Repository: mne-tools/mne-python

Issue:
Starting iPython with ipython -pylab -qt
In the tutorial it advises the user to start iPython using 

`$ ipython -pylab -qt`

However, this no longer works and should be (at least on a linux machine)

`$ ipython --matplotlib=qt`

Correct me if I'm wrong :)



Discussion:
> However, this no longer works and should be (at least on a linux machine)

@Mensen which Linux machine are you running? This works fine with recent Debian/Ubuntu systems.

@dengemann I'm running Ubuntu 12.04, I haven't tried with 13.10 at home. If this is just an issue with older versions of ubuntu, I won't have that issue since 14.04 will come out next week. Thanks for the help!

@Mensen this is strange. I know that this works nicely on ubuntu 12.04 (I'm using that too) ...

The command above doesn't work for me either. The second one works. You can also try

`$ipython --pylab`

I think that automatically uses the Qt4Agg backend. @dengemann may know better though :)

@Mensen did this get resolved?

pylab mode is disprefered anyways isn't it?

yes the recommended way now is --matplotlib


Resolving patch:
diff --git a/mne/channels/montage.py b/mne/channels/montage.py
--- a/mne/channels/montage.py
+++ b/mne/channels/montage.py
@@ -51,8 +51,8 @@ def __init__(self, pos, ch_names, kind, selection):
         self.selection = selection
 
     def __repr__(self):
-        s = '<Montage | %s - %d Channels: %s ...>'
-        s %= self.kind, len(self.ch_names), ', '.join(self.ch_names[:3])
+        s = ('<Montage | %s - %d channels: %s ...>'
+             % (self.kind, len(self.ch_names), ', '.join(self.ch_names[:3])))
         return s
 
     def plot(self, scale_factor=1.5, show_names=False):
@@ -153,6 +153,9 @@ def read_montage(kind, ch_names=None, path=None, unit='m', transform=False):
     -----
     Built-in montages are not scaled or transformed by default.
 
+    Montages can contain fiducial points in addition to electrode
+    locations, e.g. ``biosemi-64`` contains 67 total channels.
+
     .. versionadded:: 0.9.0
     """
 
diff --git a/tutorials/plot_introduction.py b/tutorials/plot_introduction.py
--- a/tutorials/plot_introduction.py
+++ b/tutorials/plot_introduction.py
@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 """
 .. _intro_tutorial:
 
@@ -16,40 +17,44 @@
 -------------------------------
 
    - **Raw data visualization** to visualize recordings, can also use
-   *mne_browse_raw* for extended functionality (see :ref:`ch_browse`)
+     *mne_browse_raw* for extended functionality (see :ref:`ch_browse`)
    - **Epoching**: Define epochs, baseline correction, handle conditions etc.
    - **Averaging** to get Evoked data
    - **Compute SSP pojectors** to remove ECG and EOG artifacts
    - **Compute ICA** to remove artifacts or select latent sources.
+   - **Maxwell filtering** to remove environmental noise.
    - **Boundary Element Modeling**: single and three-layer BEM model
      creation and solution computation.
    - **Forward modeling**: BEM computation and mesh creation
-   (see :ref:`ch_forward`)
+     (see :ref:`ch_forward`)
    - **Linear inverse solvers** (dSPM, sLORETA, MNE, LCMV, DICS)
    - **Sparse inverse solvers** (L1/L2 mixed norm MxNE, Gamma Map,
-   Time-Frequency MxNE)
+     Time-Frequency MxNE)
    - **Connectivity estimation** in sensor and source space
    - **Visualization of sensor and source space data**
    - **Time-frequency** analysis with Morlet wavelets (induced power,
-   intertrial coherence, phase lock value) also in the source space
+     intertrial coherence, phase lock value) also in the source space
    - **Spectrum estimation** using multi-taper method
    - **Mixed Source Models** combining cortical and subcortical structures
    - **Dipole Fitting**
    - **Decoding** multivariate pattern analyis of M/EEG topographies
    - **Compute contrasts** between conditions, between sensors, across
-   subjects etc.
+     subjects etc.
    - **Non-parametric statistics** in time, space and frequency
-   (including cluster-level)
+     (including cluster-level)
    - **Scripting** (batch and parallel computing)
 
 What you're not supposed to do with MNE Python
 ----------------------------------------------
 
-    - **Brain and head surface segmentation** for use with BEM models -- use Freesurfer.
+    - **Brain and head surface segmentation** for use with BEM
+      models -- use Freesurfer.
+    - **Raw movement compensation** -- use Elekta Maxfilter™
 
 
-.. note:: Package based on the FIF file format from Neuromag. It can read and
-          convert CTF, BTI/4D, KIT and various EEG formats to FIF.
+.. note:: This package is based on the FIF file format from Neuromag. It
+          can read and convert CTF, BTI/4D, KIT and various EEG formats to
+          FIF.
 
 
 Installation of the required materials


Test patch: