submitt changes

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saschuta 2024-02-19 15:46:02 +01:00
parent dd2d40cf0c
commit 542fd17056
117 changed files with 201663 additions and 5966 deletions

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741,9.078749999999957,9.123099999999965,9.38655,9.167899999999975
742,9.090649999999982,9.136949999999956,9.396199999999965,9.180450000000008
743,9.104499999999973,9.15179999999998,9.408549999999991,9.186849999999993
744,9.12319999999994,9.163999999999987,9.426349999999957,9.197749999999985
745,9.128799999999956,9.169549999999958,9.433099999999968,9.204850000000022
746,9.136949999999956,9.185799999999972,9.445149999999956,9.21289999999999
747,9.151749999999936,9.197149999999965,9.45474999999999,9.223599999999976
748,9.166899999999941,9.211649999999963,9.472349999999949,9.237149999999986
749,9.18734999999998,9.222699999999975,9.482049999999958,9.247000000000014
750,9.200999999999965,9.235049999999944,9.502699999999948,9.267200000000003
751,9.213299999999947,9.247099999999989,9.512099999999975,9.276700000000005
752,9.229499999999973,9.266149999999982,9.524499999999989,9.28895
753,9.239349999999945,9.277299999999968,9.53649999999999,9.299750000000017
754,9.245949999999937,9.289249999999981,9.549999999999955,9.308150000000012
755,9.259549999999933,9.30279999999999,9.56774999999999,9.317650000000015
756,9.274399999999957,9.319299999999998,9.583949999999959,9.332650000000001
757,9.284099999999967,9.334249999999997,9.592149999999947,9.347699999999975
758,9.295099999999934,9.345349999999996,9.609899999999982,9.363549999999975
759,9.307149999999979,9.360149999999976,9.618200000000002,9.379999999999995
760,9.318049999999971,9.379099999999994,9.622099999999989,9.389599999999973
761,9.333199999999977,9.38594999999998,9.642449999999997,9.404650000000004
762,9.340999999999951,9.396949999999947,9.654799999999966,9.415500000000009
763,9.356299999999976,9.410799999999995,9.66289999999998,9.430700000000002
764,9.364199999999983,9.4255,9.671249999999986,9.442850000000021
765,9.373899999999935,9.43104999999997,9.677999999999997,9.456299999999999
766,9.388749999999959,9.445799999999963,9.695749999999975,9.472550000000012
767,9.404899999999941,9.454199999999958,9.715899999999976,9.483699999999999
768,9.419949999999972,9.459449999999947,9.72954999999996,9.501199999999983
769,9.431099999999958,9.471749999999986,9.747199999999964,9.509250000000009
770,9.44584999999995,9.478699999999947,9.76094999999998,9.516099999999994
771,9.459499999999935,9.48939999999999,9.774399999999957,9.52985000000001
772,9.471699999999942,9.50709999999998,9.789349999999956,9.543349999999975
773,9.485649999999964,9.523449999999968,9.801850000000002,9.556949999999972
774,9.49894999999998,9.53309999999999,9.81654999999995,9.570699999999988
775,9.507349999999974,9.545149999999978,9.826149999999984,9.579999999999984
776,9.520799999999952,9.561849999999993,9.83744999999999,9.592100000000016
777,9.535699999999963,9.569749999999999,9.850899999999967,9.599350000000015
778,9.545199999999966,9.584699999999998,9.868299999999977,9.615499999999997
779,9.556099999999958,9.591449999999952,9.881999999999948,9.6293
780,9.571299999999951,9.602749999999958,9.897149999999954,9.645100000000014
781,9.580699999999979,9.615899999999954,9.913249999999948,9.661850000000015
782,9.592749999999967,9.62134999999995,9.928199999999947,9.676600000000008
783,9.612049999999954,9.633849999999995,9.93365,9.69565
784,9.622799999999984,9.641999999999996,9.944649999999967,9.707850000000008
785,9.637749999999983,9.654149999999959,9.956849999999974,9.71629999999999
786,9.652649999999937,9.663499999999999,9.969149999999956,9.73090000000002
787,9.667799999999943,9.678499999999985,9.978599999999972,9.744500000000016
788,9.679899999999975,9.68689999999998,9.993499999999983,9.752999999999986
789,9.699149999999975,9.703049999999962,,9.771749999999997
790,9.715399999999931,9.717949999999973,,9.788200000000018
791,9.72909999999996,9.728949999999998,,9.808550000000025
792,9.735749999999939,9.745349999999974,,9.825049999999976
793,9.749499999999955,9.752049999999997,,9.842800000000011
794,9.769949999999938,9.771349999999984,,9.84944999999999
795,9.784899999999936,9.790549999999996,,9.865599999999972
796,9.798299999999983,9.807899999999961,,9.876550000000009
797,9.809499999999957,9.822949999999992,,9.890899999999988
798,9.820149999999956,9.83929999999998,,9.894400000000019
799,9.829749999999933,9.851349999999968,,9.907899999999984
800,9.840649999999982,9.871849999999995,,9.914750000000026
801,9.85009999999994,9.88009999999997,,9.929449999999974
802,9.866299999999967,9.890999999999963,,9.935099999999977
803,9.88009999999997,9.903249999999957,,9.944700000000012
804,9.901849999999968,9.91964999999999,,9.95675
805,9.914049999999975,9.93444999999997,,9.970500000000015
806,9.929049999999961,9.94399999999996,,9.979949999999974
807,9.934499999999957,9.955299999999966,,9.992650000000026
808,9.944199999999967,9.967499999999973,,
809,9.956399999999974,9.979299999999967,,
810,9.968549999999937,9.994649999999979,,
811,9.977999999999952,,,
812,9.989199999999983,,,
813,9.995699999999943,,,
1 1 0 2 3
2 0 0.005699999999933425 0.003349999999954889 0.010999999999967258 0.004349999999988086
3 1 0.01259999999996353 0.014149999999972351 0.02584999999999127 0.012650000000007822
4 2 0.02489999999994552 0.026399999999966894 0.037099999999952615 0.026099999999985357
5 3 0.037099999999952615 0.03989999999998872 0.05169999999998254 0.04385000000002037
6 4 0.051999999999964075 0.04939999999999145 0.06014999999996462 0.0535499999999729
7 5 0.06309999999996307 0.06039999999995871 0.06944999999996071 0.06450000000000955
8 6 0.07384999999993624 0.0684999999999718 0.08189999999996189 0.08494999999999209
9 7 0.0847999999999729 0.07974999999999 0.09669999999999845 0.09544999999997117
10 8 0.0969499999999357 0.09309999999999263 0.10774999999995316 0.11164999999999736
11 9 0.10789999999997235 0.10669999999998936 0.12545000000000073 0.12824999999998
12 10 0.1268999999999778 0.11104999999997744 0.14054999999996198 0.13929999999999154
13 11 0.14084999999994352 0.12029999999998608 0.1564499999999498 0.15534999999999854
14 12 0.1542999999999779 0.12844999999998663 0.16604999999998427 0.1662499999999909
15 13 0.16244999999997844 0.14194999999995161 0.1783999999999537 0.1775000000000091
16 14 0.16789999999997463 0.15284999999994398 0.1946999999999548 0.18830000000002656
17 15 0.17874999999997954 0.16409999999996217 0.21105000000000018 0.2004000000000019
18 16 0.19224999999994452 0.17739999999997735 0.22319999999996298 0.21255000000002156
19 17 0.19899999999995543 0.18289999999996098 0.23954999999995152 0.23570000000000846
20 18 0.2167499999999336 0.19779999999997244 0.25099999999997635 0.24389999999999645
21 19 0.2236499999999637 0.20769999999998845 0.26004999999997835 0.2588000000000079
22 20 0.24254999999993743 0.21544999999997572 0.2749499999999898 0.2738499999999817
23 21 0.2525499999999852 0.231899999999996 0.28754999999995334 0.2860499999999888
24 22 0.2589999999999577 0.24304999999998245 0.29814999999996417 0.29845000000000255
25 23 0.26979999999997517 0.25084999999995716 0.3118999999999801 0.3077999999999861
26 24 0.29429999999996426 0.2645999999999731 0.32279999999997244 0.31869999999997844
27 25 0.30104999999997517 0.2780499999999506 0.3388999999999669 0.32954999999998336
28 26 0.3161999999999807 0.2919499999999857 0.349899999999991 0.34199999999998454
29 27 0.3270999999999731 0.3012499999999818 0.35914999999999964 0.35924999999997453
30 28 0.33784999999994625 0.314899999999966 0.3686999999999898 0.36899999999997135
31 29 0.3463999999999601 0.33399999999994634 0.3795499999999947 0.37855000000001837
32 30 0.3594999999999686 0.34874999999999545 0.39214999999995825 0.39224999999999
33 31 0.369549999999947 0.3573499999999967 0.4044499999999971 0.40440000000000964
34 32 0.3799999999999386 0.37079999999997426 0.4138499999999681 0.41969999999997754
35 33 0.39229999999997744 0.3869999999999436 0.4260999999999626 0.4291499999999928
36 34 0.40449999999998454 0.3953499999999508 0.43959999999998445 0.4397999999999911
37 35 0.4182499999999436 0.40739999999999554 0.446550000000002 0.4560999999999922
38 36 0.43339999999994916 0.42134999999996126 0.4572499999999877 0.4630000000000223
39 37 0.439899999999966 0.427949999999953 0.47369999999995116 0.4737999999999829
40 38 0.456299999999942 0.44029999999997926 0.4873999999999796 0.4793000000000234
41 39 0.4726499999999305 0.45644999999996116 0.49699999999995725 0.49295000000000755
42 40 0.482249999999965 0.4714999999999918 0.509399999999971 0.5091499999999769
43 41 0.4874499999999671 0.4876499999999737 0.513149999999996 0.520150000000001
44 42 0.49589999999994916 0.49594999999999345 0.5268499999999676 0.5282000000000266
45 43 0.5106499999999414 0.509399999999971 0.5390499999999747 0.5408499999999776
46 44 0.5218999999999596 0.5217999999999847 0.5581999999999994 0.5499500000000239
47 45 0.5338999999999601 0.5421499999999924 0.56899999999996 0.5635500000000206
48 46 0.5462999999999738 0.5544499999999744 0.5798499999999649 0.5718499999999835
49 47 0.561149999999941 0.5639499999999771 0.59375 0.5827499999999759
50 48 0.5719999999999459 0.5723499999999717 0.6110999999999649 0.5921500000000037
51 49 0.58299999999997 0.585899999999981 0.623299999999972 0.6044999999999732
52 50 0.591199999999958 0.5978999999999814 0.6327999999999747 0.6098499999999945
53 51 0.6033499999999776 0.6088999999999487 0.6477999999999611 0.6233500000000163
54 52 0.6197499999999536 0.6186499999999455 0.6600499999999556 0.6412500000000136
55 53 0.6360499999999547 0.6239999999999668 0.6777500000000032 0.6492999999999824
56 54 0.649599999999964 0.6333999999999946 0.6900499999999852 0.6643500000000131
57 55 0.6603499999999372 0.6481999999999744 0.7066499999999678 0.6764999999999759
58 56 0.6754499999999553 0.6595999999999549 0.7131499999999846 0.682200000000023
59 57 0.6890999999999394 0.6782499999999914 0.7212999999999852 0.6915500000000065
60 58 0.7029499999999302 0.6931999999999903 0.7294999999999732 0.7051500000000033
61 59 0.7203999999999837 0.7042999999999893 0.7430999999999699 0.7201000000000022
62 60 0.7339999999999804 0.7164999999999964 0.7511499999999955 0.733949999999993
63 61 0.7434999999999832 0.7274499999999762 0.7636499999999842 0.7418499999999995
64 62 0.7502499999999372 0.7421999999999684 0.7787500000000023 0.7513000000000147
65 63 0.7651999999999362 0.7492999999999483 0.8029999999999973 0.7608000000000175
66 64 0.7816499999999564 0.7601999999999975 0.8141499999999837 0.7705000000000268
67 65 0.8048499999999308 0.7721499999999537 0.8234499999999798 0.7814000000000192
68 66 0.8156999999999357 0.7804999999999609 0.8342499999999973 0.8032000000000039
69 67 0.8197499999999422 0.8021999999999707 0.8455999999999904 0.8154499999999985
70 68 0.833349999999939 0.8116999999999734 0.860149999999976 0.8220999999999776
71 69 0.8467999999999734 0.8214499999999703 0.8721999999999639 0.8359500000000253
72 70 0.8605999999999767 0.8348499999999603 0.8930999999999472 0.8468500000000176
73 71 0.8714499999999816 0.8457499999999527 0.898399999999981 0.8602999999999952
74 72 0.882349999999974 0.8606999999999516 0.9184999999999945 0.8725000000000023
75 73 0.8918499999999767 0.871599999999944 0.9241999999999848 0.8875499999999761
76 74 0.9027999999999565 0.8840499999999452 0.9322499999999536 0.8997499999999832
77 75 0.9096999999999298 0.89874999999995 0.9446999999999548 0.9173999999999864
78 76 0.9177499999999554 0.9181499999999687 0.9581499999999892 0.9282499999999914
79 77 0.9313499999999522 0.9314999999999714 0.973199999999963 0.9474999999999909
80 78 0.9411999999999807 0.9448999999999614 0.9787999999999784 0.955600000000004
81 79 0.9465499999999452 0.9506999999999834 0.9882499999999936 0.9692499999999882
82 80 0.9505499999999643 0.955949999999973 1.002999999999986 0.9828499999999849
83 81 0.9558999999999855 0.9722999999999615 1.0191999999999553 0.9963000000000193
84 82 0.9668499999999653 0.9873499999999922 1.0300999999999476 1.007349999999974
85 83 0.9843999999999369 1.0007499999999823 1.042699999999968 1.0194999999999936
86 84 0.9926999999999566 1.009149999999977 1.0533499999999663 1.0304499999999734
87 85 1.0008499999999572 1.0225999999999544 1.0656499999999482 1.0428999999999746
88 86 1.011699999999962 1.0309499999999616 1.0766499999999724 1.053449999999998
89 87 1.0226499999999419 1.0457499999999982 1.0901999999999816 1.0670999999999822
90 88 1.033649999999966 1.0566499999999905 1.1038999999999533 1.0726000000000226
91 89 1.0442999999999643 1.0731999999999857 1.1203499999999735 1.0862000000000194
92 90 1.0524499999999648 1.0906999999999698 1.1281499999999482 1.1014499999999998
93 91 1.063799999999958 1.1042999999999665 1.1430499999999597 1.1107000000000085
94 92 1.0674999999999386 1.1111999999999966 1.1593499999999608 1.1243000000000052
95 93 1.071899999999971 1.1260999999999513 1.1707499999999982 1.1351999999999975
96 94 1.0799499999999398 1.1437999999999988 1.1825999999999794 1.1516500000000178
97 95 1.0919999999999845 1.1587499999999977 1.189549999999997 1.1652500000000146
98 96 1.102899999999977 1.169749999999965 1.2056499999999915 1.1802999999999884
99 97 1.109649999999931 1.1804499999999507 1.216700000000003 1.195350000000019
100 98 1.127399999999966 1.1900499999999852 1.2288499999999658 1.2101000000000113
101 99 1.1355499999999665 1.2066999999999553 1.2439999999999714 1.220899999999972
102 100 1.1505999999999403 1.2200999999999453 1.2548999999999637 1.2387499999999818
103 101 1.1654999999999518 1.2257499999999482 1.2709999999999582 1.250850000000014
104 102 1.1803999999999633 1.2349999999999568 1.283299999999997 1.2575499999999806
105 103 1.1899999999999409 1.244549999999947 1.290049999999951 1.2769999999999868
106 104 1.206199999999967 1.256749999999954 1.2983499999999708 1.2903499999999894
107 105 1.2172499999999786 1.2663499999999885 1.3063999999999965 1.3039499999999862
108 106 1.2350999999999317 1.2800499999999602 1.3241499999999746 1.3175999999999704
109 107 1.2538999999999305 1.2909999999999968 1.3311499999999796 1.3260000000000218
110 108 1.2661999999999694 1.2990499999999656 1.3485499999999888 1.3405500000000075
111 109 1.2825999999999453 1.3124999999999432 1.3621499999999855 1.3530000000000086
112 110 1.2909499999999525 1.3302499999999782 1.3784499999999866 1.360900000000015
113 111 1.3030499999999847 1.349299999999971 1.386699999999962 1.3773499999999785
114 112 1.3164999999999623 1.3613999999999464 1.4017999999999802 1.388249999999971
115 113 1.3291999999999575 1.3725499999999897 1.4140999999999622 1.406000000000006
116 114 1.3368999999999573 1.387399999999957 1.4274999999999523 1.4127500000000168
117 115 1.3532499999999459 1.405149999999992 1.4410499999999615 1.4277999999999906
118 116 1.3626999999999612 1.4185999999999694 1.4533999999999878 1.4399999999999977
119 117 1.377749999999935 1.4308999999999514 1.4641999999999484 1.4562999999999988
120 118 1.387299999999982 1.440499999999986 1.4765999999999622 1.464749999999981
121 119 1.4040499999999838 1.4555999999999472 1.4887499999999818 1.476699999999994
122 120 1.4118499999999585 1.4731499999999755 1.5092499999999518 1.484899999999982
123 121 1.4268499999999449 1.4934999999999832 1.520150000000001 1.4971499999999764
124 122 1.4404499999999416 1.5056999999999903 1.5309499999999616 1.511000000000024
125 123 1.4528999999999428 1.5179999999999723 1.540649999999971 1.5271000000000186
126 124 1.463549999999941 1.5301999999999794 1.5555499999999824 1.5446000000000026
127 125 1.4744499999999334 1.5411999999999466 1.5690499999999474 1.5597500000000082
128 126 1.488399999999956 1.557549999999992 1.5799499999999966 1.565150000000017
129 127 1.4975999999999772 1.5685499999999593 1.5948499999999513 1.578800000000001
130 128 1.5099499999999466 1.5791999999999575 1.6124999999999545 1.5882500000000164
131 129 1.5234499999999684 1.5861999999999625 1.6290499999999497 1.6018000000000256
132 130 1.5317499999999313 1.6076999999999657 1.6423999999999523 1.6141999999999825
133 131 1.5454999999999472 1.6147999999999456 1.653449999999964 1.6211999999999875
134 132 1.5563999999999396 1.620399999999961 1.6672999999999547 1.6317999999999984
135 133 1.569849999999974 1.632599999999968 1.683499999999981 1.6440000000000055
136 134 1.5791999999999575 1.6417999999999893 1.6981999999999857 1.656349999999975
137 135 1.5914999999999395 1.6487999999999943 1.7120499999999765 1.6646000000000072
138 136 1.6023499999999444 1.659599999999955 1.7228999999999814 1.6798000000000002
139 137 1.613299999999981 1.6690499999999702 1.7418499999999995 1.6958500000000072
140 138 1.6309499999999844 1.6826999999999543 1.75 1.7094000000000165
141 139 1.6433499999999412 1.7002999999999702 1.7621999999999503 1.7229500000000257
142 140 1.6486999999999625 1.7098999999999478 1.7799999999999727 1.73660000000001
143 141 1.663599999999974 1.7235999999999763 1.79789999999997 1.7463000000000193
144 142 1.6760499999999752 1.731899999999996 1.8071999999999662 1.7624000000000137
145 143 1.6907999999999674 1.750599999999963 1.8220999999999776 1.780399999999986
146 144 1.703399999999931 1.7629999999999768 1.8344999999999914 1.7977500000000077
147 145 1.712949999999978 1.78064999999998 1.8480500000000006 1.811399999999992
148 146 1.7234999999999445 1.7917999999999665 1.857499999999959 1.8238000000000056
149 147 1.7331999999999539 1.7985499999999774 1.8615499999999656 1.8335500000000025
150 148 1.7452499999999418 1.8147499999999468 1.8737999999999602 1.8482000000000198
151 149 1.750699999999938 1.825649999999996 1.8913499999999885 1.8563500000000204
152 150 1.7615999999999303 1.83239999999995 1.9025999999999499 1.8633500000000254
153 151 1.78064999999998 1.8490999999999644 1.9160499999999843 1.8754000000000133
154 152 1.7970999999999435 1.8608999999999583 1.9282499999999914 1.8825499999999806
155 153 1.811999999999955 1.8733499999999594 1.933999999999969 1.8944999999999936
156 154 1.8269499999999539 1.8909499999999753 1.946299999999951 1.9084500000000162
157 155 1.848799999999983 1.8976499999999987 1.9584999999999582 1.916249999999991
158 156 1.8557499999999436 1.91274999999996 1.9638499999999794 1.931600000000003
159 157 1.8652999999999338 1.9331999999999994 1.9786999999999466 1.9489500000000248
160 158 1.874749999999949 1.9481499999999983 1.996499999999969 1.958500000000015
161 159 1.8908499999999435 1.9603499999999485 2.011349999999993 1.9773500000000013
162 160 1.8963999999999714 1.969949999999983 2.026149999999973 1.9966499999999883
163 161 1.9085999999999785 1.980799999999988 2.038599999999974 2.015449999999987
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170 168 1.9998999999999683 2.0789499999999634 2.125349999999969 2.097249999999974
171 169 2.010849999999948 2.0909999999999513 2.1352999999999724 2.1056500000000256
172 170 2.0165499999999383 2.1074499999999716 2.1459499999999707 2.1148999999999774
173 171 2.0270999999999617 2.1193999999999846 2.1622999999999593 2.1260500000000206
174 172 2.0365999999999644 2.1289999999999623 2.181349999999952 2.1353500000000167
175 173 2.0517999999999574 2.1345999999999776 2.194949999999949 2.1488499999999817
176 174 2.067799999999977 2.1467499999999973 2.2155499999999506 2.16255000000001
177 175 2.0843999999999596 2.1538999999999646 2.2290499999999724 2.1815500000000156
178 176 2.0964499999999475 2.164549999999963 2.2398000000000025 2.189799999999991
179 177 2.1183999999999514 2.1700499999999465 2.2509000000000015 2.1978500000000167
180 178 2.129099999999937 2.180849999999964 2.262900000000002 2.2156499999999824
181 179 2.144049999999936 2.1904499999999985 2.2777999999999565 2.2305999999999813
182 180 2.149599999999964 2.1957499999999754 2.2903499999999894 2.2442500000000223
183 181 2.1536499999999705 2.2137499999999477 2.2983999999999583 2.2621500000000196
184 182 2.165899999999965 2.2272499999999695 2.3132999999999697 2.27679999999998
185 183 2.180949999999939 2.2391999999999825 2.3296499999999583 2.2876999999999725
186 184 2.1904499999999416 2.2516499999999837 2.3460999999999785 2.2972500000000196
187 185 2.194699999999955 2.26279999999997 2.35979999999995 2.3137499999999704
188 186 2.212349999999958 2.2787499999999454 2.374499999999955 2.332550000000026
189 187 2.22584999999998 2.287149999999997 2.390799999999956 2.349049999999977
190 188 2.2422999999999433 2.300649999999962 2.4006499999999846 2.3625000000000114
191 189 2.247599999999977 2.312849999999969 2.4058499999999867 2.3734999999999786
192 190 2.2557499999999777 2.3292999999999893 2.4155999999999835 2.3842500000000086
193 191 2.264099999999985 2.3482499999999504 2.4261999999999944 2.395150000000001
194 192 2.2788999999999646 2.3605499999999893 2.4398499999999785 2.406150000000025
195 193 2.2911999999999466 2.3768499999999904 2.4466999999999643 2.4184999999999945
196 194 2.2992499999999723 2.383699999999976 2.4588999999999714 2.4277999999999906
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794,9.6216,9.639299999999992,9.764849999999967,9.733949999999993,9.933600000000013,9.937899999999956,9.923849999999959,9.906499999999994,9.801199999999994,9.947699999999998
795,9.633350000000007,9.651549999999986,9.779200000000003,9.750900000000001,9.9409,9.945549999999969,9.936849999999993,9.919849999999997,9.819399999999973,9.962300000000027
796,9.646749999999997,9.65889999999996,9.793099999999981,9.76505000000003,9.956700000000012,9.958749999999952,9.938499999999976,9.936549999999954,9.827999999999975,9.978150000000028
797,9.655750000000012,9.674449999999979,9.80279999999999,9.766650000000027,9.968999999999994,9.970899999999972,9.95519999999999,9.938199999999995,9.839099999999974,9.989350000000002
798,9.667399999999986,9.685399999999959,9.821750000000009,9.778599999999983,9.979649999999992,9.981749999999977,9.97339999999997,9.950099999999964,9.853499999999997,
799,9.679750000000013,9.70134999999999,9.830399999999997,9.794300000000021,,,9.980849999999975,9.959799999999973,9.870449999999948,
800,9.690699999999993,9.715849999999989,9.84114999999997,9.805200000000013,,,,9.97969999999998,9.882449999999949,
801,9.707600000000014,9.721249999999998,9.852300000000014,9.818899999999985,,,,,9.89489999999995,
802,9.718549999999993,9.738949999999988,9.866049999999973,9.827400000000011,,,,,9.906049999999993,
803,9.727350000000001,9.751049999999964,9.881849999999986,9.83805000000001,,,,,9.921449999999993,
804,9.745299999999986,9.764399999999966,9.893449999999973,9.850300000000004,,,,,9.937199999999962,
805,9.755299999999977,9.774099999999976,9.903300000000002,9.864950000000022,,,,,9.938849999999945,
806,9.768399999999986,9.789850000000001,9.916650000000004,9.878150000000005,,,,,9.948349999999948,
807,9.78789999999998,9.801949999999977,9.93104999999997,9.885550000000023,,,,,9.964049999999986,
808,9.793799999999976,9.821249999999964,9.938600000000008,9.902249999999981,,,,,9.978449999999953,
809,9.813150000000007,9.828599999999994,9.9495,9.912399999999991,,,,,9.980099999999993,
810,9.826399999999978,9.841700000000003,9.960300000000018,9.926699999999983,,,,,,
811,9.83159999999998,9.855399999999975,9.971400000000017,9.937549999999987,,,,,,
812,9.842449999999985,9.869899999999973,9.98354999999998,9.947450000000003,,,,,,
813,9.85435000000001,9.884399999999971,,9.959400000000016,,,,,,
814,9.866499999999974,9.900350000000003,,9.97399999999999,,,,,,
815,9.882299999999987,9.914899999999989,,9.981449999999995,,,,,,
816,9.899400000000014,9.930549999999982,,,,,,,,
817,9.905449999999973,9.937849999999969,,,,,,,,
818,9.916449999999998,9.947450000000003,,,,,,,,
819,9.93470000000002,9.959450000000004,,,,,,,,
820,9.945350000000019,9.971699999999998,,,,,,,,
821,9.958649999999977,9.981400000000008,,,,,,,,
822,9.971099999999979,,,,,,,,,
823,9.98169999999999,,,,,,,,,
1 3 0 1 2 4 5 6 7 8 9
2 0 0.007049999999992451 0.011949999999956162 0.005650000000002819 0.009650000000021919 0.013149999999995998 0.002949999999998454 0.002949999999998454 0.007649999999955526 0.008999999999957708 0.006349999999997635
3 1 0.015699999999981173 0.026499999999998636 0.015049999999973807 0.015800000000012915 0.026349999999979445 0.014449999999953889 0.014949999999998909 0.021949999999947067 0.01619999999996935 0.01485000000002401
4 2 0.02924999999999045 0.03284999999999627 0.0272499999999809 0.027899999999988268 0.03370000000001028 0.023749999999949978 0.029349999999965348 0.029749999999978627 0.029399999999952797 0.026700000000005275
5 3 0.04354999999998199 0.04589999999996053 0.04194999999998572 0.04370000000000118 0.044549999999958345 0.03829999999999245 0.04424999999997681 0.042749999999955435 0.04424999999997681 0.039249999999981355
6 4 0.04964999999998554 0.05824999999998681 0.049350000000004 0.06319999999999482 0.055749999999989086 0.04919999999998481 0.05604999999997062 0.06219999999996162 0.053849999999954434 0.0484000000000151
7 5 0.0644500000000221 0.07019999999999982 0.06509999999997262 0.07300000000003593 0.06790000000000873 0.06509999999997262 0.06844999999998436 0.07314999999999827 0.06619999999998072 0.06319999999999482
8 6 0.0716499999999769 0.078550000000007 0.07630000000000337 0.08859999999998536 0.07634999999999081 0.07599999999996498 0.08039999999999736 0.0876999999999839 0.07929999999998927 0.07519999999999527
9 7 0.08494999999999209 0.095799999999997 0.09404999999998154 0.09854999999998881 0.09224999999997863 0.08919999999994843 0.09534999999999627 0.0987999999999829 0.09249999999997272 0.07695000000001073
10 8 0.09809999999998809 0.11050000000000182 0.103999999999985 0.11324999999999363 0.1078499999999849 0.10014999999998508 0.10839999999996053 0.1120499999999538 0.10599999999999454 0.09834999999998217
11 9 0.11290000000002465 0.11889999999999645 0.1150000000000091 0.1240500000000111 0.113900000000001 0.11369999999999436 0.11579999999997881 0.1268999999999778 0.11444999999997663 0.11065000000002101
12 10 0.11905000000001564 0.13354999999995698 0.13049999999998363 0.1349999999999909 0.12999999999999545 0.12969999999995707 0.13159999999999172 0.14239999999995234 0.13019999999994525 0.12274999999999636
13 11 0.13220000000001164 0.14449999999999363 0.14159999999998263 0.14470000000000027 0.14314999999999145 0.1431999999999789 0.1423499999999649 0.15559999999999263 0.14179999999998927 0.13254999999998063
14 12 0.14229999999997744 0.155399999999986 0.14879999999999427 0.15775000000002137 0.1540499999999838 0.15009999999995216 0.1560999999999808 0.16804999999999382 0.154549999999972 0.14470000000000027
15 13 0.15625 0.1696499999999901 0.16345000000001164 0.16919999999998936 0.16499999999996362 0.1634999999999991 0.16674999999997908 0.1812999999999647 0.16784999999998718 0.15800000000001546
16 14 0.16874999999998863 0.18064999999995734 0.17914999999999281 0.18205000000000382 0.1782000000000039 0.17669999999998254 0.18029999999998836 0.19739999999995916 0.18014999999996917 0.16879999999997608
17 15 0.18074999999998909 0.19149999999996226 0.18399999999996908 0.18394999999998163 0.18360000000001264 0.18514999999996462 0.18859999999995125 0.20794999999998254 0.19959999999997535 0.18209999999999127
18 16 0.19695000000001528 0.2036499999999819 0.20114999999998417 0.20050000000003365 0.20024999999998272 0.19995000000000118 0.20219999999994798 0.21904999999998154 0.2127999999999588 0.2005500000000211
19 17 0.20265000000000555 0.21580000000000155 0.21429999999998017 0.21494999999998754 0.21600000000000819 0.21804999999994834 0.21414999999996098 0.23699999999996635 0.22029999999995198 0.21154999999998836
20 18 0.21715000000000373 0.22800000000000864 0.22629999999998063 0.22604999999998654 0.2258499999999799 0.22554999999999836 0.2288499999999658 0.2388499999999567 0.23599999999999 0.2246499999999969
21 19 0.225750000000005 0.246249999999975 0.24225000000001273 0.241800000000012 0.23894999999998845 0.24144999999998618 0.24339999999995143 0.25184999999999036 0.2492499999999609 0.241800000000012
22 20 0.24540000000001783 0.25884999999999536 0.25324999999998 0.25380000000001246 0.2514499999999771 0.25234999999997854 0.254099999999994 0.2639499999999657 0.2577999999999747 0.251700000000028
23 21 0.2534999999999741 0.26704999999998336 0.26539999999999964 0.2669999999999959 0.2669999999999959 0.26559999999994943 0.2662499999999568 0.2782999999999447 0.27139999999997144 0.2626000000000204
24 22 0.26679999999998927 0.27899999999999636 0.2765499999999861 0.27820000000002665 0.27780000000001337 0.27769999999998163 0.2784499999999639 0.2799999999999727 0.2811999999999557 0.27800000000002
25 23 0.27694999999999936 0.29845000000000255 0.29189999999999827 0.28899999999998727 0.29599999999999227 0.2981999999999516 0.29814999999996417 0.29719999999997526 0.30034999999998035 0.29200000000003
26 24 0.296100000000024 0.30579999999997654 0.304300000000012 0.3024000000000342 0.3050000000000068 0.3042499999999677 0.3050999999999817 0.3151499999999601 0.31019999999995207 0.30355000000002974
27 25 0.30450000000001864 0.31894999999997253 0.31639999999998736 0.3170000000000073 0.31829999999996517 0.31879999999995334 0.31979999999998654 0.32349999999996726 0.3223499999999717 0.305150000000026
28 26 0.3192500000000109 0.3287499999999568 0.32859999999999445 0.3254000000000019 0.3291999999999575 0.330949999999973 0.32734999999996717 0.3392999999999802 0.3307999999999538 0.319500000000005
29 27 0.32994999999999663 0.3395999999999617 0.3393499999999676 0.33244999999999436 0.3397499999999809 0.3409499999999639 0.3403500000000008 0.35024999999996 0.3475499999999556 0.3304499999999848
30 28 0.34104999999999563 0.34814999999997553 0.3503499999999917 0.34719999999998663 0.35464999999999236 0.3504499999999666 0.35129999999998063 0.36104999999997744 0.3491999999999962 0.34134999999997717
31 29 0.3521000000000072 0.3614000000000033 0.35994999999996935 0.360650000000021 0.36424999999997 0.3627999999999929 0.362199999999973 0.3748499999999808 0.3623499999999922 0.35214999999999463
32 30 0.3616999999999848 0.37259999999997717 0.3720000000000141 0.37299999999999045 0.3727000000000089 0.37464999999997417 0.3734499999999912 0.38179999999999836 0.37224999999995134 0.3692500000000223
33 31 0.37259999999997717 0.38239999999996144 0.38179999999999836 0.38110000000000355 0.381149999999991 0.38239999999996144 0.38434999999998354 0.4037999999999897 0.38199999999994816 0.3754000000000133
34 32 0.3809999999999718 0.393100000000004 0.39274999999997817 0.3958499999999958 0.3957499999999641 0.3978499999999485 0.4012499999999477 0.4136999999999489 0.39419999999995525 0.38290000000000646
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36 34 0.411549999999977 0.41604999999998427 0.4195500000000152 0.42005000000000337 0.42039999999997235 0.42809999999997217 0.42674999999997 0.4401999999999475 0.4186499999999569 0.4107999999999947
37 35 0.42455000000001064 0.4257999999999811 0.4282000000000039 0.4286999999999921 0.43079999999997654 0.43694999999996753 0.4352999999999838 0.44974999999999454 0.42919999999998026 0.4216000000000122
38 36 0.4309000000000083 0.43789999999995644 0.44020000000000437 0.4410000000000309 0.4417500000000132 0.4487999999999488 0.443649999999991 0.462099999999964 0.44124999999996817 0.4303499999999758
39 37 0.4442500000000109 0.44540000000000646 0.4510500000000093 0.4568500000000313 0.4563999999999737 0.4610500000000002 0.4593999999999596 0.47549999999995407 0.45489999999995234 0.443300000000022
40 38 0.456450000000018 0.45974999999998545 0.46084999999999354 0.46264999999999645 0.471149999999966 0.47549999999995407 0.4672499999999786 0.4914999999999736 0.4619499999999448 0.4592000000000098
41 39 0.4649000000000001 0.4755999999999858 0.48014999999998054 0.4784000000000219 0.48204999999995835 0.49014999999997144 0.4815999999999576 0.5023499999999785 0.48024999999995543 0.46084999999999354
42 40 0.480700000000013 0.4867999999999597 0.49014999999997144 0.489750000000015 0.49049999999999727 0.49724999999995134 0.4888499999999567 0.5166499999999701 0.4899499999999648 0.47860000000002856
43 41 0.4914999999999736 0.49680000000000746 0.4995000000000118 0.4988999999999919 0.5000999999999749 0.5134499999999775 0.5059499999999844 0.5251499999999965 0.49999999999994316 0.4895999999999958
44 42 0.49894999999997935 0.513499999999965 0.5128500000000145 0.5127499999999827 0.5133999999999901 0.5253499999999462 0.516399999999976 0.542349999999999 0.5156499999999937 0.49860000000001037
45 43 0.5145499999999856 0.5253999999999905 0.5239500000000135 0.5231499999999869 0.5256499999999846 0.5374999999999659 0.5252499999999714 0.5591499999999883 0.526299999999992 0.5138000000000034
46 44 0.5230999999999995 0.5326999999999771 0.5384500000000116 0.532950000000028 0.5320499999999697 0.5536999999999921 0.5364499999999452 0.5701499999999555 0.5338499999999726 0.525950000000023
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55 53 0.6340000000000146 0.6490999999999758 0.6499999999999773 0.6494999999999891 0.6435999999999922 0.6639999999999873 0.6432999999999538 0.6855499999999779 0.6527999999999565 0.639349999999979
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58 56 0.6750000000000114 0.6855999999999653 0.6890500000000088 0.6896500000000287 0.6858499999999594 0.7073499999999626 0.6808999999999514 0.7198499999999513 0.6918499999999881 0.6802500000000009
59 57 0.6906500000000051 0.6967500000000086 0.7033999999999878 0.699450000000013 0.695699999999988 0.7184499999999616 0.6952499999999873 0.7332499999999982 0.7049999999999841 0.6901000000000295
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61 59 0.7087500000000091 0.7184499999999616 0.724049999999977 0.7237999999999829 0.721149999999966 0.7440999999999462 0.7171999999999912 0.7468999999999824 0.7267999999999688 0.7145500000000311
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89 87 1.046199999999999 1.0476999999999634 1.0728500000000167 1.0686000000000035 1.0537499999999795 1.0899499999999875 1.056099999999958 1.0962999999999852 1.0696499999999673 1.055749999999989
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91 89 1.0730500000000234 1.073849999999993 1.0932500000000118 1.0937000000000126 1.0756000000000085 1.114499999999964 1.0803999999999974 1.1193499999999972 1.093999999999994 1.082400000000007
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93 91 1.0900500000000193 1.0944499999999948 1.1148999999999774 1.1132499999999936 1.1037000000000035 1.1445999999999685 1.1062499999999886 1.1544999999999845 1.1134499999999434 1.1059999999999945
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855,9.593999999999994,9.884299999999996,9.918050000000001,9.934599999999996,,9.87424999999999,9.840599999999995,9.681299999999993,9.829549999999983,9.719799999999992,9.720349999999996,9.654749999999979,9.76384999999999,9.633199999999988,9.664799999999985,9.77355,9.719049999999982,9.829399999999993,9.793749999999989,9.7192,9.825600000000009
856,9.605799999999988,9.899949999999997,9.937199999999997,9.938099999999999,,9.886099999999999,9.852699999999999,9.703349999999986,9.840299999999985,9.732749999999982,9.731249999999989,9.665549999999996,9.77494999999999,9.647950000000009,9.680299999999988,9.790400000000005,9.72059999999999,9.840100000000007,9.810249999999996,9.732249999999965,9.829049999999995
857,9.619399999999985,9.917349999999999,9.938749999999999,9.946599999999997,,9.899749999999997,9.867349999999988,9.71799999999999,9.853599999999986,9.734499999999997,9.751750000000015,9.679949999999991,9.792849999999987,9.655049999999989,9.681849999999997,9.795850000000002,9.73154999999997,9.852499999999992,9.822499999999991,9.733799999999974,9.840300000000013
858,9.626749999999987,9.927799999999998,9.945900000000002,9.958849999999991,,9.91664999999999,9.880849999999995,9.731249999999989,9.872099999999989,9.751499999999993,9.76509999999999,9.681549999999987,9.794549999999987,9.665099999999995,9.690449999999998,9.811499999999995,9.749749999999977,9.870599999999996,9.828749999999985,9.751750000000015,9.852149999999995
859,9.646199999999993,9.937399999999997,9.9591,9.979549999999996,,9.931399999999996,9.900049999999993,9.737249999999989,9.881799999999984,9.768149999999991,9.775900000000007,9.70304999999999,9.810000000000002,9.679599999999994,9.707349999999991,9.826750000000004,9.763249999999971,9.883700000000005,9.840699999999998,9.773649999999975,9.858099999999979
860,9.654349999999994,9.939050000000002,9.97155,9.981299999999997,,9.938299999999998,9.90724999999999,9.754099999999994,9.898849999999982,9.776600000000002,9.791899999999998,9.718849999999975,9.82695000000001,9.686800000000005,9.719099999999997,9.828249999999997,9.776199999999989,9.893750000000011,9.854199999999992,9.779699999999991,9.879999999999995
861,9.665300000000002,9.947200000000002,9.981000000000002,,,9.9469,9.927899999999994,9.769849999999991,9.904849999999982,9.792450000000002,9.801449999999988,9.720399999999984,9.828450000000004,9.703849999999989,9.720699999999994,9.840100000000007,9.793199999999985,9.903449999999992,9.871449999999982,9.792849999999987,9.89224999999999
862,9.679899999999975,9.95935,9.982799999999997,,,9.958100000000002,9.936399999999992,9.777449999999988,9.91834999999999,9.803249999999991,9.819999999999993,9.73229999999998,9.840550000000007,9.718349999999987,9.732499999999987,9.854449999999986,9.794849999999968,9.916699999999992,9.884749999999997,9.794600000000003,9.903050000000007
863,9.681449999999984,9.980049999999999,,,,9.980800000000002,9.939549999999997,9.794349999999994,9.935349999999985,9.823949999999996,9.827249999999992,9.733899999999977,9.852450000000005,9.719850000000008,9.746250000000003,9.870299999999986,9.81049999999999,9.93350000000001,9.898899999999998,9.810999999999979,9.904899999999998
864,9.701849999999979,9.981549999999999,,,,9.98245,9.94619999999999,9.808999999999983,9.938799999999986,9.828749999999985,9.840450000000004,9.754349999999988,9.854099999999988,9.731750000000005,9.762499999999989,9.882550000000009,9.827249999999992,9.938400000000001,9.905249999999995,9.82695000000001,9.9298
865,9.709399999999988,,,,,,9.958499999999987,9.827049999999986,9.946099999999987,9.830449999999985,9.842299999999994,9.7654,9.877900000000011,9.747500000000002,9.775549999999981,9.899550000000005,9.828749999999985,9.945799999999991,9.925549999999987,9.82850000000002,9.937000000000012
866,9.718999999999994,,,,,,9.971949999999993,9.828549999999993,9.958249999999992,9.84099999999998,9.8553,9.776150000000001,9.879649999999998,9.755200000000002,9.790949999999981,9.9058,9.840649999999982,9.957850000000008,9.936700000000002,9.83929999999998,9.938750000000027
867,9.73139999999998,,,,,,9.981249999999989,9.837949999999992,9.972799999999992,9.853349999999978,9.873449999999991,9.791849999999982,9.897600000000011,9.765899999999988,9.796050000000008,9.922650000000004,9.851799999999969,9.97845000000001,9.938299999999998,9.853549999999984,9.94810000000001
868,9.75279999999998,,,,,,,9.852499999999992,9.980999999999995,9.871699999999976,9.884050000000002,9.799149999999997,9.904599999999988,9.778050000000007,9.8228,9.937099999999987,9.867499999999978,9.990749999999991,9.946249999999992,9.865049999999997,9.959000000000003
869,9.763649999999984,,,,,,,9.85759999999999,,9.88379999999998,9.89885000000001,9.825849999999974,9.927850000000007,9.791300000000007,9.828450000000004,9.938700000000011,9.883099999999985,,9.959499999999991,9.88394999999997,9.971450000000004
870,9.775899999999979,,,,,,,9.879049999999992,,9.899499999999989,9.90485000000001,9.827399999999983,9.937449999999984,9.803650000000005,9.830299999999994,9.946850000000012,9.884749999999968,,9.972099999999983,9.898500000000013,9.980849999999975
871,9.791649999999976,,,,,,,9.885449999999992,,9.906549999999982,9.926700000000011,9.840449999999976,9.938999999999993,9.820400000000006,9.841049999999996,9.958300000000008,9.900199999999984,,9.980249999999984,9.900300000000016,
872,9.80249999999998,,,,,,,9.90249999999999,,9.91764999999998,9.936599999999999,9.847849999999994,9.947100000000006,9.82759999999999,9.85299999999998,9.971299999999985,9.915999999999968,,9.981949999999983,9.918450000000007,
873,9.804449999999974,,,,,,,9.918099999999995,,9.9358,9.93825000000001,9.857649999999978,9.959450000000004,9.829199999999986,9.87254999999999,9.980950000000007,9.932849999999974,,,9.932450000000017,
874,9.826949999999982,,,,,,,9.932549999999992,,9.938449999999989,9.947300000000013,9.879449999999991,9.974099999999993,9.841099999999983,9.8844,,9.93889999999999,,,9.938449999999989,
875,9.828449999999975,,,,,,,9.938649999999996,,9.94659999999999,9.959699999999998,9.88655,9.981099999999998,9.854500000000002,9.901350000000008,,9.946249999999992,,,9.945899999999995,
876,9.838499999999982,,,,,,,9.946149999999989,,9.958799999999997,9.97305,9.901249999999976,,9.871700000000004,9.915999999999997,,9.95829999999998,,,9.959450000000004,
877,9.851349999999996,,,,,,,9.959399999999988,,9.972649999999987,9.981300000000005,9.924449999999979,,9.884600000000006,9.930999999999983,,9.979149999999976,,,9.971699999999998,
878,9.858599999999996,,,,,,,9.971799999999988,,9.980649999999997,,9.936399999999992,,9.899349999999998,9.937700000000007,,9.980849999999975,,,9.980999999999995,
879,9.876899999999978,,,,,,,9.981199999999987,,,,9.93795,,9.905650000000009,9.946249999999992,,,,,9.982849999999985,
880,9.885149999999982,,,,,,,9.998199999999983,,,,9.946149999999989,,9.928650000000005,9.9478,,,,,,
881,9.898799999999994,,,,,,,,,,,9.958499999999987,,9.936949999999996,9.97199999999998,,,,,,
882,9.9058,,,,,,,,,,,9.972799999999978,,9.938500000000005,9.980400000000003,,,,,,
883,9.92874999999998,,,,,,,,,,,9.980399999999975,,9.946650000000005,,,,,,,
884,9.9375,,,,,,,,,,,,,9.970949999999988,,,,,,,
885,9.939099999999996,,,,,,,,,,,,,9.98075,,,,,,,
886,9.947299999999984,,,,,,,,,,,,,9.98230000000001,,,,,,,
887,9.958399999999983,,,,,,,,,,,,,,,,,,,,
888,9.971699999999998,,,,,,,,,,,,,,,,,,,,
889,9.980399999999975,,,,,,,,,,,,,,,,,,,,
1 9 0 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 19 20
2 0 0.013849999999990814 0.0031499999999979877 0.0007999999999981355 0.001799999999995805 0.012499999999988631 0.007699999999999818 0.0014499999999912916 0.003949999999989018 0.007999999999981355 0.013999999999995794 0.007499999999993179 0.012999999999976808 0.009050000000002001 0.009600000000006048 0.0033999999999991815 0.0023500000000069576 0.0012499999999704414 0.014150000000000773 0.00735000000000241 0.009349999999983538 0.003150000000005093
3 1 0.015449999999987085 0.014449999999996521 0.014099999999999113 0.013649999999998386 0.014150000000000773 0.014899999999997249 0.013649999999998386 0.01474999999999227 0.014949999999984698 0.015549999999990405 0.015749999999997044 0.014599999999973079 0.01490000000001146 0.015749999999997044 0.013999999999981583 0.008549999999985403 0.014699999999976399 0.015649999999993724 0.014250000000004093 0.015699999999981173 0.014099999999984902
4 2 0.02744999999998754 0.0251000000000019 0.02120000000000033 0.015249999999994657 0.02719999999999345 0.022750000000002046 0.020599999999987517 0.027149999999991792 0.027049999999988472 0.022949999999994475 0.026749999999992724 0.02759999999997831 0.02719999999999345 0.027899999999988268 0.015549999999990405 0.016150000000010323 0.01624999999998522 0.02760000000000673 0.02179999999998472 0.022850000000005366 0.015699999999981173
5 3 0.03829999999999245 0.03979999999999961 0.03190000000000026 0.028149999999996567 0.039599999999992974 0.028499999999993975 0.03634999999999877 0.031999999999982265 0.035899999999983834 0.037299999999987676 0.03745000000000687 0.02924999999999045 0.035750000000007276 0.0388499999999965 0.028500000000008185 0.027899999999988268 0.024549999999976535 0.02934999999999377 0.03274999999999295 0.04245000000000232 0.028599999999983083
6 4 0.04800000000000182 0.047100000000000364 0.043250000000000455 0.04399999999999693 0.046700000000001296 0.04434999999999434 0.044899999999998386 0.04624999999998636 0.04539999999998656 0.04554999999999154 0.04619999999999891 0.04354999999998199 0.046649999999999636 0.04550000000000409 0.043049999999993815 0.03665000000000873 0.03804999999996994 0.044649999999990087 0.044799999999980855 0.054550000000006094 0.04349999999999454
7 5 0.06499999999999773 0.06519999999999726 0.05425000000000324 0.04560000000000031 0.0648999999999944 0.06354999999999222 0.056099999999986494 0.05484999999998763 0.05744999999998868 0.06394999999999129 0.06444999999999368 0.055999999999983174 0.06479999999999109 0.06340000000000146 0.044749999999993406 0.04744999999999777 0.04624999999998636 0.055849999999992406 0.055949999999995725 0.06524999999999181 0.05525000000000091
8 6 0.07124999999999204 0.07394999999999641 0.07085000000000008 0.06465000000000032 0.07144999999999868 0.0651500000000027 0.07034999999999059 0.06624999999998238 0.06974999999998488 0.07119999999999038 0.07089999999999463 0.06529999999997926 0.07210000000000605 0.07040000000000646 0.06414999999998372 0.06544999999999845 0.06444999999996526 0.06665000000000987 0.06529999999997926 0.07264999999998167 0.067450000000008
9 7 0.08584999999999354 0.08504999999999541 0.07874999999999943 0.07074999999999676 0.086749999999995 0.07854999999999279 0.0768499999999932 0.07669999999998822 0.08554999999998358 0.08579999999999188 0.08789999999999054 0.07259999999999422 0.0856499999999869 0.09010000000000673 0.07269999999999754 0.07165000000000532 0.0705499999999688 0.07159999999998945 0.0724999999999909 0.08714999999997985 0.07589999999999009
10 8 0.09789999999998145 0.0981000000000023 0.09754999999999825 0.0852999999999966 0.09824999999999307 0.09669999999999845 0.09609999999999275 0.09164999999998713 0.09779999999999234 0.09854999999998881 0.09864999999999213 0.09109999999998308 0.09770000000000323 0.09850000000000136 0.08570000000000277 0.08744999999998981 0.08404999999999063 0.08744999999998981 0.08719999999999573 0.10165000000000646 0.0886499999999728
11 9 0.11354999999997517 0.1136499999999998 0.11274999999999835 0.09854999999999592 0.11254999999999882 0.11224999999998886 0.11314999999999031 0.10829999999998563 0.11209999999998388 0.11269999999998959 0.11305000000001542 0.1104999999999734 0.11209999999999809 0.11324999999999363 0.09924999999998363 0.09745000000000914 0.09954999999996517 0.0983500000000106 0.09805000000000064 0.11394999999998845 0.10034999999999172
12 10 0.11504999999999654 0.11540000000000106 0.11845000000000283 0.11224999999999596 0.1183499999999924 0.11379999999999768 0.11909999999998888 0.11439999999998918 0.11359999999999104 0.11974999999999625 0.11459999999999582 0.11744999999999095 0.11375000000001023 0.11930000000000973 0.11230000000000473 0.1129500000000121 0.11309999999997444 0.11375000000001023 0.11274999999997704 0.12014999999999532 0.11360000000001946
13 11 0.1297999999999888 0.13459999999999894 0.1316500000000005 0.11379999999999768 0.13154999999999006 0.12094999999999345 0.13144999999998674 0.13124999999999432 0.13154999999999006 0.13219999999998322 0.13015000000001464 0.13100000000000023 0.13145000000000095 0.13229999999998654 0.11384999999998513 0.11465000000001169 0.12034999999997353 0.12520000000000664 0.11444999999997663 0.13124999999999432 0.11540000000002237
14 12 0.14289999999999736 0.14405 0.14305000000000234 0.1314999999999955 0.1425999999999874 0.13434999999999775 0.14229999999999166 0.14124999999998522 0.1428499999999815 0.14289999999998315 0.14220000000000255 0.14194999999998004 0.14279999999999404 0.14449999999999363 0.12344999999999118 0.1313000000000102 0.13154999999997585 0.1323500000000024 0.13214999999999577 0.14424999999999955 0.13074999999997772
15 13 0.14784999999997694 0.14564999999999628 0.15740000000000265 0.1331999999999951 0.15625 0.14494999999999436 0.15324999999998568 0.15099999999998204 0.14989999999998815 0.15509999999999025 0.14985000000001492 0.14904999999998836 0.15375000000000227 0.15569999999999595 0.1416499999999985 0.14214999999998668 0.143449999999973 0.14424999999999955 0.14324999999999477 0.14584999999999582 0.14389999999997372
16 14 0.1601999999999748 0.15879999999999939 0.16834999999999667 0.14364999999999384 0.16834999999998956 0.15724999999999056 0.15934999999998922 0.16059999999998809 0.16804999999999382 0.16474999999998374 0.15925000000001432 0.15885000000000105 0.16560000000001196 0.165300000000002 0.14519999999998845 0.14930000000001087 0.14504999999996926 0.15745000000001141 0.1554999999999893 0.15879999999998518 0.1453999999999951
17 15 0.16934999999998013 0.16980000000000217 0.16984999999999673 0.15714999999999435 0.18019999999999925 0.16839999999999122 0.1703499999999849 0.1693999999999818 0.16959999999998843 0.17954999999999188 0.16919999999998936 0.16969999999997754 0.16949999999999932 0.1797499999999843 0.15850000000000364 0.15899999999999181 0.1580499999999745 0.15905000000000769 0.16960000000000264 0.16870000000000118 0.1585499999999911
18 16 0.18114999999997394 0.17974999999999852 0.18155000000000143 0.1681999999999988 0.18179999999999552 0.18030000000000257 0.1821999999999946 0.18154999999998722 0.18029999999998836 0.1832499999999868 0.18029999999998836 0.1810999999999865 0.18055000000001087 0.1814499999999839 0.16835000000000377 0.16909999999998604 0.16884999999999195 0.16980000000000928 0.18059999999999832 0.18184999999999718 0.16840000000001965
19 17 0.18294999999997685 0.18554999999999922 0.19760000000000133 0.1801499999999976 0.2009999999999934 0.1938999999999993 0.1839999999999975 0.1863999999999919 0.19969999999999288 0.20134999999999081 0.19700000000000273 0.18454999999997312 0.1983500000000049 0.20034999999998604 0.18035000000000423 0.1810999999999865 0.18104999999997062 0.18074999999998909 0.1821999999999946 0.18574999999998454 0.17944999999997435
20 18 0.2008999999999901 0.20239999999999725 0.209699999999998 0.1851999999999947 0.21554999999999325 0.20109999999999673 0.2015999999999849 0.2018499999999932 0.20954999999999302 0.20999999999999375 0.2030499999999904 0.20134999999999081 0.20830000000000837 0.20204999999998563 0.1842499999999916 0.19909999999998718 0.18474999999997976 0.1856999999999971 0.2012499999999875 0.2002999999999986 0.189200000000028
21 19 0.21459999999999013 0.2181500000000014 0.21835000000000093 0.20109999999999673 0.2252499999999884 0.2154999999999916 0.21759999999999025 0.2176499999999919 0.22409999999999286 0.21819999999999595 0.21880000000001587 0.21274999999999977 0.21920000000000073 0.21854999999999336 0.20149999999998158 0.20070000000001187 0.20059999999998013 0.20134999999999081 0.21699999999998454 0.208849999999984 0.20120000000002847
22 20 0.22539999999997917 0.22679999999999723 0.22800000000000153 0.21314999999999884 0.24714999999999065 0.22544999999999504 0.2258499999999941 0.22519999999998674 0.23749999999998295 0.23089999999999122 0.22630000000000905 0.2184999999999775 0.23499999999998522 0.22614999999998986 0.21479999999999677 0.2178000000000111 0.213799999999992 0.21739999999999782 0.2184999999999775 0.21819999999999595 0.2155999999999949
23 21 0.24719999999999231 0.24985000000000213 0.2450499999999991 0.22519999999999385 0.25319999999999254 0.2462499999999892 0.24549999999999272 0.2371999999999872 0.25064999999999316 0.24619999999998754 0.2467499999999916 0.23644999999999072 0.2509499999999889 0.2477000000000089 0.22534999999999172 0.2261000000000024 0.2223499999999774 0.2266999999999939 0.22709999999997876 0.2376999999999896 0.22525000000001683
24 22 0.2533499999999833 0.25164999999999793 0.2533999999999992 0.24724999999999397 0.2665499999999952 0.25199999999999534 0.25294999999999845 0.2527999999999935 0.26524999999999466 0.25344999999998663 0.2554499999999962 0.25234999999997854 0.2617999999999938 0.25270000000000437 0.2462500000000034 0.24780000000001223 0.2358499999999708 0.2497500000000059 0.24869999999998527 0.2522999999999911 0.23980000000000246
25 23 0.26539999999999964 0.26565000000000083 0.26550000000000296 0.25414999999999566 0.27874999999998806 0.26549999999998875 0.26599999999999113 0.2616999999999905 0.27624999999999034 0.2661499999999961 0.2663000000000011 0.2620499999999879 0.2761999999999887 0.2659500000000037 0.2538499999999999 0.25380000000001246 0.2515499999999804 0.2514500000000055 0.2560499999999877 0.2630999999999801 0.252049999999997
26 24 0.2752999999999872 0.27805000000000035 0.27660000000000196 0.2654499999999942 0.3017499999999984 0.27514999999999645 0.27709999999999013 0.2733999999999952 0.29809999999999093 0.27684999999999604 0.2781999999999982 0.275449999999978 0.2896000000000072 0.27719999999999345 0.26694999999998004 0.26609999999999445 0.26134999999996467 0.2646000000000015 0.27574999999998795 0.26489999999998304 0.2654499999999871
27 25 0.2982499999999959 0.297150000000002 0.2997000000000014 0.27754999999999797 0.30339999999999634 0.29460000000000264 0.29169999999999163 0.29044999999999277 0.3040499999999895 0.29744999999998356 0.3001999999999896 0.29119999999997503 0.30179999999998586 0.2977999999999952 0.27994999999998527 0.2793000000000063 0.27354999999997176 0.27789999999998827 0.2898500000000013 0.2770500000000027 0.2752500000000282
28 26 0.2998999999999796 0.30449999999999733 0.3042999999999978 0.295499999999997 0.32004999999999484 0.30409999999999116 0.3038999999999987 0.3037999999999954 0.3199999999999932 0.3044999999999902 0.30879999999999086 0.3024499999999932 0.3150499999999852 0.30384999999998286 0.29829999999998336 0.30025000000000546 0.29659999999998377 0.29060000000001196 0.3032000000000039 0.30314999999998804 0.2995999999999981
29 27 0.31895000000000095 0.31929999999999836 0.32039999999999935 0.3040499999999966 0.32969999999998834 0.30579999999999075 0.31604999999998995 0.3195999999999941 0.3286499999999819 0.3192499999999825 0.31964999999999577 0.31564999999997667 0.3248999999999853 0.320699999999988 0.30424999999999613 0.30484999999998763 0.30384999999998286 0.30434999999999945 0.3047499999999843 0.30469999999999686 0.30444999999997435
30 28 0.3252499999999827 0.3291499999999985 0.3297499999999971 0.31979999999999364 0.33884999999999366 0.3211999999999904 0.3232499999999874 0.3293999999999926 0.3385499999999837 0.32994999999999663 0.3293999999999926 0.32289999999997576 0.33089999999998554 0.32909999999998263 0.3181500000000028 0.31809999999998695 0.31939999999997326 0.30604999999999905 0.3201999999999998 0.3203499999999906 0.3176000000000272
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ampullary.py Normal file
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from utils_suseptibility import ampullary_punit
from utils_all import p_units_to_show, update_cell_names, load_folder_name
#from utils_all import load_folder_name
#from plt_RAM import plt_punit
from IPython import embed
if __name__ == '__main__':
test = False
if test:
#'2011-09-21-ab' ist echt gut
# '2010-06-21-am' auch gut
# '2012-05-15-ac', auch gut
# '2012-04-26-ae' auch gut
# '2012-05-07-ac' ist gut
# '2010-06-21-ac' die ist geil
# '2010-11-11-al' hat jetzt halt einen peka beim eof
# '2012-05-30-aa' hat beim EODf mehrfahcen einen peak
# '2012-03-23-ad' zweite linie sehr dünn
# '2012-03-30-af' EODf peak
# '2012-04-30-af' Peak beim Mehrcahen
#'2012-07-12-al' fehlt noch
cells_search = [
'2012-05-07-ac',
'2012-04-30-af',
'2012-07-12-al',
'2010-06-21-ac',
'2012-05-15-ac',
'2012-03-30-af',
'2012-04-26-ae',
'2011-09-21-ab',
'2010-11-11-al',
'2010-06-21-am',
'2012-05-30-aa',
'2012-03-23-ad',
]
cells_plot = update_cell_names(cells_search)
for cell in cells_plot:
cells_plot2 = [cell]
ampullary_punit(cells_plot2=cells_plot2, RAM=False)
else:
cells_plot2 = p_units_to_show(type_here='amp')
ampullary_punit(titles=['Low-CV ampullary cell,'],cells_plot2=cells_plot2, RAM=False, scale_val = False, add_texts = [0.25,1.3])

