Changeset 298d2d4 in sasmodels for doc

Timestamp:

Mar 26, 2019 11:26:07 AM (6 years ago)

Author:

awashington

Branches:

master

Children:

d522352

Parents:

c9d052d (diff), 598a354 (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

Message:

Merge branch 'master' into log_sesans

Location:

doc

Files:

• guide/direct_call.png (added)
• guide/gpu_setup.rst (modified) (5 diffs)
• guide/magnetism/magnetism.rst (modified) (1 diff)
• guide/pd/polydispersity.rst (modified) (5 diffs)
• guide/plugin.rst (modified) (18 diffs)
• guide/scripting.rst (modified) (3 diffs)
• rst_prolog (modified) (1 diff)
• guide/sesans/sans_to_sesans.rst (modified) (1 diff)

doc/guide/gpu_setup.rst

@@ -94 +94 @@
 Device Selection
 ================
+**OpenCL drivers**
+
 If you have multiple GPU devices you can tell the program which device to use.
 By default, the program looks for one GPU and one CPU device from available
@@ -104 +106 @@
 was used to run the model.
 
-**If you don't want to use OpenCL, you can set** *SAS_OPENCL=None*
-**in your environment settings, and it will only use normal programs.**
-
-If you want to use one of the other devices, you can run the following
+If you want to use a specific driver and devices, you can run the following
 from the python console::
 
@@ -115 +114 @@
 This will provide a menu of different OpenCL drivers available.
 When one is selected, it will say "set PYOPENCL_CTX=..."
-Use that value as the value of *SAS_OPENCL*.
+Use that value as the value of *SAS_OPENCL=driver:device*.
+
+To use the default OpenCL device (rather than CUDA or None),
+set *SAS_OPENCL=opencl*.
+
+In batch queues, you may need to set *XDG_CACHE_HOME=~/.cache* 
+(Linux only) to a different directory, depending on how the filesystem 
+is configured.  You should also set *SAS_DLL_PATH* for CPU-only modules.
+
+    -DSAS_MODELPATH=path sets directory containing custom models
+    -DSAS_OPENCL=vendor:device|cuda:device|none sets the target GPU device
+    -DXDG_CACHE_HOME=~/.cache sets the pyopencl cache root (linux only)
+    -DSAS_COMPILER=tinycc|msvc|mingw|unix sets the DLL compiler
+    -DSAS_OPENMP=1 turns on OpenMP for the DLLs
+    -DSAS_DLL_PATH=path sets the path to the compiled modules
+
+
+**CUDA drivers**
+
+If OpenCL drivers are not available on your system, but NVidia CUDA
+drivers are available, then set *SAS_OPENCL=cuda* or
+*SAS_OPENCL=cuda:n* for a particular device number *n*.  If no device
+number is specified, then the CUDA drivers looks for look for
+*CUDA_DEVICE=n* or a file ~/.cuda-device containing n for the device number.
+
+In batch queues, the SLURM command *sbatch --gres=gpu:1 ...* will set
+*CUDA_VISIBLE_DEVICES=n*, which ought to set the correct device
+number for *SAS_OPENCL=cuda*.  If not, then set
+*CUDA_DEVICE=$CUDA_VISIBLE_DEVICES* within the batch script.  You may
+need to set the CUDA cache directory to a folder accessible across the
+cluster with *PYCUDA_CACHE_DIR* (or *PYCUDA_DISABLE_CACHE* to disable
+caching), and you may need to set environment specific compiler flags
+with *PYCUDA_DEFAULT_NVCC_FLAGS*.  You should also set *SAS_DLL_PATH* 
+for CPU-only modules.
+
+**No GPU support**
+
+If you don't want to use OpenCL or CUDA, you can set *SAS_OPENCL=None*
+in your environment settings, and it will only use normal programs.
+
+In batch queues, you may need to set *SAS_DLL_PATH* to a directory
+accessible on the compute node.
+
 
 Device Testing
@@ -139 +180 @@
 the compiler.
 
