1 | """ |
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2 | Conversion of scattering cross section from SANS (I(q), or rather, ds/dO) in absolute |
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3 | units (cm-1)into SESANS correlation function G using a Hankel transformation, then converting |
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4 | the SESANS correlation function into polarisation from the SESANS experiment |
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5 | |
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6 | Everything is in units of metres except specified otherwise (NOT TRUE!!!) |
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7 | Everything is in conventional units (nm for spin echo length) |
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8 | |
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9 | Wim Bouwman (w.g.bouwman@tudelft.nl), June 2013 |
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10 | """ |
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11 | |
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12 | from __future__ import division |
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13 | |
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14 | import numpy as np # type: ignore |
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15 | from numpy import pi # type: ignore |
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16 | from scipy.special import j0 |
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17 | |
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18 | |
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19 | class SesansTransform(object): |
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20 | """ |
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21 | Spin-Echo SANS transform calculator. Similar to a resolution function, |
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22 | the SesansTransform object takes I(q) for the set of *q_calc* values and |
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23 | produces a transformed dataset |
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24 | |
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25 | *SElength* (A) is the set of spin-echo lengths in the measured data. |
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26 | |
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27 | *zaccept* (1/A) is the maximum acceptance of scattering vector in the spin |
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28 | echo encoding dimension (for ToF: Q of min(R) and max(lam)). |
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29 | |
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30 | *Rmax* (A) is the maximum size sensitivity; larger radius requires more |
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31 | computation time. |
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32 | """ |
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33 | #: SElength from the data in the original data units; not used by transform |
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34 | #: but the GUI uses it, so make sure that it is present. |
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35 | q = None # type: np.ndarray |
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36 | |
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37 | #: q values to calculate when computing transform |
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38 | q_calc = None # type: np.ndarray |
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39 | |
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40 | # transform arrays |
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41 | _H = None # type: np.ndarray |
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42 | _H0 = None # type: np.ndarray |
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43 | |
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44 | def __init__(self, z, SElength, lam, zaccept, Rmax, log_spacing=1.0003): |
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45 | # type: (np.ndarray, float, float) -> None |
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46 | self.q = z |
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47 | self.log_spacing = log_spacing |
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48 | self._set_hankel(SElength, lam, zaccept, Rmax) |
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49 | |
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50 | def apply(self, Iq): |
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51 | # type: (np.ndarray) -> np.ndarray |
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52 | """ |
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53 | Apply the SESANS transform to the computed I(q). |
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54 | """ |
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55 | G0 = np.dot(self._H0, Iq) |
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56 | G = np.dot(self._H.T, Iq) |
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57 | P = G - G0 |
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58 | return P |
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59 | |
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60 | def _set_hankel(self, SElength, lam, zaccept, Rmax): |
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61 | # type: (np.ndarray, float, float) -> None |
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62 | SElength = np.asarray(SElength) |
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63 | q_max = 2*pi / (SElength[1] - SElength[0]) |
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64 | q_min = 0.1 * 2*pi / (np.size(SElength) * SElength[-1]) |
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65 | q = np.exp(np.arange(np.log(q_min), np.log(q_max), |
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66 | np.log(self.log_spacing))) |
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67 | |
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68 | dq = np.diff(q) |
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69 | dq = np.insert(dq, 0, dq[0]) |
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70 | |
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71 | H0 = dq/(2*pi) * q |
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72 | |
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73 | H = np.outer(q, SElength) |
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74 | j0(H, out=H) |
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75 | H *= (dq * q / (2*pi)).reshape((-1, 1)) |
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76 | |
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77 | reptheta = np.outer(q, lam/(2*pi)) |
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78 | np.arcsin(reptheta, out=reptheta) |
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79 | mask = reptheta > zaccept |
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80 | H[mask] = 0 |
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81 | |
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82 | self.q_calc = q |
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83 | self._H, self._H0 = H, H0 |
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