def binary(i):
"""Returns a binary string representation of number.
Example:
>>> binary(2)
'10'
>>> binary(1337)
'10100111001'
>>> int(binary(1337), 2)
1337
"""
if not i:
return "" # finished, no more bits
bit = i & 1 # front bit
i = i >> 1 # shift away front bit
if bit: # bit was set
return binary(i) + "1"
else:
return binary(i) + "0"
def getPopRecursion(n, logR_mean, a2_mean, a2_std, dtilt, kick, kick_args):
"""
global trials
if trials>0:
r = (200.-n)/trials
if r<0.001 and 1/np.sqrt(200.-n)<0.1:
print("too few!")
trials = 0
return
trials += n
"""
#logR_min = np.log10(R_min)
#logR_max = np.log10(R_max)
mMax = 75.
mMin = 5.
bq = 1.3
alpha = -2.2
if n == 0:
trials = 0
return
else:
# Tilt angles
t1 = genTruncatedNorm(n, 0, dtilt, -np.pi, np.pi)
t2 = genTruncatedNorm(n, 0, dtilt, -np.pi, np.pi)
# Azimuthal angles
phi1 = 2. * np.pi * np.random.random(n)
phi2 = 2. * np.pi * np.random.random(n)
# Spin magnitudes
a1 = np.zeros(n)
a2 = genTruncatedNorm(n, a2_mean, a2_std, 0, 1)
# Masses
m1 = np.power(
(mMax**(1. + alpha) - mMin**(1. + alpha)) * np.random.random(n) +
mMin**(1. + alpha), 1. / (1. + alpha))
m2 = np.power((m1**(1. + bq) - mMin**(1. + bq)) * np.random.random(n) +
mMin**(1. + bq), 1. / (1. + bq))
# Separation
#sep = 10.**(logR_min + (logR_max-logR_min)*np.random.random(n))
#sep = 10.**(np.random.normal(size=n,loc=logR_mean,scale=0.5))
#sep = 10.**logR_mean*np.ones(n)
#logSep = genTruncatedNorm(n,logR_mean,0.5,np.log10(3.),np.inf)
#sep = 10.**logSep
sep = 10.**(np.log10(5.) +
(np.log10(300) - np.log10(5.)) * np.random.random(n))
#period = 10.**(3.*np.random.random(n)-1)*24.*3600
#sep = np.power(G*(m1+m2)*Msun*period**2./(4.*np.pi**2.),1./3.)/Rsun
binaries = np.array([
binary(m1[i], m2[i], a1[i], a2[i], t1[i], t2[i], phi1[i], phi2[i],
sep[i] * Rsun) for i in range(n)
])
if kick == "maxwellian":
survived = np.array(
[b.kick(random_kick(*kick_args), 1) for b in binaries])
elif kick == "directed":
survived = np.array([
b.kick(random_lognormal_kick(*kick_args), 1) for b in binaries
])
elif kick == "polar_maxwellian":
survived = np.array(
[b.kick(polar_maxwellian(*kick_args), 1) for b in binaries])
else:
sys.exit()
mergerTimes = np.array([b.time_to_merger()
for b in binaries]) / 1e10 / year
to_replace = ((survived == 0) + (mergerTimes > 1))
binaries[to_replace] = getPopRecursion(len(binaries[to_replace]),
logR_mean, a2_mean, a2_std,
dtilt, kick, kick_args)
return binaries
def getPopRecursion(n,
logR_mean,
a2_mean,
a2_std,
dtilt,
kick,
kick_args,
beta,
efficiencyThreshold=1e-3,
dist='logUniform'):
genTime = 0.
mMax = 75.
mMin = 5.