-On Windows, set *SASCOMPILER=tinycc* for the tinycc compiler,
-*SASCOMPILER=msvc* for the Microsoft Visual C compiler,
-or *SASCOMPILER=mingw* for the MinGW compiler. If TinyCC is available
+On Windows, set *SAS_COMPILER=tinycc* for the tinycc compiler,
+*SAS_COMPILER=msvc* for the Microsoft Visual C compiler,
+or *SAS_COMPILER=mingw* for the MinGW compiler. If TinyCC is available
 on the python path (it is provided with SasView), that will be the
 default. If you want one of the other compilers, be sure to have it
@@ -154 +195 @@
 *Document History*
 
-| 2017-09-27 Paul Kienzle
+| 2018-10-15 Paul Kienzle

doc/guide/magnetism/magnetism.rst

@@ -89 +89 @@
 
 ===========   ================================================================
- M0:sld       $D_M M_0$
- mtheta:sld   $\theta_M$
- mphi:sld     $\phi_M$
- up:angle     $\theta_\mathrm{up}$
- up:frac_i    $u_i$ = (spin up)/(spin up + spin down) *before* the sample
- up:frac_f    $u_f$ = (spin up)/(spin up + spin down) *after* the sample
+ sld_M0       $D_M M_0$
+ sld_mtheta   $\theta_M$
+ sld_mphi     $\phi_M$
+ up_frac_i    $u_i$ = (spin up)/(spin up + spin down) *before* the sample
+ up_frac_f    $u_f$ = (spin up)/(spin up + spin down) *after* the sample
+ up_angle     $\theta_\mathrm{up}$
 ===========   ================================================================
 
 .. note::
-    The values of the 'up:frac_i' and 'up:frac_f' must be in the range 0 to 1.
+    The values of the 'up_frac_i' and 'up_frac_f' must be in the range 0 to 1.
 
 *Document History*

doc/guide/pd/polydispersity.rst

@@ -8 +8 @@
 .. _polydispersityhelp:
 
-Polydispersity Distributions
-----------------------------
-
-With some models in sasmodels we can calculate the average intensity for a
-population of particles that exhibit size and/or orientational
-polydispersity. The resultant intensity is normalized by the average
-particle volume such that
+Polydispersity & Orientational Distributions
+--------------------------------------------
+
+For some models we can calculate the average intensity for a population of 
+particles that possess size and/or orientational (ie, angular) distributions. 
+In SasView we call the former *polydispersity* but use the parameter *PD* to 
+parameterise both. In other words, the meaning of *PD* in a model depends on 
+the actual parameter it is being applied too.
+
+The resultant intensity is then normalized by the average particle volume such 
+that
 
 .. math::
@@ -20 +24 @@
   P(q) = \text{scale} \langle F^* F \rangle / V + \text{background}
 
-where $F$ is the scattering amplitude and $\langle\cdot\rangle$ denotes an
-average over the size distribution.
+where $F$ is the scattering amplitude and $\langle\cdot\rangle$ denotes an 
+average over the distribution $f(x; \bar x, \sigma)$, giving
+
+.. math::
+
+  P(q) = \frac{\text{scale}}{V} \int_\mathbb{R} 
+  f(x; \bar x, \sigma) F^2(q, x)\, dx + \text{background}
 
 Each distribution is characterized by a center value $\bar x$ or
 $x_\text{med}$, a width parameter $\sigma$ (note this is *not necessarily*
-the standard deviation, so read the description carefully), the number of
-sigmas $N_\sigma$ to include from the tails of the distribution, and the
-number of points used to compute the average. The center of the distribution
-is set by the value of the model parameter. The meaning of a polydispersity 
-parameter *PD* (not to be confused with a molecular weight distributions 
-in polymer science) in a model depends on the type of parameter it is being 
-applied too.
+the standard deviation, so read the description of the distribution carefully), 
+the number of sigmas $N_\sigma$ to include from the tails of the distribution, 
+and the number of points used to compute the average. The center of the 
+distribution is set by the value of the model parameter.
 
 The distribution width applied to *volume* (ie, shape-describing) parameters 
 is relative to the center value such that $\sigma = \mathrm{PD} \cdot \bar x$. 
-However, the distribution width applied to *orientation* (ie, angle-describing) 
-parameters is just $\sigma = \mathrm{PD}$.
+However, the distribution width applied to *orientation* parameters is just 
+$\sigma = \mathrm{PD}$.
 
 $N_\sigma$ determines how far into the tails to evaluate the distribution,
 with larger values of $N_\sigma$ required for heavier tailed distributions.
 The scattering in general falls rapidly with $qr$ so the usual assumption
-that $G(r - 3\sigma_r)$ is tiny and therefore $f(r - 3\sigma_r)G(r - 3\sigma_r)$
+that $f(r - 3\sigma_r)$ is tiny and therefore $f(r - 3\sigma_r)f(r - 3\sigma_r)$
 will not contribute much to the average may not hold when particles are large.
 This, too, will require increasing $N_\sigma$.
 
 Users should note that the averaging computation is very intensive. Applying
-polydispersion to multiple parameters at the same time or increasing the
-number of points in the distribution will require patience! However, the
-calculations are generally more robust with more data points or more angles.
+polydispersion and/or orientational distributions to multiple parameters at 
+the same time, or increasing the number of points in the distribution, will 
+require patience! However, the calculations are generally more robust with 
+more data points or more angles.
 
 The following distribution functions are provided:
@@ -64 +71 @@
 Additional distributions are under consideration.
 
+**Beware: when the Polydispersity & Orientational Distribution panel in SasView is**
+**first opened, the default distribution for all parameters is the Gaussian Distribution.**
+**This may not be suitable. See Suggested Applications below.**
+
+.. note:: In 2009 IUPAC decided to introduce the new term 'dispersity' to replace 
+           the term 'polydispersity' (see `Pure Appl. Chem., (2009), 81(2), 
+           351-353 <http://media.iupac.org/publications/pac/2009/pdf/8102x0351.pdf>`_ 
+           in order to make the terminology describing distributions of chemical 
+           properties unambiguous. However, these terms are unrelated to the 
+           proportional size distributions and orientational distributions used in 
+           SasView models.
+
 Suggested Applications
 ^^^^^^^^^^^^^^^^^^^^^^
 
-If applying polydispersion to parameters describing particle sizes, use
+If applying polydispersion to parameters describing particle sizes, consider using
 the Lognormal or Schulz distributions.
 
 If applying polydispersion to parameters describing interfacial thicknesses
-or angular orientations, use the Gaussian or Boltzmann distributions.
+or angular orientations, consider using the Gaussian or Boltzmann distributions.
 
 If applying polydispersion to parameters describing angles, use the Uniform 
@@ -318 +337 @@
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Many commercial Dynamic Light Scattering (DLS) instruments produce a size
-polydispersity parameter, sometimes even given the symbol $p$\ ! This
-parameter is defined as the relative standard deviation coefficient of
-variation of the size distribution and is NOT the same as the polydispersity
-parameters in the Lognormal and Schulz distributions above (though they all
-related) except when the DLS polydispersity parameter is <0.13.
-
-.. math::
-
-    p_{DLS} = \sqrt(\nu / \bar x^2)
-
-where $\nu$ is the variance of the distribution and $\bar x$ is the mean
-value of $x$.
+Several measures of polydispersity abound in Dynamic Light Scattering (DLS) and 
+it should not be assumed that any of the following can be simply equated with 
+the polydispersity *PD* parameter used in SasView.
+
+The dimensionless **Polydispersity Index (PI)** is a measure of the width of the 
+distribution of autocorrelation function decay rates (*not* the distribution of 
+particle sizes itself, though the two are inversely related) and is defined by 
+ISO 22412:2017 as
+
+.. math::
+
+    PI = \mu_{2} / \bar \Gamma^2
+
+where $\mu_\text{2}$ is the second cumulant, and $\bar \Gamma^2$ is the 
+intensity-weighted average value, of the distribution of decay rates.
+
+*If the distribution of decay rates is Gaussian* then
+
+.. math::
+
+    PI = \sigma^2 / 2\bar \Gamma^2
+
+where $\sigma$ is the standard deviation, allowing a **Relative Polydispersity (RP)** 
+to be defined as
+
+.. math::
+
+    RP = \sigma / \bar \Gamma = \sqrt{2 \cdot PI}
+
+PI values smaller than 0.05 indicate a highly monodisperse system. Values 
+greater than 0.7 indicate significant polydispersity.
+
+The **size polydispersity P-parameter** is defined as the relative standard 
+deviation coefficient of variation  
+
+.. math::
+
+    P = \sqrt\nu / \bar R
+
+where $\nu$ is the variance of the distribution and $\bar R$ is the mean
+value of $R$. Here, the product $P \bar R$ is *equal* to the standard 
+deviation of the Lognormal distribution.
+
+P values smaller than 0.13 indicate a monodisperse system.
 
