In [1]:
from teensyio import *
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
In [2]:
t = TeensyIO(find_teensy())
In [3]:
t.send_cmd('p');
t.setup_cyclic_voltamettry(V_start=2048, V_max=3196, V_min=2048, V_end=2048, N_cycles=2)
t.send_cmd('p');
V_start=2048
V_max=3196
V_min=2048
V_end=2048
N_cycles=2
Read: -CyclicVoltamettry
V_start=2048
V_max=3196
V_min=2048
V_end=2048
N_cycles=2
V_start=2048
V_max=3196
V_min=2048
V_end=2048
N_cycles=2
In [4]:
t.run_and_plot()
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In [5]:
t.df['x_scaled'] = (t.df.x - 2048.)/2048. * 3.3
In [6]:
t.df['y_scaled'] = (t.df.y - 2048) / 2048. * 10
In [7]:
t.df
Out[7]:
x
y
x_scaled
y_scaled
0
2048
2050
0.000000
0.009766
1
2049
2049
0.001611
0.004883
2
2050
2050
0.003223
0.009766
3
2051
2050
0.004834
0.009766
4
2052
2050
0.006445
0.009766
5
2053
2046
0.008057
-0.009766
6
2054
2051
0.009668
0.014648
7
2055
2049
0.011279
0.004883
8
2056
2049
0.012891
0.004883
9
2057
2050
0.014502
0.009766
10
2058
2050
0.016113
0.009766
11
2059
2050
0.017725
0.009766
12
2060
2049
0.019336
0.004883
13
2061
2050
0.020947
0.009766
14
2062
2050
0.022559
0.009766
15
2063
2050
0.024170
0.009766
16
2064
2049
0.025781
0.004883
17
2065
2048
0.027393
0.000000
18
2066
2050
0.029004
0.009766
19
2067
2049
0.030615
0.004883
20
2068
2050
0.032227
0.009766
21
2069
2050
0.033838
0.009766
22
2070
2050
0.035449
0.009766
23
2071
2050
0.037061
0.009766
24
2072
2050
0.038672
0.009766
25
2073
2049
0.040283
0.004883
26
2074
2050
0.041895
0.009766
27
2075
2050
0.043506
0.009766
28
2076
2049
0.045117
0.004883
29
2077
2050
0.046729
0.009766
...
...
...
...
...
11697
2265
2050
0.349658
0.009766
11698
2266
2050
0.351270
0.009766
11699
2267
2049
0.352881
0.004883
11700
2268
2049
0.354492
0.004883
11701
2269
2050
0.356104
0.009766
11702
2270
2049
0.357715
0.004883
11703
2271
2050
0.359326
0.009766
11704
2272
2050
0.360937
0.009766
11705
2273
2050
0.362549
0.009766
11706
2274
2050
0.364160
0.009766
11707
2275
2050
0.365771
0.009766
11708
2276
2049
0.367383
0.004883
11709
2277
2049
0.368994
0.004883
11710
2278
2050
0.370605
0.009766
11711
2279
2050
0.372217
0.009766
11712
2280
2048
0.373828
0.000000
11713
2281
2050
0.375439
0.009766
11714
2282
2050
0.377051
0.009766
11715
2283
2050
0.378662
0.009766
11716
2284
2049
0.380273
0.004883
11717
2285
2050
0.381885
0.009766
11718
2286
2050
0.383496
0.009766
11719
2287
2051
0.385107
0.014648
11720
2288
2050
0.386719
0.009766
11721
2289
2050
0.388330
0.009766
11722
2290
2049
0.389941
0.004883
11723
2291
2050
0.391553
0.009766
11724
2292
2050
0.393164
0.009766
11725
2293
2050
0.394775
0.009766
11726
2294
2050
0.396387
0.009766
11727 rows × 4 columns
In [9]:
t.df.to_csv('red_led.csv')
In [18]:
plt.plot(t.df.x-2048, t.df.y-2048)
Out[18]:
[<matplotlib.lines.Line2D at 0x11af95610>]
In [25]:
1/mb[0]*-1
Out[25]:
1.5052704162329249
Content source: ryanpdwyer/teensyio
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