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I have a function with different parameters that I want to optimize to fit some existing data.
The function runs fine on its own, but when I try to pass it through the scipy.optimize.curve_fit function, I get this error :
IndexError: invalid index to scalar variable.
I don't understand why the function would work on its own, and I would not get any errors.
What can I do ?
The original function used dictionnaries and I thought that might be the problem but I modified it and it still doesn't work.
This is the function I'm using :
def function_test(xy,X1,X2,X3,X4):
precip = xy\[0\]
potential_evap = xy\[1\]
nUH1 = int(math.ceil(X4))
nUH2 = int(math.ceil(2.0*X4))
uh1_ordinates = [0] * nUH1
uh2_ordinates = [0] * nUH2
UH1 = [0] * nUH1
UH2 = [0] * nUH2
for t in range(1, nUH1 + 1):
uh1_ordinates[t - 1] = s_curves1(t, X4) - s_curves1(t-1, X4)
for t in range(1, nUH2 + 1):
uh2_ordinates[t - 1] = s_curves2(t, X4) - s_curves2(t-1, X4)
production_store = X1*0.60# S
routing_store = X3*0.70# R
qsim = []
for j in range(2191):
if precip[j] > potential_evap[j]:
net_evap = 0
scaled_net_precip = (precip[j] - potential_evap[j])/X1
if scaled_net_precip > 13:
scaled_net_precip = 13.
tanh_scaled_net_precip = tanh(scaled_net_precip)
reservoir_production = (X1 * (1 - (production_store/X1)**2) * tanh_scaled_net_precip) / (1 + production_store/X1 * tanh_scaled_net_precip)
routing_pattern = precip[j]-potential_evap[j]-reservoir_production
else:
scaled_net_evap = (potential_evap[j] - precip[j])/X1
if scaled_net_evap > 13:
scaled_net_evap = 13.
tanh_scaled_net_evap = tanh(scaled_net_evap)
ps_div_x1 = (2 - production_store/X1) * tanh_scaled_net_evap
net_evap = production_store * (ps_div_x1) / \
(1 + (1 - production_store/X1) * tanh_scaled_net_evap)
reservoir_production = 0
routing_pattern = 0
production_store = production_store - net_evap + reservoir_production
percolation = production_store / (1 + (production_store/2.25/X1)**4)**0.25
routing_pattern = routing_pattern + (production_store-percolation)
production_store = percolation
for i in range(0, len(UH1) - 1):
UH1[i] = UH1[i+1] + uh1_ordinates[i]*routing_pattern
UH1[-1] = uh1_ordinates[-1] * routing_pattern
for j in range(0, len(UH2) - 1):
UH2[j] = UH2[j+1] + uh2_ordinates[j]*routing_pattern
UH2[-1] = uh2_ordinates[-1] * routing_pattern
groundwater_exchange = X2 * (routing_store / X3)**3.5
routing_store = max(0, routing_store + UH1[0] * 0.9 + groundwater_exchange)
R2 = routing_store / (1 + (routing_store / X3)**4)**0.25
QR = routing_store - R2
routing_store = R2
QD = max(0, UH2[0]*0.1+groundwater_exchange)
Q = QR + QD
qsim.append(Q)
return qsim
I'm trying solve a system of coupled ordinary differential equations, formed by 7 ODEs in python, using solve_ivp or either implement a fuction for RK4.
The general physical problem is as follows:
Cooling of photovoltaic modules with heat exchanger coupling to the module. In this way, the module generates electrical energy and thermal energy.
I have a polynomial function, G(t) = 9.8385e-13*t^4 - 1.82918e-8*t^3 + 5.991355e-05*t^2 + 2.312059e-1*t + 25, which works for an approximate range of 0 < t < 9000, which represents solar radiation as a function of time of day.
This function was obtained through a "polyfit" applied to real data (file upload here. Its a CSV - https://files.fm/u/9y4evkf6c).
This function is used as input for the ODEs, which represent an electrical and a thermal system as a function of time.
To solve the electrical model, I created some scripts that solve the diode equation for the photovoltaic module in question, and the output of this script is the photovoltaic power (called in the PPV thermal model) generated as a function of the module temperature and radiation. This script works great and solves part of my problem.
My difficulty lies in solving the equations of the thermal model, which receives as input parameters G(t) and PPV.
