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updating figures

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saschuta 2024-06-06 21:10:19 +02:00
parent 39e5146ade
commit 1309e4cfca
25 changed files with 120804 additions and 121086 deletions
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ampullary.pdf
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ampullary.py
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@ -51,5 +51,5 @@ if __name__ == '__main__':
ampullary_punit(cells_plot2=cells_plot2, RAM=False) ampullary_punit(cells_plot2=cells_plot2, RAM=False)
else: else:
cells_plot2 = p_units_to_show(type_here='amp')#permuted = True, cells_plot2 = p_units_to_show(type_here='amp')#permuted = True,
print(cells_plot2) #print(cells_plot2)
ampullary_punit(eod_metrice = False, base_extra = True,color_same = False, fr_name = '$f_{base}$', tags_individual = True, isi_delta = 5, titles=[''],cells_plot2=cells_plot2, RAM=False, scale_val = False, add_texts = [0.25,1.3])#Low-CV ampullary cell, ampullary_punit(eod_metrice = False, base_extra = True,color_same = False, fr_name = '$f_{base}$', tags_individual = True, isi_delta = 5, titles=[''],cells_plot2=cells_plot2, RAM=False, scale_val = False, add_texts = [0.25,1.3])#Low-CV ampullary cell,

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cells_suscept.pdf
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cells_suscept.py
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@ -7,5 +7,5 @@ if __name__ == '__main__':
#stack_file = pd.read_csv('..\calc_RAM\calc_nix_RAM-eod_2022-01-28-ag-invivo-1_all__amp_20.0_filename_InputArr_400hz_30_P-unitApteronotusleptorhynchus.csv') #stack_file = pd.read_csv('..\calc_RAM\calc_nix_RAM-eod_2022-01-28-ag-invivo-1_all__amp_20.0_filename_InputArr_400hz_30_P-unitApteronotusleptorhynchus.csv')
cells_plot2 = p_units_to_show(type_here = 'contrasts')#permuted = True, cells_plot2 = p_units_to_show(type_here = 'contrasts')#permuted = True,
print(cells_plot2) #print(cells_plot2)
ampullary_punit(eod_metrice = False, color_same = False, base_extra = True, fr_name = label_fr_name_rm(), cells_plot2=[cells_plot2[0]], isi_delta = 5, titles=[''], tags_individual = True, xlim_p = [0, 1.15])#Low-CV P-unit, ampullary_punit(eod_metrice = False, color_same = False, base_extra = True, fr_name = label_fr_name_rm(), cells_plot2=[cells_plot2[0]], isi_delta = 5, titles=[''], tags_individual = True, xlim_p = [0, 1.15])#Low-CV P-unit,

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cells_suscept_high_CV.pdf
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cells_suscept_high_CV.py
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@ -6,5 +6,5 @@ from threefish.RAM.plots import ampullary_punit
if __name__ == '__main__': if __name__ == '__main__':
cells_plot2 = p_units_to_show(type_here='contrasts')#permuted = True, cells_plot2 = p_units_to_show(type_here='contrasts')#permuted = True,
print(cells_plot2) #print(cells_plot2)
ampullary_punit(base_extra = True, eod_metrice = False, color_same = False, fr_name = label_fr_name_rm(), tags_individual = True, isi_delta = 5, cells_plot2=[cells_plot2[1]], titles=['', 'Ampullary cell,'], )#High-CV P-unit, ampullary_punit(base_extra = True, eod_metrice = False, color_same = False, fr_name = label_fr_name_rm(), tags_individual = True, isi_delta = 5, cells_plot2=[cells_plot2[1]], titles=['', 'Ampullary cell,'], )#High-CV P-unit,

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data/flowchart___c_sig_0_a_fe_0.02.csv
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data/flowchart___c_sig_0_a_fe_0.csv
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data/flowchart__spikes_c_sig_0_a_fe_0.02.csv
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@ -1,243 +1,242 @@
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1 spikes
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457
data/flowchart__spikes_c_sig_0_a_fe_0.csv
View File

@ -1,241 +1,242 @@
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80000
data/flowchart_additiv_cv_adapt_factor_scaled__c_sig_0.9_a_fe_0.csv
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File diff suppressed because it is too large Load Diff

478
data/flowchart_additiv_cv_adapt_factor_scaled_spikes_c_sig_0.9_a_fe_0.csv
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@ -1,241 +1,239 @@
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68,0.5672 68,0.5790000000000001
69,0.57475 69,0.58765
70,0.58335 70,0.5973
71,0.5941000000000001 71,0.60375
72,0.60275 72,0.61345
73,0.6081500000000001 73,0.6199
74,0.61775 74,0.63175
75,0.6264000000000001 75,0.6371
76,0.635 76,0.64695
77,0.64575 77,0.65435
78,0.65225 78,0.6641
79,0.66405 79,0.67055
80,0.6705 80,0.68025
81,0.6759000000000001 81,0.6878000000000001
82,0.68455 82,0.6975
83,0.69315 83,0.7040000000000001
84,0.7029000000000001 84,0.7126
85,0.7115 85,0.7212500000000001
86,0.7189500000000001 86,0.7298
87,0.72655 87,0.7373000000000001
88,0.7352000000000001 88,0.7481
89,0.74485 89,0.75565
90,0.75455 90,0.76425
91,0.7621 91,0.77295
92,0.77075 92,0.7815500000000001
93,0.7782 93,0.788
94,0.7869 94,0.7955500000000001
95,0.7965500000000001 95,0.8063
96,0.80305 96,0.8127000000000001
97,0.8116500000000001 97,0.82455
98,0.8181 98,0.8321500000000001
99,0.8257 99,0.83855
100,0.83745 100,0.8450500000000001
101,0.8451000000000001 101,0.85585
102,0.8558 102,0.8645
103,0.8601500000000001 103,0.8731
104,0.8687 104,0.87955
105,0.8773500000000001 105,0.8914000000000001
106,0.8881 106,0.8989
107,0.89575 107,0.9064000000000001
108,0.90325 108,0.91295
109,0.91395 109,0.9258500000000001
110,0.9193 110,0.9312
111,0.928 111,0.94305
112,0.9376000000000001 112,0.9484
113,0.9463 113,0.9592
114,0.9538000000000001 114,0.9657
115,0.9635 115,0.9732000000000001
116,0.9700000000000001 116,0.9830000000000001
117,0.9786 117,0.9894000000000001
118,0.9883000000000001 118,0.9969
119,0.99795 119,1.0055
120,1.0044 120,1.01415
121,1.01095 121,1.02275
