6Oc@ sdZddlmZdZddlmZddlZddlZejdZddl Z e j ddddl j Zdd lmZmZgZyddlZejd Wnejd nXyddlZejd Wnejd nXdefdYZdefdYZdefdYZddMdYZeded ddgeed dgegDZdZdZdZ dZ!dZ"dZ#d dNd!YZ$d"efd#YZ%d$efd%YZ&d&e&fd'YZ'd(e'fd)YZ(d*e'fd+YZ)d,e'fd-YZ*d.e'fd/YZ+d0e'fd1YZ,d2dOd3YZ-d4Z.d5Z/d6d6d7d8Z0e1d9Z2d:d;Z3e2d<dd=e5e j6d>d>gd?Z7d@Z8e5dAZ9dBZ:dCZ;d6d6d6dDZ<dEZ=dFZ>ddGZ?d6d6dHZ@dIZAdJZBdKZCeDdLkrddlEZEeEjFndS(PsH Base class providing common functionality for analyzing Leed patterns. i(tdivisiong333333?(tUserListNtleedbasetalltraise(toptimizetndimagetFITSs-The pyfits package is not properly installed.tIMGs2The pil package (Image) is not properly installed.t ImageLoadercB sMeZdZdZdZdZdZdZdZdZ RS(s Abstract base class for a class loading LEED images. Subclasses need to provide - get_energy(image_path) - get_image(image_path) cC s\i|_x*|D]"}|j|}||j|   "c C st|d|}|j|}|j}|d|dd|d|ddf}tjd||dd d d}tj|SWdQXdS( NR'R0R.iR1R/tFtrawsF;16i(R(R)R7tImaget fromstringtnptasarray(RRR*R<tcontenttsizetpilimage((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRys  ,!(R"R#RR R)R(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyR%Xs   tFitsImageLoadercB s&eZddZdZdZRS(s\d{1,3}(?=.fit)cC s||_tj||dS(N(tregexR R(RR RI((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs cC s%tj|j|}t|jS(N(tretsearchRIR3tgroup(RRtm((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyR scC sXy.tj|}|dj}|j|SWn#tk rSd|GHtnXdS(NisIOError while processing %s(tpyfitsR(tdatatclosetIOError(RRthdulistRO((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs    (R"R#RR R(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRHs  t ImageFormatcB s eZdZdZdZRS(s!Class describing an image format.cC s||_||_||_dS(s abbrev: abbreviation (e.g. FITS) extensions: list of corresponding file extensions (e.g. .fit, .fits) loader: ImageLoader subclass for this format N(tabbrevt extensionstloader(RRTRURV((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs  cC sdj|jdj|jS(Ns{0}-Files ({1})t (tformatRTtjoinRU(R((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyt__str__s(R"R#R$RRZ(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRSs cc s0|]&}|jtkrt||gVqdS(N(RTtformats_availabletstr(t.0tformat_((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pys ss*.fits*.fitss*.imgcC s`|j|j}}|r,||}nd||}|dkrS||}n|jdS(s% Returns a normalized array of uint8.go@g?tuint8(tmintmaxtastype(tarraytnmintnmaxtscale((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyt _normalize255s   cC sv|j\}}t|}tj|j||tjj}x3tdD]%}|j|tj|||qIW|S(s* Converts numpy grayscale image to qimage.i( tshapeRgt_qttQImageROtFormat_Indexed8trangetsetColortqRgb(tnpimagethtwtqimageti((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytnpimage2qimages  !#c s7tt| t|ddfdDS(s returns int points lying in a square centered on 0,0 with given size. >>> [point for point in points_in_square(1)] [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 0), (0, 1), (1, -1), (1, 0), (1, 1)] ic3 s(|]}D]}||fVq qdS(N((R]txty(tcoords(s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pys s(txrangeR3(RF((Rws5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytpoints_in_squares#cc sQ|d}x@t|D]2\}}|d|d|kr||fVqqWdS(sI returns int points lying in a circle centered on 0, 0 with given radius.iN(Ry(tradiustradius_squaredRuRv((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytpoints_in_circles cc s\d|}d}d|}d}|}|||fV|||fV|||fV|||fVx||krW|dkr|d8}|d7}||7}n|d7}|d7}||7}||||fV||||fV||||fV||||fV||||fV||||fV||||fV||||fVqeWdS(sv Yields int points lying at the edge of an circle centered on x0, y0. Uses the Midpoint circle algorithm. iiiiN((tx0ty0RzR*tddf_xtddf_yRuRv((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytpoints_at_circles2         cc sq|d}|d}xVt|D]H\}}|d|d}||koV|knr!