, but I'm only 2 blocks from San Mateo County, so I ride in both. Wandrer.earth says that Jason Molenda is a whopping 1800 miles ahead of me in SMC and Megan Gardner is 1100 miles ahead of me in SCC, so I'm unlikely to catch up to either of them. However, I do occupy a small section of the [Pareto front](https://en.wikipedia.org/wiki/Pareto_front) for the two counties together: no other rider on wandrer.earth has done more than me in *both* counties. Here are the leaders where the dotted line indicates the five riders on the Parewto front, and small circles indicate the three other ridsers who are close."
+ "# Wandering \n",
+ "\n",
+ "The website [**Wandrer.earth**](https://wandrer.earth) tracks the distinct roads a user has biked on. It provides a fun incentive to get out and explore new roads. The site is gamified in a way that there is a reward for first reaching 25% of the road-miles in each city, and further rewards for higher percentages. (You get no credit for repeating a road you've already been on.) \n",
+ "\n",
+ "The wandrer.earth site does a good job of showing my current status, but it requires clicking around a bit, so I summarize it all in one place here. Each line gives the county (e.g. SMC for San Mateo County); city preceeded by the percentage of roads I've rode there; miles rode/total miles in city; and miles to go to the next big reward level."
]
},
{
"cell_type": "code",
- "execution_count": 451,
+ "execution_count": 95,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "SMC 100.0% Los Trancos OSP 0.3/0.3 mi \n",
+ "SMC 99.9% Los Trancos Woods 5.3/5.3 mi \n",
+ "SMC 99.9% Menlo Oaks 3.5/3.5 mi \n",
+ "SMC 99.9% Kensington Square 0.6/0.6 mi \n",
+ "SMC 99.9% Ladera 8.1/8.1 mi \n",
+ "SMC 99.9% Palomar Park 4.0/4.0 mi \n",
+ "SMC 99.8% North Fair Oaks 27/27 mi \n",
+ "SMC 99.8% Emerald Lake Hills 25/25 mi \n",
+ "SMC 99.8% West Menlo Park 11/11 mi \n",
+ "SMC 99.8% Atherton 56/56 mi \n",
+ "SMC 99.7% Sequoia Tract 11/11 mi \n",
+ "SCC 99.6% Los Altos 138/138 mi \n",
+ "SCC 99.6% Loyola 18/18 mi \n",
+ "SMC 99.6% East Palo Alto 48/48 mi \n",
+ "SMC 99.5% Menlo Park 139/140 mi \n",
+ "SMC 99.5% Woodside 75/75 mi \n",
+ "SMC 99.5% Portola Valley 48/48 mi \n",
+ "SMC 99.4% Sky Londa 12/12 mi \n",
+ "SCC 99.4% Los Altos Hills 91/91 mi \n",
+ "SMC 99.3% Redwood City 239/240 mi \n",
+ "SCC 99.2% Mountain View 206/208 mi \n",
+ "SMC 99.1% Windy Hill Preserve 4.1/4.1 mi \n",
+ "SMC 99.0% San Carlos 98/99 mi \n",
+ "SCC 99.0% Palo Alto 294/297 mi \n",
+ "SMC 91.6% Burleigh Murray Park 1.9/2.1 mi 0.2 mi for 99%\n",
+ "SCC 86.8% Foothills Preserve 1.0/1.1 mi 0.0 mi for 90%\n",
+ "SMC 80.7% Foster City 121/150 mi 14 mi for 90%\n",
+ "SMC 77.9% San Mateo Highlands 14/18 mi 2.2 mi for 90%\n",
+ "SMC 74.9% Skyline Ridge OSP 0.6/0.8 mi 0.1 mi for 90%\n",
+ "SMC 71.4% Burlingame Hills 4.3/6.0 mi 1.1 mi for 90%\n",
+ "SMC 66.7% Coal Creek Preserve 2.6/3.9 mi 0.9 mi for 90%\n",
+ "SCC 62.7% San Francisco Bay Trail 164/261 mi 71 mi for 90%\n",
+ "--- 61.6% San Mateo County 1,732/2,814 mi 800 mi for 90%\n",
+ "SMC 54.3% Burlingame 48/88 mi 32 mi for 90%\n",
+ "SMC 54.2% Belmont 53/98 mi 35 mi for 90%\n",
+ "SCC 52.4% Sunnyvale 187/357 mi 134 mi for 90%\n",
+ "SMC 52.3% Hillsborough 45/85 mi 32 mi for 90%\n",
+ "SMC 51.8% San Mateo 133/256 mi 98 mi for 90%\n",
+ "SMC 51.5% Half Moon Bay State Beach 2.3/4.4 mi 1.7 mi for 90%\n",
+ "SMC 51.2% Russian Ridge Preserve 6.2/12 mi 4.7 mi for 90%\n",
+ "SMC 51.2% Long Ridge Preserve 5.6/11 mi 4.3 mi for 90%\n",
+ "SCC 51.2% Castle Rock State Park 5.7/11 mi 4.3 mi for 90%\n",
+ "NSW 47.3% Barangaroo 0.8/1.7 mi 0.0 mi for 50%\n",
+ "SCC 47.2% Gardner 11/23 mi 0.7 mi for 50%\n",
+ "SCC 47.0% Edenvale 14/30 mi 0.9 mi for 50%\n",
+ "SMC 46.7% Brisbane 19/41 mi 1.3 mi for 50%\n",
+ "ALA 45.9% Newark 67/147 mi 6.0 mi for 50%\n",
+ "SCC 44.7% Monte Sereno 9.1/20 mi 1.1 mi for 50%\n",
+ "SMC 44.3% Moss Beach 8.7/20 mi 1.1 mi for 50%\n",
+ "SFC 43.9% Presidio Terrace 1.2/2.8 mi 0.2 mi for 50%\n",
+ "ALA 43.3% Hayward Acres 1.5/3.5 mi 0.2 mi for 50%\n",
+ "ALA 40.8% San Lorenzo 23/56 mi 5.1 mi for 50%\n",
+ "SMC 40.8% Millbrae 27/65 mi 6.0 mi for 50%\n",
+ "SFC 39.6% Lincoln Park 1.8/4.5 mi 0.5 mi for 50%\n",
+ "SCC 39.5% Communications Hill 11/28 mi 2.9 mi for 50%\n",
+ "SMC 38.7% Purisima Creek Preserve 6.4/16 mi 1.9 mi for 50%\n",
+ "MAR 38.7% Mt Tamalpais State Park 12/32 mi 3.6 mi for 50%\n",
+ "SMC 38.4% El Granada 19/49 mi 5.7 mi for 50%\n",
+ "SMC 38.3% Colma 5.2/14 mi 1.6 mi for 50%\n",
+ "SMC 38.2% Broadmoor 3.4/8.8 mi 1.0 mi for 50%\n",
+ "SFC 37.4% South Beach 1.8/4.8 mi 0.6 mi for 50%\n",
+ "SCC 37.3% Milpitas 84/224 mi 28 mi for 50%\n",
+ "MAR 37.1% Muir Beach 1.7/4.6 mi 0.6 mi for 50%\n",
+ "SFC 36.8% Lake Street 1.4/3.9 mi 0.5 mi for 50%\n",
+ "SCC 35.7% Spartan Keyes 23/64 mi 9.2 mi for 50%\n",
+ "ALA 35.7% Ashland 13/35 mi 5.0 mi for 50%\n",
+ "SCC 34.9% Willow Glen 28/82 mi 12 mi for 50%\n",
+ "MAS 34.7% MIT 3.3/9.6 mi 1.5 mi for 50%\n",
+ "NSW 34.3% Millers Point 1.1/3.2 mi 0.5 mi for 50%\n",
+ "SCC 34.0% Santa Clara 118/348 mi 56 mi for 50%\n",
+ "SCC 33.6% Seven Trees 14/41 mi 6.7 mi for 50%\n",
+ "SCC 33.4% Parkview 14/42 mi 7.1 mi for 50%\n",
+ "SCC 33.2% Cupertino 57/172 mi 29 mi for 50%\n",
+ "SCC 33.2% Los Gatos 49/148 mi 25 mi for 50%\n",
+ "MAR 32.9% Stinson Beach 3.7/11 mi 1.9 mi for 50%\n",
+ "--- 32.8% Santa Clara County 2,483/7,569 mi 1,302 mi for 50%\n",
+ "SMC 32.7% Montara 9.1/28 mi 4.8 mi for 50%\n",
+ "SMC 32.7% Pacifica 49/151 mi 26 mi for 50%\n",
+ "SCC 32.7% Branham 14/44 mi 7.6 mi for 50%\n",
+ "SMC 32.2% Half Moon Bay 22/68 mi 12 mi for 50%\n",
+ "ALA 32.2% Fremont 251/780 mi 139 mi for 50%\n",
+ "MAR 31.9% Marin Headlands GGNRA 21/66 mi 12 mi for 50%\n",
+ "SCC 31.1% San Martin 11/35 mi 6.7 mi for 50%\n",
+ "SCC 30.7% Forest of Nisene Marks SP 14/44 mi 8.5 mi for 50%\n",
+ "ALA 30.7% Union City 64/209 mi 40 mi for 50%\n",
+ "SCC 30.5% Willow Glen South 19/63 mi 12 mi for 50%\n",
+ "ALA 30.0% Hayward 133/444 mi 89 mi for 50%\n",
+ "SCC 29.7% Saratoga 53/180 mi 37 mi for 50%\n",
+ "SMC 29.5% San Bruno 34/114 mi 23 mi for 50%\n",
+ "SFC 29.4% Golden Gate Park 12/41 mi 8.4 mi for 50%\n",
+ "SFC 29.3% Seacliff 1.2/4.1 mi 0.8 mi for 50%\n",
+ "CCC 29.3% Rosie Riveter Park 1.6/5.5 mi 1.1 mi for 50%\n",
+ "NSW 29.2% Dawes Point 0.5/1.8 mi 0.4 mi for 50%\n",
+ "SCC 28.8% Campbell 34/119 mi 25 mi for 50%\n",
+ "SCC 27.7% San Jose 725/2,619 mi 584 mi for 50%\n",
+ "ALA 27.5% San Leandro 63/231 mi 52 mi for 50%\n",
+ "SMC 27.2% South San Francisco 50/185 mi 42 mi for 50%\n",
+ "SMC 27.2% Daly City 40/148 mi 34 mi for 50%\n",
+ "ALA 27.1% Cherryland 5.7/21 mi 4.8 mi for 50%\n",
+ "CAL 26.8% Mokelumne Hill 3.9/15 mi 3.4 mi for 50%\n",
+ "SFC 26.7% Presidio National Park 12/44 mi 10 mi for 50%\n",
+ "SON 23.6% Guerneville 5.4/23 mi 0.3 mi for 25%\n",
+ "SFC 21.6% Presidio Heights 1.4/6.5 mi 0.2 mi for 25%\n",
+ "SMC 21.4% Bay Area Ridge Trail 85/396 mi 14 mi for 25%\n",
+ "SFC 20.6% Panhandle 1.5/7.3 mi 0.3 mi for 25%\n",
+ "SFC 18.2% Polk Gulch 0.7/4.0 mi 0.3 mi for 25%\n",
+ "SFC 18.2% Balboa Terrace 0.6/3.4 mi 0.2 mi for 25%\n",
+ "SFC 18.0% Cole Valley 0.3/1.7 mi 0.1 mi for 25%\n",
+ "SON 17.8% Healdsburg 9.6/54 mi 3.9 mi for 25%\n",
+ "SON 17.0% Bodega Bay 4.9/29 mi 2.3 mi for 25%\n",
+ "SFC 15.9% Forest Hill 1.0/6.1 mi 0.6 mi for 25%\n",
+ "SFC 15.5% Northern Waterfront 0.9/5.6 mi 0.5 mi for 25%\n",
+ "SFC 15.4% Aquatic Park Fort Mason 1.0/6.4 mi 0.6 mi for 25%\n",