122,1.0207 122,1.03145
123,1.0324 123,1.03895
124,1.0379 124,1.04755
125,1.0475 125,1.05295
126,1.0551000000000001 126,1.0637
127,1.06155 127,1.0755000000000001
128,1.0712000000000002 128,1.082
129,1.0788 129,1.0885500000000001
130,1.08955 130,1.0971
131,1.09705 131,1.1046500000000001
132,1.1068 132,1.11535
133,1.1122 133,1.12395
134,1.11975 134,1.13155
135,1.1305 135,1.14015
136,1.1401000000000001 136,1.1509500000000001
137,1.14445 137,1.1574
138,1.1541000000000001 138,1.1682000000000001
139,1.1617 139,1.1735
140,1.1693 140,1.1811
141,1.1789 141,1.18975
142,1.18645 142,1.1972500000000001
143,1.1951 143,1.2059
144,1.2015500000000001 144,1.2177
145,1.2112500000000002 145,1.22525
146,1.21775 146,1.235
147,1.2316500000000001 147,1.2425000000000002
148,1.2371 148,1.25
149,1.24675 149,1.25655
150,1.2544 150,1.2673
151,1.2619 151,1.2758
152,1.2726 152,1.2856
153,1.2791000000000001 153,1.29305
154,1.2867 154,1.3017
155,1.2942 155,1.3071000000000002
156,1.30275 156,1.31475
157,1.3146 157,1.3233000000000001
158,1.3233000000000001 158,1.33295
159,1.3308 159,1.34275
160,1.33945 160,1.3523500000000002
161,1.347 161,1.35775
162,1.3566500000000001 162,1.36415
163,1.3631 163,1.3728
164,1.3707 164,1.3793
165,1.3793 165,1.3911
166,1.38565 166,1.4029500000000001
167,1.39545 167,1.4094
168,1.4029500000000001 168,1.41805
169,1.41165 169,1.4245
170,1.4234 170,1.4331500000000001
171,1.4288500000000002 171,1.44175
172,1.43645 172,1.45025
173,1.4460000000000002 173,1.45795
174,1.45245 174,1.4676
175,1.45895 175,1.47615
176,1.4719 176,1.4826000000000001
177,1.4794500000000002 177,1.49125
178,1.48595 178,1.4999500000000001
179,1.4934 179,1.5064
180,1.50215 180,1.5172
181,1.51075 181,1.5257500000000002
182,1.51935 182,1.5322
183,1.5279500000000001 183,1.5419
184,1.53755 184,1.54945
185,1.5452000000000001 185,1.5602500000000001
186,1.5558500000000002 186,1.5656
187,1.5624 187,1.5742500000000001
188,1.5678 188,1.5839
189,1.5763500000000001 189,1.59365
190,1.58395 190,1.60005
191,1.59575 191,1.60765
192,1.60335 192,1.6173000000000002
193,1.61195 193,1.6226500000000001
194,1.6184 194,1.6324
195,1.62815 195,1.6389500000000001
196,1.63995 196,1.64965
197,1.64635 197,1.65815
198,1.6528500000000002 198,1.66575
199,1.6647 199,1.6776
200,1.6711 200,1.683
201,1.67655 201,1.69055
202,1.6852 202,1.70235
203,1.69375 203,1.7089
204,1.7034500000000001 204,1.7174500000000001
205,1.711 205,1.7261000000000002
206,1.7186000000000001 206,1.7336
207,1.72615 207,1.74115
208,1.7357500000000001 208,1.7519500000000001
209,1.7454500000000002 209,1.7615500000000002
210,1.75515 210,1.7692
211,1.7627000000000002 211,1.7799500000000001
212,1.7703 212,1.7864
213,1.77885 213,1.79285
214,1.7885 214,1.80045
215,1.7972000000000001 215,1.8101
216,1.8036 216,1.8187
217,1.8122500000000001 217,1.8284
218,1.8177 218,1.83485
219,1.8274000000000001 219,1.8425
220,1.838 220,1.8542
221,1.8457000000000001 221,1.86175
222,1.8564 222,1.8672000000000002
223,1.86175 223,1.8758000000000001
224,1.8747 224,1.8865500000000002
225,1.8801 225,1.8941000000000001
226,1.88775 226,1.9006500000000002
227,1.8963 227,1.9081000000000001
228,1.90595 228,1.91995
229,1.9146 229,1.92755
230,1.9222000000000001 230,1.9350500000000002
231,1.9286 231,1.9437
232,1.9383000000000001 232,1.95015
233,1.9501000000000002 233,1.9619000000000002
234,1.9577 234,1.9695500000000001
235,1.96415 235,1.9760000000000002
236,1.97055 236,1.9846000000000001
237,1.9792 237,1.99425
238,1.99
239,1.99855

1 spikes
2 0 0.0006500000000000001 0.007050000000000001
3 1 0.009300000000000001 0.015700000000000002
4 2 0.015700000000000002 0.025400000000000002
5 3 0.0275 0.03615
6 4 0.034050000000000004 0.0448
7 5 0.041600000000000005 0.051250000000000004
8 6 0.0545 0.05775
9 7 0.05985 0.06745000000000001
10 8 0.06745000000000001 0.0771
11 9 0.0771 0.08365
12 10 0.08675000000000001 0.09430000000000001
13 11 0.0944 0.10185000000000001
14 12 0.10295 0.1116
15 13 0.11045 0.11805
16 14 0.11705 0.12445
17 15 0.12875 0.13745000000000002
18 16 0.1342 0.14395
19 17 0.14175000000000001 0.15245
20 18 0.1514 0.16010000000000002
21 19 0.16110000000000002 0.16970000000000002
22 20 0.16965 0.1784
23 21 0.17835 0.18485000000000001
24 22 0.18585000000000002 0.1945
25 23 0.19345 0.19985
26 24 0.19985 0.2107
27 25 0.2096 0.22035000000000002
28 26 0.21715 0.22790000000000002
29 27 0.23115000000000002 0.2376
30 28 0.23650000000000002 0.2441
31 29 0.24515 0.25270000000000004
32 30 0.25270000000000004 0.26015
33 31 0.2591 0.26990000000000003
34 32 0.26990000000000003 0.2775
35 33 0.27740000000000004 0.28715
36 34 0.28395000000000004 0.2947
37 35 0.29255000000000003 0.30225
38 36 0.30115000000000003 0.30865000000000004
39 37 0.3109 0.3194
40 38 0.31735 0.32815
41 39 0.32805 0.33455
42 40 0.33665 0.34315
43 41 0.34425 0.3539
44 42 0.3528 0.3603
45 43 0.36150000000000004 0.3679
46 44 0.36895 0.37645
47 45 0.3765 0.38405
48 46 0.3831 0.39380000000000004
49 47 0.3937 0.4045
50 48 0.40130000000000005 0.41425
51 49 0.40885000000000005 0.42175
52 50 0.41645000000000004 0.42715000000000003
53 51 0.42710000000000004 0.43575
54 52 0.43570000000000003 0.44755
55 53 0.44545 0.4531
56 54 0.45085000000000003 0.45955
57 55 0.45735000000000003 0.47240000000000004
58 56 0.4713 0.47885
59 57 0.47675 0.48535
60 58 0.48425 0.49610000000000004
61 59 0.49175 0.5036
62 60 0.5025000000000001 0.5101
63 61 0.509 0.5187
64 62 0.5165500000000001 0.52945
65 63 0.5263 0.5391
66 64 0.53695 0.54445
67 65 0.54455 0.5543
68 66 0.55325 0.5628000000000001
69 67 0.5596 0.5704
70 68 0.5672 0.5790000000000001
71 69 0.57475 0.58765
72 70 0.58335 0.5973
73 71 0.5941000000000001 0.60375
74 72 0.60275 0.61345
75 73 0.6081500000000001 0.6199
76 74 0.61775 0.63175
77 75 0.6264000000000001 0.6371
78 76 0.635 0.64695
79 77 0.64575 0.65435
80 78 0.65225 0.6641
81 79 0.66405 0.67055
82 80 0.6705 0.68025
83 81 0.6759000000000001 0.6878000000000001
84 82 0.68455 0.6975
85 83 0.69315 0.7040000000000001
86 84 0.7029000000000001 0.7126
87 85 0.7115 0.7212500000000001
88 86 0.7189500000000001 0.7298
89 87 0.72655 0.7373000000000001
90 88 0.7352000000000001 0.7481
91 89 0.74485 0.75565
92 90 0.75455 0.76425
93 91 0.7621 0.77295
94 92 0.77075 0.7815500000000001
95 93 0.7782 0.788
96 94 0.7869 0.7955500000000001
97 95 0.7965500000000001 0.8063
98 96 0.80305 0.8127000000000001
99 97 0.8116500000000001 0.82455
100 98 0.8181 0.8321500000000001
101 99 0.8257 0.83855
102 100 0.83745 0.8450500000000001
103 101 0.8451000000000001 0.85585
104 102 0.8558 0.8645
105 103 0.8601500000000001 0.8731
106 104 0.8687 0.87955
107 105 0.8773500000000001 0.8914000000000001
108 106 0.8881 0.8989
109 107 0.89575 0.9064000000000001
110 108 0.90325 0.91295
111 109 0.91395 0.9258500000000001
112 110 0.9193 0.9312
113 111 0.928 0.94305
114 112 0.9376000000000001 0.9484
115 113 0.9463 0.9592
116 114 0.9538000000000001 0.9657
117 115 0.9635 0.9732000000000001
118 116 0.9700000000000001 0.9830000000000001
119 117 0.9786 0.9894000000000001
120 118 0.9883000000000001 0.9969
121 119 0.99795 1.0055
122 120 1.0044 1.01415
123 121 1.01095 1.02275
124 122 1.0207 1.03145
125 123 1.0324 1.03895
126 124 1.0379 1.04755
127 125 1.0475 1.05295
128 126 1.0551000000000001 1.0637
129 127 1.06155 1.0755000000000001
130 128 1.0712000000000002 1.082
131 129 1.0788 1.0885500000000001
132 130 1.08955 1.0971
133 131 1.09705 1.1046500000000001
134 132 1.1068 1.11535
135 133 1.1122 1.12395
136 134 1.11975 1.13155
137 135 1.1305 1.14015
138 136 1.1401000000000001 1.1509500000000001
139 137 1.14445 1.1574
140 138 1.1541000000000001 1.1682000000000001
141 139 1.1617 1.1735
142 140 1.1693 1.1811
143 141 1.1789 1.18975
144 142 1.18645 1.1972500000000001
145 143 1.1951 1.2059
146 144 1.2015500000000001 1.2177
147 145 1.2112500000000002 1.22525
148 146 1.21775 1.235
149 147 1.2316500000000001 1.2425000000000002
150 148 1.2371 1.25
151 149 1.24675 1.25655
152 150 1.2544 1.2673
153 151 1.2619 1.2758
154 152 1.2726 1.2856
155 153 1.2791000000000001 1.29305
156 154 1.2867 1.3017
157 155 1.2942 1.3071000000000002
158 156 1.30275 1.31475
159 157 1.3146 1.3233000000000001
160 158 1.3233000000000001 1.33295
161 159 1.3308 1.34275
162 160 1.33945 1.3523500000000002
163 161 1.347 1.35775
164 162 1.3566500000000001 1.36415
165 163 1.3631 1.3728
166 164 1.3707 1.3793
167 165 1.3793 1.3911
168 166 1.38565 1.4029500000000001
169 167 1.39545 1.4094
170 168 1.4029500000000001 1.41805
171 169 1.41165 1.4245
172 170 1.4234 1.4331500000000001
173 171 1.4288500000000002 1.44175
174 172 1.43645 1.45025
175 173 1.4460000000000002 1.45795
176 174 1.45245 1.4676
177 175 1.45895 1.47615
178 176 1.4719 1.4826000000000001
179 177 1.4794500000000002 1.49125
180 178 1.48595 1.4999500000000001
181 179 1.4934 1.5064
182 180 1.50215 1.5172
183 181 1.51075 1.5257500000000002
184 182 1.51935 1.5322
185 183 1.5279500000000001 1.5419
186 184 1.53755 1.54945
187 185 1.5452000000000001 1.5602500000000001
188 186 1.5558500000000002 1.5656
189 187 1.5624 1.5742500000000001
190 188 1.5678 1.5839
191 189 1.5763500000000001 1.59365
192 190 1.58395 1.60005
193 191 1.59575 1.60765
194 192 1.60335 1.6173000000000002
195 193 1.61195 1.6226500000000001
196 194 1.6184 1.6324
197 195 1.62815 1.6389500000000001
198 196 1.63995 1.64965
199 197 1.64635 1.65815
200 198 1.6528500000000002 1.66575
201 199 1.6647 1.6776
202 200 1.6711 1.683
203 201 1.67655 1.69055
204 202 1.6852 1.70235
205 203 1.69375 1.7089
206 204 1.7034500000000001 1.7174500000000001
207 205 1.711 1.7261000000000002
208 206 1.7186000000000001 1.7336
209 207 1.72615 1.74115
210 208 1.7357500000000001 1.7519500000000001
211 209 1.7454500000000002 1.7615500000000002
212 210 1.75515 1.7692
213 211 1.7627000000000002 1.7799500000000001
214 212 1.7703 1.7864
215 213 1.77885 1.79285
216 214 1.7885 1.80045
217 215 1.7972000000000001 1.8101
218 216 1.8036 1.8187
219 217 1.8122500000000001 1.8284
220 218 1.8177 1.83485
221 219 1.8274000000000001 1.8425
222 220 1.838 1.8542
223 221 1.8457000000000001 1.86175
224 222 1.8564 1.8672000000000002
225 223 1.86175 1.8758000000000001
226 224 1.8747 1.8865500000000002
227 225 1.8801 1.8941000000000001
228 226 1.88775 1.9006500000000002
229 227 1.8963 1.9081000000000001
230 228 1.90595 1.91995
231 229 1.9146 1.92755
232 230 1.9222000000000001 1.9350500000000002
233 231 1.9286 1.9437
234 232 1.9383000000000001 1.95015
235 233 1.9501000000000002 1.9619000000000002
236 234 1.9577 1.9695500000000001
237 235 1.96415 1.9760000000000002
238 236 1.97055 1.9846000000000001
239 237 1.9792 1.99425
238 1.99
239 1.99855

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data_overview_mod.pdf
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210
data_overview_mod.py
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@ -21,45 +21,20 @@ except:
def data_overview3(): 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() plot_style()
default_figsize(column=2, length=6.2) #65.5 default_figsize(column=2, length=6.2) #65.5
#fig, ax = plt.subplots(4, 2) # , figsize=(14, 7.5) constrained_layout=True,
var_it = 'Response Modulation [Hz]' var_it = 'Response Modulation [Hz]'
var_it2 = '' 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 right = 0.85
ws = 0.75 ws = 0.75
print(right) #print(right)
grid0 = gridspec.GridSpec(3, 2, wspace=ws, bottom=0.07, grid0 = gridspec.GridSpec(3, 2, wspace=ws, bottom=0.07,
hspace=0.45, left=0.1, right=right, top=0.95) hspace=0.45, 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 # Auswahl: wir nehmen den mean um nicht Stimulus abhängigen Noise rauszumitteln
@ -88,7 +63,7 @@ def data_overview3():
'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr'] # + '_diagonal_proj' 'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr'] # + '_diagonal_proj'
scores = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr', scores = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr',
] # + '_diagonal_proj' ] # + '_diagonal_proj'