 For more information see:
-S King, C Washington & R Heenan, *Phys Chem Chem Phys*, (2005), 7, 143
+
+`ISO 22412:2017, International Standards Organisation (2017) <https://www.iso.org/standard/65410.html>`_.
+
+`Polydispersity: What does it mean for DLS and Chromatography <http://www.materials-talks.com/blog/2014/10/23/polydispersity-what-does-it-mean-for-dls-and-chromatography/>`_.
+
+`Dynamic Light Scattering: Common Terms Defined, Whitepaper WP111214. Malvern Instruments (2011) <http://www.biophysics.bioc.cam.ac.uk/wp-content/uploads/2011/02/DLS_Terms_defined_Malvern.pdf>`_.
+
+S King, C Washington & R Heenan, *Phys Chem Chem Phys*, (2005), 7, 143.
+
+T Allen, in *Particle Size Measurement*, 4th Edition, Chapman & Hall, London (1990).
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
@@ -343 +402 @@
 | 2018-03-20 Steve King
 | 2018-04-04 Steve King
+| 2018-08-09 Steve King

doc/guide/plugin.rst

@@ -273 +273 @@
 `Form_Factors`_ for more details.
 
+**model_info = ...** lets you define a model directly, for example, by
+loading and modifying existing models.  This is done implicitly by
+:func:`sasmodels.core.load_model_info`, which can create a mixture model
+from a pair of existing models.  For example::
+
+    from sasmodels.core import load_model_info
+    model_info = load_model_info('sphere+cylinder')
+
+See :class:`sasmodels.modelinfo.ModelInfo` for details about the model
+attributes that are defined.
+
 Model Parameters
 ................
@@ -291 +302 @@
 
 **Note: The order of the parameters in the definition will be the order of the
-parameters in the user interface and the order of the parameters in Iq(),
-Iqac(), Iqabc() and form_volume(). And** *scale* **and** *background*
-**parameters are implicit to all models, so they do not need to be included
-in the parameter table.**
+parameters in the user interface and the order of the parameters in Fq(), Iq(),
+Iqac(), Iqabc(), form_volume() and shell_volume().
+And** *scale* **and** *background* **parameters are implicit to all models,
+so they do not need to be included in the parameter table.**
 
 - **"name"** is the name of the parameter shown on the FitPage.
@@ -363 +374 @@
     scattered intensity.
 
-  - "volume" parameters are passed to Iq(), Iqac(), Iqabc() and form_volume(),
-    and have polydispersity loops generated automatically.
+  - "volume" parameters are passed to Fq(), Iq(), Iqac(), Iqabc(), form_volume()
+    and shell_volume(), and have polydispersity loops generated automatically.
 
   - "orientation" parameters are not passed, but instead are combined with
@@ -424 +435 @@
 appropriately smeared pattern.
 
+Each .py file also contains a function::
+
+        def random():
+        ...
+
+This function provides a model-specific random parameter set which shows model
+features in the USANS to SANS range.  For example, core-shell sphere sets the
+outer radius of the sphere logarithmically in `[20, 20,000]`, which sets the Q
+value for the transition from flat to falling.  It then uses a beta distribution
+to set the percentage of the shape which is shell, giving a preference for very
+thin or very thick shells (but never 0% or 100%).  Using `-sets=10` in sascomp
+should show a reasonable variety of curves over the default sascomp q range.
+The parameter set is returned as a dictionary of `{parameter: value, ...}`.
+Any model parameters not included in the dictionary will default according to
+the code in the `_randomize_one()` function from sasmodels/compare.py.
+
 Python Models
 .............
@@ -476 +503 @@
 used.
 
+Hollow shapes, where the volume fraction of particle corresponds to the
+material in the shell rather than the volume enclosed by the shape, must
+also define a *shell_volume(par1, par2, ...)* function.  The parameters
+are the same as for *form_volume*.  The *I(q)* calculation should use
+*shell_volume* squared as its scale factor for the volume normalization.
+The structure factor calculation needs *form_volume* in order to properly
+scale the volume fraction parameter, so both functions are required for
+hollow shapes.
+
+**Note: Pure python models do not yet support direct computation of the**
+**average of $F(q)$ and $F^2(q)$. Neither do they support orientational**
+**distributions or magnetism (use C models if these are required).**
+
 Embedded C Models
 .................
@@ -487 +527 @@
 This expands into the equivalent C code::
 
-    #include <math.h>
     double Iq(double q, double par1, double par2, ...);
     double Iq(double q, double par1, double par2, ...)
@@ -496 +535 @@
 *form_volume* defines the volume of the shape. As in python models, it
 includes only the volume parameters.
+
+*form_volume* defines the volume of the shell for hollow shapes. As in
+python models, it includes only the volume parameters.
 
