Merge pull request #315 from sbillinge/sidocs

doc: simon tweaks on the docs before release

doc/source/examples/examples.rst

@@ -7,8 +7,8 @@ Examples
 Landing page for diffpy.utils examples.
 
 .. toctree::
-    parsers_example
+    diffraction_objects_example
+    transforms_example
     resample_example
+    parsers_example
     tools_example
-    transforms_example
-    diffraction_objects_example

doc/source/examples/parsers_example.rst

@@ -9,49 +9,65 @@ This example will demonstrate how diffpy.utils lets us easily process and serial
 Using the parsers module, we can load file data into simple and easy-to-work-with Python objects.
 
 1) To begin, unzip :download:`parser_data<./example_data/parser_data.zip>` and take a look at ``data.txt``.
-   Our goal will be to extract and serialize the data table as well as the parameters listed in the header of this file.
+   This is a fairly standard format for 1D powder diffraction data.
+   Our goal will be to extract the data, and the parameters listed in the header, from this file and
+   load it into our program.
 
 2) To get the data table, we will use the ``loadData`` function. The default behavior of this
-   function is to find and extract a data table from a file.::
+   function is to find and extract a data table from a file.
+
+.. code-block:: python
 
      from diffpy.utils.parsers.loaddata import loadData
      data_table = loadData('<PATH to data.txt>')
 
-   While this will work with most datasets, on our ``data.txt`` file, we got a ``ValueError``. The reason for this is
-   due to the comments ``$ Phase Transition Near This Temperature Range`` and ``--> Note Significant Jump in Rw <--``
-   embedded within the dataset. To fix this, try using the ``comments`` parameter. ::
+While this will work with most datasets, on our ``data.txt`` file, we got a ``ValueError``. The reason for this is
+due to the comments ``$ Phase Transition Near This Temperature Range`` and ``--> Note Significant Jump in Rw <--``
+embedded within the dataset. To fix this, try using the ``comments`` parameter.
+
+.. code-block:: python
 
      data_table = loadData('<PATH to data.txt>', comments=['$', '-->'])
 
-   This parameter tells ``loadData`` that any lines beginning with ``$`` and ``-->`` are just comments and
-   more entries in our data table may follow.
+This parameter tells ``loadData`` that any lines beginning with ``$`` and ``-->`` are just comments and
+more entries in our data table may follow.
 
-   Here are a few other parameters to test out:
+Here are a few other parameters to test out:
 
    * ``delimiter=','``: Look for a comma-separated data table. Useful for csv file types.
-     However, since ``data.txt`` is whitespace separated, running ::
+     However, since ``data.txt`` is whitespace separated, running
+
+.. code-block:: python
 
        loadData('<PATH to data.txt>', comments=['$', '-->'], delimiter=',')
 
-     returns an empty list.
+returns an empty list.
    * ``minrows=50``: Only look for data tables with at least 50 rows. Since our data table has much less than that many
-     rows, running ::
+     rows, running
+
+.. code-block:: python
 
        loadData('<PATH to data.txt>', comments=['$', '-->'], minrows=50)
 
-     returns an empty list.
+returns an empty list.
    * ``usecols=[0, 3]``: Only return the 0th and 3rd columns (zero-indexed) of the data table. For ``data.txt``, this
-     corresponds to the temperature and rw columns. ::
+     corresponds to the temperature and rw columns.
+
+.. code-block:: python
 
        loadData('<PATH to data.txt>', comments=['$', '-->'], usecols=[0, 3])
 
 3) Next, to get the header information, we can again use ``loadData``,
-   but this time with the ``headers`` parameter enabled. ::
+   but this time with the ``headers`` parameter enabled.
+
+.. code-block:: python
 
      hdata = loadData('<PATH to data.txt>', comments=['$', '-->'], headers=True)
 
 4) Rather than working with separate ``data_table`` and ``hdata`` objects, it may be easier to combine them into a single
-   dictionary. We can do so using the ``serialize_data`` function. ::
+   dictionary. We can do so using the ``serialize_data`` function.
+
+.. code-block:: python
 
      from diffpy.utils.parsers.loaddata import serialize_data
      file_data = serialize_data('<PATH to data.txt', hdata, data_table)
@@ -60,45 +76,59 @@ Using the parsers module, we can load file data into simple and easy-to-work-wit
      # The entry is a dictionary containing data from hdata and data_table
      data_dict = file_data['data.txt']
 
