{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
Peter Norvig, Oct 2017
Last update: Jan 2024
\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. In this notebook I track [my cycling performance](https://www.strava.com/athletes/575579) against various goals:\n", "- **Distance**: I do about 6,000 miles a year.\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 68 miles or more on 68 different days. So 68 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." ] }, { "cell_type": "markdown", "metadata": {}, "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. The columns keep track of the total **hours** on the bike, distance traveled in **miles**, and total **feet** climbed. Then there are some columns that are dervided from these: **mph** is **miles / hour**; **vam** is vertical meters ascended per hour (or **feet × 0.3048 / hours**); **fpmi** is **feet / miles**; **pct** is the grade in percent (or **feet × 100 / miles / 5280**), and finally **kms** and **meters** are the metric equivalents of **miles** and **feet**.\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearhoursmilesfeetmphvamfpmipctkmsmeters
2023541.68631624310011.66137.038.00.7310162.4474097.0
2022532.93602836232311.31207.060.01.149699.05110436.0
2021490.53606419663412.36122.032.00.619756.9859934.0
2020438.8853419477712.1766.018.00.348593.6728888.0
2019476.32601614979712.6396.025.00.479679.7445658.0
2018475.93610115864212.82102.026.00.499816.5148354.0
2017567.33735620209612.97109.027.00.5211835.8061599.0
2016486.38633920145313.03126.032.00.6010199.4561403.0
2015419.95545220985912.98152.038.00.738772.2763965.0
2014191.03246911848112.92189.048.00.913972.6236113.0
\n", "
" ], "text/plain": [ " year hours miles feet mph vam fpmi pct kms meters\n", " 2023 541.68 6316 243100 11.66 137.0 38.0 0.73 10162.44 74097.0\n", " 2022 532.93 6028 362323 11.31 207.0 60.0 1.14 9699.05 110436.0\n", " 2021 490.53 6064 196634 12.36 122.0 32.0 0.61 9756.98 59934.0\n", " 2020 438.88 5341 94777 12.17 66.0 18.0 0.34 8593.67 28888.0\n", " 2019 476.32 6016 149797 12.63 96.0 25.0 0.47 9679.74 45658.0\n", " 2018 475.93 6101 158642 12.82 102.0 26.0 0.49 9816.51 48354.0\n", " 2017 567.33 7356 202096 12.97 109.0 27.0 0.52 11835.80 61599.0\n", " 2016 486.38 6339 201453 13.03 126.0 32.0 0.60 10199.45 61403.0\n", " 2015 419.95 5452 209859 12.98 152.0 38.0 0.73 8772.27 63965.0\n", " 2014 191.03 2469 118481 12.92 189.0 48.0 0.91 3972.62 36113.0" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%run BikeCode.ipynb\n", "\n", "yearly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here's the same data on a per day basis, assuming I ride 6 days a week:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearhoursmilesfeetmphvamfpmipctkmsmeters
20231.720.2779.211.66137.038.00.7332.6237.5
20221.719.31161.311.31207.060.01.1431.1354.0
20211.619.4630.212.36122.032.00.6131.3192.1
20201.417.1303.812.1766.018.00.3427.592.6
20191.519.3480.112.6396.025.00.4731.0146.3
20181.519.6508.512.82102.026.00.4931.5155.0
20171.823.6647.712.97109.027.00.5237.9197.4
20161.620.3645.713.03126.032.00.6032.7196.8
20151.317.5672.612.98152.038.00.7328.1205.0
20140.67.9379.712.92189.048.00.9112.7115.7
\n", "
" ], "text/plain": [ " year hours miles feet mph vam fpmi pct kms meters\n", " 2023 1.7 20.2 779.2 11.66 137.0 38.0 0.73 32.6 237.5\n", " 2022 1.7 19.3 1161.3 11.31 207.0 60.0 1.14 31.1 354.0\n", " 2021 1.6 19.4 630.2 12.36 122.0 32.0 0.61 31.3 192.1\n", " 2020 1.4 17.1 303.8 12.17 66.0 18.0 0.34 27.5 92.6\n", " 2019 1.5 19.3 480.1 12.63 96.0 25.0 0.47 31.0 146.3\n", " 2018 1.5 19.6 508.5 12.82 102.0 26.0 0.49 31.5 155.0\n", " 2017 1.8 23.6 647.7 12.97 109.0 27.0 0.52 37.9 197.4\n", " 2016 1.6 20.3 645.7 13.03 126.0 32.0 0.60 32.7 196.8\n", " 2015 1.3 17.5 672.6 12.98 152.0 38.0 0.73 28.1 205.0\n", " 2014 0.6 7.9 379.7 12.92 189.0 48.0 0.91 12.7 115.7" ] }, "execution_count": 46, "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 the [Kármán line](https://en.wikipedia.org/wiki/K%C3%A1rm%C3%A1n_line) is 100 kilometers; in 2022 I surpassed 100 kilometers of climbing (over 1,100 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), [Statshunter](https://www.statshunters.com/), [RideEveryTile](https://rideeverytile.com/), and [SquadRats](https://squadrats.com/map) 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 tiles). \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 into my main cluster (such as connecting San Francisco and Marin). I have a [separate page](???) documenting my explorations, but here are a few key points along the way:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 datesquareclustertotalcomment
02/25/20241411963279Expanding through Santa Cruz and to the South
01/01/20241410563105Start of this year
12/08/20231410423084Benicia ride connects East Bay and Napa clusters
11/05/2023149322914Alum Rock ride gets 14x14 max square
06/30/2023136892640Rides in east Bay fill in holes
04/14/2023136302595Black Sands Beach low-tide hike connects Marin to max cluster
03/04/2023135832574Almaden rides connects Gilroy to max cluster
10/22/2022133962495Alviso levees to get to 13x13 max square
10/16/2022123932492Milpitas ride connects East Bay to max cluster
09/08/2022113002487First started tracking tiles
\n" ], "text/plain": [ "" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiles" ] }, { "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 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 percent of roads/trails that I have traveled on for each place (specified by **county** and city **name**), as well as the **total** miles of road in the place, the miles I have **done**, and the amount I need to hit the **next badge**. " ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
pctcountynametotaldoneto next badge
100.0%SMCLos Trancos Woods5.35.3
100.0%SMCMenlo Oaks3.53.5
100.0%SMCLos Trancos OSP0.30.3
100.0%SMCLadera8.18.1
100.0%SMCKensington Square0.60.6
100.0%SMCNorth Fair Oaks26.727
100.0%SMCWest Menlo Park11.211
100.0%SMCSequoia Tract11.011
99.9%SCCLoyola18.318
99.9%SMCPalomar Park4.04.0
99.9%SMCEmerald Lake Hills24.625
99.8%SMCAtherton56.356
99.8%SMCWindy Hill Preserve4.14.1
99.7%SMCMenlo Park139.5139
99.7%SMCEast Palo Alto48.348
99.6%SMCSky Londa11.812
99.6%SCCLos Altos138.2138
99.5%SMCPortola Valley48.248
99.4%SMCWoodside75.275
99.3%SMCRedwood City240.5239
99.3%SCCMountain View208.1207
99.2%SCCLos Altos Hills91.391
99.0%SCCPalo Alto297.2294
99.0%SMCSan Carlos99.098
90.9%SMCBurleigh Murray Park2.11.90.2 mi to 99%
90.4%SMCFoster City150.013613 mi to 99%
86.8%SCCFoothills Preserve1.11.00.0 mi to 90%
77.9%SMCSan Mateo Highlands18.0142.2 mi to 90%
74.5%SMCSkyline Ridge OSP0.80.60.1 mi to 90%
74.1%SCCSan Francisco Bay Trail260.819341 mi to 90%
73.2%CCCRosie Riveter Park5.54.00.9 mi to 90%
71.5%SMCBurlingame Hills6.04.31.1 mi to 90%
66.7%SMCCoal Creek Preserve3.92.60.9 mi to 90%
63.5%---San Mateo County2814.01,787746 mi to 90%
59.5%SMCRussian Ridge Preserve12.27.33.7 mi to 90%
59.3%SMCMontara27.8168.5 mi to 90%
56.2%SMCBurlingame88.45030 mi to 90%
54.7%SMCBelmont98.15435 mi to 90%
53.3%SCCSunnyvale357.0190131 mi to 90%
52.3%SMCHillsborough85.34532 mi to 90%
51.8%SMCSan Mateo256.013398 mi to 90%
51.5%SMCHalf Moon Bay State Beach4.42.31.7 mi to 90%
51.2%SMCLong Ridge Preserve11.05.64.3 mi to 90%
51.2%SCCCastle Rock State Park11.25.74.3 mi to 90%
51.0%ALANewark147.07557 mi to 90%
50.2%SMCBrisbane40.92116 mi to 90%
47.7%SCCEdenvale30.0140.7 mi to 50%
47.3%NSWBarangaroo1.70.80.0 mi to 50%
47.2%SCCGardner23.4110.7 mi to 50%
45.1%SCCMonte Sereno20.49.21.0 mi to 50%
43.9%SFCPresidio Terrace2.81.20.2 mi to 50%
43.6%SMCEl Granada49.2213.1 mi to 50%
43.6%SMCMoss Beach19.78.61.3 mi to 50%
43.3%ALAHayward Acres3.51.50.2 mi to 50%
42.6%SMCMillbrae65.0284.8 mi to 50%
40.8%ALASan Lorenzo55.5235.1 mi to 50%
39.6%SFCLincoln Park4.51.80.5 mi to 50%
39.5%SCCCommunications Hill27.8112.9 mi to 50%
39.0%SMCPurisima Creek Preserve16.56.41.8 mi to 50%
38.9%SMCColma13.75.31.5 mi to 50%
38.7%MARMt Tamalpais State Park31.7123.6 mi to 50%
38.2%SMCBroadmoor8.83.41.0 mi to 50%
37.4%SFCSouth Beach4.81.80.6 mi to 50%
37.1%MARMuir Beach4.61.70.6 mi to 50%
36.8%SFCLake Street3.91.40.5 mi to 50%
36.8%SCCSpartan Keyes64.3248.5 mi to 50%
36.7%SCCMilpitas224.08230 mi to 50%
36.1%ALAAshland35.1134.9 mi to 50%
36.0%SCCWillow Glen81.62911 mi to 50%
34.8%SCCSanta Clara348.012153 mi to 50%
34.7%MASMIT9.63.31.5 mi to 50%
34.3%NSWMillers Point3.21.10.5 mi to 50%
33.7%SCCParkview42.5146.9 mi to 50%
33.6%SMCHalf Moon Bay68.02311 mi to 50%
33.6%SCCLos Gatos148.05024 mi to 50%
33.6%SCCCupertino172.05828 mi to 50%
33.2%SMCPacifica150.95025 mi to 50%
33.0%---Santa Clara County7569.02,4981,287 mi to 50%
33.0%SCCSeven Trees40.9137.0 mi to 50%
32.9%ALAUnion City208.86936 mi to 50%
32.9%MARStinson Beach11.23.71.9 mi to 50%
32.9%ALAFremont780.2257133 mi to 50%
32.7%SCCBranham44.0147.6 mi to 50%
32.7%ALAHayward444.514577 mi to 50%
31.9%MARMarin Headlands GGNRA65.72112 mi to 50%
31.5%SCCSan Martin35.3116.5 mi to 50%
31.0%SMCSan Bruno114.03522 mi to 50%
30.9%SCCWillow Glen South63.32012 mi to 50%
30.7%SCCForest of Nisene Marks SP44.0148.5 mi to 50%
30.0%SCCSaratoga180.05436 mi to 50%
29.6%SMCSouth San Francisco185.35538 mi to 50%
29.4%SFCGolden Gate Park40.8128.4 mi to 50%
29.3%SCCCampbell119.03525 mi to 50%
29.3%SFCSeacliff4.11.20.8 mi to 50%
29.2%NSWDawes Point1.80.50.4 mi to 50%
28.2%ALAFairview34.49.77.5 mi to 50%
28.0%SCCSan Jose2618.7733576 mi to 50%
27.8%ALACherryland20.95.84.6 mi to 50%
27.7%ALASan Leandro230.66451 mi to 50%
27.4%SMCDaly City148.14133 mi to 50%
26.8%CALMokelumne Hill14.73.93.4 mi to 50%
26.7%SFCPresidio National Park43.51210 mi to 50%
26.1%ALACastro Valley192.55046 mi to 50%
25.6%SMCBay Area Ridge Trail395.610197 mi to 50%
23.6%SONGuerneville22.75.40.3 mi to 25%
21.6%SFCPresidio Heights6.51.40.2 mi to 25%
20.6%SFCPanhandle7.31.50.3 mi to 25%
18.2%SFCBalboa Terrace3.40.60.2 mi to 25%
18.2%SFCPolk Gulch4.00.70.3 mi to 25%
18.0%SFCCole Valley1.70.30.1 mi to 25%
17.8%SONHealdsburg53.79.63.9 mi to 25%
17.0%SONBodega Bay28.94.92.3 mi to 25%
16.6%---Alameda County5818.0965490 mi to 25%
15.9%SFCForest Hill6.11.00.6 mi to 25%
15.5%SFCNorthern Waterfront5.60.90.5 mi to 25%
15.4%SFCAquatic Park Fort Mason6.41.00.6 mi to 25%
15.2%SFCLittle Hollywood3.70.60.4 mi to 25%
14.2%SFCClarendon Heights6.00.90.6 mi to 25%
13.8%SFCFisherman's Wharf6.20.90.7 mi to 25%
13.2%SFCSutro Heights7.10.90.8 mi to 25%
13.0%SFCAshbury Heights3.70.50.4 mi to 25%
12.9%MARCorte Madera51.06.66.2 mi to 25%
12.9%MARSausalito32.74.24.0 mi to 25%
12.3%SFCDogpatch5.10.60.6 mi to 25%
12.2%ALAAlameda206.72526 mi to 25%
12.1%SCCGilroy188.92324 mi to 25%
11.9%SFCCow Hollow12.01.41.6 mi to 25%
10.9%---Marin County2333.0255328 mi to 25%
10.7%SFCPacific Heights18.01.92.6 mi to 25%
10.7%SFCGolden Gate Heights17.81.92.5 mi to 25%
10.2%SFCFinancial District9.41.01.4 mi to 25%
9.3%---San Francisco County1217.0113192 mi to 25%
9.1%MARMill Valley92.28.415 mi to 25%
8.9%---Napa County1609.0143259 mi to 25%
8.6%SFCMission Bay13.81.22.3 mi to 25%
7.7%ALAEmeryville28.12.24.9 mi to 25%
7.6%ALABerkeley260.32045 mi to 25%
7.1%---Santa Cruz County2718.0194486 mi to 25%
6.8%ALAAlbany42.72.97.8 mi to 25%
6.2%MASCambridge180.81134 mi to 25%
6.0%SFCCentral Waterfront10.20.61.9 mi to 25%
5.1%---Sonoma County4895.0251973 mi to 25%
3.8%---Contra Costa County5945.02261,260 mi to 25%
3.7%MARSan Rafael260.09.655 mi to 25%
1.8%---California377037.06,719822 mi to 2%
0.113%---USA6406754.07,2675,546 mi to 0.2%
0.017%---Earth41974536.07,1591,236 mi to 0.02%
\n", "
" ], "text/plain": [ " pct county name total done \\\n", " 100.0% SMC Los Trancos Woods 5.3 5.3 \n", " 100.0% SMC Menlo Oaks 3.5 3.5 \n", " 100.0% SMC Los Trancos OSP 0.3 0.3 \n", " 100.0% SMC Ladera 8.1 8.1 \n", " 100.0% SMC Kensington Square 0.6 0.6 \n", " 100.0% SMC North Fair Oaks 26.7 27 \n", " 100.0% SMC West Menlo Park 11.2 11 \n", " 100.0% SMC Sequoia Tract 11.0 11 \n", " 99.9% SCC Loyola 18.3 18 \n", " 99.9% SMC Palomar Park 4.0 4.0 \n", " 99.9% SMC Emerald Lake Hills 24.6 25 \n", " 99.8% SMC Atherton 56.3 56 \n", " 99.8% SMC Windy Hill Preserve 4.1 4.1 \n", " 99.7% SMC Menlo Park 139.5 139 \n", " 99.7% SMC East Palo Alto 48.3 48 \n", " 99.6% SMC Sky Londa 11.8 12 \n", " 99.6% SCC Los Altos 138.2 138 \n", " 99.5% SMC Portola Valley 48.2 48 \n", " 99.4% SMC Woodside 75.2 75 \n", " 99.3% SMC Redwood City 240.5 239 \n", " 99.3% SCC Mountain View 208.1 207 \n", " 99.2% SCC Los Altos Hills 91.3 91 \n", " 99.0% SCC Palo Alto 297.2 294 \n", " 99.0% SMC San Carlos 99.0 98 \n", " 90.9% SMC Burleigh Murray Park 2.1 1.9 \n", " 90.4% SMC Foster City 150.0 136 \n", " 86.8% SCC Foothills Preserve 1.1 1.0 \n", " 77.9% SMC San Mateo Highlands 18.0 14 \n", " 74.5% SMC Skyline Ridge OSP 0.8 0.6 \n", " 74.1% SCC San Francisco Bay Trail 260.8 193 \n", " 73.2% CCC Rosie Riveter Park 5.5 4.0 \n", " 71.5% SMC Burlingame Hills 6.0 4.3 \n", " 66.7% SMC Coal Creek Preserve 3.9 2.6 \n", " 63.5% --- San Mateo County 2814.0 1,787 \n", " 59.5% SMC Russian Ridge Preserve 12.2 7.3 \n", " 59.3% SMC Montara 27.8 16 \n", " 56.2% SMC Burlingame 88.4 50 \n", " 54.7% SMC Belmont 98.1 54 \n", " 53.3% SCC Sunnyvale 357.0 190 \n", " 52.3% SMC Hillsborough 85.3 45 \n", " 51.8% SMC San Mateo 256.0 133 \n", " 51.5% SMC Half Moon Bay State Beach 4.4 2.3 \n", " 51.2% SMC Long Ridge Preserve 11.0 5.6 \n", " 51.2% SCC Castle Rock State Park 11.2 5.7 \n", " 51.0% ALA Newark 147.0 75 \n", " 50.2% SMC Brisbane 40.9 21 \n", " 47.7% SCC Edenvale 30.0 14 \n", " 47.3% NSW Barangaroo 1.7 0.8 \n", " 47.2% SCC Gardner 23.4 11 \n", " 45.1% SCC Monte Sereno 20.4 9.2 \n", " 43.9% SFC Presidio Terrace 2.8 1.2 \n", " 43.6% SMC El Granada 49.2 21 \n", " 43.6% SMC Moss Beach 19.7 8.6 \n", " 43.3% ALA Hayward Acres 3.5 1.5 \n", " 42.6% SMC Millbrae 65.0 28 \n", " 40.8% ALA San Lorenzo 55.5 23 \n", " 39.6% SFC Lincoln Park 4.5 1.8 \n", " 39.5% SCC Communications Hill 27.8 11 \n", " 39.0% SMC Purisima Creek Preserve 16.5 6.4 \n", " 38.9% SMC Colma 13.7 5.3 \n", " 38.7% MAR Mt Tamalpais State Park 31.7 12 \n", " 38.2% SMC Broadmoor 8.8 3.4 \n", " 37.4% SFC South Beach 4.8 1.8 \n", " 37.1% MAR Muir Beach 4.6 1.7 \n", " 36.8% SFC Lake Street 3.9 1.4 \n", " 36.8% SCC Spartan Keyes 64.3 24 \n", " 36.7% SCC Milpitas 224.0 82 \n", " 36.1% ALA Ashland 35.1 13 \n", " 36.0% SCC Willow Glen 81.6 29 \n", " 34.8% SCC Santa Clara 348.0 121 \n", " 34.7% MAS MIT 9.6 3.3 \n", " 34.3% NSW Millers Point 3.2 1.1 \n", " 33.7% SCC Parkview 42.5 14 \n", " 33.6% SMC Half Moon Bay 68.0 23 \n", " 33.6% SCC Los Gatos 148.0 50 \n", " 33.6% SCC Cupertino 172.0 58 \n", " 33.2% SMC Pacifica 150.9 50 \n", " 33.0% --- Santa Clara County 7569.0 2,498 \n", " 33.0% SCC Seven Trees 40.9 13 \n", " 32.9% ALA Union City 208.8 69 \n", " 32.9% MAR Stinson Beach 11.2 3.7 \n", " 32.9% ALA Fremont 780.2 257 \n", " 32.7% SCC Branham 44.0 14 \n", " 32.7% ALA Hayward 444.5 145 \n", " 31.9% MAR Marin Headlands GGNRA 65.7 21 \n", " 31.5% SCC San Martin 