Subir archivos a ''

in-vitro/23072019/B12784O18-T3/2019-07-23T16-59-52B12784O18-T3-ACDC-PostEth-LowFreqNoise-0.25V.h5

@@ -0,0 +1 @@
+/annex/objects/MD5-s67434980--f3ad646646dbeabd05bafb07deea1431

in-vitro/23072019/B12784O18-T3/2019-07-23T16-59-52B12784O18-T3-ACDC-PostEth-LowFreqNoise-0.25V_2.h5

@@ -0,0 +1 @@
+/annex/objects/MD5-s183878089--e3a3eb9271a5661fc22f77f5af0bfac0

in-vitro/23072019/B12784O18-T3/2019-07-23T18-55-56B12784O18-T3-ACDC-PostEth-PostLong.h5

@@ -0,0 +1 @@
+/annex/objects/MD5-s12059947--a5d594bf16082c568d969a81e1fd0780

in-vitro/23072019/B12784O18-T3/2019-07-23T18-55-56B12784O18-T3-ACDC-PostEth-PostLong_2.h5

@@ -0,0 +1 @@
+/annex/objects/MD5-s12948137--2c483e2d7f4534cd0884c1714c309372

in-vitro/23072019/B12784O18-T3/20190723_044114 Current vs Vgs

@@ -0,0 +1,15 @@
+Vgs [V]	Result A5 [µA]	Result A7 [µA]	Result A29 [µA]	Result A31 [µA]	Result B5 [µA]	Result B7 [µA]	Result B29 [µA]	Result B31 [µA]
+0	70,19	84,65	76,69	77,97	72,54	72,97	81,08	65,87
+0,0307692307692308	69,30	83,61	75,93	77,07	71,66	72,17	80,13	65,11
+0,0615384615384615	68,24	82,38	75,00	75,99	70,59	71,19	78,98	64,17
+0,0923076923076923	67,03	80,95	73,91	74,73	69,37	70,06	77,66	63,09
+0,123076923076923	65,91	79,63	72,91	73,58	68,23	69,01	76,41	62,07
+0,153846153846154	64,57	78,04	71,69	72,18	66,88	67,74	74,92	60,84
+0,184615384615385	63,11	76,33	70,36	70,67	65,41	66,37	73,29	59,47
+0,215384615384615	61,43	74,34	68,77	68,90	63,72	64,75	71,41	57,86
+0,246153846153846	59,58	72,17	67,03	66,95	61,85	62,97	69,34	56,06
+0,276923076923077	57,77	70,04	65,30	65,02	60,04	61,22	67,29	54,25
+0,307692307692308	55,67	67,60	63,30	62,81	57,94	59,19	64,93	52,17
+0,338461538461538	53,30	64,82	60,99	60,31	55,60	56,88	62,25	49,91
+0,369230769230769	50,70	61,79	58,42	57,57	53,03	54,31	59,34	47,54
+0,4	47,99	58,64	55,74	54,71	50,40	51,63	56,32	45,15

in-vitro/23072019/B12784O18-T3/20190723_044114 Current vs time

@@ -0,0 +1,878 @@
+Time [s]	Result A5 [µA]	Result A7 [µA]	Result A29 [µA]	Result A31 [µA]	Result B5 [µA]	Result B7 [µA]	Result B29 [µA]	Result B31 [µA]
+0	70,19	84,66	76,71	77,98	0,00	0,00	0,00	0,00
+0,1	70,20	84,67	76,72	77,99	71,96	72,39	80,44	65,35
+0,2	70,20	84,67	76,72	77,99	72,57	73,00	81,10	65,92
+0,3	70,18	84,66	76,70	77,97	72,55	72,99	81,09	65,90
+0,4	70,18	84,66	76,70	77,97	72,56	72,99	81,09	65,91
+0,5	70,17	84,65	76,69	77,97	72,56	72,99	81,09	65,90
+0,6	70,16	84,64	76,68	77,96	72,56	72,99	81,09	65,90
+0,7	70,16	84,64	76,68	77,96	72,56	72,99	81,09	65,90
+0,8	70,16	84,63	76,68	77,96	72,55	72,99	81,09	65,90
+0,9	70,16	84,63	76,68	77,96	72,55	72,98	81,09	65,90
+1	70,16	84,64	76,69	77,95	72,55	72,99	81,09	65,90
+1,1	70,16	84,64	76,69	77,95	72,55	72,99	81,09	65,89
+1,2	70,18	84,65	76,69	77,97	72,55	72,98	81,09	65,90
+1,3	70,17	84,65	76,69	77,97	72,55	72,99	81,09	65,90
+1,4	70,17	84,65	76,69	77,97	72,55	72,98	81,10	65,90
+1,5	70,17	84,65	76,69	77,97	72,55	72,99	81,10	65,90
+1,6	70,17	84,65	76,69	77,97	72,56	72,99	81,10	65,90
+1,7	70,18	84,65	76,69	77,97	72,55	72,98	81,09	65,89
+1,8	70,18	84,65	76,69	77,97	72,54	72,98	81,09	65,89
+1,9	70,18	84,66	76,70	77,97	72,56	72,99	81,10	65,90
+2	70,17	84,65	76,68	77,96	72,56	72,99	81,10	65,90
+2,1	70,19	84,65	76,70	77,97	72,56	72,98	81,10	65,89
+2,2	70,19	84,65	76,70	77,97	72,56	72,98	81,09	65,89
+2,3	70,19	84,65	76,70	77,97	72,55	72,97	81,08	65,88
+2,4	70,18	84,65	76,69	77,97	72,54	72,97	81,08	65,88
+2,5	70,18	84,65	76,69	77,97	72,54	72,97	81,08	65,88
+2,6	70,19	84,65	76,69	77,97	72,54	72,97	81,09	65,88
+2,7	70,19	84,65	76,69	77,97	72,56	72,98	81,09	65,89
+2,8	70,19	84,65	76,69	77,97	72,56	72,99	81,10	65,89
+2,9	70,18	84,65	76,69	77,97	72,55	72,98	81,09	65,89
+3	70,18	84,65	76,69	77,97	72,55	72,98	81,09	65,89
+3,1	70,18	84,65	76,70	77,97	72,55	72,98	81,09	65,89
+3,2	70,18	84,65	76,70	77,97	72,55	72,98	81,09	65,89
+3,3	70,18	84,65	76,70	77,97	72,56	72,99	81,09	65,89
+3,4	70,19	84,65	76,69	77,97	72,56	72,98	81,09	65,89
+3,5	70,19	84,65	76,69	77,97	72,56	72,99	81,09	65,89
+3,6	70,19	84,65	76,69	77,97	72,54	72,98	81,09	65,88
+3,7	70,19	84,65	76,69	77,97	72,55	72,98	81,09	65,88
+3,8	70,19	84,65	76,69	77,97	72,56	72,99	81,10	65,89
+3,9	70,19	84,65	76,69	77,97	72,56	72,99	81,09	65,89
+4	70,19	84,65	76,69	77,97	72,55	72,98	81,09	65,89
+4,1	70,19	84,66	76,70	77,97	72,55	72,98	81,09	65,89
+4,2	70,19	84,66	76,70	77,97	72,56	72,98	81,09	65,89
+4,3	70,19	84,66	76,70	77,97	72,56	72,99	81,09	65,89
+4,4	70,19	84,66	76,70	77,97	72,54	72,98	81,08	65,88
+4,5	70,20	84,66	76,69	77,97	72,54	72,98	81,08	65,87
+4,6	70,20	84,66	76,69	77,97	72,54	72,97	81,08	65,87
+4,7	70,20	84,65	76,70	77,97	72,55	72,98	81,09	65,88
+4,8	70,19	84,65	76,70	77,97	72,56	72,99	81,10	65,89
+4,9	70,19	84,65	76,70	77,97	72,55	72,99	81,09	65,89
+5	70,19	84,66	76,70	77,97	72,54	72,98	81,08	65,88
+5,1	70,19	84,66	76,70	77,97	72,53	72,97	81,08	65,87
+5,2	70,19	84,66	76,69	77,97	72,54	72,97	81,08	65,88
+5,3	70,19	84,66	76,69	77,97	72,53	72,97	81,08	65,88
+5,4	70,19	84,65	76,69	77,97	72,55	72,99	81,09	65,88
+5,5	70,19	84,66	76,69	77,97	72,54	72,98	81,08	65,87
+5,6	70,19	84,66	76,69	77,97	72,54	72,97	81,08	65,87
+5,7	70,19	84,65	76,69	77,97	75,10	75,53	83,64	68,43
+5,8	70,19	84,65	76,69	77,97	75,10	75,53	83,65	68,44
+5,9	72,75	87,21	79,25	80,53	74,68	75,11	83,21	68,02
+6	72,75	87,21	79,25	80,53	71,93	72,40	80,37	65,35
+6,1	72,75	87,21	79,25	80,53	71,90	72,37	80,34	65,32
+6,2	72,75	87,21	79,25	80,53	71,88	72,35	80,32	65,30
+6,3	72,75	87,21	79,25	80,53	71,85	72,33	80,30	65,28
+6,4	69,45	83,80	76,09	77,24	71,85	72,33	80,30	65,27
+6,5	69,45	83,80	76,09	77,24	71,84	72,32	80,29	65,26
+6,6	69,42	83,76	76,05	77,21	71,83	72,31	80,28	65,26
+6,7	69,41	83,75	76,06	77,20	71,82	72,31	80,27	65,26
+6,8	69,41	83,75	76,06	77,20	71,82	72,30	80,27	65,25
+6,9	69,41	83,75	76,06	77,21	71,81	72,29	80,26	65,24
+7	69,41	83,75	76,06	77,21	71,80	72,29	80,26	65,24
+7,1	69,39	83,72	76,03	77,18	71,79	72,28	80,25	65,23
+7,2	69,39	83,72	76,03	77,18	71,76	72,26	80,23	65,21
+7,3	69,38	83,71	76,02	77,16	71,77	72,25	80,22	65,20
+7,4	69,37	83,70	76,01	77,15	71,76	72,25	80,22	65,20
+7,5	69,37	83,70	76,01	77,15	71,75	72,24	80,21	65,19
+7,6	69,36	83,69	76,00	77,15	71,74	72,24	80,21	65,18
+7,7	69,36	83,69	76,00	77,15	71,75	72,23	80,20	65,18
+7,8	69,36	83,69	76,00	77,14	71,73	72,22	80,19	65,18
+7,9	69,36	83,69	76,00	77,14	71,73	72,23	80,19	65,18
+8	69,35	83,68	75,99	77,13	71,73	72,22	80,19	65,17
+8,1	69,35	83,68	75,99	77,13	71,72	72,22	80,19	65,17
+8,2	69,34	83,68	75,98	77,13	71,72	72,22	80,18	65,17
+8,3	69,33	83,67	75,97	77,12	71,72	72,21	80,18	65,16
+8,4	69,33	83,67	75,97	77,12	71,70	72,21	80,17	65,16
+8,5	69,33	83,66	75,97	77,12	71,70	72,21	80,17	65,15
+8,6	69,33	83,66	75,97	77,12	71,70	72,20	80,17	65,15
+8,7	69,33	83,66	75,97	77,12	71,70	72,20	80,16	65,14
+8,8	69,33	83,65	75,96	77,11	71,69	72,20	80,16	65,14
+8,9	69,33	83,65	75,96	77,11	71,69	72,20	80,15	65,13
+9	69,32	83,64	75,96	77,10	71,69	72,20	80,15	65,14
+9,1	69,32	83,64	75,96	77,10	71,69	72,19	80,15	65,13
+9,2	69,32	83,64	75,96	77,09	71,69	72,19	80,15	65,13
+9,3	69,32	83,64	75,96	77,09	71,69	72,19	80,15	65,12
+9,4	69,32	83,64	75,95	77,09	71,69	72,19	80,15	65,13
+9,5	69,32	83,63	75,95	77,09	71,68	72,19	80,14	65,13
+9,6	69,32	83,63	75,95	77,09	71,69	72,19	80,15	65,13
+9,7	69,32	83,63	75,95	77,09	71,68	72,19	80,14	65,13
+9,8	69,32	83,63	75,95	77,09	71,68	72,18	80,14	65,12
+9,9	69,31	83,63	75,95	77,09	71,68	72,18	80,14	65,13
+10	69,31	83,63	75,95	77,09	71,67	72,18	80,14	65,13
+10,1	69,31	83,62	75,94	77,08	71,68	72,18	80,13	65,12
+10,2	69,31	83,63	75,94	77,08	71,68	72,18	80,13	65,12
+10,3	69,31	83,63	75,94	77,08	71,66	72,17	80,13	65,12
+10,4	69,30	83,62	75,94	77,08	71,66	72,17	80,13	65,12
+10,5	69,30	83,62	75,94	77,08	71,66	72,17	80,13	65,12
+10,6	69,30	83,62	75,94	77,08	71,67	72,18	80,13	65,12
+10,7	69,30	83,62	75,94	77,08	71,66	72,18	80,13	65,12
+10,8	69,30	83,62	75,94	77,08	71,66	72,17	80,13	65,12
+10,9	69,30	83,62	75,94	77,08	71,67	72,17	80,13	65,12
+11	69,30	83,62	75,94	77,08	71,66	72,17	80,13	65,11
+11,1	69,31	83,62	75,94	77,08	71,67	72,17	80,13	65,11
+11,2	69,31	83,62	75,94	77,08	71,67	72,17	80,13	65,11
+11,3	69,31	83,62	75,93	77,07	71,66	72,17	80,13	65,11
+11,4	69,31	83,62	75,93	77,07	71,67	72,17	80,13	65,11
+11,5	69,30	83,62	75,94	77,07	71,66	72,17	80,13	65,11
+11,6	69,30	83,61	75,93	77,07	74,22	74,73	82,70	67,67
+11,7	69,30	83,61	75,93	77,07	74,22	74,73	82,69	67,67
+11,8	71,86	86,18	78,50	79,63	74,22	74,73	82,69	67,67
+11,9	71,86	86,18	78,50	79,63	71,86	72,39	80,24	65,37
+12	71,86	86,18	78,50	79,63	70,89	71,44	79,24	64,44
+12,1	71,86	86,18	78,50	79,63	70,86	71,41	79,22	64,41
+12,2	68,45	82,64	75,24	76,24	70,85	71,40	79,20	64,40
+12,3	68,39	82,58	75,18	76,18	70,82	71,38	79,18	64,38
+12,4	68,39	82,58	75,18	76,18	70,82	71,38	79,18	64,38
+12,5	68,39	82,57	75,17	76,17	70,81	71,37	79,17	64,37
+12,6	68,39	82,57	75,17	76,17	70,81	71,37	79,16	64,37
+12,7	68,38	82,56	75,17	76,17	70,80	71,37	79,15	64,36
+12,8	68,38	82,55	75,16	76,16	70,78	71,36	79,15	64,35
+12,9	68,38	82,55	75,16	76,16	70,77	71,35	79,13	64,34
+13	68,36	82,54	75,14	76,14	70,78	71,35	79,13	64,34
+13,1	68,36	82,54	75,14	76,14	70,77	71,34	79,13	64,34
+13,2	68,36	82,53	75,13	76,13	70,74	71,33	79,12	64,32
+13,3	68,36	82,53	75,13	76,13	70,74	71,31	79,10	64,31
+13,4	68,35	82,52	75,13	76,12	70,74	71,30	79,10	64,30
+13,5	68,34	82,50	75,11	76,11	70,73	71,30	79,09	64,30
+13,6	68,34	82,50	75,11	76,11	70,72	71,30	79,08	64,29
+13,7	68,33	82,50	75,11	76,10	70,72	71,29	79,08	64,28
+13,8	68,33	82,50	75,11	76,10	70,71	71,29	79,08	64,28
+13,9	68,32	82,49	75,10	76,09	70,70	71,28	79,06	64,27
+14	68,32	82,49	75,10	76,09	70,69	71,28	79,06	64,27
+14,1	68,32	82,48	75,09	76,09	70,69	71,29	79,06	64,27
+14,2	68,31	82,48	75,09	76,08	70,69	71,28	79,06	64,27
+14,3	68,31	82,48	75,09	76,08	70,69	71,27	79,05	64,26
+14,4	68,31	82,47	75,08	76,08	70,68	71,27	79,05	64,26
+14,5	68,31	82,47	75,08	76,08	70,67	71,27	79,05	64,25
+14,6	68,30	82,46	75,08	76,07	70,67	71,26	79,04	64,25
+14,7	68,30	82,46	75,08	76,07	70,67	71,26	79,05	64,25
+14,8	68,29	82,45	75,07	76,06	70,66	71,26	79,04	64,24
+14,9	68,29	82,45	75,07	76,06	70,66	71,25	79,04	64,24
+15	68,29	82,45	75,07	76,06	70,66	71,25	79,03	64,24
+15,1	68,29	82,45	75,06	76,05	70,65	71,25	79,03	64,24
+15,2	68,29	82,45	75,06	76,05	70,65	71,25	79,03	64,23
+15,3	68,28	82,43	75,05	76,03	70,64	71,24	79,02	64,22
+15,4	68,28	82,43	75,05	76,03	70,64	71,24	79,02	64,22
+15,5	68,27	82,42	75,04	76,03	70,63	71,23	79,01	64,22
+15,6	68,27	82,42	75,03	76,02	70,63	71,23	79,01	64,21
+15,7	68,27	82,42	75,03	76,02	70,62	71,22	79,01	64,21
+15,8	68,26	82,41	75,03	76,02	70,62	71,22	79,01	64,21
+15,9	68,26	82,41	75,03	76,02	70,62	71,22	79,01	64,21
+16	68,26	82,41	75,03	76,02	70,61	71,21	79,00	64,21
+16,1	68,26	82,41	75,03	76,02	70,62	71,21	79,00	64,21
+16,2	68,26	82,41	75,03	76,01	70,61	71,21	79,00	64,20
+16,3	68,26	82,41	75,03	76,01	70,61	71,21	79,00	64,20
+16,4	68,26	82,41	75,03	76,01	70,61	71,20	79,00	64,20
+16,5	68,25	82,40	75,02	76,00	70,61	71,20	79,00	64,19
+16,6	68,25	82,40	75,02	76,00	70,60	71,20	78,99	64,19
+16,7	68,25	82,39	75,01	76,00	70,60	71,20	78,99	64,19
+16,8	68,25	82,39	75,01	76,00	70,60	71,20	78,99	64,18
+16,9	68,24	82,39	75,01	76,00	70,60	71,20	78,99	64,18
+17	68,24	82,38	75,00	75,99	70,60	71,19	78,98	64,18
+17,1	68,24	82,38	75,00	75,99	70,60	71,19	78,98	64,18
+17,2	68,24	82,38	75,00	75,99	70,60	71,19	78,98	64,17
+17,3	68,24	82,38	75,00	75,99	70,58	71,19	78,98	64,17
+17,4	68,24	82,38	75,00	75,99	70,58	71,19	78,98	64,17
+17,5	68,24	82,38	75,00	75,99	70,59	71,19	78,98	64,17
+17,6	68,24	82,38	75,00	75,99	73,16	73,75	81,54	66,73
+17,7	70,80	84,95	77,57	78,55	73,16	73,76	81,55	66,74
+17,8	70,80	84,95	77,57	78,55	72,91	73,51	81,29	66,50
+17,9	70,80	84,95	77,57	78,55	69,75	70,39	78,00	63,44
+18	70,80	84,95	77,57	78,55	69,71	70,35	77,96	63,40
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+82,5	48,34	59,04	56,22	55,19	50,80	52,08	56,63	45,61
+82,6	48,32	59,01	56,18	55,16	50,79	52,06	56,61	45,59
+82,7	48,30	58,99	56,15	55,14	50,77	52,04	56,60	45,58
+82,8	48,30	58,99	56,15	55,14	50,77	52,03	56,59	45,56
+82,9	48,29	58,97	56,14	55,12	50,75	52,02	56,58	45,55
+83	48,29	58,97	56,14	55,12	50,72	52,00	56,57	45,53
+83,1	48,26	58,94	56,11	55,09	50,72	51,99	56,56	45,51
+83,2	48,26	58,94	56,11	55,09	50,71	51,98	56,55	45,50
+83,3	48,25	58,92	56,08	55,06	50,69	51,96	56,54	45,48
+83,4	48,25	58,92	56,08	55,06	50,68	51,95	56,54	45,48
+83,5	48,24	58,91	56,06	55,04	50,67	51,94	56,53	45,47
+83,6	48,24	58,91	56,06	55,04	50,67	51,92	56,51	45,45
+83,7	48,24	58,91	56,06	55,04	50,66	51,91	56,50	45,45
+83,8	48,20	58,87	56,02	54,99	50,64	51,90	56,50	45,43
+83,9	48,20	58,87	56,02	54,99	50,63	51,89	56,49	45,42
+84	48,18	58,85	55,99	54,97	50,61	51,88	56,48	45,41
+84,1	48,17	58,84	55,98	54,96	50,60	51,86	56,47	45,40
+84,2	48,17	58,84	55,98	54,96	50,59	51,85	56,47	45,39
+84,3	48,15	58,82	55,95	54,94	50,58	51,84	56,45	45,37
+84,4	48,15	58,82	55,95	54,94	50,57	51,83	56,45	45,36
+84,5	48,14	58,80	55,93	54,92	50,57	51,82	56,44	45,35
+84,6	48,14	58,80	55,93	54,92	50,54	51,81	56,43	45,34
+84,7	48,13	58,79	55,92	54,91	50,53	51,80	56,42	45,33
+84,8	48,12	58,77	55,90	54,88	50,54	51,79	56,42	45,32
+84,9	48,12	58,77	55,90	54,88	50,53	51,78	56,41	45,31
+85	48,10	58,76	55,88	54,86	50,51	51,77	56,41	45,29
+85,1	48,10	58,76	55,88	54,86	50,50	51,76	56,40	45,28
+85,2	48,09	58,74	55,86	54,84	50,49	51,75	56,39	45,27
+85,3	48,09	58,74	55,86	54,84	50,49	51,74	56,38	45,26
+85,4	48,08	58,73	55,85	54,82	50,48	51,73	56,38	45,25
+85,5	48,06	58,71	55,83	54,80	50,48	51,72	56,38	45,24
+85,6	48,06	58,71	55,83	54,80	50,46	51,71	56,37	45,24
+85,7	48,05	58,70	55,81	54,78	50,45	51,70	56,36	45,22
+85,8	48,05	58,70	55,81	54,78	50,43	51,70	56,36	45,21
+85,9	48,04	58,69	55,80	54,78	50,44	51,69	56,35	45,20
+86	48,04	58,69	55,80	54,78	50,43	51,68	56,34	45,19
+86,1	48,03	58,67	55,78	54,76	50,43	51,66	56,34	45,18
+86,2	48,02	58,66	55,77	54,74	50,42	51,66	56,33	45,17
+86,3	48,02	58,66	55,77	54,74	50,40	51,65	56,33	45,17
+86,4	48,01	58,65	55,76	54,73	50,40	51,64	56,32	45,16
+86,5	48,01	58,65	55,76	54,73	50,40	51,63	56,32	45,15
+86,6	47,99	58,64	55,74	54,71	50,39	51,62	56,32	45,15
+86,7	47,99	58,64	55,74	54,71	50,38	51,61	56,31	45,14
+86,8	47,97	58,62	55,72	54,69	50,36	51,61	56,31	45,13
+86,9	47,97	58,62	55,72	54,69	50,38	51,61	56,32	45,14
+87	47,97	58,62	55,72	54,69	50,37	51,61	56,33	45,14
+87,1	47,97	58,62	55,72	54,69	50,37	51,60	56,32	45,13
+87,2	47,97	58,62	55,72	54,69	50,36	51,59	56,31	45,12
+87,3	47,97	58,62	55,72	54,69	50,35	51,58	56,31	45,11
+87,4	47,97	58,62	55,72	54,69	50,34	51,57	56,30	45,10
+87,5	47,97	58,62	55,72	54,69	50,33	51,56	56,29	45,09
+87,6	47,97	58,62	55,72	54,69	50,33	51,56	56,29	45,08

