Changes in / [fa26e78:36a2418] in sasmodels
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README.rst
re30d645 r2a64722 10 10 is available. 11 11 12 Example 12 Install 13 13 ------- 14 15 The easiest way to use sasmodels is from `SasView <http://www.sasview.org/>`_. 16 17 You can also install sasmodels as a standalone package in python. Use 18 `miniconda <https://docs.conda.io/en/latest/miniconda.html>`_ 19 or `anaconda <https://www.anaconda.com/>`_ 20 to create a python environment with the sasmodels dependencies:: 21 22 $ conda create -n sasmodels -c conda-forge numpy scipy matplotlib pyopencl 23 24 The option ``-n sasmodels`` names the environment sasmodels, and the option 25 ``-c conda-forge`` selects the conda-forge package channel because pyopencl 26 is not part of the base anaconda distribution. 27 28 Activate the environment and install sasmodels:: 29 30 $ conda activate sasmodels 31 (sasmodels) $ pip install sasmodels 32 33 Install `bumps <https://github.com/bumps/bumps>`_ if you want to use it to fit 34 your data:: 35 36 (sasmodels) $ pip install bumps 37 38 Usage 39 ----- 40 41 Check that the works:: 42 43 (sasmodels) $ python -m sasmodels.compare cylinder 44 45 To show the orientation explorer:: 46 47 (sasmodels) $ python -m sasmodels.jitter 48 49 Documentation is available online as part of the SasView 50 `fitting perspective <http://www.sasview.org/docs/index.html>`_ 51 as well as separate pages for 52 `individual models <http://www.sasview.org/docs/user/sasgui/perspectives/fitting/models/index.html>`_. 53 Programming details for sasmodels are available in the 54 `developer documentation <http://www.sasview.org/docs/dev/dev.html>`_. 55 56 57 Fitting Example 58 --------------- 14 59 15 60 The example directory contains a radial+tangential data set for an oriented 16 61 rod-like shape. 17 62 18 The data is loaded by sas.dataloader from the sasview package, so sasview 19 is needed to run the example. 63 To load the example data, you will need the SAS data loader from the sasview 64 package. This is not yet available on PyPI, so you will need a copy of the 65 SasView source code to run it. Create a directory somewhere to hold the 66 sasview and sasmodels source code, which we will refer to as $SOURCE. 20 67 21 To run the example, you need sasview, sasmodels and bumps. Assuming these 22 repositories are installed side by side, change to the sasmodels/example 23 directory and enter:: 68 Use the following to install sasview, and the sasmodels examples:: 24 69 25 PYTHONPATH=..:../../sasview/src ../../bumps/run.py fit.py \ 26 cylinder --preview 70 (sasmodels) $ cd $SOURCE 71 (sasmodels) $ conda install git 72 (sasmodels) $ git clone https://github.com/sasview/sasview.git 73 (sasmodels) $ git clone https://github.com/sasview/sasmodels.git 27 74 28 See bumps documentation for instructions on running the fit. With the 29 python packages installed, e.g., into a virtual environment, then the 30 python path need not be set, and the command would be:: 75 Set the path to the sasview source on your python path within the sasmodels 76 environment. On Windows, this will be:: 31 77 32 bumps fit.py cylinder --preview 78 (sasmodels)> set PYTHONPATH="$SOURCE\sasview\src" 79 (sasmodels)> cd $SOURCE/sasmodels/example 80 (sasmodels)> python -m bumps.cli fit.py cylinder --preview 81 82 On Mac/Linux with the standard shell this will be:: 83 84 (sasmodels) $ export PYTHONPATH="$SOURCE/sasview/src" 85 (sasmodels) $ cd $SOURCE/sasmodels/example 86 (sasmodels) $ bumps fit.py cylinder --preview 33 87 34 88 The fit.py model accepts up to two arguments. The first argument is the … … 38 92 both radial and tangential simultaneously, use the word "both". 39 93 40 Notes 41 ----- 42 43 cylinder.c + cylinder.py is the cylinder model with renamed variables and 44 sld scaled by 1e6 so the numbers are nicer. The model name is "cylinder" 45 46 lamellar.py is an example of a single file model with embedded C code. 94 See `bumps documentation <https://bumps.readthedocs.io/>`_ for detailed 95 instructions on running the fit. 47 96 48 97 |TravisStatus|_ -
explore/beta/sasfit_compare.py
r2a12351b r119073a 505 505 } 506 506 507 Q, IQ = load_sasfit(data_file(' richard_test.txt'))508 Q, IQSD = load_sasfit(data_file(' richard_test2.txt'))509 Q, IQBD = load_sasfit(data_file(' richard_test3.txt'))507 Q, IQ = load_sasfit(data_file('sasfit_sphere_schulz_IQD.txt')) 508 Q, IQSD = load_sasfit(data_file('sasfit_sphere_schulz_IQSD.txt')) 509 Q, IQBD = load_sasfit(data_file('sasfit_sphere_schulz_IQBD.txt')) 510 510 target = Theory(Q=Q, F1=None, F2=None, P=IQ, S=None, I=IQSD, Seff=None, Ibeta=IQBD) 511 511 actual = sphere_r(Q, norm="sasfit", **pars) … … 526 526 } 527 527 528 Q, IQ = load_sasfit(data_file(' richard_test4.txt'))529 Q, IQSD = load_sasfit(data_file(' richard_test5.txt'))530 Q, IQBD = load_sasfit(data_file(' richard_test6.txt'))528 Q, IQ = load_sasfit(data_file('sasfit_ellipsoid_shulz_IQD.txt')) 529 Q, IQSD = load_sasfit(data_file('sasfit_ellipsoid_shulz_IQSD.txt')) 530 Q, IQBD = load_sasfit(data_file('sasfit_ellipsoid_shulz_IQBD.txt')) 531 531 target = Theory(Q=Q, F1=None, F2=None, P=IQ, S=None, I=IQSD, Seff=None, Ibeta=IQBD) 532 532 actual = ellipsoid_pe(Q, norm="sasfit", **pars) -
