-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathNC_fit.py
executable file
·206 lines (170 loc) · 6.44 KB
/
NC_fit.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import optparse as op
from ParIO import *
import scipy.optimize as optimize
parser=op.OptionParser(description='Makes estimates of NC particle flux based on a neoclassical simulation that is not entirely converged.')
parser.add_option('--number','-n',type='int',action='store',dest='number',help = 'Select column 1.Gamma 2.Q 3.Momentum 4.Jboot.',default=1)
parser.add_option('--niterations','-i',type='int',action='store',dest='niterations',help = 'Number of variations for each initial guess.',default=10)
#parser.add_option('--time','-t',type = 'float',action='store',dest="time0",help = 'Time to plot mode structure.',default=-1)
options,args=parser.parse_args()
print("options.number",options.number)
column = int(options.number)-1
niterations = int(options.niterations)
if column == 0:
print("Calculating particle flux.")
elif column == 1:
print("Calculating heat flux.")
elif column == 2:
print("Calculating momentum flux.")
elif column == 3:
print("Calculating Jboot.")
if len(args)!=1:
exit("""
Please enter file suffix\n""")
suffix = args[0]
if 'dat' in suffix:
suffix = '.dat'
elif '_' not in suffix:
suffix = '_'+suffix
filename = 'neoclass'+suffix
par = Parameters()
par.Read_Pars('parameters'+suffix)
pars = par.pardict
f=open(filename,'r')
data = f.read()
lines = data.split('\n')
keep_going = True
i=2
while keep_going:
ls = lines[i].split()
if len(ls) == 1:
num_spec = i-2
keep_going = False
i += 1
print("Number of species:",num_spec )
time = np.empty(0)
# delete header
lines = np.delete(lines,0)
temp = lines[0::num_spec+1]
for i in range(len(temp)):
if temp[i]:
time = np.append(time,float(temp[i]))
ntime = len(time)
fluxes = np.empty((ntime,4,num_spec))
for i in range(num_spec):
temp = lines[i+1::num_spec+1]
for j in range(len(temp)):
if temp[j]:
ls = np.array(temp[j].split())
ls = ls.astype(np.float)
fluxes[j,:,i] = ls[:]
ambipolarity = 0
for i in range(num_spec):
print("species "+pars['name'+str(i+1)])
print( "Charge of species "+str(i+1)+": "+str(pars['charge'+str(i+1)]))
print("Flux/FluxGB",fluxes[-1,column,i])
ambipolarity += fluxes[-1,column,i]*pars['charge'+str(i+1)]
print("Ambipolarity:",ambipolarity)
print("Normalized ambipolarity:",ambipolarity/(abs(fluxes[-1,column,0])+abs(fluxes[-1,column,1])+abs(fluxes[-1,column,2])))
def fit_func(t,G0,c0,gam):
return G0+c0*np.e**(-gam*t)
bvals = np.empty((3,num_spec))
for s in range(num_spec):
G0 = fluxes[-1,column,s]
c0 = fluxes[0,column,s] - G0
#gam = 0.001
gam = (fluxes[-2,column,s]-fluxes[-1,column,s])/(time[-1]-time[-2])/c0
if s > 0:
gam = bvals[2,s-1]
print("gam0",gam)
plt.plot(time,fluxes[:,column,s])
plt.plot(time,G0+c0*np.e**(-gam*(time-time[0])) )
plt.show()
f0 = np.linspace(0.1*G0,10*G0,num=niterations)
f1 = np.linspace(-10*c0,10*c0,num=niterations)
f2 = np.linspace(0.1*gam,10*gam,num=niterations)
minerr = 1.0
prec = 1e-6
for i in range(len(f0)):
if minerr < prec:
break
for j in range(len(f1)):
if minerr < prec:
break
for k in range(len(f2)):
x0 = np.array([f0[i],f1[j],f2[k]])
print("i,j,k",i,j,k)
fit,pcov= optimize.curve_fit(fit_func,time-time[0],fluxes[:,column,s],p0=x0,maxfev = 10000)
thiserr = np.sum(np.diag(pcov))
if abs(thiserr) < minerr:
minerr = thiserr
bestvals = np.array([fit[0],fit[1],fit[2]])
print("##############")
print(i,j,k)
print("fit",fit[0],fit[1],fit[2])
print("minerr",minerr)
if minerr < prec:
break
plt.plot(time-time[0],fluxes[:,column,s],label='data')
plt.plot(time-time[0],G0+c0*np.e**(-gam*(time-time[0])),label='guess' )
plt.plot(time-time[0],bestvals[0]+bestvals[1]*np.e**(-bestvals[2]*(time-time[0])),'--',label='fit' )
plt.legend()
plt.show()
bvals[:,s] = bestvals
print("bvals",bvals)
ambipolarity = 0
for s in range(num_spec):
print("species "+pars['name'+str(s+1)])
print("Charge of species "+str(s+1)+": "+str(pars['charge'+str(s+1)]))
print("Flux/FluxGB",bvals[0,s])
ambipolarity += bvals[0,s]*pars['charge'+str(s+1)]
if column==0:
print("Ambipolarity:",ambipolarity)
print("Normalized ambipolarity:",ambipolarity/np.sum(abs(bvals[0,:])))
#proceed = input("Proceed with fit (0 = no)?:")
#if proceed != '0':
# gam0 = (fluxes[-1,0,i] - fluxes[-2,0,i]) / (time[-1] - time[-2])
# start_time = float(input("Enter start time:"))
# istart = np.argmin(abs(time - start_time))
# x0 = np.array([1.0,1.0,0.01])
# for i in range(num_spec):
# fit,dummy= optimize.curve_fit(fit_func,time[istart:],fluxes[istart:,0,i],x0)
# print("fit",fit)
# plt.plot(time,fluxes[:,0,i],label='data')
# plt.plot(time,fit[0]+fit[1]*np.e**(-fit[2]*time),label='fit')
# plt.legend()
# plt.show()
if column == 0:
dGdt = np.empty(num_spec)
q = np.empty(num_spec)
num = 0
denom = 0
for s in range(num_spec):
dGdt[s] = (fluxes[-1,column,s] - fluxes[0,column,s])/(time[-1] - time[0])
q[s] = pars['charge'+str(s+1)]
denom += q[s]*dGdt[s]
num += q[s]*fluxes[0,column,s]
t_ambi = -num/denom
ambi_test = 0
Gs = np.empty(num_spec)
print("Estimates based on linear extrapolation to ambipolar condition.")
for s in range(num_spec):
temp = fluxes[0,column,s]*q[s] + q[s]*dGdt[s]*t_ambi
Gs[s] = fluxes[0,column,s] + dGdt[s]*t_ambi
#print(pars['charge'+str(s+1)],"Gs",Gs[s])
print(pars['name'+str(s+1)],"Gs",Gs[s])
ambi_test += temp
print("Test of ambipolarity:",ambi_test)
tnew = np.linspace(0,10000,num=1000)
for s in range(num_spec):
plt.plot(tnew,bvals[0,s]+bvals[1,s]*np.e**(-bvals[2,s]*(tnew)),'x-',label='Exponential fit '+pars['name'+str(s+1)] )
plt.plot(time-time[0],bvals[0,s]+bvals[1,s]*np.e**(-bvals[2,s]*(time-time[0])) )
#plt.hlines(Gs[s],0,10000,label='Ambipolar estimate '+pars['name'+str(s+1)])
if column == 0:
plt.plot(tnew,tnew/tnew*Gs[s],label='Ambipolar estimate '+pars['name'+str(s+1)])
plt.legend()
plt.title("Column: "+str(column))
plt.show()