||fVq!q!WdS(s Yields integer points lying between two circles. >>> [point for point in points_in_annulus(2, 1)] [(-2, 0), (-1, -1), (-1, 1), (0, -2), (0, 2), (1, -1), (1, 1), (2, 0)] iN(Ry(t big_radiust small_radiustbig_radius_squaredtsmall_radius_squaredRuRvtdistance_squared((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytpoints_in_annuluss   t SpotModelcB s eZdZdZdZRS(sM Data model for a Spot that stores all the information in various lists. cC s1g|_g|_g|_g|_g|_dS(N(RuRvt intensityRRz(R((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs     cC sT|jj||jj||jj||jj||jj|dS(N(RutappendRvRRRz(RRuRvRRRz((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytupdates (R"R#R$RR(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs t ImageRegioncB s eZdZdZdZRS(s/ Region of a image defined by contained spots. cC s1t|j\}}tj|tj|fS(N(tzipRORCtmean(Rtylisttxlist((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytmiddlescC sttj}|tjdS(Ng?(RRRORCtpi(tspottnitems((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRzs(R"R#R$RRz(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs tAbstractKalmanFiltercB sVeZdZdZejddZd dZdZ dZ d dZ RS( s Abstract implementation of a Kalman filter. Matrices and Vectors can be given in any input format np.matrix() understands. Vectors are internally transposed and should therefore be given as column vectors. cC swtt|jtj|j|_tj||_tj||_tjtj t |jj |_ dS(s Initialize Kalman filter. x: start state vector P: start state covariance matrix H: measurement matrix N( tsuperRRRCtasmatrixtTRutPtHtidentityRaRht_1(RRuRR((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyR!s icC sMtj|}tj|}||j|_||j|j||_dS(sf Predict next state. F: state transition matrix Q: process covariance matrix N(RCRRuRR(RR?tQ((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytpredict0scC sD|dk r(|j|j|jj|S|j|j|jjSdS(s; Returns the covariance matrix of a predicted measurement. N(tNoneRRR(RtR((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyt_calcS<s cC s|j|jS(s Predicted measurement result. (RRu(R((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyt_calczCscC stj|j}tj|}|j|}|j}|j|jj|j}|j||||_|j ||j|j|_dS(sh Update state estimate. z: measurement vector R: measurement covariance matrix N( RCRRRRRRtIRuR(RtzRtSt z_predictedtK((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRGs cC sItj|j}|j|}|j}||}|j|j|S(sr Returns the normalized distance squared of the given measurement. z: measurement vector (RCRRRRR(RRRRRtdiff((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytmeasurement_distanceVs   (iiN( R"R#R$RRCtzerosRRRRRR(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs    tAbstractPVKalmanFiltercB s)eZdZdZdZdZRS(sN Kalman filter for 2d-tracking using position and velocity as state variables.cC s_||_||ddg}ddddgddddgg}tt|j|||dS(Nii(told_timeRRR(Rtx_inty_inRttimeRuR((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRcs $cC s$t|jdt|jdfS(Nii(tfloatRu(R((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyt get_positioniscC s,t|jddt|jddfS(Nig?i(ii(ii(RR(R((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytget_position_errls(R"R#R$RRR(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRas  tPVKalmanFilter0cB seZdZRS(cO sx||j}dd|dgddd|gddddgddddgg}tt|j|||||_dS(Nii(RRRR(RRtargsR&tdtR?