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root/osprai/osprai/trunk/modelparallel.py
Revision: 25
Committed: Wed Apr 28 20:22:24 2010 UTC (9 years, 2 months ago) by rjaynes
File size: 4753 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 PARALLEL REACTION model.
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 as mp
15 >m = mp.parallelmodel()
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 parallelmodel(modelclass):
30 """The data attributes of the class:
31 parainfo: the parameter information
32 sim_data: the simulated data
33 """
34
35 def __init__ (self, parainfo=[], sim_data=[] ):
36 modelclass.__init__(self,parainfo,sim_data)
37
38 def wizard(self, ):
39 '''This function helps you to create a parameter information list.
40 This is a competing reactions model, where there are 10 parameters:
41 rmax1: Maximum analyte binding capacity for L1(RU),
42 ka1: Association rate constant for L1+A=L1A(M-1S-1),
43 kd1: Dissociation rate constant for L1A=L1+A(S-1),
44 rmax2: Maximum analyte binding capacity for L2(RU),
45 ka2: Association rate constant for L2+A=L2A(M-1S-1),
46 kd2: Dissociation rate constant for L2A=L2+A(S-1),
47 ca: Analyte concentration(M),
48 ton: Starting time for sample injection(s),
49 toff: Ending time for sample injection(s),
50 tout: Total time(s).'''
51 print ('')
52 pname = ['rmax1','ka1','kd1','rmax2','ka2','kd2','ca','ton','toff','tout']
53 parainfo = []
54 for i in range(10):
55 print i
56 tmp={}
57 tmp['name'] = pname[i]
58 vstr = raw_input("input the value of parameter '%s', if a series, seperate with ',': " %pname[i])
59 vtmp = vstr.strip().split(',')
60 tmp['number'] = len(vtmp)
61 if len(vtmp) == 1:
62 vtmp = float(vtmp[0])
63 else:
64 vtmp = map(float, vtmp)
65 tmp['value'] = vtmp
66 tmp['fixed'] = bool(input("Is '%s' fixed? (1/0): " %pname[i]))
67
68 parainfo.append(tmp)
69 ##parainfo.append({'name':'', 'value':0., 'fixed':0, 'number':0})
70 self.parainfo = parainfo
71
72
73 def function(self, t, paralist):
74 '''This function calculates the theoretical curve through the
75 parameter list you give.
76 '''
77 # for parallel reactions model
78
79 ## Assign the parameters to calculate the curve
80 for p in paralist:
81 if p['name'] == 'rmax1': rmax1 = p['value']
82 elif p['name'] == 'ka1': ka1 = p['value']
83 elif p['name'] == 'kd1': kd1 = p['value']
84 elif p['name'] == 'rmax2': rmax2 = p['value']
85 elif p['name'] == 'ka2': ka2 = p['value']
86 elif p['name'] == 'kd2': kd2 = p['value']
87 elif p['name'] == 'ca': ca = p['value']
88 elif p['name'] == 'ton': ton = p['value']
89 elif p['name'] == 'toff': toff = p['value']
90 elif p['name'] == 'tout': tout = p['value']
91 else: print p['name'], p['value']
92 if type(ca1) == list or type(ca2) == list:
93 print "Error: This function can only generate data for a single concentration."
94 return
95
96 t1 = ton ##100 ##234 # Initial injection
97 t2 = toff ##178 ##570 # Wash, begin of the dissociation
98 t3 = tout ##400 ##840 # End wash
99
100 ## Must iterate through data, numerical integration.
101 g = np.zeros(len(t), dtype=float)
102 g1 = np.zeros(len(t), dtype=float)
103 g2 = np.zeros(len(t), dtype=float)
104
105 for i in range(1,len(t)):
106 if (t[i] > t3): break #Speed things up.
107 if (t1 < t[i] < t2):
108 ## Association
109 dG1 = ka1*ca*(rmax1-g1[i-1]) - kd1*g1[i-1]
110 dG2 = ka2*ca*(rmax2-g2[i-1]) - kd2*g2[i-1]
111 elif (t2 < t[i] < t3):
112 ## Dissociation
113 dG1 = 0 - kd1*g1[i-1]
114 dG2 = 0 - kd2*g2[i-1]
115 else:
116 dG1 = 0
117 dG2 = 0
118 if (abs(g1[i]) > 999999999): dG1 = 0
119 if (abs(g2[i]) > 999999999): dG2 = 0
120
121 g1[i] = g1[i-1] + dG1 * (t[i] - t[i-1])
122 g2[i] = g2[i-1] + dG2 * (t[i] - t[i-1])
123 g[i] = g1[i] + g2[i]
124
125 return g
126
127
128 ##### End of parallel model class definition.