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root/osprai/osprai/trunk/models2010/modeltwostate_varyC.py
Revision: 41
Committed: Tue Jan 18 00:35:23 2011 UTC (8 years, 9 months ago) by clausted
File size: 4751 byte(s)
Log Message:
Moved old data class "SPRdataclass" and accompanying surface interaction model modules to /models2010 subdirectory.  The plan is to implement these models for use with the "ba_class" and the modules in the parent directory.  

Should all the models be added to mdl_module or should they each go in their own module?  I am undecided.  
Line File contents
1 """
2 Provide the model for curvefitting.
3 This is a TWO STATE REACTION model with time variable concentration..
4 The reaction equations are:
5 L + A ==ka1=> LA
6 L + A <=kd1== LA
7 LA ==ka2=> LA*
8 LA <=kd2== LA*
9
10 Rui Hou, Yuhang Wan
11 Last modified on 100427 (yymmdd) by YW
12
13 Typical Pipeline:(work together with "modelclass.py" the father class):
14 >import modeltwostate_varyC as mtvc
15 >m = mtvc.twostatemodel_varyC()
16 >...
17
18 """
19 __version__ = "100427"
20
21 import numpy as np
22 import pylab as plt
23 import copy
24 import os
25 import pickle
26 from modelclass import *
27
28
29 class twostatemodel_varyC(modelclass):
30 """The data attributes of the class:
31 parainfo: the parameter information
32 sim_data: the simulated data
33 conc: the time variable concentration data
34 """
35 def __init__ (self, parainfo=[], sim_data=[], conc=[] ):
36 modelclass.__init__(self,parainfo,sim_data)
37 self.conc = conc
38
39 ##------------ not finished yet---------------
40 def load_conc(self, fname):
41 ##fp = file(fname, 'r+')
42 conc = np.loadtxt(fname)
43 self.conc = conc
44 return
45
46 def create_conc(self, t):
47 # create a step-function as concentration curve for debugging now
48 c = np.zeros(len(t), dtype=float)
49 conc = np.zeros((2,len(t)), dtype=float)
50 t = np.array(t)
51 t1 = input("start time:")
52 t2 = input("end time:")
53 ind = plt.find((t>t1-(t[1]-t[0]))&(t<t2-(t[1]-t[0])))
54 c[ind] = 1
55 conc[0] = t
56 conc[1] = c
57 self.conc = conc
58 plt.plot(t,c,'.')
59 plt.show()
60 return
61
62 ##----------------for debugging now------------
63
64 def wizard(self, ):
65 '''This function helps you to create a parameter information list.
66 This is a competing reactions model, where there are 6 parameters:
67 rmax: Maximum analyte binding capacity(RU),
68 ka1: Association rate constant for L+A=LA(M-1S-1),
69 kd1: Dissociation rate constant for LA=L+A(S-1),
70 ka2: Forward rate constant for LA=LA*(S-1),
71 kd2: Backward rate constant for LA*=LA(S-1),
72 ca: Analyte concentration(M).'''
73 print ('')
74 pname = ['rmax','ka1','kd1','ka2','kd2','ca']
75 parainfo = []
76 for i in range(6):
77 print i
78 tmp={}
79 tmp['name'] = pname[i]
80 vstr = raw_input("input the value of parameter '%s', if a series, seperate with ',': " %pname[i])
81 vtmp = vstr.strip().split(',')
82 tmp['number'] = len(vtmp)
83 if len(vtmp) == 1:
84 vtmp = float(vtmp[0])
85 else:
86 vtmp = map(float, vtmp)
87 tmp['value'] = vtmp
88 tmp['fixed'] = bool(input("Is '%s' fixed? (1/0): " %pname[i]))
89
90 parainfo.append(tmp)
91 ##parainfo.append({'name':'', 'value':0., 'fixed':0, 'number':0})
92 self.parainfo = parainfo
93
94
95 def function(self, t, paralist):
96 '''This function calculates the theoretical curve through the
97 parameter list you give.
98 '''
99 # for two state reaction model
100 conc = copy.deepcopy(self.conc)
101 C = np.interp(t,conc[0],conc[1])
102 ## Assign the parameters to calculate the curve
103 for p in paralist:
104 if p['name'] == 'rmax': rmax = p['value']
105 elif p['name'] == 'ka1': ka1 = p['value']
106 elif p['name'] == 'kd1': kd1 = p['value']
107 elif p['name'] == 'ka2': ka2 = p['value']
108 elif p['name'] == 'kd2': kd2 = p['value']
109 elif p['name'] == 'ca': ca = p['value']
110 else: print p['name'], p['value']
111 if type(ca1) == list or type(ca2) == list:
112 print "Error: This function can only generate data for a single concentration."
113 return
114
115 ## Must iterate through data, numerical integration.
116 g = np.zeros(len(C), dtype=float)
117 g1 = np.zeros(len(C), dtype=float)
118 g2 = np.zeros(len(C), dtype=float)
119
120 for i in range(1,len(C)):
121 dG1 = (ka1*ca*C[i-1]*(rmax-g1[i-1]-g2[i-1]) - kd1*g1[i-1])-(ka2*g1[i-1]- kd2*g2[i-1])
122 dG2 = ka2*g1[i-1]- kd2*g2[i-1]
123
124 if (abs(g1[i]) > 999999999): dG1 = 0
125 if (abs(g2[i]) > 999999999): dG2 = 0
126
127 g1[i] = g1[i-1] + dG1 * (t[i] - t[i-1])
128 g2[i] = g2[i-1] + dG2 * (t[i] - t[i-1])
129 g[i] = g1[i] + g2[i]
130
131 return g
132
133 ##### End of time variable concentrated two state model class definition.