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burst_add.py Normal file
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#from plt_RAM import plt_squares_bursts_single_psd
from utils_suseptibility import *
def plt_squares_bursts_single_psd2(col = 6, cells = ['2012-07-12-ap-invivo-1', '2018-06-26-ah-invivo-1',
'2012-12-20-ae-invivo-1',
'2012-06-27-ah-invivo-1', '2011-10-25-ad-invivo-1',
'2012-12-21-ai-invivo-1',
'2012-05-10-ad-invivo-1', '2012-12-20-ad-invivo-1',
'2012-04-20-ak-invivo-1',
'2012-12-13-ah-invivo-1', '2012-12-20-ab-invivo-1'],nr_clim=10, many=False, width=0.02, row='no', HZ50=True, fs=8, hs=0.39, redo=False, nffts=['whole'],
powers=[1], col_desired=2, var_items=['contrasts'], show=False,
contrasts=[0], noises_added=[''], fft_i='forward', fft_o='forward', spikes_unit='Hz', mV_unit='mV',
D_extraction_method=['additiv_visual_d_4_scaled'], internal_noise=['eRAM'],
external_noise=['eRAM'], level_extraction=['_RAMdadjusted'], cut_off2=300, repeats=[1000000],
receiver_contrast=[1], dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9],
cut_offs1=[300],burst_corrs = [''], clims='all', restrict='restrict', label=r'$\frac{1}{mV^2S}$'):
plot_style()
duration_noise = '_short',
formula = 'code' ##'formula'
# ,int(2 ** 16) int(2 ** 16), int(2 ** 15),
stimulus_length = 1 # 20#550 # 30 # 15#45#0.5#1.5 15 45 100
trials_nrs = [1] # [100, 500, 1000, 3000, 10000, 100000, 1000000] # 500
stimulus_type = '_StimulusOrig_' # ,#
# ,3]#, 3, 1, 1.5, 0.5, ] # ,1,1.5, 0.5] #[1,1.5, 0.5] # 1.5,0.5]3, 1,
variant = 'sinz'
mimick = 'no'
cell_recording_save_name = ''
trans = 1 # 5
aa = 0
for burst_corr, cell, var_type, stim_type_afe, trials_stim, stim_type_noise, power, nfft, dendrid, cut_off1, trial_nrs, c_sig, c_noise, ref_type, adapt_type, noise_added, extract, a_fr, a_fe, in it.product(
burst_corrs, cells, D_extraction_method, external_noise
, repeats, internal_noise, powers, nffts, dendrids, cut_offs1, trials_nrs, c_signal,
c_noises,
ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ):
#print(cell, burst_corr, cell, var_type, stim_type_afe, trials_stim,stim_type_noise, power, nfft, a_fe,a_fr, dendrid, cut_off1,trial_nrs)
# print(trial_nrs, stim_type_noise, trials_stim, power, nfft, a_fe, a_fr, var_type, cut_off1,trial_nrs)
aa += 1
default_figsize(column=2, length=4.2) #5.25.5. 6.8 2+2.25+2.25
a = 0
maxs = []
mins = []
ims = []
perc05 = []
perc95 = []
iternames = [D_extraction_method, external_noise,
internal_noise, powers, nffts, dendrids, cut_offs1, trials_nrs, c_signal,
c_noises,
ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ]
nr = '2'
n = 2
b = 0
ax_lines = []
ax_psds = []
ax_diagonals = []
axss = []
resize_val = None
for c, cell in enumerate(cells):
axps = []
axpsl = []
#pss = []
lines = []
diags = []
pss = []
mats = []
a = 0
#fig = plt.figure(figsize=(12, 5.5))
fig = plt.figure()
for all in it.product(*iternames):
var_type, stim_type_afe, stim_type_noise, power, nfft, dendrid, cut_off1, trial_nrs, c_sig, c_noise, ref_type, adapt_type, noise_added, extract, a_fr, a_fe = all
# print(trials_stim,stim_type_noise, power, nfft, a_fe,a_fr, dendrid, var_type, cut_off1,trial_nrs)
#072
grid0 = gridspec.GridSpec(4, 1, wspace=0.24, bottom=0.1,
hspace=0.04, left=0.08, right=0.93, top=0.98, height_ratios = [1.1,1.3,1,5.8])#7.8
carrier, corrs_all, spikes_tr, spikes_tr_bef = save_spikes_burst_add(
a_fe, a_fr, adapt_type, burst_corr, c_noise, c_sig, cell, cell_recording_save_name, cut_off1,
cut_off2, dendrid, duration_noise, extract, fft_i, fft_o, formula, mimick, nfft, noise_added,
power, ref_type, stim_type_afe, stim_type_noise, stimulus_length, stimulus_type, trans,
trial_nrs, var_type, variant, trial_nrs_base=1, burst_corrs=burst_corrs)
# embed()
grid_stim = gridspec.GridSpecFromSubplotSpec(1, 1, grid0[0], wspace=0.34,
hspace=0.3)
ax_stim = plt.subplot(grid_stim[0])
model_cells = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
model_params = model_cells[model_cells.cell == cell].iloc[0]
cell = model_params.pop('cell')
eod_fr = model_params['EODf']
deltat = model_params.pop("deltat")
carrier = np.array(carrier['0'])
time_s = np.arange(0, len(carrier)*deltat, deltat)
carrier_RAM = np.random.normal(0, 1, size=len(time_s )) # *0.2
#sine = np.sin(2*np.pi*750)*(1+carrier_RAM*0.2)
extracted_am, time_am = extract_am(carrier, time_s, norm=False)
time_am = time_s * 1000
ax_stim.plot(time_am, extracted_am + 0.05, color='red')
ax_stim.plot(time_am, carrier, color = 'grey', linewidth = '0.5')
ax_stim.set_xlim(0, 200)
ax_stim.show_spines('')
grid_upper = gridspec.GridSpecFromSubplotSpec(1, 1, grid0[1], wspace=0.34,
hspace=0.3)
ax_spikes = plt.subplot(grid_upper[0])
#tags.append(ax_spikes)
#axss = plt.subplot(grid_data[0])
#todo: das modell hier einmal machen für eine variante
alpha = 1/(len(burst_corrs)+1)
#ax_spikes = plt.subplot(1,1,1)
colors = colors_overview()
for i in range(len(corrs_all)):
ax_spikes.eventplot(np.array(corrs_all[i]) * 1000, lineoffsets = len(corrs_all)-i, linelengths = 0.85, alpha =alphafunc_burstadd(
alpha, i), color=colors[' P-unit'])
#ax_upper.eventplot(spikes_tr_bef, color='red')
ax_spikes.set_xlim(0, 200)
ax_spikes.xscalebar(0.9, -0.02, 20, 'ms', va='right', ha='bottom')
#ax_diagonal.xscalebar(1, 0.5, 20, 'dB', va='center', ha='right')
ax_spikes.show_spines('')
#ax_spikes.set_yticks([])
##################################
grid_lower = gridspec.GridSpecFromSubplotSpec(row,col,grid0[3], wspace=0.25,
hspace=0.3)
labelpad = 0
for r, trials_stim in enumerate(repeats):
for b, burst_corr in enumerate(burst_corrs):
save_name = save_ram_model(stimulus_length, cut_off1, duration_noise, nfft, a_fe, formula,
stim_type_noise, mimick, variant, trials_stim, power,
stimulus_type,
cell_recording_save_name, nr=nr, fft_i=fft_i, fft_o=fft_o, Hz=spikes_unit,
mV=mV_unit, burst_corr=burst_corr, stim_type_afe=stim_type_afe, extract=extract,
noise_added=noise_added,
c_noise=c_noise, c_sig=c_sig, ref_type=ref_type, adapt_type=adapt_type,
var_type=var_type, cut_off2=cut_off2, dendrid=dendrid, a_fr=a_fr,
trials_nr=trial_nrs, trans=trans, zeros='ones')
path = save_name + '.pkl' # '../'+
print(save_name)
print(a)
grid_data = grid_matrices(a, grid_lower)
#grid1 = gridspec.GridSpecFromSubplotSpec(1, 2, grid[c], hspace=0)
##########596#####################
#frame = frame.sort_values(by='cv')
#fr = frame[frame.cell == cell].fr # np.array(model_cells['cell'])
#cv = frame[frame.cell == cell].cv
model_cells = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
model_params = model_cells[model_cells.cell == cell].iloc[0]
eod_fr = model_params['EODf']
#if cont_cell: # cont_cell
#grid_p_i = gridspec.GridSpecFromSubplotSpec(1, 2, grid_data[1])
#axp = plt.subplot(grid_p_i[0])
#axp.set_xlim(0,600)
sampling_calc = 40000
spikes_mat = [[]]*len(spikes_tr)
p_arrays = []
nfft_here = 2**16
diffs = []
f_array = []
#embed()
#spikes_tr = np.array(spikes_tr)
for s, sp in enumerate(corrs_all[b]):
if len(sp)>0:
#try:
np.isnan(sp[-1])
if np.isnan(sp[-1]):
sp = sp[~np.isnan(sp)]
spikes_mat[s] = cr_spikes_mat(np.array(sp), sampling_rate=sampling_calc,
length=int(sampling_calc * np.array(sp[-1])))
#embed()
p_ar, f_array = ml.psd(spikes_mat[s] - np.mean(spikes_mat[s]), Fs=sampling_calc,
NFFT=nfft_here,
noverlap=nfft_here / 2)
p_arrays.append(p_ar)
diffs.extend(np.diff(sp))
ps = np.mean(p_arrays, axis = 0)[f_array<600]
fss = f_array[f_array<600]
#axi, axs = hist_part(grid1, cell_type, burst_corrs[b], colors, cell, spikes, eod_fr, )
###############################
# square
axs = plt.subplot(grid_data[0])
axss.append(axs)
model = load_model_susept(path, cells, save_name)
#if cont:,
if len(model) > 0:
titles = ''
model_show = model[
(model.cell == cell)] # & (model_cell.file_name == file)& (model_cell.power == power)]
#embed()
new_keys = model_show.index.unique() # [0:490]
# np.abs(
try:
stack_plot = model_show[list(map(str, new_keys))]
except:
stack_plot = model_show[new_keys]
stack_plot = stack_plot.iloc[np.arange(0, len(new_keys), 1)]
stack_plot.columns = list(map(float, stack_plot.columns))
axs.set_xlim(0, 300)
axs.set_ylim(0, 300)
axs.set_xticks_delta(100)
axs.set_yticks_delta(100)
axs.set_aspect('equal')
model_params = model_cells[model_cells['cell'] == cell]
if len(model_show) > 0:
noise_strength = model_params.noise_strength.iloc[0] # **2/2
stack_plot = RAM_norm(stack_plot, trials_stim,model_show=model_show)
if many == True:
titles = titles + ' Ef=' + str(int(model_params.EODf.iloc[0]))
color = title_color(cell)
#embed()
stack_plot, add_nonlin_title, resize_val = rescale_colorbar_and_values(stack_plot, resize_val = resize_val)
#cbar[0].set_label(nonlin_title(add_nonlin_title)) # , labelpad=100
fr = int(np.round(model_show.fr.iloc[0]))
#burst_corr +
axs.text(1, 1.15, titles + ' $f_{Base}=%s$' %(int(np.round(model_show.fr_stim.iloc[0]))) + '\,Hz \n'+ r'$\rm{CV}=%s$' %(
np.round(model_show.cv_stim.iloc[0], 2)), ha = 'right', va = 'center',
transform=axs.transAxes, alpha=alphafunc_burstadd(
alpha, a), color=colors[' P-unit']) # color=color,fontsize=fs,+\n $cv_{B}$='$_{stim} + str(np.round(model_show.cv.iloc[0], 2)) + ' $fr_{B}$=' + str(fr) + '\n $D_{sig}$=' + str( np.round(D_derived, 5)) + ' s=' + str(np.round(model_show.ser_sum_stim.iloc[0], 2)
perc = '' # 'perc'
im = plt_RAM_perc(axs, perc, stack_plot)
pos = np.argmin(np.abs(stack_plot.index -fr/2))
# wenn _ am Anfang des Labels ist verändert das irgendwas, deswgen muss man das auswechseln!
if burst_corr == '':
burst_corr_name = 'no burst corr'
else:
burst_corr_name = burst_corr.replace('_', '-')
######################################
# psds
lines.append(stack_plot.iloc[pos])
diag, diagonals_prj_l = get_mat_diagonals(np.array(stack_plot))
diags.append(diagonals_prj_l)
pss.append(ps)
# embed()
##########################################
ims.append(im)
mats.append(stack_plot)
maxs.append(np.max(np.array(stack_plot)))
mins.append(np.min(np.array(stack_plot)))
perc05.append(np.percentile(stack_plot, 5))
perc95.append(np.percentile(stack_plot, 95))
plt_triangle(axs, model_show.fr.iloc[0], np.round(model_show.fr_stim.iloc[0]),
model_show.eod_fr.iloc[0], 300)
#plt_peaks_several(['',''], [model_show.fr.iloc[0],np.round(model_show.fr_stim.iloc[0])],ps,0,axp,ps,['brown','red'], fss)
axs.set_aspect('equal')
if a in np.arange(col - 1, 100, col):
cbar = colorbar_outside(axs, im, fig, add=0, width=width)
cbar[0].set_label(nonlin_title(' ['+add_nonlin_title))#, labelpad=100
#if b > col-1:
# axs.set_xlabel(F1_xlabel(), labelpad=20)
#axs.text(1.05, -0.35, F1_xlabel(), ha='center', va='center',
# transform=axs.transAxes)
set_xlabel_arrow(axs, ypos=-0.28)
#else:
# remove_xticks(axs)
#axs.set_ylabel(F2_xlabel())
#if a in np.arange(0, 10, 1) * col:
if b in np.arange(0, 100,col):
#axs.set_ylabel(F2_xlabel())
axs.text(-0.35, 0.97, F2_xlabel(), ha='center', va='center',
transform=axs.transAxes, rotation=90)
else:
remove_yticks(axs)
axs.arrow_spines('lb')
#else:
# remove_tick_ymarks(axs)
else:
print('no model there')
embed()
axs.set_title(var_type[7::] + ' \n' + burst_corr, fontsize=fs,
color=color) # + '\n $D_{sig}$=' + str( np.round(D_derived, 5)) + ' s=' + str(np.round(model_show.ser_sum_stim.iloc[0], 2)
#####################################################
####################################################
# plt psds
save_name = save_ram_model(stimulus_length, cut_off1, duration_noise, nfft, a_fe, formula,
stim_type_noise, mimick, variant, trials_stim, power,
stimulus_type,
cell_recording_save_name, nr=nr, fft_i=fft_i, fft_o=fft_o,
Hz=spikes_unit,
mV=mV_unit, burst_corr=burst_corr, stim_type_afe=stim_type_afe,
extract=extract,
noise_added=noise_added,
c_noise=c_noise, c_sig=c_sig, ref_type=ref_type, adapt_type=adapt_type,
var_type=var_type, cut_off2=cut_off2, dendrid=dendrid, a_fr=a_fr,
trials_nr=trial_nrs, trans=trans, zeros='ones')
path = save_name + '.pkl' # '../'+
grid_data = grid_matrices(a, grid_lower)
#grid_data = gridspec.GridSpecFromSubplotSpec(2, 1, grid_lower[b],
# hspace=1, height_ratios = [1.4,1]) #1.2 height_ratios=[1,1,1, 6],
grid_pds = gridspec.GridSpecFromSubplotSpec(1, 1, grid_data[1], wspace=0.34,
hspace = 0.8)#1.5
#ax_psd = plt.subplot(grid_pds[0])
#ax_psds.append(ax_psd)
ax_diagonal = plt.subplot(grid_pds[0])
ax_diagonals.append(ax_diagonal)
line = False
if line:
ax_line = plt.subplot(grid_pds[2])
ax_lines.append(ax_line)
colors = colors_overview()
psd_all = False
if psd_all:
psd_plot = False
if psd_plot:
##############################
# psd
max_val = pss[a] # np.array(diagonals_prj_l)
loag_val = 10 * np.log10(max_val / np.max(pss)) # / np.max(max_val)
ax_psd.plot(fss[fss < np.max(stack_plot.columns)], loag_val[fss < np.max(stack_plot.columns)],
zorder=100 - a, alpha=alphafunc_burstadd(
alpha, a), color=colors[' P-unit'])
else:
model = load_model_susept(path, cells, save_name)
#frame = pd.read_pickle('../calc_model/noise2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_10000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV.pkl')
# if cont:,
if len(model) > 0:
titles = ''
model_show = model[
(model.cell == cell)] # & (model_cell.file_name == file)& (model_cell.power == power)]
try:
model_show['io_cross'] = model_show['io_cross'].astype('complex')
except:
print('io cross something')
embed()
vals = np.abs(np.array(model_show['io_cross']))/model_show['isf_psd']
#embed()
ax_psd.plot(stack_plot.columns, vals, alpha=alphafunc_burstadd(
alpha, a),
color=colors[' P-unit'])#[fss < np.max(stack_plot.columns)]
arrow = False
if arrow:
if b == 3:
set_xlabel_arrow(tranfer_xlabel(),xpos=1.15, ypos=-0.35)
#ax_psd.set_xlabel(tranfer_xlabel(), labelpad = labelpad)
else:
set_xlabel_arrow('', xpos=1.15, ypos=-0.35)
ax_psd.text(0.45, -0.6, tranfer_xlabel(), va='center', ha = 'center', transform=ax_psd.transAxes)
if b == 0:
ax_psd.set_ylabel(trasnfer_ylabel())
else:
remove_yticks(ax_psd)
#vals = np.abs(vals) / (powers / counter)
#if 'osf' in model_show.keys():
# plt_transferfunction(alpha, ax_psd, color, model_show, eod_fr=eod_fr)
#######################################
# line throuhg
if line:
max_val = np.array(lines[a]) # stack_plot.iloc[pos]
loag_val = 10 * np.log10(max_val / np.max(lines)) # / np.max(max_val)
ax_line.plot(np.array(stack_plot.columns), loag_val, label=burst_corr_name, zorder=100 - b,
alpha=alphafunc_burstadd(
alpha, a), color=colors[' P-unit'])
colors = colors_overview()
# diag, diagonals_prj_l = get_mat_diagonals(np.array(stack_plot))
axis_d = axis_projection(stack_plot, axis='')
if psd_all:
ax_psd.show_spines('lb')
ax_psd.set_xlim(0, axis_d[-1])
a += 1
alpha = 1 / (len(burst_corrs) + 1)
a = 0
for b, burst_corr in enumerate(burst_corrs):
#######################################
# diagonal
axis_d = axis_projection(stack_plot, axis='')
max_val = np.array(diags[b])
loag_val = 10 * np.log10(max_val / np.max(diags)) # / np.max(max_val)
colors = colors_overview()
print(np.max(loag_val))
ax_diagonal = ax_diagonals[b]
ax_diagonal.plot(axis_d, loag_val,
label=burst_corr_name, zorder=100 - a, alpha=alphafunc_burstadd(
alpha, a),
color=colors[' P-unit'])
a += 1
ax_diagonal.set_xlim(0, axis_d[-1])
#remove_xticks(ax_psd)
#ax_psd.set_ylim(-12, 0)
ax_diagonal.set_ylim(-14, 0)
scalebar = False
if scalebar:
if b == 0:
ax_diagonal.text(-0.2, 0.5, chi_name(), ha='center', va='center', rotation=90,
transform=ax_diagonal.transAxes) # diagonal_xlabel())
if b == 3:#a == len(lines) - 1:
ax_diagonal.show_spines('b')
ax_diagonal.yscalebar(1, 0.5, 10, 'dB', va='center', ha='right')
# ax_line.yscalebar(1, 0.1, 10, 'dB', va='top', ha='right')
#ax_psd.yscalebar(1, 0.5, 10, 'dB', va='center', ha='right')
#ax_diagonal.text(1.25, 0.5, 'Projection', ha='center', va='center',
# transform=ax_diagonal.transAxes, rotation=90)
#ax_psd.text(1.25, 0.5, 'Output', ha='center', va='center',
# transform=ax_psd.transAxes, rotation=90)
if line:
ax_line.text(1.25, 0.5, 'Line', ha='center', va='center',
transform=ax_line.transAxes, rotation=90)
ax_line.show_spines('b')
else:
if b == 0:
ax_diagonal.set_ylabel('dB', va='center', ha='right')
else:
remove_yticks(ax_diagonal)
# ax_line.yscalebar(1, 0.1, 10, 'dB', va='top', ha='right')
# ax_psd.yscalebar(1, 0.5, 10, 'dB', va='center', ha='right')
# ax_diagonal.text(1.25, 0.5, 'Projection', ha='center', va='center',
# transform=ax_diagonal.transAxes, rotation=90)
# ax_psd.text(1.25, 0.5, 'Output', ha='center', va='center',
# transform=ax_psd.transAxes, rotation=90)
#if line:
# ax_line.text(1.25, 0.5, 'Line', ha='center', va='center',
# transform=ax_line.transAxes, rotation=90)
# ax_line.show_spines('b')
# else:
if line:
ax_line.set_xlim(0, axis_d[-1])
ax_line.set_ylim(-25, 0)
remove_xticks(ax_diagonal)
else:
arrow = False
if arrow:
if b == 3:
set_xlabel_arrow(diagonal_xlabel(), xpos=1.15, ypos=-0.35)
else:
set_xlabel_arrow(diagonal_xlabel(), xpos=1.15, ypos=-0.35)
#ax_diagonal.text(0.45, -0.6,diagonal_xlabel(), va='center', ha='center', transform=ax_diagonal.transAxes)
#ax_psd.text(0.45, -0.6, tranfer_xlabel(), va='center', ha='center', transform=ax_psd.transAxes)
ax_diagonal.set_xlabel(diagonal_xlabel(), labelpad = labelpad)
for a in axps:
a.set_ylim(0,np.max(axpsl))
#set_clim_same_remainer(clims, ims, maxs, mins, nr_clim, perc05, perc95)
#set_same_clim_perc(ims, perc05, perc95)
set_clim_same_here(ims, nr_clim='perc',mats = mats, percnr = 85,perc05=perc05, perc95=perc95, lim_type='up')
if line:
ax_lines[0].get_shared_y_axes().join(*ax_lines)
ax_diagonals[0].get_shared_y_axes().join(*ax_diagonals)
#ax_psds[0].get_shared_y_axes().join(*ax_psds)ax_psds,
tags = np.transpose([axss])#,ax_diagonals,
xoffs = -3.1#2.9
fig.tag(ax_stim, xoffs=xoffs)
fig.tag(ax_spikes,xoffs=xoffs)#,-4.5
tag2(fig,axss,xoffs=xoffs, yoffs = 1.9)#.35#, -0.2
save_visualization(pdf=True)
def grid_matrices(a, grid_lower):
grid_data = gridspec.GridSpecFromSubplotSpec(2, 1, grid_lower[a], hspace=0.5,
height_ratios=[1.7, 1]) # height_ratios=[1,1,1, 6],
return grid_data
def alphafunc_burstadd(alpha, i):
return 1 - alpha * i
def set_same_clim_perc(ims, perc05, perc95):
for i, im in enumerate(ims):
im.set_clim(np.min(perc05), np.max(perc95))
def set_clim_same_remainer(clims, ims, maxs, mins, nr_clim, perc05, perc95):
if 'all' in clims:
remainer = clims.replace('all', '')
if remainer == '':
remainer = 0
else:
remainer = int(remainer)
# embed()
# remainer = 0
# set_clim_same_here()
for i, im in enumerate(ims):
if i < remainer:
# im.set_clim(np.min(np.min(mins)) * nr_clim, np.max(np.max(maxs) / nr_clim))
im.set_clim(perc05[i], perc95[i])
else:
if nr_clim == 'perc':
im.set_clim(perc05[i], perc95[i])
else:
im.set_clim(np.min(perc05), np.max(perc95))
# im.set_clim(mins[i] * nr_clim, maxs[i] / nr_clim)
else:
for i, im in enumerate(ims):
if nr_clim == 'perc':
im.set_clim(perc05[i], perc95[i])
else:
im.set_clim(mins[i] * nr_clim, maxs[i] / nr_clim)
#####################################################
if __name__ == '__main__':
##################################################
# compare all scales with 0.2, 0.25, 0.3, 0.35
model = resave_small_files("models_big_fit_d_right.csv", load_folder = 'calc_model_core')
#e#mbed()
cells = model.cell.unique()
cells_m = np.array(model.cell)
cells = []
cells = ['2018-05-08-af-invivo-1', '2018-05-08-ad-invivo-1', '2012-12-20-ae-invivo-1', '2015-01-20-af-invivo-1',
'2012-07-12-ap-invivo-1', '2018-05-08-ae-invivo-1',
'2014-12-11-aa-invivo-1',
'2015-01-15-ab-invivo-1', '2018-06-25-ad-invivo-1',
'2011-10-25-ad-invivo-1', '2018-05-08-ad-invivo-1',
'2018-05-08-ab-invivo-1', '2014-06-06-ag-invivo-1',
'2014-06-06-ac-invivo-1', '2012-04-20-ak-invivo-1',
'2018-05-08-aa-invivo-1']
cells.extend(cells_m)
cells = ["2013-01-08-aa-invivo-1"]
#cells = ['2012-04-20-ak-invivo-1']
burst_corrs = ['', '_burst_added1_','_burst_added2_','_burst_added3_',]#'_burst_added2only_',]#'_burst_added2_',
params = {'burst_corrs': burst_corrs}
########
# das mit den niedriegeren CVs machen
redo = True
show = True
repeats = [1000000] #250000 250000**params,
#repeats = [10000]
D_extraction_method = ['additiv_cv_adapt_factor_scaled0.2','additiv_cv_adapt_factor_scaled0.25',
'additiv_cv_adapt_factor_scaled0.3','additiv_cv_adapt_factor_scaled0.35',]
D_extraction_method = ['additiv_cv_adapt_factor_scaled0.3','additiv_cv_adapt_factor_scaled0.35',
'additiv_cv_adapt_factor_scaled0.4','additiv_cv_adapt_factor_scaled0.45',]
D_extraction_method = ['additiv_cv_adapt_factor_scaled0.4','additiv_cv_adapt_factor_scaled0.45',]
D_extraction_method = ['additiv_cv_adapt_factor_scaled',
]#'additiv_visual_d_4_scaled',
#cells = ['2018-05-08-ad-invivo-1','2018-05-08-af-invivo-1','2018-06-25-ad-invivo-1']
#todo:hier das mit dem psd machen
#embed()#'perc'
plt_squares_bursts_single_psd2(row = 1, level_extraction=[''], col = 4, cells = cells, burst_corrs=burst_corrs, D_extraction_method=D_extraction_method, nr_clim=10,
many=False,internal_noise=['RAM'],
external_noise=['RAM'], width=0.005, HZ50=False, fs=7, hs=0.8, redo=redo,
var_items=[k for k in params], clims='all0', repeats=repeats, show=show, label=r'$\frac{1}{S}$')#r'$\frac{1}{mV^2S}$'
#embed()

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from utils_suseptibility import *#p_units_to_show,burst_cells
#from plt_RAM import plt_punit
# plt_cellbody_singlecell
def plt_cellbody_singlecell_bursts2(grid1, frame, save_names, cells_plot, cell_type_type, plus=1, ax3=[],
burst_corr='_burst_corr_individual', cells_plot2 = []):
#plt.rcParams["font.family"] = "Times New Roman"
colors = colors_overview()
stack = []
axis = []
ims = []
tags_cell = []
#titles = ['Low CV P-unit', 'High CV P-unit', 'Ampullary cell']
for c, cell in enumerate(cells_plot):
print(cell)
frame_cell = frame[(frame['cell'] == cell)]
frame_cell = unify_cell_names(frame_cell, cell_type=cell_type_type)
try:
cell_type = frame_cell[cell_type_type].iloc[0]
except:
print('cell type prob')
embed()
spikes = frame_cell.spikes.iloc[0]
wss = [0.15, 0.66]
# embed()
# eod, sampling_rate, ds, time_eod = find_eod(frame_cell)
tags = []
spikes_base = []
isi = []
frs_calc = []
fr = frame_cell.fr.iloc[0]
cv = frame_cell.cv.iloc[0]
vs = frame_cell.vs.iloc[0]
eod_fr = frame_cell.EODf.iloc[0]
print('EODF' + str(eod_fr))
spikes_base, isi, frs_calc, cont_spikes = load_spikes(spikes, eod_fr)
save_name_type = ['_allspikes_', '_burstIndividual_']
colors_b = ['grey', colors[cell_type]]
ims = []
wr_l = [1, 0.1, 1, 1]
wr_l = wr_l_cells_susept()
wr_u = [1, 0.1, 1, 1]
grid_cell, grid_upper = grids_upper_susept_pics(c, grid1, hs=0.75, row = 2, hr = [1, 0.8], wr_u = wr_u)
wss = ws_for_susept_pic()
# todo: das wenn die zwei ungleich ist ist noch ein Problem
widht_ratios = [2 + wss[0], 2 + wss[1]]
grid_lower = gridspec.GridSpecFromSubplotSpec(1, len(save_names), grid_cell[1], hspace=0.1, wspace=0.15,
width_ratios=widht_ratios)
'''grid_s1 = gridspec.GridSpecFromSubplotSpec(2, 2, grid_lower[0],
hspace=0.1, wspace=wss[0],
width_ratios=[0.8,
1]) # height_ratios=[1.5, 1.5, 5],
# plot the same also to the next plot
grid_s2 = gridspec.GridSpecFromSubplotSpec(2, 2, grid_lower[1],
hspace=0.1, wspace=wss[1],
width_ratios=[0.8,
1]) # height_ratios=[1.5, 1.5, 5],
'''
several = False
extra_input = False#>
axes = []
axos = []
axds = []
axd2, axi, axo2, grid_lower, grid_s1, grid_s2 = grids_for_psds(save_names, extra_input, grid_cell,
several, widht_ratios = widht_ratios, wss = wss, wr = wr_l)
#axo2 = None
#axd2 = None
test_clim = False
zorder = [100,1]
mats = []
ax_psds = []
add_nonlin_title = None
title_squares = ['All spikes, ', 'First spike, ']
var = ['fr','fr_burst_corr_individual']
for aa, save_name in enumerate(save_names):
add_save = '_cell' + cell + save_name_type[aa] #
# grid_lower = gridspec.GridSpecFromSubplotSpec(1, len(save_names), grid_cell[1], hspace=0.1,
# wspace=0.15)
title_square = title_squares[aa]
load_name = load_folder_name('calc_RAM') + '/' + save_name + '_' + cell
# embed()
# axes = []
stack = load_data_susept(load_name + '.pkl', load_name, add=add_save, load_version='csv', cells = cells_plot2)
if len(stack) > 0:
files = stack['file_name'].unique()
amps = stack['amp'].unique()
file_name = files[0]
stack_file = stack[stack['file_name'] == file_name]
amps_defined = [np.min(stack_file['amp'].unique())]
# embed()
xpos_xlabel = -0.24
for aaa, amp in enumerate(amps_defined):
if amp in np.array(stack_file['amp']):
alpha = find_alpha_val(aa, save_names)
#add_save = '_cell' + str(cell) + '_amp_' + str(amp)
xlim = [0,1.1]#
if not several:
#embed()
#lim = find_lim_here(cell, 'individual')
fr = frame[frame.cell == cell][var[aa]].iloc[0]
fr_bc = frame[frame.cell == cell][var[-1]].iloc[0]
#embed()
diagonals_prj_l, axi, eod_fr, fr, stack_final1, axds, axos, ax_square, axo2, axd2,mat, add_nonlin_title = plt_psds_in_one_squares_next(
aa, add_save, amp, amps_defined, axds, axes, axis, axos, c, cells_plot, colors_b,
eod_fr, file_name, grid_lower, ims, load_name, save_names, stack_file, wss, xlim = [],
test_clim=test_clim, zorder=zorder[aa], alpha=alpha, extra_input=extra_input, fr=fr,
title_square=title_square,fr_diag = fr_bc, xpos_xlabel=xpos_xlabel, add_nonlin_title=add_nonlin_title,
color=colors[cell_type], axo2=axo2, peaks_extra = True, axd2=axd2, axi=axi, iterate_var=save_names, amp_give = False)
mats.append(mat)
print(np.max(np.max(mat)))
else:
axi, eod_fr, fr, stack_final1, stack_spikes, axds, axos, ax_square, axo2, axd2 = plt_psds_in_one_squares(
aa, add, amp,
amps_defined, axds, axes,
axis, axos, c, cells_plot,
colors_b, eod_fr,
file_name, files, fr,
grid_s1, grid_s2, ims,
load_name, save_names,
stack_file, wss, xlim, axo2=axo2, axd2=axd2, iterate_var=save_names)
if aa == 0:
if extra_input:
tags.append(axi)
else:
tags.append(axo2)
tags.append(ax_square)
if aa == 1:
tags.append(axd2)
ax_psds.append(axo2)
ax_psds.append(axd2)
################################
# do the scatter of these cells
add = ['', '_burst_corr', ]
add = ['', '_burst_corr_individual']
# embed()
if len(stack) > 0:
load_name = load_folder_name('calc_RAM') + '/' + save_names[aa] + '_' + cell
if ax3 != []:
try:
frame_g = base_to_stim(load_name, frame, cell_type_type, cell_type, stack=stack)
except:
print('stim problem')
embed()
try:
ax3.scatter(frame_g['cv'], frame_g['cv_stim'], zorder=2, alpha=1,
label=cell_type, s=15,
color=colors[str(cell_type)], facecolor='white')
except:
print('scatter problem')
embed()
######################################################
#if aa == 0:
# color_here = 'grey'
#else:
color_here = ['grey', colors[str(cell_type)]][aa]#[colors[str(cell_type)]
add = ['', '_burst_corr', ]
add = ['', '_burst_corr_individual']
#colors_hist = ['grey', colors[str(cell_type)]]
# if len(hists_both) > 1:
# colors_hist = ['grey', colors[str(cell_type)]]
# else:
# colors_hist = [colors[str(cell_type)]]
# for gg in range(len(hists_both)):
# if len(hists_both) > 1:
# hists_here = hists_both[gg]
# embed()
# spikes_base, hists, frs_calc, cont_spikes = load_spikes(spikes, eod_fr, spikes_base, hists, frs_calc)
xlim_e = [0, 200]
#if 'spikes' in stack_final1.keys():
if aa == 0:
axss = plt.subplot(grid_upper[1, -2::])
# axii = plt.subplot(grid_upper[:, 0])
# try:
# embed()
# ax_spikes = plt.subplot(grid_upper[1, 1::])
eod_fr, length, stack_final, stack_final1, trial_nr = stack_preprocessing(amp, stack_file)
stack_spikes = load_data_susept(load_name + '.pkl', load_name, add=add_save, load_version='csv',
load_type='spikes',
trial_nr=trial_nr, stimulus_length=length, amp=amp,
file_name=file_name, redo = True)
# das mache ich damit der Stimulus für Mascha hier einmal mit abgespeichert ist
#stack_stim = load_data_susept(load_name + '.pkl', load_name, add=add + '_sampling_' + str(sampling),
# load_version='csv', load_type='stimulus',
# trial_nr=trial_nr, stimulus_length=length, redo=True, amp=amp,
# file_name=file_name)
plt_spikes(amps_defined, aa, c, cell, cell_type, cells_plot, color_here, eod_fr, fr, axss,
stack_final1, stack_spikes, xlim, axi=axi,xlim_e = [0,150], alpha = alpha, spikes_max = 3)
# print('spikes not there yet')
#else:
# eod_mt, sampling, spikes_loaded = nix_load(cell, stack_final1)
##############################
# isi
if len(isi) > 0:
if aa == 0:
grid_p = gridspec.GridSpecFromSubplotSpec(1, 2, grid_upper[:, 0], width_ratios=[1.4, 2],
wspace=0.3,
hspace=0.55)
#grid_p = gridspec.GridSpecFromSubplotSpec(2, 1, grid_upper[:, 0], height_ratios=[1.4, 2],
# hspace=0.55)
# hspace=0.25)
ax_isi = plt.subplot(grid_p[0])
ax_p = plt.subplot(grid_p[1])
tags.insert(0, ax_isi)
lim_here = find_lim_here(cell, burst_corr=burst_corr)
if np.min(np.concatenate(isi)) < lim_here:
_, spikes_ex, frs_calc2 = correct_burstiness(isi, spikes_base,
[eod_fr] * len(spikes_base),
[eod_fr] * len(spikes_base), lim=lim_here,
burst_corr=burst_corr)
else:
# da machen wir die Burst corr spikes anders
lim_here = find_lim_here(cell, burst_corr=burst_corr)
if np.min(np.concatenate(isi)) < lim_here:
isi, spikes_ex, frs_calc2 = correct_burstiness(isi, spikes_base,
[eod_fr] * len(spikes_base),
[eod_fr] * len(spikes_base), lim=lim_here,
burst_corr=burst_corr)
#embed()
#if len(isi[0])<1:
# print('len thing')
# embed()
right = False
ax_isi = base_cells_susept(ax_isi, ax_p, c, cell, cell_type, cells_plot, colors, eod_fr, frame,
isi, right, spikes_ex,stack,xlim,add_texts = [-3.1,0],texts_left = [250.3,0],peaks = True, pos = -0.55, titles = ['Bursty P-unit,','Bursty P-unit,'],fr_name = '$f_{BaseCorrected}$')
#plt_susept_isi_base(c, cell_type, cells_plot,'grey', ax_isi, isi, xlim=[])# color_here
#ax_p = plt_susept_psd_base(cell_type, 'grey', eod_fr, ax_p, spikes_base, xlim,
# right) #colors[str(cell_type)]
#remove_xticks(ax_p)
#else:
# embed()
##################################
# stimulus
xlim_e = [0, 100]
if aa == 0:
axe = plt.subplot(grid_upper[0, -2::])
plt_stimulus(eod_fr, axe, stack_final1, xlim_e, files[0])
tags.insert(1, axe)
#embed()
set_same_ylimscale(ax_psds)
labels_for_psds(axd2, axi, axo2, extra_input, right = right, xpos_xlabel = xpos_xlabel)
tags_cell.append(tags)
if not test_clim:
set_clim_same_here(ims, mats=mats, lim_type='up', mean_type = True, percnr = 94)
#set_clim_same_here(ims, clims='all', same = 'same', lim_type='up')
#set_clim_same_here(ims, clims='all', same = 'same', lim_type='up')
# join_y(axes)
# axds[0].get_shared_y_axes().join(*axds)
# embed()
# todo: das muss noch der gleiche ylim sein
try:
set_same_ylim(axos)
set_same_ylim(axds)
except:
print('axo thing')
#fig = plt.gcf()
#fig.tag(tags_cell, xoffs=-4, yoffs=1.9) # -1.5diese Offsets sind nicht intuitiv
if not test_clim:
try:
if len(cells_plot2) ==1:
tags_susept_pictures(tags_cell)
else:
tags_susept_pictures(tags_cell,yoffs=np.array([1.1, 1.1, 2.9, 2.9, 2.9, 2.9]))
except:
print('tag thing')
embed()
def burst_cells(amp_desired=[0.5, 1, 5], cell_class=' Ampullary', cells_plot2=[], show=False, annotate=False):
plot_style()
default_figsize(column=2, width=12, length=7.7) #ts=10, fs=10, ls=10,
save_names = ['noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr']
# 'noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s',#__burstIndividual_
# ]
save_names = ['noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s__burstIndividual_']
save_names = ['noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr']
save_names = ['noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr',
'noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr',
'noise_data10_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s_spikes_']
save_names = [
'noise_data10_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s_spikes_',
'noise_data10_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s__burstIndividual__spikes_']
save_names = [version_final(),version_final()+'__burstIndividual_']
# amps_desired, cell_type_type, cells_plot, frame, cell_types = load_isis(save_names, amps_desired = amp_desired, cell_class = cell_class)
cell_type_type = 'cell_type_reclassified'
#frame = load_cv_base_frame(cells_plot2, cell_type_type=cell_type_type)
#
frame, frame_spikes = load_cv_vals_susept(cells_plot2, EOD_type='synch',
names_keep=['spikes','gwn', 'fs', 'EODf', 'cv', 'fr', 'width_75', 'vs',
'cv_burst_corr_individual',
'fr_burst_corr_individual',
'width_75_burst_corr_individual',
'vs_burst_corr_individual', 'cell_type_reclassified',
'cell'], path_sp = '/calc_base_data-base_frame_overview.pkl',frame_general = False)
#print(cell)
#embed()
# if len(cells_plot2)>0:
cells_plot = cells_plot2
# '2012-05-15-ac-invivo-1','2010-06-21-ac-invivo-1','2010-06-18-ah-invivo-1'
# elif len(cells_plot)>6:
# cells_plot = cells_plot[0:6]
# cells_plot = cells_plot[0:6]
# embed()
################################################
# fig = plt.figure(figsize=(11.5, 6))
# das entscheided ob wir ein burst pic haben werden
#default_settings(ts=10, fs=10, ls=10, width=12, length=3.25 * len(cells_plot), column=2)
default_settings_cells_susept(cells_plot, l=3.65)
if len(cells_plot) > 1:
grid1 = big_grid_susept_pics(cells_plot, top=0.96, bottom=0.065)
else:
grid1 = big_grid_susept_pics(cells_plot, top=0.9, bottom=0.12)
plt_cellbody_singlecell_bursts2(grid1, frame, save_names, cells_plot, cell_type_type,
plus=1, burst_corr='_burst_corr_individual', cells_plot2 = cells_plot2)
# embed()
# plt.show()
fig = plt.gcf()
save_visualization(pdf=True)
# fig.savefig()
# save_all(0, cell+save_name+cell_sorted, show, '')
# show_func(show = show)
if __name__ == '__main__':
cells_plot1 = p_units_to_show(type_here = 'contrasts')
cells_plot1 = p_units_to_show(type_here='contrasts')
cells_plot2 = p_units_to_show(type_here = 'bursts')
burst_cells(cells_plot2 = [cells_plot2[0]], show = True, cell_class =' P-unit')#

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from utils_suseptibility import *#p_units_to_show,burst_cells
#from plt_RAM import plt_punit
from burst_cells_suscept import burst_cells
# plt_cellbody_singlecell
def plt_cellbody_singlecell_bursts2(grid1, frame, save_names, cells_plot, cell_type_type, plus=1, ax3=[],
burst_corr='_burst_corr_individual'):
#plt.rcParams["font.family"] = "Times New Roman"
colors = colors_overview()
stack = []
axis = []
ims = []
tags_cell = []
#titles = ['Low CV P-unit', 'High CV P-unit', 'Ampullary cell']
for c, cell in enumerate(cells_plot):
print(cell)
frame_cell = frame[(frame['cell'] == cell)]
frame_cell = unify_cell_names(frame_cell, cell_type=cell_type_type)
try:
cell_type = frame_cell[cell_type_type].iloc[0]
except:
print('cell type prob')
embed()
spikes = frame_cell.spikes.iloc[0]
wss = [0.15, 0.66]
# embed()
# eod, sampling_rate, ds, time_eod = find_eod(frame_cell)
tags = []
spikes_base = []
isi = []
frs_calc = []
fr = frame_cell.fr.iloc[0]
cv = frame_cell.cv.iloc[0]
vs = frame_cell.vs.iloc[0]
eod_fr = frame_cell.EODf.iloc[0]
print('EODF' + str(eod_fr))
spikes_base, isi, frs_calc, cont_spikes = load_spikes(spikes, eod_fr)
save_name_type = ['_allspikes_', '_burstIndividual_']
colors_b = ['grey', colors[cell_type]]
ims = []
wr_l = [1, 0.1, 1, 1]
wr_l = wr_l_cells_susept()
wr_u = [1, 0.1, 1, 1]
grid_cell, grid_upper = grids_upper_susept_pics(c, grid1, hs=0.75, row = 2, hr = [1, 0.8], wr_u = wr_u)
wss = ws_for_susept_pic()
# todo: das wenn die zwei ungleich ist ist noch ein Problem
widht_ratios = [2 + wss[0], 2 + wss[1]]
grid_lower = gridspec.GridSpecFromSubplotSpec(1, len(save_names), grid_cell[1], hspace=0.1, wspace=0.15,
width_ratios=widht_ratios)
'''grid_s1 = gridspec.GridSpecFromSubplotSpec(2, 2, grid_lower[0],
hspace=0.1, wspace=wss[0],
width_ratios=[0.8,
1]) # height_ratios=[1.5, 1.5, 5],
# plot the same also to the next plot
grid_s2 = gridspec.GridSpecFromSubplotSpec(2, 2, grid_lower[1],
hspace=0.1, wspace=wss[1],
width_ratios=[0.8,
1]) # height_ratios=[1.5, 1.5, 5],
'''
several = False
extra_input = False#>
axes = []
axos = []
axds = []
axd2, axi, axo2, grid_lower, grid_s1, grid_s2 = grids_for_psds(save_names, extra_input, grid_cell,
several, widht_ratios = widht_ratios, wss = wss, wr = wr_l)
#axo2 = None
#axd2 = None
test_clim = False
zorder = [100,1]
mats = []
ax_psds = []
add_nonlin_title = None
title_squares = ['All spikes, ', 'First spike, ']
var = ['fr','fr_burst_corr_individual']
for aa, save_name in enumerate(save_names):
add_save = '_cell' + cell + save_name_type[aa] #
# grid_lower = gridspec.GridSpecFromSubplotSpec(1, len(save_names), grid_cell[1], hspace=0.1,
# wspace=0.15)
title_square = title_squares[aa]
load_name = load_folder_name('calc_RAM') + '/' + save_name + '_' + cell
# embed()
# axes = []
stack = load_data_susept(load_name + '.pkl', load_name, add=add_save, load_version='csv', cells = cells_plot2)
if len(stack) > 0:
files = stack['file_name'].unique()
amps = stack['amp'].unique()
file_name = files[0]
stack_file = stack[stack['file_name'] == file_name]
amps_defined = [np.min(stack_file['amp'].unique())]
# embed()
xpos_xlabel = -0.24
for aaa, amp in enumerate(amps_defined):
if amp in np.array(stack_file['amp']):
alpha = find_alpha_val(aa, save_names)
#add_save = '_cell' + str(cell) + '_amp_' + str(amp)
xlim = [0,1.1]#
if not several:
#embed()
#lim = find_lim_here(cell, 'individual')
fr = frame[frame.cell == cell][var[aa]].iloc[0]
fr_bc = frame[frame.cell == cell][var[-1]].iloc[0]
#embed()
diagonals_prj_l, axi, eod_fr, fr, stack_final1, axds, axos, ax_square, axo2, axd2,mat, add_nonlin_title = plt_psds_in_one_squares_next(
aa, add_save, amp, amps_defined, axds, axes, axis, axos, c, cells_plot, colors_b,
eod_fr, file_name, grid_lower, ims, load_name, save_names, stack_file, wss, xlim = [],
test_clim=test_clim, zorder=zorder[aa], alpha=alpha, extra_input=extra_input, fr=fr,
title_square=title_square,fr_diag = fr_bc, xpos_xlabel=xpos_xlabel, add_nonlin_title=add_nonlin_title,
color=colors[cell_type], axo2=axo2, peaks_extra = True, axd2=axd2, axi=axi, iterate_var=save_names, amp_give = False)
mats.append(mat)
print(np.max(np.max(mat)))
else:
axi, eod_fr, fr, stack_final1, stack_spikes, axds, axos, ax_square, axo2, axd2 = plt_psds_in_one_squares(
aa, add, amp,
amps_defined, axds, axes,
axis, axos, c, cells_plot,
colors_b, eod_fr,
file_name, files, fr,
grid_s1, grid_s2, ims,
load_name, save_names,
stack_file, wss, xlim, axo2=axo2, axd2=axd2, iterate_var=save_names)
if aa == 0:
if extra_input:
tags.append(axi)
else:
tags.append(axo2)
tags.append(ax_square)
if aa == 1:
tags.append(axd2)
ax_psds.append(axo2)
ax_psds.append(axd2)
################################
# do the scatter of these cells
add = ['', '_burst_corr', ]
add = ['', '_burst_corr_individual']
# embed()
if len(stack) > 0:
load_name = load_folder_name('calc_RAM') + '/' + save_names[aa] + '_' + cell
if ax3 != []:
try:
frame_g = base_to_stim(load_name, frame, cell_type_type, cell_type, stack=stack)
except:
print('stim problem')
embed()
try:
ax3.scatter(frame_g['cv'], frame_g['cv_stim'], zorder=2, alpha=1,
label=cell_type, s=15,
color=colors[str(cell_type)], facecolor='white')
except:
print('scatter problem')
embed()
######################################################
#if aa == 0:
# color_here = 'grey'
#else:
color_here = ['grey', colors[str(cell_type)]][aa]#[colors[str(cell_type)]
add = ['', '_burst_corr', ]
add = ['', '_burst_corr_individual']
#colors_hist = ['grey', colors[str(cell_type)]]
# if len(hists_both) > 1:
# colors_hist = ['grey', colors[str(cell_type)]]
# else:
# colors_hist = [colors[str(cell_type)]]
# for gg in range(len(hists_both)):
# if len(hists_both) > 1:
# hists_here = hists_both[gg]
# embed()
# spikes_base, hists, frs_calc, cont_spikes = load_spikes(spikes, eod_fr, spikes_base, hists, frs_calc)
xlim_e = [0, 200]
#if 'spikes' in stack_final1.keys():
if aa == 0:
axss = plt.subplot(grid_upper[1, -2::])
# axii = plt.subplot(grid_upper[:, 0])
# try:
# embed()
# ax_spikes = plt.subplot(grid_upper[1, 1::])
eod_fr, length, stack_final, stack_final1, trial_nr = stack_preprocessing(amp, stack_file)
stack_spikes = load_data_susept(load_name + '.pkl', load_name, add=add_save, load_version='csv',
load_type='spikes',
trial_nr=trial_nr, stimulus_length=length, amp=amp,
file_name=file_name, redo = True)
# das mache ich damit der Stimulus für Mascha hier einmal mit abgespeichert ist
#stack_stim = load_data_susept(load_name + '.pkl', load_name, add=add + '_sampling_' + str(sampling),
# load_version='csv', load_type='stimulus',
# trial_nr=trial_nr, stimulus_length=length, redo=True, amp=amp,
# file_name=file_name)
plt_spikes(amps_defined, aa, c, cell, cell_type, cells_plot, color_here, eod_fr, fr, axss,
stack_final1, stack_spikes, xlim, axi=axi,xlim_e = [0,150], alpha = alpha, spikes_max = 3)
# print('spikes not there yet')
#else:
# eod_mt, sampling, spikes_loaded = nix_load(cell, stack_final1)
##############################
# isi
if len(isi) > 0:
if aa == 0:
grid_p = gridspec.GridSpecFromSubplotSpec(1, 2, grid_upper[:, 0], width_ratios=[1.4, 2],
wspace=0.3,
hspace=0.55)
#grid_p = gridspec.GridSpecFromSubplotSpec(2, 1, grid_upper[:, 0], height_ratios=[1.4, 2],
# hspace=0.55)
# hspace=0.25)
ax_isi = plt.subplot(grid_p[0])
ax_p = plt.subplot(grid_p[1])
tags.insert(0, ax_isi)
lim_here = find_lim_here(cell, burst_corr=burst_corr)
if np.min(np.concatenate(isi)) < lim_here:
_, spikes_ex, frs_calc2 = correct_burstiness(isi, spikes_base,
[eod_fr] * len(spikes_base),
[eod_fr] * len(spikes_base), lim=lim_here,
burst_corr=burst_corr)
else:
# da machen wir die Burst corr spikes anders
lim_here = find_lim_here(cell, burst_corr=burst_corr)
if np.min(np.concatenate(isi)) < lim_here:
isi, spikes_ex, frs_calc2 = correct_burstiness(isi, spikes_base,
[eod_fr] * len(spikes_base),
[eod_fr] * len(spikes_base), lim=lim_here,
burst_corr=burst_corr)
#embed()
#if len(isi[0])<1:
# print('len thing')
# embed()
right = False
ax_isi = base_cells_susept(ax_isi, ax_p, c, cell, cell_type, cells_plot, colors, eod_fr, frame,
isi, right, spikes_ex,stack,xlim,add_texts = [-3.1,0],texts_left = [250.3,0],peaks = True, pos = -0.55, titles = ['Bursty P-unit,','Bursty P-unit,'],fr_name = '$f_{BaseCorrected}$')
#plt_susept_isi_base(c, cell_type, cells_plot,'grey', ax_isi, isi, xlim=[])# color_here
#ax_p = plt_susept_psd_base(cell_type, 'grey', eod_fr, ax_p, spikes_base, xlim,
# right) #colors[str(cell_type)]
#remove_xticks(ax_p)
#else:
# embed()
##################################
# stimulus
xlim_e = [0, 100]
if aa == 0:
axe = plt.subplot(grid_upper[0, -2::])
plt_stimulus(eod_fr, axe, stack_final1, xlim_e, files[0])
tags.insert(1, axe)
set_same_ylimscale(ax_psds)
labels_for_psds(axd2, axi, axo2, extra_input, right = right, xpos_xlabel = xpos_xlabel)
tags_cell.append(tags)
if not test_clim:
set_clim_same_here(ims, mats=mats, lim_type='up', mean_type = True, percnr = 94)
#set_clim_same_here(ims, clims='all', same = 'same', lim_type='up')
#set_clim_same_here(ims, clims='all', same = 'same', lim_type='up')
# join_y(axes)
# axds[0].get_shared_y_axes().join(*axds)
# embed()
# todo: das muss noch der gleiche ylim sein
try:
set_same_ylim(axos)
set_same_ylim(axds)
except:
print('axo thing')
#fig = plt.gcf()
#fig.tag(tags_cell, xoffs=-4, yoffs=1.9) # -1.5diese Offsets sind nicht intuitiv
if not test_clim:
try:
if len(cells_plot2) ==1:
tags_susept_pictures(tags_cell)
else:
tags_susept_pictures(tags_cell,yoffs=np.array([1.1, 1.1, 2.9, 2.9, 2.9, 2.9]))
except:
print('tag thing')
embed()
if __name__ == '__main__':
cells_plot2 = p_units_to_show(type_here = 'bursts')
burst_cells( cells_plot2 = [cells_plot2[1]], show = True, cell_class =' P-unit')

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from utils_suseptibility import cells_eigen
from utils_all import p_units_to_show
#from utils_all import load_folder_name
#from plt_RAM import plt_punit
from IPython import embed
if __name__ == '__main__':
#'2010-06-21-ac-invivo-1'
# Bei dem ampullary cells machen wir auch die Bursts weg
['2010-11-26-al-invivo-1', '2010-11-26-am-invivo-1',
'2010-11-08-ab-invivo-1', '2010-11-08-ag-invivo-1', '2010-11-08-ah-invivo-1', '2010-11-08-ai-invivo-1',
'2010-11-11-ak-invivo-1', '2010-11-11-ac-invovo-1',
'2013-04-11-ab-invivo-1',
]
cells = ['2010-11-26-al-invivo-1', '2010-11-26-am-invivo-1'
]
# 10-08-11-ar
# 5 + 10+20
# 0.5, 1, 5, 10, 20
# 2.5 5 10
# 5 10 20 (2012-06-21-av, 2010-08-27-ag)
# 0.5, 1, 5 (2021-08-03-ab)
# 0.1, 0.5, 1, 5 (2021-08-03-ac)
# 1 5 10 20, (2020-10-21-ac)
# 0.1,1,10 (2021-12-17-ad)
# 0.1, 0.5, 1, 5, 10 ,20 (2020-10-27-af)
# 1, 2, 5, 10, 20 (2021-06-18-ae)
unklar = ['2010-07-29-ae-invivo-1']
# für mehrere
# ampullary
## 2.5, 5, 10 (2011-10-25-ab, 2012-07-11-ag, 2012-12-13-ao)
# amp_desired = [2.5, 10, 20]# '2010-08-31-ad-invivo-1',
# cells_plot2 = ['2011-10-25-ac-invivo-1', '2012-05-15-ac-invivo-1','2012-06-08-ae-invivo-1']#['2010-06-21-av-invivo-1', '2010-08-27-ag-invivo-1','2020-10-27-ae-invivo-1']
amp_desired = [1, 5, 10, 20]
amp_desired = [0.1, 1, 10]
amp_desired = [0.5, 1, 5]
amp_desired = [0.5, 1, 5],
# 0.1, 0.5, 1, 5 (2021-08-03-ac,2021-08-03-ae)
amp_desired = [0.1, 0.5, 1, 5]
cells_plot2 = []
# 0.5, 1, 5 (2021-08-03-ab)
amp_desired = [0.5, 1, 5]
cells_plot2 = ['2021-08-03-ab-invivo-1','2021-08-03-ac-invivo-1','2021-08-03-ae-invivo-1']
# 1 5 10 20, (2020-10-21-ac)
amp_desired = [1,5,10,20]
cells_plot2 = []
# 0.1,1,10 (2021-12-17-ad)
# 0.1, 0.5, 1, 5, 10 ,20 (2020-10-20-ab, 2020-10-27-af)
#amp_desired = [1,5,10,20]
#cells_plot2 = []
# das hat uns glaube ich ganz gut gefallen
# 5 10 20 (2010-06-21-av, 2010-08-27-ag, 2010-08-31-ad)
amp_desired = [5,20]# '2010-08-31-ad-invivo-1', 10,
cells_plot2 = p_units_to_show(type_here = 'eigen_small')
# I like: '2020-10-21-ac' '2010-06-21-av-invivo-1', '2010-08-27-ag-invivo-1' '2021-08-03-ag-invivo-1, '20-10-21-aa-invivo-1
#2021-12-17-ad
#embed()
#'2017-10-25-am-invivo-1', '2010-11-26-an-invivo-1'
#save_names = ['noise_data10_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s_spikes_']
#load_name = load_folder_name('calc_RAM') + '/' + save_names[0] + '_' +'2017-10-25-am-invivo-1'# '2010-11-26-an-invivo-1'#'2017-10-25-am-invivo-1'
#stack = pd.read_pickle(load_name + '.pkl')
#embed()#[0, 250]#'High CV P-unit', at Fr \n at 1/2 EODf'
cells_eigen(titles = ['Low-CV P-unit,','Low-CV P-unit,'], cells_plot2 = cells_plot2, amp_desired = amp_desired, show = True,cell_class = ' P-unit')

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from utils_suseptibility import ampullary_punit
from utils_all import p_units_to_show
#from utils_all import load_folder_name
#from plt_RAM import plt_punit
from IPython import embed
if __name__ == '__main__':
cells_plot2 = p_units_to_show(type_here = 'contrasts')
ampullary_punit( cells_plot2=[cells_plot2[0]], titles=['Low-CV P-unit,'])

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from utils_suseptibility import ampullary_punit
from utils_all import p_units_to_show
#from utils_all import load_folder_name
#from plt_RAM import plt_punit
from IPython import embed
if __name__ == '__main__':
cells_plot2 = p_units_to_show(type_here='bursts')
ampullary_punit(cells_plot2=[cells_plot2[1]], titles=['High-CV P-unit,', 'Ampullary cell,'],)