in-vitro/23072019/B12784O18-T3/20190723_044752 Current vs Vgs

@@ -0,0 +1,15 @@
+Vgs [V]	Result A5 [µA]	Result A7 [µA]	Result A29 [µA]	Result A31 [µA]	Result B5 [µA]	Result B7 [µA]	Result B29 [µA]	Result B31 [µA]
+0	65,69	75,87	71,51	71,85	65,36	68,12	76,27	61,04
+0,0307692307692308	63,04	73,12	68,87	68,95	62,87	65,51	73,49	58,76
+0,0615384615384615	59,93	69,44	65,72	65,56	59,72	62,44	70,12	56,01
+0,0923076923076923	56,38	65,16	62,08	61,70	56,11	58,90	66,21	52,82
+0,123076923076923	52,48	60,49	58,06	57,46	52,15	55,00	61,88	49,28
+0,153846153846154	47,91	55,03	53,24	52,46	47,51	50,33	56,73	45,05
+0,184615384615385	42,66	48,85	47,62	46,69	42,24	44,91	50,75	40,11
+0,215384615384615	36,60	41,87	41,04	40,02	36,31	38,56	43,78	34,33
+0,246153846153846	29,95	34,32	33,71	32,70	29,83	31,57	36,08	27,92
+0,276923076923077	23,36	26,94	26,46	25,55	23,47	24,68	28,36	21,63
+0,307692307692308	18,08	21,15	20,80	20,05	18,35	19,33	21,97	16,90
+0,338461538461538	15,98	19,00	18,70	18,16	16,29	17,63	19,33	15,76
+0,369230769230769	17,48	21,00	20,42	20,13	17,78	19,78	21,20	18,00
+0,4	21,33	25,84	24,76	24,58	21,60	24,27	26,04	22,02

in-vitro/23072019/B12784O18-T3/20190723_044752 Current vs Vgs.txt

@@ -0,0 +1,15 @@
+Vgs [V]	Result A5 [µA]	Result A7 [µA]	Result A29 [µA]	Result A31 [µA]	Result B5 [µA]	Result B7 [µA]	Result B29 [µA]	Result B31 [µA]
+0	65,69	75,87	71,51	71,85	65,36	68,12	76,27	61,04
+0,0307692307692308	63,04	73,12	68,87	68,95	62,87	65,51	73,49	58,76
+0,0615384615384615	59,93	69,44	65,72	65,56	59,72	62,44	70,12	56,01
+0,0923076923076923	56,38	65,16	62,08	61,70	56,11	58,90	66,21	52,82
+0,123076923076923	52,48	60,49	58,06	57,46	52,15	55,00	61,88	49,28
+0,153846153846154	47,91	55,03	53,24	52,46	47,51	50,33	56,73	45,05
+0,184615384615385	42,66	48,85	47,62	46,69	42,24	44,91	50,75	40,11
+0,215384615384615	36,60	41,87	41,04	40,02	36,31	38,56	43,78	34,33
+0,246153846153846	29,95	34,32	33,71	32,70	29,83	31,57	36,08	27,92
+0,276923076923077	23,36	26,94	26,46	25,55	23,47	24,68	28,36	21,63
+0,307692307692308	18,08	21,15	20,80	20,05	18,35	19,33	21,97	16,90
+0,338461538461538	15,98	19,00	18,70	18,16	16,29	17,63	19,33	15,76
+0,369230769230769	17,48	21,00	20,42	20,13	17,78	19,78	21,20	18,00
+0,4	21,33	25,84	24,76	24,58	21,60	24,27	26,04	22,02

in-vitro/23072019/B12784O18-T3/20190723_044752 Current vs time

@@ -0,0 +1,888 @@
+Time [s]	Result A5 [µA]	Result A7 [µA]	Result A29 [µA]	Result A31 [µA]	Result B5 [µA]	Result B7 [µA]	Result B29 [µA]	Result B31 [µA]
+0	-10,88	-4,71	-7,35	-7,44	0,00	0,00	0,00	0,00
+0,1	-11,02	-4,87	-7,50	-7,59	54,29	64,75	68,74	56,37
+0,2	-11,02	-4,87	-7,50	-7,59	57,35	67,34	71,93	58,99
+0,3	63,83	63,45	69,61	70,47	58,94	67,65	73,07	59,48
+0,4	63,83	63,45	69,61	70,47	60,00	67,78	73,75	59,76
+0,5	64,70	65,74	70,51	71,32	60,79	67,86	74,21	59,97
+0,6	65,07	68,54	70,89	71,58	61,39	67,91	74,54	60,12
+0,7	65,07	68,54	70,89	71,58	61,86	67,94	74,78	60,23
+0,8	65,25	70,24	71,06	71,68	62,25	67,95	74,97	60,31
+0,9	65,25	70,24	71,06	71,68	62,57	67,96	75,12	60,38
+1	65,37	71,84	71,19	71,74	62,84	67,97	75,24	60,44
+1,1	65,37	71,84	71,19	71,74	63,06	67,97	75,33	60,48
+1,2	65,43	72,55	71,23	71,75	63,27	67,98	75,42	60,52
+1,3	65,47	73,11	71,29	71,76	63,44	67,98	75,49	60,55
+1,4	65,47	73,11	71,29	71,76	63,59	67,97	75,55	60,58
+1,5	65,48	73,34	71,30	71,77	63,72	67,97	75,60	60,60
+1,6	65,48	73,34	71,30	71,77	63,84	67,98	75,65	60,63
+1,7	65,52	73,72	71,34	71,79	63,96	67,99	75,70	60,67
+1,8	65,52	73,72	71,34	71,79	64,08	68,00	75,75	60,70
+1,9	65,56	74,05	71,37	71,81	64,16	68,01	75,79	60,73
+2	65,57	74,19	71,39	71,82	64,26	68,03	75,83	60,75
+2,1	65,57	74,19	71,39	71,82	64,35	68,04	75,88	60,79
+2,2	65,57	74,19	71,39	71,82	64,42	68,05	75,91	60,81
+2,3	65,57	74,19	71,39	71,82	64,49	68,07	75,94	60,83
+2,4	65,61	74,64	71,43	71,83	64,55	68,07	75,96	60,85
+2,5	65,61	74,64	71,43	71,83	64,61	68,08	75,99	60,87
+2,6	65,63	74,82	71,45	71,85	64,67	68,09	76,01	60,88
+2,7	65,63	74,89	71,46	71,85	64,72	68,10	76,04	60,90
+2,8	65,63	74,89	71,46	71,85	64,77	68,10	76,06	60,91
+2,9	65,65	75,03	71,47	71,85	64,81	68,10	76,08	60,92
+3	65,65	75,03	71,47	71,85	64,86	68,11	76,10	60,93
+3,1	65,66	75,14	71,47	71,85	64,89	68,11	76,11	60,94
+3,2	65,66	75,14	71,47	71,85	64,92	68,11	76,12	60,95
+3,3	65,66	75,20	71,48	71,85	64,96	68,11	76,13	60,95
+3,4	65,66	75,29	71,49	71,86	64,98	68,10	76,14	60,96
+3,5	65,66	75,29	71,49	71,86	65,00	68,10	76,14	60,96
+3,6	65,67	75,38	71,49	71,86	65,02	68,10	76,15	60,96
+3,7	65,67	75,38	71,49	71,86	65,04	68,10	76,16	60,97
+3,8	65,67	75,38	71,49	71,86	65,09	68,11	76,18	60,99
+3,9	65,67	75,38	71,49	71,86	65,12	68,12	76,19	61,00
+4	65,68	75,42	71,49	71,86	65,13	68,12	76,20	61,00
+4,1	65,68	75,56	71,50	71,86	65,16	68,12	76,21	61,01
+4,2	65,68	75,56	71,50	71,86	65,18	68,12	76,22	61,02
+4,3	65,69	75,63	71,51	71,86	65,20	68,12	76,22	61,02
+4,4	65,69	75,63	71,51	71,86	65,21	68,12	76,22	61,02
+4,5	65,69	75,65	71,51	71,86	65,23	68,12	76,23	61,02
+4,6	65,69	75,65	71,51	71,86	65,23	68,11	76,22	61,02
+4,7	65,69	75,69	71,51	71,86	65,24	68,11	76,22	61,01
+4,8	65,69	75,74	71,51	71,85	65,27	68,12	76,24	61,03
+4,9	65,69	75,74	71,51	71,85	65,26	68,11	76,24	61,02
+5	65,70	75,76	71,51	71,86	65,30	68,12	76,25	61,04
+5,1	65,70	75,76	71,51	71,86	65,30	68,12	76,26	61,03
+5,2	65,70	75,80	71,52	71,86	65,32	68,12	76,26	61,03
+5,3	65,70	75,80	71,52	71,86	65,32	68,11	76,26	61,03
+5,4	65,70	75,83	71,52	71,85	65,33	68,11	76,26	61,04
+5,5	65,69	75,86	71,51	71,85	65,34	68,11	76,26	61,04
+5,6	65,69	75,86	71,51	71,85	65,36	68,12	76,27	61,04
+5,7	65,69	75,87	71,51	71,85	67,93	70,67	78,83	63,61
+5,8	65,69	75,87	71,51	71,85	67,94	70,67	78,83	63,61
+5,9	68,26	78,46	74,07	74,41	67,47	70,14	78,32	63,10
+6	68,26	78,46	74,07	74,41	63,38	65,68	73,88	58,93
+6,1	68,25	78,47	74,07	74,40	63,17	65,59	73,70	58,85
+6,2	68,25	78,47	74,07	74,40	63,07	65,57	73,62	58,83
+6,3	68,25	78,47	74,07	74,40	63,01	65,55	73,58	58,81
+6,4	68,25	78,47	74,07	74,40	62,97	65,55	73,55	58,80
+6,5	68,25	78,47	74,07	74,40	62,93	65,54	73,54	58,79
+6,6	63,08	73,21	68,88	68,96	62,91	65,55	73,53	58,79
+6,7	63,07	73,18	68,88	68,96	62,90	65,55	73,52	58,79
+6,8	63,07	73,18	68,88	68,96	62,89	65,54	73,51	58,79
+6,9	63,07	73,13	68,87	68,96	62,88	65,54	73,51	58,79
+7	63,07	73,13	68,87	68,96	62,88	65,55	73,51	58,79
+7,1	63,06	73,11	68,87	68,96	62,87	65,55	73,51	58,79
+7,2	63,06	73,11	68,87	68,96	62,87	65,55	73,50	58,79
+7,3	63,07	73,10	68,88	68,97	62,86	65,54	73,49	58,78
+7,4	63,07	73,09	68,88	68,97	62,86	65,54	73,49	58,78
+7,5	63,07	73,09	68,88	68,97	62,85	65,54	73,49	58,77
+7,6	63,06	73,07	68,87	68,96	62,85	65,53	73,49	58,77
+7,7	63,06	73,07	68,87	68,96	62,84	65,52	73,48	58,76
+7,8	63,06	73,07	68,87	68,96	62,83	65,52	73,47	58,75
+7,9	63,06	73,07	68,87	68,96	62,82	65,51	73,46	58,75
+8	63,06	73,06	68,87	68,96	62,82	65,51	73,46	58,75
+8,1	63,05	73,06	68,86	68,95	62,81	65,50	73,46	58,75
+8,2	63,05	73,06	68,86	68,95	62,82	65,50	73,46	58,74
+8,3	63,04	73,06	68,85	68,95	62,83	65,50	73,46	58,75
+8,4	63,04	73,06	68,85	68,95	62,81	65,50	73,45	58,74
+8,5	63,04	73,06	68,85	68,95	62,82	65,50	73,45	58,74
+8,6	63,04	73,06	68,85	68,95	62,82	65,50	73,45	58,75
+8,7	63,04	73,06	68,85	68,95	62,82	65,50	73,45	58,74
+8,8	63,04	73,06	68,85	68,95	62,81	65,50	73,45	58,74
+8,9	63,04	73,06	68,85	68,95	62,81	65,50	73,46	58,74
+9	63,04	73,06	68,85	68,94	62,82	65,50	73,46	58,74
+9,1	63,04	73,06	68,85	68,94	62,83	65,50	73,46	58,74
+9,2	63,04	73,07	68,85	68,95	62,83	65,51	73,46	58,75
+9,3	63,04	73,07	68,85	68,95	62,83	65,51	73,46	58,74
+9,4	63,04	73,07	68,86	68,95	62,83	65,51	73,46	58,75
+9,5	63,04	73,08	68,86	68,95	62,84	65,51	73,47	58,75
+9,6	63,04	73,08	68,86	68,95	62,84	65,51	73,47	58,75
+9,7	63,04	73,08	68,86	68,95	62,84	65,51	73,47	58,75
+9,8	63,04	73,08	68,86	68,95	62,85	65,51	73,47	58,75
+9,9	63,05	73,09	68,86	68,96	62,85	65,51	73,48	58,76
+10	63,05	73,09	68,86	68,96	62,84	65,51	73,48	58,76
+10,1	63,05	73,09	68,86	68,96	62,85	65,51	73,48	58,76
+10,2	63,05	73,10	68,87	68,96	62,86	65,52	73,49	58,77
+10,3	63,05	73,10	68,87	68,96	62,86	65,51	73,49	58,76
+10,4	63,05	73,10	68,87	68,96	62,86	65,51	73,49	58,76
+10,5	63,05	73,10	68,87	68,96	62,87	65,52	73,49	58,76
+10,6	63,05	73,11	68,87	68,96	62,86	65,52	73,49	58,76
+10,7	63,05	73,11	68,87	68,96	62,86	65,52	73,49	58,76
+10,8	63,05	73,11	68,87	68,96	62,88	65,51	73,49	58,76
+10,9	63,05	73,11	68,87	68,96	62,88	65,51	73,49	58,76
+11	63,05	73,11	68,87	68,96	62,88	65,51	73,49	58,76
+11,1	63,05	73,11	68,87	68,96	62,88	65,51	73,49	58,76
+11,2	63,05	73,11	68,87	68,96	62,87	65,51	73,49	58,76
+11,3	63,05	73,11	68,86	68,95	62,87	65,51	73,49	58,76
+11,4	63,05	73,11	68,86	68,95	62,88	65,51	73,49	58,77
+11,5	63,04	73,11	68,86	68,95	62,87	65,51	73,49	58,76
+11,6	63,04	73,12	68,87	68,95	62,88	65,51	73,49	58,76
+11,7	63,04	73,12	68,87	68,95	62,87	65,51	73,49	58,76
+11,8	63,04	73,12	68,87	68,95	65,44	68,08	76,05	61,33
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+76,2	17,74	21,03	20,66	20,39	17,92	20,03	21,39	18,25
+76,3	17,73	21,04	20,64	20,38	17,92	20,03	21,39	18,24
+76,4	17,73	21,04	20,64	20,38	17,92	20,02	21,39	18,23
+76,5	17,71	21,04	20,63	20,37	17,91	20,01	21,38	18,23
+76,6	17,71	21,05	20,62	20,36	17,90	20,00	21,37	18,22
+76,7	17,71	21,05	20,62	20,36	17,92	19,99	21,37	18,21
+76,8	17,69	21,05	20,61	20,35	17,90	19,98	21,36	18,20
+76,9	17,69	21,05	20,61	20,35	17,92	19,98	21,36	18,20
+77	17,68	21,05	20,60	20,33	17,91	19,97	21,35	18,19
+77,1	17,68	21,05	20,60	20,33	17,90	19,96	21,35	18,19
+77,2	17,67	21,05	20,60	20,33	17,89	19,96	21,34	18,18
+77,3	17,66	21,05	20,59	20,31	17,88	19,95	21,34	18,17
+77,4	17,66	21,05	20,59	20,31	17,90	19,95	21,33	18,17
+77,5	17,65	21,05	20,57	20,30	17,88	19,94	21,33	18,16
+77,6	17,65	21,05	20,57	20,30	17,88	19,93	21,33	18,15
+77,7	17,65	21,05	20,57	20,30	17,89	19,93	21,32	18,15
+77,8	17,65	21,05	20,57	20,30	17,88	19,92	21,32	18,14
+77,9	17,62	21,04	20,55	20,27	17,86	19,91	21,31	18,14
+78	17,62	21,04	20,55	20,27	17,88	19,91	21,31	18,13
+78,1	17,62	21,04	20,55	20,27	17,88	19,90	21,30	18,13
+78,2	17,61	21,04	20,54	20,26	17,87	19,90	21,30	18,12
+78,3	17,61	21,04	20,54	20,26	17,87	19,89	21,30	18,12
+78,4	17,60	21,04	20,53	20,25	17,86	19,89	21,29	18,11
+78,5	17,60	21,04	20,53	20,25	17,84	19,88	21,29	18,11
+78,6	17,59	21,04	20,52	20,24	17,83	19,88	21,28	18,10
+78,7	17,58	21,03	20,51	20,23	17,84	19,87	21,28	18,09
+78,8	17,58	21,03	20,51	20,23	17,85	19,87	21,27	18,09
+78,9	17,58	21,03	20,51	20,22	17,84	19,86	21,27	18,08
+79	17,58	21,03	20,51	20,22	17,83	19,86	21,26	18,08
+79,1	17,56	21,03	20,50	20,21	17,83	19,85	21,26	18,07
+79,2	17,56	21,03	20,50	20,21	17,84	19,85	21,26	18,07
+79,3	17,55	21,02	20,49	20,20	17,83	19,84	21,25	18,06
+79,4	17,54	21,02	20,48	20,19	17,83	19,84	21,25	18,06
+79,5	17,54	21,02	20,48	20,19	17,81	19,83	21,24	18,05
+79,6	17,54	21,02	20,47	20,19	17,82	19,83	21,24	18,05
+79,7	17,54	21,02	20,47	20,19	17,81	19,82	21,23	18,04
+79,8	17,53	21,02	20,46	20,18	17,82	19,82	21,23	18,04
+79,9	17,53	21,02	20,46	20,18	17,81	19,81	21,23	18,03
+80	17,52	21,01	20,46	20,17	17,79	19,81	21,22	18,03
+80,1	17,51	21,01	20,45	20,16	17,79	19,80	21,22	18,03
+80,2	17,51	21,01	20,45	20,16	17,80	19,80	21,22	18,02
+80,3	17,50	21,01	20,44	20,15	17,79	19,79	21,21	18,02
+80,4	17,50	21,01	20,44	20,15	17,77	19,79	21,21	18,01
+80,5	17,49	21,01	20,43	20,14	17,77	19,78	21,21	18,01
+80,6	17,49	21,00	20,43	20,14	17,79	19,78	21,20	18,00
+80,7	17,49	21,00	20,43	20,14	17,78	19,78	21,20	18,00
+80,8	17,48	21,00	20,42	20,13	20,34	22,33	23,76	20,56
+80,9	17,48	21,00	20,42	20,13	20,32	22,33	23,75	20,56
+81	20,03	23,56	22,97	22,69	20,33	22,43	23,83	20,62
+81,1	20,03	23,56	22,97	22,69	21,18	24,55	25,82	22,26
+81,2	20,03	23,56	22,97	22,68	21,56	24,75	26,21	22,44
+81,3	21,83	25,58	25,18	25,00	21,71	24,77	26,33	22,47
+81,4	21,83	25,58	25,18	25,00	21,79	24,78	26,38	22,48
+81,5	21,83	25,58	25,18	25,00	21,83	24,77	26,40	22,48
+81,6	21,83	25,58	25,18	25,00	21,86	24,76	26,41	22,47
+81,7	21,86	25,75	25,24	25,06	21,88	24,75	26,42	22,47
+81,8	21,86	25,75	25,24	25,06	21,89	24,74	26,42	22,46
+81,9	21,84	25,79	25,22	25,04	21,90	24,73	26,42	22,45
+82	21,79	25,88	25,17	24,98	21,90	24,71	26,41	22,44
+82,1	21,79	25,88	25,17	24,98	21,91	24,70	26,41	22,43
+82,2	21,79	25,88	25,17	24,98	21,90	24,69	26,40	22,42
+82,3	21,79	25,88	25,17	24,98	21,90	24,67	26,39	22,40
+82,4	21,76	25,91	25,15	24,95	21,89	24,66	26,38	22,38
+82,5	21,76	25,91	25,15	24,95	21,89	24,65	26,37	22,38
+82,6	21,76	25,91	25,15	24,95	21,87	24,62	26,35	22,36
+82,7	21,73	25,92	25,12	24,93	21,86	24,61	26,34	22,34
+82,8	21,73	25,92	25,12	24,93	21,86	24,60	26,33	22,33
+82,9	21,69	25,92	25,10	24,90	21,85	24,59	26,32	22,32
+83	21,69	25,92	25,10	24,90	21,84	24,58	26,31	22,31
+83,1	21,68	25,92	25,09	24,89	21,84	24,57	26,30	22,30
+83,2	21,68	25,92	25,09	24,89	21,84	24,56	26,30	22,29
+83,3	21,64	25,92	25,05	24,85	21,82	24,55	26,29	22,29
+83,4	21,63	25,92	25,04	24,84	21,81	24,54	26,28	22,28
+83,5	21,63	25,92	25,04	24,84	21,81	24,53	26,27	22,27
+83,6	21,61	25,92	25,02	24,82	21,81	24,52	26,27	22,26
+83,7	21,61	25,92	25,02	24,82	21,79	24,51	26,26	22,25
+83,8	21,59	25,92	25,00	24,81	21,80	24,50	26,25	22,24
+83,9	21,59	25,92	25,00	24,81	21,78	24,50	26,25	22,24
+84	21,57	25,92	24,98	24,79	21,78	24,49	26,24	22,23
+84,1	21,56	25,92	24,97	24,78	21,78	24,48	26,23	22,22
+84,2	21,56	25,92	24,97	24,78	21,77	24,47	26,22	22,21
+84,3	21,54	25,91	24,96	24,77	21,76	24,46	26,22	22,21
+84,4	21,54	25,91	24,96	24,77	21,76	24,46	26,21	22,20
+84,5	21,53	25,91	24,94	24,75	21,76	24,45	26,21	22,19
+84,6	21,53	25,91	24,94	24,75	21,75	24,44	26,20	22,18
+84,7	21,52	25,91	24,93	24,74	21,75	24,43	26,19	22,18
+84,8	21,50	25,90	24,92	24,73	21,74	24,43	26,19	22,17
+84,9	21,50	25,90	24,92	24,73	21,74	24,42	26,18	22,16
+85	21,48	25,90	24,90	24,71	21,74	24,41	26,17	22,16
+85,1	21,48	25,90	24,90	24,71	21,72	24,40	26,17	22,15
+85,2	21,47	25,89	24,89	24,70	21,70	24,39	26,16	22,14
+85,3	21,47	25,89	24,89	24,70	21,69	24,39	26,15	22,14
+85,4	21,46	25,89	24,88	24,69	21,69	24,38	26,15	22,13
+85,5	21,44	25,88	24,86	24,68	21,69	24,38	26,14	22,12
+85,6	21,44	25,88	24,86	24,68	21,68	24,37	26,14	22,12
+85,7	21,43	25,88	24,85	24,66	21,67	24,36	26,13	22,11
+85,8	21,43	25,88	24,85	24,66	21,68	24,36	26,12	22,10
+85,9	21,43	25,88	24,85	24,66	21,67	24,35	26,12	22,10
+86	21,43	25,88	24,85	24,66	21,66	24,34	26,11	22,09
+86,1	21,40	25,87	24,83	24,64	21,66	24,34	26,11	22,09
+86,2	21,39	25,87	24,82	24,63	21,65	24,33	26,10	22,08
+86,3	21,39	25,87	24,82	24,63	21,65	24,32	26,09	22,07
+86,4	21,38	25,86	24,81	24,62	21,64	24,32	26,09	22,07
+86,5	21,38	25,86	24,81	24,62	21,64	24,31	26,08	22,06
+86,6	21,37	25,86	24,80	24,61	21,62	24,30	26,07	22,05
+86,7	21,37	25,86	24,80	24,61	21,62	24,30	26,07	22,05
+86,8	21,35	25,85	24,78	24,60	21,62	24,29	26,06	22,04
+86,9	21,33	25,84	24,76	24,58	21,60	24,28	26,05	22,04
+87	21,33	25,84	24,76	24,58	21,61	24,28	26,05	22,03
+87,1	21,33	25,84	24,76	24,58	21,60	24,27	26,05	22,03
+87,2	21,33	25,84	24,76	24,58	21,60	24,27	26,04	22,02
+87,3	21,31	25,83	24,74	24,56	21,59	24,26	26,03	22,02
+87,4	21,31	25,83	24,74	24,56	21,58	24,26	26,03	22,01
+87,5	21,31	25,83	24,74	24,56	21,60	24,27	26,05	22,03
+87,6	21,31	25,83	24,74	24,56	21,60	24,27	26,04	22,03
+87,7	21,31	25,83	24,74	24,56	21,59	24,26	26,04	22,02
+87,8	21,31	25,83	24,74	24,56	21,59	24,26	26,04	22,01
+87,9	21,31	25,83	24,74	24,56	21,58	24,25	26,03	22,01
+88	21,31	25,83	24,74	24,55	21,58	24,25	26,03	22,00
+88,1	21,31	25,83	24,74	24,55	21,57	24,24	26,02	22,00
+88,2	21,31	25,83	24,74	24,55	21,57	24,24	26,01	21,99
+88,3	21,31	25,83	24,74	24,55	21,56	24,23	26,01	21,98
+88,4	21,31	25,83	24,74	24,55	21,56	24,22	26,00	21,98
+88,5	21,31	25,83	24,74	24,55	21,55	24,22	26,00	21,97
+88,6	21,31	25,83	24,74	24,55	21,55	24,21	25,99	21,97

