def getMinString(inFile, stringSize): l = getCompSizes(inFile, stringSize) i = l.index(min(l)) f = open(inFile) f.seek(i) s = f.read(stringSize) f.close() return s
def getMinString(inFile, stringSize): l= getCompSizes(inFile,stringSize) i = l.index(min(l)) f=open(inFile) f.seek(i) s=f.read(stringSize) f.close() return s
def main(): t=time.time() l = getCompSizes(sys.argv[1],int(sys.argv[2])) t=time.time()-t plt.plot(l,'green')#,marker=u'.') plt.plot(len(l)*[mean(l)],label='mean') plt.plot(len(l)*[median(l)], 'r--',label='median') plt.legend() plt.ylabel('Compressed Size') plt.xlabel('Position') plt.show() print t return
import matplotlib.pyplot as plt from getStats import * from varyGraph import getCompSizes import matplotlib.mlab as mlab import sys,math l = getCompSizes(sys.argv[1],int(sys.argv[2])) n, bins, patches = plt.hist(l,50,normed=True) #y = mlab.normpdf(bins, mean(l),deviation(l)) plt.xlabel('Compressed string length') #plt.plot(bins, y, 'r') plt.show()
import sys from getStats import * from varyGraph import getCompSizes import matplotlib.pyplot as plt import matplotlib.mlab as mlab stringSize=int(sys.argv[1]) l = getCompSizes('englishText.txt', stringSize) n = getCompSizes('spanishText.txt', stringSize) m, bins, p = plt.hist(l,50,normed=True,visible=False) m1, bins1, p1 = plt.hist(n,50,normed=True,visible=False) y = mlab.normpdf(bins, mean(l),deviation(l)) y1 = mlab.normpdf(bins1, mean(n),deviation(n)) plt.plot(bins,y,label='English') plt.plot(bins1,y1,'b--',label='Spanish') plt.xlabel('Compressed Size') plt.legend() plt.show()
import sys from getStats import deviation from varyGraph import getCompSizes f = open('standardDeviations.csv', 'a') for i in range(2, 8): f.write( str(i) + ',' + str(deviation(getCompSizes(sys.argv[1], 2**i))) + '\n') f.close()
import sys from getStats import * from varyGraph import getCompSizes import matplotlib.pyplot as plt import matplotlib.mlab as mlab stringSize = int(sys.argv[1]) l = getCompSizes('englishText.txt', stringSize) n = getCompSizes('spanishText.txt', stringSize) m, bins, p = plt.hist(l, 50, normed=True, visible=False) m1, bins1, p1 = plt.hist(n, 50, normed=True, visible=False) y = mlab.normpdf(bins, mean(l), deviation(l)) y1 = mlab.normpdf(bins1, mean(n), deviation(n)) plt.plot(bins, y, label='English') plt.plot(bins1, y1, 'b--', label='Spanish') plt.xlabel('Compressed Size') plt.legend() plt.show()
from varyGraph import getCompSizes import matplotlib.pyplot as plt from getStats import mean x,res=[],[] for i in range(1,100): l = getCompSizes('genome.fa',i) print i,mean(l) res+=[i/mean(l)] x+=[i] plt.plot(x,res) plt.ylabel('Mean Compression Rate') plt.xlabel('String Length') plt.show()
import matplotlib.pyplot as plt from getStats import * from varyGraph import getCompSizes import matplotlib.mlab as mlab import sys, math l = getCompSizes(sys.argv[1], int(sys.argv[2])) n, bins, patches = plt.hist(l, 50, normed=True) #y = mlab.normpdf(bins, mean(l),deviation(l)) plt.xlabel('Compressed string length') #plt.plot(bins, y, 'r') plt.show()