((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRps B(R"R#R(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRostPVKalmanFilter1cB seZdZRS(c O s||j}d|j}d||}|d||d}dd|dgddd|gdd|dgddd|gg}tt|j|||||_dS(Ngig?ii(RRRR( RRRR&Rtatv_uptpos_upR?((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRws  B(R"R#R(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRvstPVKalmanFilter2cB seZdZRS(c O s||j}d|j}d|jd}d||||d}|d||dd ||d}dd |d gd dd |gd d |d gd d d |gg} tt|j| ||||_dS( Ngg?iig?g?g@iigUUUUUU?(RRRR( RRRR&RRta_dotRRR?((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs  &B(R"R#R(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRstPVKalmanFilter3cB seZdZRS(c O s||j}d|j}d|jd}d|jd}d||||d||d}|d||dd ||dd||d } dd | d gd dd | gd d |d gd d d |gg} tt|j| ||||_dS(Ngg?igiig?g?g@g@iigUUUUUU?g?(RRRR( RRRR&RRRta_ddotRRR?((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs  &6B(R"R#R(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRstPVKalmanFilterExactVcB seZdZRS(c O s||j}d|j}d|jd}|j|d}|d||dd ||d}d d |d gd d d |gd d |d gd d d |gg} tt|j| ||||_dS( Ngg?ig?g?g?g@iiigUUUUUU?(RRRR( RRRR&RRRRRR?((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs  &B(R"R#R(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRstTrackercB s)eZdZdeddZdZRS(sE Tracks spots through intensity information and velocity prediction. iic C sy||_tj||ddg}t|||||_||_|jru||_|d|j|j|_ndS(s$ x_in, y_in: start position of spot ig?N(RzRCtdiagRtkalmantwindow_scalingtmin_window_sizetc_size( RRRRzRtinput_precisionRRt cov_input((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs    c C s|\}}|jr3|j|d|j|_n|jj|tjdddd g|jj\}}t ||||jd|j\}}}|jj ||g||jj\} } t || | |j} | | | ||jfS( Ng?i iiRg{Gz?g{Gz?gư>gư>( RRRRzRRRCRRtguesserRtcalc_intensity( RtimageRoRtx_pty_ptx_thty_tht guess_covRuRvR((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyt feed_images  (*(R"R#R$tFalseRR(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs cO s|t|k}t||S(N(totsutcom(RRR&t bin_image((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytguess_from_comscO s"ttjtj||jS(N(treversedRCt unravel_indextargmaxRh(RRR&((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytguess_from_brightestsiic s jj}tjj}|||||||g}fd}fd}d} d |d| |d| f|d| |d| fd d d g} yItj|tj|d| d t} | d} | d} | d}WndSX| |fS(Nc s)tjt|tjjS(N(RCtravelt gaussian2dtindicesRh(tp(R(s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytsc s3tjt|tjjdjS(Ni(RCRRRRhtsum(R(R(s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRsi iiig?tboundst approx_grad(iN(g?i (g?i (iN( tflattenRaRCRRRt fmin_l_bfgs_bRDtTrue(Rtx_midty_midRFtmax_tbacktparamsterrfuncterrfuncSquaredtoffsetRtvaluet params_opttx_resty_res((Rs5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytguess_from_fitsD'   cK st|}|j|k}t|}|rdg|D]$}t||j|j^q7}ng|D]}|j^qk}i|d6S(Ntspots(Rtcopyt find_spotst com_circleRRz(Rtuse_comR&t thresholdRRRt spots_pos((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytguess_from_spotss  4icK sKt|}t|j}||d}|d|}||k} t| } g| D]!