| import io | |
| import itertools | |
| import shutil | |
| import sys | |
| BLOCK = chr(9608) | |
| ESC = "\x1b[" | |
| RST = ESC + "0m" |
| 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." |
| """ 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): |
| FROM python:3.6 | |
| RUN mkdir /app | |
| ADD requirements.txt /app/ | |
| WORKDIR /app/ | |
| RUN pip install -r requirements.txt | |
| CMD [ \ | |
| "watchmedo", \ | |
| "auto-restart", \ |
| cube: | |
| gcc boring_old_cube.cpp -framework OpenGL -framework GLUT -o boring_old_cube -w |
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)
| 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)] |
| 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)) |
| 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( ( |
| #!/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"]' |