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| <!DOCTYPE html> | |
| <head> | |
| <meta http-equiv="content-type" content="text/html; charset=UTF-8" /> | |
| <script> | |
| L_NO_TOUCH = false; | |
| L_DISABLE_3D = false; | |
| </script> | |
| <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style> |
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| from tqdm import tqdm | |
| import csv | |
| order = [ | |
| "", | |
| "ID", | |
| "AC", | |
| "DT", | |
| "DE", | |
| "GN", |
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| import random | |
| class RedisDisjointSet(object): | |
| """ | |
| https://en.wikipedia.org/wiki/Disjoint-set_data_structure | |
| UTF-8 based persistent disjoint set structure that just assumes there's a local redis | |
| """ | |
| def __init__(self, seed=None): |
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| echo "echo 'sleep 1' >> ~/.profile" >> ~/.profile |
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| create table penglish_scratch.naics_desc_lookup ( | |
| code varchar(6) sortkey distkey, | |
| description varchar(255) | |
| ); | |
| insert into penglish_scratch.naics_desc_lookup values | |
| ('11', 'Agriculture, Forestry, Fishing and Hunting'), | |
| ('111', 'Crop Production'), | |
| ('1111', 'Oilseed and Grain Farming'), |
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| from lark import Lark | |
| l = Lark(''' | |
| ?start: elixir_item | json_value | xml | node | |
| /////////////////////// | |
| // JSON | |
| ?json_value: json_object | |
| | json_array |
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| import requests | |
| from slackclient import SlackClient | |
| DAYS = 1 | |
| FTL_URL = "https://backend.thefoodtruckleague.com/events/?days=%s&name=" % DAYS | |
| SLACK_TOKEN = "" | |
| def post_food_trucks_for_the_day(): | |
| sc = SlackClient(SLACK_TOKEN) |
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| var g = 7; | |
| var p = [ | |
| 0.99999999999980993, | |
| 676.5203681218851, | |
| -1259.1392167224028, | |
| 771.32342877765313, | |
| -176.61502916214059, | |
| 12.507343278686905, | |
| -0.13857109526572012, | |
| 9.9843695780195716e-6, |
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| CREATE FUNCTION levenshtein(s1 varchar, s2 varchar) RETURNS integer IMMUTABLE AS $$ | |
| import numpy as np | |
| # https://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Levenshtein_distance#Python | |
| def levenshtein(source, target): | |
| source = source or "" | |
| target = target or "" | |
| if len(source) < len(target): | |
| return levenshtein(target, source) |
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| import random | |
| import numpy as np | |
| from sacred import Experiment | |
| ex = Experiment() | |
| @ex.config | |
| def config(): | |
| n_sims = 1000 |