printing = False # print values for paper
max_xs = [[[],[],[]],[[],[],[]]] max_xs = [[[],[],[]],[[],[],[]]]
for c, cell_type_here in enumerate(cell_types): 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) frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
@ -105,19 +80,7 @@ def data_overview3():
# modulatoin comparison for both cell_types # modulatoin comparison for both cell_types
################################ ################################
# Modulation, cell type comparison # Modulation, cell type comparison
# todo: hier die diff werte über die zellen
#ax_here = []
#axd = plt.subplot(grid_lower_lower[0, c])
#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','cv_stim','response_modulation']#,'fr_base']# x_axis = ['cv_base','cv_stim','response_modulation']#,'fr_base']#
var_item_names = [var_it,var_it,var_it2]#,var_it2]#['Response Modulation [Hz]',] var_item_names = [var_it,var_it,var_it2]#,var_it2]#['Response Modulation [Hz]',]
@ -154,10 +117,6 @@ def data_overview3():
ymin = 0 ymin = 0
xmin = 0 xmin = 0
if (' P-unit' in cell_type_here) & ('cv' in x_axis[v]):
xlimk = [0, 2]
else:
xlimk = None
xlimk = None xlimk = None
labelpad = 0.5#-1 labelpad = 0.5#-1
@ -168,8 +127,9 @@ def data_overview3():
xlim=xlimk, x_pos=1, burst_fraction=burst_fraction[c], xlim=xlimk, x_pos=1, burst_fraction=burst_fraction[c],
ha='right') ha='right')
print(cell_type_here + ' median '+scores_here[v]+''+str(np.nanmedian(frame_file[scores_here[v]]))) if printing:
print(cell_type_here + ' max ' + x_axis[v] + '' + str(np.nanmax(frame_file[x_axis[v]]))) print(cell_type_here + ' median '+scores_here[v]+''+str(np.nanmedian(frame_file[scores_here[v]])))
print(cell_type_here + ' max ' + x_axis[v] + '' + str(np.nanmax(frame_file[x_axis[v]])))
if v == 0: if v == 0:
colors = colors_overview() colors = colors_overview()
@ -208,44 +168,61 @@ def data_overview3():
tags.append(axx) tags.append(axx)
counter += 1 counter += 1
#plt.show() #plt.show()
if printing:
printing_values_data_overview(cell_types, frame_load_sp, scores, species, x_axis, y_axis)
show = False#True
fig = plt.gcf()
#embed()
tags_final = np.concatenate([tags[0::3],tags[1::3],tags[2::3]])#,tags[2::3]
fig.tag(tags_final, xoffs=-4.2, yoffs=1.12)
save_visualization(pdf = True, show = show)
def printing_values_data_overview(cell_types, frame_load_sp, scores, species, x_axis, y_axis):
############################ ############################
# 1 Print scores # 1 Print scores
print('\n') print('\n')
speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens'] speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
#for species in speciess: # for species in speciess:
for c, cell_type_here in enumerate(cell_types): 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) frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
print(cell_type_here + str(len(frame_file.cell.unique()))) print(cell_type_here + str(len(frame_file.cell.unique())))
#embed() # embed()
cells_amp = ['2011-09-21-ab', '2010-06-21-am', '2012-05-15-ac', '2012-04-26-ae', '2012-05-07-ac', 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'] '2010-06-21-ac']
cells_amp = update_cell_names(cells_amp) cells_amp = update_cell_names(cells_amp)
frame_amps = frame_file[frame_file.cell.isin(cells_amp)] frame_amps = frame_file[frame_file.cell.isin(cells_amp)]
frame_amps.cell.unique() 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'] # ['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() # embed()
#if len(frame_amps) # if len(frame_amps)
################################################################# #################################################################
# print the corrs # print the corrs
score = scores[c]#'ser_base', score = scores[c] # 'ser_base',
score_print = [score] score_print = [score]
score_print = [scores[c],scores[c],scores[c],scores[c],scores[c],'burst_fraction_burst_corr_individual_base'] score_print = [scores[c], scores[c], scores[c], scores[c], scores[c],
corr_vars = ['cv_base','response_modulation','fr_base','ser_first_base','burst_fraction_burst_corr_individual_stim','cv_base'] 'burst_fraction_burst_corr_individual_base']
#for score in score_print: corr_vars = ['cv_base', 'response_modulation', 'fr_base', 'ser_first_base',
for c_nr, corr_var in enumerate(corr_vars):#,'ser_sum_corr' '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] score = score_print[c_nr]
x = frame_file['fr_base'] x = frame_file['fr_base']
y = frame_file['ser_first'] y = frame_file['ser_first']
c = frame_file['cv_base'] c = frame_file['cv_base']
try: try:
c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, corr_var, cv_name=corr_var , score=score) c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, corr_var, cv_name=corr_var,
score=score)
except: except:
print('frame file lost') print('frame file lost')
embed() embed()
corr, p_value = stats.pearsonr(x_axis, y_axis) corr, p_value = stats.pearsonr(x_axis, y_axis)
pears_l = label_pearson(corr, p_value, y_axis, n=True) pears_l = label_pearson(corr, p_value, y_axis, n=True)
print(start_name(cell_type_here, species) + ' ' + corr_var + ' to '+str(score) + pears_l) print(start_name(cell_type_here, species) + ' ' + corr_var + ' to ' + str(score) + pears_l)
#if corr_var == 'ser_first_base':# # if corr_var == 'ser_first_base':#
# embed() # embed()
print('\n') print('\n')
################################ ################################
@ -254,17 +231,20 @@ def data_overview3():
score='cv_base') score='cv_base')
corr, p_value = stats.pearsonr(x_axis, y_axis) corr, p_value = stats.pearsonr(x_axis, y_axis)
pears_l = label_pearson(corr, p_value, y_axis, n=True) pears_l = label_pearson(corr, p_value, y_axis, n=True)
print(start_name(cell_type_here, species) + ' ' + 'fr_base' + ' to ' + 'cv_base' + pears_l)
print(start_name(cell_type_here, species) + ' ' + 'fr_base' + ' to ' + 'cv_base' + pears_l)
################################ ################################
