# Grand averaging and difference waves
```python
%matplotlib qt
```
```python
import mne
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
```
```python
evoked1, evoked2 = mne.read_evokeds('001-ave.fif')
print(evoked1)
print(evoked2)
```
Reading 001-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (words)
0 CTF compensation matrices available
nave = 144 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (pseudo)
0 CTF compensation matrices available
nave = 143 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
<Evoked | 'words' (mean, N=144), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
<Evoked | 'pseudo' (mean, N=143), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
```python
evoked1, evoked2 = mne.read_evokeds('002-ave.fif')
print(evoked1)
print(evoked2)
```
Reading 002-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (words)
0 CTF compensation matrices available
nave = 24 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (pseudo)
0 CTF compensation matrices available
nave = 28 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
<Evoked | 'words' (mean, N=24), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
<Evoked | 'pseudo' (mean, N=28), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
```python
evoked1, evoked2 = mne.read_evokeds('002-ica_ave.fif')
print(evoked1)
print(evoked2)
```
Reading 002-ica_ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (words)
0 CTF compensation matrices available
nave = 69 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (pseudo)
0 CTF compensation matrices available
nave = 70 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
<Evoked | 'words' (mean, N=69), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
<Evoked | 'pseudo' (mean, N=70), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
```python
ave_names = ['001-ave.fif', '002-ica_ave.fif', '003-ave.fif', '004-ave.fif', '005-ave.fif', '015-ave.fif']
ave_names
```
['001-ave.fif',
'002-ica_ave.fif',
'003-ave.fif',
'004-ave.fif',
'005-ave.fif',
'015-ave.fif']
```python
all_words = [mne.read_evokeds(ave_name, 'words') for ave_name in ave_names]
all_pseudo = [mne.read_evokeds(ave_name, 'pseudo') for ave_name in ave_names]
```
Reading 001-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (words)
0 CTF compensation matrices available
nave = 144 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 002-ica_ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (words)
0 CTF compensation matrices available
nave = 69 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 003-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (words)
0 CTF compensation matrices available
nave = 142 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 004-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (words)
0 CTF compensation matrices available
nave = 40 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 005-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (words)
0 CTF compensation matrices available
nave = 29 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 015-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (words)
0 CTF compensation matrices available
nave = 140 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 001-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (pseudo)
0 CTF compensation matrices available
nave = 143 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 002-ica_ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (pseudo)
0 CTF compensation matrices available
nave = 70 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 003-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (pseudo)
0 CTF compensation matrices available
nave = 142 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 004-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (pseudo)
0 CTF compensation matrices available
nave = 43 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 005-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (pseudo)
0 CTF compensation matrices available
nave = 24 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
Reading 015-ave.fif ...
Isotrak not found
Found the data of interest:
t = -100.00 ... 700.00 ms (pseudo)
0 CTF compensation matrices available
nave = 144 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.
No baseline correction applied
```python
all_words
```
[<Evoked | 'words' (mean, N=144), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>,
<Evoked | 'words' (mean, N=69), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>,
<Evoked | 'words' (mean, N=142), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>,
<Evoked | 'words' (mean, N=40), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>,
<Evoked | 'words' (mean, N=29), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>,
<Evoked | 'words' (mean, N=140), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>]
```python
all_pseudo
```
[<Evoked | 'pseudo' (mean, N=143), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>,
<Evoked | 'pseudo' (mean, N=70), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>,
<Evoked | 'pseudo' (mean, N=142), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>,
<Evoked | 'pseudo' (mean, N=43), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>,
<Evoked | 'pseudo' (mean, N=24), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>,
<Evoked | 'pseudo' (mean, N=144), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>]
```python
words_grouping = mne.grand_average(all_words)
print(words_grouping)
words_grouping.comment = 'words (n=6)'
print(words_grouping)
```
Identifying common channels ...
all channels are corresponding, nothing to do.
<Evoked | 'Grand average (n = 6)' (mean, N=6), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
<Evoked | 'words (n=6)' (mean, N=6), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
```python
pseudo_grouping = mne.grand_average(all_pseudo)
print(pseudo_grouping)
pseudo_grouping.comment = 'pseudo (n=6)'
print(pseudo_grouping)
```
Identifying common channels ...
all channels are corresponding, nothing to do.
<Evoked | 'Grand average (n = 6)' (mean, N=6), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
<Evoked | 'pseudo (n=6)' (mean, N=6), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
```python
times = np.arange(0.05, 0.5, 0.05)
words_grouping.plot_topomap(times=times, ch_type='mag', time_unit='s')
```
```python
ts_args = dict(gfp=True, time_unit='s')
topomap_args = dict(sensors=False, time_unit='s')
words_grouping.plot_joint(title='Real Words', times=[.13, .20, 0.31],
ts_args=ts_args, topomap_args=topomap_args)
```
```python
tmp = [words_grouping, pseudo_grouping]
colors = 'green', 'red'
mne.viz.plot_evoked_topo(tmp, color = colors, title = 'LDT / lexicality')
```
```python
evoked_diff = mne.combine_evoked([pseudo_grouping, -words_grouping],
weights='equal') # calculate difference wave
ts_args = dict(gfp=True, time_unit='s')
topomap_args = dict(sensors=False, time_unit='s')
evoked_diff.plot_joint(title='Lexicality', times=[0.32, 0.5],
ts_args=ts_args, topomap_args=topomap_args)
```
```python
```
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