mobiusklein

import numpy as np

# These methods assume that x and y have been normalized by dividing by their maximum value in order
# to get xcorr scores comparable to those seen in common Sequest-ish search engines.

def xcorr_score(x, y, k=75):
    '''Based on blogpost by Will Fondrie:
    https://willfondrie.com/2019/02/an-intuitive-look-at-the-xcorr-score-function-in-proteomics/
    '''
    y_p = y * 0

mobiusklein

cimport cython

import numpy as np
cimport numpy as np

np.import_array()

ctypedef cython.floating floating_t

ctypedef fused numeric_collection:

mobiusklein

/*
 * levmarq.c
 *
 * This file contains an implementation of the Levenberg-Marquardt algorithm
 * for solving least-squares problems, together with some supporting routines
 * for Cholesky decomposition and inversion.  No attempt has been made at
 * optimization.  In particular, memory use in the matrix routines could be
 * cut in half with a little effort (and some loss of clarity).
 *
 * It is assumed that the compiler supports variable-length arrays as

mobiusklein

#!/home/inorton/bin/pywin
import comtypes, comtypes.client
from ctypes import *
from comtypes.automation import *
import sys
import numpy

# GetMassListFromScanNum(long FAR* pnScanNumber, LPCTSTR szFilter,  
# long nIntensityCutoffType, long nIntensityCutoffValue,  
# long nMaxNumberOfPeaks, BOOL bCentroidResult, 

mobiusklein

// Utility
function getParameterByName(name) {
    name = name.replace(/[\[]/, "\\[").replace(/[\]]/, "\\]");
    var regex = new RegExp("[\\?&]" + name + "=([^&#]*)"),
        results = regex.exec(location.search);
    return results === null ? "" : decodeURIComponent(results[1].replace(/\+/g, " "));
}

// On Ready Handler
$(function(){

mobiusklein

import sqlitedict
import psutil
from uuid import uuid4

class ResourceTracker(object):
	def __init__(self, cpu_usage_total=0, memory_usage_total=0, runtime_total=0, pid=None):
		self.cpu_total = cpu_usage_total
		self.memory_total = memory_usage_total
		self.runtime_total = runtime_total
		self.pid = pid

mobiusklein

var zlib = require("zlib"),
fs = require("fs");

var datum = new Buffer(JSON.stringify({cats: [1, 2]}))

zlib.gzip(datum, function(error, results){
    if (error !== null){
        throw error
    }
    console.log(results)

mobiusklein

import os
import random
import subprocess

root_log_dir = "./log_home"

def get_random_file_name(length=25):
  rand_part = ''.join([chr(random.randint(65,122)) for i in range(length)])
  return "logfile_%s.txt" % randpart

mobiusklein

matrixHierarchicalClustering <- function (x, Rowv = NULL, Colv = if (symm) "Rowv" else NULL, 
    distfun = dist, hclustfun = hclust, reorderfun = function(d, 
        w) reorder(d, w), add.expr, symm = FALSE, revC = identical(Colv, 
        "Rowv"), scale = c("row", "column", "none"), na.rm = TRUE, 
    margins = c(5, 5), ColSideColors, RowSideColors, cexRow = 0.2 + 
        1/log10(nr), cexCol = 0.2 + 1/log10(nc), labRow = NULL, 
    labCol = NULL, main = NULL, xlab = NULL, ylab = NULL, keep.dendro = FALSE, 
    verbose = getOption("verbose"), ...) 
{
    scale <- if (symm && missing(scale)) 

mobiusklein

import csv

tsvreader = csv.reader(open('my_file_name', 'rb'), delimiter = '\t')

best_matches = [match for match in [line for line in tsvreader] if int(match[2]) == 100]