-
Notifications
You must be signed in to change notification settings - Fork 0
/
fit_contour.py
331 lines (287 loc) · 8.34 KB
/
fit_contour.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
#Code by Maria Alejandra Ramirez
#Script: fit_contour.py
#---------------CONTOUR_GRAPH_FIT--------------
#Performs the fit of countour graphs that show the cost of each
# one-on-one competition
#The fit allows to find the relationship between c,KA and KB
#Proposed function for the fit
#Function to graph results (2)
def fun(x,y,a,b):
return (a*np.log(y)) - (b*np.log(x))
#Function to obtain the fit (1)
def fun1(data,a,b):
return a*np.log(data[0]) - b*np.log(data[1])
#RANGE OF KS USED FOR EACH HILL COEFFICIENT (H)
#Short
#kh3 = np.array([1,2,3])
#Medium
#kh2 = np.array([2,6,10,14,18])
#kh3 = np.array([4,10,16,22,28,34])
#kh4 = np.array([5,13,21,29,37,45])
#Long Sample
kh2 = np.array([2,4,6,8,10,12,14,16,18])
kh3 = np.array([4,7,10,13,16,19,22,25,28,31,34])
kh4 = np.array([5,9,13,17,21,25,29,33,37,41,45])
kh23 = np.array([3,6,9,12,15,18,21,24])
kh27 = np.array([3,6,9,12,15,18,21,24,27,30])
kh33 = np.array([4,8,12,16,20,24,28,32,36,40])
kh37 = np.array([4,10,16,22,28,34,40,46])
kh43 = np.array([5,10,15,20,25,30,35,40,45,50])
kh47 = np.array([5,12,19,26,33,40,47,54])
kh5 = np.array([6,12,18,24,30,36,42,48,54])
#Function to graph the fit result contour graph
# with the same color bar range than in the results, in order to
# make a clear visual comparisons of the results with the fit
#Param: st Negative start of the color bar range
#Param: hop Interval to the next color value limit
#Return: L Array with the values of the color bar
def barRange(st,hop):
s = st
ini = s
L = []
br = True
while br == True:
if s == (-1*ini):
br = False
L.append(s)
s = round(s+hop,3)
return L
#Performs the fit of the cost results given a Hill coefficient value
#Param: h Hill coefficient
#Return: Graph of the fit results
# CSV file with param of fit (CCte.csv)
# and with variance of the fit (Var_Fit.csv)
def fitCC(h):
#Obtains the results from the txt files
#First column of cost matrix txt file
ColIni = np.genfromtxt("Results/DataCost_"+str(h)+".txt", delimiter=",",usecols=0)
#The cost matrix in nxn, so #columns = #rows
#Lenght of first colum
n = np.size(ColIni)
#Define size of matrix
#The size is different for each Hill coefficient that depends
# on the Ks range stated previously
if h==2:
#KA
KKA = kh2
#KB
KKB = kh2
kh = kh2
elif h==2.3:
#KA
KKA = kh23
#KB
KKB = kh23
kh = kh23
elif h==2.7:
#KA
KKA = kh27
#KB
KKB = kh27
kh = kh27
elif h==3:
#KA
KKA = kh3
#KB
KKB = kh3
kh = kh3
elif h==3.3:
#KA
KKA = kh33
#KB
KKB = kh33
kh = kh33
elif h==3.7:
#KA
KKA = kh37
#KB
KKB = kh37
kh = kh37
elif h==4:
#KA
KKA = kh4
#KB
KKB = kh4
kh = kh4
elif h==4.3:
#KA
KKA = kh43
#KB
KKB = kh43
kh = kh43
elif h==4.7:
#KA
KKA = kh47
#KB
KKB = kh47
kh = kh47
elif h==5:
#KA
KKA = kh5
#KB
KKB = kh5
kh = kh5
#Generate grid - KKA is Y and KKB is X
X, Y = np.meshgrid(KKA, KKB)
#Obtains the rest of cost results data
for i in range(1,n):
Col = np.genfromtxt("Results/DataCost_"+str(h)+".txt", delimiter=",",usecols=i)
ColIni = np.append(ColIni, Col)
#Array where the cost results from the txt file are arranged.