 **source=['fn.c', ...]** includes the listed C source files in the
@@ -533 +575 @@
 listed in *source*.
 
+Structure Factors
+.................
+
+Structure factor calculations may need the underlying $<F(q)>$ and $<F^2(q)>$
+rather than $I(q)$.  This is used to compute $\beta = <F(q)>^2/<F^2(q)>$ in
+the decoupling approximation to the structure factor.
+
+Instead of defining the *Iq* function, models can define *Fq* as
+something like::
+
+    double Fq(double q, double *F1, double *F2, double par1, double par2, ...);
+    double Fq(double q, double *F1, double *F2, double par1, double par2, ...)
+    {
+        // Polar integration loop over all orientations.
+        ...
+        *F1 = 1e-2 * total_F1 * contrast * volume;
+        *F2 = 1e-4 * total_F2 * square(contrast * volume);
+        return I(q, par1, par2, ...);
+    }
+
+If the volume fraction scale factor is built into the model (as occurs for
+the vesicle model, for example), then scale *F1* by $\surd V_f$ so that
+$\beta$ is computed correctly.
+
+Structure factor calculations are not yet supported for oriented shapes.
+
+Note: only available as a separate C file listed in *source*, or within
+a *c_code* block within the python model definition file.
+
 Oriented Shapes
 ...............
@@ -544 +615 @@
 laboratory frame and beam travelling along $-z$.
 
-The oriented C model is called using *Iqabc(qa, qb, qc, par1, par2, ...)* where
+The oriented C model (oriented pure Python models are not supported) 
+is called using *Iqabc(qa, qb, qc, par1, par2, ...)* where
 *par1*, etc. are the parameters to the model.  If the shape is rotationally
 symmetric about *c* then *psi* is not needed, and the model is called
@@ -648 +720 @@
 to compute the proper magnetism and orientation, which you can implement
 using *Iqxy(qx, qy, par1, par2, ...)*.
+
+**Note: Magnetism is not supported in pure Python models.**
 
 Special Functions
@@ -685 +759 @@
     erf, erfc, tgamma, lgamma:  **do not use**
         Special functions that should be part of the standard, but are missing
-        or inaccurate on some platforms. Use sas_erf, sas_erfc and sas_gamma
-        instead (see below). Note: lgamma(x) has not yet been tested.
+        or inaccurate on some platforms. Use sas_erf, sas_erfc, sas_gamma
+        and sas_lgamma instead (see below).
 
 Some non-standard constants and functions are also provided:
@@ -753 +827 @@
         Gamma function sas_gamma\ $(x) = \Gamma(x)$.
 
-        The standard math function, tgamma(x) is unstable for $x < 1$
+        The standard math function, tgamma(x), is unstable for $x < 1$
         on some platforms.
 
         :code:`source = ["lib/sas_gamma.c", ...]`
         (`sas_gamma.c <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_gamma.c>`_)
+
+    sas_gammaln(x):
+        log gamma function sas_gammaln\ $(x) = \log \Gamma(|x|)$.
+
+        The standard math function, lgamma(x), is incorrect for single
+        precision on some platforms.
+
+        :code:`source = ["lib/sas_gammainc.c", ...]`
+        (`sas_gammainc.c <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_gammainc.c>`_)
+
+    sas_gammainc(a, x), sas_gammaincc(a, x):
+        Incomplete gamma function
+        sas_gammainc\ $(a, x) = \int_0^x t^{a-1}e^{-t}\,dt / \Gamma(a)$
+        and complementary incomplete gamma function
+        sas_gammaincc\ $(a, x) = \int_x^\infty t^{a-1}e^{-t}\,dt / \Gamma(a)$
+
+        :code:`source = ["lib/sas_gammainc.c", ...]`
+        (`sas_gammainc.c <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_gammainc.c>`_)
 
     sas_erf(x), sas_erfc(x):
@@ -795 +887 @@
         If $n$ = 0 or 1, it uses sas_J0($x$) or sas_J1($x$), respectively.
 
+        Warning: JN(n,x) can be very inaccurate (0.1%) for x not in [0.1, 100].
+
         The standard math function jn(n, x) is not available on all platforms.
 
@@ -803 +897 @@
         Sine integral Si\ $(x) = \int_0^x \tfrac{\sin t}{t}\,dt$.
 
