def test_sqrt(self):
import math
from numpypy import sqrt
nan, inf = float("nan"), float("inf")
data = [1, 2, 3, inf]
results = [math.sqrt(1), math.sqrt(2), math.sqrt(3), inf]
assert (sqrt(data) == results).all()
assert math.isnan(sqrt(-1))
assert math.isnan(sqrt(nan))
def RR_histogram(X,Y,Xr,Yr,distance,th_min,th_max,theta_bins,cat_number=3):
n_points=len(X)
#Xmin=1.0*np.floor( np.amin(X) )
#Xmax=1.0*np.ceil( np.amax(X) )
#Ymin=1.0*np.floor( np.amin(Y) )
#Ymax=1.0*np.ceil( np.amax(Y) )
#Xr= Xmin + ( Xmax - Xmin )*np.random.random_sample(n_points*cat_number)
#Yr=Ymin + ( Ymax - Ymin )*np.random.random_sample(n_points*cat_number)
d_arr=[]
for m in range(cat_number):
print "random cat=",m
for i in range(n_points):
for j in range(i+1,n_points):
d=(Xr[i + n_points*m ] - Xr[j + n_points*m ])*(Xr[i + n_points*m ] - Xr[j + n_points*m ]) + (Yr[i + n_points*m ] - Yr[j + n_points*m ])*(Yr[i + n_points*m ] - Yr[j + n_points*m ])
d=np.sqrt(d)/distance
d_arr=np.append(d_arr,d)
theta=206265*d_arr
RR,bins=np.histogram(theta,bins=theta_bins, range=(th_min,th_max))
N=1.0*n_points*(1.0*n_points-1.0)/2.0
N=N*cat_number
RR=RR/N
return RR,bins
def DR_histogram(X,Y,Xr,Yr,distance,th_min,th_max,theta_bins,cat_number=3):
n_points=len(X)
#Xmin=1.0*np.floor( np.amin(X) )
#Xmax=1.0*np.ceil( np.amax(X) )
#Ymin=1.0*np.floor( np.amin(Y) )
#Ymax=1.0*np.ceil( np.amax(Y) )
#Xr= Xmin + ( Xmax - Xmin )*np.random.random_sample(n_points*cat_number)
#Yr=Ymin + ( Ymax - Ymin )*np.random.random_sample(n_points*cat_number)
d_arr=[]
print "number of LAEs=" + str(n_points)
for m in range(cat_number):
for i in range(n_points):
if(i%100==0):
print "LAE_"+str(i)
for j in range(n_points):
d=( X[i] - Xr[j + n_points*m] )*( X[i] - Xr[j + n_points*m] ) + ( Y[i] - Yr[j + n_points*m] )*( Y[i] - Yr[j + n_points*m] )
d=np.sqrt(d)/distance
d_arr=np.append(d_arr,d)
theta=206265*d_arr
DR,bins=np.histogram(theta,bins=theta_bins, range=(th_min,th_max))
N=1.0*n_points*(n_points)
N=(N*cat_number)
DR=DR/N
return DR,bins
def only_first_factor(n):
primes = []
candidates = np.arange(2, int(np.sqrt(n)))
for candidate in candidates:
if n % candidate == 0:
primes.append(candidate)
break
return primes
def only_first_factor(n):
primes = []
candidates = np.arange(2, int(np.sqrt(n)))
for candidate in candidates:
if n % candidate == 0:
primes.append(candidate)
break
return primes
def DD_histogram(X,Y,distance,th_min,th_max,theta_bins):
n_points=len(X)
d_arr=[]
print "number of LAEs=" + str(n_points)
for i in range(n_points):
if(i%100==0):
print "LAE_"+str(i)
for j in range(i+1,n_points):
d=(X[i] - X[j])*(X[i] - X[j]) + (Y[i] - Y[j])*(Y[i] - Y[j])
d=np.sqrt(d)/distance
d_arr=np.append(d_arr,d)
theta=206265.0*d_arr
DD,bins=np.histogram(theta,bins=theta_bins, range=(th_min,th_max))
N=1.0*n_points*(1.0*n_points-1.0)/2.0
DD=DD/N
return DD,bins
def isPrime(n):
for x in np.arange(2, int(np.sqrt(n))):
if n % x == 0:
return False
return True
def test_sub_call1(self):
from numpypy import array, sqrt
a = array(range(12)).view(self.NoNew)
b = sqrt(a)
assert b.called_finalize == True
def isPrime(n):
for x in np.arange(2, int(np.sqrt(n))):
if n % x == 0:
return False
return True
def get_dist(data,i,j):
return np.sqrt((data[i][0]-data[j][0])*(data[i][0]-data[j][0])+
(data[i][1]-data[j][1])*(data[i][1]-data[j][1]))
import random
import math
import numpypy as np
a = np.arange(2, int(np.sqrt(10000000)))
def MillerRabin(n):
if np.any(n % a == 0):
return False
return True
def keygen(bits):
p = q = 3
while p == q:
p = random.randint(2**(bits/2-2),2**(bits/2))
q = random.randint(2**(bits/2-2),2**(bits/2))
p += not(p&1) # changes the values from
q += not(q&1) # even to odd
while MillerRabin(p) == False: # checks for primality
p -= 2
while MillerRabin(q) == False:
q -= 2
n = p * q
tot = (p-1) * (q-1)
e = tot
return e
print keygen(200)
