nomeyer

var fs = require('fs');
fs.appendFile(filePath, text, function(err) {
    console.log('Error writing to file');
});

nomeyer

@app.route('/', methods=['GET'], defaults={'var': 'default'})
@app.route('/<string:var>', methods=['GET'])
def page(var):
    # do stuff with the given var or default
    return render_template('page.html', var=var)

nomeyer

select schemaname||'.'||tablename
from pg_tables
where has_table_privilege (
    '~~user_name~~',
    schemaname||'.'||tablename, 
    '~~permission_type~~'
) 
and schemaname not in (
    'pg_catalog',
    'information_schema'

nomeyer

select
    schemaname,
    tablename,
    pg_size_pretty(size) as size_pretty,
    pg_size_pretty(total_size) as total_size_pretty

from (
    select
        *,
        pg_relation_size(schemaname ||'.'|| tablename) as size,

nomeyer

var start = performance.now();
var end = performance.now();
console.log('Doing stuff took ' + (end-start) + 'ms');

nomeyer

git fetch origin 8fd8e58c2599a9d31f9ca8cad1fdb7524dc794e5:refs/remotes/origin/~~branch_name~~

nomeyer

case extract(isodow from ~~date~~)
    when 1 then 'Mon ' || cast(~~date~~ as date)
    when 2 then 'Tue ' || cast(~~date~~ as date)
    when 3 then 'Wed ' || cast(~~date~~ as date)
    when 4 then 'Thu ' || cast(~~date~~ as date)
    when 5 then 'Fri ' || cast(~~date~~ as date)
    when 6 then 'Sat ' || cast(~~date~~ as date)
    when 7 then 'Sun ' || cast(~~date~~ as date)
end date

nomeyer

/*dragon*/var b=X=Y=T=L=0;document.addEventListener("click",function(a){a.preventDefault()},!0);document.addEventListener("mousedown",c);document.addEventListener("touchstart",c);function c(a){a.preventDefault();a.target!==document.documentElement&&a.target!==document.body&&(b=Date.now(),a.target.setAttribute("data-drag",b),a.target.style.position="relative",T=a.target.style.top.split("px")[0]||0,L=a.target.style.left.split("px")[0]||0);X=a.clientX||a.touches[0].clientX;Y=a.clientY||a.touches[0].clientY}document.addEventListener("mousemove",d);document.addEventListener("touchmove",d);function d(a){if(""!==b){var e=document.querySelector('[data-drag="'+b+'"]');e.style.top=parseInt(T)+parseInt((a.clientY||a.touches[0].clientY)-Y)+"px";e.style.left=parseInt(L)+parseInt((a.clientX||a.touches[0].clientX)-X)+"px"}}document.addEventListener("mouseup",f);document.addEventListener("touchend",f);function f(){b=""}document.addEventListener("mouseover",g);function g(a){a.target.style.cursor="move";a.target.style.boxShado

nomeyer

# Visualise given word embeddings
# words is a list of words
# data is the vector representation of each word

# Train the algorithm
from sklearn.manifold import TSNE
vis_algo = TSNE(random_state=0, verbose=10, init='pca', n_iter=200)
vis = vis_algo.fit_transform(data)

# Plot the resulting visualisation

nomeyer

#import <AppKit/AppKit.h>
NSRunningApplication* frontApp = [[NSWorkspace sharedWorkspace] frontmostApplication];
pid_t pid = [frontApp processIdentifier];
// fprintf(stdout, "%s\n", [[NSString stringWithFormat:@"%ld", (long)pid] UTF8String]);
// fflush(stdout);