| 1 | <!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"> |
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| 2 | <html> <head> |
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| 3 | <title>Scripting GSAS-II</title> |
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| 4 | </head> |
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| 5 | |
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| 6 | <body> |
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| 7 | <h1>Running a GSAS-II Refinement From a Python Script</h1> |
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| 8 | In this training example we create a Python script to duplicate the |
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| 9 | refinement in the |
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| 10 | <A |
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| 11 | href="https://subversion.xray.aps.anl.gov/pyGSAS/Tutorials/CWCombined/Combined%20refinement.htm" |
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| 12 | target="_blank">GSAS-II CW Combined Refinement</A> |
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| 13 | tutorial. This uses the |
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| 14 | <A href="http://gsas-ii.readthedocs.io/en/latest/GSASIIscripts.html" target="_blank"> |
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| 15 | GSASIIscriptable module</A> |
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| 16 | to perform the same refinements steps, but without use of the GSAS-II |
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| 17 | graphical user interface. |
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| 18 | |
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| 19 | <h2>Prerequisites</h2> |
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| 20 | This exercise assumes that the reader has reasonable familiarity with |
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| 21 | the Python language. |
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| 22 | Before beginning this exercise, the documentation on the |
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| 23 | <A href="http://gsas-ii.readthedocs.io/en/latest/GSASIIscripts.html" |
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| 24 | target="_blank"> |
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| 25 | Scripting Tools in the GSAS-II GSASIIscriptable module</A> should be |
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| 26 | read from here: <A href="http://gsas-ii.readthedocs.io/en/latest/GSASIIscripts.html" |
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| 27 | target="_blank">http://gsas-ii.readthedocs.io/en/latest/GSASIIscripts.html</A>. It |
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| 28 | is also wise to read the <A href="https://subversion.xray.aps.anl.gov/pyGSAS/Tutorials/CWCombined/Combined%20refinement.htm" |
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| 29 | target="_blank">GSAS-II CW Combined Refinement</A> tutorial that this |
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| 30 | exercise is modeled upon, which explains why each refinement step is |
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| 31 | being used. |
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| 32 | <P> |
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| 33 | The exercise can be performed by placing all of the Python commands |
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| 34 | into a script, (which can also be |
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| 35 | <A href="https://subversion.xray.aps.anl.gov/trac/pyGSAS/export/3131/Tutorials/PythonScript/data/example.py"> |
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| 36 | downloaded here</A>), |
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| 37 | but a more pedagogical approach will be to enter the |
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| 38 | commands into a Python interpreter. Use of IPython or Jupyter to run |
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| 39 | Python will make this a more pleasant experience. |
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| 40 | |
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| 41 | <h4>0: Load the GSASIIscriptable module</H4> |
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| 42 | <blockquote> |
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| 43 | The first step in writing any Python module is to load the modules |
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| 44 | that will be needed. Here we will need the <tt>os</tt> and |
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| 45 | <tt>sys</tt> modules from the Python standard library and the |
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| 46 | GSASIIscriptable from the GSAS-II code. The location where the |
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| 47 | GSAS-II source code is installed must usually be hard-coded into the |
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| 48 | script as is done in the example below. Note that a common location |
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| 49 | for this will be |
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| 50 | <TT>os.path.expanduser("~/g2conda/GSASII/")</TT>. Thus the script will |
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| 51 | begin with: |
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| 52 | |
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| 53 | <blockquote><textarea rows="3" cols="60" readonly> |
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| 54 | import os,sys |
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| 55 | sys.path.insert(0,'/Users/toby/software/G2/GSASII') |
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| 56 | import GSASIIscriptable as G2sc</textarea></blockquote> |
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| 57 | |
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| 58 | If a <tt>ImportError: No module named GSASIIscriptable</tt> error |
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| 59 | occurs, then the wrong directory location has been supplied for the |
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| 60 | GSAS-II files. |
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| 61 | </blockquote> |
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| 62 | |
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| 63 | <h4>1: Define some other prerequisite code</H4> |
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| 64 | <blockquote> |
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| 65 | To simplify this example, we will define the location where files will |
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| 66 | be written as <tt>workdir</tt> (this directory must exist, but it may |
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| 67 | be empty) and the location where the input files for this exercise may |
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| 68 | be found as <tt>datadir</tt>. (Note that this directory must have the |
