docs: updating links to api docs 2

CHANGELOG

@@ -1,4 +1,5 @@
 0.1.4
+ - ref, doc: fix api doc link references
  - add initial (useable) tests in test directory
  - add CHANGELOG
  - ref: change the directory structure to include topotem (#52)

doc/PTO_Junction_moore.rst

@@ -81,8 +81,8 @@ Load the line profile positions:
     >>> line_profile_positions = np.load('line_profile_positions.npy')
 
 Note: You can also choose your own line_profile_positions with
-:python:`am.add_atoms_with_gui(image)` and use the :python:`skimage.profile_line`
-for customisability.
+:py:func:`temul.topotem.fft_mapping.choose_points_on_image` and use the
+:python:`skimage.profile_line` for customisability.
 
 
 Create the Lattice Strain Map
@@ -104,7 +104,7 @@ Note that sometimes the 0 and 1 axes directions are constructed vice versa.
 .. image:: ../publication_examples/PTO_Junction_moore/data/strain_map.png
     :scale: 50 %
 
-Plot the line profiles with :python:`temul.signal_plotting` functions and a kwarg dictionary.
+Plot the line profiles with :py:mod:`temul.signal_plotting` functions and a kwarg dictionary.
 For more details on this function, see :ref:`this tutorial <line_profile_tutorial>`.
 
 .. code-block:: python
@@ -140,7 +140,7 @@ the turning of the lattice across the junction.
     :scale: 50 %
 
 
-Plot the line profiles with :python:`temul.signal_plotting` functions and a kwarg dictionary.
+Plot the line profiles with :py:mod:`temul.signal_plotting` functions and a kwarg dictionary.
 For more details on this function, see :ref:`this tutorial <line_profile_tutorial>`.
 
 .. code-block:: python

doc/_build/html/_sources/dg_visualiser_tutorial.rst.txt

@@ -44,7 +44,7 @@ There are lots of other parameters too for customisation.
 As we can see, an interactive window appears, showing the FFT ("FFT Widget")
 of the image with the positions of the inner and outer Gaussian full width
 at half maximums (FWHMs). The inital FWHMs can be changed with the
-:python:`visualise_dg_filter.d_inner` and :python:`d_outer`
+:python:`d_inner` and :python:`d_outer`
 parameters (limits can also be changed).
 
 To change the two FWHMs interactively

doc/_build/html/_sources/index.rst.txt

@@ -18,10 +18,12 @@ Interactive Examples
 .. image:: https://mybinder.org/badge_logo.svg
    :target: https://mybinder.org/v2/gh/PinkShnack/TEMUL/master
 
-Click the button above to start some data analysis (it may take a few minutes to load). 
+Click the button above to start some data analysis
+(it may take a few minutes to load).
 The "code_tutorials" folder contains walkthroughs of some of the documentation
 examples from this website.
-The "publication_examples" folder will allow you to analyse data from published scientific papers!
+The "publication_examples" folder will allow you to analyse data from
+published scientific papers!
 Just navigate to whichever of these folders you want click on the ".ipynb" files.
 
 
@@ -74,7 +76,9 @@ To cite the latest TEMUL Toolkit version, use the following DOI:
 .. image:: https://www.zenodo.org/badge/203785298.svg
    :target: https://www.zenodo.org/badge/latestdoi/203785298
 
-For example: Eoghan O'Connell, Michael Hennessy, & Eoin Moynihan. (2020, November 2). PinkShnack/TEMUL: Initial Temul-Toolkit Release (Version 0.1.1). Zenodo. http://doi.org/10.5281/zenodo.4185974
+For example: Eoghan O'Connell, Michael Hennessy, & Eoin Moynihan.
+(2021). PinkShnack/TEMUL: (Version 0.1.3).
+Zenodo. http://doi.org/10.5281/zenodo.4543963
 
 If you wish to cite an older release of the TEMUL Toolkit, click on the above
 badge to find the relevant version.

doc/_build/html/_sources/install.rst.txt

@@ -6,31 +6,44 @@
 Installation
 ------------
 
-The TEMUL Toolkit can be installed easily with PIP (those using Windows may need to download VS C++ Build Tools, see below).  
+The TEMUL Toolkit can be installed easily with PIP (those using Windows may
+need to download VS C++ Build Tools, see below).
 
 :bash:`$ pip install temul-toolkit`
 
-Then, it can be imported with the name "temul". For example, to import most of the temul functionality use:
+Then, it can be imported with the name "temul". For example, to import most
+of the temul functionality use:
 
 :python:`import temul.api as tml`
 
-Matplotlib 3.3 currently has compatability issues with iPython, see below for the fix.
-
 
 -----------------------------
 Installation Problems & Notes
 -----------------------------
 
-* If installing on Windows, you will need Visual Studio C++ Build Tools. Download it `here <https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019>`_. After downloading, choose the "C++ Build Tools" Workload and click install.
-
-* Matplotlib seems to have issues with iPython at the moment. Install matplotlib==3.2 until this issue is resolved by Matplotlib. See `here <https://stackoverflow.com/questions/64291087/matplotlib-module-sip-has-no-attribute-setapi>`_ for more details.
-
-* If you want to use the :python:`io.write_cif_from_dataframe` function, you will need to install pyCifRW version 4.3. This requires Visual Studio.
-
-* If you wish to use the :python:`simulations.py` or :python:`model_refiner.py` modules, you will need to install PyPrismatic. This requires Visual Studio and other dependencies.
-
-* If you're using any of the functions or classes that require element quantification:
-
-   * navigate to the "temul/external" directory, copy the "atomap_devel_012" folder and paste that in your "site-packages" directory. 
-   * Then, when using atomap to create sublattices and quantify elements call atomap like this: :python:`import atomap_devel_012.api as am`.
-   * This development version is slowly being folded into the master branch here: https://gitlab.com/atomap/atomap/-/issues/93 and any help or tips on implementation are welcome!
+* If installing on Windows, you will need Visual Studio C++ Build Tools.
+  Download it `here <https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019>`_.
+  After downloading, choose the "C++ Build Tools" Workload and click install.
+
+* If you want to use the :py:func:`temul.io.write_cif_from_dataframe`
+  function, you will need to install pyCifRW version 4.3. This requires
+  Visual Studio.
+
+* If you wish to use the :py:mod:`temul.simulations` or
+  :py:mod:`temul.model_refiner` modules, you will need to install PyPrismatic.
+  This requires Visual Studio and other dependencies.
+  **It is unfortunately not guaranteed to work**. If you want to help develop
+  the :py:class:`temul.model_refiner.Model_Refiner`, please create an issue
+  and/or a pull request on the
+  `TEMUL github <https://github.com/PinkShnack/TEMUL>`_.
+
+* If you're using any of the functions or classes that require element
+  quantification:
+
+   * navigate to the "temul/external" directory, copy the "atomap_devel_012"
+     folder and paste that in your "site-packages" directory.
+   * Then, when using atomap to create sublattices and quantify elements call
+     atomap like this: :python:`import atomap_devel_012.api as am`.
+   * This development version is slowly being folded into the master branch
+     here: https://gitlab.com/atomap/atomap/-/issues/93 and any help or
+     tips on implementation are welcome!

doc/_build/html/_sources/line_profile_tutorial.rst.txt

@@ -7,11 +7,13 @@
 Line Intensity Profile Comparisons
 ==================================
 
-The :python:`temul.signal_plotting` module allows one to draw line intensity profiles
-over images. The :python:`compare_images_line_profile_one_image` can be used to
+The :py:mod:`temul.signal_plotting` module allows one to draw line
+intensity profiles over images. The
+:py:func`temul.signal_plotting.compare_images_line_profile_one_image` can be used to
 draw two line profiles on one image for comparison. In future we hope to expand
 this function to allow for multiple line profiles on one image. The
-:python:`compare_images_line_profile_two_images` function allows you to draw a line
+:py:func:`temul.signal_plotting.compare_images_line_profile_two_images`
+function allows you to draw a line
 profile on an image, and apply that same profile to another image (of the same shape).
 This can be useful for comparing subsequent images in series or comparing experimental
 and simulated images.
@@ -24,7 +26,7 @@ Load the Example Images
 -----------------------
 
 Here we load some dummy data using a variation of Atomap's
-:python:`get_simple_cubic_signal` function.
+:py:func:`temul.dummy_data.get_simple_cubic_signal` function.
 
