def md(mu=1019,sigma=209): phi = [] for i in range(400,1600,50): phi.append(gaussian.pdf(i,mu,sigma)) stddraw.setYscale(0,1.1*max(phi)) stdstats.plotLines(phi) stddraw.show()
def main(): hurst_ex = float(sys.argv[1]) scale = 2**(2 * hurst_ex) fill_brownian(array, i, i1, variance, scale) stddraw.setYscale(-1, 1) stddraw.setXscale(0, len(array)) stddraw.setPenRadius(0.0) stdstats.plotLines(array) stddraw.show()
def main(): sh = [] ch = [] x = -5 while x < 5: sh.append(sinh(x)) ch.append(cosh(x)) x += 0.2 y = max(max(sh), max(ch)) print(sh) print(ch) print(y) stddraw.setYscale(-y, y) stdstats.plotPoints(sh) stdstats.plotLines(ch) stddraw.show()
def main(): n = int(sys.argv[1]) p = float(sys.argv[2]) trials = int(sys.argv[3]) t = int(sys.argv[4]) q = evaluate(n, p, trials) stdio.writeln(q) norm = exTimes(n, p, trials, t) phi = stdarray.create1D(n + 1, 0.0) stddev = math.sqrt(n) / 2.0 for i in range(n + 1): phi[i] = gaussian.pdf(i, n / 2.0, stddev) stddraw.setCanvasSize(1000, 400) stddraw.setYscale(0, 1.1 * max(max(norm), max(phi))) stdstats.plotBars(norm) stdstats.plotLines(phi) stddraw.show()
def main(argv): n = int(argv[1]) t = int(argv[2]) stddraw.createWindow() stddraw.setYscale(0, 0.2) freq = [0] * (n+1) for t in range(t): freq[binomial(n)] += 1 norm = [0.0] * (n+1) for i in range(n+1): norm[i] = float(freq[i]) / float(t) stdstats.plotBars(norm) stddev = math.sqrt(n) / 2.0 phi = [0.0] * (n+1) for i in range(n+1): phi[i] = gaussian.phi(i, n/2.0, stddev) stdstats.plotLines(phi) stddraw.show() stddraw.wait()
end = i year = year[begin:end] volume = volume[begin:end] price = price[begin:end] t = 0 for i in range(len(volume)): volume[i] = eval(volume[i]) / 100000 t += volume[i] if (i + 1) % bili == 0: hVolume.append(t / bili) t = 0 t = 0 for i in range(len(price)): price[i] = eval(price[i]) t += price[i] if (i + 1) % bili == 0: hPrice.append(t / bili) t = 0 print(len(hVolume)) stddraw.setCanvasSize(1000, 500) stddraw.setYscale(0, 1.1 * max(max(hVolume), max(hPrice))) stdstats.plotLines(hVolume) stddraw.setPenColor(stddraw.BLUE) stdstats.plotLines(hPrice) stddraw.show()
import stdstats import stddraw #a = [1,1,9,3,5,6,8,10] a = [1] print('mean:', stdstats.mean(a)) print('var:', stdstats.var(a)) print('stddev:', stdstats.stddev(a)) print('median:', stdstats.median(a)) stddraw.setYscale(min(a) - 2, max(a) + 2) stdstats.plotPoints(a) stdstats.plotLines(a) stdstats.plotBars(a) stddraw.show()
# draw the results to standard draw. Also draw the predicted Gaussian # distribution function, thereby allowing easy comparison of the # experimental results to the theoretically predicted results. n = int(sys.argv[1]) trials = int(sys.argv[2]) freq = stdarray.create1D(n + 1, 0) for t in range(trials): heads = stdrandom.binomial(n, 0.5) freq[heads] += 1 norm = stdarray.create1D(n + 1, 0.0) for i in range(n + 1): norm[i] = 1.0 * freq[i] / trials phi = stdarray.create1D(n + 1, 0.0) stddev = math.sqrt(n) / 2.0 for i in range(n + 1): phi[i] = gaussian.pdf(i, n / 2.0, stddev) stddraw.setCanvasSize(1000, 400) stddraw.setYscale(0, 1.1 * max(max(norm), max(phi))) stdstats.plotBars(norm) stdstats.plotLines(phi) stddraw.show() #----------------------------------------------------------------------- # python bernoulli.py 20 100000
r = [0] * 13 for i in range(n): t = twodice() r[t] += 1 return r def crapS(n): r = [0] * 13 for i in range(n): t = SicheDice() r[t] += 1 return r def drawDice(a=[]): stddraw.setYscale(0, 1.1 * max(a)) stdstats.plotBars(a) n = 1000000 d = crap(n) dS = crapS(n) print(d) print(dS) print(((d[7] + d[11])) / sum(d)) print((d[2] + d[3] + d[12]) / sum(d)) drawDice(d) stdstats.plotLines(dS) stddraw.show()
def plot(self): #stddraw.setPenColor('GRAY') stdstats.plotPoints(self._l) stdstats.plotLines(self._l)
# distribution function, thereby allowing easy comparison of the # experimental results to the theoretically predicted results. n = int(sys.argv[1]) trials = int(sys.argv[2]) freq = stdarray.create1D(n+1, 0) for t in range(trials): heads = stdrandom.binomial(n, 0.5) freq[heads] += 1 norm = stdarray.create1D(n+1, 0.0) for i in range(n+1): norm[i] = 1.0 * freq[i] / trials phi = stdarray.create1D(n+1, 0.0) stddev = math.sqrt(n)/2.0 for i in range(n+1): phi[i] = gaussian.pdf(i, n/2.0, stddev) stddraw.setCanvasSize(1000, 400) stddraw.setYscale(0, 1.1 * max(max(norm), max(phi))) stdstats.plotBars(norm) stdstats.plotLines(phi) stddraw.show() #----------------------------------------------------------------------- # python bernoulli.py 20 100000