Rename flags and methods (#152)

* ENH: Make flags and methods more readable

* FIX: formatting

examples/plot_10_run_pipeline.py

@@ -3,8 +3,7 @@
 Run pyLossless on a BIDS dataset.
 =================================
 
-In this notebook, we will run the pyLossless pipeline on a publicly available dataset
-that is already in BIDS format.
+In this notebook, we will run the pyLossless pipeline on a publicly available dataset.
 """
 
 # %%

pylossless/assets/ll_default_config.yaml

@@ -8,10 +8,9 @@ sd_t_pad: 1
 in_path: []
 montage_info: [0.0, -16.0, 0.0, -0.02, 0.0, -1.58, 10.7, 11.5, 11.5]
 
-
 ################## General info about the project ####################
 project:
-  readme: '# Q1K ACAR Dataset'
+  readme: "# Q1K ACAR Dataset"
 
   # Montage use to make file BIDS compliant.
   # Can be path to digitized montage OR a string of one of mne's built in
@@ -23,22 +22,22 @@ project:
   # if empty, the pipeline will use the electrodes.tsv sidecar file, if created
   # during the BIDS conversion.
   # If specified, needs to be a string of one of mne's built in standard montages.
-  analysis_montage: ''
-  
+  analysis_montage: ""
+
   set_montage_kwargs: {}
 
   coordsys:
     EEGCoordinateSystem: Other
     EEGCoordinateUnits: metres
-    
+
   general_info:
     authors: [Q1K Neuroimaging group]
     institution_Name: McGill University
     institution_address: 3775 Rue University, Montreal, QC
     dataset_doi: []
     funding: Azrieli Foundationt
     how_to_acknowledge: tba
-    license: ''
+    license: ""
     name: Q1K Mismatched Negativity
     references_and_links: []
 
@@ -52,22 +51,21 @@ project:
     power_line_frequency: 60
     software_filters: n/a
     software_versions: NetStation V4.5
-    
+
 ######################## Task break detection ########################
 # See arguments definition from mne.preprocessing.annotate_breaks
 find_breaks:
 
 ############################## epoching ##############################
 epoching:
   overlap: 0
-  
+
   # See arguments definition from mne.Epochs
   epochs_args:
     baseline: null
     tmax: 1
     tmin: 0
 
-
 ########################### EEG filtering ############################
 # See arguments definition from mne.io.Raw.filter & mne.io.Raw.notch_filter
 filtering:
@@ -77,7 +75,6 @@ filtering:
   notch_filter_args:
     freqs: []
 
-
 ############################## SLURM #################################
 # Options for running the pipeline on a cluster
 # through SLURM
@@ -95,41 +92,37 @@ slurm_options:
 nearest_neighbors:
   n_nbr_ch: 3
   n_nbr_epoch: 3
-
 
 ####################### Pipeline steps config ########################
-bridge:
+bridged_channels:
   bridge_trim: 40
   bridge_z: 6
 
-ch_ch_sd:
+noisy_channels:
   flag_crit: 0.2
   outlier_method: quantile
   outliers_kwargs:
     k: 16
     lower: 0.3
     upper: 0.7
 
-ch_low_r:
+uncorrelated_channels:
   flag_crit: 0.2
   outlier_method: quantile
   outliers_kwargs:
     k: 16
     lower: 0.3
     upper: 0.7
 
-epoch_ch_sd:
+noisy_epochs:
   flag_crit: 0.2
   outlier_method: quantile
   outliers_kwargs:
     k: 16
     lower: 0.3
     upper: 0.7
 
-epoch_gap:
-  min_gap_ms: 2000
-
-epoch_low_r:
+uncorrelated_epochs:
   flag_crit: 0.2
   outlier_method: quantile
   outliers_kwargs:
@@ -139,7 +132,7 @@ epoch_low_r:
 
 ################################ ICA #################################
 ica:
-  ic_ic_sd:
+  noisy_ic_epochs:
     flag_crit: 0.2
     outlier_method: quantile
     outliers_kwargs:

pylossless/config/rejection.py

@@ -18,8 +18,8 @@ class RejectionPolicy(ConfigMixin):
         in for the rejection policy.
     ch_flags_to_reject : list of str
         List of channel flags to apply. "all" is the same as
-        ``["ch_sd", "low_r", "bridge"]``, meaning that any channels that
-        have been flagged ``"ch_sd"``, ``"low_r"``, or ``"bridge"`` will
+        ``["noisy", "uncorrelated", "bridged"]``, meaning that any channels that
+        have been flagged ``"noisy"``, ``"uncorrelated"``, or ``"bridged"`` will
         be applied. Defaults to "all".
     ic_flags_to_reject : list of str
         List of IC flags to apply. "all" is the same as
@@ -65,7 +65,7 @@ def __init__(
         remove_flagged_ics=True,
     ):
         if ch_flags_to_reject == "all":
-            ch_flags_to_reject = ["ch_sd", "low_r", "bridge"]
+            ch_flags_to_reject = ["noisy", "uncorrelated", "bridged"]
         if ic_flags_to_reject == "all":
             ic_flags = ["muscle", "ecg", "eog", "channel_noise", "line_noise"]
             ic_flags_to_reject = ic_flags

pylossless/flagging.py

@@ -150,8 +150,8 @@ class FlaggedEpochs(dict):
         :class:`mne.Epochs`) to the ``'manual'`` dictionary key.
 
     load_from_raw:
-        Add any :class:`mne.Annotations` in a loaded :class:`mne.io.Raw` file
-        that start with ``'bad_pylossless'`` to the FlaggedEpochs class.
+        Add any pylossless :class:`mne.Annotations` in a loaded :class:`mne.io.Raw`
+        file to the FlaggedEpochs class.
 
     Notes
     -----
@@ -181,7 +181,7 @@ def add_flag_cat(self, kind, bad_epoch_inds, epochs):
         Parameters
         ----------
         kind : str
-            Should be one of ``'ch_sd'``, ``'low_r'``, ``'ic_sd1'``.
+            Should be one of ``'noisy'``, ``'uncorrelated'``, ``'noisy_ICs'``.
         bad_epochs_inds : list | tuple
             Indices for the epochs in an :class:`mne.Epochs` object. Will be
             the values for the ``kind`` dictionary key.
@@ -195,10 +195,11 @@ def add_flag_cat(self, kind, bad_epoch_inds, epochs):
         self.ll.add_pylossless_annotations(bad_epoch_inds, kind, epochs)
 
     def load_from_raw(self, raw):
-        """Load ``'bad_pylossless'`` annotations from raw object."""
+        """Load pylossless annotations from raw object."""
         sfreq = raw.info["sfreq"]
+        lossless_descriptions = ["bad_noisy", "bad_uncorrelated", "bad_noisy_ICs"]
         for annot in raw.annotations:
-            if annot["description"].startswith("bad_pylossless"):
+            if annot["description"] in lossless_descriptions:
                 ind_onset = int(np.round(annot["onset"] * sfreq))
                 ind_dur = int(np.round(annot["duration"] * sfreq))
                 inds = np.arange(ind_onset, ind_onset + ind_dur)