fix according to MNE 1.x.x (#128)

* fix according to MNE 1.x.x * workshop practicalmeeg * update doc
neuropycon · Dec 11, 2022 · fe81548 · fe81548
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diff --git a/doc/api.rst b/doc/api.rst
@@ -22,10 +22,15 @@ Pipelines (:py:mod:`ephypype.pipelines`):
 
 Nodes (:py:mod:`ephypype.nodes`):
 
-.. currentmodule:: ephypype.pipelines
+.. currentmodule:: ephypype.interfaces.mne
 
 .. autosummary::
    :toctree: generated/
+
+   LFComputation
+   NoiseCovariance
+   InverseSolution
+
 
 CLI commands (:py:mod:`ephypype.commands.neuropycon`):
 
@@ -53,4 +58,3 @@ Utils (:py:mod:`ephypype.nodes`):
 
    create_iterator
    create_datagrabber
-   Reference
diff --git a/doc/conf.py b/doc/conf.py
@@ -55,6 +55,7 @@
 
 # generate autosummary even if no references
 autosummary_generate = True
+numpydoc_show_class_members = False 
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
@@ -90,7 +91,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+# language = None
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

diff --git a/doc/img/dspm-contrast.png b/doc/img/dspm-contrast.png
diff --git a/doc/img/graph_FS.png b/doc/img/graph_FS.png
diff --git a/doc/index.rst b/doc/index.rst
@@ -92,15 +92,9 @@ The ephypype package provides the following **pipelines**:
 Installation
 =============
 
-ephypype works with **python3**
+To install the ephypype package, we recommend you first install MNE python by following the |installation instructions|. 
 
-* mne>0.19
-* nipype
-* h5py
-
-These dependencies are automatically installed during ephypype installation.
-
-We also recommend to install MNE python by following the |installation instructions|.
+The dependencies (mne, nipype, h5py) are automatically installed during ephypype installation.
 
 .. |installation instructions| raw:: html
 
@@ -109,12 +103,28 @@ We also recommend to install MNE python by following the |installation instructi
 
 Install ephypype
 ^^^^^^^^^^^^^^^^
+Then, to install ephypype package, you can use the Pypi version
 
 .. code-block:: bash
 
    $ pip install ephypype
    $ pip install jupyter
     
+or alternatively, you can download from |github| the last version and install it:
+
+.. code-block:: bash
+
+        $ git clone https://github.com/neuropycon/ephypype.git
+        $ cd ephypype
+        $ python setup.py develop
+
+.. |github| raw:: html
+
+   <a href="https://github.com/neuropycon/ephypype" target="_blank">github</a>
+
+
+
+.. note:: ephypype works with **python3**
 
 Freesurfer
 ^^^^^^^^^^

diff --git a/doc/tutorials/howto.rst b/doc/tutorials/howto.rst
@@ -70,19 +70,19 @@ the list of subjects and sessions, ...)
 
     
 Now we define some variables specific for the pipelines we use (frequency band of interest, method to compute the PSD, ...).
-in a :download:`json <https://github.com/neuropycon/ephypype/blob/master/examples/params_power.json>` file and load it in the script. 
+in a :download:`json <https://github.com/neuropycon/ephypype/blob/master/examples/params.json>` file and load it in the script. 
 
 .. code-block:: python
     :emphasize-lines: 1
 
-    data = json.load(open("params_power.json"))
+    params = json.load(open("params.json"))
 
-    freq_band_names = data['freq_band_names']
-    freq_bands = data['freq_bands']
-    is_epoched = data['is_epoched']
-    fmin = data['fmin']
-    fmax = data['fmax']
-    power_method = data['method']
+    freq_band_names = params["power"]['freq_band_names']
+    freq_bands = params["power"]['freq_bands']
+    is_epoched = params["power"]['is_epoched']
+    fmin = params["power"]['fmin']
+    fmax = params["power"]['fmax']
+    power_method = params["power"]['method']
 
 Specify Nodes
 -------------
@@ -176,7 +176,7 @@ Each pipeline provided by NeuroPycon requires two different kind of inputs:
 * inputs of the pipeline
 * **inputnode**: these particular inputs are defined after the creation of the pipeline; an inputnode of a pipeline is defined by an output of a previous Node
 
-For example, looking at the definition of `create_pipeline_power <https://neuropycon.github.io/ephypype/generated/ephypype.pipelines.create_pipeline_power.html#ephypype.pipelines.create_pipeline_power>`_  module
+For example, looking at the definition of `create_pipeline_power <https://neuropycon.github.io/ephypype/generated/ephypype.pipelines.create_pipeline_power.html#ephypype.pipelines.create_pipeline_power>`_
 ``main_path``, ``freq_bands`` are inputs of the pipeline while ``fif_file`` is an inputnode. In the next section :ref:`workflow` we'll see how to specify this inputnode.
 
 To create the pipeline node we pass the input to the ``create_pipeline_power`` function:
@@ -215,6 +215,7 @@ Then,  we connect the nodes two at a time. First, we connect the two outputs (su
 So, these two nodes taken together can grab data.
 
