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main.py
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main.py
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#Importing libraries
import numpy as np
import multiprocessing as mp ;from multiprocessing import Queue, cpu_count
import matplotlib.pyplot as plt
import time
import os
import webbrowser
import tkinter; import tkinter as tk
import pandas as pd
from pandastable import Table
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg; from matplotlib.figure import Figure
import matplotlib; matplotlib.use('TkAgg')
# Display customization
TITLE_FONT = ("Arial", 16, "bold")
violet_bg = '#ccb6e4'
white_bg = '#FFFFFF'
Title_Application = "Light-skin simulation for PPG application"
#Simulation options
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', 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'))
# correction
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
# define the optical properties of each tissue
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(): #WATER
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(): #epidermis
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(): #dermis
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(): #skull
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(): #muscle
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(): #'grey matter'
# 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
# print('Medium definition good')
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))
# Define the organization layer (in this example, there are two layers of air up and down, and seven layers of tissue in the middle)
# print('mode=wrist')
self.layer = [Medium('AIR', nummua), \
Medium('TISSUE1', nummua), \
Medium('TISSUE2', nummua), \
Medium('TISSUE2', nummua), \
Medium('TISSUE1', 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]
# excluding the upper and lower air layers
# total 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] #Remove the last value, but this value is still a list, take the second value of the list
#z coordinate at the bottom of the last layer
# calculate the critical angle cosines for each layer
# critical 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])
# print('LayerStructure good')
def calc_r_specular(self):
# direct reflections from the 1st and 2nd layers. mirror reflection
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 PhotonModel(ModelInput):
"""PhotonModel 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)
def run_photon_simulation(model, wavelength, N):
# print('processing:'+str(wavelength)+'/total:316')
## remove model parameters - 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
## remove model parameters - end
### --do_one_run-- #start
for i in range(numloops):
## photon journey -start
# initial photon state
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
# Photon starts activity
# layer:[n,mua,mus,g]
# Whether the first layer (index=1) after the air layer is air
if (tissuelayers[layer_index][1] == 0.0) and (tissuelayers[layer_index][2] == 0.0):
layer_index = 2 #Jump directly to the next level
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
# Whether the photon penetrates or reflects at the upper boundary of the current layer (uz <0).
# If "layer" is the first layer, if PARTIALREFLECTION is set to 1, the photon will be partially penetrated and partially reflected
# If PARTIALREFLECTION is set to 0, the photon will directly calculate and record the weight of the photon penetrating upward as the reflectance r.
# If "Layer" is not the first layer and the photon penetrates upwards, move the photon to "Layer-1".
# Update photon 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
# Whether the photon penetrates or reflects at the upper boundary of the current layer (uz <0).
# If "layer" is the first layer, if PARTIALREFLECTION is set to 1, the photon will be partially penetrated and partially reflected
# If PARTIALREFLECTION is set to 0, the photon will directly calculate and record the weight of the photon penetrating upward as the reflectance r.
# If "Layer" is not the first layer and the photon penetrates upwards, move the photon to "Layer-1".
# Update photon 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
## PHOTON JOURNEY ##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
# print('run_photon_simulation definition good')
# Support CPU multi-core accelerated calculation code
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)
# print('Job definition good')
class Application(object):
"""Definition of the GUI"""
def __init__(self):
"""Constructs the main window"""