35.3 11 \n", " 31.0% SMC San Bruno 114.0 35 \n", " 30.9% SCC Willow Glen South 63.3 20 \n", " 30.7% SCC Forest of Nisene Marks SP 44.0 14 \n", " 30.0% SCC Saratoga 180.0 54 \n", " 29.6% SMC South San Francisco 185.3 55 \n", " 29.4% SFC Golden Gate Park 40.8 12 \n", " 29.3% SCC Campbell 119.0 35 \n", " 29.3% SFC Seacliff 4.1 1.2 \n", " 29.2% NSW Dawes Point 1.8 0.5 \n", " 28.2% ALA Fairview 34.4 9.7 \n", " 28.0% SCC San Jose 2618.7 733 \n", " 27.8% ALA Cherryland 20.9 5.8 \n", " 27.7% ALA San Leandro 230.6 64 \n", " 27.4% SMC Daly City 148.1 41 \n", " 26.8% CAL Mokelumne Hill 14.7 3.9 \n", " 26.7% SFC Presidio National Park 43.5 12 \n", " 26.1% ALA Castro Valley 192.5 50 \n", " 25.6% SMC Bay Area Ridge Trail 395.6 101 \n", " 23.6% SON Guerneville 22.7 5.4 \n", " 21.6% SFC Presidio Heights 6.5 1.4 \n", " 20.6% SFC Panhandle 7.3 1.5 \n", " 18.2% SFC Balboa Terrace 3.4 0.6 \n", " 18.2% SFC Polk Gulch 4.0 0.7 \n", " 18.0% SFC Cole Valley 1.7 0.3 \n", " 17.8% SON Healdsburg 53.7 9.6 \n", " 17.0% SON Bodega Bay 28.9 4.9 \n", " 16.6% --- Alameda County 5818.0 965 \n", " 15.9% SFC Forest Hill 6.1 1.0 \n", " 15.5% SFC Northern Waterfront 5.6 0.9 \n", " 15.4% SFC Aquatic Park Fort Mason 6.4 1.0 \n", " 15.2% SFC Little Hollywood 3.7 0.6 \n", " 14.2% SFC Clarendon Heights 6.0 0.9 \n", " 13.8% SFC Fisherman's Wharf 6.2 0.9 \n", " 13.2% SFC Sutro Heights 7.1 0.9 \n", " 13.0% SFC Ashbury Heights 3.7 0.5 \n", " 12.9% MAR Corte Madera 51.0 6.6 \n", " 12.9% MAR Sausalito 32.7 4.2 \n", " 12.3% SFC Dogpatch 5.1 0.6 \n", " 12.2% ALA Alameda 206.7 25 \n", " 12.1% SCC Gilroy 188.9 23 \n", " 11.9% SFC Cow Hollow 12.0 1.4 \n", " 10.9% --- Marin County 2333.0 255 \n", " 10.7% SFC Pacific Heights 18.0 1.9 \n", " 10.7% SFC Golden Gate Heights 17.8 1.9 \n", " 10.2% SFC Financial District 9.4 1.0 \n", " 9.3% --- San Francisco County 1217.0 113 \n", " 9.1% MAR Mill Valley 92.2 8.4 \n", " 8.9% --- Napa County 1609.0 143 \n", " 8.6% SFC Mission Bay 13.8 1.2 \n", " 7.7% ALA Emeryville 28.1 2.2 \n", " 7.6% ALA Berkeley 260.3 20 \n", " 7.1% --- Santa Cruz County 2718.0 194 \n", " 6.8% ALA Albany 42.7 2.9 \n", " 6.2% MAS Cambridge 180.8 11 \n", " 6.0% SFC Central Waterfront 10.2 0.6 \n", " 5.1% --- Sonoma County 4895.0 251 \n", " 3.8% --- Contra Costa County 5945.0 226 \n", " 3.7% MAR San Rafael 260.0 9.6 \n", " 1.8% --- California 377037.0 6,719 \n", " 0.113% --- USA 6406754.0 7,267 \n", " 0.017% --- Earth 41974536.0 7,159 \n", "\n", " to next badge \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 0.2 mi to 99% \n", " 13 mi to 99% \n", " 0.0 mi to 90% \n", " 2.2 mi to 90% \n", " 0.1 mi to 90% \n", " 41 mi to 90% \n", " 0.9 mi to 90% \n", " 1.1 mi to 90% \n", " 0.9 mi to 90% \n", " 746 mi to 90% \n", " 3.7 mi to 90% \n", " 8.5 mi to 90% \n", " 30 mi to 90% \n", " 35 mi to 90% \n", " 131 mi to 90% \n", " 32 mi to 90% \n", " 98 mi to 90% \n", " 1.7 mi to 90% \n", " 4.3 mi to 90% \n", " 4.3 mi to 90% \n", " 57 mi to 90% \n", " 16 mi to 90% \n", " 0.7 mi to 50% \n", " 0.0 mi to 50% \n", " 0.7 mi to 50% \n", " 1.0 mi to 50% \n", " 0.2 mi to 50% \n", " 3.1 mi to 50% \n", " 1.3 mi to 50% \n", " 0.2 mi to 50% \n", " 4.8 mi to 50% \n", " 5.1 mi to 50% \n", " 0.5 mi to 50% \n", " 2.9 mi to 50% \n", " 1.8 mi to 50% \n", " 1.5 mi to 50% \n", " 3.6 mi to 50% \n", " 1.0 mi to 50% \n", " 0.6 mi to 50% \n", " 0.6 mi to 50% \n", " 0.5 mi to 50% \n", " 8.5 mi to 50% \n", " 30 mi to 50% \n", " 4.9 mi to 50% \n", " 11 mi to 50% \n", " 53 mi to 50% \n", " 1.5 mi to 50% \n", " 0.5 mi to 50% \n", " 6.9 mi to 50% \n", " 11 mi to 50% \n", " 24 mi to 50% \n", " 28 mi to 50% \n", " 25 mi to 50% \n", " 1,287 mi to 50% \n", " 7.0 mi to 50% \n", " 36 mi to 50% \n", " 1.9 mi to 50% \n", " 133 mi to 50% \n", " 7.6 mi to 50% \n", " 77 mi to 50% \n", " 12 mi to 50% \n", " 6.5 mi to 50% \n", " 22 mi to 50% \n", " 12 mi to 50% \n", " 8.5 mi to 50% \n", " 36 mi to 50% \n", " 38 mi to 50% \n", " 8.4 mi to 50% \n", " 25 mi to 50% \n", " 0.8 mi to 50% \n", " 0.4 mi to 50% \n", " 7.5 mi to 50% \n", " 576 mi to 50% \n", " 4.6 mi to 50% \n", " 51 mi to 50% \n", " 33 mi to 50% \n", " 3.4 mi to 50% \n", " 10 mi to 50% \n", " 46 mi to 50% \n", " 97 mi to 50% \n", " 0.3 mi to 25% \n", " 0.2 mi to 25% \n", " 0.3 mi to 25% \n", " 0.2 mi to 25% \n", " 0.3 mi to 25% \n", " 0.1 mi to 25% \n", " 3.9 mi to 25% \n", " 2.3 mi to 25% \n", " 490 mi to 25% \n", " 0.6 mi to 25% \n", " 0.5 mi to 25% \n", " 0.6 mi to 25% \n", " 0.4 mi to 25% \n", " 0.6 mi to 25% \n", " 0.7 mi to 25% \n", " 0.8 mi to 25% \n", " 0.4 mi to 25% \n", " 6.2 mi to 25% \n", " 4.0 mi to 25% \n", " 0.6 mi to 25% \n", " 26 mi to 25% \n", " 24 mi to 25% \n", " 1.6 mi to 25% \n", " 328 mi to 25% \n", " 2.6 mi to 25% \n", " 2.5 mi to 25% \n", " 1.4 mi to 25% \n", " 192 mi to 25% \n", " 15 mi to 25% \n", " 259 mi to 25% \n", " 2.3 mi to 25% \n", " 4.9 mi to 25% \n", " 45 mi to 25% \n", " 486 mi to 25% \n", " 7.8 mi to 25% \n", " 34 mi to 25% \n", " 1.9 mi to 25% \n", " 973 mi to 25% \n", " 1,260 mi to 25% \n", " 55 mi to 25% \n", " 822 mi to 2% \n", " 5,546 mi to 0.2% \n", " 1,236 mi to 0.02% " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "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 SCC and Megan Gardner is 1,000 miles ahead of me in SMC. Barry Mann is the leader in total miles in the two counties, and Megan leads in 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 December 2023), where the dotted line indicates the Pareto front." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameInitialsSMC %SCC %SMC milesSCC milesTotal milesAvg %
Megan GardnerMG99.0113.6027861029381556.305
Barry MannBM77.4130.3821782299447753.895
Peter NorvigPN63.5033.0017872498428548.250
Brian FeinbergBF32.5043.909153323423838.200
Jason MolendaJM7.5656.252134258447131.905
\n", "
" ], "text/plain": [ " Name Initials SMC % SCC % SMC miles SCC miles Total miles \\\n", " Megan Gardner MG 99.01 13.60 2786 1029 3815 \n", " Barry Mann BM 77.41 30.38 2178 2299 4477 \n", " Peter Norvig PN 63.50 33.00 1787 2498 4285 \n", " Brian Feinberg BF 32.50 43.90 915 3323 4238 \n", " Jason Molenda JM 7.56 56.25 213 4258 4471 \n", "\n", " Avg % \n", " 56.305 \n", " 53.895 \n", " 48.250 \n", " 38.200 \n", " 31.905 " ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pareto_front(leaders)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Eddington Number\n", "\n", "The physicist/bicyclist [Sir Arthur Eddington](https://en.wikipedia.org/wiki/Arthur_Eddington), a contemporary of Einstein defined the [**Eddington Number**](https://www.triathlete.com/2011/04/training/measuring-bike-miles-eddington-number_301789) as the largest integer **E** such that you have cycled at least **E** miles on at least **E** days.\n", "\n", "My Eddington number progress over the years, in both kilometers and miles:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearEd_kmEd_mi
202410268
202310167
20229666
20219365
20208762
20198056
20187754
20177351
20166747
20156142
20144635
\n", "
" ], "text/plain": [ " year Ed_km Ed_mi\n", " 2024 102 68\n", " 2023 101 67\n", " 2022 96 66\n", " 2021 93 65\n", " 2020 87 62\n", " 2019 80 56\n", " 2018 77 54\n", " 2017 73 51\n", " 2016 67 47\n", " 2015 61 42\n", " 2014 46 35" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ed_progress(rides)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My current Eddington Number is **102** in kilometers and **68** in miles (I've ridden at least 68 miles on at least 68 days, but not 69 miles on 69 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, weather, bicycles, and navigation aids were not nearly as nice as mine, so bravo zulu to him. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many more rides will I need to reach higher Eddington numbers? I call that the *Eddington Gap*:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
kmskms gapmilesmiles gap
10336912
104107024
105167134
106207237
107287341
108327443
109387551
110467653
111547756
\n", "
" ], "text/plain": [ " kms kms gap miles miles gap\n", " 103 3 69 12\n", " 104 10 70 24\n", " 105 16 71 34\n", " 106 20 72 37\n", " 107 28 73 41\n", " 108 32 74 43\n", " 109 38 75 51\n", " 110 46 76 53\n", " 111 54 77 56" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ed_gaps(rides)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I need 3 rides of 103 kms or 12 rides of 69 miles to increase my Eddington numbers.\n", "\n", "Here are some properties of Eddington numbers:\n", "- Your Eddington number is monotonic: it can never decrease over time. \n", "- To improve from an Eddington number of *n* to *n* + 1 can take as few as 1 ride, or as many as *n* + 1 rides.\n", " + *Suppose you have done 9 rides, each of exactly 10 miles. Your Eddington number is 9.*\n", " + *You would need 1 ride of 10 miles to improve from a number of 9 to 10.*\n", " + *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.\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", "\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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Hill-Index: Speed versus Grade on Short Climbs\n", "\n", "The Eddington number reminds me of the [**h-index**](https://en.wikipedia.org/wiki/H-index) metric for scientific publications. I invented another metric:\n", "\n", "> *Your **hill-index** is the maximum integer **h** where you can regularly climb an **h** percent grade at **h** miles per hour.*\n", "\n", "I'll plot grade versus speed for segments (not rides) with two best-fit curves: a blue quadratic and an orange cubic. I'll also superimpose a red dotted line where grade = speed." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAGDCAYAAADQ9S0AAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACsc0lEQVR4nOydd3xb1f2/nyN575XEcYazE0IICQl771HC3lAwo0AHhdIVSvstHZT0VyiFQsssZm/KDGWPsAnEQEjIjjO8Yite8pBk3d8fV9exFQ9JvsfSuTnP66WXbEt663Oee3R9dHR1rjAMA41Go9FoNBqNRtM/rngXoNFoNBqNRqPRJDp60KzRaDQajUaj0QyCHjRrNBqNRqPRaDSDoAfNGo1Go9FoNBrNIOhBs0aj0Wg0Go1GMwh60KzRaDQajUaj0QyCHjRrNA5ACHGXEOJ3oZ8PE0JsiXdN0SCEKBNCfBDvOjS7BkKICUIIQwiRFMNjfyOEuE9GXRqNJrHRg2aNJoERQmwUQviEEEVhf68I/dOfAGAYxpWGYfwpLkVqNFEwlAFrImAYxl8Mw7gs3nXIQr+B1Wj6Rw+aNZrEZwNwrvWLEGIPID1+5QyNeA6WZD+3qgNBldlVnQsh3PGuQaPZ1dCDZo0m8XkYuLDH7xcBD/W8gxCiXAjx574eLIQoEUI8K4TYJoTYIIT4aY/b9hFCLBVCNAshaoUQf+8n4zAhxJbQR9P1oRnw83vcniqEuFkIsSmUc5cQIj3ssb8WQtQAD/TX0FDG9lCdx4e14UUhhEcIsVYI8YP+2h5+eEqo1l8LIb4GvOGDrFCtN4f97QUhxLUR+LtBCPGMEOIRIUQzUNaf074OmwnVdlSU26JICPGyEKIx5GOJEMIVQa3pQogHQ35XCiF+1YenXwohvhZCeIUQ9wshRgkhXhVCtAgh3hRC5Pe4/35CiI9CdXwlhDisx23vCiH+JIT4MPTY13t8WvJ+6LpRCNEqhNi/jzZGUmuvbSqEWCiEWBd6vhVCiFN73N8d6lv1Qoj1wPfCni831N5qIcRWIcSfRT+D0tA2fyT0szVrflGo79cLIa7v63Gh+5eH+tsboTrfE0KU9rh9Rug2jxBilRDirLDH/lsIsVgI4QUOF0KME0I8F9reDUKIO3rc/5KQu+1CiNfCnscQQlwphFgTuv1OYbIbcBewf2jbNPbXFo1ml8QwDH3RF31J0AuwETgKWAXsBriBzUApYAATQvcrB/4c+vkwYEvoZxfwBfB/QAowCVgPHBu6/WPg+6Gfs4D9+qnjMCAA/B1IBQ4FvMD00O3/AF4ECoBs4CXgprDH/jX02PQ+8ssAP/CDUBt/CFQBInT7e8C/gDRgDrANODK87eHt7+GwAhjXz3MfEnJqPVc+0A6URODvhlDdp4Tum96f0/C6em7fKLfFTZgDm+TQ5WBARFDropDHfGAs8HUfnj4BRgFjgDrgS2BuaLu9Dfw+dN8xQANwQuh5jw79PiJ0+7vAOmBayMm7wKLQbRMw+27SAP0+klp7bVPgzB7b7GzM/jk6dNuVwHeh+xcA7/SsAXgeuBvIBEYCnwFX9FPbDcAjYW25N9TOPYFOYLd+HlsOtGD2uVTgNuCD0G2ZmP3wYiAJ2AuoB3bv8dgm4MBQGzOBr4BbQz+nAQeF7nsKsBZzn5EE/Bb4qEcdBvAykAeMx3w9HdfjtfhBvPd9+qIviXjRM80ajRpYs81HY/7z3xrh4/bGHMj80TAMn2EY6zH/wZ8Tut0PTBFCFBmG0WoYxieD5P3OMIxOwzDeA14BzhJCCMzB7s8Mw/AYhtEC/KXHcwAEMQdcnYZhtPeTXWkYxr2GYXQBDwKjgVFCiHHAQcCvDcPoMAyjArgP+H6EDgBuNwxjcz/PvQRzEHFw6PczgI8Nw6hicH+E7vu8YRjBUH60Ti0ifZwf002pYRh+wzCWGIZhRFDrWcBfDMPYbhjGFuD2PrL/aRhGrWEYW0NePjUMY5lhGJ3AfzEH0AAXAIsNw1gcavcbwFLMQbTFA4ZhrA45eQrzzU6kRFJrr21qGMbThmFUhep5ElgD7NMj7x+h+3sw33gAIIQYBRwPXGMYhtcwjDrMgeg5RM4fDMNoNwzjK8yB7J4D3PcVwzDeDzm9HnNWdxxwIrDRMIwHDMMIGIbxJfAsZn+0eMEwjA8NwwgCszHfJPwyVHeHYRjWschXYL5pXWkYRgDz9Tin52wz5puYRsMwNmG+iZgTRXs1ml0SPWjWaNTgYeA8zFmghwa+ay9KgZLQR+iNoY9bf4M5mwhwKeZs4HdCiM+FECcOkLXdMAxvj98rMf9pjwAygC96PMf/Qn+32GYYRscgtdZYPxiG0Rb6MSv0HNZgvOdzjxkkryeb+7shNOB8gh3HjZ8HPBr6eTB/fWVH4zSWx/0NcxbxdSHEeiHEwghrLQmrtS8ntT1+bu/j96wez3Vm2HMdhDmYt6jp8XNbj8dGQiS19vqbEOJCYX5B1qpnFmAdEhKeV9nj51LMGfvqHo+9G3PGOVKiaWt3HYZhtAKeUH2lwL5hTs8Hivt6LOaseWVoUBxOKXBbjxwP5qcRPV8zQ9k+Gs0uyS75BQqNRjUMw6gUQmzAnMm7NIqHbgY2GIYxtZ/cNcC5wjwm9jTgGSFEYdjg2CJfCJHZ47bxwHLMj5DbMT9G7m8G3Iii5nCqgAIhRHaPgfN4dsy2ezEH7RY9BxmRPv/jmIPQRcC+gHU87ID++sruz2l4naFjZkcM9rjwbRFy8HPg50KI3YF3hBCfR1BrNeahDitCv48boE2DsRl42DCMHwx6z52JpC9EUmt3TmgG9V7gSMyZ/y4hRAXmQNHK65kxvsfPmzEPqSjqZwBqN911CCGyMA8XqQrV8Z5hGEcP8Nie7jYD44UQSX3UvRm40TCMR4meobxWNRpHo2eaNRp1uBQ4op8BbX98BjSHvjSVHvpC1CwhxN4AQogLhBAjQh/3NoYe0zVA3h+EEClCiIMxP05+OvTYe4FbhRAjQ7ljhBDHRtm+PjEMYzPwEXCTECJNCDEb04U1IKgAThBCFAghioFrYniOZZjHdd4HvGYYRmPopgH99cUATlcDaUKI7wkhkjGPM02N4HHh+ScKIaaEDotpDt2nK4JanwKuE0LkCyHGAD+J1lMPHgEWCCGODT1PmjC/6Dg2gsduwzxcZ9IA94m21kzMwd42ACHExZgzzT3zfiqEGCvMLzNas/MYhlENvA7cIoTIEUK4hBCThRCHRtCWWDhBCHGQECIF+BPmITCbMY8xniaE+L4QIjl02Tv05by++AzzzcAiIURmaBscGLrtLkx/u0P3Fx3PjLC+WmBsqD6NRtMDPWjWaBTBMIx1hmEsjfIxXcACzOMVN2DOCt8H5IbuchzwrRCiFfNLSecMcBhFDbAdc1bsUeBKwzC+C932a8xDBj4R5ioSbwLTo6l1EM7F/NJVFeaxtb8PHUcL5qErX2F+Oex14MkYn+NxzC9dPmb9IQJ/fdGnU8MwmoAfhR6/FXPmectgj+sjfyqm31bMLw/+yzCMdyOo9Y+h59sQevwzmDOsURMa5J2MefjHNsyZzV8Swf+U0KE3NwIfhg4f2K+Pu0VVq2EYK4BbMH3UAnsAH/a4y73Aa5j95EvgubCICzG/PLkCs48/Q+9DTezkMeD3mIdMzMM8BMP6BOEYzGOpqzBfb9aXZ3eix/aeAmzC9HV26Lb/hh77ROj1uBzzuO1IeBv4FqgRQtRH3zyNxrlY3xbXaDSafhHmcmKPGIYRyUyiRgGEED/EHJjLmlG1DZVqHQghRDnmKiC/jXctGo0mevRMs0aj0ewCCCFGCyEODB1+MB3zuOj/xruuvlCpVo1Gs+ugvwio0Wg0uwYpmKtCTMQ8ZvoJzLWvExGVatVoNLsI+vAMjUaj0Wg0Go1mEPThGRqNRqPRaDQazSDoQbNGo9FoNBqNRjMICX1MsxBiAbAgKyvrB1Onmuv1G4aBEIJgMIjb7aarq4ukpCT8fj/Jyck7XQcCge77uVyu7sdbWS6Xq98Mr9dLZmYmfr+fpKQkurq6cLvdBIPBPjPcbjeBQKDPOsLrCc8Yrjb1vI53mwKBAElJSY5qUyJtp5791yltSpTtFO7WCW1KpO3Uc9/glDYl0nbqq/+q3qZE2U79uVW5TYm0naznlN2mL7/8st4wjJ5ntaU7KNEv8+bNMwaitrZ2wNtjZdmyZVJyZdWrWq72KzdX+9VuVc3VfuXmquRXuzVRzYPqfoGlRh/jUUccnpGfnx/vEqJCVr2q5cpCNQ/ar7xc7VbNXFmo5kH71W4tVPPgVL+OGDS3tLTEu4SokFWvarmyUM2D9isvV7tVM1cWqnnQfrVbC9U8ONWvIwbN6enp8S4hKmTVq1quLFTzoP3Ky9Vu1cyVhWoetF/t1kI1D07164hBs9/vj3cJUSGrXtVyZaGaB+1XXq52q2auLFTzoP1qtxaqeXCqX0cMmq1vR6qCrHpVy5WFah60X3m52q2aubJQzYP2q91aqObBqX4dMWh2u93xLiEqZNWrWq4sVPOg/crL1W7VzJWFah60X+3WQjUPTvXriEGzz+eLdwlRIate1XJloZoH7VdernarZq4sVPOg/Wq3Fqp5cKpfaYNmIcR/hBB1Qojlfdz2CyGEIYQosuO5MjIy7IgZNmTVq1quLFTzoP3Ky9Vu1cyVhWoetF/t1kI1D071K3OmuRw4LvyPQohxwNHAJrueqLm52a6oYUFWvarlykI1D9qvvFztVs1cWajmQfvVbi1U8+BUv9IGzYZhvA94+rjpVuBXgGHXcxUUFNgVtRPl5eWUl5fbmimrXtVyZaGaB+1XXq52q2auLFTzoP1qtxaqeXCq3yTJdfRCCHESsNUwjK8G+6aiEOJy4HKA0aNHU1FR0e9929vbpawJ2NDQ0D1lP9DzR4uselXLbWhosNWrhWoetF95udqtmrnar9xclfxqtyaqeXCq32EbNAshMoDrgWMiub9hGPcA9wDMnz/fmDNnjrzi+qC8vJyMjAxWrlwJQFtbGwBlZWXDWoeTqaioYLi3666E9isP7VYu2q9ctF95aLdyibff4Vw9YzIwEfhKCLERGAt8KYQoHmpwVVXVUCOGFVn1qpYrC9U8aL/ycrVbNXNloZoH7Ve7tVDNg1P9DttMs2EY3wAjrd9DA+f5hmHUDzW7pKRkqBE7UVZWRkVFhZQZZhn1qpgrC9U8aL/ycrVbNXNloZoH7Ve7tVDNg1P9ylxy7nHgY2C6EGKLEOJSWc/l1Hc0Ts+VhWoetF95udqtmrmyUM2D9qvdWqjmwal+pc00G4Zx7iC3T7DruWS+o5FxDLNq7+yc+o7R6bmyUMmDdqtmrixU86D9arcWqnlwql9HnBGwtrY23iVEhax6VcuVhWoetF95udqtmrmyUM2D9