-   This dictionary ``data_dict`` contains all entries in ``hdata`` and an additional entry named
-   ``data table`` containing ``data_table``. ::
+This dictionary ``data_dict`` contains all entries in ``hdata`` and an additional entry named
+``data table`` containing ``data_table``.
+
+.. code-block:: python
 
      here_is_the_data_table = data_dict['data table']
 
-   There is also an option to name columns in the data table and save those columns as entries instead. ::
+There is also an option to name columns in the data table and save those columns as entries instead.
+
+.. code-block:: python
 
      data_table_column_names = ['temperature', 'scale', 'stretch', 'rw']  # names of the columns in data.txt
      file_data = serialize_data('<PATH to data.txt>', hdata, data_table, dt_colnames=data_table_column_names)
      data_dict = file_data['data.txt']
 
-   Now we can extract specific data table columns from the dictionary. ::
+Now we can extract specific data table columns from the dictionary.
 
+.. code-block:: python
      data_table_temperature_column = data_dict['temperature']
      data_table_rw_column = data_dict['rw']
 
-5) When we are done working with the data, we can store it on disc for later use. This can also be done using the
-   ``serialize_data`` function with an additional ``serial_file`` parameter.::
+5) When we are done working with the data, we can store it on disk for later use. This can also be done using the
+   ``serialize_data`` function with an additional ``serial_file`` parameter.
+
+.. code-block:: python
 
      parsed_file_data = serialize_data('<PATH to data.txt>', hdata, data_table, serial_file='<PATH to serialfile.json>')
 
    The returned value, ``parsed_file_data``, is the dictionary we just added to ``serialfile.json``.
-   To extract the data from the serial file, we use ``deserialize_data``. ::
+   To extract the data from the serial file, we use ``deserialize_data``.
+
+.. code-block:: python
 
      from diffpy.utils.parsers.serialization import deserialize_data
      parsed_file_data = deserialize_data('<PATH to serialdata.json>')
 
-6) Finally, ``serialize_data`` allows us to store data from multiple text file in a single serial file. For one last bit
-   of practice, we will extract and add the data from ``moredata.txt`` into the same ``serialdata.json`` file.::
+6) Finally, ``serialize_data`` allows us to store data from multiple text files in a single serial file. For one last bit
+   of practice, we will extract and add the data from ``moredata.txt`` into the same ``serialdata.json`` file.
+
+.. code-block:: python
 
      data_table = loadData('<PATH to moredata.txt>')
      hdata = loadData('<PATH to moredata.txt>', headers=True)
      serialize_data('<PATH to moredata.txt>', hdata, data_table, serial_file='<PATH to serialdata.json>')
 
-   The serial file ``serialfile.json`` should now contain two entries: ``data.txt`` and ``moredata.txt``.
-   The data from each file can be accessed using ::
+The serial file ``serialfile.json`` should now contain two entries: ``data.txt`` and ``moredata.txt``.
+The data from each file can be accessed using
+
+.. code-block:: python
 
      serial_data = deserialize_data('<PATH to serialdata.json>')
      data_txt_data = serial_data['data.txt']  # Access data.txt data
      moredata_txt_data = serial_data['moredata.txt']  # Access moredata.txt data
 
 For more information, check out the :ref:`documentation<Parsers Documentation>` of the ``parsers`` module.
+b

doc/source/examples/resample_example.rst

@@ -13,14 +13,29 @@ given enough datapoints.
 1) To start, unzip :download:`parser_data<./example_data/parser_data.zip>`. Then, load the data table from ``Nickel.gr``
    and ``NiTarget.gr``. These datasets are based on data from `Atomic Pair Distribution Function Analysis: A Primer
    <https://global.oup.com/academic/product/atomic-pair-distribution-function-analysis-9780198885801?cc=us&lang=en&>`_.
-   ::
 
-     from diffpy.utils.parsers.loaddata import loadData
-     nickel_datatable = loadData('<PATH to Nickel.gr>')
-     nitarget_datatable = loadData('<PATH to NiTarget.gr>')
+.. code-block:: python
 
