Add preliminary pyfai morph

Author: Sparks29032

docs/source/funcxy.rst

@@ -26,14 +26,102 @@ scaling factor ``bgscale``.
 
 We will showcase an example of how one would refine over the
 ``PDFGetter`` parameters using ``funcxy`` to obtain a PDF.
-.. code-block::
 
-    from diffpy.pdfgetx.pdfgetter import PDFGetter
-    from diffpy.morph.morphpy import morph_arrays
-    from diffpy.utils.parsers.loaddata import loadData
+Let's say you have a measured I(Q) with Q in angstroms of a lead
+nanoparticle (composition PbS) named ``sample.chi`` taken on a
+glass background. We want to match a target calculated PDF G(r)
+stored in a file named ``target.cgr``.
+Let's also say we have a measured I(Q) of the
+glass background ``background.chi``.
+.. code-block:: python
 
+     from diffpy.pdfgetx.pdfgetter import PDFGetter
+     from diffpy.morph.morphpy import morph_arrays
+     from diffpy.utils.parsers.loaddata import loadData
 
+     pg = PDFGetter()
+
+     backgroundfile = loadData("background.chi")
+     composition = "PbS"
+
+
+     def wrap(x, y, **kwargs):
+         xy_out = pg.__call__(
+                         x=x, y=y, dataformat="QA",
+                         composition=composition,
+                         backgroundfile=backgroundfile,
+                         **kwargs
+                     )
+         r = xy_out[0]
+         gr = xy_out[1]
+         return (r, gr)
+
+
+     sample_iq = loadData("sample.chi")
+     target_gr = loadData("target.cgr")
+     params_to_morph = {
+         "bgscale": 1.0,
+         "qmin": 0.0, "qmax": 25.0,
+         "qmaxinst": 25.0, "rpoly": 0.9
+     }
+
+     morph_info, morph_table = morph_arrays(
+                                     sample_iq, target_gr,
+                                     funcxy=(wrap, params_to_morph)
+                                 )
+
+
+You can now plot ``morph_table`` against your ``target_gr`` to see
+how well your morphing refinement of the PDF-getting parameters
+as done!
+To see what the refined values of the parameters are,
+print out ``morph_info``.
+You can freely add and remove entries in
+``params_to_morph`` to include or not include them as
+parameters to refine over.
+
+If you expect to see thermal effect differences between your
+measured PDF and ``target_gr``, you can also include
+the ``stretch``, ``scale``, and ``smear`` morphs in your
+call to ``morph_arrays``.
 
 
 Performing Detector Calibration with PyFai
 ==========================================
+When performing azimuthal integration, it is important to
+ensure your beam center and detector distances are calibrated.
+However, it is possible that they have shifted
+across measurements. Here, we will use morphing to the rescue!
+
+Let's say we just measured a diffraction pattern stored
+as a NumPy object in ``diffraction_image.npy``, but some
+of the detector geometries are off.
+Before this measurement, you measured an amazing
+I(Q) pattern ``target.chi`` with a perfectly calibrated
+sample-to-detector distance and beam center.
+We will use morphing to try to recalibrate.
+
+.. code-block:: python
+
+     import numpy as np
+     import pyFAI.integrator.azimuthal as pyfai
+     import pyFAI.detectors as pfd
+     from diffpy.morph.morphpy import morph_arrays
+     from diffpy.utils.parsers.loaddata import loadData
+
+     pattern_2d = np.load("diffraction_image.npy")
+     wavelength = #
+     pixel1 = #
+     pixel2 = #
+
+     ai = pyfai.AzimuthalIntegrator()
+     ai.wavelength = wavelength
+     detector = pfd.Detector()
+     detector.max_shape = pattern_2d.shape
+
+     def wrap(x, y, sample_to_detector_dist, cent_offset_x, cent_offset_y):
+         detector.pixel1 = pixel1
+         detector.pixel2 = pixel2
+
+
+         ai.detector = detector