The equations result in this system:
System of EDOS
Labels:
Tvidro = Tglass = T1
Tcel = Tpv = T2
Ttedlar = T3
Tabs = Tabsorber = T4
Ttubo = Ttube = T5
Tfsai = Tfluid_out = T6
Tiso = Tinsulation = T7
Using method/function for RK4, the complete code is like this (you can go direct to part "#DEFINE MODEL EQUATIONS - ODES)" :
import numpy as np
import matplotlib.pyplot as plt
import csv
from numpy.polynomial.polynomial import polyval
############################################################
with open('directory of data called teste_dados_radiacao',"r") as i:
rawdata = list(csv.reader(i, delimiter = ";"))
exampledata = np.array(rawdata[1:], dtype=float)
xdata = exampledata[:,0]
ydata = exampledata[:,1]
curve = np.array(np.polyfit(xdata, ydata, 4))
rev_curve = np.array(list(reversed(curve)), dtype=float)
print(rev_curve)
#G_ajustado = polyval(xdata, rev_curve)
""" plt.plot(xdata, ydata, label = "dados experimentais")
plt.plot(xdata, model, label = "model")
plt.legend()
plt.show() """
#############################################################
#CONSTANTS
Tamb = 25 #°C #ambient temperatura
SIGMA = 5.67e-8 #W/m2K4
E_VIDRO = 0.90 #between 0.85 e 0.83 #nasrin2017 0.04
VENTO = 2 #m/s
T_GROUND = Tamb + 2 #°C
T_CEU = 0.00552*Tamb**1.5
Vf = 1 #m/s
Do = 10e-3 #m
Di = 8e-3 #m
NS = 6*10 #number of cells
T_F_ENT = 20 #°C
#INPUTS
Tcel = 25
Tv = 25
Tiso = 30
Av = 1.638*0.982
ALPHA_VIDRO = 0.9
L_VIDRO = 3e-3 #m
RHO_VIDRO = 2500 #kg/m3
M_VIDRO = Av*L_VIDRO*RHO_VIDRO #kg
CP_VIDRO = 500 #j/kgK
K_VIDRO = 2 #W/mK
TAU_VIDRO = 0.95
Pac = 0.85
H_CELL = 0.156 #m
A_CELL = NS*H_CELL**2
ALPHA_CELL = 0.9
L_CEL = 3e-3
RHO_CEL = 2330
M_CEL = A_CELL*L_CEL*RHO_CEL #kg - estimated
CP_CEL = 900 #J/kgK
K_CEL = 140 #W/mK
BETA_T = 0.43/100 # %/°C
N_ELE_REF = 0.1368 #13.68%
N_ELE = N_ELE_REF*(1 - BETA_T*(Tcel - 25)) #273 + 25 - tcel kelvin
A_tedlar = Av
L_TEDLAR = 0.33e-3
RHO_TEDLAR = 1500
M_TEDLAR = Av*L_TEDLAR*RHO_TEDLAR
CP_TEDLAR = 1090 #1090 OU 2090
K_TEDLAR = 0.35
ALPHA_TEDLAR = 0.34 #doc nasa ou zero
#parameters
RHO_ABS = 2700
A_ABS = Av
CP_ABS =900
L_ABS = 3e-3 #mm
M_ABS = A_ABS*RHO_ABS*L_ABS
K_ABS = 300