-   Each data table has two columns: first is the grid and second is the function value.
-   To extract the columns, we can utilize the serialize function ... ::
+       from diffpy.utils.parsers.loaddata import loadData
+       nickel_datatable = loadData('<PATH to Nickel.gr>')
+       nitarget_datatable = loadData('<PATH to NiTarget.gr>')
+
+Each data table has two columns: first is the grid and second is the function value.
+To extract the columns, we can utilize the serialize function ...
+
+.. code-block:: python
+
+     from diffpy.utils.parsers.serialization import serialize_data
+     nickel_data = serialize_data('Nickel.gr', {}, nickel_datatable, dt_colnames=['grid', 'func'])
+     nickel_grid = nickel_data['Nickel.gr']['grid']
+     nickel_func = nickel_data['Nickel.gr']['func']
+     target_data = serialize_data('NiTarget.gr', {}, nitarget_datatable, dt_colnames=['grid', 'function'])
+     target_grid = nickel_data['Nickel.gr']['grid']
+     target_func = nickel_data['Nickel.gr']['func']
+
+To extract the columns, we can utilize the serialize function ...
+
+.. code-block:: python
 
      from diffpy.utils.parsers.serialization import serialize_data
      nickel_data = serialize_data('Nickel.gr', {}, nickel_datatable, dt_colnames=['grid', 'func'])
@@ -30,42 +45,52 @@ given enough datapoints.
      target_grid = nickel_data['Nickel.gr']['grid']
      target_func = nickel_data['Nickel.gr']['func']
 
-   ... or you can use any other column extracting method you prefer.
+... or you can use any other column extracting method you prefer.
 
-2) If we plot the two on top of each other ::
+2) If we plot the two on top of each other
+
+.. code-block:: python
 
      import matplotlib.pyplot as plt
      plt.plot(target_grid, target_func, linewidth=3)
      plt.plot(nickel_grid, nickel_func, linewidth=1)
 
-   they look pretty similar, but to truly see the difference, we should plot the difference between the two.
-   We may want to run something like ... ::
+they look pretty similar, but to truly see the difference, we should plot the difference between the two.
+We may want to run something like ...
+
+.. code-block:: python
 
      import numpy as np
      difference = np.subtract(target_func, nickel_func)
 
-   ... but this will only produce the right result if the ``target_func`` and ``nickel_func`` are on the same grid.
-   Checking the lengths of ``target_grid`` and ``nickel_grid`` shows that these grids are clearly distinct.
+... but this will only produce the right result if the ``target_func`` and ``nickel_func`` are on the same grid.
+Checking the lengths of ``target_grid`` and ``nickel_grid`` shows that these grids are clearly distinct.
 
 3) However, we can resample the two functions to be on the same grid. Since both functions have grids spanning
-   ``[0, 60]``, let us define a new grid ... ::
+   ``[0, 60]``, let us define a new grid ...
+
+.. code-block:: python
 
      grid = np.linspace(0, 60, 6001)
 
-   ... and use the diffpy.utils ``wsinterp`` function to resample on this grid.::
+... and use the diffpy.utils ``wsinterp`` function to resample on this grid.
+
+.. code-block:: python
 
      from diffpy.utils.resampler import wsinterp
      nickel_resample = wsinterp(grid, nickel_grid, nickel_func)
      target_resample = wsinterp(grid, target_grid, target_func)
 
-   We can now plot the difference to see that these two functions are quite similar.::
+We can now plot the difference to see that these two functions are quite similar.
+
+.. code-block:: python
 
      plt.plot(grid, target_resample)
      plt.plot(grid, nickel_resample)
      plt.plot(grid, target_resample - nickel_resample)
 
-   This is the desired result as the data in ``Nickel.gr`` is every tenth data point in ``NiTarget.gr``.
-   This also shows us that ``wsinterp`` can help us reconstruct a function from incomplete data.
+This is the desired result as the data in ``Nickel.gr`` is every tenth data point in ``NiTarget.gr``.
+This also shows us that ``wsinterp`` can help us reconstruct a function from incomplete data.
 
 4) In order for our function reconstruction to be perfect up to a truncation error, we require that (a) the function is
    a Fourier transform of a band-limited dataset and (b) the original grid has enough equally-spaced datapoints based on
@@ -79,7 +104,9 @@ given enough datapoints.
    Thus, our original grid requires :math:`25.0 * 60.0 / \pi < 478`. Since our grid has :math:`601` datapoints, our
    reconstruction was perfect as shown from the comparison between ``Nickel.gr`` and ``NiTarget.gr``.
 
