Changeset 992299c in sasmodels for doc/guide


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Timestamp:
Sep 3, 2018 5:55:57 AM (6 years ago)
Author:
GitHub <noreply@…>
Branches:
master, core_shell_microgels, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests
Children:
da1c8d1, 475ff58
Parents:
39c2de9 (diff), bd7630d (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
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git-author:
Steve K <smk78@…> (09/03/18 05:55:57)
git-committer:
GitHub <noreply@…> (09/03/18 05:55:57)
Message:

Merge pull request #76 from SasView?/doc_update

update scripting docs. Refs #1141

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1 edited

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  • doc/guide/scripting.rst

    r4aa5dce rbd7630d  
    1010The key functions are :func:`sasmodels.core.load_model` for loading the 
    1111model definition and compiling the kernel and 
    12 :func:`sasmodels.data.load_data` for calling sasview to load the data. Need 
    13 the data because that defines the resolution function and the q values to 
    14 evaluate. If there is no data, then use :func:`sasmodels.data.empty_data1D` 
    15 or :func:`sasmodels.data.empty_data2D` to create some data with a given $q$. 
    16  
    17 Using sasmodels through bumps 
    18 ============================= 
    19  
    20 With the data and the model, you can wrap it in a *bumps* model with 
     12:func:`sasmodels.data.load_data` for calling sasview to load the data. 
     13 
     14Preparing data 
     15============== 
     16 
     17Usually you will load data via the sasview loader, with the 
     18:func:`sasmodels.data.load_data` function.  For example:: 
     19 
     20    from sasmodels.data import load_data 
     21    data = load_data("sasmodels/example/093191_201.dat") 
     22 
     23You may want to apply a data mask, such a beam stop, and trim high $q$:: 
     24 
     25    from sasmodels.data import set_beam_stop 
     26    set_beam_stop(data, qmin, qmax) 
     27 
     28The :func:`sasmodels.data.set_beam_stop` method simply sets the *mask* 
     29attribute for the data. 
     30 
     31The data defines the resolution function and the q values to evaluate, so 
     32even if you simulating experiments prior to making measurements, you still 
     33need a data object for reference. Use :func:`sasmodels.data.empty_data1D` 
     34or :func:`sasmodels.data.empty_data2D` to create a container with a 
     35given $q$ and $\Delta q/q$.  For example:: 
     36 
     37    import numpy as np 
     38    from sasmodels.data import empty_data1D 
     39 
     40    # 120 points logarithmically spaced from 0.005 to 0.2, with dq/q = 5% 
     41    q = np.logspace(np.log10(5e-3), np.log10(2e-1), 120) 
     42    data = empty_data1D(q, resolution=0.05) 
     43 
     44To use a more realistic model of resolution, or to load data from a file 
     45format not understood by SasView, you can use :class:`sasmodels.data.Data1D` 
     46or :class:`sasmodels.data.Data2D` directly.  The 1D data uses 
     47*x*, *y*, *dx* and *dy* for $x = q$ and $y = I(q)$, and 2D data uses 
     48*x*, *y*, *z*, *dx*, *dy*, *dz* for $x, y = qx, qy$ and $z = I(qx, qy)$. 
     49[Note: internally, the Data2D object uses SasView conventions, 
     50*qx_data*, *qy_data*, *data*, *dqx_data*, *dqy_data*, and *err_data*.] 
     51 
     52For USANS data, use 1D data, but set *dxl* and *dxw* attributes to 
     53indicate slit resolution:: 
     54 
     55    data.dxl = 0.117 
     56 
     57See :func:`sasmodels.resolution.slit_resolution` for details. 
     58 
     59SESANS data is more complicated; if your SESANS format is not supported by 
     60SasView you need to define a number of attributes beyond *x*, *y*.  For 
     61example:: 
     62 
     63    SElength = np.linspace(0, 2400, 61) # [A] 
     64    data = np.ones_like(SElength) 
     65    err_data = np.ones_like(SElength)*0.03 
     66 
     67    class Source: 
     68        wavelength = 6 # [A] 
     69        wavelength_unit = "A" 
     70    class Sample: 
     71        zacceptance = 0.1 # [A^-1] 
     72        thickness = 0.2 # [cm] 
     73 
     74    class SESANSData1D: 
     75        #q_zmax = 0.23 # [A^-1] 
     76        lam = 0.2 # [nm] 
     77        x = SElength 
     78        y = data 
     79        dy = err_data 
     80        sample = Sample() 
     81    data = SESANSData1D() 
     82 
     83    x, y = ... # create or load sesans 
     84    data = smd.Data 
     85 
     86The *data* module defines various data plotters as well. 
     87 
     88Using sasmodels directly 
     89======================== 
     90 
     91Once you have a computational kernel and a data object, you can evaluate 
     92the model for various parameters using 
     93:class:`sasmodels.direct_model.DirectModel`.  The resulting object *f* 
     94will be callable as *f(par=value, ...)*, returning the $I(q)$ for the $q$ 
     95values in the data.  For example:: 
     96 
     97    import numpy as np 
     98    from sasmodels.data import empty_data1D 
     99    from sasmodels.core import load_model 
     100    from sasmodels.direct_model import DirectModel 
     101 
     102    # 120 points logarithmically spaced from 0.005 to 0.2, with dq/q = 5% 
     103    q = np.logspace(np.log10(5e-3), np.log10(2e-1), 120) 
     104    data = empty_data1D(q, resolution=0.05) 
     105    kernel = load_model("ellipsoid) 
     106    f = DirectModel(data, kernel) 
     107    Iq = f(radius_polar=100) 
     108 
     109Polydispersity information is set with special parameter names: 
     110 
     111    * *par_pd* for polydispersity width, $\Delta p/p$, 
     112    * *par_pd_n* for the number of points in the distribution, 
     113    * *par_pd_type* for the distribution type (as a string), and 
     114    * *par_pd_nsigmas* for the limits of the distribution. 
     115 
     116Using sasmodels through the bumps optimizer 
     117=========================================== 
     118 
     119Like DirectModel, you can wrap data and a kernel in a *bumps* model with 
    21120class:`sasmodels.bumps_model.Model` and create an 
    22 class:`sasmodels.bump_model.Experiment` that you can fit with the *bumps* 
     121class:`sasmodels.bumps_model.Experiment` that you can fit with the *bumps* 
    23122interface. Here is an example from the *example* directory such as 
    24123*example/model.py*:: 
     
    75174    SasViewCom bumps.cli example/model.py --preview 
    76175 
    77 Using sasmodels directly 
    78 ======================== 
    79  
    80 Bumps has a notion of parameter boxes in which you can set and retrieve 
    81 values.  Instead of using bumps, you can create a directly callable function 
    82 with :class:`sasmodels.direct_model.DirectModel`.  The resulting object *f* 
    83 will be callable as *f(par=value, ...)*, returning the $I(q)$ for the $q$ 
    84 values in the data.  Polydisperse parameters use the same naming conventions 
    85 as in the bumps model, with e.g., radius_pd being the polydispersity associated 
    86 with radius. 
     176Calling the computation kernel 
     177============================== 
    87178 
    88179Getting a simple function that you can call on a set of q values and return 
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