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| 69 | the following files: "PBSO4.XRA", "INST_XRY.PRM", "PBSO4.CWN", |
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| 70 | "inst_d1a.prm" and "PbSO4-Wyckoff.cif". These files can be downloaded |
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| 71 | from |
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| 72 | <A href="https://subversion.xray.aps.anl.gov/pyGSAS/Tutorials/PythonScript/data/"> |
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| 73 | https://subversion.xray.aps.anl.gov/pyGSAS/Tutorials/PythonScript/data/</A>) |
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| 74 | <P> |
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| 75 | We also define here a short function to display the weighted profile R |
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| 76 | factor for every histogram in a project, <tt>HistStats</tt>. This will |
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| 77 | be <A href="#HistStats">discussed later</A>. |
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| 78 | |
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| 79 | <blockquote><textarea rows="10" cols="70" readonly> |
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| 80 | workdir = "/Users/toby/Scratch/PythonScript" |
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| 81 | datadir = "/Users/toby/software/G2/Tutorials/PythonScript/data" |
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| 82 | |
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| 83 | def HistStats(gpx): |
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| 84 | '''prints profile rfactors for all histograms''' |
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| 85 | print(u"*** profile Rwp, "+os.path.split(gpx.filename)[1]) |
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| 86 | for hist in gpx.histograms(): |
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| 87 | print("\t{:20s}: {:.2f}".format(hist.name,hist.get_wR())) |
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| 88 | print("") |
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| 89 | gpx.save()</textarea></blockquote> |
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| 90 | |
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| 91 | </blockquote> |
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| 92 | |
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| 93 | <h4>2: Create a GSAS-II project</H4> |
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| 94 | <blockquote> |
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| 95 | The first step in creating a GSASIIscriptable script to to create or |
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| 96 | access a GSAS-II project, which is done with a call to |
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| 97 | <TT>GSASIIscriptable.G2Project()</tt>. This can be done in one of two |
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| 98 | ways, a call with <tt>G2sc.G2Project(filename=</tt><I>file</I><tt>)</tt> creates a new |
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| 99 | (empty) project, while a call with |
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| 100 | <tt>G2sc.G2Project(gpxfile=</tt><I>file</I><tt>)</tt> opens and |
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| 101 | reads an existing project (.gpx) file. Both return a Project wrapper |
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| 102 | object that is used to access a number of methods and variables. |
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| 103 | Note that <TT>GSASIIscriptable.G2Project()</tt> can read .gpx files |
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| 104 | written by both previous <TT>GSASIIscriptable.G2Project()</tt> runs or |
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| 105 | the GSAS-II GUI. |
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| 106 | <P> |
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| 107 | In this example, we create a new project by using the |
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| 108 | <tt>filename=</tt><I>file</I> argument. Note that this will not |
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| 109 | actually create a file until a save operation is done. |
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| 110 | |
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| 111 | <blockquote><textarea rows="3" cols="70" readonly> |
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| 112 | # create a project with a default project name |
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| 113 | gpx = G2sc.G2Project(filename='PbSO4.gpx')</textarea></blockquote> |
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| 114 | |
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| 115 | </blockquote> |
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| 116 | <h4>3: Add Histograms and Phase to the GSAS-II project</H4> |
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| 117 | <blockquote> |
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| 118 | To add two powder diffraction datasets (histograms) to the |
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| 119 | project we use the <I>project</I><tt>.add_powder_histogram()</tt> method in |
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| 120 | the <TT>GSASIIscriptable</tt> class. The two arguments to |
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| 121 | <tt>.add_powder_histogram()</tt> are the powder dataset and the |
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| 122 | instrument parameter file. While neither powder data file uses a |
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| 123 | standard extension, the importer is able to determine the file |
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| 124 | format anyway. This will not be true for all file formats. |
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| 125 | <UL><LI> |
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| 126 | Note that <I>project</I><tt>.add_powder_histogram()</tt> returns a |
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| 127 | powder histogram objects which are saved for later reference. It is |
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| 128 | also possible to obtain these using <tt>gpx.histograms()</tt>, which |
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| 129 | returns a list of defined histograms. |
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| 130 | </UL> |
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| 131 | <P> |
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| 132 | Then we add a phase to the project using |
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| 133 | <I>project</I><tt>.add_phase()</tt>. This specifies a CIF containing the |
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| 134 | structural information, a name for the phase and specifies that the |
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| 135 | two histograms are "added" (linked) to the phase. |
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| 136 | <UL><LI> |