+        Warning: Si(x) can be very inaccurate (0.1%) for x in [0.1, 100].
+
         This function uses Taylor series for small and large arguments:
 
-        For large arguments,
+        For large arguments use the following Taylor series,
 
         .. math::
@@ -822 +918 @@
 
         :code:`source = ["lib/Si.c", ...]`
-        (`Si.c <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/Si.c>`_)
+        (`Si.c <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_Si.c>`_)
 
     sas_3j1x_x(x):
@@ -974 +1070 @@
           "radius": 120., "radius_pd": 0.2, "radius_pd_n":45},
          0.2, 0.228843],
-        [{"radius": 120., "radius_pd": 0.2, "radius_pd_n":45}, "ER", 120.],
-        [{"radius": 120., "radius_pd": 0.2, "radius_pd_n":45}, "VR", 1.],
+        [{"radius": 120., "radius_pd": 0.2, "radius_pd_n":45},
+         0.1, None, None, 120., None, 1.],  # q, F, F^2, R_eff, V, form:shell
+        [{"@S": "hardsphere"}, 0.1, None],
     ]
 
 
-**tests=[[{parameters}, q, result], ...]** is a list of lists.
+**tests=[[{parameters}, q, Iq], ...]** is a list of lists.
 Each list is one test and contains, in order:
 
@@ -991 +1088 @@
 - input and output values can themselves be lists if you have several
   $q$ values to test for the same model parameters.
-- for testing *ER* and *VR*, give the inputs as "ER" and "VR" respectively;
-  the output for *VR* should be the sphere/shell ratio, not the individual
-  sphere and shell values.
+- for testing effective radius, volume and form:shell volume ratio, use the
+  extended form of the tests results, with *None, None, R_eff, V, V_r*
+  instead of *Iq*.  This calls the kernel *Fq* function instead of *Iq*.
+- for testing F and F^2 (used for beta approximation) do the same as the
+  effective radius test, but include values for the first two elements,
+  $<F(q)>$ and $<F^2(q)>$.
+- for testing interaction between form factor and structure factor, specify
+  the structure factor name in the parameters as *{"@S": "name", ...}* with
+  the remaining list of parameters defined by the *P@S* product model.
 
 .. _Test_Your_New_Model:
@@ -1009 +1112 @@
 and a check that the model runs.
 
-If you are not using sasmodels from SasView, skip this step.
-
 Recommended Testing
 ...................
+
+**NB: For now, this more detailed testing is only possible if you have a 
+SasView build environment available!**
 