# This array helps to establish how to properly array the txt data in
# numpy arrays
Ans = np.zeros((n,n))
#Loop to organize the data from the txt files into a numpy array
z=0
for j in range(n):
for i in range(n):
Ans[i,j] = ColIni[z]
z+=1
#K range for the x and y axis obtained from the arrays previously stated
# This range is different for each Hill coefficient
fir = []
sec = []
#Organizes the K ranges appropiately
for i in kh:
for j in kh:
fir.append(j)
sec.append(i)
ink = np.array([fir,sec])
#-----FIT----------
#Fit Contour
#Performs the fit of the data from the txt files of cost results
# curve_fit(function, xdata, ydata)
# xdata refers to the independent variable and
# ydata to the depende variable
# xdata--> Ks range ydata--> cost results
mAA, covAA = curve_fit(fun1, ink, ColIni)
p1 = mAA[0]
p2 = mAA[1]
#Print the fit parameters and their variance
print "Param"
print mAA
print covAA
#Calculate and print the average of the parameters
Cavg = (p1+p2)/2.0
print "CAvg"
print Cavg
#Print Cavg in Cost cte file
text_file = open("Results/Fit/CCte.csv", "a+")
n = text_file.write(str(h)+","+ str(Cavg)+"\n")
text_file.close()
#Print Fit Var in file
text_file = open("Results/Fit/Var_Fit.csv", "a+")
n = text_file.write(str(h)+","+str(covAA[0][0])+","+str(covAA[0][1])+"\n")
text_file.close()
#Graphs the fit results
Fi = np.ones((len(KKA),len(KKB)))
for i in range(len(KKA)):
for j in range(len(KKB)):
Fi[i,j] = fun(KKB[j], KKA[i],p1,p2)
#Assigns the correct color bar for each contour graph
# This allows to make a direct visual comparison between the results
# and their fit
if h ==2:
#Med and long
L = barRange(-0.056,0.008)
elif h ==2.3:
L = barRange(-0.064,0.008)
elif h ==2.7:
L = barRange(-0.09,0.01)
elif h ==3:
#Short
#L = barRange(-0.04,0.005)
#Med and long
L = barRange(-0.105,0.015)
elif h ==3.3:
L = barRange(-0.12,0.015)
elif h ==3.7:
L = barRange(-0.16,0.02)
elif h ==4:
#Med
#L = barRange(-0.175,0.025)
#Long sample
L = barRange(-0.18,0.02)
elif h ==4.3:
L = barRange(-0.2,0.025)
elif h ==4.7:
L = barRange(-0.24,0.03)
elif h ==5:
L = barRange(-0.27,0.03)
#Graph of the fit
plt.title("Fit Cost for plasmids type A ($h$ = "+str(h)+")")
plt.contourf(X, Y, Fi, L, cmap="RdBu_r")
plt.colorbar()
plt.xlabel("$K_{A}$")
plt.ylabel("$K_{B}$")
plt.xticks(kh)
plt.yticks(kh)
plt.savefig("Results/Fit/Fit_"+str(h)+".png")
#plt.show()
plt.clf()
#Graph of the Results
# This graph is used to check that the values are being shown in the
# correct way
"""
plt.title("Check Cost for plasmids type A ($h$ = "+str(h)+")")
plt.contourf(X, Y, Ans, 16, cmap="RdBu_r")
plt.colorbar()
plt.xlabel("$K_{A}$")
plt.ylabel("$K_{B}$")
plt.xticks(kh)
plt.yticks(kh)
plt.savefig("Results/Fit/Check_"+str(h)+".png")
#plt.show()
plt.clf()
"""
#Executes the code for each set of results
# Each set is characterized by its Hill function
fitCC(2)
fitCC(2.3)
fitCC(2.7)
fitCC(3)
fitCC(3.3)
fitCC(3.7)
fitCC(4)
fitCC(4.3)
fitCC(4.7)
fitCC(5)
#--------------------FIT_OF_CTE-----------------------------
#Performs the fit needed to find the relationship between the Constant
# previously found with the Hill coefficient
#Return: Graphs Cte vs h
def Fit_Cte():
#Obtains the fit cte recorded in a CSV file, along with its
# corresponding Hill coefficient
HS = np.genfromtxt("Results/Fit/CCte.csv", delimiter=",",usecols=0)
CV = np.genfromtxt("Results/Fit/CCte.csv", delimiter=",",usecols=1)
#Proposed fit Function
#Linear function
#m: slope
#c: y-intercept
def funA(x,m,c):
return m*x + c
#Ph: parameters obtained of fit
#Ph[0]: slope, Ph[1]: y-intercept
#coh: covariance of parameters
#curve_fit(function, xdata, ydata)
Ph1, coh1 = curve_fit(funA, HS, CV)
print Ph1, coh1
#Graphs Cte vs h
plt.title("Fit Cost Constant ($A$) vs Hill Coefficient ($h$)")
plt.plot(HS,CV,c="r",label="Data")
#Shows the fit results in the graph
plt.plot(HS,funA(HS,Ph1[0],Ph1[1]),c="g",label="Fit")
plt.plot(0,0,c="white",label="$A$="+str(round(Ph1[0],3))+"$h$"+str(round(Ph1[1],3)))
plt.xlabel("Hill Coefficient ($h$)")
plt.ylabel("Fit cost constant ($A$)")
plt.xticks(HS)
plt.xlim((2,5))
plt.legend(loc=2,fontsize="small")
plt.savefig("Results/Fit/Cte_Fit.png")
plt.clf()
#Execute the code
Fit_Cte()