+ "--- 15.4% Alameda County 895/5,818 mi 560 mi for 25%\n",
+ "SFC 15.2% Little Hollywood 0.6/3.7 mi 0.4 mi for 25%\n",
+ "SFC 14.2% Clarendon Heights 0.9/6.0 mi 0.6 mi for 25%\n",
+ "SFC 13.8% Fisherman's Wharf 0.9/6.2 mi 0.7 mi for 25%\n",
+ "SFC 13.2% Sutro Heights 0.9/7.1 mi 0.8 mi for 25%\n",
+ "SFC 13.0% Ashbury Heights 0.5/3.7 mi 0.4 mi for 25%\n",
+ "MAR 12.9% Corte Madera 6.6/51 mi 6.2 mi for 25%\n",
+ "MAR 12.9% Sausalito 4.2/33 mi 4.0 mi for 25%\n",
+ "SFC 12.3% Dogpatch 0.6/5.1 mi 0.6 mi for 25%\n",
+ "ALA 12.2% Alameda 25/207 mi 26 mi for 25%\n",
+ "SCC 12.1% Gilroy 23/189 mi 24 mi for 25%\n",
+ "SFC 11.9% Cow Hollow 1.4/12 mi 1.6 mi for 25%\n",
+ "--- 10.8% Marin County 251/2,333 mi 332 mi for 25%\n",
+ "SFC 10.7% Golden Gate Heights 1.9/18 mi 2.5 mi for 25%\n",
+ "SFC 10.7% Pacific Heights 1.9/18 mi 2.6 mi for 25%\n",
+ "SFC 10.2% Financial District 1.0/9.4 mi 1.4 mi for 25%\n",
+ "MAR 9.1% Mill Valley 8.4/92 mi 15 mi for 25%\n",
+ "SFC 8.6% Mission Bay 1.2/14 mi 2.3 mi for 25%\n",
+ "ALA 8.0% Emeryville 2.2/28 mi 4.8 mi for 25%\n",
+ "ALA 7.4% Berkeley 19/260 mi 46 mi for 25%\n",
+ "--- 7.2% San Francisco County 88/1,217 mi 217 mi for 25%\n",
+ "--- 7.1% Santa Cruz County 194/2,718 mi 486 mi for 25%\n",
+ "ALA 6.8% Albany 2.9/43 mi 7.8 mi for 25%\n",
+ "MAS 6.2% Cambridge 11/181 mi 34 mi for 25%\n",
+ "SFC 6.0% Central Waterfront 0.6/10 mi 1.9 mi for 25%\n",
+ "--- 5.1% Sonoma County 251/4,895 mi 973 mi for 25%\n",
+ "--- 4.8% Napa County 78/1,609 mi 324 mi for 25%\n",
+ "MAR 3.7% San Rafael 9.6/260 mi 55 mi for 25%\n",
+ "--- 2.0% Contra Costa County 122/5,945 mi 1,364 mi for 25%\n",
+ "--- 1.7% California 6,450/0.38M mi 1,090 mi for 2%\n",
+ "--- 0.1% USA 6,840/6.4M mi 5,973 mi for 0.2%\n",
+ "--- 0.017% Earth 7,202/42M mi 1,192 mi for 0.02%\n"
+ ]
+ }
+ ],
+ "source": [
+ "wandering(by='pct')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As part of my wandering, in April 2022 I was able to get to 25% of every city that rings the San Francisco Bay and is below San Francisco or Oakland (see map [with](ring2.jpeg) or [without](ring1.jpeg) roads traveled; as soon as you get 25% of a city, it lights up with a color).\n",
+ "\n",
+ "I live at the border of Santa Clara County (SCC) and San Mateo County (SMC), so I ride in both. Wandrer.earth says that Jason Molenda is a whopping 1,700 miles ahead of me in SMC and Megan Gardner is 1,000 miles ahead of me in SCC. Barry Mann is the leader in both total miles in the two counties and average percent. Kudos to all of them! However, I do occupy a small section of the [Pareto front](https://en.wikipedia.org/wiki/Pareto_front) for the two counties together: no single rider on wandrer.earth has done more than me in *both* counties. Here are the leaders (as of September 2023), where the dotted line indicates the five riders on the Pareto front."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
"metadata": {},
"outputs": [
{
@@ -497,7 +728,7 @@
"
\n",
" \n",
"\n",
""
],
"text/plain": [
" year Ed_km Ed_mi\n",
+ " 2023 101 67\n",
" 2022 96 66\n",
" 2021 93 65\n",
" 2020 87 62\n",
@@ -755,11 +987,10 @@
" 2017 73 51\n",
" 2016 67 47\n",
" 2015 61 42\n",
- " 2014 46 35\n",
- " 2013 31 24"
+ " 2014 46 35"
]
},
- "execution_count": 452,
+ "execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
@@ -772,7 +1003,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "My current Eddington Number is **96 in kilometers and 66 in miles**: I've ridden at least 66 miles on at least 66 days (but not 67 miles on 67 days). My number is above [the median for Strava](https://swinny.net/Cycling/-4687-Calculate-your-Eddington-Number), but not nearly as good as Eddington himself: his number was **84 in miles** 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. "
+ "My current Eddington Number is **101** in kilometers and **67** in miles: I've ridden at least 67 miles on at least 67 days (but not 68 miles on 68 days). My number is above [the median for Strava](https://swinny.net/Cycling/-4687-Calculate-your-Eddington-Number), but not nearly as good as Eddington himself: his number was **84** in miles 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. "
]
},
{
@@ -784,7 +1015,7 @@
},
{
"cell_type": "code",
- "execution_count": 453,
+ "execution_count": 98,
"metadata": {},
"outputs": [
{
@@ -809,101 +1040,133 @@
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+ "
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+ "
61
\n",
+ "
49
\n",
+ "
77
\n",
+ "
21
\n",
+ "
56
\n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " kms kms gap miles miles gap\n",
- " 96 -2 66 -6\n",
- " 97 4 67 3\n",
- " 98 8 68 8\n",
- " 99 11 69 21\n",
- " 100 13 70 29\n",
- " 101 15 71 36\n",
- " 102 18 72 39\n",
- " 103 21 73 43\n",
- " 104 25 74 45\n",
- " 105 30 75 52"
+ " kms km rides kms gap miles miles rides miles gap\n",
+ " 100 107 -7 67 74 -7\n",
+ " 101 105 -4 68 67 1\n",
+ " 102 99 3 69 54 15\n",
+ " 103 97 6 70 44 26\n",
+ " 104 91 13 71 37 34\n",
+ " 105 86 19 72 35 37\n",
+ " 106 83 23 73 32 41\n",
+ " 107 76 31 74 31 43\n",
+ " 108 73 35 75 24 51\n",
+ " 109 68 41 76 23 53\n",
+ " 110 61 49 77 21 56"
]
},
- "execution_count": 453,
+ "execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
@@ -916,9 +1179,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "In kilometers, I need 4 rides to improve from 96 to 97, and 13 rides to improve to 100.\n",
- "\n",
- "In miles, I need 3 rides to improve from 66 to 67, and 29 rides to improve to 70.\n",
+ "I need three 102-km rides to improve from 101 to 102; In miles, I need one 68-mile ride to improve from 67 to 68.\n",
"\n",
"Here are some properties of Eddington numbers:\n",
"- Your Eddington number is monotonic: it can never decrease over time. \n",
@@ -928,18 +1189,13 @@
" + *You would then need 11 rides of 11 miles to improve from a number 10 to 11.*\n",
"- Your metric Eddington number will always be greater than or equal to your imperial Eddington number.\n",
"- Your metric Eddington number will never be more than 1.609344 times your imperial Eddington number.\n",
- "- Of two riders, it is possible that one has a higher metric number and the other a higher imperial number."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
+ "- Of two riders, it is possible that one has a higher metric number and the other a higher imperial number.\n",
+ "\n",
"*Note:* the definition of Eddington Number seems precise, but what exactly does ***day*** mean? The New Oxford dictionary has three senses:\n",
"\n",
- "1. a period of 24 hours; \n",
- "2. a unit of time, reckoned from one midnight to the next;\n",
- "3. the part of a day when it is light. \n",
+ "1. *a period of 24 hours;*\n",
+ "2. *a unit of time, reckoned from one midnight to the next;*\n",
+ "3. *the part of a day when it is light.* \n",
"\n",
"I originally assumed sense 2, but I wanted to accept sense 1 for what [bikepackers](https://bikepacking.com/) call a [sub-24-hour overnight](https://oneofsevenproject.com/s24o-bikepacking-guide/) (S24O): a ride to a camping site in the afternoon, pitching a tent for the night, and returning back home the next morning. And then COVID struck, the camping sites closed, so why not allow an S24O where I sleep in my own home? I realize Eddington had a lot more hardships than we have (World War I, the 1918 pandemic, and World War II, for example), but I hope he would approve of this modest accomodation on my part."
]
@@ -959,7 +1215,7 @@
},
{
"cell_type": "code",
- "execution_count": 454,
+ "execution_count": 99,
"metadata": {},
"outputs": [
{
@@ -999,12 +1255,12 @@
},
{
"cell_type": "code",
- "execution_count": 455,
+ "execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
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\n",
+ "image/png": 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\n",
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@@ -1023,14 +1279,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "So, I average a little under 14 mph when the overall route is fairly flat, with a lot of variability from 12 to 16 mph, depending more on my level of effort (and maybe the wind) than on the grade of the road. But when the grade is steeper than 50 ft/mile, my speed falls off quickly: down to 12mph at 80 ft/mile; 11 mph at 100 ft/mile; and around 10 mph at 120 ft/mile. Note that 120 ft/mile is only 2.3% grade, but if you figure a typical route is 1/3 up, 1/3 down, and 1/3 flat, then that's 6.9% average grade on the up part.\n",
+ "So, I average a little under 14 mph when the overall route is fairly flat, with a lot of variability, depending more on my level of effort (and maybe the wind) than on the grade of the road. But when the grade is steeper than 50 ft/mile, my speed falls off quickly: down to 12mph at 80 ft/mile; 11 mph at 100 ft/mile; and around 10 mph at 120 ft/mile. Note that 120 ft/mile is only 2.3% grade, but if you figure a typical route is 1/3 up, 1/3 down, and 1/3 flat, then that's 7% average grade on the up part.\n",
"\n",
"I can use this to predict the time of a ride. For example, if I'm in La Honda and want to get to Pescadero, which way is faster: the [coast route](https://www.google.com/maps/dir/La+Honda,+California/Pescadero,+California/@37.2905834,-122.3896683,12z/data=!4m19!4m18!1m10!1m1!1s0x808faed4dc6265bd:0x51a109d3306a7219!2m2!1d-122.274227!2d37.3190255!3m4!1m2!1d-122.4039496!2d37.3116594!3s0x808f062b7d7585e7:0x942480c22f110b74!1m5!1m1!1s0x808f00b4b613c4c1:0x43c609077878b77!2m2!1d-122.3830152!2d37.2551636!3e1) (15.7 miles, 361 ft climb), or the [creek route](https://www.google.com/maps/dir/La+Honda,+California/Pescadero,+California/@37.2905834,-122.3896683,12z/data=!4m19!4m18!1m10!1m1!1s0x808faed4dc6265bd:0x51a109d3306a7219!2m2!1d-122.274227!2d37.3190255!3m4!1m2!1d-122.3658887!2d37.2538867!3s0x808f00acf265bd43:0xb7e2a0c9ee355c3a!1m5!1m1!1s0x808f00b4b613c4c1:0x43c609077878b77!2m2!1d-122.3830152!2d37.2551636!3e1) (13.5 miles, 853 ft climb)? We can estimate:"
]
},
{
"cell_type": "code",
- "execution_count": 456,
+ "execution_count": 101,
"metadata": {},
"outputs": [
{
@@ -1039,7 +1295,7 @@
"'Coast: 70 min, Creek: 64 min.'"
]
},
- "execution_count": 456,
+ "execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
@@ -1068,7 +1324,7 @@
},
{
"cell_type": "code",
- "execution_count": 457,
+ "execution_count": 102,
"metadata": {},
"outputs": [
{
@@ -1097,7 +1353,7 @@
},
{
"cell_type": "code",
- "execution_count": 458,
+ "execution_count": 103,
"metadata": {},
"outputs": [
{
@@ -1130,6 +1386,7 @@
"
"
- ],
- "text/plain": [
- " year hours miles feet mph vam fpm pct kms\n",
- " 2022 398.80 4382 292575 10.99 224.0 67.0 1.26 7050.64\n",
- " 2021 490.53 6064 196634 12.36 122.0 32.0 0.61 9756.98\n",
- " 2020 438.88 5341 94777 12.17 66.0 18.0 0.34 8593.67\n",
- " 2019 476.32 6016 149797 12.63 96.0 25.0 0.47 9679.74\n",
- " 2018 475.93 6101 158642 12.82 102.0 26.0 0.49 9816.51\n",
- " 2017 567.33 7356 202096 12.97 109.0 27.0 0.52 11835.80\n",
- " 2016 486.38 6339 201453 13.03 126.0 32.0 0.60 10199.45\n",
- " 2015 419.95 5452 209859 12.98 152.0 38.0 0.73 8772.27\n",
- " 2014 191.03 2469 118481 12.92 189.0 48.0 0.91 3972.62"
- ]
- },
- "execution_count": 468,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "yearly"
- ]
}
],
"metadata": {
@@ -4460,7 +4730,9 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
- }
+ },
+ "toc-autonumbering": true,
+ "toc-showmarkdowntxt": false
},
"nbformat": 4,
"nbformat_minor": 4
diff --git a/ipynb/BikeCode.ipynb b/ipynb/BikeCode.ipynb
index 2a6ef22..768ccf7 100644
--- a/ipynb/BikeCode.ipynb
+++ b/ipynb/BikeCode.ipynb
@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -42,7 +42,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -67,7 +67,8 @@
" vam=round(ft / hr / 3.28084),\n",
" fpm=round(ft / mi),\n",
" pct=round(ft / mi * 100 / 5280, 2),\n",
- " kms=round(mi * 1.609, 2))\n",
+ " kms=round(mi * 1.609, 2),\n",
+ " km_up=round(ft * 0.0003048, 1))\n",
"\n",
"def drop_index(frame) -> pd.DataFrame:\n",
" \"\"\"Drop the index column.\"\"\"\n",
@@ -77,30 +78,35 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"rides = parse_rides(open('bikerides.tsv'))\n",
- "yearly = parse_rides(open('bikeyears.tsv')).drop(columns=['date', 'title'])"
+ "yearly = parse_rides(open('bikeyears.tsv')).drop(columns=['date', 'title'])\n",
+ "daily = yearly.copy()\n",
+ "for name in 'hours miles feet kms km_up'.split():\n",
+ " daily[name] = round(daily[name].map(lambda x: x / 350), 3 if name == 'km_up' else 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Reading Data: `segments`\n",
+ "# Reading Data: `segments`, `places`, and `tiles`\n",
"\n",
"I picked some representative climbing segments ([`bikesegments.csv`](bikesegments.csv)) with the segment length in miles and climb in feet, along with several of my times on the segment. A line like\n",
"\n",
" Old La Honda, 2.98, 1255, 28:49, 34:03, 36:44\n",
" \n",
- "means that this segment of Old La Honda Rd is 2.98 miles long, 1255 feet of climbing, and I've selected three times for my rides on that segment: the fastest, middle, and slowest of the times that Strava shows. (However, I ended up dropping the slowest time in the charts to make them less busy.)"
