#CLEANX FUNCTION PHP SOFTWARE#
Some of the more common causes of reinfections are issues like cross- site contamination or unpatched website software security vulnerabilities that get re-exploited. Py, ylab, status = kepplot.cleany(py,1.Website reinfections are a serious problem for website owners, and it can often be difficult to determine the cause behind the reinfection - especially if you lack access to necessary logs, which is usually the case for shared hosting services. Py = s - rx - (s - rx) # clean-up y-axis units # plot arclength fits vs drift along strongest eigenvector
Kepplot.labels('CCD Column','CCD Row','k',16) # labels ot(centr1_good,centr2_good,color='#009900',markersize=5,marker='D',ls='') # plot dataįor tick in ax.xaxis.get_major_ticks(): _fontsize(14)įor tick in ax.yaxis.get_major_ticks(): _fontsize(14) Xlim(pxmin - delx - pxr * pad, pxmax + delx + pxr * pad)
Ylim(pymin - pyr * pad, pymax + pyr * pad) Ylim(pymin - dely - pyr * pad, pymax + dely + pyr * pad) Xlim(pxmin - pxr * pad, pxmax + pxr * pad) Py = copy(centr2) # clean-up y-axis units Px = copy(centr1) # clean-up x-axis units Status = fine(labelsize,ticksize,logfile,verbose)Īx = kepplot.location() # plot location Kepfit.lsqclip(functype,pinit,s_pnt,flux_pnt,None,sigma_arfl,sigma_arfl,100,logfile,verbose)Ĭentr = concatenate( - mean(centr1_good), - mean(centr2_good)])Ĭfac = cfac + ccoeffs * numpy.power(xx,i) Kepfit.lsqclip(functype,pinit,t,dx,None,3.0,3.0,10,logfile,verbose)ĭfit = dfit + dcoeffs * numpy.power(t,i)Ĭentr1_pnt = append(centr1_pnt,centr1_good)Ĭentr2_pnt = append(centr2_pnt,centr2_good)Ĭcoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plx, ply, status = \ # fit polynomial to derivative and flag outliers (thruster firings)ĭcoeffs, errors, covar, iiter, dsigma, chi2, dof, fit, dumx, dumy, status = \ # calculate time derivative of arclength sĭx = (x - x) / (t - t) # correlate arclength with detrended flux # fit arclength as a function of strongest eigenvectorĪcoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \ Rx = linspace(nanmin(centr_rot),nanmax(centr_rot),100) Message = 'ERROR - KEPSFF: could not fit rotated centroid data with polynomial' Either increase the stepsize (with an appreciation of the effects on light curve quality this will have!), or better yet - cut the timeseries up to remove large gaps in the input light curve using kepclip.' % (t1,t2) There are no data points within the range of input rows %d - %d. Message = 'ERROR - KEPSFF: could not fit centroid data with polynomial. Kepfit.lsqclip(functype,pinit,centr1,centr2,None,sigma_cxcy,sigma_cxcy,10,logfile,verbose)Ĭfit += coeffs * numpy.power(centr1,j) # fit centroid data with low-order polynomialĬoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \ Winedge = append(winedge,len(table.field('TIME'))) If len(table.field('TIME')) > winedge + 1: Table, status = kepio.readfitstab(infile,instr,logfile,verbose)
Instr = kepkey.emptykeys(instr,file,logfile,verbose) # fudge non-compliant FITS keywords with no values Tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status) Instr, status = kepio.openfits(infile,'readonly',logfile,verbose) Status = kepmsg.err(logfile,message,verbose) Message = 'ERROR - KEPSFF: ' + outfile + ' exists. If clobber: status = kepio.clobber(outfile,logfile,verbose) Kepmsg.clock('KEPSFF started at',logfile,verbose) Npoly_dsdt,sigma_dsdt,npoly_arfl,sigma_arfl,plotres,clobber,verbose,logfile, Def kepsff(infile,outfile,datacol,cenmethod,stepsize,npoly_cxcy,sigma_cxcy,npoly_ardx,
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