in-vitro/23072019/B12784O18-T3/20190723_070553 Current vs Vgs

@@ -0,0 +1,15 @@
+Vgs [V]	Result A5 [µA]	Result A7 [µA]	Result A29 [µA]	Result A31 [µA]	Result B5 [µA]	Result B7 [µA]	Result B29 [µA]	Result B31 [µA]
+0	66,21	77,99	72,16	72,51	66,52	68,57	77,35	61,51
+0,0307692307692308	63,59	74,90	69,60	69,68	63,83	66,06	74,60	59,21
+0,0615384615384615	60,54	71,28	66,56	66,38	60,68	63,10	71,34	56,49
+0,0923076923076923	57,07	67,16	63,07	62,61	57,12	59,70	67,59	53,35
+0,123076923076923	53,29	62,66	59,20	58,49	53,25	55,95	63,45	49,90
+0,153846153846154	48,82	57,35	54,56	53,61	48,70	51,45	58,49	45,76
+0,184615384615385	43,67	51,26	49,12	47,95	43,50	46,20	52,70	40,93
+0,215384615384615	37,69	44,25	42,69	41,38	37,54	39,99	45,85	35,22
+0,246153846153846	31,05	36,55	35,42	34,07	31,02	33,04	38,18	28,84
+0,276923076923077	24,32	28,79	27,94	26,70	24,44	25,99	30,28	22,42
+0,307692307692308	18,62	22,16	21,55	20,63	18,91	20,04	23,19	17,21
+0,338461538461538	15,87	18,76	18,47	18,03	16,21	17,46	19,25	15,21
+0,369230769230769	16,77	19,55	19,42	19,41	16,96	18,87	19,85	16,88
+0,4	20,25	23,44	23,23	23,44	20,17	22,84	23,87	20,68

in-vitro/23072019/B12784O18-T3/20190723_070553 Current vs Vgs.txt

@@ -0,0 +1,15 @@
+Vgs [V]	Result A5 [µA]	Result A7 [µA]	Result A29 [µA]	Result A31 [µA]	Result B5 [µA]	Result B7 [µA]	Result B29 [µA]	Result B31 [µA]
+0	66,21	77,99	72,16	72,51	66,52	68,57	77,35	61,51
+0,0307692307692308	63,59	74,90	69,60	69,68	63,83	66,06	74,60	59,21
+0,0615384615384615	60,54	71,28	66,56	66,38	60,68	63,10	71,34	56,49
+0,0923076923076923	57,07	67,16	63,07	62,61	57,12	59,70	67,59	53,35
+0,123076923076923	53,29	62,66	59,20	58,49	53,25	55,95	63,45	49,90
+0,153846153846154	48,82	57,35	54,56	53,61	48,70	51,45	58,49	45,76
+0,184615384615385	43,67	51,26	49,12	47,95	43,50	46,20	52,70	40,93
+0,215384615384615	37,69	44,25	42,69	41,38	37,54	39,99	45,85	35,22
+0,246153846153846	31,05	36,55	35,42	34,07	31,02	33,04	38,18	28,84
+0,276923076923077	24,32	28,79	27,94	26,70	24,44	25,99	30,28	22,42
+0,307692307692308	18,62	22,16	21,55	20,63	18,91	20,04	23,19	17,21
+0,338461538461538	15,87	18,76	18,47	18,03	16,21	17,46	19,25	15,21
+0,369230769230769	16,77	19,55	19,42	19,41	16,96	18,87	19,85	16,88
+0,4	20,25	23,44	23,23	23,44	20,17	22,84	23,87	20,68

in-vitro/23072019/B12784O18-T3/20190723_070553 Current vs time

@@ -0,0 +1,909 @@
+Time [s]	Result A5 [µA]	Result A7 [µA]	Result A29 [µA]	Result A31 [µA]	Result B5 [µA]	Result B7 [µA]	Result B29 [µA]	Result B31 [µA]
+0	66,24	78,01	72,19	72,54	0,00	0,00	0,00	0,00
+0,1	66,24	78,01	72,19	72,54	66,63	68,67	77,44	61,60
+0,2	66,24	78,01	72,19	72,54	66,58	68,62	77,40	61,55
+0,3	66,29	78,06	72,23	72,59	66,58	68,62	77,40	61,55
+0,4	66,23	78,00	72,18	72,53	66,56	68,60	77,37	61,53
+0,5	66,23	78,00	72,18	72,53	66,56	68,60	77,37	61,53
+0,6	66,24	78,01	72,19	72,53	66,56	68,59	77,38	61,53
+0,7	66,24	78,01	72,19	72,53	66,56	68,59	77,37	61,53
+0,8	66,24	78,01	72,18	72,53	66,56	68,59	77,37	61,53
+0,9	66,24	78,01	72,18	72,53	66,56	68,59	77,37	61,53
+1	66,24	78,00	72,18	72,53	66,56	68,59	77,37	61,53
+1,1	66,24	78,00	72,18	72,53	66,55	68,58	77,37	61,52
+1,2	66,23	78,00	72,18	72,53	66,55	68,58	77,36	61,52
+1,3	66,23	78,00	72,18	72,53	66,55	68,58	77,36	61,52
+1,4	66,23	78,00	72,18	72,53	66,55	68,58	77,36	61,52
+1,5	66,23	78,00	72,18	72,53	66,56	68,57	77,36	61,52
+1,6	66,23	78,00	72,18	72,53	66,54	68,58	77,36	61,52
+1,7	66,23	78,00	72,18	72,53	66,55	68,58	77,36	61,52
+1,8	66,23	78,00	72,18	72,53	66,55	68,58	77,36	61,52
+1,9	66,24	78,00	72,18	72,53	66,55	68,58	77,37	61,52
+2	66,23	78,00	72,18	72,53	66,54	68,58	77,36	61,52
+2,1	66,23	78,00	72,18	72,53	66,54	68,57	77,36	61,52
+2,2	66,23	78,00	72,18	72,53	66,54	68,57	77,36	61,52
+2,3	66,23	78,00	72,18	72,53	66,54	68,58	77,36	61,52
+2,4	66,23	78,00	72,18	72,52	66,55	68,58	77,36	61,52
+2,5	66,23	78,00	72,18	72,52	66,55	68,58	77,36	61,52
+2,6	66,23	78,00	72,17	72,52	66,54	68,58	77,37	61,52
+2,7	66,23	78,00	72,17	72,52	66,54	68,58	77,36	61,52
+2,8	66,23	78,00	72,17	72,52	66,54	68,58	77,36	61,52
+2,9	66,23	78,00	72,17	72,52	66,55	68,59	77,37	61,53
+3	66,23	78,00	72,17	72,52	66,54	68,57	77,36	61,51
+3,1	66,22	78,00	72,17	72,52	66,55	68,58	77,36	61,52
+3,2	66,22	78,00	72,17	72,52	66,55	68,58	77,36	61,52
+3,3	66,22	78,00	72,17	72,52	66,55	68,58	77,37	61,52
+3,4	66,22	77,99	72,17	72,52	66,55	68,58	77,36	61,52
+3,5	66,22	77,99	72,17	72,52	66,54	68,58	77,36	61,52
+3,6	66,22	77,99	72,16	72,52	66,54	68,57	77,36	61,51
+3,7	66,22	77,99	72,16	72,52	66,54	68,57	77,36	61,52
+3,8	66,22	77,99	72,16	72,52	66,54	68,57	77,35	61,51
+3,9	66,22	77,99	72,16	72,52	66,54	68,57	77,36	61,52
+4	66,22	77,99	72,16	72,52	66,54	68,57	77,36	61,52
+4,1	66,22	77,99	72,17	72,52	66,55	68,57	77,36	61,52
+4,2	66,22	77,99	72,17	72,52	66,54	68,57	77,36	61,52
+4,3	66,22	77,99	72,17	72,52	66,54	68,57	77,36	61,51
+4,4	66,22	77,99	72,17	72,52	66,54	68,57	77,35	61,51
+4,5	66,22	78,00	72,17	72,52	66,54	68,57	77,35	61,51
+4,6	66,22	78,00	72,17	72,52	66,53	68,57	77,36	61,51
+4,7	66,22	78,00	72,17	72,52	66,54	68,57	77,36	61,51
+4,8	66,22	78,00	72,17	72,52	66,54	68,57	77,36	61,51
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+90,3	20,24	23,44	23,22	23,43	20,15	22,83	23,86	20,68
+90,4	20,24	23,44	23,22	23,43	20,14	22,82	23,85	20,67
+90,5	20,24	23,44	23,22	23,43	20,14	22,82	23,85	20,67
+90,6	20,20	23,39	23,17	23,38	20,13	22,81	23,85	20,66
+90,7	20,20	23,39	23,17	23,38	20,12	22,80	23,84	20,65

