yjzhang

#!/bin/bash
# run using 'nohup sh run_split_seq.sh &'

nohup split-seq all --fq1 Matthew_77_S1_R1_001.fastq.gz --fq2 Matthew_77_S1_R2_001.fastq.gz --output_dir output --chemistry v2 --genome_dir /data/reference_genomes/mm10/ --nthreads 16

yjzhang

#!/bin/bash

for i in $(seq 1 18); do
    aws s3 cp --recursive s3://sdfas/S${i}_L1_outputDGE_filtered S${i}_L1_outputDGE_filtered
    aws s3 cp --recursive s3://sdfas/S${i}_L2_outputDGE_filtered S${i}_L2_outputDGE_filtered
done

yjzhang

#!/usr/bin/env python

# merge DGE matrices from multiple runs of split-seq
# joins on genes...

import os
import sys

import numpy as np
import pandas as pd

yjzhang

#!/bin/bash

# kills all gunicorn processes with 8889

pids=`ps ax | grep gunicorn | grep "8889" | awk '{split($0,a," "); print a[1]}'`
# TODO
for pid in $pids; do
    killall -9 $pid
    echo "killed gunicorn process $pid"
done

yjzhang

#!/bin/bash

for var in "$@"; do
    filename=${var%.*}
    #echo $filename
    #echo $@
    echo writing to $filename.html
    pandoc $var --self-contained --toc -c /home/yjzhang/Dropbox/Notes/buttondown.css -s --mathml -o $filename.html
done

yjzhang

#!/bin/bash

# downloads all MEDLINE/Pubmed citations in the annual baseline.

for i in $(seq 1 972); do
    fname="1"
    if ((i < 10)); then
        fname="ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed19n000$i.xml.gz"
    elif ((i < 100)); then
        fname="ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed19n00$i.xml.gz"

yjzhang

def flatten(l):
    output = []
    for x in l:
        try:
            output += flatten(x)
        except:
            output.append(x)
    return output

yjzhang

class TrieNode(object):

    def __init__(self, char = '', children = {}, parent = None, wordEnd = False, value = True):
        self.char = char
        self.children = children
        self.parent = parent
        self.wordEnd = wordEnd
        self.value = value

yjzhang

# simple implementation of quantile normalization? 
import numpy as np 

def quantile_norm(data):
    """
    Source: https://en.wikipedia.org/wiki/Quantile_normalization 

    Note: this doesn't deal with ties very well. 

    Args:

yjzhang

import numpy as np
from scipy.stats import norm

# particle filter for localization using echolocation

# given: measurements, accelerometer readings, heading

# user's state: [x, y, theta]

# control: d(theta), d(position)