bq = 1.3
alpha = -2.2
trials = 0
surviveSN = 0
merge = 0
final_binaries = np.array([])
while final_binaries.size < n:
trials += n
# Tilt angles
t1 = genTruncatedNorm(n, 0, dtilt, -np.pi, np.pi)
t2 = genTruncatedNorm(n, 0, dtilt, -np.pi, np.pi)
# Azimuthal angles
phi1 = 2. * np.pi * np.random.random(n)
phi2 = 2. * np.pi * np.random.random(n)
# Spin magnitudes
a1 = np.zeros(n)
a2 = genTruncatedNorm(n, a2_mean, a2_std, 0, 1)
# Masses
m1 = np.power(
(mMax**(1. + alpha) - mMin**(1. + alpha)) * np.random.random(n) +
mMin**(1. + alpha), 1. / (1. + alpha))
m2 = np.power((m1**(1. + bq) - mMin**(1. + bq)) * np.random.random(n) +
mMin**(1. + bq), 1. / (1. + bq)) / beta
# Separation
if dist == 'logUniform':
sep = 10.**(np.log10(5.) +
(np.log10(300) - np.log10(5.)) * np.random.random(n))
elif dist == 'logNormal':
sep = 10.**np.random.normal(loc=1.1, scale=0.3, size=n)
elif dist == 'delta':
sep = 10.**logR_mean * np.ones(n)
else:
sys.exit()
binaries = [
binary(m1[i], m2[i], a1[i], a2[i], t1[i], t2[i], phi1[i], phi2[i],
sep[i] * Rsun) for i in range(n)
]
if kick == "maxwellian":
survived = np.array(
[b.kick(random_kick(*kick_args), beta) for b in binaries])
elif kick == "directed":
survived = np.array([
b.kick(random_lognormal_kick(*kick_args), beta)
for b in binaries
])
else:
sys.exit()
mergerTimes = np.array([b.time_to_merger()
for b in binaries]) / 1e10 / year
surviveSN += np.where(survived == 1)[0].size
merge += np.where(mergerTimes < 1)[0].size
successful = ((survived == 1) * (mergerTimes < 1))
final_binaries = np.append(final_binaries,
np.array(binaries)[successful])
# Estimate success probability
# If none have yet been successful, take an upper limit of 1/trials
p_hat = max(1. * final_binaries.size / trials, 1. / trials)
error = np.sqrt(p_hat * (1. - p_hat) / trials)
if (trials > 1. / efficiencyThreshold
) and (efficiencyThreshold - p_hat) / error > 3:
return final_binaries, trials, surviveSN, merge, False
return final_binaries, trials, surviveSN, merge, True
from PIL import Image
import os,sys
from binary import *
from horizontal_segmentation import *
from vertical_segmentation import *
from hchar_segmentation import *
from feature_extraction import *
#Entering scanned image
print "Enter the scanned image:"
input_image = raw_input()
#Entering binarisation phase
ocr_input = Image.open(input_image)
binary_image = binary(ocr_input)
binary_image.binarize()
#Entering horizontal segmentation phase
#Segmented output is obtained as "\temp\hsegments" folder
h_segmentation_input = Image.open("../temp/binary_image.png")
hsegments = hsegmentation(h_segmentation_input)
hsegments.addlines()
#Entering vertical segmentation phase
#Segmented output is obtained in "\temp\vsegments" folder
hsegment_list = os.listdir("../temp/hsegments")
hsegment_list.sort()
from PIL import Image
import os, sys
from binary import *
from horizontal_segmentation import *
from vertical_segmentation import *
from hchar_segmentation import *
from feature_extraction import *
#Entering scanned image
print "Enter the scanned image:"
input_image = raw_input()
#Entering binarisation phase
ocr_input = Image.open(input_image)
binary_image = binary(ocr_input)
binary_image.binarize()
#Entering horizontal segmentation phase
#Segmented output is obtained as "\temp\hsegments" folder
h_segmentation_input = Image.open("../temp/binary_image.png")
hsegments = hsegmentation(h_segmentation_input)
hsegments.addlines()
#Entering vertical segmentation phase
#Segmented output is obtained in "\temp\vsegments" folder
hsegment_list = os.listdir("../temp/hsegments")
hsegment_list.sort()