in-vitro/23072019/B12784O18-T3/B12784O18-T3-ACDC-PostEth

@@ -0,0 +1,15 @@
+Date/Time	Vgs	Vds	Vref	AD/DC (Top)	AC/DC (Bottom)	Calibration	Headstage SN (Top)	Headstage SN (Bottom)
+23/07/2019 16:52:03	0	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:52:25	0,0307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:52:48	0,0615384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:53:10	0,0923076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:53:33	0,123076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:53:55	0,153846153846154	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:54:18	0,184615384615385	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:54:40	0,215384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:55:03	0,246153846153846	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:55:25	0,276923076923077	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:55:48	0,307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:56:11	0,338461538461538	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:56:33	0,369230769230769	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:56:55	0,4	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  

in-vitro/23072019/B12784O18-T3/B12784O18-T3-ACDC-PostEth-PostLong

@@ -0,0 +1,15 @@
+Date/Time	Vgs	Vds	Vref	AD/DC (Top)	AC/DC (Bottom)	Calibration	Headstage SN (Top)	Headstage SN (Bottom)
+23/07/2019 18:56:22	0	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:56:44	0,0307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:57:07	0,0615384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:57:30	0,0923076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:57:53	0,123076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:58:15	0,153846153846154	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:58:38	0,184615384615385	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:59:01	0,215384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:59:24	0,246153846153846	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:59:46	0,276923076923077	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:00:09	0,307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:00:32	0,338461538461538	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:00:55	0,369230769230769	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:01:17	0,4	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  

in-vitro/23072019/B12784O18-T3/B12784O18-T3-ACDC-PostEth-PostLong.txt

@@ -0,0 +1,15 @@
+Date/Time	Vgs	Vds	Vref	AD/DC (Top)	AC/DC (Bottom)	Calibration	Headstage SN (Top)	Headstage SN (Bottom)
+23/07/2019 18:56:22	0	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:56:44	0,0307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:57:07	0,0615384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:57:30	0,0923076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:57:53	0,123076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:58:15	0,153846153846154	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:58:38	0,184615384615385	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:59:01	0,215384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:59:24	0,246153846153846	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 18:59:46	0,276923076923077	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:00:09	0,307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:00:32	0,338461538461538	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:00:55	0,369230769230769	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:01:17	0,4	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  

in-vitro/23072019/B12784O18-T3/B12784O18-T3-DC-PostEth

@@ -0,0 +1,15 @@
+Date/Time	Vgs	Vds	Vref	AD/DC (Top)	AC/DC (Bottom)	Calibration	Headstage SN (Top)	Headstage SN (Bottom)
+23/07/2019 16:45:39	0	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:45:45	0,0307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:45:51	0,0615384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:45:57	0,0923076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:46:03	0,123076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:46:10	0,153846153846154	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:46:16	0,184615384615385	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:46:22	0,215384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:46:28	0,246153846153846	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:46:35	0,276923076923077	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:46:41	0,307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:46:48	0,338461538461538	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:46:54	0,369230769230769	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:47:01	0,4	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  

in-vitro/23072019/B12784O18-T3/B12784O18-T3-DC-PostEth-PostLong

@@ -0,0 +1,15 @@
+Date/Time	Vgs	Vds	Vref	AD/DC (Top)	AC/DC (Bottom)	Calibration	Headstage SN (Top)	Headstage SN (Bottom)
+23/07/2019 19:02:07	0	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:02:14	0,0307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:02:20	0,0615384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:02:26	0,0923076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:02:33	0,123076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:02:39	0,153846153846154	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:02:46	0,184615384615385	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:02:52	0,215384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:02:59	0,246153846153846	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:03:05	0,276923076923077	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:03:12	0,307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:03:19	0,338461538461538	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:03:25	0,369230769230769	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 19:03:32	0,4	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  

in-vitro/23072019/B12784O18-T3/B12784O18-T3-DC-PreEth

@@ -0,0 +1,15 @@
+Date/Time	Vgs	Vds	Vref	AD/DC (Top)	AC/DC (Bottom)	Calibration	Headstage SN (Top)	Headstage SN (Bottom)
+23/07/2019 16:39:17	0	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:39:23	0,0307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:39:29	0,0615384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:39:35	0,0923076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:39:41	0,123076923076923	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:39:48	0,153846153846154	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:39:54	0,184615384615385	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:40:00	0,215384615384615	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:40:06	0,246153846153846	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:40:13	0,276923076923077	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:40:19	0,307692307692308	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:40:25	0,338461538461538	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:40:32	0,369230769230769	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  
+23/07/2019 16:40:38	0,4	0,1	1,25	DC	DC	76	SN:0001 / Type: W32 (Custom HS 60) BC_Top 	SN:0002 / Type: W32 (Custom HS 61)  

in-vitro/CalcGM_Noise_LMU.py

@@ -0,0 +1,460 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Oct 05 15:49:46 2018
+
+@author: aemdlabs
+"""
+
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Sep  5 12:05:21 2018
+
+@author: aguimera
+"""
+
+from PhyREC.NeoInterface import NeoSegment#, ReadMCSFile
+import PhyREC.SignalAnalysis as Ran
+import PhyREC.PlotWaves as Rplt
+import quantities as pq
+import matplotlib.pyplot as plt
+import numpy as np
+import neo
+import csv
+from datetime import datetime
+from scipy import integrate
+
+
+def ReadMCSFile(McsFile, OutSeg=None, SigNamePrefix=''):
+    import McsPy.McsData as McsData
+
+    Dat = McsData.RawData(McsFile)
+    Rec = Dat.recordings[0]
+    NSamps = Rec.duration
+
+    if OutSeg is None:
+        OutSeg = NeoSegment()
+
+    for AnaStrn, AnaStr in Rec.analog_streams.iteritems():
+        if len(AnaStr.channel_infos) == 1:
+            continue
+
+        for Chn, Chinfo in AnaStr.channel_infos.iteritems():
+            print 'Analog Stream ', Chinfo.label, Chinfo.sampling_frequency
+            ChName = str(SigNamePrefix + Chinfo.label)
+            print ChName
+
+            Fs = Chinfo.sampling_frequency
+            Var, Unit = AnaStr.get_channel_in_range(Chn, 0, NSamps)
+            sig = neo.AnalogSignal(pq.Quantity(Var, Chinfo.info['Unit']),
+                                   t_start=0*pq.s,
+                                   sampling_rate=Fs.magnitude*pq.Hz,
+                                   name=ChName)
+
+            OutSeg.AddSignal(sig)
+    return OutSeg
+
+def ReadLogFile(File):
+    Fin = open(File)
+    
+    reader = csv.reader(Fin, delimiter='\t')
+    
+    LogVals = {}
+    ValsPos = {}
+    for il, e in enumerate(reader):
+        if il == 0:
+            for ih, v in enumerate(e):
+                ValsPos[ih] = v
+                LogVals[v] = []
+        else:
+            for ih, v in enumerate(e):
+                par = ValsPos[ih]
+                if (par=='Vgs') or (par=='Vds') or (par=='Vref'):
+                    LogVals[par].append(float(v.replace(',','.')))
+                elif par == 'Date/Time':
+                    LogVals[par].append(datetime.strptime(v, '%d/%m/%Y %H:%M:%S'))
+                else:
+                    LogVals[par].append(v)
+    
+    deltas = np.array(LogVals['Date/Time'])[:]-LogVals['Date/Time'][0]
+    LogVals['Time'] = []
+    for d in deltas:
+        LogVals['Time'].append(d.total_seconds())
+        
+    Fin.close()
+    
+    return LogVals
+
+def GetSwitchTimes(Sig, Thres=-1e-4, Plot=True):
+    s = Sig.GetSignal(None)
+    ds = np.abs(np.diff(np.array(s), axis=0))
+
+    if Plot:
+        plt.figure()
+        plt.plot(s.times, s)
+        plt.plot(s.times[1:], ds)
+
+    ds = Sig.duplicate_with_new_array(signal=ds)
+    Times = Ran.threshold_detection(ds,
+                                    threshold=Thres,
+                                    RelaxTime=5*pq.s)
+    return Times
+
+def MeanStd(Data, var):
+    Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]][var])])
+    for iT,TrtName in enumerate(Data.keys()):
+        Arr[iT,:] = Data[TrtName][var][:,0]
+    
+    return np.mean(Arr,0), np.std(Arr,0)
+
+def MeanStdGM(Data):
+    Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]])])
+    for iT,TrtName in enumerate(Data.keys()):
+        Arr[iT,:] = Data[TrtName]
+    
+    return np.mean(Arr,0), np.std(Arr,0)
+
+def Integrate(PSD, Freqs, Fmin, Fmax):
+    indices = np.where((Freqs >= Fmin) & (Freqs<=Fmax))
+    print( Freqs[indices])
+    Irms = np.sqrt(integrate.trapz(PSD[indices], Freqs[indices]))      
+    return Irms 
+
+
+MCSMapI={'SE1':'Ch03',
+         'SE2':'Ch05',
+         'SE3':'Ch01',
+         'SE4':'Ch02',
+         'SE5':'Ch22',
+         'SE6':'Ch06',
+         'SE7':'Ch16',
+         'SE8':'Ch37',
+         'SE9':'Ch20',
+         'SE10':'Ch10',
+         'SE11':'Ch24',
+         'SE12':'Ch08',
+         'SE13':'Ch14',
+         'SE14':'Ch04',
+         'SE15':'Ch18',
+         'SE16':'Ch33',
+         'SE17':'Ch34',
+         'SE18':'Ch60',
+         'SE19':'Ch38',
+         'SE20':'Ch64',
+         'SE21':'Ch40',
+         'SE22':'Ch56',
+         'SE23':'Ch42',
+         'SE24':'Ch70',
+         'SE25':'Ch66',
+         'SE26':'Ch65',
+         'SE27':'Ch68',
+         'SE28':'Ch67',
+         'SE29':'Ch55',
+         'SE30':'Ch62',
+         'SE31':'Ch58',
+         'SE32':'Ch69',
+         'ME1':'Ch57',
+         'ME2':'Ch61',
+         'ME3':'Ch53',
+         'ME4':'Ch63',
+         'ME5':'Ch52',
+         'ME6':'Ch41',
+         'ME7':'Ch49',
+         'ME8':'Ch51',
+         'ME9':'Ch46',
+         'ME10':'Ch45',
+         'ME11':'Ch44',
+         'ME12':'Ch39',
+         'ME13':'Ch54',
+         'ME14':'Ch43',
+         'ME15':'Ch50',
+         'ME16':'Ch47',
+         'ME17':'Ch32',
+         'ME18':'Ch27',
+         'ME19':'Ch30',
+         'ME20':'Ch29',
+         'ME21':'Ch28',
+         'ME22':'Ch25',
+         'ME23':'Ch26',
+         'ME24':'Ch07',
+         'ME25':'Ch21',
+         'ME26':'Ch11',
+         'ME27':'Ch17',
+         'ME28':'Ch15',
+         'ME29':'Ch13',
+         'ME30':'Ch31',
+         'ME31':'Ch19',
+         'ME32':'Ch09'}
+
+                          #Col, Row  
+MCSMapFacingDown={'Ch58':(0,1),
+                  'Ch57':(0,2),
+                  'Ch56':(0,3),
+                  'Ch55':(0,4),
+                  'Ch54':(0,5),
+                  'Ch53':(0,6),
+                  'Ch52':(0,7),
+                  'Ch51':(0,8),
+                  'Ch50':(0,9),
+                  'Ch49':(0,10),
+                  'Ch60':(1,0),
+                  'Ch61':(1,1),
+                  'Ch62':(1,2),
+                  'Ch63':(1,3),
+                  'Ch64':(1,4),
+                  'Ch65':(1,5),
+                  'Ch43':(1,6),
+                  'Ch44':(1,7),
+                  'Ch45':(1,8),
+                  'Ch46':(1,9),
+                  'Ch47':(1,10),
+                  'Ch70':(2,0),
+                  'Ch69':(2,1),
+                  'Ch68':(2,2),
+                  'Ch67':(2,3),
+                  'Ch66':(2,4),
+                  'Ch42':(2,5),
+                  'Ch41':(2,6),
+                  'Ch40':(2,7),
+                  'Ch39':(2,8),
+                  'Ch38':(2,9),
+                  'Ch37':(2,10),
+                  'Ch01':(3,0),
+                  'Ch02':(3,1),
+                  'Ch03':(3,2),
+                  'Ch04':(3,3),
+                  'Ch05':(3,4),
+                  'Ch06':(3,5),
+                  'Ch30':(3,6),
+                  'Ch31':(3,7),
+                  'Ch32':(3,8),
+                  'Ch33':(3,9),
+                  'Ch34':(3,10),
+                  'Ch11':(4,0),
+                  'Ch10':(4,1),
+                  'Ch09':(4,2),
+                  'Ch08':(4,3),
+                  'Ch07':(4,4),
+                  'Ch29':(4,5),
+                  'Ch28':(4,6),
+                  'Ch27':(4,7),
+                  'Ch26':(4,8),
+                  'Ch25':(4,9),
+                  'Ch24':(4,10),
+                  'Ch12':None,
+                  'Ch59':None,
+                  'Ch13':(5,1),
+                  'Ch14':(5,2),
+                  'Ch15':(5,3),
+                  'Ch16':(5,4),
+                  'Ch17':(5,5),
+                  'Ch18':(5,6),
+                  'Ch19':(5,7),
+                  'Ch20':(5,8),
+                  'Ch21':(5,9),
+                  'Ch22':(5,10)}
+
+if __name__ == '__main__':
+
+
+    Path = '23072019/B12784O18-T3/'
+    InFileM = Path + '2019-07-23T18-55-56B12784O18-T3-ACDC-PostEth-PostLong.h5' ############
+    InFileS = Path + '2019-07-23T18-55-56B12784O18-T3-ACDC-PostEth-PostLong_2.h5' ##########
+    LogFile = Path + 'B12784O18-T3-ACDC-PostEth-PostLong.txt'
+    
+    StartCycle = -1
+
+    LogVals = ReadLogFile(LogFile)
+    delta = np.mean([t-LogVals['Time'][it] for it, t in enumerate(LogVals['Time'][1:])])*pq.s
+    Delay = delta * StartCycle
+    
+    DCch = ('ME5', 'ME7', 'ME29', 'ME31', 'SE5', 'SE7', 'SE29', 'SE31')
+
+    TrigChannel = 'SE31'
+    TrigThres = 5e-5
+    Vgs = np.array(LogVals['Vgs'])
+    Vds = LogVals['Vds'][0]
+    
+    ivgain1 = 12e3*pq.V  
+    ivgain2 = 101
+    ACgain = 10*1
+    DCgain = 1*pq.V
+    Fsig = 10
+    StabTime = 10*pq.s
+    GuardTime = 1*pq.s
+    BW = 100
+    ivgainDC = 118.8*pq.V
+    ivgainAC = 1188*pq.V 
+
+
+    Rec = ReadMCSFile(InFileM,
+                      OutSeg=None,
+                      SigNamePrefix='M')
+
+    Rec = ReadMCSFile(InFileS,
+                      OutSeg=Rec,
+                      SigNamePrefix='S')
+
+# %%
+    plt.close('all')  
+    plt.ion() 
+    
+    SwTimes = GetSwitchTimes(Sig=Rec.GetSignal(TrigChannel),
+                             Thres=TrigThres,
+                             Plot=True)
+   
+    SlotsDC = []
+    for sig in Rec.Signals():
+        if sig.name not in DCch:
+            continue
+
+        if sig.name.startswith('M'):
+            col = 'r'
+        else:
+            col = 'g'
+        SlotsDC.append(Rplt.WaveSlot(sig,
+                                     Position=0,
+                                     Color=col,
+                                     Alpha=0.5))
+    
+    SwTimes = LogVals['Time']*pq.s+SwTimes[0]+Delay
+#%% calc IV  DC
+    DevDCVals = {}
+    
+    fig, Axt = plt.subplots()
+    Ids = {}
+    for sl in SlotsDC:
+        Ids[sl.name] = []
+        for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
+            ts = SwTimes[isw]+delta
+            TWind = (t+StabTime, ts-GuardTime)
+            s = sl.GetSignal(TWind, Units='V')
+            Axt.plot(s)
+            vio = np.mean(s).magnitude
+            ids = (vio*101-(-vg+Vds))/12e3
+            Ids[sl.name].append(ids)
+        
+
+    
+#%%  Calc GM
+    GM = {}
+    Irms = {}
+    Urms = {}
+
+    fig, (AxPsd, Axt) = plt.subplots(2,1)     
+    fig2, (AxPs, Axt) = plt.subplots(2,1)  
+    for sig in Rec.Signals():
+        if sig.name[0:3] == 'SEn':
+            continue
+
+#        
+        GM[sig.name] = []
+        Irms[sig.name] = []
+        Urms[sig.name] = []
+
+        for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
+            if isw == len(SwTimes)-1:
+                ts = SwTimes[isw]-Delay
+            else:
+                ts = SwTimes[isw+1]
+                
+            TWind = (t+StabTime, ts-GuardTime)
+            s = sig.GetSignal(TWind, Units ='V')
+            
+            
+            if s.name in DCch:
+                s = (s*ivgain2-(-vg+Vds)*pq.V)/ivgain1
+            else:
+                s = s/(ivgain1*ACgain/ivgain2)
+    
+        
+            Axt.plot(s.times, s, label=vg, alpha=0.5)     
+
+            PS = Ran.PlotPSD((s,),
+                              Time = TWind,
+                              Ax=AxPs,
+                              FMin=1,
+                              Label=str(vg),
+                              scaling='spectrum')
+            
+            ps = PS[sig.name]['psd']
+            Fps = PS[sig.name]['ff']
+           
+            indicesPeak = np.where( ((Fps >= Fsig-4) & (Fps<=Fsig+4)))   
+
+            IDSpeak = np.sqrt(ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]]+
+                             ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]+1]+
+                             ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]-1])
+            
+            gm = IDSpeak*1000/0.707
+            GM[sig.name] = np.append(GM[sig.name],gm)
+            
+            PSD = Ran.PlotPSD((s,),
+                  Time = TWind,
+                  Ax=AxPsd,
+                  FMin=1,
+                  Label=str(vg),
+                  scaling='density')
+
+            psd = PSD[sig.name]['psd'][:,0]
+            Fpsd = PSD[sig.name]['ff']
+            
+#            irms = Integrate(psd, Fpsd, 1.9, 1.9*3.2)
+#            Irms[sig.name] = np.append(Irms[sig.name],irms*np.sqrt(2))
+            irms = Integrate(psd, Fpsd, 63, 200)
+            Irms[sig.name] = np.append(Irms[sig.name],irms*np.sqrt(2))
+            
+            Irms[sig.name] = Irms[sig.name]
+            
+            Urms[sig.name] = Irms[sig.name]/GM[sig.name]
+
+
+    
+    plt.figure(12)
+    GMmean, GMstd = MeanStdGM(GM)
+    plt.plot(Vgs, GMmean*1000/0.1,'k',label = '1 metal layer')
+    plt.fill_between(Vgs, GMmean*1000/0.1-GMstd*1000/0.1, GMmean*1000/0.1+GMstd*1000/0.1,color = 'k',alpha =0.3)
+    plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
+    plt.ylabel('G$_m$ (mS/V)')
+    plt.legend()
+    
+    plt.figure(7)
+    IrmsMean, IrmsStd = MeanStdGM(Irms)
+    plt.semilogy(Vgs, IrmsMean,'k',label = 'rms')
+    plt.fill_between(Vgs, IrmsMean-IrmsStd, IrmsMean+IrmsStd ,color = 'k',alpha =0.3)
+
+    plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
+    plt.ylabel('I$_{rms}$ (A)')
+    plt.legend()
+    
+    plt.figure(8)
+    UrmsMean, UrmsStd = MeanStdGM(Urms)
+    plt.semilogy(Vgs, UrmsMean,'k',label = '1 metal layer')
+    plt.fill_between(Vgs, UrmsMean-UrmsStd, UrmsMean+UrmsStd ,color = 'k',alpha =0.3)
+    plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
+    plt.ylabel('U$_{rms}$ (A)')
+    plt.legend()
+    
+
+
+#%% plot map Urms
+import scipy.io
+scipy.io.savemat('UrmsLow.mat',Urms)
+    
+plt.figure()
+A=np.log10(np.ones((11,6))*5e-17)
+
+import matplotlib.colors as colors
+for Trt in Urms.keys():
+    ch = MCSMapI[Trt]
+#    if Trt in DCch:
+#        continue
+    
+    A[MCSMapFacingDown[ch][1],MCSMapFacingDown[ch][0]] = (Urms[Trt][9])*1e6
+
+plt.imshow(A, interpolation='nearest', vmin=1, vmax=15, norm=colors.LogNorm(vmin=1, vmax=15))
+plt.grid(True)
+cbar=plt.colorbar()
+plt.xlabel('column',fontsize=12)
+plt.ylabel('row',fontsize=12)
+cbar.set_label('U$_{gs-rms}$ ($\mu$V)', rotation=270, labelpad=15,fontsize=13)

