- Timestamp:
- Jan 17, 2018 4:14:03 PM (7 years ago)
- Branches:
- master, core_shell_microgels, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests
- Children:
- c11d09f
- Parents:
- 92d330fd (diff), 1258e32 (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. - Location:
- doc/guide
- Files:
-
- 2 added
- 3 edited
Legend:
- Unmodified
- Added
- Removed
-
doc/guide/orientation/orientation.rst
r82592da r5fb0634 4 4 ================== 5 5 6 With two dimensional small angle diffraction data SasViewwill calculate6 With two dimensional small angle diffraction data sasmodels will calculate 7 7 scattering from oriented particles, applicable for example to shear flow 8 8 or orientation in a magnetic field. 9 9 10 10 In general we first need to define the reference orientation 11 of the particles with respect to the incoming neutron or X-ray beam. This 12 is done using three angles: $\theta$ and $\phi$ define the orientation of 13 the axis of the particle, angle $\Psi$ is defined as the orientation of 14 the major axis of the particle cross section with respect to its starting 15 position along the beam direction. The figures below are for an elliptical 16 cross section cylinder, but may be applied analogously to other shapes of 17 particle. 11 of the particle's $a$-$b$-$c$ axes with respect to the incoming 12 neutron or X-ray beam. This is done using three angles: $\theta$ and $\phi$ 13 define the orientation of the $c$-axis of the particle, and angle $\Psi$ is 14 defined as the orientation of the major axis of the particle cross section 15 with respect to its starting position along the beam direction (or 16 equivalently, as rotation about the $c$ axis). There is an unavoidable 17 ambiguity when $c$ is aligned with $z$ in that $\phi$ and $\Psi$ both 18 serve to rotate the particle about $c$, but this symmetry is destroyed 19 when $\theta$ is not a multiple of 180. 20 21 The figures below are for an elliptical cross section cylinder, but may 22 be applied analogously to other shapes of particle. 18 23 19 24 .. note:: … … 29 34 30 35 Definition of angles for oriented elliptical cylinder, where axis_ratio 31 b/a is shown >1 ,Note that rotation $\theta$, initially in the $x$-$z$36 b/a is shown >1. Note that rotation $\theta$, initially in the $x$-$z$ 32 37 plane, is carried out first, then rotation $\phi$ about the $z$-axis, 33 38 finally rotation $\Psi$ is around the axis of the cylinder. The neutron 34 or X-ray beam is along the $ z$ axis.39 or X-ray beam is along the $-z$ axis. 35 40 36 41 .. figure:: … … 40 45 with $\Psi$ = 0. 41 46 42 Having established the mean direction of the particle we can then apply 43 angular orientation distributions. This is done by a numerical integration 44 over a range of angles in a similar way to particle size dispersity. 45 In the current version of sasview the orientational dispersity is defined 46 with respect to the axes of the particle. 47 Having established the mean direction of the particle (the view) we can then 48 apply angular orientation distributions (jitter). This is done by a numerical 49 integration over a range of angles in a similar way to particle size 50 dispersity. The orientation dispersity is defined with respect to the 51 $a$-$b$-$c$ axes of the particle, with roll angle $\Psi$ about the $c$-axis, 52 yaw angle $\theta$ about the $b$-axis and pitch angle $\phi$ about the 53 $a$-axis. 54 55 More formally, starting with axes $a$-$b$-$c$ of the particle aligned 56 with axes $x$-$y$-$z$ of the laboratory frame, the orientation dispersity 57 is applied first, using the 58 `Tait-Bryan <https://en.wikipedia.org/wiki/Euler_angles#Conventions_2>`_ 59 $x$-$y'$-$z''$ convention with angles $\Delta\phi$-$\Delta\theta$-$\Delta\Psi$. 60 The reference orientation then follows, using the 61 `Euler angles <https://en.wikipedia.org/wiki/Euler_angles#Conventions>`_ 62 $z$-$y'$-$z''$ with angles $\phi$-$\theta$-$\Psi$. This is implemented 63 using rotation matrices as 64 65 .. math:: 66 67 R = R_z(\phi)\, R_y(\theta)\, R_z(\Psi)\, 68 R_x(\Delta\phi)\, R_y(\Delta\theta)\, R_z(\Delta\Psi) 69 70 To transform detector $(q_x, q_y)$ values into $(q_a, q_b, q_c)$ for the 71 shape in its canonical orientation, use 72 73 .. math:: 74 75 [q_a, q_b, q_c]^T = R^{-1} \, [q_x, q_y, 0]^T 76 77 78 The inverse rotation is easily calculated by rotating the opposite directions 79 in the reverse order, so 80 81 .. math:: 82 83 R^{-1} = R_z(-\Delta\Psi)\, R_y(-\Delta\theta)\, R_x(-\Delta\phi)\, 84 R_z(-\Psi)\, R_y(-\theta)\, R_z(-\phi) 85 47 86 48 87 The $\theta$ and $\phi$ orientation parameters for the cylinder only appear 49 when fitting 2d data. On introducing "Orientational Distribution" in 50 theangles, "distribution of theta" and "distribution of phi" parameters will88 when fitting 2d data. On introducing "Orientational Distribution" in the 89 angles, "distribution of theta" and "distribution of phi" parameters will 51 90 appear. These are actually rotations about the axes $\delta_1$ and $\delta_2$ 52 of the cylinder, the $b$ and $a$ axes of the cylinder cross section. (When 53 $\theta = \phi = 0$ these are parallel to the $Y$ and $X$ axes of the 54 instrument.) The third orientation distribution, in $\Psi$, is about the $c$ 55 axis of the particle. Some experimentation may be required to understand the 56 2d patterns fully. A number of different shapes of distribution are 57 available, as described for polydispersity, see :ref:`polydispersityhelp` . 91 of the cylinder, which correspond to the $b$ and $a$ axes of the cylinder 92 cross section. (When $\theta = \phi = 0$ these are parallel to the $Y$ and 93 $X$ axes of the instrument.) The third orientation distribution, in $\Psi$, 94 is about the $c$ axis of the particle. Some experimentation may be required 95 to understand the 2d patterns fully. A number of different shapes of 96 distribution are available, as described for size dispersity, see 97 :ref:`polydispersityhelp`. 58 98 59 Earlier versions of SasView had numerical integration issues in some 60 circumstances when distributions passed through 90 degrees. The distributions 61 in particle coordinates are more robust, but should still be approached with 62 care for large ranges of angle. 99 Given that the angular dispersion distribution is defined in cartesian space, 100 over a cube defined by 101 102 .. math:: 103 104 [-\Delta \theta, \Delta \theta] \times 105 [-\Delta \phi, \Delta \phi] \times 106 [-\Delta \Psi, \Delta \Psi] 107 108 but the orientation is defined over a sphere, we are left with a 109 `map projection <https://en.wikipedia.org/wiki/List_of_map_projections>`_ 110 problem, with different tradeoffs depending on how values in $\Delta\theta$ 111 and $\Delta\phi$ are translated into latitude/longitude on the sphere. 112 113 Sasmodels is using the 114 `equirectangular projection <https://en.wikipedia.org/wiki/Equirectangular_projection>`_. 115 In this projection, square patches in angular dispersity become wedge-shaped 116 patches on the sphere. To correct for the changing point density, there is a 117 scale factor of $\sin(\Delta\theta)$ that applies to each point in the 118 integral. This is not enough, though. Consider a shape which is tumbling 119 freely around the $b$ axis, with $\Delta\theta$ uniform in $[-180, 180]$. At 120 $\pm 90$, all points in $\Delta\phi$ map to the pole, so the jitter will have 121 a distinct angular preference. If the spin axis is along the beam (which 122 will be the case for $\theta=90$ and $\Psi=90$) the scattering pattern 123 should be circularly symmetric, but it will go to zero at $q_x = 0$ due to the 124 $\sin(\Delta\theta)$ correction. This problem does not appear for a shape 125 that is tumbling freely around the $a$ axis, with $\Delta\phi$ uniform in 126 $[-180, 180]$, so swap the $a$ and $b$ axes so $\Delta\theta < \Delta\phi$ 127 and adjust $\Psi$ by 90. This works with the current sasmodels shapes due to 128 symmetry. 129 130 Alternative projections were considered. 131 The `sinusoidal projection <https://en.wikipedia.org/wiki/Sinusoidal_projection>`_ 132 works by scaling $\Delta\phi$ as $\Delta\theta$ increases, and dropping those 133 points outside $[-180, 180]$. The distortions are a little less for middle 134 ranges of $\Delta\theta$, but they are still severe for large $\Delta\theta$ 135 and the model is much harder to explain. 136 The `azimuthal equidistance projection <https://en.wikipedia.org/wiki/Azimuthal_equidistant_projection>`_ 137 also improves on the equirectangular projection by extending the range of 138 reasonable values for the $\Delta\theta$ range, with $\Delta\phi$ forming a 139 wedge that cuts to the opposite side of the sphere rather than cutting to the 140 pole. This projection has the nice property that distance from the center are 141 preserved, and that $\Delta\theta$ and $\Delta\phi$ act the same. 142 The `azimuthal equal area projection <https://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection>`_ 143 is like the azimuthal equidistance projection, but it preserves area instead 144 of distance. It also has the same behaviour for $\Delta\theta$ and $\Delta\phi$. 145 The `Guyou projection <https://en.wikipedia.org/wiki/Guyou_hemisphere-in-a-square_projection>`_ 146 has an excellent balance with reasonable distortion in both $\Delta\theta$ 147 and $\Delta\phi$, as well as preserving small patches. However, it requires 148 considerably more computational overhead, and we have not yet derived the 149 formula for the distortion correction, measuring the degree of stretch at 150 the point $(\Delta\theta, \Delta\phi)$ on the map. 63 151 64 152 .. note:: 65 Note that the form factors for oriented particles are also performing 66 numerical integrations over one or more variables, so care should be taken, 67 especially with very large particles or more extreme aspect ratios. In such 68 cases results may not be accurate, particularly at very high Q, unless the model 69 has been specifically coded to use limiting forms of the scattering equations. 70 71 For best numerical results keep the $\theta$ distribution narrower than the $\phi$ 72 distribution. Thus for asymmetric particles, such as elliptical_cylinder, you may 73 need to reorder the sizes of the three axes to acheive the desired result. 74 This is due to the issues of mapping a rectangular distribution onto the 75 surface of a sphere. 153 Note that the form factors for oriented particles are performing 154 numerical integrations over one or more variables, so care should be 155 taken, especially with very large particles or more extreme aspect 156 ratios. In such cases results may not be accurate, particularly at very 157 high Q, unless the model has been specifically coded to use limiting 158 forms of the scattering equations. 76 159 77 Users can experiment with the values of *Npts* and *Nsigs*, the number of steps 78 used in the integration and the range spanned in number of standard deviations. 79 The standard deviation is entered in units of degrees. For a "rectangular" 80 distribution the full width should be $\pm \sqrt(3)$ ~ 1.73 standard deviations. 81 The new "uniform" distribution avoids this by letting you directly specify the 160 For best numerical results keep the $\theta$ distribution narrower than 161 the $\phi$ distribution. Thus for asymmetric particles, such as 162 elliptical_cylinder, you may need to reorder the sizes of the three axes 163 to acheive the desired result. This is due to the issues of mapping a 164 rectanglar distribution onto the surface of a sphere. 165 166 Users can experiment with the values of *Npts* and *Nsigs*, the number of steps 167 used in the integration and the range spanned in number of standard deviations. 168 The standard deviation is entered in units of degrees. For a "rectangular" 169 distribution the full width should be $\pm \sqrt(3)$ ~ 1.73 standard deviations. 170 The new "uniform" distribution avoids this by letting you directly specify the 82 171 half width. 83 172 84 The angular distributions will be truncated outside of the range -180 to +18085 degrees, so beware of using saying a broad Gaussian distribution with large value86 of *Nsigs*, as the array of *Npts* may be truncated to many fewer points than would 87 give a good integration,as well as becoming rather meaningless. (At some point 88 in the future the actual polydispersity arrays may be made available to the user 89 for inspection.)173 The angular distributions may be truncated outside of the range -180 to +180 174 degrees, so beware of using saying a broad Gaussian distribution with large 175 value of *Nsigs*, as the array of *Npts* may be truncated to many fewer 176 points than would give a good integration,as well as becoming rather 177 meaningless. (At some point in the future the actual dispersion arrays may be 178 made available to the user for inspection.) 90 179 91 180 Some more detailed technical notes are provided in the developer section of 92 181 this manual :ref:`orientation_developer` . 93 182 183 This definition of orientation is new to SasView 4.2. In earlier versions, 184 the orientation distribution appeared as a distribution of view angles. 185 This led to strange effects when $c$ was aligned with $z$, where changes 186 to the $\phi$ angle served only to rotate the shape about $c$, rather than 187 having a consistent interpretation as the pitch of the shape relative to 188 the flow field defining the reference orientation. Prior to SasView 4.1, 189 the reference orientation was defined using a Tait-Bryan convention, making 190 it difficult to control. Now, rotation in $\theta$ modifies the spacings 191 in the refraction pattern, and rotation in $\phi$ rotates it in the detector 192 plane. 193 194 94 195 *Document History* 95 196 96 197 | 2017-11-06 Richard Heenan 198 | 2017-12-20 Paul Kienzle -
doc/guide/plugin.rst
r3048ec6 r7e6bc45e 292 292 **Note: The order of the parameters in the definition will be the order of the 293 293 parameters in the user interface and the order of the parameters in Iq(), 294 Iqxy() and form_volume(). And** *scale* **and** *background* **parameters are 295 implicit to all models, so they do not need to be included in the parameter table.** 294 Iqac(), Iqabc() and form_volume(). And** *scale* **and** *background* 295 **parameters are implicit to all models, so they do not need to be included 296 in the parameter table.** 296 297 297 298 - **"name"** is the name of the parameter shown on the FitPage. … … 362 363 scattered intensity. 363 364 364 - "volume" parameters are passed to Iq(), Iqxy(), and form_volume(), and 365 have polydispersity loops generated automatically. 366 367 - "orientation" parameters are only passed to Iqxy(), and have angular 368 dispersion. 365 - "volume" parameters are passed to Iq(), Iqac(), Iqabc() and form_volume(), 366 and have polydispersity loops generated automatically. 367 368 - "orientation" parameters are not passed, but instead are combined with 369 orientation dispersity to translate *qx* and *qy* to *qa*, *qb* and *qc*. 370 These parameters should appear at the end of the table with the specific 371 names *theta*, *phi* and for asymmetric shapes *psi*, in that order. 369 372 370 373 Some models will have integer parameters, such as number of pearls in the … … 419 422 That is, the individual models do not need to include polydispersity 420 423 calculations, but instead rely on numerical integration to compute the 421 appropriately smeared pattern. Angular dispersion values over polar angle 422 $\theta$ requires an additional $\cos \theta$ weighting due to decreased 423 arc length for the equatorial angle $\phi$ with increasing latitude. 424 appropriately smeared pattern. 424 425 425 426 Python Models … … 468 469 barbell). If I(q; pars) is NaN for any $q$, then those parameters will be 469 470 ignored, and not included in the calculation of the weighted polydispersity. 470 471 Similar to *Iq*, you can define *Iqxy(qx, qy, par1, par2, ...)* where the472 parameter list includes any orientation parameters. If *Iqxy* is not defined,473 then it will default to *Iqxy = Iq(sqrt(qx**2+qy**2), par1, par2, ...)*.474 471 475 472 Models should define *form_volume(par1, par2, ...)* where the parameter … … 497 494 } 498 495 499 *Iqxy* is similar to *Iq*, except it uses parameters *qx, qy* instead of *q*,500 and it includes orientation parameters.501 502 496 *form_volume* defines the volume of the shape. As in python models, it 503 497 includes only the volume parameters. 504 498 505 *Iqxy* will default to *Iq(sqrt(qx**2 + qy**2), par1, ...)* and506 *form_volume* will default to 1.0.507 508 499 **source=['fn.c', ...]** includes the listed C source files in the 509 program before *Iq* and *Iqxy* are defined. This allows you to extend the 510 library of C functions available to your model. 500 program before *Iq* and *form_volume* are defined. This allows you to 501 extend the library of C functions available to your model. 502 503 *c_code* includes arbitrary C code into your kernel, which can be 504 handy for defining helper functions for *Iq* and *form_volume*. Note that 505 you can put the full function definition for *Iq* and *form_volume* 506 (include function declaration) into *c_code* as well, or put them into an 507 external C file and add that file to the list of sources. 511 508 512 509 Models are defined using double precision declarations for the … … 533 530 #define INVALID(v) (v.bell_radius < v.radius) 534 531 532 The INVALID define can go into *Iq*, or *c_code*, or an external C file 533 listed in *source*. 534 535 Oriented Shapes 536 ............... 537 538 If the scattering is dependent on the orientation of the shape, then you 539 will need to include *orientation* parameters *theta*, *phi* and *psi* 540 at the end of the parameter table. As described in the section 541 :ref:`orientation`, the individual $(q_x, q_y)$ points on the detector will 542 be rotated into $(q_a, q_b, q_c)$ points relative to the sample in its 543 canonical orientation with $a$-$b$-$c$ aligned with $x$-$y$-$z$ in the 544 laboratory frame and beam travelling along $-z$. 545 546 The oriented C model is called using *Iqabc(qa, qb, qc, par1, par2, ...)* where 547 *par1*, etc. are the parameters to the model. If the shape is rotationally 548 symmetric about *c* then *psi* is not needed, and the model is called 549 as *Iqac(qab, qc, par1, par2, ...)*. In either case, the orientation 550 parameters are not included in the function call. 551 552 For 1D oriented shapes, an integral over all angles is usually needed for 553 the *Iq* function. Given symmetry and the substitution $u = \cos(\alpha)$, 554 $du = -\sin(\alpha)\,d\alpha$ this becomes 555 556 .. math:: 557 558 I(q) &= \frac{1}{4\pi} \int_{-\pi/2}^{pi/2} \int_{-pi}^{pi} 559 F(q_a, q_b, q_c)^2 \sin(\alpha)\,d\beta\,d\alpha \\ 560 &= \frac{8}{4\pi} \int_{0}^{pi/2} \int_{0}^{\pi/2} 561 F^2 \sin(\alpha)\,d\beta\,d\alpha \\ 562 &= \frac{8}{4\pi} \int_1^0 \int_{0}^{\pi/2} - F^2 \,d\beta\,du \\ 563 &= \frac{8}{4\pi} \int_0^1 \int_{0}^{\pi/2} F^2 \,d\beta\,du 564 565 for 566 567 .. math:: 568 569 q_a &= q \sin(\alpha)\sin(\beta) = q \sqrt{1-u^2} \sin(\beta) \\ 570 q_b &= q \sin(\alpha)\cos(\beta) = q \sqrt{1-u^2} \cos(\beta) \\ 571 q_c &= q \cos(\alpha) = q u 572 573 Using the $z, w$ values for Gauss-Legendre integration in "lib/gauss76.c", the 574 numerical integration is then:: 575 576 double outer_sum = 0.0; 577 for (int i = 0; i < GAUSS_N; i++) { 578 const double cos_alpha = 0.5*GAUSS_Z[i] + 0.5; 579 const double sin_alpha = sqrt(1.0 - cos_alpha*cos_alpha); 580 const double qc = cos_alpha * q; 581 double inner_sum = 0.0; 582 for (int j = 0; j < GAUSS_N; j++) { 583 const double beta = M_PI_4 * GAUSS_Z[j] + M_PI_4; 584 double sin_beta, cos_beta; 585 SINCOS(beta, sin_beta, cos_beta); 586 const double qa = sin_alpha * sin_beta * q; 587 const double qb = sin_alpha * cos_beta * q; 588 const double form = Fq(qa, qb, qc, ...); 589 inner_sum += GAUSS_W[j] * form * form; 590 } 591 outer_sum += GAUSS_W[i] * inner_sum; 592 } 593 outer_sum *= 0.25; // = 8/(4 pi) * outer_sum * (pi/2) / 4 594 595 The *z* values for the Gauss-Legendre integration extends from -1 to 1, so 596 the double sum of *w[i]w[j]* explains the factor of 4. Correcting for the 597 average *dz[i]dz[j]* gives $(1-0) \cdot (\pi/2-0) = \pi/2$. The $8/(4 \pi)$ 598 factor comes from the integral over the quadrant. With less symmetry (eg., 599 in the bcc and fcc paracrystal models), then an integral over the entire 600 sphere may be necessary. 601 602 For simpler models which are rotationally symmetric a single integral 603 suffices: 604 605 .. math:: 606 607 I(q) &= \frac{1}{\pi}\int_{-\pi/2}^{\pi/2} 608 F(q_{ab}, q_c)^2 \sin(\alpha)\,d\alpha/\pi \\ 609 &= \frac{2}{\pi} \int_0^1 F^2\,du 610 611 for 612 613 .. math:: 614 615 q_{ab} &= q \sin(\alpha) = q \sqrt{1 - u^2} \\ 616 q_c &= q \cos(\alpha) = q u 617 618 619 with integration loop:: 620 621 double sum = 0.0; 622 for (int i = 0; i < GAUSS_N; i++) { 623 const double cos_alpha = 0.5*GAUSS_Z[i] + 0.5; 624 const double sin_alpha = sqrt(1.0 - cos_alpha*cos_alpha); 625 const double qab = sin_alpha * q; 626 const double qc = cos_alpha * q; 627 const double form = Fq(qab, qc, ...); 628 sum += GAUSS_W[j] * form * form; 629 } 630 sum *= 0.5; // = 2/pi * sum * (pi/2) / 2 631 632 Magnetism 633 ......... 634 635 Magnetism is supported automatically for all shapes by modifying the 636 effective SLD of particle according to the Halpern-Johnson vector 637 describing the interaction between neutron spin and magnetic field. All 638 parameters marked as type *sld* in the parameter table are treated as 639 possibly magnetic particles with magnitude *M0* and direction 640 *mtheta* and *mphi*. Polarization parameters are also provided 641 automatically for magnetic models to set the spin state of the measurement. 642 643 For more complicated systems where magnetism is not uniform throughout 644 the individual particles, you will need to write your own models. 645 You should not mark the nuclear sld as type *sld*, but instead leave 646 them unmarked and provide your own magnetism and polarization parameters. 647 For 2D measurements you will need $(q_x, q_y)$ values for the measurement 648 to compute the proper magnetism and orientation, which you can implement 649 using *Iqxy(qx, qy, par1, par2, ...)*. 650 535 651 Special Functions 536 652 ................. … … 543 659 M_PI, M_PI_2, M_PI_4, M_SQRT1_2, M_E: 544 660 $\pi$, $\pi/2$, $\pi/4$, $1/\sqrt{2}$ and Euler's constant $e$ 545 exp, log, pow(x,y), expm1, sqrt:546 Power functions $e^x$, $\ln x$, $x^y$, $e^x - 1$, $\ sqrt{x}$.547 The function expm1(x) is accurate across all $x$, including $x$548 very close to zero.661 exp, log, pow(x,y), expm1, log1p, sqrt, cbrt: 662 Power functions $e^x$, $\ln x$, $x^y$, $e^x - 1$, $\ln 1 + x$, 663 $\sqrt{x}$, $\sqrt[3]{x}$. The functions expm1(x) and log1p(x) 664 are accurate across all $x$, including $x$ very close to zero. 549 665 sin, cos, tan, asin, acos, atan: 550 666 Trigonometry functions and inverses, operating on radians. … … 557 673 atan(y/x) would return a value in quadrant I. Similarly for 558 674 quadrants II and IV when $x$ and $y$ have opposite sign. 559 f min(x,y), fmax(x,y), trunc, rint:675 fabs(x), fmin(x,y), fmax(x,y), trunc, rint: 560 676 Floating point functions. rint(x) returns the nearest integer. 561 677 NAN: 562 678 NaN, Not a Number, $0/0$. Use isnan(x) to test for NaN. Note that 563 679 you cannot use :code:`x == NAN` to test for NaN values since that 564 will always return false. NAN does not equal NAN! 680 will always return false. NAN does not equal NAN! The alternative, 681 :code:`x != x` may fail if the compiler optimizes the test away. 565 682 INFINITY: 566 683 $\infty, 1/0$. Use isinf(x) to test for infinity, or isfinite(x) … … 734 851 Similar arrays are available in :code:`gauss20.c` for 20-point 735 852 quadrature and in :code:`gauss150.c` for 150-point quadrature. 