guppy_basecaller --disable_pings --compress_fastq -c dna_r9.4.1_450bps_fast.cfg -i flongle_fast5_pass/ -s flongle_test2 -x 'auto' --recursive
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require(shiny); | |
require(caret); | |
require(e1071); | |
require(randomForest); | |
require(nnet); | |
require(glmnet); | |
#code for plotting nnets, taken from: http://beckmw.wordpress.com/2013/03/04/visualizing-neural-networks-from-the-nnet-package/ | |
# the formula of the output of train is off and doesn't work correctly though. | |
#Function for plotting nnets, not working right now |
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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) |
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# 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) |
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#!/bin/bash | |
# Change to your directory. Change this for directory. | |
cd /Users/mchirico/Dropbox/kaggle/iris | |
# Change machine environment | |
eval $(docker-machine env big2) | |
# Function to launch browser and container. | |
kjupyter() { |
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! PUBLIC DOMAIN LICENSE 2017 BY ANDERS S. CHRISTENSEN | |
! | |
! I WROTE THIS BECAUSE I WAS BORED - I DON'T RECOMMEND | |
! WRITING FILE PARSERS IN FORTRAN BECAUSE IT IS NOT | |
! PRODUCTIVE. | |
program convert | |
implicit none |
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################################### Keras2DML: Parallely training neural network with SystemML####################################### | |
import tensorflow as tf | |
import keras | |
from keras.models import Sequential | |
from keras.layers import Input, Dense, Conv1D, Conv2D, MaxPooling2D, Dropout,Flatten | |
from keras import backend as K | |
from keras.models import Model | |
import numpy as np | |
import matplotlib.pyplot as plt |
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$ 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 |
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************************************************************** | |
* 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/ |
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#!/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 |
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