qvdWqjmwal+HTFoLiwsjHcJUSGrXtVyZaGaB+1XXq52q2auLFTzoP1qtxaqeXCqX0cMmpuamuJdQlTIqle1XFmo5kH7lZer3aqZKwvVPGi/2q2Fah6c6tcRg+bMzMx4lxAVsupVLVcWqnnQfuXlardq5spCNQ/ar3ZroZoHp/p1xKC5s7Mz3iVEhax6VcuVhWoetF95udqtmrmyUM2D9qvdWqjmwal+HTFoTkoa1rOBDxlZ9aqWKwvVPGi/8nK1WzVzZaGaB+1Xu7VQzYNT/Tpi0LyrUV5eTnl5ebzL0Gg0Go1Go9llSOhBsxBigRDiHo/HQ1tbGy0tLTQ3N9Pe3o7H48Hv91NXV4ff76e6uhrYsUB1dXU1hmF03+7xeGhvb6e5uZmWlhba2tpobGzE5/NRX19PMBikpqamV4Z1NsDa2loCgQANDQ10dHTQ1NSE1+vF6/XS1NRER0cHDQ0NBAKB7mVLrAzruqamhmAwSH19Pe3t7TQ2Ng7YJsMw+m2T9Y4ovE2tra2Dtsm6trtNPp9vSG0aynYarjY1NjZKaZPP55PSJqv/2r2dOjo6pGynlpYW2/teR0eHlL5nubW77/XsE3a+nhobGxPu9eSkfURra6uUNrW3t0tpU3j/tWs79fyfbNd2amtrk9L3vF6vlL4X7tauvhcIBJR6PW3fvl2pfYSVabWpP4RhGP3emCjMnz/fWLp0ab+3d3R0kJaWZvvzVlRUMGfOHNtzY63Xml2urKwEoLS0FNhxAhZZHnYVv07L1X61W1VztV+5uSr51W5NVPOgul8hxBeGYcwPv19CzzRHitfrjXcJUSGrXtVyZaGaB+1XXq52q2auLFTzoP1qtxaqeXCqX7WO1O6H3NzceJcQFbHWa80oWzPO4af4luVhV/HrtFxZqORBu1UzVxaqedB+tVsL1Tw41a8jZpobGhriXUJUyKpXtVxZqOZB+5WXq92qmSsL1Txov9qthWoenOrXETPNo0aNincJUTHUesNnmO3K7Y9dza9TcmWhkgftVs1cWajmQfvVbi1U8+BUv46Yaba+/agKsuqNJjeaZeu0XzVzZaGSB+1WzVxZqOZB+9VuLVTz4FS/jhg0l5SUxLuEqJBVr2q5slDNg/YrL1e7VTNXFqp50H61WwvVPDjVryMGzU59RyMj15phrqyspLKyMqIZZ+1XzVxZqORBu1UzVxaqedB+tVsL1Tw41a8jBs1OfUfj9FxZqOZB+5WXq92qmSsL1Txov9qthWoenOrXEYNm68wuqiCr3khyy8rKKCsro7S0lNLS0u7fh5qbSMTTbyLlykIlD9qtmrmyUM2D9qvdWqjmwal+HTFoHjlyZLxLiApZ9aqWKwvVPGi/8nK1WzVzZaGaB+1Xu7VQzYNT/Sb0oFkIsUAIcY/H4xnwXOQNDQ1SzkVunUN+qOeMh97nV6+trZVyfvWqqqqIzxl//PHHc8EFF0TUpnXr1g3apnieM966trbTxo0bbdlO4W3avHmzlDbV19cP2qZY+p7Vf+3eTnV1dbZsp/A2bd261Zbt1LNNVr129z3LrR3bqWebrP5g9+tp8+bNtm2nnm3auHGjLdtpuPYR69ats3VfbrWpqqpKSptqa2tt2U7hbQrvv3Ztp4aGBlv35Q0NDdTU1Ni+L29ubqa6ulpK3wt3a9c+wuPxSHk9VVZW2r4v93q9bNq0ydZ9uex9xJo1a3q1qT+EYRj93pgozJ8/31i6dGm/t/t8PlJSUmx/3oqKCioqKoD+10aOBVn1qpYr6xzyqnnQfuXlardq5mq/cnNV8qvdmqjmQXW/QogvDMOYH36/hJ5pjhTrnZ0qyKpXtVxZqOZB+5WXq92qmSsL1Txov9qthWoenOrXEWcElPFupry8nIyMDCorK7t/B3tmnGXUq2KuLFTzoP3Ky9Vu1cyVhWoetF/t1kI1D07164iZ5q6urniXEBWy6lUtVxaqedB+5eVqt2rmykI1D9qvdmuhmgen+nXETLOM47LLysqoqKjonrK385hmWceRq5YrC9U8aL/ycrVbNXNloZoH7Ve7tVDNg1P9OmLQnJycHO8SImLRokUAXH311VLyZXlQxa+Fah60X3m52q2aubJQzYP2q91aqObBqX4dcXhGe3u7tOxITv4RLVa9kZzCOpZcu5HpVwaqedB+5eVqt2rmykI1D9qvdmuhmgen+nXETHN2dna8SxgQa4a5s7MTgPvuu49gMEhxcbGtzyPLQ6L7DUc1D9qvvFztVs1cWajmQfvVbi1U8+BUv46Yad6+fXu8S4iKpKQkfD4flZWVVFZW2jbjLMuDan5V86D9ysvVbtXMlYVqHrRf7dZCNQ9O9euIQfOIESPkhXc0Q9WyIUUsXLiQhQsXkpqaSmpqKgUFBYwfP96mAncgy4NUvxJQzYP2Ky9Xu1UzVxaqedB+tVsL1Tw41a8jBs3W6RCl8OJP4OFTwbPBtshjjz2WsrIySktLKS0tte24aVkepPqVgGoetF95udqtmrmyUM2D9qvdWqjmwal+HXFM8+jRo+WFH3UD3HsEPH4uXPYGpMZ+nM7ChQvtq6sPZHmQ6lcCqnnQfuXlardq5spCNQ/ar3ZroZoHp/pN6JlmIcQCIcQ9Ho+HtrY2WlpaaG5upr29HY/Hg9/vp66ujq1bt1JdXQ1AVVUVANXV1RiGQV1dHX6/H4/HQ3t7O83NzbS0tNDW1kZjYyM+n4/6+nqCwWD3Ow0ro62tDQomsf2oWzHqV+N74mI62ttoamrC6/Xi9Xppamqio6ODhoYGAoEAtbW1vTKs65qaGoLBIPX19WzatInGxkbOOussTj/99D7bZBhG1G3asGHDoG2yrmtrawkEAjQ0NNDR0TFgm1auXDlom3w+H42NjQNup1jaFMl2Cm/TqlWrBm1TJNspvE1r166V0qYtW7bYsp3C22StMW73dtq0aZMt2ym8TevXr7dlO/Vs06ZNm6T0PcutHdupZ5u2bNki5fW0du1a27ZTzzatWrXKlu00XPuIlStX2rKdwtu0YcMGKW3atGmTrftyq03h/deu7bR161Zb9+UNDQ1UVlbavi9vbm5m48aNUvpeuFu79hFVVVVSXk+rV6+2fV/u9XpZu3atrfty2fuI5cuX92pTfwgVFqCeP3++sXTp0mF/3oqKCubMmWP+8sm/4X8L4ZBfwRHXD3stTqSXX43taL/y0G7lov3KRfuVh3Yrl+HyK4T4wjCM+eF/T+iZ5kix3nVIZd8rYc4F8P7/g2//O6QoWfWqlisL1Txov/JytVs1c2WhmgftV7u1UM2DU/06YtBs93rHfSIEnPh3GLsPPP8jqP465ihZ9cYj1+4TtNiBk/wmIip50G7VzJWFah60X+3WQjUPTvXriEHztm3bhueJklLh7EcgLQ+eOB+89THFyKpXtVxZqOZB+5WXq92qmSsL1Txov9qthWoenOrXEatn5OfnD9+TZY+Ccx6FB46Hpy6E7z8PSSlRRciqdzhzrdnlysrKXr/bfcrxWHCC30RGJQ/arZq5slDNg/ar3Vqo5sGpfh0x09zS0jK8TzhmLzjpDqj8EF65FqL8MqWseltaWli0aFH3abvtzFUJmX5VypWFSh60WzVzZaGaB+1Xu7VQzYNT/Tpipjk9PX34n3T2mbDtO1hyM4yYDgdcFfFDZdU7nLnWjHIizTBbOMFvIqOSB+1WzVxZqOZB+9VuLVTz4FS/jphp9vv98Xniw6+HmSfD67+DVf+L+GEy6l20aBF33XUXnZ2ddHZ22jrjHDe/MSKrXtVyZaGSB+1WzVxZqOZB+9VuLVTz4FS/0maahRD/AU4E6gzDmBX629+ABYAPWAdcbBhGow3PNdSI2HC54JS7YHslPHspXPIaFM8a9GGy6pW15vZA9SbSDLOFLL+JlhuvWf5E8zDcmTJRya3MXFmo5kH71W4tVPPgVL8yZ5rLgePC/vYGMMswjNnAauA6O57I7XbbERMbKRlw7uPm6bUfPwda6wZ9iIx6Fy5cyJVXXklqaiqpqaksXLjQttN2x9VvDMiqV7VcWajkQbtVM1cWqnnQfrVbC9U8ONWvtEGzYRjvA56wv71uGIZ1fsJPgLF2PJfP57MjJnZySsyBs7feXIrO3zHg3WXVq1quLFTzEG2utTZ2ZWUllZWVw75WdqJ4iFemTFRyKzNXFqp50H61WwvVPDjVr9TTaAshJgAvW4dnhN32EvCkYRiP9PPYy4HLAUaPHj1v8eLF/T5PMBjE5bJ//N/Q0EBhYWHE98+tep+Jn/0Oz9ij2DTvt+YJUfpAVr2q5UbrN1JU8xBt7urVq4Ed3/bNzs4GYNq0ab3up/3qvqtqrvYrN1clv9qtiWoeVPc7d+7cPk+jjWEY0i7ABGB5H3+/HvgvoUH7YJd58+YZA7Ft27YBb4+VZcuWRf+g9/5mGL/PMYy3/9LvXWTVq1puTH4jQDUPseY+8MADxgMPPNDv7dqv7ruq5mq/cnNV8qvdmqjmQXW/wFKjj/HosC85J4S4CPMLgkeGChsyBQUFdsTYw8E/B896eG8R5E+AOefudBdZ9aqWKwvVPGi/8nK1WzVzZaGaB+1Xu7VQzYNT/Q7rknNCiOOAXwMnGYbRZlduXd3gX74bNoSAE/8BEw+BF6+CDUt2uouselXLlYVqHmLNLSsri8vqJYnmYbgzZaKSW5m5slDNg/ar3Vqo5sGpfqUNmoUQjwMfA9OFEFuEEJcCdwDZwBtCiAohxF12PFdxcbEdMfaRlAJnPQwFk+DJ82Hb6l43y6pXtVxZqOZB+5WXq92qmSsL1Txov9qthWoenOpX5uoZ5xqGMdowjGTDMMYahnG/YRhTDMMYZxjGnNDlSjueq6qqyo4Ye0nPg/OfBncKPHoGtG7rvklWvarlykI1D9qvvFztVs1cWajmQfvVbi1U8+BUv444I2BJSYnU/JgPvc4vhXOfNNdufuJc8LcD8upVLVcWqnnQfuXlardq5spCNQ/ar3ZroZoHp/p1xKBZ5juaxz7dxDVPVtDu64otYOw8OP1e2LIUnrscgkHl3tk59R2j03NloZIH7VbNXFmo5kH71W4tVPPgVL+OGDTLfEfT3OHnxa+qOP3fH7HZE+N3F3dbAMf8GVa+CG/8Trl3dk59x+j0XFmo5EG7VTNXFqp50H61WwvVPDjVryMGzbW1tdKyrzx0Mv8p25st29tYcMcHLFmzbfAH9cX+P4Z9roCP76Dl9UX2FhlClgeZfmWgmgftV16udqtmrixU86D9arcWqnlwql9HDJplnB2mJ4dPH8mLPzmIUdlpXPSfz7jrvXXRH+csBBx3E+x2Etkf3QTLn7O9TlkeZPu1G9U8aL/ycrVbNXNloZoH7Ve7tVDNg1P9OmLQ3NTUJP05JhRl8tyPDuD4PUaz6NXv+Mnjy2jzBaILcbnhtHvxj54P/70CNn5ga42yPAyHXztRzYP2Ky9Xu1UzVxaqedB+tVsL1Tw41a8jBs2ZmZnD8zypSdxx7lyuO34Gr35Tzal3fsSGem90IclpdJ31CORPhMfPg9oV9tUXhYfy8nLKy8ttz00EZNWrWq4sVPKg3aqZKwvVPGi/2q2Fah6c6jehB81CiAVCiHs8Hg9tbW20tLTQ3NxMe3s7Ho8Hv99PXV0dHR0dVFdXAzu+AVldXY1hGNTV1eH3+/F4PLS3t9Pc3ExLSwttbW00Njbi8/mor68nGAxSU1PTK6OtzfziX21tLYFAgIaGBjo7OzlnThF3nzeb2uZ2TvrnEhZ/tZmGhgYCgUD3cTFWhnVdU1NDMBikvr6e1q5kmk76D8GkNIIPn0bL1lU7tckwjKjb1NTUNGibrOukJPMM6g0NDXR0dNDU1ITX68Xr9dLU1ERHR0d3m8If21ebfD4fjY2NA26nWNoUyXayrq3tZPWJgdoUyXYKb1NDQ4OUNrW3tw/apki2U3ibrP5r93byer22bKfwNjU2NtqynXq2yev1Sul7lls7tlPPNrW3t0t5PTU0NNi2nXq2qa6uzpbtNFz7iKqqKlu2U3ibmpqapLSptbXVlu0U3qbw/mvXdrL6hF37cqvf2r0vb25uprm5WUrfC3dr1z6is7NTyutp27Zttu/LvV4v9fX1tu7LZe8jtmzZ0qtN/SFiXoN4GJk/f76xdOnSfm/3er1S3tVUVFQwZ86cfm/fsr2NHz7yJd9sbeKqI6ZwzVHTcLvEoLnd9dZ8A/85HvLGwcWvmidEGQKReLBmlysrKwEoLS0FGPB0zPHyGyuy6lUtV/vVblXN1X7l5qrkV7s1Uc2D6n6FEF8YhjE//H4JPdOc6IzNz+DpK/fnrPlj+efba7mk/HMa23yRBxTvAec8AvVr4Inzuk9+otFoNBqNRqNJLBwxaB5oKl02aclu/nr6bP5y6h58tK6eBXd8wPKtAx9Q3qveSYfBqXdB5UfwzKXQFXtbIvFQVlZGWVkZpaWllJaWdv8+1NxEQla9quXKQiUP2q2aubJQzYP2q91aqObBqX4dMWhOTU2N6/MLIThv3/E8dcX++AMGp//7I55eurnf++9U7x5nwPF/hVWvwMtXQ4yHzMjyEG+/0aKaB+1XXq52q2auLFTzoP1qtxaqeXCqX0cMmr3eKFewkMTc8fm8/NODmFeazy+f+ZrrnvuaDv/Op9/us959r4BDfgXLHoG3/jDg8/S38kU0HiKZYY4lNxGQVa9qubJQyYN2q2auLFTzoP1qtxaqeXCqX0cMmnNzc+NdQjdFWak8dMk+/PCwyTz+2WbOvOvjnU6/3W+9h/8G5l0MH9wKH98Z9XPL8pBIfiNBNQ/ar7xc7VbNXFmo5kH71W4tVPPgVL+OGDQ3NDTEu4ReJLld/Pq4Gdzz/XlsrPey4I4PeHdVXfft/dYrBHzvFph5Mrz2G/jqyV43WzPMlZWVVFZW7jTjLMtDovkdDNU8aL/ycrVbNXNloZoH7Ve7tVDNg1P9OmLQPGrUqHiX0CfH7F7MS1cdRHFOGheXf84/3lxNMGgMXG/orIFMPARe+BGsfi3i55PlIVH99odqHrRfebnarZq5slDNg/ar3Vqo5sGpfh0xaLYWp05EJhRl8t8fHcipc8bwjzfXUFb+OSvWbRr4QUmpcM5jMGoWPHVh9+m2B1v5QpaHRPG7aNEiFi1aNOj9httDNGdXjCY3UVGpn2m3aubKQjUP2q92a6GaB6f6dcSguaSkJN4lDEh6iptbztqTG0+dxSfrGrj0qTV8uWn7wA9KzYYLnoO8UnjsHNj65aDPI8tDovsNRzUP2q+8XO1WzVxZqOZB+9VuLVTz4FS/jhg0q/CORgjB+fuW8uwPDwAjyNl3f0z5hxsY8IyMmYVw4fOQUQCPnAZ1K4H+V75w6jtGa4a5s7OTzs7OQWech8vDYMeYx5qb6KjUz7RbNXNloZoH7Ve7tVDNg1P9JvSgWQixQAhxj8fjGfBc5KNHj5ZyLnLrHPJDPWc87Di/+ug0Py/++AAOmJjHDS+t4MqHPqd6m6f/86vnlFB73L3gTqWrfAGGZ0O/bcrLy7PlnPHhbXK73QO2SfY549PS0nC73WRlZSGEICsra8A2paamDnk79dWmrKysXm1KSkoCdnzrNjk5Oaa+V1xcbMt2Cm+T1X/t3k4jRoyI6fU0WJtyc3Nt2U492zRixIgh9b3+2mS5tWM79WxTcXGxlNdTVlaWbdupZ5tSU1Nt2U5D3UdE2ia3223bvrxnm/Ly8qS0qaioyNZ9udWm8P5r13YaPXq0LdupZ5sKCgps35c3NzeTn58vpe+Fu7VrH1FSUiLl9ZSWlmb7vtw6JbWd+3LZ+wgLq039YhhGwl/mzZtnDER1dfWAt8fKsmXLpORWV1cbXV1B49/vrjUmLnzZOPzmd4zvqpsHflDtCsNYNMEwbp1lGE1b+82VQaL4vemmm4ybbrpp0PsNt4cHHnjAeOCBB2zPHSoy+68qudqtmrnar9xclfxqtyaqeVDdL7DU6GM8mtAzzZEycuTIeJcQFSNHjsTlElx56GQe+8F+tHQEOPnOD3hqgLMIMnI3uOBZaNsOD50C3vo+c2HoX0zrL1cVZNWrWq4sVPKg3aqZKwvVPGi/2q2Fah6c6tcRg2aPxxPvEqKiZ737TSrklZ8exNxx+fzqma/5xdNf0e7b+SyCAIzZC857Ahor4eFTob33lwlleUgUvwsXLmThwoWD3m+4PURzdsVochMVlfqZdqtmrixU86D9arcWqnlwql9HDJpzcnLiXUJUhNc7MjuNRy7bl58eOZVnv9zCyXd+wNq6lr4fPOEgOPtR2PYdPHIGdDR33/TKK6/Y8sW0wepNdGTVq1quLFTyoN2qmSsL1Txov9qthWoenOrXEYNm68B7VeirXrdLcO3R03jw4n1oaPVx0h0f8vyyrX0HTD0KziyH6gp47CzwmedMd7nkbE4n+N0Vc2WhkgftVs1cWajmQfvVbi1U8+BUv44YNKekpMS7hKgYqN5Dpo3glZ8ezKySXK55soLrnvuaDn8fh2vM+B6cfh9s/hQePwf87Zx++ukDnvxERr2JiKx6VcuVhUoetFs1c2WhmgftV7u1UM2DU/06YtDc1dXPMcAJymD1Fuem8dgP9uWHh03m8c82c8qdH7K2rnXnO+5+Kpx6N2xYAk+cT1ennHd2TvO7q+TKQiUP2q2aubJQzYP2q91aqObBqX4dMWg2BjpBSAISSb1Jbhe/Pm4G5RfvTV1LJyfd8QHPfbll5zvOPgtO+iese4u0l66AgM+2GeZo6k0kZNWrWq4sVPKg3aqZKwvVPGi/2q2Fah6c6tcRg2brpBKqEE29h00fyeKfHsysMblc+9RX/LKv1TX2+j587xaS178Jz14KXf641ZsIyKpXtVxZqORBu1UzVxaqedB+tVsL1Tw41a8jBs3t7e3xLiEqrHojXd2iODeNxy7bl6uOmMIzX27hpDs+YHVt2Ooae1+G95D/g5UvwrOX2TpwVtWvE3LtXnPbDlTyq/uumrmyUM2D9qvdWqjmwal+HTFozs7OjncJURFLvUluFz8/ZjoPX7Iv29v8nHTHBzzx2aZeHymkHPxTOPYvsOJ5WwfOu4JfJ+bKQiUP2q2aubJQzYP2q91aqObBqX4dMWjevn374HdKIJ588smY11M+aGoRi68+iPmlBSx87huuenwZzR3m4Hj79u2w/49tHzir5ldWvcOZa/UJu9fctgOV/Oq+q2auLFTzoP1qtxaqeXCq34QeNAshFggh7vF4PLS1tdHS0kJzczPt7e14PB78fj91dXUUFRVRXV0NQFVVFQDV1dUYhkFdXR1+vx+Px0N7ezvNzc20tLTQ1tZGY2MjPp+P+vp6gsEgNTU1vTKsdftqa2sJBAI0NDTQ0dFBU1MTXq8Xr9dLU1MTHR0dNDQ0EAgEqK2t7ZVhXdfU1BAMBqmvr6erqwu3201ycjKpqam4XC6EEL3aZBhGv22ivZn7vz+Xnxwynle/qeGEf7zPx6uqyMzMNNs07we0HvgbWPE87Y9eAF3+neqJpk3WbPZAbfL5fDQ2Ng64nQZq01C2U3ibXC6XLdspvE2pqalS2lRYWLhTm6zjq7Kzs3G5XLjdboQQUbXJ6r92b6e8vDxbtlN438vIyLBlO/VsU15enpS+Z7mN5fU0UJsKCwulvJ5SU1Nt20492+RyuWzZTsO1jzAMw9Z9udWmzMxMKW3Kzc21ZTuFtym8/9q1nYqKimzdlzc0NJCdnW37vry5uZmsrCwpfS/crV37iBEjRkh5PSUlJdm+L/d6vaSkpNi6L5e9j/D7/b3a1C+GYST8Zd68ecZAVFVVDXh7rCxbtkxKrlXvAw88YDzwwANDylq6scE44Ka3jMnXvWLc/PIyo6sruOPGD/9pGL/PMYwnLzSMgG/I9dqNbL9OyB1KH9F+dd9VNVf7lZurkl/t1kQ1D6r7BZYafYxHE3qmOVJGjx4d7xKiws5655UW8MpPD+LI3UbyzyVbueTBz6lv7TRvPOAncMyfQ4dqxL6qxq7sV+VcWajkQbtVM1cWqnnQfrVbC9U8ONWvIwbN1vS6Klj12rWecl5GCnddMI+fHzqWj9Y1cPxtS/hgTb154wFXwTE3wooX4KmLINAZc72qIKveeOTavea2HajkV/ddNXNloZoH7Ve7tVDNg1P9OmLQXFJSEu8SokJGvUIIrjp+T1748YHkpifz/f98yqJXv8PfFTRnnI//f7DqFXjyAvB3xL1emciqV7VcWajkQbtVM1cWqnnQfrVbC9U8ONWvIwbN1kHhqiCr3urqanYbncNLPzmIc/Yez13vreOMuz6mssEL+14BJ/4D1rwBj58NvshPua39qpkrC5U8aLdq5spCNQ/ar3ZroZoHp/p1xKC5uLg43iVEhax6rdz0FDc3nbYH/zp/LzZsa+V7t3/A88u2wvyL4ZR/wYb34dEzobM1rvXKQrZfVXJloZIH7VbNXFmo5kH71W4tVPPgVL+OGDRv27Yt3iVEhax6w3NP2GM0r15zCLuNzuaaJyv42ZMVtMw4E067FzZ9DI+cBh1NcatXFsPlN9FzZaGSB+1WzVxZqOZB+9VuLVTz4FS/jhg05+fnx7uEqJBVb1+5Y/LSefwH+3HNUVN5oWIrJ9y+hC9yjoQzH4CtX8BDJ0ObJy71ymI4/SZyrixU8qDdqpkrC9U8aL/arYVqHpzqV9qgWQjxHyFEnRBieY+/FQgh3hBCrAld22K1paXFjphhQ1a9/eUmuV1cc9Q0nr5yfwwDzrr7Y26vnknXmQ9D7Qoo/x601Ax7vXbS84x5w+03UXNloZIH7XbnXDvPLqn9qpkrC5Vew9qtmrmyiLRemTPN5cBxYX9bCLxlGMZU4K3Q70MmPT3djphhQ1a9g+XOKy1g8dUHs2D2aP7+xmrOeS+PbSc9DNsr4T/Hmdcx5CYa8fKbaLmyUMmDdqtmrixU86D9arcWqnlwql9pg2bDMN4Hwj/3Pxl4MPTzg8ApdjyXdfpDVZBVbyS5OWnJ/OOcudx69p6srG7hiP/C+/vfB+0ec+C8bXVMufHCmjWrrKyksrKS8vJynn76aSnPFc/tlkio5EG73ZHb12tlqDPO2q+aubJQ6TWs3aqZK4tI6xXm2QLlIISYALxsGMas0O+NhmHk9bh9u2EYfR6iIYS4HLgcYPTo0fMWL17c7/P4/X6Sk5NtrNykoaGBwsJC23Nl1Rttbk1rgFs/3s6qBh8XlNTyf61/xEWQdQfcTHveNOn12uF39WpzkG99tJKdnQ3AtGnT+n1MrCTKdosUp/ffeGWCmm43bNgA2Pta0X7VzFXJr3ZropoH1f3OnTv3C8Mw5offL2EHzT2ZP3++sXTp0n5vb2trIyMjY+gFh1FRUcGcOXNsz5VVbyy5ga4gd76zjtvfXsP8LA8PJd9IaqAVznsKSveXWq+dfq0Zs7KysoTyG8/cXaH/xiMT1Hbb87ViZ66dqOxXhVyV/Gq3Jqp5UN2vEKLPQfNwr55RK4QYHSpoNFBnR6jP57MjZtiQVW8suUluF1cfNZWnr9yfmuQxHOFZiEfkYTx8qnkilEFy7fxSkV0kkt945spCJQ/arZq5slDNg/ar3Vqo5sGpfod70PwicFHo54uAF+wIlfFuRiay6h1K7l7j83nlpwdz0Ly5HNN4HeuNEozHz4Gvn1LCb1lZWffMWSL6jUeuLFTyoN3unNvztWJnrgqo1Hdl5spCpdewdqtmriwirVfmknOPAx8D04UQW4QQlwKLgKOFEGuAo0O/D5nm5mY7YoYNWfUONTcrNYm/njGbP19wOBcFf8dngWnw3A/wvf+Pne4r40tFdpGofoc7VxYqedBu1cyVhWoetF/t1kI1D071mySrAMMwzu3npiPtfq6CggK7I6Uiq167co+bNZq9xh/P9c+MwLPhBo7/4Ea8vhYyj/8jCGHLc8gk0f0OV64sVPKg3aqZKwvVPGi/2q2Fah6c6tcRZwSsq7Pl0OhhQ1a9duaOzEnjnosPpOGEe3gieCSZn93OpvLLoCsA7PiIt7S0lNLSUls/8h0qKvgdjlxZqORBu1UzVxaqedB+tVsL1Tw41a8jBs3FxcXxLiEqZNVrd64Qggv2n8Q+P3mQJ9PPYXzlM3x968k0JfjHLqr4lZ0rC5U8aLdq5spCNQ/ar3ZroZoHp/p1xKC5qqoq3iVEhax6ZeWmBVo47Rf/5p1Jv2B26wdsvPVoPlm+FrD3S0V2oZpf3X/l5Wq3aubKQjUP2q92a6GaB6f6dcSguaSkJN4lRIWsemXmJrtdHH7h79h4+J3sZqyl6KkF/OPpN2n3dUl5zqGgol+VUMmDdqtmrixU86D9arcWqnlwql9HDJqd+o4mEXMnHHoBxvefZ0xyC+ctv5Srb32Qis2NUp43VlT2qwIqedBu1cyVhWoetF/t1kI1D07164hBs1Pf0SRqburkg0m/4k1yszK4tf06/nHXv7nl9VX4AkEpzx8tqvtNdFTyoN2qmSsL1Txov9qthWoenOo3oQfNQogFQoh7PB4PbW1ttLS00NzcTHt7Ox6PB7/fT11dHTU1NVRXVwM73i1UV1djGAZ1dXX4/X48Hg/t7e00NzfT0tJCW1sbjY2N+Hw+6uvrCQaD1NTU9Mpoa2sDoLa2lkAgQENDAx0dHTQ1NeH1evF6vTQ1NdHR0UFDQwOBQIDa2tpeGdZ1TU0NwWCQ+vp6tmzZQmNj44BtMgwj6jZt2rRp0DZZ19G0adWqVTtnjJxB02lPkDZyMvcn/z+q37ufBbe/T8WGOlvbFMl2Cm/T2rVrbdlOPp+v13basGGDlDZVV1fbsp3C22T134HaFMt22rp1qy3bKbxNlZWVtmynnm3aunWrlL5nubVjO/VsU3V1tW3bqWebNmzYYNt26tmmtWvX2rKdhmsfsWrVKlv35VabNm3aJKVNW7ZssXVfbrUpvP/atZ1qamps3Zc3NDSwefNm2/flzc3NbN68WUrfC3dr1z6itrZWyutp7dq1tu/LvV4v69evt3VfLnsfsXLlyl5t6g9hGEa/NyYK8+fPN5YuXdrv7YFAgKQk+5eclnWOc1n1xiW3oxme+j6sf5d7xJn8zXcq1xw1nSsOmUSSe+D3ZNqv3FztV7tVNVf7lZurkl/t1kQ1D6r7FUJ8YRjG/PD7JfRMc6Q0NTXFu4SokFVvXHLTcuC8p2HP87jceJpHCx7gtteWc/pdH7O2rlVKPYPhKL8JiEoetFs1c2WhmgftV7u1UM2DU/06YtCcmZkZ7xKiQla9cctNSoFT/gVH/JZ9Wt7g4zG301hfw/duX8J9S9bTFRzeTzMc5zfBUMmDdqtmrixU86D9arcWqnlwql9HDJo7OzvjXUJUyKo3rrlCwCG/hNPvp7BxOW/l/okzJnTw51dWcvbdH7Oh3iultr5wpN8EQiUP2q2aubJQzYP2q91aqObBqX4dMWiWcdyMTGTVmxC5e5wBF71Ekq+ZPzf8jAeP8LO6toXjb3uf+z/YQHAYZp0TwkMC5MpCJQ/arZq5slDNg/ar3Vqo5sGpfh0xaNb0pry8nPLy8vgVMH5fuOxNREYRh35yGUuOreGAyUX86eUVnH3P8M46azQajUaj0diBIwbNAy0PkojIqjehcgsmwWVvQOn+5L72U+4veYlbzpjFqpoes86SVm5JKA9xzJWFSh60WzVzZaGaB+1Xu7VQzYNT/ao1f94Pqamp8S4hKmTV+/zzz2MYBpWVlQDds81lZWVDyo253vR8uOA5ePVXiI9u4/Rpqzj4J3ey8OUN/OnlFexWlMKdY1uZNCJrSPXZVq/DcmWhkgftVs1cWajmQfvVbi1U8+BUv46YafZ61fq4X1a9LpeczTmket3JcOKtcMLNsOYNRj61gPtPKuKWM/dkU5Of429bwt3vrSPQZd/ZBGX5VS1XFip50G7VzJWFah60X+3WQjUPTvXriJnm3NzceJcQFbLqPffcc0lKSrJthtnClnr3+QEUToGnL0LcewSnn/0I+SeM4ok1Bje9+h2Lv6nm/52xJ9OLsxOjXgfkykIlD9qtmrmyUM2D9qvdWqjmwal+HTHT3NDQEO8SokJWvQmfO/lw+ME7kFEID53ElNrF3P39edxx3lw2b2/nxH8u4fa31uAf4qxzwnsYplxZqORBu1UzVxaqedB+tVsL1Tw41a8jZppHjRoV7xKiQla9Vq5dM8zhubZQOBkuexOeuYTxFTdDkocTj/sr+086hD+8tIK/v7GaV5fX8P9On80eY2N7pyrbryq5slDJg3arZq4sVPOg/Wq3Fqp5cKrfhJ5pFkIsEELc4/F4aGtro6WlhebmZtrb2/F4PPj9furq6ti6dSvV1dUAVFVVAVBdXY1hGNTV1eH3+/F4PLS3t9Pc3ExLSwttbW00Njbi8/mor68nGAxSU1PTK6OtrQ2A2tpaAoEADQ0NdHR00NTUhNfrxev10tTUREdHBw0NDQQCAWpra3tlWNc1NTUEg0Hq6+vZtGkTjY2NA7bJMIyo27Rhw4ZB22RdR9OmlStXDtomn88XeZvS86g6/DZqp54HS/+D8eACjPq13HLGLP5+6nQaWjs4+c4P+MPzX9HQ1BJ1m1atWmXLdgpv09q1a23ZTuF9b8uWLbZsp/A2Wf035u3UT5s2bdoU0+tpsDatX7/elu3Us02bNm2ybTv1bJPl1o7t1LNNW7ZssW079WzT2rVrbdtOPdu0atUqW7aTna+ngdq0cuVKW/flVps2bNggpU2bNm2ydV9utSm8/9q1nbZu3WrLdurZpsrKStv35c3NzWzcuFFK3wt3a9c+oqqqSsrrafXq1bbvy71eL2vXrrV1Xy57H7F8+fJebeoPYUha9stO5s+fbyxdunTYn7eiooI5c+YM+/PuKlRUVDAnaR288BNIy4OzH4Gx82hq97Po1ZU8/tlmJhRmcNNps9l/cmG8y1UO3X/lod3KRfuVi/