 If the model compiles and runs, you can next run the unit tests that

doc/guide/scripting.rst

@@ -10 +10 @@
 The key functions are :func:`sasmodels.core.load_model` for loading the
 model definition and compiling the kernel and
-:func:`sasmodels.data.load_data` for calling sasview to load the data. Need
-the data because that defines the resolution function and the q values to
-evaluate. If there is no data, then use :func:`sasmodels.data.empty_data1D`
-or :func:`sasmodels.data.empty_data2D` to create some data with a given $q$.
-
-Using sasmodels through bumps
-=============================
-
-With the data and the model, you can wrap it in a *bumps* model with
+:func:`sasmodels.data.load_data` for calling sasview to load the data.
+
+Preparing data
+==============
+
+Usually you will load data via the sasview loader, with the
+:func:`sasmodels.data.load_data` function.  For example::
+
+    from sasmodels.data import load_data
+    data = load_data("sasmodels/example/093191_201.dat")
+
+You may want to apply a data mask, such a beam stop, and trim high $q$::
+
+    from sasmodels.data import set_beam_stop
+    set_beam_stop(data, qmin, qmax)
+
+The :func:`sasmodels.data.set_beam_stop` method simply sets the *mask*
+attribute for the data.
+
+The data defines the resolution function and the q values to evaluate, so
+even if you simulating experiments prior to making measurements, you still
+need a data object for reference. Use :func:`sasmodels.data.empty_data1D`
+or :func:`sasmodels.data.empty_data2D` to create a container with a
+given $q$ and $\Delta q/q$.  For example::
+
+    import numpy as np
+    from sasmodels.data import empty_data1D
+
+    # 120 points logarithmically spaced from 0.005 to 0.2, with dq/q = 5%
+    q = np.logspace(np.log10(5e-3), np.log10(2e-1), 120)
+    data = empty_data1D(q, resolution=0.05)
+
+To use a more realistic model of resolution, or to load data from a file
+format not understood by SasView, you can use :class:`sasmodels.data.Data1D`
+or :class:`sasmodels.data.Data2D` directly.  The 1D data uses
+*x*, *y*, *dx* and *dy* for $x = q$ and $y = I(q)$, and 2D data uses
+*x*, *y*, *z*, *dx*, *dy*, *dz* for $x, y = qx, qy$ and $z = I(qx, qy)$.
+[Note: internally, the Data2D object uses SasView conventions,
+*qx_data*, *qy_data*, *data*, *dqx_data*, *dqy_data*, and *err_data*.]
+
+For USANS data, use 1D data, but set *dxl* and *dxw* attributes to
+indicate slit resolution::
+
+    data.dxl = 0.117
+
+See :func:`sasmodels.resolution.slit_resolution` for details.
+
+SESANS data is more complicated; if your SESANS format is not supported by
+SasView you need to define a number of attributes beyond *x*, *y*.  For
+example::
+
+    SElength = np.linspace(0, 2400, 61) # [A]
+    data = np.ones_like(SElength)
+    err_data = np.ones_like(SElength)*0.03
+
+    class Source:
+        wavelength = 6 # [A]
+        wavelength_unit = "A"
+    class Sample:
+        zacceptance = 0.1 # [A^-1]
+        thickness = 0.2 # [cm]
+
+    class SESANSData1D:
+        #q_zmax = 0.23 # [A^-1]
+        lam = 0.2 # [nm]
+        x = SElength
+        y = data
+        dy = err_data
+        sample = Sample()
+    data = SESANSData1D()
+
+    x, y = ... # create or load sesans
+    data = smd.Data
+
+The *data* module defines various data plotters as well.
+
+Using sasmodels directly
+========================
+
+Once you have a computational kernel and a data object, you can evaluate
+the model for various parameters using
+:class:`sasmodels.direct_model.DirectModel`.  The resulting object *f*
+will be callable as *f(par=value, ...)*, returning the $I(q)$ for the $q$
+values in the data.  For example::
+
+    import numpy as np
+    from sasmodels.data import empty_data1D
+    from sasmodels.core import load_model
+    from sasmodels.direct_model import DirectModel
+
+    # 120 points logarithmically spaced from 0.005 to 0.2, with dq/q = 5%
+    q = np.logspace(np.log10(5e-3), np.log10(2e-1), 120)
+    data = empty_data1D(q, resolution=0.05)
+    kernel = load_model("ellipsoid)
+    f = DirectModel(data, kernel)
+    Iq = f(radius_polar=100)
+
+Polydispersity information is set with special parameter names:
+
+    * *par_pd* for polydispersity width, $\Delta p/p$,
+    * *par_pd_n* for the number of points in the distribution,
+    * *par_pd_type* for the distribution type (as a string), and
+    * *par_pd_nsigmas* for the limits of the distribution.
+
+Using sasmodels through the bumps optimizer
+===========================================
+
+Like DirectModel, you can wrap data and a kernel in a *bumps* model with
 class:`sasmodels.bumps_model.Model` and create an
-class:`sasmodels.bump_model.Experiment` that you can fit with the *bumps*
+class:`sasmodels.bumps_model.Experiment` that you can fit with the *bumps*
 interface. Here is an example from the *example* directory such as
 *example/model.py*::
@@ -75 +174 @@
     SasViewCom bumps.cli example/model.py --preview
 
-Using sasmodels directly
-========================
-
-Bumps has a notion of parameter boxes in which you can set and retrieve
-values.  Instead of using bumps, you can create a directly callable function
-with :class:`sasmodels.direct_model.DirectModel`.  The resulting object *f*
-will be callable as *f(par=value, ...)*, returning the $I(q)$ for the $q$
-values in the data.  Polydisperse parameters use the same naming conventions
-as in the bumps model, with e.g., radius_pd being the polydispersity associated
-with radius.
+Calling the computation kernel
+==============================
 
 Getting a simple function that you can call on a set of q values and return
@@ -97 +188 @@
 python kernel.  Once the kernel is in hand, we can then marshal a set of
 parameters into a :class:`sasmodels.details.CallDetails` object and ship it to
-the kernel using the :func:`sansmodels.direct_model.call_kernel` function.  An
-example should help, *example/cylinder_eval.py*::
-
-    from numpy import logspace
+the kernel using the :func:`sansmodels.direct_model.call_kernel` function.  To
+accesses the underlying $<F(q)>$ and $<F^2(q)>$, use
+:func:`sasmodels.direct_model.call_Fq` instead.
+
+The following example should
+help, *example/cylinder_eval.py*::
+
+    from numpy import logspace, sqrt
     from matplotlib import pyplot as plt
     from sasmodels.core import load_model
-    from sasmodels.direct_model import call_kernel
+    from sasmodels.direct_model import call_kernel, call_Fq
 