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| 137 | Note that <I>project</I><tt>.add_phase()</tt> |
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| 138 | returns a phase object</li> |
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| 139 | <LI>Also, the previously saved histogram objects are used in the |
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| 140 | <I>project</I><tt>.add_phase()</tt> call. Note that it is |
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| 141 | also possible to reference the two histgrams by their numbers in the |
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| 142 | project (here <tt>histograms=[0,1]</tt>) or by the histogram names |
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| 143 | (here <BR><TT>histograms=['PWDR PBSO4.XRA Bank 1', 'PWDR PBSO4.CWN |
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| 144 | Bank 1']</TT>). |
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| 145 | These three ways to use the <tt>histograms</tt> parameter produce |
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| 146 | the same result. |
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| 147 | |
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| 148 | <blockquote><textarea rows="10" cols="70" readonly> |
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| 149 | # setup step 1: add two histograms to the project |
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| 150 | hist1 = gpx.add_powder_histogram(os.path.join(datadir,"PBSO4.XRA"), |
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| 151 | os.path.join(datadir,"INST_XRY.PRM")) |
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| 152 | hist2 = gpx.add_powder_histogram(os.path.join(datadir,"PBSO4.CWN"), |
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| 153 | os.path.join(datadir,"inst_d1a.prm")) |
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| 154 | # setup step 2: add a phase and link it to the previous histograms |
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| 155 | phase0 = gpx.add_phase(os.path.join(datadir,"PbSO4-Wyckoff.cif"), |
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| 156 | phasename="PbSO4", |
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| 157 | histograms=[hist1,hist2])</textarea></blockquote> |
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| 158 | |
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| 159 | |
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| 160 | </blockquote> |
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| 161 | <A name="ChangeCycles"> |
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| 162 | <h4>4: Change the number of refinement cycles</H4> |
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| 163 | </A><blockquote> |
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| 164 | While this is not noted in the original tutorial, this exercise will |
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| 165 | not complete properly if more variables are added to the refinement |
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| 166 | without converging the refinement (or at least coming close to |
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| 167 | convergence) at each refinement step. This is best accomplished by |
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| 168 | increasing the number of least-squares cycles. There at present is |
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| 169 | no method in the project object that allows this parameter to be |
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| 170 | set, so this must be done by finding appropriate parameter |
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| 171 | dictionary entry. |
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| 172 | <P> |
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| 173 | At this point, the Python command <tt>gpx.data.keys()</tt> shows the names of the |
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| 174 | entries in the GSAS-II tree; 'Controls' is the tree item |
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| 175 | corresponding to the section where Least Squares cycles are |
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| 176 | set. Command <tt>gpx.data['Controls'].keys()</tt> shows that all |
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| 177 | values are located in an entry labeled 'data' and |
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| 178 | <tt>gpx.data['Controls']['data'].keys()</tt> shows the entries in |
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| 179 | this section. Examination of |
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| 180 | <BR><tt>gpx.data['Controls']['data']['max cyc']</tt>shows the value 3, |
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| 181 | which is default number of cycles. Thus, |
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| 182 | the number of cycles is changed with this Python command: |
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| 183 | |
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| 184 | <blockquote><textarea rows="3" cols="70" readonly> |
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| 185 | # not in tutorial: increase # of cycles to improve convergence |
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| 186 | gpx.data['Controls']['data']['max cyc'] = 8 # not in API </textarea></blockquote> |
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| 187 | |
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| 188 | </blockquote> |
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| 189 | <h4>5: Set initial variables and refine</H4> |
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| 190 | <blockquote> |
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| 191 | |
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| 192 | In Step 4 of the original tutorial, the refinement is performed with |
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| 193 | the variables that are on by default. These variables are the two |
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| 194 | histogram scale factors and three background parameters for each |
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| 195 | histogram (8 in total). With GSASIIscriptable, the background |
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| 196 | refinement flags are not turned on by default, so a dictionary must be |
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| 197 | created to set these histogram variables. This code: |
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| 198 | <UL> |
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| 199 | <LI>defines a dictionary (refdict0) that will be used to set the number of background coefficients (not really |
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| 200 | needed, since the default is 3) and sets the background refinement |
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| 201 | flag "on".</li> |
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| 202 | <LI>The project is saved under a new name, so that we can later look at |
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| 203 | the results from each step separately.</li> |
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| 204 | <LI>The parameters in refdict0 are set using |
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| 205 | <I>project</i><tt>.do_refinements()</tt> for both histograms in the |
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| 206 | project and the a refinement is run. |
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| 207 | <LI>The weighted profile R-factor values for all histograms in the project |
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| 208 | are printed using the previously defined <tt>HistStats()</tt> function. |
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| 209 | </UL> |
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| 210 | |
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| 211 | <A name="HistStats"> |
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| 212 | Function <tt>HistStats()</tt> works by using <tt>gpx.histograms()</tt> |
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| 213 | to iterate over all histograms in the project, setting <tt>hist</tt> to each histogram |
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| 214 | object. Class member <tt>hist.name</tt> provides the histogram name |
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| 215 | and method <tt>hist.get_wR()</tt> looks up the profile R-factor and |
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| 216 | prints them. The function also writes the final refinement results |
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| 217 | into the current project file. </A> |
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| 218 | |
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| 219 | <blockquote><textarea rows="5" cols="75" readonly> |
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| 220 | # tutorial step 4: turn on background refinement (Hist) |
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| 221 | refdict0 = {"set": {"Background": { "no. coeffs": 3, "refine": True }}} |
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| 222 | gpx.save('step4.gpx') |
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| 223 | gpx.do_refinements([refdict0]) |
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| 224 | HistStats(gpx)</textarea></blockquote> |
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| 225 | |
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| 226 | The output from this will be:<blockquote><pre> |
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| 227 | Hessian Levenburg-Marquardt SVD refinement on 8 variables: |
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| 228 | initial chi^2 9.6912e+06 |
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| 229 | Cycle: 0, Time: 1.88s, Chi**2: 6.7609e+05, Lambda: 0.001, Delta: 0.93 |
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| 230 | initial chi^2 6.7609e+05 |
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| 231 | Cycle: 1, Time: 1.84s, Chi**2: 6.7602e+05, Lambda: 0.001, Delta: 0.000104 |
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| 232 | converged |
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| 233 | Found 0 SVD zeros |
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| 234 | Read from file: /Users/toby/software/G2/Tutorials/PythonScript/step4.bak0.gpx |
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| 235 | Save to file : /Users/toby/software/G2/Tutorials/PythonScript/step4.gpx |
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| 236 | GPX file save successful |
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| 237 | Refinement results are in file: /Users/toby/software/G2/Tutorials/PythonScript/step4.lst |
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| 238 | ***** Refinement successful ***** |
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| 239 | *** profile Rwp, step4.gpx |
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| 240 | PWDR PBSO4.XRA Bank 1: 40.88 |
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| 241 | PWDR PBSO4.CWN Bank 1: 18.65 |
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| 242 | </pre></blockquote>Note that the Rwp values agree with what is |
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| 243 | expected from the original tutorial. |
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| 244 | <P> |
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| 245 | <B>Note:</B> that there are several equivalent ways to set the histogram |
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| 246 | parameters using |
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| 247 | <tt>G2PwdrData.set_refinements()</tt>, |
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| 248 | <tt>G2Project.set_refinement()</tt> or |
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| 249 | <tt>my_project.do_refinements()</tt>, as |
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| 250 | <A href="http://gsas-ii.readthedocs.io/en/latest/GSASIIscripts.html#refinement-parameters"> |
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| 251 | described here</A>. The <tt>gpx.do_refinements([refdict0])</tt> |
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| 252 | statement above could be replaced with: |
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| 253 | <blockquote><pre> |
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| 254 | gpx.set_refinement({"Background": { "no. coeffs": 3, "refine": True }}) |
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| 255 | gpx.do_refinements([{}]) |
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| 256 | </pre></blockquote> |
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| 257 | or |
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| 258 | <blockquote><pre> |
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| 259 | for hist in gpx.histograms(): |
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| 260 | hist.set_refinements({"Background": { "no. coeffs": 3, "refine": True }}) |
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| 261 | gpx.do_refinements([{}]) |
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| 262 | </pre></blockquote> |
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| 263 | |
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| 264 | </blockquote> |
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| 265 | <h4>6: Add unit cell parameters to refinement</h4> |
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| 266 | <blockquote> |
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| 267 | |
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| 268 | In Step 5 of the original tutorial, the refinement is performed again, |
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| 269 | but the unit cell is refined in the phase. |
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| 270 | |
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| 271 | <blockquote><textarea rows="6" cols="75" readonly> |
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| 272 | # tutorial step 5: add unit cell refinement (Phase) |