+ "means that this segment of Old La Honda Rd is 2.98 miles long, 1255 feet of climbing, and I've selected three times for my rides on that segment: the fastest, middle, and slowest of the times that Strava shows. (However, I ended up dropping the slowest time in the charts to make them less busy.)\n",
+ "\n",
+ "I keep track of percentage of roads ridden in various places in `'bikeplaceshort.csv'`, which comes from wandrer.earth."
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
@@ -117,94 +123,55 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"segments = parse_segments(open('bikesegments.csv'))"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Reading Data: `places` and `tiles`\n",
- "\n",
- "Monthly, I will take my [summary data from wandrer.earth](https://wandrer.earth/athletes/3534/santa-clara-county-california) and enter it in the file [bikeplaces.csv](bikeplaces.csv), in a format where\n",
- "\n",
- " San Carlos,99.0,SMC,22.2,26.0,32.9,,37.2,39.0,40.5,,41.4,,,41.7,,,,,,59.5,78.7\n",
- "means that San Carlos has 99.0 miles of roads, is in San Mateo County (SMC), and in the first month that I had ridden 22.2% of the roads in the first month that I kept track, and 78.7% in the most recent month. In months with no entry, there was no change."
- ]
- },
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
- "places = pd.read_csv('bikeplaces.csv', comment='#')\n",
- "months = [m for m in places.columns if '/' in m]\n",
- "places['maxpct'] = [max(p for p in place[4:] if not pd.isna(p))\n",
- " for place in places.itertuples()]"
+ "places = pd.read_table(open('bikeplaceshort.csv'), sep=',', comment='#')"
]
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 50,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "
date
\n",
- "
tiles
\n",
- "
square
\n",
- "
cluster
\n",
- "
\n",
- " \n",
- " \n",
- "
\n",
- "
\n",
- "
Sep 2022
\n",
- "
2481
\n",
- "
11x11
\n",
- "
295
\n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " date tiles square cluster\n",
- " Sep 2022 2481 11x11 295"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "tiles = drop_index(pd.DataFrame(columns='date tiles square cluster'.split(),\n",
- " data=[('Sep 2022', 2481, '11x11', 295)]))"
+ "def make_clickable(comment) -> str:\n",
+ " \"\"\"Make a clickable link for a pandas dataframe.\"\"\"\n",
+ " if '!' not in comment:\n",
+ " return comment\n",
+ " anchor, number = comment.split('!')\n",
+ " return f'{anchor}'\n",
+ "\n",
+ "def link_date(date) -> str:\n",
+ " \"\"\"Make the date into a clickable link.\"\"\"\n",
+ " m, d, y = date.split('/')\n",
+ " return f'{date}'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tiles = drop_index(pd.DataFrame(columns='date square cluster total comment'.split(), data=[\n",
+ " ('06/30/2023', 13, 689, 2640, 'Rides in east Bay!9298603815'),\n",
+ " ('04/14/2023', 13, 630, 2595, 'Black Sands Beach connects Marin to max cluster!8891171008'),\n",
+ " ('03/04/2023', 13, 583, 2574, 'Almaden rides connects Gilroy to max cluster!8654437264'),\n",
+ " ('10/22/2022', 13, 396, 2495, 'Alviso levees to get to 13x13 max square!8003921626'),\n",
+ " ('10/16/2022', 12, 393, 2492, 'Milpitas ride connects East Bay to max cluster!7974994605'),\n",
+ " ('09/08/2022', 11, 300, 2487, 'First started tracking tiles')])\n",
+ " ).style.format({'comment': make_clickable, 'date': link_date})"
]
},
{
@@ -216,7 +183,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -256,69 +223,35 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Plotting Wandrer Places"
+ "# Wandrer Places "
]
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
- "special_areas = dict(sf='San Francisco Neighborhoods', sj='San Jose Neighborhoods', \n",
- " far='Far Away Places', county='Bay Area Counties', big='California, USA and Earth')\n",
- "\n",
- "def wandering(places, pcts=(100, 99, 90, 50, 33.3, 25, 0), special_areas=special_areas):\n",
- " \"Plot charts within the various percent ranges, and special groups.\"\n",
- " for i in range(len(pcts) - 1):\n",
- " hi, lo = pcts[i], pcts[i + 1]\n",
- " inrange = places[(places.maxpct > lo) & (places.maxpct <= hi) & ~places.area.isin(special_areas)]\n",
- " wandrer_plot(f'Places with {lo}% to {hi}% roads traveled', inrange)\n",
- " for area in special_areas:\n",
- " wandrer_plot(special_areas[area], places[places.area == area])\n",
+ "def wandering(places=places, by=['pct']):\n",
+ " \"All those who wander are not lost.\" # Also try by=['cat', 'pct']\n",
+ " frame = places.sort_values(by=by, ascending=('pct' not in by))\n",
+ " M = 1_000_000\n",
+ " for i, (name, miles, county, pct) in frame.iterrows():\n",
+ " # Some fiddling to get the format right\n",
+ " p = f'{pct:.1f}' if (pct > 0.1) else f'{pct:.3f}'\n",
+ " mymiles = pct / 100 * miles\n",
+ " done = f'{rounded(mymiles)}/{rounded(miles)} mi'\n",
+ " togo = next((f'{rounded(target / 100 * miles - mymiles):>5} mi for {target}%' \n",
+ " for target in (0.02, 0.1, 0.2, 1, 2, 25, 50, 90, 99)\n",
+ " if mymiles < target / 100 * miles), '')\n",
+ " print(f'{county} {p:>5}% {name:25} {done:>15} {togo}') \n",
" \n",
- "def wandrer_plot(title, places):\n",
- " \"\"\"Plot Wandrer.earth data for the places with given abbrevs.\"\"\"\n",
- " if len(places) == 0:\n",
- " return # Don't make an empty plot\n",
- " places = places.sort_values(by='maxpct', ascending=False)\n",
- " fig, ax = plt.figure(), plt.subplot(111); \n",
- " plt.plot()\n",
- " for (_, name, miles, area, *pcts, maxpct), marker in zip(places.itertuples(), markers):\n",
- " pcts = replace_nans(pcts)\n",
- " ax.plot(pcts, ':', marker=marker, label=label(pcts, name, miles))\n",
- " ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), shadow=True,\n",
- " prop=matplotlib.font_manager.FontProperties(family='monospace'))\n",
- " plt.xticks(range(len(pcts)), labels=months, rotation=90, fontfamily='monospace')\n",
- " plt.ylabel('Percent of Roads Ridden')\n",
- " plt.title(title); plt.tight_layout(); grid(axis='y'); plt.show()\n",
- " \n",
- "markers = '^v> str:\n",
- " \"\"\"Make a label for the legend.\"\"\"\n",
- " pct = f'{rounded(pcts[-1]):>3}' if pcts[-1] > 1.4 else f'{pcts[-1]}'\n",
- " done = miles * pcts[-1]\n",
- " bonus = next((f' {rounded((p - pcts[-1]) / 100 * miles):>3} to {p}%' \n",
- " for p in bonuses if p >= pcts[-1]), '')\n",
- " return f'{pct}% ({rounded(done / 100):>3}/{rounded(miles):<3} mi){bonus} {name}'\n",
- " \n",
- "def replace_nans(numbers) -> list:\n",
- " \"\"\"Replace NaN (not a number) values with the previous actual number.\"\"\"\n",
- " result = []\n",
- " prev = 0\n",
- " for x in numbers:\n",
- " if x == x:\n",
- " prev = x\n",
- " else: # Not a Number \n",
- " x = prev\n",
- " result.append(x)\n",
- " return result \n",
- "\n",
"def rounded(x: float) -> str: \n",
" \"\"\"Round x to 3 spaces wide (if possible).\"\"\"\n",
- " return f'{round(x):,d}' if x > 10 else f'{x:.1f}'"
+ " return (rounded(x/1e6) + 'M' if x > 1e6\n",
+ " else f'{x/1e6:4.2f}M' if x > 1e5\n",
+ " else f'{round(x):,d}' if x > 10 \n",
+ " else f'{x:.1f}')"
]
},
{
@@ -330,24 +263,24 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def make_leaders(data):\n",
" \"\"\"Make a dataframe of leaders in two counties.\"\"\"\n",
" leaders = pd.DataFrame(data, columns=['Name', 'Initials', 'SMC %', 'SCC %', 'Front?'])\n",
- " leaders['SMC miles'] = [round(29.51 * d[2]) for d in data]\n",
- " leaders['SCC miles'] = [round(75.64 * d[3]) for d in data]\n",
+ " leaders['SMC miles'] = [round(2814 * d[2] / 100) for d in data]\n",
+ " leaders['SCC miles'] = [round(7569 * d[3] / 100) for d in data]\n",
" leaders['Total miles'] = leaders['SMC miles'] + leaders['SCC miles']\n",
- " leaders['Total %'] = leaders['SMC %'] + leaders['SCC %']\n",
- " return drop_index(leaders.sort_values('Total %', ascending=False))\n",
+ " leaders['Avg %'] = (leaders['SMC %'] + leaders['SCC %']) / 2\n",
+ " return drop_index(leaders.sort_values('Avg %', ascending=False))\n",
"\n",
- "leaders = make_leaders([ # Data as of Sept 8, 2022\n",
- " ('Barry Mann', 'BM', 75.34, 29.32, 1), ('Jason Molenda', 'JM', 7.13, 54.59, 1), \n",
- " ('Peter Norvig', 'PN', 55.26, 30.31, 1), ('Brian Feinberg', 'BF', 29.72, 35.93, 1),\n",
- " ('Jim Brooks', 'JB', 4.23, 43.53, 0), ('Megan Gardner', 'MG', 92.51, 8.69, 1),\n",
- " ('Matthew Ring', 'MR', 75.53, 1.48, 0), ('Elliot Huff', 'EF', 51.78, 8.14, 0)])\n",
+ "leaders = make_leaders([ # Data as of Sept 20, 2023 (Name, Initials, SMC, SCC, Frontier?)\n",
+ " ('Barry Mann', 'BM', 76.97, 30.21, 1), ('Jason Molenda', 'JM', 7.13, 55.39, 1), \n",
+ " ('Peter Norvig', 'PN', 61.56, 32.8, 1), ('Brian Feinberg', 'BF', 32.5, 43.68, 1),\n",
+ " ('Jim Brooks', 'JB', 4.23, 44.36, 0), ('Megan Gardner', 'MG', 97.62, 8.69, 1),\n",
+ " ('Matthew Ring', 'MR', 78.85, 1.48, 0), ('Elliot Hoff', 'EF', 52.88, 8.14, 0)])\n",
" \n",
"def pareto_front(leaders):\n",
" ax = leaders.plot('SMC %', 'SCC %', grid=True, kind='scatter')\n",
@@ -367,7 +300,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -381,52 +314,22 @@
" \"\"\"The number of rides needed to reach an Eddington number target.\"\"\"\n",
" return target - sum(distances >= target)\n",
"\n",
- "def Ed_gaps(rides, N=10) -> dict:\n",
- " \"\"\"A table of gaps to Eddington numbers by year, and a plot..\"\"\"\n",
- " E_km = Ed_number(rides, 'kms')\n",
- " E_mi = Ed_number(rides, 'miles')\n",
- " data = [(E_km + d, Ed_gap(rides.kms, E_km + d), \n",
- " E_mi + d, Ed_gap(rides.miles, E_mi + d))\n",
+ "def Ed_gaps(rides, E_km=100, E_mi=67, N=11) -> dict:\n",
+ " \"\"\"A table of gaps to Eddington numbers by year.\"\"\"\n",
+ " data = [(E_km + d, sum(rides.kms >= E_km + d), Ed_gap(rides.kms, E_km + d), \n",
+ " E_mi + d, sum(rides.miles >= E_mi + d), Ed_gap(rides.miles, E_mi + d))\n",
" for d in range(N)]\n",
- " df = pd.DataFrame(data, columns=['kms', 'kms gap', 'miles', 'miles gap'])\n",
+ " df = pd.DataFrame(data, columns=['kms', 'km rides', 'kms gap', \n",
+ " 'miles', 'miles rides', 'miles gap'])\n",
" return drop_index(df)\n",
"\n",
- "def Ed_progress(rides, years=reversed(range(2013, 2022 + 1))) -> pd.DataFrame:\n",
+ "def Ed_progress(rides, years=range(2023, 2013, -1)) -> pd.DataFrame:\n",
" \"\"\"A table of Eddington numbers by year, and a plot.\"\"\"\n",
" def Ed(year, unit): return Ed_number(rides[rides['year'] <= year], unit)\n",
" data = [(y, Ed(y, 'kms'), Ed(y, 'miles')) for y in years]\n",
" df = pd.DataFrame(data, columns=['year', 'Ed_km', 'Ed_mi'])\n",
" return drop_index(df)"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Climbing to Space"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [],
- "source": [
- "per_month_climbing = [35.491, 31.765, 39.186, 33.641, 32.782, 14.809, 46.731, 38.556]\n",
- "\n",
- "space = {'100 kms': 328.204, '10 Everests': 290.320, '50 miles': 50 * 5.280}\n",
- "\n",
- "def climbing(per_month=per_month_climbing, space=space):\n",
- " \"\"\"Plot progress in climbing\"\"\"\n",
- " total = np.cumsum(per_month)\n",
- " for label in space:\n",
- " plt.plot(range(12), [space[label]] * 12, ':', label=label)\n",
- " plt.plot(range(len(total)), total, 'o-', label='my total')\n",
- " plt.plot(range(len(total)), per_month, 's-.', label='per month')\n",
- " plt.legend(loc=(1.04, .64), fontsize='large'); grid()\n",
- " plt.xlabel('Month of 2022'); plt.ylabel('Total climbing (Kft)')\n",
- " plt.xticks(range(12), 'Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec'.split())"
- ]
}
],
"metadata": {
@@ -446,7 +349,8 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
- }
+ },
+ "toc-autonumbering": true
},
"nbformat": 4,
"nbformat_minor": 4
diff --git a/ipynb/OneLetterOff.ipynb b/ipynb/OneLetterOff.ipynb
new file mode 100644
index 0000000..2dc2c40
--- /dev/null
+++ b/ipynb/OneLetterOff.ipynb
@@ -0,0 +1,602 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "a6233239-1cf1-4eee-affc-d96a0747ee68",
+ "metadata": {},
+ "source": [
+ "
Peter Norvig, Aug 2023
\n",
+ "\n",
+ "# One Letter Off: Word Game with Large Language Models\n",
+ "\n",
+ "Professor [**Serge Belongie**](https://en.wikipedia.org/wiki/Serge_Belongie) invented a word game: \n",
+ "- *Pick a word and drop the last letter to form a second word.*\n",
+ "- *Come up with a crossword-puzzle-style clue to help someone guess the resulting two-word phrase.*\n",
+ "\n",
+ "I'm calling the game **One Letter Off**, because that's one of the names that [Bard](https://bard.google.com) suggested when I asked. I'll also introduce a variant of the game where *any* letter in the word can be dropped, not just the last one.\n",
+ "\n",
+ "As an example, pick *board*, drop the last letter to get the phrase *boar board* and write the clue *pig plank.* In the variant, you could drop the letter *o* to get *board bard* and write the clue *pine poet.* (Note the pair of words can be in either order.)\n",
+ "\n",
+ "I thought it would be interesting to write a program to generate interesting word pairs and create clues. My plan is:\n",
+ "\n",
+ "1. Obtain a dictionary of words.\n",
+ "2. Write code to generate all one-letter-off word pairs and sort them so the best ones come first.\n",
+ "3. Inspect some of the pairs and manually write some clues for them, just to get a feel for the task.\n",
+ "4. Write code to prompt a large language model and see what clues it can produce.\n",
+ "\n",
+ "# 1. A dictionary of words, with word vectors\n",
+ "\n",
+ "Given a dictionary, it is easy to find all pairs of words that are one letter off. The tricky part is to decide which are the \"good\" ones. Clearly, pairing the word *race* with *races* or *raced* or *racer* is bad, because they are all just forms of the same root word. The game is interesting when two words are very different in meaning, even though they are only one letter apart. Pairing *quaker* with *quake* is ok, because a Quaker is not just someone who quakes. So I can't rely on simple heuristic rules like \"don't drop a final -r.\" \n",
+ "\n",
+ "I can, however, associate each word with a [**word vector**](https://en.wikipedia.org/wiki/Word_embedding) (also called *word embedding*) and define the goodness of a pair as the distance between the pairs. Multiple research teams have published open-source dictionaries that map words to word vectors. Two words that are similar in meaning or usage have similar word vectors (and so the distance between them is small), but words with distinct usage should have dissimilar word vectors (and a larger distance between them). \n",
+ "\n",
+ "I downloaded a [file of word vectors](http://vectors.nlpl.eu/repository/20/0.zip) from the [NLPL Word Vectors Repository](http://vectors.nlpl.eu/repository/) and truncated it to consider only the [30,000 most common words](model30k.txt), because I didn't want to be giving clues for words that the guesser is unlikely to know. Each line in the word vector file has a word, followed by its part of speech (which we will ignore), followed by a vector of 300 numbers. For example:\n",
+ "\n",
+ " say_VERB -0.008861 0.097097 0.100236 0.070044 -0.079279 0.000923 ...\n",
+ " \n",
+ "I'll read the file into a dict of `{word: vector}` called `vectors`, but I will eliminate words that are too short, or have a non-alphabetic or uppercase character. Also, if a word appears twice with two different parts of speech, I'll use the first one (because the file is sorted with more frequent words first)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "bfedfb19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import *\n",
+ "import numpy as np\n",
+ "\n",