YrD+1WLsPlVwjxhWEY88P/ntAzzZFivVNQBVn1qpYLwKzT4dLXwZ0EDxwHyx4hNz2Zm06bzWM/2BcDOPfeT7juuW9o7vDHtV7VcmWhkgftVs1cWajmQfvVbi1U8+BUv44YNJeUlMS7hKiQVa9qud0U7wGXvwelB8ALP4ZXfg4BHwdMLuJ/Vx/C5YdM4snPN3H039/jf8tr4lavarmyUMmDdqtmrixU86D9arcWqnlwql9HDJqtY1NUQVa9quX2IqMAzn8WDrgKPr8Pyk+Apq2kp7j5zQm78fyPD6QgM5UrH/mCKx5eSm1zx7DXq1quLFTyoN2qmSsL1Txov9qthWoenOrXEYPmkSNHxruEqJBVr2q5O+FOgmP+DGc+CHUr4e6DYf27AMwem8eLPzmQXx83g3dXbeOoW97jkU8qCQZ3PiZfNQ+6/8rL1W7VzJWFah60X+3WQjUPTvXriEGzx+OJdwlRIate1XL7ZfdT4PJ3IXMEPHwqLLkFgkGS3S5+eNhkXrvmEPYYm8tvn1/O2fd8zNq61mGpV7VcWajkQbtVM1cWqnnQfrVbC9U8ONWvIwbNOTk58S4hKmTVq1rugBRNhcvegt1Phbf+CE+cB+2NAEwoyuTRy/blb2fMZnVtKyfctoRb31hNh79Lar2q5cpCJQ/arZq5slDNg/ar3Vqo5sGpfh0xaLbWRVQFWfWqljsoqVlw+v1w3F9h7Rtwz6FQVQGAEIIz54/jzWsP5fg9irntrTWccNsSPl7XYFu95eXl3ackBwf6jRGVPGi3aubKQjUP2q92a6GaB6f6dcSgOSUlJd4lRIWsehMxN3zgGTVCwH5XQtli6PLD/UfDZ/dCaH3xEdmp3HbOXB66ZB8CQYNz7/2EG15dx3avL/bn7IdE9BsPVPKg3aqZKwvVPGi/2q2Fah6c6tcRp9Hu6uqKdwlRIate1XKjYvy+cMUSeP5KWPwL2LgETvonpJmn2j5k2gheu+YQbn97Dfe+v57313q4/oTdOG2vMQghonoqa5BfWVnZ6/fTTz/dtub0JCH8RoFK/Uy7VTNXFqp50H61WwvVPDjVryMGzSqc1bAnsupNpNz+Bp5lZWWxF5JZCOc+CR/dbh7nXP2VudJGyRwA0lPc/Pq4GRw5JZe/vL6enz/9Fc98sYU/nTKLKSOzYn/eEInkN56o5EG7VTNXFqp50H61WwvVPDjVb0IfniGEWCCEuMfj8Qx4LvKkpCQp5yK3jnEZ6jnjoff51Q3DkHJ+9UAgYMs548Pb1NTUNGibws8Z73K5EEKQkZGBy+UiKSmpuw1D2k5AzZRz4OLFdPk64P6jaXzjb2AY3W0qyTB4pGwvfnvcZL6tauK4f7zPjS9+TWOLN6LtdOGFF3LiiScyYcIEJk2axFlnncXpp59OR0eHLdspvE1ut9uW7RTeJqv/DrSdYul7QEyvp8Ha5Pf7Y3o99demf/zjH9x33322baeebbLc2rGderbJ7Xbbtp16tqmjo8O27dSzTa2trUPeTna/ngZqU1NTk637cqtNgUBASpsMw7B1X261Kbz/2rWdkpKSbNlOPdsUDAZt204929TV1SWl74W7tWsfkZycLOX15PV6bd+XW5l27cuHYx9hrZ5htak/hArvBubPn28sXbq039s9Hg8FBQW2P6+sc5zLqjcRcweaYR6yX2+DebjGmtdhxonm4RoZBb3q3dbSyU2LV/Lcsq2UFmbwx5Nncei0ETHVnoh+B2JX77+LFi0iLS2Na665xrZMi13drexc7Vdurkp+tVsT1Tyo7lcI8YVhGPPD7+eIwzOys7PjXUJUyKpXtdwhYx2u8cm/4M0b4K6D4LR7yB6zb/ddRmSn8vez53DGvLH89oXlXPSfz/je7NH834kzGZWTNmB8z4H+okWLcLlc/OpXv7K9GQnrtx8SvZ8tWrQIgM7OTvx+f/fvCxcutCVfJonudrhyZaGaB+1Xu7VQzYNT/Sb04RmRsn379niXEBWy6k3E3LKysqEdxzwYLhcc8BO47A1ISoPyE/G99nvo6v3xygFTinj16oP5+dHTeGNFLUfe8h73f7CBQFcw4qdKSxt4kB0ruv/Ky83IyLA9UyYquZWZKwvVPGi/2q2Fah6c6tcRM80jRkT2cXuiIKte1XJtpWQuXPE+vPorMpfeCbVL4bR7Ib+0+y6pSW6uOnIqJ80p4YYXv+VPL6/g6aWb+fMps5g/oe+PkXrOWnZ2dkqZtVTCbw8SvZ9Z22bRokX4/X4lZpgtEt3tcOXKQjUP2q92a6GaB6f6jXimWQhxgBDiPCHEhdYl5upsxjqgWxVk1ataru2kZsEp/2L7kTdD3Uq462D4+umd7lZamMl/yvbmrgvm0dzu54y7PuYXT39FQ2vngPG5ublSylbGbwiV+llW1tBXTRlOVHIrM1cWqnnQfrVbC9U8ONVvRF8EFEI8DEwGKgBrMTvDMIyfxlhfVAz2RUBZyDrgXGMi1e/2jfDsD2DLZzDrdPjeLZCev9Pd2nwBbn9rLfctWU9mahK/OHY65+0zHrer99rOKh0Xa6H7rzy0W7lov3LRfuWh3cpluPz290XASGea5wMHGobxI8MwrgpdhmXAHAnWkiGqIKveRMsd8tkAY6SqqgryJ8DFr8Lhv4UVL8C/D4T17+1034yUJBYeP4P/XXMwM0fn8Lvnl3PynR/wReXOxzfJ+mKD7r/ycrVbNXNloZoH7Ve7tVDNg1P9RjpoXg4Ux1yNZEpKSuJdQlTIqle1XFl01+tOgkN/CZe+AckZ8NBJ8Nr14O/Y6TFTRmbz2A/25Z/nzqW+xcfp//6IXz79FfWhQzYWLlzIj3/8Y7n1KoJK/Uy7VTNXFqp50H61WwvVPDjV74CDZiHES0KIF4EiYIUQ4jUhxIvWJdbihBA/E0J8K4RYLoR4XAgxpGUJrIWuVUFWvYmSa80wV1ZWUllZOewzzjvVO2Yv80uCe18GH98B9x4BNd/s9DghBAv2LOGtnx/KFYdO4vmKrRx+87uUf2iuspEofuONSh60WzVzZaGaB+1Xu7VQzYNT/Q62esbNQy+lN0KIMcBPgZmGYbQLIZ4CzgHKY80sLk7YSfA+kVWvarmy6LPelAzzuOZpx8ELP4Z7DoNDfw0H/Qzcyb3umpmaxHXH78aZ88bxh5e+5YaXVvDE55v5/YKZjB6uehOYRO5n4SekiSXTllO+x0giux3OXFmo5kH71W4tVPPgVL8DzjQbhvGedQFWAblADrAq9LdYSQLShRBJQAYwpINftm3bNpSHDzuy6k2UXGtt5tLSUkpLS+Wv1RzGgPVOPRp+9AnMPAXeuRHuO8pcaaMPpozM4qFL9uHf5+9FS0eAc+/9lKseX0ZVY/vw1ZuAJEo/i1emTFRyKzNXFqp50H61WwvVPDjVb0TrNAshLgP+D3gbEMA/hRB/NAzjP9EWZhjGViHEzcAmoB143TCM16PN6Ul+/s6rIiQysupVLVcWg9abUQBn3A8zT4KXfwZ3HwKHXw8HXAUud6+7CiE4fo/RHDZ9JP96ZzX3LNnImytq+fHhk7ns4EmkJbv7eRIb600wErGfWbPDlZWVvX4///zzh5wxnG/4EtFtPHJloZoH7Ve7tVDNg1P9Rrrk3CrgAMMwGkK/FwIfGYYxPdrChBD5wLPA2UAj8DTwjGEYj4Td73LgcoDRo0fPW7x4cb+ZnZ2dpKamRlvKoDQ0NFBYWGh7rqx6VctNBL9JndsZW/F38qrfx5s/k017XUdn9vh+cxv9bsormvh4SwejMt1cMjeXfcakIYTo8zF21xsNieB3uHJXr14NQEtLC7BjpZPS0tKIM/vLmDZt2k733ZXcxiNX+5Wbq5Jf7dZENQ+q+507d26fS85FOmh+CzjeMAxf6PcUYLFhGEdFW5gQ4kzgOMMwLg39fiGwn2EYP+rvMYOt09ze3k56enq0pQyKrPUAZdWbaLmDzdQljF/DgOXPwis/B387HH4d7H+VufpGP7kfrKnnDy99y5q6Vg6aUsTvTpzJ9OLYlqTT/de+3PA+F0tmJDPMu6Lb4czVfuXmquRXuzVRzYPqfoe6TvNW4FMhxA1CiBuAT4C1QohrhRDXRlnbJmA/IUSGMKfnjgT6Pqg0Qvx+/1AePuzIqle1XFlEXa8QsMcZ8OPPYNox8OYNcN/OK2z0zD1oahGLrz6YGxbM5OstjZxw+xJ+/8JyGtt8MdUbrzWtY0Glfub4vuvQXFmo5kH71W4tVPPgVL8RHdMMrAtdrGnpF0I/Rz21ZhjGp0KIZ4AvgQCwDLgn2pyeDOWj8Xggq95EyY33saExe8geBWc/At8+D4t/Ya6wcdDP4JBfQlLqTrnJbhdlB07kpDljuPWN1Tz8SSXPV1Rx7dHTOH/f8SS5I3tPqvuvfbnhfSyWTDv7abR9P5HdDmeuLFTzoP1qtxaqeXCq30gHzYuB3wATejzGMAxjdtSVmQ/8PfD7WB7bF2730L+MNZzIqle1XFkMud7dT4GJh8Brv4H3/wYrXoST78BdOKvPuxdkpvCnU2Zx/n7j+eNLK/j9i9/yyCeV/PbEmRw6bcSAT1VeXo7L5Yrrl8+iRaV+tsv1XYfkykI1D9qvdmuhmgen+o308IxHgP8ApwEnhi4LYqpMAj5f9B+JxxNZ9SZKbryXnLPFQ0YBnHoXnP8M+Lxw/zG4//dL6Gjq9yEzinN49LJ9ueuCefi6glz0n88oe+Az1ta1DPhUqr0jT5R+Fq/MSIj1BD8quZWZKwvVPGi/2q2Fah6c6jfSmeZthmG8FHs5csnIyIh3CVEhq17VcmVha71Tj4YffwJv30jKZ3fD+jfh+L/CzJPNY6HDEEJw3KxiDp8xggc/2sg/31rLsf9YwgX7jueao6aRn5nS6/5lZWX4fD4ee+yx7t8THdn9zM7Z9l267yqcKwvVPGi/2q2Fah6c6jfSmebfCyHuE0KcK4Q4zbrEXp69NDc3x7uEqJBVb6LlDvcMs4XtHlKz4fhFNJ3xLGSNgKcvgsfPgcbN/T8kyc3lh0zm3V8exrn7jOPhTyo59G/vcN+S9fgCQbn1hrFo0SIWLVpkW16i9bPhzoyEWD9tUcmtzFxZqOZB+9VuLVTz4FS/kc40XwzMAJIB6z++ATwXdWUSKCgoiHcJUSGrXtVyZSGr3pzdDoMZ78Kn/4Z3/gJ37msuT7fvlTudituiMCuVP5+yBxfuP4E/v7KSP7+ykoc/qWThcTM4blYxQggKCgqUmGG2kOX35ZdfBuz9Aqnuu2rmykI1D9qvdmuhmgen+o10pnlPwzDmG4ZxkWEYF4cul8Renr3U1dXFu4SokFWvarmykOrBnWSeOfDHn8KEg+D138Ldh0LlRwM+dtqobB68eG/KL96b1CQXP3z0S86862OWbdourd6KigoWLVpEZ2cnnZ2dts04y6o3ObnvNx5DId59N9pPW1R7Dcfbb7So5kH71W4tVPPgVL+RzjR/IoSYaRjGithLkkdxcXG8S4gKWfWqliuLYfGQNx7OexK+ewX+txAeOB72PA+O/qN5CEcfCCE4bPpIDppSxFNLt/D3N1Zx6r8+YsGeJfzq2BzGFahxDJgsv9Ypr+08pln3XTVzZaGaB+1Xu7VQzYNT/UY603wQUCGEWCWE+FoI8Y0Q4uuYq7OZqqqqeJcQFbLqVS1XFsPmQQjY7URz1vmga+Gbp+GOefDZvRDs6jcnye3ivH3H8+4vD+eqI6bw+rfVHHnLe/xl8Uqa2uxbEH7OnDksXLiQ1NRUUlNTWbhwIQsXLhxyrkr9TPddNXNloZoH7Ve7tVDNg1P9RjpoPg6YChyDudTcsCw5J4RYIIS4x+Px0NbWRktLC83NzbS3t+PxePD7/dTV1TF69Giqq6uBHQ2vrq7GMAzq6urw+/14PB7a29tpbm6mpaWFtrY2Ghsb8fl81NfXEwwGqamp6ZXR1tYGQG1tLYFAgIaGBjo6OmhqasLr9eL1emlqaqKjo4OGhgYCgQC1tbW9MqzrmpoagsEg9fX1FBUV0djYOGCbDMOIuk15eXmDtsm6jqZN1vqFA7XJ5/PZ0qaHH36YBx98MKrtFN6m1NRUW7ZTeJuysrL6blNDMxz1e+rOeB5G7wmLf4Fx7+Fs/+rVAfteighy0V6FvP3zQzlqWj73LlnPQX99i3vfX8/GTVuG3Pes/puVlQVg23YaMWJETK+nwfpebm4uTU1NnHPOOSxYsCDm7dSzTSNGjIj59TRQmyy3sbyeBmpTcXGxra8nq01ZWVm2baeebUpNTbVlOw1lvxdNm9xut637cqtNeXl5UtpUVFRk677calN4/7VrO40ePdqW7dSzTQUFBbbvy5ubm8nPz5fS98Ld2rWPKCkpkfJ6SktLs31f7vV6yczMtGU7Ddc+wsJqU78YhpHwl3nz5hkDsXXr1gFvj5Vly5ZJyZVVr2q5ffl94IEHjAceeGBIuYPVG+tzROQhGDSMr582jJtnGMbvcwzjmcsMo2ngx1m5325tMr5//6dG6a9fNg5c9Jbx/LItRldXMOo6LXT/1fsGVXO1X7m5KvnVbk1U86C6X2Cp0cd4NNJjmhOakpKSeJcQFbLqTcTcSI9PtfPU2zI9DFqXELDHGTD9eFjyd/jon+ZxzwdfC/v/BJLT+q13ZkkOD12yD0vWbOOmxd9x9RMV3LtkPQuP242DphZJaVMsJGI/G85MmajkVmauLFTzoP1qtxaqeXCq30gPz0horGl/VZBVr2q5suiv3ljP0jZYbp+kZMKRvzOPd558OLz9J/jXvuYA2jAGzD146ghevuogbj17T7Z7/Vxw/6d8//5PWb61/7MRDicq9TOn9N1dLVcWqnnQfrVbC9U8ONWvI2aaCwsL411CVMiqN5Fyo505tv5ux8oJMjzEPBNeMBHOeRTWvQ2vLoQnzoMJB8OxN5rHP/dTr8slOHXuWI6fNZpHPqnkjnfWcuI/P2DBniX84phplBZm2tW0qEmkfhaPTJmo5FZmrixU86D9arcWqnlwql9HzDQ3NSXGDFykyKpXtVxZ9FdvrGdps7C+EBkTk4+AH34IJ9wMtd+aazs//2Norh7Qb1qym8sOnsT7vzqcHx8+mTdW1HDkLe/xfy8sZ1tLZ+z1DAGV+plT+u6ulisL1Txov9qthWoenOrXETPNmZnxm3WLBVn1JlJurDPHdqzNK8NDWVkZHR0dPPHEE92/R407Gfb5AexxJiy5GT69G759jpx9fwKHXGMe0tEPOWnJ/PLYGVy4/wRue2sNj366iWe+2MIlB07k8kMnkZNm/4lB+iOR+lk8MmWikluZubJQzYP2q91aqObBqX4dMdPc2RmfGbdYkVWvarmyGKzeaGeYI82NmPQ8OObP8OPPYOoxJH/w/+Cf8+HLh6BrgKVugFE5afzl1D1442eHcMSMkdzxzloO/us73PXeOtp9/a8NbScq9TOn9d1dJVcWqnnQfrVbC9U8ONWvI2aak5LUaoasehMxN9rBqR3HNMv0YMdMeDcFE+GsB2lf9Q7p7/8ZXrwKPr4Tjvy9ufqGEP0+dNKILO44by+uPLSJm19fxaJXv+OBDzdw1RFT6Vz5Dm5hntxEBonYz4YzUyYquZWZKwvVPGi/2q2Fah6c6tcRM80ajcoEx+4Dl70JZz0MwQA8cS785zjY9Mmgj501Jpfyi/fhqSv2Z1x+Br99fjl3rC/gq6ZUuoLGoI/XaDQajUYTGY4YNA949pYERFa9quX2ZKjLwfVENQ+BQMCcVZ55EvzoEz4qPIu2qhXwn2PZ9NeDeP6emwbN2GdiAd9LW815Y5sQgU7+W53Dj17cwq9uf4ygzYNn2z08+ihMmEBGdjZMmGD+bhPR1DqUPmcXSvbdfkgEn+EMhwc7263/t6n3mpCFah6c6tcRg+bU1NR4lxAVsupVLVcWqnnoletOZnX2gTw35rdwxG8p7ljNyVV/hecuB8/6AXOEgGlZPk5KXcHhKeswgKeqcllwxwe88515+lHb6x0qjz4Kl18OlZUIw4DKSvP30MB5qAMQ3XfVzJWFah60X+3WQjUPTvWr1kEn/eD1eklL2/lMa4mKrHpVy+2Jnes0q+bByg1fC3pRXSou4xIO5HP2/eY5XF8/jWvehXDIryB3zE45PR1OAH4yeyyVjOAfb67h4vLP2Wt8HtcePZ0DpxQiBjheOtJ6beG666Ctrfff2trg+uvh/POHHB9JrXaejXKoqNp3e5JIPsOR6cFaWcfOduv/beq9JmShmgen+k3omWYhxAIhxD0ej4e2tjZaWlpobm6mvb0dj8eD3++nrq6OnJwcqqurAaiqqgKguroawzCoq6vD7/fj8Xhob2+nubmZlpYW2traaGxsxOfzUV9fTzAYpKampldGW+ifeW1tLYFAgIaGBjo6OmhqasLr9eL1emlqaqKjo4OGhgYCgUD3WWWsDOu6pqaGYDBIfX096enpNDY2DtgmwzCiblNycvKgbbKuo2mT9a3Sgdrk8/lsaZPb7UYIEdV2Cm9TV1eXLdspvE0ul8uW7RTepuzs7O42JSebS8fl5uYCkJQzkrfdh/H8+BtYnX0gxrJHMW6fi/+lX9BctbbPNlkZnR3tnLbXWB47fwY3nrI7W7e3ccH9n3L6nR/w7oqtMW+njIyMmF5PvbaTz0dDQwPGli19vvaN0CE6Ho+HyspKHn30UcrLy6PuexkZGYO2yfoCSEZGBsnJybhcLlwu14BtsvYNsbyeBup72dnZtr6erO3kcrlifj0N1Kaurq4+22T1wZycHLMfJyXZ3qZY+l5nZ6et+3KrTT37TXJyMhkZGd3tHkqb0tPTbd2XW20K77927ctzcnJs2U4925SWlmb7vry5uZnU1FQpfS/crV37iNzcXCn/c4PBoC3bKbxNgC3babj2EeHjvf4Qdn1kK5P58+cbS5cu7ff22tpaRo0aZfvzVlRUSFmBQFa9quVqv33n9pyh2mm2anslvPf/4KvHwJ0Ke18KB14NWSN3yg332xno4qnPN3PHO2upbe5k/0mFXHvMNPaeUDCkeqPmhRdg4UL4+GOYM8c8JCOM1sJCnrn55u5Zu9LSUiD6Wbtoao1mZlD33cFzhzLTqrJfO2fW9b5XvdeESm5VzB0uv0KILwzDmB9+v4SeaY4UGRtGJrLqVS1XFqp5iCo3vxROuRN+/DnMPBk++Rf8Yza8dj201g340NQkN9/ffwLv/fJwfr9gJmvqWjnzro+54L5PWbrRI6fenljv3sePh7FjoakJbrwRQrNy3WRkkHXbbUM6e+OQa40TCdHHEiBXFqp50H61WwvVPDjVryMGzdb0uirIqle1XFmo5iE8t+cAsd/BYtEUOO3uvgfPLbUDPl9aspuLD5zIkl8dzvUn7MZ3Nc2ccdfHnH/fJ3y2YfDBc9QeAgE44QT4zW/M3+fOhTfegNJS87jle+6B0lIMIcy/3XOPLcczR1trrANzO1G97/YkEXyGMxwe7Gy33veq95qQhWoenOrXEYdnyELWxwAaE+3XRurXwvt/g2+eAncK7HUh3+Ydxe4HHDvoQ9t9XTz6aSV3vbee+tZODphcyNVHTmXfSYVDq6mzE6xvJF97LUyeDD/+8dAyEwTdd+Wi/cpF+5WHdiuX4fLr6MMznPqOxum5slDNgy251szzT5bCHmfC0geY+ca58PyPzQH1AKSnuLns4Eks+dXh/PZ7u7G6tpWz7/mEc+/5hI/W1e+0VF1E9b72mnkYxvrQMnl///ugA2aVZqlkkdB9bBhzZaGaB+1Xu7VQzYNT/Tpi0FxSUhLvEqJCVr2q5cpCNQ+25hZOhpPvgKsrqJ94Mix/Bu7cG56+GGq+GfChPQfPvztxJuu2tXLevZ9y5l0f897qbd2D537rNQwIfWuaPfaAAw+MqnQZfnXfVTNXFqp50H61WwvVPDjVryMGzdbSIaogq17VcmWhmgcpublj2Tr7arjmGzjgp7DmDbjrIHj4NFj/njnA7Yf0FDeXHjSR9391OH88eXeqGtu56D+fccqdH/Lmitru5X56YRhw0klw0UXm7yUl8NxzMGlSxCXL8KD7rpq5slDNg/ar3Vqo5sGpfh0xaB45cuflthIZWfWqlisL1TxI9Zs1Eo7+A/xsORz5e3O2+aGT4J7DYPlz0NX/epRpyW4u3H8C7/7ycG46bQ88bT4ue2gplzy1hpe/rqIraJgrYIB5OsKjjoIjjxxwQD4QfXkY6hkBdd9VM1cWqnnQfrVbC9U8ONWvIwbNHk/ky2UlArLqVS1XFqp58Hg8Qx4cDkp6Hhx8rTnzvOA28LXCMxfDHfPgs3vB5+33oSlJLs7dZzxv//wwbj5zT9o6/fzksWX87Ko78JeMIfDue+Ydr74afvhDcwAdAzL86r6rZq4sVPOg/Wq3Fqp5cKpfR5xG2zrrlCrIqle1XFmo5mFY/SanwbwymPt9WLUYPrwNFv8C3v6T+fd9rujzFN0AyW4XZ+w1hu+NEryz3cW9/0vh2SkH8twrm/leykbO3nscacnumEvr6cGuUzHrvqtmrixU86D9arcWqnlwql9HzDRbpz9UBVn1qpYrC5U8lJeX8+STT1JZWUll6NTRUmecLVxu2G0BXPoGXPI6TDoMPvon3DYbnrkUtn7R9+POOYfkBSdywu6jeO4XRzHqqYcJTprE71/8loP++jZ3vrOWpnZ/TCXJ8Kv7rpq5slDNg/ar3Vqo5sGpfhN60CyEWCCEuMfj8Qx4LvLk5ORhORd5rOeMh97nVweknF+9q6vLlnPGh7epubl50DbF85zx4W1qa2uzZTuFt8nn8/Hggw/y8MMP29ampKQkDMPofpebnJxMTU0Njz766JD7ntV/w9v08MMP89BDD5nbqaWF9hGz8Rz1D/w/WkrbHhdirH4N7j0C7j8Wz5L7YHMl1Vu3YhgGTccdh++SS/B4PHR0dDBvdBoPXLAHD100l2kjMvjba6s44KY3+cviFSxftzmqvhcIBLrbtGDBAi644AImT55MaWkpxxxzDGVlZVH3PSGElL5nuY3l9TRQ30tKSpLyevL5fDG/ngZqU1tbW8yvp3jsI5qbm23dl1tt6urqktImwNZ9udWm8P5r13ZKTk62dV/e0NBg7nds3pc3NzcTDAal9L1wt3btI1JSUqS8ntrb223ZTuFt6uzstGU7Ddc+Yvv27b3a1B+OOLlJS0sL2dnZtj+vrEW0ZdWrWq6Kfp999lkg+sMEosmN9VCEcPrzO2h+RzMsewQ+uwe+Wwv3t8EvzoTrbofsUQP6Xb61ibvfX88rX1eR5HJx+ryxXHHIJCYUZQ5ab1+5Q3Wh+66audqv3FyV/Gq3Jqp5UN1vfyc3ccQxzSoM/Hsiq17VcmUho97y8nJcLteQj7HtC6vempoaysvLpTxHz7xB81OyoPh4uOpKWP06VF0DbYvh1rdg91Nw7X4+TD+szy/8zRqTyz/PncsvjpnGPe+v5+kvtvDE55s4bvdiLj9kEnPH5/dbX1/bbaht131XzVxZqOZB+9VuLVTz4FS/CX14RqQkJyfHu4SokFWvarmykFXvUHYCAx2rnJycTFlZGcXFxTHn28rll8PBB0NHB8w4Dl74Dn67DPa+DFa/RuYTp8BdB8Pn90NnS58RpYWZ3HjqHnz46yP44aGT+XBtPaf+6yPOuvtj3lpZSzC4s0sZ2033XTVzZaGaB+1Xu7VQzYNT/Tpiprm9vZ309PR4lxExsupVLVcWMuotKyvD4/Hw4osvdv9uF1a9VqbdM8wWA+Zv2ACjRkFGBlx2GRx2GKSl7bi9aAocvwiO+C3eTx4gc8WT8Mq18Mb/maftnn8JjJ6903OOyE7lV8fN4EeHT+GJzzbxnw82cOmDS5kyMovLD57EyXNLSE1y9/JgJ7rvqpkrC9U8aL/arYVqHpzq1xGDZhnHzchEVr2q5cZCJAPKRPIQySERcfe7eTPsthtcfz387new337mpS9Ss0g54Eo45CfmChtL/wNfPQ5fPABj5sO8i2D30yA1q9fDslKTuOzgSVx0wARe+bqau99fz6+e/Zq/vb6KsgMmcN4+47s92PmmIe5uoySR+m48c2WhmgftV7u1UM2DU/064vAM61uPqiCrXtVyZSHTQ1lZme0zwOH1yniOnfK//31Ytsz8w7hxcMstcPHFET1++/bt5vHMY+fDKf+Cn38Hxy0yD9V48Sq4Zbp5vWXpTmcHTHa7OGXuGBb/9CAevnQfdhudY664sehtrnumgo31/Z9kJRZ031UzVxaqedB+tVsL1Tw41a8jVs8wDAMR41nIBkLWtzRl1atabjR+w2dsS0tLgb5nIwerN9aZzKF4GOg54+L3F7+Af/8b1q83D8uIgn7rNQzY/Bkse8g8Rbe/DUbsBntdCLPPgsyiPvNW1bRw35L1PPflZroMGO9qZPekWvaZWIgQQ5txToS+Gw2qvYa1XzVzVfKr3Zqo5kF1v/2tnuGImWZrvT1VkFWvarmyUM3DsPldvRqs5/rRj6C8HEaOjDqm33qFgPH7wsl3ws9XmafrTsmE164zZ58fPw9WvgwBX6+HTS/O5m9n7skvpzdzcGEbtcFsXvXN4J6NeXzVlIovEIy6xkFrTVBU62Par5q5spBRr3ZropoHp/qNy0yzECIPuA+YBRjAJYZhfNzf/QebaZaFrHc0GpNY/C5atAiAhQsXRv180cxWK82jj8L112Ns2oQYPx5uvBFOPBHGjoXzzoO77+7zYUNxOyC1K+Crx+Drp6C1FjIKzS8P7nkujN5zp6Xr7vlPOV83pbEyWMy6bV5GZKdy4X6lnLfveAqzUu2tLUaGsm+Q9UVPJ6H3vXLpy6/ul/ag+65chstvos003wb8zzCMGcCewMqhhFlndFEFWfWqlhsN1pJtnZ2ddHZ2DriEm2oebM199FFzybjKSoRhQGWl+fvLL5szy3/4w5CfIup6R82EY/4MP1sB5z0NEw42v0B4z6Hwr/1gyS3QuKk7N8UF8/M7eONnh/LgJfswc3QOt7yxmgMWvc2vn/maFVXN8mqNM0r0sWHIlYVqHrRf7dZCNQ9O9TvsM81CiBzgK2CSEeGT65lmZyLrmOZIsxw5ozJhgjlQDqe0FDZu7PMh1gxzZ2cnAKmp5myu7TPOPWnzwLfPwddPw+ZPzL+N39889nnmKZBR0Ovua+ta+M+HG/nvl1tp93ex78QCyg6YwNEzR5HkHv73/rHsG3aZTzpsQO975dLTr+6X9qL7rlziPdMcj0HzHOAeYAXmLPMXwNWGYXjD7nc5cDnA6NGj5y1evLjfzLa2NjIyMmyvtaGhgcLCQttzZdWrWm4sflevXg3AtGnT+r3PYPVGkhFLbqzPY6ffPffay5xhDsMQgqefeKLPeioqKgDo6uoCwO02103ub8dkd39I8VaTv+VNcje9RoZ3M0GRRMuofWgccwRNxQcSTN7xXK2+IG+u97J4jZc6bxdFGW5OmJrJUZMyyEl1S6/VYih9t6XFPCGMtcRRz+2h2ms4kfxGgmoehsNvJP0yUmTUq7JbO1HNg+p+586dmzCD5vnAJ8CBhmF8KoS4DWg2DON3/T1Gr57hzNyhzNYNNAuSKB4indG2rV6fD9LTIdjHF+dKSym/4YYB64n0mGZpfoNBRO035rHP3/4XmrdCUhpMPQZmnW5ep5g7ta6gwVsra3nw4418uLaB1CQXC/Ys4cL9S5k9Ns/WWvvajrKOaU6UvhvvXL3vlZsr65hmvXqG7ruyc3fF1TO2AFsMw/g09PszwF5DCdy2bduQixpOZNWrWm4sRLKGcbw9WMdbV1ZWUllZOeDx19Hk9onPB48/bi73lpJinqAk7KxGgZQU3jv22IjrGQxpfuvrzS8GHnsjXLMcLnnNXK5u0yfw9EXwtynwzCWw4gXcgTaO2b2YRy/bj9d/dghnzh/L4m+qOemODzn5zg959ostdPi7EqrvRkK8+26i5MpCNQ/ar3ZroZoHp/od9jMCGoZRI4TYLISYbhjGKuBIzEM1YiY/P9+e4oYJWfWqliuLRPRQU1NDeXl5nwP+IdX72GPmSUnGjoWDD4Y//hGmT++1esaHxx7Lhv337/tY5x5EegzzsPh1uWD8fubluEWw8QPzGOiVL8HyZyEpHaYeDTNPZtrUY/jzKXvwq+Nm8NwXW3j4k0p+/vRX3Lh4JWfsVcL3989iXEH0HxNGcjbHWBjo8YnYd+ORKwvVPAynXzuOYZZRrxPc2oFqHpzqN16rZ1wFPCqE+BqYA/xlKGHWsViqIKte1XJlEW8P1mx4aWkppaWllJWVUVxcPOTcbioqYMkS8+cLLoC33jIHzBbnnw8bN/LVl1/Cxo0cevfdO9XT1z/ISGegh92vyw2TDoUFt/HgiOv536ifwNzzYfOn8Oyl5gz0Y2eTs+JxyvbM4s1rD+XRy/Zl7wn53PfBRg752zuUPfAZb6yopSuY2CdzinffTZRcWajmQfvVbi1U8+BUv8M+0wxgGEYFsNOxIrGSHvZxdKIjq17VcmWRSB6sGeaBZiujyjUMuPBCyMyEjz+GpCQ44oio6xoK8fRrCDc16VPhe2Vw/N/MgfOKF+C7V2D1/wCBGL8fB04/gQNP+B4bjpnMf7/exhOfbeIHDy2lJDeNc/cZz9l7j2NkTtqAz2Vto+FcbSWR+m48c2WhmgftV7u1UM2DU/3GZdBsN36/X6kNJKte1XJlkSgeysrKIpq5HTS3vR3uuw+uuMI8bvnxx2HMmIjrCCd8EBjtYQjx8DtgjaX7w3E3Qc035uB51Svwxu/gjd8xvnAa1844gZ+edwxvts7g0c+2cssbq7ntrTUctdsozt13PAdPKcLlsv8LK7GQKH033rmyUM2D9qvdWqjmwal+HTFolvENTZnIqle1XFkkkodIZisHzV2yBH76U/O45VNPhd13j7oOO0kkvz0eDKNnm5fDr4PtlbDqVYwVL8LHd5D04T84