[c97724e] | 1 | """ |
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[d459d4e] | 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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[c97724e] | 5 | |
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[d459d4e] | 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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[c97724e] | 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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[7ae2b7f] | 14 | import numpy as np # type: ignore |
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[fa79f5c] | 15 | from numpy import pi # type: ignore |
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[54f1d96] | 16 | from scipy.special import j0 |
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[94d13f1] | 17 | |
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[54f1d96] | 18 | class SesansTransform(object): |
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[94d13f1] | 19 | """ |
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| 20 | Spin-Echo SANS transform calculator. Similar to a resolution function, |
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| 21 | the SesansTransform object takes I(q) for the set of *q_calc* values and |
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| 22 | produces a transformed dataset |
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| 23 | |
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| 24 | *SElength* (A) is the set of spin-echo lengths in the measured data. |
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| 25 | |
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| 26 | *zaccept* (1/A) is the maximum acceptance of scattering vector in the spin |
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| 27 | echo encoding dimension (for ToF: Q of min(R) and max(lam)). |
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| 28 | |
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| 29 | *Rmax* (A) is the maximum size sensitivity; larger radius requires more |
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| 30 | computation time. |
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| 31 | """ |
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| 32 | #: SElength from the data in the original data units; not used by transform |
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| 33 | #: but the GUI uses it, so make sure that it is present. |
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[54f1d96] | 34 | q = None # type: np.ndarray |
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| 35 | |
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[94d13f1] | 36 | #: q values to calculate when computing transform |
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| 37 | q_calc = None # type: np.ndarray |
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| 38 | |
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[54f1d96] | 39 | # transform arrays |
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[94d13f1] | 40 | _H = None # type: np.ndarray |
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| 41 | _H0 = None # type: np.ndarray |
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[54f1d96] | 42 | |
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[9f91afe] | 43 | def __init__(self, z, SElength, lam, zaccept, Rmax): |
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[94d13f1] | 44 | # type: (np.ndarray, float, float) -> None |
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| 45 | #import logging; logging.info("creating SESANS transform") |
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| 46 | self.q = z |
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[9f91afe] | 47 | self._set_hankel(SElength, lam, zaccept, Rmax) |
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[54f1d96] | 48 | |
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| 49 | def apply(self, Iq): |
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[b297ba9] | 50 | # type: (np.ndarray) -> np.ndarray |
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| 51 | """ |
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| 52 | Apply the SESANS transform to the computed I(q). |
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| 53 | """ |
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[94d13f1] | 54 | G0 = np.dot(self._H0, Iq) |
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| 55 | G = np.dot(self._H.T, Iq) |
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[54f1d96] | 56 | P = G - G0 |
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| 57 | return P |
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| 58 | |
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[9f91afe] | 59 | def _set_hankel(self, SElength, lam, zaccept, Rmax): |
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[94d13f1] | 60 | # type: (np.ndarray, float, float) -> None |
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| 61 | # Force float32 arrays, otherwise run into memory problems on some machines |
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| 62 | SElength = np.asarray(SElength, dtype='float32') |
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[54f1d96] | 63 | |
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[94d13f1] | 64 | #Rmax = #value in text box somewhere in FitPage? |
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| 65 | q_max = 2*pi / (SElength[1] - SElength[0]) |
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| 66 | q_min = 0.1 * 2*pi / (np.size(SElength) * SElength[-1]) |
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| 67 | q = np.arange(q_min, q_max, q_min, dtype='float32') |
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| 68 | dq = q_min |
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[54f1d96] | 69 | |
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[94d13f1] | 70 | H0 = np.float32(dq/(2*pi)) * q |
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[54f1d96] | 71 | |
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[94d13f1] | 72 | repq = np.tile(q, (SElength.size, 1)).T |
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| 73 | repSE = np.tile(SElength, (q.size, 1)) |
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| 74 | H = np.float32(dq/(2*pi)) * j0(repSE*repq) * repq |
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| 75 | |
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[9f91afe] | 76 | replam = np.tile(lam, (q.size, 1)) |
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| 77 | reptheta = np.arcsin(repq*replam/2*np.pi) |
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| 78 | mask = reptheta > zaccept |
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| 79 | H[mask] = 0 |
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| 80 | |
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[94d13f1] | 81 | self.q_calc = q |
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| 82 | self._H, self._H0 = H, H0 |
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