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| 273 | gpx.save('step5.gpx') |
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| 274 | refdict1 = {"set": {"Cell": True}} # set the cell flag (for all phases) |
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| 275 | gpx.set_refinement(refdict1) |
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| 276 | gpx.do_refinements([{}]) |
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| 277 | HistStats(gpx)</textarea></blockquote> |
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| 278 | |
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| 279 | In this case the <tt>gpx.set_refinement(refdict1)</tt> statement can |
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| 280 | be replaced with:<blockquote><pre> |
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| 281 | phase0.set_refinements({"Cell": True}) |
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| 282 | </pre></blockquote> |
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| 283 | |
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| 284 | Note that it is also possible to combine the refinement in the current |
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| 285 | and previous section using |
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| 286 | <blockquote><pre> |
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| 287 | gpx.do_refinements([refdict0,refdict1]) |
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| 288 | </pre></blockquote> |
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| 289 | but then the results are not saved in separate project files and it is |
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| 290 | not possible to see the Rwp values after each refinement. |
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| 291 | |
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| 292 | </blockquote> |
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| 293 | <h4>7: Add hydrostatic strain for just one histogram</h4> |
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| 294 | <blockquote> |
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| 295 | |
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| 296 | In Step 6 of the original tutorial, the refinement is performed again |
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| 297 | after adding Histogram/Phase (HAP) parameters that allow different |
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| 298 | lattice constants for the first histogram only. |
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| 299 | |
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| 300 | <blockquote><textarea rows="6" cols="75" readonly> |
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| 301 | # tutorial step 6: add Dij terms (HAP) for histogram 1 only |
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| 302 | gpx.save('step6.gpx') |
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| 303 | refdict2 = {"set": {"HStrain": True}} # set HAP parameters |
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| 304 | gpx.set_refinement(refdict2,phase=phase0,histogram=[hist1]) |
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| 305 | gpx.do_refinements([{}]) # refine after setting |
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| 306 | HistStats(gpx)</textarea></blockquote> |
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| 307 | |
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| 308 | Here we cannot use <tt>gpx.do_refinements([refdict2])</tt> because |
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| 309 | that would turn on refinement of the Hstrain terms for both histograms |
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| 310 | (if there were more than one phase, it would be all phases and all |
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| 311 | histograms), but in the above the <tt>gpx.set_refinement(...)</tt> |
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| 312 | statement alternately can be replaced with this: |
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| 313 | <blockquote><pre> |
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| 314 | phase0.set_HAP_refinements({"HStrain": True},histograms=[hist1]) |
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| 315 | </pre></blockquote> |
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| 316 | |
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| 317 | </blockquote> |
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| 318 | <h4>8: Add X-ray Sample broadening terms</h4> |
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| 319 | <blockquote> |
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| 320 | The next step in the original tutorial is to treat sample broadening |
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| 321 | by turning on refinement of the "Mustrain" (microstrain) and |
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| 322 | "Size" (Scherrer broadening) terms using an isotropic (single-term) |
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| 323 | model. As described in the |
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| 324 | <A |
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| 325 | href="http://gsas-ii.readthedocs.io/en/latest/GSASIIscripts.html#histogram-and-phase-parameters"> |
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| 326 | documentation for Histogram/Phase parameters</A>, type must always be |
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| 327 | specfied even as in this case where it is not being changed from the |
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| 328 | existing default. Again, since these parameters are being set only for |
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| 329 | one histogram, either <tt>phase0.set_HAP_refinements()</tt> or |
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| 330 | <tt>gpx.set_refinement()</tt> must be used. |
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| 331 | |
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| 332 | <blockquote><textarea rows="8" cols="75" readonly> |
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| 333 | # tutorial step 7: add size & strain broadening (HAP) for histogram 1 only |
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| 334 | gpx.save('step7.gpx') |
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| 335 | refdict2 = {"set": {"Mustrain": {"type":"isotropic","refine":True}, |
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| 336 | "Size":{"type":"isotropic","refine":True}, |
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| 337 | }} |
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| 338 | gpx.set_refinement(refdict2,phase=phase0,histogram=[hist1]) |
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| 339 | gpx.do_refinements([{}]) # refine after setting |
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| 340 | HistStats(gpx)</textarea></blockquote> |
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| 341 | |
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| 342 | </blockquote> |
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| 343 | <h4>9: Add Structural and Sample Displacement Parameters</h4> |
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| 344 | <blockquote> |
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| 345 | The step 8 in the original tutorial is to treat sample displacement |
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| 346 | for each histogram/phase. These parameters are different because of |
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| 347 | the differing diffractometer geometries. We also add refinement of |
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| 348 | sample parameters using <tt>phase0.set_refinements()</tt> to set the |
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| 349 | "X" (coordinate) and "U" (displacement) refinement flags for all |
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| 350 | atoms. The original tutorial |
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| 351 | calls for the diffractometer radius to be changed. This parameter |
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| 352 | cannot be set from any GSASIIscriptable routines, but following a |
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| 353 | similar |
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| 354 | <A href="#ChangeCycles">process, as before</A>, the location for this |
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| 355 | setting can be located in the histogram's 'Sample Parameters' section |
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| 356 | (<tt>hist2.data['Sample Parameters']['Gonio. radius']</tt>). |
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| 357 | |
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| 358 | <blockquote><textarea rows="9" cols="75" readonly> |
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| 359 | # tutorial step 8: add sample parameters & set radius (Hist); refine atom parameters (phase) |
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| 360 | gpx.save('step8.gpx') |
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| 361 | hist1.set_refinements({'Sample Parameters': ['Shift']}) |
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| 362 | hist2.set_refinements({'Sample Parameters': ['DisplaceX', 'DisplaceY']}) |
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| 363 | hist2.data['Sample Parameters']['Gonio. radius'] = 650. # not in API |
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| 364 | phase0.set_refinements({"Atoms":{"all":"XU"}}) |
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| 365 | gpx.do_refinements([{}]) # refine after setting |
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| 366 | HistStats(gpx)</textarea></blockquote> |
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| 367 | |
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| 368 | Note that the settings provided in the |
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| 369 | <tt>phase0.set_refinements()</tt> statement could have been done using |
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| 370 | the <tt>gpx.do_refinements()</tt> call, but not those in the |
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| 371 | <tt>hist</tt><I>X</I><tt>.set_refinements()</tt> calls, since they |
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| 372 | must be different for each histogram. |
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| 373 | |
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| 374 | </blockquote> |
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| 375 | <h4>10: Change Data Limits; Vary Gaussian Profile Terms</h4> |
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| 376 | <blockquote> |
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| 377 | The final step in the original tutorial is to trim the range of data |
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| 378 | used in the refinement to exclude data where no reflections occur and |
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| 379 | where the peaks are cut off at high angle. Also, additional parameters |
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| 380 | are refined here because the supplied instrumental profile parameters |
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| 381 | are not very accurate descriptions for the datasets that are used |
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| 382 | here. It is not possible to refine the Lorentzian x-ray instrumental |
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| 383 | broadening terms, since this broadening is treated as sample |
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| 384 | broadening. The Lorentzian neutron broadening is negligible. |
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| 385 | |
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| 386 | <blockquote><textarea rows="6" cols="75" readonly> |
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| 387 | # tutorial step 9: change data limits & inst. parm refinements (Hist) |
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| 388 | gpx.save('step9.gpx') |
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| 389 | hist1.set_refinements({'Limits': [16.,158.4]}) |
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| 390 | hist2.set_refinements({'Limits': [19.,153.]}) |
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| 391 | gpx.do_refinements([{"set": {'Instrument Parameters': ['U', 'V', 'W']}}]) |
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| 392 | HistStats(gpx)</textarea></blockquote> |
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| 393 | |
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| 394 | Note that the final <tt>gpx.do_refinements()</tt> statement can be |
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| 395 | replaced with calls to |
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| 396 | <tt>hist</tt><I>X</I><tt>.set_refinements()</tt> or |
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| 397 | <tt>gpx.set_refinement()</tt>, such as |
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| 398 | <blockquote><pre> |
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| 399 | hist1.set_refinements({'Instrument Parameters': ['U', 'V', 'W']}) |
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| 400 | hist2.set_refinements({'Instrument Parameters': ['U', 'V', 'W']}) |
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| 401 | gpx.do_refinements([{}]) |
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| 402 | </pre></blockquote> |
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| 403 | |
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| 404 | |
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| 405 | </blockquote><hr> |
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| 406 | <address></address> |
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| 407 | <!-- hhmts start -->Last modified: Thu Oct 12 10:45:51 CDT 2017 <!-- hhmts end --> |
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| 408 | </body> </html> |
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