-   This computation is implemented in the function ``nsinterp``.::
+   This computation is implemented in the function ``nsinterp``.
+
+.. code-block:: python
 
      from diffpy.utils.resampler import nsinterp
      qmin = 0

doc/source/examples/transforms_example.rst

@@ -16,6 +16,7 @@ This example will demonstrate how to use the functions in the
 
     # Example: convert q to 2theta
     from diffpy.utils.transforms import q_to_tth
+
     wavelength = 0.71
     q = np.array([0, 0.2, 0.4, 0.6, 0.8, 1])
     tth = q_to_tth(q, wavelength)
@@ -32,6 +33,7 @@ This example will demonstrate how to use the functions in the
 
     # Example: convert 2theta to q
     from diffpy.utils.transforms import tth_to_q
+
     wavelength = 0.71
     tth = np.array([0, 30, 60, 90, 120, 180])
     q = tth_to_q(tth, wavelength)
@@ -49,11 +51,13 @@ This example will demonstrate how to use the functions in the
 
     # Example: convert d to q
     from diffpy.utils.transforms import d_to_q
+
     d = np.array([1.0, 0.8, 0.6, 0.4, 0.2])
     q = d_to_q(d)
 
     # Example: convert d to 2theta
     from diffpy.utils.transforms import d_to_tth
+
     wavelength = 0.71
     d = np.array([1.0, 0.8, 0.6, 0.4, 0.2])
     tth = d_to_tth(d, wavelength)

doc/source/index.rst

@@ -4,14 +4,18 @@
 
 .. |title| replace:: diffpy.utils documentation
 
-diffpy.utils - Shared utilities for diffpy packages.
+diffpy.utils - General utilities for analyzing diffraction data
 
 | Software version |release|.
 | Last updated |today|.
 
-The diffpy.utils package provides general functions for extracting data from variously formatted text files as well as
-some PDF-specific functionality. These include wx GUI utilities used by the PDFgui program and an interpolation function
-based on the Whittaker-Shannon formula for resampling a bandlimited PDF or other profile function.
+The diffpy.utils package provides a number of functions and classes designed to help
+researchers analyze their diffraction data.  It also includes some functionality for
+carrying out PDF analysis.  Examples are parsers for reading common format diffraction
+data files, ``DiffractionObjects`` that allow you to do algebra on diffraction patterns,
+tools for better capture and propagation of metadata,
+diffraction-friendly interpolation routines, as well as some other tools used across
+diffpy libraries.
 
 Click :ref:`here<Utilities>` for a full list of utilities offered by diffpy.utils.
 
@@ -20,6 +24,7 @@ Examples
 ========
 Illustrations of when and how one would use various diffpy.utils functions.
 
+* :ref:`Manipulate and do algebra on diffraction data<Diffraction Objects Example>`
 * :ref:`File Data Extraction<Parsers Example>`
 * :ref:`Resampling and Data Reconstruction<Resample Example>`
 * :ref:`Load and Manage User and Package Information<Tools Example>`
@@ -30,8 +35,9 @@ Authors
 
 diffpy.utils is developed by members of the Billinge Group at
 Columbia University and at Brookhaven National Laboratory including
-Pavol Juhás, Christopher L. Farrow, the Billinge Group
-and its community contributors.
+Pavol Juhás, Christopher L. Farrow, Simon J. L. Billinge, Andrew Yang,
+with contributions from many Billinge Group members and
+members of the diffpy community.
 
 For a detailed list of contributors see
 https://github.com/diffpy/diffpy.utils/graphs/contributors.
@@ -43,6 +49,14 @@ Installation
 See the `README <https://github.com/diffpy/diffpy.utils#installation>`_
 file included with the distribution.
 
+========
+Citation
+========
+
+If you use this program for a scientific research that leads to publication, we ask that you acknowledge use of the program by citing the following paper in your publication:
+
+    P. Juhás, C. L. Farrow, X. Yang, K. R. Knox and S. J. L. Billinge, Complex modeling: a strategy and software program for combining multiple information sources to solve ill posed structure and nanostructure inverse problems, Acta Crystallogr. A 71, 562-568 (2015).
+
 =================
 Table of contents
 =================

doc/source/license.rst

@@ -14,10 +14,10 @@ OPEN SOURCE LICENSE AGREEMENT
   Lawrence Berkeley National Laboratory
 | Copyright (c) 2014, Australian Synchrotron Research Program Inc., ("ASRP")
 | Copyright (c) 2006-2007, Board of Trustees of Michigan State University
-| Copyright (c) 2008-2012, The Trustees of Columbia University in
-  the City of New York
 | Copyright (c) 2014-2019, Brookhaven Science Associates,
   Brookhaven National Laboratory
+| Copyright (c) 2008-2025, The Trustees of Columbia University in
+  the City of New York
 
 
 The "DiffPy-CMI" is distributed subject to the following license conditions: