Refactor: Add suggestions from @hakonanes

doc/user_guide/distributed_big_data.rst

@@ -10,8 +10,8 @@ discuss how to work with large datasets in ``pyxem``.
 
 .. note::
     If you want more information on working with large datasets in ``pyxem``, please see the
-    `Big Data<https://hyperspy.org/hyperspy-doc/current/user_guide/big_data.html>_` section of
-    the `HyperSpy User Guide<https://hyperspy.org/hyperspy-doc/current/user_guide/index.html>_`.
+    `Big Data <https://hyperspy.org/hyperspy-doc/current/user_guide/big_data.html>_` section of
+    the `HyperSpy User Guide <https://hyperspy.org/hyperspy-doc/current/user_guide/index.html>_`.
 
 Loading and Plotting a Dataset
 ------------------------------
@@ -50,7 +50,7 @@ You can also set the navigator so that by default it is used when plotting.
 
     haadf = hs.load("haadf.zspy") # load the HAADF dataset
     s.navigator = haadf
-    s.plot() # (navigator=haadf) happens instantaneously
+    s.plot()
 
 
 Distributed Computing

doc/user_guide/installing.rst

@@ -6,7 +6,7 @@ Installing pyxem
 
 There are a couple of different ways to install pyxem. The easiest way is to use the
 hyperspy-bundle. If you don't have much experience with managing Python environments, we
-highly recommend using the `hyperspy-bundle<https://hyperspy.org/hyperspy-bundle/download.html>`.
+highly recommend using the `hyperspy-bundle<https://hyperspy.org/hyperspy-bundle/download.html>_`.
 The bundle is a pre-configured Python environment that includes pyxem and all its dependencies.
 
 It includes many other packages that are useful for electron microscopy data analysis, such
@@ -28,39 +28,31 @@ into an existing environment. The easiest way to do this is using conda/mamba.
    but is particularly a pain sometimes. If you have having trouble you can always change
    your url from ``http://localhost:8888/lab`` to ``http://localhost:8888/notebooks`` and use the
    classic notebook instead. There are less features but it is more stable in my experience.
-
+::
 
     conda install -c conda-forge pyxem
 
-
-
 Installing using pip
 --------------------
 
 You can also install pyxem using pip if you want. In that case you can install pyxem
-using
+using::
 
     pip install pyxem
 
-
 Installing from source
 ----------------------
 
 If you want to install the development version of pyxem, you can install it from source.
-First clone the repository using
-
+First clone the repository using::
 
     git clone https://github.com/pyxem/pyxem.git
 
-
-Then navigate into the pyxem directory and install the dependencies using
-
+Then navigate into the pyxem directory and install the dependencies using::
 
     pip install -r requirements.txt
 
-
-Finally, install pyxem using
-
+Finally, install pyxem using::
 
     pip install -e .
 

pyxem/signals/insitu_diffraction2d.py

@@ -42,9 +42,9 @@ class InSituDiffraction2D(Diffraction2D):
 
     Parameters
     ----------
-    *args
+    *args:
         See :class:`~hyperspy._signals.signal2d.Signal2D`.
-    **kwargs
+    **kwargs:
         See :class:`~hyperspy._signals.signal2d.Signal2D`
     """
 
@@ -100,7 +100,7 @@ def get_drift_vectors(
         sub_pixel_factor: float
             sub_pixel_factor passed to :meth:`~hyperspy.api.signals.Signal2D.estimate_shift2D`
             function. Default is 10
-        kwargs:
+        **kwargs:
             Passed to the :meth:`~pyxem.signals.InSituDiffraction2D.get_time_series` function
 
         Returns