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@sirselim
sirselim / guppy_basecalling.md
Last active August 30, 2023 03:46
My notes on setting up basecalling on Google Colab

Nanopore basecalling on Google Colab

NOTE: this whole idea is the brain child of Jürgen Hench. He got it up and running and posted about it here. I am merely wrapping the idea in a hopefully easy to follow set of instructions for people to test themseleves.

This notebook describes processing of Nanopore sequencing data (fast5 files) in a Google Colab interactive notebook environment. This is made possible by utalising the GPU enabled runtime that is available via Colab.

@andersx
andersx / xyz2om2.py
Created June 14, 2020 15:54
XYZ to OM2
#!/usr/bin/env python2
import numpy as np
import sys
elements = dict()
elements["H"] = 1
elements["C"] = 6
elements["N"] = 7
elements["O"] = 8
elements["F"] = 9
@sirselim
sirselim / basecalling_notes.md
Last active August 1, 2023 01:27
a collection of my notes while working on nanopore basecalling on the Jetson Xavier

Jetson Xavier basecalling notes

initial basecalling runs

'fast' flip-flop calling on the Jetson Xavier

guppy_basecaller --disable_pings --compress_fastq -c dna_r9.4.1_450bps_fast.cfg -i flongle_fast5_pass/ -s flongle_test2 -x 'auto' --recursive
@tobigithub
tobigithub / ramdisk-ubuntu.txt
Created March 25, 2019 00:21
Ramdisk under ubuntu increases throughput and latency
**************************************************************
* Using a Ramdisk under Ubuntu
* Increases throughput 1:12 (151 Mbyte/sec to 1900 Mbyte/sec)
* Increases latency 1:300 (0.15 Mbyte/sec to 126 Mbyte/sec)
***************************************************************
# create 10 Gbyte ramdisk ubuntu
sudo mkdir -p /media/ramdisk
sudo mount -t tmpfs -o size=10G tmpfs /media/ramdisk/
@elrubio
elrubio / mono_crash_maxquant_20181029T091700.log
Last active July 8, 2021 19:24
mono crashes during execution of of MaxQuant
$ uname -rm
4.18.5-arch1-1-ARCH x86_64
$ mono --version
Mono JIT compiler version 5.21.0 (master/05bc22015c9 Thu Oct 25 13:31:44 CEST 2018)
Copyright (C) 2002-2014 Novell, Inc, Xamarin Inc and Contributors. www.mono-project.com
TLS: __thread
SIGSEGV: altstack
Notifications: epoll
Architecture: amd64
@NiloyPurkait
NiloyPurkait / MNIST_Keras2DML.py
Last active October 20, 2021 05:26
An example of using Apache SparkML to train a convolutional neural network in parallel using the MNIST dataset, on IBM watson studio. Written for medium article: https://medium.com/@niloypurkait/how-to-train-your-neural-networks-in-parallel-with-keras-and-apache-spark-ea8a3f48cae6
################################### Keras2DML: Parallely training neural network with SystemML#######################################
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Input, Dense, Conv1D, Conv2D, MaxPooling2D, Dropout,Flatten
from keras import backend as K
from keras.models import Model
import numpy as np
import matplotlib.pyplot as plt
@andersx
andersx / xyz2om2.f90
Last active January 7, 2021 20:13
Stupid xyz2om2 converter in fortran (cuz it was slow to do 1M files with a python converter)
! PUBLIC DOMAIN LICENSE 2017 BY ANDERS S. CHRISTENSEN
!
! I WROTE THIS BECAUSE I WAS BORED - I DON'T RECOMMEND
! WRITING FILE PARSERS IN FORTRAN BECAUSE IT IS NOT
! PRODUCTIVE.
program convert
implicit none
@mchirico
mchirico / irisKaggle.sh
Created April 9, 2016 13:47
jupyter notebook for Kaggle Docker Image
#!/bin/bash
# Change to your directory. Change this for directory.
cd /Users/mchirico/Dropbox/kaggle/iris
# Change machine environment
eval $(docker-machine env big2)
# Function to launch browser and container.
kjupyter() {
@tobigithub
tobigithub / Kind-sequence.R
Last active July 6, 2016 22:22
# Kind Sequence
# https://oeis.org/A262602
# https://en.wikipedia.org/wiki/Kind_sequence
# 1, 1, 3, 5, 7, 9, 13, 19, 27, 39, 57, 83, 119, 171, 247, 357, 515, 743, 1073, 1549, 2235, 3225, 4655, 6719, 9697,
# 13995, 20199, 29153, 42075, 60725, 87643, 126493, 182563, 263487, 380283, 548851, 792139, 1143269, 1650045,
# 2381459, 3437085, 4960637, 7159533
# The sequence seems simply odd first, but then in a gentle
# and kind way escapes to higher numbers
# Tobias Kind (2015)
@mbohun
mbohun / pubchem_convert_SMILES_to_IUPAC.py
Last active March 15, 2023 14:32
pubchem_convert_SMILES_to_IUPAC.py use pubchem PUG REST to get IUPAC names/strings for SMILES
import sys
import requests
from lxml import etree
if __name__=="__main__":
smiles = sys.argv[1]
html_doc = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/smiles/" + smiles + "/record/XML")
root = etree.XML(html_doc.text)