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] #
