Note
Click here to download the full example code
Data from Greenwald et al. (2009)¶
This example shows how to use the Greenwald et al. (2009) dataset.
[Greenwald2009] investigated the relationship between electrical stimulation amplitude and phosphene brightness in two Argus I users.
Important
You will need to install Pandas
(pip install pandas) for this dataset.
Loading the dataset¶
The dataset can be loaded as a Pandas DataFrame:
from pulse2percept.datasets import load_greenwald2009
data = load_greenwald2009()
print(data)
subject implant electrode ... interphase_dur pulse_type threshold
0 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
1 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
2 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
3 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
4 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
.. ... ... ... ... ... ... ...
78 S06 ArgusI C4 ... 0.000975 cathodicfirst 22.324
79 S06 ArgusI C4 ... 0.000975 cathodicfirst 22.324
80 S06 ArgusI C4 ... 0.000975 cathodicfirst 22.324
81 S06 ArgusI C4 ... 0.000975 cathodicfirst 22.324
82 S06 ArgusI C4 ... 0.000975 cathodicfirst 22.324
[83 rows x 12 columns]
Inspecting the DataFrame tells us that there are 83 measurements (the rows) each with 12 different attributes (the columns).
These attributes include specifiers such as “subject”, “electrode”, and “task”. We can print all column names using:
print(data.columns)
Index(['subject', 'implant', 'electrode', 'task', 'stim_class', 'stim_dur',
'amp', 'brightness', 'pulse_dur', 'interphase_dur', 'pulse_type',
'threshold'],
dtype='object')
Note
The meaning of all column names is explained in the docstring of
the load_greenwald2009 function.
For example, “amp” corresponds to the amplitude of the stimulation used in a particular measurement:
data.amp.unique()
array([ 41.328, 29.805, 52.714, ..., 316.007, 385.932, 248.806])
To select all the rows where the same subject was used, such as ‘S05’ we can index into the DataFrame as follows:
print(data[data.subject == 'S05'])
subject implant electrode ... interphase_dur pulse_type threshold
0 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
1 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
2 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
3 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
4 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
5 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
6 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
7 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
8 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
9 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
10 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
11 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
12 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
13 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
14 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
15 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
16 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
17 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
18 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
19 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
20 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
21 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
33 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
34 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
35 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
36 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
37 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
38 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
39 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
40 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
41 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
42 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
43 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
44 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
45 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
46 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
56 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
57 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
58 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
59 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
60 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
61 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
62 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
63 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
64 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
65 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
66 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
67 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
[48 rows x 12 columns]
Likewise, we can perform the same operation when initially loading the data as follows:
print(load_greenwald2009(subjects='S05'))
subject implant electrode ... interphase_dur pulse_type threshold
0 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
1 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
2 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
3 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
4 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
5 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
6 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
7 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
8 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
9 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
10 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
11 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
12 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
13 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
14 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
15 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
16 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
17 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
18 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
19 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
20 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
21 S05 ArgusI B2 ... 0.000975 cathodicfirst 20.688
22 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
23 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
24 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
25 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
26 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
27 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
28 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
29 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
30 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
31 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
32 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
33 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
34 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
35 S05 ArgusI C3 ... 0.000975 cathodicfirst 35.408
36 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
37 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
38 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
39 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
40 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
41 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
42 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
43 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
44 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
45 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
46 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
47 S05 ArgusI C4 ... 0.000975 cathodicfirst 24.000
[48 rows x 12 columns]
Note
Please see the documentation for load_greenwald2009
to see all available parameters for data subset loading.
Plotting the data¶
To see the relationship between electrical stimulation amplitude and phosphene brightness, let us demonstrate partially recreating part of Figure 2 from the paper. Specifically, we will look at subject S06 and electrode C4. Please note, we omit the power fits in this demonstration.
import matplotlib.pyplot as plt
data = load_greenwald2009(subjects='S06', electrodes='C4')
# Adjust the x-axis scaling, and add title
plt.xlim(0, 400)
plt.xticks(ticks=[0, 200, 400])
plt.xlabel("Current (µA)")
# Adjust the y-axis scaling, and add title
plt.ylabel("Rating")
plt.title("S06")
plt.ylim(0, 25)
# Add figure title
plt.title("S06 Current Amplitude vs Brightness, Electrode C4")
# Plot the data
plt.scatter(data.amp, data.brightness)
plt.show()
Total running time of the script: ( 0 minutes 0.192 seconds)