Lj2f46YeQ93Jh/Hwtik8+pWH/31bw9j8dM7Zexxnzh/HqD5mn4dzvdqEdBuHXFmo5kH71W4tVPPgVL+OGDRb68qqgqx6VcuVhWoe+sz97DPYtAnOOAOOOQa++AL2im2RmfBZ2vC/R3sYQjz8Rn2oRH4p7HclvtkXkuTyw7q3YfVrsOZ1Rn79JD8Xbn42dh9W5ezHQ3VTufn1Nm59cw1HzhjJOfuM45CpI+Jy0hRH9N0ERjUP2q92a6GaB6f6dcSg2efzSVlEWxay6lUtVxaJ6GGgQV6fub//vXkGv9NOM1eSiHHALINE9DtgZl4e7H6qeQl2wdYvYPX/cK15g92+vZWbgD8VjGRFxt48tnE6166YTlrOCM6cP5az5o+LaeWNIdWriFuZubJQzYP2q91aqObBqX4dMWhWacOAvHpVy5WFah4yMjLA64Vbb4Uf/hAKC+HeeyE31xwwD5HwWdrwv/f3+4D1SiCS3GgPldgp0+WGcfuYlyP/D1pqYd1bJK15g9nr3mZ28BVuShNsZCqL39+NX767BykT9+P0vSdx7O7FpCXLnT1Rsu8qhGoetF/t1kI1D071G68l52ylubk53iVEhax6VcuVhWoempubzVnlG26AF14w/zh2LIROZ5toqOR30MzsUTDnPDjzAfjlOrj0DcRh1zFxVAE/Sn6ZJ1L+zF1bzyD72fP4543XcMfjz/HVJg+yzqSqkluZubJQzYP2q91aqObBqX4dMdNcUFAQ7xKiQla9quXKQhkPH34IS5dScNVVUFQEa9bAxIn2PkcP7PpCmzJ+o810J+2YhT7s14iOZtj4Aenr3mb2ly9wRFcFrHqIhu+y+cq9Ox98dwQzD1hAwfjdzC8iDne9Ds6VhWoetF/t1kI1D071m9AzzUKIBUKIezweD21tbbS0tNDc3Ex7ezsejwe/309dXR21tbVUV1cDUFVVBUB1dTWGYVBXV4ff78fj8dDe3k5zczMtLS20tbXR2NiIz+ejvr6eYDBITU1Nr4y2tjYAamtrCQQCNDQ00NHRQVNTE16vF6/XS1NTEx0dHTQ0NBAIBKitre2VYV3X1NQQDAapr69n69atNDY2DtgmwzCibtPmzZsHbZN1HU2bVq9ePWibfD6flDZFsp3C27Ru3TpbtlN4mzZu3Ghrm7oefJCuv/+dmg0bzDZNnDik7RTeJqv/2r2dqqqqbNlO4W3atGmTLdupZ5uqqqpi307BJBqLD8B/9F/438Tf89TYP1B78CKaxh7OLGMNB636CwUP7M/2P09m47/PpPPje9m+5jM62ttj7ns1NTVSXk8bN260bTv1bNO6dets2U7DtY9YvXq1rftyq02bN2+W0qatW7faui+32mTtG+zeTrW1tbbuyxsaGti6davt+/Lm5ma2bNkipe+Fu7VjX27tx2S8ntavX2/7vtzr9bJhwwZb9+Wy9xHfffddrzb1h5D1MaOdzJ8/31i6dOmwP29FRQVz5swZ9ufdVdjl/La0wF/+AhddBDNmQGMjJCebJyqRgCp+h/PkIdESvvJIaWkpAHP23JP8LDcrPnwZ16YPmdO1nFGiEQB/ehFJEw9ClB5oriE9cqZ5LLUmYlTpu6qi/cpDu5XLcPkVQnxhGMZOJ+FL6JnmSLHeKaiCrHpVy5VFrPUOdhrpIXtob4e77oLXXjN/z8uDzMxdxm88cqW5FYLSqbM5vuw3HPXbl1l13uf8edIj/DZ4OS+1zmDbyiXw6i/hroPgrxPhkTPg/Zth44fgbx/2elXLlYVqHrRf7dZCNQ9O9euIY5pLSkriXUJUyKpXtVxZJJSHd9+Fl1+Gm2+GkSPN45aLioaeG0fsqjd8Fvf1118H7J1xHmqt/a0PXVFR0X0ft0uw/uPFjAVO/81feHV5DVct3cyWjauZ71rF99I3sk/NKvLWvmE+wJVsnoRl7D47jqHOHWtLvf2hWq4sVPOg/Wq3Fqp5cKpfPdMcB1R7Z+d0v9YMc2VlJZWVlf3OOMfk4bPP4Nlnob7e/D1swBxzbhyRVW9ycrLtmbHUGr79y8vLu49/G4zstGTOmj+OJ688gKcWns30Yy7lb8lXMqf+T8z338s/R/2JtVPK6HKlwhfl8MzFcOvu8PeZ8NSFNL36Z6j8CHxtUdc9EHrfYKKah3j5HexTt/7QM83yUM2DU/3qmeY4oNo7O+03itzWVvi//4PvfQ+OPBKuvto8BXbazqdnjio3gbCr3vBZ3PPPP9+W3J7YVWtxcXGfM+Dhs+U9Z6TH5KXzo8Om8MNDJ7Oiupnnl23lka8KuKVyMpkpR3HczCLOHd/EXLEG99bPYMvn5Da+AJ/+DYTbPBZ67DwYMw9K9oIRM8wVPmJA7xtMVPOg/Wq3Fqp5cKpfRwyaa2trGTVqVLzLiBhZ9aqWK4to6430FM0R5SYlwYsvmjPKRx4Jqam21xtvVOpn0WSGD4AXLVoEQGdnZ6/boz18RAjB7iW57F6Sy8Ljd+PT9Q28+FUVi7+p5tmKAPkZkzhhjwM56aQSJlDNKP8W2LoUtiyF5f81Z6QBktLNwzpK5u64FE6J6EuGKm0zmajmYbj9DvQmMBLi/RpOhFxZqObBqX4dMWguLCyMdwlRIate1XJlMewe3nkH/v1vePxxc0b5668hirMh7ep+rX/IAy3zEyuy3Ub6hsvC7RIcMKWIA6YU8YeTd+f91fW8ULGVZ7/cwqOfbmJUdionzB7HibP3Za/D8xCGAZ51ULVsx+XLh+DTu8zA5Ewo3gNG72kOqEfvGZqR7n2oi943mKjmQfvVbi1U8+BUv44YNDc1NSm1gWTVq1quLGKtd7ABT7+59fWwbBls3gwTJkQ1YB4wN0Gxu15rwLlgwQLbPURTa38DYFlL4qUmuTl65iiOnjkKb2eAN1fW8tzSSh79ZBMPfLiRMXnpnLBHMSfOLmH2HmciZp9lPjDYBdtWmQPomq+h+itY9gh85jVvd6fCyBnmYHrUHlC8B80pJRSU2H/inF2976qa2x9D7fMy6nWK26Gimgen+nXEoDlT0jq3spBVr2q5spDuoaMDfv5zmD8fLr4YzjgDTj4ZUlKGljvMxPqPcTj6Way1hT8u1lpramooLy+P+PmHOqDOTE3i5DljOHZGIT7cvLmilpe/rqb8o43cu2RD9wD6+D1GM3dcHmLUTBg1EwgdBx7sAs96cwBdXQE1y2HV/8zBNFAAkDceRs0yj5Uetbv5c8GkmI+Thsj9Jspa3KrtI/W+d9dz299rRTUPieq3PyKt1xGD5s7OTtIG+KJVoiGrXtVyZSHdQ2oqrFixYyUMIWIeMPfKVQS76g0/fvLpp5+mq6vL1oFVLLWWlZX1WjlgOAd6nZ2d5ObmctpeYzltr7E0tfl5fUUNry6v6R5Al+Smcdys0ZywRzF7jc/H5RLmcc1FU83LHmeYYYYBrbVQ8w0dGz8nrXEN1H4Lq18Do8u8jzUrPXKmeVjHyJnm77njIjo1+K7ad1XPHYxY+7yMep3mNlZU8+BUv44YNCclqdUMWfWqlisLKfW+9x5Z111nnpgkOxveeMP80p8NDLffoX7ZR1a9hmF0z/BGW1t/bTrzzDOjqmGoboZKuNvcjGTOnD+OM+ePo6ndz1sra1n8TTWPfFLJfz7cwIjsVI6ZOYrjZhWz36RCkt09VhEVArKLIbuYrpIDdpx50t8B9augdgXUfWsOpNe/C189vuOxKdkwYro5gB4xA4qmm7/njgPXjucYrC/E22c4qu0j9b5313E72GtFNQ+J5ncwIq03oddpFkIsEELc4/F44nIucusc8kM9ZzwMz/nV29vbbTlnfHib6kNrDCfqOePD29TY2Gjfdtq+nba2NrzBIIbHQ8fatWabQjUPV5ti6XtW/w1vk9vtxuVykZqaSlpaGkII3G53xNspEAjY0qYFCxZwzjnnMGnSJCZOnMjxxx/PmDFjEEKQmZmJy+Xq3pENtp36a1MgEIhqOwkhunOSk5Nxu90IIXZqk+XWju0U6esphQCHTcjg3+fN4X9XzuEfZ+/JrFHpPPflVr5//2fM+9MbXFn+Ma8tr2bT1upefa+1tXXHdvJ24CvcjfqxRxM86o/UHP1v+Pl3VF/0CVz8PxoPugHmnIvPcGOsfh1e/y08dibcNhvjphK6/n0QvifK8L/1Fzq+fJJA1TfUbt3UZ5ustbezsrJwu93d2yle+4j6+nop26m9vT3u+71o2hTefxN1X97Q0EBnZ6eU11NHR4eUNoW7jXQ7Wfssl8tFdnZ2r9ePzO3U1NQkpe+1tLQkzNgokjbV1dX1alN/CMMw+r0xUZg/f76xdOnSfm9vamoiNzfX9ueVdY5zWfWqlpvQfoNBuOIKKC6GP/3JzN2+ndz8fBsq7E28/A7lyz521mvVceqpp3bnWsu9LVy4MKqs8MfFWutgXwZMpL7b4e9iyZp6/re8hjdX1tLU7ict2cVBU0aQsX0t07M6ueDMU2LfZm0e84uH9avM623fQf0aaNq84z7CZR4zXTTNXAav5yWnhPIHHwQi72vx9BvL60Lve01k1Lurue2v/6nmIVH99kd4vUKILwzDmB9+P7Xmz/shNYK1cBMJWfWqliuLIdVrGObH2i6XOXAOBnfkpqfbUN3O7FJ++8D659DR0WFrLgyf26EedmA9/pxzzon4MT3fIFircPi7gny2wcMbK2p5/dsaqpqygSzefvQbjp01miN3G8XkEVnRFZdRAKX7mxer1nwo+/GZdFatILWlEupXhy5rYMMSCLTveHxyBgtEPi1JI+DNSiicDAWTzS8hZo2M6Nhpu1BtH7mr7xtkZcrMlYVqHpzq1xGDZq/Xq9QB57LqVS1XFjHX+/HHcMkl8L//QWkp3Hdfr3/oTvMb6wBPpocnnngCiP6EItb9wh+3YMGCmGoNn2EOP85QxkwHDN1tstvFgVOKWPPBy/ygGJZ11LGpK5+tdV38ZXETf1n8HROLMjlyxkiO3G0U8yfk9z4OOhpSMmnNnkTqhL17/z0YhJYqaFgbuqyjsH4NhZ718NHtEOzx0WdKFhRMhPyJ5iA69HNyW4e5IkgEJ26JhoH8DuX4a6ftG2JFRr27mtv++ptqHhLVb39EWq8jBs0yPgKQiax6VcuVRdT1dnWB2w1jx0J+Pmzfbg6aw2bAtF8TlTzIdjvUL7qFP/6ll14a9PHhZyrs71AWIaDQ1U6hq53S0mS2+1wUzDqEt76r46GPK7nvgw3kpCVx6PSRHDljJIdMG0FBZv+rwETcVpcLcseal0mH9b6ty28e1tGw3jxpS8M62L4B6lbCqlch6Adgd4C3UsxDPvIn9LhMhPxSyCuFtJx+a+0PlfquzFxZqPQa1m7VzJVFpPU6YtDc0NCg1OkaZdWrWq4soqr3iiugtRUefRTGjYOPPrInNwoc7TfK3FhPrtDf44Z6KteBjmmWQVJS0qBnRvT5fIPmhNd9/PHHd3u46IAJtHYG+GBNPW+trOWdVXW89FUVQsCccXkcPn0kR8wYyczROeZydoPUGxXu5NCM8iTgqN63BbugeSt41rP5q/cZlxWA7RvNy+bPobOp9/3T883Bc/6EHQPpvFJzoJ03DpJ3PpxqoL47lBN76H2DiYx6tVsT1Tw41a8jBs0qbRiQV69qubIYtF6fb8e6yuPHQ1vbjmOZh5IbI47zm0C5st0O9Qxq4Y+/4IILBn3M+PHjAbq//R3JlyXDPWSlJnHcrGKOm1VMMGhw092PssabwnYjj1vfXM3f31jNiOxUDp02gkOmjeDgKUXSz5CIyx0a8I6noTmPceGHwLRvB88GaKyE7ZWh641Q8w2sWgxdYW8mskZ155E7DvLGMypvPIhx5qA6xb6TL6j0mpCJSq9h7VbNXFlEWq8jBs1VVVWUlJTEu4yIkVWvarmyGLDeZcvgpJPg6adhv/3g+uvtyR0CjvJrU26sA7Lwx8Vaa/jAcLjWFR6o3vDDMkQEX6Cz6h4o1+USjEkPMCY9QFnZgdS3dvL+6m28/V0db66s5ZkvtiAEzB6bx6HTRtDalsSY9MCgubaTng9j8mHMXjvfFgxCaw00bgoNqDeZg+rGStj6Jax4sfvQj24yCkOHkYwLDazNn8uO2dP8WwRvpC2cvG+I5k2SjHqd7DYaVPPgVL+OGDSrtGFAXr2q5cqiz3rb2yE9HaZOhTlzILT+5ZBzbcARfhM0d7jcDnVQHcvjrRnnSOjPw0DHKZ+211i6ggZfb2nkvdXbeG/1Nu54ew1BI5/stCQ+e3gpB00dwcEpXkoLMyIaxEvD5YKcEvMyfr+dbw92mWdHbNxsDqibNpk/N202V/xY9zb423o/Jiltx6C61/UY8+ecMZBsfnFIpdeETFR6DWu3aubKItJ6HTFoduo7GqfnymKneq+6Cr74Aj78ELKyIPRlqyHn2oTyfhM4N9rMeJ/Brq96w1cGsZZGiqam/jxYh3j0h9slmDs+n7nj87nmqGk0tvlYsqaeD9bU88Hael771jxhwdj8dA6eWsRBU0aw/+TCAb9QGBdcbsgpoaoVSmbvu/PthmEe/tG4yRxIN20NXW8xL2veMGeyw8kogtyxtKcWkj5qqjmozgkNqnPHmIeIDGEFkHjuG2J5LSTCazjeubJQzYNT/Tpi0KzShgH13tkp6belxRwgC2EehlFYCIFATDPMvXIlEO9/itEOCFXyoGTfHcbc4uLiXr8P1hfyMlJYsGcJC/YswTAMNtR7+WBtPUvW1PPyV9U8/pl5wpOZo3M4YHIhB04pYp+JBWSmJsa/mn79CmGuR51RACVz+r5PoBOaq3YMpJu2dA+s05u3wrJPwdfa+zGuJMge3WMwPQZyxu6Ysc4Zaz5nP7P0uv+qtb+RSSJ4iOZ/hlP9JsaebIjU1NTstPNPZGTVq1quLLZ99BEjTj0VbrsNzjkHzj/fllzt10QlD9FmSv+y2yD0Va8dNYXnhs8iRnMigp51ZHS1cuH+E7hw/wkEuoJ8taWJj9fV8+HaBh76xFzWLskl2HNcHvtNKmC/SYXMK80nIyU+/3qG1MeSUs11pAsm9p07ahR0NJkrgFgz1d0/b4GtS2HFCzsfW52UHhpA95ihzhkDuWPZ5k9jxKQ9ITXKE9LYQCz9LhFew/HOlYVqHpzq1xGD5pEjR8a7hKiQVa9qubbj8UBBAYV77w0nn2wev2wjTvE71EMQVPKgTN8NES+3sf5z65mb5HYxrzSfeaX5/OSIqXT4u/iicjsfrq3no3UN3PXeeu58Z12vQfS+E81B9HDNREv1KwSk55mXUbv3fcdgELzboHnLjsF0c4/rdW9DSzVgADDCelxa3o61r8MPAckdC9kl4I7/v3OVXsN63xB5biz/M5zqN/6vsgEQQiwAFkycOJG2tja6urowDIPk5GTa29vJzs5m+/btCCEIBAKMHj26+7iU6upqiouL2bZtG/n5+bS0tJCeno7f70cIgdvtxufzkZGRQXNzMwUFBdTV1VFcXNyd0dZmfjGktraWwsJCmpqayMzMpLOzs3t90kAgQGpqKl6vl9zc3O61/qwM67qmpoaRI0fi8Xjw+/2kp6eTkpLSb5tGjBhBTU1NVG1qamoiKytrwDZZ19G0acOGDUydOnXANuXk5NDW1hZVmwBbtlNVVRUlt91G12OPYaxYwaZt2yi5/XazTV5vzNspvE0ej4eRI0cOeTuFtykYDBIMBoe8ncLbZPXf8Da53W4MwyA1NRUhBEIIXC4Xfr8/ojYFg0FSUlJi204DtMmqb6jbqWff8/l8uFyuqLfT6aefjtvtprGxsc82WW7t2E492+RyuXC5XH226eyzz6a9vT3i7dSzTXV1dRQUFHRvp/POO4/m5mZefvllkpOTOf/887tfk/216emnn8YwDKqrq3G73Tz44IOkpKRw5pln9tumyVkB9j9mGh6PB1dqBp+sqeXLra18tnE7d727jjvfWYdbwMySHPYoTufQ3cYwPqP3yhy27SNKSli3bh0TJ060bV9ubafW1lZyc3MH3+/V1Jhtag1SvNtefbcpGCCpfRsBzyYCDRtIaa8jw++hc9t60pq2Eqz8GFfYmtWGcBHMHIXIHUsgyzwcxMgZSyCzmKTCiXiT8skZOa67TeH9d6DX02mnnUZycjIej2fQ/V5SUhKGYQx5O/Xse52dnWRmZtqynXq+nqz67NyXFxQU7OTWrn1ESkoKgUBgSP9z+2rTli1bKC4uHrBNycnJ+P1+cnNzaWpq6v59oDZt27aN4uJi2/blVpusfZDd4701a9Ywffr07jb1Oy41DCOacWxcmD9/vrF06dJ+b/f5fKSk2P/Fk4qKCimny5VVr2q5Q/bb2AhpaeblvffM02D/7Gf4hFDKQ7z8xvpxv0oeYsmMxItT9g3l5eXdH0tGeqpya7aptLQUIQQXXXRRTDV5OwN8Ubmdzzd6+HSDh4rNjfgCQQDG5SRx0IzRzCstYH5pvm2rc6jUdwfM7Ww1Z6YbN/eetba+vNi8ded1q1NzzVnpvHFs86czYsrc0FJ7oZPBZI6IeIm9qOtNsEyZuU7ZN/RFNP8zVPcrhPjCMIz54fdL6JnmSLHemaiCrHpVyx0StbWw++7w85/DddfBoYeaF6CtsVEpDwnpdwBU8qDdDpxbVlbW/Y9wMPo6xrWxsTHmmjJTkzgkdPIUgM5AF19vaeKzDR7e+nojL3+944uFRVmpzCvNY35pAXuV5rN7SQ5pydGvSqFS3x0wNzULRkw3L30RDIK3zhxAN27aMaAOLbNX0LARNvy392OS0s3Bc4+TwZiXUvOMixEMqlV6Det9g5q5soi0XkcMmhNtwwz2bkxWvarlxkRlJZSWwqhR8NOfwgkn7HQX1TzEy2+sX3JTyUM0mfFebg6G122k7R3Ig531pia52XtCAXtPKODA/FZmz96T1XUtLN24nS8qt7O00tO9xF2K28XMkhzmjs9j7vh89hqfx5i89EFnoyOpN5btnnCvCZcLsovNy9idJsv4pqKCOTMm7lirunFT70vVMmhr6P2gpHRzEG2dsjx/Qo9LKaRmx/01nAi5skgED4nwmpBFpPU6YtDc1dUV7xKiQla9quVGzZ//DH/9K6xZA8XF8H//1+fdVPOQMH4jRCUP2q39uT3/ccr063IJZhTn8Hz5vxgFLFm4kLrmDr7c1MiyzdtZVtnI459t4oEPNwLmbPSccbnsOTaPPcflMXtsLnkZvf8RquB3OHIBSMuF4lwontX37Z2tPQbSPU5d3lgJmz6FsOOqySgkNWccFE2G/NAqI9Z1VrE5kI8BJd1KQDUPTvXriEFzohyXHenMjax6VcuNiPp687qoCM4+GzIyoKBgwIeo5iFR+m+kqOQhmsx4LzcHw+t2sPZGsj8b7r47MieN42YVc9wsc7UPf1eQVTUtfLlpOxWbG/lqcyNvrqzrvv+Ewgxmj81jjzG57DE2l/HZguzsvrOH8kmDSq+JiEnNglEzzUtftG+H7Rt7XCoxtq2FrV/At8+D0WMQkpRmzkgXTNpxKZxsXueMGfAEMI50GwOqeXCqX0cMmpOHcMKKeCCrXtVyB8XrhZkz4ZRT4J57zCXkrr120Iep5kH3X3m52q2auQCLFi0CdpwJ0fp94cKFO57f7WLWmFxmjcnlwv3NvzV3+Fm+pYmKLeYgeulGDy9+VdX9mElFmcwak8seY3LZvSSHmSU5O81IR4uKfiOl3zcP6fnmpWRu95/87e0kpadDl9887MOzATzrzUG19fO6tyHQsSPHnRIaRE/ZcW1dskY62m00qObBqX4dMWhub28nPT093mVEPFMlq17VcvvEMOCrr2DOHMjMhJtugv33jypCNQ+J0n8jRSUPsWTGY4bZIh5u+2tvJPuzROy7OWnJHDCliAOmFHX/rb61k2+2NvH5mhrWenx8HjaQHpOXzsySWcwcnUN7SwqjUgNcdNH3I16xQ6XXhEy663Un75hR5sjedwoGoaXKHEB71kPDWmgIXa95vfeqH6k5JOdNgFG7QeFUKApdCiZDcpo9tSqCan3MjlzDMGxZNScSIq3XEYPm7P4+b0tQZNWrWm6f3HknXH21OXCeNQsuvTTqCNU86P4rL1e7VTMXdswo9zXDHC1FWakcPn0kB03K755Ramjt5NuqZlZUN/NtVTPfVjXx5spaDCMXgPtveJ0ZxdnMGJ3NjOIcdhudzdRR2eSk7TwjpaLfwYjlcJWI6nW5dpykZeIhvW8Ldpkz1A3rzEF0/Rpc9ath44fw9ZM77idc5pcRR0yHommh69BqImk5EbVP7xsSJ9cwDKqbOviuppnvalpYVdPCd9UtpCa7ePEnB0mpL5xI643boFkI4QaWAlsNwzhxKFnbt29PqLPPDDZTJate1XK7qamBtjaYNMk85XVSEsyYEXOcah4Srf8OhkoenO420uNuh+JhoGwZfsvLy8nIyJCyFmvPeguzUnsteQfQ5gvwXegf9nc1zXxX3cILFVU80rGp+z4luWlMHZXN9OJspo7MYnpxNjm0M2HsaKn1qsCQ63W5d6zIMcWcoa6vqzMzfd7ugTT1q2HbKvN63du9Z6dzxsCIGTByt9CyfLvByBmQ2ntQtMu5TYBcwzDY1tLJ6tpW1tS1mNe1LayqbaGlI9B9vzF56UwvzmbWmFzb64qm3r6I50zz1cBKILK3hQMwYsSIwe+UQMiqV7VcAAIB2G8/89jlxYshPx+uvHJIkap50P1XXq52q2ZuT4YywxzOYPVmpCSx1/h89hqf3/03wzCoaupgZVUzq+taWFPbyqqaFj5e39B9MhYwB9OTR2YxdWQ2U0ZmMWVkFpNGZFKYmRLzR8zx7L+xfDFW6ms4JRNG72leetIVMFf02LYKtq00r+tWwucf9j52One8+aXGkeZlxMjdIJAHSWosjabSa7graOAVGby1spZ121pZV+dl7bZW1ta10tTu775fXkYy00Zmc/KcEqYX5zCjOJtpo7LJTR/+46Ej9RCXQbMQYizwPeBGYPBvdg2CdZpIVZBVrzK5hgFvv22ugpGUZB6SMW2abfHKeJCcKwuVPDjVbbQfnauwzXq2aezYsVJWMYmlXiEEY/LSGZOXzlEzR3X/PdAVZJOnjdW1LSxbV01tu2BNXSuPfVZJh3/HYDonLYmJI7KYXJTJxKJMJo3IYkJRBqWFmWSlJg3YTqf2X1sz3UnmShyFk2FGj3X7g13mFxC3fQd1K6B2hTmYXvsmBAMIAFeyOStdPAtGzYJRu0PxHpBZ1M+TxY9Eew0bhkF9q48N9V421ntZX+9lQ31r6Pc2fF07XgNFWSlMGpHFibNHM3VkFtNGmYc6FWXF/obSbiL1EJfTaAshngFuArKBX/R1eIYQ4nLgcoDRo0fPW7x48fAWCTQ0NAx4DnJNbOS9+ioTfvMbvvzrX3Edc0y8y3Esuv/KI95uV69eDUBLSwuw43i8aTa++RxuerYpMzMTV2hdX9XaFDQMtnm72NIcYGtLgKqWAFubzeuG9t5rwealuchz+ylKDTJ9TCGjstwUZyUxMtNNbqpL2oAi3v03noguH6mtm0lvXk9a8zrSm9aR3ryO5I4dJ3PxpRXRnjvVvOSZ176M4ohOM+4kt11Bg4b2Lmpau6hpDfS4mL+3+XeMH5NcUJyVxOisJMbkJDE2dBmTnUx2amxrdPfFcPmdO3dun6fRHvZBsxDiROAEwzB+JIQ4jH4GzT2ZP3++sXTp0n5vr6qqoqSkxNY6Qd45zmXVm9C5W7aYp76eNw/8fnjqKSpmzGDOvHn2FNmDhPYwjLm6/zrfbaSzsSptM+uY5rPOOsvWXIi/hzZfgA31Xiob2nj29ffx+NxUtQRoCabSRu/DBDJS3BRnJzNpZC5j89MZm2/Odo8JXRcM4bCPROm/8crsM9dbD7XLoWY51HwDNV+bh3pY602n5sLo2ebhISVzzUv+xJ1O2qKS20BXkK/XbqIrNYet29vZ7Gljy/Z2Nm83r6sa2wkEd4wRk92CcfkZjC/MoLQgg4lFmUwoymRSURYleWkkuXe4UH3fK4Toc9Acj8MzDgROEkKcAKQBOUKIRwzDuCDWQBkbRiay6k3YXMOAk04ylxpatgySk80v/FVU2FJfOAnrYZhzZaGSB+1WzVxZxNtDRkoSu5fksntJLnVftANQ6TMPsRk9rpTtfjfzDj6Gzdvb2ORpY7PHHMh8vK4er6/3LHV6spuSvDRG56YzOjfNvOSlUxz6eVR2GnkZycP68bdKr+GdcjOLYNJh5sXC324e2lH9tTmIrqqAz+6FLnPt8O6BdMlcGDPPvEiaiIzWQ6AryLbWTqqbOqht6jCvm83rqkZzQFzT3EEwrNwR2amMy09nzrg8Fuw5mrH55gB5fGEGo3PTcbsi609O3TcM+6DZMIzrgOsAesw0xzxgBqiurlbquC9Z9SZUbjAIzz0HJ59sDpLvugtGjozo462hklAe4pgrC5U8ON1tpMf7qrTNysrKqJD0hjqRPETyRTsr1zAMmtr9bNneztbGdraGrqsa26lu6mDJmnrqWnYeAKW4XYzMSWVkdiqjctIYlZNGUVYK7du9NKTWUpSVyojsVAqzUkhN6v+sfJGi0ms4otzk9B2DYYsuv3lsdNUyqK4wB9Kf3tW9gsfuqQWwcp/Q4/Yyr9Pz+4yPtt7i4mJaOgPUt3TS4PVR39LJttZO6po72dbSSV1LB3UtndS1dNLQ2rlzf0hyUZyTxpi8dPafXMSYvDQyhY/dSospyTM/zUhLHno/sOp14r7XEes0FxcXx7uEqJBVb0LlvvsunHkmPPKIOau8zz6219UfCeUhjrmyUMmDdqtmrixU82DlCiHIy0ghLyOl32W4/F1BtrV0Ut1kDqTrmjupbTGv61o6WFPXygdr63cs7fV570Mes1OTKMhKIT8jhcLMFAoyU7p/z0tPJi8jOVRDMnnpKeSmJ5OW3Pu4a5VewzHnupNDh2nMBi4y/xbohNpvYesXtHzzJgXbN8Lq14DQqLVoGozdB8bOh7F7w8jdCOKipSNAU7ufpnY/je0+mtr9bG/zs93rw+P1sb1tx3VDq4+G1opeX7CzcAlzCcWR2eZlVkkuo3JSKc5Npzg3leIc8xOI/D4+eZB1AhGn7hviOmg2DONd4N2h5mzbtk2p9RZl1Rv33I0bYdUqOPZYOPxwePVViMMX/eLuIUFyZaGSB+1WzVxZJKKHgT4tiCY32e2iJC+dkryBz2rW4e/i/c+WMXL8FLa1dFLfas5SekIDNY/XR3VTB99WNePx+vocpO14TkF2WjI5aUnkpCeT6jIozM4gMzWJrFS3eZ2WRFZqEhkpSWSkuElPdpOe4u7+OS10SU12kZrkIsXdeyCeCNssGDTwdQXpDATp8HfR7uui3d9l/hz63esrpo1jWZ21G3kTRuEf30R+43JGNn3DWO9yJn31EjkVjwDgJY1lwSl83jWdpcY0KoJT8NJ7u2WnJZGfkUJ+ZgpFWalMyEthTFEORZmprFj2KZlJQc499URGZKVSmJUa8WETQ/GQCLmyiLReR8w05+cP/aOP4URWvXHP/dGPYPlyWL/eXEruuOOk1DMYcfeQILmyUMmDdqtmrixU8yAjNy3ZzcjMJOaMyxv0voZh0O7vorHNz/Y2H01tfhrbzZ+b2wM0d/hp6fB3/9zY5mN9fSutHQFaOwN4fV10hR8jEAGpSeYAOtntIsktSElykeza8bvbJXAJ89otBC4XuF0CYS4k1+dRgEHDIBiELsPAMAy6ggZdxhq6gkECXQaBoEGgK0ggaOAPDZB9oUsg6jY0kZLkIjOlhOy0UrLSTiE7183kpG3sHlzFVN8KprUt58C25xAYGMJFW/5MfCV745pwAOlTDiYlr/ehAn6/v/tsluXr3wdg95Khn/xDpb4rk0jrdcSguaWlhYKCgniXETGy6h323K4uePBBOPVU86Qkt98OaWnmgDmOOMZvgqKSh0R0O9AxrCq5lZkrC9U8hOfKWLt6IIQQoRnipEFnsAE8Hk+veg3DoDMQNAfQnQHa/V20+bro8JnX1ixtZ1eQTn8XnYEe14Eg/q4grW3tuJNS8HWZg1t/V5Cu0KA3aF0HzcNTrOeE7gMjMAzz8AWXMAfaQoDb5UIYAbLSUkh2mYPvZLcLt0uQ5BYku1ykJpuz3ilJOy6pSdZMuYu0JDdpKW7SksyZ86xUNxkpSaxfvZJ9580h2R3BMmsdTbDlc8SmT8nc/AmZq56C5Q+YtxVOgdIDoPRAKD2AlmAWL774IhDdqc0HI5q+G83zOXXf4IhBc3r64C/mREJWvcOe+913cNll0NwM11wDU6ZIef5ocYzfBEUlD9qtmrlDYaB/7Kp5SES/AxFerxCi+/CLoqzUmDLb29uleJCVW5fiimzADJCWC1OOMi9gfsmw+muo/BAqP4IVL8CXDwGQlzuOA7tKqE6bisdIp0VkDxAcObrvmkRaryMGzX6/X6kNJKveYcldswY++gguugh23x0+/RTm77SUYVxR2q8CqOQhkdxGchY/ldzKzJWFah6s3GjPABkvVHoNJ2TfdSfD2Hnm5cCfmqtQ1a2Ayg/pWvMOU7d+wtT6TzkEaHYXkVNwAkw81FxjOsazGEbiIZb+l5B+ByDSeh0xaE6U0zBGiqx6hyX3b3+DZ5+F00+HrCzYe28pzzkUlParACp50G7VzI2FSP6xq+YhkfxGgkqvYSXculzmKb6LZ9Ex8zySMzOhdjmfPXULxR1ryfl2x0w0xXvsWGd6/AGQkhHRU+zSfnsQab2OGDS73fasKzhcyKpXSm4gQOr998P3vgdTp8KNN8If/2gOmBMUpfxKzJWFSh4SyW0k6/Kq5FZmrixU82DlRtJ3EgGVXsNK9l2XC0bPZp+rHzT/GOwy14le/w6sfxc+vRs++ie4U2DcvqFDP46EUbP6PUdCJB5i6X9K+o0ARwyafT4fGRmRvatKBGTVKyW3vp7k3/4Wtm+HP/wBRoywN18CSvmVmCsLlTxot2rmxkIk/9hV85BIfiNBpdewI9y63DsO5zjkF+Brg00fm4Pode/Am783L1nFMPkIcwA9+QjIKBg4V1a9CUyk9UZ4tHp8EEIsEELc4/F4aGtro6WlhebmZtrb2/F4PPj9furq6khPT6e6uhowzx8O5tldDMOgrq4Ov9+Px+Ohvb2d5uZmWlpaaGtro7GxEZ/PR319PcFgkJqaml4ZbW1tANTW1hIIBGhoaKCjo4Ompia8Xi9er5empiY6OjpoaGggEAhQW1vbK8O6rqmpIRgMUl9fT1JSEo2NjQO2yTCMqNskhBi0Tdb1QG1q+fRT/H/6k9mmoiJq//c/uOGGAdvk8/mktCmS7RTeps7OTlu2U3iburq6pLQpLS0tpu00WJus/mv3dkpOTrZlO4W3yTAMW7ZTzzYlJydL6XuW21i20/HHH09ZWVmfbUpLS5Pyeurq6rJtO/VsU2dnpy3byc7XE9C9NFd4PV6v15Z9eXl5OY888kh3m4QQUtqUlJTUazuVlZVxTGjt+6HsI8L7r13bKT093dZ9eUNDA2632/Z9eXNzMy6XS8r/p3C3duzLq6qqyMjIGHw7dQk8BXPxH/576k7/L8a1K2k87C9QegDB716BZy/F+NtkjPuOpnXxDfg3f4mvszPi7XTMMcf02ncN1KZAIGDrvlz2OKK5ublXm/pDWMuzJDLz5883li5d2u/t9fX1FBXFdhD8QFRUVDBnzhzbc2XVa1vujTfCzTfDihUwerT2q2iu9qvdqpqb6H7DZ7O1XxOVXsO7nNtgl3nq7zVvwJrXzJ+BrsxRuKcfC9OOM4+HTslMjHr7Ybj8CiG+MAxjp1UOHHF4hkprAYK8emPO9fngzjvNVTAOPhiuvRauuAJCHUj7VTNXFip50G7VzJXFUOvt78uGF1544ZBy+2NX8ztcmTJzZTHk9bpd7tBpvOfD4ddBSy2sfQPX6tfh2+fNLxQmpZkD5+knmIPo7FG21KsCkdab0IdnREpdXV28S4gKWfXGnNvVBbfeCv/9r/l7enr3gHlIuXEi4fzGKVcWKnnQbtXMlYVqHrRf7dbC9nqzR8HcC6g99P/BL9fBhS/AvIvNJe5e+incMh3uOwqW3AL1a+Jfr2QirdcRh2fIQtbHAAnB11/D3XfDP/9pfiO3rg6G+TzxjvabAGi/8tBu5ZLofhN9FYvBSHS/KiPTbfgnHaWlpYDN/dAwzIHzd4th1WKo+tL8+4gZsNsC81I8u9/VOGQzXH23v8MzHDHTbB3IrQqy6o0qd/lyePJJ82QlMOCAWftVM1cWKnnQbtXMlYVqHrRf7dZi2DwIAaN2h0N/CZe/A9euhOP/BpkjzFnnuw+B22bDa9fD5s/ME7AMY72yiLRePdM8AI56N+73wy23QGkpnHuu+W6yuRlyc+NWkqP8JiDarzy0W7lov3LRfuUxHG7j9kmHt8GcfV75krmsXZcPcsbC7qfA7qfCmHnSZ6D1TLMNOPUdja25bjc8/zy89575uxARD5i1XzVzZaGSB+1WzVxZqOZB+9VuLRLCQ2Yh7PV9OP8p+OVaOPVu84yFn94N9x0J/5gNr/8WqpZRtXWrlHplEakHR6yeUVJSEu8SokJWvTvlfv21efa+Bx+EzEx4882YzuSn/aqZKwuVPGi3aubKQjUP2q92a9GzXjtnmGP2kJYLe55jXtobYdWr8O1/4ZO74KN/UlI4BfY4E2adAUVTbKtXFpF6cMRMs7VotirIqnen3NZW+OADWLnS/D3GU19rv2rmykIlD9qtmrmyUM2D9qvdWiS0h/Q8mHNuaAZ6DSy4nc60Inh3EdwxD+4+FD66A5qrI44sLy/vPgxlOIjUgyNmmgsLC+NdQlTIqrewoMA81XVqKixcCAccABs3Qlra0HK1XyVzZaGSB+1WzVxZqOZB+9VuLZTxkJ4P8y7Cvef50FZnzj5/8zS8fj288TtzHeg9z4UZ37PtRCp2EKkHR8w0NzU1xbuEqJBVb1NzM3z3HaxateOPQxwwg/araq4sVPKg3aqZKwvVPGi/2q2Fah6ampogpwT2/zFc/i785As4+BdQvxae+wHcPA2e/xFseL/XChzWDHNlZSWVlZXDNuMcqYeEHjQLIRYIIe7xeDwDnos8IyNDyrnIrXPID/Wc8dD7/OrJycm2nV+9Y+lSAgcfTOt33+FyuWi8/XZ8d9/db5siOWd8eJssD4l6zvjwNvl8Plu2U3ibgsGglDalp6fbsp3C22RtN7u3U0pKii3bKbxNQghbtlPPNqWkpEjpe+GvCbv2Eenp6VJeT8Fg0Lbt1LNNPp/Plu00XPuItrY2W/flVptcLpeUNiUnJ9u6L7faFN5/7dpOGRkZtu7LGxoaSEpKsn1f3tzcjNvtltL3+vt/OdR9RGZmppTXk9/vt31f7vV66erq6t2mrmw44nqqznoVyhbjnXgsrHgRHlyAcdsetL3yO3x1a3G73bhcLlJTU0lLS0MIgdvtlr6PaG1t7dWm/nDEknNNTU3kSlg6TdbSJrbWu3EjHHUUlJfTtMceUjzs0n4VztV+tVtVc7Vfubkq+U10t+HLv6nkNu65vjZzCbuKR2HdO4ABEw+Fud/n4aXb6XKl7PSlx+Hy29+Sc444pjkpSa1mDLneP/4Rtm41z+g3YQKsXg0uF0lery31hbPL+XVIrixU8qDdqpkrC9U8aL/arYVqHiLKTcmAPc4wL42b4avHYdnD8NxlnC3SWZc1D4LfB5dbSo1R14tDBs27BIaxY9Hwjg7o7ISuLnP9ZVdCH2Wj0Wg0Go2jCD+ltfW7PmlMjOSNg0N/ZR73XPkBKcseYbe2hmEZMEeDIwbNAx1/kohEXe+qVeZZ/O65B+bPhxtv7POsO7I8ON6vQ3NloZIH7VbNXFmo5kH71W4tVPMQc67LBRMPMS/DePhwpPU6YtCcmpoa7xKiIuJ6g0GzA40eDSkp5mmvod/TVMry4Fi/Ds+VhUoetFs1c2WhmgftN3HdWsfa9nVMswwS1YPUXMmn5O5JpPU64nN9r6RjeWURUb033ghHH22+08rJgY8/hiOOGHpuDDjS7y6QKwuVPGi3aubKQjUP2q92a6GaB6f6dcRMs4xvfsqk33oDAfMYZSFg1P9v72xj5CrPM3w9sYm/jQ02eBdTm1QlBBywjaOQQivxETWiCfAPKpJitRGiSlNa0Q8QUvMjoUUtoo1S1MoC6kSxklbEtBBBBEpRokYNjQHzFafJj9jEeD9nvDvDzq53l33648yY9bDr3fWed4+f4/uSVjszO77nfq8ze/zuO2fmnA8XXZQdv7xs2az+4krloTR+z7DcVETyILcxc1MRzYP8nv5u8zyl9ck43T0sVG4qZtu3FCvNlUql6ApzYsq+b70FV1wBe/dm1z//eXj00WzCPJ/cHCiF3zMwNxWRPMhtzNxURPMgv3LbIpqHsvotxaT5/PPPL7rCnDih77Fj2ffOTrj44uxQjDxycyS03zM4NxWRPMhtzNxURPMgv3LbIpqHsvotxaS5dUaXKBzv+9BDcPnl2cR58WJ48snsOOb55uZMWL9neG4qInmQ25i5qYjmQX7ltkU0D2X1W4pJc2dnZ9EVZs/oKJ3r1mWXt27NzubXWm2eJ6k8hPJLPA/ymy5XbmPmpiKaB/mV2xbRPJTVbykmzWH+oqlW4bLLGPzKV7LrN9wAjzwyr0MyJqO/GDOieZDfdLlyGzM3FdE8yK/ctojmoax+T+tJs5l9xsx2VatVGo0G9XqdWq3G8PAw1WqVsbExent76ejooKurC3hv4F1dXbg7vb29jI2NUa1WGR4eplarUa/XaTQaDAwMMDo6Sn9/PxMTE3R3d5+Q0Wg0AOjp6WF8fJxKpcLIyAiDg4MMDQ0xNDTE4OAgIyMjVCoVxsfH6enpOSHjyJEjMDBAd3c3E2vWMHzttSz/+McZGBg46Zjcfc5jWrNmzYxjan2fy5gWLVo0ZUZ3dzcTExP09/czOjqaZEyz2U7tY1qyZMmpbacZxrRy5cokY9qwYUMu26l9TK3nb97baf369blsp/YxnX322blsp8ljWr9+fZLnXsttHttp8pg2bNiQ5Pdp5cqVuW2nyWNasmRJLttpofYRixYtymU7tY9pzZo1Sca0bt26XPflrTG1P3/z2k4dHR257ssrlQrnnHNO7vvyWq3G2rVrkzz32t3mtY/o7OxM8vu0dOnS3PflQ0NDrFixItd9eep9RIvWmKbF3U/7ryuvvNJPRldX10l/fqq88sor8w955BH3tWvde3uP35Sqb7TcXPxOQTQP8psuV25j5spv2txIfuU2I5qH6H6BfT7FfPS0XmmeLeedd17RFU5kZOS9s/dddx3s3Jmd0a9Jqr7RclMRzYP8psuV25i5qYjmQX7ltkU0D2X1W4pJc7VaLbrCe4yMwEc/Cvfdl12/5BJ4+GGY9MHZqfpGy01FNA/ymy5XbmPmpiKaB/mV2xbRPJTVbynOCLg6pzfSzYvDh2HjRli6FO66K/tkjGlI1TdabiqieZDfdLlyGzM3FdE8yK/ctkjtYffu3UB+Zzgsq99SrDS3DrwvjG98Izvl9ZtvZtfvuQeuv37au6fqGy03FdE8yG+6XLmNmZuKaB7kV25bRPNQVr+lWGn+4KTjhReMoSEYHMzO5HfjjXDvvXDhhbP6p6n6RstNRTQP8psuV25j5qYimgf5Pf3d5r1COx2pPDz11FNMTExw6NAhIL/xlPW5W4pJ87vvvruwDzgxAVddlR2O8eyzsG4dfPnLs/7nqfpGy01FNA/ymy5XbmPmpiKaB/mV2xbqm5bZ9i3FpDn7dJAF4I03YMsW+MAH4P77s0nzKZCqb7TcVETzIL/pcuU2Zm4qonmQ39PXbWtFtn2FdutJ3s80H1J5uOWWW1i9enXuK+Zlfe4u+DHNZnahmb1gZgfM7E0zu3u+mWeddVYe1d5jzx7YvJkrtm+HzZuz608/nX0qxnPPZfe57Ta45ppTis+9b9DcVETzIL/pcuU2Zm4qonmQX7ltEc1DWf0W8UbAceAed/8IcBXwBTO7dD6Bw8PDuRQDsgnynXfCoUOYOxw6lF2vVuGhh+Dqq+f9ELn2DZybimge5DddrtzGzE1FNA/ye/q63blzJzt37mTTpk1s2rTp+PVUpPaQd/+yPncX/PAMd+8CupqX62Z2ALgA+OmpZq5atSqndmSHXbS/i7LRgC99CQ4ezOUhcu0bODcV0TzIb7pcuY2Zm4poHuRXbltE81BWv1bkcSdmthn4IbDF3WttP7sTuBOgo6PjymeeeWbanOHhYZYtW5ZLpyu2b89WmNtwM159+eVcHiPPvpFzK5UK5557bu650TzIb7pcuY2ZK79pcyP5lduMaB6i+922bdtL7r6j/X6FTZrNbCXwA+ABd997svvu2LHD9+3bN+3P3R0zy6fY5s3ZIRntbNqU20pzrn0D5+7fvz/JmyaieZDfdLlyGzNXftPmRvIrtxnRPET3a2ZTTpoLObmJmZ0FfAfYM9OEeTZ0d3fPv1SLBx6A5ctPvG358uz2nMi1b+DcVETzIL/pcuU2Zm4qonmQX7ltEc1DWf0W8ekZBjwGHHD3h/PI7OjoyCMm4/bbYdcu2LQJN8tWmHftym7PiVz7Bs5NRTQP8psuV25j5qYimgf5ldsW0TyU1W8RK81XA58DrjOz/c2vG+cTeOTIkXyatbj9djh4MDuG+eDBXCfMkKBv0NxURPMgv+ly5TZmbiqieZBfuW0RzUNZ/Rbx6Rn/DeR6oEtnZ2eecclJ1TdabiqieZDfdLlyGzM3FdE8yK/ctojmoax+CzmmOW+6urqKrjAnUvWNlpuKaB7kN12u3MbMTUU0D/Irty2ieSir31JMmjds2FB0hTmRqm+03FRE8yC/6XLlNmZuKqJ5kF+5bRHNQ1n9lmLS3NfXV3SFOZGqb7TcVETzIL/pcuU2Zm4qonmQX7ltEc1DWf2WYtK8du3aoivMiVR9o+WmIpoH+U2XK7cxc1MRzYP8ym2LaB7K6rcUk+Z6vV50hTmRqm+03FRE8yC/6XLlNmZuKqJ5kF+5bRHNQ1n9lmLSnOJUjSlJ1TdabiqieZDfdLlyGzM3FdE8yK/ctojmoax+T+tJs5l9xsx2VatVGo0G9XqdWq3G8PAw1WqVsbExent7GR0dPf7Ox9Zn7XV1deHu9Pb2MjY2RrVaZXh4mFqtRr1ep9FoMDAwwOjoKP39/UxMTBw/I0wro9FoANDT08P4+DiVSoWRkREGBwcZGhpiaGiIwcFBRkZGqFQqjI+P09PTc0JG63t3dzcTExP09/cff+yTjcnd5zymer0+45ha3+cypukyJo9pdHQ0yZhms53ax9TX15fLdmof09GjR5OM6dixY7lsp/YxtZ6/eW+n1v3nu53ax1Sr1XLZTpPHNDw8nOS513Kbx3aaPKZjx44l+X06evRobttp8pj6+vpy2U4LtY/o7u7OdV/eGlNr/5v3mBqNRq778taY2p+/eW2n0dHRXPfllUrl+Djy3JfXajXeeeedJM+9drd57SPGxsaS/D719/fnvi8fGhqiWq3mui9f6HnEdJi7T/vD04UdO3b4vn37pv15vV5n1apVuT9uqnOcp+obLVd+0+bKr9xGzZXftLmR/MptRjQP0f2a2UvuvqP9fqf1SvNsWbRoUdEV5kSqvtFyUxHNg/ymy5XbmLmpiOZBfuW2RTQPZfUbYqXZzPqAQye5y9nAYIKHXgf0J8hN1TdarvymzZVfuY2aK79pcyP5lduMaB6i+93k7uvfdy93D/8F7EqUuy9Y32i58iu/SXPlNmyu/Mqv3Mb2UEq/pTg8A3i66AJzJFXfaLmpiOZBftPlym3M3FRE8yC/ctsimodS+g1xeEZRmNk+n+JAcJEP8psW+U2H3KZFftMiv+mQ27QU7bcsK82p2FV0gZIjv2mR33TIbVrkNy3ymw65TUuhfrXSLIQQQgghxAxopVkIIYQQQogZ0KR5CszsQjN7wcwOmNmbZnZ30Z3KhpktMrNXzOy7RXcpG2a2xsyeMLOfNZ/Dnyi6U5kwsz9r7hfeMLNvmdnSojtFxsweN7NeM3tj0m3nmNnzZvaL5ve1RXaMyjRu/765b3jNzJ40szUFVgzNVH4n/ezPzczNbF0R3crAdH7N7Itm9n/N/fDfLWQnTZqnZhy4x90/AlwFfMHMLi24U9m4GzhQdImS8lXge+5+CXAF8pwbZnYB8CfADnffAiwCbiu2VXh2A59qu+1e4Pvu/hvA95vXxdzZzfvdPg9scffLgZ8D9y10qRKxm/f7xcwuBD4JvLXQhUrGbtr8mtm1wM3A5e5+GfDQQhbSpHkK3L3L3V9uXq6TTTouKLZVeTCzjcDvAo8W3aVsmNlq4LeBxwDcfdTdBwotVT4WA8vMbDGwHDhScJ/QuPsPgWrbzTcDX29e/jpwy0J2KgtTuXX359x9vHn1x8DGBS9WEqZ57gL8A/CXgN40Ng+m8ftHwIPufqx5n96F7KRJ8wyY2WZgG/BiwVXKxD+S7VAmCu5RRj4E9AH/2jz85VEzW1F0qbLg7m+TrWy8BXQBg+7+XLGtSsn57t4F2SIGcF7BfcrKHwDPFl2iTJjZTcDb7v5q0V1KysXAb5nZi2b2AzP72EI+uCbNJ8HMVgLfAf7U3WtF9ykDZvZpoNfdXyq6S0lZDGwH/tndtwFD6KXt3GgeW3szcBHQCawws88W20qIuWNm95Mdirin6C5lwcyWA/cDf110lxKzGFhLdujsXwD/bma2UA+uSfM0mNlZZBPmPe6+t+g+JeJq4CYzOwh8G7jOzL5ZbKVScRg47O6tV0aeIJtEi3y4Afilu/e5+xiwF/jNgjuVkR4z6wBofl/Ql2DLjpndAXwauN31ubN58utkf1C/2vw/biPwspltKLRVuTgM7PWM/yV7xXrB3mypSfMUNP9qeQw44O4PF92nTLj7fe6+0d03k72B6r/cXSt1OeHu3cCvzOzDzZuuB35aYKWy8RZwlZktb+4nrkdvtEzBU8Adzct3AP9ZYJdSYWafAv4KuMndG0X3KRPu/rq7n+fum5v/xx0Gtjf3yyIf/gO4DsDMLgY+CPQv1INr0jw1VwOfI1sF3d/8urHoUkLMki8Ce8zsNWAr8DfF1ikPzRX8J4CXgdfJ9qE6A9g8MLNvAf8DfNjMDpvZHwIPAp80s1+QfQrBg0V2jMo0bv8JWAU83/y/7V8KLRmYafyKnJjG7+PAh5ofQ/dt4I6FfLVEZwQUQgghhBBiBrTSLIQQQgghxAxo0iyEEEIIIcQMaNIshBBCCCHEDGjSLIQQQgghxAxo0iyEEEIIIcQMaNIshBBnCGa2VR+fKYQQp4YmzUIIceawFdCkWQghTgF9TrMQQgTGzDYD3wNeBLYBPwd+H7gM+CqwAjhGdpKQ14FlwNvA37r7vxVQWQghQqJJsxBCBKY5af4lcI27/8jMHgd+BtwF3OruPzGz1UAD+Cyww93/uLDCQggRFB2eIYQQ8fmVu/+oefmbwO8AXe7+EwB3r7n7eGHthBCiBGjSLIQQ8Wl/ybA2xW1CCCHmgSbNQggRn18zs080L/8e8GOg08w+BmBmq8xsMVAHVhXUUQghQqNJsxBCxOcAcIeZvQacA3wNuBX4mpm9CjwPLAVeAC41s/1mdmthbYUQIiB6I6AQQgSm+UbA77r7lqK7CCFEmdFKsxBCCCGEEDOglWYhhBBCCCFmQCvNQgghhBBCzIAmzUIIIYQQQsyAJs1CCCGEEELMgCbNQgghhBBCzIAmzUIIIYQQQsyAJs1CCCGEEELMwP8D1ssa+PzgC88AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "show('pct', 'mph', segments[segments.pct > 2], \n", " 'Miles per hour versus segment grade in percent')\n", "plt.plot((2, 6, 7), (2, 6, 7), 'ro:');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both best-fit curves are above the red circle at 6% and below the red circle for 7%, so **my hill-index is 6**. We also see that I can cruise at 14 mph on a 2% grade, but only about 7 mph at 6% grade, and around 5.5 mph on 8% grades." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " # Speed versus Grade on Long Rides\n", "\n", "The plot above tell me how fast I should expect to climb a particular hill, but what about average time on longer rides? Here's a plot of my speed versus steepness (measured in feet climbed per mile rather than in percent)." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "show('fpmi', 'mph', rides, 'Speed (miles per hour) versus Ride Grade (feet per mile)')" ] }, { "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, 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": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Coast: 70 min, Creek: 64 min.'" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f'Coast: {estimate(15.7, 361)} min, Creek: {estimate(13.5, 853)} min.'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This predicts the shorter but steeper creek route would be about 6 minutes faster (whereas Google Maps predicts the creek route would be 80 minutes, 2 more than the coast route—I guess Google lacks confidence in my climbing ability). This is all good to know, but other factors (like the scenery and whether I want to stop at the San Gregorio store) are probably more important in making the choice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# VAM\n", "\n", "Climbing speed is measured by [VAM](https://en.wikipedia.org/wiki/VAM_%28bicycling%29), which stands for *velocità ascensionale media* (for native Campagnolo speakers) or *vertical ascent in meters per hour* (for SRAM) or 平均上昇率 (for Shimano), or *Vm/h* (for physicists). The theory is that for fairly steep climbs, most of your power is going into lifting against gravity, so your VAM should be about constant no matter what the grade. (For flatish segments power is spent on wind and rolling resistance, and for the very steepest of climbs, in my experience, power goes largely to cursing *sotto voce*, as they say in Italian.) \n", "\n", "Here's a plot of my VAM versus grade over short segments:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "show('pct', 'vam', segments, 'VAM (vertical meters per hour) versus segment grade in percent')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Champion cyclists can do over 1800 meters/hour over a 10 km climb, and can sustain [1400 meters/hour for 7 hours](https://www.strava.com/activities/4996833865). My VAM numbers range mostly from 400 to 800 meters/hour, and I can sustain the higher numbers for only a couple of minutes:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
titlehoursmilesfeetmphvamfpmipctkmsmeters
Camaritas climb0.010.104810.001463.0480.09.090.1615.0
Paloma Climb0.020.14827.001250.0586.011.090.2325.0
Klamath Dr.0.020.12776.001173.0642.012.150.1923.0
Entrance Way Hill Repeats0.020.10765.001158.0760.014.390.1623.0
Davenport Kicker0.020.247412.001128.0308.05.840.3923.0
Valparaiso steep0.040.181454.501105.0806.015.260.2944.0
Invernes to Firecrest Climb0.040.281437.001090.0511.09.670.4544.0
Kings Mountain final sprint0.040.311357.751029.0435.08.250.5041.0
Limantour Spit0.090.473035.221026.0645.012.210.7692.0
Tunitas flattens0.050.421668.401012.0395.07.490.6851.0
Tunitas flattens0.050.421668.401012.0395.07.490.6851.0
Cemetery Sprint0.010.08338.001006.0412.07.810.1310.0
Skyline Bump at OLH0.020.216310.50960.0300.05.680.3419.0
Laning Bump0.030.24948.00955.0392.07.420.3929.0
Faught Turn0.020.226011.00914.0273.05.170.3518.0
Westridge 3min0.080.372404.62914.0649.012.290.6073.0
Valparaiso steep0.050.181453.60884.0806.015.260.2944.0
Sharon Park steep part0.030.21867.00874.0410.07.760.3426.0
Old La Honda Mile 10.130.993707.62868.0374.07.081.59113.0
Joaquin0.090.332543.67860.0770.014.580.5377.0
\n", "
" ], "text/plain": [ " title hours miles feet mph vam fpmi \\\n", " Camaritas climb 0.01 0.10 48 10.00 1463.0 480.0 \n", " Paloma Climb 0.02 0.14 82 7.00 1250.0 586.0 \n", " Klamath Dr. 0.02 0.12 77 6.00 1173.0 642.0 \n", " Entrance Way Hill Repeats 0.02 0.10 76 5.00 1158.0 760.0 \n", " Davenport Kicker 0.02 0.24 74 12.00 1128.0 308.0 \n", " Valparaiso steep 0.04 0.18 145 4.50 1105.0 806.0 \n", " Invernes to Firecrest Climb 0.04 0.28 143 7.00 1090.0 511.0 \n", " Kings Mountain final sprint 0.04 0.31 135 7.75 1029.0 435.0 \n", " Limantour Spit 0.09 0.47 303 5.22 1026.0 645.0 \n", " Tunitas flattens 0.05 0.42 166 8.40 1012.0 395.0 \n", " Tunitas flattens 0.05 0.42 166 8.40 1012.0 395.0 \n", " Cemetery Sprint 0.01 0.08 33 8.00 1006.0 412.0 \n", " Skyline Bump at OLH 0.02 0.21 63 10.50 960.0 300.0 \n", " Laning Bump 0.03 0.24 94 8.00 955.0 392.0 \n", " Faught Turn 0.02 0.22 60 11.00 914.0 273.0 \n", " Westridge 3min 0.08 0.37 240 4.62 914.0 649.0 \n", " Valparaiso steep 0.05 0.18 145 3.60 884.0 806.0 \n", " Sharon Park steep part 0.03 0.21 86 7.00 874.0 410.0 \n", " Old La Honda Mile 1 0.13 0.99 370 7.62 868.0 374.0 \n", " Joaquin 0.09 0.33 254 3.67 860.0 770.0 \n", "\n", " pct kms meters \n", " 9.09 0.16 15.0 \n", " 11.09 0.23 25.0 \n", " 12.15 0.19 23.0 \n", " 14.39 0.16 23.0 \n", " 5.84 0.39 23.0 \n", " 15.26 0.29 44.0 \n", " 9.67 0.45 44.0 \n", " 8.25 0.50 41.0 \n", " 12.21 0.76 92.0 \n", " 7.49 0.68 51.0 \n", " 7.49 0.68 51.0 \n", " 7.81 0.13 10.0 \n", " 5.68 0.34 19.0 \n", " 7.42 0.39 29.0 \n", " 5.17 0.35 18.0 \n", " 12.29 0.60 73.0 \n", " 15.26 0.29 44.0 \n", " 7.76 0.34 26.0 \n", " 7.08 1.59 113.0 \n", " 14.58 0.53 77.0 " ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(segments, 'vam')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On segments that are at least a kilometer long my VAM tops out at about 800 meters/hour:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
