legowerewolf

↗️ This gist has been transferred to tailscale-dev/deck-tailscale

shenwei356

Functions:

# encoding: ACTG

def nuc2int_nochecking(b):
    return (ord(b) >> 1) & 3, True
    
def nuc2int_if(b):
    if b == 'a' or b == 'c' or b == 'g' or b == 't' \

or b == 'A' or b == 'C' or b == 'G' or b == 'T':

jamestwebber

import torch

# cupy fails to import when there are no GPUs. This check means that the module can
# still be imported in that case, but of course the classes won't work.
if torch.cuda.is_available():
    import cupy

from torch.utils.data import DataLoader, Dataset
from torch.utils.dlpack import from_dlpack

egasimus

({ lib, newScope, stdenv, pkgs }: let

  # nicer aliases
  derive = stdenv.mkDerivation;
  concat = builtins.concatStringsSep " ";

  # vendored libuvc: don't build, just make sources available
  libuvc-src = derive {
    name = "libuvc-src";
    # using fetchgit instead fetchFromGitHub because

mikeckennedy

import asyncio
import time

from unsync import unsync


@unsync
async def heartbeat():
    while True:
        start = time.time()

hamelsmu

name: Binder
on: 
  pull_request:
    types: [opened, reopened]

jobs:
  Create-Binder-Badge:
    runs-on: ubuntu-latest
    steps:

lkuper

Making Progress Under Uncertainty in SMT Solving, Research, and Life

Abstract

SAT and Satisfiability Modulo Theories (SMT) solvers have many important applications in PL, including verification, testing, type checking and inference, and program analysis – but they are often a mysterious black box to their users, even when those users are PL researchers with lots of solver experience! This talk will be partly a tutorial introduction to the inner workings of SAT and SMT solvers, and partly an extended analogy to navigating life as a researcher: making decisions when you have only incomplete information to go on, learning from decisions that turned out to be bad, and determining when to give up and when to try again. I’ll also highlight a variety of papers in this year’s POPL program that make use of SAT and SMT solving, and discuss why I think it’s worthwhile to learn about solver internals.

Outline

• Introduction
• Self-intro

kytulendu

#!/bin/bash

# This script will install AMDGPU-PRO OpenCL and Vulkan support.
#
# For Ubuntu and it's flavor, just install the package using this command
# in extracted driver directory instread.
#
#     ./amdgpu-pro-install --opencl=legacy,pal --headless --no-dkms
#
# For Arch Linux or Manjaro, use the opencl-amd or rocm-opencl-runtime on AUR instread.

twiecki

import dask
import dask.array as da
import dask.dataframe as dd
import sparse

@dask.delayed(pure=True)
def corr_on_chunked(chunk1, chunk2, corr_thresh=0.9):
    return sparse.COO.from_numpy((np.dot(chunk1, chunk2.T) > corr_thresh))
        
def chunked_corr_sparse_dask(data, chunksize=5000, corr_thresh=0.9):

jamesmunns

Install ARCH Linux with encrypted file-system and UEFI

The official installation guide (https://wiki.archlinux.org/index.php/Installation_Guide) contains a more verbose description.

Download the Arch ISO

• Image from https://www.archlinux.org/

Copy to a USB drive