DOC: update tutorials

examples/plot_10_run_pipeline.py

@@ -60,8 +60,10 @@
 #
 # The :class:`~pylossless.LosslessPipeline` object stores flagged channels and ICs in
 # the :attr:`~pylossless.LosslessPipeline.flags` attribute:
-print(f"flagged channels: {pipeline.flags['ch']}")
-print(f"flagged ICs: {pipeline.flags['ic']}")
+pipeline
+
+# %%
+pipeline.flags["ic"]
 
 # %%
 # Get the cleaned data

examples/test_config.yaml

@@ -0,0 +1,117 @@
+aref_trim: 30
+bridged_channels:
+    bridge_trim: 40
+    bridge_z: 6
+epoching:
+    epochs_args:
+        baseline: null
+        tmax: 1
+        tmin: 0
+    overlap: 0
+filtering:
+    filter_args:
+        h_freq: 100
+        l_freq: 1
+    notch_filter_args:
+        freqs: []
+find_breaks: null
+ica:
+    ica_args:
+        run1:
+            method: fastica
+        run2:
+            fit_params:
+                extended: true
+            method: infomax
+    noisy_ic_epochs:
+        flag_crit: 0.2
+        outlier_method: quantile
+        outliers_kwargs:
+            k: 6
+            lower: 0.3
+            upper: 0.7
+in_path: []
+montage_info:
+- 0.0
+- -16.0
+- 0.0
+- -0.02
+- 0.0
+- -1.58
+- 10.7
+- 11.5
+- 11.5
+nearest_neighbors:
+    n_nbr_ch: 3
+    n_nbr_epoch: 3
+noisy_channels:
+    flag_crit: 0.3
+    outlier_method: quantile
+    outliers_kwargs:
+        k: 16
+        lower: 0.25
+        upper: 0.75
+noisy_epochs:
+    flag_crit: 0.2
+    outlier_method: quantile
+    outliers_kwargs:
+        k: 16
+        lower: 0.3
+        upper: 0.7
+order: 1
+out_path: derivatives/EEG-IP-L
+project:
+    analysis_montage: ''
+    bids_montage: GSN-HydroCel-129
+    coordsys:
+        EEGCoordinateSystem: Other
+        EEGCoordinateUnits: metres
+    general_info:
+        authors:
+        - Q1K Neuroimaging group
+        dataset_doi: []
+        funding: Azrieli Foundationt
+        how_to_acknowledge: tba
+        institution_Name: McGill University
+        institution_address: 3775 Rue University, Montreal, QC
+        license: ''
+        name: Q1K Mismatched Negativity
+        references_and_links: []
+    readme: '# Q1K ACAR Dataset'
+    set_montage_kwargs: {}
+    t_info:
+        EEG_placement_scheme: EGI 129
+        cap_manufacturer: EGI
+        cap_manufacturers_model_name: Hydrocel 129 Channel
+        hardware_filters: n/a
+        manufacturer: Electrical Geodesics
+        manufacturers_model_name: NetAmps300
+        power_line_frequency: 60
+        software_filters: n/a
+        software_versions: NetStation V4.5
+ref_loc_file: derivatives/EEG-IP-L/code/misc/standard_1020_ll_ref19.elc
+save_f_res: 1
+sd_t_pad: 1
+slurm_options:
+    account: def-emayada
+    job_name: pylossless
+    memory: 16g
+    mpi: false
+    num_tasks: 1
+    program_options: []
+    threads_per_task: []
+    time_limit: 2h
+uncorrelated_channels:
+    flag_crit: 0.2
+    outlier_method: quantile
+    outliers_kwargs:
+        k: 16
+        lower: 0.3
+        upper: 0.7
+uncorrelated_epochs:
+    flag_crit: 0.2
+    outlier_method: quantile
+    outliers_kwargs:
+        k: 16
+        lower: 0.3
+        upper: 0.7

pylossless/pipeline.py

@@ -585,7 +585,7 @@ def add_pylossless_annotations(self, inds, event_type, epochs):
         # it's end will coincide with the first sample of the
         # next epoch, causing it to erroneously be rejected.
         df["duration"] = 1 / epochs.info["sfreq"] * len(epochs.times[:-1])
-        df["description"] = f"bad_pylossless_{event_type}"
+        df["description"] = f"bad_{event_type}"
 
         # Merge close onsets to prevent a bunch of 1-second annotations of the same name
         # find onsets close enough to be considered the same

pyproject.toml

@@ -18,7 +18,7 @@ ignore-decorators = [
 ]
 
 [tool.black]
-exclude = "(dist/)|(build/)|(.*\\.ipynb)" # Exclude build artifacts and notebooks
+exclude = "(dist/)|(build/)|(examples/)|(.*\\.ipynb)" # Exclude build artifacts and notebooks
 
 [tool.pytest.ini_options]
 filterwarnings = ["error",