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.maplibregl-map{font:12px/20px Helvetica Neue,Arial,Helvetica,sans-serif;overflow:hidden;position:relative;-webkit-tap-highlight-color:rgb(0 0 0/0)}.maplibregl-canvas{left:0;position:absolute;top:0}.maplibregl-map:fullscreen{height:100%;width:100%}.maplibregl-ctrl-group button.maplibregl-ctrl-compass{touch-action:none}.maplibregl-canvas-container.maplibregl-interactive,.maplibregl-ctrl-group button.maplibregl-ctrl-compass{cursor:grab;-webkit-user-select:none;-moz-user-select:none;user-select:none}.maplibregl-canvas-container.maplibregl-interactive.maplibregl-track-pointer{cursor:pointer}.maplibregl-canvas-container.maplibregl-interactive:active,.maplibregl-ctrl-group button.maplibregl-ctrl-compass:active{cursor:grabbing}.maplibregl-canvas-container.maplibregl-touch-zoom-rotate,.maplibregl-canvas-container.maplibregl-touch-zoom-rotate .maplibregl-canvas{touch-action:pan-x pan-y}.maplibregl-canvas-container.maplibregl-touch-drag-pan,.maplibregl-canvas-container.maplibregl-touch-drag-pan .maplibregl-canvas{touch

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autocmd BufNewFile,BufRead *.tsx set filetype=typescript.tsx

call plug#begin('~/.local/share/nvim/plugged') 

Plug 'neoclide/coc.nvim', {'branch': 'release'}
let g:coc_global_extensions = ['coc-emmet', 'coc-css', 'coc-html', 'coc-json', 'coc-prettier', 'coc-tsserver']
Plug 'ctrlpvim/ctrlp.vim'
Plug 'leafgarland/typescript-vim'
Plug 'dyng/ctrlsf.vim'
Plug 'rakr/vim-one'

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C-Em7
C-Em7

C
Em7
Am
Dm
F-G

C-Em7

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import React, {Component} from 'react'

export default DecoratedComponent =>
  class extends Component {
    state = {
      hover: false,
    }
    toggleHover = () =>
      this.setState({hover: !this.state.hover})
    render() {

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# Cet ensemble de scripts pour obtenir les stats du simulateur d'embauche est complètement archaïque... 
# mais on le fait tourner seulement tous les 2-3 mois...

# Sur le serveur

mkdir logs-DATE
sudo su
cp /var/log/nginx/openfisca.* /home/pousse/logs-10juillet2017/
# on compresse les logs pour les transférer sur un autre ordi...
tar -czvf logs-DATE.tar.gz logs-DATE

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import math
from scipy.optimize import fsolve

def calculate_net_from(brut):
    print brut[0]
    scenario = tax_benefit_system.new_scenario()
    scenario.init_single_entity(
        period = "2015-11",
        parent1 = dict(
            age = 30,

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<script src="https://rawgit.com/sgmap/cout-embauche/gh-pages/dist/cout-embauche-widget.js"></script>

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https://news.ycombinator.com/item?id=9808021

No, reducers are about reducing things. Transducers are about creating reducers that can be used on arbitrary collection-like things.

Reducers come from the observation that many higher order operations on collections can be built on reduce. You just supply the right reducing function and you get the equivalent to 'map' or 'filter'. This is useful because when you start chaining these higher order functions you can gain performance be replacing the multiple function calls with a single reducer based function.

To take the example from the main docs:

    (fold + (filter even? (map inc [1 1 1 2])))

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(defmacro forever [& body] 
  `(while true ~@body))

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sudo docker exec -i -t RUNNING_DOCKER_NAME bash

sudo openssl req -x509 -nodes -newkey rsa:2048 -keyout /etc/cozy/server.key -out /etc/cozy/server.crt -days 365 -subj '/CN=DOMAIN_NAME'