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-- Wireshark dissector for the MAVLink protocol (please see http://qgroundcontrol.org/mavlink/start for details) | |
mavlink_proto = Proto("mavlink_proto", "MAVLink protocol") | |
f = mavlink_proto.fields | |
payload_fns = {} | |
messageName = { | |
[0] = 'HEARTBEAT', | |
[1] = 'SYS_STATUS', |
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# ------------------------------------------------------------------ | |
# EDIT: I eventually found a faster way to run SD on macOS, via MPSGraph (~0.8s / step on M1 Pro): | |
# https://github.com/madebyollin/maple-diffusion | |
# The original CoreML-related code & discussion is preserved below :) | |
# ------------------------------------------------------------------ | |
# you too can run stable diffusion on the apple silicon GPU (no ANE sadly) | |
# | |
# quick test portraits (each took 50 steps x 2s / step ~= 100s on my M1 Pro): | |
# * https://i.imgur.com/5ywISvm.png |
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#!/usr/bin/env python | |
""" | |
The Needleman-Wunsch Algorithm | |
============================== | |
This is a dynamic programming algorithm for finding the optimal alignment of | |
two strings. | |
Example | |
------- |
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var request = require('request'); | |
var unzip = require('unzip'); | |
var csv2 = require('csv2'); | |
request.get('http://s3.amazonaws.com/alexa-static/top-1m.csv.zip') | |
.pipe(unzip.Parse()) | |
.on('entry', function (entry) { | |
entry.pipe(csv2()).on('data', console.log); | |
}) | |
; |