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run.py
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import numpy as np
import multiprocessing as mp
from multiprocessing import Queue, cpu_count
import matplotlib.pyplot as plt
import time
import os
WEIGHT = 1e-4 # critical weight for roulette
CHANCE = 0.1 # Chance of roulette survival
PARTIALREFLECTION = 0 # 1=split photon, 0=statistical reflection.
COSZERO = 1.0 - 1.0e-12 # cosine of about 1e-6 rad
COS90D = 1.0e-6 # cosine of about 1.57 - 1e-6 rad
path = os.path.dirname(__file__)
modelfile = np.genfromtxt(os.path.join(path,'model_input.txt'), dtype=['<U20', np.float])
class Medium:
"""Medium class - optical medium class defining the optical properties
Class instance variables:
n - refractive index
mua - absorption coefficient. [1/cm]
mus - scattering coefficient. [1/cm]
g - anisotropy
Methods:
"""
def __init__(self, mediumName, nummua):
wavelength = np.loadtxt(os.path.join(path,'wavelength.csv'))
water = np.loadtxt(os.path.join(path,'mua_water.csv'))
melanin = np.loadtxt(os.path.join(path,'mua_melanin.csv'))
deoxy = np.loadtxt(os.path.join(path,'mua_deoxy.csv'))
oxy = np.loadtxt(os.path.join(path,'mua_oxy.csv'))
# 修正
water = water*10
deoxy = deoxy*0.15
oxy = oxy*0.15
melanin = melanin*0.1
# Rayleigh
Rayleigh = (np.power(wavelength,-3.255))*7.84e+8
# t3~t7 contain hemoglobin
S = 0.75
gamma = 0.25*0.99*0.45
# 定義各組織光學特性
if mediumName.lower() == 'AIR'.lower():
self.n = 1.0
self.mua = np.zeros(316)[nummua]
self.mus = 0.0
self.g = 1.0
elif mediumName.lower() == 'TISSUE1'.lower():
Cwater = 0.05
tissue1 = ((0.1-(0.3e-4*wavelength))+1.25*Rayleigh)*(1-Cwater)+Cwater*water
self.n = 1.5
self.mua = tissue1[nummua]
self.mus = 1000.0
self.g = 0.86
elif mediumName.lower() == 'TISSUE2'.lower():
Cwater = 0.2
Cmel = 0.13
tissue2 = (Cmel*melanin+(1-Cmel)*Rayleigh)*(1-Cwater)+Cwater*water
self.n = 1.34
self.mua = tissue2[nummua]
self.mus = 450.0
self.g = 0.80
elif mediumName.lower() == 'TISSUE3'.lower():
Cb = 0.04
Cwater = 0.5
tissue3 = ((1-S)*gamma*Cb*deoxy)+(S*gamma*Cb*oxy)+((1-gamma*Cb)*Cwater*water)+((1-gamma*Cb)*(1-Cwater)*Rayleigh)
self.n = 1.4
self.mua = tissue3[nummua]
self.mus = 300.0
self.g = 0.90
elif mediumName.lower() == 'TISSUE4'.lower():
Cb = 0.3
Cwater = 0.6
tissue4 = ((1-S)*gamma*Cb*deoxy)+(S*gamma*Cb*oxy)+((1-gamma*Cb)*Cwater*water)+((1-gamma*Cb)*(1-Cwater)*Rayleigh)
self.n = 1.39
self.mua = tissue4[nummua]
self.mus = 350.0
self.g = 0.95
elif mediumName.lower() == 'TISSUE5'.lower():
Cb = 0.04
Cwater = 0.7
tissue5 = ((1-S)*gamma*Cb*deoxy)+(S*gamma*Cb*oxy)+((1-gamma*Cb)*Cwater*water)+((1-gamma*Cb)*(1-Cwater)*Rayleigh)
self.n = 1.4
self.mua = tissue5[nummua]
self.mus = 250.0