A_ABS_TUBO = 10*1.60*0.01+0.154*9*0.01
A_ABS_ISO = Av-A_ABS_TUBO
RHO_TUBO = 2700
CP_TUBO = 900
N_TUBOS = 10
L_TUBO = N_TUBOS*1.6
M_TUBO = RHO_TUBO*L_TUBO*(3.1415/4)*(Do**2 - Di**2)
K_TUBO = 300
A_TUBO_F = 0.387 #pi*Di*(L*10 VOLTAS + R(156MM)*9)
A_TUBO_ISO = 0.484 #pi*Do*(L*10 VOLTAS + R(156MM)*9)
A_ISO = Av
RHO_ISO = 50
L_ISO = 40e-3
M_ISO = A_ISO*RHO_ISO*L_ISO
CP_ISO = 670
K_ISO = 0.0375
E_ISO = 0.75 #ESTIMATED
RHO_FLUIDO = 997
M_FLUIDO = L_TUBO*(3.1415/4)*Di**2*RHO_FLUIDO
CP_FLUIDO = 4186 #j/kgK
MI_FLUIDO = 0.890e-3 #Pa*s ou N/m2 * s
K_FLUIDO = 0.607
M_PONTO = 0.05 #kg/s ou 0.5 kg/m3
#DIMENSIONLESS
Pr = CP_FLUIDO*MI_FLUIDO/K_FLUIDO #water 25°C
Re = RHO_FLUIDO*Vf*Di/MI_FLUIDO
if (Re<=2300):
Nuf = 4.364
else:
Nuf = 0.023*(Re**0.8)*(Pr*0.4)*Re
#COEFFICIENTS
h_rad_vidro_ceu = SIGMA*E_VIDRO*(Tv**2 - T_CEU)*(Tv + T_CEU)
h_conv_vidro_amb = 2.8 + 3*VENTO
h_conv_tubo_fluido = 0.5*30#Nuf
h_cond_vidro_cel = 1/((L_VIDRO/K_VIDRO) + (L_CEL/K_CEL))
h_cond_cel_tedlar = 1/((L_TEDLAR/K_TEDLAR) + (L_CEL/K_CEL))
h_cond_tedlar_abs = 1/((L_TEDLAR/K_TEDLAR) + (L_ABS/K_ABS))
h_cond_abs_tubo = 1/((L_TUBO/K_TUBO) + (L_ABS/K_ABS))
h_cond_abs_iso = 1/((L_ISO/K_ISO) + (L_ABS/K_ABS))
h_cond_tubo_iso = 1/((L_ISO/K_ISO) + (L_TUBO/K_TUBO))
h_conv_iso_amb = h_conv_vidro_amb
h_rad_iso_ground = SIGMA*E_ISO*(Tiso**2 - T_GROUND**2)*(Tiso + T_GROUND)
#GROUPS
A1 = (1/(M_VIDRO*CP_VIDRO))*(ALPHA_VIDRO*Av)#*G(t)) G_ajustado = polyval(dt,rev_curve)
A2 = (1/(M_VIDRO*CP_VIDRO))*(Av*(h_rad_vidro_ceu + h_conv_vidro_amb + h_cond_vidro_cel))
A3 = (1/(M_VIDRO*CP_VIDRO))*Av*h_cond_vidro_cel
A4 = (1/(M_VIDRO*CP_VIDRO))*Av*(h_conv_vidro_amb + h_rad_vidro_ceu)
A5 = (1/(M_CEL*CP_CEL))*(Pac*A_CELL*TAU_VIDRO*ALPHA_CELL) #*G(t)
A6 = -1*A5*N_ELE #*G(t)
A7 = (1/(M_CEL*CP_CEL))*A_CELL*h_cond_vidro_cel
A8 = (1/(M_CEL*CP_CEL))*A_CELL*(h_cond_vidro_cel + h_cond_cel_tedlar)
A9 = (1/(M_CEL*CP_CEL))*A_CELL*h_cond_cel_tedlar
A10 = (1/(M_TEDLAR*CP_TEDLAR))*A_tedlar*(1 - Pac)*TAU_VIDRO*ALPHA_TEDLAR#G(t)
A11 = (1/(M_TEDLAR*CP_TEDLAR))*A_tedlar*(h_cond_cel_tedlar + h_cond_tedlar_abs)
A12 = (1/(M_TEDLAR*CP_TEDLAR))*A_tedlar*h_cond_cel_tedlar
A13 = (1/(M_TEDLAR*CP_TEDLAR))*A_tedlar*h_cond_tedlar_abs
A14 = (1/(M_ABS*CP_ABS))*A_ABS*h_cond_tedlar_abs
A15 = (1/(M_ABS*CP_ABS))*(A_ABS*h_cond_tedlar_abs + A_ABS_TUBO*h_cond_abs_tubo + A_ABS_ISO*h_cond_abs_iso)
A16 = (1/(M_ABS*CP_ABS))*A_ABS_TUBO*h_cond_abs_tubo
A17 = (1/(M_ABS*CP_ABS))*A_ABS_ISO*h_cond_abs_iso
A18 = (1/(M_TUBO*CP_TUBO))*A_ABS_TUBO*h_cond_abs_tubo
A19 = (1/(M_TUBO*CP_TUBO))*(A_ABS_TUBO*h_cond_abs_tubo + A_TUBO_F*h_conv_tubo_fluido + A_TUBO_ISO*h_cond_tubo_iso)
A20 = (1/(M_TUBO*CP_TUBO))*A_TUBO_F*h_conv_tubo_fluido*0.5
A21 = (1/(M_TUBO*CP_TUBO))*A_TUBO_ISO*h_cond_tubo_iso
A22 = (1/(M_FLUIDO*CP_FLUIDO))*A_TUBO_F*h_conv_tubo_fluido
A23 = (1/(M_FLUIDO*CP_FLUIDO))*(A_TUBO_F*h_conv_tubo_fluido*0.5 + M_PONTO*CP_FLUIDO)
A24 = (1/(M_FLUIDO*CP_FLUIDO))*(T_F_ENT*(M_PONTO*CP_FLUIDO - h_conv_tubo_fluido*A_TUBO_F*0.5))
A25 = (1/(M_ISO*CP_ISO))*A_ABS_ISO*h_cond_abs_iso
A26 = (1/(M_ISO*CP_ISO))*(A_ABS_ISO*h_cond_abs_iso + A_TUBO_ISO*h_cond_tubo_iso + A_ISO*h_conv_iso_amb + A_ISO*h_rad_iso_ground)
A27 = (1/(M_ISO*CP_ISO))*A_TUBO_ISO*h_cond_tubo_iso
A28 = (1/(M_ISO*CP_ISO))*A_ISO*(h_conv_iso_amb*Tamb + h_rad_iso_ground*T_GROUND)
#DEFINE MODEL EQUATIONS - ODES - (GLASS, PV CELL, TEDLAR, ABSORBER, TUBE, FLUID, INSULATION) # dT1dt = A1*G_ajustado - A2*x[0] + A3*x[1] + A4 # dT2dt = A5*G_ajustado - A6*G_ajustado + A7*x[0] - A8*x[1] + A9*x[2]# dT3dt = A10*G_ajustado - A11*x[2] + A12*x[1] +A13*x[3]
def SysEdo(x, k):#tv-x[0] tcel-x[1] ttedlar-x[2] tabs-x[3] ttubo-x[4] tiso-x[5] tfs-x[6]
dT1dt = A1*polyval(k,rev_curve) - A2*x[0] + A3*x[1] + A4
dT2dt = A5*polyval(k,rev_curve) - A6*polyval(k,rev_curve) + A7*x[0] - A8*x[1] + A9*x[2]
dT3dt = A10*polyval(k,rev_curve) - A11*x[2] + A12*x[1] +A13*x[3]
dT4dt = A14*x[2] - A15*x[3] + A16*x[4] + A17*x[5]
dT5dt = A18*x[3] - A19*x[4] + A20*x[6] + A20*T_F_ENT + A21*x[5]
dT6dt = A22*x[4] - A23*x[6] + A24
dT7dt = A25*x[3] - A26*x[5] + A27*x[4] + A28
Tdot = np.array([dT1dt, dT2dt, dT3dt, dT4dt, dT5dt, dT6dt, dT7dt])
return Tdot
#RungeKutta4
def RK4(f, x0, t0, tf, dt):
t = np.arange(t0, tf, dt) #time vector
nt = t.size #lenght of time vector
nx = x0.size #length of state variables?
