Skip to content

Report generation

smoothing_steps module-attribute

Python
smoothing_steps: int | None = None

Specifies the number of smoothing steps to use when morphing from individual subject data to the average subject. See the smooth parameter of mne.compute_source_morph for details.

Pipeline steps using this setting

The following steps are directly affected by changes to smoothing_steps:

  • source/_99_group_average

report_evoked_n_time_points module-attribute

Python
report_evoked_n_time_points: int | None = None

Specifies the number of time points to display for each evoked in the report. If None, it defaults to the current default in MNE-Python.

Example

Only display 5 time points per evoked

Python
report_evoked_n_time_points = 5

Pipeline steps using this setting

The following steps are directly affected by changes to report_evoked_n_time_points:

  • sensor/_01_make_evoked
  • sensor/_99_group_average

report_stc_n_time_points module-attribute

Python
report_stc_n_time_points: int | None = None

Specifies the number of time points to display for each source estimates in the report. If None, it defaults to the current default in MNE-Python.

Example

Only display 5 images per source estimate:

Python
report_stc_n_time_points = 5

Pipeline steps using this setting

The following steps are directly affected by changes to report_stc_n_time_points:

  • source/_05_make_inverse
  • source/_99_group_average

report_image_format module-attribute

Python
report_image_format: dict[
    Literal["raster", "vector"], Literal["webp", "webp-lossy", "png", "svg"]
] = dict(raster="webp", vector="svg")

The formats used to store images embedded in the reports. The "raster" entry applies to inherently pixel-based content (topographic map sliders, ICA properties, epochs images, and similar) and can be "webp", "webp-lossy", or "png" — WebP produces the smallest reports, but lossless WebP is considerably slower to encode than PNG, which can make a difference on report-heavy runs; lossy WebP encodes about as fast as PNG and still yields much smaller reports. The "vector" entry applies to line-art figures and can additionally be "svg". Missing keys keep their default values.

Example

Trade larger reports for faster processing:

Python
report_image_format = dict(raster="png")

Pipeline steps using this setting

The following steps are directly affected by changes to report_image_format:

  • init/_01_init_derivatives_dir
  • init/_02_find_empty_room
  • preprocessing/_01_data_quality
  • preprocessing/_02_head_pos
  • preprocessing/_03_maxfilter
  • preprocessing/_04_frequency_filter
  • preprocessing/_05_regress_artifact
  • preprocessing/_06a1_fit_ica
  • preprocessing/_06a2_find_ica_artifacts
  • preprocessing/_06b_run_ssp
  • preprocessing/_07_make_epochs
  • preprocessing/_08a_apply_ica
  • preprocessing/_08b_apply_ssp
  • preprocessing/_09_ptp_reject
  • sensor/_01_make_evoked
  • sensor/_02_decoding_full_epochs
  • sensor/_03_decoding_time_by_time
  • sensor/_04_time_frequency
  • sensor/_05_decoding_csp
  • sensor/_06_make_cov
  • sensor/_99_group_average
  • source/_01_make_bem_surfaces
  • source/_04_make_forward
  • source/_05_make_inverse
  • source/_99_group_average

report_add_epochs_image_kwargs module-attribute

Python
report_add_epochs_image_kwargs: dict[str, Any] | None = None

Specifies the limits for the color scales of the epochs_image in the report. If None, it defaults to the current default in MNE-Python.

Example

Set vmin and vmax to the epochs rejection thresholds (with unit conversion):

Python
report_add_epochs_image_kwargs = {
    "grad": {"vmin": 0, "vmax": 1e13 * reject["grad"]},  # fT/cm
    "mag": {"vmin": 0, "vmax": 1e15 * reject["mag"]},  # fT
}
Pipeline steps using this setting

The following steps are directly affected by changes to report_add_epochs_image_kwargs:

  • preprocessing/_07_make_epochs
  • preprocessing/_09_ptp_reject