meeg_utils.qc.DatasetQC#

class meeg_utils.qc.DatasetQC(reports, errors, *, min_recordings=5)[source]#

Quality-check results of many recordings.

Parameters:

  • reports (dict) – Recording mapped to its QCReport.

  • errors (dict) – Recording mapped to the error that stopped its checks.

  • min_recordings (int) – Outliers are only computed for metrics measured on at least this many recordings.

__init__(reports, errors, *, min_recordings=5)[source]#

Methods

__init__(reports, errors, *[, min_recordings])

plot([kind])

Draw the dataset overview: the level of every check on every recording.

to_csv(fname)

Write to_records() as a CSV file.

to_records()

One row per recording: its overall level and every scalar metric.

Attributes

levels

level}.

plot_kinds

plot_kinds: ClassVar[dict[str, bool]] = {'levels': False}#
property levels: dict[str, dict[str, str]]#

level}.

Type:

Recording mapped to {check

to_records()[source]#

One row per recording: its overall level and every scalar metric.

to_csv(fname)[source]#

Write to_records() as a CSV file.

Parameters:

fname (str | Path) – Output file.

Returns:

The file written.

Return type:

Path

plot(kind=None)[source]#

Draw the dataset overview: the level of every check on every recording.

Parameters:

kind (str | None) – "levels" or None (all figures).

Returns:

Kind mapped to its Figure.

Return type:

dict