#! /usr/bin/env python # -*- coding: iso-8859-15 -*- """ Created on Mon May 25 13:51:41 2015 @author: kervella """ # ATTENTION: it is necessary to install pdfrw library (sudo port install py27-pdfrw) import matplotlib as mpl mpl.use('Agg') # necessary to be able to generate the reports even without X server import matplotlib.pyplot as plt #import sys #import bootstrap as boot import datetime import numpy as np from . import binary #import pdb from . import gravi_astrometry_fit from . import gravi_astrometry_class from . import gravi_astrometry_lib as gl #import warnings #import math #from scipy.interpolate import interp1d #try: # from scipy.signal import welch # welchpresent = True #except ImportError, e: # welchpresent = False # pass # module doesn't exist, deal with it. #============================================================================== # Preparation of the report using reportlab #============================================================================== def produce_astrometry_report(dualseries,filename): from reportlab.platypus import SimpleDocTemplate, Spacer, Table, TableStyle, PageBreak from reportlab.lib import colors from reportlab.rl_config import defaultPageSize from reportlab.lib.units import inch, cm, mm import io PAGE_HEIGHT=defaultPageSize[1] PAGE_WIDTH=defaultPageSize[0] print ("Start produce_astrometry_report") if (dualseries is None): print ("Input data is NULL") return None Title = "GRAVITY ASTROMETRY Quality Control Report" pageinfo = "First dual mode file: "+filename+".fits" def myFirstPage(canvas, doc): canvas.saveState() canvas.setFont('Helvetica',16) canvas.drawCentredString(PAGE_WIDTH/2.0, PAGE_HEIGHT-60, Title) canvas.setFont('Helvetica',9) canvas.drawString(inch, 0.75 * inch,"Page 1 / %s" % pageinfo) canvas.restoreState() def myLaterPages(canvas, doc): canvas.saveState() canvas.setFont('Helvetica', 9) canvas.drawString(inch, 0.75 * inch,"Page %d / %s" % (doc.page, pageinfo)) canvas.restoreState() reportname = filename+'-ASTROMETRY.pdf' doc = SimpleDocTemplate(reportname) nbase = 6 # number of bases #============================================================================== # First page #============================================================================== Story = [Spacer(1,1*mm)] Story.append(Spacer(1,2*mm)) gl.plotSubtitle(Story,"Report on SWAP sequence from the ASTROREDUCED files.") gl.plotSubtitle(Story,"Header info from 1st file in series.") # FIXME: VLTI baseline correction (from Julien Woillez) if True: dualseries.ucoord *= 0.99888521339323 dualseries.vcoord *= 0.99888521339323 gl.plotSubtitle(Story,"0.998885 SCALING FACTOR ON UCOORD and VCOORD.") if False: gl.plotSubtitle(Story,"NO SCALING FACTOR ON UCOORD and VCOORD.") obj1name = gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO FT ROBJ NAME','') obj1alpha = gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO FT ROBJ ALPHA','') obj1delta = gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO FT ROBJ DELTA','') obj1mag = str(gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO FT ROBJ MAG','')) obj2name = gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO INS SOBJ NAME','') obj2dalpha = gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO INS SOBJ X',0)/1000. # in arcseconds obj2ddelta = gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO INS SOBJ Y',0)/1000. obj2mag = str(gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO INS SOBJ MAG','')) now = datetime.datetime.now() keywords = ["First file name: ", "Last file name: ", # "OB start date:", "First observing date:", "Last observing date:", "Processing date:", "Report date", "Data type:", "SC & Polar setup:", "SC NDIT per file x DIT:", "FT DIT:", "Number of files:", "1st object name", "1st object RA Dec Kmag", "2nd object name", "2nd object dRA, dDec, Kmag", "Swap status of 1st file", "Telescopes", "Pipeline version: " ] nfiles = dualseries.nfiles nfiles_swapped = 0 for i in range(0,dualseries.nfiles): if dualseries.headers[i]['HIERARCH ESO INS SOBJ SWAP']=='YES': nfiles_swapped += 1 keyvals = [dualseries.filename[0]+'.fits', dualseries.filename[nfiles-1]+'.fits', # dualseries.headers[0]['HIERARCH ESO OBS START']+ " (UT time)", dualseries.headers[0]['DATE-OBS']+ " (UT time)", dualseries.headers[nfiles-1]['DATE-OBS']+ " (UT time)", dualseries.headers[0]['DATE']+ " (local time)", now.strftime("%Y-%m-%dT%H:%M:%S")+ " (local time)", 'ASTROREDUCED', #tech, dualseries.headers[0]['HIERARCH ESO INS SPEC RES']+', '+ dualseries.headers[0]['HIERARCH ESO FT POLA MODE'] , str(dualseries.headers[0]['HIERARCH ESO DET2 NDIT'])+' x '+str(dualseries.headers[0]['HIERARCH ESO DET2 SEQ1 DIT'])+' s', str(dualseries.headers[0]['HIERARCH ESO DET3 SEQ1 DIT'])+' s', str(dualseries.nfiles)+', Swap NO='+str(nfiles-nfiles_swapped)+\ ', Swap YES='+str(nfiles_swapped), obj1name, obj1alpha +' '+ obj1delta +' K='+ obj1mag , obj2name, '%(val).3f'%{"val":obj2dalpha} +'" '+ '%(val).3f'%{"val":obj2ddelta} +'" K='+ obj2mag, dualseries.headers[0]['HIERARCH ESO INS SOBJ SWAP'], 'T1='+gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO ISS CONF STATION1','S1')+' '+ 'T2='+gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO ISS CONF STATION2','S2')+' '+ 'T3='+gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO ISS CONF STATION3','S3')+' '+ 'T4='+gl.get_key_withdefault(dualseries.headers[0],'HIERARCH ESO ISS CONF STATION4','S4'), #str(oifits.exptime_sc)+' s', dualseries.headers[0]['HIERARCH ESO PRO REC1 PIPE ID'], ] hsize = [5*cm, 10*cm] vsize = 5*mm textmatrix = list(zip(keywords,keyvals)) t = Table(textmatrix,colWidths=hsize,rowHeights=vsize) t.setStyle(TableStyle([('INNERGRID', (0,0), (-1,-1), 0.25, colors.black), ('BOX', (0,0), (-1,-1), 2, colors.black),])) Story.append(t) Story.append(PageBreak()) # Get all files Story.append(Spacer(1,5*mm)) gl.plotTitle(Story,"Files in this report with swap state") Story.append(Spacer(1,5*mm)) keyvals = [] keywords = dualseries.filename for i in range(len(keywords)): keyvals.append(dualseries.headers[i]['HIERARCH ESO INS SOBJ SWAP']) hsize = [11.5*cm,3*cm] vsize = 5*mm textmatrix = list(zip(keywords,keyvals)) t = Table(textmatrix,colWidths=hsize,rowHeights=vsize) t.setStyle(TableStyle([('INNERGRID', (0,0), (-1,-1), 0.25, colors.black), ('BOX', (0,0), (-1,-1), 2, colors.black),])) Story.append(t) #plotSubtitle(Story,"GD ad hoc scaling factor = ", dualseries.GDscale) Story.append(PageBreak()) # Time scale in decimal hours timemjd = dualseries.mjd - np.nanmin(dualseries.mjd) timescale = timemjd*24.0 timescale0 = timescale[np.where(dualseries.swapstate==0)][::nbase] timescale1 = timescale[np.where(dualseries.swapstate==1)][::nbase] #============================================================================== # User parameters #============================================================================== # horizontal axis limits xmaxval = np.max(timescale) xminval = np.min(timescale) # Limits for the GD residual plots (in microns) yminval = -10 ymaxval = +10 # Limits for the phase residual plots phaseminval = -1.25 phasemaxval = 1.25 # Limits for the TEL-FC met plots metminval = -1.0 metmaxval = 1.0 PlotTELfit = True # Plot TEL diode astrometry if True PlotWaterfall = True # Plot the waterfall charts of the phase vs time and wavelength #============================================================================== # Astrometric model fits #============================================================================== # First guess for Mu Vel # dualseries.relposx = -1911. # dualseries.relposy = -1242. #dualseries.relposx = 143. #dualseries.relposy = -2200. # WITH 3 PARAMETERS dopdFits = gravi_astrometry_fit.dopdFit(dualseries,nZP=3) # WITH 6 PARAMETERS # dopdFits = gravi_astrometry_fit.dopdFit(dualseries,nZP=6) fitG_GDonly = dopdFits['GD'] # GD only, FC only at first step mod_GDonly = fitG_GDonly['model'] fitG_GDPD2 = dopdFits['phase2'] # GD+PD # mod_GDPD2 = fitG_GDPD2['model'] fitG_GDonly_telmet = dopdFits['GD_telmet'] # GD only, FC+TEL # mod_GDonly_telmet = fitG_GDonly_telmet['model'] fitG_GDPD2_telmet = dopdFits['phase2_telmet'] # GD+PD # mod_GDPD2_telmet = fitG_GDPD2_telmet['model'] rmsphasor_res = np.std(np.angle(dualseries.phasor_res)) rmsphasor_res2 = np.std(np.angle(dualseries.phasor_res2)) rmsphasor_res2_telmet = np.std(np.angle(dualseries.phasor_res2_telmet)) #============================================================================== # Astrometric model parameters using FC metrology only #============================================================================== hsize = [5*cm,5*cm,5*cm] vsize = 5*mm def tableParams(fitpar): varnames = list(fitpar['best'].keys()); varnames.sort(); varvals =[]; varerrs=[]; for varname in varnames: varvals.append(fitpar['best'].get(varname)) varerrs.append(fitpar['uncer'].get(varname)) varnames.append('Reduced Chi2') varvals.append(fitpar['chi2']) varerrs.append('-') textmatrix = list(zip(varnames,varvals,varerrs)) t = Table(textmatrix,colWidths=hsize,rowHeights=vsize) t.setStyle(TableStyle([('INNERGRID', (0,0), (-1,-1), 0.25, colors.black), ('BOX', (0,0), (-1,-1), 2, colors.black),])) return t def polarValues(fitX,errX,fitY,errY): fitR = np.sqrt(np.square(fitX)+np.square(fitY)) fitTheta = np.arctan2(fitX,fitY) # errR = np.sqrt(np.square(errX*np.cos(fitTheta))+np.square(errY*np.sin(fitTheta))) # errTheta = np.arctan2(np.square(errX*np.sin(fitTheta))+np.square(errY*np.cos(fitTheta)),fitR) errR = np.abs((errX*fitX+errY*fitY)/fitR) errTheta = np.sqrt( ((fitY*errX)**2 + (fitX*errY)**2)/fitR**4 ) fitTheta *= 180./np.pi errTheta *= 180./np.pi return [fitR,errR,fitTheta,errTheta] if ("WDS_J10468-4925" in obj1name): Story.append(Spacer(1,5*mm)) gl.plotTitle(Story,"1) NACO measured separation of Mu Vel (epoch 2016):") Separ = 2279.36 # mas from NACO errSepar = 0.3 # mas Thetapos = 56.9177 # deg errThetapos = 0.2 # deg gl.plotTitle(Story,"R = %(Rval).3f +/- %(Rerr).3f mas, theta = %(Tval).5f +/- %(Terr).5f deg"\ %{"Rval":Separ, "Rerr":errSepar, "Tval":Thetapos, "Terr":errThetapos}) elif ("GJ65" in obj1name): Story.append(Spacer(1,5*mm)) t = np.mean(dualseries.mjd) gl.plotTitle(Story,"1) Expected relative position of GJ65AB:") param = {'a': 2.05843, 'i': 52.177, 'OMEGA': 145.789, 'e':0.6185, 'omega':283.266, 'T0': 41359.090, 'P':9600.23} xyz = np.array(binary.orbit(t, param, Vrad=False, verbose=True)) Separ = np.sqrt(xyz[0]**2+xyz[1]**2)*1000 # milliarcsec errSepar = 0.0047*Separ # milliarcsec Thetapos = np.arctan2(xyz[0],xyz[1])*180./np.pi # deg errThetapos = 0.2 # deg gl.plotTitle(Story,"R = %(Rval).3f +/- %(Rerr).3f mas, theta = %(Tval).5f +/- %(Terr).5f deg"\ %{"Rval":Separ, "Rerr":errSepar, "Tval":Thetapos, "Terr":errThetapos}) gl.plotSubtitle(Story,"MJD=%.2f, orbit from Kervella et al. (2016, A&A, 593, A127)"%(t)) elif ("AlfCen" in obj1name) or ("AlphaCen" in obj1name) or ("alfCen" in obj1name) or ("alfCen" in obj1name): Story.append(Spacer(1,5*mm)) t = np.mean(dualseries.mjd) gl.plotTitle(Story,"1) Expected relative position of Alpha Cen A wrt. B:") param = {'a': 17.592, 'i': -100.6803, 'OMEGA': 205.064, 'e':0.5208, 'omega':232.006, 'T0': 35328.373, 'P':29194.151} xyz = np.array(binary.orbit(t, param, Vrad=False, verbose=True)) Separ = np.sqrt(xyz[0]**2+xyz[1]**2)*1000 # milliarcsec errSepar = Separ*0.013/17.592 # milliarcsec, from relative error on semi-major axis Thetapos = np.arctan2(xyz[0],xyz[1])*180./np.pi + 180. # deg BEWARE: position of A relative to B ! errThetapos = 0.2 # deg gl.plotTitle(Story,"R = %(Rval).3f +/- %(Rerr).3f mas, theta = %(Tval).5f +/- %(Terr).5f deg"\ %{"Rval":Separ, "Rerr":errSepar, "Tval":Thetapos, "Terr":errThetapos}) gl.plotSubtitle(Story,"MJD=%.2f, orbit from Kervella et al. (2016, A&A, 594, A107)"%(t)) else: Story.append(Spacer(1,5*mm)) [Separ,errSepar,Thetapos,errThetapos] = polarValues(obj2dalpha*1000., 0.0, obj2ddelta*1000., 0.0) gl.plotTitle(Story,"1) Expected astrometry of "+obj1name+" (from header):") gl.plotTitle(Story,"R = %(Rval).3f +/- %(Rerr).3f mas, theta = %(Tval).5f +/- %(Terr).5f deg"\ %{"Rval":Separ, "Rerr":errSepar, "Tval":Thetapos, "Terr":errThetapos}) #Story.append(Spacer(1,5*mm)) gl.plotTitle(Story,"2) GD only solution") Story.append(Spacer(1,2*mm)) t = tableParams(fitG_GDonly) Story.append(t) fitX = fitG_GDonly['best'].get('X[mas]') errX = fitG_GDonly['uncer'].get('X[mas]') fitY = fitG_GDonly['best'].get('Y[mas]') errY = fitG_GDonly['uncer'].get('Y[mas]') [fitR,errR,fitTheta,errTheta] = polarValues(fitX,errX,fitY,errY) gl.plotTitle(Story,"R = %(Rval).3f +/- %(Rerr).3f mas, theta = %(Tval).5f +/- %(Terr).5f deg"\ %{"Rval":fitR, "Rerr":errR, "Tval":fitTheta, "Terr":errTheta}) Rsig = (fitR-Separ)/np.sqrt(np.square(errSepar) + np.square(errR)) Tsig = (fitTheta-Thetapos)/np.sqrt(np.square(errThetapos) + np.square(errTheta)) gl.plotSubtitle(Story,"rms phase = %(rms).3f rad, dR = %(Rval).3f mas = %(Rsig).1f sigmas, dtheta = %(Tval).5f deg = %(Tsig).1f sigmas"\ %{"rms":rmsphasor_res, "Rval":fitR-Separ, "Rsig":Rsig, "Tval":fitTheta-Thetapos, "Tsig":Tsig}) #Story.append(Spacer(1,5*mm)) gl.plotTitle(Story,"3) Second fit of residual phasor of GD fit with FC MET ONLY") Story.append(Spacer(1,2*mm)) t = tableParams(fitG_GDPD2) Story.append(t) fitX = fitG_GDonly['best'].get('X[mas]')+fitG_GDPD2['best'].get('X[mas]') errX = fitG_GDPD2['uncer'].get('X[mas]') fitY = fitG_GDonly['best'].get('Y[mas]')+fitG_GDPD2['best'].get('Y[mas]') errY = fitG_GDPD2['uncer'].get('Y[mas]') [fitR,errR,fitTheta,errTheta] = polarValues(fitX,errX,fitY,errY) gl.plotTitle(Story,"R = %(Rval).3f +/- %(Rerr).3f mas, theta = %(Tval).5f +/- %(Terr).5f deg"\ %{"Rval":fitR, "Rerr":errR, "Tval":fitTheta, "Terr":errTheta}) Rsig = (fitR-Separ)/np.sqrt(np.square(errSepar) + np.square(errR)) Tsig = (fitTheta-Thetapos)/np.sqrt(np.square(errThetapos) + np.square(errTheta)) gl.plotSubtitle(Story,"rms phase = %(rms).3f rad, dR = %(Rval).3f mas = %(Rsig).1f sigmas, dtheta = %(Tval).5f deg = %(Tsig).1f sigmas"\ %{"rms":rmsphasor_res2, "Rval":fitR-Separ, "Rsig":Rsig, "Tval":fitTheta-Thetapos, "Tsig":Tsig}) hsize = [5*cm,5*cm,5*cm] vsize = 5*mm gl.plotTitle(Story,"4) Second fit of residual phasor of GD fit with FC+TEL MET") Story.append(Spacer(1,2*mm)) t = tableParams(fitG_GDPD2_telmet) Story.append(t) fitX = fitG_GDonly_telmet['best'].get('X[mas]')+fitG_GDPD2_telmet['best'].get('X[mas]') errX = fitG_GDPD2_telmet['uncer'].get('X[mas]') fitY = fitG_GDonly_telmet['best'].get('Y[mas]')+fitG_GDPD2_telmet['best'].get('Y[mas]') errY = fitG_GDPD2_telmet['uncer'].get('Y[mas]') [fitR,errR,fitTheta,errTheta] = polarValues(fitX,errX,fitY,errY) gl.plotTitle(Story,"R = %(Rval).3f +/- %(Rerr).3f mas, theta = %(Tval).5f +/- %(Terr).5f deg"\ %{"Rval":fitR, "Rerr":errR, "Tval":fitTheta, "Terr":errTheta}) Rsig = (fitR-Separ)/np.sqrt(np.square(errSepar) + np.square(errR)) Tsig = (fitTheta-Thetapos)/np.sqrt(np.square(errThetapos) + np.square(errTheta)) gl.plotSubtitle(Story,"rms phase = %(rms).3f rad, dR = %(Rval).3f mas = %(Rsig).1f sigmas, dtheta = %(Tval).5f deg = %(Tsig).1f sigmas"\ %{"rms":rmsphasor_res2_telmet, "Rval":fitR-Separ, "Rsig":Rsig, "Tval":fitTheta-Thetapos, "Tsig":Tsig}) Story.append(PageBreak()) #============================================================================== # Indices of the two swap states and baseline names #============================================================================== swap0 = np.where(dualseries.swapstate.flatten()==0) swap1 = np.where(dualseries.swapstate.flatten()==1) basenames = dualseries.base[0,:] #============================================================================== # Astrometric model fit residuals from Group Delay only #============================================================================== gl.plotTitle(Story,"(1/2) Group delay + FC MET only dOPD model fit residuals") gl.plotSubtitle(Story,"dOPD (microns) vs. time (decimal hours).",spacer=Spacer(1,2*mm)) gl.plotSubtitle(Story,"Swap WITH disp. corr.: NO=Red, YES=Orange x (-1).") hsize=16*cm vsize=6.5*cm residuals = 1E6*(dualseries.dopd_gd.flatten()-mod_GDonly) residuals0 = residuals[swap0] residuals1 = (-1)*residuals[swap1] for baseline in range(0,3): ydata0 = residuals0[baseline::nbase] ydata1 = residuals1[baseline::nbase] rms = np.std(residuals[baseline::nbase]) data = [] data.append( tuple(zip(timescale0, gl.clipdata(ydata0,yminval,ymaxval))) ) data.append( tuple(zip(timescale1, gl.clipdata(ydata1,yminval,ymaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,yminval,ymaxval,hsize,vsize,\ None,None,'Base '+basenames[baseline]+' RMS = %.3f'%rms+' microns') Story.append(a) if baseline < nbase-1: Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) gl.plotTitle(Story,"(2/2) Group