self.g = 0.80
elif mediumName.lower() == 'TISSUE6'.lower():
Cb = 0.1
Cwater = 0.7
tissue6 = ((1-S)*gamma*Cb*deoxy)+(S*gamma*Cb*oxy)+((1-gamma*Cb)*Cwater*water)+((1-gamma*Cb)*(1-Cwater)*Rayleigh)
self.n = 1.38
self.mua = tissue6[nummua]
self.mus = 300.0
self.g = 0.95
elif mediumName.lower() == 'TISSUE7'.lower():
Cb = 0.05
Cwater = 0.7
tissue7 = ((1-S)*gamma*Cb*deoxy)+(S*gamma*Cb*oxy)+((1-gamma*Cb)*Cwater*water)+((1-gamma*Cb)*(1-Cwater)*Rayleigh)
self.n = 1.44
self.mua = tissue7[nummua]
self.mus = 50.0
self.g = 0.75
class LayerStruct:
"""LayerStruct class - multi-layered structure
Class instance variables:
nIn - refractive index of the incidence medium
nOut - refractive index of the exit medium
numLayers - number of layers
layer - list of layer objects
layerThickness - layer thickness in [cm]
layerZ - layer depth z coordinates, list表示[top bottom] [cm]
cosCrit - ciritical angle cosines of each layer, list表示[top bottom]
Methods:
"""
def __init__(self, nummua=0):
self.numLayers = int(modelfile[4].__getitem__(1))
# 定義組織層(此例為上下兩層空氣，中間七層組織)
self.layer = [Medium('AIR', nummua), \
Medium('TISSUE1', nummua), \
Medium('TISSUE2', nummua), \
Medium('TISSUE3', nummua), \
Medium('TISSUE4', nummua), \
Medium('TISSUE5', nummua), \
Medium('TISSUE6', nummua), \
Medium('TISSUE7', nummua), \
Medium('AIR', nummua)]
self.layerThickness = np.empty([self.numLayers])
for i in range (self.numLayers):
self.layerThickness[i] = modelfile[5+i].__getitem__(1)
# [20e-4, 80e-4, 150e-4, 80e-4, 1500e-4, 80e-4, 6000e-4]
# 不包括上下兩層空氣層
# 總共8000e-4 [cm]
self.layerZ = []
self.cosCrit = []
z = 0 # incidence first medium z coordinate [cm]
self.layerZ.append([0, 0]) # first incidence medium, not used
self.cosCrit.append([0, 0]) # first incidence medium, not used
# find the z depth coordinates and cosine critical angles for each
# layer
for i in range(1, self.numLayers+1):
print('layer'+str(i)+'-->done!')
self.layerZ.append([z, z+self.layerThickness[i-1]])
z = self.layerZ[-1][1] #取出最後一個值，但此值還是列表，取該列表的第二個值
#也就是最後一層layer底部的z點座標
# calculate the critical angle cosines for each layer
# crticial angle at top interface of the current layer
n1 = self.layer[i].n
n2 = self.layer[i-1].n
if n1 > n2:
cosCrit0 = (1.0 - n2*n2/(n1*n1))**0.5
else:
cosCrit0 = 0.0
# crticial angle at bottom interface of the current layer
n2 = self.layer[i+1].n
if n1 > n2:
cosCrit1 = (1.0 - n2*n2/(n1*n1))**0.5
else:
cosCrit1 = 0.0
self.cosCrit.append([cosCrit0, cosCrit1])
def calc_r_specular(self):
# direct reflections from the 1st and 2nd layers. 鏡面反射
temp = (self.layer[0].n - self.layer[1].n)/(self.layer[0].n + \
self.layer[1].n)
r1 = temp*temp
if ((self.layer[1].mua == 0.0) and (self.layer[1].mus == 0.0)):
