Note

Click here to download the full example code

mkh5 Quickstart

  1. In a terminal window use mamba (recommended) or conda (if necessary) to install mkpy along with jupyter or jupyterlab into a fresh environment and activate it:

    $ mamba create --name mkpy_041221 mkpy jupyterlab \
     -c kutaslab -c conda-forge -c defaults \
     --strict-channel-priority
$ conda activate mkpy_041221

  2. In a terminal window navigate to the directory where you want to work and run:

    $ jupyter notebook

  3. Open a notebook with a Python 3 kernel and proceed.

Single subject single session workflow

from mkpy import mkh5 as mkh5

DATA_H5_F = "../mkh5_data/_sub000p3.h5"
data_h5 = mkh5.mkh5(DATA_H5_F)
data_h5.reset_all()

# load the crw, log, and YAML header
data_h5.create_mkdata(
    "sub000",
    "../mkdig_data/sub000p3.crw",
    "../mkdig_data/sub000p3.x.log",
    "../mkdig_data/sub000p3.yhdr",
)

If calibration pulses are not in the .crw file, append them like so:

data_h5.append_mkdata(
    "sub000",
    "../mkdig_data/sub000c.crw",
    "../mkdig_data/sub000c.log",
    "../mkdig_data/sub000c.yhdr",
)

# Visually check the calibration parameters.
pts, pulse, lo, hi, ccode = 3, 10, -50, 50, 0
fig, ax = data_h5.plotcals(
    DATA_H5_F,
    "sub000",
    n_points=pts,  # pts to average, either side of cursor
    cal_size=pulse,  # uV
    lo_cursor=lo,  # lo_cursor ms
    hi_cursor=hi,  # hi_cursor ms
    cal_ccode=ccode,
)
fig.show()
calibration pulses (recorded N=209) from: ../mkh5_data/_sub000p3.h5 sub000 n_points=3 cal_size=10 lo_cursor=-50 hi_cursor=50 cal_ccode=0, scale=17.67, trimmed 5, scale=20.30, trimmed 6, scale=20.05, trimmed 8, scale=20.79, trimmed 5, scale=20.59, trimmed 2, scale=40.13, trimmed 6, scale=39.63, trimmed 9, scale=40.82, trimmed 6, scale=40.47, trimmed 3, scale=40.01, trimmed 11, scale=38.82, trimmed 6, scale=35.58, trimmed 16, scale=36.46, trimmed 3, scale=40.63, trimmed 14, scale=37.88, trimmed 15, scale=39.60, trimmed 13, scale=42.02, trimmed 15, scale=39.58, trimmed 16, scale=41.42, trimmed 5, scale=40.49, trimmed 12, scale=40.11, trimmed 7, scale=40.81, trimmed 7, scale=40.65, trimmed 6, scale=39.84, trimmed 12, scale=40.59, trimmed 24, scale=40.37, trimmed 11, scale=40.81, trimmed 9, scale=41.27, trimmed 7, scale=40.73, trimmed 9, scale=40.92, trimmed 4, scale=20.57, trimmed 5, scale=20.59, trimmed 14

Out:

Plotting cals
/usr/share/miniconda/envs/env_3.8/lib/python3.8/site-packages/mkpy/mkh5.py:3666: UserWarning: negative event code(s) found for cal condition code 0 -16384
  warnings.warn(msg)
Found cals in /sub000/dblock_4

Scale the EEG A/D to microvolts.

data_h5.calibrate_mkdata(
    "sub000",
    n_points=pts,
    cal_size=pulse,
    lo_cursor=lo,
    hi_cursor=hi,
    cal_ccode=ccode,
    use_cals=None,
)

Out:

/usr/share/miniconda/envs/env_3.8/lib/python3.8/site-packages/mkpy/mkh5.py:3666: UserWarning: negative event code(s) found for cal condition code 0 -16384
  warnings.warn(msg)
Found cals in /sub000/dblock_4
Calibrating block /sub000/dblock_0 of 5: (31232,)
Calibrating block /sub000/dblock_1 of 5: (32768,)
Calibrating block /sub000/dblock_2 of 5: (31744,)
Calibrating block /sub000/dblock_3 of 5: (32512,)
Calibrating block /sub000/dblock_4 of 5: (28416,)

Examine some header info with mkh5.headinfo()

data_h5.headinfo("dblock_0.*samplerate")
data_h5.headinfo("dblock_0.*MiPa")

Out:

sub000/dblock_0/samplerate: 250.0
sub000/dblock_0/apparatus/streams/MiPa/pos: MiPa
sub000/dblock_0/apparatus/streams/MiPa/neg: A1
sub000/dblock_0/apparatus/streams/MiPa/gain: 20000
sub000/dblock_0/apparatus/streams/MiPa/hphz: 0.01
sub000/dblock_0/apparatus/streams/MiPa/lphz: 100.0
sub000/dblock_0/apparatus/streams/MiPa/mne_type: eeg
sub000/dblock_0/apparatus/sensors/MiPa/x: 0.0
sub000/dblock_0/apparatus/sensors/MiPa/y: -2.7
sub000/dblock_0/apparatus/sensors/MiPa/z: 13.2
sub000/dblock_0/streams/MiPa/jdx: 23
sub000/dblock_0/streams/MiPa/source: dig_chan_0016
sub000/dblock_0/streams/MiPa/name: MiPa
sub000/dblock_0/streams/MiPa/dt: <f2
sub000/dblock_0/streams/MiPa/calibrated: True
sub000/dblock_0/streams/MiPa/cals/cal_srate: 250.0
sub000/dblock_0/streams/MiPa/cals/cal_dblock/0: ../mkh5_data/_sub000p3.h5/sub000/dblock_4
sub000/dblock_0/streams/MiPa/cals/cal_args/n_points: 3
sub000/dblock_0/streams/MiPa/cals/cal_args/cal_size: 10.0
sub000/dblock_0/streams/MiPa/cals/cal_args/lo_cursor: -50.0
sub000/dblock_0/streams/MiPa/cals/cal_args/hi_cursor: 50.0
sub000/dblock_0/streams/MiPa/cals/cal_args/cal_ccode: 0
sub000/dblock_0/streams/MiPa/cals/scale_by: 42.019882179675996
sub000/dblock_0/streams/MiPa/cals/var: 0.24502864170431732
sub000/dblock_0/streams/MiPa/cals/median: 42.0
sub000/dblock_0/streams/MiPa/cals/iqr: 0.7142857142857153
sub000/dblock_0/streams/MiPa/cals/n_cals: 194
sub000/dblock_0/streams/MiPa/cals/n_trimmed: 15
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/0: 27
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/1: 30
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/2: 35
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/3: 58
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/4: 68
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/5: 73
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/6: 76
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/7: 94
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/8: 101
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/9: 121
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/10: 159
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/11: 178
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/12: 181
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/13: 189
sub000/dblock_0/streams/MiPa/cals/trimmed_idxs/14: 206

Total running time of the script: ( 0 minutes 7.490 seconds)

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