x = np.zeros((nx,nt)) #initialize 2D vector
x[:,0] = x0 #initial conditions
#RK4 constants
for k in range(nt-1):
k1 = dt*f(t[k], x[:,k],k)
k2 = dt*f(t[k] + dt/2, x[:,k] + k1/2, k)
k3 = dt*f(t[k] + dt/2, x[:,k] + k2/2, k)
k4 = dt*f(t[k] + dt, x[:,k] + k3, k)
dx = (k1 + 2*k2 + 2*k2 + k4)/6
x[:,k+1] = x[:,k] + dx
return x,t
#Define problems
f = lambda t, x, k : SysEdo(x, k)
#initial state - t0 is initial time - tf is final time - dt is time step
x0 = np.array([30, 30, 30, 30, 30, 30, 30])
t0 = 0
tf = 1000
dt = 1
#EDO SOLVE
x, t = RK4(f, x0, t0, tf, dt)
plt.figure()
plt.plot(t, x[0], '-', label='Tvidro')
"""
plt.plot(t, x[1], '-', label='Tpv')
plt.plot(t, x[2], '-', label='Ttedlar')
plt.plot(t, x[3], '-', label='Tabs')
plt.plot(t, x[4], '-', label='Tiso')
plt.plot(t, x[5], '-', label='Ttubo')
plt.plot(t, x[6], '-', label='Tfsai')"""
plt.title('Gráfico')
plt.legend(['Tvidro', 'Tpv', 'Ttedlar', 'Tabs', 'Tiso', 'Ttubo', 'Tfsai'], shadow=False)
plt.xlabel('t (s)')
plt.ylabel('Temperatura (°C)')
plt.xlim(0,20)
plt.ylim(0,150)
plt.grid('on')
plt.show()
Thank you in advance, I am also open to completely start the implementation from scratch if there is a better way to do this with python or matlab.
You can just replace
x, t = RK4(f, x0, t0, tf, dt)
with
t = arange(t0,tf+0.5*dt,dt)
res = solve_ivp(f,(t0,tf),x0,t_eval=t,args=(k,), method="DOP853", atol=1e-6,rtol=1e-8)
x = res.y[0]
Adapt the last 3 parameters to your liking.
I am getting an error that says I am not accounting for obscured light and that my specular is getting added when the light is obscured. This is what the specular part that is being added onto is with x representing r, g, orb of my Color class: light.color.x * s.finish.specular * specIntense
def in_shadow (sphere_list, sphere, ray_to_light, light):
new_list = list()
for s in sphere_list:
if sphere != s:
new_list.append(s)
for s in new_list:
if sphere_intersection_point(ray_to_light, s):
x1 = ray_to_light.pt.x - light.pt.x
y1 = ray_to_light.pt.y - light.pt.y
z1 = ray_to_light.pt.z - light.pt.z
dist1 = math.sqrt(x1 + y1 + z1)
x2 = ray_to_light.pt.x - s.center.x
y2 = ray_to_light.pt.y - s.center.y
z2 = ray_to_light.pt.z - s.center.z
dist2 = math.sqrt(x2 + y2 + z2)
# distance to light, distance to sphere
# check if distance to sphere < distance to light
# if so return 0
if dist2 < dist1:
return 0
return 1
def cast_ray(ray, sphere_list, color, light, point):
# count = 0
dist = -1
cp = Color(1.0, 1.0, 1.0)
for s in sphere_list:
if sphere_intersection_point(ray, s):
# count += 1
p = sphere_intersection_point(ray, s)
vec = vector_from_to(s.center, p)
N = normalize_vector(vec)
norm_scaled = scale_vector(N, 0.01)
pe = translate_point(p, norm_scaled)
l = vector_from_to(pe, light.pt)
l_dir = normalize_vector(l)
dot = dot_vector(N, l_dir)
r = Ray(pe, l_dir)
dotNScaled = dot * 2
reflecVec = difference_vector(l_dir, scale_vector(N, dotNScaled))
V = vector_from_to(point, pe)
Vdir = normalize_vector(V)
spec = dot_vector(reflecVec, Vdir)
m = in_shadow(sphere_list, s, r, light)
if (dot <= 0):
m = 0
x = (ray.pt.x - p.x) ** 2
y = (ray.pt.y - p.y) ** 2
z = (ray.pt.z - p.z) ** 2
curdist = math.sqrt(x + y + z)
# print curdist
if (dist < 0) or (dist > curdist):
dist = curdist
if (spec <= 0 ):
r = ( s.color.r * s.finish.ambient * color.r ) \
+ ( light.color.r * s.finish.diffuse * dot * s.color.r * m )
g = ( s.color.g * s.finish.ambient * color.g ) \
+ (light.color.g * s.finish.diffuse * dot * s.color.g * m )
b = ( s.color.b * s.finish.ambient * color.b ) \
+ (light.color.b * s.finish.diffuse * dot * s.color.b * m )
cp = Color(r, g, b)
if ( spec >= 0 ):
specIntense = spec ** (1/s.finish.roughness)
print type(s.finish.diffuse)
r = (s.color.r * s.finish.ambient * color.r) \
+ (light.color.r * s.finish.diffuse * dot * s.color.r * m) \
+ (light.color.r * s.finish.specular * specIntense)
g = (s.color.g * s.finish.ambient * color.g) \
+ (light.color.g * s.finish.diffuse * dot * s.color.g * m) \
+ (light.color.g * s.finish.specular * specIntense)
b = (s.color.b * s.finish.ambient * color.b) \
+ (light.color.b * s.finish.diffuse * dot * s.color.b * m) \
+ (light.color.b * s.finish.specular * specIntense)
cp = Color(r, g, b)