in-vitro/CalcGM_Noise_LMU_multipleFiles.py

@@ -0,0 +1,495 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Oct 05 15:49:46 2018
+
+@author: aemdlabs
+"""
+
+
+from PhyREC.NeoInterface import NeoSegment#, ReadMCSFile
+import PhyREC.SignalAnalysis as Ran
+import PhyREC.PlotWaves as Rplt
+import quantities as pq
+import matplotlib.pyplot as plt
+import numpy as np
+import neo
+import deepdish as dd
+import csv
+from datetime import datetime
+from scipy import integrate
+
+
+def ReadMCSFile(McsFile, OutSeg=None, SigNamePrefix=''):
+    import McsPy.McsData as McsData
+
+    Dat = McsData.RawData(McsFile)
+    Rec = Dat.recordings[0]
+    NSamps = Rec.duration
+
+    if OutSeg is None:
+        OutSeg = NeoSegment()
+
+    for AnaStrn, AnaStr in Rec.analog_streams.iteritems():
+        if len(AnaStr.channel_infos) == 1:
+            continue
+
+        for Chn, Chinfo in AnaStr.channel_infos.iteritems():
+            print 'Analog Stream ', Chinfo.label, Chinfo.sampling_frequency
+            ChName = str(SigNamePrefix + Chinfo.label)
+            print ChName
+
+            Fs = Chinfo.sampling_frequency
+            Var, Unit = AnaStr.get_channel_in_range(Chn, 0, NSamps)
+            sig = neo.AnalogSignal(pq.Quantity(Var, Chinfo.info['Unit']),
+                                   t_start=0*pq.s,
+                                   sampling_rate=Fs.magnitude*pq.Hz,
+                                   name=ChName)
+
+            OutSeg.AddSignal(sig)
+    return OutSeg
+
+def ReadLogFile(File):
+    Fin = open(File)
+    
+    reader = csv.reader(Fin, delimiter='\t')
+    
+    LogVals = {}
+    ValsPos = {}
+    for il, e in enumerate(reader):
+        if il == 0:
+            for ih, v in enumerate(e):
+                ValsPos[ih] = v
+                LogVals[v] = []
+        else:
+            for ih, v in enumerate(e):
+                par = ValsPos[ih]
+                if (par=='Vgs') or (par=='Vds') or (par=='Vref'):
+                    LogVals[par].append(float(v.replace(',','.')))
+                elif par == 'Date/Time':
+                    LogVals[par].append(datetime.strptime(v, '%d/%m/%Y %H:%M:%S'))
+                else:
+                    LogVals[par].append(v)
+    
+    deltas = np.array(LogVals['Date/Time'])[:]-LogVals['Date/Time'][0]
+    LogVals['Time'] = []
+    for d in deltas:
+        LogVals['Time'].append(d.total_seconds())
+        
+    Fin.close()
+    
+    return LogVals
+
+def GetSwitchTimes(Sig, Thres=-1e-4, Plot=True):
+    s = Sig.GetSignal(None)
+    ds = np.abs(np.diff(np.array(s), axis=0))
+
+    if Plot:
+        plt.figure()
+        plt.plot(s.times, s)
+        plt.plot(s.times[1:], ds)
+
+    ds = Sig.duplicate_with_new_array(signal=ds)
+    Times = Ran.threshold_detection(ds,
+                                    threshold=Thres,
+                                    RelaxTime=5*pq.s)
+    return Times
+
+def MeanStd(Data, var):
+    Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]][var])])
+    for iT,TrtName in enumerate(Data.keys()):
+        Arr[iT,:] = Data[TrtName][var][:,0]
+    
+    return np.mean(Arr,0), np.std(Arr,0)
+
+def MeanStdGM(Data):
+    Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]])])
+    for iT,TrtName in enumerate(Data.keys()):
+        Arr[iT,:] = Data[TrtName]
+    
+    return np.mean(Arr,0), np.std(Arr,0)
+
+def Integrate(PSD, Freqs, Fmin, Fmax):
+    indices = np.where((Freqs >= Fmin) & (Freqs<=Fmax))
+    print( Freqs[indices])
+    Irms = np.sqrt(integrate.trapz(PSD[indices], Freqs[indices]))      
+    return Irms 
+
+
+MCSMapI={'SE1':'Ch03',
+         'SE2':'Ch05',
+         'SE3':'Ch01',
+         'SE4':'Ch02',
+         'SE5':'Ch22',
+         'SE6':'Ch06',
+         'SE7':'Ch16',
+         'SE8':'Ch37',
+         'SE9':'Ch20',
+         'SE10':'Ch10',
+         'SE11':'Ch24',
+         'SE12':'Ch08',
+         'SE13':'Ch14',
+         'SE14':'Ch04',
+         'SE15':'Ch18',
+         'SE16':'Ch33',
+         'SE17':'Ch34',
+         'SE18':'Ch60',
+         'SE19':'Ch38',
+         'SE20':'Ch64',
+         'SE21':'Ch40',
+         'SE22':'Ch56',
+         'SE23':'Ch42',
+         'SE24':'Ch70',
+         'SE25':'Ch66',
+         'SE26':'Ch65',
+         'SE27':'Ch68',
+         'SE28':'Ch67',
+         'SE29':'Ch55',
+         'SE30':'Ch62',
+         'SE31':'Ch58',
+         'SE32':'Ch69',
+         'ME1':'Ch57',
+         'ME2':'Ch61',
+         'ME3':'Ch53',
+         'ME4':'Ch63',
+         'ME5':'Ch52',
+         'ME6':'Ch41',
+         'ME7':'Ch49',
+         'ME8':'Ch51',
+         'ME9':'Ch46',
+         'ME10':'Ch45',
+         'ME11':'Ch44',
+         'ME12':'Ch39',
+         'ME13':'Ch54',
+         'ME14':'Ch43',
+         'ME15':'Ch50',
+         'ME16':'Ch47',
+         'ME17':'Ch32',
+         'ME18':'Ch27',
+         'ME19':'Ch30',
+         'ME20':'Ch29',
+         'ME21':'Ch28',
+         'ME22':'Ch25',
+         'ME23':'Ch26',
+         'ME24':'Ch07',
+         'ME25':'Ch21',
+         'ME26':'Ch11',
+         'ME27':'Ch17',
+         'ME28':'Ch15',
+         'ME29':'Ch13',
+         'ME30':'Ch31',
+         'ME31':'Ch19',
+         'ME32':'Ch09'}
+
+                          #Col, Row  
+MCSMapFacingDown={'Ch58':(0,1),
+                  'Ch57':(0,2),
+                  'Ch56':(0,3),
+                  'Ch55':(0,4),
+                  'Ch54':(0,5),
+                  'Ch53':(0,6),
+                  'Ch52':(0,7),
+                  'Ch51':(0,8),
+                  'Ch50':(0,9),
+                  'Ch49':(0,10),
+                  'Ch60':(1,0),
+                  'Ch61':(1,1),
+                  'Ch62':(1,2),
+                  'Ch63':(1,3),
+                  'Ch64':(1,4),
+                  'Ch65':(1,5),
+                  'Ch43':(1,6),
+                  'Ch44':(1,7),
+                  'Ch45':(1,8),
+                  'Ch46':(1,9),
+                  'Ch47':(1,10),
+                  'Ch70':(2,0),
+                  'Ch69':(2,1),
+                  'Ch68':(2,2),
+                  'Ch67':(2,3),
+                  'Ch66':(2,4),
+                  'Ch42':(2,5),
+                  'Ch41':(2,6),
+                  'Ch40':(2,7),
+                  'Ch39':(2,8),
+                  'Ch38':(2,9),
+                  'Ch37':(2,10),
+                  'Ch01':(3,0),
+                  'Ch02':(3,1),
+                  'Ch03':(3,2),
+                  'Ch04':(3,3),
+                  'Ch05':(3,4),
+                  'Ch06':(3,5),
+                  'Ch30':(3,6),
+                  'Ch31':(3,7),
+                  'Ch32':(3,8),
+                  'Ch33':(3,9),
+                  'Ch34':(3,10),
+                  'Ch11':(4,0),
+                  'Ch10':(4,1),
+                  'Ch09':(4,2),
+                  'Ch08':(4,3),
+                  'Ch07':(4,4),
+                  'Ch29':(4,5),
+                  'Ch28':(4,6),
+                  'Ch27':(4,7),
+                  'Ch26':(4,8),
+                  'Ch25':(4,9),
+                  'Ch24':(4,10),
+                  'Ch12':None,
+                  'Ch59':None,
+                  'Ch13':(5,1),
+                  'Ch14':(5,2),
+                  'Ch15':(5,3),
+                  'Ch16':(5,4),
+                  'Ch17':(5,5),
+                  'Ch18':(5,6),
+                  'Ch19':(5,7),
+                  'Ch20':(5,8),
+                  'Ch21':(5,9),
+                  'Ch22':(5,10)}
+
+if __name__ == '__main__':
+
+
+    Path = 'C:/Users/RGarcia1/Dropbox (ICN2 AEMD - GAB GBIO)/TeamFolderLMU/Wireless/Characterization/'
+    
+    
+    Files = [
+             '28062019/B12142O30-T4/2019-06-29T17-07-11B12142O30-T4-1mVVgsSweep-ICN2-2',
+             '28062019/B12142O37-T2/2019-06-29T18-00-36B12142O37-T2-1mVVgsSweep-ICN2-PostEthx2-Pt',
+             '03072019/B12142O37-T5/2019-07-03T09-55-56B12142O37-T5-ACDC-1mVsine-Pt-PostEth',
+             '03072019/B12142O37-T6/2019-07-03T10-18-36B12142O37-T6-ACDC-1mVsine-Pt-PostEth',
+             '03072019/B12142O30-T6/2019-07-03T08-31-00B12142O30-T6-ACDC-1mVsine-Pt-PostEth',
+             '03072019/B12142O30-T9/2019-07-03T10-43-12B12142O30-T9-ACDC-1mVsine-Pt-PostEth',
+             '04072019/B12784O18-T1/2019-07-05T08-04-13B12784O18-T1-1mVVgsSweep-PostEth-ICN2',
+             '23072019/B12784O18-T2/2019-07-23T16-18-20B12784O18-T2-ACDC-PostEth-1mV_2',
+             '23072019/B12784O18-T3/2019-07-23T18-55-56B12784O18-T3-ACDC-PostEth-PostLong',]############
+    LogFiles = [
+                '28062019/B12142O30-T4/B12142O30-T4-PreEth-ACDC1mV-AgAgCl-2.txt',
+                '28062019/B12142O37-T2/B12142O37-T2-ACDC-PostEthx2-Pt.txt',
+                '03072019/B12142O37-T5/B12142O37-T5-ACDC-Pt-PostEth.txt',
+                '03072019/B12142O37-T6/B12142O37-T6-ACDC-Pt-PostEth.txt',
+                '03072019/B12142O30-T6/B12142O30-T6_Pt_PostEth-ACDC.txt',
+                '03072019/B12142O30-T9/B12142o30-T9-ACDC-Pt-PostEth.txt',
+                '04072019/B12784O18-T1/B12784O18-T1-ACdc-postTH.txt',
+                '23072019/B12784O18-T2/B12784O18-T2-ACDC-PostEth_Cy2.txt',
+                '23072019/B12784O18-T3/B12784O18-T3-ACDC-PostEth-PostLong.txt']
+
+    UrmsTot = {}
+    IrmsTot = {}
+    GmTot = {}
+    IdsTot = {}
+    VGS = {}
+    for iFile, File in enumerate(Files):
+        StartCycle = -1
+
+        LogFile = Path + LogFiles[iFile]
+        InFileM = Path + File + '.h5'
+        InFileS = Path + File + '_2.h5'
+        
+        LogVals = ReadLogFile(LogFile)
+        delta = np.mean([t-LogVals['Time'][it] for it, t in enumerate(LogVals['Time'][1:])])*pq.s
+        Delay = delta * StartCycle
+        
+        DCch = ('ME5', 'ME7', 'ME29', 'ME31', 'SE5', 'SE7', 'SE29', 'SE31')
+    
+        TrigChannel = 'SE29'
+        TrigThres = 5e-5
+        Vgs = np.array(LogVals['Vgs'])
+        Vds = LogVals['Vds'][0]
+        VGS[File] = Vgs
+        
+        ivgain1 = 12e3*pq.V  
+        ivgain2 = 101
+        ACgain = 10*1
+        DCgain = 1*pq.V
+        Fsig = 10
+        StabTime = 10*pq.s
+        GuardTime = 1*pq.s
+        BW = 100
+        ivgainDC = 118.8*pq.V #the gain (1e6) is already applied to the saved signal  ## Check this gain
+        ivgainAC = 1188*pq.V 
+    
+
+    
+    
+        Rec = ReadMCSFile(InFileM,
+                          OutSeg=None,
+                          SigNamePrefix='M')
+    
+        Rec = ReadMCSFile(InFileS,
+                          OutSeg=Rec,
+                          SigNamePrefix='S')
+    
+    # %% Detect Vgs switch time and arrange DC signals into SlotsDC 
+        plt.close('all')  
+        plt.ion() 
+        
+        SwTimes = GetSwitchTimes(Sig=Rec.GetSignal(TrigChannel),
+                                 Thres=TrigThres,
+                                 Plot=False)
+       
+        SlotsDC = []
+        for sig in Rec.Signals():
+            if sig.name not in DCch:
+                continue
+    
+            SlotsDC.append(Rplt.WaveSlot(sig,))
+    
+        
+        SwTimes = LogVals['Time']*pq.s+SwTimes[0]+Delay
+        
+    #%% calc IV  DC
+        DevDCVals = {}
+        
+        Ids = {}
+        for sl in SlotsDC:
+            Ids[sl.name] = []
+            for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
+                ts = SwTimes[isw]+delta
+                TWind = (t+StabTime, ts-GuardTime)
+                s = sl.GetSignal(TWind, Units='V')
+
+                vio = np.mean(s).magnitude
+                ids = (vio*101-(-vg+Vds))/12e3 #apply hardware gain calibration
+                Ids[sl.name].append(ids)
+            
+        IdsTot[File.split('/')[1]] = Ids    
+
+
+        
+    #%%  Calc GM and integrated noise Irms
+    
+        GM = {}
+        Irms = {}
+        Urms = {}
+        
+        fig, (AxPsd, Axt) = plt.subplots(2,1)     
+        fig2, (AxPs, Axt) = plt.subplots(2,1)  
+        for sig in Rec.Signals():
+            if sig.name[0:3] == 'SEn': #discard encoder channels
+                continue
+
+           
+            GM[sig.name] = []
+            Irms[sig.name] = []
+            Urms[sig.name] = []
+
+    
+            for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
+                if isw == len(SwTimes)-1:
+                    ts = sl.Signal.t_stop
+                else:
+                    ts = SwTimes[isw+1]
+                TWind = (t+StabTime, ts-GuardTime)
+                s = sig.GetSignal(TWind, Units ='V')
+                           
+                if s.name in DCch:
+                    s = (s*ivgain2-(-vg+Vds)*pq.V)/ivgain1
+                else:
+                    s = s/(ivgain1*ACgain/ivgain2)
+    
+                PS = Ran.PlotPSD((s,),
+                                  Time = TWind,
+                                  Ax=AxPs,
+                                  FMin=1,
+                                  Label=str(vg),
+                                  scaling='spectrum')
+                
+                ps = PS[sig.name]['psd']
+                Fps = PS[sig.name]['ff']
+               
+                indicesPeak = np.where( ((Fps >= Fsig-4) & (Fps<=Fsig+4)))   
+    
+                IDSpeak = np.sqrt(ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]]+
+                                 ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]+1]+
+                                 ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]-1])
+                
+                gm = IDSpeak*1000/0.707
+                GM[sig.name] = np.append(GM[sig.name],gm)
+                
+                PSD = Ran.PlotPSD((s,),
+                      Time = TWind,
+                      Ax=AxPsd,
+                      FMin=1,
+                      Label=str(vg),
+                      scaling='density')
+    
+                psd = PSD[sig.name]['psd'][:,0]
+                Fpsd = PSD[sig.name]['ff']
+                
+                irms = Integrate(psd, Fpsd, 1.9, 1.9*3.3)
+                Irms[sig.name] = np.append(Irms[sig.name],irms*2) #
+                # square root of 2 adjusts the 1/f integrated noise to a 1 order of magnitude frequency range
+                Irms[sig.name] = Irms[sig.name]
+                Urms[sig.name] = Irms[sig.name]/GM[sig.name]
+    
+            plt.close('all')   #close psd figures created
+
+        
+        UrmsTot[File.split('/')[1]] = Urms
+        IrmsTot[File.split('/')[1]] = Irms
+        GmTot[File.split('/')[1]] = GM
+        
+    
+    plt.figure()
+    GMmean, GMstd = MeanStdGM(GM)
+    plt.plot(Vgs-0.70, GMmean*1000/0.1,'k',label = '1 metal layer')
+    plt.fill_between(Vgs-0.70, GMmean*1000/0.1-GMstd*1000/0.1, GMmean*1000/0.1+GMstd*1000/0.1,color = 'k',alpha =0.3)
+    plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
+    plt.ylabel('G$_m$ (mS/V)')
+    plt.legend()
+    
+    plt.figure()
+    IrmsMean, IrmsStd = MeanStdGM(Irms)
+    plt.semilogy(Vgs-0.70, IrmsMean,'k',label = 'rms')
+    plt.fill_between(Vgs-0.70, IrmsMean-IrmsStd, IrmsMean+IrmsStd ,color = 'k',alpha =0.3)
+
+
+    plt.figure()
+    UrmsMean, UrmsStd = MeanStdGM(Urms)
+    plt.semilogy(Vgs-0.70, UrmsMean,'k',label = '1 metal layer')
+    plt.fill_between(Vgs-0.70, UrmsMean-UrmsStd, UrmsMean+UrmsStd ,color = 'k',alpha =0.3)
+    plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
+    plt.ylabel('U$_{rms}$ (A)')
+    plt.legend()
+    
+
+
+    dd.io.save('GmIrmsUrmsIds10Probe_2.h5',(GmTot, IrmsTot, UrmsTot, IdsTot))
+
+    #%% plot map Urms
+        
+    plt.figure()
+    A=np.log10(np.ones((11,6))*5e-17)
+    
+    import matplotlib.colors as colors
+    for Trt in Urms.keys():
+        ch = MCSMapI[Trt]
+
+        A[MCSMapFacingDown[ch][1],MCSMapFacingDown[ch][0]] = (Urms[Trt][9])*1e6
+    
+    plt.imshow(A, interpolation='nearest', vmin=3, vmax=30, norm=colors.LogNorm(vmin=3, vmax=30))
+    plt.grid(True)
+    cbar=plt.colorbar()
+    plt.xlabel('column',fontsize=12)
+    plt.ylabel('row',fontsize=12)
+    cbar.set_label('U$_{gs-rms}$ ($\mu$V)', rotation=270, labelpad=15,fontsize=13)
+    
+    #%% plot map Gm
+        
+    plt.figure()
+    A=np.log10(np.ones((11,6))*5e-17)
+    
+    import matplotlib.colors as colors
+    for Trt in Urms.keys():
+        ch = MCSMapI[Trt]
+        
+        A[MCSMapFacingDown[ch][1], MCSMapFacingDown[ch][0]] = (GM[Trt][9]*1000/0.1)
+    
+    plt.imshow(A, interpolation='nearest', vmin=1, vmax=3, norm=colors.LogNorm(vmin=1, vmax=3))
+    plt.grid(True)
+    cbar=plt.colorbar()
+    plt.xlabel('column',fontsize=12)
+    plt.ylabel('row',fontsize=12)
+    cbar.set_label('G$_{m}$ (mS/V)', rotation=270, labelpad=15,fontsize=13)