# glass layer.
temp = (self.layer[1].n - self.layer[2].n)/(self.layer[1].n + \
self.layer[2].n)
r2 = temp*temp
r1 = r1 + (1 - r1)*(1 - r1)*r2/(1 - r1*r2)
return r1
class ModelInput:
"""ModelInput class - multi-layered photon scattering model input
Class instance variables:
Wth - play roulette if photon weight < Wth
dz - z grid separation [cm]
dr - r grid separation [cm]
da - alpha grid separation [radian]
nz - array range 0..nz-1
nr - array range 0..nr-1
na - array range 0..na-1
layerObj - medium layer structure class instance
Methods:
"""
def __init__(self, nummua=0):
self.layerObj = LayerStruct(nummua)
self.dz = modelfile[0].__getitem__(1)
self.dr = modelfile[1].__getitem__(1)
self.nz = int(modelfile[2].__getitem__(1))
self.nr = int(modelfile[3].__getitem__(1))
self.na = 10
self.Wth = WEIGHT
self.da = 0.5*np.pi/self.na
class MCMLModel(ModelInput):
"""MCMLModel class - multi-layered photon scattering model, inherits from
ModelInput layer structure setup
Class instance variables:
Rsp - specular reflectance [-]
Rd - total diffuse reflectance [-]
A - total absorption probability [-]
Tt - total transmittance [-]
Rd_ra - 2D distribution of diffuse reflectance [1/(cm2 sr)]
Rd_r - 1D radial distribution of diffuse reflectance [1/cm2]
Rd_a - 1D angular distribution of diffuse reflectance [1/sr]
A_rz - 2D probability density in turbid media over r & z [1/cm3]
A_z - 1D probability density over z [1/cm]
A_l - each layer's absorption probability [-]
Tt_ra - 2D distribution of total transmittance [1/(cm2 sr)]
Tt_r - 1D radial distribution of transmittance [1/cm2]
Tt_a - 1D angular distribution of transmittance [1/sr]
Methods:
"""
def __init__(self, nummua=0):
# extend the ModelInput base class instance variables
ModelInput.__init__(self, nummua)
self.numPhotons = 0
# initialize the model grid arrays
self.Rsp = self.layerObj.calc_r_specular()
self.Rd = 0.0
self.A = 0.0
self.Tt = 0.0
self.Rd_ra = np.matrix(np.zeros((self.nr, self.na)))
self.Rd_r = np.zeros(self.nr)
self.Rd_a = np.zeros(self.na)
self.A_rz = np.matrix(np.zeros((self.nr, self.nz)))
self.A_z = np.zeros(self.nz)
self.A_l = np.zeros(2 + self.layerObj.numLayers)
self.Tt_ra = np.matrix(np.zeros((self.nr, self.na)))
self.Tt_r = np.zeros(self.nr)
self.Tt_a = np.zeros(self.na)
# 主要MCML光子行進的程序碼
def run_photon_simulation(model, wavelength, N):
print('processing:'+str(wavelength)+'/total:316')
## --取出model參數start-- ##
numloops = N
numLayers = model.layerObj.numLayers
list_ = LayerStruct(wavelength).layer
tissuelayers = []
for i in range(numLayers+2):
tissuelayers.append([list_[i].n, list_[i].mua, list_[i].mus, list_[i].g])
layerThickness = model.layerObj.layerThickness
layerZ = model.layerObj.layerZ
cosCrit = model.layerObj.cosCrit
dz = model.dz
dr = model.nz
nz = model.nz
nr = model.nr
na = model.na
Wth = model.Wth
da = model.da
Rsp = model.Rsp
Rd = model.Rd
Tt = model.Tt
Rd_ra = model.Rd_ra
Rd_r = model.Rd_r
Rd_a = model.Rd_a
Tt_ra = model.Tt_ra
Tt_r = model.Tt_r
Tt_a = model.Tt_a
## --取出model參數end-- ##
### --do_one_run-- #start
for i in range(numloops):
## --光子旅程-- ##start
# initial光子狀態
numPhotons = 0
x = 0.0
y = 0.0
z = 0.0
ux = 0.0
uy = 0.0
uz = 1.0
w = 1.0 - Rsp
dead = False
layer_index = 1
s = 0
sleft = 0
# 光子開始活動
# layer:[n,mua,mus,g]
# 空氣層後的第一層(index=1)是否為air
if (tissuelayers[layer_index][1] == 0.0) and (tissuelayers[layer_index][2] == 0.0):
layer_index = 2 # 直接跳往下一層
z = layerZ[layer_index][0] # use z0 from the next layer
## --run_one_photon-- ##start
while dead == False:
# hop_drop_spin
# hop_in_glass
if (tissuelayers[layer_index][1] == 0.0) and (tissuelayers[layer_index][2] == 0.0):
if uz == 0.0: # horizontal photon in glass is killed
dead = True
else: # Move the photon packet in glass layer.