+ "def read_vectors(lines: Iterable[str]) -> Dict[str, np.array]:\n",
+ " \"\"\"Read lines of text into a dict of {word: vector} pairs.\"\"\"\n",
+ " vectors = {}\n",
+ " for line in lines:\n",
+ " entry, *numbers = line.split()\n",
+ " word, POS = entry.split('_') # Ignore the part of speech\n",
+ " if word not in vectors and len(word) >= 4 and word.isalpha() and word.islower():\n",
+ " vectors[word] = np.array([float(x) for x in numbers])\n",
+ " return vectors\n",
+ "\n",
+ "vectors = read_vectors(open('model30k.txt'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "efdc80fe",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "18331"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(vectors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ad74e30",
+ "metadata": {},
+ "source": [
+ "There are 18,331 distinct entries, out of the 30,000 lines in the original file.\n",
+ "\n",
+ "The [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance) between two word vectors is the [norm](https://en.wikipedia.org/wiki/Norm_(mathematics)) of their difference:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "d02a7b33",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def distance(word1, word2, vectors) -> float:\n",
+ " \"\"\"Distance between vectors for the two words.\"\"\"\n",
+ " return np.linalg.norm(vectors[word1] - vectors[word2])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "02fa9df6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.3230686663760125"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "distance('quaker', 'quake', vectors)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "d40b7470",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7579947920361987"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "distance('smoker', 'smoke', vectors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f2c125d7",
+ "metadata": {},
+ "source": [
+ "# 2. Word pairs, best first\n",
+ "\n",
+ "The next step is to form word pairs by considering each word, looking at all ways to drop a letter from each word, and checking if those are words. Then we'll sort them so the \"best\" pairs come first."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "e2061a00",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sorted_pairs(vectors, drop_fn: callable) -> List[Tuple[str, str]]:\n",
+ " \"\"\"List of (word1, word2) pairs, biggest word-vector distance first.\"\"\"\n",
+ " pairs = [(w1, w2) for w1 in vectors for w2 in drop_fn(w1, vectors)]\n",
+ " pairs.sort(key=lambda pair: distance(*pair, vectors), reverse=True)\n",
+ " return pairs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6c65db76",
+ "metadata": {},
+ "source": [
+ "Here are the two functions to drop letters (either just the last letter, or any letter) and the function `words`, which checks which of the resulting strings are in fact words."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "227cbcc7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def drop_last_letter(word, vectors) -> Set[str]: \n",
+ " \"\"\"All ways to drop the last letter from word to form a word in `vectors`.\"\"\"\n",
+ " return words({word[:-1]}, vectors)\n",
+ "\n",
+ "def drop_any_letter(word, vectors) -> Set[str]: \n",
+ " \"\"\"All ways to drop one letter from word to form a word in `vectors`.\"\"\"\n",
+ " return words({word[:i] + word[i + 1:] for i in range(len(word))}, vectors)\n",
+ "\n",
+ "def words(candidates, vectors) -> Set[str]:\n",
+ " \"\"\"All candidate strings that are words in the `vectors` dict.\"\"\"\n",
+ " return {w for w in candidates if w in vectors}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "6d82b297",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'clam'}"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "drop_last_letter('clamp', vectors)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "1e4c8df5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'camp', 'clam', 'clap', 'lamp'}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "drop_any_letter('clamp', vectors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5f9cfe4c",
+ "metadata": {},
+ "source": [
+ "We're ready to generate a sorted list of word pairs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "0b3e1834",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1129"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pairs = sorted_pairs(vectors, drop_last_letter)\n",
+ "len(pairs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21903917",
+ "metadata": {},
+ "source": [
+ "There are 1,129 pairs, but I'll just look at the first 50:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "c55e51a6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('seedy', 'seed'),\n",
+ " ('depth', 'dept'),\n",
+ " ('hindu', 'hind'),\n",
+ " ('sloth', 'slot'),\n",
+ " ('plumb', 'plum'),\n",
+ " ('tense', 'tens'),\n",
+ " ('reverb', 'rever'),\n",
+ " ('irish', 'iris'),\n",
+ " ('siren', 'sire'),\n",
+ " ('trusty', 'trust'),\n",
+ " ('meter', 'mete'),\n",
+ " ('pleat', 'plea'),\n",
+ " ('sinew', 'sine'),\n",
+ " ('chancel', 'chance'),\n",
+ " ('heath', 'heat'),\n",
+ " ('aspiring', 'aspirin'),\n",
+ " ('forth', 'fort'),\n",
+ " ('combo', 'comb'),\n",
+ " ('drama', 'dram'),\n",
+ " ('paste', 'past'),\n",
+ " ('outwith', 'outwit'),\n",
+ " ('filly', 'fill'),\n",
+ " ('board', 'boar'),\n",
+ " ('livery', 'liver'),\n",
+ " ('forcep', 'force'),\n",
+ " ('stocky', 'stock'),\n",
+ " ('corporal', 'corpora'),\n",
+ " ('photon', 'photo'),\n",
+ " ('forte', 'fort'),\n",
+ " ('rabbit', 'rabbi'),\n",
+ " ('median', 'media'),\n",
+ " ('cello', 'cell'),\n",
+ " ('chart', 'char'),\n",
+ " ('spark', 'spar'),\n",
+ " ('liver', 'live'),\n",
+ " ('caster', 'caste'),\n",
+ " ('irony', 'iron'),\n",
+ " ('insider', 'inside'),\n",
+ " ('heron', 'hero'),\n",
+ " ('macho', 'mach'),\n",
+ " ('heroine', 'heroin'),\n",
+ " ('polyp', 'poly'),\n",
+ " ('gravely', 'gravel'),\n",
+ " ('primer', 'prime'),\n",
+ " ('honey', 'hone'),\n",
+ " ('quaker', 'quake'),\n",
+ " ('tablet', 'table'),\n",
+ " ('grant', 'gran'),\n",
+ " ('prime', 'prim'),\n",
+ " ('valet', 'vale')]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pairs[:50]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e87f893",
+ "metadata": {},
+ "source": [
+ "Now I'll consider the variant where we can drop any letter:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "09dfc2fa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('mitre', 'mite'),\n",
+ " ('seedy', 'seed'),\n",
+ " ('posit', 'post'),\n",
+ " ('resign', 'resin'),\n",
+ " ('insect', 'inset'),\n",
+ " ('score', 'core'),\n",
+ " ('parse', 'arse'),\n",
+ " ('depth', 'dept'),\n",
+ " ('convert', 'covert'),\n",
+ " ('thank', 'tank'),\n",
+ " ('hindu', 'hind'),\n",
+ " ('orally', 'rally'),\n",
+ " ('stigma', 'sigma'),\n",
+ " ('naive', 'nave'),\n",
+ " ('harmful', 'armful'),\n",
+ " ('sloth', 'slot'),\n",
+ " ('canyon', 'canon'),\n",
+ " ('bassist', 'assist'),\n",
+ " ('preach', 'peach'),\n",
+ " ('launder', 'lander'),\n",
+ " ('congenital', 'congenial'),\n",
+ " ('supper', 'upper'),\n",
+ " ('duress', 'dress'),\n",
+ " ('usher', 'user'),\n",
+ " ('crate', 'rate'),\n",
+ " ('platitude', 'latitude'),\n",
+ " ('ironic', 'ionic'),\n",
+ " ('sever', 'seer'),\n",
+ " ('quilt', 'quit'),\n",
+ " ('timer', 'tier'),\n",
+ " ('yeast', 'east'),\n",
+ " ('sturdy', 'study'),\n",
+ " ('tamper', 'taper'),\n",
+ " ('crater', 'cater'),\n",
+ " ('frame', 'fame'),\n",
+ " ('broach', 'roach'),\n",
+ " ('stage', 'sage'),\n",
+ " ('leaver', 'lever'),\n",
+ " ('restate', 'estate'),\n",
+ " ('sluice', 'slice'),\n",
+ " ('blinding', 'binding'),\n",
+ " ('gamble', 'gable'),\n",
+ " ('strait', 'trait'),\n",
+ " ('pledge', 'ledge'),\n",
+ " ('clean', 'clan'),\n",
+ " ('shite', 'site'),\n",