# Window settings
self.fen = tk.Tk()
global frame
frame = self.fen
self.fen.configure(background='white')
self.fen.title(Title_Application)
self.fen.minsize(1000, 400)
# Initialisation de df
global patient_input;patient_input = []
self.df = pd.DataFrame(patient_input)
# Frames initialization
global f
f = tk.Frame(self.fen, bg=white_bg) # frame for the database
global f2
f2 = tk.Frame(self.fen, bg=white_bg) # frame for the buttons
f.pack(fill='y', expand=0, side='top')
f2.pack(fill='y', expand=0, side='left')
#Display options
global Title
Title=tk.Label(frame, text="\n Light-Skin Interaction Simulation in a PPG device \n Infineon Technologies AG", font=("Arial", 16, "bold"),bg=white_bg)
Title.pack(fill='x',side='top')
global Description_lbl
Description_lbl = tk.Label(frame, text="This desktop app aims at simulating the light-skin interaction for PPG monitoring. \n It takes into account personal clinical parameters to optimize the dependant parameters and provide a greater accuracy of the cardiac data for the patient.\n The theoretical model and details about the code are provided in the internship report, which is linked in all pages of this app.", font=("Arial", 10, "italic"), bg=white_bg)
Description_lbl.pack(expand='yes')
global Desc1; global Desc2; global Desc3
Desc1 = tk.Label(self.fen,
text="\n This simulation is held within Infineon Technologies AG \n",
font=("Arial", 12), bg=white_bg)
Desc1.pack(side='top')
Desc2 = tk.Label(self.fen,text="Supervisors :",font=("Arial", 11, 'bold'), bg=white_bg)
Desc2.pack(side='top')
Desc3 = tk.Label(self.fen, text="Patrick Moder \n Hans Ehm \n",font=("Arial", 11), bg=white_bg)
Desc3.pack(side='top')
def internship_link():
# filename = '\\mucsdn32.eu.infineon.com\csc_bs\02_public\_Benkirane\InternshipReport_27.07.2021_KenzaBenkirane.pdf'
# os.system(filename) #Open file [Same as Right-click Open]
# os.system('notepad '+filename) #Open in notepad
webbrowser.open_new(r"https://insalyonfrance-my.sharepoint.com/:w:/g/personal/kbenkirane_insa-lyon_fr/Ea3b3krYh2FIoSjloJnkMFIBDCaX6n_tRHGWTMwzPl797Q")
global Link_bttn
Link_bttn = tk.Button(frame, text="Open the internship report",font=("Arial", 8, 'italic'), command=lambda: internship_link(), bg=white_bg)
Link_bttn.pack(side="bottom")
global Choice
Choice = tk.Label(self.fen,text="\n The default mode is Reflection mode : \n please answer the following questionnaire \n", font=("Arial", 11), bg=white_bg)
Choice.pack(side='top')
global Next_bttn
Next_bttn= tk.Button(frame, text='Go to questionnaire', bg=white_bg,command=lambda: Questionnaire_window(self))
Next_bttn.pack()
global NumberOfTry;NumberOfTry=0
global Questionnaire_window
def Questionnaire_window(self):
# Modifying the page visualization
Choice.pack_forget();
Desc1.pack_forget(); Desc2.pack_forget(); Desc3.pack_forget() # Deleting the text
Description_lbl.pack_forget();
Title.config(font=("Arial", 8, "italic"))
Title.pack(side='bottom')
#Displaying and saving the questionnaire
global empty; empty=tk.Label(frame,text="",font=("Arial", 11, 'bold'), bg=white_bg);empty.pack(side='top')
global please_label ; please_label=tk.Label(frame,text="Please answer the following questions :",font=("Arial", 11, 'bold'), bg=white_bg);please_label.pack(side='top')
#saving locally the data for the simulation
#NB: the data will not be saved to any file or location : it's only used for the simulation and then automatically deleted
global Columns ; Columns=[]
#Type of acquisition
global mode_des; mode_des = tk.Label(frame,text="Are you using finger or wrist :",font=("Arial", 11), bg=white_bg) ;mode_des.pack()
global mode_input;mode_input=tk.StringVar()
frame_mode = tk.Frame(frame,bg=white_bg,width=500,height=300);frame_mode.pack()
global R1_mode;R1_mode = tk.Radiobutton(frame_mode, text="Finger", variable=mode_input, value=1, bg=white_bg)
R1_mode.pack(side='left')
global R2_mode;R2_mode = tk.Radiobutton(frame_mode, text="Wrist", variable=mode_input, value=2, bg=white_bg)
R2_mode.pack(side='right')
#Gender
global gender_des; gender_des = tk.Label(frame,text="Gender :",font=("Arial", 11), bg=white_bg) ;gender_des.pack()
global gender_input; gender_input=tk.StringVar()
frame_gender = tk.Frame(frame,bg=white_bg,width=500,height=300);frame_gender.pack()
global R1_gender;R1_gender = tk.Radiobutton(frame_gender, text="Female", variable=gender_input, value=1, bg=white_bg)
R1_gender.pack(side='left')
global R2_gender; R2_gender = tk.Radiobutton(frame_gender, text="Male", variable=gender_input, value=2, bg=white_bg)
R2_gender.pack(side='right')
#Height
empty.pack(side='top')
global height_des; height_des = tk.Label(frame,text="Height (cm) :",font=("Arial", 11), bg=white_bg)
height_des.pack()