delay + FC MET only dOPD model fit residuals") gl.plotSubtitle(Story,"dOPD (microns) vs. time (decimal hours).",spacer=Spacer(1,2*mm)) gl.plotSubtitle(Story,"Swap WITH disp. corr.: NO=Red, YES=Orange x (-1).") for baseline in range(3,nbase): ydata0 = residuals0[baseline::nbase] ydata1 = residuals1[baseline::nbase] rms = np.std(residuals[baseline::nbase]) data = [] data.append( tuple(zip(timescale0, gl.clipdata(ydata0,yminval,ymaxval))) ) data.append( tuple(zip(timescale1, gl.clipdata(ydata1,yminval,ymaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,yminval,ymaxval,hsize,vsize,\ None,None,'Base '+basenames[baseline]+' RMS = %.3f'%rms+' microns') Story.append(a) if baseline < nbase-1: Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) #============================================================================== # Phase residual after GD FC only model subtracion #============================================================================== gl.plotTitle(Story,"(1/2) Residual dOPD from phase after FC ONLY GD fit") gl.plotSubtitle(Story,"dOPD (mic) from WRAPPED phase vs. time (h), median over lbd.",spacer=Spacer(1,2*mm)) gl.plotSubtitle(Story,"Swap with disp. corr.: NO=Red, YES=Orange x (-1).") hsize=16*cm vsize=6.5*cm residuals = 1E6*dualseries.dopd_phasor_res.flatten() * ((-1)**(dualseries.swapstate.flatten())) # in meters residuals0 = residuals[swap0] residuals1 = (-1)*residuals[swap1] # max step to avoid counting wraps in rms stepmax = (1E6*dualseries.lambda_mean)/2.0 for baseline in range(0,3): ydata0 = residuals0[baseline::nbase] ydata1 = residuals1[baseline::nbase] rms0 = np.std(np.abs(ydata0)%stepmax) rms1 = np.std(np.abs(ydata1)%stepmax) data=[] data.append(tuple(zip(timescale0, gl.clipdata(ydata0,phaseminval,phasemaxval))) ) data.append(tuple(zip(timescale1, gl.clipdata(ydata1,phaseminval,phasemaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,phaseminval,phasemaxval,hsize,vsize,\ None,None,'Base %s RMS(swapNO)= %.3f microns RMS(swapYES)= %.3f microns'\ %(basenames[baseline],rms0,rms1)) Story.append(a) if baseline < nbase-1: Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) gl.plotTitle(Story,"(2/2) Residual dOPD from phase after FC ONLY GD fit") gl.plotSubtitle(Story,"dOPD (mic) from WRAPPED phase vs. time (h), median over lbd.",spacer=Spacer(1,2*mm)) gl.plotSubtitle(Story,"Swap with disp. corr.: NO=Red, YES=Orange x (-1).") for baseline in range(3,nbase): ydata0 = residuals0[baseline::nbase] ydata1 = residuals1[baseline::nbase] rms0 = np.std(np.abs(ydata0)%stepmax) rms1 = np.std(np.abs(ydata1)%stepmax) data=[] data.append(tuple(zip(timescale0, gl.clipdata(ydata0,phaseminval,phasemaxval))) ) data.append(tuple(zip(timescale1, gl.clipdata(ydata1,phaseminval,phasemaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,phaseminval,phasemaxval,hsize,vsize,\ None,None,'Base %s RMS(swapNO)= %.3f microns RMS(swapYES)= %.3f microns'\ %(basenames[baseline],rms0,rms1)) Story.append(a) if baseline < nbase-1: Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) #============================================================================== # Phase residual after GD FC+TEL model subtraction #============================================================================== if PlotTELfit: gl.plotTitle(Story,"(1/2) Residual dOPD from phase after FC+TEL GD fit") gl.plotSubtitle(Story,"dOPD (mic) from WRAPPED phase vs. time (h), median over lbd.",spacer=Spacer(1,2*mm)) gl.plotSubtitle(Story,"Swap with disp. corr.: NO=Red, YES=Orange x (-1).") hsize=16*cm vsize=6.5*cm residuals = 1E6 * dualseries.dopd_phasor_res_telmet.flatten() * ((-1)**(dualseries.swapstate.flatten())) residuals0 = residuals[swap0] residuals1 = (-1)*residuals[swap1] for baseline in range(0,3): ydata0 = residuals0[baseline::nbase] ydata1 = residuals1[baseline::nbase] rms0 = np.std(np.abs(ydata0)%stepmax) rms1 = np.std(np.abs(ydata1)%stepmax) data=[] data.append(tuple(zip(timescale0, gl.clipdata(ydata0,phaseminval,phasemaxval))) ) data.append(tuple(zip(timescale1, gl.clipdata(ydata1,phaseminval,phasemaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,phaseminval,phasemaxval,hsize,vsize,\ None,None,'Base %s RMS(swapNO)= %.3f microns RMS(swapYES)= %.3f microns'\ %(basenames[baseline],rms0,rms1)) Story.append(a) if baseline < nbase-1: Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) gl.plotTitle(Story,"(2/2) Residual dOPD from phase after FC+TEL GD fit") gl.plotSubtitle(Story,"dOPD (mic) from WRAPPED phase vs. time (h), median over lbd.",spacer=Spacer(1,2*mm)) gl.plotSubtitle(Story,"Swap with disp. corr.: NO=Red, YES=Orange x (-1).") for baseline in range(3,nbase): ydata0 = residuals0[baseline::nbase] ydata1 = residuals1[baseline::nbase] rms0 = np.std(np.abs(residuals0[baseline::nbase])%stepmax) rms1 = np.std(np.abs(residuals1[baseline::nbase])%stepmax) data=[] data.append(tuple(zip(timescale0, gl.clipdata(ydata0,phaseminval,phasemaxval))) ) data.append(tuple(zip(timescale1, gl.clipdata(ydata1,phaseminval,phasemaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,phaseminval,phasemaxval,hsize,vsize,\ None,None,'Base %s RMS(swapNO)= %.3f microns RMS(swapYES)= %.3f microns'\ %(basenames[baseline],rms0,rms1)) Story.append(a) if baseline < nbase-1: Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) #============================================================================== # Phase residual after second fit of phase #============================================================================== if PlotTELfit: gl.plotTitle(Story,"(1/2) Residual dOPD from phase (FC+TEL) after second phase fit") gl.plotSubtitle(Story,"dOPD (mic) from phase vs. time (h), median over lbd.",spacer=Spacer(1,2*mm)) gl.plotSubtitle(Story,"Swap with disp. corr.: NO=Red, YES=Orange x (-1).") hsize=16*cm vsize=6.5*cm residuals = 1E6 * dualseries.dopd_phasor_res2_telmet.flatten() * ((-1)**(dualseries.swapstate.flatten())) residuals0 = residuals[swap0] residuals1 = (-1)*residuals[swap1] for baseline in range(0,3): ydata0 = residuals0[baseline::nbase] ydata1 = residuals1[baseline::nbase] rms0 = np.std(np.abs(ydata0)%stepmax) rms1 = np.std(np.abs(ydata1)%stepmax) data=[] data.append(tuple(zip(timescale0, gl.clipdata(ydata0,phaseminval,phasemaxval))) ) data.append(tuple(zip(timescale1, gl.clipdata(ydata1,phaseminval,phasemaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,phaseminval,phasemaxval,hsize,vsize,\ None,None,'Base %s RMS(swapNO)= %.3f microns RMS(swapYES)= %.3f microns'\ %(basenames[baseline],rms0,rms1)) Story.append(a) if baseline < nbase-1: Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) gl.plotTitle(Story,"(2/2) Residual dOPD from phase (FC+TEL) after second phase fit") gl.plotSubtitle(Story,"dOPD (mic) from phase vs. time (h), median over lbd.",spacer=Spacer(1,2*mm)) gl.plotSubtitle(Story,"Swap with disp. corr.: NO=Red, YES=Orange x (-1).") for baseline in range(3,nbase): ydata0 = residuals0[baseline::nbase] ydata1 = residuals1[baseline::nbase] rms0 = np.std(np.abs(residuals0[baseline::nbase])%stepmax) rms1 = np.std(np.abs(residuals1[baseline::nbase])%stepmax) data=[] data.append(tuple(zip(timescale0, gl.clipdata(ydata0,phaseminval,phasemaxval))) ) data.append(tuple(zip(timescale1, gl.clipdata(ydata1,phaseminval,phasemaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,phaseminval,phasemaxval,hsize,vsize,\ None,None,'Base %s RMS(swapNO)= %.3f microns RMS(swapYES)= %.3f microns'\ %(basenames[baseline],rms0,rms1)) Story.append(a) if baseline < nbase-1: Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) #============================================================================== # Difference between TEL and FC metrology #============================================================================== gl.plotTitle(Story,"Difference between TEL and FC metrology signals") gl.plotSubtitle(Story,"dOPD (microns) vs. time (decimal hours).",spacer=Spacer(1,2*mm)) gl.plotSubtitle(Story,"Swap: NO=Red, YES=Orange.") hsize=16*cm vsize=3*cm residuals = 1E6*(dualseries.met_delta.flatten()) residuals0 = (residuals[swap0]) residuals1 = (-1)*(residuals[swap1]) for baseline in range(0,nbase): ydata0 = residuals0[baseline::nbase] ydata1 = residuals1[baseline::nbase] #yminval = np.min(residuals[baseline::nbase]) #ymaxval = np.max(residuals[baseline::nbase]) rms = np.std(residuals[baseline::nbase]) data = [] data.append( tuple(zip(timescale0, gl.clipdata(ydata0,metminval,metmaxval))) ) data.append( tuple(zip(timescale1, gl.clipdata(ydata1,metminval,metmaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,metminval,metmaxval,hsize,vsize,\ None,None,'Base '+basenames[baseline]+' RMS = %.3f'%rms+' microns') Story.append(a) if baseline < nbase-1: Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) #============================================================================== # Waterfall display of second fit phasor residuals GD+TEL+FC #============================================================================== if PlotWaterfall: gl.plotTitle(Story,"Waterfall of residual phasor of GD+FC only fit") gl.plotSubtitle(Story,"RMS = %.3f rad, wavelength in horizontal axis, time in vertical axis."%(rmsphasor_res)) valmin = -np.pi valmax = np.pi imgdata = io.StringIO() plt.close('all') plt.rc('xtick', labelsize='x-small') plt.rc('ytick', labelsize='x-small') fig, axarr = plt.subplots(nrows=nbase, ncols=1, sharex=True, sharey=True, figsize=(5,8)) for baseline in range(0,nbase): im = axarr[baseline].imshow(np.angle(dualseries.phasor_res[:,baseline,:]),\ vmin=valmin,vmax=valmax,cmap='bwr', interpolation='nearest', aspect='auto') axarr[baseline].set_title('Baseline '+basenames[baseline], fontsize=7) fig.subplots_adjust(hspace=.2) # Color bar plot fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(im, cax=cbar_ax) widthfig = 20 * cm heightfig= 22 * cm plt.savefig(imgdata, format='PDF', dpi=72, bbox_inches='tight') pi1 = gl.PdfImage(imgdata, width=widthfig, height=heightfig, kind='bound') plt.close('all') Story.append(pi1) Story.append(PageBreak()) gl.plotTitle(Story,"Waterfall of residual phasor of second fit with FC only") gl.plotSubtitle(Story,"RMS = %.3f rad, wavelength in horizontal axis, time in vertical axis."%(rmsphasor_res2)) valmin = -np.pi valmax = np.pi imgdata = io.StringIO() plt.close('all') plt.rc('xtick', labelsize='x-small') plt.rc('ytick', labelsize='x-small') fig, axarr = plt.subplots(nrows=nbase, ncols=1, sharex=True, sharey=True, figsize=(5,8)) for baseline in range(0,nbase): im = axarr[baseline].imshow(np.angle(dualseries.phasor_res2[:,baseline,:]),\ vmin=valmin,vmax=valmax,cmap='bwr', interpolation='nearest', aspect='auto') axarr[baseline].set_title('Baseline '+basenames[baseline], fontsize=7) fig.subplots_adjust(hspace=.2) # Color bar plot fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(im, cax=cbar_ax) widthfig = 20 * cm heightfig= 22 * cm plt.savefig(imgdata, format='PDF', dpi=72, bbox_inches='tight') pi1 = gl.PdfImage(imgdata, width=widthfig, height=heightfig, kind='bound') plt.close('all') Story.append(pi1) Story.append(PageBreak()) gl.plotTitle(Story,"Waterfall of residual phasor of second fit with FC+TEL") gl.plotSubtitle(Story,"RMS = %.3f rad, wavelength in horizontal axis, time in vertical axis."