-
Notifications
You must be signed in to change notification settings - Fork 0
/
falc_vs_falp.py
246 lines (202 loc) · 9.23 KB
/
falc_vs_falp.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
import numpy as np
import glob
import matplotlib.pyplot as plt
from natsort import natsorted
import copy
custom_bins = True
file_list = natsorted(glob.glob('odf_spectra*Comparison'))
#for item in file_list:
# int(item.split('Comparison')[0].split('a')[-1])
sub_bins_path = '/home/cernetic/Documents/sorting/lopa-sorting/subBins'
def running_mean(x, N):
cumsum = np.cumsum(np.insert(x, 0, 0))
return (cumsum[N:] - cumsum[:-N]) / N
def sort_array(array, column):
array = array[np.argsort(array[:, column])]
return array
"""Every element of data consists of one Contrast file"""
raw_data = []
for item in file_list:
print item
raw_data.append(np.loadtxt(item))
if custom_bins:
"""Perform merging"""
lower_bound, step = 1000, 10
averaging_bins, data = range(lower_bound, 9000 + step, step), []
for item in raw_data:
data.append([])
for j, item in enumerate(raw_data):
averaged_item = []
for i in range(len(averaging_bins)-1):
interval = item[(item[:, 0] >= averaging_bins[i]) & (averaging_bins[i+1] > item[:, 0])][:, -1]
interval = (np.ones(len(interval)) - interval)**2
averaged_item.append(np.sum(interval))
data[j] = np.c_[np.array(averaging_bins[:-1]), np.array(averaged_item)]
"""sub_bins contains all chi2 values of 1 sub bins across all Contrast files"""
sub_bins = []
for x in range(len(data[0])):
sub_bins.append([])
for i in range(len(data)):
for j in range(len(data[i])):
sub_bins[j].append(data[i][j])
"""sub_bins contains all values of 1 sub bins across all Contrast files"""
raw_sub_bins = []
for x in range(len(raw_data[0])):
raw_sub_bins.append([])
for i in range(len(raw_data)):
for j in range(len(raw_data[i])):
raw_sub_bins[j].append(raw_data[i][j])
"""min_indexes contains the index of the minimum value for each sub bin"""
min_indexes = []
for item in sub_bins:
min_indexes.append([item[np.argmin([x[-1] for x in item])],np.argmin([x[-1] for x in item])])
"""Create best combination"""
best_combination = []
for i, item in enumerate([x[-1] for x in min_indexes]):
print i, item
best_combination.append(sub_bins[i][item])
# np.savetxt("chi2_results", best_combination, fmt='%s')
falc_ratio = []
for i, item in enumerate(min_indexes):
falc_ratio.append(raw_sub_bins[i][item[-1]])
falc_ratio = np.array(falc_ratio)
falc_indices = list(min_indexes)
falc_raw_sub_bins = list(raw_sub_bins)
##############################################################
file_list = natsorted(glob.glob('odf_fac_spect*Comparison'))
for item in file_list:
int(item.split('Comparison')[0].split('a')[-1])
sub_bins_path = '/home/cernetic/Documents/sorting/lopa-sorting/subBins'
"""Every element of data consists of one Contrast file"""
raw_data = []
for item in file_list:
print item
raw_data.append(np.loadtxt(item))
"""Perform merging"""
averaging_bins, data = range(lower_bound, 9000 + step, step), []
for item in raw_data:
data.append([])
for j, item in enumerate(raw_data):
averaged_item = []
for i in range(len(averaging_bins)-1):
interval = item[(item[:, 0] >= averaging_bins[i]) & (averaging_bins[i+1] > item[:, 0])][:, -1]
interval = (np.ones(len(interval)) - interval)**2
averaged_item.append(np.sum(interval))
data[j] = np.c_[np.array(averaging_bins[:-1]), np.array(averaged_item)]
"""sub_bins contains all chi2 values of 1 sub bins across all Contrast files"""
sub_bins = []
for x in range(len(data[0])):
sub_bins.append([])
for i in range(len(data)):
for j in range(len(data[i])):
sub_bins[j].append(data[i][j])
"""sub_bins contains all values of 1 sub bins across all Contrast files"""
raw_sub_bins = []
for x in range(len(raw_data[0])):
raw_sub_bins.append([])
for i in range(len(raw_data)):
for j in range(len(raw_data[i])):
raw_sub_bins[j].append(raw_data[i][j])
"""min_indexes contains the index of the minimum value for each sub bin"""
min_indexes = []
for item in sub_bins:
min_indexes.append([item[np.argmin([x[-1] for x in item])],np.argmin([x[-1] for x in item])])
"""Create best combination"""
best_combination = []
for i, item in enumerate([x[-1] for x in min_indexes]):
print i, item
best_combination.append(sub_bins[i][item])
# falp_ratio = []
# for i, item in enumerate(min_indexes):
# falp_ratio.append(raw_sub_bins[i][item[-1]])
# falp_ratio = np.array(falp_ratio)
# falp_indices = list(min_indexes)
# falp_raw_sub_bins = list(raw_sub_bins)
#
# falp_ratio_on_falc = []
# for i, item in enumerate(falp_indices):
# falp_ratio_on_falc.append(falc_raw_sub_bins[i][item[-1]])
# falp_ratio_on_falc = np.array(falp_ratio_on_falc)
#
# falc_ratio_on_falp = []
# for i, item in enumerate(falc_indices):
# falc_ratio_on_falp.append(falp_raw_sub_bins[i][item[-1]])
# falc_ratio_on_falp = np.array(falc_ratio_on_falp)
fig, (ax1, ax2) = plt.subplots(2)
ax1.set_xlim((1000, 9000))
ax1.set_ylim((.5, 1.1))
ax1.grid(True)
ax2.set_xlim((1000, 9000))
ax2.set_ylim((.5, 1.1))
ax2.grid(True)
ax1.plot(falc_ratio[:, 0], falc_ratio[:, -1], label='falc_best')
ax1.plot(falp_ratio_on_falc[:, 0], falp_ratio_on_falc[:, -1], label='falc_on_falp')
ax1.legend(loc='best')
ax2.plot(falp_ratio[:, 0], falp_ratio[:, -1], label='falp_best')
ax2.plot(falc_ratio_on_falp[:, 0], falc_ratio_on_falp[:, -1], label='falp_on_falc')
#ax1.plot(falp_ratio[:, 0], falp_ratio[:, -1], label='falp_best')
#ax1.plot(falp_ratio_on_falc[:, 0], falp_ratio_on_falc[:, -1], label='falp_on_falc')
ax2.legend(loc='best')
plt.savefig('best_comparison.pdf')
plt.show()
else:
"""First for FALC"""
for i, item in enumerate(raw_data):
raw_data[i] = item.tolist()
"""Perform merging"""
for j, item in enumerate(raw_data):
for i, line in enumerate(item):
raw_data[j][i].append((1. - float(line[-1]))**2)
"""best_combination contains the best sub bins and their indexes"""
best_combination = []
raw_data = np.array(raw_data)
for i in range(len(raw_data[0])):
best_combination.append([raw_data[:, i, 0][0], raw_data[:, i, 2][np.argmin(raw_data[:, i, -1])], raw_data[:, i, -1][np.argmin(raw_data[:, i, -1])], np.argmin(raw_data[:, i, -1])])
"""Store FALC results and do the same for FALP"""
falc_raw_data = copy.copy(raw_data)
falc_best_combination = copy.copy(best_combination)
file_list = natsorted(glob.glob('odf_fac_spectra*Comparison'))
"""Every element of data consists of one Contrast file"""
raw_data = []
for item in file_list:
print item
raw_data.append(np.loadtxt(item))
for i, item in enumerate(raw_data):
raw_data[i] = item.tolist()
"""Perform merging"""
for j, item in enumerate(raw_data):
for i, line in enumerate(item):
raw_data[j][i].append((1. - float(line[-1]))**2)
"""best_combination contains the best sub bins and their indexes"""
best_combination = []
raw_data = np.array(raw_data)
for i in range(len(raw_data[0])):
best_combination.append([raw_data[:, i, 0][0], raw_data[:, i, 2][np.argmin(raw_data[:, i, -1])], raw_data[:, i, -1][np.argmin(raw_data[:, i, -1])], np.argmin(raw_data[:, i, -1])])
falp_raw_data = copy.copy(raw_data)
falp_best_combination = copy.copy(best_combination)
"""Create best combination of FALC on FALP best indices and vice versa"""
falp_indices_on_falc, falc_indices_on_falp = [], []
for i, item in enumerate(falp_best_combination):
falp_indices_on_falc.append([falc_raw_data[:, i][item[-1]][0], falc_raw_data[:, i][item[-1]][2]])
falp_indices_on_falc = np.array(falp_indices_on_falc)
for i, item in enumerate(falc_best_combination):
falc_indices_on_falp.append([falp_raw_data[:, i][item[-1]][0], falp_raw_data[:, i][item[-1]][2]])
falc_indices_on_falp = np.array(falc_indices_on_falp)
falc_best_combination, falp_best_combination = np.array(falc_best_combination), np.array(falp_best_combination)
fig, (ax1, ax2) = plt.subplots(2)
ax1.set_xlim((1000, 9000))
ax1.set_ylim((.8, 1.1))
ax1.grid(True)
ax2.set_xlim((1000, 9000))
ax2.set_ylim((.8, 1.1))
ax2.grid(True)
ax1.plot(falc_best_combination[:, 0], falc_best_combination[:, 1], label='falc_best', linewidth=.2)
ax1.plot(falp_indices_on_falc[:, 0], falp_indices_on_falc[:, -1], label='falc_on_falp', linewidth=.2)
ax1.legend(loc='best')
ax2.plot(falp_best_combination[:, 0], falp_best_combination[:, 1], label='falp_best', linewidth=.2)
ax2.plot(falc_indices_on_falp[:, 0], falc_indices_on_falp[:, -1], label='falc_on_falp', linewidth=.2)
#ax1.plot(falp_ratio[:, 0], falp_ratio[:, -1], label='falp_best')
#ax1.plot(falp_ratio_on_falc[:, 0], falp_ratio_on_falc[:, -1], label='falp_on_falc')
ax2.legend(loc='best')
plt.savefig('best_comparison.pdf')
plt.show()