{"url":"https://api.github.com/gists/4341954","forks_url":"https://api.github.com/gists/4341954/forks","commits_url":"https://api.github.com/gists/4341954/commits","id":"4341954","node_id":"MDQ6R2lzdDQzNDE5NTQ=","git_pull_url":"https://gist.github.com/4341954.git","git_push_url":"https://gist.github.com/4341954.git","html_url":"https://gist.github.com/mbostock/4341954","files":{".block":{"filename":".block","type":"text/plain","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/4341954/raw/33436af8434588dd75e07cfb6e17fcfe48cec4fe/.block","size":82,"truncated":false,"content":"license: gpl-3.0\nredirect: https://observablehq.com/@d3/kernel-density-estimation\n","encoding":"utf-8"},"README.md":{"filename":"README.md","type":"text/markdown","language":"Markdown","raw_url":"https://gist.githubusercontent.com/mbostock/4341954/raw/1f6f54c0d794f3835d2857135b870209c29c484d/README.md","size":1450,"truncated":false,"content":"[Kernel density estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation) is a method of estimating the probability distribution of a random variable based on a random sample. In contrast to a [histogram](/mbostock/3048450), kernel density estimation produces a smooth estimate. The smoothness can be tuned via the kernel’s _bandwidth_ parameter. With the correct choice of bandwidth, important features of the distribution can be seen, while an incorrect choice results in undersmoothing or oversmoothing and obscured features.\n\nThis example shows a histogram and a kernel density estimation for times between eruptions of [Old Faithful Geyser](https://en.wikipedia.org/wiki/Old_Faithful) in Yellowstone National Park, taken from R’s [`faithful`](https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/faithful.html) dataset. The data follow a [bimodal distribution](https://en.wikipedia.org/wiki/Bimodal_distribution); short eruptions are followed by a wait time averaging about 55 minutes, and long eruptions by a wait time averaging about 80 minutes. In recent years, wait times have been increasing, possibly due to the effects of earthquakes on the geyser’s geohydrology.\n\nThis example is based on a Protovis version by [John Firebaugh](/jfirebaugh/900762). See also a [two-dimensional density estimation](/mbostock/e3f4376d54e02d5d43ae32a7cf0e6aa9) of this dataset using [d3-contour](https://github.com/d3/d3-contour).