import urllib import os.path import sys from sklearn import linear_model from data_import import percolator_import, summary_stats try: import cPickle as pickle kwargs = {} except: import _pickle as pickle kwargs = {'encoding':'bytes'} import gzip data, true_hits, peptide_number = percolator_import(sys.argv[1]) data2, true_hits_2, peptide_number_2 = percolator_import(sys.argv[2]) means, sdevs = summary_stats(data) pre_trans_data = data pre_trans_data2 = data2 #save scans and labels scan_nums = data[:,0] labels = data[:,1] scan_nums_2 = data2[:,0] labels_2 = data2[:,1]
import string import numpy as np import matplotlib import matplotlib.pyplot as plt import time import urllib import os.path import sys import random from datetime import datetime from sklearn import linear_model from data_import import percolator_import, summary_stats data, true_hits, peptide_number = percolator_import(sys.argv[1]) pre_trans_data = data scan_nums = data[:,0] orig_labels = data[:,1] labels = orig_labels #strip scans and peps out data = data [:, [i for i in range(2, 20)]] # # Creat First N Scan Subset # count = 0 N_scans = 50000 for scan in scan_nums: if int(scan) > N_scans: