def getPoint(maxL=10):
type_dist = "loguniform"
RStarSample = sample_value(
0, 2,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fPlanets = sample_value(
-1, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
nEnvironment = sample_value(
-1, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fIntelligence = sample_value(
-3, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fCivilization = sample_value(
-2, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
L = sample_value(
2, maxL,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fLife = lifeDist(mean=0, sigma=50)
fLifeEks = float(mp.log(fLife, 10))
nStars = random.uniform(11, 11.60205999132)
E3 = nStars + fPlanets + nEnvironment
E4 = E3 + fLife
E5 = E4 + fIntelligence
resitev = RStarSample + fPlanets + nEnvironment + fLifeEks + fIntelligence + fCivilization + L # calculates N
# thresholds
# rand_tresh = 4
# if random.random()<0.5:
rand_tresh = 3
glajenje = 0.2
'''
if (E4 < np.random.normal(math.log(2, 10), glajenje) \
or E3 < np.random.normal(math.log(3, 10), glajenje) \
or resitev > np.random.normal(3.5, glajenje)
or resitev < np.random.normal(-3, glajenje)):
# return getPoint(maxL)
return False
'''
if resitev < -6:
return False
return 10**resitev
def getPoint(maxN=10):
dist = "uniform"
RStarSample = sample_value(0, 2, dist) # logaritmic
Nstar = 11.4771212547 # total number of stars. Paper persumes there are 2*10^22 oz. 22.30103 in our observable universe - only for our galaxy 3*10^11 oz. 11.4771212547
fPlanets = sample_value(
-1, 0, dist) # paper suggests 1 np.random.normal(1)
nEnvironment = sample_value(
-1, 0, dist) # paper suggests 0.2 np.random.normal(0.2)
astrophysicsProbability2 = np.random.normal(
0.155, 0.73
) # distributuin that is similar to the one if we calculate it our self
if (astrophysicsProbability2 >
2): # so that the distribution look more similar
astrophysicsProbability2 = sample_value(1.65, 1.95, dist)
if (astrophysicsProbability2 < -2):
astrophysicsProbability2 = sample_value(-1.95, -1.65, dist)
fInteligence = sample_value(-3, 0, dist)
fCivilization = sample_value(-2, 0,
dist) # in the paper it is mentioned as ft
fLife = lifeDist(mean=0, sigma=50)
fLifeEks = float(mp.log(fLife, 10))
# L = random.uniform(2, maxL) #loguniform
# L = math.log10(sample_value(10 ** 2, 10 ** maxL, dist)) # uniform
N = sample_value(0, maxN, dist)
# biotechnicalProbability - lower bound calcuclated to be 2.5*10^-24 aka -23.60206 - only for our galaxy 1.7*10^-11 aka -10.76955
# astrophysicsProbability = RStarSample + fPlanets + nEnvironment # use Nstar instead of rstarsample if no L
# biotechnicalProbability = fInteligence + fCivilization + fLifeEks
biotechnicalProbability = sample_value(-10.76955, 0, dist)
# resitev = astrophysicsProbability + biotechnicalProbability #calculates A = number of civ that have ever apperared in the world
resitev = N - (
astrophysicsProbability2 + biotechnicalProbability
) # calculates A = number of civ that have ever apperared in the world
E3 = Nstar + fPlanets + nEnvironment
E4 = E3 + fLife
# E5 = E4 + fInteligence
# threshold if N values are very low
# if (resitev < 0): #treshold from min possible solution
# if (resitev < 0) or (resitev > 5): #threshold min and L possible solution
glajenje = 0.4
if resitev < np.random.normal(0, glajenje) or resitev > np.random.normal(
5, glajenje): # bokal?
return False
# x.append(astrophysicsProbability) #from our distribution calculated distribution
# y.append(astrophysicsProbability2) #from our distribution
return resitev
def getPoint(maxN=10):
type_dist = "loguniform"
RStarSample = sample_value(0, 2, type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fPlanets = sample_value(-1, 0, type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
nEnvironment = sample_value(-1, 0, type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fIntelligence = sample_value(-3, 0, type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fCivilization = sample_value(-2, 0, type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
# N = sample_value(0, maxN, type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
N = np.log10(maxN)
#N = maxN
fLife = lifeDist(mean=0, sigma=50)
fLifeEks = float(mp.log(fLife, 10))
nStars = random.uniform(11, 11.60205999132)
E3 = nStars + fPlanets + nEnvironment
E4 = E3 + fLife
E5 = E4 + fIntelligence
# resitev = RStarSample + fPlanets + nEnvironment + fLifeEks + fInteligence + fCivilization + L
L = N - (RStarSample + fPlanets + nEnvironment + fLifeEks + fIntelligence + fCivilization)
glajenje = 0.4
# thresholds
# if (L < np.random.normal(0, glajenje)
'''
if (E4 < np.random.normal(math.log(2, 10), glajenje)
or E3 < np.random.normal(math.log(3, 10), glajenje)
or L > np.random.normal(10.139879,
glajenje)): # maxL - age of the universe - if we are taking drake equation for this moment
# return getPoint(maxN)
return False
'''
return L
def getPoint(maxL=10, type_dist="loguniform"):
RStarSample = sample_value(
0, 2,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fPlanets = sample_value(
-1, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
nEnvironment = sample_value(
-1, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fIntelligence = sample_value(
-3, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fCivilization = sample_value(
-2, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
L = sample_value(
2, maxL,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fLife = lifeDist(mean=0, sigma=50)
fLifeEks = float(mp.log(fLife, 10))
# fLifeEks = np.random.lognormal(-2 , 0)
resitev = RStarSample + fPlanets + nEnvironment + fLifeEks + fIntelligence + fCivilization + L # calculates N
# threshold if N values are very low
# if (resitev < 0):
# return False
resitev1 = 10**resitev # rounded de-logarithmised solution
# for i in range(resitev1):
# A = abs(random.gauss(1, 0.2))
A = 1
v = abs(random.gauss(0.016 * 300000, 10)) * 365 * 24 * 60 * 60
# L1 = random.uniform(2, maxL)
L1 = 10**L
B = 0.004 / (
(9.461 * 10**(12))**3) # number density of stars as per Wikipedia
R = v * random.uniform(
0, L1
) # radius of inhabited zone, I assume they have been expanding since the became detectable, which is random
# integral = integrate.quad(lambda r: r ** 2 * math.exp(-(R - r) / (v * 10 ** L1)), 0, R)
integral = L1 * v * (R**2 - 2 * L1 * R * v + 2 *
(1 - math.e**(-R / (L1 * v))) * L1**2 * v**2)
n = B * 10**fPlanets * 10**nEnvironment * 4 * math.pi * integral
koncnaResitev0 = A * (n + 1) * resitev1
if koncnaResitev0 < 0:
return False, False
koncnaResitev1 = math.log10(koncnaResitev0)
# print('n ',n,' integral ',integral,' resitev1', resitev1,'resitev', resitev,' koncnaResitev0 ',koncnaResitev0,' koncnaResitev1 ',koncnaResitev1)
glajenje = 0.2
nStars = random.uniform(11, 11.60205999132)
E3 = nStars + fPlanets + nEnvironment
E4 = E3 + fLife
#if koncnaResitev1 < np.random.normal(0, glajenje) or koncnaResitev1 > np.random.normal(4, glajenje):
'''
if E4 < np.random.normal(math.log(2, 10), glajenje) \
or E3 < np.random.normal(math.log(3, 10), glajenje) \
or koncnaResitev1 < -6 :
#or koncnaResitev1 > np.random.normal(3.5, glajenje):
return False, False
# if koncnaResitev1 < -6 or koncnaResitev1 > np.random.normal(3.5, glajenje):
# return False
if koncnaResitev1 > nStars:
return False, False
'''
return koncnaResitev1, koncnaResitev0 / resitev1
def getPoint(maxN=10, type_dist="loguniform"):
RStarSample = sample_value(
0, 2,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fPlanets = sample_value(
-1, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
nEnvironment = sample_value(
-1, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fIntelligence = sample_value(
-3, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fCivilization = sample_value(
-2, 0,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
N = 10**sample_value(
0, maxN,
type_dist) # loguniform - uniform - halfgauss - lognormal - fixed
fLife = float(lifeDist(mean=0, sigma=50))
fLifeEks = float(mp.log(fLife, 10))
f = 10**(RStarSample + fPlanets + nEnvironment + fLifeEks + fIntelligence +
fCivilization)
nStars = random.uniform(11, 11.60205999132)
E3 = nStars + fPlanets + nEnvironment
E4 = E3 + fLife
meje = RStarSample + fPlanets + nEnvironment + fLifeEks + fIntelligence + fCivilization + 10
#if E4 < 2 or E3 < 3 or meje < 0:
# return False
# A = abs(random.gauss(1, 0.2))
A = 1
v = abs(random.gauss(0.016 * 300000, 10)) * 365 * 24 * 60 * 60
############################################################################# TODO
# R = v * random.uniform(0, L) # radius of inhabited zone, I assume they have been expanding since the became detectable, which is random
# ok tle ^ not je dejansko tudi L, nisem tega vidu prej... to je treba se nekako vkljucit
# bom dal V*L/2
# Tle je enacba more bit 0 na eni strani in vse ostalo na drugi
# function = lambda L: 1 / nStars * f * A * L * (L * v * (R ** 2 - 2 * L * R * v + 2 * L ^ 2 * v ^ 2 * (1 - mp.e ** (-R / (L * v))))) - N
B = 0.004 / (
(9.461 * 10**(12))**3) # number density of stars as per Wikipedia
function = lambda L: f * A * L * (
B * 10**fPlanets * 10**nEnvironment * 4 * math.pi *
(L * v * ((v * L / 2)**2 - 2 * L *
(v * L / 2) * v + 2 * L**2 * v**2 * 0.393469)) + 1) - N
function1 = lambda L: f * A * (L + 5.13342 * 10**10 * 10**
(fPlanets + nEnvironment) * B * L**4) - N
L_initial_guess = 10**2 # to je se za malo probat
L_solution, info, ier, mes = fsolve(function1,
L_initial_guess,
full_output=1) # numerical solver
# print(np.log10(L_solution[0]), ' sol ', L_solution, ' info ', info, ' ier ', ier, ' mes ', mes)
if ier != 1:
return False
# print(np.log10(L_solution[0]), ' sol ', L_solution, ' ier ', ier, ' f ', f)
L = math.log(L_solution[0], 10)
#if (L < 0) or E4 < math.log(2, 10) or E3 < math.log(3, 10) or L > 10.139879: # maxL - age of the universe
# return False
return L