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root/osprai/osprai/trunk/modelparallel_varyC.py
Revision: 25
Committed: Wed Apr 28 20:22:24 2010 UTC (9 years, 7 months ago) by rjaynes
File size: 4965 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 User Rev File contents
1 rjaynes 25 """
2     Provide the model for curvefitting.
3     This is a PARALLEL REACTION model with time variable concentration..
4     The reaction equations:
5     L1 + A ==ka1=> L1A
6     L1 + A <=kd1== L1A
7     L2 + A ==ka2=> L2A
8     L2 + A <=kd2== L2A
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 modelparallel_varyC as mpvc
15     >m = mpvc.parallelmodel_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     class parallelmodel_varyC(modelclass):
29     """The data attributes of the class:
30     parainfo: the parameter information
31     sim_data: the simulated data
32     conc: the time variable concentration data
33     """
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 7 parameters:
67     rmax1: Maximum analyte binding capacity for L1(RU),
68     ka1: Association rate constant for L1+A=L1A(M-1S-1),
69     kd1: Dissociation rate constant for L1A=L1+A(S-1),
70     rmax2: Maximum analyte binding capacity for L2(RU),
71     ka2: Association rate constant for L2+A=L2A(M-1S-1),
72     kd2: Dissociation rate constant for L2A=L2+A(S-1),
73     ca: Analyte concentration(M).'''
74     print ('')
75     pname = ['rmax1','ka1','kd1','rmax2','ka2','kd2','ca']
76     parainfo = []
77     for i in range(7):
78     print i
79     tmp={}
80     tmp['name'] = pname[i]
81     vstr = raw_input("input the value of parameter '%s', if a series, seperate with ',': " %pname[i])
82     vtmp = vstr.strip().split(',')
83     tmp['number'] = len(vtmp)
84     if len(vtmp) == 1:
85     vtmp = float(vtmp[0])
86     else:
87     vtmp = map(float, vtmp)
88     tmp['value'] = vtmp
89     tmp['fixed'] = bool(input("Is '%s' fixed? (1/0): " %pname[i]))
90    
91     parainfo.append(tmp)
92     ##parainfo.append({'name':'', 'value':0., 'fixed':0, 'number':0})
93     self.parainfo = parainfo
94    
95    
96     def function(self, t, paralist):
97     '''This function calculates the theoretical curve through the
98     parameter list you give.
99     '''
100     # for parallel reactions model
101     ## Assign the parameters to calculate the curve
102     conc = copy.deepcopy(self.conc)
103     C = np.interp(t,conc[0],conc[1])
104     for p in paralist:
105     if p['name'] == 'rmax1': rmax1 = p['value']
106     elif p['name'] == 'ka1': ka1 = p['value']
107     elif p['name'] == 'kd1': kd1 = p['value']
108     elif p['name'] == 'rmax2': rmax2 = p['value']
109     elif p['name'] == 'ka2': ka2 = p['value']
110     elif p['name'] == 'kd2': kd2 = p['value']
111     elif p['name'] == 'ca': ca = p['value']
112     else: print p['name'], p['value']
113     if type(ca1) == list or type(ca2) == list:
114     print "Error: This function can only generate data for a single concentration."
115     return
116    
117     ## Must iterate through data, numerical integration.
118     g = np.zeros(len(C), dtype=float)
119     g1 = np.zeros(len(C), dtype=float)
120     g2 = np.zeros(len(C), dtype=float)
121    
122     for i in range(1,len(C)):
123     dG1 = ka1*ca*C[i-1]*(rmax1-g1[i-1]) - kd1*g1[i-1]
124     dG2 = ka2*ca*C[i-1]*(rmax2-g2[i-1]) - kd2*g2[i-1]
125     elif (t2 < t[i] < t3):
126    
127     if (abs(g1[i]) > 999999999): dG1 = 0
128     if (abs(g2[i]) > 999999999): dG2 = 0
129    
130     g1[i] = g1[i-1] + dG1 * (t[i] - t[i-1])
131     g2[i] = g2[i-1] + dG2 * (t[i] - t[i-1])
132     g[i] = g1[i] + g2[i]
133    
134     return g
135    
136     ##### End of time variable concentrated parallel model class definition.