def get_similar_paintings(n): # Return random painting every once in a while for spread js = request.get_json() t = get_tree() # Get index that we added at get_random_painting() mainimgindex = js['index'] # Calculate distance to all other paintings distlist = [None] * len(t) t1 = c() for i, img in enumerate(t): distlist[i] = utils_dist(js['index'], i) # Distance to itself is 0 of course if mainimgindex == i: distlist[i] = float('inf') t2 = c() # Return best n similarimgindexes = sorted(enumerate(distlist), key=lambda x: x[1])[:n] time = t2 - t1 logger.info( "Calculated distance from painting #{} to all other paintings in {:.4f}s, chose #{}" .format(mainimgindex, time, similarimgindexes)) # Add distance to json obj for later analysis return json.dumps( list( dict((k, v) for (k, v) in t[similarimgindex].items() if k != 'features') for similarimgindex, _ in similarimgindexes))
def get_similar_painting(): # Return random painting every once in a while for spread js = request.get_json() t = get_tree() feature = js['feature'] # Get index that we added at get_random_painting() mainimgindex = js['index'] # Calculate distance to all other paintings distlist = [None] * len(t) feat1 = t[mainimgindex]['features'][feature] t1 = c() for i, img in enumerate(t): feat2 = img['features'][feature] d = dist(feature, feat1, feat2) distlist[i] = d # Distance to itself is 0 of course if mainimgindex == i: distlist[i] = float('inf') t2 = c() # Return best or random for spread mindist = min(distlist) similarimgindex = distlist.index(mindist) selectrandom = True if random.random() < .2 else False if selectrandom: logger.info( "get_similar_painting is getting random painting for spread") similarimgindex = random.randint(0, len(distlist) - 1) logger.info( "Calculated distance from painting #{index} for feature {feature} to all other paintings in {time:.4f}s, chose #{similarimgindex}" .format(index=mainimgindex, time=t2 - t1, feature=feature, similarimgindex=similarimgindex)) # Add distance to json obj for later analysis return json.dumps( dict(((k, v) for (k, v) in t[similarimgindex].items() if k != 'features'), dist=mindist, feature=feature, index=similarimgindex, random=selectrandom))
########################################################################## if __name__ == '__main__': from time import clock as c from pylab import hist, show, hold, ion, plot, xlim, draw, ioff, close #---make a mixed distribution------------------------------ norm1 = random.normal(loc=0.0, scale= 1.0, size=1000) lim = 100 unif2 = random.normal(loc=0.0, scale=50.0, size=3000) mix = concatenate((norm1,unif2)) #---------------------------------------------------------- t1 = c() mean1, stdev1, portion1 = do_em_nomr_n_unif(mix, 100) mean2 = mean1 stdev2 = 50 t2 = c() print('DONE in %s sec' % (t2-t1)) #---Diagnostic Plot--------------------------------------------- def dnorm(x, m, s): return (1/(s*(2*pi)**0.5))*exp(-(x-m)**2/(2*s**2)) tracepoints = arange(-lim,+lim+1,1) dist1 = portion1*dnorm(tracepoints,mean1,stdev1) dist2 = (1-portion1)*dnorm(tracepoints,mean2,stdev2) total = len(mix)*(portion1*dnorm(tracepoints,mean1,stdev1)+\ (1-portion1)*dnorm(tracepoints,mean2,stdev2))
) #print(type(extract)) extract = bs4.BeautifulSoup(extract.text, "lxml") total = extract.select(".cscore_score") k = total[1].text.split() #k=total[1].text print(k[1]) if k[1] == prev: continue else: prev = k[1] for i in extract.select('.over-score'): x = i.text #print(x) break #print(x) if x == "4": playsound('/home/harshit/Documents/script_pro/four.mp3') continue if x == '6': playsound('/home/harshit/Documents/script_pro/SIX.mp3') continue if x == 'W': playsound('/home/harshit/Documents/script_pro/wicket.mp3') continue time.c(10)
dtaEntriesFH = open('dta_entries.txt', 'r') csv_reader = csv.reader(dtaEntriesFH, delimiter='\t') dtaEntries = [x for x in csv_reader] dtaEntriesFH.close() # -------------------------------------------------- a = array([[float(y) for y in x] for x in xTandemInput[1:]]) aColumns = xTandemInput[0] # ('parent_scan', 'mz', 'intensity', 'ppm') b = array([float(x[3]) for x in dtaEntries[1:]]) x = a[:, 0] y = a[:, 3] tic_1 = c() # spar range 0.6 .. 1.2 yFit, runMed = R_runmed_smooth_spline(x, y, x, runMedSpan=0.1, spar=0.6, sc=sc) tic_2 = c() print('done in %s seconds' % (tic_2 - tic_1)) sc.Close() plot(x, y, 'bo') plot(runMed[0], runMed[1], 'y^') plot(x, yFit, 'r+')
def sieve(limit): primes = set() s = [False] * (limit + 1) for i in range(2, limit + 1): if s[i]: continue primes.add(i) for j in range(i * i, limit + 1, i): s[j] = True return primes primes = set() while True: largest = 0 try: limit = int(input("Enter number (600851475143 by default): ")) except ValueError: limit = 600851475143 if len(primes) == 0 or limit > max(primes): primes = sieve(int(sqrt(limit))) start = c() for i in primes: if limit % i == 0: if i > largest: largest = i print("The largest prime factor of {} is {}".format(limit, largest)) end = c() print("The program took {} seconds to run".format(end - start))
def sleep(minutos): from time import sleep as c c(minutos*60)
# prime generator made by gXLg # from sys import argv as a from time import perf_counter as c t = c() n = set() p = [] if str.isdigit(a[-1]): m = int(a[-1]) else: m = 1000 l = 1 for i in range(2, m): if i not in n: p.append(i) n.update(j for j in range(i * i, m, i * l)) l = 2 print(c() - t) print(p) # usage: python prime.py [maxnum] #