 .. code-block:: python
 
@@ -46,8 +48,10 @@ Compare two Line Profiles in one Image
 --------------------------------------
 
 As with the :ref:`Masked FFT and iFFT <masked_fft_tutorial>` tutorial, we can
-choose points on the image. This time we use :python:`choose_points_on_image`.
-We need to choose four points for the :python:`compare_images_line_profile_one_image`
+choose points on the image. This time we use
+:py:func:`temul.topotem.fft_mapping.choose_points_on_image`.
+We need to choose four points for the
+:py:func:`temul.signal_plotting.compare_images_line_profile_one_image`
 function, as it draws two line profiles over one image.
 
 .. code-block:: python
@@ -80,7 +84,8 @@ Now run the comparison function to display the two line intensity profiles.
 Compare two Images with Line Profile
 ------------------------------------
 
-Using :python:`choose_points_on_image`, we now choose two points on one image.
+Using :py:func:`temul.topotem.fft_mapping.choose_points_on_image`,
+we now choose two points on one image.
 Then, we plot this line intensity profile over the same position in two images.
 
 .. code-block:: python

doc/_build/html/_sources/masked_fft_tutorial.rst.txt

@@ -7,9 +7,12 @@
 Masked FFT and iFFT
 ===================
 
-The :python:`temul.signal_processing` module allows one to choose the masking coordinates with
-:python:`choose_mask_coordinates` and easily returnt the masked fast Fourier Transform (FFT) with
-:python:`get_masked_ifft`. This can useful in various scenarios, from understanding
+The :py:mod:`temul.signal_processing` module allows one to choose the
+masking coordinates with
+:py:func:`temul.topotem.fft_mapping.choose_mask_coordinates` and easily
+return the masked fast Fourier Transform (FFT) with
+:py:func:`temul.topotem.fft_mapping.get_masked_ifft`. This can useful in
+various scenarios, from understanding
 the diffraction space spots and how they relate to the real space structure,
 to `revealing domain walls <https://onlinelibrary.wiley.com/doi/abs/10.1111/jmi.12876>`_
 and finding initial atom positions for difficult images.

doc/_build/html/_sources/polarisation_vectors_tutorial.rst.txt

@@ -14,13 +14,13 @@ of each.
 
 Current functions:
 
-1. Using Atomap's :python:`get_polarization_from_second_sublattice` Sublattice
-   method. Great for "standard" polarised structures with two sublattices.
-2. Using the TEMUL :python:`find_polarisation_vectors` function. Useful for
-   structures that Atomap's :python:`get_polarization_from_second_sublattice` can't
-   handle.
-3. Using the TEMUL :python:`atom_deviation_from_straight_line_fit` function.
-   Useful for calculating polarisation from a single sublattice, similar to and
+1. Using Atomap's `get_polarization_from_second_sublattice <https://atomap.org/api_documentation.html?highlight=get_polarization#atomap.sublattice.Sublattice.get_polarization_from_second_sublattice>`_
+   Sublattice method. Great for "standard" polarised structures with two sublattices.
+2. Using the TEMUL :py:func:`temul.topotem.polarisation.find_polarisation_vectors`
+   function. Useful for structures that Atomap's
+   :python:`get_polarization_from_second_sublattice` can't handle.
+3. Using the TEMUL :py:func:`temul.topotem.polarisation.atom_deviation_from_straight_line_fit`
+   function. Useful for calculating polarisation from a single sublattice, similar to and
    based off: J. Gonnissen *et al*, Direct Observation of Ferroelectric Domain Walls in
    LiNbO3: Wall‐Meanders, Kinks, and Local Electric Charges, 26, 42, 2016, DOI: 10.1002/adfm.201603489.
 
@@ -36,12 +36,13 @@ where you will find the python scripts and interactive python notebooks:
 
 For standard Polarised Structures (e.g., PTO)
 ---------------------------------------------
-Atomap's :python:`get_polarization_from_second_sublattice` Sublattice method will
+Atomap's `get_polarization_from_second_sublattice <https://atomap.org/api_documentation.html?highlight=get_polarization#atomap.sublattice.Sublattice.get_polarization_from_second_sublattice>`_
+Sublattice method will
 be sufficent for most users when dealing with the classic PTO-style polarisation,
 wherein the atoms in a sublattice are polarised with respect to a second sublattice.
 
 See the second section of this tutorial on how to plot this in many different ways
-using :python:`plot_polarisation_vectors`!
+using :py:func:`temul.topotem.polarisation.plot_polarisation_vectors`!
 
 .. code-block:: python
 
@@ -72,13 +73,14 @@ using :python:`plot_polarisation_vectors`!
 For nonstandard Polarised Structures (e.g., Boracites)
 ------------------------------------------------------
 When the above function can't isn't suitable, the TEMUL
-:python:`find_polarisation_vectors` function may be an option. It is useful for
-structures that Atomap's :python:`get_polarization_from_second_sublattice` can't
+:py:func:`temul.topotem.polarisation.find_polarisation_vectors` function may
+be an option. It is useful for structures that Atomap's
+:python:`get_polarization_from_second_sublattice` can't
 handle. It is a little more involved and requires some extra preparation when 
 creating the sublattices. 
 
 See the second section of this tutorial on how to plot this in many different ways
-using :python:`plot_polarisation_vectors`!
+using :py:func:`temul.topotem.polarisation.plot_polarisation_vectors`!
 
 .. code-block:: python
 
@@ -130,13 +132,13 @@ For single Polarised Sublattices (e.g., LNO)
 --------------------------------------------
 When dealing with structures in which the polarisation must be extracted from a
 single sublattice (one type of chemical atomic column, the TEMUL
-:python:`atom_deviation_from_straight_line_fit` function
+:py:func:`temul.topotem.polarisation.atom_deviation_from_straight_line_fit` function
 may be an option. It is based off the description by J. Gonnissen *et al*,
 Direct Observation of Ferroelectric Domain Walls in LiNbO3: Wall‐Meanders,
 Kinks, and Local Electric Charges, 26, 42, 2016, DOI: 10.1002/adfm.201603489.
 
 See the second section of this tutorial on how to plot this in many different ways
-using :python:`plot_polarisation_vectors`!
+using :py:func:`temul.topotem.polarisation.plot_polarisation_vectors`!
 
 .. code-block:: python
 
@@ -191,9 +193,10 @@ Let's look at some rotated data
 Plotting Polarisation and Movement Vectors
 ==========================================
 
-The :python:`temul.polarisation` module allows one to visualise the
+The :py:func:`temul.topotem.polarisation.temul.polarisation` module allows one to visualise the
 polarisation/movement of atoms in an atomic resolution image. In this tutorial,
-we will use a dummy dataset to show the different ways the :python:`plot_polarisation_vectors`
+we will use a dummy dataset to show the different ways the
+:py:func:`temul.topotem.polarisation.plot_polarisation_vectors`
 function can display data. In future, tutorials on published experimental data
 will also be available.
 
@@ -256,7 +259,8 @@ polarisation vectors for regular structures.
     >>> x, y = [i[0] for i in vector_list], [i[1] for i in vector_list]
     >>> u, v = [i[2] for i in vector_list], [i[3] for i in vector_list]
 
-Now we can display all of the variations that :python:`plot_polarisation_vectors`
+Now we can display all of the variations that
+:py:func:`temul.topotem.polarisation.plot_polarisation_vectors`
 gives us! You can specify sampling (scale) and units, or use a calibrated image
 so that they are automatically set.
 

doc/_build/html/dg_visualiser_tutorial.html

@@ -205,7 +205,7 @@ <h2>Interactively Filter the Experimental Image<a class="headerlink" href="#inte
 <p>As we can see, an interactive window appears, showing the FFT (“FFT Widget”)
 of the image with the positions of the inner and outer Gaussian full width
 at half maximums (FWHMs). The inital FWHMs can be changed with the
-<code class="code python docutils literal notranslate"><span class="name"><span class="pre">visualise_dg_filter</span></span><span class="operator"><span class="pre">.</span></span><span class="name"><span class="pre">d_inner</span></span></code> and <code class="code python docutils literal notranslate"><span class="name"><span class="pre">d_outer</span></span></code>
+<code class="code python docutils literal notranslate"><span class="name"><span class="pre">d_inner</span></span></code> and <code class="code python docutils literal notranslate"><span class="name"><span class="pre">d_outer</span></span></code>
 parameters (limits can also be changed).</p>
 <p>To change the two FWHMs interactively
 just use the sliders at the bottom of the window. Reset can be used to reset the