853 The macros :code:`GAUSS_N`, :code:`GAUSS_Z` and :code:`GAUSS_W` are 854 defined so that you can change the order of the integration by 855 selecting an different source without touching the C code. 736 856 737 857 :code:`source = ["lib/gauss76.c", ...]` … … 791 911 can be a model that runs 1000x faster on a good card. Even your laptop may 792 912 show a 50x improvement or more over the equivalent pure python model. 793 794 External C Models795 .................796 797 External C models are very much like embedded C models, except that798 *Iq*, *Iqxy* and *form_volume* are defined in an external source file799 loaded using the *source=[...]* statement. You need to supply the function800 declarations for each of these that you need instead of building them801 automatically from the parameter table.802 913 803 914 … … 1002 1113 variable name *Rg* for example because $R_g$ is the right name for the model 1003 1114 parameter then ignore the lint errors. Also, ignore *missing-docstring* 1004 for standard model functions *Iq*, *Iq xy*, etc.1115 for standard model functions *Iq*, *Iqac*, etc. 1005 1116 1006 1117 We will have delinting sessions at the SasView Code Camps, where we can -
doc/guide/pd/polydispersity.rst
reda8b30 r92d330fd 42 42 calculations are generally more robust with more data points or more angles. 43 43 44 The following fivedistribution functions are provided:44 The following distribution functions are provided: 45 45 46 46 * *Rectangular Distribution* 47 * *Uniform Distribution* 47 48 * *Gaussian Distribution* 48 49 * *Lognormal Distribution* 49 50 * *Schulz Distribution* 50 51 * *Array Distribution* 52 * *Boltzmann Distribution* 51 53 52 54 These are all implemented as *number-average* distributions. … … 85 87 Rectangular distribution. 86 88 89 90 91 Uniform Distribution 92 ^^^^^^^^^^^^^^^^^^^^^^^^ 93 94 The Uniform Distribution is defined as 95 96 .. math:: 97 98 f(x) = \frac{1}{\text{Norm}} 99 \begin{cases} 100 1 & \text{for } |x - \bar x| \leq \sigma \\ 101 0 & \text{for } |x - \bar x| > \sigma 102 \end{cases} 103 104 where $\bar x$ is the mean of the distribution, $\sigma$ is the half-width, and 105 *Norm* is a normalization factor which is determined during the numerical 106 calculation. 107 108 Note that the polydispersity is given by 109 110 .. math:: \text{PD} = \sigma / \bar x 111 112 .. figure:: pd_uniform.jpg 113 114 Uniform distribution. 115 116 The value $N_\sigma$ is ignored for this distribution. 117 87 118 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ 88 119 … … 183 214 ^^^^^^^^^^^^^^^^^^ 184 215 185 This user-definable distribution should be given as a s asimple ASCII text216 This user-definable distribution should be given as a simple ASCII text 186 217 file where the array is defined by two columns of numbers: $x$ and $f(x)$. 187 218 The $f(x)$ will be normalized to 1 during the computation. … … 202 233 given for the model will have no affect, and will be ignored when computing 203 234 the average. This means that any parameter with an array distribution will 204 not be fittable. 235 not be fitable. 236 237 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ 238 239 Boltzmann Distribution 240 ^^^^^^^^^^^^^^^^^^^^^^ 241 242 The Boltzmann Distribution is defined as 243 244 .. math:: 245 246 f(x) = \frac{1}{\text{Norm}} 247 \exp\left(-\frac{ | x - \bar x | }{\sigma}\right) 248 249 where $\bar x$ is the mean of the distribution and *Norm* is a normalization 250 factor which is determined during the numerical calculation. 251 The width is defined as 252 253 .. math:: \sigma=\frac{k T}{E} 254 255 which is the inverse Boltzmann factor, 256 where $k$ is the Boltzmann constant, $T$ the temperature in Kelvin and $E$ a 257 characteristic energy per particle. 258 259 .. figure:: pd_boltzmann.jpg 260 261 Boltzmann distribution. 205 262 206 263 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
Note: See TracChangeset
for help on using the changeset viewer.