Changeset 9150036 in sasmodels for doc


Ignore:
Timestamp:
Mar 6, 2019 6:05:28 PM (6 years ago)
Author:
Paul Kienzle <pkienzle@…>
Branches:
master, core_shell_microgels, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests
Children:
f64b154, bd91e8f, c11d09f, 02226a2
Parents:
e589e9a (diff), 37f38ff (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 beta_approx

Location:
doc/guide
Files:
1 added
3 edited

Legend:

Unmodified
Added
Removed
  • doc/guide/plugin.rst

    raa8c6e0 r9150036  
    272272structure factor to account for interactions between particles.  See 
    273273`Form_Factors`_ for more details. 
     274 
     275**model_info = ...** lets you define a model directly, for example, by 
     276loading and modifying existing models.  This is done implicitly by 
     277:func:`sasmodels.core.load_model_info`, which can create a mixture model 
     278from a pair of existing models.  For example:: 
     279 
     280    from sasmodels.core import load_model_info 
     281    model_info = load_model_info('sphere+cylinder') 
     282 
     283See :class:`sasmodels.modelinfo.ModelInfo` for details about the model 
     284attributes that are defined. 
    274285 
    275286Model Parameters 
     
    894905             - \frac{\sin(x)}{x}\left(\frac{1}{x} - \frac{3!}{x^3} + \frac{5!}{x^5} - \frac{7!}{x^7}\right) 
    895906 
    896         For small arguments , 
     907        For small arguments, 
    897908 
    898909        .. math:: 
  • doc/guide/gpu_setup.rst

    r63602b1 r8b31efa  
    9494Device Selection 
    9595================ 
     96**OpenCL drivers** 
     97 
    9698If you have multiple GPU devices you can tell the program which device to use. 
    9799By default, the program looks for one GPU and one CPU device from available 
     
    104106was used to run the model. 
    105107 
    106 **If you don't want to use OpenCL, you can set** *SAS_OPENCL=None* 
    107 **in your environment settings, and it will only use normal programs.** 
    108  
    109 If you want to use one of the other devices, you can run the following 
     108If you want to use a specific driver and devices, you can run the following 
    110109from the python console:: 
    111110 
     
    115114This will provide a menu of different OpenCL drivers available. 
    116115When one is selected, it will say "set PYOPENCL_CTX=..." 
    117 Use that value as the value of *SAS_OPENCL*. 
     116Use that value as the value of *SAS_OPENCL=driver:device*. 
     117 
     118To use the default OpenCL device (rather than CUDA or None), 
     119set *SAS_OPENCL=opencl*. 
     120 
     121In batch queues, you may need to set *XDG_CACHE_HOME=~/.cache*  
     122(Linux only) to a different directory, depending on how the filesystem  
     123is configured.  You should also set *SAS_DLL_PATH* for CPU-only modules. 
     124 
     125    -DSAS_MODELPATH=path sets directory containing custom models 
     126    -DSAS_OPENCL=vendor:device|cuda:device|none sets the target GPU device 
     127    -DXDG_CACHE_HOME=~/.cache sets the pyopencl cache root (linux only) 
     128    -DSAS_COMPILER=tinycc|msvc|mingw|unix sets the DLL compiler 
     129    -DSAS_OPENMP=1 turns on OpenMP for the DLLs 
     130    -DSAS_DLL_PATH=path sets the path to the compiled modules 
     131 
     132 
     133**CUDA drivers** 
     134 
     135If OpenCL drivers are not available on your system, but NVidia CUDA 
     136drivers are available, then set *SAS_OPENCL=cuda* or 
     137*SAS_OPENCL=cuda:n* for a particular device number *n*.  If no device 
     138number is specified, then the CUDA drivers looks for look for 
     139*CUDA_DEVICE=n* or a file ~/.cuda-device containing n for the device number. 
     140 
     141In batch queues, the SLURM command *sbatch --gres=gpu:1 ...* will set 
     142*CUDA_VISIBLE_DEVICES=n*, which ought to set the correct device 
     143number for *SAS_OPENCL=cuda*.  If not, then set 
     144*CUDA_DEVICE=$CUDA_VISIBLE_DEVICES* within the batch script.  You may 
     145need to set the CUDA cache directory to a folder accessible across the 
     146cluster with *PYCUDA_CACHE_DIR* (or *PYCUDA_DISABLE_CACHE* to disable 
     147caching), and you may need to set environment specific compiler flags 
     148with *PYCUDA_DEFAULT_NVCC_FLAGS*.  You should also set *SAS_DLL_PATH*  
     149for CPU-only modules. 
     150 
     151**No GPU support** 
     152 
     153If you don't want to use OpenCL or CUDA, you can set *SAS_OPENCL=None* 
     154in your environment settings, and it will only use normal programs. 
     155 
     156In batch queues, you may need to set *SAS_DLL_PATH* to a directory 
     157accessible on the compute node. 
     158 
    118159 
    119160Device Testing 
     
