grmwld

Keybase proof

I hereby claim:

• I am grmwld on github.
• I am shroompfette (https://keybase.io/shroompfette) on keybase.
• I have a public key ASA_qNRyFyh_NJqZw8lOUzy59a3Fy-HedaAcxytXivMbyQo

To claim this, I am signing this object:

grmwld

Keybase proof

I hereby claim:

• I am grmwld on github.
• I am shr00mpfette (https://keybase.io/shr00mpfette) on keybase.
• I have a public key ASDsvP0cSe64D7UiMXqQ71pbE07XlI5qonhveTZ6r5Mnhwo

To claim this, I am signing this object:

grmwld

#!/bin/bash
# By haxus 2017


PATH_BS=.
DATA_DIR=data
src=$PATH_BS/$DATA_DIR/bitcoin-history.json
tar=$PATH_BS/$DATA_DIR/bitcoin-history.sql
tmp=$PATH_BS/$DATA_DIR/bitcoin-history.tmp
tmp2=$PATH_BS/$DATA_DIR/bitcoin-history.tmp2

grmwld

#!/bin/bash

# explain.sh begins
explain () {
  if [ "$#" -eq 0 ]; then
    while read  -p "Command: " cmd; do
      curl -Gs "https://www.mankier.com/api/explain/?cols="$(tput cols) --data-urlencode "q=$cmd"
    done
    echo "Bye!"
  elif [ "$#" -eq 1 ]; then

grmwld

let barposition = "top"
let blacklists = ["https://inbox.google.com/*","https://mail.google.com/*"]

grmwld

contexte A : 

> table(a$Date)

2010-06-23 2010-06-24 2010-06-25 2010-06-28 2010-06-29 2010-06-30 2010-07-02 2010-07-05 2010-07-06 2010-07-07 2010-07-08
        30         30         30         30         30         30         30         30         30         20         10

contexte B :

> table(a$Date)

grmwld

track = myGlyph.AnnotationLine(glyph='xyplot', subtype='density', height=60, min_score=0, shadow=True, foreground='crimson', label='PET coverage')
items = [(p['head']['fref'], p['head']['fstart'], p['tail']['fend'], p['tail']['fend'] - p['head']['fstart'], 'PET') for p in pets.pets]
for i in items:
    track.add(i)
svg = myGlyph.mySVGRenderer(annotation_lines=[track],
            documentWidth=800,
            documentHeight=300,
            genomic_span=30000,
            fref='scaffold_1',
            genome_offset=pets.pets[0]['head']['fstart']-2000,

grmwld

#!/usr/bin/env python2.7

import scipy.ndimage as ndimage
import numpy

def local_maxima(array, min_distance = 1, periodic=False, edges_allowed=True): 
    """Find all local maxima of the array, separated by at least min_distance."""
    array = numpy.asarray(array)
    cval = 0 
    if periodic: