-
Notifications
You must be signed in to change notification settings - Fork 1
/
nest.py
325 lines (315 loc) · 14.2 KB
/
nest.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
import batchent
import entropy
import spectral_pca
from scipy.spatial import distance
import csv
import index
import numpy
import previous_ent
import dkl
import pandas as pd
import imp
import math
import spread
import pickle_commands as pc
import Song_D_KL_calc_MK as sdkl_mk
import syllabify
import acoustic_transition_entropy
imp.reload(spectral_pca)
imp.reload(dkl)
imp.reload(batchent)
imp.reload(syllabify)
meta_nest_dict = {
'BrownBlue': ['br82bl42', 'br81bl41', 'tutor_bl5wh5'],
'GreenBlack': ['gn56bk56', 'gn55bk55', 'tutor_or152br44'],
'GreyCyan': ['gy6cy6', 'gy5cy5', 'gy4cy4', 'tutor_br34bl20'],
'OrangeBrown': ['or189br53', 'or188br52', 'tutor_bk'],
'PurpleGreen': ['pu12gn8', 'tutor_si933'],
'PurpleYellow': ['pu17ye34', 'pu14ye31', 'tutor_ye20gy31'],
'RedYellow': ['re38ye2', 're37ye1', 're10ye6', 'tutor_si935'],
'WhiteOrange': ['wh96or142', 'wh100or80', 'tutor_si933(2)'],
'YellowBlack': ['ye73bk73', 'ye84bk64', 'tutor_si936'],
'YellowGrey' : ['ye44gy44', 'tutor_or172br12']
}
directory = './data/BFs_logan/data/'
prefix = 'fathers_and_sons_from_logan - '
prevalence_dict = {}
category_dict = {}
prevalence_data = pd.read_csv('./data/Data4Malcolm - main.csv')
for tutor_syllable,pupil_syllable,prevalence,category in zip(prevalence_data['TutorID_Syllable'],
prevalence_data['PupilID_Syllable'],
prevalence_data['perTut'],
prevalence_data['Category']):
prevalence_dict[tutor_syllable] = prevalence
category_dict[pupil_syllable] = category
def branch_point_differences(n,mode):
out_dict = {}
syllables_dict = {}
for nest, birds_list in meta_nest_dict.items():
nest_dict = {}
nest_syllable_dict = {}
pupil_IDs = birds_list[:-1]
tutor_ID = birds_list[-1]
for pupil_ID in pupil_IDs:
fp1 = directory + prefix + tutor_ID + '.csv'
fp2 = directory + prefix + pupil_ID + '.csv'
distrib_1 = entropy.branchpoints(fp1, [2, n + 1])[n]
distrib_2 = entropy.branchpoints(fp2, [2, n + 1])[n]
bird1_branchpoints = []
bird2_branchpoints = []
for branchpoint_1 in distrib_1.keys():
bird1_branchpoints.append(branchpoint_1)
for branchpoint_2 in distrib_2.keys():
bird2_branchpoints.append(branchpoint_2)
branchpoints_to_analyze = [value for value in bird1_branchpoints if value in bird2_branchpoints]
branchpoints_dict = {}
for branchpoint in branchpoints_to_analyze:
differences_dict={}
if branchpoint in distrib_1.values():
count1 = distrib_1[branchpoint]['count']
else:
count1 = 0
if branchpoint in distrib_2.values():
count2 = distrib_1[branchpoint]['count']
else:
count2 = 0
transitions_to_analyze = list(distrib_1[branchpoint]['transitions'].keys())+list(distrib_2[branchpoint]['transitions'].keys())
for transition in transitions_to_analyze:
if transition not in distrib_1[branchpoint]['transitions'].keys():
bird1_value = 0.00000001
else:
bird1_value = distrib_1[branchpoint]['transitions'][transition]
if transition not in distrib_2[branchpoint]['transitions'].keys():
bird2_value = 0.00000001
else:
bird2_value = distrib_2[branchpoint]['transitions'][transition]
if mode == 'euclidean':
difference = abs(bird1_value-bird2_value)
if mode == 'dkl':
difference = bird1_value * math.log2(bird1_value/bird2_value)
if mode == 'log':
difference = abs(math.log2(bird1_value)-math.log2(bird2_value))
differences_dict[transition] = difference
divergence = sum(differences_dict.values())
branchpoints_dict[branchpoint] = {
'tutor_count': count1,
'pupil_count': count2,
'divergence': divergence}
divergences = []
counts = []
for branchpoint, subdict in branchpoints_dict.items():
divergences.append(subdict['divergence'])
shared_branchpoints = len(branchpoints_dict.keys())
out_dict[nest] = nest_dict
syllables_dict[nest] = nest_syllable_dict
matrix_version = []
syllables_matrix_version = []
for nest, nestdict in out_dict.items():
for bird, birdresult in nestdict.items():
matrix_version.append(birdresult)
for nest, nestdict in syllables_dict.items():
for bird, birddict in nestdict.items():
for syl, syldict in birddict.items():
syllables_matrix_version.append(
[nest, bird, syl, syldict['divergence']])
with open("./output/bird_divergence.csv", 'w') as output_file:
writer = csv.writer(output_file)
for row in matrix_version:
writer.writerow([row])
with open("./output/syllable_divergence.csv", 'w') as output_file:
writer = csv.writer(output_file)
writer.writerow(['Nest', 'BirdID', 'Syllable', 'Divergence'])
for row in syllables_matrix_version:
writer.writerow(row)
return [matrix_version,syllables_dict]
def tutor_compare(n_for_previous_ent=2):
pca_data = spectral_pca.get_medians()
token_pca_data = spectral_pca.tokens_by_type_5D()
forwards_ent_data = pc.depickle('forwards_ent_data')
backwards_ent_data = pc.depickle('backwards_ent_data')
fEP = pc.depickle('fEP')
bEP = pc.depickle('bEP')
divergence_data = branch_point_differences(2,'euclidean')[1]
dkl_data = branch_point_differences(2,'dkl')[1]
log_data = branch_point_differences(2,'log')[1]
previous_ent_data = previous_ent.batch_pe(n=n_for_previous_ent)
tutor_previous_spread_dict = acoustic_transition_entropy.acoustic_spread()
tutor_next_spread_dict = acoustic_transition_entropy.acoustic_spread(mode='forward')
out_dict = {}
for nest, birds_list in meta_nest_dict.items():
nest_dict = {}
pupil_IDs = birds_list[:-1]
tutor_ID = birds_list[-1]
tutor_syllables = pca_data[tutor_ID].keys()
for pupil_ID in pupil_IDs:
pupil_dict = {}
tutor_syllables = pca_data[tutor_ID].keys()
pupil_syllables = pca_data[pupil_ID].keys()
retained_syllables = [
value for value in tutor_syllables if value in pupil_syllables]
dropped_syllables = [
value for value in tutor_syllables if value not in pupil_syllables]
for syllable in [syllable for syllable in tutor_syllables if syllable != 'i']:
try:
prevalence = prevalence_dict[tutor_ID+'_'+syllable]
category = category_dict[pupil_ID+'_'+syllable]
except:
prevalence = ''
category = ''
pupil_entropy=''
direction_dict={'forwards':{'tutor':'','pupil':''},'backwards':{'tutor':'','pupil':''},'fEP':{'tutor':'','pupil':''},'bEP':{'tutor':'','pupil':''}}
for direction,direction_data in zip(['forwards','backwards','fEP','bEP'],[forwards_ent_data,backwards_ent_data,fEP,bEP]):
for row in direction_data:
if row[0] == pupil_ID and row[1] == syllable:
direction_dict[direction]['pupil'] = row[2]
if row[0] == tutor_ID and row[1] == syllable:
direction_dict[direction]['tutor'] = row[2]
tutor_spectral_data = row[-6:]
spectral_distance = ''
divergence=''
dkl_value = ''
log_value = ''
SDKL1 = ''
SDKL2 = ''
tutor_previous_spread = ''
try:
tutor_spread = spread.spread(token_pca_data[tutor_ID + '_' + syllable])
except:
tutor_spread = ''
try:
pupil_spread = spread.spread(token_pca_data[pupil_ID + '_' + syllable])
except:
pupil_spread = ''
try:
cloud_distance = spread.sim(token_pca_data[tutor_ID + '_' + syllable],
token_pca_data[pupil_ID + '_' + syllable])
except:
cloud_distance = ''
try:
tutor_pca = pca_data[tutor_ID][syllable]
pupil_pca = pca_data[pupil_ID][syllable]
spectral_distance = distance.euclidean(
tuple(tutor_pca), tuple(pupil_pca))
except:
pass
try:
tutor_previous_spread = tutor_previous_spread_dict[tutor_ID+'_'+syllable]
tutor_next_spread = tutor_next_spread_dict[tutor_ID+'_'+syllable]
except BaseException:
tutor_previous_spread = ''
tutor_next_spread = ''
if category == 'Retained':
try:
tutor_fp = 'C:/Users/SakataWoolleyLab/Desktop/BFfromLogan/'+nest+'/'+tutor_ID+'/'
pupil_fp = 'C:/Users/SakataWoolleyLab/Desktop/BFfromLogan/'+nest+'/'+pupil_ID+'/'
SDKL_output_list = sdkl_mk.main_program(tutor_fp, tutor_fp+'syllables/'+syllable+'/', pupil_fp+'syllables/'+syllable+'/', 1, 1)
SDKL1 = SDKL_output_list[5]
SDKL2 = SDKL_output_list[6]
except:
pass
print('SDKL1: '+SDKL1+'; SDKL2: '+SDKL2)
try:
divergence = divergence_data[nest][pupil_ID][tuple(
syllable)]['divergence']
dkl_value = dkl_data[nest][pupil_ID][tuple(
syllable)]['divergence']
log_value = log_data[nest][pupil_ID][tuple(
syllable)]['divergence']
except:
pass
try:
tutor_previous_ent = previous_ent_data[tutor_ID][syllable]
pupil_previous_ent = ''
except:
pass
try:
pupil_previous_ent = previous_ent_data[pupil_ID][syllable]
except:
pass
pupil_dict[syllable] = [
prevalence,
category,
direction_dict['forwards']['tutor'],
direction_dict['forwards']['pupil'],
direction_dict['backwards']['tutor'],
direction_dict['backwards']['pupil'],
direction_dict['fEP']['tutor'],
direction_dict['fEP']['pupil'],
direction_dict['bEP']['tutor'],
direction_dict['bEP']['pupil'],
spectral_distance,
tutor_previous_spread,
tutor_next_spread,
tutor_spread,
pupil_spread,
cloud_distance,
divergence,
dkl_value,
log_value,
tutor_previous_ent,
pupil_previous_ent,
SDKL1,
SDKL2]
for feature in tutor_spectral_data:
pupil_dict[syllable].append(feature)
nest_dict[pupil_ID] = pupil_dict
out_dict[nest] = nest_dict
matrix_version = []
for nest, nestdict in out_dict.items():
for bird, birddict in nestdict.items():
for syl, syllist in birddict.items():
matrix_version.append([nest, bird, syl] + syllist)
with open("./output/nest_learning.csv", 'w') as output_file:
writer = csv.writer(output_file)
writer.writerow(['Nest',
'BirdID',
'Syllable',
'Prevalence',
'Category',
'TutorForwardsEntropy',
'PupilForwardsEntropy',
'TutorBackwardsEntropy',
'PupilBackwardsEntropy',
'TutorfEP',
'PupilfEP',
'TutorbEP',
'PupilbEP',
'SpectralDistance',
'TutorPreviousSpread',
'TutorNextSpread',
'TutorSpread',
'PupilSpread',
'CloudDistance',
'EuclideanDistance',
'DKL',
'LogDistance',
'TutorPreviousEnt',
'PupilPreviousEnt',
'SDKL1',
'SDKL2',
'MeanFreq',
'SpecDense',
'Duration',
'LoudEnt',
'SpecTempEnt',
'meanLoud',])
for row in matrix_version:
writer.writerow(row)
return matrix_version
def average(previous_result):
out_dict = {}
bird_dict = {}
for row in previous_result:
if row[1] not in bird_dict.keys():
bird_dict[row[1]] = []
bird_dict[row[1]].append(row[-1])
for key, value in bird_dict.items():
out_dict[key] = sum(value) / len(value)
matrix_version = []
for bird, birdresult in out_dict.items():
matrix_version.append(birdresult)
with open("./output/average_spectral_distances.csv", 'w') as output_file:
writer = csv.writer(output_file)
for row in matrix_version:
writer.writerow([row])
return matrix_version