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root/osprai/osprai/trunk/mdl_module.py
Revision: 18
Committed: Thu Apr 22 07:36:31 2010 UTC (9 years, 6 months ago) by clausted
File size: 2843 byte(s)
Log Message:
Add a simple 1to1 model function.
Line File contents
1 """
2 mdl: Example model functions module for SPRI data.
3 Christopher Lausted, Institute for Systems Biology,
4 OSPRAI developers
5 Last modified on 100421 (yymmdd)
6
7 Example:
8 #import mdl_module as mdl
9 #params = {'Rate': {'value':1, 'min':-100.0, 'max':100.0, 'fixed':False} }
10 #params = dict(Rate = dict(value=1, min=0, max=10, fixed=False))
11 #times = range(100)
12 #data1 = drift(time, data0, params)
13 print data1
14 """
15 __version__ = "100421"
16
17
18 ## Import libraries
19 import numpy as np
20 from copy import deepcopy
21
22
23 def drift(time, data, params):
24 """
25 This function simply models a constant signal drift in units/second.
26 It requires numpy arrays of times and starting data values,
27 It only requires one parameter in the params list.
28 params['rate']['value']
29 """
30 y = np.zeros(len(time), dtype=float)
31 try:
32 rate = params['rate']['value']
33 except KeyError:
34 print "Error: Rate parameter not supplied to drift() model."
35 return y
36
37 ## Numerical integration.
38 y[0] = data[0]
39 for i in range(1,len(time)):
40 dy = rate * (time[i]-time[i-1])
41 y[i] = y[i-1] + dy
42
43 return y
44
45
46 def simple1to1(time, data, params):
47 """
48 This function simply models a 1:1 interaction
49 It requires numpy arrays of times and starting data values,
50 params['t1']['value'] is time of injection for binding
51 params['rmax']['value'] is maximum response.
52 params['conc']['value'] is time of concentration of analyte
53 params['kon']['value'] is on-rate of analyte
54 params['t2']['value'] is time of end binding / begin washing
55 params['koff']['value'] is off-rate of analyte
56 params['t3']['value'] is time end of washing / data fitting.
57 """
58 ## Skip parameter validation steps for now.
59 t1 = params['t1']['value']
60 rmax = params['rmax']['value']
61 conc = params['conc']['value']
62 kon = params['kon']['value']
63 t2 = params['t2']['value']
64 koff = params['koff']['value']
65 t3 = params['t3']['value']
66
67 y = np.zeros(len(time), dtype=float)
68 base = y[0] = data[0] ## Baseline SPR signal.
69
70 ## Must iterate through data, numerical integration.
71 for i in range(1,len(time)):
72 if (time[i] <= t1):
73 ## Pre-binding phase.
74 base = y[i] = data[i]
75 elif (t1 < time[i] <= t2):
76 ## Binding phase
77 yb = y[i-1] - base
78 dy = conc*kon*(rmax-yb) - koff*yb
79 if (abs(y[i-1]) > 999999999): dy = 0 ## Is this useful?
80 y[i] = y[i-1] + dy * (time[i] - time[i-1])
81 elif (t2 < time[i] <= t3):
82 ## Dissociation
83 yb = y[i-1]-base
84 dy = 0 - koff*yb
85 y[i] = y[i-1] + dy * (time[i] - time[i-1])
86
87 return y
88