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#sys.path.insert(0, '..')
#from plt_RAM import plt_RAM_overview_nice
#from utils_susept import
from IPython import embed
from matplotlib import gridspec as gridspec, pyplot as plt
import numpy as np
#embed()
from utils_all_down import default_settings, resave_small_files,update_cell_names
from utils_suseptibility import default_figsize, NLI_scorename, kernel_scatter, plt_burst_modulation_hists, \
plt_model_overview2, version_final
from utils_all import colors_overview, cv_base_name, load_overview_susept, make_log_ticks, p_units_to_show, \
save_visualization, \
setting_overview_score, update_ssh_file, load_folder_name
#import lstat
try:
from plotstyle import plot_style, spines_params
except:
a = 5
from heterogeneity import plt_fr_cv_base, get_grids_for_cv_fr, get_frame_for_base_plot
def data_overview2():
# calcdf_RAM_overview()
save_name = 'calc_RAM_overview-noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr'
save_name = 'calc_RAM_overview-noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s'
save_name = 'calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__CutatBeginning_0.05_s_NeurDelay_0.005_s'
save_name = 'calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s'
col = 4
row = 2 # sharex=True,
plot_style()
default_figsize(column=2, length=7.35)
#fig, ax = plt.subplots(4, 2) # , figsize=(14, 7.5) constrained_layout=True,
three = True
if three:
top = True
grid00 = gridspec.GridSpec(2, 1, wspace=0.37, bottom=0.15,
hspace=0.25, left=0.085, right=0.95, top=0.94, height_ratios = [1,2.7])
else:
grid00 = gridspec.GridSpec(4, 2, wspace=0.75, bottom=0.04,
hspace=0.6, left=0.15, right=0.99, top=0.95)
top = False
#grid0 = gridspec.GridSpec(3, 3, wspace=0.35, bottom=0.2,
# hspace=1, left=0.085, right=0.95, top=0.94)
grid0 = gridspec.GridSpecFromSubplotSpec(1, 5, grid00[0],width_ratios = [1,0.4, 1,0.55,1], hspace=0.35,wspace=0)
grid1 = gridspec.GridSpecFromSubplotSpec(2, 5, grid00[1],width_ratios = [1,0.4, 1,0.55,1], hspace=1.1, wspace=0)
###################################
###############################
# Das ist der Finale Score
scoreall = 'perc99/med'
###################################
#scores = [scoreall+'_diagonal_proj']
#score = scores[0]
##########################
# Auswahl: wir nehmen den mean um nicht Stimulus abhängigen Noise rauszumitteln
#save_names = []
save_names = ['calc_RAM_overview-_simplified_'+version_final()]#'calc_RAM_overview-_simplified_noise_data9_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s__burstIndividual_','calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s__burstIndividual_',
save_names = [version_final_overview()]#_abs_
#save_names = ['calc_RAM_overview-_simplified_noise_data12_nfft0.5sec_original__StimPreSaved4__abs_']
counter = 0
colors = colors_overview()
ax_j = []
axls = []
axss = []
cell_types = [' Ampullary', ' P-unit', ]
counter = 0
tags = []
log = False
counterplus = 2
max_val = 1.5
score_burst_corr = 'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr'#scores[0]
score_n_burst_corr= 'max(diag5Hz)/med \n diagonal_proj \n fr_base_w_burstcorr' # , 'max(diag5Hz)/med \n diagonal_proj \n fr_base_w_burstcorr']
score_n_burst_corr = 'Fr$_{BurstCorr}$/Median' # , 'max(diag5Hz)/med \n diagonal_proj \n fr_base_w_burstcorr']
score_n_burst_corr = NLI_burstcorr_name()
score = 'max(diag5Hz)/med_diagonal_proj_fr'
score_n = 'max(diag5Hz)\n/med_diagonal_proj_fr'
score_n = 'Fr/Median' # , 'max(diag5Hz)/med \n diagonal_proj \n fr_base_w_burstcorr']
score_n = NLI_scorename()
#score = 'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr' # scores[0]
redo = False#True#True
frame_load_sp = load_overview_susept(save_names[0], redo=redo, redo_class=redo)
#embed()
test_amps = False
if test_amps:
cells_amp = ['2011-09-21-ab', '2010-06-21-am', '2012-05-15-ac', '2012-04-26-ae', '2012-05-07-ac', '2010-06-21-ac']
cells_amp = update_cell_names(cells_amp)
frame_amps = frame_load_sp[frame_load_sp.cell.isin(cells_amp)]
fish_plot = False
if fish_plot:
axls, axss, counter, frame_load_sp, max_val, score_burst_corr = fish_plt_false(ax_j, axls, axss, cell_types,
colors, counter, grid0, log,
max_val, save_names,
score_burst_corr)
alpha = 0.3
####################################################################
# base plots
# cells = p_units_to_show()
# cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells)
burst_choice = ['', '_burst_corr_individual']
xmax_burst = [[1.5,'no'],[0.8,'no']]
for gg in range(len(burst_choice)):
cells = p_units_to_show()
species = ' Apteronotus leptorhynchus'
cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells, save_names = save_names, species = species)
ax0, ax_cv, ax_fr = get_grids_for_cv_fr(gg, grid0[counter])#counter
if gg == 0:
#ax_cv.text(1.5, 2, r'\bf{A} \textit{Apteronotus leptorhynchus}', ha = 'center', va = 'center', transform=ax_cv.transAxes)
text_punits(ax_cv, colors)
ax_cv.text(0.35, 0.45, r'Ampullary cells', ha='left', va='center',
transform=ax_cv.transAxes, color = colors[' Ampullary'])#' Ampullary',
species_letter(ax_cv)
print(species)
x_axis, y_axis = plt_fr_cv_base(ax0, ax_cv, ax_fr, burst_choice,
frame_load, gg, alpha = alpha, xmax = xmax_burst[gg], species = species)
ax0.set_xticks_delta(0.5)
tags.append(ax_cv)
counter += 2
#frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
####################################################################
# base plots
# cells = p_units_to_show()
# cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells)
burst_choice = ['']#, '_burst_corr_individual']
#counter += 1
nrs = [1,3]
for gg in range(len(burst_choice)):
cells = p_units_to_show()
species = ' Eigenmannia virescens'
cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells,save_names = save_names, species = species)
ax0, ax_cv, ax_fr = get_grids_for_cv_fr(gg, grid0[counter])#counter
#ax_cv.set_title('Eigenmannia virescens')
#ax_cv.text(0.5, 2, B, ha='center', va='center', transform=ax_cv.transAxes)
species_letter(ax_cv, letter='B', species=r'\textit{Eigenmannia virescens}')
print(species)
x_axis, y_axis = plt_fr_cv_base(ax0, ax_cv, ax_fr, burst_choice,
frame_load, gg, alpha = alpha, xmax = ['no','no'], species = species)
ax0.set_xticks_delta(0.5)
tags.append(ax_cv)
counter += 2
#embed()
###########################
###########################
###########################
# counterreset!
counter = 0
#grid0 = grid1
# base ready
####################################################################
####################################################################
####################################################################
####################################################################
#grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
#
cv_name = "cv_base"
species = ' Apteronotus leptorhynchus'
cell_type_here = ' P-unit'
#for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
#embed()
#setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
x_axis = ['cv_base',score]#,'burst_fraction_burst_corr_individual_stim']#, 'cv_base']#,'cv_base',]
x_axis_names = [cv_base_name(), score_n] # ,'Burst Fraction$_{Stim}$' 'CV$_{Base}$']#,'Burst Fraction$_{Base}$',]
y_axis = ['burst_fraction_burst_corr_individual_base', score_burst_corr] # , 'coherence_', ]
y_axis_name = ['Burst Fraction$_{Base}$', score_n_burst_corr]
#
var_item_names = ['Response Modulation [Hz]']#,'Response Modulation [Hz]']#,'Burst Fraction$_{Base}$']#'Response Modulation',]#,score_n_burst_corr,'Response Modulation [Hz]']#'Burst Fraction$_{Stim}$',['Modulation']#,
#'Response Modulation [Hz]',
var_types = ['response_modulation']#,'response_modulation']#,'burst_fraction_burst_corr_individual_base']#'response_modulation']#,score_burst_corr,'response_modulation']#,'burst_fraction_burst_corr_individual_stim','cv_base''response_modulation'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
logs = [False,False]#,False]
xmins = [0,0]#,'no']
#'response_modulation',
#var_item_names = ['burst_diff']#['Modulation [Hz]']#,
#var_types = ['burst_diff'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
#['burst_fraction_burst_corr_individual_stim']
#score_name = ['Burst Fraction$_{Stim}$']# 'CV$_{Base}$']#,'Burst Fraction', ]
frame_file['burst_diff'] = np.abs(frame_file['burst_fraction_burst_corr_individual_stim']-frame_file['burst_fraction_burst_corr_individual_base'])
burst_fraction = [1,1,1]#, 0.5, 1, 1]#05burst_fraction_burst_corr_individual_base
#score_n = ['Perc99/Med','Perc99/Med']
#score = ['max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr','max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr']
n = False
xlim_here0 = [0,1.5]
ax_j, axls, axs, axss, counter = plt_var_axis([], axls, axss,y_axis_name, burst_fraction, cell_type_here, counter, cv_name,
frame_file, grid1, max_val, score_burst_corr, y_axis, var_item_names,
var_types, x_axis, x_axis_names, counterplus = counterplus, n = n, xlim = xlim_here0, log = logs, top = top, xmins = xmins, extra_cells = False)
text_punits(ax_j[0], colors, xpos = 0.65, ypos = 0.9)
#plt.show()
#hier beide anhängen
tags.append(ax_j[-1])
#tags.extend(ax_j)
xlim_here = axss[-1].get_xlim()
ylim_here = axss[-1].get_ylim()
axss[-1].set_xlim(xlim_here0)
ax_j[-1].set_xlim(xlim_here0)
start = np.max([xlim_here[0],ylim_here[0]])
end = np.min([xlim_here[1], ylim_here[1]])
#axss[-1].plot([start, end], [start, end], color='grey', linewidth=0.5)
#plt.show()
####################################################################
#grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
#
cv_name = "cv_base"
species = ' Apteronotus leptorhynchus'
cell_type_here = ' P-unit'
#for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
x_axis = ['burst_fraction_burst_corr_individual_base','cv_base',score]#,'burst_fraction_burst_corr_individual_stim']#, 'cv_base']#,'cv_base',]
x_axis_names = ['Burst Fraction$_{Base}$', 'CV$_{Base}$',score_n] # ,'Burst Fraction$_{Stim}$' 'CV$_{Base}$']#,'Burst Fraction$_{Base}$',]
y_axis = [score_burst_corr, 'burst_fraction_burst_corr_individual_base', score_burst_corr] # , 'coherence_', ]
y_axis_name = [score_n_burst_corr, 'Burst Fraction$_{Base}$', score_n_burst_corr]
#
var_item_names = ['Response Modulation [Hz]']#,'Response Modulation [Hz]']#,'Burst Fraction$_{Base}$']#'Response Modulation',]#,score_n_burst_corr,'Response Modulation [Hz]']#'Burst Fraction$_{Stim}$',['Modulation']#,
#'Response Modulation [Hz]',
var_types = ['response_modulation']#,'response_modulation']#,'burst_fraction_burst_corr_individual_base']#'response_modulation']#,score_burst_corr,'response_modulation']#,'burst_fraction_burst_corr_individual_stim','cv_base''response_modulation'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
logs = [False,False]#,False]
xmins = [0,0]#,'no']
#'response_modulation',
#var_item_names = ['burst_diff']#['Modulation [Hz]']#,
#var_types = ['burst_diff'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
#['burst_fraction_burst_corr_individual_stim']
#score_name = ['Burst Fraction$_{Stim}$']# 'CV$_{Base}$']#,'Burst Fraction', ]
frame_file['burst_diff'] = np.abs(frame_file['burst_fraction_burst_corr_individual_stim']-frame_file['burst_fraction_burst_corr_individual_base'])
burst_fraction = [1,1,1]#, 0.5, 1, 1]#05burst_fraction_burst_corr_individual_base
#score_n = ['Perc99/Med','Perc99/Med']
#score = ['max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr','max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr']
xmax = 6
ax_j, axls, axs, axss, counter = plt_var_axis([], axls, axss,y_axis_name, burst_fraction, cell_type_here, counter, cv_name,
frame_file, grid1, max_val, score_burst_corr, y_axis, var_item_names,
var_types, x_axis, x_axis_names, counterplus = counterplus, n = n, log = logs, top = top, xmins = xmins, ymaxs = [xmax], extra_cells = True)
#text_punits(axs, colors)
#hier beide anhängen
reset_xlims(axls, axss, xmax)
tags.append(ax_j[-1])
#tags.extend(ax_j)
xlim_here = axss[-1].get_xlim()
ylim_here = axss[-1].get_ylim()
start = np.max([xlim_here[0],ylim_here[0]])
end = np.min([xlim_here[1], ylim_here[1]])
#axss[-1].plot([start, end], [start, end], color='grey', linewidth=0.5)
#plt.show()
########################################################################
#####################################################################
# Eigemania Zellen
ylim_eigen = [0, 6]
cell_types = [' P-unit',' Ampullary'] # ,' P-unit',' Ampullary', ]
x_axis = ["cv_base"] # , "cv_base_w_burstcorr","cv_base", ]
cv_name_title = [cv_base_name()] # ,'CV$_{BurstCorr}$','CV']
species_all = [' Eigenmannia virescens'] # ,' Apteronotus leptorhynchus',' Eigenmannia virescens']
#counter += counterplus
ax_j, axls, axss, cell_types, _, max_val, _ = species_with_both_cells(grid1[counter], cell_types, ax_j,
axls, axss, colors,
cv_name_title,
save_names, score,
species_all, x_axis,ylim = ylim_eigen,
max_val=max_val,n = n, alpha = alpha, log = False, score_n = score_n)
tags.append(ax_j[-1])
axss[-1].set_title('')
ax_j[-1].set_title('')
counter += 1
####################################################################
# grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
#
cv_name = "cv_base"
species = ' Apteronotus leptorhynchus'
cell_type_here = ' P-unit'
# for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
x_axis = [
score] # 'burst_fraction_burst_corr_individual_base','cv_base']#,score]#,'burst_fraction_burst_corr_individual_stim']#, 'cv_base']#,'cv_base',]
x_axis_names = [
score_n] # ,'Burst Fraction$_{Base}$', 'CV$_{Base}$']#,score_n] # ,'Burst Fraction$_{Stim}$' 'CV$_{Base}$']#,'Burst Fraction$_{Base}$',]
y_axis = [
score_burst_corr] # , 'burst_fraction_burst_corr_individual_base']#, score_burst_corr] # , 'coherence_', ]
y_axis_name = [score_n_burst_corr] # , 'Burst Fraction$_{Base}$']#, score_n_burst_corr]
#
var_item_names = [
'Burst Fraction$_{Base}$'] # 'Response Modulation',]#,score_n_burst_corr,'Response Modulation [Hz]']#'Burst Fraction$_{Stim}$',['Modulation']#,
# 'Response Modulation [Hz]',
var_types = [
'burst_fraction_burst_corr_individual_base'] # 'response_modulation']#,score_burst_corr,'response_modulation']#,'burst_fraction_burst_corr_individual_stim','cv_base''response_modulation'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
logs = [False]
xmins = ['no']
ymins = ['no']
# 'response_modulation',
# var_item_names = ['burst_diff']#['Modulation [Hz]']#,
# var_types = ['burst_diff'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
# ['burst_fraction_burst_corr_individual_stim']
# score_name = ['Burst Fraction$_{Stim}$']# 'CV$_{Base}$']#,'Burst Fraction', ]
frame_file['burst_diff'] = np.abs(frame_file['burst_fraction_burst_corr_individual_stim'] - frame_file[
'burst_fraction_burst_corr_individual_base'])
burst_fraction = [1, 1, 1] # , 0.5, 1, 1]#05burst_fraction_burst_corr_individual_base
# score_n = ['Perc99/Med','Perc99/Med']
# score = ['max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr','max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr']
xmax = 7
ax_j, axls, axs, axss, counter = plt_var_axis(ax_j, axls, axss, y_axis_name, burst_fraction, cell_type_here,
counter, cv_name,
frame_file, grid1, max_val, score_burst_corr, y_axis, var_item_names,
var_types, x_axis, x_axis_names, counterplus = counterplus,n = n, log=logs, top=top, xmins=xmins, ymaxs = [xmax], extra_cells= False)
reset_xlims(axls, axss, xmax)
axss[-1].set_xticks_delta(2)
axss[-1].set_yticks_delta(2)
axss[-1].set_xlim(0.5, xmax)
ax_j[-1].get_shared_x_axes().join(*[axss[-1], ax_j[-1]])
#text_punits(axs, colors)
# embed()
tags.append(ax_j[-1])
xlim_here = axss[-1].get_xlim()
ylim_here = axss[-1].get_ylim()
start = np.max([xlim_here[0], ylim_here[0]])
end = np.min([xlim_here[1], ylim_here[1]])
axss[-1].plot([start, end], [start, end], color='grey', linewidth=0.5)
# embed()
####################################################################
cv_name = "cv_base"
species = ' Apteronotus leptorhynchus'
cell_type_here = ' P-unit'
# for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
var_types = ['response_modulation']#,'response_modulation']
x_axis = ['burst_fraction_burst_corr_individual_base']#,score]
var_item_names = ['Response Modulation [Hz]']#,'Response Modulation [Hz]']#'Modulatoin'
x_axis_names = ['Burst Fraction$_{Base}$']#,score_n]
burst_fraction = [1, 1, 1, 1] # burst_fraction_burst_corr_individual_base
y_axis = ['burst_fraction_burst_corr_individual_stim']#,score_burst_corr]
y_axis_name = ['Burst Fraction$_{Stim}$']#,score_n_burst_corr]
#embed()
test = False
if test:
burst_fraction, var_item_names, var_types, x_axis, x_axis_names, y_axis, y_axis_name = test_eodfr(
burst_fraction, var_item_names, var_types, x_axis, x_axis_names, y_axis, y_axis_name)
#embed()
for v, var_type in enumerate(var_types):
# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid1[counter])
cmap, _, _ = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
x_axis[v], frame_file, max_val, y_axis[v],
burst_fraction=burst_fraction[v], n = n,top = top, var_item=var_type)
axs.set_ylabel(y_axis_name[v])
axs.set_xlabel(x_axis_names[v])
#text_punits(axs, colors)
plot_extra_cells(axs, frame_file, y_axis, v, x_axis, type_here = 'contrasts')
#if v == 1:
if test == False:
axs.plot([0, 1.01], [0, 1.01], color='grey', linewidth=0.5)
tags.append(axk)
counter += counterplus
####################################################################
#grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
#
mutual_information = False
if mutual_information:
cv_name = "cv_base"
species = ' Apteronotus leptorhynchus'
cell_type_here = ' P-unit'
#for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
x_axis = ['burst_fraction_burst_corr_individual_base','cv_base',score]#,'burst_fraction_burst_corr_individual_stim']#, 'cv_base']#,'cv_base',]
x_axis_names = ['Burst Fraction$_{Base}$', 'CV$_{Base}$',score_n] # ,'Burst Fraction$_{Stim}$' 'CV$_{Base}$']#,'Burst Fraction$_{Base}$',]
y_axis = ['contrast_integral', 'burst_fraction_burst_corr_individual_base', score_burst_corr] # , 'coherence_', ]
y_axis_name = ['(MI$_{R}$-MI$_{C}$)/MI$_{C}$', 'Burst Fraction$_{Base}$', score_n_burst_corr]
#
var_item_names = ['Response Modulation [Hz]']#,'Response Modulation [Hz]']#,'Burst Fraction$_{Base}$']#'Response Modulation',]#,score_n_burst_corr,'Response Modulation [Hz]']#'Burst Fraction$_{Stim}$',['Modulation']#,
#'Response Modulation [Hz]',
var_types = ['response_modulation']#,'response_modulation']#,'burst_fraction_burst_corr_individual_base']#'response_modulation']#,score_burst_corr,'response_modulation']#,'burst_fraction_burst_corr_individual_stim','cv_base''response_modulation'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
logs = [False,False]#,False]
xmins = [0,0]#,'no']
#'response_modulation',
#var_item_names = ['burst_diff']#['Modulation [Hz]']#,
#var_types = ['burst_diff'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
#['burst_fraction_burst_corr_individual_stim']
#score_name = ['Burst Fraction$_{Stim}$']# 'CV$_{Base}$']#,'Burst Fraction', ]
frame_file['burst_diff'] = np.abs(frame_file['burst_fraction_burst_corr_individual_stim']-frame_file['burst_fraction_burst_corr_individual_base'])
burst_fraction = [1,1,1]#, 0.5, 1, 1]#05burst_fraction_burst_corr_individual_base
#score_n = ['Perc99/Med','Perc99/Med']
#score = ['max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr','max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr']
ax_j, axls, axs, axss, counter = plt_var_axis([], axls, axss,y_axis_name, burst_fraction, cell_type_here, counter, cv_name,
frame_file, grid0, max_val, score_burst_corr, y_axis, var_item_names,
var_types, x_axis, x_axis_names,counterplus = coutnerplus, n = n, extra_cells = True, log = logs, top = top, xmins = xmins)
#hier beide anhängen
tags.append(ax_j[-1])
#tags.extend(ax_j)
xlim_here = axss[-1].get_xlim()
ylim_here = axss[-1].get_ylim()
start = np.max([xlim_here[0],ylim_here[0]])
end = np.min([xlim_here[1], ylim_here[1]])
#axss[-1].plot([start, end], [start, end], color='grey', linewidth=0.5)
#####################################################################
# Eigemania Zellen
cell_types = [' P-unit',' Ampullary'] # ,' P-unit',' Ampullary', ]
x_axis = ["response_modulation"] # , "cv_base_w_burstcorr","cv_base", ]
cv_name_title = ['Response Modulation [Hz]'] # ,'CV$_{BurstCorr}$','CV']
species_all = [' Eigenmannia virescens'] # ,' Apteronotus leptorhynchus',' Eigenmannia virescens']
#counter += 1
ax_j, axls, axss, cell_types, frame_load_sp, max_val, score_burst_corr = species_with_both_cells(grid1[counter], cell_types, ax_j,
axls, axss, colors,
cv_name_title,
save_names, score,
species_all, x_axis, alpha = alpha,
max_val=max_val, n = n, ylim = ylim_eigen, log = False, score_n = score_n)
tags.append(ax_j[-1])
axss[-1].set_title('')
ax_j[-1].set_title('')
counter+= 1#counterplus
#####################################################
#grid_lower_lower = gridspec.GridSpecFromSubplotSpec(1, 2, grid0[1], wspace = 0.5, hspace=0.55)#, height_ratios = [1,3]
cell_types = [' P-unit']#,' P-unit',' Ampullary', ]
restrict = False
if restrict:
ax_j, axls, axss, counter = modulation_restrict(ax_j, axls, axs, axss, cell_types, counter, cv_name,
frame_load_sp, grid0, log, max_val, score_burst_corr, score_n,
species, tags, top)
################################
# coherence
coherence_plot = False
if coherence_plot:
coherence_plot_modulation(ax_j, axls, axss, counter, frame_load_sp, grid0, max_val, tags, top)
##############################################
# jetzt kommen die extra P-unit Eigen statistiken
#species = ' Eigenmannia virescens'
#cv_name = 'cv_base'
#ax = plt.subplot(grid[4, 0])
#for c, cell_type_here in enumerate(cell_types):
# frame_file = setting_overview_score(cell_type_here, frame_load_sp, species=species)
# plt_eigen(cv_name, ax, c, cell_type_here, cells_extra, colors, frame_file, max_val, score,
# species, x_axis, y_axis)
########################
# modell
model_plot = False
if model_plot:
model_overview_plot(grid0, scoreall)
#ax_j[0].get_shared_y_axes().join(*[ax_j[0],ax_j[2],ax_j[3]])
#ax_j[0].get_shared_y_axes().join(*[ax_j[1], ax_j[3], ax_j[5], axls[0], axls[1], axls[2]])
#ax_j[0].get_shared_x_axes().join(*ax_j)
#ax_j[0].get_shared_x_axes().join(*[ax_j[0],ax_j[1]])
#ax_j[2].get_shared_x_axes().join(*[ax_j[2], ax_j[3]])
#ax_j[4].get_shared_x_axes().join(*[ax_j[4], ax_j[5]])
#plt.show()
fig = plt.gcf()
fig.tag([[tags[0], tags[1], tags[3], tags[4], tags[6], tags[7]],[tags[2], tags[5], tags[8]]], xoffs=-4.2, yoffs=1.12)
show = False#True
save_visualization(pdf = True, show = show)
def reset_xlims(axls, axss, xmax):
axss[-1].set_ylim(0.5, xmax)
axls[-1].get_shared_y_axes().join(*[axss[-1], axls[-1]])
def species_letter(ax_cv, letter = 'A', species = r'\textit{Apteronotus leptorhynchus}'):
ax_cv.text(-0.26, 1.8, letter, ha='left',
transform=ax_cv.transAxes, fontsize=13.5)
ax_cv.text(-0.1, 1.8, species, ha='left',
transform=ax_cv.transAxes) # va='center',
def text_punits(ax_cv, colors, xpos = 0.35, ypos = 0.85):
ax_cv.text(xpos, ypos, r'P-units', ha='left', va='center',
transform=ax_cv.transAxes, color=colors[' P-unit'])
def fish_plt_false(ax_j, axls, axss, cell_types, colors, counter, grid0, log, max_val, save_names, score_burst_corr):
#####################################################################
x_axis = ["cv_base"] # , "cv_base_w_burstcorr","cv_base", ]
cv_name_title = ['CV'] # ,'CV$_{BurstCorr}$','CV']
species_all = [' Apteronotus leptorhynchus'] # ,' Apteronotus leptorhynchus',' Eigenmannia virescens']
ax_j, axls, axss, cell_types, frame_load_sp, max_val, score_burst_corr = species_with_both_cells(grid0[counter],
cell_types, ax_j,
axls, axss, colors,
cv_name_title,
save_names,
score_burst_corr,
species_all,
x_axis, log=log,
max_val=max_val)
counter += 1
#####################################################################
x_axis = ["cv_base"] # , "cv_base_w_burstcorr","cv_base", ]
cv_name_title = ['CV'] # ,'CV$_{BurstCorr}$','CV']
# embed()
species_all = [' Apteronotus albifrons'] # ,' Apteronotus leptorhynchus',' Eigenmannia virescens']
ax_j, axls, axss, cell_types, frame_load_sp, max_val, score_burst_corr = species_with_both_cells(grid0[-2],
cell_types, ax_j,
axls, axss, colors,
cv_name_title,
save_names,
score_burst_corr,
species_all,
x_axis,
max_val=max_val)
return axls, axss, counter, frame_load_sp, max_val, score_burst_corr
def version_final_overview():
return 'calc_RAM_overview-_simplified_noise_data12_nfft0.5sec_original__StimPreSaved4__direct_'
def NLI_burstcorr_name():
return 'NLI$(f_{BaseCorrected})$'#f_{BaseCorr}
def test_eodfr(burst_fraction, var_item_names, var_types, x_axis, x_axis_names, y_axis, y_axis_name):
########################################
# test
var_types = ['response_modulation']
x_axis = ['burst_fraction_burst_corr_individual_base']
var_item_names = ['Response Modulation [Hz]'] # 'Modulatoin'
x_axis_names = ['Burst Fraction$_{Base}$']
burst_fraction = [1, 1, 1, 1] # burst_fraction_burst_corr_individual_base
y_axis = ['eod_fr']
y_axis_name = ['EOD Fr']
return burst_fraction, var_item_names, var_types, x_axis, x_axis_names, y_axis, y_axis_name
def model_overview_plot(grid0, scoreall):
model = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
cells = model.cell.unique()
axm = plt.subplot(grid0[-3])
plt_model_overview2(axm, cells, scores=[scoreall + '_'])
plt.subplots_adjust(left=0.07, right=0.95, top=0.98, bottom=0.05, wspace=0.45, hspace=0.55)
def coherence_plot_modulation(ax_j, axls, axss, counter, frame_load_sp, grid0, max_val, tags, top):
cv_name = "cv_base"
species = ' Apteronotus leptorhynchus'
cell_type_here = ' P-unit'
# for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
var_types = ['burst_fraction_burst_corr_individual_base'] # ,'response_modulation']
x_axis = ['cv_base'] # ,'burst_fraction_burst_corr_individual_base']
var_item_names = ['Burst Fraction'] # , 'Modulatoin' ]
x_axis_names = ['Burst Fraction$_{Base}$'] # ,'Burst Fraction$_{Base}$']
burst_fraction = [1] # , 1, 1, 1]#burst_fraction_burst_corr_individual_base
y_axis = ['mutual_informaiton_'] # , 'coherence_''burst_fraction_burst_corr_individual_stim']
y_axis_name = ['Mutual Information'] # , 'coherence''Burst Fraction$_{Stim}$']
# embed()
for v, var_type in enumerate(var_types):
# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[counter])
cmap, _, y_axis = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
x_axis[v], frame_file, max_val, y_axis[v],
burst_fraction=burst_fraction[v], xmin=0, ymin=0, top=top,
var_item=var_type)
axs.set_ylabel(y_axis_name[v])
axs.set_xlabel(x_axis_names[v])
# if v == 1:
# axs.plot([0, 1], [0, 1], color='grey', linewidth=0.5)
tags.append(axk)
counter += 1
# ax_j
def modulation_restrict(ax_j, axls, axs, axss, cell_types, counter, cv_name, frame_load_sp, grid0, log, max_val,
score_burst_corr, score_n, species, tags, top):
burst_corr_reset = 'response_modulation'
burst_fraction = [50] # , 1000, 1000] # ,1,1]
burst_fraction = [0.01] # , 1, 1] # ,1,1]
burst_corr_reset = 'burst_fraction_burst_corr_individual_stim'
for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
##############################################
# modulatoin comparison for both cell_types
################################
# Modulation, cell type comparison
# todo: hier die diff werte über die zellen
# ax_here = []
# axd = plt.subplot(grid_lower_lower[0, c])
# embed()
# kernel_histogram(axk, colors[str(cell_type_here)], np.array(x_axis), norm=True, step=0.03, alpha=0.5)
# embed()
# axk.show_spines('lb')
# axs = plt.subplot(grid0[6+c])
var_types = ['response_modulation']
x_axis = ['cv_base']
var_item_names = ['Response Modulation [Hz]', ] # 'Modulatoin'
x_axis_names = ['CV$_{Base}$']
y_axis = [score_burst_corr]
y_axis_name = [score_n]
for v, var_type in enumerate(var_types):
# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[counter])
cmap, _, y_axis = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
x_axis[v], frame_file, max_val, y_axis[v],
burst_fraction=burst_fraction[c], top=top,
burst_fraction_reset=burst_corr_reset, var_item=var_type)
axs.set_ylabel(y_axis_name[v])
axs.set_xlabel(x_axis_names[v])
# remove_yticks(axl)
if log:
axl.set_yscale('log')
axs.set_yscale('log')
axl.minorticks_off()
axl.set_yticks_blank()
tags.append(axk)
counter += 1
if c in [0, 2]:
######################################################
# hier kommen die kontrast Punkte dazu
# für die Zellen spielt Burst correctin ja keine Rolle
# if cv_n == 0:
if cell_type_here == ' P-unit':
cells_plot2 = p_units_to_show(type_here='contrasts')[0:2]
else:
cells_plot2 = [p_units_to_show(type_here='contrasts')[-1]]
# for cell_plt in cells_plot2:
cells_extra = frame_file[frame_file['cell'].isin(cells_plot2)].index
# ax = plt.subplot(grid[1, cv_n])
# todo: hier nur noch die kleinste und größte Amplitude nehmen
# embed()
axs.scatter(frame_file[cv_name].loc[cells_extra], frame_file[score_burst_corr].loc[cells_extra],
s=5, color='white', edgecolor='black', alpha=0.5, clip_on=False) # colors[str(cell_type_here)]
# cmap, _, y_axis = plt_modulation_overview(axs, c, cell_type_here,
# cv_name, frame_file, max_val, score,
# species)
# axs.set_ylabel(score)
# embed()#frame_file[(frame_file.cv_base < 0.65) & (frame_file.response_modulation > 200)].response_modulation
# axs.set_xlabel(cv_name)
# axs.get_shared_x_axes().join(*[axs, axd])
# elif species == ' Apteronotus albifrons':
# plt_albi(ax[4, 1], cell_type_here, colors, max_val, species, x_axis, y_axis)
# ax[1,cv_n].set_xlim(0, max_val)
# set_same_ylim(np.concatenate(ax[1::, :]))
# set_same_ylim(np.concatenate(ax[1::, :]),ylim_type ='xlim')
# set_same_ylim(ax[0, :], ylim_type='xlim')
# set_ylim_same()
# ax[1, 1].get_shared_y_axes().join(*ax[1, 1::])
# embed()
# counter += 1
# embed()
return ax_j, axls, axss, counter
def plt_var_axis(ax_j, axls, axss,score_name, burst_fraction, cell_type_here, counter, cv_name, frame_file, grid0, max_val, score,
scores_here, var_item_names, var_types, x_axis, x_axis_names, ymaxs = ['no'], n = True,counterplus = 1, extra_cells_false = False, extra_cells = True, ymins = ['no'], xmins = [], xlim = None, log = [False], top = False):
for v, var_type in enumerate(var_types):
# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[counter])
counter += counterplus
try:
cmap, _, y_axis = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
x_axis[v], frame_file, max_val, scores_here[v],
ymax = ymaxs[v], burst_fraction=burst_fraction[v], n = n, xlim = xlim, xmin = xmins[v],ymin =ymins[v], var_item=var_type, top = top)
except:
print('burst thing')
embed()
axk.show_spines('')
axl.show_spines('')
axs.set_ylabel(score_name[v])
axs.set_xlabel(x_axis_names[v])
if v in [0, 1]:
if log[v]:
axl.set_yscale('log')
axs.set_yscale('log')
axes = [axl, axs]
make_log_ticks(axes)
axl.set_yticks_blank()
# remove_yticks(axl)
axl.minorticks_off()
if extra_cells:
plot_extra_cells(axs, frame_file, scores_here, v, x_axis)
return ax_j, axls, axs, axss, counter
def plot_extra_cells(axs, frame_file, scores_here, v, x_axis, type_here = 'bursts'):
############################
# extra Zellen Scatter
# todo: diese Zellen müssen noch runter konvertiert werden
# todo: extra funktion für Zellen über 9 Snippets schreiben und die nochmal extra machen
cells_plot2 = p_units_to_show(type_here=type_here)
# for cell_plt in cells_plot2:
cells_extra = frame_file[frame_file['cell'].isin(cells_plot2)].index
# ax = plt.subplot(grid[1, cv_n])
#embed()
test_amps = False
if test_amps:
cells_here = frame_file.cell.unique()
cells_amp = ['2011-09-21-ab', '2010-06-21-am', '2012-05-15-ac', '2012-04-26-ae', '2012-05-07-ac',
'2010-06-21-ac']
cells_amp = update_cell_names(cells_amp)
# frame_amps = frame_file[frame_file.cell.isin(cells_amp)]
for cell_amp in cells_amp:
if cell_amp in cells_here:
print(cell_amp)
# cells_plot = update_cell_names(cells_search)
try:
axs.scatter(frame_file[x_axis[v]].loc[cells_extra], frame_file[scores_here[v]].loc[cells_extra],
s=5, color='white', edgecolor='black', alpha=0.5,
clip_on=False) # colors[str(cell_type_here)]
except:
print('small things')
embed()
def species_with_both_cells(grid0, cell_types, ax_j, axls, axss, colors, cv_name_title, save_names, score, species_all, x_axis, log = True, n = True,ylim = None, alpha = 1, max_val = 1.5, score_n ='Perc(99)/Median'):
for cv_n, cv_name in enumerate(x_axis):
if cv_n == 0:
redo = False
else:
redo = False
redo = False
frame_load_sp = load_overview_susept(save_names[0], redo=redo, redo_class=redo)
if len(frame_load_sp.cell.unique()) <470:
# double check if the file is the newest
#hostname, password, root, username = credencials0()
dated_up = update_ssh_file(load_folder_name('calc_RAM') + '/' + save_names[0] + '.csv')
if dated_up == 'yes':
frame_load_sp = load_overview_susept(save_names[0], redo=True, redo_class=redo)
#res = pysftp.put(filepath, destination)
#ssh_stdin, ssh_stdout, ssh_stderr = ssh.exec_command(cmd_to_execute)
#ssh
#embed()
# frame_file = setting_overview_score(cell_type_here, frame_load_sp, min_amp=True, species=species)
# print(np.isnan(species))
for c, cell_type_here in enumerate(cell_types):
#if c == 1:
# embed()
species = species_all[cv_n]
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
##################################
# modulation and species comparison
# x_axis, y_axis = get_axis(cv_name, frame_file, score)
# if cv_n == 0:
###############################
#######################
# Kernel Histogram
# plot the histograms of the values above the according vals
# grid = gridspec.GridSpecFromSubplotSpec(1, 1, grid0[0],hspace=0, wspace = 0.15)#grid[0, cv_n]
#
if c == 0:
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0)
if c in [0, 2]:
axk.set_title(species)
# embed()
if len(frame_file) > 0:
if log:
ymin = 'no'
else:
ymin = 0
xmin = 0
if ylim:
axs.set_ylim(ylim)
axs, x_axis = kernel_scatter(axl, cell_types, axk, axs, ax_j, c, cell_type_here, colors, cv_n, cv_name,
frame_file, grid0, max_val,
score, log = log,alpha = alpha, n = n,xmin = xmin, ymin = ymin)
if log:
axl.set_yscale('log')
make_log_ticks([axs])
axl.set_yticks_blank()
axl.minorticks_off()
axs.set_xlabel(cv_name_title[cv_n])
if cv_n == 0:
axs.set_ylabel(score_n)
axl.show_spines('')
axk.show_spines('')
if cv_n == 0:
axm = [axs]
return ax_j, axls, axss, cell_types, frame_load_sp, max_val, score
def get_grid_4(ax_j, axls, axss, grid0):
grid_k = gridspec.GridSpecFromSubplotSpec(2, 2, grid0,
hspace=0.1, wspace=0.1, height_ratios=[0.35, 3], width_ratios=[3, 0.5])
axk = plt.subplot(grid_k[0, 0])
ax_j.append(axk)
axs = plt.subplot(grid_k[1, 0])
axss.append(axs)
axl = plt.subplot(grid_k[1, 1])
axls.append(axl)
return axk, axl, axs, axls, axss, ax_j
if __name__ == '__main__':
data_overview2()

521
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#sys.path.insert(0, '..')
#from plt_RAM import plt_RAM_overview_nice
#from utils_susept import
from IPython import embed
from matplotlib import gridspec as gridspec, pyplot as plt
import numpy as np
from utils_all_down import default_settings
from utils_suseptibility import colors_overview
from utils_suseptibility import default_figsize, NLI_scorename,pearson_label, exclude_nans_for_corr, kernel_scatter, \
plt_burst_modulation_hists, \
version_final
from utils_all import update_cell_names, load_overview_susept, make_log_ticks, p_units_to_show, save_visualization, setting_overview_score
from scipy import stats
try:
from plotstyle import plot_style, spines_params
except:
a = 5
def data_overview3():
# calcdf_RAM_overview()
save_name = 'calc_RAM_overview-noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr'
save_name = 'calc_RAM_overview-noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s'
save_name = 'calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__CutatBeginning_0.05_s_NeurDelay_0.005_s'
save_name = 'calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s'
col = 4
row = 2 # sharex=True,
plot_style()
default_figsize(column=2, length=4.2) #65.5
#fig, ax = plt.subplots(4, 2) # , figsize=(14, 7.5) constrained_layout=True,
var_it = 'Response Modulation [Hz]'
var_it2 = ''
if var_it == '':
ws = 0.35
else:
ws = 0.65
if var_it2 != '':
right = 0.9
else:
right = 0.98
right = 0.85
ws = 0.75
print(right)
grid0 = gridspec.GridSpec(2, 2, wspace=ws, bottom=0.13,
hspace=0.3, left=0.1, right=right, top=0.95)
###################################
###############################
# Das ist der Finale Score
scoreall = 'perc99/med_diagonal_proj'
scoreall = 'max(diag5Hz)/med_diagonal_proj_fr'#'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr'#'perc99/med_diagonal_proj'
#'max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr',
###################################
#scores = [scoreall+'_diagonal_proj']
##########################
# Auswahl: wir nehmen den mean um nicht Stimulus abhängigen Noise rauszumitteln
#save_names = []
save_names = ['calc_RAM_overview-_simplified_'+version_final()]#'calc_RAM_overview-_simplified_noise_data9_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s__burstIndividual_','calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s__burstIndividual_',
save_names = ['calc_RAM_overview-_simplified_noise_data12_nfft0.5sec_original__StimPreSaved4__abs__direct_']
save_names = ['calc_RAM_overview-_simplified_noise_data12_nfft0.5sec_original__StimPreSaved4__direct_']
#save_names = ['calc_RAM_overview-_simplified_noise_data12_nfft0.5sec_original__StimPreSaved4__abs_']
#####################################################
#grid_lower_lower = gridspec.GridSpecFromSubplotSpec(1, 2, grid0[1], wspace = 0.5, hspace=0.55)#, height_ratios = [1,3]
cell_types = [' Ampullary', ' P-unit']#, ' P-unit',]#' P-unit',
species = ' Apteronotus leptorhynchus'
burst_corr_reset = 'response_modulation'
burst_fraction = [1000, 1000]# 50, # ,1,1]
burst_fraction = [1, 1] # ,1,1]
burst_corr_reset = 'burst_fraction_burst_corr_individual_stim'
redo = False
#embed()
counter = 0
tags = []
frame_load_sp = load_overview_susept(save_names[0], redo=redo, redo_class=redo)
scores = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr',
'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr'] # + '_diagonal_proj'
scores = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr',
] # + '_diagonal_proj'
cell_types_name = ['Ampullary cells','P-units']
for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
##############################################
# modulatoin comparison for both cell_types
################################
# Modulation, cell type comparison
# todo: hier die diff werte über die zellen
#ax_here = []
#axd = plt.subplot(grid_lower_lower[0, c])
# embed()
#kernel_histogram(axk, colors[str(cell_type_here)], np.array(x_axis), norm=True, step=0.03, alpha=0.5)
#embed()
#axk.show_spines('lb')
#axs = plt.subplot(grid0[6+c])
colorbar = False
#if colorbar:
x_axis = ['cv_base','response_modulation']#,'fr_base']#
var_item_names = [var_it,var_it2]#,var_it2]#['Response Modulation [Hz]',]
var_types = ['response_modulation','']#,'']#'response_modulation'
x_axis_names = ['CV','Response Modulation [Hz]']#$_{Base}$,'Fr$_{Base}$',]
#score = scores[0]
score_n = ['Perc99/Med', 'Perc99/Med']
score = scores[c]
scores_here = [score,score]#,score]
score_name = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr']#,'max(diag5Hz)/med_diagonal_proj_fr']#'Perc99/Med'
score_name = ['Fr/Med', 'Fr/Med']#'Fr/Med'] # 'Perc99/Med'
score_name = [NLI_scorename(), NLI_scorename()]#NLI_scorename()] # 'Fr/Med''Perc99/Med'
ax_j = []
axls = []
axss = []
#embed()
#frame_max = frame_file[frame_file[score]>5]
cv_name = "cv_base"
max_val = 1.5
log = False
for v, var_type in enumerate(var_types):
# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[v,counter])
if log:
ymin = 'no'
else:
ymin = 0
xmin = 0
cmap, _, y_axis = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
x_axis[v], frame_file, max_val, scores_here[v],
burst_fraction=burst_fraction[c], ha = 'right', x_pos = 1, xmin = xmin, ymin = ymin, burst_fraction_reset = burst_corr_reset,var_item=var_type)
if v == 0:
colors = colors_overview()
axk.set_title(cell_types_name[c], color = colors[cell_type_here])
axk.show_spines('')
axl.show_spines('')
axs.set_ylabel(score_name[v])
axs.set_xlabel(x_axis_names[v])
if (cell_type_here == ' P-unit') & (x_axis[v] == 'cv_base' ):
axs.set_xlim(0,1.7)
#remove_yticks(axl)
if log:
axl.set_yscale('log')
axs.set_yscale('log')
make_log_ticks([axs])
axl.minorticks_off()
axl.set_yticks_blank()
plt_specific_cells(axs, cell_type_here, x_axis[v], frame_file, scores_here[v])
tags.append(axk)
counter += 1
#plt.show()
############################
# 1 Print scores
print('\n')
speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
#for species in speciess:
for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
#embed()
cells_amp = ['2011-09-21-ab', '2010-06-21-am', '2012-05-15-ac', '2012-04-26-ae', '2012-05-07-ac',
'2010-06-21-ac']
cells_amp = update_cell_names(cells_amp)
frame_amps = frame_file[frame_file.cell.isin(cells_amp)]
frame_amps.cell.unique()
#['2012-05-07-ac-invivo-1', '2012-04-26-ae','2012-05-15-ac-invivo-1', '2010-06-21-ac-invivo-1','2010-06-21-am-invivo-1', '2011-09-21-ab-invivo-1']
#embed()
#if len(frame_amps)
#################################################################
# print the corrs
score = scores[c]#'ser_base',
score_print = [score]
score_print = [scores[c],scores[c],scores[c],scores[c],scores[c],'burst_fraction_burst_corr_individual_base']
corr_vars = ['cv_base','response_modulation','fr_base','ser_first_base','burst_fraction_burst_corr_individual_stim','cv_base']
#for score in score_print:
for c_nr, corr_var in enumerate(corr_vars):#,'ser_sum_corr'
score = score_print[c_nr]
x = frame_file['fr_base']
y = frame_file['ser_first']
c = frame_file['cv_base']
try:
c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, corr_var, cv_name=corr_var , score=score)
except:
print('frame file lost')
embed()
corr, p_value = stats.pearsonr(x_axis, y_axis)
pears_l = pearson_label(corr, p_value, y_axis, n=True)
print(start_name(cell_type_here, species) + ' ' + corr_var + ' to '+str(score) + pears_l)
#if corr_var == 'ser_first_base':#
# embed()
print('\n')
################################
# cv to fr correlation
c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, 'fr_base', cv_name='fr_base',
score='cv_base')
corr, p_value = stats.pearsonr(x_axis, y_axis)
pears_l = pearson_label(corr, p_value, y_axis, n=True)
print(start_name(cell_type_here, species) + ' ' + 'fr_base' + ' to ' + 'cv_base' + pears_l)
################################
# cv to fr correlation
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, 'burst_fraction_burst_corr_individual_base', cv_name='burst_fraction_burst_corr_individual_base',
score='cv_base')
corr, p_value = stats.pearsonr(x_axis, y_axis)
pears_l = pearson_label(corr, p_value, y_axis, n=True)
print(start_name(cell_type_here, species) + ' amprange: ' + 'burst_fraction_individual_base' + ' to ' + 'cv_base' + pears_l)
###############################
# fr to nonline but for both modulations
c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, 'fr_base', cv_name='fr_base',
score=score)
corr, p_value = stats.pearsonr(x_axis, y_axis)
pears_l = pearson_label(corr, p_value, y_axis, n=True)
print(start_name(cell_type_here, species) + ' amprange: ' + ' ' + 'fr_base' + ' to score' + pears_l)
##################################
print('\n')
#embed()
#todo: hier noch die Werte für die Methodik printen
test = False
#embed()
if test:
plt.hist(frame_file['lim_individual'])
#print(frame_file['lim_individual'])
############################################
############################
# 2 Print names
# printe um welchen Zeitraum es sich handelt
frame_file = setting_overview_score(frame_load_sp, cell_type_here = '', f_exclude=False, snippet=None, min_amp='min',
species='')
print('\n')
print('From ' +str(np.sort(frame_file.cell)[0])+' to '+str(np.sort(frame_file.cell)[-1])+' nr '+str(len(frame_file.cell)))
#embed()
#embed()
#####################################################################
############################
# 3 Print , EODF
print('\n')
speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
for species in speciess:
fish = []
for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
if len(frame_file)> 0:
# ja der fisch hatte halt eine ziemlich niedriege EODf
frame_here = frame_file[frame_file.cell != '2022-01-05-af-invivo-1']
try:
print(start_name(cell_type_here,
species) + ' eodf_min ' + str(int(np.round(np.nanmin(frame_here.eod_fr)))) + ' eodf_max ' + str(int(np.round(np.nanmax(frame_here.eod_fr))))+' n '+str(len(frame_here)))
except:
print('eodf thing')
embed()
my_list = np.unique(frame_here.cell)
new_list = [item[0:10] for item in my_list]
fish.extend(new_list)
print(species[0:7]+' n_fish '+str(len(np.unique(fish))))
#####################################################################
# Burst corr limits
print('\n')
for species in speciess:
fish = []
for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
lims = np.unique(frame_file['lim_individual'])
lims_name = ''
for lim in lims:
lims_name = lims_name + str(lim) + ': ' + str(np.sum(frame_file['lim_individual'] == lim)) + ','
print(start_name(cell_type_here, species) + ' ' + lims_name)
#####################################################################
############################
# 3 Print , EODF
# Amplituden
print('\n')
speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
for species in speciess:
for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here,min_amp = '',species=species)
if len(frame_file)> 0:
#embed()
print(start_name(cell_type_here, species) + ' amp_min ' + str(
np.nanmin(frame_file.amp)) + ' amp_max ' + str(np.nanmax(frame_file[~np.isinf(frame_file.amp)].amp)))
test = False
#embed()
if test:
frame_file[['cv_stim','cv_base']]
frame_file['cv_stim']
embed()
############################
# CVs min max
for species in speciess:
for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='', species=species)
if len(frame_file) > 0:
try:
print(start_name(cell_type_here, species) + ' CV_min ' + str(np.round(np.nanmin(frame_file.cv_base), 2)) + ' CV max ' + str(np.round(np.nanmax(frame_file.cv_base), 2)) + ' FR max ' + str(np.round(np.nanmin(frame_file.fr_base))) + ' FR max ' + str(np.round(np.nanmax(frame_file.fr_base))))
except:
print('min here')
embed()
############################################
#######
# N
print('\n N')
speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
for species in speciess:
for c, cell_type_here in enumerate(cell_types):
##################
# printe wie viele Zellen es am Anfang gab
frame_file = setting_overview_score(frame_load_sp, cell_type_here, f_exclude = False, snippet = None, min_amp='min', species=species)
print(start_name(cell_type_here, species) + ' nr ' + str(len(frame_file)))
#embed()
#if c in [0,2]:
#cmap, _, y_axis = plt_modulation_overview(axs, c, cell_type_here,
# cv_name, frame_file, max_val, score,
# species)
#axs.set_ylabel(score)
#embed()#frame_file[(frame_file.cv_base < 0.65) & (frame_file.response_modulation > 200)].response_modulation
#axs.set_xlabel(cv_name)
#axs.get_shared_x_axes().join(*[axs, axd])
# elif species == ' Apteronotus albifrons':
# plt_albi(ax[4, 1], cell_type_here, colors, max_val, species, x_axis, y_axis)
#ax[1,cv_n].set_xlim(0, max_val)
#set_same_ylim(np.concatenate(ax[1::, :]))
#set_same_ylim(np.concatenate(ax[1::, :]),ylim_type ='xlim')
#set_same_ylim(ax[0, :], ylim_type='xlim')
#set_ylim_same()
#ax[1, 1].get_shared_y_axes().join(*ax[1, 1::])
# embed()
#counter += 1
#embed()
############################################
# printe um welchen Zeitraum es sich handelt
frame_file = setting_overview_score(frame_load_sp, cell_type_here = '', f_exclude=False, snippet=None, min_amp='min',
species='')
print('\n sampling'+str(frame_file.sampling.unique()))
print('P-units Fr: 50-450 Hz CV: 0.15 - 1.35')
print('Ampullary Fr: 80-200 Hz, CV: 0.08 - 0.22')
#plt.show()
show = False#True
fig = plt.gcf()
#embed()
tags_final = np.concatenate([tags[0::2],tags[1::2]])#,tags[2::3]
fig.tag(tags_final, xoffs=-4.2, yoffs=1.12)
save_visualization(pdf = True, show = show)
def start_name(cell_type_here, species):
return species[0:7] + ' ' + cell_type_here[0:7]
def plt_specific_cells(axs, cell_type_here, cv_name, frame_file, score):
######################################################
# hier kommen die kontrast Punkte dazu
# für die Zellen spielt Burst correctin ja keine Rolle
# if cv_n == 0:
if cell_type_here == ' P-unit':
cells_plot2 = p_units_to_show(type_here='contrasts')[0:2]
else:
cells_plot2 = [p_units_to_show(type_here='amp')[0]]
# for cell_plt in cells_plot2:
cells_extra = frame_file[frame_file['cell'].isin(cells_plot2)].index
# ax = plt.subplot(grid[1, cv_n])
# todo: hier nur noch die kleinste und größte Amplitude nehmen
# embed()
axs.scatter(frame_file[cv_name].loc[cells_extra], frame_file[score].loc[cells_extra],
s=9, facecolor="None", edgecolor='black', alpha=0.7, clip_on=False) # colors[str(cell_type_here)]
#embed()
def plt_var_axis(ax_j, axls, axss,score_name, burst_fraction, cell_type_here, counter, cv_name, frame_file, grid0, max_val, score,
scores_here, var_item_names, var_types, x_axis, x_axis_names, log = False):
for v, var_type in enumerate(var_types):
# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[counter])
counter += 1
cmap, _, y_axis = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
x_axis[v], frame_file, max_val, scores_here[v],
burst_fraction=burst_fraction[v], var_item=var_type)
axs.set_ylabel(score_name[v])
axs.set_xlabel(x_axis_names[v])
if v in [0, 1]:
if log:
axl.set_yscale('log')
axs.set_yscale('log')
axl.set_yticks_blank()
# remove_yticks(axl)
axl.minorticks_off()
if v == 0:
############################
# extra Zellen Scatter
# todo: diese Zellen müssen noch runter konvertiert werden
# todo: extra funktion für Zellen über 9 Snippets schreiben und die nochmal extra machen
cells_plot2 = p_units_to_show(type_here='bursts')
# for cell_plt in cells_plot2:
cells_extra = frame_file[frame_file['cell'].isin(cells_plot2)].index
# ax = plt.subplot(grid[1, cv_n])
axs.scatter(frame_file[x_axis[v]].loc[cells_extra], frame_file[score].loc[cells_extra],
s=5, color='white', edgecolor='black', alpha=0.5,
clip_on=False) # colors[str(cell_type_here)]
return ax_j, axls, axs, axss, counter
def species_with_both_cells(grid0, cell_types,ax_j, axls, axss, colors, cv_name_title, save_names, scores, species_all, x_axis, log = True):
for cv_n, cv_name in enumerate(x_axis):
if cv_n == 0:
redo = False
else:
redo = False
redo = False
frame_load_sp = load_overview_susept(save_names[0], redo=redo, redo_class=redo)
# frame_file = setting_overview_score(cell_type_here, frame_load_sp, min_amp=True, species=species)
# print(np.isnan(species))
score = scores[0]
max_val = 1.5
for c, cell_type_here in enumerate(cell_types):
#if c == 1:
# embed()
species = species_all[cv_n]
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
##################################
# modulation and species comparison
# x_axis, y_axis = get_axis(cv_name, frame_file, score)
# if cv_n == 0:
###############################
#######################
# Kernel Histogram
# plot the histograms of the values above the according vals
# grid = gridspec.GridSpecFromSubplotSpec(1, 1, grid0[0],hspace=0, wspace = 0.15)#grid[0, cv_n]
#
if c == 0:
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0)
if c in [0, 2]:
axk.set_title(species)
# embed()
if len(frame_file) > 0:
axs, x_axis = kernel_scatter(axl, cell_types, axk, axs, ax_j, c, cell_type_here, colors, cv_n, cv_name,
frame_file, grid0, max_val,
score, log = log)
if log:
axl.set_yscale('log')
axl.set_yticks_blank()
axl.minorticks_off()
axs.set_xlabel(cv_name_title[cv_n])
if cv_n == 0:
axs.set_ylabel('Perc(99)/Median')
if cv_n == 0:
axm = [axs]
return ax_j, axls, axss, cell_types, frame_load_sp, max_val, score
def get_grid_4(ax_j, axls, axss, grid0):
grid_k = gridspec.GridSpecFromSubplotSpec(2, 2, grid0,
hspace=0.1, wspace=0.1, height_ratios=[0.35, 3], width_ratios=[3, 0.5])
axk = plt.subplot(grid_k[0, 0])
ax_j.append(axk)
axs = plt.subplot(grid_k[1, 0])
axss.append(axs)
axl = plt.subplot(grid_k[1, 1])
axls.append(axl)
return axk, axl, axs, axls, axss, ax_j
if __name__ == '__main__':
data_overview3()

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flowchart.py Normal file
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#from utils_suseptibility import default_settings
#from plt_RAM import model_and_data_isi, model_cells
from IPython import embed
import numpy as np
import pandas as pd
from plotstyle import plot_style, spines_params
from utils_suseptibility import model_sheme_in_one
from utils_all import default_figsize, model_sheme_split, remove_yticks, save_visualization
#from utils_all import default_settings, resave_small_files
#from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
import matplotlib.gridspec as gridspec
from matplotlib import pyplot as plt
def flowchart():
#fig = plt.figure(figsize=(6.8, 5.5))
plot_style()
default_figsize(column=2, length=4.7) #5.3 5.8
grid_orig = gridspec.GridSpec(1, 1, wspace=0.15, bottom=0.07,
hspace=0.1, left=0.05, right=0.96,
top=0.92) # , height_ratios = [0.4,3]
model_sheme_split(grid_orig[0]) # grid_sheme grid_lower[3]
# model_sheme_only(grid[2])
save_visualization()
#fig.savefig()
if __name__ == '__main__':
flowchart()

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@STRING( ActaPhysiolScand = "Acta Physiol Scand" )
@STRING( AmZool = "Am Zool" )
@STRING( AnnNYAcadSci = "Ann NY Acad Sci" )
@STRING( AnimBehav = "Anim Behav" )
@STRING( AnimBiol = "Anim Biol" )
@STRING( AnnuRevNeurosci = "Annu Rev Neurosci" )
@STRING( AnnuRevPhysiol = "Annu Rev Physiol" )
@STRING( AnnuRevPsychol = "Annu Rev Psychol" )
@STRING( BehavBrainSci = "Behav Brain Sci" )
@STRING( BehavBrainRes = "Behav Brain Res" )
@STRING( BehavEcol = "Behav Ecol" )
@STRING( BehavEcolSociobiol = "Behav Ecol Sociobiol" )
@STRING( BiolBull = "Biol Bull" )
@STRING( BiolCell = "Biol Cell" )
@STRING( BiolCons = "Biological Conservation" )
@STRING( BiolCybern = "Biol Cybern" )
@STRING( BiolLett = "Biol Lett" )
@STRING( BiolRev = "Biol Rev" )
@STRING( Biometrika = "Biometrika" )
@STRING( BiophysJ = "Biophys J" )
@STRING( Biosystems = "Biosyst" )
@STRING( BrainBehavEvol = "Brain Behav Evol" )
@STRING( BrainRes = "Brain Res" )
@STRING( BrainResRev = "Brain Res Rev" )
@STRING( BrainResBull = "Brain Res Bull" )
@STRING( BrainResCognBrainRes = "Brain Res Cogn Brain Res" )
@STRING( BullMathBiol = "Bull Math Biol" )
@STRING( CanJZool = "Can J Zool" )
@STRING( CellTissueRes = "Cell Tissue Res" )
@STRING( CerebCortex = "Cereb Cortex" )
@STRING{ CompBiochemPhysiol = "Comp Biochem Physiol"}
@STRING( CurrBiol = "Curr Biol" )
@STRING( CurrOpinNeurobiol = "Curr Opin Neurobiol" )
@STRING( eLife = "eLife" )
@STRING( eNeuro = "eNeuro" )
@STRING( EuropBiophysJ = "Europ Biophys J" )
@STRING( EuroJNeurosci = "Euro J Neurosci" )
@STRING( ExpBrainRes = "Exp Brain Res" )
@STRING( FluctNoiseLett = "Fluct Noise Lett" )
@STRING( FrontComputNeurosci = "Front Comput Neurosci" )
@STRING( FrontNeuroInform = "Front Neuroinform" )
@STRING( FrontPsychol = "Front Psychol" )
@STRING( HearRes = "Hear Res" )
@STRING( Hippocampus = "Hippocampus" )
@STRING( HormBehav = "Horm Behav" )
@STRING( IEEETNN = "IEEE TNN" )
@STRING( IEEETransInfTechBiomed = "IEEE Trans Inf Tech Biomed" )
@STRING( IntegrCompBiol = "Integr Comp Biol" )
@STRING( IntJBifChaos = "Int J Bif Chaos" )
@STRING( IntJInsMorpholEmbryol = "Int J Ins Morphol Embryol" )
@STRING( IntJNeuralSyst = "Int J Neural Syst" )
@STRING( JAcoustSocAm = "J Acoust Soc Am" )
@STRING( JAnimEcol = "J Anim Ecol" )
@STRING( JARO = "JARO" )
@STRING( JBiolChem = "J Biol Chem" )
@STRING( JCellBiol = "J Cell Biol" )
@STRING( JChemNeuroanat = "J Chem Neuroanat" )
@STRING( JCompNeurol = "J Comp Neurol" )
@STRING( JCompPhysiol = "J Comp Physiol" )
@STRING( JCompPhysiolA = "J Comp Physiol A" )
@STRING( JCompPhysiolB = "J Comp Physiol B" )
@STRING( JComputNeurosci = "J Comput Neurosci" )
@STRING( JEvolBiol = "J Evol Biol" )
@STRING( JExpBiol = "J Exp Biol" )
@STRING( JExpZool = "J Exp Zool" )
@STRING( JFishBiol = "J Fish Biol" )
@STRING( JGenPhysiol = "J Gen Physiol" )
@STRING( JInsectBehav = "J Insect Behav" )
@STRING( JInsectPhysiol = "J Insect Physiol" )
@STRING( JMathBiol = "J Math Biol" )
@STRING( JMembrBiol = "J Membr Biol" )
@STRING( JMorph = "J Morph" )
@STRING( JNeuralEng = "J Neural Eng " )
@STRING( JNeurocytol = "J Neurocytol" )
@STRING( JNeuroGenetics = "J Neuro Genetics" )
@STRING( JNeurophysiol = "J Neurophysiol" )
@STRING( JNeurosci = "J Neurosci" )
@STRING( JNeurosciMethods = "J Neurosci Methods" )
@STRING( JOSA = "J Opt Soc Am A" )
@STRING( JPharmacolExpTher = "J Pharmaco Exp Ther" )
@STRING( JPhysiol = "J Physiol" )
@STRING( JPhysiolParis = "J Physiol Paris" )
@STRING{ JPlanktonRes = "J Plankton Res" }
@STRING( JpnJPhysiol = "Jpn J Physiol" )
@STRING( JStatPhys = "J Stat Phys" )
@STRING( JTheorBiol = "J Theor Biol" )
@STRING( JVis = "J Vis" )
@STRING( MathBiosci = "Math Biosci" )
@STRING( MicroscResTech = "Microsc Res Tech" )
@STRING( NatNeurosci = "Nat Neurosci" )
@STRING( Nature = "Nature" )
@STRING( NatCommun = "Nat Commun" )
@STRING( NatRevNeurosci = "Nat Rev Neurosci" )
@STRING( Naturforsch = "Naturforsch" )
@STRING( Naturwissenschaften = "Naturwissenschaften" )
@STRING( Network = "Network Comput Neural Syst" )
@STRING( NeuralComput = "Neural Comput" )
@STRING( NeuralNetw = "Neural Netw" )
@STRING( Neurocomput = "Neurocomput" )
@STRING( Neuron = "Neuron" )
@STRING( NeuroReport = "NeuroReport" )
@STRING( Neuroscience = "Neurosci" )
@STRING( NeurosciLett = "Neurosci Lett" )
@STRING( NeurosciRes = "Neurosci Res" )
@STRING( PflugersArch = {Pflugers Arch} )
@STRING( PLoSBiol = "PLoS Biol" )
@STRING( PLoSComputBiol = "PLoS Comput Biol" )
@STRING( PLoSONE = "PLoS ONE" )
@STRING( PNAS = "P Natl Acad Sci" )
@STRING( PhilTransRSocLondBBiolSci = "Phil Trans R Soc Lond B Biol Sci" )
@STRING( PhysicaD = "Physica D" )
@STRING( PhysiolRep = "Physiol Rep" )
@STRING( PhysiolRev = "Physiol Rev" )
@STRING( PhysRep = "Phys Rep" )
@STRING( PhysRevA = "Phys Rev A" )
@STRING( PhysRevE = "Phys Rev E" )
@STRING( PhysRevLett = "Phys Rev Lett" )
@STRING( ProcRSocLondBBiolSci = "Proc R Soc Lond B Biol Sci" )
@STRING( ProcSPIE = "Proc SPIE" )
@STRING( ProgBrainRes = "Prog Brain Res" )
@STRING( ProgRetinEyeRes = "Prog Retin Eye Res" )
@STRING( Science = "Science" )
@STRING( SIAMJApplMath = "SIAM J Appl Math" )
@STRING( TheorPopulBiol = "Theor Popul Biol" )
@STRING{ TICS = "Trends Cog Sci"}
@STRING( TIEE = "Trends Ecol Evol" )
@STRING( TINS = "Trends Neurosci" )
@STRING( VisNeurosci = "Vis Neurosci" )
@STRING( VisionRes = "Vision Res" )

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@STRING( ActaPhysiolScand = "Acta Physiol Scand" )
@STRING( AmZool = "Am Zool" )
@STRING( AnnNYAcadSci = "Ann NY Acad Sci" )
@STRING( AnimBehav = "Anim Behav" )
@STRING( AnimBiol = "Anim Biol" )
@STRING( AnnuRevNeurosci = "Annu Rev Neurosci" )
@STRING( AnnuRevPhysiol = "Annu Rev Physiol" )
@STRING( AnnuRevPsychol = "Annu Rev Psychol" )
@STRING( BehavBrainSci = "Behav Brain Sci" )
@STRING( BehavBrainRes = "Behav Brain Res" )
@STRING( BehavEcol = "Behav Ecol" )
@STRING( BehavEcolSociobiol = "Behav Ecol Sociobiol" )
@STRING( BiolBull = "Biol Bull" )
@STRING( BiolCell = "Biol Cell" )
@STRING( BiolCons = "Biological Conservation" )
@STRING( BiolCybern = "Biol Cybern" )
@STRING( BiolLett = "Biol Lett" )
@STRING( BiolRev = "Biol Rev" )
@STRING( Biometrika = "Biometrika" )
@STRING( BiophysJ = "Biophys J" )
@STRING( Biosystems = "Biosyst" )
@STRING( BrainBehavEvol = "Brain Behav Evol" )
@STRING( BrainRes = "Brain Res" )
@STRING( BrainResRev = "Brain Res Rev" )
@STRING( BrainResBull = "Brain Res Bull" )
@STRING( BrainResCognBrainRes = "Brain Res Cogn Brain Res" )
@STRING( BullMathBiol = "Bull Math Biol" )
@STRING( CanJZool = "Can J Zool" )
@STRING( CellTissueRes = "Cell Tissue Res" )
@STRING( CerebCortex = "Cereb Cortex" )
@STRING{ CompBiochemPhysiol = "Comp Biochem Physiol"}
@STRING( CurrBiol = "Curr Biol" )
@STRING( CurrOpinNeurobiol = "Curr Opin Neurobiol" )
@STRING( eLife = "eLife" )
@STRING( eNeuro = "eNeuro" )
@STRING( EuropBiophysJ = "Europ Biophys J" )
@STRING( EuroJNeurosci = "Euro J Neurosci" )
@STRING( ExpBrainRes = "Exp Brain Res" )
@STRING( FluctNoiseLett = "Fluct Noise Lett" )
@STRING( FrontComputNeurosci = "Front Comput Neurosci" )
@STRING( FrontNeuroInform = "Front Neuroinform" )
@STRING( FrontPsychol = "Front Psychol" )
@STRING( HearRes = "Hear Res" )
@STRING( Hippocampus = "Hippocampus" )
@STRING( HormBehav = "Horm Behav" )
@STRING( IEEETNN = "IEEE TNN" )
@STRING( IEEETransInfTechBiomed = "IEEE Trans Inf Tech Biomed" )
@STRING( IntegrCompBiol = "Integr Comp Biol" )
@STRING( IntJBifChaos = "Int J Bif Chaos" )
@STRING( IntJInsMorpholEmbryol = "Int J Ins Morphol Embryol" )
@STRING( IntJNeuralSyst = "Int J Neural Syst" )
@STRING( JAcoustSocAm = "J Acoust Soc Am" )
@STRING( JAnimEcol = "J Anim Ecol" )
@STRING( JARO = "JARO" )
@STRING( JBiolChem = "J Biol Chem" )
@STRING( JCellBiol = "J Cell Biol" )
@STRING( JChemNeuroanat = "J Chem Neuroanat" )
@STRING( JCompNeurol = "J Comp Neurol" )
@STRING( JCompPhysiol = "J Comp Physiol" )
@STRING( JCompPhysiolA = "J Comp Physiol A" )
@STRING( JCompPhysiolB = "J Comp Physiol B" )
@STRING( JComputNeurosci = "J Comput Neurosci" )
@STRING( JEvolBiol = "J Evol Biol" )
@STRING( JExpBiol = "J Exp Biol" )
@STRING( JExpZool = "J Exp Zool" )
@STRING( JFishBiol = "J Fish Biol" )
@STRING( JGenPhysiol = "J Gen Physiol" )
@STRING( JInsectBehav = "J Insect Behav" )
@STRING( JInsectPhysiol = "J Insect Physiol" )
@STRING( JMathBiol = "J Math Biol" )
@STRING( JMembrBiol = "J Membr Biol" )
@STRING( JMorph = "J Morph" )
@STRING( JNeuralEng = "J Neural Eng " )
@STRING( JNeurocytol = "J Neurocytol" )
@STRING( JNeuroGenetics = "J Neuro Genetics" )
@STRING( JNeurophysiol = "J Neurophysiol" )
@STRING( JNeurosci = "J Neurosci" )
@STRING( JNeurosciMethods = "J Neurosci Methods" )
@STRING( JOSA = "J Opt Soc Am A" )
@STRING( JPharmacolExpTher = "J Pharmaco Exp Ther" )
@STRING( JPhysiol = "J Physiol" )
@STRING( JPhysiolParis = "J Physiol Paris" )
@STRING{ JPlanktonRes = "J Plankton Res" }
@STRING( JpnJPhysiol = "Jpn J Physiol" )
@STRING( JStatPhys = "J Stat Phys" )
@STRING( JTheorBiol = "J Theor Biol" )
@STRING( JVis = "J Vis" )
@STRING( MathBiosci = "Math Biosci" )
@STRING( MicroscResTech = "Microsc Res Tech" )
@STRING( NatNeurosci = "Nat Neurosci" )
@STRING( Nature = "Nature" )
@STRING( NatCommun = "Nat Commun" )
@STRING( NatRevNeurosci = "Nat Rev Neurosci" )
@STRING( Naturforsch = "Naturforsch" )
@STRING( Naturwissenschaften = "Naturwissenschaften" )
@STRING( Network = "Network Comput Neural Syst" )
@STRING( NeuralComput = "Neural Comput" )
@STRING( NeuralNetw = "Neural Netw" )
@STRING( Neurocomput = "Neurocomput" )
@STRING( Neuron = "Neuron" )
@STRING( NeuroReport = "NeuroReport" )
@STRING( Neuroscience = "Neurosci" )
@STRING( NeurosciLett = "Neurosci Lett" )
@STRING( NeurosciRes = "Neurosci Res" )
@STRING( PflugersArch = {Pflugers Arch} )
@STRING( PLoSBiol = "PLoS Biol" )
@STRING( PLoSComputBiol = "PLoS Comput Biol" )
@STRING( PLoSONE = "PLoS ONE" )
@STRING( PNAS = "P Natl Acad Sci" )
@STRING( PhilTransRSocLondBBiolSci = "Phil Trans R Soc Lond B Biol Sci" )
@STRING( PhysicaD = "Physica D" )
@STRING( PhysiolRep = "Physiol Rep" )
@STRING( PhysiolRev = "Physiol Rev" )
@STRING( PhysRep = "Phys Rep" )
@STRING( PhysRevA = "Phys Rev A" )
@STRING( PhysRevE = "Phys Rev E" )
@STRING( PhysRevLett = "Phys Rev Lett" )
@STRING( ProcRSocLondBBiolSci = "Proc R Soc Lond B Biol Sci" )
@STRING( ProcSPIE = "Proc SPIE" )
@STRING( ProgBrainRes = "Prog Brain Res" )
@STRING( ProgRetinEyeRes = "Prog Retin Eye Res" )
@STRING( Science = "Science" )
@STRING( SIAMJApplMath = "SIAM J Appl Math" )
@STRING( TheorPopulBiol = "Theor Popul Biol" )
@STRING{ TICS = "Trends Cog Sci"}
@STRING( TIEE = "Trends Ecol Evol" )
@STRING( TINS = "Trends Neurosci" )
@STRING( VisNeurosci = "Vis Neurosci" )
@STRING( VisionRes = "Vision Res" )

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#from utils_suseptibility import default_settings
#from plt_RAM import model_and_data_isi, model_cells
#from utils_suseptibility import model_and_data, remove_yticks
#from utils_suseptibility import *
#from utils_susept import nonlin_title, plt_data_susept, plt_single_square_modl, set_clim_same_here, set_xlabel_arrow, \
# set_ylabel_arrow, \
# xpos_y_modelanddata
#from utils_all import default_settings, find_cell_add, get_flowchart_params, load_folder_name, load_model_susept, \
# overlap_cells, \
# plot_lowpass2, plt_time_arrays, remove_xticks, remove_yticks, resave_small_files, save_visualization, set_same_ylim
from utils_suseptibility import *#model_and_data
#from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
def table_printen(table):
print(table.keys())
for l in range(len(table)):
list_here = np.array(table.iloc[l])
l1 = "& ".join(list_here)
print(l1)
def model_and_data2(width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08-aa-invivo-1"], show=False,
contrasts=[0], noises_added=[''], D_extraction_method=['additiv_cv_adapt_factor_scaled'],
internal_noise=['RAM'], external_noise=['RAM'], level_extraction=[''], receiver_contrast=[1],
dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9], cut_offs1=[300],
label=r'$\frac{1}{mV^2S}$'): # ['eRAM']
# plot_style()#['_RAMscaled']'_RAMscaled'
duration_noise = '_short',
formula = 'code' ##'formula'
# ,int(2 ** 16) int(2 ** 16), int(2 ** 15),
stimulus_length = 1 # 20#550 # 30 # 15#45#0.5#1.5 15 45 100
trials_nrs = [1] # [100, 500, 1000, 3000, 10000, 100000, 1000000] # 500
stimulus_type = '_StimulusOrig_' # ,#
# ,3]#, 3, 1, 1.5, 0.5, ] # ,1,1.5, 0.5] #[1,1.5, 0.5] # 1.5,0.5]3, 1,
variant = 'sinz'
mimick = 'no'
cell_recording_save_name = ''
trans = 1 # 5
rep = 1000000 # 500000#0
repeats = [20, rep] # 250000
aa = 0
good_data, remaining = overlap_cells()
cells_all = [good_data[0]]
plot_style()
default_figsize(column=2, length=4.75) # 0.75
grid = gridspec.GridSpec(3, 4, wspace=0.85, bottom=0.07,
hspace=0.18, left=0.09, right=0.93, top=0.94)
a = 0
maxs = []
mins = []
mats = []
ims = []
perc05 = []
perc95 = []
iternames = [D_extraction_method, external_noise,
internal_noise, powers, nffts, dendrids, cut_offs1, trials_nrs, c_signal,
c_noises,
ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ]
nr = '2'
# embed()
# cell_contrasts = ["2013-01-08-aa-invivo-1"]
# cells_triangl_contrast = np.concatenate([cells_all,cell_contrasts])
# cells_triangl_contrast = 1
# cell_contrasts = 1
rows = len(cells_all) # len(good_data)+len(cell_contrasts)
perc = 'perc'
lp = 2
label_model = r'Nonlinearity $\frac{1}{S}$'
for all in it.product(*iternames):
var_type, stim_type_afe, stim_type_noise, power, nfft, dendrid, cut_off1, trial_nrs, c_sig, c_noise, ref_type, adapt_type, noise_added, extract, a_fr, a_fe = all
# print(trials_stim,stim_type_noise, power, nfft, a_fe,a_fr, dendrid, var_type, cut_off1,trial_nrs)
fig = plt.figure()
hs = 0.45
#################################
# data cells
# embed()
grid_data = gridspec.GridSpecFromSubplotSpec(1, 1, grid[0, 1],
hspace=hs)
#ypos_x_modelanddata()
nr = 1
ax_data, stack_spikes_all, eod_frs = plt_data_susept(fig, grid_data, cells_all, cell_type='p-unit', width=width,
cbar_label=True, nr = nr, amp_given = 1,xlabel = False, lp=lp, title=True)
for ax_external in ax_data:
# ax.set_ylabel(F2_xlabel())
# remove_xticks(ax)
ax_external.set_xticks_delta(100)
set_ylabel_arrow(ax_external, xpos=xpos_y_modelanddata(), ypos=0.87)
#embed()
set_xlabel_arrow(ax_external, ypos=ypos_x_modelanddata())
# ax.text(-0.42, 0.87, F2_xlabel(), ha='center', va='center',
# transform=ax.transAxes, rotation = 90)
# ax.text(1.66, 0.5, nonlin_title(), rotation=90, ha='center', va='center',
# transform=ax.transAxes)
ax_external.arrow_spines('lb')
#embed()
#plt.show()
##################################
# model part
trial_nr = 500000
cell = '2013-01-08-aa-invivo-1'
cell = '2012-07-03-ak-invivo-1'
print('cell'+str(cell))
cells_given = [cell]
save_name_rev = load_folder_name(
'calc_model') + '/' + 'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_revQuadrant_'
# for trial in trials:#.009
trial_nr = 1000000#1000000
save_names = [
'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_500000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_500000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_500000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
]
nrs_s = [2, 3, 6, 7, 10, 11]
#embed()
tr_name = trial_nr/1000000
if tr_name == 1:
tr_name = 1
titles = ['Model\n$N=11$ \n $c=1\,\%$', 'Model\n$N=%s $' % (tr_name) +'\,million \n $c=1\,\%$',
'Model\,('+noise_name().lower()+')' + '\n' + '$N=11$\n $c=0\,\%$',
'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million \n $c=0\,\%$',
'Model\,('+noise_name().lower()+')' + '\n' + '$N=11$\n $c=1\,\%$',
'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million\n $c=1\,\%$ ']#%
ax_model = []
for s, sav_name in enumerate(save_names):
try:
ax_external = plt.subplot(grid[nrs_s[s]])
except:
print('vers something')
embed()
ax_model.append(ax_external)
save_name = load_folder_name('calc_model') + '/' + sav_name
cell_add, cells_save = find_cell_add(cells_given)
perc = 'perc'
path = save_name + '.pkl' # '../'+
# stack = get_stack_one_quadrant(cell, cell_add, cells_save, path, save_name)
# full_matrix = create_full_matrix2(np.array(stack), np.array(stack_rev))
# stack_final = get_axis_on_full_matrix(full_matrix, stack)
# im = plt_RAM_perc(ax, perc, np.abs(stack))
# embed()
stack = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
if len(stack)> 0:
add_nonlin_title, cbar, fig, stack_plot, im = plt_single_square_modl(ax_external, cell, stack, perc, titles[s],
width, titles_plot=True,
resize=True, nr = nr)
# if s in [1,3,5]:
ims.append(im)
mats.append(stack_plot)
maxs.append(np.max(np.array(stack_plot)))
mins.append(np.min(np.array(stack_plot)))
col = 2
row = 3
ax_external.set_xticks_delta(100)
ax_external.set_yticks_delta(100)
# cbar[0].set_label(nonlin_title(add_nonlin_title)) # , labelpad=100
cbar.set_label(nonlin_title(' ['+add_nonlin_title), labelpad=lp) # rotation=270,
if (s in np.arange(col - 1, 100, col)) | (s == 0):
remove_yticks(ax_external)
else:
set_ylabel_arrow(ax_external, xpos=xpos_y_modelanddata(), ypos=0.87)
if s >= row * col - col:
set_xlabel_arrow(ax_external, ypos=ypos_x_modelanddata())
# ax.set_xlabel(F1_xlabel(), labelpad=20)
else:
remove_xticks(ax_external)
if len(cells) > 1:
a += 1
set_clim_same_here(ims, mats=mats, lim_type='up', nr_clim='perc', clims='', percnr=95)
#################################################
# Flowcharts
var_types = ['', 'additiv_cv_adapt_factor_scaled', 'additiv_cv_adapt_factor_scaled']
##additiv_cv_adapt_factor_scaled
a_fes = [0.009, 0, 0.009]
eod_fe = [750, 750, 750]
ylim = [-0.5, 0.5]
c_sigs = [0, 0.9, 0.9]
grid_left = [[], grid[1, 0], grid[2, 0]]
ax_ams = []
for g, grid_here in enumerate([grid[0, 0], grid[1, 0], grid[2, 0]]):
grid_lowpass = gridspec.GridSpecFromSubplotSpec(4, 1,
subplot_spec=grid_here, hspace=0.3,
height_ratios=[1, 1,1, 0.1])
models = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
model_params = models[models['cell'] == '2012-07-03-ak-invivo-1'].iloc[0]
cell = model_params.pop('cell') # .iloc[0]# Werte für das Paper nachschauen
eod_fr = model_params['EODf'] # .iloc[0]
deltat = model_params.pop("deltat") # .iloc[0]
v_offset = model_params.pop("v_offset") # .iloc[0]
# embed()eod_fr = stack.eod_fr.iloc[0]
print(var_types[g] + ' a_fe ' + str(a_fes[g]))
noise_final_c, spike_times, stimulus, stimulus_here, time, v_dent_output, v_mem_output, frame = get_flowchart_params(
a_fes, a_fr, g, c_sigs[g], cell, deltat, eod_fr, model_params, stimulus_length, v_offset, var_types,
eod_fe=eod_fe)
# embed()
if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1):
grid_lowpass2 = gridspec.GridSpecFromSubplotSpec(4, 1,
subplot_spec=grid_here,height_ratios=[1, 1,1, 0.1], hspace=0.2)
# if (np.unique(frame.RAM_afe) != 0):grid_left[g]
ax_external = plt_time_arrays('red', grid_lowpass2, 1, (frame.RAM_afe)*100, time=time, nr=0)
# if (np.unique(frame.RAM_noise) != 0):
remove_xticks(ax_external)
ax_intrinsic = plt_time_arrays('purple', grid_lowpass2, 1, (frame.RAM_noise)*100, time=time, nr=1)
ax_intrinsic.text(-0.6, 0.5, '$\%$', rotation=90, va='center', transform=ax_intrinsic.transAxes)
ax_intrinsic.show_spines('l')
ax_external.show_spines('l')
# ax_ams.append(axt_p2)
#color_timeseries = 'black'
#axt_p2.set_xlabel('Time [ms]')
#axt_p2.text(-0.6, 0.5, '$\%$', rotation=90, va='center', transform=axt_p2.transAxes)
#ax_ams.append(axt_p2)
vers = 'all'
elif (len(np.unique(frame.RAM_afe)) > 1):
color_timeseries = 'red'
nr_plot = 0
print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
try:
ax_external, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time,
(frame.RAM_afe + frame.RAM_noise)*100,
deltat, eod_fr,
color1=color_timeseries, lw=1, extract=False)
except:
print('add up thing')
embed()
ax_external.show_spines('l')
ax_intrinsic = plt.subplot(grid_lowpass[1])
ax_intrinsic.show_spines('l')
ax_intrinsic.axhline(0, color='black', lw=0.5)
ax_intrinsic.axhline(0, color='purple', lw=0.5)
remove_xticks(ax_external)
remove_xticks(ax_intrinsic)
join_x([ax_intrinsic, ax_external])
join_y([ax_intrinsic, ax_external])
vers = 'first'
elif (len(np.unique(frame.RAM_noise)) > 1):
color_timeseries = 'purple'
nr_plot = 1
print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
try:
ax_intrinsic, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time, (frame.RAM_afe + frame.RAM_noise)*100,
deltat, eod_fr,
color1=color_timeseries, lw=1, extract=False)
except:
print('add up thing')
embed()
ax_external = plt.subplot(grid_lowpass[0])
ax_external.show_spines('l')
ax_intrinsic.show_spines('l')
ax_external.axhline(0, color='black', lw=0.5)
ax_external.axhline(0, color='red', lw=0.5)
join_x([ax_intrinsic,ax_external])
join_y([ax_intrinsic, ax_external])
vers = 'second'
ax_external.text(-0.6, 0.5, '$\%$', va='center', rotation=90, transform=ax_external.transAxes)
ax_intrinsic.text(-0.6, 0.5, '$\%$', va='center', rotation=90, transform=ax_intrinsic.transAxes)
remove_xticks(ax_intrinsic)
# if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1):
ax_external.set_xlabel('')
# remove_yticks(ax)
ax_ams.append(ax_external)
remove_xticks(ax_external)
# embed()
ax_n, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[2]], time, noise_final_c, deltat, eod_fr,
extract=False, color1='grey', lw=1)
remove_yticks(ax_n)
if g == 2:
ax_n.set_xlabel('Time [ms]', labelpad = -0.5)
else:
remove_xticks(ax_n)
ax_n.set_ylim(ylim)
if vers == 'first':
ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes)
ax_n.text(start_pos_modeldata(), 1.1, noise_component_name(), ha='right', color='gray',
transform=ax_n.transAxes)
elif vers == 'second':
ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes)
ax_intrinsic.text(start_pos_modeldata(), 1, signal_component_name(), ha='right', color='purple',
transform=ax_intrinsic.transAxes)
ax_n.text(start_pos_modeldata(), 0.8, noise_component_name(), ha='right', color='gray',
transform=ax_n.transAxes)
else:
ax_n.text(start_pos_modeldata(), 0.8, noise_component_name(), ha='right', color='gray',
transform=ax_n.transAxes)
ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes)
ax_intrinsic.text(start_pos_modeldata(), 1, signal_component_name(), ha='right', color='purple',
transform=ax_intrinsic.transAxes)
set_same_ylim(ax_ams, up='up')
# embed()
axes = np.concatenate([ax_data, ax_model])
axes = [ax_ams[0], axes[0], axes[1], axes[2], ax_ams[1], axes[3], axes[4], ax_ams[2], axes[5],
axes[6], ]
axd1 = plt.subplot(grid[1, 1])
axd2 = plt.subplot(grid[2, 1])
#ax_data.extend([,])
axd1.show_spines('')
axd2.show_spines('')
#embed()
#axes = [[ax_ams[0],ax_data[0],axes[2], axes[3]],[ax_ams[1],axd1,axes[4], axes[5]],[axd2,axd2, axes[6], axes[7]]]
fig.tag([axes[0:4]], xoffs=-3, yoffs=1.6) # ax_ams[3],
fig.tag([[axes[4]]], xoffs=-3, yoffs=1.6, minor_index=0) # ax_ams[3],
fig.tag([axes[5:7]], xoffs=-3, yoffs=1.6, major_index = 1, minor_index = 2) # ax_ams[3],
fig.tag([[axes[7]]], xoffs=-3, yoffs=1.6, major_index=2,minor_index=0) # ax_ams[3],
fig.tag([axes[8::]], xoffs=-3, yoffs=1.6, major_index=2, minor_index=2) # ax_ams[3],
#fig.tag([axes[7::]], xoffs=-3, yoffs=1.6) # ax_ams[3],
#fig.tag([ax_ams[0],ax_data[0],axes[2], axes[3]], xoffs=-3, yoffs=1.6)#ax_ams[3],
save_visualization(pdf=True)
def start_pos_modeldata():
return 1.03
def signal_component_name():
return 'Signal component'#'signal noise'
def noise_component_name():
return 'Noise component'#'intrinsic noise'
def ypos_x_modelanddata():
return -0.45
if __name__ == '__main__':
model = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
cells = model.cell.unique()
# embed()
params = {'cells': cells}
show = True
# if show == False:
# low CV: cells = ['2012-07-03-ak-invivo-1']
plot_style()
default_settings(lw=0.5, column=2, length=3.35) #8.5
redo = False
D_extraction_method = ['additiv_cv_adapt_factor_scaled']
# D_extraction_method = ['additiv_visual_d_4_scaled']
##########################
# hier printen wir die table Werte zum kopieren in den Text
table = pd.read_csv('print_table_suscept-model_params_suscept_table.csv')
table_printen(table)
table = pd.read_csv('print_table_all-model_params_suscept_table.csv')
print('model big')
table_printen(table)
#embed()
##########################
#embed()
model_and_data2(width=0.005, show=show, D_extraction_method=D_extraction_method,
label=r'$\frac{1}{mV^2S}$') #r'$\frac{1}{mV^2S}$'

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from utils_suseptibility import *
def model_full():
plot_style()
default_figsize(column=2, length=2.3)
grid = gridspec.GridSpec(1, 2, wspace=0.6, bottom = 0.1,hspace=0.15, top=0.95, left=0.075, right=0.87)
axes = []
##################################
# model part
ls = '--'
lw = 0.5
ax = plt.subplot(grid[1])
axes.append(ax)
perc,im,stack_final = plt_model_big(ax, ls = ls, lw = 0.5)
# if len(cbar) > 0:
###############################
# data part
ax = plt.subplot(grid[0])
axes.append(ax)
cell = '2012-07-03-ak-invivo-1'
mat_rev,stack_final_rev = load_stack_data_susept(cell, save_name = version_final(), end = '_revQuadrant_')
mat, stack = load_stack_data_susept(cell, save_name=version_final(), end = '')
#embed()
#try:
full_matrix = create_full_matrix2(np.array(mat),np.array(mat_rev))
#except:
# print('full matrix something')
# embed()
stack_final = get_axis_on_full_matrix(full_matrix, mat)
abs_matrix = np.abs(stack_final)
#embed()
#if np.
abs_matrix, add_nonlin_title, resize_val = rescale_colorbar_and_values(abs_matrix)
ax.axhline(0, color = 'white', linestyle = ls, linewidth = lw)
ax.axvline(0, color='white', linestyle = ls, linewidth = lw)
im = plt_RAM_perc(ax, perc, abs_matrix)
cbar, left, bottom, width, height = colorbar_outside(ax, im, add=5, width=0.01)
set_clim_same_here([im], mats=[abs_matrix], lim_type='up', nr_clim='perc', clims='', percnr=95)
#clim = im.get_clim()
#if clim[1]> 1000:
#todo: change clim values with different Hz values
#embed()
cbar.set_label(nonlin_title(add_nonlin_title = ' ['+add_nonlin_title), rotation=90, labelpad=8)
set_ylabel_arrow(ax, xpos = -0.07, ypos = 0.97)
set_xlabel_arrow(ax, xpos=1, ypos=-0.07)
''' eod_fr, stack_spikes = plt_data_suscept_single(ax, cbar_label, cell, cells, f, fig, file_names_exclude, lp, title,
width)'''
cbar, left, bottom, width, height = colorbar_outside(ax, im, add=5, width=0.01)
#print('finished model_full')
fig = plt.gcf()
#axes = plt.gca()
fig.tag(axes[::-1], xoffs=-4.5, yoffs=0.4) # ax_ams[3],
save_visualization()
def load_stack_data_susept(cell, save_name, end = ''):
load_name = load_folder_name('calc_RAM') + '/' + save_name+end
add = '_cell' + cell +end# str(f) # + '_amp_' + str(amp)
#embed()
stack_cell = load_data_susept(load_name + '_' + cell + '.pkl', load_name + '_' + cell, add=add,
load_version='csv')
file_names_exclude = get_file_names_exclude()
stack_cell = stack_cell[~stack_cell['file_name'].isin(file_names_exclude)]
# if len(stack_cell):
file_names = stack_cell.file_name.unique()
#embed()
file_names = exclude_file_name_short(file_names)
cut_off_nr = get_cutoffs_nr(file_names)
try:
maxs = list(map(float, cut_off_nr))
except:
embed()
file_names = file_names[np.argmax(maxs)]
#embed()
stack_file = stack_cell[stack_cell['file_name'] == file_names]
amps = [np.min(stack_file.amp.unique())]
amps = restrict_punits(cell, amps)
amp = np.min(amps)#[0]
# for amp in amps:
stack_amps = stack_file[stack_file['amp'] == amp]
lengths = stack_amps.stimulus_length.unique()
trial_nr_double = stack_amps.trial_nr.unique()
trial_nr = np.max(trial_nr_double)
stack_final = stack_amps[
(stack_amps['stimulus_length'] == np.max(lengths)) & (stack_amps.trial_nr == trial_nr)]
mat, new_keys = get_mat_susept(stack_final)
return mat,stack_final
if __name__ == '__main__':
model_full()