} | | jt| g^qY} x||kr|} ||8}gtj|D].\} }||ko| knr| ^q}x0|D](\}}g}xnt| D]`\}} || ddd|| dddd| dtj d}|j ||fq W|j |dd|kr|dd} | | dj ||f| | dj| | d<| | dcd7>> image = np.ones((2, 2)) >>> adjust_slice(image, 0, 1.5, 0, 2) (0, 1, 0, 2) >>> adjust_slice(image, -5.5, 2, -0.5, 10) (0, 2, 0, 2) >>> adjust_slice(image, -5, -4, -0.5, 10) Traceback (most recent call last): ... IndexError iis&slice had to be adjusted to fit image.ii( RhRR3RRRtwarningRttuple( Rtx_sl_mintx_sl_maxty_sl_minty_sl_maxtymaxtxmaxtadjustedRRsR((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyR1s(*          2 cC sKt|t|||\}}|rG|t|||||8}n|S(sR Calculates the intensity of the point. Uses background substraction. (t_integral_intensityR|tbackground_intensity(RoRuRvRztbackground_substractionRtcnt((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRVs! cC stt|||}g|D]\}}|||^q}|jtdt|}||| !}tj|S(s+ Returns trimmed mean of points at circle. g?(RRR3RRCR(RoRuRvRztpixelst intensitiestoff((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyR._s ' cC sOd}x<t|D].\}}||||d||d7}qW||fS(su Sums the values of the image at the given pixel increments. returns: summed intensity, number of pixels ii(R(RoR1RuRvRR0tincs((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyR-hs&c sfdS(sEReturns a two dimensional gaussian function with the given parametersc s?tjtj|d|d dS(Ni(RCRDtexp(RuRv(Rtheighttcenter_xtcenter_ytwidth_xtwidth_y(s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRws((R6R7R8R9R:Rtslope_xtslope_y((R:R6R7R8R9Rs5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRtsc C s|j}tj|j\}}||j|}||j|}|ddt|f}tjttj|j|d|j|j}|t|ddf}tjttj|j|d|j|j} |j } | |||| gS(szReturns [height, x, y, width_x, width_y] the gaussian parameters of a 2D distribution by calculating its moments. Ni( RRCRRhR3tsqrttabstarangeRFRa( ROttotaltXtYRuRvtcolR9trowR:R6((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pytmoments{s == c C s|\}}t|}t|}tj|}xNt|dD]<\}}yd|||||fs(RRCRtpopt_expandtaddRR(RRt white_pixelst startpixeltpoint((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs   c s|\tfddddgD}|j|}||8}|sW|S|sjt|gS|j}x$|D]}|jt||q}W|S(Nc3 s9|]/}dddgD]}||fVqqdS(iiiN((R]Rstj(RvRu(s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pys siii(Rt intersectionRRRR(tpixelRTt next_pixelsRVt next_pixel((RvRus5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRRs (    cC s%d}tj|d|\}}|d dtj|}|j}t||}|d|d}|d}d} d} d} xtd|dD]} ||| || 7}||| 7}|||} | r||||}||| } nd} | | kr| } | } qqW|| S(s Finds threshold using Otsu's method. image: image for which threshold is to be found. C++ code by Jordan Bevik ported to ImageJ plugin by G.Landini ported to python by A.Mayer i,tbinsig?ii(RCt histogramRRFRRl(Rtnbinsthistt bin_edgestbin_midtNRtSktN1tBCVtBCVmaxtkStartktdenomtnum((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyRs,     t__main__((((GR$t __future__Rt __version__RRJtloggingt getLoggerRtnumpyRCtseterrt PyQt4.QtGuitQtGuiRitscipyRRR[RNRR$RAtobjectR R%RHRStdictt IMAGE_FORMATSRgRtRyR|RRRRRRRRRRRRRRRRRRRRRRRRR.R-RRERRRPRRRRR"tdoctestttestmod(((s5/home/salopaasi/Documents/Moo/Easyleednew/leedbase.pyts     6,     G      " *# %      '