jamestwebber

jamestwebber

jamestwebber

➜  packer-images git:(specops) ✗ PACKER_LOG=1 packer build -force nanopore.json 
2018/01/30 19:06:30 [INFO] Packer version: 1.1.0
2018/01/30 19:06:30 Packer Target OS/Arch: darwin amd64
2018/01/30 19:06:30 Built with Go Version: go1.9
2018/01/30 19:06:30 Detected home directory from env var: /Users/james.webber
2018/01/30 19:06:30 Using internal plugin for vmware-iso
2018/01/30 19:06:30 Using internal plugin for vmware-vmx
2018/01/30 19:06:30 Using internal plugin for googlecompute
2018/01/30 19:06:30 Using internal plugin for hyperv-iso
2018/01/30 19:06:30 Using internal plugin for parallels-pvm

jamestwebber

jamestwebber

jamestwebber

jamestwebber

#!/usr/bin/env python3

import argparse
import gzip
import itertools
import os

import multiprocessing

from collections import Counter, defaultdict

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

jamestwebber

use std::collections::LinkedList;
use std::hash::Hash;

use counter::Counter;
use rayon::prelude::*;

#[derive(Default)]
pub struct ParCounter<T: Hash + Eq, N = usize>(Counter<T, N>);

impl<T: Hash + Eq + Clone, N: Clone + Ord> ParCounter<T, N> {

jamestwebber

import numpy as np
import numba as nb

import scipy.stats


@nb.njit(parallel=True)
def tiecorrect(rankvals):
    """
    parallelized version of scipy.stats.tiecorrect