# step_size_in_glass
if uz > 0.0:
dl_b = (layerZ[layer_index][1] - z)/uz
elif uz < 0.0:
dl_b = (layerZ[layer_index][0] - z)/uz
else:
dl_b = 0.0
s = dl_b
# hop
x += s*ux
y += s*uy
z += s*uz
# cross_or_not
if uz < 0.0: # cross_up_or_not
# 光子是否在目前層的上邊界（uz <0）穿透或反射。
# 如果“層”是第一層，若PARTIALREFLECTION設置為1，則光子將被部分穿透並部分反射
# 如果PARTIALREFLECTION設置為0，則光子將直接計算記錄往上穿透的光子權重作為反射率r。
# 如果“層”不是第一層並且光子往上穿透，將光子移動到“圖層-1”。
# 更新光子parmameters。
r = 0.0 # reflectance
ni = tissuelayers[layer_index][0]
nt = tissuelayers[layer_index-1][0]
if -uz <= cosCrit[layer_index][0]:
r = 1.0 # total internal reflection
else:
# --RFresnel(n1, n2, ca1)-- #
n1, n2, ca1 = ni, nt, -uz
if n1 == n2: # matched boundary
ca2 = ca1
r = 0.0
elif ca1 > COSZERO: # normal incident
ca2 = ca1
r = (n2-n1)/(n2+n1)
r *= r
elif ca1 < COS90D: # very slant
ca2 = 0.0
r = 1.0
else: # general
# sine of the incident and transmission angles
sa1 = (1.0 - ca1*ca1)**0.5
sa2 = n1*sa1/n2
if sa2 >= 1.0:
# double check for total internal reflection
ca2 = 0.0
r = 1.0
else:
# cosines of the sum ap or
# difference am of the two
# angles. ap = a1+a2
# am = a1 - a2
ca2 = (1.0 - sa2*sa2)**0.5;
cap = ca1*ca2 - sa1*sa2 # c+ = cc - ss
cam = ca1*ca2 + sa1*sa2 # c- = cc + ss
sap = sa1*ca2 + ca1*sa2 # s+ = sc + cs
sam = sa1*ca2 - ca1*sa2 # s- = sc - cs
r = 0.5*sam*sam*(cam*cam+cap*cap)/(sap*sap*cam*cam)
uz1 = ca2
# --RFresnel(n1, n2, ca1)-- #
if np.random.random_sample() > r: # transmitted to layer-1
if layer_index == 1:
uz = -uz1
# --record_R-- #
ir = int((x*x + y*y)**0.5/dr)
if ir > (nr - 1):
ir = (nr -1)
ia = int(np.arccos(-uz)/da)
if ia > (na -1):
ia = (na -1)
Rd_ra[ir, ia] += w*(1.0 - 0.0)
w *= 0.0
# --record_R-- #
dead = True
else:
layer_index -= 1
ux *= ni/nt
uy *= ni/nt
uz = -uz1
else: # reflected
uz = -uz
else: # cross_dn_or_not
r = 0.0
ni = tissuelayers[layer_index][0]
nt = tissuelayers[layer_index+1][0]
if uz <= cosCrit[layer_index][1]:
r = 1.0 # TIF
else:
# --RFresnel(n1, n2, ca1)-- #
n1, n2, ca1 = ni, nt, uz
if n1 == n2: # matched boundary
ca2 = ca1
r = 0.0
elif ca1 > COSZERO: # normal incident
ca2 = ca1
r = (n2-n1)/(n2+n1)
r *= r
elif ca1 < COS90D: # very slant
ca2 = 0.0
r = 1.0
else: # general
# sine of the incident and transmission angles
sa1 = (1.0 - ca1*ca1)**0.5
sa2 = n1*sa1/n2
if sa2 >= 1.0:
# double check for total internal reflection
ca2 = 0.0
r = 1.0
else:
# cosines of the sum ap or
# difference am of the two
# angles. ap = a1+a2
# am = a1 - a2
ca2 = (1.0 - sa2*sa2)**0.5;
cap = ca1*ca2 - sa1*sa2 # c+ = cc - ss
cam = ca1*ca2 + sa1*sa2 # c- = cc + ss
sap = sa1*ca2 + ca1*sa2 # s+ = sc + cs
sam = sa1*ca2 - ca1*sa2 # s- = sc - cs
r = 0.5*sam*sam*(cam*cam+cap*cap)/(sap*sap*cam*cam)
uz1 = ca2
# --RFresnel(n1, n2, ca1)-- #
if np.random.random_sample() > r: # transmitted to layer+1
if layer_index == numLayers:
uz = uz1
# --record_T-- #
ir = int((x*x + y*y)**0.5/dr)
if ir > (nr - 1):
ir = (nr - 1)
ia = int(np.arccos(uz)/da)
if ia > (na - 1):
ia = (na - 1)
Tt_ra[ir, ia] += w*(1.0 - 0.0)
w *= 0.0
# --record_T-- #
dead = True
else:
layer_index += 1
ux *= ni/nt
uy *= ni/nt
uz = uz1
else: # reflected
uz = -uz
# hop_drop_spin_in_tissue
else:
# step_size_in_tissue
mua = tissuelayers[layer_index][1]
mus = tissuelayers[layer_index][2]
if sleft == 0.0: # make a new step
rnd = np.random.random_sample()
s = -np.log(rnd)/(mua + mus)
else: # take the leftover
s = sleft/(mua + mus)
