print("[","|"*int(t/times*10)," "*int(10-t/times*10),"]",izpis, "% time: ",time.time()-timeStart)
print()
izpis += 10
for s in range(0,noStars):
all1 = 1
for i in range(0,noParameters):
if random.random()>random.uniform(0,0.2):
all1 = 0
if(all1==1):
summ+=1
elements[t]=summ
'''
for s in range(0, noStars):
if random.random() <= 0.1**noParameters:
summ += 1
elements[t] = summ
(xaxis, yaxis) = createHistogram(elements, range(0, 200))
save(xaxis, yaxis, 'Toy model 4 parameters brez')
#yaxis = fl.gaussian_filter(yaxis, 10)
#hist1 = np.histogram(elements,range(0,100))
#plt.hist(elements,bins=range(0,400))
#plt.plot(hist1)
#plt.show()
@author: benos
'''
from IO import readFile, save
import numpy as np
from logHistogramAdd import logHistogramAddMult
from mpSampleMultiple import scale
(xaxis, yaxis) = readFile("sigma is 50.csv")
size = len(yaxis)
start = 0
end = np.log(xaxis[-1])
dist = [0] * size
izpis = 0
lastIndex = 1
for i in range(0, size):
if yaxis[i] == 0:
continue
(lastIndex, dist) = logHistogramAddMult(start, end, size, dist,
xaxis[i] + 1, yaxis[i], lastIndex)
if i % (size / 10) == 0:
print(izpis, "%")
izpis += 10
save(scale(start, end, size), dist, 'Sigma is 50 plus 1')
print('done')
elements = [0] * (times + 1)
izpis = 0
for t in range(0, times + 1):
summ = 0
'''
if t % (times / 10) == 0:
print("[","|"*int(t/times*10)," "*int(10-t/times*10),"]",izpis, "% time: ",time.time()-timeStart)
print()
izpis += 10
for s in range(0,noStars):
all1 = 1
for i in range(0,noParameters):
if random.random()>random.uniform(0,0.2):
all1 = 0
if(all1==1):
summ+=1
elements[t]=summ
'''
for s in range(0, noStars):
if random.random() <= 0.1**noParameters:
summ += 1
elements[t] = summ
(xaxis, yaxis) = createHistogram(elements, range(0, 200))
save(xaxis, yaxis, 'Toy model 6 parameters')
from nicePsevdoPDF import getPDFNice
#dejanski program:
start = -100
stop = 100
pdfSize = 10000
size = 30000
xOs, pdf = getDistributionOfEks(size, pdfSize, low=start, high=stop, printOn=1)
cdf = getCDFNIC(pdf)
pdf[0] = pdf[1]
#pdf=normalizePDF(pdf)
l = mp.linspace(start, stop, pdfSize)
xOs = [mp.power(10, x) for x in l]
pdf = fl.gaussian_filter(pdf, 10)
save(xOs, pdf, "L with laplace")
save(xOs, cdf, "L with laplace cdf")
#cdf = getCDFNIC(pdf)
pdfNice = getPDFNice(xOs, pdf)
print("done")
#save(xOs, pdfNice , "pdfNice 200")
#plt.xscale('log')
#plt.plot(xOs, pdfNice, 'blue', label = 'pdfNice' )
#plt.plot(xOs, cdf, 'red', label = 'cdf' )
#plt.legend( loc=4 )
#plt.plot([1,1],[0,1],'green')
#p.fill(xOs, pdf, facecolor='blue', alpha=0.5)
from IO import save
def key_gen(n):
#生成大素数
for _ in range(4):
p = get_large_prime_length(n)
q = get_large_prime_length(n)
while (p == q):
q = get_large_prime_length(n)
print(p)
print(q)
n = p * q
n1 = (p - 1) * (q - 1)
#默认为65537
e = 65537
r, d, l = t.ext_gcd(e, n1)
if d < 0:
d = d + n1
return p, q, n, e, d
if __name__ == "__main__":