%(rmsphasor_res2_telmet)) valmin = -np.pi valmax = np.pi imgdata = io.StringIO() plt.close('all') plt.rc('xtick', labelsize='x-small') plt.rc('ytick', labelsize='x-small') fig, axarr = plt.subplots(nrows=nbase, ncols=1, sharex=True, sharey=True, figsize=(5,8)) for baseline in range(0,nbase): im = axarr[baseline].imshow(np.angle(dualseries.phasor_res2_telmet[:,baseline,:]),\ vmin=valmin,vmax=valmax,cmap='bwr', interpolation='nearest', aspect='auto') axarr[baseline].set_title('Baseline '+basenames[baseline], fontsize=7) fig.subplots_adjust(hspace=.2) # Color bar plot fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(im, cax=cbar_ax) widthfig = 20 * cm heightfig= 22 * cm plt.savefig(imgdata, format='PDF', dpi=72, bbox_inches='tight') pi1 = gl.PdfImage(imgdata, width=widthfig, height=heightfig, kind='bound') plt.close('all') Story.append(pi1) Story.append(PageBreak()) if True: #============================================================================== # GDELAY versus time #============================================================================== gl.plotTitle(Story,"GDELAY_SC and GDELAY_FT versus time") gl.plotSubtitle(Story,"Group delay (microns) vs. time (decimal hours), Red = SC, Orange = FT.") hsize=16*cm vsize=3*cm ydata0 = np.array(dualseries.gd_sc * 1E6) ydata1 = np.array(dualseries.gd_ft * 1E6) gdminval = np.nanmin([ydata0,ydata1]) gdmaxval = np.nanmax([ydata0,ydata1]) for baseline in range(0,nbase): data = [] data.append( tuple(zip(timescale[:,baseline], gl.clipdata(ydata0[:,baseline],gdminval,gdmaxval))) ) data.append( tuple(zip(timescale[:,baseline], gl.clipdata(ydata1[:,baseline],gdminval,gdmaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,gdminval,gdmaxval,hsize,vsize,\ None,None,'Base '+basenames[baseline]) Story.append(a) if baseline < nbase-1: Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) #============================================================================== # GDELAY difference versus time #============================================================================== gl.plotTitle(Story,"GDELAY_SC - GDELAY_FT versus time") gl.plotSubtitle(Story,"Group delay (microns) vs. time (decimal hours). Swap NO=Red, YES=Orange.") hsize=16*cm vsize=3*cm residuals = 1E6*(dualseries.gd_sc - dualseries.gd_ft) residuals0 = 1E6*(dualseries.gd_sc[np.where(dualseries.swapstate==0)]-dualseries.gd_ft[np.where(dualseries.swapstate==0)]) residuals1 = 1E6*(dualseries.gd_sc[np.where(dualseries.swapstate==1)]-dualseries.gd_ft[np.where(dualseries.swapstate==1)]) gddiffminval = np.nanmin(residuals) gddiffmaxval = np.nanmax(residuals) for baseline in range(0,nbase): ydata0 = residuals0[baseline::nbase] ydata1 = residuals1[baseline::nbase] rms = np.std(residuals[baseline::nbase]) data=[] data.append(tuple(zip(timescale0, gl.clipdata(ydata0,gddiffminval,gddiffmaxval))) ) data.append(tuple(zip(timescale1, gl.clipdata(ydata1,gddiffminval,gddiffmaxval))) ) a = gl.graphscatteraxes (data, xminval, xmaxval,gddiffminval,gddiffmaxval,hsize,vsize,\ None,None,'Base '+basenames[baseline]+' RMS = %.3f'%rms+' microns') Story.append(a) Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) #============================================================================== # GD_DISP - GD model versus time #============================================================================== gl.plotTitle(Story,"GD_DISP - GD model versus time") gl.plotSubtitle(Story,"dOPD (microns) vs. time (decimal hours)") hsize=16*cm vsize=3*cm ydata1 = (dualseries.gd_disp.flatten() - mod_GDonly) * 1e6 gdresminval = np.nanmin(ydata1) gdresmaxval = np.nanmax(ydata1) for baseline in range(0,nbase): data = [tuple(zip(timescale[:,baseline], gl.clipdata(ydata1[baseline::nbase],gdresminval,gdresmaxval)))] a = gl.graphscatteraxes (data, xminval, xmaxval,gdresminval,gdresmaxval,hsize,vsize,None,None,'Base '+basenames[baseline]) Story.append(a) Story.append(Spacer(1,7*mm)) Story.append(PageBreak()) #============================================================================== # Create the PDF #============================================================================== print("Creating ASTROMETRY PDF report:") doc.build(Story, onFirstPage=myFirstPage, onLaterPages=myLaterPages) print((" "+reportname)) #============================================================================== # MAIN PROGRAM #============================================================================== if __name__ == '__main__': # fits_keys = {'HIERARCH ESO PRO CATG': ['P2VMREDUCED'],\ # 'HIERARCH ESO FT POLA MODE': ['COMBINED','SPLIT']} fits_keys = {'HIERARCH ESO PRO CATG': ['ASTROREDUCED'],\ 'HIERARCH ESO FT POLA MODE': ['COMBINED','SPLIT']} # Four UTs ## no dispersion: # files = [ ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-14T23:23:16.657_astroreduced', bad ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-14T23:30:01.679_astroreduced', bad ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-14T23:34:43.695_astroreduced', bad ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T00:33:25.894_astroreduced', bad ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T00:39:49.915_astroreduced', bad #'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T01:01:22.988_astroreduced', # 1 #'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T01:07:44.010_astroreduced', #'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T01:16:38.040_astroreduced', # 1 ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T01:22:59.061_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T01:32:32.094_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T01:38:56.115_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T01:47:44.145_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T01:54:08.167_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T02:07:56.213_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T02:14:20.235_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T02:22:20.262_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T02:28:41.283_astroreduced',# 1 ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T02:52:26.364_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T02:59:08.386_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T03:09:41.422_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T03:16:02.444_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T03:24:20.472_astroreduced', ##'astroreduced/2016-08-14-nodisp/GRAVI.2016-08-15T03:30:44.493_astroreduced' # ] ## with dispersion: # files = [ ##'astroreduced/GRAVI.2016-08-14T23:23:16.657_astroreduced', bad ##'astroreduced/GRAVI.2016-08-14T23:30:01.679_astroreduced', bad ##'astroreduced/GRAVI.2016-08-14T23:34:43.695_astroreduced', bad ##'astroreduced/GRAVI.2016-08-15T00:33:25.894_astroreduced', bad ##'astroreduced/GRAVI.2016-08-15T00:39:49.915_astroreduced', bad #'astroreduced/GRAVI.2016-08-15T01:01:22.988_astroreduced', # 1 #'astroreduced/GRAVI.2016-08-15T01:07:44.010_astroreduced', # 1 #'astroreduced/GRAVI.2016-08-15T01:16:38.040_astroreduced', # 1 ##'astroreduced/GRAVI.2016-08-15T01:22:59.061_astroreduced', ##'astroreduced/GRAVI.2016-08-15T01:32:32.094_astroreduced', ##'astroreduced/GRAVI.2016-08-15T01:38:56.115_astroreduced', ##'astroreduced/GRAVI.2016-08-15T01:47:44.145_astroreduced', ##'astroreduced/GRAVI.2016-08-15T01:54:08.167_astroreduced', ##'astroreduced/GRAVI.2016-08-15T02:07:56.213_astroreduced', ##'astroreduced/GRAVI.2016-08-15T02:14:20.235_astroreduced', ##'astroreduced/GRAVI.2016-08-15T02:22:20.262_astroreduced', ##'astroreduced/GRAVI.2016-08-15T02:28:41.283_astroreduced', ##'astroreduced/GRAVI.2016-08-15T02:52:26.364_astroreduced', ##'astroreduced/GRAVI.2016-08-15T02:59:08.386_astroreduced', ##'astroreduced/GRAVI.2016-08-15T03:09:41.422_astroreduced', ##'astroreduced/GRAVI.2016-08-15T03:16:02.444_astroreduced', ##'astroreduced/GRAVI.2016-08-15T03:24:20.472_astroreduced', ##'astroreduced/GRAVI.2016-08-15T03:30:44.493_astroreduced', ##'astroreduced/GRAVI.2016-08-15T03:30:44.493_astroreduced', ##'astroreduced/GRAVI.2016-08-15T03:37:56.518_astroreduced', ##'astroreduced/GRAVI.2016-08-15T03:44:32.540_astroreduced', ##'astroreduced/GRAVI.2016-08-15T03:51:38.564_astroreduced' # ] # # WDS_J20452-3120 B and C series # files = [ # 'astroreduced/GRAVITY.2016-07-12T04-56-13_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T05-02-28_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T05-09-55_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T05-16-07_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T06-22-37_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T06-29-40_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T06-36-52_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T06-43-04_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T07-44-22_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T07-50-31_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T07-57-22_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T08-03-31_astroreduced' # ] # files = [ # 'astroreduced/GRAVITY.2016-07-12T05-41-37_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T05-47-46_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T05-58-10_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T06-04-34_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T07-03-01_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T07-09-10_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T07-20-16_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T07-26-43_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T08-24-22_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T08-32-40_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T08-46-55_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T08-50-41_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T08-59-32_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T09-13-38_astroreduced', # 'astroreduced/GRAVITY.2016-07-12T09-20-44_astroreduced' # ] if False: # # Mu Vel series # files = [ # #'astroreduced/GRAVITY.2016-03-23T05-31-27_astroreduced', # bad met # #'astroreduced/GRAVITY.2016-03-23T05-51-09_astroreduced', # bad met # #'astroreduced/GRAVITY.2016-03-23T06-00-45_astroreduced', # bad met # 'astroreduced/GRAVITY.2016-03-23T06-08-30_astroreduced', # 'astroreduced/GRAVITY.2016-03-23T06-19-48_astroreduced', # 'astroreduced/GRAVITY.2016-03-23T06-30-28_astroreduced', # 'astroreduced/GRAVITY.2016-03-23T06-42-06_astroreduced', # 'astroreduced/GRAVITY.2016-03-23T06-51-18_astroreduced'] files = [ #'astroreduced/GRAVI.2016-04-10T01:45:11.406_astroreduced', #'astroreduced/GRAVI.2016-04-10T02:02:14.463_astroreduced', 'astroreduced/GRAVI.2016-04-10T02:22:02.530_astroreduced', 'astroreduced/GRAVI.2016-04-10T02:29:53.557_astroreduced', # 'astroreduced/GRAVI.2016-04-10T02:33:20.568_astroreduced', # bad synchro 'astroreduced/GRAVI.2016-04-10T02:36:32.579_astroreduced', 'astroreduced/GRAVI.2016-04-10T02:42:41.600_astroreduced', 'astroreduced/GRAVI.2016-04-10T02:50:29.626_astroreduced', 'astroreduced/GRAVI.2016-04-10T02:54:26.639_astroreduced', 'astroreduced/GRAVI.2016-04-10T02:59:02.655_astroreduced', 'astroreduced/GRAVI.2016-04-10T03:06:08.679_astroreduced', 'astroreduced/GRAVI.2016-04-10T03:14:44.708_astroreduced', 'astroreduced/GRAVI.2016-04-10T03:18:44.722_astroreduced', 'astroreduced/GRAVI.2016-04-10T03:21:53.732_astroreduced', 'astroreduced/GRAVI.2016-04-10T03:28:02.753_astroreduced', 'astroreduced/GRAVI.2016-04-10T03:46:41.815_astroreduced', 'astroreduced/GRAVI.2016-04-10T03:49:56.826_astroreduced', #'astroreduced/GRAVI.2016-04-10T03:53:08.837_astroreduced', #'astroreduced/GRAVI.2016-04-10T04:00:29.862_astroreduced', #'astroreduced/GRAVI.2016-04-10T04:09:11.892_astroreduced', #'astroreduced/GRAVI.2016-04-10T04:13:44.907_astroreduced', #'astroreduced/GRAVI.2016-04-10T04:17:08.918_astroreduced', 'astroreduced/GRAVI.2016-04-10T04:24:08.942_astroreduced', 'astroreduced/GRAVI.2016-04-10T04:32:35.970_astroreduced', 'astroreduced/GRAVI.2016-04-10T04:35:44.981_astroreduced', 'astroreduced/GRAVI.2016-04-10T04:40:08.996_astroreduced', 'astroreduced/GRAVI.2016-04-10T04:46:45.019_astroreduced', 'astroreduced/GRAVI.2016-04-10T04:53:57.043_astroreduced', 'astroreduced/GRAVI.2016-04-10T04:57:12.054_astroreduced', 'astroreduced/GRAVI.2016-04-10T05:00:18.064_astroreduced', 'astroreduced/GRAVI.2016-04-10T05:07:18.087_astroreduced', 'astroreduced/GRAVI.2016-04-10T05:14:48.113_astroreduced', 'astroreduced/GRAVI.2016-04-10T05:18:00.124_astroreduced', 'astroreduced/GRAVI.2016-04-10T05:21:09.134_astroreduced', 'astroreduced/GRAVI.2016-04-10T05:29:18.162_astroreduced', 'astroreduced/GRAVI.2016-04-10T05:36:15.185_astroreduced', 'astroreduced/GRAVI.2016-04-10T05:39:27.196_astroreduced', 'astroreduced/GRAVI.2016-04-10T05:42:36.207_astroreduced' ] # files = [ # 'astroreduced/GRAVI.2016-06-03T00:37:14.206_astroreduced', # 'astroreduced/GRAVI.2016-06-03T00:44:20.230_astroreduced' # ] # files = [ # 'astroreduced/GRAVI.2016-06-03T02:56:35.676_astroreduced', # 'astroreduced/GRAVI.2016-06-03T03:03:20.699_astroreduced', # 'astroreduced/GRAVI.2016-06-03T03:09:53.722_astroreduced', # 'astroreduced/GRAVI.2016-06-03T03:17:35.748_astroreduced', # 'astroreduced/GRAVI.2016-06-03T03:23:53.768_astroreduced', # 'astroreduced/GRAVI.2016-06-03T03:30:17.790_astroreduced', # 'astroreduced/GRAVI.2016-06-03T03:36:35.812_astroreduced', # 'astroreduced/GRAVI.2016-06-03T03:43:05.834_astroreduced', # 'astroreduced/GRAVI.2016-06-03T03:49:23.854_astroreduced', # 'astroreduced/GRAVI.2016-06-03T03:56:56.881_astroreduced', # 'astroreduced/GRAVI.2016-06-03T04:03:26.902_astroreduced', # 'astroreduced/GRAVI.2016-06-03T04:09:26.923_astroreduced', # 'astroreduced/GRAVI.2016-06-03T04:15:44.944_astroreduced', # 'astroreduced/GRAVI.2016-06-03T04:24:14.973_astroreduced', # 'astroreduced/GRAVI.2016-06-03T04:30:32.994_astroreduced', # 'astroreduced/GRAVI.2016-06-03T04:36:54.015_astroreduced' # ] # GJ65 2017-01-04 if False: files = [ '/Volumes/GRAVITY-1TB/2017-01-04/astroreduced/GRAVITY.2017-01-04T01-46-25_astroreduced', '/Volumes/GRAVITY-1TB/2017-01-04/astroreduced/GRAVITY.2017-01-04T01-53-51_astroreduced', '/Volumes/GRAVITY-1TB/2017-01-04/astroreduced/GRAVITY.2017-01-04T02-04-15_astroreduced', '/Volumes/GRAVITY-1TB/2017-01-04/astroreduced/GRAVITY.2017-01-04T02-11-11_astroreduced' ] # GJ65 2017-01-05 if False: files = [ '/Volumes/GRAVITY-1TB/2017-01-05/astroreduced/GRAVITY.2017-01-05T01-09-28_astroreduced', '/Volumes/GRAVITY-1TB/2017-01-05/astroreduced/GRAVITY.2017-01-05T01-25-11_astroreduced', '/Volumes/GRAVITY-1TB/2017-01-05/astroreduced/GRAVITY.2017-01-05T01-32-13_astroreduced', '/Volumes/GRAVITY-1TB/2017-01-05/astroreduced/GRAVITY.2017-01-05T01-59-54_astroreduced', '/Volumes/GRAVITY-1TB/2017-01-05/astroreduced/GRAVITY.2017-01-05T02-25-46_astroreduced', '/Volumes/GRAVITY-1TB/2017-01-05/astroreduced/GRAVITY.2017-01-05T02-38-42_astroreduced', #'/Volumes/GRAVITY-1TB/2017-01-05/astroreduced/GRAVITY.2017-01-05T02-46-17_astroreduced' ] # # GJ65 2017-01-06 # files = [ # '/Volumes/GRAVITY-1TB/2017-01-06/astroreduced/GRAVITY.2017-01-06T01-26-29_astroreduced', # '/Volumes/GRAVITY-1TB/2017-01-06/astroreduced/GRAVITY.2017-01-06T01-41-08_astroreduced', # '/Volumes/GRAVITY-1TB/2017-01-06/astroreduced/GRAVITY.2017-01-06T01-47-39_astroreduced', # '/Volumes/GRAVITY-1TB/2017-01-06/astroreduced/GRAVITY.2017-01-06T01-56-29_astroreduced', # #'/Volumes/GRAVITY-1TB/2017-01-06/astroreduced/GRAVITY.2017-01-06T02-03-05_astroreduced', # '/Volumes/GRAVITY-1TB/2017-01-06/astroreduced/GRAVITY.2017-01-06T02-15-34_astroreduced', # '/Volumes/GRAVITY-1TB/2017-01-06/astroreduced/GRAVITY.2017-01-06T02-22-25_astroreduced', # #'/Volumes/GRAVITY-1TB/2017-01-06/astroreduced/GRAVITY.2017-01-06T02-29-21_astroreduced', # '/Volumes/GRAVITY-1TB/2017-01-06/astroreduced/GRAVITY.2017-01-06T02-35-53_astroreduced', # ] if True: directory = '/Users/kervella/Pipelines/python_tools/gravi_astrometry/2017-01-06/' files = [ directory+'GRAVITY.2017-01-06T01-26-29_astroreduced', directory+'GRAVITY.2017-01-06T01-41-08_astroreduced', directory+'GRAVITY.2017-01-06T01-47-39_astroreduced', directory+'GRAVITY.2017-01-06T01-56-29_astroreduced', directory+'GRAVITY.2017-01-06T02-03-05_astroreduced', directory+'GRAVITY.2017-01-06T02-15-34_astroreduced', directory+'GRAVITY.2017-01-06T02-22-25_astroreduced', directory+'GRAVITY.2017-01-06T02-35-53_astroreduced' ] files.sort() filename = files[0] # name of the first file in the series for the report plt.close('all') astroreduced = gravi_astrometry_class.Astroreduced(files,fits_keys) produce_astrometry_report(astroreduced,filename)