# cv to fr correlation # cv to fr correlation
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species) 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', 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') score='cv_base')
corr, p_value = stats.pearsonr(x_axis, y_axis) corr, p_value = stats.pearsonr(x_axis, y_axis)
pears_l = label_pearson(corr, p_value, y_axis, n=True) pears_l = label_pearson(corr, p_value, y_axis, n=True)
print(start_name(cell_type_here, species) + ' amprange: ' + 'burst_fraction_individual_base' + ' to ' + 'cv_base' + pears_l) print(start_name(cell_type_here,
species) + ' amprange: ' + 'burst_fraction_individual_base' + ' to ' + 'cv_base' + pears_l)
############################### ###############################
# fr to nonline but for both modulations # fr to nonline but for both modulations
@ -275,29 +255,25 @@ def data_overview3():
print(start_name(cell_type_here, species) + ' amprange: ' + ' ' + 'fr_base' + ' to score' + pears_l) print(start_name(cell_type_here, species) + ' amprange: ' + ' ' + 'fr_base' + ' to score' + pears_l)
################################## ##################################
print('\n') print('\n')
#embed() # embed()
#todo: hier noch die Werte für die Methodik printen # todo: hier noch die Werte für die Methodik printen
test = False test = False
#embed() # embed()
if test: if test:
plt.hist(frame_file['lim_individual']) plt.hist(frame_file['lim_individual'])
#print(frame_file['lim_individual']) # print(frame_file['lim_individual'])
############################################ ############################################
############################ ############################
# 2 Print names # 2 Print names
# printe um welchen Zeitraum es sich handelt # 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', frame_file = setting_overview_score(frame_load_sp, cell_type_here='', f_exclude=False, snippet=None, min_amp='min',
species='') species='')
print('\n') 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))) print('From ' + str(np.sort(frame_file.cell)[0]) + ' to ' + str(np.sort(frame_file.cell)[-1]) + ' nr ' + str(
#embed() len(frame_file.cell)))
# embed()
# embed()
#embed()
##################################################################### #####################################################################
############################ ############################
# 3 Print , EODF # 3 Print , EODF
@ -308,12 +284,14 @@ def data_overview3():
for c, cell_type_here in enumerate(cell_types): 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) frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
if len(frame_file)> 0: if len(frame_file) > 0:
# ja der fisch hatte halt eine ziemlich niedriege EODf # ja der fisch hatte halt eine ziemlich niedriege EODf
frame_here = frame_file[frame_file.cell != '2022-01-05-af-invivo-1'] frame_here = frame_file[frame_file.cell != '2022-01-05-af-invivo-1']
try: try:
print(start_name(cell_type_here, 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))) 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: except:
print('eodf thing') print('eodf thing')
embed() embed()
@ -321,9 +299,7 @@ def data_overview3():
new_list = [item[0:10] for item in my_list] new_list = [item[0:10] for item in my_list]
fish.extend(new_list) fish.extend(new_list)
print(species[0:7] + ' n_fish ' + str(len(np.unique(fish))))
print(species[0:7]+' n_fish '+str(len(np.unique(fish))))
##################################################################### #####################################################################
# Burst corr limits # Burst corr limits
print('\n') print('\n')
@ -336,8 +312,7 @@ def data_overview3():
lims_name = '' lims_name = ''
for lim in lims: for lim in lims:
lims_name = lims_name + str(lim) + ': ' + str(np.sum(frame_file['lim_individual'] == lim)) + ',' lims_name = lims_name + str(lim) + ': ' + str(np.sum(frame_file['lim_individual'] == lim)) + ','
print(start_name(cell_type_here, species) + ' ' + lims_name) print(start_name(cell_type_here, species) + ' ' + lims_name)
##################################################################### #####################################################################
############################ ############################
# 3 Print , EODF # 3 Print , EODF
@ -346,18 +321,13 @@ def data_overview3():
speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens'] speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
for species in speciess: for species in speciess:
for c, cell_type_here in enumerate(cell_types): for c, cell_type_here in enumerate(cell_types):
frame_file = setting_overview_score(frame_load_sp, cell_type_here,min_amp = '',species=species) frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='', species=species)
if len(frame_file)> 0: if len(frame_file) > 0:
#embed() # embed()
print(start_name(cell_type_here, species) + ' amp_min ' + str( 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))) np.nanmin(frame_file.amp)) + ' amp_max ' + str(
test = False np.nanmax(frame_file[~np.isinf(frame_file.amp)].amp)))
#embed()
if test:
frame_file[['cv_stim','cv_base']]
frame_file['cv_stim']
embed()
############################ ############################
# CVs min max # CVs min max
for species in speciess: for species in speciess:
@ -366,11 +336,14 @@ def data_overview3():
if len(frame_file) > 0: if len(frame_file) > 0:
try: 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)))) 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: except:
print('min here') print('min here')
embed() embed()
############################################ ############################################
####### #######
# N # N
@ -380,51 +353,42 @@ def data_overview3():
for c, cell_type_here in enumerate(cell_types): for c, cell_type_here in enumerate(cell_types):
################## ##################
