doc updates [skip ci]

Author: Circle Ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 63bbd22fd3d6c992c53e407d3efe1329
+config: 411e7400163acc21866bd705432ec5b0
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07841a9269d1816a3add41f32d8a5ed2/read_bids_datasets.ipynb

@@ -337,7 +337,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.2"
+      "version": "3.13.0"
     }
   },
   "nbformat": 4,

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "# Download the data for subject 1, for the 2 minutes of eyes closed rest task.\n# From the online documentation of the data we know that run \"2\" corresponds\n# to the \"eyes closed\" task.\nsubject = 1\nrun = 2\neegbci.load_data(subject=subject, runs=run, update_path=True)"
+        "# Download the data for subject 1, for the 2 minutes of eyes closed rest task.\n# From the online documentation of the data we know that run \"2\" corresponds\n# to the \"eyes closed\" task.\nsubject = 1\nrun = 2\neegbci.load_data(subjects=subject, runs=run, update_path=True)"
       ]
     },
     {
@@ -94,7 +94,7 @@
       },
       "outputs": [],
       "source": [
-        "# Load the data from \"2 minutes eyes closed rest\"\nedf_path = eegbci.load_data(subject=subject, runs=run)[0]\nraw = mne.io.read_raw_edf(edf_path, preload=False)\nraw.info[\"line_freq\"] = 50  # specify power line frequency as required by BIDS"
+        "# Load the data from \"2 minutes eyes closed rest\"\nedf_path = eegbci.load_data(subjects=subject, runs=run)[0]\nraw = mne.io.read_raw_edf(edf_path, preload=False)\nraw.info[\"line_freq\"] = 50  # specify power line frequency as required by BIDS"
       ]
     },
     {
@@ -283,7 +283,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.2"
+      "version": "3.13.0"
     }
   },
   "nbformat": 4,

dev/_downloads/0bc5481d861907f3ba86dd37d953b980/convert_eeg_to_bids.ipynb

@@ -312,7 +312,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.2"
+      "version": "3.13.0"
     }
   },
   "nbformat": 4,

dev/_downloads/0c7e85abda2fce1fe2c484ad9f9aaa81/bidspath.ipynb

@@ -50,7 +50,7 @@
 run_map = dict(zip(runs, range(1, 4)))
 
 for subject_id in subject_ids:
-    eegbci.load_data(subject=subject_id, runs=runs, update_path=True)
+    eegbci.load_data(subjects=subject_id, runs=runs, update_path=True)
 
 # get path to MNE directory with the downloaded example data
 mne_data_dir = mne.get_config("MNE_DATASETS_EEGBCI_PATH")
@@ -81,7 +81,7 @@
 bids_list = list()
 for subject_id in subject_ids:
     for run in runs:
-        raw_fname = eegbci.load_data(subject=subject_id, runs=run)[0]
+        raw_fname = eegbci.load_data(subjects=subject_id, runs=run)[0]
         raw = mne.io.read_raw_edf(raw_fname)
         raw.info["line_freq"] = 50  # specify power line frequency
         raw_list.append(raw)

dev/_downloads/1eed244ec75de0e6a9392a728e24219b/convert_group_studies.py

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "subject_ids = [1, 2]\n\n# The run numbers in the eegbci are not consecutive ... we follow the online\n# documentation to get the 1st, 2nd, and 3rd run of one of the the motor\n# imagery task\nruns = [\n    4,  # This is run #1 of imagining to open/close left or right fist\n    8,  # ... run #2\n    12,  # ... run #3\n]\n\n# map the eegbci run numbers to the number of the run in the motor imagery task\nrun_map = dict(zip(runs, range(1, 4)))\n\nfor subject_id in subject_ids:\n    eegbci.load_data(subject=subject_id, runs=runs, update_path=True)\n\n# get path to MNE directory with the downloaded example data\nmne_data_dir = mne.get_config(\"MNE_DATASETS_EEGBCI_PATH\")\ndata_dir = op.join(mne_data_dir, \"MNE-eegbci-data\")"
+        "subject_ids = [1, 2]\n\n# The run numbers in the eegbci are not consecutive ... we follow the online\n# documentation to get the 1st, 2nd, and 3rd run of one of the the motor\n# imagery task\nruns = [\n    4,  # This is run #1 of imagining to open/close left or right fist\n    8,  # ... run #2\n    12,  # ... run #3\n]\n\n# map the eegbci run numbers to the number of the run in the motor imagery task\nrun_map = dict(zip(runs, range(1, 4)))\n\nfor subject_id in subject_ids:\n    eegbci.load_data(subjects=subject_id, runs=runs, update_path=True)\n\n# get path to MNE directory with the downloaded example data\nmne_data_dir = mne.get_config(\"MNE_DATASETS_EEGBCI_PATH\")\ndata_dir = op.join(mne_data_dir, \"MNE-eegbci-data\")"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "raw_list = list()\nbids_list = list()\nfor subject_id in subject_ids:\n    for run in runs:\n        raw_fname = eegbci.load_data(subject=subject_id, runs=run)[0]\n        raw = mne.io.read_raw_edf(raw_fname)\n        raw.info[\"line_freq\"] = 50  # specify power line frequency\n        raw_list.append(raw)\n        bids_path = BIDSPath(\n            subject=f\"{subject_id:03}\",\n            session=\"01\",\n            task=\"MotorImagery\",\n            run=f\"{run_map[run]:02}\",\n            root=bids_root,\n        )\n        bids_list.append(bids_path)\n\ndaysback_min, daysback_max = get_anonymization_daysback(raw_list)\n\nfor raw, bids_path in zip(raw_list, bids_list):\n    # By using the same anonymization `daysback` number we can\n    # preserve the longitudinal structure of multiple sessions for a\n    # single subject and the relation between subjects. Be sure to\n    # change or delete this number before putting code online, you\n    # wouldn't want to inadvertently de-anonymize your data.\n    #\n    # Note that we do not need to pass any events, as the dataset is already\n    # equipped with annotations, which will be converted to BIDS events\n    # automatically.\n    write_raw_bids(\n        raw, bids_path, anonymize=dict(daysback=daysback_min + 2117), overwrite=True\n    )"
+        "raw_list = list()\nbids_list = list()\nfor subject_id in subject_ids:\n    for run in runs:\n        raw_fname = eegbci.load_data(subjects=subject_id, runs=run)[0]\n        raw = mne.io.read_raw_edf(raw_fname)\n        raw.info[\"line_freq\"] = 50  # specify power line frequency\n        raw_list.append(raw)\n        bids_path = BIDSPath(\n            subject=f\"{subject_id:03}\",\n            session=\"01\",\n            task=\"MotorImagery\",\n            run=f\"{run_map[run]:02}\",\n            root=bids_root,\n        )\n        bids_list.append(bids_path)\n\ndaysback_min, daysback_max = get_anonymization_daysback(raw_list)\n\nfor raw, bids_path in zip(raw_list, bids_list):\n    # By using the same anonymization `daysback` number we can\n    # preserve the longitudinal structure of multiple sessions for a\n    # single subject and the relation between subjects. Be sure to\n    # change or delete this number before putting code online, you\n    # wouldn't want to inadvertently de-anonymize your data.\n    #\n    # Note that we do not need to pass any events, as the dataset is already\n    # equipped with annotations, which will be converted to BIDS events\n    # automatically.\n    write_raw_bids(\n        raw, bids_path, anonymize=dict(daysback=daysback_min + 2117), overwrite=True\n    )"
       ]
     },
     {
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.2"
+      "version": "3.13.0"
     }
   },
   "nbformat": 4,