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#from matplotlib import gridspec as gridspec, pyplot as plt
#from plotstyle import plot_style
#from utils_all import chose_mat_max_value, default_settings, find_code_vs_not, save_visualization
#from utils_susept import check_var_substract_method, chose_certain_group, divergence_title_add_on, extract_waves, \
# find_all_dir_cells, \
# load_b_public, \
# load_cells_three, \
# plot_arrays_ROC_psd_single3, plot_shemes4, plt_coherences, predefine_grouping_frame, \
# restrict_cell_type, save_arrays_susept
from utils_suseptibility import *
#sys.path.insert(0, '..')
#from utils_suseptibility import motivation_small
#from utils import divergence_title_add_on,chose_certain_group,predefine_grouping_frame,check_nix_fish,find_all_dir_cells,load_cells_three,restrict_cell_type,plot_shemes2,plot_arrays_ROC_psd_single
def motivation_all_small(ylim=[-1.25, 1.25], c1=10, dfs=['m1', 'm2'], mult_type='_multsorted2_', top=0.94, devs=['2'],
figsize=None, redo=False, save=True, end='0', cut_matrix='malefemale', chose_score='mean_nrs',
a_fr=1, restrict='modulation', adapt='adaptoffsetallall2', step=str(30),
detections=['AllTrialsIndex'], variant='no', sorted_on='LocalReconst0.2Norm'):
autodefines = [
'triangle_diagonal_fr'] # ['triangle_fr', 'triangle_diagonal_fr', 'triangle_df2_fr','triangle_df2_eodf''triangle_df1_eodf', ] # ,'triangle_df2_fr''triangle_df1_fr','_triangle_diagonal__fr',]
cells = ['2021-08-03-ac-invivo-1'] ##'2021-08-03-ad-invivo-1',,[10, ][5 ]
c1s = [10] # 1, 10,
c2s = [10]
plot_style()
default_figsize(column=2, length=3.3) #6.7 ts=12, ls=12, fs=12
show = True
DF2_desired = [0.8]
DF1_desired = [0.87]
DF2_desired = [-0.23]
DF1_desired = [0.94]
# mean_type = '_MeanTrialsIndexPhaseSort_Min0.25sExcluded_'
extract = ''
datasets, data_dir = find_all_dir_cells()
cells = ['2022-01-28-ah-invivo-1'] # , '2022-01-28-af-invivo-1', '2022-01-28-ab-invivo-1',
# '2022-01-27-ab-invivo-1', ] # ,'2022-01-28-ah-invivo-1', '2022-01-28-af-invivo-1', ]
append_others = 'apend_others' # '#'apend_others'#'apend_others'#'apend_others'##'apend_others'
autodefine = '_DFdesired_'
autodefine = 'triangle_diagonal_fr' # ['triangle_fr', 'triangle_diagonal_fr', 'triangle_df2_fr','triangle_df2_eodf''triangle_df1_eodf', ] # ,'triangle_df2_fr''triangle_df1_fr','_triangle_diagonal__fr',]
DF2_desired = [-33]
DF1_desired = [133]
autodefine = '_dfchosen_closest_'
autodefine = '_dfchosen_closest_first_'
cells = ['2021-08-03-ac-invivo-1'] ##'2021-08-03-ad-invivo-1',,[10, ][5 ]
# c1s = [10] # 1, 10,
# c2s = [10]
minsetting = 'Min0.25sExcluded'
c2 = 10
# detections = ['MeanTrialsIndexPhaseSort'] # ['AllTrialsIndex'] # ,'MeanTrialsIndexPhaseSort''DetectionAnalysis''_MeanTrialsPhaseSort'
# detections = ['AllTrialsIndex'] # ['_MeanTrialsIndexPhaseSort_Min0.25sExcluded_extended_eod_loc_synch']
extend_trials = '' # 'extended'#''#'extended'#''#'extended'#''#'extended'#''#'extended'#''#'extended'# ok kein Plan was das hier ist
# phase_sorting = ''#'PhaseSort'
eodftype = '_psdEOD_'
concat = '' # 'TrialsConcat'
indices = ['_allindices_']
chirps = [
''] # '_ChirpsDelete3_',,'_ChirpsDelete3_'','','',''#'_ChirpsDelete3_'#''#'_ChirpsDelete3_'#'#'_ChirpsDelete2_'#''#'_ChirpsDelete_'#''#'_ChirpsDelete_'#''#'_ChirpsDelete_'#''#'_ChirpsCache_'
extract = '' # '_globalmax_'
devs_savename = ['original', '05'] # ['05']#####################
# control = pd.read_pickle(
# load_folder_name(
# 'calc_model') + '/modell_all_cell_no_sinz3_afe0.1__afr1__afj0.1__length1.5_adaptoffsetallall2___stepefish' + step + 'Hz_ratecorrrisidual35__modelbigfit_nfft4096.pkl')
if len(cells) < 1:
data_dir, cells = load_cells_three(end, data_dir=data_dir, datasets=datasets)
cells, p_units_cells, pyramidals = restrict_cell_type(cells, 'p-units')
# default_settings(fs=8)
start = 'min' #
cells = ['2021-08-03-ac-invivo-1']
tag_cells = []
for c, cell in enumerate(cells):
counter_pic = 0
contrasts = [c2]
tag_cell = []
for c, contrast in enumerate(contrasts):
contrast_small = 'c2'
contrast_big = 'c1'
contrasts1 = [c1]
for contrast1 in contrasts1:
for devname_orig in devs:
datapoints = [1000]
for d in datapoints:
################################
# prepare DF1 desired
# chose_score = 'auci02_012-auci_base_01'
# hier muss das halt stimmen mit der auswahl
# hier wollen wir eigntlich kein autodefine
# sondern wir wollen so ein diagonal ding haben
divergnce, fr, pivot_chosen, max_val, max_x, max_y, mult, DF1_desired, DF2_desired, min_y, min_x, min_val, diff_cut = chose_mat_max_value(
DF1_desired, DF2_desired, '', mult_type, eodftype, indices, cell, contrast_small,
contrast_big, contrast1, dfs, start, devname_orig, contrast, autodefine=autodefine,
cut_matrix='cut', chose_score=chose_score) # chose_score = 'auci02_012-auci_base_01'
DF1_desired = DF1_desired # [::-1]
DF2_desired = DF2_desired # [::-1]
# embed()
#######################################
# ROC part
# fr, celltype = get_fr_from_info(cell, data_dir[c])
version_comp, subfolder, mod_name_slash, mod_name, subfolder_path = find_code_vs_not()
b = load_b_public(c, cell, data_dir)
mt_sorted = predefine_grouping_frame(b, eodftype=eodftype, cell_name=cell)
counter_waves = 0
mt_sorted = mt_sorted[(mt_sorted['c2'] == c2) & (mt_sorted['c1'] == c1)]
for gg in range(len(DF1_desired)):
# embed()
# try:
t3 = time.time()
# except:
# print('time thing')
# embed()
ax_w = []
###################
# all trials in one
grouped = mt_sorted.groupby(
['c1', 'c2', 'm1, m2'],
as_index=False)
# try:
grouped_mean = chose_certain_group(DF1_desired[gg],
DF2_desired[gg], grouped,
several=True, emb=False,
concat=True)
# except:
# print('grouped thing')
# embed()
###################
# groups sorted by repro tag
# todo: evnetuell die tuples gleich hier umspeichern vom csv ''
# embed()
grouped = mt_sorted.groupby(
['c1', 'c2', 'm1, m2', 'repro_tag_id'],
as_index=False)
grouped_orig = chose_certain_group(DF1_desired[gg],
DF2_desired[gg],
grouped,
several=True)
gr_trials = len(grouped_orig)
###################
groups_variants = [grouped_mean]
group_mean = [grouped_orig[0][0], grouped_mean]
for d, detection in enumerate(detections):
mean_type = '_' + detection # + '_' + minsetting + '_' + extend_trials + concat
##############################################################
# load plotting arrays
arrays, arrays_original, spikes_pure = save_arrays_susept(
data_dir, cell, c, b, chirps, devs, extract, group_mean, mean_type, plot_group=0,
rocextra=False, sorted_on=sorted_on)
####################################################
####################################################
# hier checken wir ob für diesen einen Punkt das funkioniert mit der standardabweichung
# embed()
try:
check_var_substract_method(spikes_pure)
except:
print('var checking not possible')
# fig = plt.figure()
# grid = gridspec.GridSpec(2, 1, wspace=0.7, left=0.05, top=0.95, bottom=0.15,
# right=0.98)
if figsize:
fig = plt.figure(figsize=figsize)
else:
fig = plt.figure()
grid = gridspec.GridSpec(1, 1, wspace=0.7, hspace=0.35, left=0.055, top=top,
bottom=0.15,
right=0.935) # height_ratios=[1, 2], height_ratios = [1,6]bottom=0.25, top=0.8,
hr = [1, 0.35, 1.2, 0, 3, ] # 1
# embed()#0.75, 0.55, 0.75,
# hr = [1, 0.8, 1, 1, 1, 1.4]
##########################################################################
# several coherence plot
# frame_psd = pd.read_pickle(load_folder_name('calc_RAM')+'/noise_data11_nfft1sec_original__StimPreSaved4__first__CutatBeginning_0.05_s_NeurDelay_0.005_s_2021-08-03-ab-invivo-1.pkl')
# frame_psd = pd.read_pickle(load_folder_name('calc_RAM') + '/noise_data11_nfft1sec_original__StimPreSaved4__first__CutatBeginning_0.05_s_NeurDelay_0.005_s_2021-08-03-ab-invivo-1.pkl')
coh = False
if coh:
ax_w, d, data_dir, devs = plt_coherences(ax_w, d, data_dir, devs, grid)
# ax_cohs = plt.subplot(grid[0,1])
##########################################################################
# part with the power spectra
grid0 = gridspec.GridSpecFromSubplotSpec(5, 4, wspace=0.15, hspace=0.35,
subplot_spec=grid[:, :],
height_ratios=hr)
xlim = [0, 100]
# plt.savefig(r'C:\Users\alexi\OneDrive - bwedu\Präsentations\latex\experimental_protocol.pdf')
# embed()
fr_end = divergence_title_add_on(group_mean, fr[gg], autodefine)
###########################################
stimulus_length = 0.3
deltat = 1 / 40000
eodf = np.mean(group_mean[1].eodf)
eod_fr = eodf
# embed()
a_fr = 1
# embed()
eod_fe = eodf + np.mean(
group_mean[1].DF2) # data.eodf.iloc[0] + 10 # cell_model.eode.iloc[0]
a_fe = group_mean[0][1] / 100
eod_fj = eodf + np.mean(
group_mean[1].DF1) # data.eodf.iloc[0] + 50 # cell_model.eodj.iloc[0]
a_fj = group_mean[0][0] / 100
variant_cell = 'no' # 'receiver_emitter_jammer'
print('f0' + str(eod_fr))
print('f1'+str(eod_fe))
print('f2' + str(eod_fj))
eod_fish_j, time_array, time_fish_r, eod_fish_r, time_fish_e, eod_fish_e, time_fish_sam, eod_fish_sam, stimulus_am, stimulus_sam = extract_waves(
variant_cell, '',
stimulus_length, deltat, eod_fr, a_fr, a_fe, [eod_fe], 0, eod_fj, a_fj)
jammer_name = 'female'
cocktail_names = False
if cocktail_names:
titles = ['receiver ',
'+' + 'intruder ',
'+' + jammer_name,
'+' + jammer_name + '+intruder',
[]] ##'receiver + ' + 'receiver + receiver
else:
titles = title_motivation()
gs = [0, 1, 2, 3, 4]
waves_presents = [['receiver', '', '', 'all'],
['receiver', 'emitter', '', 'all'],
['receiver', '', 'jammer', 'all'],
['receiver', 'emitter', 'jammer', 'all'],
] # ['', '', '', ''],['receiver', '', '', 'all'],
# ['receiver', '', 'jammer', 'all'],
# ['receiver', 'emitter', '', 'all'],'receiver', 'emitter', 'jammer',
symbols = [''] # '$+$', '$-$', '$-$', '$=$',
symbols = ['', '', '', '', '']
time_array = time_array * 1000
color0 = 'green'
color0_burst = 'darkgreen'
color01 = 'green'
color02 = 'red'
color012 = 'orange'
color01_2 = 'purple'
colors_am = ['black', 'black', 'black', 'black'] # color01, color02, color012]
extracted = [False, True, True, True]
extracted2 = [False, False, False, False]
for i in range(len(waves_presents)):
ax = plot_shemes4(eod_fish_r, eod_fish_e, eod_fish_j, grid0, time_array,
g=gs[i], title_top=True, add=0.1, eod_fr=eod_fr,
waves_present=waves_presents[i], ylim=ylim,
xlim=xlim, jammer_label='f2', color_am=colors_am[i],
emitter_label='f1',color_am2 = color01_2, receiver_label='f0',
extracted=extracted[i],extracted2=extracted2[i],
title=titles[i]) # 'intruder','receiver'#jammer_name
ax_w.append(ax)
if ax != []:
ax.text(1.1, 0.45, symbols[i], fontsize=35, transform=ax.transAxes)
bar = False
if bar:
if i == 0:
ax.plot([0, 20], [ylim[0] + 0.01, ylim[0] + 0.01], color='black')
ax.text(0, -0.16, '20 ms', va='center', fontsize=10,
transform=ax.transAxes)
printing = True
if printing:
print(time.time() - t3)
# embed()
##########################################
# spike response
array_chosen = 1
if d == 0: #
# embed()
fr_isi, ax_ps, ax_as = plot_arrays_ROC_psd_single3(
[arrays[0], arrays[2], arrays[1], arrays[3]],
[arrays_original[0], arrays_original[2], arrays_original[1],
arrays_original[3]], spikes_pure, fr, cell, grid0, chirps, extract,
mean_type,
group_mean, b, devs,
xlim=xlim, row=1 + d * 3,
array_chosen=array_chosen,
color0_burst=color0_burst, mean_types=[mean_type],
color01=color01, color02=color02,
color012=color012,
color01_2=color01_2)
##########################################################################
# save_visualization()
# if 'same' in mean_type:
# mean_type_name = mean_type
# else:
# mean_type_name = ''
# plt.show()
suptitle = cell + ' c1: ' + str(group_mean[0][0]) + '$\%$ m1: ' + str(
group_mean[0][2][0]) + ' DF1: ' + str(
group_mean[1]['DF1, DF2'].iloc[0][0]) + ' c2: ' + str(
group_mean[0][1]) + '$\%$ m2: ' + str(
group_mean[0][2][1]) + ' DF2: ' + str(
group_mean[1]['DF1, DF2'].iloc[0][1]) + ' Trials nr ' + str(
len(group_mean[1])) + fr_end
# plt.suptitle(suptitle)
# embed()
individual_tag = 'DF1' + str(DF1_desired[gg]) + 'DF2' + str(
DF2_desired[gg]) + cell + '_c1_' + str(c1) + '_c2_' + str(c2) + mean_type
# save_all(individual_tag, show, counter_contrast=0, savename='')
# print('individual_tag')
# embed()
axes = []
axes.append(ax_w)
# axes.extend(np.transpose(ax_as))
# axes.append(np.transpose(ax_ps))
# np.transpose(axes)
# embed()
#fig.tag(ax_w[0:3], xoffs=-2.3, yoffs=1.7)
#fig.tag(ax_w[3::], xoffs=-1.9, yoffs=1.4)
fig.tag(ax_w, xoffs=-1.9, yoffs=1.4)
# ax_w, np.transpose(ax_as), ax_ps
if save:
save_visualization(individual_tag=individual_tag, show=show, pdf=True)
# fig = plt.gcf()
# fig.savefig()
# plt.show()
return suptitle
if __name__ == '__main__':
motivation_all_small(c1=10, mult_type='_multsorted2_', devs=['05'], redo=True, save=True, end='all',
cut_matrix='malefemale', chose_score='mean_nrs', restrict='modulation_no_classes', step='50',
detections=['MeanTrialsIndexPhaseSort'], sorted_on='LocalReconst0.2NormAm')

