davidaknowles

from Bio import Entrez
import csv
from collections import defaultdict
from datetime import datetime

AUTHOR_NAME = "Knowles DA"
START_YEAR = datetime.now().year - 4
EMAIL = "you@example.com"
OUTFILE = "nsf_coauthors.csv"

davidaknowles

matplotlib
numpy==1.26.4
pandas
scanpy
scikit_learn
scipy
plotnine
seaborn
jupyterlab
Cython

davidaknowles

function mmwrite(filename,A)

    mmfile = fopen(filename,'w');
    
    [M,N] = size(A);
    
    [I,J,V] = find(A);
    NZ = length(V);
    

davidaknowles

#!/bin/bash

bcftools view -H input.vcf.gz | \
awk '{print $1 "\t" $2  "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" "0/1"}' | \
cat <(bcftools view -h -s SOME_SAMPLE input.vcf.gz) - | \
bgzip > \
output.vcf.gz

bcftools view -H output.vcf.gz | awk '{ print $1 "\t" $2 - 1 "\t" $2 }' | bgzip > output.bed.gz

davidaknowles

import pyro
from pyro.infer import SVI, Trace_ELBO, TraceEnum_ELBO, config_enumerate
from torch.distributions import constraints

data = 2. * torch.Tensor( [-1., -0.5, -0.5, .5, .8, 1.] )

def model(data):
    guide_efficacy = pyro.sample('guide_efficacy', dist.Beta(1., 1.).expand([len(data)]).to_event(1) )
    gene_essentiality = pyro.sample("gene_essentiality", dist.Normal(0., 5.))
    mean = gene_essentiality * guide_efficacy

davidaknowles

require(VariantAnnotation)
require(GenomicRanges)

get_cis_geno = function(tab, chrom, left, right, genome_build = "GRCh38") {
  gr = GRanges(chrom, IRanges(left, right))
  sp = ScanVcfParam(which = gr)
  vcf = readVcf(tab, genome_build, param = sp)
  gt = geno(vcf)$GT
  if (nrow(gt) == 0) return(NULL)
  allele1 = substr(gt, 1, 1)

davidaknowles

#' Consistent implementation of diag
fix_diag=function(x) {
  if(length(x)==1) matrix(x) else diag(x)
}

unscale = function(x) {
  x = sweep(x, 1, attr(x, "scaled:scale"), "*")
  sweep(x, 1, attr(x, "scaled:center"), "+")
}

davidaknowles

def pixel_shuffle_1d(x, upscale_factor):
    batch_size, channels, steps = x.size()
    channels //= upscale_factor
    input_view = x.contiguous().view(batch_size, channels, upscale_factor, steps)
    shuffle_out = input_view.permute(0, 1, 3, 2).contiguous()
    return shuffle_out.view(batch_size, channels, steps * upscale_factor)

davidaknowles

#!/bin/bash 
set -e
outfile=$2
if [ "$#" -eq 1 ]; then
    outfile=$1
fi
gs -sDEVICE=pdfwrite -dNOPAUSE -dBATCH -dSAFER -dCompatibilityLevel=1.5 -sOutputFile=temp.pdf $1
# so you can run on the same file
mv temp.pdf $outfile

davidaknowles

library(ggplot2)  ## devtools::install_github("hadley/ggplot2)
library(grid)     ## rasterGrob
library(EBImage)  ## readImage
library(ggthemes) ## theme_minimal

## ##########
## INDEPENDENT CODE TO BE SOURCED:
## ##########
# user-level interface to the element grob