in-vitro/LongRec.py

@@ -0,0 +1,229 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jul  5 09:21:49 2019
+
+@author: rgarcia1
+"""
+
+from PhyREC.NeoInterface import NeoSegment#, ReadMCSFile
+import PhyREC.SignalAnalysis as Ran
+import PhyREC.PlotWaves as Rplt
+import quantities as pq
+import matplotlib.pyplot as plt
+import numpy as np
+import neo
+import PhyREC.SignalProcess as RPro
+import deepdish as dd
+import csv
+from datetime import datetime
+
+
+def ReadMCSFile(McsFile, OutSeg=None, SigNamePrefix=''):
+    import McsPy.McsData as McsData
+
+    Dat = McsData.RawData(McsFile)
+    Rec = Dat.recordings[0]
+    NSamps = Rec.duration
+
+    if OutSeg is None:
+        OutSeg = NeoSegment()
+
+    for AnaStrn, AnaStr in Rec.analog_streams.iteritems():
+        if len(AnaStr.channel_infos) == 1:
+            continue
+
+        for Chn, Chinfo in AnaStr.channel_infos.iteritems():
+            print 'Analog Stream ', Chinfo.label, Chinfo.sampling_frequency
+            ChName = str(SigNamePrefix + Chinfo.label)
+            print ChName
+
+            Fs = Chinfo.sampling_frequency
+            Var, Unit = AnaStr.get_channel_in_range(Chn, 0, NSamps)
+            sig = neo.AnalogSignal(pq.Quantity(Var, Chinfo.info['Unit']),
+                                   t_start=0*pq.s,
+                                   sampling_rate=Fs.magnitude*pq.Hz,
+                                   name=ChName)
+
+            OutSeg.AddSignal(sig)
+    return OutSeg
+
+def ReadLogFile(File):
+    Fin = open(File)
+    
+    reader = csv.reader(Fin, delimiter='\t')
+    
+    LogVals = {}
+    ValsPos = {}
+    for il, e in enumerate(reader):
+        if il == 0:
+            for ih, v in enumerate(e):
+                ValsPos[ih] = v
+                LogVals[v] = []
+        else:
+            for ih, v in enumerate(e):
+                par = ValsPos[ih]
+                if (par=='Vgs') or (par=='Vds') or (par=='Vref'):
+                    LogVals[par].append(float(v.replace(',','.')))
+                elif par == 'Date/Time':
+                    LogVals[par].append(datetime.strptime(v, '%d/%m/%Y %H:%M:%S'))
+                else:
+                    LogVals[par].append(v)
+    
+    deltas = np.array(LogVals['Date/Time'])[:]-LogVals['Date/Time'][0]
+    LogVals['Time'] = []
+    for d in deltas:
+        LogVals['Time'].append(d.total_seconds())
+        
+    Fin.close()
+    
+    return LogVals
+
+def MeanStd(Data):
+    Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]]['psd'])])
+    for iT,TrtName in enumerate(Data.keys()):
+        Arr[iT,:] = Data[TrtName]['psd'][:,0]
+    
+    return np.mean(Arr,0), np.std(Arr,0)
+
+
+Path = '23072019/B12784O18-T3/'
+InFileM = Path + '2019-07-23T16-59-52B12784O18-T3-ACDC-PostEth-LowFreqNoise-0.25V.h5' ############
+InFileS = Path + '2019-07-23T16-59-52B12784O18-T3-ACDC-PostEth-LowFreqNoise-0.25V_2.h5' ##########
+
+Gm = dd.io.load(Path + 'GM-B12784O18-T3')
+
+
+BW = 100
+ivgainDC = 118.8*pq.V
+ivgainAC = 1188*pq.V
+TWind = (140*pq.s, 2350*pq.s)
+
+DCch = ('ME5', 'ME7', 'ME29', 'ME31', 'SE5', 'SE7', 'SE29', 'SE31')
+
+Rec = ReadMCSFile(InFileM,
+                  OutSeg=None,
+                  SigNamePrefix='M')
+
+Rec = ReadMCSFile(InFileS,
+                  OutSeg=Rec,
+                  SigNamePrefix='S')
+
+
+#plt.close('all')
+
+#%% Plot Sig High Freq
+plt.close('all')
+SlotsAC= []
+for sig in Rec.Signals():
+    SigProDC = [{'function': RPro.Gain, 'args': {'Gain': pq.A/(Gm[sig.name][8]*(pq.A/pq.V)*ivgainAC)}},
+            {'function': RPro.SetZero, 'args' : {'TWind' :TWind}},
+            {'function': RPro.Filter, 'args': {'Type':'bandpass',
+                                               'Order':2,
+                                               'Freqs':(20,200)}},
+            ]
+    if sig.name in DCch+('SE30',):
+        continue
+    sig.ProcessChain = SigProDC
+    sig = sig.GetSignal(TWind)
+    SlotsAC.append(Rplt.WaveSlot(sig,
+                                 Position=0,
+                                 Alpha=0.5))
+
+Splots = Rplt.PlotSlots(SlotsAC)
+Splots.PlotChannels(Time=TWind,
+                    Units='V')    
+plt.xlabel('time (s)')
+plt.ylabel('Sig (uV)')
+
+
+
+#%% Plot PSD
+fig, AxPs = plt.subplots()  
+PSD = Ran.PlotPSD((s for s in SlotsAC),
+                  Time = TWind,
+                  Ax=AxPs,
+                  FMin=1,
+                  scaling='density',
+                  Units = 'V',
+                  label = '20-200Hz',
+                  color='orange')
+MeanPSDhigh, StdPSDhigh = MeanStd(PSD)
+FpsdHigh = PSD[PSD.keys()[0]]['ff']
+#%%Plot hystogram
+
+HistData = NeoSegment()
+plt.figure(5)
+
+signal = np.array([])
+for iSl, sl in enumerate(SlotsAC):
+    signal = np.append(signal,np.array(sl.GetSignal(TWind))[:,0]*1000000)
+hist, vect = np.histogram(signal, bins=1000 , range = (np.min(signal),np.max(signal)))        
+plt.hist(signal, bins =1000, density = True, range = (np.min(signal),np.max(signal)),alpha=0.3,color='orange',label = '1-100Hz')
+
+
+
+plt.legend()
+
+
+#%% Plot Sig
+Slots= []
+for sig in Rec.Signals():
+    SigProAC = [{'function': RPro.Gain, 'args': {'Gain': pq.A/(Gm[sig.name][8]*(pq.A/pq.V)*ivgainDC)}},
+            {'function': RPro.SetZero, 'args' : {'TWind' :TWind}},
+            {'function': RPro.Filter, 'args': {'Type':'bandpass',
+                                               'Order':2,
+                                               'Freqs':(0.05,0.5)}},
+            ]
+    if sig.name not in DCch:
+        continue
+    sig.ProcessChain = SigProAC
+    sig = sig.GetSignal(TWind)
+    Slots.append(Rplt.WaveSlot(sig,
+                                 Position=0,
+                                 Alpha=0.5))
+
+Splots = Rplt.PlotSlots(Slots)
+Splots.PlotChannels(Time=TWind,
+                    Units='V')    
+
+plt.xlabel('time (s)')
+plt.ylabel('Sig (uV)')
+
+
+#%% Plot PSD
+#fig, (AxPs, Axt) = plt.subplots(2,1)  
+PSD = Ran.PlotPSD((s for s in Slots),
+                  Time = TWind,
+                  Ax=AxPs,
+                  FMin=0.01,
+                  scaling='density',
+                  Units = 'V',
+                  label = '0.05-0.5Hz',
+                  color= 'k')
+MeanPSDlow, StdPSDlow = MeanStd(PSD)
+FpsdLow = PSD[PSD.keys()[0]]['ff']
+#%%Plot hystogram
+
+HistData = NeoSegment()
+plt.figure(5)
+
+signal = np.array([])
+for iSl, sl in enumerate(Slots):
+    signal = np.append(signal,np.array(sl.GetSignal(TWind))[:,0]*1000000)
+hist, vect = np.histogram(signal, bins=1000 , range = (np.min(signal),np.max(signal)))        
+plt.hist(signal, bins =1000, density = True, range = (np.min(signal),np.max(signal)),alpha=0.3,color='k', label = '0.01-1Hz')
+
+
+
+plt.xlabel('Signal amplitude ($\mu$V)')
+plt.ylabel('Probability density')
+
+#%%Plot PSD average
+plt.figure()
+plt.loglog(FpsdLow,MeanPSDlow,'k')
+plt.fill_between(FpsdLow,MeanPSDlow+StdPSDlow,MeanPSDlow-StdPSDlow, color ='k',alpha=0.3)
+plt.loglog(FpsdHigh,MeanPSDhigh,'orange')
+plt.fill_between(FpsdHigh,MeanPSDhigh+StdPSDhigh,MeanPSDhigh-StdPSDhigh, color ='orange',alpha=0.3)
+
+
+

in-vitro/McsPy/LICENCE.txt

@@ -0,0 +1,24 @@
+Copyright (c) 2018, Multi Channel Systems MCS GmbH
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of the Multi Channel Systems MCS GmbH nor the
+      names of its contributors may be used to endorse or promote products
+      derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY
+DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

in-vitro/McsPy/McsCMOS.py

@@ -0,0 +1,123 @@
+"""
+    McsCMOS
+    ~~~~~~~
+
+    Wrapper and Helper to access MCS CMOS Data within H5 Files 
+    
+    :copyright: (c) 2015 by Multi Channel Systems MCS GmbH
+    :license: see LICENSE for more details
+"""
+
+import h5py
+import numpy
+
+class CMOSData(h5py.File):
+    """
+    Wrapper for a HDF5 File containing CMOS Data
+    """
+    def __init__(self, path):
+        """
+        Creates a CMOSData file and links it to a H5 File
+        :param path: Path to a H5 File containing CMOS Data
+        :type path: string 
+        """
+        super(CMOSData, self).__init__(path, mode='r')
+        
+        # -- map raw data --
+        self.raw_data= self['/Data/Recording_0/FrameStream/Stream_0/FrameDataEntity_0/FrameData']
+        self.conv_factors= self['/Data/Recording_0/FrameStream/Stream_0/FrameDataEntity_0/ConversionFactors']
+
+        # - map proxy data -
+        self.conv_data = CMOSConvProxy(self)
+
+        # -- map meta --
+        self.meta={}
+        
+        # - from InfoFrame
+        info_frame= self['/Data/Recording_0/FrameStream/Stream_0/InfoFrame']
+        
+        for key in info_frame.dtype.names:
+            self.meta[key]=info_frame[key][0]
+
+        if("Tick" in self.meta):
+            self.meta["FrameRate"] = 10.0**6/self.meta["Tick"]
+        
+        # - from File
+        for key, value in self.attrs.items():
+            self.meta[key]= value
+
+        # - from Data Group
+        for key, value in self['/Data'].attrs.items():
+            self.meta[key]= value
+
+        # -- map events --
+        if("EventStream" in self["Data/Recording_0/"].keys()):
+            event_group = self["Data/Recording_0/EventStream/Stream_0/"]
+            event_info = self["Data/Recording_0/EventStream/Stream_0/InfoEvent"]
+
+            self.events={}
+            self.event_frames={}
+        
+            for key in event_group.keys():
+                if "EventEntity" in key:
+                    info = event_info["Label"][event_info["EventID"]==int(key.split("_")[1])][0]
+                    self.events[info] = event_group[key][0, 0]
+                    self.event_frames[info] = event_group[key][0, 0]/self.meta["Tick"]
+
+
+
+class CMOSConvProxy:
+    """
+    Private Class, should be embedded within a CMOSData Object.
+    A proxy that transparently converts raw data to calibrated data. 
+    """
+
+    def __init__(self, parent):
+        """
+        Creates a new CMOSConvProxy
+        :param parent: Object that can provide raw_data and conv_factors
+        :type parent: CMOSData
+        """
+        self._parent = parent
+        self.dtype = numpy.int32
+
+    def __getitem__(self, slices):
+        """
+        Sliced access to converted data
+        :param slices: Data-slices to retrieve
+        :returns: converted data
+        """
+        raw_data = self._parent.raw_data.__getitem__(slices)
+        conv_fasctors = self._parent.conv_factors.__getitem__((slices[0], slices[1]))
+        return (raw_data*conv_fasctors).astype(self.dtype)
+
+    @property
+    def shape(self):
+        """
+        Shape of the data
+        """
+        return self._parent.raw_data.shape
+
+
+class CMOSSpikes(h5py.File):
+    """
+    Wrapper for a HDF5 File containing CMOS Spike Data.
+    Spike Information is accessible through the .spike Member,
+    Waveform Information (if available) through the .waveforms Member.
+    """
+    def __init__(self, path):
+        super(CMOSSpikeFile, self).__init__(path)
+
+        # -- Check for right structure --
+        if("data" in self.keys() and "spikes" in self['data'].keys()):
+            
+            # -- Map Spike-Data to RecordArray
+            self.spikes = np.core.records.fromarrays(self['data/spikes'][:,:], 
+                                                 names='time, col, row',
+                                                 formats = 'int64, int64, int64')
+            # -- Map Waveforms to Array
+            if("waveforms" in self['data'].keys()):
+                self.waveforms = self['data/waveforms'][:,:].transpose()
+                
+        else:
+            raise IOError(path+ " has no valid CMOSSpikeFile Structure")

in-vitro/McsPy/Test/TestCMOS.py

@@ -0,0 +1,85 @@
+import unittest
+import McsPy.McsCMOS
+import os
+import numpy
+
+FILENAME = os.path.join(os.path.dirname(__file__), 'TestData\\CMOSTestRec.h5')
+
+class TestMcsCMOS(unittest.TestCase):
+
+    def setUp(self):
+        self.test_file=McsPy.McsCMOS.CMOSData(FILENAME)
+
+    def test_CMOSData_meta(self):
+
+        #File Meta
+        self.assertEqual(self.test_file.meta["McsHdf5ProtocolType"], "RawData")
+        self.assertEqual(self.test_file.meta["McsHdf5ProtocolVersion"], 1)
+
+        # Data Meta
+        self.assertEqual(self.test_file.meta["ProgramName"].strip(), "CMOS-MEA-Control")
+        self.assertEqual(self.test_file.meta["ProgramVersion"].strip(), "0.7.0.0")
+        self.assertEqual(self.test_file.meta["MeaName"].strip(), "nMos32?")
+        self.assertEqual(self.test_file.meta["MeaLayout"], "")
+        self.assertEqual(self.test_file.meta["MeaSN"].strip(), "unknown")
+        self.assertEqual(self.test_file.meta["Date"].strip(), "Tuesday, November 04, 2014")
+        self.assertEqual(self.test_file.meta["DateInTicks"], 635506934728348929)
+        self.assertEqual(self.test_file.meta["FileGUID"], "67ced1bf-c1a7-4a3d-9df3-2e56fd459cbd")
+        self.assertEqual(self.test_file.meta["Comment"], "")
+
+        # InfoFrame Meta
+        self.assertEqual(self.test_file.meta["FrameID"], 1)
+        self.assertEqual(self.test_file.meta["FrameDataID"], 0)
+        self.assertEqual(self.test_file.meta["GroupID"], 1)
+        self.assertEqual(self.test_file.meta["Label"], "ROI 1")
+        self.assertEqual(self.test_file.meta["RawDataType"], "Short")
+        self.assertEqual(self.test_file.meta["Unit"], "V")
+        self.assertEqual(self.test_file.meta["Exponent"], -9)
+        self.assertEqual(self.test_file.meta["ADZero"], 0)
+        self.assertEqual(self.test_file.meta["Tick"], 50)
+        self.assertEqual(self.test_file.meta["HighPassFilterType"], "")
+        self.assertEqual(self.test_file.meta["HighPassFilterCutOffFrequency"], "-1")
+        self.assertEqual(self.test_file.meta["HighPassFilterOrder"], -1)
+        self.assertEqual(self.test_file.meta["LowPassFilterType"], "")
+        self.assertEqual(self.test_file.meta["LowPassFilterCutOffFrequency"], "-1")
+        self.assertEqual(self.test_file.meta["LowPassFilterOrder"], -1)
+        self.assertEqual(self.test_file.meta["SensorSpacing"], 1)
+        self.assertEqual(self.test_file.meta["FrameLeft"], 1)
+        self.assertEqual(self.test_file.meta["FrameTop"], 1)
+        self.assertEqual(self.test_file.meta["FrameRight"], 65)
+        self.assertEqual(self.test_file.meta["FrameBottom"], 65)
+        self.assertEqual(self.test_file.meta["ReferenceFrameLeft"], 1)
+        self.assertEqual(self.test_file.meta["ReferenceFrameTop"], 1)
+        self.assertEqual(self.test_file.meta["ReferenceFrameRight"], 65)
+        self.assertEqual(self.test_file.meta["ReferenceFrameBottom"], 65)
+
+    def test_CMOSData_data(self):
+
+        # Dataset Dimensions
+        self.assertEqual(self.test_file.raw_data.shape[0],65)
+        self.assertEqual(self.test_file.raw_data.shape[1],65)
+        self.assertEqual(self.test_file.raw_data.shape[2],2000)
+
+        #Some Random Datapoints
+        self.assertEqual(self.test_file.raw_data[56,45,33],10)
+        self.assertEqual(self.test_file.raw_data[1,11,203],-3)
+        self.assertEqual(self.test_file.raw_data[23,64,870],0)
+
+    def test_CMOSData_conversion_factors(self):
+        self.assertEqual(self.test_file.conv_factors[56,45],6456)
+        self.assertEqual(self.test_file.conv_factors[1,11],1)
+        self.assertEqual(self.test_file.conv_factors[23,64],1)
+
+    def test_CMOSData_conversion_proxy(self):
+
+        #Data Access
+        self.assertEqual(self.test_file.conv_data[56,45,33],64560)
+        self.assertEqual(self.test_file.conv_data[1,11,203],-3)
+        self.assertEqual(self.test_file.conv_data[23,64,870],0)
+
+        #Attribute proxy
+        self.assertEqual(self.test_file.conv_data.dtype,numpy.int32)
+        self.assertEqual(self.test_file.conv_data[23,64,870].dtype,numpy.int32)
+
+if __name__ == '__main__':
+    unittest.main()

in-vitro/McsPy/Test/TestRawDataStructures.py

@@ -0,0 +1,344 @@
+import unittest
+import McsPy.McsData
+import datetime
+import exceptions
+import os
+import numpy as np
+
+from McsPy import *
+
+test_raw_frame_data_file_path = os.path.join(os.path.dirname(__file__), 'TestData\\Sensors-10x100ms-10kHz.h5')
+
+test_data_file_path = os.path.join(os.path.dirname(__file__), 'TestData\\2014-07-09T10-17-35W8 Standard all 500 Hz.h5')
+
+average_segment_data_file_path = os.path.join(os.path.dirname(__file__), 'TestData\\20150402_00 Atrium_002.h5')
+
+#@unittest.skip("showing the principle structure of python unit tests")
+#class Test_TestRawDataStructures(unittest.TestCase):
+#    def test_A(self):
+#        self.fail("Not implemented")
+
+class Test_RawData(unittest.TestCase):
+    def setUp(self):
+        self.data = McsData.RawData(test_data_file_path)
+        self.raw_frame_data = McsData.RawData(test_raw_frame_data_file_path)
+        self.average_segments = McsData.RawData(average_segment_data_file_path)
+
+class Test_RawDataContainer(Test_RawData):
+    # Test MCS-HDF5 version
+    def test_mcs_hdf5_version(self):
+        self.assertEqual(self.data.mcs_hdf5_protocol_type, "RawData", 
+                         "The MCS-HDF5 protocol type was '%s' and not '%s' as expected!" % (self.data.mcs_hdf5_protocol_type, "RawData"))
+        self.assertEqual(self.data.mcs_hdf5_protocol_type_version, 1, 
+                         "The MCS-HDF5 protocol version was '%s' and not '1' as expected!" % self.data.mcs_hdf5_protocol_type_version)
+
+    def test_mcs_hdf5_version_frame(self):
+        self.assertEqual(self.data.mcs_hdf5_protocol_type,  "RawData", 
+                         "The MCS-HDF5 protocol type was '%s' and not '%s' as expected!" % (self.data.mcs_hdf5_protocol_type, "RawData"))
+        self.assertEqual(self.data.mcs_hdf5_protocol_type_version, 1, 
+                         "The MCS-HDF5 protocol version was '%s' and not '1' as expected!" % self.data.mcs_hdf5_protocol_type_version)
+
+    # Test session:
+    def test_session_attributes(self):
+        self.assertEqual(self.data.comment, '', 'Comment is different!')
+        self.assertEqual(self.data.clr_date, 'Mittwoch, 9. Juli 2014', 'Clr-Date is different!')
+        self.assertEqual(self.data.date_in_clr_ticks, 635404978551720981, 'Clr-Date-Ticks are different!')
+        self.assertEqual(self.data.date, datetime.datetime(2014, 7, 9, 10, 17, 35, 172098), 'Date is different!');
+        self.assertEqual(str(self.data.file_guid), '700b3ec2-d406-4943-bcef-79d73f0ac4d3', 'FileGUID is different!')
+        self.assertEqual(str(self.data.mea_layout), 'Linear8', 'Mea-Layout is different!')
+        self.assertEqual(self.data.mea_sn, '', 'MeaSN is different!')
+        self.assertEqual(self.data.mea_name, 'Linear8', 'MeaName is different!')
+        self.assertEqual(self.data.program_name, 'Multi Channel Experimenter', 'Program name is different!')
+        self.assertEqual(self.data.program_version, '0.9.8.2', 'Program version is different!') 
+
+
+    # Test recording:
+    def test_count_recordings(self):
+        self.assertEqual(len(self.data.recordings), 1, 'There should be only one recording!')
+
+    def test_recording_attributes(self):
+        first_recording = self.data.recordings[0]
+        self.assertEqual(first_recording.comment, '', 'Recording comment is different!')
+        self.assertEqual(first_recording.duration, 19700000, 'Recording duration is different!')
+        self.assertEqual(first_recording.label, '', 'Recording label is different!')
+        self.assertEqual(first_recording.recording_id, 0, 'Recording ID is different!')
+        self.assertEqual(first_recording.recording_type, '', 'Recording type is different!')
+        self.assertEqual(first_recording.timestamp, 0, 'Recording timestamp is different!')
+        self.assertAlmostEqual(first_recording.duration_time.to(ureg.sec).magnitude, 19.7, places = 1, msg = 'Recording timestamp is different!')
+
+    # Test analog streams:
+    def test_count_analog_streams(self):
+        self.assertEqual(len(self.data.recordings[0].analog_streams), 3, 'There should be only one analog stream inside the recording!')
+
+    def test_analog_stream_attributes(self):
+        first_analog_stream = self.data.recordings[0].analog_streams[0]
+        self.assertEqual(first_analog_stream.info_version, 1, "Version of the Stream-Info was %s and not as expected 1!" % first_analog_stream.info_version)
+        self.assertEqual(first_analog_stream.data_subtype, 'Electrode', 'Analog stream data sub type is different!')
+        self.assertEqual(first_analog_stream.label, 'Filter (1) Filter Data', 'Analog stream label is different!')
+        self.assertEqual(str(first_analog_stream.source_stream_guid), '43f795b0-7881-408f-a840-0207bc8e203c', 'Analog stream source GUID is different!')
+        self.assertEqual(str(first_analog_stream.stream_guid), 'a9d1ab04-2cf8-489c-a861-595e662fba4e', 'Analog stream GUID is different!')
+        self.assertEqual(first_analog_stream.stream_type, 'Analog', 'Analog stream type is different!')
+    
+    def test_analog_stream(self):
+        data_set = self.data.recordings[0].analog_streams[0].channel_data
+        self.assertEqual(data_set.shape, (8, 9850), 'Shape of dataset is different!')
+        
+        timestamp_index = self.data.recordings[0].analog_streams[0].timestamp_index
+        self.assertEqual(timestamp_index.shape, (1, 3), 'Shape of timestamp index is different!')
+
+        channel_infos = self.data.recordings[0].analog_streams[0].channel_infos
+        self.assertEqual(len(channel_infos), 8, 'Number of channel info objects is different!')
+        self.assertEqual(len(channel_infos[0].info), 16, 'Number of of components of an channel info object is different!')
+        self.assertEqual(channel_infos[0].version, 1, 'InfoChannel-Type version 1 expected but was %s' % channel_infos[0].version)
+
+    def test_analog_stream_data(self):
+        analog_stream = self.data.recordings[0].analog_streams[0]
+        signal = analog_stream.get_channel_in_range(0, 1569, 1584)
+        sig = signal[0]
+        scale = 381469 * 10**-9
+        expected_sig = np.array([4, 5, 0, -3, 2, -1, -6, 6, 0, 0, 0, 0, 0, 0, 3, -9], dtype=np.float) * scale
+        np.testing.assert_almost_equal(sig, expected_sig, decimal = 5)
+        #self.assertEquals(map(lambda x: x, sig), expected_sig, "Signal values were '%s' and not as expected '%s'" % (sig, expected_sig))
+        self.assertEqual(str(signal[1]), 'volt', "Unit of sampled values was expected to be 'volt' but was '%s'!" % str(signal[1]))
+
+    def test_analog_stream_data_timestamps(self):
+        analog_stream = self.data.recordings[0].analog_streams[0]
+        signal_ts = analog_stream.get_channel_sample_timestamps(6, 1996, 2000)
+        sig_ts = signal_ts[0]
+        expected_ts = [3992000, 3994000, 3996000, 3998000, 4000000]
+        self.assertEquals(map(lambda x: x, sig_ts), expected_ts, "Selected timestamps were '%s' and not as expected '%s'" % (sig_ts, expected_ts))
+        self.assertEqual(str(signal_ts[1]), 'microsecond', "Unit of timestamps was expected to be 'microsecond' but was '%s'!" % str(signal_ts[1]))
+
+    # Test frame streams:
+    def test_count_frame_streams(self):
+        self.assertEqual(len(self.raw_frame_data.recordings[0].frame_streams), 1, 'There should be only one frame stream!')
+        self.assertEqual(len(self.raw_frame_data.recordings[0].frame_streams[0].frame_entity), 1, 'There should be only one frame entity inside the stream!')
+
+    def test_frame_stream_attributes(self):
+        first_frame_stream = self.raw_frame_data.recordings[0].frame_streams[0]
+        self.assertEqual(first_frame_stream.info_version, 1, "Version of the Stream-Info was %s and not as expected 1!" % first_frame_stream.info_version)
+        self.assertEqual(first_frame_stream.data_subtype, 'Unknown', 'Frame stream data sub type is different!')
+        self.assertEqual(first_frame_stream.label, '', 'Frame stream label is different!')
+        self.assertEqual(str(first_frame_stream.source_stream_guid), '11bee63c-8714-4b2b-8cf9-228b1915f183', 'Frame stream source GUID is different!')
+        self.assertEqual(str(first_frame_stream.stream_guid), '784bf2ba-0e1b-4f3a-acc6-825af9bd1bf1', 'Frame stream GUID is different!')
+        self.assertEqual(first_frame_stream.stream_type, 'Frame', 'Frame stream type is different!')
+    
+    def test_frame_infos(self):
+        conv_fact_expected = np.zeros(shape=(65,65), dtype=np.int32) + 1000
+        info_expected = {
+                     'FrameLeft': 1, 'Exponent': -9, 'RawDataType': 'Short', 'LowPassFilterCutOffFrequency': '-1', 'Label': 'ROI 1', 
+                     'FrameTop': 1, 'ADZero': 0, 'LowPassFilterOrder': -1, 'ReferenceFrameTop': 1, 'FrameRight': 65, 'HighPassFilterType': '', 
+                     'Tick': 50, 'SensorSpacing': 1, 'HighPassFilterCutOffFrequency': '-1', 'FrameDataID': 0, 'FrameID': 1, 'GroupID': 1, 
+                     'ReferenceFrameRight': 65, 'ReferenceFrameBottom': 65, 'LowPassFilterType': '', 'HighPassFilterOrder': -1, 
+                     'ReferenceFrameLeft': 1, 'FrameBottom': 65, 'Unit': 'V'
+        }
+        frame_info = self.raw_frame_data.recordings[0].frame_streams[0].frame_entity[1].info
+        self.assertEqual(len(frame_info.info), 24, 'Number of of components of an channel info object is different!')
+        info_key_diff = set(frame_info.info.keys()) - set(info_expected.keys())
+        if not info_key_diff:
+            for key, value in frame_info.info.items():
+                self.assertEqual(
+                    value, info_expected[key], 
+                    "Frame info object for key '%(k)s' is ('%(val)s') not as expected ('%(ex_val)s')!" % {'k':key, 'val':value, 'ex_val':info_expected[key]}
+                )
+        self.assertEqual(frame_info.frame.height, 65, "Frame height was '%s' and not '65' as expected!" % frame_info.frame.height)
+        self.assertEqual(frame_info.frame.width, 65, "Frame width was '%s' and not '65' as expected!" % frame_info.frame.width)
+        self.assertEqual(frame_info.reference_frame.height, 65, "Frame height was '%s' and not '65' as expected!" % frame_info.reference_frame.height)
+        self.assertEqual(frame_info.reference_frame.width, 65, "Frame width was '%s' and not '65' as expected!" % frame_info.reference_frame.width)
+        self.assertEqual(frame_info.adc_basic_step.magnitude, 10**-9, "ADC step was '%s' and not '10^-9 V' as expected!" % frame_info.adc_basic_step)
+        self.assertEqual(frame_info.adc_step_for_sensor(0,0).magnitude, 1000 * 10**-9, "ADC step was '%s' and not '1000 * 10^-9 V' as expected!" % frame_info.adc_step_for_sensor(0,0))
+        self.assertEqual(frame_info.adc_step_for_sensor(1,1).magnitude, 1000 * 10**-9, "ADC step was '%s' and not '1000 * 10^-9 V' as expected!" % frame_info.adc_step_for_sensor(1,1))
+        self.assertEqual(frame_info.adc_step_for_sensor(63,63).magnitude, 1000 * 10**-9, "ADC step was '%s' and not '1000 * 10^-9 V' as expected!" % frame_info.adc_step_for_sensor(63,63))
+        self.assertTrue((frame_info.conversion_factors == conv_fact_expected).all(), "Frame sensor conversion factors matrix is different from the expected one!")
+        self.assertRaises(exceptions.IndexError, frame_info.adc_step_for_sensor, 65,65)          
+
+    def test_frame_data(self):
+        frame_entity = self.raw_frame_data.recordings[0].frame_streams[0].frame_entity[1]
+        frame_data = frame_entity.data
+        frame = frame_data[:,:,1]
+        self.assertEqual(frame.shape, (65,65), "Second slice was '%s' and not '(65,65)' as expected!" % str(frame.shape))
+        selected_values = [frame[0,0], frame[9,3], frame[0,5]]
+        expected_values = [    -10000,        211,       -727]
+        self.assertEquals(selected_values, expected_values, "Selected ADC values were '%s' and not as expected '%s'" % (selected_values, expected_values))
+        sensor_signal = frame_entity.get_sensor_signal(30, 30, 0, 1000)
+        sig = sensor_signal[0]
+        self.assertEquals(len(sig), 1001, "Length of sensor signal was '%s' and not as expected '1001'" % len(sig))
+
+    def test_frame_data_timestamps(self):
+        frame_entity = self.raw_frame_data.recordings[0].frame_streams[0].frame_entity[1]
+        timestamps = frame_entity.get_frame_timestamps(0,2000)
+        ts = timestamps[0]
+        self.assertEqual(len(ts), 2001, "Number oftime stamps were '%s' and not as expected '2001'" % len(ts))
+        timestamps = frame_entity.get_frame_timestamps(1995,2005)
+        ts = timestamps[0]
+        self.assertEqual(len(ts), 11, "Number of timestamps were '%s' and not as expected '11'" % len(ts))
+        expected_ts = [199750, 199800, 199850, 199900, 199950, 200000,  1000000,  1000050,  1000100, 1000150, 1000200]
+        self.assertEquals(map(lambda x: x, ts), expected_ts, "Timestamps were '%s' and not as expected '%s'" % (ts, expected_ts))
+        timestamps = frame_entity.get_frame_timestamps(0,5000)
+        ts = timestamps[0]
+        self.assertEqual(len(ts), 5001, "Number of timestamps were '%s' and not as expected '5001'" % len(ts))
+        selected_ts = [ ts[0], ts[1], ts[2000], ts[2001], ts[2002], ts[4001], ts[4002], ts[4003]]
+        expected_ts = [100000,100050,   200000,  1000000,  1000050,  1100000,  3000000,  3000050]
+        self.assertEquals(selected_ts, expected_ts, "Selected timestamps were '%s' and not as expected '%s'" % (selected_ts, expected_ts))
+        timestamps = frame_entity.get_frame_timestamps(16008,16008)
+        ts = timestamps[0]
+        self.assertEqual(len(ts), 1, "Number of timestamps were '%s' and not as expected '1'" % len(ts))
+        self.assertEquals(ts[0], 12500000, "Timestamps were '%s' and not as expected '%s'" % (ts, expected_ts))
+        self.assertEqual(str(timestamps[1]), 'microsecond', "Unit of timestamps was expected to be 'microsecond' but was '%s'!" % str(timestamps[1]))
+
+    # Test event streams:
+    def test_count_event_streams(self):
+        self.assertEqual(len(self.data.recordings[0].event_streams), 1, 'There should be only one event stream inside the recording!')
+        self.assertEqual(len(self.data.recordings[0].event_streams[0].event_entity), 1, 'There should be 1 event entities inside the stream!')
+
+    def test_event_stream_attributes(self):
+        first_event_stream = self.data.recordings[0].event_streams[0]
+        self.assertEqual(first_event_stream.info_version, 1, "Version of the Stream-Info was %s and not as expected 1!" % first_event_stream.info_version)
+        self.assertEqual(first_event_stream.data_subtype, 'DigitalPort', 'Event stream data sub type is different from expected \'DigitalPort\'!')
+        self.assertEqual(first_event_stream.label, 'Digital Events 1', 'Event stream label is different!')
+        self.assertEqual(str(first_event_stream.source_stream_guid), '0696bca6-7c30-4024-8e58-72da383aa248', 'Event stream source GUID is different!')
+        self.assertEqual(str(first_event_stream.stream_guid), '92bc437b-7655-4673-adfa-abbeca2c53e0', 'Event stream GUID is different!')
+        self.assertEqual(first_event_stream.stream_type, 'Event', 'Event stream type is different!')
+
+    def test_event_infos(self):
+        first_event_entity = self.data.recordings[0].event_streams[0].event_entity[0]
+        self.assertEqual(first_event_entity.info.version, 1, "EventEntityInfo-Version was %s and not \'1\' as expected!" % first_event_entity.info.version)
+        self.assertEqual(first_event_entity.info.id, 0, "ID is not as expected!")
+        self.assertEqual(first_event_entity.info.raw_data_bytes, 4, "RawDataBytes is not as expected!")
+        self.assertEquals(first_event_entity.info.source_channel_ids, [8],"Source channel IDs are different!") 
+        self.assertEquals(first_event_entity.info.source_channel_labels.values(), 
+                          ["1"],"Source channels label is different (was '%s' instead of '['1']')!" % 
+                          first_event_entity.info.source_channel_labels.values()) 
+
+    def test_event_data(self):
+        first_event_entity = self.data.recordings[0].event_streams[0].event_entity[0]
+        self.assertEqual(first_event_entity.count, 12, "Count was expected to be 12 but was %s!" % first_event_entity.count)
+        events = first_event_entity.get_events()
+        self.assertEqual(str(events[1]), 'microsecond', "Event time unit was expected to be 'microsecond' but was '%s'!" % str(events[1]))
+        self.assertEqual((events[0]).shape, (2,12), "Event structured was expected to be (2,12) but was %s!" % str(events[0].shape))
+        events_ts = first_event_entity.get_event_timestamps(0,3)
+        #self.assertAlmostEquals(events[0],[1.204050, 2.099150, 2.106800] , places = 5, msg = "Event timestamps were not as expected!")
+        np.testing.assert_almost_equal(events_ts[0],[216000, 1916000, 3616000], decimal = 5)
+        events_ts = first_event_entity.get_event_timestamps(4,5)
+        self.assertAlmostEqual(events_ts[0][0], 7016000, places = 4, msg = "Last event timestamp was %s and not as expected 216000!" % events[0][0])
+        events_duration = first_event_entity.get_event_durations(2,8)
+        np.testing.assert_almost_equal(events_duration[0],[0, 0, 0, 0, 0, 0], decimal = 5)
+        self.assertRaises(exceptions.IndexError, first_event_entity.get_events, 16, 4)
+        self.assertRaises(exceptions.IndexError, first_event_entity.get_events, 412, 500)   
+        self.assertRaises(exceptions.IndexError, first_event_entity.get_events, -1, 5) 
+
+    # Test segment streams:
+    def test_count_segment_streams(self):
+        self.assertEqual(len(self.data.recordings[0].segment_streams), 1, 'There should be only one segment stream inside the recording!')
+        self.assertEqual(len(self.data.recordings[0].segment_streams[0].segment_entity), 8, 'There should be 8 segment entities inside the stream!')
+
+    def test_segment_stream_attributes(self):
+        first_segment_stream = self.data.recordings[0].segment_streams[0]
+        self.assertEqual(first_segment_stream.info_version, 1, "Version of the Stream-Info was %s and not as expected 1!" % first_segment_stream.info_version)
+        self.assertEqual(first_segment_stream.stream_type, 'Segment', "Segment stream type was '%s' and not 'Segment'!" % first_segment_stream.stream_type)
+        self.assertEqual(first_segment_stream.data_subtype, 'Spike', "Segment stream data sub type was '%s' and not 'Spike' as expected!" % first_segment_stream.data_subtype)
+        self.assertEqual(first_segment_stream.label, 'Spike Detector (1) Spike Data', "Segment label was '%s' and not '' as expected!" % first_segment_stream.label)
+        self.assertEqual(str(first_segment_stream.source_stream_guid), 'a9d1ab04-2cf8-489c-a861-595e662fba4e', 
+                         "Segment stream source GUID was '%s' and not 'a9d1ab04-2cf8-489c-a861-595e662fba4e' as expected!" % str(first_segment_stream.source_stream_guid))
+        self.assertEqual(str(first_segment_stream.stream_guid), '7c2105e5-5ea4-4fdc-91d8-6b85f47773c2', 
+                         "Segment stream GUID was '%s' and not '7c2105e5-5ea4-4fdc-91d8-6b85f47773c2' as expected!" % str(first_segment_stream.stream_guid))
+
+    def test_segment_infos(self):
+        fifth_segment_entity = self.data.recordings[0].segment_streams[0].segment_entity[4]
+        self.assertEqual(fifth_segment_entity.info.version, 1, "SegmentEntityInfo-Version was '%s' and not '1' as expected!" % fifth_segment_entity.info.version)
+        self.assertEqual(fifth_segment_entity.info.id, 4, "ID was '%s' and not '4' as expected!" % fifth_segment_entity.info.id)
+        self.assertEqual(fifth_segment_entity.info.group_id, 0, "Group ID was '%s' and not '0' as expected!" % fifth_segment_entity.info.group_id)
+        self.assertEqual(fifth_segment_entity.info.pre_interval.magnitude, 1000, "Pre-Interval was '%s' and not '1000' as expected!" % fifth_segment_entity.info.pre_interval.magnitude)
+        self.assertEqual(str(fifth_segment_entity.info.pre_interval.units), 'microsecond', "Pre-Interval unit was '%s' and not 'microsecond' as expected!" % str(fifth_segment_entity.info.pre_interval.units))
+        self.assertEqual(fifth_segment_entity.info.post_interval.magnitude, 2000, "Post-Interval was '%s' and not '2000' as expected!" % fifth_segment_entity.info.post_interval.magnitude)
+        self.assertEqual(str(fifth_segment_entity.info.post_interval.units), 'microsecond', "Post-Interval unit was '%s' and not 'microsecond' as expected!" % str(fifth_segment_entity.info.post_interval.units))
+        self.assertEqual(fifth_segment_entity.info.type, 'Cutout', "Type was '%s' and not 'Cutout' as expected!" % fifth_segment_entity.info.type)
+        self.assertEqual(fifth_segment_entity.info.count, 1, "Count of segments was '%s' and not '1' as expected!" % fifth_segment_entity.info.count)
+        self.assertEquals(fifth_segment_entity.info.source_channel_of_segment.keys(), [0], 
+                          "Source channel dataset index was different (was '%s' instead of '['0']')!" % fifth_segment_entity.info.source_channel_of_segment.keys()) 
+        self.assertEquals(fifth_segment_entity.info.source_channel_of_segment[0].channel_id, 4, 
+                          "Source channel ID was different (was '%s' instead of '4')!" % fifth_segment_entity.info.source_channel_of_segment[0].channel_id) 
+
+    def test_segment_data(self):
+        first_segment_entity = self.data.recordings[0].segment_streams[0].segment_entity[0]
+        self.assertEqual(first_segment_entity.segment_sample_count, 26, "Segment sample count was expected to be  but was %s!" % first_segment_entity.segment_sample_count)
+        signal = first_segment_entity.get_segment_in_range(0)
+        self.assertEqual(signal[0].shape, (2, 26), "Matrix of segment signal points was expected to be '(2,26)' but was '%s'!" % str(signal[0].shape))
+        self.assertEqual(str(signal[1]), 'volt', "Unit of segment signal was expected to be 'volt' but was '%s'!" % str(signal[1]))
+        signal_flat = first_segment_entity.get_segment_in_range(0, flat = True)
+        self.assertEqual(len(signal_flat[0]), 52, "Vector ('flat = True') of segment signal points was expected to be '52' but was '%s'!" % len(signal_flat[0]))
+        self.assertRaises(exceptions.IndexError, first_segment_entity.get_segment_in_range, segment_id = 0, flat = False, idx_start = 16, idx_end = 4)
+        self.assertRaises(exceptions.IndexError, first_segment_entity.get_segment_in_range, segment_id = 0, flat = False, idx_start = 40, idx_end = 49)
+        self.assertRaises(exceptions.IndexError, first_segment_entity.get_segment_in_range, segment_id = 0, flat = False, idx_start = -1, idx_end = 10)
+
+    def test_segment_data_timestamps(self):
+        first_segment_entity = self.data.recordings[0].segment_streams[0].segment_entity[0]
+        signal_ts = first_segment_entity.get_segment_sample_timestamps(0)
+        self.assertEqual(signal_ts[0].shape, (2, 26), "Matrix of segment timestamps was expected to be '(2,26)' but was '%s'!" % str(signal_ts[0].shape))
+        self.assertEqual(str(signal_ts[1]), 'microsecond', "Unit of timestamps was expected to be 'microsecond' but was '%s'!" % str(signal_ts[1]))
+        ts_selected = (signal_ts[0][:,0]).tolist()
+        expected_ts_first_segment = [943000, 945000]
+        self.assertEquals(ts_selected, expected_ts_first_segment, "Timestamps for the first segment were '%s' and not as expected '%s" % (ts_selected, expected_ts_first_segment))
+        ts_selected = (signal_ts[0][:,2]).tolist()
+        expected_ts_third_segment = [963000, 965000]
+        self.assertEquals(ts_selected, expected_ts_third_segment, "Timestamps for the third segment were '%s' and not as expected '%s" % (ts_selected, expected_ts_third_segment))
+        signal_flat_ts = first_segment_entity.get_segment_sample_timestamps(0, flat = True)
+        self.assertEqual(len(signal_flat_ts[0]), 52, "Vector ('flat = True') of segment signal points was expected to be '52' but was '%s'!" % len(signal_flat_ts[0]))
+        self.assertRaises(exceptions.IndexError, first_segment_entity.get_segment_sample_timestamps, segment_id = 0, flat = False, idx_start = 16, idx_end = 4)
+        self.assertRaises(exceptions.IndexError, first_segment_entity.get_segment_sample_timestamps, segment_id = 0, flat = False, idx_start = 40, idx_end = 49)
+        self.assertRaises(exceptions.IndexError, first_segment_entity.get_segment_sample_timestamps, segment_id = 0, flat = False, idx_start = -1, idx_end = 10)
+
+    # Test average segment streams:
+    def test_average_segment_stream_counts(self):
+        self.assertEqual(len(self.average_segments.recordings[0].segment_streams), 1, 'There should be one segment streams inside the recording!')
+        self.assertEqual(self.average_segments.recordings[0].segment_streams[0].data_subtype, 'Average', "The data subtype of the first segment stream should be 'Average'!")
+        self.assertEqual(len(self.average_segments.recordings[0].segment_streams[0].segment_entity), 11, 'There should be 11 average segment entities inside the stream!')
+
+    def test_average_segment_data(self):
+        first_average_segment_entity = self.average_segments.recordings[0].segment_streams[0].segment_entity[18]
+        self.assertEqual(first_average_segment_entity.number_of_averages, 8, "Number of averages was expected to be '8' but was %s!" % first_average_segment_entity.number_of_averages)
+
+    # Test timestamp streams:
+    def test_count_timestamp_streams(self):
+        self.assertEqual(len(self.data.recordings[0].timestamp_streams), 1, 'There should be only one timestamp stream inside the recording!')
+        self.assertEqual(len(self.data.recordings[0].timestamp_streams[0].timestamp_entity), 8, 
+                         'There should be 8 event entities inside the stream (found %s)!' % len(self.data.recordings[0].timestamp_streams[0].timestamp_entity))
+
+    def test_timestamp_stream_attributes(self):
+        first_timestamp_stream = self.data.recordings[0].timestamp_streams[0]
+        self.assertEqual(first_timestamp_stream.info_version, 1, "Version of the Stream-Info was %s and not as expected 1!" % first_timestamp_stream.info_version)
+        self.assertEqual(first_timestamp_stream.data_subtype, 'NeuralSpike', 'Timestamp stream data sub type is different from expected \'NeuralSpike\'!')
+        self.assertEqual(first_timestamp_stream.label, 'Spike Detector (1) Spike Timestamps', 'Timestamp stream label is different!')
+        self.assertEqual(str(first_timestamp_stream.source_stream_guid), 'a9d1ab04-2cf8-489c-a861-595e662fba4e', 'Timestamp stream source GUID is different!')
+        self.assertEqual(str(first_timestamp_stream.stream_guid), 'b71fc432-be6a-4135-9d15-3c7c1a4b4ed6', 'TimeStamp stream GUID is different!')
+        self.assertEqual(first_timestamp_stream.stream_type, 'TimeStamp', 'Timestamp stream type is different!')
+
+    def test_timestamp_infos(self):
+        first_timestamp_entity = self.data.recordings[0].timestamp_streams[0].timestamp_entity[0]
+        self.assertEqual(first_timestamp_entity.info.version, 1, "TimeStampEntityInfo-Version was '%s' and not '1' as expected!" % first_timestamp_entity.info.version)
+        self.assertEqual(first_timestamp_entity.info.id, 0, "ID is not as expected!")
+        self.assertEqual(first_timestamp_entity.info.group_id, 0, "Group ID is not as expected!")
+        self.assertEqual(first_timestamp_entity.info.data_type, 'Long', "DataType is not as expected!")
+        self.assertEqual(first_timestamp_entity.info.unit, 's', "Unit is not as expected (was %s instead of \'s\')!" % first_timestamp_entity.info.unit)
+        self.assertEqual(first_timestamp_entity.info.exponent, -6, "Exponent is not as expected (was %s instead of \'-6\')!" % first_timestamp_entity.info.exponent)
+        self.assertEqual(first_timestamp_entity.info.measuring_unit, 1 * ureg.us , 
+                          "Exponent is not as expected (was %s instead of \'us\')!" % first_timestamp_entity.info.measuring_unit)
+        self.assertEquals(first_timestamp_entity.info.source_channel_ids, [0],"Source channel IDs are different!") 
+        self.assertEquals(first_timestamp_entity.info.source_channel_labels.values(), 
+                           ["E1"],"Source channels label is different (was '%s' instead of '['E1']')!" % 
+                           first_timestamp_entity.info.source_channel_labels.values())
+
+    def test_timestamp_data(self):
+        first_timestamp_entity = self.data.recordings[0].timestamp_streams[0].timestamp_entity[0]
+        self.assertEqual(first_timestamp_entity.count, 26, "Count was expected to be 26 but was %s!" % first_timestamp_entity.count)
+        timestamps = first_timestamp_entity.get_timestamps()
+        self.assertEqual(timestamps[1], 1 * ureg.us, "Timestamp time unit was expected to be 'us' but was '%s'!" % timestamps[1])
+        expected_ts = [944000, 954000, 964000, 3030000, 3040000, 3052000, 
+                       3096000, 5104000, 5116000, 5126000, 7204000, 7212000, 
+                       7226000, 9290000, 9298000, 11376000, 11386000, 11442000, 
+                       13462000, 13472000, 13528000, 15548000, 15558000, 17634000, 
+                       17644000, 17686000]
+        self.assertEquals(timestamps[0][0].tolist(), expected_ts, "Timestamps of the first TS-Entity were '%s' and not as expected '%s" % (timestamps[0], expected_ts))
+
+if __name__ == '__main__':
+    unittest.main()

in-vitro/McsPy/__init__.py

@@ -0,0 +1,54 @@
+"""
+    McsPy
+    ~~~~~
+
+    McsPy is a Python module/package to read, handle and operate on HDF5-based raw data
+    files converted from recordings of devices of the Multi Channel Systems MCS GmbH.
+
+    :copyright: (c) 2015 by Multi Channel Systems MCS GmbH
+    :license: see LICENSE for more details
+"""
+
+#print("McsPy init!")
+version = 0.02
+
+# Supported MCS-HDF5 protocol types and versions:
+class McsHdf5Protocols:
+    """
+    Class of supported MCS-HDF5 protocol types and version ranges
+
+    Entry: (Protocol Type Name => Tuple of supported version range from (including) the first version entry up to (including) the second version entry)
+    """
+    SUPPORTED_PROTOCOLS = {"RawData" : (1, 3),  # from first to second version number and including this versions
+                           "InfoChannel" : (1, 1), # Info-Object Versions
+                           "FrameEntityInfo" : (1, 1),
+                           "EventEntityInfo" : (1, 1),
+                           "SegmentEntityInfo" : (1, 1),
+                           "TimeStampEntityInfo" : (1, 1),
+                           "AnalogStreamInfoVersion" : (1, 1), # StreamInfo-Object Versions
+                           "FrameStreamInfoVersion" : (1, 1),
+                           "EventStreamInfoVersion" : (1, 1),
+                           "SegmentStreamInfoVersion" : (1, 1),
+                           "TimeStampStreamInfoVersion" : (1, 1)}
+
+    @classmethod
+    def check_protocol_type_version(self, protocol_type_name, version):
+        """
+        Check if the given version of a protocol is supported by the implementation
+
+        :param protocol_type_name: name of the protocol that is tested
+        :param version: version number that should be checked
+        :returns: is true if the given protocol and version is supported
+        """
+        if McsHdf5Protocols.SUPPORTED_PROTOCOLS.has_key(protocol_type_name):
+            supported_versions = McsHdf5Protocols.SUPPORTED_PROTOCOLS[protocol_type_name]
+            if (version < supported_versions[0]) or (supported_versions[1] < version):
+                raise IOError('Given HDF5 file contains \'%s\' type of version %s and supported are only all versions from %s up to %s' % 
+                               (protocol_type_name, version, supported_versions[0], supported_versions[1]))
+        else:
+            raise IOError("The given HDF5 contains a type \'%s\' that is unknown in this implementation!" % protocol_type_name)
+        return True
+
+from pint import UnitRegistry
+ureg = UnitRegistry()
+Q_ = ureg.Quantity

