pansapiens

#!/bin/bash
# Ollama Model Export Script
# Usage: bash ollama-export.sh vicuna:7b
# License: MIT (https://ncurl.xyz/s/o_o6DVqIR)
# https://gist.github.com/supersonictw/f6cf5e599377132fe5e180b3d495c553

set -e

echo "Ollama Model Export Script"
echo "License: MIT (https://ncurl.xyz/s/RD0Yl5fSg)"

pansapiens

#!/usr/bin/env python2.7

# Export from Google App Engine Datastore Backup LevelDB format to JSON flat file
# Based on: https://gist.github.com/xlfe/af25f160256e4d52f499dee7e8fa212f

##
# 2024 instructions:
##
# Using the Google Cloud console (https://console.cloud.google.com), find "Firestore"
# and export your database to a Cloud Storage "Bucket". Download the content of the Bucket.

pansapiens

#!/usr/bin/env python

###
# bam2fq_softclip.py
###
# A script for exploring soft-clipping of aligned reads.
# Extracts mapped reads from a BAM file as a FASTQ, but encodes 'soft-clipped' regions
# as lowercase. Soft-clipped regions can be quickly visualized in the terminal like:
# 
# ./bam2fq_softclip.py aligned.bam | grep --color=always [atcg] |less -R

pansapiens

A simple Docker Compose recipe for schollz/hostyoself.

pansapiens

#!/usr/bin/env python

import argparse
import pandas as pd
import re
import sys
from typing import Optional, List
import logging
import glob
import io

pansapiens

#!/bin/bash

# Given a directory of fastq files, use bbmerge to find adapters sequences for every sample,
# align and find the consensus. 
# Final consensus is in  r1_adapter.consensus.fa and r2_adapter.consensus.fa - trim by hand
# if the automatic trimming up the the first 'n' isn't sensible

# Requires: bbmerge.sh (bbmap), muscle, emboss (cons)

pansapiens

#!/bin/bash

R_TMUX_SESSION_NAME="${R_TMUX_SESSION_NAME:-vscode-r}"
TMUX_CMD=$(command -v tmux)

if ! [ -x "$(command -v tmux)" ]; then
    echo "Error: tmux is not installed."
    exit 1
fi

pansapiens

Gzip compression heatmap

Generates a heatmap plot showing the compression ratio of different block through a file.

Show interesting patterns in FASTQ files, and may be useful for diagnosing pathological or unusual data. I find using the zscore transformation for the plot is more informative.

Run like:

./compression_heatmap.py -b 1048576 -c rainbow -t zscore SRR11794587_2.fastq.gz

pansapiens

#!/bin/bash
# This script deinterleaves a FASTQ file generated by an MGI sequencer with two flowcell lanes
# where headers are in the format: @v300009551L1C002R003000000/1 with L1 or L2 indicating the lane
# This can be useful for some analyses since each lane can behave like a technical replicate (eg DADA2 error correction?)

set -e
set -o pipefail

input_fastq_gz="$1"  # input FASTQ file
samplename="$(basename $input_fastq_gz .fastq.gz)"

pansapiens

#!/usr/bin/env python
import os
import shutil
import re
import sys
import difflib
import argparse


def parse_ssh_config(config_path):