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[2490] Utils.pm:395> WARN - Duplicate entry key: 'Triefenbach2008' in file 'references.bib', skipping ...
[2494] Utils.pm:395> WARN - Duplicate entry key: 'Westby1988' in file 'references.bib', skipping ...
[2781] Utils.pm:395> WARN - Datamodel: Entry 'Fernald2014' (references.bib): Field 'owner' invalid in data model - ignoring
[2782] Utils.pm:395> WARN - Datamodel: Entry 'Fernald2014' (references.bib): Field 'timestamp' invalid in data model - ignoring
[2981] Utils.pm:395> WARN - Datamodel: Entry 'Baktoft2015' (references.bib): Field 'timestamp' invalid in data model - ignoring
[3069] Utils.pm:395> WARN - Datamodel: Entry 'Egnor2016' (references.bib): Field 'owner' invalid in data model - ignoring
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[3111] Utils.pm:395> WARN - Datamodel: Entry 'Serrano2003' (references.bib): Field 'owner' invalid in data model - ignoring
[3112] Utils.pm:395> WARN - Datamodel: Entry 'Serrano2003' (references.bib): Field 'timestamp' invalid in data model - ignoring
[3267] Utils.pm:395> WARN - Datamodel: Entry 'Cavigelli1999' (references.bib): Field 'owner' invalid in data model - ignoring
[3268] Utils.pm:395> WARN - Datamodel: Entry 'Cavigelli1999' (references.bib): Field 'timestamp' invalid in data model - ignoring
[3484] Utils.pm:395> WARN - Datamodel: Entry 'Markham2015' (references.bib): Field 'owner' invalid in data model - ignoring
[3485] Utils.pm:395> WARN - Datamodel: Entry 'Markham2015' (references.bib): Field 'timestamp' invalid in data model - ignoring
[3661] Utils.pm:395> WARN - Datamodel: Entry 'Chivers1995' (references.bib): Field 'owner' invalid in data model - ignoring
[3662] Utils.pm:395> WARN - Datamodel: Entry 'Chivers1995' (references.bib): Field 'timestamp' invalid in data model - ignoring
[3727] Utils.pm:395> WARN - Datamodel: Entry 'Menzel2005' (references.bib): Field 'owner' invalid in data model - ignoring
[3729] Utils.pm:395> WARN - Datamodel: Entry 'Menzel2005' (references.bib): Field 'timestamp' invalid in data model - ignoring
[3931] Utils.pm:395> WARN - Datamodel: Entry 'Chapman1995' (references.bib): Field 'owner' invalid in data model - ignoring
[3931] Utils.pm:395> WARN - Datamodel: Entry 'Chapman1995' (references.bib): Field 'timestamp' invalid in data model - ignoring
[3993] Utils.pm:395> WARN - Datamodel: Entry 'Sapolsky2005' (references.bib): Field 'owner' invalid in data model - ignoring
[3993] Utils.pm:395> WARN - Datamodel: Entry 'Sapolsky2005' (references.bib): Field 'timestamp' invalid in data model - ignoring
[4014] Utils.pm:395> WARN - Datamodel: Entry 'Arnold1994' (references.bib): Field 'owner' invalid in data model - ignoring
[4018] Utils.pm:395> WARN - Datamodel: Entry 'Arnold1994' (references.bib): Field 'timestamp' invalid in data model - ignoring
[4213] Utils.pm:395> WARN - Datamodel: Entry 'Stamper2010' (references.bib): Field 'owner' invalid in data model - ignoring
[4214] Utils.pm:395> WARN - Datamodel: Entry 'Stamper2010' (references.bib): Field 'timestamp' invalid in data model - ignoring
[4270] Utils.pm:395> WARN - Datamodel: Entry 'Sonerud2001' (references.bib): Field 'owner' invalid in data model - ignoring
[4271] Utils.pm:395> WARN - Datamodel: Entry 'Sonerud2001' (references.bib): Field 'timestamp' invalid in data model - ignoring
[4295] Utils.pm:395> WARN - Datamodel: Entry 'Hagedorn1985' (references.bib): Field 'owner' invalid in data model - ignoring
[4296] Utils.pm:395> WARN - Datamodel: Entry 'Hagedorn1985' (references.bib): Field 'timestamp' invalid in data model - ignoring
[4341] Utils.pm:395> WARN - Datamodel: Entry 'Stoddard2007' (references.bib): Field 'owner' invalid in data model - ignoring
[4342] Utils.pm:395> WARN - Datamodel: Entry 'Stoddard2007' (references.bib): Field 'timestamp' invalid in data model - ignoring
[4565] Utils.pm:395> WARN - Datamodel: Entry 'Cote1995' (references.bib): Field 'owner' invalid in data model - ignoring
[4566] Utils.pm:395> WARN - Datamodel: Entry 'Cote1995' (references.bib): Field 'timestamp' invalid in data model - ignoring
[4708] Utils.pm:395> WARN - Datamodel: Entry 'Hager1991' (references.bib): Field 'owner' invalid in data model - ignoring
[4721] Utils.pm:395> WARN - Datamodel: Entry 'Hager1991' (references.bib): Field 'timestamp' invalid in data model - ignoring
[4782] Utils.pm:395> WARN - Datamodel: Entry 'Korstjens2006' (references.bib): Field 'owner' invalid in data model - ignoring
[4783] Utils.pm:395> WARN - Datamodel: Entry 'Korstjens2006' (references.bib): Field 'timestamp' invalid in data model - ignoring
[4905] Utils.pm:395> WARN - Datamodel: Entry 'Cvikel2015' (references.bib): Field 'owner' invalid in data model - ignoring
[4906] Utils.pm:395> WARN - Datamodel: Entry 'Cvikel2015' (references.bib): Field 'timestamp' invalid in data model - ignoring
[5259] Utils.pm:395> WARN - Datamodel: Entry 'scipy2020' (references.bib): Field 'adsurl' invalid in data model - ignoring
[5644] Utils.pm:395> WARN - legacy month field 'Oct' in entry 'Engelmann2010' is not an integer - this will probably not sort properly.
[5646] Utils.pm:395> WARN - Datamodel: Entry 'Engelmann2010' (references.bib): Field 'timestamp' invalid in data model - ignoring
[5715] Utils.pm:395> WARN - Datamodel: Entry 'Lavoue2000' (references.bib): Field 'owner' invalid in data model - ignoring
[5716] Utils.pm:395> WARN - Datamodel: Entry 'Lavoue2000' (references.bib): Field 'timestamp' invalid in data model - ignoring
[5772] Utils.pm:395> WARN - Datamodel: Entry 'Geffen1996' (references.bib): Field 'owner' invalid in data model - ignoring
[5773] Utils.pm:395> WARN - Datamodel: Entry 'Geffen1996' (references.bib): Field 'timestamp' invalid in data model - ignoring
[5859] Utils.pm:395> WARN - Datamodel: Entry 'Jason1990' (references.bib): Field 'owner' invalid in data model - ignoring
[5860] Utils.pm:395> WARN - Datamodel: Entry 'Jason1990' (references.bib): Field 'timestamp' invalid in data model - ignoring
[5926] Utils.pm:395> WARN - Datamodel: Entry 'Hass2002' (references.bib): Field 'owner' invalid in data model - ignoring
[5926] Utils.pm:395> WARN - Datamodel: Entry 'Hass2002' (references.bib): Field 'timestamp' invalid in data model - ignoring
[6043] Utils.pm:395> WARN - Datamodel: Entry 'Hojesjo1998' (references.bib): Field 'owner' invalid in data model - ignoring
[6044] Utils.pm:395> WARN - Datamodel: Entry 'Hojesjo1998' (references.bib): Field 'timestamp' invalid in data model - ignoring
[6265] Utils.pm:395> WARN - Datamodel: Entry 'Dell2014' (references.bib): Field 'owner' invalid in data model - ignoring
[6266] Utils.pm:395> WARN - Datamodel: Entry 'Dell2014' (references.bib): Field 'timestamp' invalid in data model - ignoring
[6491] Utils.pm:395> WARN - Datamodel: Entry 'Mathis2018' (references.bib): Field 'owner' invalid in data model - ignoring
[6493] Utils.pm:395> WARN - Datamodel: Entry 'Mathis2018' (references.bib): Field 'timestamp' invalid in data model - ignoring
[6524] Utils.pm:395> WARN - Datamodel: Entry 'Triefenbach2003' (references.bib): Field 'owner' invalid in data model - ignoring
[6525] Utils.pm:395> WARN - Datamodel: Entry 'Triefenbach2003' (references.bib): Field 'timestamp' invalid in data model - ignoring
[6584] Utils.pm:395> WARN - Datamodel: Entry 'Tallarovic2005' (references.bib): Field 'owner' invalid in data model - ignoring
[6585] Utils.pm:395> WARN - Datamodel: Entry 'Tallarovic2005' (references.bib): Field 'timestamp' invalid in data model - ignoring
[6696] Utils.pm:395> WARN - Datamodel: Entry 'Engler2000' (references.bib): Field 'owner' invalid in data model - ignoring
[6697] Utils.pm:395> WARN - Datamodel: Entry 'Engler2000' (references.bib): Field 'timestamp' invalid in data model - ignoring
[6724] Utils.pm:395> WARN - Datamodel: Entry 'Janson1985' (references.bib): Field 'owner' invalid in data model - ignoring
[6725] Utils.pm:395> WARN - Datamodel: Entry 'Janson1985' (references.bib): Field 'timestamp' invalid in data model - ignoring
[7018] Utils.pm:395> WARN - Datamodel: Entry 'Sword2005' (references.bib): Field 'owner' invalid in data model - ignoring
[7019] Utils.pm:395> WARN - Datamodel: Entry 'Sword2005' (references.bib): Field 'timestamp' invalid in data model - ignoring
[7133] Utils.pm:395> WARN - Datamodel: Entry 'Raab2019' (references.bib): Field 'owner' invalid in data model - ignoring
[7135] Utils.pm:395> WARN - Datamodel: Entry 'Raab2019' (references.bib): Field 'timestamp' invalid in data model - ignoring
[7771] Utils.pm:395> WARN - Datamodel: Entry 'zuniga2017' (references.bib): Field 'article_type' invalid in data model - ignoring
[7772] Utils.pm:395> WARN - Datamodel: Entry 'zuniga2017' (references.bib): Field 'timestamp' invalid in data model - ignoring
[7844] Utils.pm:395> WARN - Datamodel: Entry 'Kappeler2008' (references.bib): Field 'owner' invalid in data model - ignoring
[7845] Utils.pm:395> WARN - Datamodel: Entry 'Kappeler2008' (references.bib): Field 'timestamp' invalid in data model - ignoring
[7883] Utils.pm:395> WARN - Datamodel: Entry 'Kirschbaum2002' (references.bib): Field 'owner' invalid in data model - ignoring
[7884] Utils.pm:395> WARN - Datamodel: Entry 'Kirschbaum2002' (references.bib): Field 'timestamp' invalid in data model - ignoring
[7939] Utils.pm:395> WARN - Datamodel: Entry 'Rizzo2009' (references.bib): Field 'owner' invalid in data model - ignoring
[7940] Utils.pm:395> WARN - Datamodel: Entry 'Rizzo2009' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8000] Utils.pm:395> WARN - Datamodel: Entry 'Hupe2008' (references.bib): Field 'owner' invalid in data model - ignoring
[8000] Utils.pm:395> WARN - Datamodel: Entry 'Hupe2008' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8024] Utils.pm:395> WARN - Datamodel: Entry 'Miramontes1996' (references.bib): Field 'owner' invalid in data model - ignoring
[8028] Utils.pm:395> WARN - Datamodel: Entry 'Miramontes1996' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8087] Utils.pm:395> WARN - Datamodel: Entry 'Hughey2018' (references.bib): Field 'owner' invalid in data model - ignoring
[8088] Utils.pm:395> WARN - Datamodel: Entry 'Hughey2018' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8234] Utils.pm:395> WARN - Datamodel: Entry 'Parrish1999' (references.bib): Field 'owner' invalid in data model - ignoring
[8234] Utils.pm:395> WARN - Datamodel: Entry 'Parrish1999' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8316] Utils.pm:395> WARN - Datamodel: Entry 'Rodriguez2010' (references.bib): Field 'owner' invalid in data model - ignoring
[8317] Utils.pm:395> WARN - Datamodel: Entry 'Rodriguez2010' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8347] Utils.pm:395> WARN - Datamodel: Entry 'Strandburg2015' (references.bib): Field 'owner' invalid in data model - ignoring
[8349] Utils.pm:395> WARN - Datamodel: Entry 'Strandburg2015' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8394] Utils.pm:395> WARN - Datamodel: Entry 'Madhav2018' (references.bib): Field 'owner' invalid in data model - ignoring
[8395] Utils.pm:395> WARN - Datamodel: Entry 'Madhav2018' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8520] Utils.pm:395> WARN - Datamodel: Entry 'Charpentier2005' (references.bib): Field 'owner' invalid in data model - ignoring
[8521] Utils.pm:395> WARN - Datamodel: Entry 'Charpentier2005' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8599] Utils.pm:395> WARN - Datamodel: Entry 'Kerth2003' (references.bib): Field 'owner' invalid in data model - ignoring
[8600] Utils.pm:395> WARN - Datamodel: Entry 'Kerth2003' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8625] Utils.pm:395> WARN - legacy month field 'Jan' in entry 'Krahe2004' is not an integer - this will probably not sort properly.
[8628] Utils.pm:395> WARN - Datamodel: Entry 'Krahe2004' (references.bib): Field 'pii' invalid in data model - ignoring
[8633] Utils.pm:395> WARN - Datamodel: Entry 'Krahe2004' (references.bib): Field 'pmid' invalid in data model - ignoring
[8633] Utils.pm:395> WARN - Datamodel: Entry 'Krahe2004' (references.bib): Field 'refid' invalid in data model - ignoring
[8634] Utils.pm:395> WARN - Datamodel: Entry 'Krahe2004' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8697] Utils.pm:395> WARN - Datamodel: Entry 'Nelson1997' (references.bib): Field 'owner' invalid in data model - ignoring
[8698] Utils.pm:395> WARN - Datamodel: Entry 'Nelson1997' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8740] Utils.pm:395> WARN - Datamodel: Entry 'Engh2002' (references.bib): Field 'owner' invalid in data model - ignoring
[8740] Utils.pm:395> WARN - Datamodel: Entry 'Engh2002' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8811] Utils.pm:395> WARN - Datamodel: Entry 'Henninger2020' (references.bib): Field 'owner' invalid in data model - ignoring
[8812] Utils.pm:395> WARN - Datamodel: Entry 'Henninger2020' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8872] Utils.pm:395> WARN - Datamodel: Entry 'Meyer1987' (references.bib): Field 'owner' invalid in data model - ignoring
[8873] Utils.pm:395> WARN - Datamodel: Entry 'Meyer1987' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8917] Utils.pm:395> WARN - Datamodel: Entry 'DeWoody2000' (references.bib): Field 'owner' invalid in data model - ignoring
[8919] Utils.pm:395> WARN - Datamodel: Entry 'DeWoody2000' (references.bib): Field 'timestamp' invalid in data model - ignoring
[8961] Utils.pm:395> WARN - legacy month field 'dec' in entry 'VanHemmen2011' is not an integer - this will probably not sort properly.
[9081] Utils.pm:395> WARN - Datamodel: Entry 'Clutton-Brock1999' (references.bib): Field 'owner' invalid in data model - ignoring
[9082] Utils.pm:395> WARN - Datamodel: Entry 'Clutton-Brock1999' (references.bib): Field 'timestamp' invalid in data model - ignoring
[9188] Utils.pm:395> WARN - Datamodel: Entry 'Metcalfe1995' (references.bib): Field 'owner' invalid in data model - ignoring
[9190] Utils.pm:395> WARN - Datamodel: Entry 'Metcalfe1995' (references.bib): Field 'timestamp' invalid in data model - ignoring
[9234] Utils.pm:395> WARN - Datamodel: Entry 'Blumstein2001' (references.bib): Field 'owner' invalid in data model - ignoring
[9235] Utils.pm:395> WARN - Datamodel: Entry 'Blumstein2001' (references.bib): Field 'timestamp' invalid in data model - ignoring
[9688] Utils.pm:395> WARN - Datamodel: Entry 'Todd1999' (references.bib): Field 'owner' invalid in data model - ignoring
[9689] Utils.pm:395> WARN - Datamodel: Entry 'Todd1999' (references.bib): Field 'timestamp' invalid in data model - ignoring
[9746] Utils.pm:395> WARN - Datamodel: Entry 'Bilde2007' (references.bib): Field 'owner' invalid in data model - ignoring
[9746] Utils.pm:395> WARN - Datamodel: Entry 'Bilde2007' (references.bib): Field 'timestamp' invalid in data model - ignoring
[9761] Utils.pm:395> WARN - Datamodel: Entry 'Creel1996' (references.bib): Field 'owner' invalid in data model - ignoring
[9761] Utils.pm:395> WARN - Datamodel: Entry 'Creel1996' (references.bib): Field 'timestamp' invalid in data model - ignoring
[9902] Utils.pm:395> WARN - Datamodel: Entry 'Markham2017' (references.bib): Field 'owner' invalid in data model - ignoring
[9903] Utils.pm:395> WARN - Datamodel: Entry 'Markham2017' (references.bib): Field 'timestamp' invalid in data model - ignoring
[9920] Utils.pm:395> WARN - Datamodel: Entry 'Jun2013' (references.bib): Field 'owner' invalid in data model - ignoring
[9922] Utils.pm:395> WARN - Datamodel: Entry 'Jun2013' (references.bib): Field 'timestamp' invalid in data model - ignoring
[9974] Utils.pm:395> WARN - Datamodel: Entry 'Barber2000' (references.bib): Field 'owner' invalid in data model - ignoring
[9975] Utils.pm:395> WARN - Datamodel: Entry 'Barber2000' (references.bib): Field 'timestamp' invalid in data model - ignoring
[10006] Utils.pm:395> WARN - Datamodel: Entry 'StrandburgPeshkin2019' (references.bib): Field 'owner' invalid in data model - ignoring
[10007] Utils.pm:395> WARN - Datamodel: Entry 'StrandburgPeshkin2019' (references.bib): Field 'timestamp' invalid in data model - ignoring
[10012] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 39, warning: undefined macro "JExpBiol"
[10013] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 49, warning: undefined macro "CurrBiol"
[10014] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 83, warning: undefined macro "PNAS"
[10030] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 110, warning: undefined macro "Neuron"
[10030] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 131, warning: undefined macro "ProcRSocLondBBiolSci"
[10032] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 140, warning: undefined macro "JExpBiol"
[10033] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 183, warning: undefined macro "CurrOpinNeurobiol"
[10035] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 192, warning: undefined macro "JExpBiol"
[10036] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 212, warning: undefined macro "Network"
[10037] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 253, warning: undefined macro "JExpBiol"
[10038] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 262, warning: undefined macro "PLOSComputBiol"
[10039] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 283, warning: undefined macro "BiolCybern"
[10040] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 292, warning: undefined macro "PLOSOne"
[10040] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 316, warning: undefined macro "TINS"
[10041] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 334, warning: undefined macro "ProcRSocLondBBiolSci"
[10042] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 382, warning: undefined macro "Network"
[10043] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 392, warning: undefined macro "JCompPhysiolA"
[10044] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 412, warning: undefined macro "JCompPhysiolA"
[10045] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 421, warning: undefined macro "JCompPhysiolA"
[10046] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 430, warning: undefined macro "JNeurosci"
[10076] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 440, warning: undefined macro "JCompPhysiolA"
[10079] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 449, warning: undefined macro "JNeurosci"
[10079] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 469, warning: undefined macro "JCompPhysiolA"
[10081] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 479, warning: undefined macro "JCompPhysiolA"
[10082] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 488, warning: undefined macro "JCompPhysiolA"
[10085] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 499, warning: undefined macro "JNeurophysiol"
[10086] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 509, warning: undefined macro "JNeurophysiol"
[10087] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 519, warning: undefined macro "JNeurophysiol"
[10087] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 528, warning: undefined macro "JNeurophysiol"
[10088] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 569, warning: undefined macro "JCompPhysiol"
[10089] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 607, warning: undefined macro "JCompPhysiol"
[10090] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 618, warning: undefined macro "JNeurosci"
[10090] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 628, warning: undefined macro "Neuron"
[10091] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 637, warning: undefined macro "JNeurophysiol"
[10092] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 666, warning: undefined macro "AnnuRevPhysiol"
[10093] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 684, warning: undefined macro "PhilTransRSocLondBBiolSci"
[10093] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 726, warning: undefined macro "BiolCybern"
[10116] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 735, warning: undefined macro "JEvolBiol"
[10118] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 771, warning: undefined macro "JCompPhysiolA"
[10123] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 800, warning: undefined macro "PLoSComputBiol"
[10123] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 810, warning: undefined macro "JNeurosci"
[10124] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 820, warning: undefined macro "NeuralNetw"
[10125] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 839, warning: undefined macro "JNeurosci"
[10126] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 850, warning: undefined macro "CompBiochemPhysiol"
[10140] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 891, warning: undefined macro "JExpBiol"
[10141] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 900, warning: undefined macro "JPhysiolParis"
[10142] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 910, warning: undefined macro "BrainBehavEvol"
[10142] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 918, warning: undefined macro "JGenPhysiol"
[10143] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 929, warning: undefined macro "JCompPhysiol"
[10144] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 939, warning: undefined macro "JCompPhysiol"
[10145] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 949, warning: undefined macro "JCompPhysiol"
[10146] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 958, warning: undefined macro "TINS"
[10147] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 968, warning: undefined macro "BrainResRev"
[10148] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 983, warning: undefined macro "Science"
[10149] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1034, warning: undefined macro "JExpBiol"
[10150] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1044, warning: undefined macro "BrainBehavEvol"
[10151] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1055, warning: undefined macro "JExpBiol"
[10152] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1064, warning: undefined macro "JCompPhysiolA"
[10153] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1073, warning: undefined macro "JPhysiolParis"
[10154] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1093, warning: undefined macro "HormBehav"
[10155] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1106, warning: undefined macro "JPhysiolParis"
[10156] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1115, warning: undefined macro "Science"
[10156] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1125, warning: undefined macro "JNeuroGenetics"
[10157] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1135, warning: undefined macro "JCompNeurol"
[10158] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1164, warning: undefined macro "JExpBiol"
[10190] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1173, warning: undefined macro "Science"
[10193] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1183, warning: undefined macro "NatCommun"
[10194] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1203, warning: undefined macro "AnimBehav"
[10195] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1271, warning: undefined macro "JPhysiol"
[10195] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1302, warning: undefined macro "BehavEcolSociobiol"
[10196] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1313, warning: undefined macro "BehavEcol"
[10198] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1326, warning: undefined macro "JCompPhysiolA"
[10198] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1346, warning: undefined macro "VisionRes"
[10200] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1408, warning: undefined macro "Science"
[10201] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1417, warning: undefined macro "BrainBehavEvol"
[10202] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1427, warning: undefined macro "BehavEcol"
[10203] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1440, warning: undefined macro "Science"
[10204] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1471, warning: undefined macro "JExpBiol"
[10204] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1494, warning: undefined macro "PNAS"
[10205] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1503, warning: undefined macro "JAnimEcol"
[10206] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1523, warning: undefined macro "JMorph"
[10228] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1532, warning: undefined macro "BrainResBull"
[10231] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1606, warning: undefined macro "PNAS"
[10232] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1627, warning: undefined macro "TIEE"
[10233] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1660, warning: undefined macro "NatCommun"
[10236] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1691, warning: undefined macro "CompBiochemPhysiol"
[10236] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1701, warning: undefined macro "ProcRSocLondBBiolSci"
[10246] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1724, warning: undefined macro "Network"
[10251] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1732, warning: undefined macro "JCompPhysiolA"
[10252] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1746, warning: undefined macro "BrainBehavEvol"
[10253] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1755, warning: undefined macro "JCompPhysiolA"
[10254] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1773, warning: undefined macro "JCompPhysiolA"
[10255] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1791, warning: undefined macro "JCompPhysiolA"
[10255] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1812, warning: undefined macro "AnimBiol"
[10256] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1849, warning: undefined macro "JNeurosci"
[10257] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1859, warning: undefined macro "AnnuRevNeurosci"
[10258] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1882, warning: undefined macro "JCompPhysiolA"
[10259] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1900, warning: undefined macro "JExpBiol"
[10260] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1909, warning: undefined macro "JCompPhysiolA"
[10260] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1918, warning: undefined macro "JCompPhysiolA"
[10261] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1927, warning: undefined macro "Nature"
[10262] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1948, warning: undefined macro "JCompPhysiolA"
[10263] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1957, warning: undefined macro "Ethology"
[10264] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1983, warning: undefined macro "CompBiochemPhysiol"
[10265] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 1992, warning: undefined macro "BehavEcol"
[10266] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2005, warning: undefined macro "JCompPhysiolA"
[10271] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2067, warning: undefined macro "JPhysiolParis"
[10305] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2076, warning: undefined macro "ProgBrainRes"
[10308] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2085, warning: undefined macro "CurrOpinNeurobiol"
[10309] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2116, warning: undefined macro "BiolLett"
[10310] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2125, warning: undefined macro "NeuralComput"
[10310] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2134, warning: undefined macro "NatNeurosci"
[10311] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2178, warning: undefined macro "JNeurosci"
[10312] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2202, warning: undefined macro "JCompPhysiolA"
[10313] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2211, warning: undefined macro "NatNeurosci"
[10313] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2232, warning: undefined macro "JTheorBiol"
[10314] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2249, warning: undefined macro "AnimBehav"
[10316] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2258, warning: undefined macro "NatNeurosci"
[10336] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2267, warning: undefined macro "JExpBiol"
[10337] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2276, warning: undefined macro "BiolLett"
[10339] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2285, warning: undefined macro "BiolCybern"
[10339] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2294, warning: undefined macro "BiolCybern"
[10340] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2344, warning: undefined macro "AnimBehav"
[10341] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2365, warning: undefined macro "AnnuRevPsychol"
[10342] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2372, warning: undefined macro "NeuralComput"
[10342] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2381, warning: undefined macro "Neuron"
[10343] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2404, warning: undefined macro "NatNeurosci"
[10344] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2413, warning: undefined macro "NatNeurosci"
[10345] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2422, warning: undefined macro "JExpBiol"
[10345] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2431, warning: undefined macro "CurrBiol"
[10346] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2491, warning: undefined macro "AnimBehav"
[10347] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2506, warning: undefined macro "JCompPhysiolA"
[10373] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2526, warning: undefined macro "BehavEcolSociobiol"
[10376] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2540, warning: undefined macro "JNeurophysiol"
[10376] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2569, warning: undefined macro "JCompNeurol"
[10377] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2578, warning: undefined macro "JCompNeurol"
[10377] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2587, warning: undefined macro "JNeurophysiol"
[10378] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2597, warning: undefined macro "BehavEcolSociobiol"
[10379] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2657, warning: undefined macro "JCompPhysiol"
[10379] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2666, warning: undefined macro "JCompPhysiol"
[10380] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2675, warning: undefined macro "JCompPhysiol"
[10381] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2684, warning: undefined macro "Science"
[10381] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2723, warning: undefined macro "JCompPhysiolA"
[10382] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2749, warning: undefined macro "MicroscResTech"
[10385] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2777, warning: undefined macro "JExpBiol"
[10386] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2813, warning: undefined macro "TINS"
[10387] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2834, warning: undefined macro "JPhysiol"
[10387] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2843, warning: undefined macro "JExpBiol"
[10388] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2853, warning: undefined macro "JPhysiolParis"
[10389] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2862, warning: undefined macro "JNeurosci"
[10389] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2871, warning: undefined macro "BehavEcol"
[10390] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2886, warning: undefined macro "Science"
[10390] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2913, warning: undefined macro "JCompPhysiol"
[10391] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2922, warning: undefined macro "BehavEcolSociobiol"
[10392] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2931, warning: undefined macro "BehavEcolSociobiol"
[10393] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2940, warning: undefined macro "Science"
[10393] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2949, warning: undefined macro "BrainBehavEvol"
[10394] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2958, warning: undefined macro "BrainBehavEvol"
[10419] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2967, warning: undefined macro "BrainBehavEvol"
[10424] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2976, warning: undefined macro "JExpBiol"
[10425] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2985, warning: undefined macro "JCompPhysiol"
[10438] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 2994, warning: undefined macro "Nature"
[10439] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3065, warning: undefined macro "HearRes"
[10440] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3074, warning: undefined macro "PhilTransRSocLondBBiolSci"
[10441] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3098, warning: undefined macro "JExpBiol"
[10442] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3112, warning: undefined macro "JPhysiolParis"
[10443] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3138, warning: undefined macro "BehavEcolSociobiol"
[10444] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3166, warning: undefined macro "AnimBehav"
[10445] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3182, warning: undefined macro "NatNeurosci"
[10445] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3234, warning: undefined macro "PLOSOne"
[10446] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3249, warning: undefined macro "JExpBiol"
[10447] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3278, warning: undefined macro "BehavEcolSociobiol"
[10448] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3315, warning: undefined macro "BiolCybern"
[10449] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3335, warning: undefined macro "PLoSBiol"
[10450] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3380, warning: undefined macro "PhysiolRep"
[10451] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3410, warning: undefined macro "JPhysiolParis"
[10452] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3437, warning: undefined macro "JGenPhysiol"
[10453] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3443, warning: undefined macro "JCompPhysiolA"
[10454] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3455, warning: undefined macro "JCompPhysiolA"
[10455] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3468, warning: undefined macro "JCompPhysiol"
[10455] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3477, warning: undefined macro "JCompPhysiol"
[10456] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3486, warning: undefined macro "JNeurophysiol"
[10457] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3539, warning: undefined macro "JNeurophysiol"
[10458] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3569, warning: undefined macro "BehavEcolSociobiol"
[10490] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3630, warning: undefined macro "CurrOpinNeurobiol"
[10491] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3648, warning: undefined macro "BehavEcolSociobiol"
[10495] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3657, warning: undefined macro "JExpBiol"
[10496] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3739, warning: undefined macro "Neuroscience"
[10496] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3747, warning: undefined macro "Naturforsch"
[10497] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3782, warning: undefined macro "JOSA"
[10498] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3803, warning: undefined macro "NatNeurosci"
[10499] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3813, warning: undefined macro "FrontPsychol"
[10500] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3857, warning: undefined macro "Nature"
[10501] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3864, warning: undefined macro "JExpBiol"
[10502] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3873, warning: undefined macro "JExpBiol"
[10503] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3891, warning: undefined macro "PLoSComputBiol"
[10504] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3901, warning: undefined macro "CurrOpinNeurobiol"
[10505] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3942, warning: undefined macro "JExpBiol"
[10506] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3951, warning: undefined macro "PLOSComputBiol"
[10507] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3985, warning: undefined macro "JCompNeurol"
[10529] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 3994, warning: undefined macro "JCompNeurol"
[10532] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4061, warning: undefined macro "IntegrCompBiol"
[10533] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4071, warning: undefined macro "PhilTransRSocLondBBiolSci"
[10534] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4083, warning: undefined macro "JCompNeurol"
[10537] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4092, warning: undefined macro "JExpBiol"
[10538] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4122, warning: undefined macro "CurrOpinNeurobiol"
[10554] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4131, warning: undefined macro "JNeurophysiol"
[10555] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4151, warning: undefined macro "NatNeurosci"
[10556] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4175, warning: undefined macro "JNeurosci"
[10557] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4184, warning: undefined macro "JNeurosci"
[10558] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4193, warning: undefined macro "JCompPhysiol"
[10559] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4213, warning: undefined macro "JNeurosci"
[10560] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4223, warning: undefined macro "AnimBehav"
[10561] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4232, warning: undefined macro "JCompPhysiolA"
[10561] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4251, warning: undefined macro "PLoSComputBiol"
[10562] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4299, warning: undefined macro "ProcRSocLondBBiolSci"
[10563] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4342, warning: undefined macro "Science"
[10564] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4351, warning: undefined macro "JCompPhysiol"
[10565] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4362, warning: undefined macro "JCompPhysiolA"
[10566] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4388, warning: undefined macro "PNAS"
[10567] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4397, warning: undefined macro "PhysRevE"
[10574] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4406, warning: undefined macro "PLOSComputBiol"
[10605] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4464, warning: undefined macro "CompBiochemPhysiol"
[10607] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4484, warning: undefined macro "PNAS"
[10609] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4562, warning: undefined macro "JExpBiol"
[10611] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4572, warning: undefined macro "JCompPhysiolA"
[10612] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4582, warning: undefined macro "JCompPhysiol"
[10614] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4623, warning: undefined macro "JCompNeurol"
[10617] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4632, warning: undefined macro "CerebCortex"
[10620] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4676, warning: undefined macro "Nature"
[10639] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4763, warning: undefined macro "PLOSComputBiol"
[10640] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4772, warning: undefined macro "PNAS"
[10641] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4791, warning: undefined macro "JExpBiol"
[10642] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4800, warning: undefined macro "JCompPhysiolA"
[10643] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4810, warning: undefined macro "JCompPhysiol"
[10644] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4817, warning: undefined macro "BiolRev"
[10645] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4837, warning: undefined macro "JPhysiolParis"
[10646] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4868, warning: undefined macro "ProcRSocLondBBiolSci"
[10647] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4886, warning: undefined macro "NatNeurosci"
[10648] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4917, warning: undefined macro "JExpBiol"
[10649] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4959, warning: undefined macro "NatNeurosci"
[10650] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4969, warning: undefined macro "Neuroreport"
[10674] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 4990, warning: undefined macro "JCompPhysiolA"
[10677] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5010, warning: undefined macro "JExpBiol"
[10678] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5104, warning: undefined macro "JExpBiol"
[10679] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5154, warning: undefined macro "JCompPhysiol"
[10680] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5163, warning: undefined macro "BioSystems"
[10683] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5183, warning: undefined macro "PhysRevLett"
[10699] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5226, warning: undefined macro "Neuron"
[10704] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5308, warning: undefined macro "PLOSOne"
[10706] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5387, warning: undefined macro "JExpBiol"
[10710] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5476, warning: undefined macro "NatNeurosci"
[10713] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5486, warning: undefined macro "JCompPhysiolA"
[10731] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5495, warning: undefined macro "JCompPhysiolA"
[10733] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5538, warning: undefined macro "JCompPhysiolA"
[10735] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5554, warning: undefined macro "CurrOpinNeurobiol"
[10736] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5563, warning: undefined macro "JExpBiol"
[10739] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5582, warning: undefined macro "Nature"
[10740] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5591, warning: undefined macro "JCompPhysiolA"
[10741] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5610, warning: undefined macro "JPhysiolParis"
[10742] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5630, warning: undefined macro "JExpBiol"
[10743] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5639, warning: undefined macro "JCompPhysiolA"
[10744] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5648, warning: undefined macro "AnnuRevNeurosci"
[10745] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5657, warning: undefined macro "JAcoustSocAm"
[10762] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5687, warning: undefined macro "Nature"
[10763] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5697, warning: undefined macro "JExpBiol"
[10765] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5706, warning: undefined macro "JPhysiolParis"
[10766] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5715, warning: undefined macro "PLOSBiol"
[10767] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5736, warning: undefined macro "AnimBehav"
[10769] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5745, warning: undefined macro "BehavBrainRes"
[10774] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5758, warning: undefined macro "JExpBiol"
[10775] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5778, warning: undefined macro "JExpBiol"
[10776] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5787, warning: undefined macro "JExpBiol"
[10777] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5796, warning: undefined macro "BiolBull"
[10793] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5805, warning: undefined macro "BrainBehavEvol"
[10794] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5834, warning: undefined macro "JCompPhysiolA"
[10796] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5874, warning: undefined macro "JNeurophysiol"
[10797] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5906, warning: 5 characters of junk seen at toplevel
[10798] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5921, warning: undefined macro "BehavEcol"
[10799] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5932, warning: undefined macro "JCompNeurol"
[10799] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 5998, warning: undefined macro "AnimBehav"
[10800] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6013, warning: undefined macro "BehavBrainRes"
[10801] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6038, warning: undefined macro "JCompPhysiol"
[10804] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6047, warning: undefined macro "JCompPhysiol"
[10804] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6068, warning: undefined macro "NeurosciLett"
[10805] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6077, warning: undefined macro "AnimBehav"
[10806] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6103, warning: undefined macro "AnimBehav"
[10807] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6115, warning: undefined macro "JExpBiol"
[10808] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6200, warning: undefined macro "JNeurosci"
[10809] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6221, warning: undefined macro "TINS"
[10833] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6243, warning: undefined macro "JMorph"
[10837] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6250, warning: undefined macro "JFishBiol"
[10838] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6278, warning: undefined macro "JPhysiolParis"
[10839] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6288, warning: undefined macro "JNeurophysiol"
[10840] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6296, warning: undefined macro "HearRes"
[10841] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6305, warning: undefined macro "JExpBiol"
[10841] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6334, warning: undefined macro "TINS"
[10857] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6343, warning: undefined macro "JCompPhysiol"
[10858] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6352, warning: undefined macro "BehavEcolSociobiol"
[10859] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6362, warning: undefined macro "BehavEcolSociobiol"
[10859] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6371, warning: undefined macro "JPhysiol"
[10860] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6389, warning: undefined macro "TICS"
[10861] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6400, warning: undefined macro "NeuralComput"
[10862] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6431, warning: undefined macro "JExpBiol"
[10862] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6461, warning: undefined macro "JCompPhysiolA"
[10863] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6491, warning: undefined macro "PLOSComputBiol"
[10864] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6500, warning: undefined macro "TICS"
[10865] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6510, warning: undefined macro "JPhysiolParis"
[10865] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6521, warning: undefined macro "JExpBiol"
[10866] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6555, warning: undefined macro "AnimBehav"
[10867] Utils.pm:395> WARN - BibTeX subsystem: C:\Users\alexi\AppData\Local\Temp\biber_tmp_Sslf\33e59b5ca9ec342068223d604c3f8d55_22820.utf8, line 6567, warning: undefined macro "JCompPhysiolA"
[10888] Biber.pm:1641> INFO - Datamodel validation starting
[10897] Utils.pm:395> WARN - Datamodel: Entry 'Abel2009' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10898] Utils.pm:395> WARN - Datamodel: Entry 'Adelson1985' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10900] Utils.pm:395> WARN - Datamodel: Entry 'Aertsen1981' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10902] Utils.pm:395> WARN - Datamodel: Entry 'Aguilera2003' (references.bib): Missing mandatory field 'journaltitle'
[10903] Utils.pm:395> WARN - Datamodel: Entry 'Akre2010' (references.bib): Missing mandatory field 'journaltitle'
[10903] Utils.pm:395> WARN - Datamodel: Entry 'Albert2001' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10904] Utils.pm:395> WARN - Datamodel: Entry 'Albert2001' (references.bib): Missing mandatory field 'journaltitle'
[10905] Utils.pm:395> WARN - Datamodel: Entry 'Amidror2015Consonance' (references.bib): Missing mandatory field 'institution'
[10913] Utils.pm:395> WARN - Datamodel: Entry 'Ammari2014' (references.bib): Missing mandatory field 'journaltitle'
[10918] Utils.pm:395> WARN - Datamodel: Entry 'Amoore1986' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10919] Utils.pm:395> WARN - Datamodel: Entry 'Anderson2014' (references.bib): Missing mandatory field 'journaltitle'
[10921] Utils.pm:395> WARN - Datamodel: Entry 'Arendt2008' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10930] Utils.pm:395> WARN - Datamodel: Entry 'Arnegard2005' (references.bib): Missing mandatory field 'journaltitle'
[10935] Utils.pm:395> WARN - Datamodel: Entry 'Arnegard2006' (references.bib): Missing mandatory field 'journaltitle'
[10936] Utils.pm:395> WARN - Datamodel: Entry 'Arthur1971' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10937] Utils.pm:395> WARN - Datamodel: Entry 'Ashida2011' (references.bib): Missing mandatory field 'journaltitle'
[10938] Utils.pm:395> WARN - Datamodel: Entry 'Assad1999' (references.bib): Missing mandatory field 'journaltitle'
[10939] Utils.pm:395> WARN - Datamodel: Entry 'Attick1991' (references.bib): Missing mandatory field 'journaltitle'
[10939] Utils.pm:395> WARN - Datamodel: Entry 'Audouze2014' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10940] Utils.pm:395> WARN - Datamodel: Entry 'Averbeck2006' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10941] Utils.pm:395> WARN - Datamodel: Entry 'Babineau2006' (references.bib): Missing mandatory field 'journaltitle'
[10941] Utils.pm:395> WARN - Datamodel: Entry 'Babineau2007' (references.bib): Missing mandatory field 'journaltitle'
[10942] Utils.pm:395> WARN - Datamodel: Entry 'Bach2008' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10943] Utils.pm:395> WARN - Datamodel: Entry 'Bacher1983' (references.bib): Missing mandatory field 'journaltitle'
[10943] Utils.pm:395> WARN - Datamodel: Entry 'Baktoft2015' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10944] Utils.pm:395> WARN - Datamodel: Entry 'Baktoft2015' (references.bib): Missing mandatory field 'journaltitle'
[10945] Utils.pm:395> WARN - Datamodel: Entry 'Ball1971' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10946] Utils.pm:395> WARN - Datamodel: Entry 'Baluch2011' (references.bib): Missing mandatory field 'journaltitle'
[10946] Utils.pm:395> WARN - Datamodel: Entry 'Barayeu2023' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10947] Utils.pm:395> WARN - Datamodel: Entry 'Barber2000' (references.bib): Missing mandatory field 'journaltitle'
[10948] Utils.pm:395> WARN - Datamodel: Entry 'Barlow1957' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10949] Utils.pm:395> WARN - Datamodel: Entry 'Barlow1972' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10950] Utils.pm:395> WARN - Datamodel: Entry 'Barlow2001a' (references.bib): Missing mandatory field 'journaltitle'
[10950] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1980' (references.bib): Missing mandatory field 'journaltitle'
[10951] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1981' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10952] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1981a' (references.bib): Missing mandatory field 'journaltitle'
[10954] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1981b' (references.bib): Missing mandatory field 'journaltitle'
[10976] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1985' (references.bib): Missing mandatory field 'journaltitle'
[10979] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1987' (references.bib): Missing mandatory field 'journaltitle'
[10979] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1990' (references.bib): Missing mandatory field 'journaltitle'
[10980] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1991' (references.bib): Invalid field 'publisher' for entrytype 'article'
[10981] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1993a' (references.bib): Missing mandatory field 'journaltitle'
[10981] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1993b' (references.bib): Missing mandatory field 'journaltitle'
[10982] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1995' (references.bib): Missing mandatory field 'journaltitle'
[10983] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1996a' (references.bib): Missing mandatory field 'journaltitle'
[10984] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1996b' (references.bib): Missing mandatory field 'journaltitle'
[10985] Utils.pm:395> WARN - Datamodel: Entry 'Bastian1998' (references.bib): Missing mandatory field 'journaltitle'
[11003] Utils.pm:395> WARN - Datamodel: Entry 'Bastian2001' (references.bib): Missing mandatory field 'journaltitle'
[11003] Utils.pm:395> WARN - Datamodel: Entry 'Bastian2002' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11004] Utils.pm:395> WARN - Datamodel: Entry 'Bastian2004' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11005] Utils.pm:395> WARN - Datamodel: Entry 'Behrend1977' (references.bib): Missing mandatory field 'journaltitle'
[11005] Utils.pm:395> WARN - Datamodel: Entry 'Beiran2018' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11006] Utils.pm:395> WARN - Datamodel: Entry 'Beiran2018coding' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11007] Utils.pm:395> WARN - Datamodel: Entry 'Bell1976' (references.bib): Missing mandatory field 'journaltitle'
[11008] Utils.pm:395> WARN - Datamodel: Entry 'Benda2005' (references.bib): Missing mandatory field 'journaltitle'
[11008] Utils.pm:395> WARN - Datamodel: Entry 'Benda2006' (references.bib): Missing mandatory field 'journaltitle'
[11009] Utils.pm:395> WARN - Datamodel: Entry 'Benda2010' (references.bib): Missing mandatory field 'journaltitle'
[11010] Utils.pm:395> WARN - Datamodel: Entry 'Benda2021' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11011] Utils.pm:395> WARN - Datamodel: Entry 'Bennett1970' (references.bib): Missing mandatory field 'journaltitle'
[11012] Utils.pm:395> WARN - Datamodel: Entry 'Berdahl2018' (references.bib): Missing mandatory field 'journaltitle'
[11012] Utils.pm:395> WARN - Datamodel: Entry 'Berman1995' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11013] Utils.pm:395> WARN - Datamodel: Entry 'Berman1999' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11014] Utils.pm:395> WARN - Datamodel: Entry 'Betsch2004' (references.bib): Missing mandatory field 'journaltitle'
[11014] Utils.pm:395> WARN - Datamodel: Entry 'Bilde2007' (references.bib): Missing mandatory field 'journaltitle'
[11015] Utils.pm:395> WARN - Datamodel: Entry 'Birmingham1999' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11038] Utils.pm:395> WARN - Datamodel: Entry 'Bleckmann1993' (references.bib): Missing mandatory field 'journaltitle'
[11039] Utils.pm:395> WARN - Datamodel: Entry 'Boerlin2013' (references.bib): Missing mandatory field 'journaltitle'
[11040] Utils.pm:395> WARN - Datamodel: Entry 'Bol2011' (references.bib): Missing mandatory field 'journaltitle'
[11041] Utils.pm:395> WARN - Datamodel: Entry 'Bol2013' (references.bib): Missing mandatory field 'journaltitle'
[11042] Utils.pm:395> WARN - Datamodel: Entry 'Bratton1990' (references.bib): Missing mandatory field 'journaltitle'
[11042] Utils.pm:395> WARN - Datamodel: Entry 'Bretschneider1985' (references.bib): Missing mandatory field 'journaltitle'
[11043] Utils.pm:395> WARN - Datamodel: Entry 'Briand2016' (references.bib): Missing mandatory field 'editor'
[11044] Utils.pm:395> WARN - Datamodel: Entry 'Brincat2004' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11044] Utils.pm:395> WARN - Datamodel: Entry 'Brownell1990' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11045] Utils.pm:395> WARN - Datamodel: Entry 'Budelli2000' (references.bib): Missing mandatory field 'journaltitle'
[11046] Utils.pm:395> WARN - Datamodel: Entry 'Budelli2002' (references.bib): Missing mandatory field 'journaltitle'
[11046] Utils.pm:395> WARN - Datamodel: Entry 'Bullock1969' (references.bib): Missing mandatory field 'journaltitle'
[11047] Utils.pm:395> WARN - Datamodel: Entry 'Bullock1970' (references.bib): Missing mandatory field 'journaltitle'
[11049] Utils.pm:395> WARN - Datamodel: Entry 'Bullock1972a' (references.bib): Missing mandatory field 'journaltitle'
[11065] Utils.pm:395> WARN - Datamodel: Entry 'Bullock1972b' (references.bib): Missing mandatory field 'journaltitle'
[11067] Utils.pm:395> WARN - Datamodel: Entry 'Bullock1972General' (references.bib): Missing mandatory field 'journaltitle'
[11068] Utils.pm:395> WARN - Datamodel: Entry 'Bullock1982' (references.bib): Missing mandatory field 'journaltitle'
[11074] Utils.pm:395> WARN - Datamodel: Entry 'Bullock1983' (references.bib): Missing mandatory field 'journaltitle'
[11075] Utils.pm:395> WARN - Datamodel: Entry 'Burham1969' (references.bib): Missing mandatory field 'journaltitle'
[11075] Utils.pm:395> WARN - Datamodel: Entry 'Bushdid2014' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11076] Utils.pm:395> WARN - Datamodel: Entry 'Cai1996' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11078] Utils.pm:395> WARN - Datamodel: Entry 'Caldwell1978' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11079] Utils.pm:395> WARN - Datamodel: Entry 'Caputi1998' (references.bib): Missing mandatory field 'journaltitle'
[11091] Utils.pm:395> WARN - Datamodel: Entry 'Caputi1998Brachy' (references.bib): Missing mandatory field 'journaltitle'
[11093] Utils.pm:395> WARN - Datamodel: Entry 'Caputi1999' (references.bib): Missing mandatory field 'journaltitle'
[11094] Utils.pm:395> WARN - Datamodel: Entry 'Caputi2006' (references.bib): Missing mandatory field 'journaltitle'
[11095] Utils.pm:395> WARN - Datamodel: Entry 'Caputi2008' (references.bib): Missing mandatory field 'journaltitle'
[11097] Utils.pm:395> WARN - Datamodel: Entry 'Cariani2001' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11098] Utils.pm:395> WARN - Datamodel: Entry 'Carlson2000' (references.bib): Missing mandatory field 'journaltitle'
[11099] Utils.pm:395> WARN - Datamodel: Entry 'Carlson2002' (references.bib): Missing mandatory field 'journaltitle'
[11100] Utils.pm:395> WARN - Datamodel: Entry 'Carlson2011' (references.bib): Missing mandatory field 'journaltitle'
[11101] Utils.pm:395> WARN - Datamodel: Entry 'Carlson2013' (references.bib): Missing mandatory field 'journaltitle'
[11102] Utils.pm:395> WARN - Datamodel: Entry 'Carr1982' (references.bib): Missing mandatory field 'journaltitle'
[11103] Utils.pm:395> WARN - Datamodel: Entry 'Carr1986' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11104] Utils.pm:395> WARN - Datamodel: Entry 'Carriot2017' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11105] Utils.pm:395> WARN - Datamodel: Entry 'Castello2000' (references.bib): Missing mandatory field 'journaltitle'
[11106] Utils.pm:395> WARN - Datamodel: Entry 'Catania2014' (references.bib): Missing mandatory field 'journaltitle'
[11106] Utils.pm:395> WARN - Datamodel: Entry 'Catania2015a' (references.bib): Missing mandatory field 'journaltitle'
[11107] Utils.pm:395> WARN - Datamodel: Entry 'Cattaneo1981' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11108] Utils.pm:395> WARN - Datamodel: Entry 'Cavigelli1999' (references.bib): Missing mandatory field 'journaltitle'
[11109] Utils.pm:395> WARN - Datamodel: Entry 'Chacron2000' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11110] Utils.pm:395> WARN - Datamodel: Entry 'Chacron2001' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11137] Utils.pm:395> WARN - Datamodel: Entry 'Chacron2004' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11144] Utils.pm:395> WARN - Datamodel: Entry 'Chacron2005' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11145] Utils.pm:395> WARN - Datamodel: Entry 'Chacron2006' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11146] Utils.pm:395> WARN - Datamodel: Entry 'Chagnaud2008' (references.bib): Missing mandatory field 'journaltitle'
[11147] Utils.pm:395> WARN - Datamodel: Entry 'Chakravarthy2018' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11150] Utils.pm:395> WARN - Datamodel: Entry 'Chan2005' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11152] Utils.pm:395> WARN - Datamodel: Entry 'Chapman1995' (references.bib): Missing mandatory field 'journaltitle'
[11152] Utils.pm:395> WARN - Datamodel: Entry 'Charpentier2005' (references.bib): Missing mandatory field 'journaltitle'
[11154] Utils.pm:395> WARN - Datamodel: Entry 'Chen2005' (references.bib): Missing mandatory field 'journaltitle'
[11154] Utils.pm:395> WARN - Datamodel: Entry 'Cherry1953' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11157] Utils.pm:395> WARN - Datamodel: Entry 'Chikkerur2010' (references.bib): Missing mandatory field 'journaltitle'
[11177] Utils.pm:395> WARN - Datamodel: Entry 'Chocholle1957' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11180] Utils.pm:395> WARN - Datamodel: Entry 'Coates1954' (references.bib): Missing mandatory field 'journaltitle'
[11180] Utils.pm:395> WARN - Datamodel: Entry 'Collin2012' (references.bib): Missing mandatory field 'journaltitle'
[11181] Utils.pm:395> WARN - Datamodel: Entry 'Cote1995' (references.bib): Missing mandatory field 'journaltitle'
[11182] Utils.pm:395> WARN - Datamodel: Entry 'Cox2004' (references.bib): Missing mandatory field 'journaltitle'
[11184] Utils.pm:395> WARN - Datamodel: Entry 'Crampton2013' (references.bib): Missing mandatory field 'journaltitle'
[11185] Utils.pm:395> WARN - Datamodel: Entry 'Crick1984' (references.bib): Missing mandatory field 'journaltitle'
[11186] Utils.pm:395> WARN - Datamodel: Entry 'Crook2004' (references.bib): Missing mandatory field 'journaltitle'
[11189] Utils.pm:395> WARN - Datamodel: Entry 'Csicsvari1998' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11207] Utils.pm:395> WARN - Datamodel: Entry 'Cuadrado1987' (references.bib): Missing mandatory field 'journaltitle'
[11207] Utils.pm:395> WARN - Datamodel: Entry 'Cuadrado1992' (references.bib): Missing mandatory field 'journaltitle'
[11208] Utils.pm:395> WARN - Datamodel: Entry 'Cunningham2004' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11209] Utils.pm:395> WARN - Datamodel: Entry 'Cunningham2017' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11210] Utils.pm:395> WARN - Datamodel: Entry 'Dalton2000' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11211] Utils.pm:395> WARN - Datamodel: Entry 'Dau1997' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11212] Utils.pm:395> WARN - Datamodel: Entry 'Deemyad2013' (references.bib): Missing mandatory field 'journaltitle'
[11212] Utils.pm:395> WARN - Datamodel: Entry 'Delgutte1990' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11213] Utils.pm:395> WARN - Datamodel: Entry 'Dell2014' (references.bib): Missing mandatory field 'journaltitle'
[11214] Utils.pm:395> WARN - Datamodel: Entry 'DeSantana2019' (references.bib): Missing mandatory field 'journaltitle'
[11215] Utils.pm:395> WARN - Datamodel: Entry 'Destexhe1993' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11216] Utils.pm:395> WARN - Datamodel: Entry 'DeValois1982' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11217] Utils.pm:395> WARN - Datamodel: Entry 'DeWeille1983' (references.bib): Missing mandatory field 'journaltitle'
[11218] Utils.pm:395> WARN - Datamodel: Entry 'DeWoody2000' (references.bib): Missing mandatory field 'journaltitle'
[11221] Utils.pm:395> WARN - Datamodel: Entry 'Doiron2002' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11243] Utils.pm:395> WARN - Datamodel: Entry 'Dong1995' (references.bib): Missing mandatory field 'journaltitle'
[11244] Utils.pm:395> WARN - Datamodel: Entry 'Dunlap1998' (references.bib): Missing mandatory field 'journaltitle'
[11246] Utils.pm:395> WARN - Datamodel: Entry 'Dunlap2000' (references.bib): Missing mandatory field 'journaltitle'
[11247] Utils.pm:395> WARN - Datamodel: Entry 'Dunlap2002' (references.bib): Missing mandatory field 'journaltitle'
[11248] Utils.pm:395> WARN - Datamodel: Entry 'Dunlap2003b' (references.bib): Missing mandatory field 'journaltitle'
[11249] Utils.pm:395> WARN - Datamodel: Entry 'Dux2006' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11250] Utils.pm:395> WARN - Datamodel: Entry 'Dye1987' (references.bib): Missing mandatory field 'journaltitle'
[11250] Utils.pm:395> WARN - Datamodel: Entry 'Edwards2010' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11252] Utils.pm:395> WARN - Datamodel: Entry 'Eeuwes2008' (references.bib): Missing mandatory field 'journaltitle'
[11253] Utils.pm:395> WARN - Datamodel: Entry 'Egerland2020' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11254] Utils.pm:395> WARN - Datamodel: Entry 'Egerland2020' (references.bib): Missing mandatory field 'journaltitle'
[11255] Utils.pm:395> WARN - Datamodel: Entry 'Eggermont1993' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11256] Utils.pm:395> WARN - Datamodel: Entry 'Egner2010' (references.bib): Missing mandatory field 'journaltitle'
[11257] Utils.pm:395> WARN - Datamodel: Entry 'Egnor2016' (references.bib): Missing mandatory field 'journaltitle'
[11257] Utils.pm:395> WARN - Datamodel: Entry 'Elfwing2018' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11258] Utils.pm:395> WARN - Datamodel: Entry 'Emde1990' (references.bib): Missing mandatory field 'journaltitle'
[11259] Utils.pm:395> WARN - Datamodel: Entry 'Emde1993' (references.bib): Missing mandatory field 'journaltitle'
[11260] Utils.pm:395> WARN - Datamodel: Entry 'Emde1994' (references.bib): Missing mandatory field 'journaltitle'
[11261] Utils.pm:395> WARN - Datamodel: Entry 'Emde1998' (references.bib): Missing mandatory field 'journaltitle'
[11261] Utils.pm:395> WARN - Datamodel: Entry 'Emde1998Distance' (references.bib): Missing mandatory field 'journaltitle'
[11262] Utils.pm:395> WARN - Datamodel: Entry 'Emde2006' (references.bib): Missing mandatory field 'journaltitle'
[11263] Utils.pm:395> WARN - Datamodel: Entry 'Emde2010Signalling' (references.bib): Missing mandatory field 'journaltitle'
[11264] Utils.pm:395> WARN - Datamodel: Entry 'Engelmann2010' (references.bib): Invalid field 'institution' for entrytype 'article'
[11265] Utils.pm:395> WARN - Datamodel: Entry 'Enger1968' (references.bib): Missing mandatory field 'journaltitle'
[11265] Utils.pm:395> WARN - Datamodel: Entry 'Engh2002' (references.bib): Missing mandatory field 'journaltitle'
[11295] Utils.pm:395> WARN - Datamodel: Entry 'Engler2000' (references.bib): Missing mandatory field 'journaltitle'
[11297] Utils.pm:395> WARN - Datamodel: Entry 'Evans1972' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11299] Utils.pm:395> WARN - Datamodel: Entry 'Evans1981' (references.bib): Missing mandatory field 'editor'
[11300] Utils.pm:395> WARN - Datamodel: Entry 'Ewert2000' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11301] Utils.pm:395> WARN - Datamodel: Entry 'Fechler2013' (references.bib): Missing mandatory field 'journaltitle'
[11302] Utils.pm:395> WARN - Datamodel: Entry 'Fenske2006' (references.bib): Missing mandatory field 'journaltitle'
[11304] Utils.pm:395> WARN - Datamodel: Entry 'Fernald2014' (references.bib): Missing mandatory field 'journaltitle'
[11305] Utils.pm:395> WARN - Datamodel: Entry 'Feulner2009' (references.bib): Missing mandatory field 'journaltitle'
[11305] Utils.pm:395> WARN - Datamodel: Entry 'Field1994' (references.bib): Missing mandatory field 'journaltitle'
[11306] Utils.pm:395> WARN - Datamodel: Entry 'Fischer2011' (references.bib): Missing mandatory field 'journaltitle'
[11307] Utils.pm:395> WARN - Datamodel: Entry 'Fleischer2010' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11308] Utils.pm:395> WARN - Datamodel: Entry 'Ford1973' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11308] Utils.pm:395> WARN - Datamodel: Entry 'Foster1985' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11309] Utils.pm:395> WARN - Datamodel: Entry 'Fotowat2013' (references.bib): Missing mandatory field 'journaltitle'
[11310] Utils.pm:395> WARN - Datamodel: Entry 'Fourcaud-Trocme2003' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11313] Utils.pm:395> WARN - Datamodel: Entry 'Franchina1998' (references.bib): Missing mandatory field 'journaltitle'
[11340] Utils.pm:395> WARN - Datamodel: Entry 'Freeman2013' (references.bib): Missing mandatory field 'journaltitle'
[11341] Utils.pm:395> WARN - Datamodel: Entry 'French1973' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11342] Utils.pm:395> WARN - Datamodel: Entry 'Freund2002' (references.bib): Missing mandatory field 'journaltitle'
[11343] Utils.pm:395> WARN - Datamodel: Entry 'Friedman1996' (references.bib): Missing mandatory field 'journaltitle'
[11344] Utils.pm:395> WARN - Datamodel: Entry 'Froudarakis2014' (references.bib): Missing mandatory field 'journaltitle'
[11345] Utils.pm:395> WARN - Datamodel: Entry 'Fugere2010' (references.bib): Missing mandatory field 'journaltitle'
[11345] Utils.pm:395> WARN - Datamodel: Entry 'Fugere2011' (references.bib): Missing mandatory field 'journaltitle'
[11346] Utils.pm:395> WARN - Datamodel: Entry 'Fujita2010' (references.bib): Missing mandatory field 'journaltitle'
[11347] Utils.pm:395> WARN - Datamodel: Entry 'Fujita2019' (references.bib): Missing mandatory field 'journaltitle'
[11348] Utils.pm:395> WARN - Datamodel: Entry 'Fukutomi2020' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11351] Utils.pm:395> WARN - Datamodel: Entry 'Gabbiani1996' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11352] Utils.pm:395> WARN - Datamodel: Entry 'Gabbiani1999' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11353] Utils.pm:395> WARN - Datamodel: Entry 'Gavassa2012' (references.bib): Missing mandatory field 'journaltitle'
[11355] Utils.pm:395> WARN - Datamodel: Entry 'Geisler2008' (references.bib): Missing mandatory field 'journaltitle'
[11356] Utils.pm:395> WARN - Datamodel: Entry 'Gershman2012' (references.bib): Missing mandatory field 'journaltitle'
[11357] Utils.pm:395> WARN - Datamodel: Entry 'Gilbert2007' (references.bib): Missing mandatory field 'journaltitle'
[11359] Utils.pm:395> WARN - Datamodel: Entry 'Goldstein1966' (references.bib): Missing mandatory field 'institution'
[11360] Utils.pm:395> WARN - Datamodel: Entry 'Gomez2014' (references.bib): Missing mandatory field 'journaltitle'
[11361] Utils.pm:395> WARN - Datamodel: Entry 'Goris2014' (references.bib): Missing mandatory field 'journaltitle'
[11390] Utils.pm:395> WARN - Datamodel: Entry 'Gottwald2017' (references.bib): Missing mandatory field 'journaltitle'
[11391] Utils.pm:395> WARN - Datamodel: Entry 'Gottwald2018' (references.bib): Missing mandatory field 'journaltitle'
[11392] Utils.pm:395> WARN - Datamodel: Entry 'Grahn2012' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11394] Utils.pm:395> WARN - Datamodel: Entry 'Gross2002' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11395] Utils.pm:395> WARN - Datamodel: Entry 'Hagedorn1985' (references.bib): Missing mandatory field 'journaltitle'
[11395] Utils.pm:395> WARN - Datamodel: Entry 'Hagedorn1985Hypopomus' (references.bib): Missing mandatory field 'journaltitle'
[11396] Utils.pm:395> WARN - Datamodel: Entry 'Hager1991' (references.bib): Missing mandatory field 'journaltitle'
[11397] Utils.pm:395> WARN - Datamodel: Entry 'Hagiwara1963' (references.bib): Missing mandatory field 'journaltitle'
[11398] Utils.pm:395> WARN - Datamodel: Entry 'Hartmann1979' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11399] Utils.pm:395> WARN - Datamodel: Entry 'Harvey-Girard2013' (references.bib): Missing mandatory field 'journaltitle'
[11400] Utils.pm:395> WARN - Datamodel: Entry 'Harvey2010' (references.bib): Missing mandatory field 'journaltitle'
[11401] Utils.pm:395> WARN - Datamodel: Entry 'Harvey2013' (references.bib): Missing mandatory field 'journaltitle'
[11401] Utils.pm:395> WARN - Datamodel: Entry 'Hass2002' (references.bib): Missing mandatory field 'journaltitle'
[11404] Utils.pm:395> WARN - Datamodel: Entry 'Hawken1996' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11405] Utils.pm:395> WARN - Datamodel: Entry 'Haykin2005' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11406] Utils.pm:395> WARN - Datamodel: Entry 'Heil2015' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11407] Utils.pm:395> WARN - Datamodel: Entry 'Heil2016' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11408] Utils.pm:395> WARN - Datamodel: Entry 'Heiligenberg1973' (references.bib): Missing mandatory field 'journaltitle'
[11408] Utils.pm:395> WARN - Datamodel: Entry 'Heiligenberg1975' (references.bib): Missing mandatory field 'journaltitle'
[11409] Utils.pm:395> WARN - Datamodel: Entry 'Heiligenberg1976' (references.bib): Missing mandatory field 'journaltitle'
[11439] Utils.pm:395> WARN - Datamodel: Entry 'Heiligenberg1978' (references.bib): Missing mandatory field 'journaltitle'
[11443] Utils.pm:395> WARN - Datamodel: Entry 'Heiligenberg1981' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11444] Utils.pm:395> WARN - Datamodel: Entry 'Heiligenberg1982' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11445] Utils.pm:395> WARN - Datamodel: Entry 'Heiligenberg1996' (references.bib): Missing mandatory field 'journaltitle'
[11446] Utils.pm:395> WARN - Datamodel: Entry 'Hennig2004' (references.bib): Missing mandatory field 'journaltitle'
[11447] Utils.pm:395> WARN - Datamodel: Entry 'Henninger2017' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11448] Utils.pm:395> WARN - Datamodel: Entry 'Henninger2020' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11448] Utils.pm:395> WARN - Datamodel: Entry 'Henninger2020' (references.bib): Missing mandatory field 'journaltitle'
[11449] Utils.pm:395> WARN - Datamodel: Entry 'Henry2017' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11451] Utils.pm:395> WARN - Datamodel: Entry 'Hirabayashi2014' (references.bib): Missing mandatory field 'journaltitle'
[11452] Utils.pm:395> WARN - Datamodel: Entry 'Hodgkin1952' (references.bib): Missing mandatory field 'journaltitle'
[11453] Utils.pm:395> WARN - Datamodel: Entry 'Hofmann2008' (references.bib): Missing mandatory field 'journaltitle'
[11455] Utils.pm:395> WARN - Datamodel: Entry 'Hofmann2013' (references.bib): Missing mandatory field 'journaltitle'
[11456] Utils.pm:395> WARN - Datamodel: Entry 'Hofmann2017' (references.bib): Missing mandatory field 'journaltitle'
[11456] Utils.pm:395> WARN - Datamodel: Entry 'Hojesjo1998' (references.bib): Missing mandatory field 'journaltitle'
[11457] Utils.pm:395> WARN - Datamodel: Entry 'Hopkins1972' (references.bib): Missing mandatory field 'journaltitle'
[11479] Utils.pm:395> WARN - Datamodel: Entry 'Hopkins1976' (references.bib): Missing mandatory field 'journaltitle'
[11480] Utils.pm:395> WARN - Datamodel: Entry 'Hopkins1978' (references.bib): Missing mandatory field 'journaltitle'
[11481] Utils.pm:395> WARN - Datamodel: Entry 'Hopkins1980' (references.bib): Missing mandatory field 'journaltitle'
[11482] Utils.pm:395> WARN - Datamodel: Entry 'Hopkins1981' (references.bib): Missing mandatory field 'journaltitle'
[11482] Utils.pm:395> WARN - Datamodel: Entry 'Hopkins1986Temporal' (references.bib): Missing mandatory field 'journaltitle'
[11483] Utils.pm:395> WARN - Datamodel: Entry 'Hopkins1986Time' (references.bib): Missing mandatory field 'journaltitle'
[11483] Utils.pm:395> WARN - Datamodel: Entry 'Hopkins1990' (references.bib): Missing mandatory field 'journaltitle'
[11484] Utils.pm:395> WARN - Datamodel: Entry 'Hopkins1999' (references.bib): Missing mandatory field 'journaltitle'
[11485] Utils.pm:395> WARN - Datamodel: Entry 'Hoshimiya1980' (references.bib): Missing mandatory field 'journaltitle'
[11486] Utils.pm:395> WARN - Datamodel: Entry 'Hosoya2005' (references.bib): Missing mandatory field 'journaltitle'
[11490] Utils.pm:395> WARN - Datamodel: Entry 'Hubel1959' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11506] Utils.pm:395> WARN - Datamodel: Entry 'Hubel1962' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11507] Utils.pm:395> WARN - Datamodel: Entry 'Hudspeth1988kinetic' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11508] Utils.pm:395> WARN - Datamodel: Entry 'Hudspeth1988model' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11509] Utils.pm:395> WARN - Datamodel: Entry 'Huetz2011' (references.bib): Missing mandatory field 'journaltitle'
[11510] Utils.pm:395> WARN - Datamodel: Entry 'Hughey2018' (references.bib): Missing mandatory field 'journaltitle'
[11510] Utils.pm:395> WARN - Datamodel: Entry 'Hunter2007' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11511] Utils.pm:395> WARN - Datamodel: Entry 'Hupe2008' (references.bib): Missing mandatory field 'journaltitle'
[11512] Utils.pm:395> WARN - Datamodel: Entry 'Hupe2008DF' (references.bib): Missing mandatory field 'journaltitle'
[11514] Utils.pm:395> WARN - Datamodel: Entry 'Izhikevich2000' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11515] Utils.pm:395> WARN - Datamodel: Entry 'Janson1985' (references.bib): Missing mandatory field 'journaltitle'
[11516] Utils.pm:395> WARN - Datamodel: Entry 'Janson2022' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11517] Utils.pm:395> WARN - Datamodel: Entry 'Jason1990' (references.bib): Missing mandatory field 'journaltitle'
[11517] Utils.pm:395> WARN - Datamodel: Entry 'Jazayeri2006' (references.bib): Missing mandatory field 'journaltitle'
[11518] Utils.pm:395> WARN - Datamodel: Entry 'Joris1992' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11520] Utils.pm:395> WARN - Datamodel: Entry 'Juergens1999' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11521] Utils.pm:395> WARN - Datamodel: Entry 'Julicher2001' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11545] Utils.pm:395> WARN - Datamodel: Entry 'Jun2013' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11545] Utils.pm:395> WARN - Datamodel: Entry 'Jun2013' (references.bib): Missing mandatory field 'journaltitle'
[11551] Utils.pm:395> WARN - Datamodel: Entry 'Jun2014' (references.bib): Missing mandatory field 'journaltitle'
[11555] Utils.pm:395> WARN - Datamodel: Entry 'Kappeler2008' (references.bib): Missing mandatory field 'journaltitle'
[11563] Utils.pm:395> WARN - Datamodel: Entry 'Karri2020' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11564] Utils.pm:395> WARN - Datamodel: Entry 'Kashimori1996' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11569] Utils.pm:395> WARN - Datamodel: Entry 'Kelly2008' (references.bib): Missing mandatory field 'journaltitle'
[11570] Utils.pm:395> WARN - Datamodel: Entry 'Kemp1978' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11572] Utils.pm:395> WARN - Datamodel: Entry 'Kern2005' (references.bib): Missing mandatory field 'journaltitle'
[11585] Utils.pm:395> WARN - Datamodel: Entry 'Khanbabaie2010' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11591] Utils.pm:395> WARN - Datamodel: Entry 'Khanna1989' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11592] Utils.pm:395> WARN - Datamodel: Entry 'Khosravi-Hashemi2014' (references.bib): Missing mandatory field 'journaltitle'
[11592] Utils.pm:395> WARN - Datamodel: Entry 'King2019' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11593] Utils.pm:395> WARN - Datamodel: Entry 'Kirschbaum2002' (references.bib): Missing mandatory field 'journaltitle'
[11594] Utils.pm:395> WARN - Datamodel: Entry 'Kiskinis2018' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11595] Utils.pm:395> WARN - Datamodel: Entry 'Knight1972a' (references.bib): Missing mandatory field 'journaltitle'
[11596] Utils.pm:395> WARN - Datamodel: Entry 'Knudsen1974' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11596] Utils.pm:395> WARN - Datamodel: Entry 'Knudsen1974' (references.bib): Missing mandatory field 'journaltitle'
[11597] Utils.pm:395> WARN - Datamodel: Entry 'Knudsen1975' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11599] Utils.pm:395> WARN - Datamodel: Entry 'Knudsen1975' (references.bib): Missing mandatory field 'journaltitle'
[11600] Utils.pm:395> WARN - Datamodel: Entry 'Knudsen1976a' (references.bib): Missing mandatory field 'journaltitle'
[11601] Utils.pm:395> WARN - Datamodel: Entry 'Knudsen1976b' (references.bib): Missing mandatory field 'journaltitle'
[11602] Utils.pm:395> WARN - Datamodel: Entry 'Knudsen1978' (references.bib): Missing mandatory field 'journaltitle'
[11604] Utils.pm:395> WARN - Datamodel: Entry 'Knudsen2007' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11628] Utils.pm:395> WARN - Datamodel: Entry 'Koelsch2005' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11630] Utils.pm:395> WARN - Datamodel: Entry 'Koenig1876' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11633] Utils.pm:395> WARN - Datamodel: Entry 'Koenig1881' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11634] Utils.pm:395> WARN - Datamodel: Entry 'Koerding2004' (references.bib): Missing mandatory field 'journaltitle'
[11636] Utils.pm:395> WARN - Datamodel: Entry 'Korstjens2006' (references.bib): Missing mandatory field 'journaltitle'
[11638] Utils.pm:395> WARN - Datamodel: Entry 'Kozmik2008' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11639] Utils.pm:395> WARN - Datamodel: Entry 'Krahe2004' (references.bib): Invalid field 'institution' for entrytype 'article'
[11640] Utils.pm:395> WARN - Datamodel: Entry 'Krahe2008' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11641] Utils.pm:395> WARN - Datamodel: Entry 'Krahe2014' (references.bib): Missing mandatory field 'journaltitle'
[11642] Utils.pm:395> WARN - Datamodel: Entry 'Kramer1988' (references.bib): Missing mandatory field 'journaltitle'
[11643] Utils.pm:395> WARN - Datamodel: Entry 'Kramer1999' (references.bib): Missing mandatory field 'journaltitle'
[11644] Utils.pm:395> WARN - Datamodel: Entry 'Kubo1957' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11644] Utils.pm:395> WARN - Datamodel: Entry 'Kujawa1995' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11646] Utils.pm:395> WARN - Datamodel: Entry 'Lamore1986' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11648] Utils.pm:395> WARN - Datamodel: Entry 'Land1977' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11649] Utils.pm:395> WARN - Datamodel: Entry 'Langley1996' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11650] Utils.pm:395> WARN - Datamodel: Entry 'Larson2014' (references.bib): Missing mandatory field 'journaltitle'
[11677] Utils.pm:395> WARN - Datamodel: Entry 'Laughlin1981' (references.bib): Missing mandatory field 'journaltitle'
[11680] Utils.pm:395> WARN - Datamodel: Entry 'Lavoue2012' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11682] Utils.pm:395> WARN - Datamodel: Entry 'Lee2003' (references.bib): Missing mandatory field 'journaltitle'
[11682] Utils.pm:395> WARN - Datamodel: Entry 'Leek2001' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11683] Utils.pm:395> WARN - Datamodel: Entry 'Lewicki2002' (references.bib): Missing mandatory field 'journaltitle'
[11684] Utils.pm:395> WARN - Datamodel: Entry 'Lewicki2014' (references.bib): Missing mandatory field 'journaltitle'
[11685] Utils.pm:395> WARN - Datamodel: Entry 'Liberman1982' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11689] Utils.pm:395> WARN - Datamodel: Entry 'Lindner2022' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11690] Utils.pm:395> WARN - Datamodel: Entry 'Lisman1997' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11691] Utils.pm:395> WARN - Datamodel: Entry 'Lissmann1951' (references.bib): Missing mandatory field 'journaltitle'
[11692] Utils.pm:395> WARN - Datamodel: Entry 'Lissmann1958Function' (references.bib): Missing mandatory field 'journaltitle'
[11692] Utils.pm:395> WARN - Datamodel: Entry 'Lissmann1958Mechanism' (references.bib): Missing mandatory field 'journaltitle'
[11694] Utils.pm:395> WARN - Datamodel: Entry 'Litwin-Kumar2012' (references.bib): Missing mandatory field 'journaltitle'
[11695] Utils.pm:395> WARN - Datamodel: Entry 'Lochmann2011' (references.bib): Missing mandatory field 'journaltitle'
[11696] Utils.pm:395> WARN - Datamodel: Entry 'Longtin1996' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11697] Utils.pm:395> WARN - Datamodel: Entry 'Longtin2008' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11729] Utils.pm:395> WARN - Datamodel: Entry 'Machnik2008' (references.bib): Missing mandatory field 'journaltitle'
[11731] Utils.pm:395> WARN - Datamodel: Entry 'MacIver2010' (references.bib): Missing mandatory field 'journaltitle'
[11732] Utils.pm:395> WARN - Datamodel: Entry 'Maier2008' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11733] Utils.pm:395> WARN - Datamodel: Entry 'Maler1974' (references.bib): Missing mandatory field 'journaltitle'
[11734] Utils.pm:395> WARN - Datamodel: Entry 'Maler1979' (references.bib): Missing mandatory field 'journaltitle'
[11736] Utils.pm:395> WARN - Datamodel: Entry 'Malone2010' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11738] Utils.pm:395> WARN - Datamodel: Entry 'Manley1990' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11739] Utils.pm:395> WARN - Datamodel: Entry 'Margolskee2002' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11743] Utils.pm:395> WARN - Datamodel: Entry 'Markham2015' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11746] Utils.pm:395> WARN - Datamodel: Entry 'Markham2016' (references.bib): Missing mandatory field 'journaltitle'
[11765] Utils.pm:395> WARN - Datamodel: Entry 'Markham2017' (references.bib): Missing mandatory field 'journaltitle'
[11766] Utils.pm:395> WARN - Datamodel: Entry 'Marquez2013a' (references.bib): Missing mandatory field 'journaltitle'
[11767] Utils.pm:395> WARN - Datamodel: Entry 'Marquez2013b' (references.bib): Missing mandatory field 'journaltitle'
[11768] Utils.pm:395> WARN - Datamodel: Entry 'Marsat2010burst' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11774] Utils.pm:395> WARN - Datamodel: Entry 'Marsat2012a' (references.bib): Missing mandatory field 'journaltitle'
[11778] Utils.pm:395> WARN - Datamodel: Entry 'Marsat2012b' (references.bib): Missing mandatory field 'journaltitle'
[11779] Utils.pm:395> WARN - Datamodel: Entry 'Marsat2012bursting' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11781] Utils.pm:395> WARN - Datamodel: Entry 'Mathis2018' (references.bib): Missing mandatory field 'journaltitle'
[11783] Utils.pm:395> WARN - Datamodel: Entry 'McAdams1999' (references.bib): Missing mandatory field 'journaltitle'
[11784] Utils.pm:395> WARN - Datamodel: Entry 'McAnelly2000' (references.bib): Missing mandatory field 'journaltitle'
[11785] Utils.pm:395> WARN - Datamodel: Entry 'McCreery1977' (references.bib): Missing mandatory field 'journaltitle'
[11786] Utils.pm:395> WARN - Datamodel: Entry 'Mcdermott2009' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11790] Utils.pm:395> WARN - Datamodel: Entry 'McGillivray2012' (references.bib): Missing mandatory field 'journaltitle'
[11792] Utils.pm:395> WARN - Datamodel: Entry 'McGregor1992' (references.bib): Missing mandatory field 'journaltitle'
[11794] Utils.pm:395> WARN - Datamodel: Entry 'McKibben1993' (references.bib): Missing mandatory field 'journaltitle'
[11820] Utils.pm:395> WARN - Datamodel: Entry 'Mejias2013' (references.bib): Missing mandatory field 'journaltitle'
[11825] Utils.pm:395> WARN - Datamodel: Entry 'Menzel2005' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11837] Utils.pm:395> WARN - Datamodel: Entry 'Meredith2002' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11844] Utils.pm:395> WARN - Datamodel: Entry 'Metcalfe1995' (references.bib): Missing mandatory field 'journaltitle'
[11846] Utils.pm:395> WARN - Datamodel: Entry 'Metzen2016' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11850] Utils.pm:395> WARN - Datamodel: Entry 'Metzen2018descending' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11852] Utils.pm:395> WARN - Datamodel: Entry 'Meyer1982' (references.bib): Missing mandatory field 'journaltitle'
[11855] Utils.pm:395> WARN - Datamodel: Entry 'Meyer1982Impedances' (references.bib): Missing mandatory field 'journaltitle'
[11858] Utils.pm:395> WARN - Datamodel: Entry 'Meyer1987' (references.bib): Missing mandatory field 'journaltitle'
[11881] Utils.pm:395> WARN - Datamodel: Entry 'Middleton2006' (references.bib): Missing mandatory field 'journaltitle'
[11885] Utils.pm:395> WARN - Datamodel: Entry 'Middleton2007' (references.bib): Missing mandatory field 'journaltitle'
[11889] Utils.pm:395> WARN - Datamodel: Entry 'Migliaro2005' (references.bib): Missing mandatory field 'journaltitle'
[11908] Utils.pm:395> WARN - Datamodel: Entry 'Millman2002' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11921] Utils.pm:395> WARN - Datamodel: Entry 'Moller1972' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11924] Utils.pm:395> WARN - Datamodel: Entry 'Moller1976' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11934] Utils.pm:395> WARN - Datamodel: Entry 'Montgomery1984' (references.bib): Missing mandatory field 'journaltitle'
[11940] Utils.pm:395> WARN - Datamodel: Entry 'Moore1999' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11948] Utils.pm:395> WARN - Datamodel: Entry 'Moortgat1998' (references.bib): Missing mandatory field 'journaltitle'
[11953] Utils.pm:395> WARN - Datamodel: Entry 'Moreno2014' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11967] Utils.pm:395> WARN - Datamodel: Entry 'Myers2003' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11975] Utils.pm:395> WARN - Datamodel: Entry 'Nauhaus2012' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11978] Utils.pm:395> WARN - Datamodel: Entry 'Neiman2011fish' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11981] Utils.pm:395> WARN - Datamodel: Entry 'Nelson1997' (references.bib): Invalid field 'publisher' for entrytype 'article'
[11988] Utils.pm:395> WARN - Datamodel: Entry 'Nelson1999' (references.bib): Missing mandatory field 'journaltitle'
[12000] Utils.pm:395> WARN - Datamodel: Entry 'Nelson2006' (references.bib): Missing mandatory field 'journaltitle'
[12008] Utils.pm:395> WARN - Datamodel: Entry 'Neuhofer2011' (references.bib): Missing mandatory field 'journaltitle'
[12010] Utils.pm:395> WARN - Datamodel: Entry 'Neuweiler1970' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12012] Utils.pm:395> WARN - Datamodel: Entry 'Nikias1993' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12024] Utils.pm:395> WARN - Datamodel: Entry 'Northcutt2000' (references.bib): Missing mandatory field 'journaltitle'
[12033] Utils.pm:395> WARN - Datamodel: Entry 'Nortmann2013' (references.bib): Missing mandatory field 'journaltitle'
[12038] Utils.pm:395> WARN - Datamodel: Entry 'Nowak2003' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12041] Utils.pm:395> WARN - Datamodel: Entry 'Ohm1839' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12043] Utils.pm:395> WARN - Datamodel: Entry 'Olshausen1996' (references.bib): Missing mandatory field 'journaltitle'
[12045] Utils.pm:395> WARN - Datamodel: Entry 'Oswald2004' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12063] Utils.pm:395> WARN - Datamodel: Entry 'Ott2021' (references.bib): Missing mandatory field 'author'
[12065] Utils.pm:395> WARN - Datamodel: Entry 'Ott2021' (references.bib): Missing mandatory field 'journaltitle'
[12067] Utils.pm:395> WARN - Datamodel: Entry 'Ott2021' (references.bib): Missing mandatory field 'title'
[12072] Utils.pm:395> WARN - Datamodel: Entry 'Palmer1982' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12074] Utils.pm:395> WARN - Datamodel: Entry 'Palmer1986' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12076] Utils.pm:395> WARN - Datamodel: Entry 'Parrish1999' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12079] Utils.pm:395> WARN - Datamodel: Entry 'Pedraja2014' (references.bib): Missing mandatory field 'journaltitle'
[12099] Utils.pm:395> WARN - Datamodel: Entry 'Pedraja2018' (references.bib): Missing mandatory field 'journaltitle'
[12101] Utils.pm:395> WARN - Datamodel: Entry 'Pedregosa2011' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12106] Utils.pm:395> WARN - Datamodel: Entry 'Pereira2012' (references.bib): Missing mandatory field 'journaltitle'
[12107] Utils.pm:395> WARN - Datamodel: Entry 'Perrone2009' (references.bib): Missing mandatory field 'journaltitle'
[12110] Utils.pm:395> WARN - Datamodel: Entry 'Peters1972' (references.bib): Missing mandatory field 'journaltitle'
[12118] Utils.pm:395> WARN - Datamodel: Entry 'Peters2007' (references.bib): Missing mandatory field 'journaltitle'
[12131] Utils.pm:395> WARN - Datamodel: Entry 'Peterson2019' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12148] Utils.pm:395> WARN - Datamodel: Entry 'Petzold2016' (references.bib): Missing mandatory field 'journaltitle'
[12150] Utils.pm:395> WARN - Datamodel: Entry 'Phillips1990' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12156] Utils.pm:395> WARN - Datamodel: Entry 'Poggio1985' (references.bib): Missing mandatory field 'journaltitle'
[12158] Utils.pm:395> WARN - Datamodel: Entry 'Pouget2013' (references.bib): Missing mandatory field 'journaltitle'
[12167] Utils.pm:395> WARN - Datamodel: Entry 'Preyer1996' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12176] Utils.pm:395> WARN - Datamodel: Entry 'Pugh1999' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12179] Utils.pm:395> WARN - Datamodel: Entry 'Pusch2008' (references.bib): Missing mandatory field 'journaltitle'
[12181] Utils.pm:395> WARN - Datamodel: Entry 'Quiroga2005' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12184] Utils.pm:395> WARN - Datamodel: Entry 'Quiroga2010' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12189] Utils.pm:395> WARN - Datamodel: Entry 'Quiroga2010' (references.bib): Missing mandatory field 'journaltitle'
[12207] Utils.pm:395> WARN - Datamodel: Entry 'Rao1999' (references.bib): Missing mandatory field 'journaltitle'
[12209] Utils.pm:395> WARN - Datamodel: Entry 'Rao2005' (references.bib): Missing mandatory field 'journaltitle'
[12211] Utils.pm:395> WARN - Datamodel: Entry 'Rasch1984' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12212] Utils.pm:395> WARN - Datamodel: Entry 'Rasnow1996' (references.bib): Missing mandatory field 'journaltitle'
[12213] Utils.pm:395> WARN - Datamodel: Entry 'Ratnam2000' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12214] Utils.pm:395> WARN - Datamodel: Entry 'Reardon2011' (references.bib): Missing mandatory field 'journaltitle'
[12215] Utils.pm:395> WARN - Datamodel: Entry 'Rhode1977' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12217] Utils.pm:395> WARN - Datamodel: Entry 'Ridder2010Auditory' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12218] Utils.pm:395> WARN - Datamodel: Entry 'Ridder2010Cord' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12220] Utils.pm:395> WARN - Datamodel: Entry 'Roddey2000' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12242] Utils.pm:395> WARN - Datamodel: Entry 'Rodriguez2013' (references.bib): Missing mandatory field 'journaltitle'
[12244] Utils.pm:395> WARN - Datamodel: Entry 'Roeber1834' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12247] Utils.pm:395> WARN - Datamodel: Entry 'Romani1982' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12250] Utils.pm:395> WARN - Datamodel: Entry 'Romei2009' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12252] Utils.pm:395> WARN - Datamodel: Entry 'Rose1971' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12267] Utils.pm:395> WARN - Datamodel: Entry 'Roth1972' (references.bib): Missing mandatory field 'journaltitle'
[12269] Utils.pm:395> WARN - Datamodel: Entry 'Rother2003' (references.bib): Missing mandatory field 'journaltitle'
[12272] Utils.pm:395> WARN - Datamodel: Entry 'Roux2006' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12275] Utils.pm:395> WARN - Datamodel: Entry 'Ruderman1994' (references.bib): Missing mandatory field 'journaltitle'
[12278] Utils.pm:395> WARN - Datamodel: Entry 'Ruggero1992' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12281] Utils.pm:395> WARN - Datamodel: Entry 'Ruggero1997' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12284] Utils.pm:395> WARN - Datamodel: Entry 'Russell1978' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12295] Utils.pm:395> WARN - Datamodel: Entry 'Rust2005' (references.bib): Missing mandatory field 'journaltitle'
[12297] Utils.pm:395> WARN - Datamodel: Entry 'Sachs1968' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12300] Utils.pm:395> WARN - Datamodel: Entry 'Sachs1974' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12302] Utils.pm:395> WARN - Datamodel: Entry 'Sakmann1969' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12305] Utils.pm:395> WARN - Datamodel: Entry 'Salazar2013' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12308] Utils.pm:395> WARN - Datamodel: Entry 'Salazar2022' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12310] Utils.pm:395> WARN - Datamodel: Entry 'Salminen2018' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12312] Utils.pm:395> WARN - Datamodel: Entry 'Sanguinetti2011' (references.bib): Missing mandatory field 'journaltitle'
[12315] Utils.pm:395> WARN - Datamodel: Entry 'Sapolsky2005' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12333] Utils.pm:395> WARN - Datamodel: Entry 'Savard2011' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12335] Utils.pm:395> WARN - Datamodel: Entry 'Sayles2013' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12338] Utils.pm:395> WARN - Datamodel: Entry 'Schiff1962' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12340] Utils.pm:395> WARN - Datamodel: Entry 'Schluger1987' (references.bib): Missing mandatory field 'journaltitle'
[12342] Utils.pm:395> WARN - Datamodel: Entry 'Schmiedt1982' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12345] Utils.pm:395> WARN - Datamodel: Entry 'Schneider1976' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12347] Utils.pm:395> WARN - Datamodel: Entry 'Schneider2011' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12362] Utils.pm:395> WARN - Datamodel: Entry 'Schreiner1988' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12364] Utils.pm:395> WARN - Datamodel: Entry 'Schuller1984' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12367] Utils.pm:395> WARN - Datamodel: Entry 'Schwartz2001' (references.bib): Missing mandatory field 'journaltitle'
[12369] Utils.pm:395> WARN - Datamodel: Entry 'Schwarz2001' (references.bib): Missing mandatory field 'journaltitle'
[12371] Utils.pm:395> WARN - Datamodel: Entry 'Schweitzer1986' (references.bib): Missing mandatory field 'journaltitle'
[12374] Utils.pm:395> WARN - Datamodel: Entry 'Serrano2003' (references.bib): Missing mandatory field 'journaltitle'
[12376] Utils.pm:395> WARN - Datamodel: Entry 'Sharpee2011' (references.bib): Missing mandatory field 'journaltitle'
[12379] Utils.pm:395> WARN - Datamodel: Entry 'Shieh1996' (references.bib): Missing mandatory field 'journaltitle'
[12394] Utils.pm:395> WARN - Datamodel: Entry 'Sillito1994' (references.bib): Missing mandatory field 'journaltitle'
[12396] Utils.pm:395> WARN - Datamodel: Entry 'Silva1999' (references.bib): Missing mandatory field 'journaltitle'
[12399] Utils.pm:395> WARN - Datamodel: Entry 'Silva2008' (references.bib): Missing mandatory field 'journaltitle'
[12401] Utils.pm:395> WARN - Datamodel: Entry 'Sim2011' (references.bib): Missing mandatory field 'journaltitle'
[12403] Utils.pm:395> WARN - Datamodel: Entry 'Simmons2013' (references.bib): Missing mandatory field 'journaltitle'
[12406] Utils.pm:395> WARN - Datamodel: Entry 'Simoncelli2001' (references.bib): Missing mandatory field 'journaltitle'
[12408] Utils.pm:395> WARN - Datamodel: Entry 'Singh2003' (references.bib): Missing mandatory field 'journaltitle'
[12410] Utils.pm:395> WARN - Datamodel: Entry 'Smalt2012' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12426] Utils.pm:395> WARN - Datamodel: Entry 'Smith2006' (references.bib): Missing mandatory field 'journaltitle'
[12428] Utils.pm:395> WARN - Datamodel: Entry 'Smith2013' (references.bib): Missing mandatory field 'journaltitle'
[12431] Utils.pm:395> WARN - Datamodel: Entry 'Smith2016' (references.bib): Missing mandatory field 'journaltitle'
[12433] Utils.pm:395> WARN - Datamodel: Entry 'Snyder2007' (references.bib): Missing mandatory field 'journaltitle'
[12436] Utils.pm:395> WARN - Datamodel: Entry 'Squire1982' (references.bib): Missing mandatory field 'journaltitle'
[12438] Utils.pm:395> WARN - Datamodel: Entry 'Stamper2010' (references.bib): Missing mandatory field 'journaltitle'
[12441] Utils.pm:395> WARN - Datamodel: Entry 'Stamper2012Envelope' (references.bib): Missing mandatory field 'journaltitle'
[12454] Utils.pm:395> WARN - Datamodel: Entry 'stamper2012JAR' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12457] Utils.pm:395> WARN - Datamodel: Entry 'Stamper2012Shuttle' (references.bib): Missing mandatory field 'journaltitle'
[12459] Utils.pm:395> WARN - Datamodel: Entry 'Stamper2013' (references.bib): Missing mandatory field 'journaltitle'
[12461] Utils.pm:395> WARN - Datamodel: Entry 'Steinbach1970' (references.bib): Missing mandatory field 'journaltitle'
[12463] Utils.pm:395> WARN - Datamodel: Entry 'Stephens2013' (references.bib): Missing mandatory field 'journaltitle'
[12465] Utils.pm:395> WARN - Datamodel: Entry 'Stocks2000' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12467] Utils.pm:395> WARN - Datamodel: Entry 'Stoddard1999Brachy' (references.bib): Missing mandatory field 'journaltitle'
[12470] Utils.pm:395> WARN - Datamodel: Entry 'Stoekl2014' (references.bib): Missing mandatory field 'journaltitle'
[12472] Utils.pm:395> WARN - Datamodel: Entry 'Strandburg2015' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12474] Utils.pm:395> WARN - Datamodel: Entry 'StrandburgPeshkin2019' (references.bib): Missing mandatory field 'journaltitle'
[12488] Utils.pm:395> WARN - Datamodel: Entry 'Striedter1991' (references.bib): Missing mandatory field 'journaltitle'
[12490] Utils.pm:395> WARN - Datamodel: Entry 'Su2001' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12492] Utils.pm:395> WARN - Datamodel: Entry 'Sumner2012' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12495] Utils.pm:395> WARN - Datamodel: Entry 'Surlykke1988' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12497] Utils.pm:395> WARN - Datamodel: Entry 'Szabo1965' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12500] Utils.pm:395> WARN - Datamodel: Entry 'Tallarovic2005' (references.bib): Missing mandatory field 'journaltitle'
[12502] Utils.pm:395> WARN - Datamodel: Entry 'Tan2005' (references.bib): Missing mandatory field 'journaltitle'
[12504] Utils.pm:395> WARN - Datamodel: Entry 'Tong1982a' (references.bib): Missing mandatory field 'journaltitle'
[12517] Utils.pm:395> WARN - Datamodel: Entry 'Tong1982b' (references.bib): Missing mandatory field 'journaltitle'
[12521] Utils.pm:395> WARN - Datamodel: Entry 'Treutwein1999' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12523] Utils.pm:395> WARN - Datamodel: Entry 'Tricas1995' (references.bib): Missing mandatory field 'journaltitle'
[12526] Utils.pm:395> WARN - Datamodel: Entry 'Triefenbach2003' (references.bib): Missing mandatory field 'journaltitle'
[12528] Utils.pm:395> WARN - Datamodel: Entry 'Triefenbach2008' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12531] Utils.pm:395> WARN - Datamodel: Entry 'Turner2007' (references.bib): Missing mandatory field 'journaltitle'
[12533] Utils.pm:395> WARN - Datamodel: Entry 'Viancour1979' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12535] Utils.pm:395> WARN - Datamodel: Entry 'Viemeister1988' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12539] Utils.pm:395> WARN - Datamodel: Entry 'Vila1999' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12550] Utils.pm:395> WARN - Datamodel: Entry 'Vinje2002' (references.bib): Missing mandatory field 'journaltitle'
[12554] Utils.pm:395> WARN - Datamodel: Entry 'Virtanen2020' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12556] Utils.pm:395> WARN - Datamodel: Entry 'Vonderschen2014' (references.bib): Missing mandatory field 'journaltitle'
[12558] Utils.pm:395> WARN - Datamodel: Entry 'Voronenko2017' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12560] Utils.pm:395> WARN - Datamodel: Entry 'Wachtel1966' (references.bib): Missing mandatory field 'journaltitle'
[12561] Utils.pm:395> WARN - Datamodel: Entry 'Waddell2020' (references.bib): Missing mandatory field 'journaltitle'
[12563] Utils.pm:395> WARN - Datamodel: Entry 'Walt2011' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12564] Utils.pm:395> WARN - Datamodel: Entry 'Walz2013' (references.bib): Missing mandatory field 'journaltitle'
[12565] Utils.pm:395> WARN - Datamodel: Entry 'Walz2014' (references.bib): Missing mandatory field 'journaltitle'
[12566] Utils.pm:395> WARN - Datamodel: Entry 'Wang2007' (references.bib): Missing mandatory field 'journaltitle'
[12568] Utils.pm:395> WARN - Datamodel: Entry 'Watanabe1963' (references.bib): Missing mandatory field 'journaltitle'
[12585] Utils.pm:395> WARN - Datamodel: Entry 'Wen2009' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12588] Utils.pm:395> WARN - Datamodel: Entry 'Westby1981JAR' (references.bib): Missing mandatory field 'journaltitle'
[12590] Utils.pm:395> WARN - Datamodel: Entry 'Westby1982' (references.bib): Missing mandatory field 'journaltitle'
[12593] Utils.pm:395> WARN - Datamodel: Entry 'Westby1988' (references.bib): Missing mandatory field 'journaltitle'
[12595] Utils.pm:395> WARN - Datamodel: Entry 'Wilkens2002' (references.bib): Missing mandatory field 'journaltitle'
[12597] Utils.pm:395> WARN - Datamodel: Entry 'Winkler2009' (references.bib): Missing mandatory field 'journaltitle'
[12599] Utils.pm:395> WARN - Datamodel: Entry 'Wiskott2002' (references.bib): Missing mandatory field 'journaltitle'
[12600] Utils.pm:395> WARN - Datamodel: Entry 'Wohr2007' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12612] Utils.pm:395> WARN - Datamodel: Entry 'Womack2004' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12617] Utils.pm:395> WARN - Datamodel: Entry 'Wong2007' (references.bib): Missing mandatory field 'journaltitle'
[12619] Utils.pm:395> WARN - Datamodel: Entry 'Wypych2012' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12621] Utils.pm:395> WARN - Datamodel: Entry 'Xu1996' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12626] Utils.pm:395> WARN - Datamodel: Entry 'Yager1993' (references.bib): Missing mandatory field 'journaltitle'
[12629] Utils.pm:395> WARN - Datamodel: Entry 'Young1979' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12631] Utils.pm:395> WARN - Datamodel: Entry 'Yu2005' (references.bib): Missing mandatory field 'journaltitle'
[12635] Utils.pm:395> WARN - Datamodel: Entry 'Yuille2006' (references.bib): Missing mandatory field 'journaltitle'
[12649] Utils.pm:395> WARN - Datamodel: Entry 'Zakon2002' (references.bib): Missing mandatory field 'journaltitle'
[12651] Utils.pm:395> WARN - Datamodel: Entry 'Zakon2008' (references.bib): Missing mandatory field 'journaltitle'
[12653] Utils.pm:395> WARN - Datamodel: Entry 'Zeldenrust2018' (references.bib): Invalid field 'publisher' for entrytype 'article'
[12657] Utils.pm:395> WARN - Datamodel: Entry 'Zupanc2001' (references.bib): Missing mandatory field 'journaltitle'
[12658] Utils.pm:395> WARN - Datamodel: Entry 'Zupanc2006' (references.bib): Missing mandatory field 'journaltitle'
[12660] Biber.pm:1690> INFO - Datamodel validation complete
[12849] UCollate.pm:68> INFO - Overriding locale 'en_US' defaults 'variable = shifted' with 'variable = non-ignorable'
[12849] UCollate.pm:68> INFO - Overriding locale 'en_US' defaults 'normalization = NFD' with 'normalization = prenormalized'
[12854] Biber.pm:4168> INFO - Sorting list 'tool/global//global/global' of type 'entry' with template 'tool' and locale 'en_US'
[12867] Biber.pm:4174> INFO - No sort tailoring available for locale 'en_US'
[14244] bibtex.pm:439> INFO - Writing 'references_bibertool.bib' with encoding 'UTF-8'
[14261] bibtex.pm:479> INFO - Output to references_bibertool.bib
[14262] Biber.pm:128> INFO - WARNINGS: 966

5957
references_bibertool.bib Normal file

File diff suppressed because it is too large Load Diff

View File

@ -17,6 +17,7 @@
\providecommand\HyField@AuxAddToFields[1]{}
\providecommand\HyField@AuxAddToCoFields[2]{}
\emailauthor{jan.greqe@uni-tuebingen.de}{Jan Grewe\corref {cor1}}
\emailauthor{jan.greqe@uni-tuebingen.de}{Jan Grewe\corref {cor1}}
\Newlabel{cor1}{1}
\citation{Salazar2013}
\citation{Knudsen1975,Henninger2020}
@ -28,21 +29,15 @@
\citation{Hladnik2023}
\citation{Grewe2017,Hladnik2023}
\citation{Grewe2017,Hladnik2023}
\citation{Chacron2004}
\citation{Nowak2003}
\citation{Cunningham2004}
\citation{Womack2004}
\citation{Destexhe1993}
\citation{Eggermont1996}
\citation{Oswald2004}
\Newlabel{1}{a}
\Newlabel{2}{b}
\Newlabel{3}{c}
\@writefile{toc}{\contentsline {section}{Introduction}{1}{section*.2}\protected@file@percent }
\citation{Xu1996,Benda2005,Gussin2007,Grewe2017}
\citation{Nelson1997}
\citation{Chacron2004}
\citation{Voronenko2017}
\Newlabel{1}{a}
\Newlabel{4}{b}
\Newlabel{5}{c}
\Newlabel{2}{d}
\Newlabel{3}{e}
\@writefile{toc}{\contentsline {section}{Introduction}{1}{section*.1}\protected@file@percent }
\citation{Roeber1834,Plomp1967,Joris2004,Grahn2012}
\citation{Engler2001,Hupe2008,Henninger2018,Benda2020}
\citation{Henninger2018}
@ -52,53 +47,46 @@
\citation{Voronenko2017}
\citation{Egerland2020}
\citation{Egerland2020}
\citation{Chacron2005,Grewe2017}
\citation{Grewe2017,Neiman2011fish}
\citation{Voronenko2017,Savard2011}
\citation{Grewe2017,Hladnik2023}
\citation{Voronenko2017}
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Nonlinearity in an electrophysiologically recorded P-unit of \textit {Apteronotus leptorhynchus}{} in a three-fish setting. Receiver with EOD frequency $\ensuremath {f_{EOD}}{} =664$\,Hz encounters fish with EOD frequencies $f_{1}=631$\,Hz and $f_{2}=797$\,Hz. Both encountered fish lead to a beat contrast of 10\,\%. Top: Interference of the receiver EOD with the EODs of other fish. Second row: Spike trains of the P-unit. Third row: Firing rate, retrieved as the convolution of the spike trains with a Gaussian kernel ($\sigma = 1$\,ms). Bottom row: Power spectrum of the firing rate. \figitem {A} Baseline condition: Only the receiver is present. The mean baseline firing rate \ensuremath {f_{Base}}{} dominates the power spectrum of the firing rate. \figitem {B} The receiver and the fish with EOD frequency $f_{1}=631$\,Hz are present. \figitem {C} The receiver and the fish with EOD frequency $f_{2}=797$\,Hz are present. \figitem {D} All three fish with the EOD frequencies \ensuremath {f_{EOD}}{}, $f_{1}$ and $f_{2}$ are present. Nonlinear peaks occur at the sum and difference of the two beat frequencies in the power spectrum of the firing rate. \relax }}{3}{figure.caption.3}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {A.9}{\ignorespaces Response of experimentally measured P-units to RAM stimuli. Light purple -- low RAM contrast. Dark purple -- high RAM contrast. \figitem {A} Regular firing low-CV P-unit. \figitem [i]{A} Left: Interspike intervals (ISI) distribution during baseline. Right: Baseline power spectrum of the firing rate. \figitem [ii]{A} Top: EOD carrier (gray) with RAM (red). Middle: Spike trains in response to a low RAM contrast. Bottom: Spike trains in response to a high RAM contrast. \figitem [iii]{A} First-order susceptibility (see Eq.\nobreakspace {}(\ref {linearencoding_methods})). \figitem [iv]{A} Absolute value of the second-order susceptibility, Eq.\nobreakspace {}(\ref {susceptibility}), for the low RAM contrast. Pink lines -- edges of the structure when $f_{1}$, $f_{2}${} or \ensuremath {f_{1} + f_{2}}{} are equal to \ensuremath {f_{Base}}{}. Orange line -- part of the structure when \ensuremath {f_{1} + f_{2}}{} is equal to half \ensuremath {f_{EOD}}. \figitem [v]{A} Absolute value of the second-order susceptibility for the higher RAM contrast. \figitem [vi]{A} Projected diagonals, calculated as the mean of the anti-diagonals of the matrices in \panel [iv,v]{A}. Gray dots: \ensuremath {f_{Base}}{}. Gray area: $\ensuremath {f_{Base}}{} \pm 5$\,Hz. Dashed lines: Medians of the projected diagonals. \figitem {B} Noisy high-CV P-Unit. Panels as in \panel {A}. \relax }}{18}{figure.caption.46}\protected@file@percent }
\newlabel{cells_suscept_high_CV}{{A.9}{18}{Response of experimentally measured P-units to RAM stimuli. Light purple -- low RAM contrast. Dark purple -- high RAM contrast. \figitem {A} Regular firing low-CV P-unit. \figitem [i]{A} Left: Interspike intervals (ISI) distribution during baseline. Right: Baseline power spectrum of the firing rate. \figitem [ii]{A} Top: EOD carrier (gray) with RAM (red). Middle: Spike trains in response to a low RAM contrast. Bottom: Spike trains in response to a high RAM contrast. \figitem [iii]{A} First-order susceptibility (see \eqnref {linearencoding_methods}). \figitem [iv]{A} Absolute value of the second-order susceptibility, \eqnref {susceptibility}, for the low RAM contrast. Pink lines -- edges of the structure when \fone , \ftwo {} or \fsum {} are equal to \fbase {}. Orange line -- part of the structure when \fsum {} is equal to half \feod . \figitem [v]{A} Absolute value of the second-order susceptibility for the higher RAM contrast. \figitem [vi]{A} Projected diagonals, calculated as the mean of the anti-diagonals of the matrices in \panel [iv,v]{A}. Gray dots: \fbase {}. Gray area: $\fbase {} \pm 5$\,Hz. Dashed lines: Medians of the projected diagonals. \figitem {B} Noisy high-CV P-Unit. Panels as in \panel {A}. \relax }{figure.caption.46}{}}
\bibcite{Destexhe1993}{{10}{1993}{{Destexhe et~al.}}{{}}}
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Savard, M., Krahe, R., and Chacron, M. (2011).
\newblock Neural heterogeneities influence envelope and temporal coding at the
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\newblock {\em Neuroscience}, 172:270--284.
\bibitem[Schlungbaum and Lindner, 2023]{Schlungbaum2023}
Schlungbaum, M. and Lindner, B. (2023).
\newblock Detecting a periodic signal by a population of spiking neurons in the
weakly nonlinear response regime.
\newblock {\em arXiv preprint arXiv:2305.13100}.
\bibitem[Schneider et~al., 2011]{Schneider2011}
Schneider, A.~D., Cullen, K.~E., and Chacron, M.~J. (2011).
\newblock In vivo conditions induce faithful encoding of stimuli by reducing
nonlinear synchronization in vestibular sensory neurons.
\newblock {\em PLoS Computational Biology}, 7(7):e1002120.
\bibitem[Sinz et~al., 2020]{Sinz2020}
Sinz, F.~H., Sachgau, C., Henninger, J., Benda, J., and Grewe, J. (2020).
\newblock Simultaneous spike-time locking to multiple frequencies.
\newblock {\em Journal of Neurophysiology}, 123(6):2355--2372.
\bibitem[Stamper et~al., 2010]{Stamper2010}
Stamper, S.~A., Carrera-G, E., Tan, E.~W., Fug\`ere, V., Krahe, R., and
Fortune, E.~S. (2010).
\newblock Species differences in group size and electrosensory interference in
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\bibitem[Stamper et~al., 2012]{Stamper2012Envelope}
Stamper, S.~A., Madhav, M.~S., Cowan, N.~J., and Fortune, E.~S. (2012).
\newblock Beyond the {Jamming Avoidance Response}: weakly electric fish respond
to the envelope of social electrosensory signals.
\newblock {\em Journal of Experimental Biology}, 215:4196--4207.
\bibitem[Stoewer et~al., 2014]{Stoewer2014}
Stoewer, A., Kellner, C.~J., Benda, J., Wachtler, T., and Grewe, J. (2014).
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Tan, E.~W., Nizar, J.~M., Carrera-G, E., and Fortune, E.~S. (2005).
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Voronenko, S.~O. and Lindner, B. (2017).
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\newblock Special cutaneous receptor organs of fish: the tuberous organs of
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\newblock {\em Journal of Morphology}, 119:51--80.
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Walz, H., Grewe, J., and Benda, J. (2014).
\newblock Static frequency tuning accounts for changes in neural synchrony
evoked by transient communication signals.
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Womack, M.~D. and Khodakhah, K. (2004).
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Xu, Z., Payne, J.~R., and Nelson, M.~E. (1996).
\newblock Logarithmic time course of sensory adaptation in electrosensory
afferent nerve fibers in a weakly electric fish.
\newblock {\em Journal of Neurophysiology}, 76(3):2020--2032.
\end{thebibliography}

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