Merge branch 'xraypy:master' into bm16_bliss_update

doc/index.rst

@@ -108,6 +108,7 @@ Table of Contents
    guis.rst
    xasviewer.rst
    wxmap_viewer.rst
+   xrd1d_viewer.rst
    qtrixs.rst
    python.rst
    larchlang.rst

doc/wxmap_viewer.rst

@@ -1,20 +1,18 @@
 .. _mapviewer_app:
 
-==========================
-GSECARS Mapviewr
+XRFMap Viewr
 ==========================
 
 .. _wxmplot:  https://newville.github.io/wxmplot
 .. _matplotlib: https://matplotlib.org/
 
 
-The GSECARS Mapviewer is one of the main GUI applications of Larch,
+The GSECARS XRFMap Viewer is one of the main GUI applications of Larch,
 allowing users to read and display HDF5 files containing X-ray fluorescence
-maps from synchrotron X-ray microprobes.  The program plays the dual role
-of displaying existing X-ray microprobe maps in HDF5 format and assembling
-those HDF5 X-ray microprobe map files from the raw data as collected APS
-beamline 13-ID-E or other microprobes using the same Epics scanning
-software used there.
+maps from synchrotron X-ray microprobes.  The program plays the dual role of
+displaying existing X-ray microprobe maps in HDF5 format and assembling those
+HDF5 X-ray microprobe map files from the raw data as collected APS beamline
+13-ID-E or other microprobes using the same Epics scanning software used there.
 
 
 

doc/xasviewer.rst

@@ -6,17 +6,17 @@
 Larix (was XAS Viewer)
 ===========================
 
-The Larix Application gives a graphical user interface (GUI) for the
+The `Larix` Application gives a graphical user interface (GUI) for the
 visualization and analyis of X-ray absorption spectroscopy (XAS) data, both
-XANES and EXAFS. It is deliberately patterned after the Demeter Package
-(Athena and Artemis programs), and shares many concepts and presentation ideas
-with these progrqms.  As a GUI Program, Larix should seem very "Athena-like"
-though of course there will be several differences.  We hope that many of
-these differences will be "improvements".  By using Larch for all of its
-analysis steps, Larix not only provides interactive data visualization,
-exploration, and analysis of XAS data, but also records those steps as Larch /
-Python commands that can be saved and reproduced outside of the GUI, say to
-enable batch processing of large volumes of data.
+XANES and EXAFS. It is deliberately patterned after the Demeter Package (Athena
+and Artemis programs), and shares many concepts and presentation ideas with
+these progrqms.  As a GUI Program, Larix should seem very "Athena-like" though
+of course there will be several differences.  We hope that many of these
+differences will be "improvements".  By using Larch for all of its analysis
+steps, Larix not only provides interactive data visualization, exploration, and
+analysis of XAS data, but also records those steps as Larch / Python commands
+that can be saved and reproduced outside of the GUI, say to enable batch
+processing of large volumes of data.
 
 Larix is still in active development with new features driving much of the
 development and releases of Larch version for the past few years. At this

doc/xrd1d_viewer.rst

@@ -0,0 +1,21 @@
+.. _larchxrd1d-chapter:
+
+.. _larchxrd1d_app:
+
+Larch XRD1D Viewer
+==========================
+
+
+The `Larch XRD1D` application allows simple viewing and manipulation of
+1-dimensional XRD patterns of diffracted intensity as a function of scattering
+wavenumber or angle: :math:`I(q)`, :math:`I(2\theta)`.  The application allows
+
+  1. reading and comparison of multiple XRD patterns.
+  2. fitting and subtracting a background from an XRD pattern.
+
+
+
+
+.. image:: _images/xrd1d_main_with_tiff.png
+    :target: _images/xrd1d_main_with_tiff.png
+    :width: 70%

examples/feffit/doc_feffit1.out

@@ -1,41 +1,41 @@
 =================== FEFFIT RESULTS ====================
 [[Statistics]]
-   n_function_calls   = 36
-   n_variables        = 4
-   n_data_points      = 104
-   n_independent      = 15.2602829
-   chi_square         = 112.916714
-   reduced chi_square = 10.0278754
-   r-factor           = 0.00272181
-   Akaike info crit   = 38.5418835
-   Bayesian info crit = 41.4428977
-
-[[Dataset]]
-   unique_id          = 'dc5vezx7'
-   fit space          = 'r'
-   r-range            = 1.400, 3.000
-   k-range            = 3.000, 17.000
-   k window, dk       = 'kaiser', 4.000
-   paths used in fit  = ['feffcu01.dat']
-   k-weight           = 2
-   epsilon_k          = Array(mean=6.1910e-4, std=0.0016545)
-   epsilon_r          = 0.0208085
-   n_independent      = 15.260
+  n_function_calls     = 36
+  n_variables          = 4
+  n_data_points        = 104
+  n_independent        = 15.2602829
+  chi_square           = 112.916714
+  reduced chi_square   = 10.0278754
+  r-factor             = 0.00272181
+  Akaike info crit     = 38.5418835
+  Bayesian info crit   = 41.4428977
 
