ViewVC Help
View File | Revision Log | Show Annotations | View Changeset | Root Listing
root/osprai/osprai/trunk/modelparallel_varyC.py
Revision: 25
Committed: Wed Apr 28 20:22:24 2010 UTC (9 years 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 File contents
1 """
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.