def Weightloss_Shooting():
age, sex = 38. , 'female'
H, BW = 1.73, 72.7
T = 12*7. # Time frame = 3 months
PALf, EIf = 1.7, 2025
def EI(t): return EIf
def PAL(t): return PALf
from solution import fat_mass, compute_weight_curve, weight_odes
F = fat_mass(BW,age,H,sex)
L = BW-F
# Fix activity level and determine Energy Intake to achieve Target weight of 145 lbs = 65.90909 kg
init_FL = np.array([F,L])
EI0, EI1 = 2000, 1950 # Variable Energy Intake
beta = 65.90909*2.2
reltol, abstol = 1e-9,1e-8
dim, iterate = 2, 15
print '\nj = 1', '\nt = ', EI0
for j in range(2,iterate):
print '\nj = ', j, '\nt = ', EI1
ode_f = lambda t,y: weight_odes(t,y,EI1,PAL(t))
example1 = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example1.set_initial_value(init_FL, 0.)
y1 = 2.2*sum(example1.integrate(T) )
if abs(y1-beta)<1e-8:
print '\n--Solution computed successfully--'
break
# Update
ode_f = lambda t,y: weight_odes(t,y,EI0,PAL(t))
example0 = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example0.set_initial_value(init_FL, 0.)
y0 = 2.2*sum(example0.integrate(T))
EI2 = EI1 - (y1 - beta)*(EI1-EI0)/(y1- y0)
EI0 = EI1
EI1 = EI2
# Here we plot the solution
ode_f = lambda t,y: weight_odes(t,y,EI1,PAL(t))
example = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example.set_initial_value(init_FL, 0.)
X = np.linspace(0.,T,801)
Y = np.zeros((len(X),dim))
Y[0,:] = init_FL
for j in range(1,len(X)): Y[j,:] = example.integrate(X[j])
print '\n|y(T) - Target Weight| = ', np.abs(beta-2.2*sum(Y[-1,:]) ),'\n'
plt.plot(X,2.2*(Y[:,0]+Y[:,1]),'-k',linewidth=2.0)
plt.axis([-1,T+1,0,170])
plt.show(); plt.clf()
return
def Predicted_Weight(PAL, EI, Target_Weight, age, sex, H, BW, T):
from solution import fat_mass, compute_weight_curve, weight_odes
F = fat_mass(BW, age, H, sex)
L = BW - F
# Fix activity level and determine Energy Intake to achieve Target weight of 145 lbs = 65.90909 kg
init_FL = np.array([F, L])
reltol, abstol = 1e-9, 1e-8
ode_f = lambda t, y: weight_odes(t, y, EI, PAL)
ode_object = ode(ode_f).set_integrator('dopri5', atol=abstol, rtol=reltol)
ode_object.set_initial_value(init_FL, 0.)
y1 = sum(ode_object.integrate(T))
return y1 - Target_Weight
def Predicted_Weight(PAL,EI,Target_Weight,age,sex,H,BW,T):
from solution import fat_mass, compute_weight_curve, weight_odes
F = fat_mass(BW,age,H,sex)
L = BW-F
# Fix activity level and determine Energy Intake to achieve Target weight of 145 lbs = 65.90909 kg
init_FL = np.array([F,L])
reltol, abstol = 1e-9,1e-8
ode_f = lambda t,y: weight_odes(t,y,EI,PAL)
ode_object = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
ode_object.set_initial_value(init_FL, 0.)
y1 = sum(ode_object.integrate(T) )
return y1-Target_Weight
import numpy as np
import matplotlib.pyplot as plt
def weightloss_calculator(age, sex, H, BW, T, (PAL, EI)):
# Initial stats
# Age (y), Gender ('male' or 'female'), Height (m), Body Weight (kg)
# Time (d)
#
# Diet/Lifestyle Change
# (PAL, EI) = Future Physical Activity Level and Energy Intake
#
# Call the IVP Solver
########################################
from solution import fat_mass, compute_weight_curve
F = fat_mass(BW, age, H, sex)
L = BW - F
t, y = compute_weight_curve(F, L, T, EI, PAL)
# Plot the Results
####################################
fig, ax = plt.subplots()
plt.plot(t, 2.2 * y[:, 0], '-b', label='Fat', linewidth=2.0)
plt.plot(t, 2.2 * y[:, 1], '-g', label='Lean', linewidth=2.0)
plt.plot(t, 2.2 * (y[:, 0] + y[:, 1]), '-r', label='Total', linewidth=2.0)
plt.legend(loc=1) # Upper right placement
plt.xlabel('days', fontsize=16)
plt.ylabel('lbs', fontsize=16)
plt.axis([0, np.max(t), 20, 180])
plt.plot(t, 2.2 * 25 * H**2 * np.ones(t.shape),
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import solution
def weightloss_calculator(age, sex, H, BW, T, (PAL, EI)):
# Initial stats
# Age (y), Gender ('male' or 'female'), Height (m), Body Weight (kg)
# Time (d)
#
# Diet/Lifestyle Change
# (PAL, EI) = Future Physical Activity Level and Energy Intake
#
# Call the IVP Solver
########################################
F = solution.fat_mass(BW, age, H, sex)
L = BW - F