 .. code-block:: python
+   :emphasize-lines: 1-2
       
     main_workflow.connect(infosource, 'subject_id', datasource, 'subject_id')
     main_workflow.connect(infosource, 'session_id', datasource, 'session_id')

diff --git a/doc/workshop/meg/01-run-smri_reconall.py → doc/workshop/01_meg/01-run-smri_reconall.py b/doc/workshop/meg/01-run-smri_reconall.py → doc/workshop/01_meg/01-run-smri_reconall.py
@@ -5,6 +5,10 @@
 01. Freesurfer anatomical pipeline
 ==================================
 
+This workflow runs the `Nipype <http://nipype.readthedocs.io/en/latest/#>`_
+Interface wrapping the `recon-all <https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all>`_
+command of Freesurfer.
+
 The solution of MEG inverse problem requires knowledge of the lead field
 matrix. A cortical segmentation of the anatomical MRI is necessary to generate
 the source space, where the neural activity will be estimated.
@@ -16,10 +20,18 @@
 Freesurfer. The output of :ref:`reconallnode` node is used as input of another node that
 creates the BEM surfaces using the FreeSurfer watershed algorithm.
 
+The workflow generates an HTML report displaying the BEM surfaces as
+colored contours overlaid on the T1 MRI images to verify that the surfaces do not intersect.
+
 .. warning:: Make sure that Freesurfer is properly configured before
     running this script.
 """
 
+# Authors: Annalisa Pascarella <[email protected]>
+# License: BSD (3-clause)
+
+# sphinx_gallery_thumbnail_number = 1
+
 ###############################################################################
 # Import modules
 # ^^^^^^^^^^^^^^
@@ -41,8 +53,10 @@
 # Let us specify the variables that are specific for the data analysis (the
 # main directories where the data are stored, the list of subjects and
 # sessions, ...) and the variable specific for the particular pipeline
-# (MRI path, Freesurfer fir, ...) in a
-# :download:`json <https://github.com/neuropycon/ephypype/tree/master/doc/workshop/meg/params.json>` file 
+# (MRI path, Freesurfer fir, ...) in a |params.json| file
+#
+# .. |params.json| replace::
+#   :download:`json <https://github.com/neuropycon/ephypype/tree/master/doc/workshop/01_meg/params.json>`
 
 # Read experiment params as json
 params = json.load(open("params.json"))
@@ -61,7 +75,7 @@
 if not os.environ.get('FREESURFER_HOME'):
     raise RuntimeError('FREESURFER_HOME environment variable not set')
 os.environ["SUBJECTS_DIR"] = subjects_dir
-print('SUBJECTS_DIR %s ' % os.environ["SUBJECTS_DIR"])
+print(f'SUBJECTS_DIR {os.environ["SUBJECTS_DIR"]} ')
 
 
 ###############################################################################
@@ -158,4 +172,12 @@
 # ^^^^^^^
 # The output of this workflow is the cortical segmentation of the
 # structural data that we find in the ``subjects_dir`` and will be used in
-# :ref:`plot_events_inverse`
+# :ref:`plot_events_inverse`
+#
+# .. note:: The main advantage to use this workflow lies in the parallel
+#       processing provided by nipype engine, that allows segmenting the 19 MRI
+#       data in less than two days while processing a single MRI generally
+#       takes one day.
+# .. image:: ../../img/graph_FS.png
+#    :width: 50%
+#    :align: center
diff --git a/doc/workshop/meg/README.rst → doc/workshop/01_meg/README.rst b/doc/workshop/meg/README.rst → doc/workshop/01_meg/README.rst
@@ -1,19 +1,20 @@
-.. _facedataset:
-
-
-FACE dataset
-^^^^^^^^^^^^
-
+
+
+
+FACE dataset
+^^^^^^^^^^^^
+
 These examples demonstrate how to process 1 participant of the |FACE| dataset from |Wakeman_Henson|. The data consist of simultaneous MEG/EEG recordings
 from 19 healthy participants performing a visual recognition task. Subjects were presented images of famous, unfamiliar and scrambled faces.
-Each subject participated in 6 runs, each 7.5 min in duration.
-
-.. |FACE| raw:: html
-
-        <a href="https://openneuro.org/datasets/ds000117/versions/1.0.4" target="_blank">FACE</a>
-
-.. |Wakeman_Henson| raw:: html
-
-        <a href="https://www.nature.com/articles/sdata20151" target="_blank">Wakeman and Henson (2015)</a>
-
-Here, we focus only on MEG data and use :func:`~ephypype.pipelines.create_pipeline_preproc_meeg` to preprocess the MEG raw data and :func:`~ephypype.pipelines.create_pipeline_source_reconstruction` to perform source reconstruction of time-locked event-related fields.
+Each subject participated in 6 runs, each 7.5 min in duration.
+
+.. |FACE| raw:: html
+
+        <a href="https://openneuro.org/datasets/ds000117/versions/1.0.4" target="_blank">FACE</a>
+
+.. |Wakeman_Henson| raw:: html
+
+        <a href="https://www.nature.com/articles/sdata20151" target="_blank">Wakeman and
+ Henson (2015)</a>
+
+Here, we focus only on MEG data and use :func:`~ephypype.pipelines.create_pipeline_preproc_meeg` to preprocess the MEG raw data and :func:`~ephypype.pipelines.create_pipeline_source_reconstruction` to perform source reconstruction of time-locked event-related fields.