# printe wie viele Zellen es am Anfang gab # 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) 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))) print(start_name(cell_type_here, species) + ' nr ' + str(len(frame_file)))
#embed() # embed()
#if c in [0,2]: # if c in [0,2]:
#cmap, _, y_axis = plt_modulation_overview(axs, c, cell_type_here, # cmap, _, y_axis = plt_modulation_overview(axs, c, cell_type_here,
# cv_name, frame_file, max_val, score, # cv_name, frame_file, max_val, score,
# species) # species)
#axs.set_ylabel(score) # axs.set_ylabel(score)
#embed()#frame_file[(frame_file.cv_base < 0.65) & (frame_file.response_modulation > 200)].response_modulation # embed()#frame_file[(frame_file.cv_base < 0.65) & (frame_file.response_modulation > 200)].response_modulation
#axs.set_xlabel(cv_name) # axs.set_xlabel(cv_name)
#axs.get_shared_x_axes().join(*[axs, axd])
# axs.get_shared_x_axes().join(*[axs, axd])
# elif species == ' Apteronotus albifrons': # elif species == ' Apteronotus albifrons':
# plt_albi(ax[4, 1], cell_type_here, colors, max_val, species, x_axis, y_axis) # 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) # ax[1,cv_n].set_xlim(0, max_val)
#set_same_ylim(np.concatenate(ax[1::, :])) # set_same_ylim(np.concatenate(ax[1::, :]))
#set_same_ylim(np.concatenate(ax[1::, :]),ylim_type ='xlim') # set_same_ylim(np.concatenate(ax[1::, :]),ylim_type ='xlim')
#set_same_ylim(ax[0, :], ylim_type='xlim') # set_same_ylim(ax[0, :], ylim_type='xlim')
#set_ylim_same() # set_ylim_same()
#ax[1, 1].get_shared_y_axes().join(*ax[1, 1::]) # ax[1, 1].get_shared_y_axes().join(*ax[1, 1::])
#counter += 1
#embed()
# counter += 1
# embed()
############################################ ############################################
# printe um welchen Zeitraum es sich handelt # 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', frame_file = setting_overview_score(frame_load_sp, cell_type_here='', f_exclude=False, snippet=None, min_amp='min',
species='') species='')
print('\n sampling'+str(frame_file.sampling.unique())) print('\n sampling' + str(frame_file.sampling.unique()))
print('P-units Fr: 50-450 Hz CV: 0.15 - 1.35') print('P-units Fr: 50-450 Hz CV: 0.15 - 1.35')
print('Ampullary Fr: 80-200 Hz, CV: 0.08 - 0.22') print('Ampullary Fr: 80-200 Hz, CV: 0.08 - 0.22')
#plt.show() # plt.show()
show = False#True
fig = plt.gcf()
#embed()
tags_final = np.concatenate([tags[0::3],tags[1::3],tags[2::3]])#,tags[2::3]
fig.tag(tags_final, xoffs=-4.2, yoffs=1.12)
save_visualization(pdf = True, show = show)
if __name__ == '__main__': if __name__ == '__main__':

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model_and_data.py
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@ -74,7 +74,6 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
for all in it.product(*iternames): 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 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() fig = plt.figure()
hs = 0.45 hs = 0.45
@ -103,7 +102,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
trial_nr = 500000 trial_nr = 500000
cell = '2013-01-08-aa-invivo-1' cell = '2013-01-08-aa-invivo-1'
cell = '2012-07-03-ak-invivo-1' cell = '2012-07-03-ak-invivo-1'
print('cell' + str(cell))
cells_given = [cell] cells_given = [cell]
save_name_rev = find_folder_name( save_name_rev = find_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( '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(
@ -287,7 +286,7 @@ def plt_model_flowcharts(a_fr, ax_external, c, cs, grid, stack, stimulus_length)
deltat = model_params.pop("deltat") # .iloc[0] deltat = model_params.pop("deltat") # .iloc[0]
v_offset = model_params.pop("v_offset") # .iloc[0] v_offset = model_params.pop("v_offset") # .iloc[0]
eod_fr = stack.eod_fr.iloc[0] 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( 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, a_fes, a_fr, g, c_sigs[g], cell, deltat, eod_fr, model_params, stimulus_length, v_offset, var_types,
eod_fe=eod_fe) eod_fe=eod_fe)
@ -315,7 +314,7 @@ def plt_model_flowcharts(a_fr, ax_external, c, cs, grid, stack, stimulus_length)
elif len(np.unique(frame.RAM_afe)) > 1: elif len(np.unique(frame.RAM_afe)) > 1:
color_timeseries = 'red' color_timeseries = 'red'
nr_plot = 0 nr_plot = 0
print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise))) #print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
try: try:
ax_external, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time, ax_external, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time,
(frame.RAM_afe + frame.RAM_noise) * 100, (frame.RAM_afe + frame.RAM_noise) * 100,
@ -339,7 +338,7 @@ def plt_model_flowcharts(a_fr, ax_external, c, cs, grid, stack, stimulus_length)
elif len(np.unique(frame.RAM_noise)) > 1: elif len(np.unique(frame.RAM_noise)) > 1:
color_timeseries = 'purple' color_timeseries = 'purple'
nr_plot = 1 nr_plot = 1
print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise))) #print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
try: try:
ax_intrinsic, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time, ax_intrinsic, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time,
(frame.RAM_afe + frame.RAM_noise) * 100, (frame.RAM_afe + frame.RAM_noise) * 100,
@ -454,5 +453,5 @@ if __name__ == '__main__':
########################## ##########################
#embed() #embed()
print('hi') #print('hi')
model_and_data2(eod_metrice=False, width=0.005, D_extraction_method=D_extraction_method) #r'$\frac{1}{mV^2S}$' model_and_data2(eod_metrice=False, width=0.005, D_extraction_method=D_extraction_method) #r'$\frac{1}{mV^2S}$'

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@ -86,7 +86,7 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
trial_nr = 500000 trial_nr = 500000
cell = '2013-01-08-aa-invivo-1' cell = '2013-01-08-aa-invivo-1'
cell = '2012-07-03-ak-invivo-1' cell = '2012-07-03-ak-invivo-1'
print('cell'+str(cell)) #print('cell'+str(cell))
cells_given = [cell] cells_given = [cell]