 [[Variables]]
-   amp            =  0.9315590 +/- 0.0391633   (init=  1.0000000)
-   del_e0         =  4.3577673 +/- 0.5113492   (init=  0.0000000)
-   del_r          = -0.0060167 +/- 0.0026133   (init=  0.0000000)
-   sig2           =  0.0086697 +/- 3.0785e-4   (init=  0.0000000)
+  amp                  =  0.9315590 +/- 0.0391633  (init= 1.0000000)
+  del_e0               =  4.3577673 +/- 0.5113492  (init= 0.0000000)
+  del_r                = -0.0060167 +/- 0.0026133  (init= 0.0000000)
+  sig2                 =  0.0086697 +/- 3.0785e-4  (init= 0.0000000)
 
-[[Correlations]]    (unreported correlations are <  0.100)
-   amp, sig2            =  0.928
-   del_e0, del_r        =  0.920
-   del_r, sig2          =  0.159
-   amp, del_r           =  0.137
+[[Correlations]] (unreported correlations are <  0.100)
+  amp, sig2            = +0.928
+  del_e0, del_r        = +0.920
+  del_r, sig2          = +0.159
+  amp, del_r           = +0.137
+
+[[Dataset]]
+  unique_id            = 'dc5vezx7'
+  fit space            = 'r'
+  r-range              = 1.400, 3.000
+  k-range              = 3.000, 17.000
+  k window, dk         = 'kaiser', 4.000
+  paths used in fit    = ['feffcu01.dat']
+  k-weight             = 2
+  epsilon_k            = Array(mean=6.1910e-4, std=0.0016545)
+  epsilon_r            = 0.0208085
+  n_independent        = 15.260
 
 [[Paths]]
- = Path 'p3tf6mewgr' = Cu K Edge
+ = Path 'p3tf6mewg' = Cu K Edge
     feffdat file = feffcu01.dat, from feff run ''
     geometry  atom      x        y        z      ipot
               Cu       0.0000,  0.0000,  0.0000  0 (absorber)

examples/feffit/doc_feffit2.out

@@ -1,58 +1,58 @@
 =================== FEFFIT RESULTS ====================
 [[Statistics]]
-   n_function_calls   = 107
-   n_variables        = 7
-   n_data_points      = 138
-   n_independent      = 19.7166213
-   chi_square         = 12.9083391
-   reduced chi_square = 1.01507616
-   r-factor           = 0.00251319
-   Akaike info crit   = 5.64826681
-   Bayesian info crit = 12.5185008
-
-[[Dataset]]
-   unique_id          = 'ds3qtgjs'
-   fit space          = 'r'
-   r-range            = 1.400, 3.500
-   k-range            = 3.000, 17.000
-   k window, dk       = 'kaiser', 4.000
-   paths used in fit  = ['feff0001.dat', 'feff0002.dat', 'feff0003.dat']
-   k-weight           = 2
-   epsilon_k          = Array(mean=0.0017550, std=0.0058069)
-   epsilon_r          = 0.0589866
-   n_independent      = 19.717
+  n_function_calls     = 107
+  n_variables          = 7
+  n_data_points        = 138
+  n_independent        = 19.7166213
+  chi_square           = 12.9083391
+  reduced chi_square   = 1.01507616
+  r-factor             = 0.00251319
+  Akaike info crit     = 5.64826681
+  Bayesian info crit   = 12.5185008
 
 [[Variables]]
-   alpha          =  0.0028114 +/- 0.0029801   (init=  0.0000000)
-   amp            =  0.9362805 +/- 0.0362098   (init=  1.0000000)
-   c3_1           =  1.4440e-4 +/- 8.2197e-5   (init=  0.0020000)
-   del_e0         =  5.7042623 +/- 0.8197285   (init=  3.0000000)
-   sig2_1         =  0.0086805 +/- 2.8390e-4   (init=  0.0020000)
-   sig2_2         =  0.0131046 +/- 0.0011358   (init=  0.0020000)
-   sig2_3         =  0.0063292 +/- 0.0030901   (init=  0.0020000)
+  alpha                =  0.0028114 +/- 0.0029801  (init= 0.0000000)
+  amp                  =  0.9362805 +/- 0.0362098  (init= 1.0000000)
+  c3_1                 =  1.4440e-4 +/- 8.2197e-5  (init= 0.0020000)
+  del_e0               =  5.7042623 +/- 0.8197285  (init= 3.0000000)
+  sig2_1               =  0.0086805 +/- 2.8390e-4  (init= 0.0020000)
+  sig2_2               =  0.0131046 +/- 0.0011358  (init= 0.0020000)
+  sig2_3               =  0.0063292 +/- 0.0030901  (init= 0.0020000)
 
-[[Correlations]]    (unreported correlations are <  0.100)
-   alpha, c3_1          =  0.952
-   alpha, del_e0        =  0.950
-   amp, sig2_1          =  0.929
-   c3_1, del_e0         =  0.837
-   amp, sig2_2          =  0.298
-   sig2_1, sig2_2       =  0.276
-   sig2_1, sig2_3       =  0.213
-   amp, sig2_3          =  0.213
-   c3_1, sig2_3         = -0.189
-   alpha, amp           =  0.185
-   alpha, sig2_1        =  0.172
-   c3_1, sig2_2         =  0.168
-   alpha, sig2_2        =  0.152
-   alpha, sig2_3        = -0.148
-   amp, del_e0          =  0.148
-   amp, c3_1            =  0.145
-   del_e0, sig2_1       =  0.143
-   c3_1, sig2_1         =  0.124
+[[Correlations]] (unreported correlations are <  0.100)
+  alpha, c3_1          =  0.952
+  alpha, del_e0        =  0.950
+  amp, sig2_1          =  0.929
+  c3_1, del_e0         =  0.837
+  amp, sig2_2          =  0.298
+  sig2_1, sig2_2       =  0.276
+  sig2_1, sig2_3       =  0.213
+  amp, sig2_3          =  0.213
+  c3_1, sig2_3         = -0.189
+  alpha, amp           =  0.185
+  alpha, sig2_1        =  0.172
+  c3_1, sig2_2         =  0.168
+  alpha, sig2_2        =  0.152
+  alpha, sig2_3        = -0.148
+  amp, del_e0          =  0.148
+  amp, c3_1            =  0.145
+  del_e0, sig2_1       =  0.143
+  c3_1, sig2_1         =  0.124
+
+[[Dataset]]
+  unique_id            = 'ds3qtgjs'
+  fit space            = 'r'
+  r-range              = 1.400, 3.500
+  k-range              = 3.000, 17.000
+  k window, dk         = 'kaiser', 4.000
+  paths used in fit    = ['feff0001.dat', 'feff0002.dat', 'feff0003.dat']
+  k-weight             = 2
+  epsilon_k            = Array(mean=0.0017550, std=0.0058069)
+  epsilon_r            = 0.0589866
+  n_independent        = 19.717
 
 [[Paths]]
- = Path 'p3tf6mewgr' = Cu K Edge
+ = Path 'p3tf6mewg' = Cu K Edge
     feffdat file = feff0001.dat, from feff run ''
     geometry  atom      x        y        z      ipot
               Cu       0.0000,  0.0000,  0.0000  0 (absorber)
@@ -66,7 +66,7 @@
      sigma2 =  0.0086805 +/- 2.8390e-4  := 'sig2_1'
      third  =  1.4440e-4 +/- 8.2197e-5  := 'c3_1'
 
- = Path 'pj3m4qvfyl' = Cu K Edge
+ = Path 'pj3m4qvfy' = Cu K Edge
     feffdat file = feff0002.dat, from feff run ''
     geometry  atom      x        y        z      ipot
               Cu       0.0000,  0.0000,  0.0000  0 (absorber)
@@ -79,7 +79,7 @@
      deltar =  0.0101300 +/- 0.0107377  := 'alpha*reff'
      sigma2 =  0.0131046 +/- 0.0011358  := 'sig2_2'
 
- = Path 'pibakl7xet' = Cu K Edge
+ = Path 'pibakl7xe' = Cu K Edge
     feffdat file = feff0003.dat, from feff run ''
     geometry  atom      x        y        z      ipot
               Cu       0.0000,  0.0000,  0.0000  0 (absorber)