in-vitro/PlotStatistics.py

@@ -0,0 +1,106 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul 24 11:17:18 2019
+
+@author: rgarcia1
+"""
+
+import deepdish as dd
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+
+def set_box_color(bp, color):
+    plt.setp(bp['boxes'], color=color)
+    plt.setp(bp['whiskers'], color=color)
+    plt.setp(bp['caps'], color=color)
+
+#%% Load Data from 10 probes, one of them is  discarded because of conection problems during characterization
+plt.close('all')
+GmAll, IrmsAll, UrmsAll, Ids = dd.io.load('GmIrmsUrmsIds10Probe_2.h5')
+
+#%%
+#plot histogram
+Gm = np.array([])
+
+BestProbes = ['B12784O18-T3',] #select 3 best probes for specific analysis
+for keyProbe in GmAll.keys():
+    if keyProbe in BestProbes:
+        for keyTrt in GmAll[keyProbe].keys():
+
+            Gm = np.append(Gm,np.max(GmAll[keyProbe][keyTrt][:-1]))
+           
+
+hist, vect = np.histogram(Gm*1000/0.1, bins=22 , range = (1.3,3.3))        
+plt.figure(1)
+plt.hist(Gm*1000/0.1, bins =22, range = (1.3,3.3), color = 'firebrick', label = '192 g-SGFETs from 3 probes')
+xbin = np.linspace(1.2,3.5,100)
+Gm = np.sort(Gm)[1:]
+
+plt.plot(xbin, 14*np.exp(-(xbin-np.mean(Gm)*10000)**2/(2*(np.std(Gm)*10000)**2)),'k',label = 'Gaussian, $\mu$ = '+'{} - '.format(round(np.mean(Gm)*10000),1)+'$\sigma$ = '+'{}'.format(round(np.std(Gm)*10000),1) + '[mS/V]')
+plt.xlabel('G$_m$ (mS/V)')
+plt.ylabel('# counts')
+plt.legend()
+
+Urms = np.array([])
+for keyProbe in UrmsAll.keys():
+    for keyTrt in UrmsAll[keyProbe].keys():
+        if keyProbe in BestProbes:
+            Urms = np.append(Urms,np.min(UrmsAll[keyProbe][keyTrt][:-1]))
+hist, vect = np.histogram(np.log10(Urms*1e6), bins=50 , range = (0,2))  
+xbin = np.linspace(0,3.5,1000)      
+plt.figure(2)
+plt.hist(np.log10(Urms*1e6), bins =50, range = (0,2),color= 'blue',label = '192 g-SGFETs from 3 probes')
+Urms = np.sort(Urms)[:-8]
+plt.plot(xbin, 15*np.exp(-(xbin-np.mean(np.log10(Urms*1e6)))**2/(2*(np.std(np.log10(Urms*1e6)))**2)),'k',label = 'Gaussian, $\mu$ = '+'{} - '.format(round(np.mean(Gm)*10000),1)+'$\sigma$ = '+'{}'.format(round(np.std(Gm)*10000),1) + '[mS/V]')
+
+plt.xlabel('log(U$_{gs-rms}$)')
+plt.ylabel('# counts')
+plt.legend()
+
+
+
+#%% plot boxplots
+GM = []
+for keyProbe in GmAll.keys():
+    if keyProbe == 'B12142O37-T4': #probe discarded due to wrong characterization
+        continue
+    
+    Gm = np.array([]) 
+
+    for keyTrt in GmAll[keyProbe].keys():
+
+        Gm = np.append(Gm,np.max(GmAll[keyProbe][keyTrt][:-1]))
+    Gm = Gm*10000
+    Gm = np.ndarray.tolist(Gm)
+    GM.append(Gm) 
+    
+URMS = []
+for keyProbe in UrmsAll.keys():
+    if keyProbe == 'B12142O37-T4':
+        continue
+    Urms = np.array([]) 
+    
+    for keyTrt in UrmsAll[keyProbe].keys():
+
+        va = np.min(UrmsAll[keyProbe][keyTrt][:-1])
+        if math.isnan(va):
+            Urms = 1e-3  #sets Nan to an extremely bad sensitivity
+        else:
+            Urms = np.append(Urms,va)
+    Urms = Urms*1e6
+    Urms = np.ndarray.tolist(Urms)
+    URMS.append(Urms)    
+    
+Fig, ax = plt.subplots()
+bpr = ax.boxplot(GM,flierprops={'markeredgecolor': 'firebrick'})
+ax.set_ylabel('G$_m$ (mS/V)')
+set_box_color(bpr, 'firebrick')
+plt.xlabel('Device number')
+
+Fig, ax2 = plt.subplots()
+bpr2 = ax2.boxplot(URMS,positions=[2,6,3,5,1,4,7,8,9],flierprops={'markeredgecolor': 'blue'})
+set_box_color(bpr2, 'blue')
+ax2.set_ylabel('U$_{gs-rms}$ ($\mu$V)')
+ax2.set_yscale('log')
+plt.xlabel('Device number')