titlehoursmilesfeetmphvamfpmipctkmsmeters
Old La Honda Mile 10.130.993707.62868.0374.07.081.59113.0
Westridge0.140.683854.86838.0566.010.721.09117.0
Old La Honda (Bridge to Stop)0.483.3312556.94797.0377.07.145.36383.0
Old La Honda (Bridge to Stop)0.513.3312556.53750.0377.07.145.36383.0
Westridge0.160.683854.25733.0566.010.721.09117.0
Tunitas steep0.251.205994.80730.0499.09.451.93183.0
Old La Honda Mile 10.160.993706.19705.0374.07.081.59113.0
Woodside Climb0.131.7129513.15692.0173.03.272.7590.0
Huddart0.170.923855.41690.0418.07.931.48117.0
Top of Groton Rd heading west0.130.922917.08682.0316.05.991.4889.0
Watts (Sonoma)0.141.203138.57681.0261.04.941.9395.0
Tunitas steep0.271.205994.44676.0499.09.451.93183.0
Canon to No Cycling0.090.751988.33671.0264.05.001.2160.0
Canon to No Cycling0.090.751988.33671.0264.05.001.2160.0
Coe Second Switchback to flat0.221.004834.55669.0483.09.151.61147.0
Lower Redwood Gulch0.221.034744.68657.0460.08.721.66144.0
Lobitas Creek0.200.964304.80655.0448.08.481.54131.0
West Alpine switchback0.150.783225.20654.0413.07.821.2698.0
Kaboom Portola Rd0.050.6710213.40622.0152.02.881.0831.0
Huddart0.190.923854.84618.0418.07.931.48117.0
Kings Greer to Skyline0.783.9215365.03600.0392.07.426.31468.0
Woodside Climb0.151.7129511.40599.0173.03.272.7590.0
Stage Rd0.191.013735.32598.0369.06.991.63114.0
Try not to fall back0.210.714103.38595.0577.010.941.14125.0
Sand Gill Sharon-top0.070.8513612.14592.0160.03.031.3741.0
Sand Gill Sharon-top0.070.8513612.14592.0160.03.031.3741.0
Mt Eden climb0.141.022727.29592.0267.05.051.6483.0
Tunitas lower climb0.221.304215.91583.0324.06.132.09128.0
Kings Greer to Skyline0.813.9215364.84578.0392.07.426.31468.0
Haskins0.301.515665.03575.0375.07.102.43173.0
\n", "
" ], "text/plain": [ " title hours miles feet mph vam fpmi \\\n", " Old La Honda Mile 1 0.13 0.99 370 7.62 868.0 374.0 \n", " Westridge 0.14 0.68 385 4.86 838.0 566.0 \n", " Old La Honda (Bridge to Stop) 0.48 3.33 1255 6.94 797.0 377.0 \n", " Old La Honda (Bridge to Stop) 0.51 3.33 1255 6.53 750.0 377.0 \n", " Westridge 0.16 0.68 385 4.25 733.0 566.0 \n", " Tunitas steep 0.25 1.20 599 4.80 730.0 499.0 \n", " Old La Honda Mile 1 0.16 0.99 370 6.19 705.0 374.0 \n", " Woodside Climb 0.13 1.71 295 13.15 692.0 173.0 \n", " Huddart 0.17 0.92 385 5.41 690.0 418.0 \n", " Top of Groton Rd heading west 0.13 0.92 291 7.08 682.0 316.0 \n", " Watts (Sonoma) 0.14 1.20 313 8.57 681.0 261.0 \n", " Tunitas steep 0.27 1.20 599 4.44 676.0 499.0 \n", " Canon to No Cycling 0.09 0.75 198 8.33 671.0 264.0 \n", " Canon to No Cycling 0.09 0.75 198 8.33 671.0 264.0 \n", " Coe Second Switchback to flat 0.22 1.00 483 4.55 669.0 483.0 \n", " Lower Redwood Gulch 0.22 1.03 474 4.68 657.0 460.0 \n", " Lobitas Creek 0.20 0.96 430 4.80 655.0 448.0 \n", " West Alpine switchback 0.15 0.78 322 5.20 654.0 413.0 \n", " Kaboom Portola Rd 0.05 0.67 102 13.40 622.0 152.0 \n", " Huddart 0.19 0.92 385 4.84 618.0 418.0 \n", " Kings Greer to Skyline 0.78 3.92 1536 5.03 600.0 392.0 \n", " Woodside Climb 0.15 1.71 295 11.40 599.0 173.0 \n", " Stage Rd 0.19 1.01 373 5.32 598.0 369.0 \n", " Try not to fall back 0.21 0.71 410 3.38 595.0 577.0 \n", " Sand Gill Sharon-top 0.07 0.85 136 12.14 592.0 160.0 \n", " Sand Gill Sharon-top 0.07 0.85 136 12.14 592.0 160.0 \n", " Mt Eden climb 0.14 1.02 272 7.29 592.0 267.0 \n", " Tunitas lower climb 0.22 1.30 421 5.91 583.0 324.0 \n", " Kings Greer to Skyline 0.81 3.92 1536 4.84 578.0 392.0 \n", " Haskins 0.30 1.51 566 5.03 575.0 375.0 \n", "\n", " pct kms meters \n", " 7.08 1.59 113.0 \n", " 10.72 1.09 117.0 \n", " 7.14 5.36 383.0 \n", " 7.14 5.36 383.0 \n", " 10.72 1.09 117.0 \n", " 9.45 1.93 183.0 \n", " 7.08 1.59 113.0 \n", " 3.27 2.75 90.0 \n", " 7.93 1.48 117.0 \n", " 5.99 1.48 89.0 \n", " 4.94 1.93 95.0 \n", " 9.45 1.93 183.0 \n", " 5.00 1.21 60.0 \n", " 5.00 1.21 60.0 \n", " 9.15 1.61 147.0 \n", " 8.72 1.66 144.0 \n", " 8.48 1.54 131.0 \n", " 7.82 1.26 98.0 \n", " 2.88 1.08 31.0 \n", " 7.93 1.48 117.0 \n", " 7.42 6.31 468.0 \n", " 3.27 2.75 90.0 \n", " 6.99 1.63 114.0 \n", " 10.94 1.14 125.0 \n", " 3.03 1.37 41.0 \n", " 3.03 1.37 41.0 \n", " 5.05 1.64 83.0 \n", " 6.13 2.09 128.0 \n", " 7.42 6.31 468.0 \n", " 7.10 2.43 173.0 " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(segments[segments.kms >= 1], 'vam', n=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I can also look at VAM numbers for complete rides. I would expect the ride VAM to be half the segment VAM (or less) since most of my rides are circuits where I return to the start, and thus no more than half the ride is climbing. Sure enough, the best I can do is about 400 meters/hour:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dateyeartitlehoursmilesfeetmphvamfpmipctkmsmeters
Sun, 11/29/20152015Mt. Hamilton3.6837.00490210.05406.0132.02.5159.531494.0
Fri, 4/2/20212021Everesting 5: climb 2×(OLH + WOLH)3.2731.4843449.63405.0138.02.6150.651324.0
Mon, 3/29/20212021Everesting 1: Mt Diablo2.6022.2234068.55399.0153.02.9035.751038.0
Tue, 3/30/20212021Everesting 2: Kings + WOLH + OLH3.3435.99437710.78399.0122.02.3057.911334.0
Sun, 12/1/20132013Mt. Hamilton3.7837.5649219.94397.0131.02.4860.431500.0
Sat, 11/25/20172017Mt. Hamilton3.6936.6548069.93397.0131.02.4858.971465.0
Fri, 10/30/20152015OLH / West Alpine3.4839.51450511.35395.0114.02.1663.571373.0
Sat, 4/26/20142014OLH / Tunitas Creek5.2658.69674211.16391.0115.02.1894.432055.0
Sat, 4/18/20152015Tunitas + Lobitos Creeks5.2461.27661111.69385.0108.02.0498.582015.0
Wed, 10/14/20152015Half Moon Bay6.1372.97764411.90380.0105.01.98117.412330.0
Sat, 7/25/20152015Palo Alto, California4.0443.62481910.80364.0110.02.0970.181469.0
Sun, 6/4/20172017Sequoia Challenge6.2966.52752010.58364.0113.02.14107.032292.0
Sat, 10/11/20142014OLH / Tunitas5.0958.29604411.45362.0104.01.9693.791842.0
Sat, 8/13/20162016Petaluma / Point Reyes4.5054.75528612.17358.097.01.8388.091611.0
Fri, 8/28/20152015Pescadaro via OLH5.3166.01613712.43352.093.01.76106.211871.0
Sun, 4/4/20212021Everesting 7: Mill Creek / Morrison Canyon3.0829.3835179.54348.0120.02.2747.271072.0
Sat, 2/10/20242024Seacliff, etc.4.7263.41536513.43346.085.01.60102.031635.0
Wed, 6/18/20142014Sierra to the Sea Day 44.9657.64556111.62342.096.01.8392.741695.0
Sun, 6/3/20182018The Sequoia5.9764.92667710.87341.0103.01.95104.462035.0
Sat, 5/9/20152015OLH2.5032.33278812.93340.086.01.6352.02850.0
\n", "
" ], "text/plain": [ " date year title hours \\\n", " Sun, 11/29/2015 2015 Mt. Hamilton 3.68 \n", " Fri, 4/2/2021 2021 Everesting 5: climb 2×(OLH + WOLH) 3.27 \n", " Mon, 3/29/2021 2021 Everesting 1: Mt Diablo 2.60 \n", " Tue, 3/30/2021 2021 Everesting 2: Kings + WOLH + OLH 3.34 \n", " Sun, 12/1/2013 2013 Mt. Hamilton 3.78 \n", " Sat, 11/25/2017 2017 Mt. Hamilton 3.69 \n", " Fri, 10/30/2015 2015 OLH / West Alpine 3.48 \n", " Sat, 4/26/2014 2014 OLH / Tunitas Creek 5.26 \n", " Sat, 4/18/2015 2015 Tunitas + Lobitos Creeks 5.24 \n", " Wed, 10/14/2015 2015 Half Moon Bay 6.13 \n", " Sat, 7/25/2015 2015 Palo Alto, California 4.04 \n", " Sun, 6/4/2017 2017 Sequoia Challenge 6.29 \n", " Sat, 10/11/2014 2014 OLH / Tunitas 5.09 \n", " Sat, 8/13/2016 2016 Petaluma / Point Reyes 4.50 \n", " Fri, 8/28/2015 2015 Pescadaro via OLH 5.31 \n", " Sun, 4/4/2021 2021 Everesting 7: Mill Creek / Morrison Canyon 3.08 \n", " Sat, 2/10/2024 2024 Seacliff, etc. 4.72 \n", " Wed, 6/18/2014 2014 Sierra to the Sea Day 4 4.96 \n", " Sun, 6/3/2018 2018 The Sequoia 5.97 \n", " Sat, 5/9/2015 2015 OLH 2.50 \n", "\n", " miles feet mph vam fpmi pct kms meters \n", " 37.00 4902 10.05 406.0 132.0 2.51 59.53 1494.0 \n", " 31.48 4344 9.63 405.0 138.0 2.61 50.65 1324.0 \n", " 22.22 3406 8.55 399.0 153.0 2.90 35.75 1038.0 \n", " 35.99 4377 10.78 399.0 122.0 2.30 57.91 1334.0 \n", " 37.56 4921 9.94 397.0 131.0 2.48 60.43 1500.0 \n", " 36.65 4806 9.93 397.0 131.0 2.48 58.97 1465.0 \n", " 39.51 4505 11.35 395.0 114.0 2.16 63.57 1373.0 \n", " 58.69 6742 11.16 391.0 115.0 2.18 94.43 2055.0 \n", " 61.27 6611 11.69 385.0 108.0 2.04 98.58 2015.0 \n", " 72.97 7644 11.90 380.0 105.0 1.98 117.41 2330.0 \n", " 43.62 4819 10.80 364.0 110.0 2.09 70.18 1469.0 \n", " 66.52 7520 10.58 364.0 113.0 2.14 107.03 2292.0 \n", " 58.29 6044 11.45 362.0 104.0 1.96 93.79 1842.0 \n", " 54.75 5286 12.17 358.0 97.0 1.83 88.09 1611.0 \n", " 66.01 6137 12.43 352.0 93.0 1.76 106.21 1871.0 \n", " 29.38 3517 9.54 348.0 120.0 2.27 47.27 1072.0 \n", " 63.41 5365 13.43 346.0 85.0 1.60 102.03 1635.0 \n", " 57.64 5561 11.62 342.0 96.0 1.83 92.74 1695.0 \n", " 64.92 6677 10.87 341.0 103.0 1.95 104.46 2035.0 \n", " 32.33 2788 12.93 340.0 86.0 1.63 52.02 850.0 " ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(rides, 'vam')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploring the Data\n", "\n", "\n", "Some more ways to look at the data, both rides and segments." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearhoursmilesfeetmphvamfpmipctkmsmeters
count538.000000538.000000538.000000538.000000538.000000538.000000538.000000538.000000538.000000538.000000
mean2016.9460973.34816042.7744611817.26022312.980186157.82527941.5631970.78695268.823996553.905204
std2.5041631.44403217.2914961493.1883731.31440190.08919227.2746690.51645127.822002455.121271
min2012.0000001.54000020.96000068.0000008.55000010.0000003.0000000.05000033.72000021.000000
25%2015.0000002.21000029.037500731.25000012.16250081.00000020.0000000.37000046.722500223.000000
50%2017.0000002.86000036.7100001368.00000013.130000152.50000036.0000000.69000059.070000417.000000
75%2018.0000004.35000056.2750002322.25000013.777500218.75000056.0000001.07000090.542500707.750000
max2024.0000008.140000102.4100007644.00000016.750000406.000000153.0000002.900000164.7800002330.000000
\n", "
" ], "text/plain": [ " year hours miles feet mph \\\n", "count 538.000000 538.000000 538.000000 538.000000 538.000000 \n", "mean 2016.946097 3.348160 42.774461 1817.260223 12.980186 \n", "std 2.504163 1.444032 17.291496 1493.188373 1.314401 \n", "min 2012.000000 1.540000 20.960000 68.000000 8.550000 \n", "25% 2015.000000 2.210000 29.037500 731.250000 12.162500 \n", "50% 2017.000000 2.860000 36.710000 1368.000000 13.130000 \n", "75% 2018.000000 4.350000 56.275000 2322.250000 13.777500 \n", "max 2024.000000 8.140000 102.410000 7644.000000 16.750000 \n", "\n", " vam fpmi pct kms meters \n", "count 538.000000 538.000000 538.000000 538.000000 538.000000 \n", "mean 157.825279 41.563197 0.786952 68.823996 553.905204 \n", "std 90.089192 27.274669 0.516451 27.822002 455.121271 \n", "min 10.000000 3.000000 0.050000 33.720000 21.000000 \n", "25% 81.000000 20.000000 0.370000 46.722500 223.000000 \n", "50% 152.500000 36.000000 0.690000 59.070000 417.000000 \n", "75% 218.750000 56.000000 1.070000 90.542500 707.750000 \n", "max 406.000000 153.000000 2.900000 164.780000 2330.000000 " ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rides.describe() # Summary statistics for the rides" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
hoursmilesfeetmphvamfpmipctkmsmeters
count141.000000141.000000141.000000141.000000141.000000141.000000141.000000141.000000141.000000
mean0.1417020.932979268.9787237.444539641.893617353.5035466.6948941.50106481.978723
std0.1737110.989832295.0606593.557336211.923595186.8163203.5379741.59226789.957408
min0.0100000.08000021.0000002.120000111.00000018.0000000.3500000.1300006.000000
25%0.0500000.330000104.0000004.830000503.000000219.0000004.1400000.53000032.000000
50%0.0900000.600000166.0000006.190000630.000000333.0000006.3000000.97000051.000000
75%0.1500001.190000303.0000009.800000724.000000462.0000008.7600001.91000092.000000
max1.3900007.3800001887.00000019.7900001463.000000839.00000015.89000011.870000575.000000
\n", "
" ], "text/plain": [ " hours miles feet mph vam \\\n", "count 141.000000 141.000000 141.000000 141.000000 141.000000 \n", "mean 0.141702 0.932979 268.978723 7.444539 641.893617 \n", "std 0.173711 0.989832 295.060659 3.557336 211.923595 \n", "min 0.010000 0.080000 21.000000 2.120000 111.000000 \n", "25% 0.050000 0.330000 104.000000 4.830000 503.000000 \n", "50% 0.090000 0.600000 166.000000 6.190000 630.000000 \n", "75% 0.150000 1.190000 303.000000 9.800000 724.000000 \n", "max 1.390000 7.380000 1887.000000 19.790000 1463.000000 \n", "\n", " fpmi pct kms meters \n", "count 141.000000 141.000000 141.000000 141.000000 \n", "mean 353.503546 6.694894 1.501064 81.978723 \n", "std 186.816320 3.537974 1.592267 89.957408 \n", "min 18.000000 0.350000 0.130000 6.000000 \n", "25% 219.000000 4.140000 0.530000 32.000000 \n", "50% 333.000000 6.300000 0.970000 51.000000 \n", "75% 462.000000 8.760000 1.910000 92.000000 \n", "max 839.000000 15.890000 11.870000 575.000000 " ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "segments.describe() # Summary statistics for the segments" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dateyeartitlehoursmilesfeetmphvamfpmipctkmsmeters
Sun, 5/22/20162016Canada2.1936.68133216.75185.036.00.6959.02406.0
Wed, 9/13/20172017Healdburg / Jimtown2.1334.4591216.17131.026.00.5055.43278.0
Sat, 1/25/20142014Woodside1.5625.08124316.08243.050.00.9440.35379.0
Sat, 4/11/20152015Woodside1.5424.73103516.06205.042.00.7939.79315.0
Sun, 7/11/20212021San Jose4.1065.10108615.8881.017.00.32104.75331.0
Sun, 1/18/20152015Woodside1.6426.02125715.87234.048.00.9141.87383.0
Fri, 6/24/20162016Foothill Expway1.5925.1162315.79119.025.00.4740.40190.0
Sun, 1/26/20142014Canada Rd2.1033.12144615.77210.044.00.8353.29441.0
Fri, 1/6/20122012Omarama to Wanaka New Zealand4.4870.35326215.70222.046.00.88113.19994.0
Sun, 4/12/20152015Palo Alto Cycling2.0331.76121015.65182.038.00.7251.10369.0
Sun, 10/15/20172017Los Gatos2.8644.71143715.63153.032.00.6171.94438.0
Sun, 8/5/20182018Bike Ride Northwest Day 13.5855.77182415.58155.033.00.6289.73556.0
Sun, 2/28/20162016Woodside Loop1.7326.9384315.57149.031.00.5943.33257.0
Sun, 6/26/20162016Los Gatos3.2850.78118115.48110.023.00.4481.71360.0
Mon, 1/19/20152015Canada Rd, etc.2.9545.64183615.47190.040.00.7673.43560.0
Sun, 1/19/20142014Palo Alto, CA1.6225.01120115.44226.048.00.9140.24366.0
Sun, 12/6/20152015Canada Rd2.2534.67123715.41168.036.00.6855.78377.0
Tue, 6/18/20132013work etc (headwinds)2.0631.4880915.28120.026.00.4950.65247.0
Fri, 9/23/20162016Los Gatos2.8943.93133915.20141.030.00.5870.68408.0
Sat, 7/9/20162016Santa Cruz3.8458.23404215.16321.069.01.3193.691232.0
\n", "
" ], "text/plain": [ " date year title hours miles feet \\\n", " Sun, 5/22/2016 2016 Canada 2.19 36.68 1332 \n", " Wed, 9/13/2017 2017 Healdburg / Jimtown 2.13 34.45 912 \n", " Sat, 1/25/2014 2014 Woodside 1.56 25.08 1243 \n", " Sat, 4/11/2015 2015 Woodside 1.54 24.73 1035 \n", " Sun, 7/11/2021 2021 San Jose 4.10 65.10 1086 \n", " Sun, 1/18/2015 2015 Woodside 1.64 26.02 1257 \n", " Fri, 6/24/2016 2016 Foothill Expway 1.59 25.11 623 \n", " Sun, 1/26/2014 2014 Canada Rd 2.10 33.12 1446 \n", " Fri, 1/6/2012 2012 Omarama to Wanaka New Zealand 4.48 70.35 3262 \n", " Sun, 4/12/2015 2015 Palo Alto Cycling 2.03 31.76 1210 \n", " Sun, 10/15/2017 2017 Los Gatos 2.86 44.71 1437 \n", " Sun, 8/5/2018 2018 Bike Ride Northwest Day 1 3.58 55.77 1824 \n", " Sun, 2/28/2016 2016 Woodside Loop 1.73 26.93 843 \n", " Sun, 6/26/2016 2016 Los Gatos 3.28 50.78 1181 \n", " Mon, 1/19/2015 2015 Canada Rd, etc. 2.95 45.64 1836 \n", " Sun, 1/19/2014 2014 Palo Alto, CA 1.62 25.01 1201 \n", " Sun, 12/6/2015 2015 Canada Rd 2.25 34.67 1237 \n", " Tue, 6/18/2013 2013 work etc (headwinds) 2.06 31.48 809 \n", " Fri, 9/23/2016 2016 Los Gatos 2.89 43.93 1339 \n", " Sat, 7/9/2016 2016 Santa Cruz 3.84 58.23 4042 \n", "\n", " mph vam fpmi pct kms meters \n", " 16.75 185.0 36.0 0.69 59.02 406.0 \n", " 16.17 131.0 26.0 0.50 55.43 278.0 \n", " 16.08 243.0 50.0 0.94 40.35 379.0 \n", " 16.06 205.0 42.0 0.79 39.79 315.0 \n", " 15.88 81.0 17.0 0.32 104.75 331.0 \n", " 15.87 234.0 48.0 0.91 41.87 383.0 \n", " 15.79 119.0 25.0 0.47 40.40 190.0 \n", " 15.77 210.0 44.0 0.83 53.29 441.0 \n", " 15.70 222.0 46.0 0.88 113.19 994.0 \n", " 15.65 182.0 38.0 0.72 51.10 369.0 \n", " 15.63 153.0 32.0 0.61 71.94 438.0 \n", " 15.58 155.0 33.0 0.62 89.73 556.0 \n", " 15.57 149.0 31.0 0.59 43.33 257.0 \n", " 15.48 110.0 23.0 0.44 81.71 360.0 \n", " 15.47 190.0 40.0 0.76 73.43 560.0 \n", " 15.44 226.0 48.0 0.91 40.24 366.0 \n", " 15.41 168.0 36.0 0.68 55.78 377.0 \n", " 15.28 120.0 26.0 0.49 50.65 247.0 \n", " 15.20 141.0 30.0 0.58 70.68 408.0 \n", " 15.16 321.0 69.0 1.31 93.69 1232.0 " ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(rides, 'mph') # Fastest rides (of more than 20 miles, that I sampled into database)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
titlehoursmilesfeetmphvamfpmipctkmsmeters
PCH Pescadero to Bean Hollow0.142.775119.79111.018.00.354.4616.0
Highway 1 Cascanoa to Cascade0.091.618917.89301.055.01.052.5927.0
Vickrey Fruitvale0.060.996816.50345.069.01.301.5921.0
Highway 9 Mantalvo0.030.453515.00356.078.01.470.7211.0
Highway 9 Mantalvo0.030.453515.00356.078.01.470.7211.0
The Boneyard0.101.4813514.80411.091.01.732.3841.0
Vickrey Fruitvale0.070.996814.14296.069.01.301.5921.0
Sand Hill Alpine to 2800.121.6718013.92457.0108.02.042.6955.0
Canada to College0.101.3711913.70363.087.01.652.2036.0
Foothill Homestead0.091.2212613.56427.0103.01.961.9638.0
The Boneyard0.111.4813513.45374.091.01.732.3841.0
Kaboom Portola Rd0.050.6710213.40622.0152.02.881.0831.0
Woodside Climb0.131.7129513.15692.0173.03.272.7590.0
Sand Hill Alpine to 2800.131.6718012.85422.0108.02.042.6955.0
Alpine Westridge0.060.769912.67503.0130.02.471.2230.0
Alpine Westridge0.060.769912.67503.0130.02.471.2230.0
Stanford Ave0.050.638512.60518.0135.02.561.0126.0
Canada to College0.111.3711912.45330.087.01.652.2036.0
Sand Hill 280 to horse0.040.499512.25724.0194.03.670.7929.0
Stevens Country Park0.101.2211212.20341.092.01.741.9634.0
\n", "
" ], "text/plain": [ " title hours miles feet mph vam fpmi \\\n", " PCH Pescadero to Bean Hollow 0.14 2.77 51 19.79 111.0 18.0 \n", " Highway 1 Cascanoa to Cascade 0.09 1.61 89 17.89 301.0 55.0 \n", " Vickrey Fruitvale 0.06 0.99 68 16.50 345.0 69.0 \n", " Highway 9 Mantalvo 0.03 0.45 35 15.00 356.0 78.0 \n", " Highway 9 Mantalvo 0.03 0.45 35 15.00 356.0 78.0 \n", " The Boneyard 0.10 1.48 135 14.80 411.0 91.0 \n", " Vickrey Fruitvale 0.07 0.99 68 14.14 296.0 69.0 \n", " Sand Hill Alpine to 280 0.12 1.67 180 13.92 457.0 108.0 \n", " Canada to College 0.10 1.37 119 13.70 363.0 87.0 \n", " Foothill Homestead 0.09 1.22 126 13.56 427.0 103.0 \n", " The Boneyard 0.11 1.48 135 13.45 374.0 91.0 \n", " Kaboom Portola Rd 0.05 0.67 102 13.40 622.0 152.0 \n", " Woodside Climb 0.13 1.71 295 13.15 692.0 173.0 \n", " Sand Hill Alpine to 280 0.13 1.67 180 12.85 422.0 108.0 \n", " Alpine Westridge 0.06 0.76 99 12.67 503.0 130.0 \n", " Alpine Westridge 0.06 0.76 99 12.67 503.0 130.0 \n", " Stanford Ave 0.05 0.63 85 12.60 518.0 135.0 \n", " Canada to College 0.11 1.37 119 12.45 330.0 87.0 \n", " Sand Hill 280 to horse 0.04 0.49 95 12.25 724.0 194.0 \n", " Stevens Country Park 0.10 1.22 112 12.20 341.0 92.0 \n", "\n", " pct kms meters \n", " 0.35 4.46 16.0 \n", " 1.05 2.59 27.0 \n", " 1.30 1.59 21.0 \n", " 1.47 0.72 11.0 \n", " 1.47 0.72 11.0 \n", " 1.73 2.38 41.0 \n", " 1.30 1.59 21.0 \n", " 2.04 2.69 55.0 \n", " 1.65 2.20 36.0 \n", " 1.96 1.96 38.0 \n", " 1.73 2.38 41.0 \n", " 2.88 1.08 31.0 \n", " 3.27 2.75 90.0 \n", " 2.04 2.69 55.0 \n", " 2.47 1.22 30.0 \n", " 2.47 1.22 30.0 \n", " 2.56 1.01 26.0 \n", " 1.65 2.20 36.0 \n", " 3.67 0.79 29.0 \n", " 1.74 1.96 34.0 " ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(segments, 'mph') # Fastest segments (there are no descent segments in the database)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
titlehoursmilesfeetmphvamfpmipctkmsmeters
West Alpine full1.397.3818875.31414.0256.04.8411.87575.0
Kings Greer to Skyline0.783.9215365.03600.0392.07.426.31468.0
Kings Greer to Skyline0.813.9215364.84578.0392.07.426.31468.0
Old La Honda (Bridge to Stop)0.483.3312556.94797.0377.07.145.36383.0
Old La Honda (Bridge to Stop)0.513.3312556.53750.0377.07.145.36383.0
Alma Mountain Charlie0.533.128755.89503.0280.05.315.02267.0
Kings half way0.462.898206.28543.0284.05.374.65250.0
Kings half way0.502.898205.78500.0284.05.374.65250.0
Alpine Portola to top Joaquin0.573.528016.18428.0228.04.315.66244.0
Alpine Portola to top Joaquin0.583.528016.07421.0228.04.315.66244.0
Tunitas steep0.271.205994.44676.0499.09.451.93183.0
Tunitas steep0.251.205994.80730.0499.09.451.93183.0
Haskins0.301.515665.03575.0375.07.102.43173.0
Haskins0.311.515664.87557.0375.07.102.43173.0
Coe Second Switchback to flat0.221.004834.55669.0483.09.151.61147.0
Lower Redwood Gulch0.221.034744.68657.0460.08.721.66144.0
Alpine Willowbrook to Joaquin0.292.274617.83485.0203.03.853.65141.0
Alpine Willowbrook to Joaquin0.282.274618.11502.0203.03.853.65141.0
Lobitas Creek0.200.964304.80655.0448.08.481.54131.0
Tunitas lower climb0.221.304215.91583.0324.06.132.09128.0
\n", "
" ], "text/plain": [ " title hours miles feet mph vam fpmi pct \\\n", " West Alpine full 1.39 7.38 1887 5.31 414.0 256.0 4.84 \n", " Kings Greer to Skyline 0.78 3.92 1536 5.03 600.0 392.0 7.42 \n", " Kings Greer to Skyline 0.81 3.92 1536 4.84 578.0 392.0 7.42 \n", " Old La Honda (Bridge to Stop) 0.48 3.33 1255 6.94 797.0 377.0 7.14 \n", " Old La Honda (Bridge to Stop) 0.51 3.33 1255 6.53 750.0 377.0 7.14 \n", " Alma Mountain Charlie 0.53 3.12 875 5.89 503.0 280.0 5.31 \n", " Kings half way 0.46 2.89 820 6.28 543.0 284.0 5.37 \n", " Kings half way 0.50 2.89 820 5.78 500.0 284.0 5.37 \n", " Alpine Portola to top Joaquin 0.57 3.52 801 6.18 428.0 228.0 4.31 \n", " Alpine Portola to top Joaquin 0.58 3.52 801 6.07 421.0 228.0 4.31 \n", " Tunitas steep 0.27 1.20 599 4.44 676.0 499.0 9.45 \n", " Tunitas steep 0.25 1.20 599 4.80 730.0 499.0 9.45 \n", " Haskins 0.30 1.51 566 5.03 575.0 375.0 7.10 \n", " Haskins 0.31 1.51 566 4.87 557.0 375.0 7.10 \n", " Coe Second Switchback to flat 0.22 1.00 483 4.55 669.0 483.0 9.15 \n", " Lower Redwood Gulch 0.22 1.03 474 4.68 657.0 460.0 8.72 \n", " Alpine Willowbrook to Joaquin 0.29 2.27 461 7.83 485.0 203.0 3.85 \n", " Alpine Willowbrook to Joaquin 0.28 2.27 461 8.11 502.0 203.0 3.85 \n", " Lobitas Creek 0.20 0.96 430 4.80 655.0 448.0 8.48 \n", " Tunitas lower climb 0.22 1.30 421 5.91 583.0 324.0 6.13 \n", "\n", " kms meters \n", " 11.87 575.0 \n", " 6.31 468.0 \n", " 6.31 468.0 \n", " 5.36 383.0 \n", " 5.36 383.0 \n", " 5.02 267.0 \n", " 4.65 250.0 \n", " 4.65 250.0 \n", " 5.66 244.0 \n", " 5.66 244.0 \n", " 1.93 183.0 \n", " 1.93 183.0 \n", " 2.43 173.0 \n", " 2.43 173.0 \n", " 1.61 147.0 \n", " 1.66 144.0 \n", " 3.65 141.0 \n", " 3.65 141.0 \n", " 1.54 131.0 \n", " 2.09 128.0 " ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(segments, 'feet') # Biggest climbing segments" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
titlehoursmilesfeetmphvamfpmipctkmsmeters
Redwood Gulch hits0.060.181513.00767.0839.015.890.2946.0
Valparaiso steep0.040.181454.501105.0806.015.260.2944.0
Valparaiso steep0.050.181453.60884.0806.015.260.2944.0
Limantour steepest0.090.201592.22538.0795.015.060.3248.0
Joaquin0.100.332543.30774.0770.014.580.5377.0
Joaquin0.090.332543.67860.0770.014.580.5377.0
Entrance Way Hill Repeats0.020.10765.001158.0760.014.390.1623.0
Stirrup Wall0.060.171252.83635.0735.013.930.2738.0
Stirrup Wall0.080.171252.12476.0735.013.930.2738.0
Westridge 3min0.080.372404.62914.0649.012.290.6073.0
Westridge 3min0.090.372404.11813.0649.012.290.6073.0
Limantour Spit0.090.473035.221026.0645.012.210.7692.0
Klamath Dr.0.020.12776.001173.0642.012.150.1923.0
green valley kicker0.080.291783.62678.0614.011.620.4754.0
Redwood Gulch wall0.110.432583.91715.0600.011.360.6979.0
Paloma Climb0.020.14827.001250.0586.011.090.2325.0
Try not to fall back0.210.714103.38595.0577.010.941.14125.0
Westridge0.140.683854.86838.0566.010.721.09117.0
Westridge0.160.683854.25733.0566.010.721.09117.0
Stair Step0.090.321753.56593.0547.010.360.5153.0
\n", "
" ], "text/plain": [ " title hours miles feet mph vam fpmi pct \\\n", " Redwood Gulch hits 0.06 0.18 151 3.00 767.0 839.0 15.89 \n", " Valparaiso steep 0.04 0.18 145 4.50 1105.0 806.0 15.26 \n", " Valparaiso steep 0.05 0.18 145 3.60 884.0 806.0 15.26 \n", " Limantour steepest 0.09 0.20 159 2.22 538.0 795.0 15.06 \n", " Joaquin 0.10 0.33 254 3.30 774.0 770.0 14.58 \n", " Joaquin 0.09 0.33 254 3.67 860.0 770.0 14.58 \n", " Entrance Way Hill Repeats 0.02 0.10 76 5.00 1158.0 760.0 14.39 \n", " Stirrup Wall 0.06 0.17 125 2.83 635.0 735.0 13.93 \n", " Stirrup Wall 0.08 0.17 125 2.12 476.0 735.0 13.93 \n", " Westridge 3min 0.08 0.37 240 4.62 914.0 649.0 12.29 \n", " Westridge 3min 0.09 0.37 240 4.11 813.0 649.0 12.29 \n", " Limantour Spit 0.09 0.47 303 5.22 1026.0 645.0 12.21 \n", " Klamath Dr. 0.02 0.12 77 6.00 1173.0 642.0 12.15 \n", " green valley kicker 0.08 0.29 178 3.62 678.0 614.0 11.62 \n", " Redwood Gulch wall 0.11 0.43 258 3.91 715.0 600.0 11.36 \n", " Paloma Climb 0.02 0.14 82 7.00 1250.0 586.0 11.09 \n", " Try not to fall back 0.21 0.71 410 3.38 595.0 577.0 10.94 \n", " Westridge 0.14 0.68 385 4.86 838.0 566.0 10.72 \n", " Westridge 0.16 0.68 385 4.25 733.0 566.0 10.72 \n", " Stair Step 0.09 0.32 175 3.56 593.0 547.0 10.36 \n", "\n", " kms meters \n", " 0.29 46.0 \n", " 0.29 44.0 \n", " 0.29 44.0 \n", " 0.32 48.0 \n", " 0.53 77.0 \n", " 0.53 77.0 \n", " 0.16 23.0 \n", " 0.27 38.0 \n", " 0.27 38.0 \n", " 0.60 73.0 \n", " 0.60 73.0 \n", " 0.76 92.0 \n", " 0.19 23.0 \n", " 0.47 54.0 \n", " 0.69 79.0 \n", " 0.23 25.0 \n", " 1.14 125.0 \n", " 1.09 117.0 \n", " 1.09 117.0 \n", " 0.51 53.0 " ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(segments, 'pct') # Steepest climbs" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dateyeartitlehoursmilesfeetmphvamfpmipctkmsmeters
Fri, 1/9/20122012Otago Rail Trail Century7.87102.41228613.0189.022.00.42164.78697.0
Sat, 5/7/20222022Wine Country Century6.65100.26525315.08241.052.00.99161.321601.0
Thu, 6/14/20122012Coyote Creek Century with Juliet8.14100.07151312.2957.015.00.29161.01461.0
Sat, 5/13/20172017Morgan Hill iCare Classic7.46100.05459613.41188.046.00.87160.981401.0
Sat, 5/12/20182018ICare Classic, Morgan Hill6.8091.29416013.42186.046.00.86146.891268.0
Sat, 5/6/20172017Wine Country Century7.2689.49524612.33220.059.01.11143.991599.0
Fri, 8/10/20182018Bike Ride Northwest Day 66.2484.70438013.57214.052.00.98136.281335.0
Fri, 2/28/20202020Sawyer Camp Trail6.4184.43344813.17164.041.00.77135.851051.0
Wed, 6/7/20232023Los Altos7.0581.54211011.5791.026.00.49131.20643.0
Sun, 8/30/20202020Los Gatos6.3680.92210012.72101.026.00.49130.20640.0
Sat, 9/17/20222022San Gregorio / Tunitas6.5680.53601512.28279.075.01.41129.571833.0
Sat, 10/1/20162016Half Moon Bay overnight campout7.5180.07603910.66245.075.01.43128.831841.0
Mon, 10/5/20202020Half way around the bay on bay trail6.4480.0554112.4326.07.00.13128.80165.0
Sun, 6/21/20202020Sawyer Camp Trail6.5979.78173812.1180.022.00.41128.37530.0
Thu, 1/5/20122012Tekapo Lake to Omarama New Zealand5.4679.42214514.55120.027.00.51127.79654.0
Tue, 8/7/20182018Bike Ride Northwest Day 36.1878.96509212.78251.064.01.22127.051552.0
Sun, 6/15/20142014Sierra to the Sea Day 15.5778.53477714.10261.061.01.15126.351456.0
Sun, 2/7/20212021Saratoga / Campbell5.8978.38227013.31117.029.00.55126.11692.0
Sat, 7/2/20222022Bear Gulch, West Side6.4977.73699111.98328.090.01.70125.072131.0
Sun, 6/2/20192019The Sequoia6.6877.51646711.60295.083.01.58124.711971.0
\n", "
" ], "text/plain": [ " date year title hours miles \\\n", " Fri, 1/9/2012 2012 Otago Rail Trail Century 7.87 102.41 \n", " Sat, 5/7/2022 2022 Wine Country Century 6.65 100.26 \n", " Thu, 6/14/2012 2012 Coyote Creek Century with Juliet 8.14 100.07 \n", " Sat, 5/13/2017 2017 Morgan Hill iCare Classic 7.46 100.05 \n", " Sat, 5/12/2018 2018 ICare Classic, Morgan Hill 6.80 91.29 \n", " Sat, 5/6/2017 2017 Wine Country Century 7.26 89.49 \n", " Fri, 8/10/2018 2018 Bike Ride Northwest Day 6 6.24 84.70 \n", " Fri, 2/28/2020 2020 Sawyer Camp Trail 6.41 84.43 \n", " Wed, 6/7/2023 2023 Los Altos 7.05 81.54 \n", " Sun, 8/30/2020 2020 Los Gatos 6.36 80.92 \n", " Sat, 9/17/2022 2022 San Gregorio / Tunitas 6.56 80.53 \n", " Sat, 10/1/2016 2016 Half Moon Bay overnight campout 7.51 80.07 \n", " Mon, 10/5/2020 2020 Half way around the bay on bay trail 6.44 80.05 \n", " Sun, 6/21/2020 2020 Sawyer Camp Trail 6.59 79.78 \n", " Thu, 1/5/2012 2012 Tekapo Lake to Omarama New Zealand 5.46 79.42 \n", " Tue, 8/7/2018 2018 Bike Ride Northwest Day 3 6.18 78.96 \n", " Sun, 6/15/2014 2014 Sierra to the Sea Day 1 5.57 78.53 \n", " Sun, 2/7/2021 2021 Saratoga / Campbell 5.89 78.38 \n", " Sat, 7/2/2022 2022 Bear Gulch, West Side 6.49 77.73 \n", " Sun, 6/2/2019 2019 The Sequoia 6.68 77.51 \n", "\n", " feet mph vam fpmi pct kms meters \n", " 2286 13.01 89.0 22.0 0.42 164.78 697.0 \n", " 5253 15.08 241.0 52.0 0.99 161.32 1601.0 \n", " 1513 12.29 57.0 15.0 0.29 161.01 461.0 \n", " 4596 13.41 188.0 46.0 0.87 160.98 1401.0 \n", " 4160 13.42 186.0 46.0 0.86 146.89 1268.0 \n", " 5246 12.33 220.0 59.0 1.11 143.99 1599.0 \n", " 4380 13.57 214.0 52.0 0.98 136.28 1335.0 \n", " 3448 13.17 164.0 41.0 0.77 135.85 1051.0 \n", " 2110 11.57 91.0 26.0 0.49 131.20 643.0 \n", " 2100 12.72 101.0 26.0 0.49 130.20 640.0 \n", " 6015 12.28 279.0 75.0 1.41 129.57 1833.0 \n", " 6039 10.66 245.0 75.0 1.43 128.83 1841.0 \n", " 541 12.43 26.0 7.0 0.13 128.80 165.0 \n", " 1738 12.11 80.0 22.0 0.41 128.37 530.0 \n", " 2145 14.55 120.0 27.0 0.51 127.79 654.0 \n", " 5092 12.78 251.0 64.0 1.22 127.05 1552.0 \n", " 4777 14.10 261.0 61.0 1.15 126.35 1456.0 \n", " 2270 13.31 117.0 29.0 0.55 126.11 692.0 \n", " 6991 11.98 328.0 90.0 1.70 125.07 2131.0 \n", " 6467 11.60 295.0 83.0 1.58 124.71 1971.0 " ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(rides, 'miles') # Longest rides" ] } ], "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" }, "toc-autonumbering": true, "toc-showmarkdowntxt": false }, "nbformat": 4, "nbformat_minor": 4 }