# import a CON file
In this example, we read a con file. And save these data in a epoch file
and a evoked file.
```python
%matplotlib qt
```
```python
import mne
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
data_path = '/Users/kevinhsu/Documents/D/000_experiment/compounding_VR/2013_megdata'
# import raw CON
sid = 6
confile = data_path + '/de%.3dnn.con' % sid #
# /Users/kevinhsu/Documents/D/000_experiment/compounding_VR/2013_megdata/de001nn.con
raw = mne.io.kit.read_raw_kit(confile, stim = [192, 193, 194], slope = '+')
events = mne.find_events(raw, stim_channel='STI 014')
raw.plot()
```
Extracting SQD Parameters from /Users/kevinhsu/Documents/D/000_experiment/compounding_VR/2013_megdata/de006nn.con...
Creating Raw.info structure...
Setting channel info structure...
Creating Info structure...
Current compensation grade : 0
Ready.
300 events found
Event IDs: [1 4]
```python
# browse events: three columns [onsets, zeros, conditions]
events
```
array([[ 13963, 0, 4],
[ 17444, 0, 1],
[ 21925, 0, 4],
...
[1217254, 0, 1],
[1220929, 0, 4],
[1224333, 0, 1],
[1227606, 0, 1],
[1231091, 0, 1],
[1234853, 0, 4],
[1238734, 0, 1],
[1241954, 0, 1],
[1245831, 0, 4],
[1249235, 0, 4]])
```python
kit_lay = mne.channels.layout.read_layout(data_path + '/KIT-TW-157.lout')
# preload raw MEG data
raw.load_data()
# low pass fillter
raw.filter(0, 30, phase= 'zero-double')
# duration of epochs
tmin = -0.1 # pre stimulis interval (in seconds) #
tmax = 0.7 # post stimulus interval #
# artifact rejection criteria
reject = dict(mag=1.5e-12)
# using strings (words) to label conditions
event_id = {'words':1, 'pseudo': 4}
# keep MEG channels
picks = mne.pick_types(raw.info, meg= True, stim = False) # channels to use in epochs #
# baseline correction
baseline = (None, 0) # what to use as baseline comparison - here it's pre-stim interval #
# do the actual epoching processes
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj = True,
picks = picks, baseline=baseline, preload = True,
reject=reject)
```
Reading 0 ... 1264999 = 0.000 ... 1264.999 secs...
Filtering raw data in 1 contiguous segment
Setting up low-pass filter at 30 Hz
FIR filter parameters
---------------------
Designing a two-pass forward and reverse, zero-phase, non-causal lowpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Upper passband edge: 30.00 Hz
- Upper transition bandwidth: 7.50 Hz (-12 dB cutoff frequency: 33.75 Hz)
- Filter length: 441 samples (0.441 sec)
300 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 300 events and 801 original time points ...
Rejecting epoch based on MAG : ['MEG 145']
Rejecting epoch based on MAG : ['MEG 130']
Rejecting epoch based on MAG : ['MEG 145']
Rejecting epoch based on MAG : ['MEG 131']
...
Rejecting epoch based on MAG : ['MEG 131', 'MEG 145']
Rejecting epoch based on MAG : ['MEG 130']
Rejecting epoch based on MAG : ['MEG 130', 'MEG 131']
Rejecting epoch based on MAG : ['MEG 131']
Rejecting epoch based on MAG : ['MEG 130', 'MEG 131']
218 bad epochs dropped
```python
# check results of artifact rejection
drop_log = pd.DataFrame(epochs.drop_log)
drop_log.to_csv('s%.3d_droplog.csv' % sid)
```
```python
# saving an epoch object into a fif file
epochs.save(data_path + '/%.3dnn_raw_epo.fif' % sid, overwrite= True)
# averaging
evoked1 = epochs['words'].average()
evoked2 = epochs['pseudo'].average()
# saving multiple evoked objects into a fif file
mne.write_evokeds('%.3d-ave.fif' % sid, [evoked1, evoked2])
```
```python
evoked1.plot()
```
Show MEG data
```python
raw
```
<RawKIT | de006nn.con, n_channels x n_times : 257 x 1265000 (1265.0 sec), ~2.42 GB, data loaded>
```python
epochs
```
<Epochs | 82 events (all good), -0.1 - 0.7 sec, baseline [None, 0], ~79.0 MB, data loaded,
'pseudo': 48
'words': 34>
```python
evoked1
```
<Evoked | 'words' (mean, N=34), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
```python
evoked2
```
<Evoked | 'pseudo' (mean, N=48), [-0.1, 0.7] sec, 157 ch, ~1.3 MB>
```python
tmp = [evoked1, evoked2]
colors = 'green', 'red'
mne.viz.plot_evoked_topo(tmp, color = colors, title = 'LDT / lexicality', layout = kit_lay)
```
```python
times = np.arange(0.05, 0.5, 0.05)
evoked1.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')
evoked1.plot_joint(title='Real Words', times=[.13, .20, 0.31],
ts_args=ts_args, topomap_args=topomap_args)
```
```python
raw.info['ch_names']
```
['MEG 001',
'MEG 002',
...
'TRIGGER 006',
'TRIGGER 007',
'TRIGGER 008',
...
'MISC 031',
'MISC 032',
'STI 014']
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