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root/osprai/osprai/trunk/modeltwostate_varyC.py
Revision: 25
Committed: Wed Apr 28 20:22:24 2010 UTC (9 years, 5 months ago) by rjaynes
File size: 4751 byte(s)
Log Message:
Add py and obj files to allow modeling of more SPR experiments with converter and curvefitting modules.  This is the work of Yuhang Wan and Rui Hou.

1. In "converter.py": 
      Add the saving and reading function for the sprclass data object.
      Also add function "keyfile_read_fake" to provide default information for SPRit and ICM formats in case of the bug when do background_subtract.
      Fix the bugs in "background_subtract".
      Tested by DAM and ICM formats.
2. In model modules:
      "modelclass.py" is the parent class for all the other model classes that performs the theoretical simulating, loading and saving of the parameter or simulated data. Rui and I also add some other model modules like competing model, twostate model, parallel model, and the time variable concentrated models, where the simulated result is compared with Clamp's simulation to make sure the equations are correct. 
       The basicmodel and basicmodel_varyC class are tested. 
3. In "curvefitting.py":
      Add typical pipeline for operation. The examples are packed with the file. 
      Add function to show the Elapsed time for each fitting.
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.