explore/precision.py
rfba9ca0 rcd28947 207 207 return model_info 208 208 209 # Hack to allow second parameter A in two parameter functions 209 # Hack to allow second parameter A in the gammainc and gammaincc functions. 210 # Create a 2-D variant of the precision test if we need to handle other two 211 # parameter functions. 210 212 A = 1 211 213 def parse_extra_pars(): 214 """ 215 Parse the command line looking for the second parameter "A=..." for the 216 gammainc/gammaincc functions. 217 """ 212 218 global A 213 219 … … 333 339 ) 334 340 add_function( 341 # Note: "a" is given as A=... on the command line via parse_extra_pars 335 342 name="gammainc(x)", 336 343 mp_function=lambda x, a=A: mp.gammainc(a, a=0, b=x)/mp.gamma(a), … … 339 346 ) 340 347 add_function( 348 # Note: "a" is given as A=... on the command line via parse_extra_pars 341 349 name="gammaincc(x)", 342 350 mp_function=lambda x, a=A: mp.gammainc(a, a=x, b=mp.inf)/mp.gamma(a), -
sasmodels/generate.py
r39a06c9 rcd28947 703 703 """ 704 704 for code in source: 705 m = _FQ_PATTERN.search(code) 706 if m is not None: 705 if _FQ_PATTERN.search(code) is not None: 707 706 return True 708 707 return False … … 712 711 # type: (List[str]) -> bool 713 712 """ 714 Return True if C source defines " void Fq(".713 Return True if C source defines "double shell_volume(". 715 714 """ 716 715 for code in source: 717 m = _SHELL_VOLUME_PATTERN.search(code) 718 if m is not None: 716 if _SHELL_VOLUME_PATTERN.search(code) is not None: 719 717 return True 720 718 return False -
sasmodels/kernel.py
r3199b17 r3199b17 135 135 nout = 2 if self.info.have_Fq and self.dim == '1d' else 1 136 136 total_weight = self.result[nout*self.q_input.nq + 0] 137 # Note: total_weight = sum(weight > cutoff), with cutoff >= 0, so it 138 # is okay to test directly against zero. If weight is zero then I(q), 139 # etc. must also be zero. 137 140 if total_weight == 0.: 138 141 total_weight = 1. -
sasmodels/models/pearl_necklace.c
r4453136 r4453136 40 40 const double si = sas_sinx_x(q*A_s); 41 41 const double omsi = 1.0 - si; 42 const double pow_si = pow (si, num_pearls);42 const double pow_si = pown(si, num_pearls); 43 43 44 44 // form factor for num_pearls -
sasmodels/sasview_model.py
r5024a56 r3a1afed 695 695 return self._calculate_Iq(qx, qy) 696 696 697 def _calculate_Iq(self, qx, qy=None , Fq=False, effective_radius_type=1):697 def _calculate_Iq(self, qx, qy=None): 698 698 if self._model is None: 699 699 self._model = core.build_model(self._model_info) … … 715 715 #print("values", values) 716 716 #print("is_mag", is_magnetic) 717 if Fq:718 result = calculator.Fq(call_details, values, cutoff=self.cutoff,719 magnetic=is_magnetic,720 effective_radius_type=effective_radius_type)721 717 result = calculator(call_details, values, cutoff=self.cutoff, 722 718 magnetic=is_magnetic) … … 736 732 Calculate the effective radius for P(q)*S(q) 737 733 734 *mode* is the R_eff type, which defaults to 1 to match the ER 735 calculation for sasview models from version 3.x. 736 738 737 :return: the value of the effective radius 739 738 """ 740 Fq = self._calculate_Iq([0.1], True, mode) 741 return Fq[2] 739 # ER and VR are only needed for old multiplication models, based on 740 # sas.sascalc.fit.MultiplicationModel. Fail for now. If we want to 741 # continue supporting them then add some test cases so that the code 742 # is exercised. We can access ER/VR using the kernel Fq function by 743 # extending _calculate_Iq so that it calls: 744 # if er_mode > 0: 745 # res = calculator.Fq(call_details, values, cutoff=self.cutoff, 746 # magnetic=False, effective_radius_type=mode) 747 # R_eff, form_shell_ratio = res[2], res[4] 748 # return R_eff, form_shell_ratio 749 # Then use the following in calculate_ER: 750 # ER, VR = self._calculate_Iq(q=[0.1], er_mode=mode) 751 # return ER 752 # Similarly, for calculate_VR: 753 # ER, VR = self._calculate_Iq(q=[0.1], er_mode=1) 754 # return VR 755 # Obviously a combined calculate_ER_VR method would be better, but 756 # we only need them to support very old models, so ignore the 2x 757 # performance hit. 758 raise NotImplementedError("ER function is no longer available.") 742 759 743 760 def calculate_VR(self): … … 748 765 :return: the value of the form:shell volume ratio 749 766 """ 750 Fq = self._calculate_Iq([0.1], True, mode)751 r eturn Fq[4]767 # See comments in calculate_ER. 768 raise NotImplementedError("VR function is no longer available.") 752 769 753 770 def set_dispersion(self, parameter, dispersion):
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