sleft = 0.0
# hit_boundary?
if uz > 0.0:
dl_b = (layerZ[layer_index][1] - z)/uz
elif uz < 0.0:
dl_b = (layerZ[layer_index][0] - z)/uz
if (uz != 0.0) and (s > dl_b):
mut = tissuelayers[layer_index][1] + \
tissuelayers[layer_index][2]
sleft = (s - dl_b)*mut
s = dl_b
hit = True
else:
hit = False
if hit == True: # hit_boundary
# hop
x += s*ux
y += s*uy
z += s*uz
# cross_or_not
if uz < 0.0: # cross_up_or_not
# 光子是否在目前層的上邊界（uz <0）穿透或反射。
# 如果“層”是第一層，若PARTIALREFLECTION設置為1，則光子將被部分穿透並部分反射
# 如果PARTIALREFLECTION設置為0，則光子將直接計算記錄往上穿透的光子權重作為反射率r。
# 如果“層”不是第一層並且光子往上穿透，將光子移動到“圖層-1”。
# 更新光子parmameters。
r = 0.0 # reflectance
ni = tissuelayers[layer_index][0]
nt = tissuelayers[layer_index-1][0]
if -uz <= cosCrit[layer_index][0]:
r = 1.0 # total internal reflection
else:
# --RFresnel(n1, n2, ca1)-- #
n1, n2, ca1 = ni, nt, -uz
if n1 == n2: # matched boundary
ca2 = ca1
r = 0.0
elif ca1 > COSZERO: # normal incident
ca2 = ca1
r = (n2-n1)/(n2+n1)
r *= r
elif ca1 < COS90D: # very slant
ca2 = 0.0
r = 1.0
else: # general
# sine of the incident and transmission angles
sa1 = (1.0 - ca1*ca1)**0.5
sa2 = n1*sa1/n2
if sa2 >= 1.0:
# double check for total internal reflection
ca2 = 0.0
r = 1.0
else:
# cosines of the sum ap or
# difference am of the two
# angles. ap = a1+a2
# am = a1 - a2
ca2 = (1.0 - sa2*sa2)**0.5;
cap = ca1*ca2 - sa1*sa2 # c+ = cc - ss
cam = ca1*ca2 + sa1*sa2 # c- = cc + ss
sap = sa1*ca2 + ca1*sa2 # s+ = sc + cs
sam = sa1*ca2 - ca1*sa2 # s- = sc - cs
r = 0.5*sam*sam*(cam*cam+cap*cap)/(sap*sap*cam*cam)
uz1 = ca2
# --RFresnel(n1, n2, ca1)-- #
if np.random.random_sample() > r: # transmitted to layer-1
if layer_index == 1:
uz = -uz1
# --record_R-- #
ir = int((x*x + y*y)**0.5/dr)
if ir > (nr - 1):
ir = (nr -1)
ia = int(np.arccos(-uz)/da)
if ia > (na -1):
ia = (na -1)
Rd_ra[ir, ia] += w*(1.0 - 0.0)
w *= 0.0
# --record_R-- #
dead = True
else:
layer_index -= 1
ux *= ni/nt
uy *= ni/nt
uz = -uz1
else: # reflected
uz = -uz
else: # cross_dn_or_not
r = 0.0
ni = tissuelayers[layer_index][0]
nt = tissuelayers[layer_index+1][0]
if uz <= cosCrit[layer_index][1]:
r = 1.0 # TIF
else:
# --RFresnel(n1, n2, ca1)-- #
n1, n2, ca1 = ni, nt, uz
if n1 == n2: # matched boundary
ca2 = ca1
r = 0.0
elif ca1 > COSZERO: # normal incident
ca2 = ca1
r = (n2-n1)/(n2+n1)
r *= r
elif ca1 < COS90D: # very slant
ca2 = 0.0
r = 1.0
else: # general
# sine of the incident and transmission angles
sa1 = (1.0 - ca1*ca1)**0.5
sa2 = n1*sa1/n2
if sa2 >= 1.0:
# double check for total internal reflection
ca2 = 0.0
r = 1.0
else:
# cosines of the sum ap or
# difference am of the two
# angles. ap = a1+a2
# am = a1 - a2
ca2 = (1.0 - sa2*sa2)**0.5;
cap = ca1*ca2 - sa1*sa2 # c+ = cc - ss
cam = ca1*ca2 + sa1*sa2 # c- = cc + ss
sap = sa1*ca2 + ca1*sa2 # s+ = sc + cs