global height_input;height_input=tk.StringVar()
global height; height=tk.Entry(frame,textvariable=height_input)
height.pack()
#Weight
empty.pack(side='top')
global weight_des; weight_des = tk.Label(frame,text="Weight (kg) :",font=("Arial", 11), bg=white_bg)
weight_des.pack()
global weight_input; weight_input=tk.StringVar()
global weight; weight=tk.Entry(frame,textvariable=weight_input)
weight.pack()
#Skin type
empty.pack(side='top')
global skintype_des; skintype_des = tk.Label(frame,text="What is your skin type ? ",font=("Arial", 11), bg=white_bg)
skintype_des.pack()
def skintype_link():
webbrowser.open_new(r"https://healthengine.com.au/info/skin-colour#fitz")
global skintype_bttn
global skintype_bttn;skintype_bttn = tk.Button(frame, text="Discover my skin type", font=("Arial", 7,'italic'), command=lambda: skintype_link(), bg=white_bg)
skintype_bttn.pack()
global skintype_input; skintype_input = tk.StringVar()
frame_skintype = tk.Frame(frame,bg=white_bg,width=500,height=300);frame_skintype.pack()
global R1_skintype; R1_skintype = tk.Radiobutton(frame_skintype, text="Type I", variable=skintype_input, value=1, bg=white_bg)
R1_skintype.pack(side='left')
global R2_skintype; R2_skintype = tk.Radiobutton(frame_skintype, text="Type II", variable=skintype_input, value=2, bg=white_bg)
R2_skintype.pack(side='left')
global R3_skintype; R3_skintype = tk.Radiobutton(frame_skintype, text="Type III", variable=skintype_input, value=3, bg=white_bg)
R3_skintype.pack(side='left')
global R4_skintype; R4_skintype = tk.Radiobutton(frame_skintype, text="Type IV", variable=skintype_input, value=4, bg=white_bg)
R4_skintype.pack(side='left')
global R5_skintype; R5_skintype = tk.Radiobutton(frame_skintype, text="Type V", variable=skintype_input, value=5, bg=white_bg)
R5_skintype.pack(side='left')
global R6_skintype; R6_skintype = tk.Radiobutton(frame_skintype, text="Type VI", variable=skintype_input, value=6, bg=white_bg)
R6_skintype.pack(side='left')
Next_bttn.config(text='Next', bg=white_bg,command=lambda: simulation_questions(self))
Next_bttn.pack(side='bottom')
global simulation_questions
def simulation_questions(self):
#Displaying options
Next_bttn.config(text='Go!', bg=white_bg,command=lambda: database_window(self))
Next_bttn.pack(side='bottom')
gender_des.forget(); height_des.forget(); #name_des.forget()
R1_skintype.forget();R2_skintype.forget();R3_skintype.forget();R4_skintype.forget();R5_skintype.forget();R6_skintype.forget();
weight.forget();height.forget();R1_gender.forget();R2_gender.forget(); gender_des.forget();mode_des.forget(); skintype_des.forget();
please_label.forget(); R1_mode.forget();R2_mode.forget();skintype_bttn.forget();weight_des.forget()
empty.forget()
#Reset possible
def reset_function():
photonnumber_des.forget();photonnumber.forget()
print('Reset done')
lambda: Questionnaire_window(self)
#Get the data from the questionnaire entries
patient_input.append(mode_input.get());Columns.append('Acquisition mode');
mode=patient_input[0]
patient_input.append(gender_input.get());Columns.append('Gender')
patient_input.append(height_input.get()); Columns.append('Height')
patient_input.append(weight_input.get());Columns.append('Weight')
patient_input.append(skintype_input.get());Columns.append('Skin type')
#Simulation questions
global simulation_parameters;simulation_parameters=[]; global Parameters; Parameters=[]
#Number of photons
empty.pack(side='top')
global photonnumber_des; photonnumber_des = tk.Label(frame,text="What is the number of photons :",font=("Arial", 11), bg=white_bg)
photonnumber_des.pack()
global photonnumber_input; photonnumber_input=tk.IntVar()
global photonnumber; photonnumber=tk.Entry(frame,textvariable=photonnumber_input)
photonnumber.pack()
#Database Actualization
self.df=pd.DataFrame(patient_input)
global database_window
def database_window(self):
Next_bttn.forget()
photonnumber_des.forget(); empty.forget();photonnumber.forget()
simulation_parameters.append(photonnumber_input.get());
global NumberOfPhotons; NumberOfPhotons=int(simulation_parameters[0])
#Adding the BMI: BMI calculation
# w=float(patient_input[3])
# h=float(patient_input[4])*0.001
# BMI=w/(h*h)
# patient_input.append(BMI); Columns.append('BMI')
# print(patient_input)
#Creating the dataframe
# print('patient_input',patient_input) #have an idea of the table
# print('Columns',Columns)
df_patient=pd.DataFrame(patient_input)
df_patient=df_patient.transpose()
self.df=df_patient
#simulation
if __name__ == "__main__":
N = NumberOfPhotons
N = int(N)
cpu_number = 1#int(CPUNumber)
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 = PhotonModel()
c = {}
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()