save_name_rev = find_folder_name( save_name_rev = find_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( '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(
@ -127,12 +127,8 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
] ]
nrs_s = [3, 4, 8, 9]#, 10, 11 nrs_s = [3, 4, 8, 9]#, 10, 11
save_name = save_names[s] save_name = save_names[s]
#embed()
tr_name = trial_nr/1000000 tr_name = trial_nr/1000000
if tr_name == 1: if tr_name == 1:
tr_name = 1
ax_model = []
save_name = find_folder_name('calc_model') + '/' + save_name save_name = find_folder_name('calc_model') + '/' + save_name
@ -141,12 +137,7 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
perc = 'perc' perc = 'perc'
path = save_name + '.pkl' # '../'+ 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))
#
stack = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add) stack = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
if len(stack)> 0: if len(stack)> 0:
@ -184,14 +175,8 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
perc05_wo_norm.append(float('nan')) perc05_wo_norm.append(float('nan'))
median_wo_norm.append(float('nan')) median_wo_norm.append(float('nan'))
counter.append(float('nan')) counter.append(float('nan'))
# vars, cv_stims, fr_stims
print((np.array(counter)+1)/trial_nrs_here)
print((np.array(counter)) / trial_nrs_here)
#embed()
#ax.plot(trial_nrs_here, perc05, color = 'grey')
ax[0].plot(trial_nrs_here, perc95, color = 'black', clip_on = False, label = '99.99th percentile', alpha = alphas[s]) ax[0].plot(trial_nrs_here, perc95, color = 'black', clip_on = False, label = '99.99th percentile', alpha = alphas[s])
#ax.plot(trial_nrs_here, median, color = 'black', label = 'median')
#ax.scatter(trial_nrs_here, perc05, color = 'grey')
ax[0].scatter(trial_nrs_here, perc95, color = 'black', clip_on = False, alpha = alphas[s]) ax[0].scatter(trial_nrs_here, perc95, color = 'black', clip_on = False, alpha = alphas[s])
ax[0].set_xscale('log')#colors[s] ax[0].set_xscale('log')#colors[s]
ax[0].set_yscale('log') ax[0].set_yscale('log')
@ -203,52 +188,12 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
if s == 1: if s == 1:
ax[0].plot(trial_nrs_here, perc05, color='lightgrey', clip_on=False, label = '10th percentile', alpha = alphas[s]) ax[0].plot(trial_nrs_here, perc05, color='lightgrey', clip_on=False, label = '10th percentile', alpha = alphas[s])
ax[0].scatter(trial_nrs_here, perc05, color='lightgrey', clip_on=False, alpha = alphas[s]) ax[0].scatter(trial_nrs_here, perc05, color='lightgrey', clip_on=False, alpha = alphas[s])
ax[0].plot(trial_nrs_here, perc90, color='grey', clip_on=False, label='90th percentile', alpha = alphas[s]) ax[0].plot(trial_nrs_here, perc90, color='grey', clip_on=False, label='90th percentile', alpha = alphas[s])
ax[0].scatter(trial_nrs_here, perc90, color='grey', clip_on=False, alpha = alphas[s]) ax[0].scatter(trial_nrs_here, perc90, color='grey', clip_on=False, alpha = alphas[s])
ax[0].legend() ax[0].legend()
#ax[0].set_xscale('log')
#ax[0].set_yscale('log')
#ax[0].set_xlabel('Trials [$N$]')
#ax[0].set_ylabel('$\chi_{2}$\,[Hz]')
#ax[0].legend()
''' ax = plt.subplot(1,3,2)
ax.plot(trial_nrs_here, perc05_wo_norm, color = 'grey')
ax.plot(trial_nrs_here, perc95_wo_norm, color = 'grey')
ax.plot(trial_nrs_here, median_wo_norm, color = 'black', label = 'median')
ax.fill_between(trial_nrs_here, perc05_wo_norm, perc95_wo_norm, color='grey')
#ax.scatter(trial_nrs_here, median, color='black', label='measured points')
ax = plt.subplot(1,3,3)
ax.plot(trial_nrs_here, perc05_wo_norm, color = 'grey')
ax.plot(trial_nrs_here, perc95_wo_norm, color = 'grey')
ax.plot(trial_nrs_here, median_wo_norm, color = 'black', label = 'median')
ax.fill_between(trial_nrs_here, perc05_wo_norm, perc95_wo_norm, color='grey')
#ax.scatter(trial_nrs_here, median, color='black', label='measured points')
ax.legend()
ax.set_xscale('log')
ax.set_yscale('log')
'''
plt.subplots_adjust(left = 0.1, right = 0.9, bottom = 0.2, top = 0.95) plt.subplots_adjust(left = 0.1, right = 0.9, bottom = 0.2, top = 0.95)
#plt.set_scale
#embed()
#embed()
#fig.tag([axes[0:3]], 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[3:6]], 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],
#plt.show()
save_visualization(pdf=True) save_visualization(pdf=True)

188
update_project.py
View File

@ -1,188 +0,0 @@
'''import os
try:
from numba import jit
except ImportError:
def jit(nopython):
def decorator_jit(func):
return func
return decorator_jit
import inspect
if 'cv_cell_types' not in inspect.stack()[-1][1]:
try:
from plotstyle import plot_style, spines_params
except:
a = 5
import sys
from IPython import embed
# utils susept wird in utils paper copiert und von utisl_susepitbility gestartet
utils_suseptibility_name = 'utils1'
utils_susept_name = 'utils1_project'
utils_suseptibility_name2 = 'utils_susept2'
utils_susept_name2 = 'utils_paper2'
utils_suseptibility_name_all = 'utils0'
utils_susept_name_all = 'utils0_project'#_down
try:# this will not load but I want this to be reference for the refractoring in pycharm
from threefish.utils1 import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
cont_other_dir = True#
except:
cont_other_dir = False#then I know that I am on alexandras PC and I can update my code
#embed()
if cont_other_dir == False:
import filecmp
# I can also update the folder in another directy to double check its dependencies
if not os.path.exists('../'+utils_suseptibility_name+'.py'):
from utils1_project import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
#from utils_paper2 import * # resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
else:
#embed()
if filecmp.cmp('../'+utils_suseptibility_name+'.py', utils_susept_name+'.py'):
from utils1_project import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
#from utils_paper2 import * # resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
if os.path.exists('../'+utils_suseptibility_name+'.py'):# das mache ich um in dem richtigen embed zu arbeiten
sys.path.insert(0, '..')