# if count > 1:
# print 'intersects two!'
return cp
I think somewhere I am not accounting for the case where the sphere has another one in front of it therefore the specular part is being added to it when it shouldn't, creating this weird white light behind the first sphere that isn't supposed to be there. I'm sure there is a bug in this code somewhere but I cannot find it.
I've set up numpy.seterr as follows:
np.seterr(invalid='raise', over ='raise', under='raise')
And I'm getting the following error:
c = beta[j,i] + oneminusbeta[j,i]
FloatingPointError: overflow encountered in double_scalars
I've checked what beta[j,i] and oneminusbeta[j,i] are at the point of crash, and these are their values:
beta: -131.340389182
oneminusbeta: 0.0
Please note, this line of addition (beta[j,i] + oneminusbeta[j,i]) has run for thousands of lines in a loop (that performs image classification) before crashing here at this point.
How can I deal with this? Is it necessary to change the type of the numpy arrays?
This is how I've initialized them:
beta = np.empty([m,n])
oneminusbeta = np.empty([m,n])
Is it possible to cast the individual values before adding them up? Rather than changing the entire array declarations? Or is this even a serious issue? Would it be safe to simply turn off the numpy.seterr configuration and let the calculations go ahead without raising the error?
Edit
Someone suggested below, and I suspected as well, that the values being added shouldn't cause an overflow. Then how can I find out where the overflow is really happening?
This is my code:
epthreshold = 709
enthreshold = -708
f.write("weights["+str(i)+", " + str(j)+"] = math.exp(beta: " +str(beta[j,i])+ " + oneminusbeta: " + str(oneminusbeta[j,i])+")\n" )
c = beta[j,i] + oneminusbeta[j,i]
weights[i,j] = math.exp(np.clip(c, enthreshold, epthreshold))
And when I check my log file, this is the line I get:
weights[5550, 13] = math.exp(beta: -131.340389182 + oneminusbeta: 0.0)
Edit 2
Here's the rest of my code, where variables n,m and H have already been initialized to integer values:
import numba
import numpy as np
import statsmodels.api as sm
weights = np.empty([n,m])
for curr_n in range(n):
for curr_m in range(m):
weights[curr_n,curr_m] = 1.0/(n)
beta = np.empty([m,n])
oneminusbeta = np.empty([m,n])
for curr_class in range(m):
for curr_sample in range(n):
beta[curr_class,curr_sample] = 1./m
epthreshold = 709 # positive exponential threshold
enthreshold = -708
for h in range(H):
print "Boosting round %d ... " % h
z = np.empty([n,m])
for j in range(m): # computing working responses and weights, Step 2(a)(i)
for i in range(no_samples):
i_class = y[i] #get the correct class for the current sample
if h == 0:
z[i,j] = (int(j==i_class) - beta[j,i])/((beta[j,i])*(1. - beta[j,i]))
weights[i,j] = beta[j,i]*(1. - beta[j,i])
else:
if j == i_class:
z[i,j] = math.exp(np.clip(-beta[j,i],enthreshold, epthreshold))
else:
z[i,j] = -math.exp(np.clip(oneminusbeta[j,i], enthreshold, epthreshold))
f.write("weights["+str(i)+", " + str(j)+"] = math.exp(beta: " +str(beta[j,i])+ " + oneminusbeta: " + str(oneminusbeta[j,i])+")\n" )
c = beta[j,i] + oneminusbeta[j,i]
weights[i,j] = math.exp(np.clip(c, enthreshold, epthreshold))
g_h = np.zeros([1,1])
j = 0
# Calculating regression coefficients per class
# building the parameters per j class
for y1_w in zip(z.T, weights.T):
y1, w = y1_w
temp_g = sm.WLS(y1, X, w).fit() # Step 2(a)(ii)
if np.allclose(g_h,0):
g_h = temp_g.params
else:
g_h = np.c_[g_h, temp_g.params]
j = j + 1
if np.allclose(g,0):
g = g_h
else:
g = g + g_h # Step(2)(a)(iii)
# now set g(x), function coefficients according to new formula, step (2)(b)
sum_g = g.sum(axis=1)
for j in range(m):
diff = (g[:,j] - ((1./m) * sum_g))
g[:,j] = ((m-1.)/m) * diff
g_per_round[h,:,j] = g[:,j]
#Now computing beta, Step 2(c)...."
Q = 0.
e = 0.
for j in range(m):
# Calculating beta and oneminusbeta for class j
aj = 0.0
for i in range(no_samples):
i_class = y[i]
X1 = X[i].reshape(1, no_features)
g1 = g[:,j].reshape(no_features, 1)
gc = g[:,i_class].reshape(no_features, 1)
dot = 1. + float(np.dot(X1, g1)) - float(np.dot(X1,gc))
aj = dot
sum_e = 0.
a_q = []
a_q.append(0.)
for j2 in range(m): # calculating sum of e's except for all j except where j=i_class
if j2 != i_class: # g based on j2, not necessarily g1?
g2 = g[:,j2].reshape(no_features, 1)
dot1 = 1. + float(np.dot(X1, g2)) - float(np.dot(X1,gc))
e2 = math.exp(np.clip(dot1,enthreshold, epthreshold))
sum_e = sum_e + e2
a_q.append(dot1)
if (int(j==i_class) == 1):
a_q_arr = np.array(a_q)
alpha = np.array(a_q_arr[1:])
Q = mylogsumexp(f,a_q_arr, 1, 0)
sumalpha = mylogsumexp(f,alpha, 1, 0)
beta[j,i] = -Q
oneminusbeta[j,i] = sumalpha - Q
else:
alpha = a_q
alpha = np.array(alpha[1:])
a_q_arr = np.array(a_q)
Q = mylogsumexp(f,a_q_arr, 0, aj)
sumalpha = log(math.exp(np.clip(Q, enthreshold, epthreshold)) - math.exp(np.clip(aj, enthreshold, epthreshold)))
beta[j,i] = aj - Q
oneminusbeta[j,i] = sumalpha - Q
and the function mylogsumexp is:
def mylogsumexp(f, a, is_class, maxaj, axis=None, b=None):
np.seterr(over="raise", under="raise", invalid="raise")
threshold = -sys.float_info.max
maxthreshold = sys.float_info.max
epthreshold = 709 # positive exponential threshold
enthreshold = -708
a = asarray(a)
if axis is None:
a = a.ravel()
else:
a = rollaxis(a, axis)
if is_class == 1:
a_max = a.max(axis=0)
else:
a_max = maxaj
#bnone = " none "
if b is not None:
a_max = maxaj
b = asarray(b)
if axis is None:
b = b.ravel()
else:
b = rollaxis(b, axis)
a = np.clip(a - a_max, enthreshold, epthreshold)
midout = np.sum(np.exp(a), axis=0)
midout = 1.0 + np.clip(midout - math.exp(a_max), threshold, maxthreshold)
out = np.log(midout)
else:
a = np.clip(a - a_max, enthreshold, epthreshold)
out = np.log(np.sum(np.exp(a)))
out += a_max
if out == float("inf"):
out = maxthreshold
if out == float("-inf"):
out = threshold
return out
Having a few problems basically inserting a bunch of flags generated by a loop:
def drawHelix(radius, length, coils):
numPoints = int(8)
degrees = float((360 / numPoints))
centerX = float(0)
centerZ = float(0)
xLoc = float(0)
zLoc = float(0)
yLoc = float(0)
yOffset = float(((length / coils) / numPoints))
vectorIndex = int(0)
pointStr = ""
knotStr = ""
for i in range(1, (360 * coils), 20):
t = i + degrees
xLoc = centerX + (math.cos(t) * radius)
zLoc = centerZ - (math.sin(t) * radius)
pointStr = (pointStr + " p=(" + str(xLoc) + "," + str(yLoc) + "," + str(zLoc) + "),")
knotStr = (knotStr + "k=(" + str(vectorIndex) + ")")
vectorIndex = i + 1
yLoc = yLoc + yOffset
print pointStr
spiral = cmds.curve(d=float(1.0), pointStr, knotStr)
cmds.rebuildCurve (spiral, ch=1, rpo=1, rt=0, end=1, kr=1, kcp=0, kep=0, kt=0, s=0, d=3, tol=0.001)
return spiral
Which I then run with: drawHelix (2.00, 3.00, 5.00)
The problem is that Maya doesn't recognise the "pointStr" as a flag for the curve command, when I print pointStr it does give me exactly what I want, but struggling on how to actually make this work!
The Python interpreter will not expand your strings before calling the function (you could achieve this using eval but this is generally considered bad practice -- see this post on SO).
It should work when passing the arguments as a dict of keywords.
Look it up here:
Section 4.7 More on Defining Functions in the Official Python Tutorial
PEP 3102 Keyword-Only Arguments
Section 5.3.4 Calls in the Python Reference Manual
So instead of:
pointStr = (pointStr + " p=(" + str(xLoc) + "," + str(yLoc) + "," + str(zLoc) + "),")
knotStr = (knotStr + "k=(" + str(vectorIndex) + ")")
You should do
kwargs['d'] = 1.0
kwargs['p'] = []
for i in range(1, (360 * coils), 20):
...
kwargs['p'].append((xloc, yloc, zloc))
kwargs['k'].append(vectorIndex)
spiral = cmds.curve(**kwargs)
Apart from that there are a few other issues in your code:
float((360 / numPoints)) will evaluate differently in Python2.x and Python3.x. This is what happens in 2.x:
In [5]: float(7 / 6)
Out[5]: 1.0
In [6]: 7. / 6
Out[6]: 1.1666666666666667
In If you wanted to ensure that floating point division is performed in your case use degrees = 360. / numPoints.
The potential implications are worse in this line of your code: yOffset = float(((length / coils) / numPoints)).
You declare float and int constants just by writing them either with or without a decimal point. No need to wrap them in a call to float() or int()
I assume this is what you had in mind:
from maya import cmds
import math
def drawHelix(radius, length, coils):
numPoints = int(8)
degrees = float((360 / numPoints))
centerX = float(0)
centerZ = float(0)
xLoc = float(0)
zLoc = float(0)
yLoc = float(0)
yOffset = float(((length / float(coils)) / float(numPoints)))
vectorIndex = int(0)
pointStr = []
knotStr = []
yLoc = 0
for i in range(1, (360 * coils), 20):
t = i + degrees
xLoc = centerX + (math.cos(t) * radius)
zLoc = centerZ - (math.sin(t) * radius)
pointStr.append((xLoc, yLoc,zLoc))
knotStr.append(vectorIndex)
vectorIndex = i + 1
yLoc = yLoc + yOffset
print pointStr
spiral = cmds.curve(p= pointStr, k=knotStr,d=float(1.0))
cmds.rebuildCurve (spiral, ch=1, rpo=1,
rt=0, end=1, kr=1, kcp=0, kep=0,
kt=0, s=0, d=3, tol=0.001)
return spiral
There is just a way much better way to do this. This is how your supposed to use Maya, use nodes to build your stuff. So here goes, a unnecessarily commented and verbose version:
from maya import cmds
def getHistoryShape(name):
history = cmds.listHistory(name)
filteredShape = cmds.ls(history, shapes=1)[0]
return filteredShape
def drawHelix(radius, length, coils):
cyl = cmds.cylinder( ch=True, radius=radius, ax=(0,1,0),
hr=float(length)/float(radius) )
# build a curve on the cylinders surface
crv = cmds.curveOnSurface(cyl[0], d=1,
uv=[(0,0),(length, coils*4)],
k=[0,1])
# make a duplicate that is visible
crv = cmds.duplicateCurve(ch=1, rn=0, local=1)
# tell maya to ignore the cylinder as a piece of geometry
shape = getHistoryShape(cyl[0])
cmds.setAttr(shape+'.intermediateObject', 1)
cmds.rename(cyl[0],'helix1')
return crv
Now you can change the helixes parameters later, live. You could expose the parameters radius, length and coils for the user, so they can be animated. See Maya factory scripts for example.