\n","encoding":"utf-8"},"faithful.json":{"filename":"faithful.json","type":"application/json","language":"JSON","raw_url":"https://gist.githubusercontent.com/mbostock/4341954/raw/c03602b70ea5241bafd034638055b9c0d48eac69/faithful.json","size":818,"truncated":false,"content":"[79,54,74,62,85,55,88,85,51,85,54,84,78,47,83,52,62,84,52,79,51,47,78,69,74,83,55,76,78,79,73,77,66,80,74,52,48,80,59,90,80,58,84,58,73,83,64,53,82,59,75,90,54,80,54,83,71,64,77,81,59,84,48,82,60,92,78,78,65,73,82,56,79,71,62,76,60,78,76,83,75,82,70,65,73,88,76,80,48,86,60,90,50,78,63,72,84,75,51,82,62,88,49,83,81,47,84,52,86,81,75,59,89,79,59,81,50,85,59,87,53,69,77,56,88,81,45,82,55,90,45,83,56,89,46,82,51,86,53,79,81,60,82,77,76,59,80,49,96,53,77,77,65,81,71,70,81,93,53,89,45,86,58,78,66,76,63,88,52,93,49,57,77,68,81,81,73,50,85,74,55,77,83,83,51,78,84,46,83,55,81,57,76,84,77,81,87,77,51,78,60,82,91,53,78,46,77,84,49,83,71,80,49,75,64,76,53,94,55,76,50,82,54,75,78,79,78,78,70,79,70,54,86,50,90,54,54,77,79,64,75,47,86,63,85,82,57,82,67,74,54,83,73,73,88,80,71,83,56,79,78,84,58,83,43,60,75,81,46,90,46,74]\n","encoding":"utf-8"},"index.html":{"filename":"index.html","type":"text/html","language":"HTML","raw_url":"https://gist.githubusercontent.com/mbostock/4341954/raw/359dbbeee68a5a5839a87283828c50e40d8cc32a/index.html","size":2329,"truncated":false,"content":"\n\n\n\n\n","encoding":"utf-8"},"thumbnail.png":{"filename":"thumbnail.png","type":"image/png","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/4341954/raw/992cc6995683e3f977d4f8383386961e3fdb230d/thumbnail.png","size":9875,"truncated":false,"content":"iVBORw0KGgoAAAANSUhEUgAAAOYAAAB4CAIAAABpZBnfAAAKn2lDQ1BpY20A\nAEjHlZYHUFPpFse/e9MbLRABKaH3Ll16DaAgVRCVkIQQSgiBICI2RFyBFUVE\nBGyIIKLgWmhrQUSxsAhYsKALsigo62IBCyrvAo/w3rzZebNn5uT75cyZ//3O\nd+838weA3MQUCBJgKQAS+anCQC9X+srwCDquH5AABRCBOjBkslIELgEBfuBv\nY/IhgGbWe0YzWuCfhTSbk8ICAApAOJqdwkpE+DySh1gCYSoAqGCkrrkuVTDD\n6QjLCpENIpw/w9w5PjLD0XN8frYnONAN4ZsA4MlMppALAOk+UqensbiIDukj\nwqZ8No8PAFkTYUdWLJONMJLAMDExaYZ3I6wb/R863P/SjBZrMplcMc/NMht4\nd16KIIG5/h8ex/+PxATR/DNUkCSnxAf5Iqs2cmbpLKZH0DzHchh+8yxIdQ2c\nZ14qI1jcI/IOmWdRfIjLPMcn+Yr7+dHL/cX6KW4R85wRGxw2z2yOu8c8C5MC\nxf0paUEeC/1uy+c5jukTMM9M4ewss8xJ8Apc2HOAeJ/8hOXiWWKEnuIeTsrC\nvKmxwd5iRj4AcT/PkyGeV+i9oJ8QINYUigLF58Dhh4g12Ux38dmCYBALRIAP\n2IADhCAaJIEEkArowB3wQAoQIP+YAHndqZz01JmB3JIE64U8bmwq3QW5FRxD\nOoPPMjakm5uamYOZOzb3Cj/QZu8ORLu9UEtuBcA2FylyF2pMDQCaXwJAnVyo\nabyf+04vdbNEwrS5GnrmB4PcXEkgCxSQL0QD6AIjYA6sgD1wBh7AB/gjk4SD\nNYCFzJOITLIOZIKtIAfkgd1gHygFh8ExcAKcBmdBI7gIroIb4A7oBg/AUzAA\nhsEbMA4mwRQEQTiIAlEhBUgV0oIMIHPIBnKEPCA/KBAKh6IgLsSHRFAmtA3K\ngwqhUugoVAP9AjVDV6FbUA/0GBqERqH30FcYBZNhWVgZ1oZNYBvYBfaFg+HV\nMBdOhjPgbHgXXAJXwKfgBvgqfAd+AA/Ab+AJFECRUDSUGsoIZYNyQ/mjIlAx\nKCFqEyoXVYyqQNWhWlAdqHuoAdQY6gsai6ai6WgjtD3aGx2CZqGT0ZvQ+ehS\n9Al0A7odfQ89iB5H/8BQMEoYA4wdhoFZieFi1mFyMMWYKswFzHXMA8wwZhKL\nxdKwOlhrrDc2HBuH3YDNxx7E1mNbsT3YIewEDodTwBngHHD+OCYuFZeDO4A7\nhbuC68UN4z7jSXhVvDneEx+B5+Oz8MX4k/jL+F78K/wUQYqgRbAj+BPYhPWE\nAkIloYVwlzBMmCJKE3WIDsRgYhxxK7GEWEe8TuwnfiCRSOokW9IKEo+0hVRC\nOkO6SRokfSHLkPXJbuRIsoi8i1xNbiU/Jn+gUCjaFGdKBCWVsotSQ7lGeU75\nLEGVMJZgSLAlNkuUSTRI9Eq8lSRIakm6SK6RzJAsljwneVdyTIogpS3lJsWU\n2iRVJtUs1Sc1IU2VNpP2l06Uzpc+KX1LekQGJ6Mt4yHDlsmWOSZzTWaIiqJq\nUN2oLOo2aiX1OnVYFiurI8uQjZPNkz0t2yU7Licjt0QuVC5drkzuktwADUXT\npjFoCbQC2lnaQ9rXRcqLXBZxFu1cVLeod9En+cXyzvIc+Vz5evkH8l8V6Aoe\nCvEKexQaFZ4pohX1FVcorlM8pHhdcWyx7GL7xazFuYvPLn6iBCvpKwUqbVA6\nptSpNKGsouylLFA+oHxNeUyFpuKsEqdSpHJZZVSVquqoylMtUr2i+pouR3eh\nJ9BL6O30cTUlNW81kdpRtS61KXUd9RD1LPV69WcaRA0bjRiNIo02jXFNVc1l\nmpmatZpPtAhaNlqxWvu1OrQ+aetoh2nv0G7UHtGR12HoZOjU6vTrUnSddJN1\nK3Tv62H1bPTi9Q7qdevD+pb6sfpl+ncNYAMrA57BQYMeQ4yhrSHfsMKwz4hs\n5GKUZlRrNGhMM/YzzjJuNH5romkSYbLHpMPkh6mlaYJppelTMxkzH7Mssxaz\n9+b65izzMvP7FhQLT4vNFk0W75YYLOEsObTkkSXVcpnlDss2y+9W1lZCqzqr\nUWtN6yjrcus+G1mbAJt8m5u2GFtX2822F22/2FnZpdqdtfvL3sg+3v6k/chS\nnaWcpZVLhxzUHZgORx0GHOmOUY5HHAec1JyYThVOL5w1nNnOVc6vXPRc4lxO\nubx1NXUVul5w/eRm57bRrdUd5e7lnuve5SHjEeJR6vHcU92T61nrOe5l6bXB\nq9Ub4+3rvce7j6HMYDFqGOM+1j4bfdp9yb5BvqW+L/z0/YR+LcvgZT7L9i7r\nX661nL+80R/4M/z3+j8L0AlIDvh1BXZFwIqyFS8DzQIzAzuCqEFrg04GTQa7\nBhcEPw3RDRGFtIVKhkaG1oR+CnMPKwwbWGmycuPKO+GK4bzwpghcRGhEVcTE\nKo9V+1YNR1pG5kQ+XK2zOn31rTWKaxLWXForuZa59lwUJios6mTUN6Y/s4I5\nEc2ILo8eZ7mx9rPesJ3ZRexRjgOnkPMqxiGmMGaE68Ddyx2NdYotjh3jufFK\nee/ivOMOx32K94+vjp9OCEuoT8QnRiU282X48fz2JJWk9KQegYEgRzCQbJe8\nL3lc6CusSoFSVqc0pcoiZqZTpCvaLhpMc0wrS/u8LnTduXTpdH5653r99TvX\nv8rwzDi+Ab2BtaEtUy1za+bgRpeNRzdBm6I3tW3W2Jy9eXiL15YTW4lb47f+\nlmWaVZj1cVvYtpZs5ewt2UPbvbbX5kjkCHP6dtjvOPwT+ifeT107LXYe2Pkj\nl517O880rzjvWz4r//bPZj+X/Dy9K2ZXV4FVwaHd2N383Q/3OO05UShdmFE4\ntHfZ3oYielFu0cd9a/fdKl5SfHg/cb9o/0CJX0nTAc0Duw98K40tfVDmWlZf\nrlS+s/zTQfbB3kPOh+oOKx/OO/z1CO/Io6NeRxsqtCuKj2GPpR17WRla2XHc\n5nhNlWJVXtX3an71wInAE+011jU1J5VOFtTCtaLa0VORp7pPu59uqjOqO1pP\nq887A86Izrz+JeqXh2d9z7adszlXd17rfPkF6oXcBqhhfcN4Y2zjQFN4U0+z\nT3Nbi33LhV+Nf62+qHax7JLcpYLLxMvZl6evZFyZaBW0jl3lXh1qW9v29NrK\na/fbV7R3Xfe9fvOG541rHS4dV2463Lx4y+5W822b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