sam = sa1*ca2 - ca1*sa2 # s- = sc - cs
r = 0.5*sam*sam*(cam*cam+cap*cap)/(sap*sap*cam*cam)
uz1 = ca2
# --RFresnel(n1, n2, ca1)-- #
if np.random.random_sample() > r: # transmitted to layer+1
if layer_index == numLayers:
uz = uz1
# --record_T-- #
ir = int((x*x + y*y)**0.5/dr)
if ir > (nr - 1):
ir = (nr - 1)
ia = int(np.arccos(uz)/da)
if ia > (na - 1):
ia = (na - 1)
Tt_ra[ir, ia] += w*(1.0 - 0.0)
w *= 0.0
# --record_T-- #
dead = True
else:
layer_index += 1
ux *= ni/nt
uy *= ni/nt
uz = uz1
else: # reflected
uz = -uz
else: # same_layer
# hop
x += s*ux
y += s*uy
z += s*uz
# drop
iz = int(z/dz)
if iz > (nz - 1):
iz = (nz - 1)
ir = int((x*x + y*y)**0.5/dr)
if ir > (nr - 1):
ir = (nr - 1)
dwa = w * mua/(mua+mus)
w -=dwa
# spin
# --SpinTheta-- #
g = tissuelayers[layer_index][3]
if g == 0.0:
cost = 2*np.random.random_sample() - 1
else:
temp = (1 - g*g)/(1 - g + 2*g*np.random.random_sample())
cost = (1 + g*g - temp*temp)/(2*g)
if cost < -1:
cost = -1.0
elif cost > 1:
cost = 1.0
# --SpinTheta-- #
sint = (1.0 - cost*cost)**0.5
psi = 2.0*np.pi*np.random.random_sample()
cosp = np.cos(psi)
if psi < np.pi:
sinp = (1.0 - cosp*cosp)**0.5
# sqrt() is faster than sin().
else:
sinp = -(1.0 - cosp*cosp)**0.5
if np.fabs(uz) > COSZERO: # normal incident
ux = sint*cosp
uy = sint*sinp
uz = cost*np.sign(uz)
else: # regular incident
temp = (1.0 - uz*uz)**0.5
ux = sint*(ux*uz*cosp - uy*sinp)/temp + ux*cost
uy = sint*(uy*uz*cosp + ux*sinp)/temp + uy*cost
uz = -sint*cosp*temp + uz*cost
# roulette
if (w < Wth) and (dead == False):
if w == 0.0:
dead = True
elif np.random.random_sample() < CHANCE: # survived the roulette
w /= CHANCE
else:
dead = True
numPhotons += 1
## --run_one_photon-- ##end
## --光子旅程-- ##end
### --do_one_run-- #end
### --sum_scale_result-- #start
for ir in range(nr):
sum = 0.0
for ia in range(na):
sum += Rd_ra[ir, ia]
Rd_r[ir] = sum
for ia in range(na):
sum = 0.0
for ir in range(nr):
sum += Rd_ra[ir, ia]
Rd_a[ia] = sum
sum = 0.0
for ir in range(nr):
sum += Rd_r[ir]
Rd = sum
scale1 = 4.0*np.pi*np.pi*dr*np.sin(da/2)*dr*numloops
for ir in range(nr):
for ia in range(na):
scale2 = 1.0/((ir+0.5)*np.sin(2.0*(ia+0.5)*da)*scale1)
Rd_ra[ir, ia] *= scale2
scale1 = 2.0*np.pi*dr*dr*numloops
for ir in range(nr):
scale2 = 1.0/((ir+0.5)*scale1)
Rd_r[ir] *= scale2
scale1 = 2.0*np.pi*da*numloops
for ia in range(na):
scale2 = 1.0/(np.sin((ia+0.5)*da)*scale1)
Rd_a[ia] *= scale2
scale2 = 1.0/numloops
Rd *= scale2
### --sum_scale_result-- #end
return Rd
# 支持CPU多核心加速運算程式碼
def job(q, model, N, boundary):
R_df = []
for wavelength in range(boundary[0], boundary[1], 5):
Rd = run_photon_simulation(model, wavelength, N)
R_df.append(Rd)
q.put(R_df)
if __name__ == "__main__":
save = input('save the Reflection Spectrum?(y/n):')
if save == 'y':
print ('save ON !')