p, q, n, e, d = key_gen(20)
save(p, "p.txt")
save(q, "q.txt")
save(n, "n.txt")
save(e, "e.txt")
save(d, "d.txt")
from lifeDist import lifeDist, lifeDist2
from logHistogramAdd import logHistogramAddMult
from mpLogspace import mpLogspace
data = readData("laplace_cutoff_correction")
siz = len(data)
formatted = [0] * siz
sortedData = np.sort(data)
saveData(sortedData, "laplace_cutoff_correction_sorted")
lastIndex = 0
dist = [0] * siz
start = -40
end = 15
size = siz
for value in sortedData:
(lastIndex, dist) = logHistogramAddMult(start, end, size, dist, value, 1,
lastIndex)
xaxis = mpLogspace(10**-40, 10**15, siz)
save(xaxis, dist, "laplace_cutoff_correction_sorted")
print("done")
Fintelligence = mpLogUniform(0.001, 1, size)
Fcivilization = mpLogUniform(0.01, 1, size)
Length = mpLogUniform(100, 10000000000, size)
'''
Rstar = (1, 100)
Fplanets = (0.1, 1)
Nhabitable = (0.1, 1)
# Flife = lognormal(10**(-40),1,size)
# Flife = loguniform(1,1000,size)
# Flife = (0,1,lifeDist(size,size/2))
# Flife = (mpmathify(10 ** (-156)), 1)
Fintelligence = (0.001, 1)
Fcivilization = (0.01, 1)
Length = (100, 10000000000)
'''
Flife = (1, -35, 15, 14, 17, 0, 50)
N = readFile('sigma is 50.csv')
(xaxis, yaxis) = sampleL([Rstar, Fplanets, Nhabitable, Fintelligence, Fcivilization], -100, 100, size, 230000, Flife, (1, N))
save(xaxis, yaxis, 'What is L with N sig50')
Flife = (1,-35,15,14,17,0,100)
(xaxis, yaxis) = mpSampleMultipleTime([Rstar, Fplanets, Nhabitable, Fintelligence, Fcivilization, Length],-120,15, size, 50000,Flife)
save(xaxis, yaxis, 'sigma is 100')
'''
Flife = (1,-35,15,14,17,0,200)
'''
Created on 13 Aug 2018
@author: benos
'''
import time
from meanMedian import meanMedian
from IO import save, readFile
import scipy.ndimage.filters as fl
import matplotlib.pyplot as plt
from createGraph import createGraph
from StandardizeDistribution import StandardizeDistributionW
import numpy as np
(xaxis, yaxis) = readFile("laplace_new")
'''
xaxis2=[0]*len(xaxis)
xaxis2[0]=np.round(np.log10(xaxis[0]))
yaxis[0]=int(yaxis[0])
for i in range(1,len(xaxis)):
xaxis2[i]=int(np.round(np.log10(xaxis[i])))
yaxis[i]=int(yaxis[i])
save(xaxis2,yaxis,"sigma is 100 exponents")
'''
avg = 0
for i in range(0, len(xaxis)):
avg += yaxis[i] * xaxis[i]
avg /= sum(yaxis)
print(sum(yaxis))
allRand *= random.random() * 0.2
'''
for s in range(0, noStars):
allRand = 1
for i in range(0, noParameters):
#allRand *= random.uniform(0, 0.2)
allRand *= random.random()*0.2
if(random.random() < allRand):
summ += 1
elements[t] = summ
'''
for s in range(0,noStars):
if random.random()<=0.1**noParameters:
summ+=1
elements[t]=summ
'''
(xaxis, yaxis) = createHistogram(elements, range(0, 200))
save(xaxis, yaxis, 'Toy model 5 parameters uniform bad')
# yaxis = fl.gaussian_filter(yaxis, 10)
# hist1 = np.histogram(elements,range(0,100))
# plt.hist(elements,bins=range(0,400))
# plt.plot(hist1)
# plt.show()