jsheedy

import io
import itertools
import shutil
import sys


BLOCK = chr(9608)
ESC = "\x1b["
RST = ESC + "0m"

jsheedy

function run() {
  # usage: run "command string --with-args"
  cmd_output=$(eval $1)
  return_value=$?
  if [ $return_value != 0 ]; then
    echo "Command $1 failed"
    exit -1
  else
    echo "output: $cmd_output"
    echo "Command succeeded."

jsheedy

""" quick hack to press the buttons on the Spotify desktop app based on nanoKONTROL transport buttons using cliclick.
The volume control on slider 1 is especially janky """

import subprocess

import rtmidi

midiin = rtmidi.RtMidiIn()

def print_message(midi):

jsheedy

FROM python:3.6

RUN mkdir /app
ADD requirements.txt /app/
WORKDIR /app/
RUN pip install -r requirements.txt

CMD [ \
  "watchmedo", \
  "auto-restart", \

jsheedy

cube:
	gcc boring_old_cube.cpp -framework OpenGL -framework GLUT -o boring_old_cube -w

jsheedy

asyncio + uvloop echo server benchmark

This is an attempt at a bare minimum client/server benchmark of asyncio with optional use of uvloop. On my 2015 macbook pro, I get almost 2.5X improvement using uvloop in the server.

The client runs PARALLEL tasks at once. Running only a single task results in about 1/8 the throughput of 100 simultaneous tasks.

# with uvloop
$ python client.py
satisfied 100000 requests in 1.41021 seconds (70911.42 reqs/s)

jsheedy

import time

import psycopg2

from iter_file import IteratorFile

conn = psycopg2.connect(host="localhost", database="test")

# args = [(1,2), (3,4), (5,6)]
args = [(i,i+1) for i in range(1,1*10**4,2)]

jsheedy

def skeletonize(img):
    """ OpenCV function to return a skeletonized version of img, a Mat object"""

    #  hat tip to http://felix.abecassis.me/2011/09/opencv-morphological-skeleton/

    img = img.copy() # don't clobber original
    skel = img.copy()

    skel[:,:] = 0
    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))

jsheedy

def floyd_steinberg_dither(data):

    result = data.astype(np.float64)

    fs_matrix = np.array( (
        ( np.nan, np.nan, 7/16 ),
        ( 3/16, 5/16, 1/16),
        ), dtype=np.float64 );

    fs_mask = np.array( (

jsheedy

#!/usr/bin/env python

""" parses a JSON document on stdin, and prints to stdout the portion of the document
referenced by the Python getter substring given as the first argument.

This implementation uses eval(), so use with care! No warranty implied.

For example

$ curl 'http://api.duckduckgo.com/?q=ramen&format=json' 2> /dev/null | ~/bin/extract_data.py '["RelatedTopics"][0]["Text"]'