from threefish.utils1 import *#resave_small_files,remove_yticks, unify_cell_names,load_cv_table, colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
else:
# wir schauen erstmal ohne sys dass das immer zu teilen da ist
import shutil
if os.path.exists('../'+utils_suseptibility_name+'.py'):
shutil.copyfile('../'+utils_suseptibility_name+'.py', utils_susept_name+'.py')
import shutil
if os.path.exists('../' + utils_suseptibility_name + '.py'):
shutil.copyfile('../' + utils_suseptibility_name_all + '.py', utils_susept_name_all + '.py')
print('copied utils all')
# wenn wir auf meinem Computer sind ziehen wir es aber immer vom code
# damit das refractors und wenn wir wo anders sind von dem extra kopierten file
if not os.path.exists('../'+utils_suseptibility_name+'.py'):
from utils1_project import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
#from utils_paper2 import * # resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
sys.path.insert(0, '..')
from threefish.utils1 import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
else:
sys.path.insert(0, '..')
from threefish.utils1 import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
##############################
# find out if we are in the code or develop mode (alexandra) or the public mode!
names_extra_modules = []
version = 'code'
# for name in names_extra_modules:
if 'suseptibility' in inspect.stack()[-1][1]: # 'code' not in # da ist jetzt die Starter Directory
version = 'develop'
if ('code' not in inspect.stack()[-1][1]) | (not (('alex' in os.getlogin()) | ('rudnaya' in os.getlogin()))):
version = 'public' # für alle sollte version public sein!
# this we do only in the develop mode
if version == 'develop': #
copy = True
if copy:
if __name__ == '__main__':
from distutils.dir_util import copy_tree
import time
import shutil
#shutil.rmtree("../../make_folder/suseptibility")#delet
t1 = time.time()
dirs = os.listdir("../suseptibility")
d_count = 0
for d, directory in enumerate(dirs):
if (('.csv' in directory) | ('.npy' in directory) | ('.pkl' in directory) | ('.py' in directory)| ('suseptibility' in directory)| ('make' in dir.lower()) | ('.dat' in directory)) & ('__pycache__' not in directory):
new_folder_make = '../../../make_folder'
if not os.path.exists(new_folder_make):
os.mkdir(new_folder_make)
new_dir = new_folder_make+"/suseptibility/"
if not os.path.exists(new_dir):
os.mkdir(new_dir)
# clean the previous directory
#embed()
if d_count == 0:
dirs_new = os.listdir(new_dir)
for dir_n in dirs_new:
if '__pycache__' not in dir_n:
os.remove(new_dir + dir_n)
# nicht remove three sonst macht es alles weg
#shutil.rmtree(new_dir)
#os.mkdir(new_dir)
# redo the directory
#
if os.path.exists(new_dir + directory):
embed()
#print(new_dir+dir)
#try:
shutil.copy2("../suseptibility/" + directory,new_dir + directory)
#print(new_dir + directory)
#shutil.copy("../suseptibility/" + directory,new_dir + directory)
#shutil.copyfile("../suseptibility/"+dir, new_dir+dir)
#except:
# print('shutil stuff')
# embed()
d_count += 1
t2 = time.time() -t1
print('Time='+str(t2))
print('copied')
#copy_tree("../suseptibility", "../../make_folder/suseptibility")
def plt_scatter_four(grid, frame, cell_types, cell_type_type, annotate, colors):
grid2 = gridspec.GridSpecFromSubplotSpec(1, 4, grid[0], wspace=0.4,
hspace=0.2)
add = ['', '_burst_corr', ]
ax0 = plt.subplot(grid2[0])
# ok hier plotten wir nur den scatter der auch ein gwn hat, aber was ist wenn es mehr sind?
# ok im prinzip sollte das zwar schon stimmen aber für das Bild kann man wirklich mehr machen
for c, cell_type_it in enumerate(cell_types):
frame_g = frame[
(frame[cell_type_type] == cell_type_it) & ((frame.gwn == True) | (frame.fs == True))]
plt_cv_fr(annotate, ax0, add[0], frame_g, colors, cell_type_it)
ax1 = plt.subplot(grid2[1])
for c, cell_type_it in enumerate(cell_types):
frame_g = frame[
(frame[cell_type_type] == cell_type_it) & ((frame.gwn == True) | (frame.fs == True))]
plt_cv_vs(frame_g, ax1, add[0], annotate, colors, cell_type_it)
ax2 = plt.subplot(grid2[2])
ax2.set_title('burst')
for c, cell_type_it in enumerate(cell_types):
# frame_all = frame[(frame[cell_type_type] == cell_type)]
frame_g = frame[
(frame[cell_type_type] == cell_type_it) & ((frame.gwn == True) | (frame.fs == True))]
plt_cv_fr(annotate, ax2, add[1], frame_g, colors, cell_type_it)
return ax0, ax1, ax2,'''