filename = input('the filename?(do NOT add extension):')
else:
print ('save OFF !')
n = input('How many photons for simulation?(1000 photons spend about 4 mins):')
N = int(n)
cpu_number = input('How many multicore operations to use?')
cpu_number = int(cpu_number)
# 本程式只有製作到8核運算，如果想擴充更多核心運算，請修改以下cpu_number<=8 的8改成想要的最高核心數
if cpu_number>=1 and cpu_number<=8 and cpu_number<=cpu_count():
cpu_number = int(cpu_number)
elif cpu_number>cpu_count():
cpu_number = cpu_count()
else:
cpu_number = 1
tStart = time.time()
model = MCMLModel()
c = {}
# 本程式只有製作到8核運算，如果想擴充更多核心運算，請增加以下程式碼如:
# 在m8.start()以下依序增加核心數
# if c.get(8):
# boundary = c[8]
# q9 = Queue()
# m9 = mp.Process(target=job, args=(q9,model,N,boundary))
# m9.start()
# 以此類推.....
for i in range(0,cpu_number):
c[i] = [int(0+i*316/cpu_number), int((i+1)*316/cpu_number)]
if c.get(0):
boundary = c[0]
q1 = Queue()
m1 = mp.Process(target=job, args=(q1,model,N,boundary))
m1.start()
if c.get(1):
boundary = c[1]
q2 = Queue()
m2 = mp.Process(target=job, args=(q2,model,N,boundary))
m2.start()
if c.get(2):
boundary = c[2]
q3 = Queue()
m3 = mp.Process(target=job, args=(q3,model,N,boundary))
m3.start()
if c.get(3):
boundary = c[3]
q4 = Queue()
m4 = mp.Process(target=job, args=(q4,model,N,boundary))
m4.start()
if c.get(4):
boundary = c[4]
q5 = Queue()
m5 = mp.Process(target=job, args=(q5,model,N,boundary))
m5.start()
if c.get(5):
boundary = c[5]
q6 = Queue()
m6 = mp.Process(target=job, args=(q6,model,N,boundary))
m6.start()
if c.get(6):
boundary = c[6]
q7 = Queue()
m7 = mp.Process(target=job, args=(q7,model,N,boundary))
m7.start()
if c.get(7):
boundary = c[7]
q8 = Queue()
m8 = mp.Process(target=job, args=(q8,model,N,boundary))
m8.start()
print('Currently using %d cores in the operation'%(i+1))
# 本程式只有製作到8核運算，如果想擴充更多核心運算，請增加以下程式碼如:
# 在R.append(q8.get())以下依序增加核心數
# if c.get(8):
# m9.join()
# R.append(q9.get())
# 以此類推.....
R = []
if c.get(0):
m1.join()
R.append(q1.get())
if c.get(1):
m2.join()
R.append(q2.get())
if c.get(2):
m3.join()
R.append(q3.get())
if c.get(3):
m4.join()
R.append(q4.get())
if c.get(4):
m5.join()
R.append(q5.get())
if c.get(5):
m6.join()
R.append(q6.get())
if c.get(6):
m7.join()
R.append(q7.get())
if c.get(7):
m8.join()
R.append(q8.get())
print('operation done!')
import itertools
out = list(itertools.chain.from_iterable(R))
tEnd = time.time()#計時結束
print ('spend time:'+str(tEnd - tStart)+'seconds')
if save == 'y':
np.savetxt(str(filename)+'.csv', out, delimiter='\t')
path = os.path.dirname(__file__)
WL = np.loadtxt(os.path.join(path,'wavelength.csv'))
plt.plot(WL[::5],out)
plt.show()
print ('finish!')
input('Press any key to end')