t, y = solution.compute_weight_curve(F, L, T, EI, PAL)
# Plot the Results
####################################
fig, ax = plt.subplots()
plt.plot(t, 2.2 * y[:, 0], '-b', label='Fat', linewidth=2.0)
plt.plot(t, 2.2 * y[:, 1], '-g', label='Lean', linewidth=2.0)
plt.plot(t, 2.2 * (y[:, 0] + y[:, 1]), '-r', label='Total', linewidth=2.0)
plt.legend(loc=1) # Upper right placement
plt.xlabel('days', fontsize=16)
plt.ylabel('lbs', fontsize=16)
plt.axis([0, np.max(t), 20, 180])
# High end of normal weight range
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import solution
def weightloss_calculator(age, sex, H, BW, T, (PAL, EI)):
# Initial stats
# Age (y), Gender ('male' or 'female'), Height (m), Body Weight (kg)
# Time (d)
#
# Diet/Lifestyle Change
# (PAL, EI) = Future Physical Activity Level and Energy Intake
#
# Call the IVP Solver
########################################
F = solution.fat_mass(BW, age, H, sex)
L = BW - F
t, y = solution.compute_weight_curve(F, L, T, EI, PAL)
# Plot the Results
####################################
fig, ax = plt.subplots()
plt.plot(t, 2.2 * y[:, 0], '-b', label='Fat', linewidth=2.0)
plt.plot(t, 2.2 * y[:, 1], '-g', label='Lean', linewidth=2.0)
plt.plot(t, 2.2 * (y[:, 0] + y[:, 1]), '-r', label='Total', linewidth=2.0)
plt.legend(loc=1) # Upper right placement
plt.xlabel('days', fontsize=16)
plt.ylabel('lbs', fontsize=16)
plt.axis([0, np.max(t), 20, 180])
# High end of normal weight range
import numpy as np
import matplotlib.pyplot as plt
def weightloss_calculator(age, sex, H, BW, T, (PAL,EI) ):
# Initial stats
# Age (y), Gender ('male' or 'female'), Height (m), Body Weight (kg)
# Time (d)
#
# Diet/Lifestyle Change
# (PAL, EI) = Future Physical Activity Level and Energy Intake
#
# Call the IVP Solver
########################################
from solution import fat_mass, compute_weight_curve
F = fat_mass(BW,age,H,sex)
L = BW-F
t,y = compute_weight_curve(F,L,T,EI,PAL)
# Plot the Results
####################################
fig, ax = plt.subplots()
plt.plot(t,2.2*y[:,0],'-b',label='Fat',linewidth=2.0)
plt.plot(t,2.2*y[:,1],'-g',label='Lean',linewidth=2.0)
plt.plot(t,2.2*(y[:,0]+y[:,1]),'-r',label='Total',linewidth=2.0)
plt.legend(loc=1)# Upper right placement
plt.xlabel('days',fontsize=16)
plt.ylabel('lbs',fontsize=16)
plt.axis([0, np.max(t),20, 180])
plt.plot(t, 2.2*25*H**2*np.ones(t.shape),'-.k') # High end of normal weight range
def Weightloss_Shooting():
age, sex = 38., 'female'
H, BW = 1.73, 72.7
T = 12 * 7. # Time frame = 3 months
PALf, EIf = 1.7, 2025
def EI(t):
return EIf
def PAL(t):
return PALf
from solution import fat_mass, compute_weight_curve, weight_odes
F = fat_mass(BW, age, H, sex)
L = BW - F
# Fix activity level and determine Energy Intake to achieve Target weight of 145 lbs = 65.90909 kg
init_FL = np.array([F, L])
EI0, EI1 = 2000, 1950 # Variable Energy Intake
beta = 65.90909 * 2.2
reltol, abstol = 1e-9, 1e-8
dim, iterate = 2, 15
print '\nj = 1', '\nt = ', EI0
for j in range(2, iterate):
print '\nj = ', j, '\nt = ', EI1
ode_f = lambda t, y: weight_odes(t, y, EI1, PAL(t))
example1 = ode(ode_f).set_integrator('dopri5',
atol=abstol,
rtol=reltol)
example1.set_initial_value(init_FL, 0.)
y1 = 2.2 * sum(example1.integrate(T))
if abs(y1 - beta) < 1e-8:
print '\n--Solution computed successfully--'
break
# Update
ode_f = lambda t, y: weight_odes(t, y, EI0, PAL(t))
example0 = ode(ode_f).set_integrator('dopri5',
atol=abstol,
rtol=reltol)
example0.set_initial_value(init_FL, 0.)
y0 = 2.2 * sum(example0.integrate(T))
EI2 = EI1 - (y1 - beta) * (EI1 - EI0) / (y1 - y0)
EI0 = EI1
EI1 = EI2
# Here we plot the solution
ode_f = lambda t, y: weight_odes(t, y, EI1, PAL(t))
example = ode(ode_f).set_integrator('dopri5', atol=abstol, rtol=reltol)
example.set_initial_value(init_FL, 0.)
X = np.linspace(0., T, 801)
Y = np.zeros((len(X), dim))
Y[0, :] = init_FL
for j in range(1, len(X)):
Y[j, :] = example.integrate(X[j])
print '\n|y(T) - Target Weight| = ', np.abs(beta -
2.2 * sum(Y[-1, :])), '\n'
plt.plot(X, 2.2 * (Y[:, 0] + Y[:, 1]), '-k', linewidth=2.0)
plt.axis([-1, T + 1, 0, 170])
plt.show()
plt.clf()
return