    154195*Document History* 
    155196 
    156 | 2017-09-27 Paul Kienzle 
     197| 2018-10-15 Paul Kienzle 
  • doc/guide/scripting.rst

    rbd7630d r23df833  
    188188python kernel.  Once the kernel is in hand, we can then marshal a set of 
    189189parameters into a :class:`sasmodels.details.CallDetails` object and ship it to 
    190 the kernel using the :func:`sansmodels.direct_model.call_kernel` function.  An 
    191 example should help, *example/cylinder_eval.py*:: 
    192  
    193     from numpy import logspace 
     190the kernel using the :func:`sansmodels.direct_model.call_kernel` function.  To 
     191accesses the underlying $<F(q)>$ and $<F^2(q)>$, use 
     192:func:`sasmodels.direct_model.call_Fq` instead. 
     193 
     194The following example should 
     195help, *example/cylinder_eval.py*:: 
     196 
     197    from numpy import logspace, sqrt 
    194198    from matplotlib import pyplot as plt 
    195199    from sasmodels.core import load_model 
    196     from sasmodels.direct_model import call_kernel 
     200    from sasmodels.direct_model import call_kernel, call_Fq 
    197201 
    198202    model = load_model('cylinder') 
    199203    q = logspace(-3, -1, 200) 
    200204    kernel = model.make_kernel([q]) 
    201     Iq = call_kernel(kernel, dict(radius=200.)) 
    202     plt.loglog(q, Iq) 
     205    pars = {'radius': 200, 'radius_pd': 0.1, 'scale': 2} 
     206    Iq = call_kernel(kernel, pars) 
     207    F, Fsq, Reff, V, Vratio = call_Fq(kernel, pars) 
     208 
     209    plt.loglog(q, Iq, label='2 I(q)') 
     210    plt.loglog(q, F**2/V, label='<F(q)>^2/V') 
     211    plt.loglog(q, Fsq/V, label='<F^2(q)>/V') 
     212    plt.xlabel('q (1/A)') 
     213    plt.ylabel('I(q) (1/cm)') 
     214    plt.title('Cylinder with radius 200.') 
     215    plt.legend() 
    203216    plt.show() 
    204217 
    205 On windows, this can be called from the cmd prompt using sasview as:: 
     218.. figure:: direct_call.png 
     219 
     220    Comparison between $I(q)$, $<F(q)>$ and $<F^2(q)>$ for cylinder model. 
     221 
     222This compares $I(q)$ with $<F(q)>$ and $<F^2(q)>$ for a cylinder 
     223with *radius=200 +/- 20* and *scale=2*. Note that *call_Fq* does not 
     224include scale and background, nor does it normalize by the average volume. 
     225The definition of $F = \rho V \hat F$ scaled by the contrast and 
     226volume, compared to the canonical cylinder $\hat F$, with $\hat F(0) = 1$. 
     227Integrating over polydispersity and orientation, the returned values are 
     228$\sum_{r,w\in N(r_o, r_o/10)} \sum_\theta w F(q,r_o,\theta)\sin\theta$ and 
     229$\sum_{r,w\in N(r_o, r_o/10)} \sum_\theta w F^2(q,r_o,\theta)\sin\theta$. 
     230 
     231On windows, this example can be called from the cmd prompt using sasview as 
     232as the python interpreter:: 
    206233 
    207234    SasViewCom example/cylinder_eval.py 
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