     model = load_model('cylinder')
     q = logspace(-3, -1, 200)
     kernel = model.make_kernel([q])
-    Iq = call_kernel(kernel, dict(radius=200.))
-    plt.loglog(q, Iq)
+    pars = {'radius': 200, 'radius_pd': 0.1, 'scale': 2}
+    Iq = call_kernel(kernel, pars)
+    F, Fsq, Reff, V, Vratio = call_Fq(kernel, pars)
+
+    plt.loglog(q, Iq, label='2 I(q)')
+    plt.loglog(q, F**2/V, label='<F(q)>^2/V')
+    plt.loglog(q, Fsq/V, label='<F^2(q)>/V')
+    plt.xlabel('q (1/A)')
+    plt.ylabel('I(q) (1/cm)')
+    plt.title('Cylinder with radius 200.')
+    plt.legend()
     plt.show()
 
-On windows, this can be called from the cmd prompt using sasview as::
+.. figure:: direct_call.png
+
+    Comparison between $I(q)$, $<F(q)>$ and $<F^2(q)>$ for cylinder model.
+
+This compares $I(q)$ with $<F(q)>$ and $<F^2(q)>$ for a cylinder
+with *radius=200 +/- 20* and *scale=2*. Note that *call_Fq* does not
+include scale and background, nor does it normalize by the average volume.
+The definition of $F = \rho V \hat F$ scaled by the contrast and
+volume, compared to the canonical cylinder $\hat F$, with $\hat F(0) = 1$.
+Integrating over polydispersity and orientation, the returned values are
+$\sum_{r,w\in N(r_o, r_o/10)} \sum_\theta w F(q,r_o,\theta)\sin\theta$ and
+$\sum_{r,w\in N(r_o, r_o/10)} \sum_\theta w F^2(q,r_o,\theta)\sin\theta$.
+
+On windows, this example can be called from the cmd prompt using sasview as
+as the python interpreter::
 
     SasViewCom example/cylinder_eval.py

doc/rst_prolog

@@ -9 +9 @@
 .. |Ang^-3| replace:: |Ang|\ :sup:`-3`
 .. |Ang^-4| replace:: |Ang|\ :sup:`-4`
+.. |nm^-1| replace:: nm\ :sup:`-1`
 .. |cm^-1| replace:: cm\ :sup:`-1`
 .. |cm^2| replace:: cm\ :sup:`2`

doc/guide/sesans/sans_to_sesans.rst

@@ -31 +31 @@
 
 in which :math:`t` is the thickness of the sample and :math:`\lambda` is the wavelength of the neutrons.
+
+Log Spaced SESANS
+-----------------
+
+For computational efficiency, the integral in the Hankel transform is
+converted into a Reimann sum
+
+
+.. math:: G(\delta) \approx
+          2 \pi
+          \sum_{Q=q_{min}}^{q_{max}} J_0(Q \delta)
+          \frac{d \Sigma}{d \Omega} (Q)
+          Q \Delta Q \!
+
+However, this model approximates more than is strictly necessary.
+Specifically, it is approximating the entire integral, when it is only
+the scattering function that cannot be handled analytically.  A better
+approximation might be
+
+.. math:: G(\delta) \approx
+          \sum_{n=0} 2 \pi \frac{d \Sigma}{d \Omega} (q_n)
+          \int_{q_{n-1}}^{q_n} J_0(Q \delta) Q dQ
+          =
+          \sum_{n=0} \frac{2 \pi}{\delta} \frac{d \Sigma}{d \Omega} (q_n)
+          (q_n J_1(q_n \delta) - q_{n-1}J_1(q_{n-1} \delta))\!,
+
+Assume that vectors :math:`q_n` and :math:`I_n` represent the q points
+and corresponding intensity data, respectively.  Further assume that
+:math:`\delta_m` and :math:`G_m` are the spin echo lengths and
+corresponding Hankel transform value.
+
+.. math:: G_m = H_{nm} I_n
+
+where
+
+.. math:: H_{nm} = \frac{2 \pi}{\delta_m}
+          (q_n J_1(q_n \delta_m) - q_{n-1} J_1(q_{n-1} \delta_m))
+
+Also not that, for the limit as :math:`\delta_m` approaches zero,
+
+.. math:: G(0)
+          =
+          \sum_{n=0} \pi \frac{d \Sigma}{d \Omega} (q_n) (q_n^2 - q_{n-1}^2)