+ " ('sched', 'shed'),\n",
+ " ('holist', 'hoist'),\n",
+ " ('plumb', 'plum'),\n",
+ " ('waive', 'wave')]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorted_pairs(vectors, drop_any_letter)[:50]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "44a30715",
+ "metadata": {},
+ "source": [
+ "Both lists provide pretty promising pairs! \n",
+ "\n",
+ "But did I really accomplish the goal of eliminating bad pairs? I think so! The pairs at the end of the list are exactly the kind of thing I wanted to get rid of:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "56cf7c96",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('northwards', 'northward'),\n",
+ " ('decentralised', 'decentralise'),\n",
+ " ('insured', 'insure'),\n",
+ " ('thanks', 'thank'),\n",
+ " ('surpluse', 'surplus'),\n",
+ " ('eastwards', 'eastward'),\n",
+ " ('devoted', 'devote'),\n",
+ " ('opposed', 'oppose'),\n",
+ " ('alleged', 'allege'),\n",
+ " ('waken', 'wake'),\n",
+ " ('diall', 'dial'),\n",
+ " ('increased', 'increase'),\n",
+ " ('randomised', 'randomise'),\n",
+ " ('brewery', 'brewer'),\n",
+ " ('involved', 'involve'),\n",
+ " ('vaginal', 'vagina'),\n",
+ " ('larval', 'larva'),\n",
+ " ('fabliaux', 'fabliau'),\n",
+ " ('towards', 'toward'),\n",
+ " ('mucosal', 'mucosa')]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pairs[-20:]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5fa3c30e",
+ "metadata": {},
+ "source": [
+ "# 3 Manually writing clues\n",
+ "\n",
+ "I can take some of these suggested pairs, and make up clues on my own:\n",
+ "\n",
+ "|pair|**clue**|\n",
+ "|----|----|\n",
+ "|alley ally|**Bowling teammate**|\n",
+ "|binding blinding|**Mandatory punishment for the cyclops Polyphemus**|\n",
+ "|class lass|**Sophisticated young lady**|\n",
+ "|finnish finish|**Scandinavian ending**|\n",
+ "|harmful armful|**Dangerous bundle**|\n",
+ "|latitude platitude|**Parallel cliche**|\n",
+ "|plum plumb|**Most excellent toilet installation job**|"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d502d513",
+ "metadata": {},
+ "source": [
+ "# 3. Prompting an LLM to write clues\n",
+ "\n",
+ "I wrote some code to submit pairs of words to a large language model (LLM) and get back a clue. The basic approach looks like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "36a92c46",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from requests import post\n",
+ "\n",
+ "def get_a_clue(pair, url, api_key) -> str:\n",
+ " \"\"\"Ask a LLM to generate a clue for the pair of words.\"\"\"\n",
+ " phrase = ' '.join(pair)\n",
+ " return ask(f'Write a clever crossword puzzle clue for the phrase \"{phrase}\"', url, api_key)\n",
+ "\n",
+ "def ask(query, url, api_key) -> str:\n",
+ " \"\"\"Ask PaLM model to respond to the query.\"\"\"\n",
+ " response = post(url, headers={\"Authorization\": f\"Bearer {api_key}\"}, json={\"query\": query})\n",
+ " response.raise_for_status()\n",
+ " return response.json()[\"queryResult\"][\"fulfillmentText\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1c4bbb58",
+ "metadata": {},
+ "source": [
+ "However, I'm not going to run the above code in this notebook, for three reasons:\n",
+ "1. Every time the code is re-run it produces a different response, and some runs are not very good.\n",
+ "2. For security, I can't show the API key in this notebook, so you would need to get your own API key.\n",
+ "3. There are many LLMs and I don't want to imply that this is the only one to use.\n",
+ "\n",
+ "Instead I'll show in the table below some of the previously-generated responses, edited for brevity, and selecting mostly good responses, with a few bad ones to illustrate specific points. The model gets a lot of things right:\n",
+ "- Some of the clues are really good!\n",
+ "- The model mostly understands how clues work: short, not too hard, not too easy.\n",
+ "- The model knows that word play or puns are desireable.\n",
+ "- The model gives an explanation of the clue as well as the clue itself.\n",
+ "- Twice the model used a question mark, a crossword puzzle convention to signal that the clue is not straightforward.\n",
+ "\n",
+ "\n",
+ "The model also gets some things wrong:\n",
+ "- Some of the clues are really bad!\n",
+ "- Many clues break a cardinal rule by mentioning one of the answer words (see \"board boar\", etc).\n",
+ "- The model often claims that a clue is \"a play on\" two meanings of a word (so far so good), but then gets the words or meanings confused (see \"frame fame\", \"pleat plea\", \"stocky stock\").\n",
+ "\n",
+ "\n",
+ "|pair|drop|clue|explanation|\n",
+ "| :---- | :---- | :---- | :----------- |\n",
+ "|aspiring aspirin|any|**Wannabe pain reliever**|The clue suggests that an \"aspiring aspirin\" is a something that wants to be a pain reliever, but is not yet one. |\n",
+ "|blinding binding|any|**A dazzling cover-up**|The clue suggests that a \"blinding binding\" is a very bright or dazzling cover-up, such as the cover of a book with a shiny metallic finish or a book with a cover made of sequins.|\n",
+ "|board boar|last|**A pig that's a member of the board of directors**|This clue is clever because it is a pun on the words \"board\" and \"boar\". It also plays on the stereotype of pigs being greedy and wanting to be in positions of power.|\n",
+ "|cello cell|last|**Musical prison?**|This clue is clever because it is a pun on the word \"cell.\" A \"cell\" can refer to a room in a prison, or it can refer to the part of a cello that contains the strings. |\n",
+ "|covert convert|any|**Agent of change**|This clue is clever because it is a play on words. \"Agent of change\" can refer to someone who is trying to make a difference in the world, but it can also refer to a spy or undercover agent.|\n",
+ "|crater cater|any|**A space rock that serves food**|The word \"crater\" refers to a large bowl-shaped depression in the ground, often formed by the impact of a meteorite or asteroid.|\n",
+ "|finnish finish|any|**How a Suomi might end a race**|The word \"Suomi\" is the Finnish word for Finland. The clue suggests how a Finnish person might end a race, because it is a play on the words \"Finnish\" and \"finish.\"|\n",
+ "|frame fame|any|**What kind of fame is displayed on a wall?**|This clue is clever because it is a play on words. The word \"fame\" can mean both \"celebrity\" and \"a picture in a frame.\" |\n",
+ "|flying fling|any|**Airy affair**|This clue is a bit of a pun, as it plays on the two meanings of the word \"fling\": a brief and passionate romantic relationship, and the act of throwing something. |\n",
+ "|ledge pledge|any|**A promise to hang on**|The clue suggests that a \"pledge ledge\" is a promise to hang on to something, such as a promise to keep a secret or to repay a debt.|\n",
+ "|pleat plea|last|**Fashionable plea for more folds?**|This clue is clever because it is a pun on the two meanings of the word \"pleat\": (1) A fold in a piece of fabric (2) A request or appeal|\n",
+ "|plum plumb|last|**Perfectly perpendicular**|The clue suggests that a \"plum plumb\" is a line that is perfectly perpendicular to the ground, like a plumb line.|\n",
+ "|quaker quake|any|**A religious tremor**|The clue suggests that a \"quaker quake\" is a type of religious tremor, which could be interpreted in a number of ways. For example, it could refer to a powerful spiritual experience, or to a time of social and political upheaval.|\n",
+ "|quilt quit|any|**Sewing machine strike**|The clue suggests that a \"quilt quit\" is a strike by sewing machines, which would prevent people from making quilts.|\n",
+ "|rabbit rabbi|last|**What kind of religious leader is all ears**|This clue is clever because it plays on the double meaning of the word \"ears.\" On the one hand, rabbits are known for having large ears. On the other hand, \"all ears\" is an idiom that means to be very attentive and eager to listen.|\n",
+ "|rally orally|any|**Shout for support**|The answer is \"rally orally\", which is a phrase that could be used to describe someone shouting for support.|\n",
+ "|seedy seed|last|**The kernel of a shady deal**|The clue suggests that a \"seedy seed\" is the beginning of something shady or immoral. This could refer to a criminal activity, a broken promise, or even a relationship that is doomed.|\n",
+ "|stage sage|any|**wise actor**|The clue suggests that a \"stage sage\" is a wise actor who is at the top of their game.|\n",
+ "|stocky stock|last|**Beefy broth**|This clue is a play on the two meanings of the word \"stock\": (1) A type of broth made from meat or bones. (2) A large quantity of something, especially goods or money.|\n",
+ "|timer tier|any|**A level of timekeeping**|The clue suggests that a \"timer tier\" is a level of timekeeping, such as the different tiers of accuracy that are available for timers. For example, a timer used for cooking may have a lower tier of accuracy than a timer used for scientific research.|\n",
+ "|yeast east|any|**Where the dough rises**|The clue suggests that \"yeast east\" is the direction in which the sun rises because yeast causes dough to rise.|\n",
+ "\n",
+ "What do you think? Are these clues any good? Can you do better, either by manually writing your own, or by writing a better program (e.g. by finding better word pairs, by constructing a better prompt, or by using a different LLM that performs better on this task)?"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ipynb/bikeplaceshort.csv b/ipynb/bikeplaceshort.csv
new file mode 100644
index 0000000..3ae250e
--- /dev/null
+++ b/ipynb/bikeplaceshort.csv
@@ -0,0 +1,165 @@
+name,miles,county,pct
+#,,,
+# San Mateo County,,SMC,
+#,,,
+Atherton,56.3,SMC,99.8
+Bay Area Ridge Trail,395.6,SMC,21.4
+Belmont,98.1,SMC,54.2
+Brisbane,40.9,SMC,46.7
+Broadmoor,8.8,SMC,38.2
+Burlingame Hills,6,SMC,71.4
+Burlingame,88.4,SMC,54.3
+Burleigh Murray Park,2.1,SMC,91.6
+Coal Creek Preserve,3.9,SMC,66.7
+Colma,13.7,SMC,38.3
+Daly City,148.1,SMC,27.2
+East Palo Alto,48.3,SMC,99.6
+El Granada,49.2,SMC,38.4
+Emerald Lake Hills,24.6,SMC,99.8
+Foster City,150,SMC,80.7
+Half Moon Bay,68,SMC,32.2
+Half Moon Bay State Beach,4.4,SMC,51.5
+Hillsborough,85.3,SMC,52.3
+Kensington Square,0.6,SMC,99.9
+Ladera,8.1,SMC,99.9
+Long Ridge Preserve,11.0,SMC,51.2
+Los Trancos OSP,0.3,SMC,100
+Los Trancos Woods,5.3,SMC,99.9
+Menlo Oaks,3.5,SMC,99.9
+Menlo Park,139.5,SMC,99.5
+Millbrae,65,SMC,40.8
+Montara,27.8,SMC,32.7
+Moss Beach,19.7,SMC,44.3
+North Fair Oaks,26.7,SMC,99.8
+Pacifica,150.9,SMC,32.7
+Palomar Park,4,SMC,99.9
+Portola Valley,48.2,SMC,99.5
+Purisima Creek Preserve,16.5,SMC,38.7
+Redwood City,240.5,SMC,99.3
+Russian Ridge Preserve,12.2,SMC,51.2
+San Bruno,114,SMC,29.5
+San Carlos,99,SMC,99.0
+San Mateo,256,SMC,51.8
+San Mateo Highlands,18,SMC,77.9
+Sequoia Tract,11,SMC,99.7
+Sky Londa,11.8,SMC,99.4
+Skyline Ridge OSP,0.8,SMC,74.9
+South San Francisco,185.3,SMC,27.2
+West Menlo Park,11.2,SMC,99.8
+Windy Hill Preserve,4.1,SMC,99.1
+Woodside,75.2,SMC,99.5
+#,,,
+# Santa Clara County,,SCC,
+#,,,
+Branham,44,SCC,32.7
+Campbell,119,SCC,28.8
+Communications Hill,27.8,SCC,39.5
+Cupertino,172,SCC,33.2
+Edenvale,30,SCC,47
+Foothills Preserve,1.1,SCC,86.8
+Gardner,23.4,SCC,47.2
+Gilroy,188.9,SCC,12.1
+Los Altos,138.2,SCC,99.6
+Los Altos Hills,91.3,SCC,99.4
+Los Gatos,148,SCC,33.2
+Loyola,18.3,SCC,99.6
+Milpitas,224,SCC,37.3
+Monte Sereno,20.4,SCC,44.7
+Mountain View,208.1,SCC,99.2
+Palo Alto,297.2,SCC,99.0
+Parkview,42.5,SCC,33.4
+San Francisco Bay Trail,260.8,SCC,62.7
+San Jose,2618.7,SCC,27.7
+Santa Clara,348,SCC,34
+San Martin,35.3,SCC,31.1
+Saratoga,180,SCC,29.7
+Seven Trees,40.9,SCC,33.6
+Spartan Keyes,64.3,SCC,35.7
+Sunnyvale,357,SCC,52.4
+Willow Glen,81.6,SCC,34.9
+Willow Glen South,63.3,SCC,30.5
+#,,,
+# Alameda County,,,
+#,,,
+Alameda,206.7,ALA,12.2
+Albany,42.7,ALA,6.8
+Ashland,35.1,ALA,35.7
+Berkeley,260.3,ALA,7.4
+Cherryland,20.9,ALA,27.1
+Emeryville,28.1,ALA,8
+Fremont,780.2,ALA,32.2
+Hayward,444.5,ALA,30.0
+Hayward Acres,3.5,ALA,43.3
+Newark,147,ALA,45.9
+San Leandro,230.6,ALA,27.5
+San Lorenzo,55.5,ALA,40.8
+Union City,208.8,ALA,30.7
+#,,,
+# SF County,,SFC,
+#,,,
+Aquatic Park Fort Mason,6.4,SFC,15.4
+Ashbury Heights,3.7,SFC,13
+Balboa Terrace,3.4,SFC,18.2
+Central Waterfront,10.2,SFC,6
+Clarendon Heights,6,SFC,14.2
+Cole Valley,1.7,SFC,18
+Cow Hollow,12,SFC,11.9
+Dogpatch,5.1,SFC,12.3
+Financial District,9.4,SFC,10.2
+Fisherman's Wharf,6.2,SFC,13.8
+Forest Hill,6.1,SFC,15.9
+Golden Gate Heights,17.8,SFC,10.7
+Golden Gate Park,40.8,SFC,29.4
+Lake Street,3.9,SFC,36.8
+Lincoln Park,4.5,SFC,39.6
+Little Hollywood,3.7,SFC,15.2
+Mission Bay,13.8,SFC,8.6
+Northern Waterfront,5.6,SFC,15.5
+Pacific Heights,18,SFC,10.7
+Panhandle,7.3,SFC,20.6
+Polk Gulch,4,SFC,18.2
+Presidio Heights,6.5,SFC,21.6
+Presidio National Park,43.5,SFC,26.7
+Presidio Terrace,2.8,SFC,43.9
+Seacliff,4.1,SFC,29.3
+South Beach,4.8,SFC,37.4
+Sutro Heights,7.1,SFC,13.2
+#,,,
+# Far Away Places,,,
+#,,,
+Barangaroo,1.7,NSW,47.3
+Bodega Bay,28.9,SON,17
+Cambridge,180.8,MAS,6.2
+Castle Rock State Park,11.2,SCC,51.2
+Corte Madera,51,MAR,12.9
+Dawes Point,1.8,NSW,29.2
+Forest of Nisene Marks SP,44.0,SCC,30.7
+Guerneville,22.7,SON,23.6
+Healdsburg,53.7,SON,17.8
+Marin Headlands GGNRA,65.7,MAR,31.9
+Mill Valley,92.2,MAR,9.1
+Millers Point,3.2,NSW,34.3
+MIT,9.6,MAS,34.7
+Mokelumne Hill,14.7,CAL,26.8
+Mt Tamalpais State Park,31.7,MAR,38.7
+Muir Beach,4.6,MAR,37.1
+Rosie Riveter Park,5.5,CCC,29.3
+San Rafael,260,MAR,3.7
+Sausalito,32.7,MAR,12.9
+Stinson Beach,11.2,MAR,32.9
+#,,,
+# Counties and Bigger,,,
+#,,,
+San Mateo County,2814,---,61.56
+Santa Clara County,7569,---,32.8
+Alameda County,5818,---,15.38
+Marin County,2333,---,10.75
+San Francisco County,1217,---,7.21
+Napa County,1609,---,4.85
+Sonoma County,4895,---,5.12
+Santa Cruz County,2718,---,7.12
+Contra Costa County,5945,---,2.05
+#,,,
+California,377037,---,1.7108
+USA,6406754,---,0.10677
+Earth,41974536,---,0.017159
\ No newline at end of file
diff --git a/ipynb/bikerides.tsv b/ipynb/bikerides.tsv
index 61c4dac..77204ae 100644
--- a/ipynb/bikerides.tsv
+++ b/ipynb/bikerides.tsv
@@ -1,5 +1,25 @@
date year title hours miles feet
-##### 2019-2022: Mostly Eddington rides; most recent first
+##### 2019-2023: Mostly Eddington rides; most recent first
+Sat, 10/7 2023 Morning Ride 5:33:41 70.25 3,038
+Sat, 9/23 2023 Dumbarton / Niles 4:45:06 65.02 1,122
+Sat, 9/9 2023 Alameda to Treasure Island 4:43:08 62.97 1,378
+Fri, 9/1 2023 Santa Cruz + Ladera 6:14:38 67.59 4,990
+Sat, 8/12 2023 Norris Canyon + Ladera 6:53:48 74.97 5,470
+Fri, 7/28 2023 San Carlos completed 5:24:45 69.49 4,479
+Wed, 7/19 2023 MTV + RWC 5:22:20 64.14 1,145
+Wed, 6/28 2023 Los Altos Hills + MTV 5:51:01 62.77 1,687
+Tue, 6/20 2023 Ed Levin Park with Ted MTB 6:10:19 66.55 4,547
+Fri, 6/9 2023 Completed Los Altos and 50% Sunnyvale 5:37:26 67.20 1,266
+Wed, 6/7 2023 Los Altos 7:03:03 81.54 2,110
+Fri, 6/2 2023 Los Altos Hills 6:47:13 68.33 5,221
+Tue, 5/16 2023 Los Altos Hills paths 5:56:04 64.03 1,869
+Mon, 5/1 2023 Eden Landing 5:43:00 64.24 1,425
+Sun, 4/23 2023 Portola Loop with Juliet 5:09:21 62.43 2,805
+Sun, 3/26 2023 Redwood City 5:42:04 69.11 1,015
+Sat, 3/18 2023 Mostly Palo Alto 5:54:50 69.31 2,549
+Wed, 12/21 2022 Canada / Portola 4:18:54 62.83 2,226
+Sat, 11/19 2022 Cañada 5:02:48 63.06 3,340
+Fri, 10/21 2022 Alviso Levees + Seaport 5:06:26 67.14 931
Sat, 9/17 2022 San Gregorio / Tunitas 6:33:44 80.53 6,015
Thu, 7/14 2022 Bike Hut Classic 6:36:30 74.16 6,070
Sat, 7/9 2022 Purisima Creek Trail 7:20:20 62.93 7,139