| // Includes functions for exporting active sheet or all sheets as JSON object (also Python object syntax compatible). | |
| // Tweak the makePrettyJSON_ function to customize what kind of JSON to export. | |
| var FORMAT_ONELINE = 'One-line'; | |
| var FORMAT_MULTILINE = 'Multi-line'; | |
| var FORMAT_PRETTY = 'Pretty'; | |
| var LANGUAGE_JS = 'JavaScript'; | |
| var LANGUAGE_PYTHON = 'Python'; |
As pointed out by @johntyree in the comments, using git reflog is easier and more reliable. Thanks for the suggestion!
$ git reflog
1ed7510 HEAD@{1}: checkout: moving from develop to 1ed7510
3970d09 HEAD@{2}: checkout: moving from b-fix-build to develop
1ed7510 HEAD@{3}: commit: got everything working the way I want
70b3696 HEAD@{4}: commit: upgrade rails, do some refactoring
How to create the topojson map:
curl -o estados.zip http://mapserver.inegi.org.mx/MGN/mge2010v5_0.zip
curl -o municipios.zip http://mapserver.inegi.org.mx/MGN/mgm2010v5_0.zip
unzip estados.zip
unzip municipios.zip
ogr2ogr states.shp Entidades_2010_5.shp -t_srs "+proj=longlat +ellps=WGS84 +no_defs +towgs84=0,0,0"
ogr2ogr municipalities.shp Municipios_2010_5.shp -t_srs "+proj=longlat +ellps=WGS84 +no_defs +towgs84=0,0,0"
topojson -o mx_tj.json -s 1e-7 -q 1e5 states.shp municipalities.shp -p state_code=+CVE_ENT,state_name=NOM_ENT,mun_code=+CVE_MUN,mun_name=NOM_MUN
| section .text | |
| global _start | |
| _start: | |
| xor eax, eax ; init eax 0 | |
| xor ebx, ebx ; init ebx 0 | |
| xor esi, esi ; init esi 0 | |
| jmp _socket ; jmp to _socket | |
| _socket_call: |
| // Just before switching jobs: | |
| // Add one of these. | |
| // Preferably into the same commit where you do a large merge. | |
| // | |
| // This started as a tweet with a joke of "C++ pro-tip: #define private public", | |
| // and then it quickly escalated into more and more evil suggestions. | |
| // I've tried to capture interesting suggestions here. | |
| // | |
| // Contributors: @r2d2rigo, @joeldevahl, @msinilo, @_Humus_, | |
| // @YuriyODonnell, @rygorous, @cmuratori, @mike_acton, @grumpygiant, |
| license: gpl-3.0 | |
| redirect: https://observablehq.com/@d3/brush-snapping-transitions |
| height: 650 | |
| license: mit |
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, 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.
This example shows a histogram and a kernel density estimation for times between eruptions of Old Faithful Geyser in Yellowstone National Park, taken from R’s faithful dataset. The data follow a bimodal distribution; short eruptions are followed by a wait time averaging about 55 minutes, and long eruptions by a wa
| license: gpl-3.0 |
| license: gpl-3.0 |
